diff --git a/shared/package-files/0c/0c00bafaf4e827a7eac83842bd6efcff7c52acc4b74808ec32a253bdbdb43bd3/add_if_then_else.h b/shared/package-files/0c/0c00bafaf4e827a7eac83842bd6efcff7c52acc4b74808ec32a253bdbdb43bd3/add_if_then_else.h
new file mode 100644
index 0000000000000000000000000000000000000000..c6b3f9376d6b328f1d7eaf6cda14b007a6fc8e45
--- /dev/null
+++ b/shared/package-files/0c/0c00bafaf4e827a7eac83842bd6efcff7c52acc4b74808ec32a253bdbdb43bd3/add_if_then_else.h
@@ -0,0 +1,11 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+TORCH_API bool AddIfThenElseOp(std::shared_ptr<Graph>& graph);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/0c/0c02ab85a5006c26645efe48e107c8667cbc71cac2e994791233c6833d45b75a/fmax_meta_dispatch.h b/shared/package-files/0c/0c02ab85a5006c26645efe48e107c8667cbc71cac2e994791233c6833d45b75a/fmax_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c51ba2002c4422ab5f3279397efb82e76e312ad
--- /dev/null
+++ b/shared/package-files/0c/0c02ab85a5006c26645efe48e107c8667cbc71cac2e994791233c6833d45b75a/fmax_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor fmax(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/0c/0c04462503d275fff1c68227b8404128de24ba7d08ceef8b197f1cd7e862baf9/distance_regular.py b/shared/package-files/0c/0c04462503d275fff1c68227b8404128de24ba7d08ceef8b197f1cd7e862baf9/distance_regular.py
new file mode 100644
index 0000000000000000000000000000000000000000..bef9f01316748a122ba7a646657a7d140c4afcde
--- /dev/null
+++ b/shared/package-files/0c/0c04462503d275fff1c68227b8404128de24ba7d08ceef8b197f1cd7e862baf9/distance_regular.py
@@ -0,0 +1,231 @@
+"""
+=======================
+Distance-regular graphs
+=======================
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+from .distance_measures import diameter
+
+__all__ = [
+    "is_distance_regular",
+    "is_strongly_regular",
+    "intersection_array",
+    "global_parameters",
+]
+
+
+def is_distance_regular(G):
+    """Returns True if the graph is distance regular, False otherwise.
+
+    A connected graph G is distance-regular if for any nodes x,y
+    and any integers i,j=0,1,...,d (where d is the graph
+    diameter), the number of vertices at distance i from x and
+    distance j from y depends only on i,j and the graph distance
+    between x and y, independently of the choice of x and y.
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    bool
+      True if the graph is Distance Regular, False otherwise
+
+    Examples
+    --------
+    >>> G = nx.hypercube_graph(6)
+    >>> nx.is_distance_regular(G)
+    True
+
+    See Also
+    --------
+    intersection_array, global_parameters
+
+    Notes
+    -----
+    For undirected and simple graphs only
+
+    References
+    ----------
+    .. [1] Brouwer, A. E.; Cohen, A. M.; and Neumaier, A.
+        Distance-Regular Graphs. New York: Springer-Verlag, 1989.
+    .. [2] Weisstein, Eric W. "Distance-Regular Graph."
+        http://mathworld.wolfram.com/Distance-RegularGraph.html
+
+    """
+    try:
+        intersection_array(G)
+        return True
+    except nx.NetworkXError:
+        return False
+
+
+def global_parameters(b, c):
+    """Returns global parameters for a given intersection array.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    Thus, a distance regular graph has the global parameters,
+    [[c_0,a_0,b_0],[c_1,a_1,b_1],......,[c_d,a_d,b_d]] for the
+    intersection array  [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+    where a_i+b_i+c_i=k , k= degree of every vertex.
+
+    Parameters
+    ----------
+    b : list
+
+    c : list
+
+    Returns
+    -------
+    iterable
+       An iterable over three tuples.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> b, c = nx.intersection_array(G)
+    >>> list(nx.global_parameters(b, c))
+    [(0, 0, 3), (1, 0, 2), (1, 1, 1), (1, 1, 1), (2, 0, 1), (3, 0, 0)]
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Global Parameters."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/GlobalParameters.html
+
+    See Also
+    --------
+    intersection_array
+    """
+    return ((y, b[0] - x - y, x) for x, y in zip(b + [0], [0] + c))
+
+
+@not_implemented_for("directed", "multigraph")
+def intersection_array(G):
+    """Returns the intersection array of a distance-regular graph.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    A distance regular graph's intersection array is given by,
+    [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    b,c: tuple of lists
+
+    Examples
+    --------
+    >>> G = nx.icosahedral_graph()
+    >>> nx.intersection_array(G)
+    ([5, 2, 1], [1, 2, 5])
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Intersection Array."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/IntersectionArray.html
+
+    See Also
+    --------
+    global_parameters
+    """
+    # test for regular graph (all degrees must be equal)
+    degree = iter(G.degree())
+    (_, k) = next(degree)
+    for _, knext in degree:
+        if knext != k:
+            raise nx.NetworkXError("Graph is not distance regular.")
+        k = knext
+    path_length = dict(nx.all_pairs_shortest_path_length(G))
+    diameter = max(max(path_length[n].values()) for n in path_length)
+    bint = {}  # 'b' intersection array
+    cint = {}  # 'c' intersection array
+    for u in G:
+        for v in G:
+            try:
+                i = path_length[u][v]
+            except KeyError as err:  # graph must be connected
+                raise nx.NetworkXError("Graph is not distance regular.") from err
+            # number of neighbors of v at a distance of i-1 from u
+            c = len([n for n in G[v] if path_length[n][u] == i - 1])
+            # number of neighbors of v at a distance of i+1 from u
+            b = len([n for n in G[v] if path_length[n][u] == i + 1])
+            # b,c are independent of u and v
+            if cint.get(i, c) != c or bint.get(i, b) != b:
+                raise nx.NetworkXError("Graph is not distance regular")
+            bint[i] = b
+            cint[i] = c
+    return (
+        [bint.get(j, 0) for j in range(diameter)],
+        [cint.get(j + 1, 0) for j in range(diameter)],
+    )
+
+
+# TODO There is a definition for directed strongly regular graphs.
+@not_implemented_for("directed", "multigraph")
+def is_strongly_regular(G):
+    """Returns True if and only if the given graph is strongly
+    regular.
+
+    An undirected graph is *strongly regular* if
+
+    * it is regular,
+    * each pair of adjacent vertices has the same number of neighbors in
+      common,
+    * each pair of nonadjacent vertices has the same number of neighbors
+      in common.
+
+    Each strongly regular graph is a distance-regular graph.
+    Conversely, if a distance-regular graph has diameter two, then it is
+    a strongly regular graph. For more information on distance-regular
+    graphs, see :func:`is_distance_regular`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    Returns
+    -------
+    bool
+        Whether `G` is strongly regular.
+
+    Examples
+    --------
+
+    The cycle graph on five vertices is strongly regular. It is
+    two-regular, each pair of adjacent vertices has no shared neighbors,
+    and each pair of nonadjacent vertices has one shared neighbor::
+
+        >>> G = nx.cycle_graph(5)
+        >>> nx.is_strongly_regular(G)
+        True
+
+    """
+    # Here is an alternate implementation based directly on the
+    # definition of strongly regular graphs:
+    #
+    #     return (all_equal(G.degree().values())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in G.edges())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in non_edges(G)))
+    #
+    # We instead use the fact that a distance-regular graph of diameter
+    # two is strongly regular.
+    return is_distance_regular(G) and diameter(G) == 2
diff --git a/shared/package-files/0c/0c05cad261bb0815b7eecb9bb3c62e4e48fffe64f7563c7dd18e8a391eb33570/__init__.py b/shared/package-files/0c/0c05cad261bb0815b7eecb9bb3c62e4e48fffe64f7563c7dd18e8a391eb33570/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e94ab1bb9d636d2ebe4d57ba8230eaf597c344ee
--- /dev/null
+++ b/shared/package-files/0c/0c05cad261bb0815b7eecb9bb3c62e4e48fffe64f7563c7dd18e8a391eb33570/__init__.py
@@ -0,0 +1,52 @@
+
+import sys
+import torch
+
+
+def is_available():
+    return hasattr(torch._C, "_dist_autograd_init")
+
+
+if is_available() and not torch._C._dist_autograd_init():
+    raise RuntimeError("Failed to initialize torch.distributed.autograd")
+
+if is_available():
+    from torch._C._distributed_autograd import (
+        get_gradients,
+        backward,
+        _init,
+        _new_context,
+        _release_context,
+        _get_max_id,
+        _is_valid_context,
+        _retrieve_context,
+        _current_context,
+        _get_debug_info,
+        DistAutogradContext,
+    )
+
+
+class context:
+    '''
+    Context object to wrap forward and backward passes when using
+    distributed autograd. The ``context_id`` generated in the ``with``
+    statement  is required to uniquely identify a distributed backward pass
+    on all workers. Each worker stores metadata associated with this
+    ``context_id``, which is required to correctly execute a distributed
+    autograd pass.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> with dist_autograd.context() as context_id:
+        >>>     t1 = torch.rand((3, 3), requires_grad=True)
+        >>>     t2 = torch.rand((3, 3), requires_grad=True)
+        >>>     loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
+        >>>     dist_autograd.backward(context_id, [loss])
+    '''
+    def __enter__(self):
+        self.autograd_context = _new_context()
+        return self.autograd_context._context_id()
+
+    def __exit__(self, type, value, traceback):
+        _release_context(self.autograd_context._context_id())
diff --git a/shared/package-files/0c/0c0634e1aaf55e2c7012c725ead69275d922e18feb62742757ccc5eb5fb3d596/test_describe.py b/shared/package-files/0c/0c0634e1aaf55e2c7012c725ead69275d922e18feb62742757ccc5eb5fb3d596/test_describe.py
new file mode 100644
index 0000000000000000000000000000000000000000..5beb09940acf32a4a597819f5b130863d90261e5
--- /dev/null
+++ b/shared/package-files/0c/0c0634e1aaf55e2c7012c725ead69275d922e18feb62742757ccc5eb5fb3d596/test_describe.py
@@ -0,0 +1,417 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameDescribe:
+    def test_describe_bool_in_mixed_frame(self):
+        df = DataFrame(
+            {
+                "string_data": ["a", "b", "c", "d", "e"],
+                "bool_data": [True, True, False, False, False],
+                "int_data": [10, 20, 30, 40, 50],
+            }
+        )
+
+        # Integer data are included in .describe() output,
+        # Boolean and string data are not.
+        result = df.describe()
+        expected = DataFrame(
+            {"int_data": [5, 30, df.int_data.std(), 10, 20, 30, 40, 50]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Top value is a boolean value that is False
+        result = df.describe(include=["bool"])
+
+        expected = DataFrame(
+            {"bool_data": [5, 2, False, 3]}, index=["count", "unique", "top", "freq"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_empty_object(self):
+        # GH#27183
+        df = DataFrame({"A": [None, None]}, dtype=object)
+        result = df.describe()
+        expected = DataFrame(
+            {"A": [0, 0, np.nan, np.nan]},
+            dtype=object,
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = df.iloc[:0].describe()
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_bool_frame(self):
+        # GH#13891
+        df = DataFrame(
+            {
+                "bool_data_1": [False, False, True, True],
+                "bool_data_2": [False, True, True, True],
+            }
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"bool_data_1": [4, 2, False, 2], "bool_data_2": [4, 2, True, 3]},
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "bool_data": [False, False, True, True, False],
+                "int_data": [0, 1, 2, 3, 4],
+            }
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"int_data": [5, 2, df.int_data.std(), 0, 1, 2, 3, 4]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {"bool_data": [False, False, True, True], "str_data": ["a", "b", "c", "a"]}
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"bool_data": [4, 2, False, 2], "str_data": [4, 3, "a", 2]},
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_categorical(self):
+        df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)})
+        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
+        cat_labels = Categorical(labels, labels)
+
+        df = df.sort_values(by=["value"], ascending=True)
+        df["value_group"] = pd.cut(
+            df.value, range(0, 10500, 500), right=False, labels=cat_labels
+        )
+        cat = df
+
+        # Categoricals should not show up together with numerical columns
+        result = cat.describe()
+        assert len(result.columns) == 1
+
+        # In a frame, describe() for the cat should be the same as for string
+        # arrays (count, unique, top, freq)
+
+        cat = Categorical(
+            ["a", "b", "b", "b"], categories=["a", "b", "c"], ordered=True
+        )
+        s = Series(cat)
+        result = s.describe()
+        expected = Series([4, 2, "b", 3], index=["count", "unique", "top", "freq"])
+        tm.assert_series_equal(result, expected)
+
+        cat = Series(Categorical(["a", "b", "c", "c"]))
+        df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]})
+        result = df3.describe()
+        tm.assert_numpy_array_equal(result["cat"].values, result["s"].values)
+
+    def test_describe_empty_categorical_column(self):
+        # GH#26397
+        # Ensure the index of an empty categorical DataFrame column
+        # also contains (count, unique, top, freq)
+        df = DataFrame({"empty_col": Categorical([])})
+        result = df.describe()
+        expected = DataFrame(
+            {"empty_col": [0, 0, np.nan, np.nan]},
+            index=["count", "unique", "top", "freq"],
+            dtype="object",
+        )
+        tm.assert_frame_equal(result, expected)
+        # ensure NaN, not None
+        assert np.isnan(result.iloc[2, 0])
+        assert np.isnan(result.iloc[3, 0])
+
+    def test_describe_categorical_columns(self):
+        # GH#11558
+        columns = pd.CategoricalIndex(["int1", "int2", "obj"], ordered=True, name="XXX")
+        df = DataFrame(
+            {
+                "int1": [10, 20, 30, 40, 50],
+                "int2": [10, 20, 30, 40, 50],
+                "obj": ["A", 0, None, "X", 1],
+            },
+            columns=columns,
+        )
+        result = df.describe()
+
+        exp_columns = pd.CategoricalIndex(
+            ["int1", "int2"],
+            categories=["int1", "int2", "obj"],
+            ordered=True,
+            name="XXX",
+        )
+        expected = DataFrame(
+            {
+                "int1": [5, 30, df.int1.std(), 10, 20, 30, 40, 50],
+                "int2": [5, 30, df.int2.std(), 10, 20, 30, 40, 50],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            columns=exp_columns,
+        )
+
+        tm.assert_frame_equal(result, expected)
+        tm.assert_categorical_equal(result.columns.values, expected.columns.values)
+
+    def test_describe_datetime_columns(self):
+        columns = pd.DatetimeIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01"],
+            freq="MS",
+            tz="US/Eastern",
+            name="XXX",
+        )
+        df = DataFrame(
+            {
+                0: [10, 20, 30, 40, 50],
+                1: [10, 20, 30, 40, 50],
+                2: ["A", 0, None, "X", 1],
+            }
+        )
+        df.columns = columns
+        result = df.describe()
+
+        exp_columns = pd.DatetimeIndex(
+            ["2011-01-01", "2011-02-01"], freq="MS", tz="US/Eastern", name="XXX"
+        )
+        expected = DataFrame(
+            {
+                0: [5, 30, df.iloc[:, 0].std(), 10, 20, 30, 40, 50],
+                1: [5, 30, df.iloc[:, 1].std(), 10, 20, 30, 40, 50],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        expected.columns = exp_columns
+        tm.assert_frame_equal(result, expected)
+        assert result.columns.freq == "MS"
+        assert result.columns.tz == expected.columns.tz
+
+    def test_describe_timedelta_values(self):
+        # GH#6145
+        t1 = pd.timedelta_range("1 days", freq="D", periods=5)
+        t2 = pd.timedelta_range("1 hours", freq="h", periods=5)
+        df = DataFrame({"t1": t1, "t2": t2})
+
+        expected = DataFrame(
+            {
+                "t1": [
+                    5,
+                    pd.Timedelta("3 days"),
+                    df.iloc[:, 0].std(),
+                    pd.Timedelta("1 days"),
+                    pd.Timedelta("2 days"),
+                    pd.Timedelta("3 days"),
+                    pd.Timedelta("4 days"),
+                    pd.Timedelta("5 days"),
+                ],
+                "t2": [
+                    5,
+                    pd.Timedelta("3 hours"),
+                    df.iloc[:, 1].std(),
+                    pd.Timedelta("1 hours"),
+                    pd.Timedelta("2 hours"),
+                    pd.Timedelta("3 hours"),
+                    pd.Timedelta("4 hours"),
+                    pd.Timedelta("5 hours"),
+                ],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+
+        result = df.describe()
+        tm.assert_frame_equal(result, expected)
+
+        exp_repr = (
+            "                              t1                         t2\n"
+            "count                          5                          5\n"
+            "mean             3 days 00:00:00            0 days 03:00:00\n"
+            "std    1 days 13:56:50.394919273  0 days 01:34:52.099788303\n"
+            "min              1 days 00:00:00            0 days 01:00:00\n"
+            "25%              2 days 00:00:00            0 days 02:00:00\n"
+            "50%              3 days 00:00:00            0 days 03:00:00\n"
+            "75%              4 days 00:00:00            0 days 04:00:00\n"
+            "max              5 days 00:00:00            0 days 05:00:00"
+        )
+        assert repr(result) == exp_repr
+
+    def test_describe_tz_values(self, tz_naive_fixture):
+        # GH#21332
+        tz = tz_naive_fixture
+        s1 = Series(range(5))
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s2 = Series(date_range(start, end, tz=tz))
+        df = DataFrame({"s1": s1, "s2": s2})
+
+        expected = DataFrame(
+            {
+                "s1": [5, 2, 0, 1, 2, 3, 4, 1.581139],
+                "s2": [
+                    5,
+                    Timestamp(2018, 1, 3).tz_localize(tz),
+                    start.tz_localize(tz),
+                    s2[1],
+                    s2[2],
+                    s2[3],
+                    end.tz_localize(tz),
+                    np.nan,
+                ],
+            },
+            index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
+        )
+        result = df.describe(include="all")
+        tm.assert_frame_equal(result, expected)
+
+    def test_datetime_is_numeric_includes_datetime(self):
+        df = DataFrame({"a": date_range("2012", periods=3), "b": [1, 2, 3]})
+        result = df.describe()
+        expected = DataFrame(
+            {
+                "a": [
+                    3,
+                    Timestamp("2012-01-02"),
+                    Timestamp("2012-01-01"),
+                    Timestamp("2012-01-01T12:00:00"),
+                    Timestamp("2012-01-02"),
+                    Timestamp("2012-01-02T12:00:00"),
+                    Timestamp("2012-01-03"),
+                    np.nan,
+                ],
+                "b": [3, 2, 1, 1.5, 2, 2.5, 3, 1],
+            },
+            index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_tz_values2(self):
+        tz = "CET"
+        s1 = Series(range(5))
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s2 = Series(date_range(start, end, tz=tz))
+        df = DataFrame({"s1": s1, "s2": s2})
+
+        s1_ = s1.describe()
+        s2_ = s2.describe()
+        idx = [
+            "count",
+            "mean",
+            "min",
+            "25%",
+            "50%",
+            "75%",
+            "max",
+            "std",
+        ]
+        expected = pd.concat([s1_, s2_], axis=1, keys=["s1", "s2"]).reindex(
+            idx, copy=False
+        )
+
+        result = df.describe(include="all")
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_percentiles_integer_idx(self):
+        # GH#26660
+        df = DataFrame({"x": [1]})
+        pct = np.linspace(0, 1, 10 + 1)
+        result = df.describe(percentiles=pct)
+
+        expected = DataFrame(
+            {"x": [1.0, 1.0, np.nan, 1.0, *(1.0 for _ in pct), 1.0]},
+            index=[
+                "count",
+                "mean",
+                "std",
+                "min",
+                "0%",
+                "10%",
+                "20%",
+                "30%",
+                "40%",
+                "50%",
+                "60%",
+                "70%",
+                "80%",
+                "90%",
+                "100%",
+                "max",
+            ],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_does_not_raise_error_for_dictlike_elements(self):
+        # GH#32409
+        df = DataFrame([{"test": {"a": "1"}}, {"test": {"a": "2"}}])
+        expected = DataFrame(
+            {"test": [2, 2, {"a": "1"}, 1]}, index=["count", "unique", "top", "freq"]
+        )
+        result = df.describe()
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("exclude", ["x", "y", ["x", "y"], ["x", "z"]])
+    def test_describe_when_include_all_exclude_not_allowed(self, exclude):
+        """
+        When include is 'all', then setting exclude != None is not allowed.
+        """
+        df = DataFrame({"x": [1], "y": [2], "z": [3]})
+        msg = "exclude must be None when include is 'all'"
+        with pytest.raises(ValueError, match=msg):
+            df.describe(include="all", exclude=exclude)
+
+    def test_describe_with_duplicate_columns(self):
+        df = DataFrame(
+            [[1, 1, 1], [2, 2, 2], [3, 3, 3]],
+            columns=["bar", "a", "a"],
+            dtype="float64",
+        )
+        result = df.describe()
+        ser = df.iloc[:, 0].describe()
+        expected = pd.concat([ser, ser, ser], keys=df.columns, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_with_na(self, any_numeric_ea_dtype):
+        # GH#48778
+
+        df = DataFrame({"a": [1, pd.NA, pd.NA], "b": pd.NA}, dtype=any_numeric_ea_dtype)
+        result = df.describe()
+        expected = DataFrame(
+            {"a": [1.0, 1.0, pd.NA] + [1.0] * 5, "b": [0.0] + [pd.NA] * 7},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype="Float64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_exclude_pa_dtype(self):
+        # GH#52570
+        pa = pytest.importorskip("pyarrow")
+        df = DataFrame(
+            {
+                "a": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int8())),
+                "b": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int16())),
+                "c": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int32())),
+            }
+        )
+        result = df.describe(
+            include=pd.ArrowDtype(pa.int8()), exclude=pd.ArrowDtype(pa.int32())
+        )
+        expected = DataFrame(
+            {"a": [3, 2, 1, 1, 1.5, 2, 2.5, 3]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype=pd.ArrowDtype(pa.float64()),
+        )
+        tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/0c/0c065a2cdd070b66d5d80715cf2c6c2acaf13ad2c85dbfcd9ffe99f8b806a7cc/arima112_css_results.py b/shared/package-files/0c/0c065a2cdd070b66d5d80715cf2c6c2acaf13ad2c85dbfcd9ffe99f8b806a7cc/arima112_css_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dc7e07bd9b73fb59a6ed092277659ceae838d83
--- /dev/null
+++ b/shared/package-files/0c/0c065a2cdd070b66d5d80715cf2c6c2acaf13ad2c85dbfcd9ffe99f8b806a7cc/arima112_css_results.py
@@ -0,0 +1,1315 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+llf = np.array([-244.3852892951])
+
+nobs = np.array([202])
+
+k = np.array([5])
+
+k_exog = np.array([1])
+
+sigma = np.array([.81130812929037])
+
+chi2 = np.array([73901.783883385])
+
+df_model = np.array([3])
+
+k_ar = np.array([1])
+
+k_ma = np.array([2])
+
+params = np.array([
+    .91963917600489,
+    -.89804855498306,
+    1.3032353997768,
+    .30230174935463,
+    .65822088065264])
+
+cov_params = np.array([
+    .00622027554245,
+    .00026933156699,
+    -.00014478009121,
+    -.00010527901395,
+    .00006880952561,
+    .00026933156699,
+    .0023661521973,
+    -.00263264462948,
+    -.00241927046074,
+    -.00069258998629,
+    -.00014478009121,
+    -.00263264462948,
+    .003637275855,
+    .0033625395431,
+    .00072507981262,
+    -.00010527901395,
+    -.00241927046074,
+    .0033625395431,
+    .00312013997649,
+    .00067166747844,
+    .00006880952561,
+    -.00069258998629,
+    .00072507981262,
+    .00067166747844,
+    .00108477120916]).reshape(5, 5)
+
+xb = np.array([
+    .91963917016983,
+    .91963917016983,
+    .69261693954468,
+    .76115423440933,
+    .63710719347,
+    .77478265762329,
+    .61527073383331,
+    .80501782894135,
+    .62182641029358,
+    .72158020734787,
+    .66619211435318,
+    .72980058193207,
+    .64935982227325,
+    .77660953998566,
+    .6069877743721,
+    .79818457365036,
+    .60795724391937,
+    .7583304643631,
+    .68147450685501,
+    .72765469551086,
+    .69699174165726,
+    .69102382659912,
+    .67753201723099,
+    .72617518901825,
+    .69977235794067,
+    .713603079319,
+    .72478419542313,
+    .68503832817078,
+    .75677126646042,
+    .79034942388535,
+    .68609654903412,
+    .83208250999451,
+    .60605573654175,
+    .80849212408066,
+    .69862711429596,
+    .80978256464005,
+    .7383074760437,
+    .78789436817169,
+    .79390448331833,
+    .79169547557831,
+    .76283228397369,
+    .87939429283142,
+    .75783687829971,
+    .85010063648224,
+    .80657452344894,
+    .86508285999298,
+    .72368890047073,
+    .86846202611923,
+    .75351697206497,
+    .74047154188156,
+    .82022970914841,
+    .73184186220169,
+    .7623735666275,
+    .74929028749466,
+    .75702118873596,
+    .79036456346512,
+    .81429827213287,
+    1.0007030963898,
+    .70464313030243,
+    1.2375881671906,
+    .82733017206192,
+    1.1780800819397,
+    .83767229318619,
+    1.3407131433487,
+    .7835128903389,
+    .99667322635651,
+    .82677388191223,
+    1.0330017805099,
+    .78713357448578,
+    .80603551864624,
+    .91298097372055,
+    .94862020015717,
+    .83088356256485,
+    1.1405943632126,
+    .72683191299438,
+    1.0197489261627,
+    .88344657421112,
+    1.1016070842743,
+    1.0485582351685,
+    1.1717364788055,
+    .94894939661026,
+    1.418029665947,
+    1.2063212394714,
+    1.3504880666733,
+    1.4053744077682,
+    1.5106836557388,
+    .91192328929901,
+    1.4546687602997,
+    1.2100585699081,
+    1.2459771633148,
+    1.2914154529572,
+    1.1733019351959,
+    .80550068616867,
+    .88859277963638,
+    1.5257360935211,
+    .49089628458023,
+    .75268715620041,
+    .92090040445328,
+    .99410575628281,
+    .87882828712463,
+    1.1253950595856,
+    .89082646369934,
+    .9317963719368,
+    .90858340263367,
+    .82737028598785,
+    1.0978132486343,
+    .74325948953629,
+    .98125350475311,
+    1.0478370189667,
+    .03625157848001,
+    1.3422871828079,
+    .51377469301224,
+    1.3643686771393,
+    .70055514574051,
+    1.2559896707535,
+    .71517109870911,
+    1.1997950077057,
+    .75360465049744,
+    1.2862613201141,
+    .79965251684189,
+    1.2606881856918,
+    1.018030166626,
+    1.1752370595932,
+    .69517260789871,
+    1.597958445549,
+    .65335071086884,
+    1.4763361215591,
+    1.2708671092987,
+    1.0432199239731,
+    .561203956604,
+    1.2630445957184,
+    .66821777820587,
+    1.2384748458862,
+    .70777904987335,
+    1.2246036529541,
+    .75373476743698,
+    1.199233174324,
+    .69312900304794,
+    1.0659650564194,
+    .80386221408844,
+    .99243313074112,
+    .78622406721115,
+    1.1766475439072,
+    .74267518520355,
+    1.1679803133011,
+    .85658311843872,
+    .99335825443268,
+    .79920876026154,
+    1.1595865488052,
+    .92043119668961,
+    .98299539089203,
+    .94316083192825,
+    1.0661553144455,
+    .6393609046936,
+    .9456650018692,
+    .91597771644592,
+    .80332309007645,
+    .65838772058487,
+    1.1093089580536,
+    .68860310316086,
+    1.0485997200012,
+    .89771980047226,
+    .94581252336502,
+    1.0480616092682,
+    1.0014315843582,
+    1.1307729482651,
+    1.1770483255386,
+    .89873492717743,
+    1.2652103900909,
+    .66434383392334,
+    1.1431220769882,
+    .44322970509529,
+    .9269899725914,
+    1.0786435604095,
+    .82789659500122,
+    1.0368362665176,
+    1.0712716579437,
+    .70438456535339,
+    .88966482877731,
+    1.009087562561,
+    1.0887442827225,
+    .88976800441742,
+    1.2735350131989,
+    1.0157470703125,
+    .95522791147232,
+    1.3003809452057,
+    .73179203271866,
+    2.4736785888672,
+    -.25176140666008,
+    1.9082181453705,
+    .53501582145691,
+    .7591078877449,
+    1.0281100273132,
+    1.6240043640137,
+    .60095232725143,
+    1.6211705207825,
+    1.344465970993,
+    1.124480009079,
+    2.1775946617126,
+    -.71973150968552,
+    -.37754261493683,
+    1.2329530715942,
+    1.127131819725])
+
+y = np.array([
+    np.nan,
+    29.899639129639,
+    29.842617034912,
+    30.111154556274,
+    30.007108688354,
+    30.314783096313,
+    30.165269851685,
+    30.555017471313,
+    30.461826324463,
+    30.531579971313,
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+    30.709800720215,
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+    30.986608505249,
+    30.826986312866,
+    31.178184509277,
+    31.047958374023,
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+    220.78759765625,
+    216.16928100586,
+    211.79644775391,
+    213.90396118164,
+    215.59613037109])
+
+resid = np.array([
+    np.nan,
+    -.74963909387589,
+    -.49261617660522,
+    -.74115377664566,
+    -.46710711717606,
+    -.76478433609009,
+    -.41527000069618,
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+    -.53765416145325,
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+    -.6210241317749,
+    -.57753163576126,
+    -.56617534160614,
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+
+results = Bunch(
+    llf=llf,
+    nobs=nobs,
+    k=k,
+    k_exog=k_exog,
+    sigma=sigma,
+    chi2=chi2,
+    df_model=df_model,
+    k_ar=k_ar,
+    k_ma=k_ma,
+    params=params,
+    cov_params=cov_params,
+    xb=xb,
+    y=y,
+    resid=resid,
+    yr=yr,
+    mse=mse,
+    stdp=stdp,
+    icstats=icstats
+)
diff --git a/shared/package-files/0c/0c0df17bfece897a1da7765c822453b09866573028cecced13e2efee02bcccc4/gb12345.enc b/shared/package-files/0c/0c0df17bfece897a1da7765c822453b09866573028cecced13e2efee02bcccc4/gb12345.enc
new file mode 100644
index 0000000000000000000000000000000000000000..3f3f4d254586a07e90d26155dda61b886029fbc4
--- /dev/null
+++ b/shared/package-files/0c/0c0df17bfece897a1da7765c822453b09866573028cecced13e2efee02bcccc4/gb12345.enc
@@ -0,0 +1,1414 @@
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diff --git a/shared/package-files/0c/0c11a425195e3e8ba1e86ee002716cda3a0ec653c88187a7946c79402380e793/Conda.psm1 b/shared/package-files/0c/0c11a425195e3e8ba1e86ee002716cda3a0ec653c88187a7946c79402380e793/Conda.psm1
new file mode 100644
index 0000000000000000000000000000000000000000..9de2a14c683ecf63135c360736590d1cc06924c8
--- /dev/null
+++ b/shared/package-files/0c/0c11a425195e3e8ba1e86ee002716cda3a0ec653c88187a7946c79402380e793/Conda.psm1
@@ -0,0 +1,271 @@
+param([parameter(Position=0,Mandatory=$false)] [Hashtable] $CondaModuleArgs=@{})
+
+# Defaults from before we had arguments.
+if (-not $CondaModuleArgs.ContainsKey('ChangePs1')) {
+    $CondaModuleArgs.ChangePs1 = $True
+}
+
+## ENVIRONMENT MANAGEMENT ######################################################
+
+<#
+    .SYNOPSIS
+        Obtains a list of valid conda environments.
+
+    .EXAMPLE
+        Get-CondaEnvironment
+
+    .EXAMPLE
+        genv
+#>
+function Get-CondaEnvironment {
+    [CmdletBinding()]
+    param();
+
+    begin {}
+
+    process {
+        # NB: the JSON output of conda env list does not include the names
+        #     of each env, so we need to parse the fragile output instead.
+        & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA env list | `
+            Where-Object { -not $_.StartsWith("#") } | `
+            Where-Object { -not $_.Trim().Length -eq 0 } | `
+            ForEach-Object {
+                $envLine = $_ -split "\s+";
+                $Active = $envLine[1] -eq "*";
+                [PSCustomObject] @{
+                    Name = $envLine[0];
+                    Active = $Active;
+                    Path = if ($Active) {$envLine[2]} else {$envLine[1]};
+                } | Write-Output;
+            }
+    }
+
+    end {}
+}
+
+<#
+    .SYNOPSIS
+        Activates a conda environment, placing its commands and packages at
+        the head of $Env:PATH.
+
+    .EXAMPLE
+        Enter-CondaEnvironment base
+
+    .EXAMPLE
+        etenv base
+
+    .NOTES
+        This command does not currently support activating environments stored
+        in a non-standard location.
+#>
+function Enter-CondaEnvironment {
+    [CmdletBinding()]
+    param(
+        [Parameter(Mandatory=$false)][switch]$Stack,
+        [Parameter(Position=0)][string]$Name
+    );
+
+    begin {
+        If ($Stack) {
+            $activateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell activate --stack $Name | Out-String);
+        } Else {
+            $activateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell activate $Name | Out-String);
+        }
+
+        Write-Verbose "[conda shell.powershell activate $Name]`n$activateCommand";
+        Invoke-Expression -Command $activateCommand;
+    }
+
+    process {}
+
+    end {}
+
+}
+
+<#
+    .SYNOPSIS
+        Deactivates the current conda environment, if any.
+
+    .EXAMPLE
+        Exit-CondaEnvironment
+
+    .EXAMPLE
+        exenv
+#>
+function Exit-CondaEnvironment {
+    [CmdletBinding()]
+    param();
+
+    begin {
+        $deactivateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell deactivate | Out-String);
+
+        # If deactivate returns an empty string, we have nothing more to do,
+        # so return early.
+        if ($deactivateCommand.Trim().Length -eq 0) {
+            return;
+        }
+        Write-Verbose "[conda shell.powershell deactivate]`n$deactivateCommand";
+        Invoke-Expression -Command $deactivateCommand;
+    }
+    process {}
+    end {}
+}
+
+## CONDA WRAPPER ###############################################################
+
+<#
+    .SYNOPSIS
+        conda is a tool for managing and deploying applications, environments
+        and packages.
+
+    .PARAMETER Command
+        Subcommand to invoke.
+
+    .EXAMPLE
+        conda install toolz
+#>
+function Invoke-Conda() {
+    # Don't use any explicit args here, we'll use $args and tab completion
+    # so that we can capture everything, INCLUDING short options (e.g. -n).
+    if ($Args.Count -eq 0) {
+        # No args, just call the underlying conda executable.
+        & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA;
+    }
+    else {
+        $Command = $Args[0];
+        if ($Args.Count -ge 2) {
+            $OtherArgs = $Args[1..($Args.Count - 1)];
+        } else {
+            $OtherArgs = @();
+        }
+        switch ($Command) {
+            "activate" {
+                Enter-CondaEnvironment @OtherArgs;
+            }
+            "deactivate" {
+                Exit-CondaEnvironment;
+            }
+
+            default {
+                # There may be a command we don't know want to handle
+                # differently in the shell wrapper, pass it through
+                # verbatim.
+                & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA $Command @OtherArgs;
+            }
+        }
+    }
+}
+
+## TAB COMPLETION ##############################################################
+# We borrow the approach used by posh-git, in which we override any existing
+# functions named TabExpansion, look for commands we can complete on, and then
+# default to the previously defined TabExpansion function for everything else.
+
+if (Test-Path Function:\TabExpansion) {
+    # Since this technique is common, we encounter an infinite loop if it's
+    # used more than once unless we give our backup a unique name.
+    Rename-Item Function:\TabExpansion CondaTabExpansionBackup
+}
+
+function Expand-CondaEnv() {
+    param(
+        [string]
+        $Filter
+    );
+
+    $ValidEnvs = Get-CondaEnvironment;
+    $ValidEnvs `
+        | Where-Object { $_.Name -like "$filter*" } `
+        | ForEach-Object { $_.Name } `
+        | Write-Output;
+    $ValidEnvs `
+        | Where-Object { $_.Path -like "$filter*" } `
+        | ForEach-Object { $_.Path } `
+        | Write-Output;
+
+}
+
+function Expand-CondaSubcommands() {
+    param(
+        [string]
+        $Filter
+    );
+
+    $ValidCommands = Invoke-Conda shell.powershell commands;
+
+    # Add in the commands defined within this wrapper, filter, sort, and return.
+    $ValidCommands + @('activate', 'deactivate') `
+        | Where-Object { $_ -like "$Filter*" } `
+        | Sort-Object `
+        | Write-Output;
+
+}
+
+function TabExpansion($line, $lastWord) {
+    $lastBlock = [regex]::Split($line, '[|;]')[-1].TrimStart()
+
+    switch -regex ($lastBlock) {
+        # Pull out conda commands we recognize first before falling through
+        # to the general patterns for conda itself.
+        "^conda activate (.*)" { Expand-CondaEnv $lastWord; break; }
+        "^etenv (.*)" { Expand-CondaEnv $lastWord; break; }
+
+        # If we got down to here, check arguments to conda itself.
+        "^conda (.*)" { Expand-CondaSubcommands $lastWord; break; }
+
+        # Finally, fall back on existing tab expansion.
+        default {
+            if (Test-Path Function:\CondaTabExpansionBackup) {
+                CondaTabExpansionBackup $line $lastWord
+            }
+        }
+    }
+}
+
+## PROMPT MANAGEMENT ###########################################################
+
+<#
+    .SYNOPSIS
+        Modifies the current prompt to show the currently activated conda
+        environment, if any.
+    .EXAMPLE
+        Add-CondaEnvironmentToPrompt
+
+        Causes the current session's prompt to display the currently activated
+        conda environment.
+#>
+if ($CondaModuleArgs.ChangePs1) {
+    # We use the same procedure to nest prompts as we did for nested tab completion.
+    if (Test-Path Function:\prompt) {
+        Rename-Item Function:\prompt CondaPromptBackup
+    } else {
+        function CondaPromptBackup() {
+            # Restore a basic prompt if the definition is missing.
+            "PS $($executionContext.SessionState.Path.CurrentLocation)$('>' * ($nestedPromptLevel + 1)) ";
+        }
+    }
+
+    function global:prompt() {
+        if ($Env:CONDA_PROMPT_MODIFIER) {
+            $Env:CONDA_PROMPT_MODIFIER | Write-Host -NoNewline
+        }
+        CondaPromptBackup;
+    }
+}
+
+## ALIASES #####################################################################
+
+New-Alias conda Invoke-Conda -Force
+New-Alias genv Get-CondaEnvironment -Force
+New-Alias etenv Enter-CondaEnvironment -Force
+New-Alias exenv Exit-CondaEnvironment -Force
+
+## EXPORTS ###################################################################
+
+Export-ModuleMember `
+    -Alias * `
+    -Function `
+        Invoke-Conda, `
+        Get-CondaEnvironment, `
+        Enter-CondaEnvironment, Exit-CondaEnvironment, `
+        TabExpansion, prompt
diff --git a/shared/package-files/0c/0c12ae80850415330fee0ea22f56e4f7ce84ea5742adc35d04258e25b4256bf6/test_function_base.py b/shared/package-files/0c/0c12ae80850415330fee0ea22f56e4f7ce84ea5742adc35d04258e25b4256bf6/test_function_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bb73b600380a99c6754f8c0a4fe350c0a9068c4
--- /dev/null
+++ b/shared/package-files/0c/0c12ae80850415330fee0ea22f56e4f7ce84ea5742adc35d04258e25b4256bf6/test_function_base.py
@@ -0,0 +1,4201 @@
+import operator
+import warnings
+import sys
+import decimal
+from fractions import Fraction
+import math
+import pytest
+import hypothesis
+from hypothesis.extra.numpy import arrays
+import hypothesis.strategies as st
+from functools import partial
+
+import numpy as np
+from numpy import ma
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_raises, assert_allclose, IS_PYPY,
+    assert_warns, assert_raises_regex, suppress_warnings, HAS_REFCOUNT, IS_WASM
+    )
+import numpy.lib.function_base as nfb
+from numpy.random import rand
+from numpy.lib import (
+    add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov,
+    delete, diff, digitize, extract, flipud, gradient, hamming, hanning,
+    i0, insert, interp, kaiser, meshgrid, msort, piecewise, place, rot90,
+    select, setxor1d, sinc, trapz, trim_zeros, unwrap, unique, vectorize
+    )
+from numpy.core.numeric import normalize_axis_tuple
+
+
+def get_mat(n):
+    data = np.arange(n)
+    data = np.add.outer(data, data)
+    return data
+
+
+def _make_complex(real, imag):
+    """
+    Like real + 1j * imag, but behaves as expected when imag contains non-finite
+    values
+    """
+    ret = np.zeros(np.broadcast(real, imag).shape, np.complex_)
+    ret.real = real
+    ret.imag = imag
+    return ret
+
+
+class TestRot90:
+    def test_basic(self):
+        assert_raises(ValueError, rot90, np.ones(4))
+        assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(0,1,2))
+        assert_raises(ValueError, rot90, np.ones((2,2)), axes=(0,2))
+        assert_raises(ValueError, rot90, np.ones((2,2)), axes=(1,1))
+        assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(-2,1))
+
+        a = [[0, 1, 2],
+             [3, 4, 5]]
+        b1 = [[2, 5],
+              [1, 4],
+              [0, 3]]
+        b2 = [[5, 4, 3],
+              [2, 1, 0]]
+        b3 = [[3, 0],
+              [4, 1],
+              [5, 2]]
+        b4 = [[0, 1, 2],
+              [3, 4, 5]]
+
+        for k in range(-3, 13, 4):
+            assert_equal(rot90(a, k=k), b1)
+        for k in range(-2, 13, 4):
+            assert_equal(rot90(a, k=k), b2)
+        for k in range(-1, 13, 4):
+            assert_equal(rot90(a, k=k), b3)
+        for k in range(0, 13, 4):
+            assert_equal(rot90(a, k=k), b4)
+
+        assert_equal(rot90(rot90(a, axes=(0,1)), axes=(1,0)), a)
+        assert_equal(rot90(a, k=1, axes=(1,0)), rot90(a, k=-1, axes=(0,1)))
+
+    def test_axes(self):
+        a = np.ones((50, 40, 3))
+        assert_equal(rot90(a).shape, (40, 50, 3))
+        assert_equal(rot90(a, axes=(0,2)), rot90(a, axes=(0,-1)))
+        assert_equal(rot90(a, axes=(1,2)), rot90(a, axes=(-2,-1)))
+
+    def test_rotation_axes(self):
+        a = np.arange(8).reshape((2,2,2))
+
+        a_rot90_01 = [[[2, 3],
+                       [6, 7]],
+                      [[0, 1],
+                       [4, 5]]]
+        a_rot90_12 = [[[1, 3],
+                       [0, 2]],
+                      [[5, 7],
+                       [4, 6]]]
+        a_rot90_20 = [[[4, 0],
+                       [6, 2]],
+                      [[5, 1],
+                       [7, 3]]]
+        a_rot90_10 = [[[4, 5],
+                       [0, 1]],
+                      [[6, 7],
+                       [2, 3]]]
+
+        assert_equal(rot90(a, axes=(0, 1)), a_rot90_01)
+        assert_equal(rot90(a, axes=(1, 0)), a_rot90_10)
+        assert_equal(rot90(a, axes=(1, 2)), a_rot90_12)
+
+        for k in range(1,5):
+            assert_equal(rot90(a, k=k, axes=(2, 0)),
+                         rot90(a_rot90_20, k=k-1, axes=(2, 0)))
+
+
+class TestFlip:
+
+    def test_axes(self):
+        assert_raises(np.AxisError, np.flip, np.ones(4), axis=1)
+        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=2)
+        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=-3)
+        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=(0, 3))
+
+    def test_basic_lr(self):
+        a = get_mat(4)
+        b = a[:, ::-1]
+        assert_equal(np.flip(a, 1), b)
+        a = [[0, 1, 2],
+             [3, 4, 5]]
+        b = [[2, 1, 0],
+             [5, 4, 3]]
+        assert_equal(np.flip(a, 1), b)
+
+    def test_basic_ud(self):
+        a = get_mat(4)
+        b = a[::-1, :]
+        assert_equal(np.flip(a, 0), b)
+        a = [[0, 1, 2],
+             [3, 4, 5]]
+        b = [[3, 4, 5],
+             [0, 1, 2]]
+        assert_equal(np.flip(a, 0), b)
+
+    def test_3d_swap_axis0(self):
+        a = np.array([[[0, 1],
+                       [2, 3]],
+                      [[4, 5],
+                       [6, 7]]])
+
+        b = np.array([[[4, 5],
+                       [6, 7]],
+                      [[0, 1],
+                       [2, 3]]])
+
+        assert_equal(np.flip(a, 0), b)
+
+    def test_3d_swap_axis1(self):
+        a = np.array([[[0, 1],
+                       [2, 3]],
+                      [[4, 5],
+                       [6, 7]]])
+
+        b = np.array([[[2, 3],
+                       [0, 1]],
+                      [[6, 7],
+                       [4, 5]]])
+
+        assert_equal(np.flip(a, 1), b)
+
+    def test_3d_swap_axis2(self):
+        a = np.array([[[0, 1],
+                       [2, 3]],
+                      [[4, 5],
+                       [6, 7]]])
+
+        b = np.array([[[1, 0],
+                       [3, 2]],
+                      [[5, 4],
+                       [7, 6]]])
+
+        assert_equal(np.flip(a, 2), b)
+
+    def test_4d(self):
+        a = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
+        for i in range(a.ndim):
+            assert_equal(np.flip(a, i),
+                         np.flipud(a.swapaxes(0, i)).swapaxes(i, 0))
+
+    def test_default_axis(self):
+        a = np.array([[1, 2, 3],
+                      [4, 5, 6]])
+        b = np.array([[6, 5, 4],
+                      [3, 2, 1]])
+        assert_equal(np.flip(a), b)
+
+    def test_multiple_axes(self):
+        a = np.array([[[0, 1],
+                       [2, 3]],
+                      [[4, 5],
+                       [6, 7]]])
+
+        assert_equal(np.flip(a, axis=()), a)
+
+        b = np.array([[[5, 4],
+                       [7, 6]],
+                      [[1, 0],
+                       [3, 2]]])
+
+        assert_equal(np.flip(a, axis=(0, 2)), b)
+
+        c = np.array([[[3, 2],
+                       [1, 0]],
+                      [[7, 6],
+                       [5, 4]]])
+
+        assert_equal(np.flip(a, axis=(1, 2)), c)
+
+
+class TestAny:
+
+    def test_basic(self):
+        y1 = [0, 0, 1, 0]
+        y2 = [0, 0, 0, 0]
+        y3 = [1, 0, 1, 0]
+        assert_(np.any(y1))
+        assert_(np.any(y3))
+        assert_(not np.any(y2))
+
+    def test_nd(self):
+        y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
+        assert_(np.any(y1))
+        assert_array_equal(np.any(y1, axis=0), [1, 1, 0])
+        assert_array_equal(np.any(y1, axis=1), [0, 1, 1])
+
+
+class TestAll:
+
+    def test_basic(self):
+        y1 = [0, 1, 1, 0]
+        y2 = [0, 0, 0, 0]
+        y3 = [1, 1, 1, 1]
+        assert_(not np.all(y1))
+        assert_(np.all(y3))
+        assert_(not np.all(y2))
+        assert_(np.all(~np.array(y2)))
+
+    def test_nd(self):
+        y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
+        assert_(not np.all(y1))
+        assert_array_equal(np.all(y1, axis=0), [0, 0, 1])
+        assert_array_equal(np.all(y1, axis=1), [0, 0, 1])
+
+
+class TestCopy:
+
+    def test_basic(self):
+        a = np.array([[1, 2], [3, 4]])
+        a_copy = np.copy(a)
+        assert_array_equal(a, a_copy)
+        a_copy[0, 0] = 10
+        assert_equal(a[0, 0], 1)
+        assert_equal(a_copy[0, 0], 10)
+
+    def test_order(self):
+        # It turns out that people rely on np.copy() preserving order by
+        # default; changing this broke scikit-learn:
+        # github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783  # noqa
+        a = np.array([[1, 2], [3, 4]])
+        assert_(a.flags.c_contiguous)
+        assert_(not a.flags.f_contiguous)
+        a_fort = np.array([[1, 2], [3, 4]], order="F")
+        assert_(not a_fort.flags.c_contiguous)
+        assert_(a_fort.flags.f_contiguous)
+        a_copy = np.copy(a)
+        assert_(a_copy.flags.c_contiguous)
+        assert_(not a_copy.flags.f_contiguous)
+        a_fort_copy = np.copy(a_fort)
+        assert_(not a_fort_copy.flags.c_contiguous)
+        assert_(a_fort_copy.flags.f_contiguous)
+
+    def test_subok(self):
+        mx = ma.ones(5)
+        assert_(not ma.isMaskedArray(np.copy(mx, subok=False)))
+        assert_(ma.isMaskedArray(np.copy(mx, subok=True)))
+        # Default behavior
+        assert_(not ma.isMaskedArray(np.copy(mx)))
+
+
+class TestAverage:
+
+    def test_basic(self):
+        y1 = np.array([1, 2, 3])
+        assert_(average(y1, axis=0) == 2.)
+        y2 = np.array([1., 2., 3.])
+        assert_(average(y2, axis=0) == 2.)
+        y3 = [0., 0., 0.]
+        assert_(average(y3, axis=0) == 0.)
+
+        y4 = np.ones((4, 4))
+        y4[0, 1] = 0
+        y4[1, 0] = 2
+        assert_almost_equal(y4.mean(0), average(y4, 0))
+        assert_almost_equal(y4.mean(1), average(y4, 1))
+
+        y5 = rand(5, 5)
+        assert_almost_equal(y5.mean(0), average(y5, 0))
+        assert_almost_equal(y5.mean(1), average(y5, 1))
+
+    @pytest.mark.parametrize(
+        'x, axis, expected_avg, weights, expected_wavg, expected_wsum',
+        [([1, 2, 3], None, [2.0], [3, 4, 1], [1.75], [8.0]),
+         ([[1, 2, 5], [1, 6, 11]], 0, [[1.0, 4.0, 8.0]],
+          [1, 3], [[1.0, 5.0, 9.5]], [[4, 4, 4]])],
+    )
+    def test_basic_keepdims(self, x, axis, expected_avg,
+                            weights, expected_wavg, expected_wsum):
+        avg = np.average(x, axis=axis, keepdims=True)
+        assert avg.shape == np.shape(expected_avg)
+        assert_array_equal(avg, expected_avg)
+
+        wavg = np.average(x, axis=axis, weights=weights, keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+
+        wavg, wsum = np.average(x, axis=axis, weights=weights, returned=True,
+                                keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+        assert wsum.shape == np.shape(expected_wsum)
+        assert_array_equal(wsum, expected_wsum)
+
+    def test_weights(self):
+        y = np.arange(10)
+        w = np.arange(10)
+        actual = average(y, weights=w)
+        desired = (np.arange(10) ** 2).sum() * 1. / np.arange(10).sum()
+        assert_almost_equal(actual, desired)
+
+        y1 = np.array([[1, 2, 3], [4, 5, 6]])
+        w0 = [1, 2]
+        actual = average(y1, weights=w0, axis=0)
+        desired = np.array([3., 4., 5.])
+        assert_almost_equal(actual, desired)
+
+        w1 = [0, 0, 1]
+        actual = average(y1, weights=w1, axis=1)
+        desired = np.array([3., 6.])
+        assert_almost_equal(actual, desired)
+
+        # This should raise an error. Can we test for that ?
+        # assert_equal(average(y1, weights=w1), 9./2.)
+
+        # 2D Case
+        w2 = [[0, 0, 1], [0, 0, 2]]
+        desired = np.array([3., 6.])
+        assert_array_equal(average(y1, weights=w2, axis=1), desired)
+        assert_equal(average(y1, weights=w2), 5.)
+
+        y3 = rand(5).astype(np.float32)
+        w3 = rand(5).astype(np.float64)
+
+        assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3))
+
+        # test weights with `keepdims=False` and `keepdims=True`
+        x = np.array([2, 3, 4]).reshape(3, 1)
+        w = np.array([4, 5, 6]).reshape(3, 1)
+
+        actual = np.average(x, weights=w, axis=1, keepdims=False)
+        desired = np.array([2., 3., 4.])
+        assert_array_equal(actual, desired)
+
+        actual = np.average(x, weights=w, axis=1, keepdims=True)
+        desired = np.array([[2.], [3.], [4.]])
+        assert_array_equal(actual, desired)
+
+    def test_returned(self):
+        y = np.array([[1, 2, 3], [4, 5, 6]])
+
+        # No weights
+        avg, scl = average(y, returned=True)
+        assert_equal(scl, 6.)
+
+        avg, scl = average(y, 0, returned=True)
+        assert_array_equal(scl, np.array([2., 2., 2.]))
+
+        avg, scl = average(y, 1, returned=True)
+        assert_array_equal(scl, np.array([3., 3.]))
+
+        # With weights
+        w0 = [1, 2]
+        avg, scl = average(y, weights=w0, axis=0, returned=True)
+        assert_array_equal(scl, np.array([3., 3., 3.]))
+
+        w1 = [1, 2, 3]
+        avg, scl = average(y, weights=w1, axis=1, returned=True)
+        assert_array_equal(scl, np.array([6., 6.]))
+
+        w2 = [[0, 0, 1], [1, 2, 3]]
+        avg, scl = average(y, weights=w2, axis=1, returned=True)
+        assert_array_equal(scl, np.array([1., 6.]))
+
+    def test_subclasses(self):
+        class subclass(np.ndarray):
+            pass
+        a = np.array([[1,2],[3,4]]).view(subclass)
+        w = np.array([[1,2],[3,4]]).view(subclass)
+
+        assert_equal(type(np.average(a)), subclass)
+        assert_equal(type(np.average(a, weights=w)), subclass)
+
+    def test_upcasting(self):
+        typs = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
+                 ('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')]
+        for at, wt, rt in typs:
+            a = np.array([[1,2],[3,4]], dtype=at)
+            w = np.array([[1,2],[3,4]], dtype=wt)
+            assert_equal(np.average(a, weights=w).dtype, np.dtype(rt))
+
+    def test_object_dtype(self):
+        a = np.array([decimal.Decimal(x) for x in range(10)])
+        w = np.array([decimal.Decimal(1) for _ in range(10)])
+        w /= w.sum()
+        assert_almost_equal(a.mean(0), average(a, weights=w))
+
+    def test_average_class_without_dtype(self):
+        # see gh-21988
+        a = np.array([Fraction(1, 5), Fraction(3, 5)])
+        assert_equal(np.average(a), Fraction(2, 5))
+
+class TestSelect:
+    choices = [np.array([1, 2, 3]),
+               np.array([4, 5, 6]),
+               np.array([7, 8, 9])]
+    conditions = [np.array([False, False, False]),
+                  np.array([False, True, False]),
+                  np.array([False, False, True])]
+
+    def _select(self, cond, values, default=0):
+        output = []
+        for m in range(len(cond)):
+            output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
+        return output
+
+    def test_basic(self):
+        choices = self.choices
+        conditions = self.conditions
+        assert_array_equal(select(conditions, choices, default=15),
+                           self._select(conditions, choices, default=15))
+
+        assert_equal(len(choices), 3)
+        assert_equal(len(conditions), 3)
+
+    def test_broadcasting(self):
+        conditions = [np.array(True), np.array([False, True, False])]
+        choices = [1, np.arange(12).reshape(4, 3)]
+        assert_array_equal(select(conditions, choices), np.ones((4, 3)))
+        # default can broadcast too:
+        assert_equal(select([True], [0], default=[0]).shape, (1,))
+
+    def test_return_dtype(self):
+        assert_equal(select(self.conditions, self.choices, 1j).dtype,
+                     np.complex_)
+        # But the conditions need to be stronger then the scalar default
+        # if it is scalar.
+        choices = [choice.astype(np.int8) for choice in self.choices]
+        assert_equal(select(self.conditions, choices).dtype, np.int8)
+
+        d = np.array([1, 2, 3, np.nan, 5, 7])
+        m = np.isnan(d)
+        assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0])
+
+    def test_deprecated_empty(self):
+        assert_raises(ValueError, select, [], [], 3j)
+        assert_raises(ValueError, select, [], [])
+
+    def test_non_bool_deprecation(self):
+        choices = self.choices
+        conditions = self.conditions[:]
+        conditions[0] = conditions[0].astype(np.int_)
+        assert_raises(TypeError, select, conditions, choices)
+        conditions[0] = conditions[0].astype(np.uint8)
+        assert_raises(TypeError, select, conditions, choices)
+        assert_raises(TypeError, select, conditions, choices)
+
+    def test_many_arguments(self):
+        # This used to be limited by NPY_MAXARGS == 32
+        conditions = [np.array([False])] * 100
+        choices = [np.array([1])] * 100
+        select(conditions, choices)
+
+
+class TestInsert:
+
+    def test_basic(self):
+        a = [1, 2, 3]
+        assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
+        assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
+        assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
+        assert_equal(insert(a, 1, [1, 2, 3]), [1, 1, 2, 3, 2, 3])
+        assert_equal(insert(a, [1, -1, 3], 9), [1, 9, 2, 9, 3, 9])
+        assert_equal(insert(a, slice(-1, None, -1), 9), [9, 1, 9, 2, 9, 3])
+        assert_equal(insert(a, [-1, 1, 3], [7, 8, 9]), [1, 8, 2, 7, 3, 9])
+        b = np.array([0, 1], dtype=np.float64)
+        assert_equal(insert(b, 0, b[0]), [0., 0., 1.])
+        assert_equal(insert(b, [], []), b)
+        # Bools will be treated differently in the future:
+        # assert_equal(insert(a, np.array([True]*4), 9), [9, 1, 9, 2, 9, 3, 9])
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', FutureWarning)
+            assert_equal(
+                insert(a, np.array([True] * 4), 9), [1, 9, 9, 9, 9, 2, 3])
+            assert_(w[0].category is FutureWarning)
+
+    def test_multidim(self):
+        a = [[1, 1, 1]]
+        r = [[2, 2, 2],
+             [1, 1, 1]]
+        assert_equal(insert(a, 0, [1]), [1, 1, 1, 1])
+        assert_equal(insert(a, 0, [2, 2, 2], axis=0), r)
+        assert_equal(insert(a, 0, 2, axis=0), r)
+        assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]])
+
+        a = np.array([[1, 1], [2, 2], [3, 3]])
+        b = np.arange(1, 4).repeat(3).reshape(3, 3)
+        c = np.concatenate(
+            (a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T,
+             a[:, 1:2]), axis=1)
+        assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b)
+        assert_equal(insert(a, [1], [1, 2, 3], axis=1), c)
+        # scalars behave differently, in this case exactly opposite:
+        assert_equal(insert(a, 1, [1, 2, 3], axis=1), b)
+        assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c)
+
+        a = np.arange(4).reshape(2, 2)
+        assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a)
+        assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a)
+
+        # negative axis value
+        a = np.arange(24).reshape((2, 3, 4))
+        assert_equal(insert(a, 1, a[:,:, 3], axis=-1),
+                     insert(a, 1, a[:,:, 3], axis=2))
+        assert_equal(insert(a, 1, a[:, 2,:], axis=-2),
+                     insert(a, 1, a[:, 2,:], axis=1))
+
+        # invalid axis value
+        assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=3)
+        assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=-4)
+
+        # negative axis value
+        a = np.arange(24).reshape((2, 3, 4))
+        assert_equal(insert(a, 1, a[:, :, 3], axis=-1),
+                     insert(a, 1, a[:, :, 3], axis=2))
+        assert_equal(insert(a, 1, a[:, 2, :], axis=-2),
+                     insert(a, 1, a[:, 2, :], axis=1))
+
+    def test_0d(self):
+        a = np.array(1)
+        with pytest.raises(np.AxisError):
+            insert(a, [], 2, axis=0)
+        with pytest.raises(TypeError):
+            insert(a, [], 2, axis="nonsense")
+
+    def test_subclass(self):
+        class SubClass(np.ndarray):
+            pass
+        a = np.arange(10).view(SubClass)
+        assert_(isinstance(np.insert(a, 0, [0]), SubClass))
+        assert_(isinstance(np.insert(a, [], []), SubClass))
+        assert_(isinstance(np.insert(a, [0, 1], [1, 2]), SubClass))
+        assert_(isinstance(np.insert(a, slice(1, 2), [1, 2]), SubClass))
+        assert_(isinstance(np.insert(a, slice(1, -2, -1), []), SubClass))
+        # This is an error in the future:
+        a = np.array(1).view(SubClass)
+        assert_(isinstance(np.insert(a, 0, [0]), SubClass))
+
+    def test_index_array_copied(self):
+        x = np.array([1, 1, 1])
+        np.insert([0, 1, 2], x, [3, 4, 5])
+        assert_equal(x, np.array([1, 1, 1]))
+
+    def test_structured_array(self):
+        a = np.array([(1, 'a'), (2, 'b'), (3, 'c')],
+                     dtype=[('foo', 'i'), ('bar', 'a1')])
+        val = (4, 'd')
+        b = np.insert(a, 0, val)
+        assert_array_equal(b[0], np.array(val, dtype=b.dtype))
+        val = [(4, 'd')] * 2
+        b = np.insert(a, [0, 2], val)
+        assert_array_equal(b[[0, 3]], np.array(val, dtype=b.dtype))
+
+    def test_index_floats(self):
+        with pytest.raises(IndexError):
+            np.insert([0, 1, 2], np.array([1.0, 2.0]), [10, 20])
+        with pytest.raises(IndexError):
+            np.insert([0, 1, 2], np.array([], dtype=float), [])
+
+    @pytest.mark.parametrize('idx', [4, -4])
+    def test_index_out_of_bounds(self, idx):
+        with pytest.raises(IndexError, match='out of bounds'):
+            np.insert([0, 1, 2], [idx], [3, 4])
+
+
+class TestAmax:
+
+    def test_basic(self):
+        a = [3, 4, 5, 10, -3, -5, 6.0]
+        assert_equal(np.amax(a), 10.0)
+        b = [[3, 6.0, 9.0],
+             [4, 10.0, 5.0],
+             [8, 3.0, 2.0]]
+        assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0])
+        assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0])
+
+
+class TestAmin:
+
+    def test_basic(self):
+        a = [3, 4, 5, 10, -3, -5, 6.0]
+        assert_equal(np.amin(a), -5.0)
+        b = [[3, 6.0, 9.0],
+             [4, 10.0, 5.0],
+             [8, 3.0, 2.0]]
+        assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0])
+        assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0])
+
+
+class TestPtp:
+
+    def test_basic(self):
+        a = np.array([3, 4, 5, 10, -3, -5, 6.0])
+        assert_equal(a.ptp(axis=0), 15.0)
+        b = np.array([[3, 6.0, 9.0],
+                      [4, 10.0, 5.0],
+                      [8, 3.0, 2.0]])
+        assert_equal(b.ptp(axis=0), [5.0, 7.0, 7.0])
+        assert_equal(b.ptp(axis=-1), [6.0, 6.0, 6.0])
+
+        assert_equal(b.ptp(axis=0, keepdims=True), [[5.0, 7.0, 7.0]])
+        assert_equal(b.ptp(axis=(0,1), keepdims=True), [[8.0]])
+
+
+class TestCumsum:
+
+    def test_basic(self):
+        ba = [1, 2, 10, 11, 6, 5, 4]
+        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+        for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
+                      np.uint32, np.float32, np.float64, np.complex64,
+                      np.complex128]:
+            a = np.array(ba, ctype)
+            a2 = np.array(ba2, ctype)
+
+            tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
+            assert_array_equal(np.cumsum(a, axis=0), tgt)
+
+            tgt = np.array(
+                [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
+            assert_array_equal(np.cumsum(a2, axis=0), tgt)
+
+            tgt = np.array(
+                [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
+            assert_array_equal(np.cumsum(a2, axis=1), tgt)
+
+
+class TestProd:
+
+    def test_basic(self):
+        ba = [1, 2, 10, 11, 6, 5, 4]
+        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
+                      np.float32, np.float64, np.complex64, np.complex128]:
+            a = np.array(ba, ctype)
+            a2 = np.array(ba2, ctype)
+            if ctype in ['1', 'b']:
+                assert_raises(ArithmeticError, np.prod, a)
+                assert_raises(ArithmeticError, np.prod, a2, 1)
+            else:
+                assert_equal(a.prod(axis=0), 26400)
+                assert_array_equal(a2.prod(axis=0),
+                                   np.array([50, 36, 84, 180], ctype))
+                assert_array_equal(a2.prod(axis=-1),
+                                   np.array([24, 1890, 600], ctype))
+
+
+class TestCumprod:
+
+    def test_basic(self):
+        ba = [1, 2, 10, 11, 6, 5, 4]
+        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
+                      np.float32, np.float64, np.complex64, np.complex128]:
+            a = np.array(ba, ctype)
+            a2 = np.array(ba2, ctype)
+            if ctype in ['1', 'b']:
+                assert_raises(ArithmeticError, np.cumprod, a)
+                assert_raises(ArithmeticError, np.cumprod, a2, 1)
+                assert_raises(ArithmeticError, np.cumprod, a)
+            else:
+                assert_array_equal(np.cumprod(a, axis=-1),
+                                   np.array([1, 2, 20, 220,
+                                             1320, 6600, 26400], ctype))
+                assert_array_equal(np.cumprod(a2, axis=0),
+                                   np.array([[1, 2, 3, 4],
+                                             [5, 12, 21, 36],
+                                             [50, 36, 84, 180]], ctype))
+                assert_array_equal(np.cumprod(a2, axis=-1),
+                                   np.array([[1, 2, 6, 24],
+                                             [5, 30, 210, 1890],
+                                             [10, 30, 120, 600]], ctype))
+
+
+class TestDiff:
+
+    def test_basic(self):
+        x = [1, 4, 6, 7, 12]
+        out = np.array([3, 2, 1, 5])
+        out2 = np.array([-1, -1, 4])
+        out3 = np.array([0, 5])
+        assert_array_equal(diff(x), out)
+        assert_array_equal(diff(x, n=2), out2)
+        assert_array_equal(diff(x, n=3), out3)
+
+        x = [1.1, 2.2, 3.0, -0.2, -0.1]
+        out = np.array([1.1, 0.8, -3.2, 0.1])
+        assert_almost_equal(diff(x), out)
+
+        x = [True, True, False, False]
+        out = np.array([False, True, False])
+        out2 = np.array([True, True])
+        assert_array_equal(diff(x), out)
+        assert_array_equal(diff(x, n=2), out2)
+
+    def test_axis(self):
+        x = np.zeros((10, 20, 30))
+        x[:, 1::2, :] = 1
+        exp = np.ones((10, 19, 30))
+        exp[:, 1::2, :] = -1
+        assert_array_equal(diff(x), np.zeros((10, 20, 29)))
+        assert_array_equal(diff(x, axis=-1), np.zeros((10, 20, 29)))
+        assert_array_equal(diff(x, axis=0), np.zeros((9, 20, 30)))
+        assert_array_equal(diff(x, axis=1), exp)
+        assert_array_equal(diff(x, axis=-2), exp)
+        assert_raises(np.AxisError, diff, x, axis=3)
+        assert_raises(np.AxisError, diff, x, axis=-4)
+
+        x = np.array(1.11111111111, np.float64)
+        assert_raises(ValueError, diff, x)
+
+    def test_nd(self):
+        x = 20 * rand(10, 20, 30)
+        out1 = x[:, :, 1:] - x[:, :, :-1]
+        out2 = out1[:, :, 1:] - out1[:, :, :-1]
+        out3 = x[1:, :, :] - x[:-1, :, :]
+        out4 = out3[1:, :, :] - out3[:-1, :, :]
+        assert_array_equal(diff(x), out1)
+        assert_array_equal(diff(x, n=2), out2)
+        assert_array_equal(diff(x, axis=0), out3)
+        assert_array_equal(diff(x, n=2, axis=0), out4)
+
+    def test_n(self):
+        x = list(range(3))
+        assert_raises(ValueError, diff, x, n=-1)
+        output = [diff(x, n=n) for n in range(1, 5)]
+        expected = [[1, 1], [0], [], []]
+        assert_(diff(x, n=0) is x)
+        for n, (expected, out) in enumerate(zip(expected, output), start=1):
+            assert_(type(out) is np.ndarray)
+            assert_array_equal(out, expected)
+            assert_equal(out.dtype, np.int_)
+            assert_equal(len(out), max(0, len(x) - n))
+
+    def test_times(self):
+        x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
+        expected = [
+            np.array([1, 1], dtype='timedelta64[D]'),
+            np.array([0], dtype='timedelta64[D]'),
+        ]
+        expected.extend([np.array([], dtype='timedelta64[D]')] * 3)
+        for n, exp in enumerate(expected, start=1):
+            out = diff(x, n=n)
+            assert_array_equal(out, exp)
+            assert_equal(out.dtype, exp.dtype)
+
+    def test_subclass(self):
+        x = ma.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]],
+                     mask=[[False, False], [True, False],
+                           [False, True], [True, True], [False, False]])
+        out = diff(x)
+        assert_array_equal(out.data, [[1], [1], [1], [1], [1]])
+        assert_array_equal(out.mask, [[False], [True],
+                                      [True], [True], [False]])
+        assert_(type(out) is type(x))
+
+        out3 = diff(x, n=3)
+        assert_array_equal(out3.data, [[], [], [], [], []])
+        assert_array_equal(out3.mask, [[], [], [], [], []])
+        assert_(type(out3) is type(x))
+
+    def test_prepend(self):
+        x = np.arange(5) + 1
+        assert_array_equal(diff(x, prepend=0), np.ones(5))
+        assert_array_equal(diff(x, prepend=[0]), np.ones(5))
+        assert_array_equal(np.cumsum(np.diff(x, prepend=0)), x)
+        assert_array_equal(diff(x, prepend=[-1, 0]), np.ones(6))
+
+        x = np.arange(4).reshape(2, 2)
+        result = np.diff(x, axis=1, prepend=0)
+        expected = [[0, 1], [2, 1]]
+        assert_array_equal(result, expected)
+        result = np.diff(x, axis=1, prepend=[[0], [0]])
+        assert_array_equal(result, expected)
+
+        result = np.diff(x, axis=0, prepend=0)
+        expected = [[0, 1], [2, 2]]
+        assert_array_equal(result, expected)
+        result = np.diff(x, axis=0, prepend=[[0, 0]])
+        assert_array_equal(result, expected)
+
+        assert_raises(ValueError, np.diff, x, prepend=np.zeros((3,3)))
+
+        assert_raises(np.AxisError, diff, x, prepend=0, axis=3)
+
+    def test_append(self):
+        x = np.arange(5)
+        result = diff(x, append=0)
+        expected = [1, 1, 1, 1, -4]
+        assert_array_equal(result, expected)
+        result = diff(x, append=[0])
+        assert_array_equal(result, expected)
+        result = diff(x, append=[0, 2])
+        expected = expected + [2]
+        assert_array_equal(result, expected)
+
+        x = np.arange(4).reshape(2, 2)
+        result = np.diff(x, axis=1, append=0)
+        expected = [[1, -1], [1, -3]]
+        assert_array_equal(result, expected)
+        result = np.diff(x, axis=1, append=[[0], [0]])
+        assert_array_equal(result, expected)
+
+        result = np.diff(x, axis=0, append=0)
+        expected = [[2, 2], [-2, -3]]
+        assert_array_equal(result, expected)
+        result = np.diff(x, axis=0, append=[[0, 0]])
+        assert_array_equal(result, expected)
+
+        assert_raises(ValueError, np.diff, x, append=np.zeros((3,3)))
+
+        assert_raises(np.AxisError, diff, x, append=0, axis=3)
+
+
+class TestDelete:
+
+    def setup_method(self):
+        self.a = np.arange(5)
+        self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2)
+
+    def _check_inverse_of_slicing(self, indices):
+        a_del = delete(self.a, indices)
+        nd_a_del = delete(self.nd_a, indices, axis=1)
+        msg = 'Delete failed for obj: %r' % indices
+        assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a,
+                           err_msg=msg)
+        xor = setxor1d(nd_a_del[0,:, 0], self.nd_a[0, indices, 0])
+        assert_array_equal(xor, self.nd_a[0,:, 0], err_msg=msg)
+
+    def test_slices(self):
+        lims = [-6, -2, 0, 1, 2, 4, 5]
+        steps = [-3, -1, 1, 3]
+        for start in lims:
+            for stop in lims:
+                for step in steps:
+                    s = slice(start, stop, step)
+                    self._check_inverse_of_slicing(s)
+
+    def test_fancy(self):
+        self._check_inverse_of_slicing(np.array([[0, 1], [2, 1]]))
+        with pytest.raises(IndexError):
+            delete(self.a, [100])
+        with pytest.raises(IndexError):
+            delete(self.a, [-100])
+
+        self._check_inverse_of_slicing([0, -1, 2, 2])
+
+        self._check_inverse_of_slicing([True, False, False, True, False])
+
+        # not legal, indexing with these would change the dimension
+        with pytest.raises(ValueError):
+            delete(self.a, True)
+        with pytest.raises(ValueError):
+            delete(self.a, False)
+
+        # not enough items
+        with pytest.raises(ValueError):
+            delete(self.a, [False]*4)
+
+    def test_single(self):
+        self._check_inverse_of_slicing(0)
+        self._check_inverse_of_slicing(-4)
+
+    def test_0d(self):
+        a = np.array(1)
+        with pytest.raises(np.AxisError):
+            delete(a, [], axis=0)
+        with pytest.raises(TypeError):
+            delete(a, [], axis="nonsense")
+
+    def test_subclass(self):
+        class SubClass(np.ndarray):
+            pass
+        a = self.a.view(SubClass)
+        assert_(isinstance(delete(a, 0), SubClass))
+        assert_(isinstance(delete(a, []), SubClass))
+        assert_(isinstance(delete(a, [0, 1]), SubClass))
+        assert_(isinstance(delete(a, slice(1, 2)), SubClass))
+        assert_(isinstance(delete(a, slice(1, -2)), SubClass))
+
+    def test_array_order_preserve(self):
+        # See gh-7113
+        k = np.arange(10).reshape(2, 5, order='F')
+        m = delete(k, slice(60, None), axis=1)
+
+        # 'k' is Fortran ordered, and 'm' should have the
+        # same ordering as 'k' and NOT become C ordered
+        assert_equal(m.flags.c_contiguous, k.flags.c_contiguous)
+        assert_equal(m.flags.f_contiguous, k.flags.f_contiguous)
+
+    def test_index_floats(self):
+        with pytest.raises(IndexError):
+            np.delete([0, 1, 2], np.array([1.0, 2.0]))
+        with pytest.raises(IndexError):
+            np.delete([0, 1, 2], np.array([], dtype=float))
+
+    @pytest.mark.parametrize("indexer", [np.array([1]), [1]])
+    def test_single_item_array(self, indexer):
+        a_del_int = delete(self.a, 1)
+        a_del = delete(self.a, indexer)
+        assert_equal(a_del_int, a_del)
+
+        nd_a_del_int = delete(self.nd_a, 1, axis=1)
+        nd_a_del = delete(self.nd_a, np.array([1]), axis=1)
+        assert_equal(nd_a_del_int, nd_a_del)
+
+    def test_single_item_array_non_int(self):
+        # Special handling for integer arrays must not affect non-integer ones.
+        # If `False` was cast to `0` it would delete the element:
+        res = delete(np.ones(1), np.array([False]))
+        assert_array_equal(res, np.ones(1))
+
+        # Test the more complicated (with axis) case from gh-21840
+        x = np.ones((3, 1))
+        false_mask = np.array([False], dtype=bool)
+        true_mask = np.array([True], dtype=bool)
+
+        res = delete(x, false_mask, axis=-1)
+        assert_array_equal(res, x)
+        res = delete(x, true_mask, axis=-1)
+        assert_array_equal(res, x[:, :0])
+
+        # Object or e.g. timedeltas should *not* be allowed
+        with pytest.raises(IndexError):
+            delete(np.ones(2), np.array([0], dtype=object))
+
+        with pytest.raises(IndexError):
+            # timedeltas are sometimes "integral, but clearly not allowed:
+            delete(np.ones(2), np.array([0], dtype="m8[ns]"))
+
+
+class TestGradient:
+
+    def test_basic(self):
+        v = [[1, 1], [3, 4]]
+        x = np.array(v)
+        dx = [np.array([[2., 3.], [2., 3.]]),
+              np.array([[0., 0.], [1., 1.]])]
+        assert_array_equal(gradient(x), dx)
+        assert_array_equal(gradient(v), dx)
+
+    def test_args(self):
+        dx = np.cumsum(np.ones(5))
+        dx_uneven = [1., 2., 5., 9., 11.]
+        f_2d = np.arange(25).reshape(5, 5)
+
+        # distances must be scalars or have size equal to gradient[axis]
+        gradient(np.arange(5), 3.)
+        gradient(np.arange(5), np.array(3.))
+        gradient(np.arange(5), dx)
+        # dy is set equal to dx because scalar
+        gradient(f_2d, 1.5)
+        gradient(f_2d, np.array(1.5))
+
+        gradient(f_2d, dx_uneven, dx_uneven)
+        # mix between even and uneven spaces and
+        # mix between scalar and vector
+        gradient(f_2d, dx, 2)
+
+        # 2D but axis specified
+        gradient(f_2d, dx, axis=1)
+
+        # 2d coordinate arguments are not yet allowed
+        assert_raises_regex(ValueError, '.*scalars or 1d',
+            gradient, f_2d, np.stack([dx]*2, axis=-1), 1)
+
+    def test_badargs(self):
+        f_2d = np.arange(25).reshape(5, 5)
+        x = np.cumsum(np.ones(5))
+
+        # wrong sizes
+        assert_raises(ValueError, gradient, f_2d, x, np.ones(2))
+        assert_raises(ValueError, gradient, f_2d, 1, np.ones(2))
+        assert_raises(ValueError, gradient, f_2d, np.ones(2), np.ones(2))
+        # wrong number of arguments
+        assert_raises(TypeError, gradient, f_2d, x)
+        assert_raises(TypeError, gradient, f_2d, x, axis=(0,1))
+        assert_raises(TypeError, gradient, f_2d, x, x, x)
+        assert_raises(TypeError, gradient, f_2d, 1, 1, 1)
+        assert_raises(TypeError, gradient, f_2d, x, x, axis=1)
+        assert_raises(TypeError, gradient, f_2d, 1, 1, axis=1)
+
+    def test_datetime64(self):
+        # Make sure gradient() can handle special types like datetime64
+        x = np.array(
+            ['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12',
+             '1910-10-12', '1910-12-12', '1912-12-12'],
+            dtype='datetime64[D]')
+        dx = np.array(
+            [-5, -3, 0, 31, 61, 396, 731],
+            dtype='timedelta64[D]')
+        assert_array_equal(gradient(x), dx)
+        assert_(dx.dtype == np.dtype('timedelta64[D]'))
+
+    def test_masked(self):
+        # Make sure that gradient supports subclasses like masked arrays
+        x = np.ma.array([[1, 1], [3, 4]],
+                        mask=[[False, False], [False, False]])
+        out = gradient(x)[0]
+        assert_equal(type(out), type(x))
+        # And make sure that the output and input don't have aliased mask
+        # arrays
+        assert_(x._mask is not out._mask)
+        # Also check that edge_order=2 doesn't alter the original mask
+        x2 = np.ma.arange(5)
+        x2[2] = np.ma.masked
+        np.gradient(x2, edge_order=2)
+        assert_array_equal(x2.mask, [False, False, True, False, False])
+
+    def test_second_order_accurate(self):
+        # Testing that the relative numerical error is less that 3% for
+        # this example problem. This corresponds to second order
+        # accurate finite differences for all interior and boundary
+        # points.
+        x = np.linspace(0, 1, 10)
+        dx = x[1] - x[0]
+        y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
+        analytical = 6 * x ** 2 + 8 * x + 2
+        num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1)
+        assert_(np.all(num_error < 0.03) == True)
+
+        # test with unevenly spaced
+        np.random.seed(0)
+        x = np.sort(np.random.random(10))
+        y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
+        analytical = 6 * x ** 2 + 8 * x + 2
+        num_error = np.abs((np.gradient(y, x, edge_order=2) / analytical) - 1)
+        assert_(np.all(num_error < 0.03) == True)
+
+    def test_spacing(self):
+        f = np.array([0, 2., 3., 4., 5., 5.])
+        f = np.tile(f, (6,1)) + f.reshape(-1, 1)
+        x_uneven = np.array([0., 0.5, 1., 3., 5., 7.])
+        x_even = np.arange(6.)
+
+        fdx_even_ord1 = np.tile([2., 1.5, 1., 1., 0.5, 0.], (6,1))
+        fdx_even_ord2 = np.tile([2.5, 1.5, 1., 1., 0.5, -0.5], (6,1))
+        fdx_uneven_ord1 = np.tile([4., 3., 1.7, 0.5, 0.25, 0.], (6,1))
+        fdx_uneven_ord2 = np.tile([5., 3., 1.7, 0.5, 0.25, -0.25], (6,1))
+
+        # evenly spaced
+        for edge_order, exp_res in [(1, fdx_even_ord1), (2, fdx_even_ord2)]:
+            res1 = gradient(f, 1., axis=(0,1), edge_order=edge_order)
+            res2 = gradient(f, x_even, x_even,
+                            axis=(0,1), edge_order=edge_order)
+            res3 = gradient(f, x_even, x_even,
+                            axis=None, edge_order=edge_order)
+            assert_array_equal(res1, res2)
+            assert_array_equal(res2, res3)
+            assert_almost_equal(res1[0], exp_res.T)
+            assert_almost_equal(res1[1], exp_res)
+
+            res1 = gradient(f, 1., axis=0, edge_order=edge_order)
+            res2 = gradient(f, x_even, axis=0, edge_order=edge_order)
+            assert_(res1.shape == res2.shape)
+            assert_almost_equal(res2, exp_res.T)
+
+            res1 = gradient(f, 1., axis=1, edge_order=edge_order)
+            res2 = gradient(f, x_even, axis=1, edge_order=edge_order)
+            assert_(res1.shape == res2.shape)
+            assert_array_equal(res2, exp_res)
+
+        # unevenly spaced
+        for edge_order, exp_res in [(1, fdx_uneven_ord1), (2, fdx_uneven_ord2)]:
+            res1 = gradient(f, x_uneven, x_uneven,
+                            axis=(0,1), edge_order=edge_order)
+            res2 = gradient(f, x_uneven, x_uneven,
+                            axis=None, edge_order=edge_order)
+            assert_array_equal(res1, res2)
+            assert_almost_equal(res1[0], exp_res.T)
+            assert_almost_equal(res1[1], exp_res)
+
+            res1 = gradient(f, x_uneven, axis=0, edge_order=edge_order)
+            assert_almost_equal(res1, exp_res.T)
+
+            res1 = gradient(f, x_uneven, axis=1, edge_order=edge_order)
+            assert_almost_equal(res1, exp_res)
+
+        # mixed
+        res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=1)
+        res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=1)
+        assert_array_equal(res1[0], res2[1])
+        assert_array_equal(res1[1], res2[0])
+        assert_almost_equal(res1[0], fdx_even_ord1.T)
+        assert_almost_equal(res1[1], fdx_uneven_ord1)
+
+        res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=2)
+        res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=2)
+        assert_array_equal(res1[0], res2[1])
+        assert_array_equal(res1[1], res2[0])
+        assert_almost_equal(res1[0], fdx_even_ord2.T)
+        assert_almost_equal(res1[1], fdx_uneven_ord2)
+
+    def test_specific_axes(self):
+        # Testing that gradient can work on a given axis only
+        v = [[1, 1], [3, 4]]
+        x = np.array(v)
+        dx = [np.array([[2., 3.], [2., 3.]]),
+              np.array([[0., 0.], [1., 1.]])]
+        assert_array_equal(gradient(x, axis=0), dx[0])
+        assert_array_equal(gradient(x, axis=1), dx[1])
+        assert_array_equal(gradient(x, axis=-1), dx[1])
+        assert_array_equal(gradient(x, axis=(1, 0)), [dx[1], dx[0]])
+
+        # test axis=None which means all axes
+        assert_almost_equal(gradient(x, axis=None), [dx[0], dx[1]])
+        # and is the same as no axis keyword given
+        assert_almost_equal(gradient(x, axis=None), gradient(x))
+
+        # test vararg order
+        assert_array_equal(gradient(x, 2, 3, axis=(1, 0)),
+                           [dx[1]/2.0, dx[0]/3.0])
+        # test maximal number of varargs
+        assert_raises(TypeError, gradient, x, 1, 2, axis=1)
+
+        assert_raises(np.AxisError, gradient, x, axis=3)
+        assert_raises(np.AxisError, gradient, x, axis=-3)
+        # assert_raises(TypeError, gradient, x, axis=[1,])
+
+    def test_timedelta64(self):
+        # Make sure gradient() can handle special types like timedelta64
+        x = np.array(
+            [-5, -3, 10, 12, 61, 321, 300],
+            dtype='timedelta64[D]')
+        dx = np.array(
+            [2, 7, 7, 25, 154, 119, -21],
+            dtype='timedelta64[D]')
+        assert_array_equal(gradient(x), dx)
+        assert_(dx.dtype == np.dtype('timedelta64[D]'))
+
+    def test_inexact_dtypes(self):
+        for dt in [np.float16, np.float32, np.float64]:
+            # dtypes should not be promoted in a different way to what diff does
+            x = np.array([1, 2, 3], dtype=dt)
+            assert_equal(gradient(x).dtype, np.diff(x).dtype)
+
+    def test_values(self):
+        # needs at least 2 points for edge_order ==1
+        gradient(np.arange(2), edge_order=1)
+        # needs at least 3 points for edge_order ==1
+        gradient(np.arange(3), edge_order=2)
+
+        assert_raises(ValueError, gradient, np.arange(0), edge_order=1)
+        assert_raises(ValueError, gradient, np.arange(0), edge_order=2)
+        assert_raises(ValueError, gradient, np.arange(1), edge_order=1)
+        assert_raises(ValueError, gradient, np.arange(1), edge_order=2)
+        assert_raises(ValueError, gradient, np.arange(2), edge_order=2)
+
+    @pytest.mark.parametrize('f_dtype', [np.uint8, np.uint16,
+                                         np.uint32, np.uint64])
+    def test_f_decreasing_unsigned_int(self, f_dtype):
+        f = np.array([5, 4, 3, 2, 1], dtype=f_dtype)
+        g = gradient(f)
+        assert_array_equal(g, [-1]*len(f))
+
+    @pytest.mark.parametrize('f_dtype', [np.int8, np.int16,
+                                         np.int32, np.int64])
+    def test_f_signed_int_big_jump(self, f_dtype):
+        maxint = np.iinfo(f_dtype).max
+        x = np.array([1, 3])
+        f = np.array([-1, maxint], dtype=f_dtype)
+        dfdx = gradient(f, x)
+        assert_array_equal(dfdx, [(maxint + 1) // 2]*2)
+
+    @pytest.mark.parametrize('x_dtype', [np.uint8, np.uint16,
+                                         np.uint32, np.uint64])
+    def test_x_decreasing_unsigned(self, x_dtype):
+        x = np.array([3, 2, 1], dtype=x_dtype)
+        f = np.array([0, 2, 4])
+        dfdx = gradient(f, x)
+        assert_array_equal(dfdx, [-2]*len(x))
+
+    @pytest.mark.parametrize('x_dtype', [np.int8, np.int16,
+                                         np.int32, np.int64])
+    def test_x_signed_int_big_jump(self, x_dtype):
+        minint = np.iinfo(x_dtype).min
+        maxint = np.iinfo(x_dtype).max
+        x = np.array([-1, maxint], dtype=x_dtype)
+        f = np.array([minint // 2, 0])
+        dfdx = gradient(f, x)
+        assert_array_equal(dfdx, [0.5, 0.5])
+
+    def test_return_type(self):
+        res = np.gradient(([1, 2], [2, 3]))
+        if np._using_numpy2_behavior():
+            assert type(res) is tuple
+        else:
+            assert type(res) is list
+
+
+class TestAngle:
+
+    def test_basic(self):
+        x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2,
+             1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
+        y = angle(x)
+        yo = [
+            np.arctan(3.0 / 1.0),
+            np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0,
+            -np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)]
+        z = angle(x, deg=True)
+        zo = np.array(yo) * 180 / np.pi
+        assert_array_almost_equal(y, yo, 11)
+        assert_array_almost_equal(z, zo, 11)
+
+    def test_subclass(self):
+        x = np.ma.array([1 + 3j, 1, np.sqrt(2)/2 * (1 + 1j)])
+        x[1] = np.ma.masked
+        expected = np.ma.array([np.arctan(3.0 / 1.0), 0, np.arctan(1.0)])
+        expected[1] = np.ma.masked
+        actual = angle(x)
+        assert_equal(type(actual), type(expected))
+        assert_equal(actual.mask, expected.mask)
+        assert_equal(actual, expected)
+
+
+class TestTrimZeros:
+
+    a = np.array([0, 0, 1, 0, 2, 3, 4, 0])
+    b = a.astype(float)
+    c = a.astype(complex)
+    d = a.astype(object)
+
+    def values(self):
+        attr_names = ('a', 'b', 'c', 'd')
+        return (getattr(self, name) for name in attr_names)
+
+    def test_basic(self):
+        slc = np.s_[2:-1]
+        for arr in self.values():
+            res = trim_zeros(arr)
+            assert_array_equal(res, arr[slc])
+
+    def test_leading_skip(self):
+        slc = np.s_[:-1]
+        for arr in self.values():
+            res = trim_zeros(arr, trim='b')
+            assert_array_equal(res, arr[slc])
+
+    def test_trailing_skip(self):
+        slc = np.s_[2:]
+        for arr in self.values():
+            res = trim_zeros(arr, trim='F')
+            assert_array_equal(res, arr[slc])
+
+    def test_all_zero(self):
+        for _arr in self.values():
+            arr = np.zeros_like(_arr, dtype=_arr.dtype)
+
+            res1 = trim_zeros(arr, trim='B')
+            assert len(res1) == 0
+
+            res2 = trim_zeros(arr, trim='f')
+            assert len(res2) == 0
+
+    def test_size_zero(self):
+        arr = np.zeros(0)
+        res = trim_zeros(arr)
+        assert_array_equal(arr, res)
+
+    @pytest.mark.parametrize(
+        'arr',
+        [np.array([0, 2**62, 0]),
+         np.array([0, 2**63, 0]),
+         np.array([0, 2**64, 0])]
+    )
+    def test_overflow(self, arr):
+        slc = np.s_[1:2]
+        res = trim_zeros(arr)
+        assert_array_equal(res, arr[slc])
+
+    def test_no_trim(self):
+        arr = np.array([None, 1, None])
+        res = trim_zeros(arr)
+        assert_array_equal(arr, res)
+
+    def test_list_to_list(self):
+        res = trim_zeros(self.a.tolist())
+        assert isinstance(res, list)
+
+
+class TestExtins:
+
+    def test_basic(self):
+        a = np.array([1, 3, 2, 1, 2, 3, 3])
+        b = extract(a > 1, a)
+        assert_array_equal(b, [3, 2, 2, 3, 3])
+
+    def test_place(self):
+        # Make sure that non-np.ndarray objects
+        # raise an error instead of doing nothing
+        assert_raises(TypeError, place, [1, 2, 3], [True, False], [0, 1])
+
+        a = np.array([1, 4, 3, 2, 5, 8, 7])
+        place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
+        assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
+
+        place(a, np.zeros(7), [])
+        assert_array_equal(a, np.arange(1, 8))
+
+        place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9])
+        assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9])
+        assert_raises_regex(ValueError, "Cannot insert from an empty array",
+                            lambda: place(a, [0, 0, 0, 0, 0, 1, 0], []))
+
+        # See Issue #6974
+        a = np.array(['12', '34'])
+        place(a, [0, 1], '9')
+        assert_array_equal(a, ['12', '9'])
+
+    def test_both(self):
+        a = rand(10)
+        mask = a > 0.5
+        ac = a.copy()
+        c = extract(mask, a)
+        place(a, mask, 0)
+        place(a, mask, c)
+        assert_array_equal(a, ac)
+
+
+# _foo1 and _foo2 are used in some tests in TestVectorize.
+
+def _foo1(x, y=1.0):
+    return y*math.floor(x)
+
+
+def _foo2(x, y=1.0, z=0.0):
+    return y*math.floor(x) + z
+
+
+class TestVectorize:
+
+    def test_simple(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract)
+        r = f([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_scalar(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract)
+        r = f([0, 3, 6, 9], 5)
+        assert_array_equal(r, [5, 8, 1, 4])
+
+    def test_large(self):
+        x = np.linspace(-3, 2, 10000)
+        f = vectorize(lambda x: x)
+        y = f(x)
+        assert_array_equal(y, x)
+
+    def test_ufunc(self):
+        f = vectorize(math.cos)
+        args = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
+        r1 = f(args)
+        r2 = np.cos(args)
+        assert_array_almost_equal(r1, r2)
+
+    def test_keywords(self):
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo)
+        args = np.array([1, 2, 3])
+        r1 = f(args)
+        r2 = np.array([2, 3, 4])
+        assert_array_equal(r1, r2)
+        r1 = f(args, 2)
+        r2 = np.array([3, 4, 5])
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order1(self):
+        # gh-1620: The second call of f would crash with
+        # `ValueError: invalid number of arguments`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0), 1.0)
+        r2 = f(np.arange(3.0))
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order2(self):
+        # gh-1620: The second call of f would crash with
+        # `ValueError: non-broadcastable output operand with shape ()
+        # doesn't match the broadcast shape (3,)`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0))
+        r2 = f(np.arange(3.0), 1.0)
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order3(self):
+        # gh-1620: The third call of f would crash with
+        # `ValueError: invalid number of arguments`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0))
+        r2 = f(np.arange(3.0), y=1.0)
+        r3 = f(np.arange(3.0))
+        assert_array_equal(r1, r2)
+        assert_array_equal(r1, r3)
+
+    def test_keywords_with_otypes_several_kwd_args1(self):
+        # gh-1620 Make sure different uses of keyword arguments
+        # don't break the vectorized function.
+        f = vectorize(_foo2, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(10.4, z=100)
+        r2 = f(10.4, y=-1)
+        r3 = f(10.4)
+        assert_equal(r1, _foo2(10.4, z=100))
+        assert_equal(r2, _foo2(10.4, y=-1))
+        assert_equal(r3, _foo2(10.4))
+
+    def test_keywords_with_otypes_several_kwd_args2(self):
+        # gh-1620 Make sure different uses of keyword arguments
+        # don't break the vectorized function.
+        f = vectorize(_foo2, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(z=100, x=10.4, y=-1)
+        r2 = f(1, 2, 3)
+        assert_equal(r1, _foo2(z=100, x=10.4, y=-1))
+        assert_equal(r2, _foo2(1, 2, 3))
+
+    def test_keywords_no_func_code(self):
+        # This needs to test a function that has keywords but
+        # no func_code attribute, since otherwise vectorize will
+        # inspect the func_code.
+        import random
+        try:
+            vectorize(random.randrange)  # Should succeed
+        except Exception:
+            raise AssertionError()
+
+    def test_keywords2_ticket_2100(self):
+        # Test kwarg support: enhancement ticket 2100
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo)
+        args = np.array([1, 2, 3])
+        r1 = f(a=args)
+        r2 = np.array([2, 3, 4])
+        assert_array_equal(r1, r2)
+        r1 = f(b=1, a=args)
+        assert_array_equal(r1, r2)
+        r1 = f(args, b=2)
+        r2 = np.array([3, 4, 5])
+        assert_array_equal(r1, r2)
+
+    def test_keywords3_ticket_2100(self):
+        # Test excluded with mixed positional and kwargs: ticket 2100
+        def mypolyval(x, p):
+            _p = list(p)
+            res = _p.pop(0)
+            while _p:
+                res = res * x + _p.pop(0)
+            return res
+
+        vpolyval = np.vectorize(mypolyval, excluded=['p', 1])
+        ans = [3, 6]
+        assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
+        assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
+        assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3]))
+
+    def test_keywords4_ticket_2100(self):
+        # Test vectorizing function with no positional args.
+        @vectorize
+        def f(**kw):
+            res = 1.0
+            for _k in kw:
+                res *= kw[_k]
+            return res
+
+        assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8])
+
+    def test_keywords5_ticket_2100(self):
+        # Test vectorizing function with no kwargs args.
+        @vectorize
+        def f(*v):
+            return np.prod(v)
+
+        assert_array_equal(f([1, 2], [3, 4]), [3, 8])
+
+    def test_coverage1_ticket_2100(self):
+        def foo():
+            return 1
+
+        f = vectorize(foo)
+        assert_array_equal(f(), 1)
+
+    def test_assigning_docstring(self):
+        def foo(x):
+            """Original documentation"""
+            return x
+
+        f = vectorize(foo)
+        assert_equal(f.__doc__, foo.__doc__)
+
+        doc = "Provided documentation"
+        f = vectorize(foo, doc=doc)
+        assert_equal(f.__doc__, doc)
+
+    def test_UnboundMethod_ticket_1156(self):
+        # Regression test for issue 1156
+        class Foo:
+            b = 2
+
+            def bar(self, a):
+                return a ** self.b
+
+        assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
+                           np.arange(9) ** 2)
+        assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
+                           np.arange(9) ** 2)
+
+    def test_execution_order_ticket_1487(self):
+        # Regression test for dependence on execution order: issue 1487
+        f1 = vectorize(lambda x: x)
+        res1a = f1(np.arange(3))
+        res1b = f1(np.arange(0.1, 3))
+        f2 = vectorize(lambda x: x)
+        res2b = f2(np.arange(0.1, 3))
+        res2a = f2(np.arange(3))
+        assert_equal(res1a, res2a)
+        assert_equal(res1b, res2b)
+
+    def test_string_ticket_1892(self):
+        # Test vectorization over strings: issue 1892.
+        f = np.vectorize(lambda x: x)
+        s = '0123456789' * 10
+        assert_equal(s, f(s))
+
+    def test_cache(self):
+        # Ensure that vectorized func called exactly once per argument.
+        _calls = [0]
+
+        @vectorize
+        def f(x):
+            _calls[0] += 1
+            return x ** 2
+
+        f.cache = True
+        x = np.arange(5)
+        assert_array_equal(f(x), x * x)
+        assert_equal(_calls[0], len(x))
+
+    def test_otypes(self):
+        f = np.vectorize(lambda x: x)
+        f.otypes = 'i'
+        x = np.arange(5)
+        assert_array_equal(f(x), x)
+
+    def test_parse_gufunc_signature(self):
+        assert_equal(nfb._parse_gufunc_signature('(x)->()'), ([('x',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x,y)->()'),
+                     ([('x', 'y')], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x),(y)->()'),
+                     ([('x',), ('y',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x)->(y)'),
+                     ([('x',)], [('y',)]))
+        assert_equal(nfb._parse_gufunc_signature('(x)->(y),()'),
+                     ([('x',)], [('y',), ()]))
+        assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'),
+                     ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
+
+        # Tests to check if whitespaces are ignored
+        assert_equal(nfb._parse_gufunc_signature('(x )->()'), ([('x',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('( x , y )->(  )'),
+                     ([('x', 'y')], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x),( y) ->()'),
+                     ([('x',), ('y',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(  x)-> (y )  '),
+                     ([('x',)], [('y',)]))
+        assert_equal(nfb._parse_gufunc_signature(' (x)->( y),( )'),
+                     ([('x',)], [('y',), ()]))
+        assert_equal(nfb._parse_gufunc_signature(
+                     '(  ), ( a,  b,c )  ,(  d)   ->   (d  ,  e)'),
+                     ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
+
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('(x)(y)->()')
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('(x),(y)->')
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('((x))->(x)')
+
+    def test_signature_simple(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract, signature='(),()->()')
+        r = f([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_signature_mean_last(self):
+        def mean(a):
+            return a.mean()
+
+        f = vectorize(mean, signature='(n)->()')
+        r = f([[1, 3], [2, 4]])
+        assert_array_equal(r, [2, 3])
+
+    def test_signature_center(self):
+        def center(a):
+            return a - a.mean()
+
+        f = vectorize(center, signature='(n)->(n)')
+        r = f([[1, 3], [2, 4]])
+        assert_array_equal(r, [[-1, 1], [-1, 1]])
+
+    def test_signature_two_outputs(self):
+        f = vectorize(lambda x: (x, x), signature='()->(),()')
+        r = f([1, 2, 3])
+        assert_(isinstance(r, tuple) and len(r) == 2)
+        assert_array_equal(r[0], [1, 2, 3])
+        assert_array_equal(r[1], [1, 2, 3])
+
+    def test_signature_outer(self):
+        f = vectorize(np.outer, signature='(a),(b)->(a,b)')
+        r = f([1, 2], [1, 2, 3])
+        assert_array_equal(r, [[1, 2, 3], [2, 4, 6]])
+
+        r = f([[[1, 2]]], [1, 2, 3])
+        assert_array_equal(r, [[[[1, 2, 3], [2, 4, 6]]]])
+
+        r = f([[1, 0], [2, 0]], [1, 2, 3])
+        assert_array_equal(r, [[[1, 2, 3], [0, 0, 0]],
+                               [[2, 4, 6], [0, 0, 0]]])
+
+        r = f([1, 2], [[1, 2, 3], [0, 0, 0]])
+        assert_array_equal(r, [[[1, 2, 3], [2, 4, 6]],
+                               [[0, 0, 0], [0, 0, 0]]])
+
+    def test_signature_computed_size(self):
+        f = vectorize(lambda x: x[:-1], signature='(n)->(m)')
+        r = f([1, 2, 3])
+        assert_array_equal(r, [1, 2])
+
+        r = f([[1, 2, 3], [2, 3, 4]])
+        assert_array_equal(r, [[1, 2], [2, 3]])
+
+    def test_signature_excluded(self):
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo, signature='()->()', excluded={'b'})
+        assert_array_equal(f([1, 2, 3]), [2, 3, 4])
+        assert_array_equal(f([1, 2, 3], b=0), [1, 2, 3])
+
+    def test_signature_otypes(self):
+        f = vectorize(lambda x: x, signature='(n)->(n)', otypes=['float64'])
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+
+    def test_signature_invalid_inputs(self):
+        f = vectorize(operator.add, signature='(n),(n)->(n)')
+        with assert_raises_regex(TypeError, 'wrong number of positional'):
+            f([1, 2])
+        with assert_raises_regex(
+                ValueError, 'does not have enough dimensions'):
+            f(1, 2)
+        with assert_raises_regex(
+                ValueError, 'inconsistent size for core dimension'):
+            f([1, 2], [1, 2, 3])
+
+        f = vectorize(operator.add, signature='()->()')
+        with assert_raises_regex(TypeError, 'wrong number of positional'):
+            f(1, 2)
+
+    def test_signature_invalid_outputs(self):
+
+        f = vectorize(lambda x: x[:-1], signature='(n)->(n)')
+        with assert_raises_regex(
+                ValueError, 'inconsistent size for core dimension'):
+            f([1, 2, 3])
+
+        f = vectorize(lambda x: x, signature='()->(),()')
+        with assert_raises_regex(ValueError, 'wrong number of outputs'):
+            f(1)
+
+        f = vectorize(lambda x: (x, x), signature='()->()')
+        with assert_raises_regex(ValueError, 'wrong number of outputs'):
+            f([1, 2])
+
+    def test_size_zero_output(self):
+        # see issue 5868
+        f = np.vectorize(lambda x: x)
+        x = np.zeros([0, 5], dtype=int)
+        with assert_raises_regex(ValueError, 'otypes'):
+            f(x)
+
+        f.otypes = 'i'
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='()->()')
+        with assert_raises_regex(ValueError, 'otypes'):
+            f(x)
+
+        f = np.vectorize(lambda x: x, signature='()->()', otypes='i')
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='(n)->(n)', otypes='i')
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='(n)->(n)')
+        assert_array_equal(f(x.T), x.T)
+
+        f = np.vectorize(lambda x: [x], signature='()->(n)', otypes='i')
+        with assert_raises_regex(ValueError, 'new output dimensions'):
+            f(x)
+
+    def test_subclasses(self):
+        class subclass(np.ndarray):
+            pass
+
+        m = np.array([[1., 0., 0.],
+                      [0., 0., 1.],
+                      [0., 1., 0.]]).view(subclass)
+        v = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]).view(subclass)
+        # generalized (gufunc)
+        matvec = np.vectorize(np.matmul, signature='(m,m),(m)->(m)')
+        r = matvec(m, v)
+        assert_equal(type(r), subclass)
+        assert_equal(r, [[1., 3., 2.], [4., 6., 5.], [7., 9., 8.]])
+
+        # element-wise (ufunc)
+        mult = np.vectorize(lambda x, y: x*y)
+        r = mult(m, v)
+        assert_equal(type(r), subclass)
+        assert_equal(r, m * v)
+
+    def test_name(self):
+        #See gh-23021
+        @np.vectorize
+        def f2(a, b):
+            return a + b
+
+        assert f2.__name__ == 'f2'
+
+    def test_decorator(self):
+        @vectorize
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_docstring(self):
+        @vectorize
+        def f(x):
+            """Docstring"""
+            return x
+
+        if sys.flags.optimize < 2:
+            assert f.__doc__ == "Docstring"
+
+    def test_partial(self):
+        def foo(x, y):
+            return x + y
+
+        bar = partial(foo, 3)
+        vbar = np.vectorize(bar)
+        assert vbar(1) == 4
+
+    def test_signature_otypes_decorator(self):
+        @vectorize(signature='(n)->(n)', otypes=['float64'])
+        def f(x):
+            return x
+
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+        assert f.__name__ == 'f'
+
+    def test_bad_input(self):
+        with assert_raises(TypeError):
+            A = np.vectorize(pyfunc = 3)
+
+    def test_no_keywords(self):
+        with assert_raises(TypeError):
+            @np.vectorize("string")
+            def foo():
+                return "bar"
+
+    def test_positional_regression_9477(self):
+        # This supplies the first keyword argument as a positional,
+        # to ensure that they are still properly forwarded after the
+        # enhancement for #9477
+        f = vectorize((lambda x: x), ['float64'])
+        r = f([2])
+        assert_equal(r.dtype, np.dtype('float64'))
+
+
+class TestLeaks:
+    class A:
+        iters = 20
+
+        def bound(self, *args):
+            return 0
+
+        @staticmethod
+        def unbound(*args):
+            return 0
+
+    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+    @pytest.mark.parametrize('name, incr', [
+            ('bound', A.iters),
+            ('unbound', 0),
+            ])
+    def test_frompyfunc_leaks(self, name, incr):
+        # exposed in gh-11867 as np.vectorized, but the problem stems from
+        # frompyfunc.
+        # class.attribute = np.frompyfunc(<method>) creates a
+        # reference cycle if <method> is a bound class method. It requires a
+        # gc collection cycle to break the cycle (on CPython 3)
+        import gc
+        A_func = getattr(self.A, name)
+        gc.disable()
+        try:
+            refcount = sys.getrefcount(A_func)
+            for i in range(self.A.iters):
+                a = self.A()
+                a.f = np.frompyfunc(getattr(a, name), 1, 1)
+                out = a.f(np.arange(10))
+            a = None
+            # A.func is part of a reference cycle if incr is non-zero
+            assert_equal(sys.getrefcount(A_func), refcount + incr)
+            for i in range(5):
+                gc.collect()
+            assert_equal(sys.getrefcount(A_func), refcount)
+        finally:
+            gc.enable()
+
+
+class TestDigitize:
+
+    def test_forward(self):
+        x = np.arange(-6, 5)
+        bins = np.arange(-5, 5)
+        assert_array_equal(digitize(x, bins), np.arange(11))
+
+    def test_reverse(self):
+        x = np.arange(5, -6, -1)
+        bins = np.arange(5, -5, -1)
+        assert_array_equal(digitize(x, bins), np.arange(11))
+
+    def test_random(self):
+        x = rand(10)
+        bin = np.linspace(x.min(), x.max(), 10)
+        assert_(np.all(digitize(x, bin) != 0))
+
+    def test_right_basic(self):
+        x = [1, 5, 4, 10, 8, 11, 0]
+        bins = [1, 5, 10]
+        default_answer = [1, 2, 1, 3, 2, 3, 0]
+        assert_array_equal(digitize(x, bins), default_answer)
+        right_answer = [0, 1, 1, 2, 2, 3, 0]
+        assert_array_equal(digitize(x, bins, True), right_answer)
+
+    def test_right_open(self):
+        x = np.arange(-6, 5)
+        bins = np.arange(-6, 4)
+        assert_array_equal(digitize(x, bins, True), np.arange(11))
+
+    def test_right_open_reverse(self):
+        x = np.arange(5, -6, -1)
+        bins = np.arange(4, -6, -1)
+        assert_array_equal(digitize(x, bins, True), np.arange(11))
+
+    def test_right_open_random(self):
+        x = rand(10)
+        bins = np.linspace(x.min(), x.max(), 10)
+        assert_(np.all(digitize(x, bins, True) != 10))
+
+    def test_monotonic(self):
+        x = [-1, 0, 1, 2]
+        bins = [0, 0, 1]
+        assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3])
+        assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3])
+        bins = [1, 1, 0]
+        assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0])
+        assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0])
+        bins = [1, 1, 1, 1]
+        assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4])
+        assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4])
+        bins = [0, 0, 1, 0]
+        assert_raises(ValueError, digitize, x, bins)
+        bins = [1, 1, 0, 1]
+        assert_raises(ValueError, digitize, x, bins)
+
+    def test_casting_error(self):
+        x = [1, 2, 3 + 1.j]
+        bins = [1, 2, 3]
+        assert_raises(TypeError, digitize, x, bins)
+        x, bins = bins, x
+        assert_raises(TypeError, digitize, x, bins)
+
+    def test_return_type(self):
+        # Functions returning indices should always return base ndarrays
+        class A(np.ndarray):
+            pass
+        a = np.arange(5).view(A)
+        b = np.arange(1, 3).view(A)
+        assert_(not isinstance(digitize(b, a, False), A))
+        assert_(not isinstance(digitize(b, a, True), A))
+
+    def test_large_integers_increasing(self):
+        # gh-11022
+        x = 2**54  # loses precision in a float
+        assert_equal(np.digitize(x, [x - 1, x + 1]), 1)
+
+    @pytest.mark.xfail(
+        reason="gh-11022: np.core.multiarray._monoticity loses precision")
+    def test_large_integers_decreasing(self):
+        # gh-11022
+        x = 2**54  # loses precision in a float
+        assert_equal(np.digitize(x, [x + 1, x - 1]), 1)
+
+
+class TestUnwrap:
+
+    def test_simple(self):
+        # check that unwrap removes jumps greater that 2*pi
+        assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1])
+        # check that unwrap maintains continuity
+        assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi))
+
+    def test_period(self):
+        # check that unwrap removes jumps greater that 255
+        assert_array_equal(unwrap([1, 1 + 256], period=255), [1, 2])
+        # check that unwrap maintains continuity
+        assert_(np.all(diff(unwrap(rand(10) * 1000, period=255)) < 255))
+        # check simple case
+        simple_seq = np.array([0, 75, 150, 225, 300])
+        wrap_seq = np.mod(simple_seq, 255)
+        assert_array_equal(unwrap(wrap_seq, period=255), simple_seq)
+        # check custom discont value
+        uneven_seq = np.array([0, 75, 150, 225, 300, 430])
+        wrap_uneven = np.mod(uneven_seq, 250)
+        no_discont = unwrap(wrap_uneven, period=250)
+        assert_array_equal(no_discont, [0, 75, 150, 225, 300, 180])
+        sm_discont = unwrap(wrap_uneven, period=250, discont=140)
+        assert_array_equal(sm_discont, [0, 75, 150, 225, 300, 430])
+        assert sm_discont.dtype == wrap_uneven.dtype
+
+
+@pytest.mark.parametrize(
+    "dtype", "O" + np.typecodes["AllInteger"] + np.typecodes["Float"]
+)
+@pytest.mark.parametrize("M", [0, 1, 10])
+class TestFilterwindows:
+
+    def test_hanning(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = hanning(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.500, 4)
+
+    def test_hamming(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = hamming(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.9400, 4)
+
+    def test_bartlett(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = bartlett(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.4444, 4)
+
+    def test_blackman(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = blackman(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 3.7800, 4)
+
+    def test_kaiser(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = kaiser(scalar, 0)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 10, 15)
+
+
+class TestTrapz:
+
+    def test_simple(self):
+        x = np.arange(-10, 10, .1)
+        r = trapz(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1)
+        # check integral of normal equals 1
+        assert_almost_equal(r, 1, 7)
+
+    def test_ndim(self):
+        x = np.linspace(0, 1, 3)
+        y = np.linspace(0, 2, 8)
+        z = np.linspace(0, 3, 13)
+
+        wx = np.ones_like(x) * (x[1] - x[0])
+        wx[0] /= 2
+        wx[-1] /= 2
+        wy = np.ones_like(y) * (y[1] - y[0])
+        wy[0] /= 2
+        wy[-1] /= 2
+        wz = np.ones_like(z) * (z[1] - z[0])
+        wz[0] /= 2
+        wz[-1] /= 2
+
+        q = x[:, None, None] + y[None,:, None] + z[None, None,:]
+
+        qx = (q * wx[:, None, None]).sum(axis=0)
+        qy = (q * wy[None, :, None]).sum(axis=1)
+        qz = (q * wz[None, None, :]).sum(axis=2)
+
+        # n-d `x`
+        r = trapz(q, x=x[:, None, None], axis=0)
+        assert_almost_equal(r, qx)
+        r = trapz(q, x=y[None,:, None], axis=1)
+        assert_almost_equal(r, qy)
+        r = trapz(q, x=z[None, None,:], axis=2)
+        assert_almost_equal(r, qz)
+
+        # 1-d `x`
+        r = trapz(q, x=x, axis=0)
+        assert_almost_equal(r, qx)
+        r = trapz(q, x=y, axis=1)
+        assert_almost_equal(r, qy)
+        r = trapz(q, x=z, axis=2)
+        assert_almost_equal(r, qz)
+
+    def test_masked(self):
+        # Testing that masked arrays behave as if the function is 0 where
+        # masked
+        x = np.arange(5)
+        y = x * x
+        mask = x == 2
+        ym = np.ma.array(y, mask=mask)
+        r = 13.0  # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
+        assert_almost_equal(trapz(ym, x), r)
+
+        xm = np.ma.array(x, mask=mask)
+        assert_almost_equal(trapz(ym, xm), r)
+
+        xm = np.ma.array(x, mask=mask)
+        assert_almost_equal(trapz(y, xm), r)
+
+
+class TestSinc:
+
+    def test_simple(self):
+        assert_(sinc(0) == 1)
+        w = sinc(np.linspace(-1, 1, 100))
+        # check symmetry
+        assert_array_almost_equal(w, flipud(w), 7)
+
+    def test_array_like(self):
+        x = [0, 0.5]
+        y1 = sinc(np.array(x))
+        y2 = sinc(list(x))
+        y3 = sinc(tuple(x))
+        assert_array_equal(y1, y2)
+        assert_array_equal(y1, y3)
+
+
+class TestUnique:
+
+    def test_simple(self):
+        x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0])
+        assert_(np.all(unique(x) == [0, 1, 2, 3, 4]))
+        assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1]))
+        x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
+        assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
+        x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
+        assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
+
+
+class TestCheckFinite:
+
+    def test_simple(self):
+        a = [1, 2, 3]
+        b = [1, 2, np.inf]
+        c = [1, 2, np.nan]
+        np.lib.asarray_chkfinite(a)
+        assert_raises(ValueError, np.lib.asarray_chkfinite, b)
+        assert_raises(ValueError, np.lib.asarray_chkfinite, c)
+
+    def test_dtype_order(self):
+        # Regression test for missing dtype and order arguments
+        a = [1, 2, 3]
+        a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64)
+        assert_(a.dtype == np.float64)
+
+
+class TestCorrCoef:
+    A = np.array(
+        [[0.15391142, 0.18045767, 0.14197213],
+         [0.70461506, 0.96474128, 0.27906989],
+         [0.9297531, 0.32296769, 0.19267156]])
+    B = np.array(
+        [[0.10377691, 0.5417086, 0.49807457],
+         [0.82872117, 0.77801674, 0.39226705],
+         [0.9314666, 0.66800209, 0.03538394]])
+    res1 = np.array(
+        [[1., 0.9379533, -0.04931983],
+         [0.9379533, 1., 0.30007991],
+         [-0.04931983, 0.30007991, 1.]])
+    res2 = np.array(
+        [[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523],
+         [0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386],
+         [-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601],
+         [0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113],
+         [0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823],
+         [0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]])
+
+    def test_non_array(self):
+        assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]),
+                            [[1., -1.], [-1.,  1.]])
+
+    def test_simple(self):
+        tgt1 = corrcoef(self.A)
+        assert_almost_equal(tgt1, self.res1)
+        assert_(np.all(np.abs(tgt1) <= 1.0))
+
+        tgt2 = corrcoef(self.A, self.B)
+        assert_almost_equal(tgt2, self.res2)
+        assert_(np.all(np.abs(tgt2) <= 1.0))
+
+    def test_ddof(self):
+        # ddof raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1)
+            sup.filter(DeprecationWarning)
+            # ddof has no or negligible effect on the function
+            assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
+            assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
+            assert_almost_equal(corrcoef(self.A, ddof=3), self.res1)
+            assert_almost_equal(corrcoef(self.A, self.B, ddof=3), self.res2)
+
+    def test_bias(self):
+        # bias raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0)
+            assert_warns(DeprecationWarning, corrcoef, self.A, bias=0)
+            sup.filter(DeprecationWarning)
+            # bias has no or negligible effect on the function
+            assert_almost_equal(corrcoef(self.A, bias=1), self.res1)
+
+    def test_complex(self):
+        x = np.array([[1, 2, 3], [1j, 2j, 3j]])
+        res = corrcoef(x)
+        tgt = np.array([[1., -1.j], [1.j, 1.]])
+        assert_allclose(res, tgt)
+        assert_(np.all(np.abs(res) <= 1.0))
+
+    def test_xy(self):
+        x = np.array([[1, 2, 3]])
+        y = np.array([[1j, 2j, 3j]])
+        assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]]))
+
+    def test_empty(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(corrcoef(np.array([])), np.nan)
+            assert_array_equal(corrcoef(np.array([]).reshape(0, 2)),
+                               np.array([]).reshape(0, 0))
+            assert_array_equal(corrcoef(np.array([]).reshape(2, 0)),
+                               np.array([[np.nan, np.nan], [np.nan, np.nan]]))
+
+    def test_extreme(self):
+        x = [[1e-100, 1e100], [1e100, 1e-100]]
+        with np.errstate(all='raise'):
+            c = corrcoef(x)
+        assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]]))
+        assert_(np.all(np.abs(c) <= 1.0))
+
+    @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
+    def test_corrcoef_dtype(self, test_type):
+        cast_A = self.A.astype(test_type)
+        res = corrcoef(cast_A, dtype=test_type)
+        assert test_type == res.dtype
+
+
+class TestCov:
+    x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
+    res1 = np.array([[1., -1.], [-1., 1.]])
+    x2 = np.array([0.0, 1.0, 2.0], ndmin=2)
+    frequencies = np.array([1, 4, 1])
+    x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T
+    res2 = np.array([[0.4, -0.4], [-0.4, 0.4]])
+    unit_frequencies = np.ones(3, dtype=np.int_)
+    weights = np.array([1.0, 4.0, 1.0])
+    res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]])
+    unit_weights = np.ones(3)
+    x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])
+
+    def test_basic(self):
+        assert_allclose(cov(self.x1), self.res1)
+
+    def test_complex(self):
+        x = np.array([[1, 2, 3], [1j, 2j, 3j]])
+        res = np.array([[1., -1.j], [1.j, 1.]])
+        assert_allclose(cov(x), res)
+        assert_allclose(cov(x, aweights=np.ones(3)), res)
+
+    def test_xy(self):
+        x = np.array([[1, 2, 3]])
+        y = np.array([[1j, 2j, 3j]])
+        assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]]))
+
+    def test_empty(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(cov(np.array([])), np.nan)
+            assert_array_equal(cov(np.array([]).reshape(0, 2)),
+                               np.array([]).reshape(0, 0))
+            assert_array_equal(cov(np.array([]).reshape(2, 0)),
+                               np.array([[np.nan, np.nan], [np.nan, np.nan]]))
+
+    def test_wrong_ddof(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(cov(self.x1, ddof=5),
+                               np.array([[np.inf, -np.inf],
+                                         [-np.inf, np.inf]]))
+
+    def test_1D_rowvar(self):
+        assert_allclose(cov(self.x3), cov(self.x3, rowvar=False))
+        y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501])
+        assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=False))
+
+    def test_1D_variance(self):
+        assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1))
+
+    def test_fweights(self):
+        assert_allclose(cov(self.x2, fweights=self.frequencies),
+                        cov(self.x2_repeats))
+        assert_allclose(cov(self.x1, fweights=self.frequencies),
+                        self.res2)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies),
+                        self.res1)
+        nonint = self.frequencies + 0.5
+        assert_raises(TypeError, cov, self.x1, fweights=nonint)
+        f = np.ones((2, 3), dtype=np.int_)
+        assert_raises(RuntimeError, cov, self.x1, fweights=f)
+        f = np.ones(2, dtype=np.int_)
+        assert_raises(RuntimeError, cov, self.x1, fweights=f)
+        f = -1 * np.ones(3, dtype=np.int_)
+        assert_raises(ValueError, cov, self.x1, fweights=f)
+
+    def test_aweights(self):
+        assert_allclose(cov(self.x1, aweights=self.weights), self.res3)
+        assert_allclose(cov(self.x1, aweights=3.0 * self.weights),
+                        cov(self.x1, aweights=self.weights))
+        assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1)
+        w = np.ones((2, 3))
+        assert_raises(RuntimeError, cov, self.x1, aweights=w)
+        w = np.ones(2)
+        assert_raises(RuntimeError, cov, self.x1, aweights=w)
+        w = -1.0 * np.ones(3)
+        assert_raises(ValueError, cov, self.x1, aweights=w)
+
+    def test_unit_fweights_and_aweights(self):
+        assert_allclose(cov(self.x2, fweights=self.frequencies,
+                            aweights=self.unit_weights),
+                        cov(self.x2_repeats))
+        assert_allclose(cov(self.x1, fweights=self.frequencies,
+                            aweights=self.unit_weights),
+                        self.res2)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.unit_weights),
+                        self.res1)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.weights),
+                        self.res3)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=3.0 * self.weights),
+                        cov(self.x1, aweights=self.weights))
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.unit_weights),
+                        self.res1)
+
+    @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
+    def test_cov_dtype(self, test_type):
+        cast_x1 = self.x1.astype(test_type)
+        res = cov(cast_x1, dtype=test_type)
+        assert test_type == res.dtype
+
+
+class Test_I0:
+
+    def test_simple(self):
+        assert_almost_equal(
+            i0(0.5),
+            np.array(1.0634833707413234))
+
+        # need at least one test above 8, as the implementation is piecewise
+        A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549, 10.0])
+        expected = np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049, 2815.71662847])
+        assert_almost_equal(i0(A), expected)
+        assert_almost_equal(i0(-A), expected)
+
+        B = np.array([[0.827002, 0.99959078],
+                      [0.89694769, 0.39298162],
+                      [0.37954418, 0.05206293],
+                      [0.36465447, 0.72446427],
+                      [0.48164949, 0.50324519]])
+        assert_almost_equal(
+            i0(B),
+            np.array([[1.17843223, 1.26583466],
+                      [1.21147086, 1.03898290],
+                      [1.03633899, 1.00067775],
+                      [1.03352052, 1.13557954],
+                      [1.05884290, 1.06432317]]))
+        # Regression test for gh-11205
+        i0_0 = np.i0([0.])
+        assert_equal(i0_0.shape, (1,))
+        assert_array_equal(np.i0([0.]), np.array([1.]))
+
+    def test_non_array(self):
+        a = np.arange(4)
+
+        class array_like:
+            __array_interface__ = a.__array_interface__
+
+            def __array_wrap__(self, arr):
+                return self
+
+        # E.g. pandas series survive ufunc calls through array-wrap:
+        assert isinstance(np.abs(array_like()), array_like)
+        exp = np.i0(a)
+        res = np.i0(array_like())
+
+        assert_array_equal(exp, res)
+
+    def test_complex(self):
+        a = np.array([0, 1 + 2j])
+        with pytest.raises(TypeError, match="i0 not supported for complex values"):
+            res = i0(a)
+
+
+class TestKaiser:
+
+    def test_simple(self):
+        assert_(np.isfinite(kaiser(1, 1.0)))
+        assert_almost_equal(kaiser(0, 1.0),
+                            np.array([]))
+        assert_almost_equal(kaiser(2, 1.0),
+                            np.array([0.78984831, 0.78984831]))
+        assert_almost_equal(kaiser(5, 1.0),
+                            np.array([0.78984831, 0.94503323, 1.,
+                                      0.94503323, 0.78984831]))
+        assert_almost_equal(kaiser(5, 1.56789),
+                            np.array([0.58285404, 0.88409679, 1.,
+                                      0.88409679, 0.58285404]))
+
+    def test_int_beta(self):
+        kaiser(3, 4)
+
+
+class TestMsort:
+
+    def test_simple(self):
+        A = np.array([[0.44567325, 0.79115165, 0.54900530],
+                      [0.36844147, 0.37325583, 0.96098397],
+                      [0.64864341, 0.52929049, 0.39172155]])
+        with pytest.warns(DeprecationWarning, match="msort is deprecated"):
+            assert_almost_equal(
+                msort(A),
+                np.array([[0.36844147, 0.37325583, 0.39172155],
+                          [0.44567325, 0.52929049, 0.54900530],
+                          [0.64864341, 0.79115165, 0.96098397]]))
+
+
+class TestMeshgrid:
+
+    def test_simple(self):
+        [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
+        assert_array_equal(X, np.array([[1, 2, 3],
+                                        [1, 2, 3],
+                                        [1, 2, 3],
+                                        [1, 2, 3]]))
+        assert_array_equal(Y, np.array([[4, 4, 4],
+                                        [5, 5, 5],
+                                        [6, 6, 6],
+                                        [7, 7, 7]]))
+
+    def test_single_input(self):
+        [X] = meshgrid([1, 2, 3, 4])
+        assert_array_equal(X, np.array([1, 2, 3, 4]))
+
+    def test_no_input(self):
+        args = []
+        assert_array_equal([], meshgrid(*args))
+        assert_array_equal([], meshgrid(*args, copy=False))
+
+    def test_indexing(self):
+        x = [1, 2, 3]
+        y = [4, 5, 6, 7]
+        [X, Y] = meshgrid(x, y, indexing='ij')
+        assert_array_equal(X, np.array([[1, 1, 1, 1],
+                                        [2, 2, 2, 2],
+                                        [3, 3, 3, 3]]))
+        assert_array_equal(Y, np.array([[4, 5, 6, 7],
+                                        [4, 5, 6, 7],
+                                        [4, 5, 6, 7]]))
+
+        # Test expected shapes:
+        z = [8, 9]
+        assert_(meshgrid(x, y)[0].shape == (4, 3))
+        assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4))
+        assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2))
+        assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2))
+
+        assert_raises(ValueError, meshgrid, x, y, indexing='notvalid')
+
+    def test_sparse(self):
+        [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True)
+        assert_array_equal(X, np.array([[1, 2, 3]]))
+        assert_array_equal(Y, np.array([[4], [5], [6], [7]]))
+
+    def test_invalid_arguments(self):
+        # Test that meshgrid complains about invalid arguments
+        # Regression test for issue #4755:
+        # https://github.com/numpy/numpy/issues/4755
+        assert_raises(TypeError, meshgrid,
+                      [1, 2, 3], [4, 5, 6, 7], indices='ij')
+
+    def test_return_type(self):
+        # Test for appropriate dtype in returned arrays.
+        # Regression test for issue #5297
+        # https://github.com/numpy/numpy/issues/5297
+        x = np.arange(0, 10, dtype=np.float32)
+        y = np.arange(10, 20, dtype=np.float64)
+
+        X, Y = np.meshgrid(x,y)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+        # copy
+        X, Y = np.meshgrid(x,y, copy=True)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+        # sparse
+        X, Y = np.meshgrid(x,y, sparse=True)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+    def test_writeback(self):
+        # Issue 8561
+        X = np.array([1.1, 2.2])
+        Y = np.array([3.3, 4.4])
+        x, y = np.meshgrid(X, Y, sparse=False, copy=True)
+
+        x[0, :] = 0
+        assert_equal(x[0, :], 0)
+        assert_equal(x[1, :], X)
+
+    def test_nd_shape(self):
+        a, b, c, d, e = np.meshgrid(*([0] * i for i in range(1, 6)))
+        expected_shape = (2, 1, 3, 4, 5)
+        assert_equal(a.shape, expected_shape)
+        assert_equal(b.shape, expected_shape)
+        assert_equal(c.shape, expected_shape)
+        assert_equal(d.shape, expected_shape)
+        assert_equal(e.shape, expected_shape)
+
+    def test_nd_values(self):
+        a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5])
+        assert_equal(a, [[[0, 0, 0]], [[0, 0, 0]]])
+        assert_equal(b, [[[1, 1, 1]], [[2, 2, 2]]])
+        assert_equal(c, [[[3, 4, 5]], [[3, 4, 5]]])
+
+    def test_nd_indexing(self):
+        a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5], indexing='ij')
+        assert_equal(a, [[[0, 0, 0], [0, 0, 0]]])
+        assert_equal(b, [[[1, 1, 1], [2, 2, 2]]])
+        assert_equal(c, [[[3, 4, 5], [3, 4, 5]]])
+
+
+class TestPiecewise:
+
+    def test_simple(self):
+        # Condition is single bool list
+        x = piecewise([0, 0], [True, False], [1])
+        assert_array_equal(x, [1, 0])
+
+        # List of conditions: single bool list
+        x = piecewise([0, 0], [[True, False]], [1])
+        assert_array_equal(x, [1, 0])
+
+        # Conditions is single bool array
+        x = piecewise([0, 0], np.array([True, False]), [1])
+        assert_array_equal(x, [1, 0])
+
+        # Condition is single int array
+        x = piecewise([0, 0], np.array([1, 0]), [1])
+        assert_array_equal(x, [1, 0])
+
+        # List of conditions: int array
+        x = piecewise([0, 0], [np.array([1, 0])], [1])
+        assert_array_equal(x, [1, 0])
+
+        x = piecewise([0, 0], [[False, True]], [lambda x:-1])
+        assert_array_equal(x, [0, -1])
+
+        assert_raises_regex(ValueError, '1 or 2 functions are expected',
+            piecewise, [0, 0], [[False, True]], [])
+        assert_raises_regex(ValueError, '1 or 2 functions are expected',
+            piecewise, [0, 0], [[False, True]], [1, 2, 3])
+
+    def test_two_conditions(self):
+        x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
+        assert_array_equal(x, [3, 4])
+
+    def test_scalar_domains_three_conditions(self):
+        x = piecewise(3, [True, False, False], [4, 2, 0])
+        assert_equal(x, 4)
+
+    def test_default(self):
+        # No value specified for x[1], should be 0
+        x = piecewise([1, 2], [True, False], [2])
+        assert_array_equal(x, [2, 0])
+
+        # Should set x[1] to 3
+        x = piecewise([1, 2], [True, False], [2, 3])
+        assert_array_equal(x, [2, 3])
+
+    def test_0d(self):
+        x = np.array(3)
+        y = piecewise(x, x > 3, [4, 0])
+        assert_(y.ndim == 0)
+        assert_(y == 0)
+
+        x = 5
+        y = piecewise(x, [True, False], [1, 0])
+        assert_(y.ndim == 0)
+        assert_(y == 1)
+
+        # With 3 ranges (It was failing, before)
+        y = piecewise(x, [False, False, True], [1, 2, 3])
+        assert_array_equal(y, 3)
+
+    def test_0d_comparison(self):
+        x = 3
+        y = piecewise(x, [x <= 3, x > 3], [4, 0])  # Should succeed.
+        assert_equal(y, 4)
+
+        # With 3 ranges (It was failing, before)
+        x = 4
+        y = piecewise(x, [x <= 3, (x > 3) * (x <= 5), x > 5], [1, 2, 3])
+        assert_array_equal(y, 2)
+
+        assert_raises_regex(ValueError, '2 or 3 functions are expected',
+            piecewise, x, [x <= 3, x > 3], [1])
+        assert_raises_regex(ValueError, '2 or 3 functions are expected',
+            piecewise, x, [x <= 3, x > 3], [1, 1, 1, 1])
+
+    def test_0d_0d_condition(self):
+        x = np.array(3)
+        c = np.array(x > 3)
+        y = piecewise(x, [c], [1, 2])
+        assert_equal(y, 2)
+
+    def test_multidimensional_extrafunc(self):
+        x = np.array([[-2.5, -1.5, -0.5],
+                      [0.5, 1.5, 2.5]])
+        y = piecewise(x, [x < 0, x >= 2], [-1, 1, 3])
+        assert_array_equal(y, np.array([[-1., -1., -1.],
+                                        [3., 3., 1.]]))
+
+    def test_subclasses(self):
+        class subclass(np.ndarray):
+            pass
+        x = np.arange(5.).view(subclass)
+        r = piecewise(x, [x<2., x>=4], [-1., 1., 0.])
+        assert_equal(type(r), subclass)
+        assert_equal(r, [-1., -1., 0., 0., 1.])
+
+
+class TestBincount:
+
+    def test_simple(self):
+        y = np.bincount(np.arange(4))
+        assert_array_equal(y, np.ones(4))
+
+    def test_simple2(self):
+        y = np.bincount(np.array([1, 5, 2, 4, 1]))
+        assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))
+
+    def test_simple_weight(self):
+        x = np.arange(4)
+        w = np.array([0.2, 0.3, 0.5, 0.1])
+        y = np.bincount(x, w)
+        assert_array_equal(y, w)
+
+    def test_simple_weight2(self):
+        x = np.array([1, 2, 4, 5, 2])
+        w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
+        y = np.bincount(x, w)
+        assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
+
+    def test_with_minlength(self):
+        x = np.array([0, 1, 0, 1, 1])
+        y = np.bincount(x, minlength=3)
+        assert_array_equal(y, np.array([2, 3, 0]))
+        x = []
+        y = np.bincount(x, minlength=0)
+        assert_array_equal(y, np.array([]))
+
+    def test_with_minlength_smaller_than_maxvalue(self):
+        x = np.array([0, 1, 1, 2, 2, 3, 3])
+        y = np.bincount(x, minlength=2)
+        assert_array_equal(y, np.array([1, 2, 2, 2]))
+        y = np.bincount(x, minlength=0)
+        assert_array_equal(y, np.array([1, 2, 2, 2]))
+
+    def test_with_minlength_and_weights(self):
+        x = np.array([1, 2, 4, 5, 2])
+        w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
+        y = np.bincount(x, w, 8)
+        assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))
+
+    def test_empty(self):
+        x = np.array([], dtype=int)
+        y = np.bincount(x)
+        assert_array_equal(x, y)
+
+    def test_empty_with_minlength(self):
+        x = np.array([], dtype=int)
+        y = np.bincount(x, minlength=5)
+        assert_array_equal(y, np.zeros(5, dtype=int))
+
+    def test_with_incorrect_minlength(self):
+        x = np.array([], dtype=int)
+        assert_raises_regex(TypeError,
+                            "'str' object cannot be interpreted",
+                            lambda: np.bincount(x, minlength="foobar"))
+        assert_raises_regex(ValueError,
+                            "must not be negative",
+                            lambda: np.bincount(x, minlength=-1))
+
+        x = np.arange(5)
+        assert_raises_regex(TypeError,
+                            "'str' object cannot be interpreted",
+                            lambda: np.bincount(x, minlength="foobar"))
+        assert_raises_regex(ValueError,
+                            "must not be negative",
+                            lambda: np.bincount(x, minlength=-1))
+
+    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+    def test_dtype_reference_leaks(self):
+        # gh-6805
+        intp_refcount = sys.getrefcount(np.dtype(np.intp))
+        double_refcount = sys.getrefcount(np.dtype(np.double))
+
+        for j in range(10):
+            np.bincount([1, 2, 3])
+        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
+        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
+
+        for j in range(10):
+            np.bincount([1, 2, 3], [4, 5, 6])
+        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
+        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
+
+    @pytest.mark.parametrize("vals", [[[2, 2]], 2])
+    def test_error_not_1d(self, vals):
+        # Test that values has to be 1-D (both as array and nested list)
+        vals_arr = np.asarray(vals)
+        with assert_raises(ValueError):
+            np.bincount(vals_arr)
+        with assert_raises(ValueError):
+            np.bincount(vals)
+
+
+class TestInterp:
+
+    def test_exceptions(self):
+        assert_raises(ValueError, interp, 0, [], [])
+        assert_raises(ValueError, interp, 0, [0], [1, 2])
+        assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0)
+        assert_raises(ValueError, interp, 0, [], [], period=360)
+        assert_raises(ValueError, interp, 0, [0], [1, 2], period=360)
+
+    def test_basic(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.linspace(0, 1, 50)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_right_left_behavior(self):
+        # Needs range of sizes to test different code paths.
+        # size ==1 is special cased, 1 < size < 5 is linear search, and
+        # size >= 5 goes through local search and possibly binary search.
+        for size in range(1, 10):
+            xp = np.arange(size, dtype=np.double)
+            yp = np.ones(size, dtype=np.double)
+            incpts = np.array([-1, 0, size - 1, size], dtype=np.double)
+            decpts = incpts[::-1]
+
+            incres = interp(incpts, xp, yp)
+            decres = interp(decpts, xp, yp)
+            inctgt = np.array([1, 1, 1, 1], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, left=0)
+            decres = interp(decpts, xp, yp, left=0)
+            inctgt = np.array([0, 1, 1, 1], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, right=2)
+            decres = interp(decpts, xp, yp, right=2)
+            inctgt = np.array([1, 1, 1, 2], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, left=0, right=2)
+            decres = interp(decpts, xp, yp, left=0, right=2)
+            inctgt = np.array([0, 1, 1, 2], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+    def test_scalar_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = 0
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = .3
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float32(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float64(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.nan
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_non_finite_behavior_exact_x(self):
+        x = [1, 2, 2.5, 3, 4]
+        xp = [1, 2, 3, 4]
+        fp = [1, 2, np.inf, 4]
+        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.inf, np.inf, 4])
+        fp = [1, 2, np.nan, 4]
+        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.nan, np.nan, 4])
+
+    @pytest.fixture(params=[
+        lambda x: np.float_(x),
+        lambda x: _make_complex(x, 0),
+        lambda x: _make_complex(0, x),
+        lambda x: _make_complex(x, np.multiply(x, -2))
+    ], ids=[
+        'real',
+        'complex-real',
+        'complex-imag',
+        'complex-both'
+    ])
+    def sc(self, request):
+        """ scale function used by the below tests """
+        return request.param
+
+    def test_non_finite_any_nan(self, sc):
+        """ test that nans are propagated """
+        assert_equal(np.interp(0.5, [np.nan,      1], sc([     0,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0, np.nan], sc([     0,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0,      1], sc([np.nan,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0,      1], sc([     0, np.nan])), sc(np.nan))
+
+    def test_non_finite_inf(self, sc):
+        """ Test that interp between opposite infs gives nan """
+        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([      0,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0,       1], sc([-np.inf, +np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0,       1], sc([+np.inf, -np.inf])), sc(np.nan))
+
+        # unless the y values are equal
+        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([     10,      10])), sc(10))
+
+    def test_non_finite_half_inf_xf(self, sc):
+        """ Test that interp where both axes have a bound at inf gives nan """
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([-np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([+np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, -np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, +np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([-np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([+np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, -np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, +np.inf])), sc(np.nan))
+
+    def test_non_finite_half_inf_x(self, sc):
+        """ Test interp where the x axis has a bound at inf """
+        assert_equal(np.interp(0.5, [-np.inf, -np.inf], sc([0, 10])), sc(10))
+        assert_equal(np.interp(0.5, [-np.inf, 1      ], sc([0, 10])), sc(10))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([0, 10])), sc(0))
+        assert_equal(np.interp(0.5, [+np.inf, +np.inf], sc([0, 10])), sc(0))
+
+    def test_non_finite_half_inf_f(self, sc):
+        """ Test interp where the f axis has a bound at inf """
+        assert_equal(np.interp(0.5, [0, 1], sc([      0, -np.inf])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([      0, +np.inf])), sc(+np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf,      10])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf,      10])), sc(+np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, -np.inf])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, +np.inf])), sc(+np.inf))
+
+    def test_complex_interp(self):
+        # test complex interpolation
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5))*1.0j
+        x0 = 0.3
+        y0 = x0 + (1+x0)*1.0j
+        assert_almost_equal(np.interp(x0, x, y), y0)
+        # test complex left and right
+        x0 = -1
+        left = 2 + 3.0j
+        assert_almost_equal(np.interp(x0, x, y, left=left), left)
+        x0 = 2.0
+        right = 2 + 3.0j
+        assert_almost_equal(np.interp(x0, x, y, right=right), right)
+        # test complex non finite
+        x = [1, 2, 2.5, 3, 4]
+        xp = [1, 2, 3, 4]
+        fp = [1, 2+1j, np.inf, 4]
+        y = [1, 2+1j, np.inf+0.5j, np.inf, 4]
+        assert_almost_equal(np.interp(x, xp, fp), y)
+        # test complex periodic
+        x = [-180, -170, -185, 185, -10, -5, 0, 365]
+        xp = [190, -190, 350, -350]
+        fp = [5+1.0j, 10+2j, 3+3j, 4+4j]
+        y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j,
+             3.5+3.5j, 3.75+3.75j]
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+
+    def test_zero_dimensional_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.array(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+        xp = np.array([0, 2, 4])
+        fp = np.array([1, -1, 1])
+
+        actual = np.interp(np.array(1), xp, fp)
+        assert_equal(actual, 0)
+        assert_(isinstance(actual, np.float64))
+
+        actual = np.interp(np.array(4.5), xp, fp, period=4)
+        assert_equal(actual, 0.5)
+        assert_(isinstance(actual, np.float64))
+
+    def test_if_len_x_is_small(self):
+        xp = np.arange(0, 10, 0.0001)
+        fp = np.sin(xp)
+        assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)
+
+    def test_period(self):
+        x = [-180, -170, -185, 185, -10, -5, 0, 365]
+        xp = [190, -190, 350, -350]
+        fp = [5, 10, 3, 4]
+        y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+        x = np.array(x, order='F').reshape(2, -1)
+        y = np.array(y, order='C').reshape(2, -1)
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+
+
+class TestPercentile:
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.percentile(x, 0), 0.)
+        assert_equal(np.percentile(x, 100), 3.5)
+        assert_equal(np.percentile(x, 50), 1.75)
+        x[1] = np.nan
+        assert_equal(np.percentile(x, 0), np.nan)
+        assert_equal(np.percentile(x, 0, method='nearest'), np.nan)
+
+    def test_fraction(self):
+        x = [Fraction(i, 2) for i in range(8)]
+
+        p = np.percentile(x, Fraction(0))
+        assert_equal(p, Fraction(0))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, Fraction(100))
+        assert_equal(p, Fraction(7, 2))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, Fraction(50))
+        assert_equal(p, Fraction(7, 4))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, [Fraction(50)])
+        assert_equal(p, np.array([Fraction(7, 4)]))
+        assert_equal(type(p), np.ndarray)
+
+    def test_api(self):
+        d = np.ones(5)
+        np.percentile(d, 5, None, None, False)
+        np.percentile(d, 5, None, None, False, 'linear')
+        o = np.ones((1,))
+        np.percentile(d, 5, None, o, False, 'linear')
+
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+
+    def test_2D(self):
+        x = np.array([[1, 1, 1],
+                      [1, 1, 1],
+                      [4, 4, 3],
+                      [1, 1, 1],
+                      [1, 1, 1]])
+        assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1])
+
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
+    def test_linear_nan_1D(self, dtype):
+        # METHOD 1 of H&F
+        arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype)
+        res = np.percentile(
+            arr,
+            40.0,
+            method="linear")
+        np.testing.assert_equal(res, np.NAN)
+        np.testing.assert_equal(res.dtype, arr.dtype)
+
+    H_F_TYPE_CODES = [(int_type, np.float64)
+                      for int_type in np.typecodes["AllInteger"]
+                      ] + [(np.float16, np.float16),
+                           (np.float32, np.float32),
+                           (np.float64, np.float64),
+                           (np.longdouble, np.longdouble),
+                           (np.dtype("O"), np.float64)]
+
+    @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES)
+    @pytest.mark.parametrize(["method", "expected"],
+                             [("inverted_cdf", 20),
+                              ("averaged_inverted_cdf", 27.5),
+                              ("closest_observation", 20),
+                              ("interpolated_inverted_cdf", 20),
+                              ("hazen", 27.5),
+                              ("weibull", 26),
+                              ("linear", 29),
+                              ("median_unbiased", 27),
+                              ("normal_unbiased", 27.125),
+                              ])
+    def test_linear_interpolation(self,
+                                  method,
+                                  expected,
+                                  input_dtype,
+                                  expected_dtype):
+        expected_dtype = np.dtype(expected_dtype)
+        if np._get_promotion_state() == "legacy":
+            expected_dtype = np.promote_types(expected_dtype, np.float64)
+
+        arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype)
+        actual = np.percentile(arr, 40.0, method=method)
+
+        np.testing.assert_almost_equal(
+            actual, expected_dtype.type(expected), 14)
+
+        if method in ["inverted_cdf", "closest_observation"]:
+            if input_dtype == "O":
+                np.testing.assert_equal(np.asarray(actual).dtype, np.float64)
+            else:
+                np.testing.assert_equal(np.asarray(actual).dtype,
+                                        np.dtype(input_dtype))
+        else:
+            np.testing.assert_equal(np.asarray(actual).dtype,
+                                    np.dtype(expected_dtype))
+
+    TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["Float"] + "O"
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_lower_higher(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 50,
+                                   method='lower'), 4)
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 50,
+                                   method='higher'), 5)
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_midpoint(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 51,
+                                   method='midpoint'), 4.5)
+        assert_equal(np.percentile(np.arange(9, dtype=dtype) + 1, 50,
+                                   method='midpoint'), 5)
+        assert_equal(np.percentile(np.arange(11, dtype=dtype), 51,
+                                   method='midpoint'), 5.5)
+        assert_equal(np.percentile(np.arange(11, dtype=dtype), 50,
+                                   method='midpoint'), 5)
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_nearest(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 51,
+                                   method='nearest'), 5)
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 49,
+                                   method='nearest'), 4)
+
+    def test_linear_interpolation_extrapolation(self):
+        arr = np.random.rand(5)
+
+        actual = np.percentile(arr, 100)
+        np.testing.assert_equal(actual, arr.max())
+
+        actual = np.percentile(arr, 0)
+        np.testing.assert_equal(actual, arr.min())
+
+    def test_sequence(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75])
+
+    def test_axis(self):
+        x = np.arange(12).reshape(3, 4)
+
+        assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0])
+
+        r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
+        assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0)
+
+        r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]]
+        assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T)
+
+        # ensure qth axis is always first as with np.array(old_percentile(..))
+        x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
+        assert_equal(np.percentile(x, (25, 50)).shape, (2,))
+        assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,))
+        assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5))
+        assert_equal(
+            np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50),
+                                   method="higher").shape, (2,))
+        assert_equal(np.percentile(x, (25, 50, 75),
+                                   method="higher").shape, (3,))
+        assert_equal(np.percentile(x, (25, 50), axis=0,
+                                   method="higher").shape, (2, 4, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=1,
+                                   method="higher").shape, (2, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=2,
+                                   method="higher").shape, (2, 3, 4, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=3,
+                                   method="higher").shape, (2, 3, 4, 5))
+        assert_equal(np.percentile(x, (25, 50, 75), axis=1,
+                                   method="higher").shape, (3, 3, 5, 6))
+
+    def test_scalar_q(self):
+        # test for no empty dimensions for compatibility with old percentile
+        x = np.arange(12).reshape(3, 4)
+        assert_equal(np.percentile(x, 50), 5.5)
+        assert_(np.isscalar(np.percentile(x, 50)))
+        r0 = np.array([4.,  5.,  6.,  7.])
+        assert_equal(np.percentile(x, 50, axis=0), r0)
+        assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape)
+        r1 = np.array([1.5,  5.5,  9.5])
+        assert_almost_equal(np.percentile(x, 50, axis=1), r1)
+        assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape)
+
+        out = np.empty(1)
+        assert_equal(np.percentile(x, 50, out=out), 5.5)
+        assert_equal(out, 5.5)
+        out = np.empty(4)
+        assert_equal(np.percentile(x, 50, axis=0, out=out), r0)
+        assert_equal(out, r0)
+        out = np.empty(3)
+        assert_equal(np.percentile(x, 50, axis=1, out=out), r1)
+        assert_equal(out, r1)
+
+        # test for no empty dimensions for compatibility with old percentile
+        x = np.arange(12).reshape(3, 4)
+        assert_equal(np.percentile(x, 50, method='lower'), 5.)
+        assert_(np.isscalar(np.percentile(x, 50)))
+        r0 = np.array([4.,  5.,  6.,  7.])
+        c0 = np.percentile(x, 50, method='lower', axis=0)
+        assert_equal(c0, r0)
+        assert_equal(c0.shape, r0.shape)
+        r1 = np.array([1.,  5.,  9.])
+        c1 = np.percentile(x, 50, method='lower', axis=1)
+        assert_almost_equal(c1, r1)
+        assert_equal(c1.shape, r1.shape)
+
+        out = np.empty((), dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', out=out)
+        assert_equal(c, 5)
+        assert_equal(out, 5)
+        out = np.empty(4, dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', axis=0, out=out)
+        assert_equal(c, r0)
+        assert_equal(out, r0)
+        out = np.empty(3, dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', axis=1, out=out)
+        assert_equal(c, r1)
+        assert_equal(out, r1)
+
+    def test_exception(self):
+        assert_raises(ValueError, np.percentile, [1, 2], 56,
+                      method='foobar')
+        assert_raises(ValueError, np.percentile, [1], 101)
+        assert_raises(ValueError, np.percentile, [1], -1)
+        assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101])
+        assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1])
+
+    def test_percentile_list(self):
+        assert_equal(np.percentile([1, 2, 3], 0), 1)
+
+    def test_percentile_out(self):
+        x = np.array([1, 2, 3])
+        y = np.zeros((3,))
+        p = (1, 2, 3)
+        np.percentile(x, p, out=y)
+        assert_equal(np.percentile(x, p), y)
+
+        x = np.array([[1, 2, 3],
+                      [4, 5, 6]])
+
+        y = np.zeros((3, 3))
+        np.percentile(x, p, axis=0, out=y)
+        assert_equal(np.percentile(x, p, axis=0), y)
+
+        y = np.zeros((3, 2))
+        np.percentile(x, p, axis=1, out=y)
+        assert_equal(np.percentile(x, p, axis=1), y)
+
+        x = np.arange(12).reshape(3, 4)
+        # q.dim > 1, float
+        r0 = np.array([[2.,  3.,  4., 5.], [4., 5., 6., 7.]])
+        out = np.empty((2, 4))
+        assert_equal(np.percentile(x, (25, 50), axis=0, out=out), r0)
+        assert_equal(out, r0)
+        r1 = np.array([[0.75,  4.75,  8.75], [1.5,  5.5,  9.5]])
+        out = np.empty((2, 3))
+        assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1)
+        assert_equal(out, r1)
+
+        # q.dim > 1, int
+        r0 = np.array([[0,  1,  2, 3], [4, 5, 6, 7]])
+        out = np.empty((2, 4), dtype=x.dtype)
+        c = np.percentile(x, (25, 50), method='lower', axis=0, out=out)
+        assert_equal(c, r0)
+        assert_equal(out, r0)
+        r1 = np.array([[0,  4,  8], [1,  5,  9]])
+        out = np.empty((2, 3), dtype=x.dtype)
+        c = np.percentile(x, (25, 50), method='lower', axis=1, out=out)
+        assert_equal(c, r1)
+        assert_equal(out, r1)
+
+    def test_percentile_empty_dim(self):
+        # empty dims are preserved
+        d = np.arange(11 * 2).reshape(11, 1, 2, 1)
+        assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2))
+        assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2))
+        assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1))
+
+        assert_array_equal(np.percentile(d, 50, axis=2,
+                                         method='midpoint').shape,
+                           (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-2,
+                                         method='midpoint').shape,
+                           (11, 1, 1))
+
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape,
+                           (2, 1, 2, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape,
+                           (2, 11, 2, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape,
+                           (2, 11, 1, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape,
+                           (2, 11, 1, 2))
+
+    def test_percentile_no_overwrite(self):
+        a = np.array([2, 3, 4, 1])
+        np.percentile(a, [50], overwrite_input=False)
+        assert_equal(a, np.array([2, 3, 4, 1]))
+
+        a = np.array([2, 3, 4, 1])
+        np.percentile(a, [50])
+        assert_equal(a, np.array([2, 3, 4, 1]))
+
+    def test_no_p_overwrite(self):
+        p = np.linspace(0., 100., num=5)
+        np.percentile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, np.linspace(0., 100., num=5))
+        p = np.linspace(0., 100., num=5).tolist()
+        np.percentile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, np.linspace(0., 100., num=5).tolist())
+
+    def test_percentile_overwrite(self):
+        a = np.array([2, 3, 4, 1])
+        b = np.percentile(a, [50], overwrite_input=True)
+        assert_equal(b, np.array([2.5]))
+
+        b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True)
+        assert_equal(b, np.array([2.5]))
+
+    def test_extended_axis(self):
+        o = np.random.normal(size=(71, 23))
+        x = np.dstack([o] * 10)
+        assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30))
+        x = np.moveaxis(x, -1, 0)
+        assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30))
+        x = x.swapaxes(0, 1).copy()
+        assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30))
+        x = x.swapaxes(0, 1).copy()
+
+        assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)),
+                     np.percentile(x, [25, 60], axis=None))
+        assert_equal(np.percentile(x, [25, 60], axis=(0,)),
+                     np.percentile(x, [25, 60], axis=0))
+
+        d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
+        np.random.shuffle(d.ravel())
+        assert_equal(np.percentile(d, 25,  axis=(0, 1, 2))[0],
+                     np.percentile(d[:,:,:, 0].flatten(), 25))
+        assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1],
+                     np.percentile(d[:,:, 1,:].flatten(), [10, 90]))
+        assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2],
+                     np.percentile(d[:,:, 2,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2],
+                     np.percentile(d[2,:,:,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1],
+                     np.percentile(d[2, 1,:,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1],
+                     np.percentile(d[2,:,:, 1].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2],
+                     np.percentile(d[2,:, 2,:].flatten(), 25))
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(np.AxisError, np.percentile, d, axis=-5, q=25)
+        assert_raises(np.AxisError, np.percentile, d, axis=(0, -5), q=25)
+        assert_raises(np.AxisError, np.percentile, d, axis=4, q=25)
+        assert_raises(np.AxisError, np.percentile, d, axis=(0, 4), q=25)
+        # each of these refers to the same axis twice
+        assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)
+        assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25)
+        assert_raises(ValueError, np.percentile, d, axis=(3, -1), q=25)
+
+    def test_keepdims(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
+                     (1, 1, 7, 11))
+        assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
+                     (1, 5, 7, 1))
+        assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
+                     (3, 1, 7, 11))
+        assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
+                     (1, 1, 7, 1))
+
+        assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
+                                   keepdims=True).shape, (2, 1, 1, 7, 1))
+        assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
+                                   keepdims=True).shape, (2, 1, 5, 7, 1))
+
+    @pytest.mark.parametrize('q', [7, [1, 7]])
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1,),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, q, axis):
+        d = np.ones((3, 5, 7, 11))
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        shape_out = np.shape(q) + shape_out
+
+        out = np.empty(shape_out)
+        result = np.percentile(d, q, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    def test_out(self):
+        o = np.zeros((4,))
+        d = np.ones((3, 4))
+        assert_equal(np.percentile(d, 0, 0, out=o), o)
+        assert_equal(np.percentile(d, 0, 0, method='nearest', out=o), o)
+        o = np.zeros((3,))
+        assert_equal(np.percentile(d, 1, 1, out=o), o)
+        assert_equal(np.percentile(d, 1, 1, method='nearest', out=o), o)
+
+        o = np.zeros(())
+        assert_equal(np.percentile(d, 2, out=o), o)
+        assert_equal(np.percentile(d, 2, method='nearest', out=o), o)
+
+    def test_out_nan(self):
+        with warnings.catch_warnings(record=True):
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            o = np.zeros((4,))
+            d = np.ones((3, 4))
+            d[2, 1] = np.nan
+            assert_equal(np.percentile(d, 0, 0, out=o), o)
+            assert_equal(
+                np.percentile(d, 0, 0, method='nearest', out=o), o)
+            o = np.zeros((3,))
+            assert_equal(np.percentile(d, 1, 1, out=o), o)
+            assert_equal(
+                np.percentile(d, 1, 1, method='nearest', out=o), o)
+            o = np.zeros(())
+            assert_equal(np.percentile(d, 1, out=o), o)
+            assert_equal(
+                np.percentile(d, 1, method='nearest', out=o), o)
+
+    def test_nan_behavior(self):
+        a = np.arange(24, dtype=float)
+        a[2] = np.nan
+        assert_equal(np.percentile(a, 0.3), np.nan)
+        assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
+        assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
+                     np.array([np.nan] * 2))
+
+        a = np.arange(24, dtype=float).reshape(2, 3, 4)
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_equal(np.percentile(a, 0.3), np.nan)
+        assert_equal(np.percentile(a, 0.3).ndim, 0)
+
+        # axis0 zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.percentile(a, 0.3, 0), b)
+
+        # axis0 not zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], 0)
+        b[:, 2, 3] = np.nan
+        b[:, 1, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], 0), b)
+
+        # axis1 zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.percentile(a, 0.3, 1), b)
+        # axis1 not zerod
+        b = np.percentile(
+            np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1)
+        b[:, 1, 3] = np.nan
+        b[:, 1, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], 1), b)
+
+        # axis02 zerod
+        b = np.percentile(
+            np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.percentile(a, 0.3, (0, 2)), b)
+        # axis02 not zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], (0, 2))
+        b[:, 1] = np.nan
+        b[:, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
+        # axis02 not zerod with method='nearest'
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], (0, 2), method='nearest')
+        b[:, 1] = np.nan
+        b[:, 2] = np.nan
+        assert_equal(np.percentile(
+            a, [0.3, 0.6], (0, 2), method='nearest'), b)
+
+    def test_nan_q(self):
+        # GH18830
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], np.nan)
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], [np.nan])
+        q = np.linspace(1.0, 99.0, 16)
+        q[0] = np.nan
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], q)
+
+    @pytest.mark.parametrize("dtype", ["m8[D]", "M8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_basic(self, dtype, pos):
+        # TODO: Note that times have dubious rounding as of fixing NaTs!
+        # NaT and NaN should behave the same, do basic tests for NaT:
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.percentile(a, 30)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.percentile(a, 30, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
+
+quantile_methods = [
+    'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+    'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
+    'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher',
+    'midpoint']
+
+
+class TestQuantile:
+    # most of this is already tested by TestPercentile
+
+    def V(self, x, y, alpha):
+        # Identification function used in several tests.
+        return (x >= y) - alpha
+
+    def test_max_ulp(self):
+        x = [0.0, 0.2, 0.4]
+        a = np.quantile(x, 0.45)
+        # The default linear method would result in 0 + 0.2 * (0.45/2) = 0.18.
+        # 0.18 is not exactly representable and the formula leads to a 1 ULP
+        # different result. Ensure it is this exact within 1 ULP, see gh-20331.
+        np.testing.assert_array_max_ulp(a, 0.18, maxulp=1)
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.quantile(x, 0), 0.)
+        assert_equal(np.quantile(x, 1), 3.5)
+        assert_equal(np.quantile(x, 0.5), 1.75)
+
+    def test_correct_quantile_value(self):
+        a = np.array([True])
+        tf_quant = np.quantile(True, False)
+        assert_equal(tf_quant, a[0])
+        assert_equal(type(tf_quant), a.dtype)
+        a = np.array([False, True, True])
+        quant_res = np.quantile(a, a)
+        assert_array_equal(quant_res, a)
+        assert_equal(quant_res.dtype, a.dtype)
+
+    def test_fraction(self):
+        # fractional input, integral quantile
+        x = [Fraction(i, 2) for i in range(8)]
+        q = np.quantile(x, 0)
+        assert_equal(q, 0)
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, 1)
+        assert_equal(q, Fraction(7, 2))
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, .5)
+        assert_equal(q, 1.75)
+        assert_equal(type(q), np.float64)
+
+        q = np.quantile(x, Fraction(1, 2))
+        assert_equal(q, Fraction(7, 4))
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, [Fraction(1, 2)])
+        assert_equal(q, np.array([Fraction(7, 4)]))
+        assert_equal(type(q), np.ndarray)
+
+        q = np.quantile(x, [[Fraction(1, 2)]])
+        assert_equal(q, np.array([[Fraction(7, 4)]]))
+        assert_equal(type(q), np.ndarray)
+
+        # repeat with integral input but fractional quantile
+        x = np.arange(8)
+        assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2))
+
+    def test_complex(self):
+        #See gh-22652
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+
+    def test_no_p_overwrite(self):
+        # this is worth retesting, because quantile does not make a copy
+        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
+        p = p0.copy()
+        np.quantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+        p0 = p0.tolist()
+        p = p.tolist()
+        np.quantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
+    def test_quantile_preserve_int_type(self, dtype):
+        res = np.quantile(np.array([1, 2], dtype=dtype), [0.5],
+                          method="nearest")
+        assert res.dtype == dtype
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    def test_quantile_monotonic(self, method):
+        # GH 14685
+        # test that the return value of quantile is monotonic if p0 is ordered
+        # Also tests that the boundary values are not mishandled.
+        p0 = np.linspace(0, 1, 101)
+        quantile = np.quantile(np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9,
+                                         8, 8, 7]) * 0.1, p0, method=method)
+        assert_equal(np.sort(quantile), quantile)
+
+        # Also test one where the number of data points is clearly divisible:
+        quantile = np.quantile([0., 1., 2., 3.], p0, method=method)
+        assert_equal(np.sort(quantile), quantile)
+
+    @hypothesis.given(
+            arr=arrays(dtype=np.float64,
+                       shape=st.integers(min_value=3, max_value=1000),
+                       elements=st.floats(allow_infinity=False, allow_nan=False,
+                                          min_value=-1e300, max_value=1e300)))
+    def test_quantile_monotonic_hypo(self, arr):
+        p0 = np.arange(0, 1, 0.01)
+        quantile = np.quantile(arr, p0)
+        assert_equal(np.sort(quantile), quantile)
+
+    def test_quantile_scalar_nan(self):
+        a = np.array([[10., 7., 4.], [3., 2., 1.]])
+        a[0][1] = np.nan
+        actual = np.quantile(a, 0.5)
+        assert np.isscalar(actual)
+        assert_equal(np.quantile(a, 0.5), np.nan)
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_identification_equation(self, method, alpha):
+        # Test that the identification equation holds for the empirical
+        # CDF:
+        #   E[V(x, Y)] = 0  <=>  x is quantile
+        # with Y the random variable for which we have observed values and
+        # V(x, y) the canonical identification function for the quantile (at
+        # level alpha), see
+        # https://doi.org/10.48550/arXiv.0912.0902        
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we cover 3 cases:
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        x = np.quantile(y, alpha, method=method)
+        if method in ("higher",):
+            # These methods do not fulfill the identification equation.
+            assert np.abs(np.mean(self.V(x, y, alpha))) > 0.1 / n
+        elif int(n * alpha) == n * alpha:
+            # We can expect exact results, up to machine precision.
+            assert_allclose(np.mean(self.V(x, y, alpha)), 0, atol=1e-14)
+        else:
+            # V = (x >= y) - alpha cannot sum to zero exactly but within
+            # "sample precision".
+            assert_allclose(np.mean(self.V(x, y, alpha)), 0,
+                atol=1 / n / np.amin([alpha, 1 - alpha]))
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_add_and_multiply_constant(self, method, alpha):
+        # Test that
+        #  1. quantile(c + x) = c + quantile(x)
+        #  2. quantile(c * x) = c * quantile(x)
+        #  3. quantile(-x) = -quantile(x, 1 - alpha)
+        #     On empirical quantiles, this equation does not hold exactly.
+        # Koenker (2005) "Quantile Regression" Chapter 2.2.3 calls these
+        # properties equivariance.
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we have cases for
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        q = np.quantile(y, alpha, method=method)
+        c = 13.5
+
+        # 1
+        assert_allclose(np.quantile(c + y, alpha, method=method), c + q)
+        # 2
+        assert_allclose(np.quantile(c * y, alpha, method=method), c * q)
+        # 3
+        q = -np.quantile(-y, 1 - alpha, method=method)
+        if method == "inverted_cdf":
+            if (
+                n * alpha == int(n * alpha)
+                or np.round(n * alpha) == int(n * alpha) + 1
+            ):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "closest_observation":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            elif np.round(n * alpha) == int(n * alpha) + 1:
+                assert_allclose(
+                    q, np.quantile(y, alpha + 1/n, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "interpolated_inverted_cdf":
+            assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+        elif method == "nearest":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method=method))
+        elif method == "lower":
+            assert_allclose(q, np.quantile(y, alpha, method="higher"))
+        elif method == "higher":
+            assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        else:
+            # "averaged_inverted_cdf", "hazen", "weibull", "linear",
+            # "median_unbiased", "normal_unbiased", "midpoint"
+            assert_allclose(q, np.quantile(y, alpha, method=method))
+
+
+class TestLerp:
+    @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
+                                   min_value=0, max_value=1),
+                      t1=st.floats(allow_nan=False, allow_infinity=False,
+                                   min_value=0, max_value=1),
+                      a = st.floats(allow_nan=False, allow_infinity=False,
+                                    min_value=-1e300, max_value=1e300),
+                      b = st.floats(allow_nan=False, allow_infinity=False,
+                                    min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b):
+        l0 = nfb._lerp(a, b, t0)
+        l1 = nfb._lerp(a, b, t1)
+        if t0 == t1 or a == b:
+            assert l0 == l1  # uninteresting
+        elif (t0 < t1) == (a < b):
+            assert l0 <= l1
+        else:
+            assert l0 >= l1
+
+    @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=0, max_value=1),
+                      a=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300),
+                      b=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_bounded(self, t, a, b):
+        if a <= b:
+            assert a <= nfb._lerp(a, b, t) <= b
+        else:
+            assert b <= nfb._lerp(a, b, t) <= a
+
+    @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=0, max_value=1),
+                      a=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300),
+                      b=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_symmetric(self, t, a, b):
+        # double subtraction is needed to remove the extra precision of t < 0.5
+        left = nfb._lerp(a, b, 1 - (1 - t))
+        right = nfb._lerp(b, a, 1 - t)
+        assert_allclose(left, right)
+
+    def test_linear_interpolation_formula_0d_inputs(self):
+        a = np.array(2)
+        b = np.array(5)
+        t = np.array(0.2)
+        assert nfb._lerp(a, b, t) == 2.6
+
+
+class TestMedian:
+
+    def test_basic(self):
+        a0 = np.array(1)
+        a1 = np.arange(2)
+        a2 = np.arange(6).reshape(2, 3)
+        assert_equal(np.median(a0), 1)
+        assert_allclose(np.median(a1), 0.5)
+        assert_allclose(np.median(a2), 2.5)
+        assert_allclose(np.median(a2, axis=0), [1.5,  2.5,  3.5])
+        assert_equal(np.median(a2, axis=1), [1, 4])
+        assert_allclose(np.median(a2, axis=None), 2.5)
+
+        a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775])
+        assert_almost_equal((a[1] + a[3]) / 2., np.median(a))
+        a = np.array([0.0463301, 0.0444502, 0.141249])
+        assert_equal(a[0], np.median(a))
+        a = np.array([0.0444502, 0.141249, 0.0463301])
+        assert_equal(a[-1], np.median(a))
+        # check array scalar result
+        assert_equal(np.median(a).ndim, 0)
+        a[1] = np.nan
+        assert_equal(np.median(a).ndim, 0)
+
+    def test_axis_keyword(self):
+        a3 = np.array([[2, 3],
+                       [0, 1],
+                       [6, 7],
+                       [4, 5]])
+        for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]:
+            orig = a.copy()
+            np.median(a, axis=None)
+            for ax in range(a.ndim):
+                np.median(a, axis=ax)
+            assert_array_equal(a, orig)
+
+        assert_allclose(np.median(a3, axis=0), [3,  4])
+        assert_allclose(np.median(a3.T, axis=1), [3,  4])
+        assert_allclose(np.median(a3), 3.5)
+        assert_allclose(np.median(a3, axis=None), 3.5)
+        assert_allclose(np.median(a3.T), 3.5)
+
+    def test_overwrite_keyword(self):
+        a3 = np.array([[2, 3],
+                       [0, 1],
+                       [6, 7],
+                       [4, 5]])
+        a0 = np.array(1)
+        a1 = np.arange(2)
+        a2 = np.arange(6).reshape(2, 3)
+        assert_allclose(np.median(a0.copy(), overwrite_input=True), 1)
+        assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5)
+        assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5)
+        assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0),
+                        [1.5,  2.5,  3.5])
+        assert_allclose(
+            np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4])
+        assert_allclose(
+            np.median(a2.copy(), overwrite_input=True, axis=None), 2.5)
+        assert_allclose(
+            np.median(a3.copy(), overwrite_input=True, axis=0), [3,  4])
+        assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1),
+                        [3,  4])
+
+        a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
+        np.random.shuffle(a4.ravel())
+        assert_allclose(np.median(a4, axis=None),
+                        np.median(a4.copy(), axis=None, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=0),
+                        np.median(a4.copy(), axis=0, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=1),
+                        np.median(a4.copy(), axis=1, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=2),
+                        np.median(a4.copy(), axis=2, overwrite_input=True))
+
+    def test_array_like(self):
+        x = [1, 2, 3]
+        assert_almost_equal(np.median(x), 2)
+        x2 = [x]
+        assert_almost_equal(np.median(x2), 2)
+        assert_allclose(np.median(x2, axis=0), x)
+
+    def test_subclass(self):
+        # gh-3846
+        class MySubClass(np.ndarray):
+
+            def __new__(cls, input_array, info=None):
+                obj = np.asarray(input_array).view(cls)
+                obj.info = info
+                return obj
+
+            def mean(self, axis=None, dtype=None, out=None):
+                return -7
+
+        a = MySubClass([1, 2, 3])
+        assert_equal(np.median(a), -7)
+
+    @pytest.mark.parametrize('arr',
+                             ([1., 2., 3.], [1., np.nan, 3.], np.nan, 0.))
+    def test_subclass2(self, arr):
+        """Check that we return subclasses, even if a NaN scalar."""
+        class MySubclass(np.ndarray):
+            pass
+
+        m = np.median(np.array(arr).view(MySubclass))
+        assert isinstance(m, MySubclass)
+
+    def test_out(self):
+        o = np.zeros((4,))
+        d = np.ones((3, 4))
+        assert_equal(np.median(d, 0, out=o), o)
+        o = np.zeros((3,))
+        assert_equal(np.median(d, 1, out=o), o)
+        o = np.zeros(())
+        assert_equal(np.median(d, out=o), o)
+
+    def test_out_nan(self):
+        with warnings.catch_warnings(record=True):
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            o = np.zeros((4,))
+            d = np.ones((3, 4))
+            d[2, 1] = np.nan
+            assert_equal(np.median(d, 0, out=o), o)
+            o = np.zeros((3,))
+            assert_equal(np.median(d, 1, out=o), o)
+            o = np.zeros(())
+            assert_equal(np.median(d, out=o), o)
+
+    def test_nan_behavior(self):
+        a = np.arange(24, dtype=float)
+        a[2] = np.nan
+        assert_equal(np.median(a), np.nan)
+        assert_equal(np.median(a, axis=0), np.nan)
+
+        a = np.arange(24, dtype=float).reshape(2, 3, 4)
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_equal(np.median(a), np.nan)
+        assert_equal(np.median(a).ndim, 0)
+
+        # axis0
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.median(a, 0), b)
+
+        # axis1
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.median(a, 1), b)
+
+        # axis02
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.median(a, (0, 2)), b)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work correctly")
+    def test_empty(self):
+        # mean(empty array) emits two warnings: empty slice and divide by 0
+        a = np.array([], dtype=float)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a), np.nan)
+            assert_(w[0].category is RuntimeWarning)
+            assert_equal(len(w), 2)
+
+        # multiple dimensions
+        a = np.array([], dtype=float, ndmin=3)
+        # no axis
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a), np.nan)
+            assert_(w[0].category is RuntimeWarning)
+
+        # axis 0 and 1
+        b = np.array([], dtype=float, ndmin=2)
+        assert_equal(np.median(a, axis=0), b)
+        assert_equal(np.median(a, axis=1), b)
+
+        # axis 2
+        b = np.array(np.nan, dtype=float, ndmin=2)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a, axis=2), b)
+            assert_(w[0].category is RuntimeWarning)
+
+    def test_object(self):
+        o = np.arange(7.)
+        assert_(type(np.median(o.astype(object))), float)
+        o[2] = np.nan
+        assert_(type(np.median(o.astype(object))), float)
+
+    def test_extended_axis(self):
+        o = np.random.normal(size=(71, 23))
+        x = np.dstack([o] * 10)
+        assert_equal(np.median(x, axis=(0, 1)), np.median(o))
+        x = np.moveaxis(x, -1, 0)
+        assert_equal(np.median(x, axis=(-2, -1)), np.median(o))
+        x = x.swapaxes(0, 1).copy()
+        assert_equal(np.median(x, axis=(0, -1)), np.median(o))
+
+        assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None))
+        assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0))
+        assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1))
+
+        d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
+        np.random.shuffle(d.ravel())
+        assert_equal(np.median(d, axis=(0, 1, 2))[0],
+                     np.median(d[:,:,:, 0].flatten()))
+        assert_equal(np.median(d, axis=(0, 1, 3))[1],
+                     np.median(d[:,:, 1,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 1, -4))[2],
+                     np.median(d[:,:, 2,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 1, 2))[2],
+                     np.median(d[2,:,:,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 2))[2, 1],
+                     np.median(d[2, 1,:,:].flatten()))
+        assert_equal(np.median(d, axis=(1, -2))[2, 1],
+                     np.median(d[2,:,:, 1].flatten()))
+        assert_equal(np.median(d, axis=(1, 3))[2, 2],
+                     np.median(d[2,:, 2,:].flatten()))
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(np.AxisError, np.median, d, axis=-5)
+        assert_raises(np.AxisError, np.median, d, axis=(0, -5))
+        assert_raises(np.AxisError, np.median, d, axis=4)
+        assert_raises(np.AxisError, np.median, d, axis=(0, 4))
+        assert_raises(ValueError, np.median, d, axis=(1, 1))
+
+    def test_keepdims(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_equal(np.median(d, axis=None, keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape,
+                     (1, 1, 7, 11))
+        assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape,
+                     (1, 5, 7, 1))
+        assert_equal(np.median(d, axis=(1,), keepdims=True).shape,
+                     (3, 1, 7, 11))
+        assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape,
+                     (1, 1, 7, 1))
+
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1, ),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, axis):
+        d = np.ones((3, 5, 7, 11))
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        out = np.empty(shape_out)
+        result = np.median(d, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    @pytest.mark.parametrize("dtype", ["m8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_behavior(self, dtype, pos):
+        # TODO: Median does not support Datetime, due to `mean`.
+        # NaT and NaN should behave the same, do basic tests for NaT.
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.median(a)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.median(a, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
+
+class TestAdd_newdoc_ufunc:
+
+    def test_ufunc_arg(self):
+        assert_raises(TypeError, add_newdoc_ufunc, 2, "blah")
+        assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah")
+
+    def test_string_arg(self):
+        assert_raises(TypeError, add_newdoc_ufunc, np.add, 3)
+
+
+class TestAdd_newdoc:
+
+    @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
+    @pytest.mark.xfail(IS_PYPY, reason="PyPy does not modify tp_doc")
+    def test_add_doc(self):
+        # test that np.add_newdoc did attach a docstring successfully:
+        tgt = "Current flat index into the array."
+        assert_equal(np.core.flatiter.index.__doc__[:len(tgt)], tgt)
+        assert_(len(np.core.ufunc.identity.__doc__) > 300)
+        assert_(len(np.lib.index_tricks.mgrid.__doc__) > 300)
+
+    @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
+    def test_errors_are_ignored(self):
+        prev_doc = np.core.flatiter.index.__doc__
+        # nothing changed, but error ignored, this should probably
+        # give a warning (or even error) in the future.
+        np.add_newdoc("numpy.core", "flatiter", ("index", "bad docstring"))
+        assert prev_doc == np.core.flatiter.index.__doc__
+
+
+class TestAddDocstring():
+    # Test should possibly be moved, but it also fits to be close to
+    # the newdoc tests...
+    @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
+    @pytest.mark.skipif(IS_PYPY, reason="PyPy does not modify tp_doc")
+    def test_add_same_docstring(self):
+        # test for attributes (which are C-level defined)
+        np.add_docstring(np.ndarray.flat, np.ndarray.flat.__doc__)
+        # And typical functions:
+        def func():
+            """docstring"""
+            return
+
+        np.add_docstring(func, func.__doc__)
+
+    @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
+    def test_different_docstring_fails(self):
+        # test for attributes (which are C-level defined)
+        with assert_raises(RuntimeError):
+            np.add_docstring(np.ndarray.flat, "different docstring")
+        # And typical functions:
+        def func():
+            """docstring"""
+            return
+
+        with assert_raises(RuntimeError):
+            np.add_docstring(func, "different docstring")
+
+
+class TestSortComplex:
+
+    @pytest.mark.parametrize("type_in, type_out", [
+        ('l', 'D'),
+        ('h', 'F'),
+        ('H', 'F'),
+        ('b', 'F'),
+        ('B', 'F'),
+        ('g', 'G'),
+        ])
+    def test_sort_real(self, type_in, type_out):
+        # sort_complex() type casting for real input types
+        a = np.array([5, 3, 6, 2, 1], dtype=type_in)
+        actual = np.sort_complex(a)
+        expected = np.sort(a).astype(type_out)
+        assert_equal(actual, expected)
+        assert_equal(actual.dtype, expected.dtype)
+
+    def test_sort_complex(self):
+        # sort_complex() handling of complex input
+        a = np.array([2 + 3j, 1 - 2j, 1 - 3j, 2 + 1j], dtype='D')
+        expected = np.array([1 - 3j, 1 - 2j, 2 + 1j, 2 + 3j], dtype='D')
+        actual = np.sort_complex(a)
+        assert_equal(actual, expected)
+        assert_equal(actual.dtype, expected.dtype)
diff --git a/shared/package-files/0c/0c15552da76e6541d25a5289e5a0579b181d366fd88cbbd68328934dc42ad387/test_cdft_asymptotic.py b/shared/package-files/0c/0c15552da76e6541d25a5289e5a0579b181d366fd88cbbd68328934dc42ad387/test_cdft_asymptotic.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b1ad41243f0865c205963d938ab61a346ee8e88
--- /dev/null
+++ b/shared/package-files/0c/0c15552da76e6541d25a5289e5a0579b181d366fd88cbbd68328934dc42ad387/test_cdft_asymptotic.py
@@ -0,0 +1,49 @@
+# gh-14777 regression tests
+# Test stdtr and stdtrit with infinite df and large values of df
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy.special import stdtr, stdtrit, ndtr, ndtri
+
+
+def test_stdtr_vs_R_large_df():
+    df = [1e10, 1e12, 1e120, np.inf]
+    t = 1.
+    res = stdtr(df, t)
+    # R Code:
+    #   options(digits=20)
+    #   pt(1., c(1e10, 1e12, 1e120, Inf))
+    res_R = [0.84134474605644460343,
+             0.84134474606842180044,
+             0.84134474606854281475,
+             0.84134474606854292578]
+    assert_allclose(res, res_R, rtol=2e-15)
+    # last value should also agree with ndtr
+    assert_equal(res[3], ndtr(1.))
+
+
+def test_stdtrit_vs_R_large_df():
+    df = [1e10, 1e12, 1e120, np.inf]
+    p = 0.1
+    res = stdtrit(df, p)
+    # R Code:
+    #   options(digits=20)
+    #   qt(0.1, c(1e10, 1e12, 1e120, Inf))
+    res_R = [-1.2815515656292593150,
+             -1.2815515655454472466,
+             -1.2815515655446008125,
+             -1.2815515655446008125]
+    assert_allclose(res, res_R, rtol=1e-14, atol=1e-15)
+    # last value should also agree with ndtri
+    assert_equal(res[3], ndtri(0.1))
+
+
+def test_stdtr_stdtri_invalid():
+    # a mix of large and inf df with t/p equal to nan
+    df = [1e10, 1e12, 1e120, np.inf]
+    x = np.nan
+    res1 = stdtr(df, x)
+    res2 = stdtrit(df, x)
+    res_ex = 4*[np.nan]
+    assert_equal(res1, res_ex)
+    assert_equal(res2, res_ex)
diff --git a/shared/package-files/0c/0c1580ed02b4765ba89278a8b8e8b3675879a0021c5a2e3b37b8ba5224834625/console.pyi b/shared/package-files/0c/0c1580ed02b4765ba89278a8b8e8b3675879a0021c5a2e3b37b8ba5224834625/console.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d2fc1d36fd5c7f8fb9302cc65511d7d262d2d0b5
--- /dev/null
+++ b/shared/package-files/0c/0c1580ed02b4765ba89278a8b8e8b3675879a0021c5a2e3b37b8ba5224834625/console.pyi
@@ -0,0 +1,44 @@
+import code
+from typing import Any, Optional
+
+class HTMLStringO:
+    def __init__(self): ...
+    def isatty(self): ...
+    def close(self): ...
+    def flush(self): ...
+    def seek(self, n, mode: int = ...): ...
+    def readline(self): ...
+    def reset(self): ...
+    def write(self, x): ...
+    def writelines(self, x): ...
+
+class ThreadedStream:
+    @staticmethod
+    def push(): ...
+    @staticmethod
+    def fetch(): ...
+    @staticmethod
+    def displayhook(obj): ...
+    def __setattr__(self, name, value): ...
+    def __dir__(self): ...
+    def __getattribute__(self, name): ...
+
+class _ConsoleLoader:
+    def __init__(self): ...
+    def register(self, code, source): ...
+    def get_source_by_code(self, code): ...
+
+class _InteractiveConsole(code.InteractiveInterpreter):
+    globals: Any
+    more: Any
+    buffer: Any
+    def __init__(self, globals, locals): ...
+    def runsource(self, source): ...
+    def runcode(self, code): ...
+    def showtraceback(self): ...
+    def showsyntaxerror(self, filename: Optional[Any] = ...): ...
+    def write(self, data): ...
+
+class Console:
+    def __init__(self, globals: Optional[Any] = ..., locals: Optional[Any] = ...): ...
+    def eval(self, code): ...
diff --git a/shared/package-files/0c/0c15ad816413e201d7ee66f88998d185f0cc11f728586cc8ca710e050a76719e/random_clustered.py b/shared/package-files/0c/0c15ad816413e201d7ee66f88998d185f0cc11f728586cc8ca710e050a76719e/random_clustered.py
new file mode 100644
index 0000000000000000000000000000000000000000..622fb911bfb45e6ef607e111fa860d19a54a55c5
--- /dev/null
+++ b/shared/package-files/0c/0c15ad816413e201d7ee66f88998d185f0cc11f728586cc8ca710e050a76719e/random_clustered.py
@@ -0,0 +1,116 @@
+"""Generate graphs with given degree and triangle sequence.
+"""
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_clustered_graph"]
+
+
+@py_random_state(2)
+def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
+    r"""Generate a random graph with the given joint independent edge degree and
+    triangle degree sequence.
+
+    This uses a configuration model-like approach to generate a random graph
+    (with parallel edges and self-loops) by randomly assigning edges to match
+    the given joint degree sequence.
+
+    The joint degree sequence is a list of pairs of integers of the form
+    $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list,
+    vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other
+    edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number
+    $d_{u,i}$ is the *independent edge degree*.
+
+    Parameters
+    ----------
+    joint_degree_sequence : list of integer pairs
+        Each list entry corresponds to the independent edge degree and
+        triangle degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence. Nodes are labeled
+        starting at 0 with an index corresponding to the position in
+        deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the independent edge degree sequence sum is not even
+        or the triangle degree sequence sum is not divisible by 3.
+
+    Notes
+    -----
+    As described by Miller [1]_ (see also Newman [2]_ for an equivalent
+    description).
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the
+    independent degree sequence does not have an even sum or the
+    triangle degree sequence sum is not divisible by 3.
+
+    This configuration model-like construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Joel C. Miller. "Percolation and epidemics in random clustered
+           networks". In: Physical review. E, Statistical, nonlinear, and soft
+           matter physics 80 (2 Part 1 August 2009).
+    .. [2] M. E. J. Newman. "Random Graphs with Clustering".
+           In: Physical Review Letters 103 (5 July 2009)
+
+    Examples
+    --------
+    >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    >>> G = nx.random_clustered_graph(deg)
+
+    To remove parallel edges:
+
+    >>> G = nx.Graph(G)
+
+    To remove self loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    # In Python 3, zip() returns an iterator. Make this into a list.
+    joint_degree_sequence = list(joint_degree_sequence)
+
+    N = len(joint_degree_sequence)
+    G = nx.empty_graph(N, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    ilist = []
+    tlist = []
+    for n in G:
+        degrees = joint_degree_sequence[n]
+        for icount in range(degrees[0]):
+            ilist.append(n)
+        for tcount in range(degrees[1]):
+            tlist.append(n)
+
+    if len(ilist) % 2 != 0 or len(tlist) % 3 != 0:
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    seed.shuffle(ilist)
+    seed.shuffle(tlist)
+    while ilist:
+        G.add_edge(ilist.pop(), ilist.pop())
+    while tlist:
+        n1 = tlist.pop()
+        n2 = tlist.pop()
+        n3 = tlist.pop()
+        G.add_edges_from([(n1, n2), (n1, n3), (n2, n3)])
+    return G
diff --git a/shared/package-files/0c/0c15dd9c87b6f2fadb7f80be6abed981e1486b49060f8a43049e183fecf84074/nodata.arff b/shared/package-files/0c/0c15dd9c87b6f2fadb7f80be6abed981e1486b49060f8a43049e183fecf84074/nodata.arff
new file mode 100644
index 0000000000000000000000000000000000000000..5766aeb229a1b31378026274c366e8e9e44fd487
--- /dev/null
+++ b/shared/package-files/0c/0c15dd9c87b6f2fadb7f80be6abed981e1486b49060f8a43049e183fecf84074/nodata.arff
@@ -0,0 +1,11 @@
+@RELATION iris
+
+@ATTRIBUTE sepallength  REAL
+@ATTRIBUTE sepalwidth   REAL
+@ATTRIBUTE petallength  REAL
+@ATTRIBUTE petalwidth   REAL
+@ATTRIBUTE class    {Iris-setosa,Iris-versicolor,Iris-virginica}
+
+@DATA
+
+% This file has no data
diff --git a/shared/package-files/0c/0c1cd30fcdcc877a1a71f6737d4e9f42e182f3b82fb67186683b9ef2cf4cd16b/uuid.h b/shared/package-files/0c/0c1cd30fcdcc877a1a71f6737d4e9f42e182f3b82fb67186683b9ef2cf4cd16b/uuid.h
new file mode 100644
index 0000000000000000000000000000000000000000..8c9660c463b08f799f71a3a710f3d3f2eadadd96
--- /dev/null
+++ b/shared/package-files/0c/0c1cd30fcdcc877a1a71f6737d4e9f42e182f3b82fb67186683b9ef2cf4cd16b/uuid.h
@@ -0,0 +1,63 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include "arrow/extension_type.h"
+
+namespace arrow::extension {
+
+/// \brief UuidArray stores array of UUIDs. Underlying storage type is
+/// FixedSizeBinary(16).
+class ARROW_EXPORT UuidArray : public ExtensionArray {
+ public:
+  using ExtensionArray::ExtensionArray;
+};
+
+/// \brief UuidType is a canonical arrow extension type for UUIDs.
+/// UUIDs are stored as FixedSizeBinary(16) with big-endian notation and this
+/// does not interpret the bytes in any way. Specific UUID version is not
+/// required or guaranteed.
+class ARROW_EXPORT UuidType : public ExtensionType {
+ public:
+  /// \brief Construct a UuidType.
+  UuidType() : ExtensionType(fixed_size_binary(16)) {}
+
+  std::string extension_name() const override { return "arrow.uuid"; }
+  std::string ToString(bool show_metadata = false) const override;
+
+  bool ExtensionEquals(const ExtensionType& other) const override;
+
+  /// Create a UuidArray from ArrayData
+  std::shared_ptr<Array> MakeArray(std::shared_ptr<ArrayData> data) const override;
+
+  Result<std::shared_ptr<DataType>> Deserialize(
+      std::shared_ptr<DataType> storage_type,
+      const std::string& serialized) const override;
+
+  std::string Serialize() const override { return ""; }
+
+  /// \brief Create a UuidType instance
+  static Result<std::shared_ptr<DataType>> Make() { return std::make_shared<UuidType>(); }
+
+  static bool IsSupportedStorageType(const std::shared_ptr<DataType>& storage_type);
+};
+
+/// \brief Return a UuidType instance.
+ARROW_EXPORT std::shared_ptr<DataType> uuid();
+
+}  // namespace arrow::extension
diff --git a/shared/package-files/0c/0c2152360d3ce31214f7ff496a3d8e61b6d9b4b85e0af08d02fe49d16d0ecfbe/hann_window_compositeexplicitautograd_dispatch.h b/shared/package-files/0c/0c2152360d3ce31214f7ff496a3d8e61b6d9b4b85e0af08d02fe49d16d0ecfbe/hann_window_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e9da8ffd2a973c3fbee731e8cdad442f8038adc1
--- /dev/null
+++ b/shared/package-files/0c/0c2152360d3ce31214f7ff496a3d8e61b6d9b4b85e0af08d02fe49d16d0ecfbe/hann_window_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,30 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor hann_window(int64_t window_length, at::TensorOptions options={});
+TORCH_API at::Tensor hann_window(int64_t window_length, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+TORCH_API at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length);
+TORCH_API at::Tensor & hann_window_outf(int64_t window_length, at::Tensor & out);
+TORCH_API at::Tensor hann_window(int64_t window_length, bool periodic, at::TensorOptions options={});
+TORCH_API at::Tensor hann_window(int64_t window_length, bool periodic, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+TORCH_API at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length, bool periodic);
+TORCH_API at::Tensor & hann_window_outf(int64_t window_length, bool periodic, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/0c/0c215ffc3b7970db2e4962676100067c9af1d72d99d064224938746a34098630/test_to_timestamp.py b/shared/package-files/0c/0c215ffc3b7970db2e4962676100067c9af1d72d99d064224938746a34098630/test_to_timestamp.py
new file mode 100644
index 0000000000000000000000000000000000000000..3867f9e3245dc10a90ab4fcb1458b861ee7e2f86
--- /dev/null
+++ b/shared/package-files/0c/0c215ffc3b7970db2e4962676100067c9af1d72d99d064224938746a34098630/test_to_timestamp.py
@@ -0,0 +1,142 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    PeriodIndex,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestToTimestamp:
+    def test_to_timestamp_non_contiguous(self):
+        # GH#44100
+        dti = date_range("2021-10-18", periods=9, freq="D")
+        pi = dti.to_period()
+
+        result = pi[::2].to_timestamp()
+        expected = dti[::2]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::2].to_timestamp()
+        expected = dti._data[::2]
+        # TODO: can we get the freq to round-trip?
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+        result = pi[::-1].to_timestamp()
+        expected = dti[::-1]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::-1].to_timestamp()
+        expected = dti._data[::-1]
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+        result = pi[::2][::-1].to_timestamp()
+        expected = dti[::2][::-1]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::2][::-1].to_timestamp()
+        expected = dti._data[::2][::-1]
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+    def test_to_timestamp_freq(self):
+        idx = period_range("2017", periods=12, freq="Y-DEC")
+        result = idx.to_timestamp()
+        expected = date_range("2017", periods=12, freq="YS-JAN")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_pi_nat(self):
+        # GH#7228
+        index = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="M", name="idx")
+
+        result = index.to_timestamp("D")
+        expected = DatetimeIndex(
+            [NaT, datetime(2011, 1, 1), datetime(2011, 2, 1)],
+            dtype="M8[ns]",
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.name == "idx"
+
+        result2 = result.to_period(freq="M")
+        tm.assert_index_equal(result2, index)
+        assert result2.name == "idx"
+
+        result3 = result.to_period(freq="3M")
+        exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="3M", name="idx")
+        tm.assert_index_equal(result3, exp)
+        assert result3.freqstr == "3M"
+
+        msg = "Frequency must be positive, because it represents span: -2Y"
+        with pytest.raises(ValueError, match=msg):
+            result.to_period(freq="-2Y")
+
+    def test_to_timestamp_preserve_name(self):
+        index = period_range(freq="Y", start="1/1/2001", end="12/1/2009", name="foo")
+        assert index.name == "foo"
+
+        conv = index.to_timestamp("D")
+        assert conv.name == "foo"
+
+    def test_to_timestamp_quarterly_bug(self):
+        years = np.arange(1960, 2000).repeat(4)
+        quarters = np.tile(list(range(1, 5)), 40)
+
+        pindex = PeriodIndex.from_fields(year=years, quarter=quarters)
+
+        stamps = pindex.to_timestamp("D", "end")
+        expected = DatetimeIndex([x.to_timestamp("D", "end") for x in pindex])
+        tm.assert_index_equal(stamps, expected)
+        assert stamps.freq == expected.freq
+
+    def test_to_timestamp_pi_mult(self):
+        idx = PeriodIndex(["2011-01", "NaT", "2011-02"], freq="2M", name="idx")
+
+        result = idx.to_timestamp()
+        expected = DatetimeIndex(
+            ["2011-01-01", "NaT", "2011-02-01"], dtype="M8[ns]", name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E")
+        expected = DatetimeIndex(
+            ["2011-02-28", "NaT", "2011-03-31"], dtype="M8[ns]", name="idx"
+        )
+        expected = expected + Timedelta(1, "D") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_pi_combined(self):
+        idx = period_range(start="2011", periods=2, freq="1D1h", name="idx")
+
+        result = idx.to_timestamp()
+        expected = DatetimeIndex(
+            ["2011-01-01 00:00", "2011-01-02 01:00"], dtype="M8[ns]", name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E")
+        expected = DatetimeIndex(
+            ["2011-01-02 00:59:59", "2011-01-03 01:59:59"], name="idx", dtype="M8[ns]"
+        )
+        expected = expected + Timedelta(1, "s") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E", freq="h")
+        expected = DatetimeIndex(
+            ["2011-01-02 00:00", "2011-01-03 01:00"], dtype="M8[ns]", name="idx"
+        )
+        expected = expected + Timedelta(1, "h") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_1703(self):
+        index = period_range("1/1/2012", periods=4, freq="D")
+
+        result = index.to_timestamp()
+        assert result[0] == Timestamp("1/1/2012")
diff --git a/shared/package-files/0c/0c21d293265526a2b3535f6de026771f36d49019c5edb86d4bdeba473f7188f9/ViewFuncs.h b/shared/package-files/0c/0c21d293265526a2b3535f6de026771f36d49019c5edb86d4bdeba473f7188f9/ViewFuncs.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f422749d3ef1710fe63aeeff344d219154041b2
--- /dev/null
+++ b/shared/package-files/0c/0c21d293265526a2b3535f6de026771f36d49019c5edb86d4bdeba473f7188f9/ViewFuncs.h
@@ -0,0 +1,953 @@
+#pragma once
+
+// @generated from ../tools/autograd/templates/ViewFuncs.h
+
+#include <torch/library.h>
+#include <torch/csrc/autograd/variable.h>
+#include <c10/core/SymIntArrayRef.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Operators.h>
+#else
+#include <ATen/ops/_conj_ops.h>
+#include <ATen/ops/_indices_ops.h>
+#include <ATen/ops/_neg_view_ops.h>
+#include <ATen/ops/_nested_get_values_ops.h>
+#include <ATen/ops/_nested_view_from_buffer_ops.h>
+#include <ATen/ops/_nested_view_from_jagged_ops.h>
+#include <ATen/ops/_reshape_alias_ops.h>
+#include <ATen/ops/_test_autograd_multiple_dispatch_view_ops.h>
+#include <ATen/ops/_values_ops.h>
+#include <ATen/ops/alias_ops.h>
+#include <ATen/ops/as_strided_ops.h>
+#include <ATen/ops/ccol_indices_ops.h>
+#include <ATen/ops/chunk_ops.h>
+#include <ATen/ops/col_indices_ops.h>
+#include <ATen/ops/crow_indices_ops.h>
+#include <ATen/ops/diagonal_ops.h>
+#include <ATen/ops/expand_ops.h>
+#include <ATen/ops/indices_ops.h>
+#include <ATen/ops/narrow_ops.h>
+#include <ATen/ops/permute_ops.h>
+#include <ATen/ops/row_indices_ops.h>
+#include <ATen/ops/select_ops.h>
+#include <ATen/ops/slice_ops.h>
+#include <ATen/ops/slice_inverse_ops.h>
+#include <ATen/ops/split_ops.h>
+#include <ATen/ops/split_with_sizes_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/t_ops.h>
+#include <ATen/ops/transpose_ops.h>
+#include <ATen/ops/unbind_ops.h>
+#include <ATen/ops/unfold_ops.h>
+#include <ATen/ops/unsqueeze_ops.h>
+#include <ATen/ops/values_ops.h>
+#include <ATen/ops/view_ops.h>
+#include <ATen/ops/view_ops.h>
+#include <ATen/ops/view_as_complex_ops.h>
+#include <ATen/ops/view_as_real_ops.h>
+#endif
+
+namespace torch::autograd::generated {
+
+using at::Scalar;
+using at::Tensor;
+using at::IntArrayRef;
+using at::ArrayRef;
+using at::Type;
+using at::ScalarType;
+using c10::optional;
+using c10::fmap;
+
+#define _CONJ_VIEW_FUNC_AVAILABLE
+struct _ConjViewFunc : public torch::autograd::ViewFunc {
+  _ConjViewFunc() 
+  {};
+  virtual ~_ConjViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _INDICES_VIEW_FUNC_AVAILABLE
+struct _IndicesViewFunc : public torch::autograd::ViewFunc {
+  _IndicesViewFunc() 
+  {};
+  virtual ~_IndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NEG_VIEW_VIEW_FUNC_AVAILABLE
+struct _NegViewViewFunc : public torch::autograd::ViewFunc {
+  _NegViewViewFunc() 
+  {};
+  virtual ~_NegViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NESTED_GET_VALUES_VIEW_FUNC_AVAILABLE
+struct _NestedGetValuesViewFunc : public torch::autograd::ViewFunc {
+  _NestedGetValuesViewFunc() 
+  {};
+  virtual ~_NestedGetValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NESTED_VIEW_FROM_BUFFER_VIEW_FUNC_AVAILABLE
+struct _NestedViewFromBufferViewFunc : public torch::autograd::ViewFunc {
+  _NestedViewFromBufferViewFunc(const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) : nested_size(nested_size), nested_strides(nested_strides), offsets(offsets)
+  {};
+  virtual ~_NestedViewFromBufferViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor nested_size;
+  at::Tensor nested_strides;
+  at::Tensor offsets;
+};
+
+#define _NESTED_VIEW_FROM_JAGGED_VIEW_FUNC_AVAILABLE
+struct _NestedViewFromJaggedViewFunc : public torch::autograd::ViewFunc {
+  _NestedViewFromJaggedViewFunc(const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional<at::Tensor> & lengths, int64_t ragged_idx) : offsets(offsets), dummy(dummy), lengths(lengths), ragged_idx(ragged_idx)
+  {};
+  virtual ~_NestedViewFromJaggedViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor offsets;
+  at::Tensor dummy;
+  c10::optional<at::Tensor> lengths;
+  int64_t ragged_idx;
+};
+
+#define _RESHAPE_ALIAS_VIEW_FUNC_AVAILABLE
+struct _ReshapeAliasViewFunc : public torch::autograd::ViewFunc {
+  _ReshapeAliasViewFunc(c10::SymIntArrayRef size, c10::SymIntArrayRef stride) : size(size.vec()), stride(stride.vec())
+  {};
+  virtual ~_ReshapeAliasViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  ::std::vector<c10::SymInt> stride;
+};
+
+#define _TEST_AUTOGRAD_MULTIPLE_DISPATCH_VIEW_VIEW_FUNC_AVAILABLE
+struct _TestAutogradMultipleDispatchViewViewFunc : public torch::autograd::ViewFunc {
+  _TestAutogradMultipleDispatchViewViewFunc() 
+  {};
+  virtual ~_TestAutogradMultipleDispatchViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _VALUES_VIEW_FUNC_AVAILABLE
+struct _ValuesViewFunc : public torch::autograd::ViewFunc {
+  _ValuesViewFunc() 
+  {};
+  virtual ~_ValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define ALIAS_VIEW_FUNC_AVAILABLE
+struct AliasViewFunc : public torch::autograd::ViewFunc {
+  AliasViewFunc() 
+  {};
+  virtual ~AliasViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define AS_STRIDED_VIEW_FUNC_AVAILABLE
+struct AsStridedViewFunc : public torch::autograd::ViewFunc {
+  AsStridedViewFunc(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) : size(size.vec()), stride(stride.vec()), storage_offset(storage_offset)
+  {};
+  virtual ~AsStridedViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  ::std::vector<c10::SymInt> stride;
+  c10::optional<c10::SymInt> storage_offset;
+};
+
+#define CCOL_INDICES_VIEW_FUNC_AVAILABLE
+struct CcolIndicesViewFunc : public torch::autograd::ViewFunc {
+  CcolIndicesViewFunc() 
+  {};
+  virtual ~CcolIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define CHUNK_VIEW_FUNC_AVAILABLE
+struct ChunkViewFunc : public torch::autograd::ViewFunc {
+  ChunkViewFunc(int64_t chunks, int64_t dim, int64_t view_idx) : chunks(chunks), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~ChunkViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t chunks;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define COL_INDICES_VIEW_FUNC_AVAILABLE
+struct ColIndicesViewFunc : public torch::autograd::ViewFunc {
+  ColIndicesViewFunc() 
+  {};
+  virtual ~ColIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define CROW_INDICES_VIEW_FUNC_AVAILABLE
+struct CrowIndicesViewFunc : public torch::autograd::ViewFunc {
+  CrowIndicesViewFunc() 
+  {};
+  virtual ~CrowIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define DIAGONAL_VIEW_FUNC_AVAILABLE
+struct DiagonalViewFunc : public torch::autograd::ViewFunc {
+  DiagonalViewFunc(int64_t offset, int64_t dim1, int64_t dim2) : offset(offset), dim1(dim1), dim2(dim2)
+  {};
+  virtual ~DiagonalViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t offset;
+  int64_t dim1;
+  int64_t dim2;
+};
+
+#define EXPAND_VIEW_FUNC_AVAILABLE
+struct ExpandViewFunc : public torch::autograd::ViewFunc {
+  ExpandViewFunc(c10::SymIntArrayRef size, bool implicit) : size(size.vec()), implicit(implicit)
+  {};
+  virtual ~ExpandViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  bool implicit;
+};
+
+#define INDICES_VIEW_FUNC_AVAILABLE
+struct IndicesViewFunc : public torch::autograd::ViewFunc {
+  IndicesViewFunc() 
+  {};
+  virtual ~IndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define NARROW_VIEW_FUNC_AVAILABLE
+struct NarrowViewFunc : public torch::autograd::ViewFunc {
+  NarrowViewFunc(int64_t dim, c10::SymInt start, c10::SymInt length) : dim(dim), start(start), length(length)
+  {};
+  virtual ~NarrowViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::SymInt start;
+  c10::SymInt length;
+};
+
+#define PERMUTE_VIEW_FUNC_AVAILABLE
+struct PermuteViewFunc : public torch::autograd::ViewFunc {
+  PermuteViewFunc(at::IntArrayRef dims) : dims(dims.vec())
+  {};
+  virtual ~PermuteViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<int64_t> dims;
+};
+
+#define ROW_INDICES_VIEW_FUNC_AVAILABLE
+struct RowIndicesViewFunc : public torch::autograd::ViewFunc {
+  RowIndicesViewFunc() 
+  {};
+  virtual ~RowIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define SELECT_INT_VIEW_FUNC_AVAILABLE
+struct SelectIntViewFunc : public torch::autograd::ViewFunc {
+  SelectIntViewFunc(int64_t dim, c10::SymInt index) : dim(dim), index(index)
+  {};
+  virtual ~SelectIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::SymInt index;
+};
+
+#define SLICE_TENSOR_VIEW_FUNC_AVAILABLE
+struct SliceTensorViewFunc : public torch::autograd::ViewFunc {
+  SliceTensorViewFunc(int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) : dim(dim), start(start), end(end), step(step)
+  {};
+  virtual ~SliceTensorViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::optional<c10::SymInt> start;
+  c10::optional<c10::SymInt> end;
+  c10::SymInt step;
+};
+
+#define SLICE_INVERSE_VIEW_FUNC_AVAILABLE
+struct SliceInverseViewFunc : public torch::autograd::ViewFunc {
+  SliceInverseViewFunc(const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) : src(src), dim(dim), start(start), end(end), step(step)
+  {};
+  virtual ~SliceInverseViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor src;
+  int64_t dim;
+  c10::optional<c10::SymInt> start;
+  c10::optional<c10::SymInt> end;
+  c10::SymInt step;
+};
+
+#define SPLIT_TENSOR_VIEW_FUNC_AVAILABLE
+struct SplitTensorViewFunc : public torch::autograd::ViewFunc {
+  SplitTensorViewFunc(c10::SymInt split_size, int64_t dim, int64_t view_idx) : split_size(split_size), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~SplitTensorViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  c10::SymInt split_size;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define SPLIT_WITH_SIZES_VIEW_FUNC_AVAILABLE
+struct SplitWithSizesViewFunc : public torch::autograd::ViewFunc {
+  SplitWithSizesViewFunc(c10::SymIntArrayRef split_sizes, int64_t dim, int64_t view_idx) : split_sizes(split_sizes.vec()), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~SplitWithSizesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> split_sizes;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define SQUEEZE_VIEW_FUNC_AVAILABLE
+struct SqueezeViewFunc : public torch::autograd::ViewFunc {
+  SqueezeViewFunc() 
+  {};
+  virtual ~SqueezeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define SQUEEZE_DIM_VIEW_FUNC_AVAILABLE
+struct SqueezeDimViewFunc : public torch::autograd::ViewFunc {
+  SqueezeDimViewFunc(int64_t dim) : dim(dim)
+  {};
+  virtual ~SqueezeDimViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+};
+
+#define SQUEEZE_DIMS_VIEW_FUNC_AVAILABLE
+struct SqueezeDimsViewFunc : public torch::autograd::ViewFunc {
+  SqueezeDimsViewFunc(at::IntArrayRef dim) : dim(dim.vec())
+  {};
+  virtual ~SqueezeDimsViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<int64_t> dim;
+};
+
+#define T_VIEW_FUNC_AVAILABLE
+struct TViewFunc : public torch::autograd::ViewFunc {
+  TViewFunc() 
+  {};
+  virtual ~TViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define TRANSPOSE_INT_VIEW_FUNC_AVAILABLE
+struct TransposeIntViewFunc : public torch::autograd::ViewFunc {
+  TransposeIntViewFunc(int64_t dim0, int64_t dim1) : dim0(dim0), dim1(dim1)
+  {};
+  virtual ~TransposeIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim0;
+  int64_t dim1;
+};
+
+#define UNBIND_INT_VIEW_FUNC_AVAILABLE
+struct UnbindIntViewFunc : public torch::autograd::ViewFunc {
+  UnbindIntViewFunc(int64_t dim, int64_t view_idx) : dim(dim), view_idx(view_idx)
+  {};
+  virtual ~UnbindIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define UNFOLD_VIEW_FUNC_AVAILABLE
+struct UnfoldViewFunc : public torch::autograd::ViewFunc {
+  UnfoldViewFunc(int64_t dimension, int64_t size, int64_t step) : dimension(dimension), size(size), step(step)
+  {};
+  virtual ~UnfoldViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dimension;
+  int64_t size;
+  int64_t step;
+};
+
+#define UNSQUEEZE_VIEW_FUNC_AVAILABLE
+struct UnsqueezeViewFunc : public torch::autograd::ViewFunc {
+  UnsqueezeViewFunc(int64_t dim) : dim(dim)
+  {};
+  virtual ~UnsqueezeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+};
+
+#define VALUES_VIEW_FUNC_AVAILABLE
+struct ValuesViewFunc : public torch::autograd::ViewFunc {
+  ValuesViewFunc() 
+  {};
+  virtual ~ValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define VIEW_VIEW_FUNC_AVAILABLE
+struct ViewViewFunc : public torch::autograd::ViewFunc {
+  ViewViewFunc(c10::SymIntArrayRef size) : size(size.vec())
+  {};
+  virtual ~ViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+};
+
+#define VIEW_DTYPE_VIEW_FUNC_AVAILABLE
+struct ViewDtypeViewFunc : public torch::autograd::ViewFunc {
+  ViewDtypeViewFunc(at::ScalarType dtype) : dtype(dtype)
+  {};
+  virtual ~ViewDtypeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::ScalarType dtype;
+};
+
+#define VIEW_AS_COMPLEX_VIEW_FUNC_AVAILABLE
+struct ViewAsComplexViewFunc : public torch::autograd::ViewFunc {
+  ViewAsComplexViewFunc() 
+  {};
+  virtual ~ViewAsComplexViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define VIEW_AS_REAL_VIEW_FUNC_AVAILABLE
+struct ViewAsRealViewFunc : public torch::autograd::ViewFunc {
+  ViewAsRealViewFunc() 
+  {};
+  virtual ~ViewAsRealViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+} // namespace torch::autograd::generated
diff --git a/shared/package-files/0c/0c228e0a4cf2e60236e37d2b6b49d892dd86323096d27188a6125edd358d782b/ts_backend_impl.h b/shared/package-files/0c/0c228e0a4cf2e60236e37d2b6b49d892dd86323096d27188a6125edd358d782b/ts_backend_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..d238e8263e57781d09bbc744db5c92f9684ac09a
--- /dev/null
+++ b/shared/package-files/0c/0c228e0a4cf2e60236e37d2b6b49d892dd86323096d27188a6125edd358d782b/ts_backend_impl.h
@@ -0,0 +1,52 @@
+#pragma once
+
+#include <torch/csrc/lazy/backend/backend_interface.h>
+
+namespace torch {
+namespace lazy {
+
+class TORCH_API TSData : public torch::lazy::BackendData {
+ public:
+  TSData(const at::Scalar& scalar, const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, torch::lazy::Shape(scalar.type(), {})),
+        scalar(scalar) {}
+
+  TSData(
+      const at::Tensor& data,
+      const torch::lazy::Shape& shape,
+      const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, shape), data_(data) {}
+
+  TSData(
+      const torch::lazy::Shape& shape,
+      const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, shape) {}
+
+  Handle GetHandle() override {
+    return reinterpret_cast<int64_t>(this);
+  }
+
+  void Assign(const torch::lazy::BackendData& data) override {
+    data_ = static_cast<const TSData&>(data).data_;
+  }
+
+  bool HasValue() const override {
+    return data_.defined();
+  }
+
+  at::Tensor data() {
+    return data_;
+  }
+
+  c10::optional<at::Scalar> scalar;
+
+ private:
+  at::Tensor data_;
+};
+
+TORCH_API torch::lazy::BackendImplInterface* GetTSBackendImpl();
+
+TORCH_API void InitTorchScriptBackend();
+
+} // namespace lazy
+} // namespace torch
diff --git a/shared/package-files/0c/0c22e0d886b5d782dca4f5371425bc1cc3990a49f92bb9217d744c7f667d5b22/dialogs.py b/shared/package-files/0c/0c22e0d886b5d782dca4f5371425bc1cc3990a49f92bb9217d744c7f667d5b22/dialogs.py
new file mode 100644
index 0000000000000000000000000000000000000000..c47c15b4f96d07faec417d4cde62c52ec2263195
--- /dev/null
+++ b/shared/package-files/0c/0c22e0d886b5d782dca4f5371425bc1cc3990a49f92bb9217d744c7f667d5b22/dialogs.py
@@ -0,0 +1,107 @@
+"""
+Collection of reusable components for building full screen applications.
+"""
+from __future__ import annotations
+
+from typing import Sequence
+
+from prompt_toolkit.filters import has_completions, has_focus
+from prompt_toolkit.formatted_text import AnyFormattedText
+from prompt_toolkit.key_binding.bindings.focus import focus_next, focus_previous
+from prompt_toolkit.key_binding.key_bindings import KeyBindings
+from prompt_toolkit.layout.containers import (
+    AnyContainer,
+    DynamicContainer,
+    HSplit,
+    VSplit,
+)
+from prompt_toolkit.layout.dimension import AnyDimension
+from prompt_toolkit.layout.dimension import Dimension as D
+
+from .base import Box, Button, Frame, Shadow
+
+__all__ = [
+    "Dialog",
+]
+
+
+class Dialog:
+    """
+    Simple dialog window. This is the base for input dialogs, message dialogs
+    and confirmation dialogs.
+
+    Changing the title and body of the dialog is possible at runtime by
+    assigning to the `body` and `title` attributes of this class.
+
+    :param body: Child container object.
+    :param title: Text to be displayed in the heading of the dialog.
+    :param buttons: A list of `Button` widgets, displayed at the bottom.
+    """
+
+    def __init__(
+        self,
+        body: AnyContainer,
+        title: AnyFormattedText = "",
+        buttons: Sequence[Button] | None = None,
+        modal: bool = True,
+        width: AnyDimension = None,
+        with_background: bool = False,
+    ) -> None:
+        self.body = body
+        self.title = title
+
+        buttons = buttons or []
+
+        # When a button is selected, handle left/right key bindings.
+        buttons_kb = KeyBindings()
+        if len(buttons) > 1:
+            first_selected = has_focus(buttons[0])
+            last_selected = has_focus(buttons[-1])
+
+            buttons_kb.add("left", filter=~first_selected)(focus_previous)
+            buttons_kb.add("right", filter=~last_selected)(focus_next)
+
+        frame_body: AnyContainer
+        if buttons:
+            frame_body = HSplit(
+                [
+                    # Add optional padding around the body.
+                    Box(
+                        body=DynamicContainer(lambda: self.body),
+                        padding=D(preferred=1, max=1),
+                        padding_bottom=0,
+                    ),
+                    # The buttons.
+                    Box(
+                        body=VSplit(buttons, padding=1, key_bindings=buttons_kb),
+                        height=D(min=1, max=3, preferred=3),
+                    ),
+                ]
+            )
+        else:
+            frame_body = body
+
+        # Key bindings for whole dialog.
+        kb = KeyBindings()
+        kb.add("tab", filter=~has_completions)(focus_next)
+        kb.add("s-tab", filter=~has_completions)(focus_previous)
+
+        frame = Shadow(
+            body=Frame(
+                title=lambda: self.title,
+                body=frame_body,
+                style="class:dialog.body",
+                width=(None if with_background is None else width),
+                key_bindings=kb,
+                modal=modal,
+            )
+        )
+
+        self.container: Box | Shadow
+        if with_background:
+            self.container = Box(body=frame, style="class:dialog", width=width)
+        else:
+            self.container = frame
+
+    def __pt_container__(self) -> AnyContainer:
+        return self.container
diff --git a/shared/package-files/0c/0c23c1c4d58ff81bdd4ff4448fafdae138d4acd69feac0aafd7a31537a446df7/cython_blas.pxd b/shared/package-files/0c/0c23c1c4d58ff81bdd4ff4448fafdae138d4acd69feac0aafd7a31537a446df7/cython_blas.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7ed44f6ea8611f926e3ea5fd2670446cdf9b398c
--- /dev/null
+++ b/shared/package-files/0c/0c23c1c4d58ff81bdd4ff4448fafdae138d4acd69feac0aafd7a31537a446df7/cython_blas.pxd
@@ -0,0 +1,169 @@
+"""
+This file was generated by _generate_pyx.py.
+Do not edit this file directly.
+"""
+
+# Within scipy, these wrappers can be used via relative or absolute cimport.
+# Examples:
+# from ..linalg cimport cython_blas
+# from scipy.linalg cimport cython_blas
+# cimport scipy.linalg.cython_blas as cython_blas
+# cimport ..linalg.cython_blas as cython_blas
+
+# Within SciPy, if BLAS functions are needed in C/C++/Fortran,
+# these wrappers should not be used.
+# The original libraries should be linked directly.
+
+ctypedef float s
+ctypedef double d
+ctypedef float complex c
+ctypedef double complex z
+
+cdef void caxpy(int *n, c *ca, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void ccopy(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef c cdotc(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef c cdotu(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void cgbmv(char *trans, int *m, int *n, int *kl, int *ku, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cgemm(char *transa, char *transb, int *m, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void cgemv(char *trans, int *m, int *n, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cgerc(int *m, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void cgeru(int *m, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void chbmv(char *uplo, int *n, int *k, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void chemm(char *side, char *uplo, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void chemv(char *uplo, int *n, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cher(char *uplo, int *n, s *alpha, c *x, int *incx, c *a, int *lda) noexcept nogil
+cdef void cher2(char *uplo, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void cher2k(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, s *beta, c *c, int *ldc) noexcept nogil
+cdef void cherk(char *uplo, char *trans, int *n, int *k, s *alpha, c *a, int *lda, s *beta, c *c, int *ldc) noexcept nogil
+cdef void chpmv(char *uplo, int *n, c *alpha, c *ap, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void chpr(char *uplo, int *n, s *alpha, c *x, int *incx, c *ap) noexcept nogil
+cdef void chpr2(char *uplo, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *ap) noexcept nogil
+cdef void crotg(c *ca, c *cb, s *c, c *s) noexcept nogil
+cdef void cscal(int *n, c *ca, c *cx, int *incx) noexcept nogil
+cdef void csrot(int *n, c *cx, int *incx, c *cy, int *incy, s *c, s *s) noexcept nogil
+cdef void csscal(int *n, s *sa, c *cx, int *incx) noexcept nogil
+cdef void cswap(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void csymm(char *side, char *uplo, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void csyr2k(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void csyrk(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *beta, c *c, int *ldc) noexcept nogil
+cdef void ctbmv(char *uplo, char *trans, char *diag, int *n, int *k, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctbsv(char *uplo, char *trans, char *diag, int *n, int *k, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctpmv(char *uplo, char *trans, char *diag, int *n, c *ap, c *x, int *incx) noexcept nogil
+cdef void ctpsv(char *uplo, char *trans, char *diag, int *n, c *ap, c *x, int *incx) noexcept nogil
+cdef void ctrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb) noexcept nogil
+cdef void ctrmv(char *uplo, char *trans, char *diag, int *n, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb) noexcept nogil
+cdef void ctrsv(char *uplo, char *trans, char *diag, int *n, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef d dasum(int *n, d *dx, int *incx) noexcept nogil
+cdef void daxpy(int *n, d *da, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef d dcabs1(z *z) noexcept nogil
+cdef void dcopy(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef d ddot(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef void dgbmv(char *trans, int *m, int *n, int *kl, int *ku, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dgemm(char *transa, char *transb, int *m, int *n, int *k, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dgemv(char *trans, int *m, int *n, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dger(int *m, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *a, int *lda) noexcept nogil
+cdef d dnrm2(int *n, d *x, int *incx) noexcept nogil
+cdef void drot(int *n, d *dx, int *incx, d *dy, int *incy, d *c, d *s) noexcept nogil
+cdef void drotg(d *da, d *db, d *c, d *s) noexcept nogil
+cdef void drotm(int *n, d *dx, int *incx, d *dy, int *incy, d *dparam) noexcept nogil
+cdef void drotmg(d *dd1, d *dd2, d *dx1, d *dy1, d *dparam) noexcept nogil
+cdef void dsbmv(char *uplo, int *n, int *k, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dscal(int *n, d *da, d *dx, int *incx) noexcept nogil
+cdef d dsdot(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void dspmv(char *uplo, int *n, d *alpha, d *ap, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dspr(char *uplo, int *n, d *alpha, d *x, int *incx, d *ap) noexcept nogil
+cdef void dspr2(char *uplo, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *ap) noexcept nogil
+cdef void dswap(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef void dsymm(char *side, char *uplo, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dsymv(char *uplo, int *n, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dsyr(char *uplo, int *n, d *alpha, d *x, int *incx, d *a, int *lda) noexcept nogil
+cdef void dsyr2(char *uplo, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *a, int *lda) noexcept nogil
+cdef void dsyr2k(char *uplo, char *trans, int *n, int *k, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dsyrk(char *uplo, char *trans, int *n, int *k, d *alpha, d *a, int *lda, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dtbmv(char *uplo, char *trans, char *diag, int *n, int *k, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtbsv(char *uplo, char *trans, char *diag, int *n, int *k, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtpmv(char *uplo, char *trans, char *diag, int *n, d *ap, d *x, int *incx) noexcept nogil
+cdef void dtpsv(char *uplo, char *trans, char *diag, int *n, d *ap, d *x, int *incx) noexcept nogil
+cdef void dtrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb) noexcept nogil
+cdef void dtrmv(char *uplo, char *trans, char *diag, int *n, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb) noexcept nogil
+cdef void dtrsv(char *uplo, char *trans, char *diag, int *n, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef d dzasum(int *n, z *zx, int *incx) noexcept nogil
+cdef d dznrm2(int *n, z *x, int *incx) noexcept nogil
+cdef int icamax(int *n, c *cx, int *incx) noexcept nogil
+cdef int idamax(int *n, d *dx, int *incx) noexcept nogil
+cdef int isamax(int *n, s *sx, int *incx) noexcept nogil
+cdef int izamax(int *n, z *zx, int *incx) noexcept nogil
+cdef bint lsame(char *ca, char *cb) noexcept nogil
+cdef s sasum(int *n, s *sx, int *incx) noexcept nogil
+cdef void saxpy(int *n, s *sa, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s scasum(int *n, c *cx, int *incx) noexcept nogil
+cdef s scnrm2(int *n, c *x, int *incx) noexcept nogil
+cdef void scopy(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s sdot(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s sdsdot(int *n, s *sb, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void sgbmv(char *trans, int *m, int *n, int *kl, int *ku, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sgemm(char *transa, char *transb, int *m, int *n, int *k, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void sgemv(char *trans, int *m, int *n, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sger(int *m, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *a, int *lda) noexcept nogil
+cdef s snrm2(int *n, s *x, int *incx) noexcept nogil
+cdef void srot(int *n, s *sx, int *incx, s *sy, int *incy, s *c, s *s) noexcept nogil
+cdef void srotg(s *sa, s *sb, s *c, s *s) noexcept nogil
+cdef void srotm(int *n, s *sx, int *incx, s *sy, int *incy, s *sparam) noexcept nogil
+cdef void srotmg(s *sd1, s *sd2, s *sx1, s *sy1, s *sparam) noexcept nogil
+cdef void ssbmv(char *uplo, int *n, int *k, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sscal(int *n, s *sa, s *sx, int *incx) noexcept nogil
+cdef void sspmv(char *uplo, int *n, s *alpha, s *ap, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sspr(char *uplo, int *n, s *alpha, s *x, int *incx, s *ap) noexcept nogil
+cdef void sspr2(char *uplo, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *ap) noexcept nogil
+cdef void sswap(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void ssymm(char *side, char *uplo, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void ssymv(char *uplo, int *n, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void ssyr(char *uplo, int *n, s *alpha, s *x, int *incx, s *a, int *lda) noexcept nogil
+cdef void ssyr2(char *uplo, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *a, int *lda) noexcept nogil
+cdef void ssyr2k(char *uplo, char *trans, int *n, int *k, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void ssyrk(char *uplo, char *trans, int *n, int *k, s *alpha, s *a, int *lda, s *beta, s *c, int *ldc) noexcept nogil
+cdef void stbmv(char *uplo, char *trans, char *diag, int *n, int *k, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void stbsv(char *uplo, char *trans, char *diag, int *n, int *k, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void stpmv(char *uplo, char *trans, char *diag, int *n, s *ap, s *x, int *incx) noexcept nogil
+cdef void stpsv(char *uplo, char *trans, char *diag, int *n, s *ap, s *x, int *incx) noexcept nogil
+cdef void strmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb) noexcept nogil
+cdef void strmv(char *uplo, char *trans, char *diag, int *n, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void strsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb) noexcept nogil
+cdef void strsv(char *uplo, char *trans, char *diag, int *n, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void zaxpy(int *n, z *za, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zcopy(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef z zdotc(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef z zdotu(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zdrot(int *n, z *cx, int *incx, z *cy, int *incy, d *c, d *s) noexcept nogil
+cdef void zdscal(int *n, d *da, z *zx, int *incx) noexcept nogil
+cdef void zgbmv(char *trans, int *m, int *n, int *kl, int *ku, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zgemm(char *transa, char *transb, int *m, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zgemv(char *trans, int *m, int *n, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zgerc(int *m, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zgeru(int *m, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zhbmv(char *uplo, int *n, int *k, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zhemm(char *side, char *uplo, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zhemv(char *uplo, int *n, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zher(char *uplo, int *n, d *alpha, z *x, int *incx, z *a, int *lda) noexcept nogil
+cdef void zher2(char *uplo, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zher2k(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, d *beta, z *c, int *ldc) noexcept nogil
+cdef void zherk(char *uplo, char *trans, int *n, int *k, d *alpha, z *a, int *lda, d *beta, z *c, int *ldc) noexcept nogil
+cdef void zhpmv(char *uplo, int *n, z *alpha, z *ap, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zhpr(char *uplo, int *n, d *alpha, z *x, int *incx, z *ap) noexcept nogil
+cdef void zhpr2(char *uplo, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *ap) noexcept nogil
+cdef void zrotg(z *ca, z *cb, d *c, z *s) noexcept nogil
+cdef void zscal(int *n, z *za, z *zx, int *incx) noexcept nogil
+cdef void zswap(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zsymm(char *side, char *uplo, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zsyr2k(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zsyrk(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *beta, z *c, int *ldc) noexcept nogil
+cdef void ztbmv(char *uplo, char *trans, char *diag, int *n, int *k, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztbsv(char *uplo, char *trans, char *diag, int *n, int *k, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztpmv(char *uplo, char *trans, char *diag, int *n, z *ap, z *x, int *incx) noexcept nogil
+cdef void ztpsv(char *uplo, char *trans, char *diag, int *n, z *ap, z *x, int *incx) noexcept nogil
+cdef void ztrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb) noexcept nogil
+cdef void ztrmv(char *uplo, char *trans, char *diag, int *n, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb) noexcept nogil
+cdef void ztrsv(char *uplo, char *trans, char *diag, int *n, z *a, int *lda, z *x, int *incx) noexcept nogil
diff --git a/shared/package-files/0c/0c26e96fb439c6485b0e2206edf3b80b4a45e0ccdd7c14f298cb22bab5fd1401/digamma.h b/shared/package-files/0c/0c26e96fb439c6485b0e2206edf3b80b4a45e0ccdd7c14f298cb22bab5fd1401/digamma.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5a6c9a0cce2b6a3ac65f1233ad8ecd0d6795cb3
--- /dev/null
+++ b/shared/package-files/0c/0c26e96fb439c6485b0e2206edf3b80b4a45e0ccdd7c14f298cb22bab5fd1401/digamma.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/digamma_ops.h>
+
+namespace at {
+
+
+// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & digamma_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::digamma_out::call(self, out);
+}
+// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & digamma_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::digamma_out::call(self, out);
+}
+
+// aten::digamma(Tensor self) -> Tensor
+inline at::Tensor digamma(const at::Tensor & self) {
+    return at::_ops::digamma::call(self);
+}
+
+}
diff --git a/shared/package-files/0c/0c2d4669bc3b13d6f7073c1d029c6513aa0433bde7990a18c41b9c2fc8c0a005/test_tracing.py b/shared/package-files/0c/0c2d4669bc3b13d6f7073c1d029c6513aa0433bde7990a18c41b9c2fc8c0a005/test_tracing.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ab895efa2c73baac757857e568825e25794de03
--- /dev/null
+++ b/shared/package-files/0c/0c2d4669bc3b13d6f7073c1d029c6513aa0433bde7990a18c41b9c2fc8c0a005/test_tracing.py
@@ -0,0 +1,182 @@
+from io import StringIO
+import logging
+
+import unittest
+from numba.core import tracing
+
+logger = logging.getLogger('trace')
+logger.setLevel(logging.INFO)
+
+# Make sure tracing is enabled
+orig_trace = tracing.trace
+tracing.trace = tracing.dotrace
+
+class CapturedTrace:
+    """Capture the trace temporarily for validation."""
+
+    def __init__(self):
+        self.buffer = StringIO()
+        self.handler = logging.StreamHandler(self.buffer)
+    def __enter__(self):
+        self._handlers = logger.handlers
+        self.buffer = StringIO()
+        logger.handlers = [logging.StreamHandler(self.buffer)]
+    def __exit__(self, type, value, traceback):
+        logger.handlers = self._handlers
+    def getvalue(self):
+
+        # Depending on how the tests are run, object names may be
+        # qualified by their containing module.
+        # Remove that to make the trace output independent from the testing mode.
+        log = self.buffer.getvalue()
+        log = log.replace(__name__ + '.','')
+        return log
+
+class Class(object):
+
+    @tracing.trace
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @tracing.trace
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+
+    def _test_get(self):
+        return self.__test
+
+    def _test_set(self, value):
+        self.__test = value
+
+    test = tracing.trace(property(_test_get, _test_set))
+        
+    @tracing.trace
+    def method(self, some, other='value', *args, **kwds):
+        pass
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class instance>'
+
+class Class2(object):
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+    @property
+    def test(self):
+        return self.__test
+    @test.setter
+    def test(self, value):
+        self.__test = value
+
+    def method(self):
+        pass
+
+    def __str__(self):
+        return 'Test(' + str(self.test) + ')'
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class2 instance>'
+
+
+@tracing.trace
+def test(x, y, z = True):
+    a = x + y
+    b = x * y
+    if z: return a
+    else: return b
+
+class TestTracing(unittest.TestCase):
+
+    def __init__(self, *args):
+        super(TestTracing, self).__init__(*args)
+
+    def setUp(self):
+        self.capture = CapturedTrace()
+
+    def tearDown(self):
+        del self.capture
+        
+    def test_method(self):
+
+        with self.capture:
+            Class().method('foo', bar='baz')
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.method(self=<Class instance>, some='foo', other='value', bar='baz')\n" +
+                         "<< Class.method\n")
+
+    def test_class_method(self):
+
+        with self.capture:
+            Class.class_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.class_method(cls=<class 'Class'>)\n" +
+                         "<< Class.class_method\n")
+
+    def test_static_method(self):
+
+        with self.capture:
+            Class.static_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> static_method()\n" +
+                         "<< static_method\n")
+
+
+    def test_property(self):
+
+        with self.capture:
+            test = Class()
+            test.test = 1
+            assert 1 == test.test
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class._test_set(self=<Class instance>, value=1)\n" +
+                         "<< Class._test_set\n" +
+                         ">> Class._test_get(self=<Class instance>)\n" +
+                         "<< Class._test_get -> 1\n")
+
+    def test_function(self):
+
+        with self.capture:
+            test(5, 5)
+            test(5, 5, False)
+        self.assertEqual(self.capture.getvalue(),
+                         ">> test(x=5, y=5, z=True)\n" +
+                         "<< test -> 10\n" +
+                         ">> test(x=5, y=5, z=False)\n" +
+                         "<< test -> 25\n")
+
+    @unittest.skip("recursive decoration not yet implemented")
+    def test_injected(self):
+
+        with self.capture:
+            tracing.trace(Class2, recursive=True)
+            Class2.class_method()
+            Class2.static_method()
+            test = Class2()
+            test.test = 1
+            assert 1 == test.test
+            test.method()
+
+            self.assertEqual(self.capture.getvalue(),
+                         ">> Class2.class_method(cls=<type 'Class2'>)\n" +
+                         "<< Class2.class_method\n"
+                         ">> static_method()\n"
+                         "<< static_method\n")
+
+            
+# Reset tracing to its original value
+tracing.trace = orig_trace
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/0c/0c2f3a0c2146f970dbba975536dd8fb3af0ed8406db6756463c1858f0df8fcb8/vt100-nav b/shared/package-files/0c/0c2f3a0c2146f970dbba975536dd8fb3af0ed8406db6756463c1858f0df8fcb8/vt100-nav
new file mode 100644
index 0000000000000000000000000000000000000000..4a3765973a90f8f3cf7e8336776fc0641336c994
Binary files /dev/null and b/shared/package-files/0c/0c2f3a0c2146f970dbba975536dd8fb3af0ed8406db6756463c1858f0df8fcb8/vt100-nav differ
diff --git a/shared/package-files/0c/0c38936b6626aa40a1a4bccaafcd27f9939f2405d66fa618074444474d77747c/msvcrt.pyi b/shared/package-files/0c/0c38936b6626aa40a1a4bccaafcd27f9939f2405d66fa618074444474d77747c/msvcrt.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ede80c9fbb66f18430361688c419675043497b6a
--- /dev/null
+++ b/shared/package-files/0c/0c38936b6626aa40a1a4bccaafcd27f9939f2405d66fa618074444474d77747c/msvcrt.pyi
@@ -0,0 +1,24 @@
+import sys
+from typing import Text
+
+# This module is only available on Windows
+if sys.platform == "win32":
+    LK_LOCK: int
+    LK_NBLCK: int
+    LK_NBRLCK: int
+    LK_RLCK: int
+    LK_UNLCK: int
+    def locking(__fd: int, __mode: int, __nbytes: int) -> None: ...
+    def setmode(__fd: int, __mode: int) -> int: ...
+    def open_osfhandle(__handle: int, __flags: int) -> int: ...
+    def get_osfhandle(__fd: int) -> int: ...
+    def kbhit() -> bool: ...
+    def getch() -> bytes: ...
+    def getwch() -> Text: ...
+    def getche() -> bytes: ...
+    def getwche() -> Text: ...
+    def putch(__char: bytes) -> None: ...
+    def putwch(__unicode_char: Text) -> None: ...
+    def ungetch(__char: bytes) -> None: ...
+    def ungetwch(__unicode_char: Text) -> None: ...
+    def heapmin() -> None: ...
diff --git a/shared/package-files/0c/0c38a332607070a1ab616daa6f0cfc5b7bc3dd4abac295fff70edf60a6275763/textfmts.py b/shared/package-files/0c/0c38a332607070a1ab616daa6f0cfc5b7bc3dd4abac295fff70edf60a6275763/textfmts.py
new file mode 100644
index 0000000000000000000000000000000000000000..42d52e95632501d75a66a73036fe4e4980c49333
--- /dev/null
+++ b/shared/package-files/0c/0c38a332607070a1ab616daa6f0cfc5b7bc3dd4abac295fff70edf60a6275763/textfmts.py
@@ -0,0 +1,431 @@
+"""
+    pygments.lexers.textfmts
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for various text formats.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexers import guess_lexer, get_lexer_by_name
+from pygments.lexer import RegexLexer, bygroups, default, include
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Generic, Literal, Punctuation
+from pygments.util import ClassNotFound
+
+__all__ = ['IrcLogsLexer', 'TodotxtLexer', 'HttpLexer', 'GettextLexer',
+           'NotmuchLexer', 'KernelLogLexer']
+
+
+class IrcLogsLexer(RegexLexer):
+    """
+    Lexer for IRC logs in *irssi*, *xchat* or *weechat* style.
+    """
+
+    name = 'IRC logs'
+    aliases = ['irc']
+    filenames = ['*.weechatlog']
+    mimetypes = ['text/x-irclog']
+
+    flags = re.VERBOSE | re.MULTILINE
+    timestamp = r"""
+        (
+          # irssi / xchat and others
+          (?: \[|\()?                  # Opening bracket or paren for the timestamp
+            (?:                        # Timestamp
+                (?: (?:\d{1,4} [-/])*  # Date as - or /-separated groups of digits
+                    (?:\d{1,4})
+                 [T ])?                # Date/time separator: T or space
+                (?: \d?\d [:.])*       # Time as :/.-separated groups of 1 or 2 digits
+                    (?: \d?\d)
+            )
+          (?: \]|\))?\s+               # Closing bracket or paren for the timestamp
+        |
+          # weechat
+          \d{4}\s\w{3}\s\d{2}\s        # Date
+          \d{2}:\d{2}:\d{2}\s+         # Time + Whitespace
+        |
+          # xchat
+          \w{3}\s\d{2}\s               # Date
+          \d{2}:\d{2}:\d{2}\s+         # Time + Whitespace
+        )?
+    """
+    tokens = {
+        'root': [
+            # log start/end
+            (r'^\*\*\*\*(.*)\*\*\*\*$', Comment),
+            # hack
+            ("^" + timestamp + r'(\s*<[^>]*>\s*)$', bygroups(Comment.Preproc, Name.Tag)),
+            # normal msgs
+            ("^" + timestamp + r"""
+                (\s*<.*?>\s*)          # Nick """,
+             bygroups(Comment.Preproc, Name.Tag), 'msg'),
+            # /me msgs
+            ("^" + timestamp + r"""
+                (\s*[*]\s+)            # Star
+                (\S+\s+.*?\n)          # Nick + rest of message """,
+             bygroups(Comment.Preproc, Keyword, Generic.Inserted)),
+            # join/part msgs
+            ("^" + timestamp + r"""
+                (\s*(?:\*{3}|<?-[!@=P]?->?)\s*)  # Star(s) or symbols
+                (\S+\s+)                     # Nick + Space
+                (.*?\n)                         # Rest of message """,
+             bygroups(Comment.Preproc, Keyword, String, Comment)),
+            (r"^.*?\n", Text),
+        ],
+        'msg': [
+            (r"\S+:(?!//)", Name.Attribute),  # Prefix
+            (r".*\n", Text, '#pop'),
+        ],
+    }
+
+
+class GettextLexer(RegexLexer):
+    """
+    Lexer for Gettext catalog files.
+
+    .. versionadded:: 0.9
+    """
+    name = 'Gettext Catalog'
+    aliases = ['pot', 'po']
+    filenames = ['*.pot', '*.po']
+    mimetypes = ['application/x-gettext', 'text/x-gettext', 'text/gettext']
+
+    tokens = {
+        'root': [
+            (r'^#,\s.*?$', Keyword.Type),
+            (r'^#:\s.*?$', Keyword.Declaration),
+            # (r'^#$', Comment),
+            (r'^(#|#\.\s|#\|\s|#~\s|#\s).*$', Comment.Single),
+            (r'^(")([A-Za-z-]+:)(.*")$',
+             bygroups(String, Name.Property, String)),
+            (r'^".*"$', String),
+            (r'^(msgid|msgid_plural|msgstr|msgctxt)(\s+)(".*")$',
+             bygroups(Name.Variable, Text, String)),
+            (r'^(msgstr\[)(\d)(\])(\s+)(".*")$',
+             bygroups(Name.Variable, Number.Integer, Name.Variable, Text, String)),
+        ]
+    }
+
+
+class HttpLexer(RegexLexer):
+    """
+    Lexer for HTTP sessions.
+
+    .. versionadded:: 1.5
+    """
+
+    name = 'HTTP'
+    aliases = ['http']
+
+    flags = re.DOTALL
+
+    def get_tokens_unprocessed(self, text, stack=('root',)):
+        """Reset the content-type state."""
+        self.content_type = None
+        return RegexLexer.get_tokens_unprocessed(self, text, stack)
+
+    def header_callback(self, match):
+        if match.group(1).lower() == 'content-type':
+            content_type = match.group(5).strip()
+            if ';' in content_type:
+                content_type = content_type[:content_type.find(';')].strip()
+            self.content_type = content_type
+        yield match.start(1), Name.Attribute, match.group(1)
+        yield match.start(2), Text, match.group(2)
+        yield match.start(3), Operator, match.group(3)
+        yield match.start(4), Text, match.group(4)
+        yield match.start(5), Literal, match.group(5)
+        yield match.start(6), Text, match.group(6)
+
+    def continuous_header_callback(self, match):
+        yield match.start(1), Text, match.group(1)
+        yield match.start(2), Literal, match.group(2)
+        yield match.start(3), Text, match.group(3)
+
+    def content_callback(self, match):
+        content_type = getattr(self, 'content_type', None)
+        content = match.group()
+        offset = match.start()
+        if content_type:
+            from pygments.lexers import get_lexer_for_mimetype
+            possible_lexer_mimetypes = [content_type]
+            if '+' in content_type:
+                # application/calendar+xml can be treated as application/xml
+                # if there's not a better match.
+                general_type = re.sub(r'^(.*)/.*\+(.*)$', r'\1/\2',
+                                      content_type)
+                possible_lexer_mimetypes.append(general_type)
+
+            for i in possible_lexer_mimetypes:
+                try:
+                    lexer = get_lexer_for_mimetype(i)
+                except ClassNotFound:
+                    pass
+                else:
+                    for idx, token, value in lexer.get_tokens_unprocessed(content):
+                        yield offset + idx, token, value
+                    return
+        yield offset, Text, content
+
+    tokens = {
+        'root': [
+            (r'(GET|POST|PUT|DELETE|HEAD|OPTIONS|TRACE|PATCH|CONNECT)( +)([^ ]+)( +)'
+             r'(HTTP)(/)(1\.[01]|2(?:\.0)?|3)(\r?\n|\Z)',
+             bygroups(Name.Function, Text, Name.Namespace, Text,
+                      Keyword.Reserved, Operator, Number, Text),
+             'headers'),
+            (r'(HTTP)(/)(1\.[01]|2(?:\.0)?|3)( +)(\d{3})(?:( +)([^\r\n]*))?(\r?\n|\Z)',
+             bygroups(Keyword.Reserved, Operator, Number, Text, Number, Text,
+                      Name.Exception, Text),
+             'headers'),
+        ],
+        'headers': [
+            (r'([^\s:]+)( *)(:)( *)([^\r\n]+)(\r?\n|\Z)', header_callback),
+            (r'([\t ]+)([^\r\n]+)(\r?\n|\Z)', continuous_header_callback),
+            (r'\r?\n', Text, 'content')
+        ],
+        'content': [
+            (r'.+', content_callback)
+        ]
+    }
+
+    def analyse_text(text):
+        return text.startswith(('GET /', 'POST /', 'PUT /', 'DELETE /', 'HEAD /',
+                                'OPTIONS /', 'TRACE /', 'PATCH /', 'CONNECT '))
+
+
+class TodotxtLexer(RegexLexer):
+    """
+    Lexer for Todo.txt todo list format.
+
+    .. versionadded:: 2.0
+    """
+
+    name = 'Todotxt'
+    url = 'http://todotxt.com/'
+    aliases = ['todotxt']
+    # *.todotxt is not a standard extension for Todo.txt files; including it
+    # makes testing easier, and also makes autodetecting file type easier.
+    filenames = ['todo.txt', '*.todotxt']
+    mimetypes = ['text/x-todo']
+
+    # Aliases mapping standard token types of Todo.txt format concepts
+    CompleteTaskText = Operator  # Chosen to de-emphasize complete tasks
+    IncompleteTaskText = Text    # Incomplete tasks should look like plain text
+
+    # Priority should have most emphasis to indicate importance of tasks
+    Priority = Generic.Heading
+    # Dates should have next most emphasis because time is important
+    Date = Generic.Subheading
+
+    # Project and context should have equal weight, and be in different colors
+    Project = Generic.Error
+    Context = String
+
+    # If tag functionality is added, it should have the same weight as Project
+    # and Context, and a different color. Generic.Traceback would work well.
+
+    # Regex patterns for building up rules; dates, priorities, projects, and
+    # contexts are all atomic
+    # TODO: Make date regex more ISO 8601 compliant
+    date_regex = r'\d{4,}-\d{2}-\d{2}'
+    priority_regex = r'\([A-Z]\)'
+    project_regex = r'\+\S+'
+    context_regex = r'@\S+'
+
+    # Compound regex expressions
+    complete_one_date_regex = r'(x )(' + date_regex + r')'
+    complete_two_date_regex = (complete_one_date_regex + r'( )(' +
+                               date_regex + r')')
+    priority_date_regex = r'(' + priority_regex + r')( )(' + date_regex + r')'
+
+    tokens = {
+        # Should parse starting at beginning of line; each line is a task
+        'root': [
+            # Complete task entry points: two total:
+            # 1. Complete task with two dates
+            (complete_two_date_regex, bygroups(CompleteTaskText, Date,
+                                               CompleteTaskText, Date),
+             'complete'),
+            # 2. Complete task with one date
+            (complete_one_date_regex, bygroups(CompleteTaskText, Date),
+             'complete'),
+
+            # Incomplete task entry points: six total:
+            # 1. Priority plus date
+            (priority_date_regex, bygroups(Priority, IncompleteTaskText, Date),
+             'incomplete'),
+            # 2. Priority only
+            (priority_regex, Priority, 'incomplete'),
+            # 3. Leading date
+            (date_regex, Date, 'incomplete'),
+            # 4. Leading context
+            (context_regex, Context, 'incomplete'),
+            # 5. Leading project
+            (project_regex, Project, 'incomplete'),
+            # 6. Non-whitespace catch-all
+            (r'\S+', IncompleteTaskText, 'incomplete'),
+        ],
+
+        # Parse a complete task
+        'complete': [
+            # Newline indicates end of task, should return to root
+            (r'\s*\n', CompleteTaskText, '#pop'),
+            # Tokenize contexts and projects
+            (context_regex, Context),
+            (project_regex, Project),
+            # Tokenize non-whitespace text
+            (r'\S+', CompleteTaskText),
+            # Tokenize whitespace not containing a newline
+            (r'\s+', CompleteTaskText),
+        ],
+
+        # Parse an incomplete task
+        'incomplete': [
+            # Newline indicates end of task, should return to root
+            (r'\s*\n', IncompleteTaskText, '#pop'),
+            # Tokenize contexts and projects
+            (context_regex, Context),
+            (project_regex, Project),
+            # Tokenize non-whitespace text
+            (r'\S+', IncompleteTaskText),
+            # Tokenize whitespace not containing a newline
+            (r'\s+', IncompleteTaskText),
+        ],
+    }
+
+
+class NotmuchLexer(RegexLexer):
+    """
+    For Notmuch email text format.
+
+    .. versionadded:: 2.5
+
+    Additional options accepted:
+
+    `body_lexer`
+        If given, highlight the contents of the message body with the specified
+        lexer, else guess it according to the body content (default: ``None``).
+    """
+
+    name = 'Notmuch'
+    url = 'https://notmuchmail.org/'
+    aliases = ['notmuch']
+
+    def _highlight_code(self, match):
+        code = match.group(1)
+
+        try:
+            if self.body_lexer:
+                lexer = get_lexer_by_name(self.body_lexer)
+            else:
+                lexer = guess_lexer(code.strip())
+        except ClassNotFound:
+            lexer = get_lexer_by_name('text')
+
+        yield from lexer.get_tokens_unprocessed(code)
+
+    tokens = {
+        'root': [
+            (r'\fmessage\{\s*', Keyword, ('message', 'message-attr')),
+        ],
+        'message-attr': [
+            (r'(\s*id:\s*)(\S+)', bygroups(Name.Attribute, String)),
+            (r'(\s*(?:depth|match|excluded):\s*)(\d+)',
+             bygroups(Name.Attribute, Number.Integer)),
+            (r'(\s*filename:\s*)(.+\n)',
+             bygroups(Name.Attribute, String)),
+            default('#pop'),
+        ],
+        'message': [
+            (r'\fmessage\}\n', Keyword, '#pop'),
+            (r'\fheader\{\n', Keyword, 'header'),
+            (r'\fbody\{\n', Keyword, 'body'),
+        ],
+        'header': [
+            (r'\fheader\}\n', Keyword, '#pop'),
+            (r'((?:Subject|From|To|Cc|Date):\s*)(.*\n)',
+             bygroups(Name.Attribute, String)),
+            (r'(.*)(\s*\(.*\))(\s*\(.*\)\n)',
+             bygroups(Generic.Strong, Literal, Name.Tag)),
+        ],
+        'body': [
+            (r'\fpart\{\n', Keyword, 'part'),
+            (r'\f(part|attachment)\{\s*', Keyword, ('part', 'part-attr')),
+            (r'\fbody\}\n', Keyword, '#pop'),
+        ],
+        'part-attr': [
+            (r'(ID:\s*)(\d+)', bygroups(Name.Attribute, Number.Integer)),
+            (r'(,\s*)((?:Filename|Content-id):\s*)([^,]+)',
+             bygroups(Punctuation, Name.Attribute, String)),
+            (r'(,\s*)(Content-type:\s*)(.+\n)',
+             bygroups(Punctuation, Name.Attribute, String)),
+            default('#pop'),
+        ],
+        'part': [
+            (r'\f(?:part|attachment)\}\n', Keyword, '#pop'),
+            (r'\f(?:part|attachment)\{\s*', Keyword, ('#push', 'part-attr')),
+            (r'^Non-text part: .*\n', Comment),
+            (r'(?s)(.*?(?=\f(?:part|attachment)\}\n))', _highlight_code),
+        ],
+    }
+
+    def analyse_text(text):
+        return 1.0 if text.startswith('\fmessage{') else 0.0
+
+    def __init__(self, **options):
+        self.body_lexer = options.get('body_lexer', None)
+        RegexLexer.__init__(self, **options)
+
+
+class KernelLogLexer(RegexLexer):
+    """
+    For Linux Kernel log ("dmesg") output.
+
+    .. versionadded:: 2.6
+    """
+    name = 'Kernel log'
+    aliases = ['kmsg', 'dmesg']
+    filenames = ['*.kmsg', '*.dmesg']
+
+    tokens = {
+        'root': [
+            (r'^[^:]+:debug : (?=\[)', Text, 'debug'),
+            (r'^[^:]+:info  : (?=\[)', Text, 'info'),
+            (r'^[^:]+:warn  : (?=\[)', Text, 'warn'),
+            (r'^[^:]+:notice: (?=\[)', Text, 'warn'),
+            (r'^[^:]+:err   : (?=\[)', Text, 'error'),
+            (r'^[^:]+:crit  : (?=\[)', Text, 'error'),
+            (r'^(?=\[)', Text, 'unknown'),
+        ],
+        'unknown': [
+            (r'^(?=.+(warning|notice|audit|deprecated))', Text, 'warn'),
+            (r'^(?=.+(error|critical|fail|Bug))', Text, 'error'),
+            default('info'),
+        ],
+        'base': [
+            (r'\[[0-9. ]+\] ', Number),
+            (r'(?<=\] ).+?:', Keyword),
+            (r'\n', Text, '#pop'),
+        ],
+        'debug': [
+            include('base'),
+            (r'.+\n', Comment, '#pop')
+        ],
+        'info': [
+            include('base'),
+            (r'.+\n', Text, '#pop')
+        ],
+        'warn': [
+            include('base'),
+            (r'.+\n', Generic.Strong, '#pop')
+        ],
+        'error': [
+            include('base'),
+            (r'.+\n', Generic.Error, '#pop')
+        ]
+    }
diff --git a/shared/package-files/0c/0c3b8cc2b2263040b0e8c85f9dc9f6c4c3a390570a515d4ed79f7bfdf485480b/chunked_builder.h b/shared/package-files/0c/0c3b8cc2b2263040b0e8c85f9dc9f6c4c3a390570a515d4ed79f7bfdf485480b/chunked_builder.h
new file mode 100644
index 0000000000000000000000000000000000000000..93b327bf3ae2b63bc4439d77440b54d10e45810a
--- /dev/null
+++ b/shared/package-files/0c/0c3b8cc2b2263040b0e8c85f9dc9f6c4c3a390570a515d4ed79f7bfdf485480b/chunked_builder.h
@@ -0,0 +1,68 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <memory>
+#include <vector>
+
+#include "arrow/status.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/type_fwd.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace json {
+
+class PromotionGraph;
+
+class ARROW_EXPORT ChunkedArrayBuilder {
+ public:
+  virtual ~ChunkedArrayBuilder() = default;
+
+  /// Spawn a task that will try to convert and insert the given JSON block
+  virtual void Insert(int64_t block_index,
+                      const std::shared_ptr<Field>& unconverted_field,
+                      const std::shared_ptr<Array>& unconverted) = 0;
+
+  /// Return the final chunked array.
+  /// Every chunk must be inserted before this is called!
+  virtual Status Finish(std::shared_ptr<ChunkedArray>* out) = 0;
+
+  /// Finish current task group and substitute a new one
+  virtual Status ReplaceTaskGroup(
+      const std::shared_ptr<arrow::internal::TaskGroup>& task_group) = 0;
+
+ protected:
+  explicit ChunkedArrayBuilder(
+      const std::shared_ptr<arrow::internal::TaskGroup>& task_group)
+      : task_group_(task_group) {}
+
+  std::shared_ptr<arrow::internal::TaskGroup> task_group_;
+};
+
+/// create a chunked builder
+///
+/// if unexpected fields and promotion need to be handled, promotion_graph must be
+/// non-null
+ARROW_EXPORT Status MakeChunkedArrayBuilder(
+    const std::shared_ptr<arrow::internal::TaskGroup>& task_group, MemoryPool* pool,
+    const PromotionGraph* promotion_graph, const std::shared_ptr<DataType>& type,
+    std::shared_ptr<ChunkedArrayBuilder>* out);
+
+}  // namespace json
+}  // namespace arrow
diff --git a/shared/package-files/0c/0c3bf5261f951c3c1c6373588b8fbd4a9c7fe7dbba9face51bb58c8b74ad989b/distro.py b/shared/package-files/0c/0c3bf5261f951c3c1c6373588b8fbd4a9c7fe7dbba9face51bb58c8b74ad989b/distro.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8bec474cdd9482eb88d10585d5a33d512862b2
--- /dev/null
+++ b/shared/package-files/0c/0c3bf5261f951c3c1c6373588b8fbd4a9c7fe7dbba9face51bb58c8b74ad989b/distro.py
@@ -0,0 +1,1107 @@
+# Copyright 2015,2016 Nir Cohen
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+The ``distro`` package (``distro`` stands for Linux Distribution) provides
+information about the Linux distribution it runs on, such as a reliable
+machine-readable distro ID, or version information.
+
+It is a renewed alternative implementation for Python's original
+:py:func:`platform.linux_distribution` function, but it provides much more
+functionality. An alternative implementation became necessary because Python
+3.5 deprecated this function, and Python 3.7 is expected to remove it
+altogether. Its predecessor function :py:func:`platform.dist` was already
+deprecated since Python 2.6 and is also expected to be removed in Python 3.7.
+Still, there are many cases in which access to Linux distribution information
+is needed. See `Python issue 1322 <https://bugs.python.org/issue1322>`_ for
+more information.
+"""
+
+import os
+import re
+import sys
+import json
+import shlex
+import logging
+import argparse
+import subprocess
+
+
+if not sys.platform.startswith('linux'):
+    raise ImportError('Unsupported platform: {0}'.format(sys.platform))
+
+_UNIXCONFDIR = os.environ.get('UNIXCONFDIR', '/etc')
+_OS_RELEASE_BASENAME = 'os-release'
+
+#: Translation table for normalizing the "ID" attribute defined in os-release
+#: files, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as defined in the os-release file, translated to lower case,
+#:   with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_OS_ID = {}
+
+#: Translation table for normalizing the "Distributor ID" attribute returned by
+#: the lsb_release command, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as returned by the lsb_release command, translated to lower
+#:   case, with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_LSB_ID = {
+    'enterpriseenterprise': 'oracle',  # Oracle Enterprise Linux
+    'redhatenterpriseworkstation': 'rhel',  # RHEL 6, 7 Workstation
+    'redhatenterpriseserver': 'rhel',  # RHEL 6, 7 Server
+}
+
+#: Translation table for normalizing the distro ID derived from the file name
+#: of distro release files, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as derived from the file name of a distro release file,
+#:   translated to lower case, with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_DISTRO_ID = {
+    'redhat': 'rhel',  # RHEL 6.x, 7.x
+}
+
+# Pattern for content of distro release file (reversed)
+_DISTRO_RELEASE_CONTENT_REVERSED_PATTERN = re.compile(
+    r'(?:[^)]*\)(.*)\()? *(?:STL )?([\d.+\-a-z]*\d) *(?:esaeler *)?(.+)')
+
+# Pattern for base file name of distro release file
+_DISTRO_RELEASE_BASENAME_PATTERN = re.compile(
+    r'(\w+)[-_](release|version)$')
+
+# Base file names to be ignored when searching for distro release file
+_DISTRO_RELEASE_IGNORE_BASENAMES = (
+    'debian_version',
+    'lsb-release',
+    'oem-release',
+    _OS_RELEASE_BASENAME,
+    'system-release'
+)
+
+
+def linux_distribution(full_distribution_name=True):
+    """
+    Return information about the current Linux distribution as a tuple
+    ``(id_name, version, codename)`` with items as follows:
+
+    * ``id_name``:  If *full_distribution_name* is false, the result of
+      :func:`distro.id`. Otherwise, the result of :func:`distro.name`.
+
+    * ``version``:  The result of :func:`distro.version`.
+
+    * ``codename``:  The result of :func:`distro.codename`.
+
+    The interface of this function is compatible with the original
+    :py:func:`platform.linux_distribution` function, supporting a subset of
+    its parameters.
+
+    The data it returns may not exactly be the same, because it uses more data
+    sources than the original function, and that may lead to different data if
+    the Linux distribution is not consistent across multiple data sources it
+    provides (there are indeed such distributions ...).
+
+    Another reason for differences is the fact that the :func:`distro.id`
+    method normalizes the distro ID string to a reliable machine-readable value
+    for a number of popular Linux distributions.
+    """
+    return _distro.linux_distribution(full_distribution_name)
+
+
+def id():
+    """
+    Return the distro ID of the current Linux distribution, as a
+    machine-readable string.
+
+    For a number of Linux distributions, the returned distro ID value is
+    *reliable*, in the sense that it is documented and that it does not change
+    across releases of the distribution.
+
+    This package maintains the following reliable distro ID values:
+
+    ==============  =========================================
+    Distro ID       Distribution
+    ==============  =========================================
+    "ubuntu"        Ubuntu
+    "debian"        Debian
+    "rhel"          RedHat Enterprise Linux
+    "centos"        CentOS
+    "fedora"        Fedora
+    "sles"          SUSE Linux Enterprise Server
+    "opensuse"      openSUSE
+    "amazon"        Amazon Linux
+    "arch"          Arch Linux
+    "cloudlinux"    CloudLinux OS
+    "exherbo"       Exherbo Linux
+    "gentoo"        GenToo Linux
+    "ibm_powerkvm"  IBM PowerKVM
+    "kvmibm"        KVM for IBM z Systems
+    "linuxmint"     Linux Mint
+    "mageia"        Mageia
+    "mandriva"      Mandriva Linux
+    "parallels"     Parallels
+    "pidora"        Pidora
+    "raspbian"      Raspbian
+    "oracle"        Oracle Linux (and Oracle Enterprise Linux)
+    "scientific"    Scientific Linux
+    "slackware"     Slackware
+    "xenserver"     XenServer
+    ==============  =========================================
+
+    If you have a need to get distros for reliable IDs added into this set,
+    or if you find that the :func:`distro.id` function returns a different
+    distro ID for one of the listed distros, please create an issue in the
+    `distro issue tracker`_.
+
+    **Lookup hierarchy and transformations:**
+
+    First, the ID is obtained from the following sources, in the specified
+    order. The first available and non-empty value is used:
+
+    * the value of the "ID" attribute of the os-release file,
+
+    * the value of the "Distributor ID" attribute returned by the lsb_release
+      command,
+
+    * the first part of the file name of the distro release file,
+
+    The so determined ID value then passes the following transformations,
+    before it is returned by this method:
+
+    * it is translated to lower case,
+
+    * blanks (which should not be there anyway) are translated to underscores,
+
+    * a normalization of the ID is performed, based upon
+      `normalization tables`_. The purpose of this normalization is to ensure
+      that the ID is as reliable as possible, even across incompatible changes
+      in the Linux distributions. A common reason for an incompatible change is
+      the addition of an os-release file, or the addition of the lsb_release
+      command, with ID values that differ from what was previously determined
+      from the distro release file name.
+    """
+    return _distro.id()
+
+
+def name(pretty=False):
+    """
+    Return the name of the current Linux distribution, as a human-readable
+    string.
+
+    If *pretty* is false, the name is returned without version or codename.
+    (e.g. "CentOS Linux")
+
+    If *pretty* is true, the version and codename are appended.
+    (e.g. "CentOS Linux 7.1.1503 (Core)")
+
+    **Lookup hierarchy:**
+
+    The name is obtained from the following sources, in the specified order.
+    The first available and non-empty value is used:
+
+    * If *pretty* is false:
+
+      - the value of the "NAME" attribute of the os-release file,
+
+      - the value of the "Distributor ID" attribute returned by the lsb_release
+        command,
+
+      - the value of the "<name>" field of the distro release file.
+
+    * If *pretty* is true:
+
+      - the value of the "PRETTY_NAME" attribute of the os-release file,
+
+      - the value of the "Description" attribute returned by the lsb_release
+        command,
+
+      - the value of the "<name>" field of the distro release file, appended
+        with the value of the pretty version ("<version_id>" and "<codename>"
+        fields) of the distro release file, if available.
+    """
+    return _distro.name(pretty)
+
+
+def version(pretty=False, best=False):
+    """
+    Return the version of the current Linux distribution, as a human-readable
+    string.
+
+    If *pretty* is false, the version is returned without codename (e.g.
+    "7.0").
+
+    If *pretty* is true, the codename in parenthesis is appended, if the
+    codename is non-empty (e.g. "7.0 (Maipo)").
+
+    Some distributions provide version numbers with different precisions in
+    the different sources of distribution information. Examining the different
+    sources in a fixed priority order does not always yield the most precise
+    version (e.g. for Debian 8.2, or CentOS 7.1).
+
+    The *best* parameter can be used to control the approach for the returned
+    version:
+
+    If *best* is false, the first non-empty version number in priority order of
+    the examined sources is returned.
+
+    If *best* is true, the most precise version number out of all examined
+    sources is returned.
+
+    **Lookup hierarchy:**
+
+    In all cases, the version number is obtained from the following sources.
+    If *best* is false, this order represents the priority order:
+
+    * the value of the "VERSION_ID" attribute of the os-release file,
+    * the value of the "Release" attribute returned by the lsb_release
+      command,
+    * the version number parsed from the "<version_id>" field of the first line
+      of the distro release file,
+    * the version number parsed from the "PRETTY_NAME" attribute of the
+      os-release file, if it follows the format of the distro release files.
+    * the version number parsed from the "Description" attribute returned by
+      the lsb_release command, if it follows the format of the distro release
+      files.
+    """
+    return _distro.version(pretty, best)
+
+
+def version_parts(best=False):
+    """
+    Return the version of the current Linux distribution as a tuple
+    ``(major, minor, build_number)`` with items as follows:
+
+    * ``major``:  The result of :func:`distro.major_version`.
+
+    * ``minor``:  The result of :func:`distro.minor_version`.
+
+    * ``build_number``:  The result of :func:`distro.build_number`.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.version_parts(best)
+
+
+def major_version(best=False):
+    """
+    Return the major version of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The major version is the first
+    part of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.major_version(best)
+
+
+def minor_version(best=False):
+    """
+    Return the minor version of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The minor version is the second
+    part of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.minor_version(best)
+
+
+def build_number(best=False):
+    """
+    Return the build number of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The build number is the third part
+    of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.build_number(best)
+
+
+def like():
+    """
+    Return a space-separated list of distro IDs of distributions that are
+    closely related to the current Linux distribution in regards to packaging
+    and programming interfaces, for example distributions the current
+    distribution is a derivative from.
+
+    **Lookup hierarchy:**
+
+    This information item is only provided by the os-release file.
+    For details, see the description of the "ID_LIKE" attribute in the
+    `os-release man page
+    <http://www.freedesktop.org/software/systemd/man/os-release.html>`_.
+    """
+    return _distro.like()
+
+
+def codename():
+    """
+    Return the codename for the release of the current Linux distribution,
+    as a string.
+
+    If the distribution does not have a codename, an empty string is returned.
+
+    Note that the returned codename is not always really a codename. For
+    example, openSUSE returns "x86_64". This function does not handle such
+    cases in any special way and just returns the string it finds, if any.
+
+    **Lookup hierarchy:**
+
+    * the codename within the "VERSION" attribute of the os-release file, if
+      provided,
+
+    * the value of the "Codename" attribute returned by the lsb_release
+      command,
+
+    * the value of the "<codename>" field of the distro release file.
+    """
+    return _distro.codename()
+
+
+def info(pretty=False, best=False):
+    """
+    Return certain machine-readable information items about the current Linux
+    distribution in a dictionary, as shown in the following example:
+
+    .. sourcecode:: python
+
+        {
+            'id': 'rhel',
+            'version': '7.0',
+            'version_parts': {
+                'major': '7',
+                'minor': '0',
+                'build_number': ''
+            },
+            'like': 'fedora',
+            'codename': 'Maipo'
+        }
+
+    The dictionary structure and keys are always the same, regardless of which
+    information items are available in the underlying data sources. The values
+    for the various keys are as follows:
+
+    * ``id``:  The result of :func:`distro.id`.
+
+    * ``version``:  The result of :func:`distro.version`.
+
+    * ``version_parts -> major``:  The result of :func:`distro.major_version`.
+
+    * ``version_parts -> minor``:  The result of :func:`distro.minor_version`.
+
+    * ``version_parts -> build_number``:  The result of
+      :func:`distro.build_number`.
+
+    * ``like``:  The result of :func:`distro.like`.
+
+    * ``codename``:  The result of :func:`distro.codename`.
+
+    For a description of the *pretty* and *best* parameters, see the
+    :func:`distro.version` method.
+    """
+    return _distro.info(pretty, best)
+
+
+def os_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the os-release file data source of the current Linux distribution.
+
+    See `os-release file`_ for details about these information items.
+    """
+    return _distro.os_release_info()
+
+
+def lsb_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the lsb_release command data source of the current Linux distribution.
+
+    See `lsb_release command output`_ for details about these information
+    items.
+    """
+    return _distro.lsb_release_info()
+
+
+def distro_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the distro release file data source of the current Linux distribution.
+
+    See `distro release file`_ for details about these information items.
+    """
+    return _distro.distro_release_info()
+
+
+def os_release_attr(attribute):
+    """
+    Return a single named information item from the os-release file data source
+    of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `os-release file`_ for details about these information items.
+    """
+    return _distro.os_release_attr(attribute)
+
+
+def lsb_release_attr(attribute):
+    """
+    Return a single named information item from the lsb_release command output
+    data source of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `lsb_release command output`_ for details about these information
+    items.
+    """
+    return _distro.lsb_release_attr(attribute)
+
+
+def distro_release_attr(attribute):
+    """
+    Return a single named information item from the distro release file
+    data source of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `distro release file`_ for details about these information items.
+    """
+    return _distro.distro_release_attr(attribute)
+
+
+class LinuxDistribution(object):
+    """
+    Provides information about a Linux distribution.
+
+    This package creates a private module-global instance of this class with
+    default initialization arguments, that is used by the
+    `consolidated accessor functions`_ and `single source accessor functions`_.
+    By using default initialization arguments, that module-global instance
+    returns data about the current Linux distribution (i.e. the distro this
+    package runs on).
+
+    Normally, it is not necessary to create additional instances of this class.
+    However, in situations where control is needed over the exact data sources
+    that are used, instances of this class can be created with a specific
+    distro release file, or a specific os-release file, or without invoking the
+    lsb_release command.
+    """
+
+    def __init__(self,
+                 include_lsb=True,
+                 os_release_file='',
+                 distro_release_file=''):
+        """
+        The initialization method of this class gathers information from the
+        available data sources, and stores that in private instance attributes.
+        Subsequent access to the information items uses these private instance
+        attributes, so that the data sources are read only once.
+
+        Parameters:
+
+        * ``include_lsb`` (bool): Controls whether the
+          `lsb_release command output`_ is included as a data source.
+
+          If the lsb_release command is not available in the program execution
+          path, the data source for the lsb_release command will be empty.
+
+        * ``os_release_file`` (string): The path name of the
+          `os-release file`_ that is to be used as a data source.
+
+          An empty string (the default) will cause the default path name to
+          be used (see `os-release file`_ for details).
+
+          If the specified or defaulted os-release file does not exist, the
+          data source for the os-release file will be empty.
+
+        * ``distro_release_file`` (string): The path name of the
+          `distro release file`_ that is to be used as a data source.
+
+          An empty string (the default) will cause a default search algorithm
+          to be used (see `distro release file`_ for details).
+
+          If the specified distro release file does not exist, or if no default
+          distro release file can be found, the data source for the distro
+          release file will be empty.
+
+        Public instance attributes:
+
+        * ``os_release_file`` (string): The path name of the
+          `os-release file`_ that is actually used as a data source. The
+          empty string if no distro release file is used as a data source.
+
+        * ``distro_release_file`` (string): The path name of the
+          `distro release file`_ that is actually used as a data source. The
+          empty string if no distro release file is used as a data source.
+
+        Raises:
+
+        * :py:exc:`IOError`: Some I/O issue with an os-release file or distro
+          release file.
+
+        * :py:exc:`subprocess.CalledProcessError`: The lsb_release command had
+          some issue (other than not being available in the program execution
+          path).
+
+        * :py:exc:`UnicodeError`: A data source has unexpected characters or
+          uses an unexpected encoding.
+        """
+        self.os_release_file = os_release_file or \
+            os.path.join(_UNIXCONFDIR, _OS_RELEASE_BASENAME)
+        self.distro_release_file = distro_release_file or ''  # updated later
+        self._os_release_info = self._get_os_release_info()
+        self._lsb_release_info = self._get_lsb_release_info() \
+            if include_lsb else {}
+        self._distro_release_info = self._get_distro_release_info()
+
+    def __repr__(self):
+        """Return repr of all info
+        """
+        return \
+            "LinuxDistribution(" \
+            "os_release_file={0!r}, " \
+            "distro_release_file={1!r}, " \
+            "_os_release_info={2!r}, " \
+            "_lsb_release_info={3!r}, " \
+            "_distro_release_info={4!r})".format(
+                self.os_release_file,
+                self.distro_release_file,
+                self._os_release_info,
+                self._lsb_release_info,
+                self._distro_release_info)
+
+    def linux_distribution(self, full_distribution_name=True):
+        """
+        Return information about the Linux distribution that is compatible
+        with Python's :func:`platform.linux_distribution`, supporting a subset
+        of its parameters.
+
+        For details, see :func:`distro.linux_distribution`.
+        """
+        return (
+            self.name() if full_distribution_name else self.id(),
+            self.version(),
+            self.codename()
+        )
+
+    def id(self):
+        """Return the distro ID of the Linux distribution, as a string.
+
+        For details, see :func:`distro.id`.
+        """
+        def normalize(distro_id, table):
+            distro_id = distro_id.lower().replace(' ', '_')
+            return table.get(distro_id, distro_id)
+
+        distro_id = self.os_release_attr('id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_OS_ID)
+
+        distro_id = self.lsb_release_attr('distributor_id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_LSB_ID)
+
+        distro_id = self.distro_release_attr('id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_DISTRO_ID)
+
+        return ''
+
+    def name(self, pretty=False):
+        """
+        Return the name of the Linux distribution, as a string.
+
+        For details, see :func:`distro.name`.
+        """
+        name = self.os_release_attr('name') \
+            or self.lsb_release_attr('distributor_id') \
+            or self.distro_release_attr('name')
+        if pretty:
+            name = self.os_release_attr('pretty_name') \
+                or self.lsb_release_attr('description')
+            if not name:
+                name = self.distro_release_attr('name')
+                version = self.version(pretty=True)
+                if version:
+                    name = name + ' ' + version
+        return name or ''
+
+    def version(self, pretty=False, best=False):
+        """
+        Return the version of the Linux distribution, as a string.
+
+        For details, see :func:`distro.version`.
+        """
+        versions = [
+            self.os_release_attr('version_id'),
+            self.lsb_release_attr('release'),
+            self.distro_release_attr('version_id'),
+            self._parse_distro_release_content(
+                self.os_release_attr('pretty_name')).get('version_id', ''),
+            self._parse_distro_release_content(
+                self.lsb_release_attr('description')).get('version_id', '')
+        ]
+        version = ''
+        if best:
+            # This algorithm uses the last version in priority order that has
+            # the best precision. If the versions are not in conflict, that
+            # does not matter; otherwise, using the last one instead of the
+            # first one might be considered a surprise.
+            for v in versions:
+                if v.count(".") > version.count(".") or version == '':
+                    version = v
+        else:
+            for v in versions:
+                if v != '':
+                    version = v
+                    break
+        if pretty and version and self.codename():
+            version = u'{0} ({1})'.format(version, self.codename())
+        return version
+
+    def version_parts(self, best=False):
+        """
+        Return the version of the Linux distribution, as a tuple of version
+        numbers.
+
+        For details, see :func:`distro.version_parts`.
+        """
+        version_str = self.version(best=best)
+        if version_str:
+            version_regex = re.compile(r'(\d+)\.?(\d+)?\.?(\d+)?')
+            matches = version_regex.match(version_str)
+            if matches:
+                major, minor, build_number = matches.groups()
+                return major, minor or '', build_number or ''
+        return '', '', ''
+
+    def major_version(self, best=False):
+        """
+        Return the major version number of the current distribution.
+
+        For details, see :func:`distro.major_version`.
+        """
+        return self.version_parts(best)[0]
+
+    def minor_version(self, best=False):
+        """
+        Return the minor version number of the Linux distribution.
+
+        For details, see :func:`distro.minor_version`.
+        """
+        return self.version_parts(best)[1]
+
+    def build_number(self, best=False):
+        """
+        Return the build number of the Linux distribution.
+
+        For details, see :func:`distro.build_number`.
+        """
+        return self.version_parts(best)[2]
+
+    def like(self):
+        """
+        Return the IDs of distributions that are like the Linux distribution.
+
+        For details, see :func:`distro.like`.
+        """
+        return self.os_release_attr('id_like') or ''
+
+    def codename(self):
+        """
+        Return the codename of the Linux distribution.
+
+        For details, see :func:`distro.codename`.
+        """
+        return self.os_release_attr('codename') \
+            or self.lsb_release_attr('codename') \
+            or self.distro_release_attr('codename') \
+            or ''
+
+    def info(self, pretty=False, best=False):
+        """
+        Return certain machine-readable information about the Linux
+        distribution.
+
+        For details, see :func:`distro.info`.
+        """
+        return dict(
+            id=self.id(),
+            version=self.version(pretty, best),
+            version_parts=dict(
+                major=self.major_version(best),
+                minor=self.minor_version(best),
+                build_number=self.build_number(best)
+            ),
+            like=self.like(),
+            codename=self.codename(),
+        )
+
+    def os_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the os-release file data source of the Linux distribution.
+
+        For details, see :func:`distro.os_release_info`.
+        """
+        return self._os_release_info
+
+    def lsb_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the lsb_release command data source of the Linux
+        distribution.
+
+        For details, see :func:`distro.lsb_release_info`.
+        """
+        return self._lsb_release_info
+
+    def distro_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the distro release file data source of the Linux
+        distribution.
+
+        For details, see :func:`distro.distro_release_info`.
+        """
+        return self._distro_release_info
+
+    def os_release_attr(self, attribute):
+        """
+        Return a single named information item from the os-release file data
+        source of the Linux distribution.
+
+        For details, see :func:`distro.os_release_attr`.
+        """
+        return self._os_release_info.get(attribute, '')
+
+    def lsb_release_attr(self, attribute):
+        """
+        Return a single named information item from the lsb_release command
+        output data source of the Linux distribution.
+
+        For details, see :func:`distro.lsb_release_attr`.
+        """
+        return self._lsb_release_info.get(attribute, '')
+
+    def distro_release_attr(self, attribute):
+        """
+        Return a single named information item from the distro release file
+        data source of the Linux distribution.
+
+        For details, see :func:`distro.distro_release_attr`.
+        """
+        return self._distro_release_info.get(attribute, '')
+
+    def _get_os_release_info(self):
+        """
+        Get the information items from the specified os-release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if os.path.isfile(self.os_release_file):
+            with open(self.os_release_file) as release_file:
+                return self._parse_os_release_content(release_file)
+        return {}
+
+    @staticmethod
+    def _parse_os_release_content(lines):
+        """
+        Parse the lines of an os-release file.
+
+        Parameters:
+
+        * lines: Iterable through the lines in the os-release file.
+                 Each line must be a unicode string or a UTF-8 encoded byte
+                 string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        props = {}
+        lexer = shlex.shlex(lines, posix=True)
+        lexer.whitespace_split = True
+
+        # The shlex module defines its `wordchars` variable using literals,
+        # making it dependent on the encoding of the Python source file.
+        # In Python 2.6 and 2.7, the shlex source file is encoded in
+        # 'iso-8859-1', and the `wordchars` variable is defined as a byte
+        # string. This causes a UnicodeDecodeError to be raised when the
+        # parsed content is a unicode object. The following fix resolves that
+        # (... but it should be fixed in shlex...):
+        if sys.version_info[0] == 2 and isinstance(lexer.wordchars, bytes):
+            lexer.wordchars = lexer.wordchars.decode('iso-8859-1')
+
+        tokens = list(lexer)
+        for token in tokens:
+            # At this point, all shell-like parsing has been done (i.e.
+            # comments processed, quotes and backslash escape sequences
+            # processed, multi-line values assembled, trailing newlines
+            # stripped, etc.), so the tokens are now either:
+            # * variable assignments: var=value
+            # * commands or their arguments (not allowed in os-release)
+            if '=' in token:
+                k, v = token.split('=', 1)
+                if isinstance(v, bytes):
+                    v = v.decode('utf-8')
+                props[k.lower()] = v
+                if k == 'VERSION':
+                    # this handles cases in which the codename is in
+                    # the `(CODENAME)` (rhel, centos, fedora) format
+                    # or in the `, CODENAME` format (Ubuntu).
+                    codename = re.search(r'(\(\D+\))|,(\s+)?\D+', v)
+                    if codename:
+                        codename = codename.group()
+                        codename = codename.strip('()')
+                        codename = codename.strip(',')
+                        codename = codename.strip()
+                        # codename appears within paranthese.
+                        props['codename'] = codename
+                    else:
+                        props['codename'] = ''
+            else:
+                # Ignore any tokens that are not variable assignments
+                pass
+        return props
+
+    def _get_lsb_release_info(self):
+        """
+        Get the information items from the lsb_release command output.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        cmd = 'lsb_release -a'
+        # conda customization: On Ubuntu 17.10, lsb_release calls the
+        # system Python and it will not find our custom sysconfigdata
+        env = os.environ.copy()
+        if '_PYTHON_SYSCONFIGDATA_NAME' in env:
+            del env['_PYTHON_SYSCONFIGDATA_NAME']
+        process = subprocess.Popen(
+            cmd,
+            shell=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env)
+        stdout, stderr = process.communicate()
+        stdout, stderr = stdout.decode('utf-8'), stderr.decode('utf-8')
+        code = process.returncode
+        if code == 0:
+            content = stdout.splitlines()
+            return self._parse_lsb_release_content(content)
+        elif code == 127:  # Command not found
+            return {}
+        else:
+            if sys.version_info[:2] >= (3, 5):
+                raise subprocess.CalledProcessError(code, cmd, stdout, stderr)
+            elif sys.version_info[:2] >= (2, 7):
+                raise subprocess.CalledProcessError(code, cmd, stdout)
+            elif sys.version_info[:2] == (2, 6):
+                raise subprocess.CalledProcessError(code, cmd)
+
+    @staticmethod
+    def _parse_lsb_release_content(lines):
+        """
+        Parse the output of the lsb_release command.
+
+        Parameters:
+
+        * lines: Iterable through the lines of the lsb_release output.
+                 Each line must be a unicode string or a UTF-8 encoded byte
+                 string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        props = {}
+        for line in lines:
+            line = line.decode('utf-8') if isinstance(line, bytes) else line
+            kv = line.strip('\n').split(':', 1)
+            if len(kv) != 2:
+                # Ignore lines without colon.
+                continue
+            k, v = kv
+            props.update({k.replace(' ', '_').lower(): v.strip()})
+        return props
+
+    def _get_distro_release_info(self):
+        """
+        Get the information items from the specified distro release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if self.distro_release_file:
+            # If it was specified, we use it and parse what we can, even if
+            # its file name or content does not match the expected pattern.
+            distro_info = self._parse_distro_release_file(
+                self.distro_release_file)
+            basename = os.path.basename(self.distro_release_file)
+            # The file name pattern for user-specified distro release files
+            # is somewhat more tolerant (compared to when searching for the
+            # file), because we want to use what was specified as best as
+            # possible.
+            match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
+            if match:
+                distro_info['id'] = match.group(1)
+            return distro_info
+        else:
+            try:
+                basenames = os.listdir(_UNIXCONFDIR)
+                # We sort for repeatability in cases where there are multiple
+                # distro specific files; e.g. CentOS, Oracle, Enterprise all
+                # containing `redhat-release` on top of their own.
+                basenames.sort()
+            except OSError:
+                # This may occur when /etc is not readable but we can't be
+                # sure about the *-release files. Check common entries of
+                # /etc for information. If they turn out to not be there the
+                # error is handled in `_parse_distro_release_file()`.
+                basenames = ['SuSE-release',
+                             'arch-release',
+                             'base-release',
+                             'centos-release',
+                             'fedora-release',
+                             'gentoo-release',
+                             'mageia-release',
+                             'manjaro-release',
+                             'oracle-release',
+                             'redhat-release',
+                             'sl-release',
+                             'slackware-version']
+            for basename in basenames:
+                if basename in _DISTRO_RELEASE_IGNORE_BASENAMES:
+                    continue
+                match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
+                if match:
+                    filepath = os.path.join(_UNIXCONFDIR, basename)
+                    distro_info = self._parse_distro_release_file(filepath)
+                    if 'name' in distro_info:
+                        # The name is always present if the pattern matches
+                        self.distro_release_file = filepath
+                        distro_info['id'] = match.group(1)
+                        return distro_info
+            return {}
+
+    def _parse_distro_release_file(self, filepath):
+        """
+        Parse a distro release file.
+
+        Parameters:
+
+        * filepath: Path name of the distro release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        try:
+            with open(filepath) as fp:
+                # Only parse the first line. For instance, on SLES there
+                # are multiple lines. We don't want them...
+                return self._parse_distro_release_content(fp.readline())
+        except (OSError, IOError):
+            # Ignore not being able to read a specific, seemingly version
+            # related file.
+            # See https://github.com/nir0s/distro/issues/162
+            return {}
+
+    @staticmethod
+    def _parse_distro_release_content(line):
+        """
+        Parse a line from a distro release file.
+
+        Parameters:
+        * line: Line from the distro release file. Must be a unicode string
+                or a UTF-8 encoded byte string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if isinstance(line, bytes):
+            line = line.decode('utf-8')
+        matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(
+            line.strip()[::-1])
+        distro_info = {}
+        if matches:
+            # regexp ensures non-None
+            distro_info['name'] = matches.group(3)[::-1]
+            if matches.group(2):
+                distro_info['version_id'] = matches.group(2)[::-1]
+            if matches.group(1):
+                distro_info['codename'] = matches.group(1)[::-1]
+        elif line:
+            distro_info['name'] = line.strip()
+        return distro_info
+
+
+_distro = LinuxDistribution()
+
+
+def main():
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+    logger.addHandler(logging.StreamHandler(sys.stdout))
+
+    parser = argparse.ArgumentParser(description="Linux distro info tool")
+    parser.add_argument(
+        '--json',
+        '-j',
+        help="Output in machine readable format",
+        action="store_true")
+    args = parser.parse_args()
+
+    if args.json:
+        logger.info(json.dumps(info(), indent=4, sort_keys=True))
+    else:
+        logger.info('Name: %s', name(pretty=True))
+        distribution_version = version(pretty=True)
+        logger.info('Version: %s', distribution_version)
+        distribution_codename = codename()
+        logger.info('Codename: %s', distribution_codename)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/shared/package-files/0c/0c3d6692d733af00fbf4d4770346c07a05d64a577fe3bae941f26d66bd3d31d9/maxcut.py b/shared/package-files/0c/0c3d6692d733af00fbf4d4770346c07a05d64a577fe3bae941f26d66bd3d31d9/maxcut.py
new file mode 100644
index 0000000000000000000000000000000000000000..59dfa63c49f4f413170e9bd671d77ea0b980b561
--- /dev/null
+++ b/shared/package-files/0c/0c3d6692d733af00fbf4d4770346c07a05d64a577fe3bae941f26d66bd3d31d9/maxcut.py
@@ -0,0 +1,111 @@
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for, py_random_state
+
+__all__ = ["randomized_partitioning", "one_exchange"]
+
+
+@not_implemented_for("directed", "multigraph")
+@py_random_state(1)
+def randomized_partitioning(G, seed=None, p=0.5, weight=None):
+    """Compute a random partitioning of the graph nodes and its cut value.
+
+    A partitioning is calculated by observing each node
+    and deciding to add it to the partition with probability `p`,
+    returning a random cut and its corresponding value (the
+    sum of weights of edges connecting different partitions).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    p : scalar
+        Probability for each node to be part of the first partition.
+        Should be in [0,1]
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_size : scalar
+        Value of the minimum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a minimum cut.
+    """
+    cut = {node for node in G.nodes() if seed.random() < p}
+    cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    partition = (cut, G.nodes - cut)
+    return cut_size, partition
+
+
+def _swap_node_partition(cut, node):
+    return cut - {node} if node in cut else cut.union({node})
+
+
+@not_implemented_for("directed", "multigraph")
+@py_random_state(2)
+def one_exchange(G, initial_cut=None, seed=None, weight=None):
+    """Compute a partitioning of the graphs nodes and the corresponding cut value.
+
+    Use a greedy one exchange strategy to find a locally maximal cut
+    and its value, it works by finding the best node (one that gives
+    the highest gain to the cut value) to add to the current cut
+    and repeats this process until no improvement can be made.
+
+    Parameters
+    ----------
+    G : networkx Graph
+        Graph to find a maximum cut for.
+
+    initial_cut : set
+        Cut to use as a starting point. If not supplied the algorithm
+        starts with an empty cut.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_value : scalar
+        Value of the maximum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a maximum cut.
+    """
+    if initial_cut is None:
+        initial_cut = set()
+    cut = set(initial_cut)
+    current_cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    while True:
+        nodes = list(G.nodes())
+        # Shuffling the nodes ensures random tie-breaks in the following call to max
+        seed.shuffle(nodes)
+        best_node_to_swap = max(
+            nodes,
+            key=lambda v: nx.algorithms.cut_size(
+                G, _swap_node_partition(cut, v), weight=weight
+            ),
+            default=None,
+        )
+        potential_cut = _swap_node_partition(cut, best_node_to_swap)
+        potential_cut_size = nx.algorithms.cut_size(G, potential_cut, weight=weight)
+
+        if potential_cut_size > current_cut_size:
+            cut = potential_cut
+            current_cut_size = potential_cut_size
+        else:
+            break
+
+    partition = (cut, G.nodes - cut)
+    return current_cut_size, partition
diff --git a/shared/package-files/0c/0c3e225c027aebf0653a8e3bff13cb34bd0968bf1f084d5f506af347c8440e55/_standard_gamma_grad_ops.h b/shared/package-files/0c/0c3e225c027aebf0653a8e3bff13cb34bd0968bf1f084d5f506af347c8440e55/_standard_gamma_grad_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..5ea2afa19db4a68848d774e2609704ad8f4f8c3a
--- /dev/null
+++ b/shared/package-files/0c/0c3e225c027aebf0653a8e3bff13cb34bd0968bf1f084d5f506af347c8440e55/_standard_gamma_grad_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _standard_gamma_grad {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_standard_gamma_grad")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_standard_gamma_grad(Tensor self, Tensor output) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & output);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output);
+};
+
+struct TORCH_API _standard_gamma_grad_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_standard_gamma_grad")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & output, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0c4aa958f21646900836e9bf94a761f3e16f2acc7ef09f939c672e6ebc7303a9/compat.pyi b/shared/package-files/0c/0c4aa958f21646900836e9bf94a761f3e16f2acc7ef09f939c672e6ebc7303a9/compat.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3e8fd859c803f10af5ac04ab16718e3112f86ffa
--- /dev/null
+++ b/shared/package-files/0c/0c4aa958f21646900836e9bf94a761f3e16f2acc7ef09f939c672e6ebc7303a9/compat.pyi
@@ -0,0 +1,39 @@
+import sys
+from io import TextIOWrapper
+from typing import Any, Optional, Tuple
+
+PY2: bool
+PY3: bool
+WIN: bool
+string_types: Tuple[
+    str,
+]
+integer_types: Tuple[
+    int,
+]
+class_types: Tuple[
+    type,
+]
+text_type = str
+binary_type = bytes
+long = int
+
+def unquote_bytes_to_wsgi(bytestring: bytes) -> str: ...
+def text_(s: str, encoding: str = ..., errors: str = ...) -> str: ...
+def tostr(s: str) -> str: ...
+def tobytes(s: str) -> bytes: ...
+
+exec_: Any
+
+def reraise(tp: Any, value: BaseException, tb: Optional[str] = ...) -> None: ...
+
+MAXINT: int
+HAS_IPV6: bool
+IPPROTO_IPV6: int
+IPV6_V6ONLY: int
+
+def set_nonblocking(fd: TextIOWrapper) -> None: ...
+
+ResourceWarning: Warning
+
+def qualname(cls: Any) -> str: ...
diff --git a/shared/package-files/0c/0c51168439117527a818479f180221ffba8cdd9b04ec71a03065582d006b69c7/pipe.py b/shared/package-files/0c/0c51168439117527a818479f180221ffba8cdd9b04ec71a03065582d006b69c7/pipe.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e61341d9ad9f36199ead474245f81eaaa95ef6f
--- /dev/null
+++ b/shared/package-files/0c/0c51168439117527a818479f180221ffba8cdd9b04ec71a03065582d006b69c7/pipe.py
@@ -0,0 +1,490 @@
+# Copyright 2019 Kakao Brain
+#
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+"""The Pipe interface."""
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Any, Iterable, Iterator, List, Optional, Union, Sequence, Tuple, cast
+
+import torch
+from torch import Tensor, nn
+from torch.distributed.rpc import RRef
+import torch.autograd
+import torch.cuda
+
+from . import microbatch
+from .batchnorm import DeferredBatchNorm
+from .pipeline import Pipeline
+from .skip.layout import inspect_skip_layout
+from .skip.skippable import verify_skippables
+from .stream import AbstractStream, new_stream
+
+__all__ = ["Pipe", "BalanceError", "PipeSequential", "WithDevice"]
+
+
+Device = Union[torch.device, int, str]
+Devices = Union[Iterable[Device], List[Device]]
+
+Tensors = Sequence[Tensor]
+TensorOrTensors = Union[Tensor, Tensors]
+
+if TYPE_CHECKING:
+    # Typechecking: nn.Module is not a Generic
+    Module = nn.Module[TensorOrTensors]  # type: ignore[type-arg]
+    NamedModules = OrderedDict[str, Module]
+else:
+    Module = nn.Module
+    NamedModules = OrderedDict
+
+
+def _recommend_auto_balance(message: str) -> str:
+    """Expands a message with recommendation to :mod:`torchpipe.balance`."""
+    return f"""{message}
+
+If your model is still under development, its optimal balance would change
+frequently. In this case, we highly recommend 'torch.distributed.pipeline.sync.balance' for
+naive automatic balancing:
+
+  from torch.distributed.pipeline.sync import Pipe
+  from torch.distributed.pipeline.sync.balance import balance_by_time
+
+  partitions = torch.cuda.device_count()
+  sample = torch.empty(...)
+  balance = balance_by_time(partitions, model, sample)
+
+  model = Pipe(model, balance, ...)
+"""
+
+
+def _verify_module(module: nn.Sequential) -> None:
+    if not isinstance(module, nn.Sequential):
+        raise TypeError("module must be nn.Sequential to be partitioned")
+
+    named_children = list(module.named_children())
+    if len(named_children) != len(module):
+        raise ValueError("module with duplicate children is not supported")
+
+
+def _verify_splitting(
+    module: nn.Sequential, partitions: List[nn.Sequential], devices: List[torch.device]
+) -> None:
+    num_parameters = len(list(module.parameters()))
+    num_child_parameters = sum(len(list(child.parameters())) for child in module.children())
+    if num_parameters == num_child_parameters:
+        return
+
+    for i in range(len(partitions)):
+        for j in range(i + 1, len(partitions)):
+            parti = partitions[i]
+            partj = partitions[j]
+            if devices[i] == devices[j]:
+                continue
+            for p in parti.parameters():
+                for q in partj.parameters():
+                    if p is q:
+                        raise ValueError("module with duplicate parameters on distinct devices is not supported")
+
+
+class BalanceError(ValueError):
+    pass
+
+
+def _retrieve_device(module: nn.Module) -> torch.device:
+    """Validates all parameters in the Module have the same device and returns
+    the appropriate device.
+
+    Args:
+        An ``nn.Module`` to process.
+
+    Returns:
+        ``torch.Device`` for the entire module.
+
+    Raises:
+        ValueError:
+            If devices for ``nn.Module`` parameters are not all same.
+    """
+
+    device = None
+    for parameter in module.parameters():
+        if device is None:
+            device = parameter.device
+        elif device != parameter.device:
+            raise ValueError(
+                f'nn.Module: {module}, should have all parameters on a single device,'
+                ' please use .to() to place the module on a single device')
+
+    return device if device is not None else torch.device("cpu")
+
+
+class PipeSequential(nn.Sequential):
+    """
+    Pipe variant of ``nn.Sequential`` which supports multiple inputs.
+    """
+
+    def forward(self, *inputs):
+        for module in self:
+            if isinstance(inputs, Tuple):  # type: ignore[arg-type]
+                inputs = module(*inputs)
+            else:
+                # Don't expand single variables (ex: lists/Tensor)
+                inputs = module(inputs)
+        return inputs
+
+
+class WithDevice(nn.Module):
+    """
+    Wraps an ``nn.Module`` which is part of ``nn.Sequential`` passed into :class:`Pipe`
+    that overrides the device for that module. In cases where :class:`Pipe`
+    can't implicitly determine the device for the module and places it on CPU,
+    this wrapper can be used to override the implicit behavior and explicitly
+    specify which device a module should run on.
+
+    The provided module is also moved to the given device via ``.to(device)``
+    by :class:`Pipe`
+
+    Args:
+        module(:class:`torch.nn.Module`): The module to be wrapped.
+        device(:class:`torch.device`): The device to run the module on.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> fc1 = nn.Linear(16, 8).cuda(0)
+        >>> fc2 = nn.Linear(8, 4).cuda(1)
+        >>> dropout = nn.Dropout()
+        >>>
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
+        >>> # Dropout does not have any parameters/buffers, but we want to
+        >>> # run it on cuda:1 to avoid any GPU to CPU transfers.
+        >>> model = nn.Sequential(fc1, fc2, WithDevice(dropout, 'cuda:1'))
+        >>> # xdoctest: +SKIP("Needs RPC framework init")
+        >>> model = Pipe(model, chunks=8)
+    """
+    def __init__(self, module: nn.Module, device: torch.device):
+        super().__init__()
+        self._module = module
+        self._device = torch.device(device)
+
+    def forward(self, *args, **kwargs):
+        return self._module(*args, **kwargs)
+
+    @property
+    def module(self):
+        return self._module
+
+    @property
+    def device(self):
+        return self._device
+
+
+def _assemble_partition(modules: List[nn.Module]):
+    modules_list: List[nn.Module] = []
+    for module in modules:
+        if isinstance(module, nn.Sequential):
+            modules_list.extend(module.children())
+        else:
+            modules_list.append(module)
+    return PipeSequential(*modules_list)
+
+
+def _split_module(modules: nn.Sequential) -> Tuple[List[nn.Sequential], List[torch.device]]:
+    partitions = []
+    devices = []
+
+    current_partition = []
+    current_device = None
+    for name, module in modules.named_children():
+        if isinstance(module, WithDevice):
+            # Process device override and move module to appropriate device.
+            device = module.device
+            module = module.module
+            module.to(device)
+        else:
+            device = _retrieve_device(module)
+        if current_device is not None and (current_device != device or device.type == 'cpu'):
+            partitions.append(_assemble_partition(current_partition))
+            devices.append(current_device)
+            current_partition = []
+        current_device = device
+        current_partition.append(module)
+
+    if current_device is not None:
+        partitions.append(_assemble_partition(current_partition))
+        devices.append(current_device)
+
+    partitions = cast(List[nn.Sequential], nn.ModuleList(partitions))
+
+    return partitions, devices
+
+
+MOVING_DENIED = TypeError("denied to move parameters and buffers, because Pipe should manage device placement")
+
+
+class Pipe(Module):
+    """Wraps an arbitrary :class:`nn.Sequential <torch.nn.Sequential>` module
+    to train on using synchronous pipeline parallelism. If the module requires
+    lots of memory and doesn't fit on a single GPU, pipeline parallelism is a
+    useful technique to employ for training.
+
+    The implementation is based on the torchgpipe_ paper.
+
+    .. _torchgpipe: https://arxiv.org/abs/2004.09910
+
+    Pipe combines pipeline parallelism with checkpointing to reduce peak
+    memory required to train while minimizing device under-utilization.
+
+    You should place all the modules on the appropriate devices and wrap them
+    into an :class:`nn.Sequential <torch.nn.Sequential>` module defining the
+    desired order of execution. If a module does not contain any
+    parameters/buffers, it is assumed this module should be executed on CPU
+    and appropriate input tensors to the module are moved to CPU before
+    execution. This behavior can be overridden by the :class:`WithDevice`
+    wrapper which can be used to explicitly specify which device a module
+    should run on.
+
+    Args:
+        module (:class:`nn.Sequential <torch.nn.Sequential>`):
+            sequential module to be parallelized using pipelining. Each module
+            in the sequence has to have all of its parameters on a single
+            device. Each module in the sequence has to either be an nn.Module
+            or :class:`nn.Sequential <torch.nn.Sequential>` (to combine multiple
+            sequential modules on a single device)
+        chunks (int):
+            number of micro-batches (default: ``1``)
+        checkpoint (str):
+            when to enable checkpointing, one of ``'always'``,
+            ``'except_last'``, or ``'never'`` (default: ``'except_last'``).
+            ``'never'`` disables checkpointing completely, ``'except_last'``
+            enables checkpointing for all micro-batches except the last one
+            and ``'always'`` enables checkpointing for all micro-batches.
+        deferred_batch_norm (bool):
+            whether to use deferred ``BatchNorm`` moving statistics (default:
+            :data:`False`). If set to :data:`True`, we track statistics across
+            multiple micro-batches to update the running statistics per
+            mini-batch.
+
+    Raises:
+        TypeError:
+            the module is not a :class:`nn.Sequential <torch.nn.Sequential>`.
+        ValueError:
+            invalid arguments
+
+    Example::
+        Pipeline of two FC layers across GPUs 0 and 1.
+
+        >>> # Need to initialize RPC framework first.
+        >>> # xdoctest: +SKIP
+        >>> os.environ['MASTER_ADDR'] = 'localhost'
+        >>> os.environ['MASTER_PORT'] = '29500'
+        >>> torch.distributed.rpc.init_rpc('worker', rank=0, world_size=1)
+        >>>
+        >>> # Build pipe.
+        >>> fc1 = nn.Linear(16, 8).cuda(0)
+        >>> fc2 = nn.Linear(8, 4).cuda(1)
+        >>> model = nn.Sequential(fc1, fc2)
+        >>> model = Pipe(model, chunks=8)
+        >>> input = torch.rand(16, 16).cuda(0)
+        >>> output_rref = model(input)
+
+    .. note::
+        You can wrap a :class:`Pipe` model with
+        :class:`torch.nn.parallel.DistributedDataParallel` only when the
+        checkpoint parameter of :class:`Pipe` is ``'never'``.
+
+    .. note::
+        :class:`Pipe` only supports intra-node pipelining currently, but
+        will be expanded to support inter-node pipelining in the future.
+        The forward function returns an :class:`~torch.distributed.rpc.RRef`
+        to allow for inter-node pipelining in the future, where the output
+        might be on a remote host. For intra-node pipelining you can use
+        :meth:`~torch.distributed.rpc.RRef.local_value` to retrieve the
+        output locally.
+
+    .. warning::
+        :class:`Pipe` is experimental and subject to change.
+    """
+
+    def __init__(
+        self,
+        module: nn.Sequential,
+        chunks: int = 1,
+        checkpoint: str = "except_last",
+        deferred_batch_norm: bool = False,
+    ) -> None:
+        super().__init__()
+
+        # Check if RPC framework is initialized.
+        if not torch.distributed.rpc._is_current_rpc_agent_set():
+            raise RuntimeError(
+                'Please initialize RPC framework for Pipe using '
+                'torch.distributed.rpc.init_rpc')
+
+        chunks = int(chunks)
+        checkpoint = str(checkpoint)
+
+        if chunks <= 0:
+            raise ValueError("number of chunks must be positive integer")
+        if checkpoint not in ["always", "except_last", "never"]:
+            raise ValueError("checkpoint is not one of 'always', 'except_last', or 'never'")
+
+        _verify_module(module)
+
+        # Verify if the underlying skippable modules satisfy integrity. The
+        # integrity can be verified before forward() because it is static.
+        verify_skippables(module)
+
+        self.chunks = chunks
+        self.checkpoint = checkpoint
+
+        if deferred_batch_norm:
+            module = DeferredBatchNorm.convert_deferred_batch_norm(module, chunks)
+
+        self.partitions, self.devices = _split_module(module)
+        _verify_splitting(module, self.partitions, self.devices)
+
+        self._copy_streams: List[List[AbstractStream]] = []
+        self._skip_layout = inspect_skip_layout(self.partitions)
+
+        # Separate CUDA streams for copy.
+        copy_streams = self._ensure_copy_streams()
+
+        # The micro-batch index where the checkpointing stops.
+        checkpoint_stop = {"always": self.chunks, "except_last": self.chunks - 1, "never": 0}[self.checkpoint]
+
+        self.pipeline = Pipeline(self.partitions, self.devices, copy_streams, self._skip_layout, checkpoint_stop)
+
+    def __len__(self) -> int:
+        """Counts the length of the underlying sequential module."""
+        return sum(len(p) for p in self.partitions)
+
+    def __getitem__(self, index: int) -> nn.Module:
+        """Gets a layer in the underlying sequential module."""
+        partitions = self.partitions
+        if index < 0:
+            partitions = partitions[::-1]
+
+        for partition in partitions:
+            try:
+                return partition[index]
+            except IndexError:
+                pass
+
+            shift = len(partition)
+
+            if index < 0:
+                index += shift
+            else:
+                index -= shift
+
+        raise IndexError
+
+    def __iter__(self) -> Iterator[nn.Module]:
+        """Iterates over children of the underlying sequential module."""
+        for partition in self.partitions:
+            yield from partition
+
+    # Pipe should manage the device of each partition.
+    # Deny cuda(), cpu(), and to() with device, by TypeError.
+    def cuda(self, device: Optional[Device] = None) -> "Pipe":
+        raise MOVING_DENIED
+
+    def cpu(self) -> "Pipe":
+        raise MOVING_DENIED
+
+    def to(self, *args: Any, **kwargs: Any) -> "Pipe":
+        # Deny these usages:
+        #
+        # - to(device[, dtype, non_blocking])
+        # - to(tensor[, non_blocking])
+        #
+        # But allow this:
+        #
+        # - to(dtype[, non_blocking])
+        #
+        if "device" in kwargs or "tensor" in kwargs:
+            raise MOVING_DENIED
+
+        if args:
+            if isinstance(args[0], (torch.device, int, str)):
+                raise MOVING_DENIED
+            if torch.is_tensor(args[0]):
+                raise MOVING_DENIED
+
+        return super().to(*args, **kwargs)
+
+    def _ensure_copy_streams(self) -> List[List[AbstractStream]]:
+        """Ensures that :class:`Pipe` caches CUDA streams for copy.
+
+        It's worth to cache CUDA streams although PyTorch already manages a
+        pool of pre-allocated CUDA streams, because it may reduce GPU memory
+        fragmentation when the number of micro-batches is small.
+
+        """
+        if not self._copy_streams:
+            for device in self.devices:
+                self._copy_streams.append([new_stream(device) for _ in range(self.chunks)])
+
+        return self._copy_streams
+
+    def forward(self, *inputs) -> RRef:
+        """
+        Processes a single input mini-batch through the pipe and returns an
+        :class:`~torch.distributed.rpc.RRef` pointing to the output.
+        :class:`Pipe` is a fairly transparent module wrapper. It doesn't
+        modify the input and output signature of the underlying module. But
+        there's type restriction. Input and output have to contain at least one
+        tensor. This restriction is applied at partition boundaries too.
+
+        The sequence of inputs are fed into the first stage of the pipeline as
+        ``*inputs``. As a result the positional args for this function should
+        match the positional args for the first stage of the pipeline. The same
+        condition applies for output of one stage of the pipeline which is the
+        input for the next stage.
+
+        The input tensor is split into multiple micro-batches based on the
+        ``chunks`` parameter used to initialize :class:`Pipe`. The batch size
+        is assumed to be the first dimension of the tensor and if the batch
+        size is less than ``chunks``, the number of micro-batches is equal to
+        the batch size.
+
+        Only tensors are split into multiple micro-batches, non-Tensor inputs
+        are just replicated as-is in each micro-batch. For non-Tensor outputs
+        in the last stage of the pipeline, they are aggregated as a ``List``
+        and returned the user. For example, if you have 2 micro-batches
+        returning the integer 5, the user would receive the consolidated
+        output of `[5, 5]`
+
+        All the input tensors need to be on the same device as the first
+        partition of the pipeline.
+
+        If a tensor is wrapped with the :class:`NoChunk` wrapper, the tensor
+        is not split across micro-batches and is replicated as-is similar to
+        non-tensors.
+
+        Args:
+            inputs: input mini-batch
+
+        Returns:
+            :class:`~torch.distributed.rpc.RRef` to the output of the mini-batch
+
+        Raises:
+            TypeError: input doesn't contain at least one tensor
+
+        """
+        first_partition_device = self.devices[0] if len(self.devices) != 0 else torch.device("cpu")
+        microbatch.check(first_partition_device, *inputs)
+
+        if not self.devices:
+            # Empty sequential module is not illegal.
+            return RRef(*inputs)
+
+        # Divide a mini-batch into micro-batches.
+        batches = microbatch.scatter(*inputs, chunks=self.chunks)
+
+        # Run pipeline parallelism.
+        self.pipeline.run(batches)
+
+        # Merge the micro-batches into one mini-batch.
+        output = microbatch.gather(batches)
+        return RRef(output)
diff --git a/shared/package-files/0c/0c5230e53eb4f80a1d08664f0b06cccdb111a441774c46c0685242b86cfb90ea/vecm_e6_jmulti_cili.txt b/shared/package-files/0c/0c5230e53eb4f80a1d08664f0b06cccdb111a441774c46c0685242b86cfb90ea/vecm_e6_jmulti_cili.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5062579e4daa9d7b00e6a299792313a338046b33
--- /dev/null
+++ b/shared/package-files/0c/0c5230e53eb4f80a1d08664f0b06cccdb111a441774c46c0685242b86cfb90ea/vecm_e6_jmulti_cili.txt
@@ -0,0 +1,149 @@
+*** Mon, 25 Jul 2016 21:32:41 ***
+VEC REPRESENTATION
+endogenous variables:     Dp R 
+exogenous variables:       
+deterministic variables:  CONST TREND 
+endogenous lags (diffs):  3 
+exogenous lags:           0 
+sample range:             [1973 Q2, 1998 Q4], T = 103
+estimation procedure:     One stage. Johansen approach 
+
+
+Lagged endogenous term:
+=======================
+              d(Dp)      d(R)  
+------------------------------
+d(Dp)(t-1)|   -0.531    -0.324  
+          |   (0.151)   (0.131) 
+          |   {0.000}   {0.013} 
+          |  [-3.514]  [-2.479] 
+d(R) (t-1)|    0.051     0.260  
+          |   (0.117)   (0.101) 
+          |   {0.660}   {0.010} 
+          |   [0.440]   [2.581] 
+d(Dp)(t-2)|   -0.665    -0.202  
+          |   (0.103)   (0.089) 
+          |   {0.000}   {0.024} 
+          |  [-6.427]  [-2.254] 
+d(R) (t-2)|    0.122     0.017  
+          |   (0.116)   (0.101) 
+          |   {0.295}   {0.863} 
+          |   [1.047]   [0.172] 
+d(Dp)(t-3)|   -0.808    -0.071  
+          |   (0.055)   (0.047) 
+          |   {0.000}   {0.137} 
+          | [-14.712]  [-1.487] 
+d(R) (t-3)|   -0.050     0.226  
+          |   (0.114)   (0.098) 
+          |   {0.659}   {0.022} 
+          |  [-0.441]   [2.295] 
+------------------------------
+
+
+
+
+Loading coefficients:
+=====================
+             d(Dp)      d(R)  
+-----------------------------
+ec1(t-1)|   -0.619     0.426  
+        |   (0.198)   (0.171) 
+        |   {0.002}   {0.013} 
+        |  [-3.127]   [2.490] 
+-----------------------------
+
+Estimated cointegration relation(s):
+====================================
+           ec1(t-1)  
+--------------------
+Dp(t-1)   |    1.000  
+          |   (0.000) 
+          |   {0.000} 
+          |   [0.000] 
+R (t-1)   |   -0.281  
+          |   (0.063) 
+          |   {0.000} 
+          |  [-4.429] 
+CONST     |    0.013  
+          |   (0.006) 
+          |   {0.032} 
+          |   [2.150] 
+TREND(t-1)|    0.000  
+          |   (0.000) 
+          |   {0.901} 
+          |  [-0.124] 
+--------------------
+
+
+
+VAR REPRESENTATION
+
+modulus of the eigenvalues of the reverse characteristic polynomial:
+|z| = ( 1.0095     1.0117     1.0117     1.0000     1.3224     1.3224     1.7259     1.7259     )
+
+Legend:
+=======
+              Equation 1   Equation 2  ...
+------------------------------------------
+Variable 1 | Coefficient          ...
+           | (Std. Dev.)
+           | {p - Value}
+           | [t - Value]
+Variable 2 |         ...
+...
+------------------------------------------
+
+
+Lagged endogenous term:
+=======================
+                Dp         R  
+-----------------------------
+ Dp(t-1)|   -0.150     0.102  
+        |   (0.249)   (0.215) 
+        |   {0.547}   {0.634} 
+        |  [-0.603]   [0.475] 
+ R (t-1)|    0.225     1.140  
+        |   (0.129)   (0.112) 
+        |   {0.081}   {0.000} 
+        |   [1.745]  [10.216] 
+ Dp(t-2)|   -0.134     0.122  
+        |   (0.055)   (0.048) 
+        |   {0.016}   {0.011} 
+        |  [-2.415]   [2.552] 
+ R (t-2)|    0.071    -0.243  
+        |   (0.170)   (0.147) 
+        |   {0.678}   {0.099} 
+        |   [0.415]  [-1.649] 
+ Dp(t-3)|   -0.143     0.131  
+        |   (0.056)   (0.048) 
+        |   {0.010}   {0.007} 
+        |  [-2.561]   [2.716] 
+ R (t-3)|   -0.172     0.208  
+        |   (0.170)   (0.147) 
+        |   {0.312}   {0.157} 
+        |  [-1.011]   [1.415] 
+ Dp(t-4)|    0.808     0.071  
+        |   (0.055)   (0.047) 
+        |   {0.000}   {0.137} 
+        |  [14.712]   [1.487] 
+ R (t-4)|    0.050    -0.226  
+        |   (0.114)   (0.098) 
+        |   {0.659}   {0.022} 
+        |   [0.441]  [-2.295] 
+-----------------------------
+
+
+Deterministic term:
+===================
+                    Dp         R  
+---------------------------------
+CONST     (t)|   -0.008     0.006  
+             |   (0.000)   (0.000) 
+             |   {0.000}   {0.000} 
+             |   [0.000]   [0.000] 
+TREND(t-1)(t)|    0.000     0.000  
+             |   (0.000)   (0.000) 
+             |   {0.000}   {0.000} 
+             |   [0.000]   [0.000] 
+---------------------------------
+
diff --git a/shared/package-files/0c/0c527a38a57a811c38a7d3ae55fcb510d4de2c909b612d86ba9a70a499f04064/test_backend.py b/shared/package-files/0c/0c527a38a57a811c38a7d3ae55fcb510d4de2c909b612d86ba9a70a499f04064/test_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..933e9c0302d46faf33b3dc015d58996e3e46c058
--- /dev/null
+++ b/shared/package-files/0c/0c527a38a57a811c38a7d3ae55fcb510d4de2c909b612d86ba9a70a499f04064/test_backend.py
@@ -0,0 +1,98 @@
+from functools import partial
+
+import numpy as np
+import scipy.fft
+from scipy.fft import _fftlog, _pocketfft, set_backend
+from scipy.fft.tests import mock_backend
+
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+
+fnames = ('fft', 'fft2', 'fftn',
+          'ifft', 'ifft2', 'ifftn',
+          'rfft', 'rfft2', 'rfftn',
+          'irfft', 'irfft2', 'irfftn',
+          'dct', 'idct', 'dctn', 'idctn',
+          'dst', 'idst', 'dstn', 'idstn',
+          'fht', 'ifht')
+
+np_funcs = (np.fft.fft, np.fft.fft2, np.fft.fftn,
+            np.fft.ifft, np.fft.ifft2, np.fft.ifftn,
+            np.fft.rfft, np.fft.rfft2, np.fft.rfftn,
+            np.fft.irfft, np.fft.irfft2, np.fft.irfftn,
+            np.fft.hfft, _pocketfft.hfft2, _pocketfft.hfftn,  # np has no hfftn
+            np.fft.ihfft, _pocketfft.ihfft2, _pocketfft.ihfftn,
+            _pocketfft.dct, _pocketfft.idct, _pocketfft.dctn, _pocketfft.idctn,
+            _pocketfft.dst, _pocketfft.idst, _pocketfft.dstn, _pocketfft.idstn,
+            # must provide required kwargs for fht, ifht
+            partial(_fftlog.fht, dln=2, mu=0.5),
+            partial(_fftlog.ifht, dln=2, mu=0.5))
+
+funcs = (scipy.fft.fft, scipy.fft.fft2, scipy.fft.fftn,
+         scipy.fft.ifft, scipy.fft.ifft2, scipy.fft.ifftn,
+         scipy.fft.rfft, scipy.fft.rfft2, scipy.fft.rfftn,
+         scipy.fft.irfft, scipy.fft.irfft2, scipy.fft.irfftn,
+         scipy.fft.hfft, scipy.fft.hfft2, scipy.fft.hfftn,
+         scipy.fft.ihfft, scipy.fft.ihfft2, scipy.fft.ihfftn,
+         scipy.fft.dct, scipy.fft.idct, scipy.fft.dctn, scipy.fft.idctn,
+         scipy.fft.dst, scipy.fft.idst, scipy.fft.dstn, scipy.fft.idstn,
+         # must provide required kwargs for fht, ifht
+         partial(scipy.fft.fht, dln=2, mu=0.5),
+         partial(scipy.fft.ifht, dln=2, mu=0.5))
+
+mocks = (mock_backend.fft, mock_backend.fft2, mock_backend.fftn,
+         mock_backend.ifft, mock_backend.ifft2, mock_backend.ifftn,
+         mock_backend.rfft, mock_backend.rfft2, mock_backend.rfftn,
+         mock_backend.irfft, mock_backend.irfft2, mock_backend.irfftn,
+         mock_backend.hfft, mock_backend.hfft2, mock_backend.hfftn,
+         mock_backend.ihfft, mock_backend.ihfft2, mock_backend.ihfftn,
+         mock_backend.dct, mock_backend.idct,
+         mock_backend.dctn, mock_backend.idctn,
+         mock_backend.dst, mock_backend.idst,
+         mock_backend.dstn, mock_backend.idstn,
+         mock_backend.fht, mock_backend.ifht)
+
+
+@pytest.mark.parametrize("func, np_func, mock", zip(funcs, np_funcs, mocks))
+def test_backend_call(func, np_func, mock):
+    x = np.arange(20).reshape((10,2))
+    answer = np_func(x.astype(np.float64))
+    assert_allclose(func(x), answer, atol=1e-10)
+
+    with set_backend(mock_backend, only=True):
+        mock.number_calls.c = 0
+        y = func(x)
+        assert_equal(y, mock.return_value)
+        assert_equal(mock.number_calls.c, 1)
+
+    assert_allclose(func(x), answer, atol=1e-10)
+
+
+plan_funcs = (scipy.fft.fft, scipy.fft.fft2, scipy.fft.fftn,
+              scipy.fft.ifft, scipy.fft.ifft2, scipy.fft.ifftn,
+              scipy.fft.rfft, scipy.fft.rfft2, scipy.fft.rfftn,
+              scipy.fft.irfft, scipy.fft.irfft2, scipy.fft.irfftn,
+              scipy.fft.hfft, scipy.fft.hfft2, scipy.fft.hfftn,
+              scipy.fft.ihfft, scipy.fft.ihfft2, scipy.fft.ihfftn)
+
+plan_mocks = (mock_backend.fft, mock_backend.fft2, mock_backend.fftn,
+              mock_backend.ifft, mock_backend.ifft2, mock_backend.ifftn,
+              mock_backend.rfft, mock_backend.rfft2, mock_backend.rfftn,
+              mock_backend.irfft, mock_backend.irfft2, mock_backend.irfftn,
+              mock_backend.hfft, mock_backend.hfft2, mock_backend.hfftn,
+              mock_backend.ihfft, mock_backend.ihfft2, mock_backend.ihfftn)
+
+
+@pytest.mark.parametrize("func, mock", zip(plan_funcs, plan_mocks))
+def test_backend_plan(func, mock):
+    x = np.arange(20).reshape((10, 2))
+
+    with pytest.raises(NotImplementedError, match='precomputed plan'):
+        func(x, plan='foo')
+
+    with set_backend(mock_backend, only=True):
+        mock.number_calls.c = 0
+        y = func(x, plan='foo')
+        assert_equal(y, mock.return_value)
+        assert_equal(mock.number_calls.c, 1)
+        assert_equal(mock.last_args.l[1]['plan'], 'foo')
diff --git a/shared/package-files/0c/0c52c03003c5cd7d832358800048d563f9ccf53ba86260a37b79bb435ec0d045/test_formats.py b/shared/package-files/0c/0c52c03003c5cd7d832358800048d563f9ccf53ba86260a37b79bb435ec0d045/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..81c79f7d18f2fc87bcbbac3159f873ddb76f9559
--- /dev/null
+++ b/shared/package-files/0c/0c52c03003c5cd7d832358800048d563f9ccf53ba86260a37b79bb435ec0d045/test_formats.py
@@ -0,0 +1,350 @@
+from contextlib import nullcontext
+from datetime import (
+    datetime,
+    time,
+)
+import locale
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    PeriodIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+def get_local_am_pm():
+    """Return the AM and PM strings returned by strftime in current locale."""
+    am_local = time(1).strftime("%p")
+    pm_local = time(13).strftime("%p")
+    return am_local, pm_local
+
+
+def test_get_values_for_csv():
+    index = PeriodIndex(["2017-01-01", "2017-01-02", "2017-01-03"], freq="D")
+
+    # First, with no arguments.
+    expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv()
+    tm.assert_numpy_array_equal(result, expected)
+
+    # No NaN values, so na_rep has no effect
+    result = index._get_values_for_csv(na_rep="pandas")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # Make sure date formatting works
+    expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype=object)
+
+    result = index._get_values_for_csv(date_format="%m-%Y-%d")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # NULL object handling should work
+    index = PeriodIndex(["2017-01-01", pd.NaT, "2017-01-03"], freq="D")
+    expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv(na_rep="NaT")
+    tm.assert_numpy_array_equal(result, expected)
+
+    expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv(na_rep="pandas")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+class TestPeriodIndexRendering:
+    def test_format_empty(self):
+        # GH#35712
+        empty_idx = PeriodIndex([], freq="Y")
+        msg = r"PeriodIndex\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format() == []
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format(name=True) == [""]
+
+    @pytest.mark.parametrize("method", ["__repr__", "__str__"])
+    def test_representation(self, method):
+        # GH#7601
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+        idx10 = PeriodIndex(["2011-01-01", "2011-02-01"], freq="3D")
+
+        exp1 = "PeriodIndex([], dtype='period[D]')"
+
+        exp2 = "PeriodIndex(['2011-01-01'], dtype='period[D]')"
+
+        exp3 = "PeriodIndex(['2011-01-01', '2011-01-02'], dtype='period[D]')"
+
+        exp4 = (
+            "PeriodIndex(['2011-01-01', '2011-01-02', '2011-01-03'], "
+            "dtype='period[D]')"
+        )
+
+        exp5 = "PeriodIndex(['2011', '2012', '2013'], dtype='period[Y-DEC]')"
+
+        exp6 = (
+            "PeriodIndex(['2011-01-01 09:00', '2012-02-01 10:00', 'NaT'], "
+            "dtype='period[h]')"
+        )
+
+        exp7 = "PeriodIndex(['2013Q1'], dtype='period[Q-DEC]')"
+
+        exp8 = "PeriodIndex(['2013Q1', '2013Q2'], dtype='period[Q-DEC]')"
+
+        exp9 = "PeriodIndex(['2013Q1', '2013Q2', '2013Q3'], dtype='period[Q-DEC]')"
+
+        exp10 = "PeriodIndex(['2011-01-01', '2011-02-01'], dtype='period[3D]')"
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9, idx10],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9, exp10],
+        ):
+            result = getattr(idx, method)()
+            assert result == expected
+
+    # TODO: These are Series.__repr__ tests
+    def test_representation_to_series(self):
+        # GH#10971
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+
+        exp1 = """Series([], dtype: period[D])"""
+
+        exp2 = """0    2011-01-01
+dtype: period[D]"""
+
+        exp3 = """0    2011-01-01
+1    2011-01-02
+dtype: period[D]"""
+
+        exp4 = """0    2011-01-01
+1    2011-01-02
+2    2011-01-03
+dtype: period[D]"""
+
+        exp5 = """0    2011
+1    2012
+2    2013
+dtype: period[Y-DEC]"""
+
+        exp6 = """0    2011-01-01 09:00
+1    2012-02-01 10:00
+2                 NaT
+dtype: period[h]"""
+
+        exp7 = """0    2013Q1
+dtype: period[Q-DEC]"""
+
+        exp8 = """0    2013Q1
+1    2013Q2
+dtype: period[Q-DEC]"""
+
+        exp9 = """0    2013Q1
+1    2013Q2
+2    2013Q3
+dtype: period[Q-DEC]"""
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9],
+        ):
+            result = repr(Series(idx))
+            assert result == expected
+
+    def test_summary(self):
+        # GH#9116
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+
+        exp1 = """PeriodIndex: 0 entries
+Freq: D"""
+
+        exp2 = """PeriodIndex: 1 entries, 2011-01-01 to 2011-01-01
+Freq: D"""
+
+        exp3 = """PeriodIndex: 2 entries, 2011-01-01 to 2011-01-02
+Freq: D"""
+
+        exp4 = """PeriodIndex: 3 entries, 2011-01-01 to 2011-01-03
+Freq: D"""
+
+        exp5 = """PeriodIndex: 3 entries, 2011 to 2013
+Freq: Y-DEC"""
+
+        exp6 = """PeriodIndex: 3 entries, 2011-01-01 09:00 to NaT
+Freq: h"""
+
+        exp7 = """PeriodIndex: 1 entries, 2013Q1 to 2013Q1
+Freq: Q-DEC"""
+
+        exp8 = """PeriodIndex: 2 entries, 2013Q1 to 2013Q2
+Freq: Q-DEC"""
+
+        exp9 = """PeriodIndex: 3 entries, 2013Q1 to 2013Q3
+Freq: Q-DEC"""
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9],
+        ):
+            result = idx._summary()
+            assert result == expected
+
+
+class TestPeriodIndexFormat:
+    def test_period_format_and_strftime_default(self):
+        per = PeriodIndex([datetime(2003, 1, 1, 12), None], freq="h")
+
+        # Default formatting
+        msg = "PeriodIndex.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format()
+        assert formatted[0] == "2003-01-01 12:00"  # default: minutes not shown
+        assert formatted[1] == "NaT"
+        # format is equivalent to strftime(None)...
+        assert formatted[0] == per.strftime(None)[0]
+        assert per.strftime(None)[1] is np.nan  # ...except for NaTs
+
+        # Same test with nanoseconds freq
+        per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="ns")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format()
+        assert (formatted == per.strftime(None)).all()
+        assert formatted[0] == "2003-01-01 12:01:01.123456789"
+        assert formatted[1] == "2003-01-01 12:01:01.123456790"
+
+    def test_period_custom(self):
+        # GH#46252 custom formatting directives %l (ms) and %u (us)
+        msg = "PeriodIndex.format is deprecated"
+
+        # 3 digits
+        per = pd.period_range("2003-01-01 12:01:01.123", periods=2, freq="ms")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123000 ns=123000000)"
+        assert formatted[1] == "03 12:01:01 (ms=124 us=124000 ns=124000000)"
+
+        # 6 digits
+        per = pd.period_range("2003-01-01 12:01:01.123456", periods=2, freq="us")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456000)"
+        assert formatted[1] == "03 12:01:01 (ms=123 us=123457 ns=123457000)"
+
+        # 9 digits
+        per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="ns")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456789)"
+        assert formatted[1] == "03 12:01:01 (ms=123 us=123456 ns=123456790)"
+
+    def test_period_tz(self):
+        # Formatting periods created from a datetime with timezone.
+        msg = r"PeriodIndex\.format is deprecated"
+        # This timestamp is in 2013 in Europe/Paris but is 2012 in UTC
+        dt = pd.to_datetime(["2013-01-01 00:00:00+01:00"], utc=True)
+
+        # Converting to a period looses the timezone information
+        # Since tz is currently set as utc, we'll see 2012
+        with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+            per = dt.to_period(freq="h")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert per.format()[0] == "2012-12-31 23:00"
+
+        # If tz is currently set as paris before conversion, we'll see 2013
+        dt = dt.tz_convert("Europe/Paris")
+        with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+            per = dt.to_period(freq="h")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert per.format()[0] == "2013-01-01 00:00"
+
+    @pytest.mark.parametrize(
+        "locale_str",
+        [
+            pytest.param(None, id=str(locale.getlocale())),
+            "it_IT.utf8",
+            "it_IT",  # Note: encoding will be 'ISO8859-1'
+            "zh_CN.utf8",
+            "zh_CN",  # Note: encoding will be 'gb2312'
+        ],
+    )
+    def test_period_non_ascii_fmt(self, locale_str):
+        # GH#46468 non-ascii char in input format string leads to wrong output
+
+        # Skip if locale cannot be set
+        if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+            pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+        # Change locale temporarily for this test.
+        with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+            # Scalar
+            per = pd.Period("2018-03-11 13:00", freq="h")
+            assert per.strftime("%y é") == "18 é"
+
+            # Index
+            per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+            msg = "PeriodIndex.format is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = per.format(date_format="%y é")
+            assert formatted[0] == "03 é"
+            assert formatted[1] == "03 é"
+
+    @pytest.mark.parametrize(
+        "locale_str",
+        [
+            pytest.param(None, id=str(locale.getlocale())),
+            "it_IT.utf8",
+            "it_IT",  # Note: encoding will be 'ISO8859-1'
+            "zh_CN.utf8",
+            "zh_CN",  # Note: encoding will be 'gb2312'
+        ],
+    )
+    def test_period_custom_locale_directive(self, locale_str):
+        # GH#46319 locale-specific directive leads to non-utf8 c strftime char* result
+
+        # Skip if locale cannot be set
+        if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+            pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+        # Change locale temporarily for this test.
+        with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+            # Get locale-specific reference
+            am_local, pm_local = get_local_am_pm()
+
+            # Scalar
+            per = pd.Period("2018-03-11 13:00", freq="h")
+            assert per.strftime("%p") == pm_local
+
+            # Index
+            per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+            msg = "PeriodIndex.format is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = per.format(date_format="%y %I:%M:%S%p")
+            assert formatted[0] == f"03 01:00:00{am_local}"
+            assert formatted[1] == f"03 01:00:00{pm_local}"
diff --git a/shared/package-files/0c/0c56d1cc902aa31c536cb523a2357e1eec061df3a70003caa4e7415c54705d2e/adaptive.h b/shared/package-files/0c/0c56d1cc902aa31c536cb523a2357e1eec061df3a70003caa4e7415c54705d2e/adaptive.h
new file mode 100644
index 0000000000000000000000000000000000000000..939d57dd5d5107e2e4f6975d84c1a87cbc17e154
--- /dev/null
+++ b/shared/package-files/0c/0c56d1cc902aa31c536cb523a2357e1eec061df3a70003caa4e7415c54705d2e/adaptive.h
@@ -0,0 +1,109 @@
+#pragma once
+
+#include <torch/nn/cloneable.h>
+#include <torch/nn/functional/activation.h>
+#include <torch/nn/module.h>
+#include <torch/nn/modules/container/modulelist.h>
+#include <torch/nn/modules/container/sequential.h>
+#include <torch/nn/modules/linear.h>
+#include <torch/nn/options/adaptive.h>
+
+namespace torch {
+namespace nn {
+
+/// The output of a single invocation of an AdaptiveLogSoftmaxWithLoss
+/// module's `forward()` method.
+struct TORCH_API ASMoutput {
+  ASMoutput(Tensor output_, double loss_);
+
+  /// Tensor containing computed target log probabilities for each example
+  Tensor output;
+
+  /// Scalar representing the computed negative log likelihood loss
+  double loss;
+};
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AdaptiveLogSoftmaxWithLoss
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Efficient softmax approximation as described in
+/// `Efficient softmax approximation for GPUs`_ by Edouard Grave, Armand Joulin,
+/// Moustapha Cissé, David Grangier, and Hervé Jégou.
+/// See
+/// https://pytorch.org/docs/master/nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss
+/// to learn about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::AdaptiveLogSoftmaxWithLossOptions`
+/// class to learn what constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// AdaptiveLogSoftmaxWithLoss model(AdaptiveLogSoftmaxWithLossOptions(8, 10,
+/// {4, 8}).div_value(2.).head_bias(true));
+/// ```
+class TORCH_API AdaptiveLogSoftmaxWithLossImpl
+    : public Cloneable<AdaptiveLogSoftmaxWithLossImpl> {
+ public:
+  AdaptiveLogSoftmaxWithLossImpl(
+      int64_t in_features,
+      int64_t n_classes,
+      std::vector<int64_t> cutoffs)
+      : AdaptiveLogSoftmaxWithLossImpl(AdaptiveLogSoftmaxWithLossOptions(
+            in_features,
+            n_classes,
+            cutoffs)) {}
+
+  explicit AdaptiveLogSoftmaxWithLossImpl(
+      AdaptiveLogSoftmaxWithLossOptions options_);
+
+  ASMoutput forward(const Tensor& input, const Tensor& target);
+
+  void reset() override;
+
+  void reset_parameters();
+
+  /// Pretty prints the `AdaptiveLogSoftmaxWithLoss` module into the given
+  /// `stream`.
+  void pretty_print(std::ostream& stream) const override;
+
+  /// Given input tensor, and output of `head`, computes the log of the full
+  /// distribution
+  Tensor _get_full_log_prob(const Tensor& input, const Tensor& head_output);
+
+  /// Computes log probabilities for all n_classes
+  Tensor log_prob(const Tensor& input);
+
+  /// This is equivalent to `log_pob(input).argmax(1)` but is more efficient in
+  /// some cases
+  Tensor predict(const Tensor& input);
+
+  /// The options with which this `Module` was constructed
+  AdaptiveLogSoftmaxWithLossOptions options;
+
+  /// Cutoffs used to assign targets to their buckets. It should be an ordered
+  /// Sequence of integers sorted in the increasing order
+  std::vector<int64_t> cutoffs;
+
+  int64_t shortlist_size;
+
+  /// Number of clusters
+  int64_t n_clusters;
+
+  /// Output size of head classifier
+  int64_t head_size;
+
+  Linear head = nullptr;
+
+  ModuleList tail;
+};
+
+/// A `ModuleHolder` subclass for `AdaptiveLogSoftmaxWithLossImpl`.
+/// See the documentation for `AdaptiveLogSoftmaxWithLossImpl` class to learn
+/// what methods it provides, and examples of how to use
+/// `AdaptiveLogSoftmaxWithLoss` with
+/// `torch::nn::AdaptiveLogSoftmaxWithLossOptions`. See the documentation for
+/// `ModuleHolder` to learn about PyTorch's module storage semantics.
+TORCH_MODULE(AdaptiveLogSoftmaxWithLoss);
+
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/0c/0c5a3106c098456aa876635a0cd3e742b6534d789a4afc0d664c2bc9b7bb725d/test_tools.py b/shared/package-files/0c/0c5a3106c098456aa876635a0cd3e742b6534d789a4afc0d664c2bc9b7bb725d/test_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..f507e64d88b06b5862de3e98c693ab9f85306116
--- /dev/null
+++ b/shared/package-files/0c/0c5a3106c098456aa876635a0cd3e742b6534d789a4afc0d664c2bc9b7bb725d/test_tools.py
@@ -0,0 +1,52 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Period,
+    PeriodIndex,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestPeriodRepresentation:
+    """
+    Wish to match NumPy units
+    """
+
+    @pytest.mark.parametrize(
+        "freq, base_date",
+        [
+            ("W-THU", "1970-01-01"),
+            ("D", "1970-01-01"),
+            ("B", "1970-01-01"),
+            ("h", "1970-01-01"),
+            ("min", "1970-01-01"),
+            ("s", "1970-01-01"),
+            ("ms", "1970-01-01"),
+            ("us", "1970-01-01"),
+            ("ns", "1970-01-01"),
+            ("M", "1970-01"),
+            ("Y", 1970),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_freq(self, freq, base_date):
+        rng = period_range(start=base_date, periods=10, freq=freq)
+        exp = np.arange(10, dtype=np.int64)
+
+        tm.assert_numpy_array_equal(rng.asi8, exp)
+
+
+class TestPeriodIndexConversion:
+    def test_tolist(self):
+        index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
+        rs = index.tolist()
+        for x in rs:
+            assert isinstance(x, Period)
+
+        recon = PeriodIndex(rs)
+        tm.assert_index_equal(index, recon)
diff --git a/shared/package-files/0c/0c5bab22964a970533cb61ecf356133c51b3ea5224f1d6a59827dcc64cff74b1/linalg.py b/shared/package-files/0c/0c5bab22964a970533cb61ecf356133c51b3ea5224f1d6a59827dcc64cff74b1/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0f8ff0c99c5911852de942fc0d398d4e90b4f55
--- /dev/null
+++ b/shared/package-files/0c/0c5bab22964a970533cb61ecf356133c51b3ea5224f1d6a59827dcc64cff74b1/linalg.py
@@ -0,0 +1,2970 @@
+"""
+Implementation of linear algebra operations.
+"""
+
+
+import contextlib
+import warnings
+
+from llvmlite import ir
+
+import numpy as np
+import operator
+
+from numba.core.imputils import (lower_builtin, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked)
+from numba.core.typing import signature
+from numba.core.extending import intrinsic, overload, register_jitable
+from numba.core import types, cgutils, config
+from numba.core.errors import TypingError, NumbaTypeError, \
+    NumbaPerformanceWarning
+from .arrayobj import make_array, _empty_nd_impl, array_copy
+from numba.np import numpy_support as np_support
+from numba import _helperlib
+
+ll_char = ir.IntType(8)
+ll_char_p = ll_char.as_pointer()
+ll_void_p = ll_char_p
+ll_intc = ir.IntType(32)
+ll_intc_p = ll_intc.as_pointer()
+intp_t = cgutils.intp_t
+ll_intp_p = intp_t.as_pointer()
+
+
+def _lapack_runtime_is_ilp64():
+    """
+    Whether the scipy actually installed *right now* provides ILP64 (64-bit
+    Fortran integer) scipy.linalg.cython_blas / cython_lapack, as opposed to
+    the usual 32-bit ("LP64") ones. This may differ from what numba's own
+    _lapack.c wrappers were *built* for (see _LAPACK_BUILD_ILP64 below and
+    _check_lapack_int_width()) -- scipy can be upgraded/downgraded/swapped
+    independently of numba. SciPy records which ABI it built for in
+    scipy.__config__ (see scipy.linalg.blas.HAS_LP64, which reads the same
+    flag).
+    """
+    try:
+        from scipy.__config__ import CONFIG
+        return bool(CONFIG['Build Dependencies']['blas']['cython blas ilp64'])
+    except (ImportError, KeyError, TypeError):
+        # scipy predates this __config__ entry (pre-Meson builds); those
+        # releases only ever shipped LP64 cython_blas/cython_lapack.
+        return False
+
+
+# Which Fortran integer width numba/_lapack.c was built to target (see
+# NUMBA_LAPACK_ILP64 in the "Build time environment variables" install
+# docs, and setup.py). numba/_lapack.c is only ever compiled for this one
+# width -- it is fixed at build time, not re-probed on every import, and is
+# compiled into numba._helperlib itself so it can't drift from the actual
+# binary.
+_LAPACK_BUILD_ILP64 = bool(getattr(_helperlib, "LAPACK_BUILD_ILP64", False))
+_LAPACK_ILP64 = _LAPACK_BUILD_ILP64
+
+_LAPACK_INSTALL_DOCS_URL = (
+    "https://numba.readthedocs.io/en/stable/user/installing.html"
+    "#numba-source-install-env_vars"
+)
+
+
+def _check_lapack_int_width():
+    """
+    Guard against a numba built for one Fortran integer width being used
+    against a scipy providing the other one: the compiled numba_* wrappers
+    assume a fixed width, so calling through the wrong one is silent memory
+    corruption or wrong answers, not a clean error -- refuse outright
+    instead. Called from ensure_blas()/ensure_lapack(), once scipy is
+    confirmed importable.
+    """
+    runtime_ilp64 = _lapack_runtime_is_ilp64()
+    if runtime_ilp64 != _LAPACK_BUILD_ILP64:
+        built_as = "ILP64" if _LAPACK_BUILD_ILP64 else "LP64"
+        found_as = "ILP64" if runtime_ilp64 else "LP64"
+        raise RuntimeError(
+            f"This copy of Numba was built for {built_as} BLAS/LAPACK, but "
+            f"the scipy installed now provides {found_as} "
+            f"scipy.linalg.cython_blas/cython_lapack. Numba's compiled "
+            f"BLAS/LAPACK wrappers assume a fixed integer width and will "
+            f"produce wrong results or crash if called against the wrong "
+            f"one. Either install a matching SciPy or rebuild Numba "
+            f"-- see {_LAPACK_INSTALL_DOCS_URL} for details."
+        )
+
+
+# fortran int type, this needs to match the F_INT typedef in _lapack.c.
+F_INT_nptype = np.int64 if _LAPACK_ILP64 else np.int32
+F_INT_nbtype = types.int64 if _LAPACK_ILP64 else types.int32
+
+# BLAS kinds as letters
+_blas_kinds = {
+    types.float32: 's',
+    types.float64: 'd',
+    types.complex64: 'c',
+    types.complex128: 'z',
+}
+
+
+def get_blas_kind(dtype, func_name="<BLAS function>"):
+    kind = _blas_kinds.get(dtype)
+    if kind is None:
+        raise NumbaTypeError("unsupported dtype for %s()" % (func_name,))
+    return kind
+
+
+def ensure_blas():
+    try:
+        import scipy.linalg.cython_blas
+    except ImportError:
+        raise ImportError("scipy 0.16+ is required for linear algebra")
+    _check_lapack_int_width()
+
+
+def ensure_lapack():
+    try:
+        import scipy.linalg.cython_lapack
+    except ImportError:
+        raise ImportError("scipy 0.16+ is required for linear algebra")
+    _check_lapack_int_width()
+
+
+def make_constant_slot(context, builder, ty, val):
+    const = context.get_constant_generic(builder, ty, val)
+    return cgutils.alloca_once_value(builder, const)
+
+
+class _BLAS:
+    """
+    Functions to return type signatures for wrapped
+    BLAS functions.
+    """
+
+    def __init__(self):
+        ensure_blas()
+
+    @classmethod
+    def numba_xxnrm2(cls, dtype):
+        rtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(types.char,             # kind
+                         types.intp,             # n
+                         types.CPointer(dtype),  # x
+                         types.intp,             # incx
+                         types.CPointer(rtype))  # returned
+
+        return types.ExternalFunction("numba_xxnrm2", sig)
+
+    @classmethod
+    def numba_xxgemm(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.char,             # transa
+            types.char,             # transb
+            types.intp,             # m
+            types.intp,             # n
+            types.intp,             # k
+            types.CPointer(dtype),  # alpha
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # b
+            types.intp,             # ldb
+            types.CPointer(dtype),  # beta
+            types.CPointer(dtype),  # c
+            types.intp              # ldc
+        )
+        return types.ExternalFunction("numba_xxgemm", sig)
+
+
+class _LAPACK:
+    """
+    Functions to return type signatures for wrapped
+    LAPACK functions.
+    """
+
+    def __init__(self):
+        ensure_lapack()
+
+    @classmethod
+    def numba_xxgetrf(cls, dtype):
+        sig = types.intc(types.char,                   # kind
+                         types.intp,                   # m
+                         types.intp,                   # n
+                         types.CPointer(dtype),        # a
+                         types.intp,                   # lda
+                         types.CPointer(F_INT_nbtype)  # ipiv
+                         )
+        return types.ExternalFunction("numba_xxgetrf", sig)
+
+    @classmethod
+    def numba_ez_xxgetri(cls, dtype):
+        sig = types.intc(types.char,                   # kind
+                         types.intp,                   # n
+                         types.CPointer(dtype),        # a
+                         types.intp,                   # lda
+                         types.CPointer(F_INT_nbtype)  # ipiv
+                         )
+        return types.ExternalFunction("numba_ez_xxgetri", sig)
+
+    @classmethod
+    def numba_ez_rgeev(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobvl
+                         types.char,             # jobvr
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(dtype),  # wr
+                         types.CPointer(dtype),  # wi
+                         types.CPointer(dtype),  # vl
+                         types.intp,             # ldvl
+                         types.CPointer(dtype),  # vr
+                         types.intp              # ldvr
+                         )
+        return types.ExternalFunction("numba_ez_rgeev", sig)
+
+    @classmethod
+    def numba_ez_cgeev(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobvl
+                         types.char,             # jobvr
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(dtype),  # w
+                         types.CPointer(dtype),  # vl
+                         types.intp,             # ldvl
+                         types.CPointer(dtype),  # vr
+                         types.intp              # ldvr
+                         )
+        return types.ExternalFunction("numba_ez_cgeev", sig)
+
+    @classmethod
+    def numba_ez_xxxevd(cls, dtype):
+        wtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobz
+                         types.char,             # uplo
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(wtype),  # w
+                         )
+        return types.ExternalFunction("numba_ez_xxxevd", sig)
+
+    @classmethod
+    def numba_xxpotrf(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # uplo
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp              # lda
+                         )
+        return types.ExternalFunction("numba_xxpotrf", sig)
+
+    @classmethod
+    def numba_ez_gesdd(cls, dtype):
+        stype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(
+            types.char,             # kind
+            types.char,             # jobz
+            types.intp,             # m
+            types.intp,             # n
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(stype),  # s
+            types.CPointer(dtype),  # u
+            types.intp,             # ldu
+            types.CPointer(dtype),  # vt
+            types.intp              # ldvt
+        )
+
+        return types.ExternalFunction("numba_ez_gesdd", sig)
+
+    @classmethod
+    def numba_ez_geqrf(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.intp,             # m
+            types.intp,             # n
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # tau
+        )
+        return types.ExternalFunction("numba_ez_geqrf", sig)
+
+    @classmethod
+    def numba_ez_xxgqr(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.intp,             # m
+            types.intp,             # n
+            types.intp,             # k
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # tau
+        )
+        return types.ExternalFunction("numba_ez_xxgqr", sig)
+
+    @classmethod
+    def numba_ez_gelsd(cls, dtype):
+        rtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(
+            types.char,                 # kind
+            types.intp,                 # m
+            types.intp,                 # n
+            types.intp,                 # nrhs
+            types.CPointer(dtype),      # a
+            types.intp,                 # lda
+            types.CPointer(dtype),      # b
+            types.intp,                 # ldb
+            types.CPointer(rtype),      # S
+            types.float64,              # rcond
+            types.CPointer(F_INT_nbtype)  # rank
+        )
+        return types.ExternalFunction("numba_ez_gelsd", sig)
+
+    @classmethod
+    def numba_xgesv(cls, dtype):
+        sig = types.intc(
+            types.char,                    # kind
+            types.intp,                    # n
+            types.intp,                    # nhrs
+            types.CPointer(dtype),         # a
+            types.intp,                    # lda
+            types.CPointer(F_INT_nbtype),  # ipiv
+            types.CPointer(dtype),         # b
+            types.intp                     # ldb
+        )
+        return types.ExternalFunction("numba_xgesv", sig)
+
+
+@contextlib.contextmanager
+def make_contiguous(context, builder, sig, args):
+    """
+    Ensure that all array arguments are contiguous, if necessary by
+    copying them.
+    A new (sig, args) tuple is yielded.
+    """
+    newtys = []
+    newargs = []
+    copies = []
+    for ty, val in zip(sig.args, args):
+        if not isinstance(ty, types.Array) or ty.layout in 'CF':
+            newty, newval = ty, val
+        else:
+            newty = ty.copy(layout='C')
+            copysig = signature(newty, ty)
+            newval = array_copy(context, builder, copysig, (val,))
+            copies.append((newty, newval))
+        newtys.append(newty)
+        newargs.append(newval)
+    yield signature(sig.return_type, *newtys), tuple(newargs)
+    for ty, val in copies:
+        context.nrt.decref(builder, ty, val)
+
+
+def check_c_int(context, builder, n):
+    """
+    Check whether *n* fits in a C `int`.
+    """
+    _maxint = 2**31 - 1
+
+    def impl(n):
+        if n > _maxint:
+            raise OverflowError("array size too large to fit in C int")
+
+    context.compile_internal(builder, impl,
+                             signature(types.none, types.intp), (n,))
+
+
+def check_blas_return(context, builder, res):
+    """
+    Check the integer error return from one of the BLAS wrappers in
+    _helperlib.c.
+    """
+    with builder.if_then(cgutils.is_not_null(builder, res), likely=False):
+        # Those errors shouldn't happen, it's easier to just abort the process
+        pyapi = context.get_python_api(builder)
+        pyapi.gil_ensure()
+        pyapi.fatal_error("BLAS wrapper returned with an error")
+
+
+def check_lapack_return(context, builder, res):
+    """
+    Check the integer error return from one of the LAPACK wrappers in
+    _helperlib.c.
+    """
+    with builder.if_then(cgutils.is_not_null(builder, res), likely=False):
+        # Those errors shouldn't happen, it's easier to just abort the process
+        pyapi = context.get_python_api(builder)
+        pyapi.gil_ensure()
+        pyapi.fatal_error("LAPACK wrapper returned with an error")
+
+
+def call_xxdot(context, builder, conjugate, dtype,
+               n, a_data, b_data, out_data):
+    """
+    Call the BLAS vector * vector product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char, ll_char, intp_t,    # kind, conjugate, n
+                            ll_void_p, ll_void_p, ll_void_p,  # a, b, out
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxdot")
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+    conjugate = ir.Constant(ll_char, int(conjugate))
+
+    res = builder.call(fn, (kind_val, conjugate, n,
+                            builder.bitcast(a_data, ll_void_p),
+                            builder.bitcast(b_data, ll_void_p),
+                            builder.bitcast(out_data, ll_void_p)))
+    check_blas_return(context, builder, res)
+
+
+def call_xxgemv(context, builder, do_trans,
+                m_type, m_shapes, m_data, v_data, out_data):
+    """
+    Call the BLAS matrix * vector product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char, ll_char,                 # kind, trans
+                            intp_t, intp_t,                   # m, n
+                            ll_void_p, ll_void_p, intp_t,     # alpha, a, lda
+                            ll_void_p, ll_void_p, ll_void_p,  # x, beta, y
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxgemv")
+
+    dtype = m_type.dtype
+    alpha = make_constant_slot(context, builder, dtype, 1.0)
+    beta = make_constant_slot(context, builder, dtype, 0.0)
+
+    if m_type.layout == 'F':
+        m, n = m_shapes
+        lda = m_shapes[0]
+    else:
+        n, m = m_shapes
+        lda = m_shapes[1]
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+    trans = ir.Constant(ll_char, ord('t') if do_trans else ord('n'))
+
+    res = builder.call(fn, (kind_val, trans, m, n,
+                            builder.bitcast(alpha, ll_void_p),
+                            builder.bitcast(m_data, ll_void_p), lda,
+                            builder.bitcast(v_data, ll_void_p),
+                            builder.bitcast(beta, ll_void_p),
+                            builder.bitcast(out_data, ll_void_p)))
+    check_blas_return(context, builder, res)
+
+
+def call_xxgemm(context, builder,
+                x_type, x_shapes, x_data,
+                y_type, y_shapes, y_data,
+                out_type, out_shapes, out_data):
+    """
+    Call the BLAS matrix * matrix product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char,                       # kind
+                            ll_char, ll_char,              # transa, transb
+                            intp_t, intp_t, intp_t,        # m, n, k
+                            ll_void_p, ll_void_p, intp_t,  # alpha, a, lda
+                            ll_void_p, intp_t, ll_void_p,  # b, ldb, beta
+                            ll_void_p, intp_t,             # c, ldc
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxgemm")
+
+    m, k = x_shapes
+    _k, n = y_shapes
+    dtype = x_type.dtype
+    alpha = make_constant_slot(context, builder, dtype, 1.0)
+    beta = make_constant_slot(context, builder, dtype, 0.0)
+
+    trans = ir.Constant(ll_char, ord('t'))
+    notrans = ir.Constant(ll_char, ord('n'))
+
+    def get_array_param(ty, shapes, data):
+        return (
+            # Transpose if layout different from result's
+            notrans if ty.layout == out_type.layout else trans,
+            # Size of the inner dimension in physical array order
+            shapes[1] if ty.layout == 'C' else shapes[0],
+            # The data pointer, unit-less
+            builder.bitcast(data, ll_void_p),
+        )
+
+    transa, lda, data_a = get_array_param(y_type, y_shapes, y_data)
+    transb, ldb, data_b = get_array_param(x_type, x_shapes, x_data)
+    _, ldc, data_c = get_array_param(out_type, out_shapes, out_data)
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+
+    res = builder.call(fn, (kind_val, transa, transb, n, m, k,
+                            builder.bitcast(alpha, ll_void_p), data_a, lda,
+                            data_b, ldb, builder.bitcast(beta, ll_void_p),
+                            data_c, ldc))
+    check_blas_return(context, builder, res)
+
+
+def dot_2_mm(context, builder, sig, args):
+    """
+    np.dot(matrix, matrix)
+    """
+    def dot_impl(a, b):
+        m, k = a.shape
+        _k, n = b.shape
+        if k == 0:
+            return np.zeros((m, n), a.dtype)
+        out = np.empty((m, n), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_vm(context, builder, sig, args):
+    """
+    np.dot(vector, matrix)
+    """
+    def dot_impl(a, b):
+        m, = a.shape
+        _m, n = b.shape
+        if m == 0:
+            return np.zeros((n, ), a.dtype)
+        out = np.empty((n, ), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_mv(context, builder, sig, args):
+    """
+    np.dot(matrix, vector)
+    """
+    def dot_impl(a, b):
+        m, n = a.shape
+        _n, = b.shape
+        if n == 0:
+            return np.zeros((m, ), a.dtype)
+        out = np.empty((m, ), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_vv(context, builder, sig, args, conjugate=False):
+    """
+    np.dot(vector, vector)
+    np.vdot(vector, vector)
+    """
+    aty, bty = sig.args
+    dtype = sig.return_type
+    a = make_array(aty)(context, builder, args[0])
+    b = make_array(bty)(context, builder, args[1])
+    n, = cgutils.unpack_tuple(builder, a.shape)
+
+    def check_args(a, b):
+        m, = a.shape
+        n, = b.shape
+        if m != n:
+            raise ValueError("incompatible array sizes for np.dot(a, b) "
+                             "(vector * vector)")
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+    check_c_int(context, builder, n)
+
+    out = cgutils.alloca_once(builder, context.get_value_type(dtype))
+    call_xxdot(context, builder, conjugate, dtype, n, a.data, b.data, out)
+    return builder.load(out)
+
+
+@overload(np.dot)
+def dot_2(a, b):
+    """
+    np.dot(a, b)
+    """
+    return dot_2_impl('np.dot()', a, b)
+
+
+@overload(operator.matmul)
+def matmul_2(a, b):
+    """
+    a @ b
+    """
+    return dot_2_impl("'@'", a, b)
+
+
+def dot_2_impl(name, a, b):
+    if isinstance(a, types.Array) and isinstance(b, types.Array):
+        @intrinsic
+        def _impl(typingcontext, a, b):
+            ndims = (a.ndim, b.ndim)
+
+            def _dot2_codegen(context, builder, sig, args):
+                ensure_blas()
+
+                with make_contiguous(context, builder, sig, args) as (sig, args):
+                    if ndims == (2, 2):
+                        return dot_2_mm(context, builder, sig, args)
+                    elif ndims == (2, 1):
+                        return dot_2_mv(context, builder, sig, args)
+                    elif ndims == (1, 2):
+                        return dot_2_vm(context, builder, sig, args)
+                    elif ndims == (1, 1):
+                        return dot_2_vv(context, builder, sig, args)
+                    else:
+                        raise AssertionError('unreachable')
+
+            if a.dtype != b.dtype:
+                raise TypingError(
+                    "%s arguments must all have the same dtype" % name)
+
+            if ndims == (2, 2):
+                return_type = types.Array(a.dtype, 2, 'C')
+            elif ndims == (2, 1) or ndims == (1, 2):
+                return_type = types.Array(a.dtype, 1, 'C')
+            elif ndims == (1, 1):
+                return_type = a.dtype
+            else:
+                raise TypingError(("%s: inputs must have compatible "
+                                   "dimensions") % name)
+            return signature(return_type, a, b), _dot2_codegen
+
+        if (
+            a.layout not in "CF" or b.layout not in "CF"
+        ) and not config.DISABLE_PERFORMANCE_WARNINGS:
+            warnings.warn(
+                "%s is faster on contiguous arrays, called on %s" % (
+                    name, (a, b),), NumbaPerformanceWarning)
+
+        return lambda a, b: _impl(a, b)
+
+
+@overload(np.vdot)
+def vdot(left, right):
+    """
+    np.vdot(a, b)
+    """
+    if isinstance(left, types.Array) and isinstance(right, types.Array):
+        @intrinsic
+        def _impl(typingcontext, left, right):
+            def codegen(context, builder, sig, args):
+                ensure_blas()
+
+                with make_contiguous(context, builder, sig, args) as\
+                        (sig, args):
+                    return dot_2_vv(context, builder, sig, args, conjugate=True)
+
+            if left.ndim != 1 or right.ndim != 1:
+                raise TypingError("np.vdot() only supported on 1-D arrays")
+
+            if left.dtype != right.dtype:
+                raise TypingError(
+                    "np.vdot() arguments must all have the same dtype")
+            return signature(left.dtype, left, right), codegen
+
+        if (
+            left.layout not in "CF" or right.layout not in "CF"
+        ) and not config.DISABLE_PERFORMANCE_WARNINGS:
+            warnings.warn(
+                "np.vdot() is faster on contiguous arrays, called on %s"
+                % ((left, right),), NumbaPerformanceWarning)
+
+        return lambda left, right: _impl(left, right)
+
+
+def dot_3_vm_check_args(a, b, out):
+    m, = a.shape
+    _m, n = b.shape
+    if m != _m:
+        raise ValueError("incompatible array sizes for "
+                         "np.dot(a, b) (vector * matrix)")
+    if out.shape != (n,):
+        raise ValueError("incompatible output array size for "
+                         "np.dot(a, b, out) (vector * matrix)")
+
+
+def dot_3_mv_check_args(a, b, out):
+    m, _n = a.shape
+    n, = b.shape
+    if n != _n:
+        raise ValueError("incompatible array sizes for np.dot(a, b) "
+                         "(matrix * vector)")
+    if out.shape != (m,):
+        raise ValueError("incompatible output array size for "
+                         "np.dot(a, b, out) (matrix * vector)")
+
+
+def dot_3_vm(context, builder, sig, args):
+    """
+    np.dot(vector, matrix, out)
+    np.dot(matrix, vector, out)
+    """
+    xty, yty, outty = sig.args
+    assert outty == sig.return_type
+    dtype = xty.dtype
+
+    x = make_array(xty)(context, builder, args[0])
+    y = make_array(yty)(context, builder, args[1])
+    out = make_array(outty)(context, builder, args[2])
+    x_shapes = cgutils.unpack_tuple(builder, x.shape)
+    y_shapes = cgutils.unpack_tuple(builder, y.shape)
+    out_shapes = cgutils.unpack_tuple(builder, out.shape)
+    if xty.ndim < yty.ndim:
+        # Vector * matrix
+        # Asked for x * y, we will compute y.T * x
+        mty = yty
+        m_shapes = y_shapes
+        v_shape = x_shapes[0]
+        lda = m_shapes[1]
+        do_trans = yty.layout == 'F'
+        m_data, v_data = y.data, x.data
+        check_args = dot_3_vm_check_args
+    else:
+        # Matrix * vector
+        # We will compute x * y
+        mty = xty
+        m_shapes = x_shapes
+        v_shape = y_shapes[0]
+        lda = m_shapes[0]
+        do_trans = xty.layout == 'C'
+        m_data, v_data = x.data, y.data
+        check_args = dot_3_mv_check_args
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+    for val in m_shapes:
+        check_c_int(context, builder, val)
+
+    zero = context.get_constant(types.intp, 0)
+    both_empty = builder.icmp_signed('==', v_shape, zero)
+    matrix_empty = builder.icmp_signed('==', lda, zero)
+    is_empty = builder.or_(both_empty, matrix_empty)
+    with builder.if_else(is_empty, likely=False) as (empty, nonempty):
+        with empty:
+            cgutils.memset(builder, out.data,
+                           builder.mul(out.itemsize, out.nitems), 0)
+        with nonempty:
+            call_xxgemv(context, builder, do_trans, mty, m_shapes, m_data,
+                        v_data, out.data)
+
+    return impl_ret_borrowed(context, builder, sig.return_type,
+                             out._getvalue())
+
+
+def dot_3_mm(context, builder, sig, args):
+    """
+    np.dot(matrix, matrix, out)
+    """
+    xty, yty, outty = sig.args
+    assert outty == sig.return_type
+    dtype = xty.dtype
+
+    x = make_array(xty)(context, builder, args[0])
+    y = make_array(yty)(context, builder, args[1])
+    out = make_array(outty)(context, builder, args[2])
+    x_shapes = cgutils.unpack_tuple(builder, x.shape)
+    y_shapes = cgutils.unpack_tuple(builder, y.shape)
+    out_shapes = cgutils.unpack_tuple(builder, out.shape)
+    m, k = x_shapes
+    _k, n = y_shapes
+
+    # The only case Numpy supports
+    assert outty.layout == 'C'
+
+    def check_args(a, b, out):
+        m, k = a.shape
+        _k, n = b.shape
+        if k != _k:
+            raise ValueError("incompatible array sizes for np.dot(a, b) "
+                             "(matrix * matrix)")
+        if out.shape != (m, n):
+            raise ValueError("incompatible output array size for "
+                             "np.dot(a, b, out) (matrix * matrix)")
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+
+    check_c_int(context, builder, m)
+    check_c_int(context, builder, k)
+    check_c_int(context, builder, n)
+
+    x_data = x.data
+    y_data = y.data
+    out_data = out.data
+
+    # If eliminated dimension is zero, set all entries to zero and return
+    zero = context.get_constant(types.intp, 0)
+    both_empty = builder.icmp_signed('==', k, zero)
+    x_empty = builder.icmp_signed('==', m, zero)
+    y_empty = builder.icmp_signed('==', n, zero)
+    is_empty = builder.or_(both_empty, builder.or_(x_empty, y_empty))
+    with builder.if_else(is_empty, likely=False) as (empty, nonempty):
+        with empty:
+            cgutils.memset(builder, out.data,
+                           builder.mul(out.itemsize, out.nitems), 0)
+        with nonempty:
+            # Check if any of the operands is really a 1-d vector represented
+            # as a (1, k) or (k, 1) 2-d array.  In those cases, it is pessimal
+            # to call the generic matrix * matrix product BLAS function.
+            one = context.get_constant(types.intp, 1)
+            is_left_vec = builder.icmp_signed('==', m, one)
+            is_right_vec = builder.icmp_signed('==', n, one)
+
+            with builder.if_else(is_right_vec) as (r_vec, r_mat):
+                with r_vec:
+                    with builder.if_else(is_left_vec) as (v_v, m_v):
+                        with v_v:
+                            # V * V
+                            call_xxdot(context, builder, False, dtype,
+                                       k, x_data, y_data, out_data)
+                        with m_v:
+                            # M * V
+                            do_trans = xty.layout == outty.layout
+                            call_xxgemv(context, builder, do_trans,
+                                        xty, x_shapes, x_data, y_data, out_data)
+                with r_mat:
+                    with builder.if_else(is_left_vec) as (v_m, m_m):
+                        with v_m:
+                            # V * M
+                            do_trans = yty.layout != outty.layout
+                            call_xxgemv(context, builder, do_trans,
+                                        yty, y_shapes, y_data, x_data, out_data)
+                        with m_m:
+                            # M * M
+                            call_xxgemm(context, builder,
+                                        xty, x_shapes, x_data,
+                                        yty, y_shapes, y_data,
+                                        outty, out_shapes, out_data)
+
+    return impl_ret_borrowed(context, builder, sig.return_type,
+                             out._getvalue())
+
+
+@overload(np.dot)
+def dot_3(a, b, out):
+    """
+    np.dot(a, b, out)
+    """
+    if (isinstance(a, types.Array) and isinstance(b, types.Array) and
+            isinstance(out, types.Array)):
+        @intrinsic
+        def _impl(typingcontext, a, b, out):
+            def codegen(context, builder, sig, args):
+                ensure_blas()
+
+                with make_contiguous(context, builder, sig, args) as (sig,
+                                                                      args):
+                    ndims = set(x.ndim for x in sig.args[:2])
+                    if ndims == {2}:
+                        return dot_3_mm(context, builder, sig, args)
+                    elif ndims == {1, 2}:
+                        return dot_3_vm(context, builder, sig, args)
+                    else:
+                        raise AssertionError('unreachable')
+            if a.dtype != b.dtype or a.dtype != out.dtype:
+                raise TypingError(
+                    "np.dot() arguments must all have the same dtype")
+
+            return signature(out, a, b, out), codegen
+
+        if (
+            a.layout not in "CF"
+            or b.layout not in "CF"
+            or out.layout not in "CF"
+        ) and not config.DISABLE_PERFORMANCE_WARNINGS:
+            warnings.warn(
+                "np.vdot() is faster on contiguous arrays, called on %s"
+                % ((a, b),), NumbaPerformanceWarning)
+
+        return lambda a, b, out: _impl(a, b, out)
+
+
+fatal_error_func = types.ExternalFunction("numba_fatal_error", types.intc())
+
+
+@register_jitable
+def _check_finite_matrix(a):
+    for v in np.nditer(a):
+        if not np.isfinite(v.item()):
+            raise np.linalg.LinAlgError(
+                "Array must not contain infs or NaNs.")
+
+
+def _check_linalg_matrix(a, func_name, la_prefix=True):
+    # la_prefix is present as some functions, e.g. np.trace()
+    # are documented under "linear algebra" but aren't in the
+    # module
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # Unpack optional type
+    if isinstance(a, types.Optional):
+        a = a.type
+    if not isinstance(a, types.Array):
+        msg = "%s.%s() only supported for array types" % interp
+        raise TypingError(msg, highlighting=False)
+    if not a.ndim == 2:
+        msg = "%s.%s() only supported on 2-D arrays." % interp
+        raise TypingError(msg, highlighting=False)
+    if not isinstance(a.dtype, (types.Float, types.Complex)):
+        msg = "%s.%s() only supported on "\
+            "float and complex arrays." % interp
+        raise TypingError(msg, highlighting=False)
+
+
+def _check_homogeneous_types(func_name, *types):
+    t0 = types[0].dtype
+    for t in types[1:]:
+        if t.dtype != t0:
+            msg = "np.linalg.%s() only supports inputs that have homogeneous dtypes." % func_name
+            raise TypingError(msg, highlighting=False)
+
+
+def _copy_to_fortran_order():
+    pass
+
+
+@overload(_copy_to_fortran_order)
+def ol_copy_to_fortran_order(a):
+    # This function copies the array 'a' into a new array with fortran order.
+    # This exists because the copy routines don't take order flags yet.
+    F_layout = a.layout == 'F'
+    A_layout = a.layout == 'A'
+    def impl(a):
+        if F_layout:
+            # it's F ordered at compile time, just copy
+            acpy = np.copy(a)
+        elif A_layout:
+            # decide based on runtime value
+            flag_f = a.flags.f_contiguous
+            if flag_f:
+                # it's already F ordered, so copy but in a round about way to
+                # ensure that the copy is also F ordered
+                acpy = np.copy(a.T).T
+            else:
+                # it's something else ordered, so let asfortranarray deal with
+                # copying and making it fortran ordered
+                acpy = np.asfortranarray(a)
+        else:
+            # it's C ordered at compile time, asfortranarray it.
+            acpy = np.asfortranarray(a)
+        return acpy
+    return impl
+
+
+@register_jitable
+def _inv_err_handler(r):
+    if r != 0:
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+        if r > 0:
+            raise np.linalg.LinAlgError(
+                "Matrix is singular to machine precision.")
+
+@register_jitable
+def _dummy_liveness_func(a):
+    """pass a list of variables to be preserved through dead code elimination"""
+    return a[0]
+
+
+@overload(np.linalg.inv)
+def inv_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "inv")
+
+    numba_xxgetrf = _LAPACK().numba_xxgetrf(a.dtype)
+
+    numba_xxgetri = _LAPACK().numba_ez_xxgetri(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "inv"))
+
+    def inv_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        if n == 0:
+            return acpy
+
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xxgetrf(kind, n, n, acpy.ctypes, n, ipiv.ctypes)
+        _inv_err_handler(r)
+
+        r = numba_xxgetri(kind, n, acpy.ctypes, n, ipiv.ctypes)
+        _inv_err_handler(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, ipiv.size])
+        return acpy
+
+    return inv_impl
+
+
+@register_jitable
+def _handle_err_maybe_convergence_problem(r):
+    if r != 0:
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+        if r > 0:
+            raise ValueError("Internal algorithm failed to converge.")
+
+
+def _check_linalg_1_or_2d_matrix(a, func_name, la_prefix=True):
+    # la_prefix is present as some functions, e.g. np.trace()
+    # are documented under "linear algebra" but aren't in the
+    # module
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # checks that a matrix is 1 or 2D
+    if not isinstance(a, types.Array):
+        raise TypingError("%s.%s() only supported for array types "
+                          % interp)
+    if not a.ndim <= 2:
+        raise TypingError("%s.%s() only supported on 1 and 2-D arrays "
+                          % interp)
+    if not isinstance(a.dtype, (types.Float, types.Complex)):
+        raise TypingError("%s.%s() only supported on "
+                          "float and complex arrays." % interp)
+
+
+@overload(np.linalg.cholesky)
+def cho_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "cholesky")
+
+    numba_xxpotrf = _LAPACK().numba_xxpotrf(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "cholesky"))
+    UP = ord('U')
+    LO = ord('L')
+
+    def cho_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        # The output is allocated in C order
+        out = a.copy()
+
+        if n == 0:
+            return out
+
+        # Pass UP since xxpotrf() operates in F order
+        # The semantics ensure this works fine
+        # (out is really its Hermitian in F order, but UP instructs
+        #  xxpotrf to compute the Hermitian of the upper triangle
+        #  => they cancel each other)
+        r = numba_xxpotrf(kind, UP, n, out.ctypes, n)
+        if r != 0:
+            if r < 0:
+                fatal_error_func()
+                assert 0   # unreachable
+            if r > 0:
+                raise np.linalg.LinAlgError(
+                    "Matrix is not positive definite.")
+        # Zero out upper triangle, in F order
+        for col in range(n):
+            out[:col, col] = 0
+        return out
+
+    return cho_impl
+
+@overload(np.linalg.eig)
+def eig_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eig")
+
+    numba_ez_rgeev = _LAPACK().numba_ez_rgeev(a.dtype)
+    numba_ez_cgeev = _LAPACK().numba_ez_cgeev(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eig"))
+
+    JOBVL = ord('N')
+    JOBVR = ord('V')
+
+    # NumPy >= 2.5 always returns complex from eig for real input.
+    _eig_always_complex = np_support.numpy_version >= (2, 5)
+    _eig_real_ty = getattr(a.dtype, "underlying_float", a.dtype)
+    _eig_cmplx_dtype = np_support.as_dtype(
+        getattr(types, f"complex{2 * _eig_real_ty.bitwidth}"))
+
+    def real_eig_impl(a):
+        """
+        eig() implementation for real arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = n
+        wr = np.empty(n, dtype=a.dtype)
+        wi = np.empty(n, dtype=a.dtype)
+        vl = np.empty((n, ldvl), dtype=a.dtype)
+        vr = np.empty((n, ldvr), dtype=a.dtype)
+
+        if n == 0:
+            if _eig_always_complex:
+                w = np.empty(n, dtype=_eig_cmplx_dtype)
+                v = np.empty((n, n), dtype=_eig_cmplx_dtype)
+                return (w, v.T)
+            else:
+                return (wr, vr.T)
+
+        r = numba_ez_rgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            wr.ctypes,
+                            wi.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+
+        _handle_err_maybe_convergence_problem(r)
+
+        if _eig_always_complex:
+            # Unpack real LAPACK wr/wi and vr into complex eigenvalues/vectors.
+            # Conjugate pairs: LAPACK stores Re/Im parts in consecutive columns.
+            # vr rows hold eigenvectors (same layout as the real-output path).
+            w = np.empty(n, dtype=_eig_cmplx_dtype)
+            v = np.empty((n, n), dtype=_eig_cmplx_dtype)
+            i = 0
+            while i < n:
+                if wi[i] == 0.0:
+                    w[i] = complex(wr[i], wi[i])
+                    for j in range(n):
+                        v[i, j] = complex(vr[i, j], 0.0)
+                    i += 1
+                else:
+                    w[i] = complex(wr[i], wi[i])
+                    w[i + 1] = complex(wr[i + 1], wi[i + 1])
+                    for j in range(n):
+                        re = vr[i, j]
+                        im = vr[i + 1, j]
+                        v.real[i, j] = re
+                        v.imag[i, j] = im
+                        v.real[i + 1, j] = re
+                        v.imag[i + 1, j] = -im
+                    i += 2
+
+            # put these in to help with liveness analysis,
+            # `.ctypes` doesn't keep the vars alive
+            _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size,
+                                  wi.size, w.size, v.size])
+            return (w, v.T)
+
+        # By design numba does not support dynamic return types, however,
+        # Numpy does. Numpy uses this ability in the case of returning
+        # eigenvalues/vectors of a real matrix. The return type of
+        # np.linalg.eig(), when operating on a matrix in real space
+        # depends on the values present in the matrix itself (recalling
+        # that eigenvalues are the roots of the characteristic polynomial
+        # of the system matrix, which will by construction depend on the
+        # values present in the system matrix). As numba cannot handle
+        # the case of a runtime decision based domain change relative to
+        # the input type, if it is required numba raises as below.
+        if np.any(wi):
+            raise ValueError(
+                "eig() argument must not cause a domain change.")
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size, wi.size])
+
+        return (wr, vr.T)
+
+    def cmplx_eig_impl(a):
+        """
+        eig() implementation for complex arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = n
+        w = np.empty(n, dtype=a.dtype)
+        vl = np.empty((n, ldvl), dtype=a.dtype)
+        vr = np.empty((n, ldvr), dtype=a.dtype)
+
+        if n == 0:
+            return (w, vr.T)
+
+        r = numba_ez_cgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            w.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, w.size])
+        return (w, vr.T)
+
+    if isinstance(a.dtype, types.scalars.Complex):
+        return cmplx_eig_impl
+    else:
+        return real_eig_impl
+
+@overload(np.linalg.eigvals)
+def eigvals_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigvals")
+
+    numba_ez_rgeev = _LAPACK().numba_ez_rgeev(a.dtype)
+    numba_ez_cgeev = _LAPACK().numba_ez_cgeev(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigvals"))
+
+    JOBVL = ord('N')
+    JOBVR = ord('N')
+
+    # NumPy >= 2.5 always returns complex from eigvals for real input.
+    _eigvals_always_complex = np_support.numpy_version >= (2, 5)
+    _eigvals_real_ty = getattr(a.dtype, "underlying_float", a.dtype)
+    _eigvals_cmplx_dtype = np_support.as_dtype(
+        getattr(types, f"complex{2 * _eigvals_real_ty.bitwidth}"))
+
+    def real_eigvals_impl(a):
+        """
+        eigvals() implementation for real arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = 1
+        wr = np.empty(n, dtype=a.dtype)
+
+        if n == 0:
+            if _eigvals_always_complex:
+                return np.empty(n, dtype=_eigvals_cmplx_dtype)
+            else:
+                return wr
+
+        wi = np.empty(n, dtype=a.dtype)
+
+        # not referenced but need setting for MKL null check
+        vl = np.empty((1), dtype=a.dtype)
+        vr = np.empty((1), dtype=a.dtype)
+
+        r = numba_ez_rgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            wr.ctypes,
+                            wi.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        if _eigvals_always_complex:
+            w = np.empty(n, dtype=_eigvals_cmplx_dtype)
+            for i in range(n):
+                w[i] = complex(wr[i], wi[i])
+
+            # put these in to help with liveness analysis,
+            # `.ctypes` doesn't keep the vars alive
+            _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size,
+                                  wi.size, w.size])
+            return w
+
+        # By design numba does not support dynamic return types, however,
+        # Numpy does. Numpy uses this ability in the case of returning
+        # eigenvalues/vectors of a real matrix. The return type of
+        # np.linalg.eigvals(), when operating on a matrix in real space
+        # depends on the values present in the matrix itself (recalling
+        # that eigenvalues are the roots of the characteristic polynomial
+        # of the system matrix, which will by construction depend on the
+        # values present in the system matrix). As numba cannot handle
+        # the case of a runtime decision based domain change relative to
+        # the input type, if it is required numba raises as below.
+        if np.any(wi):
+            raise ValueError(
+                "eigvals() argument must not cause a domain change.")
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size, wi.size])
+        return wr
+
+    def cmplx_eigvals_impl(a):
+        """
+        eigvals() implementation for complex arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = 1
+        w = np.empty(n, dtype=a.dtype)
+
+        if n == 0:
+            return w
+
+        vl = np.empty((1), dtype=a.dtype)
+        vr = np.empty((1), dtype=a.dtype)
+
+        r = numba_ez_cgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            w.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, w.size])
+        return w
+
+    if isinstance(a.dtype, types.scalars.Complex):
+        return cmplx_eigvals_impl
+    else:
+        return real_eigvals_impl
+
+@overload(np.linalg.eigh)
+def eigh_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigh")
+
+    # convert typing floats to numpy floats for use in the impl
+    w_type = getattr(a.dtype, "underlying_float", a.dtype)
+    w_dtype = np_support.as_dtype(w_type)
+
+    numba_ez_xxxevd = _LAPACK().numba_ez_xxxevd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigh"))
+
+    JOBZ = ord('V')
+    UPLO = ord('L')
+
+    def eigh_impl(a):
+        n = a.shape[-1]
+
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        w = np.empty(n, dtype=w_dtype)
+
+        if n == 0:
+            return (w, acpy)
+
+        r = numba_ez_xxxevd(kind,  # kind
+                            JOBZ,  # jobz
+                            UPLO,  # uplo
+                            n,  # n
+                            acpy.ctypes,  # a
+                            n,  # lda
+                            w.ctypes  # w
+                            )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, w.size])
+        return (w, acpy)
+
+    return eigh_impl
+
+@overload(np.linalg.eigvalsh)
+def eigvalsh_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigvalsh")
+
+    # convert typing floats to numpy floats for use in the impl
+    w_type = getattr(a.dtype, "underlying_float", a.dtype)
+    w_dtype = np_support.as_dtype(w_type)
+
+    numba_ez_xxxevd = _LAPACK().numba_ez_xxxevd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigvalsh"))
+
+    JOBZ = ord('N')
+    UPLO = ord('L')
+
+    def eigvalsh_impl(a):
+        n = a.shape[-1]
+
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        w = np.empty(n, dtype=w_dtype)
+
+        if n == 0:
+            return w
+
+        r = numba_ez_xxxevd(kind,  # kind
+                            JOBZ,  # jobz
+                            UPLO,  # uplo
+                            n,  # n
+                            acpy.ctypes,  # a
+                            n,  # lda
+                            w.ctypes  # w
+                            )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, w.size])
+        return w
+
+    return eigvalsh_impl
+
+@overload(np.linalg.svd)
+def svd_impl(a, full_matrices=1):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "svd")
+
+    # convert typing floats to numpy floats for use in the impl
+    s_type = getattr(a.dtype, "underlying_float", a.dtype)
+    s_dtype = np_support.as_dtype(s_type)
+
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "svd"))
+
+    JOBZ_A = ord('A')
+    JOBZ_S = ord('S')
+
+    def svd_impl(a, full_matrices=1):
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        if n == 0 or m == 0:
+            raise np.linalg.LinAlgError("Arrays cannot be empty")
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldu = m
+        minmn = min(m, n)
+
+        if full_matrices:
+            JOBZ = JOBZ_A
+            ucol = m
+            ldvt = n
+        else:
+            JOBZ = JOBZ_S
+            ucol = minmn
+            ldvt = minmn
+
+        u = np.empty((ucol, ldu), dtype=a.dtype)
+        s = np.empty(minmn, dtype=s_dtype)
+        vt = np.empty((n, ldvt), dtype=a.dtype)
+
+        r = numba_ez_gesdd(
+            kind,  # kind
+            JOBZ,  # jobz
+            m,  # m
+            n,  # n
+            acpy.ctypes,  # a
+            m,  # lda
+            s.ctypes,  # s
+            u.ctypes,  # u
+            ldu,  # ldu
+            vt.ctypes,  # vt
+            ldvt          # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size])
+        return (u.T, s, vt.T)
+
+    return svd_impl
+
+
+@overload(np.linalg.qr)
+def qr_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "qr")
+
+    # Need two functions, the first computes R, storing it in the upper
+    # triangle of A with the below diagonal part of A containing elementary
+    # reflectors needed to construct Q. The second turns the below diagonal
+    # entries of A into Q, storing Q in A (creates orthonormal columns from
+    # the elementary reflectors).
+
+    numba_ez_geqrf = _LAPACK().numba_ez_geqrf(a.dtype)
+    numba_ez_xxgqr = _LAPACK().numba_ez_xxgqr(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "qr"))
+
+    def qr_impl(a):
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        if n == 0 or m == 0:
+            raise np.linalg.LinAlgError("Arrays cannot be empty")
+
+        _check_finite_matrix(a)
+
+        # copy A as it will be destroyed
+        q = _copy_to_fortran_order(a)
+
+        lda = m
+
+        minmn = min(m, n)
+        tau = np.empty((minmn), dtype=a.dtype)
+
+        ret = numba_ez_geqrf(
+            kind,  # kind
+            m,  # m
+            n,  # n
+            q.ctypes,  # a
+            m,  # lda
+            tau.ctypes  # tau
+        )
+        if ret < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+
+        # pull out R, this is transposed because of Fortran
+        r = np.zeros((n, minmn), dtype=a.dtype).T
+
+        # the triangle in R
+        for i in range(minmn):
+            for j in range(i + 1):
+                r[j, i] = q[j, i]
+
+        # and the possible square in R
+        for i in range(minmn, n):
+            for j in range(minmn):
+                r[j, i] = q[j, i]
+
+        ret = numba_ez_xxgqr(
+            kind,  # kind
+            m,  # m
+            minmn,  # n
+            minmn,  # k
+            q.ctypes,  # a
+            m,  # lda
+            tau.ctypes  # tau
+        )
+        _handle_err_maybe_convergence_problem(ret)
+
+        # help liveness analysis
+        _dummy_liveness_func([tau.size, q.size])
+        return (q[:, :minmn], r)
+
+    return qr_impl
+
+
+# helpers and jitted specialisations required for np.linalg.lstsq
+# and np.linalg.solve. These functions have "system" in their name
+# as a differentiator.
+
+def _system_copy_in_b(bcpy, b, nrhs):
+    """
+    Correctly copy 'b' into the 'bcpy' scratch space.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_copy_in_b)
+def _system_copy_in_b_impl(bcpy, b, nrhs):
+    if b.ndim == 1:
+        def oneD_impl(bcpy, b, nrhs):
+            bcpy[:b.shape[-1], 0] = b
+        return oneD_impl
+    else:
+        def twoD_impl(bcpy, b, nrhs):
+            bcpy[:b.shape[-2], :nrhs] = b
+        return twoD_impl
+
+
+def _system_compute_nrhs(b):
+    """
+    Compute the number of right hand sides in the system of equations
+    """
+    raise NotImplementedError
+
+
+@overload(_system_compute_nrhs)
+def _system_compute_nrhs_impl(b):
+    if b.ndim == 1:
+        def oneD_impl(b):
+            return 1
+        return oneD_impl
+    else:
+        def twoD_impl(b):
+            return b.shape[-1]
+        return twoD_impl
+
+
+def _system_check_dimensionally_valid(a, b):
+    """
+    Check that AX=B style system input is dimensionally valid.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_check_dimensionally_valid)
+def _system_check_dimensionally_valid_impl(a, b):
+    ndim = b.ndim
+    if ndim == 1:
+        def oneD_impl(a, b):
+            am = a.shape[-2]
+            bm = b.shape[-1]
+            if am != bm:
+                raise np.linalg.LinAlgError(
+                    "Incompatible array sizes, system is not dimensionally valid.")
+        return oneD_impl
+    else:
+        def twoD_impl(a, b):
+            am = a.shape[-2]
+            bm = b.shape[-2]
+            if am != bm:
+                raise np.linalg.LinAlgError(
+                    "Incompatible array sizes, system is not dimensionally valid.")
+        return twoD_impl
+
+
+def _system_check_non_empty(a, b):
+    """
+    Check that AX=B style system input is not empty.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_check_non_empty)
+def _system_check_non_empty_impl(a, b):
+    ndim = b.ndim
+    if ndim == 1:
+        def oneD_impl(a, b):
+            am = a.shape[-2]
+            an = a.shape[-1]
+            bm = b.shape[-1]
+            if am == 0 or bm == 0 or an == 0:
+                raise np.linalg.LinAlgError('Arrays cannot be empty')
+        return oneD_impl
+    else:
+        def twoD_impl(a, b):
+            am = a.shape[-2]
+            an = a.shape[-1]
+            bm = b.shape[-2]
+            bn = b.shape[-1]
+            if am == 0 or bm == 0 or an == 0 or bn == 0:
+                raise np.linalg.LinAlgError('Arrays cannot be empty')
+        return twoD_impl
+
+
+def _lstsq_residual(b, n, nrhs):
+    """
+    Compute the residual from the 'b' scratch space.
+    """
+    raise NotImplementedError
+
+
+@overload(_lstsq_residual)
+def _lstsq_residual_impl(b, n, nrhs):
+    ndim = b.ndim
+    dtype = b.dtype
+    real_dtype = np_support.as_dtype(getattr(dtype, "underlying_float", dtype))
+
+    if ndim == 1:
+        if isinstance(dtype, (types.Complex)):
+            def cmplx_impl(b, n, nrhs):
+                res = np.empty((1,), dtype=real_dtype)
+                res[0] = np.sum(np.abs(b[n:, 0])**2)
+                return res
+            return cmplx_impl
+        else:
+            def real_impl(b, n, nrhs):
+                res = np.empty((1,), dtype=real_dtype)
+                res[0] = np.sum(b[n:, 0]**2)
+                return res
+            return real_impl
+    else:
+        assert ndim == 2
+        if isinstance(dtype, (types.Complex)):
+            def cmplx_impl(b, n, nrhs):
+                res = np.empty((nrhs), dtype=real_dtype)
+                for k in range(nrhs):
+                    res[k] = np.sum(np.abs(b[n:, k])**2)
+                return res
+            return cmplx_impl
+        else:
+            def real_impl(b, n, nrhs):
+                res = np.empty((nrhs), dtype=real_dtype)
+                for k in range(nrhs):
+                    res[k] = np.sum(b[n:, k]**2)
+                return res
+            return real_impl
+
+
+def _lstsq_solution(b, bcpy, n):
+    """
+    Extract 'x' (the lstsq solution) from the 'bcpy' scratch space.
+    Note 'b' is only used to check the system input dimension...
+    """
+    raise NotImplementedError
+
+
+@overload(_lstsq_solution)
+def _lstsq_solution_impl(b, bcpy, n):
+    if b.ndim == 1:
+        def oneD_impl(b, bcpy, n):
+            return bcpy.T.ravel()[:n]
+        return oneD_impl
+    else:
+        def twoD_impl(b, bcpy, n):
+            return bcpy[:n, :].copy()
+        return twoD_impl
+
+
+@overload(np.linalg.lstsq)
+def lstsq_impl(a, b, rcond=-1.0):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "lstsq")
+
+    # B can be 1D or 2D.
+    _check_linalg_1_or_2d_matrix(b, "lstsq")
+
+    _check_homogeneous_types("lstsq", a, b)
+
+    np_dt = np_support.as_dtype(a.dtype)
+    nb_dt = a.dtype
+
+    # convert typing floats to np floats for use in the impl
+    r_type = getattr(nb_dt, "underlying_float", nb_dt)
+    real_dtype = np_support.as_dtype(r_type)
+
+    # lapack solver
+    numba_ez_gelsd = _LAPACK().numba_ez_gelsd(a.dtype)
+
+    kind = ord(get_blas_kind(nb_dt, "lstsq"))
+
+    # The following functions select specialisations based on
+    # information around 'b', a lot of this effort is required
+    # as 'b' can be either 1D or 2D, and then there are
+    # some optimisations available depending on real or complex
+    # space.
+
+    def lstsq_impl(a, b, rcond=-1.0):
+        n = a.shape[-1]
+        m = a.shape[-2]
+        nrhs = _system_compute_nrhs(b)
+
+        # check the systems have no inf or NaN
+        _check_finite_matrix(a)
+        _check_finite_matrix(b)
+
+        # check the system is not empty
+        _system_check_non_empty(a, b)
+
+        # check the systems are dimensionally valid
+        _system_check_dimensionally_valid(a, b)
+
+        minmn = min(m, n)
+        maxmn = max(m, n)
+
+        # a is destroyed on exit, copy it
+        acpy = _copy_to_fortran_order(a)
+
+        # b is overwritten on exit with the solution, copy allocate
+        bcpy = np.empty((nrhs, maxmn), dtype=np_dt).T
+        # specialised copy in due to b being 1 or 2D
+        _system_copy_in_b(bcpy, b, nrhs)
+
+        # Allocate returns
+        s = np.empty(minmn, dtype=real_dtype)
+        rank_ptr = np.empty(1, dtype=F_INT_nptype)
+
+        r = numba_ez_gelsd(
+            kind,  # kind
+            m,  # m
+            n,  # n
+            nrhs,  # nrhs
+            acpy.ctypes,  # a
+            m,  # lda
+            bcpy.ctypes,  # a
+            maxmn,  # ldb
+            s.ctypes,  # s
+            rcond,  # rcond
+            rank_ptr.ctypes  # rank
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # set rank to that which was computed
+        rank = rank_ptr[0]
+
+        # compute residuals
+        if rank < n or m <= n:
+            res = np.empty((0), dtype=real_dtype)
+        else:
+            # this requires additional dispatch as there's a faster
+            # impl if the result is in the real domain (no abs() required)
+            res = _lstsq_residual(bcpy, n, nrhs)
+
+        # extract 'x', the solution
+        x = _lstsq_solution(b, bcpy, n)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, bcpy.size, s.size, rank_ptr.size])
+        return (x, res, rank, s[:minmn])
+
+    return lstsq_impl
+
+
+def _solve_compute_return(b, bcpy):
+    """
+    Extract 'x' (the solution) from the 'bcpy' scratch space.
+    Note 'b' is only used to check the system input dimension...
+    """
+    raise NotImplementedError
+
+
+@overload(_solve_compute_return)
+def _solve_compute_return_impl(b, bcpy):
+    if b.ndim == 1:
+        def oneD_impl(b, bcpy):
+            return bcpy.T.ravel()
+        return oneD_impl
+    else:
+        def twoD_impl(b, bcpy):
+            return bcpy
+        return twoD_impl
+
+
+@overload(np.linalg.solve)
+def solve_impl(a, b):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "solve")
+    _check_linalg_1_or_2d_matrix(b, "solve")
+
+    _check_homogeneous_types("solve", a, b)
+
+    np_dt = np_support.as_dtype(a.dtype)
+    nb_dt = a.dtype
+
+    # the lapack solver
+    numba_xgesv = _LAPACK().numba_xgesv(a.dtype)
+
+    kind = ord(get_blas_kind(nb_dt, "solve"))
+
+    def solve_impl(a, b):
+        n = a.shape[-1]
+        nrhs = _system_compute_nrhs(b)
+
+        # check the systems have no inf or NaN
+        _check_finite_matrix(a)
+        _check_finite_matrix(b)
+
+        # check the systems are dimensionally valid
+        _system_check_dimensionally_valid(a, b)
+
+        # a is destroyed on exit, copy it
+        acpy = _copy_to_fortran_order(a)
+
+        # b is overwritten on exit with the solution, copy allocate
+        bcpy = np.empty((nrhs, n), dtype=np_dt).T
+        if n == 0:
+            return _solve_compute_return(b, bcpy)
+
+        # specialised copy in due to b being 1 or 2D
+        _system_copy_in_b(bcpy, b, nrhs)
+
+        # allocate pivot array (needs to be fortran int size)
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xgesv(
+            kind,        # kind
+            n,           # n
+            nrhs,        # nhrs
+            acpy.ctypes,  # a
+            n,           # lda
+            ipiv.ctypes,  # ipiv
+            bcpy.ctypes,  # b
+            n            # ldb
+        )
+        _inv_err_handler(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, bcpy.size, ipiv.size])
+        return _solve_compute_return(b, bcpy)
+
+    return solve_impl
+
+
+@overload(np.linalg.pinv)
+def pinv_impl(a, rcond=1.e-15):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "pinv")
+
+    # convert typing floats to numpy floats for use in the impl
+    s_type = getattr(a.dtype, "underlying_float", a.dtype)
+    s_dtype = np_support.as_dtype(s_type)
+
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    numba_xxgemm = _BLAS().numba_xxgemm(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "pinv"))
+    JOB = ord('S')
+
+    # need conjugate transposes
+    TRANSA = ord('C')
+    TRANSB = ord('C')
+
+    # scalar constants
+    dt = np_support.as_dtype(a.dtype)
+    zero = np.array([0.], dtype=dt)
+    one = np.array([1.], dtype=dt)
+
+    def pinv_impl(a, rcond=1.e-15):
+
+        # The idea is to build the pseudo-inverse via inverting the singular
+        # value decomposition of a matrix `A`. Mathematically, this is roughly
+        # A = U*S*V^H        [The SV decomposition of A]
+        # A^+ = V*(S^+)*U^H  [The inverted SV decomposition of A]
+        # where ^+ is pseudo inversion and ^H is Hermitian transpose.
+        # As V and U are unitary, their inverses are simply their Hermitian
+        # transpose. S has singular values on its diagonal and zero elsewhere,
+        # it is inverted trivially by reciprocal of the diagonal values with
+        # the exception that zero singular values remain as zero.
+        #
+        # The practical implementation can take advantage of a few things to
+        # gain a few % performance increase:
+        # * A is destroyed by the SVD algorithm from LAPACK so a copy is
+        #   required, this memory is exactly the right size in which to return
+        #   the pseudo-inverse and so can be reused for this purpose.
+        # * The pseudo-inverse of S can be applied to either V or U^H, this
+        #   then leaves a GEMM operation to compute the inverse via either:
+        #   A^+ = (V*(S^+))*U^H
+        #   or
+        #   A^+ = V*((S^+)*U^H)
+        #   however application of S^+ to V^H or U is more convenient as they
+        #   are the result of the SVD algorithm. The application of the
+        #   diagonal system is just a matrix multiplication which results in a
+        #   row/column scaling (direction depending). To save effort, this
+        #   "matrix multiplication" is applied to the smallest of U or V^H and
+        #   only up to the point of "cut-off" (see next note) just as a direct
+        #   scaling.
+        # * The cut-off level for application of S^+ can be used to reduce
+        #   total effort, this cut-off can come via rcond or may just naturally
+        #   be present as a result of zeros in the singular values. Regardless
+        #   there's no need to multiply by zeros in the application of S^+ to
+        #   V^H or U as above. Further, the GEMM operation can be shrunk in
+        #   effort by noting that the possible zero block generated by the
+        #   presence of zeros in S^+ has no effect apart from wasting cycles as
+        #   it is all fmadd()s where one operand is zero. The inner dimension
+        #   of the GEMM operation can therefore be set as shrunk accordingly!
+
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        if m == 0 or n == 0:
+            return acpy.T.ravel().reshape(a.shape).T
+
+        minmn = min(m, n)
+
+        u = np.empty((minmn, m), dtype=a.dtype)
+        s = np.empty(minmn, dtype=s_dtype)
+        vt = np.empty((n, minmn), dtype=a.dtype)
+
+        r = numba_ez_gesdd(
+            kind,         # kind
+            JOB,          # job
+            m,            # m
+            n,            # n
+            acpy.ctypes,  # a
+            m,            # lda
+            s.ctypes,     # s
+            u.ctypes,     # u
+            m,            # ldu
+            vt.ctypes,    # vt
+            minmn         # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # Invert singular values under threshold. Also find the index of
+        # the threshold value as this is the upper limit for the application
+        # of the inverted singular values. Finding this value saves
+        # multiplication by a block of zeros that would be created by the
+        # application of these values to either U or V^H ahead of multiplying
+        # them together. This is done by simply in BLAS parlance via
+        # restricting the `k` dimension to `cut_idx` in `xgemm` whilst keeping
+        # the leading dimensions correct.
+
+        cut_at = s[0] * rcond
+        cut_idx = 0
+        for k in range(minmn):
+            if s[k] > cut_at:
+                s[k] = 1. / s[k]
+                cut_idx = k
+        cut_idx += 1
+
+        # Use cut_idx so there's no scaling by 0.
+        if m >= n:
+            # U is largest so apply S^+ to V^H.
+            for i in range(n):
+                for j in range(cut_idx):
+                    vt[i, j] = vt[i, j] * s[j]
+        else:
+            # V^H is largest so apply S^+ to U.
+            for i in range(cut_idx):
+                s_local = s[i]
+                for j in range(minmn):
+                    u[i, j] = u[i, j] * s_local
+
+        # Do (v^H)^H*U^H (obviously one of the matrices includes the S^+
+        # scaling) and write back to acpy. Note the innner dimension of cut_idx
+        # taking account of the possible zero block.
+        # We can store the result in acpy, given we had to create it
+        # for use in the SVD, and it is now redundant and the right size
+        # but wrong shape.
+
+        r = numba_xxgemm(
+            kind,
+            TRANSA,       # TRANSA
+            TRANSB,       # TRANSB
+            n,            # M
+            m,            # N
+            cut_idx,      # K
+            one.ctypes,   # ALPHA
+            vt.ctypes,    # A
+            minmn,        # LDA
+            u.ctypes,     # B
+            m,            # LDB
+            zero.ctypes,  # BETA
+            acpy.ctypes,  # C
+            n             # LDC
+        )
+
+        # help liveness analysis
+        #acpy.size
+        #vt.size
+        #u.size
+        #s.size
+        #one.size
+        #zero.size
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size, one.size,
+            zero.size])
+        return acpy.T.ravel().reshape(a.shape).T
+
+    return pinv_impl
+
+
+def _get_slogdet_diag_walker(a):
+    """
+    Walks the diag of a LUP decomposed matrix
+    uses that det(A) = prod(diag(lup(A)))
+    and also that log(a)+log(b) = log(a*b)
+    The return sign is adjusted based on the values found
+    such that the log(value) stays in the real domain.
+    """
+    if isinstance(a.dtype, types.Complex):
+        @register_jitable
+        def cmplx_diag_walker(n, a, sgn):
+            # walk diagonal
+            csgn = sgn + 0.j
+            acc = 0.
+            for k in range(n):
+                absel = np.abs(a[k, k])
+                csgn = csgn * (a[k, k] / absel)
+                acc = acc + np.log(absel)
+            return (csgn, acc)
+        return cmplx_diag_walker
+    else:
+        @register_jitable
+        def real_diag_walker(n, a, sgn):
+            # walk diagonal
+            acc = 0.
+            for k in range(n):
+                v = a[k, k]
+                if v < 0.:
+                    sgn = -sgn
+                    v = -v
+                acc = acc + np.log(v)
+            # sgn is a float dtype
+            return (sgn + 0., acc)
+        return real_diag_walker
+
+
+@overload(np.linalg.slogdet)
+def slogdet_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "slogdet")
+
+    numba_xxgetrf = _LAPACK().numba_xxgetrf(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "slogdet"))
+
+    diag_walker = _get_slogdet_diag_walker(a)
+
+    ONE = a.dtype(1)
+    ZERO = getattr(a.dtype, "underlying_float", a.dtype)(0)
+
+    def slogdet_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        if n == 0:
+            return (ONE, ZERO)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xxgetrf(kind, n, n, acpy.ctypes, n, ipiv.ctypes)
+
+        if r > 0:
+            # factorisation failed, return same defaults as np
+            return (0., -np.inf)
+        _inv_err_handler(r)  # catch input-to-lapack problem
+
+        # The following, prior to the call to diag_walker, is present
+        # to account for the effect of possible permutations to the
+        # sign of the determinant.
+        # This is the same idea as in numpy:
+        # File name `umath_linalg.c.src` e.g.
+        # https://github.com/numpy/numpy/blob/master/numpy/linalg/umath_linalg.c.src
+        # in function `@TYPE@_slogdet_single_element`.
+        sgn = 1
+        for k in range(n):
+            sgn = sgn + (ipiv[k] != (k + 1))
+
+        sgn = sgn & 1
+        if sgn == 0:
+            sgn = -1
+
+        # help liveness analysis
+        _dummy_liveness_func([ipiv.size])
+        return diag_walker(n, acpy, sgn)
+
+    return slogdet_impl
+
+
+@overload(np.linalg.det)
+def det_impl(a):
+
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "det")
+
+    def det_impl(a):
+        (sgn, slogdet) = np.linalg.slogdet(a)
+        return sgn * np.exp(slogdet)
+
+    return det_impl
+
+
+def _compute_singular_values(a):
+    """
+    Compute singular values of *a*.
+    """
+    raise NotImplementedError
+
+
+@overload(_compute_singular_values)
+def _compute_singular_values_impl(a):
+    """
+    Returns a function to compute singular values of `a`
+    """
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "svd"))
+
+    # Flag for "only compute `S`" to give to xgesdd
+    JOBZ_N = ord('N')
+
+    nb_ret_type = getattr(a.dtype, "underlying_float", a.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+    np_dtype = np_support.as_dtype(a.dtype)
+
+    # These are not referenced in the computation but must be set
+    # for MKL.
+    u = np.empty((1, 1), dtype=np_dtype)
+    vt = np.empty((1, 1), dtype=np_dtype)
+
+    def sv_function(a):
+        """
+        Computes singular values.
+        """
+        # Don't use the np.linalg.svd impl instead
+        # call LAPACK to shortcut doing the "reconstruct
+        # singular vectors from reflectors" step and just
+        # get back the singular values.
+        n = a.shape[-1]
+        m = a.shape[-2]
+        if m == 0 or n == 0:
+            raise np.linalg.LinAlgError('Arrays cannot be empty')
+        _check_finite_matrix(a)
+
+        ldu = m
+        minmn = min(m, n)
+
+        # need to be >=1 but aren't referenced
+        ucol = 1
+        ldvt = 1
+
+        acpy = _copy_to_fortran_order(a)
+
+        # u and vt are not referenced however need to be
+        # allocated (as done above) for MKL as it
+        # checks for ref is nullptr.
+        s = np.empty(minmn, dtype=np_ret_type)
+
+        r = numba_ez_gesdd(
+            kind,        # kind
+            JOBZ_N,      # jobz
+            m,           # m
+            n,           # n
+            acpy.ctypes,  # a
+            m,           # lda
+            s.ctypes,    # s
+            u.ctypes,    # u
+            ldu,         # ldu
+            vt.ctypes,   # vt
+            ldvt         # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size])
+        return s
+
+    return sv_function
+
+
+def _oneD_norm_2(a):
+    """
+    Compute the L2-norm of 1D-array *a*.
+    """
+    raise NotImplementedError
+
+
+@overload(_oneD_norm_2)
+def _oneD_norm_2_impl(a):
+    nb_ret_type = getattr(a.dtype, "underlying_float", a.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+
+    xxnrm2 = _BLAS().numba_xxnrm2(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "norm"))
+
+    def impl(a):
+        # Just ignore order, calls are guarded to only come
+        # from cases where order=None or order=2.
+        n = len(a)
+        # Call L2-norm routine from BLAS
+        ret = np.empty((1,), dtype=np_ret_type)
+        jmp = int(a.strides[0] / a.itemsize)
+        r = xxnrm2(
+            kind,      # kind
+            n,         # n
+            a.ctypes,  # x
+            jmp,       # incx
+            ret.ctypes  # result
+        )
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+
+        # help liveness analysis
+        #ret.size
+        #a.size
+        _dummy_liveness_func([ret.size, a.size])
+        return ret[0]
+
+    return impl
+
+
+def _get_norm_impl(x, ord_flag):
+    # This function is quite involved as norm supports a large
+    # range of values to select different norm types via kwarg `ord`.
+    # The implementation below branches on dimension of the input
+    # (1D or 2D). The default for `ord` is `None` which requires
+    # special handling in numba, this is dealt with first in each of
+    # the dimension branches. Following this the various norms are
+    # computed via code that is in most cases simply a loop version
+    # of a ufunc based version as found in numpy.
+
+    # The following is common to both 1D and 2D cases.
+    # Convert typing floats to numpy floats for use in the impl.
+    # The return type is always a float, numba differs from numpy in
+    # that it returns an input precision specific value whereas numpy
+    # always returns np.float64.
+    nb_ret_type = getattr(x.dtype, "underlying_float", x.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+
+    np_dtype = np_support.as_dtype(x.dtype)
+
+    xxnrm2 = _BLAS().numba_xxnrm2(x.dtype)
+
+    kind = ord(get_blas_kind(x.dtype, "norm"))
+
+    if x.ndim == 1:
+        # 1D cases
+
+        # handle "ord" being "None", must be done separately
+        if ord_flag in (None, types.none):
+            def oneD_impl(x, ord=None):
+                return _oneD_norm_2(x)
+        else:
+            def oneD_impl(x, ord=None):
+                n = len(x)
+
+                # Shortcut to handle zero length arrays
+                # this differs slightly to numpy in that
+                # numpy raises a ValueError for kwarg ord=
+                # +/-np.inf as the reduction operations like
+                # max() and min() don't accept zero length
+                # arrays
+                if n == 0:
+                    return 0.0
+
+                # Note: on order == 2
+                # This is the same as for ord=="None" but because
+                # we have to handle "None" specially this condition
+                # is separated
+                if ord == 2:
+                    return _oneD_norm_2(x)
+                elif ord == np.inf:
+                    # max(abs(x))
+                    ret = abs(x[0])
+                    for k in range(1, n):
+                        val = abs(x[k])
+                        if val > ret:
+                            ret = val
+                    return ret
+
+                elif ord == -np.inf:
+                    # min(abs(x))
+                    ret = abs(x[0])
+                    for k in range(1, n):
+                        val = abs(x[k])
+                        if val < ret:
+                            ret = val
+                    return ret
+
+                elif ord == 0:
+                    # sum(x != 0)
+                    ret = 0.0
+                    for k in range(n):
+                        if x[k] != 0.:
+                            ret += 1.
+                    return ret
+
+                elif ord == 1:
+                    # sum(abs(x))
+                    ret = 0.0
+                    for k in range(n):
+                        ret += abs(x[k])
+                    return ret
+
+                else:
+                    # sum(abs(x)**ord)**(1./ord)
+                    ret = 0.0
+                    for k in range(n):
+                        ret += abs(x[k])**ord
+                    return ret**(1. / ord)
+        return oneD_impl
+
+    elif x.ndim == 2:
+        # 2D cases
+
+        # handle "ord" being "None"
+        if ord_flag in (None, types.none):
+            # Force `x` to be C-order, so that we can take a contiguous
+            # 1D view.
+            if x.layout == 'C':
+                @register_jitable
+                def array_prepare(x):
+                    return x
+            elif x.layout == 'F':
+                @register_jitable
+                def array_prepare(x):
+                    # Legal since L2(x) == L2(x.T)
+                    return x.T
+            else:
+                @register_jitable
+                def array_prepare(x):
+                    return x.copy()
+
+            # Compute the Frobenius norm, this is the L2,2 induced norm of `x`
+            # which is the L2-norm of x.ravel() and so can be computed via BLAS
+            def twoD_impl(x, ord=None):
+                n = x.size
+                if n == 0:
+                    # reshape() currently doesn't support zero-sized arrays
+                    return 0.0
+                x_c = array_prepare(x)
+                return _oneD_norm_2(x_c.reshape(n))
+        else:
+            # max value for this dtype
+            max_val = np.finfo(np_ret_type.type).max
+
+            def twoD_impl(x, ord=None):
+                n = x.shape[-1]
+                m = x.shape[-2]
+
+                # Shortcut to handle zero size arrays
+                # this differs slightly to numpy in that
+                # numpy raises errors for some ord values
+                # and in other cases returns zero.
+                if x.size == 0:
+                    return 0.0
+
+                if ord == np.inf:
+                    # max of sum of abs across rows
+                    # max(sum(abs(x)), axis=1)
+                    global_max = 0.
+                    for ii in range(m):
+                        tmp = 0.
+                        for jj in range(n):
+                            tmp += abs(x[ii, jj])
+                        if tmp > global_max:
+                            global_max = tmp
+                    return global_max
+
+                elif ord == -np.inf:
+                    # min of sum of abs across rows
+                    # min(sum(abs(x)), axis=1)
+                    global_min = max_val
+                    for ii in range(m):
+                        tmp = 0.
+                        for jj in range(n):
+                            tmp += abs(x[ii, jj])
+                        if tmp < global_min:
+                            global_min = tmp
+                    return global_min
+                elif ord == 1:
+                    # max of sum of abs across cols
+                    # max(sum(abs(x)), axis=0)
+                    global_max = 0.
+                    for ii in range(n):
+                        tmp = 0.
+                        for jj in range(m):
+                            tmp += abs(x[jj, ii])
+                        if tmp > global_max:
+                            global_max = tmp
+                    return global_max
+
+                elif ord == -1:
+                    # min of sum of abs across cols
+                    # min(sum(abs(x)), axis=0)
+                    global_min = max_val
+                    for ii in range(n):
+                        tmp = 0.
+                        for jj in range(m):
+                            tmp += abs(x[jj, ii])
+                        if tmp < global_min:
+                            global_min = tmp
+                    return global_min
+
+                # Results via SVD, singular values are sorted on return
+                # by definition.
+                elif ord == 2:
+                    # max SV
+                    return _compute_singular_values(x)[0]
+                elif ord == -2:
+                    # min SV
+                    return _compute_singular_values(x)[-1]
+                else:
+                    # replicate numpy error
+                    raise ValueError("Invalid norm order for matrices.")
+        return twoD_impl
+    else:
+        assert 0  # unreachable
+
+
+@overload(np.linalg.norm)
+def norm_impl(x, ord=None):
+    ensure_lapack()
+
+    _check_linalg_1_or_2d_matrix(x, "norm")
+
+    return _get_norm_impl(x, ord)
+
+
+@overload(np.linalg.cond)
+def cond_impl(x, p=None):
+    ensure_lapack()
+
+    _check_linalg_matrix(x, "cond")
+
+    def impl(x, p=None):
+        # This is extracted for performance, numpy does approximately:
+        # `condition = norm(x) * norm(inv(x))`
+        # in the cases of `p == 2` or `p ==-2` singular values are used
+        # for computing norms. This costs numpy an svd of `x` then an
+        # inversion of `x` and another svd of `x`.
+        # Below is a different approach, which also gives a more
+        # accurate answer as there is no inversion involved.
+        # Recall that the singular values of an inverted matrix are the
+        # reciprocal of singular values of the original matrix.
+        # Therefore calling `svd(x)` once yields all the information
+        # needed about both `x` and `inv(x)` without the cost or
+        # potential loss of accuracy incurred through inversion.
+        # For the case of `p == 2`, the result is just the ratio of
+        # `largest singular value/smallest singular value`, and for the
+        # case of `p==-2` the result is simply the
+        # `smallest singular value/largest singular value`.
+        # As a result of this, numba accepts non-square matrices as
+        # input when p==+/-2 as well as when p==None.
+        if p == 2 or p == -2 or p is None:
+            s = _compute_singular_values(x)
+            if p == 2 or p is None:
+                r = np.divide(s[0], s[-1])
+            else:
+                r = np.divide(s[-1], s[0])
+        else:  # cases np.inf, -np.inf, 1, -1
+            norm_x = np.linalg.norm(x, p)
+            norm_inv_x = np.linalg.norm(np.linalg.inv(x), p)
+            r = norm_x * norm_inv_x
+        # NumPy uses a NaN mask, if the input has a NaN, it will return NaN,
+        # Numba calls ban NaN through the use of _check_finite_matrix but this
+        # catches cases where NaN occurs through floating point use
+        if np.isnan(r):
+            return np.inf
+        else:
+            return r
+    return impl
+
+
+@register_jitable
+def _get_rank_from_singular_values(sv, t):
+    """
+    Gets rank from singular values with cut-off at a given tolerance
+    """
+    rank = 0
+    for k in range(len(sv)):
+        if sv[k] > t:
+            rank = rank + 1
+        else:  # sv is ordered big->small so break on condition not met
+            break
+    return rank
+
+
+@overload(np.linalg.matrix_rank)
+def matrix_rank_impl(A, tol=None):
+    """
+    Computes rank for matrices and vectors.
+    The only issue that may arise is that because numpy uses double
+    precision lapack calls whereas numba uses type specific lapack
+    calls, some singular values may differ and therefore counting the
+    number of them above a tolerance may lead to different counts,
+    and therefore rank, in some cases.
+    """
+    ensure_lapack()
+
+    _check_linalg_1_or_2d_matrix(A, "matrix_rank")
+
+    def _2d_matrix_rank_impl(A, tol):
+
+        # handle the tol==None case separately for type inference to work
+        if tol in (None, types.none):
+            nb_type = getattr(A.dtype, "underlying_float", A.dtype)
+            np_type = np_support.as_dtype(nb_type)
+            eps_val = np.finfo(np_type).eps
+
+            def _2d_tol_none_impl(A, tol=None):
+                s = _compute_singular_values(A)
+                # replicate numpy default tolerance calculation
+                r = A.shape[0]
+                c = A.shape[1]
+                l = max(r, c)
+                t = s[0] * l * eps_val
+                return _get_rank_from_singular_values(s, t)
+            return _2d_tol_none_impl
+        else:
+            def _2d_tol_not_none_impl(A, tol=None):
+                s = _compute_singular_values(A)
+                return _get_rank_from_singular_values(s, tol)
+            return _2d_tol_not_none_impl
+
+    def _get_matrix_rank_impl(A, tol):
+        ndim = A.ndim
+        if ndim == 1:
+            # NOTE: Technically, the numpy implementation could be argued as
+            # incorrect for the case of a vector (1D matrix). If a tolerance
+            # is provided and a vector with a singular value below tolerance is
+            # encountered this should report a rank of zero, the numpy
+            # implementation does not do this and instead elects to report that
+            # if any value in the vector is nonzero then the rank is 1.
+            # An example would be [0, 1e-15, 0, 2e-15] which numpy reports as
+            # rank 1 invariant of `tol`. The singular value for this vector is
+            # obviously sqrt(5)*1e-15 and so a tol of e.g. sqrt(6)*1e-15 should
+            # lead to a reported rank of 0 whereas a tol of 1e-15 should lead
+            # to a reported rank of 1, numpy reports 1 regardless.
+            # The code below replicates the numpy behaviour.
+            def _1d_matrix_rank_impl(A, tol=None):
+                for k in range(len(A)):
+                    if A[k] != 0.:
+                        return 1
+                return 0
+            return _1d_matrix_rank_impl
+        elif ndim == 2:
+            return _2d_matrix_rank_impl(A, tol)
+        else:
+            assert 0  # unreachable
+
+    return _get_matrix_rank_impl(A, tol)
+
+
+@overload(np.linalg.matrix_power)
+def matrix_power_impl(a, n):
+    """
+    Computes matrix power. Only integer powers are supported in numpy.
+    """
+
+    _check_linalg_matrix(a, "matrix_power")
+    np_dtype = np_support.as_dtype(a.dtype)
+
+    nt = getattr(n, 'dtype', n)
+    if not isinstance(nt, types.Integer):
+        raise NumbaTypeError("Exponent must be an integer.")
+
+    def matrix_power_impl(a, n):
+
+        if n == 0:
+            # this should be eye() but it doesn't support
+            # the dtype kwarg yet so do it manually to save
+            # the copy required by eye(a.shape[0]).asdtype()
+            A = np.zeros(a.shape, dtype=np_dtype)
+            for k in range(a.shape[0]):
+                A[k, k] = 1.
+            return A
+
+        am, an = a.shape[-1], a.shape[-2]
+        if am != an:
+            raise ValueError('input must be a square array')
+
+        # empty, return a copy
+        if am == 0:
+            return a.copy()
+
+        # note: to be consistent over contiguousness, C order is
+        # returned as that is what dot() produces and the most common
+        # paths through matrix_power will involve that. Therefore
+        # copies are made here to ensure the data ordering is
+        # correct for paths not going via dot().
+
+        if n < 0:
+            A = np.linalg.inv(a).copy()
+            if n == -1:  # return now
+                return A
+            n = -n
+        else:
+            if n == 1:  # return a copy now
+                return a.copy()
+            A = a  # this is safe, `a` is only read
+
+        if n < 4:
+            if n == 2:
+                return np.dot(A, A)
+            if n == 3:
+                return np.dot(np.dot(A, A), A)
+        else:
+
+            acc = A
+            exp = n
+
+            # Initialise ret, SSA cannot see the loop will execute, without this
+            # it appears as uninitialised.
+            ret = acc
+            # tried a loop split and branchless using identity matrix as
+            # input but it seems like having a "first entry" flag is quicker
+            flag = True
+            while exp != 0:
+                if exp & 1:
+                    if flag:
+                        ret = acc
+                        flag = False
+                    else:
+                        ret = np.dot(ret, acc)
+                acc = np.dot(acc, acc)
+                exp = exp >> 1
+
+            return ret
+
+    return matrix_power_impl
+
+# This is documented under linalg despite not being in the module
+
+
+@overload(np.trace)
+def matrix_trace_impl(a, offset=0):
+    """
+    Computes the trace of an array.
+    """
+
+    _check_linalg_matrix(a, "trace", la_prefix=False)
+
+    if not isinstance(offset, (int, types.Integer)):
+        raise NumbaTypeError("integer argument expected, got %s" % offset)
+
+    def matrix_trace_impl(a, offset=0):
+        rows, cols = a.shape
+        k = offset
+        if k < 0:
+            rows = rows + k
+        if k > 0:
+            cols = cols - k
+        n = max(min(rows, cols), 0)
+        ret = 0
+        if k >= 0:
+            for i in range(n):
+                ret += a[i, k + i]
+        else:
+            for i in range(n):
+                ret += a[i - k, i]
+        return ret
+
+    return matrix_trace_impl
+
+
+def _check_scalar_or_lt_2d_mat(a, func_name, la_prefix=True):
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # checks that a matrix is 1 or 2D
+    if isinstance(a, types.Array):
+        if not a.ndim <= 2:
+            raise TypingError("%s.%s() only supported on 1 and 2-D arrays "
+                              % interp, highlighting=False)
+
+
+@register_jitable
+def outer_impl_none(a, b, out):
+    aa = np.asarray(a)
+    bb = np.asarray(b)
+    return np.multiply(aa.ravel().reshape((aa.size, 1)),
+                        bb.ravel().reshape((1, bb.size)))
+
+
+@register_jitable
+def outer_impl_arr(a, b, out):
+    aa = np.asarray(a)
+    bb = np.asarray(b)
+    np.multiply(aa.ravel().reshape((aa.size, 1)),
+                bb.ravel().reshape((1, bb.size)),
+                out)
+    return out
+
+
+def _get_outer_impl(a, b, out):
+    if out in (None, types.none):
+        return outer_impl_none
+    else:
+        return outer_impl_arr
+
+
+@overload(np.outer)
+def outer_impl(a, b, out=None):
+
+    _check_scalar_or_lt_2d_mat(a, "outer", la_prefix=False)
+    _check_scalar_or_lt_2d_mat(b, "outer", la_prefix=False)
+
+    impl = _get_outer_impl(a, b, out)
+
+    def outer_impl(a, b, out=None):
+        return impl(a, b, out)
+
+    return outer_impl
+
+
+def _kron_normaliser_impl(x):
+    # makes x into a 2d array
+    if isinstance(x, types.Array):
+        if x.layout not in ('C', 'F'):
+            raise TypingError("np.linalg.kron only supports 'C' or 'F' layout "
+                              "input arrays. Received an input of "
+                              "layout '{}'.".format(x.layout))
+        elif x.ndim == 2:
+            @register_jitable
+            def nrm_shape(x):
+                xn = x.shape[-1]
+                xm = x.shape[-2]
+                return x.reshape(xm, xn)
+            return nrm_shape
+        else:
+            @register_jitable
+            def nrm_shape(x):
+                xn = x.shape[-1]
+                return x.reshape(1, xn)
+            return nrm_shape
+    else:  # assume its a scalar
+        @register_jitable
+        def nrm_shape(x):
+            a = np.empty((1, 1), type(x))
+            a[0] = x
+            return a
+        return nrm_shape
+
+
+def _kron_return(a, b):
+    # transforms c into something that kron would return
+    # based on the shapes of a and b
+    a_is_arr = isinstance(a, types.Array)
+    b_is_arr = isinstance(b, types.Array)
+    if a_is_arr and b_is_arr:
+        if a.ndim == 2 or b.ndim == 2:
+            @register_jitable
+            def ret(a, b, c):
+                return c
+            return ret
+        else:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(c.size)
+            return ret
+    else:  # at least one of (a, b) is a scalar
+        if a_is_arr:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(a.shape)
+            return ret
+        elif b_is_arr:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(b.shape)
+            return ret
+        else:  # both scalars
+            @register_jitable
+            def ret(a, b, c):
+                return c[0]
+            return ret
+
+
+@overload(np.kron)
+def kron_impl(a, b):
+
+    _check_scalar_or_lt_2d_mat(a, "kron", la_prefix=False)
+    _check_scalar_or_lt_2d_mat(b, "kron", la_prefix=False)
+
+    fix_a = _kron_normaliser_impl(a)
+    fix_b = _kron_normaliser_impl(b)
+    ret_c = _kron_return(a, b)
+
+    # this is fine because the ufunc for the Hadamard product
+    # will reject differing dtypes in a and b.
+    dt = getattr(a, 'dtype', a)
+
+    def kron_impl(a, b):
+
+        aa = fix_a(a)
+        bb = fix_b(b)
+
+        am = aa.shape[-2]
+        an = aa.shape[-1]
+        bm = bb.shape[-2]
+        bn = bb.shape[-1]
+
+        cm = am * bm
+        cn = an * bn
+
+        # allocate c
+        C = np.empty((cm, cn), dtype=dt)
+
+        # In practice this is runs quicker than the more obvious
+        # `each element of A multiplied by B and assigned to
+        # a block in C` like alg.
+
+        # loop over rows of A
+        for i in range(am):
+            # compute the column offset into C
+            rjmp = i * bm
+            # loop over rows of B
+            for k in range(bm):
+                # compute row the offset into C
+                irjmp = rjmp + k
+                # slice a given row of B
+                slc = bb[k, :]
+                # loop over columns of A
+                for j in range(an):
+                    # vectorized assignment of an element of A
+                    # multiplied by the current row of B into
+                    # a slice of a row of C
+                    cjmp = j * bn
+                    C[irjmp, cjmp:cjmp + bn] = aa[i, j] * slc
+
+        return ret_c(a, b, C)
+
+    return kron_impl
diff --git a/shared/package-files/0c/0c5bdff5e2602627390d41edda5ac992c4aee57e9e5d17eb877d2de720154b36/gdscript.py b/shared/package-files/0c/0c5bdff5e2602627390d41edda5ac992c4aee57e9e5d17eb877d2de720154b36/gdscript.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f4f6d4315b7e1fac10cfc8db37e4276134c1b94
--- /dev/null
+++ b/shared/package-files/0c/0c5bdff5e2602627390d41edda5ac992c4aee57e9e5d17eb877d2de720154b36/gdscript.py
@@ -0,0 +1,188 @@
+"""
+    pygments.lexers.gdscript
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexer for GDScript.
+
+    Modified by Daniel J. Ramirez <djrmuv@gmail.com> based on the original
+    python.py.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexer import RegexLexer, include, bygroups, default, words, \
+    combined
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Whitespace
+
+__all__ = ["GDScriptLexer"]
+
+
+class GDScriptLexer(RegexLexer):
+    """
+    For GDScript source code.
+    """
+
+    name = "GDScript"
+    url = 'https://www.godotengine.org'
+    aliases = ["gdscript", "gd"]
+    filenames = ["*.gd"]
+    mimetypes = ["text/x-gdscript", "application/x-gdscript"]
+
+    def innerstring_rules(ttype):
+        return [
+            # the old style '%s' % (...) string formatting
+            (r"%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?"
+             "[hlL]?[E-GXc-giorsux%]",
+             String.Interpol),
+            # backslashes, quotes and formatting signs must be parsed one at a time
+            (r'[^\\\'"%\n]+', ttype),
+            (r'[\'"\\]', ttype),
+            # unhandled string formatting sign
+            (r"%", ttype),
+            # newlines are an error (use "nl" state)
+        ]
+
+    tokens = {
+        "root": [
+            (r"\n", Whitespace),
+            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
+             bygroups(Whitespace, String.Affix, String.Doc)),
+            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
+             bygroups(Whitespace, String.Affix, String.Doc)),
+            (r"[^\S\n]+", Whitespace),
+            (r"#.*$", Comment.Single),
+            (r"[]{}:(),;[]", Punctuation),
+            (r"(\\)(\n)", bygroups(Text, Whitespace)),
+            (r"\\", Text),
+            (r"(in|and|or|not)\b", Operator.Word),
+            (r"!=|==|<<|>>|&&|\+=|-=|\*=|/=|%=|&=|\|=|\|\||[-~+/*%=<>&^.!|$]",
+             Operator),
+            include("keywords"),
+            (r"(func)(\s+)", bygroups(Keyword, Whitespace), "funcname"),
+            (r"(class)(\s+)", bygroups(Keyword, Whitespace), "classname"),
+            include("builtins"),
+            ('([rR]|[uUbB][rR]|[rR][uUbB])(""")',
+             bygroups(String.Affix, String.Double),
+             "tdqs"),
+            ("([rR]|[uUbB][rR]|[rR][uUbB])(''')",
+             bygroups(String.Affix, String.Single),
+             "tsqs"),
+            ('([rR]|[uUbB][rR]|[rR][uUbB])(")',
+             bygroups(String.Affix, String.Double),
+             "dqs"),
+            ("([rR]|[uUbB][rR]|[rR][uUbB])(')",
+             bygroups(String.Affix, String.Single),
+             "sqs"),
+            ('([uUbB]?)(""")',
+             bygroups(String.Affix, String.Double),
+             combined("stringescape", "tdqs")),
+            ("([uUbB]?)(''')",
+             bygroups(String.Affix, String.Single),
+             combined("stringescape", "tsqs")),
+            ('([uUbB]?)(")',
+             bygroups(String.Affix, String.Double),
+             combined("stringescape", "dqs")),
+            ("([uUbB]?)(')",
+             bygroups(String.Affix, String.Single),
+             combined("stringescape", "sqs")),
+            include("name"),
+            include("numbers"),
+        ],
+        "keywords": [
+            (words(("and", "in", "not", "or", "as", "breakpoint", "class",
+                    "class_name", "extends", "is", "func", "setget", "signal",
+                    "tool", "const", "enum", "export", "onready", "static",
+                    "var", "break", "continue", "if", "elif", "else", "for",
+                    "pass", "return", "match", "while", "remote", "master",
+                    "puppet", "remotesync", "mastersync", "puppetsync"),
+                   suffix=r"\b"), Keyword),
+        ],
+        "builtins": [
+            (words(("Color8", "ColorN", "abs", "acos", "asin", "assert", "atan",
+                    "atan2", "bytes2var", "ceil", "char", "clamp", "convert",
+                    "cos", "cosh", "db2linear", "decimals", "dectime", "deg2rad",
+                    "dict2inst", "ease", "exp", "floor", "fmod", "fposmod",
+                    "funcref", "hash", "inst2dict", "instance_from_id", "is_inf",
+                    "is_nan", "lerp", "linear2db", "load", "log", "max", "min",
+                    "nearest_po2", "pow", "preload", "print", "print_stack",
+                    "printerr", "printraw", "prints", "printt", "rad2deg",
+                    "rand_range", "rand_seed", "randf", "randi", "randomize",
+                    "range", "round", "seed", "sign", "sin", "sinh", "sqrt",
+                    "stepify", "str", "str2var", "tan", "tan", "tanh",
+                    "type_exist", "typeof", "var2bytes", "var2str", "weakref",
+                    "yield"), prefix=r"(?<!\.)", suffix=r"\b"),
+             Name.Builtin),
+            (r"((?<!\.)(self|false|true)|(PI|TAU|NAN|INF)" r")\b",
+             Name.Builtin.Pseudo),
+            (words(("bool", "int", "float", "String", "NodePath", "Vector2",
+                    "Rect2", "Transform2D", "Vector3", "Rect3", "Plane", "Quat",
+                    "Basis", "Transform", "Color", "RID", "Object", "NodePath",
+                    "Dictionary", "Array", "PackedByteArray", "PackedInt32Array",
+                    "PackedInt64Array", "PackedFloat32Array", "PackedFloat64Array",
+                    "PackedStringArray", "PackedVector2Array", "PackedVector3Array",
+                    "PackedColorArray", "null", "void"),
+                   prefix=r"(?<!\.)", suffix=r"\b"),
+             Name.Builtin.Type),
+        ],
+        "numbers": [
+            (r"(\d+\.\d*|\d*\.\d+)([eE][+-]?[0-9]+)?j?", Number.Float),
+            (r"\d+[eE][+-]?[0-9]+j?", Number.Float),
+            (r"0[xX][a-fA-F0-9]+", Number.Hex),
+            (r"\d+j?", Number.Integer),
+        ],
+        "name": [(r"[a-zA-Z_]\w*", Name)],
+        "funcname": [(r"[a-zA-Z_]\w*", Name.Function, "#pop"), default("#pop")],
+        "classname": [(r"[a-zA-Z_]\w*", Name.Class, "#pop")],
+        "stringescape": [
+            (
+                r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
+                r"U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})",
+                String.Escape,
+            )
+        ],
+        "strings-single": innerstring_rules(String.Single),
+        "strings-double": innerstring_rules(String.Double),
+        "dqs": [
+            (r'"', String.Double, "#pop"),
+            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
+            include("strings-double"),
+        ],
+        "sqs": [
+            (r"'", String.Single, "#pop"),
+            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
+            include("strings-single"),
+        ],
+        "tdqs": [
+            (r'"""', String.Double, "#pop"),
+            include("strings-double"),
+            (r"\n", Whitespace),
+        ],
+        "tsqs": [
+            (r"'''", String.Single, "#pop"),
+            include("strings-single"),
+            (r"\n", Whitespace),
+        ],
+    }
+
+    def analyse_text(text):
+        score = 0.0
+
+        if re.search(
+            r"func (_ready|_init|_input|_process|_unhandled_input)", text
+        ):
+            score += 0.8
+
+        if re.search(
+            r"(extends |class_name |onready |preload|load|setget|func [^_])",
+            text
+        ):
+            score += 0.4
+
+        if re.search(r"(var|const|enum|export|signal|tool)", text):
+            score += 0.2
+
+        return min(score, 1.0)
diff --git a/shared/package-files/0c/0c5c5c2d1ff01de4c6740e3d565f9dafc934cdd9bb2e592c07a0ea8f7643e588/well_known_types.pyi b/shared/package-files/0c/0c5c5c2d1ff01de4c6740e3d565f9dafc934cdd9bb2e592c07a0ea8f7643e588/well_known_types.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b8a64b319166286a46b3c530ff2b4aba1335c7ff
--- /dev/null
+++ b/shared/package-files/0c/0c5c5c2d1ff01de4c6740e3d565f9dafc934cdd9bb2e592c07a0ea8f7643e588/well_known_types.pyi
@@ -0,0 +1,94 @@
+from datetime import datetime, timedelta
+from typing import Any as tAny, Dict, Optional, Text, Type
+
+class Error(Exception): ...
+class ParseError(Error): ...
+
+class Any:
+    type_url: tAny = ...
+    value: tAny = ...
+    def Pack(self, msg: tAny, type_url_prefix: bytes = ..., deterministic: Optional[tAny] = ...) -> None: ...
+    def Unpack(self, msg: tAny): ...
+    def TypeName(self): ...
+    def Is(self, descriptor: tAny): ...
+
+class Timestamp:
+    def ToJsonString(self) -> str: ...
+    seconds: int = ...
+    nanos: int = ...
+    def FromJsonString(self, value: str) -> None: ...
+    def GetCurrentTime(self) -> None: ...
+    def ToNanoseconds(self) -> int: ...
+    def ToMicroseconds(self) -> int: ...
+    def ToMilliseconds(self) -> int: ...
+    def ToSeconds(self) -> int: ...
+    def FromNanoseconds(self, nanos: int) -> None: ...
+    def FromMicroseconds(self, micros: int) -> None: ...
+    def FromMilliseconds(self, millis: int) -> None: ...
+    def FromSeconds(self, seconds: int) -> None: ...
+    def ToDatetime(self) -> datetime: ...
+    def FromDatetime(self, dt: datetime) -> None: ...
+
+class Duration:
+    def ToJsonString(self) -> str: ...
+    seconds: int = ...
+    nanos: int = ...
+    def FromJsonString(self, value: tAny) -> None: ...
+    def ToNanoseconds(self) -> int: ...
+    def ToMicroseconds(self) -> int: ...
+    def ToMilliseconds(self) -> int: ...
+    def ToSeconds(self) -> int: ...
+    def FromNanoseconds(self, nanos: int) -> None: ...
+    def FromMicroseconds(self, micros: int) -> None: ...
+    def FromMilliseconds(self, millis: int) -> None: ...
+    def FromSeconds(self, seconds: int) -> None: ...
+    def ToTimedelta(self) -> timedelta: ...
+    def FromTimedelta(self, td: timedelta) -> None: ...
+
+class FieldMask:
+    def ToJsonString(self) -> str: ...
+    def FromJsonString(self, value: tAny) -> None: ...
+    def IsValidForDescriptor(self, message_descriptor: tAny): ...
+    def AllFieldsFromDescriptor(self, message_descriptor: tAny) -> None: ...
+    def CanonicalFormFromMask(self, mask: tAny) -> None: ...
+    def Union(self, mask1: tAny, mask2: tAny) -> None: ...
+    def Intersect(self, mask1: tAny, mask2: tAny) -> None: ...
+    def MergeMessage(
+        self, source: tAny, destination: tAny, replace_message_field: bool = ..., replace_repeated_field: bool = ...
+    ) -> None: ...
+
+class _FieldMaskTree:
+    def __init__(self, field_mask: Optional[tAny] = ...) -> None: ...
+    def MergeFromFieldMask(self, field_mask: tAny) -> None: ...
+    def AddPath(self, path: tAny): ...
+    def ToFieldMask(self, field_mask: tAny) -> None: ...
+    def IntersectPath(self, path: tAny, intersection: tAny): ...
+    def AddLeafNodes(self, prefix: tAny, node: tAny) -> None: ...
+    def MergeMessage(self, source: tAny, destination: tAny, replace_message: tAny, replace_repeated: tAny) -> None: ...
+
+class Struct:
+    def __getitem__(self, key: tAny): ...
+    def __contains__(self, item: tAny): ...
+    def __setitem__(self, key: tAny, value: tAny) -> None: ...
+    def __delitem__(self, key: tAny) -> None: ...
+    def __len__(self): ...
+    def __iter__(self): ...
+    def keys(self): ...
+    def values(self): ...
+    def items(self): ...
+    def get_or_create_list(self, key: tAny): ...
+    def get_or_create_struct(self, key: tAny): ...
+    def update(self, dictionary: tAny) -> None: ...
+
+class ListValue:
+    def __len__(self): ...
+    def append(self, value: tAny) -> None: ...
+    def extend(self, elem_seq: tAny) -> None: ...
+    def __getitem__(self, index: tAny): ...
+    def __setitem__(self, index: tAny, value: tAny) -> None: ...
+    def __delitem__(self, key: tAny) -> None: ...
+    def items(self) -> None: ...
+    def add_struct(self): ...
+    def add_list(self): ...
+
+WKTBASES: Dict[Text, Type]
diff --git a/shared/package-files/0c/0c5c72165a73591652f7374186c8c82397c550e318f5b5c79f7108ac8be638ca/builder_decimal.h b/shared/package-files/0c/0c5c72165a73591652f7374186c8c82397c550e318f5b5c79f7108ac8be638ca/builder_decimal.h
new file mode 100644
index 0000000000000000000000000000000000000000..a0bf0a04220842cceada0d0754ad6be4e41a3093
--- /dev/null
+++ b/shared/package-files/0c/0c5c72165a73591652f7374186c8c82397c550e318f5b5c79f7108ac8be638ca/builder_decimal.h
@@ -0,0 +1,164 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <memory>
+
+#include "arrow/array/array_decimal.h"
+#include "arrow/array/builder_base.h"
+#include "arrow/array/builder_binary.h"
+#include "arrow/array/data.h"
+#include "arrow/status.h"
+#include "arrow/type.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+
+/// \addtogroup numeric-builders
+///
+/// @{
+
+class ARROW_EXPORT Decimal32Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal32Type;
+  using ValueType = Decimal32;
+
+  explicit Decimal32Builder(const std::shared_ptr<DataType>& type,
+                            MemoryPool* pool = default_memory_pool(),
+                            int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal32 val);
+  void UnsafeAppend(Decimal32 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal32Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal32Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal64Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal64Type;
+  using ValueType = Decimal64;
+
+  explicit Decimal64Builder(const std::shared_ptr<DataType>& type,
+                            MemoryPool* pool = default_memory_pool(),
+                            int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal64 val);
+  void UnsafeAppend(Decimal64 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal64Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal64Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal128Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal128Type;
+  using ValueType = Decimal128;
+
+  explicit Decimal128Builder(const std::shared_ptr<DataType>& type,
+                             MemoryPool* pool = default_memory_pool(),
+                             int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal128 val);
+  void UnsafeAppend(Decimal128 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal128Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal128Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal256Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal256Type;
+  using ValueType = Decimal256;
+
+  explicit Decimal256Builder(const std::shared_ptr<DataType>& type,
+                             MemoryPool* pool = default_memory_pool(),
+                             int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(const Decimal256& val);
+  void UnsafeAppend(const Decimal256& val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal256Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal256Type> decimal_type_;
+};
+
+using DecimalBuilder = Decimal128Builder;
+
+/// @}
+
+}  // namespace arrow
diff --git a/shared/package-files/0c/0c5c98d884018f7af619992f6d121e64ed84118e3aeb16eec1ce0f4a1025e12c/test_classes.py b/shared/package-files/0c/0c5c98d884018f7af619992f6d121e64ed84118e3aeb16eec1ce0f4a1025e12c/test_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6322062f29ece2f52754ac7aedf2591b3a983709
--- /dev/null
+++ b/shared/package-files/0c/0c5c98d884018f7af619992f6d121e64ed84118e3aeb16eec1ce0f4a1025e12c/test_classes.py
@@ -0,0 +1,600 @@
+"""Test inter-conversion of different polynomial classes.
+
+This tests the convert and cast methods of all the polynomial classes.
+
+"""
+import operator as op
+from numbers import Number
+
+import pytest
+import numpy as np
+from numpy.polynomial import (
+    Polynomial, Legendre, Chebyshev, Laguerre, Hermite, HermiteE)
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+from numpy.polynomial.polyutils import RankWarning
+
+#
+# fixtures
+#
+
+classes = (
+    Polynomial, Legendre, Chebyshev, Laguerre,
+    Hermite, HermiteE
+    )
+classids = tuple(cls.__name__ for cls in classes)
+
+@pytest.fixture(params=classes, ids=classids)
+def Poly(request):
+    return request.param
+
+#
+# helper functions
+#
+random = np.random.random
+
+
+def assert_poly_almost_equal(p1, p2, msg=""):
+    try:
+        assert_(np.all(p1.domain == p2.domain))
+        assert_(np.all(p1.window == p2.window))
+        assert_almost_equal(p1.coef, p2.coef)
+    except AssertionError:
+        msg = f"Result: {p1}\nTarget: {p2}"
+        raise AssertionError(msg)
+
+
+#
+# Test conversion methods that depend on combinations of two classes.
+#
+
+Poly1 = Poly
+Poly2 = Poly
+
+
+def test_conversion(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = p1.convert(kind=Poly2, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+def test_cast(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = Poly2.cast(p1, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+#
+# test methods that depend on one class
+#
+
+
+def test_identity(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    x = np.linspace(d[0], d[1], 11)
+    p = Poly.identity(domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_almost_equal(p(x), x)
+
+
+def test_basis(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.basis(5, domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_equal(p.coef, [0]*5 + [1])
+
+
+def test_fromroots(Poly):
+    # check that requested roots are zeros of a polynomial
+    # of correct degree, domain, and window.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    r = random((5,))
+    p1 = Poly.fromroots(r, domain=d, window=w)
+    assert_equal(p1.degree(), len(r))
+    assert_equal(p1.domain, d)
+    assert_equal(p1.window, w)
+    assert_almost_equal(p1(r), 0)
+
+    # check that polynomial is monic
+    pdom = Polynomial.domain
+    pwin = Polynomial.window
+    p2 = Polynomial.cast(p1, domain=pdom, window=pwin)
+    assert_almost_equal(p2.coef[-1], 1)
+
+
+def test_bad_conditioned_fit(Poly):
+
+    x = [0., 0., 1.]
+    y = [1., 2., 3.]
+
+    # check RankWarning is raised
+    with pytest.warns(RankWarning) as record:
+        Poly.fit(x, y, 2)
+    assert record[0].message.args[0] == "The fit may be poorly conditioned"
+
+
+def test_fit(Poly):
+
+    def f(x):
+        return x*(x - 1)*(x - 2)
+    x = np.linspace(0, 3)
+    y = f(x)
+
+    # check default value of domain and window
+    p = Poly.fit(x, y, 3)
+    assert_almost_equal(p.domain, [0, 3])
+    assert_almost_equal(p(x), y)
+    assert_equal(p.degree(), 3)
+
+    # check with given domains and window
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.fit(x, y, 3, domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+    p = Poly.fit(x, y, [0, 1, 2, 3], domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+
+    # check with class domain default
+    p = Poly.fit(x, y, 3, [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+    p = Poly.fit(x, y, [0, 1, 2, 3], [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+
+    # check that fit accepts weights.
+    w = np.zeros_like(x)
+    z = y + random(y.shape)*.25
+    w[::2] = 1
+    p1 = Poly.fit(x[::2], z[::2], 3)
+    p2 = Poly.fit(x, z, 3, w=w)
+    p3 = Poly.fit(x, z, [0, 1, 2, 3], w=w)
+    assert_almost_equal(p1(x), p2(x))
+    assert_almost_equal(p2(x), p3(x))
+
+
+def test_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(p1 == p1)
+    assert_(not p1 == p2)
+    assert_(not p1 == p3)
+    assert_(not p1 == p4)
+
+
+def test_not_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(not p1 != p1)
+    assert_(p1 != p2)
+    assert_(p1 != p3)
+    assert_(p1 != p4)
+
+
+def test_add(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 + p2
+    assert_poly_almost_equal(p2 + p1, p3)
+    assert_poly_almost_equal(p1 + c2, p3)
+    assert_poly_almost_equal(c2 + p1, p3)
+    assert_poly_almost_equal(p1 + tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) + p1, p3)
+    assert_poly_almost_equal(p1 + np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) + p1, p3)
+    assert_raises(TypeError, op.add, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.add, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.add, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.add, p1, Polynomial([0]))
+
+
+def test_sub(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 - p2
+    assert_poly_almost_equal(p2 - p1, -p3)
+    assert_poly_almost_equal(p1 - c2, p3)
+    assert_poly_almost_equal(c2 - p1, -p3)
+    assert_poly_almost_equal(p1 - tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) - p1, -p3)
+    assert_poly_almost_equal(p1 - np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) - p1, -p3)
+    assert_raises(TypeError, op.sub, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.sub, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.sub, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.sub, p1, Polynomial([0]))
+
+
+def test_mul(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 * p2
+    assert_poly_almost_equal(p2 * p1, p3)
+    assert_poly_almost_equal(p1 * c2, p3)
+    assert_poly_almost_equal(c2 * p1, p3)
+    assert_poly_almost_equal(p1 * tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * 2, p1 * Poly([2]))
+    assert_poly_almost_equal(2 * p1, p1 * Poly([2]))
+    assert_raises(TypeError, op.mul, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mul, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mul, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mul, p1, Polynomial([0]))
+
+
+def test_floordiv(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 // p2, p1)
+    assert_poly_almost_equal(p4 // c2, p1)
+    assert_poly_almost_equal(c4 // p2, p1)
+    assert_poly_almost_equal(p4 // tuple(c2), p1)
+    assert_poly_almost_equal(tuple(c4) // p2, p1)
+    assert_poly_almost_equal(p4 // np.array(c2), p1)
+    assert_poly_almost_equal(np.array(c4) // p2, p1)
+    assert_poly_almost_equal(2 // p2, Poly([0]))
+    assert_poly_almost_equal(p2 // 2, 0.5*p2)
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.floordiv, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.floordiv, p1, Polynomial([0]))
+
+
+def test_truediv(Poly):
+    # true division is valid only if the denominator is a Number and
+    # not a python bool.
+    p1 = Poly([1,2,3])
+    p2 = p1 * 5
+
+    for stype in np.ScalarType:
+        if not issubclass(stype, Number) or issubclass(stype, bool):
+            continue
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in (int, float):
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in [complex]:
+        s = stype(5, 0)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for s in [tuple(), list(), dict(), bool(), np.array([1])]:
+        assert_raises(TypeError, op.truediv, p2, s)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for ptype in classes:
+        assert_raises(TypeError, op.truediv, p2, ptype(1))
+
+
+def test_mod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 % p2, p3)
+    assert_poly_almost_equal(p4 % c2, p3)
+    assert_poly_almost_equal(c4 % p2, p3)
+    assert_poly_almost_equal(p4 % tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c4) % p2, p3)
+    assert_poly_almost_equal(p4 % np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c4) % p2, p3)
+    assert_poly_almost_equal(2 % p2, Poly([2]))
+    assert_poly_almost_equal(p2 % 2, Poly([0]))
+    assert_raises(TypeError, op.mod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mod, p1, Polynomial([0]))
+
+
+def test_divmod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    quo, rem = divmod(p4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, c2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(c4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, tuple(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(tuple(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, np.array(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(np.array(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p2, 2)
+    assert_poly_almost_equal(quo, 0.5*p2)
+    assert_poly_almost_equal(rem, Poly([0]))
+    quo, rem = divmod(2, p2)
+    assert_poly_almost_equal(quo, Poly([0]))
+    assert_poly_almost_equal(rem, Poly([2]))
+    assert_raises(TypeError, divmod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, divmod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, divmod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, divmod, p1, Polynomial([0]))
+
+
+def test_roots(Poly):
+    d = Poly.domain * 1.25 + .25
+    w = Poly.window
+    tgt = np.linspace(d[0], d[1], 5)
+    res = np.sort(Poly.fromroots(tgt, domain=d, window=w).roots())
+    assert_almost_equal(res, tgt)
+    # default domain and window
+    res = np.sort(Poly.fromroots(tgt).roots())
+    assert_almost_equal(res, tgt)
+
+
+def test_degree(Poly):
+    p = Poly.basis(5)
+    assert_equal(p.degree(), 5)
+
+
+def test_copy(Poly):
+    p1 = Poly.basis(5)
+    p2 = p1.copy()
+    assert_(p1 == p2)
+    assert_(p1 is not p2)
+    assert_(p1.coef is not p2.coef)
+    assert_(p1.domain is not p2.domain)
+    assert_(p1.window is not p2.window)
+
+
+def test_integ(Poly):
+    P = Polynomial
+    # Check defaults
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+    # Check with k
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(k=1))
+    p2 = P.cast(p0.integ(2, k=[1, 1]))
+    assert_poly_almost_equal(p1, P([1, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([1, 1, 1, 1, 1]))
+    # Check with lbnd
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(lbnd=1))
+    p2 = P.cast(p0.integ(2, lbnd=1))
+    assert_poly_almost_equal(p1, P([-9, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([6, -9, 1, 1, 1]))
+    # Check scaling
+    d = 2*Poly.domain
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]), domain=d)
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+
+
+def test_deriv(Poly):
+    # Check that the derivative is the inverse of integration. It is
+    # assumes that the integration has been checked elsewhere.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p1 = Poly([1, 2, 3], domain=d, window=w)
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+    # default domain and window
+    p1 = Poly([1, 2, 3])
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+
+
+def test_linspace(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly([1, 2, 3], domain=d, window=w)
+    # check default domain
+    xtgt = np.linspace(d[0], d[1], 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20)
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+    # check specified domain
+    xtgt = np.linspace(0, 2, 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20, domain=[0, 2])
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+
+
+def test_pow(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    tgt = Poly([1], domain=d, window=w)
+    tst = Poly([1, 2, 3], domain=d, window=w)
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # default domain and window
+    tgt = Poly([1])
+    tst = Poly([1, 2, 3])
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # check error for invalid powers
+    assert_raises(ValueError, op.pow, tgt, 1.5)
+    assert_raises(ValueError, op.pow, tgt, -1)
+
+
+def test_call(Poly):
+    P = Polynomial
+    d = Poly.domain
+    x = np.linspace(d[0], d[1], 11)
+
+    # Check defaults
+    p = Poly.cast(P([1, 2, 3]))
+    tgt = 1 + x*(2 + 3*x)
+    res = p(x)
+    assert_almost_equal(res, tgt)
+
+
+def test_cutdeg(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.cutdeg, .5)
+    assert_raises(ValueError, p.cutdeg, -1)
+    assert_equal(len(p.cutdeg(3)), 3)
+    assert_equal(len(p.cutdeg(2)), 3)
+    assert_equal(len(p.cutdeg(1)), 2)
+    assert_equal(len(p.cutdeg(0)), 1)
+
+
+def test_truncate(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.truncate, .5)
+    assert_raises(ValueError, p.truncate, 0)
+    assert_equal(len(p.truncate(4)), 3)
+    assert_equal(len(p.truncate(3)), 3)
+    assert_equal(len(p.truncate(2)), 2)
+    assert_equal(len(p.truncate(1)), 1)
+
+
+def test_trim(Poly):
+    c = [1, 1e-6, 1e-12, 0]
+    p = Poly(c)
+    assert_equal(p.trim().coef, c[:3])
+    assert_equal(p.trim(1e-10).coef, c[:2])
+    assert_equal(p.trim(1e-5).coef, c[:1])
+
+
+def test_mapparms(Poly):
+    # check with defaults. Should be identity.
+    d = Poly.domain
+    w = Poly.window
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([0, 1], p.mapparms())
+    #
+    w = 2*d + 1
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([1, 2], p.mapparms())
+
+
+def test_ufunc_override(Poly):
+    p = Poly([1, 2, 3])
+    x = np.ones(3)
+    assert_raises(TypeError, np.add, p, x)
+    assert_raises(TypeError, np.add, x, p)
+
+
+#
+# Test class method that only exists for some classes
+#
+
+
+class TestInterpolate:
+
+    def f(self, x):
+        return x * (x - 1) * (x - 2)
+
+    def test_raises(self):
+        assert_raises(ValueError, Chebyshev.interpolate, self.f, -1)
+        assert_raises(TypeError, Chebyshev.interpolate, self.f, 10.)
+
+    def test_dimensions(self):
+        for deg in range(1, 5):
+            assert_(Chebyshev.interpolate(self.f, deg).degree() == deg)
+
+    def test_approximation(self):
+
+        def powx(x, p):
+            return x**p
+
+        x = np.linspace(0, 2, 10)
+        for deg in range(0, 10):
+            for t in range(0, deg + 1):
+                p = Chebyshev.interpolate(powx, deg, domain=[0, 2], args=(t,))
+                assert_almost_equal(p(x), powx(x, t), decimal=11)
diff --git a/shared/package-files/0c/0c5d6ff1c27a7f3c67f28b51143d6b0f55508cab2b554b7fb9ce3ddd1c3188fe/d413-unix-s b/shared/package-files/0c/0c5d6ff1c27a7f3c67f28b51143d6b0f55508cab2b554b7fb9ce3ddd1c3188fe/d413-unix-s
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index 0000000000000000000000000000000000000000..70868c484e14f6961cece95c7d7dfb8b5f4c49f2
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diff --git a/shared/package-files/0c/0c5d6ff1c27a7f3c67f28b51143d6b0f55508cab2b554b7fb9ce3ddd1c3188fe/d463-unix-s b/shared/package-files/0c/0c5d6ff1c27a7f3c67f28b51143d6b0f55508cab2b554b7fb9ce3ddd1c3188fe/d463-unix-s
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Binary files /dev/null and b/shared/package-files/0c/0c5d6ff1c27a7f3c67f28b51143d6b0f55508cab2b554b7fb9ce3ddd1c3188fe/d463-unix-s differ
diff --git a/shared/package-files/0c/0c5e8c0e2db9f345b7a68e92b90e246437a366cd2f089b02a3027ad847e05c85/upsample_linear1d_cuda_dispatch.h b/shared/package-files/0c/0c5e8c0e2db9f345b7a68e92b90e246437a366cd2f089b02a3027ad847e05c85/upsample_linear1d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..02f1645e3bd3d978bef611e5197b3b15689814b5
--- /dev/null
+++ b/shared/package-files/0c/0c5e8c0e2db9f345b7a68e92b90e246437a366cd2f089b02a3027ad847e05c85/upsample_linear1d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/0c/0c5f2c66f5f8df9c0b965029eaebe9513826b36e477df135720292741281ab22/max_pool1d_compositeimplicitautograd_dispatch.h b/shared/package-files/0c/0c5f2c66f5f8df9c0b965029eaebe9513826b36e477df135720292741281ab22/max_pool1d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee83af7db0996e8fea9dd3db1807496bda078dc7
--- /dev/null
+++ b/shared/package-files/0c/0c5f2c66f5f8df9c0b965029eaebe9513826b36e477df135720292741281ab22/max_pool1d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/0c/0c62da0125fd540f733823d1962e19ed355de7b928015a0605e2d67aec185073/multicomp.py b/shared/package-files/0c/0c62da0125fd540f733823d1962e19ed355de7b928015a0605e2d67aec185073/multicomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..5736a5a6842a7e3118b5d6581f722e0685a969e2
--- /dev/null
+++ b/shared/package-files/0c/0c62da0125fd540f733823d1962e19ed355de7b928015a0605e2d67aec185073/multicomp.py
@@ -0,0 +1,87 @@
+"""
+
+Created on Fri Mar 30 18:27:25 2012
+Author: Josef Perktold
+"""
+
+from statsmodels.sandbox.stats.multicomp import MultiComparison, tukeyhsd
+
+__all__ = ["MultiComparison", "tukeyhsd"]
+
+
+def pairwise_tukeyhsd(endog, groups, alpha=0.05, use_var="equal"):
+    """
+    Calculate all pairwise comparisons with TukeyHSD or Games-Howell
+
+    Parameters
+    ----------
+    endog : array_like, 1d
+        response variable
+    groups : array_like, 1d
+        array with groups, can be string or integers
+    alpha : float, optional
+        significance level for the test
+    use_var : {"unequal", "equal"}, optional
+        If ``use_var`` is "equal", then the Tukey-hsd pvalues are returned.
+        Tukey-hsd assumes that (within) variances are the same across groups.
+        If ``use_var`` is "unequal", then the Games-Howell pvalues are
+        returned. This uses Welch's t-test for unequal variances with
+        Satterthwaite's corrected degrees of freedom for each pairwise
+        comparison.
+
+    Returns
+    -------
+    results : TukeyHSDResults instance
+        A results class containing relevant data and some post-hoc
+        calculations, including adjusted p-value.
+
+    See Also
+    --------
+    MultiComparison
+        Class for pairwise comparisons of multiple groups.
+    tukeyhsd
+        Compute simultaneous Tukey HSD comparisons from summary data.
+    statsmodels.sandbox.stats.multicomp.TukeyHSDResults
+        Results from a Tukey HSD comparison.
+
+    Notes
+    -----
+    The results include the following attributes and methods:
+
+    * ``reject`` is a boolean array indicating whether each comparison is
+      statistically significant.
+    * ``pvalues`` contains the adjusted p-values for each comparison.
+    * ``summary()`` returns a printable table that includes the reject column.
+    * ``summary_frame()`` returns a DataFrame with the comparison results.
+
+    This is just a wrapper around tukeyhsd method of MultiComparison.
+    Tukey-hsd is not robust to heteroscedasticity, i.e., variance differ across
+    groups, especially if group sizes also vary. In those cases, the actual
+    size (rejection rate under the Null hypothesis) might be far from the
+    nominal size of the test.
+    The Games-Howell method uses pairwise t-tests that are robust to differences
+    in variances and approximately maintains size unless samples are very
+    small.
+
+    .. versionadded:: 0.15
+
+        The `use_var` keyword and option for Games-Howell test.
+
+    Examples
+    --------
+    The reject decisions and adjusted p-values can be accessed directly from
+    the results instance.
+
+    >>> import numpy as np
+    >>> endog = np.array([1, 2, 3, 4, 5, 6])
+    >>> groups = np.array(["a", "a", "b", "b", "c", "c"])
+    >>> res = pairwise_tukeyhsd(endog, groups)
+    >>> res.reject
+    array([False,  True, False])
+    >>> res.pvalues.round(3)
+    array([0.129, 0.022, 0.129])
+
+    """
+
+    return MultiComparison(endog, groups).tukeyhsd(alpha=alpha,
+                                                   use_var=use_var)
diff --git a/shared/package-files/0c/0c6571e2379b940a9b69816133705f3d25b947ec0496d69fe8abbad37883f11c/libdeflate-targets.cmake b/shared/package-files/0c/0c6571e2379b940a9b69816133705f3d25b947ec0496d69fe8abbad37883f11c/libdeflate-targets.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..dbd96298474d4560471a96ff1c3fe4db8558312b
--- /dev/null
+++ b/shared/package-files/0c/0c6571e2379b940a9b69816133705f3d25b947ec0496d69fe8abbad37883f11c/libdeflate-targets.cmake
@@ -0,0 +1,103 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 2.8.0 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.8.3...3.24)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS libdeflate::libdeflate_shared)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target libdeflate::libdeflate_shared
+add_library(libdeflate::libdeflate_shared SHARED IMPORTED)
+
+set_target_properties(libdeflate::libdeflate_shared PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "LIBDEFLATE_DLL"
+  INTERFACE_INCLUDE_DIRECTORIES "/opt/conda/include"
+)
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/libdeflate-targets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/shared/package-files/0c/0c66cae5e8d23befe26d90e878ce51943358edaf19f1e192727ab7713850d49b/test_arrayfuncs.py b/shared/package-files/0c/0c66cae5e8d23befe26d90e878ce51943358edaf19f1e192727ab7713850d49b/test_arrayfuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5c99427cbd003eebe0eb4228667be056a0ab642
--- /dev/null
+++ b/shared/package-files/0c/0c66cae5e8d23befe26d90e878ce51943358edaf19f1e192727ab7713850d49b/test_arrayfuncs.py
@@ -0,0 +1,40 @@
+import numpy as np
+import pytest
+
+from sklearn.utils._testing import assert_allclose
+from sklearn.utils.arrayfuncs import _all_with_any_reduction_axis_1, min_pos
+
+
+def test_min_pos():
+    # Check that min_pos returns a positive value and that it's consistent
+    # between float and double
+    X = np.random.RandomState(0).randn(100)
+
+    min_double = min_pos(X)
+    min_float = min_pos(X.astype(np.float32))
+
+    assert_allclose(min_double, min_float)
+    assert min_double >= 0
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_min_pos_no_positive(dtype):
+    # Check that the return value of min_pos is the maximum representable
+    # value of the input dtype when all input elements are <= 0 (#19328)
+    X = np.full(100, -1.0).astype(dtype, copy=False)
+
+    assert min_pos(X) == np.finfo(dtype).max
+
+
+@pytest.mark.parametrize(
+    "dtype", [np.int16, np.int32, np.int64, np.float32, np.float64]
+)
+@pytest.mark.parametrize("value", [0, 1.5, -1])
+def test_all_with_any_reduction_axis_1(dtype, value):
+    # Check that return value is False when there is no row equal to `value`
+    X = np.arange(12, dtype=dtype).reshape(3, 4)
+    assert not _all_with_any_reduction_axis_1(X, value=value)
+
+    # Make a row equal to `value`
+    X[1, :] = value
+    assert _all_with_any_reduction_axis_1(X, value=value)
diff --git a/shared/package-files/0c/0c698b1a4e136e1906841889be76de352719ccb7679beea72685adf3786bcfeb/ivp.py b/shared/package-files/0c/0c698b1a4e136e1906841889be76de352719ccb7679beea72685adf3786bcfeb/ivp.py
new file mode 100644
index 0000000000000000000000000000000000000000..8186982e4fddbd8c1058b59c745ca66ab3a9c224
--- /dev/null
+++ b/shared/package-files/0c/0c698b1a4e136e1906841889be76de352719ccb7679beea72685adf3786bcfeb/ivp.py
@@ -0,0 +1,755 @@
+import inspect
+import numpy as np
+from .bdf import BDF
+from .radau import Radau
+from .rk import RK23, RK45, DOP853
+from .lsoda import LSODA
+from scipy.optimize import OptimizeResult
+from .common import EPS, OdeSolution
+from .base import OdeSolver
+
+
+METHODS = {'RK23': RK23,
+           'RK45': RK45,
+           'DOP853': DOP853,
+           'Radau': Radau,
+           'BDF': BDF,
+           'LSODA': LSODA}
+
+
+MESSAGES = {0: "The solver successfully reached the end of the integration interval.",
+            1: "A termination event occurred."}
+
+
+class OdeResult(OptimizeResult):
+    pass
+
+
+def prepare_events(events):
+    """Standardize event functions and extract attributes."""
+    if callable(events):
+        events = (events,)
+
+    max_events = np.empty(len(events))
+    direction = np.empty(len(events))
+    for i, event in enumerate(events):
+        terminal = getattr(event, 'terminal', None)
+        direction[i] = getattr(event, 'direction', 0)
+
+        message = ('The `terminal` attribute of each event '
+                   'must be a boolean or positive integer.')
+        if terminal is None or terminal == 0:
+            max_events[i] = np.inf
+        elif int(terminal) == terminal and terminal > 0:
+            max_events[i] = terminal
+        else:
+            raise ValueError(message)
+
+    return events, max_events, direction
+
+
+def solve_event_equation(event, sol, t_old, t):
+    """Solve an equation corresponding to an ODE event.
+
+    The equation is ``event(t, y(t)) = 0``, here ``y(t)`` is known from an
+    ODE solver using some sort of interpolation. It is solved by
+    `scipy.optimize.brentq` with xtol=atol=4*EPS.
+
+    Parameters
+    ----------
+    event : callable
+        Function ``event(t, y)``.
+    sol : callable
+        Function ``sol(t)`` which evaluates an ODE solution between `t_old`
+        and  `t`.
+    t_old, t : float
+        Previous and new values of time. They will be used as a bracketing
+        interval.
+
+    Returns
+    -------
+    root : float
+        Found solution.
+    """
+    from scipy.optimize import brentq
+    return brentq(lambda t: event(t, sol(t)), t_old, t,
+                  xtol=4 * EPS, rtol=4 * EPS)
+
+
+def handle_events(sol, events, active_events, event_count, max_events,
+                  t_old, t):
+    """Helper function to handle events.
+
+    Parameters
+    ----------
+    sol : DenseOutput
+        Function ``sol(t)`` which evaluates an ODE solution between `t_old`
+        and  `t`.
+    events : list of callables, length n_events
+        Event functions with signatures ``event(t, y)``.
+    active_events : ndarray
+        Indices of events which occurred.
+    event_count : ndarray
+        Current number of occurrences for each event.
+    max_events : ndarray, shape (n_events,)
+        Number of occurrences allowed for each event before integration
+        termination is issued.
+    t_old, t : float
+        Previous and new values of time.
+
+    Returns
+    -------
+    root_indices : ndarray
+        Indices of events which take zero between `t_old` and `t` and before
+        a possible termination.
+    roots : ndarray
+        Values of t at which events occurred.
+    terminate : bool
+        Whether a terminal event occurred.
+    """
+    roots = [solve_event_equation(events[event_index], sol, t_old, t)
+             for event_index in active_events]
+
+    roots = np.asarray(roots)
+
+    if np.any(event_count[active_events] >= max_events[active_events]):
+        if t > t_old:
+            order = np.argsort(roots)
+        else:
+            order = np.argsort(-roots)
+        active_events = active_events[order]
+        roots = roots[order]
+        t = np.nonzero(event_count[active_events]
+                       >= max_events[active_events])[0][0]
+        active_events = active_events[:t + 1]
+        roots = roots[:t + 1]
+        terminate = True
+    else:
+        terminate = False
+
+    return active_events, roots, terminate
+
+
+def find_active_events(g, g_new, direction):
+    """Find which event occurred during an integration step.
+
+    Parameters
+    ----------
+    g, g_new : array_like, shape (n_events,)
+        Values of event functions at a current and next points.
+    direction : ndarray, shape (n_events,)
+        Event "direction" according to the definition in `solve_ivp`.
+
+    Returns
+    -------
+    active_events : ndarray
+        Indices of events which occurred during the step.
+    """
+    g, g_new = np.asarray(g), np.asarray(g_new)
+    up = (g <= 0) & (g_new >= 0)
+    down = (g >= 0) & (g_new <= 0)
+    either = up | down
+    mask = (up & (direction > 0) |
+            down & (direction < 0) |
+            either & (direction == 0))
+
+    return np.nonzero(mask)[0]
+
+
+def solve_ivp(fun, t_span, y0, method='RK45', t_eval=None, dense_output=False,
+              events=None, vectorized=False, args=None, **options):
+    """Solve an initial value problem for a system of ODEs.
+
+    This function numerically integrates a system of ordinary differential
+    equations given an initial value::
+
+        dy / dt = f(t, y)
+        y(t0) = y0
+
+    Here t is a 1-D independent variable (time), y(t) is an
+    N-D vector-valued function (state), and an N-D
+    vector-valued function f(t, y) determines the differential equations.
+    The goal is to find y(t) approximately satisfying the differential
+    equations, given an initial value y(t0)=y0.
+
+    Some of the solvers support integration in the complex domain, but note
+    that for stiff ODE solvers, the right-hand side must be
+    complex-differentiable (satisfy Cauchy-Riemann equations [11]_).
+    To solve a problem in the complex domain, pass y0 with a complex data type.
+    Another option always available is to rewrite your problem for real and
+    imaginary parts separately.
+
+    Parameters
+    ----------
+    fun : callable
+        Right-hand side of the system: the time derivative of the state ``y``
+        at time ``t``. The calling signature is ``fun(t, y)``, where ``t`` is a
+        scalar and ``y`` is an ndarray with ``len(y) = len(y0)``. Additional
+        arguments need to be passed if ``args`` is used (see documentation of
+        ``args`` argument). ``fun`` must return an array of the same shape as
+        ``y``. See `vectorized` for more information.
+    t_span : 2-member sequence
+        Interval of integration (t0, tf). The solver starts with t=t0 and
+        integrates until it reaches t=tf. Both t0 and tf must be floats
+        or values interpretable by the float conversion function.
+    y0 : array_like, shape (n,)
+        Initial state. For problems in the complex domain, pass `y0` with a
+        complex data type (even if the initial value is purely real).
+    method : string or `OdeSolver`, optional
+        Integration method to use:
+
+            * 'RK45' (default): Explicit Runge-Kutta method of order 5(4) [1]_.
+              The error is controlled assuming accuracy of the fourth-order
+              method, but steps are taken using the fifth-order accurate
+              formula (local extrapolation is done). A quartic interpolation
+              polynomial is used for the dense output [2]_. Can be applied in
+              the complex domain.
+            * 'RK23': Explicit Runge-Kutta method of order 3(2) [3]_. The error
+              is controlled assuming accuracy of the second-order method, but
+              steps are taken using the third-order accurate formula (local
+              extrapolation is done). A cubic Hermite polynomial is used for the
+              dense output. Can be applied in the complex domain.
+            * 'DOP853': Explicit Runge-Kutta method of order 8 [13]_.
+              Python implementation of the "DOP853" algorithm originally
+              written in Fortran [14]_. A 7-th order interpolation polynomial
+              accurate to 7-th order is used for the dense output.
+              Can be applied in the complex domain.
+            * 'Radau': Implicit Runge-Kutta method of the Radau IIA family of
+              order 5 [4]_. The error is controlled with a third-order accurate
+              embedded formula. A cubic polynomial which satisfies the
+              collocation conditions is used for the dense output.
+            * 'BDF': Implicit multi-step variable-order (1 to 5) method based
+              on a backward differentiation formula for the derivative
+              approximation [5]_. The implementation follows the one described
+              in [6]_. A quasi-constant step scheme is used and accuracy is
+              enhanced using the NDF modification. Can be applied in the
+              complex domain.
+            * 'LSODA': Adams/BDF method with automatic stiffness detection and
+              switching [7]_, [8]_. This is a wrapper of the Fortran solver
+              from ODEPACK.
+
+        Explicit Runge-Kutta methods ('RK23', 'RK45', 'DOP853') should be used
+        for non-stiff problems and implicit methods ('Radau', 'BDF') for
+        stiff problems [9]_. Among Runge-Kutta methods, 'DOP853' is recommended
+        for solving with high precision (low values of `rtol` and `atol`).
+
+        If not sure, first try to run 'RK45'. If it makes unusually many
+        iterations, diverges, or fails, your problem is likely to be stiff and
+        you should use 'Radau' or 'BDF'. 'LSODA' can also be a good universal
+        choice, but it might be somewhat less convenient to work with as it
+        wraps old Fortran code.
+
+        You can also pass an arbitrary class derived from `OdeSolver` which
+        implements the solver.
+    t_eval : array_like or None, optional
+        Times at which to store the computed solution, must be sorted and lie
+        within `t_span`. If None (default), use points selected by the solver.
+    dense_output : bool, optional
+        Whether to compute a continuous solution. Default is False.
+    events : callable, or list of callables, optional
+        Events to track. If None (default), no events will be tracked.
+        Each event occurs at the zeros of a continuous function of time and
+        state. Each function must have the signature ``event(t, y)`` where
+        additional argument have to be passed if ``args`` is used (see
+        documentation of ``args`` argument). Each function must return a
+        float. The solver will find an accurate value of `t` at which
+        ``event(t, y(t)) = 0`` using a root-finding algorithm. By default,
+        all zeros will be found. The solver looks for a sign change over
+        each step, so if multiple zero crossings occur within one step,
+        events may be missed. Additionally each `event` function might
+        have the following attributes:
+
+            terminal: bool or int, optional
+                When boolean, whether to terminate integration if this event occurs.
+                When integral, termination occurs after the specified the number of
+                occurrences of this event.
+                Implicitly False if not assigned.
+            direction: float, optional
+                Direction of a zero crossing. If `direction` is positive,
+                `event` will only trigger when going from negative to positive,
+                and vice versa if `direction` is negative. If 0, then either
+                direction will trigger event. Implicitly 0 if not assigned.
+
+        You can assign attributes like ``event.terminal = True`` to any
+        function in Python.
+    vectorized : bool, optional
+        Whether `fun` can be called in a vectorized fashion. Default is False.
+
+        If ``vectorized`` is False, `fun` will always be called with ``y`` of
+        shape ``(n,)``, where ``n = len(y0)``.
+
+        If ``vectorized`` is True, `fun` may be called with ``y`` of shape
+        ``(n, k)``, where ``k`` is an integer. In this case, `fun` must behave
+        such that ``fun(t, y)[:, i] == fun(t, y[:, i])`` (i.e. each column of
+        the returned array is the time derivative of the state corresponding
+        with a column of ``y``).
+
+        Setting ``vectorized=True`` allows for faster finite difference
+        approximation of the Jacobian by methods 'Radau' and 'BDF', but
+        will result in slower execution for other methods and for 'Radau' and
+        'BDF' in some circumstances (e.g. small ``len(y0)``).
+    args : tuple, optional
+        Additional arguments to pass to the user-defined functions.  If given,
+        the additional arguments are passed to all user-defined functions.
+        So if, for example, `fun` has the signature ``fun(t, y, a, b, c)``,
+        then `jac` (if given) and any event functions must have the same
+        signature, and `args` must be a tuple of length 3.
+    **options
+        Options passed to a chosen solver. All options available for already
+        implemented solvers are listed below.
+    first_step : float or None, optional
+        Initial step size. Default is `None` which means that the algorithm
+        should choose.
+    max_step : float, optional
+        Maximum allowed step size. Default is np.inf, i.e., the step size is not
+        bounded and determined solely by the solver.
+    rtol, atol : float or array_like, optional
+        Relative and absolute tolerances. The solver keeps the local error
+        estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
+        relative accuracy (number of correct digits), while `atol` controls
+        absolute accuracy (number of correct decimal places). To achieve the
+        desired `rtol`, set `atol` to be smaller than the smallest value that
+        can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
+        allowable error. If `atol` is larger than ``rtol * abs(y)`` the
+        number of correct digits is not guaranteed. Conversely, to achieve the
+        desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
+        than `atol`. If components of y have different scales, it might be
+        beneficial to set different `atol` values for different components by
+        passing array_like with shape (n,) for `atol`. Default values are
+        1e-3 for `rtol` and 1e-6 for `atol`.
+    jac : array_like, sparse_matrix, callable or None, optional
+        Jacobian matrix of the right-hand side of the system with respect
+        to y, required by the 'Radau', 'BDF' and 'LSODA' method. The
+        Jacobian matrix has shape (n, n) and its element (i, j) is equal to
+        ``d f_i / d y_j``.  There are three ways to define the Jacobian:
+
+            * If array_like or sparse_matrix, the Jacobian is assumed to
+              be constant. Not supported by 'LSODA'.
+            * If callable, the Jacobian is assumed to depend on both
+              t and y; it will be called as ``jac(t, y)``, as necessary.
+              Additional arguments have to be passed if ``args`` is
+              used (see documentation of ``args`` argument).
+              For 'Radau' and 'BDF' methods, the return value might be a
+              sparse matrix.
+            * If None (default), the Jacobian will be approximated by
+              finite differences.
+
+        It is generally recommended to provide the Jacobian rather than
+        relying on a finite-difference approximation.
+    jac_sparsity : array_like, sparse matrix or None, optional
+        Defines a sparsity structure of the Jacobian matrix for a finite-
+        difference approximation. Its shape must be (n, n). This argument
+        is ignored if `jac` is not `None`. If the Jacobian has only few
+        non-zero elements in *each* row, providing the sparsity structure
+        will greatly speed up the computations [10]_. A zero entry means that
+        a corresponding element in the Jacobian is always zero. If None
+        (default), the Jacobian is assumed to be dense.
+        Not supported by 'LSODA', see `lband` and `uband` instead.
+    lband, uband : int or None, optional
+        Parameters defining the bandwidth of the Jacobian for the 'LSODA'
+        method, i.e., ``jac[i, j] != 0 only for i - lband <= j <= i + uband``.
+        Default is None. Setting these requires your jac routine to return the
+        Jacobian in the packed format: the returned array must have ``n``
+        columns and ``uband + lband + 1`` rows in which Jacobian diagonals are
+        written. Specifically ``jac_packed[uband + i - j , j] = jac[i, j]``.
+        The same format is used in `scipy.linalg.solve_banded` (check for an
+        illustration).  These parameters can be also used with ``jac=None`` to
+        reduce the number of Jacobian elements estimated by finite differences.
+    min_step : float, optional
+        The minimum allowed step size for 'LSODA' method.
+        By default `min_step` is zero.
+
+    Returns
+    -------
+    Bunch object with the following fields defined:
+    t : ndarray, shape (n_points,)
+        Time points.
+    y : ndarray, shape (n, n_points)
+        Values of the solution at `t`.
+    sol : `OdeSolution` or None
+        Found solution as `OdeSolution` instance; None if `dense_output` was
+        set to False.
+    t_events : list of ndarray or None
+        Contains for each event type a list of arrays at which an event of
+        that type event was detected. None if `events` was None.
+    y_events : list of ndarray or None
+        For each value of `t_events`, the corresponding value of the solution.
+        None if `events` was None.
+    nfev : int
+        Number of evaluations of the right-hand side.
+    njev : int
+        Number of evaluations of the Jacobian.
+    nlu : int
+        Number of LU decompositions.
+    status : int
+        Reason for algorithm termination:
+
+            * -1: Integration step failed.
+            *  0: The solver successfully reached the end of `tspan`.
+            *  1: A termination event occurred.
+
+    message : string
+        Human-readable description of the termination reason.
+    success : bool
+        True if the solver reached the interval end or a termination event
+        occurred (``status >= 0``).
+
+    References
+    ----------
+    .. [1] J. R. Dormand, P. J. Prince, "A family of embedded Runge-Kutta
+           formulae", Journal of Computational and Applied Mathematics, Vol. 6,
+           No. 1, pp. 19-26, 1980.
+    .. [2] L. W. Shampine, "Some Practical Runge-Kutta Formulas", Mathematics
+           of Computation,, Vol. 46, No. 173, pp. 135-150, 1986.
+    .. [3] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas",
+           Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989.
+    .. [4] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations II:
+           Stiff and Differential-Algebraic Problems", Sec. IV.8.
+    .. [5] `Backward Differentiation Formula
+            <https://en.wikipedia.org/wiki/Backward_differentiation_formula>`_
+            on Wikipedia.
+    .. [6] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
+           COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
+    .. [7] A. C. Hindmarsh, "ODEPACK, A Systematized Collection of ODE
+           Solvers," IMACS Transactions on Scientific Computation, Vol 1.,
+           pp. 55-64, 1983.
+    .. [8] L. Petzold, "Automatic selection of methods for solving stiff and
+           nonstiff systems of ordinary differential equations", SIAM Journal
+           on Scientific and Statistical Computing, Vol. 4, No. 1, pp. 136-148,
+           1983.
+    .. [9] `Stiff equation <https://en.wikipedia.org/wiki/Stiff_equation>`_ on
+           Wikipedia.
+    .. [10] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
+            sparse Jacobian matrices", Journal of the Institute of Mathematics
+            and its Applications, 13, pp. 117-120, 1974.
+    .. [11] `Cauchy-Riemann equations
+             <https://en.wikipedia.org/wiki/Cauchy-Riemann_equations>`_ on
+             Wikipedia.
+    .. [12] `Lotka-Volterra equations
+            <https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations>`_
+            on Wikipedia.
+    .. [13] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
+            Equations I: Nonstiff Problems", Sec. II.
+    .. [14] `Page with original Fortran code of DOP853
+            <http://www.unige.ch/~hairer/software.html>`_.
+
+    Examples
+    --------
+    Basic exponential decay showing automatically chosen time points.
+
+    >>> import numpy as np
+    >>> from scipy.integrate import solve_ivp
+    >>> def exponential_decay(t, y): return -0.5 * y
+    >>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8])
+    >>> print(sol.t)
+    [ 0.          0.11487653  1.26364188  3.06061781  4.81611105  6.57445806
+      8.33328988 10.        ]
+    >>> print(sol.y)
+    [[2.         1.88836035 1.06327177 0.43319312 0.18017253 0.07483045
+      0.03107158 0.01350781]
+     [4.         3.7767207  2.12654355 0.86638624 0.36034507 0.14966091
+      0.06214316 0.02701561]
+     [8.         7.5534414  4.25308709 1.73277247 0.72069014 0.29932181
+      0.12428631 0.05403123]]
+
+    Specifying points where the solution is desired.
+
+    >>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8],
+    ...                 t_eval=[0, 1, 2, 4, 10])
+    >>> print(sol.t)
+    [ 0  1  2  4 10]
+    >>> print(sol.y)
+    [[2.         1.21305369 0.73534021 0.27066736 0.01350938]
+     [4.         2.42610739 1.47068043 0.54133472 0.02701876]
+     [8.         4.85221478 2.94136085 1.08266944 0.05403753]]
+
+    Cannon fired upward with terminal event upon impact. The ``terminal`` and
+    ``direction`` fields of an event are applied by monkey patching a function.
+    Here ``y[0]`` is position and ``y[1]`` is velocity. The projectile starts
+    at position 0 with velocity +10. Note that the integration never reaches
+    t=100 because the event is terminal.
+
+    >>> def upward_cannon(t, y): return [y[1], -0.5]
+    >>> def hit_ground(t, y): return y[0]
+    >>> hit_ground.terminal = True
+    >>> hit_ground.direction = -1
+    >>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10], events=hit_ground)
+    >>> print(sol.t_events)
+    [array([40.])]
+    >>> print(sol.t)
+    [0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02
+     1.11088891e-01 1.11098890e+00 1.11099890e+01 4.00000000e+01]
+
+    Use `dense_output` and `events` to find position, which is 100, at the apex
+    of the cannonball's trajectory. Apex is not defined as terminal, so both
+    apex and hit_ground are found. There is no information at t=20, so the sol
+    attribute is used to evaluate the solution. The sol attribute is returned
+    by setting ``dense_output=True``. Alternatively, the `y_events` attribute
+    can be used to access the solution at the time of the event.
+
+    >>> def apex(t, y): return y[1]
+    >>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10],
+    ...                 events=(hit_ground, apex), dense_output=True)
+    >>> print(sol.t_events)
+    [array([40.]), array([20.])]
+    >>> print(sol.t)
+    [0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02
+     1.11088891e-01 1.11098890e+00 1.11099890e+01 4.00000000e+01]
+    >>> print(sol.sol(sol.t_events[1][0]))
+    [100.   0.]
+    >>> print(sol.y_events)
+    [array([[-5.68434189e-14, -1.00000000e+01]]),
+     array([[1.00000000e+02, 1.77635684e-15]])]
+
+    As an example of a system with additional parameters, we'll implement
+    the Lotka-Volterra equations [12]_.
+
+    >>> def lotkavolterra(t, z, a, b, c, d):
+    ...     x, y = z
+    ...     return [a*x - b*x*y, -c*y + d*x*y]
+    ...
+
+    We pass in the parameter values a=1.5, b=1, c=3 and d=1 with the `args`
+    argument.
+
+    >>> sol = solve_ivp(lotkavolterra, [0, 15], [10, 5], args=(1.5, 1, 3, 1),
+    ...                 dense_output=True)
+
+    Compute a dense solution and plot it.
+
+    >>> t = np.linspace(0, 15, 300)
+    >>> z = sol.sol(t)
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(t, z.T)
+    >>> plt.xlabel('t')
+    >>> plt.legend(['x', 'y'], shadow=True)
+    >>> plt.title('Lotka-Volterra System')
+    >>> plt.show()
+
+    A couple examples of using solve_ivp to solve the differential
+    equation ``y' = Ay`` with complex matrix ``A``.
+
+    >>> A = np.array([[-0.25 + 0.14j, 0, 0.33 + 0.44j],
+    ...               [0.25 + 0.58j, -0.2 + 0.14j, 0],
+    ...               [0, 0.2 + 0.4j, -0.1 + 0.97j]])
+
+    Solving an IVP with ``A`` from above and ``y`` as 3x1 vector:
+
+    >>> def deriv_vec(t, y):
+    ...     return A @ y
+    >>> result = solve_ivp(deriv_vec, [0, 25],
+    ...                    np.array([10 + 0j, 20 + 0j, 30 + 0j]),
+    ...                    t_eval=np.linspace(0, 25, 101))
+    >>> print(result.y[:, 0])
+    [10.+0.j 20.+0.j 30.+0.j]
+    >>> print(result.y[:, -1])
+    [18.46291039+45.25653651j 10.01569306+36.23293216j
+     -4.98662741+80.07360388j]
+
+    Solving an IVP with ``A`` from above with ``y`` as 3x3 matrix :
+
+    >>> def deriv_mat(t, y):
+    ...     return (A @ y.reshape(3, 3)).flatten()
+    >>> y0 = np.array([[2 + 0j, 3 + 0j, 4 + 0j],
+    ...                [5 + 0j, 6 + 0j, 7 + 0j],
+    ...                [9 + 0j, 34 + 0j, 78 + 0j]])
+
+    >>> result = solve_ivp(deriv_mat, [0, 25], y0.flatten(),
+    ...                    t_eval=np.linspace(0, 25, 101))
+    >>> print(result.y[:, 0].reshape(3, 3))
+    [[ 2.+0.j  3.+0.j  4.+0.j]
+     [ 5.+0.j  6.+0.j  7.+0.j]
+     [ 9.+0.j 34.+0.j 78.+0.j]]
+    >>> print(result.y[:, -1].reshape(3, 3))
+    [[  5.67451179 +12.07938445j  17.2888073  +31.03278837j
+        37.83405768 +63.25138759j]
+     [  3.39949503 +11.82123994j  21.32530996 +44.88668871j
+        53.17531184+103.80400411j]
+     [ -2.26105874 +22.19277664j -15.1255713  +70.19616341j
+       -38.34616845+153.29039931j]]
+
+
+    """
+    if method not in METHODS and not (
+            inspect.isclass(method) and issubclass(method, OdeSolver)):
+        raise ValueError(f"`method` must be one of {METHODS} or OdeSolver class.")
+
+    t0, tf = map(float, t_span)
+
+    if args is not None:
+        # Wrap the user's fun (and jac, if given) in lambdas to hide the
+        # additional parameters.  Pass in the original fun as a keyword
+        # argument to keep it in the scope of the lambda.
+        try:
+            _ = [*(args)]
+        except TypeError as exp:
+            suggestion_tuple = (
+                "Supplied 'args' cannot be unpacked. Please supply `args`"
+                f" as a tuple (e.g. `args=({args},)`)"
+            )
+            raise TypeError(suggestion_tuple) from exp
+
+        def fun(t, x, fun=fun):
+            return fun(t, x, *args)
+        jac = options.get('jac')
+        if callable(jac):
+            options['jac'] = lambda t, x: jac(t, x, *args)
+
+    if t_eval is not None:
+        t_eval = np.asarray(t_eval)
+        if t_eval.ndim != 1:
+            raise ValueError("`t_eval` must be 1-dimensional.")
+
+        if np.any(t_eval < min(t0, tf)) or np.any(t_eval > max(t0, tf)):
+            raise ValueError("Values in `t_eval` are not within `t_span`.")
+
+        d = np.diff(t_eval)
+        if tf > t0 and np.any(d <= 0) or tf < t0 and np.any(d >= 0):
+            raise ValueError("Values in `t_eval` are not properly sorted.")
+
+        if tf > t0:
+            t_eval_i = 0
+        else:
+            # Make order of t_eval decreasing to use np.searchsorted.
+            t_eval = t_eval[::-1]
+            # This will be an upper bound for slices.
+            t_eval_i = t_eval.shape[0]
+
+    if method in METHODS:
+        method = METHODS[method]
+
+    solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)
+
+    if t_eval is None:
+        ts = [t0]
+        ys = [y0]
+    elif t_eval is not None and dense_output:
+        ts = []
+        ti = [t0]
+        ys = []
+    else:
+        ts = []
+        ys = []
+
+    interpolants = []
+
+    if events is not None:
+        events, max_events, event_dir = prepare_events(events)
+        event_count = np.zeros(len(events))
+        if args is not None:
+            # Wrap user functions in lambdas to hide the additional parameters.
+            # The original event function is passed as a keyword argument to the
+            # lambda to keep the original function in scope (i.e., avoid the
+            # late binding closure "gotcha").
+            events = [lambda t, x, event=event: event(t, x, *args)
+                      for event in events]
+        g = [event(t0, y0) for event in events]
+        t_events = [[] for _ in range(len(events))]
+        y_events = [[] for _ in range(len(events))]
+    else:
+        t_events = None
+        y_events = None
+
+    status = None
+    while status is None:
+        message = solver.step()
+
+        if solver.status == 'finished':
+            status = 0
+        elif solver.status == 'failed':
+            status = -1
+            break
+
+        t_old = solver.t_old
+        t = solver.t
+        y = solver.y
+
+        if dense_output:
+            sol = solver.dense_output()
+            interpolants.append(sol)
+        else:
+            sol = None
+
+        if events is not None:
+            g_new = [event(t, y) for event in events]
+            active_events = find_active_events(g, g_new, event_dir)
+            if active_events.size > 0:
+                if sol is None:
+                    sol = solver.dense_output()
+
+                event_count[active_events] += 1
+                root_indices, roots, terminate = handle_events(
+                    sol, events, active_events, event_count, max_events,
+                    t_old, t)
+
+                for e, te in zip(root_indices, roots):
+                    t_events[e].append(te)
+                    y_events[e].append(sol(te))
+
+                if terminate:
+                    status = 1
+                    t = roots[-1]
+                    y = sol(t)
+
+            g = g_new
+
+        if t_eval is None:
+            donot_append = (len(ts) > 1 and
+                            ts[-1] == t and
+                            dense_output)
+            if not donot_append:
+                ts.append(t)
+                ys.append(y)
+            else:
+                if len(interpolants) > 0:
+                    interpolants.pop()
+        else:
+            # The value in t_eval equal to t will be included.
+            if solver.direction > 0:
+                t_eval_i_new = np.searchsorted(t_eval, t, side='right')
+                t_eval_step = t_eval[t_eval_i:t_eval_i_new]
+            else:
+                t_eval_i_new = np.searchsorted(t_eval, t, side='left')
+                # It has to be done with two slice operations, because
+                # you can't slice to 0th element inclusive using backward
+                # slicing.
+                t_eval_step = t_eval[t_eval_i_new:t_eval_i][::-1]
+
+            if t_eval_step.size > 0:
+                if sol is None:
+                    sol = solver.dense_output()
+                ts.append(t_eval_step)
+                ys.append(sol(t_eval_step))
+                t_eval_i = t_eval_i_new
+
+        if t_eval is not None and dense_output:
+            ti.append(t)
+
+    message = MESSAGES.get(status, message)
+
+    if t_events is not None:
+        t_events = [np.asarray(te) for te in t_events]
+        y_events = [np.asarray(ye) for ye in y_events]
+
+    if t_eval is None:
+        ts = np.array(ts)
+        ys = np.vstack(ys).T
+    elif ts:
+        ts = np.hstack(ts)
+        ys = np.hstack(ys)
+
+    if dense_output:
+        if t_eval is None:
+            sol = OdeSolution(
+                ts, interpolants, alt_segment=True if method in [BDF, LSODA] else False
+            )
+        else:
+            sol = OdeSolution(
+                ti, interpolants, alt_segment=True if method in [BDF, LSODA] else False
+            )
+    else:
+        sol = None
+
+    return OdeResult(t=ts, y=ys, sol=sol, t_events=t_events, y_events=y_events,
+                     nfev=solver.nfev, njev=solver.njev, nlu=solver.nlu,
+                     status=status, message=message, success=status >= 0)
diff --git a/shared/package-files/0c/0c6afc23568d55b3e9ac914f9c5361e3033e778aa5b58d3cc82835fc5c638679/difflib.py b/shared/package-files/0c/0c6afc23568d55b3e9ac914f9c5361e3033e778aa5b58d3cc82835fc5c638679/difflib.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba0b256969ebff7cdc23ef2c51df29107ad2d867
--- /dev/null
+++ b/shared/package-files/0c/0c6afc23568d55b3e9ac914f9c5361e3033e778aa5b58d3cc82835fc5c638679/difflib.py
@@ -0,0 +1,2056 @@
+"""
+Module difflib -- helpers for computing deltas between objects.
+
+Function get_close_matches(word, possibilities, n=3, cutoff=0.6):
+    Use SequenceMatcher to return list of the best "good enough" matches.
+
+Function context_diff(a, b):
+    For two lists of strings, return a delta in context diff format.
+
+Function ndiff(a, b):
+    Return a delta: the difference between `a` and `b` (lists of strings).
+
+Function restore(delta, which):
+    Return one of the two sequences that generated an ndiff delta.
+
+Function unified_diff(a, b):
+    For two lists of strings, return a delta in unified diff format.
+
+Class SequenceMatcher:
+    A flexible class for comparing pairs of sequences of any type.
+
+Class Differ:
+    For producing human-readable deltas from sequences of lines of text.
+
+Class HtmlDiff:
+    For producing HTML side by side comparison with change highlights.
+"""
+
+__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',
+           'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff',
+           'unified_diff', 'diff_bytes', 'HtmlDiff', 'Match']
+
+from heapq import nlargest as _nlargest
+from collections import namedtuple as _namedtuple
+from types import GenericAlias
+
+Match = _namedtuple('Match', 'a b size')
+
+def _calculate_ratio(matches, length):
+    if length:
+        return 2.0 * matches / length
+    return 1.0
+
+class SequenceMatcher:
+
+    """
+    SequenceMatcher is a flexible class for comparing pairs of sequences of
+    any type, so long as the sequence elements are hashable.  The basic
+    algorithm predates, and is a little fancier than, an algorithm
+    published in the late 1980's by Ratcliff and Obershelp under the
+    hyperbolic name "gestalt pattern matching".  The basic idea is to find
+    the longest contiguous matching subsequence that contains no "junk"
+    elements (R-O doesn't address junk).  The same idea is then applied
+    recursively to the pieces of the sequences to the left and to the right
+    of the matching subsequence.  This does not yield minimal edit
+    sequences, but does tend to yield matches that "look right" to people.
+
+    SequenceMatcher tries to compute a "human-friendly diff" between two
+    sequences.  Unlike e.g. UNIX(tm) diff, the fundamental notion is the
+    longest *contiguous* & junk-free matching subsequence.  That's what
+    catches peoples' eyes.  The Windows(tm) windiff has another interesting
+    notion, pairing up elements that appear uniquely in each sequence.
+    That, and the method here, appear to yield more intuitive difference
+    reports than does diff.  This method appears to be the least vulnerable
+    to syncing up on blocks of "junk lines", though (like blank lines in
+    ordinary text files, or maybe "<P>" lines in HTML files).  That may be
+    because this is the only method of the 3 that has a *concept* of
+    "junk" <wink>.
+
+    Example, comparing two strings, and considering blanks to be "junk":
+
+    >>> s = SequenceMatcher(lambda x: x == " ",
+    ...                     "private Thread currentThread;",
+    ...                     "private volatile Thread currentThread;")
+    >>>
+
+    .ratio() returns a float in [0, 1], measuring the "similarity" of the
+    sequences.  As a rule of thumb, a .ratio() value over 0.6 means the
+    sequences are close matches:
+
+    >>> print(round(s.ratio(), 3))
+    0.866
+    >>>
+
+    If you're only interested in where the sequences match,
+    .get_matching_blocks() is handy:
+
+    >>> for block in s.get_matching_blocks():
+    ...     print("a[%d] and b[%d] match for %d elements" % block)
+    a[0] and b[0] match for 8 elements
+    a[8] and b[17] match for 21 elements
+    a[29] and b[38] match for 0 elements
+
+    Note that the last tuple returned by .get_matching_blocks() is always a
+    dummy, (len(a), len(b), 0), and this is the only case in which the last
+    tuple element (number of elements matched) is 0.
+
+    If you want to know how to change the first sequence into the second,
+    use .get_opcodes():
+
+    >>> for opcode in s.get_opcodes():
+    ...     print("%6s a[%d:%d] b[%d:%d]" % opcode)
+     equal a[0:8] b[0:8]
+    insert a[8:8] b[8:17]
+     equal a[8:29] b[17:38]
+
+    See the Differ class for a fancy human-friendly file differencer, which
+    uses SequenceMatcher both to compare sequences of lines, and to compare
+    sequences of characters within similar (near-matching) lines.
+
+    See also function get_close_matches() in this module, which shows how
+    simple code building on SequenceMatcher can be used to do useful work.
+
+    Timing:  Basic R-O is cubic time worst case and quadratic time expected
+    case.  SequenceMatcher is quadratic time for the worst case and has
+    expected-case behavior dependent in a complicated way on how many
+    elements the sequences have in common; best case time is linear.
+    """
+
+    def __init__(self, isjunk=None, a='', b='', autojunk=True):
+        """Construct a SequenceMatcher.
+
+        Optional arg isjunk is None (the default), or a one-argument
+        function that takes a sequence element and returns true iff the
+        element is junk.  None is equivalent to passing "lambda x: 0", i.e.
+        no elements are considered to be junk.  For example, pass
+            lambda x: x in " \\t"
+        if you're comparing lines as sequences of characters, and don't
+        want to synch up on blanks or hard tabs.
+
+        Optional arg a is the first of two sequences to be compared.  By
+        default, an empty string.  The elements of a must be hashable.  See
+        also .set_seqs() and .set_seq1().
+
+        Optional arg b is the second of two sequences to be compared.  By
+        default, an empty string.  The elements of b must be hashable. See
+        also .set_seqs() and .set_seq2().
+
+        Optional arg autojunk should be set to False to disable the
+        "automatic junk heuristic" that treats popular elements as junk
+        (see module documentation for more information).
+        """
+
+        # Members:
+        # a
+        #      first sequence
+        # b
+        #      second sequence; differences are computed as "what do
+        #      we need to do to 'a' to change it into 'b'?"
+        # b2j
+        #      for x in b, b2j[x] is a list of the indices (into b)
+        #      at which x appears; junk and popular elements do not appear
+        # fullbcount
+        #      for x in b, fullbcount[x] == the number of times x
+        #      appears in b; only materialized if really needed (used
+        #      only for computing quick_ratio())
+        # matching_blocks
+        #      a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];
+        #      ascending & non-overlapping in i and in j; terminated by
+        #      a dummy (len(a), len(b), 0) sentinel
+        # opcodes
+        #      a list of (tag, i1, i2, j1, j2) tuples, where tag is
+        #      one of
+        #          'replace'   a[i1:i2] should be replaced by b[j1:j2]
+        #          'delete'    a[i1:i2] should be deleted
+        #          'insert'    b[j1:j2] should be inserted
+        #          'equal'     a[i1:i2] == b[j1:j2]
+        # isjunk
+        #      a user-supplied function taking a sequence element and
+        #      returning true iff the element is "junk" -- this has
+        #      subtle but helpful effects on the algorithm, which I'll
+        #      get around to writing up someday <0.9 wink>.
+        #      DON'T USE!  Only __chain_b uses this.  Use "in self.bjunk".
+        # bjunk
+        #      the items in b for which isjunk is True.
+        # bpopular
+        #      nonjunk items in b treated as junk by the heuristic (if used).
+
+        self.isjunk = isjunk
+        self.a = self.b = None
+        self.autojunk = autojunk
+        self.set_seqs(a, b)
+
+    def set_seqs(self, a, b):
+        """Set the two sequences to be compared.
+
+        >>> s = SequenceMatcher()
+        >>> s.set_seqs("abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        """
+
+        self.set_seq1(a)
+        self.set_seq2(b)
+
+    def set_seq1(self, a):
+        """Set the first sequence to be compared.
+
+        The second sequence to be compared is not changed.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.set_seq1("bcde")
+        >>> s.ratio()
+        1.0
+        >>>
+
+        SequenceMatcher computes and caches detailed information about the
+        second sequence, so if you want to compare one sequence S against
+        many sequences, use .set_seq2(S) once and call .set_seq1(x)
+        repeatedly for each of the other sequences.
+
+        See also set_seqs() and set_seq2().
+        """
+
+        if a is self.a:
+            return
+        self.a = a
+        self.matching_blocks = self.opcodes = None
+
+    def set_seq2(self, b):
+        """Set the second sequence to be compared.
+
+        The first sequence to be compared is not changed.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.set_seq2("abcd")
+        >>> s.ratio()
+        1.0
+        >>>
+
+        SequenceMatcher computes and caches detailed information about the
+        second sequence, so if you want to compare one sequence S against
+        many sequences, use .set_seq2(S) once and call .set_seq1(x)
+        repeatedly for each of the other sequences.
+
+        See also set_seqs() and set_seq1().
+        """
+
+        if b is self.b:
+            return
+        self.b = b
+        self.matching_blocks = self.opcodes = None
+        self.fullbcount = None
+        self.__chain_b()
+
+    # For each element x in b, set b2j[x] to a list of the indices in
+    # b where x appears; the indices are in increasing order; note that
+    # the number of times x appears in b is len(b2j[x]) ...
+    # when self.isjunk is defined, junk elements don't show up in this
+    # map at all, which stops the central find_longest_match method
+    # from starting any matching block at a junk element ...
+    # b2j also does not contain entries for "popular" elements, meaning
+    # elements that account for more than 1 + 1% of the total elements, and
+    # when the sequence is reasonably large (>= 200 elements); this can
+    # be viewed as an adaptive notion of semi-junk, and yields an enormous
+    # speedup when, e.g., comparing program files with hundreds of
+    # instances of "return NULL;" ...
+    # note that this is only called when b changes; so for cross-product
+    # kinds of matches, it's best to call set_seq2 once, then set_seq1
+    # repeatedly
+
+    def __chain_b(self):
+        # Because isjunk is a user-defined (not C) function, and we test
+        # for junk a LOT, it's important to minimize the number of calls.
+        # Before the tricks described here, __chain_b was by far the most
+        # time-consuming routine in the whole module!  If anyone sees
+        # Jim Roskind, thank him again for profile.py -- I never would
+        # have guessed that.
+        # The first trick is to build b2j ignoring the possibility
+        # of junk.  I.e., we don't call isjunk at all yet.  Throwing
+        # out the junk later is much cheaper than building b2j "right"
+        # from the start.
+        b = self.b
+        self.b2j = b2j = {}
+
+        for i, elt in enumerate(b):
+            indices = b2j.setdefault(elt, [])
+            indices.append(i)
+
+        # Purge junk elements
+        self.bjunk = junk = set()
+        isjunk = self.isjunk
+        if isjunk:
+            for elt in b2j.keys():
+                if isjunk(elt):
+                    junk.add(elt)
+            for elt in junk: # separate loop avoids separate list of keys
+                del b2j[elt]
+
+        # Purge popular elements that are not junk
+        self.bpopular = popular = set()
+        n = len(b)
+        if self.autojunk and n >= 200:
+            ntest = n // 100 + 1
+            for elt, idxs in b2j.items():
+                if len(idxs) > ntest:
+                    popular.add(elt)
+            for elt in popular: # ditto; as fast for 1% deletion
+                del b2j[elt]
+
+    def find_longest_match(self, alo=0, ahi=None, blo=0, bhi=None):
+        """Find longest matching block in a[alo:ahi] and b[blo:bhi].
+
+        By default it will find the longest match in the entirety of a and b.
+
+        If isjunk is not defined:
+
+        Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
+            alo <= i <= i+k <= ahi
+            blo <= j <= j+k <= bhi
+        and for all (i',j',k') meeting those conditions,
+            k >= k'
+            i <= i'
+            and if i == i', j <= j'
+
+        In other words, of all maximal matching blocks, return one that
+        starts earliest in a, and of all those maximal matching blocks that
+        start earliest in a, return the one that starts earliest in b.
+
+        >>> s = SequenceMatcher(None, " abcd", "abcd abcd")
+        >>> s.find_longest_match(0, 5, 0, 9)
+        Match(a=0, b=4, size=5)
+
+        If isjunk is defined, first the longest matching block is
+        determined as above, but with the additional restriction that no
+        junk element appears in the block.  Then that block is extended as
+        far as possible by matching (only) junk elements on both sides.  So
+        the resulting block never matches on junk except as identical junk
+        happens to be adjacent to an "interesting" match.
+
+        Here's the same example as before, but considering blanks to be
+        junk.  That prevents " abcd" from matching the " abcd" at the tail
+        end of the second sequence directly.  Instead only the "abcd" can
+        match, and matches the leftmost "abcd" in the second sequence:
+
+        >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
+        >>> s.find_longest_match(0, 5, 0, 9)
+        Match(a=1, b=0, size=4)
+
+        If no blocks match, return (alo, blo, 0).
+
+        >>> s = SequenceMatcher(None, "ab", "c")
+        >>> s.find_longest_match(0, 2, 0, 1)
+        Match(a=0, b=0, size=0)
+        """
+
+        # CAUTION:  stripping common prefix or suffix would be incorrect.
+        # E.g.,
+        #    ab
+        #    acab
+        # Longest matching block is "ab", but if common prefix is
+        # stripped, it's "a" (tied with "b").  UNIX(tm) diff does so
+        # strip, so ends up claiming that ab is changed to acab by
+        # inserting "ca" in the middle.  That's minimal but unintuitive:
+        # "it's obvious" that someone inserted "ac" at the front.
+        # Windiff ends up at the same place as diff, but by pairing up
+        # the unique 'b's and then matching the first two 'a's.
+
+        a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__
+        if ahi is None:
+            ahi = len(a)
+        if bhi is None:
+            bhi = len(b)
+        besti, bestj, bestsize = alo, blo, 0
+        # find longest junk-free match
+        # during an iteration of the loop, j2len[j] = length of longest
+        # junk-free match ending with a[i-1] and b[j]
+        j2len = {}
+        nothing = []
+        for i in range(alo, ahi):
+            # look at all instances of a[i] in b; note that because
+            # b2j has no junk keys, the loop is skipped if a[i] is junk
+            j2lenget = j2len.get
+            newj2len = {}
+            for j in b2j.get(a[i], nothing):
+                # a[i] matches b[j]
+                if j < blo:
+                    continue
+                if j >= bhi:
+                    break
+                k = newj2len[j] = j2lenget(j-1, 0) + 1
+                if k > bestsize:
+                    besti, bestj, bestsize = i-k+1, j-k+1, k
+            j2len = newj2len
+
+        # Extend the best by non-junk elements on each end.  In particular,
+        # "popular" non-junk elements aren't in b2j, which greatly speeds
+        # the inner loop above, but also means "the best" match so far
+        # doesn't contain any junk *or* popular non-junk elements.
+        while besti > alo and bestj > blo and \
+              not isbjunk(b[bestj-1]) and \
+              a[besti-1] == b[bestj-1]:
+            besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
+        while besti+bestsize < ahi and bestj+bestsize < bhi and \
+              not isbjunk(b[bestj+bestsize]) and \
+              a[besti+bestsize] == b[bestj+bestsize]:
+            bestsize += 1
+
+        # Now that we have a wholly interesting match (albeit possibly
+        # empty!), we may as well suck up the matching junk on each
+        # side of it too.  Can't think of a good reason not to, and it
+        # saves post-processing the (possibly considerable) expense of
+        # figuring out what to do with it.  In the case of an empty
+        # interesting match, this is clearly the right thing to do,
+        # because no other kind of match is possible in the regions.
+        while besti > alo and bestj > blo and \
+              isbjunk(b[bestj-1]) and \
+              a[besti-1] == b[bestj-1]:
+            besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
+        while besti+bestsize < ahi and bestj+bestsize < bhi and \
+              isbjunk(b[bestj+bestsize]) and \
+              a[besti+bestsize] == b[bestj+bestsize]:
+            bestsize = bestsize + 1
+
+        return Match(besti, bestj, bestsize)
+
+    def get_matching_blocks(self):
+        """Return list of triples describing matching subsequences.
+
+        Each triple is of the form (i, j, n), and means that
+        a[i:i+n] == b[j:j+n].  The triples are monotonically increasing in
+        i and in j.  New in Python 2.5, it's also guaranteed that if
+        (i, j, n) and (i', j', n') are adjacent triples in the list, and
+        the second is not the last triple in the list, then i+n != i' or
+        j+n != j'.  IOW, adjacent triples never describe adjacent equal
+        blocks.
+
+        The last triple is a dummy, (len(a), len(b), 0), and is the only
+        triple with n==0.
+
+        >>> s = SequenceMatcher(None, "abxcd", "abcd")
+        >>> list(s.get_matching_blocks())
+        [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]
+        """
+
+        if self.matching_blocks is not None:
+            return self.matching_blocks
+        la, lb = len(self.a), len(self.b)
+
+        # This is most naturally expressed as a recursive algorithm, but
+        # at least one user bumped into extreme use cases that exceeded
+        # the recursion limit on their box.  So, now we maintain a list
+        # ('queue`) of blocks we still need to look at, and append partial
+        # results to `matching_blocks` in a loop; the matches are sorted
+        # at the end.
+        queue = [(0, la, 0, lb)]
+        matching_blocks = []
+        while queue:
+            alo, ahi, blo, bhi = queue.pop()
+            i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi)
+            # a[alo:i] vs b[blo:j] unknown
+            # a[i:i+k] same as b[j:j+k]
+            # a[i+k:ahi] vs b[j+k:bhi] unknown
+            if k:   # if k is 0, there was no matching block
+                matching_blocks.append(x)
+                if alo < i and blo < j:
+                    queue.append((alo, i, blo, j))
+                if i+k < ahi and j+k < bhi:
+                    queue.append((i+k, ahi, j+k, bhi))
+        matching_blocks.sort()
+
+        # It's possible that we have adjacent equal blocks in the
+        # matching_blocks list now.  Starting with 2.5, this code was added
+        # to collapse them.
+        i1 = j1 = k1 = 0
+        non_adjacent = []
+        for i2, j2, k2 in matching_blocks:
+            # Is this block adjacent to i1, j1, k1?
+            if i1 + k1 == i2 and j1 + k1 == j2:
+                # Yes, so collapse them -- this just increases the length of
+                # the first block by the length of the second, and the first
+                # block so lengthened remains the block to compare against.
+                k1 += k2
+            else:
+                # Not adjacent.  Remember the first block (k1==0 means it's
+                # the dummy we started with), and make the second block the
+                # new block to compare against.
+                if k1:
+                    non_adjacent.append((i1, j1, k1))
+                i1, j1, k1 = i2, j2, k2
+        if k1:
+            non_adjacent.append((i1, j1, k1))
+
+        non_adjacent.append( (la, lb, 0) )
+        self.matching_blocks = list(map(Match._make, non_adjacent))
+        return self.matching_blocks
+
+    def get_opcodes(self):
+        """Return list of 5-tuples describing how to turn a into b.
+
+        Each tuple is of the form (tag, i1, i2, j1, j2).  The first tuple
+        has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the
+        tuple preceding it, and likewise for j1 == the previous j2.
+
+        The tags are strings, with these meanings:
+
+        'replace':  a[i1:i2] should be replaced by b[j1:j2]
+        'delete':   a[i1:i2] should be deleted.
+                    Note that j1==j2 in this case.
+        'insert':   b[j1:j2] should be inserted at a[i1:i1].
+                    Note that i1==i2 in this case.
+        'equal':    a[i1:i2] == b[j1:j2]
+
+        >>> a = "qabxcd"
+        >>> b = "abycdf"
+        >>> s = SequenceMatcher(None, a, b)
+        >>> for tag, i1, i2, j1, j2 in s.get_opcodes():
+        ...    print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
+        ...           (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))
+         delete a[0:1] (q) b[0:0] ()
+          equal a[1:3] (ab) b[0:2] (ab)
+        replace a[3:4] (x) b[2:3] (y)
+          equal a[4:6] (cd) b[3:5] (cd)
+         insert a[6:6] () b[5:6] (f)
+        """
+
+        if self.opcodes is not None:
+            return self.opcodes
+        i = j = 0
+        self.opcodes = answer = []
+        for ai, bj, size in self.get_matching_blocks():
+            # invariant:  we've pumped out correct diffs to change
+            # a[:i] into b[:j], and the next matching block is
+            # a[ai:ai+size] == b[bj:bj+size].  So we need to pump
+            # out a diff to change a[i:ai] into b[j:bj], pump out
+            # the matching block, and move (i,j) beyond the match
+            tag = ''
+            if i < ai and j < bj:
+                tag = 'replace'
+            elif i < ai:
+                tag = 'delete'
+            elif j < bj:
+                tag = 'insert'
+            if tag:
+                answer.append( (tag, i, ai, j, bj) )
+            i, j = ai+size, bj+size
+            # the list of matching blocks is terminated by a
+            # sentinel with size 0
+            if size:
+                answer.append( ('equal', ai, i, bj, j) )
+        return answer
+
+    def get_grouped_opcodes(self, n=3):
+        """ Isolate change clusters by eliminating ranges with no changes.
+
+        Return a generator of groups with up to n lines of context.
+        Each group is in the same format as returned by get_opcodes().
+
+        >>> from pprint import pprint
+        >>> a = list(map(str, range(1,40)))
+        >>> b = a[:]
+        >>> b[8:8] = ['i']     # Make an insertion
+        >>> b[20] += 'x'       # Make a replacement
+        >>> b[23:28] = []      # Make a deletion
+        >>> b[30] += 'y'       # Make another replacement
+        >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
+        [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
+         [('equal', 16, 19, 17, 20),
+          ('replace', 19, 20, 20, 21),
+          ('equal', 20, 22, 21, 23),
+          ('delete', 22, 27, 23, 23),
+          ('equal', 27, 30, 23, 26)],
+         [('equal', 31, 34, 27, 30),
+          ('replace', 34, 35, 30, 31),
+          ('equal', 35, 38, 31, 34)]]
+        """
+
+        codes = self.get_opcodes()
+        if not codes:
+            codes = [("equal", 0, 1, 0, 1)]
+        # Fixup leading and trailing groups if they show no changes.
+        if codes[0][0] == 'equal':
+            tag, i1, i2, j1, j2 = codes[0]
+            codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2
+        if codes[-1][0] == 'equal':
+            tag, i1, i2, j1, j2 = codes[-1]
+            codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)
+
+        nn = n + n
+        group = []
+        for tag, i1, i2, j1, j2 in codes:
+            # End the current group and start a new one whenever
+            # there is a large range with no changes.
+            if tag == 'equal' and i2-i1 > nn:
+                group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))
+                yield group
+                group = []
+                i1, j1 = max(i1, i2-n), max(j1, j2-n)
+            group.append((tag, i1, i2, j1 ,j2))
+        if group and not (len(group)==1 and group[0][0] == 'equal'):
+            yield group
+
+    def ratio(self):
+        """Return a measure of the sequences' similarity (float in [0,1]).
+
+        Where T is the total number of elements in both sequences, and
+        M is the number of matches, this is 2.0*M / T.
+        Note that this is 1 if the sequences are identical, and 0 if
+        they have nothing in common.
+
+        .ratio() is expensive to compute if you haven't already computed
+        .get_matching_blocks() or .get_opcodes(), in which case you may
+        want to try .quick_ratio() or .real_quick_ratio() first to get an
+        upper bound.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.quick_ratio()
+        0.75
+        >>> s.real_quick_ratio()
+        1.0
+        """
+
+        matches = sum(triple[-1] for triple in self.get_matching_blocks())
+        return _calculate_ratio(matches, len(self.a) + len(self.b))
+
+    def quick_ratio(self):
+        """Return an upper bound on ratio() relatively quickly.
+
+        This isn't defined beyond that it is an upper bound on .ratio(), and
+        is faster to compute.
+        """
+
+        # viewing a and b as multisets, set matches to the cardinality
+        # of their intersection; this counts the number of matches
+        # without regard to order, so is clearly an upper bound
+        if self.fullbcount is None:
+            self.fullbcount = fullbcount = {}
+            for elt in self.b:
+                fullbcount[elt] = fullbcount.get(elt, 0) + 1
+        fullbcount = self.fullbcount
+        # avail[x] is the number of times x appears in 'b' less the
+        # number of times we've seen it in 'a' so far ... kinda
+        avail = {}
+        availhas, matches = avail.__contains__, 0
+        for elt in self.a:
+            if availhas(elt):
+                numb = avail[elt]
+            else:
+                numb = fullbcount.get(elt, 0)
+            avail[elt] = numb - 1
+            if numb > 0:
+                matches = matches + 1
+        return _calculate_ratio(matches, len(self.a) + len(self.b))
+
+    def real_quick_ratio(self):
+        """Return an upper bound on ratio() very quickly.
+
+        This isn't defined beyond that it is an upper bound on .ratio(), and
+        is faster to compute than either .ratio() or .quick_ratio().
+        """
+
+        la, lb = len(self.a), len(self.b)
+        # can't have more matches than the number of elements in the
+        # shorter sequence
+        return _calculate_ratio(min(la, lb), la + lb)
+
+    __class_getitem__ = classmethod(GenericAlias)
+
+
+def get_close_matches(word, possibilities, n=3, cutoff=0.6):
+    """Use SequenceMatcher to return list of the best "good enough" matches.
+
+    word is a sequence for which close matches are desired (typically a
+    string).
+
+    possibilities is a list of sequences against which to match word
+    (typically a list of strings).
+
+    Optional arg n (default 3) is the maximum number of close matches to
+    return.  n must be > 0.
+
+    Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
+    that don't score at least that similar to word are ignored.
+
+    The best (no more than n) matches among the possibilities are returned
+    in a list, sorted by similarity score, most similar first.
+
+    >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
+    ['apple', 'ape']
+    >>> import keyword as _keyword
+    >>> get_close_matches("wheel", _keyword.kwlist)
+    ['while']
+    >>> get_close_matches("Apple", _keyword.kwlist)
+    []
+    >>> get_close_matches("accept", _keyword.kwlist)
+    ['except']
+    """
+
+    if not n >  0:
+        raise ValueError("n must be > 0: %r" % (n,))
+    if not 0.0 <= cutoff <= 1.0:
+        raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
+    result = []
+    s = SequenceMatcher()
+    s.set_seq2(word)
+    for x in possibilities:
+        s.set_seq1(x)
+        if s.real_quick_ratio() >= cutoff and \
+           s.quick_ratio() >= cutoff and \
+           s.ratio() >= cutoff:
+            result.append((s.ratio(), x))
+
+    # Move the best scorers to head of list
+    result = _nlargest(n, result)
+    # Strip scores for the best n matches
+    return [x for score, x in result]
+
+
+def _keep_original_ws(s, tag_s):
+    """Replace whitespace with the original whitespace characters in `s`"""
+    return ''.join(
+        c if tag_c == " " and c.isspace() else tag_c
+        for c, tag_c in zip(s, tag_s)
+    )
+
+
+
+class Differ:
+    r"""
+    Differ is a class for comparing sequences of lines of text, and
+    producing human-readable differences or deltas.  Differ uses
+    SequenceMatcher both to compare sequences of lines, and to compare
+    sequences of characters within similar (near-matching) lines.
+
+    Each line of a Differ delta begins with a two-letter code:
+
+        '- '    line unique to sequence 1
+        '+ '    line unique to sequence 2
+        '  '    line common to both sequences
+        '? '    line not present in either input sequence
+
+    Lines beginning with '? ' attempt to guide the eye to intraline
+    differences, and were not present in either input sequence.  These lines
+    can be confusing if the sequences contain tab characters.
+
+    Note that Differ makes no claim to produce a *minimal* diff.  To the
+    contrary, minimal diffs are often counter-intuitive, because they synch
+    up anywhere possible, sometimes accidental matches 100 pages apart.
+    Restricting synch points to contiguous matches preserves some notion of
+    locality, at the occasional cost of producing a longer diff.
+
+    Example: Comparing two texts.
+
+    First we set up the texts, sequences of individual single-line strings
+    ending with newlines (such sequences can also be obtained from the
+    `readlines()` method of file-like objects):
+
+    >>> text1 = '''  1. Beautiful is better than ugly.
+    ...   2. Explicit is better than implicit.
+    ...   3. Simple is better than complex.
+    ...   4. Complex is better than complicated.
+    ... '''.splitlines(keepends=True)
+    >>> len(text1)
+    4
+    >>> text1[0][-1]
+    '\n'
+    >>> text2 = '''  1. Beautiful is better than ugly.
+    ...   3.   Simple is better than complex.
+    ...   4. Complicated is better than complex.
+    ...   5. Flat is better than nested.
+    ... '''.splitlines(keepends=True)
+
+    Next we instantiate a Differ object:
+
+    >>> d = Differ()
+
+    Note that when instantiating a Differ object we may pass functions to
+    filter out line and character 'junk'.  See Differ.__init__ for details.
+
+    Finally, we compare the two:
+
+    >>> result = list(d.compare(text1, text2))
+
+    'result' is a list of strings, so let's pretty-print it:
+
+    >>> from pprint import pprint as _pprint
+    >>> _pprint(result)
+    ['    1. Beautiful is better than ugly.\n',
+     '-   2. Explicit is better than implicit.\n',
+     '-   3. Simple is better than complex.\n',
+     '+   3.   Simple is better than complex.\n',
+     '?     ++\n',
+     '-   4. Complex is better than complicated.\n',
+     '?            ^                     ---- ^\n',
+     '+   4. Complicated is better than complex.\n',
+     '?           ++++ ^                      ^\n',
+     '+   5. Flat is better than nested.\n']
+
+    As a single multi-line string it looks like this:
+
+    >>> print(''.join(result), end="")
+        1. Beautiful is better than ugly.
+    -   2. Explicit is better than implicit.
+    -   3. Simple is better than complex.
+    +   3.   Simple is better than complex.
+    ?     ++
+    -   4. Complex is better than complicated.
+    ?            ^                     ---- ^
+    +   4. Complicated is better than complex.
+    ?           ++++ ^                      ^
+    +   5. Flat is better than nested.
+    """
+
+    def __init__(self, linejunk=None, charjunk=None):
+        """
+        Construct a text differencer, with optional filters.
+
+        The two optional keyword parameters are for filter functions:
+
+        - `linejunk`: A function that should accept a single string argument,
+          and return true iff the string is junk. The module-level function
+          `IS_LINE_JUNK` may be used to filter out lines without visible
+          characters, except for at most one splat ('#').  It is recommended
+          to leave linejunk None; the underlying SequenceMatcher class has
+          an adaptive notion of "noise" lines that's better than any static
+          definition the author has ever been able to craft.
+
+        - `charjunk`: A function that should accept a string of length 1. The
+          module-level function `IS_CHARACTER_JUNK` may be used to filter out
+          whitespace characters (a blank or tab; **note**: bad idea to include
+          newline in this!).  Use of IS_CHARACTER_JUNK is recommended.
+        """
+
+        self.linejunk = linejunk
+        self.charjunk = charjunk
+
+    def compare(self, a, b):
+        r"""
+        Compare two sequences of lines; generate the resulting delta.
+
+        Each sequence must contain individual single-line strings ending with
+        newlines. Such sequences can be obtained from the `readlines()` method
+        of file-like objects.  The delta generated also consists of newline-
+        terminated strings, ready to be printed as-is via the writelines()
+        method of a file-like object.
+
+        Example:
+
+        >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),
+        ...                                'ore\ntree\nemu\n'.splitlines(True))),
+        ...       end="")
+        - one
+        ?  ^
+        + ore
+        ?  ^
+        - two
+        - three
+        ?  -
+        + tree
+        + emu
+        """
+
+        cruncher = SequenceMatcher(self.linejunk, a, b)
+        for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():
+            if tag == 'replace':
+                g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
+            elif tag == 'delete':
+                g = self._dump('-', a, alo, ahi)
+            elif tag == 'insert':
+                g = self._dump('+', b, blo, bhi)
+            elif tag == 'equal':
+                g = self._dump(' ', a, alo, ahi)
+            else:
+                raise ValueError('unknown tag %r' % (tag,))
+
+            yield from g
+
+    def _dump(self, tag, x, lo, hi):
+        """Generate comparison results for a same-tagged range."""
+        for i in range(lo, hi):
+            yield '%s %s' % (tag, x[i])
+
+    def _plain_replace(self, a, alo, ahi, b, blo, bhi):
+        assert alo < ahi and blo < bhi
+        # dump the shorter block first -- reduces the burden on short-term
+        # memory if the blocks are of very different sizes
+        if bhi - blo < ahi - alo:
+            first  = self._dump('+', b, blo, bhi)
+            second = self._dump('-', a, alo, ahi)
+        else:
+            first  = self._dump('-', a, alo, ahi)
+            second = self._dump('+', b, blo, bhi)
+
+        for g in first, second:
+            yield from g
+
+    def _fancy_replace(self, a, alo, ahi, b, blo, bhi):
+        r"""
+        When replacing one block of lines with another, search the blocks
+        for *similar* lines; the best-matching pair (if any) is used as a
+        synch point, and intraline difference marking is done on the
+        similar pair. Lots of work, but often worth it.
+
+        Example:
+
+        >>> d = Differ()
+        >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,
+        ...                            ['abcdefGhijkl\n'], 0, 1)
+        >>> print(''.join(results), end="")
+        - abcDefghiJkl
+        ?    ^  ^  ^
+        + abcdefGhijkl
+        ?    ^  ^  ^
+        """
+
+        # don't synch up unless the lines have a similarity score of at
+        # least cutoff; best_ratio tracks the best score seen so far
+        best_ratio, cutoff = 0.74, 0.75
+        cruncher = SequenceMatcher(self.charjunk)
+        eqi, eqj = None, None   # 1st indices of equal lines (if any)
+
+        # search for the pair that matches best without being identical
+        # (identical lines must be junk lines, & we don't want to synch up
+        # on junk -- unless we have to)
+        for j in range(blo, bhi):
+            bj = b[j]
+            cruncher.set_seq2(bj)
+            for i in range(alo, ahi):
+                ai = a[i]
+                if ai == bj:
+                    if eqi is None:
+                        eqi, eqj = i, j
+                    continue
+                cruncher.set_seq1(ai)
+                # computing similarity is expensive, so use the quick
+                # upper bounds first -- have seen this speed up messy
+                # compares by a factor of 3.
+                # note that ratio() is only expensive to compute the first
+                # time it's called on a sequence pair; the expensive part
+                # of the computation is cached by cruncher
+                if cruncher.real_quick_ratio() > best_ratio and \
+                      cruncher.quick_ratio() > best_ratio and \
+                      cruncher.ratio() > best_ratio:
+                    best_ratio, best_i, best_j = cruncher.ratio(), i, j
+        if best_ratio < cutoff:
+            # no non-identical "pretty close" pair
+            if eqi is None:
+                # no identical pair either -- treat it as a straight replace
+                yield from self._plain_replace(a, alo, ahi, b, blo, bhi)
+                return
+            # no close pair, but an identical pair -- synch up on that
+            best_i, best_j, best_ratio = eqi, eqj, 1.0
+        else:
+            # there's a close pair, so forget the identical pair (if any)
+            eqi = None
+
+        # a[best_i] very similar to b[best_j]; eqi is None iff they're not
+        # identical
+
+        # pump out diffs from before the synch point
+        yield from self._fancy_helper(a, alo, best_i, b, blo, best_j)
+
+        # do intraline marking on the synch pair
+        aelt, belt = a[best_i], b[best_j]
+        if eqi is None:
+            # pump out a '-', '?', '+', '?' quad for the synched lines
+            atags = btags = ""
+            cruncher.set_seqs(aelt, belt)
+            for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes():
+                la, lb = ai2 - ai1, bj2 - bj1
+                if tag == 'replace':
+                    atags += '^' * la
+                    btags += '^' * lb
+                elif tag == 'delete':
+                    atags += '-' * la
+                elif tag == 'insert':
+                    btags += '+' * lb
+                elif tag == 'equal':
+                    atags += ' ' * la
+                    btags += ' ' * lb
+                else:
+                    raise ValueError('unknown tag %r' % (tag,))
+            yield from self._qformat(aelt, belt, atags, btags)
+        else:
+            # the synch pair is identical
+            yield '  ' + aelt
+
+        # pump out diffs from after the synch point
+        yield from self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi)
+
+    def _fancy_helper(self, a, alo, ahi, b, blo, bhi):
+        g = []
+        if alo < ahi:
+            if blo < bhi:
+                g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
+            else:
+                g = self._dump('-', a, alo, ahi)
+        elif blo < bhi:
+            g = self._dump('+', b, blo, bhi)
+
+        yield from g
+
+    def _qformat(self, aline, bline, atags, btags):
+        r"""
+        Format "?" output and deal with tabs.
+
+        Example:
+
+        >>> d = Differ()
+        >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
+        ...                      '  ^ ^  ^      ', '  ^ ^  ^      ')
+        >>> for line in results: print(repr(line))
+        ...
+        '- \tabcDefghiJkl\n'
+        '? \t ^ ^  ^\n'
+        '+ \tabcdefGhijkl\n'
+        '? \t ^ ^  ^\n'
+        """
+        atags = _keep_original_ws(aline, atags).rstrip()
+        btags = _keep_original_ws(bline, btags).rstrip()
+
+        yield "- " + aline
+        if atags:
+            yield f"? {atags}\n"
+
+        yield "+ " + bline
+        if btags:
+            yield f"? {btags}\n"
+
+# With respect to junk, an earlier version of ndiff simply refused to
+# *start* a match with a junk element.  The result was cases like this:
+#     before: private Thread currentThread;
+#     after:  private volatile Thread currentThread;
+# If you consider whitespace to be junk, the longest contiguous match
+# not starting with junk is "e Thread currentThread".  So ndiff reported
+# that "e volatil" was inserted between the 't' and the 'e' in "private".
+# While an accurate view, to people that's absurd.  The current version
+# looks for matching blocks that are entirely junk-free, then extends the
+# longest one of those as far as possible but only with matching junk.
+# So now "currentThread" is matched, then extended to suck up the
+# preceding blank; then "private" is matched, and extended to suck up the
+# following blank; then "Thread" is matched; and finally ndiff reports
+# that "volatile " was inserted before "Thread".  The only quibble
+# remaining is that perhaps it was really the case that " volatile"
+# was inserted after "private".  I can live with that <wink>.
+
+import re
+
+def IS_LINE_JUNK(line, pat=re.compile(r"\s*(?:#\s*)?$").match):
+    r"""
+    Return True for ignorable line: iff `line` is blank or contains a single '#'.
+
+    Examples:
+
+    >>> IS_LINE_JUNK('\n')
+    True
+    >>> IS_LINE_JUNK('  #   \n')
+    True
+    >>> IS_LINE_JUNK('hello\n')
+    False
+    """
+
+    return pat(line) is not None
+
+def IS_CHARACTER_JUNK(ch, ws=" \t"):
+    r"""
+    Return True for ignorable character: iff `ch` is a space or tab.
+
+    Examples:
+
+    >>> IS_CHARACTER_JUNK(' ')
+    True
+    >>> IS_CHARACTER_JUNK('\t')
+    True
+    >>> IS_CHARACTER_JUNK('\n')
+    False
+    >>> IS_CHARACTER_JUNK('x')
+    False
+    """
+
+    return ch in ws
+
+
+########################################################################
+###  Unified Diff
+########################################################################
+
+def _format_range_unified(start, stop):
+    'Convert range to the "ed" format'
+    # Per the diff spec at http://www.unix.org/single_unix_specification/
+    beginning = start + 1     # lines start numbering with one
+    length = stop - start
+    if length == 1:
+        return '{}'.format(beginning)
+    if not length:
+        beginning -= 1        # empty ranges begin at line just before the range
+    return '{},{}'.format(beginning, length)
+
+def unified_diff(a, b, fromfile='', tofile='', fromfiledate='',
+                 tofiledate='', n=3, lineterm='\n'):
+    r"""
+    Compare two sequences of lines; generate the delta as a unified diff.
+
+    Unified diffs are a compact way of showing line changes and a few
+    lines of context.  The number of context lines is set by 'n' which
+    defaults to three.
+
+    By default, the diff control lines (those with ---, +++, or @@) are
+    created with a trailing newline.  This is helpful so that inputs
+    created from file.readlines() result in diffs that are suitable for
+    file.writelines() since both the inputs and outputs have trailing
+    newlines.
+
+    For inputs that do not have trailing newlines, set the lineterm
+    argument to "" so that the output will be uniformly newline free.
+
+    The unidiff format normally has a header for filenames and modification
+    times.  Any or all of these may be specified using strings for
+    'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
+    The modification times are normally expressed in the ISO 8601 format.
+
+    Example:
+
+    >>> for line in unified_diff('one two three four'.split(),
+    ...             'zero one tree four'.split(), 'Original', 'Current',
+    ...             '2005-01-26 23:30:50', '2010-04-02 10:20:52',
+    ...             lineterm=''):
+    ...     print(line)                 # doctest: +NORMALIZE_WHITESPACE
+    --- Original        2005-01-26 23:30:50
+    +++ Current         2010-04-02 10:20:52
+    @@ -1,4 +1,4 @@
+    +zero
+     one
+    -two
+    -three
+    +tree
+     four
+    """
+
+    _check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm)
+    started = False
+    for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
+        if not started:
+            started = True
+            fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
+            todate = '\t{}'.format(tofiledate) if tofiledate else ''
+            yield '--- {}{}{}'.format(fromfile, fromdate, lineterm)
+            yield '+++ {}{}{}'.format(tofile, todate, lineterm)
+
+        first, last = group[0], group[-1]
+        file1_range = _format_range_unified(first[1], last[2])
+        file2_range = _format_range_unified(first[3], last[4])
+        yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm)
+
+        for tag, i1, i2, j1, j2 in group:
+            if tag == 'equal':
+                for line in a[i1:i2]:
+                    yield ' ' + line
+                continue
+            if tag in {'replace', 'delete'}:
+                for line in a[i1:i2]:
+                    yield '-' + line
+            if tag in {'replace', 'insert'}:
+                for line in b[j1:j2]:
+                    yield '+' + line
+
+
+########################################################################
+###  Context Diff
+########################################################################
+
+def _format_range_context(start, stop):
+    'Convert range to the "ed" format'
+    # Per the diff spec at http://www.unix.org/single_unix_specification/
+    beginning = start + 1     # lines start numbering with one
+    length = stop - start
+    if not length:
+        beginning -= 1        # empty ranges begin at line just before the range
+    if length <= 1:
+        return '{}'.format(beginning)
+    return '{},{}'.format(beginning, beginning + length - 1)
+
+# See http://www.unix.org/single_unix_specification/
+def context_diff(a, b, fromfile='', tofile='',
+                 fromfiledate='', tofiledate='', n=3, lineterm='\n'):
+    r"""
+    Compare two sequences of lines; generate the delta as a context diff.
+
+    Context diffs are a compact way of showing line changes and a few
+    lines of context.  The number of context lines is set by 'n' which
+    defaults to three.
+
+    By default, the diff control lines (those with *** or ---) are
+    created with a trailing newline.  This is helpful so that inputs
+    created from file.readlines() result in diffs that are suitable for
+    file.writelines() since both the inputs and outputs have trailing
+    newlines.
+
+    For inputs that do not have trailing newlines, set the lineterm
+    argument to "" so that the output will be uniformly newline free.
+
+    The context diff format normally has a header for filenames and
+    modification times.  Any or all of these may be specified using
+    strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
+    The modification times are normally expressed in the ISO 8601 format.
+    If not specified, the strings default to blanks.
+
+    Example:
+
+    >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True),
+    ...       'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')),
+    ...       end="")
+    *** Original
+    --- Current
+    ***************
+    *** 1,4 ****
+      one
+    ! two
+    ! three
+      four
+    --- 1,4 ----
+    + zero
+      one
+    ! tree
+      four
+    """
+
+    _check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm)
+    prefix = dict(insert='+ ', delete='- ', replace='! ', equal='  ')
+    started = False
+    for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
+        if not started:
+            started = True
+            fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
+            todate = '\t{}'.format(tofiledate) if tofiledate else ''
+            yield '*** {}{}{}'.format(fromfile, fromdate, lineterm)
+            yield '--- {}{}{}'.format(tofile, todate, lineterm)
+
+        first, last = group[0], group[-1]
+        yield '***************' + lineterm
+
+        file1_range = _format_range_context(first[1], last[2])
+        yield '*** {} ****{}'.format(file1_range, lineterm)
+
+        if any(tag in {'replace', 'delete'} for tag, _, _, _, _ in group):
+            for tag, i1, i2, _, _ in group:
+                if tag != 'insert':
+                    for line in a[i1:i2]:
+                        yield prefix[tag] + line
+
+        file2_range = _format_range_context(first[3], last[4])
+        yield '--- {} ----{}'.format(file2_range, lineterm)
+
+        if any(tag in {'replace', 'insert'} for tag, _, _, _, _ in group):
+            for tag, _, _, j1, j2 in group:
+                if tag != 'delete':
+                    for line in b[j1:j2]:
+                        yield prefix[tag] + line
+
+def _check_types(a, b, *args):
+    # Checking types is weird, but the alternative is garbled output when
+    # someone passes mixed bytes and str to {unified,context}_diff(). E.g.
+    # without this check, passing filenames as bytes results in output like
+    #   --- b'oldfile.txt'
+    #   +++ b'newfile.txt'
+    # because of how str.format() incorporates bytes objects.
+    if a and not isinstance(a[0], str):
+        raise TypeError('lines to compare must be str, not %s (%r)' %
+                        (type(a[0]).__name__, a[0]))
+    if b and not isinstance(b[0], str):
+        raise TypeError('lines to compare must be str, not %s (%r)' %
+                        (type(b[0]).__name__, b[0]))
+    for arg in args:
+        if not isinstance(arg, str):
+            raise TypeError('all arguments must be str, not: %r' % (arg,))
+
+def diff_bytes(dfunc, a, b, fromfile=b'', tofile=b'',
+               fromfiledate=b'', tofiledate=b'', n=3, lineterm=b'\n'):
+    r"""
+    Compare `a` and `b`, two sequences of lines represented as bytes rather
+    than str. This is a wrapper for `dfunc`, which is typically either
+    unified_diff() or context_diff(). Inputs are losslessly converted to
+    strings so that `dfunc` only has to worry about strings, and encoded
+    back to bytes on return. This is necessary to compare files with
+    unknown or inconsistent encoding. All other inputs (except `n`) must be
+    bytes rather than str.
+    """
+    def decode(s):
+        try:
+            return s.decode('ascii', 'surrogateescape')
+        except AttributeError as err:
+            msg = ('all arguments must be bytes, not %s (%r)' %
+                   (type(s).__name__, s))
+            raise TypeError(msg) from err
+    a = list(map(decode, a))
+    b = list(map(decode, b))
+    fromfile = decode(fromfile)
+    tofile = decode(tofile)
+    fromfiledate = decode(fromfiledate)
+    tofiledate = decode(tofiledate)
+    lineterm = decode(lineterm)
+
+    lines = dfunc(a, b, fromfile, tofile, fromfiledate, tofiledate, n, lineterm)
+    for line in lines:
+        yield line.encode('ascii', 'surrogateescape')
+
+def ndiff(a, b, linejunk=None, charjunk=IS_CHARACTER_JUNK):
+    r"""
+    Compare `a` and `b` (lists of strings); return a `Differ`-style delta.
+
+    Optional keyword parameters `linejunk` and `charjunk` are for filter
+    functions, or can be None:
+
+    - linejunk: A function that should accept a single string argument and
+      return true iff the string is junk.  The default is None, and is
+      recommended; the underlying SequenceMatcher class has an adaptive
+      notion of "noise" lines.
+
+    - charjunk: A function that accepts a character (string of length
+      1), and returns true iff the character is junk. The default is
+      the module-level function IS_CHARACTER_JUNK, which filters out
+      whitespace characters (a blank or tab; note: it's a bad idea to
+      include newline in this!).
+
+    Tools/scripts/ndiff.py is a command-line front-end to this function.
+
+    Example:
+
+    >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+    ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+    >>> print(''.join(diff), end="")
+    - one
+    ?  ^
+    + ore
+    ?  ^
+    - two
+    - three
+    ?  -
+    + tree
+    + emu
+    """
+    return Differ(linejunk, charjunk).compare(a, b)
+
+def _mdiff(fromlines, tolines, context=None, linejunk=None,
+           charjunk=IS_CHARACTER_JUNK):
+    r"""Returns generator yielding marked up from/to side by side differences.
+
+    Arguments:
+    fromlines -- list of text lines to compared to tolines
+    tolines -- list of text lines to be compared to fromlines
+    context -- number of context lines to display on each side of difference,
+               if None, all from/to text lines will be generated.
+    linejunk -- passed on to ndiff (see ndiff documentation)
+    charjunk -- passed on to ndiff (see ndiff documentation)
+
+    This function returns an iterator which returns a tuple:
+    (from line tuple, to line tuple, boolean flag)
+
+    from/to line tuple -- (line num, line text)
+        line num -- integer or None (to indicate a context separation)
+        line text -- original line text with following markers inserted:
+            '\0+' -- marks start of added text
+            '\0-' -- marks start of deleted text
+            '\0^' -- marks start of changed text
+            '\1' -- marks end of added/deleted/changed text
+
+    boolean flag -- None indicates context separation, True indicates
+        either "from" or "to" line contains a change, otherwise False.
+
+    This function/iterator was originally developed to generate side by side
+    file difference for making HTML pages (see HtmlDiff class for example
+    usage).
+
+    Note, this function utilizes the ndiff function to generate the side by
+    side difference markup.  Optional ndiff arguments may be passed to this
+    function and they in turn will be passed to ndiff.
+    """
+    import re
+
+    # regular expression for finding intraline change indices
+    change_re = re.compile(r'(\++|\-+|\^+)')
+
+    # create the difference iterator to generate the differences
+    diff_lines_iterator = ndiff(fromlines,tolines,linejunk,charjunk)
+
+    def _make_line(lines, format_key, side, num_lines=[0,0]):
+        """Returns line of text with user's change markup and line formatting.
+
+        lines -- list of lines from the ndiff generator to produce a line of
+                 text from.  When producing the line of text to return, the
+                 lines used are removed from this list.
+        format_key -- '+' return first line in list with "add" markup around
+                          the entire line.
+                      '-' return first line in list with "delete" markup around
+                          the entire line.
+                      '?' return first line in list with add/delete/change
+                          intraline markup (indices obtained from second line)
+                      None return first line in list with no markup
+        side -- indice into the num_lines list (0=from,1=to)
+        num_lines -- from/to current line number.  This is NOT intended to be a
+                     passed parameter.  It is present as a keyword argument to
+                     maintain memory of the current line numbers between calls
+                     of this function.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        num_lines[side] += 1
+        # Handle case where no user markup is to be added, just return line of
+        # text with user's line format to allow for usage of the line number.
+        if format_key is None:
+            return (num_lines[side],lines.pop(0)[2:])
+        # Handle case of intraline changes
+        if format_key == '?':
+            text, markers = lines.pop(0), lines.pop(0)
+            # find intraline changes (store change type and indices in tuples)
+            sub_info = []
+            def record_sub_info(match_object,sub_info=sub_info):
+                sub_info.append([match_object.group(1)[0],match_object.span()])
+                return match_object.group(1)
+            change_re.sub(record_sub_info,markers)
+            # process each tuple inserting our special marks that won't be
+            # noticed by an xml/html escaper.
+            for key,(begin,end) in reversed(sub_info):
+                text = text[0:begin]+'\0'+key+text[begin:end]+'\1'+text[end:]
+            text = text[2:]
+        # Handle case of add/delete entire line
+        else:
+            text = lines.pop(0)[2:]
+            # if line of text is just a newline, insert a space so there is
+            # something for the user to highlight and see.
+            if not text:
+                text = ' '
+            # insert marks that won't be noticed by an xml/html escaper.
+            text = '\0' + format_key + text + '\1'
+        # Return line of text, first allow user's line formatter to do its
+        # thing (such as adding the line number) then replace the special
+        # marks with what the user's change markup.
+        return (num_lines[side],text)
+
+    def _line_iterator():
+        """Yields from/to lines of text with a change indication.
+
+        This function is an iterator.  It itself pulls lines from a
+        differencing iterator, processes them and yields them.  When it can
+        it yields both a "from" and a "to" line, otherwise it will yield one
+        or the other.  In addition to yielding the lines of from/to text, a
+        boolean flag is yielded to indicate if the text line(s) have
+        differences in them.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        lines = []
+        num_blanks_pending, num_blanks_to_yield = 0, 0
+        while True:
+            # Load up next 4 lines so we can look ahead, create strings which
+            # are a concatenation of the first character of each of the 4 lines
+            # so we can do some very readable comparisons.
+            while len(lines) < 4:
+                lines.append(next(diff_lines_iterator, 'X'))
+            s = ''.join([line[0] for line in lines])
+            if s.startswith('X'):
+                # When no more lines, pump out any remaining blank lines so the
+                # corresponding add/delete lines get a matching blank line so
+                # all line pairs get yielded at the next level.
+                num_blanks_to_yield = num_blanks_pending
+            elif s.startswith('-?+?'):
+                # simple intraline change
+                yield _make_line(lines,'?',0), _make_line(lines,'?',1), True
+                continue
+            elif s.startswith('--++'):
+                # in delete block, add block coming: we do NOT want to get
+                # caught up on blank lines yet, just process the delete line
+                num_blanks_pending -= 1
+                yield _make_line(lines,'-',0), None, True
+                continue
+            elif s.startswith(('--?+', '--+', '- ')):
+                # in delete block and see an intraline change or unchanged line
+                # coming: yield the delete line and then blanks
+                from_line,to_line = _make_line(lines,'-',0), None
+                num_blanks_to_yield,num_blanks_pending = num_blanks_pending-1,0
+            elif s.startswith('-+?'):
+                # intraline change
+                yield _make_line(lines,None,0), _make_line(lines,'?',1), True
+                continue
+            elif s.startswith('-?+'):
+                # intraline change
+                yield _make_line(lines,'?',0), _make_line(lines,None,1), True
+                continue
+            elif s.startswith('-'):
+                # delete FROM line
+                num_blanks_pending -= 1
+                yield _make_line(lines,'-',0), None, True
+                continue
+            elif s.startswith('+--'):
+                # in add block, delete block coming: we do NOT want to get
+                # caught up on blank lines yet, just process the add line
+                num_blanks_pending += 1
+                yield None, _make_line(lines,'+',1), True
+                continue
+            elif s.startswith(('+ ', '+-')):
+                # will be leaving an add block: yield blanks then add line
+                from_line, to_line = None, _make_line(lines,'+',1)
+                num_blanks_to_yield,num_blanks_pending = num_blanks_pending+1,0
+            elif s.startswith('+'):
+                # inside an add block, yield the add line
+                num_blanks_pending += 1
+                yield None, _make_line(lines,'+',1), True
+                continue
+            elif s.startswith(' '):
+                # unchanged text, yield it to both sides
+                yield _make_line(lines[:],None,0),_make_line(lines,None,1),False
+                continue
+            # Catch up on the blank lines so when we yield the next from/to
+            # pair, they are lined up.
+            while(num_blanks_to_yield < 0):
+                num_blanks_to_yield += 1
+                yield None,('','\n'),True
+            while(num_blanks_to_yield > 0):
+                num_blanks_to_yield -= 1
+                yield ('','\n'),None,True
+            if s.startswith('X'):
+                return
+            else:
+                yield from_line,to_line,True
+
+    def _line_pair_iterator():
+        """Yields from/to lines of text with a change indication.
+
+        This function is an iterator.  It itself pulls lines from the line
+        iterator.  Its difference from that iterator is that this function
+        always yields a pair of from/to text lines (with the change
+        indication).  If necessary it will collect single from/to lines
+        until it has a matching pair from/to pair to yield.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        line_iterator = _line_iterator()
+        fromlines,tolines=[],[]
+        while True:
+            # Collecting lines of text until we have a from/to pair
+            while (len(fromlines)==0 or len(tolines)==0):
+                try:
+                    from_line, to_line, found_diff = next(line_iterator)
+                except StopIteration:
+                    return
+                if from_line is not None:
+                    fromlines.append((from_line,found_diff))
+                if to_line is not None:
+                    tolines.append((to_line,found_diff))
+            # Once we have a pair, remove them from the collection and yield it
+            from_line, fromDiff = fromlines.pop(0)
+            to_line, to_diff = tolines.pop(0)
+            yield (from_line,to_line,fromDiff or to_diff)
+
+    # Handle case where user does not want context differencing, just yield
+    # them up without doing anything else with them.
+    line_pair_iterator = _line_pair_iterator()
+    if context is None:
+        yield from line_pair_iterator
+    # Handle case where user wants context differencing.  We must do some
+    # storage of lines until we know for sure that they are to be yielded.
+    else:
+        context += 1
+        lines_to_write = 0
+        while True:
+            # Store lines up until we find a difference, note use of a
+            # circular queue because we only need to keep around what
+            # we need for context.
+            index, contextLines = 0, [None]*(context)
+            found_diff = False
+            while(found_diff is False):
+                try:
+                    from_line, to_line, found_diff = next(line_pair_iterator)
+                except StopIteration:
+                    return
+                i = index % context
+                contextLines[i] = (from_line, to_line, found_diff)
+                index += 1
+            # Yield lines that we have collected so far, but first yield
+            # the user's separator.
+            if index > context:
+                yield None, None, None
+                lines_to_write = context
+            else:
+                lines_to_write = index
+                index = 0
+            while(lines_to_write):
+                i = index % context
+                index += 1
+                yield contextLines[i]
+                lines_to_write -= 1
+            # Now yield the context lines after the change
+            lines_to_write = context-1
+            try:
+                while(lines_to_write):
+                    from_line, to_line, found_diff = next(line_pair_iterator)
+                    # If another change within the context, extend the context
+                    if found_diff:
+                        lines_to_write = context-1
+                    else:
+                        lines_to_write -= 1
+                    yield from_line, to_line, found_diff
+            except StopIteration:
+                # Catch exception from next() and return normally
+                return
+
+
+_file_template = """
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+          "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+
+<html>
+
+<head>
+    <meta http-equiv="Content-Type"
+          content="text/html; charset=%(charset)s" />
+    <title></title>
+    <style type="text/css">%(styles)s
+    </style>
+</head>
+
+<body>
+    %(table)s%(legend)s
+</body>
+
+</html>"""
+
+_styles = """
+        table.diff {font-family:Courier; border:medium;}
+        .diff_header {background-color:#e0e0e0}
+        td.diff_header {text-align:right}
+        .diff_next {background-color:#c0c0c0}
+        .diff_add {background-color:#aaffaa}
+        .diff_chg {background-color:#ffff77}
+        .diff_sub {background-color:#ffaaaa}"""
+
+_table_template = """
+    <table class="diff" id="difflib_chg_%(prefix)s_top"
+           cellspacing="0" cellpadding="0" rules="groups" >
+        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>
+        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>
+        %(header_row)s
+        <tbody>
+%(data_rows)s        </tbody>
+    </table>"""
+
+_legend = """
+    <table class="diff" summary="Legends">
+        <tr> <th colspan="2"> Legends </th> </tr>
+        <tr> <td> <table border="" summary="Colors">
+                      <tr><th> Colors </th> </tr>
+                      <tr><td class="diff_add">&nbsp;Added&nbsp;</td></tr>
+                      <tr><td class="diff_chg">Changed</td> </tr>
+                      <tr><td class="diff_sub">Deleted</td> </tr>
+                  </table></td>
+             <td> <table border="" summary="Links">
+                      <tr><th colspan="2"> Links </th> </tr>
+                      <tr><td>(f)irst change</td> </tr>
+                      <tr><td>(n)ext change</td> </tr>
+                      <tr><td>(t)op</td> </tr>
+                  </table></td> </tr>
+    </table>"""
+
+class HtmlDiff(object):
+    """For producing HTML side by side comparison with change highlights.
+
+    This class can be used to create an HTML table (or a complete HTML file
+    containing the table) showing a side by side, line by line comparison
+    of text with inter-line and intra-line change highlights.  The table can
+    be generated in either full or contextual difference mode.
+
+    The following methods are provided for HTML generation:
+
+    make_table -- generates HTML for a single side by side table
+    make_file -- generates complete HTML file with a single side by side table
+
+    See tools/scripts/diff.py for an example usage of this class.
+    """
+
+    _file_template = _file_template
+    _styles = _styles
+    _table_template = _table_template
+    _legend = _legend
+    _default_prefix = 0
+
+    def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None,
+                 charjunk=IS_CHARACTER_JUNK):
+        """HtmlDiff instance initializer
+
+        Arguments:
+        tabsize -- tab stop spacing, defaults to 8.
+        wrapcolumn -- column number where lines are broken and wrapped,
+            defaults to None where lines are not wrapped.
+        linejunk,charjunk -- keyword arguments passed into ndiff() (used by
+            HtmlDiff() to generate the side by side HTML differences).  See
+            ndiff() documentation for argument default values and descriptions.
+        """
+        self._tabsize = tabsize
+        self._wrapcolumn = wrapcolumn
+        self._linejunk = linejunk
+        self._charjunk = charjunk
+
+    def make_file(self, fromlines, tolines, fromdesc='', todesc='',
+                  context=False, numlines=5, *, charset='utf-8'):
+        """Returns HTML file of side by side comparison with change highlights
+
+        Arguments:
+        fromlines -- list of "from" lines
+        tolines -- list of "to" lines
+        fromdesc -- "from" file column header string
+        todesc -- "to" file column header string
+        context -- set to True for contextual differences (defaults to False
+            which shows full differences).
+        numlines -- number of context lines.  When context is set True,
+            controls number of lines displayed before and after the change.
+            When context is False, controls the number of lines to place
+            the "next" link anchors before the next change (so click of
+            "next" link jumps to just before the change).
+        charset -- charset of the HTML document
+        """
+
+        return (self._file_template % dict(
+            styles=self._styles,
+            legend=self._legend,
+            table=self.make_table(fromlines, tolines, fromdesc, todesc,
+                                  context=context, numlines=numlines),
+            charset=charset
+        )).encode(charset, 'xmlcharrefreplace').decode(charset)
+
+    def _tab_newline_replace(self,fromlines,tolines):
+        """Returns from/to line lists with tabs expanded and newlines removed.
+
+        Instead of tab characters being replaced by the number of spaces
+        needed to fill in to the next tab stop, this function will fill
+        the space with tab characters.  This is done so that the difference
+        algorithms can identify changes in a file when tabs are replaced by
+        spaces and vice versa.  At the end of the HTML generation, the tab
+        characters will be replaced with a nonbreakable space.
+        """
+        def expand_tabs(line):
+            # hide real spaces
+            line = line.replace(' ','\0')
+            # expand tabs into spaces
+            line = line.expandtabs(self._tabsize)
+            # replace spaces from expanded tabs back into tab characters
+            # (we'll replace them with markup after we do differencing)
+            line = line.replace(' ','\t')
+            return line.replace('\0',' ').rstrip('\n')
+        fromlines = [expand_tabs(line) for line in fromlines]
+        tolines = [expand_tabs(line) for line in tolines]
+        return fromlines,tolines
+
+    def _split_line(self,data_list,line_num,text):
+        """Builds list of text lines by splitting text lines at wrap point
+
+        This function will determine if the input text line needs to be
+        wrapped (split) into separate lines.  If so, the first wrap point
+        will be determined and the first line appended to the output
+        text line list.  This function is used recursively to handle
+        the second part of the split line to further split it.
+        """
+        # if blank line or context separator, just add it to the output list
+        if not line_num:
+            data_list.append((line_num,text))
+            return
+
+        # if line text doesn't need wrapping, just add it to the output list
+        size = len(text)
+        max = self._wrapcolumn
+        if (size <= max) or ((size -(text.count('\0')*3)) <= max):
+            data_list.append((line_num,text))
+            return
+
+        # scan text looking for the wrap point, keeping track if the wrap
+        # point is inside markers
+        i = 0
+        n = 0
+        mark = ''
+        while n < max and i < size:
+            if text[i] == '\0':
+                i += 1
+                mark = text[i]
+                i += 1
+            elif text[i] == '\1':
+                i += 1
+                mark = ''
+            else:
+                i += 1
+                n += 1
+
+        # wrap point is inside text, break it up into separate lines
+        line1 = text[:i]
+        line2 = text[i:]
+
+        # if wrap point is inside markers, place end marker at end of first
+        # line and start marker at beginning of second line because each
+        # line will have its own table tag markup around it.
+        if mark:
+            line1 = line1 + '\1'
+            line2 = '\0' + mark + line2
+
+        # tack on first line onto the output list
+        data_list.append((line_num,line1))
+
+        # use this routine again to wrap the remaining text
+        self._split_line(data_list,'>',line2)
+
+    def _line_wrapper(self,diffs):
+        """Returns iterator that splits (wraps) mdiff text lines"""
+
+        # pull from/to data and flags from mdiff iterator
+        for fromdata,todata,flag in diffs:
+            # check for context separators and pass them through
+            if flag is None:
+                yield fromdata,todata,flag
+                continue
+            (fromline,fromtext),(toline,totext) = fromdata,todata
+            # for each from/to line split it at the wrap column to form
+            # list of text lines.
+            fromlist,tolist = [],[]
+            self._split_line(fromlist,fromline,fromtext)
+            self._split_line(tolist,toline,totext)
+            # yield from/to line in pairs inserting blank lines as
+            # necessary when one side has more wrapped lines
+            while fromlist or tolist:
+                if fromlist:
+                    fromdata = fromlist.pop(0)
+                else:
+                    fromdata = ('',' ')
+                if tolist:
+                    todata = tolist.pop(0)
+                else:
+                    todata = ('',' ')
+                yield fromdata,todata,flag
+
+    def _collect_lines(self,diffs):
+        """Collects mdiff output into separate lists
+
+        Before storing the mdiff from/to data into a list, it is converted
+        into a single line of text with HTML markup.
+        """
+
+        fromlist,tolist,flaglist = [],[],[]
+        # pull from/to data and flags from mdiff style iterator
+        for fromdata,todata,flag in diffs:
+            try:
+                # store HTML markup of the lines into the lists
+                fromlist.append(self._format_line(0,flag,*fromdata))
+                tolist.append(self._format_line(1,flag,*todata))
+            except TypeError:
+                # exceptions occur for lines where context separators go
+                fromlist.append(None)
+                tolist.append(None)
+            flaglist.append(flag)
+        return fromlist,tolist,flaglist
+
+    def _format_line(self,side,flag,linenum,text):
+        """Returns HTML markup of "from" / "to" text lines
+
+        side -- 0 or 1 indicating "from" or "to" text
+        flag -- indicates if difference on line
+        linenum -- line number (used for line number column)
+        text -- line text to be marked up
+        """
+        try:
+            linenum = '%d' % linenum
+            id = ' id="%s%s"' % (self._prefix[side],linenum)
+        except TypeError:
+            # handle blank lines where linenum is '>' or ''
+            id = ''
+        # replace those things that would get confused with HTML symbols
+        text=text.replace("&","&amp;").replace(">","&gt;").replace("<","&lt;")
+
+        # make space non-breakable so they don't get compressed or line wrapped
+        text = text.replace(' ','&nbsp;').rstrip()
+
+        return '<td class="diff_header"%s>%s</td><td nowrap="nowrap">%s</td>' \
+               % (id,linenum,text)
+
+    def _make_prefix(self):
+        """Create unique anchor prefixes"""
+
+        # Generate a unique anchor prefix so multiple tables
+        # can exist on the same HTML page without conflicts.
+        fromprefix = "from%d_" % HtmlDiff._default_prefix
+        toprefix = "to%d_" % HtmlDiff._default_prefix
+        HtmlDiff._default_prefix += 1
+        # store prefixes so line format method has access
+        self._prefix = [fromprefix,toprefix]
+
+    def _convert_flags(self,fromlist,tolist,flaglist,context,numlines):
+        """Makes list of "next" links"""
+
+        # all anchor names will be generated using the unique "to" prefix
+        toprefix = self._prefix[1]
+
+        # process change flags, generating middle column of next anchors/links
+        next_id = ['']*len(flaglist)
+        next_href = ['']*len(flaglist)
+        num_chg, in_change = 0, False
+        last = 0
+        for i,flag in enumerate(flaglist):
+            if flag:
+                if not in_change:
+                    in_change = True
+                    last = i
+                    # at the beginning of a change, drop an anchor a few lines
+                    # (the context lines) before the change for the previous
+                    # link
+                    i = max([0,i-numlines])
+                    next_id[i] = ' id="difflib_chg_%s_%d"' % (toprefix,num_chg)
+                    # at the beginning of a change, drop a link to the next
+                    # change
+                    num_chg += 1
+                    next_href[last] = '<a href="#difflib_chg_%s_%d">n</a>' % (
+                         toprefix,num_chg)
+            else:
+                in_change = False
+        # check for cases where there is no content to avoid exceptions
+        if not flaglist:
+            flaglist = [False]
+            next_id = ['']
+            next_href = ['']
+            last = 0
+            if context:
+                fromlist = ['<td></td><td>&nbsp;No Differences Found&nbsp;</td>']
+                tolist = fromlist
+            else:
+                fromlist = tolist = ['<td></td><td>&nbsp;Empty File&nbsp;</td>']
+        # if not a change on first line, drop a link
+        if not flaglist[0]:
+            next_href[0] = '<a href="#difflib_chg_%s_0">f</a>' % toprefix
+        # redo the last link to link to the top
+        next_href[last] = '<a href="#difflib_chg_%s_top">t</a>' % (toprefix)
+
+        return fromlist,tolist,flaglist,next_href,next_id
+
+    def make_table(self,fromlines,tolines,fromdesc='',todesc='',context=False,
+                   numlines=5):
+        """Returns HTML table of side by side comparison with change highlights
+
+        Arguments:
+        fromlines -- list of "from" lines
+        tolines -- list of "to" lines
+        fromdesc -- "from" file column header string
+        todesc -- "to" file column header string
+        context -- set to True for contextual differences (defaults to False
+            which shows full differences).
+        numlines -- number of context lines.  When context is set True,
+            controls number of lines displayed before and after the change.
+            When context is False, controls the number of lines to place
+            the "next" link anchors before the next change (so click of
+            "next" link jumps to just before the change).
+        """
+
+        # make unique anchor prefixes so that multiple tables may exist
+        # on the same page without conflict.
+        self._make_prefix()
+
+        # change tabs to spaces before it gets more difficult after we insert
+        # markup
+        fromlines,tolines = self._tab_newline_replace(fromlines,tolines)
+
+        # create diffs iterator which generates side by side from/to data
+        if context:
+            context_lines = numlines
+        else:
+            context_lines = None
+        diffs = _mdiff(fromlines,tolines,context_lines,linejunk=self._linejunk,
+                      charjunk=self._charjunk)
+
+        # set up iterator to wrap lines that exceed desired width
+        if self._wrapcolumn:
+            diffs = self._line_wrapper(diffs)
+
+        # collect up from/to lines and flags into lists (also format the lines)
+        fromlist,tolist,flaglist = self._collect_lines(diffs)
+
+        # process change flags, generating middle column of next anchors/links
+        fromlist,tolist,flaglist,next_href,next_id = self._convert_flags(
+            fromlist,tolist,flaglist,context,numlines)
+
+        s = []
+        fmt = '            <tr><td class="diff_next"%s>%s</td>%s' + \
+              '<td class="diff_next">%s</td>%s</tr>\n'
+        for i in range(len(flaglist)):
+            if flaglist[i] is None:
+                # mdiff yields None on separator lines skip the bogus ones
+                # generated for the first line
+                if i > 0:
+                    s.append('        </tbody>        \n        <tbody>\n')
+            else:
+                s.append( fmt % (next_id[i],next_href[i],fromlist[i],
+                                           next_href[i],tolist[i]))
+        if fromdesc or todesc:
+            header_row = '<thead><tr>%s%s%s%s</tr></thead>' % (
+                '<th class="diff_next"><br /></th>',
+                '<th colspan="2" class="diff_header">%s</th>' % fromdesc,
+                '<th class="diff_next"><br /></th>',
+                '<th colspan="2" class="diff_header">%s</th>' % todesc)
+        else:
+            header_row = ''
+
+        table = self._table_template % dict(
+            data_rows=''.join(s),
+            header_row=header_row,
+            prefix=self._prefix[1])
+
+        return table.replace('\0+','<span class="diff_add">'). \
+                     replace('\0-','<span class="diff_sub">'). \
+                     replace('\0^','<span class="diff_chg">'). \
+                     replace('\1','</span>'). \
+                     replace('\t','&nbsp;')
+
+del re
+
+def restore(delta, which):
+    r"""
+    Generate one of the two sequences that generated a delta.
+
+    Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
+    lines originating from file 1 or 2 (parameter `which`), stripping off line
+    prefixes.
+
+    Examples:
+
+    >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+    ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+    >>> diff = list(diff)
+    >>> print(''.join(restore(diff, 1)), end="")
+    one
+    two
+    three
+    >>> print(''.join(restore(diff, 2)), end="")
+    ore
+    tree
+    emu
+    """
+    try:
+        tag = {1: "- ", 2: "+ "}[int(which)]
+    except KeyError:
+        raise ValueError('unknown delta choice (must be 1 or 2): %r'
+                           % which) from None
+    prefixes = ("  ", tag)
+    for line in delta:
+        if line[:2] in prefixes:
+            yield line[2:]
+
+def _test():
+    import doctest, difflib
+    return doctest.testmod(difflib)
+
+if __name__ == "__main__":
+    _test()
diff --git a/shared/package-files/0c/0c6d8bdb8291793894f271695cb8a06df9642a8b8a5933d47faa2c42cea6c626/align_util.h b/shared/package-files/0c/0c6d8bdb8291793894f271695cb8a06df9642a8b8a5933d47faa2c42cea6c626/align_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..71920e49f4aa2b1d92312b4aabaffafe35d323c7
--- /dev/null
+++ b/shared/package-files/0c/0c6d8bdb8291793894f271695cb8a06df9642a8b8a5933d47faa2c42cea6c626/align_util.h
@@ -0,0 +1,221 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <algorithm>
+
+#include "arrow/memory_pool.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/bit_util.h"
+
+namespace arrow {
+namespace internal {
+
+struct BitmapWordAlignParams {
+  int64_t leading_bits;
+  int64_t trailing_bits;
+  int64_t trailing_bit_offset;
+  const uint8_t* aligned_start;
+  int64_t aligned_bits;
+  int64_t aligned_words;
+};
+
+// Compute parameters for accessing a bitmap using aligned word instructions.
+// The returned parameters describe:
+// - a leading area of size `leading_bits` before the aligned words
+// - a word-aligned area of size `aligned_bits`
+// - a trailing area of size `trailing_bits` after the aligned words
+template <uint64_t ALIGN_IN_BYTES>
+inline BitmapWordAlignParams BitmapWordAlign(const uint8_t* data, int64_t bit_offset,
+                                             int64_t length) {
+  static_assert(bit_util::IsPowerOf2(ALIGN_IN_BYTES),
+                "ALIGN_IN_BYTES should be a positive power of two");
+  constexpr uint64_t ALIGN_IN_BITS = ALIGN_IN_BYTES * 8;
+
+  BitmapWordAlignParams p;
+
+  // Compute a "bit address" that we can align up to ALIGN_IN_BITS.
+  // We don't care about losing the upper bits since we are only interested in the
+  // difference between both addresses.
+  const uint64_t bit_addr =
+      reinterpret_cast<size_t>(data) * 8 + static_cast<uint64_t>(bit_offset);
+  const uint64_t aligned_bit_addr = bit_util::RoundUpToPowerOf2(bit_addr, ALIGN_IN_BITS);
+
+  p.leading_bits = std::min<int64_t>(length, aligned_bit_addr - bit_addr);
+  p.aligned_words = (length - p.leading_bits) / ALIGN_IN_BITS;
+  p.aligned_bits = p.aligned_words * ALIGN_IN_BITS;
+  p.trailing_bits = length - p.leading_bits - p.aligned_bits;
+  p.trailing_bit_offset = bit_offset + p.leading_bits + p.aligned_bits;
+
+  p.aligned_start = data + (bit_offset + p.leading_bits) / 8;
+  return p;
+}
+}  // namespace internal
+
+namespace util {
+
+// Functions to check if the provided Arrow object is aligned by the specified alignment
+
+/// \brief Special alignment value to use data type-specific alignment
+///
+/// If this is passed as the `alignment` in one of the CheckAlignment or EnsureAlignment
+/// functions, then the function will ensure each buffer is suitably aligned
+/// for the data type of the array.  For example, given an int32 buffer the values
+/// buffer's address must be a multiple of 4.  Given a large_string buffer the offsets
+/// buffer's address must be a multiple of 8.
+constexpr int64_t kValueAlignment = -3;
+
+/// \brief Calculate if the buffer's address is a multiple of `alignment`
+///
+/// If `alignment` is less than or equal to 0 then this method will always return true
+/// \param buffer the buffer to check
+/// \param alignment the alignment (in bytes) to check for
+ARROW_EXPORT bool CheckAlignment(const Buffer& buffer, int64_t alignment);
+/// \brief Calculate if all buffers in the array data are aligned
+///
+/// This will also check the buffers in the dictionary and any children
+/// \param array the array data to check
+/// \param alignment the alignment (in bytes) to check for
+ARROW_EXPORT bool CheckAlignment(const ArrayData& array, int64_t alignment);
+/// \brief Calculate if all buffers in the array are aligned
+///
+/// This will also check the buffers in the dictionary and any children
+/// \param array the array to check
+/// \param alignment the alignment (in bytes) to check for
+ARROW_EXPORT bool CheckAlignment(const Array& array, int64_t alignment);
+
+// Following functions require an additional boolean vector which stores the
+// alignment check bits of the constituent objects.
+// For example, needs_alignment vector for a ChunkedArray will contain the
+// check bits of the constituent Arrays.
+// The boolean vector check was introduced to minimize the repetitive checks
+// of the constituent objects during the EnsureAlignment function where certain
+// objects can be ignored for further checking if we already know that they are
+// completely aligned.
+
+/// \brief Calculate which (if any) chunks in a chunked array are unaligned
+/// \param array the array to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param needs_alignment an output vector that will store the results of the check
+///        it must be set to a valid vector.  Extra elements will be added to the end
+///        of the vector for each chunk that is checked.  `true` will be stored if
+///        the chunk is unaligned.
+/// \param offset the index of the chunk to start checking
+/// \return true if all chunks (starting at `offset`) are aligned, false otherwise
+ARROW_EXPORT bool CheckAlignment(const ChunkedArray& array, int64_t alignment,
+                                 std::vector<bool>* needs_alignment, int offset = 0);
+
+/// \brief calculate which (if any) columns in a record batch are unaligned
+/// \param batch the batch to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param needs_alignment an output vector that will store the results of the
+///        check.  It must be set to a valid vector.  Extra elements will be added
+///        to the end of the vector for each column that is checked.  `true` will be
+///        stored if the column is unaligned.
+ARROW_EXPORT bool CheckAlignment(const RecordBatch& batch, int64_t alignment,
+                                 std::vector<bool>* needs_alignment);
+
+/// \brief calculate which (if any) columns in a table are unaligned
+/// \param table the table to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param needs_alignment an output vector that will store the results of the
+///        check.  It must be set to a valid vector.  Extra elements will be added
+///        to the end of the vector for each column that is checked.  `true` will be
+///        stored if the column is unaligned.
+ARROW_EXPORT bool CheckAlignment(const Table& table, int64_t alignment,
+                                 std::vector<bool>* needs_alignment);
+
+/// \brief return a buffer that has the given alignment and the same data as the input
+/// buffer
+///
+/// If the input buffer is already aligned then this method will return the input buffer
+/// If the input buffer is not already aligned then this method will allocate a new
+/// buffer.  The alignment of the new buffer will have at least
+/// max(kDefaultBufferAlignment, alignment) bytes of alignment.
+///
+/// \param buffer the buffer to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate a new buffer if the
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<Buffer>> EnsureAlignment(
+    std::shared_ptr<Buffer> buffer, int64_t alignment, MemoryPool* memory_pool);
+
+/// \brief return an array data where all buffers are aligned by the given alignment
+///
+/// If any input buffer is already aligned then this method will reuse that same input
+/// buffer.
+///
+/// \param array_data the array data to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate new buffers if any
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<ArrayData>> EnsureAlignment(
+    std::shared_ptr<ArrayData> array_data, int64_t alignment, MemoryPool* memory_pool);
+
+/// \brief return an array where all buffers are aligned by the given alignment
+///
+/// If any input buffer is already aligned then this method will reuse that same input
+/// buffer.
+///
+/// \param array the array to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate new buffers if any
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<Array>> EnsureAlignment(std::shared_ptr<Array> array,
+                                                            int64_t alignment,
+                                                            MemoryPool* memory_pool);
+
+/// \brief return a chunked array where all buffers are aligned by the given alignment
+///
+/// If any input buffer is already aligned then this method will reuse that same input
+/// buffer.
+///
+/// \param array the chunked array to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate new buffers if any
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<ChunkedArray>> EnsureAlignment(
+    std::shared_ptr<ChunkedArray> array, int64_t alignment, MemoryPool* memory_pool);
+
+/// \brief return a record batch where all buffers are aligned by the given alignment
+///
+/// If any input buffer is already aligned then this method will reuse that same input
+/// buffer.
+///
+/// \param batch the batch to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate new buffers if any
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<RecordBatch>> EnsureAlignment(
+    std::shared_ptr<RecordBatch> batch, int64_t alignment, MemoryPool* memory_pool);
+
+/// \brief return a table where all buffers are aligned by the given alignment
+///
+/// If any input buffer is already aligned then this method will reuse that same input
+/// buffer.
+///
+/// \param table the table to check
+/// \param alignment the alignment (in bytes) to check for
+/// \param memory_pool a memory pool that will be used to allocate new buffers if any
+///        input buffer is not sufficiently aligned
+ARROW_EXPORT Result<std::shared_ptr<Table>> EnsureAlignment(std::shared_ptr<Table> table,
+                                                            int64_t alignment,
+                                                            MemoryPool* memory_pool);
+
+}  // namespace util
+}  // namespace arrow
diff --git a/shared/package-files/0c/0c6dd5acd3805522bc24e458889268241b92f5c1c808640535ebc07393f3c191/__init__.py b/shared/package-files/0c/0c6dd5acd3805522bc24e458889268241b92f5c1c808640535ebc07393f3c191/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a4892032ea570532d6a262d70501e8827128d4
--- /dev/null
+++ b/shared/package-files/0c/0c6dd5acd3805522bc24e458889268241b92f5c1c808640535ebc07393f3c191/__init__.py
@@ -0,0 +1,11 @@
+from torch.utils.data.datapipes.dataframe.dataframes import (
+    CaptureDataFrame, DFIterDataPipe,
+)
+from torch.utils.data.datapipes.dataframe.datapipes import (
+    DataFramesAsTuplesPipe,
+)
+
+__all__ = ['CaptureDataFrame', 'DFIterDataPipe', 'DataFramesAsTuplesPipe']
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
diff --git a/shared/package-files/0c/0c6f08a14b710b2d9a7168d41d450abb86d1b13c576de14b16328c2c0d8674b6/test_deprecated_conv_modules.py b/shared/package-files/0c/0c6f08a14b710b2d9a7168d41d450abb86d1b13c576de14b16328c2c0d8674b6/test_deprecated_conv_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..b41b6105410a308e7774fce760b235497d0303bb
--- /dev/null
+++ b/shared/package-files/0c/0c6f08a14b710b2d9a7168d41d450abb86d1b13c576de14b16328c2c0d8674b6/test_deprecated_conv_modules.py
@@ -0,0 +1,22 @@
+from sympy import MatrixSymbol, symbols, Sum
+from sympy.tensor.array.expressions import conv_array_to_indexed, from_array_to_indexed, ArrayTensorProduct, \
+    ArrayContraction, conv_array_to_matrix, from_array_to_matrix, conv_matrix_to_array, from_matrix_to_array, \
+    conv_indexed_to_array, from_indexed_to_array
+from sympy.testing.pytest import warns
+from sympy.utilities.exceptions import SymPyDeprecationWarning
+
+
+def test_deprecated_conv_module_results():
+
+    M = MatrixSymbol("M", 3, 3)
+    N = MatrixSymbol("N", 3, 3)
+    i, j, d = symbols("i j d")
+
+    x = ArrayContraction(ArrayTensorProduct(M, N), (1, 2))
+    y = Sum(M[i, d]*N[d, j], (d, 0, 2))
+
+    with warns(SymPyDeprecationWarning, test_stacklevel=False):
+        assert conv_array_to_indexed.convert_array_to_indexed(x, [i, j]).dummy_eq(from_array_to_indexed.convert_array_to_indexed(x, [i, j]))
+        assert conv_array_to_matrix.convert_array_to_matrix(x) == from_array_to_matrix.convert_array_to_matrix(x)
+        assert conv_matrix_to_array.convert_matrix_to_array(M*N) == from_matrix_to_array.convert_matrix_to_array(M*N)
+        assert conv_indexed_to_array.convert_indexed_to_array(y) == from_indexed_to_array.convert_indexed_to_array(y)
diff --git a/shared/package-files/0c/0c6ffc1a1b209eec52328186e01112d0f72a6e28da1577a845e4a625a330d982/_to_dense_ops.h b/shared/package-files/0c/0c6ffc1a1b209eec52328186e01112d0f72a6e28da1577a845e4a625a330d982/_to_dense_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b2cb945619d55510ec0782f8e403f1945ddeaeb3
--- /dev/null
+++ b/shared/package-files/0c/0c6ffc1a1b209eec52328186e01112d0f72a6e28da1577a845e4a625a330d982/_to_dense_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _to_dense {
+  using schema = at::Tensor (const at::Tensor &, c10::optional<at::ScalarType>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_to_dense")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_to_dense(Tensor self, ScalarType? dtype=None, bool? masked_grad=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad);
+};
+
+struct TORCH_API _to_dense_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::optional<at::ScalarType>, c10::optional<bool>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_to_dense")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0c707778707429803e13448179b286dfa60b93e4f97a06f0c2bca77b74702073/mpsig.py b/shared/package-files/0c/0c707778707429803e13448179b286dfa60b93e4f97a06f0c2bca77b74702073/mpsig.py
new file mode 100644
index 0000000000000000000000000000000000000000..d129de74e5df00c22bc0b82c7d3f7b52483941f9
--- /dev/null
+++ b/shared/package-files/0c/0c707778707429803e13448179b286dfa60b93e4f97a06f0c2bca77b74702073/mpsig.py
@@ -0,0 +1,122 @@
+"""
+Some signal functions implemented using mpmath.
+"""
+
+try:
+    import mpmath
+except ImportError:
+    mpmath = None
+
+
+def _prod(seq):
+    """Returns the product of the elements in the sequence `seq`."""
+    p = 1
+    for elem in seq:
+        p *= elem
+    return p
+
+
+def _relative_degree(z, p):
+    """
+    Return relative degree of transfer function from zeros and poles.
+
+    This is simply len(p) - len(z), which must be nonnegative.
+    A ValueError is raised if len(p) < len(z).
+    """
+    degree = len(p) - len(z)
+    if degree < 0:
+        raise ValueError("Improper transfer function. "
+                         "Must have at least as many poles as zeros.")
+    return degree
+
+
+def _zpkbilinear(z, p, k, fs):
+    """Bilinear transformation to convert a filter from analog to digital."""
+
+    degree = _relative_degree(z, p)
+
+    fs2 = 2*fs
+
+    # Bilinear transform the poles and zeros
+    z_z = [(fs2 + z1) / (fs2 - z1) for z1 in z]
+    p_z = [(fs2 + p1) / (fs2 - p1) for p1 in p]
+
+    # Any zeros that were at infinity get moved to the Nyquist frequency
+    z_z.extend([-1] * degree)
+
+    # Compensate for gain change
+    numer = _prod(fs2 - z1 for z1 in z)
+    denom = _prod(fs2 - p1 for p1 in p)
+    k_z = k * numer / denom
+
+    return z_z, p_z, k_z.real
+
+
+def _zpklp2lp(z, p, k, wo=1):
+    """Transform a lowpass filter to a different cutoff frequency."""
+
+    degree = _relative_degree(z, p)
+
+    # Scale all points radially from origin to shift cutoff frequency
+    z_lp = [wo * z1 for z1 in z]
+    p_lp = [wo * p1 for p1 in p]
+
+    # Each shifted pole decreases gain by wo, each shifted zero increases it.
+    # Cancel out the net change to keep overall gain the same
+    k_lp = k * wo**degree
+
+    return z_lp, p_lp, k_lp
+
+
+def _butter_analog_poles(n):
+    """
+    Poles of an analog Butterworth lowpass filter.
+
+    This is the same calculation as scipy.signal.buttap(n) or
+    scipy.signal.butter(n, 1, analog=True, output='zpk'), but mpmath is used,
+    and only the poles are returned.
+    """
+    poles = [-mpmath.exp(1j*mpmath.pi*k/(2*n)) for k in range(-n+1, n, 2)]
+    return poles
+
+
+def butter_lp(n, Wn):
+    """
+    Lowpass Butterworth digital filter design.
+
+    This computes the same result as scipy.signal.butter(n, Wn, output='zpk'),
+    but it uses mpmath, and the results are returned in lists instead of NumPy
+    arrays.
+    """
+    zeros = []
+    poles = _butter_analog_poles(n)
+    k = 1
+    fs = 2
+    warped = 2 * fs * mpmath.tan(mpmath.pi * Wn / fs)
+    z, p, k = _zpklp2lp(zeros, poles, k, wo=warped)
+    z, p, k = _zpkbilinear(z, p, k, fs=fs)
+    return z, p, k
+
+
+def zpkfreqz(z, p, k, worN=None):
+    """
+    Frequency response of a filter in zpk format, using mpmath.
+
+    This is the same calculation as scipy.signal.freqz, but the input is in
+    zpk format, the calculation is performed using mpath, and the results are
+    returned in lists instead of NumPy arrays.
+    """
+    if worN is None or isinstance(worN, int):
+        N = worN or 512
+        ws = [mpmath.pi * mpmath.mpf(j) / N for j in range(N)]
+    else:
+        ws = worN
+
+    h = []
+    for wk in ws:
+        zm1 = mpmath.exp(1j * wk)
+        numer = _prod([zm1 - t for t in z])
+        denom = _prod([zm1 - t for t in p])
+        hk = k * numer / denom
+        h.append(hk)
+    return ws, h
diff --git a/shared/package-files/0c/0c709c5e3969c9e4854c5e7c3a67425d424bde1ad746e8d7fa122003c617ba43/jit.h b/shared/package-files/0c/0c709c5e3969c9e4854c5e7c3a67425d424bde1ad746e8d7fa122003c617ba43/jit.h
new file mode 100644
index 0000000000000000000000000000000000000000..703eed0d04248044edfa136324be0eb4828f76a4
--- /dev/null
+++ b/shared/package-files/0c/0c709c5e3969c9e4854c5e7c3a67425d424bde1ad746e8d7fa122003c617ba43/jit.h
@@ -0,0 +1,36 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/api/module.h>
+
+#include <memory>
+#include <string>
+
+namespace torch {
+namespace jit {
+
+/// Compiles script code into an executable graph.
+///
+/// Takes a string containing functions in script syntax and compiles them into
+/// a module (graph). The returned module provides a `run_method` function
+/// that may be used to invoke the compiled functions.
+///
+/// For example:
+/// \rst
+/// .. code-block:: cpp
+///
+///   auto module = torch::jit::compile(R"JIT(
+///     def relu_script(a, b):
+///       return torch.relu(a + b)
+///     def test_while(a, i):
+///       while i < 10:
+///         a += a
+///         i += 1
+///       return a
+///   )JIT");
+///   IValue output = module->run_method("relu_script", a, b);
+/// \endrst
+TORCH_API std::shared_ptr<CompilationUnit> compile(const std::string& source);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/0c/0c72c6badd1d0d7da7dca4744665788924d1b96658345d8a7e239bc0bb87ab02/test_unicode_array.py b/shared/package-files/0c/0c72c6badd1d0d7da7dca4744665788924d1b96658345d8a7e239bc0bb87ab02/test_unicode_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..d291ffd90d14a2325e86cf479af2afde22bcc2bb
--- /dev/null
+++ b/shared/package-files/0c/0c72c6badd1d0d7da7dca4744665788924d1b96658345d8a7e239bc0bb87ab02/test_unicode_array.py
@@ -0,0 +1,889 @@
+import numpy as np
+
+import unittest
+from numba import jit, from_dtype
+from numba.core import types
+from numba.typed import Dict
+from numba.tests.support import (TestCase, skip_ppc64le_issue4563)
+
+
+def getitem(x, i):
+    return x[i]
+
+
+def getitem2(x, i, j):
+    return x[i][j]
+
+
+def setitem(x, i, v):
+    x[i] = v
+    return x
+
+
+def setitem2(x, i, y, j):
+    x[i] = y[j]
+    return x
+
+
+def setitem_literal(x, i):
+    x[i] = '123'
+    return x
+
+
+def getitem_key(x, y, j):
+    x[y[j]] = 123
+
+
+def return_len(x, i):
+    return len(x[i])
+
+
+def return_bool(x, i):
+    return bool(x[i])
+
+
+def equal_getitem(x, i, j):
+    return x[i] == x[j]
+
+
+def notequal_getitem(x, i, j):
+    return x[i] != x[j]
+
+
+def lessthan_getitem(x, i, j):
+    return x[i] < x[j]
+
+
+def greaterthan_getitem(x, i, j):
+    return x[i] > x[j]
+
+
+def lessequal_getitem(x, i, j):
+    return x[i] <= x[j]
+
+
+def greaterequal_getitem(x, i, j):
+    return x[i] >= x[j]
+
+
+def contains_getitem2(x, i, y, j):
+    return x[i] in y[j]
+
+
+def equal_getitem_value(x, i, v):
+    r1 = x[i] == v
+    r2 = v == x[i]
+    if r1 == r2:
+        return r1
+    raise ValueError('x[i] == v and v == x[i] are unequal')
+
+
+def notequal_getitem_value(x, i, v):
+    r1 = x[i] != v
+    r2 = v != x[i]
+    if r1 == r2:
+        return r1
+    raise ValueError('x[i] != v and v != x[i] are unequal')
+
+
+def return_isascii(x, i):
+    return x[i].isascii()
+
+
+def return_isupper(x, i):
+    return x[i].isupper()
+
+
+def return_upper(x, i):
+    return x[i].upper()
+
+
+def return_str(x, i):
+    return str(x[i])
+
+
+def return_bytes(x, i):
+    return bytes(x[i])
+
+
+def return_hash(x, i):
+    return hash(x[i])
+
+
+def return_find(x, i, y, j):
+    return x[i].find(y[j])
+
+
+def return_rfind(x, i, y, j):
+    return x[i].rfind(y[j])
+
+
+def return_startswith(x, i, y, j):
+    return x[i].startswith(y[j])
+
+
+def return_endswith(x, i, y, j):
+    return x[i].endswith(y[j])
+
+
+def return_split1(x, i):
+    return x[i].split()
+
+
+def return_split2(x, i, y, j):
+    return x[i].split(y[j])
+
+
+def return_split3(x, i, y, j, maxsplit):
+    return x[i].split(sep=y[j], maxsplit=maxsplit)
+
+
+# NOT IMPLEMENTED: tests for rsplit
+
+
+def return_center1(x, i, w):
+    return x[i].center(w)
+
+
+def return_center2(x, i, w, y, j):
+    return x[i].center(w, y[j])
+
+
+def return_ljust1(x, i, w):
+    return x[i].ljust(w)
+
+
+def return_ljust2(x, i, w, y, j):
+    return x[i].ljust(w, y[j])
+
+
+def return_rjust1(x, i, w):
+    return x[i].rjust(w)
+
+
+def return_rjust2(x, i, w, y, j):
+    return x[i].rjust(w, y[j])
+
+
+def return_join(x, i, y, j, z, k):
+    return x[i].join([y[j], z[k]])
+
+
+def return_zfill(x, i, w):
+    return x[i].zfill(w)
+
+
+def return_lstrip1(x, i):
+    return x[i].lstrip()
+
+
+def return_lstrip2(x, i, y, j):
+    return x[i].lstrip(y[j])
+
+
+def return_rstrip1(x, i):
+    return x[i].rstrip()
+
+
+def return_rstrip2(x, i, y, j):
+    return x[i].rstrip(y[j])
+
+
+def return_strip1(x, i):
+    return x[i].strip()
+
+
+def return_strip2(x, i, y, j):
+    return x[i].strip(y[j])
+
+
+def return_add(x, i, y, j):
+    return x[i] + y[j]
+
+
+def return_iadd(x, i, y, j):
+    x[i] += y[j]
+    return x[i]
+
+
+def return_mul(x, i, y, j):
+    return x[i] * y[j]  # one of the operants must be integer
+
+
+def return_not(x, i):
+    return not x[i]
+
+
+def join_string_array(str_arr):
+    return ",".join(str_arr)
+
+
+@skip_ppc64le_issue4563
+class TestUnicodeArray(TestCase):
+
+    def _test(self, pyfunc, cfunc, *args, **kwargs):
+        expected = pyfunc(*args, **kwargs)
+        self.assertPreciseEqual(cfunc(*args, **kwargs), expected)
+
+    def test_getitem2(self):
+        cgetitem2 = jit(nopython=True)(getitem2)
+
+        arr = np.array(b'12')
+        self.assertPreciseEqual(cgetitem2(arr, (), 0), getitem2(arr, (), 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, (), 2)
+
+        arr = np.array('12')
+        self.assertPreciseEqual(cgetitem2(arr, (), 0), getitem2(arr, (), 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, (), 2)
+
+        arr = np.array([b'12', b'3'])
+        self.assertPreciseEqual(cgetitem2(arr, 0, 0), getitem2(arr, 0, 0))
+        self.assertPreciseEqual(cgetitem2(arr, 0, 1), getitem2(arr, 0, 1))
+        self.assertPreciseEqual(cgetitem2(arr, 1, 0), getitem2(arr, 1, 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, 1, 1)
+
+        arr = np.array(['12', '3'])
+        self.assertPreciseEqual(cgetitem2(arr, 0, 0), getitem2(arr, 0, 0))
+        self.assertPreciseEqual(cgetitem2(arr, 0, 1), getitem2(arr, 0, 1))
+        self.assertPreciseEqual(cgetitem2(arr, 1, 0), getitem2(arr, 1, 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, 1, 1)
+
+    def test_getitem(self):
+        pyfunc = getitem
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, b'12', 1)
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1)
+
+        self._test(pyfunc, cfunc, '12', 1)
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1)
+
+    def test_getitem_key(self):
+        pyfunc = getitem_key
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for x, i in [
+                (np.array('123'), ()),
+                (np.array(['123']), 0),
+                (np.array(b'123'), ()),
+                (np.array([b'123']), 0)
+        ]:
+            d1 = {}
+            d2 = Dict.empty(from_dtype(x.dtype), types.int64)
+            pyfunc(d1, x, i)
+            cfunc(d2, x, i)
+            self.assertEqual(d1, d2)
+            # check for charseq to str conversion:
+            str(d2)
+
+    def test_setitem(self):
+        pyfunc = setitem
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array(12)
+        self._test(pyfunc, cfunc, x, (), 34)
+
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        y1 = pyfunc(x1, (), b'34')
+        y2 = cfunc(x2, (), b'34')
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['123'])
+        x2 = np.array(['123'])
+        y1 = pyfunc(x1, 0, '34')
+        y2 = cfunc(x2, 0, '34')
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_setitem2(self):
+        pyfunc = setitem2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x1 = np.array(['123', 'ABC'])
+        x2 = np.array(['123', 'ABC'])
+        y1 = pyfunc(x1, 0, x1, 1)
+        y2 = cfunc(x2, 0, x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array([b'123', b'ABC'])
+        x2 = np.array([b'123', b'ABC'])
+        y1 = pyfunc(x1, 0, x1, 1)
+        y2 = cfunc(x2, 0, x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = np.array('ABC')
+        z2 = np.array('ABC')
+        y1 = pyfunc(x1, (), z1, ())
+        y2 = cfunc(x2, (), z2, ())
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(123)
+        x2 = np.array(123)
+        z1 = 456,
+        z2 = 456,
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'ABC',
+        z2 = b'ABC',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'ABC',
+        z2 = 'ABC',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-16
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB\u01e9',
+        z2 = 'AB\u01e9',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-32
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB\U00108a0e',
+        z2 = 'AB\U00108a0e',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8, assign longer value (truncates as in numpy)
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'ABCD',
+        z2 = 'ABCD',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8, assign shorter value
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB',
+        z2 = 'AB',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes, assign longer value (truncates as in numpy)
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'ABCD',
+        z2 = b'ABCD',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes, assign shorter value
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'AB',
+        z2 = b'AB',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_setitem_literal(self):
+        pyfunc = setitem_literal
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x1 = np.array('ABC')
+        x2 = np.array('ABC')
+        y1 = pyfunc(x1, ())
+        y2 = cfunc(x2, ())
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['ABC', '5678'])
+        x2 = np.array(['ABC', '5678'])
+        y1 = pyfunc(x1, 0)
+        y2 = cfunc(x2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['ABC', '5678'])
+        x2 = np.array(['ABC', '5678'])
+        y1 = pyfunc(x1, 1)
+        y2 = cfunc(x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_return_len(self):
+        pyfunc = return_len
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1)
+
+    def test_return_bool(self):
+        pyfunc = return_bool
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'']), 1)
+        self._test(pyfunc, cfunc, np.array(['12', '']), 1)
+
+    def _test_op_getitem(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array([1, 2]), 0, 1)
+        self._test(pyfunc, cfunc, '12', 0, 1)
+        self._test(pyfunc, cfunc, b'12', 0, 1)
+        self._test(pyfunc, cfunc, np.array(b'12'), (), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ())
+
+        self._test(pyfunc, cfunc, np.array([b'1', b'2']), 0, 0)
+        self._test(pyfunc, cfunc, np.array([b'1', b'2']), 0, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0, 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1, 0)
+
+        self._test(pyfunc, cfunc, np.array(['1', '2']), 0, 0)
+        self._test(pyfunc, cfunc, np.array(['1', '2']), 0, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0, 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1, 0)
+
+    def test_equal_getitem(self):
+        self._test_op_getitem(equal_getitem)
+
+    def test_notequal_getitem(self):
+        self._test_op_getitem(notequal_getitem)
+
+    def test_lessthan_getitem(self):
+        self._test_op_getitem(lessthan_getitem)
+
+    def test_greaterthan_getitem(self):
+        self._test_op_getitem(greaterthan_getitem)
+
+    def test_lessequal_getitem(self):
+        self._test_op_getitem(lessequal_getitem)
+
+    def test_greaterequal_getitem(self):
+        self._test_op_getitem(greaterequal_getitem)
+
+    def _test_op_getitem_value(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array([1, 2]), 0, 1)
+        self._test(pyfunc, cfunc, '12', 0, '1')
+        self._test(pyfunc, cfunc, '12', 1, '3')
+        self._test(pyfunc, cfunc, np.array('1234'), (), '1234')
+        self._test(pyfunc, cfunc, np.array(['1234']), 0, '1234')
+        self._test(pyfunc, cfunc, np.array(['1234']), 0, 'abc')
+        # fails: No conversion from array(bool, 1d, C) to bool
+        #self._test(pyfunc, cfunc, b'12', 0, b'1')
+        self._test(pyfunc, cfunc, np.array(b'12'), (), b'12')
+        self._test(pyfunc, cfunc, np.array([b'12']), 0, b'12')
+        self._test(pyfunc, cfunc, np.array([b'12']), 0, b'a')
+
+    def test_equal_getitem_value(self):
+        self._test_op_getitem_value(equal_getitem_value)
+
+    def test_notequal_getitem_value(self):
+        self._test_op_getitem_value(notequal_getitem_value)
+
+    def test_contains_getitem2(self):
+        pyfunc = contains_getitem2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array('123')
+        y = np.array('12345')
+        self._test(pyfunc, cfunc, x, (), y, ())
+        self._test(pyfunc, cfunc, y, (), x, ())
+
+        x = np.array(b'123')
+        y = np.array(b'12345')
+        self._test(pyfunc, cfunc, x, (), y, ())
+        self._test(pyfunc, cfunc, y, (), x, ())
+
+        x = ('123',)
+        y = np.array('12345')
+        self._test(pyfunc, cfunc, x, 0, y, ())
+        self._test(pyfunc, cfunc, y, (), x, 0)
+
+        x = (b'123',)
+        y = np.array(b'12345')
+        self._test(pyfunc, cfunc, x, 0, y, ())
+        self._test(pyfunc, cfunc, y, (), x, 0)
+
+    def test_return_isascii(self):
+        pyfunc = return_isascii
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+        self._test(pyfunc, cfunc, np.array('1234\u00e9'), ())
+        self._test(pyfunc, cfunc, np.array(['1234\u00e9']), 0)
+
+    def test_return_isupper(self):
+        pyfunc = return_isupper
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('abc'), ())
+        self._test(pyfunc, cfunc, np.array(['abc']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'abc'), ())
+        self._test(pyfunc, cfunc, np.array([b'abc']), 0)
+
+    def test_return_str(self):
+        pyfunc = return_str
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+
+    def test_return_bytes(self):
+        pyfunc = return_bytes
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+        self._test(pyfunc, cfunc, np.array([b'1234']), 0)
+
+    def test_return_upper(self):
+        pyfunc = return_upper
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('abc'), ())
+        self._test(pyfunc, cfunc, np.array(['abc']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'abc'), ())
+        self._test(pyfunc, cfunc, np.array([b'abc']), 0)
+
+    def test_hash(self):
+        pyfunc = return_hash
+        cfunc = jit(nopython=True)(pyfunc)
+
+        hash1 = pyfunc(np.array('123'), ())
+        hash2 = hash('123')
+        hash3 = hash(np.array('123')[()])
+        self.assertTrue(hash1 == hash2 == hash3)
+
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+
+        self._test(pyfunc, cfunc, np.array('1234\u00e9'), ())
+        self._test(pyfunc, cfunc, np.array(['1234u00e9']), 0)
+
+        self._test(pyfunc, cfunc, np.array('1234\U00108a0e'), ())
+        self._test(pyfunc, cfunc, np.array(['1234\U00108a0e']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+        self._test(pyfunc, cfunc, np.array([b'1234']), 0)
+
+    def test_return_find(self):
+        pyfunc = return_find
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_rfind(self):
+        pyfunc = return_rfind
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_startswith(self):
+        pyfunc = return_startswith
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_endswith(self):
+        pyfunc = return_endswith
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_split1(self):
+        pyfunc = return_split1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('12 34'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+
+    def test_return_split2(self):
+        pyfunc = return_split2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('12 34'), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('12 34'), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('12 34',), 0, np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array(b'12 34'), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'12 34'), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'12 34',), 0, np.array(b' '), ())
+
+    def test_return_split3(self):
+        pyfunc = return_split3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (),
+                   np.array(' '), (), 2)
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), (' ',), 0, 2)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, np.array(' '), (), 2)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (),
+                   np.array(b' '), (), 2)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), (b' ',), 0, 2)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, np.array(b' '), (), 2)
+
+    def test_return_ljust1(self):
+        pyfunc = return_ljust1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_ljust2(self):
+        pyfunc = return_ljust2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_rjust1(self):
+        pyfunc = return_rjust1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_rjust2(self):
+        pyfunc = return_rjust2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_center1(self):
+        pyfunc = return_center1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_center2(self):
+        pyfunc = return_center2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_join(self):
+        pyfunc = return_join
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(','), (), np.array('abc'), (),
+                   np.array('123'), ())
+        self._test(pyfunc, cfunc, np.array(','), (), np.array('abc'), (),
+                   ('123',), 0)
+        self._test(pyfunc, cfunc, (',',), 0, np.array('abc'), (),
+                   np.array('123'), ())
+        self._test(pyfunc, cfunc, (',',), 0, np.array('abc'), (),
+                   ('123',), 0)
+        self._test(pyfunc, cfunc, np.array(b','), (), np.array(b'abc'), (),
+                   np.array(b'123'), ())
+        self._test(pyfunc, cfunc, np.array(b','), (), np.array(b'abc'), (),
+                   (b'123',), 0)
+        self._test(pyfunc, cfunc, (b',',), 0, np.array(b'abc'), (),
+                   np.array(b'123'), ())
+        self._test(pyfunc, cfunc, (b',',), 0, np.array(b'abc'), (),
+                   (b'123',), 0)
+
+    def test_return_zfill(self):
+        pyfunc = return_zfill
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_lstrip1(self):
+        pyfunc = return_lstrip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_lstrip2(self):
+        pyfunc = return_lstrip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_rstrip1(self):
+        pyfunc = return_rstrip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_rstrip2(self):
+        pyfunc = return_rstrip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_strip1(self):
+        pyfunc = return_strip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_strip2(self):
+        pyfunc = return_strip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_add(self):
+        pyfunc = return_add
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), np.array('cd'), ())
+        self._test(pyfunc, cfunc, np.array('ab'), (), ('cd',), 0)
+        self._test(pyfunc, cfunc, ('ab',), 0, np.array('cd'), ())
+
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), np.array(b'cd'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (b'cd',), 0)
+        self._test(pyfunc, cfunc, (b'ab',), 0, np.array(b'cd'), ())
+
+    def test_return_iadd(self):
+        pyfunc = return_iadd
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), np.array('cd'), ())
+        self._test(pyfunc, cfunc, np.array('ab'), (), ('cd',), 0)
+        expected = pyfunc(['ab'], 0, np.array('cd'), ())
+        result = pyfunc(['ab'], 0, np.array('cd'), ())
+        self.assertPreciseEqual(result, expected)
+
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), np.array(b'cd'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (b'cd',), 0)
+        expected = pyfunc([b'ab'], 0, np.array(b'cd'), ())
+        result = pyfunc([b'ab'], 0, np.array(b'cd'), ())
+        self.assertPreciseEqual(result, expected)
+
+    def test_return_mul(self):
+        pyfunc = return_mul
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), (5,), 0)
+        self._test(pyfunc, cfunc, (5,), 0, np.array('ab'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (5,), 0)
+        self._test(pyfunc, cfunc, (5,), 0, np.array(b'ab'), ())
+
+    def test_return_not(self):
+        pyfunc = return_not
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), ())
+        self._test(pyfunc, cfunc, (b'ab',), 0)
+
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, (b'',), 0)
+
+    def test_join(self):
+        pyfunc = join_string_array
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array(["hi", "there"]))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/0c/0c73100794e9d473412117cb6020696b953577b1dccf85b68c407a29e9689525/randn_like_ops.h b/shared/package-files/0c/0c73100794e9d473412117cb6020696b953577b1dccf85b68c407a29e9689525/randn_like_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf2e0f77aafee619d70e2fe4b32e04c0e2f9e003
--- /dev/null
+++ b/shared/package-files/0c/0c73100794e9d473412117cb6020696b953577b1dccf85b68c407a29e9689525/randn_like_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randn_like {
+  using schema = at::Tensor (const at::Tensor &, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>, c10::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randn_like")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API randn_like_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randn_like")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0c769d208bcbd0d3a2d6659197eccd6507a78250a0350e85476a9fee99fcdcc3/split_ops.h b/shared/package-files/0c/0c769d208bcbd0d3a2d6659197eccd6507a78250a0350e85476a9fee99fcdcc3/split_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf0450777a795e56925f4d0a3df3c21072055353
--- /dev/null
+++ b/shared/package-files/0c/0c769d208bcbd0d3a2d6659197eccd6507a78250a0350e85476a9fee99fcdcc3/split_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API split_Tensor {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymInt, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::split")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]")
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+};
+
+struct TORCH_API split_sizes {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::split")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "sizes")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]")
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0c77a53c4f43936392b925bf8b8c6881345457afeb2bde40d95396052f1f3988/_limiter_utils.py b/shared/package-files/0c/0c77a53c4f43936392b925bf8b8c6881345457afeb2bde40d95396052f1f3988/_limiter_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..efb5b3ba5ae1f25291ecbf98b8b67a46dce0cc9b
--- /dev/null
+++ b/shared/package-files/0c/0c77a53c4f43936392b925bf8b8c6881345457afeb2bde40d95396052f1f3988/_limiter_utils.py
@@ -0,0 +1,33 @@
+import collections
+from typing import Deque, Optional
+
+import torch
+
+
+class _FreeEventQueue:
+    """
+    This tracks all pending frees corresponding to inflight all-gathers. The
+    queueing pattern is iterative enqueues with a single dequeue per iteration
+    once the limit ``_max_num_inflight_all_gathers`` is reached.
+    """
+
+    def __init__(self) -> None:
+        self._queue: Deque[torch.cuda.Event] = collections.deque()
+        self._max_num_inflight_all_gathers = 2  # empirically chosen
+
+    def enqueue(self, free_event: torch.cuda.Event) -> None:
+        """Enqueues a free event."""
+        self._queue.append(free_event)
+
+    def dequeue_if_needed(self) -> Optional[torch.cuda.Event]:
+        """Dequeues a single event if the limit is reached."""
+        if len(self._queue) >= self._max_num_inflight_all_gathers:
+            return self._dequeue()
+        return None
+
+    def _dequeue(self) -> Optional[torch.cuda.Event]:
+        """Dequeues a free event if possible."""
+        if self._queue:
+            event = self._queue.popleft()
+            return event
+        return None
diff --git a/shared/package-files/0c/0c7884671a1790d603c73b8ee614f271ac7b7c6fd8e77de69371a8b3e56b99b8/prelu_compositeimplicitautograd_dispatch.h b/shared/package-files/0c/0c7884671a1790d603c73b8ee614f271ac7b7c6fd8e77de69371a8b3e56b99b8/prelu_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..15753a36ddb31bbf04ea3432d342f5bf3456c69b
--- /dev/null
+++ b/shared/package-files/0c/0c7884671a1790d603c73b8ee614f271ac7b7c6fd8e77de69371a8b3e56b99b8/prelu_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor prelu(const at::Tensor & self, const at::Tensor & weight);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/0c/0c7a84ffdf5cceb7ac8fc201f48fb973e0bec84bed959187c40ddbb0c5a08368/wy325-42 b/shared/package-files/0c/0c7a84ffdf5cceb7ac8fc201f48fb973e0bec84bed959187c40ddbb0c5a08368/wy325-42
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diff --git a/shared/package-files/0c/0c7d1bc1b2a97630f55825b55629e75c4b952d8d3df2e1ca922aefec315cf493/__init__.py b/shared/package-files/0c/0c7d1bc1b2a97630f55825b55629e75c4b952d8d3df2e1ca922aefec315cf493/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ebe59b499c214dd82954bff84824cfea574b415
--- /dev/null
+++ b/shared/package-files/0c/0c7d1bc1b2a97630f55825b55629e75c4b952d8d3df2e1ca922aefec315cf493/__init__.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# flake8: noqa
+
+from .from_dataframe import from_dataframe
diff --git a/shared/package-files/0c/0c7fdbb107ee5272b6a1b75bd3a2a08ac3b85cbaa1b75d815ddae052c659bde8/New_Salem b/shared/package-files/0c/0c7fdbb107ee5272b6a1b75bd3a2a08ac3b85cbaa1b75d815ddae052c659bde8/New_Salem
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diff --git a/shared/package-files/0c/0c826023dea90f244201659382a0d67bb7423fb9ff5ccaf73b522f9bc70c0a82/test_smoothing2.R b/shared/package-files/0c/0c826023dea90f244201659382a0d67bb7423fb9ff5ccaf73b522f9bc70c0a82/test_smoothing2.R
new file mode 100644
index 0000000000000000000000000000000000000000..695037abb500e15d273fae70e951acc75014b930
--- /dev/null
+++ b/shared/package-files/0c/0c826023dea90f244201659382a0d67bb7423fb9ff5ccaf73b522f9bc70c0a82/test_smoothing2.R
@@ -0,0 +1,70 @@
+library(KFAS)
+options(digits=10)
+
+# should run this from the statsmodels/statsmodels directory
+dta <- read.csv('datasets/macrodata/macrodata.csv')
+
+obs <- diff(log(data.matrix(dta[c('realgdp','realcons','realinv')])))
+
+#T <- t(matrix(
+#  c(-0.1119908792, 0.8441841604,  0.0238725303,
+#     0.2629347724, 0.4996718412, -0.0173023305,
+#    -3.2192369082, 4.1536028244,  0.4514379215), nrow=3, ncol=3))
+#Q <- t(matrix(
+#  c(0.0000640649, 0.0000388496, 0.0002148769,
+#    0.0000388496, 0.0000572802, 0.000001555,
+#    0.0002148769, 0.000001555,  0.0017088585), nrow=3, ncol=3))
+#H <- matrix(0, 3, 3)
+T <- t(matrix(
+  c(-0.1119908792, 0.8441841604,  0.0238725303,
+     0.2629347724, 0.4996718412, -0.0173023305,
+    -3.2192369082, 4.1536028244,  0.4514379215), nrow=3, ncol=3))
+Q <- t(matrix(
+  c(0.0000640649, 0.0000388496, 0.0002148769,
+    0.0000388496, 0.0000572802, 0.000001555,
+    0.0002148769, 0.000001555,  0.0017088585), nrow=3, ncol=3))
+H <- t(matrix(
+  c(0.0000640649, 0.,           0.,
+    0.,           0.0000572802, 0.,
+    0.,           0.,           0.0017088585), nrow=3, ncol=3))
+
+mod <- SSModel(obs ~ -1 + SSMcustom(Z=diag(3), T=T, R=diag(3), Q=Q, P1=diag(3)*1e6),  H=H)
+kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=FALSE)
+
+# kf$logLik # 1695.34872
+
+# r = scaled_smoothed_estimator
+# N = scaled_smoothed_estimator_cov
+# m = forecasts
+# v = forecasts_error
+# F = forecasts_error_cov
+# a = predicted_state
+# P = predicted_state_cov
+# mu = filtered_forecasts
+# alphahat = smoothed_state
+# V = smoothed_state_cov
+# muhat = smoothed_forecasts
+# etahat = smoothed_state_disturbance
+# V_eta = smoothed_state_disturbance_cov
+# epshat = smoothed_measurement_disturbance
+# V_eps = smoothed_measurement_disturbance_cov
+
+out <- as.data.frame(with(kf, cbind(
+  t(r)[2:203,], apply(N, 3, det)[2:203],
+  m, apply(P_mu, 3, det), v, t(F),
+  a[2:203,], apply(P, 3, det)[2:203],
+  alphahat, apply(V, 3, det),
+  muhat, apply(V_mu, 3, det),
+  etahat, apply(V_eta, 3, det), epshat, t(V_eps)
+)))
+names(out) <- c(
+  "r1", "r2", "r3", "detN",
+  "m1", "m2", "m3", "detPmu",
+  "v1", "v2", "v3", "F1", "F2", "F3",
+  "a1", "a2", "a3", "detP",
+  "alphahat1", "alphahat2", "alphahat3", "detV",
+  "muhat1", "muhat2", "muhat3", "detVmu",
+  "etahat1", "etahat2", "etahat3", "detVeta",
+  "epshat1", "epshat2", "epshat3", "Veps1", "Veps2", "Veps3"
+)
+write.csv(out, 'tsa/statespace/tests/results/results_smoothing2_R.csv', row.names=FALSE)
diff --git a/shared/package-files/0c/0c82c7d3deca5c04314d451d3c4d130e77c3f4bebaa437763ffea75fb3392660/activation.h b/shared/package-files/0c/0c82c7d3deca5c04314d451d3c4d130e77c3f4bebaa437763ffea75fb3392660/activation.h
new file mode 100644
index 0000000000000000000000000000000000000000..52030da2aa9233fc8dbbe0b4da36afd3e939a006
--- /dev/null
+++ b/shared/package-files/0c/0c82c7d3deca5c04314d451d3c4d130e77c3f4bebaa437763ffea75fb3392660/activation.h
@@ -0,0 +1,966 @@
+#pragma once
+
+#include <ATen/Dispatch.h>
+#include <torch/nn/functional/dropout.h>
+#include <torch/nn/functional/linear.h>
+#include <torch/nn/options/activation.h>
+#include <torch/nn/options/dropout.h>
+#include <torch/nn/options/linear.h>
+#include <torch/types.h>
+#include <limits>
+#include <utility>
+
+namespace torch {
+namespace nn {
+namespace functional {
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor elu(Tensor input, double alpha, bool inplace) {
+  if (inplace) {
+    return torch::elu_(input, alpha);
+  } else {
+    return torch::elu(input, alpha);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.elu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::ELUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::elu(x, F::ELUFuncOptions().alpha(0.42).inplace(true));
+/// ```
+inline Tensor elu(Tensor input, const ELUFuncOptions& options = {}) {
+  return detail::elu(std::move(input), options.alpha(), options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor selu(Tensor input, bool inplace) {
+  if (inplace) {
+    return torch::selu_(input);
+  } else {
+    return torch::selu(input);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.selu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::SELUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::selu(input, F::SELUFuncOptions(false));
+/// ```
+inline Tensor selu(Tensor input, const SELUFuncOptions& options = {}) {
+  return detail::selu(std::move(input), options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor hardshrink(const Tensor& input, double lambda) {
+  return torch::hardshrink(input, lambda);
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardshrink
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::HardshrinkFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::hardshrink(x, F::HardshrinkFuncOptions().lambda(0.42));
+/// ```
+inline Tensor hardshrink(
+    const Tensor& input,
+    const HardshrinkFuncOptions& options = {}) {
+  return detail::hardshrink(input, options.lambda());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor hardtanh(
+    Tensor input,
+    double min_val,
+    double max_val,
+    bool inplace) {
+  if (inplace) {
+    return torch::hardtanh_(input, min_val, max_val);
+  } else {
+    return torch::hardtanh(input, min_val, max_val);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardtanh
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::HardtanhFuncOptions` class
+/// to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::hardtanh(x,
+/// F::HardtanhFuncOptions().min_val(-1.0).max_val(1.0).inplace(true));
+/// ```
+inline Tensor hardtanh(Tensor input, const HardtanhFuncOptions& options = {}) {
+  return detail::hardtanh(
+      std::move(input),
+      options.min_val(),
+      options.max_val(),
+      options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor leaky_relu(Tensor input, double negative_slope, bool inplace) {
+  if (inplace) {
+    return torch::leaky_relu_(input, negative_slope);
+  } else {
+    return torch::leaky_relu(input, negative_slope);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.leaky_relu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::LeakyReLUFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::leaky_relu(x,
+/// F::LeakyReLUFuncOptions().negative_slope(0.42).inplace(true));
+/// ```
+inline Tensor leaky_relu(
+    Tensor input,
+    const LeakyReLUFuncOptions& options = {}) {
+  return detail::leaky_relu(
+      std::move(input), options.negative_slope(), options.inplace());
+}
+
+// ============================================================================
+
+inline Tensor logsigmoid(const Tensor& input) {
+  return torch::log_sigmoid(input);
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor gumbel_softmax(
+    const Tensor& logits,
+    double tau,
+    bool hard,
+    int dim) {
+  auto gumbels =
+      -torch::empty_like(logits).exponential_().log(); // ~Gumbel(0,1)
+  gumbels = (logits + gumbels) / tau; // ~Gumbel(logits, tau)
+  auto y_soft = gumbels.softmax(dim);
+
+  torch::Tensor ret;
+  if (hard) {
+    // Straight through.
+    auto index = std::get<1>(y_soft.max(dim, /*keepdim=*/true));
+    auto y_hard = torch::zeros_like(logits).scatter_(dim, index, 1.0);
+    ret = y_hard - y_soft.detach() + y_soft;
+  } else {
+    ret = y_soft;
+  }
+  return ret;
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.gumbel_softmax
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::GumbelSoftmaxFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::gumbel_softmax(logits, F::GumbelSoftmaxFuncOptions().hard(true).dim(-1));
+/// ```
+inline Tensor gumbel_softmax(
+    const Tensor& logits,
+    const GumbelSoftmaxFuncOptions& options = {}) {
+  return detail::gumbel_softmax(
+      logits, options.tau(), options.hard(), options.dim());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor softmax(
+    const Tensor& input,
+    int64_t dim,
+    c10::optional<torch::Dtype> dtype) {
+  Tensor ret;
+
+  if (dtype == c10::nullopt) {
+    ret = input.softmax(dim);
+  } else {
+    ret = input.softmax(dim, dtype);
+  }
+
+  return ret;
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmax
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::SoftmaxFuncOptions` class
+/// to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::softmax(input, F::SoftmaxFuncOptions(1));
+/// ```
+inline Tensor softmax(const Tensor& input, const SoftmaxFuncOptions& options) {
+  return detail::softmax(input, options.dim(), options.dtype());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor softmin(
+    const Tensor& input,
+    int64_t dim,
+    c10::optional<torch::Dtype> dtype) {
+  Tensor ret;
+
+  if (dtype == c10::nullopt) {
+    ret = (-input).softmax(dim);
+  } else {
+    ret = (-input).softmax(dim, dtype);
+  }
+
+  return ret;
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmin
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::SoftminFuncOptions` class
+/// to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::softmin(input, F::SoftminFuncOptions(1));
+/// ```
+inline Tensor softmin(const Tensor& input, const SoftminFuncOptions& options) {
+  return detail::softmin(input, options.dim(), options.dtype());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor log_softmax(
+    const Tensor& input,
+    int64_t dim,
+    c10::optional<torch::Dtype> dtype) {
+  Tensor ret;
+
+  if (dtype == c10::nullopt) {
+    ret = input.log_softmax(dim);
+  } else {
+    ret = input.log_softmax(dim, dtype);
+  }
+
+  return ret;
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.log_softmax
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::LogSoftmaxFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::log_softmax(input, LogSoftmaxFuncOptions(1));
+/// ```
+inline Tensor log_softmax(
+    const Tensor& input,
+    const LogSoftmaxFuncOptions& options) {
+  return detail::log_softmax(input, options.dim(), options.dtype());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor glu(const Tensor& input, int64_t dim) {
+  TORCH_CHECK(
+      input.dim() != 0,
+      "glu does not suppport scalars because halving size must be even");
+  return torch::glu(input, dim);
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.glu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::GLUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::glu(input, GLUFuncOptions(1));
+/// ```
+inline Tensor glu(const Tensor& input, const GLUFuncOptions& options = {}) {
+  return detail::glu(input, options.dim());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor gelu(const Tensor& input, string approximate) {
+  return torch::gelu(input, approximate);
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+inline Tensor gelu(const Tensor& input, const GELUFuncOptions& options = {}) {
+  return detail::gelu(input, options.approximate());
+}
+
+// ============================================================================
+
+inline Tensor silu(const Tensor& input) {
+  return torch::silu(input);
+}
+
+// ============================================================================
+
+inline Tensor mish(const Tensor& input) {
+  return torch::mish(input);
+}
+
+// ============================================================================
+
+inline Tensor prelu(const Tensor& input, const Tensor& weight) {
+  return torch::prelu(input, weight);
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor relu(Tensor input, bool inplace) {
+  if (inplace) {
+    return torch::relu_(input);
+  } else {
+    return torch::relu(input);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::ReLUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::relu(x, F::ReLUFuncOptions().inplace(true));
+/// ```
+inline Tensor relu(Tensor input, const ReLUFuncOptions& options = {}) {
+  return detail::relu(std::move(input), options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor relu6(Tensor input, bool inplace) {
+  if (inplace) {
+    return torch::relu6_(input);
+  } else {
+    return torch::relu6(input);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu6
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::ReLU6FuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::relu6(x, F::ReLU6FuncOptions().inplace(true));
+/// ```
+inline Tensor relu6(Tensor input, const ReLU6FuncOptions& options = {}) {
+  return detail::relu6(std::move(input), options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor rrelu(
+    Tensor input,
+    double lower,
+    double upper,
+    bool training,
+    bool inplace) {
+  if (inplace) {
+    return torch::rrelu_(input, lower, upper, training);
+  } else {
+    return torch::rrelu(input, lower, upper, training);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.rrelu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::RReLUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::rrelu(x, F::RReLUFuncOptions().lower(0.1).upper(0.4).inplace(true));
+/// ```
+inline Tensor rrelu(Tensor input, const RReLUFuncOptions& options = {}) {
+  return detail::rrelu(
+      std::move(input),
+      options.lower(),
+      options.upper(),
+      options.training(),
+      options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor celu(Tensor input, double alpha, bool inplace) {
+  if (inplace) {
+    return torch::celu_(input, alpha);
+  } else {
+    return torch::celu(input, alpha);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.celu
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::CELUFuncOptions` class to
+/// learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::celu(x, F::CELUFuncOptions().alpha(0.42).inplace(true));
+/// ```
+inline Tensor celu(Tensor input, const CELUFuncOptions& options = {}) {
+  return detail::celu(std::move(input), options.alpha(), options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor softplus(const Tensor& input, double beta, double threshold) {
+  return torch::softplus(input, beta, threshold);
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softplus
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::SoftplusFuncOptions` class
+/// to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::softplus(x, F::SoftplusFuncOptions().beta(0.5).threshold(3.0));
+/// ```
+inline Tensor softplus(
+    const Tensor& input,
+    const SoftplusFuncOptions& options = {}) {
+  return detail::softplus(input, options.beta(), options.threshold());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor softshrink(const Tensor& input, double lambda) {
+  return torch::softshrink(input, lambda);
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softshrink
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::SoftshrinkFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::softshrink(x, F::SoftshrinkFuncOptions(0.42));
+/// ```
+inline Tensor softshrink(
+    const Tensor& input,
+    const SoftshrinkFuncOptions& options = {}) {
+  return detail::softshrink(input, options.lambda());
+}
+
+// ============================================================================
+
+inline Tensor softsign(const Tensor& input) {
+  return input / (input.abs() + 1);
+}
+
+// ============================================================================
+
+inline Tensor tanhshrink(const Tensor& input) {
+  return input - input.tanh();
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor threshold(
+    Tensor input,
+    double threshold,
+    double value,
+    bool inplace) {
+  if (inplace) {
+    return torch::threshold_(input, threshold, value);
+  } else {
+    return torch::threshold(input, threshold, value);
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See
+/// https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.threshold
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::ThresholdFuncOptions`
+/// class to learn what optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::threshold(x, F::ThresholdFuncOptions(0.5, 0.5).inplace(true));
+/// ```
+inline Tensor threshold(Tensor input, const ThresholdFuncOptions& options) {
+  return detail::threshold(
+      std::move(input),
+      options.threshold(),
+      options.value(),
+      options.inplace());
+}
+
+// ============================================================================
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline std::tuple<Tensor, Tensor> multi_head_attention_forward(
+    const Tensor& query,
+    const Tensor& key,
+    const Tensor& value,
+    int64_t embed_dim_to_check,
+    int64_t num_heads,
+    const Tensor& in_proj_weight,
+    const Tensor& in_proj_bias,
+    const Tensor& bias_k,
+    const Tensor& bias_v,
+    bool add_zero_attn,
+    double dropout_p,
+    const Tensor& out_proj_weight,
+    const Tensor& out_proj_bias,
+    bool training = true,
+    const Tensor& key_padding_mask = {},
+    bool need_weights = true,
+    const Tensor& attn_mask = {},
+    bool use_separate_proj_weight = false,
+    const Tensor& q_proj_weight = {},
+    const Tensor& k_proj_weight = {},
+    const Tensor& v_proj_weight = {},
+    const Tensor& static_k = {},
+    const Tensor& static_v = {},
+    bool average_attn_weights = true) {
+  namespace F = torch::nn::functional;
+
+  const auto query_sizes = query.sizes();
+  const auto& tgt_len = query_sizes[0];
+  const auto& bsz = query_sizes[1];
+  const auto& embed_dim = query_sizes[2];
+  TORCH_INTERNAL_ASSERT(embed_dim == embed_dim_to_check);
+  TORCH_INTERNAL_ASSERT(key.sizes() == value.sizes());
+
+  const auto head_dim = embed_dim / num_heads;
+  TORCH_CHECK(
+      head_dim * num_heads == embed_dim,
+      "embed_dim must be divisible by num_heads");
+  const auto scaling = 1 / std::sqrt(head_dim);
+
+  Tensor q, k, v;
+  if (!use_separate_proj_weight) {
+    if (torch::equal(query, key) && torch::equal(key, value)) {
+      // self-attention
+      const auto chunks =
+          F::linear(query, in_proj_weight, in_proj_bias).chunk(3, /*dim=*/-1);
+      q = chunks[0];
+      k = chunks[1];
+      v = chunks[2];
+    } else if (torch::equal(key, value)) {
+      // encoder-decoder attention
+      // This is inline in_proj function with in_proj_weight and in_proj_bias
+      auto _b = in_proj_bias;
+      auto _start = 0;
+      auto _end = embed_dim;
+      auto _w = in_proj_weight.slice(/*dim=*/0, _start, _end);
+      if (_b.defined()) {
+        _b = _b.slice(/*dim=*/0, _start, _end);
+      }
+      q = F::linear(query, _w, _b);
+
+      if (!key.defined()) {
+        TORCH_INTERNAL_ASSERT(!value.defined());
+        k.reset();
+        v.reset();
+      } else {
+        // This is inline in_proj function with in_proj_weight and in_proj_bias
+        _b = in_proj_bias;
+        _start = embed_dim;
+        _w = in_proj_weight.slice(/*dim=*/0, _start);
+        if (_b.defined()) {
+          _b = _b.slice(/*dim=*/0, _start);
+        }
+        const auto chunks = F::linear(key, _w, _b).chunk(2, /*dim=*/-1);
+        k = chunks[0];
+        v = chunks[1];
+      }
+    } else {
+      // This is inline in_proj function with in_proj_weight and in_proj_bias
+      auto _b = in_proj_bias;
+      auto _start = 0;
+      auto _end = embed_dim;
+      auto _w = in_proj_weight.slice(/*dim=*/0, _start, _end);
+      if (_b.defined()) {
+        _b = _b.slice(/*dim=*/0, _start, _end);
+      }
+      q = F::linear(query, _w, _b);
+
+      // This is inline in_proj function with in_proj_weight and in_proj_bias
+      _b = in_proj_bias;
+      _start = embed_dim;
+      _end = embed_dim * 2;
+      _w = in_proj_weight.slice(/*dim=*/0, _start, _end);
+      if (_b.defined()) {
+        _b = _b.slice(/*dim=*/0, _start, _end);
+      }
+      k = F::linear(key, _w, _b);
+
+      // This is inline in_proj function with in_proj_weight and in_proj_bias
+      _b = in_proj_bias;
+      _start = embed_dim * 2;
+      _w = in_proj_weight.slice(/*dim=*/0, _start);
+      if (_b.defined()) {
+        _b = _b.slice(0, _start);
+      }
+      v = F::linear(value, _w, _b);
+    }
+  } else {
+    const auto& q_proj_weight_non_opt = q_proj_weight;
+    {
+      const auto sizes = q_proj_weight_non_opt.sizes();
+      const auto len1 = sizes[0];
+      const auto len2 = sizes[1];
+      TORCH_CHECK(len1 == embed_dim && len2 == query.size(-1));
+    }
+
+    const auto& k_proj_weight_non_opt = k_proj_weight;
+    {
+      const auto sizes = k_proj_weight_non_opt.sizes();
+      const auto len1 = sizes[0];
+      const auto len2 = sizes[1];
+      TORCH_CHECK(len1 == embed_dim && len2 == key.size(-1));
+    }
+
+    const auto& v_proj_weight_non_opt = v_proj_weight;
+    {
+      const auto sizes = v_proj_weight_non_opt.sizes();
+      const auto len1 = sizes[0];
+      const auto len2 = sizes[1];
+      TORCH_CHECK(len1 == embed_dim && len2 == value.size(-1));
+    }
+
+    if (in_proj_bias.defined()) {
+      q = F::linear(
+          query,
+          q_proj_weight_non_opt,
+          in_proj_bias.slice(/*dim=*/0, 0, embed_dim));
+      k = F::linear(
+          key,
+          k_proj_weight_non_opt,
+          in_proj_bias.slice(/*dim=*/0, embed_dim, (embed_dim * 2)));
+      v = F::linear(
+          value,
+          v_proj_weight_non_opt,
+          in_proj_bias.slice(/*dim=*/0, (embed_dim * 2)));
+    } else {
+      q = F::linear(query, q_proj_weight_non_opt, in_proj_bias);
+      k = F::linear(key, k_proj_weight_non_opt, in_proj_bias);
+      v = F::linear(value, v_proj_weight_non_opt, in_proj_bias);
+    }
+  }
+  q = q * scaling;
+  Tensor attn_mask_ = attn_mask;
+  Tensor key_padding_mask_ = key_padding_mask;
+  if (bias_k.defined() && bias_v.defined()) {
+    if (!static_k.defined() && !static_v.defined()) {
+      k = torch::cat({k, bias_k.repeat({1, bsz, 1})});
+      v = torch::cat({v, bias_v.repeat({1, bsz, 1})});
+      if (attn_mask_.defined()) {
+        attn_mask_ = torch::cat(
+            {attn_mask_,
+             torch::zeros(
+                 {attn_mask_.size(0), 1},
+                 at::TensorOptions(attn_mask_.dtype())
+                     .device(attn_mask_.device()))},
+            /*dim=*/1);
+      }
+      if (key_padding_mask_.defined()) {
+        key_padding_mask_ = torch::cat(
+            {key_padding_mask_,
+             torch::zeros(
+                 {key_padding_mask_.size(0), 1},
+                 at::TensorOptions(key_padding_mask_.dtype())
+                     .device(key_padding_mask_.device()))},
+            /*dim=*/1);
+      }
+    } else {
+      TORCH_CHECK(!static_k.defined(), "bias cannot be added to static key.");
+      TORCH_CHECK(!static_v.defined(), "bias cannot be added to static value.");
+    }
+  } else {
+    TORCH_CHECK(!bias_k.defined());
+    TORCH_CHECK(!bias_v.defined());
+  }
+  q = q.contiguous().view({tgt_len, bsz * num_heads, head_dim}).transpose(0, 1);
+  if (k.defined()) {
+    k = k.contiguous().view({-1, bsz * num_heads, head_dim}).transpose(0, 1);
+  }
+  if (v.defined()) {
+    v = v.contiguous().view({-1, bsz * num_heads, head_dim}).transpose(0, 1);
+  }
+  if (static_k.defined()) {
+    TORCH_CHECK(static_k.size(0) == bsz * num_heads);
+    TORCH_CHECK(static_k.size(2) == head_dim);
+    k = static_k;
+  }
+  if (static_v.defined()) {
+    TORCH_CHECK(static_v.size(0) == bsz * num_heads);
+    TORCH_CHECK(static_v.size(2) == head_dim);
+    v = static_v;
+  }
+  auto src_len = k.size(1);
+  if (key_padding_mask_.defined()) {
+    TORCH_CHECK(key_padding_mask_.size(0) == bsz);
+    TORCH_CHECK(key_padding_mask_.size(1) == src_len);
+  }
+  if (add_zero_attn) {
+    src_len += 1;
+    auto k_sizes = k.sizes().vec();
+    k_sizes[1] = 1;
+    k = torch::cat(
+        {k,
+         torch::zeros(
+             k_sizes, at::TensorOptions(k.dtype()).device(k.device()))},
+        /*dim=*/1);
+    auto v_sizes = v.sizes().vec();
+    v_sizes[1] = 1;
+    v = torch::cat(
+        {v,
+         torch::zeros(
+             v_sizes, at::TensorOptions(v.dtype()).device(v.device()))},
+        /*dim=*/1);
+    if (attn_mask_.defined()) {
+      attn_mask_ = torch::cat(
+          {attn_mask_,
+           torch::zeros(
+               {attn_mask_.size(0), 1},
+               at::TensorOptions(attn_mask_.dtype())
+                   .device(attn_mask_.device()))},
+          /*dim=*/1);
+    }
+    if (key_padding_mask_.defined()) {
+      key_padding_mask_ = torch::cat(
+          {key_padding_mask_,
+           torch::zeros(
+               {key_padding_mask_.size(0), 1},
+               at::TensorOptions(key_padding_mask_.dtype())
+                   .device(key_padding_mask_.device()))},
+          /*dim=*/1);
+    }
+  }
+  auto attn_output_weights = torch::bmm(q, k.transpose(1, 2));
+  TORCH_CHECK(
+      attn_output_weights.sizes() ==
+      IntArrayRef({bsz * num_heads, tgt_len, src_len}));
+  if (attn_mask_.defined()) {
+    attn_mask_ = attn_mask_.unsqueeze(0);
+    attn_output_weights += attn_mask_;
+  }
+  if (key_padding_mask_.defined()) {
+    attn_output_weights =
+        attn_output_weights.view({bsz, num_heads, tgt_len, src_len});
+    attn_output_weights = AT_DISPATCH_FLOATING_TYPES(
+        attn_output_weights.scalar_type(),
+        "attn_output_weights.masked_fill",
+        [&]() {
+          return attn_output_weights.masked_fill(
+              key_padding_mask_.unsqueeze(1).unsqueeze(2),
+              -std::numeric_limits<scalar_t>::infinity());
+        });
+    attn_output_weights =
+        attn_output_weights.view({bsz * num_heads, tgt_len, src_len});
+  }
+  // NOLINTNEXTLINE(bugprone-argument-comment)
+  attn_output_weights = F::softmax(attn_output_weights, /*dim=*/-1);
+  attn_output_weights = F::dropout(
+      attn_output_weights,
+      F::DropoutFuncOptions().p(dropout_p).training(training));
+  auto attn_output = torch::bmm(attn_output_weights, v);
+  TORCH_CHECK(
+      attn_output.sizes() == IntArrayRef({bsz * num_heads, tgt_len, head_dim}));
+  attn_output =
+      attn_output.transpose(0, 1).contiguous().view({tgt_len, bsz, embed_dim});
+  attn_output = F::linear(attn_output, out_proj_weight, out_proj_bias);
+  if (need_weights) {
+    attn_output_weights =
+        attn_output_weights.view({bsz, num_heads, tgt_len, src_len});
+    if (average_attn_weights) {
+      // average attention weights over heads
+      attn_output_weights = attn_output_weights.sum(/*dim=*/1) / num_heads;
+    }
+    return std::make_tuple(attn_output, attn_output_weights);
+  } else {
+    return std::make_tuple(attn_output, Tensor());
+  }
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+inline std::tuple<Tensor, Tensor> multi_head_attention_forward(
+    const Tensor& query,
+    const Tensor& key,
+    const Tensor& value,
+    const MultiheadAttentionForwardFuncOptions& options) {
+  return detail::multi_head_attention_forward(
+      query,
+      key,
+      value,
+      options.embed_dim_to_check(),
+      options.num_heads(),
+      options.in_proj_weight(),
+      options.in_proj_bias(),
+      options.bias_k(),
+      options.bias_v(),
+      options.add_zero_attn(),
+      options.dropout_p(),
+      options.out_proj_weight(),
+      options.out_proj_bias(),
+      options.training(),
+      options.key_padding_mask(),
+      options.need_weights(),
+      options.attn_mask(),
+      options.use_separate_proj_weight(),
+      options.q_proj_weight(),
+      options.k_proj_weight(),
+      options.v_proj_weight(),
+      options.static_k(),
+      options.static_v(),
+      options.average_attn_weights());
+}
+
+} // namespace functional
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/0c/0c83ea71841b26950454793cecc8126b89af14683babef5a1790951ca17c9b09/rename_ops.h b/shared/package-files/0c/0c83ea71841b26950454793cecc8126b89af14683babef5a1790951ca17c9b09/rename_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..16e26207676dc41320545f6172f7184d64ac6cb1
--- /dev/null
+++ b/shared/package-files/0c/0c83ea71841b26950454793cecc8126b89af14683babef5a1790951ca17c9b09/rename_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API rename_ {
+  using schema = at::Tensor & (at::Tensor &, c10::optional<at::DimnameList>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::rename_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, c10::optional<at::DimnameList> names);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<at::DimnameList> names);
+};
+
+struct TORCH_API rename {
+  using schema = at::Tensor (const at::Tensor &, c10::optional<at::DimnameList>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::rename")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "rename(Tensor(a) self, Dimname[]? names) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, c10::optional<at::DimnameList> names);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::DimnameList> names);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0c861405d3a863c5bd865c49622ab560c6a71967e259c7595b46bee74f18fce2/DLConvertor.h b/shared/package-files/0c/0c861405d3a863c5bd865c49622ab560c6a71967e259c7595b46bee74f18fce2/DLConvertor.h
new file mode 100644
index 0000000000000000000000000000000000000000..b35c9657527d84c022ffeea845b9b37fdd17ee70
--- /dev/null
+++ b/shared/package-files/0c/0c861405d3a863c5bd865c49622ab560c6a71967e259c7595b46bee74f18fce2/DLConvertor.h
@@ -0,0 +1,25 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <ATen/Tensor.h>
+#include <ATen/dlpack.h>
+
+// this convertor will:
+// 1) take a Tensor object and wrap it in the DLPack tensor
+// 2) take a dlpack tensor and convert it to the ATen Tensor
+
+namespace at {
+
+TORCH_API ScalarType toScalarType(const DLDataType& dtype);
+TORCH_API DLManagedTensor* toDLPack(const Tensor& src);
+TORCH_API Tensor fromDLPack(DLManagedTensor* src);
+C10_DEPRECATED_MESSAGE("Please migrate to a non-const variant")
+inline Tensor fromDLPack(const DLManagedTensor* src) {
+  return fromDLPack(const_cast<DLManagedTensor*>(src));
+}
+TORCH_API Tensor
+fromDLPack(DLManagedTensor* src, std::function<void(void*)> deleter);
+TORCH_API DLDataType getDLDataType(const Tensor& t);
+TORCH_API DLDevice getDLContext(const Tensor& tensor, const int64_t& device_id);
+
+} // namespace at
diff --git a/shared/package-files/0c/0c8ae8d3e763d753594c0f2bbbd6f636f5a5f4758ec3da827a31341bd413497f/linalg_matrix_rank_native.h b/shared/package-files/0c/0c8ae8d3e763d753594c0f2bbbd6f636f5a5f4758ec3da827a31341bd413497f/linalg_matrix_rank_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..03a27457ac112b04d4d11d33dd4a89a1219f4cd0
--- /dev/null
+++ b/shared/package-files/0c/0c8ae8d3e763d753594c0f2bbbd6f636f5a5f4758ec3da827a31341bd413497f/linalg_matrix_rank_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor linalg_matrix_rank(const at::Tensor & input, const c10::optional<at::Tensor> & atol={}, const c10::optional<at::Tensor> & rtol={}, bool hermitian=false);
+TORCH_API at::Tensor & linalg_matrix_rank_out(const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out);
+TORCH_API at::Tensor linalg_matrix_rank(const at::Tensor & self, c10::optional<double> atol=c10::nullopt, c10::optional<double> rtol=c10::nullopt, bool hermitian=false);
+TORCH_API at::Tensor & linalg_matrix_rank_out(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out);
+TORCH_API at::Tensor linalg_matrix_rank(const at::Tensor & self, double tol, bool hermitian=false);
+TORCH_API at::Tensor & linalg_matrix_rank_out(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out);
+TORCH_API at::Tensor linalg_matrix_rank(const at::Tensor & input, const at::Tensor & tol, bool hermitian=false);
+TORCH_API at::Tensor & linalg_matrix_rank_out(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/0c/0c8bc1b51b021366ed9cc7b011d3c535cb8f56ad773aed96c08d80dd8369c414/kde.py b/shared/package-files/0c/0c8bc1b51b021366ed9cc7b011d3c535cb8f56ad773aed96c08d80dd8369c414/kde.py
new file mode 100644
index 0000000000000000000000000000000000000000..6add4e19127895817b42f8602b5deb43ba3b725d
--- /dev/null
+++ b/shared/package-files/0c/0c8bc1b51b021366ed9cc7b011d3c535cb8f56ad773aed96c08d80dd8369c414/kde.py
@@ -0,0 +1,380 @@
+"""
+This module was copied from the scipy project.
+
+In the process of copying, some methods were removed because they depended on
+other parts of scipy (especially on compiled components), allowing seaborn to
+have a simple and pure Python implementation. These include:
+
+- integrate_gaussian
+- integrate_box
+- integrate_box_1d
+- integrate_kde
+- logpdf
+- resample
+
+Additionally, the numpy.linalg module was substituted for scipy.linalg,
+and the examples section (with doctests) was removed from the docstring
+
+The original scipy license is copied below:
+
+Copyright (c) 2001-2002 Enthought, Inc.  2003-2019, SciPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+   copyright notice, this list of conditions and the following
+   disclaimer in the documentation and/or other materials provided
+   with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+"""
+
+# -------------------------------------------------------------------------------
+#
+#  Define classes for (uni/multi)-variate kernel density estimation.
+#
+#  Currently, only Gaussian kernels are implemented.
+#
+#  Written by: Robert Kern
+#
+#  Date: 2004-08-09
+#
+#  Modified: 2005-02-10 by Robert Kern.
+#              Contributed to SciPy
+#            2005-10-07 by Robert Kern.
+#              Some fixes to match the new scipy_core
+#
+#  Copyright 2004-2005 by Enthought, Inc.
+#
+# -------------------------------------------------------------------------------
+
+import numpy as np
+from numpy import (asarray, atleast_2d, reshape, zeros, newaxis, dot, exp, pi,
+                   sqrt, power, atleast_1d, sum, ones, cov)
+from numpy import linalg
+
+
+__all__ = ['gaussian_kde']
+
+
+class gaussian_kde:
+    """Representation of a kernel-density estimate using Gaussian kernels.
+
+    Kernel density estimation is a way to estimate the probability density
+    function (PDF) of a random variable in a non-parametric way.
+    `gaussian_kde` works for both uni-variate and multi-variate data.   It
+    includes automatic bandwidth determination.  The estimation works best for
+    a unimodal distribution; bimodal or multi-modal distributions tend to be
+    oversmoothed.
+
+    Parameters
+    ----------
+    dataset : array_like
+        Datapoints to estimate from. In case of univariate data this is a 1-D
+        array, otherwise a 2-D array with shape (# of dims, # of data).
+    bw_method : str, scalar or callable, optional
+        The method used to calculate the estimator bandwidth.  This can be
+        'scott', 'silverman', a scalar constant or a callable.  If a scalar,
+        this will be used directly as `kde.factor`.  If a callable, it should
+        take a `gaussian_kde` instance as only parameter and return a scalar.
+        If None (default), 'scott' is used.  See Notes for more details.
+    weights : array_like, optional
+        weights of datapoints. This must be the same shape as dataset.
+        If None (default), the samples are assumed to be equally weighted
+
+    Attributes
+    ----------
+    dataset : ndarray
+        The dataset with which `gaussian_kde` was initialized.
+    d : int
+        Number of dimensions.
+    n : int
+        Number of datapoints.
+    neff : int
+        Effective number of datapoints.
+
+        .. versionadded:: 1.2.0
+    factor : float
+        The bandwidth factor, obtained from `kde.covariance_factor`, with which
+        the covariance matrix is multiplied.
+    covariance : ndarray
+        The covariance matrix of `dataset`, scaled by the calculated bandwidth
+        (`kde.factor`).
+    inv_cov : ndarray
+        The inverse of `covariance`.
+
+    Methods
+    -------
+    evaluate
+    __call__
+    integrate_gaussian
+    integrate_box_1d
+    integrate_box
+    integrate_kde
+    pdf
+    logpdf
+    resample
+    set_bandwidth
+    covariance_factor
+
+    Notes
+    -----
+    Bandwidth selection strongly influences the estimate obtained from the KDE
+    (much more so than the actual shape of the kernel).  Bandwidth selection
+    can be done by a "rule of thumb", by cross-validation, by "plug-in
+    methods" or by other means; see [3]_, [4]_ for reviews.  `gaussian_kde`
+    uses a rule of thumb, the default is Scott's Rule.
+
+    Scott's Rule [1]_, implemented as `scotts_factor`, is::
+
+        n**(-1./(d+4)),
+
+    with ``n`` the number of data points and ``d`` the number of dimensions.
+    In the case of unequally weighted points, `scotts_factor` becomes::
+
+        neff**(-1./(d+4)),
+
+    with ``neff`` the effective number of datapoints.
+    Silverman's Rule [2]_, implemented as `silverman_factor`, is::
+
+        (n * (d + 2) / 4.)**(-1. / (d + 4)).
+
+    or in the case of unequally weighted points::
+
+        (neff * (d + 2) / 4.)**(-1. / (d + 4)).
+
+    Good general descriptions of kernel density estimation can be found in [1]_
+    and [2]_, the mathematics for this multi-dimensional implementation can be
+    found in [1]_.
+
+    With a set of weighted samples, the effective number of datapoints ``neff``
+    is defined by::
+
+        neff = sum(weights)^2 / sum(weights^2)
+
+    as detailed in [5]_.
+
+    References
+    ----------
+    .. [1] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and
+           Visualization", John Wiley & Sons, New York, Chicester, 1992.
+    .. [2] B.W. Silverman, "Density Estimation for Statistics and Data
+           Analysis", Vol. 26, Monographs on Statistics and Applied Probability,
+           Chapman and Hall, London, 1986.
+    .. [3] B.A. Turlach, "Bandwidth Selection in Kernel Density Estimation: A
+           Review", CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.
+    .. [4] D.M. Bashtannyk and R.J. Hyndman, "Bandwidth selection for kernel
+           conditional density estimation", Computational Statistics & Data
+           Analysis, Vol. 36, pp. 279-298, 2001.
+    .. [5] Gray P. G., 1969, Journal of the Royal Statistical Society.
+           Series A (General), 132, 272
+
+    """
+    def __init__(self, dataset, bw_method=None, weights=None):
+        self.dataset = atleast_2d(asarray(dataset))
+        if not self.dataset.size > 1:
+            raise ValueError("`dataset` input should have multiple elements.")
+
+        self.d, self.n = self.dataset.shape
+
+        if weights is not None:
+            self._weights = atleast_1d(weights).astype(float)
+            self._weights /= sum(self._weights)
+            if self.weights.ndim != 1:
+                raise ValueError("`weights` input should be one-dimensional.")
+            if len(self._weights) != self.n:
+                raise ValueError("`weights` input should be of length n")
+            self._neff = 1/sum(self._weights**2)
+
+        self.set_bandwidth(bw_method=bw_method)
+
+    def evaluate(self, points):
+        """Evaluate the estimated pdf on a set of points.
+
+        Parameters
+        ----------
+        points : (# of dimensions, # of points)-array
+            Alternatively, a (# of dimensions,) vector can be passed in and
+            treated as a single point.
+
+        Returns
+        -------
+        values : (# of points,)-array
+            The values at each point.
+
+        Raises
+        ------
+        ValueError : if the dimensionality of the input points is different than
+                     the dimensionality of the KDE.
+
+        """
+        points = atleast_2d(asarray(points))
+
+        d, m = points.shape
+        if d != self.d:
+            if d == 1 and m == self.d:
+                # points was passed in as a row vector
+                points = reshape(points, (self.d, 1))
+                m = 1
+            else:
+                msg = f"points have dimension {d}, dataset has dimension {self.d}"
+                raise ValueError(msg)
+
+        output_dtype = np.common_type(self.covariance, points)
+        result = zeros((m,), dtype=output_dtype)
+
+        whitening = linalg.cholesky(self.inv_cov)
+        scaled_dataset = dot(whitening, self.dataset)
+        scaled_points = dot(whitening, points)
+
+        if m >= self.n:
+            # there are more points than data, so loop over data
+            for i in range(self.n):
+                diff = scaled_dataset[:, i, newaxis] - scaled_points
+                energy = sum(diff * diff, axis=0) / 2.0
+                result += self.weights[i]*exp(-energy)
+        else:
+            # loop over points
+            for i in range(m):
+                diff = scaled_dataset - scaled_points[:, i, newaxis]
+                energy = sum(diff * diff, axis=0) / 2.0
+                result[i] = sum(exp(-energy)*self.weights, axis=0)
+
+        result = result / self._norm_factor
+
+        return result
+
+    __call__ = evaluate
+
+    def scotts_factor(self):
+        """Compute Scott's factor.
+
+        Returns
+        -------
+        s : float
+            Scott's factor.
+        """
+        return power(self.neff, -1./(self.d+4))
+
+    def silverman_factor(self):
+        """Compute the Silverman factor.
+
+        Returns
+        -------
+        s : float
+            The silverman factor.
+        """
+        return power(self.neff*(self.d+2.0)/4.0, -1./(self.d+4))
+
+    #  Default method to calculate bandwidth, can be overwritten by subclass
+    covariance_factor = scotts_factor
+    covariance_factor.__doc__ = """Computes the coefficient (`kde.factor`) that
+        multiplies the data covariance matrix to obtain the kernel covariance
+        matrix. The default is `scotts_factor`.  A subclass can overwrite this
+        method to provide a different method, or set it through a call to
+        `kde.set_bandwidth`."""
+
+    def set_bandwidth(self, bw_method=None):
+        """Compute the estimator bandwidth with given method.
+
+        The new bandwidth calculated after a call to `set_bandwidth` is used
+        for subsequent evaluations of the estimated density.
+
+        Parameters
+        ----------
+        bw_method : str, scalar or callable, optional
+            The method used to calculate the estimator bandwidth.  This can be
+            'scott', 'silverman', a scalar constant or a callable.  If a
+            scalar, this will be used directly as `kde.factor`.  If a callable,
+            it should take a `gaussian_kde` instance as only parameter and
+            return a scalar.  If None (default), nothing happens; the current
+            `kde.covariance_factor` method is kept.
+
+        Notes
+        -----
+        .. versionadded:: 0.11
+
+        """
+        if bw_method is None:
+            pass
+        elif bw_method == 'scott':
+            self.covariance_factor = self.scotts_factor
+        elif bw_method == 'silverman':
+            self.covariance_factor = self.silverman_factor
+        elif np.isscalar(bw_method) and not isinstance(bw_method, str):
+            self._bw_method = 'use constant'
+            self.covariance_factor = lambda: bw_method
+        elif callable(bw_method):
+            self._bw_method = bw_method
+            self.covariance_factor = lambda: self._bw_method(self)
+        else:
+            msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
+                  "or a callable."
+            raise ValueError(msg)
+
+        self._compute_covariance()
+
+    def _compute_covariance(self):
+        """Computes the covariance matrix for each Gaussian kernel using
+        covariance_factor().
+        """
+        self.factor = self.covariance_factor()
+        # Cache covariance and inverse covariance of the data
+        if not hasattr(self, '_data_inv_cov'):
+            self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,
+                                               bias=False,
+                                               aweights=self.weights))
+            self._data_inv_cov = linalg.inv(self._data_covariance)
+
+        self.covariance = self._data_covariance * self.factor**2
+        self.inv_cov = self._data_inv_cov / self.factor**2
+        self._norm_factor = sqrt(linalg.det(2*pi*self.covariance))
+
+    def pdf(self, x):
+        """
+        Evaluate the estimated pdf on a provided set of points.
+
+        Notes
+        -----
+        This is an alias for `gaussian_kde.evaluate`.  See the ``evaluate``
+        docstring for more details.
+
+        """
+        return self.evaluate(x)
+
+    @property
+    def weights(self):
+        try:
+            return self._weights
+        except AttributeError:
+            self._weights = ones(self.n)/self.n
+            return self._weights
+
+    @property
+    def neff(self):
+        try:
+            return self._neff
+        except AttributeError:
+            self._neff = 1/sum(self.weights**2)
+            return self._neff
diff --git a/shared/package-files/0c/0c8d5daec1d6405d7aae709ef502d555497695bd99afd2f24b94d3b7be95fbf7/macrodata_jmulti_ncs_granger_causality_realcons.txt b/shared/package-files/0c/0c8d5daec1d6405d7aae709ef502d555497695bd99afd2f24b94d3b7be95fbf7/macrodata_jmulti_ncs_granger_causality_realcons.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b8a4a191026416169eeb79f899572574ce687019
--- /dev/null
+++ b/shared/package-files/0c/0c8d5daec1d6405d7aae709ef502d555497695bd99afd2f24b94d3b7be95fbf7/macrodata_jmulti_ncs_granger_causality_realcons.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:16:34 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realcons" do not Granger-cause "realgdp, realinv"
+
+Test statistic l = 11.4249
+pval-F( l; 8, 552) = 0.0000 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realcons" and "realgdp, realinv"
+
+Test statistic: c = 71.7504
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/0c/0c8e6244aa08839d6523ff85cac290cb26899f0addf02a8b8e4edbee2a85229d/test_constructors.py b/shared/package-files/0c/0c8e6244aa08839d6523ff85cac290cb26899f0addf02a8b8e4edbee2a85229d/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4bc00b923434f8d62c8332b3104696051fd287e
--- /dev/null
+++ b/shared/package-files/0c/0c8e6244aa08839d6523ff85cac290cb26899f0addf02a8b8e4edbee2a85229d/test_constructors.py
@@ -0,0 +1,51 @@
+import pytest
+
+from pandas import (
+    Interval,
+    Period,
+    Timestamp,
+)
+
+
+class TestIntervalConstructors:
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            ("a", "z"),
+            (("a", "b"), ("c", "d")),
+            (list("AB"), list("ab")),
+            (Interval(0, 1), Interval(1, 2)),
+            (Period("2018Q1", freq="Q"), Period("2018Q1", freq="Q")),
+        ],
+    )
+    def test_construct_errors(self, left, right):
+        # GH#23013
+        msg = "Only numeric, Timestamp and Timedelta endpoints are allowed"
+        with pytest.raises(ValueError, match=msg):
+            Interval(left, right)
+
+    def test_constructor_errors(self):
+        msg = "invalid option for 'closed': foo"
+        with pytest.raises(ValueError, match=msg):
+            Interval(0, 1, closed="foo")
+
+        msg = "left side of interval must be <= right side"
+        with pytest.raises(ValueError, match=msg):
+            Interval(1, 0)
+
+    @pytest.mark.parametrize(
+        "tz_left, tz_right", [(None, "UTC"), ("UTC", None), ("UTC", "US/Eastern")]
+    )
+    def test_constructor_errors_tz(self, tz_left, tz_right):
+        # GH#18538
+        left = Timestamp("2017-01-01", tz=tz_left)
+        right = Timestamp("2017-01-02", tz=tz_right)
+
+        if tz_left is None or tz_right is None:
+            error = TypeError
+            msg = "Cannot compare tz-naive and tz-aware timestamps"
+        else:
+            error = ValueError
+            msg = "left and right must have the same time zone"
+        with pytest.raises(error, match=msg):
+            Interval(left, right)
diff --git a/shared/package-files/0c/0c8ee216c1204f82e9b8fcd7291797a82582c215e5b0c5af841887fed9002936/_cubature.py b/shared/package-files/0c/0c8ee216c1204f82e9b8fcd7291797a82582c215e5b0c5af841887fed9002936/_cubature.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6d8911d13eeaa2420ef65a12e9b4ba34400ca0
--- /dev/null
+++ b/shared/package-files/0c/0c8ee216c1204f82e9b8fcd7291797a82582c215e5b0c5af841887fed9002936/_cubature.py
@@ -0,0 +1,728 @@
+import math
+import heapq
+import itertools
+
+from dataclasses import dataclass, field
+from types import ModuleType
+from typing import Any, TypeAlias
+
+from scipy._lib._array_api import (
+    array_namespace,
+    xp_size,
+    xp_copy,
+    xp_broadcast_promote
+)
+from scipy._lib._util import MapWrapper
+
+from scipy.integrate._rules import (
+    ProductNestedFixed,
+    GaussKronrodQuadrature,
+    GenzMalikCubature,
+)
+from scipy.integrate._rules._base import _split_subregion
+
+__all__ = ['cubature']
+
+Array: TypeAlias = Any  # To be changed to an array-api-typing Protocol later
+
+
+@dataclass
+class CubatureRegion:
+    estimate: Array
+    error: Array
+    a: Array
+    b: Array
+    _xp: ModuleType = field(repr=False)
+
+    def __lt__(self, other):
+        # Consider regions with higher error estimates as being "less than" regions with
+        # lower order estimates, so that regions with high error estimates are placed at
+        # the top of the heap.
+
+        this_err = self._xp.max(self._xp.abs(self.error))
+        other_err = self._xp.max(self._xp.abs(other.error))
+
+        return this_err > other_err
+
+
+@dataclass
+class CubatureResult:
+    estimate: Array
+    error: Array
+    status: str
+    regions: list[CubatureRegion]
+    subdivisions: int
+    atol: float
+    rtol: float
+
+
+def cubature(f, a, b, *, rule="gk21", rtol=1e-8, atol=0, max_subdivisions=10000,
+             args=(), workers=1, points=None):
+    r"""
+    Adaptive cubature of multidimensional array-valued function.
+
+    Given an arbitrary integration rule, this function returns an estimate of the
+    integral to the requested tolerance over the region defined by the arrays `a` and
+    `b` specifying the corners of a hypercube.
+
+    Convergence is not guaranteed for all integrals.
+
+    Parameters
+    ----------
+    f : callable
+        Function to integrate. `f` must have the signature::
+
+            f(x : ndarray, *args) -> ndarray
+
+        `f` should accept arrays ``x`` of shape::
+
+            (npoints, ndim)
+
+        and output arrays of shape::
+
+            (npoints, output_dim_1, ..., output_dim_n)
+
+        In this case, `cubature` will return arrays of shape::
+
+            (output_dim_1, ..., output_dim_n)
+    a, b : array_like
+        Lower and upper limits of integration as 1D arrays specifying the left and right
+        endpoints of the intervals being integrated over. Limits can be infinite.
+    rule : str, optional
+        Rule used to estimate the integral. If passing a string, the options are
+        "gauss-kronrod" (21 node), or "genz-malik" (degree 7). If a rule like
+        "gauss-kronrod" is specified for an ``n``-dim integrand, the corresponding
+        Cartesian product rule is used. "gk21", "gk15" are also supported for
+        compatibility with `quad_vec`. See Notes.
+    rtol, atol : float, optional
+        Relative and absolute tolerances. Iterations are performed until the error is
+        estimated to be less than ``atol + rtol * abs(est)``. Here `rtol` controls
+        relative accuracy (number of correct digits), while `atol` controls absolute
+        accuracy (number of correct decimal places). To achieve the desired `rtol`, set
+        `atol` to be smaller than the smallest value that can be expected from
+        ``rtol * abs(y)`` so that `rtol` dominates the allowable error. If `atol` is
+        larger than ``rtol * abs(y)`` the number of correct digits is not guaranteed.
+        Conversely, to achieve the desired `atol`, set `rtol` such that
+        ``rtol * abs(y)`` is always smaller than `atol`. Default values are 1e-8 for
+        `rtol` and 0 for `atol`.
+    max_subdivisions : int, optional
+        Upper bound on the number of subdivisions to perform. Default is 10,000.
+    args : tuple, optional
+        Additional positional args passed to `f`, if any.
+    workers : int or map-like callable, optional
+        If `workers` is an integer, part of the computation is done in parallel
+        subdivided to this many tasks (using :class:`python:multiprocessing.pool.Pool`).
+        Supply `-1` to use all cores available to the Process. Alternatively, supply a
+        map-like callable, such as :meth:`python:multiprocessing.pool.Pool.map` for
+        evaluating the population in parallel. This evaluation is carried out as
+        ``workers(func, iterable)``.
+    points : list of array_like, optional
+        List of points to avoid evaluating `f` at, under the condition that the rule
+        being used does not evaluate `f` on the boundary of a region (which is the
+        case for all Genz-Malik and Gauss-Kronrod rules). This can be useful if `f` has
+        a singularity at the specified point. This should be a list of array-likes where
+        each element has length ``ndim``. Default is empty. See Examples.
+
+    Returns
+    -------
+    res : object
+        Object containing the results of the estimation. It has the following
+        attributes:
+
+        estimate : ndarray
+            Estimate of the value of the integral over the overall region specified.
+        error : ndarray
+            Estimate of the error of the approximation over the overall region
+            specified.
+        status : str
+            Whether the estimation was successful. Can be either: "converged",
+            "not_converged".
+        subdivisions : int
+            Number of subdivisions performed.
+        atol, rtol : float
+            Requested tolerances for the approximation.
+        regions: list of object
+            List of objects containing the estimates of the integral over smaller
+            regions of the domain.
+
+        Each object in ``regions`` has the following attributes:
+
+        a, b : ndarray
+            Points describing the corners of the region. If the original integral
+            contained infinite limits or was over a region described by `region`,
+            then `a` and `b` are in the transformed coordinates.
+        estimate : ndarray
+            Estimate of the value of the integral over this region.
+        error : ndarray
+            Estimate of the error of the approximation over this region.
+
+    Notes
+    -----
+    The algorithm uses a similar algorithm to `quad_vec`, which itself is based on the
+    implementation of QUADPACK's DQAG* algorithms, implementing global error control and
+    adaptive subdivision.
+
+    The source of the nodes and weights used for Gauss-Kronrod quadrature can be found
+    in [1]_, and the algorithm for calculating the nodes and weights in Genz-Malik
+    cubature can be found in [2]_.
+
+    The rules currently supported via the `rule` argument are:
+
+    - ``"gauss-kronrod"``, 21-node Gauss-Kronrod
+    - ``"genz-malik"``, n-node Genz-Malik
+
+    If using Gauss-Kronrod for an ``n``-dim integrand where ``n > 2``, then the
+    corresponding Cartesian product rule will be found by taking the Cartesian product
+    of the nodes in the 1D case. This means that the number of nodes scales
+    exponentially as ``21^n`` in the Gauss-Kronrod case, which may be problematic in a
+    moderate number of dimensions.
+
+    Genz-Malik is typically less accurate than Gauss-Kronrod but has much fewer nodes,
+    so in this situation using "genz-malik" might be preferable.
+
+    Infinite limits are handled with an appropriate variable transformation. Assuming
+    ``a = [a_1, ..., a_n]`` and ``b = [b_1, ..., b_n]``:
+
+    If :math:`a_i = -\infty` and :math:`b_i = \infty`, the i-th integration variable
+    will use the transformation :math:`x = \frac{1-|t|}{t}` and :math:`t \in (-1, 1)`.
+
+    If :math:`a_i \ne \pm\infty` and :math:`b_i = \infty`, the i-th integration variable
+    will use the transformation :math:`x = a_i + \frac{1-t}{t}` and
+    :math:`t \in (0, 1)`.
+
+    If :math:`a_i = -\infty` and :math:`b_i \ne \pm\infty`, the i-th integration
+    variable will use the transformation :math:`x = b_i - \frac{1-t}{t}` and
+    :math:`t \in (0, 1)`.
+
+    References
+    ----------
+    .. [1] R. Piessens, E. de Doncker, Quadpack: A Subroutine Package for Automatic
+        Integration, files: dqk21.f, dqk15.f (1983).
+
+    .. [2] A.C. Genz, A.A. Malik, Remarks on algorithm 006: An adaptive algorithm for
+        numerical integration over an N-dimensional rectangular region, Journal of
+        Computational and Applied Mathematics, Volume 6, Issue 4, 1980, Pages 295-302,
+        ISSN 0377-0427
+        :doi:`10.1016/0771-050X(80)90039-X`
+
+    Examples
+    --------
+    **1D integral with vector output**:
+
+    .. math::
+
+        \int^1_0 \mathbf f(x) \text dx
+
+    Where ``f(x) = x^n`` and ``n = np.arange(10)`` is a vector. Since no rule is
+    specified, the default "gk21" is used, which corresponds to Gauss-Kronrod
+    integration with 21 nodes.
+
+    >>> import numpy as np
+    >>> from scipy.integrate import cubature
+    >>> def f(x, n):
+    ...    # Make sure x and n are broadcastable
+    ...    return x[:, np.newaxis]**n[np.newaxis, :]
+    >>> res = cubature(
+    ...     f,
+    ...     a=[0],
+    ...     b=[1],
+    ...     args=(np.arange(10),),
+    ... )
+    >>> res.estimate
+     array([1.        , 0.5       , 0.33333333, 0.25      , 0.2       ,
+            0.16666667, 0.14285714, 0.125     , 0.11111111, 0.1       ])
+
+    **7D integral with arbitrary-shaped array output**::
+
+        f(x) = cos(2*pi*r + alphas @ x)
+
+    for some ``r`` and ``alphas``, and the integral is performed over the unit
+    hybercube, :math:`[0, 1]^7`. Since the integral is in a moderate number of
+    dimensions, "genz-malik" is used rather than the default "gauss-kronrod" to
+    avoid constructing a product rule with :math:`21^7 \approx 2 \times 10^9` nodes.
+
+    >>> import numpy as np
+    >>> from scipy.integrate import cubature
+    >>> def f(x, r, alphas):
+    ...     # f(x) = cos(2*pi*r + alphas @ x)
+    ...     # Need to allow r and alphas to be arbitrary shape
+    ...     npoints, ndim = x.shape[0], x.shape[-1]
+    ...     alphas = alphas[np.newaxis, ...]
+    ...     x = x.reshape(npoints, *([1]*(len(alphas.shape) - 1)), ndim)
+    ...     return np.cos(2*np.pi*r + np.sum(alphas * x, axis=-1))
+    >>> rng = np.random.default_rng()
+    >>> r, alphas = rng.random((2, 3)), rng.random((2, 3, 7))
+    >>> res = cubature(
+    ...     f=f,
+    ...     a=np.array([0, 0, 0, 0, 0, 0, 0]),
+    ...     b=np.array([1, 1, 1, 1, 1, 1, 1]),
+    ...     rtol=1e-5,
+    ...     rule="genz-malik",
+    ...     args=(r, alphas),
+    ... )
+    >>> res.estimate
+     array([[-0.79812452,  0.35246913, -0.52273628],
+            [ 0.88392779,  0.59139899,  0.41895111]])
+
+    **Parallel computation with** `workers`:
+
+    >>> from concurrent.futures import ThreadPoolExecutor
+    >>> with ThreadPoolExecutor() as executor:
+    ...     res = cubature(
+    ...         f=f,
+    ...         a=np.array([0, 0, 0, 0, 0, 0, 0]),
+    ...         b=np.array([1, 1, 1, 1, 1, 1, 1]),
+    ...         rtol=1e-5,
+    ...         rule="genz-malik",
+    ...         args=(r, alphas),
+    ...         workers=executor.map,
+    ...      )
+    >>> res.estimate
+     array([[-0.79812452,  0.35246913, -0.52273628],
+            [ 0.88392779,  0.59139899,  0.41895111]])
+
+    **2D integral with infinite limits**:
+
+    .. math::
+
+        \int^{ \infty }_{ -\infty }
+        \int^{ \infty }_{ -\infty }
+            e^{-x^2-y^2}
+        \text dy
+        \text dx
+
+    >>> def gaussian(x):
+    ...     return np.exp(-np.sum(x**2, axis=-1))
+    >>> res = cubature(gaussian, [-np.inf, -np.inf], [np.inf, np.inf])
+    >>> res.estimate
+     3.1415926
+
+    **1D integral with singularities avoided using** `points`:
+
+    .. math::
+
+        \int^{ 1 }_{ -1 }
+          \frac{\sin(x)}{x}
+        \text dx
+
+    It is necessary to use the `points` parameter to avoid evaluating `f` at the origin.
+
+    >>> def sinc(x):
+    ...     return np.sin(x)/x
+    >>> res = cubature(sinc, [-1], [1], points=[[0]])
+    >>> res.estimate
+     1.8921661
+    """
+
+    # It is also possible to use a custom rule, but this is not yet part of the public
+    # API. An example of this can be found in the class scipy.integrate._rules.Rule.
+
+    xp = array_namespace(a, b)
+    max_subdivisions = float("inf") if max_subdivisions is None else max_subdivisions
+    points = [] if points is None else points
+
+    # Convert a and b to arrays and convert each point in points to an array, promoting
+    # each to a common floating dtype.
+    a, b, *points = xp_broadcast_promote(a, b, *points, force_floating=True)
+    result_dtype = a.dtype
+
+    if xp_size(a) == 0 or xp_size(b) == 0:
+        raise ValueError("`a` and `b` must be nonempty")
+
+    if a.ndim != 1 or b.ndim != 1:
+        raise ValueError("`a` and `b` must be 1D arrays")
+
+    # If the rule is a string, convert to a corresponding product rule
+    if isinstance(rule, str):
+        ndim = xp_size(a)
+
+        if rule == "genz-malik":
+            rule = GenzMalikCubature(ndim, xp=xp)
+        else:
+            quadratues = {
+                "gauss-kronrod": GaussKronrodQuadrature(21, xp=xp),
+
+                # Also allow names quad_vec uses:
+                "gk21": GaussKronrodQuadrature(21, xp=xp),
+                "gk15": GaussKronrodQuadrature(15, xp=xp),
+            }
+
+            base_rule = quadratues.get(rule)
+
+            if base_rule is None:
+                raise ValueError(f"unknown rule {rule}")
+
+            rule = ProductNestedFixed([base_rule] * ndim)
+
+    # If any of limits are the wrong way around (a > b), flip them and keep track of
+    # the sign.
+    sign = (-1) ** xp.sum(xp.astype(a > b, xp.int8), dtype=result_dtype)
+
+    a_flipped = xp.min(xp.stack([a, b]), axis=0)
+    b_flipped = xp.max(xp.stack([a, b]), axis=0)
+
+    a, b = a_flipped, b_flipped
+
+    # If any of the limits are infinite, apply a transformation
+    if xp.any(xp.isinf(a)) or xp.any(xp.isinf(b)):
+        f = _InfiniteLimitsTransform(f, a, b, xp=xp)
+        a, b = f.transformed_limits
+
+        # Map points from the original coordinates to the new transformed coordinates.
+        #
+        # `points` is a list of arrays of shape (ndim,), but transformations are applied
+        # to arrays of shape (npoints, ndim).
+        #
+        # It is not possible to combine all the points into one array and then apply
+        # f.inv to all of them at once since `points` needs to remain iterable.
+        # Instead, each point is reshaped to an array of shape (1, ndim), `f.inv` is
+        # applied, and then each is reshaped back to (ndim,).
+        points = [xp.reshape(point, (1, -1)) for point in points]
+        points = [f.inv(point) for point in points]
+        points = [xp.reshape(point, (-1,)) for point in points]
+
+        # Include any problematic points introduced by the transformation
+        points.extend(f.points)
+
+    # If any problematic points are specified, divide the initial region so that these
+    # points lie on the edge of a subregion.
+    #
+    # This means ``f`` won't be evaluated there if the rule being used has no evaluation
+    # points on the boundary.
+    if len(points) == 0:
+        initial_regions = [(a, b)]
+    else:
+        initial_regions = _split_region_at_points(a, b, points, xp)
+
+    regions = []
+    est = 0.0
+    err = 0.0
+
+    for a_k, b_k in initial_regions:
+        est_k = rule.estimate(f, a_k, b_k, args)
+        err_k = rule.estimate_error(f, a_k, b_k, args)
+        regions.append(CubatureRegion(est_k, err_k, a_k, b_k, xp))
+
+        est += est_k
+        err += err_k
+
+    subdivisions = 0
+    success = True
+
+    with MapWrapper(workers) as mapwrapper:
+        while xp.any(err > atol + rtol * xp.abs(est)):
+            # region_k is the region with highest estimated error
+            region_k = heapq.heappop(regions)
+
+            est_k = region_k.estimate
+            err_k = region_k.error
+
+            a_k, b_k = region_k.a, region_k.b
+
+            # Subtract the estimate of the integral and its error over this region from
+            # the current global estimates, since these will be refined in the loop over
+            # all subregions.
+            est -= est_k
+            err -= err_k
+
+            # Find all 2^ndim subregions formed by splitting region_k along each axis,
+            # e.g. for 1D integrals this splits an estimate over an interval into an
+            # estimate over two subintervals, for 3D integrals this splits an estimate
+            # over a cube into 8 subcubes.
+            #
+            # For each of the new subregions, calculate an estimate for the integral and
+            # the error there, and push these regions onto the heap for potential
+            # further subdividing.
+
+            executor_args = zip(
+                itertools.repeat(f),
+                itertools.repeat(rule),
+                itertools.repeat(args),
+                _split_subregion(a_k, b_k, xp),
+            )
+
+            for subdivision_result in mapwrapper(_process_subregion, executor_args):
+                a_k_sub, b_k_sub, est_sub, err_sub = subdivision_result
+
+                est += est_sub
+                err += err_sub
+
+                new_region = CubatureRegion(est_sub, err_sub, a_k_sub, b_k_sub, xp)
+
+                heapq.heappush(regions, new_region)
+
+            subdivisions += 1
+
+            if subdivisions >= max_subdivisions:
+                success = False
+                break
+
+        status = "converged" if success else "not_converged"
+
+        # Apply sign change to handle any limits which were initially flipped.
+        est = sign * est
+
+        return CubatureResult(
+            estimate=est,
+            error=err,
+            status=status,
+            subdivisions=subdivisions,
+            regions=regions,
+            atol=atol,
+            rtol=rtol,
+        )
+
+
+def _process_subregion(data):
+    f, rule, args, coord = data
+    a_k_sub, b_k_sub = coord
+
+    est_sub = rule.estimate(f, a_k_sub, b_k_sub, args)
+    err_sub = rule.estimate_error(f, a_k_sub, b_k_sub, args)
+
+    return a_k_sub, b_k_sub, est_sub, err_sub
+
+
+def _is_strictly_in_region(a, b, point, xp):
+    if xp.all(point == a) or xp.all(point == b):
+        return False
+
+    return xp.all(a <= point) and xp.all(point <= b)
+
+
+def _split_region_at_points(a, b, points, xp):
+    """
+    Given the integration limits `a` and `b` describing a rectangular region and a list
+    of `points`, find the list of ``[(a_1, b_1), ..., (a_l, b_l)]`` which breaks up the
+    initial region into smaller subregion such that no `points` lie strictly inside
+    any of the subregions.
+    """
+
+    regions = [(a, b)]
+
+    for point in points:
+        if xp.any(xp.isinf(point)):
+            # If a point is specified at infinity, ignore.
+            #
+            # This case occurs when points are given by the user to avoid, but after
+            # applying a transformation, they are removed.
+            continue
+
+        new_subregions = []
+
+        for a_k, b_k in regions:
+            if _is_strictly_in_region(a_k, b_k, point, xp):
+                subregions = _split_subregion(a_k, b_k, xp, point)
+
+                for left, right in subregions:
+                    # Skip any zero-width regions.
+                    if xp.any(left == right):
+                        continue
+                    else:
+                        new_subregions.append((left, right))
+
+                new_subregions.extend(subregions)
+
+            else:
+                new_subregions.append((a_k, b_k))
+
+        regions = new_subregions
+
+    return regions
+
+
+class _VariableTransform:
+    """
+    A transformation that can be applied to an integral.
+    """
+
+    @property
+    def transformed_limits(self):
+        """
+        New limits of integration after applying the transformation.
+        """
+
+        raise NotImplementedError
+
+    @property
+    def points(self):
+        """
+        Any problematic points introduced by the transformation.
+
+        These should be specified as points where ``_VariableTransform(f)(self, point)``
+        would be problematic.
+
+        For example, if the transformation ``x = 1/((1-t)(1+t))`` is applied to a
+        univariate integral, then points should return ``[ [1], [-1] ]``.
+        """
+
+        return []
+
+    def inv(self, x):
+        """
+        Map points ``x`` to ``t`` such that if ``f`` is the original function and ``g``
+        is the function after the transformation is applied, then::
+
+            f(x) = g(self.inv(x))
+        """
+
+        raise NotImplementedError
+
+    def __call__(self, t, *args, **kwargs):
+        """
+        Apply the transformation to ``f`` and multiply by the Jacobian determinant.
+        This should be the new integrand after the transformation has been applied so
+        that the following is satisfied::
+
+            f_transformed = _VariableTransform(f)
+
+            cubature(f, a, b) == cubature(
+                f_transformed,
+                *f_transformed.transformed_limits(a, b),
+            )
+        """
+
+        raise NotImplementedError
+
+
+class _InfiniteLimitsTransform(_VariableTransform):
+    r"""
+    Transformation for handling infinite limits.
+
+    Assuming ``a = [a_1, ..., a_n]`` and ``b = [b_1, ..., b_n]``:
+
+    If :math:`a_i = -\infty` and :math:`b_i = \infty`, the i-th integration variable
+    will use the transformation :math:`x = \frac{1-|t|}{t}` and :math:`t \in (-1, 1)`.
+
+    If :math:`a_i \ne \pm\infty` and :math:`b_i = \infty`, the i-th integration variable
+    will use the transformation :math:`x = a_i + \frac{1-t}{t}` and
+    :math:`t \in (0, 1)`.
+
+    If :math:`a_i = -\infty` and :math:`b_i \ne \pm\infty`, the i-th integration
+    variable will use the transformation :math:`x = b_i - \frac{1-t}{t}` and
+    :math:`t \in (0, 1)`.
+    """
+
+    def __init__(self, f, a, b, xp):
+        self._xp = xp
+
+        self._f = f
+        self._orig_a = a
+        self._orig_b = b
+
+        # (-oo, oo) will be mapped to (-1, 1).
+        self._double_inf_pos = (a == -math.inf) & (b == math.inf)
+
+        # (start, oo) will be mapped to (0, 1).
+        start_inf_mask = (a != -math.inf) & (b == math.inf)
+
+        # (-oo, end) will be mapped to (0, 1).
+        inf_end_mask = (a == -math.inf) & (b != math.inf)
+
+        # This is handled by making the transformation t = -x and reducing it to
+        # the other semi-infinite case.
+        self._semi_inf_pos = start_inf_mask | inf_end_mask
+
+        # Since we flip the limits, we don't need to separately multiply the
+        # integrand by -1.
+        self._orig_a[inf_end_mask] = -b[inf_end_mask]
+        self._orig_b[inf_end_mask] = -a[inf_end_mask]
+
+        self._num_inf = self._xp.sum(
+            self._xp.astype(self._double_inf_pos | self._semi_inf_pos, self._xp.int64),
+        ).__int__()
+
+    @property
+    def transformed_limits(self):
+        a = xp_copy(self._orig_a)
+        b = xp_copy(self._orig_b)
+
+        a[self._double_inf_pos] = -1
+        b[self._double_inf_pos] = 1
+
+        a[self._semi_inf_pos] = 0
+        b[self._semi_inf_pos] = 1
+
+        return a, b
+
+    @property
+    def points(self):
+        # If there are infinite limits, then the origin becomes a problematic point
+        # due to a division by zero there.
+
+        # If the function using this class only wraps f when a and b contain infinite
+        # limits, this condition will always be met (as is the case with cubature).
+        #
+        # If a and b do not contain infinite limits but f is still wrapped with this
+        # class, then without this condition the initial region of integration will
+        # be split around the origin unnecessarily.
+        if self._num_inf != 0:
+            return [self._xp.zeros(self._orig_a.shape)]
+        else:
+            return []
+
+    def inv(self, x):
+        t = xp_copy(x)
+        npoints = x.shape[0]
+
+        double_inf_mask = self._xp.tile(
+            self._double_inf_pos[self._xp.newaxis, :],
+            (npoints, 1),
+        )
+
+        semi_inf_mask = self._xp.tile(
+            self._semi_inf_pos[self._xp.newaxis, :],
+            (npoints, 1),
+        )
+
+        # If any components of x are 0, then this component will be mapped to infinity
+        # under the transformation used for doubly-infinite limits.
+        #
+        # Handle the zero values and non-zero values separately to avoid division by
+        # zero.
+        zero_mask = x[double_inf_mask] == 0
+        non_zero_mask = double_inf_mask & ~zero_mask
+        t[zero_mask] = math.inf
+        t[non_zero_mask] = 1/(x[non_zero_mask] + self._xp.sign(x[non_zero_mask]))
+
+        start = self._xp.tile(self._orig_a[self._semi_inf_pos], (npoints,))
+        t[semi_inf_mask] = 1/(x[semi_inf_mask] - start + 1)
+
+        return t
+
+    def __call__(self, t, *args, **kwargs):
+        x = xp_copy(t)
+        npoints = t.shape[0]
+
+        double_inf_mask = self._xp.tile(
+            self._double_inf_pos[self._xp.newaxis, :],
+            (npoints, 1),
+        )
+
+        semi_inf_mask = self._xp.tile(
+            self._semi_inf_pos[self._xp.newaxis, :],
+            (npoints, 1),
+        )
+
+        # For (-oo, oo) -> (-1, 1), use the transformation x = (1-|t|)/t.
+        x[double_inf_mask] = (
+            (1 - self._xp.abs(t[double_inf_mask])) / t[double_inf_mask]
+        )
+
+        start = self._xp.tile(self._orig_a[self._semi_inf_pos], (npoints,))
+
+        # For (start, oo) -> (0, 1), use the transformation x = start + (1-t)/t.
+        x[semi_inf_mask] = start + (1 - t[semi_inf_mask]) / t[semi_inf_mask]
+
+        jacobian_det = 1/self._xp.prod(
+            self._xp.reshape(
+                t[semi_inf_mask | double_inf_mask]**2,
+                (-1, self._num_inf),
+            ),
+            axis=-1,
+        )
+
+        f_x = self._f(x, *args, **kwargs)
+        jacobian_det = self._xp.reshape(jacobian_det, (-1, *([1]*(len(f_x.shape) - 1))))
+
+        return f_x * jacobian_det
diff --git a/shared/package-files/0c/0c90569a85ff1da6aeeea9609af5b27db852ceb5f6a8beb7e919ecec21b4d699/__init__.py b/shared/package-files/0c/0c90569a85ff1da6aeeea9609af5b27db852ceb5f6a8beb7e919ecec21b4d699/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..156cb232a7aa80eee1526c7598f72043de10473f
--- /dev/null
+++ b/shared/package-files/0c/0c90569a85ff1da6aeeea9609af5b27db852ceb5f6a8beb7e919ecec21b4d699/__init__.py
@@ -0,0 +1 @@
+"""Empty __init__.py file to signal Python this directory is a package."""
diff --git a/shared/package-files/0c/0c97b573e59d6e02da0a5330263b9cc05c1b19e3c5aa79b4bcf49d5f2190cb65/_hierarchical_fast.pyx b/shared/package-files/0c/0c97b573e59d6e02da0a5330263b9cc05c1b19e3c5aa79b4bcf49d5f2190cb65/_hierarchical_fast.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..36ae0ab0d241432df9f5833901580dc88c30d925
--- /dev/null
+++ b/shared/package-files/0c/0c97b573e59d6e02da0a5330263b9cc05c1b19e3c5aa79b4bcf49d5f2190cb65/_hierarchical_fast.pyx
@@ -0,0 +1,507 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+cimport cython
+
+from ..metrics._dist_metrics cimport DistanceMetric64
+from ..utils._fast_dict cimport IntFloatDict
+from ..utils._typedefs cimport float64_t, intp_t, uint8_t
+
+# C++
+from cython.operator cimport dereference as deref, preincrement as inc
+from libcpp.map cimport map as cpp_map
+from libc.math cimport fmax, INFINITY
+
+
+###############################################################################
+# Utilities for computing the ward momentum
+
+def compute_ward_dist(
+    const float64_t[::1] m_1,
+    const float64_t[:, ::1] m_2,
+    const intp_t[::1] coord_row,
+    const intp_t[::1] coord_col,
+    float64_t[::1] res
+):
+    cdef intp_t size_max = coord_row.shape[0]
+    cdef intp_t n_features = m_2.shape[1]
+    cdef intp_t i, j, row, col
+    cdef float64_t pa, n
+
+    for i in range(size_max):
+        row = coord_row[i]
+        col = coord_col[i]
+        n = (m_1[row] * m_1[col]) / (m_1[row] + m_1[col])
+        pa = 0.
+        for j in range(n_features):
+            pa += (m_2[row, j] / m_1[row] - m_2[col, j] / m_1[col]) ** 2
+        res[i] = pa * n
+
+
+###############################################################################
+# Utilities for cutting and exploring a hierarchical tree
+
+def _hc_get_descendent(intp_t node, children, intp_t n_leaves):
+    """
+    Function returning all the descendent leaves of a set of nodes in the tree.
+
+    Parameters
+    ----------
+    node : integer
+        The node for which we want the descendents.
+
+    children : list of pairs, length n_nodes
+        The children of each non-leaf node. Values less than `n_samples` refer
+        to leaves of the tree. A greater value `i` indicates a node with
+        children `children[i - n_samples]`.
+
+    n_leaves : integer
+        Number of leaves.
+
+    Returns
+    -------
+    descendent : list of int
+    """
+    ind = [node]
+    if node < n_leaves:
+        return ind
+    descendent = []
+
+    # It is actually faster to do the accounting of the number of
+    # elements is the list ourselves: len is a lengthy operation on a
+    # chained list
+    cdef intp_t i, n_indices = 1
+
+    while n_indices:
+        i = ind.pop()
+        if i < n_leaves:
+            descendent.append(i)
+            n_indices -= 1
+        else:
+            ind.extend(children[i - n_leaves])
+            n_indices += 1
+    return descendent
+
+
+def hc_get_heads(intp_t[:] parents, copy=True):
+    """Returns the heads of the forest, as defined by parents.
+
+    Parameters
+    ----------
+    parents : array of integers
+        The parent structure defining the forest (ensemble of trees)
+    copy : boolean
+        If copy is False, the input 'parents' array is modified inplace
+
+    Returns
+    -------
+    heads : array of integers of same shape as parents
+        The indices in the 'parents' of the tree heads
+
+    """
+    cdef intp_t parent, node0, node, size
+    if copy:
+        parents = np.copy(parents)
+    size = parents.size
+
+    # Start from the top of the tree and go down
+    for node0 in range(size - 1, -1, -1):
+        node = node0
+        parent = parents[node]
+        while parent != node:
+            parents[node0] = parent
+            node = parent
+            parent = parents[node]
+    return parents
+
+
+def _get_parents(
+    nodes,
+    heads,
+    const intp_t[:] parents,
+    uint8_t[::1] not_visited
+):
+    """Returns the heads of the given nodes, as defined by parents.
+
+    Modifies 'heads' and 'not_visited' in-place.
+
+    Parameters
+    ----------
+    nodes : list of integers
+        The nodes to start from
+    heads : list of integers
+        A list to hold the results (modified inplace)
+    parents : array of integers
+        The parent structure defining the tree
+    not_visited
+        The tree nodes to consider (modified inplace)
+
+    """
+    cdef intp_t parent, node
+
+    for node in nodes:
+        parent = parents[node]
+        while parent != node:
+            node = parent
+            parent = parents[node]
+        if not_visited[node]:
+            not_visited[node] = 0
+            heads.append(node)
+
+
+###############################################################################
+# merge strategies implemented on IntFloatDicts
+
+# These are used in the hierarchical clustering code, to implement
+# merging between two clusters, defined as a dict containing node number
+# as keys and edge weights as values.
+
+
+def max_merge(
+    IntFloatDict a,
+    IntFloatDict b,
+    const intp_t[:] mask,
+    intp_t n_a,
+    intp_t n_b
+):
+    """Merge two IntFloatDicts with the max strategy: when the same key is
+    present in the two dicts, the max of the two values is used.
+
+    Parameters
+    ==========
+    a, b : IntFloatDict object
+        The IntFloatDicts to merge
+    mask : ndarray array of dtype integer and of dimension 1
+        a mask for keys to ignore: if not mask[key] the corresponding key
+        is skipped in the output dictionary
+    n_a, n_b : float
+        n_a and n_b are weights for a and b for the merge strategy.
+        They are not used in the case of a max merge.
+
+    Returns
+    =======
+    out : IntFloatDict object
+        The IntFloatDict resulting from the merge
+    """
+    cdef IntFloatDict out_obj = IntFloatDict.__new__(IntFloatDict)
+    cdef cpp_map[intp_t, float64_t].iterator a_it = a.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator a_end = a.my_map.end()
+    cdef intp_t key
+    cdef float64_t value
+    # First copy a into out
+    while a_it != a_end:
+        key = deref(a_it).first
+        if mask[key]:
+            out_obj.my_map[key] = deref(a_it).second
+        inc(a_it)
+
+    # Then merge b into out
+    cdef cpp_map[intp_t, float64_t].iterator out_it = out_obj.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator out_end = out_obj.my_map.end()
+    cdef cpp_map[intp_t, float64_t].iterator b_it = b.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator b_end = b.my_map.end()
+    while b_it != b_end:
+        key = deref(b_it).first
+        value = deref(b_it).second
+        if mask[key]:
+            out_it = out_obj.my_map.find(key)
+            if out_it == out_end:
+                # Key not found
+                out_obj.my_map[key] = value
+            else:
+                deref(out_it).second = fmax(deref(out_it).second, value)
+        inc(b_it)
+    return out_obj
+
+
+def average_merge(
+    IntFloatDict a,
+    IntFloatDict b,
+    const intp_t[:] mask,
+    intp_t n_a,
+    intp_t n_b
+):
+    """Merge two IntFloatDicts with the average strategy: when the
+    same key is present in the two dicts, the weighted average of the two
+    values is used.
+
+    Parameters
+    ==========
+    a, b : IntFloatDict object
+        The IntFloatDicts to merge
+    mask : ndarray array of dtype integer and of dimension 1
+        a mask for keys to ignore: if not mask[key] the corresponding key
+        is skipped in the output dictionary
+    n_a, n_b : float
+        n_a and n_b are weights for a and b for the merge strategy.
+        They are used for a weighted mean.
+
+    Returns
+    =======
+    out : IntFloatDict object
+        The IntFloatDict resulting from the merge
+    """
+    cdef IntFloatDict out_obj = IntFloatDict.__new__(IntFloatDict)
+    cdef cpp_map[intp_t, float64_t].iterator a_it = a.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator a_end = a.my_map.end()
+    cdef intp_t key
+    cdef float64_t value
+    cdef float64_t n_out = <float64_t> (n_a + n_b)
+    # First copy a into out
+    while a_it != a_end:
+        key = deref(a_it).first
+        if mask[key]:
+            out_obj.my_map[key] = deref(a_it).second
+        inc(a_it)
+
+    # Then merge b into out
+    cdef cpp_map[intp_t, float64_t].iterator out_it = out_obj.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator out_end = out_obj.my_map.end()
+    cdef cpp_map[intp_t, float64_t].iterator b_it = b.my_map.begin()
+    cdef cpp_map[intp_t, float64_t].iterator b_end = b.my_map.end()
+    while b_it != b_end:
+        key = deref(b_it).first
+        value = deref(b_it).second
+        if mask[key]:
+            out_it = out_obj.my_map.find(key)
+            if out_it == out_end:
+                # Key not found
+                out_obj.my_map[key] = value
+            else:
+                deref(out_it).second = (n_a * deref(out_it).second
+                                        + n_b * value) / n_out
+        inc(b_it)
+    return out_obj
+
+
+###############################################################################
+# An edge object for fast comparisons
+
+cdef class WeightedEdge:
+    cdef public intp_t a
+    cdef public intp_t b
+    cdef public float64_t weight
+
+    def __init__(self, float64_t weight, intp_t a, intp_t b):
+        self.weight = weight
+        self.a = a
+        self.b = b
+
+    def __richcmp__(self, WeightedEdge other, int op):
+        """Cython-specific comparison method.
+
+        op is the comparison code::
+            <   0
+            ==  2
+            >   4
+            <=  1
+            !=  3
+            >=  5
+        """
+        if op == 0:
+            return self.weight < other.weight
+        elif op == 1:
+            return self.weight <= other.weight
+        elif op == 2:
+            return self.weight == other.weight
+        elif op == 3:
+            return self.weight != other.weight
+        elif op == 4:
+            return self.weight > other.weight
+        elif op == 5:
+            return self.weight >= other.weight
+
+    def __repr__(self):
+        return "%s(weight=%f, a=%i, b=%i)" % (self.__class__.__name__,
+                                              self.weight,
+                                              self.a, self.b)
+
+
+################################################################################
+# Efficient labelling/conversion of MSTs to single linkage hierarchies
+
+cdef class UnionFind(object):
+
+    def __init__(self, N):
+        self.parent = np.full(2 * N - 1, -1., dtype=np.intp, order='C')
+        self.next_label = N
+        self.size = np.hstack((np.ones(N, dtype=np.intp),
+                               np.zeros(N - 1, dtype=np.intp)))
+
+    cdef void union(self, intp_t m, intp_t n) noexcept:
+        self.parent[m] = self.next_label
+        self.parent[n] = self.next_label
+        self.size[self.next_label] = self.size[m] + self.size[n]
+        self.next_label += 1
+        return
+
+    @cython.wraparound(True)
+    cdef intp_t fast_find(self, intp_t n) noexcept:
+        cdef intp_t p
+        p = n
+        # find the highest node in the linkage graph so far
+        while self.parent[n] != -1:
+            n = self.parent[n]
+        # provide a shortcut up to the highest node
+        while self.parent[p] != n:
+            p, self.parent[p] = self.parent[p], n
+        return n
+
+
+def _single_linkage_label(const float64_t[:, :] L):
+    """
+    Convert an linkage array or MST to a tree by labelling clusters at merges.
+    This is done by using a Union find structure to keep track of merges
+    efficiently. This is the private version of the function that assumes that
+    ``L`` has been properly validated. See ``single_linkage_label`` for the
+    user facing version of this function.
+
+    Parameters
+    ----------
+    L: array of shape (n_samples - 1, 3)
+        The linkage array or MST where each row specifies two samples
+        to be merged and a distance or weight at which the merge occurs. This
+         array is assumed to be sorted by the distance/weight.
+
+    Returns
+    -------
+    A tree in the format used by scipy.cluster.hierarchy.
+    """
+
+    cdef float64_t[:, ::1] result_arr
+
+    cdef intp_t left, left_cluster, right, right_cluster, index
+    cdef float64_t delta
+
+    result_arr = np.zeros((L.shape[0], 4), dtype=np.float64)
+    U = UnionFind(L.shape[0] + 1)
+
+    for index in range(L.shape[0]):
+
+        left = <intp_t> L[index, 0]
+        right = <intp_t> L[index, 1]
+        delta = L[index, 2]
+
+        left_cluster = U.fast_find(left)
+        right_cluster = U.fast_find(right)
+
+        result_arr[index][0] = left_cluster
+        result_arr[index][1] = right_cluster
+        result_arr[index][2] = delta
+        result_arr[index][3] = U.size[left_cluster] + U.size[right_cluster]
+
+        U.union(left_cluster, right_cluster)
+
+    return np.asarray(result_arr)
+
+
+@cython.wraparound(True)
+def single_linkage_label(L):
+    """
+    Convert an linkage array or MST to a tree by labelling clusters at merges.
+    This is done by using a Union find structure to keep track of merges
+    efficiently.
+
+    Parameters
+    ----------
+    L: array of shape (n_samples - 1, 3)
+        The linkage array or MST where each row specifies two samples
+        to be merged and a distance or weight at which the merge occurs. This
+         array is assumed to be sorted by the distance/weight.
+
+    Returns
+    -------
+    A tree in the format used by scipy.cluster.hierarchy.
+    """
+    # Validate L
+    if L[:, :2].min() < 0 or L[:, :2].max() >= 2 * L.shape[0] + 1:
+        raise ValueError("Input MST array is not a validly formatted MST array")
+
+    is_sorted = lambda x: np.all(x[:-1] <= x[1:])
+    if not is_sorted(L[:, 2]):
+        raise ValueError("Input MST array must be sorted by weight")
+
+    return _single_linkage_label(L)
+
+
+# Implements MST-LINKAGE-CORE from https://arxiv.org/abs/1109.2378
+def mst_linkage_core(
+        const float64_t [:, ::1] raw_data,
+        DistanceMetric64 dist_metric):
+    """
+    Compute the necessary elements of a minimum spanning
+    tree for computation of single linkage clustering. This
+    represents the MST-LINKAGE-CORE algorithm (Figure 6) from
+    :arxiv:`Daniel Mullner, "Modern hierarchical, agglomerative clustering
+    algorithms" <1109.2378>`.
+
+    In contrast to the scipy implementation is never computes
+    a full distance matrix, generating distances only as they
+    are needed and releasing them when no longer needed.
+
+    Parameters
+    ----------
+    raw_data: array of shape (n_samples, n_features)
+        The array of feature data to be clustered. Must be C-aligned
+
+    dist_metric: DistanceMetric64
+        A DistanceMetric64 object conforming to the API from
+        ``sklearn.metrics._dist_metrics.pxd`` that will be
+        used to compute distances.
+
+    Returns
+    -------
+    mst_core_data: array of shape (n_samples, 3)
+        An array providing information from which one
+        can either compute an MST, or the linkage hierarchy
+        very efficiently. See :arxiv:`Daniel Mullner, "Modern hierarchical,
+        agglomerative clustering algorithms" <1109.2378>` algorithm
+        MST-LINKAGE-CORE for more details.
+    """
+    cdef:
+        intp_t n_samples = raw_data.shape[0]
+        uint8_t[:] in_tree = np.zeros(n_samples, dtype=bool)
+        float64_t[:, ::1] result = np.zeros((n_samples - 1, 3))
+
+        intp_t current_node = 0
+        intp_t new_node
+        intp_t i
+        intp_t j
+        intp_t num_features = raw_data.shape[1]
+
+        float64_t right_value
+        float64_t left_value
+        float64_t new_distance
+
+        float64_t[:] current_distances = np.full(n_samples, INFINITY)
+
+    for i in range(n_samples - 1):
+
+        in_tree[current_node] = 1
+
+        new_distance = INFINITY
+        new_node = 0
+
+        for j in range(n_samples):
+            if in_tree[j]:
+                continue
+
+            right_value = current_distances[j]
+            left_value = dist_metric.dist(&raw_data[current_node, 0],
+                                          &raw_data[j, 0],
+                                          num_features)
+
+            if left_value < right_value:
+                current_distances[j] = left_value
+
+            if current_distances[j] < new_distance:
+                new_distance = current_distances[j]
+                new_node = j
+
+        result[i, 0] = current_node
+        result[i, 1] = new_node
+        result[i, 2] = new_distance
+        current_node = new_node
+
+    return np.array(result)
diff --git a/shared/package-files/0c/0c97db6b339e0896aa30d147992654524c7e394072f146691eb677e769ee27a2/linalg_lstsq_compositeexplicitautograd_dispatch.h b/shared/package-files/0c/0c97db6b339e0896aa30d147992654524c7e394072f146691eb677e769ee27a2/linalg_lstsq_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a4bae4781dd3089499e07d18467a539d3e531c85
--- /dev/null
+++ b/shared/package-files/0c/0c97db6b339e0896aa30d147992654524c7e394072f146691eb677e769ee27a2/linalg_lstsq_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> linalg_lstsq(const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond=c10::nullopt, c10::optional<c10::string_view> driver=c10::nullopt);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/0c/0c97e9d18d6fe79d0f30167baf30238a5fca8447ebabfc3a78ad7ab8afc5cfad/cytoscape.py b/shared/package-files/0c/0c97e9d18d6fe79d0f30167baf30238a5fca8447ebabfc3a78ad7ab8afc5cfad/cytoscape.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0c0e3f3b7743bb9c9efc00e1441ee40bcfafbae
--- /dev/null
+++ b/shared/package-files/0c/0c97e9d18d6fe79d0f30167baf30238a5fca8447ebabfc3a78ad7ab8afc5cfad/cytoscape.py
@@ -0,0 +1,173 @@
+import networkx as nx
+
+__all__ = ["cytoscape_data", "cytoscape_graph"]
+
+
+def cytoscape_data(G, name="name", ident="id"):
+    """Returns data in Cytoscape JSON format (cyjs).
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to convert to cytoscape format
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    data: dict
+        A dictionary with cyjs formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If the values for `name` and `ident` are identical.
+
+    See Also
+    --------
+    cytoscape_graph: convert a dictionary in cyjs format to a graph
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> G = nx.path_graph(2)
+    >>> nx.cytoscape_data(G)  # doctest: +SKIP
+    {'data': [],
+     'directed': False,
+     'multigraph': False,
+     'elements': {'nodes': [{'data': {'id': '0', 'value': 0, 'name': '0'}},
+       {'data': {'id': '1', 'value': 1, 'name': '1'}}],
+      'edges': [{'data': {'source': 0, 'target': 1}}]}}
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    jsondata = {"data": list(G.graph.items())}
+    jsondata["directed"] = G.is_directed()
+    jsondata["multigraph"] = G.is_multigraph()
+    jsondata["elements"] = {"nodes": [], "edges": []}
+    nodes = jsondata["elements"]["nodes"]
+    edges = jsondata["elements"]["edges"]
+
+    for i, j in G.nodes.items():
+        n = {"data": j.copy()}
+        n["data"]["id"] = j.get(ident) or str(i)
+        n["data"]["value"] = i
+        n["data"]["name"] = j.get(name) or str(i)
+        nodes.append(n)
+
+    if G.is_multigraph():
+        for e in G.edges(keys=True):
+            n = {"data": G.adj[e[0]][e[1]][e[2]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            n["data"]["key"] = e[2]
+            edges.append(n)
+    else:
+        for e in G.edges():
+            n = {"data": G.adj[e[0]][e[1]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            edges.append(n)
+    return jsondata
+
+
+def cytoscape_graph(data, name="name", ident="id"):
+    """
+    Create a NetworkX graph from a dictionary in cytoscape JSON format.
+
+    Parameters
+    ----------
+    data : dict
+        A dictionary of data conforming to cytoscape JSON format.
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    graph : a NetworkX graph instance
+        The `graph` can be an instance of `Graph`, `DiGraph`, `MultiGraph`, or
+        `MultiDiGraph` depending on the input data.
+
+    Raises
+    ------
+    NetworkXError
+        If the `name` and `ident` attributes are identical.
+
+    See Also
+    --------
+    cytoscape_data: convert a NetworkX graph to a dict in cyjs format
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> data_dict = {
+    ...     'data': [],
+    ...     'directed': False,
+    ...     'multigraph': False,
+    ...     'elements': {'nodes': [{'data': {'id': '0', 'value': 0, 'name': '0'}},
+    ...       {'data': {'id': '1', 'value': 1, 'name': '1'}}],
+    ...      'edges': [{'data': {'source': 0, 'target': 1}}]}
+    ... }
+    >>> G = nx.cytoscape_graph(data_dict)
+    >>> G.name
+    ''
+    >>> G.nodes()
+    NodeView((0, 1))
+    >>> G.nodes(data=True)[0]
+    {'id': '0', 'value': 0, 'name': '0'}
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'source': 0, 'target': 1})])
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    multigraph = data.get("multigraph")
+    directed = data.get("directed")
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+    graph.graph = dict(data.get("data"))
+    for d in data["elements"]["nodes"]:
+        node_data = d["data"].copy()
+        node = d["data"]["value"]
+
+        if d["data"].get(name):
+            node_data[name] = d["data"].get(name)
+        if d["data"].get(ident):
+            node_data[ident] = d["data"].get(ident)
+
+        graph.add_node(node)
+        graph.nodes[node].update(node_data)
+
+    for d in data["elements"]["edges"]:
+        edge_data = d["data"].copy()
+        sour = d["data"]["source"]
+        targ = d["data"]["target"]
+        if multigraph:
+            key = d["data"].get("key", 0)
+            graph.add_edge(sour, targ, key=key)
+            graph.edges[sour, targ, key].update(edge_data)
+        else:
+            graph.add_edge(sour, targ)
+            graph.edges[sour, targ].update(edge_data)
+    return graph
diff --git a/shared/package-files/0c/0c9a4c623c5e60a29077035c30bdbf174bed021faa9ca4d87be0a94f141efb88/format_control.py b/shared/package-files/0c/0c9a4c623c5e60a29077035c30bdbf174bed021faa9ca4d87be0a94f141efb88/format_control.py
new file mode 100644
index 0000000000000000000000000000000000000000..db3995eac9f9ec2450e0e2d4a18e666c0b178681
--- /dev/null
+++ b/shared/package-files/0c/0c9a4c623c5e60a29077035c30bdbf174bed021faa9ca4d87be0a94f141efb88/format_control.py
@@ -0,0 +1,80 @@
+from typing import FrozenSet, Optional, Set
+
+from pip._vendor.packaging.utils import canonicalize_name
+
+from pip._internal.exceptions import CommandError
+
+
+class FormatControl:
+    """Helper for managing formats from which a package can be installed."""
+
+    __slots__ = ["no_binary", "only_binary"]
+
+    def __init__(
+        self,
+        no_binary: Optional[Set[str]] = None,
+        only_binary: Optional[Set[str]] = None,
+    ) -> None:
+        if no_binary is None:
+            no_binary = set()
+        if only_binary is None:
+            only_binary = set()
+
+        self.no_binary = no_binary
+        self.only_binary = only_binary
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, self.__class__):
+            return NotImplemented
+
+        if self.__slots__ != other.__slots__:
+            return False
+
+        return all(getattr(self, k) == getattr(other, k) for k in self.__slots__)
+
+    def __repr__(self) -> str:
+        return "{}({}, {})".format(
+            self.__class__.__name__, self.no_binary, self.only_binary
+        )
+
+    @staticmethod
+    def handle_mutual_excludes(value: str, target: Set[str], other: Set[str]) -> None:
+        if value.startswith("-"):
+            raise CommandError(
+                "--no-binary / --only-binary option requires 1 argument."
+            )
+        new = value.split(",")
+        while ":all:" in new:
+            other.clear()
+            target.clear()
+            target.add(":all:")
+            del new[: new.index(":all:") + 1]
+            # Without a none, we want to discard everything as :all: covers it
+            if ":none:" not in new:
+                return
+        for name in new:
+            if name == ":none:":
+                target.clear()
+                continue
+            name = canonicalize_name(name)
+            other.discard(name)
+            target.add(name)
+
+    def get_allowed_formats(self, canonical_name: str) -> FrozenSet[str]:
+        result = {"binary", "source"}
+        if canonical_name in self.only_binary:
+            result.discard("source")
+        elif canonical_name in self.no_binary:
+            result.discard("binary")
+        elif ":all:" in self.only_binary:
+            result.discard("source")
+        elif ":all:" in self.no_binary:
+            result.discard("binary")
+        return frozenset(result)
+
+    def disallow_binaries(self) -> None:
+        self.handle_mutual_excludes(
+            ":all:",
+            self.no_binary,
+            self.only_binary,
+        )
diff --git a/shared/package-files/0c/0c9bbc09c677c70c7c9e3a630de48eca1cb17aa6589a17dbedd912094fa597b6/test_multivariate_resultants.py b/shared/package-files/0c/0c9bbc09c677c70c7c9e3a630de48eca1cb17aa6589a17dbedd912094fa597b6/test_multivariate_resultants.py
new file mode 100644
index 0000000000000000000000000000000000000000..0799feb41fc875cf038723916a3efd62ff31b1b4
--- /dev/null
+++ b/shared/package-files/0c/0c9bbc09c677c70c7c9e3a630de48eca1cb17aa6589a17dbedd912094fa597b6/test_multivariate_resultants.py
@@ -0,0 +1,294 @@
+"""Tests for Dixon's and Macaulay's classes. """
+
+from sympy.matrices.dense import Matrix
+from sympy.polys.polytools import factor
+from sympy.core import symbols
+from sympy.tensor.indexed import IndexedBase
+
+from sympy.polys.multivariate_resultants import (DixonResultant,
+                                                 MacaulayResultant)
+
+c, d = symbols("a, b")
+x, y = symbols("x, y")
+
+p =  c * x + y
+q =  x + d * y
+
+dixon = DixonResultant(polynomials=[p, q], variables=[x, y])
+macaulay = MacaulayResultant(polynomials=[p, q], variables=[x, y])
+
+def test_dixon_resultant_init():
+    """Test init method of DixonResultant."""
+    a = IndexedBase("alpha")
+
+    assert dixon.polynomials == [p, q]
+    assert dixon.variables == [x, y]
+    assert dixon.n == 2
+    assert dixon.m == 2
+    assert dixon.dummy_variables == [a[0], a[1]]
+
+def test_get_dixon_polynomial_numerical():
+    """Test Dixon's polynomial for a numerical example."""
+    a = IndexedBase("alpha")
+
+    p = x + y
+    q = x ** 2 + y **3
+    h = x ** 2 + y
+
+    dixon = DixonResultant([p, q, h], [x, y])
+    polynomial = -x * y ** 2 * a[0] - x * y ** 2 * a[1] - x * y * a[0] \
+    * a[1] - x * y * a[1] ** 2 - x * a[0] * a[1] ** 2 + x * a[0] - \
+    y ** 2 * a[0] * a[1] + y ** 2 * a[1] - y * a[0] * a[1] ** 2 + y * \
+    a[1] ** 2
+
+    assert dixon.get_dixon_polynomial().as_expr().expand() == polynomial
+
+def test_get_max_degrees():
+    """Tests max degrees function."""
+
+    p = x + y
+    q = x ** 2 + y **3
+    h = x ** 2 + y
+
+    dixon = DixonResultant(polynomials=[p, q, h], variables=[x, y])
+    dixon_polynomial = dixon.get_dixon_polynomial()
+
+    assert dixon.get_max_degrees(dixon_polynomial) == [1, 2]
+
+def test_get_dixon_matrix():
+    """Test Dixon's resultant for a numerical example."""
+
+    x, y = symbols('x, y')
+
+    p = x + y
+    q = x ** 2 + y ** 3
+    h = x ** 2 + y
+
+    dixon = DixonResultant([p, q, h], [x, y])
+    polynomial = dixon.get_dixon_polynomial()
+
+    assert dixon.get_dixon_matrix(polynomial).det() == 0
+
+def test_get_dixon_matrix_example_two():
+    """Test Dixon's matrix for example from [Palancz08]_."""
+    x, y, z = symbols('x, y, z')
+
+    f = x ** 2 + y ** 2 - 1 + z * 0
+    g = x ** 2 + z ** 2 - 1 + y * 0
+    h = y ** 2 + z ** 2 - 1
+
+    example_two = DixonResultant([f, g, h], [y, z])
+    poly = example_two.get_dixon_polynomial()
+    matrix = example_two.get_dixon_matrix(poly)
+
+    expr = 1 - 8 * x ** 2 + 24 * x ** 4 - 32 * x ** 6 + 16 * x ** 8
+    assert (matrix.det() - expr).expand() == 0
+
+def test_KSY_precondition():
+    """Tests precondition for KSY Resultant."""
+    A, B, C = symbols('A, B, C')
+
+    m1 = Matrix([[1, 2, 3],
+                 [4, 5, 12],
+                 [6, 7, 18]])
+
+    m2 = Matrix([[0, C**2],
+                 [-2 * C, -C ** 2]])
+
+    m3 = Matrix([[1, 0],
+                 [0, 1]])
+
+    m4 = Matrix([[A**2, 0, 1],
+                 [A, 1, 1 / A]])
+
+    m5 = Matrix([[5, 1],
+                 [2, B],
+                 [0, 1],
+                 [0, 0]])
+
+    assert dixon.KSY_precondition(m1) == False
+    assert dixon.KSY_precondition(m2) == True
+    assert dixon.KSY_precondition(m3) == True
+    assert dixon.KSY_precondition(m4) == False
+    assert dixon.KSY_precondition(m5) == True
+
+def test_delete_zero_rows_and_columns():
+    """Tests method for deleting rows and columns containing only zeros."""
+    A, B, C = symbols('A, B, C')
+
+    m1 = Matrix([[0, 0],
+                 [0, 0],
+                 [1, 2]])
+
+    m2 = Matrix([[0, 1, 2],
+                 [0, 3, 4],
+                 [0, 5, 6]])
+
+    m3 = Matrix([[0, 0, 0, 0],
+                 [0, 1, 2, 0],
+                 [0, 3, 4, 0],
+                 [0, 0, 0, 0]])
+
+    m4 = Matrix([[1, 0, 2],
+                 [0, 0, 0],
+                 [3, 0, 4]])
+
+    m5 = Matrix([[0, 0, 0, 1],
+                 [0, 0, 0, 2],
+                 [0, 0, 0, 3],
+                 [0, 0, 0, 4]])
+
+    m6 = Matrix([[0, 0, A],
+                 [B, 0, 0],
+                 [0, 0, C]])
+
+    assert dixon.delete_zero_rows_and_columns(m1) == Matrix([[1, 2]])
+
+    assert dixon.delete_zero_rows_and_columns(m2) == Matrix([[1, 2],
+                                                             [3, 4],
+                                                             [5, 6]])
+
+    assert dixon.delete_zero_rows_and_columns(m3) == Matrix([[1, 2],
+                                                             [3, 4]])
+
+    assert dixon.delete_zero_rows_and_columns(m4) == Matrix([[1, 2],
+                                                             [3, 4]])
+
+    assert dixon.delete_zero_rows_and_columns(m5) == Matrix([[1],
+                                                             [2],
+                                                             [3],
+                                                             [4]])
+
+    assert dixon.delete_zero_rows_and_columns(m6) == Matrix([[0, A],
+                                                             [B, 0],
+                                                             [0, C]])
+
+def test_product_leading_entries():
+    """Tests product of leading entries method."""
+    A, B = symbols('A, B')
+
+    m1 = Matrix([[1, 2, 3],
+                 [0, 4, 5],
+                 [0, 0, 6]])
+
+    m2 = Matrix([[0, 0, 1],
+                 [2, 0, 3]])
+
+    m3 = Matrix([[0, 0, 0],
+                 [1, 2, 3],
+                 [0, 0, 0]])
+
+    m4 = Matrix([[0, 0, A],
+                 [1, 2, 3],
+                 [B, 0, 0]])
+
+    assert dixon.product_leading_entries(m1) == 24
+    assert dixon.product_leading_entries(m2) == 2
+    assert dixon.product_leading_entries(m3) == 1
+    assert dixon.product_leading_entries(m4) == A * B
+
+def test_get_KSY_Dixon_resultant_example_one():
+    """Tests the KSY Dixon resultant for example one"""
+    x, y, z = symbols('x, y, z')
+
+    p = x * y * z
+    q = x**2 - z**2
+    h = x + y + z
+    dixon = DixonResultant([p, q, h], [x, y])
+    dixon_poly = dixon.get_dixon_polynomial()
+    dixon_matrix = dixon.get_dixon_matrix(dixon_poly)
+    D = dixon.get_KSY_Dixon_resultant(dixon_matrix)
+
+    assert D == -z**3
+
+def test_get_KSY_Dixon_resultant_example_two():
+    """Tests the KSY Dixon resultant for example two"""
+    x, y, A = symbols('x, y, A')
+
+    p = x * y + x * A + x - A**2 - A + y**2 + y
+    q = x**2 + x * A - x + x * y + y * A - y
+    h = x**2 + x * y + 2 * x - x * A - y * A - 2 * A
+
+    dixon = DixonResultant([p, q, h], [x, y])
+    dixon_poly = dixon.get_dixon_polynomial()
+    dixon_matrix = dixon.get_dixon_matrix(dixon_poly)
+    D = factor(dixon.get_KSY_Dixon_resultant(dixon_matrix))
+
+    assert D == -8*A*(A - 1)*(A + 2)*(2*A - 1)**2
+
+def test_macaulay_resultant_init():
+    """Test init method of MacaulayResultant."""
+
+    assert macaulay.polynomials == [p, q]
+    assert macaulay.variables == [x, y]
+    assert macaulay.n == 2
+    assert macaulay.degrees == [1, 1]
+    assert macaulay.degree_m == 1
+    assert macaulay.monomials_size == 2
+
+def test_get_degree_m():
+    assert macaulay._get_degree_m() == 1
+
+def test_get_size():
+    assert macaulay.get_size() == 2
+
+def test_macaulay_example_one():
+    """Tests the Macaulay for example from [Bruce97]_"""
+
+    x, y, z = symbols('x, y, z')
+    a_1_1, a_1_2, a_1_3 = symbols('a_1_1, a_1_2, a_1_3')
+    a_2_2, a_2_3, a_3_3 = symbols('a_2_2, a_2_3, a_3_3')
+    b_1_1, b_1_2, b_1_3 = symbols('b_1_1, b_1_2, b_1_3')
+    b_2_2, b_2_3, b_3_3 = symbols('b_2_2, b_2_3, b_3_3')
+    c_1, c_2, c_3 = symbols('c_1, c_2, c_3')
+
+    f_1 = a_1_1 * x ** 2 + a_1_2 * x * y + a_1_3 * x * z + \
+          a_2_2 * y ** 2 + a_2_3 * y * z + a_3_3 * z ** 2
+    f_2 = b_1_1 * x ** 2 + b_1_2 * x * y + b_1_3 * x * z + \
+          b_2_2 * y ** 2 + b_2_3 * y * z + b_3_3 * z ** 2
+    f_3 = c_1 * x + c_2 * y + c_3 * z
+
+    mac = MacaulayResultant([f_1, f_2, f_3], [x, y, z])
+
+    assert mac.degrees == [2, 2, 1]
+    assert mac.degree_m == 3
+
+    assert mac.monomial_set == [x ** 3, x ** 2 * y, x ** 2 * z,
+                                x * y ** 2,
+                                x * y * z, x * z ** 2, y ** 3,
+                                y ** 2 *z, y * z ** 2, z ** 3]
+    assert mac.monomials_size == 10
+    assert mac.get_row_coefficients() == [[x, y, z], [x, y, z],
+                                          [x * y, x * z, y * z, z ** 2]]
+
+    matrix = mac.get_matrix()
+    assert matrix.shape == (mac.monomials_size, mac.monomials_size)
+    assert mac.get_submatrix(matrix) == Matrix([[a_1_1, a_2_2],
+                                                [b_1_1, b_2_2]])
+
+def test_macaulay_example_two():
+    """Tests the Macaulay formulation for example from [Stiller96]_."""
+
+    x, y, z = symbols('x, y, z')
+    a_0, a_1, a_2 = symbols('a_0, a_1, a_2')
+    b_0, b_1, b_2 = symbols('b_0, b_1, b_2')
+    c_0, c_1, c_2, c_3, c_4 = symbols('c_0, c_1, c_2, c_3, c_4')
+
+    f = a_0 * y -  a_1 * x + a_2 * z
+    g = b_1 * x ** 2 + b_0 * y ** 2 - b_2 * z ** 2
+    h = c_0 * y - c_1 * x ** 3 + c_2 * x ** 2 * z - c_3 * x * z ** 2 + \
+        c_4 * z ** 3
+
+    mac = MacaulayResultant([f, g, h], [x, y, z])
+
+    assert mac.degrees == [1, 2, 3]
+    assert mac.degree_m == 4
+    assert mac.monomials_size == 15
+    assert len(mac.get_row_coefficients()) == mac.n
+
+    matrix = mac.get_matrix()
+    assert matrix.shape == (mac.monomials_size, mac.monomials_size)
+    assert mac.get_submatrix(matrix) == Matrix([[-a_1, a_0, a_2, 0],
+                                                [0, -a_1, 0, 0],
+                                                [0, 0, -a_1, 0],
+                                                [0, 0, 0, -a_1]])
diff --git a/shared/package-files/0c/0c9f1d9147f6584fbd491876be5e7d86b4e201c1167040bb939d2d3e92c1595a/batch_norm_replacement.py b/shared/package-files/0c/0c9f1d9147f6584fbd491876be5e7d86b4e201c1167040bb939d2d3e92c1595a/batch_norm_replacement.py
new file mode 100644
index 0000000000000000000000000000000000000000..29715581837e19fab7c2ab511abf289ec35ef662
--- /dev/null
+++ b/shared/package-files/0c/0c9f1d9147f6584fbd491876be5e7d86b4e201c1167040bb939d2d3e92c1595a/batch_norm_replacement.py
@@ -0,0 +1,24 @@
+import torch.nn as nn
+from torch._functorch.utils import exposed_in
+
+
+def batch_norm_without_running_stats(module: nn.Module):
+    if isinstance(module, nn.modules.batchnorm._BatchNorm) and module.track_running_stats:
+        module.running_mean = None
+        module.running_var = None
+        module.num_batches_tracked = None
+        module.track_running_stats = False
+
+
+@exposed_in("torch.func")
+def replace_all_batch_norm_modules_(root: nn.Module) -> nn.Module:
+    """
+    In place updates :attr:`root` by setting the ``running_mean`` and ``running_var`` to be None and
+    setting track_running_stats to be False for any nn.BatchNorm module in :attr:`root`
+    """
+    # base case
+    batch_norm_without_running_stats(root)
+
+    for obj in root.modules():
+        batch_norm_without_running_stats(obj)
+    return root
diff --git a/shared/package-files/0c/0ca62aeb75257a573dc2f72749b72a650080bc28a08f281e14b768f1e3f3a9e6/embedding_expanded_weights.py b/shared/package-files/0c/0ca62aeb75257a573dc2f72749b72a650080bc28a08f281e14b768f1e3f3a9e6/embedding_expanded_weights.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7956a3a1b1f666708eefbec69d031af2da18592
--- /dev/null
+++ b/shared/package-files/0c/0ca62aeb75257a573dc2f72749b72a650080bc28a08f281e14b768f1e3f3a9e6/embedding_expanded_weights.py
@@ -0,0 +1,54 @@
+import torch
+import torch.nn.functional as F
+from .expanded_weights_impl import implements_per_sample_grads
+from .expanded_weights_utils import standard_kwargs, forward_helper, set_grad_sample_if_exists
+
+from typing import List, Optional
+
+@implements_per_sample_grads(F.embedding)
+class EmbeddingPerSampleGrad(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, kwarg_names, _, *expanded_args_and_kwargs):
+        expanded_args, expanded_kwargs = standard_kwargs(kwarg_names, expanded_args_and_kwargs)
+        if len(expanded_args[0].shape) == 1:
+            raise RuntimeError(f"Expanded Weights needs an input with a batch size, got a 1D tensor, {expanded_args[0]}")
+        output = forward_helper(F.embedding, expanded_args, expanded_kwargs)
+        ctx.input, ctx.weight = expanded_args
+        ctx.padding_idx, ctx.scale_grad_by_freq = expanded_kwargs['padding_idx'], expanded_kwargs['scale_grad_by_freq']
+        ctx.sparse = expanded_kwargs['sparse']
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, weight = ctx.input, ctx.weight
+        padding_idx, scale_grad_by_freq, sparse = ctx.padding_idx, ctx.scale_grad_by_freq, ctx.sparse
+
+        def weight_per_sample_grad(weight):
+            batch_size = input.shape[0]
+            embedding_dim = weight.shape[1]
+            index = (
+                input.unsqueeze(-1)
+                .expand(*input.shape, embedding_dim)
+                .reshape(batch_size, -1, embedding_dim)
+            )
+            grad_sample = torch.zeros(
+                batch_size, *weight.shape, device=weight.device, dtype=grad_output.dtype
+            )
+            return grad_sample.scatter_add_(1, index, grad_output.reshape(batch_size, -1, embedding_dim))
+
+        results: List[Optional[torch.Tensor]] = []
+        results.append(None)  # for kwarg names
+        results.append(None)  # for op reference
+
+        if input.requires_grad:
+            bw_fn = torch.ops.aten.embedding_backward
+            results.append(bw_fn(grad_output, input, weight.shape[0], padding_idx, scale_grad_by_freq, sparse))
+        else:
+            results.append(None)
+
+        # weight doesn't compute batched gradients; no other arguments are differentiable (2 not saved from forward)
+        results = results + [None] * 6
+
+        # set grad_sample field for weight with per sample gradients
+        set_grad_sample_if_exists(weight, weight_per_sample_grad)
+        return tuple(results)
diff --git a/shared/package-files/0c/0ca6643919572d533da962fa993ecce4d2fd6bafc3f2d2959ff79726019ab0c1/test_compiler_flags.py b/shared/package-files/0c/0ca6643919572d533da962fa993ecce4d2fd6bafc3f2d2959ff79726019ab0c1/test_compiler_flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe31e5452070c84de426caa2da6b8d4dd9285675
--- /dev/null
+++ b/shared/package-files/0c/0ca6643919572d533da962fa993ecce4d2fd6bafc3f2d2959ff79726019ab0c1/test_compiler_flags.py
@@ -0,0 +1,119 @@
+import re
+
+from numba import njit
+from numba.core.extending import overload
+from numba.core.targetconfig import ConfigStack
+from numba.core.compiler import Flags, DEFAULT_FLAGS
+from numba.core import types
+from numba.core.funcdesc import default_mangler
+
+from numba.tests.support import TestCase, unittest
+
+
+class TestCompilerFlags(TestCase):
+    def test_setting_invalid_attribute(self):
+        flags = Flags()
+        msg = "'Flags' object has no attribute 'this_really_does_not_exist'"
+        with self.assertRaisesRegex(AttributeError, msg):
+            flags.this_really_does_not_exist = True
+
+
+class TestCompilerFlagCachedOverload(TestCase):
+    def test_fastmath_in_overload(self):
+        def fastmath_status():
+            pass
+
+        @overload(fastmath_status)
+        def ov_fastmath_status():
+            flags = ConfigStack().top()
+            val = "Has fastmath" if flags.fastmath else "No fastmath"
+
+            def codegen():
+                return val
+
+            return codegen
+
+        @njit(fastmath=True)
+        def set_fastmath():
+            return fastmath_status()
+
+        @njit()
+        def foo():
+            a = fastmath_status()
+            b = set_fastmath()
+            return (a, b)
+
+        a, b = foo()
+        self.assertEqual(a, "No fastmath")
+        self.assertEqual(b, "Has fastmath")
+
+
+class TestFlagMangling(TestCase):
+
+    def test_demangle(self):
+
+        def check(flags):
+            mangled = flags.get_mangle_string()
+            out = flags.demangle(mangled)
+            # Demangle result MUST match summary()
+            self.assertEqual(out, flags.summary())
+
+        # test empty flags
+        flags = Flags()
+        check(flags)
+
+        # test default
+        check(DEFAULT_FLAGS)
+
+        # test other
+        flags = Flags()
+        flags.no_cpython_wrapper = True
+        flags.nrt = True
+        flags.fastmath = True
+        check(flags)
+
+    def test_mangled_flags_is_shorter(self):
+        # at least for these control cases
+        flags = Flags()
+        flags.nrt = True
+        flags.auto_parallel = True
+        self.assertLess(len(flags.get_mangle_string()), len(flags.summary()))
+
+    def test_mangled_flags_with_fastmath_parfors_inline(self):
+        # at least for these control cases
+        flags = Flags()
+        flags.nrt = True
+        flags.auto_parallel = True
+        flags.fastmath = True
+        flags.inline = "always"
+        self.assertLess(len(flags.get_mangle_string()), len(flags.summary()))
+        demangled = flags.demangle(flags.get_mangle_string())
+        # There should be no pointer value in the demangled string.
+        self.assertNotIn("0x", demangled)
+
+    def test_demangling_from_mangled_symbols(self):
+        """Test demangling of flags from mangled symbol"""
+        # Use default mangler to mangle the string
+        fname = 'foo'
+        argtypes = types.int32,
+        flags = Flags()
+        flags.nrt = True
+        flags.inline = "always"
+        name = default_mangler(
+            fname, argtypes, abi_tags=[flags.get_mangle_string()],
+        )
+        # Find the ABI-tag. Starts with "B"
+        prefix = "_Z3fooB"
+        # Find the length of the ABI-tag
+        m = re.match("[0-9]+", name[len(prefix):])
+        size = m.group(0)
+        # Extract the ABI tag
+        base = len(prefix) + len(size)
+        abi_mangled = name[base:base + int(size)]
+        # Demangle and check
+        demangled = Flags.demangle(abi_mangled)
+        self.assertEqual(demangled, flags.summary())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/shared/package-files/0c/0ca8b0b86cd9dede165795b1650bfc102fc4ef7845ccd3d5a02d5294a5acec0a/_validate_sparse_csr_tensor_args.h b/shared/package-files/0c/0ca8b0b86cd9dede165795b1650bfc102fc4ef7845ccd3d5a02d5294a5acec0a/_validate_sparse_csr_tensor_args.h
new file mode 100644
index 0000000000000000000000000000000000000000..34f28baa7dc79f1d66c7c9d7a8a0afd2bd0f8271
--- /dev/null
+++ b/shared/package-files/0c/0ca8b0b86cd9dede165795b1650bfc102fc4ef7845ccd3d5a02d5294a5acec0a/_validate_sparse_csr_tensor_args.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_validate_sparse_csr_tensor_args_ops.h>
+
+namespace at {
+
+
+// aten::_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()
+inline void _validate_sparse_csr_tensor_args(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) {
+    return at::_ops::_validate_sparse_csr_tensor_args::call(crow_indices, col_indices, values, size);
+}
+
+}
diff --git a/shared/package-files/0c/0ca93ed5bfab66c245c458b526827b4e612f204434adff82f470195b0be3b8cd/__init__.py b/shared/package-files/0c/0ca93ed5bfab66c245c458b526827b4e612f204434adff82f470195b0be3b8cd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a124bf6486bf3d8e9e8221738e3dcef97b14ece
--- /dev/null
+++ b/shared/package-files/0c/0ca93ed5bfab66c245c458b526827b4e612f204434adff82f470195b0be3b8cd/__init__.py
@@ -0,0 +1 @@
+__version__ = "2.2.0"
diff --git a/shared/package-files/0c/0cab5a123f1f43ddb26c84d3594e019b5eb44bda732665156e36964677a7c54e/Santo_Domingo b/shared/package-files/0c/0cab5a123f1f43ddb26c84d3594e019b5eb44bda732665156e36964677a7c54e/Santo_Domingo
new file mode 100644
index 0000000000000000000000000000000000000000..4fe36fd4c11f998ba3f626c5e23d97bd82d12791
Binary files /dev/null and b/shared/package-files/0c/0cab5a123f1f43ddb26c84d3594e019b5eb44bda732665156e36964677a7c54e/Santo_Domingo differ
diff --git a/shared/package-files/0c/0cab8487657622424ebbe067bab5af6b1f0cab911c407b46f8afb39dc199469a/test_deprecated_kwargs.py b/shared/package-files/0c/0cab8487657622424ebbe067bab5af6b1f0cab911c407b46f8afb39dc199469a/test_deprecated_kwargs.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc88fc3ba18263ac78f7057bfb5950f0420646b4
--- /dev/null
+++ b/shared/package-files/0c/0cab8487657622424ebbe067bab5af6b1f0cab911c407b46f8afb39dc199469a/test_deprecated_kwargs.py
@@ -0,0 +1,21 @@
+"""
+Tests for the deprecated keyword arguments for `read_json`.
+"""
+from io import StringIO
+
+import pandas as pd
+import pandas._testing as tm
+
+from pandas.io.json import read_json
+
+
+def test_good_kwargs():
+    df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
+
+    with tm.assert_produces_warning(None):
+        data1 = StringIO(df.to_json(orient="split"))
+        tm.assert_frame_equal(df, read_json(data1, orient="split"))
+        data2 = StringIO(df.to_json(orient="columns"))
+        tm.assert_frame_equal(df, read_json(data2, orient="columns"))
+        data3 = StringIO(df.to_json(orient="index"))
+        tm.assert_frame_equal(df, read_json(data3, orient="index"))
diff --git a/shared/package-files/0c/0cb491f3cf55dde1f10d3c930e01588c115b633c90e1efe0efc2bc856dd51ea5/deg2rad_compositeexplicitautograd_dispatch.h b/shared/package-files/0c/0cb491f3cf55dde1f10d3c930e01588c115b633c90e1efe0efc2bc856dd51ea5/deg2rad_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..26458e6b05ed233ed19076ce3b5f7909070903a7
--- /dev/null
+++ b/shared/package-files/0c/0cb491f3cf55dde1f10d3c930e01588c115b633c90e1efe0efc2bc856dd51ea5/deg2rad_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor deg2rad(const at::Tensor & self);
+TORCH_API at::Tensor & deg2rad_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & deg2rad_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & deg2rad_(at::Tensor & self);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/0c/0cb97368024272a4027d632861ffb0a05a851f3193f4e5ad765260ac3b2ca32b/logaddexp_meta_dispatch.h b/shared/package-files/0c/0cb97368024272a4027d632861ffb0a05a851f3193f4e5ad765260ac3b2ca32b/logaddexp_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..085c5c9f76013ae71668447cb7834c8a691f1832
--- /dev/null
+++ b/shared/package-files/0c/0cb97368024272a4027d632861ffb0a05a851f3193f4e5ad765260ac3b2ca32b/logaddexp_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor logaddexp(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & logaddexp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & logaddexp_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/0c/0cba3dec9870c8627a77f76e92fa8db7376eac0749aefe58c7dd5867333fb633/_augment.py b/shared/package-files/0c/0cba3dec9870c8627a77f76e92fa8db7376eac0749aefe58c7dd5867333fb633/_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc645d6c8a8a49ec159e872f1a6e293fb5414a62
--- /dev/null
+++ b/shared/package-files/0c/0cba3dec9870c8627a77f76e92fa8db7376eac0749aefe58c7dd5867333fb633/_augment.py
@@ -0,0 +1,355 @@
+import math
+import numbers
+import warnings
+from typing import Any, Callable, Dict, List, Sequence, Tuple, Union
+
+import PIL.Image
+import torch
+from torch.nn.functional import one_hot
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torchvision import transforms as _transforms, tv_tensors
+from torchvision.transforms.v2 import functional as F
+
+from ._transform import _RandomApplyTransform, Transform
+from ._utils import _check_sequence_input, _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size
+
+
+class RandomErasing(_RandomApplyTransform):
+    """Randomly select a rectangle region in the input image or video and erase its pixels.
+
+    This transform does not support PIL Image.
+    'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
+
+    Args:
+        p (float, optional): probability that the random erasing operation will be performed.
+        scale (tuple of float, optional): range of proportion of erased area against input image.
+        ratio (tuple of float, optional): range of aspect ratio of erased area.
+        value (number or tuple of numbers): erasing value. Default is 0. If a single int, it is used to
+            erase all pixels. If a tuple of length 3, it is used to erase
+            R, G, B channels respectively.
+            If a str of 'random', erasing each pixel with random values.
+        inplace (bool, optional): boolean to make this transform inplace. Default set to False.
+
+    Returns:
+        Erased input.
+
+    Example:
+        >>> from torchvision.transforms import v2 as transforms
+        >>>
+        >>> transform = transforms.Compose([
+        >>>   transforms.RandomHorizontalFlip(),
+        >>>   transforms.PILToTensor(),
+        >>>   transforms.ConvertImageDtype(torch.float),
+        >>>   transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        >>>   transforms.RandomErasing(),
+        >>> ])
+    """
+
+    _v1_transform_cls = _transforms.RandomErasing
+
+    def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+        return dict(
+            super()._extract_params_for_v1_transform(),
+            value="random" if self.value is None else self.value,
+        )
+
+    def __init__(
+        self,
+        p: float = 0.5,
+        scale: Tuple[float, float] = (0.02, 0.33),
+        ratio: Tuple[float, float] = (0.3, 3.3),
+        value: float = 0.0,
+        inplace: bool = False,
+    ):
+        super().__init__(p=p)
+        if not isinstance(value, (numbers.Number, str, tuple, list)):
+            raise TypeError("Argument value should be either a number or str or a sequence")
+        if isinstance(value, str) and value != "random":
+            raise ValueError("If value is str, it should be 'random'")
+        if not isinstance(scale, (tuple, list)):
+            raise TypeError("Scale should be a sequence")
+        if not isinstance(ratio, (tuple, list)):
+            raise TypeError("Ratio should be a sequence")
+        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+            warnings.warn("Scale and ratio should be of kind (min, max)")
+        if scale[0] < 0 or scale[1] > 1:
+            raise ValueError("Scale should be between 0 and 1")
+        self.scale = scale
+        self.ratio = ratio
+        if isinstance(value, (int, float)):
+            self.value = [float(value)]
+        elif isinstance(value, str):
+            self.value = None
+        elif isinstance(value, (list, tuple)):
+            self.value = [float(v) for v in value]
+        else:
+            self.value = value
+        self.inplace = inplace
+
+        self._log_ratio = torch.log(torch.tensor(self.ratio))
+
+    def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any:
+        if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)):
+            warnings.warn(
+                f"{type(self).__name__}() is currently passing through inputs of type "
+                f"tv_tensors.{type(inpt).__name__}. This will likely change in the future."
+            )
+        return super()._call_kernel(functional, inpt, *args, **kwargs)
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        img_c, img_h, img_w = query_chw(flat_inputs)
+
+        if self.value is not None and not (len(self.value) in (1, img_c)):
+            raise ValueError(
+                f"If value is a sequence, it should have either a single value or {img_c} (number of inpt channels)"
+            )
+
+        area = img_h * img_w
+
+        log_ratio = self._log_ratio
+        for _ in range(10):
+            erase_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
+            aspect_ratio = torch.exp(
+                torch.empty(1).uniform_(
+                    log_ratio[0],  # type: ignore[arg-type]
+                    log_ratio[1],  # type: ignore[arg-type]
+                )
+            ).item()
+
+            h = int(round(math.sqrt(erase_area * aspect_ratio)))
+            w = int(round(math.sqrt(erase_area / aspect_ratio)))
+            if not (h < img_h and w < img_w):
+                continue
+
+            if self.value is None:
+                v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
+            else:
+                v = torch.tensor(self.value)[:, None, None]
+
+            i = torch.randint(0, img_h - h + 1, size=(1,)).item()
+            j = torch.randint(0, img_w - w + 1, size=(1,)).item()
+            break
+        else:
+            i, j, h, w, v = 0, 0, img_h, img_w, None
+
+        return dict(i=i, j=j, h=h, w=w, v=v)
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        if params["v"] is not None:
+            inpt = self._call_kernel(F.erase, inpt, **params, inplace=self.inplace)
+
+        return inpt
+
+
+class _BaseMixUpCutMix(Transform):
+    def __init__(self, *, alpha: float = 1.0, num_classes: int, labels_getter="default") -> None:
+        super().__init__()
+        self.alpha = float(alpha)
+        self._dist = torch.distributions.Beta(torch.tensor([alpha]), torch.tensor([alpha]))
+
+        self.num_classes = num_classes
+
+        self._labels_getter = _parse_labels_getter(labels_getter)
+
+    def forward(self, *inputs):
+        inputs = inputs if len(inputs) > 1 else inputs[0]
+        flat_inputs, spec = tree_flatten(inputs)
+        needs_transform_list = self._needs_transform_list(flat_inputs)
+
+        if has_any(flat_inputs, PIL.Image.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask):
+            raise ValueError(f"{type(self).__name__}() does not support PIL images, bounding boxes and masks.")
+
+        labels = self._labels_getter(inputs)
+        if not isinstance(labels, torch.Tensor):
+            raise ValueError(f"The labels must be a tensor, but got {type(labels)} instead.")
+        elif labels.ndim != 1:
+            raise ValueError(
+                f"labels tensor should be of shape (batch_size,) " f"but got shape {labels.shape} instead."
+            )
+
+        params = {
+            "labels": labels,
+            "batch_size": labels.shape[0],
+            **self._get_params(
+                [inpt for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) if needs_transform]
+            ),
+        }
+
+        # By default, the labels will be False inside needs_transform_list, since they are a torch.Tensor coming
+        # after an image or video. However, we need to handle them in _transform, so we make sure to set them to True
+        needs_transform_list[next(idx for idx, inpt in enumerate(flat_inputs) if inpt is labels)] = True
+        flat_outputs = [
+            self._transform(inpt, params) if needs_transform else inpt
+            for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list)
+        ]
+
+        return tree_unflatten(flat_outputs, spec)
+
+    def _check_image_or_video(self, inpt: torch.Tensor, *, batch_size: int):
+        expected_num_dims = 5 if isinstance(inpt, tv_tensors.Video) else 4
+        if inpt.ndim != expected_num_dims:
+            raise ValueError(
+                f"Expected a batched input with {expected_num_dims} dims, but got {inpt.ndim} dimensions instead."
+            )
+        if inpt.shape[0] != batch_size:
+            raise ValueError(
+                f"The batch size of the image or video does not match the batch size of the labels: "
+                f"{inpt.shape[0]} != {batch_size}."
+            )
+
+    def _mixup_label(self, label: torch.Tensor, *, lam: float) -> torch.Tensor:
+        label = one_hot(label, num_classes=self.num_classes)
+        if not label.dtype.is_floating_point:
+            label = label.float()
+        return label.roll(1, 0).mul_(1.0 - lam).add_(label.mul(lam))
+
+
+class MixUp(_BaseMixUpCutMix):
+    """Apply MixUp to the provided batch of images and labels.
+
+    Paper: `mixup: Beyond Empirical Risk Minimization <https://arxiv.org/abs/1710.09412>`_.
+
+    .. note::
+        This transform is meant to be used on **batches** of samples, not
+        individual images. See
+        :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage
+        examples.
+        The sample pairing is deterministic and done by matching consecutive
+        samples in the batch, so the batch needs to be shuffled (this is an
+        implementation detail, not a guaranteed convention.)
+
+    In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed
+    into a tensor of shape ``(batch_size, num_classes)``.
+
+    Args:
+        alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
+        num_classes (int): number of classes in the batch. Used for one-hot-encoding.
+        labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
+            common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``.
+            It can also be a callable that takes the same input as the transform, and returns the labels.
+    """
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        return dict(lam=float(self._dist.sample(())))  # type: ignore[arg-type]
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        lam = params["lam"]
+
+        if inpt is params["labels"]:
+            return self._mixup_label(inpt, lam=lam)
+        elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt):
+            self._check_image_or_video(inpt, batch_size=params["batch_size"])
+
+            output = inpt.roll(1, 0).mul_(1.0 - lam).add_(inpt.mul(lam))
+
+            if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)):
+                output = tv_tensors.wrap(output, like=inpt)
+
+            return output
+        else:
+            return inpt
+
+
+class CutMix(_BaseMixUpCutMix):
+    """Apply CutMix to the provided batch of images and labels.
+
+    Paper: `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
+    <https://arxiv.org/abs/1905.04899>`_.
+
+    .. note::
+        This transform is meant to be used on **batches** of samples, not
+        individual images. See
+        :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage
+        examples.
+        The sample pairing is deterministic and done by matching consecutive
+        samples in the batch, so the batch needs to be shuffled (this is an
+        implementation detail, not a guaranteed convention.)
+
+    In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed
+    into a tensor of shape ``(batch_size, num_classes)``.
+
+    Args:
+        alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
+        num_classes (int): number of classes in the batch. Used for one-hot-encoding.
+        labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
+            common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``.
+            It can also be a callable that takes the same input as the transform, and returns the labels.
+    """
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        lam = float(self._dist.sample(()))  # type: ignore[arg-type]
+
+        H, W = query_size(flat_inputs)
+
+        r_x = torch.randint(W, size=(1,))
+        r_y = torch.randint(H, size=(1,))
+
+        r = 0.5 * math.sqrt(1.0 - lam)
+        r_w_half = int(r * W)
+        r_h_half = int(r * H)
+
+        x1 = int(torch.clamp(r_x - r_w_half, min=0))
+        y1 = int(torch.clamp(r_y - r_h_half, min=0))
+        x2 = int(torch.clamp(r_x + r_w_half, max=W))
+        y2 = int(torch.clamp(r_y + r_h_half, max=H))
+        box = (x1, y1, x2, y2)
+
+        lam_adjusted = float(1.0 - (x2 - x1) * (y2 - y1) / (W * H))
+
+        return dict(box=box, lam_adjusted=lam_adjusted)
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        if inpt is params["labels"]:
+            return self._mixup_label(inpt, lam=params["lam_adjusted"])
+        elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt):
+            self._check_image_or_video(inpt, batch_size=params["batch_size"])
+
+            x1, y1, x2, y2 = params["box"]
+            rolled = inpt.roll(1, 0)
+            output = inpt.clone()
+            output[..., y1:y2, x1:x2] = rolled[..., y1:y2, x1:x2]
+
+            if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)):
+                output = tv_tensors.wrap(output, like=inpt)
+
+            return output
+        else:
+            return inpt
+
+
+class JPEG(Transform):
+    """Apply JPEG compression and decompression to the given images.
+
+    If the input is a :class:`torch.Tensor`, it is expected
+    to be of dtype uint8, on CPU, and have [..., 3 or 1, H, W] shape,
+    where ... means an arbitrary number of leading dimensions.
+
+    Args:
+        quality (sequence or number): JPEG quality, from 1 to 100. Lower means more compression.
+            If quality is a sequence like (min, max), it specifies the range of JPEG quality to
+            randomly select from (inclusive of both ends).
+
+    Returns:
+        image with JPEG compression.
+    """
+
+    def __init__(self, quality: Union[int, Sequence[int]]):
+        super().__init__()
+        if isinstance(quality, int):
+            quality = [quality, quality]
+        else:
+            _check_sequence_input(quality, "quality", req_sizes=(2,))
+
+        if not (1 <= quality[0] <= quality[1] <= 100 and isinstance(quality[0], int) and isinstance(quality[1], int)):
+            raise ValueError(f"quality must be an integer from 1 to 100, got {quality =}")
+
+        self.quality = quality
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        quality = torch.randint(self.quality[0], self.quality[1] + 1, ()).item()
+        return dict(quality=quality)
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return self._call_kernel(F.jpeg, inpt, quality=params["quality"])
diff --git a/shared/package-files/0c/0cbb05e9b413bf862d5cfa25f118cca8ffc61cb645edea22e044e571c56b5a09/darwin-256x96 b/shared/package-files/0c/0cbb05e9b413bf862d5cfa25f118cca8ffc61cb645edea22e044e571c56b5a09/darwin-256x96
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diff --git a/shared/package-files/0c/0cbb05e9b413bf862d5cfa25f118cca8ffc61cb645edea22e044e571c56b5a09/xnuppc-256x96 b/shared/package-files/0c/0cbb05e9b413bf862d5cfa25f118cca8ffc61cb645edea22e044e571c56b5a09/xnuppc-256x96
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diff --git a/shared/package-files/0c/0cbcdd5870fab6d5b84b4344bc641bb07fd41098e167286f3b8397263a32bd94/literal.py b/shared/package-files/0c/0cbcdd5870fab6d5b84b4344bc641bb07fd41098e167286f3b8397263a32bd94/literal.py
new file mode 100644
index 0000000000000000000000000000000000000000..d06431eed4da31ada71eeb3947f6b238e7b2fb74
--- /dev/null
+++ b/shared/package-files/0c/0cbcdd5870fab6d5b84b4344bc641bb07fd41098e167286f3b8397263a32bd94/literal.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from functools import partial
+from collections.abc import Callable
+
+import pytest  # type: ignore
+import numpy as np
+
+AR = np.array(0)
+AR.setflags(write=False)
+
+KACF = frozenset({None, "K", "A", "C", "F"})
+ACF = frozenset({None, "A", "C", "F"})
+CF = frozenset({None, "C", "F"})
+
+order_list: list[tuple[frozenset, Callable]] = [
+    (KACF, partial(np.ndarray, 1)),
+    (KACF, AR.tobytes),
+    (KACF, partial(AR.astype, int)),
+    (KACF, AR.copy),
+    (ACF, partial(AR.reshape, 1)),
+    (KACF, AR.flatten),
+    (KACF, AR.ravel),
+    (KACF, partial(np.array, 1)),
+    (CF, partial(np.zeros, 1)),
+    (CF, partial(np.ones, 1)),
+    (CF, partial(np.empty, 1)),
+    (CF, partial(np.full, 1, 1)),
+    (KACF, partial(np.zeros_like, AR)),
+    (KACF, partial(np.ones_like, AR)),
+    (KACF, partial(np.empty_like, AR)),
+    (KACF, partial(np.full_like, AR, 1)),
+    (KACF, partial(np.add, 1, 1)),  # i.e. np.ufunc.__call__
+    (ACF, partial(np.reshape, AR, 1)),
+    (KACF, partial(np.ravel, AR)),
+    (KACF, partial(np.asarray, 1)),
+    (KACF, partial(np.asanyarray, 1)),
+]
+
+for order_set, func in order_list:
+    for order in order_set:
+        func(order=order)
+
+    invalid_orders = KACF - order_set
+    for order in invalid_orders:
+        with pytest.raises(ValueError):
+            func(order=order)
diff --git a/shared/package-files/0c/0cbf3dd574865acd2f1701820529456f1722dd1941e16bdff58986efb2f1a9c5/_smoothers_lowess.pyx b/shared/package-files/0c/0cbf3dd574865acd2f1701820529456f1722dd1941e16bdff58986efb2f1a9c5/_smoothers_lowess.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..ae8ba3f84a9e8f1f98d9c434ed51efe42595b7b0
--- /dev/null
+++ b/shared/package-files/0c/0cbf3dd574865acd2f1701820529456f1722dd1941e16bdff58986efb2f1a9c5/_smoothers_lowess.pyx
@@ -0,0 +1,676 @@
+#cython: boundscheck=False, wraparound=False, cdivision=True, language_level=3
+"""
+Univariate lowess function, like in R.
+
+References
+----------
+Hastie, Tibshirani, Friedman. (2009) The Elements of Statistical Learning: Data
+Mining, Inference, and Prediction, Second Edition: Chapter 6.
+
+Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing
+Scatterplots". Journal of the American Statistical Association 74 (368): 829-836.
+"""
+
+cimport cython
+import numpy as np
+cimport numpy as np
+from libc.math cimport NAN, fabs, fmax
+
+DTYPE = np.double
+ctypedef np.double_t DTYPE_t
+
+np.import_array()
+
+def lowess(np.ndarray[DTYPE_t, ndim = 1] endog,
+           np.ndarray[DTYPE_t, ndim = 1] exog,
+           np.ndarray[DTYPE_t, ndim = 1] xvals,
+           np.ndarray[DTYPE_t, ndim = 1] resid_weights,
+           double frac = 2.0 / 3.0,
+           Py_ssize_t it = 3,
+           double delta = 0.0,
+           bint given_xvals = 0):
+    """
+    lowess(endog, exog, xvals, resid_weights, frac=2/3, it=3, delta=0, given_xvals=False)
+
+    LOWESS (Locally Weighted Scatterplot Smoothing)
+
+    A lowess function that outs smoothed estimates of endog
+    at the given exog values from points (exog, endog)
+
+    Parameters
+    ----------
+    endog : 1-D numpy array
+        The y-values of the observed points
+    exog : 1-D numpy array
+        The x-values of the observed points. exog has to be increasing.
+    xvals : ndarray, 1d
+        The x-values of the points at which to perform the regression
+    resid_weights : 1-D numpy array
+        The initial weightings of the observed points based off residuals
+    frac : float
+        Between 0 and 1. The fraction of the data used
+        when estimating each y-value.
+    it : int
+        The number of residual-based reweightings
+        to perform.
+    delta : float
+        Distance within which to use linear-interpolation
+        instead of weighted regression.
+    given_xvals : bool
+        Whether xvals was provided as a an argument or whether
+        we are just using xvals = exog
+
+    Returns
+    -------
+    out : numpy array
+        A numpy array with two columns. The first column
+        is the sorted x values and the second column the
+        associated estimated y-values.
+    resid_weights: ndarray
+        A numpy array containing the final residual weightings
+        of the data points
+
+    Notes
+    -----
+    This lowess function implements the algorithm given in the
+    reference below using local linear estimates.
+
+    Suppose the input data has N points. The algorithm works by
+    estimating the `smooth` y_i by taking the frac*N closest points
+    to (x_i,y_i) based on their x values and estimating y_i
+    using a weighted linear regression. The weight for (x_j,y_j)
+    is tricube function applied to |x_i-x_j|.
+
+    If it > 1, then further weighted local linear regressions
+    are performed, where the weights are the same as above
+    times the _lowess_bisquare function of the residuals. Each iteration
+    takes approximately the same amount of time as the original fit,
+    so these iterations are expensive. They are most useful when
+    the noise has extremely heavy tails, such as Cauchy noise.
+    Noise with less heavy-tails, such as t-distributions with df>2,
+    are less problematic. The weights downgrade the influence of
+    points with large residuals. In the extreme case, points whose
+    residuals are larger than 6 times the median absolute residual
+    are given weight 0.
+
+    delta can be used to save computations. For each x_i, regressions
+    are skipped for points closer than delta. The next regression is
+    fit for the farthest point within delta of x_i and all points in
+    between are estimated by linearly interpolating between the two
+    regression fits.
+
+    Judicious choice of delta can cut computation time considerably
+    for large data (N > 5000). A good choice is delta = 0.01 *
+    range(exog).
+
+    Some experimentation is likely required to find a good
+    choice of frac and iter for a particular dataset.
+
+    References
+    ----------
+    Cleveland, W.S. (1979) "Robust Locally Weighted Regression
+    and Smoothing Scatterplots". Journal of the American Statistical
+    Association 74 (368): 829-836.
+
+    Examples
+    --------
+    The below allows a comparison between how different the fits from
+    lowess for different values of frac can be.
+
+    >>> import numpy as np
+    >>> import statsmodels.api as sm
+    >>> lowess = sm.nonparametric.lowess
+    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
+    >>> y = np.sin(x) + np.random.normal(size=len(x))
+    >>> z = lowess(y, x)
+    >>> w = lowess(y, x, frac=1./3)
+
+    This gives a similar comparison for when it is 0 vs not.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import statsmodels.api as sm
+    >>> lowess = sm.nonparametric.lowess
+    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
+    >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x))
+    >>> z = lowess(y, x, frac= 1./3, it=0)
+    >>> w = lowess(y, x, frac=1./3)
+
+    """
+    cdef:
+        Py_ssize_t j
+        int k
+        Py_ssize_t robiter, i, left_end, right_end
+        np.npy_intp n, out_n, last_fit_i
+        np.ndarray[DTYPE_t, ndim = 1] x, y
+        np.ndarray[DTYPE_t, ndim = 1] y_fit
+        np.ndarray[DTYPE_t, ndim = 1] weights
+        DTYPE_t xval, radius
+
+    y = endog   # now just alias
+    x = exog
+
+
+    if not 0 <= frac <= 1:
+           raise ValueError("Lowess `frac` must be in the range [0,1]!")
+
+    n = np.PyArray_DIMS(x)[0]
+    out_n = np.PyArray_DIMS(xvals)[0]
+
+    # The number of neighbors in each regression.
+    # round up if close to integer
+    k =  int(frac * n + 1e-10)
+
+    # frac should be set, so that 2 <= k <= n.
+    # Conform them instead of throwing error.
+    if k < 2:
+        k = 2
+    if k > n:
+        k = n
+
+    y_fit = np.empty(out_n)
+    weights = np.empty(n)
+
+    it += 1 # Add one to it for initial run.
+    for robiter in range(it):
+        i = 0
+        last_fit_i = -1
+        left_end = 0
+        right_end = k
+        # Reinitialize y_fit each iteration
+        for j in range(out_n):
+            y_fit[j] = 0.0
+
+        # 'do' Fit y[i]'s 'until' the end of the regression
+        while True:
+            # The x value at which we will fit this time
+            xval  = xvals[i]
+
+            # Re-initialize the weights for each point xval.
+            for j in range(n):
+                weights[j] = 0.0
+
+            # Describe the neighborhood around the current xval.
+            left_end, right_end, radius = update_neighborhood(x, xval, n,
+                                                              left_end,
+                                                              right_end)
+
+            # Calculate the weights for the regression in this neighborhood.
+            # Determine if at least some weights are positive, so a regression
+            # is ok.
+            reg_ok = calculate_weights(x, weights, resid_weights, xval, left_end,
+                                       right_end, radius)
+
+            # If ok, run the regression
+            calculate_y_fit(x, y, i, xval, y_fit, weights, left_end, right_end,
+                            reg_ok, fill_with_nans=given_xvals)
+
+            # If we skipped some points (because of how delta was set), go back
+            # and fit them by linear interpolation.
+            if last_fit_i < (i - 1):
+                interpolate_skipped_fits(xvals, y_fit, i, last_fit_i)
+
+            # Update the last fit counter to indicate we've now fit this point.
+            # Find the next i for which we'll run a regression.
+            i, last_fit_i = update_indices(xvals, y_fit, delta, i, out_n, last_fit_i)
+
+            if last_fit_i >= out_n-1:
+                break
+
+        # Calculate residual weights
+        if not given_xvals:
+            resid_weights = calculate_residual_weights(y, y_fit)
+
+    return np.array([xvals, y_fit]).T, resid_weights
+
+
+cpdef update_neighborhood(const double[::1] x,
+                          double xval,
+                          Py_ssize_t n,
+                          Py_ssize_t left_end,
+                          Py_ssize_t right_end):
+    """
+    Find the indices bounding the k-nearest-neighbors of the current point.
+
+    Parameters
+    ----------
+    x : 1-D numpy array
+        The input x-values
+    xval : float
+        The x-value of the point currently being fit.
+    n : indexing integer
+        The length of the input vectors, x and y.
+    left_end : indexing integer
+        The index of the left-most point in the neighborhood
+        of the previously-fit x-value.
+    right_end: indexing integer
+        The index of the right-most point in the neighborhood
+        of the previous x-value. Non-inclusive, s.t. the neighborhood is
+        x[left_end] <= x < x[right_end].
+
+    Returns
+    -------
+    left_end : indexing integer
+        The index of the left-most point in the neighborhood
+        of xval (the current point).
+    right_end: indexing integer
+        The index of the right-most point in the neighborhood
+               of xval. Non-inclusive, s.t. the neighborhood is
+               x[left_end] <= x < x[right_end].
+    radius : float
+        The radius of the current neighborhood. The larger of
+        distances between xval and its left-most or right-most
+        neighbor.
+    """
+
+    cdef double radius
+    # A subtle loop. Start from the current neighborhood range:
+    # [left_end, right_end). Shift both ends rightwards by one
+    # (so that the neighborhood still contains k points), until
+    # the current point is in the center (or just to the left of
+    # the center) of the neighborhood. This neighborhood will
+    # contain the k-nearest neighbors of xval.
+    #
+    # Once the right end hits the end of the data, hold the
+    # neighborhood the same for the remaining xvals.
+    while True:
+        if right_end < n:
+            if xval > ((x[left_end] + x[right_end]) / 2.0):
+                left_end += 1
+                right_end += 1
+            else:
+                break
+        else:
+            break
+
+    radius = fmax(xval - x[left_end], x[right_end-1] - xval)
+
+    return left_end, right_end, radius
+
+cdef bint calculate_weights(np.ndarray[DTYPE_t, ndim = 1] x,
+                            np.ndarray[DTYPE_t, ndim = 1] weights,
+                            np.ndarray[DTYPE_t, ndim = 1] resid_weights,
+                            DTYPE_t xval,
+                            Py_ssize_t left_end,
+                            Py_ssize_t right_end,
+                            double radius):
+    """
+    Calculate weights
+
+    Parameters
+    ----------
+    x : 1-D vector
+        The input x-values.
+    weights : 1-D numpy array
+        The vector of regression weights.
+    resid_weights : 1-D numpy array
+        The vector of residual weights from the last iteration.
+    xval: float
+        The x-value of the point currently being fit.
+    left_end: indexing integer
+        The index of the left-most point in the neighborhood of
+        x[i].
+    right_end : indexing integer
+        The index of the right-most point in the neighborhood
+        of x[i]. Non-inclusive, s.t. the neighborhood is
+        x[left_end] <= x < x[right_end].
+    radius : float
+        The radius of the current neighborhood. The larger of
+        distances between x[i] and its left-most or right-most
+        neighbor.
+
+    Returns
+    -------
+    reg_ok : bool
+        If True, at least some points have positive weight, and the
+        regression will be run. If False, the regression is skipped
+        and y_fit[i] is set to equal y[i].
+    Also, changes elements of weights in-place.
+    """
+
+    cdef:
+        np.ndarray[DTYPE_t, ndim = 1] x_j = x[left_end:right_end]
+        np.ndarray[DTYPE_t, ndim = 1] dist_i_j = np.abs(x_j - xval) / radius
+        bint reg_ok = 1  # True
+        double sum_weights
+        int num_nonzero_weights = 0
+
+    # Assign the distance measure to the weights, then apply the tricube
+    # function to change in-place.
+    weights[left_end:right_end] = dist_i_j
+
+    tricube(weights[left_end:right_end])
+    weights[left_end:right_end] = (weights[left_end:right_end] *
+                                      resid_weights[left_end:right_end])
+
+    sum_weights = np.sum(weights[left_end:right_end])
+    for j in range(left_end, right_end):
+        num_nonzero_weights += weights[j] > 1e-12
+
+    if num_nonzero_weights < 2:
+        # Need at least 2 non-zero weights to get an okay regression fit
+        # see 1960
+        return 0  # False
+    for j in range(left_end, right_end):
+        weights[j] /= sum_weights
+
+    return reg_ok
+
+
+cdef void calculate_y_fit(np.ndarray[DTYPE_t, ndim = 1] x,
+                          np.ndarray[DTYPE_t, ndim = 1] y,
+                          Py_ssize_t i,
+                          DTYPE_t xval,
+                          np.ndarray[DTYPE_t, ndim = 1] y_fit,
+                          np.ndarray[DTYPE_t, ndim = 1] weights,
+                          Py_ssize_t left_end,
+                          Py_ssize_t right_end,
+                          bint reg_ok,
+                          bint fill_with_nans = 0):
+    """
+    Calculate smoothed/fitted y-value by weighted regression.
+
+    Parameters
+    ----------
+    x : 1-D numpy array
+        The vector of input x-values.
+    y : 1-D numpy array
+        The vector of input y-values.
+    i : indexing integer
+        The index of the point currently being fit.
+    xval: float
+        The x-value of the point currently being fit.
+    y_fit: 1-D numpy array
+        The vector of fitted y-values.
+    weights : 1-D numpy array
+        The vector of regression weights.
+    left_end : indexing integer
+        The index of the left-most point in the neighborhood of
+        xval.
+    right_end: indexing integers
+        The index of the right-most point in the neighborhood
+        of xval. Non-inclusive, s.t. the neighborhood is
+        x[left_end] <= x < x[right_end].
+    reg_ok : bool
+        If True, at least some points have positive weight, and the
+        regression will be run. If False, the regression is skipped
+        and y_fit[i] is set to equal y[i].
+    fill_with_nans: bool
+        If True, values with no valid regression will be filled with NaN
+        Otherwise, we use the original value in `y` of that point.
+        If x and xval are not the same, this must be False.
+
+    Returns
+    -------
+    Nothing. Changes y_fit[i] in-place.
+
+    Notes
+    -----
+    No regression function (e.g., lstsq) is called. Instead "projection
+    vector" p_i_j is calculated, and y_fit[i] = sum(p_i_j * y[j]) = y_fit[i]
+    for j s.t. x[j] is in the neighborhood of xval. p_i_j is a function of
+    the weights, xval, and its neighbors.
+    """
+
+    cdef:
+       double sum_weighted_x = 0, weighted_sqdev_x = 0, p_i_j
+
+    if not reg_ok:
+        if fill_with_nans:
+            # Fill a bad regression (weights all zeros) with nans
+            y_fit[i] = NAN
+        else:
+            # Fill a bad regression with the original value
+            # only possible when not using xvals distinct from x
+            y_fit[i] = y[i]
+    else:
+        for j in range(left_end, right_end):
+            sum_weighted_x += weights[j] * x[j]
+        for j in range(left_end, right_end):
+            weighted_sqdev_x += weights[j] * (x[j] - sum_weighted_x) ** 2
+        # force this to always be a positive value
+        weighted_sqdev_x = fmax(weighted_sqdev_x, 1e-12)
+        for j in range(left_end, right_end):
+            p_i_j = weights[j] * (1.0 + (xval - sum_weighted_x) *
+                             (x[j] - sum_weighted_x) / weighted_sqdev_x)
+            y_fit[i] += p_i_j * y[j]
+
+cdef void interpolate_skipped_fits(const double[::1] xvals,
+                                   double[::1] y_fit,
+                                   Py_ssize_t i,
+                                   Py_ssize_t last_fit_i):
+    """
+    Calculate smoothed/fitted y by linear interpolation between the current
+    and previous y fitted by weighted regression.
+    Called only if delta > 0.
+
+    Parameters
+    ----------
+    xvals : 1-D numpy array
+        The vector of x-values where regression is performed.
+    y_fit : 1-D numpy array
+        The vector of fitted y-values
+    i : indexing integer
+        The index of the point currently being fit by weighted
+        regression.
+    last_fit_i : indexing integer
+        The index of the last point fit by weighted regression.
+
+    Returns
+    -------
+    None
+        Values changed in-place.
+    """
+
+    cdef:
+        double[::1] a_view
+        Py_ssize_t j
+        Py_ssize_t offset = last_fit_i + 1
+        Py_ssize_t n = i - offset
+        double denom, a
+
+    denom = xvals[i] - xvals[last_fit_i]
+    for j in range(n):
+        a = (xvals[offset + j] - xvals[last_fit_i]) / denom
+        y_fit[offset+j] = a * y_fit[i] + (1.0 - a) * y_fit[last_fit_i]
+
+
+cpdef update_indices(const double[::1] xvals,
+                     double[::1] y_fit,
+                     double delta,
+                     Py_ssize_t i,
+                     Py_ssize_t out_n,
+                     Py_ssize_t last_fit_i):
+    """
+    Update the counters of the local regression.
+
+    Parameters
+    ----------
+    xvals : 1-D numpy array
+        The vector of x-values where regression is performed.
+    y_fit : 1-D numpy array
+        The vector of fitted y-values
+    delta : float
+        Indicates the range of x values within which linear
+        interpolation should be used to estimate y_fit instead
+        of weighted regression.
+    i : indexing integer
+        The index of the current point being fit.
+    out_n : indexing integer
+        The length of the input vector xvals.
+    last_fit_i : indexing integer
+        The last point at which y_fit was calculated.
+
+    Returns
+    -------
+    i : indexing integer
+        The next point at which to run a weighted regression.
+    last_fit_i : indexing integer
+        The updated last point at which y_fit was calculated
+
+    Notes
+    -----
+    The relationship between the outputs is s.t. xvals[i+1] >
+    xvals[last_fit_i] + delta.
+    """
+    cdef:
+        Py_ssize_t k
+        double cutpoint
+
+    last_fit_i = i
+    k = last_fit_i
+    # For most points within delta of the current point, we skip the
+    # weighted linear regression (which save much computation of
+    # weights and fitted points). Instead, we'll jump to the last
+    # point within delta, fit the weighted regression at that point,
+    # and linearly interpolate in between.
+
+    # This loop increments until we fall just outside of delta distance,
+    # copying the results for any repeated x's along the way.
+    cutpoint = xvals[last_fit_i] + delta
+    for k in range(last_fit_i + 1, out_n):
+        if xvals[k] > cutpoint:
+            break
+        if xvals[k] == xvals[last_fit_i]:
+            # if tied with previous x-value, just use the already
+            # fitted y, and update the last-fit counter.
+            y_fit[k] = y_fit[last_fit_i]
+            last_fit_i = k
+
+    # i, which indicates the next point to fit the regression at, is
+    # either one prior to k (since k should be the first point outside
+    # of delta) or is just incremented + 1 if k = i+1. This insures we
+    # always step forward.
+    i = max(k-1, last_fit_i + 1)
+
+    return i, last_fit_i
+
+
+cpdef np.ndarray calculate_residual_weights(const double[::1] y, const double[::1] y_fit):
+    """
+    Calculate residual weights for the next `robustifying` iteration.
+
+    Parameters
+    ----------
+    y: 1-D numpy array
+        The vector of actual input y-values.
+    y_fit: 1-D numpy array
+        The vector of fitted y-values from the current
+        iteration.
+
+    Returns
+    -------
+    resid_weights: 1-D numpy array
+        The vector of residual weights, to be used in the
+        next iteration of regressions.
+    """
+    cdef:
+        Py_ssize_t j
+        np.npy_intp n = y.size
+        np.ndarray std_resid = np.empty(n)
+        double median, scale
+        double* std_resid_data = <double *>np.PyArray_DATA(std_resid)
+
+    for j in range(n):
+        std_resid_data[j] = fabs(y[j] - y_fit[j])
+
+    median = <double>np.median(std_resid)
+    if median == 0:
+        for j in range(n):
+            std_resid_data[j] = <double>(std_resid_data[j] > 0)
+    else:
+        scale = 6.0 * median
+        for j in range(n):
+            std_resid_data[j] /= scale
+
+    # Some trimming of outlier residuals.
+    for j in range(n):
+        if std_resid_data[j] > 1:
+            std_resid_data[j] = 1.0
+    # std_resid[std_resid >= 0.999] = 1.0
+    # std_resid[std_resid <= 0.001] = 0.0
+
+    return bisquare(std_resid)
+
+
+cdef void tricube(double[::1] x):
+    """
+    The tri-cubic function (1 - x**3)**3. Used to weight neighboring
+    points along the x-axis based on their distance to the current point.
+
+    Parameters
+    ----------
+    x: 1-D numpy array
+        A vector of neighbors` distances from the current point,
+        in units of the neighborhood radius.
+
+    Returns
+    -------
+    Nothing. Changes array elements in-place
+    """
+
+    # fast_array_cube is an elementwise, in-place cubed-power
+    # operator.
+    cdef:
+        np.npy_intp n = x.size
+        Py_ssize_t j
+        double tmp
+
+    fast_array_cube(x)
+    for j in range(n):
+        x[j] = 1.0 - x[j]
+    fast_array_cube(x)
+
+
+cdef void fast_array_cube(double[::1] x):
+    """
+    A fast, elementwise, in-place cube operator. Called by the
+    tricube function.
+
+    Parameters
+    ----------
+    x: 1-D numpy array
+
+    Returns
+    -------
+    Nothing. Changes array elements in-place.
+    """
+    cdef:
+        np.npy_intp n = x.size
+        Py_ssize_t j
+        double tmp
+
+    for j in range(n):
+        tmp = x[j]
+        x[j] = tmp * tmp * tmp
+
+
+cpdef np.ndarray bisquare(const double[::1] x):
+    """
+    The bi-square function (1 - x**2)**2.
+
+    Used to weight the residuals in the `robustifying`
+    iterations. Called by the calculate_residual_weights function.
+
+    Parameters
+    ----------
+    x: 1-D numpy array
+        A vector of absolute regression residuals, in units of
+        6 times the median absolute residual.
+
+    Returns
+    -------
+    A 1-D numpy array of residual weights.
+    """
+    cdef:
+        np.npy_intp n = x.size
+        np.ndarray out = <np.ndarray>np.empty(n)
+        double* out_data = <double *>np.PyArray_DATA(out)
+        Py_ssize_t j
+        double tmp
+
+    for j in range(n):
+        tmp = 1.0 - x[j] * x[j]
+        out_data[j] = tmp * tmp
+
+    return out
diff --git a/shared/package-files/0c/0cbfddfdad0a7d05a48e1e625a6b28748eb36eead51815c6d7c1908ec6708393/stata_medpar1_glm.csv b/shared/package-files/0c/0cbfddfdad0a7d05a48e1e625a6b28748eb36eead51815c6d7c1908ec6708393/stata_medpar1_glm.csv
new file mode 100644
index 0000000000000000000000000000000000000000..05ae405e8e3435f65959caef1c3410d42dcf5f29
--- /dev/null
+++ b/shared/package-files/0c/0cbfddfdad0a7d05a48e1e625a6b28748eb36eead51815c6d7c1908ec6708393/stata_medpar1_glm.csv
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diff --git a/shared/package-files/0c/0cc3e002b9fea895c56e8304b40c5e901835e973de52647a5ec818ee1dfd1999/fake_quantize_per_channel_affine_ops.h b/shared/package-files/0c/0cc3e002b9fea895c56e8304b40c5e901835e973de52647a5ec818ee1dfd1999/fake_quantize_per_channel_affine_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..50596d03fe06e591ed571a513c02821724a16b2f
--- /dev/null
+++ b/shared/package-files/0c/0cc3e002b9fea895c56e8304b40c5e901835e973de52647a5ec818ee1dfd1999/fake_quantize_per_channel_affine_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API fake_quantize_per_channel_affine {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, int64_t, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fake_quantize_per_channel_affine")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0cc55b28bd6cd2ad6ed263963156ae1a8033ed7272ebbd67d5ab15f1e53266fa/_foreach_acos_ops.h b/shared/package-files/0c/0cc55b28bd6cd2ad6ed263963156ae1a8033ed7272ebbd67d5ab15f1e53266fa/_foreach_acos_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1328a65a554e012a2ac6a82b341556e68c12ad00
--- /dev/null
+++ b/shared/package-files/0c/0cc55b28bd6cd2ad6ed263963156ae1a8033ed7272ebbd67d5ab15f1e53266fa/_foreach_acos_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_acos {
+  using schema = ::std::vector<at::Tensor> (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_acos")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_acos(Tensor[] self) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_acos_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_acos_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_acos_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_acos_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_acos")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/0c/0cc757d419b5f25ccdba2096cf07de1d43111973d2f2fe405a787efc45ff019c/Norfolk b/shared/package-files/0c/0cc757d419b5f25ccdba2096cf07de1d43111973d2f2fe405a787efc45ff019c/Norfolk
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diff --git a/shared/package-files/0c/0ccb3b4c5165e57d95ff7b11220f7ea9a8a20bde31f6cd27d1211d61fd926871/NamedTensor.h b/shared/package-files/0c/0ccb3b4c5165e57d95ff7b11220f7ea9a8a20bde31f6cd27d1211d61fd926871/NamedTensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7606b0a668a43800b89755af1371551909b23d5
--- /dev/null
+++ b/shared/package-files/0c/0ccb3b4c5165e57d95ff7b11220f7ea9a8a20bde31f6cd27d1211d61fd926871/NamedTensor.h
@@ -0,0 +1 @@
+#include <ATen/core/NamedTensor.h>
diff --git a/shared/package-files/0c/0ccec651141b7527dc75d1f3db733139285ab4115cdbab4ffec7771bca460901/__init__.py b/shared/package-files/0c/0ccec651141b7527dc75d1f3db733139285ab4115cdbab4ffec7771bca460901/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd86a505d8c4b8026bd91cde27d441e00223a8bc
--- /dev/null
+++ b/shared/package-files/0c/0ccec651141b7527dc75d1f3db733139285ab4115cdbab4ffec7771bca460901/__init__.py
@@ -0,0 +1,138 @@
+"""Plotting module that can plot 2D and 3D functions
+"""
+
+from sympy.utilities.decorator import doctest_depends_on
+
+@doctest_depends_on(modules=('pyglet',))
+def PygletPlot(*args, **kwargs):
+    """
+
+    Plot Examples
+    =============
+
+    See examples/advanced/pyglet_plotting.py for many more examples.
+
+    >>> from sympy.plotting.pygletplot import PygletPlot as Plot
+    >>> from sympy.abc import x, y, z
+
+    >>> Plot(x*y**3-y*x**3)
+    [0]: -x**3*y + x*y**3, 'mode=cartesian'
+
+    >>> p = Plot()
+    >>> p[1] = x*y
+    >>> p[1].color = z, (0.4,0.4,0.9), (0.9,0.4,0.4)
+
+    >>> p = Plot()
+    >>> p[1] =  x**2+y**2
+    >>> p[2] = -x**2-y**2
+
+
+    Variable Intervals
+    ==================
+
+    The basic format is [var, min, max, steps], but the
+    syntax is flexible and arguments left out are taken
+    from the defaults for the current coordinate mode:
+
+    >>> Plot(x**2) # implies [x,-5,5,100]
+    [0]: x**2, 'mode=cartesian'
+
+    >>> Plot(x**2, [], []) # [x,-1,1,40], [y,-1,1,40]
+    [0]: x**2, 'mode=cartesian'
+    >>> Plot(x**2-y**2, [100], [100]) # [x,-1,1,100], [y,-1,1,100]
+    [0]: x**2 - y**2, 'mode=cartesian'
+    >>> Plot(x**2, [x,-13,13,100])
+    [0]: x**2, 'mode=cartesian'
+    >>> Plot(x**2, [-13,13]) # [x,-13,13,100]
+    [0]: x**2, 'mode=cartesian'
+    >>> Plot(x**2, [x,-13,13]) # [x,-13,13,100]
+    [0]: x**2, 'mode=cartesian'
+    >>> Plot(1*x, [], [x], mode='cylindrical')
+    ... # [unbound_theta,0,2*Pi,40], [x,-1,1,20]
+    [0]: x, 'mode=cartesian'
+
+
+    Coordinate Modes
+    ================
+
+    Plot supports several curvilinear coordinate modes, and
+    they independent for each plotted function. You can specify
+    a coordinate mode explicitly with the 'mode' named argument,
+    but it can be automatically determined for Cartesian or
+    parametric plots, and therefore must only be specified for
+    polar, cylindrical, and spherical modes.
+
+    Specifically, Plot(function arguments) and Plot[n] =
+    (function arguments) will interpret your arguments as a
+    Cartesian plot if you provide one function and a parametric
+    plot if you provide two or three functions. Similarly, the
+    arguments will be interpreted as a curve if one variable is
+    used, and a surface if two are used.
+
+    Supported mode names by number of variables:
+
+    1: parametric, cartesian, polar
+    2: parametric, cartesian, cylindrical = polar, spherical
+
+    >>> Plot(1, mode='spherical')
+
+
+    Calculator-like Interface
+    =========================
+
+    >>> p = Plot(visible=False)
+    >>> f = x**2
+    >>> p[1] = f
+    >>> p[2] = f.diff(x)
+    >>> p[3] = f.diff(x).diff(x)
+    >>> p
+    [1]: x**2, 'mode=cartesian'
+    [2]: 2*x, 'mode=cartesian'
+    [3]: 2, 'mode=cartesian'
+    >>> p.show()
+    >>> p.clear()
+    >>> p
+    <blank plot>
+    >>> p[1] =  x**2+y**2
+    >>> p[1].style = 'solid'
+    >>> p[2] = -x**2-y**2
+    >>> p[2].style = 'wireframe'
+    >>> p[1].color = z, (0.4,0.4,0.9), (0.9,0.4,0.4)
+    >>> p[1].style = 'both'
+    >>> p[2].style = 'both'
+    >>> p.close()
+
+
+    Plot Window Keyboard Controls
+    =============================
+
+    Screen Rotation:
+        X,Y axis      Arrow Keys, A,S,D,W, Numpad 4,6,8,2
+        Z axis        Q,E, Numpad 7,9
+
+    Model Rotation:
+        Z axis        Z,C, Numpad 1,3
+
+    Zoom:             R,F, PgUp,PgDn, Numpad +,-
+
+    Reset Camera:     X, Numpad 5
+
+    Camera Presets:
+        XY            F1
+        XZ            F2
+        YZ            F3
+        Perspective   F4
+
+    Sensitivity Modifier: SHIFT
+
+    Axes Toggle:
+        Visible       F5
+        Colors        F6
+
+    Close Window:     ESCAPE
+
+    =============================
+    """
+
+    from sympy.plotting.pygletplot.plot import PygletPlot
+    return PygletPlot(*args, **kwargs)
diff --git a/shared/package-files/0c/0cd40859afd1f67e885de75d1b5c3fa5752e218ee3dcc0efeb3b9fe29bb3a7fd/pty b/shared/package-files/0c/0cd40859afd1f67e885de75d1b5c3fa5752e218ee3dcc0efeb3b9fe29bb3a7fd/pty
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diff --git a/shared/package-files/0c/0cd4e60e00d63aece5427adc78447a609d0dbad514c19f6da3354c056426bae6/scalar_byte_descr.sav b/shared/package-files/0c/0cd4e60e00d63aece5427adc78447a609d0dbad514c19f6da3354c056426bae6/scalar_byte_descr.sav
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index 0000000000000000000000000000000000000000..182e29bc57dc05154388553a71876820025bca8d
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diff --git a/shared/package-files/0c/0cd64c29b5f4c43a806dff70cd02b01e547b99d04eedad4409202e5d4a390844/test_set_index.py b/shared/package-files/0c/0cd64c29b5f4c43a806dff70cd02b01e547b99d04eedad4409202e5d4a390844/test_set_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c8d365f0d6c056c8c023e5173c9b3671cbfbd48
--- /dev/null
+++ b/shared/package-files/0c/0cd64c29b5f4c43a806dff70cd02b01e547b99d04eedad4409202e5d4a390844/test_set_index.py
@@ -0,0 +1,734 @@
+"""
+See also: test_reindex.py:TestReindexSetIndex
+"""
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+    period_range,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def frame_of_index_cols():
+    """
+    Fixture for DataFrame of columns that can be used for indexing
+
+    Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')];
+    'A' & 'B' contain duplicates (but are jointly unique), the rest are unique.
+
+         A      B  C         D         E  (tuple, as, label)
+    0  foo    one  a  0.608477 -0.012500           -1.664297
+    1  foo    two  b -0.633460  0.249614           -0.364411
+    2  foo  three  c  0.615256  2.154968           -0.834666
+    3  bar    one  d  0.234246  1.085675            0.718445
+    4  bar    two  e  0.533841 -0.005702           -3.533912
+    """
+    df = DataFrame(
+        {
+            "A": ["foo", "foo", "foo", "bar", "bar"],
+            "B": ["one", "two", "three", "one", "two"],
+            "C": ["a", "b", "c", "d", "e"],
+            "D": np.random.default_rng(2).standard_normal(5),
+            "E": np.random.default_rng(2).standard_normal(5),
+            ("tuple", "as", "label"): np.random.default_rng(2).standard_normal(5),
+        }
+    )
+    return df
+
+
+class TestSetIndex:
+    def test_set_index_multiindex(self):
+        # segfault in GH#3308
+        d = {"t1": [2, 2.5, 3], "t2": [4, 5, 6]}
+        df = DataFrame(d)
+        tuples = [(0, 1), (0, 2), (1, 2)]
+        df["tuples"] = tuples
+
+        index = MultiIndex.from_tuples(df["tuples"])
+        # it works!
+        df.set_index(index)
+
+    def test_set_index_empty_column(self):
+        # GH#1971
+        df = DataFrame(
+            [
+                {"a": 1, "p": 0},
+                {"a": 2, "m": 10},
+                {"a": 3, "m": 11, "p": 20},
+                {"a": 4, "m": 12, "p": 21},
+            ],
+            columns=["a", "m", "p", "x"],
+        )
+
+        result = df.set_index(["a", "x"])
+
+        expected = df[["m", "p"]]
+        expected.index = MultiIndex.from_arrays([df["a"], df["x"]], names=["a", "x"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_set_index_empty_dataframe(self):
+        # GH#38419
+        df1 = DataFrame(
+            {"a": Series(dtype="datetime64[ns]"), "b": Series(dtype="int64"), "c": []}
+        )
+
+        df2 = df1.set_index(["a", "b"])
+        result = df2.index.to_frame().dtypes
+        expected = df1[["a", "b"]].dtypes
+        tm.assert_series_equal(result, expected)
+
+    def test_set_index_multiindexcolumns(self):
+        columns = MultiIndex.from_tuples([("foo", 1), ("foo", 2), ("bar", 1)])
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 3)), columns=columns
+        )
+
+        result = df.set_index(df.columns[0])
+
+        expected = df.iloc[:, 1:]
+        expected.index = df.iloc[:, 0].values
+        expected.index.names = [df.columns[0]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_set_index_timezone(self):
+        # GH#12358
+        # tz-aware Series should retain the tz
+        idx = DatetimeIndex(["2014-01-01 10:10:10"], tz="UTC").tz_convert("Europe/Rome")
+        df = DataFrame({"A": idx})
+        assert df.set_index(idx).index[0].hour == 11
+        assert DatetimeIndex(Series(df.A))[0].hour == 11
+        assert df.set_index(df.A).index[0].hour == 11
+
+    def test_set_index_cast_datetimeindex(self):
+        df = DataFrame(
+            {
+                "A": [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)],
+                "B": np.random.default_rng(2).standard_normal(1000),
+            }
+        )
+
+        idf = df.set_index("A")
+        assert isinstance(idf.index, DatetimeIndex)
+
+    def test_set_index_dst(self):
+        di = date_range("2006-10-29 00:00:00", periods=3, freq="h", tz="US/Pacific")
+
+        df = DataFrame(data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=di).reset_index()
+        # single level
+        res = df.set_index("index")
+        exp = DataFrame(
+            data={"a": [0, 1, 2], "b": [3, 4, 5]},
+            index=Index(di, name="index"),
+        )
+        exp.index = exp.index._with_freq(None)
+        tm.assert_frame_equal(res, exp)
+
+        # GH#12920
+        res = df.set_index(["index", "a"])
+        exp_index = MultiIndex.from_arrays([di, [0, 1, 2]], names=["index", "a"])
+        exp = DataFrame({"b": [3, 4, 5]}, index=exp_index)
+        tm.assert_frame_equal(res, exp)
+
+    def test_set_index(self, float_string_frame):
+        df = float_string_frame
+        idx = Index(np.arange(len(df))[::-1])
+
+        df = df.set_index(idx)
+        tm.assert_index_equal(df.index, idx)
+        with pytest.raises(ValueError, match="Length mismatch"):
+            df.set_index(idx[::2])
+
+    def test_set_index_names(self):
+        df = DataFrame(
+            np.ones((10, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(10)]),
+        )
+        df.index.name = "name"
+
+        assert df.set_index(df.index).index.names == ["name"]
+
+        mi = MultiIndex.from_arrays(df[["A", "B"]].T.values, names=["A", "B"])
+        mi2 = MultiIndex.from_arrays(
+            df[["A", "B", "A", "B"]].T.values, names=["A", "B", "C", "D"]
+        )
+
+        df = df.set_index(["A", "B"])
+
+        assert df.set_index(df.index).index.names == ["A", "B"]
+
+        # Check that set_index isn't converting a MultiIndex into an Index
+        assert isinstance(df.set_index(df.index).index, MultiIndex)
+
+        # Check actual equality
+        tm.assert_index_equal(df.set_index(df.index).index, mi)
+
+        idx2 = df.index.rename(["C", "D"])
+
+        # Check that [MultiIndex, MultiIndex] yields a MultiIndex rather
+        # than a pair of tuples
+        assert isinstance(df.set_index([df.index, idx2]).index, MultiIndex)
+
+        # Check equality
+        tm.assert_index_equal(df.set_index([df.index, idx2]).index, mi2)
+
+    # A has duplicate values, C does not
+    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_drop_inplace(self, frame_of_index_cols, drop, inplace, keys):
+        df = frame_of_index_cols
+
+        if isinstance(keys, list):
+            idx = MultiIndex.from_arrays([df[x] for x in keys], names=keys)
+        else:
+            idx = Index(df[keys], name=keys)
+        expected = df.drop(keys, axis=1) if drop else df
+        expected.index = idx
+
+        if inplace:
+            result = df.copy()
+            return_value = result.set_index(keys, drop=drop, inplace=True)
+            assert return_value is None
+        else:
+            result = df.set_index(keys, drop=drop)
+
+        tm.assert_frame_equal(result, expected)
+
+    # A has duplicate values, C does not
+    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_append(self, frame_of_index_cols, drop, keys):
+        df = frame_of_index_cols
+
+        keys = keys if isinstance(keys, list) else [keys]
+        idx = MultiIndex.from_arrays(
+            [df.index] + [df[x] for x in keys], names=[None] + keys
+        )
+        expected = df.drop(keys, axis=1) if drop else df.copy()
+        expected.index = idx
+
+        result = df.set_index(keys, drop=drop, append=True)
+
+        tm.assert_frame_equal(result, expected)
+
+    # A has duplicate values, C does not
+    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_append_to_multiindex(self, frame_of_index_cols, drop, keys):
+        # append to existing multiindex
+        df = frame_of_index_cols.set_index(["D"], drop=drop, append=True)
+
+        keys = keys if isinstance(keys, list) else [keys]
+        expected = frame_of_index_cols.set_index(["D"] + keys, drop=drop, append=True)
+
+        result = df.set_index(keys, drop=drop, append=True)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_set_index_after_mutation(self):
+        # GH#1590
+        df = DataFrame({"val": [0, 1, 2], "key": ["a", "b", "c"]})
+        expected = DataFrame({"val": [1, 2]}, Index(["b", "c"], name="key"))
+
+        df2 = df.loc[df.index.map(lambda indx: indx >= 1)]
+        result = df2.set_index("key")
+        tm.assert_frame_equal(result, expected)
+
+    # MultiIndex constructor does not work directly on Series -> lambda
+    # Add list-of-list constructor because list is ambiguous -> lambda
+    # also test index name if append=True (name is duplicate here for B)
+    @pytest.mark.parametrize(
+        "box",
+        [
+            Series,
+            Index,
+            np.array,
+            list,
+            lambda x: [list(x)],
+            lambda x: MultiIndex.from_arrays([x]),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "append, index_name", [(True, None), (True, "B"), (True, "test"), (False, None)]
+    )
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_pass_single_array(
+        self, frame_of_index_cols, drop, append, index_name, box
+    ):
+        df = frame_of_index_cols
+        df.index.name = index_name
+
+        key = box(df["B"])
+        if box == list:
+            # list of strings gets interpreted as list of keys
+            msg = "['one', 'two', 'three', 'one', 'two']"
+            with pytest.raises(KeyError, match=msg):
+                df.set_index(key, drop=drop, append=append)
+        else:
+            # np.array/list-of-list "forget" the name of B
+            name_mi = getattr(key, "names", None)
+            name = [getattr(key, "name", None)] if name_mi is None else name_mi
+
+            result = df.set_index(key, drop=drop, append=append)
+
+            # only valid column keys are dropped
+            # since B is always passed as array above, nothing is dropped
+            expected = df.set_index(["B"], drop=False, append=append)
+            expected.index.names = [index_name] + name if append else name
+
+            tm.assert_frame_equal(result, expected)
+
+    # MultiIndex constructor does not work directly on Series -> lambda
+    # also test index name if append=True (name is duplicate here for A & B)
+    @pytest.mark.parametrize(
+        "box", [Series, Index, np.array, list, lambda x: MultiIndex.from_arrays([x])]
+    )
+    @pytest.mark.parametrize(
+        "append, index_name",
+        [(True, None), (True, "A"), (True, "B"), (True, "test"), (False, None)],
+    )
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_pass_arrays(
+        self, frame_of_index_cols, drop, append, index_name, box
+    ):
+        df = frame_of_index_cols
+        df.index.name = index_name
+
+        keys = ["A", box(df["B"])]
+        # np.array/list "forget" the name of B
+        names = ["A", None if box in [np.array, list, tuple, iter] else "B"]
+
+        result = df.set_index(keys, drop=drop, append=append)
+
+        # only valid column keys are dropped
+        # since B is always passed as array above, only A is dropped, if at all
+        expected = df.set_index(["A", "B"], drop=False, append=append)
+        expected = expected.drop("A", axis=1) if drop else expected
+        expected.index.names = [index_name] + names if append else names
+
+        tm.assert_frame_equal(result, expected)
+
+    # MultiIndex constructor does not work directly on Series -> lambda
+    # We also emulate a "constructor" for the label -> lambda
+    # also test index name if append=True (name is duplicate here for A)
+    @pytest.mark.parametrize(
+        "box2",
+        [
+            Series,
+            Index,
+            np.array,
+            list,
+            iter,
+            lambda x: MultiIndex.from_arrays([x]),
+            lambda x: x.name,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "box1",
+        [
+            Series,
+            Index,
+            np.array,
+            list,
+            iter,
+            lambda x: MultiIndex.from_arrays([x]),
+            lambda x: x.name,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "append, index_name", [(True, None), (True, "A"), (True, "test"), (False, None)]
+    )
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_pass_arrays_duplicate(
+        self, frame_of_index_cols, drop, append, index_name, box1, box2
+    ):
+        df = frame_of_index_cols
+        df.index.name = index_name
+
+        keys = [box1(df["A"]), box2(df["A"])]
+        result = df.set_index(keys, drop=drop, append=append)
+
+        # if either box is iter, it has been consumed; re-read
+        keys = [box1(df["A"]), box2(df["A"])]
+
+        # need to adapt first drop for case that both keys are 'A' --
+        # cannot drop the same column twice;
+        # plain == would give ambiguous Boolean error for containers
+        first_drop = (
+            False
+            if (
+                isinstance(keys[0], str)
+                and keys[0] == "A"
+                and isinstance(keys[1], str)
+                and keys[1] == "A"
+            )
+            else drop
+        )
+        # to test against already-tested behaviour, we add sequentially,
+        # hence second append always True; must wrap keys in list, otherwise
+        # box = list would be interpreted as keys
+        expected = df.set_index([keys[0]], drop=first_drop, append=append)
+        expected = expected.set_index([keys[1]], drop=drop, append=True)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("append", [True, False])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_pass_multiindex(self, frame_of_index_cols, drop, append):
+        df = frame_of_index_cols
+        keys = MultiIndex.from_arrays([df["A"], df["B"]], names=["A", "B"])
+
+        result = df.set_index(keys, drop=drop, append=append)
+
+        # setting with a MultiIndex will never drop columns
+        expected = df.set_index(["A", "B"], drop=False, append=append)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_construction_with_categorical_index(self):
+        ci = CategoricalIndex(list("ab") * 5, name="B")
+
+        # with Categorical
+        df = DataFrame(
+            {"A": np.random.default_rng(2).standard_normal(10), "B": ci.values}
+        )
+        idf = df.set_index("B")
+        tm.assert_index_equal(idf.index, ci)
+
+        # from a CategoricalIndex
+        df = DataFrame({"A": np.random.default_rng(2).standard_normal(10), "B": ci})
+        idf = df.set_index("B")
+        tm.assert_index_equal(idf.index, ci)
+
+        # round-trip
+        idf = idf.reset_index().set_index("B")
+        tm.assert_index_equal(idf.index, ci)
+
+    def test_set_index_preserve_categorical_dtype(self):
+        # GH#13743, GH#13854
+        df = DataFrame(
+            {
+                "A": [1, 2, 1, 1, 2],
+                "B": [10, 16, 22, 28, 34],
+                "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),
+                "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),
+            }
+        )
+        for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]:
+            result = df.set_index(cols).reset_index()
+            result = result.reindex(columns=df.columns)
+            tm.assert_frame_equal(result, df)
+
+    def test_set_index_datetime(self):
+        # GH#3950
+        df = DataFrame(
+            {
+                "label": ["a", "a", "a", "b", "b", "b"],
+                "datetime": [
+                    "2011-07-19 07:00:00",
+                    "2011-07-19 08:00:00",
+                    "2011-07-19 09:00:00",
+                    "2011-07-19 07:00:00",
+                    "2011-07-19 08:00:00",
+                    "2011-07-19 09:00:00",
+                ],
+                "value": range(6),
+            }
+        )
+        df.index = to_datetime(df.pop("datetime"), utc=True)
+        df.index = df.index.tz_convert("US/Pacific")
+
+        expected = DatetimeIndex(
+            ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
+            name="datetime",
+        )
+        expected = expected.tz_localize("UTC").tz_convert("US/Pacific")
+
+        df = df.set_index("label", append=True)
+        tm.assert_index_equal(df.index.levels[0], expected)
+        tm.assert_index_equal(df.index.levels[1], Index(["a", "b"], name="label"))
+        assert df.index.names == ["datetime", "label"]
+
+        df = df.swaplevel(0, 1)
+        tm.assert_index_equal(df.index.levels[0], Index(["a", "b"], name="label"))
+        tm.assert_index_equal(df.index.levels[1], expected)
+        assert df.index.names == ["label", "datetime"]
+
+        df = DataFrame(np.random.default_rng(2).random(6))
+        idx1 = DatetimeIndex(
+            [
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+            ],
+            tz="US/Eastern",
+        )
+        idx2 = DatetimeIndex(
+            [
+                "2012-04-01 09:00",
+                "2012-04-01 09:00",
+                "2012-04-01 09:00",
+                "2012-04-02 09:00",
+                "2012-04-02 09:00",
+                "2012-04-02 09:00",
+            ],
+            tz="US/Eastern",
+        )
+        idx3 = date_range("2011-01-01 09:00", periods=6, tz="Asia/Tokyo")
+        idx3 = idx3._with_freq(None)
+
+        df = df.set_index(idx1)
+        df = df.set_index(idx2, append=True)
+        df = df.set_index(idx3, append=True)
+
+        expected1 = DatetimeIndex(
+            ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
+            tz="US/Eastern",
+        )
+        expected2 = DatetimeIndex(
+            ["2012-04-01 09:00", "2012-04-02 09:00"], tz="US/Eastern"
+        )
+
+        tm.assert_index_equal(df.index.levels[0], expected1)
+        tm.assert_index_equal(df.index.levels[1], expected2)
+        tm.assert_index_equal(df.index.levels[2], idx3)
+
+        # GH#7092
+        tm.assert_index_equal(df.index.get_level_values(0), idx1)
+        tm.assert_index_equal(df.index.get_level_values(1), idx2)
+        tm.assert_index_equal(df.index.get_level_values(2), idx3)
+
+    def test_set_index_period(self):
+        # GH#6631
+        df = DataFrame(np.random.default_rng(2).random(6))
+        idx1 = period_range("2011-01-01", periods=3, freq="M")
+        idx1 = idx1.append(idx1)
+        idx2 = period_range("2013-01-01 09:00", periods=2, freq="h")
+        idx2 = idx2.append(idx2).append(idx2)
+        idx3 = period_range("2005", periods=6, freq="Y")
+
+        df = df.set_index(idx1)
+        df = df.set_index(idx2, append=True)
+        df = df.set_index(idx3, append=True)
+
+        expected1 = period_range("2011-01-01", periods=3, freq="M")
+        expected2 = period_range("2013-01-01 09:00", periods=2, freq="h")
+
+        tm.assert_index_equal(df.index.levels[0], expected1)
+        tm.assert_index_equal(df.index.levels[1], expected2)
+        tm.assert_index_equal(df.index.levels[2], idx3)
+
+        tm.assert_index_equal(df.index.get_level_values(0), idx1)
+        tm.assert_index_equal(df.index.get_level_values(1), idx2)
+        tm.assert_index_equal(df.index.get_level_values(2), idx3)
+
+
+class TestSetIndexInvalid:
+    def test_set_index_verify_integrity(self, frame_of_index_cols):
+        df = frame_of_index_cols
+
+        with pytest.raises(ValueError, match="Index has duplicate keys"):
+            df.set_index("A", verify_integrity=True)
+        # with MultiIndex
+        with pytest.raises(ValueError, match="Index has duplicate keys"):
+            df.set_index([df["A"], df["A"]], verify_integrity=True)
+
+    @pytest.mark.parametrize("append", [True, False])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_raise_keys(self, frame_of_index_cols, drop, append):
+        df = frame_of_index_cols
+
+        with pytest.raises(KeyError, match="['foo', 'bar', 'baz']"):
+            # column names are A-E, as well as one tuple
+            df.set_index(["foo", "bar", "baz"], drop=drop, append=append)
+
+        # non-existent key in list with arrays
+        with pytest.raises(KeyError, match="X"):
+            df.set_index([df["A"], df["B"], "X"], drop=drop, append=append)
+
+        msg = "[('foo', 'foo', 'foo', 'bar', 'bar')]"
+        # tuples always raise KeyError
+        with pytest.raises(KeyError, match=msg):
+            df.set_index(tuple(df["A"]), drop=drop, append=append)
+
+        # also within a list
+        with pytest.raises(KeyError, match=msg):
+            df.set_index(["A", df["A"], tuple(df["A"])], drop=drop, append=append)
+
+    @pytest.mark.parametrize("append", [True, False])
+    @pytest.mark.parametrize("drop", [True, False])
+    @pytest.mark.parametrize("box", [set], ids=["set"])
+    def test_set_index_raise_on_type(self, frame_of_index_cols, box, drop, append):
+        df = frame_of_index_cols
+
+        msg = 'The parameter "keys" may be a column key, .*'
+        # forbidden type, e.g. set
+        with pytest.raises(TypeError, match=msg):
+            df.set_index(box(df["A"]), drop=drop, append=append)
+
+        # forbidden type in list, e.g. set
+        with pytest.raises(TypeError, match=msg):
+            df.set_index(["A", df["A"], box(df["A"])], drop=drop, append=append)
+
+    # MultiIndex constructor does not work directly on Series -> lambda
+    @pytest.mark.parametrize(
+        "box",
+        [Series, Index, np.array, iter, lambda x: MultiIndex.from_arrays([x])],
+        ids=["Series", "Index", "np.array", "iter", "MultiIndex"],
+    )
+    @pytest.mark.parametrize("length", [4, 6], ids=["too_short", "too_long"])
+    @pytest.mark.parametrize("append", [True, False])
+    @pytest.mark.parametrize("drop", [True, False])
+    def test_set_index_raise_on_len(
+        self, frame_of_index_cols, box, length, drop, append
+    ):
+        # GH 24984
+        df = frame_of_index_cols  # has length 5
+
+        values = np.random.default_rng(2).integers(0, 10, (length,))
+
+        msg = "Length mismatch: Expected 5 rows, received array of length.*"
+
+        # wrong length directly
+        with pytest.raises(ValueError, match=msg):
+            df.set_index(box(values), drop=drop, append=append)
+
+        # wrong length in list
+        with pytest.raises(ValueError, match=msg):
+            df.set_index(["A", df.A, box(values)], drop=drop, append=append)
+
+
+class TestSetIndexCustomLabelType:
+    def test_set_index_custom_label_type(self):
+        # GH#24969
+
+        class Thing:
+            def __init__(self, name, color) -> None:
+                self.name = name
+                self.color = color
+
+            def __str__(self) -> str:
+                return f"<Thing {repr(self.name)}>"
+
+            # necessary for pretty KeyError
+            __repr__ = __str__
+
+        thing1 = Thing("One", "red")
+        thing2 = Thing("Two", "blue")
+        df = DataFrame({thing1: [0, 1], thing2: [2, 3]})
+        expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))
+
+        # use custom label directly
+        result = df.set_index(thing2)
+        tm.assert_frame_equal(result, expected)
+
+        # custom label wrapped in list
+        result = df.set_index([thing2])
+        tm.assert_frame_equal(result, expected)
+
+        # missing key
+        thing3 = Thing("Three", "pink")
+        msg = "<Thing 'Three'>"
+        with pytest.raises(KeyError, match=msg):
+            # missing label directly
+            df.set_index(thing3)
+
+        with pytest.raises(KeyError, match=msg):
+            # missing label in list
+            df.set_index([thing3])
+
+    def test_set_index_custom_label_hashable_iterable(self):
+        # GH#24969
+
+        # actual example discussed in GH 24984 was e.g. for shapely.geometry
+        # objects (e.g. a collection of Points) that can be both hashable and
+        # iterable; using frozenset as a stand-in for testing here
+
+        class Thing(frozenset):
+            # need to stabilize repr for KeyError (due to random order in sets)
+            def __repr__(self) -> str:
+                tmp = sorted(self)
+                joined_reprs = ", ".join(map(repr, tmp))
+                # double curly brace prints one brace in format string
+                return f"frozenset({{{joined_reprs}}})"
+
+        thing1 = Thing(["One", "red"])
+        thing2 = Thing(["Two", "blue"])
+        df = DataFrame({thing1: [0, 1], thing2: [2, 3]})
+        expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))
+
+        # use custom label directly
+        result = df.set_index(thing2)
+        tm.assert_frame_equal(result, expected)
+
+        # custom label wrapped in list
+        result = df.set_index([thing2])
+        tm.assert_frame_equal(result, expected)
+
+        # missing key
+        thing3 = Thing(["Three", "pink"])
+        msg = r"frozenset\(\{'Three', 'pink'\}\)"
+        with pytest.raises(KeyError, match=msg):
+            # missing label directly
+            df.set_index(thing3)
+
+        with pytest.raises(KeyError, match=msg):
+            # missing label in list
+            df.set_index([thing3])
+
+    def test_set_index_custom_label_type_raises(self):
+        # GH#24969
+
+        # purposefully inherit from something unhashable
+        class Thing(set):
+            def __init__(self, name, color) -> None:
+                self.name = name
+                self.color = color
+
+            def __str__(self) -> str:
+                return f"<Thing {repr(self.name)}>"
+
+        thing1 = Thing("One", "red")
+        thing2 = Thing("Two", "blue")
+        df = DataFrame([[0, 2], [1, 3]], columns=[thing1, thing2])
+
+        msg = 'The parameter "keys" may be a column key, .*'
+
+        with pytest.raises(TypeError, match=msg):
+            # use custom label directly
+            df.set_index(thing2)
+
+        with pytest.raises(TypeError, match=msg):
+            # custom label wrapped in list
+            df.set_index([thing2])
+
+    def test_set_index_periodindex(self):
+        # GH#6631
+        df = DataFrame(np.random.default_rng(2).random(6))
+        idx1 = period_range("2011/01/01", periods=6, freq="M")
+        idx2 = period_range("2013", periods=6, freq="Y")
+
+        df = df.set_index(idx1)
+        tm.assert_index_equal(df.index, idx1)
+        df = df.set_index(idx2)
+        tm.assert_index_equal(df.index, idx2)
diff --git a/shared/package-files/0c/0cd6f8d61f44dc84c6bc0249bbcea2e527ec2abc2cb50274139813fdba534bf9/arrow_16597.py b/shared/package-files/0c/0cd6f8d61f44dc84c6bc0249bbcea2e527ec2abc2cb50274139813fdba534bf9/arrow_16597.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ab9d6cc94eee919ef999893442bc1a2c4f0b942
--- /dev/null
+++ b/shared/package-files/0c/0cd6f8d61f44dc84c6bc0249bbcea2e527ec2abc2cb50274139813fdba534bf9/arrow_16597.py
@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This file is called from a test in test_flight.py.
+import time
+
+import pyarrow as pa
+import pyarrow.flight as flight
+
+
+class Server(flight.FlightServerBase):
+    def do_put(self, context, descriptor, reader, writer):
+        time.sleep(1)
+        raise flight.FlightCancelledError("")
+
+
+if __name__ == "__main__":
+    server = Server("grpc://localhost:0")
+    client = flight.connect(f"grpc://localhost:{server.port}")
+    schema = pa.schema([])
+    writer, reader = client.do_put(
+        flight.FlightDescriptor.for_command(b""), schema)
+    writer.done_writing()
diff --git a/shared/package-files/0c/0cd7c179805aff9f24486ecf339225a3ac67cde93c952880197d35b8c15162a2/test_operatorset.py b/shared/package-files/0c/0cd7c179805aff9f24486ecf339225a3ac67cde93c952880197d35b8c15162a2/test_operatorset.py
new file mode 100644
index 0000000000000000000000000000000000000000..fff038bb12a7e6aa100ac00b0e145dc323a77e4d
--- /dev/null
+++ b/shared/package-files/0c/0cd7c179805aff9f24486ecf339225a3ac67cde93c952880197d35b8c15162a2/test_operatorset.py
@@ -0,0 +1,68 @@
+from sympy.core.singleton import S
+
+from sympy.physics.quantum.operatorset import (
+    operators_to_state, state_to_operators
+)
+
+from sympy.physics.quantum.cartesian import (
+    XOp, XKet, PxOp, PxKet, XBra, PxBra
+)
+
+from sympy.physics.quantum.state import Ket, Bra
+from sympy.physics.quantum.operator import Operator
+from sympy.physics.quantum.spin import (
+    JxKet, JyKet, JzKet, JxBra, JyBra, JzBra,
+    JxOp, JyOp, JzOp, J2Op
+)
+
+from sympy.testing.pytest import raises
+
+
+def test_spin():
+    assert operators_to_state({J2Op, JxOp}) == JxKet
+    assert operators_to_state({J2Op, JyOp}) == JyKet
+    assert operators_to_state({J2Op, JzOp}) == JzKet
+    assert operators_to_state({J2Op(), JxOp()}) == JxKet
+    assert operators_to_state({J2Op(), JyOp()}) == JyKet
+    assert operators_to_state({J2Op(), JzOp()}) == JzKet
+
+    assert state_to_operators(JxKet) == {J2Op, JxOp}
+    assert state_to_operators(JyKet) == {J2Op, JyOp}
+    assert state_to_operators(JzKet) == {J2Op, JzOp}
+    assert state_to_operators(JxBra) == {J2Op, JxOp}
+    assert state_to_operators(JyBra) == {J2Op, JyOp}
+    assert state_to_operators(JzBra) == {J2Op, JzOp}
+
+    assert state_to_operators(JxKet(S.Half, S.Half)) == {J2Op(), JxOp()}
+    assert state_to_operators(JyKet(S.Half, S.Half)) == {J2Op(), JyOp()}
+    assert state_to_operators(JzKet(S.Half, S.Half)) == {J2Op(), JzOp()}
+    assert state_to_operators(JxBra(S.Half, S.Half)) == {J2Op(), JxOp()}
+    assert state_to_operators(JyBra(S.Half, S.Half)) == {J2Op(), JyOp()}
+    assert state_to_operators(JzBra(S.Half, S.Half)) == {J2Op(), JzOp()}
+
+
+def test_op_to_state():
+    assert operators_to_state(XOp) == XKet()
+    assert operators_to_state(PxOp) == PxKet()
+    assert operators_to_state(Operator) == Ket()
+
+    assert state_to_operators(operators_to_state(XOp("Q"))) == XOp("Q")
+    assert state_to_operators(operators_to_state(XOp())) == XOp()
+
+    raises(NotImplementedError, lambda: operators_to_state(XKet))
+
+
+def test_state_to_op():
+    assert state_to_operators(XKet) == XOp()
+    assert state_to_operators(PxKet) == PxOp()
+    assert state_to_operators(XBra) == XOp()
+    assert state_to_operators(PxBra) == PxOp()
+    assert state_to_operators(Ket) == Operator()
+    assert state_to_operators(Bra) == Operator()
+
+    assert operators_to_state(state_to_operators(XKet("test"))) == XKet("test")
+    assert operators_to_state(state_to_operators(XBra("test"))) == XKet("test")
+    assert operators_to_state(state_to_operators(XKet())) == XKet()
+    assert operators_to_state(state_to_operators(XBra())) == XKet()
+
+    raises(NotImplementedError, lambda: state_to_operators(XOp))
diff --git a/shared/package-files/0c/0cdb59e255ccd7dcf4af847c9b020aeaee78ce7fcf5f214ebcf123328acf9f24/cp1258.enc b/shared/package-files/0c/0cdb59e255ccd7dcf4af847c9b020aeaee78ce7fcf5f214ebcf123328acf9f24/cp1258.enc
new file mode 100644
index 0000000000000000000000000000000000000000..95fdef882959ac09db866fab45506ef2188e0faf
--- /dev/null
+++ b/shared/package-files/0c/0cdb59e255ccd7dcf4af847c9b020aeaee78ce7fcf5f214ebcf123328acf9f24/cp1258.enc
@@ -0,0 +1,20 @@
+# Encoding file: cp1258, single-byte
+S
+003F 0 1
+00
+0000000100020003000400050006000700080009000A000B000C000D000E000F
+0010001100120013001400150016001700180019001A001B001C001D001E001F
+0020002100220023002400250026002700280029002A002B002C002D002E002F
+0030003100320033003400350036003700380039003A003B003C003D003E003F
+0040004100420043004400450046004700480049004A004B004C004D004E004F
+0050005100520053005400550056005700580059005A005B005C005D005E005F
+0060006100620063006400650066006700680069006A006B006C006D006E006F
+0070007100720073007400750076007700780079007A007B007C007D007E007F
+20AC0081201A0192201E20262020202102C62030008A20390152008D008E008F
+009020182019201C201D20222013201402DC2122009A203A0153009D009E0178
+00A000A100A200A300A400A500A600A700A800A900AA00AB00AC00AD00AE00AF
+00B000B100B200B300B400B500B600B700B800B900BA00BB00BC00BD00BE00BF
+00C000C100C2010200C400C500C600C700C800C900CA00CB030000CD00CE00CF
+011000D1030900D300D401A000D600D700D800D900DA00DB00DC01AF030300DF
+00E000E100E2010300E400E500E600E700E800E900EA00EB030100ED00EE00EF
+011100F1032300F300F401A100F600F700F800F900FA00FB00FC01B020AB00FF
diff --git a/shared/package-files/0c/0cdc69a1a9d76f90782c9033fdaca7867d761dde7ae2cbd1277d8b1a2e84a859/screen.Eterm b/shared/package-files/0c/0cdc69a1a9d76f90782c9033fdaca7867d761dde7ae2cbd1277d8b1a2e84a859/screen.Eterm
new file mode 100644
index 0000000000000000000000000000000000000000..8ed159d35a476aeed062d3783b6c7a82790ebfee
Binary files /dev/null and b/shared/package-files/0c/0cdc69a1a9d76f90782c9033fdaca7867d761dde7ae2cbd1277d8b1a2e84a859/screen.Eterm differ
diff --git a/shared/package-files/0c/0ce0f616d28393a3bd93a53487d50a590fbae7053ccf5b523e7de05254700aca/environment.py b/shared/package-files/0c/0ce0f616d28393a3bd93a53487d50a590fbae7053ccf5b523e7de05254700aca/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecd4d998927961f185dd0ddb498136a4f3581d0e
--- /dev/null
+++ b/shared/package-files/0c/0ce0f616d28393a3bd93a53487d50a590fbae7053ccf5b523e7de05254700aca/environment.py
@@ -0,0 +1,88 @@
+import os
+from distutils.dist import Distribution
+
+__metaclass__ = type
+
+class EnvironmentConfig:
+    def __init__(self, distutils_section='ALL', **kw):
+        self._distutils_section = distutils_section
+        self._conf_keys = kw
+        self._conf = None
+        self._hook_handler = None
+
+    def dump_variable(self, name):
+        conf_desc = self._conf_keys[name]
+        hook, envvar, confvar, convert, append = conf_desc
+        if not convert:
+            convert = lambda x : x
+        print('%s.%s:' % (self._distutils_section, name))
+        v = self._hook_handler(name, hook)
+        print('  hook   : %s' % (convert(v),))
+        if envvar:
+            v = os.environ.get(envvar, None)
+            print('  environ: %s' % (convert(v),))
+        if confvar and self._conf:
+            v = self._conf.get(confvar, (None, None))[1]
+            print('  config : %s' % (convert(v),))
+
+    def dump_variables(self):
+        for name in self._conf_keys:
+            self.dump_variable(name)
+
+    def __getattr__(self, name):
+        try:
+            conf_desc = self._conf_keys[name]
+        except KeyError:
+            raise AttributeError(
+                f"'EnvironmentConfig' object has no attribute '{name}'"
+            ) from None
+
+        return self._get_var(name, conf_desc)
+
+    def get(self, name, default=None):
+        try:
+            conf_desc = self._conf_keys[name]
+        except KeyError:
+            return default
+        var = self._get_var(name, conf_desc)
+        if var is None:
+            var = default
+        return var
+
+    def _get_var(self, name, conf_desc):
+        hook, envvar, confvar, convert, append = conf_desc
+        if convert is None:
+            convert = lambda x: x
+        var = self._hook_handler(name, hook)
+        if envvar is not None:
+            envvar_contents = os.environ.get(envvar)
+            if envvar_contents is not None:
+                envvar_contents = convert(envvar_contents)
+                if var and append:
+                    if os.environ.get('NPY_DISTUTILS_APPEND_FLAGS', '1') == '1':
+                        var.extend(envvar_contents)
+                    else:
+                        # NPY_DISTUTILS_APPEND_FLAGS was explicitly set to 0
+                        # to keep old (overwrite flags rather than append to
+                        # them) behavior
+                        var = envvar_contents
+                else:
+                    var = envvar_contents
+        if confvar is not None and self._conf:
+            if confvar in self._conf:
+                source, confvar_contents = self._conf[confvar]
+                var = convert(confvar_contents)
+        return var
+
+
+    def clone(self, hook_handler):
+        ec = self.__class__(distutils_section=self._distutils_section,
+                            **self._conf_keys)
+        ec._hook_handler = hook_handler
+        return ec
+
+    def use_distribution(self, dist):
+        if isinstance(dist, Distribution):
+            self._conf = dist.get_option_dict(self._distutils_section)
+        else:
+            self._conf = dist
diff --git a/shared/package-files/0c/0ce667b5554d764c9aabc34e1b914c579b825ce30ee5dab736698c3e0735d952/fmax.h b/shared/package-files/0c/0ce667b5554d764c9aabc34e1b914c579b825ce30ee5dab736698c3e0735d952/fmax.h
new file mode 100644
index 0000000000000000000000000000000000000000..672569e3f03a1e37302aea16ea848c542c9f831f
--- /dev/null
+++ b/shared/package-files/0c/0ce667b5554d764c9aabc34e1b914c579b825ce30ee5dab736698c3e0735d952/fmax.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/fmax_ops.h>
+
+namespace at {
+
+
+// aten::fmax(Tensor self, Tensor other) -> Tensor
+inline at::Tensor fmax(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::fmax::call(self, other);
+}
+
+// aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::fmax_out::call(self, other, out);
+}
+// aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::fmax_out::call(self, other, out);
+}
+
+}
diff --git a/shared/package-files/0c/0ce8cbe50e344983fe9f4131e08fdb5b8dc1e3bfc297fc0c88bf518f2059afbb/_embedding_bag_native.h b/shared/package-files/0c/0ce8cbe50e344983fe9f4131e08fdb5b8dc1e3bfc297fc0c88bf518f2059afbb/_embedding_bag_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..09dc3340dc3c8b0cd749549571185229830a3fd2
--- /dev/null
+++ b/shared/package-files/0c/0ce8cbe50e344983fe9f4131e08fdb5b8dc1e3bfc297fc0c88bf518f2059afbb/_embedding_bag_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_out(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_cpu(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_cuda(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/0c/0ce9606f87251142e3301a74706dd9742ad737cbc57712599920dffb5ddb5920/test_inference.py b/shared/package-files/0c/0ce9606f87251142e3301a74706dd9742ad737cbc57712599920dffb5ddb5920/test_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee6a0a12a89e74ebf43e3952b45e3b310a30072c
--- /dev/null
+++ b/shared/package-files/0c/0ce9606f87251142e3301a74706dd9742ad737cbc57712599920dffb5ddb5920/test_inference.py
@@ -0,0 +1,315 @@
+"""For more tests on satisfiability, see test_dimacs"""
+
+from sympy.assumptions.ask import Q
+from sympy.core.symbol import symbols
+from sympy.logic.boolalg import And, Implies, Equivalent, true, false
+from sympy.logic.inference import literal_symbol, \
+     pl_true, satisfiable, valid, entails, PropKB
+from sympy.logic.algorithms.dpll import dpll, dpll_satisfiable, \
+    find_pure_symbol, find_unit_clause, unit_propagate, \
+    find_pure_symbol_int_repr, find_unit_clause_int_repr, \
+    unit_propagate_int_repr
+from sympy.logic.algorithms.dpll2 import dpll_satisfiable as dpll2_satisfiable
+from sympy.testing.pytest import raises
+
+
+def test_literal():
+    A, B = symbols('A,B')
+    assert literal_symbol(True) is True
+    assert literal_symbol(False) is False
+    assert literal_symbol(A) is A
+    assert literal_symbol(~A) is A
+
+
+def test_find_pure_symbol():
+    A, B, C = symbols('A,B,C')
+    assert find_pure_symbol([A], [A]) == (A, True)
+    assert find_pure_symbol([A, B], [~A | B, ~B | A]) == (None, None)
+    assert find_pure_symbol([A, B, C], [ A | ~B, ~B | ~C, C | A]) == (A, True)
+    assert find_pure_symbol([A, B, C], [~A | B, B | ~C, C | A]) == (B, True)
+    assert find_pure_symbol([A, B, C], [~A | ~B, ~B | ~C, C | A]) == (B, False)
+    assert find_pure_symbol(
+        [A, B, C], [~A | B, ~B | ~C, C | A]) == (None, None)
+
+
+def test_find_pure_symbol_int_repr():
+    assert find_pure_symbol_int_repr([1], [{1}]) == (1, True)
+    assert find_pure_symbol_int_repr([1, 2],
+                [{-1, 2}, {-2, 1}]) == (None, None)
+    assert find_pure_symbol_int_repr([1, 2, 3],
+                [{1, -2}, {-2, -3}, {3, 1}]) == (1, True)
+    assert find_pure_symbol_int_repr([1, 2, 3],
+                [{-1, 2}, {2, -3}, {3, 1}]) == (2, True)
+    assert find_pure_symbol_int_repr([1, 2, 3],
+                [{-1, -2}, {-2, -3}, {3, 1}]) == (2, False)
+    assert find_pure_symbol_int_repr([1, 2, 3],
+                [{-1, 2}, {-2, -3}, {3, 1}]) == (None, None)
+
+
+def test_unit_clause():
+    A, B, C = symbols('A,B,C')
+    assert find_unit_clause([A], {}) == (A, True)
+    assert find_unit_clause([A, ~A], {}) == (A, True)  # Wrong ??
+    assert find_unit_clause([A | B], {A: True}) == (B, True)
+    assert find_unit_clause([A | B], {B: True}) == (A, True)
+    assert find_unit_clause(
+        [A | B | C, B | ~C, A | ~B], {A: True}) == (B, False)
+    assert find_unit_clause([A | B | C, B | ~C, A | B], {A: True}) == (B, True)
+    assert find_unit_clause([A | B | C, B | ~C, A ], {}) == (A, True)
+
+
+def test_unit_clause_int_repr():
+    assert find_unit_clause_int_repr(map(set, [[1]]), {}) == (1, True)
+    assert find_unit_clause_int_repr(map(set, [[1], [-1]]), {}) == (1, True)
+    assert find_unit_clause_int_repr([{1, 2}], {1: True}) == (2, True)
+    assert find_unit_clause_int_repr([{1, 2}], {2: True}) == (1, True)
+    assert find_unit_clause_int_repr(map(set,
+        [[1, 2, 3], [2, -3], [1, -2]]), {1: True}) == (2, False)
+    assert find_unit_clause_int_repr(map(set,
+        [[1, 2, 3], [3, -3], [1, 2]]), {1: True}) == (2, True)
+
+    A, B, C = symbols('A,B,C')
+    assert find_unit_clause([A | B | C, B | ~C, A ], {}) == (A, True)
+
+
+def test_unit_propagate():
+    A, B, C = symbols('A,B,C')
+    assert unit_propagate([A | B], A) == []
+    assert unit_propagate([A | B, ~A | C, ~C | B, A], A) == [C, ~C | B, A]
+
+
+def test_unit_propagate_int_repr():
+    assert unit_propagate_int_repr([{1, 2}], 1) == []
+    assert unit_propagate_int_repr(map(set,
+        [[1, 2], [-1, 3], [-3, 2], [1]]), 1) == [{3}, {-3, 2}]
+
+
+def test_dpll():
+    """This is also tested in test_dimacs"""
+    A, B, C = symbols('A,B,C')
+    assert dpll([A | B], [A, B], {A: True, B: True}) == {A: True, B: True}
+
+
+def test_dpll_satisfiable():
+    A, B, C = symbols('A,B,C')
+    assert dpll_satisfiable( A & ~A ) is False
+    assert dpll_satisfiable( A & ~B ) == {A: True, B: False}
+    assert dpll_satisfiable(
+        A | B ) in ({A: True}, {B: True}, {A: True, B: True})
+    assert dpll_satisfiable(
+        (~A | B) & (~B | A) ) in ({A: True, B: True}, {A: False, B: False})
+    assert dpll_satisfiable( (A | B) & (~B | C) ) in ({A: True, B: False},
+            {A: True, C: True}, {B: True, C: True})
+    assert dpll_satisfiable( A & B & C  ) == {A: True, B: True, C: True}
+    assert dpll_satisfiable( (A | B) & (A >> B) ) == {B: True}
+    assert dpll_satisfiable( Equivalent(A, B) & A ) == {A: True, B: True}
+    assert dpll_satisfiable( Equivalent(A, B) & ~A ) == {A: False, B: False}
+
+
+def test_dpll2_satisfiable():
+    A, B, C = symbols('A,B,C')
+    assert dpll2_satisfiable( A & ~A ) is False
+    assert dpll2_satisfiable( A & ~B ) == {A: True, B: False}
+    assert dpll2_satisfiable(
+        A | B ) in ({A: True}, {B: True}, {A: True, B: True})
+    assert dpll2_satisfiable(
+        (~A | B) & (~B | A) ) in ({A: True, B: True}, {A: False, B: False})
+    assert dpll2_satisfiable( (A | B) & (~B | C) ) in ({A: True, B: False, C: True},
+        {A: True, B: True, C: True})
+    assert dpll2_satisfiable( A & B & C  ) == {A: True, B: True, C: True}
+    assert dpll2_satisfiable( (A | B) & (A >> B) ) in ({B: True, A: False},
+        {B: True, A: True})
+    assert dpll2_satisfiable( Equivalent(A, B) & A ) == {A: True, B: True}
+    assert dpll2_satisfiable( Equivalent(A, B) & ~A ) == {A: False, B: False}
+
+
+def test_minisat22_satisfiable():
+    A, B, C = symbols('A,B,C')
+    minisat22_satisfiable = lambda expr: satisfiable(expr, algorithm="minisat22")
+    assert minisat22_satisfiable( A & ~A ) is False
+    assert minisat22_satisfiable( A & ~B ) == {A: True, B: False}
+    assert minisat22_satisfiable(
+        A | B ) in ({A: True}, {B: False}, {A: False, B: True}, {A: True, B: True}, {A: True, B: False})
+    assert minisat22_satisfiable(
+        (~A | B) & (~B | A) ) in ({A: True, B: True}, {A: False, B: False})
+    assert minisat22_satisfiable( (A | B) & (~B | C) ) in ({A: True, B: False, C: True},
+        {A: True, B: True, C: True}, {A: False, B: True, C: True}, {A: True, B: False, C: False})
+    assert minisat22_satisfiable( A & B & C  ) == {A: True, B: True, C: True}
+    assert minisat22_satisfiable( (A | B) & (A >> B) ) in ({B: True, A: False},
+        {B: True, A: True})
+    assert minisat22_satisfiable( Equivalent(A, B) & A ) == {A: True, B: True}
+    assert minisat22_satisfiable( Equivalent(A, B) & ~A ) == {A: False, B: False}
+
+def test_minisat22_minimal_satisfiable():
+    A, B, C = symbols('A,B,C')
+    minisat22_satisfiable = lambda expr, minimal=True: satisfiable(expr, algorithm="minisat22", minimal=True)
+    assert minisat22_satisfiable( A & ~A ) is False
+    assert minisat22_satisfiable( A & ~B ) == {A: True, B: False}
+    assert minisat22_satisfiable(
+        A | B ) in ({A: True}, {B: False}, {A: False, B: True}, {A: True, B: True}, {A: True, B: False})
+    assert minisat22_satisfiable(
+        (~A | B) & (~B | A) ) in ({A: True, B: True}, {A: False, B: False})
+    assert minisat22_satisfiable( (A | B) & (~B | C) ) in ({A: True, B: False, C: True},
+        {A: True, B: True, C: True}, {A: False, B: True, C: True}, {A: True, B: False, C: False})
+    assert minisat22_satisfiable( A & B & C  ) == {A: True, B: True, C: True}
+    assert minisat22_satisfiable( (A | B) & (A >> B) ) in ({B: True, A: False},
+        {B: True, A: True})
+    assert minisat22_satisfiable( Equivalent(A, B) & A ) == {A: True, B: True}
+    assert minisat22_satisfiable( Equivalent(A, B) & ~A ) == {A: False, B: False}
+    g = satisfiable((A | B | C),algorithm="minisat22",minimal=True,all_models=True)
+    sol = next(g)
+    first_solution = {key for key, value in sol.items() if value}
+    sol=next(g)
+    second_solution = {key for key, value in sol.items() if value}
+    sol=next(g)
+    third_solution = {key for key, value in sol.items() if value}
+    assert not first_solution <= second_solution
+    assert not second_solution <= third_solution
+    assert not first_solution <= third_solution
+
+def test_satisfiable():
+    A, B, C = symbols('A,B,C')
+    assert satisfiable(A & (A >> B) & ~B) is False
+
+
+def test_valid():
+    A, B, C = symbols('A,B,C')
+    assert valid(A >> (B >> A)) is True
+    assert valid((A >> (B >> C)) >> ((A >> B) >> (A >> C))) is True
+    assert valid((~B >> ~A) >> (A >> B)) is True
+    assert valid(A | B | C) is False
+    assert valid(A >> B) is False
+
+
+def test_pl_true():
+    A, B, C = symbols('A,B,C')
+    assert pl_true(True) is True
+    assert pl_true( A & B, {A: True, B: True}) is True
+    assert pl_true( A | B, {A: True}) is True
+    assert pl_true( A | B, {B: True}) is True
+    assert pl_true( A | B, {A: None, B: True}) is True
+    assert pl_true( A >> B, {A: False}) is True
+    assert pl_true( A | B | ~C, {A: False, B: True, C: True}) is True
+    assert pl_true(Equivalent(A, B), {A: False, B: False}) is True
+
+    # test for false
+    assert pl_true(False) is False
+    assert pl_true( A & B, {A: False, B: False}) is False
+    assert pl_true( A & B, {A: False}) is False
+    assert pl_true( A & B, {B: False}) is False
+    assert pl_true( A | B, {A: False, B: False}) is False
+
+    #test for None
+    assert pl_true(B, {B: None}) is None
+    assert pl_true( A & B, {A: True, B: None}) is None
+    assert pl_true( A >> B, {A: True, B: None}) is None
+    assert pl_true(Equivalent(A, B), {A: None}) is None
+    assert pl_true(Equivalent(A, B), {A: True, B: None}) is None
+
+    # Test for deep
+    assert pl_true(A | B, {A: False}, deep=True) is None
+    assert pl_true(~A & ~B, {A: False}, deep=True) is None
+    assert pl_true(A | B, {A: False, B: False}, deep=True) is False
+    assert pl_true(A & B & (~A | ~B), {A: True}, deep=True) is False
+    assert pl_true((C >> A) >> (B >> A), {C: True}, deep=True) is True
+
+
+def test_pl_true_wrong_input():
+    from sympy.core.numbers import pi
+    raises(ValueError, lambda: pl_true('John Cleese'))
+    raises(ValueError, lambda: pl_true(42 + pi + pi ** 2))
+    raises(ValueError, lambda: pl_true(42))
+
+
+def test_entails():
+    A, B, C = symbols('A, B, C')
+    assert entails(A, [A >> B, ~B]) is False
+    assert entails(B, [Equivalent(A, B), A]) is True
+    assert entails((A >> B) >> (~A >> ~B)) is False
+    assert entails((A >> B) >> (~B >> ~A)) is True
+
+
+def test_PropKB():
+    A, B, C = symbols('A,B,C')
+    kb = PropKB()
+    assert kb.ask(A >> B) is False
+    assert kb.ask(A >> (B >> A)) is True
+    kb.tell(A >> B)
+    kb.tell(B >> C)
+    assert kb.ask(A) is False
+    assert kb.ask(B) is False
+    assert kb.ask(C) is False
+    assert kb.ask(~A) is False
+    assert kb.ask(~B) is False
+    assert kb.ask(~C) is False
+    assert kb.ask(A >> C) is True
+    kb.tell(A)
+    assert kb.ask(A) is True
+    assert kb.ask(B) is True
+    assert kb.ask(C) is True
+    assert kb.ask(~C) is False
+    kb.retract(A)
+    assert kb.ask(C) is False
+
+
+def test_propKB_tolerant():
+    """"tolerant to bad input"""
+    kb = PropKB()
+    A, B, C = symbols('A,B,C')
+    assert kb.ask(B) is False
+
+def test_satisfiable_non_symbols():
+    x, y = symbols('x y')
+    assumptions = Q.zero(x*y)
+    facts = Implies(Q.zero(x*y), Q.zero(x) | Q.zero(y))
+    query = ~Q.zero(x) & ~Q.zero(y)
+    refutations = [
+        {Q.zero(x): True, Q.zero(x*y): True},
+        {Q.zero(y): True, Q.zero(x*y): True},
+        {Q.zero(x): True, Q.zero(y): True, Q.zero(x*y): True},
+        {Q.zero(x): True, Q.zero(y): False, Q.zero(x*y): True},
+        {Q.zero(x): False, Q.zero(y): True, Q.zero(x*y): True}]
+    assert not satisfiable(And(assumptions, facts, query), algorithm='dpll')
+    assert satisfiable(And(assumptions, facts, ~query), algorithm='dpll') in refutations
+    assert not satisfiable(And(assumptions, facts, query), algorithm='dpll2')
+    assert satisfiable(And(assumptions, facts, ~query), algorithm='dpll2') in refutations
+
+def test_satisfiable_bool():
+    from sympy.core.singleton import S
+    assert satisfiable(true) == {true: true}
+    assert satisfiable(S.true) == {true: true}
+    assert satisfiable(false) is False
+    assert satisfiable(S.false) is False
+
+
+def test_satisfiable_all_models():
+    from sympy.abc import A, B
+    assert next(satisfiable(False, all_models=True)) is False
+    assert list(satisfiable((A >> ~A) & A, all_models=True)) == [False]
+    assert list(satisfiable(True, all_models=True)) == [{true: true}]
+
+    models = [{A: True, B: False}, {A: False, B: True}]
+    result = satisfiable(A ^ B, all_models=True)
+    models.remove(next(result))
+    models.remove(next(result))
+    raises(StopIteration, lambda: next(result))
+    assert not models
+
+    assert list(satisfiable(Equivalent(A, B), all_models=True)) == \
+    [{A: False, B: False}, {A: True, B: True}]
+
+    models = [{A: False, B: False}, {A: False, B: True}, {A: True, B: True}]
+    for model in satisfiable(A >> B, all_models=True):
+        models.remove(model)
+    assert not models
+
+    # This is a santiy test to check that only the required number
+    # of solutions are generated. The expr below has 2**100 - 1 models
+    # which would time out the test if all are generated at once.
+    from sympy.utilities.iterables import numbered_symbols
+    from sympy.logic.boolalg import Or
+    sym = numbered_symbols()
+    X = [next(sym) for i in range(100)]
+    result = satisfiable(Or(*X), all_models=True)
+    for i in range(10):
+        assert next(result)
diff --git a/shared/package-files/0c/0ceac51c81e7b3ebed913f0c2f014cba392ae50b16d975cabbb72342ec643d97/__init__.py b/shared/package-files/0c/0ceac51c81e7b3ebed913f0c2f014cba392ae50b16d975cabbb72342ec643d97/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c9fb032a2bb3d5e4452b48d0a870615c186f365
--- /dev/null
+++ b/shared/package-files/0c/0ceac51c81e7b3ebed913f0c2f014cba392ae50b16d975cabbb72342ec643d97/__init__.py
@@ -0,0 +1,9 @@
+from .activation import MultiheadAttention
+from .rnn import LSTM
+from .rnn import LSTMCell
+
+__all__ = [
+    'LSTM',
+    'LSTMCell',
+    'MultiheadAttention',
+]
diff --git a/shared/package-files/0c/0cf137da97ad0151335e03a4252c614ca423ceebb129a9e24bfe7f5ab00078ea/vecm_e6_jmulti_cis_ir.txt b/shared/package-files/0c/0cf137da97ad0151335e03a4252c614ca423ceebb129a9e24bfe7f5ab00078ea/vecm_e6_jmulti_cis_ir.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e46079f25c3a16b57f7abffe5c6979a52b515bee
--- /dev/null
+++ b/shared/package-files/0c/0cf137da97ad0151335e03a4252c614ca423ceebb129a9e24bfe7f5ab00078ea/vecm_e6_jmulti_cis_ir.txt
@@ -0,0 +1,29 @@
+*** Tue, 16 Aug 2016 19:30:53 ***
+VECM Forecast Error Impulse Responses
+
+                   Selected  Impulse  Responses: "impulse variable -> response variable"
+
+time                            Dp                   R                  Dp                   R    
+                              ->Dp                ->Dp                 ->R                 ->R    
+
+    point estimate          1.0000              0.0000              0.0000              1.0000    
+  1 point estimate          0.0343              0.2262              0.1949              1.1727    
+  2 point estimate         -0.0083              0.2023              0.2196              1.1306    
+  3 point estimate          0.0755              0.1911              0.3116              1.2648    
+  4 point estimate          0.4097              0.2183              0.4733              1.2744    
+  5 point estimate          0.1077              0.2852              0.5556              1.2246    
+  6 point estimate          0.0771              0.2694              0.5629              1.2141    
+  7 point estimate          0.1328              0.2612              0.6113              1.1860    
+  8 point estimate          0.2484              0.2670              0.6620              1.1509    
+  9 point estimate          0.1502              0.2849              0.6771              1.1331    
+ 10 point estimate          0.1332              0.2774              0.6723              1.1187    
+ 11 point estimate          0.1600              0.2713              0.6807              1.1029    
+ 12 point estimate          0.1993              0.2720              0.6909              1.0937    
+ 13 point estimate          0.1658              0.2773              0.6907              1.0893    
+ 14 point estimate          0.1573              0.2742              0.6848              1.0857    
+ 15 point estimate          0.1685              0.2714              0.6847              1.0829    
+ 16 point estimate          0.1817              0.2716              0.6865              1.0818    
+ 17 point estimate          0.1699              0.2734              0.6852              1.0817    
+ 18 point estimate          0.1660              0.2723              0.6824              1.0818    
+ 19 point estimate          0.1704              0.2713              0.6819              1.0817    
+ 20 point estimate          0.1749              0.2715              0.6825              1.0819    
diff --git a/shared/package-files/0c/0cf46a9b6a01431e8a2a2e6d97d7435695c47403bef96d16a51404c8b790a5fe/vode.py b/shared/package-files/0c/0cf46a9b6a01431e8a2a2e6d97d7435695c47403bef96d16a51404c8b790a5fe/vode.py
new file mode 100644
index 0000000000000000000000000000000000000000..f92927901084ce33cdeb006057d85dd501b13aae
--- /dev/null
+++ b/shared/package-files/0c/0cf46a9b6a01431e8a2a2e6d97d7435695c47403bef96d16a51404c8b790a5fe/vode.py
@@ -0,0 +1,15 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__: list[str] = []
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="integrate", module="vode",
+                                   private_modules=["_vode"], all=__all__,
+                                   attribute=name)
diff --git a/shared/package-files/0c/0cf4ece93e05f94870cc541387ed5f484a50cdacc7da37f6c6922ba3722d178a/utils.py b/shared/package-files/0c/0cf4ece93e05f94870cc541387ed5f484a50cdacc7da37f6c6922ba3722d178a/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..826b9567b47bc704902eb959fd21376c8969f695
--- /dev/null
+++ b/shared/package-files/0c/0cf4ece93e05f94870cc541387ed5f484a50cdacc7da37f6c6922ba3722d178a/utils.py
@@ -0,0 +1,24 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+from cryptography.hazmat.bindings._rust import asn1
+from cryptography.hazmat.primitives import hashes
+
+decode_dss_signature = asn1.decode_dss_signature
+encode_dss_signature = asn1.encode_dss_signature
+
+
+class Prehashed:
+    def __init__(self, algorithm: hashes.HashAlgorithm):
+        if not isinstance(algorithm, hashes.HashAlgorithm):
+            raise TypeError("Expected instance of HashAlgorithm.")
+
+        self._algorithm = algorithm
+        self._digest_size = algorithm.digest_size
+
+    @property
+    def digest_size(self) -> int:
+        return self._digest_size
diff --git a/shared/package-files/0c/0cf63617fb41fcd993742cb0b4277a1d8e71880a2f3c711aa2b2c229ba8700e0/positive.h b/shared/package-files/0c/0cf63617fb41fcd993742cb0b4277a1d8e71880a2f3c711aa2b2c229ba8700e0/positive.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7742351f24b2f0455d4cd3d447bd12743f95f54
--- /dev/null
+++ b/shared/package-files/0c/0cf63617fb41fcd993742cb0b4277a1d8e71880a2f3c711aa2b2c229ba8700e0/positive.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/positive_ops.h>
+
+namespace at {
+
+
+// aten::positive(Tensor(a) self) -> Tensor(a)
+inline at::Tensor positive(const at::Tensor & self) {
+    return at::_ops::positive::call(self);
+}
+
+}
diff --git a/shared/package-files/0c/0cf7b4a8d39584bc799920c89e4241360c5dfb6da786428b8c195157ee9f5988/__init__.py b/shared/package-files/0c/0cf7b4a8d39584bc799920c89e4241360c5dfb6da786428b8c195157ee9f5988/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..de86a59e07113dcc7f9c656e65c7708ee230afa6
--- /dev/null
+++ b/shared/package-files/0c/0cf7b4a8d39584bc799920c89e4241360c5dfb6da786428b8c195157ee9f5988/__init__.py
@@ -0,0 +1,56 @@
+"""Popular unsupervised clustering algorithms."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from ._affinity_propagation import AffinityPropagation, affinity_propagation
+from ._agglomerative import (
+    AgglomerativeClustering,
+    FeatureAgglomeration,
+    linkage_tree,
+    ward_tree,
+)
+from ._bicluster import SpectralBiclustering, SpectralCoclustering
+from ._birch import Birch
+from ._bisect_k_means import BisectingKMeans
+from ._dbscan import DBSCAN, dbscan
+from ._hdbscan.hdbscan import HDBSCAN
+from ._kmeans import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus
+from ._mean_shift import MeanShift, estimate_bandwidth, get_bin_seeds, mean_shift
+from ._optics import (
+    OPTICS,
+    cluster_optics_dbscan,
+    cluster_optics_xi,
+    compute_optics_graph,
+)
+from ._spectral import SpectralClustering, spectral_clustering
+
+__all__ = [
+    "DBSCAN",
+    "HDBSCAN",
+    "OPTICS",
+    "AffinityPropagation",
+    "AgglomerativeClustering",
+    "Birch",
+    "BisectingKMeans",
+    "FeatureAgglomeration",
+    "KMeans",
+    "MeanShift",
+    "MiniBatchKMeans",
+    "SpectralBiclustering",
+    "SpectralClustering",
+    "SpectralCoclustering",
+    "affinity_propagation",
+    "cluster_optics_dbscan",
+    "cluster_optics_xi",
+    "compute_optics_graph",
+    "dbscan",
+    "estimate_bandwidth",
+    "get_bin_seeds",
+    "k_means",
+    "kmeans_plusplus",
+    "linkage_tree",
+    "mean_shift",
+    "spectral_clustering",
+    "ward_tree",
+]
diff --git a/shared/package-files/0c/0cfed03b7bba2ecf26f732da043073569cff23be8888a484242993828f6db05c/_to_dense_compositeexplicitautograd_dispatch.h b/shared/package-files/0c/0cfed03b7bba2ecf26f732da043073569cff23be8888a484242993828f6db05c/_to_dense_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7896e26f7f8ea5190502f37d493f61564fad3c76
--- /dev/null
+++ b/shared/package-files/0c/0cfed03b7bba2ecf26f732da043073569cff23be8888a484242993828f6db05c/_to_dense_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _to_dense_out(at::Tensor & out, const at::Tensor & self, c10::optional<at::ScalarType> dtype=c10::nullopt, c10::optional<bool> masked_grad=c10::nullopt);
+TORCH_API at::Tensor & _to_dense_outf(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/1b/1b00229df5a979a040339bbc72d448f39968fee5cc24f07241c9f6129a9b53dd/kl.msg b/shared/package-files/1b/1b00229df5a979a040339bbc72d448f39968fee5cc24f07241c9f6129a9b53dd/kl.msg
new file mode 100644
index 0000000000000000000000000000000000000000..d877bfe6af30d0d4b3dcb51d7b65bade8b5999eb
--- /dev/null
+++ b/shared/package-files/1b/1b00229df5a979a040339bbc72d448f39968fee5cc24f07241c9f6129a9b53dd/kl.msg
@@ -0,0 +1,47 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset kl DAYS_OF_WEEK_ABBREV [list \
+        "sab"\
+        "ata"\
+        "mar"\
+        "pin"\
+        "sis"\
+        "tal"\
+        "arf"]
+    ::msgcat::mcset kl DAYS_OF_WEEK_FULL [list \
+        "sabaat"\
+        "ataasinngorneq"\
+        "marlunngorneq"\
+        "pingasunngorneq"\
+        "sisamanngorneq"\
+        "tallimanngorneq"\
+        "arfininngorneq"]
+    ::msgcat::mcset kl MONTHS_ABBREV [list \
+        "jan"\
+        "feb"\
+        "mar"\
+        "apr"\
+        "maj"\
+        "jun"\
+        "jul"\
+        "aug"\
+        "sep"\
+        "okt"\
+        "nov"\
+        "dec"\
+        ""]
+    ::msgcat::mcset kl MONTHS_FULL [list \
+        "januari"\
+        "februari"\
+        "martsi"\
+        "aprili"\
+        "maji"\
+        "juni"\
+        "juli"\
+        "augustusi"\
+        "septemberi"\
+        "oktoberi"\
+        "novemberi"\
+        "decemberi"\
+        ""]
+}
diff --git a/shared/package-files/1b/1b013ee8a342e4851d0c7b7142e6b4a7f9454c556b71f9a5ab313a9458cc3928/_upsample_nearest_exact3d_meta_dispatch.h b/shared/package-files/1b/1b013ee8a342e4851d0c7b7142e6b4a7f9454c556b71f9a5ab313a9458cc3928/_upsample_nearest_exact3d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bffa26fc3c83042fa141cd93f7155b43f8cd3774
--- /dev/null
+++ b/shared/package-files/1b/1b013ee8a342e4851d0c7b7142e6b4a7f9454c556b71f9a5ab313a9458cc3928/_upsample_nearest_exact3d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d=c10::nullopt, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor _upsample_nearest_exact3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d=c10::nullopt, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & _upsample_nearest_exact3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d=c10::nullopt, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & _upsample_nearest_exact3d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & _upsample_nearest_exact3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d=c10::nullopt, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & _upsample_nearest_exact3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/1b/1b059ce22a65a04ee8812f4b4ce9248ec8a3d666cf310c5b576fe9bcdae261b1/root_system.py b/shared/package-files/1b/1b059ce22a65a04ee8812f4b4ce9248ec8a3d666cf310c5b576fe9bcdae261b1/root_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..60c516b07c7693a38bb8814f61917bd552cdfd70
--- /dev/null
+++ b/shared/package-files/1b/1b059ce22a65a04ee8812f4b4ce9248ec8a3d666cf310c5b576fe9bcdae261b1/root_system.py
@@ -0,0 +1,199 @@
+from .cartan_type import CartanType
+from sympy.core.basic import Atom
+
+class RootSystem(Atom):
+    """Represent the root system of a simple Lie algebra
+
+    Every simple Lie algebra has a unique root system.  To find the root
+    system, we first consider the Cartan subalgebra of g, which is the maximal
+    abelian subalgebra, and consider the adjoint action of g on this
+    subalgebra.  There is a root system associated with this action. Now, a
+    root system over a vector space V is a set of finite vectors Phi (called
+    roots), which satisfy:
+
+    1.  The roots span V
+    2.  The only scalar multiples of x in Phi are x and -x
+    3.  For every x in Phi, the set Phi is closed under reflection
+        through the hyperplane perpendicular to x.
+    4.  If x and y are roots in Phi, then the projection of y onto
+        the line through x is a half-integral multiple of x.
+
+    Now, there is a subset of Phi, which we will call Delta, such that:
+    1.  Delta is a basis of V
+    2.  Each root x in Phi can be written x = sum k_y y for y in Delta
+
+    The elements of Delta are called the simple roots.
+    Therefore, we see that the simple roots span the root space of a given
+    simple Lie algebra.
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Root_system
+    .. [2] Lie Algebras and Representation Theory - Humphreys
+
+    """
+
+    def __new__(cls, cartantype):
+        """Create a new RootSystem object
+
+        This method assigns an attribute called cartan_type to each instance of
+        a RootSystem object.  When an instance of RootSystem is called, it
+        needs an argument, which should be an instance of a simple Lie algebra.
+        We then take the CartanType of this argument and set it as the
+        cartan_type attribute of the RootSystem instance.
+
+        """
+        obj = Atom.__new__(cls)
+        obj.cartan_type = CartanType(cartantype)
+        return obj
+
+    def simple_roots(self):
+        """Generate the simple roots of the Lie algebra
+
+        The rank of the Lie algebra determines the number of simple roots that
+        it has.  This method obtains the rank of the Lie algebra, and then uses
+        the simple_root method from the Lie algebra classes to generate all the
+        simple roots.
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> roots = c.simple_roots()
+        >>> roots
+        {1: [1, -1, 0, 0], 2: [0, 1, -1, 0], 3: [0, 0, 1, -1]}
+
+        """
+        n = self.cartan_type.rank()
+        roots = {}
+        for i in range(1, n+1):
+            root = self.cartan_type.simple_root(i)
+            roots[i] = root
+        return roots
+
+
+    def all_roots(self):
+        """Generate all the roots of a given root system
+
+        The result is a dictionary where the keys are integer numbers.  It
+        generates the roots by getting the dictionary of all positive roots
+        from the bases classes, and then taking each root, and multiplying it
+        by -1 and adding it to the dictionary.  In this way all the negative
+        roots are generated.
+
+        """
+        alpha = self.cartan_type.positive_roots()
+        keys = list(alpha.keys())
+        k = max(keys)
+        for val in keys:
+            k += 1
+            root = alpha[val]
+            newroot = [-x for x in root]
+            alpha[k] = newroot
+        return alpha
+
+    def root_space(self):
+        """Return the span of the simple roots
+
+        The root space is the vector space spanned by the simple roots, i.e. it
+        is a vector space with a distinguished basis, the simple roots.  This
+        method returns a string that represents the root space as the span of
+        the simple roots, alpha[1],...., alpha[n].
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> c.root_space()
+        'alpha[1] + alpha[2] + alpha[3]'
+
+        """
+        n = self.cartan_type.rank()
+        rs = " + ".join("alpha["+str(i) +"]" for i in range(1, n+1))
+        return rs
+
+    def add_simple_roots(self, root1, root2):
+        """Add two simple roots together
+
+        The function takes as input two integers, root1 and root2.  It then
+        uses these integers as keys in the dictionary of simple roots, and gets
+        the corresponding simple roots, and then adds them together.
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> newroot = c.add_simple_roots(1, 2)
+        >>> newroot
+        [1, 0, -1, 0]
+
+        """
+
+        alpha = self.simple_roots()
+        if root1 > len(alpha) or root2 > len(alpha):
+            raise ValueError("You've used a root that doesn't exist!")
+        a1 = alpha[root1]
+        a2 = alpha[root2]
+        newroot = [_a1 + _a2 for _a1, _a2 in zip(a1, a2)]
+        return newroot
+
+    def add_as_roots(self, root1, root2):
+        """Add two roots together if and only if their sum is also a root
+
+        It takes as input two vectors which should be roots.  It then computes
+        their sum and checks if it is in the list of all possible roots.  If it
+        is, it returns the sum.  Otherwise it returns a string saying that the
+        sum is not a root.
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> c.add_as_roots([1, 0, -1, 0], [0, 0, 1, -1])
+        [1, 0, 0, -1]
+        >>> c.add_as_roots([1, -1, 0, 0], [0, 0, -1, 1])
+        'The sum of these two roots is not a root'
+
+        """
+        alpha = self.all_roots()
+        newroot = [r1 + r2 for r1, r2 in zip(root1, root2)]
+        if newroot in alpha.values():
+            return newroot
+        else:
+            return "The sum of these two roots is not a root"
+
+
+    def cartan_matrix(self):
+        """Cartan matrix of Lie algebra associated with this root system
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> c.cartan_matrix()
+        Matrix([
+            [ 2, -1,  0],
+            [-1,  2, -1],
+            [ 0, -1,  2]])
+        """
+        return self.cartan_type.cartan_matrix()
+
+    def dynkin_diagram(self):
+        """Dynkin diagram of the Lie algebra associated with this root system
+
+        Examples
+        ========
+
+        >>> from sympy.liealgebras.root_system import RootSystem
+        >>> c = RootSystem("A3")
+        >>> print(c.dynkin_diagram())
+        0---0---0
+        1   2   3
+        """
+        return self.cartan_type.dynkin_diagram()
diff --git a/shared/package-files/1b/1b0d063f6e303f6acf9f7f16c256bd5bb070587e09b421a96a9ad96f95ba2556/test_type_B.py b/shared/package-files/1b/1b0d063f6e303f6acf9f7f16c256bd5bb070587e09b421a96a9ad96f95ba2556/test_type_B.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f2a9011f96bc647e48d39e16cf10703a99d86b3
--- /dev/null
+++ b/shared/package-files/1b/1b0d063f6e303f6acf9f7f16c256bd5bb070587e09b421a96a9ad96f95ba2556/test_type_B.py
@@ -0,0 +1,17 @@
+from sympy.liealgebras.cartan_type import CartanType
+from sympy.matrices import Matrix
+
+def test_type_B():
+    c = CartanType("B3")
+    m = Matrix(3, 3, [2, -1, 0, -1, 2, -2, 0, -1, 2])
+    assert m == c.cartan_matrix()
+    assert c.dimension() == 3
+    assert c.roots() == 18
+    assert c.simple_root(3) == [0, 0, 1]
+    assert c.basis() == 3
+    assert c.lie_algebra() == "so(6)"
+    diag = "0---0=>=0\n1   2   3"
+    assert c.dynkin_diagram() == diag
+    assert c.positive_roots() ==  {1: [1, -1, 0], 2: [1, 1, 0], 3: [1, 0, -1],
+            4: [1, 0, 1], 5: [0, 1, -1], 6: [0, 1, 1], 7: [1, 0, 0],
+            8: [0, 1, 0], 9: [0, 0, 1]}
diff --git a/shared/package-files/1b/1b1105d5621f94f3528653ab65cf0065c87623a1b10e56e92261ea5fad7f3f9a/tree.py b/shared/package-files/1b/1b1105d5621f94f3528653ab65cf0065c87623a1b10e56e92261ea5fad7f3f9a/tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea81e3882d891d0bc10fd053b48191d9f4baab60
--- /dev/null
+++ b/shared/package-files/1b/1b1105d5621f94f3528653ab65cf0065c87623a1b10e56e92261ea5fad7f3f9a/tree.py
@@ -0,0 +1,175 @@
+def pprint_nodes(subtrees):
+    """
+    Prettyprints systems of nodes.
+
+    Examples
+    ========
+
+    >>> from sympy.printing.tree import pprint_nodes
+    >>> print(pprint_nodes(["a", "b1\\nb2", "c"]))
+    +-a
+    +-b1
+    | b2
+    +-c
+
+    """
+    def indent(s, type=1):
+        x = s.split("\n")
+        r = "+-%s\n" % x[0]
+        for a in x[1:]:
+            if a == "":
+                continue
+            if type == 1:
+                r += "| %s\n" % a
+            else:
+                r += "  %s\n" % a
+        return r
+    if not subtrees:
+        return ""
+    f = ""
+    for a in subtrees[:-1]:
+        f += indent(a)
+    f += indent(subtrees[-1], 2)
+    return f
+
+
+def print_node(node, assumptions=True):
+    """
+    Returns information about the "node".
+
+    This includes class name, string representation and assumptions.
+
+    Parameters
+    ==========
+
+    assumptions : bool, optional
+        See the ``assumptions`` keyword in ``tree``
+    """
+    s = "%s: %s\n" % (node.__class__.__name__, str(node))
+
+    if assumptions:
+        d = node._assumptions
+    else:
+        d = None
+
+    if d:
+        for a in sorted(d):
+            v = d[a]
+            if v is None:
+                continue
+            s += "%s: %s\n" % (a, v)
+
+    return s
+
+
+def tree(node, assumptions=True):
+    """
+    Returns a tree representation of "node" as a string.
+
+    It uses print_node() together with pprint_nodes() on node.args recursively.
+
+    Parameters
+    ==========
+
+    asssumptions : bool, optional
+        The flag to decide whether to print out all the assumption data
+        (such as ``is_integer`, ``is_real``) associated with the
+        expression or not.
+
+        Enabling the flag makes the result verbose, and the printed
+        result may not be determinisitic because of the randomness used
+        in backtracing the assumptions.
+
+    See Also
+    ========
+
+    print_tree
+
+    """
+    subtrees = []
+    for arg in node.args:
+        subtrees.append(tree(arg, assumptions=assumptions))
+    s = print_node(node, assumptions=assumptions) + pprint_nodes(subtrees)
+    return s
+
+
+def print_tree(node, assumptions=True):
+    """
+    Prints a tree representation of "node".
+
+    Parameters
+    ==========
+
+    asssumptions : bool, optional
+        The flag to decide whether to print out all the assumption data
+        (such as ``is_integer`, ``is_real``) associated with the
+        expression or not.
+
+        Enabling the flag makes the result verbose, and the printed
+        result may not be determinisitic because of the randomness used
+        in backtracing the assumptions.
+
+    Examples
+    ========
+
+    >>> from sympy.printing import print_tree
+    >>> from sympy import Symbol
+    >>> x = Symbol('x', odd=True)
+    >>> y = Symbol('y', even=True)
+
+    Printing with full assumptions information:
+
+    >>> print_tree(y**x)
+    Pow: y**x
+    +-Symbol: y
+    | algebraic: True
+    | commutative: True
+    | complex: True
+    | even: True
+    | extended_real: True
+    | finite: True
+    | hermitian: True
+    | imaginary: False
+    | infinite: False
+    | integer: True
+    | irrational: False
+    | noninteger: False
+    | odd: False
+    | rational: True
+    | real: True
+    | transcendental: False
+    +-Symbol: x
+      algebraic: True
+      commutative: True
+      complex: True
+      even: False
+      extended_nonzero: True
+      extended_real: True
+      finite: True
+      hermitian: True
+      imaginary: False
+      infinite: False
+      integer: True
+      irrational: False
+      noninteger: False
+      nonzero: True
+      odd: True
+      rational: True
+      real: True
+      transcendental: False
+      zero: False
+
+    Hiding the assumptions:
+
+    >>> print_tree(y**x, assumptions=False)
+    Pow: y**x
+    +-Symbol: y
+    +-Symbol: x
+
+    See Also
+    ========
+
+    tree
+
+    """
+    print(tree(node, assumptions=assumptions))
diff --git a/shared/package-files/1b/1b125a3f94289c3ff62f4dee59bcc46ded67aa4f759d3dbc035d84ce92d85d1e/message.py b/shared/package-files/1b/1b125a3f94289c3ff62f4dee59bcc46ded67aa4f759d3dbc035d84ce92d85d1e/message.py
new file mode 100644
index 0000000000000000000000000000000000000000..6752ce0fa138255f7e31320d2840abe5c903278a
--- /dev/null
+++ b/shared/package-files/1b/1b125a3f94289c3ff62f4dee59bcc46ded67aa4f759d3dbc035d84ce92d85d1e/message.py
@@ -0,0 +1,1173 @@
+# Copyright (C) 2001-2007 Python Software Foundation
+# Author: Barry Warsaw
+# Contact: email-sig@python.org
+
+"""Basic message object for the email package object model."""
+
+__all__ = ['Message', 'EmailMessage']
+
+import re
+import uu
+import quopri
+from io import BytesIO, StringIO
+
+# Intrapackage imports
+from email import utils
+from email import errors
+from email._policybase import Policy, compat32
+from email import charset as _charset
+from email._encoded_words import decode_b
+Charset = _charset.Charset
+
+SEMISPACE = '; '
+
+# Regular expression that matches `special' characters in parameters, the
+# existence of which force quoting of the parameter value.
+tspecials = re.compile(r'[ \(\)<>@,;:\\"/\[\]\?=]')
+
+
+def _splitparam(param):
+    # Split header parameters.  BAW: this may be too simple.  It isn't
+    # strictly RFC 2045 (section 5.1) compliant, but it catches most headers
+    # found in the wild.  We may eventually need a full fledged parser.
+    # RDM: we might have a Header here; for now just stringify it.
+    a, sep, b = str(param).partition(';')
+    if not sep:
+        return a.strip(), None
+    return a.strip(), b.strip()
+
+def _formatparam(param, value=None, quote=True):
+    """Convenience function to format and return a key=value pair.
+
+    This will quote the value if needed or if quote is true.  If value is a
+    three tuple (charset, language, value), it will be encoded according
+    to RFC2231 rules.  If it contains non-ascii characters it will likewise
+    be encoded according to RFC2231 rules, using the utf-8 charset and
+    a null language.
+    """
+    if value is not None and len(value) > 0:
+        # A tuple is used for RFC 2231 encoded parameter values where items
+        # are (charset, language, value).  charset is a string, not a Charset
+        # instance.  RFC 2231 encoded values are never quoted, per RFC.
+        if isinstance(value, tuple):
+            # Encode as per RFC 2231
+            param += '*'
+            value = utils.encode_rfc2231(value[2], value[0], value[1])
+            return '%s=%s' % (param, value)
+        else:
+            try:
+                value.encode('ascii')
+            except UnicodeEncodeError:
+                param += '*'
+                value = utils.encode_rfc2231(value, 'utf-8', '')
+                return '%s=%s' % (param, value)
+        # BAW: Please check this.  I think that if quote is set it should
+        # force quoting even if not necessary.
+        if quote or tspecials.search(value):
+            return '%s="%s"' % (param, utils.quote(value))
+        else:
+            return '%s=%s' % (param, value)
+    else:
+        return param
+
+def _parseparam(s):
+    # RDM This might be a Header, so for now stringify it.
+    s = ';' + str(s)
+    plist = []
+    while s[:1] == ';':
+        s = s[1:]
+        end = s.find(';')
+        while end > 0 and (s.count('"', 0, end) - s.count('\\"', 0, end)) % 2:
+            end = s.find(';', end + 1)
+        if end < 0:
+            end = len(s)
+        f = s[:end]
+        if '=' in f:
+            i = f.index('=')
+            f = f[:i].strip().lower() + '=' + f[i+1:].strip()
+        plist.append(f.strip())
+        s = s[end:]
+    return plist
+
+
+def _unquotevalue(value):
+    # This is different than utils.collapse_rfc2231_value() because it doesn't
+    # try to convert the value to a unicode.  Message.get_param() and
+    # Message.get_params() are both currently defined to return the tuple in
+    # the face of RFC 2231 parameters.
+    if isinstance(value, tuple):
+        return value[0], value[1], utils.unquote(value[2])
+    else:
+        return utils.unquote(value)
+
+
+
+class Message:
+    """Basic message object.
+
+    A message object is defined as something that has a bunch of RFC 2822
+    headers and a payload.  It may optionally have an envelope header
+    (a.k.a. Unix-From or From_ header).  If the message is a container (i.e. a
+    multipart or a message/rfc822), then the payload is a list of Message
+    objects, otherwise it is a string.
+
+    Message objects implement part of the `mapping' interface, which assumes
+    there is exactly one occurrence of the header per message.  Some headers
+    do in fact appear multiple times (e.g. Received) and for those headers,
+    you must use the explicit API to set or get all the headers.  Not all of
+    the mapping methods are implemented.
+    """
+    def __init__(self, policy=compat32):
+        self.policy = policy
+        self._headers = []
+        self._unixfrom = None
+        self._payload = None
+        self._charset = None
+        # Defaults for multipart messages
+        self.preamble = self.epilogue = None
+        self.defects = []
+        # Default content type
+        self._default_type = 'text/plain'
+
+    def __str__(self):
+        """Return the entire formatted message as a string.
+        """
+        return self.as_string()
+
+    def as_string(self, unixfrom=False, maxheaderlen=0, policy=None):
+        """Return the entire formatted message as a string.
+
+        Optional 'unixfrom', when true, means include the Unix From_ envelope
+        header.  For backward compatibility reasons, if maxheaderlen is
+        not specified it defaults to 0, so you must override it explicitly
+        if you want a different maxheaderlen.  'policy' is passed to the
+        Generator instance used to serialize the message; if it is not
+        specified the policy associated with the message instance is used.
+
+        If the message object contains binary data that is not encoded
+        according to RFC standards, the non-compliant data will be replaced by
+        unicode "unknown character" code points.
+        """
+        from email.generator import Generator
+        policy = self.policy if policy is None else policy
+        fp = StringIO()
+        g = Generator(fp,
+                      mangle_from_=False,
+                      maxheaderlen=maxheaderlen,
+                      policy=policy)
+        g.flatten(self, unixfrom=unixfrom)
+        return fp.getvalue()
+
+    def __bytes__(self):
+        """Return the entire formatted message as a bytes object.
+        """
+        return self.as_bytes()
+
+    def as_bytes(self, unixfrom=False, policy=None):
+        """Return the entire formatted message as a bytes object.
+
+        Optional 'unixfrom', when true, means include the Unix From_ envelope
+        header.  'policy' is passed to the BytesGenerator instance used to
+        serialize the message; if not specified the policy associated with
+        the message instance is used.
+        """
+        from email.generator import BytesGenerator
+        policy = self.policy if policy is None else policy
+        fp = BytesIO()
+        g = BytesGenerator(fp, mangle_from_=False, policy=policy)
+        g.flatten(self, unixfrom=unixfrom)
+        return fp.getvalue()
+
+    def is_multipart(self):
+        """Return True if the message consists of multiple parts."""
+        return isinstance(self._payload, list)
+
+    #
+    # Unix From_ line
+    #
+    def set_unixfrom(self, unixfrom):
+        self._unixfrom = unixfrom
+
+    def get_unixfrom(self):
+        return self._unixfrom
+
+    #
+    # Payload manipulation.
+    #
+    def attach(self, payload):
+        """Add the given payload to the current payload.
+
+        The current payload will always be a list of objects after this method
+        is called.  If you want to set the payload to a scalar object, use
+        set_payload() instead.
+        """
+        if self._payload is None:
+            self._payload = [payload]
+        else:
+            try:
+                self._payload.append(payload)
+            except AttributeError:
+                raise TypeError("Attach is not valid on a message with a"
+                                " non-multipart payload")
+
+    def get_payload(self, i=None, decode=False):
+        """Return a reference to the payload.
+
+        The payload will either be a list object or a string.  If you mutate
+        the list object, you modify the message's payload in place.  Optional
+        i returns that index into the payload.
+
+        Optional decode is a flag indicating whether the payload should be
+        decoded or not, according to the Content-Transfer-Encoding header
+        (default is False).
+
+        When True and the message is not a multipart, the payload will be
+        decoded if this header's value is `quoted-printable' or `base64'.  If
+        some other encoding is used, or the header is missing, or if the
+        payload has bogus data (i.e. bogus base64 or uuencoded data), the
+        payload is returned as-is.
+
+        If the message is a multipart and the decode flag is True, then None
+        is returned.
+        """
+        # Here is the logic table for this code, based on the email5.0.0 code:
+        #   i     decode  is_multipart  result
+        # ------  ------  ------------  ------------------------------
+        #  None   True    True          None
+        #   i     True    True          None
+        #  None   False   True          _payload (a list)
+        #   i     False   True          _payload element i (a Message)
+        #   i     False   False         error (not a list)
+        #   i     True    False         error (not a list)
+        #  None   False   False         _payload
+        #  None   True    False         _payload decoded (bytes)
+        # Note that Barry planned to factor out the 'decode' case, but that
+        # isn't so easy now that we handle the 8 bit data, which needs to be
+        # converted in both the decode and non-decode path.
+        if self.is_multipart():
+            if decode:
+                return None
+            if i is None:
+                return self._payload
+            else:
+                return self._payload[i]
+        # For backward compatibility, Use isinstance and this error message
+        # instead of the more logical is_multipart test.
+        if i is not None and not isinstance(self._payload, list):
+            raise TypeError('Expected list, got %s' % type(self._payload))
+        payload = self._payload
+        # cte might be a Header, so for now stringify it.
+        cte = str(self.get('content-transfer-encoding', '')).lower()
+        # payload may be bytes here.
+        if isinstance(payload, str):
+            if utils._has_surrogates(payload):
+                bpayload = payload.encode('ascii', 'surrogateescape')
+                if not decode:
+                    try:
+                        payload = bpayload.decode(self.get_param('charset', 'ascii'), 'replace')
+                    except LookupError:
+                        payload = bpayload.decode('ascii', 'replace')
+            elif decode:
+                try:
+                    bpayload = payload.encode('ascii')
+                except UnicodeError:
+                    # This won't happen for RFC compliant messages (messages
+                    # containing only ASCII code points in the unicode input).
+                    # If it does happen, turn the string into bytes in a way
+                    # guaranteed not to fail.
+                    bpayload = payload.encode('raw-unicode-escape')
+        if not decode:
+            return payload
+        if cte == 'quoted-printable':
+            return quopri.decodestring(bpayload)
+        elif cte == 'base64':
+            # XXX: this is a bit of a hack; decode_b should probably be factored
+            # out somewhere, but I haven't figured out where yet.
+            value, defects = decode_b(b''.join(bpayload.splitlines()))
+            for defect in defects:
+                self.policy.handle_defect(self, defect)
+            return value
+        elif cte in ('x-uuencode', 'uuencode', 'uue', 'x-uue'):
+            in_file = BytesIO(bpayload)
+            out_file = BytesIO()
+            try:
+                uu.decode(in_file, out_file, quiet=True)
+                return out_file.getvalue()
+            except uu.Error:
+                # Some decoding problem
+                return bpayload
+        if isinstance(payload, str):
+            return bpayload
+        return payload
+
+    def set_payload(self, payload, charset=None):
+        """Set the payload to the given value.
+
+        Optional charset sets the message's default character set.  See
+        set_charset() for details.
+        """
+        if hasattr(payload, 'encode'):
+            if charset is None:
+                self._payload = payload
+                return
+            if not isinstance(charset, Charset):
+                charset = Charset(charset)
+            payload = payload.encode(charset.output_charset)
+        if hasattr(payload, 'decode'):
+            self._payload = payload.decode('ascii', 'surrogateescape')
+        else:
+            self._payload = payload
+        if charset is not None:
+            self.set_charset(charset)
+
+    def set_charset(self, charset):
+        """Set the charset of the payload to a given character set.
+
+        charset can be a Charset instance, a string naming a character set, or
+        None.  If it is a string it will be converted to a Charset instance.
+        If charset is None, the charset parameter will be removed from the
+        Content-Type field.  Anything else will generate a TypeError.
+
+        The message will be assumed to be of type text/* encoded with
+        charset.input_charset.  It will be converted to charset.output_charset
+        and encoded properly, if needed, when generating the plain text
+        representation of the message.  MIME headers (MIME-Version,
+        Content-Type, Content-Transfer-Encoding) will be added as needed.
+        """
+        if charset is None:
+            self.del_param('charset')
+            self._charset = None
+            return
+        if not isinstance(charset, Charset):
+            charset = Charset(charset)
+        self._charset = charset
+        if 'MIME-Version' not in self:
+            self.add_header('MIME-Version', '1.0')
+        if 'Content-Type' not in self:
+            self.add_header('Content-Type', 'text/plain',
+                            charset=charset.get_output_charset())
+        else:
+            self.set_param('charset', charset.get_output_charset())
+        if charset != charset.get_output_charset():
+            self._payload = charset.body_encode(self._payload)
+        if 'Content-Transfer-Encoding' not in self:
+            cte = charset.get_body_encoding()
+            try:
+                cte(self)
+            except TypeError:
+                # This 'if' is for backward compatibility, it allows unicode
+                # through even though that won't work correctly if the
+                # message is serialized.
+                payload = self._payload
+                if payload:
+                    try:
+                        payload = payload.encode('ascii', 'surrogateescape')
+                    except UnicodeError:
+                        payload = payload.encode(charset.output_charset)
+                self._payload = charset.body_encode(payload)
+                self.add_header('Content-Transfer-Encoding', cte)
+
+    def get_charset(self):
+        """Return the Charset instance associated with the message's payload.
+        """
+        return self._charset
+
+    #
+    # MAPPING INTERFACE (partial)
+    #
+    def __len__(self):
+        """Return the total number of headers, including duplicates."""
+        return len(self._headers)
+
+    def __getitem__(self, name):
+        """Get a header value.
+
+        Return None if the header is missing instead of raising an exception.
+
+        Note that if the header appeared multiple times, exactly which
+        occurrence gets returned is undefined.  Use get_all() to get all
+        the values matching a header field name.
+        """
+        return self.get(name)
+
+    def __setitem__(self, name, val):
+        """Set the value of a header.
+
+        Note: this does not overwrite an existing header with the same field
+        name.  Use __delitem__() first to delete any existing headers.
+        """
+        max_count = self.policy.header_max_count(name)
+        if max_count:
+            lname = name.lower()
+            found = 0
+            for k, v in self._headers:
+                if k.lower() == lname:
+                    found += 1
+                    if found >= max_count:
+                        raise ValueError("There may be at most {} {} headers "
+                                         "in a message".format(max_count, name))
+        self._headers.append(self.policy.header_store_parse(name, val))
+
+    def __delitem__(self, name):
+        """Delete all occurrences of a header, if present.
+
+        Does not raise an exception if the header is missing.
+        """
+        name = name.lower()
+        newheaders = []
+        for k, v in self._headers:
+            if k.lower() != name:
+                newheaders.append((k, v))
+        self._headers = newheaders
+
+    def __contains__(self, name):
+        return name.lower() in [k.lower() for k, v in self._headers]
+
+    def __iter__(self):
+        for field, value in self._headers:
+            yield field
+
+    def keys(self):
+        """Return a list of all the message's header field names.
+
+        These will be sorted in the order they appeared in the original
+        message, or were added to the message, and may contain duplicates.
+        Any fields deleted and re-inserted are always appended to the header
+        list.
+        """
+        return [k for k, v in self._headers]
+
+    def values(self):
+        """Return a list of all the message's header values.
+
+        These will be sorted in the order they appeared in the original
+        message, or were added to the message, and may contain duplicates.
+        Any fields deleted and re-inserted are always appended to the header
+        list.
+        """
+        return [self.policy.header_fetch_parse(k, v)
+                for k, v in self._headers]
+
+    def items(self):
+        """Get all the message's header fields and values.
+
+        These will be sorted in the order they appeared in the original
+        message, or were added to the message, and may contain duplicates.
+        Any fields deleted and re-inserted are always appended to the header
+        list.
+        """
+        return [(k, self.policy.header_fetch_parse(k, v))
+                for k, v in self._headers]
+
+    def get(self, name, failobj=None):
+        """Get a header value.
+
+        Like __getitem__() but return failobj instead of None when the field
+        is missing.
+        """
+        name = name.lower()
+        for k, v in self._headers:
+            if k.lower() == name:
+                return self.policy.header_fetch_parse(k, v)
+        return failobj
+
+    #
+    # "Internal" methods (public API, but only intended for use by a parser
+    # or generator, not normal application code.
+    #
+
+    def set_raw(self, name, value):
+        """Store name and value in the model without modification.
+
+        This is an "internal" API, intended only for use by a parser.
+        """
+        self._headers.append((name, value))
+
+    def raw_items(self):
+        """Return the (name, value) header pairs without modification.
+
+        This is an "internal" API, intended only for use by a generator.
+        """
+        return iter(self._headers.copy())
+
+    #
+    # Additional useful stuff
+    #
+
+    def get_all(self, name, failobj=None):
+        """Return a list of all the values for the named field.
+
+        These will be sorted in the order they appeared in the original
+        message, and may contain duplicates.  Any fields deleted and
+        re-inserted are always appended to the header list.
+
+        If no such fields exist, failobj is returned (defaults to None).
+        """
+        values = []
+        name = name.lower()
+        for k, v in self._headers:
+            if k.lower() == name:
+                values.append(self.policy.header_fetch_parse(k, v))
+        if not values:
+            return failobj
+        return values
+
+    def add_header(self, _name, _value, **_params):
+        """Extended header setting.
+
+        name is the header field to add.  keyword arguments can be used to set
+        additional parameters for the header field, with underscores converted
+        to dashes.  Normally the parameter will be added as key="value" unless
+        value is None, in which case only the key will be added.  If a
+        parameter value contains non-ASCII characters it can be specified as a
+        three-tuple of (charset, language, value), in which case it will be
+        encoded according to RFC2231 rules.  Otherwise it will be encoded using
+        the utf-8 charset and a language of ''.
+
+        Examples:
+
+        msg.add_header('content-disposition', 'attachment', filename='bud.gif')
+        msg.add_header('content-disposition', 'attachment',
+                       filename=('utf-8', '', Fußballer.ppt'))
+        msg.add_header('content-disposition', 'attachment',
+                       filename='Fußballer.ppt'))
+        """
+        parts = []
+        for k, v in _params.items():
+            if v is None:
+                parts.append(k.replace('_', '-'))
+            else:
+                parts.append(_formatparam(k.replace('_', '-'), v))
+        if _value is not None:
+            parts.insert(0, _value)
+        self[_name] = SEMISPACE.join(parts)
+
+    def replace_header(self, _name, _value):
+        """Replace a header.
+
+        Replace the first matching header found in the message, retaining
+        header order and case.  If no matching header was found, a KeyError is
+        raised.
+        """
+        _name = _name.lower()
+        for i, (k, v) in zip(range(len(self._headers)), self._headers):
+            if k.lower() == _name:
+                self._headers[i] = self.policy.header_store_parse(k, _value)
+                break
+        else:
+            raise KeyError(_name)
+
+    #
+    # Use these three methods instead of the three above.
+    #
+
+    def get_content_type(self):
+        """Return the message's content type.
+
+        The returned string is coerced to lower case of the form
+        `maintype/subtype'.  If there was no Content-Type header in the
+        message, the default type as given by get_default_type() will be
+        returned.  Since according to RFC 2045, messages always have a default
+        type this will always return a value.
+
+        RFC 2045 defines a message's default type to be text/plain unless it
+        appears inside a multipart/digest container, in which case it would be
+        message/rfc822.
+        """
+        missing = object()
+        value = self.get('content-type', missing)
+        if value is missing:
+            # This should have no parameters
+            return self.get_default_type()
+        ctype = _splitparam(value)[0].lower()
+        # RFC 2045, section 5.2 says if its invalid, use text/plain
+        if ctype.count('/') != 1:
+            return 'text/plain'
+        return ctype
+
+    def get_content_maintype(self):
+        """Return the message's main content type.
+
+        This is the `maintype' part of the string returned by
+        get_content_type().
+        """
+        ctype = self.get_content_type()
+        return ctype.split('/')[0]
+
+    def get_content_subtype(self):
+        """Returns the message's sub-content type.
+
+        This is the `subtype' part of the string returned by
+        get_content_type().
+        """
+        ctype = self.get_content_type()
+        return ctype.split('/')[1]
+
+    def get_default_type(self):
+        """Return the `default' content type.
+
+        Most messages have a default content type of text/plain, except for
+        messages that are subparts of multipart/digest containers.  Such
+        subparts have a default content type of message/rfc822.
+        """
+        return self._default_type
+
+    def set_default_type(self, ctype):
+        """Set the `default' content type.
+
+        ctype should be either "text/plain" or "message/rfc822", although this
+        is not enforced.  The default content type is not stored in the
+        Content-Type header.
+        """
+        self._default_type = ctype
+
+    def _get_params_preserve(self, failobj, header):
+        # Like get_params() but preserves the quoting of values.  BAW:
+        # should this be part of the public interface?
+        missing = object()
+        value = self.get(header, missing)
+        if value is missing:
+            return failobj
+        params = []
+        for p in _parseparam(value):
+            try:
+                name, val = p.split('=', 1)
+                name = name.strip()
+                val = val.strip()
+            except ValueError:
+                # Must have been a bare attribute
+                name = p.strip()
+                val = ''
+            params.append((name, val))
+        params = utils.decode_params(params)
+        return params
+
+    def get_params(self, failobj=None, header='content-type', unquote=True):
+        """Return the message's Content-Type parameters, as a list.
+
+        The elements of the returned list are 2-tuples of key/value pairs, as
+        split on the `=' sign.  The left hand side of the `=' is the key,
+        while the right hand side is the value.  If there is no `=' sign in
+        the parameter the value is the empty string.  The value is as
+        described in the get_param() method.
+
+        Optional failobj is the object to return if there is no Content-Type
+        header.  Optional header is the header to search instead of
+        Content-Type.  If unquote is True, the value is unquoted.
+        """
+        missing = object()
+        params = self._get_params_preserve(missing, header)
+        if params is missing:
+            return failobj
+        if unquote:
+            return [(k, _unquotevalue(v)) for k, v in params]
+        else:
+            return params
+
+    def get_param(self, param, failobj=None, header='content-type',
+                  unquote=True):
+        """Return the parameter value if found in the Content-Type header.
+
+        Optional failobj is the object to return if there is no Content-Type
+        header, or the Content-Type header has no such parameter.  Optional
+        header is the header to search instead of Content-Type.
+
+        Parameter keys are always compared case insensitively.  The return
+        value can either be a string, or a 3-tuple if the parameter was RFC
+        2231 encoded.  When it's a 3-tuple, the elements of the value are of
+        the form (CHARSET, LANGUAGE, VALUE).  Note that both CHARSET and
+        LANGUAGE can be None, in which case you should consider VALUE to be
+        encoded in the us-ascii charset.  You can usually ignore LANGUAGE.
+        The parameter value (either the returned string, or the VALUE item in
+        the 3-tuple) is always unquoted, unless unquote is set to False.
+
+        If your application doesn't care whether the parameter was RFC 2231
+        encoded, it can turn the return value into a string as follows:
+
+            rawparam = msg.get_param('foo')
+            param = email.utils.collapse_rfc2231_value(rawparam)
+
+        """
+        if header not in self:
+            return failobj
+        for k, v in self._get_params_preserve(failobj, header):
+            if k.lower() == param.lower():
+                if unquote:
+                    return _unquotevalue(v)
+                else:
+                    return v
+        return failobj
+
+    def set_param(self, param, value, header='Content-Type', requote=True,
+                  charset=None, language='', replace=False):
+        """Set a parameter in the Content-Type header.
+
+        If the parameter already exists in the header, its value will be
+        replaced with the new value.
+
+        If header is Content-Type and has not yet been defined for this
+        message, it will be set to "text/plain" and the new parameter and
+        value will be appended as per RFC 2045.
+
+        An alternate header can be specified in the header argument, and all
+        parameters will be quoted as necessary unless requote is False.
+
+        If charset is specified, the parameter will be encoded according to RFC
+        2231.  Optional language specifies the RFC 2231 language, defaulting
+        to the empty string.  Both charset and language should be strings.
+        """
+        if not isinstance(value, tuple) and charset:
+            value = (charset, language, value)
+
+        if header not in self and header.lower() == 'content-type':
+            ctype = 'text/plain'
+        else:
+            ctype = self.get(header)
+        if not self.get_param(param, header=header):
+            if not ctype:
+                ctype = _formatparam(param, value, requote)
+            else:
+                ctype = SEMISPACE.join(
+                    [ctype, _formatparam(param, value, requote)])
+        else:
+            ctype = ''
+            for old_param, old_value in self.get_params(header=header,
+                                                        unquote=requote):
+                append_param = ''
+                if old_param.lower() == param.lower():
+                    append_param = _formatparam(param, value, requote)
+                else:
+                    append_param = _formatparam(old_param, old_value, requote)
+                if not ctype:
+                    ctype = append_param
+                else:
+                    ctype = SEMISPACE.join([ctype, append_param])
+        if ctype != self.get(header):
+            if replace:
+                self.replace_header(header, ctype)
+            else:
+                del self[header]
+                self[header] = ctype
+
+    def del_param(self, param, header='content-type', requote=True):
+        """Remove the given parameter completely from the Content-Type header.
+
+        The header will be re-written in place without the parameter or its
+        value. All values will be quoted as necessary unless requote is
+        False.  Optional header specifies an alternative to the Content-Type
+        header.
+        """
+        if header not in self:
+            return
+        new_ctype = ''
+        for p, v in self.get_params(header=header, unquote=requote):
+            if p.lower() != param.lower():
+                if not new_ctype:
+                    new_ctype = _formatparam(p, v, requote)
+                else:
+                    new_ctype = SEMISPACE.join([new_ctype,
+                                                _formatparam(p, v, requote)])
+        if new_ctype != self.get(header):
+            del self[header]
+            self[header] = new_ctype
+
+    def set_type(self, type, header='Content-Type', requote=True):
+        """Set the main type and subtype for the Content-Type header.
+
+        type must be a string in the form "maintype/subtype", otherwise a
+        ValueError is raised.
+
+        This method replaces the Content-Type header, keeping all the
+        parameters in place.  If requote is False, this leaves the existing
+        header's quoting as is.  Otherwise, the parameters will be quoted (the
+        default).
+
+        An alternative header can be specified in the header argument.  When
+        the Content-Type header is set, we'll always also add a MIME-Version
+        header.
+        """
+        # BAW: should we be strict?
+        if not type.count('/') == 1:
+            raise ValueError
+        # Set the Content-Type, you get a MIME-Version
+        if header.lower() == 'content-type':
+            del self['mime-version']
+            self['MIME-Version'] = '1.0'
+        if header not in self:
+            self[header] = type
+            return
+        params = self.get_params(header=header, unquote=requote)
+        del self[header]
+        self[header] = type
+        # Skip the first param; it's the old type.
+        for p, v in params[1:]:
+            self.set_param(p, v, header, requote)
+
+    def get_filename(self, failobj=None):
+        """Return the filename associated with the payload if present.
+
+        The filename is extracted from the Content-Disposition header's
+        `filename' parameter, and it is unquoted.  If that header is missing
+        the `filename' parameter, this method falls back to looking for the
+        `name' parameter.
+        """
+        missing = object()
+        filename = self.get_param('filename', missing, 'content-disposition')
+        if filename is missing:
+            filename = self.get_param('name', missing, 'content-type')
+        if filename is missing:
+            return failobj
+        return utils.collapse_rfc2231_value(filename).strip()
+
+    def get_boundary(self, failobj=None):
+        """Return the boundary associated with the payload if present.
+
+        The boundary is extracted from the Content-Type header's `boundary'
+        parameter, and it is unquoted.
+        """
+        missing = object()
+        boundary = self.get_param('boundary', missing)
+        if boundary is missing:
+            return failobj
+        # RFC 2046 says that boundaries may begin but not end in w/s
+        return utils.collapse_rfc2231_value(boundary).rstrip()
+
+    def set_boundary(self, boundary):
+        """Set the boundary parameter in Content-Type to 'boundary'.
+
+        This is subtly different than deleting the Content-Type header and
+        adding a new one with a new boundary parameter via add_header().  The
+        main difference is that using the set_boundary() method preserves the
+        order of the Content-Type header in the original message.
+
+        HeaderParseError is raised if the message has no Content-Type header.
+        """
+        missing = object()
+        params = self._get_params_preserve(missing, 'content-type')
+        if params is missing:
+            # There was no Content-Type header, and we don't know what type
+            # to set it to, so raise an exception.
+            raise errors.HeaderParseError('No Content-Type header found')
+        newparams = []
+        foundp = False
+        for pk, pv in params:
+            if pk.lower() == 'boundary':
+                newparams.append(('boundary', '"%s"' % boundary))
+                foundp = True
+            else:
+                newparams.append((pk, pv))
+        if not foundp:
+            # The original Content-Type header had no boundary attribute.
+            # Tack one on the end.  BAW: should we raise an exception
+            # instead???
+            newparams.append(('boundary', '"%s"' % boundary))
+        # Replace the existing Content-Type header with the new value
+        newheaders = []
+        for h, v in self._headers:
+            if h.lower() == 'content-type':
+                parts = []
+                for k, v in newparams:
+                    if v == '':
+                        parts.append(k)
+                    else:
+                        parts.append('%s=%s' % (k, v))
+                val = SEMISPACE.join(parts)
+                newheaders.append(self.policy.header_store_parse(h, val))
+
+            else:
+                newheaders.append((h, v))
+        self._headers = newheaders
+
+    def get_content_charset(self, failobj=None):
+        """Return the charset parameter of the Content-Type header.
+
+        The returned string is always coerced to lower case.  If there is no
+        Content-Type header, or if that header has no charset parameter,
+        failobj is returned.
+        """
+        missing = object()
+        charset = self.get_param('charset', missing)
+        if charset is missing:
+            return failobj
+        if isinstance(charset, tuple):
+            # RFC 2231 encoded, so decode it, and it better end up as ascii.
+            pcharset = charset[0] or 'us-ascii'
+            try:
+                # LookupError will be raised if the charset isn't known to
+                # Python.  UnicodeError will be raised if the encoded text
+                # contains a character not in the charset.
+                as_bytes = charset[2].encode('raw-unicode-escape')
+                charset = str(as_bytes, pcharset)
+            except (LookupError, UnicodeError):
+                charset = charset[2]
+        # charset characters must be in us-ascii range
+        try:
+            charset.encode('us-ascii')
+        except UnicodeError:
+            return failobj
+        # RFC 2046, $4.1.2 says charsets are not case sensitive
+        return charset.lower()
+
+    def get_charsets(self, failobj=None):
+        """Return a list containing the charset(s) used in this message.
+
+        The returned list of items describes the Content-Type headers'
+        charset parameter for this message and all the subparts in its
+        payload.
+
+        Each item will either be a string (the value of the charset parameter
+        in the Content-Type header of that part) or the value of the
+        'failobj' parameter (defaults to None), if the part does not have a
+        main MIME type of "text", or the charset is not defined.
+
+        The list will contain one string for each part of the message, plus
+        one for the container message (i.e. self), so that a non-multipart
+        message will still return a list of length 1.
+        """
+        return [part.get_content_charset(failobj) for part in self.walk()]
+
+    def get_content_disposition(self):
+        """Return the message's content-disposition if it exists, or None.
+
+        The return values can be either 'inline', 'attachment' or None
+        according to the rfc2183.
+        """
+        value = self.get('content-disposition')
+        if value is None:
+            return None
+        c_d = _splitparam(value)[0].lower()
+        return c_d
+
+    # I.e. def walk(self): ...
+    from email.iterators import walk
+
+
+class MIMEPart(Message):
+
+    def __init__(self, policy=None):
+        if policy is None:
+            from email.policy import default
+            policy = default
+        super().__init__(policy)
+
+
+    def as_string(self, unixfrom=False, maxheaderlen=None, policy=None):
+        """Return the entire formatted message as a string.
+
+        Optional 'unixfrom', when true, means include the Unix From_ envelope
+        header.  maxheaderlen is retained for backward compatibility with the
+        base Message class, but defaults to None, meaning that the policy value
+        for max_line_length controls the header maximum length.  'policy' is
+        passed to the Generator instance used to serialize the message; if it
+        is not specified the policy associated with the message instance is
+        used.
+        """
+        policy = self.policy if policy is None else policy
+        if maxheaderlen is None:
+            maxheaderlen = policy.max_line_length
+        return super().as_string(unixfrom, maxheaderlen, policy)
+
+    def __str__(self):
+        return self.as_string(policy=self.policy.clone(utf8=True))
+
+    def is_attachment(self):
+        c_d = self.get('content-disposition')
+        return False if c_d is None else c_d.content_disposition == 'attachment'
+
+    def _find_body(self, part, preferencelist):
+        if part.is_attachment():
+            return
+        maintype, subtype = part.get_content_type().split('/')
+        if maintype == 'text':
+            if subtype in preferencelist:
+                yield (preferencelist.index(subtype), part)
+            return
+        if maintype != 'multipart' or not self.is_multipart():
+            return
+        if subtype != 'related':
+            for subpart in part.iter_parts():
+                yield from self._find_body(subpart, preferencelist)
+            return
+        if 'related' in preferencelist:
+            yield (preferencelist.index('related'), part)
+        candidate = None
+        start = part.get_param('start')
+        if start:
+            for subpart in part.iter_parts():
+                if subpart['content-id'] == start:
+                    candidate = subpart
+                    break
+        if candidate is None:
+            subparts = part.get_payload()
+            candidate = subparts[0] if subparts else None
+        if candidate is not None:
+            yield from self._find_body(candidate, preferencelist)
+
+    def get_body(self, preferencelist=('related', 'html', 'plain')):
+        """Return best candidate mime part for display as 'body' of message.
+
+        Do a depth first search, starting with self, looking for the first part
+        matching each of the items in preferencelist, and return the part
+        corresponding to the first item that has a match, or None if no items
+        have a match.  If 'related' is not included in preferencelist, consider
+        the root part of any multipart/related encountered as a candidate
+        match.  Ignore parts with 'Content-Disposition: attachment'.
+        """
+        best_prio = len(preferencelist)
+        body = None
+        for prio, part in self._find_body(self, preferencelist):
+            if prio < best_prio:
+                best_prio = prio
+                body = part
+                if prio == 0:
+                    break
+        return body
+
+    _body_types = {('text', 'plain'),
+                   ('text', 'html'),
+                   ('multipart', 'related'),
+                   ('multipart', 'alternative')}
+    def iter_attachments(self):
+        """Return an iterator over the non-main parts of a multipart.
+
+        Skip the first of each occurrence of text/plain, text/html,
+        multipart/related, or multipart/alternative in the multipart (unless
+        they have a 'Content-Disposition: attachment' header) and include all
+        remaining subparts in the returned iterator.  When applied to a
+        multipart/related, return all parts except the root part.  Return an
+        empty iterator when applied to a multipart/alternative or a
+        non-multipart.
+        """
+        maintype, subtype = self.get_content_type().split('/')
+        if maintype != 'multipart' or subtype == 'alternative':
+            return
+        payload = self.get_payload()
+        # Certain malformed messages can have content type set to `multipart/*`
+        # but still have single part body, in which case payload.copy() can
+        # fail with AttributeError.
+        try:
+            parts = payload.copy()
+        except AttributeError:
+            # payload is not a list, it is most probably a string.
+            return
+
+        if maintype == 'multipart' and subtype == 'related':
+            # For related, we treat everything but the root as an attachment.
+            # The root may be indicated by 'start'; if there's no start or we
+            # can't find the named start, treat the first subpart as the root.
+            start = self.get_param('start')
+            if start:
+                found = False
+                attachments = []
+                for part in parts:
+                    if part.get('content-id') == start:
+                        found = True
+                    else:
+                        attachments.append(part)
+                if found:
+                    yield from attachments
+                    return
+            parts.pop(0)
+            yield from parts
+            return
+        # Otherwise we more or less invert the remaining logic in get_body.
+        # This only really works in edge cases (ex: non-text related or
+        # alternatives) if the sending agent sets content-disposition.
+        seen = []   # Only skip the first example of each candidate type.
+        for part in parts:
+            maintype, subtype = part.get_content_type().split('/')
+            if ((maintype, subtype) in self._body_types and
+                    not part.is_attachment() and subtype not in seen):
+                seen.append(subtype)
+                continue
+            yield part
+
+    def iter_parts(self):
+        """Return an iterator over all immediate subparts of a multipart.
+
+        Return an empty iterator for a non-multipart.
+        """
+        if self.is_multipart():
+            yield from self.get_payload()
+
+    def get_content(self, *args, content_manager=None, **kw):
+        if content_manager is None:
+            content_manager = self.policy.content_manager
+        return content_manager.get_content(self, *args, **kw)
+
+    def set_content(self, *args, content_manager=None, **kw):
+        if content_manager is None:
+            content_manager = self.policy.content_manager
+        content_manager.set_content(self, *args, **kw)
+
+    def _make_multipart(self, subtype, disallowed_subtypes, boundary):
+        if self.get_content_maintype() == 'multipart':
+            existing_subtype = self.get_content_subtype()
+            disallowed_subtypes = disallowed_subtypes + (subtype,)
+            if existing_subtype in disallowed_subtypes:
+                raise ValueError("Cannot convert {} to {}".format(
+                    existing_subtype, subtype))
+        keep_headers = []
+        part_headers = []
+        for name, value in self._headers:
+            if name.lower().startswith('content-'):
+                part_headers.append((name, value))
+            else:
+                keep_headers.append((name, value))
+        if part_headers:
+            # There is existing content, move it to the first subpart.
+            part = type(self)(policy=self.policy)
+            part._headers = part_headers
+            part._payload = self._payload
+            self._payload = [part]
+        else:
+            self._payload = []
+        self._headers = keep_headers
+        self['Content-Type'] = 'multipart/' + subtype
+        if boundary is not None:
+            self.set_param('boundary', boundary)
+
+    def make_related(self, boundary=None):
+        self._make_multipart('related', ('alternative', 'mixed'), boundary)
+
+    def make_alternative(self, boundary=None):
+        self._make_multipart('alternative', ('mixed',), boundary)
+
+    def make_mixed(self, boundary=None):
+        self._make_multipart('mixed', (), boundary)
+
+    def _add_multipart(self, _subtype, *args, _disp=None, **kw):
+        if (self.get_content_maintype() != 'multipart' or
+                self.get_content_subtype() != _subtype):
+            getattr(self, 'make_' + _subtype)()
+        part = type(self)(policy=self.policy)
+        part.set_content(*args, **kw)
+        if _disp and 'content-disposition' not in part:
+            part['Content-Disposition'] = _disp
+        self.attach(part)
+
+    def add_related(self, *args, **kw):
+        self._add_multipart('related', *args, _disp='inline', **kw)
+
+    def add_alternative(self, *args, **kw):
+        self._add_multipart('alternative', *args, **kw)
+
+    def add_attachment(self, *args, **kw):
+        self._add_multipart('mixed', *args, _disp='attachment', **kw)
+
+    def clear(self):
+        self._headers = []
+        self._payload = None
+
+    def clear_content(self):
+        self._headers = [(n, v) for n, v in self._headers
+                         if not n.lower().startswith('content-')]
+        self._payload = None
+
+
+class EmailMessage(MIMEPart):
+
+    def set_content(self, *args, **kw):
+        super().set_content(*args, **kw)
+        if 'MIME-Version' not in self:
+            self['MIME-Version'] = '1.0'
diff --git a/shared/package-files/1b/1b138da714861ce7fd46c97d72dc3cb3e1df6cf8c2b9ee9d7a7fdb9ca3d1536b/_convert_weight_to_int4pack_cuda_dispatch.h b/shared/package-files/1b/1b138da714861ce7fd46c97d72dc3cb3e1df6cf8c2b9ee9d7a7fdb9ca3d1536b/_convert_weight_to_int4pack_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..14df73b867581d86492a36a9154747711d7d952f
--- /dev/null
+++ b/shared/package-files/1b/1b138da714861ce7fd46c97d72dc3cb3e1df6cf8c2b9ee9d7a7fdb9ca3d1536b/_convert_weight_to_int4pack_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor _convert_weight_to_int4pack(const at::Tensor & self, int64_t innerKTiles);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/1b/1b13fcb5be7eb002564ed0570ca6974f32bf16ded8fb18ff1211530fd679cb67/training_provenance.json b/shared/package-files/1b/1b13fcb5be7eb002564ed0570ca6974f32bf16ded8fb18ff1211530fd679cb67/training_provenance.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ece116933581b8ecfcae6b10e66dc9921d2e0d3
--- /dev/null
+++ b/shared/package-files/1b/1b13fcb5be7eb002564ed0570ca6974f32bf16ded8fb18ff1211530fd679cb67/training_provenance.json
@@ -0,0 +1,125566 @@
+{
+  "initialization": {
+    "distribution": "Normal(0,0.01)",
+    "seed": 20261021,
+    "pretrained_weights": false
+  },
+  "training_data": "all 19880 supplied training shots per condition; all 130 conditions",
+  "train_manifest_sha256": "ce46c21e3595cfc5885bbe2b6481105317d2e6267498ec4572601469825914b8",
+  "validation_manifest_sha256": "482bb7268c6c827ebbf1f756ee6994750acfcd011adab08eaa2ada703e5faea1",
+  "calibration_exception": "Supplied even-fitted DEMs include validation syndromes but no logical labels or test data.",
+  "optimizer": "L-BFGS-B binary cross entropy plus 1e-5 L2, maximum 300 iterations",
+  "selection": "First trained candidate; validation reported without parameter fitting or hyperparameter selection",
+  "test_data_used": false,
+  "released_per_shot_predictions_used": false,
+  "history": [
+    {
+      "group": "d3_bX_center_3_5",
+      "train_loss": 0.49244210477855016,
+      "iterations": 88,
+      "failure": 0.26941105769230766,
+      "plain_failure": 0.2847205528846154,
+      "correlated_failure": 0.2704326923076923,
+      "coset_failure": 0.2847205528846154,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_5_3",
+      "train_loss": 0.4947914557869911,
+      "iterations": 85,
+      "failure": 0.2706430288461538,
+      "plain_failure": 0.29103064903846154,
+      "correlated_failure": 0.27076322115384616,
+      "coset_failure": 0.29103064903846154,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_5_7",
+      "train_loss": 0.4786369068642458,
+      "iterations": 79,
+      "failure": 0.25964543269230766,
+      "plain_failure": 0.2741887019230769,
+      "correlated_failure": 0.2598106971153846,
+      "coset_failure": 0.2741887019230769,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_7_5",
+      "train_loss": 0.48299120621578956,
+      "iterations": 81,
+      "failure": 0.26062199519230766,
+      "plain_failure": 0.2762920673076923,
+      "correlated_failure": 0.2615234375,
+      "coset_failure": 0.2762920673076923,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_3_5",
+      "train_loss": 0.4903180822446507,
+      "iterations": 62,
+      "failure": 0.2718299278846154,
+      "plain_failure": 0.2878605769230769,
+      "correlated_failure": 0.2736778846153846,
+      "coset_failure": 0.2878605769230769,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_5_3",
+      "train_loss": 0.4886621962442568,
+      "iterations": 90,
+      "failure": 0.25896935096153845,
+      "plain_failure": 0.27548076923076925,
+      "correlated_failure": 0.2588942307692308,
+      "coset_failure": 0.27548076923076925,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_5_7",
+      "train_loss": 0.4822654448634678,
+      "iterations": 71,
+      "failure": 0.2594200721153846,
+      "plain_failure": 0.2823016826923077,
+      "correlated_failure": 0.25961538461538464,
+      "coset_failure": 0.2823016826923077,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_7_5",
+      "train_loss": 0.47379411844328495,
+      "iterations": 69,
+      "failure": 0.2542067307692308,
+      "plain_failure": 0.27393329326923077,
+      "correlated_failure": 0.2547776442307692,
+      "coset_failure": 0.27393329326923077,
+      "optimizer_success": true
+    },
+    {
+      "group": "d5_bX_center_5_5",
+      "train_loss": 0.4801955727360068,
+      "iterations": 85,
+      "failure": 0.2616887019230769,
+      "plain_failure": 0.293359375,
+      "correlated_failure": 0.2609375,
+      "coset_failure": 0.293359375,
+      "optimizer_success": true
+    },
+    {
+      "group": "d5_bZ_center_5_5",
+      "train_loss": 0.4661361209861219,
+      "iterations": 87,
+      "failure": 0.2465294471153846,
+      "plain_failure": 0.2799729567307692,
+      "correlated_failure": 0.24618389423076922,
+      "coset_failure": 0.2799729567307692,
+      "optimizer_success": true
+    }
+  ],
+  "elapsed_seconds": 30.247901678085327,
+  "experiment_2": {
+    "method": "Exact contraction of supplied DEM for d3; learned logistic mixture with matching features",
+    "initialization": {
+      "distribution": "Normal(0,0.01)",
+      "seed": 20261022,
+      "pretrained_weights": false
+    },
+    "selection": "Minimum validation condition-mean failure per group among raw exact, calibrated exact, exact plus matching ensemble",
+    "history": [
+      {
+        "group": "d3_bX_center_3_5",
+        "candidates": {
+          "exact_raw": {
+            "failure": 0.253125
+          },
+          "exact_calibrated": {
+            "failure": 0.25378605769230766,
+            "train_loss": 0.46127817271552574,
+            "iterations": 8
+          },
+          "exact_ensemble": {
+            "failure": 0.2522686298076923,
+            "train_loss": 0.459112999306209,
+            "iterations": 115
+          }
+        },
+        "selected": "exact_ensemble"
+      },
+      {
+        "group": "d3_bX_center_5_3",
+        "candidates": {
+          "exact_raw": {
+            "failure": 0.2569711538461538
+          },
+          "exact_calibrated": {
+            "failure": 0.25790264423076925,
+            "train_loss": 0.46654812103904,
+            "iterations": 8
+          },
+          "exact_ensemble": {
+            "failure": 0.257421875,
+            "train_loss": 0.4647701206688181,
+            "iterations": 80
+          }
+        },
+        "selected": "exact_raw"
+      },
+      {
+        "group": "d3_bX_center_5_7",
+        "candidates": {
+          "exact_raw": {
+            "failure": 0.23622295673076923
+          },
+          "exact_calibrated": {
+            "failure": 0.23608774038461539,
+            "train_loss": 0.43883548729746,
+            "iterations": 8
+          },
+          "exact_ensemble": {
+            "failure": 0.2357421875,
+            "train_loss": 0.43779428254411507,
+            "iterations": 82
+          }
+        },
+        "selected": "exact_ensemble"
+      },
+      {
+        "group": "d3_bX_center_7_5",
+        "candidates": {
+          "exact_raw": {
+            "failure": 0.24507211538461537
+          },
+          "exact_calibrated": {
+            "failure": 0.24541766826923078,
+            "train_loss": 0.4551514021617466,
+            "iterations": 8
+          },
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+              "combiner_expert_selection": "For each new expert and basis, minimum raw full-validation mean failure across0/512/1024/1536; earlier listed checkpoint wins ties. Freeze before using reserved labels",
+              "combiner_plan": "Fit small regularized syndrome-dependent mixtures using only the512 gate rows/condition. Diagnostic512 rows/condition must not fit or select weights/checkpoints/hyperparameters",
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+            "maximum_probability_difference": 0.0
+          }
+        ],
+        "prediction_generation": "All26 d5 conditions replayed from the assembled package; unchanged d3 predictions retained. Full trusted scoring and relocated arbitrary-ID/subset checks.",
+        "runtime_evidence": "Full133120-shot d5 replay took 342.34s on one H100; d3 paths unchanged from the recorded incumbent.",
+        "d5_validation_runtime_seconds": 342.34057784080505,
+        "runtime_vs_cached": {
+          "scope": "Assembled runtime versus cached candidate predictions, including physical-prior recomputation and compilation/batching differences; descriptive, not a causal precision attribution",
+          "maximum_probability_difference": 6.0498714447021484e-05,
+          "decision_differences": 1,
+          "source_sha256": "2e3e81d122f9d626d486d050c38d86bfd3e55bc6cd8e0e270b9e8eabbf69539a"
+        },
+        "contract_execution": {
+          "requested_verifier_uid": 10001,
+          "effective_uid": 1000,
+          "effective_gid": 1000,
+          "supplementary_groups": [],
+          "identity_status": "Development process is already unprivileged and cannot change UID; verifier UID10001 was not executed",
+          "model_and_input_copy": "Owned by development UID 1000,directories0555,files0444,relocated under a fresh accessible temporary parent",
+          "output_directory": "Owned by execution UID 1000,mode0700",
+          "PYTHONPATH": "",
+          "backend": "Runtime default; full selected subset cases",
+          "scope": "Local contract check; not a Docker/network/mount-isolation test"
+        }
+      }
+    },
+    "selection": "Choose each group new raw checkpoint by full-validation mean failure, earlier listed checkpoint wins ties; compare incumbent with fixed equal old/new temporal average and its equal small-contextual blend across all13 durations",
+    "history": [
+      {
+        "group": "d3_bX_center_3_5",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.24606370192307692,
+          "mean3": 0.2459735576923077
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.2444110576923077,
+          "equal-temporal": 0.24516225961538465,
+          "equal-temporal-small": 0.24615384615384617
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bX_center_5_3",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.25446213942307694,
+          "mean3": 0.25443209134615385
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.2526592548076923,
+          "equal-temporal": 0.2533954326923077,
+          "equal-temporal-small": 0.2537109375
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bX_center_5_7",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.23493088942307694,
+          "mean3": 0.23461538461538461
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.233203125,
+          "equal-temporal": 0.2339092548076923,
+          "equal-temporal-small": 0.2338641826923077
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bX_center_7_5",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.23937800480769234,
+          "mean3": 0.23886718750000002
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.23880709134615385,
+          "equal-temporal": 0.2385667067307692,
+          "equal-temporal-small": 0.23901742788461539
+        },
+        "selected": "equal-temporal"
+      },
+      {
+        "group": "d3_bZ_center_3_5",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.2459435096153846,
+          "mean3": 0.24672475961538462
+        },
+        "new_raw_checkpoint": "mean2",
+        "candidates": {
+          "incumbent": 0.24447115384615387,
+          "equal-temporal": 0.24526742788461536,
+          "equal-temporal-small": 0.24586838942307693
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bZ_center_5_3",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.23780048076923077,
+          "mean3": 0.23737980769230768
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.23652343749999996,
+          "equal-temporal": 0.2369441105769231,
+          "equal-temporal-small": 0.23743990384615382
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bZ_center_5_7",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.23716947115384618,
+          "mean3": 0.2373497596153846
+        },
+        "new_raw_checkpoint": "mean2",
+        "candidates": {
+          "incumbent": 0.2356670673076923,
+          "equal-temporal": 0.23614783653846153,
+          "equal-temporal-small": 0.23659855769230773
+        },
+        "selected": "incumbent"
+      },
+      {
+        "group": "d3_bZ_center_7_5",
+        "new_raw_checkpoint_failures": {
+          "mean2": 0.2333383413461538,
+          "mean3": 0.23312800480769236
+        },
+        "new_raw_checkpoint": "mean3",
+        "candidates": {
+          "incumbent": 0.23266225961538464,
+          "equal-temporal": 0.23317307692307693,
+          "equal-temporal-small": 0.2332481971153846
+        },
+        "selected": "incumbent"
+      }
+    ],
+    "runtime_control": {
+      "status": "passed",
+      "device": "CPU",
+      "cases": [
+        {
+          "condition": "surface_code_bX_d3_r01_center_3_5",
+          "shots": 1,
+          "old_path_exact": true,
+          "mixture_exact": true,
+          "order_and_singleton_exact": true
+        },
+        {
+          "condition": "surface_code_bZ_d3_r03_center_3_5",
+          "shots": 7,
+          "old_path_exact": true,
+          "mixture_exact": true,
+          "order_and_singleton_exact": true
+        },
+        {
+          "condition": "surface_code_bX_d3_r25_center_3_5",
+          "shots": 3,
+          "old_path_exact": true,
+          "mixture_exact": true,
+          "order_and_singleton_exact": true
+        }
+      ],
+      "sources": {
+        "temporal_contextual.py": "5cdd9c9bdaf0bb526cd7922ce1a990d1206a4bfba72dc76751cd3a09964ad95d",
+        "temporal_ensemble_contextual.py": "0ecb0608637d040b2d167953403a230f659886663df884348011959427edec98",
+        "temporal_model.py": "76fd01d5ae50f7d0b5790800898e7103ddb86db8f6a2ace883407515add2e186"
+      },
+      "test_data_used": false
+    },
+    "test_data_used": false
+  },
+  "joint_mixture1": {
+    "declared_plan": {
+      "hypothesis": "Joint logical-likelihood training may learn useful shot-dependent complementarity beyond frozen independent expert averages",
+      "parents": [
+        "fresh2_full/weights-1536.npz",
+        "reserved1_full/weights-1536.npz"
+      ],
+      "initialization": "Both experts retain their session-trained parameters and exact Adam moments/count2048; new41-input32-hidden SiLU gate has random hidden weights(seed20261310),zero output and bias,giving equal mixture",
+      "features": "Gate uses only24site densities,lag1/2/4/8coincidence/covariance,6density/frame summaries,duration/duration_squared/basis;fixed numeric scales,no fitted normalization,no IDs/labels/shot positions/expert probabilities",
+      "loss": "Logical BCE of two-expert probability mixture; expert BCE adjoints weighted by posterior responsibility,free gate-logit adjoint=coefficient-responsibility; gradients controlled against independent autodiff",
+      "training": "2048updates B1024;uniform sampling over all516880training rows/26conditions(seed20261311);sharedphysicalpass3 priors;expertLR.0001,gateLR.001;separate AdamW clocks,moments restored for experts,freshgate count0;one joint norm1 clip,decay1e-4",
+      "selection_steps": [
+        0,
+        512,
+        1024,
+        2048
+      ],
+      "validation": "Full133120 supplied validation shots per listed checkpoint; perbasis choose incumbent or rawjoint/equalphysical,all13durations; tiesretainincumbent. Step0 is fixedmean of trainedparents.1536has screening only and is not a full-selection checkpoint.",
+      "audit_scope": "All training labels are eligible; no independent reserved-training-label audit claim. Parent initialization provenance retained.",
+      "controls": "CPUscalar finite differences,exactchain autodiff,feature order/subset checks; two-update serialization/resume/sampling/Adam controls; portableCPUruntime;GPUactualgeometrygradient control before training",
+      "runtime": "Two exact neural passes share one physical-prior computation; preserve all retained incumbent dispatches;fullreplay/scoring/readonlyrelocation/projection before any publication",
+      "interpretation": "Learned branches are not identified physical noise regimes; this is a joint model recipe,not an isolated causal test of gating",
+      "gpu": "One H100,serialized afterfresh_temporal1_finished",
+      "test_data_used": false,
+      "parameter_counts": {
+        "expert0": 29505,
+        "expert1": 29505,
+        "gate": 1377,
+        "total": 60387
+      },
+      "physical_blend_clarification_before_training": "Equal physical blends use the existing calibrated/ensemble physical pass3 decoder output from turbo1/predictions, checked against runtime config and DEM/plans/weights. Raw feedback5_cache pass3 is only the neural conditioning prior/diagnostic. Corrected before any GPU training or selection in this study."
+    },
+    "physical_blend_reference": {
+      "package": "/run-state/workspace/research/context_upgrades/turbo1/package",
+      "predictions": "/run-state/workspace/research/context_upgrades/turbo1/predictions",
+      "component": "Existing physical pass3 decoder output, including its training-fitted calibration/ensemble; exactly the hybrid3.py blend_turbo path",
+      "verified_config": "turbo settings equal; physical DEM/plans and calibration/ensemble weights have identical SHA256",
+      "verified_files": [
+        "dems/surface_code_bX_d5_r01_center_5_5.dem",
+        "dems/surface_code_bX_d5_r03_center_5_5.dem",
+        "dems/surface_code_bX_d5_r05_center_5_5.dem",
+        "dems/surface_code_bX_d5_r07_center_5_5.dem",
+        "dems/surface_code_bX_d5_r09_center_5_5.dem",
+        "dems/surface_code_bX_d5_r11_center_5_5.dem",
+        "dems/surface_code_bX_d5_r13_center_5_5.dem",
+        "dems/surface_code_bX_d5_r15_center_5_5.dem",
+        "dems/surface_code_bX_d5_r17_center_5_5.dem",
+        "dems/surface_code_bX_d5_r19_center_5_5.dem",
+        "dems/surface_code_bX_d5_r21_center_5_5.dem",
+        "dems/surface_code_bX_d5_r23_center_5_5.dem",
+        "dems/surface_code_bX_d5_r25_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r01_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r03_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r05_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r07_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r09_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r11_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r13_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r15_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r17_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r19_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r21_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r23_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r25_center_5_5.dem",
+        "plans/surface_code_bX_d5_r01_center_5_5.npz",
+        "plans/surface_code_bZ_d5_r01_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r03_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r05_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r07_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r09_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r11_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r13_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r15_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r17_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r19_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r21_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r23_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r25_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r03_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r05_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r07_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r09_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r11_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r13_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r15_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r17_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r19_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r21_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r23_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r25_center_5_5.npz",
+        "weights/turbo1-d5_bX_center_5_5.npz",
+        "weights/turbo1-d5_bZ_center_5_5.npz"
+      ],
+      "clarification": "Use these ID-aligned probabilities, not the raw pass3 column in feedback5_cache. Physical priors supplied to neural networks remain unchanged."
+    },
+    "selection_history": [
+      {
+        "basis": "X",
+        "group": "d5_bX_center_5_5",
+        "candidates": {
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+          "1024-physical0.5": 0.2324669471153846,
+          "2048-physical0": 0.2327824519230769,
+          "2048-physical0.5": 0.23300781249999997
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+        "selected": "1024-physical0"
+      },
+      {
+        "basis": "Z",
+        "group": "d5_bZ_center_5_5",
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+          "2048-physical0": 0.22035757211538462,
+          "2048-physical0.5": 0.2203876201923077
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+        "selected": "incumbent"
+      }
+    ],
+    "selected_checkpoints": {
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+        "step": 1024,
+        "training_draws": 1048576,
+        "adam_counts": {
+          "expert0": 3072,
+          "expert1": 3072,
+          "gate": 1024
+        },
+        "distinct_phase_rows": 446971,
+        "distinct_by_condition": {
+          "surface_code_bX_d5_r01_center_5_5": 17445,
+          "surface_code_bX_d5_r03_center_5_5": 17875,
+          "surface_code_bX_d5_r05_center_5_5": 16246,
+          "surface_code_bX_d5_r07_center_5_5": 16070,
+          "surface_code_bX_d5_r09_center_5_5": 16429,
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+          "surface_code_bX_d5_r13_center_5_5": 17193,
+          "surface_code_bX_d5_r15_center_5_5": 17571,
+          "surface_code_bX_d5_r17_center_5_5": 17502,
+          "surface_code_bX_d5_r19_center_5_5": 17754,
+          "surface_code_bX_d5_r21_center_5_5": 17567,
+          "surface_code_bX_d5_r23_center_5_5": 16091,
+          "surface_code_bX_d5_r25_center_5_5": 17260,
+          "surface_code_bZ_d5_r01_center_5_5": 16790,
+          "surface_code_bZ_d5_r03_center_5_5": 17857,
+          "surface_code_bZ_d5_r05_center_5_5": 17484,
+          "surface_code_bZ_d5_r07_center_5_5": 17491,
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+          "surface_code_bZ_d5_r13_center_5_5": 18278,
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+          "surface_code_bZ_d5_r17_center_5_5": 16049,
+          "surface_code_bZ_d5_r19_center_5_5": 17173,
+          "surface_code_bZ_d5_r21_center_5_5": 17037,
+          "surface_code_bZ_d5_r23_center_5_5": 17805,
+          "surface_code_bZ_d5_r25_center_5_5": 17175
+        },
+        "weights_sha256": "d3193fe31df6de065d5f9c45edccb24076c9635c52711920feabfc03cf82c8e3",
+        "complete": true
+      }
+    },
+    "training": {
+      "run_config": {
+        "parents": [
+          "/run-state/workspace/research/learned_d5/fresh2_full/weights-1536.npz",
+          "/run-state/workspace/research/learned_d5/reserved1_full/weights-1536.npz"
+        ],
+        "output": "/run-state/workspace/research/learned_d5/joint_mixture1",
+        "training_cache": "/run-state/workspace/research/learned_d5/full_training_priors3",
+        "resume": null,
+        "steps": 2048,
+        "batch": 1024,
+        "expert_lr": 0.0001,
+        "gate_lr": 0.001,
+        "gate_seed": 20261310,
+        "seed": 20261311,
+        "eval_every": 512,
+        "validation_shots": 1024,
+        "validation_batch": 512,
+        "training_source_sha256": {
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+          "exact_d3__exact.py": "869dfacb295c419c156d1070fc2afdd6e20d02ac1840e45e72346b2b289f4825"
+        },
+        "parent_records": [
+          {
+            "part": "expert0",
+            "weights": "/run-state/workspace/research/learned_d5/fresh2_full/weights-1536.npz",
+            "weights_sha256": "20746e71c276e720074318b0978e6ea0ddc0df206954834f13c7f36354933cea",
+            "optimizer": "/run-state/workspace/research/learned_d5/fresh2_full/optimizer-1536.npz",
+            "optimizer_sha256": "ebd3d11cf11c9cd7e262e15695c3ec0ac227c1b9688d781f3d13e3ae6f98d00b",
+            "initial_adam_count": 2048,
+            "moments_restored": true
+          },
+          {
+            "part": "expert1",
+            "weights": "/run-state/workspace/research/learned_d5/reserved1_full/weights-1536.npz",
+            "weights_sha256": "65a762bc0623024c3fbc416ff5df00d8ea275f6c7d691a54c6e0492f459a362f",
+            "optimizer": "/run-state/workspace/research/learned_d5/reserved1_full/optimizer-1536.npz",
+            "optimizer_sha256": "b8c13e9f941a0b89696d5eb79c8396a8870bcb80cf4f11b69854f52de0c3a22a",
+            "initial_adam_count": 2048,
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+        ],
+        "initial_adam_counts": {
+          "expert0": 2048,
+          "expert1": 2048,
+          "gate": 0
+        },
+        "initialization": "Both experts copied with exact Adam state from declared session-trained parents; new gate has random hidden weights and zero output, giving equal initial mixture",
+        "sampling": "Fresh seed for joint study; resumed phases restore only their own saved sampling state",
+        "runtime_versions": {
+          "jax": "0.7.2",
+          "numpy": "2.3.3"
+        }
+      },
+      "parent_provenance": [
+        {
+          "initialization": "Continue from the recorded session-trained checkpoint; original random initialization seed 20261134",
+          "prior_pass": 3,
+          "initial_seed": 20261134,
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+            "expert1": {
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+            "equal_trained_experts": {
+              "failure": 0.3724609375,
+              "nll": 0.6249072789556098
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+            "dynamic_gate": {
+              "failure": 0.3728515625,
+              "nll": 0.6248442052611475
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+            "condition_mean_gate_diagnostic": {
+              "failure": 0.3734375,
+              "nll": 0.6247616863727404
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+            "gate_corrected_equal_errors": 14,
+            "gate_introduced_equal_errors": 16,
+            "mean_expert0_coefficient": 0.4255400598049164,
+            "std_expert0_coefficient": 0.04466654360294342,
+            "original_joint_inference_comparison": {
+              "max_probability_difference": 0.0,
+              "decision_differences": 0
+            },
+            "elapsed_seconds": 66.79905652999878
+          }
+        ],
+        "seconds": 66.8079903125763,
+        "test_data_used": false,
+        "source_sha256": {
+          "joint_gate_ablation.py": "ea765d87c0c7495f3871bec5abe05200c866c6dcd52493f6068c99ac9ebfa818",
+          "joint_mixture.py": "07b111c55b7a380d387ef0955cb4ce894f6c76f6f84a5488c6ac80ae09415137",
+          "model.py": "3b2d4b34a8945ddfe82d76e28b4aa917f11933ed885c0cdcd2bc389448a871fb"
+        }
+      }
+    },
+    "new_optimizer_updates": 0,
+    "new_trainable_parameters": 0,
+    "test_data_used": false,
+    "initialization": "All extracted parameters descend from the joint study session-trained random-initialization parents; exact tensors are serialized without further fitting",
+    "selection_scope": "Adaptive follow-up proposed after observing validation branch diagnostics. All13 durations perbasis are included. Transductive condition-mean gates are excluded. This is not an independent confirmation dataset."
+  },
+  "confidence_joint1": {
+    "declared_plan": {
+      "hypothesis": "Adding fully differentiated expert confidence may let the jointly trained gate use complementary expert certainty beyond syndrome density and history alone",
+      "parents": [
+        "fresh2_full/weights-1536.npz",
+        "reserved1_full/weights-1536.npz"
+      ],
+      "initialization": "Same session-trained parents and exact Adam states as joint_mixture1; same gate seed20261310 preserves its41input hidden weights; append4zero input rows; zero output gives same initial equal mixture",
+      "features": "Existing41syndrome/basis/duration features plus clipped logits/8 for each expert and sqrt(z^2+1e-4) for each;clip probabilities to[1e-6,1-1e-6] and logit to[-8,8];45inputs,32SiLU hidden;shot-local,label-free,ID-free,fixedscales;full derivative through predictions",
+      "loss": "Stable mixture BCE; total multiplier on expert BCE gradient is responsibility+(1-2*y)*exp(-expert_BCE)*(gate-only derivative dL/dq); all prediction-to-gate paths differentiated; labels used only in loss",
+      "training": "Matched joint_mixture1:2048updates B1024;seed20261311 yields same uniform draws over516880rows/26conditions;expertLR.0001,gateLR.001;separate restored expert Adam clocks,fresh gate Adam;globalnorm1,decay1e-4",
+      "selection_steps": [
+        0,
+        512,
+        1024,
+        2048
+      ],
+      "validation": "Full133120 supplied validation shots per listed checkpoint; perbasis choose incumbent or rawjoint/equalphysical,all13durations; tiesretainincumbent. Step0 is fixedmean of trainedparents.1536has screening only and is not a full-selection checkpoint.",
+      "audit_scope": "All training labels are eligible; no independent reserved-training-label audit claim. Parent initialization provenance retained.",
+      "controls": "CPU scalar finite differences/exact autodiff with nonzero confidence couplings passed;two1update CPU restoration/sampling audit passed;four-case portable CPU runtime passed (order/subsets/delegation exact,independent mixture max5.96e-8,CPU priors vs GPUcache max3.58e-7);actual GPUcontrol still required before training; composed joint->robust->confidence CPU runtime passed after using distinct confidence cache attributes; actual joint/robust sources unchanged; constructed joint->logicalresidual->confidence CPU runtime also passed retained paths/order/distinct caches",
+      "runtime": "Two exact neural passes share one physical-prior computation; preserve all retained incumbent dispatches;fullreplay/scoring/readonlyrelocation/projection before any publication",
+      "interpretation": "Matched recipe extension; no physical-regime identification or independent reserved-label audit claim",
+      "gpu": "One H100; serialized after completed branch verification",
+      "test_data_used": false,
+      "parameter_counts": {
+        "expert0": 29505,
+        "expert1": 29505,
+        "gate": 1505,
+        "total": 60515
+      },
+      "physical_blend_clarification_before_training": "Equal physical blends use the existing calibrated/ensemble physical pass3 decoder output from turbo1/predictions, checked against runtime config and DEM/plans/weights. Raw feedback5_cache pass3 is only the neural conditioning prior/diagnostic. Corrected before any GPU training or selection in this study.",
+      "status": "Launched after fixed branch extraction verification",
+      "queued_utc": "2026-10-21T16:04:11.785757+00:00",
+      "launch_rationale": "Fixed branch diagnostics show modest, basis-dependent gate gains and a stronger individual Zexpert. The previously prepared matched confidence-input extension tests whether the gate can better use expert certainty. Recipe and selection checkpoints remain unchanged.",
+      "launch_utc": "2026-10-21T16:11:35.399436+00:00"
+    },
+    "physical_blend_reference": {
+      "package": "/run-state/workspace/research/context_upgrades/turbo1/package",
+      "predictions": "/run-state/workspace/research/context_upgrades/turbo1/predictions",
+      "component": "Existing physical pass3 decoder output, including its training-fitted calibration/ensemble; exactly the hybrid3.py blend_turbo path",
+      "verified_config": "turbo settings equal; physical DEM/plans and calibration/ensemble weights have identical SHA256",
+      "verified_files": [
+        "dems/surface_code_bX_d5_r01_center_5_5.dem",
+        "dems/surface_code_bX_d5_r03_center_5_5.dem",
+        "dems/surface_code_bX_d5_r05_center_5_5.dem",
+        "dems/surface_code_bX_d5_r07_center_5_5.dem",
+        "dems/surface_code_bX_d5_r09_center_5_5.dem",
+        "dems/surface_code_bX_d5_r11_center_5_5.dem",
+        "dems/surface_code_bX_d5_r13_center_5_5.dem",
+        "dems/surface_code_bX_d5_r15_center_5_5.dem",
+        "dems/surface_code_bX_d5_r17_center_5_5.dem",
+        "dems/surface_code_bX_d5_r19_center_5_5.dem",
+        "dems/surface_code_bX_d5_r21_center_5_5.dem",
+        "dems/surface_code_bX_d5_r23_center_5_5.dem",
+        "dems/surface_code_bX_d5_r25_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r01_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r03_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r05_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r07_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r09_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r11_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r13_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r15_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r17_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r19_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r21_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r23_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r25_center_5_5.dem",
+        "plans/surface_code_bX_d5_r01_center_5_5.npz",
+        "plans/surface_code_bZ_d5_r01_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r03_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r05_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r07_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r09_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r11_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r13_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r15_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r17_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r19_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r21_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r23_center_5_5.npz",
+        "turbo_plans/surface_code_bX_d5_r25_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r03_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r05_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r07_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r09_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r11_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r13_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r15_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r17_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r19_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r21_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r23_center_5_5.npz",
+        "turbo_plans/surface_code_bZ_d5_r25_center_5_5.npz",
+        "weights/turbo1-d5_bX_center_5_5.npz",
+        "weights/turbo1-d5_bZ_center_5_5.npz"
+      ],
+      "clarification": "Use these ID-aligned probabilities, not the raw pass3 column in feedback5_cache. Physical priors supplied to neural networks remain unchanged."
+    },
+    "selection_history": [
+      {
+        "basis": "X",
+        "group": "d5_bX_center_5_5",
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+          "2048-physical0.5": 0.23300781250000002
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+        "selected": "1024-physical0"
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+      {
+        "basis": "Z",
+        "group": "d5_bZ_center_5_5",
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+        "weights_sha256": "bf3eecca4cabbe147d19aca5c2539e527baf0683b8de673b360630d4517d3e9a",
+        "complete": true
+      }
+    },
+    "training": {
+      "run_config": {
+        "parents": [
+          "/run-state/workspace/research/learned_d5/fresh2_full/weights-1536.npz",
+          "/run-state/workspace/research/learned_d5/reserved1_full/weights-1536.npz"
+        ],
+        "output": "/run-state/workspace/research/learned_d5/confidence_joint1",
+        "training_cache": "/run-state/workspace/research/learned_d5/full_training_priors3",
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+        "steps": 2048,
+        "batch": 1024,
+        "expert_lr": 0.0001,
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+        "initialization": "Both experts copied with exact Adam state from declared session-trained parents; new gate retains the original seeded 41-input hidden weights, appends four zero confidence-input rows, and has zero output, giving the same initial equal mixture",
+        "sampling": "Same seed and draws as syndrome-only joint_mixture1; resumed phases restore only their own saved sampling state",
+        "runtime_versions": {
+          "jax": "0.7.2",
+          "numpy": "2.3.3"
+        }
+      },
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+          "initialization": "Continue from the recorded session-trained checkpoint; original random initialization seed 20261134",
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+          "hidden_width": 64,
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+          "feature_mode": "local events, reconstructed measurements, shot densities, physical edge support and conditioned prior",
+          "architecture": "Two 64-unit SiLU layers; 394 features; bounded 2*tanh correction to relevant-CSS edge logits from the recorded physical feedback pass; exact width-14 contraction predicts logical probability. D5 joint conditioning remains approximate.",
+          "training": "All supplied training shots per each of all26 d5 conditions; uniform condition and within-condition sampling for optimizer batches.",
+          "training_cache": "/run-state/workspace/research/learned_d5/full_training_priors3",
+          "training_cache_provenance": {
+            "method": "Three undamped physical CSS extrinsic-message passes, clipping8, pass1/pass3 priors captured before logical readout",
+            "scope": "All supplied training rows in all26 d5 conditions; no validation or test labels used in cache generation",
+            "reused_rows": "4096 rows per condition from feedback5_cache, sampled with seed20261023; same first three passes",
+            "remaining_rows": "Computed now with last-only logical tracking; previously controlled against full logical tracking",
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+          "axial_residual/model.py": "a62d91d239325f45d9f1a6af6e91095b8d6028a52959dcb7b98ba109df097537",
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+}
diff --git a/shared/package-files/1b/1b1558a72507a85a40136f08e33bb09cc0e07ba867e1558fa210f4471940df70/segment_reduce_native.h b/shared/package-files/1b/1b1558a72507a85a40136f08e33bb09cc0e07ba867e1558fa210f4471940df70/segment_reduce_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..49fa3d1d59ef131e919900d3016140c89174c9ab
--- /dev/null
+++ b/shared/package-files/1b/1b1558a72507a85a40136f08e33bb09cc0e07ba867e1558fa210f4471940df70/segment_reduce_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & segment_reduce_out(const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & indices, const c10::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const c10::optional<at::Scalar> & initial, at::Tensor & out);
+TORCH_API at::Tensor segment_reduce_kernel(const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths={}, const c10::optional<at::Tensor> & indices={}, const c10::optional<at::Tensor> & offsets={}, int64_t axis=0, bool unsafe=false, const c10::optional<at::Scalar> & initial=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b15bfead5ee8ae59aa7ea5ce13d2fd4f31c7f0f055daab799211fe743a42e85/bsplines.py b/shared/package-files/1b/1b15bfead5ee8ae59aa7ea5ce13d2fd4f31c7f0f055daab799211fe743a42e85/bsplines.py
new file mode 100644
index 0000000000000000000000000000000000000000..6adabb32711447cbba60bbd963104e444599773e
--- /dev/null
+++ b/shared/package-files/1b/1b15bfead5ee8ae59aa7ea5ce13d2fd4f31c7f0f055daab799211fe743a42e85/bsplines.py
@@ -0,0 +1,351 @@
+from sympy.core import S, sympify
+from sympy.core.symbol import (Dummy, symbols)
+from sympy.functions import Piecewise, piecewise_fold
+from sympy.logic.boolalg import And
+from sympy.sets.sets import Interval
+
+from functools import lru_cache
+
+
+def _ivl(cond, x):
+    """return the interval corresponding to the condition
+
+    Conditions in spline's Piecewise give the range over
+    which an expression is valid like (lo <= x) & (x <= hi).
+    This function returns (lo, hi).
+    """
+    if isinstance(cond, And) and len(cond.args) == 2:
+        a, b = cond.args
+        if a.lts == x:
+            a, b = b, a
+        return a.lts, b.gts
+    raise TypeError('unexpected cond type: %s' % cond)
+
+
+def _add_splines(c, b1, d, b2, x):
+    """Construct c*b1 + d*b2."""
+
+    if S.Zero in (b1, c):
+        rv = piecewise_fold(d * b2)
+    elif S.Zero in (b2, d):
+        rv = piecewise_fold(c * b1)
+    else:
+        new_args = []
+        # Just combining the Piecewise without any fancy optimization
+        p1 = piecewise_fold(c * b1)
+        p2 = piecewise_fold(d * b2)
+
+        # Search all Piecewise arguments except (0, True)
+        p2args = list(p2.args[:-1])
+
+        # This merging algorithm assumes the conditions in
+        # p1 and p2 are sorted
+        for arg in p1.args[:-1]:
+            expr = arg.expr
+            cond = arg.cond
+
+            lower = _ivl(cond, x)[0]
+
+            # Check p2 for matching conditions that can be merged
+            for i, arg2 in enumerate(p2args):
+                expr2 = arg2.expr
+                cond2 = arg2.cond
+
+                lower_2, upper_2 = _ivl(cond2, x)
+                if cond2 == cond:
+                    # Conditions match, join expressions
+                    expr += expr2
+                    # Remove matching element
+                    del p2args[i]
+                    # No need to check the rest
+                    break
+                elif lower_2 < lower and upper_2 <= lower:
+                    # Check if arg2 condition smaller than arg1,
+                    # add to new_args by itself (no match expected
+                    # in p1)
+                    new_args.append(arg2)
+                    del p2args[i]
+                    break
+
+            # Checked all, add expr and cond
+            new_args.append((expr, cond))
+
+        # Add remaining items from p2args
+        new_args.extend(p2args)
+
+        # Add final (0, True)
+        new_args.append((0, True))
+
+        rv = Piecewise(*new_args, evaluate=False)
+
+    return rv.expand()
+
+
+@lru_cache(maxsize=128)
+def bspline_basis(d, knots, n, x):
+    """
+    The $n$-th B-spline at $x$ of degree $d$ with knots.
+
+    Explanation
+    ===========
+
+    B-Splines are piecewise polynomials of degree $d$. They are defined on a
+    set of knots, which is a sequence of integers or floats.
+
+    Examples
+    ========
+
+    The 0th degree splines have a value of 1 on a single interval:
+
+        >>> from sympy import bspline_basis
+        >>> from sympy.abc import x
+        >>> d = 0
+        >>> knots = tuple(range(5))
+        >>> bspline_basis(d, knots, 0, x)
+        Piecewise((1, (x >= 0) & (x <= 1)), (0, True))
+
+    For a given ``(d, knots)`` there are ``len(knots)-d-1`` B-splines
+    defined, that are indexed by ``n`` (starting at 0).
+
+    Here is an example of a cubic B-spline:
+
+        >>> bspline_basis(3, tuple(range(5)), 0, x)
+        Piecewise((x**3/6, (x >= 0) & (x <= 1)),
+                  (-x**3/2 + 2*x**2 - 2*x + 2/3,
+                  (x >= 1) & (x <= 2)),
+                  (x**3/2 - 4*x**2 + 10*x - 22/3,
+                  (x >= 2) & (x <= 3)),
+                  (-x**3/6 + 2*x**2 - 8*x + 32/3,
+                  (x >= 3) & (x <= 4)),
+                  (0, True))
+
+    By repeating knot points, you can introduce discontinuities in the
+    B-splines and their derivatives:
+
+        >>> d = 1
+        >>> knots = (0, 0, 2, 3, 4)
+        >>> bspline_basis(d, knots, 0, x)
+        Piecewise((1 - x/2, (x >= 0) & (x <= 2)), (0, True))
+
+    It is quite time consuming to construct and evaluate B-splines. If
+    you need to evaluate a B-spline many times, it is best to lambdify them
+    first:
+
+        >>> from sympy import lambdify
+        >>> d = 3
+        >>> knots = tuple(range(10))
+        >>> b0 = bspline_basis(d, knots, 0, x)
+        >>> f = lambdify(x, b0)
+        >>> y = f(0.5)
+
+    Parameters
+    ==========
+
+    d : integer
+        degree of bspline
+
+    knots : list of integer values
+        list of knots points of bspline
+
+    n : integer
+        $n$-th B-spline
+
+    x : symbol
+
+    See Also
+    ========
+
+    bspline_basis_set
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/B-spline
+
+    """
+    # make sure x has no assumptions so conditions don't evaluate
+    xvar = x
+    x = Dummy()
+
+    knots = tuple(sympify(k) for k in knots)
+    d = int(d)
+    n = int(n)
+    n_knots = len(knots)
+    n_intervals = n_knots - 1
+    if n + d + 1 > n_intervals:
+        raise ValueError("n + d + 1 must not exceed len(knots) - 1")
+    if d == 0:
+        result = Piecewise(
+            (S.One, Interval(knots[n], knots[n + 1]).contains(x)), (0, True)
+        )
+    elif d > 0:
+        denom = knots[n + d + 1] - knots[n + 1]
+        if denom != S.Zero:
+            B = (knots[n + d + 1] - x) / denom
+            b2 = bspline_basis(d - 1, knots, n + 1, x)
+        else:
+            b2 = B = S.Zero
+
+        denom = knots[n + d] - knots[n]
+        if denom != S.Zero:
+            A = (x - knots[n]) / denom
+            b1 = bspline_basis(d - 1, knots, n, x)
+        else:
+            b1 = A = S.Zero
+
+        result = _add_splines(A, b1, B, b2, x)
+    else:
+        raise ValueError("degree must be non-negative: %r" % n)
+
+    # return result with user-given x
+    return result.xreplace({x: xvar})
+
+
+def bspline_basis_set(d, knots, x):
+    """
+    Return the ``len(knots)-d-1`` B-splines at *x* of degree *d*
+    with *knots*.
+
+    Explanation
+    ===========
+
+    This function returns a list of piecewise polynomials that are the
+    ``len(knots)-d-1`` B-splines of degree *d* for the given knots.
+    This function calls ``bspline_basis(d, knots, n, x)`` for different
+    values of *n*.
+
+    Examples
+    ========
+
+    >>> from sympy import bspline_basis_set
+    >>> from sympy.abc import x
+    >>> d = 2
+    >>> knots = range(5)
+    >>> splines = bspline_basis_set(d, knots, x)
+    >>> splines
+    [Piecewise((x**2/2, (x >= 0) & (x <= 1)),
+               (-x**2 + 3*x - 3/2, (x >= 1) & (x <= 2)),
+               (x**2/2 - 3*x + 9/2, (x >= 2) & (x <= 3)),
+               (0, True)),
+    Piecewise((x**2/2 - x + 1/2, (x >= 1) & (x <= 2)),
+              (-x**2 + 5*x - 11/2, (x >= 2) & (x <= 3)),
+              (x**2/2 - 4*x + 8, (x >= 3) & (x <= 4)),
+              (0, True))]
+
+    Parameters
+    ==========
+
+    d : integer
+        degree of bspline
+
+    knots : list of integers
+        list of knots points of bspline
+
+    x : symbol
+
+    See Also
+    ========
+
+    bspline_basis
+
+    """
+    n_splines = len(knots) - d - 1
+    return [bspline_basis(d, tuple(knots), i, x) for i in range(n_splines)]
+
+
+def interpolating_spline(d, x, X, Y):
+    """
+    Return spline of degree *d*, passing through the given *X*
+    and *Y* values.
+
+    Explanation
+    ===========
+
+    This function returns a piecewise function such that each part is
+    a polynomial of degree not greater than *d*. The value of *d*
+    must be 1 or greater and the values of *X* must be strictly
+    increasing.
+
+    Examples
+    ========
+
+    >>> from sympy import interpolating_spline
+    >>> from sympy.abc import x
+    >>> interpolating_spline(1, x, [1, 2, 4, 7], [3, 6, 5, 7])
+    Piecewise((3*x, (x >= 1) & (x <= 2)),
+            (7 - x/2, (x >= 2) & (x <= 4)),
+            (2*x/3 + 7/3, (x >= 4) & (x <= 7)))
+    >>> interpolating_spline(3, x, [-2, 0, 1, 3, 4], [4, 2, 1, 1, 3])
+    Piecewise((7*x**3/117 + 7*x**2/117 - 131*x/117 + 2, (x >= -2) & (x <= 1)),
+            (10*x**3/117 - 2*x**2/117 - 122*x/117 + 77/39, (x >= 1) & (x <= 4)))
+
+    Parameters
+    ==========
+
+    d : integer
+        Degree of Bspline strictly greater than equal to one
+
+    x : symbol
+
+    X : list of strictly increasing real values
+        list of X coordinates through which the spline passes
+
+    Y : list of real values
+        list of corresponding Y coordinates through which the spline passes
+
+    See Also
+    ========
+
+    bspline_basis_set, interpolating_poly
+
+    """
+    from sympy.solvers.solveset import linsolve
+    from sympy.matrices.dense import Matrix
+
+    # Input sanitization
+    d = sympify(d)
+    if not (d.is_Integer and d.is_positive):
+        raise ValueError("Spline degree must be a positive integer, not %s." % d)
+    if len(X) != len(Y):
+        raise ValueError("Number of X and Y coordinates must be the same.")
+    if len(X) < d + 1:
+        raise ValueError("Degree must be less than the number of control points.")
+    if not all(a < b for a, b in zip(X, X[1:])):
+        raise ValueError("The x-coordinates must be strictly increasing.")
+    X = [sympify(i) for i in X]
+
+    # Evaluating knots value
+    if d.is_odd:
+        j = (d + 1) // 2
+        interior_knots = X[j:-j]
+    else:
+        j = d // 2
+        interior_knots = [
+            (a + b)/2 for a, b in zip(X[j : -j - 1], X[j + 1 : -j])
+        ]
+
+    knots = [X[0]] * (d + 1) + list(interior_knots) + [X[-1]] * (d + 1)
+
+    basis = bspline_basis_set(d, knots, x)
+
+    A = [[b.subs(x, v) for b in basis] for v in X]
+
+    coeff = linsolve((Matrix(A), Matrix(Y)), symbols("c0:{}".format(len(X)), cls=Dummy))
+    coeff = list(coeff)[0]
+    intervals = {c for b in basis for (e, c) in b.args if c != True}
+
+    # Sorting the intervals
+    #  ival contains the end-points of each interval
+    ival = [_ivl(c, x) for c in intervals]
+    com = zip(ival, intervals)
+    com = sorted(com, key=lambda x: x[0])
+    intervals = [y for x, y in com]
+
+    basis_dicts = [{c: e for (e, c) in b.args} for b in basis]
+    spline = []
+    for i in intervals:
+        piece = sum(
+            [c * d.get(i, S.Zero) for (c, d) in zip(coeff, basis_dicts)], S.Zero
+        )
+        spline.append((piece, i))
+    return Piecewise(*spline)
diff --git a/shared/package-files/1b/1b173d48b21234a316aca76c549f5f285145c1fc716737c5f9873edabfffcf93/laguerre.pyi b/shared/package-files/1b/1b173d48b21234a316aca76c549f5f285145c1fc716737c5f9873edabfffcf93/laguerre.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e546bc20a54c0e522cd7ea851ad8e8a42d895980
--- /dev/null
+++ b/shared/package-files/1b/1b173d48b21234a316aca76c549f5f285145c1fc716737c5f9873edabfffcf93/laguerre.pyi
@@ -0,0 +1,46 @@
+from typing import Any
+
+from numpy import ndarray, dtype, int_
+from numpy.polynomial._polybase import ABCPolyBase
+from numpy.polynomial.polyutils import trimcoef
+
+__all__: list[str]
+
+lagtrim = trimcoef
+
+def poly2lag(pol): ...
+def lag2poly(c): ...
+
+lagdomain: ndarray[Any, dtype[int_]]
+lagzero: ndarray[Any, dtype[int_]]
+lagone: ndarray[Any, dtype[int_]]
+lagx: ndarray[Any, dtype[int_]]
+
+def lagline(off, scl): ...
+def lagfromroots(roots): ...
+def lagadd(c1, c2): ...
+def lagsub(c1, c2): ...
+def lagmulx(c): ...
+def lagmul(c1, c2): ...
+def lagdiv(c1, c2): ...
+def lagpow(c, pow, maxpower=...): ...
+def lagder(c, m=..., scl=..., axis=...): ...
+def lagint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ...
+def lagval(x, c, tensor=...): ...
+def lagval2d(x, y, c): ...
+def laggrid2d(x, y, c): ...
+def lagval3d(x, y, z, c): ...
+def laggrid3d(x, y, z, c): ...
+def lagvander(x, deg): ...
+def lagvander2d(x, y, deg): ...
+def lagvander3d(x, y, z, deg): ...
+def lagfit(x, y, deg, rcond=..., full=..., w=...): ...
+def lagcompanion(c): ...
+def lagroots(c): ...
+def laggauss(deg): ...
+def lagweight(x): ...
+
+class Laguerre(ABCPolyBase):
+    domain: Any
+    window: Any
+    basis_name: Any
diff --git a/shared/package-files/1b/1b18b978b7f2e2a587aa77f0bc7a6130718c4b680dd19cc749eb2ee7eb8b9590/cmd.py b/shared/package-files/1b/1b18b978b7f2e2a587aa77f0bc7a6130718c4b680dd19cc749eb2ee7eb8b9590/cmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..859e91096d8f57d906c00023ef1a1c0e663178d6
--- /dev/null
+++ b/shared/package-files/1b/1b18b978b7f2e2a587aa77f0bc7a6130718c4b680dd19cc749eb2ee7eb8b9590/cmd.py
@@ -0,0 +1,401 @@
+"""A generic class to build line-oriented command interpreters.
+
+Interpreters constructed with this class obey the following conventions:
+
+1. End of file on input is processed as the command 'EOF'.
+2. A command is parsed out of each line by collecting the prefix composed
+   of characters in the identchars member.
+3. A command `foo' is dispatched to a method 'do_foo()'; the do_ method
+   is passed a single argument consisting of the remainder of the line.
+4. Typing an empty line repeats the last command.  (Actually, it calls the
+   method `emptyline', which may be overridden in a subclass.)
+5. There is a predefined `help' method.  Given an argument `topic', it
+   calls the command `help_topic'.  With no arguments, it lists all topics
+   with defined help_ functions, broken into up to three topics; documented
+   commands, miscellaneous help topics, and undocumented commands.
+6. The command '?' is a synonym for `help'.  The command '!' is a synonym
+   for `shell', if a do_shell method exists.
+7. If completion is enabled, completing commands will be done automatically,
+   and completing of commands args is done by calling complete_foo() with
+   arguments text, line, begidx, endidx.  text is string we are matching
+   against, all returned matches must begin with it.  line is the current
+   input line (lstripped), begidx and endidx are the beginning and end
+   indexes of the text being matched, which could be used to provide
+   different completion depending upon which position the argument is in.
+
+The `default' method may be overridden to intercept commands for which there
+is no do_ method.
+
+The `completedefault' method may be overridden to intercept completions for
+commands that have no complete_ method.
+
+The data member `self.ruler' sets the character used to draw separator lines
+in the help messages.  If empty, no ruler line is drawn.  It defaults to "=".
+
+If the value of `self.intro' is nonempty when the cmdloop method is called,
+it is printed out on interpreter startup.  This value may be overridden
+via an optional argument to the cmdloop() method.
+
+The data members `self.doc_header', `self.misc_header', and
+`self.undoc_header' set the headers used for the help function's
+listings of documented functions, miscellaneous topics, and undocumented
+functions respectively.
+"""
+
+import string, sys
+
+__all__ = ["Cmd"]
+
+PROMPT = '(Cmd) '
+IDENTCHARS = string.ascii_letters + string.digits + '_'
+
+class Cmd:
+    """A simple framework for writing line-oriented command interpreters.
+
+    These are often useful for test harnesses, administrative tools, and
+    prototypes that will later be wrapped in a more sophisticated interface.
+
+    A Cmd instance or subclass instance is a line-oriented interpreter
+    framework.  There is no good reason to instantiate Cmd itself; rather,
+    it's useful as a superclass of an interpreter class you define yourself
+    in order to inherit Cmd's methods and encapsulate action methods.
+
+    """
+    prompt = PROMPT
+    identchars = IDENTCHARS
+    ruler = '='
+    lastcmd = ''
+    intro = None
+    doc_leader = ""
+    doc_header = "Documented commands (type help <topic>):"
+    misc_header = "Miscellaneous help topics:"
+    undoc_header = "Undocumented commands:"
+    nohelp = "*** No help on %s"
+    use_rawinput = 1
+
+    def __init__(self, completekey='tab', stdin=None, stdout=None):
+        """Instantiate a line-oriented interpreter framework.
+
+        The optional argument 'completekey' is the readline name of a
+        completion key; it defaults to the Tab key. If completekey is
+        not None and the readline module is available, command completion
+        is done automatically. The optional arguments stdin and stdout
+        specify alternate input and output file objects; if not specified,
+        sys.stdin and sys.stdout are used.
+
+        """
+        if stdin is not None:
+            self.stdin = stdin
+        else:
+            self.stdin = sys.stdin
+        if stdout is not None:
+            self.stdout = stdout
+        else:
+            self.stdout = sys.stdout
+        self.cmdqueue = []
+        self.completekey = completekey
+
+    def cmdloop(self, intro=None):
+        """Repeatedly issue a prompt, accept input, parse an initial prefix
+        off the received input, and dispatch to action methods, passing them
+        the remainder of the line as argument.
+
+        """
+
+        self.preloop()
+        if self.use_rawinput and self.completekey:
+            try:
+                import readline
+                self.old_completer = readline.get_completer()
+                readline.set_completer(self.complete)
+                readline.parse_and_bind(self.completekey+": complete")
+            except ImportError:
+                pass
+        try:
+            if intro is not None:
+                self.intro = intro
+            if self.intro:
+                self.stdout.write(str(self.intro)+"\n")
+            stop = None
+            while not stop:
+                if self.cmdqueue:
+                    line = self.cmdqueue.pop(0)
+                else:
+                    if self.use_rawinput:
+                        try:
+                            line = input(self.prompt)
+                        except EOFError:
+                            line = 'EOF'
+                    else:
+                        self.stdout.write(self.prompt)
+                        self.stdout.flush()
+                        line = self.stdin.readline()
+                        if not len(line):
+                            line = 'EOF'
+                        else:
+                            line = line.rstrip('\r\n')
+                line = self.precmd(line)
+                stop = self.onecmd(line)
+                stop = self.postcmd(stop, line)
+            self.postloop()
+        finally:
+            if self.use_rawinput and self.completekey:
+                try:
+                    import readline
+                    readline.set_completer(self.old_completer)
+                except ImportError:
+                    pass
+
+
+    def precmd(self, line):
+        """Hook method executed just before the command line is
+        interpreted, but after the input prompt is generated and issued.
+
+        """
+        return line
+
+    def postcmd(self, stop, line):
+        """Hook method executed just after a command dispatch is finished."""
+        return stop
+
+    def preloop(self):
+        """Hook method executed once when the cmdloop() method is called."""
+        pass
+
+    def postloop(self):
+        """Hook method executed once when the cmdloop() method is about to
+        return.
+
+        """
+        pass
+
+    def parseline(self, line):
+        """Parse the line into a command name and a string containing
+        the arguments.  Returns a tuple containing (command, args, line).
+        'command' and 'args' may be None if the line couldn't be parsed.
+        """
+        line = line.strip()
+        if not line:
+            return None, None, line
+        elif line[0] == '?':
+            line = 'help ' + line[1:]
+        elif line[0] == '!':
+            if hasattr(self, 'do_shell'):
+                line = 'shell ' + line[1:]
+            else:
+                return None, None, line
+        i, n = 0, len(line)
+        while i < n and line[i] in self.identchars: i = i+1
+        cmd, arg = line[:i], line[i:].strip()
+        return cmd, arg, line
+
+    def onecmd(self, line):
+        """Interpret the argument as though it had been typed in response
+        to the prompt.
+
+        This may be overridden, but should not normally need to be;
+        see the precmd() and postcmd() methods for useful execution hooks.
+        The return value is a flag indicating whether interpretation of
+        commands by the interpreter should stop.
+
+        """
+        cmd, arg, line = self.parseline(line)
+        if not line:
+            return self.emptyline()
+        if cmd is None:
+            return self.default(line)
+        self.lastcmd = line
+        if line == 'EOF' :
+            self.lastcmd = ''
+        if cmd == '':
+            return self.default(line)
+        else:
+            try:
+                func = getattr(self, 'do_' + cmd)
+            except AttributeError:
+                return self.default(line)
+            return func(arg)
+
+    def emptyline(self):
+        """Called when an empty line is entered in response to the prompt.
+
+        If this method is not overridden, it repeats the last nonempty
+        command entered.
+
+        """
+        if self.lastcmd:
+            return self.onecmd(self.lastcmd)
+
+    def default(self, line):
+        """Called on an input line when the command prefix is not recognized.
+
+        If this method is not overridden, it prints an error message and
+        returns.
+
+        """
+        self.stdout.write('*** Unknown syntax: %s\n'%line)
+
+    def completedefault(self, *ignored):
+        """Method called to complete an input line when no command-specific
+        complete_*() method is available.
+
+        By default, it returns an empty list.
+
+        """
+        return []
+
+    def completenames(self, text, *ignored):
+        dotext = 'do_'+text
+        return [a[3:] for a in self.get_names() if a.startswith(dotext)]
+
+    def complete(self, text, state):
+        """Return the next possible completion for 'text'.
+
+        If a command has not been entered, then complete against command list.
+        Otherwise try to call complete_<command> to get list of completions.
+        """
+        if state == 0:
+            import readline
+            origline = readline.get_line_buffer()
+            line = origline.lstrip()
+            stripped = len(origline) - len(line)
+            begidx = readline.get_begidx() - stripped
+            endidx = readline.get_endidx() - stripped
+            if begidx>0:
+                cmd, args, foo = self.parseline(line)
+                if cmd == '':
+                    compfunc = self.completedefault
+                else:
+                    try:
+                        compfunc = getattr(self, 'complete_' + cmd)
+                    except AttributeError:
+                        compfunc = self.completedefault
+            else:
+                compfunc = self.completenames
+            self.completion_matches = compfunc(text, line, begidx, endidx)
+        try:
+            return self.completion_matches[state]
+        except IndexError:
+            return None
+
+    def get_names(self):
+        # This method used to pull in base class attributes
+        # at a time dir() didn't do it yet.
+        return dir(self.__class__)
+
+    def complete_help(self, *args):
+        commands = set(self.completenames(*args))
+        topics = set(a[5:] for a in self.get_names()
+                     if a.startswith('help_' + args[0]))
+        return list(commands | topics)
+
+    def do_help(self, arg):
+        'List available commands with "help" or detailed help with "help cmd".'
+        if arg:
+            # XXX check arg syntax
+            try:
+                func = getattr(self, 'help_' + arg)
+            except AttributeError:
+                try:
+                    doc=getattr(self, 'do_' + arg).__doc__
+                    if doc:
+                        self.stdout.write("%s\n"%str(doc))
+                        return
+                except AttributeError:
+                    pass
+                self.stdout.write("%s\n"%str(self.nohelp % (arg,)))
+                return
+            func()
+        else:
+            names = self.get_names()
+            cmds_doc = []
+            cmds_undoc = []
+            help = {}
+            for name in names:
+                if name[:5] == 'help_':
+                    help[name[5:]]=1
+            names.sort()
+            # There can be duplicates if routines overridden
+            prevname = ''
+            for name in names:
+                if name[:3] == 'do_':
+                    if name == prevname:
+                        continue
+                    prevname = name
+                    cmd=name[3:]
+                    if cmd in help:
+                        cmds_doc.append(cmd)
+                        del help[cmd]
+                    elif getattr(self, name).__doc__:
+                        cmds_doc.append(cmd)
+                    else:
+                        cmds_undoc.append(cmd)
+            self.stdout.write("%s\n"%str(self.doc_leader))
+            self.print_topics(self.doc_header,   cmds_doc,   15,80)
+            self.print_topics(self.misc_header,  list(help.keys()),15,80)
+            self.print_topics(self.undoc_header, cmds_undoc, 15,80)
+
+    def print_topics(self, header, cmds, cmdlen, maxcol):
+        if cmds:
+            self.stdout.write("%s\n"%str(header))
+            if self.ruler:
+                self.stdout.write("%s\n"%str(self.ruler * len(header)))
+            self.columnize(cmds, maxcol-1)
+            self.stdout.write("\n")
+
+    def columnize(self, list, displaywidth=80):
+        """Display a list of strings as a compact set of columns.
+
+        Each column is only as wide as necessary.
+        Columns are separated by two spaces (one was not legible enough).
+        """
+        if not list:
+            self.stdout.write("<empty>\n")
+            return
+
+        nonstrings = [i for i in range(len(list))
+                        if not isinstance(list[i], str)]
+        if nonstrings:
+            raise TypeError("list[i] not a string for i in %s"
+                            % ", ".join(map(str, nonstrings)))
+        size = len(list)
+        if size == 1:
+            self.stdout.write('%s\n'%str(list[0]))
+            return
+        # Try every row count from 1 upwards
+        for nrows in range(1, len(list)):
+            ncols = (size+nrows-1) // nrows
+            colwidths = []
+            totwidth = -2
+            for col in range(ncols):
+                colwidth = 0
+                for row in range(nrows):
+                    i = row + nrows*col
+                    if i >= size:
+                        break
+                    x = list[i]
+                    colwidth = max(colwidth, len(x))
+                colwidths.append(colwidth)
+                totwidth += colwidth + 2
+                if totwidth > displaywidth:
+                    break
+            if totwidth <= displaywidth:
+                break
+        else:
+            nrows = len(list)
+            ncols = 1
+            colwidths = [0]
+        for row in range(nrows):
+            texts = []
+            for col in range(ncols):
+                i = row + nrows*col
+                if i >= size:
+                    x = ""
+                else:
+                    x = list[i]
+                texts.append(x)
+            while texts and not texts[-1]:
+                del texts[-1]
+            for col in range(len(texts)):
+                texts[col] = texts[col].ljust(colwidths[col])
+            self.stdout.write("%s\n"%str("  ".join(texts)))
diff --git a/shared/package-files/1b/1b198246b3ffee4a281205e29184616c5ec4fcdd20e5aa088d4e4b6db0df6ed2/norm_except_dim_native.h b/shared/package-files/1b/1b198246b3ffee4a281205e29184616c5ec4fcdd20e5aa088d4e4b6db0df6ed2/norm_except_dim_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8918e5c22c1e63b7ee57c56d3e5163a7c672252e
--- /dev/null
+++ b/shared/package-files/1b/1b198246b3ffee4a281205e29184616c5ec4fcdd20e5aa088d4e4b6db0df6ed2/norm_except_dim_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor norm_except_dim(const at::Tensor & v, int64_t pow=2, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b19a94a633c3294be69db79c8025fd05c28eb0efc5b129033542f29ab8747fb/libswresample.pc b/shared/package-files/1b/1b19a94a633c3294be69db79c8025fd05c28eb0efc5b129033542f29ab8747fb/libswresample.pc
new file mode 100644
index 0000000000000000000000000000000000000000..98d40b1455f7ef325e4f08f4e108e793af6af8da
--- /dev/null
+++ b/shared/package-files/1b/1b19a94a633c3294be69db79c8025fd05c28eb0efc5b129033542f29ab8747fb/libswresample.pc
@@ -0,0 +1,14 @@
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=/opt/conda/lib
+includedir=/opt/conda/include
+
+Name: libswresample
+Description: FFmpeg audio resampling library
+Version: 3.7.100
+Requires: 
+Requires.private: libavutil >= 56.51.100
+Conflicts:
+Libs: -L${libdir}  -lswresample 
+Libs.private: -lm
+Cflags: -I${includedir}
diff --git a/shared/package-files/1b/1b1a3c4d0bbde63022fb9dac5f6990887c4b0d97233f2f557b11e600ef91f987/_hypothesis_globals.py b/shared/package-files/1b/1b1a3c4d0bbde63022fb9dac5f6990887c4b0d97233f2f557b11e600ef91f987/_hypothesis_globals.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3eb6fd6d7a8ece2eb771185287075d74c29623
--- /dev/null
+++ b/shared/package-files/1b/1b1a3c4d0bbde63022fb9dac5f6990887c4b0d97233f2f557b11e600ef91f987/_hypothesis_globals.py
@@ -0,0 +1,28 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Copyright the Hypothesis Authors.
+# Individual contributors are listed in AUTHORS.rst and the git log.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+
+"""
+Module for globals shared between plugin(s) and the main hypothesis module, without
+depending on either. This file should have no imports outside of stdlib.
+"""
+
+import os
+
+in_initialization = 1
+"""If >0, indicates that hypothesis is still initializing (importing or loading
+the test environment). `import hypothesis` will cause this number to be decremented,
+and the pytest plugin increments at load time, then decrements it just before each test
+session starts. However, this leads to a hole in coverage if another pytest plugin
+imports hypothesis before our plugin is loaded. HYPOTHESIS_EXTEND_INITIALIZATION may
+be set to pre-increment the value on behalf of _hypothesis_pytestplugin, plugging the
+hole."""
+
+if os.environ.get("HYPOTHESIS_EXTEND_INITIALIZATION"):
+    in_initialization += 1
diff --git a/shared/package-files/1b/1b1b582048fbff5fc5351b5056be31413a60dc2ceabaa6644ce8b7d06f011d57/exp_native.h b/shared/package-files/1b/1b1b582048fbff5fc5351b5056be31413a60dc2ceabaa6644ce8b7d06f011d57/exp_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c769f4ab95877e77733a454bc7574ec849fea5e3
--- /dev/null
+++ b/shared/package-files/1b/1b1b582048fbff5fc5351b5056be31413a60dc2ceabaa6644ce8b7d06f011d57/exp_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/exp_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_exp_out : public at::meta::structured_exp {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b1b87f4ca611a170407a757c3517a51d7237350e6335e91d1ae23e48ef1e3e2/mem_dependency_checker.h b/shared/package-files/1b/1b1b87f4ca611a170407a757c3517a51d7237350e6335e91d1ae23e48ef1e3e2/mem_dependency_checker.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b5bb538459c668481610d778c13a96e5142c23e
--- /dev/null
+++ b/shared/package-files/1b/1b1b87f4ca611a170407a757c3517a51d7237350e6335e91d1ae23e48ef1e3e2/mem_dependency_checker.h
@@ -0,0 +1,415 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <utility>
+#include <vector>
+
+#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+enum class AccessType {
+  Input,
+  Output,
+  Load,
+  Store,
+  Call,
+  AtomicAdd,
+  Alloc,
+  Free
+};
+const char* AccessToString(AccessType a);
+
+class AccessInfo;
+using DependencySet = std::unordered_set<std::shared_ptr<AccessInfo>>;
+
+/* AccessInfo
+ *
+ * Represents a single bounded memory access to a buffer, for instance a Load or
+ * a Store. Holds information relating to the specific access and links to
+ * connected accesses in the dependency graph.
+ */
+class TORCH_API AccessInfo {
+ public:
+  AccessInfo(
+      size_t id,
+      AccessType type,
+      StmtPtr stmt,
+      VarPtr var,
+      IndexBounds bounds)
+      : id_(id),
+        type_(type),
+        stmt_(std::move(stmt)),
+        expr_(nullptr),
+        var_(std::move(var)),
+        bounds_(std::move(bounds)) {}
+
+  AccessInfo(
+      size_t id,
+      AccessType type,
+      ExprPtr expr,
+      StmtPtr stmt,
+      VarPtr var,
+      IndexBounds bounds)
+      : id_(id),
+        type_(type),
+        stmt_(std::move(stmt)),
+        expr_(std::move(expr)),
+        var_(std::move(var)),
+        bounds_(std::move(bounds)) {}
+
+  // Id is a unique int representing the order this access occurred in the
+  // graph.
+  size_t id() const {
+    return id_;
+  }
+
+  // The type of the access (Load, Store, etc).
+  AccessType type() const {
+    return type_;
+  }
+
+  // The enclosing Stmt this access represents. E.g. if this is a Store then
+  // Stmt is the Store itself, while if the access is caused by an Expr, this is
+  // the most immediate parent Stmt.
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  // If the access is represented by an Expr (such as Load or Call) then this is
+  // it, otherwise it's nullptr.
+  ExprPtr expr() const {
+    return expr_;
+  }
+
+  // The Var representing the underlying Buffer.
+  VarPtr var() const {
+    return var_;
+  }
+
+  // A vector of Bounds representing the start and end expression for each
+  // dimension.
+  IndexBounds& bounds() {
+    return bounds_;
+  }
+
+  // Each access that this depends upon,
+  // eg. if this is a Load, then it contains every Store that immediately
+  // contributes to a load of the bounds.
+  // or: if this is a Store, it contains all reads on the RHS of the Store.
+  const std::map<size_t, std::shared_ptr<AccessInfo>>& dependencies() const {
+    return dependencies_;
+  }
+
+  // Each access that depends on this one.
+  // ie. this access is present in the dependencies map of all accesses that are
+  // dependent.
+  std::map<size_t, std::shared_ptr<AccessInfo>> dependents() const {
+    std::map<size_t, std::shared_ptr<AccessInfo>> res;
+    for (const auto& kv : dependents_) {
+      res.emplace(kv.first, kv.second.lock());
+    }
+    return res;
+  }
+
+  // Returns the symbolic expression of the indices of this access.
+  std::vector<ExprPtr> getIndices() const;
+
+  // Establishes a dependency or dependent relationship with another access.
+  void addDependency(const std::shared_ptr<AccessInfo>& write);
+  void addDependent(const std::shared_ptr<AccessInfo>& read);
+
+  // helper for checking dependencies.
+  bool hasDependency(const std::shared_ptr<AccessInfo>& info) const;
+
+  // Returns the set of all nodes that are direct (immediate) dependencies of
+  // this access.
+  DependencySet getDirectDependencies();
+  // likewise, returns all nodes that directly depend on this one.
+  DependencySet getDirectDependents();
+
+  // Returns the full list of all nodes in the graph that this access depends
+  // on, and all nodes they depend on, and so forth, back to the inputs.
+  DependencySet getIndirectDependencies();
+  // likewise, returns the full list of all nodes that depend on this node, and
+  // all nodes that depend on those nodes and so on down to the outputs.
+  DependencySet getIndirectDependents();
+
+  // Does this access represent a read of memory (Load, ReduceOp, Call, etc).
+  bool isRead() const;
+  // Does this access represent a write of memory (Store, etc).
+  bool isWrite() const;
+
+  // Helpers for dumping accesses in various formats.
+  void print() const;
+  void dumpDOT(std::ostream& os) const;
+  const char* AccessTypeColour() const;
+
+ private:
+  size_t id_;
+  AccessType type_;
+  StmtPtr stmt_;
+  ExprPtr expr_;
+  VarPtr var_;
+  IndexBounds bounds_;
+
+  // Yes these should be sorted.
+  std::map<size_t, std::shared_ptr<AccessInfo>> dependencies_;
+  std::map<size_t, std::weak_ptr<AccessInfo>> dependents_;
+};
+
+using VarBoundMap = std::unordered_map<VarPtr, Bound>;
+
+/* MemDependencyChecker analyses a IR fragment and builds a dependency graph of
+ * accesses contained within.
+ *
+ * It's possible to retrieve the entire graph in node-object form, or can be
+ * used as an oracle for answering dependency questions. e.g:
+ *
+ *  analyzer.hasIndirectDependency(BufA, BufB); or,
+ *  analyzer.hasDirectDependency(LoadA, StoreB);
+ */
+class TORCH_API MemDependencyChecker : public IRVisitor {
+  struct Scope;
+
+ public:
+  MemDependencyChecker();
+  MemDependencyChecker(
+      const std::unordered_set<BufPtr>& inputs,
+      const std::unordered_set<BufPtr>& outputs);
+  MemDependencyChecker(
+      const std::vector<BufHandle>& inputs,
+      const std::vector<BufHandle>& outputs);
+
+  ~MemDependencyChecker() override = default;
+
+  // Whether or not to allow loop execution order to influence dependency
+  // calculation. If the loop may later be parallelized you don't want this.
+  bool allowLoopExecutionOrderAnalysis(bool allow = true);
+
+  // Dependency Checking API.
+  // The goal is to have enough overloads here so you don't really have to think
+  // about it.
+
+  // Returns true if any read in A has a direct dependence on a write in B.
+  bool dependsDirectly(StmtPtr A, StmtPtr B);
+  bool dependsDirectly(ExprPtr A, StmtPtr B);
+
+  // Returns true of the output depends directly on a write contained in B.
+  bool dependsDirectly(BufPtr output, StmtPtr B);
+
+  // Returns true if a read in A depends directly on the provided input.
+  bool dependsDirectly(StmtPtr A, BufPtr input);
+  bool dependsDirectly(ExprPtr A, BufPtr input);
+
+  // Outputs/inputs cannot depend directly.
+
+  // Returns true if the access A has B as an immediate dependency.
+  bool dependsDirectly(
+      const std::shared_ptr<AccessInfo>& A,
+      const std::shared_ptr<AccessInfo>& B);
+
+  // Returns true if any read in A has an ancestor write contained in B.
+  bool dependsIndirectly(StmtPtr A, StmtPtr B);
+  bool dependsIndirectly(ExprPtr A, StmtPtr B);
+
+  // Returns true of the output depends indirectly on a write contained in B.
+  bool dependsIndirectly(BufPtr output, StmtPtr B);
+
+  // Returns true if a read in A depends indirectly on the provided input.
+  bool dependsIndirectly(StmtPtr A, BufPtr input);
+  bool dependsIndirectly(ExprPtr A, BufPtr input);
+
+  // returns true if the output uses any load of the input.
+  bool dependsIndirectly(BufPtr output, BufPtr input);
+
+  // Returns true if the access A has a dependency chain to access B.
+  bool dependsIndirectly(
+      const std::shared_ptr<AccessInfo>& A,
+      const std::shared_ptr<AccessInfo>& B);
+
+  // Returns the AccessInfo
+  std::shared_ptr<AccessInfo> accessFor(StmtPtr A) const;
+  std::shared_ptr<AccessInfo> accessFor(ExprPtr A) const;
+
+  // Returns all AccessInfos.
+  std::unordered_set<std::shared_ptr<AccessInfo>> accessesWithin(
+      StmtPtr A) const;
+  // TODO: this will return only the AccessInfo for A. It's included for
+  // completeness but be aware it wont return accesses used in the computation
+  // of A.
+  std::unordered_set<std::shared_ptr<AccessInfo>> accessesWithin(
+      ExprPtr A) const;
+
+  // Accesses relating to input and output buffers.
+  std::shared_ptr<AccessInfo> input(BufPtr B) const;
+  std::shared_ptr<AccessInfo> output(BufPtr B) const;
+
+  // Returns the full history of reads and writes.
+  const std::vector<std::shared_ptr<AccessInfo>>& getHistory() const;
+
+  // Dumps the dependency graph in DOT format.
+  void dumpDAG(const std::string& filename) const;
+
+ private:
+  // Node visitors.
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+  void visit(CondPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(CompareSelectPtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(LetPtr v) override;
+  void visit(AtomicAddPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+
+  using BoundRelationship = std::pair<IndexBounds, std::shared_ptr<AccessInfo>>;
+
+  // An internal struct holding the accesses found within a scope Block.
+  struct Scope {
+    Scope(BlockPtr b, std::shared_ptr<Scope> p)
+        : block(std::move(b)), parent(std::move(p)) {}
+
+    BlockPtr block;
+    std::shared_ptr<Scope> parent;
+
+    std::unordered_map<VarPtr, Bound> shadowedVarBounds;
+    std::unordered_set<VarPtr> localVars;
+
+    std::vector<std::shared_ptr<AccessInfo>> accesses_;
+
+    std::unordered_map<VarPtr, std::list<BoundRelationship>> openWrites_;
+  };
+  std::shared_ptr<Scope> currentScope_;
+
+  bool allowExecutionOrderAnalysis_{false};
+
+  std::unordered_multimap<StmtPtr, std::shared_ptr<AccessInfo>> stmtToAccess_;
+  std::unordered_multimap<ExprPtr, std::shared_ptr<AccessInfo>> exprToAccess_;
+  std::unordered_map<StmtPtr, std::vector<std::shared_ptr<AccessInfo>>>
+      scopeToAccesses_;
+
+  VarBoundMap knownVarBounds_;
+
+  // Finds all accesses that are reads within the scope of v.
+  template <typename StmtOrExprPtr>
+  DependencySet getAllReadsWithin(StmtOrExprPtr v) {
+    DependencySet reads;
+    auto insertAllReads = [&](const auto& nodes) {
+      for (const auto& l : nodes) {
+        auto bound = exprToAccess_.equal_range(l);
+        for (auto it = bound.first; it != bound.second; ++it) {
+          if (it->second->isRead()) {
+            reads.insert(it->second);
+          }
+        }
+      }
+    };
+
+    // Look for and insert accesses belonging to all nodes that act like
+    // reads.
+    insertAllReads(NodeFinder<Load>::find(v));
+    insertAllReads(NodeFinder<ReduceOp>::find(v));
+
+    return reads;
+  }
+
+  // Finds all accesses that are writes within the scope of v.
+  // Writes cannot occur in Exprs, so this is a little simpler.
+  DependencySet getAllWritesWithin(StmtPtr v) {
+    DependencySet writes;
+
+    // writes just Store currently.
+    auto stores = NodeFinder<Store>::find(std::move(v));
+    for (const auto& s : stores) {
+      auto bound = stmtToAccess_.equal_range(s);
+      for (auto it = bound.first; it != bound.second; ++it) {
+        if (it->second->isWrite()) {
+          writes.insert(it->second);
+        }
+      }
+    }
+    return writes;
+  }
+
+  // Templated helpers to work on either Exprs or Stmts.
+  template <typename StmtOrExprPtr>
+  bool dependsDirectlyHelper(StmtOrExprPtr A, StmtPtr B) {
+    auto aReads = getAllReadsWithin(A);
+    auto bWrites = getAllWritesWithin(B);
+
+    for (auto& read : aReads) {
+      for (auto& depPair : read->dependencies()) {
+        if (bWrites.count(depPair.second) != 0) {
+          return true;
+        }
+      }
+    }
+
+    return false;
+  }
+
+  template <typename StmtOrExprPtr>
+  bool dependsIndirectlyHelper(StmtOrExprPtr A, StmtPtr B) {
+    auto aReads = getAllReadsWithin(A);
+    auto bWrites = getAllWritesWithin(B);
+
+    auto aDeps = getAllWriteDependencies(aReads);
+
+    for (auto& dependency : aDeps) {
+      if (bWrites.count(dependency) != 0) {
+        return true;
+      }
+    }
+
+    return false;
+  }
+
+  DependencySet getAllWriteDependencies(const DependencySet& products);
+
+  // Maps for inputs and outputs, since they aren't present directly in the IR.
+  std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>> inputs_;
+  std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>> outputs_;
+  std::unordered_map<VarPtr, std::shared_ptr<AccessInfo>> intermediates_;
+
+  // Inserts accesses for Buf's: specifically for inputs and outputs.
+  void insertBuffers(
+      std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>>& bufs,
+      AccessType type);
+
+  // Update the write history with a new write, adding dependencies and closing
+  // any overlapped writes (if possible).
+  void updateWriteHistory(
+      std::list<BoundRelationship>& writeHistory,
+      const std::shared_ptr<AccessInfo>& info,
+      size_t latestAccessToClose,
+      bool closeOverlapped = true,
+      bool insert = true);
+
+  // Merge a child scope into a parent scope, adding dependencies for open
+  // writes in the parent to accesses in the child.
+  void mergeScope(
+      const std::shared_ptr<Scope>& child,
+      const std::shared_ptr<Scope>& parent,
+      bool closeOverlapped = true);
+
+  // Binds symbolic vars in indices with the low and high bound for those vars.
+  std::vector<Bound> getIndicesBounds(const std::vector<ExprPtr>& indices);
+
+  size_t nextAccess_{0};
+  StmtPtr lastStmt_{nullptr};
+};
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/1b/1b1e9c8de941d1a5c60ca3301b93bec293e3cadeab14955a35e9cd983e506f71/test_bessel.py b/shared/package-files/1b/1b1e9c8de941d1a5c60ca3301b93bec293e3cadeab14955a35e9cd983e506f71/test_bessel.py
new file mode 100644
index 0000000000000000000000000000000000000000..5effdefcbfa95423f0eca7c09c1ec9eadf065c3f
--- /dev/null
+++ b/shared/package-files/1b/1b1e9c8de941d1a5c60ca3301b93bec293e3cadeab14955a35e9cd983e506f71/test_bessel.py
@@ -0,0 +1,759 @@
+from itertools import product
+
+from sympy.concrete.summations import Sum
+from sympy.core.function import (diff, expand_func)
+from sympy.core.numbers import (I, Rational, oo, pi)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.complexes import (conjugate, polar_lift)
+from sympy.functions.elementary.exponential import (exp, exp_polar, log)
+from sympy.functions.elementary.hyperbolic import (cosh, sinh)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.functions.special.bessel import (besseli, besselj, besselk, bessely, hankel1, hankel2, hn1, hn2, jn, jn_zeros, yn)
+from sympy.functions.special.gamma_functions import (gamma, uppergamma)
+from sympy.functions.special.hyper import hyper
+from sympy.integrals.integrals import Integral
+from sympy.series.order import O
+from sympy.series.series import series
+from sympy.functions.special.bessel import (airyai, airybi,
+                                            airyaiprime, airybiprime, marcumq)
+from sympy.core.random import (random_complex_number as randcplx,
+                                      verify_numerically as tn,
+                                      test_derivative_numerically as td,
+                                      _randint)
+from sympy.simplify import besselsimp
+from sympy.testing.pytest import raises, slow
+
+from sympy.abc import z, n, k, x
+
+randint = _randint()
+
+
+def test_bessel_rand():
+    for f in [besselj, bessely, besseli, besselk, hankel1, hankel2]:
+        assert td(f(randcplx(), z), z)
+
+    for f in [jn, yn, hn1, hn2]:
+        assert td(f(randint(-10, 10), z), z)
+
+
+def test_bessel_twoinputs():
+    for f in [besselj, bessely, besseli, besselk, hankel1, hankel2, jn, yn]:
+        raises(TypeError, lambda: f(1))
+        raises(TypeError, lambda: f(1, 2, 3))
+
+
+def test_besselj_leading_term():
+    assert besselj(0, x).as_leading_term(x) == 1
+    assert besselj(1, sin(x)).as_leading_term(x) == x/2
+    assert besselj(1, 2*sqrt(x)).as_leading_term(x) == sqrt(x)
+
+    # https://github.com/sympy/sympy/issues/21701
+    assert (besselj(z, x)/x**z).as_leading_term(x) == 1/(2**z*gamma(z + 1))
+
+
+def test_bessely_leading_term():
+    assert bessely(0, x).as_leading_term(x) == (2*log(x) - 2*log(2) + 2*S.EulerGamma)/pi
+    assert bessely(1, sin(x)).as_leading_term(x) == -2/(pi*x)
+    assert bessely(1, 2*sqrt(x)).as_leading_term(x) == -1/(pi*sqrt(x))
+
+
+def test_besseli_leading_term():
+    assert besseli(0, x).as_leading_term(x) == 1
+    assert besseli(1, sin(x)).as_leading_term(x) == x/2
+    assert besseli(1, 2*sqrt(x)).as_leading_term(x) == sqrt(x)
+
+
+def test_besselk_leading_term():
+    assert besselk(0, x).as_leading_term(x) == -log(x) - S.EulerGamma + log(2)
+    assert besselk(1, sin(x)).as_leading_term(x) == 1/x
+    assert besselk(1, 2*sqrt(x)).as_leading_term(x) == 1/(2*sqrt(x))
+
+
+def test_besselj_series():
+    assert besselj(0, x).series(x) == 1 - x**2/4 + x**4/64 + O(x**6)
+    assert besselj(0, x**(1.1)).series(x) == 1 + x**4.4/64 - x**2.2/4 + O(x**6)
+    assert besselj(0, x**2 + x).series(x) == 1 - x**2/4 - x**3/2\
+        - 15*x**4/64 + x**5/16 + O(x**6)
+    assert besselj(0, sqrt(x) + x).series(x, n=4) == 1 - x/4 - 15*x**2/64\
+        + 215*x**3/2304 - x**Rational(3, 2)/2 + x**Rational(5, 2)/16\
+        + 23*x**Rational(7, 2)/384 + O(x**4)
+    assert besselj(0, x/(1 - x)).series(x) == 1 - x**2/4 - x**3/2 - 47*x**4/64\
+        - 15*x**5/16 + O(x**6)
+    assert besselj(0, log(1 + x)).series(x) == 1 - x**2/4 + x**3/4\
+        - 41*x**4/192 + 17*x**5/96 + O(x**6)
+    assert besselj(1, sin(x)).series(x) == x/2 - 7*x**3/48 + 73*x**5/1920 + O(x**6)
+    assert besselj(1, 2*sqrt(x)).series(x) == sqrt(x) - x**Rational(3, 2)/2\
+        + x**Rational(5, 2)/12 - x**Rational(7, 2)/144 + x**Rational(9, 2)/2880\
+        - x**Rational(11, 2)/86400 + O(x**6)
+    assert besselj(-2, sin(x)).series(x, n=4) == besselj(2, sin(x)).series(x, n=4)
+
+
+def test_bessely_series():
+    const = 2*S.EulerGamma/pi - 2*log(2)/pi + 2*log(x)/pi
+    assert bessely(0, x).series(x, n=4) == const + x**2*(-log(x)/(2*pi)\
+        + (2 - 2*S.EulerGamma)/(4*pi) + log(2)/(2*pi)) + O(x**4*log(x))
+    assert bessely(1, x).series(x, n=4) == -2/(pi*x) + x*(log(x)/pi - log(2)/pi - \
+        (1 - 2*S.EulerGamma)/(2*pi)) + x**3*(-log(x)/(8*pi) + \
+        (S(5)/2 - 2*S.EulerGamma)/(16*pi) + log(2)/(8*pi)) + O(x**4*log(x))
+    assert bessely(2, x).series(x, n=4) == -4/(pi*x**2) - 1/pi + x**2*(log(x)/(4*pi) - \
+        log(2)/(4*pi) - (S(3)/2 - 2*S.EulerGamma)/(8*pi)) + O(x**4*log(x))
+    assert bessely(3, x).series(x, n=4) == -16/(pi*x**3) - 2/(pi*x) - \
+        x/(4*pi) + x**3*(log(x)/(24*pi) - log(2)/(24*pi) - \
+        (S(11)/6 - 2*S.EulerGamma)/(48*pi)) + O(x**4*log(x))
+    assert bessely(0, x**(1.1)).series(x, n=4) == 2*S.EulerGamma/pi\
+        - 2*log(2)/pi + 2.2*log(x)/pi + x**2.2*(-0.55*log(x)/pi\
+        + (2 - 2*S.EulerGamma)/(4*pi) + log(2)/(2*pi)) + O(x**4*log(x))
+    assert bessely(0, x**2 + x).series(x, n=4) == \
+        const - (2 - 2*S.EulerGamma)*(-x**3/(2*pi) - x**2/(4*pi)) + 2*x/pi\
+        + x**2*(-log(x)/(2*pi) - 1/pi + log(2)/(2*pi))\
+        + x**3*(-log(x)/pi + 1/(6*pi) + log(2)/pi) + O(x**4*log(x))
+    assert bessely(0, x/(1 - x)).series(x, n=3) == const\
+        + 2*x/pi + x**2*(-log(x)/(2*pi) + (2 - 2*S.EulerGamma)/(4*pi)\
+        + log(2)/(2*pi) + 1/pi) + O(x**3*log(x))
+    assert bessely(0, log(1 + x)).series(x, n=3) == const\
+        - x/pi + x**2*(-log(x)/(2*pi) + (2 - 2*S.EulerGamma)/(4*pi)\
+        + log(2)/(2*pi) + 5/(12*pi)) + O(x**3*log(x))
+    assert bessely(1, sin(x)).series(x, n=4) == -1/(pi*(-x**3/12 + x/2)) - \
+        (1 - 2*S.EulerGamma)*(-x**3/12 + x/2)/pi + x*(log(x)/pi - log(2)/pi) + \
+        x**3*(-7*log(x)/(24*pi) - 1/(6*pi) + (S(5)/2 - 2*S.EulerGamma)/(16*pi) +
+        7*log(2)/(24*pi)) + O(x**4*log(x))
+    assert bessely(1, 2*sqrt(x)).series(x, n=3) == -1/(pi*sqrt(x)) + \
+        sqrt(x)*(log(x)/pi - (1 - 2*S.EulerGamma)/pi) + x**(S(3)/2)*(-log(x)/(2*pi) + \
+        (S(5)/2 - 2*S.EulerGamma)/(2*pi)) + x**(S(5)/2)*(log(x)/(12*pi) - \
+        (S(10)/3 - 2*S.EulerGamma)/(12*pi)) + O(x**3*log(x))
+    assert bessely(-2, sin(x)).series(x, n=4) == bessely(2, sin(x)).series(x, n=4)
+
+
+def test_besseli_series():
+    assert besseli(0, x).series(x) == 1 + x**2/4 + x**4/64 + O(x**6)
+    assert besseli(0, x**(1.1)).series(x) == 1 + x**4.4/64 + x**2.2/4 + O(x**6)
+    assert besseli(0, x**2 + x).series(x) == 1 + x**2/4 + x**3/2 + 17*x**4/64 + \
+        x**5/16 + O(x**6)
+    assert besseli(0, sqrt(x) + x).series(x, n=4) == 1 + x/4 + 17*x**2/64 + \
+        217*x**3/2304 + x**(S(3)/2)/2 + x**(S(5)/2)/16 + 25*x**(S(7)/2)/384 + O(x**4)
+    assert besseli(0, x/(1 - x)).series(x) == 1 + x**2/4 + x**3/2 + 49*x**4/64 + \
+        17*x**5/16 + O(x**6)
+    assert besseli(0, log(1 + x)).series(x) == 1 + x**2/4 - x**3/4 + 47*x**4/192 - \
+        23*x**5/96 + O(x**6)
+    assert besseli(1, sin(x)).series(x) == x/2 - x**3/48 - 47*x**5/1920 + O(x**6)
+    assert besseli(1, 2*sqrt(x)).series(x) == sqrt(x) + x**(S(3)/2)/2 + x**(S(5)/2)/12 + \
+        x**(S(7)/2)/144 + x**(S(9)/2)/2880 + x**(S(11)/2)/86400 + O(x**6)
+    assert besseli(-2, sin(x)).series(x, n=4) == besseli(2, sin(x)).series(x, n=4)
+
+
+def test_besselk_series():
+    const = log(2) - S.EulerGamma - log(x)
+    assert besselk(0, x).series(x, n=4) == const + \
+        x**2*(-log(x)/4 - S.EulerGamma/4 + log(2)/4 + S(1)/4) + O(x**4*log(x))
+    assert besselk(1, x).series(x, n=4) == 1/x + x*(log(x)/2 - log(2)/2 - \
+        S(1)/4 + S.EulerGamma/2) + x**3*(log(x)/16 - S(5)/64 - log(2)/16 + \
+        S.EulerGamma/16) + O(x**4*log(x))
+    assert besselk(2, x).series(x, n=4) == 2/x**2 - S(1)/2 + x**2*(-log(x)/8 - \
+        S.EulerGamma/8 + log(2)/8 + S(3)/32) + O(x**4*log(x))
+    assert besselk(0, x**(1.1)).series(x, n=4) == log(2) - S.EulerGamma - \
+        1.1*log(x) + x**2.2*(-0.275*log(x) - S.EulerGamma/4 + \
+        log(2)/4 + S(1)/4) + O(x**4*log(x))
+    assert besselk(0, x**2 + x).series(x, n=4) == const + \
+        (2 - 2*S.EulerGamma)*(x**3/4 + x**2/8) - x + x**2*(-log(x)/4 + \
+        log(2)/4 + S(1)/2) + x**3*(-log(x)/2 - S(7)/12 + log(2)/2) + O(x**4*log(x))
+    assert besselk(0, x/(1 - x)).series(x, n=3) == const - x + x**2*(-log(x)/4 - \
+        S(1)/4 - S.EulerGamma/4 + log(2)/4) + O(x**3*log(x))
+    assert besselk(0, log(1 + x)).series(x, n=3) == const + x/2 + \
+        x**2*(-log(x)/4 - S.EulerGamma/4 + S(1)/24 + log(2)/4) + O(x**3*log(x))
+    assert besselk(1, 2*sqrt(x)).series(x, n=3) == 1/(2*sqrt(x)) + \
+        sqrt(x)*(log(x)/2 - S(1)/2 + S.EulerGamma) + x**(S(3)/2)*(log(x)/4 - S(5)/8 + \
+        S.EulerGamma/2) + x**(S(5)/2)*(log(x)/24 - S(5)/36 + S.EulerGamma/12) + O(x**3*log(x))
+    assert besselk(-2, sin(x)).series(x, n=4) == besselk(2, sin(x)).series(x, n=4)
+
+
+def test_diff():
+    assert besselj(n, z).diff(z) == besselj(n - 1, z)/2 - besselj(n + 1, z)/2
+    assert bessely(n, z).diff(z) == bessely(n - 1, z)/2 - bessely(n + 1, z)/2
+    assert besseli(n, z).diff(z) == besseli(n - 1, z)/2 + besseli(n + 1, z)/2
+    assert besselk(n, z).diff(z) == -besselk(n - 1, z)/2 - besselk(n + 1, z)/2
+    assert hankel1(n, z).diff(z) == hankel1(n - 1, z)/2 - hankel1(n + 1, z)/2
+    assert hankel2(n, z).diff(z) == hankel2(n - 1, z)/2 - hankel2(n + 1, z)/2
+
+
+def test_rewrite():
+    assert besselj(n, z).rewrite(jn) == sqrt(2*z/pi)*jn(n - S.Half, z)
+    assert bessely(n, z).rewrite(yn) == sqrt(2*z/pi)*yn(n - S.Half, z)
+    assert besseli(n, z).rewrite(besselj) == \
+        exp(-I*n*pi/2)*besselj(n, polar_lift(I)*z)
+    assert besselj(n, z).rewrite(besseli) == \
+        exp(I*n*pi/2)*besseli(n, polar_lift(-I)*z)
+
+    nu = randcplx()
+
+    assert tn(besselj(nu, z), besselj(nu, z).rewrite(besseli), z)
+    assert tn(besselj(nu, z), besselj(nu, z).rewrite(bessely), z)
+
+    assert tn(besseli(nu, z), besseli(nu, z).rewrite(besselj), z)
+    assert tn(besseli(nu, z), besseli(nu, z).rewrite(bessely), z)
+
+    assert tn(bessely(nu, z), bessely(nu, z).rewrite(besselj), z)
+    assert tn(bessely(nu, z), bessely(nu, z).rewrite(besseli), z)
+
+    assert tn(besselk(nu, z), besselk(nu, z).rewrite(besselj), z)
+    assert tn(besselk(nu, z), besselk(nu, z).rewrite(besseli), z)
+    assert tn(besselk(nu, z), besselk(nu, z).rewrite(bessely), z)
+
+    # check that a rewrite was triggered, when the order is set to a generic
+    # symbol 'nu'
+    assert yn(nu, z) != yn(nu, z).rewrite(jn)
+    assert hn1(nu, z) != hn1(nu, z).rewrite(jn)
+    assert hn2(nu, z) != hn2(nu, z).rewrite(jn)
+    assert jn(nu, z) != jn(nu, z).rewrite(yn)
+    assert hn1(nu, z) != hn1(nu, z).rewrite(yn)
+    assert hn2(nu, z) != hn2(nu, z).rewrite(yn)
+
+    # rewriting spherical bessel functions (SBFs) w.r.t. besselj, bessely is
+    # not allowed if a generic symbol 'nu' is used as the order of the SBFs
+    # to avoid inconsistencies (the order of bessel[jy] is allowed to be
+    # complex-valued, whereas SBFs are defined only for integer orders)
+    order = nu
+    for f in (besselj, bessely):
+        assert hn1(order, z) == hn1(order, z).rewrite(f)
+        assert hn2(order, z) == hn2(order, z).rewrite(f)
+
+    assert jn(order, z).rewrite(besselj) == sqrt(2)*sqrt(pi)*sqrt(1/z)*besselj(order + S.Half, z)/2
+    assert jn(order, z).rewrite(bessely) == (-1)**nu*sqrt(2)*sqrt(pi)*sqrt(1/z)*bessely(-order - S.Half, z)/2
+
+    # for integral orders rewriting SBFs w.r.t bessel[jy] is allowed
+    N = Symbol('n', integer=True)
+    ri = randint(-11, 10)
+    for order in (ri, N):
+        for f in (besselj, bessely):
+            assert yn(order, z) != yn(order, z).rewrite(f)
+            assert jn(order, z) != jn(order, z).rewrite(f)
+            assert hn1(order, z) != hn1(order, z).rewrite(f)
+            assert hn2(order, z) != hn2(order, z).rewrite(f)
+
+    for func, refunc in product((yn, jn, hn1, hn2),
+                                (jn, yn, besselj, bessely)):
+        assert tn(func(ri, z), func(ri, z).rewrite(refunc), z)
+
+
+def test_expand():
+    assert expand_func(besselj(S.Half, z).rewrite(jn)) == \
+        sqrt(2)*sin(z)/(sqrt(pi)*sqrt(z))
+    assert expand_func(bessely(S.Half, z).rewrite(yn)) == \
+        -sqrt(2)*cos(z)/(sqrt(pi)*sqrt(z))
+
+    # XXX: teach sin/cos to work around arguments like
+    # x*exp_polar(I*pi*n/2).  Then change besselsimp -> expand_func
+    assert besselsimp(besselj(S.Half, z)) == sqrt(2)*sin(z)/(sqrt(pi)*sqrt(z))
+    assert besselsimp(besselj(Rational(-1, 2), z)) == sqrt(2)*cos(z)/(sqrt(pi)*sqrt(z))
+    assert besselsimp(besselj(Rational(5, 2), z)) == \
+        -sqrt(2)*(z**2*sin(z) + 3*z*cos(z) - 3*sin(z))/(sqrt(pi)*z**Rational(5, 2))
+    assert besselsimp(besselj(Rational(-5, 2), z)) == \
+        -sqrt(2)*(z**2*cos(z) - 3*z*sin(z) - 3*cos(z))/(sqrt(pi)*z**Rational(5, 2))
+
+    assert besselsimp(bessely(S.Half, z)) == \
+        -(sqrt(2)*cos(z))/(sqrt(pi)*sqrt(z))
+    assert besselsimp(bessely(Rational(-1, 2), z)) == sqrt(2)*sin(z)/(sqrt(pi)*sqrt(z))
+    assert besselsimp(bessely(Rational(5, 2), z)) == \
+        sqrt(2)*(z**2*cos(z) - 3*z*sin(z) - 3*cos(z))/(sqrt(pi)*z**Rational(5, 2))
+    assert besselsimp(bessely(Rational(-5, 2), z)) == \
+        -sqrt(2)*(z**2*sin(z) + 3*z*cos(z) - 3*sin(z))/(sqrt(pi)*z**Rational(5, 2))
+
+    assert besselsimp(besseli(S.Half, z)) == sqrt(2)*sinh(z)/(sqrt(pi)*sqrt(z))
+    assert besselsimp(besseli(Rational(-1, 2), z)) == \
+        sqrt(2)*cosh(z)/(sqrt(pi)*sqrt(z))
+    assert besselsimp(besseli(Rational(5, 2), z)) == \
+        sqrt(2)*(z**2*sinh(z) - 3*z*cosh(z) + 3*sinh(z))/(sqrt(pi)*z**Rational(5, 2))
+    assert besselsimp(besseli(Rational(-5, 2), z)) == \
+        sqrt(2)*(z**2*cosh(z) - 3*z*sinh(z) + 3*cosh(z))/(sqrt(pi)*z**Rational(5, 2))
+
+    assert besselsimp(besselk(S.Half, z)) == \
+        besselsimp(besselk(Rational(-1, 2), z)) == sqrt(pi)*exp(-z)/(sqrt(2)*sqrt(z))
+    assert besselsimp(besselk(Rational(5, 2), z)) == \
+        besselsimp(besselk(Rational(-5, 2), z)) == \
+        sqrt(2)*sqrt(pi)*(z**2 + 3*z + 3)*exp(-z)/(2*z**Rational(5, 2))
+
+    n = Symbol('n', integer=True, positive=True)
+
+    assert expand_func(besseli(n + 2, z)) == \
+        besseli(n, z) + (-2*n - 2)*(-2*n*besseli(n, z)/z + besseli(n - 1, z))/z
+    assert expand_func(besselj(n + 2, z)) == \
+        -besselj(n, z) + (2*n + 2)*(2*n*besselj(n, z)/z - besselj(n - 1, z))/z
+    assert expand_func(besselk(n + 2, z)) == \
+        besselk(n, z) + (2*n + 2)*(2*n*besselk(n, z)/z + besselk(n - 1, z))/z
+    assert expand_func(bessely(n + 2, z)) == \
+        -bessely(n, z) + (2*n + 2)*(2*n*bessely(n, z)/z - bessely(n - 1, z))/z
+
+    assert expand_func(besseli(n + S.Half, z).rewrite(jn)) == \
+        (sqrt(2)*sqrt(z)*exp(-I*pi*(n + S.Half)/2) *
+         exp_polar(I*pi/4)*jn(n, z*exp_polar(I*pi/2))/sqrt(pi))
+    assert expand_func(besselj(n + S.Half, z).rewrite(jn)) == \
+        sqrt(2)*sqrt(z)*jn(n, z)/sqrt(pi)
+
+    r = Symbol('r', real=True)
+    p = Symbol('p', positive=True)
+    i = Symbol('i', integer=True)
+
+    for besselx in [besselj, bessely, besseli, besselk]:
+        assert besselx(i, p).is_extended_real is True
+        assert besselx(i, x).is_extended_real is None
+        assert besselx(x, z).is_extended_real is None
+
+    for besselx in [besselj, besseli]:
+        assert besselx(i, r).is_extended_real is True
+    for besselx in [bessely, besselk]:
+        assert besselx(i, r).is_extended_real is None
+
+    for besselx in [besselj, bessely, besseli, besselk]:
+        assert expand_func(besselx(oo, x)) == besselx(oo, x, evaluate=False)
+        assert expand_func(besselx(-oo, x)) == besselx(-oo, x, evaluate=False)
+
+
+# Quite varying time, but often really slow
+@slow
+def test_slow_expand():
+    def check(eq, ans):
+        return tn(eq, ans) and eq == ans
+
+    rn = randcplx(a=1, b=0, d=0, c=2)
+
+    for besselx in [besselj, bessely, besseli, besselk]:
+        ri = S(2*randint(-11, 10) + 1) / 2  # half integer in [-21/2, 21/2]
+        assert tn(besselsimp(besselx(ri, z)), besselx(ri, z))
+
+    assert check(expand_func(besseli(rn, x)),
+                 besseli(rn - 2, x) - 2*(rn - 1)*besseli(rn - 1, x)/x)
+    assert check(expand_func(besseli(-rn, x)),
+                 besseli(-rn + 2, x) + 2*(-rn + 1)*besseli(-rn + 1, x)/x)
+
+    assert check(expand_func(besselj(rn, x)),
+                 -besselj(rn - 2, x) + 2*(rn - 1)*besselj(rn - 1, x)/x)
+    assert check(expand_func(besselj(-rn, x)),
+                 -besselj(-rn + 2, x) + 2*(-rn + 1)*besselj(-rn + 1, x)/x)
+
+    assert check(expand_func(besselk(rn, x)),
+                 besselk(rn - 2, x) + 2*(rn - 1)*besselk(rn - 1, x)/x)
+    assert check(expand_func(besselk(-rn, x)),
+                 besselk(-rn + 2, x) - 2*(-rn + 1)*besselk(-rn + 1, x)/x)
+
+    assert check(expand_func(bessely(rn, x)),
+                 -bessely(rn - 2, x) + 2*(rn - 1)*bessely(rn - 1, x)/x)
+    assert check(expand_func(bessely(-rn, x)),
+                 -bessely(-rn + 2, x) + 2*(-rn + 1)*bessely(-rn + 1, x)/x)
+
+
+def mjn(n, z):
+    return expand_func(jn(n, z))
+
+
+def myn(n, z):
+    return expand_func(yn(n, z))
+
+
+def test_jn():
+    z = symbols("z")
+    assert jn(0, 0) == 1
+    assert jn(1, 0) == 0
+    assert jn(-1, 0) == S.ComplexInfinity
+    assert jn(z, 0) == jn(z, 0, evaluate=False)
+    assert jn(0, oo) == 0
+    assert jn(0, -oo) == 0
+
+    assert mjn(0, z) == sin(z)/z
+    assert mjn(1, z) == sin(z)/z**2 - cos(z)/z
+    assert mjn(2, z) == (3/z**3 - 1/z)*sin(z) - (3/z**2) * cos(z)
+    assert mjn(3, z) == (15/z**4 - 6/z**2)*sin(z) + (1/z - 15/z**3)*cos(z)
+    assert mjn(4, z) == (1/z + 105/z**5 - 45/z**3)*sin(z) + \
+        (-105/z**4 + 10/z**2)*cos(z)
+    assert mjn(5, z) == (945/z**6 - 420/z**4 + 15/z**2)*sin(z) + \
+        (-1/z - 945/z**5 + 105/z**3)*cos(z)
+    assert mjn(6, z) == (-1/z + 10395/z**7 - 4725/z**5 + 210/z**3)*sin(z) + \
+        (-10395/z**6 + 1260/z**4 - 21/z**2)*cos(z)
+
+    assert expand_func(jn(n, z)) == jn(n, z)
+
+    # SBFs not defined for complex-valued orders
+    assert jn(2+3j, 5.2+0.3j).evalf() == jn(2+3j, 5.2+0.3j)
+
+    assert eq([jn(2, 5.2+0.3j).evalf(10)],
+              [0.09941975672 - 0.05452508024*I])
+
+
+def test_yn():
+    z = symbols("z")
+    assert myn(0, z) == -cos(z)/z
+    assert myn(1, z) == -cos(z)/z**2 - sin(z)/z
+    assert myn(2, z) == -((3/z**3 - 1/z)*cos(z) + (3/z**2)*sin(z))
+    assert expand_func(yn(n, z)) == yn(n, z)
+
+    # SBFs not defined for complex-valued orders
+    assert yn(2+3j, 5.2+0.3j).evalf() == yn(2+3j, 5.2+0.3j)
+
+    assert eq([yn(2, 5.2+0.3j).evalf(10)],
+              [0.185250342 + 0.01489557397*I])
+
+
+def test_sympify_yn():
+    assert S(15) in myn(3, pi).atoms()
+    assert myn(3, pi) == 15/pi**4 - 6/pi**2
+
+
+def eq(a, b, tol=1e-6):
+    for u, v in zip(a, b):
+        if not (abs(u - v) < tol):
+            return False
+    return True
+
+
+def test_jn_zeros():
+    assert eq(jn_zeros(0, 4), [3.141592, 6.283185, 9.424777, 12.566370])
+    assert eq(jn_zeros(1, 4), [4.493409, 7.725251, 10.904121, 14.066193])
+    assert eq(jn_zeros(2, 4), [5.763459, 9.095011, 12.322940, 15.514603])
+    assert eq(jn_zeros(3, 4), [6.987932, 10.417118, 13.698023, 16.923621])
+    assert eq(jn_zeros(4, 4), [8.182561, 11.704907, 15.039664, 18.301255])
+
+
+def test_bessel_eval():
+    n, m, k = Symbol('n', integer=True), Symbol('m'), Symbol('k', integer=True, zero=False)
+
+    for f in [besselj, besseli]:
+        assert f(0, 0) is S.One
+        assert f(2.1, 0) is S.Zero
+        assert f(-3, 0) is S.Zero
+        assert f(-10.2, 0) is S.ComplexInfinity
+        assert f(1 + 3*I, 0) is S.Zero
+        assert f(-3 + I, 0) is S.ComplexInfinity
+        assert f(-2*I, 0) is S.NaN
+        assert f(n, 0) != S.One and f(n, 0) != S.Zero
+        assert f(m, 0) != S.One and f(m, 0) != S.Zero
+        assert f(k, 0) is S.Zero
+
+    assert bessely(0, 0) is S.NegativeInfinity
+    assert besselk(0, 0) is S.Infinity
+    for f in [bessely, besselk]:
+        assert f(1 + I, 0) is S.ComplexInfinity
+        assert f(I, 0) is S.NaN
+
+    for f in [besselj, bessely]:
+        assert f(m, S.Infinity) is S.Zero
+        assert f(m, S.NegativeInfinity) is S.Zero
+
+    for f in [besseli, besselk]:
+        assert f(m, I*S.Infinity) is S.Zero
+        assert f(m, I*S.NegativeInfinity) is S.Zero
+
+    for f in [besseli, besselk]:
+        assert f(-4, z) == f(4, z)
+        assert f(-3, z) == f(3, z)
+        assert f(-n, z) == f(n, z)
+        assert f(-m, z) != f(m, z)
+
+    for f in [besselj, bessely]:
+        assert f(-4, z) == f(4, z)
+        assert f(-3, z) == -f(3, z)
+        assert f(-n, z) == (-1)**n*f(n, z)
+        assert f(-m, z) != (-1)**m*f(m, z)
+
+    for f in [besselj, besseli]:
+        assert f(m, -z) == (-z)**m*z**(-m)*f(m, z)
+
+    assert besseli(2, -z) == besseli(2, z)
+    assert besseli(3, -z) == -besseli(3, z)
+
+    assert besselj(0, -z) == besselj(0, z)
+    assert besselj(1, -z) == -besselj(1, z)
+
+    assert besseli(0, I*z) == besselj(0, z)
+    assert besseli(1, I*z) == I*besselj(1, z)
+    assert besselj(3, I*z) == -I*besseli(3, z)
+
+
+def test_bessel_nan():
+    # FIXME: could have these return NaN; for now just fix infinite recursion
+    for f in [besselj, bessely, besseli, besselk, hankel1, hankel2, yn, jn]:
+        assert f(1, S.NaN) == f(1, S.NaN, evaluate=False)
+
+
+def test_meromorphic():
+    assert besselj(2, x).is_meromorphic(x, 1) == True
+    assert besselj(2, x).is_meromorphic(x, 0) == True
+    assert besselj(2, x).is_meromorphic(x, oo) == False
+    assert besselj(S(2)/3, x).is_meromorphic(x, 1) == True
+    assert besselj(S(2)/3, x).is_meromorphic(x, 0) == False
+    assert besselj(S(2)/3, x).is_meromorphic(x, oo) == False
+    assert besselj(x, 2*x).is_meromorphic(x, 2) == False
+    assert besselk(0, x).is_meromorphic(x, 1) == True
+    assert besselk(2, x).is_meromorphic(x, 0) == True
+    assert besseli(0, x).is_meromorphic(x, 1) == True
+    assert besseli(2, x).is_meromorphic(x, 0) == True
+    assert bessely(0, x).is_meromorphic(x, 1) == True
+    assert bessely(0, x).is_meromorphic(x, 0) == False
+    assert bessely(2, x).is_meromorphic(x, 0) == True
+    assert hankel1(3, x**2 + 2*x).is_meromorphic(x, 1) == True
+    assert hankel1(0, x).is_meromorphic(x, 0) == False
+    assert hankel2(11, 4).is_meromorphic(x, 5) == True
+    assert hn1(6, 7*x**3 + 4).is_meromorphic(x, 7) == True
+    assert hn2(3, 2*x).is_meromorphic(x, 9) == True
+    assert jn(5, 2*x + 7).is_meromorphic(x, 4) == True
+    assert yn(8, x**2 + 11).is_meromorphic(x, 6) == True
+
+
+def test_conjugate():
+    n = Symbol('n')
+    z = Symbol('z', extended_real=False)
+    x = Symbol('x', extended_real=True)
+    y = Symbol('y', positive=True)
+    t = Symbol('t', negative=True)
+
+    for f in [besseli, besselj, besselk, bessely, hankel1, hankel2]:
+        assert f(n, -1).conjugate() != f(conjugate(n), -1)
+        assert f(n, x).conjugate() != f(conjugate(n), x)
+        assert f(n, t).conjugate() != f(conjugate(n), t)
+
+    rz = randcplx(b=0.5)
+
+    for f in [besseli, besselj, besselk, bessely]:
+        assert f(n, 1 + I).conjugate() == f(conjugate(n), 1 - I)
+        assert f(n, 0).conjugate() == f(conjugate(n), 0)
+        assert f(n, 1).conjugate() == f(conjugate(n), 1)
+        assert f(n, z).conjugate() == f(conjugate(n), conjugate(z))
+        assert f(n, y).conjugate() == f(conjugate(n), y)
+        assert tn(f(n, rz).conjugate(), f(conjugate(n), conjugate(rz)))
+
+    assert hankel1(n, 1 + I).conjugate() == hankel2(conjugate(n), 1 - I)
+    assert hankel1(n, 0).conjugate() == hankel2(conjugate(n), 0)
+    assert hankel1(n, 1).conjugate() == hankel2(conjugate(n), 1)
+    assert hankel1(n, y).conjugate() == hankel2(conjugate(n), y)
+    assert hankel1(n, z).conjugate() == hankel2(conjugate(n), conjugate(z))
+    assert tn(hankel1(n, rz).conjugate(), hankel2(conjugate(n), conjugate(rz)))
+
+    assert hankel2(n, 1 + I).conjugate() == hankel1(conjugate(n), 1 - I)
+    assert hankel2(n, 0).conjugate() == hankel1(conjugate(n), 0)
+    assert hankel2(n, 1).conjugate() == hankel1(conjugate(n), 1)
+    assert hankel2(n, y).conjugate() == hankel1(conjugate(n), y)
+    assert hankel2(n, z).conjugate() == hankel1(conjugate(n), conjugate(z))
+    assert tn(hankel2(n, rz).conjugate(), hankel1(conjugate(n), conjugate(rz)))
+
+
+def test_branching():
+    assert besselj(polar_lift(k), x) == besselj(k, x)
+    assert besseli(polar_lift(k), x) == besseli(k, x)
+
+    n = Symbol('n', integer=True)
+    assert besselj(n, exp_polar(2*pi*I)*x) == besselj(n, x)
+    assert besselj(n, polar_lift(x)) == besselj(n, x)
+    assert besseli(n, exp_polar(2*pi*I)*x) == besseli(n, x)
+    assert besseli(n, polar_lift(x)) == besseli(n, x)
+
+    def tn(func, s):
+        from sympy.core.random import uniform
+        c = uniform(1, 5)
+        expr = func(s, c*exp_polar(I*pi)) - func(s, c*exp_polar(-I*pi))
+        eps = 1e-15
+        expr2 = func(s + eps, -c + eps*I) - func(s + eps, -c - eps*I)
+        return abs(expr.n() - expr2.n()).n() < 1e-10
+
+    nu = Symbol('nu')
+    assert besselj(nu, exp_polar(2*pi*I)*x) == exp(2*pi*I*nu)*besselj(nu, x)
+    assert besseli(nu, exp_polar(2*pi*I)*x) == exp(2*pi*I*nu)*besseli(nu, x)
+    assert tn(besselj, 2)
+    assert tn(besselj, pi)
+    assert tn(besselj, I)
+    assert tn(besseli, 2)
+    assert tn(besseli, pi)
+    assert tn(besseli, I)
+
+
+def test_airy_base():
+    z = Symbol('z')
+    x = Symbol('x', real=True)
+    y = Symbol('y', real=True)
+
+    assert conjugate(airyai(z)) == airyai(conjugate(z))
+    assert airyai(x).is_extended_real
+
+    assert airyai(x+I*y).as_real_imag() == (
+            airyai(x - I*y)/2 + airyai(x + I*y)/2,
+            I*(airyai(x - I*y) - airyai(x + I*y))/2)
+
+
+def test_airyai():
+    z = Symbol('z', real=False)
+    t = Symbol('t', negative=True)
+    p = Symbol('p', positive=True)
+
+    assert isinstance(airyai(z), airyai)
+
+    assert airyai(0) == 3**Rational(1, 3)/(3*gamma(Rational(2, 3)))
+    assert airyai(oo) == 0
+    assert airyai(-oo) == 0
+
+    assert diff(airyai(z), z) == airyaiprime(z)
+
+    assert series(airyai(z), z, 0, 3) == (
+        3**Rational(5, 6)*gamma(Rational(1, 3))/(6*pi) - 3**Rational(1, 6)*z*gamma(Rational(2, 3))/(2*pi) + O(z**3))
+
+    assert airyai(z).rewrite(hyper) == (
+        -3**Rational(2, 3)*z*hyper((), (Rational(4, 3),), z**3/9)/(3*gamma(Rational(1, 3))) +
+         3**Rational(1, 3)*hyper((), (Rational(2, 3),), z**3/9)/(3*gamma(Rational(2, 3))))
+
+    assert isinstance(airyai(z).rewrite(besselj), airyai)
+    assert airyai(t).rewrite(besselj) == (
+        sqrt(-t)*(besselj(Rational(-1, 3), 2*(-t)**Rational(3, 2)/3) +
+                  besselj(Rational(1, 3), 2*(-t)**Rational(3, 2)/3))/3)
+    assert airyai(z).rewrite(besseli) == (
+        -z*besseli(Rational(1, 3), 2*z**Rational(3, 2)/3)/(3*(z**Rational(3, 2))**Rational(1, 3)) +
+         (z**Rational(3, 2))**Rational(1, 3)*besseli(Rational(-1, 3), 2*z**Rational(3, 2)/3)/3)
+    assert airyai(p).rewrite(besseli) == (
+        sqrt(p)*(besseli(Rational(-1, 3), 2*p**Rational(3, 2)/3) -
+                 besseli(Rational(1, 3), 2*p**Rational(3, 2)/3))/3)
+
+    assert expand_func(airyai(2*(3*z**5)**Rational(1, 3))) == (
+        -sqrt(3)*(-1 + (z**5)**Rational(1, 3)/z**Rational(5, 3))*airybi(2*3**Rational(1, 3)*z**Rational(5, 3))/6 +
+         (1 + (z**5)**Rational(1, 3)/z**Rational(5, 3))*airyai(2*3**Rational(1, 3)*z**Rational(5, 3))/2)
+
+
+def test_airybi():
+    z = Symbol('z', real=False)
+    t = Symbol('t', negative=True)
+    p = Symbol('p', positive=True)
+
+    assert isinstance(airybi(z), airybi)
+
+    assert airybi(0) == 3**Rational(5, 6)/(3*gamma(Rational(2, 3)))
+    assert airybi(oo) is oo
+    assert airybi(-oo) == 0
+
+    assert diff(airybi(z), z) == airybiprime(z)
+
+    assert series(airybi(z), z, 0, 3) == (
+        3**Rational(1, 3)*gamma(Rational(1, 3))/(2*pi) + 3**Rational(2, 3)*z*gamma(Rational(2, 3))/(2*pi) + O(z**3))
+
+    assert airybi(z).rewrite(hyper) == (
+        3**Rational(1, 6)*z*hyper((), (Rational(4, 3),), z**3/9)/gamma(Rational(1, 3)) +
+        3**Rational(5, 6)*hyper((), (Rational(2, 3),), z**3/9)/(3*gamma(Rational(2, 3))))
+
+    assert isinstance(airybi(z).rewrite(besselj), airybi)
+    assert airyai(t).rewrite(besselj) == (
+        sqrt(-t)*(besselj(Rational(-1, 3), 2*(-t)**Rational(3, 2)/3) +
+                  besselj(Rational(1, 3), 2*(-t)**Rational(3, 2)/3))/3)
+    assert airybi(z).rewrite(besseli) == (
+        sqrt(3)*(z*besseli(Rational(1, 3), 2*z**Rational(3, 2)/3)/(z**Rational(3, 2))**Rational(1, 3) +
+                 (z**Rational(3, 2))**Rational(1, 3)*besseli(Rational(-1, 3), 2*z**Rational(3, 2)/3))/3)
+    assert airybi(p).rewrite(besseli) == (
+        sqrt(3)*sqrt(p)*(besseli(Rational(-1, 3), 2*p**Rational(3, 2)/3) +
+                         besseli(Rational(1, 3), 2*p**Rational(3, 2)/3))/3)
+
+    assert expand_func(airybi(2*(3*z**5)**Rational(1, 3))) == (
+        sqrt(3)*(1 - (z**5)**Rational(1, 3)/z**Rational(5, 3))*airyai(2*3**Rational(1, 3)*z**Rational(5, 3))/2 +
+        (1 + (z**5)**Rational(1, 3)/z**Rational(5, 3))*airybi(2*3**Rational(1, 3)*z**Rational(5, 3))/2)
+
+
+def test_airyaiprime():
+    z = Symbol('z', real=False)
+    t = Symbol('t', negative=True)
+    p = Symbol('p', positive=True)
+
+    assert isinstance(airyaiprime(z), airyaiprime)
+
+    assert airyaiprime(0) == -3**Rational(2, 3)/(3*gamma(Rational(1, 3)))
+    assert airyaiprime(oo) == 0
+
+    assert diff(airyaiprime(z), z) == z*airyai(z)
+
+    assert series(airyaiprime(z), z, 0, 3) == (
+        -3**Rational(2, 3)/(3*gamma(Rational(1, 3))) + 3**Rational(1, 3)*z**2/(6*gamma(Rational(2, 3))) + O(z**3))
+
+    assert airyaiprime(z).rewrite(hyper) == (
+        3**Rational(1, 3)*z**2*hyper((), (Rational(5, 3),), z**3/9)/(6*gamma(Rational(2, 3))) -
+        3**Rational(2, 3)*hyper((), (Rational(1, 3),), z**3/9)/(3*gamma(Rational(1, 3))))
+
+    assert isinstance(airyaiprime(z).rewrite(besselj), airyaiprime)
+    assert airyai(t).rewrite(besselj) == (
+        sqrt(-t)*(besselj(Rational(-1, 3), 2*(-t)**Rational(3, 2)/3) +
+                  besselj(Rational(1, 3), 2*(-t)**Rational(3, 2)/3))/3)
+    assert airyaiprime(z).rewrite(besseli) == (
+        z**2*besseli(Rational(2, 3), 2*z**Rational(3, 2)/3)/(3*(z**Rational(3, 2))**Rational(2, 3)) -
+        (z**Rational(3, 2))**Rational(2, 3)*besseli(Rational(-1, 3), 2*z**Rational(3, 2)/3)/3)
+    assert airyaiprime(p).rewrite(besseli) == (
+        p*(-besseli(Rational(-2, 3), 2*p**Rational(3, 2)/3) + besseli(Rational(2, 3), 2*p**Rational(3, 2)/3))/3)
+
+    assert expand_func(airyaiprime(2*(3*z**5)**Rational(1, 3))) == (
+        sqrt(3)*(z**Rational(5, 3)/(z**5)**Rational(1, 3) - 1)*airybiprime(2*3**Rational(1, 3)*z**Rational(5, 3))/6 +
+        (z**Rational(5, 3)/(z**5)**Rational(1, 3) + 1)*airyaiprime(2*3**Rational(1, 3)*z**Rational(5, 3))/2)
+
+
+def test_airybiprime():
+    z = Symbol('z', real=False)
+    t = Symbol('t', negative=True)
+    p = Symbol('p', positive=True)
+
+    assert isinstance(airybiprime(z), airybiprime)
+
+    assert airybiprime(0) == 3**Rational(1, 6)/gamma(Rational(1, 3))
+    assert airybiprime(oo) is oo
+    assert airybiprime(-oo) == 0
+
+    assert diff(airybiprime(z), z) == z*airybi(z)
+
+    assert series(airybiprime(z), z, 0, 3) == (
+        3**Rational(1, 6)/gamma(Rational(1, 3)) + 3**Rational(5, 6)*z**2/(6*gamma(Rational(2, 3))) + O(z**3))
+
+    assert airybiprime(z).rewrite(hyper) == (
+        3**Rational(5, 6)*z**2*hyper((), (Rational(5, 3),), z**3/9)/(6*gamma(Rational(2, 3))) +
+        3**Rational(1, 6)*hyper((), (Rational(1, 3),), z**3/9)/gamma(Rational(1, 3)))
+
+    assert isinstance(airybiprime(z).rewrite(besselj), airybiprime)
+    assert airyai(t).rewrite(besselj) == (
+        sqrt(-t)*(besselj(Rational(-1, 3), 2*(-t)**Rational(3, 2)/3) +
+                  besselj(Rational(1, 3), 2*(-t)**Rational(3, 2)/3))/3)
+    assert airybiprime(z).rewrite(besseli) == (
+        sqrt(3)*(z**2*besseli(Rational(2, 3), 2*z**Rational(3, 2)/3)/(z**Rational(3, 2))**Rational(2, 3) +
+                 (z**Rational(3, 2))**Rational(2, 3)*besseli(Rational(-2, 3), 2*z**Rational(3, 2)/3))/3)
+    assert airybiprime(p).rewrite(besseli) == (
+        sqrt(3)*p*(besseli(Rational(-2, 3), 2*p**Rational(3, 2)/3) + besseli(Rational(2, 3), 2*p**Rational(3, 2)/3))/3)
+
+    assert expand_func(airybiprime(2*(3*z**5)**Rational(1, 3))) == (
+        sqrt(3)*(z**Rational(5, 3)/(z**5)**Rational(1, 3) - 1)*airyaiprime(2*3**Rational(1, 3)*z**Rational(5, 3))/2 +
+        (z**Rational(5, 3)/(z**5)**Rational(1, 3) + 1)*airybiprime(2*3**Rational(1, 3)*z**Rational(5, 3))/2)
+
+
+def test_marcumq():
+    m = Symbol('m')
+    a = Symbol('a')
+    b = Symbol('b')
+
+    assert marcumq(0, 0, 0) == 0
+    assert marcumq(m, 0, b) == uppergamma(m, b**2/2)/gamma(m)
+    assert marcumq(2, 0, 5) == 27*exp(Rational(-25, 2))/2
+    assert marcumq(0, a, 0) == 1 - exp(-a**2/2)
+    assert marcumq(0, pi, 0) == 1 - exp(-pi**2/2)
+    assert marcumq(1, a, a) == S.Half + exp(-a**2)*besseli(0, a**2)/2
+    assert marcumq(2, a, a) == S.Half + exp(-a**2)*besseli(0, a**2)/2 + exp(-a**2)*besseli(1, a**2)
+
+    assert diff(marcumq(1, a, 3), a) == a*(-marcumq(1, a, 3) + marcumq(2, a, 3))
+    assert diff(marcumq(2, 3, b), b) == -b**2*exp(-b**2/2 - Rational(9, 2))*besseli(1, 3*b)/3
+
+    x = Symbol('x')
+    assert marcumq(2, 3, 4).rewrite(Integral, x=x) == \
+           Integral(x**2*exp(-x**2/2 - Rational(9, 2))*besseli(1, 3*x), (x, 4, oo))/3
+    assert eq([marcumq(5, -2, 3).rewrite(Integral).evalf(10)],
+              [0.7905769565])
+
+    k = Symbol('k')
+    assert marcumq(-3, -5, -7).rewrite(Sum, k=k) == \
+           exp(-37)*Sum((Rational(5, 7))**k*besseli(k, 35), (k, 4, oo))
+    assert eq([marcumq(1, 3, 1).rewrite(Sum).evalf(10)],
+              [0.9891705502])
+
+    assert marcumq(1, a, a, evaluate=False).rewrite(besseli) == S.Half + exp(-a**2)*besseli(0, a**2)/2
+    assert marcumq(2, a, a, evaluate=False).rewrite(besseli) == S.Half + exp(-a**2)*besseli(0, a**2)/2 + \
+           exp(-a**2)*besseli(1, a**2)
+    assert marcumq(3, a, a).rewrite(besseli) == (besseli(1, a**2) + besseli(2, a**2))*exp(-a**2) + \
+           S.Half + exp(-a**2)*besseli(0, a**2)/2
+    assert marcumq(5, 8, 8).rewrite(besseli) == exp(-64)*besseli(0, 64)/2 + \
+           (besseli(4, 64) + besseli(3, 64) + besseli(2, 64) + besseli(1, 64))*exp(-64) + S.Half
+    assert marcumq(m, a, a).rewrite(besseli) == marcumq(m, a, a)
+
+    x = Symbol('x', integer=True)
+    assert marcumq(x, a, a).rewrite(besseli) == marcumq(x, a, a)
diff --git a/shared/package-files/1b/1b22831cc261f2f112ec22a6d956f4d5bb28b424f15ef52fc07d8456ae1e2621/source_range_serialization_impl.h b/shared/package-files/1b/1b22831cc261f2f112ec22a6d956f4d5bb28b424f15ef52fc07d8456ae1e2621/source_range_serialization_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b7cd5a14ba9282090b025f23cdf28eb9bf1c5de
--- /dev/null
+++ b/shared/package-files/1b/1b22831cc261f2f112ec22a6d956f4d5bb28b424f15ef52fc07d8456ae1e2621/source_range_serialization_impl.h
@@ -0,0 +1,30 @@
+#pragma once
+
+#include <torch/csrc/jit/serialization/source_range_serialization.h>
+
+namespace torch {
+namespace jit {
+
+// Do this clownyness with virtual functions because of the split
+// between ATen core and torch
+
+class ConcreteSourceRangeUnpickler : public SourceRangeUnpickler {
+ public:
+  ConcreteSourceRangeUnpickler(at::DataPtr&& data, size_t size);
+
+  c10::optional<SourceRange> findSourceRangeThatGenerated(
+      const SourceRange& range) override;
+
+ private:
+  at::DataPtr data;
+  size_t size;
+
+  void unpickle();
+
+  std::mutex mutex;
+  std::shared_ptr<SourceRangeDeserializer> deserializer;
+  std::shared_ptr<SourceRangeRecords> unpickled_records;
+};
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/1b/1b23e81ad723329d730b471d79c3ea747532a88f6a311226339ef41175285639/transformercoder.h b/shared/package-files/1b/1b23e81ad723329d730b471d79c3ea747532a88f6a311226339ef41175285639/transformercoder.h
new file mode 100644
index 0000000000000000000000000000000000000000..64f6b998f4c657d1ca45a0123dbba379effd0979
--- /dev/null
+++ b/shared/package-files/1b/1b23e81ad723329d730b471d79c3ea747532a88f6a311226339ef41175285639/transformercoder.h
@@ -0,0 +1,76 @@
+#pragma once
+
+#include <torch/arg.h>
+#include <torch/csrc/Export.h>
+#include <torch/enum.h>
+#include <torch/types.h>
+
+#include <torch/nn/modules/container/any.h>
+#include <torch/nn/modules/transformerlayer.h>
+
+namespace torch {
+namespace nn {
+
+/// Options for the `TransformerEncoder`
+///
+/// Example:
+/// ```
+/// TransformerEncoderLayer encoderLayer(TransformerEncoderLayerOptions(512,
+/// 8).dropout(0.1)); auto options = TransformerEncoderOptions(encoderLayer,
+/// 6).norm(LayerNorm(LayerNormOptions({2})));
+/// ```
+struct TORCH_API TransformerEncoderOptions {
+  // This constructor will keep a shallow copy of encoder_layer, so it keeps all
+  // the data in encoder_layer.
+  TransformerEncoderOptions(
+      TransformerEncoderLayer encoder_layer,
+      int64_t num_layers);
+  // This constructor will create a new TransformerEncoderLayer obj based on
+  // passed in encoder_layer_options.
+  TransformerEncoderOptions(
+      const TransformerEncoderLayerOptions& encoder_layer_options,
+      int64_t num_layers);
+
+  /// transformer Encoder Layer
+  TORCH_ARG(TransformerEncoderLayer, encoder_layer) = nullptr;
+
+  /// number of encoder layers
+  TORCH_ARG(int64_t, num_layers);
+
+  /// normalization module
+  TORCH_ARG(AnyModule, norm);
+};
+
+/// Options for the `TransformerDecoder` module.
+///
+/// Example:
+/// ```
+/// TransformerDecoderLayer decoder_layer(TransformerDecoderLayerOptions(512,
+/// 8).dropout(0.1)); auto options = TransformerDecoderOptions(decoder_layer,
+/// 6)norm(LayerNorm(LayerNormOptions({2}))); TransformerDecoder
+/// transformer_decoder(options);
+/// ```
+struct TORCH_API TransformerDecoderOptions {
+  // This constructor will keep the a ref of passed in decoder_layer,
+  // so it keeps all the data in decoder_layer.
+  TransformerDecoderOptions(
+      TransformerDecoderLayer decoder_layer,
+      int64_t num_layers);
+  // This constructor will create a new TransformerDecoderLayer obj,
+  // based on passed in decoder_layer_options.
+  TransformerDecoderOptions(
+      const TransformerDecoderLayerOptions& decoder_layer_options,
+      int64_t num_layers);
+
+  /// decoder layer to be cloned
+  TORCH_ARG(TransformerDecoderLayer, decoder_layer) = nullptr;
+
+  /// number of decoder layers
+  TORCH_ARG(int64_t, num_layers);
+
+  /// normalization module
+  TORCH_ARG(AnyModule, norm);
+};
+
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/1b/1b2ad46700d398a22257447035d595b778ca398ea7d9376e95cca09d852abdf2/xterm+osc104 b/shared/package-files/1b/1b2ad46700d398a22257447035d595b778ca398ea7d9376e95cca09d852abdf2/xterm+osc104
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diff --git a/shared/package-files/1b/1b2c2dee1252ca1b5cf682060214d9525b35bbfe42fc0b6618a94e0a476ac889/trsock.pyi b/shared/package-files/1b/1b2c2dee1252ca1b5cf682060214d9525b35bbfe42fc0b6618a94e0a476ac889/trsock.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..db9efb08b58507e9aa6f03decd44f44980cce2c8
--- /dev/null
+++ b/shared/package-files/1b/1b2c2dee1252ca1b5cf682060214d9525b35bbfe42fc0b6618a94e0a476ac889/trsock.pyi
@@ -0,0 +1,86 @@
+import socket
+import sys
+from types import TracebackType
+from typing import Any, BinaryIO, Iterable, List, NoReturn, Optional, Tuple, Type, Union, overload
+
+if sys.version_info >= (3, 8):
+    # These are based in socket, maybe move them out into _typeshed.pyi or such
+    _Address = Union[tuple, str]
+    _RetAddress = Any
+    _WriteBuffer = Union[bytearray, memoryview]
+    _CMSG = Tuple[int, int, bytes]
+    class TransportSocket:
+        def __init__(self, sock: socket.socket) -> None: ...
+        def _na(self, what: str) -> None: ...
+        @property
+        def family(self) -> int: ...
+        @property
+        def type(self) -> int: ...
+        @property
+        def proto(self) -> int: ...
+        def __getstate__(self) -> NoReturn: ...
+        def fileno(self) -> int: ...
+        def dup(self) -> socket.socket: ...
+        def get_inheritable(self) -> bool: ...
+        def shutdown(self, how: int) -> None: ...
+        @overload
+        def getsockopt(self, level: int, optname: int) -> int: ...
+        @overload
+        def getsockopt(self, level: int, optname: int, buflen: int) -> bytes: ...
+        @overload
+        def setsockopt(self, level: int, optname: int, value: Union[int, bytes]) -> None: ...
+        @overload
+        def setsockopt(self, level: int, optname: int, value: None, optlen: int) -> None: ...
+        def getpeername(self) -> _RetAddress: ...
+        def getsockname(self) -> _RetAddress: ...
+        def getsockbyname(self) -> NoReturn: ...  # This method doesn't exist on socket, yet is passed through?
+        def accept(self) -> Tuple[socket.socket, _RetAddress]: ...
+        def connect(self, address: Union[_Address, bytes]) -> None: ...
+        def connect_ex(self, address: Union[_Address, bytes]) -> int: ...
+        def bind(self, address: Union[_Address, bytes]) -> None: ...
+        if sys.platform == "win32":
+            def ioctl(self, control: int, option: Union[int, Tuple[int, int, int], bool]) -> None: ...
+        else:
+            def ioctl(self, control: int, option: Union[int, Tuple[int, int, int], bool]) -> NoReturn: ...
+        def listen(self, __backlog: int = ...) -> None: ...
+        def makefile(self) -> BinaryIO: ...
+        def sendfile(self, file: BinaryIO, offset: int = ..., count: Optional[int] = ...) -> int: ...
+        def close(self) -> None: ...
+        def detach(self) -> int: ...
+        if sys.platform == "linux":
+            def sendmsg_afalg(
+                self, msg: Iterable[bytes] = ..., *, op: int, iv: Any = ..., assoclen: int = ..., flags: int = ...
+            ) -> int: ...
+        else:
+            def sendmsg_afalg(
+                self, msg: Iterable[bytes] = ..., *, op: int, iv: Any = ..., assoclen: int = ..., flags: int = ...
+            ) -> NoReturn: ...
+        def sendmsg(
+            self, __buffers: Iterable[bytes], __ancdata: Iterable[_CMSG] = ..., __flags: int = ..., __address: _Address = ...
+        ) -> int: ...
+        @overload
+        def sendto(self, data: bytes, address: _Address) -> int: ...
+        @overload
+        def sendto(self, data: bytes, flags: int, address: _Address) -> int: ...
+        def send(self, data: bytes, flags: int = ...) -> int: ...
+        def sendall(self, data: bytes, flags: int = ...) -> None: ...
+        def set_inheritable(self, inheritable: bool) -> None: ...
+        if sys.platform == "win32":
+            def share(self, process_id: int) -> bytes: ...
+        else:
+            def share(self, process_id: int) -> NoReturn: ...
+        def recv_into(self, buffer: _WriteBuffer, nbytes: int = ..., flags: int = ...) -> int: ...
+        def recvfrom_into(self, buffer: _WriteBuffer, nbytes: int = ..., flags: int = ...) -> Tuple[int, _RetAddress]: ...
+        def recvmsg_into(
+            self, __buffers: Iterable[_WriteBuffer], __ancbufsize: int = ..., __flags: int = ...
+        ) -> Tuple[int, List[_CMSG], int, Any]: ...
+        def recvmsg(self, __bufsize: int, __ancbufsize: int = ..., __flags: int = ...) -> Tuple[bytes, List[_CMSG], int, Any]: ...
+        def recvfrom(self, bufsize: int, flags: int = ...) -> Tuple[bytes, _RetAddress]: ...
+        def recv(self, bufsize: int, flags: int = ...) -> bytes: ...
+        def settimeout(self, value: Optional[float]) -> None: ...
+        def gettimeout(self) -> Optional[float]: ...
+        def setblocking(self, flag: bool) -> None: ...
+        def __enter__(self) -> socket.socket: ...
+        def __exit__(
+            self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+        ) -> None: ...
diff --git a/shared/package-files/1b/1b2c867904391a69bd69e30b5d44b3d25bf01e38bfb16e93fdf5775d0203a698/notes_fdr.txt b/shared/package-files/1b/1b2c867904391a69bd69e30b5d44b3d25bf01e38bfb16e93fdf5775d0203a698/notes_fdr.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1f660bd1d62358345e68685083ac3e3f1a7b1b3f
--- /dev/null
+++ b/shared/package-files/1b/1b2c867904391a69bd69e30b5d44b3d25bf01e38bfb16e93fdf5775d0203a698/notes_fdr.txt
@@ -0,0 +1,128 @@
+
+Multiple Tests and Multiple Comparisons
+=======================================
+
+Introduction
+------------
+
+generic multiple testing procedures, p-value corrections and fdr do not use
+any additional information, only information contained in p-values.
+I do not know if there are any underlying assumption, except that the raw
+pvalues are uniformly on [0,1] distributed under the null hypothesis.
+
+fdr for microarray or fmri can use special structure
+A special case in general statistical literature is the comparison of means
+with specific methods to correct for multiple tests, e.g., Tukey
+
+(survey by Shaffer
+
+
+General Methods
+---------------
+
+pvalue correction
+~~~~~~~~~~~~~~~~~
+
+implemented,
+
+basic fdr, BH, BY
+
+multiple testing procedures
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+pvalue correction is not available/implemented, return reject is True/False
+for a given confidence level.
+(pvalue correction were initially written this way)
+
+
+fdr bky  2-step procedure with estimation of fraction of false hypothesis.
+
+
+Multiple Comparisons
+--------------------
+
+Tukey, Dunnet, ...
+see Hothorn, Bretz, Westfall, University of Munich 2008, working paper,
+published in ...
+
+The general theory in Bretz, Genz and Hothorn, Bretz, Westfall, multcomp,
+is based on the distribution of the maximum of the statistics when the
+statistics are distributed according to a joint t or normal distribution
+distribution. pvalues, whether to reject a hypothesis and joint confidence
+intervalls can be obtained directly from integration of the joint t or
+normal distribution.
+
+Scipy does not have the cdf of a multivariate t distribution, but according
+to Bretz and Gentz it can be obtained by a univariate integration from
+the cdf of the multivariate normal distribution.
+
+The distribution for Tukey's range statistic is half (?) of the distribution
+of the maximum of t-distributed random variables if the underlying means
+are assumed to be independently distributed. (my interpretation so far, but
+I have not verified the details yet, see ... )
+
+Bretz and Genz also have a table with the summary of the contrast matrices
+for different tests, like Tukey and Dunnet. These contrast matrices are
+implemented in R, but for some of them they are not immediately obvious.
+
+As an aside, this seems to be related to getting simultaneous confidence
+bands for forecasting. But it is not clear to me yet how to define the
+confidence bands, I have the articles but have not read them yet.
+
+The older tests, Tukey, Dunnet and similar assume that the means are
+independently normally distributed, and critical values are tabulated for this
+case. Newer methods allow for arbitrary correlation (Bretz, Hothorn,...) or
+assume a specific structure for simplicity, for example factor structure for
+the covariance as in Hsu. The latter is the default approach in SAS.
+The tests based on independence are often categorized as post-hoc tests, a good
+overview of the formulas is in the SPSS description.
+
+It is also mentioned in the literature (?) that these multiple comparison tests
+loose power if they are only used conditional on the rejection of an overall
+F-test
+
+
+TODO:
+* finish Tukey, Dunnet because they will be familiar
+* add multivariate normal cdf to statsmodels
+* try Bretz/Gentz cdf of t distribution form the cdf of the multivariate normal
+
+
+
+special cases
+-------------
+
+
+nipy.neurospin
+
+follows
+
+Schwartzman, A. et al., 2009. Empirical null and false discovery rate
+analysis in neuroimaging. NeuroImage, 44(1), pp.71-82.
+
+test defined for point-wise test on a 2 component mixture distribution.
+The probability of an observation to be in the null distribution can be
+estimated from the mixture distribution (not sure)
+fdr defined on observations not a sample mean
+alternative literature: use topological properties of the picture to
+correct fdr.
+
+
+TODO
+====
+
+* fdr_bky and check whether prior q can be used with current pvalue-correction
+* multi-comparison: Tukey, convenience classes/methods expand on current
+* Monte Carlo
+* maybe some resampling, bootstrap, permutation, later
+* for the rest I'm not really interested in doing the work: k-FWER, k-FDR,
+  other estimators for fraction of true or false hypothesis
+
+
+
+
+multtest (R)
+check bootstrap references
+
+multcomp (R)
+contrMat has the contrast matrices for various standard test
+function parm parm(coef, vcov, df = 0) can work with only the estimates given
diff --git a/shared/package-files/1b/1b2d15d63d28c5affa7dd95ce55c53ceba8f0d032694a211e946d2ad36095e58/test_cut.py b/shared/package-files/1b/1b2d15d63d28c5affa7dd95ce55c53ceba8f0d032694a211e946d2ad36095e58/test_cut.py
new file mode 100644
index 0000000000000000000000000000000000000000..cab2302b3d8778811764579d6b43e9e2f855dbbb
--- /dev/null
+++ b/shared/package-files/1b/1b2d15d63d28c5affa7dd95ce55c53ceba8f0d032694a211e946d2ad36095e58/test_cut.py
@@ -0,0 +1,792 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    Series,
+    TimedeltaIndex,
+    Timestamp,
+    cut,
+    date_range,
+    interval_range,
+    isna,
+    qcut,
+    timedelta_range,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+import pandas.core.reshape.tile as tmod
+
+
+def test_simple():
+    data = np.ones(5, dtype="int64")
+    result = cut(data, 4, labels=False)
+
+    expected = np.array([1, 1, 1, 1, 1])
+    tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+
+@pytest.mark.parametrize("func", [list, np.array])
+def test_bins(func):
+    data = func([0.2, 1.4, 2.5, 6.2, 9.7, 2.1])
+    result, bins = cut(data, 3, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3))
+    intervals = intervals.take([0, 0, 0, 1, 2, 0])
+    expected = Categorical(intervals, ordered=True)
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667, 6.53333333, 9.7]))
+
+
+def test_right():
+    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
+    result, bins = cut(data, 4, right=True, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3))
+    expected = Categorical(intervals, ordered=True)
+    expected = expected.take([0, 0, 0, 2, 3, 0, 0])
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95, 7.325, 9.7]))
+
+
+def test_no_right():
+    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
+    result, bins = cut(data, 4, right=False, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3), closed="left")
+    intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
+    expected = Categorical(intervals, ordered=True)
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95, 7.325, 9.7095]))
+
+
+def test_bins_from_interval_index():
+    c = cut(range(5), 3)
+    expected = c
+    result = cut(range(5), bins=expected.categories)
+    tm.assert_categorical_equal(result, expected)
+
+    expected = Categorical.from_codes(
+        np.append(c.codes, -1), categories=c.categories, ordered=True
+    )
+    result = cut(range(6), bins=expected.categories)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_bins_from_interval_index_doc_example():
+    # Make sure we preserve the bins.
+    ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
+    c = cut(ages, bins=[0, 18, 35, 70])
+    expected = IntervalIndex.from_tuples([(0, 18), (18, 35), (35, 70)])
+    tm.assert_index_equal(c.categories, expected)
+
+    result = cut([25, 20, 50], bins=c.categories)
+    tm.assert_index_equal(result.categories, expected)
+    tm.assert_numpy_array_equal(result.codes, np.array([1, 1, 2], dtype="int8"))
+
+
+def test_bins_not_overlapping_from_interval_index():
+    # see gh-23980
+    msg = "Overlapping IntervalIndex is not accepted"
+    ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
+
+    with pytest.raises(ValueError, match=msg):
+        cut([5, 6], bins=ii)
+
+
+def test_bins_not_monotonic():
+    msg = "bins must increase monotonically"
+    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
+
+    with pytest.raises(ValueError, match=msg):
+        cut(data, [0.1, 1.5, 1, 10])
+
+
+@pytest.mark.parametrize(
+    "x, bins, expected",
+    [
+        (
+            date_range("2017-12-31", periods=3),
+            [Timestamp.min, Timestamp("2018-01-01"), Timestamp.max],
+            IntervalIndex.from_tuples(
+                [
+                    (Timestamp.min, Timestamp("2018-01-01")),
+                    (Timestamp("2018-01-01"), Timestamp.max),
+                ]
+            ),
+        ),
+        (
+            [-1, 0, 1],
+            np.array(
+                [np.iinfo(np.int64).min, 0, np.iinfo(np.int64).max], dtype="int64"
+            ),
+            IntervalIndex.from_tuples(
+                [(np.iinfo(np.int64).min, 0), (0, np.iinfo(np.int64).max)]
+            ),
+        ),
+        (
+            [
+                np.timedelta64(-1, "ns"),
+                np.timedelta64(0, "ns"),
+                np.timedelta64(1, "ns"),
+            ],
+            np.array(
+                [
+                    np.timedelta64(-np.iinfo(np.int64).max, "ns"),
+                    np.timedelta64(0, "ns"),
+                    np.timedelta64(np.iinfo(np.int64).max, "ns"),
+                ]
+            ),
+            IntervalIndex.from_tuples(
+                [
+                    (
+                        np.timedelta64(-np.iinfo(np.int64).max, "ns"),
+                        np.timedelta64(0, "ns"),
+                    ),
+                    (
+                        np.timedelta64(0, "ns"),
+                        np.timedelta64(np.iinfo(np.int64).max, "ns"),
+                    ),
+                ]
+            ),
+        ),
+    ],
+)
+def test_bins_monotonic_not_overflowing(x, bins, expected):
+    # GH 26045
+    result = cut(x, bins)
+    tm.assert_index_equal(result.categories, expected)
+
+
+def test_wrong_num_labels():
+    msg = "Bin labels must be one fewer than the number of bin edges"
+    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
+
+    with pytest.raises(ValueError, match=msg):
+        cut(data, [0, 1, 10], labels=["foo", "bar", "baz"])
+
+
+@pytest.mark.parametrize(
+    "x,bins,msg",
+    [
+        ([], 2, "Cannot cut empty array"),
+        ([1, 2, 3], 0.5, "`bins` should be a positive integer"),
+    ],
+)
+def test_cut_corner(x, bins, msg):
+    with pytest.raises(ValueError, match=msg):
+        cut(x, bins)
+
+
+@pytest.mark.parametrize("arg", [2, np.eye(2), DataFrame(np.eye(2))])
+@pytest.mark.parametrize("cut_func", [cut, qcut])
+def test_cut_not_1d_arg(arg, cut_func):
+    msg = "Input array must be 1 dimensional"
+    with pytest.raises(ValueError, match=msg):
+        cut_func(arg, 2)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0, 1, 2, 3, 4, np.inf],
+        [-np.inf, 0, 1, 2, 3, 4],
+        [-np.inf, 0, 1, 2, 3, 4, np.inf],
+    ],
+)
+def test_int_bins_with_inf(data):
+    # GH 24314
+    msg = "cannot specify integer `bins` when input data contains infinity"
+    with pytest.raises(ValueError, match=msg):
+        cut(data, bins=3)
+
+
+def test_cut_out_of_range_more():
+    # see gh-1511
+    name = "x"
+
+    ser = Series([0, -1, 0, 1, -3], name=name)
+    ind = cut(ser, [0, 1], labels=False)
+
+    exp = Series([np.nan, np.nan, np.nan, 0, np.nan], name=name)
+    tm.assert_series_equal(ind, exp)
+
+
+@pytest.mark.parametrize(
+    "right,breaks,closed",
+    [
+        (True, [-1e-3, 0.25, 0.5, 0.75, 1], "right"),
+        (False, [0, 0.25, 0.5, 0.75, 1 + 1e-3], "left"),
+    ],
+)
+def test_labels(right, breaks, closed):
+    arr = np.tile(np.arange(0, 1.01, 0.1), 4)
+
+    result, bins = cut(arr, 4, retbins=True, right=right)
+    ex_levels = IntervalIndex.from_breaks(breaks, closed=closed)
+    tm.assert_index_equal(result.categories, ex_levels)
+
+
+def test_cut_pass_series_name_to_factor():
+    name = "foo"
+    ser = Series(np.random.default_rng(2).standard_normal(100), name=name)
+
+    factor = cut(ser, 4)
+    assert factor.name == name
+
+
+def test_label_precision():
+    arr = np.arange(0, 0.73, 0.01)
+    result = cut(arr, 4, precision=2)
+
+    ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36, 0.54, 0.72])
+    tm.assert_index_equal(result.categories, ex_levels)
+
+
+@pytest.mark.parametrize("labels", [None, False])
+def test_na_handling(labels):
+    arr = np.arange(0, 0.75, 0.01)
+    arr[::3] = np.nan
+
+    result = cut(arr, 4, labels=labels)
+    result = np.asarray(result)
+
+    expected = np.where(isna(arr), np.nan, result)
+    tm.assert_almost_equal(result, expected)
+
+
+def test_inf_handling():
+    data = np.arange(6)
+    data_ser = Series(data, dtype="int64")
+
+    bins = [-np.inf, 2, 4, np.inf]
+    result = cut(data, bins)
+    result_ser = cut(data_ser, bins)
+
+    ex_uniques = IntervalIndex.from_breaks(bins)
+    tm.assert_index_equal(result.categories, ex_uniques)
+
+    assert result[5] == Interval(4, np.inf)
+    assert result[0] == Interval(-np.inf, 2)
+    assert result_ser[5] == Interval(4, np.inf)
+    assert result_ser[0] == Interval(-np.inf, 2)
+
+
+def test_cut_out_of_bounds():
+    arr = np.random.default_rng(2).standard_normal(100)
+    result = cut(arr, [-1, 0, 1])
+
+    mask = isna(result)
+    ex_mask = (arr < -1) | (arr > 1)
+    tm.assert_numpy_array_equal(mask, ex_mask)
+
+
+@pytest.mark.parametrize(
+    "get_labels,get_expected",
+    [
+        (
+            lambda labels: labels,
+            lambda labels: Categorical(
+                ["Medium"] + 4 * ["Small"] + ["Medium", "Large"],
+                categories=labels,
+                ordered=True,
+            ),
+        ),
+        (
+            lambda labels: Categorical.from_codes([0, 1, 2], labels),
+            lambda labels: Categorical.from_codes([1] + 4 * [0] + [1, 2], labels),
+        ),
+    ],
+)
+def test_cut_pass_labels(get_labels, get_expected):
+    bins = [0, 25, 50, 100]
+    arr = [50, 5, 10, 15, 20, 30, 70]
+    labels = ["Small", "Medium", "Large"]
+
+    result = cut(arr, bins, labels=get_labels(labels))
+    tm.assert_categorical_equal(result, get_expected(labels))
+
+
+def test_cut_pass_labels_compat():
+    # see gh-16459
+    arr = [50, 5, 10, 15, 20, 30, 70]
+    labels = ["Good", "Medium", "Bad"]
+
+    result = cut(arr, 3, labels=labels)
+    exp = cut(arr, 3, labels=Categorical(labels, categories=labels, ordered=True))
+    tm.assert_categorical_equal(result, exp)
+
+
+@pytest.mark.parametrize("x", [np.arange(11.0), np.arange(11.0) / 1e10])
+def test_round_frac_just_works(x):
+    # It works.
+    cut(x, 2)
+
+
+@pytest.mark.parametrize(
+    "val,precision,expected",
+    [
+        (-117.9998, 3, -118),
+        (117.9998, 3, 118),
+        (117.9998, 2, 118),
+        (0.000123456, 2, 0.00012),
+    ],
+)
+def test_round_frac(val, precision, expected):
+    # see gh-1979
+    result = tmod._round_frac(val, precision=precision)
+    assert result == expected
+
+
+def test_cut_return_intervals():
+    ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
+    result = cut(ser, 3)
+
+    exp_bins = np.linspace(0, 8, num=4).round(3)
+    exp_bins[0] -= 0.008
+
+    expected = Series(
+        IntervalIndex.from_breaks(exp_bins, closed="right").take(
+            [0, 0, 0, 1, 1, 1, 2, 2, 2]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_ret_bins():
+    # see gh-8589
+    ser = Series(np.arange(4))
+    result, bins = cut(ser, 2, retbins=True)
+
+    expected = Series(
+        IntervalIndex.from_breaks([-0.003, 1.5, 3], closed="right").repeat(2)
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "kwargs,msg",
+    [
+        ({"duplicates": "drop"}, None),
+        ({}, "Bin edges must be unique"),
+        ({"duplicates": "raise"}, "Bin edges must be unique"),
+        ({"duplicates": "foo"}, "invalid value for 'duplicates' parameter"),
+    ],
+)
+def test_cut_duplicates_bin(kwargs, msg):
+    # see gh-20947
+    bins = [0, 2, 4, 6, 10, 10]
+    values = Series(np.array([1, 3, 5, 7, 9]), index=["a", "b", "c", "d", "e"])
+
+    if msg is not None:
+        with pytest.raises(ValueError, match=msg):
+            cut(values, bins, **kwargs)
+    else:
+        result = cut(values, bins, **kwargs)
+        expected = cut(values, pd.unique(np.asarray(bins)))
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [9.0, -9.0, 0.0])
+@pytest.mark.parametrize("length", [1, 2])
+def test_single_bin(data, length):
+    # see gh-14652, gh-15428
+    ser = Series([data] * length)
+    result = cut(ser, 1, labels=False)
+
+    expected = Series([0] * length, dtype=np.intp)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array_1_writeable,array_2_writeable", [(True, True), (True, False), (False, False)]
+)
+def test_cut_read_only(array_1_writeable, array_2_writeable):
+    # issue 18773
+    array_1 = np.arange(0, 100, 10)
+    array_1.flags.writeable = array_1_writeable
+
+    array_2 = np.arange(0, 100, 10)
+    array_2.flags.writeable = array_2_writeable
+
+    hundred_elements = np.arange(100)
+    tm.assert_categorical_equal(
+        cut(hundred_elements, array_1), cut(hundred_elements, array_2)
+    )
+
+
+@pytest.mark.parametrize(
+    "conv",
+    [
+        lambda v: Timestamp(v),
+        lambda v: to_datetime(v),
+        lambda v: np.datetime64(v),
+        lambda v: Timestamp(v).to_pydatetime(),
+    ],
+)
+def test_datetime_bin(conv):
+    data = [np.datetime64("2012-12-13"), np.datetime64("2012-12-15")]
+    bin_data = ["2012-12-12", "2012-12-14", "2012-12-16"]
+
+    expected = Series(
+        IntervalIndex(
+            [
+                Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
+                Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2])),
+            ]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+
+    bins = [conv(v) for v in bin_data]
+    result = Series(cut(data, bins=bins))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [Series, Index, np.array, list])
+def test_datetime_cut(unit, box):
+    # see gh-14714
+    #
+    # Testing time data when it comes in various collection types.
+    data = to_datetime(["2013-01-01", "2013-01-02", "2013-01-03"]).astype(f"M8[{unit}]")
+    data = box(data)
+    result, _ = cut(data, 3, retbins=True)
+
+    if box is list:
+        # We don't (yet) do inference on these, so get nanos
+        unit = "ns"
+
+    if unit == "s":
+        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
+        # for why we round to 8 seconds instead of 7
+        left = DatetimeIndex(
+            ["2012-12-31 23:57:08", "2013-01-01 16:00:00", "2013-01-02 08:00:00"],
+            dtype=f"M8[{unit}]",
+        )
+    else:
+        left = DatetimeIndex(
+            [
+                "2012-12-31 23:57:07.200000",
+                "2013-01-01 16:00:00",
+                "2013-01-02 08:00:00",
+            ],
+            dtype=f"M8[{unit}]",
+        )
+    right = DatetimeIndex(
+        ["2013-01-01 16:00:00", "2013-01-02 08:00:00", "2013-01-03 00:00:00"],
+        dtype=f"M8[{unit}]",
+    )
+
+    exp_intervals = IntervalIndex.from_arrays(left, right)
+    expected = Series(exp_intervals).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(Series(result), expected)
+
+
+@pytest.mark.parametrize("box", [list, np.array, Index, Series])
+def test_datetime_tz_cut_mismatched_tzawareness(box):
+    # GH#54964
+    bins = box(
+        [
+            Timestamp("2013-01-01 04:57:07.200000"),
+            Timestamp("2013-01-01 21:00:00"),
+            Timestamp("2013-01-02 13:00:00"),
+            Timestamp("2013-01-03 05:00:00"),
+        ]
+    )
+    ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
+
+    msg = "Cannot use timezone-naive bins with timezone-aware values"
+    with pytest.raises(ValueError, match=msg):
+        cut(ser, bins)
+
+
+@pytest.mark.parametrize(
+    "bins",
+    [
+        3,
+        [
+            Timestamp("2013-01-01 04:57:07.200000", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-01 21:00:00", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-02 13:00:00", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-03 05:00:00", tz="UTC").tz_convert("US/Eastern"),
+        ],
+    ],
+)
+@pytest.mark.parametrize("box", [list, np.array, Index, Series])
+def test_datetime_tz_cut(bins, box):
+    # see gh-19872
+    tz = "US/Eastern"
+    ser = Series(date_range("20130101", periods=3, tz=tz))
+
+    if not isinstance(bins, int):
+        bins = box(bins)
+
+    result = cut(ser, bins)
+    expected = Series(
+        IntervalIndex(
+            [
+                Interval(
+                    Timestamp("2012-12-31 23:57:07.200000", tz=tz),
+                    Timestamp("2013-01-01 16:00:00", tz=tz),
+                ),
+                Interval(
+                    Timestamp("2013-01-01 16:00:00", tz=tz),
+                    Timestamp("2013-01-02 08:00:00", tz=tz),
+                ),
+                Interval(
+                    Timestamp("2013-01-02 08:00:00", tz=tz),
+                    Timestamp("2013-01-03 00:00:00", tz=tz),
+                ),
+            ]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+def test_datetime_nan_error():
+    msg = "bins must be of datetime64 dtype"
+
+    with pytest.raises(ValueError, match=msg):
+        cut(date_range("20130101", periods=3), bins=[0, 2, 4])
+
+
+def test_datetime_nan_mask():
+    result = cut(
+        date_range("20130102", periods=5), bins=date_range("20130101", periods=2)
+    )
+
+    mask = result.categories.isna()
+    tm.assert_numpy_array_equal(mask, np.array([False]))
+
+    mask = result.isna()
+    tm.assert_numpy_array_equal(mask, np.array([False, True, True, True, True]))
+
+
+@pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
+def test_datetime_cut_roundtrip(tz, unit):
+    # see gh-19891
+    ser = Series(date_range("20180101", periods=3, tz=tz, unit=unit))
+    result, result_bins = cut(ser, 2, retbins=True)
+
+    expected = cut(ser, result_bins)
+    tm.assert_series_equal(result, expected)
+
+    if unit == "s":
+        # TODO: constructing DatetimeIndex with dtype="M8[s]" without truncating
+        #  the first entry here raises in array_to_datetime. Should truncate
+        #  instead of raising?
+        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
+        # for why we round to 8 seconds instead of 7
+        expected_bins = DatetimeIndex(
+            ["2017-12-31 23:57:08", "2018-01-02 00:00:00", "2018-01-03 00:00:00"],
+            dtype=f"M8[{unit}]",
+        )
+    else:
+        expected_bins = DatetimeIndex(
+            [
+                "2017-12-31 23:57:07.200000",
+                "2018-01-02 00:00:00",
+                "2018-01-03 00:00:00",
+            ],
+            dtype=f"M8[{unit}]",
+        )
+    expected_bins = expected_bins.tz_localize(tz)
+    tm.assert_index_equal(result_bins, expected_bins)
+
+
+def test_timedelta_cut_roundtrip():
+    # see gh-19891
+    ser = Series(timedelta_range("1day", periods=3))
+    result, result_bins = cut(ser, 2, retbins=True)
+
+    expected = cut(ser, result_bins)
+    tm.assert_series_equal(result, expected)
+
+    expected_bins = TimedeltaIndex(
+        ["0 days 23:57:07.200000", "2 days 00:00:00", "3 days 00:00:00"]
+    )
+    tm.assert_index_equal(result_bins, expected_bins)
+
+
+@pytest.mark.parametrize("bins", [6, 7])
+@pytest.mark.parametrize(
+    "box, compare",
+    [
+        (Series, tm.assert_series_equal),
+        (np.array, tm.assert_categorical_equal),
+        (list, tm.assert_equal),
+    ],
+)
+def test_cut_bool_coercion_to_int(bins, box, compare):
+    # issue 20303
+    data_expected = box([0, 1, 1, 0, 1] * 10)
+    data_result = box([False, True, True, False, True] * 10)
+    expected = cut(data_expected, bins, duplicates="drop")
+    result = cut(data_result, bins, duplicates="drop")
+    compare(result, expected)
+
+
+@pytest.mark.parametrize("labels", ["foo", 1, True])
+def test_cut_incorrect_labels(labels):
+    # GH 13318
+    values = range(5)
+    msg = "Bin labels must either be False, None or passed in as a list-like argument"
+    with pytest.raises(ValueError, match=msg):
+        cut(values, 4, labels=labels)
+
+
+@pytest.mark.parametrize("bins", [3, [0, 5, 15]])
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+def test_cut_nullable_integer(bins, right, include_lowest):
+    a = np.random.default_rng(2).integers(0, 10, size=50).astype(float)
+    a[::2] = np.nan
+    result = cut(
+        pd.array(a, dtype="Int64"), bins, right=right, include_lowest=include_lowest
+    )
+    expected = cut(a, bins, right=right, include_lowest=include_lowest)
+    tm.assert_categorical_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, bins, labels, expected_codes, expected_labels",
+    [
+        ([15, 17, 19], [14, 16, 18, 20], ["A", "B", "A"], [0, 1, 0], ["A", "B"]),
+        ([1, 3, 5], [0, 2, 4, 6, 8], [2, 0, 1, 2], [2, 0, 1], [0, 1, 2]),
+    ],
+)
+def test_cut_non_unique_labels(data, bins, labels, expected_codes, expected_labels):
+    # GH 33141
+    result = cut(data, bins=bins, labels=labels, ordered=False)
+    expected = Categorical.from_codes(
+        expected_codes, categories=expected_labels, ordered=False
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, bins, labels, expected_codes, expected_labels",
+    [
+        ([15, 17, 19], [14, 16, 18, 20], ["C", "B", "A"], [0, 1, 2], ["C", "B", "A"]),
+        ([1, 3, 5], [0, 2, 4, 6, 8], [3, 0, 1, 2], [0, 1, 2], [3, 0, 1, 2]),
+    ],
+)
+def test_cut_unordered_labels(data, bins, labels, expected_codes, expected_labels):
+    # GH 33141
+    result = cut(data, bins=bins, labels=labels, ordered=False)
+    expected = Categorical.from_codes(
+        expected_codes, categories=expected_labels, ordered=False
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cut_unordered_with_missing_labels_raises_error():
+    # GH 33141
+    msg = "'labels' must be provided if 'ordered = False'"
+    with pytest.raises(ValueError, match=msg):
+        cut([0.5, 3], bins=[0, 1, 2], ordered=False)
+
+
+def test_cut_unordered_with_series_labels():
+    # https://github.com/pandas-dev/pandas/issues/36603
+    ser = Series([1, 2, 3, 4, 5])
+    bins = Series([0, 2, 4, 6])
+    labels = Series(["a", "b", "c"])
+    result = cut(ser, bins=bins, labels=labels, ordered=False)
+    expected = Series(["a", "a", "b", "b", "c"], dtype="category")
+    tm.assert_series_equal(result, expected)
+
+
+def test_cut_no_warnings():
+    df = DataFrame({"value": np.random.default_rng(2).integers(0, 100, 20)})
+    labels = [f"{i} - {i + 9}" for i in range(0, 100, 10)]
+    with tm.assert_produces_warning(False):
+        df["group"] = cut(df.value, range(0, 105, 10), right=False, labels=labels)
+
+
+def test_cut_with_duplicated_index_lowest_included():
+    # GH 42185
+    expected = Series(
+        [Interval(-0.001, 2, closed="right")] * 3
+        + [Interval(2, 4, closed="right"), Interval(-0.001, 2, closed="right")],
+        index=[0, 1, 2, 3, 0],
+        dtype="category",
+    ).cat.as_ordered()
+
+    ser = Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
+    result = cut(ser, bins=[0, 2, 4], include_lowest=True)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning")
+def test_cut_with_nonexact_categorical_indices():
+    # GH 42424
+
+    ser = Series(range(100))
+    ser1 = cut(ser, 10).value_counts().head(5)
+    ser2 = cut(ser, 10).value_counts().tail(5)
+    result = DataFrame({"1": ser1, "2": ser2})
+
+    index = pd.CategoricalIndex(
+        [
+            Interval(-0.099, 9.9, closed="right"),
+            Interval(9.9, 19.8, closed="right"),
+            Interval(19.8, 29.7, closed="right"),
+            Interval(29.7, 39.6, closed="right"),
+            Interval(39.6, 49.5, closed="right"),
+            Interval(49.5, 59.4, closed="right"),
+            Interval(59.4, 69.3, closed="right"),
+            Interval(69.3, 79.2, closed="right"),
+            Interval(79.2, 89.1, closed="right"),
+            Interval(89.1, 99, closed="right"),
+        ],
+        ordered=True,
+    )
+
+    expected = DataFrame(
+        {"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
+    )
+
+    tm.assert_frame_equal(expected, result)
+
+
+def test_cut_with_timestamp_tuple_labels():
+    # GH 40661
+    labels = [(Timestamp(10),), (Timestamp(20),), (Timestamp(30),)]
+    result = cut([2, 4, 6], bins=[1, 3, 5, 7], labels=labels)
+
+    expected = Categorical.from_codes([0, 1, 2], labels, ordered=True)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cut_bins_datetime_intervalindex():
+    # https://github.com/pandas-dev/pandas/issues/46218
+    bins = interval_range(Timestamp("2022-02-25"), Timestamp("2022-02-27"), freq="1D")
+    # passing Series instead of list is important to trigger bug
+    result = cut(Series([Timestamp("2022-02-26")]).astype("M8[ns]"), bins=bins)
+    expected = Categorical.from_codes([0], bins, ordered=True)
+    tm.assert_categorical_equal(result.array, expected)
+
+
+def test_cut_with_nullable_int64():
+    # GH 30787
+    series = Series([0, 1, 2, 3, 4, pd.NA, 6, 7], dtype="Int64")
+    bins = [0, 2, 4, 6, 8]
+    intervals = IntervalIndex.from_breaks(bins)
+
+    expected = Series(
+        Categorical.from_codes([-1, 0, 0, 1, 1, -1, 2, 3], intervals, ordered=True)
+    )
+
+    result = cut(series, bins=bins)
+
+    tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/1b/1b2ea22d7a57238deacf4099068753aee43d67305c1864205e2ffa6d2e1bd377/_sparse_softmax_compositeimplicitautograd_dispatch.h b/shared/package-files/1b/1b2ea22d7a57238deacf4099068753aee43d67305c1864205e2ffa6d2e1bd377/_sparse_softmax_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..26f7216f9f5ee887f410ebd1639e5154094453c1
--- /dev/null
+++ b/shared/package-files/1b/1b2ea22d7a57238deacf4099068753aee43d67305c1864205e2ffa6d2e1bd377/_sparse_softmax_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor _sparse_softmax(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor _sparse_softmax(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/1b/1b31e9b157251f67a49f51a66e826aefbd67d9a90ba2ef3af34a8b7518680d91/test_base.py b/shared/package-files/1b/1b31e9b157251f67a49f51a66e826aefbd67d9a90ba2ef3af34a8b7518680d91/test_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f7ead8b134dc2e09bb0cef8441cb2b75a5aee8b
--- /dev/null
+++ b/shared/package-files/1b/1b31e9b157251f67a49f51a66e826aefbd67d9a90ba2ef3af34a8b7518680d91/test_base.py
@@ -0,0 +1,5695 @@
+#
+# Authors: Travis Oliphant, Ed Schofield, Robert Cimrman, Nathan Bell, and others
+
+""" Test functions for sparse matrices. Each class in the "Matrix class
+based tests" section become subclasses of the classes in the "Generic
+tests" section. This is done by the functions in the "Tailored base
+class for generic tests" section.
+
+"""
+
+
+import contextlib
+import functools
+import operator
+import platform
+import itertools
+import sys
+
+import pytest
+from pytest import raises as assert_raises
+
+import numpy as np
+from numpy import (arange, zeros, array, dot, asarray,
+                   vstack, ndarray, transpose, diag, kron, inf, conjugate,
+                   int8)
+
+import random
+from numpy.testing import (assert_equal, assert_array_equal,
+        assert_array_almost_equal, assert_almost_equal, assert_,
+        assert_allclose, suppress_warnings)
+
+import scipy.linalg
+
+import scipy.sparse as sparse
+from scipy.sparse import (csc_matrix, csr_matrix, dok_matrix,
+        coo_matrix, lil_matrix, dia_matrix, bsr_matrix,
+        csc_array, csr_array, dok_array,
+        coo_array, lil_array, dia_array, bsr_array,
+        eye, issparse, SparseEfficiencyWarning, sparray)
+from scipy.sparse._base import _formats
+from scipy.sparse._sputils import (supported_dtypes, isscalarlike,
+                                   get_index_dtype, asmatrix, matrix)
+from scipy.sparse.linalg import splu, expm, inv
+
+from scipy._lib.decorator import decorator
+from scipy._lib._util import ComplexWarning
+
+IS_COLAB = ('google.colab' in sys.modules)
+
+
+def assert_in(member, collection, msg=None):
+    message = msg if msg is not None else f"{member!r} not found in {collection!r}"
+    assert_(member in collection, msg=message)
+
+
+def assert_array_equal_dtype(x, y, **kwargs):
+    assert_(x.dtype == y.dtype)
+    assert_array_equal(x, y, **kwargs)
+
+
+NON_ARRAY_BACKED_FORMATS = frozenset(['dok'])
+
+def sparse_may_share_memory(A, B):
+    # Checks if A and B have any numpy array sharing memory.
+
+    def _underlying_arrays(x):
+        # Given any object (e.g. a sparse array), returns all numpy arrays
+        # stored in any attribute.
+
+        arrays = []
+        for a in x.__dict__.values():
+            if isinstance(a, np.ndarray | np.generic):
+                arrays.append(a)
+        return arrays
+
+    for a in _underlying_arrays(A):
+        for b in _underlying_arrays(B):
+            if np.may_share_memory(a, b):
+                return True
+    return False
+
+
+sup_complex = suppress_warnings()
+sup_complex.filter(ComplexWarning)
+
+
+def with_64bit_maxval_limit(maxval_limit=None, random=False, fixed_dtype=None,
+                            downcast_maxval=None, assert_32bit=False):
+    """
+    Monkeypatch the maxval threshold at which scipy.sparse switches to
+    64-bit index arrays, or make it (pseudo-)random.
+
+    """
+    if maxval_limit is None:
+        maxval_limit = np.int64(10)
+    else:
+        # Ensure we use numpy scalars rather than Python scalars (matters for
+        # NEP 50 casting rule changes)
+        maxval_limit = np.int64(maxval_limit)
+
+    if assert_32bit:
+        def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
+            tp = get_index_dtype(arrays, maxval, check_contents)
+            assert_equal(np.iinfo(tp).max, np.iinfo(np.int32).max)
+            assert_(tp == np.int32 or tp == np.intc)
+            return tp
+    elif fixed_dtype is not None:
+        def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
+            return fixed_dtype
+    elif random:
+        counter = np.random.RandomState(seed=1234)
+
+        def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
+            return (np.int32, np.int64)[counter.randint(2)]
+    else:
+        def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
+            dtype = np.int32
+            if maxval is not None:
+                if maxval > maxval_limit:
+                    dtype = np.int64
+            for arr in arrays:
+                arr = np.asarray(arr)
+                if arr.dtype > np.int32:
+                    if check_contents:
+                        if arr.size == 0:
+                            # a bigger type not needed
+                            continue
+                        elif np.issubdtype(arr.dtype, np.integer):
+                            maxval = arr.max()
+                            minval = arr.min()
+                            if minval >= -maxval_limit and maxval <= maxval_limit:
+                                # a bigger type not needed
+                                continue
+                    dtype = np.int64
+            return dtype
+
+    if downcast_maxval is not None:
+        def new_downcast_intp_index(arr):
+            if arr.max() > downcast_maxval:
+                raise AssertionError("downcast limited")
+            return arr.astype(np.intp)
+
+    @decorator
+    def deco(func, *a, **kw):
+        backup = []
+        modules = [scipy.sparse._bsr, scipy.sparse._coo, scipy.sparse._csc,
+                   scipy.sparse._csr, scipy.sparse._dia, scipy.sparse._dok,
+                   scipy.sparse._lil, scipy.sparse._sputils,
+                   scipy.sparse._compressed, scipy.sparse._construct]
+        try:
+            for mod in modules:
+                backup.append((mod, 'get_index_dtype',
+                               getattr(mod, 'get_index_dtype', None)))
+                setattr(mod, 'get_index_dtype', new_get_index_dtype)
+                if downcast_maxval is not None:
+                    backup.append((mod, 'downcast_intp_index',
+                                   getattr(mod, 'downcast_intp_index', None)))
+                    setattr(mod, 'downcast_intp_index', new_downcast_intp_index)
+            return func(*a, **kw)
+        finally:
+            for mod, name, oldfunc in backup:
+                if oldfunc is not None:
+                    setattr(mod, name, oldfunc)
+
+    return deco
+
+
+def toarray(a):
+    if isinstance(a, np.ndarray) or isscalarlike(a):
+        return a
+    return a.toarray()
+
+
+class BinopTester:
+    # Custom type to test binary operations on sparse matrices.
+
+    def __add__(self, mat):
+        return "matrix on the right"
+
+    def __mul__(self, mat):
+        return "matrix on the right"
+
+    def __sub__(self, mat):
+        return "matrix on the right"
+
+    def __radd__(self, mat):
+        return "matrix on the left"
+
+    def __rmul__(self, mat):
+        return "matrix on the left"
+
+    def __rsub__(self, mat):
+        return "matrix on the left"
+
+    def __matmul__(self, mat):
+        return "matrix on the right"
+
+    def __rmatmul__(self, mat):
+        return "matrix on the left"
+
+class BinopTester_with_shape:
+    # Custom type to test binary operations on sparse matrices
+    # with object which has shape attribute.
+    def __init__(self,shape):
+        self._shape = shape
+
+    def shape(self):
+        return self._shape
+
+    def ndim(self):
+        return len(self._shape)
+
+    def __add__(self, mat):
+        return "matrix on the right"
+
+    def __mul__(self, mat):
+        return "matrix on the right"
+
+    def __sub__(self, mat):
+        return "matrix on the right"
+
+    def __radd__(self, mat):
+        return "matrix on the left"
+
+    def __rmul__(self, mat):
+        return "matrix on the left"
+
+    def __rsub__(self, mat):
+        return "matrix on the left"
+
+    def __matmul__(self, mat):
+        return "matrix on the right"
+
+    def __rmatmul__(self, mat):
+        return "matrix on the left"
+
+class ComparisonTester:
+    # Custom type to test comparison operations on sparse matrices.
+    def __eq__(self, other):
+        return "eq"
+
+    def __ne__(self, other):
+        return "ne"
+
+    def __lt__(self, other):
+        return "lt"
+
+    def __le__(self, other):
+        return "le"
+
+    def __gt__(self, other):
+        return "gt"
+
+    def __ge__(self, other):
+        return "ge"
+
+
+#------------------------------------------------------------------------------
+# Generic tests
+#------------------------------------------------------------------------------
+
+
+class _MatrixMixin:
+    """mixin to easily allow tests of both sparray and spmatrix"""
+    bsr_container = bsr_matrix
+    coo_container = coo_matrix
+    csc_container = csc_matrix
+    csr_container = csr_matrix
+    dia_container = dia_matrix
+    dok_container = dok_matrix
+    lil_container = lil_matrix
+    asdense = staticmethod(asmatrix)
+
+    def test_getrow(self):
+        assert_array_equal(self.datsp.getrow(1).toarray(), self.dat[[1], :])
+        assert_array_equal(self.datsp.getrow(-1).toarray(), self.dat[[-1], :])
+
+    def test_getcol(self):
+        assert_array_equal(self.datsp.getcol(1).toarray(), self.dat[:, [1]])
+        assert_array_equal(self.datsp.getcol(-1).toarray(), self.dat[:, [-1]])
+
+    def test_asfptype(self):
+        A = self.spcreator(arange(6,dtype='int32').reshape(2,3))
+
+        assert_equal(A.asfptype().dtype, np.dtype('float64'))
+        assert_equal(A.asfptype().format, A.format)
+        assert_equal(A.astype('int16').asfptype().dtype, np.dtype('float32'))
+        assert_equal(A.astype('complex128').asfptype().dtype, np.dtype('complex128'))
+
+        B = A.asfptype()
+        C = B.asfptype()
+        assert_(B is C)
+
+
+# TODO test prune
+# TODO test has_sorted_indices
+class _TestCommon:
+    """test common functionality shared by all sparse formats"""
+    math_dtypes = supported_dtypes
+
+    bsr_container = bsr_array
+    coo_container = coo_array
+    csc_container = csc_array
+    csr_container = csr_array
+    dia_container = dia_array
+    dok_container = dok_array
+    lil_container = lil_array
+    asdense = array
+
+    @classmethod
+    def init_class(cls):
+        # Canonical data.
+        cls.dat = array([[1, 0, 0, 2], [3, 0, 1, 0], [0, 2, 0, 0]], 'd')
+        cls.datsp = cls.spcreator(cls.dat)
+
+        # Some sparse and dense matrices with data for every supported dtype.
+        # This set union is a workaround for numpy#6295, which means that
+        # two np.int64 dtypes don't hash to the same value.
+        cls.checked_dtypes = set(supported_dtypes).union(cls.math_dtypes)
+        cls.dat_dtypes = {}
+        cls.datsp_dtypes = {}
+        for dtype in cls.checked_dtypes:
+            cls.dat_dtypes[dtype] = cls.dat.astype(dtype)
+            cls.datsp_dtypes[dtype] = cls.spcreator(cls.dat.astype(dtype))
+
+        # Check that the original data is equivalent to the
+        # corresponding dat_dtypes & datsp_dtypes.
+        assert_equal(cls.dat, cls.dat_dtypes[np.float64])
+        assert_equal(cls.datsp.toarray(),
+                     cls.datsp_dtypes[np.float64].toarray())
+
+        cls.is_array_test = isinstance(cls.datsp, sparray)
+
+    def test_bool(self):
+        def check(dtype):
+            datsp = self.datsp_dtypes[dtype]
+
+            assert_raises(ValueError, bool, datsp)
+            assert_(self.spcreator([[1]]))
+            assert_(not self.spcreator([[0]]))
+
+        if isinstance(self, TestDOK):
+            pytest.skip("Cannot create a rank <= 2 DOK matrix.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_bool_rollover(self):
+        # bool's underlying dtype is 1 byte, check that it does not
+        # rollover True -> False at 256.
+        dat = array([[True, False]])
+        datsp = self.spcreator(dat)
+
+        for _ in range(10):
+            datsp = datsp + datsp
+            dat = dat + dat
+        assert_array_equal(dat, datsp.toarray())
+
+    def test_eq(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datbsr = self.bsr_container(dat)
+            datcsr = self.csr_container(dat)
+            datcsc = self.csc_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat == dat2, (datsp == datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype(dat == dat2, (datbsr == datsp2).toarray())
+            assert_array_equal_dtype(dat == dat2, (datcsr == datsp2).toarray())
+            assert_array_equal_dtype(dat == dat2, (datcsc == datsp2).toarray())
+            assert_array_equal_dtype(dat == dat2, (datlil == datsp2).toarray())
+            # sparse/dense
+            assert_array_equal_dtype(dat == datsp2, datsp2 == dat)
+            # sparse/scalar
+            assert_array_equal_dtype(dat == 0, (datsp == 0).toarray())
+            assert_array_equal_dtype(dat == 1, (datsp == 1).toarray())
+            assert_array_equal_dtype(dat == np.nan,
+                                     (datsp == np.nan).toarray())
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_ne(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datbsr = self.bsr_container(dat)
+            datcsc = self.csc_container(dat)
+            datcsr = self.csr_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat != dat2, (datsp != datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype(dat != dat2, (datbsr != datsp2).toarray())
+            assert_array_equal_dtype(dat != dat2, (datcsc != datsp2).toarray())
+            assert_array_equal_dtype(dat != dat2, (datcsr != datsp2).toarray())
+            assert_array_equal_dtype(dat != dat2, (datlil != datsp2).toarray())
+            # sparse/dense
+            assert_array_equal_dtype(dat != datsp2, datsp2 != dat)
+            # sparse/scalar
+            assert_array_equal_dtype(dat != 0, (datsp != 0).toarray())
+            assert_array_equal_dtype(dat != 1, (datsp != 1).toarray())
+            assert_array_equal_dtype(0 != dat, (0 != datsp).toarray())
+            assert_array_equal_dtype(1 != dat, (1 != datsp).toarray())
+            assert_array_equal_dtype(dat != np.nan,
+                                     (datsp != np.nan).toarray())
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_lt(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            # data
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datcomplex = dat.astype(complex)
+            datcomplex[:,0] = 1 + 1j
+            datspcomplex = self.spcreator(datcomplex)
+            datbsr = self.bsr_container(dat)
+            datcsc = self.csc_container(dat)
+            datcsr = self.csr_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat < dat2, (datsp < datsp2).toarray())
+            assert_array_equal_dtype(datcomplex < dat2,
+                                     (datspcomplex < datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype(dat < dat2, (datbsr < datsp2).toarray())
+            assert_array_equal_dtype(dat < dat2, (datcsc < datsp2).toarray())
+            assert_array_equal_dtype(dat < dat2, (datcsr < datsp2).toarray())
+            assert_array_equal_dtype(dat < dat2, (datlil < datsp2).toarray())
+
+            assert_array_equal_dtype(dat2 < dat, (datsp2 < datbsr).toarray())
+            assert_array_equal_dtype(dat2 < dat, (datsp2 < datcsc).toarray())
+            assert_array_equal_dtype(dat2 < dat, (datsp2 < datcsr).toarray())
+            assert_array_equal_dtype(dat2 < dat, (datsp2 < datlil).toarray())
+            # sparse/dense
+            assert_array_equal_dtype(dat < dat2, datsp < dat2)
+            assert_array_equal_dtype(datcomplex < dat2, datspcomplex < dat2)
+            # sparse/scalar
+            for val in [2, 1, 0, -1, -2]:
+                val = np.int64(val)  # avoid Python scalar (due to NEP 50 changes)
+                assert_array_equal_dtype((datsp < val).toarray(), dat < val)
+                assert_array_equal_dtype((val < datsp).toarray(), val < dat)
+
+            with np.errstate(invalid='ignore'):
+                assert_array_equal_dtype((datsp < np.nan).toarray(),
+                                         dat < np.nan)
+
+            # data
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+
+            # dense rhs
+            assert_array_equal_dtype(dat < datsp2, datsp < dat2)
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_gt(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datcomplex = dat.astype(complex)
+            datcomplex[:,0] = 1 + 1j
+            datspcomplex = self.spcreator(datcomplex)
+            datbsr = self.bsr_container(dat)
+            datcsc = self.csc_container(dat)
+            datcsr = self.csr_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat > dat2, (datsp > datsp2).toarray())
+            assert_array_equal_dtype(datcomplex > dat2,
+                                     (datspcomplex > datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype(dat > dat2, (datbsr > datsp2).toarray())
+            assert_array_equal_dtype(dat > dat2, (datcsc > datsp2).toarray())
+            assert_array_equal_dtype(dat > dat2, (datcsr > datsp2).toarray())
+            assert_array_equal_dtype(dat > dat2, (datlil > datsp2).toarray())
+
+            assert_array_equal_dtype(dat2 > dat, (datsp2 > datbsr).toarray())
+            assert_array_equal_dtype(dat2 > dat, (datsp2 > datcsc).toarray())
+            assert_array_equal_dtype(dat2 > dat, (datsp2 > datcsr).toarray())
+            assert_array_equal_dtype(dat2 > dat, (datsp2 > datlil).toarray())
+            # sparse/dense
+            assert_array_equal_dtype(dat > dat2, datsp > dat2)
+            assert_array_equal_dtype(datcomplex > dat2, datspcomplex > dat2)
+            # sparse/scalar
+            for val in [2, 1, 0, -1, -2]:
+                val = np.int64(val)  # avoid Python scalar (due to NEP 50 changes)
+                assert_array_equal_dtype((datsp > val).toarray(), dat > val)
+                assert_array_equal_dtype((val > datsp).toarray(), val > dat)
+
+            with np.errstate(invalid='ignore'):
+                assert_array_equal_dtype((datsp > np.nan).toarray(),
+                                         dat > np.nan)
+
+            # data
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+
+            # dense rhs
+            assert_array_equal_dtype(dat > datsp2, datsp > dat2)
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_le(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datcomplex = dat.astype(complex)
+            datcomplex[:,0] = 1 + 1j
+            datspcomplex = self.spcreator(datcomplex)
+            datbsr = self.bsr_container(dat)
+            datcsc = self.csc_container(dat)
+            datcsr = self.csr_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat <= dat2, (datsp <= datsp2).toarray())
+            assert_array_equal_dtype(datcomplex <= dat2,
+                                     (datspcomplex <= datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype((datbsr <= datsp2).toarray(), dat <= dat2)
+            assert_array_equal_dtype((datcsc <= datsp2).toarray(), dat <= dat2)
+            assert_array_equal_dtype((datcsr <= datsp2).toarray(), dat <= dat2)
+            assert_array_equal_dtype((datlil <= datsp2).toarray(), dat <= dat2)
+
+            assert_array_equal_dtype((datsp2 <= datbsr).toarray(), dat2 <= dat)
+            assert_array_equal_dtype((datsp2 <= datcsc).toarray(), dat2 <= dat)
+            assert_array_equal_dtype((datsp2 <= datcsr).toarray(), dat2 <= dat)
+            assert_array_equal_dtype((datsp2 <= datlil).toarray(), dat2 <= dat)
+            # sparse/dense
+            assert_array_equal_dtype(datsp <= dat2, dat <= dat2)
+            assert_array_equal_dtype(datspcomplex <= dat2, datcomplex <= dat2)
+            # sparse/scalar
+            for val in [2, 1, -1, -2]:
+                val = np.int64(val)  # avoid Python scalar (due to NEP 50 changes)
+                assert_array_equal_dtype((datsp <= val).toarray(), dat <= val)
+                assert_array_equal_dtype((val <= datsp).toarray(), val <= dat)
+
+            # data
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+
+            # dense rhs
+            assert_array_equal_dtype(dat <= datsp2, datsp <= dat2)
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_ge(self):
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        @sup_complex
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+            datcomplex = dat.astype(complex)
+            datcomplex[:,0] = 1 + 1j
+            datspcomplex = self.spcreator(datcomplex)
+            datbsr = self.bsr_container(dat)
+            datcsc = self.csc_container(dat)
+            datcsr = self.csr_container(dat)
+            datlil = self.lil_container(dat)
+
+            # sparse/sparse
+            assert_array_equal_dtype(dat >= dat2, (datsp >= datsp2).toarray())
+            assert_array_equal_dtype(datcomplex >= dat2,
+                                     (datspcomplex >= datsp2).toarray())
+            # mix sparse types
+            assert_array_equal_dtype((datbsr >= datsp2).toarray(), dat >= dat2)
+            assert_array_equal_dtype((datcsc >= datsp2).toarray(), dat >= dat2)
+            assert_array_equal_dtype((datcsr >= datsp2).toarray(), dat >= dat2)
+            assert_array_equal_dtype((datlil >= datsp2).toarray(), dat >= dat2)
+
+            assert_array_equal_dtype((datsp2 >= datbsr).toarray(), dat2 >= dat)
+            assert_array_equal_dtype((datsp2 >= datcsc).toarray(), dat2 >= dat)
+            assert_array_equal_dtype((datsp2 >= datcsr).toarray(), dat2 >= dat)
+            assert_array_equal_dtype((datsp2 >= datlil).toarray(), dat2 >= dat)
+            # sparse/dense
+            assert_array_equal_dtype(datsp >= dat2, dat >= dat2)
+            assert_array_equal_dtype(datspcomplex >= dat2, datcomplex >= dat2)
+            # sparse/scalar
+            for val in [2, 1, -1, -2]:
+                val = np.int64(val)  # avoid Python scalar (due to NEP 50 changes)
+                assert_array_equal_dtype((datsp >= val).toarray(), dat >= val)
+                assert_array_equal_dtype((val >= datsp).toarray(), val >= dat)
+
+            # dense data
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+            dat2 = dat.copy()
+            dat2[:,0] = 0
+            datsp2 = self.spcreator(dat2)
+
+            # dense rhs
+            assert_array_equal_dtype(dat >= datsp2, datsp >= dat2)
+
+        if self.datsp.format not in ['bsr', 'csc', 'csr']:
+            pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_empty(self):
+        # create empty matrices
+        assert_equal(self.spcreator((3, 3)).toarray(), zeros((3, 3)))
+        assert_equal(self.spcreator((3, 3)).nnz, 0)
+        assert_equal(self.spcreator((3, 3)).count_nonzero(), 0)
+        if self.datsp.format in ["coo", "csr", "csc", "lil"]:
+            assert_equal(self.spcreator((3, 3)).count_nonzero(axis=0), array([0, 0, 0]))
+
+    def test_count_nonzero(self):
+        axis_support = self.datsp.format in ["coo", "csr", "csc", "lil"]
+        axes = [None, 0, 1, -1, -2] if axis_support else [None]
+
+        for A in (self.datsp, self.datsp.T):
+            for ax in axes:
+                expected = np.count_nonzero(A.toarray(), axis=ax)
+                assert_equal(A.count_nonzero(axis=ax), expected)
+
+        if not axis_support:
+            with assert_raises(NotImplementedError, match="not implemented .* format"):
+                self.datsp.count_nonzero(axis=0)
+
+    def test_invalid_shapes(self):
+        assert_raises(ValueError, self.spcreator, (-1,3))
+        assert_raises(ValueError, self.spcreator, (3,-1))
+        assert_raises(ValueError, self.spcreator, (-1,-1))
+
+    def test_repr(self):
+        datsp = self.spcreator([[1, 0, 0], [0, 0, 0], [0, 0, -2]])
+        extra = (
+            "(1 diagonals) " if datsp.format == "dia"
+            else "(blocksize=1x1) " if datsp.format == "bsr"
+            else ""
+        )
+        _, fmt = _formats[datsp.format]
+        sparse_cls = "array" if self.is_array_test else "matrix"
+        expected = (
+            f"<{fmt} sparse {sparse_cls} of dtype '{datsp.dtype}'\n"
+            f"\twith {datsp.nnz} stored elements {extra}and shape {datsp.shape}>"
+        )
+        assert repr(datsp) == expected
+
+    def test_str_maxprint(self):
+        datsp = self.spcreator(np.arange(75).reshape(5, 15))
+        assert datsp.maxprint == 50
+        assert len(str(datsp).split('\n')) == 51 + 3
+
+        dat = np.arange(15).reshape(5,3)
+        datsp = self.spcreator(dat)
+        # format dia reports nnz=15, but we want 14
+        nnz_small = 14 if datsp.format == 'dia' else datsp.nnz
+        datsp_mp6 = self.spcreator(dat, maxprint=6)
+
+        assert len(str(datsp).split('\n')) == nnz_small + 3
+        assert len(str(datsp_mp6).split('\n')) == 6 + 4
+
+        # Check parameter `maxprint` is keyword only
+        datsp = self.spcreator(dat, shape=(5, 3), dtype='i', copy=False, maxprint=4)
+        datsp = self.spcreator(dat, (5, 3), 'i', False, maxprint=4)
+        with pytest.raises(TypeError, match="positional argument|unpack non-iterable"):
+            self.spcreator(dat, (5, 3), 'i', False, 4)
+
+    def test_str(self):
+        datsp = self.spcreator([[1, 0, 0], [0, 0, 0], [0, 0, -2]])
+        if datsp.nnz != 2:
+            return
+        extra = (
+            "(1 diagonals) " if datsp.format == "dia"
+            else "(blocksize=1x1) " if datsp.format == "bsr"
+            else ""
+        )
+        _, fmt = _formats[datsp.format]
+        sparse_cls = "array" if self.is_array_test else "matrix"
+        expected = (
+            f"<{fmt} sparse {sparse_cls} of dtype '{datsp.dtype}'\n"
+            f"\twith {datsp.nnz} stored elements {extra}and shape {datsp.shape}>"
+            "\n  Coords\tValues"
+            "\n  (0, 0)\t1"
+            "\n  (2, 2)\t-2"
+        )
+        assert str(datsp) == expected
+
+    def test_empty_arithmetic(self):
+        # Test manipulating empty matrices. Fails in SciPy SVN <= r1768
+        shape = (5, 5)
+        for mytype in [np.dtype('int32'), np.dtype('float32'),
+                np.dtype('float64'), np.dtype('complex64'),
+                np.dtype('complex128')]:
+            a = self.spcreator(shape, dtype=mytype)
+            b = a + a
+            c = 2 * a
+            d = a @ a.tocsc()
+            e = a @ a.tocsr()
+            f = a @ a.tocoo()
+            for m in [a,b,c,d,e,f]:
+                assert_equal(m.toarray(), a.toarray()@a.toarray())
+                # These fail in all revisions <= r1768:
+                assert_equal(m.dtype,mytype)
+                assert_equal(m.toarray().dtype,mytype)
+
+    def test_abs(self):
+        A = array([[-1, 0, 17], [0, -5, 0], [1, -4, 0], [0, 0, 0]], 'd')
+        assert_equal(abs(A), abs(self.spcreator(A)).toarray())
+
+    def test_round(self):
+        decimal = 1
+        A = array([[-1.35, 0.56], [17.25, -5.98]], 'd')
+        assert_equal(np.around(A, decimals=decimal),
+                     round(self.spcreator(A), ndigits=decimal).toarray())
+
+    def test_elementwise_power(self):
+        A = array([[-4, -3, -2], [-1, 0, 1], [2, 3, 4]], 'd')
+        assert_equal(np.power(A, 2), self.spcreator(A).power(2).toarray())
+
+        #it's element-wise power function, input has to be a scalar
+        assert_raises(NotImplementedError, self.spcreator(A).power, A)
+
+    def test_neg(self):
+        A = array([[-1, 0, 17], [0, -5, 0], [1, -4, 0], [0, 0, 0]], 'd')
+        assert_equal(-A, (-self.spcreator(A)).toarray())
+
+        # see gh-5843
+        A = array([[True, False, False], [False, False, True]])
+        assert_raises(NotImplementedError, self.spcreator(A).__neg__)
+
+    def test_real(self):
+        D = array([[1 + 3j, 2 - 4j]])
+        A = self.spcreator(D)
+        assert_equal(A.real.toarray(), D.real)
+
+    def test_imag(self):
+        D = array([[1 + 3j, 2 - 4j]])
+        A = self.spcreator(D)
+        assert_equal(A.imag.toarray(), D.imag)
+
+    def test_diagonal(self):
+        # Does the matrix's .diagonal() method work?
+        mats = []
+        mats.append([[1,0,2]])
+        mats.append([[1],[0],[2]])
+        mats.append([[0,1],[0,2],[0,3]])
+        mats.append([[0,0,1],[0,0,2],[0,3,0]])
+        mats.append([[1,0],[0,0]])
+
+        mats.append(kron(mats[0],[[1,2]]))
+        mats.append(kron(mats[0],[[1],[2]]))
+        mats.append(kron(mats[1],[[1,2],[3,4]]))
+        mats.append(kron(mats[2],[[1,2],[3,4]]))
+        mats.append(kron(mats[3],[[1,2],[3,4]]))
+        mats.append(kron(mats[3],[[1,2,3,4]]))
+
+        for m in mats:
+            rows, cols = array(m).shape
+            sparse_mat = self.spcreator(m)
+            for k in range(-rows-1, cols+2):
+                assert_equal(sparse_mat.diagonal(k=k), diag(m, k=k))
+            # Test for k beyond boundaries(issue #11949)
+            assert_equal(sparse_mat.diagonal(k=10), diag(m, k=10))
+            assert_equal(sparse_mat.diagonal(k=-99), diag(m, k=-99))
+
+        # Test all-zero matrix.
+        assert_equal(self.spcreator((40, 16130)).diagonal(), np.zeros(40))
+        # Test empty matrix
+        # https://github.com/scipy/scipy/issues/11949
+        assert_equal(self.spcreator((0, 0)).diagonal(), np.empty(0))
+        assert_equal(self.spcreator((15, 0)).diagonal(), np.empty(0))
+        assert_equal(self.spcreator((0, 5)).diagonal(10), np.empty(0))
+
+    def test_trace(self):
+        # For square matrix
+        A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        B = self.spcreator(A)
+        for k in range(-2, 3):
+            assert_equal(A.trace(offset=k), B.trace(offset=k))
+
+        # For rectangular matrix
+        A = np.array([[1, 2, 3], [4, 5, 6]])
+        B = self.spcreator(A)
+        for k in range(-1, 3):
+            assert_equal(A.trace(offset=k), B.trace(offset=k))
+
+    def test_reshape(self):
+        x = self.spcreator([[1, 0, 7], [0, 0, 0], [0, 3, 0], [0, 0, 5]])
+        for order in ['C', 'F']:
+            for s in [(12, 1), (1, 12)]:
+                assert_array_equal(x.reshape(s, order=order).toarray(),
+                                   x.toarray().reshape(s, order=order))
+
+        # This example is taken from the stackoverflow answer at
+        # https://stackoverflow.com/q/16511879
+        x = self.spcreator([[0, 10, 0, 0], [0, 0, 0, 0], [0, 20, 30, 40]])
+        y = x.reshape((2, 6))  # Default order is 'C'
+        desired = [[0, 10, 0, 0, 0, 0], [0, 0, 0, 20, 30, 40]]
+        assert_array_equal(y.toarray(), desired)
+
+        # Reshape with negative indexes
+        y = x.reshape((2, -1))
+        assert_array_equal(y.toarray(), desired)
+        y = x.reshape((-1, 6))
+        assert_array_equal(y.toarray(), desired)
+        assert_raises(ValueError, x.reshape, (-1, -1))
+
+        # Reshape with star args
+        y = x.reshape(2, 6)
+        assert_array_equal(y.toarray(), desired)
+        assert_raises(TypeError, x.reshape, 2, 6, not_an_arg=1)
+
+        # Reshape with same size is noop unless copy=True
+        y = x.reshape((3, 4))
+        assert_(y is x)
+        y = x.reshape((3, 4), copy=True)
+        assert_(y is not x)
+
+        # Ensure reshape did not alter original size
+        assert_array_equal(x.shape, (3, 4))
+
+        if self.is_array_test:
+            with assert_raises(AttributeError, match="has no setter|n't set attribute"):
+                x.shape = (2, 6)
+        else:  # spmatrix test
+            # Reshape in place
+            x.shape = (2, 6)
+            assert_array_equal(x.toarray(), desired)
+
+        # Reshape to bad ndim
+        assert_raises(ValueError, x.reshape, (x.size,))
+        assert_raises(ValueError, x.reshape, (1, x.size, 1))
+
+    @pytest.mark.slow
+    def test_setdiag_comprehensive(self):
+        def dense_setdiag(a, v, k):
+            v = np.asarray(v)
+            if k >= 0:
+                n = min(a.shape[0], a.shape[1] - k)
+                if v.ndim != 0:
+                    n = min(n, len(v))
+                    v = v[:n]
+                i = np.arange(0, n)
+                j = np.arange(k, k + n)
+                a[i,j] = v
+            elif k < 0:
+                dense_setdiag(a.T, v, -k)
+
+        def check_setdiag(a, b, k):
+            # Check setting diagonal using a scalar, a vector of
+            # correct length, and too short or too long vectors
+            for r in [-1, len(np.diag(a, k)), 2, 30]:
+                if r < 0:
+                    v = np.random.choice(range(1, 20))
+                else:
+                    v = np.random.randint(1, 20, size=r)
+
+                dense_setdiag(a, v, k)
+                with suppress_warnings() as sup:
+                    sup.filter(SparseEfficiencyWarning, "Changing the sparsity structu")
+                    b.setdiag(v, k)
+
+                # check that dense_setdiag worked
+                d = np.diag(a, k)
+                if np.asarray(v).ndim == 0:
+                    assert_array_equal(d, v, err_msg="{msg} {r}")
+                else:
+                    n = min(len(d), len(v))
+                    assert_array_equal(d[:n], v[:n], err_msg="{msg} {r}")
+                # check that sparse setdiag worked
+                assert_array_equal(b.toarray(), a, err_msg="{msg} {r}")
+
+        # comprehensive test
+        np.random.seed(1234)
+        shapes = [(0,5), (5,0), (1,5), (5,1), (5,5)]
+        for dtype in [np.int8, np.float64]:
+            for m,n in shapes:
+                ks = np.arange(-m+1, n-1)
+                for k in ks:
+                    a = np.zeros((m, n), dtype=dtype)
+                    b = self.spcreator((m, n), dtype=dtype)
+
+                    check_setdiag(a, b, k)
+
+                    # check overwriting etc
+                    for k2 in np.random.choice(ks, size=min(len(ks), 5)):
+                        check_setdiag(a, b, k2)
+
+    def test_setdiag(self):
+        # simple test cases
+        m = self.spcreator(np.eye(3))
+        m2 = self.spcreator((4, 4))
+        values = [3, 2, 1]
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            assert_raises(ValueError, m.setdiag, values, k=4)
+            m.setdiag(values)
+            assert_array_equal(m.diagonal(), values)
+            m.setdiag(values, k=1)
+            assert_array_equal(m.toarray(), np.array([[3, 3, 0],
+                                                      [0, 2, 2],
+                                                      [0, 0, 1]]))
+            m.setdiag(values, k=-2)
+            assert_array_equal(m.toarray(), np.array([[3, 3, 0],
+                                                      [0, 2, 2],
+                                                      [3, 0, 1]]))
+            m.setdiag((9,), k=2)
+            assert_array_equal(m.toarray()[0,2], 9)
+            m.setdiag((9,), k=-2)
+            assert_array_equal(m.toarray()[2,0], 9)
+            # test short values on an empty matrix
+            m2.setdiag([1], k=2)
+            assert_array_equal(m2.toarray()[0], [0, 0, 1, 0])
+            # test overwriting that same diagonal
+            m2.setdiag([1, 1], k=2)
+            assert_array_equal(m2.toarray()[:2], [[0, 0, 1, 0],
+                                                  [0, 0, 0, 1]])
+
+    def test_nonzero(self):
+        A = array([[1, 0, 1],[0, 1, 1],[0, 0, 1]])
+        Asp = self.spcreator(A)
+
+        A_nz = {tuple(ij) for ij in transpose(A.nonzero())}
+        Asp_nz = {tuple(ij) for ij in transpose(Asp.nonzero())}
+
+        assert_equal(A_nz, Asp_nz)
+
+    def test_numpy_nonzero(self):
+        # See gh-5987
+        A = array([[1, 0, 1], [0, 1, 1], [0, 0, 1]])
+        Asp = self.spcreator(A)
+
+        A_nz = {tuple(ij) for ij in transpose(np.nonzero(A))}
+        Asp_nz = {tuple(ij) for ij in transpose(np.nonzero(Asp))}
+
+        assert_equal(A_nz, Asp_nz)
+
+    def test_sum(self):
+        np.random.seed(1234)
+        dat_1 = np.array([[0, 1, 2],
+                        [3, -4, 5],
+                        [-6, 7, 9]])
+        dat_2 = np.random.rand(5, 5)
+        dat_3 = np.array([[]])
+        dat_4 = np.zeros((40, 40))
+        dat_5 = sparse.rand(5, 5, density=1e-2).toarray()
+        matrices = [dat_1, dat_2, dat_3, dat_4, dat_5]
+
+        def check(dtype, j):
+            dat = self.asdense(matrices[j], dtype=dtype)
+            datsp = self.spcreator(dat, dtype=dtype)
+            with np.errstate(over='ignore'):
+                assert_array_almost_equal(dat.sum(), datsp.sum())
+                assert_equal(dat.sum().dtype, datsp.sum().dtype)
+                assert_(np.isscalar(datsp.sum(axis=None)))
+                assert_array_almost_equal(dat.sum(axis=None),
+                                          datsp.sum(axis=None))
+                assert_equal(dat.sum(axis=None).dtype,
+                             datsp.sum(axis=None).dtype)
+                assert_array_almost_equal(dat.sum(axis=0), datsp.sum(axis=0))
+                assert_equal(dat.sum(axis=0).dtype, datsp.sum(axis=0).dtype)
+                assert_array_almost_equal(dat.sum(axis=1), datsp.sum(axis=1))
+                assert_equal(dat.sum(axis=1).dtype, datsp.sum(axis=1).dtype)
+                assert_array_almost_equal(dat.sum(axis=-2), datsp.sum(axis=-2))
+                assert_equal(dat.sum(axis=-2).dtype, datsp.sum(axis=-2).dtype)
+                assert_array_almost_equal(dat.sum(axis=-1), datsp.sum(axis=-1))
+                assert_equal(dat.sum(axis=-1).dtype, datsp.sum(axis=-1).dtype)
+
+        for dtype in self.checked_dtypes:
+            for j in range(len(matrices)):
+                check(dtype, j)
+
+    def test_sum_invalid_params(self):
+        out = np.zeros((1, 3))
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        assert_raises(ValueError, datsp.sum, axis=3)
+        assert_raises(TypeError, datsp.sum, axis=(0, 1))
+        assert_raises(TypeError, datsp.sum, axis=1.5)
+        assert_raises(ValueError, datsp.sum, axis=1, out=out)
+
+    def test_sum_dtype(self):
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        def check(dtype):
+            dat_sum = dat.sum(dtype=dtype)
+            datsp_sum = datsp.sum(dtype=dtype)
+
+            assert_array_almost_equal(dat_sum, datsp_sum)
+            assert_equal(dat_sum.dtype, datsp_sum.dtype)
+
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_sum_out(self):
+        keep = not self.is_array_test
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        dat_out = array(0) if self.is_array_test else array([[0]])
+        datsp_out = array(0) if self.is_array_test else matrix([[0]])
+
+        dat.sum(out=dat_out, keepdims=keep)
+        datsp.sum(out=datsp_out)
+        assert_array_almost_equal(dat_out, datsp_out)
+
+        dat_out = np.zeros((3,)) if self.is_array_test else np.zeros((3, 1))
+        datsp_out = np.zeros((3,)) if self.is_array_test else matrix(np.zeros((3, 1)))
+
+        dat.sum(axis=1, out=dat_out, keepdims=keep)
+        datsp.sum(axis=1, out=datsp_out)
+        assert_array_almost_equal(dat_out, datsp_out)
+
+        # check that wrong shape out parameter raises
+        with assert_raises(ValueError, match="output parameter.*wrong.*dimension"):
+            datsp.sum(out=array([0]))
+        with assert_raises(ValueError, match="output parameter.*wrong.*dimension"):
+            datsp.sum(out=array([[0]] if self.is_array_test else 0))
+
+    def test_numpy_sum(self):
+        # See gh-5987
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        dat_sum = np.sum(dat)
+        datsp_sum = np.sum(datsp)
+
+        assert_array_almost_equal(dat_sum, datsp_sum)
+        assert_equal(dat_sum.dtype, datsp_sum.dtype)
+
+    def test_mean(self):
+        keep = not self.is_array_test
+        def check(dtype):
+            dat = array([[0, 1, 2],
+                         [3, 4, 5],
+                         [6, 7, 9]], dtype=dtype)
+            datsp = self.spcreator(dat, dtype=dtype)
+
+            assert_array_almost_equal(dat.mean(), datsp.mean())
+            assert_equal(dat.mean().dtype, datsp.mean().dtype)
+            assert_(np.isscalar(datsp.mean(axis=None)))
+            assert_array_almost_equal(
+                dat.mean(axis=None, keepdims=keep), datsp.mean(axis=None)
+            )
+            assert_equal(dat.mean(axis=None).dtype, datsp.mean(axis=None).dtype)
+            assert_array_almost_equal(
+                dat.mean(axis=0, keepdims=keep), datsp.mean(axis=0)
+            )
+            assert_equal(dat.mean(axis=0).dtype, datsp.mean(axis=0).dtype)
+            assert_array_almost_equal(
+                dat.mean(axis=1, keepdims=keep), datsp.mean(axis=1)
+            )
+            assert_equal(dat.mean(axis=1).dtype, datsp.mean(axis=1).dtype)
+            assert_array_almost_equal(
+                dat.mean(axis=-2, keepdims=keep), datsp.mean(axis=-2)
+            )
+            assert_equal(dat.mean(axis=-2).dtype, datsp.mean(axis=-2).dtype)
+            assert_array_almost_equal(
+                dat.mean(axis=-1, keepdims=keep), datsp.mean(axis=-1)
+            )
+            assert_equal(dat.mean(axis=-1).dtype, datsp.mean(axis=-1).dtype)
+
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_mean_invalid_params(self):
+        out = self.asdense(np.zeros((1, 3)))
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        assert_raises(ValueError, datsp.mean, axis=3)
+        assert_raises(TypeError, datsp.mean, axis=(0, 1))
+        assert_raises(TypeError, datsp.mean, axis=1.5)
+        assert_raises(ValueError, datsp.mean, axis=1, out=out)
+
+    def test_mean_dtype(self):
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        def check(dtype):
+            dat_mean = dat.mean(dtype=dtype)
+            datsp_mean = datsp.mean(dtype=dtype)
+
+            assert_array_almost_equal(dat_mean, datsp_mean)
+            assert_equal(dat_mean.dtype, datsp_mean.dtype)
+
+        for dtype in self.checked_dtypes:
+            check(dtype)
+
+    def test_mean_out(self):
+        keep = not self.is_array_test
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        dat_out = array(0) if self.is_array_test else array([[0]])
+        datsp_out = array(0) if self.is_array_test else matrix([[0]])
+
+        dat.mean(out=dat_out, keepdims=keep)
+        datsp.mean(out=datsp_out)
+        assert_array_almost_equal(dat_out, datsp_out)
+
+        dat_out = np.zeros((3,)) if self.is_array_test else np.zeros((3, 1))
+        datsp_out = np.zeros((3,)) if self.is_array_test else matrix(np.zeros((3, 1)))
+
+        dat.mean(axis=1, out=dat_out, keepdims=keep)
+        datsp.mean(axis=1, out=datsp_out)
+        assert_array_almost_equal(dat_out, datsp_out)
+
+        # check that wrong shape out parameter raises
+        with assert_raises(ValueError, match="output parameter.*wrong.*dimension"):
+            datsp.mean(out=array([0]))
+        with assert_raises(ValueError, match="output parameter.*wrong.*dimension"):
+            datsp.mean(out=array([[0]] if self.is_array_test else 0))
+
+    def test_numpy_mean(self):
+        # See gh-5987
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        dat_mean = np.mean(dat)
+        datsp_mean = np.mean(datsp)
+
+        assert_array_almost_equal(dat_mean, datsp_mean)
+        assert_equal(dat_mean.dtype, datsp_mean.dtype)
+
+    def test_expm(self):
+        M = array([[1, 0, 2], [0, 0, 3], [-4, 5, 6]], float)
+        sM = self.spcreator(M, shape=(3,3), dtype=float)
+        Mexp = scipy.linalg.expm(M)
+
+        N = array([[3., 0., 1.], [0., 2., 0.], [0., 0., 0.]])
+        sN = self.spcreator(N, shape=(3,3), dtype=float)
+        Nexp = scipy.linalg.expm(N)
+
+        with suppress_warnings() as sup:
+            sup.filter(
+                SparseEfficiencyWarning,
+                "splu converted its input to CSC format",
+            )
+            sup.filter(
+                SparseEfficiencyWarning,
+                "spsolve is more efficient when sparse b is in the CSC matrix format",
+            )
+            sup.filter(
+                SparseEfficiencyWarning,
+                "spsolve requires A be CSC or CSR matrix format",
+            )
+            sMexp = expm(sM).toarray()
+            sNexp = expm(sN).toarray()
+
+        assert_array_almost_equal((sMexp - Mexp), zeros((3, 3)))
+        assert_array_almost_equal((sNexp - Nexp), zeros((3, 3)))
+
+    def test_inv(self):
+        def check(dtype):
+            M = array([[1, 0, 2], [0, 0, 3], [-4, 5, 6]], dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning,
+                           "spsolve requires A be CSC or CSR matrix format",)
+                sup.filter(SparseEfficiencyWarning,
+                           "spsolve is more efficient when sparse b "
+                           "is in the CSC matrix format",)
+                sup.filter(SparseEfficiencyWarning,
+                           "splu converted its input to CSC format",)
+                sM = self.spcreator(M, shape=(3,3), dtype=dtype)
+                sMinv = inv(sM)
+            assert_array_almost_equal(sMinv.dot(sM).toarray(), np.eye(3))
+            assert_raises(TypeError, inv, M)
+        for dtype in [float]:
+            check(dtype)
+
+    @sup_complex
+    def test_from_array(self):
+        A = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
+        assert_array_equal(self.spcreator(A).toarray(), A)
+
+        A = array([[1.0 + 3j, 0, 0],
+                   [0, 2.0 + 5, 0],
+                   [0, 0, 0]])
+        assert_array_equal(self.spcreator(A).toarray(), A)
+        assert_array_equal(self.spcreator(A, dtype='int16').toarray(),A.astype('int16'))
+
+    @sup_complex
+    def test_from_matrix(self):
+        A = self.asdense([[1, 0, 0], [2, 3, 4], [0, 5, 0], [0, 0, 0]])
+        assert_array_equal(self.spcreator(A).todense(), A)
+
+        A = self.asdense([[1.0 + 3j, 0, 0],
+                          [0, 2.0 + 5, 0],
+                          [0, 0, 0]])
+        assert_array_equal(self.spcreator(A).todense(), A)
+        assert_array_equal(
+            self.spcreator(A, dtype='int16').todense(), A.astype('int16')
+        )
+
+    @sup_complex
+    def test_from_list(self):
+        A = [[1,0,0],[2,3,4],[0,5,0],[0,0,0]]
+        assert_array_equal(self.spcreator(A).toarray(), A)
+
+        A = [[1.0 + 3j, 0, 0],
+             [0, 2.0 + 5, 0],
+             [0, 0, 0]]
+        assert_array_equal(self.spcreator(A).toarray(), array(A))
+        assert_array_equal(
+            self.spcreator(A, dtype='int16').toarray(), array(A).astype('int16')
+        )
+
+    @sup_complex
+    def test_from_sparse(self):
+        D = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
+        S = self.csr_container(D)
+        assert_array_equal(self.spcreator(S).toarray(), D)
+        S = self.spcreator(D)
+        assert_array_equal(self.spcreator(S).toarray(), D)
+
+        D = array([[1.0 + 3j, 0, 0],
+                   [0, 2.0 + 5, 0],
+                   [0, 0, 0]])
+        S = self.csr_container(D)
+        assert_array_equal(self.spcreator(S).toarray(), D)
+        assert_array_equal(self.spcreator(S, dtype='int16').toarray(),
+                           D.astype('int16'))
+        S = self.spcreator(D)
+        assert_array_equal(self.spcreator(S).toarray(), D)
+        assert_array_equal(self.spcreator(S, dtype='int16').toarray(),
+                           D.astype('int16'))
+
+    # def test_array(self):
+    #    """test array(A) where A is in sparse format"""
+    #    assert_equal( array(self.datsp), self.dat )
+
+    def test_todense(self):
+        # Check C- or F-contiguous (default).
+        chk = self.datsp.todense()
+        assert isinstance(chk, np.ndarray if self.is_array_test else np.matrix)
+        assert_array_equal(chk, self.dat)
+        assert_(chk.flags.c_contiguous != chk.flags.f_contiguous)
+        # Check C-contiguous (with arg).
+        chk = self.datsp.todense(order='C')
+        assert_array_equal(chk, self.dat)
+        assert_(chk.flags.c_contiguous)
+        assert_(not chk.flags.f_contiguous)
+        # Check F-contiguous (with arg).
+        chk = self.datsp.todense(order='F')
+        assert_array_equal(chk, self.dat)
+        assert_(not chk.flags.c_contiguous)
+        assert_(chk.flags.f_contiguous)
+        # Check with out argument (array).
+        out = np.zeros(self.datsp.shape, dtype=self.datsp.dtype)
+        chk = self.datsp.todense(out=out)
+        assert_array_equal(self.dat, out)
+        assert_array_equal(self.dat, chk)
+        assert np.may_share_memory(chk, out)
+        # Check with out array (matrix).
+        out = self.asdense(np.zeros(self.datsp.shape, dtype=self.datsp.dtype))
+        chk = self.datsp.todense(out=out)
+        assert_array_equal(self.dat, out)
+        assert_array_equal(self.dat, chk)
+        assert np.may_share_memory(chk, out)
+        a = array([[1.,2.,3.]])
+        dense_dot_dense = a @ self.dat
+        check = a @ self.datsp.todense()
+        assert_array_equal(dense_dot_dense, check)
+        b = array([[1.,2.,3.,4.]]).T
+        dense_dot_dense = self.dat @ b
+        check2 = self.datsp.todense() @ b
+        assert_array_equal(dense_dot_dense, check2)
+        # Check bool data works.
+        spbool = self.spcreator(self.dat, dtype=bool)
+        matbool = self.dat.astype(bool)
+        assert_array_equal(spbool.todense(), matbool)
+
+    def test_toarray(self):
+        # Check C- or F-contiguous (default).
+        dat = asarray(self.dat)
+        chk = self.datsp.toarray()
+        assert_array_equal(chk, dat)
+        assert_(chk.flags.c_contiguous != chk.flags.f_contiguous)
+        # Check C-contiguous (with arg).
+        chk = self.datsp.toarray(order='C')
+        assert_array_equal(chk, dat)
+        assert_(chk.flags.c_contiguous)
+        assert_(not chk.flags.f_contiguous)
+        # Check F-contiguous (with arg).
+        chk = self.datsp.toarray(order='F')
+        assert_array_equal(chk, dat)
+        assert_(not chk.flags.c_contiguous)
+        assert_(chk.flags.f_contiguous)
+        # Check with output arg.
+        out = np.zeros(self.datsp.shape, dtype=self.datsp.dtype)
+        self.datsp.toarray(out=out)
+        assert_array_equal(chk, dat)
+        # Check that things are fine when we don't initialize with zeros.
+        out[...] = 1.
+        self.datsp.toarray(out=out)
+        assert_array_equal(chk, dat)
+        a = array([1.,2.,3.])
+        dense_dot_dense = dot(a, dat)
+        check = dot(a, self.datsp.toarray())
+        assert_array_equal(dense_dot_dense, check)
+        b = array([1.,2.,3.,4.])
+        dense_dot_dense = dot(dat, b)
+        check2 = dot(self.datsp.toarray(), b)
+        assert_array_equal(dense_dot_dense, check2)
+        # Check bool data works.
+        spbool = self.spcreator(self.dat, dtype=bool)
+        arrbool = dat.astype(bool)
+        assert_array_equal(spbool.toarray(), arrbool)
+
+    @sup_complex
+    def test_astype(self):
+        D = array([[2.0 + 3j, 0, 0],
+                   [0, 4.0 + 5j, 0],
+                   [0, 0, 0]])
+        S = self.spcreator(D)
+
+        for x in supported_dtypes:
+            # Check correctly casted
+            D_casted = D.astype(x)
+            for copy in (True, False):
+                S_casted = S.astype(x, copy=copy)
+                assert_equal(S_casted.dtype, D_casted.dtype)  # correct type
+                assert_equal(S_casted.toarray(), D_casted)    # correct values
+                assert_equal(S_casted.format, S.format)       # format preserved
+            # Check correctly copied
+            assert_(S_casted.astype(x, copy=False) is S_casted)
+            S_copied = S_casted.astype(x, copy=True)
+            assert_(S_copied is not S_casted)
+
+            def check_equal_but_not_same_array_attribute(attribute):
+                a = getattr(S_casted, attribute)
+                b = getattr(S_copied, attribute)
+                assert_array_equal(a, b)
+                assert_(a is not b)
+                i = (0,) * b.ndim
+                b_i = b[i]
+                b[i] = not b[i]
+                assert_(a[i] != b[i])
+                b[i] = b_i
+
+            if S_casted.format in ('csr', 'csc', 'bsr'):
+                for attribute in ('indices', 'indptr', 'data'):
+                    check_equal_but_not_same_array_attribute(attribute)
+            elif S_casted.format == 'coo':
+                for attribute in ('row', 'col', 'data'):
+                    check_equal_but_not_same_array_attribute(attribute)
+            elif S_casted.format == 'dia':
+                for attribute in ('offsets', 'data'):
+                    check_equal_but_not_same_array_attribute(attribute)
+
+    @sup_complex
+    def test_astype_immutable(self):
+        D = array([[2.0 + 3j, 0, 0],
+                   [0, 4.0 + 5j, 0],
+                   [0, 0, 0]])
+        S = self.spcreator(D)
+        if hasattr(S, 'data'):
+            S.data.flags.writeable = False
+        if S.format in ('csr', 'csc', 'bsr'):
+            S.indptr.flags.writeable = False
+            S.indices.flags.writeable = False
+        for x in supported_dtypes:
+            D_casted = D.astype(x)
+            S_casted = S.astype(x)
+            assert_equal(S_casted.dtype, D_casted.dtype)
+
+    def test_mul_scalar(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            assert_array_equal(dat*2, (datsp*2).toarray())
+            assert_array_equal(dat*17.3, (datsp*17.3).toarray())
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_rmul_scalar(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            assert_array_equal(2*dat, (2*datsp).toarray())
+            assert_array_equal(17.3*dat, (17.3*datsp).toarray())
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    # GitHub issue #15210
+    def test_rmul_scalar_type_error(self):
+        datsp = self.datsp_dtypes[np.float64]
+        with assert_raises(TypeError):
+            None * datsp
+
+    def test_add(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            a = dat.copy()
+            a[0,2] = 2.0
+            b = datsp
+            c = b + a
+            assert_array_equal(c, b.toarray() + a)
+
+            c = b + b.tocsr()
+            assert_array_equal(c.toarray(),
+                               b.toarray() + b.toarray())
+
+            # test broadcasting
+            c = b + a[0]
+            assert_array_equal(c, b.toarray() + a[0])
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_radd(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            a = dat.copy()
+            a[0,2] = 2.0
+            b = datsp
+            c = a + b
+            assert_array_equal(c, a + b.toarray())
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_sub(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            assert_array_equal((datsp - datsp).toarray(), np.zeros((3, 4)))
+            assert_array_equal((datsp - 0).toarray(), dat)
+
+            A = self.spcreator(
+                np.array([[1, 0, 0, 4], [-1, 0, 0, 0], [0, 8, 0, -5]], 'd')
+            )
+            assert_array_equal((datsp - A).toarray(), dat - A.toarray())
+            assert_array_equal((A - datsp).toarray(), A.toarray() - dat)
+
+            # test broadcasting
+            assert_array_equal(datsp - dat[0], dat - dat[0])
+
+        for dtype in self.math_dtypes:
+            if dtype == np.dtype('bool'):
+                # boolean array subtraction deprecated in 1.9.0
+                continue
+
+            check(dtype)
+
+    def test_rsub(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            assert_array_equal((dat - datsp),[[0,0,0,0],[0,0,0,0],[0,0,0,0]])
+            assert_array_equal((datsp - dat),[[0,0,0,0],[0,0,0,0],[0,0,0,0]])
+            assert_array_equal((0 - datsp).toarray(), -dat)
+
+            A = self.spcreator([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],dtype='d')
+            assert_array_equal((dat - A), dat - A.toarray())
+            assert_array_equal((A - dat), A.toarray() - dat)
+            assert_array_equal(A.toarray() - datsp, A.toarray() - dat)
+            assert_array_equal(datsp - A.toarray(), dat - A.toarray())
+
+            # test broadcasting
+            assert_array_equal(dat[0] - datsp, dat[0] - dat)
+
+        for dtype in self.math_dtypes:
+            if dtype == np.dtype('bool'):
+                # boolean array subtraction deprecated in 1.9.0
+                continue
+
+            check(dtype)
+
+    def test_add0(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            # Adding 0 to a sparse matrix
+            assert_array_equal((datsp + 0).toarray(), dat)
+            # use sum (which takes 0 as a starting value)
+            sumS = sum([k * datsp for k in range(1, 3)])
+            sumD = sum([k * dat for k in range(1, 3)])
+            assert_almost_equal(sumS.toarray(), sumD)
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_elementwise_multiply(self):
+        # real/real
+        A = array([[4,0,9],[2,-3,5]])
+        B = array([[0,7,0],[0,-4,0]])
+        Asp = self.spcreator(A)
+        Bsp = self.spcreator(B)
+        assert_almost_equal(Asp.multiply(Bsp).toarray(), A*B)  # sparse/sparse
+        assert_almost_equal(Asp.multiply(B).toarray(), A*B)  # sparse/dense
+
+        # complex/complex
+        C = array([[1-2j,0+5j,-1+0j],[4-3j,-3+6j,5]])
+        D = array([[5+2j,7-3j,-2+1j],[0-1j,-4+2j,9]])
+        Csp = self.spcreator(C)
+        Dsp = self.spcreator(D)
+        assert_almost_equal(Csp.multiply(Dsp).toarray(), C*D)  # sparse/sparse
+        assert_almost_equal(Csp.multiply(D).toarray(), C*D)  # sparse/dense
+
+        # real/complex
+        assert_almost_equal(Asp.multiply(Dsp).toarray(), A*D)  # sparse/sparse
+        assert_almost_equal(Asp.multiply(D).toarray(), A*D)  # sparse/dense
+
+    def test_elementwise_multiply_broadcast(self):
+        A = array([4])
+        B = array([[-9]])
+        C = array([1,-1,0])
+        D = array([[7,9,-9]])
+        E = array([[3],[2],[1]])
+        F = array([[8,6,3],[-4,3,2],[6,6,6]])
+        G = [1, 2, 3]
+        H = np.ones((3, 4))
+        J = H.T
+        K = array([[0]])
+        L = array([[[1,2],[0,1]]])
+
+        # Some arrays can't be cast as spmatrices (A,C,L) so leave
+        # them out.
+        Bsp = self.spcreator(B)
+        Dsp = self.spcreator(D)
+        Esp = self.spcreator(E)
+        Fsp = self.spcreator(F)
+        Hsp = self.spcreator(H)
+        Hspp = self.spcreator(H[0,None])
+        Jsp = self.spcreator(J)
+        Jspp = self.spcreator(J[:,0,None])
+        Ksp = self.spcreator(K)
+
+        matrices = [A, B, C, D, E, F, G, H, J, K, L]
+        spmatrices = [Bsp, Dsp, Esp, Fsp, Hsp, Hspp, Jsp, Jspp, Ksp]
+
+        # sparse/sparse
+        for i in spmatrices:
+            for j in spmatrices:
+                try:
+                    dense_mult = i.toarray() * j.toarray()
+                except ValueError:
+                    assert_raises(ValueError, i.multiply, j)
+                    continue
+                sp_mult = i.multiply(j)
+                assert_almost_equal(sp_mult.toarray(), dense_mult)
+
+        # sparse/dense
+        for i in spmatrices:
+            for j in matrices:
+                try:
+                    dense_mult = i.toarray() * j
+                except TypeError:
+                    continue
+                except ValueError:
+                    assert_raises(ValueError, i.multiply, j)
+                    continue
+                sp_mult = i.multiply(j)
+                if issparse(sp_mult):
+                    assert_almost_equal(sp_mult.toarray(), dense_mult)
+                else:
+                    assert_almost_equal(sp_mult, dense_mult)
+
+    def test_elementwise_divide(self):
+        expected = [[1,np.nan,np.nan,1],
+                    [1,np.nan,1,np.nan],
+                    [np.nan,1,np.nan,np.nan]]
+        assert_array_equal(toarray(self.datsp / self.datsp), expected)
+
+        denom = self.spcreator([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],dtype='d')
+        expected = [[1,np.nan,np.nan,0.5],
+                    [-3,np.nan,inf,np.nan],
+                    [np.nan,0.25,np.nan,0]]
+        assert_array_equal(toarray(self.datsp / denom), expected)
+
+        # complex
+        A = array([[1-2j,0+5j,-1+0j],[4-3j,-3+6j,5]])
+        B = array([[5+2j,7-3j,-2+1j],[0-1j,-4+2j,9]])
+        Asp = self.spcreator(A)
+        Bsp = self.spcreator(B)
+        assert_almost_equal(toarray(Asp / Bsp), A/B)
+
+        # integer
+        A = array([[1,2,3],[-3,2,1]])
+        B = array([[0,1,2],[0,-2,3]])
+        Asp = self.spcreator(A)
+        Bsp = self.spcreator(B)
+        with np.errstate(divide='ignore'):
+            assert_array_equal(toarray(Asp / Bsp), A / B)
+
+        # mismatching sparsity patterns
+        A = array([[0,1],[1,0]])
+        B = array([[1,0],[1,0]])
+        Asp = self.spcreator(A)
+        Bsp = self.spcreator(B)
+        with np.errstate(divide='ignore', invalid='ignore'):
+            assert_array_equal(np.array(toarray(Asp / Bsp)), A / B)
+
+    def test_pow(self):
+        A = array([[1, 0, 2, 0], [0, 3, 4, 0], [0, 5, 0, 0], [0, 6, 7, 8]])
+        B = self.spcreator(A)
+
+        if self.is_array_test:  # sparrays use element-wise power
+            # Todo: Add 1+3j to tested exponent list when np1.24 is no longer supported
+            #    Complex exponents of 0 (our implicit fill value) change in numpy-1.25
+            #    from `(nan+nanj)` to `0`. Old value makes array element-wise result
+            #    dense and is hard to check for without any `isnan` method.
+            # So while untested here, element-wise complex exponents work with np>=1.25.
+            # for exponent in [1, 2, 2.2, 3, 1+3j]:
+            for exponent in [1, 2, 2.2, 3]:
+                ret_sp = B**exponent
+                ret_np = A**exponent
+                assert_array_equal(ret_sp.toarray(), ret_np)
+                assert_equal(ret_sp.dtype, ret_np.dtype)
+
+            # invalid exponents
+            assert_raises(NotImplementedError, B.__pow__, 0)
+            assert_raises(ValueError, B.__pow__, -1)
+
+            # nonsquare matrix
+            B = self.spcreator(A[:3,:])
+            assert_equal((B**1).toarray(), B.toarray())
+        else:  # test sparse matrix. spmatrices use matrix multiplicative power
+            for exponent in [0,1,2,3]:
+                ret_sp = B**exponent
+                ret_np = np.linalg.matrix_power(A, exponent)
+                assert_array_equal(ret_sp.toarray(), ret_np)
+                assert_equal(ret_sp.dtype, ret_np.dtype)
+
+            # invalid exponents
+            for exponent in [-1, 2.2, 1 + 3j]:
+                assert_raises(ValueError, B.__pow__, exponent)
+
+            # nonsquare matrix
+            B = self.spcreator(A[:3,:])
+            assert_raises(TypeError, B.__pow__, 1)
+
+    def test_rmatvec(self):
+        M = self.spcreator([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
+        assert_array_almost_equal([1,2,3,4] @ M, dot([1,2,3,4], M.toarray()))
+        row = array([[1,2,3,4]])
+        assert_array_almost_equal(row @ M, row @ M.toarray())
+
+    def test_small_multiplication(self):
+        # test that A*x works for x with shape () (1,) (1,1) and (1,0)
+        A = self.spcreator([[1],[2],[3]])
+
+        assert_(issparse(A * array(1)))
+        assert_equal((A * array(1)).toarray(), [[1], [2], [3]])
+
+        assert_equal(A @ array([1]), array([1, 2, 3]))
+        assert_equal(A @ array([[1]]), array([[1], [2], [3]]))
+        assert_equal(A @ np.ones((1, 1)), array([[1], [2], [3]]))
+        assert_equal(A @ np.ones((1, 0)), np.ones((3, 0)))
+
+    def test_star_vs_at_sign_for_sparray_and_spmatrix(self):
+        # test that * is matmul for spmatrix and mul for sparray
+        A = np.array([[1], [2], [3]])
+        Asp = self.spcreator(A)
+
+        if self.is_array_test:
+            assert_array_almost_equal((Asp * np.ones((3, 1))).toarray(), A)
+            assert_array_almost_equal((Asp * array([[1]])).toarray(), A)
+        else:
+            assert_equal(Asp * array([1]), array([1, 2, 3]))
+            assert_equal(Asp * array([[1]]), array([[1], [2], [3]]))
+            assert_equal(Asp * np.ones((1, 0)), np.ones((3, 0)))
+
+    def test_binop_custom_type(self):
+        # Non-regression test: previously, binary operations would raise
+        # NotImplementedError instead of returning NotImplemented
+        # (https://docs.python.org/library/constants.html#NotImplemented)
+        # so overloading Custom + matrix etc. didn't work.
+        A = self.spcreator([[1], [2], [3]])
+        B = BinopTester()
+        assert_equal(A + B, "matrix on the left")
+        assert_equal(A - B, "matrix on the left")
+        assert_equal(A * B, "matrix on the left")
+        assert_equal(B + A, "matrix on the right")
+        assert_equal(B - A, "matrix on the right")
+        assert_equal(B * A, "matrix on the right")
+
+        assert_equal(A @ B, "matrix on the left")
+        assert_equal(B @ A, "matrix on the right")
+
+    def test_binop_custom_type_with_shape(self):
+        A = self.spcreator([[1], [2], [3]])
+        B = BinopTester_with_shape((3,1))
+        assert_equal(A + B, "matrix on the left")
+        assert_equal(A - B, "matrix on the left")
+        assert_equal(A * B, "matrix on the left")
+        assert_equal(B + A, "matrix on the right")
+        assert_equal(B - A, "matrix on the right")
+        assert_equal(B * A, "matrix on the right")
+
+        assert_equal(A @ B, "matrix on the left")
+        assert_equal(B @ A, "matrix on the right")
+
+    def test_mul_custom_type(self):
+        class Custom:
+            def __init__(self, scalar):
+                self.scalar = scalar
+
+            def __rmul__(self, other):
+                return other * self.scalar
+
+        scalar = 2
+        A = self.spcreator([[1],[2],[3]])
+        c = Custom(scalar)
+        A_scalar = A * scalar
+        A_c = A * c
+        assert_array_equal_dtype(A_scalar.toarray(), A_c.toarray())
+        assert_equal(A_scalar.format, A_c.format)
+
+    def test_comparisons_custom_type(self):
+        A = self.spcreator([[1], [2], [3]])
+        B = ComparisonTester()
+        assert_equal(A == B, "eq")
+        assert_equal(A != B, "ne")
+        assert_equal(A > B, "lt")
+        assert_equal(A >= B, "le")
+        assert_equal(A < B, "gt")
+        assert_equal(A <= B, "ge")
+
+    def test_dot_scalar(self):
+        M = self.spcreator(array([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
+        scalar = 10
+        actual = M.dot(scalar)
+        expected = M * scalar
+
+        assert_allclose(actual.toarray(), expected.toarray())
+
+    def test_matmul(self):
+        M = self.spcreator(array([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
+        B = self.spcreator(array([[0,1],[1,0],[0,2]],'d'))
+        col = array([[1,2,3]]).T
+
+        matmul = operator.matmul
+        # check matrix-vector
+        assert_array_almost_equal(matmul(M, col), M.toarray() @ col)
+
+        # check matrix-matrix
+        assert_array_almost_equal(matmul(M, B).toarray(), (M @ B).toarray())
+        assert_array_almost_equal(matmul(M.toarray(), B), (M @ B).toarray())
+        assert_array_almost_equal(matmul(M, B.toarray()), (M @ B).toarray())
+
+        # check error on matrix-scalar
+        assert_raises(ValueError, matmul, M, 1)
+        assert_raises(ValueError, matmul, 1, M)
+
+    def test_matvec(self):
+        M = self.spcreator([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
+        col = array([[1,2,3]]).T
+
+        assert_array_almost_equal(M @ col, M.toarray() @ col)
+
+        # check result dimensions (ticket #514)
+        assert_equal((M @ array([1,2,3])).shape,(4,))
+        assert_equal((M @ array([[1],[2],[3]])).shape,(4,1))
+        assert_equal((M @ matrix([[1],[2],[3]])).shape,(4,1))
+
+        # check result type
+        assert_(isinstance(M @ array([1,2,3]), ndarray))
+        matrix_or_array = ndarray if self.is_array_test else np.matrix
+        assert_(isinstance(M @ matrix([1,2,3]).T, matrix_or_array))
+
+        # ensure exception is raised for improper dimensions
+        bad_vecs = [array([1,2]), array([1,2,3,4]), array([[1],[2]]),
+                    matrix([1,2,3]), matrix([[1],[2]])]
+        for x in bad_vecs:
+            assert_raises(ValueError, M.__matmul__, x)
+
+        # The current relationship between sparse matrix products and array
+        # products is as follows:
+        assert_almost_equal(M@array([1,2,3]), dot(M.toarray(),[1,2,3]))
+        assert_almost_equal(M@[[1],[2],[3]], np.atleast_2d(dot(M.toarray(),[1,2,3])).T)
+        # Note that the result of M * x is dense if x has a singleton dimension.
+
+        # Currently M.matvec(asarray(col)) is rank-1, whereas M.matvec(col)
+        # is rank-2.  Is this desirable?
+
+    def test_matmat_sparse(self):
+        a = matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
+        a2 = array([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
+        b = matrix([[0,1],[1,0],[0,2]],'d')
+        asp = self.spcreator(a)
+        bsp = self.spcreator(b)
+        assert_array_almost_equal((asp @ bsp).toarray(), a @ b)
+        assert_array_almost_equal(asp @ b, a @ b)
+        assert_array_almost_equal(a @ bsp, a @ b)
+        assert_array_almost_equal(a2 @ bsp, a @ b)
+
+        # Now try performing cross-type multiplication:
+        csp = bsp.tocsc()
+        c = b
+        want = a @ c
+        assert_array_almost_equal((asp @ csp).toarray(), want)
+        assert_array_almost_equal(asp @ c, want)
+
+        assert_array_almost_equal(a @ csp, want)
+        assert_array_almost_equal(a2 @ csp, want)
+        csp = bsp.tocsr()
+        assert_array_almost_equal((asp @ csp).toarray(), want)
+        assert_array_almost_equal(asp @ c, want)
+
+        assert_array_almost_equal(a @ csp, want)
+        assert_array_almost_equal(a2 @ csp, want)
+        csp = bsp.tocoo()
+        assert_array_almost_equal((asp @ csp).toarray(), want)
+        assert_array_almost_equal(asp @ c, want)
+
+        assert_array_almost_equal(a @ csp, want)
+        assert_array_almost_equal(a2 @ csp, want)
+
+        # Test provided by Andy Fraser, 2006-03-26
+        L = 30
+        frac = .3
+        random.seed(0)  # make runs repeatable
+        A = zeros((L,2))
+        for i in range(L):
+            for j in range(2):
+                r = random.random()
+                if r < frac:
+                    A[i,j] = r/frac
+
+        A = self.spcreator(A)
+        B = A @ A.T
+        assert_array_almost_equal(B.toarray(), A.toarray() @ A.T.toarray())
+        assert_array_almost_equal(B.toarray(), A.toarray() @ A.toarray().T)
+
+        # check dimension mismatch 2x2 times 3x2
+        A = self.spcreator([[1,2],[3,4]])
+        B = self.spcreator([[1,2],[3,4],[5,6]])
+        assert_raises(ValueError, A.__matmul__, B)
+        if self.is_array_test:
+            assert_raises(ValueError, A.__mul__, B)
+
+    def test_matmat_dense(self):
+        a = [[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]
+        asp = self.spcreator(a)
+
+        # check both array and matrix types
+        bs = [array([[1,2],[3,4],[5,6]]), matrix([[1,2],[3,4],[5,6]])]
+
+        for b in bs:
+            result = asp @ b
+            assert_(isinstance(result, ndarray if self.is_array_test else type(b)))
+            assert_equal(result.shape, (4,2))
+            assert_equal(result, dot(a,b))
+
+    def test_sparse_format_conversions(self):
+        A = sparse.kron([[1,0,2],[0,3,4],[5,0,0]], [[1,2],[0,3]])
+        D = A.toarray()
+        A = self.spcreator(A)
+
+        for format in ['bsr','coo','csc','csr','dia','dok','lil']:
+            a = A.asformat(format)
+            assert_equal(a.format,format)
+            assert_array_equal(a.toarray(), D)
+
+            b = self.spcreator(D+3j).asformat(format)
+            assert_equal(b.format,format)
+            assert_array_equal(b.toarray(), D+3j)
+
+            c = self.spcreator(D).asformat(format)
+            assert_equal(c.format,format)
+            assert_array_equal(c.toarray(), D)
+
+        for format in ['array', 'dense']:
+            a = A.asformat(format)
+            assert_array_equal(a, D)
+
+            b = self.spcreator(D+3j).asformat(format)
+            assert_array_equal(b, D+3j)
+
+    def test_tobsr(self):
+        x = array([[1,0,2,0],[0,0,0,0],[0,0,4,5]])
+        y = array([[0,1,2],[3,0,5]])
+        A = kron(x,y)
+        Asp = self.spcreator(A)
+        for format in ['bsr']:
+            fn = getattr(Asp, 'to' + format)
+
+            for X in [1, 2, 3, 6]:
+                for Y in [1, 2, 3, 4, 6, 12]:
+                    assert_equal(fn(blocksize=(X, Y)).toarray(), A)
+
+    def test_transpose(self):
+        dat_1 = self.dat
+        dat_2 = np.array([[]])
+        matrices = [dat_1, dat_2]
+
+        def check(dtype, j):
+            dat = array(matrices[j], dtype=dtype)
+            datsp = self.spcreator(dat)
+
+            a = datsp.transpose()
+            b = dat.transpose()
+
+            assert_array_equal(a.toarray(), b)
+            assert_array_equal(a.transpose().toarray(), dat)
+            assert_array_equal(datsp.transpose(axes=(1, 0)).toarray(), b)
+            assert_equal(a.dtype, b.dtype)
+
+        # See gh-5987
+        empty = self.spcreator((3, 4))
+        assert_array_equal(np.transpose(empty).toarray(),
+                           np.transpose(zeros((3, 4))))
+        assert_array_equal(empty.T.toarray(), zeros((4, 3)))
+        assert_raises(ValueError, empty.transpose, axes=0)
+
+        for dtype in self.checked_dtypes:
+            for j in range(len(matrices)):
+                check(dtype, j)
+
+    def test_add_dense(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            # adding a dense matrix to a sparse matrix
+            sum1 = dat + datsp
+            assert_array_equal(sum1, dat + dat)
+            sum2 = datsp + dat
+            assert_array_equal(sum2, dat + dat)
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_sub_dense(self):
+        # subtracting a dense matrix to/from a sparse matrix
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            # Behavior is different for bool.
+            if dat.dtype == bool:
+                sum1 = dat - datsp
+                assert_array_equal(sum1, dat - dat)
+                sum2 = datsp - dat
+                assert_array_equal(sum2, dat - dat)
+            else:
+                # Manually add to avoid upcasting from scalar
+                # multiplication.
+                sum1 = (dat + dat + dat) - datsp
+                assert_array_equal(sum1, dat + dat)
+                sum2 = (datsp + datsp + datsp) - dat
+                assert_array_equal(sum2, dat + dat)
+
+        for dtype in self.math_dtypes:
+            if dtype == np.dtype('bool'):
+                # boolean array subtraction deprecated in 1.9.0
+                continue
+
+            check(dtype)
+
+    def test_maximum_minimum(self):
+        A_dense = np.array([[1, 0, 3], [0, 4, 5], [0, 0, 0]])
+        B_dense = np.array([[1, 1, 2], [0, 3, 6], [1, -1, 0]])
+
+        A_dense_cpx = np.array([[1, 0, 3], [0, 4+2j, 5], [0, 1j, -1j]])
+
+        def check(dtype, dtype2, btype):
+            if np.issubdtype(dtype, np.complexfloating):
+                A = self.spcreator(A_dense_cpx.astype(dtype))
+            else:
+                A = self.spcreator(A_dense.astype(dtype))
+            if btype == 'scalar':
+                B = dtype2.type(1)
+            elif btype == 'scalar2':
+                B = dtype2.type(-1)
+            elif btype == 'dense':
+                B = B_dense.astype(dtype2)
+            elif btype == 'sparse':
+                B = self.spcreator(B_dense.astype(dtype2))
+            else:
+                raise ValueError()
+
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning,
+                           "Taking maximum .minimum. with > 0 .< 0. number "
+                           "results to a dense matrix")
+
+                max_s = A.maximum(B)
+                min_s = A.minimum(B)
+
+            max_d = np.maximum(toarray(A), toarray(B))
+            assert_array_equal(toarray(max_s), max_d)
+            assert_equal(max_s.dtype, max_d.dtype)
+
+            min_d = np.minimum(toarray(A), toarray(B))
+            assert_array_equal(toarray(min_s), min_d)
+            assert_equal(min_s.dtype, min_d.dtype)
+
+        for dtype in self.math_dtypes:
+            for dtype2 in [np.int8, np.float64, np.complex128]:
+                for btype in ['scalar', 'scalar2', 'dense', 'sparse']:
+                    check(np.dtype(dtype), np.dtype(dtype2), btype)
+
+    def test_copy(self):
+        # Check whether the copy=True and copy=False keywords work
+        A = self.datsp
+
+        # check that copy preserves format
+        assert_equal(A.copy().format, A.format)
+        assert_equal(A.__class__(A,copy=True).format, A.format)
+        assert_equal(A.__class__(A,copy=False).format, A.format)
+
+        assert_equal(A.copy().toarray(), A.toarray())
+        assert_equal(A.__class__(A, copy=True).toarray(), A.toarray())
+        assert_equal(A.__class__(A, copy=False).toarray(), A.toarray())
+
+        # check that XXX_array.toXXX() works
+        toself = getattr(A,'to' + A.format)
+        assert_(toself() is A)
+        assert_(toself(copy=False) is A)
+        assert_equal(toself(copy=True).format, A.format)
+        assert_equal(toself(copy=True).toarray(), A.toarray())
+
+        # check whether the data is copied?
+        assert_(not sparse_may_share_memory(A.copy(), A))
+
+    # test that __iter__ is compatible with NumPy matrix
+    def test_iterator(self):
+        B = self.asdense(np.arange(50).reshape(5, 10))
+        A = self.spcreator(B)
+
+        for x, y in zip(A, B):
+            assert_equal(x.toarray(), y)
+
+    def test_size_zero_matrix_arithmetic(self):
+        # Test basic matrix arithmetic with shapes like (0,0), (10,0),
+        # (0, 3), etc.
+        mat = array([])
+        a = mat.reshape((0, 0))
+        b = mat.reshape((0, 1))
+        c = mat.reshape((0, 5))
+        d = mat.reshape((1, 0))
+        e = mat.reshape((5, 0))
+        f = np.ones([5, 5])
+
+        asp = self.spcreator(a)
+        bsp = self.spcreator(b)
+        csp = self.spcreator(c)
+        dsp = self.spcreator(d)
+        esp = self.spcreator(e)
+        fsp = self.spcreator(f)
+
+        # matrix product.
+        assert_array_equal(asp.dot(asp).toarray(), np.dot(a, a))
+        assert_array_equal(bsp.dot(dsp).toarray(), np.dot(b, d))
+        assert_array_equal(dsp.dot(bsp).toarray(), np.dot(d, b))
+        assert_array_equal(csp.dot(esp).toarray(), np.dot(c, e))
+        assert_array_equal(csp.dot(fsp).toarray(), np.dot(c, f))
+        assert_array_equal(esp.dot(csp).toarray(), np.dot(e, c))
+        assert_array_equal(dsp.dot(csp).toarray(), np.dot(d, c))
+        assert_array_equal(fsp.dot(esp).toarray(), np.dot(f, e))
+
+        # bad matrix products
+        assert_raises(ValueError, dsp.dot, e)
+        assert_raises(ValueError, asp.dot, d)
+
+        # elemente-wise multiplication
+        assert_array_equal(asp.multiply(asp).toarray(), np.multiply(a, a))
+        assert_array_equal(bsp.multiply(bsp).toarray(), np.multiply(b, b))
+        assert_array_equal(dsp.multiply(dsp).toarray(), np.multiply(d, d))
+
+        assert_array_equal(asp.multiply(a).toarray(), np.multiply(a, a))
+        assert_array_equal(bsp.multiply(b).toarray(), np.multiply(b, b))
+        assert_array_equal(dsp.multiply(d).toarray(), np.multiply(d, d))
+
+        assert_array_equal(asp.multiply(6).toarray(), np.multiply(a, 6))
+        assert_array_equal(bsp.multiply(6).toarray(), np.multiply(b, 6))
+        assert_array_equal(dsp.multiply(6).toarray(), np.multiply(d, 6))
+
+        # bad element-wise multiplication
+        assert_raises(ValueError, asp.multiply, c)
+        assert_raises(ValueError, esp.multiply, c)
+
+        # Addition
+        assert_array_equal(asp.__add__(asp).toarray(), a.__add__(a))
+        assert_array_equal(bsp.__add__(bsp).toarray(), b.__add__(b))
+        assert_array_equal(dsp.__add__(dsp).toarray(), d.__add__(d))
+
+        # bad addition
+        assert_raises(ValueError, asp.__add__, dsp)
+        assert_raises(ValueError, bsp.__add__, asp)
+
+    def test_size_zero_conversions(self):
+        mat = array([])
+        a = mat.reshape((0, 0))
+        b = mat.reshape((0, 5))
+        c = mat.reshape((5, 0))
+
+        for m in [a, b, c]:
+            spm = self.spcreator(m)
+            assert_array_equal(spm.tocoo().toarray(), m)
+            assert_array_equal(spm.tocsr().toarray(), m)
+            assert_array_equal(spm.tocsc().toarray(), m)
+            assert_array_equal(spm.tolil().toarray(), m)
+            assert_array_equal(spm.todok().toarray(), m)
+            assert_array_equal(spm.tobsr().toarray(), m)
+
+    def test_dtype_check(self):
+        a = np.array([[3.5, 0, 1.1], [0, 0, 0]], dtype=np.float16)
+        with assert_raises(ValueError, match="does not support dtype"):
+            self.spcreator(a)
+
+        A32 = self.spcreator(a.astype(np.float32))
+        with assert_raises(ValueError, match="does not support dtype"):
+            self.spcreator(A32, dtype=np.float16)
+
+    def test_pickle(self):
+        import pickle
+        sup = suppress_warnings()
+        sup.filter(SparseEfficiencyWarning)
+
+        @sup
+        def check():
+            datsp = self.datsp.copy()
+            for protocol in range(pickle.HIGHEST_PROTOCOL):
+                sploaded = pickle.loads(pickle.dumps(datsp, protocol=protocol))
+                assert_equal(datsp.shape, sploaded.shape)
+                assert_array_equal(datsp.toarray(), sploaded.toarray())
+                assert_equal(datsp.format, sploaded.format)
+                # Hacky check for class member equality. This assumes that
+                # all instance variables are one of:
+                #  1. Plain numpy ndarrays
+                #  2. Tuples of ndarrays
+                #  3. Types that support equality comparison with ==
+                for key, val in datsp.__dict__.items():
+                    if isinstance(val, np.ndarray):
+                        assert_array_equal(val, sploaded.__dict__[key])
+                    elif (isinstance(val, tuple) and val
+                          and isinstance(val[0], np.ndarray)):
+                        assert_array_equal(val, sploaded.__dict__[key])
+                    else:
+                        assert_(val == sploaded.__dict__[key])
+        check()
+
+    def test_unary_ufunc_overrides(self):
+        def check(name):
+            if name == "sign":
+                pytest.skip("sign conflicts with comparison op "
+                            "support on Numpy")
+            if self.datsp.format in ["dok", "lil"]:
+                pytest.skip("Unary ops not implemented for dok/lil")
+            ufunc = getattr(np, name)
+
+            X = self.spcreator(np.arange(20).reshape(4, 5) / 20.)
+            X0 = ufunc(X.toarray())
+
+            X2 = ufunc(X)
+            assert_array_equal(X2.toarray(), X0)
+
+        for name in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
+                     "arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
+                     "deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt",
+                     "abs"]:
+            check(name)
+
+    def test_resize(self):
+        # resize(shape) resizes the matrix in-place
+        D = np.array([[1, 0, 3, 4],
+                      [2, 0, 0, 0],
+                      [3, 0, 0, 0]])
+        S = self.spcreator(D)
+        assert_(S.resize((3, 2)) is None)
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0],
+                                         [3, 0]])
+        S.resize((2, 2))
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0]])
+        S.resize((3, 2))
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0],
+                                         [0, 0]])
+        S.resize((3, 3))
+        assert_array_equal(S.toarray(), [[1, 0, 0],
+                                         [2, 0, 0],
+                                         [0, 0, 0]])
+        # test no-op
+        S.resize((3, 3))
+        assert_array_equal(S.toarray(), [[1, 0, 0],
+                                         [2, 0, 0],
+                                         [0, 0, 0]])
+
+        # test *args
+        S.resize(3, 2)
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0],
+                                         [0, 0]])
+
+        if self.is_array_test and S.format in ["coo", "csr"]:
+            S.resize(1)
+        else:
+            assert_raises((ValueError, NotImplementedError, IndexError), S.resize, 1)
+
+        for bad_shape in [(-1, 2), (2, -1), (1, 2, 3)]:
+            assert_raises(ValueError, S.resize, bad_shape)
+
+    def test_constructor1_base(self):
+        A = self.datsp
+
+        self_format = A.format
+
+        C = A.__class__(A, copy=False)
+        assert_array_equal_dtype(A.toarray(), C.toarray())
+        if self_format not in NON_ARRAY_BACKED_FORMATS:
+            assert_(sparse_may_share_memory(A, C))
+
+        C = A.__class__(A, dtype=A.dtype, copy=False)
+        assert_array_equal_dtype(A.toarray(), C.toarray())
+        if self_format not in NON_ARRAY_BACKED_FORMATS:
+            assert_(sparse_may_share_memory(A, C))
+
+        C = A.__class__(A, dtype=np.float32, copy=False)
+        assert_array_equal(A.toarray(), C.toarray())
+
+        C = A.__class__(A, copy=True)
+        assert_array_equal_dtype(A.toarray(), C.toarray())
+        assert_(not sparse_may_share_memory(A, C))
+
+        for other_format in ['csr', 'csc', 'coo', 'dia', 'dok', 'lil']:
+            if other_format == self_format:
+                continue
+            B = A.asformat(other_format)
+            C = A.__class__(B, copy=False)
+            assert_array_equal_dtype(A.toarray(), C.toarray())
+
+            C = A.__class__(B, copy=True)
+            assert_array_equal_dtype(A.toarray(), C.toarray())
+            assert_(not sparse_may_share_memory(B, C))
+
+
+class _TestInplaceArithmetic:
+    def test_inplace_dense(self):
+        a = np.ones((3, 4))
+        b = self.spcreator(a)
+
+        x = a.copy()
+        y = a.copy()
+        x += a
+        y += b
+        assert_array_equal(x, y)
+
+        x = a.copy()
+        y = a.copy()
+        x -= a
+        y -= b
+        assert_array_equal(x, y)
+
+        if self.is_array_test:
+            # Elementwise multiply from sparray.__rmul__
+            x = a.copy()
+            y = a.copy()
+            with assert_raises(ValueError, match="inconsistent shapes"):
+                x *= b.T
+            x = x * a
+            y *= b
+            assert_array_equal(x, y.toarray())
+        else:
+            # Matrix multiply from spmatrix.__rmul__
+            x = a.copy()
+            y = a.copy()
+            with assert_raises(ValueError, match="dimension mismatch"):
+                x *= b
+            x = x.dot(a.T)
+            y *= b.T
+            assert_array_equal(x, y)
+
+        # Matrix multiply from __rmatmul__
+        y = a.copy()
+        # skip this test if numpy doesn't support __imatmul__ yet.
+        # move out of the try/except once numpy 1.24 is no longer supported.
+        try:
+            y @= b.T
+        except TypeError:
+            pass
+        else:
+            x = a.copy()
+            y = a.copy()
+            with assert_raises(ValueError, match="dimension mismatch"):
+                x @= b
+            x = x.dot(a.T)
+            y @= b.T
+            assert_array_equal(x, y)
+
+        # Floor division is not supported
+        with assert_raises(TypeError, match="unsupported operand"):
+            x //= b
+
+    def test_imul_scalar(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            # Avoid implicit casting.
+            if np.can_cast(int, dtype, casting='same_kind'):
+                a = datsp.copy()
+                a *= 2
+                b = dat.copy()
+                b *= 2
+                assert_array_equal(b, a.toarray())
+
+            if np.can_cast(float, dtype, casting='same_kind'):
+                a = datsp.copy()
+                a *= 17.3
+                b = dat.copy()
+                b *= 17.3
+                assert_array_equal(b, a.toarray())
+
+        for dtype in self.math_dtypes:
+            check(dtype)
+
+    def test_idiv_scalar(self):
+        def check(dtype):
+            dat = self.dat_dtypes[dtype]
+            datsp = self.datsp_dtypes[dtype]
+
+            if np.can_cast(int, dtype, casting='same_kind'):
+                a = datsp.copy()
+                a /= 2
+                b = dat.copy()
+                b /= 2
+                assert_array_equal(b, a.toarray())
+
+            if np.can_cast(float, dtype, casting='same_kind'):
+                a = datsp.copy()
+                a /= 17.3
+                b = dat.copy()
+                b /= 17.3
+                assert_array_equal(b, a.toarray())
+
+        for dtype in self.math_dtypes:
+            # /= should only be used with float dtypes to avoid implicit
+            # casting.
+            if not np.can_cast(dtype, np.dtype(int)):
+                check(dtype)
+
+    def test_inplace_success(self):
+        # Inplace ops should work even if a specialized version is not
+        # implemented, falling back to x = x <op> y
+        a = self.spcreator(np.eye(5))
+        b = self.spcreator(np.eye(5))
+        bp = self.spcreator(np.eye(5))
+
+        b += a
+        bp = bp + a
+        assert_allclose(b.toarray(), bp.toarray())
+
+        if self.is_array_test:
+            b *= a
+            bp = bp * a
+            assert_allclose(b.toarray(), bp.toarray())
+
+        b @= a
+        bp = bp @ a
+        assert_allclose(b.toarray(), bp.toarray())
+
+        b -= a
+        bp = bp - a
+        assert_allclose(b.toarray(), bp.toarray())
+
+        with assert_raises(TypeError, match="unsupported operand"):
+            a //= b
+
+
+class _TestGetSet:
+    def test_getelement(self):
+        def check(dtype):
+            D = array([[1,0,0],
+                       [4,3,0],
+                       [0,2,0],
+                       [0,0,0]], dtype=dtype)
+            A = self.spcreator(D)
+
+            M,N = D.shape
+
+            for i in range(-M, M):
+                for j in range(-N, N):
+                    assert_equal(A[i,j], D[i,j])
+
+            assert_equal(type(A[1,1]), dtype)
+
+            for ij in [(0,3),(-1,3),(4,0),(4,3),(4,-1), (1, 2, 3)]:
+                assert_raises((IndexError, TypeError), A.__getitem__, ij)
+
+        for dtype in supported_dtypes:
+            check(np.dtype(dtype))
+
+    def test_setelement(self):
+        def check(dtype):
+            A = self.spcreator((3,4), dtype=dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[0, 0] = dtype.type(0)  # bug 870
+                A[1, 2] = dtype.type(4.0)
+                A[0, 1] = dtype.type(3)
+                A[2, 0] = dtype.type(2.0)
+                A[0,-1] = dtype.type(8)
+                A[-1,-2] = dtype.type(7)
+                A[0, 1] = dtype.type(5)
+
+            if dtype != np.bool_:
+                assert_array_equal(
+                    A.toarray(),
+                    [
+                        [0, 5, 0, 8],
+                        [0, 0, 4, 0],
+                        [2, 0, 7, 0]
+                    ]
+                )
+
+            for ij in [(0,4),(-1,4),(3,0),(3,4),(3,-1)]:
+                assert_raises(IndexError, A.__setitem__, ij, 123.0)
+
+            for v in [[1,2,3], array([1,2,3])]:
+                assert_raises(ValueError, A.__setitem__, (0,0), v)
+
+            if (not np.issubdtype(dtype, np.complexfloating) and
+                    dtype != np.bool_):
+                for v in [3j]:
+                    assert_raises(TypeError, A.__setitem__, (0,0), v)
+
+        for dtype in supported_dtypes:
+            check(np.dtype(dtype))
+
+    def test_negative_index_assignment(self):
+        # Regression test for GitHub issue 4428.
+
+        def check(dtype):
+            A = self.spcreator((3, 10), dtype=dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[0, -4] = 1
+            assert_equal(A[0, -4], 1)
+
+        for dtype in self.math_dtypes:
+            check(np.dtype(dtype))
+
+    def test_scalar_assign_2(self):
+        n, m = (5, 10)
+
+        def _test_set(i, j, nitems):
+            msg = f"{i!r} ; {j!r} ; {nitems!r}"
+            A = self.spcreator((n, m))
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[i, j] = 1
+            assert_almost_equal(A.sum(), nitems, err_msg=msg)
+            assert_almost_equal(A[i, j], 1, err_msg=msg)
+
+        # [i,j]
+        for i, j in [(2, 3), (-1, 8), (-1, -2), (array(-1), -2), (-1, array(-2)),
+                     (array(-1), array(-2))]:
+            _test_set(i, j, 1)
+
+    def test_index_scalar_assign(self):
+        A = self.spcreator((5, 5))
+        B = np.zeros((5, 5))
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            for C in [A, B]:
+                C[0,1] = 1
+                C[3,0] = 4
+                C[3,0] = 9
+        assert_array_equal(A.toarray(), B)
+
+
+@pytest.mark.thread_unsafe
+class _TestSolve:
+    def test_solve(self):
+        # Test whether the lu_solve command segfaults, as reported by Nils
+        # Wagner for a 64-bit machine, 02 March 2005 (EJS)
+        n = 20
+        np.random.seed(0)  # make tests repeatable
+        A = zeros((n,n), dtype=complex)
+        x = np.random.rand(n)
+        y = np.random.rand(n-1)+1j*np.random.rand(n-1)
+        r = np.random.rand(n)
+        for i in range(len(x)):
+            A[i,i] = x[i]
+        for i in range(len(y)):
+            A[i,i+1] = y[i]
+            A[i+1,i] = conjugate(y[i])
+        A = self.spcreator(A)
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning,
+                       "splu converted its input to CSC format")
+            x = splu(A).solve(r)
+        assert_almost_equal(A @ x,r)
+
+
+class _TestSlicing:
+    def test_dtype_preservation(self):
+        assert_equal(self.spcreator((1,10), dtype=np.int16)[0,1:5].dtype, np.int16)
+        assert_equal(self.spcreator((1,10), dtype=np.int32)[0,1:5].dtype, np.int32)
+        assert_equal(self.spcreator((1,10), dtype=np.float32)[0,1:5].dtype, np.float32)
+        assert_equal(self.spcreator((1,10), dtype=np.float64)[0,1:5].dtype, np.float64)
+
+    def test_dtype_preservation_empty_slice(self):
+        # This should be parametrized with pytest, but something in the parent
+        # class creation used in this file breaks pytest.mark.parametrize.
+        for dt in [np.int16, np.int32, np.float32, np.float64]:
+            A = self.spcreator((3, 2), dtype=dt)
+            assert_equal(A[:, 0:0:2].dtype, dt)
+            assert_equal(A[0:0:2, :].dtype, dt)
+            assert_equal(A[0, 0:0:2].dtype, dt)
+            assert_equal(A[0:0:2, 0].dtype, dt)
+
+    def test_get_horiz_slice(self):
+        B = self.asdense(arange(50.).reshape(5,10))
+        A = self.spcreator(B)
+        r0, r1, r2 = (0, 1, 2) if self.is_array_test else ([0], [1], [2])
+        assert_array_equal(B[r1, :], A[1, :].toarray())
+        assert_array_equal(B[r1, 2:5], A[1, 2:5].toarray())
+
+        C = self.asdense([[1, 2, 1], [4, 0, 6], [0, 0, 0], [0, 0, 1]])
+        D = self.spcreator(C)
+        assert_array_equal(C[r1, 1:3], D[1, 1:3].toarray())
+
+        # Now test slicing when a row contains only zeros
+        E = self.asdense([[1, 2, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
+        F = self.spcreator(E)
+        assert_array_equal(E[r1, 1:3], F[1, 1:3].toarray())
+        assert_array_equal(E[r2, -2:], F[2, -2:].toarray())
+
+        # The following should raise exceptions:
+        assert_raises(IndexError, A.__getitem__, (slice(None), 11))
+        assert_raises(IndexError, A.__getitem__, (6, slice(3, 7)))
+
+    def test_get_vert_slice(self):
+        B = arange(50.).reshape(5, 10)
+        A = self.spcreator(B)
+        c0, c1, c2 = (0, 1, 2) if self.is_array_test else ([0], [1], [2])
+        assert_array_equal(B[2:5, c0], A[2:5, 0].toarray())
+        assert_array_equal(B[:, c1], A[:, 1].toarray())
+
+        C = array([[1, 2, 1], [4, 0, 6], [0, 0, 0], [0, 0, 1]])
+        D = self.spcreator(C)
+        assert_array_equal(C[1:3, c1], D[1:3, 1].toarray())
+        assert_array_equal(C[:, c2], D[:, 2].toarray())
+
+        # Now test slicing when a column contains only zeros
+        E = array([[1, 0, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
+        F = self.spcreator(E)
+        assert_array_equal(E[:, c1], F[:, 1].toarray())
+        assert_array_equal(E[-2:, c2], F[-2:, 2].toarray())
+
+        # The following should raise exceptions:
+        assert_raises(IndexError, A.__getitem__, (slice(None), 11))
+        assert_raises(IndexError, A.__getitem__, (6, slice(3, 7)))
+
+    def test_get_slices(self):
+        B = arange(50.).reshape(5, 10)
+        A = self.spcreator(B)
+        assert_array_equal(A[2:5, 0:3].toarray(), B[2:5, 0:3])
+        assert_array_equal(A[1:, :-1].toarray(), B[1:, :-1])
+        assert_array_equal(A[:-1, 1:].toarray(), B[:-1, 1:])
+
+        # Now test slicing when a column contains only zeros
+        E = array([[1, 0, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
+        F = self.spcreator(E)
+        assert_array_equal(E[1:2, 1:2], F[1:2, 1:2].toarray())
+        assert_array_equal(E[:, 1:], F[:, 1:].toarray())
+
+    def test_non_unit_stride_2d_indexing(self):
+        # Regression test -- used to silently ignore the stride.
+        v0 = np.random.rand(50, 50)
+        try:
+            v = self.spcreator(v0)[0:25:2, 2:30:3]
+        except ValueError:
+            # if unsupported
+            raise pytest.skip("feature not implemented")
+
+        assert_array_equal(v.toarray(), v0[0:25:2, 2:30:3])
+
+    def test_slicing_2(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        # [i,j]
+        assert_equal(A[2,3], B[2,3])
+        assert_equal(A[-1,8], B[-1,8])
+        assert_equal(A[-1,-2],B[-1,-2])
+        assert_equal(A[array(-1),-2],B[-1,-2])
+        assert_equal(A[-1,array(-2)],B[-1,-2])
+        assert_equal(A[array(-1),array(-2)],B[-1,-2])
+
+        # [i,1:2]
+        assert_equal(A[2, :].toarray(), B[2, :])
+        assert_equal(A[2, 5:-2].toarray(), B[2, 5:-2])
+        assert_equal(A[array(2), 5:-2].toarray(), B[2, 5:-2])
+
+        # [1:2,j]
+        assert_equal(A[:, 2].toarray(), B[:, 2])
+        assert_equal(A[3:4, 9].toarray(), B[3:4, 9])
+        assert_equal(A[1:4, -5].toarray(), B[1:4, -5])
+        assert_equal(A[2:-1, 3].toarray(), B[2:-1, 3])
+        assert_equal(A[2:-1, array(3)].toarray(), B[2:-1, 3])
+
+        # [1:2,1:2]
+        assert_equal(A[1:2, 1:2].toarray(), B[1:2, 1:2])
+        assert_equal(A[4:, 3:].toarray(), B[4:, 3:])
+        assert_equal(A[:4, :5].toarray(), B[:4, :5])
+        assert_equal(A[2:-1, :5].toarray(), B[2:-1, :5])
+
+        # [i]
+        assert_equal(A[1, :].toarray(), B[1, :])
+        assert_equal(A[-2, :].toarray(), B[-2, :])
+        assert_equal(A[array(-2), :].toarray(), B[-2, :])
+
+        # [1:2]
+        assert_equal(A[1:4].toarray(), B[1:4])
+        assert_equal(A[1:-2].toarray(), B[1:-2])
+
+        # Check bug reported by Robert Cimrman:
+        # http://thread.gmane.org/gmane.comp.python.scientific.devel/7986 (dead link)
+        s = slice(int8(2),int8(4),None)
+        assert_equal(A[s, :].toarray(), B[2:4, :])
+        assert_equal(A[:, s].toarray(), B[:, 2:4])
+
+    @pytest.mark.fail_slow(2)
+    def test_slicing_3(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        s_ = np.s_
+        slices = [s_[:2], s_[1:2], s_[3:], s_[3::2],
+                  s_[15:20], s_[3:2],
+                  s_[8:3:-1], s_[4::-2], s_[:5:-1],
+                  0, 1, s_[:], s_[1:5], -1, -2, -5,
+                  array(-1), np.int8(-3)]
+
+        def check_1(a):
+            x = A[a]
+            y = B[a]
+            if y.shape == ():
+                assert_equal(x, y, repr(a))
+            else:
+                if x.size == 0 and y.size == 0:
+                    pass
+                else:
+                    assert_array_equal(x.toarray(), y, repr(a))
+
+        for j, a in enumerate(slices):
+            check_1(a)
+
+        def check_2(a, b):
+            # Indexing np.matrix with 0-d arrays seems to be broken,
+            # as they seem not to be treated as scalars.
+            # https://github.com/numpy/numpy/issues/3110
+            if isinstance(a, np.ndarray):
+                ai = int(a)
+            else:
+                ai = a
+            if isinstance(b, np.ndarray):
+                bi = int(b)
+            else:
+                bi = b
+
+            x = A[a, b]
+            y = B[ai, bi]
+
+            if y.shape == ():
+                assert_equal(x, y, repr((a, b)))
+            else:
+                if x.size == 0 and y.size == 0:
+                    pass
+                else:
+                    assert_array_equal(x.toarray(), y, repr((a, b)))
+
+        for i, a in enumerate(slices):
+            for j, b in enumerate(slices):
+                check_2(a, b)
+
+        # Check out of bounds etc. systematically
+        extra_slices = []
+        for a, b, c in itertools.product(*([(None, 0, 1, 2, 5, 15,
+                                             -1, -2, 5, -15)]*3)):
+            if c == 0:
+                continue
+            extra_slices.append(slice(a, b, c))
+
+        for a in extra_slices:
+            check_2(a, a)
+            check_2(a, -2)
+            check_2(-2, a)
+
+    def test_None_slicing(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        assert A[1, 2].ndim == 0
+        assert A[None, 1, 2:4].shape == (1, 2)
+        assert A[None, 1, 2, None].shape == (1, 1)
+
+        # see gh-22458
+        assert A[None, 1].shape == (1, 10)
+        assert A[1, None].shape == (1, 10)
+        assert A[None, 1, :].shape == (1, 10)
+        assert A[1, None, :].shape == (1, 10)
+        assert A[1, :, None].shape == (10, 1)
+
+        assert A[None, 1:3, 2].shape == B[None, 1:3, 2].shape == (1, 2)
+        assert A[1:3, None, 2].shape == B[1:3, None, 2].shape == (2, 1)
+        assert A[1:3, 2, None].shape == B[1:3, 2, None].shape == (2, 1)
+        assert A[None, 1, 2:4].shape == B[None, 1, 2:4].shape == (1, 2)
+        assert A[1, None, 2:4].shape == B[1, None, 2:4].shape == (1, 2)
+        assert A[1, 2:4, None].shape == B[1, 2:4, None].shape == (2, 1)
+
+        # different for spmatrix
+        if self.is_array_test:
+            assert A[1:3, 2].shape == B[1:3, 2].shape == (2,)
+            assert A[1, 2:4].shape == B[1, 2:4].shape == (2,)
+            assert A[None, 1, 2].shape == B[None, 1, 2].shape == (1,)
+            assert A[1, None, 2].shape == B[1, None, 2].shape == (1,)
+            assert A[1, 2, None].shape == B[1, 2, None].shape == (1,)
+        else:
+            assert A[1, 2:4].shape == B[1, 2:4].shape == (1, 2)
+            assert A[1:3, 2].shape == B[1:3, 2].shape == (2, 1)
+            assert A[None, 1, 2].shape == B[None, 1, 2].shape == (1, 1)
+            assert A[1, None, 2].shape == B[1, None, 2].shape == (1, 1)
+            assert A[1, 2, None].shape == B[1, 2, None].shape == (1, 1)
+
+    def test_ellipsis_slicing(self):
+        b = self.asdense(arange(50).reshape(5,10))
+        a = self.spcreator(b)
+
+        assert_array_equal(a[...].toarray(), b[...])
+        assert_array_equal(a[...,].toarray(), b[...,])
+
+        assert_array_equal(a[4, ...].toarray(), b[4, ...])
+        assert_array_equal(a[..., 4].toarray(), b[..., 4])
+        assert_array_equal(a[..., 5].toarray(), b[..., 5])
+        with pytest.raises(IndexError, match='index .5. out of range'):
+            a[5, ...]
+        with pytest.raises(IndexError, match='index .10. out of range'):
+            a[..., 10]
+        with pytest.raises(IndexError, match='index .5. out of range'):
+            a.T[..., 5]
+
+        assert_array_equal(a[1:, ...].toarray(), b[1:, ...])
+        assert_array_equal(a[..., 1:].toarray(), b[..., 1:])
+        assert_array_equal(a[:2, ...].toarray(), b[:2, ...])
+        assert_array_equal(a[..., :2].toarray(), b[..., :2])
+
+        # check slice limit outside range
+        assert_array_equal(a[:5, ...].toarray(), b[:5, ...])
+        assert_array_equal(a[..., :5].toarray(), b[..., :5])
+        assert_array_equal(a[5:, ...].toarray(), b[5:, ...])
+        assert_array_equal(a[..., 5:].toarray(), b[..., 5:])
+        assert_array_equal(a[10:, ...].toarray(), b[10:, ...])
+        assert_array_equal(a[..., 10:].toarray(), b[..., 10:])
+
+        # ellipsis should be ignored
+        assert_array_equal(a[1:, 1, ...].toarray(), b[1:, 1, ...])
+        assert_array_equal(a[1, ..., 1:].toarray(), b[1, ..., 1:])
+        assert_array_equal(a[..., 1, 1:].toarray(), b[1, ..., 1:])
+        assert_array_equal(a[:2, 1, ...].toarray(), b[:2, 1, ...])
+        assert_array_equal(a[1, ..., :2].toarray(), b[1, ..., :2])
+        assert_array_equal(a[..., 1, :2].toarray(), b[1, ..., :2])
+        # These return ints
+        assert_equal(a[1, 1, ...], b[1, 1, ...])
+        assert_equal(a[1, ..., 1], b[1, ..., 1])
+
+    def test_ellipsis_fancy_bool(self):
+        numpy_a = self.asdense(arange(50).reshape(5, 10))
+        a = self.spcreator(numpy_a)
+
+        ix5 = [True, False, True, False, True]
+        ix10 = [False] * 5 + ix5  # same number of True values as ix5
+        ix10_6True = ix5 + ix5  # not same number of True values as ix5
+        full_ix = [ix10] * 5
+
+        assert_array_equal(toarray(a[full_ix, ...]), numpy_a[full_ix, ...])
+        assert_array_equal(toarray(a[..., full_ix]), numpy_a[..., full_ix])
+
+        assert_array_equal(toarray(a[ix5, ...]), numpy_a[ix5, ...])
+        assert_array_equal(toarray(a[..., ix10]), numpy_a[..., ix10])
+
+        assert_array_equal(toarray(a[ix5, ..., ix10]), numpy_a[ix5, ..., ix10])
+        assert_array_equal(toarray(a[..., ix5, ix10]), numpy_a[..., ix5, ix10])
+        assert_array_equal(toarray(a[ix5, ix10, ...]), numpy_a[ix5, ix10, ...])
+
+        with assert_raises(ValueError, match="shape mismatch"):
+            a[ix5, ix10_6True]
+
+    def test_ellipsis_fancy_slicing(self):
+        b = self.asdense(arange(50).reshape(5, 10))
+        a = self.spcreator(b)
+
+        assert_array_equal(a[[4], ...].toarray(), b[[4], ...])
+        assert_array_equal(a[[2, 4], ...].toarray(), b[[2, 4], ...])
+        assert_array_equal(a[..., [4]].toarray(), b[..., [4]])
+        assert_array_equal(a[..., [2, 4]].toarray(), b[..., [2, 4]])
+
+        assert_array_equal(a[[4], 1, ...].toarray(), b[[4], 1, ...])
+        assert_array_equal(a[[2, 4], 1, ...].toarray(), b[[2, 4], 1, ...])
+        assert_array_equal(a[[4], ..., 1].toarray(), b[[4], ..., 1])
+        assert_array_equal(a[..., [4], 1].toarray(), b[..., [4], 1])
+        # fancy index gives dense
+        assert_array_equal(toarray(a[[2, 4], ..., [2, 4]]), b[[2, 4], ..., [2, 4]])
+        assert_array_equal(toarray(a[..., [2, 4], [2, 4]]), b[..., [2, 4], [2, 4]])
+
+    def test_multiple_ellipsis_slicing(self):
+        a = self.spcreator(arange(6).reshape(3, 2))
+
+        with pytest.raises(IndexError,
+                           match='an index can only have a single ellipsis'):
+            a[..., ...]
+        with pytest.raises(IndexError,
+                           match='an index can only have a single ellipsis'):
+            a[..., 1, ...]
+
+
+class _TestSlicingAssign:
+    def test_slice_scalar_assign(self):
+        A = self.spcreator((5, 5))
+        B = np.zeros((5, 5))
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            for C in [A, B]:
+                C[0:1,1] = 1
+                C[3:0,0] = 4
+                C[3:4,0] = 9
+                C[0,4:] = 1
+                C[3::-1,4:] = 9
+        assert_array_equal(A.toarray(), B)
+
+    def test_slice_assign_2(self):
+        n, m = (5, 10)
+
+        def _test_set(i, j):
+            msg = f"i={i!r}; j={j!r}"
+            A = self.spcreator((n, m))
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[i, j] = 1
+            B = np.zeros((n, m))
+            B[i, j] = 1
+            assert_array_almost_equal(A.toarray(), B, err_msg=msg)
+        # [i,1:2]
+        for i, j in [(2, slice(3)), (2, slice(None, 10, 4)), (2, slice(5, -2)),
+                     (array(2), slice(5, -2))]:
+            _test_set(i, j)
+
+    def test_self_self_assignment(self):
+        # Tests whether a row of one sparse array can be assigned to another.
+        B = self.spcreator((4,3))
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            B[0,0] = 2
+            B[1,2] = 7
+            B[2,1] = 3
+            B[3,0] = 10
+
+            A = B / 10
+            B[0,:] = A[0,:]
+            assert_array_equal(A[0,:].toarray(), B[0,:].toarray())
+
+            A = B / 10
+            B[:,:] = A[:1,:1]
+            assert_array_equal(np.zeros((4,3)) + A[0,0], B.toarray())
+
+            A = B / 10
+            B[:-1,0] = A[None,0,:].T
+            assert_array_equal(A[0,:].toarray().T, B[:-1,0].toarray())
+
+    def test_slice_assignment(self):
+        B = self.spcreator((4,3))
+        expected = array([[10,0,0],
+                          [0,0,6],
+                          [0,14,0],
+                          [0,0,0]])
+        block = [[1,0],[0,4]]
+
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            B[0,0] = 5
+            B[1,2] = 3
+            B[2,1] = 7
+            B[:,:] = B+B
+            assert_array_equal(B.toarray(), expected)
+
+            B[:2,:2] = self.csc_container(array(block))
+            assert_array_equal(B.toarray()[:2, :2], block)
+
+    def test_sparsity_modifying_assignment(self):
+        B = self.spcreator((4,3))
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            B[0,0] = 5
+            B[1,2] = 3
+            B[2,1] = 7
+            B[3,0] = 10
+            B[:3] = self.csr_container(np.eye(3))
+
+        expected = array([[1,0,0],[0,1,0],[0,0,1],[10,0,0]])
+        assert_array_equal(B.toarray(), expected)
+
+    def test_set_slice(self):
+        A = self.spcreator((5,10))
+        B = array(zeros((5, 10), float))
+        s_ = np.s_
+        slices = [s_[:2], s_[1:2], s_[3:], s_[3::2],
+                  s_[8:3:-1], s_[4::-2], s_[:5:-1],
+                  0, 1, s_[:], s_[1:5], -1, -2, -5,
+                  array(-1), np.int8(-3)]
+
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            for j, a in enumerate(slices):
+                A[a] = j
+                B[a] = j
+                assert_array_equal(A.toarray(), B, repr(a))
+
+            for i, a in enumerate(slices):
+                for j, b in enumerate(slices):
+                    A[a,b] = 10*i + 1000*(j+1)
+                    B[a,b] = 10*i + 1000*(j+1)
+                    assert_array_equal(A.toarray(), B, repr((a, b)))
+
+            A[0, 1:10:2] = range(1, 10, 2)
+            B[0, 1:10:2] = range(1, 10, 2)
+            assert_array_equal(A.toarray(), B)
+            A[1:5:2, 0] = np.arange(1, 5, 2)[:, None]
+            B[1:5:2, 0] = np.arange(1, 5, 2)[:]
+            assert_array_equal(A.toarray(), B)
+
+        # The next commands should raise exceptions
+        assert_raises(ValueError, A.__setitem__, (0, 0), list(range(100)))
+        assert_raises(ValueError, A.__setitem__, (0, 0), arange(100))
+        assert_raises(ValueError, A.__setitem__, (0, slice(None)),
+                      list(range(100)))
+        assert_raises(ValueError, A.__setitem__, (slice(None), 1),
+                      list(range(100)))
+        assert_raises(ValueError, A.__setitem__, (slice(None), 1), A.copy())
+        assert_raises(ValueError, A.__setitem__,
+                      ([[1, 2, 3], [0, 3, 4]], [1, 2, 3]), [1, 2, 3, 4])
+        assert_raises(ValueError, A.__setitem__,
+                      ([[1, 2, 3], [0, 3, 4], [4, 1, 3]],
+                       [[1, 2, 4], [0, 1, 3]]), [2, 3, 4])
+        assert_raises(ValueError, A.__setitem__, (slice(4), 0),
+                      [[1, 2], [3, 4]])
+
+    def test_assign_empty(self):
+        A = self.spcreator(np.ones((2, 3)))
+        B = self.spcreator((1, 2))
+        A[1, :2] = B
+        assert_array_equal(A.toarray(), [[1, 1, 1], [0, 0, 1]])
+
+    def test_assign_1d_slice(self):
+        A = self.spcreator(np.ones((3, 3)))
+        x = np.zeros(3)
+        A[:, 0] = x
+        A[1, :] = x
+        assert_array_equal(A.toarray(), [[0, 1, 1], [0, 0, 0], [0, 1, 1]])
+
+
+class _TestFancyIndexing:
+    """Tests fancy indexing features.  The tests for any matrix formats
+    that implement these features should derive from this class.
+    """
+
+    def test_dtype_preservation_empty_index(self):
+        # This should be parametrized with pytest, but something in the parent
+        # class creation used in this file breaks pytest.mark.parametrize.
+        for dt in [np.int16, np.int32, np.float32, np.float64]:
+            A = self.spcreator((3, 2), dtype=dt)
+            assert_equal(A[:, [False, False]].dtype, dt)
+            assert_equal(A[[False, False, False], :].dtype, dt)
+            assert_equal(A[:, []].dtype, dt)
+            assert_equal(A[[], :].dtype, dt)
+
+    def test_bad_index(self):
+        A = self.spcreator(np.zeros([5, 5]))
+        assert_raises((IndexError, ValueError, TypeError), A.__getitem__, "foo")
+        assert_raises((IndexError, ValueError, TypeError), A.__getitem__, (2, "foo"))
+        assert_raises((IndexError, ValueError), A.__getitem__,
+                      ([1, 2, 3], [1, 2, 3, 4]))
+
+    def test_fancy_indexing(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        # [i]
+        assert_equal(A[[3]].toarray(), B[[3]])
+        assert_equal(A[[1, 3]].toarray(), B[[1, 3]])
+
+        # [i,[1,2]]
+        assert_equal(A[3, [3]].toarray(), B[3, [3]])
+        assert_equal(A[3, [1, 3]].toarray(), B[3, [1, 3]])
+        assert_equal(A[-1, [2, -5]].toarray(), B[-1, [2, -5]])
+        assert_equal(A[array(-1), [2, -5]].toarray(), B[-1, [2, -5]])
+        assert_equal(A[-1, array([2, -5])].toarray(), B[-1, [2, -5]])
+        assert_equal(A[array(-1), array([2, -5])].toarray(), B[-1, [2, -5]])
+
+        # [1:2,[1,2]]
+        assert_equal(A[:, [2, 8, 3, -1]].toarray(), B[:, [2, 8, 3, -1]])
+        assert_equal(A[3:4, [9]].toarray(), B[3:4, [9]])
+        assert_equal(A[1:4, [-1, -5]].toarray(), B[1:4, [-1, -5]])
+        assert_equal(A[1:4, array([-1, -5])].toarray(), B[1:4, [-1, -5]])
+
+        # [[1,2],j]
+        assert_equal(A[[3], 3].toarray(), B[[3], 3])
+        assert_equal(A[[1, 3], 3].toarray(), B[[1, 3], 3])
+        assert_equal(A[[2, -5], -4].toarray(), B[[2, -5], -4])
+        assert_equal(A[array([2, -5]), -4].toarray(), B[[2, -5], -4])
+        assert_equal(A[[2, -5], array(-4)].toarray(), B[[2, -5], -4])
+        assert_equal(A[array([2, -5]), array(-4)].toarray(), B[[2, -5], -4])
+
+        # [[1,2],1:2]
+        assert_equal(A[[3], :].toarray(), B[[3], :])
+        assert_equal(A[[1, 3], :].toarray(), B[[1, 3], :])
+        assert_equal(A[[2, -5], 8:-1].toarray(), B[[2, -5], 8:-1])
+        assert_equal(A[array([2, -5]), 8:-1].toarray(), B[[2, -5], 8:-1])
+
+        # [[1,2],[1,2]]
+        assert_equal(toarray(A[[3], [4]]), B[[3], [4]])
+        assert_equal(toarray(A[[1, 3], [2, 4]]), B[[1, 3], [2, 4]])
+        assert_equal(toarray(A[[-1, -3], [2, -4]]), B[[-1, -3], [2, -4]])
+        assert_equal(
+            toarray(A[array([-1, -3]), [2, -4]]), B[[-1, -3], [2, -4]]
+        )
+        assert_equal(
+            toarray(A[[-1, -3], array([2, -4])]), B[[-1, -3], [2, -4]]
+        )
+        assert_equal(
+            toarray(A[array([-1, -3]), array([2, -4])]), B[[-1, -3], [2, -4]]
+        )
+
+        # [[[1],[2]],[1,2]]
+        assert_equal(A[[[1], [3]], [2, 4]].toarray(), B[[[1], [3]], [2, 4]])
+        assert_equal(
+            A[[[-1], [-3], [-2]], [2, -4]].toarray(),
+            B[[[-1], [-3], [-2]], [2, -4]]
+        )
+        assert_equal(
+            A[array([[-1], [-3], [-2]]), [2, -4]].toarray(),
+            B[[[-1], [-3], [-2]], [2, -4]]
+        )
+        assert_equal(
+            A[[[-1], [-3], [-2]], array([2, -4])].toarray(),
+            B[[[-1], [-3], [-2]], [2, -4]]
+        )
+        assert_equal(
+            A[array([[-1], [-3], [-2]]), array([2, -4])].toarray(),
+            B[[[-1], [-3], [-2]], [2, -4]]
+        )
+
+        # [[1,2]]
+        assert_equal(A[[1, 3]].toarray(), B[[1, 3]])
+        assert_equal(A[[-1, -3]].toarray(), B[[-1, -3]])
+        assert_equal(A[array([-1, -3])].toarray(), B[[-1, -3]])
+
+        # [[1,2],:][:,[1,2]]
+        assert_equal(A[[3], :][:, [4]].toarray(), B[[3], :][:, [4]])
+        assert_equal(
+            A[[1, 3], :][:, [2, 4]].toarray(), B[[1, 3], :][:, [2, 4]]
+        )
+        assert_equal(
+            A[[-1, -3], :][:, [2, -4]].toarray(), B[[-1, -3], :][:, [2, -4]]
+        )
+        assert_equal(
+            A[array([-1, -3]), :][:, array([2, -4])].toarray(),
+            B[[-1, -3], :][:, [2, -4]]
+        )
+
+        # [1,[[1,2]]][[[1,2]],1]
+        assert_equal(
+            A[1, [[1, 3]]][[[0, 0]], 1].toarray(), B[1, [[1, 3]]][[[0, 0]], 1]
+        )
+        assert_equal(
+            A[1, [[-1, -3]]][[[0, -1]], 1].toarray(), B[1, [[-1, -3]]][[[0, -1]], 1]
+        )
+        # [:1,[[1,2]]][[[1,2]],:1]
+        with pytest.raises(IndexError, match="Only 1D or 2D arrays allowed"):
+            A[:1, [[1, 3]]]
+        with pytest.raises(IndexError, match="Only 1D or 2D arrays allowed"):
+            A[[[0, 0]], :1]
+
+        # [:,[1,2]][[1,2],:]
+        assert_equal(
+            A[:, [1, 3]][[2, 4], :].toarray(), B[:, [1, 3]][[2, 4], :]
+        )
+        assert_equal(
+            A[:, [-1, -3]][[2, -4], :].toarray(), B[:, [-1, -3]][[2, -4], :]
+        )
+        assert_equal(
+            A[:, array([-1, -3])][array([2, -4]), :].toarray(),
+            B[:, [-1, -3]][[2, -4], :]
+        )
+
+        # Check bug reported by Robert Cimrman:
+        # http://thread.gmane.org/gmane.comp.python.scientific.devel/7986 (dead link)
+        s = slice(int8(2),int8(4),None)
+        assert_equal(A[s, :].toarray(), B[2:4, :])
+        assert_equal(A[:, s].toarray(), B[:, 2:4])
+
+        # Regression for gh-4917: index with tuple of 2D arrays
+        i = np.array([[1]], dtype=int)
+        assert_equal(A[i, i].toarray(), B[i, i])
+
+        # Regression for gh-4917: index with tuple of empty nested lists
+        assert_equal(A[[[]], [[]]].toarray(), B[[[]], [[]]])
+
+    def test_fancy_indexing_randomized(self):
+        np.random.seed(1234)  # make runs repeatable
+
+        NUM_SAMPLES = 50
+        M = 6
+        N = 4
+
+        D = self.asdense(np.random.rand(M,N))
+        D = np.multiply(D, D > 0.5)
+
+        I = np.random.randint(-M + 1, M, size=NUM_SAMPLES)
+        J = np.random.randint(-N + 1, N, size=NUM_SAMPLES)
+
+        S = self.spcreator(D)
+
+        SIJ = S[I,J]
+        if issparse(SIJ):
+            SIJ = SIJ.toarray()
+        assert_equal(SIJ, D[I,J])
+
+        I_bad = I + M
+        J_bad = J - N
+
+        assert_raises(IndexError, S.__getitem__, (I_bad,J))
+        assert_raises(IndexError, S.__getitem__, (I,J_bad))
+
+    def test_missized_masking(self):
+        M, N = 5, 10
+
+        B = self.asdense(arange(M * N).reshape(M, N))
+        A = self.spcreator(B)
+
+        # Content of mask shouldn't matter, only its size
+        row_long = np.ones(M + 1, dtype=bool)
+        row_short = np.ones(M - 1, dtype=bool)
+        col_long = np.ones(N + 2, dtype=bool)
+        col_short = np.ones(N - 2, dtype=bool)
+
+        match="bool index .* has shape .* instead of .*"
+        for i, j in itertools.product(
+            (row_long, row_short, slice(None)),
+            (col_long, col_short, slice(None)),
+        ):
+            if isinstance(i, slice) and isinstance(j, slice):
+                continue
+            with pytest.raises(IndexError, match=match):
+                _ = A[i, j]
+
+    def test_fancy_indexing_boolean(self):
+        np.random.seed(1234)  # make runs repeatable
+
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        I = np.array(np.random.randint(0, 2, size=5), dtype=bool)
+        J = np.array(np.random.randint(0, 2, size=10), dtype=bool)
+        X = np.array(np.random.randint(0, 2, size=(5, 10)), dtype=bool)
+
+        assert_equal(toarray(A[I]), B[I])
+        assert_equal(toarray(A[:, J]), B[:, J])
+        assert_equal(toarray(A[X]), B[X])
+        assert_equal(toarray(A[B > 9]), B[B > 9])
+
+        I = np.array([True, False, True, True, False])
+        J = np.array([False, True, True, False, True,
+                      False, False, False, False, False])
+
+        assert_equal(toarray(A[I, J]), B[I, J])
+
+        Z1 = np.zeros((6, 11), dtype=bool)
+        Z2 = np.zeros((6, 11), dtype=bool)
+        Z2[0,-1] = True
+        Z3 = np.zeros((6, 11), dtype=bool)
+        Z3[-1,0] = True
+
+        assert_raises(IndexError, A.__getitem__, Z1)
+        assert_raises(IndexError, A.__getitem__, Z2)
+        assert_raises(IndexError, A.__getitem__, Z3)
+        assert_raises((IndexError, ValueError), A.__getitem__, (X, 1))
+
+    def test_fancy_indexing_sparse_boolean(self):
+        np.random.seed(1234)  # make runs repeatable
+
+        B = self.asdense(arange(50).reshape(5,10))
+        A = self.spcreator(B)
+
+        X = np.array(np.random.randint(0, 2, size=(5, 10)), dtype=bool)
+
+        Xsp = self.csr_container(X)
+
+        assert_equal(toarray(A[Xsp]), B[X])
+        assert_equal(toarray(A[A > 9]), B[B > 9])
+
+        Z = np.array(np.random.randint(0, 2, size=(5, 11)), dtype=bool)
+        Y = np.array(np.random.randint(0, 2, size=(6, 10)), dtype=bool)
+
+        Zsp = self.csr_container(Z)
+        Ysp = self.csr_container(Y)
+
+        assert_raises(IndexError, A.__getitem__, Zsp)
+        assert_raises(IndexError, A.__getitem__, Ysp)
+        assert_raises((IndexError, ValueError), A.__getitem__, (Xsp, 1))
+
+    def test_fancy_indexing_regression_3087(self):
+        mat = self.spcreator(array([[1, 0, 0], [0,1,0], [1,0,0]]))
+        desired_cols = np.ravel(mat.sum(0)) > 0
+        assert_equal(mat[:, desired_cols].toarray(), [[1, 0], [0, 1], [1, 0]])
+
+    def test_fancy_indexing_seq_assign(self):
+        mat = self.spcreator(array([[1, 0], [0, 1]]))
+        assert_raises(ValueError, mat.__setitem__, (0, 0), np.array([1,2]))
+
+    def test_fancy_indexing_2d_assign(self):
+        # regression test for gh-10695
+        mat = self.spcreator(array([[1, 0], [2, 3]]))
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            mat[[0, 1], [1, 1]] = mat[[1, 0], [0, 0]]
+        assert_equal(toarray(mat), array([[1, 2], [2, 1]]))
+
+    def test_fancy_indexing_empty(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        B[1,:] = 0
+        B[:,2] = 0
+        B[3,6] = 0
+        A = self.spcreator(B)
+
+        K = np.array([False, False, False, False, False])
+        assert_equal(toarray(A[K]), B[K])
+        K = np.array([], dtype=int)
+        assert_equal(toarray(A[K]), B[K])
+        assert_equal(toarray(A[K, K]), B[K, K])
+        J = np.array([0, 1, 2, 3, 4], dtype=int)[:,None]
+        assert_equal(toarray(A[K, J]), B[K, J])
+        assert_equal(toarray(A[J, K]), B[J, K])
+
+
+@contextlib.contextmanager
+def check_remains_sorted(X):
+    """Checks that sorted indices property is retained through an operation
+    """
+    if not hasattr(X, 'has_sorted_indices') or not X.has_sorted_indices:
+        yield
+        return
+    yield
+    indices = X.indices.copy()
+    X.has_sorted_indices = False
+    X.sort_indices()
+    assert_array_equal(indices, X.indices,
+                       'Expected sorted indices, found unsorted')
+
+
+class _TestFancyIndexingAssign:
+    def test_bad_index_assign(self):
+        A = self.spcreator(np.zeros([5, 5]))
+        assert_raises((IndexError, ValueError, TypeError), A.__setitem__, "foo", 2)
+        assert_raises((IndexError, ValueError, TypeError), A.__setitem__, (2, "foo"), 5)
+
+    def test_fancy_indexing_set(self):
+        n, m = (5, 10)
+
+        def _test_set_slice(i, j):
+            A = self.spcreator((n, m))
+            B = self.asdense(np.zeros((n, m)))
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                B[i, j] = 1
+                with check_remains_sorted(A):
+                    A[i, j] = 1
+            assert_array_almost_equal(A.toarray(), B)
+        # [1:2,1:2]
+        for i, j in [((2, 3, 4), slice(None, 10, 4)),
+                     (np.arange(3), slice(5, -2)),
+                     (slice(2, 5), slice(5, -2))]:
+            _test_set_slice(i, j)
+        for i, j in [(np.arange(3), np.arange(3)), ((0, 3, 4), (1, 2, 4))]:
+            _test_set_slice(i, j)
+
+    def test_fancy_assignment_dtypes(self):
+        def check(dtype):
+            A = self.spcreator((5, 5), dtype=dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[[0,1],[0,1]] = dtype.type(1)
+                assert_equal(A.sum(), dtype.type(1)*2)
+                A[0:2,0:2] = dtype.type(1.0)
+                assert_equal(A.sum(), dtype.type(1)*4)
+                A[2,2] = dtype.type(1.0)
+                assert_equal(A.sum(), dtype.type(1)*4 + dtype.type(1))
+
+        for dtype in supported_dtypes:
+            check(np.dtype(dtype))
+
+    def test_sequence_assignment(self):
+        A = self.spcreator((4,3))
+        B = self.spcreator(eye(3,4))
+
+        i0 = [0,1,2]
+        i1 = (0,1,2)
+        i2 = array(i0)
+
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            with check_remains_sorted(A):
+                A[0,i0] = B[i0,0].T
+                A[1,i1] = B[i1,1].T
+                A[2,i2] = B[i2,2].T
+            assert_array_equal(A.toarray(), B.T.toarray())
+
+            # column slice
+            A = self.spcreator((2,3))
+            with check_remains_sorted(A):
+                A[1,1:3] = [10,20]
+            assert_array_equal(A.toarray(), [[0, 0, 0], [0, 10, 20]])
+
+            # row slice
+            A = self.spcreator((3,2))
+            with check_remains_sorted(A):
+                A[1:3,1] = [[10],[20]]
+            assert_array_equal(A.toarray(), [[0, 0], [0, 10], [0, 20]])
+
+            # both slices
+            A = self.spcreator((3,3))
+            B = self.asdense(np.zeros((3,3)))
+            with check_remains_sorted(A):
+                for C in [A, B]:
+                    C[[0,1,2], [0,1,2]] = [4,5,6]
+            assert_array_equal(A.toarray(), B)
+
+            # both slices (2)
+            A = self.spcreator((4, 3))
+            with check_remains_sorted(A):
+                A[(1, 2, 3), (0, 1, 2)] = [1, 2, 3]
+            assert_almost_equal(A.sum(), 6)
+            B = self.asdense(np.zeros((4, 3)))
+            B[(1, 2, 3), (0, 1, 2)] = [1, 2, 3]
+            assert_array_equal(A.toarray(), B)
+
+    def test_fancy_assign_empty(self):
+        B = self.asdense(arange(50).reshape(5,10))
+        B[1,:] = 0
+        B[:,2] = 0
+        B[3,6] = 0
+        A = self.spcreator(B)
+
+        K = np.array([False, False, False, False, False])
+        A[K] = 42
+        assert_equal(toarray(A), B)
+
+        K = np.array([], dtype=int)
+        A[K] = 42
+        assert_equal(toarray(A), B)
+        A[K,K] = 42
+        assert_equal(toarray(A), B)
+
+        J = np.array([0, 1, 2, 3, 4], dtype=int)[:,None]
+        A[K,J] = 42
+        assert_equal(toarray(A), B)
+        A[J,K] = 42
+        assert_equal(toarray(A), B)
+
+
+class _TestFancyMultidim:
+    def test_fancy_indexing_ndarray(self):
+        sets = [
+            (np.array([[1], [2], [3]]), np.array([3, 4, 2])),
+            (np.array([[1], [2], [3]]), np.array([[3, 4, 2]])),
+            (np.array([[1, 2, 3]]), np.array([[3], [4], [2]])),
+            (np.array([1, 2, 3]), np.array([[3], [4], [2]])),
+            (np.array([[1, 2, 3], [3, 4, 2]]),
+             np.array([[5, 6, 3], [2, 3, 1]]))
+            ]
+        # These inputs generate 3-D outputs
+        #    (np.array([[[1], [2], [3]], [[3], [4], [2]]]),
+        #     np.array([[[5], [6], [3]], [[2], [3], [1]]])),
+
+        for I, J in sets:
+            np.random.seed(1234)
+            D = self.asdense(np.random.rand(5, 7))
+            S = self.spcreator(D)
+
+            SIJ = S[I,J]
+            if issparse(SIJ):
+                SIJ = SIJ.toarray()
+            assert_equal(SIJ, D[I,J])
+
+            I_bad = I + 5
+            J_bad = J + 7
+
+            assert_raises(IndexError, S.__getitem__, (I_bad,J))
+            assert_raises(IndexError, S.__getitem__, (I,J_bad))
+
+            # This would generate 3-D arrays -- not supported
+            assert_raises(IndexError, S.__getitem__, ([I, I], slice(None)))
+            assert_raises(IndexError, S.__getitem__, (slice(None), [J, J]))
+
+
+class _TestFancyMultidimAssign:
+    def test_fancy_assign_ndarray(self):
+        np.random.seed(1234)
+
+        D = self.asdense(np.random.rand(5, 7))
+        S = self.spcreator(D)
+        X = np.random.rand(2, 3)
+
+        I = np.array([[1, 2, 3], [3, 4, 2]])
+        J = np.array([[5, 6, 3], [2, 3, 1]])
+
+        with check_remains_sorted(S):
+            S[I,J] = X
+        D[I,J] = X
+        assert_equal(S.toarray(), D)
+
+        I_bad = I + 5
+        J_bad = J + 7
+
+        C = [1, 2, 3]
+
+        with check_remains_sorted(S):
+            S[I,J] = C
+        D[I,J] = C
+        assert_equal(S.toarray(), D)
+
+        with check_remains_sorted(S):
+            S[I,J] = 3
+        D[I,J] = 3
+        assert_equal(S.toarray(), D)
+
+        assert_raises(IndexError, S.__setitem__, (I_bad,J), C)
+        assert_raises(IndexError, S.__setitem__, (I,J_bad), C)
+
+    def test_fancy_indexing_multidim_set(self):
+        n, m = (5, 10)
+
+        def _test_set_slice(i, j):
+            A = self.spcreator((n, m))
+            with check_remains_sorted(A), suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                A[i, j] = 1
+            B = self.asdense(np.zeros((n, m)))
+            B[i, j] = 1
+            assert_array_almost_equal(A.toarray(), B)
+        # [[[1, 2], [1, 2]], [1, 2]]
+        for i, j in [(np.array([[1, 2], [1, 3]]), [1, 3]),
+                        (np.array([0, 4]), [[0, 3], [1, 2]]),
+                        ([[1, 2, 3], [0, 2, 4]], [[0, 4, 3], [4, 1, 2]])]:
+            _test_set_slice(i, j)
+
+    def test_fancy_assign_list(self):
+        np.random.seed(1234)
+
+        D = self.asdense(np.random.rand(5, 7))
+        S = self.spcreator(D)
+        X = np.random.rand(2, 3)
+
+        I = [[1, 2, 3], [3, 4, 2]]
+        J = [[5, 6, 3], [2, 3, 1]]
+
+        S[I,J] = X
+        D[I,J] = X
+        assert_equal(S.toarray(), D)
+
+        I_bad = [[ii + 5 for ii in i] for i in I]
+        J_bad = [[jj + 7 for jj in j] for j in J]
+        C = [1, 2, 3]
+
+        S[I,J] = C
+        D[I,J] = C
+        assert_equal(S.toarray(), D)
+
+        S[I,J] = 3
+        D[I,J] = 3
+        assert_equal(S.toarray(), D)
+
+        assert_raises(IndexError, S.__setitem__, (I_bad,J), C)
+        assert_raises(IndexError, S.__setitem__, (I,J_bad), C)
+
+    def test_fancy_assign_slice(self):
+        np.random.seed(1234)
+
+        D = self.asdense(np.random.rand(5, 7))
+        S = self.spcreator(D)
+
+        I = [1, 2, 3, 3, 4, 2]
+        J = [5, 6, 3, 2, 3, 1]
+
+        I_bad = [ii + 5 for ii in I]
+        J_bad = [jj + 7 for jj in J]
+
+        C1 = [1, 2, 3, 4, 5, 6, 7]
+        C2 = np.arange(5)[:, None]
+        assert_raises(IndexError, S.__setitem__, (I_bad, slice(None)), C1)
+        assert_raises(IndexError, S.__setitem__, (slice(None), J_bad), C2)
+
+
+class _TestArithmetic:
+    """
+    Test real/complex arithmetic
+    """
+    def __arith_init(self):
+        # these can be represented exactly in FP (so arithmetic should be exact)
+        __A = array([[-1.5, 6.5, 0, 2.25, 0, 0],
+                          [3.125, -7.875, 0.625, 0, 0, 0],
+                          [0, 0, -0.125, 1.0, 0, 0],
+                          [0, 0, 8.375, 0, 0, 0]], 'float64')
+        __B = array([[0.375, 0, 0, 0, -5, 2.5],
+                          [14.25, -3.75, 0, 0, -0.125, 0],
+                          [0, 7.25, 0, 0, 0, 0],
+                          [18.5, -0.0625, 0, 0, 0, 0]], 'complex128')
+        __B.imag = array([[1.25, 0, 0, 0, 6, -3.875],
+                               [2.25, 4.125, 0, 0, 0, 2.75],
+                               [0, 4.125, 0, 0, 0, 0],
+                               [-0.0625, 0, 0, 0, 0, 0]], 'float64')
+
+        # fractions are all x/16ths
+        assert_array_equal((__A*16).astype('int32'),16*__A)
+        assert_array_equal((__B.real*16).astype('int32'),16*__B.real)
+        assert_array_equal((__B.imag*16).astype('int32'),16*__B.imag)
+
+        __Asp = self.spcreator(__A)
+        __Bsp = self.spcreator(__B)
+        return __A, __B, __Asp, __Bsp
+
+    @pytest.mark.fail_slow(20)
+    def test_add_sub(self):
+        __A, __B, __Asp, __Bsp = self.__arith_init()
+
+        # basic tests
+        assert_array_equal(
+            (__Asp + __Bsp).toarray(), __A + __B
+        )
+
+        # check conversions
+        for x in supported_dtypes:
+            with np.errstate(invalid="ignore"):
+                A = __A.astype(x)
+            Asp = self.spcreator(A)
+            for y in supported_dtypes:
+                if not np.issubdtype(y, np.complexfloating):
+                    with np.errstate(invalid="ignore"):
+                        B = __B.real.astype(y)
+                else:
+                    B = __B.astype(y)
+                Bsp = self.spcreator(B)
+
+                # addition
+                D1 = A + B
+                S1 = Asp + Bsp
+
+                assert_equal(S1.dtype,D1.dtype)
+                assert_array_equal(S1.toarray(), D1)
+                assert_array_equal(Asp + B,D1)          # check sparse + dense
+                assert_array_equal(A + Bsp,D1)          # check dense + sparse
+
+                # subtraction
+                if np.dtype('bool') in [x, y]:
+                    # boolean array subtraction deprecated in 1.9.0
+                    continue
+
+                D1 = A - B
+                S1 = Asp - Bsp
+
+                assert_equal(S1.dtype,D1.dtype)
+                assert_array_equal(S1.toarray(), D1)
+                assert_array_equal(Asp - B,D1)          # check sparse - dense
+                assert_array_equal(A - Bsp,D1)          # check dense - sparse
+
+    def test_mu(self):
+        __A, __B, __Asp, __Bsp = self.__arith_init()
+
+        # basic tests
+        assert_array_equal((__Asp @ __Bsp.T).toarray(),
+                            __A @ __B.T)
+
+        for x in supported_dtypes:
+            with np.errstate(invalid="ignore"):
+                A = __A.astype(x)
+            Asp = self.spcreator(A)
+            for y in supported_dtypes:
+                if np.issubdtype(y, np.complexfloating):
+                    B = __B.astype(y)
+                else:
+                    with np.errstate(invalid="ignore"):
+                        B = __B.real.astype(y)
+                Bsp = self.spcreator(B)
+
+                D1 = A @ B.T
+                S1 = Asp @ Bsp.T
+
+                assert_allclose(S1.toarray(), D1,
+                                atol=1e-14*abs(D1).max())
+                assert_equal(S1.dtype,D1.dtype)
+
+
+class _TestMinMax:
+    def test_minmax(self):
+        for dtype in [np.float32, np.float64, np.int32, np.int64, np.complex128]:
+            D = np.arange(20, dtype=dtype).reshape(5,4)
+
+            X = self.spcreator(D)
+            assert_equal(X.min(), 0)
+            assert_equal(X.max(), 19)
+            assert_equal(X.min().dtype, dtype)
+            assert_equal(X.max().dtype, dtype)
+
+            D *= -1
+            X = self.spcreator(D)
+            assert_equal(X.min(), -19)
+            assert_equal(X.max(), 0)
+
+            D += 5
+            X = self.spcreator(D)
+            assert_equal(X.min(), -14)
+            assert_equal(X.max(), 5)
+
+        # try a fully dense matrix
+        X = self.spcreator(np.arange(1, 10).reshape(3, 3))
+        assert_equal(X.min(), 1)
+        assert_equal(X.min().dtype, X.dtype)
+
+        X = -X
+        assert_equal(X.max(), -1)
+
+        # and a fully sparse matrix
+        Z = self.spcreator(np.zeros((1, 1)))
+        assert_equal(Z.min(), 0)
+        assert_equal(Z.max(), 0)
+        assert_equal(Z.max().dtype, Z.dtype)
+
+        # another test
+        D = np.arange(20, dtype=float).reshape(5,4)
+        D[0:2, :] = 0
+        X = self.spcreator(D)
+        assert_equal(X.min(), 0)
+        assert_equal(X.max(), 19)
+
+        # zero-size matrices
+        for D in [np.zeros((0, 0)), np.zeros((0, 10)), np.zeros((10, 0))]:
+            X = self.spcreator(D)
+            assert_raises(ValueError, X.min)
+            assert_raises(ValueError, X.max)
+
+    def test_minmax_axis(self):
+        keep = not self.is_array_test
+        D = np.arange(50).reshape(5, 10)
+        # completely empty rows, leaving some completely full:
+        D[1, :] = 0
+        # empty at end for reduceat:
+        D[:, 9] = 0
+        # partial rows/cols:
+        D[3, 3] = 0
+        # entries on either side of 0:
+        D[2, 2] = -1
+        X = self.spcreator(D)
+
+        axes_even = [0, -2]
+        axes_odd = [1, -1]
+        for axis in axes_odd + axes_even:
+            assert_array_equal(
+                X.max(axis=axis).toarray(), D.max(axis=axis, keepdims=keep)
+            )
+            assert_array_equal(
+                X.min(axis=axis).toarray(), D.min(axis=axis, keepdims=keep)
+            )
+
+        for axis in axes_even:
+            assert_equal(
+                X.max(axis=axis, explicit=True).toarray(),
+                self.asdense([40, 41, 42, 43, 44, 45, 46, 47, 48, 0])
+            )
+            if np.any(X.data == 0):
+                # Noncanonical case
+                expected = self.asdense([20, 1, -1, 3, 4, 5, 0, 7, 8, 0])
+            else:
+                expected = self.asdense([20, 1, -1, 3, 4, 5, 6, 7, 8, 0])
+            assert_equal(X.min(axis=axis, explicit=True).toarray(), expected)
+
+        for axis in axes_odd:
+            expected_max = np.array([8, 0, 28, 38, 48])
+            expected_min = np.array([1, 0, -1, 30, 40])
+            if not self.is_array_test:
+                expected_max = expected_max.reshape((5, 1))
+                expected_min = expected_min.reshape((5, 1))
+            assert_equal(X.max(axis=axis, explicit=True).toarray(), expected_max)
+            assert_equal(X.min(axis=axis, explicit=True).toarray(), expected_min)
+
+        # full matrix
+        D = np.arange(1, 51).reshape(10, 5)
+        X = self.spcreator(D)
+        for axis in axes_odd + axes_even:
+            assert_array_equal(
+                X.max(axis=axis).toarray(), D.max(axis=axis, keepdims=keep)
+            )
+            assert_array_equal(
+                X.min(axis=axis).toarray(), D.min(axis=axis, keepdims=keep)
+            )
+
+        for axis in axes_even:
+            expected_max = D[-1, :]
+            expected_min = D[0, :]
+            if not self.is_array_test:
+                expected_max = D[None, -1, :]
+                expected_min = D[None, 0, :]
+            assert_equal(X.max(axis=axis, explicit=True).toarray(), expected_max)
+            assert_equal(X.min(axis=axis, explicit=True).toarray(), expected_min)
+        for axis in axes_odd:
+            expected_max = D[:, -1]
+            expected_min = D[:, 0]
+            if not self.is_array_test:
+                expected_max = D[:, -1, None]
+                expected_min = D[:, 0, None]
+            assert_equal(X.max(axis=axis, explicit=True).toarray(), expected_max)
+            assert_equal(X.min(axis=axis, explicit=True).toarray(), expected_min)
+
+        # empty matrix
+        D = self.asdense(np.zeros((10, 5)))
+        X = self.spcreator(D)
+        for axis in axes_even + axes_odd:
+            assert_equal(X.max(axis=axis, explicit=True).toarray(), D.max(axis=axis))
+            assert_equal(X.min(axis=axis, explicit=True).toarray(), D.min(axis=axis))
+
+        # zero-size matrices
+        D = self.asdense(np.zeros((0, 10)))
+        X = self.spcreator(D)
+        explicit_values = [True, False]
+        even_explicit_pairs = list(itertools.product(axes_even, explicit_values))
+        odd_explicit_pairs = list(itertools.product(axes_odd, explicit_values))
+        for axis, ex in even_explicit_pairs:
+            assert_raises(ValueError, X.min, axis=axis, explicit=ex)
+            assert_raises(ValueError, X.max, axis=axis, explicit=ex)
+        for axis, ex in odd_explicit_pairs:
+            assert_equal(X.max(axis=axis, explicit=ex).toarray(), D.max(axis=axis))
+            assert_equal(X.min(axis=axis, explicit=ex).toarray(), D.min(axis=axis))
+
+        D = self.asdense(np.zeros((10, 0)))
+        X = self.spcreator(D)
+        for axis, ex in odd_explicit_pairs:
+            assert_raises(ValueError, X.min, axis=axis, explicit=ex)
+            assert_raises(ValueError, X.max, axis=axis, explicit=ex)
+        for axis, ex in even_explicit_pairs:
+            assert_equal(X.max(axis=axis, explicit=ex).toarray(), D.max(axis=axis))
+            assert_equal(X.min(axis=axis, explicit=ex).toarray(), D.min(axis=axis))
+
+    def test_nanminmax(self):
+        D = self.asdense(np.arange(50).reshape(5,10), dtype=float)
+        D[1, :] = 0
+        D[:, 9] = 0
+        D[3, 3] = 0
+        D[2, 2] = -1
+        D[4, 2] = np.nan
+        D[1, 4] = np.nan
+        X = self.spcreator(D)
+
+        X_nan_maximum = X.nanmax()
+        assert np.isscalar(X_nan_maximum)
+        assert X_nan_maximum == np.nanmax(D)
+
+        X_nan_minimum = X.nanmin()
+        assert np.isscalar(X_nan_minimum)
+        assert X_nan_minimum == np.nanmin(D)
+
+        axes = [-2, -1, 0, 1]
+        for axis in axes:
+            X_nan_maxima = X.nanmax(axis=axis)
+            assert_allclose(X_nan_maxima.toarray(), np.nanmax(D, axis=axis))
+            assert isinstance(X_nan_maxima, self.coo_container)
+
+            X_nan_minima = X.nanmin(axis=axis)
+            assert_allclose(X_nan_minima.toarray(), np.nanmin(D, axis=axis))
+            assert isinstance(X_nan_minima, self.coo_container)
+
+    def test_minmax_invalid_params(self):
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        for fname in ('min', 'max'):
+            func = getattr(datsp, fname)
+            assert_raises(ValueError, func, axis=3)
+            assert_raises(TypeError, func, axis=(0, 1))
+            assert_raises(TypeError, func, axis=1.5)
+            assert_raises(ValueError, func, axis=1, out=1)
+
+    def test_numpy_minmax(self):
+        # See gh-5987
+        # xref gh-7460 in 'numpy'
+        from scipy.sparse import _data
+
+        dat = array([[0, 1, 2],
+                     [3, -4, 5],
+                     [-6, 7, 9]])
+        datsp = self.spcreator(dat)
+
+        # We are only testing sparse matrices who have
+        # implemented 'min' and 'max' because they are
+        # the ones with the compatibility issues with
+        # the 'numpy' implementation.
+        if isinstance(datsp, _data._minmax_mixin):
+            assert_array_equal(np.min(datsp), np.min(dat))
+            assert_array_equal(np.max(datsp), np.max(dat))
+
+    def test_argmax(self):
+        from scipy.sparse import _data
+        D1 = np.array([
+            [-1, 5, 2, 3],
+            [0, 0, -1, -2],
+            [-1, -2, -3, -4],
+            [1, 2, 3, 4],
+            [1, 2, 0, 0],
+        ])
+        D2 = D1.transpose()
+        # Non-regression test cases for gh-16929.
+        D3 = np.array([[4, 3], [7, 5]])
+        D4 = np.array([[4, 3], [7, 0]])
+        D5 = np.array([[5, 5, 3], [4, 9, 10], [3, 4, 9]])
+
+        for D in [D1, D2, D3, D4, D5]:
+            D = self.asdense(D)
+            mat = self.spcreator(D)
+            if not isinstance(mat, _data._minmax_mixin):
+                continue
+
+            assert_equal(mat.argmax(), np.argmax(D))
+            assert_equal(mat.argmin(), np.argmin(D))
+
+            assert_equal(mat.argmax(axis=0), np.argmax(D, axis=0))
+            assert_equal(mat.argmin(axis=0), np.argmin(D, axis=0))
+
+            assert_equal(mat.argmax(axis=1), np.argmax(D, axis=1))
+            assert_equal(mat.argmin(axis=1), np.argmin(D, axis=1))
+
+        # zero-size matrices
+        D6 = self.spcreator(np.empty((0, 5)))
+        D7 = self.spcreator(np.empty((5, 0)))
+        explicits = [True, False]
+
+        for mat, axis, ex in itertools.product([D6, D7], [None, 0, 1], explicits):
+            if axis is None or mat.shape[axis] == 0:
+                with pytest.raises(ValueError, match="Cannot apply"):
+                    mat.argmax(axis=axis, explicit=ex)
+                with pytest.raises(ValueError, match="Cannot apply"):
+                    mat.argmin(axis=axis, explicit=ex)
+            else:
+                if self.is_array_test:
+                    expected = np.zeros(0)
+                else:
+                    expected = np.zeros((0, 1) if axis == 1 else (1, 0))
+                assert_equal(mat.argmin(axis=axis, explicit=ex), expected)
+                assert_equal(mat.argmax(axis=axis, explicit=ex), expected)
+
+        mat = self.spcreator(D1)
+        assert_equal(mat.argmax(axis=0, explicit=True), self.asdense([3, 0, 3, 3]))
+        assert_equal(mat.argmin(axis=0, explicit=True), self.asdense([0, 2, 2, 2]))
+
+        expected_max = np.array([1, 2, 0, 3, 1])
+        expected_min = np.array([0, 3, 3, 0, 0])
+        if mat.nnz != 16:
+            # Noncanonical case
+            expected_min[-1] = 2
+        if not self.is_array_test:
+            expected_max = expected_max.reshape((5, 1))
+            expected_min = expected_min.reshape((5, 1))
+
+        assert_equal(mat.argmax(axis=1, explicit=True), expected_max)
+        assert_equal(asarray(mat.argmin(axis=1, explicit=True)), expected_min)
+
+        # all zeros
+        D = np.zeros((2, 2))
+        mat = self.spcreator(D)
+        if mat.nnz != 0:
+            # Noncanonical case
+            assert_equal(mat.argmin(axis=None, explicit=True), 0)
+            assert_equal(mat.argmax(axis=None, explicit=True), 0)
+        else:
+            # Canonical case
+            with pytest.raises(ValueError, match="Cannot apply"):
+                mat.argmin(axis=None, explicit=True)
+            with pytest.raises(ValueError, match="Cannot apply"):
+                mat.argmax(axis=None, explicit=True)
+
+
+class _TestGetNnzAxis:
+    def test_getnnz_axis(self):
+        dat = array([[0, 2],
+                     [3, 5],
+                     [-6, 9]])
+        bool_dat = dat.astype(bool)
+        datsp = self.spcreator(dat)
+
+        accepted_return_dtypes = (np.int32, np.int64)
+
+        getnnz = datsp.count_nonzero if self.is_array_test else datsp.getnnz
+        assert_array_equal(bool_dat.sum(axis=None), getnnz(axis=None))
+        assert_array_equal(bool_dat.sum(), getnnz())
+        assert_array_equal(bool_dat.sum(axis=0), getnnz(axis=0))
+        assert_in(getnnz(axis=0).dtype, accepted_return_dtypes)
+        assert_array_equal(bool_dat.sum(axis=1), getnnz(axis=1))
+        assert_in(getnnz(axis=1).dtype, accepted_return_dtypes)
+        assert_array_equal(bool_dat.sum(axis=-2), getnnz(axis=-2))
+        assert_in(getnnz(axis=-2).dtype, accepted_return_dtypes)
+        assert_array_equal(bool_dat.sum(axis=-1), getnnz(axis=-1))
+        assert_in(getnnz(axis=-1).dtype, accepted_return_dtypes)
+
+        assert_raises(ValueError, getnnz, axis=2)
+
+
+#------------------------------------------------------------------------------
+# Tailored base class for generic tests
+#------------------------------------------------------------------------------
+
+def _possibly_unimplemented(cls, require=True):
+    """
+    Construct a class that either runs tests as usual (require=True),
+    or each method skips if it encounters a common error.
+    """
+    if require:
+        return cls
+    else:
+        def wrap(fc):
+            @functools.wraps(fc)
+            def wrapper(*a, **kw):
+                try:
+                    return fc(*a, **kw)
+                except (NotImplementedError, TypeError, ValueError,
+                        IndexError, AttributeError):
+                    raise pytest.skip("feature not implemented")
+
+            return wrapper
+
+        new_dict = dict(cls.__dict__)
+        for name, func in cls.__dict__.items():
+            if name.startswith('test_'):
+                new_dict[name] = wrap(func)
+        return type(cls.__name__ + "NotImplemented",
+                    cls.__bases__,
+                    new_dict)
+
+
+def sparse_test_class(getset=True, slicing=True, slicing_assign=True,
+                      fancy_indexing=True, fancy_assign=True,
+                      fancy_multidim_indexing=True, fancy_multidim_assign=True,
+                      minmax=True, nnz_axis=True):
+    """
+    Construct a base class, optionally converting some of the tests in
+    the suite to check that the feature is not implemented.
+    """
+    bases = (_TestCommon,
+             _possibly_unimplemented(_TestGetSet, getset),
+             _TestSolve,
+             _TestInplaceArithmetic,
+             _TestArithmetic,
+             _possibly_unimplemented(_TestSlicing, slicing),
+             _possibly_unimplemented(_TestSlicingAssign, slicing_assign),
+             _possibly_unimplemented(_TestFancyIndexing, fancy_indexing),
+             _possibly_unimplemented(_TestFancyIndexingAssign,
+                                     fancy_assign),
+             _possibly_unimplemented(_TestFancyMultidim,
+                                     fancy_indexing and fancy_multidim_indexing),
+             _possibly_unimplemented(_TestFancyMultidimAssign,
+                                     fancy_multidim_assign and fancy_assign),
+             _possibly_unimplemented(_TestMinMax, minmax),
+             _possibly_unimplemented(_TestGetNnzAxis, nnz_axis))
+
+    # check that test names do not clash
+    names = {}
+    for cls in bases:
+        for name in cls.__dict__:
+            if not name.startswith('test_'):
+                continue
+            old_cls = names.get(name)
+            if old_cls is not None:
+                raise ValueError(f"Test class {cls.__name__} overloads test "
+                                 f"{name} defined in {old_cls.__name__}")
+            names[name] = cls
+
+    return type("TestBase", bases, {})
+
+
+#------------------------------------------------------------------------------
+# Matrix class based tests
+#------------------------------------------------------------------------------
+
+class TestCSR(sparse_test_class()):
+    @classmethod
+    def spcreator(cls, *args, **kwargs):
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            return csr_array(*args, **kwargs)
+    math_dtypes = [np.bool_, np.int_, np.float64, np.complex128]
+
+    def test_constructor1(self):
+        b = array([[0, 4, 0],
+                   [3, 0, 0],
+                   [0, 2, 0]], 'd')
+        bsp = self.csr_container(b)
+        assert_array_almost_equal(bsp.data,[4,3,2])
+        assert_array_equal(bsp.indices,[1,0,1])
+        assert_array_equal(bsp.indptr,[0,1,2,3])
+        assert_equal(bsp.nnz,3)
+        assert_equal(bsp.format,'csr')
+        assert_array_equal(bsp.toarray(), b)
+
+    def test_constructor2(self):
+        b = zeros((6,6),'d')
+        b[3,4] = 5
+        bsp = self.csr_container(b)
+        assert_array_almost_equal(bsp.data,[5])
+        assert_array_equal(bsp.indices,[4])
+        assert_array_equal(bsp.indptr,[0,0,0,0,1,1,1])
+        assert_array_almost_equal(bsp.toarray(), b)
+
+    def test_constructor3(self):
+        b = array([[1, 0],
+                   [0, 2],
+                   [3, 0]], 'd')
+        bsp = self.csr_container(b)
+        assert_array_almost_equal(bsp.data,[1,2,3])
+        assert_array_equal(bsp.indices,[0,1,0])
+        assert_array_equal(bsp.indptr,[0,1,2,3])
+        assert_array_almost_equal(bsp.toarray(), b)
+
+    def test_constructor4(self):
+        # using (data, ij) format
+        row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
+        col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
+        data = array([6., 10., 3., 9., 1., 4.,
+                              11., 2., 8., 5., 7.])
+
+        ij = vstack((row,col))
+        csr = self.csr_container((data,ij),(4,3))
+        assert_array_equal(arange(12).reshape(4, 3), csr.toarray())
+
+        # using Python lists and a specified dtype
+        csr = self.csr_container(([2**63 + 1, 1], ([0, 1], [0, 1])), dtype=np.uint64)
+        dense = array([[2**63 + 1, 0], [0, 1]], dtype=np.uint64)
+        assert_array_equal(dense, csr.toarray())
+
+        # with duplicates (should sum the duplicates)
+        csr = self.csr_container(([1,1,1,1], ([0,2,2,0], [0,1,1,0])))
+        assert csr.nnz == 2
+
+    def test_constructor5(self):
+        # infer dimensions from arrays
+        indptr = array([0,1,3,3])
+        indices = array([0,5,1,2])
+        data = array([1,2,3,4])
+        csr = self.csr_container((data, indices, indptr))
+        assert_array_equal(csr.shape,(3,6))
+
+    def test_constructor6(self):
+        # infer dimensions and dtype from lists
+        indptr = [0, 1, 3, 3]
+        indices = [0, 5, 1, 2]
+        data = [1, 2, 3, 4]
+        csr = self.csr_container((data, indices, indptr))
+        assert_array_equal(csr.shape, (3,6))
+        assert_(np.issubdtype(csr.dtype, np.signedinteger))
+
+    def test_constructor_smallcol(self):
+        # int64 indices not required
+        data = arange(6) + 1
+        col = array([1, 2, 1, 0, 0, 2], dtype=np.int64)
+        ptr = array([0, 2, 4, 6], dtype=np.int64)
+
+        a = self.csr_container((data, col, ptr), shape=(3, 3))
+
+        b = array([[0, 1, 2],
+                   [4, 3, 0],
+                   [5, 0, 6]], 'd')
+
+        # sparray is less aggressive in downcasting indices to int32 than spmatrix
+        expected_dtype = np.dtype(np.int64 if self.is_array_test else np.int32)
+        assert_equal(a.indptr.dtype, expected_dtype)
+        assert_equal(a.indices.dtype, expected_dtype)
+        assert_array_equal(a.toarray(), b)
+
+    def test_constructor_largecol(self):
+        # int64 indices required
+        data = arange(6) + 1
+        large = np.iinfo(np.int32).max + 100
+        col = array([0, 1, 2, large, large+1, large+2], dtype=np.int64)
+        ptr = array([0, 2, 4, 6], dtype=np.int64)
+
+        a = self.csr_container((data, col, ptr))
+
+        assert_equal(a.indptr.dtype, np.dtype(np.int64))
+        assert_equal(a.indices.dtype, np.dtype(np.int64))
+        assert_array_equal(a.shape, (3, max(col)+1))
+
+    def test_sort_indices(self):
+        data = arange(5)
+        indices = array([7, 2, 1, 5, 4])
+        indptr = array([0, 3, 5])
+        asp = self.csr_container((data, indices, indptr), shape=(2,10))
+        bsp = asp.copy()
+        asp.sort_indices()
+        assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    def test_eliminate_zeros(self):
+        data = array([1, 0, 0, 0, 2, 0, 3, 0])
+        indices = array([1, 2, 3, 4, 5, 6, 7, 8])
+        indptr = array([0, 3, 8])
+        asp = self.csr_container((data, indices, indptr), shape=(2,10))
+        bsp = asp.copy()
+        asp.eliminate_zeros()
+        assert_array_equal(asp.nnz, 3)
+        assert_array_equal(asp.data,[1, 2, 3])
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    def test_ufuncs(self):
+        X = self.csr_container(np.arange(20).reshape(4, 5) / 20.)
+        for f in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
+                  "arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
+                  "deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt"]:
+            assert_equal(hasattr(self.datsp, f), True)
+            X2 = getattr(X, f)()
+            assert_equal(X.shape, X2.shape)
+            assert_array_equal(X.indices, X2.indices)
+            assert_array_equal(X.indptr, X2.indptr)
+            assert_array_equal(X2.toarray(), getattr(np, f)(X.toarray()))
+
+    def test_unsorted_arithmetic(self):
+        data = arange(5)
+        indices = array([7, 2, 1, 5, 4])
+        indptr = array([0, 3, 5])
+        asp = self.csr_container((data, indices, indptr), shape=(2,10))
+        data = arange(6)
+        indices = array([8, 1, 5, 7, 2, 4])
+        indptr = array([0, 2, 6])
+        bsp = self.csr_container((data, indices, indptr), shape=(2,10))
+        assert_equal((asp + bsp).toarray(), asp.toarray() + bsp.toarray())
+
+    def test_fancy_indexing_broadcast(self):
+        # broadcasting indexing mode is supported
+        I = np.array([[1], [2], [3]])
+        J = np.array([3, 4, 2])
+
+        np.random.seed(1234)
+        D = self.asdense(np.random.rand(5, 7))
+        S = self.spcreator(D)
+
+        SIJ = S[I,J]
+        if issparse(SIJ):
+            SIJ = SIJ.toarray()
+        assert_equal(SIJ, D[I,J])
+
+    def test_has_sorted_indices(self):
+        "Ensure has_sorted_indices memoizes sorted state for sort_indices"
+        sorted_inds = np.array([0, 1])
+        unsorted_inds = np.array([1, 0])
+        data = np.array([1, 1])
+        indptr = np.array([0, 2])
+        M = self.csr_container((data, sorted_inds, indptr)).copy()
+        assert_equal(True, M.has_sorted_indices)
+        assert isinstance(M.has_sorted_indices, bool)
+
+        M = self.csr_container((data, unsorted_inds, indptr)).copy()
+        assert_equal(False, M.has_sorted_indices)
+
+        # set by sorting
+        M.sort_indices()
+        assert_equal(True, M.has_sorted_indices)
+        assert_array_equal(M.indices, sorted_inds)
+
+        M = self.csr_container((data, unsorted_inds, indptr)).copy()
+        # set manually (although underlyingly unsorted)
+        M.has_sorted_indices = True
+        assert_equal(True, M.has_sorted_indices)
+        assert_array_equal(M.indices, unsorted_inds)
+
+        # ensure sort bypassed when has_sorted_indices == True
+        M.sort_indices()
+        assert_array_equal(M.indices, unsorted_inds)
+
+    def test_has_canonical_format(self):
+        "Ensure has_canonical_format memoizes state for sum_duplicates"
+
+        M = self.csr_container((np.array([2]), np.array([0]), np.array([0, 1])))
+        assert_equal(True, M.has_canonical_format)
+
+        indices = np.array([0, 0])  # contains duplicate
+        data = np.array([1, 1])
+        indptr = np.array([0, 2])
+
+        M = self.csr_container((data, indices, indptr)).copy()
+        assert_equal(False, M.has_canonical_format)
+        assert isinstance(M.has_canonical_format, bool)
+
+        # set by deduplicating
+        M.sum_duplicates()
+        assert_equal(True, M.has_canonical_format)
+        assert_equal(1, len(M.indices))
+
+        M = self.csr_container((data, indices, indptr)).copy()
+        # set manually (although underlyingly duplicated)
+        M.has_canonical_format = True
+        assert_equal(True, M.has_canonical_format)
+        assert_equal(2, len(M.indices))  # unaffected content
+
+        # ensure deduplication bypassed when has_canonical_format == True
+        M.sum_duplicates()
+        assert_equal(2, len(M.indices))  # unaffected content
+
+    def test_scalar_idx_dtype(self):
+        # Check that index dtype takes into account all parameters
+        # passed to sparsetools, including the scalar ones
+        indptr = np.zeros(2, dtype=np.int32)
+        indices = np.zeros(0, dtype=np.int32)
+        vals = np.zeros(0)
+        a = self.csr_container((vals, indices, indptr), shape=(1, 2**31-1))
+        b = self.csr_container((vals, indices, indptr), shape=(1, 2**31))
+        ij = np.zeros((2, 0), dtype=np.int32)
+        c = self.csr_container((vals, ij), shape=(1, 2**31-1))
+        d = self.csr_container((vals, ij), shape=(1, 2**31))
+        e = self.csr_container((1, 2**31-1))
+        f = self.csr_container((1, 2**31))
+        assert_equal(a.indptr.dtype, np.int32)
+        assert_equal(b.indptr.dtype, np.int64)
+        assert_equal(c.indptr.dtype, np.int32)
+        assert_equal(d.indptr.dtype, np.int64)
+        assert_equal(e.indptr.dtype, np.int32)
+        assert_equal(f.indptr.dtype, np.int64)
+
+        # These shouldn't fail
+        for x in [a, b, c, d, e, f]:
+            x + x
+
+    def test_setdiag_csr(self):
+        # see gh-21791 setting mixture of existing and not when new_values < 0.001*nnz
+        D = self.dia_container(([np.arange(1002)], [0]), shape=(1002, 1002))
+        A = self.spcreator(D)
+        A.setdiag(5 * np.ones(A.shape[0]))
+        assert A[-1, -1] == 5
+
+    def test_binop_explicit_zeros(self):
+        # Check that binary ops don't introduce spurious explicit zeros.
+        # See gh-9619 for context.
+        a = self.csr_container([[0, 1, 0]])
+        b = self.csr_container([[1, 1, 0]])
+        assert (a + b).nnz == 2
+        assert a.multiply(b).nnz == 1
+
+
+TestCSR.init_class()
+
+
+class TestCSRMatrix(_MatrixMixin, TestCSR):
+    @classmethod
+    def spcreator(cls, *args, **kwargs):
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            return csr_matrix(*args, **kwargs)
+
+
+TestCSRMatrix.init_class()
+
+
+class TestCSC(sparse_test_class()):
+    @classmethod
+    def spcreator(cls, *args, **kwargs):
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            return csc_array(*args, **kwargs)
+    math_dtypes = [np.bool_, np.int_, np.float64, np.complex128]
+
+    def test_constructor1(self):
+        b = array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 2, 0, 3]], 'd')
+        bsp = self.csc_container(b)
+        assert_array_almost_equal(bsp.data,[1,2,1,3])
+        assert_array_equal(bsp.indices,[0,2,1,2])
+        assert_array_equal(bsp.indptr,[0,1,2,3,4])
+        assert_equal(bsp.nnz,4)
+        assert_equal(bsp.shape,b.shape)
+        assert_equal(bsp.format,'csc')
+
+    def test_constructor2(self):
+        b = zeros((6,6),'d')
+        b[2,4] = 5
+        bsp = self.csc_container(b)
+        assert_array_almost_equal(bsp.data,[5])
+        assert_array_equal(bsp.indices,[2])
+        assert_array_equal(bsp.indptr,[0,0,0,0,0,1,1])
+
+    def test_constructor3(self):
+        b = array([[1, 0], [0, 0], [0, 2]], 'd')
+        bsp = self.csc_container(b)
+        assert_array_almost_equal(bsp.data,[1,2])
+        assert_array_equal(bsp.indices,[0,2])
+        assert_array_equal(bsp.indptr,[0,1,2])
+
+    def test_constructor4(self):
+        # using (data, ij) format
+        row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
+        col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
+        data = array([6., 10., 3., 9., 1., 4., 11., 2., 8., 5., 7.])
+
+        ij = vstack((row,col))
+        csc = self.csc_container((data,ij),(4,3))
+        assert_array_equal(arange(12).reshape(4, 3), csc.toarray())
+
+        # with duplicates (should sum the duplicates)
+        csc = self.csc_container(([1,1,1,1], ([0,2,2,0], [0,1,1,0])))
+        assert csc.nnz == 2
+
+    def test_constructor5(self):
+        # infer dimensions from arrays
+        indptr = array([0,1,3,3])
+        indices = array([0,5,1,2])
+        data = array([1,2,3,4])
+        csc = self.csc_container((data, indices, indptr))
+        assert_array_equal(csc.shape,(6,3))
+
+    def test_constructor6(self):
+        # infer dimensions and dtype from lists
+        indptr = [0, 1, 3, 3]
+        indices = [0, 5, 1, 2]
+        data = [1, 2, 3, 4]
+        csc = self.csc_container((data, indices, indptr))
+        assert_array_equal(csc.shape,(6,3))
+        assert_(np.issubdtype(csc.dtype, np.signedinteger))
+
+    def test_eliminate_zeros(self):
+        data = array([1, 0, 0, 0, 2, 0, 3, 0])
+        indices = array([1, 2, 3, 4, 5, 6, 7, 8])
+        indptr = array([0, 3, 8])
+        asp = self.csc_container((data, indices, indptr), shape=(10,2))
+        bsp = asp.copy()
+        asp.eliminate_zeros()
+        assert_array_equal(asp.nnz, 3)
+        assert_array_equal(asp.data,[1, 2, 3])
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    def test_sort_indices(self):
+        data = arange(5)
+        row = array([7, 2, 1, 5, 4])
+        ptr = [0, 3, 5]
+        asp = self.csc_container((data, row, ptr), shape=(10,2))
+        bsp = asp.copy()
+        asp.sort_indices()
+        assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    def test_ufuncs(self):
+        X = self.csc_container(np.arange(21).reshape(7, 3) / 21.)
+        for f in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
+                  "arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
+                  "deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt"]:
+            assert_equal(hasattr(self.datsp, f), True)
+            X2 = getattr(X, f)()
+            assert_equal(X.shape, X2.shape)
+            assert_array_equal(X.indices, X2.indices)
+            assert_array_equal(X.indptr, X2.indptr)
+            assert_array_equal(X2.toarray(), getattr(np, f)(X.toarray()))
+
+    def test_unsorted_arithmetic(self):
+        data = arange(5)
+        indices = array([7, 2, 1, 5, 4])
+        indptr = array([0, 3, 5])
+        asp = self.csc_container((data, indices, indptr), shape=(10,2))
+        data = arange(6)
+        indices = array([8, 1, 5, 7, 2, 4])
+        indptr = array([0, 2, 6])
+        bsp = self.csc_container((data, indices, indptr), shape=(10,2))
+        assert_equal((asp + bsp).toarray(), asp.toarray() + bsp.toarray())
+
+    def test_fancy_indexing_broadcast(self):
+        # broadcasting indexing mode is supported
+        I = np.array([[1], [2], [3]])
+        J = np.array([3, 4, 2])
+
+        np.random.seed(1234)
+        D = self.asdense(np.random.rand(5, 7))
+        S = self.spcreator(D)
+
+        SIJ = S[I,J]
+        if issparse(SIJ):
+            SIJ = SIJ.toarray()
+        assert_equal(SIJ, D[I,J])
+
+    def test_scalar_idx_dtype(self):
+        # Check that index dtype takes into account all parameters
+        # passed to sparsetools, including the scalar ones
+        indptr = np.zeros(2, dtype=np.int32)
+        indices = np.zeros(0, dtype=np.int32)
+        vals = np.zeros(0)
+        a = self.csc_container((vals, indices, indptr), shape=(2**31-1, 1))
+        b = self.csc_container((vals, indices, indptr), shape=(2**31, 1))
+        ij = np.zeros((2, 0), dtype=np.int32)
+        c = self.csc_container((vals, ij), shape=(2**31-1, 1))
+        d = self.csc_container((vals, ij), shape=(2**31, 1))
+        e = self.csr_container((1, 2**31-1))
+        f = self.csr_container((1, 2**31))
+        assert_equal(a.indptr.dtype, np.int32)
+        assert_equal(b.indptr.dtype, np.int64)
+        assert_equal(c.indptr.dtype, np.int32)
+        assert_equal(d.indptr.dtype, np.int64)
+        assert_equal(e.indptr.dtype, np.int32)
+        assert_equal(f.indptr.dtype, np.int64)
+
+        # These shouldn't fail
+        for x in [a, b, c, d, e, f]:
+            x + x
+
+    def test_setdiag_csc(self):
+        # see gh-21791 setting mixture of existing and not when new_values < 0.001*nnz
+        D = self.dia_container(([np.arange(1002)], [0]), shape=(1002, 1002))
+        A = self.spcreator(D)
+        A.setdiag(5 * np.ones(A.shape[0]))
+        assert A[-1, -1] == 5
+
+
+TestCSC.init_class()
+
+
+class TestCSCMatrix(_MatrixMixin, TestCSC):
+    @classmethod
+    def spcreator(cls, *args, **kwargs):
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            return csc_matrix(*args, **kwargs)
+
+
+TestCSCMatrix.init_class()
+
+
+class TestDOK(sparse_test_class(minmax=False, nnz_axis=False)):
+    spcreator = dok_array
+    math_dtypes = [np.int_, np.float64, np.complex128]
+
+    def test_mult(self):
+        A = self.dok_container((10, 12))
+        A[0, 3] = 10
+        A[5, 6] = 20
+        D = A @ A.T
+        E = A @ A.T.conjugate()
+        assert_array_equal(D.toarray(), E.toarray())
+
+    def test_add_nonzero(self):
+        A = self.spcreator((3,2))
+        A[0,1] = -10
+        A[2,0] = 20
+        A = A + 10
+        B = array([[10, 0], [10, 10], [30, 10]])
+        assert_array_equal(A.toarray(), B)
+
+        A = A + 1j
+        B = B + 1j
+        assert_array_equal(A.toarray(), B)
+
+    def test_dok_divide_scalar(self):
+        A = self.spcreator((3,2))
+        A[0,1] = -10
+        A[2,0] = 20
+
+        assert_array_equal((A/1j).toarray(), A.toarray()/1j)
+        assert_array_equal((A/9).toarray(), A.toarray()/9)
+
+    def test_convert(self):
+        # Test provided by Andrew Straw.  Fails in SciPy <= r1477.
+        (m, n) = (6, 7)
+        a = self.dok_container((m, n))
+
+        # set a few elements, but none in the last column
+        a[2,1] = 1
+        a[0,2] = 2
+        a[3,1] = 3
+        a[1,5] = 4
+        a[4,3] = 5
+        a[4,2] = 6
+
+        # assert that the last column is all zeros
+        assert_array_equal(a.toarray()[:,n-1], zeros(m,))
+
+        # make sure it still works for CSC format
+        csc = a.tocsc()
+        assert_array_equal(csc.toarray()[:,n-1], zeros(m,))
+
+        # now test CSR
+        (m, n) = (n, m)
+        b = a.transpose()
+        assert_equal(b.shape, (m, n))
+        # assert that the last row is all zeros
+        assert_array_equal(b.toarray()[m-1,:], zeros(n,))
+
+        # make sure it still works for CSR format
+        csr = b.tocsr()
+        assert_array_equal(csr.toarray()[m-1,:], zeros(n,))
+
+    def test_ctor(self):
+        # Empty ctor
+        assert_raises(TypeError, self.dok_container)
+
+        # Dense ctor
+        b = array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 2, 0, 3]], 'd')
+        A = self.dok_container(b)
+        assert_equal(b.dtype, A.dtype)
+        assert_equal(A.toarray(), b)
+
+        # Sparse ctor
+        c = self.csr_container(b)
+        assert_equal(A.toarray(), c.toarray())
+
+        data = [[0, 1, 2], [3, 0, 0]]
+        d = self.dok_container(data, dtype=np.float32)
+        assert_equal(d.dtype, np.float32)
+        da = d.toarray()
+        assert_equal(da.dtype, np.float32)
+        assert_array_equal(da, data)
+
+    def test_ticket1160(self):
+        # Regression test for ticket #1160.
+        a = self.dok_container((3,3))
+        a[0,0] = 0
+        # This assert would fail, because the above assignment would
+        # incorrectly call __set_item__ even though the value was 0.
+        assert_((0,0) not in a.keys(), "Unexpected entry (0,0) in keys")
+
+        # Slice assignments were also affected.
+        b = self.dok_container((3,3))
+        b[:,0] = 0
+        assert_(len(b.keys()) == 0, "Unexpected entries in keys")
+
+
+class TestDOKMatrix(_MatrixMixin, TestDOK):
+    spcreator = dok_matrix
+
+
+TestDOK.init_class()
+TestDOKMatrix.init_class()
+
+
+class TestLIL(sparse_test_class(minmax=False)):
+    spcreator = lil_array
+    math_dtypes = [np.int_, np.float64, np.complex128]
+
+    def test_dot(self):
+        A = zeros((10, 10), np.complex128)
+        A[0, 3] = 10
+        A[5, 6] = 20j
+
+        B = self.lil_container((10, 10), dtype=np.complex128)
+        B[0, 3] = 10
+        B[5, 6] = 20j
+
+        # TODO: properly handle this assertion on ppc64le
+        if platform.machine() != 'ppc64le':
+            assert_array_equal(A @ A.T, (B @ B.T).toarray())
+
+        assert_array_equal(A @ A.conjugate().T, (B @ B.conjugate().T).toarray())
+
+    def test_scalar_mul(self):
+        x = self.lil_container((3, 3))
+        x[0, 0] = 2
+
+        x = x*2
+        assert_equal(x[0, 0], 4)
+
+        x = x*0
+        assert_equal(x[0, 0], 0)
+
+    def test_truediv_scalar(self):
+        A = self.spcreator((3, 2))
+        A[0, 1] = -10
+        A[2, 0] = 20
+
+        assert_array_equal((A / 1j).toarray(), A.toarray() / 1j)
+        assert_array_equal((A / 9).toarray(), A.toarray() / 9)
+
+    def test_inplace_ops(self):
+        A = self.lil_container([[0, 2, 3], [4, 0, 6]])
+        B = self.lil_container([[0, 1, 0], [0, 2, 3]])
+
+        data = {'add': (B, A + B),
+                'sub': (B, A - B),
+                'mul': (3, A * 3)}
+
+        for op, (other, expected) in data.items():
+            result = A.copy()
+            getattr(result, f'__i{op}__')(other)
+
+            assert_array_equal(result.toarray(), expected.toarray())
+
+        # Ticket 1604.
+        A = self.lil_container((1, 3), dtype=np.dtype('float64'))
+        B = self.asdense([0.1, 0.1, 0.1])
+        A[0, :] += B
+        assert_array_equal(A[0, :].toarray(), B)
+
+    def test_lil_iteration(self):
+        row_data = [[1, 2, 3], [4, 5, 6]]
+        B = self.lil_container(array(row_data))
+        for r, row in enumerate(B):
+            assert_array_equal(row.toarray(), array(row_data[r], ndmin=row.ndim))
+
+    def test_lil_from_csr(self):
+        # Tests whether a LIL can be constructed from a CSR.
+        B = self.lil_container((10, 10))
+        B[0, 3] = 10
+        B[5, 6] = 20
+        B[8, 3] = 30
+        B[3, 8] = 40
+        B[8, 9] = 50
+        C = B.tocsr()
+        D = self.lil_container(C)
+        assert_array_equal(C.toarray(), D.toarray())
+
+    def test_fancy_indexing_lil(self):
+        M = self.asdense(arange(25).reshape(5, 5))
+        A = self.lil_container(M)
+
+        assert_equal(A[array([1, 2, 3]), 2:3].toarray(),
+                     M[array([1, 2, 3]), 2:3])
+
+    def test_point_wise_multiply(self):
+        l = self.lil_container((4, 3))
+        l[0, 0] = 1
+        l[1, 1] = 2
+        l[2, 2] = 3
+        l[3, 1] = 4
+
+        m = self.lil_container((4, 3))
+        m[0, 0] = 1
+        m[0, 1] = 2
+        m[2, 2] = 3
+        m[3, 1] = 4
+        m[3, 2] = 4
+
+        assert_array_equal(l.multiply(m).toarray(),
+                           m.multiply(l).toarray())
+
+        assert_array_equal(l.multiply(m).toarray(),
+                           [[1, 0, 0],
+                            [0, 0, 0],
+                            [0, 0, 9],
+                            [0, 16, 0]])
+
+    def test_lil_multiply_removal(self):
+        # Ticket #1427.
+        a = self.lil_container(np.ones((3, 3)))
+        a *= 2.
+        a[0, :] = 0
+
+
+class TestLILMatrix(_MatrixMixin, TestLIL):
+    spcreator = lil_matrix
+
+
+TestLIL.init_class()
+TestLILMatrix.init_class()
+
+
+class TestCOO(sparse_test_class(getset=False,
+                                slicing=False, slicing_assign=False,
+                                fancy_indexing=False, fancy_assign=False)):
+    spcreator = coo_array
+    math_dtypes = [np.int_, np.float64, np.complex128]
+
+    def test_constructor1(self):
+        # unsorted triplet format
+        row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
+        col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
+        data = array([6., 10., 3., 9., 1., 4., 11., 2., 8., 5., 7.])
+
+        coo = self.coo_container((data,(row,col)),(4,3))
+        assert_array_equal(arange(12).reshape(4, 3), coo.toarray())
+
+        # using Python lists and a specified dtype
+        coo = self.coo_container(([2**63 + 1, 1], ([0, 1], [0, 1])), dtype=np.uint64)
+        dense = array([[2**63 + 1, 0], [0, 1]], dtype=np.uint64)
+        assert_array_equal(dense, coo.toarray())
+
+    def test_constructor2(self):
+        # unsorted triplet format with duplicates (which are summed)
+        row = array([0,1,2,2,2,2,0,0,2,2])
+        col = array([0,2,0,2,1,1,1,0,0,2])
+        data = array([2,9,-4,5,7,0,-1,2,1,-5])
+        coo = self.coo_container((data,(row,col)),(3,3))
+
+        mat = array([[4, -1, 0], [0, 0, 9], [-3, 7, 0]])
+
+        assert_array_equal(mat, coo.toarray())
+
+    def test_constructor3(self):
+        # empty matrix
+        coo = self.coo_container((4,3))
+
+        assert_array_equal(coo.shape,(4,3))
+        assert_array_equal(coo.row,[])
+        assert_array_equal(coo.col,[])
+        assert_array_equal(coo.data,[])
+        assert_array_equal(coo.toarray(), zeros((4, 3)))
+
+    def test_constructor4(self):
+        # from dense matrix
+        mat = array([[0,1,0,0],
+                     [7,0,3,0],
+                     [0,4,0,0]])
+        coo = self.coo_container(mat)
+        assert_array_equal(coo.toarray(), mat)
+
+        # upgrade rank 1 arrays to row matrix
+        mat = array([0,1,0,0])
+        coo = self.coo_container(mat)
+        expected = mat if self.is_array_test else mat.reshape(1, -1)
+        assert_array_equal(coo.toarray(), expected)
+
+        # error if second arg interpreted as shape (gh-9919)
+        with pytest.raises(TypeError, match=r'object cannot be interpreted'):
+            self.coo_container([0, 11, 22, 33], ([0, 1, 2, 3], [0, 0, 0, 0]))
+
+        # error if explicit shape arg doesn't match the dense matrix
+        with pytest.raises(ValueError, match=r'inconsistent shapes'):
+            self.coo_container([0, 11, 22, 33], shape=(4, 4))
+
+    def test_constructor_data_ij_dtypeNone(self):
+        data = [1]
+        coo = self.coo_container((data, ([0], [0])), dtype=None)
+        assert coo.dtype == np.array(data).dtype
+
+    @pytest.mark.xfail(run=False, reason='COO does not have a __getitem__')
+    def test_iterator(self):
+        pass
+
+    def test_todia_all_zeros(self):
+        zeros = [[0, 0]]
+        dia = self.coo_container(zeros).todia()
+        assert_array_equal(dia.toarray(), zeros)
+
+    def test_sum_duplicates(self):
+        coo = self.coo_container((4,3))
+        coo.sum_duplicates()
+        coo = self.coo_container(([1,2], ([1,0], [1,0])))
+        coo.sum_duplicates()
+        assert_array_equal(coo.toarray(), [[2,0],[0,1]])
+        coo = self.coo_container(([1,2], ([1,1], [1,1])))
+        coo.sum_duplicates()
+        assert_array_equal(coo.toarray(), [[0,0],[0,3]])
+        assert_array_equal(coo.row, [1])
+        assert_array_equal(coo.col, [1])
+        assert_array_equal(coo.data, [3])
+
+    def test_todok_duplicates(self):
+        coo = self.coo_container(([1,1,1,1], ([0,2,2,0], [0,1,1,0])))
+        dok = coo.todok()
+        assert_array_equal(dok.toarray(), coo.toarray())
+
+    def test_tocompressed_duplicates(self):
+        coo = self.coo_container(([1,1,1,1], ([0,2,2,0], [0,1,1,0])))
+        csr = coo.tocsr()
+        assert_equal(csr.nnz + 2, coo.nnz)
+        csc = coo.tocsc()
+        assert_equal(csc.nnz + 2, coo.nnz)
+
+    def test_eliminate_zeros(self):
+        data = array([1, 0, 0, 0, 2, 0, 3, 0])
+        row = array([0, 0, 0, 1, 1, 1, 1, 1])
+        col = array([1, 2, 3, 4, 5, 6, 7, 8])
+        asp = self.coo_container((data, (row, col)), shape=(2,10))
+        bsp = asp.copy()
+        asp.eliminate_zeros()
+        assert_((asp.data != 0).all())
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    def test_reshape_copy(self):
+        arr = [[0, 10, 0, 0], [0, 0, 0, 0], [0, 20, 30, 40]]
+        new_shape = (2, 6)
+        x = self.coo_container(arr)
+
+        y = x.reshape(new_shape)
+        assert_(y.data is x.data)
+
+        y = x.reshape(new_shape, copy=False)
+        assert_(y.data is x.data)
+
+        y = x.reshape(new_shape, copy=True)
+        assert_(not np.may_share_memory(y.data, x.data))
+
+    def test_large_dimensions_reshape(self):
+        # Test that reshape is immune to integer overflow when number of elements
+        # exceeds 2^31-1
+        mat1 = self.coo_container(([1], ([3000000], [1000])), (3000001, 1001))
+        mat2 = self.coo_container(([1], ([1000], [3000000])), (1001, 3000001))
+
+        # assert_array_equal is slow for big matrices because it expects dense
+        # Using __ne__ and nnz instead
+        assert_((mat1.reshape((1001, 3000001), order='C') != mat2).nnz == 0)
+        assert_((mat2.reshape((3000001, 1001), order='F') != mat1).nnz == 0)
+
+
+class TestCOOMatrix(_MatrixMixin, TestCOO):
+    spcreator = coo_matrix
+
+
+TestCOO.init_class()
+TestCOOMatrix.init_class()
+
+
+class TestDIA(sparse_test_class(getset=False, slicing=False, slicing_assign=False,
+                                fancy_indexing=False, fancy_assign=False,
+                                minmax=False, nnz_axis=False)):
+    spcreator = dia_array
+    math_dtypes = [np.int_, np.float64, np.complex128]
+
+    def test_constructor1(self):
+        D = array([[1, 0, 3, 0],
+                   [1, 2, 0, 4],
+                   [0, 2, 3, 0],
+                   [0, 0, 3, 4]])
+        data = np.array([[1,2,3,4]]).repeat(3,axis=0)
+        offsets = np.array([0,-1,2])
+        assert_equal(self.dia_container((data, offsets), shape=(4, 4)).toarray(), D)
+
+    @pytest.mark.xfail(run=False, reason='DIA does not have a __getitem__')
+    def test_iterator(self):
+        pass
+
+    @with_64bit_maxval_limit(3)
+    def test_setdiag_dtype(self):
+        m = self.dia_container(np.eye(3))
+        assert_equal(m.offsets.dtype, np.int32)
+        m.setdiag((3,), k=2)
+        assert_equal(m.offsets.dtype, np.int32)
+
+        m = self.dia_container(np.eye(4))
+        assert_equal(m.offsets.dtype, np.int64)
+        m.setdiag((3,), k=3)
+        assert_equal(m.offsets.dtype, np.int64)
+
+    @pytest.mark.skip(reason='DIA stores extra zeros')
+    def test_getnnz_axis(self):
+        pass
+
+    def test_convert_gh14555(self):
+        # regression test for gh-14555
+        m = self.dia_container(([[1, 1, 0]], [-1]), shape=(4, 2))
+        expected = m.toarray()
+        assert_array_equal(m.tocsc().toarray(), expected)
+        assert_array_equal(m.tocsr().toarray(), expected)
+
+    def test_tocoo_gh10050(self):
+        # regression test for gh-10050
+        m = self.dia_container([[1, 2], [3, 4]]).tocoo()
+        flat_inds = np.ravel_multi_index((m.row, m.col), m.shape)
+        inds_are_sorted = np.all(np.diff(flat_inds) > 0)
+        assert m.has_canonical_format == inds_are_sorted
+
+    def test_tocoo_tocsr_tocsc_gh19245(self):
+        # test index_dtype with tocoo, tocsr, tocsc
+        data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
+        offsets = np.array([0, -1, 2], dtype=np.int32)
+        dia = sparse.dia_array((data, offsets), shape=(4, 4))
+
+        coo = dia.tocoo()
+        assert coo.col.dtype == np.int32
+        csr = dia.tocsr()
+        assert csr.indices.dtype == np.int32
+        csc = dia.tocsc()
+        assert csc.indices.dtype == np.int32
+
+    def test_mul_scalar(self):
+        # repro for gh-20434
+        m = self.dia_container([[1, 2], [0, 4]])
+        res = m * 3
+        assert isinstance(res, m.__class__)
+        assert_array_equal(res.toarray(), [[3, 6], [0, 12]])
+
+        res2 = m.multiply(3)
+        assert isinstance(res2, m.__class__)
+        assert_array_equal(res2.toarray(), [[3, 6], [0, 12]])
+
+
+class TestDIAMatrix(_MatrixMixin, TestDIA):
+    spcreator = dia_matrix
+
+
+TestDIA.init_class()
+TestDIAMatrix.init_class()
+
+
+class TestBSR(sparse_test_class(getset=False,
+                                slicing=False, slicing_assign=False,
+                                fancy_indexing=False, fancy_assign=False,
+                                nnz_axis=False)):
+    spcreator = bsr_array
+    math_dtypes = [np.int_, np.float64, np.complex128]
+
+    def test_constructor1(self):
+        # check native BSR format constructor
+        indptr = array([0,2,2,4])
+        indices = array([0,2,2,3])
+        data = zeros((4,2,3))
+
+        data[0] = array([[0, 1, 2],
+                         [3, 0, 5]])
+        data[1] = array([[0, 2, 4],
+                         [6, 0, 10]])
+        data[2] = array([[0, 4, 8],
+                         [12, 0, 20]])
+        data[3] = array([[0, 5, 10],
+                         [15, 0, 25]])
+
+        A = kron([[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]])
+        Asp = self.bsr_container((data,indices,indptr),shape=(6,12))
+        assert_equal(Asp.toarray(), A)
+
+        # infer shape from arrays
+        Asp = self.bsr_container((data,indices,indptr))
+        assert_equal(Asp.toarray(), A)
+
+    def test_constructor2(self):
+        # construct from dense
+
+        # test zero mats
+        for shape in [(1,1), (5,1), (1,10), (10,4), (3,7), (2,1)]:
+            A = zeros(shape)
+            assert_equal(self.bsr_container(A).toarray(), A)
+        A = zeros((4,6))
+        assert_equal(self.bsr_container(A, blocksize=(2, 2)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(2, 3)).toarray(), A)
+
+        A = kron([[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]])
+        assert_equal(self.bsr_container(A).toarray(), A)
+        assert_equal(self.bsr_container(A, shape=(6, 12)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(1, 1)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(2, 3)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(2, 6)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(2, 12)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(3, 12)).toarray(), A)
+        assert_equal(self.bsr_container(A, blocksize=(6, 12)).toarray(), A)
+
+        A = kron([[1,0,2,0],[0,1,0,0],[0,0,0,0]], [[0,1,2],[3,0,5]])
+        assert_equal(self.bsr_container(A, blocksize=(2, 3)).toarray(), A)
+
+    def test_constructor3(self):
+        # construct from coo-like (data,(row,col)) format
+        arg = ([1,2,3], ([0,1,1], [0,0,1]))
+        A = array([[1,0],[2,3]])
+        assert_equal(self.bsr_container(arg, blocksize=(2, 2)).toarray(), A)
+
+    def test_constructor4(self):
+        # regression test for gh-6292: self.bsr_matrix((data, indices, indptr)) was
+        #  trying to compare an int to a None
+        n = 8
+        data = np.ones((n, n, 1), dtype=np.int8)
+        indptr = np.array([0, n], dtype=np.int32)
+        indices = np.arange(n, dtype=np.int32)
+        self.bsr_container((data, indices, indptr), blocksize=(n, 1), copy=False)
+
+    def test_constructor5(self):
+        # check for validations introduced in gh-13400
+        n = 8
+        data_1dim = np.ones(n)
+        data = np.ones((n, n, n))
+        indptr = np.array([0, n])
+        indices = np.arange(n)
+
+        with assert_raises(ValueError):
+            # data ndim check
+            self.bsr_container((data_1dim, indices, indptr))
+
+        with assert_raises(ValueError):
+            # invalid blocksize
+            self.bsr_container((data, indices, indptr), blocksize=(1, 1, 1))
+
+        with assert_raises(ValueError):
+            # mismatching blocksize
+            self.bsr_container((data, indices, indptr), blocksize=(1, 1))
+
+    def test_default_dtype(self):
+        # As a numpy array, `values` has shape (2, 2, 1).
+        values = [[[1], [1]], [[1], [1]]]
+        indptr = np.array([0, 2], dtype=np.int32)
+        indices = np.array([0, 1], dtype=np.int32)
+        b = self.bsr_container((values, indices, indptr), blocksize=(2, 1))
+        assert b.dtype == np.array(values).dtype
+
+    def test_bsr_tocsr(self):
+        # check native conversion from BSR to CSR
+        indptr = array([0, 2, 2, 4])
+        indices = array([0, 2, 2, 3])
+        data = zeros((4, 2, 3))
+
+        data[0] = array([[0, 1, 2],
+                         [3, 0, 5]])
+        data[1] = array([[0, 2, 4],
+                         [6, 0, 10]])
+        data[2] = array([[0, 4, 8],
+                         [12, 0, 20]])
+        data[3] = array([[0, 5, 10],
+                         [15, 0, 25]])
+
+        A = kron([[1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 4, 5]],
+                 [[0, 1, 2], [3, 0, 5]])
+        Absr = self.bsr_container((data, indices, indptr), shape=(6, 12))
+        Acsr = Absr.tocsr()
+        Acsr_via_coo = Absr.tocoo().tocsr()
+        assert_equal(Acsr.toarray(), A)
+        assert_equal(Acsr.toarray(), Acsr_via_coo.toarray())
+
+    def test_eliminate_zeros(self):
+        data = kron([1, 0, 0, 0, 2, 0, 3, 0], [[1,1],[1,1]]).T
+        data = data.reshape(-1,2,2)
+        indices = array([1, 2, 3, 4, 5, 6, 7, 8])
+        indptr = array([0, 3, 8])
+        asp = self.bsr_container((data, indices, indptr), shape=(4,20))
+        bsp = asp.copy()
+        asp.eliminate_zeros()
+        assert_array_equal(asp.nnz, 3*4)
+        assert_array_equal(asp.toarray(), bsp.toarray())
+
+    # GitHub issue #9687
+    def test_eliminate_zeros_all_zero(self):
+        np.random.seed(0)
+        m = self.bsr_container(np.random.random((12, 12)), blocksize=(2, 3))
+
+        # eliminate some blocks, but not all
+        m.data[m.data <= 0.9] = 0
+        m.eliminate_zeros()
+        assert_equal(m.nnz, 66)
+        assert_array_equal(m.data.shape, (11, 2, 3))
+
+        # eliminate all remaining blocks
+        m.data[m.data <= 1.0] = 0
+        m.eliminate_zeros()
+        assert_equal(m.nnz, 0)
+        assert_array_equal(m.data.shape, (0, 2, 3))
+        assert_array_equal(m.toarray(), np.zeros((12, 12)))
+
+        # test fast path
+        m.eliminate_zeros()
+        assert_equal(m.nnz, 0)
+        assert_array_equal(m.data.shape, (0, 2, 3))
+        assert_array_equal(m.toarray(), np.zeros((12, 12)))
+
+    def test_bsr_matvec(self):
+        A = self.bsr_container(arange(2*3*4*5).reshape(2*4,3*5), blocksize=(4,5))
+        x = arange(A.shape[1]).reshape(-1,1)
+        assert_equal(A @ x, A.toarray() @ x)
+
+    def test_bsr_matvecs(self):
+        A = self.bsr_container(arange(2*3*4*5).reshape(2*4,3*5), blocksize=(4,5))
+        x = arange(A.shape[1]*6).reshape(-1,6)
+        assert_equal(A @ x, A.toarray() @ x)
+
+    @pytest.mark.xfail(run=False, reason='BSR does not have a __getitem__')
+    def test_iterator(self):
+        pass
+
+    @pytest.mark.xfail(run=False, reason='BSR does not have a __setitem__')
+    def test_setdiag(self):
+        pass
+
+    def test_resize_blocked(self):
+        # test resize() with non-(1,1) blocksize
+        D = np.array([[1, 0, 3, 4],
+                      [2, 0, 0, 0],
+                      [3, 0, 0, 0]])
+        S = self.spcreator(D, blocksize=(1, 2))
+        assert_(S.resize((3, 2)) is None)
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0],
+                                         [3, 0]])
+        S.resize((2, 2))
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0]])
+        S.resize((3, 2))
+        assert_array_equal(S.toarray(), [[1, 0],
+                                         [2, 0],
+                                         [0, 0]])
+        S.resize((3, 4))
+        assert_array_equal(S.toarray(), [[1, 0, 0, 0],
+                                         [2, 0, 0, 0],
+                                         [0, 0, 0, 0]])
+        assert_raises(ValueError, S.resize, (2, 3))
+
+    @pytest.mark.xfail(run=False, reason='BSR does not have a __setitem__')
+    def test_setdiag_comprehensive(self):
+        pass
+
+    @pytest.mark.skipif(IS_COLAB, reason="exceeds memory limit")
+    def test_scalar_idx_dtype(self):
+        # Check that index dtype takes into account all parameters
+        # passed to sparsetools, including the scalar ones
+        indptr = np.zeros(2, dtype=np.int32)
+        indices = np.zeros(0, dtype=np.int32)
+        vals = np.zeros((0, 1, 1))
+        a = self.bsr_container((vals, indices, indptr), shape=(1, 2**31-1))
+        b = self.bsr_container((vals, indices, indptr), shape=(1, 2**31))
+        c = self.bsr_container((1, 2**31-1))
+        d = self.bsr_container((1, 2**31))
+        assert_equal(a.indptr.dtype, np.int32)
+        assert_equal(b.indptr.dtype, np.int64)
+        assert_equal(c.indptr.dtype, np.int32)
+        assert_equal(d.indptr.dtype, np.int64)
+
+        try:
+            vals2 = np.zeros((0, 1, 2**31-1))
+            vals3 = np.zeros((0, 1, 2**31))
+            e = self.bsr_container((vals2, indices, indptr), shape=(1, 2**31-1))
+            f = self.bsr_container((vals3, indices, indptr), shape=(1, 2**31))
+            assert_equal(e.indptr.dtype, np.int32)
+            assert_equal(f.indptr.dtype, np.int64)
+        except (MemoryError, ValueError):
+            # May fail on 32-bit Python
+            e = 0
+            f = 0
+
+        # These shouldn't fail
+        for x in [a, b, c, d, e, f]:
+            x + x
+
+
+class TestBSRMatrix(_MatrixMixin, TestBSR):
+    spcreator = bsr_matrix
+
+
+TestBSR.init_class()
+TestBSRMatrix.init_class()
+
+
+#------------------------------------------------------------------------------
+# Tests for non-canonical representations (with duplicates, unsorted indices)
+#------------------------------------------------------------------------------
+
+def _same_sum_duplicate(data, *inds, **kwargs):
+    """Duplicates entries to produce the same matrix"""
+    indptr = kwargs.pop('indptr', None)
+    if np.issubdtype(data.dtype, np.bool_) or \
+       np.issubdtype(data.dtype, np.unsignedinteger):
+        if indptr is None:
+            return (data,) + inds
+        else:
+            return (data,) + inds + (indptr,)
+
+    zeros_pos = (data == 0).nonzero()
+
+    # duplicate data
+    data = data.repeat(2, axis=0)
+    data[::2] -= 1
+    data[1::2] = 1
+
+    # don't spoil all explicit zeros
+    if zeros_pos[0].size > 0:
+        pos = tuple(p[0] for p in zeros_pos)
+        pos1 = (2*pos[0],) + pos[1:]
+        pos2 = (2*pos[0]+1,) + pos[1:]
+        data[pos1] = 0
+        data[pos2] = 0
+
+    inds = tuple(indices.repeat(2) for indices in inds)
+
+    if indptr is None:
+        return (data,) + inds
+    else:
+        return (data,) + inds + (indptr * 2,)
+
+
+class _NonCanonicalMixin:
+    def spcreator(self, D, *args, sorted_indices=False, **kwargs):
+        """Replace D with a non-canonical equivalent: containing
+        duplicate elements and explicit zeros"""
+        construct = super().spcreator
+        M = construct(D, *args, **kwargs)
+
+        zero_pos = (M.toarray() == 0).nonzero()
+        has_zeros = (zero_pos[0].size > 0)
+        if has_zeros:
+            k = zero_pos[0].size//2
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                M = self._insert_explicit_zero(M, zero_pos[0][k], zero_pos[1][k])
+
+        arg1 = self._arg1_for_noncanonical(M, sorted_indices)
+        if 'shape' not in kwargs:
+            kwargs['shape'] = M.shape
+        NC = construct(arg1, **kwargs)
+
+        # check that result is valid
+        if NC.dtype in [np.float32, np.complex64]:
+            # For single-precision floats, the differences between M and NC
+            # that are introduced by the extra operations involved in the
+            # construction of NC necessitate a more lenient tolerance level
+            # than the default.
+            rtol = 1e-05
+        else:
+            rtol = 1e-07
+        assert_allclose(NC.toarray(), M.toarray(), rtol=rtol)
+
+        # check that at least one explicit zero
+        if has_zeros:
+            assert_((NC.data == 0).any())
+        # TODO check that NC has duplicates (which are not explicit zeros)
+
+        return NC
+
+    @pytest.mark.skip(reason='bool(matrix) counts explicit zeros')
+    def test_bool(self):
+        pass
+
+    @pytest.mark.skip(reason='getnnz-axis counts explicit zeros')
+    def test_getnnz_axis(self):
+        pass
+
+    @pytest.mark.skip(reason='nnz counts explicit zeros')
+    def test_empty(self):
+        pass
+
+
+class _NonCanonicalCompressedMixin(_NonCanonicalMixin):
+    def _arg1_for_noncanonical(self, M, sorted_indices=False):
+        """Return non-canonical constructor arg1 equivalent to M"""
+        data, indices, indptr = _same_sum_duplicate(M.data, M.indices,
+                                                    indptr=M.indptr)
+        if not sorted_indices:
+            for start, stop in zip(indptr, indptr[1:]):
+                indices[start:stop] = indices[start:stop][::-1].copy()
+                data[start:stop] = data[start:stop][::-1].copy()
+        return data, indices, indptr
+
+    def _insert_explicit_zero(self, M, i, j):
+        M[i,j] = 0
+        return M
+
+
+class _NonCanonicalCSMixin(_NonCanonicalCompressedMixin):
+    def test_getelement(self):
+        def check(dtype, sorted_indices):
+            D = array([[1,0,0],
+                       [4,3,0],
+                       [0,2,0],
+                       [0,0,0]], dtype=dtype)
+            A = self.spcreator(D, sorted_indices=sorted_indices)
+
+            M,N = D.shape
+
+            for i in range(-M, M):
+                for j in range(-N, N):
+                    assert_equal(A[i,j], D[i,j])
+
+            for ij in [(0,3),(-1,3),(4,0),(4,3),(4,-1), (1, 2, 3)]:
+                assert_raises((IndexError, TypeError), A.__getitem__, ij)
+
+        for dtype in supported_dtypes:
+            for sorted_indices in [False, True]:
+                check(np.dtype(dtype), sorted_indices)
+
+    def test_setitem_sparse(self):
+        D = np.eye(3)
+        A = self.spcreator(D)
+        B = self.spcreator([[1,2,3]])
+
+        D[1,:] = B.toarray()
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            A[1,:] = B
+        assert_array_equal(A.toarray(), D)
+
+        D[:,2] = B.toarray().ravel()
+        with suppress_warnings() as sup:
+            sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+            A[:,2] = B.T
+        assert_array_equal(A.toarray(), D)
+
+    @pytest.mark.xfail(run=False, reason='inverse broken with non-canonical matrix')
+    def test_inv(self):
+        pass
+
+    @pytest.mark.xfail(run=False, reason='solve broken with non-canonical matrix')
+    def test_solve(self):
+        pass
+
+
+class TestCSRNonCanonical(_NonCanonicalCSMixin, TestCSR):
+    pass
+
+
+class TestCSRNonCanonicalMatrix(TestCSRNonCanonical, TestCSRMatrix):
+    pass
+
+
+class TestCSCNonCanonical(_NonCanonicalCSMixin, TestCSC):
+    pass
+
+
+class TestCSCNonCanonicalMatrix(TestCSCNonCanonical, TestCSCMatrix):
+    pass
+
+
+class TestBSRNonCanonical(_NonCanonicalCompressedMixin, TestBSR):
+    def _insert_explicit_zero(self, M, i, j):
+        x = M.tocsr()
+        x[i,j] = 0
+        return x.tobsr(blocksize=M.blocksize)
+
+    @pytest.mark.xfail(run=False, reason='diagonal broken with non-canonical BSR')
+    def test_diagonal(self):
+        pass
+
+    @pytest.mark.xfail(run=False, reason='expm broken with non-canonical BSR')
+    def test_expm(self):
+        pass
+
+
+class TestBSRNonCanonicalMatrix(TestBSRNonCanonical, TestBSRMatrix):
+    pass
+
+
+class TestCOONonCanonical(_NonCanonicalMixin, TestCOO):
+    def _arg1_for_noncanonical(self, M, sorted_indices=None):
+        """Return non-canonical constructor arg1 equivalent to M"""
+        data, row, col = _same_sum_duplicate(M.data, M.row, M.col)
+        return data, (row, col)
+
+    def _insert_explicit_zero(self, M, i, j):
+        M.data = np.r_[M.data.dtype.type(0), M.data]
+        M.row = np.r_[M.row.dtype.type(i), M.row]
+        M.col = np.r_[M.col.dtype.type(j), M.col]
+        return M
+
+    def test_setdiag_noncanonical(self):
+        m = self.spcreator(np.eye(3))
+        m.sum_duplicates()
+        m.setdiag([3, 2], k=1)
+        m.sum_duplicates()
+        assert_(np.all(np.diff(m.col) >= 0))
+
+
+class TestCOONonCanonicalMatrix(TestCOONonCanonical, TestCOOMatrix):
+    pass
+
+
+#Todo: Revisit 64bit tests: avoid rerun of all tests for each version of get_index_dtype
+def cases_64bit(sp_api):
+    """Yield all tests for all formats that use get_index_dtype
+
+    This is more than testing get_index_dtype. It allows checking whether upcasting
+    or downcasting the index dtypes affects test results. The approach used here
+    does not try to figure out which tests might fail due to 32/64-bit issues.
+    We just run them all.
+    So, each test method in that uses cases_64bit reruns most of the test suite!
+    """
+    if sp_api == "sparray":
+        TEST_CLASSES = [TestBSR, TestCOO, TestCSC, TestCSR, TestDIA,
+                         # lil/dok->other conversion operations use get_index_dtype
+                         # so we include lil & dok test suite even though they do not
+                         # use get_index_dtype within the class. That means many of
+                         # these tests are superfluous, but it's hard to pick which
+                         TestDOK, TestLIL
+                        ]
+    elif sp_api == "spmatrix":
+        TEST_CLASSES = [TestBSRMatrix, TestCOOMatrix, TestCSCMatrix,
+                         TestCSRMatrix, TestDIAMatrix,
+                         # lil/dok->other conversion operations use get_index_dtype
+                         TestDOKMatrix, TestLILMatrix
+                        ]
+    else:
+        raise ValueError(f"parameter {sp_api=} is not one of 'sparray' or 'spmatrix'")
+
+    # The following features are missing, so skip the tests:
+    SKIP_TESTS = {
+        'test_expm': 'expm for 64-bit indices not available',
+        'test_inv': 'linsolve for 64-bit indices not available',
+        'test_solve': 'linsolve for 64-bit indices not available',
+        'test_scalar_idx_dtype': 'test implemented in base class',
+        'test_large_dimensions_reshape': 'test actually requires 64-bit to work',
+        'test_constructor_smallcol': 'test verifies int32 indexes',
+        'test_constructor_largecol': 'test verifies int64 indexes',
+        'test_tocoo_tocsr_tocsc_gh19245': 'test verifies int32 indexes',
+    }
+
+    for cls in TEST_CLASSES:
+        for method_name in sorted(dir(cls)):
+            method = getattr(cls, method_name)
+            if (method_name.startswith('test_') and
+                    not getattr(method, 'slow', False)):
+                marks = []
+
+                msg = SKIP_TESTS.get(method_name)
+                if bool(msg):
+                    marks += [pytest.mark.skip(reason=msg)]
+
+                markers = getattr(method, 'pytestmark', [])
+                for mark in markers:
+                    if mark.name in ('skipif', 'skip', 'xfail', 'xslow'):
+                        marks.append(mark)
+
+                yield pytest.param(cls, method_name, marks=marks)
+
+
+class Test64Bit:
+    # classes that use get_index_dtype
+    MAT_CLASSES = [
+        bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dia_matrix,
+        bsr_array, coo_array, csc_array, csr_array, dia_array,
+    ]
+
+    def _compare_index_dtype(self, m, dtype):
+        dtype = np.dtype(dtype)
+        if m.format in ['csc', 'csr', 'bsr']:
+            return (m.indices.dtype == dtype) and (m.indptr.dtype == dtype)
+        elif m.format == 'coo':
+            return (m.row.dtype == dtype) and (m.col.dtype == dtype)
+        elif m.format == 'dia':
+            return (m.offsets.dtype == dtype)
+        else:
+            raise ValueError(f"matrix {m!r} has no integer indices")
+
+    @pytest.mark.thread_unsafe
+    def test_decorator_maxval_limit(self):
+        # Test that the with_64bit_maxval_limit decorator works
+
+        @with_64bit_maxval_limit(maxval_limit=10)
+        def check(mat_cls):
+            m = mat_cls(np.random.rand(10, 1))
+            assert_(self._compare_index_dtype(m, np.int32))
+            m = mat_cls(np.random.rand(11, 1))
+            assert_(self._compare_index_dtype(m, np.int64))
+
+        for mat_cls in self.MAT_CLASSES:
+            check(mat_cls)
+
+    @pytest.mark.thread_unsafe
+    def test_decorator_maxval_random(self):
+        # Test that the with_64bit_maxval_limit decorator works (2)
+
+        @with_64bit_maxval_limit(random=True)
+        def check(mat_cls):
+            seen_32 = False
+            seen_64 = False
+            for k in range(100):
+                m = mat_cls(np.random.rand(9, 9))
+                seen_32 = seen_32 or self._compare_index_dtype(m, np.int32)
+                seen_64 = seen_64 or self._compare_index_dtype(m, np.int64)
+                if seen_32 and seen_64:
+                    break
+            else:
+                raise AssertionError("both 32 and 64 bit indices not seen")
+
+        for mat_cls in self.MAT_CLASSES:
+            check(mat_cls)
+
+    @pytest.mark.thread_unsafe
+    def test_downcast_intp(self):
+        # Check that bincount and ufunc.reduceat intp downcasts are
+        # dealt with. The point here is to trigger points in the code
+        # that can fail on 32-bit systems when using 64-bit indices,
+        # due to use of functions that only work with intp-size
+        # indices.
+
+        @with_64bit_maxval_limit(fixed_dtype=np.int64, downcast_maxval=1)
+        def check_limited(csc_container, csr_container, coo_container):
+            # These involve indices larger than `downcast_maxval`
+            a = csc_container([[1, 2], [3, 4], [5, 6]])
+            assert_raises(AssertionError, a.count_nonzero, axis=1)
+            assert_raises(AssertionError, a.sum, axis=0)
+
+            a = csr_container([[1, 2, 3], [3, 4, 6]])
+            assert_raises(AssertionError, a.count_nonzero, axis=0)
+            assert_raises(AssertionError, a.sum, axis=1)
+
+            a = coo_container([[1, 2, 3], [3, 4, 5]])
+            assert_raises(AssertionError, a.count_nonzero, axis=0)
+            a.has_canonical_format = False
+            assert_raises(AssertionError, a.sum_duplicates)
+
+        @with_64bit_maxval_limit(fixed_dtype=np.int64)
+        def check_unlimited(csc_container, csr_container, coo_container):
+            # These involve indices smaller than `downcast_maxval`
+            a = csc_container([[1, 2], [3, 4], [5, 6]])
+            a.count_nonzero(axis=1)
+            a.sum(axis=0)
+
+            a = csr_container([[1, 2, 3], [3, 4, 6]])
+            a.count_nonzero(axis=0)
+            a.sum(axis=1)
+
+            a = coo_container([[1, 2, 3], [3, 4, 5]])
+            a.count_nonzero(axis=0)
+            a.has_canonical_format = False
+            a.sum_duplicates()
+
+        check_limited(csc_array, csr_array, coo_array)
+        check_unlimited(csc_array, csr_array, coo_array)
+        check_limited(csc_matrix, csr_matrix, coo_matrix)
+        check_unlimited(csc_matrix, csr_matrix, coo_matrix)
+
+
+# Testing both spmatrices and sparrays for 64bit index dtype handling is
+# expensive and double-checks the same code (e.g. _coobase)
+class RunAll64Bit:
+    def _check_resiliency(self, cls, method_name, **kw):
+        # Resiliency test, to check that sparse matrices deal reasonably
+        # with varying index data types.
+
+        @with_64bit_maxval_limit(**kw)
+        def check(cls, method_name):
+            instance = cls()
+            if hasattr(instance, 'setup_method'):
+                instance.setup_method()
+            try:
+                getattr(instance, method_name)()
+            finally:
+                if hasattr(instance, 'teardown_method'):
+                    instance.teardown_method()
+
+        check(cls, method_name)
+
+
+@pytest.mark.thread_unsafe
+@pytest.mark.slow
+class Test64BitArray(RunAll64Bit):
+    # inheritance of pytest test classes does not separate marks for subclasses.
+    # So we define these functions in both Array and Matrix versions.
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("sparray"))
+    def test_resiliency_limit_10(self, cls, method_name):
+        self._check_resiliency(cls, method_name, maxval_limit=10)
+
+    @pytest.mark.fail_slow(2)
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("sparray"))
+    def test_resiliency_random(self, cls, method_name):
+        # bsr_array.eliminate_zeros relies on csr_array constructor
+        # not making copies of index arrays --- this is not
+        # necessarily true when we pick the index data type randomly
+        self._check_resiliency(cls, method_name, random=True)
+
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("sparray"))
+    def test_resiliency_all_32(self, cls, method_name):
+        self._check_resiliency(cls, method_name, fixed_dtype=np.int32)
+
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("sparray"))
+    def test_resiliency_all_64(self, cls, method_name):
+        self._check_resiliency(cls, method_name, fixed_dtype=np.int64)
+
+
+@pytest.mark.thread_unsafe
+class Test64BitMatrix(RunAll64Bit):
+    # assert_32bit=True only for spmatrix cuz sparray does not check index content
+    @pytest.mark.fail_slow(5)
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("spmatrix"))
+    def test_no_64(self, cls, method_name):
+        self._check_resiliency(cls, method_name, assert_32bit=True)
+
+    # inheritance of pytest test classes does not separate marks for subclasses.
+    # So we define these functions in both Array and Matrix versions.
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("spmatrix"))
+    def test_resiliency_limit_10(self, cls, method_name):
+        self._check_resiliency(cls, method_name, maxval_limit=10)
+
+    @pytest.mark.fail_slow(2)
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("spmatrix"))
+    def test_resiliency_random(self, cls, method_name):
+        # bsr_array.eliminate_zeros relies on csr_array constructor
+        # not making copies of index arrays --- this is not
+        # necessarily true when we pick the index data type randomly
+        self._check_resiliency(cls, method_name, random=True)
+
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("spmatrix"))
+    def test_resiliency_all_32(self, cls, method_name):
+        self._check_resiliency(cls, method_name, fixed_dtype=np.int32)
+
+    @pytest.mark.parametrize('cls,method_name', cases_64bit("spmatrix"))
+    def test_resiliency_all_64(self, cls, method_name):
+        self._check_resiliency(cls, method_name, fixed_dtype=np.int64)
+
+def test_broadcast_to():
+    a = np.array([[1, 0, 2]])
+    b = np.array([[1], [0], [2]])
+    c = np.array([[1, 0, 2], [0, 3, 0]])
+    d = np.array([[7]])
+    e = np.array([[0]])
+    f = np.array([[0,0,0,0]])
+    for container in (csc_matrix, csc_array, csr_matrix, csr_array):
+        res_a = container(a)._broadcast_to((2,3))
+        res_b = container(b)._broadcast_to((3,4))
+        res_c = container(c)._broadcast_to((2,3))
+        res_d = container(d)._broadcast_to((4,4))
+        res_e = container(e)._broadcast_to((5,6))
+        res_f = container(f)._broadcast_to((2,4))
+        assert_array_equal(res_a.toarray(), np.broadcast_to(a, (2,3)))
+        assert_array_equal(res_b.toarray(), np.broadcast_to(b, (3,4)))
+        assert_array_equal(res_c.toarray(), c)
+        assert_array_equal(res_d.toarray(), np.broadcast_to(d, (4,4)))
+        assert_array_equal(res_e.toarray(), np.broadcast_to(e, (5,6)))
+        assert_array_equal(res_f.toarray(), np.broadcast_to(f, (2,4)))
+
+        with pytest.raises(ValueError, match="cannot be broadcast"):
+            container([[1, 2, 0], [3, 0, 1]])._broadcast_to(shape=(2, 1))
+
+        with pytest.raises(ValueError, match="cannot be broadcast"):
+            container([[0, 1, 2]])._broadcast_to(shape=(3, 2))
diff --git a/shared/package-files/1b/1b34810f4ea3be54583aabbb0f96796b9c76e65f499d382d39b9fb4b80fdcee8/dark_background.mplstyle b/shared/package-files/1b/1b34810f4ea3be54583aabbb0f96796b9c76e65f499d382d39b9fb4b80fdcee8/dark_background.mplstyle
new file mode 100644
index 0000000000000000000000000000000000000000..61a99f3c0d10c3676ec466b53f716d69df40bb1f
--- /dev/null
+++ b/shared/package-files/1b/1b34810f4ea3be54583aabbb0f96796b9c76e65f499d382d39b9fb4b80fdcee8/dark_background.mplstyle
@@ -0,0 +1,26 @@
+# Set black background default line colors to white.
+
+lines.color: white
+patch.edgecolor: white
+
+text.color: white
+
+axes.facecolor: black
+axes.edgecolor: white
+axes.labelcolor: white
+axes.prop_cycle: cycler('color', ['8dd3c7', 'feffb3', 'bfbbd9', 'fa8174', '81b1d2', 'fdb462', 'b3de69', 'bc82bd', 'ccebc4', 'ffed6f'])
+
+xtick.color: white
+ytick.color: white
+
+grid.color: white
+
+figure.facecolor: black
+figure.edgecolor: black
+
+### Boxplots
+boxplot.boxprops.color: white
+boxplot.capprops.color: white
+boxplot.flierprops.color: white
+boxplot.flierprops.markeredgecolor: white
+boxplot.whiskerprops.color: white
diff --git a/shared/package-files/1b/1b348ed35591082ad7625a7c1d2871af22894f052f70022683d17af16b8b9dcd/cloudpickle_main_class.py b/shared/package-files/1b/1b348ed35591082ad7625a7c1d2871af22894f052f70022683d17af16b8b9dcd/cloudpickle_main_class.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e29315b37219aa23e9d59051538bf6d311cbd14
--- /dev/null
+++ b/shared/package-files/1b/1b348ed35591082ad7625a7c1d2871af22894f052f70022683d17af16b8b9dcd/cloudpickle_main_class.py
@@ -0,0 +1,6 @@
+# Expected to run this module as __main__
+
+
+# Cloudpickle will think this is a dynamic class when this module is __main__
+class Klass:
+    classvar = None
diff --git a/shared/package-files/1b/1b369691c44c67316d5deea0a71bddc04d9a2ee267efba57b0afe484a3309ad2/batchnorm.py b/shared/package-files/1b/1b369691c44c67316d5deea0a71bddc04d9a2ee267efba57b0afe484a3309ad2/batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfef31268cff11f937b806701d6e6667b1c3622f
--- /dev/null
+++ b/shared/package-files/1b/1b369691c44c67316d5deea0a71bddc04d9a2ee267efba57b0afe484a3309ad2/batchnorm.py
@@ -0,0 +1,106 @@
+import torch
+import torch.ao.nn.intrinsic as nni
+
+__all__ = [
+    "BatchNorm2d",
+    "BatchNorm3d"
+]
+
+class _BatchNorm(torch.nn.modules.batchnorm._BatchNorm):
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_features, eps, momentum, True, True, **factory_kwargs)
+        self.register_buffer('scale', torch.tensor(1.0, **factory_kwargs))
+        self.register_buffer('zero_point', torch.tensor(0, **factory_kwargs))
+
+    @staticmethod
+    def from_float(cls, mod):
+        activation_post_process = mod.activation_post_process
+        if type(mod) == cls._NNI_BN_RELU_MODULE:
+            mod = mod[0]
+        scale, zero_point = activation_post_process.calculate_qparams()
+        new_mod = cls(mod.num_features, mod.eps)
+        new_mod.weight = mod.weight
+        new_mod.bias = mod.bias
+        new_mod.running_mean = mod.running_mean
+        new_mod.running_var = mod.running_var
+        new_mod.scale = scale
+        new_mod.zero_point = zero_point
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, bn, output_scale, output_zero_point):
+        qbn = cls(
+            bn.num_features,
+            bn.eps,
+            bn.momentum,
+            device=bn.weight.device,
+            dtype=bn.weight.dtype
+        )
+        qbn.weight = bn.weight
+        qbn.bias = bn.bias
+        qbn.running_mean = bn.running_mean
+        qbn.running_var = bn.running_var
+        qbn.scale = output_scale
+        qbn.zero_point = output_zero_point
+        return qbn
+
+class BatchNorm2d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm2d`.
+    """
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU2d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return 'QuantizedBatchNorm2d'
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm2d(
+            input, self.weight, self.bias, self.running_mean,
+            self.running_var, self.eps, self.scale, self.zero_point)
+
+    @classmethod
+    def from_float(cls, mod):
+        return _BatchNorm.from_float(cls, mod)
+
+class BatchNorm3d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm3d`.
+    """
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return 'QuantizedBatchNorm3d'
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm3d(
+            input, self.weight, self.bias, self.running_mean,
+            self.running_var, self.eps, self.scale, self.zero_point)
+
+    @classmethod
+    def from_float(cls, mod):
+        return _BatchNorm.from_float(cls, mod)
diff --git a/shared/package-files/1b/1b36a4ac98dd6da56d9cf83f82bd166133aefee93842d30894dc32aa02eb524a/annotation.py b/shared/package-files/1b/1b36a4ac98dd6da56d9cf83f82bd166133aefee93842d30894dc32aa02eb524a/annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..81780010fb7f28dc7deb0eff61e408e5390ec238
--- /dev/null
+++ b/shared/package-files/1b/1b36a4ac98dd6da56d9cf83f82bd166133aefee93842d30894dc32aa02eb524a/annotation.py
@@ -0,0 +1,470 @@
+"""
+PEP 0484 ( https://www.python.org/dev/peps/pep-0484/ ) describes type hints
+through function annotations. There is a strong suggestion in this document
+that only the type of type hinting defined in PEP0484 should be allowed
+as annotations in future python versions.
+"""
+
+import re
+from inspect import Parameter
+
+from parso import ParserSyntaxError, parse
+
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.base_value import ValueSet, NO_VALUES
+from jedi.inference.gradual.base import DefineGenericBaseClass, GenericClass
+from jedi.inference.gradual.generics import TupleGenericManager
+from jedi.inference.gradual.type_var import TypeVar
+from jedi.inference.helpers import is_string
+from jedi.inference.compiled import builtin_from_name
+from jedi.inference.param import get_executed_param_names
+from jedi import debug
+from jedi import parser_utils
+
+
+def infer_annotation(context, annotation):
+    """
+    Inferes an annotation node. This means that it inferes the part of
+    `int` here:
+
+        foo: int = 3
+
+    Also checks for forward references (strings)
+    """
+    value_set = context.infer_node(annotation)
+    if len(value_set) != 1:
+        debug.warning("Inferred typing index %s should lead to 1 object, "
+                      " not %s" % (annotation, value_set))
+        return value_set
+
+    inferred_value = list(value_set)[0]
+    if is_string(inferred_value):
+        result = _get_forward_reference_node(context, inferred_value.get_safe_value())
+        if result is not None:
+            return context.infer_node(result)
+    return value_set
+
+
+def _infer_annotation_string(context, string, index=None):
+    node = _get_forward_reference_node(context, string)
+    if node is None:
+        return NO_VALUES
+
+    value_set = context.infer_node(node)
+    if index is not None:
+        value_set = value_set.filter(
+            lambda value: (
+                value.array_type == 'tuple'
+                and len(list(value.py__iter__())) >= index
+            )
+        ).py__simple_getitem__(index)
+    return value_set
+
+
+def _get_forward_reference_node(context, string):
+    try:
+        new_node = context.inference_state.grammar.parse(
+            string,
+            start_symbol='eval_input',
+            error_recovery=False
+        )
+    except ParserSyntaxError:
+        debug.warning('Annotation not parsed: %s' % string)
+        return None
+    else:
+        module = context.tree_node.get_root_node()
+        parser_utils.move(new_node, module.end_pos[0])
+        new_node.parent = context.tree_node
+        return new_node
+
+
+def _split_comment_param_declaration(decl_text):
+    """
+    Split decl_text on commas, but group generic expressions
+    together.
+
+    For example, given "foo, Bar[baz, biz]" we return
+    ['foo', 'Bar[baz, biz]'].
+
+    """
+    try:
+        node = parse(decl_text, error_recovery=False).children[0]
+    except ParserSyntaxError:
+        debug.warning('Comment annotation is not valid Python: %s' % decl_text)
+        return []
+
+    if node.type in ['name', 'atom_expr', 'power']:
+        return [node.get_code().strip()]
+
+    params = []
+    try:
+        children = node.children
+    except AttributeError:
+        return []
+    else:
+        for child in children:
+            if child.type in ['name', 'atom_expr', 'power']:
+                params.append(child.get_code().strip())
+
+    return params
+
+
+@inference_state_method_cache()
+def infer_param(function_value, param, ignore_stars=False):
+    values = _infer_param(function_value, param)
+    if ignore_stars or not values:
+        return values
+    inference_state = function_value.inference_state
+    if param.star_count == 1:
+        tuple_ = builtin_from_name(inference_state, 'tuple')
+        return ValueSet([GenericClass(
+            tuple_,
+            TupleGenericManager((values,)),
+        )])
+    elif param.star_count == 2:
+        dct = builtin_from_name(inference_state, 'dict')
+        generics = (
+            ValueSet([builtin_from_name(inference_state, 'str')]),
+            values
+        )
+        return ValueSet([GenericClass(
+            dct,
+            TupleGenericManager(generics),
+        )])
+    return values
+
+
+def _infer_param(function_value, param):
+    """
+    Infers the type of a function parameter, using type annotations.
+    """
+    annotation = param.annotation
+    if annotation is None:
+        # If no Python 3-style annotation, look for a comment annotation.
+        # Identify parameters to function in the same sequence as they would
+        # appear in a type comment.
+        all_params = [child for child in param.parent.children
+                      if child.type == 'param']
+
+        node = param.parent.parent
+        comment = parser_utils.get_following_comment_same_line(node)
+        if comment is None:
+            return NO_VALUES
+
+        match = re.match(r"^#\s*type:\s*\(([^#]*)\)\s*->", comment)
+        if not match:
+            return NO_VALUES
+        params_comments = _split_comment_param_declaration(match.group(1))
+
+        # Find the specific param being investigated
+        index = all_params.index(param)
+        # If the number of parameters doesn't match length of type comment,
+        # ignore first parameter (assume it's self).
+        if len(params_comments) != len(all_params):
+            debug.warning(
+                "Comments length != Params length %s %s",
+                params_comments, all_params
+            )
+        if function_value.is_bound_method():
+            if index == 0:
+                # Assume it's self, which is already handled
+                return NO_VALUES
+            index -= 1
+        if index >= len(params_comments):
+            return NO_VALUES
+
+        param_comment = params_comments[index]
+        return _infer_annotation_string(
+            function_value.get_default_param_context(),
+            param_comment
+        )
+    # Annotations are like default params and resolve in the same way.
+    context = function_value.get_default_param_context()
+    return infer_annotation(context, annotation)
+
+
+def py__annotations__(funcdef):
+    dct = {}
+    for function_param in funcdef.get_params():
+        param_annotation = function_param.annotation
+        if param_annotation is not None:
+            dct[function_param.name.value] = param_annotation
+
+    return_annotation = funcdef.annotation
+    if return_annotation:
+        dct['return'] = return_annotation
+    return dct
+
+
+def resolve_forward_references(context, all_annotations):
+    def resolve(node):
+        if node is None or node.type != 'string':
+            return node
+
+        node = _get_forward_reference_node(
+            context,
+            context.inference_state.compiled_subprocess.safe_literal_eval(
+                node.value,
+            ),
+        )
+
+        if node is None:
+            # There was a string, but it's not a valid annotation
+            return None
+
+        # The forward reference tree has an additional root node ('eval_input')
+        # that we don't want. Extract the node we do want, that is equivalent to
+        # the nodes returned by `py__annotations__` for a non-quoted node.
+        node = node.children[0]
+
+        return node
+
+    return {name: resolve(node) for name, node in all_annotations.items()}
+
+
+@inference_state_method_cache()
+def infer_return_types(function, arguments):
+    """
+    Infers the type of a function's return value,
+    according to type annotations.
+    """
+    context = function.get_default_param_context()
+    all_annotations = resolve_forward_references(
+        context,
+        py__annotations__(function.tree_node),
+    )
+    annotation = all_annotations.get("return", None)
+    if annotation is None:
+        # If there is no Python 3-type annotation, look for an annotation
+        # comment.
+        node = function.tree_node
+        comment = parser_utils.get_following_comment_same_line(node)
+        if comment is None:
+            return NO_VALUES
+
+        match = re.match(r"^#\s*type:\s*\([^#]*\)\s*->\s*([^#]*)", comment)
+        if not match:
+            return NO_VALUES
+
+        return _infer_annotation_string(
+            context,
+            match.group(1).strip()
+        ).execute_annotation()
+
+    unknown_type_vars = find_unknown_type_vars(context, annotation)
+    annotation_values = infer_annotation(context, annotation)
+    if not unknown_type_vars:
+        return annotation_values.execute_annotation()
+
+    type_var_dict = infer_type_vars_for_execution(function, arguments, all_annotations)
+
+    return ValueSet.from_sets(
+        ann.define_generics(type_var_dict)
+        if isinstance(ann, (DefineGenericBaseClass, TypeVar)) else ValueSet({ann})
+        for ann in annotation_values
+    ).execute_annotation()
+
+
+def infer_type_vars_for_execution(function, arguments, annotation_dict):
+    """
+    Some functions use type vars that are not defined by the class, but rather
+    only defined in the function. See for example `iter`. In those cases we
+    want to:
+
+    1. Search for undefined type vars.
+    2. Infer type vars with the execution state we have.
+    3. Return the union of all type vars that have been found.
+    """
+    context = function.get_default_param_context()
+
+    annotation_variable_results = {}
+    executed_param_names = get_executed_param_names(function, arguments)
+    for executed_param_name in executed_param_names:
+        try:
+            annotation_node = annotation_dict[executed_param_name.string_name]
+        except KeyError:
+            continue
+
+        annotation_variables = find_unknown_type_vars(context, annotation_node)
+        if annotation_variables:
+            # Infer unknown type var
+            annotation_value_set = context.infer_node(annotation_node)
+            kind = executed_param_name.get_kind()
+            actual_value_set = executed_param_name.infer()
+            if kind is Parameter.VAR_POSITIONAL:
+                actual_value_set = actual_value_set.merge_types_of_iterate()
+            elif kind is Parameter.VAR_KEYWORD:
+                # TODO _dict_values is not public.
+                actual_value_set = actual_value_set.try_merge('_dict_values')
+            merge_type_var_dicts(
+                annotation_variable_results,
+                annotation_value_set.infer_type_vars(actual_value_set),
+            )
+    return annotation_variable_results
+
+
+def infer_return_for_callable(arguments, param_values, result_values):
+    all_type_vars = {}
+    for pv in param_values:
+        if pv.array_type == 'list':
+            type_var_dict = _infer_type_vars_for_callable(arguments, pv.py__iter__())
+            all_type_vars.update(type_var_dict)
+
+    return ValueSet.from_sets(
+        v.define_generics(all_type_vars)
+        if isinstance(v, (DefineGenericBaseClass, TypeVar))
+        else ValueSet({v})
+        for v in result_values
+    ).execute_annotation()
+
+
+def _infer_type_vars_for_callable(arguments, lazy_params):
+    """
+    Infers type vars for the Calllable class:
+
+        def x() -> Callable[[Callable[..., _T]], _T]: ...
+    """
+    annotation_variable_results = {}
+    for (_, lazy_value), lazy_callable_param in zip(arguments.unpack(), lazy_params):
+        callable_param_values = lazy_callable_param.infer()
+        # Infer unknown type var
+        actual_value_set = lazy_value.infer()
+        merge_type_var_dicts(
+            annotation_variable_results,
+            callable_param_values.infer_type_vars(actual_value_set),
+        )
+    return annotation_variable_results
+
+
+def merge_type_var_dicts(base_dict, new_dict):
+    for type_var_name, values in new_dict.items():
+        if values:
+            try:
+                base_dict[type_var_name] |= values
+            except KeyError:
+                base_dict[type_var_name] = values
+
+
+def merge_pairwise_generics(annotation_value, annotated_argument_class):
+    """
+    Match up the generic parameters from the given argument class to the
+    target annotation.
+
+    This walks the generic parameters immediately within the annotation and
+    argument's type, in order to determine the concrete values of the
+    annotation's parameters for the current case.
+
+    For example, given the following code:
+
+        def values(mapping: Mapping[K, V]) -> List[V]: ...
+
+        for val in values({1: 'a'}):
+            val
+
+    Then this function should be given representations of `Mapping[K, V]`
+    and `Mapping[int, str]`, so that it can determine that `K` is `int and
+    `V` is `str`.
+
+    Note that it is responsibility of the caller to traverse the MRO of the
+    argument type as needed in order to find the type matching the
+    annotation (in this case finding `Mapping[int, str]` as a parent of
+    `Dict[int, str]`).
+
+    Parameters
+    ----------
+
+    `annotation_value`: represents the annotation to infer the concrete
+        parameter types of.
+
+    `annotated_argument_class`: represents the annotated class of the
+        argument being passed to the object annotated by `annotation_value`.
+    """
+
+    type_var_dict = {}
+
+    if not isinstance(annotated_argument_class, DefineGenericBaseClass):
+        return type_var_dict
+
+    annotation_generics = annotation_value.get_generics()
+    actual_generics = annotated_argument_class.get_generics()
+
+    for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics):
+        merge_type_var_dicts(
+            type_var_dict,
+            annotation_generics_set.infer_type_vars(actual_generic_set.execute_annotation()),
+        )
+
+    return type_var_dict
+
+
+def find_type_from_comment_hint_for(context, node, name):
+    return _find_type_from_comment_hint(context, node, node.children[1], name)
+
+
+def find_type_from_comment_hint_with(context, node, name):
+    assert len(node.children[1].children) == 3, \
+        "Can only be here when children[1] is 'foo() as f'"
+    varlist = node.children[1].children[2]
+    return _find_type_from_comment_hint(context, node, varlist, name)
+
+
+def find_type_from_comment_hint_assign(context, node, name):
+    return _find_type_from_comment_hint(context, node, node.children[0], name)
+
+
+def _find_type_from_comment_hint(context, node, varlist, name):
+    index = None
+    if varlist.type in ("testlist_star_expr", "exprlist", "testlist"):
+        # something like "a, b = 1, 2"
+        index = 0
+        for child in varlist.children:
+            if child == name:
+                break
+            if child.type == "operator":
+                continue
+            index += 1
+        else:
+            return []
+
+    comment = parser_utils.get_following_comment_same_line(node)
+    if comment is None:
+        return []
+    match = re.match(r"^#\s*type:\s*([^#]*)", comment)
+    if match is None:
+        return []
+    return _infer_annotation_string(
+        context, match.group(1).strip(), index
+    ).execute_annotation()
+
+
+def find_unknown_type_vars(context, node):
+    def check_node(node):
+        if node.type in ('atom_expr', 'power'):
+            trailer = node.children[-1]
+            if trailer.type == 'trailer' and trailer.children[0] == '[':
+                for subscript_node in _unpack_subscriptlist(trailer.children[1]):
+                    check_node(subscript_node)
+        else:
+            found[:] = _filter_type_vars(context.infer_node(node), found)
+
+    found = []  # We're not using a set, because the order matters.
+    check_node(node)
+    return found
+
+
+def _filter_type_vars(value_set, found=()):
+    new_found = list(found)
+    for type_var in value_set:
+        if isinstance(type_var, TypeVar) and type_var not in found:
+            new_found.append(type_var)
+    return new_found
+
+
+def _unpack_subscriptlist(subscriptlist):
+    if subscriptlist.type == 'subscriptlist':
+        for subscript in subscriptlist.children[::2]:
+            if subscript.type != 'subscript':
+                yield subscript
+    else:
+        if subscriptlist.type != 'subscript':
+            yield subscriptlist
diff --git a/shared/package-files/1b/1b383361b51abc904bb83f6f09f2c03a4496b3f2ea57ddb42b835b7deab4f7cc/dominating.py b/shared/package-files/1b/1b383361b51abc904bb83f6f09f2c03a4496b3f2ea57ddb42b835b7deab4f7cc/dominating.py
new file mode 100644
index 0000000000000000000000000000000000000000..042e2b88892bb79c09b7e9cb0d63be4b4ba07d16
--- /dev/null
+++ b/shared/package-files/1b/1b383361b51abc904bb83f6f09f2c03a4496b3f2ea57ddb42b835b7deab4f7cc/dominating.py
@@ -0,0 +1,93 @@
+"""Functions for computing dominating sets in a graph."""
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import arbitrary_element
+
+__all__ = ["dominating_set", "is_dominating_set"]
+
+
+def dominating_set(G, start_with=None):
+    r"""Finds a dominating set for the graph G.
+
+    A *dominating set* for a graph with node set *V* is a subset *D* of
+    *V* such that every node not in *D* is adjacent to at least one
+    member of *D* [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    start_with : node (default=None)
+        Node to use as a starting point for the algorithm.
+
+    Returns
+    -------
+    D : set
+        A dominating set for G.
+
+    Notes
+    -----
+    This function is an implementation of algorithm 7 in [2]_ which
+    finds some dominating set, not necessarily the smallest one.
+
+    See also
+    --------
+    is_dominating_set
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+
+    .. [2] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    all_nodes = set(G)
+    if start_with is None:
+        start_with = arbitrary_element(all_nodes)
+    if start_with not in G:
+        raise nx.NetworkXError(f"node {start_with} is not in G")
+    dominating_set = {start_with}
+    dominated_nodes = set(G[start_with])
+    remaining_nodes = all_nodes - dominated_nodes - dominating_set
+    while remaining_nodes:
+        # Choose an arbitrary node and determine its undominated neighbors.
+        v = remaining_nodes.pop()
+        undominated_neighbors = set(G[v]) - dominating_set
+        # Add the node to the dominating set and the neighbors to the
+        # dominated set. Finally, remove all of those nodes from the set
+        # of remaining nodes.
+        dominating_set.add(v)
+        dominated_nodes |= undominated_neighbors
+        remaining_nodes -= undominated_neighbors
+    return dominating_set
+
+
+@nx._dispatch
+def is_dominating_set(G, nbunch):
+    """Checks if `nbunch` is a dominating set for `G`.
+
+    A *dominating set* for a graph with node set *V* is a subset *D* of
+    *V* such that every node not in *D* is adjacent to at least one
+    member of *D* [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch : iterable
+        An iterable of nodes in the graph `G`.
+
+    See also
+    --------
+    dominating_set
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+
+    """
+    testset = {n for n in nbunch if n in G}
+    nbrs = set(chain.from_iterable(G[n] for n in testset))
+    return len(set(G) - testset - nbrs) == 0
diff --git a/shared/package-files/1b/1b3b4f9eec96d8dea9f9c5dce9be24d76ca09b70bf291f409373027feb6822b0/_tabular.py b/shared/package-files/1b/1b3b4f9eec96d8dea9f9c5dce9be24d76ca09b70bf291f409373027feb6822b0/_tabular.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a6e5421654bb5b1b84f49dff6fbbacbea0d9f2
--- /dev/null
+++ b/shared/package-files/1b/1b3b4f9eec96d8dea9f9c5dce9be24d76ca09b70bf291f409373027feb6822b0/_tabular.py
@@ -0,0 +1,347 @@
+import logging
+
+import numpy as np
+import pandas as pd
+from numba import njit
+
+from .. import utils
+from .._serializable import Deserializer, Serializer
+from ..utils import MaskedModel
+from ..utils._exceptions import DimensionError, InvalidClusteringError
+from ._masker import Masker
+
+log = logging.getLogger("shap")
+
+
+class Tabular(Masker):
+    """A common base class for Independent and Partition."""
+
+    def __init__(self, data, max_samples=100, clustering=None, partition=None):
+        """This masks out tabular features by integrating over the given background dataset.
+
+        Parameters
+        ----------
+        data : np.array, pandas.DataFrame
+            The background dataset that is used for masking.
+
+        max_samples : int
+            The maximum number of samples to use from the passed background data. If data has more
+            than max_samples then shap.utils.sample is used to subsample the dataset. The number of
+            samples coming out of the masker (to be integrated over) matches the number of samples in
+            the background dataset. This means larger background dataset cause longer runtimes. Normally
+            about 1, 10, 100, or 1000 background samples are reasonable choices.
+
+        clustering : string or None (default) or numpy.ndarray
+            The distance metric to use for creating the clustering of the features. The
+            distance function can be any valid scipy.spatial.distance.pdist's metric argument.
+            However we suggest using 'correlation' in most cases. The full list of options is
+            `braycurtis`, `canberra`, `chebyshev`, `cityblock`, `correlation`, `cosine`, `dice`,
+            `euclidean`, `hamming`, `jaccard`, `jensenshannon`, `kulsinski`, `mahalanobis`,
+            `matching`, `minkowski`, `rogerstanimoto`, `russellrao`, `seuclidean`,
+            `sokalmichener`, `sokalsneath`, `sqeuclidean`, `yule`. These are all
+            the options from scipy.spatial.distance.pdist's metric argument.
+
+        """
+        self.output_dataframe = False
+        if isinstance(data, pd.DataFrame):
+            self.feature_names = data.columns
+            data = data.values
+            self.output_dataframe = True
+
+        if isinstance(data, dict) and "mean" in data:
+            self.mean = data.get("mean", None)
+            self.cov = data.get("cov", None)
+            data = np.expand_dims(data["mean"], 0)
+
+        if hasattr(data, "shape") and data.shape[0] > max_samples:
+            data = utils.sample(data, max_samples)
+
+        self.data = data
+        self.clustering = clustering
+        self.partition = partition
+        self.max_samples = max_samples
+
+        # # warn users about large background data sets
+        # if self.data.shape[0] > 100:
+        #     log.warning("Using " + str(self.data.shape[0]) + " background data samples could cause slower " +
+        #                 "run times. Consider shap.utils.sample(data, K) to summarize the background using only K samples.")
+
+        # compute the clustering of the data
+        if clustering is not None and partition is not None:
+            raise ValueError("You cannot pass both 'clustering' and 'partition'. Please provide only one.")
+        elif clustering is not None:
+            if isinstance(clustering, str):
+                self.clustering = utils.hclust(data, metric=clustering)
+            elif isinstance(clustering, np.ndarray):
+                self.clustering = clustering
+            else:
+                raise InvalidClusteringError(
+                    "Unknown clustering given! Make sure you pass a distance metric as a string, or a clustering as a numpy.ndarray."
+                )
+            self.partition = None
+        elif partition is not None:
+            self.partition = partition
+            self.clustering = None
+        else:
+            self.clustering = None
+            self.partition = None
+
+        # self._last_mask = np.zeros(self.data.shape[1], dtype=bool)
+        self._masked_data = data.copy()
+        self._last_mask = np.zeros(data.shape[1], dtype=bool)
+        self.shape = self.data.shape
+        self.supports_delta_masking = True
+        # self._last_x = None
+        # self._data_variance = np.ones(self.data.shape, dtype=bool)
+
+        # this is property that allows callers to check what rows actually changed since last time.
+        # self.changed_rows = np.ones(self.data.shape[0], dtype=bool)
+
+    def __call__(self, mask, x):
+        mask = self._standardize_mask(mask, x)
+
+        # make sure we are given a single sample
+        if len(x.shape) != 1 or x.shape[0] != self.data.shape[1]:
+            raise DimensionError("The input passed for tabular masking does not match the background data shape!")
+
+        # if mask is an array of integers then we are doing delta masking
+        if np.issubdtype(mask.dtype, np.integer):
+            variants = ~self.invariants(x)
+            curr_delta_inds = np.zeros(len(mask), dtype=int)
+            num_masks = (mask >= 0).sum()
+            varying_rows_out = np.zeros((num_masks, self.shape[0]), dtype=bool)
+            masked_inputs_out = np.zeros((num_masks * self.shape[0], self.shape[1]))
+            self._last_mask[:] = False
+            self._masked_data[:] = self.data
+            _delta_masking(
+                mask,
+                x,
+                curr_delta_inds,
+                varying_rows_out,
+                self._masked_data,
+                self._last_mask,
+                self.data,
+                variants,
+                masked_inputs_out,
+                MaskedModel.delta_mask_noop_value,
+            )
+            if self.output_dataframe:
+                return (pd.DataFrame(masked_inputs_out, columns=self.feature_names),), varying_rows_out
+
+            return (masked_inputs_out,), varying_rows_out
+
+        # otherwise we update the whole set of masked data for a single sample
+        self._masked_data[:] = x * mask + self.data * np.invert(mask)
+        self._last_mask[:] = mask
+
+        if self.output_dataframe:
+            return pd.DataFrame(self._masked_data, columns=self.feature_names)
+
+        return (self._masked_data,)
+
+    # def reset_delta_masking(self):
+    #     """ This resets the masker back to all zeros when delta masking.
+
+    #     Note that the presence of this function also denotes that we support delta masking.
+    #     """
+    #     self._masked_data[:] = self.data
+    #     self._last_mask[:] = False
+
+    def invariants(self, x):
+        """This returns a mask of which features change when we mask them.
+
+        This optional masking method allows explainers to avoid re-evaluating the model when
+        the features that would have been masked are all invariant.
+        """
+        # make sure we got valid data
+        if x.shape != self.data.shape[1:]:
+            raise DimensionError(
+                "The passed data does not match the background shape expected by the masker! The data of shape "
+                + str(x.shape)
+                + " was passed while the masker expected data of shape "
+                + str(self.data.shape[1:])
+                + "."
+            )
+
+        return np.isclose(x, self.data)
+
+    def save(self, out_file):
+        """Write a Tabular masker to a file stream."""
+        super().save(out_file)
+
+        # Increment the version number when the encoding changes!
+        with Serializer(out_file, "shap.maskers.Tabular", version=0) as s:
+            # save the data in the format it was given to us
+            if self.output_dataframe:
+                s.save("data", pd.DataFrame(self.data, columns=self.feature_names))
+            elif getattr(self, "mean", None) is not None:
+                s.save("data", (self.mean, self.cov))
+            else:
+                s.save("data", self.data)
+
+            s.save("max_samples", self.max_samples)
+            s.save("clustering", self.clustering)
+            s.save("partition", self.partition)
+
+    @classmethod
+    def load(cls, in_file, instantiate=True):
+        """Load a Tabular masker from a file stream."""
+        if instantiate:
+            return cls._instantiated_load(in_file)
+
+        kwargs = super().load(in_file, instantiate=False)
+        with Deserializer(in_file, "shap.maskers.Tabular", min_version=0, max_version=0) as s:
+            kwargs["data"] = s.load("data")
+            kwargs["max_samples"] = s.load("max_samples")
+            kwargs["clustering"] = s.load("clustering")
+            kwargs["partition"] = s.load("partition")
+        return kwargs
+
+
+@njit
+def _single_delta_mask(dind, masked_inputs, last_mask, data, x, noop_code):
+    if dind == noop_code:
+        pass
+    elif last_mask[dind]:
+        masked_inputs[:, dind] = data[:, dind]
+        last_mask[dind] = False
+    else:
+        masked_inputs[:, dind] = x[dind]
+        last_mask[dind] = True
+
+
+@njit
+def _delta_masking(
+    masks,
+    x,
+    curr_delta_inds,
+    varying_rows_out,
+    masked_inputs_tmp,
+    last_mask,
+    data,
+    variants,
+    masked_inputs_out,
+    noop_code,
+):
+    """Implements the special (high speed) delta masking API that only flips the positions we need to.
+
+    Note that we attempt to avoid doing any allocation inside this function for speed reasons.
+    """
+    dpos = 0
+    i = -1
+    masks_pos = 0
+    output_pos = 0
+    N = masked_inputs_tmp.shape[0]
+    while masks_pos < len(masks):
+        i += 1
+
+        # update the tmp masked inputs array
+        dpos = 0
+        curr_delta_inds[0] = masks[masks_pos]
+        while curr_delta_inds[dpos] < 0:  # negative values mean keep going
+            curr_delta_inds[dpos] = -curr_delta_inds[dpos] - 1  # -value + 1 is the original index that needs flipped
+            _single_delta_mask(curr_delta_inds[dpos], masked_inputs_tmp, last_mask, data, x, noop_code)
+            dpos += 1
+            curr_delta_inds[dpos] = masks[masks_pos + dpos]
+        _single_delta_mask(curr_delta_inds[dpos], masked_inputs_tmp, last_mask, data, x, noop_code)
+
+        # copy the tmp masked inputs array to the output
+        masked_inputs_out[output_pos : output_pos + N] = masked_inputs_tmp
+        masks_pos += dpos + 1
+
+        # mark which rows have been updated, so we can only evaluate the model on the rows we need to
+        if i == 0:
+            varying_rows_out[i, :] = True
+
+        else:
+            # only one column was changed
+            if dpos == 0:
+                varying_rows_out[i, :] = variants[:, curr_delta_inds[dpos]]
+
+            # more than one column was changed
+            else:
+                varying_rows_out[i, :] = np.sum(variants[:, curr_delta_inds[: dpos + 1]], axis=1) > 0
+
+        output_pos += N
+
+
+class Independent(Tabular):
+    """This masks out tabular features by integrating over the given background dataset."""
+
+    def __init__(self, data, max_samples=100):
+        """Build a Independent masker with the given background data.
+
+        Parameters
+        ----------
+        data : numpy.ndarray, pandas.DataFrame
+            The background dataset that is used for masking.
+
+        max_samples : int
+            The maximum number of samples to use from the passed background data. If data has more
+            than max_samples then shap.utils.sample is used to subsample the dataset. The number of
+            samples coming out of the masker (to be integrated over) matches the number of samples in
+            the background dataset. This means larger background dataset cause longer runtimes. Normally
+            about 1, 10, 100, or 1000 background samples are reasonable choices.
+
+        """
+        super().__init__(data, max_samples=max_samples, clustering=None)
+
+
+class Partition(Tabular):
+    """This masks out tabular features by integrating over the given background dataset.
+
+    Unlike Independent, Partition respects a hierarchical structure of the data.
+    """
+
+    def __init__(self, data, max_samples=100, clustering="correlation"):
+        """Build a Partition masker with the given background data and clustering.
+
+        Parameters
+        ----------
+        data : numpy.ndarray, pandas.DataFrame
+            The background dataset that is used for masking.
+
+        max_samples : int
+            The maximum number of samples to use from the passed background data. If data has more
+            than max_samples then shap.utils.sample is used to subsample the dataset. The number of
+            samples coming out of the masker (to be integrated over) matches the number of samples in
+            the background dataset. This means larger background dataset cause longer runtimes. Normally
+            about 1, 10, 100, or 1000 background samples are reasonable choices.
+
+        clustering : string or numpy.ndarray
+            If a string, then this is the distance metric to use for creating the clustering of
+            the features. The distance function can be any valid scipy.spatial.distance.pdist's metric
+            argument. However we suggest using 'correlation' in most cases. The full list of options is
+            `braycurtis`, `canberra`, `chebyshev`, `cityblock`, `correlation`, `cosine`, `dice`,
+            `euclidean`, `hamming`, `jaccard`, `jensenshannon`, `kulsinski`, `mahalanobis`,
+            `matching`, `minkowski`, `rogerstanimoto`, `russellrao`, `seuclidean`,
+            `sokalmichener`, `sokalsneath`, `sqeuclidean`, `yule`. These are all
+            the options from scipy.spatial.distance.pdist's metric argument.
+            If an array, then this is assumed to be the clustering of the features.
+
+        """
+        super().__init__(data, max_samples=max_samples, clustering=clustering, partition=None)
+
+
+class Impute(Masker):  # we should inherit from Tabular once we add support for arbitrary masking
+    """This imputes the values of missing features using the values of the observed features.
+
+    Unlike Independent, Gaussian imputes missing values based on correlations with observed data points.
+    """
+
+    def __init__(self, data, method="linear"):
+        """Build a Partition masker with the given background data and clustering.
+
+        Parameters
+        ----------
+        data : numpy.ndarray, pandas.DataFrame or {"mean: numpy.ndarray, "cov": numpy.ndarray} dictionary
+            The background dataset that is used for masking.
+
+        """
+        if data is dict and "mean" in data:
+            self.mean = data.get("mean", None)
+            self.cov = data.get("cov", None)
+            data = np.expand_dims(data["mean"], 0)
+
+        self.data = data
+        self.method = method
diff --git a/shared/package-files/1b/1b3bf17804394ca6e86b1c05330d181cbfaae4246ff214cc226c88f398b09155/einsum.h b/shared/package-files/1b/1b3bf17804394ca6e86b1c05330d181cbfaae4246ff214cc226c88f398b09155/einsum.h
new file mode 100644
index 0000000000000000000000000000000000000000..834d6ce6d690ebb8dbb63e55b9a3b1f33e9c9126
--- /dev/null
+++ b/shared/package-files/1b/1b3bf17804394ca6e86b1c05330d181cbfaae4246ff214cc226c88f398b09155/einsum.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/einsum_ops.h>
+
+namespace at {
+
+
+// aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor
+inline at::Tensor einsum(c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path=c10::nullopt) {
+    return at::_ops::einsum::call(equation, tensors, path);
+}
+
+}
diff --git a/shared/package-files/1b/1b3c1cf61f67eb394d3a127c421e9f403781f2085a46682fde4b4101f3c9c7e5/test_subscheck.py b/shared/package-files/1b/1b3c1cf61f67eb394d3a127c421e9f403781f2085a46682fde4b4101f3c9c7e5/test_subscheck.py
new file mode 100644
index 0000000000000000000000000000000000000000..799c2854e878208721b600767de350cda08cd7e5
--- /dev/null
+++ b/shared/package-files/1b/1b3c1cf61f67eb394d3a127c421e9f403781f2085a46682fde4b4101f3c9c7e5/test_subscheck.py
@@ -0,0 +1,203 @@
+from sympy.core.function import (Derivative, Function, diff)
+from sympy.core.numbers import (I, Rational, pi)
+from sympy.core.relational import Eq
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.functions.special.error_functions import (Ei, erf, erfi)
+from sympy.integrals.integrals import Integral
+
+from sympy.solvers.ode.subscheck import checkodesol, checksysodesol
+
+from sympy.functions import besselj, bessely
+
+from sympy.testing.pytest import raises, slow
+
+
+C0, C1, C2, C3, C4 = symbols('C0:5')
+u, x, y, z = symbols('u,x:z', real=True)
+f = Function('f')
+g = Function('g')
+h = Function('h')
+
+
+@slow
+def test_checkodesol():
+    # For the most part, checkodesol is well tested in the tests below.
+    # These tests only handle cases not checked below.
+    raises(ValueError, lambda: checkodesol(f(x, y).diff(x), Eq(f(x, y), x)))
+    raises(ValueError, lambda: checkodesol(f(x).diff(x), Eq(f(x, y),
+           x), f(x, y)))
+    assert checkodesol(f(x).diff(x), Eq(f(x, y), x)) == \
+        (False, -f(x).diff(x) + f(x, y).diff(x) - 1)
+    assert checkodesol(f(x).diff(x), Eq(f(x), x)) is not True
+    assert checkodesol(f(x).diff(x), Eq(f(x), x)) == (False, 1)
+    sol1 = Eq(f(x)**5 + 11*f(x) - 2*f(x) + x, 0)
+    assert checkodesol(diff(sol1.lhs, x), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x)*exp(f(x)), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x, 2), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x, 2)*exp(f(x)), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x, 3), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x, 3)*exp(f(x)), sol1) == (True, 0)
+    assert checkodesol(diff(sol1.lhs, x, 3), Eq(f(x), x*log(x))) == \
+        (False, 60*x**4*((log(x) + 1)**2 + log(x))*(
+        log(x) + 1)*log(x)**2 - 5*x**4*log(x)**4 - 9)
+    assert checkodesol(diff(exp(f(x)) + x, x)*x, Eq(exp(f(x)) + x, 0)) == \
+        (True, 0)
+    assert checkodesol(diff(exp(f(x)) + x, x)*x, Eq(exp(f(x)) + x, 0),
+        solve_for_func=False) == (True, 0)
+    assert checkodesol(f(x).diff(x, 2), [Eq(f(x), C1 + C2*x),
+        Eq(f(x), C2 + C1*x), Eq(f(x), C1*x + C2*x**2)]) == \
+        [(True, 0), (True, 0), (False, C2)]
+    assert checkodesol(f(x).diff(x, 2), {Eq(f(x), C1 + C2*x),
+        Eq(f(x), C2 + C1*x), Eq(f(x), C1*x + C2*x**2)}) == \
+        {(True, 0), (True, 0), (False, C2)}
+    assert checkodesol(f(x).diff(x) - 1/f(x)/2, Eq(f(x)**2, x)) == \
+        [(True, 0), (True, 0)]
+    assert checkodesol(f(x).diff(x) - f(x), Eq(C1*exp(x), f(x))) == (True, 0)
+    # Based on test_1st_homogeneous_coeff_ode2_eq3sol.  Make sure that
+    # checkodesol tries back substituting f(x) when it can.
+    eq3 = x*exp(f(x)/x) + f(x) - x*f(x).diff(x)
+    sol3 = Eq(f(x), log(log(C1/x)**(-x)))
+    assert not checkodesol(eq3, sol3)[1].has(f(x))
+    # This case was failing intermittently depending on hash-seed:
+    eqn = Eq(Derivative(x*Derivative(f(x), x), x)/x, exp(x))
+    sol = Eq(f(x), C1 + C2*log(x) + exp(x) - Ei(x))
+    assert checkodesol(eqn, sol, order=2, solve_for_func=False)[0]
+    eq = x**2*(f(x).diff(x, 2)) + x*(f(x).diff(x)) + (2*x**2 +25)*f(x)
+    sol = Eq(f(x), C1*besselj(5*I, sqrt(2)*x) + C2*bessely(5*I, sqrt(2)*x))
+    assert checkodesol(eq, sol) == (True, 0)
+
+    eqs = [Eq(f(x).diff(x), f(x) + g(x)), Eq(g(x).diff(x), f(x) + g(x))]
+    sol = [Eq(f(x), -C1 + C2*exp(2*x)), Eq(g(x), C1 + C2*exp(2*x))]
+    assert checkodesol(eqs, sol) == (True, [0, 0])
+
+
+def test_checksysodesol():
+    x, y, z = symbols('x, y, z', cls=Function)
+    t = Symbol('t')
+    eq = (Eq(diff(x(t),t), 9*y(t)), Eq(diff(y(t),t), 12*x(t)))
+    sol = [Eq(x(t), 9*C1*exp(-6*sqrt(3)*t) + 9*C2*exp(6*sqrt(3)*t)), \
+    Eq(y(t), -6*sqrt(3)*C1*exp(-6*sqrt(3)*t) + 6*sqrt(3)*C2*exp(6*sqrt(3)*t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), 2*x(t) + 4*y(t)), Eq(diff(y(t),t), 12*x(t) + 41*y(t)))
+    sol = [Eq(x(t), 4*C1*exp(t*(-sqrt(1713)/2 + Rational(43, 2))) + 4*C2*exp(t*(sqrt(1713)/2 + \
+    Rational(43, 2)))), Eq(y(t), C1*(-sqrt(1713)/2 + Rational(39, 2))*exp(t*(-sqrt(1713)/2 + \
+    Rational(43, 2))) + C2*(Rational(39, 2) + sqrt(1713)/2)*exp(t*(sqrt(1713)/2 + Rational(43, 2))))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), x(t) + y(t)), Eq(diff(y(t),t), -2*x(t) + 2*y(t)))
+    sol = [Eq(x(t), (C1*sin(sqrt(7)*t/2) + C2*cos(sqrt(7)*t/2))*exp(t*Rational(3, 2))), \
+    Eq(y(t), ((C1/2 - sqrt(7)*C2/2)*sin(sqrt(7)*t/2) + (sqrt(7)*C1/2 + \
+    C2/2)*cos(sqrt(7)*t/2))*exp(t*Rational(3, 2)))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), x(t) + y(t) + 9), Eq(diff(y(t),t), 2*x(t) + 5*y(t) + 23))
+    sol = [Eq(x(t), C1*exp(t*(-sqrt(6) + 3)) + C2*exp(t*(sqrt(6) + 3)) - \
+    Rational(22, 3)), Eq(y(t), C1*(-sqrt(6) + 2)*exp(t*(-sqrt(6) + 3)) + C2*(2 + \
+    sqrt(6))*exp(t*(sqrt(6) + 3)) - Rational(5, 3))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), x(t) + y(t) + 81), Eq(diff(y(t),t), -2*x(t) + y(t) + 23))
+    sol = [Eq(x(t), (C1*sin(sqrt(2)*t) + C2*cos(sqrt(2)*t))*exp(t) - Rational(58, 3)), \
+    Eq(y(t), (sqrt(2)*C1*cos(sqrt(2)*t) - sqrt(2)*C2*sin(sqrt(2)*t))*exp(t) - Rational(185, 3))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), 5*t*x(t) + 2*y(t)), Eq(diff(y(t),t), 2*x(t) + 5*t*y(t)))
+    sol = [Eq(x(t), (C1*exp(Integral(2, t).doit()) + C2*exp(-(Integral(2, t)).doit()))*\
+    exp((Integral(5*t, t)).doit())), Eq(y(t), (C1*exp((Integral(2, t)).doit()) - \
+    C2*exp(-(Integral(2, t)).doit()))*exp((Integral(5*t, t)).doit()))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), 5*t*x(t) + t**2*y(t)), Eq(diff(y(t),t), -t**2*x(t) + 5*t*y(t)))
+    sol = [Eq(x(t), (C1*cos((Integral(t**2, t)).doit()) + C2*sin((Integral(t**2, t)).doit()))*\
+    exp((Integral(5*t, t)).doit())), Eq(y(t), (-C1*sin((Integral(t**2, t)).doit()) + \
+    C2*cos((Integral(t**2, t)).doit()))*exp((Integral(5*t, t)).doit()))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), 5*t*x(t) + t**2*y(t)), Eq(diff(y(t),t), -t**2*x(t) + (5*t+9*t**2)*y(t)))
+    sol = [Eq(x(t), (C1*exp((-sqrt(77)/2 + Rational(9, 2))*(Integral(t**2, t)).doit()) + \
+    C2*exp((sqrt(77)/2 + Rational(9, 2))*(Integral(t**2, t)).doit()))*exp((Integral(5*t, t)).doit())), \
+    Eq(y(t), (C1*(-sqrt(77)/2 + Rational(9, 2))*exp((-sqrt(77)/2 + Rational(9, 2))*(Integral(t**2, t)).doit()) + \
+    C2*(sqrt(77)/2 + Rational(9, 2))*exp((sqrt(77)/2 + Rational(9, 2))*(Integral(t**2, t)).doit()))*exp((Integral(5*t, t)).doit()))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t,t), 5*x(t) + 43*y(t)), Eq(diff(y(t),t,t), x(t) + 9*y(t)))
+    root0 = -sqrt(-sqrt(47) + 7)
+    root1 = sqrt(-sqrt(47) + 7)
+    root2 = -sqrt(sqrt(47) + 7)
+    root3 = sqrt(sqrt(47) + 7)
+    sol = [Eq(x(t), 43*C1*exp(t*root0) + 43*C2*exp(t*root1) + 43*C3*exp(t*root2) + 43*C4*exp(t*root3)), \
+    Eq(y(t), C1*(root0**2 - 5)*exp(t*root0) + C2*(root1**2 - 5)*exp(t*root1) + \
+    C3*(root2**2 - 5)*exp(t*root2) + C4*(root3**2 - 5)*exp(t*root3))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t,t), 8*x(t)+3*y(t)+31), Eq(diff(y(t),t,t), 9*x(t)+7*y(t)+12))
+    root0 = -sqrt(-sqrt(109)/2 + Rational(15, 2))
+    root1 = sqrt(-sqrt(109)/2 + Rational(15, 2))
+    root2 = -sqrt(sqrt(109)/2 + Rational(15, 2))
+    root3 = sqrt(sqrt(109)/2 + Rational(15, 2))
+    sol = [Eq(x(t), 3*C1*exp(t*root0) + 3*C2*exp(t*root1) + 3*C3*exp(t*root2) + 3*C4*exp(t*root3) - Rational(181, 29)), \
+    Eq(y(t), C1*(root0**2 - 8)*exp(t*root0) + C2*(root1**2 - 8)*exp(t*root1) + \
+    C3*(root2**2 - 8)*exp(t*root2) + C4*(root3**2 - 8)*exp(t*root3) + Rational(183, 29))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t,t) - 9*diff(y(t),t) + 7*x(t),0), Eq(diff(y(t),t,t) + 9*diff(x(t),t) + 7*y(t),0))
+    sol = [Eq(x(t), C1*cos(t*(Rational(9, 2) + sqrt(109)/2)) + C2*sin(t*(Rational(9, 2) + sqrt(109)/2)) + \
+    C3*cos(t*(-sqrt(109)/2 + Rational(9, 2))) + C4*sin(t*(-sqrt(109)/2 + Rational(9, 2)))), Eq(y(t), -C1*sin(t*(Rational(9, 2) + sqrt(109)/2)) \
+    + C2*cos(t*(Rational(9, 2) + sqrt(109)/2)) - C3*sin(t*(-sqrt(109)/2 + Rational(9, 2))) + C4*cos(t*(-sqrt(109)/2 + Rational(9, 2))))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t,t), 9*t*diff(y(t),t)-9*y(t)), Eq(diff(y(t),t,t),7*t*diff(x(t),t)-7*x(t)))
+    I1 = sqrt(6)*7**Rational(1, 4)*sqrt(pi)*erfi(sqrt(6)*7**Rational(1, 4)*t/2)/2 - exp(3*sqrt(7)*t**2/2)/t
+    I2 = -sqrt(6)*7**Rational(1, 4)*sqrt(pi)*erf(sqrt(6)*7**Rational(1, 4)*t/2)/2 - exp(-3*sqrt(7)*t**2/2)/t
+    sol = [Eq(x(t), C3*t + t*(9*C1*I1 + 9*C2*I2)), Eq(y(t), C4*t + t*(3*sqrt(7)*C1*I1 - 3*sqrt(7)*C2*I2))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), 21*x(t)), Eq(diff(y(t),t), 17*x(t)+3*y(t)), Eq(diff(z(t),t), 5*x(t)+7*y(t)+9*z(t)))
+    sol = [Eq(x(t), C1*exp(21*t)), Eq(y(t), 17*C1*exp(21*t)/18 + C2*exp(3*t)), \
+    Eq(z(t), 209*C1*exp(21*t)/216 - 7*C2*exp(3*t)/6 + C3*exp(9*t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0, 0])
+
+    eq = (Eq(diff(x(t),t),3*y(t)-11*z(t)),Eq(diff(y(t),t),7*z(t)-3*x(t)),Eq(diff(z(t),t),11*x(t)-7*y(t)))
+    sol = [Eq(x(t), 7*C0 + sqrt(179)*C1*cos(sqrt(179)*t) + (77*C1/3 + 130*C2/3)*sin(sqrt(179)*t)), \
+    Eq(y(t), 11*C0 + sqrt(179)*C2*cos(sqrt(179)*t) + (-58*C1/3 - 77*C2/3)*sin(sqrt(179)*t)), \
+    Eq(z(t), 3*C0 + sqrt(179)*(-7*C1/3 - 11*C2/3)*cos(sqrt(179)*t) + (11*C1 - 7*C2)*sin(sqrt(179)*t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0, 0])
+
+    eq = (Eq(3*diff(x(t),t),4*5*(y(t)-z(t))),Eq(4*diff(y(t),t),3*5*(z(t)-x(t))),Eq(5*diff(z(t),t),3*4*(x(t)-y(t))))
+    sol = [Eq(x(t), C0 + 5*sqrt(2)*C1*cos(5*sqrt(2)*t) + (12*C1/5 + 164*C2/15)*sin(5*sqrt(2)*t)), \
+    Eq(y(t), C0 + 5*sqrt(2)*C2*cos(5*sqrt(2)*t) + (-51*C1/10 - 12*C2/5)*sin(5*sqrt(2)*t)), \
+    Eq(z(t), C0 + 5*sqrt(2)*(-9*C1/25 - 16*C2/25)*cos(5*sqrt(2)*t) + (12*C1/5 - 12*C2/5)*sin(5*sqrt(2)*t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0, 0])
+
+    eq = (Eq(diff(x(t),t),4*x(t) - z(t)),Eq(diff(y(t),t),2*x(t)+2*y(t)-z(t)),Eq(diff(z(t),t),3*x(t)+y(t)))
+    sol = [Eq(x(t), C1*exp(2*t) + C2*t*exp(2*t) + C2*exp(2*t) + C3*t**2*exp(2*t)/2 + C3*t*exp(2*t) + C3*exp(2*t)), \
+    Eq(y(t), C1*exp(2*t) + C2*t*exp(2*t) + C2*exp(2*t) + C3*t**2*exp(2*t)/2 + C3*t*exp(2*t)), \
+    Eq(z(t), 2*C1*exp(2*t) + 2*C2*t*exp(2*t) + C2*exp(2*t) + C3*t**2*exp(2*t) + C3*t*exp(2*t) + C3*exp(2*t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0, 0])
+
+    eq = (Eq(diff(x(t),t),4*x(t) - y(t) - 2*z(t)),Eq(diff(y(t),t),2*x(t) + y(t)- 2*z(t)),Eq(diff(z(t),t),5*x(t)-3*z(t)))
+    sol = [Eq(x(t), C1*exp(2*t) + C2*(-sin(t) + 3*cos(t)) + C3*(3*sin(t) + cos(t))), \
+    Eq(y(t), C2*(-sin(t) + 3*cos(t)) + C3*(3*sin(t) + cos(t))), Eq(z(t), C1*exp(2*t) + 5*C2*cos(t) + 5*C3*sin(t))]
+    assert checksysodesol(eq, sol) == (True, [0, 0, 0])
+
+    eq = (Eq(diff(x(t),t),x(t)*y(t)**3), Eq(diff(y(t),t),y(t)**5))
+    sol = [Eq(x(t), C1*exp((-1/(4*C2 + 4*t))**(Rational(-1, 4)))), Eq(y(t), -(-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), C1*exp(-1/(-1/(4*C2 + 4*t))**Rational(1, 4))), Eq(y(t), (-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), C1*exp(-I/(-1/(4*C2 + 4*t))**Rational(1, 4))), Eq(y(t), -I*(-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), C1*exp(I/(-1/(4*C2 + 4*t))**Rational(1, 4))), Eq(y(t), I*(-1/(4*C2 + 4*t))**Rational(1, 4))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(diff(x(t),t), exp(3*x(t))*y(t)**3),Eq(diff(y(t),t), y(t)**5))
+    sol = [Eq(x(t), -log(C1 - 3/(-1/(4*C2 + 4*t))**Rational(1, 4))/3), Eq(y(t), -(-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), -log(C1 + 3/(-1/(4*C2 + 4*t))**Rational(1, 4))/3), Eq(y(t), (-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), -log(C1 + 3*I/(-1/(4*C2 + 4*t))**Rational(1, 4))/3), Eq(y(t), -I*(-1/(4*C2 + 4*t))**Rational(1, 4)), \
+    Eq(x(t), -log(C1 - 3*I/(-1/(4*C2 + 4*t))**Rational(1, 4))/3), Eq(y(t), I*(-1/(4*C2 + 4*t))**Rational(1, 4))]
+    assert checksysodesol(eq, sol) == (True, [0, 0])
+
+    eq = (Eq(x(t),t*diff(x(t),t)+diff(x(t),t)*diff(y(t),t)), Eq(y(t),t*diff(y(t),t)+diff(y(t),t)**2))
+    sol = {Eq(x(t), C1*C2 + C1*t), Eq(y(t), C2**2 + C2*t)}
+    assert checksysodesol(eq, sol) == (True, [0, 0])
diff --git a/shared/package-files/1b/1b4168d2401f589e8a4c475a704f5f1c7101ea621ef85521397128e397cad0e9/scalar_tensor_native.h b/shared/package-files/1b/1b4168d2401f589e8a4c475a704f5f1c7101ea621ef85521397128e397cad0e9/scalar_tensor_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f93bd89e71256026d4cc1af36efa2bcaf3f91c88
--- /dev/null
+++ b/shared/package-files/1b/1b4168d2401f589e8a4c475a704f5f1c7101ea621ef85521397128e397cad0e9/scalar_tensor_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor scalar_tensor(const at::Scalar & s, c10::optional<at::ScalarType> dtype={}, c10::optional<at::Layout> layout={}, c10::optional<at::Device> device={}, c10::optional<bool> pin_memory={});
+TORCH_API at::Tensor & scalar_tensor_out(const at::Scalar & s, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b42df7e7d6b0feb17cb0bc8d97e6ce6899492306dd880c48a39d1a2f0279004/cp1250.enc b/shared/package-files/1b/1b42df7e7d6b0feb17cb0bc8d97e6ce6899492306dd880c48a39d1a2f0279004/cp1250.enc
new file mode 100644
index 0000000000000000000000000000000000000000..070ad901bd6aee7640caa7152a79bcc77444693c
--- /dev/null
+++ b/shared/package-files/1b/1b42df7e7d6b0feb17cb0bc8d97e6ce6899492306dd880c48a39d1a2f0279004/cp1250.enc
@@ -0,0 +1,20 @@
+# Encoding file: cp1250, single-byte
+S
+003F 0 1
+00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 --git a/shared/package-files/1b/1b47107ec12a7f027e14840c917802270487327eedf6f3dea42aee77a7d910a9/dammit.py b/shared/package-files/1b/1b47107ec12a7f027e14840c917802270487327eedf6f3dea42aee77a7d910a9/dammit.py
new file mode 100644
index 0000000000000000000000000000000000000000..692433c57a2dbb2bd20168b21ff56a9f6201a871
--- /dev/null
+++ b/shared/package-files/1b/1b47107ec12a7f027e14840c917802270487327eedf6f3dea42aee77a7d910a9/dammit.py
@@ -0,0 +1,1095 @@
+# -*- coding: utf-8 -*-
+"""Beautiful Soup bonus library: Unicode, Dammit
+
+This library converts a bytestream to Unicode through any means
+necessary. It is heavily based on code from Mark Pilgrim's Universal
+Feed Parser. It works best on XML and HTML, but it does not rewrite the
+XML or HTML to reflect a new encoding; that's the tree builder's job.
+"""
+# Use of this source code is governed by the MIT license.
+__license__ = "MIT"
+
+from html.entities import codepoint2name
+from collections import defaultdict
+import codecs
+import re
+import logging
+import string
+
+# Import a library to autodetect character encodings. We'll support
+# any of a number of libraries that all support the same API:
+#
+# * cchardet
+# * chardet
+# * charset-normalizer
+chardet_module = None
+try:
+    #  PyPI package: cchardet
+    import cchardet as chardet_module
+except ImportError:
+    try:
+        #  Debian package: python-chardet
+        #  PyPI package: chardet
+        import chardet as chardet_module
+    except ImportError:
+        try:
+            # PyPI package: charset-normalizer
+            import charset_normalizer as chardet_module
+        except ImportError:
+            # No chardet available.
+            chardet_module = None
+
+if chardet_module:
+    def chardet_dammit(s):
+        if isinstance(s, str):
+            return None
+        return chardet_module.detect(s)['encoding']
+else:
+    def chardet_dammit(s):
+        return None
+
+# Build bytestring and Unicode versions of regular expressions for finding
+# a declared encoding inside an XML or HTML document.
+xml_encoding = '^\\s*<\\?.*encoding=[\'"](.*?)[\'"].*\\?>'
+html_meta = '<\\s*meta[^>]+charset\\s*=\\s*["\']?([^>]*?)[ /;\'">]'
+encoding_res = dict()
+encoding_res[bytes] = {
+    'html' : re.compile(html_meta.encode("ascii"), re.I),
+    'xml' : re.compile(xml_encoding.encode("ascii"), re.I),
+}
+encoding_res[str] = {
+    'html' : re.compile(html_meta, re.I),
+    'xml' : re.compile(xml_encoding, re.I)
+}
+
+from html.entities import html5
+
+class EntitySubstitution(object):
+    """The ability to substitute XML or HTML entities for certain characters."""
+
+    def _populate_class_variables():
+        """Initialize variables used by this class to manage the plethora of
+        HTML5 named entities.
+
+        This function returns a 3-tuple containing two dictionaries
+        and a regular expression:
+
+        unicode_to_name - A mapping of Unicode strings like "⦨" to
+        entity names like "angmsdaa". When a single Unicode string has
+        multiple entity names, we try to choose the most commonly-used
+        name.
+
+        name_to_unicode: A mapping of entity names like "angmsdaa" to 
+        Unicode strings like "⦨".
+
+        named_entity_re: A regular expression matching (almost) any
+        Unicode string that corresponds to an HTML5 named entity.
+        """
+        unicode_to_name = {}
+        name_to_unicode = {}
+
+        short_entities = set()
+        long_entities_by_first_character = defaultdict(set)
+        
+        for name_with_semicolon, character in sorted(html5.items()):
+            # "It is intentional, for legacy compatibility, that many
+            # code points have multiple character reference names. For
+            # example, some appear both with and without the trailing
+            # semicolon, or with different capitalizations."
+            # - https://html.spec.whatwg.org/multipage/named-characters.html#named-character-references
+            #
+            # The parsers are in charge of handling (or not) character
+            # references with no trailing semicolon, so we remove the
+            # semicolon whenever it appears.
+            if name_with_semicolon.endswith(';'):
+                name = name_with_semicolon[:-1]
+            else:
+                name = name_with_semicolon
+
+            # When parsing HTML, we want to recognize any known named
+            # entity and convert it to a sequence of Unicode
+            # characters.
+            if name not in name_to_unicode:
+                name_to_unicode[name] = character
+
+            # When _generating_ HTML, we want to recognize special
+            # character sequences that _could_ be converted to named
+            # entities.
+            unicode_to_name[character] = name
+
+            # We also need to build a regular expression that lets us
+            # _find_ those characters in output strings so we can
+            # replace them.
+            #
+            # This is tricky, for two reasons.
+
+            if (len(character) == 1 and ord(character) < 128
+                and character not in '<>&'):
+                # First, it would be annoying to turn single ASCII
+                # characters like | into named entities like
+                # &verbar;. The exceptions are <>&, which we _must_
+                # turn into named entities to produce valid HTML.
+                continue
+
+            if len(character) > 1 and all(ord(x) < 128 for x in character):
+                # We also do not want to turn _combinations_ of ASCII
+                # characters like 'fj' into named entities like '&fjlig;',
+                # though that's more debateable.
+                continue
+
+            # Second, some named entities have a Unicode value that's
+            # a subset of the Unicode value for some _other_ named
+            # entity.  As an example, \u2267' is &GreaterFullEqual;,
+            # but '\u2267\u0338' is &NotGreaterFullEqual;. Our regular
+            # expression needs to match the first two characters of
+            # "\u2267\u0338foo", but only the first character of
+            # "\u2267foo".
+            #
+            # In this step, we build two sets of characters that
+            # _eventually_ need to go into the regular expression. But
+            # we won't know exactly what the regular expression needs
+            # to look like until we've gone through the entire list of
+            # named entities.
+            if len(character) == 1:
+                short_entities.add(character)
+            else:
+                long_entities_by_first_character[character[0]].add(character)
+
+        # Now that we've been through the entire list of entities, we
+        # can create a regular expression that matches any of them.
+        particles = set()
+        for short in short_entities:
+            long_versions = long_entities_by_first_character[short]
+            if not long_versions:
+                particles.add(short)
+            else:
+                ignore = "".join([x[1] for x in long_versions])
+                # This finds, e.g. \u2267 but only if it is _not_
+                # followed by \u0338.
+                particles.add("%s(?![%s])" % (short, ignore))
+        
+        for long_entities in list(long_entities_by_first_character.values()):
+            for long_entity in long_entities:
+                particles.add(long_entity)
+
+        re_definition = "(%s)" % "|".join(particles)
+                
+        # If an entity shows up in both html5 and codepoint2name, it's
+        # likely that HTML5 gives it several different names, such as
+        # 'rsquo' and 'rsquor'. When converting Unicode characters to
+        # named entities, the codepoint2name name should take
+        # precedence where possible, since that's the more easily
+        # recognizable one.
+        for codepoint, name in list(codepoint2name.items()):
+            character = chr(codepoint)
+            unicode_to_name[character] = name
+
+        return unicode_to_name, name_to_unicode, re.compile(re_definition)
+    (CHARACTER_TO_HTML_ENTITY, HTML_ENTITY_TO_CHARACTER,
+     CHARACTER_TO_HTML_ENTITY_RE) = _populate_class_variables()
+
+    CHARACTER_TO_XML_ENTITY = {
+        "'": "apos",
+        '"': "quot",
+        "&": "amp",
+        "<": "lt",
+        ">": "gt",
+        }
+
+    BARE_AMPERSAND_OR_BRACKET = re.compile("([<>]|"
+                                           "&(?!#\\d+;|#x[0-9a-fA-F]+;|\\w+;)"
+                                           ")")
+
+    AMPERSAND_OR_BRACKET = re.compile("([<>&])")
+
+    @classmethod
+    def _substitute_html_entity(cls, matchobj):
+        """Used with a regular expression to substitute the
+        appropriate HTML entity for a special character string."""
+        entity = cls.CHARACTER_TO_HTML_ENTITY.get(matchobj.group(0))
+        return "&%s;" % entity
+
+    @classmethod
+    def _substitute_xml_entity(cls, matchobj):
+        """Used with a regular expression to substitute the
+        appropriate XML entity for a special character string."""
+        entity = cls.CHARACTER_TO_XML_ENTITY[matchobj.group(0)]
+        return "&%s;" % entity
+
+    @classmethod
+    def quoted_attribute_value(self, value):
+        """Make a value into a quoted XML attribute, possibly escaping it.
+
+         Most strings will be quoted using double quotes.
+
+          Bob's Bar -> "Bob's Bar"
+
+         If a string contains double quotes, it will be quoted using
+         single quotes.
+
+          Welcome to "my bar" -> 'Welcome to "my bar"'
+
+         If a string contains both single and double quotes, the
+         double quotes will be escaped, and the string will be quoted
+         using double quotes.
+
+          Welcome to "Bob's Bar" -> "Welcome to &quot;Bob's bar&quot;
+        """
+        quote_with = '"'
+        if '"' in value:
+            if "'" in value:
+                # The string contains both single and double
+                # quotes.  Turn the double quotes into
+                # entities. We quote the double quotes rather than
+                # the single quotes because the entity name is
+                # "&quot;" whether this is HTML or XML.  If we
+                # quoted the single quotes, we'd have to decide
+                # between &apos; and &squot;.
+                replace_with = "&quot;"
+                value = value.replace('"', replace_with)
+            else:
+                # There are double quotes but no single quotes.
+                # We can use single quotes to quote the attribute.
+                quote_with = "'"
+        return quote_with + value + quote_with
+
+    @classmethod
+    def substitute_xml(cls, value, make_quoted_attribute=False):
+        """Substitute XML entities for special XML characters.
+
+        :param value: A string to be substituted. The less-than sign
+          will become &lt;, the greater-than sign will become &gt;,
+          and any ampersands will become &amp;. If you want ampersands
+          that appear to be part of an entity definition to be left
+          alone, use substitute_xml_containing_entities() instead.
+
+        :param make_quoted_attribute: If True, then the string will be
+         quoted, as befits an attribute value.
+        """
+        # Escape angle brackets and ampersands.
+        value = cls.AMPERSAND_OR_BRACKET.sub(
+            cls._substitute_xml_entity, value)
+
+        if make_quoted_attribute:
+            value = cls.quoted_attribute_value(value)
+        return value
+
+    @classmethod
+    def substitute_xml_containing_entities(
+        cls, value, make_quoted_attribute=False):
+        """Substitute XML entities for special XML characters.
+
+        :param value: A string to be substituted. The less-than sign will
+          become &lt;, the greater-than sign will become &gt;, and any
+          ampersands that are not part of an entity defition will
+          become &amp;.
+
+        :param make_quoted_attribute: If True, then the string will be
+         quoted, as befits an attribute value.
+        """
+        # Escape angle brackets, and ampersands that aren't part of
+        # entities.
+        value = cls.BARE_AMPERSAND_OR_BRACKET.sub(
+            cls._substitute_xml_entity, value)
+
+        if make_quoted_attribute:
+            value = cls.quoted_attribute_value(value)
+        return value
+
+    @classmethod
+    def substitute_html(cls, s):
+        """Replace certain Unicode characters with named HTML entities.
+
+        This differs from data.encode(encoding, 'xmlcharrefreplace')
+        in that the goal is to make the result more readable (to those
+        with ASCII displays) rather than to recover from
+        errors. There's absolutely nothing wrong with a UTF-8 string
+        containg a LATIN SMALL LETTER E WITH ACUTE, but replacing that
+        character with "&eacute;" will make it more readable to some
+        people.
+
+        :param s: A Unicode string.
+        """
+        return cls.CHARACTER_TO_HTML_ENTITY_RE.sub(
+            cls._substitute_html_entity, s)
+
+
+class EncodingDetector:
+    """Suggests a number of possible encodings for a bytestring.
+
+    Order of precedence:
+
+    1. Encodings you specifically tell EncodingDetector to try first
+    (the known_definite_encodings argument to the constructor).
+
+    2. An encoding determined by sniffing the document's byte-order mark.
+
+    3. Encodings you specifically tell EncodingDetector to try if
+    byte-order mark sniffing fails (the user_encodings argument to the
+    constructor).
+
+    4. An encoding declared within the bytestring itself, either in an
+    XML declaration (if the bytestring is to be interpreted as an XML
+    document), or in a <meta> tag (if the bytestring is to be
+    interpreted as an HTML document.)
+
+    5. An encoding detected through textual analysis by chardet,
+    cchardet, or a similar external library.
+
+    4. UTF-8.
+
+    5. Windows-1252.
+
+    """
+    def __init__(self, markup, known_definite_encodings=None,
+                 is_html=False, exclude_encodings=None,
+                 user_encodings=None, override_encodings=None):
+        """Constructor.
+
+        :param markup: Some markup in an unknown encoding.
+
+        :param known_definite_encodings: When determining the encoding
+            of `markup`, these encodings will be tried first, in
+            order. In HTML terms, this corresponds to the "known
+            definite encoding" step defined here:
+            https://html.spec.whatwg.org/multipage/parsing.html#parsing-with-a-known-character-encoding
+
+        :param user_encodings: These encodings will be tried after the
+            `known_definite_encodings` have been tried and failed, and
+            after an attempt to sniff the encoding by looking at a
+            byte order mark has failed. In HTML terms, this
+            corresponds to the step "user has explicitly instructed
+            the user agent to override the document's character
+            encoding", defined here:
+            https://html.spec.whatwg.org/multipage/parsing.html#determining-the-character-encoding
+
+        :param override_encodings: A deprecated alias for
+            known_definite_encodings. Any encodings here will be tried
+            immediately after the encodings in
+            known_definite_encodings.
+
+        :param is_html: If True, this markup is considered to be
+            HTML. Otherwise it's assumed to be XML.
+
+        :param exclude_encodings: These encodings will not be tried,
+            even if they otherwise would be.
+
+        """
+        self.known_definite_encodings = list(known_definite_encodings or [])
+        if override_encodings:
+            self.known_definite_encodings += override_encodings
+        self.user_encodings = user_encodings or []
+        exclude_encodings = exclude_encodings or []
+        self.exclude_encodings = set([x.lower() for x in exclude_encodings])
+        self.chardet_encoding = None
+        self.is_html = is_html
+        self.declared_encoding = None
+
+        # First order of business: strip a byte-order mark.
+        self.markup, self.sniffed_encoding = self.strip_byte_order_mark(markup)
+
+    def _usable(self, encoding, tried):
+        """Should we even bother to try this encoding?
+
+        :param encoding: Name of an encoding.
+        :param tried: Encodings that have already been tried. This will be modified
+            as a side effect.
+        """
+        if encoding is not None:
+            encoding = encoding.lower()
+            if encoding in self.exclude_encodings:
+                return False
+            if encoding not in tried:
+                tried.add(encoding)
+                return True
+        return False
+
+    @property
+    def encodings(self):
+        """Yield a number of encodings that might work for this markup.
+
+        :yield: A sequence of strings.
+        """
+        tried = set()
+
+        # First, try the known definite encodings
+        for e in self.known_definite_encodings:
+            if self._usable(e, tried):
+                yield e
+
+        # Did the document originally start with a byte-order mark
+        # that indicated its encoding?
+        if self._usable(self.sniffed_encoding, tried):
+            yield self.sniffed_encoding
+
+        # Sniffing the byte-order mark did nothing; try the user
+        # encodings.
+        for e in self.user_encodings:
+            if self._usable(e, tried):
+                yield e
+            
+        # Look within the document for an XML or HTML encoding
+        # declaration.
+        if self.declared_encoding is None:
+            self.declared_encoding = self.find_declared_encoding(
+                self.markup, self.is_html)
+        if self._usable(self.declared_encoding, tried):
+            yield self.declared_encoding
+
+        # Use third-party character set detection to guess at the
+        # encoding.
+        if self.chardet_encoding is None:
+            self.chardet_encoding = chardet_dammit(self.markup)
+        if self._usable(self.chardet_encoding, tried):
+            yield self.chardet_encoding
+
+        # As a last-ditch effort, try utf-8 and windows-1252.
+        for e in ('utf-8', 'windows-1252'):
+            if self._usable(e, tried):
+                yield e
+
+    @classmethod
+    def strip_byte_order_mark(cls, data):
+        """If a byte-order mark is present, strip it and return the encoding it implies.
+
+        :param data: Some markup.
+        :return: A 2-tuple (modified data, implied encoding)
+        """
+        encoding = None
+        if isinstance(data, str):
+            # Unicode data cannot have a byte-order mark.
+            return data, encoding
+        if (len(data) >= 4) and (data[:2] == b'\xfe\xff') \
+               and (data[2:4] != '\x00\x00'):
+            encoding = 'utf-16be'
+            data = data[2:]
+        elif (len(data) >= 4) and (data[:2] == b'\xff\xfe') \
+                 and (data[2:4] != '\x00\x00'):
+            encoding = 'utf-16le'
+            data = data[2:]
+        elif data[:3] == b'\xef\xbb\xbf':
+            encoding = 'utf-8'
+            data = data[3:]
+        elif data[:4] == b'\x00\x00\xfe\xff':
+            encoding = 'utf-32be'
+            data = data[4:]
+        elif data[:4] == b'\xff\xfe\x00\x00':
+            encoding = 'utf-32le'
+            data = data[4:]
+        return data, encoding
+
+    @classmethod
+    def find_declared_encoding(cls, markup, is_html=False, search_entire_document=False):
+        """Given a document, tries to find its declared encoding.
+
+        An XML encoding is declared at the beginning of the document.
+
+        An HTML encoding is declared in a <meta> tag, hopefully near the
+        beginning of the document.
+
+        :param markup: Some markup.
+        :param is_html: If True, this markup is considered to be HTML. Otherwise
+            it's assumed to be XML.
+        :param search_entire_document: Since an encoding is supposed to declared near the beginning
+            of the document, most of the time it's only necessary to search a few kilobytes of data.
+            Set this to True to force this method to search the entire document.
+        """
+        if search_entire_document:
+            xml_endpos = html_endpos = len(markup)
+        else:
+            xml_endpos = 1024
+            html_endpos = max(2048, int(len(markup) * 0.05))
+
+        if isinstance(markup, bytes):
+            res = encoding_res[bytes]
+        else:
+            res = encoding_res[str]
+
+        xml_re = res['xml']
+        html_re = res['html']
+        declared_encoding = None
+        declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)
+        if not declared_encoding_match and is_html:
+            declared_encoding_match = html_re.search(markup, endpos=html_endpos)
+        if declared_encoding_match is not None:
+            declared_encoding = declared_encoding_match.groups()[0]
+        if declared_encoding:
+            if isinstance(declared_encoding, bytes):
+                declared_encoding = declared_encoding.decode('ascii', 'replace')
+            return declared_encoding.lower()
+        return None
+
+class UnicodeDammit:
+    """A class for detecting the encoding of a *ML document and
+    converting it to a Unicode string. If the source encoding is
+    windows-1252, can replace MS smart quotes with their HTML or XML
+    equivalents."""
+
+    # This dictionary maps commonly seen values for "charset" in HTML
+    # meta tags to the corresponding Python codec names. It only covers
+    # values that aren't in Python's aliases and can't be determined
+    # by the heuristics in find_codec.
+    CHARSET_ALIASES = {"macintosh": "mac-roman",
+                       "x-sjis": "shift-jis"}
+
+    ENCODINGS_WITH_SMART_QUOTES = [
+        "windows-1252",
+        "iso-8859-1",
+        "iso-8859-2",
+        ]
+
+    def __init__(self, markup, known_definite_encodings=[],
+                 smart_quotes_to=None, is_html=False, exclude_encodings=[],
+                 user_encodings=None, override_encodings=None
+    ):
+        """Constructor.
+
+        :param markup: A bytestring representing markup in an unknown encoding.
+
+        :param known_definite_encodings: When determining the encoding
+            of `markup`, these encodings will be tried first, in
+            order. In HTML terms, this corresponds to the "known
+            definite encoding" step defined here:
+            https://html.spec.whatwg.org/multipage/parsing.html#parsing-with-a-known-character-encoding
+
+        :param user_encodings: These encodings will be tried after the
+            `known_definite_encodings` have been tried and failed, and
+            after an attempt to sniff the encoding by looking at a
+            byte order mark has failed. In HTML terms, this
+            corresponds to the step "user has explicitly instructed
+            the user agent to override the document's character
+            encoding", defined here:
+            https://html.spec.whatwg.org/multipage/parsing.html#determining-the-character-encoding
+
+        :param override_encodings: A deprecated alias for
+            known_definite_encodings. Any encodings here will be tried
+            immediately after the encodings in
+            known_definite_encodings.
+
+        :param smart_quotes_to: By default, Microsoft smart quotes will, like all other characters, be converted
+           to Unicode characters. Setting this to 'ascii' will convert them to ASCII quotes instead.
+           Setting it to 'xml' will convert them to XML entity references, and setting it to 'html'
+           will convert them to HTML entity references.
+        :param is_html: If True, this markup is considered to be HTML. Otherwise
+            it's assumed to be XML.
+        :param exclude_encodings: These encodings will not be considered, even
+            if the sniffing code thinks they might make sense.
+
+        """
+        self.smart_quotes_to = smart_quotes_to
+        self.tried_encodings = []
+        self.contains_replacement_characters = False
+        self.is_html = is_html
+        self.log = logging.getLogger(__name__)
+        self.detector = EncodingDetector(
+            markup, known_definite_encodings, is_html, exclude_encodings,
+            user_encodings, override_encodings
+        )
+
+        # Short-circuit if the data is in Unicode to begin with.
+        if isinstance(markup, str) or markup == '':
+            self.markup = markup
+            self.unicode_markup = str(markup)
+            self.original_encoding = None
+            return
+
+        # The encoding detector may have stripped a byte-order mark.
+        # Use the stripped markup from this point on.
+        self.markup = self.detector.markup
+
+        u = None
+        for encoding in self.detector.encodings:
+            markup = self.detector.markup
+            u = self._convert_from(encoding)
+            if u is not None:
+                break
+
+        if not u:
+            # None of the encodings worked. As an absolute last resort,
+            # try them again with character replacement.
+
+            for encoding in self.detector.encodings:
+                if encoding != "ascii":
+                    u = self._convert_from(encoding, "replace")
+                if u is not None:
+                    self.log.warning(
+                            "Some characters could not be decoded, and were "
+                            "replaced with REPLACEMENT CHARACTER."
+                    )
+                    self.contains_replacement_characters = True
+                    break
+
+        # If none of that worked, we could at this point force it to
+        # ASCII, but that would destroy so much data that I think
+        # giving up is better.
+        self.unicode_markup = u
+        if not u:
+            self.original_encoding = None
+
+    def _sub_ms_char(self, match):
+        """Changes a MS smart quote character to an XML or HTML
+        entity, or an ASCII character."""
+        orig = match.group(1)
+        if self.smart_quotes_to == 'ascii':
+            sub = self.MS_CHARS_TO_ASCII.get(orig).encode()
+        else:
+            sub = self.MS_CHARS.get(orig)
+            if type(sub) == tuple:
+                if self.smart_quotes_to == 'xml':
+                    sub = '&#x'.encode() + sub[1].encode() + ';'.encode()
+                else:
+                    sub = '&'.encode() + sub[0].encode() + ';'.encode()
+            else:
+                sub = sub.encode()
+        return sub
+
+    def _convert_from(self, proposed, errors="strict"):
+        """Attempt to convert the markup to the proposed encoding.
+
+        :param proposed: The name of a character encoding.
+        """
+        proposed = self.find_codec(proposed)
+        if not proposed or (proposed, errors) in self.tried_encodings:
+            return None
+        self.tried_encodings.append((proposed, errors))
+        markup = self.markup
+        # Convert smart quotes to HTML if coming from an encoding
+        # that might have them.
+        if (self.smart_quotes_to is not None
+            and proposed in self.ENCODINGS_WITH_SMART_QUOTES):
+            smart_quotes_re = b"([\x80-\x9f])"
+            smart_quotes_compiled = re.compile(smart_quotes_re)
+            markup = smart_quotes_compiled.sub(self._sub_ms_char, markup)
+
+        try:
+            #print("Trying to convert document to %s (errors=%s)" % (
+            #    proposed, errors))
+            u = self._to_unicode(markup, proposed, errors)
+            self.markup = u
+            self.original_encoding = proposed
+        except Exception as e:
+            #print("That didn't work!")
+            #print(e)
+            return None
+        #print("Correct encoding: %s" % proposed)
+        return self.markup
+
+    def _to_unicode(self, data, encoding, errors="strict"):
+        """Given a string and its encoding, decodes the string into Unicode.
+
+        :param encoding: The name of an encoding.
+        """
+        return str(data, encoding, errors)
+
+    @property
+    def declared_html_encoding(self):
+        """If the markup is an HTML document, returns the encoding declared _within_
+        the document.
+        """
+        if not self.is_html:
+            return None
+        return self.detector.declared_encoding
+
+    def find_codec(self, charset):
+        """Convert the name of a character set to a codec name.
+
+        :param charset: The name of a character set.
+        :return: The name of a codec.
+        """
+        value = (self._codec(self.CHARSET_ALIASES.get(charset, charset))
+               or (charset and self._codec(charset.replace("-", "")))
+               or (charset and self._codec(charset.replace("-", "_")))
+               or (charset and charset.lower())
+               or charset
+                )
+        if value:
+            return value.lower()
+        return None
+
+    def _codec(self, charset):
+        if not charset:
+            return charset
+        codec = None
+        try:
+            codecs.lookup(charset)
+            codec = charset
+        except (LookupError, ValueError):
+            pass
+        return codec
+
+
+    # A partial mapping of ISO-Latin-1 to HTML entities/XML numeric entities.
+    MS_CHARS = {b'\x80': ('euro', '20AC'),
+                b'\x81': ' ',
+                b'\x82': ('sbquo', '201A'),
+                b'\x83': ('fnof', '192'),
+                b'\x84': ('bdquo', '201E'),
+                b'\x85': ('hellip', '2026'),
+                b'\x86': ('dagger', '2020'),
+                b'\x87': ('Dagger', '2021'),
+                b'\x88': ('circ', '2C6'),
+                b'\x89': ('permil', '2030'),
+                b'\x8A': ('Scaron', '160'),
+                b'\x8B': ('lsaquo', '2039'),
+                b'\x8C': ('OElig', '152'),
+                b'\x8D': '?',
+                b'\x8E': ('#x17D', '17D'),
+                b'\x8F': '?',
+                b'\x90': '?',
+                b'\x91': ('lsquo', '2018'),
+                b'\x92': ('rsquo', '2019'),
+                b'\x93': ('ldquo', '201C'),
+                b'\x94': ('rdquo', '201D'),
+                b'\x95': ('bull', '2022'),
+                b'\x96': ('ndash', '2013'),
+                b'\x97': ('mdash', '2014'),
+                b'\x98': ('tilde', '2DC'),
+                b'\x99': ('trade', '2122'),
+                b'\x9a': ('scaron', '161'),
+                b'\x9b': ('rsaquo', '203A'),
+                b'\x9c': ('oelig', '153'),
+                b'\x9d': '?',
+                b'\x9e': ('#x17E', '17E'),
+                b'\x9f': ('Yuml', ''),}
+
+    # A parochial partial mapping of ISO-Latin-1 to ASCII. Contains
+    # horrors like stripping diacritical marks to turn á into a, but also
+    # contains non-horrors like turning “ into ".
+    MS_CHARS_TO_ASCII = {
+        b'\x80' : 'EUR',
+        b'\x81' : ' ',
+        b'\x82' : ',',
+        b'\x83' : 'f',
+        b'\x84' : ',,',
+        b'\x85' : '...',
+        b'\x86' : '+',
+        b'\x87' : '++',
+        b'\x88' : '^',
+        b'\x89' : '%',
+        b'\x8a' : 'S',
+        b'\x8b' : '<',
+        b'\x8c' : 'OE',
+        b'\x8d' : '?',
+        b'\x8e' : 'Z',
+        b'\x8f' : '?',
+        b'\x90' : '?',
+        b'\x91' : "'",
+        b'\x92' : "'",
+        b'\x93' : '"',
+        b'\x94' : '"',
+        b'\x95' : '*',
+        b'\x96' : '-',
+        b'\x97' : '--',
+        b'\x98' : '~',
+        b'\x99' : '(TM)',
+        b'\x9a' : 's',
+        b'\x9b' : '>',
+        b'\x9c' : 'oe',
+        b'\x9d' : '?',
+        b'\x9e' : 'z',
+        b'\x9f' : 'Y',
+        b'\xa0' : ' ',
+        b'\xa1' : '!',
+        b'\xa2' : 'c',
+        b'\xa3' : 'GBP',
+        b'\xa4' : '$', #This approximation is especially parochial--this is the
+                       #generic currency symbol.
+        b'\xa5' : 'YEN',
+        b'\xa6' : '|',
+        b'\xa7' : 'S',
+        b'\xa8' : '..',
+        b'\xa9' : '',
+        b'\xaa' : '(th)',
+        b'\xab' : '<<',
+        b'\xac' : '!',
+        b'\xad' : ' ',
+        b'\xae' : '(R)',
+        b'\xaf' : '-',
+        b'\xb0' : 'o',
+        b'\xb1' : '+-',
+        b'\xb2' : '2',
+        b'\xb3' : '3',
+        b'\xb4' : ("'", 'acute'),
+        b'\xb5' : 'u',
+        b'\xb6' : 'P',
+        b'\xb7' : '*',
+        b'\xb8' : ',',
+        b'\xb9' : '1',
+        b'\xba' : '(th)',
+        b'\xbb' : '>>',
+        b'\xbc' : '1/4',
+        b'\xbd' : '1/2',
+        b'\xbe' : '3/4',
+        b'\xbf' : '?',
+        b'\xc0' : 'A',
+        b'\xc1' : 'A',
+        b'\xc2' : 'A',
+        b'\xc3' : 'A',
+        b'\xc4' : 'A',
+        b'\xc5' : 'A',
+        b'\xc6' : 'AE',
+        b'\xc7' : 'C',
+        b'\xc8' : 'E',
+        b'\xc9' : 'E',
+        b'\xca' : 'E',
+        b'\xcb' : 'E',
+        b'\xcc' : 'I',
+        b'\xcd' : 'I',
+        b'\xce' : 'I',
+        b'\xcf' : 'I',
+        b'\xd0' : 'D',
+        b'\xd1' : 'N',
+        b'\xd2' : 'O',
+        b'\xd3' : 'O',
+        b'\xd4' : 'O',
+        b'\xd5' : 'O',
+        b'\xd6' : 'O',
+        b'\xd7' : '*',
+        b'\xd8' : 'O',
+        b'\xd9' : 'U',
+        b'\xda' : 'U',
+        b'\xdb' : 'U',
+        b'\xdc' : 'U',
+        b'\xdd' : 'Y',
+        b'\xde' : 'b',
+        b'\xdf' : 'B',
+        b'\xe0' : 'a',
+        b'\xe1' : 'a',
+        b'\xe2' : 'a',
+        b'\xe3' : 'a',
+        b'\xe4' : 'a',
+        b'\xe5' : 'a',
+        b'\xe6' : 'ae',
+        b'\xe7' : 'c',
+        b'\xe8' : 'e',
+        b'\xe9' : 'e',
+        b'\xea' : 'e',
+        b'\xeb' : 'e',
+        b'\xec' : 'i',
+        b'\xed' : 'i',
+        b'\xee' : 'i',
+        b'\xef' : 'i',
+        b'\xf0' : 'o',
+        b'\xf1' : 'n',
+        b'\xf2' : 'o',
+        b'\xf3' : 'o',
+        b'\xf4' : 'o',
+        b'\xf5' : 'o',
+        b'\xf6' : 'o',
+        b'\xf7' : '/',
+        b'\xf8' : 'o',
+        b'\xf9' : 'u',
+        b'\xfa' : 'u',
+        b'\xfb' : 'u',
+        b'\xfc' : 'u',
+        b'\xfd' : 'y',
+        b'\xfe' : 'b',
+        b'\xff' : 'y',
+        }
+
+    # A map used when removing rogue Windows-1252/ISO-8859-1
+    # characters in otherwise UTF-8 documents.
+    #
+    # Note that \x81, \x8d, \x8f, \x90, and \x9d are undefined in
+    # Windows-1252.
+    WINDOWS_1252_TO_UTF8 = {
+        0x80 : b'\xe2\x82\xac', # €
+        0x82 : b'\xe2\x80\x9a', # ‚
+        0x83 : b'\xc6\x92',     # ƒ
+        0x84 : b'\xe2\x80\x9e', # „
+        0x85 : b'\xe2\x80\xa6', # …
+        0x86 : b'\xe2\x80\xa0', # †
+        0x87 : b'\xe2\x80\xa1', # ‡
+        0x88 : b'\xcb\x86',     # ˆ
+        0x89 : b'\xe2\x80\xb0', # ‰
+        0x8a : b'\xc5\xa0',     # Š
+        0x8b : b'\xe2\x80\xb9', # ‹
+        0x8c : b'\xc5\x92',     # Œ
+        0x8e : b'\xc5\xbd',     # Ž
+        0x91 : b'\xe2\x80\x98', # ‘
+        0x92 : b'\xe2\x80\x99', # ’
+        0x93 : b'\xe2\x80\x9c', # “
+        0x94 : b'\xe2\x80\x9d', # ”
+        0x95 : b'\xe2\x80\xa2', # •
+        0x96 : b'\xe2\x80\x93', # –
+        0x97 : b'\xe2\x80\x94', # —
+        0x98 : b'\xcb\x9c',     # ˜
+        0x99 : b'\xe2\x84\xa2', # ™
+        0x9a : b'\xc5\xa1',     # š
+        0x9b : b'\xe2\x80\xba', # ›
+        0x9c : b'\xc5\x93',     # œ
+        0x9e : b'\xc5\xbe',     # ž
+        0x9f : b'\xc5\xb8',     # Ÿ
+        0xa0 : b'\xc2\xa0',     #  
+        0xa1 : b'\xc2\xa1',     # ¡
+        0xa2 : b'\xc2\xa2',     # ¢
+        0xa3 : b'\xc2\xa3',     # £
+        0xa4 : b'\xc2\xa4',     # ¤
+        0xa5 : b'\xc2\xa5',     # ¥
+        0xa6 : b'\xc2\xa6',     # ¦
+        0xa7 : b'\xc2\xa7',     # §
+        0xa8 : b'\xc2\xa8',     # ¨
+        0xa9 : b'\xc2\xa9',     # ©
+        0xaa : b'\xc2\xaa',     # ª
+        0xab : b'\xc2\xab',     # «
+        0xac : b'\xc2\xac',     # ¬
+        0xad : b'\xc2\xad',     # ­
+        0xae : b'\xc2\xae',     # ®
+        0xaf : b'\xc2\xaf',     # ¯
+        0xb0 : b'\xc2\xb0',     # °
+        0xb1 : b'\xc2\xb1',     # ±
+        0xb2 : b'\xc2\xb2',     # ²
+        0xb3 : b'\xc2\xb3',     # ³
+        0xb4 : b'\xc2\xb4',     # ´
+        0xb5 : b'\xc2\xb5',     # µ
+        0xb6 : b'\xc2\xb6',     # ¶
+        0xb7 : b'\xc2\xb7',     # ·
+        0xb8 : b'\xc2\xb8',     # ¸
+        0xb9 : b'\xc2\xb9',     # ¹
+        0xba : b'\xc2\xba',     # º
+        0xbb : b'\xc2\xbb',     # »
+        0xbc : b'\xc2\xbc',     # ¼
+        0xbd : b'\xc2\xbd',     # ½
+        0xbe : b'\xc2\xbe',     # ¾
+        0xbf : b'\xc2\xbf',     # ¿
+        0xc0 : b'\xc3\x80',     # À
+        0xc1 : b'\xc3\x81',     # Á
+        0xc2 : b'\xc3\x82',     # Â
+        0xc3 : b'\xc3\x83',     # Ã
+        0xc4 : b'\xc3\x84',     # Ä
+        0xc5 : b'\xc3\x85',     # Å
+        0xc6 : b'\xc3\x86',     # Æ
+        0xc7 : b'\xc3\x87',     # Ç
+        0xc8 : b'\xc3\x88',     # È
+        0xc9 : b'\xc3\x89',     # É
+        0xca : b'\xc3\x8a',     # Ê
+        0xcb : b'\xc3\x8b',     # Ë
+        0xcc : b'\xc3\x8c',     # Ì
+        0xcd : b'\xc3\x8d',     # Í
+        0xce : b'\xc3\x8e',     # Î
+        0xcf : b'\xc3\x8f',     # Ï
+        0xd0 : b'\xc3\x90',     # Ð
+        0xd1 : b'\xc3\x91',     # Ñ
+        0xd2 : b'\xc3\x92',     # Ò
+        0xd3 : b'\xc3\x93',     # Ó
+        0xd4 : b'\xc3\x94',     # Ô
+        0xd5 : b'\xc3\x95',     # Õ
+        0xd6 : b'\xc3\x96',     # Ö
+        0xd7 : b'\xc3\x97',     # ×
+        0xd8 : b'\xc3\x98',     # Ø
+        0xd9 : b'\xc3\x99',     # Ù
+        0xda : b'\xc3\x9a',     # Ú
+        0xdb : b'\xc3\x9b',     # Û
+        0xdc : b'\xc3\x9c',     # Ü
+        0xdd : b'\xc3\x9d',     # Ý
+        0xde : b'\xc3\x9e',     # Þ
+        0xdf : b'\xc3\x9f',     # ß
+        0xe0 : b'\xc3\xa0',     # à
+        0xe1 : b'\xa1',         # á
+        0xe2 : b'\xc3\xa2',     # â
+        0xe3 : b'\xc3\xa3',     # ã
+        0xe4 : b'\xc3\xa4',     # ä
+        0xe5 : b'\xc3\xa5',     # å
+        0xe6 : b'\xc3\xa6',     # æ
+        0xe7 : b'\xc3\xa7',     # ç
+        0xe8 : b'\xc3\xa8',     # è
+        0xe9 : b'\xc3\xa9',     # é
+        0xea : b'\xc3\xaa',     # ê
+        0xeb : b'\xc3\xab',     # ë
+        0xec : b'\xc3\xac',     # ì
+        0xed : b'\xc3\xad',     # í
+        0xee : b'\xc3\xae',     # î
+        0xef : b'\xc3\xaf',     # ï
+        0xf0 : b'\xc3\xb0',     # ð
+        0xf1 : b'\xc3\xb1',     # ñ
+        0xf2 : b'\xc3\xb2',     # ò
+        0xf3 : b'\xc3\xb3',     # ó
+        0xf4 : b'\xc3\xb4',     # ô
+        0xf5 : b'\xc3\xb5',     # õ
+        0xf6 : b'\xc3\xb6',     # ö
+        0xf7 : b'\xc3\xb7',     # ÷
+        0xf8 : b'\xc3\xb8',     # ø
+        0xf9 : b'\xc3\xb9',     # ù
+        0xfa : b'\xc3\xba',     # ú
+        0xfb : b'\xc3\xbb',     # û
+        0xfc : b'\xc3\xbc',     # ü
+        0xfd : b'\xc3\xbd',     # ý
+        0xfe : b'\xc3\xbe',     # þ
+        }
+
+    MULTIBYTE_MARKERS_AND_SIZES = [
+        (0xc2, 0xdf, 2), # 2-byte characters start with a byte C2-DF
+        (0xe0, 0xef, 3), # 3-byte characters start with E0-EF
+        (0xf0, 0xf4, 4), # 4-byte characters start with F0-F4
+        ]
+
+    FIRST_MULTIBYTE_MARKER = MULTIBYTE_MARKERS_AND_SIZES[0][0]
+    LAST_MULTIBYTE_MARKER = MULTIBYTE_MARKERS_AND_SIZES[-1][1]
+
+    @classmethod
+    def detwingle(cls, in_bytes, main_encoding="utf8",
+                  embedded_encoding="windows-1252"):
+        """Fix characters from one encoding embedded in some other encoding.
+
+        Currently the only situation supported is Windows-1252 (or its
+        subset ISO-8859-1), embedded in UTF-8.
+
+        :param in_bytes: A bytestring that you suspect contains
+            characters from multiple encodings. Note that this _must_
+            be a bytestring. If you've already converted the document
+            to Unicode, you're too late.
+        :param main_encoding: The primary encoding of `in_bytes`.
+        :param embedded_encoding: The encoding that was used to embed characters
+            in the main document.
+        :return: A bytestring in which `embedded_encoding`
+          characters have been converted to their `main_encoding`
+          equivalents.
+        """
+        if embedded_encoding.replace('_', '-').lower() not in (
+            'windows-1252', 'windows_1252'):
+            raise NotImplementedError(
+                "Windows-1252 and ISO-8859-1 are the only currently supported "
+                "embedded encodings.")
+
+        if main_encoding.lower() not in ('utf8', 'utf-8'):
+            raise NotImplementedError(
+                "UTF-8 is the only currently supported main encoding.")
+
+        byte_chunks = []
+
+        chunk_start = 0
+        pos = 0
+        while pos < len(in_bytes):
+            byte = in_bytes[pos]
+            if not isinstance(byte, int):
+                # Python 2.x
+                byte = ord(byte)
+            if (byte >= cls.FIRST_MULTIBYTE_MARKER
+                and byte <= cls.LAST_MULTIBYTE_MARKER):
+                # This is the start of a UTF-8 multibyte character. Skip
+                # to the end.
+                for start, end, size in cls.MULTIBYTE_MARKERS_AND_SIZES:
+                    if byte >= start and byte <= end:
+                        pos += size
+                        break
+            elif byte >= 0x80 and byte in cls.WINDOWS_1252_TO_UTF8:
+                # We found a Windows-1252 character!
+                # Save the string up to this point as a chunk.
+                byte_chunks.append(in_bytes[chunk_start:pos])
+
+                # Now translate the Windows-1252 character into UTF-8
+                # and add it as another, one-byte chunk.
+                byte_chunks.append(cls.WINDOWS_1252_TO_UTF8[byte])
+                pos += 1
+                chunk_start = pos
+            else:
+                # Go on to the next character.
+                pos += 1
+        if chunk_start == 0:
+            # The string is unchanged.
+            return in_bytes
+        else:
+            # Store the final chunk.
+            byte_chunks.append(in_bytes[chunk_start:])
+        return b''.join(byte_chunks)
+
diff --git a/shared/package-files/1b/1b48069d57a3fdadf209dfa29788b09cf661210881bd2d5d96fdf72ec3583693/adjoint_ops.h b/shared/package-files/1b/1b48069d57a3fdadf209dfa29788b09cf661210881bd2d5d96fdf72ec3583693/adjoint_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb3cda9e9737af4299c50e23609805f41f64b08f
--- /dev/null
+++ b/shared/package-files/1b/1b48069d57a3fdadf209dfa29788b09cf661210881bd2d5d96fdf72ec3583693/adjoint_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API adjoint {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::adjoint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "adjoint(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1b487685be0ebb8c83acb5f852e361a78fbbd613a2a0eba0cd3cea0629511732/pyobject_preservation.h b/shared/package-files/1b/1b487685be0ebb8c83acb5f852e361a78fbbd613a2a0eba0cd3cea0629511732/pyobject_preservation.h
new file mode 100644
index 0000000000000000000000000000000000000000..456095d7b7037d46bd59f8f173795e177ae269a6
--- /dev/null
+++ b/shared/package-files/1b/1b487685be0ebb8c83acb5f852e361a78fbbd613a2a0eba0cd3cea0629511732/pyobject_preservation.h
@@ -0,0 +1,7 @@
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+// This file contains utilities used for handling PyObject preservation
+
+void clear_slots(PyTypeObject* type, PyObject* self);
diff --git a/shared/package-files/1b/1b4979874c3f025317dfcf0b06fc8cee080a28ff3e8efe1de9e899f6d4f4d21e/es_co.msg b/shared/package-files/1b/1b4979874c3f025317dfcf0b06fc8cee080a28ff3e8efe1de9e899f6d4f4d21e/es_co.msg
new file mode 100644
index 0000000000000000000000000000000000000000..77e57f0be7bee914212540dff7d44ab77cb5f937
--- /dev/null
+++ b/shared/package-files/1b/1b4979874c3f025317dfcf0b06fc8cee080a28ff3e8efe1de9e899f6d4f4d21e/es_co.msg
@@ -0,0 +1,6 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset es_CO DATE_FORMAT "%e/%m/%Y"
+    ::msgcat::mcset es_CO TIME_FORMAT_12 "%I:%M:%S %P"
+    ::msgcat::mcset es_CO DATE_TIME_FORMAT "%e/%m/%Y %I:%M:%S %P %z"
+}
diff --git a/shared/package-files/1b/1b4ae5b26f6ea582d875238926df1523ff66f4480773323ee132173e5a47456c/connectivity.py b/shared/package-files/1b/1b4ae5b26f6ea582d875238926df1523ff66f4480773323ee132173e5a47456c/connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..dad28f9033cbedab80b00cfa455acbf3170a7975
--- /dev/null
+++ b/shared/package-files/1b/1b4ae5b26f6ea582d875238926df1523ff66f4480773323ee132173e5a47456c/connectivity.py
@@ -0,0 +1,121 @@
+from collections import defaultdict
+
+import networkx as nx
+
+__all__ = ["average_degree_connectivity"]
+
+
+def average_degree_connectivity(
+    G, source="in+out", target="in+out", nodes=None, weight=None
+):
+    r"""Compute the average degree connectivity of graph.
+
+    The average degree connectivity is the average nearest neighbor degree of
+    nodes with degree k. For weighted graphs, an analogous measure can
+    be computed using the weighted average neighbors degree defined in
+    [1]_, for a node `i`, as
+
+    .. math::
+
+        k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
+
+    where `s_i` is the weighted degree of node `i`,
+    `w_{ij}` is the weight of the edge that links `i` and `j`,
+    and `N(i)` are the neighbors of node `i`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source :  "in"|"out"|"in+out" (default:"in+out")
+       Directed graphs only. Use "in"- or "out"-degree for source node.
+
+    target : "in"|"out"|"in+out" (default:"in+out"
+       Directed graphs only. Use "in"- or "out"-degree for target node.
+
+    nodes : list or iterable (optional)
+        Compute neighbor connectivity for these nodes. The default is all
+        nodes.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    d : dict
+       A dictionary keyed by degree k with the value of average connectivity.
+
+    Raises
+    ------
+    NetworkXError
+        If either `source` or `target` are not one of 'in',
+        'out', or 'in+out'.
+        If either `source` or `target` is passed for an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G.edges[1, 2]["weight"] = 3
+    >>> nx.average_degree_connectivity(G)
+    {1: 2.0, 2: 1.5}
+    >>> nx.average_degree_connectivity(G, weight="weight")
+    {1: 2.0, 2: 1.75}
+
+    See Also
+    --------
+    average_neighbor_degree
+
+    References
+    ----------
+    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
+       "The architecture of complex weighted networks".
+       PNAS 101 (11): 3747–3752 (2004).
+    """
+    # First, determine the type of neighbors and the type of degree to use.
+    if G.is_directed():
+        if source not in ("in", "out", "in+out"):
+            raise nx.NetworkXError('source must be one of "in", "out", or "in+out"')
+        if target not in ("in", "out", "in+out"):
+            raise nx.NetworkXError('target must be one of "in", "out", or "in+out"')
+        direction = {"out": G.out_degree, "in": G.in_degree, "in+out": G.degree}
+        neighbor_funcs = {
+            "out": G.successors,
+            "in": G.predecessors,
+            "in+out": G.neighbors,
+        }
+        source_degree = direction[source]
+        target_degree = direction[target]
+        neighbors = neighbor_funcs[source]
+        # `reverse` indicates whether to look at the in-edge when
+        # computing the weight of an edge.
+        reverse = source == "in"
+    else:
+        if source != "in+out" or target != "in+out":
+            raise nx.NetworkXError(
+                f"source and target arguments are only supported for directed graphs"
+            )
+        source_degree = G.degree
+        target_degree = G.degree
+        neighbors = G.neighbors
+        reverse = False
+    dsum = defaultdict(int)
+    dnorm = defaultdict(int)
+    # Check if `source_nodes` is actually a single node in the graph.
+    source_nodes = source_degree(nodes)
+    if nodes in G:
+        source_nodes = [(nodes, source_degree(nodes))]
+    for n, k in source_nodes:
+        nbrdeg = target_degree(neighbors(n))
+        if weight is None:
+            s = sum(d for n, d in nbrdeg)
+        else:  # weight nbr degree by weight of (n,nbr) edge
+            if reverse:
+                s = sum(G[nbr][n].get(weight, 1) * d for nbr, d in nbrdeg)
+            else:
+                s = sum(G[n][nbr].get(weight, 1) * d for nbr, d in nbrdeg)
+        dnorm[k] += source_degree(n, weight=weight)
+        dsum[k] += s
+
+    # normalize
+    return {k: avg if dnorm[k] == 0 else avg / dnorm[k] for k, avg in dsum.items()}
diff --git a/shared/package-files/1b/1b4b996e9fee7e582ff80142ee77ba9b4b71cc0a46a8c656bedfc809fab758aa/factory.h b/shared/package-files/1b/1b4b996e9fee7e582ff80142ee77ba9b4b71cc0a46a8c656bedfc809fab758aa/factory.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1c22a1598ec765ad4768a8393250bbbc73956e2
--- /dev/null
+++ b/shared/package-files/1b/1b4b996e9fee7e582ff80142ee77ba9b4b71cc0a46a8c656bedfc809fab758aa/factory.h
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+#include <vector>
+
+#include <tensorpipe/channel/context.h>
+#include <tensorpipe/transport/context.h>
+
+namespace tensorpipe {
+namespace channel {
+namespace mpt {
+
+std::shared_ptr<Context> create(
+    std::vector<std::shared_ptr<transport::Context>> contexts,
+    std::vector<std::shared_ptr<transport::Listener>> listeners);
+
+} // namespace mpt
+} // namespace channel
+} // namespace tensorpipe
diff --git a/shared/package-files/1b/1b4c7bd4216230a6b92102ff5ccd1a52f4ef721677deba5ba8fe465156e4f96b/fmin_meta_dispatch.h b/shared/package-files/1b/1b4c7bd4216230a6b92102ff5ccd1a52f4ef721677deba5ba8fe465156e4f96b/fmin_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7c1a54fe39fcfd062131299d3fc28a37372cd6a
--- /dev/null
+++ b/shared/package-files/1b/1b4c7bd4216230a6b92102ff5ccd1a52f4ef721677deba5ba8fe465156e4f96b/fmin_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor fmin(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmin_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmin_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/1b/1b4f1a33cd6b011aa88c22a7b2e9d51bbbc907d820a27ec7f12f3115a6682bfa/test_complex_kernel.py b/shared/package-files/1b/1b4f1a33cd6b011aa88c22a7b2e9d51bbbc907d820a27ec7f12f3115a6682bfa/test_complex_kernel.py
new file mode 100644
index 0000000000000000000000000000000000000000..e72a6df006a41eafa3312fd8ff9eb11a869936d1
--- /dev/null
+++ b/shared/package-files/1b/1b4f1a33cd6b011aa88c22a7b2e9d51bbbc907d820a27ec7f12f3115a6682bfa/test_complex_kernel.py
@@ -0,0 +1,20 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaComplex(CUDATestCase):
+    def test_cuda_complex_arg(self):
+        @cuda.jit('void(complex128[:], complex128)')
+        def foo(a, b):
+            i = cuda.grid(1)
+            a[i] += b
+
+        a = np.arange(5, dtype=np.complex128)
+        a0 = a.copy()
+        foo[1, a.shape](a, 2j)
+        self.assertTrue(np.allclose(a, a0 + 2j))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/1b/1b4f5c83a97cdd234d28784e109e0a4c3d73778a2e082599e23c239807513d0f/textpeer.tcl b/shared/package-files/1b/1b4f5c83a97cdd234d28784e109e0a4c3d73778a2e082599e23c239807513d0f/textpeer.tcl
new file mode 100644
index 0000000000000000000000000000000000000000..83e8e14fec7bff773cf0f3fefe2eac302ce5f237
--- /dev/null
+++ b/shared/package-files/1b/1b4f5c83a97cdd234d28784e109e0a4c3d73778a2e082599e23c239807513d0f/textpeer.tcl
@@ -0,0 +1,62 @@
+# textpeer.tcl --
+#
+# This demonstration script creates a pair of text widgets that can edit a
+# single logical buffer. This is particularly useful when editing related text
+# in two (or more) parts of the same file.
+
+if {![info exists widgetDemo]} {
+    error "This script should be run from the \"widget\" demo."
+}
+
+package require Tk
+
+set w .textpeer
+catch {destroy $w}
+toplevel $w
+wm title $w "Text Widget Peering Demonstration"
+wm iconname $w "textpeer"
+positionWindow $w
+
+set count 0
+
+## Define a widget that we peer from; it won't ever actually be shown though
+set first [text $w.text[incr count]]
+$first insert end "This is a coupled pair of text widgets; they are peers to "
+$first insert end "each other. They have the same underlying data model, but "
+$first insert end "can show different locations, have different current edit "
+$first insert end "locations, and have different selections. You can also "
+$first insert end "create additional peers of any of these text widgets using "
+$first insert end "the Make Peer button beside the text widget to clone, and "
+$first insert end "delete a particular peer widget using the Delete Peer "
+$first insert end "button."
+
+## Procedures to make and kill clones; most of this is just so that the demo
+## looks nice...
+proc makeClone {w parent} {
+    global count
+    set t [$parent peer create $w.text[incr count] -yscroll "$w.sb$count set"\
+		  -height 10 -wrap word]
+    set sb [ttk::scrollbar $w.sb$count -command "$t yview" -orient vertical]
+    set b1 [button $w.clone$count -command "makeClone $w $t" \
+		    -text "Make Peer"]
+    set b2 [button $w.kill$count -command "killClone $w $count" \
+		    -text "Delete Peer"]
+    set row [expr {$count * 2}]
+    grid $t $sb $b1 -sticky nsew -row $row
+    grid ^  ^   $b2 -row [incr row]
+    grid configure $b1 $b2 -sticky new
+    grid rowconfigure $w $b2 -weight 1
+}
+proc killClone {w count} {
+    destroy $w.text$count $w.sb$count
+    destroy $w.clone$count $w.kill$count
+}
+
+## Now set up the GUI
+makeClone $w $first
+makeClone $w $first
+destroy $first
+
+## See Code / Dismiss buttons
+grid [addSeeDismiss $w.buttons $w] - - -sticky ew -row 5000
+grid columnconfigure $w 0 -weight 1
diff --git a/shared/package-files/1b/1b503810ff2a5f5e73ef6e1aa1373ed77700667b9297657ed866e9e068e53e30/multipart.pyi b/shared/package-files/1b/1b503810ff2a5f5e73ef6e1aa1373ed77700667b9297657ed866e9e068e53e30/multipart.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f7dd85d43b610816fd98a2c834618345b5eb21c1
--- /dev/null
+++ b/shared/package-files/1b/1b503810ff2a5f5e73ef6e1aa1373ed77700667b9297657ed866e9e068e53e30/multipart.pyi
@@ -0,0 +1,70 @@
+from typing import Any, Optional
+
+class CompleteMultiPartUpload:
+    bucket: Any
+    location: Any
+    bucket_name: Any
+    key_name: Any
+    etag: Any
+    version_id: Any
+    encrypted: Any
+    def __init__(self, bucket: Optional[Any] = ...) -> None: ...
+    def startElement(self, name, attrs, connection): ...
+    def endElement(self, name, value, connection): ...
+
+class Part:
+    bucket: Any
+    part_number: Any
+    last_modified: Any
+    etag: Any
+    size: Any
+    def __init__(self, bucket: Optional[Any] = ...) -> None: ...
+    def startElement(self, name, attrs, connection): ...
+    def endElement(self, name, value, connection): ...
+
+def part_lister(mpupload, part_number_marker: Optional[Any] = ...): ...
+
+class MultiPartUpload:
+    bucket: Any
+    bucket_name: Any
+    key_name: Any
+    id: Any
+    initiator: Any
+    owner: Any
+    storage_class: Any
+    initiated: Any
+    part_number_marker: Any
+    next_part_number_marker: Any
+    max_parts: Any
+    is_truncated: bool
+    def __init__(self, bucket: Optional[Any] = ...) -> None: ...
+    def __iter__(self): ...
+    def to_xml(self): ...
+    def startElement(self, name, attrs, connection): ...
+    def endElement(self, name, value, connection): ...
+    def get_all_parts(
+        self, max_parts: Optional[Any] = ..., part_number_marker: Optional[Any] = ..., encoding_type: Optional[Any] = ...
+    ): ...
+    def upload_part_from_file(
+        self,
+        fp,
+        part_num,
+        headers: Optional[Any] = ...,
+        replace: bool = ...,
+        cb: Optional[Any] = ...,
+        num_cb: int = ...,
+        md5: Optional[Any] = ...,
+        size: Optional[Any] = ...,
+    ): ...
+    def copy_part_from_key(
+        self,
+        src_bucket_name,
+        src_key_name,
+        part_num,
+        start: Optional[Any] = ...,
+        end: Optional[Any] = ...,
+        src_version_id: Optional[Any] = ...,
+        headers: Optional[Any] = ...,
+    ): ...
+    def complete_upload(self): ...
+    def cancel_upload(self): ...
diff --git a/shared/package-files/1b/1b52c20e407704352e0f342e5319f88c28ea5126c2c0c5ff95f9155c32defa4d/test_linsolve.py b/shared/package-files/1b/1b52c20e407704352e0f342e5319f88c28ea5126c2c0c5ff95f9155c32defa4d/test_linsolve.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d8cd7eb9feb27c59d6a32ceb3f04118eae971e2
--- /dev/null
+++ b/shared/package-files/1b/1b52c20e407704352e0f342e5319f88c28ea5126c2c0c5ff95f9155c32defa4d/test_linsolve.py
@@ -0,0 +1,111 @@
+#
+# test_linsolve.py
+#
+#    Test the internal implementation of linsolve.
+#
+
+from sympy.testing.pytest import raises
+
+from sympy.core.numbers import I
+from sympy.core.relational import Eq
+from sympy.core.singleton import S
+from sympy.abc import x, y, z
+
+from sympy.polys.matrices.linsolve import _linsolve
+from sympy.polys.solvers import PolyNonlinearError
+
+
+def test__linsolve():
+    assert _linsolve([], [x]) == {x:x}
+    assert _linsolve([S.Zero], [x]) == {x:x}
+    assert _linsolve([x-1,x-2], [x]) is None
+    assert _linsolve([x-1], [x]) == {x:1}
+    assert _linsolve([x-1, y], [x, y]) == {x:1, y:S.Zero}
+    assert _linsolve([2*I], [x]) is None
+    raises(PolyNonlinearError, lambda: _linsolve([x*(1 + x)], [x]))
+
+
+def test__linsolve_float():
+
+    # This should give the exact answer:
+    eqs = [
+        y - x,
+        y - 0.0216 * x
+    ]
+    sol = {x:0.0, y:0.0}
+    assert _linsolve(eqs, (x, y)) == sol
+
+    # Other cases should be close to eps
+
+    def all_close(sol1, sol2, eps=1e-15):
+        close = lambda a, b: abs(a - b) < eps
+        assert sol1.keys() == sol2.keys()
+        return all(close(sol1[s], sol2[s]) for s in sol1)
+
+    eqs = [
+        0.8*x +         0.8*z + 0.2,
+        0.9*x + 0.7*y + 0.2*z + 0.9,
+        0.7*x + 0.2*y + 0.2*z + 0.5
+    ]
+    sol_exact = {x:-29/42, y:-11/21, z:37/84}
+    sol_linsolve = _linsolve(eqs, [x,y,z])
+    assert all_close(sol_exact, sol_linsolve)
+
+    eqs = [
+        0.9*x + 0.3*y + 0.4*z + 0.6,
+        0.6*x + 0.9*y + 0.1*z + 0.7,
+        0.4*x + 0.6*y + 0.9*z + 0.5
+    ]
+    sol_exact = {x:-88/175, y:-46/105, z:-1/25}
+    sol_linsolve = _linsolve(eqs, [x,y,z])
+    assert all_close(sol_exact, sol_linsolve)
+
+    eqs = [
+        0.4*x + 0.3*y + 0.6*z + 0.7,
+        0.4*x + 0.3*y + 0.9*z + 0.9,
+        0.7*x + 0.9*y,
+    ]
+    sol_exact = {x:-9/5, y:7/5, z:-2/3}
+    sol_linsolve = _linsolve(eqs, [x,y,z])
+    assert all_close(sol_exact, sol_linsolve)
+
+    eqs = [
+        x*(0.7 + 0.6*I) + y*(0.4 + 0.7*I) + z*(0.9 + 0.1*I) + 0.5,
+        0.2*I*x + 0.2*I*y + z*(0.9 + 0.2*I) + 0.1,
+        x*(0.9 + 0.7*I) + y*(0.9 + 0.7*I) + z*(0.9 + 0.4*I) + 0.4,
+    ]
+    sol_exact = {
+        x:-6157/7995 - 411/5330*I,
+        y:8519/15990 + 1784/7995*I,
+        z:-34/533 + 107/1599*I,
+    }
+    sol_linsolve = _linsolve(eqs, [x,y,z])
+    assert all_close(sol_exact, sol_linsolve)
+
+    # XXX: This system for x and y over RR(z) is problematic.
+    #
+    # eqs = [
+    #     x*(0.2*z + 0.9) + y*(0.5*z + 0.8) + 0.6,
+    #     0.1*x*z + y*(0.1*z + 0.6) + 0.9,
+    # ]
+    #
+    # linsolve(eqs, [x, y])
+    # The solution for x comes out as
+    #
+    #       -3.9e-5*z**2 - 3.6e-5*z - 8.67361737988404e-20
+    #  x =  ----------------------------------------------
+    #           3.0e-6*z**3 - 1.3e-5*z**2 - 5.4e-5*z
+    #
+    # The 8e-20 in the numerator should be zero which would allow z to cancel
+    # from top and bottom. It should be possible to avoid this somehow because
+    # the inverse of the matrix only has a quadratic factor (the determinant)
+    # in the denominator.
+
+
+def test__linsolve_deprecated():
+    raises(PolyNonlinearError, lambda:
+        _linsolve([Eq(x**2, x**2 + y)], [x, y]))
+    raises(PolyNonlinearError, lambda:
+        _linsolve([(x + y)**2 - x**2], [x]))
+    raises(PolyNonlinearError, lambda:
+        _linsolve([Eq((x + y)**2, x**2)], [x]))
diff --git a/shared/package-files/1b/1b52c4c64f9d31e55361186d620702644b07939b646b093b8225dc2b84392953/defchararray.py b/shared/package-files/1b/1b52c4c64f9d31e55361186d620702644b07939b646b093b8225dc2b84392953/defchararray.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c5a30bff70ef4edfb9fc0dd616af9d99d9da39
--- /dev/null
+++ b/shared/package-files/1b/1b52c4c64f9d31e55361186d620702644b07939b646b093b8225dc2b84392953/defchararray.py
@@ -0,0 +1,2914 @@
+"""
+This module contains a set of functions for vectorized string
+operations and methods.
+
+.. note::
+   The `chararray` class exists for backwards compatibility with
+   Numarray, it is not recommended for new development. Starting from numpy
+   1.4, if one needs arrays of strings, it is recommended to use arrays of
+   `dtype` `object_`, `bytes_` or `str_`, and use the free functions
+   in the `numpy.char` module for fast vectorized string operations.
+
+Some methods will only be available if the corresponding string method is
+available in your version of Python.
+
+The preferred alias for `defchararray` is `numpy.char`.
+
+"""
+import functools
+
+from .._utils import set_module
+from .numerictypes import (
+    bytes_, str_, integer, int_, object_, bool_, character)
+from .numeric import ndarray, compare_chararrays
+from .numeric import array as narray
+from numpy.core.multiarray import _vec_string
+from numpy.core import overrides
+from numpy.compat import asbytes
+import numpy
+
+__all__ = [
+    'equal', 'not_equal', 'greater_equal', 'less_equal',
+    'greater', 'less', 'str_len', 'add', 'multiply', 'mod', 'capitalize',
+    'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs',
+    'find', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace',
+    'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'partition',
+    'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit',
+    'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase',
+    'title', 'translate', 'upper', 'zfill', 'isnumeric', 'isdecimal',
+    'array', 'asarray'
+    ]
+
+
+_globalvar = 0
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy.char')
+
+
+def _is_unicode(arr):
+    """Returns True if arr is a string or a string array with a dtype that
+    represents a unicode string, otherwise returns False.
+
+    """
+    if (isinstance(arr, str) or
+            issubclass(numpy.asarray(arr).dtype.type, str)):
+        return True
+    return False
+
+
+def _to_bytes_or_str_array(result, output_dtype_like=None):
+    """
+    Helper function to cast a result back into an array
+    with the appropriate dtype if an object array must be used
+    as an intermediary.
+    """
+    ret = numpy.asarray(result.tolist())
+    dtype = getattr(output_dtype_like, 'dtype', None)
+    if dtype is not None:
+        return ret.astype(type(dtype)(_get_num_chars(ret)), copy=False)
+    return ret
+
+
+def _clean_args(*args):
+    """
+    Helper function for delegating arguments to Python string
+    functions.
+
+    Many of the Python string operations that have optional arguments
+    do not use 'None' to indicate a default value.  In these cases,
+    we need to remove all None arguments, and those following them.
+    """
+    newargs = []
+    for chk in args:
+        if chk is None:
+            break
+        newargs.append(chk)
+    return newargs
+
+def _get_num_chars(a):
+    """
+    Helper function that returns the number of characters per field in
+    a string or unicode array.  This is to abstract out the fact that
+    for a unicode array this is itemsize / 4.
+    """
+    if issubclass(a.dtype.type, str_):
+        return a.itemsize // 4
+    return a.itemsize
+
+
+def _binary_op_dispatcher(x1, x2):
+    return (x1, x2)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def equal(x1, x2):
+    """
+    Return (x1 == x2) element-wise.
+
+    Unlike `numpy.equal`, this comparison is performed by first
+    stripping whitespace characters from the end of the string.  This
+    behavior is provided for backward-compatibility with numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    not_equal, greater_equal, less_equal, greater, less
+    """
+    return compare_chararrays(x1, x2, '==', True)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def not_equal(x1, x2):
+    """
+    Return (x1 != x2) element-wise.
+
+    Unlike `numpy.not_equal`, this comparison is performed by first
+    stripping whitespace characters from the end of the string.  This
+    behavior is provided for backward-compatibility with numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    equal, greater_equal, less_equal, greater, less
+    """
+    return compare_chararrays(x1, x2, '!=', True)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def greater_equal(x1, x2):
+    """
+    Return (x1 >= x2) element-wise.
+
+    Unlike `numpy.greater_equal`, this comparison is performed by
+    first stripping whitespace characters from the end of the string.
+    This behavior is provided for backward-compatibility with
+    numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    equal, not_equal, less_equal, greater, less
+    """
+    return compare_chararrays(x1, x2, '>=', True)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def less_equal(x1, x2):
+    """
+    Return (x1 <= x2) element-wise.
+
+    Unlike `numpy.less_equal`, this comparison is performed by first
+    stripping whitespace characters from the end of the string.  This
+    behavior is provided for backward-compatibility with numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    equal, not_equal, greater_equal, greater, less
+    """
+    return compare_chararrays(x1, x2, '<=', True)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def greater(x1, x2):
+    """
+    Return (x1 > x2) element-wise.
+
+    Unlike `numpy.greater`, this comparison is performed by first
+    stripping whitespace characters from the end of the string.  This
+    behavior is provided for backward-compatibility with numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    equal, not_equal, greater_equal, less_equal, less
+    """
+    return compare_chararrays(x1, x2, '>', True)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def less(x1, x2):
+    """
+    Return (x1 < x2) element-wise.
+
+    Unlike `numpy.greater`, this comparison is performed by first
+    stripping whitespace characters from the end of the string.  This
+    behavior is provided for backward-compatibility with numarray.
+
+    Parameters
+    ----------
+    x1, x2 : array_like of str or unicode
+        Input arrays of the same shape.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools.
+
+    See Also
+    --------
+    equal, not_equal, greater_equal, less_equal, greater
+    """
+    return compare_chararrays(x1, x2, '<', True)
+
+
+def _unary_op_dispatcher(a):
+    return (a,)
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def str_len(a):
+    """
+    Return len(a) element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of integers
+
+    See Also
+    --------
+    len
+
+    Examples
+    --------
+    >>> a = np.array(['Grace Hopper Conference', 'Open Source Day'])
+    >>> np.char.str_len(a)
+    array([23, 15])
+    >>> a = np.array([u'\u0420', u'\u043e'])
+    >>> np.char.str_len(a)
+    array([1, 1])
+    >>> a = np.array([['hello', 'world'], [u'\u0420', u'\u043e']])
+    >>> np.char.str_len(a)
+    array([[5, 5], [1, 1]])
+    """
+    # Note: __len__, etc. currently return ints, which are not C-integers.
+    # Generally intp would be expected for lengths, although int is sufficient
+    # due to the dtype itemsize limitation.
+    return _vec_string(a, int_, '__len__')
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def add(x1, x2):
+    """
+    Return element-wise string concatenation for two arrays of str or unicode.
+
+    Arrays `x1` and `x2` must have the same shape.
+
+    Parameters
+    ----------
+    x1 : array_like of str or unicode
+        Input array.
+    x2 : array_like of str or unicode
+        Input array.
+
+    Returns
+    -------
+    add : ndarray
+        Output array of `bytes_` or `str_`, depending on input types
+        of the same shape as `x1` and `x2`.
+
+    """
+    arr1 = numpy.asarray(x1)
+    arr2 = numpy.asarray(x2)
+    out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
+
+    if type(arr1.dtype) != type(arr2.dtype):
+        # Enforce this for now.  The solution to it will be implement add
+        # as a ufunc.  It never worked right on Python 3: bytes + unicode gave
+        # nonsense unicode + bytes errored, and unicode + object used the
+        # object dtype itemsize as num chars (worked on short strings).
+        # bytes + void worked but promoting void->bytes is dubious also.
+        raise TypeError(
+            "np.char.add() requires both arrays of the same dtype kind, but "
+            f"got dtypes: '{arr1.dtype}' and '{arr2.dtype}' (the few cases "
+            "where this used to work often lead to incorrect results).")
+
+    return _vec_string(arr1, type(arr1.dtype)(out_size), '__add__', (arr2,))
+
+def _multiply_dispatcher(a, i):
+    return (a,)
+
+
+@array_function_dispatch(_multiply_dispatcher)
+def multiply(a, i):
+    """
+    Return (a * i), that is string multiple concatenation,
+    element-wise.
+
+    Values in `i` of less than 0 are treated as 0 (which yields an
+    empty string).
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    i : array_like of ints
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input types
+    
+    Examples
+    --------
+    >>> a = np.array(["a", "b", "c"])
+    >>> np.char.multiply(x, 3)
+    array(['aaa', 'bbb', 'ccc'], dtype='<U3')
+    >>> i = np.array([1, 2, 3])
+    >>> np.char.multiply(a, i)
+    array(['a', 'bb', 'ccc'], dtype='<U3')
+    >>> np.char.multiply(np.array(['a']), i)
+    array(['a', 'aa', 'aaa'], dtype='<U3')
+    >>> a = np.array(['a', 'b', 'c', 'd', 'e', 'f']).reshape((2, 3))
+    >>> np.char.multiply(a, 3)
+    array([['aaa', 'bbb', 'ccc'],
+           ['ddd', 'eee', 'fff']], dtype='<U3')
+    >>> np.char.multiply(a, i)
+    array([['a', 'bb', 'ccc'],
+           ['d', 'ee', 'fff']], dtype='<U3')
+    """
+    a_arr = numpy.asarray(a)
+    i_arr = numpy.asarray(i)
+    if not issubclass(i_arr.dtype.type, integer):
+        raise ValueError("Can only multiply by integers")
+    out_size = _get_num_chars(a_arr) * max(int(i_arr.max()), 0)
+    return _vec_string(
+        a_arr, type(a_arr.dtype)(out_size), '__mul__', (i_arr,))
+
+
+def _mod_dispatcher(a, values):
+    return (a, values)
+
+
+@array_function_dispatch(_mod_dispatcher)
+def mod(a, values):
+    """
+    Return (a % i), that is pre-Python 2.6 string formatting
+    (interpolation), element-wise for a pair of array_likes of str
+    or unicode.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    values : array_like of values
+       These values will be element-wise interpolated into the string.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input types
+
+    See Also
+    --------
+    str.__mod__
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, '__mod__', (values,)), a)
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def capitalize(a):
+    """
+    Return a copy of `a` with only the first character of each element
+    capitalized.
+
+    Calls `str.capitalize` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+        Input array of strings to capitalize.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input
+        types
+
+    See Also
+    --------
+    str.capitalize
+
+    Examples
+    --------
+    >>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c
+    array(['a1b2', '1b2a', 'b2a1', '2a1b'],
+        dtype='|S4')
+    >>> np.char.capitalize(c)
+    array(['A1b2', '1b2a', 'B2a1', '2a1b'],
+        dtype='|S4')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'capitalize')
+
+
+def _center_dispatcher(a, width, fillchar=None):
+    return (a,)
+
+
+@array_function_dispatch(_center_dispatcher)
+def center(a, width, fillchar=' '):
+    """
+    Return a copy of `a` with its elements centered in a string of
+    length `width`.
+
+    Calls `str.center` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    width : int
+        The length of the resulting strings
+    fillchar : str or unicode, optional
+        The padding character to use (default is space).
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input
+        types
+
+    See Also
+    --------
+    str.center
+    
+    Notes
+    -----
+    This function is intended to work with arrays of strings.  The
+    fill character is not applied to numeric types.
+
+    Examples
+    --------
+    >>> c = np.array(['a1b2','1b2a','b2a1','2a1b']); c
+    array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='<U4')
+    >>> np.char.center(c, width=9)
+    array(['   a1b2  ', '   1b2a  ', '   b2a1  ', '   2a1b  '], dtype='<U9')
+    >>> np.char.center(c, width=9, fillchar='*')
+    array(['***a1b2**', '***1b2a**', '***b2a1**', '***2a1b**'], dtype='<U9')
+    >>> np.char.center(c, width=1)
+    array(['a', '1', 'b', '2'], dtype='<U1')
+
+    """
+    a_arr = numpy.asarray(a)
+    width_arr = numpy.asarray(width)
+    size = int(numpy.max(width_arr.flat))
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
+        fillchar = asbytes(fillchar)
+    return _vec_string(
+        a_arr, type(a_arr.dtype)(size), 'center', (width_arr, fillchar))
+
+
+def _count_dispatcher(a, sub, start=None, end=None):
+    return (a,)
+
+
+@array_function_dispatch(_count_dispatcher)
+def count(a, sub, start=0, end=None):
+    """
+    Returns an array with the number of non-overlapping occurrences of
+    substring `sub` in the range [`start`, `end`].
+
+    Calls `str.count` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    sub : str or unicode
+       The substring to search for.
+
+    start, end : int, optional
+       Optional arguments `start` and `end` are interpreted as slice
+       notation to specify the range in which to count.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of ints.
+
+    See Also
+    --------
+    str.count
+
+    Examples
+    --------
+    >>> c = np.array(['aAaAaA', '  aA  ', 'abBABba'])
+    >>> c
+    array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
+    >>> np.char.count(c, 'A')
+    array([3, 1, 1])
+    >>> np.char.count(c, 'aA')
+    array([3, 1, 0])
+    >>> np.char.count(c, 'A', start=1, end=4)
+    array([2, 1, 1])
+    >>> np.char.count(c, 'A', start=1, end=3)
+    array([1, 0, 0])
+
+    """
+    return _vec_string(a, int_, 'count', [sub, start] + _clean_args(end))
+
+
+def _code_dispatcher(a, encoding=None, errors=None):
+    return (a,)
+
+
+@array_function_dispatch(_code_dispatcher)
+def decode(a, encoding=None, errors=None):
+    r"""
+    Calls ``bytes.decode`` element-wise.
+
+    The set of available codecs comes from the Python standard library,
+    and may be extended at runtime.  For more information, see the
+    :mod:`codecs` module.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    encoding : str, optional
+       The name of an encoding
+
+    errors : str, optional
+       Specifies how to handle encoding errors
+
+    Returns
+    -------
+    out : ndarray
+
+    See Also
+    --------
+    :py:meth:`bytes.decode`
+
+    Notes
+    -----
+    The type of the result will depend on the encoding specified.
+
+    Examples
+    --------
+    >>> c = np.array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@',
+    ...               b'\x81\x82\xc2\xc1\xc2\x82\x81'])
+    >>> c
+    array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@',
+    ...    b'\x81\x82\xc2\xc1\xc2\x82\x81'], dtype='|S7')
+    >>> np.char.decode(c, encoding='cp037')
+    array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'decode', _clean_args(encoding, errors)))
+
+
+@array_function_dispatch(_code_dispatcher)
+def encode(a, encoding=None, errors=None):
+    """
+    Calls `str.encode` element-wise.
+
+    The set of available codecs comes from the Python standard library,
+    and may be extended at runtime. For more information, see the codecs
+    module.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    encoding : str, optional
+       The name of an encoding
+
+    errors : str, optional
+       Specifies how to handle encoding errors
+
+    Returns
+    -------
+    out : ndarray
+
+    See Also
+    --------
+    str.encode
+
+    Notes
+    -----
+    The type of the result will depend on the encoding specified.
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'encode', _clean_args(encoding, errors)))
+
+
+def _endswith_dispatcher(a, suffix, start=None, end=None):
+    return (a,)
+
+
+@array_function_dispatch(_endswith_dispatcher)
+def endswith(a, suffix, start=0, end=None):
+    """
+    Returns a boolean array which is `True` where the string element
+    in `a` ends with `suffix`, otherwise `False`.
+
+    Calls `str.endswith` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    suffix : str
+
+    start, end : int, optional
+        With optional `start`, test beginning at that position. With
+        optional `end`, stop comparing at that position.
+
+    Returns
+    -------
+    out : ndarray
+        Outputs an array of bools.
+
+    See Also
+    --------
+    str.endswith
+
+    Examples
+    --------
+    >>> s = np.array(['foo', 'bar'])
+    >>> s[0] = 'foo'
+    >>> s[1] = 'bar'
+    >>> s
+    array(['foo', 'bar'], dtype='<U3')
+    >>> np.char.endswith(s, 'ar')
+    array([False,  True])
+    >>> np.char.endswith(s, 'a', start=1, end=2)
+    array([False,  True])
+
+    """
+    return _vec_string(
+        a, bool_, 'endswith', [suffix, start] + _clean_args(end))
+
+
+def _expandtabs_dispatcher(a, tabsize=None):
+    return (a,)
+
+
+@array_function_dispatch(_expandtabs_dispatcher)
+def expandtabs(a, tabsize=8):
+    """
+    Return a copy of each string element where all tab characters are
+    replaced by one or more spaces.
+
+    Calls `str.expandtabs` element-wise.
+
+    Return a copy of each string element where all tab characters are
+    replaced by one or more spaces, depending on the current column
+    and the given `tabsize`. The column number is reset to zero after
+    each newline occurring in the string. This doesn't understand other
+    non-printing characters or escape sequences.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+        Input array
+    tabsize : int, optional
+        Replace tabs with `tabsize` number of spaces.  If not given defaults
+        to 8 spaces.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.expandtabs
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'expandtabs', (tabsize,)), a)
+
+
+@array_function_dispatch(_count_dispatcher)
+def find(a, sub, start=0, end=None):
+    """
+    For each element, return the lowest index in the string where
+    substring `sub` is found.
+
+    Calls `str.find` element-wise.
+
+    For each element, return the lowest index in the string where
+    substring `sub` is found, such that `sub` is contained in the
+    range [`start`, `end`].
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    sub : str or unicode
+
+    start, end : int, optional
+        Optional arguments `start` and `end` are interpreted as in
+        slice notation.
+
+    Returns
+    -------
+    out : ndarray or int
+        Output array of ints.  Returns -1 if `sub` is not found.
+
+    See Also
+    --------
+    str.find
+
+    Examples
+    --------
+    >>> a = np.array(["NumPy is a Python library"])
+    >>> np.char.find(a, "Python", start=0, end=None)
+    array([11])
+
+    """
+    return _vec_string(
+        a, int_, 'find', [sub, start] + _clean_args(end))
+
+
+@array_function_dispatch(_count_dispatcher)
+def index(a, sub, start=0, end=None):
+    """
+    Like `find`, but raises `ValueError` when the substring is not found.
+
+    Calls `str.index` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    sub : str or unicode
+
+    start, end : int, optional
+
+    Returns
+    -------
+    out : ndarray
+        Output array of ints.  Returns -1 if `sub` is not found.
+
+    See Also
+    --------
+    find, str.find
+
+    Examples
+    --------
+    >>> a = np.array(["Computer Science"])
+    >>> np.char.index(a, "Science", start=0, end=None)
+    array([9])
+
+    """
+    return _vec_string(
+        a, int_, 'index', [sub, start] + _clean_args(end))
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isalnum(a):
+    """
+    Returns true for each element if all characters in the string are
+    alphanumeric and there is at least one character, false otherwise.
+
+    Calls `str.isalnum` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.isalnum
+    """
+    return _vec_string(a, bool_, 'isalnum')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isalpha(a):
+    """
+    Returns true for each element if all characters in the string are
+    alphabetic and there is at least one character, false otherwise.
+
+    Calls `str.isalpha` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.isalpha
+    """
+    return _vec_string(a, bool_, 'isalpha')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isdigit(a):
+    """
+    Returns true for each element if all characters in the string are
+    digits and there is at least one character, false otherwise.
+
+    Calls `str.isdigit` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.isdigit
+
+    Examples
+    --------
+    >>> a = np.array(['a', 'b', '0'])
+    >>> np.char.isdigit(a)
+    array([False, False,  True])
+    >>> a = np.array([['a', 'b', '0'], ['c', '1', '2']])
+    >>> np.char.isdigit(a)
+    array([[False, False,  True], [False,  True,  True]])
+    """
+    return _vec_string(a, bool_, 'isdigit')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def islower(a):
+    """
+    Returns true for each element if all cased characters in the
+    string are lowercase and there is at least one cased character,
+    false otherwise.
+
+    Calls `str.islower` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.islower
+    """
+    return _vec_string(a, bool_, 'islower')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isspace(a):
+    """
+    Returns true for each element if there are only whitespace
+    characters in the string and there is at least one character,
+    false otherwise.
+
+    Calls `str.isspace` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.isspace
+    """
+    return _vec_string(a, bool_, 'isspace')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def istitle(a):
+    """
+    Returns true for each element if the element is a titlecased
+    string and there is at least one character, false otherwise.
+
+    Call `str.istitle` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.istitle
+    """
+    return _vec_string(a, bool_, 'istitle')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isupper(a):
+    """
+    Return true for each element if all cased characters in the
+    string are uppercase and there is at least one character, false
+    otherwise.
+
+    Call `str.isupper` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of bools
+
+    See Also
+    --------
+    str.isupper
+
+    Examples
+    --------
+    >>> str = "GHC"
+    >>> np.char.isupper(str)
+    array(True)     
+    >>> a = np.array(["hello", "HELLO", "Hello"])
+    >>> np.char.isupper(a)
+    array([False,  True, False]) 
+
+    """
+    return _vec_string(a, bool_, 'isupper')
+
+
+def _join_dispatcher(sep, seq):
+    return (sep, seq)
+
+
+@array_function_dispatch(_join_dispatcher)
+def join(sep, seq):
+    """
+    Return a string which is the concatenation of the strings in the
+    sequence `seq`.
+
+    Calls `str.join` element-wise.
+
+    Parameters
+    ----------
+    sep : array_like of str or unicode
+    seq : array_like of str or unicode
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input types
+
+    See Also
+    --------
+    str.join
+
+    Examples
+    --------
+    >>> np.char.join('-', 'osd')
+    array('o-s-d', dtype='<U5')
+
+    >>> np.char.join(['-', '.'], ['ghc', 'osd'])
+    array(['g-h-c', 'o.s.d'], dtype='<U5')
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(sep, object_, 'join', (seq,)), seq)
+
+
+
+def _just_dispatcher(a, width, fillchar=None):
+    return (a,)
+
+
+@array_function_dispatch(_just_dispatcher)
+def ljust(a, width, fillchar=' '):
+    """
+    Return an array with the elements of `a` left-justified in a
+    string of length `width`.
+
+    Calls `str.ljust` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    width : int
+        The length of the resulting strings
+    fillchar : str or unicode, optional
+        The character to use for padding
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.ljust
+
+    """
+    a_arr = numpy.asarray(a)
+    width_arr = numpy.asarray(width)
+    size = int(numpy.max(width_arr.flat))
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
+        fillchar = asbytes(fillchar)
+    return _vec_string(
+        a_arr, type(a_arr.dtype)(size), 'ljust', (width_arr, fillchar))
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def lower(a):
+    """
+    Return an array with the elements converted to lowercase.
+
+    Call `str.lower` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.lower
+
+    Examples
+    --------
+    >>> c = np.array(['A1B C', '1BCA', 'BCA1']); c
+    array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
+    >>> np.char.lower(c)
+    array(['a1b c', '1bca', 'bca1'], dtype='<U5')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'lower')
+
+
+def _strip_dispatcher(a, chars=None):
+    return (a,)
+
+
+@array_function_dispatch(_strip_dispatcher)
+def lstrip(a, chars=None):
+    """
+    For each element in `a`, return a copy with the leading characters
+    removed.
+
+    Calls `str.lstrip` element-wise.
+
+    Parameters
+    ----------
+    a : array-like, {str, unicode}
+        Input array.
+
+    chars : {str, unicode}, optional
+        The `chars` argument is a string specifying the set of
+        characters to be removed. If omitted or None, the `chars`
+        argument defaults to removing whitespace. The `chars` argument
+        is not a prefix; rather, all combinations of its values are
+        stripped.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.lstrip
+
+    Examples
+    --------
+    >>> c = np.array(['aAaAaA', '  aA  ', 'abBABba'])
+    >>> c
+    array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
+
+    The 'a' variable is unstripped from c[1] because whitespace leading.
+
+    >>> np.char.lstrip(c, 'a')
+    array(['AaAaA', '  aA  ', 'bBABba'], dtype='<U7')
+
+
+    >>> np.char.lstrip(c, 'A') # leaves c unchanged
+    array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
+    >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all()
+    ... # XXX: is this a regression? This used to return True
+    ... # np.char.lstrip(c,'') does not modify c at all.
+    False
+    >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all()
+    True
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'lstrip', (chars,))
+
+
+def _partition_dispatcher(a, sep):
+    return (a,)
+
+
+@array_function_dispatch(_partition_dispatcher)
+def partition(a, sep):
+    """
+    Partition each element in `a` around `sep`.
+
+    Calls `str.partition` element-wise.
+
+    For each element in `a`, split the element as the first
+    occurrence of `sep`, and return 3 strings containing the part
+    before the separator, the separator itself, and the part after
+    the separator. If the separator is not found, return 3 strings
+    containing the string itself, followed by two empty strings.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array
+    sep : {str, unicode}
+        Separator to split each string element in `a`.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type.
+        The output array will have an extra dimension with 3
+        elements per input element.
+
+    See Also
+    --------
+    str.partition
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'partition', (sep,)), a)
+
+
+def _replace_dispatcher(a, old, new, count=None):
+    return (a,)
+
+
+@array_function_dispatch(_replace_dispatcher)
+def replace(a, old, new, count=None):
+    """
+    For each element in `a`, return a copy of the string with all
+    occurrences of substring `old` replaced by `new`.
+
+    Calls `str.replace` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    old, new : str or unicode
+
+    count : int, optional
+        If the optional argument `count` is given, only the first
+        `count` occurrences are replaced.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.replace
+    
+    Examples
+    --------
+    >>> a = np.array(["That is a mango", "Monkeys eat mangos"])
+    >>> np.char.replace(a, 'mango', 'banana')
+    array(['That is a banana', 'Monkeys eat bananas'], dtype='<U19')
+
+    >>> a = np.array(["The dish is fresh", "This is it"])
+    >>> np.char.replace(a, 'is', 'was')
+    array(['The dwash was fresh', 'Thwas was it'], dtype='<U19')
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'replace', [old, new] + _clean_args(count)), a)
+
+
+@array_function_dispatch(_count_dispatcher)
+def rfind(a, sub, start=0, end=None):
+    """
+    For each element in `a`, return the highest index in the string
+    where substring `sub` is found, such that `sub` is contained
+    within [`start`, `end`].
+
+    Calls `str.rfind` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    sub : str or unicode
+
+    start, end : int, optional
+        Optional arguments `start` and `end` are interpreted as in
+        slice notation.
+
+    Returns
+    -------
+    out : ndarray
+       Output array of ints.  Return -1 on failure.
+
+    See Also
+    --------
+    str.rfind
+
+    """
+    return _vec_string(
+        a, int_, 'rfind', [sub, start] + _clean_args(end))
+
+
+@array_function_dispatch(_count_dispatcher)
+def rindex(a, sub, start=0, end=None):
+    """
+    Like `rfind`, but raises `ValueError` when the substring `sub` is
+    not found.
+
+    Calls `str.rindex` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    sub : str or unicode
+
+    start, end : int, optional
+
+    Returns
+    -------
+    out : ndarray
+       Output array of ints.
+
+    See Also
+    --------
+    rfind, str.rindex
+
+    """
+    return _vec_string(
+        a, int_, 'rindex', [sub, start] + _clean_args(end))
+
+
+@array_function_dispatch(_just_dispatcher)
+def rjust(a, width, fillchar=' '):
+    """
+    Return an array with the elements of `a` right-justified in a
+    string of length `width`.
+
+    Calls `str.rjust` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    width : int
+        The length of the resulting strings
+    fillchar : str or unicode, optional
+        The character to use for padding
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.rjust
+
+    """
+    a_arr = numpy.asarray(a)
+    width_arr = numpy.asarray(width)
+    size = int(numpy.max(width_arr.flat))
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
+        fillchar = asbytes(fillchar)
+    return _vec_string(
+        a_arr, type(a_arr.dtype)(size), 'rjust', (width_arr, fillchar))
+
+
+@array_function_dispatch(_partition_dispatcher)
+def rpartition(a, sep):
+    """
+    Partition (split) each element around the right-most separator.
+
+    Calls `str.rpartition` element-wise.
+
+    For each element in `a`, split the element as the last
+    occurrence of `sep`, and return 3 strings containing the part
+    before the separator, the separator itself, and the part after
+    the separator. If the separator is not found, return 3 strings
+    containing the string itself, followed by two empty strings.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+        Input array
+    sep : str or unicode
+        Right-most separator to split each element in array.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of string or unicode, depending on input
+        type.  The output array will have an extra dimension with
+        3 elements per input element.
+
+    See Also
+    --------
+    str.rpartition
+
+    """
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'rpartition', (sep,)), a)
+
+
+def _split_dispatcher(a, sep=None, maxsplit=None):
+    return (a,)
+
+
+@array_function_dispatch(_split_dispatcher)
+def rsplit(a, sep=None, maxsplit=None):
+    """
+    For each element in `a`, return a list of the words in the
+    string, using `sep` as the delimiter string.
+
+    Calls `str.rsplit` element-wise.
+
+    Except for splitting from the right, `rsplit`
+    behaves like `split`.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    sep : str or unicode, optional
+        If `sep` is not specified or None, any whitespace string
+        is a separator.
+    maxsplit : int, optional
+        If `maxsplit` is given, at most `maxsplit` splits are done,
+        the rightmost ones.
+
+    Returns
+    -------
+    out : ndarray
+       Array of list objects
+
+    See Also
+    --------
+    str.rsplit, split
+
+    """
+    # This will return an array of lists of different sizes, so we
+    # leave it as an object array
+    return _vec_string(
+        a, object_, 'rsplit', [sep] + _clean_args(maxsplit))
+
+
+def _strip_dispatcher(a, chars=None):
+    return (a,)
+
+
+@array_function_dispatch(_strip_dispatcher)
+def rstrip(a, chars=None):
+    """
+    For each element in `a`, return a copy with the trailing
+    characters removed.
+
+    Calls `str.rstrip` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    chars : str or unicode, optional
+       The `chars` argument is a string specifying the set of
+       characters to be removed. If omitted or None, the `chars`
+       argument defaults to removing whitespace. The `chars` argument
+       is not a suffix; rather, all combinations of its values are
+       stripped.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.rstrip
+
+    Examples
+    --------
+    >>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c
+    array(['aAaAaA', 'abBABba'],
+        dtype='|S7')
+    >>> np.char.rstrip(c, b'a')
+    array(['aAaAaA', 'abBABb'],
+        dtype='|S7')
+    >>> np.char.rstrip(c, b'A')
+    array(['aAaAa', 'abBABba'],
+        dtype='|S7')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'rstrip', (chars,))
+
+
+@array_function_dispatch(_split_dispatcher)
+def split(a, sep=None, maxsplit=None):
+    """
+    For each element in `a`, return a list of the words in the
+    string, using `sep` as the delimiter string.
+
+    Calls `str.split` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    sep : str or unicode, optional
+       If `sep` is not specified or None, any whitespace string is a
+       separator.
+
+    maxsplit : int, optional
+        If `maxsplit` is given, at most `maxsplit` splits are done.
+
+    Returns
+    -------
+    out : ndarray
+        Array of list objects
+
+    See Also
+    --------
+    str.split, rsplit
+
+    """
+    # This will return an array of lists of different sizes, so we
+    # leave it as an object array
+    return _vec_string(
+        a, object_, 'split', [sep] + _clean_args(maxsplit))
+
+
+def _splitlines_dispatcher(a, keepends=None):
+    return (a,)
+
+
+@array_function_dispatch(_splitlines_dispatcher)
+def splitlines(a, keepends=None):
+    """
+    For each element in `a`, return a list of the lines in the
+    element, breaking at line boundaries.
+
+    Calls `str.splitlines` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    keepends : bool, optional
+        Line breaks are not included in the resulting list unless
+        keepends is given and true.
+
+    Returns
+    -------
+    out : ndarray
+        Array of list objects
+
+    See Also
+    --------
+    str.splitlines
+
+    """
+    return _vec_string(
+        a, object_, 'splitlines', _clean_args(keepends))
+
+
+def _startswith_dispatcher(a, prefix, start=None, end=None):
+    return (a,)
+
+
+@array_function_dispatch(_startswith_dispatcher)
+def startswith(a, prefix, start=0, end=None):
+    """
+    Returns a boolean array which is `True` where the string element
+    in `a` starts with `prefix`, otherwise `False`.
+
+    Calls `str.startswith` element-wise.
+
+    Parameters
+    ----------
+    a : array_like of str or unicode
+
+    prefix : str
+
+    start, end : int, optional
+        With optional `start`, test beginning at that position. With
+        optional `end`, stop comparing at that position.
+
+    Returns
+    -------
+    out : ndarray
+        Array of booleans
+
+    See Also
+    --------
+    str.startswith
+
+    """
+    return _vec_string(
+        a, bool_, 'startswith', [prefix, start] + _clean_args(end))
+
+
+@array_function_dispatch(_strip_dispatcher)
+def strip(a, chars=None):
+    """
+    For each element in `a`, return a copy with the leading and
+    trailing characters removed.
+
+    Calls `str.strip` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    chars : str or unicode, optional
+       The `chars` argument is a string specifying the set of
+       characters to be removed. If omitted or None, the `chars`
+       argument defaults to removing whitespace. The `chars` argument
+       is not a prefix or suffix; rather, all combinations of its
+       values are stripped.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.strip
+
+    Examples
+    --------
+    >>> c = np.array(['aAaAaA', '  aA  ', 'abBABba'])
+    >>> c
+    array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
+    >>> np.char.strip(c)
+    array(['aAaAaA', 'aA', 'abBABba'], dtype='<U7')
+    >>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads
+    array(['AaAaA', '  aA  ', 'bBABb'], dtype='<U7')
+    >>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails
+    array(['aAaAa', '  aA  ', 'abBABba'], dtype='<U7')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'strip', _clean_args(chars))
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def swapcase(a):
+    """
+    Return element-wise a copy of the string with
+    uppercase characters converted to lowercase and vice versa.
+
+    Calls `str.swapcase` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.swapcase
+
+    Examples
+    --------
+    >>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c
+    array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'],
+        dtype='|S5')
+    >>> np.char.swapcase(c)
+    array(['A1b C', '1B cA', 'B cA1', 'Ca1B'],
+        dtype='|S5')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'swapcase')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def title(a):
+    """
+    Return element-wise title cased version of string or unicode.
+
+    Title case words start with uppercase characters, all remaining cased
+    characters are lowercase.
+
+    Calls `str.title` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array.
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.title
+
+    Examples
+    --------
+    >>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c
+    array(['a1b c', '1b ca', 'b ca1', 'ca1b'],
+        dtype='|S5')
+    >>> np.char.title(c)
+    array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'],
+        dtype='|S5')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'title')
+
+
+def _translate_dispatcher(a, table, deletechars=None):
+    return (a,)
+
+
+@array_function_dispatch(_translate_dispatcher)
+def translate(a, table, deletechars=None):
+    """
+    For each element in `a`, return a copy of the string where all
+    characters occurring in the optional argument `deletechars` are
+    removed, and the remaining characters have been mapped through the
+    given translation table.
+
+    Calls `str.translate` element-wise.
+
+    Parameters
+    ----------
+    a : array-like of str or unicode
+
+    table : str of length 256
+
+    deletechars : str
+
+    Returns
+    -------
+    out : ndarray
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.translate
+
+    """
+    a_arr = numpy.asarray(a)
+    if issubclass(a_arr.dtype.type, str_):
+        return _vec_string(
+            a_arr, a_arr.dtype, 'translate', (table,))
+    else:
+        return _vec_string(
+            a_arr, a_arr.dtype, 'translate', [table] + _clean_args(deletechars))
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def upper(a):
+    """
+    Return an array with the elements converted to uppercase.
+
+    Calls `str.upper` element-wise.
+
+    For 8-bit strings, this method is locale-dependent.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.upper
+
+    Examples
+    --------
+    >>> c = np.array(['a1b c', '1bca', 'bca1']); c
+    array(['a1b c', '1bca', 'bca1'], dtype='<U5')
+    >>> np.char.upper(c)
+    array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
+
+    """
+    a_arr = numpy.asarray(a)
+    return _vec_string(a_arr, a_arr.dtype, 'upper')
+
+
+def _zfill_dispatcher(a, width):
+    return (a,)
+
+
+@array_function_dispatch(_zfill_dispatcher)
+def zfill(a, width):
+    """
+    Return the numeric string left-filled with zeros
+
+    Calls `str.zfill` element-wise.
+
+    Parameters
+    ----------
+    a : array_like, {str, unicode}
+        Input array.
+    width : int
+        Width of string to left-fill elements in `a`.
+
+    Returns
+    -------
+    out : ndarray, {str, unicode}
+        Output array of str or unicode, depending on input type
+
+    See Also
+    --------
+    str.zfill
+
+    """
+    a_arr = numpy.asarray(a)
+    width_arr = numpy.asarray(width)
+    size = int(numpy.max(width_arr.flat))
+    return _vec_string(
+        a_arr, type(a_arr.dtype)(size), 'zfill', (width_arr,))
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isnumeric(a):
+    """
+    For each element, return True if there are only numeric
+    characters in the element.
+
+    Calls `str.isnumeric` element-wise.
+
+    Numeric characters include digit characters, and all characters
+    that have the Unicode numeric value property, e.g. ``U+2155,
+    VULGAR FRACTION ONE FIFTH``.
+
+    Parameters
+    ----------
+    a : array_like, unicode
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, bool
+        Array of booleans of same shape as `a`.
+
+    See Also
+    --------
+    str.isnumeric
+
+    Examples
+    --------
+    >>> np.char.isnumeric(['123', '123abc', '9.0', '1/4', 'VIII'])
+    array([ True, False, False, False, False])
+
+    """
+    if not _is_unicode(a):
+        raise TypeError("isnumeric is only available for Unicode strings and arrays")
+    return _vec_string(a, bool_, 'isnumeric')
+
+
+@array_function_dispatch(_unary_op_dispatcher)
+def isdecimal(a):
+    """
+    For each element, return True if there are only decimal
+    characters in the element.
+
+    Calls `str.isdecimal` element-wise.
+
+    Decimal characters include digit characters, and all characters
+    that can be used to form decimal-radix numbers,
+    e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``.
+
+    Parameters
+    ----------
+    a : array_like, unicode
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, bool
+        Array of booleans identical in shape to `a`.
+
+    See Also
+    --------
+    str.isdecimal
+
+    Examples
+    --------
+    >>> np.char.isdecimal(['12345', '4.99', '123ABC', ''])
+    array([ True, False, False, False])
+
+    """ 
+    if not _is_unicode(a):
+        raise TypeError(
+            "isdecimal is only available for Unicode strings and arrays")
+    return _vec_string(a, bool_, 'isdecimal')
+
+
+@set_module('numpy')
+class chararray(ndarray):
+    """
+    chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
+              strides=None, order=None)
+
+    Provides a convenient view on arrays of string and unicode values.
+
+    .. note::
+       The `chararray` class exists for backwards compatibility with
+       Numarray, it is not recommended for new development. Starting from numpy
+       1.4, if one needs arrays of strings, it is recommended to use arrays of
+       `dtype` `object_`, `bytes_` or `str_`, and use the free functions
+       in the `numpy.char` module for fast vectorized string operations.
+
+    Versus a regular NumPy array of type `str` or `unicode`, this
+    class adds the following functionality:
+
+      1) values automatically have whitespace removed from the end
+         when indexed
+
+      2) comparison operators automatically remove whitespace from the
+         end when comparing values
+
+      3) vectorized string operations are provided as methods
+         (e.g. `.endswith`) and infix operators (e.g. ``"+", "*", "%"``)
+
+    chararrays should be created using `numpy.char.array` or
+    `numpy.char.asarray`, rather than this constructor directly.
+
+    This constructor creates the array, using `buffer` (with `offset`
+    and `strides`) if it is not ``None``. If `buffer` is ``None``, then
+    constructs a new array with `strides` in "C order", unless both
+    ``len(shape) >= 2`` and ``order='F'``, in which case `strides`
+    is in "Fortran order".
+
+    Methods
+    -------
+    astype
+    argsort
+    copy
+    count
+    decode
+    dump
+    dumps
+    encode
+    endswith
+    expandtabs
+    fill
+    find
+    flatten
+    getfield
+    index
+    isalnum
+    isalpha
+    isdecimal
+    isdigit
+    islower
+    isnumeric
+    isspace
+    istitle
+    isupper
+    item
+    join
+    ljust
+    lower
+    lstrip
+    nonzero
+    put
+    ravel
+    repeat
+    replace
+    reshape
+    resize
+    rfind
+    rindex
+    rjust
+    rsplit
+    rstrip
+    searchsorted
+    setfield
+    setflags
+    sort
+    split
+    splitlines
+    squeeze
+    startswith
+    strip
+    swapaxes
+    swapcase
+    take
+    title
+    tofile
+    tolist
+    tostring
+    translate
+    transpose
+    upper
+    view
+    zfill
+
+    Parameters
+    ----------
+    shape : tuple
+        Shape of the array.
+    itemsize : int, optional
+        Length of each array element, in number of characters. Default is 1.
+    unicode : bool, optional
+        Are the array elements of type unicode (True) or string (False).
+        Default is False.
+    buffer : object exposing the buffer interface or str, optional
+        Memory address of the start of the array data.  Default is None,
+        in which case a new array is created.
+    offset : int, optional
+        Fixed stride displacement from the beginning of an axis?
+        Default is 0. Needs to be >=0.
+    strides : array_like of ints, optional
+        Strides for the array (see `ndarray.strides` for full description).
+        Default is None.
+    order : {'C', 'F'}, optional
+        The order in which the array data is stored in memory: 'C' ->
+        "row major" order (the default), 'F' -> "column major"
+        (Fortran) order.
+
+    Examples
+    --------
+    >>> charar = np.chararray((3, 3))
+    >>> charar[:] = 'a'
+    >>> charar
+    chararray([[b'a', b'a', b'a'],
+               [b'a', b'a', b'a'],
+               [b'a', b'a', b'a']], dtype='|S1')
+
+    >>> charar = np.chararray(charar.shape, itemsize=5)
+    >>> charar[:] = 'abc'
+    >>> charar
+    chararray([[b'abc', b'abc', b'abc'],
+               [b'abc', b'abc', b'abc'],
+               [b'abc', b'abc', b'abc']], dtype='|S5')
+
+    """
+    def __new__(subtype, shape, itemsize=1, unicode=False, buffer=None,
+                offset=0, strides=None, order='C'):
+        global _globalvar
+
+        if unicode:
+            dtype = str_
+        else:
+            dtype = bytes_
+
+        # force itemsize to be a Python int, since using NumPy integer
+        # types results in itemsize.itemsize being used as the size of
+        # strings in the new array.
+        itemsize = int(itemsize)
+
+        if isinstance(buffer, str):
+            # unicode objects do not have the buffer interface
+            filler = buffer
+            buffer = None
+        else:
+            filler = None
+
+        _globalvar = 1
+        if buffer is None:
+            self = ndarray.__new__(subtype, shape, (dtype, itemsize),
+                                   order=order)
+        else:
+            self = ndarray.__new__(subtype, shape, (dtype, itemsize),
+                                   buffer=buffer,
+                                   offset=offset, strides=strides,
+                                   order=order)
+        if filler is not None:
+            self[...] = filler
+        _globalvar = 0
+        return self
+
+    def __array_finalize__(self, obj):
+        # The b is a special case because it is used for reconstructing.
+        if not _globalvar and self.dtype.char not in 'SUbc':
+            raise ValueError("Can only create a chararray from string data.")
+
+    def __getitem__(self, obj):
+        val = ndarray.__getitem__(self, obj)
+
+        if isinstance(val, character):
+            temp = val.rstrip()
+            if len(temp) == 0:
+                val = ''
+            else:
+                val = temp
+
+        return val
+
+    # IMPLEMENTATION NOTE: Most of the methods of this class are
+    # direct delegations to the free functions in this module.
+    # However, those that return an array of strings should instead
+    # return a chararray, so some extra wrapping is required.
+
+    def __eq__(self, other):
+        """
+        Return (self == other) element-wise.
+
+        See Also
+        --------
+        equal
+        """
+        return equal(self, other)
+
+    def __ne__(self, other):
+        """
+        Return (self != other) element-wise.
+
+        See Also
+        --------
+        not_equal
+        """
+        return not_equal(self, other)
+
+    def __ge__(self, other):
+        """
+        Return (self >= other) element-wise.
+
+        See Also
+        --------
+        greater_equal
+        """
+        return greater_equal(self, other)
+
+    def __le__(self, other):
+        """
+        Return (self <= other) element-wise.
+
+        See Also
+        --------
+        less_equal
+        """
+        return less_equal(self, other)
+
+    def __gt__(self, other):
+        """
+        Return (self > other) element-wise.
+
+        See Also
+        --------
+        greater
+        """
+        return greater(self, other)
+
+    def __lt__(self, other):
+        """
+        Return (self < other) element-wise.
+
+        See Also
+        --------
+        less
+        """
+        return less(self, other)
+
+    def __add__(self, other):
+        """
+        Return (self + other), that is string concatenation,
+        element-wise for a pair of array_likes of str or unicode.
+
+        See Also
+        --------
+        add
+        """
+        return asarray(add(self, other))
+
+    def __radd__(self, other):
+        """
+        Return (other + self), that is string concatenation,
+        element-wise for a pair of array_likes of `bytes_` or `str_`.
+
+        See Also
+        --------
+        add
+        """
+        return asarray(add(numpy.asarray(other), self))
+
+    def __mul__(self, i):
+        """
+        Return (self * i), that is string multiple concatenation,
+        element-wise.
+
+        See Also
+        --------
+        multiply
+        """
+        return asarray(multiply(self, i))
+
+    def __rmul__(self, i):
+        """
+        Return (self * i), that is string multiple concatenation,
+        element-wise.
+
+        See Also
+        --------
+        multiply
+        """
+        return asarray(multiply(self, i))
+
+    def __mod__(self, i):
+        """
+        Return (self % i), that is pre-Python 2.6 string formatting
+        (interpolation), element-wise for a pair of array_likes of `bytes_`
+        or `str_`.
+
+        See Also
+        --------
+        mod
+        """
+        return asarray(mod(self, i))
+
+    def __rmod__(self, other):
+        return NotImplemented
+
+    def argsort(self, axis=-1, kind=None, order=None):
+        """
+        Return the indices that sort the array lexicographically.
+
+        For full documentation see `numpy.argsort`, for which this method is
+        in fact merely a "thin wrapper."
+
+        Examples
+        --------
+        >>> c = np.array(['a1b c', '1b ca', 'b ca1', 'Ca1b'], 'S5')
+        >>> c = c.view(np.chararray); c
+        chararray(['a1b c', '1b ca', 'b ca1', 'Ca1b'],
+              dtype='|S5')
+        >>> c[c.argsort()]
+        chararray(['1b ca', 'Ca1b', 'a1b c', 'b ca1'],
+              dtype='|S5')
+
+        """
+        return self.__array__().argsort(axis, kind, order)
+    argsort.__doc__ = ndarray.argsort.__doc__
+
+    def capitalize(self):
+        """
+        Return a copy of `self` with only the first character of each element
+        capitalized.
+
+        See Also
+        --------
+        char.capitalize
+
+        """
+        return asarray(capitalize(self))
+
+    def center(self, width, fillchar=' '):
+        """
+        Return a copy of `self` with its elements centered in a
+        string of length `width`.
+
+        See Also
+        --------
+        center
+        """
+        return asarray(center(self, width, fillchar))
+
+    def count(self, sub, start=0, end=None):
+        """
+        Returns an array with the number of non-overlapping occurrences of
+        substring `sub` in the range [`start`, `end`].
+
+        See Also
+        --------
+        char.count
+
+        """
+        return count(self, sub, start, end)
+
+    def decode(self, encoding=None, errors=None):
+        """
+        Calls ``bytes.decode`` element-wise.
+
+        See Also
+        --------
+        char.decode
+
+        """
+        return decode(self, encoding, errors)
+
+    def encode(self, encoding=None, errors=None):
+        """
+        Calls `str.encode` element-wise.
+
+        See Also
+        --------
+        char.encode
+
+        """
+        return encode(self, encoding, errors)
+
+    def endswith(self, suffix, start=0, end=None):
+        """
+        Returns a boolean array which is `True` where the string element
+        in `self` ends with `suffix`, otherwise `False`.
+
+        See Also
+        --------
+        char.endswith
+
+        """
+        return endswith(self, suffix, start, end)
+
+    def expandtabs(self, tabsize=8):
+        """
+        Return a copy of each string element where all tab characters are
+        replaced by one or more spaces.
+
+        See Also
+        --------
+        char.expandtabs
+
+        """
+        return asarray(expandtabs(self, tabsize))
+
+    def find(self, sub, start=0, end=None):
+        """
+        For each element, return the lowest index in the string where
+        substring `sub` is found.
+
+        See Also
+        --------
+        char.find
+
+        """
+        return find(self, sub, start, end)
+
+    def index(self, sub, start=0, end=None):
+        """
+        Like `find`, but raises `ValueError` when the substring is not found.
+
+        See Also
+        --------
+        char.index
+
+        """
+        return index(self, sub, start, end)
+
+    def isalnum(self):
+        """
+        Returns true for each element if all characters in the string
+        are alphanumeric and there is at least one character, false
+        otherwise.
+
+        See Also
+        --------
+        char.isalnum
+
+        """
+        return isalnum(self)
+
+    def isalpha(self):
+        """
+        Returns true for each element if all characters in the string
+        are alphabetic and there is at least one character, false
+        otherwise.
+
+        See Also
+        --------
+        char.isalpha
+
+        """
+        return isalpha(self)
+
+    def isdigit(self):
+        """
+        Returns true for each element if all characters in the string are
+        digits and there is at least one character, false otherwise.
+
+        See Also
+        --------
+        char.isdigit
+
+        """
+        return isdigit(self)
+
+    def islower(self):
+        """
+        Returns true for each element if all cased characters in the
+        string are lowercase and there is at least one cased character,
+        false otherwise.
+
+        See Also
+        --------
+        char.islower
+
+        """
+        return islower(self)
+
+    def isspace(self):
+        """
+        Returns true for each element if there are only whitespace
+        characters in the string and there is at least one character,
+        false otherwise.
+
+        See Also
+        --------
+        char.isspace
+
+        """
+        return isspace(self)
+
+    def istitle(self):
+        """
+        Returns true for each element if the element is a titlecased
+        string and there is at least one character, false otherwise.
+
+        See Also
+        --------
+        char.istitle
+
+        """
+        return istitle(self)
+
+    def isupper(self):
+        """
+        Returns true for each element if all cased characters in the
+        string are uppercase and there is at least one character, false
+        otherwise.
+
+        See Also
+        --------
+        char.isupper
+
+        """
+        return isupper(self)
+
+    def join(self, seq):
+        """
+        Return a string which is the concatenation of the strings in the
+        sequence `seq`.
+
+        See Also
+        --------
+        char.join
+
+        """
+        return join(self, seq)
+
+    def ljust(self, width, fillchar=' '):
+        """
+        Return an array with the elements of `self` left-justified in a
+        string of length `width`.
+
+        See Also
+        --------
+        char.ljust
+
+        """
+        return asarray(ljust(self, width, fillchar))
+
+    def lower(self):
+        """
+        Return an array with the elements of `self` converted to
+        lowercase.
+
+        See Also
+        --------
+        char.lower
+
+        """
+        return asarray(lower(self))
+
+    def lstrip(self, chars=None):
+        """
+        For each element in `self`, return a copy with the leading characters
+        removed.
+
+        See Also
+        --------
+        char.lstrip
+
+        """
+        return asarray(lstrip(self, chars))
+
+    def partition(self, sep):
+        """
+        Partition each element in `self` around `sep`.
+
+        See Also
+        --------
+        partition
+        """
+        return asarray(partition(self, sep))
+
+    def replace(self, old, new, count=None):
+        """
+        For each element in `self`, return a copy of the string with all
+        occurrences of substring `old` replaced by `new`.
+
+        See Also
+        --------
+        char.replace
+
+        """
+        return asarray(replace(self, old, new, count))
+
+    def rfind(self, sub, start=0, end=None):
+        """
+        For each element in `self`, return the highest index in the string
+        where substring `sub` is found, such that `sub` is contained
+        within [`start`, `end`].
+
+        See Also
+        --------
+        char.rfind
+
+        """
+        return rfind(self, sub, start, end)
+
+    def rindex(self, sub, start=0, end=None):
+        """
+        Like `rfind`, but raises `ValueError` when the substring `sub` is
+        not found.
+
+        See Also
+        --------
+        char.rindex
+
+        """
+        return rindex(self, sub, start, end)
+
+    def rjust(self, width, fillchar=' '):
+        """
+        Return an array with the elements of `self`
+        right-justified in a string of length `width`.
+
+        See Also
+        --------
+        char.rjust
+
+        """
+        return asarray(rjust(self, width, fillchar))
+
+    def rpartition(self, sep):
+        """
+        Partition each element in `self` around `sep`.
+
+        See Also
+        --------
+        rpartition
+        """
+        return asarray(rpartition(self, sep))
+
+    def rsplit(self, sep=None, maxsplit=None):
+        """
+        For each element in `self`, return a list of the words in
+        the string, using `sep` as the delimiter string.
+
+        See Also
+        --------
+        char.rsplit
+
+        """
+        return rsplit(self, sep, maxsplit)
+
+    def rstrip(self, chars=None):
+        """
+        For each element in `self`, return a copy with the trailing
+        characters removed.
+
+        See Also
+        --------
+        char.rstrip
+
+        """
+        return asarray(rstrip(self, chars))
+
+    def split(self, sep=None, maxsplit=None):
+        """
+        For each element in `self`, return a list of the words in the
+        string, using `sep` as the delimiter string.
+
+        See Also
+        --------
+        char.split
+
+        """
+        return split(self, sep, maxsplit)
+
+    def splitlines(self, keepends=None):
+        """
+        For each element in `self`, return a list of the lines in the
+        element, breaking at line boundaries.
+
+        See Also
+        --------
+        char.splitlines
+
+        """
+        return splitlines(self, keepends)
+
+    def startswith(self, prefix, start=0, end=None):
+        """
+        Returns a boolean array which is `True` where the string element
+        in `self` starts with `prefix`, otherwise `False`.
+
+        See Also
+        --------
+        char.startswith
+
+        """
+        return startswith(self, prefix, start, end)
+
+    def strip(self, chars=None):
+        """
+        For each element in `self`, return a copy with the leading and
+        trailing characters removed.
+
+        See Also
+        --------
+        char.strip
+
+        """
+        return asarray(strip(self, chars))
+
+    def swapcase(self):
+        """
+        For each element in `self`, return a copy of the string with
+        uppercase characters converted to lowercase and vice versa.
+
+        See Also
+        --------
+        char.swapcase
+
+        """
+        return asarray(swapcase(self))
+
+    def title(self):
+        """
+        For each element in `self`, return a titlecased version of the
+        string: words start with uppercase characters, all remaining cased
+        characters are lowercase.
+
+        See Also
+        --------
+        char.title
+
+        """
+        return asarray(title(self))
+
+    def translate(self, table, deletechars=None):
+        """
+        For each element in `self`, return a copy of the string where
+        all characters occurring in the optional argument
+        `deletechars` are removed, and the remaining characters have
+        been mapped through the given translation table.
+
+        See Also
+        --------
+        char.translate
+
+        """
+        return asarray(translate(self, table, deletechars))
+
+    def upper(self):
+        """
+        Return an array with the elements of `self` converted to
+        uppercase.
+
+        See Also
+        --------
+        char.upper
+
+        """
+        return asarray(upper(self))
+
+    def zfill(self, width):
+        """
+        Return the numeric string left-filled with zeros in a string of
+        length `width`.
+
+        See Also
+        --------
+        char.zfill
+
+        """
+        return asarray(zfill(self, width))
+
+    def isnumeric(self):
+        """
+        For each element in `self`, return True if there are only
+        numeric characters in the element.
+
+        See Also
+        --------
+        char.isnumeric
+
+        """
+        return isnumeric(self)
+
+    def isdecimal(self):
+        """
+        For each element in `self`, return True if there are only
+        decimal characters in the element.
+
+        See Also
+        --------
+        char.isdecimal
+
+        """
+        return isdecimal(self)
+
+
+@set_module("numpy.char")
+def array(obj, itemsize=None, copy=True, unicode=None, order=None):
+    """
+    Create a `chararray`.
+
+    .. note::
+       This class is provided for numarray backward-compatibility.
+       New code (not concerned with numarray compatibility) should use
+       arrays of type `bytes_` or `str_` and use the free functions
+       in :mod:`numpy.char <numpy.core.defchararray>` for fast
+       vectorized string operations instead.
+
+    Versus a regular NumPy array of type `str` or `unicode`, this
+    class adds the following functionality:
+
+      1) values automatically have whitespace removed from the end
+         when indexed
+
+      2) comparison operators automatically remove whitespace from the
+         end when comparing values
+
+      3) vectorized string operations are provided as methods
+         (e.g. `str.endswith`) and infix operators (e.g. ``+, *, %``)
+
+    Parameters
+    ----------
+    obj : array of str or unicode-like
+
+    itemsize : int, optional
+        `itemsize` is the number of characters per scalar in the
+        resulting array.  If `itemsize` is None, and `obj` is an
+        object array or a Python list, the `itemsize` will be
+        automatically determined.  If `itemsize` is provided and `obj`
+        is of type str or unicode, then the `obj` string will be
+        chunked into `itemsize` pieces.
+
+    copy : bool, optional
+        If true (default), then the object is copied.  Otherwise, a copy
+        will only be made if __array__ returns a copy, if obj is a
+        nested sequence, or if a copy is needed to satisfy any of the other
+        requirements (`itemsize`, unicode, `order`, etc.).
+
+    unicode : bool, optional
+        When true, the resulting `chararray` can contain Unicode
+        characters, when false only 8-bit characters.  If unicode is
+        None and `obj` is one of the following:
+
+          - a `chararray`,
+          - an ndarray of type `str` or `unicode`
+          - a Python str or unicode object,
+
+        then the unicode setting of the output array will be
+        automatically determined.
+
+    order : {'C', 'F', 'A'}, optional
+        Specify the order of the array.  If order is 'C' (default), then the
+        array will be in C-contiguous order (last-index varies the
+        fastest).  If order is 'F', then the returned array
+        will be in Fortran-contiguous order (first-index varies the
+        fastest).  If order is 'A', then the returned array may
+        be in any order (either C-, Fortran-contiguous, or even
+        discontiguous).
+    """
+    if isinstance(obj, (bytes, str)):
+        if unicode is None:
+            if isinstance(obj, str):
+                unicode = True
+            else:
+                unicode = False
+
+        if itemsize is None:
+            itemsize = len(obj)
+        shape = len(obj) // itemsize
+
+        return chararray(shape, itemsize=itemsize, unicode=unicode,
+                         buffer=obj, order=order)
+
+    if isinstance(obj, (list, tuple)):
+        obj = numpy.asarray(obj)
+
+    if isinstance(obj, ndarray) and issubclass(obj.dtype.type, character):
+        # If we just have a vanilla chararray, create a chararray
+        # view around it.
+        if not isinstance(obj, chararray):
+            obj = obj.view(chararray)
+
+        if itemsize is None:
+            itemsize = obj.itemsize
+            # itemsize is in 8-bit chars, so for Unicode, we need
+            # to divide by the size of a single Unicode character,
+            # which for NumPy is always 4
+            if issubclass(obj.dtype.type, str_):
+                itemsize //= 4
+
+        if unicode is None:
+            if issubclass(obj.dtype.type, str_):
+                unicode = True
+            else:
+                unicode = False
+
+        if unicode:
+            dtype = str_
+        else:
+            dtype = bytes_
+
+        if order is not None:
+            obj = numpy.asarray(obj, order=order)
+        if (copy or
+                (itemsize != obj.itemsize) or
+                (not unicode and isinstance(obj, str_)) or
+                (unicode and isinstance(obj, bytes_))):
+            obj = obj.astype((dtype, int(itemsize)))
+        return obj
+
+    if isinstance(obj, ndarray) and issubclass(obj.dtype.type, object):
+        if itemsize is None:
+            # Since no itemsize was specified, convert the input array to
+            # a list so the ndarray constructor will automatically
+            # determine the itemsize for us.
+            obj = obj.tolist()
+            # Fall through to the default case
+
+    if unicode:
+        dtype = str_
+    else:
+        dtype = bytes_
+
+    if itemsize is None:
+        val = narray(obj, dtype=dtype, order=order, subok=True)
+    else:
+        val = narray(obj, dtype=(dtype, itemsize), order=order, subok=True)
+    return val.view(chararray)
+
+
+@set_module("numpy.char")
+def asarray(obj, itemsize=None, unicode=None, order=None):
+    """
+    Convert the input to a `chararray`, copying the data only if
+    necessary.
+
+    Versus a regular NumPy array of type `str` or `unicode`, this
+    class adds the following functionality:
+
+      1) values automatically have whitespace removed from the end
+         when indexed
+
+      2) comparison operators automatically remove whitespace from the
+         end when comparing values
+
+      3) vectorized string operations are provided as methods
+         (e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
+
+    Parameters
+    ----------
+    obj : array of str or unicode-like
+
+    itemsize : int, optional
+        `itemsize` is the number of characters per scalar in the
+        resulting array.  If `itemsize` is None, and `obj` is an
+        object array or a Python list, the `itemsize` will be
+        automatically determined.  If `itemsize` is provided and `obj`
+        is of type str or unicode, then the `obj` string will be
+        chunked into `itemsize` pieces.
+
+    unicode : bool, optional
+        When true, the resulting `chararray` can contain Unicode
+        characters, when false only 8-bit characters.  If unicode is
+        None and `obj` is one of the following:
+
+          - a `chararray`,
+          - an ndarray of type `str` or 'unicode`
+          - a Python str or unicode object,
+
+        then the unicode setting of the output array will be
+        automatically determined.
+
+    order : {'C', 'F'}, optional
+        Specify the order of the array.  If order is 'C' (default), then the
+        array will be in C-contiguous order (last-index varies the
+        fastest).  If order is 'F', then the returned array
+        will be in Fortran-contiguous order (first-index varies the
+        fastest).
+    """
+    return array(obj, itemsize, copy=False,
+                 unicode=unicode, order=order)
diff --git a/shared/package-files/1b/1b5751bf50e52a165176f3a5428eccb21c8117014acd46dc92ff74ce678b7ca6/matrix.py b/shared/package-files/1b/1b5751bf50e52a165176f3a5428eccb21c8117014acd46dc92ff74ce678b7ca6/matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b99c118b62858c31b1c7db499fb0af4b4d99cb3
--- /dev/null
+++ b/shared/package-files/1b/1b5751bf50e52a165176f3a5428eccb21c8117014acd46dc92ff74ce678b7ca6/matrix.py
@@ -0,0 +1,1262 @@
+"""Functions to visualize matrices of data."""
+import warnings
+
+import matplotlib as mpl
+from matplotlib.collections import LineCollection
+import matplotlib.pyplot as plt
+from matplotlib import gridspec
+import numpy as np
+import pandas as pd
+try:
+    from scipy.cluster import hierarchy
+    _no_scipy = False
+except ImportError:
+    _no_scipy = True
+
+from . import cm
+from .axisgrid import Grid
+from ._compat import get_colormap
+from .utils import (
+    despine,
+    axis_ticklabels_overlap,
+    relative_luminance,
+    to_utf8,
+    _draw_figure,
+)
+
+
+__all__ = ["heatmap", "clustermap"]
+
+
+def _index_to_label(index):
+    """Convert a pandas index or multiindex to an axis label."""
+    if isinstance(index, pd.MultiIndex):
+        return "-".join(map(to_utf8, index.names))
+    else:
+        return index.name
+
+
+def _index_to_ticklabels(index):
+    """Convert a pandas index or multiindex into ticklabels."""
+    if isinstance(index, pd.MultiIndex):
+        return ["-".join(map(to_utf8, i)) for i in index.values]
+    else:
+        return index.values
+
+
+def _convert_colors(colors):
+    """Convert either a list of colors or nested lists of colors to RGB."""
+    to_rgb = mpl.colors.to_rgb
+
+    try:
+        to_rgb(colors[0])
+        # If this works, there is only one level of colors
+        return list(map(to_rgb, colors))
+    except ValueError:
+        # If we get here, we have nested lists
+        return [list(map(to_rgb, color_list)) for color_list in colors]
+
+
+def _matrix_mask(data, mask):
+    """Ensure that data and mask are compatible and add missing values.
+
+    Values will be plotted for cells where ``mask`` is ``False``.
+
+    ``data`` is expected to be a DataFrame; ``mask`` can be an array or
+    a DataFrame.
+
+    """
+    if mask is None:
+        mask = np.zeros(data.shape, bool)
+
+    if isinstance(mask, np.ndarray):
+        # For array masks, ensure that shape matches data then convert
+        if mask.shape != data.shape:
+            raise ValueError("Mask must have the same shape as data.")
+
+        mask = pd.DataFrame(mask,
+                            index=data.index,
+                            columns=data.columns,
+                            dtype=bool)
+
+    elif isinstance(mask, pd.DataFrame):
+        # For DataFrame masks, ensure that semantic labels match data
+        if not mask.index.equals(data.index) \
+           and mask.columns.equals(data.columns):
+            err = "Mask must have the same index and columns as data."
+            raise ValueError(err)
+
+    # Add any cells with missing data to the mask
+    # This works around an issue where `plt.pcolormesh` doesn't represent
+    # missing data properly
+    mask = mask | pd.isnull(data)
+
+    return mask
+
+
+class _HeatMapper:
+    """Draw a heatmap plot of a matrix with nice labels and colormaps."""
+
+    def __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt,
+                 annot_kws, cbar, cbar_kws,
+                 xticklabels=True, yticklabels=True, mask=None):
+        """Initialize the plotting object."""
+        # We always want to have a DataFrame with semantic information
+        # and an ndarray to pass to matplotlib
+        if isinstance(data, pd.DataFrame):
+            plot_data = data.values
+        else:
+            plot_data = np.asarray(data)
+            data = pd.DataFrame(plot_data)
+
+        # Validate the mask and convert to DataFrame
+        mask = _matrix_mask(data, mask)
+
+        plot_data = np.ma.masked_where(np.asarray(mask), plot_data)
+
+        # Get good names for the rows and columns
+        xtickevery = 1
+        if isinstance(xticklabels, int):
+            xtickevery = xticklabels
+            xticklabels = _index_to_ticklabels(data.columns)
+        elif xticklabels is True:
+            xticklabels = _index_to_ticklabels(data.columns)
+        elif xticklabels is False:
+            xticklabels = []
+
+        ytickevery = 1
+        if isinstance(yticklabels, int):
+            ytickevery = yticklabels
+            yticklabels = _index_to_ticklabels(data.index)
+        elif yticklabels is True:
+            yticklabels = _index_to_ticklabels(data.index)
+        elif yticklabels is False:
+            yticklabels = []
+
+        if not len(xticklabels):
+            self.xticks = []
+            self.xticklabels = []
+        elif isinstance(xticklabels, str) and xticklabels == "auto":
+            self.xticks = "auto"
+            self.xticklabels = _index_to_ticklabels(data.columns)
+        else:
+            self.xticks, self.xticklabels = self._skip_ticks(xticklabels,
+                                                             xtickevery)
+
+        if not len(yticklabels):
+            self.yticks = []
+            self.yticklabels = []
+        elif isinstance(yticklabels, str) and yticklabels == "auto":
+            self.yticks = "auto"
+            self.yticklabels = _index_to_ticklabels(data.index)
+        else:
+            self.yticks, self.yticklabels = self._skip_ticks(yticklabels,
+                                                             ytickevery)
+
+        # Get good names for the axis labels
+        xlabel = _index_to_label(data.columns)
+        ylabel = _index_to_label(data.index)
+        self.xlabel = xlabel if xlabel is not None else ""
+        self.ylabel = ylabel if ylabel is not None else ""
+
+        # Determine good default values for the colormapping
+        self._determine_cmap_params(plot_data, vmin, vmax,
+                                    cmap, center, robust)
+
+        # Sort out the annotations
+        if annot is None or annot is False:
+            annot = False
+            annot_data = None
+        else:
+            if isinstance(annot, bool):
+                annot_data = plot_data
+            else:
+                annot_data = np.asarray(annot)
+                if annot_data.shape != plot_data.shape:
+                    err = "`data` and `annot` must have same shape."
+                    raise ValueError(err)
+            annot = True
+
+        # Save other attributes to the object
+        self.data = data
+        self.plot_data = plot_data
+
+        self.annot = annot
+        self.annot_data = annot_data
+
+        self.fmt = fmt
+        self.annot_kws = {} if annot_kws is None else annot_kws.copy()
+        self.cbar = cbar
+        self.cbar_kws = {} if cbar_kws is None else cbar_kws.copy()
+
+    def _determine_cmap_params(self, plot_data, vmin, vmax,
+                               cmap, center, robust):
+        """Use some heuristics to set good defaults for colorbar and range."""
+
+        # plot_data is a np.ma.array instance
+        calc_data = plot_data.astype(float).filled(np.nan)
+        if vmin is None:
+            if robust:
+                vmin = np.nanpercentile(calc_data, 2)
+            else:
+                vmin = np.nanmin(calc_data)
+        if vmax is None:
+            if robust:
+                vmax = np.nanpercentile(calc_data, 98)
+            else:
+                vmax = np.nanmax(calc_data)
+        self.vmin, self.vmax = vmin, vmax
+
+        # Choose default colormaps if not provided
+        if cmap is None:
+            if center is None:
+                self.cmap = cm.rocket
+            else:
+                self.cmap = cm.icefire
+        elif isinstance(cmap, str):
+            self.cmap = get_colormap(cmap)
+        elif isinstance(cmap, list):
+            self.cmap = mpl.colors.ListedColormap(cmap)
+        else:
+            self.cmap = cmap
+
+        # Recenter a divergent colormap
+        if center is not None:
+
+            # Copy bad values
+            # in mpl<3.2 only masked values are honored with "bad" color spec
+            # (see https://github.com/matplotlib/matplotlib/pull/14257)
+            bad = self.cmap(np.ma.masked_invalid([np.nan]))[0]
+
+            # under/over values are set for sure when cmap extremes
+            # do not map to the same color as +-inf
+            under = self.cmap(-np.inf)
+            over = self.cmap(np.inf)
+            under_set = under != self.cmap(0)
+            over_set = over != self.cmap(self.cmap.N - 1)
+
+            vrange = max(vmax - center, center - vmin)
+            normlize = mpl.colors.Normalize(center - vrange, center + vrange)
+            cmin, cmax = normlize([vmin, vmax])
+            cc = np.linspace(cmin, cmax, 256)
+            self.cmap = mpl.colors.ListedColormap(self.cmap(cc))
+            self.cmap.set_bad(bad)
+            if under_set:
+                self.cmap.set_under(under)
+            if over_set:
+                self.cmap.set_over(over)
+
+    def _annotate_heatmap(self, ax, mesh):
+        """Add textual labels with the value in each cell."""
+        mesh.update_scalarmappable()
+        height, width = self.annot_data.shape
+        xpos, ypos = np.meshgrid(np.arange(width) + .5, np.arange(height) + .5)
+        for x, y, m, color, val in zip(xpos.flat, ypos.flat,
+                                       mesh.get_array().flat, mesh.get_facecolors(),
+                                       self.annot_data.flat):
+            if m is not np.ma.masked:
+                lum = relative_luminance(color)
+                text_color = ".15" if lum > .408 else "w"
+                annotation = ("{:" + self.fmt + "}").format(val)
+                text_kwargs = dict(color=text_color, ha="center", va="center")
+                text_kwargs.update(self.annot_kws)
+                ax.text(x, y, annotation, **text_kwargs)
+
+    def _skip_ticks(self, labels, tickevery):
+        """Return ticks and labels at evenly spaced intervals."""
+        n = len(labels)
+        if tickevery == 0:
+            ticks, labels = [], []
+        elif tickevery == 1:
+            ticks, labels = np.arange(n) + .5, labels
+        else:
+            start, end, step = 0, n, tickevery
+            ticks = np.arange(start, end, step) + .5
+            labels = labels[start:end:step]
+        return ticks, labels
+
+    def _auto_ticks(self, ax, labels, axis):
+        """Determine ticks and ticklabels that minimize overlap."""
+        transform = ax.figure.dpi_scale_trans.inverted()
+        bbox = ax.get_window_extent().transformed(transform)
+        size = [bbox.width, bbox.height][axis]
+        axis = [ax.xaxis, ax.yaxis][axis]
+        tick, = axis.set_ticks([0])
+        fontsize = tick.label1.get_size()
+        max_ticks = int(size // (fontsize / 72))
+        if max_ticks < 1:
+            return [], []
+        tick_every = len(labels) // max_ticks + 1
+        tick_every = 1 if tick_every == 0 else tick_every
+        ticks, labels = self._skip_ticks(labels, tick_every)
+        return ticks, labels
+
+    def plot(self, ax, cax, kws):
+        """Draw the heatmap on the provided Axes."""
+        # Remove all the Axes spines
+        despine(ax=ax, left=True, bottom=True)
+
+        # setting vmin/vmax in addition to norm is deprecated
+        # so avoid setting if norm is set
+        if kws.get("norm") is None:
+            kws.setdefault("vmin", self.vmin)
+            kws.setdefault("vmax", self.vmax)
+
+        # Draw the heatmap
+        mesh = ax.pcolormesh(self.plot_data, cmap=self.cmap, **kws)
+
+        # Set the axis limits
+        ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))
+
+        # Invert the y axis to show the plot in matrix form
+        ax.invert_yaxis()
+
+        # Possibly add a colorbar
+        if self.cbar:
+            cb = ax.figure.colorbar(mesh, cax, ax, **self.cbar_kws)
+            cb.outline.set_linewidth(0)
+            # If rasterized is passed to pcolormesh, also rasterize the
+            # colorbar to avoid white lines on the PDF rendering
+            if kws.get('rasterized', False):
+                cb.solids.set_rasterized(True)
+
+        # Add row and column labels
+        if isinstance(self.xticks, str) and self.xticks == "auto":
+            xticks, xticklabels = self._auto_ticks(ax, self.xticklabels, 0)
+        else:
+            xticks, xticklabels = self.xticks, self.xticklabels
+
+        if isinstance(self.yticks, str) and self.yticks == "auto":
+            yticks, yticklabels = self._auto_ticks(ax, self.yticklabels, 1)
+        else:
+            yticks, yticklabels = self.yticks, self.yticklabels
+
+        ax.set(xticks=xticks, yticks=yticks)
+        xtl = ax.set_xticklabels(xticklabels)
+        ytl = ax.set_yticklabels(yticklabels, rotation="vertical")
+        plt.setp(ytl, va="center")  # GH2484
+
+        # Possibly rotate them if they overlap
+        _draw_figure(ax.figure)
+
+        if axis_ticklabels_overlap(xtl):
+            plt.setp(xtl, rotation="vertical")
+        if axis_ticklabels_overlap(ytl):
+            plt.setp(ytl, rotation="horizontal")
+
+        # Add the axis labels
+        ax.set(xlabel=self.xlabel, ylabel=self.ylabel)
+
+        # Annotate the cells with the formatted values
+        if self.annot:
+            self._annotate_heatmap(ax, mesh)
+
+
+def heatmap(
+    data, *,
+    vmin=None, vmax=None, cmap=None, center=None, robust=False,
+    annot=None, fmt=".2g", annot_kws=None,
+    linewidths=0, linecolor="white",
+    cbar=True, cbar_kws=None, cbar_ax=None,
+    square=False, xticklabels="auto", yticklabels="auto",
+    mask=None, ax=None,
+    **kwargs
+):
+    """Plot rectangular data as a color-encoded matrix.
+
+    This is an Axes-level function and will draw the heatmap into the
+    currently-active Axes if none is provided to the ``ax`` argument.  Part of
+    this Axes space will be taken and used to plot a colormap, unless ``cbar``
+    is False or a separate Axes is provided to ``cbar_ax``.
+
+    Parameters
+    ----------
+    data : rectangular dataset
+        2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
+        is provided, the index/column information will be used to label the
+        columns and rows.
+    vmin, vmax : floats, optional
+        Values to anchor the colormap, otherwise they are inferred from the
+        data and other keyword arguments.
+    cmap : matplotlib colormap name or object, or list of colors, optional
+        The mapping from data values to color space. If not provided, the
+        default will depend on whether ``center`` is set.
+    center : float, optional
+        The value at which to center the colormap when plotting divergent data.
+        Using this parameter will change the default ``cmap`` if none is
+        specified.
+    robust : bool, optional
+        If True and ``vmin`` or ``vmax`` are absent, the colormap range is
+        computed with robust quantiles instead of the extreme values.
+    annot : bool or rectangular dataset, optional
+        If True, write the data value in each cell. If an array-like with the
+        same shape as ``data``, then use this to annotate the heatmap instead
+        of the data. Note that DataFrames will match on position, not index.
+    fmt : str, optional
+        String formatting code to use when adding annotations.
+    annot_kws : dict of key, value mappings, optional
+        Keyword arguments for :meth:`matplotlib.axes.Axes.text` when ``annot``
+        is True.
+    linewidths : float, optional
+        Width of the lines that will divide each cell.
+    linecolor : color, optional
+        Color of the lines that will divide each cell.
+    cbar : bool, optional
+        Whether to draw a colorbar.
+    cbar_kws : dict of key, value mappings, optional
+        Keyword arguments for :meth:`matplotlib.figure.Figure.colorbar`.
+    cbar_ax : matplotlib Axes, optional
+        Axes in which to draw the colorbar, otherwise take space from the
+        main Axes.
+    square : bool, optional
+        If True, set the Axes aspect to "equal" so each cell will be
+        square-shaped.
+    xticklabels, yticklabels : "auto", bool, list-like, or int, optional
+        If True, plot the column names of the dataframe. If False, don't plot
+        the column names. If list-like, plot these alternate labels as the
+        xticklabels. If an integer, use the column names but plot only every
+        n label. If "auto", try to densely plot non-overlapping labels.
+    mask : bool array or DataFrame, optional
+        If passed, data will not be shown in cells where ``mask`` is True.
+        Cells with missing values are automatically masked.
+    ax : matplotlib Axes, optional
+        Axes in which to draw the plot, otherwise use the currently-active
+        Axes.
+    kwargs : other keyword arguments
+        All other keyword arguments are passed to
+        :meth:`matplotlib.axes.Axes.pcolormesh`.
+
+    Returns
+    -------
+    ax : matplotlib Axes
+        Axes object with the heatmap.
+
+    See Also
+    --------
+    clustermap : Plot a matrix using hierarchical clustering to arrange the
+                 rows and columns.
+
+    Examples
+    --------
+
+    .. include:: ../docstrings/heatmap.rst
+
+    """
+    # Initialize the plotter object
+    plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
+                          annot_kws, cbar, cbar_kws, xticklabels,
+                          yticklabels, mask)
+
+    # Add the pcolormesh kwargs here
+    kwargs["linewidths"] = linewidths
+    kwargs["edgecolor"] = linecolor
+
+    # Draw the plot and return the Axes
+    if ax is None:
+        ax = plt.gca()
+    if square:
+        ax.set_aspect("equal")
+    plotter.plot(ax, cbar_ax, kwargs)
+    return ax
+
+
+class _DendrogramPlotter:
+    """Object for drawing tree of similarities between data rows/columns"""
+
+    def __init__(self, data, linkage, metric, method, axis, label, rotate):
+        """Plot a dendrogram of the relationships between the columns of data
+
+        Parameters
+        ----------
+        data : pandas.DataFrame
+            Rectangular data
+        """
+        self.axis = axis
+        if self.axis == 1:
+            data = data.T
+
+        if isinstance(data, pd.DataFrame):
+            array = data.values
+        else:
+            array = np.asarray(data)
+            data = pd.DataFrame(array)
+
+        self.array = array
+        self.data = data
+
+        self.shape = self.data.shape
+        self.metric = metric
+        self.method = method
+        self.axis = axis
+        self.label = label
+        self.rotate = rotate
+
+        if linkage is None:
+            self.linkage = self.calculated_linkage
+        else:
+            self.linkage = linkage
+        self.dendrogram = self.calculate_dendrogram()
+
+        # Dendrogram ends are always at multiples of 5, who knows why
+        ticks = 10 * np.arange(self.data.shape[0]) + 5
+
+        if self.label:
+            ticklabels = _index_to_ticklabels(self.data.index)
+            ticklabels = [ticklabels[i] for i in self.reordered_ind]
+            if self.rotate:
+                self.xticks = []
+                self.yticks = ticks
+                self.xticklabels = []
+
+                self.yticklabels = ticklabels
+                self.ylabel = _index_to_label(self.data.index)
+                self.xlabel = ''
+            else:
+                self.xticks = ticks
+                self.yticks = []
+                self.xticklabels = ticklabels
+                self.yticklabels = []
+                self.ylabel = ''
+                self.xlabel = _index_to_label(self.data.index)
+        else:
+            self.xticks, self.yticks = [], []
+            self.yticklabels, self.xticklabels = [], []
+            self.xlabel, self.ylabel = '', ''
+
+        self.dependent_coord = self.dendrogram['dcoord']
+        self.independent_coord = self.dendrogram['icoord']
+
+    def _calculate_linkage_scipy(self):
+        linkage = hierarchy.linkage(self.array, method=self.method,
+                                    metric=self.metric)
+        return linkage
+
+    def _calculate_linkage_fastcluster(self):
+        import fastcluster
+        # Fastcluster has a memory-saving vectorized version, but only
+        # with certain linkage methods, and mostly with euclidean metric
+        # vector_methods = ('single', 'centroid', 'median', 'ward')
+        euclidean_methods = ('centroid', 'median', 'ward')
+        euclidean = self.metric == 'euclidean' and self.method in \
+            euclidean_methods
+        if euclidean or self.method == 'single':
+            return fastcluster.linkage_vector(self.array,
+                                              method=self.method,
+                                              metric=self.metric)
+        else:
+            linkage = fastcluster.linkage(self.array, method=self.method,
+                                          metric=self.metric)
+            return linkage
+
+    @property
+    def calculated_linkage(self):
+
+        try:
+            return self._calculate_linkage_fastcluster()
+        except ImportError:
+            if np.prod(self.shape) >= 10000:
+                msg = ("Clustering large matrix with scipy. Installing "
+                       "`fastcluster` may give better performance.")
+                warnings.warn(msg)
+
+        return self._calculate_linkage_scipy()
+
+    def calculate_dendrogram(self):
+        """Calculates a dendrogram based on the linkage matrix
+
+        Made a separate function, not a property because don't want to
+        recalculate the dendrogram every time it is accessed.
+
+        Returns
+        -------
+        dendrogram : dict
+            Dendrogram dictionary as returned by scipy.cluster.hierarchy
+            .dendrogram. The important key-value pairing is
+            "reordered_ind" which indicates the re-ordering of the matrix
+        """
+        return hierarchy.dendrogram(self.linkage, no_plot=True,
+                                    color_threshold=-np.inf)
+
+    @property
+    def reordered_ind(self):
+        """Indices of the matrix, reordered by the dendrogram"""
+        return self.dendrogram['leaves']
+
+    def plot(self, ax, tree_kws):
+        """Plots a dendrogram of the similarities between data on the axes
+
+        Parameters
+        ----------
+        ax : matplotlib.axes.Axes
+            Axes object upon which the dendrogram is plotted
+
+        """
+        tree_kws = {} if tree_kws is None else tree_kws.copy()
+        tree_kws.setdefault("linewidths", .5)
+        tree_kws.setdefault("colors", tree_kws.pop("color", (.2, .2, .2)))
+
+        if self.rotate and self.axis == 0:
+            coords = zip(self.dependent_coord, self.independent_coord)
+        else:
+            coords = zip(self.independent_coord, self.dependent_coord)
+        lines = LineCollection([list(zip(x, y)) for x, y in coords],
+                               **tree_kws)
+
+        ax.add_collection(lines)
+        number_of_leaves = len(self.reordered_ind)
+        max_dependent_coord = max(map(max, self.dependent_coord))
+
+        if self.rotate:
+            ax.yaxis.set_ticks_position('right')
+
+            # Constants 10 and 1.05 come from
+            # `scipy.cluster.hierarchy._plot_dendrogram`
+            ax.set_ylim(0, number_of_leaves * 10)
+            ax.set_xlim(0, max_dependent_coord * 1.05)
+
+            ax.invert_xaxis()
+            ax.invert_yaxis()
+        else:
+            # Constants 10 and 1.05 come from
+            # `scipy.cluster.hierarchy._plot_dendrogram`
+            ax.set_xlim(0, number_of_leaves * 10)
+            ax.set_ylim(0, max_dependent_coord * 1.05)
+
+        despine(ax=ax, bottom=True, left=True)
+
+        ax.set(xticks=self.xticks, yticks=self.yticks,
+               xlabel=self.xlabel, ylabel=self.ylabel)
+        xtl = ax.set_xticklabels(self.xticklabels)
+        ytl = ax.set_yticklabels(self.yticklabels, rotation='vertical')
+
+        # Force a draw of the plot to avoid matplotlib window error
+        _draw_figure(ax.figure)
+
+        if len(ytl) > 0 and axis_ticklabels_overlap(ytl):
+            plt.setp(ytl, rotation="horizontal")
+        if len(xtl) > 0 and axis_ticklabels_overlap(xtl):
+            plt.setp(xtl, rotation="vertical")
+        return self
+
+
+def dendrogram(
+    data, *,
+    linkage=None, axis=1, label=True, metric='euclidean',
+    method='average', rotate=False, tree_kws=None, ax=None
+):
+    """Draw a tree diagram of relationships within a matrix
+
+    Parameters
+    ----------
+    data : pandas.DataFrame
+        Rectangular data
+    linkage : numpy.array, optional
+        Linkage matrix
+    axis : int, optional
+        Which axis to use to calculate linkage. 0 is rows, 1 is columns.
+    label : bool, optional
+        If True, label the dendrogram at leaves with column or row names
+    metric : str, optional
+        Distance metric. Anything valid for scipy.spatial.distance.pdist
+    method : str, optional
+        Linkage method to use. Anything valid for
+        scipy.cluster.hierarchy.linkage
+    rotate : bool, optional
+        When plotting the matrix, whether to rotate it 90 degrees
+        counter-clockwise, so the leaves face right
+    tree_kws : dict, optional
+        Keyword arguments for the ``matplotlib.collections.LineCollection``
+        that is used for plotting the lines of the dendrogram tree.
+    ax : matplotlib axis, optional
+        Axis to plot on, otherwise uses current axis
+
+    Returns
+    -------
+    dendrogramplotter : _DendrogramPlotter
+        A Dendrogram plotter object.
+
+    Notes
+    -----
+    Access the reordered dendrogram indices with
+    dendrogramplotter.reordered_ind
+
+    """
+    if _no_scipy:
+        raise RuntimeError("dendrogram requires scipy to be installed")
+
+    plotter = _DendrogramPlotter(data, linkage=linkage, axis=axis,
+                                 metric=metric, method=method,
+                                 label=label, rotate=rotate)
+    if ax is None:
+        ax = plt.gca()
+
+    return plotter.plot(ax=ax, tree_kws=tree_kws)
+
+
+class ClusterGrid(Grid):
+
+    def __init__(self, data, pivot_kws=None, z_score=None, standard_scale=None,
+                 figsize=None, row_colors=None, col_colors=None, mask=None,
+                 dendrogram_ratio=None, colors_ratio=None, cbar_pos=None):
+        """Grid object for organizing clustered heatmap input on to axes"""
+        if _no_scipy:
+            raise RuntimeError("ClusterGrid requires scipy to be available")
+
+        if isinstance(data, pd.DataFrame):
+            self.data = data
+        else:
+            self.data = pd.DataFrame(data)
+
+        self.data2d = self.format_data(self.data, pivot_kws, z_score,
+                                       standard_scale)
+
+        self.mask = _matrix_mask(self.data2d, mask)
+
+        self._figure = plt.figure(figsize=figsize)
+
+        self.row_colors, self.row_color_labels = \
+            self._preprocess_colors(data, row_colors, axis=0)
+        self.col_colors, self.col_color_labels = \
+            self._preprocess_colors(data, col_colors, axis=1)
+
+        try:
+            row_dendrogram_ratio, col_dendrogram_ratio = dendrogram_ratio
+        except TypeError:
+            row_dendrogram_ratio = col_dendrogram_ratio = dendrogram_ratio
+
+        try:
+            row_colors_ratio, col_colors_ratio = colors_ratio
+        except TypeError:
+            row_colors_ratio = col_colors_ratio = colors_ratio
+
+        width_ratios = self.dim_ratios(self.row_colors,
+                                       row_dendrogram_ratio,
+                                       row_colors_ratio)
+        height_ratios = self.dim_ratios(self.col_colors,
+                                        col_dendrogram_ratio,
+                                        col_colors_ratio)
+
+        nrows = 2 if self.col_colors is None else 3
+        ncols = 2 if self.row_colors is None else 3
+
+        self.gs = gridspec.GridSpec(nrows, ncols,
+                                    width_ratios=width_ratios,
+                                    height_ratios=height_ratios)
+
+        self.ax_row_dendrogram = self._figure.add_subplot(self.gs[-1, 0])
+        self.ax_col_dendrogram = self._figure.add_subplot(self.gs[0, -1])
+        self.ax_row_dendrogram.set_axis_off()
+        self.ax_col_dendrogram.set_axis_off()
+
+        self.ax_row_colors = None
+        self.ax_col_colors = None
+
+        if self.row_colors is not None:
+            self.ax_row_colors = self._figure.add_subplot(
+                self.gs[-1, 1])
+        if self.col_colors is not None:
+            self.ax_col_colors = self._figure.add_subplot(
+                self.gs[1, -1])
+
+        self.ax_heatmap = self._figure.add_subplot(self.gs[-1, -1])
+        if cbar_pos is None:
+            self.ax_cbar = self.cax = None
+        else:
+            # Initialize the colorbar axes in the gridspec so that tight_layout
+            # works. We will move it where it belongs later. This is a hack.
+            self.ax_cbar = self._figure.add_subplot(self.gs[0, 0])
+            self.cax = self.ax_cbar  # Backwards compatibility
+        self.cbar_pos = cbar_pos
+
+        self.dendrogram_row = None
+        self.dendrogram_col = None
+
+    def _preprocess_colors(self, data, colors, axis):
+        """Preprocess {row/col}_colors to extract labels and convert colors."""
+        labels = None
+
+        if colors is not None:
+            if isinstance(colors, (pd.DataFrame, pd.Series)):
+
+                # If data is unindexed, raise
+                if (not hasattr(data, "index") and axis == 0) or (
+                    not hasattr(data, "columns") and axis == 1
+                ):
+                    axis_name = "col" if axis else "row"
+                    msg = (f"{axis_name}_colors indices can't be matched with data "
+                           f"indices. Provide {axis_name}_colors as a non-indexed "
+                           "datatype, e.g. by using `.to_numpy()``")
+                    raise TypeError(msg)
+
+                # Ensure colors match data indices
+                if axis == 0:
+                    colors = colors.reindex(data.index)
+                else:
+                    colors = colors.reindex(data.columns)
+
+                # Replace na's with white color
+                # TODO We should set these to transparent instead
+                colors = colors.astype(object).fillna('white')
+
+                # Extract color values and labels from frame/series
+                if isinstance(colors, pd.DataFrame):
+                    labels = list(colors.columns)
+                    colors = colors.T.values
+                else:
+                    if colors.name is None:
+                        labels = [""]
+                    else:
+                        labels = [colors.name]
+                    colors = colors.values
+
+            colors = _convert_colors(colors)
+
+        return colors, labels
+
+    def format_data(self, data, pivot_kws, z_score=None,
+                    standard_scale=None):
+        """Extract variables from data or use directly."""
+
+        # Either the data is already in 2d matrix format, or need to do a pivot
+        if pivot_kws is not None:
+            data2d = data.pivot(**pivot_kws)
+        else:
+            data2d = data
+
+        if z_score is not None and standard_scale is not None:
+            raise ValueError(
+                'Cannot perform both z-scoring and standard-scaling on data')
+
+        if z_score is not None:
+            data2d = self.z_score(data2d, z_score)
+        if standard_scale is not None:
+            data2d = self.standard_scale(data2d, standard_scale)
+        return data2d
+
+    @staticmethod
+    def z_score(data2d, axis=1):
+        """Standarize the mean and variance of the data axis
+
+        Parameters
+        ----------
+        data2d : pandas.DataFrame
+            Data to normalize
+        axis : int
+            Which axis to normalize across. If 0, normalize across rows, if 1,
+            normalize across columns.
+
+        Returns
+        -------
+        normalized : pandas.DataFrame
+            Noramlized data with a mean of 0 and variance of 1 across the
+            specified axis.
+        """
+        if axis == 1:
+            z_scored = data2d
+        else:
+            z_scored = data2d.T
+
+        z_scored = (z_scored - z_scored.mean()) / z_scored.std()
+
+        if axis == 1:
+            return z_scored
+        else:
+            return z_scored.T
+
+    @staticmethod
+    def standard_scale(data2d, axis=1):
+        """Divide the data by the difference between the max and min
+
+        Parameters
+        ----------
+        data2d : pandas.DataFrame
+            Data to normalize
+        axis : int
+            Which axis to normalize across. If 0, normalize across rows, if 1,
+            normalize across columns.
+
+        Returns
+        -------
+        standardized : pandas.DataFrame
+            Noramlized data with a mean of 0 and variance of 1 across the
+            specified axis.
+
+        """
+        # Normalize these values to range from 0 to 1
+        if axis == 1:
+            standardized = data2d
+        else:
+            standardized = data2d.T
+
+        subtract = standardized.min()
+        standardized = (standardized - subtract) / (
+            standardized.max() - standardized.min())
+
+        if axis == 1:
+            return standardized
+        else:
+            return standardized.T
+
+    def dim_ratios(self, colors, dendrogram_ratio, colors_ratio):
+        """Get the proportions of the figure taken up by each axes."""
+        ratios = [dendrogram_ratio]
+
+        if colors is not None:
+            # Colors are encoded as rgb, so there is an extra dimension
+            if np.ndim(colors) > 2:
+                n_colors = len(colors)
+            else:
+                n_colors = 1
+
+            ratios += [n_colors * colors_ratio]
+
+        # Add the ratio for the heatmap itself
+        ratios.append(1 - sum(ratios))
+
+        return ratios
+
+    @staticmethod
+    def color_list_to_matrix_and_cmap(colors, ind, axis=0):
+        """Turns a list of colors into a numpy matrix and matplotlib colormap
+
+        These arguments can now be plotted using heatmap(matrix, cmap)
+        and the provided colors will be plotted.
+
+        Parameters
+        ----------
+        colors : list of matplotlib colors
+            Colors to label the rows or columns of a dataframe.
+        ind : list of ints
+            Ordering of the rows or columns, to reorder the original colors
+            by the clustered dendrogram order
+        axis : int
+            Which axis this is labeling
+
+        Returns
+        -------
+        matrix : numpy.array
+            A numpy array of integer values, where each indexes into the cmap
+        cmap : matplotlib.colors.ListedColormap
+
+        """
+        try:
+            mpl.colors.to_rgb(colors[0])
+        except ValueError:
+            # We have a 2D color structure
+            m, n = len(colors), len(colors[0])
+            if not all(len(c) == n for c in colors[1:]):
+                raise ValueError("Multiple side color vectors must have same size")
+        else:
+            # We have one vector of colors
+            m, n = 1, len(colors)
+            colors = [colors]
+
+        # Map from unique colors to colormap index value
+        unique_colors = {}
+        matrix = np.zeros((m, n), int)
+        for i, inner in enumerate(colors):
+            for j, color in enumerate(inner):
+                idx = unique_colors.setdefault(color, len(unique_colors))
+                matrix[i, j] = idx
+
+        # Reorder for clustering and transpose for axis
+        matrix = matrix[:, ind]
+        if axis == 0:
+            matrix = matrix.T
+
+        cmap = mpl.colors.ListedColormap(list(unique_colors))
+        return matrix, cmap
+
+    def plot_dendrograms(self, row_cluster, col_cluster, metric, method,
+                         row_linkage, col_linkage, tree_kws):
+        # Plot the row dendrogram
+        if row_cluster:
+            self.dendrogram_row = dendrogram(
+                self.data2d, metric=metric, method=method, label=False, axis=0,
+                ax=self.ax_row_dendrogram, rotate=True, linkage=row_linkage,
+                tree_kws=tree_kws
+            )
+        else:
+            self.ax_row_dendrogram.set_xticks([])
+            self.ax_row_dendrogram.set_yticks([])
+        # PLot the column dendrogram
+        if col_cluster:
+            self.dendrogram_col = dendrogram(
+                self.data2d, metric=metric, method=method, label=False,
+                axis=1, ax=self.ax_col_dendrogram, linkage=col_linkage,
+                tree_kws=tree_kws
+            )
+        else:
+            self.ax_col_dendrogram.set_xticks([])
+            self.ax_col_dendrogram.set_yticks([])
+        despine(ax=self.ax_row_dendrogram, bottom=True, left=True)
+        despine(ax=self.ax_col_dendrogram, bottom=True, left=True)
+
+    def plot_colors(self, xind, yind, **kws):
+        """Plots color labels between the dendrogram and the heatmap
+
+        Parameters
+        ----------
+        heatmap_kws : dict
+            Keyword arguments heatmap
+
+        """
+        # Remove any custom colormap and centering
+        # TODO this code has consistently caused problems when we
+        # have missed kwargs that need to be excluded that it might
+        # be better to rewrite *in*clusively.
+        kws = kws.copy()
+        kws.pop('cmap', None)
+        kws.pop('norm', None)
+        kws.pop('center', None)
+        kws.pop('annot', None)
+        kws.pop('vmin', None)
+        kws.pop('vmax', None)
+        kws.pop('robust', None)
+        kws.pop('xticklabels', None)
+        kws.pop('yticklabels', None)
+
+        # Plot the row colors
+        if self.row_colors is not None:
+            matrix, cmap = self.color_list_to_matrix_and_cmap(
+                self.row_colors, yind, axis=0)
+
+            # Get row_color labels
+            if self.row_color_labels is not None:
+                row_color_labels = self.row_color_labels
+            else:
+                row_color_labels = False
+
+            heatmap(matrix, cmap=cmap, cbar=False, ax=self.ax_row_colors,
+                    xticklabels=row_color_labels, yticklabels=False, **kws)
+
+            # Adjust rotation of labels
+            if row_color_labels is not False:
+                plt.setp(self.ax_row_colors.get_xticklabels(), rotation=90)
+        else:
+            despine(self.ax_row_colors, left=True, bottom=True)
+
+        # Plot the column colors
+        if self.col_colors is not None:
+            matrix, cmap = self.color_list_to_matrix_and_cmap(
+                self.col_colors, xind, axis=1)
+
+            # Get col_color labels
+            if self.col_color_labels is not None:
+                col_color_labels = self.col_color_labels
+            else:
+                col_color_labels = False
+
+            heatmap(matrix, cmap=cmap, cbar=False, ax=self.ax_col_colors,
+                    xticklabels=False, yticklabels=col_color_labels, **kws)
+
+            # Adjust rotation of labels, place on right side
+            if col_color_labels is not False:
+                self.ax_col_colors.yaxis.tick_right()
+                plt.setp(self.ax_col_colors.get_yticklabels(), rotation=0)
+        else:
+            despine(self.ax_col_colors, left=True, bottom=True)
+
+    def plot_matrix(self, colorbar_kws, xind, yind, **kws):
+        self.data2d = self.data2d.iloc[yind, xind]
+        self.mask = self.mask.iloc[yind, xind]
+
+        # Try to reorganize specified tick labels, if provided
+        xtl = kws.pop("xticklabels", "auto")
+        try:
+            xtl = np.asarray(xtl)[xind]
+        except (TypeError, IndexError):
+            pass
+        ytl = kws.pop("yticklabels", "auto")
+        try:
+            ytl = np.asarray(ytl)[yind]
+        except (TypeError, IndexError):
+            pass
+
+        # Reorganize the annotations to match the heatmap
+        annot = kws.pop("annot", None)
+        if annot is None or annot is False:
+            pass
+        else:
+            if isinstance(annot, bool):
+                annot_data = self.data2d
+            else:
+                annot_data = np.asarray(annot)
+                if annot_data.shape != self.data2d.shape:
+                    err = "`data` and `annot` must have same shape."
+                    raise ValueError(err)
+                annot_data = annot_data[yind][:, xind]
+            annot = annot_data
+
+        # Setting ax_cbar=None in clustermap call implies no colorbar
+        kws.setdefault("cbar", self.ax_cbar is not None)
+        heatmap(self.data2d, ax=self.ax_heatmap, cbar_ax=self.ax_cbar,
+                cbar_kws=colorbar_kws, mask=self.mask,
+                xticklabels=xtl, yticklabels=ytl, annot=annot, **kws)
+
+        ytl = self.ax_heatmap.get_yticklabels()
+        ytl_rot = None if not ytl else ytl[0].get_rotation()
+        self.ax_heatmap.yaxis.set_ticks_position('right')
+        self.ax_heatmap.yaxis.set_label_position('right')
+        if ytl_rot is not None:
+            ytl = self.ax_heatmap.get_yticklabels()
+            plt.setp(ytl, rotation=ytl_rot)
+
+        tight_params = dict(h_pad=.02, w_pad=.02)
+        if self.ax_cbar is None:
+            self._figure.tight_layout(**tight_params)
+        else:
+            # Turn the colorbar axes off for tight layout so that its
+            # ticks don't interfere with the rest of the plot layout.
+            # Then move it.
+            self.ax_cbar.set_axis_off()
+            self._figure.tight_layout(**tight_params)
+            self.ax_cbar.set_axis_on()
+            self.ax_cbar.set_position(self.cbar_pos)
+
+    def plot(self, metric, method, colorbar_kws, row_cluster, col_cluster,
+             row_linkage, col_linkage, tree_kws, **kws):
+
+        # heatmap square=True sets the aspect ratio on the axes, but that is
+        # not compatible with the multi-axes layout of clustergrid
+        if kws.get("square", False):
+            msg = "``square=True`` ignored in clustermap"
+            warnings.warn(msg)
+            kws.pop("square")
+
+        colorbar_kws = {} if colorbar_kws is None else colorbar_kws
+
+        self.plot_dendrograms(row_cluster, col_cluster, metric, method,
+                              row_linkage=row_linkage, col_linkage=col_linkage,
+                              tree_kws=tree_kws)
+        try:
+            xind = self.dendrogram_col.reordered_ind
+        except AttributeError:
+            xind = np.arange(self.data2d.shape[1])
+        try:
+            yind = self.dendrogram_row.reordered_ind
+        except AttributeError:
+            yind = np.arange(self.data2d.shape[0])
+
+        self.plot_colors(xind, yind, **kws)
+        self.plot_matrix(colorbar_kws, xind, yind, **kws)
+        return self
+
+
+def clustermap(
+    data, *,
+    pivot_kws=None, method='average', metric='euclidean',
+    z_score=None, standard_scale=None, figsize=(10, 10),
+    cbar_kws=None, row_cluster=True, col_cluster=True,
+    row_linkage=None, col_linkage=None,
+    row_colors=None, col_colors=None, mask=None,
+    dendrogram_ratio=.2, colors_ratio=0.03,
+    cbar_pos=(.02, .8, .05, .18), tree_kws=None,
+    **kwargs
+):
+    """
+    Plot a matrix dataset as a hierarchically-clustered heatmap.
+
+    This function requires scipy to be available.
+
+    Parameters
+    ----------
+    data : 2D array-like
+        Rectangular data for clustering. Cannot contain NAs.
+    pivot_kws : dict, optional
+        If `data` is a tidy dataframe, can provide keyword arguments for
+        pivot to create a rectangular dataframe.
+    method : str, optional
+        Linkage method to use for calculating clusters. See
+        :func:`scipy.cluster.hierarchy.linkage` documentation for more
+        information.
+    metric : str, optional
+        Distance metric to use for the data. See
+        :func:`scipy.spatial.distance.pdist` documentation for more options.
+        To use different metrics (or methods) for rows and columns, you may
+        construct each linkage matrix yourself and provide them as
+        `{row,col}_linkage`.
+    z_score : int or None, optional
+        Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
+        for the rows or the columns. Z scores are: z = (x - mean)/std, so
+        values in each row (column) will get the mean of the row (column)
+        subtracted, then divided by the standard deviation of the row (column).
+        This ensures that each row (column) has mean of 0 and variance of 1.
+    standard_scale : int or None, optional
+        Either 0 (rows) or 1 (columns). Whether or not to standardize that
+        dimension, meaning for each row or column, subtract the minimum and
+        divide each by its maximum.
+    figsize : tuple of (width, height), optional
+        Overall size of the figure.
+    cbar_kws : dict, optional
+        Keyword arguments to pass to `cbar_kws` in :func:`heatmap`, e.g. to
+        add a label to the colorbar.
+    {row,col}_cluster : bool, optional
+        If ``True``, cluster the {rows, columns}.
+    {row,col}_linkage : :class:`numpy.ndarray`, optional
+        Precomputed linkage matrix for the rows or columns. See
+        :func:`scipy.cluster.hierarchy.linkage` for specific formats.
+    {row,col}_colors : list-like or pandas DataFrame/Series, optional
+        List of colors to label for either the rows or columns. Useful to evaluate
+        whether samples within a group are clustered together. Can use nested lists or
+        DataFrame for multiple color levels of labeling. If given as a
+        :class:`pandas.DataFrame` or :class:`pandas.Series`, labels for the colors are
+        extracted from the DataFrames column names or from the name of the Series.
+        DataFrame/Series colors are also matched to the data by their index, ensuring
+        colors are drawn in the correct order.
+    mask : bool array or DataFrame, optional
+        If passed, data will not be shown in cells where `mask` is True.
+        Cells with missing values are automatically masked. Only used for
+        visualizing, not for calculating.
+    {dendrogram,colors}_ratio : float, or pair of floats, optional
+        Proportion of the figure size devoted to the two marginal elements. If
+        a pair is given, they correspond to (row, col) ratios.
+    cbar_pos : tuple of (left, bottom, width, height), optional
+        Position of the colorbar axes in the figure. Setting to ``None`` will
+        disable the colorbar.
+    tree_kws : dict, optional
+        Parameters for the :class:`matplotlib.collections.LineCollection`
+        that is used to plot the lines of the dendrogram tree.
+    kwargs : other keyword arguments
+        All other keyword arguments are passed to :func:`heatmap`.
+
+    Returns
+    -------
+    :class:`ClusterGrid`
+        A :class:`ClusterGrid` instance.
+
+    See Also
+    --------
+    heatmap : Plot rectangular data as a color-encoded matrix.
+
+    Notes
+    -----
+    The returned object has a ``savefig`` method that should be used if you
+    want to save the figure object without clipping the dendrograms.
+
+    To access the reordered row indices, use:
+    ``clustergrid.dendrogram_row.reordered_ind``
+
+    Column indices, use:
+    ``clustergrid.dendrogram_col.reordered_ind``
+
+    Examples
+    --------
+
+    .. include:: ../docstrings/clustermap.rst
+
+    """
+    if _no_scipy:
+        raise RuntimeError("clustermap requires scipy to be available")
+
+    plotter = ClusterGrid(data, pivot_kws=pivot_kws, figsize=figsize,
+                          row_colors=row_colors, col_colors=col_colors,
+                          z_score=z_score, standard_scale=standard_scale,
+                          mask=mask, dendrogram_ratio=dendrogram_ratio,
+                          colors_ratio=colors_ratio, cbar_pos=cbar_pos)
+
+    return plotter.plot(metric=metric, method=method,
+                        colorbar_kws=cbar_kws,
+                        row_cluster=row_cluster, col_cluster=col_cluster,
+                        row_linkage=row_linkage, col_linkage=col_linkage,
+                        tree_kws=tree_kws, **kwargs)
diff --git a/shared/package-files/1b/1b575ced6ebf9f3ca1d4b49c6acd0f2aa44a997ffa112e85087f727bc99ca11a/overrides.py b/shared/package-files/1b/1b575ced6ebf9f3ca1d4b49c6acd0f2aa44a997ffa112e85087f727bc99ca11a/overrides.py
new file mode 100644
index 0000000000000000000000000000000000000000..8076802e48a7ca9a11340045ea516f243bc6ad05
--- /dev/null
+++ b/shared/package-files/1b/1b575ced6ebf9f3ca1d4b49c6acd0f2aa44a997ffa112e85087f727bc99ca11a/overrides.py
@@ -0,0 +1,1973 @@
+"""
+Python implementation of ``__torch_function__``
+
+While most of the torch API and handling for ``__torch_function__`` happens
+at the C++ level, some of the torch API is written in Python so we need
+python-level handling for ``__torch_function__`` overrides as well. The main
+developer-facing functionality in this file are handle_torch_function and
+has_torch_function. See torch/functional.py and test/test_overrides.py
+for usage examples.
+
+Note
+----
+heavily inspired by NumPy's ``__array_function__`` (see:
+https://github.com/pytorch/pytorch/issues/24015 and
+https://www.numpy.org/neps/nep-0018-array-function-protocol.html
+)
+
+If changing this file in a way that can affect ``__torch_function__`` overhead,
+please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
+instructions in the ``README.md`` in that directory.
+"""
+
+import __future__  # noqa: F404
+
+import collections
+import functools
+import types
+import warnings
+from typing import Dict, Set, List, Any, Callable, Iterable, Type, Tuple
+from functools import wraps
+import contextlib
+
+import torch
+from torch._C import (
+    _has_torch_function, _has_torch_function_unary,
+    _has_torch_function_variadic, _add_docstr,
+    _push_on_torch_function_stack, _pop_torch_function_stack, _get_function_stack_at, _len_torch_function_stack,
+    _is_torch_function_mode_enabled)
+
+__all__ = [
+    "get_ignored_functions",
+    "get_overridable_functions",
+    "get_testing_overrides",
+    "handle_torch_function",
+    "has_torch_function",
+    "resolve_name",
+    "is_tensor_like",
+    "is_tensor_method_or_property",
+    "wrap_torch_function",
+    "enable_reentrant_dispatch",
+]
+
+
+def _disable_user_warnings(
+        func: Callable, regex: str = '.*is deprecated, please use.*', module: str = 'torch') -> Callable:
+    """
+    Decorator that temporarily disables ``UserWarning``s for the given ``module`` if the warning message matches the
+    given ``regex`` pattern.
+
+    Arguments
+    ---------
+    func : function
+        Function to disable the warnings for.
+    regex : str
+        A regex pattern compilable by ``re.compile``. This is used to match the ``UserWarning`` message.
+    module : str
+        The python module to which the filtering should be restricted.
+
+    Returns
+    -------
+    function
+        The wrapped function.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning, message=regex, module=module)
+            return func(*args, **kwargs)
+    return wrapper
+
+
+@functools.lru_cache(None)
+@_disable_user_warnings
+def get_ignored_functions() -> Set[Callable]:
+    """
+    Return public functions that cannot be overridden by ``__torch_function__``.
+
+    Returns
+    -------
+    Set[Callable]
+        A tuple of functions that are publicly available in the torch API but cannot
+        be overridden with ``__torch_function__``. Mostly this is because none of the
+        arguments of these functions are tensors or tensor-likes.
+
+    Examples
+    --------
+    >>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions()
+    True
+    >>> torch.add in torch.overrides.get_ignored_functions()
+    False
+    """
+    Tensor = torch.Tensor
+    return {
+        torch.typename,
+        torch.is_tensor,
+        torch.is_storage,
+        torch.set_default_tensor_type,
+        torch.set_default_device,
+        torch.get_default_device,
+        torch.set_rng_state,
+        torch.get_rng_state,
+        torch.manual_seed,
+        torch.initial_seed,
+        torch.seed,
+        torch.save,
+        torch.load,
+        torch.set_printoptions,
+        torch.fork,
+        torch.get_default_dtype,
+        torch.get_num_interop_threads,
+        torch.get_num_threads,
+        torch.init_num_threads,
+        torch.import_ir_module,
+        torch.import_ir_module_from_buffer,
+        torch.is_anomaly_enabled,
+        torch.is_anomaly_check_nan_enabled,
+        torch.is_grad_enabled,
+        torch.merge_type_from_type_comment,
+        torch.parse_ir,
+        torch.parse_schema,
+        torch.parse_type_comment,
+        torch.set_anomaly_enabled,
+        torch.set_flush_denormal,
+        torch.set_num_interop_threads,
+        torch.set_num_threads,
+        torch.wait,
+        torch.as_tensor,
+        torch.from_numpy,
+        torch.get_device,
+        torch.tensor,
+        torch.default_generator,
+        torch.has_cuda,
+        torch.has_cudnn,
+        torch.has_lapack,
+        torch.device,
+        torch.dtype,
+        torch.finfo,
+        torch.has_mkl,
+        torch.has_mps,
+        torch.has_mkldnn,
+        torch.has_openmp,
+        torch.iinfo,
+        torch.memory_format,
+        torch.qscheme,
+        torch.set_grad_enabled,
+        torch.no_grad,
+        torch.enable_grad,
+        torch.inference_mode,
+        torch.is_inference_mode_enabled,
+        torch.layout,
+        torch.align_tensors,
+        torch.arange,
+        torch.as_strided,
+        torch.bartlett_window,
+        torch.blackman_window,
+        torch.broadcast_shapes,
+        torch.can_cast,
+        torch.compile,
+        torch.cudnn_affine_grid_generator,
+        torch.cudnn_batch_norm,
+        torch.cudnn_convolution,
+        torch.cudnn_convolution_transpose,
+        torch.cudnn_convolution_relu,
+        torch.cudnn_convolution_add_relu,
+        torch.cudnn_grid_sampler,
+        torch.cudnn_is_acceptable,
+        torch.empty,
+        torch.empty_permuted,
+        torch.empty_strided,
+        torch.empty_quantized,
+        torch.export.dynamic_dim,
+        torch.export.export,
+        torch.export.load,
+        torch.export.register_dataclass,
+        torch.export.save,
+        torch.eye,
+        torch.fft.fftfreq,
+        torch.fft.rfftfreq,
+        torch.from_file,
+        torch.full,
+        torch.fill,
+        torch.hamming_window,
+        torch.hann_window,
+        torch.kaiser_window,
+        torch.linspace,
+        torch.logspace,
+        torch.mkldnn_adaptive_avg_pool2d,
+        torch.mkldnn_convolution,
+        torch.mkldnn_max_pool2d,
+        torch.mkldnn_max_pool3d,
+        torch.mkldnn_linear_backward_weights,
+        torch.mkldnn_rnn_layer,
+        torch.normal,
+        torch.ones,
+        torch.promote_types,
+        torch.rand,
+        torch.randn,
+        torch.randint,
+        torch.randperm,
+        torch.range,
+        torch.result_type,
+        torch.scalar_tensor,
+        torch.sparse_coo_tensor,
+        torch.sparse_compressed_tensor,
+        torch.sparse_csr_tensor,
+        torch.sparse_csc_tensor,
+        torch.sparse_bsr_tensor,
+        torch.sparse_bsc_tensor,
+        torch.sym_constrain_range,
+        torch.sym_constrain_range_for_size,
+        torch.tril_indices,
+        torch.triu_indices,
+        torch.vander,
+        torch.zeros,
+        torch._jit_internal.boolean_dispatch,
+        torch.nn.functional.assert_int_or_pair,
+        torch.nn.functional.upsample,
+        torch.nn.functional.upsample_bilinear,
+        torch.nn.functional.upsample_nearest,
+        torch.nn.functional.has_torch_function,
+        torch.nn.functional.has_torch_function_unary,
+        torch.nn.functional.has_torch_function_variadic,
+        torch.nn.functional.handle_torch_function,
+        torch.nn.functional.sigmoid,
+        torch.nn.functional.hardsigmoid,
+        torch.nn.functional.tanh,
+        torch.nn.functional._canonical_mask,
+        torch.nn.functional._none_or_dtype,
+        # Doesn't actually take or return tensor arguments
+        torch.nn.init.calculate_gain,
+        # These are deprecated; don't test them
+        torch.nn.init.uniform,
+        torch.nn.init.normal,
+        torch.nn.init.constant,
+        torch.nn.init.eye,
+        torch.nn.init.dirac,
+        torch.nn.init.xavier_uniform,
+        torch.nn.init.xavier_normal,
+        torch.nn.init.kaiming_uniform,
+        torch.nn.init.kaiming_normal,
+        torch.nn.init.orthogonal,
+        torch.nn.init.sparse,
+        torch.nested.to_padded_tensor,
+        has_torch_function,
+        handle_torch_function,
+        torch.set_autocast_enabled,
+        torch.is_autocast_enabled,
+        torch.clear_autocast_cache,
+        torch.set_autocast_cpu_enabled,
+        torch.is_autocast_cpu_enabled,
+        torch.set_autocast_xla_enabled,
+        torch.is_autocast_xla_enabled,
+        torch.set_autocast_ipu_enabled,
+        torch.is_autocast_ipu_enabled,
+        torch.set_autocast_cpu_dtype,
+        torch.get_autocast_cpu_dtype,
+        torch.set_autocast_ipu_dtype,
+        torch.get_autocast_ipu_dtype,
+        torch.get_autocast_gpu_dtype,
+        torch.set_autocast_gpu_dtype,
+        torch.get_autocast_xla_dtype,
+        torch.set_autocast_xla_dtype,
+        torch.autocast_increment_nesting,
+        torch.autocast_decrement_nesting,
+        torch.is_autocast_cache_enabled,
+        torch.set_autocast_cache_enabled,
+        torch.nn.functional.hardswish,
+        torch.is_vulkan_available,
+        torch.are_deterministic_algorithms_enabled,
+        torch.use_deterministic_algorithms,
+        torch.is_deterministic_algorithms_warn_only_enabled,
+        torch.set_deterministic_debug_mode,
+        torch.get_deterministic_debug_mode,
+        torch.set_float32_matmul_precision,
+        torch.get_float32_matmul_precision,
+        torch.unify_type_list,
+        torch.is_warn_always_enabled,
+        torch.set_warn_always,
+        torch.vitals_enabled,
+        torch.set_vital,
+        torch.read_vitals,
+        torch.vmap,
+        torch.cond,
+        torch.frombuffer,
+        torch.asarray,
+        torch._functional_sym_constrain_range,
+        torch._make_dep_token,
+        Tensor.__delitem__,
+        Tensor.__dir__,
+        Tensor.__getattribute__,
+        Tensor.__init__,
+        Tensor.__iter__,
+        Tensor.__init_subclass__,
+        Tensor.__delattr__,
+        Tensor.__setattr__,
+        Tensor.__torch_function__,
+        Tensor.__torch_dispatch__,
+        Tensor.__new__,
+        Tensor.__class__,
+        Tensor.__subclasshook__,
+        Tensor.__hash__,
+        Tensor.as_subclass,
+        Tensor.eig,
+        Tensor.lstsq,
+        Tensor.reinforce,
+        Tensor.new,
+        Tensor.new_tensor,
+        Tensor.new_empty,
+        Tensor.new_empty_strided,
+        Tensor.new_zeros,
+        Tensor.new_ones,
+        Tensor.new_full,
+        Tensor._make_subclass,
+        Tensor.solve,
+        Tensor.symeig,
+        Tensor.stride,
+        Tensor.unflatten,
+        Tensor.to_sparse_coo,
+        Tensor.to_sparse_csr,
+        Tensor.to_sparse_csc,
+        Tensor.to_sparse_bsr,
+        Tensor.to_sparse_bsc,
+        Tensor._to_sparse,
+        Tensor._to_sparse_csr,
+        Tensor._to_sparse_csc,
+        Tensor._to_sparse_bsr,
+        Tensor._to_sparse_bsc,
+        Tensor._typed_storage,
+        Tensor._reduce_ex_internal,
+        Tensor._fix_weakref,
+        Tensor._view_func,
+        Tensor._view_func_unsafe,
+        Tensor._rev_view_func_unsafe,
+        Tensor._make_wrapper_subclass,
+        Tensor._python_dispatch.__get__,
+        Tensor._has_symbolic_sizes_strides.__get__,
+        Tensor._conj,
+        Tensor._conj_physical,
+        Tensor._lazy_clone,
+        Tensor._neg_view,
+        Tensor._is_zerotensor,
+        Tensor._is_all_true,
+        Tensor._is_any_true,
+        Tensor._addmm_activation,
+        Tensor.to_padded_tensor,
+    }
+
+
+@functools.lru_cache(None)
+def get_default_nowrap_functions() -> Set[Callable]:
+    """
+    Return public functions that do not wrap in a subclass when invoked by
+    the default ``Tensor.__torch_function__`` that preserves subclasses.  Typically,
+    these functions represent field accesses (i.e., retrieving a Tensor that
+    is stored somewhere on the Tensor) as opposed to computation.  Users of
+    these functions expect object identity to be preserved over multiple accesses
+    (e.g., ``a.grad is a.grad``) which cannot be upheld if we're wrapping on
+    the fly every time (furthermore, the tensor stored here might already be
+    the subclass, in which case wrapping really ought not to happen).
+
+    Not ALL property accessors have this property; for example ``Tensor.T`` actually
+    just creates a new transposed tensor on the fly, and so we SHOULD interpose on
+    these calls (you need to check the implementation of the function to see if
+    this is the case or not).  Additionally, if a property accessor doesn't return a Tensor,
+    it doesn't have to be on this list (though it is harmless if it is).
+    """
+    Tensor = torch.Tensor
+    return {
+        Tensor._base.__get__,
+        Tensor.grad.__get__,
+        Tensor._grad.__get__,
+    }
+
+
+@functools.lru_cache(None)
+@_disable_user_warnings
+def get_testing_overrides() -> Dict[Callable, Callable]:
+    """Return a dict containing dummy overrides for all overridable functions
+
+    Returns
+    -------
+    Dict[Callable, Callable]
+        A dictionary that maps overridable functions in the PyTorch API to
+        lambda functions that have the same signature as the real function
+        and unconditionally return -1. These lambda functions are useful
+        for testing API coverage for a type that defines ``__torch_function__``.
+
+    Examples
+    --------
+    >>> import inspect
+    >>> my_add = torch.overrides.get_testing_overrides()[torch.add]
+    >>> inspect.signature(my_add)
+    <Signature (input, other, out=None)>
+    """
+    # Every function in the PyTorchAPI that can be overriden needs an entry
+    # in this dict.
+    #
+    # Optimally we would use inspect to get the function signature and define
+    # the lambda function procedurally but that is blocked by generating
+    # function signatures for native kernels that can be consumed by inspect.
+    # See Issue #28233.
+    Tensor = torch.Tensor
+    ret: Dict[Callable, Callable] = {
+        torch.abs: lambda input, out=None: -1,
+        torch.absolute: lambda input, out=None: -1,
+        torch.adaptive_avg_pool1d: lambda input, output_size: -1,
+        torch.adaptive_max_pool1d: lambda inputs, output_size: -1,
+        torch.acos: lambda input, out=None: -1,
+        torch.adjoint: lambda input: -1,
+        torch.arccos: lambda input, out=None: -1,
+        torch.acosh: lambda input, out=None: -1,
+        torch.arccosh: lambda input, out=None: -1,
+        torch.add: lambda input, other, out=None: -1,
+        torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
+        torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1,
+        torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1,
+        torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
+        torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1,
+        torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1,
+        torch.affine_grid_generator: lambda theta, size, align_corners: -1,
+        torch.all: lambda input, dim=None: -1,
+        torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1,
+        torch.alpha_dropout: lambda input, p, train, inplace=False: -1,
+        torch.amax: lambda input, dim=None: -1,
+        torch.amin: lambda input, dim=None: -1,
+        torch.aminmax: lambda input, dim=None, keepdim=False, out=None: -1,
+        torch.angle: lambda input, out=None: -1,
+        torch.any: lambda input, dim=None, keepdim=False, out=None: -1,
+        torch.argmax: lambda input: -1,
+        torch.argmin: lambda input: -1,
+        torch.argsort: lambda input, dim=None: -1,
+        torch.asin: lambda input, out=None: -1,
+        torch._assert_async: lambda input, msg: -1,
+        torch.arcsin: lambda input, out=None: -1,
+        torch.asinh: lambda input, out=None: -1,
+        torch.arcsinh: lambda input, out=None: -1,
+        torch.atan: lambda input, out=None: -1,
+        torch.arctan: lambda input, out=None: -1,
+        torch.atan2: lambda input, other, out=None: -1,
+        torch.arctan2: lambda input, other, out=None: -1,
+        torch.atanh: lambda input, out=None: -1,
+        torch.arctanh: lambda input, out=None: -1,
+        torch.atleast_1d: lambda *tensors: -1,
+        torch.atleast_2d: lambda *tensors: -1,
+        torch.atleast_3d: lambda *tensors: -1,
+        torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1,
+        torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
+        torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1,
+        torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count_tensor: -1,
+        torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1,
+        torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1,
+        torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
+        torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
+        torch.batch_norm_stats: lambda input, eps: -1,
+        torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1,
+        torch.bernoulli: lambda input, generator=None, out=None: -1,
+        torch.bilinear: lambda input1, input2, weight, bias: -1,
+        torch.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, reduce=None,
+                                                 reduction='mean', pos_weight=None: -1),
+        torch.bincount: lambda input, weights=None, minlength=0: -1,
+        torch.binomial: lambda count, prob, generator=None: -1,
+        torch.bitwise_and: lambda input, other, out=None: -1,
+        torch.bitwise_not: lambda input, out=None: -1,
+        torch.bitwise_or: lambda input, other, out=None: -1,
+        torch.bitwise_xor: lambda input, other, out=None: -1,
+        torch.bitwise_left_shift: lambda input, other, out=None: -1,
+        torch.bitwise_right_shift: lambda input, other, out=None: -1,
+        torch.block_diag: lambda *tensors: -1,
+        torch.bmm: lambda input, mat2, out=None: -1,
+        torch.broadcast_tensors: lambda *tensors: -1,
+        torch.broadcast_to: lambda self, size: -1,
+        torch.bucketize: lambda input, boundaries, out_int32=False, right=False, out=None: -1,
+        torch.cartesian_prod: lambda *tensors: -1,
+        torch.cat: lambda tensors, dim=0, out=None: -1,
+        torch.concat: lambda tensors, dim=0, out=None: -1,  # alias for torch.cat
+        torch.concatenate: lambda tensors, dim=0, out=None: -1,  # alias for torch.concatenate
+        torch.cdist: lambda x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary': -1,
+        torch.ceil: lambda input, out=None: -1,
+        torch.celu: lambda input, alpha=1., inplace=False: -1,
+        torch.chain_matmul: lambda *matrices, out=None: -1,
+        torch.channel_shuffle: lambda input, groups : -1,
+        torch.cholesky: lambda input, upper=False, out=None: -1,
+        torch.linalg.cholesky: lambda input, out=None: -1,
+        torch.linalg.cholesky_ex: lambda input, check_errors=False, out=None: -1,
+        torch.cholesky_inverse: lambda input, upper=False, out=None: -1,
+        torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1,
+        torch.choose_qparams_optimized: lambda input, numel, n_bins, ratio, bit_width: -1,
+        torch.chunk: lambda input, chunks, dim=0: -1,
+        torch.clamp: lambda input, min=None, max=None, out=None: -1,
+        torch.clip: lambda input, min=None, max=None, out=None: -1,
+        torch.clamp_min: lambda input, min, out=None: -1,
+        torch.clamp_max: lambda input, max, out=None: -1,
+        torch.column_stack: lambda tensors, out=None: -1,
+        torch.cov: lambda input, correction=1, fweights=None, aweights=None: -1,
+        torch.clone: lambda input: -1,
+        torch.combinations: lambda input, r=2, with_replacement=False: -1,
+        torch.complex: lambda real, imag: -1,
+        torch.copysign: lambda input, other, out=None: -1,
+        torch.polar: lambda abs, ang: -1,
+        torch.linalg.cond: lambda input, ord=None: -1,
+        torch.conj: lambda input, out=None: -1,
+        torch.conj_physical: lambda input, out=None: -1,
+        torch.resolve_conj: lambda input, out=None: -1,
+        torch.resolve_neg: lambda input, out=None: -1,
+        torch.constant_pad_nd: lambda input, pad, value=0: -1,
+        torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
+        torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
+        torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
+        torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1,
+        torch.conv_tbc: lambda input, weight, bias, pad=0: -1,
+        torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
+        torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
+        torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
+        torch.corrcoef: lambda input: -1,
+        torch.cos: lambda input, out=None: -1,
+        torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,
+        torch.cosh: lambda input, out=None: -1,
+        torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1,
+        torch.count_nonzero: lambda input: -1,
+        torch.cross: lambda input, other, dim=None, out=None: -1,
+        torch.linalg.cross: lambda input, other, dim=-1, out=None: -1,
+        torch.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean',
+                         zero_infinity=False: -1),
+        torch.cummax: lambda input, dim, out=None: -1,
+        torch.cummin: lambda input, dim, out=None: -1,
+        torch.cumprod: lambda input, dim, out=None, dtype=None: -1,
+        torch.cumsum: lambda input, dim, out=None, dtype=None: -1,
+        torch.cumulative_trapezoid: lambda y, x=None, dim=-1: -1,
+        torch.logcumsumexp: lambda input, dim, out=None: -1,
+        torch.deg2rad: lambda input, out=None: -1,
+        torch.dequantize: lambda input: -1,
+        torch.det: lambda input: -1,
+        torch.linalg.det: lambda input: -1,  # alias for torch.det  # type: ignore[attr-defined]
+        torch.detach: lambda input: -1,
+        torch.diag: lambda input, diagonal=0, out=None: -1,
+        torch.diag_embed: lambda input, diagonal=0, out=None: -1,
+        torch.diagflat: lambda input, offset=0: -1,
+        torch.diff: lambda input, n=1, dim=-1, prepend=None, append=None, out=None: -1,
+        torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
+        torch.linalg.diagonal: lambda input, offset=0, dim1=-2, dim2=-1: -1,
+        torch.diagonal_scatter: lambda input, src, offset=0, dim1=0, dim2=1: -1,
+        torch.as_strided_scatter: lambda self, src, size, stride, storage_offset=None: -1,
+        torch.digamma: lambda input, out=None: -1,
+        torch.dist: lambda input, other, p=2: -1,
+        torch.div: lambda input, other, rounding_mode=None, out=None: -1,
+        torch.divide: lambda input, other, rounding_mode=None, out=None: -1,
+        torch.dot: lambda input, other, out=None: -1,
+        torch.dropout: lambda input, p, train, inplace=False: -1,
+        torch.dsmm: lambda input, mat2: -1,
+        torch.hsmm: lambda mat1, mat2: -1,
+        torch.dsplit: lambda input, indices_or_sections: -1,
+        torch.dstack: lambda tensors, out=None: -1,
+        torch.linalg.eig: lambda input, out=None: -1,
+        torch.linalg.eigvals: lambda input, out=None: -1,
+        torch.linalg.eigh: lambda input, UPLO="L", out=None: -1,
+        torch.linalg.eigvalsh: lambda input, UPLO="L", out=None: -1,
+        torch.einsum: lambda equation, *operands: -1,
+        torch.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
+                          sparse=False: -1),
+        torch.embedding_bag: (lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False,
+                              mode='mean', sparse=False, per_sample_weights=None, padding_idx=None: -1),
+        torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
+        torch.eq: lambda input, other, out=None: -1,
+        torch.equal: lambda input, other: -1,
+        torch.erf: lambda input, out=None: -1,
+        torch.erfc: lambda input, out=None: -1,
+        torch.erfinv: lambda input, out=None: -1,
+        torch.exp: lambda input, out=None: -1,
+        torch.exp2: lambda input, out=None: -1,
+        torch.expm1: lambda input, out=None: -1,
+        torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1,
+        torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1,
+        torch.fused_moving_avg_obs_fake_quant: (lambda x, observer_on, fake_quant_on, averaging_const, running_min,
+                                                running_max, scale, zero_point, quant_min, quant_max, ch_axis,
+                                                per_row_fake_quant=False, symmetric_quant=False: -1),
+        torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1,
+        torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1,
+        torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1,
+        torch.fbgemm_linear_int8_weight_fp32_activation: (lambda input, weight, packed, col_offsets, weight_scale,
+                                                          weight_zero_point, bias: -1),
+        torch.fbgemm_linear_quantize_weight: lambda input: -1,
+        torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1,
+        torch.fbgemm_pack_quantized_matrix: lambda input, a, b: -1,
+        torch.feature_alpha_dropout: lambda input, p, train: -1,
+        torch.feature_dropout: lambda input, p, train: -1,
+        torch.fft.ifft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fft.rfft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fft.irfft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fft.hfft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fft.ihfft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fft.hfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.ihfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.hfftn: lambda input, s=None, dim=-1, norm=None: -1,
+        torch.fft.ihfftn: lambda input, s=None, dim=-1, norm=None: -1,
+        torch.fft.fftn: lambda input, s=None, dim=None, norm=None: -1,
+        torch.fft.ifftn: lambda input, s=None, dim=None, norm=None: -1,
+        torch.fft.rfftn: lambda input, s=None, dim=None, norm=None: -1,
+        torch.fft.irfftn: lambda input, s=None, dim=None, norm=None: -1,
+        torch.fft.fft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.ifft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.rfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.irfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
+        torch.fft.fftshift: lambda input, dim=None: -1,
+        torch.fft.ifftshift: lambda input, dim=None: -1,
+        torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
+        torch.fix: lambda input, out=None: -1,
+        torch.flatten: lambda input, start_dim=0, end_dim=-1: -1,
+        torch.flip: lambda input, dims: -1,
+        torch.fliplr: lambda input: -1,
+        torch.flipud: lambda input: -1,
+        torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1,
+        torch.floor: lambda input, out=None: -1,
+        torch.floor_divide: lambda input, other: -1,
+        torch.float_power: lambda input, exponent, out=None: -1,
+        torch.fmod: lambda input, other, out=None: -1,
+        torch.frac: lambda input, out=None: -1,
+        torch.frexp: lambda input, out=None: -1,
+        torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
+        torch._functional_assert_async: lambda input, msg, dep_token: -1,
+        torch.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1,
+        torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1,
+        torch.gcd: lambda input, other, out=None: -1,
+        torch.ge: lambda input, other, out=None: -1,
+        torch.greater_equal: lambda input, other, out=None: -1,
+        torch.geqrf: lambda input, out=None: -1,
+        torch.i0: lambda input, out=None: -1,
+        torch.inner: lambda input, other, out=None: -1,
+        torch.outer: lambda input, vec2, out=None: -1,
+        torch.ger: lambda input, vec2, out=None: -1,  # alias for torch.outer
+        torch.gradient: lambda input, spacing=None, dim=None, edge_order=1: -1,
+        torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
+        torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
+        torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
+        torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1,
+        torch.gru: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
+        torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
+        torch.gt: lambda input, other, out=None: -1,
+        torch.greater: lambda input, other, out=None: -1,
+        torch.hardshrink: lambda input, lambd=0.5: -1,
+        torch.heaviside: lambda input, values, out=None: -1,
+        torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction='mean': -1,
+        torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1,
+        torch.histogram: lambda input, bins=100, min=None, max=None, weight=None, density=False, out=None: -1,
+        torch.histogramdd: lambda input, bins, range=None, weight=None, density=False: -1,
+        torch.linalg.householder_product: lambda input, tau: -1,
+        torch.hspmm: lambda mat1, mat2, out=None: -1,
+        torch.hsplit: lambda input, indices_or_sections: -1,
+        torch.hstack: lambda tensors, out=None: -1,
+        torch.hypot: lambda input, other, out=None: -1,
+        torch.igamma: lambda input, other, out=None: -1,
+        torch.igammac: lambda input, other, out=None: -1,
+        torch.imag: lambda input, out=None: -1,
+        torch.index_add: lambda input, dim, index, source: -1,
+        torch.index_copy: lambda input, dim, index, source: -1,
+        torch.index_put: lambda input, indices, values, accumulate=False: -1,
+        torch.index_select: lambda input, dim, index, out=None: -1,
+        torch.index_fill: lambda input, dim, index, value: -1,
+        torch.index_reduce: lambda input, dim, index, source, reduce, include_input=True: -1,
+        torch.isfinite: lambda tensor: -1,
+        torch.isin: lambda e, te, assume_unique=False, invert=False: -1,
+        torch.isinf: lambda tensor: -1,
+        torch.isreal: lambda tensor: -1,
+        torch.isposinf: lambda input, out=None: -1,
+        torch.isneginf: lambda input, out=None: -1,
+        torch.instance_norm: (lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps,
+                              cudnn_enabled: -1),
+        torch.int_repr: lambda input: -1,
+        torch.inverse: lambda input, out=None: -1,
+        torch.linalg.inv: lambda input, out=None: -1,
+        torch.linalg.inv_ex: lambda input, check_errors=False, out=None: -1,
+        torch.is_complex: lambda input: -1,
+        torch.is_conj: lambda input: -1,
+        torch.is_neg: lambda input: -1,
+        torch.is_distributed: lambda input: -1,
+        torch.is_inference: lambda input: -1,
+        torch.is_floating_point: lambda input: -1,
+        torch.is_nonzero: lambda input: -1,
+        torch.is_same_size: lambda input, other: -1,
+        torch.is_signed: lambda input: -1,
+        torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1,
+        torch.isnan: lambda input: -1,
+        torch.istft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
+                      normalized=False, onesided=None, length=None, return_complex=False: -1),
+        torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
+        torch.kron: lambda input, other: -1,
+        torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
+        torch.linalg.ldl_factor_ex: lambda input, hermitian=False, check_errors=False, out=None: -1,
+        torch.linalg.ldl_factor: lambda input, hermitian=False, out=None: -1,
+        torch.linalg.ldl_solve: lambda LD, pivots, B, hermitian=False, out=None: -1,
+        torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
+        torch.lcm: lambda input, other, out=None: -1,
+        torch.ldexp: lambda input, other, out=None: -1,
+        torch.le: lambda input, other, out=None: -1,
+        torch.less_equal: lambda input, other, out=None: -1,
+        torch.lerp: lambda input, end, weight, out=None: -1,
+        torch.lgamma: lambda input, out=None: -1,
+        torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None,
+        tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1,
+        torch.log: lambda input, out=None: -1,
+        torch.log_softmax: lambda input, dim, dtype=None: -1,
+        torch.log10: lambda input, out=None: -1,
+        torch.log1p: lambda input, out=None: -1,
+        torch.log2: lambda input, out=None: -1,
+        torch.logaddexp: lambda input, other, out=None: -1,
+        torch.logaddexp2: lambda input, other, out=None: -1,
+        torch.logdet: lambda input: -1,
+        torch.xlogy: lambda x, y, out=None: -1,
+        torch.logical_and: lambda input, other, out=None: -1,
+        torch.logical_not: lambda input, out=None: -1,
+        torch.logical_or: lambda input, other, out=None: -1,
+        torch.logical_xor: lambda input, other, out=None: -1,
+        torch.logit: lambda input, eps=None: -1,
+        torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
+        torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
+        torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
+        torch.lt: lambda input, other, out=None: -1,
+        torch.less: lambda input, other, out=None: -1,
+        torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
+        torch.lu_solve: lambda b, LU_data, LU_pivots, out=None: -1,
+        torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,  # type: ignore[attr-defined]  # noqa: B950
+        torch.masked_fill: lambda input, mask, value: -1,
+        torch.masked_scatter: lambda input, mask, source: -1,
+        torch.masked_select: lambda input, mask, out=None: -1,
+        torch.matmul: lambda input, other, out=None: -1,
+        torch.linalg.lu: lambda input, pivot=True, out=None: -1,
+        torch.linalg.lu_factor: lambda input, pivot=True, out=None: -1,
+        torch.linalg.lu_factor_ex: lambda input, pivot=True, check_errors=False, out=None: -1,
+        torch.linalg.lu_solve: lambda LU, pivots, B, left=True, adjoint=False, out=None: -1,
+        torch.linalg.matmul: lambda input, other, out=None: -1,  # alias for torch.matmul
+        torch.matrix_power: lambda input, n: -1,
+        torch.linalg.matrix_power: lambda input, n, out=None: -1,
+        torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1,
+        torch.linalg.multi_dot: lambda tensors, out=None: -1,
+        torch.matrix_exp: lambda input: -1,
+        torch.linalg.matrix_exp: lambda input: -1,
+        torch.max: lambda input, out=None: -1,
+        torch.maximum: lambda input, other, out=None: -1,
+        torch.fmax: lambda input, other, out=None: -1,
+        torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
+        torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
+        torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
+        torch.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                        return_indices=False, ceil_mode=False: -1),
+        torch.mean: lambda input, dim=None: -1,
+        torch.nanmean: lambda input, dim=None, keepdim=False, dtype=None, out=None: -1,
+        torch.median: lambda input, dim=None: -1,
+        torch.nanmedian: lambda input, dim=None: -1,
+        torch.meshgrid: lambda *tensors, **kwargs: -1,
+        torch.min: lambda input, out=None: -1,
+        torch.minimum: lambda input, other, out=None: -1,
+        torch.fmin: lambda input, other, out=None: -1,
+        torch.miopen_batch_norm: (lambda input, weight, bias, running_mean, running_var, training,
+                                  exponential_average_factor, epsilon: -1),
+        torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1,
+        torch.miopen_convolution_add_relu: lambda input, weight, z, alpha, bias, stride, padding, dilation, groups: -1,
+        torch.miopen_convolution_relu: lambda input, weight, bias, stride, padding, dilation, groups: -1,
+        torch.miopen_convolution_transpose: (lambda input, weight, bias, padding, output_padding, stride, dilation,
+                                             groups, benchmark, deterministic: -1),
+        torch.miopen_depthwise_convolution: (lambda input, weight, bias, padding, stride, dilation, groups, benchmark,
+                                             deterministic: -1),
+        torch.miopen_rnn: (lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first,
+                           dropout, train, bidirectional, batch_sizes, dropout_state: -1),
+        torch.mm: lambda input, mat2, out=None: -1,
+        torch.mode: lambda input, dim=-1, keepdim=False, out=None: -1,
+        torch.movedim: lambda input, source, destination: -1,
+        torch.moveaxis: lambda input, source, destination: -1,
+        torch.msort: lambda input, descending=False, out=None: -1,
+        torch.mul: lambda input, other, out=None: -1,
+        torch.multiply: lambda input, other, out=None: -1,
+        torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1,
+        torch.mv: lambda input, vec, out=None: -1,
+        torch.mvlgamma: lambda input, p: -1,
+        torch.narrow: lambda input, dim, start, length: -1,
+        torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1,
+        torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
+        torch._native_batch_norm_legit: lambda input, weight, bias, training, momentum, eps: -1,
+        torch.native_dropout: lambda input, p, train: -1,
+        torch.native_layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
+        torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1,
+        torch.native_norm: lambda input, p=2, dim=None, keepdim=False, dtype=None: -1,
+        torch.native_channel_shuffle: lambda input, groups : -1,
+        torch.ne: lambda input, other, out=None: -1,
+        torch.not_equal: lambda input, other, out=None: -1,
+        torch.neg: lambda input, out=None: -1,
+        torch.negative: lambda input, out=None: -1,
+        torch.nextafter: lambda input, other, out=None: -1,
+        torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1,
+        torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1,
+        torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1,
+        torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1,
+        torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
+        torch.nn.functional.avg_pool2d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
+                                         count_include_pad=True, divisor_override=None: -1),
+        torch.nn.functional.avg_pool3d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
+                                         count_include_pad=True, divisor_override=None: -1),
+        torch.nn.functional.batch_norm: (lambda input, running_mean, running_var, weight=None, bias=None, training=False,
+                                         momentum=0.1, eps=1e-05: -1),
+        torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1,
+        torch.nn.functional.binary_cross_entropy: (lambda input, target, weight=None, size_average=None, reduce=None,
+                                                   reduction="mean": -1),
+        torch.nn.functional.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None,
+                                                               reduce=None, reduction="mean", pos_weight=None: -1),
+        torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1,
+        torch.nn.functional.cosine_embedding_loss: (lambda input1, input2, target, margin=0, size_average=None,
+                                                    reduce=None, reduction='mean': -1),
+        torch.nn.functional.cross_entropy: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
+                                            reduce=None, reduction="mean", label_smoothing=0.0: -1),
+        torch.nn.functional.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0,
+                                       reduction='mean', zero_infinity=False: -1),
+        torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1,
+        torch.nn.functional.dropout1d: lambda input, p=0.5, training=True, inplace=False: -1,
+        torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1,
+        torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1,
+        torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1,
+        torch.nn.functional.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0,
+                                        scale_grad_by_freq=False, sparse=False: -1),
+        torch.nn.functional.embedding_bag: (lambda input, weight, offsets=None, max_norm=None, norm_type=2,
+                                            scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None,
+                                            include_last_offset=False, padding_idx=None: -1),
+        torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
+        torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1,
+        torch.nn.functional.fractional_max_pool2d: (lambda input, kernel_size, output_size=None, output_ratio=None,
+                                                    return_indices=False, _random_samples=None: -1),
+        torch.nn.functional.fractional_max_pool2d_with_indices: (
+            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
+            _random_samples=None: -1),
+        torch.nn.functional.fractional_max_pool3d: (lambda input, kernel_size, output_size=None, output_ratio=None,
+                                                    return_indices=False, _random_samples=None: -1),
+        torch.nn.functional.fractional_max_pool3d_with_indices: (
+            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
+            _random_samples=None: -1),
+        torch.nn.functional.gaussian_nll_loss: lambda input, target, var, full=False, eps=1e-06, reduction='mean': -1,
+        torch.nn.functional.gelu: lambda input, approximate='none': -1,
+        torch.nn.functional.glu: lambda input, dim=-1: -1,
+        torch.nn.functional.grid_sample: lambda input, grid, mode='bilinear', padding_mode='zeros', align_corners=None: -1,
+        torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1,
+        torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1,
+        torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1,
+        torch.nn.functional.hardtanh: lambda input, min_val=-1., max_val=1., inplace=False: -1,
+        torch.nn.functional.hinge_embedding_loss: (lambda input, target, margin=1.0, size_average=None, reduce=None,
+                                                   reduction='mean': -1),
+        torch.nn.functional.instance_norm: (lambda input, running_mean=None, running_var=None, weight=None, bias=None,
+                                            use_input_stats=True, momentum=0.1, eps=1e-05: -1),
+        torch.nn.functional.interpolate: (lambda input, size=None, scale_factor=None, mode='nearest', align_corners=None,
+                                          recompute_scale_factor=None, antialias=False: -1),
+        torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
+        torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
+        torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
+        torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1,
+        torch.nn.functional.linear: lambda input, weight, bias=None: -1,
+        torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1,
+        torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
+        torch.nn.functional.logsigmoid: lambda input: -1,
+        torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
+        torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
+        torch.nn.functional.lp_pool3d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
+        torch.nn.functional.margin_ranking_loss: (lambda input1, input2, target, margin=0, size_average=None,
+                                                  reduce=None, reduction='mean': -1),
+        torch.nn.functional.max_pool1d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                         ceil_mode=False, return_indices=False: -1),
+        torch.nn.functional.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                                      return_indices=False, ceil_mode=False: -1),
+        torch.nn.functional.max_pool2d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                         ceil_mode=False, return_indices=False: -1),
+        torch.nn.functional.max_pool2d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                                      return_indices=False, ceil_mode=False: -1),
+        torch.nn.functional.max_pool3d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                         return_indices=False, ceil_mode=False: -1),
+        torch.nn.functional.max_pool3d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
+                                                      return_indices=False, ceil_mode=False: -1),
+        torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
+        torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
+        torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
+        torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
+        torch.nn.functional.multi_head_attention_forward: (
+            lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v,
+            add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None,
+            need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None,
+            v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=None, is_causal=False: -1),
+        torch.nn.functional.multi_margin_loss: (lambda input, target, p=1, margin=1.0, weight=None, size_average=None,
+                                                reduce=None, reduction='mean': -1),
+        torch.nn.functional.multilabel_margin_loss: (lambda input, target, size_average=None, reduce=None,
+                                                     reduction='mean': -1),
+        torch.nn.functional.multilabel_soft_margin_loss: (lambda input, target, weight=None, size_average=None,
+                                                          reduce=None, reduction='mean': -1),
+        torch.nn.functional.nll_loss: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
+                                       reduce=None, reduction='mean': -1),
+        torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1,
+        torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1,
+        torch.nn.functional.pad: lambda input, pad, mode='constant', value=0: -1,
+        torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
+        torch.nn.functional.poisson_nll_loss: (lambda input, target, log_input=True, full=False, size_average=None,
+                                               eps=1e-08, reduce=None, reduction='mean': -1),
+        torch.nn.functional.prelu: lambda input, weight: -1,
+        torch.nn.functional.relu: lambda input, inplace=False: -1,
+        torch.nn.functional.relu6: lambda input, inplace=False: -1,
+        torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1,
+        torch.nn.functional.selu: lambda input, inplace=False: -1,
+        torch.nn.functional.silu: lambda input, inplace=False: -1,
+        torch.nn.functional.mish: lambda input, inplace=False: -1,
+        torch.nn.functional.scaled_dot_product_attention: lambda query, key, value, attn_mask=None, dropout_p=0.0: -1,
+        torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean', beta=1.: -1,
+        torch.nn.functional.huber_loss: lambda input, target, reduction='mean', delta=1.: -1,
+        torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
+        torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
+        torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
+        torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1,
+        torch.nn.functional.softshrink: lambda input, lambd=0.5: -1,
+        torch.nn.functional.softsign: lambda input: -1,
+        torch.nn.functional.tanhshrink: lambda input: -1,
+        torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1,
+        torch.nn.functional.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06,
+                                                  swap=False, size_average=None, reduce=None, reduction='mean': -1),
+        torch.nn.functional.triplet_margin_with_distance_loss: (lambda anchor, positive, negative, *,
+                                                                distance_function=None, margin=1.0,
+                                                                swap=False, reduction='mean': -1),
+        torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
+        torch.nn.init.uniform_: lambda tensor, a=0., b=1., generator=None: -1,
+        torch.nn.init.normal_: lambda tensor, mean=0., std=1., generator=None: -1,
+        torch.nn.init.constant_: lambda tensor, val: -1,
+        torch.nn.init.kaiming_uniform_: lambda tensor, a=0, mode='fan_in', nonlinearity='leaky_relu', generator=None: -1,
+        torch.nonzero: lambda input, as_tuple=False: -1,
+        torch.nonzero_static: lambda input, *, size, fill_value=-1: -1,
+        torch.argwhere: lambda input: -1,
+        torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
+        torch.linalg.norm: lambda input, ord=None, dim=None, keepdim=False, out=None, dtype=None: -1,
+        torch.linalg.vector_norm: lambda input, ord=2, dim=None, keepdim=False, out=None, dtype=None: -1,
+        torch.linalg.matrix_norm: lambda input, ord='fro', dim=(-2, -1), keepdim=False, out=None, dtype=None: -1,
+        torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
+        torch.nuclear_norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
+        torch.numel: lambda input: -1,
+        torch.orgqr: lambda input, tau: -1,
+        torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1,
+        torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
+        torch.permute: lambda self, dim: -1,
+        torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1,
+        torch.pdist: lambda input, p=2: -1,
+        torch.pinverse: lambda input, rcond=1e-15: -1,
+        torch.linalg.pinv: lambda input, rcond=1e-15, hermitian=False: -1,
+        torch.pixel_shuffle: lambda input, upscale_factor: -1,
+        torch.pixel_unshuffle: lambda input, downscale_factor: -1,
+        torch.poisson: lambda input, generator=None: -1,
+        torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1,
+        torch.polygamma: lambda input, n, out=None: -1,
+        torch.positive: lambda input, out=None: -1,
+        torch.prelu: lambda input, weight: -1,
+        torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
+        torch.pow: lambda input, exponent, out=None: -1,
+        torch.prod: lambda input, dtype=None: -1,
+        torch.put: lambda input, index, source, accumulate=False: -1,
+        torch.q_per_channel_axis: lambda input: -1,
+        torch.q_per_channel_scales: lambda input: -1,
+        torch.q_per_channel_zero_points: lambda input: -1,
+        torch.q_scale: lambda input: -1,
+        torch.q_zero_point: lambda input: -1,
+        torch.qr: lambda input, some=True, out=None: -1,
+        torch.linalg.qr: lambda input, mode='reduced', out=None: -1,
+        torch.quantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
+        torch.nanquantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
+        torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
+        torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1,
+        torch.quantize_per_tensor_dynamic: lambda input, dtype, reduce_range: -1,
+        torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1,
+        torch.quantized_gru_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
+                                   col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
+
+        torch.quantized_lstm_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
+                                    col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
+        torch.quantized_max_pool1d: (lambda input, kernel_size, stride=tuple(), padding=(0,),
+                                     dilation=(1,), ceil_mode=False: -1),
+        torch.quantized_max_pool2d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0),
+                                     dilation=(1, 1), ceil_mode=False: -1),
+        torch.quantized_max_pool3d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0, 0),
+                                     dilation=(1, 1, 1), ceil_mode=False: -1),
+        torch.quantized_rnn_relu_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
+                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
+        torch.quantized_rnn_tanh_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
+                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
+        torch.rad2deg: lambda input, out=None: -1,
+        torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
+        torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
+        torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
+        torch.ravel: lambda input: -1,
+        torch.real: lambda input, out=None: -1,
+        torch.vdot: lambda input, other, out=None: -1,
+        torch.linalg.vecdot: lambda input, other, dim=-1, out=None: -1,
+        torch.view_as_real: lambda input: -1,
+        torch.view_as_complex: lambda input: -1,
+        torch.reciprocal: lambda input, out=None: -1,
+        torch.relu: lambda input, inplace=False: -1,
+        torch.remainder: lambda input, other, out=None: -1,
+        torch.renorm: lambda input, p, dim, maxnorm, out=None: -1,
+        torch.repeat_interleave: lambda input, dim=None: -1,
+        torch.reshape: lambda input, shape: -1,
+        torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
+        torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
+        torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
+        torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
+        torch.roll: lambda input, shifts, dims=None: -1,
+        torch.rot90: lambda input, k=1, dims=(0, 1): -1,
+        torch.round: lambda input, out=None: -1,
+        torch.row_stack: lambda tensors, out=None: -1,  # alias for torch.vstack
+        torch._rowwise_prune: (lambda weight, mask, compressed_indices_dtype: -1),
+        torch.rrelu: lambda input, lower=1. / 8, upper=1. / 3, training=False, inplace=False: -1,
+        torch.rsqrt: lambda input, out=None: -1,
+        torch.rsub: lambda input, other, alpha=1: -1,
+        torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
+        torch.scatter: lambda input, dim, index, src: -1,
+        torch.scatter_add: lambda input, dim, index, src: -1,
+        torch.scatter_reduce: lambda input, dim, index, src, reduce, include_self=True: -1,
+        torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1,
+        torch._segment_reduce: lambda data, reduce="max", lengths=None, indices=None, offsets=None, axis=0, unsafe=False: -1,
+        torch.select: lambda input, dim, index: -1,
+        torch.select_scatter: lambda input, src, dim, index: -1,
+        torch.slice_inverse: lambda input, src, dim=0, start=None, end=None, step=1: -1,
+        torch.slice_scatter: lambda input, src, dim=0, start=None, end=None, step=1: -1,
+        torch.selu: lambda input, inplace=False: -1,
+        torch.sigmoid: lambda input, out=None: -1,
+        torch.sign: lambda input, out=None: -1,
+        torch.signbit: lambda input, out=None: -1,
+        torch.sgn: lambda input, out=None: -1,
+        torch.sin: lambda input, out=None: -1,
+        torch.sinc: lambda input, out=None: -1,
+        torch.sinh: lambda input, out=None: -1,
+        torch.slogdet: lambda input: -1,
+        torch.linalg.slogdet: lambda input: -1,
+        torch.smm: lambda input, mat2: -1,
+        torch.spmm: lambda input, mat2: -1,
+        torch.softmax: lambda input, dim, dtype=None: -1,
+        torch.linalg.solve: lambda A, B, left=True, out=None: -1,
+        torch.linalg.solve_ex: lambda A, B, left=True, check_errors=False, out=None: -1,
+        torch.sort: lambda input, dim=-1, descending=False, *, stable=False, out=None: -1,
+        torch.split: lambda tensor, split_size_or_sections, dim=0: -1,
+        torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
+        torch.sqrt: lambda input, out=None: -1,
+        torch.square: lambda input, out=None: -1,
+        torch.squeeze: lambda input, dim=None, out=None: -1,
+        torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
+        torch.stack: lambda tensors, dim=0, out=None: -1,
+        torch.std: lambda input, dim=None: -1,
+        torch.std_mean: lambda input, dim=None: -1,
+        torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
+                     pad_mode='reflect', normalized=False, onesided=True, return_complex=None: -1),
+        torch.sub: lambda input, other, out=None: -1,
+        torch.subtract: lambda input, other, out=None: -1,
+        torch.sum: lambda input, dim=None: -1,
+        torch.sym_float: lambda input: -1,
+        torch.sym_int: lambda input: -1,
+        torch.sym_max: lambda a, b: -1,
+        torch.sym_min: lambda a, b: -1,
+        torch.sym_not: lambda input: -1,
+        torch.sym_ite: lambda a, b, c: -1,
+        torch._sym_sqrt: lambda input: -1,
+        torch._sym_cos: lambda input: -1,
+        torch._sym_cosh: lambda input: -1,
+        torch._sym_sin: lambda input: -1,
+        torch._sym_sinh: lambda input: -1,
+        torch._sym_tan: lambda input: -1,
+        torch._sym_tanh: lambda input: -1,
+        torch._sym_asin: lambda input: -1,
+        torch._sym_acos: lambda input: -1,
+        torch._sym_atan: lambda input: -1,
+        torch.nansum: lambda input, dim=None: -1,
+        torch.svd: lambda input, some=True, compute_uv=True, out=None: -1,
+        torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1,
+        torch.linalg.svd: lambda input, full_matrices=True, out=None: -1,
+        torch.linalg.svdvals: lambda input, out=None: -1,
+        torch.swapaxes: lambda input, dim0, dim1: -1,
+        torch.swapdims: lambda input, axis0, axis1: -1,
+        torch.special.airy_ai: lambda input: -1,
+        torch.special.bessel_j0: lambda input: -1,
+        torch.special.bessel_j1: lambda input: -1,
+        torch.special.bessel_y0: lambda input: -1,
+        torch.special.bessel_y1: lambda input: -1,
+        torch.special.chebyshev_polynomial_t: lambda input, n, out=None: -1,
+        torch.special.chebyshev_polynomial_u: lambda input, n, out=None: -1,
+        torch.special.chebyshev_polynomial_v: lambda input, n, out=None: -1,
+        torch.special.chebyshev_polynomial_w: lambda input, n, out=None: -1,
+        torch.special.digamma: lambda input: -1,
+        torch.special.entr: lambda input: -1,
+        torch.special.erf: lambda input: -1,
+        torch.special.erfc: lambda input: -1,
+        torch.special.erfcx: lambda input: -1,
+        torch.special.erfinv: lambda input: -1,
+        torch.special.exp2: lambda input: -1,
+        torch.special.expit: lambda input: -1,
+        torch.special.expm1: lambda input: -1,
+        torch.special.gammainc: lambda input, other, out=None: -1,
+        torch.special.gammaincc: lambda input, other, out=None: -1,
+        torch.special.gammaln: lambda input: -1,
+        torch.special.hermite_polynomial_h: lambda input, n, out=None: -1,
+        torch.special.hermite_polynomial_he: lambda input, n, out=None: -1,
+        torch.special.i0: lambda input: -1,
+        torch.special.i0e: lambda input: -1,
+        torch.special.i1: lambda input: -1,
+        torch.special.i1e: lambda input: -1,
+        torch.special.laguerre_polynomial_l: lambda input, n, out=None: -1,
+        torch.special.legendre_polynomial_p: lambda input, n, out=None: -1,
+        torch.special.log1p: lambda input: -1,
+        torch.special.log_ndtr: lambda input: -1,
+        torch.special.log_softmax: lambda input, dim, dtype=None: -1,
+        torch.special.logit: lambda input: -1,
+        torch.special.logsumexp: lambda input, dim, keepdim=False, out=None: -1,
+        torch.special.modified_bessel_i0: lambda input: -1,
+        torch.special.modified_bessel_i1: lambda input: -1,
+        torch.special.modified_bessel_k0: lambda input: -1,
+        torch.special.modified_bessel_k1: lambda input: -1,
+        torch.special.multigammaln: lambda input, p: -1,
+        torch.special.ndtr: lambda input: -1,
+        torch.special.ndtri: lambda input: -1,
+        torch.special.polygamma: lambda input, n, out=None: -1,
+        torch.special.psi: lambda input: -1,
+        torch.special.round: lambda input: -1,
+        torch.special.scaled_modified_bessel_k0: lambda input: -1,
+        torch.special.scaled_modified_bessel_k1: lambda input: -1,
+        torch.special.shifted_chebyshev_polynomial_t: lambda input, n, out=None: -1,
+        torch.special.shifted_chebyshev_polynomial_u: lambda input, n, out=None: -1,
+        torch.special.shifted_chebyshev_polynomial_v: lambda input, n, out=None: -1,
+        torch.special.shifted_chebyshev_polynomial_w: lambda input, n, out=None: -1,
+        torch.special.sinc: lambda input: -1,
+        torch.special.softmax: lambda input, dim, dtype=None: -1,
+        torch.special.spherical_bessel_j0: lambda input: -1,
+        torch.special.xlog1py: lambda input, other, out=None: -1,
+        torch.special.xlogy: lambda input, other, out=None: -1,
+        torch.special.zeta: lambda self, other, out=None: -1,
+        torch.t: lambda input: -1,
+        torch.take: lambda input, index: -1,
+        torch.take_along_dim: lambda input, indices, dim=None, out=None: -1,
+        torch.tan: lambda input, out=None: -1,
+        torch.tanh: lambda input, out=None: -1,
+        torch.linalg.tensorinv: lambda a, ind=2: -1,
+        torch.linalg.tensorsolve: lambda a, b, dims=None: -1,
+        torch.tensordot: lambda a, b, dims=2, out=None: -1,
+        torch.tensor_split: lambda input, indices_or_sections, dim=0: -1,
+        torch.threshold: lambda input, threshold, value, inplace=False: -1,
+        torch.tile: lambda input, dims: -1,
+        torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1,
+        torch.trace: lambda input: -1,
+        torch.transpose: lambda input, dim0, dim1: -1,
+        torch.trapz: lambda y, x=None, dim=-1: -1,
+        torch.trapezoid: lambda y, x=None, dim=-1: -1,
+        torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
+        torch.linalg.solve_triangular: lambda input, B, upper, left=True, unitriangular=False: -1,
+        torch.tril: lambda input, diagonal=0, out=None: -1,
+        torch.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False,
+
+                                    size_average=None, reduce=None, reduction='mean': -1),
+        torch.triu: lambda input, diagonal=0, out=None: -1,
+        torch.true_divide: lambda input, other: -1,
+        torch.trunc: lambda input, out=None: -1,
+        torch.unbind: lambda input, dim=0: -1,
+        torch.unflatten: lambda input, dim, sizes, names: -1,
+        torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1,
+        torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1,
+        torch.unravel_index: lambda indices, shape: -1,
+        torch.unsafe_chunk: lambda input, chunks, dim=0: -1,
+        torch.unsafe_split: lambda tensor, split_size_or_sections, dim=0: -1,
+        torch.unsafe_split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
+        torch.unsqueeze: lambda input, dim, out=None: -1,
+        torch.linalg.vander: lambda x, N=None: -1,
+        torch.var: lambda input, dim=None: -1,
+        torch.var_mean: lambda input, dim=None: -1,
+        torch.vsplit: lambda input, indices_or_sections: -1,
+        torch.vstack: lambda tensors, out=None: -1,
+        torch.where: lambda condition, x=None, y=None: -1,
+        torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
+        torch._fw_primal_copy: lambda self, level: -1,
+        torch._make_dual_copy: lambda primal, tangent, level: -1,
+        torch.view_as_real_copy: lambda self: -1,
+        torch.view_as_complex_copy: lambda self: -1,
+        torch._conj_copy: lambda self: -1,
+        torch._neg_view_copy: lambda self: -1,
+        torch.as_strided_copy: lambda self, size, stride, storage_offset=None: -1,
+        torch._sparse_broadcast_to_copy: lambda self, size: -1,
+        torch.diagonal_copy: lambda self, offset=0, dim1=0, dim2=1: -1,
+        torch.expand_copy: lambda self, size, *, implicit=False: -1,
+        torch.narrow_copy: lambda self, dim, start, length: -1,
+        torch.permute_copy: lambda self, dims: -1,
+        torch._reshape_alias_copy: lambda self, size, stride: -1,
+        torch.select_copy: lambda self, dim, index: -1,
+        torch.detach_copy: lambda self: -1,
+        torch.slice_copy: lambda self, dim=0, start=None, end=None, step=1: -1,
+        torch.split_copy: lambda self, split_size, dim=0: -1,
+        torch.split_with_sizes_copy: lambda self, split_sizes, dim=0: -1,
+        torch.squeeze_copy: lambda self, dim: -1,
+        torch.t_copy: lambda self: -1,
+        torch.transpose_copy: lambda self, dim0, dim1: -1,
+        torch.unsqueeze_copy: lambda self, dim: -1,
+        torch._indices_copy: lambda self: -1,
+        torch._values_copy: lambda self: -1,
+        torch.indices_copy: lambda self: -1,
+        torch.values_copy: lambda self: -1,
+        torch.crow_indices_copy: lambda self: -1,
+        torch.col_indices_copy: lambda self: -1,
+        torch.ccol_indices_copy: lambda self: -1,
+        torch.row_indices_copy: lambda self: -1,
+        torch.unbind_copy: lambda self, dim=0: -1,
+        torch.view_copy: lambda self, dtype: -1,
+        torch.unfold_copy: lambda self, dimension, size, step: -1,
+        torch.alias_copy: lambda self: -1,
+        Tensor.__floordiv__: lambda self, other: -1,
+        Tensor.__rfloordiv__: lambda self, other: -1,
+        Tensor.__ifloordiv__: lambda self, other: -1,
+        Tensor.__truediv__: lambda self, other: -1,
+        Tensor.__rtruediv__: lambda self, other: -1,
+        Tensor.__itruediv__: lambda self, other: -1,
+        Tensor.__lshift__: lambda self, other: -1,
+        Tensor.__rlshift__: lambda self, other: -1,
+        Tensor.__ilshift__: lambda self, other: -1,
+        Tensor.__rshift__: lambda self, other: -1,
+        Tensor.__rrshift__: lambda self, other: -1,
+        Tensor.__irshift__: lambda self, other: -1,
+        Tensor.__and__: lambda self, other: -1,
+        Tensor.__or__: lambda self, other: -1,
+        Tensor.__xor__: lambda self, other: -1,
+        Tensor.__float__: lambda self: -1,
+        Tensor.__complex__: lambda self: -1,
+        Tensor.__array__: lambda self, dtype: -1,
+        Tensor.__bool__: lambda self: -1,
+        Tensor.__contains__: lambda self, other: -1,
+        Tensor.__neg__: lambda self: -1,
+        Tensor.__invert__: lambda self: -1,
+        Tensor.__mod__: lambda self, other: -1,
+        Tensor.__rmod__: lambda self, other: -1,
+        Tensor.__imod__: lambda self, other: -1,
+        Tensor.__array_wrap__: lambda self, array: -1,
+        Tensor.__getitem__: lambda self, idx: -1,
+        Tensor.__deepcopy__: lambda self, memo: -1,
+        Tensor.__int__: lambda self: -1,
+        Tensor.__long__: lambda self: -1,
+        Tensor.__index__: lambda self: -1,
+        Tensor.__len__: lambda self: -1,
+        Tensor.__format__: lambda self, format_spec: -1,
+        Tensor.__reduce_ex__: lambda self, proto: -1,
+        Tensor.__reversed__: lambda self: -1,
+        Tensor.__repr__: lambda self, *, tensor_contents=None: -1,
+        Tensor.__setitem__: lambda self, k, v: -1,
+        Tensor.__setstate__: lambda self, d: -1,
+        Tensor.T.__get__: lambda self: -1,
+        Tensor.H.__get__: lambda self: -1,
+        Tensor.mT.__get__: lambda self: -1,
+        Tensor.mH.__get__: lambda self: -1,
+        Tensor._backward_hooks.__get__: lambda self: -1,
+        Tensor._post_accumulate_grad_hooks.__get__: lambda self: -1,
+        Tensor._base.__get__: lambda self: -1,
+        Tensor._cdata.__get__: lambda self: -1,
+        Tensor.grad.__get__: lambda self: -1,
+        Tensor._grad.__get__: lambda self: -1,
+        Tensor._grad_fn.__get__: lambda self: -1,
+        Tensor.grad_fn.__get__: lambda self: -1,
+        Tensor._version.__get__: lambda self: -1,
+        Tensor._autocast_to_reduced_precision: lambda self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype: -1,
+        Tensor._autocast_to_full_precision: lambda self, cuda_enabled, cpu_enabled: -1,
+        Tensor.data.__get__: lambda self: -1,
+        Tensor.device.__get__: lambda self: -1,
+        Tensor.dtype.__get__: lambda self: -1,
+        Tensor.is_cuda.__get__: lambda self: -1,
+        Tensor.is_cpu.__get__: lambda self: -1,
+        Tensor.is_xla.__get__: lambda self: -1,
+        Tensor.is_xpu.__get__: lambda self: -1,
+        Tensor.is_ipu.__get__: lambda self: -1,
+        Tensor.is_leaf.__get__: lambda self: -1,
+        Tensor.retains_grad.__get__: lambda self: -1,
+        Tensor.is_meta.__get__: lambda self: -1,
+        Tensor.is_mps.__get__: lambda self: -1,
+        Tensor.is_mtia.__get__: lambda self: -1,
+        Tensor.is_nested.__get__: lambda self: -1,
+        Tensor.is_ort.__get__: lambda self: -1,
+        Tensor.is_mkldnn.__get__: lambda self: -1,
+        Tensor.is_quantized.__get__: lambda self: -1,
+        Tensor.is_sparse.__get__: lambda self: -1,
+        Tensor.is_sparse_csr.__get__: lambda self: -1,
+        Tensor.is_vulkan.__get__: lambda self: -1,
+        Tensor.itemsize.__get__: lambda self: -1,
+        Tensor.layout.__get__: lambda self: -1,
+        Tensor.name.__get__: lambda self: -1,
+        Tensor.names.__get__: lambda self: -1,
+        Tensor.nbytes.__get__: lambda self: -1,
+        Tensor.ndim.__get__: lambda self: -1,
+        Tensor.output_nr.__get__: lambda self: -1,
+        Tensor.requires_grad.__get__: lambda self: -1,
+        Tensor.shape.__get__: lambda self: -1,
+        Tensor.volatile.__get__: lambda self: -1,
+        Tensor.real.__get__: lambda self: -1,
+        Tensor.imag.__get__: lambda self: -1,
+        Tensor.__cuda_array_interface__.__get__: lambda self: -1,
+        Tensor.type: lambda self, dtype=None, non_blocking=False, **kwargs: -1,
+        Tensor._dimI: lambda self: -1,
+        Tensor._dimV: lambda self: -1,
+        Tensor._indices: lambda self: -1,
+        Tensor._is_view: lambda self: -1,
+        Tensor._nnz: lambda self: -1,
+        Tensor.crow_indices: lambda self: -1,
+        Tensor.col_indices: lambda self: -1,
+        Tensor.ccol_indices: lambda self: -1,
+        Tensor.row_indices: lambda self: -1,
+        Tensor._update_names: lambda self, names, inplace: -1,
+        Tensor._values: lambda self: -1,
+        Tensor.adjoint: lambda self: -1,
+        Tensor.align_as: lambda self, other: -1,
+        Tensor.align_to: lambda self, order, ellipsis_idx: -1,
+        Tensor.apply_: lambda self, callable: -1,
+        Tensor.as_strided: lambda self, size, stride: -1,
+        Tensor.as_strided_: lambda self, size, stride: -1,
+        Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1,
+        Tensor.bfloat16: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.bool: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.byte: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.char: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.cauchy_: lambda self, median=0, sigma=1, *, generator=None: -1,
+        Tensor.coalesce: lambda self: -1,
+        Tensor._coalesced_: lambda self, coalesced: -1,
+        Tensor.contiguous: lambda self, memory_format=torch.contiguous_format: -1,
+        Tensor.copy_: lambda self, src, non_blocking=False: -1,
+        Tensor.cpu: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.cuda: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.xpu: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.ipu: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.data_ptr: lambda self: -1,
+        Tensor.dense_dim: lambda self: -1,
+        Tensor.diagonal_scatter: lambda self, src, offset=0, dim1=0, dim2=1: -1,
+        Tensor.dim: lambda self: -1,
+        Tensor.dim_order: lambda self: -1,
+        Tensor.double: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.cdouble: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.element_size: lambda self: -1,
+        Tensor.expand: lambda self, size: -1,
+        Tensor.expand_as: lambda self, other: -1,
+        Tensor.exponential_: lambda self, lambd=1, *, generator=None: -1,
+        Tensor.fill_: lambda self, value: -1,
+        Tensor.fill_diagonal_: lambda self, value: -1,
+        Tensor.float: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.cfloat: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.geometric_: lambda self, p, *, generator=None: -1,
+        Tensor.get_device: lambda self: -1,
+        Tensor.half: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.chalf: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.has_names: lambda self: -1,
+        Tensor.indices: lambda self: -1,
+        Tensor.int: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.is_coalesced: lambda self: -1,
+        Tensor.is_contiguous: lambda self: -1,
+        Tensor.is_inference: lambda self: -1,
+        Tensor.is_pinned: lambda self: -1,
+        Tensor.is_set_to: lambda self, tensor: -1,
+        Tensor.is_shared: lambda self: -1,
+        Tensor.item: lambda self: -1,
+        Tensor.log_normal_: lambda self, mean=1, std=2, *, generator=None: -1,
+        Tensor.log_softmax: lambda self, dim: -1,
+        Tensor.long: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.map_: lambda self, tensor, callable: -1,
+        Tensor.map2_: lambda self, x, y, callable: -1,
+        Tensor.mm: lambda self, mat2: -1,
+        Tensor.module_load: lambda self, other, assign=False: -1,
+        Tensor.narrow_copy: lambda self, dimension, start, length: -1,
+        Tensor.ndimension: lambda self: -1,
+        Tensor.nelement: lambda self: -1,
+        Tensor._nested_tensor_size: lambda self: -1,
+        Tensor._nested_tensor_storage_offsets: lambda self: -1,
+        Tensor._nested_tensor_strides: lambda self: -1,
+        Tensor.normal_: lambda self: -1,
+        Tensor.numpy: lambda self: -1,
+        Tensor.permute: lambda self, dim: -1,
+        Tensor.pin_memory: lambda self: -1,
+        Tensor.put_: lambda self, indices, tensor, accumulate=False: -1,
+        Tensor.qscheme: lambda self: -1,
+        Tensor.random_: lambda self, from_=0, to=None, *, generator=None: -1,
+        Tensor.record_stream: lambda self, stream: -1,
+        Tensor.refine_names: lambda self, names: -1,
+        Tensor.register_hook: lambda self, hook: -1,
+        Tensor.register_post_accumulate_grad_hook: lambda self, hook: -1,
+        Tensor.rename: lambda self, name: -1,
+        Tensor.repeat: lambda self, *size: -1,
+        Tensor.requires_grad_: lambda self, requires_grad=True: -1,
+        Tensor.reshape_as: lambda self, other: -1,
+        Tensor.resize: lambda self, *size: -1,
+        Tensor.resize_: lambda self, size: -1,
+        Tensor.resize_as: lambda self, other: -1,
+        Tensor.resize_as_sparse_: lambda self, other: -1,
+        Tensor.retain_grad: lambda self: -1,
+        Tensor.set_: lambda self, source=None, storage_offset=0, size=None, stride=None: -1,
+        Tensor.select_scatter: lambda self, src, dim, index: -1,
+        Tensor.share_memory_: lambda self: -1,
+        Tensor.short: lambda self, memory_format=torch.preserve_format: -1,
+        Tensor.size: lambda self: -1,
+        Tensor.slice_scatter: lambda self, src, dim=0, start=None, end=None, step=1: -1,
+        Tensor.sparse_dim: lambda self: -1,
+        Tensor.sparse_mask: lambda self, mask: -1,
+        Tensor._sparse_mask_projection: lambda self, mask, accumulate_matches=False: -1,
+        Tensor.sparse_resize_: lambda self, size1, size2, dense_dim: -1,
+        Tensor.sparse_resize_and_clear_: lambda self, size1, size2, dense_dim: -1,
+        Tensor.sspaddmm: lambda self, mat1, mat2, beta=1, alpha=1, out=None: -1,
+        Tensor.storage: lambda self: -1,
+        Tensor.untyped_storage: lambda self: -1,
+        Tensor.storage_offset: lambda self: -1,
+        Tensor.storage_type: lambda self: -1,
+        Tensor.sum_to_size: lambda self, size: -1,
+        Tensor.tile: lambda self, *reps: -1,
+        Tensor.to: lambda self, dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format: -1,
+        Tensor.to_dense: lambda self, dtype=None, *, masked_grad=None: -1,
+        Tensor._to_dense: lambda self, dtype=None, masked_grad=None: -1,
+        Tensor.to_sparse: lambda self: -1,
+        Tensor.tolist: lambda self: -1,
+        Tensor.to_mkldnn: lambda self: -1,
+        Tensor.type_as: lambda self, other: -1,
+        Tensor.unfold: lambda self, dimension, size, step: -1,
+        Tensor.uniform_: lambda self, from_=0, to=1: -1,
+        Tensor.values: lambda self: -1,
+        Tensor.view: lambda self, shape: -1,
+        Tensor.view_as: lambda self, other: -1,
+        Tensor.zero_: lambda self: -1,
+        Tensor.__dlpack__: lambda self, stream=None: -1,
+        Tensor.__dlpack_device__: lambda self: -1,
+        torch.linalg.lstsq: lambda self, b, cond=None, driver=None: -1,
+    }
+
+    ret2 = {}
+    ignored = get_ignored_functions()
+
+    for k, v in ret.items():
+        # Generate methods like __add__ and add_ by default from add
+        names = [
+            k.__name__,  # Default method
+            k.__name__ + "_",  # Inplace variant
+            "__" + k.__name__ + "__",  # Dunder method
+            "__i" + k.__name__ + "__",  # Inplace dunder method
+            "__r" + k.__name__ + "__",  # Reverse dunder method
+        ]
+
+        if k.__name__.startswith("bitwise_"):
+            # bitwise_<op> have dunder methods of the form __<op>__
+            # And so on.
+            subname = k.__name__[len("bitwise_"):]
+            names.extend([
+                "__" + subname + "__",
+                "__i" + subname + "__",
+                "__r" + subname + "__"
+            ])
+
+        for name in names:
+            func = getattr(Tensor, name, None)
+            if callable(func) and func not in ret and func not in ignored:
+                ret2[func] = v
+
+    ret.update(ret2)
+    return ret
+
+def wrap_torch_function(dispatcher: Callable):
+    """Wraps a given function with ``__torch_function__`` -related functionality.
+
+    Parameters
+    ----------
+    dispatcher: Callable
+        A callable that returns an iterable of Tensor-likes passed into the function.
+
+    Note
+    ----
+    This decorator may reduce the performance of your code. Generally, it's enough to express
+    your code as a series of functions that, themselves, support __torch_function__. If you
+    find yourself in the rare situation where this is not the case, e.g. if you're wrapping a
+    low-level library and you also need it to work for Tensor-likes, then this function is available.
+
+    Examples
+    --------
+    >>> def dispatcher(a): # Must have the same signature as func
+    ...     return (a,)
+    >>> @torch.overrides.wrap_torch_function(dispatcher)
+    >>> def func(a): # This will make func dispatchable by __torch_function__
+    ...     return a + 0
+    """
+    def inner(func):
+        @functools.wraps(func)
+        def wrapped(*args, **kwargs):
+            relevant_args = dispatcher(*args, **kwargs)
+            if has_torch_function(relevant_args):
+                return handle_torch_function(wrapped, relevant_args, *args, **kwargs)
+
+            return func(*args, **kwargs)
+
+        return wrapped
+
+    return inner
+
+def _get_overloaded_args(relevant_args: Iterable[Any], get_type_fn: Callable[[Any], Type] = None) -> List[Any]:
+    """Returns a list of arguments on which to call __torch_function__.
+
+    Checks arguments in relevant_args for __torch_function__ implementations,
+    storing references to the arguments and their types in overloaded_args and
+    overloaded_types in order of calling precedence. Only distinct types are
+    considered. If a type is a subclass of another type it will have higher
+    precedence, otherwise the precedence order is the same as the order of
+    arguments in relevant_args, that is, from left-to-right in the argument list.
+
+    The precedence-determining algorithm implemented in this function is
+    described in `NEP-0018`_.
+
+    See torch::append_overloaded_arg for the equivalent function in the C++
+    implementation.
+
+    Parameters
+    ----------
+    relevant_args : iterable of array-like
+        Iterable of array-like arguments to check for __torch_function__
+        methods.
+
+    get_type_fn : callable, optional
+        Function to call on each argument in relevant_args to get its type.
+
+    Returns
+    -------
+    overloaded_args : list
+        Arguments from relevant_args on which to call __torch_function__
+        methods, in the order in which they should be called.
+
+    .. _NEP-0018:
+       https://numpy.org/neps/nep-0018-array-function-protocol.html
+    """
+    if get_type_fn is None:
+        get_type_fn = type
+
+    # If torch function is not enabled, there are no overloaded types
+    if not torch._C._is_torch_function_enabled():
+        return []
+    # Runtime is O(num_arguments * num_unique_types)
+    overloaded_types: Set[Type] = set()
+    overloaded_args: List[Any] = []
+    for arg in relevant_args:
+        arg_type = get_type_fn(arg)
+        # We only collect arguments if they have a unique type, which ensures
+        # reasonable performance even with a long list of possibly overloaded
+        # arguments.
+        #
+        # NB: Important to exclude _disabled_torch_function_impl, otherwise
+        # https://github.com/pytorch/pytorch/issues/64687
+        if (arg_type not in overloaded_types and hasattr(arg_type, '__torch_function__') and
+                arg_type.__torch_function__ != torch._C._disabled_torch_function_impl):
+            # Create lists explicitly for the first type (usually the only one
+            # done) to avoid setting up the iterator for overloaded_args.
+            if overloaded_types:
+                overloaded_types.add(arg_type)
+                # By default, insert argument at the end, but if it is
+                # subclass of another argument, insert it before that argument.
+                # This ensures "subclasses before superclasses".
+                index = len(overloaded_args)
+                for i, old_arg in enumerate(overloaded_args):
+                    if issubclass(arg_type, get_type_fn(old_arg)):
+                        index = i
+                        break
+                overloaded_args.insert(index, arg)
+            else:
+                overloaded_types = {arg_type}
+                overloaded_args = [arg]
+    return overloaded_args
+
+
+def handle_torch_function(
+        public_api: Callable, relevant_args: Iterable[Any], *args, **kwargs) -> Any:
+    """Implement a function with checks for ``__torch_function__`` overrides.
+
+    See torch::autograd::handle_torch_function for the equivalent of this
+    function in the C++ implementation.
+
+    Arguments
+    ---------
+    public_api : function
+        Function exposed by the public torch API originally called like
+        ``public_api(*args, **kwargs)`` on which arguments are now being
+        checked.
+    relevant_args : iterable
+        Iterable of arguments to check for __torch_function__ methods.
+    args : tuple
+        Arbitrary positional arguments originally passed into ``public_api``.
+    kwargs : tuple
+        Arbitrary keyword arguments originally passed into ``public_api``.
+
+    Returns
+    -------
+    object
+        Result from calling ``implementation`` or an ``__torch_function__``
+        method, as appropriate.
+
+    Raises
+    ------
+    TypeError : if no implementation is found.
+
+    Example
+    -------
+    >>> def func(a):
+    ...     if has_torch_function_unary(a):
+    ...         return handle_torch_function(func, (a,), a)
+    ...     return a + 0
+    """
+    # Check for __torch_function__ methods.
+    overloaded_args = _get_overloaded_args(relevant_args)
+    # overloaded_args already have unique types.
+    types = tuple(map(type, overloaded_args))
+
+    # Check for __torch_function__ mode.
+    if _is_torch_function_mode_enabled():
+        # if we're here, the mode must be set to a TorchFunctionStackMode
+        # this unsets it and calls directly into TorchFunctionStackMode's torch function
+        with _pop_mode_temporarily() as mode:
+            result = mode.__torch_function__(public_api, types, args, kwargs)
+        if result is not NotImplemented:
+            return result
+
+    # Call overrides
+    for overloaded_arg in overloaded_args:
+        # This call needs to become a classmethod call in the future.
+        # See https://github.com/pytorch/pytorch/issues/63767
+        torch_func_method = overloaded_arg.__torch_function__
+        if hasattr(torch_func_method, "__self__") and torch_func_method.__self__ is overloaded_arg and \
+                torch_func_method is not torch._C._disabled_torch_function_impl:
+            warnings.warn("Defining your `__torch_function__ as a plain method is deprecated and "
+                          "will be an error in future, please define it as a classmethod.",
+                          DeprecationWarning)
+
+        # Use `public_api` instead of `implementation` so __torch_function__
+        # implementations can do equality/identity comparisons.
+        result = torch_func_method(public_api, types, args, kwargs)
+
+        if result is not NotImplemented:
+            return result
+
+    func_name = f'{public_api.__module__}.{public_api.__name__}'
+    msg = (
+        f"no implementation found for '{func_name}' on types that implement "
+        f'__torch_function__: {[type(arg) for arg in overloaded_args]}'
+    )
+    if _is_torch_function_mode_enabled():
+        msg += f" nor in mode {_get_current_function_mode()}"
+    raise TypeError(msg)
+
+has_torch_function = _add_docstr(
+    _has_torch_function,
+    r"""Check for __torch_function__ implementations in the elements of an iterable
+    or if a __torch_function__ mode is enabled.  Considers exact ``Tensor`` s
+    and ``Parameter`` s non-dispatchable.  Use this to guard a call to
+    :func:`handle_torch_function`; don't use it to test if something
+    is Tensor-like, use :func:`is_tensor_like` instead.
+    Arguments
+    ---------
+    relevant_args : iterable
+        Iterable or arguments to check for __torch_function__ methods.
+    Returns
+    -------
+    bool
+        True if any of the elements of relevant_args have __torch_function__
+        implementations, False otherwise.
+    See Also
+    ________
+    torch.is_tensor_like
+        Checks if something is a Tensor-like, including an exact ``Tensor``.
+    """
+)
+
+has_torch_function_unary = _add_docstr(
+    _has_torch_function_unary,
+    r"""Special case of `has_torch_function` for single inputs.
+    Instead of:
+      `has_torch_function((t,))`
+    call:
+      `has_torch_function_unary(t)`
+    which skips unnecessary packing and unpacking work.
+    """
+)
+
+has_torch_function_variadic = _add_docstr(
+    _has_torch_function_variadic,
+    r"""Special case of `has_torch_function` that skips tuple creation.
+
+    This uses the METH_FASTCALL protocol introduced in Python 3.7
+
+    Instead of:
+      `has_torch_function((a, b))`
+    call:
+      `has_torch_function_variadic(a, b)`
+    which skips unnecessary packing and unpacking work.
+    """
+)
+
+@functools.lru_cache(None)
+def _get_overridable_functions() -> Tuple[Dict[Any, List[Callable]], Dict[Callable, str]]:
+    overridable_funcs = collections.defaultdict(list)
+    index = {}
+    tested_namespaces = [
+        ("torch", torch, torch.__all__),
+        ("torch.functional", torch.functional, torch.functional.__all__),
+        ("torch.nn.functional", torch.nn.functional, dir(torch.nn.functional)),
+        ("torch.nn.init", torch.nn.init, dir(torch.nn.init)),
+        ("torch.Tensor", torch.Tensor, dir(torch.Tensor)),
+        ("torch.linalg", torch.linalg, dir(torch.linalg)),
+        ("torch.fft", torch.fft, dir(torch.fft)),
+        ("torch.special", torch.special, dir(torch.special)),
+    ]
+    for namespace_str, namespace, ns_funcs in tested_namespaces:
+        for func_name in ns_funcs:
+            ignore = False
+            # ignore private functions or functions that are deleted in torch.__init__
+            if namespace is not torch.Tensor:
+                if func_name.startswith('__'):
+                    continue
+                elif func_name.startswith('_'):
+                    ignore = True
+                elif func_name.endswith('_'):
+                    ignore = True
+                elif not func_name[0].islower():
+                    ignore = True
+                elif func_name == 'unique_dim':
+                    continue
+            else:
+                func = getattr(namespace, func_name)
+                if getattr(object, func_name, None) == func:
+                    continue
+                if func_name == '__weakref__':
+                    continue
+            func = getattr(namespace, func_name)
+            if namespace is torch.Tensor and getattr(object, func_name, None) == func:
+                continue
+            # ignore re-exported modules
+            if isinstance(func, types.ModuleType):
+                continue
+            # ignore __future__ imports
+            if isinstance(func, __future__._Feature):
+                continue
+
+            if not callable(func) and hasattr(func, "__get__"):
+                index[func.__get__] = f"{namespace_str}.{func_name}.__get__"
+                index[func.__set__] = f"{namespace_str}.{func_name}.__set__"
+                if ignore:
+                    continue
+                if func.__get__ in get_ignored_functions():
+                    msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
+                           "but still has an explicit override")
+                    assert func.__get__ not in get_testing_overrides(), msg.format(namespace, func.__name__)
+                    continue
+                else:
+                    overridable_funcs[func].append(func.__get__)
+                    continue
+
+            if not callable(func):
+                continue
+
+            index[func] = f"{namespace_str}.{func_name}"
+
+            if ignore:
+                continue
+
+            # cannot be overriden by __torch_function__
+            if func in get_ignored_functions():
+                msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
+                       "but still has an explicit override")
+                assert func not in get_testing_overrides(), msg.format(namespace, func.__name__)
+                continue
+            overridable_funcs[namespace].append(func)
+    return overridable_funcs, index
+
+@_disable_user_warnings
+def get_overridable_functions() -> Dict[Any, List[Callable]]:
+    """List functions that are overridable via __torch_function__
+
+    Returns
+    -------
+    Dict[Any, List[Callable]]
+        A dictionary that maps namespaces that contain overridable functions
+        to functions in that namespace that can be overridden.
+    """
+    return _get_overridable_functions()[0]
+
+@_disable_user_warnings
+def resolve_name(f):
+    """Get a human readable string name for a function passed to
+    __torch_function__
+
+    Arguments
+    ---------
+    f : Callable
+        Function to resolve the name of.
+
+    Returns
+    -------
+    str
+        Name of the function; if eval'ed it should give back the input
+        function.
+    """
+    if isinstance(f, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)):
+        return str(f)
+    return _get_overridable_functions()[1].get(f)
+
+@functools.lru_cache(None)
+def _get_tensor_methods() -> Set[Callable]:
+    """ Returns a set of the overridable methods on ``torch.Tensor`` """
+    overridable_funcs = get_overridable_functions()
+    methods = set(overridable_funcs[torch.Tensor])
+    return methods
+
+@_disable_user_warnings
+def is_tensor_method_or_property(func: Callable) -> bool:
+    """
+    Returns True if the function passed in is a handler for a
+    method or property belonging to ``torch.Tensor``, as passed
+    into ``__torch_function__``.
+
+    .. note::
+       For properties, their ``__get__`` method must be passed in.
+
+    This may be needed, in particular, for the following reasons:
+
+    1. Methods/properties sometimes don't contain a `__module__` slot.
+    2. They require that the first passed-in argument is an instance
+       of ``torch.Tensor``.
+
+    Examples
+    --------
+    >>> is_tensor_method_or_property(torch.Tensor.add)
+    True
+    >>> is_tensor_method_or_property(torch.add)
+    False
+    """
+    return func in _get_tensor_methods() or func.__name__ == "__get__"
+
+def is_tensor_like(inp):
+    """
+    Returns ``True`` if the passed-in input is a Tensor-like.
+
+    Currently, this occurs whenever there's a ``__torch_function__``
+    attribute on the type of the input.
+
+    Examples
+    --------
+    A subclass of tensor is generally a Tensor-like.
+
+    >>> class SubTensor(torch.Tensor): ...
+    >>> is_tensor_like(SubTensor([0]))
+    True
+
+    Built-in or user types aren't usually Tensor-like.
+
+    >>> is_tensor_like(6)
+    False
+    >>> is_tensor_like(None)
+    False
+    >>> class NotATensor: ...
+    >>> is_tensor_like(NotATensor())
+    False
+
+    But, they can be made Tensor-like by implementing __torch_function__.
+
+    >>> class TensorLike:
+    ...     @classmethod
+    ...     def __torch_function__(cls, func, types, args, kwargs):
+    ...         return -1
+    >>> is_tensor_like(TensorLike())
+    True
+    """
+    return type(inp) is torch.Tensor or hasattr(inp, "__torch_function__")
+
+class TorchFunctionMode:
+    """
+    A ``TorchFunctionMode`` allows you to override the meaning of all
+    ``__torch_function__`` overrideable functions within a dynamic scope,
+    without having to actually create a tensor subclass or manually
+    monkey-patch functions in the PyTorch API.  Some common situations
+    where you should use a mode:
+
+        * You want to override the meaning of factory functions, or other
+          functions that do not otherwise take a tensor as an argument
+          (these cannot be overridden with tensor subclasses).
+
+        * You want to override the behavior of all functions without needing
+          to wrap your inputs in tensor subclasses; e.g., if you are just
+          interested in logging intermediate computations.
+
+        * You want to control the order of execution of various tensor
+          subclasses explicitly, rather than implicitly via the return of
+          ``NotImplemented``.
+
+    Independent subclasses of :class:`TorchFunctionMode` are compositional:
+    modes can be pushed onto a stack using ``with MyMode():``.
+    When you call functions in the PyTorch API inside your
+    ``__torch_function__`` implementation, by default, they will forward on to
+    the next mode on the mode stack.  If you want recursively call back into
+    your current ``__torch_function__`` implementation, either explicitly
+    invoke ``self.__torch_function__(...)``, or use the context manager
+    ``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch
+    API self-referential (beware of infinite loops, in this case!)
+    """
+    inner: "TorchFunctionMode"
+
+    # Force metaclass to generate constructor at the base of the hierarchy
+    def __init__(self):
+        pass
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        raise NotImplementedError()
+
+    def __enter__(self):
+        _push_mode(self)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        _pop_mode()
+
+    @classmethod
+    def push(cls, *args, **kwargs):
+        warnings.warn("`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`")
+        instance = cls(*args, **kwargs)
+        return instance
+
+
+def _get_current_function_mode():
+    stack_len = _len_torch_function_stack()
+    return _get_function_stack_at(stack_len - 1) if stack_len > 0 else None
+
+
+def _get_current_function_mode_stack():
+    stack_len = _len_torch_function_stack()
+    return [_get_function_stack_at(i) for i in range(stack_len)]
+
+def _push_mode(mode):
+    _push_on_torch_function_stack(mode)
+
+
+def _pop_mode():
+    old = _pop_torch_function_stack()
+    return old
+
+
+@contextlib.contextmanager
+def _pop_mode_temporarily():
+    old = _pop_mode()
+    try:
+        yield old
+    finally:
+        _push_mode(old)
+
+class BaseTorchFunctionMode(TorchFunctionMode):
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        return func(*args, **kwargs)
+
+
+@contextlib.contextmanager
+def enable_reentrant_dispatch():
+    # NB: this can't simply be
+    # `enable_reentrant_dispatch = torch._C._RestorePythonTLSSnapshot`
+    # because:
+    # 1. torch._C._RestorePythonTLSSnapshot is unavailable when this file
+    #    initially gets imported. Probably an import order thing.
+    # 2. enable_reentrant_dispatch is technically public API; assigning
+    #    it the object would change the __module__ to look private.
+    with torch._C._RestorePythonTLSSnapshot():
+        try:
+            yield
+        finally:
+            pass
diff --git a/shared/package-files/1b/1b5990acadde4bfcc6ecb318bb0c82be4a61f59a0d91b40c2e92349e1239b632/test_astype.py b/shared/package-files/1b/1b5990acadde4bfcc6ecb318bb0c82be4a61f59a0d91b40c2e92349e1239b632/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..dde5f38074efb0dda0942e17022d9a22e3d44afa
--- /dev/null
+++ b/shared/package-files/1b/1b5990acadde4bfcc6ecb318bb0c82be4a61f59a0d91b40c2e92349e1239b632/test_astype.py
@@ -0,0 +1,254 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+)
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+    NaT,
+    Timedelta,
+    Timestamp,
+    interval_range,
+)
+import pandas._testing as tm
+
+
+class AstypeTests:
+    """Tests common to IntervalIndex with any subtype"""
+
+    def test_astype_idempotent(self, index):
+        result = index.astype("interval")
+        tm.assert_index_equal(result, index)
+
+        result = index.astype(index.dtype)
+        tm.assert_index_equal(result, index)
+
+    def test_astype_object(self, index):
+        result = index.astype(object)
+        expected = Index(index.values, dtype="object")
+        tm.assert_index_equal(result, expected)
+        assert not result.equals(index)
+
+    def test_astype_category(self, index):
+        result = index.astype("category")
+        expected = CategoricalIndex(index.values)
+        tm.assert_index_equal(result, expected)
+
+        result = index.astype(CategoricalDtype())
+        tm.assert_index_equal(result, expected)
+
+        # non-default params
+        categories = index.dropna().unique().values[:-1]
+        dtype = CategoricalDtype(categories=categories, ordered=True)
+        result = index.astype(dtype)
+        expected = CategoricalIndex(index.values, categories=categories, ordered=True)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "int64",
+            "uint64",
+            "float64",
+            "complex128",
+            "period[M]",
+            "timedelta64",
+            "timedelta64[ns]",
+            "datetime64",
+            "datetime64[ns]",
+            "datetime64[ns, US/Eastern]",
+        ],
+    )
+    def test_astype_cannot_cast(self, index, dtype):
+        msg = "Cannot cast IntervalIndex to dtype"
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+    def test_astype_invalid_dtype(self, index):
+        msg = "data type [\"']fake_dtype[\"'] not understood"
+        with pytest.raises(TypeError, match=msg):
+            index.astype("fake_dtype")
+
+
+class TestIntSubtype(AstypeTests):
+    """Tests specific to IntervalIndex with integer-like subtype"""
+
+    indexes = [
+        IntervalIndex.from_breaks(np.arange(-10, 11, dtype="int64")),
+        IntervalIndex.from_breaks(np.arange(100, dtype="uint64"), closed="left"),
+    ]
+
+    @pytest.fixture(params=indexes)
+    def index(self, request):
+        return request.param
+
+    @pytest.mark.parametrize(
+        "subtype", ["float64", "datetime64[ns]", "timedelta64[ns]"]
+    )
+    def test_subtype_conversion(self, index, subtype):
+        dtype = IntervalDtype(subtype, index.closed)
+        result = index.astype(dtype)
+        expected = IntervalIndex.from_arrays(
+            index.left.astype(subtype), index.right.astype(subtype), closed=index.closed
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "subtype_start, subtype_end", [("int64", "uint64"), ("uint64", "int64")]
+    )
+    def test_subtype_integer(self, subtype_start, subtype_end):
+        index = IntervalIndex.from_breaks(np.arange(100, dtype=subtype_start))
+        dtype = IntervalDtype(subtype_end, index.closed)
+        result = index.astype(dtype)
+        expected = IntervalIndex.from_arrays(
+            index.left.astype(subtype_end),
+            index.right.astype(subtype_end),
+            closed=index.closed,
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.xfail(reason="GH#15832")
+    def test_subtype_integer_errors(self):
+        # int64 -> uint64 fails with negative values
+        index = interval_range(-10, 10)
+        dtype = IntervalDtype("uint64", "right")
+
+        # Until we decide what the exception message _should_ be, we
+        #  assert something that it should _not_ be.
+        #  We should _not_ be getting a message suggesting that the -10
+        #  has been wrapped around to a large-positive integer
+        msg = "^(?!(left side of interval must be <= right side))"
+        with pytest.raises(ValueError, match=msg):
+            index.astype(dtype)
+
+
+class TestFloatSubtype(AstypeTests):
+    """Tests specific to IntervalIndex with float subtype"""
+
+    indexes = [
+        interval_range(-10.0, 10.0, closed="neither"),
+        IntervalIndex.from_arrays(
+            [-1.5, np.nan, 0.0, 0.0, 1.5], [-0.5, np.nan, 1.0, 1.0, 3.0], closed="both"
+        ),
+    ]
+
+    @pytest.fixture(params=indexes)
+    def index(self, request):
+        return request.param
+
+    @pytest.mark.parametrize("subtype", ["int64", "uint64"])
+    def test_subtype_integer(self, subtype):
+        index = interval_range(0.0, 10.0)
+        dtype = IntervalDtype(subtype, "right")
+        result = index.astype(dtype)
+        expected = IntervalIndex.from_arrays(
+            index.left.astype(subtype), index.right.astype(subtype), closed=index.closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # raises with NA
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(ValueError, match=msg):
+            index.insert(0, np.nan).astype(dtype)
+
+    @pytest.mark.parametrize("subtype", ["int64", "uint64"])
+    def test_subtype_integer_with_non_integer_borders(self, subtype):
+        index = interval_range(0.0, 3.0, freq=0.25)
+        dtype = IntervalDtype(subtype, "right")
+        result = index.astype(dtype)
+        expected = IntervalIndex.from_arrays(
+            index.left.astype(subtype), index.right.astype(subtype), closed=index.closed
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_subtype_integer_errors(self):
+        # float64 -> uint64 fails with negative values
+        index = interval_range(-10.0, 10.0)
+        dtype = IntervalDtype("uint64", "right")
+        msg = re.escape(
+            "Cannot convert interval[float64, right] to interval[uint64, right]; "
+            "subtypes are incompatible"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+    @pytest.mark.parametrize("subtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_subtype_datetimelike(self, index, subtype):
+        dtype = IntervalDtype(subtype, "right")
+        msg = "Cannot convert .* to .*; subtypes are incompatible"
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_astype_category(self, index):
+        super().test_astype_category(index)
+
+
+class TestDatetimelikeSubtype(AstypeTests):
+    """Tests specific to IntervalIndex with datetime-like subtype"""
+
+    indexes = [
+        interval_range(Timestamp("2018-01-01"), periods=10, closed="neither"),
+        interval_range(Timestamp("2018-01-01"), periods=10).insert(2, NaT),
+        interval_range(Timestamp("2018-01-01", tz="US/Eastern"), periods=10),
+        interval_range(Timedelta("0 days"), periods=10, closed="both"),
+        interval_range(Timedelta("0 days"), periods=10).insert(2, NaT),
+    ]
+
+    @pytest.fixture(params=indexes)
+    def index(self, request):
+        return request.param
+
+    @pytest.mark.parametrize("subtype", ["int64", "uint64"])
+    def test_subtype_integer(self, index, subtype):
+        dtype = IntervalDtype(subtype, "right")
+
+        if subtype != "int64":
+            msg = (
+                r"Cannot convert interval\[(timedelta64|datetime64)\[ns.*\], .*\] "
+                r"to interval\[uint64, .*\]"
+            )
+            with pytest.raises(TypeError, match=msg):
+                index.astype(dtype)
+            return
+
+        result = index.astype(dtype)
+        new_left = index.left.astype(subtype)
+        new_right = index.right.astype(subtype)
+
+        expected = IntervalIndex.from_arrays(new_left, new_right, closed=index.closed)
+        tm.assert_index_equal(result, expected)
+
+    def test_subtype_float(self, index):
+        dtype = IntervalDtype("float64", "right")
+        msg = "Cannot convert .* to .*; subtypes are incompatible"
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+    def test_subtype_datetimelike(self):
+        # datetime -> timedelta raises
+        dtype = IntervalDtype("timedelta64[ns]", "right")
+        msg = "Cannot convert .* to .*; subtypes are incompatible"
+
+        index = interval_range(Timestamp("2018-01-01"), periods=10)
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+        index = interval_range(Timestamp("2018-01-01", tz="CET"), periods=10)
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
+
+        # timedelta -> datetime raises
+        dtype = IntervalDtype("datetime64[ns]", "right")
+        index = interval_range(Timedelta("0 days"), periods=10)
+        with pytest.raises(TypeError, match=msg):
+            index.astype(dtype)
diff --git a/shared/package-files/1b/1b5b1ae9e9f5cffe35b14edc91163cf9f9498d4292a8926285ac41970cd4777b/bsplines.py b/shared/package-files/1b/1b5b1ae9e9f5cffe35b14edc91163cf9f9498d4292a8926285ac41970cd4777b/bsplines.py
new file mode 100644
index 0000000000000000000000000000000000000000..0328d45c107bda78cbdbd374148237ca09ac411d
--- /dev/null
+++ b/shared/package-files/1b/1b5b1ae9e9f5cffe35b14edc91163cf9f9498d4292a8926285ac41970cd4777b/bsplines.py
@@ -0,0 +1,21 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.signal` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__ = [  # noqa: F822
+    'spline_filter', 'gauss_spline',
+    'cspline1d', 'qspline1d', 'cspline1d_eval', 'qspline1d_eval',
+    'cspline2d', 'sepfir2d'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="signal", module="bsplines",
+                                   private_modules=["_spline_filters"], all=__all__,
+                                   attribute=name)
diff --git a/shared/package-files/1b/1b5e87e00dc87a84269cead8578b9e6462928e18a95f1f3373c9eef451a5bcc0/WHEEL b/shared/package-files/1b/1b5e87e00dc87a84269cead8578b9e6462928e18a95f1f3373c9eef451a5bcc0/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..becc9a66ea739ba941d48a749e248761cc6e658a
--- /dev/null
+++ b/shared/package-files/1b/1b5e87e00dc87a84269cead8578b9e6462928e18a95f1f3373c9eef451a5bcc0/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.37.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/shared/package-files/1b/1b60943d185de69448999a47d2e39520f89dedfcc1e0eb896631fc6ab40cac4d/test_iloc.py b/shared/package-files/1b/1b60943d185de69448999a47d2e39520f89dedfcc1e0eb896631fc6ab40cac4d/test_iloc.py
new file mode 100644
index 0000000000000000000000000000000000000000..8939ecc78000be08812afb702358e7eee1ae9499
--- /dev/null
+++ b/shared/package-files/1b/1b60943d185de69448999a47d2e39520f89dedfcc1e0eb896631fc6ab40cac4d/test_iloc.py
@@ -0,0 +1,171 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def simple_multiindex_dataframe():
+    """
+    Factory function to create simple 3 x 3 dataframe with
+    both columns and row MultiIndex using supplied data or
+    random data by default.
+    """
+
+    data = np.random.default_rng(2).standard_normal((3, 3))
+    return DataFrame(
+        data, columns=[[2, 2, 4], [6, 8, 10]], index=[[4, 4, 8], [8, 10, 12]]
+    )
+
+
+@pytest.mark.parametrize(
+    "indexer, expected",
+    [
+        (
+            lambda df: df.iloc[0],
+            lambda arr: Series(arr[0], index=[[2, 2, 4], [6, 8, 10]], name=(4, 8)),
+        ),
+        (
+            lambda df: df.iloc[2],
+            lambda arr: Series(arr[2], index=[[2, 2, 4], [6, 8, 10]], name=(8, 12)),
+        ),
+        (
+            lambda df: df.iloc[:, 2],
+            lambda arr: Series(arr[:, 2], index=[[4, 4, 8], [8, 10, 12]], name=(4, 10)),
+        ),
+    ],
+)
+def test_iloc_returns_series(indexer, expected, simple_multiindex_dataframe):
+    df = simple_multiindex_dataframe
+    arr = df.values
+    result = indexer(df)
+    expected = expected(arr)
+    tm.assert_series_equal(result, expected)
+
+
+def test_iloc_returns_dataframe(simple_multiindex_dataframe):
+    df = simple_multiindex_dataframe
+    result = df.iloc[[0, 1]]
+    expected = df.xs(4, drop_level=False)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_iloc_returns_scalar(simple_multiindex_dataframe):
+    df = simple_multiindex_dataframe
+    arr = df.values
+    result = df.iloc[2, 2]
+    expected = arr[2, 2]
+    assert result == expected
+
+
+def test_iloc_getitem_multiple_items():
+    # GH 5528
+    tup = zip(*[["a", "a", "b", "b"], ["x", "y", "x", "y"]])
+    index = MultiIndex.from_tuples(tup)
+    df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), index=index)
+    result = df.iloc[[2, 3]]
+    expected = df.xs("b", drop_level=False)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_iloc_getitem_labels():
+    # this is basically regular indexing
+    arr = np.random.default_rng(2).standard_normal((4, 3))
+    df = DataFrame(
+        arr,
+        columns=[["i", "i", "j"], ["A", "A", "B"]],
+        index=[["i", "i", "j", "k"], ["X", "X", "Y", "Y"]],
+    )
+    result = df.iloc[2, 2]
+    expected = arr[2, 2]
+    assert result == expected
+
+
+def test_frame_getitem_slice(multiindex_dataframe_random_data):
+    df = multiindex_dataframe_random_data
+    result = df.iloc[:4]
+    expected = df[:4]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_setitem_slice(multiindex_dataframe_random_data):
+    df = multiindex_dataframe_random_data
+    df.iloc[:4] = 0
+
+    assert (df.values[:4] == 0).all()
+    assert (df.values[4:] != 0).all()
+
+
+def test_indexing_ambiguity_bug_1678():
+    # GH 1678
+    columns = MultiIndex.from_tuples(
+        [("Ohio", "Green"), ("Ohio", "Red"), ("Colorado", "Green")]
+    )
+    index = MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)])
+
+    df = DataFrame(np.arange(12).reshape((4, 3)), index=index, columns=columns)
+
+    result = df.iloc[:, 1]
+    expected = df.loc[:, ("Ohio", "Red")]
+    tm.assert_series_equal(result, expected)
+
+
+def test_iloc_integer_locations():
+    # GH 13797
+    data = [
+        ["str00", "str01"],
+        ["str10", "str11"],
+        ["str20", "srt21"],
+        ["str30", "str31"],
+        ["str40", "str41"],
+    ]
+
+    index = MultiIndex.from_tuples(
+        [("CC", "A"), ("CC", "B"), ("CC", "B"), ("BB", "a"), ("BB", "b")]
+    )
+
+    expected = DataFrame(data)
+    df = DataFrame(data, index=index)
+
+    result = DataFrame([[df.iloc[r, c] for c in range(2)] for r in range(5)])
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, indexes, values, expected_k",
+    [
+        # test without indexer value in first level of MultiIndex
+        ([[2, 22, 5], [2, 33, 6]], [0, -1, 1], [2, 3, 1], [7, 10]),
+        # test like code sample 1 in the issue
+        ([[1, 22, 555], [1, 33, 666]], [0, -1, 1], [200, 300, 100], [755, 1066]),
+        # test like code sample 2 in the issue
+        ([[1, 3, 7], [2, 4, 8]], [0, -1, 1], [10, 10, 1000], [17, 1018]),
+        # test like code sample 3 in the issue
+        ([[1, 11, 4], [2, 22, 5], [3, 33, 6]], [0, -1, 1], [4, 7, 10], [8, 15, 13]),
+    ],
+)
+def test_iloc_setitem_int_multiindex_series(data, indexes, values, expected_k):
+    # GH17148
+    df = DataFrame(data=data, columns=["i", "j", "k"])
+    df = df.set_index(["i", "j"])
+
+    series = df.k.copy()
+    for i, v in zip(indexes, values):
+        series.iloc[i] += v
+
+    df["k"] = expected_k
+    expected = df.k
+    tm.assert_series_equal(series, expected)
+
+
+def test_getitem_iloc(multiindex_dataframe_random_data):
+    df = multiindex_dataframe_random_data
+    result = df.iloc[2]
+    expected = df.xs(df.index[2])
+    tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/1b/1b612dd598c8803935d928878227c42948899a30dc0f7eb5c7dbd2b326a78aec/floor_meta.h b/shared/package-files/1b/1b612dd598c8803935d928878227c42948899a30dc0f7eb5c7dbd2b326a78aec/floor_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a2f2f50375054d9bb9b9cd3e788aca274174fa7
--- /dev/null
+++ b/shared/package-files/1b/1b612dd598c8803935d928878227c42948899a30dc0f7eb5c7dbd2b326a78aec/floor_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_floor : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b68144734c4b66791f27add5d425f3620775585718a03d0f9b110ba3a4d88db/WHEEL b/shared/package-files/1b/1b68144734c4b66791f27add5d425f3620775585718a03d0f9b110ba3a4d88db/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..d8b9936dad9ab2513fa6979f411560d3b6b57e37
--- /dev/null
+++ b/shared/package-files/1b/1b68144734c4b66791f27add5d425f3620775585718a03d0f9b110ba3a4d88db/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.12.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/shared/package-files/1b/1b683913497e140070632428c1b28abcb70423c3558895da60b5b779411cbe3c/test_convert_matrix_to_array.py b/shared/package-files/1b/1b683913497e140070632428c1b28abcb70423c3558895da60b5b779411cbe3c/test_convert_matrix_to_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..142585882588df6aa0e4648d9d8881ea755f42a0
--- /dev/null
+++ b/shared/package-files/1b/1b683913497e140070632428c1b28abcb70423c3558895da60b5b779411cbe3c/test_convert_matrix_to_array.py
@@ -0,0 +1,128 @@
+from sympy import Lambda, KroneckerProduct
+from sympy.core.symbol import symbols, Dummy
+from sympy.matrices.expressions.hadamard import (HadamardPower, HadamardProduct)
+from sympy.matrices.expressions.inverse import Inverse
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions.matpow import MatPow
+from sympy.matrices.expressions.special import Identity
+from sympy.matrices.expressions.trace import Trace
+from sympy.matrices.expressions.transpose import Transpose
+from sympy.tensor.array.expressions.array_expressions import ArrayTensorProduct, ArrayContraction, \
+    PermuteDims, ArrayDiagonal, ArrayElementwiseApplyFunc, _array_contraction, _array_tensor_product, Reshape
+from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix
+from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
+
+i, j, k, l, m, n = symbols("i j k l m n")
+
+I = Identity(k)
+
+M = MatrixSymbol("M", k, k)
+N = MatrixSymbol("N", k, k)
+P = MatrixSymbol("P", k, k)
+Q = MatrixSymbol("Q", k, k)
+
+A = MatrixSymbol("A", k, k)
+B = MatrixSymbol("B", k, k)
+C = MatrixSymbol("C", k, k)
+D = MatrixSymbol("D", k, k)
+
+X = MatrixSymbol("X", k, k)
+Y = MatrixSymbol("Y", k, k)
+
+a = MatrixSymbol("a", k, 1)
+b = MatrixSymbol("b", k, 1)
+c = MatrixSymbol("c", k, 1)
+d = MatrixSymbol("d", k, 1)
+
+
+def test_arrayexpr_convert_matrix_to_array():
+
+    expr = M*N
+    result = ArrayContraction(ArrayTensorProduct(M, N), (1, 2))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = M*N*M
+    result = _array_contraction(ArrayTensorProduct(M, N, M), (1, 2), (3, 4))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = Transpose(M)
+    assert convert_matrix_to_array(expr) == PermuteDims(M, [1, 0])
+
+    expr = M*Transpose(N)
+    assert convert_matrix_to_array(expr) == _array_contraction(_array_tensor_product(M, PermuteDims(N, [1, 0])), (1, 2))
+
+    expr = 3*M*N
+    res = convert_matrix_to_array(expr)
+    rexpr = convert_array_to_matrix(res)
+    assert expr == rexpr
+
+    expr = 3*M + N*M.T*M + 4*k*N
+    res = convert_matrix_to_array(expr)
+    rexpr = convert_array_to_matrix(res)
+    assert expr == rexpr
+
+    expr = Inverse(M)*N
+    rexpr = convert_array_to_matrix(convert_matrix_to_array(expr))
+    assert expr == rexpr
+
+    expr = M**2
+    rexpr = convert_array_to_matrix(convert_matrix_to_array(expr))
+    assert expr == rexpr
+
+    expr = M*(2*N + 3*M)
+    res = convert_matrix_to_array(expr)
+    rexpr = convert_array_to_matrix(res)
+    assert expr == rexpr
+
+    expr = Trace(M)
+    result = ArrayContraction(M, (0, 1))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = 3*Trace(M)
+    result = ArrayContraction(ArrayTensorProduct(3, M), (0, 1))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = 3*Trace(Trace(M) * M)
+    result = ArrayContraction(ArrayTensorProduct(3, M, M), (0, 1), (2, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = 3*Trace(M)**2
+    result = ArrayContraction(ArrayTensorProduct(3, M, M), (0, 1), (2, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = HadamardProduct(M, N)
+    result = ArrayDiagonal(ArrayTensorProduct(M, N), (0, 2), (1, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = HadamardProduct(M*N, N*M)
+    result = ArrayDiagonal(ArrayContraction(ArrayTensorProduct(M, N, N, M), (1, 2), (5, 6)), (0, 2), (1, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = HadamardPower(M, 2)
+    result = ArrayDiagonal(ArrayTensorProduct(M, M), (0, 2), (1, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = HadamardPower(M*N, 2)
+    result = ArrayDiagonal(ArrayContraction(ArrayTensorProduct(M, N, M, N), (1, 2), (5, 6)), (0, 2), (1, 3))
+    assert convert_matrix_to_array(expr) == result
+
+    expr = HadamardPower(M, n)
+    d0 = Dummy("d0")
+    result = ArrayElementwiseApplyFunc(Lambda(d0, d0**n), M)
+    assert convert_matrix_to_array(expr).dummy_eq(result)
+
+    expr = M**2
+    assert isinstance(expr, MatPow)
+    assert convert_matrix_to_array(expr) == ArrayContraction(ArrayTensorProduct(M, M), (1, 2))
+
+    expr = a.T*b
+    cg = convert_matrix_to_array(expr)
+    assert cg == ArrayContraction(ArrayTensorProduct(a, b), (0, 2))
+
+    expr = KroneckerProduct(A, B)
+    cg = convert_matrix_to_array(expr)
+    assert cg == Reshape(PermuteDims(ArrayTensorProduct(A, B), [0, 2, 1, 3]), (k**2, k**2))
+
+    expr = KroneckerProduct(A, B, C, D)
+    cg = convert_matrix_to_array(expr)
+    assert cg == Reshape(PermuteDims(ArrayTensorProduct(A, B, C, D), [0, 2, 4, 6, 1, 3, 5, 7]), (k**4, k**4))
diff --git a/shared/package-files/1b/1b684d0128144e8c5a6eec64dfced0c7dfd83885e092e87ed36b8212a3155fe8/_histogramdd_bin_edges_compositeexplicitautograd_dispatch.h b/shared/package-files/1b/1b684d0128144e8c5a6eec64dfced0c7dfd83885e092e87ed36b8212a3155fe8/_histogramdd_bin_edges_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0fa1dbf19c80e1a4e60db8dbf6a5d957e0881cb0
--- /dev/null
+++ b/shared/package-files/1b/1b684d0128144e8c5a6eec64dfced0c7dfd83885e092e87ed36b8212a3155fe8/_histogramdd_bin_edges_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void _histogramdd_bin_edges_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range=c10::nullopt, const c10::optional<at::Tensor> & weight={}, bool density=false);
+TORCH_API void _histogramdd_bin_edges_outf(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/1b/1b69d341510c98a956b7407be3f7d400beca437600bbffc9e87722898b362325/Thimbu b/shared/package-files/1b/1b69d341510c98a956b7407be3f7d400beca437600bbffc9e87722898b362325/Thimbu
new file mode 100644
index 0000000000000000000000000000000000000000..95a9de9657f5b8b15aacb6e05c085c7e11bb8f55
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diff --git a/shared/package-files/1b/1b69d341510c98a956b7407be3f7d400beca437600bbffc9e87722898b362325/Thimphu b/shared/package-files/1b/1b69d341510c98a956b7407be3f7d400beca437600bbffc9e87722898b362325/Thimphu
new file mode 100644
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diff --git a/shared/package-files/1b/1b6bd8d2db0ec839373b2e9159dfeb3b53d9e79b25e5f5a28c973f92beafb7ea/numpy.py b/shared/package-files/1b/1b6bd8d2db0ec839373b2e9159dfeb3b53d9e79b25e5f5a28c973f92beafb7ea/numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d550061c6bb5c56d4f1287cf7268012f0f38af7
--- /dev/null
+++ b/shared/package-files/1b/1b6bd8d2db0ec839373b2e9159dfeb3b53d9e79b25e5f5a28c973f92beafb7ea/numpy.py
@@ -0,0 +1,80 @@
+"""Compatibility functions for numpy versions in lib
+
+np_new_unique
+-------------
+Optionally provides the count of the number of occurrences of each
+unique element.
+
+Copied from Numpy source, under license:
+
+Copyright (c) 2005-2015, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the following
+  disclaimer in the documentation and/or other materials provided
+  with the distribution.
+
+* Neither the name of the NumPy Developers nor the names of any
+  contributors may be used to endorse or promote products derived
+  from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import numpy as np
+from packaging.version import Version, parse
+
+__all__ = [
+    "NP_LT_2",
+    "NP_LT_24",
+    "inplace_reshape",
+    "np_matrix_rank",
+    "np_new_unique",
+]
+
+NP_LT_2 = parse(np.__version__) < Version("1.99.99")
+NP_LT_24 = parse(np.__version__) < Version("2.3.99")
+
+np_matrix_rank = np.linalg.matrix_rank
+np_new_unique = np.unique
+
+
+def inplace_reshape(arr: np.ndarray, shape: tuple[int, ...]) -> np.ndarray:
+    """
+    Reshape an array in place when possible, falling back to a copy
+
+    Parameters
+    ----------
+    arr : ndarray
+        The array to reshape.
+    shape : tuple[int, ...]
+        The new shape.
+
+    Returns
+    -------
+    ndarray
+        The reshaped array.
+    """
+    try:
+        return arr.reshape(shape, copy=False)
+    except TypeError:
+        arr.shape = shape
+        return arr
diff --git a/shared/package-files/1b/1b7341e493247c4368f2fd6ee97d34735f95d87117e16f5f46a70d89b35d18d7/random.pyi b/shared/package-files/1b/1b7341e493247c4368f2fd6ee97d34735f95d87117e16f5f46a70d89b35d18d7/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9a28024c2c391c519ae788eb428f1324a8eb5c94
--- /dev/null
+++ b/shared/package-files/1b/1b7341e493247c4368f2fd6ee97d34735f95d87117e16f5f46a70d89b35d18d7/random.pyi
@@ -0,0 +1,87 @@
+import _random
+import sys
+from typing import AbstractSet, Any, Callable, Iterable, List, MutableSequence, Optional, Sequence, Tuple, TypeVar, Union
+
+_T = TypeVar("_T")
+
+class Random(_random.Random):
+    def __init__(self, x: Any = ...) -> None: ...
+    def seed(self, a: Any = ..., version: int = ...) -> None: ...
+    def getstate(self) -> Tuple[Any, ...]: ...
+    def setstate(self, state: Tuple[Any, ...]) -> None: ...
+    def getrandbits(self, __k: int) -> int: ...
+    def randrange(self, start: int, stop: Optional[int] = ..., step: int = ...) -> int: ...
+    def randint(self, a: int, b: int) -> int: ...
+    if sys.version_info >= (3, 9):
+        def randbytes(self, n: int) -> bytes: ...
+    def choice(self, seq: Sequence[_T]) -> _T: ...
+    def choices(
+        self,
+        population: Sequence[_T],
+        weights: Optional[Sequence[float]] = ...,
+        *,
+        cum_weights: Optional[Sequence[float]] = ...,
+        k: int = ...,
+    ) -> List[_T]: ...
+    def shuffle(self, x: MutableSequence[Any], random: Optional[Callable[[], float]] = ...) -> None: ...
+    if sys.version_info >= (3, 9):
+        def sample(
+            self, population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...
+        ) -> List[_T]: ...
+    else:
+        def sample(self, population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
+    def random(self) -> float: ...
+    def uniform(self, a: float, b: float) -> float: ...
+    def triangular(self, low: float = ..., high: float = ..., mode: Optional[float] = ...) -> float: ...
+    def betavariate(self, alpha: float, beta: float) -> float: ...
+    def expovariate(self, lambd: float) -> float: ...
+    def gammavariate(self, alpha: float, beta: float) -> float: ...
+    def gauss(self, mu: float, sigma: float) -> float: ...
+    def lognormvariate(self, mu: float, sigma: float) -> float: ...
+    def normalvariate(self, mu: float, sigma: float) -> float: ...
+    def vonmisesvariate(self, mu: float, kappa: float) -> float: ...
+    def paretovariate(self, alpha: float) -> float: ...
+    def weibullvariate(self, alpha: float, beta: float) -> float: ...
+
+# SystemRandom is not implemented for all OS's; good on Windows & Linux
+class SystemRandom(Random): ...
+
+# ----- random function stubs -----
+def seed(a: Any = ..., version: int = ...) -> None: ...
+def getstate() -> object: ...
+def setstate(state: object) -> None: ...
+def getrandbits(__k: int) -> int: ...
+def randrange(start: int, stop: Union[None, int] = ..., step: int = ...) -> int: ...
+def randint(a: int, b: int) -> int: ...
+
+if sys.version_info >= (3, 9):
+    def randbytes(n: int) -> bytes: ...
+
+def choice(seq: Sequence[_T]) -> _T: ...
+def choices(
+    population: Sequence[_T],
+    weights: Optional[Sequence[float]] = ...,
+    *,
+    cum_weights: Optional[Sequence[float]] = ...,
+    k: int = ...,
+) -> List[_T]: ...
+def shuffle(x: MutableSequence[Any], random: Optional[Callable[[], float]] = ...) -> None: ...
+
+if sys.version_info >= (3, 9):
+    def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...) -> List[_T]: ...
+
+else:
+    def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
+
+def random() -> float: ...
+def uniform(a: float, b: float) -> float: ...
+def triangular(low: float = ..., high: float = ..., mode: Optional[float] = ...) -> float: ...
+def betavariate(alpha: float, beta: float) -> float: ...
+def expovariate(lambd: float) -> float: ...
+def gammavariate(alpha: float, beta: float) -> float: ...
+def gauss(mu: float, sigma: float) -> float: ...
+def lognormvariate(mu: float, sigma: float) -> float: ...
+def normalvariate(mu: float, sigma: float) -> float: ...
+def vonmisesvariate(mu: float, kappa: float) -> float: ...
+def paretovariate(alpha: float) -> float: ...
+def weibullvariate(alpha: float, beta: float) -> float: ...
diff --git a/shared/package-files/1b/1b751a27ac8f8ab584855da7daff1967daefc0121383df19ace34e509a4de0e0/test_conditional.py b/shared/package-files/1b/1b751a27ac8f8ab584855da7daff1967daefc0121383df19ace34e509a4de0e0/test_conditional.py
new file mode 100644
index 0000000000000000000000000000000000000000..44e27c36a4ba9b32dc9d98c3a9b494ffe96a072c
--- /dev/null
+++ b/shared/package-files/1b/1b751a27ac8f8ab584855da7daff1967daefc0121383df19ace34e509a4de0e0/test_conditional.py
@@ -0,0 +1,477 @@
+import numpy as np
+from numpy.testing import assert_allclose
+import pandas as pd
+import pytest
+
+from statsmodels.discrete.conditional_models import (
+    ConditionalLogit,
+    ConditionalMNLogit,
+    ConditionalPoisson,
+)
+from statsmodels.iolib.summary import Summary
+from statsmodels.tools.numdiff import approx_fprime
+
+
+def test_logit_1d():
+
+    y = np.r_[0, 1, 0, 1, 0, 1, 0, 1, 1, 1]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+
+    x = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x = x[:, None]
+
+    model = ConditionalLogit(y, x, groups=g)
+
+    # Check the gradient for the denominator of the partial likelihood
+    for x in -1, 0, 1, 2:
+        params = np.r_[x,]
+        _, grad = model._denom_grad(0, params)
+        ngrad = approx_fprime(params, lambda x: model._denom(0, x)).squeeze()
+        assert_allclose(grad, ngrad)
+
+    # Check the gradient for the loglikelihood
+    for x in -1, 0, 1, 2:
+        grad = approx_fprime(np.r_[x,], model.loglike).squeeze()
+        score = model.score(np.r_[x,])
+        assert_allclose(grad, score, rtol=1e-4)
+
+    result = model.fit()
+
+    # From Stata
+    assert_allclose(result.params, np.r_[0.9272407], rtol=1e-5)
+    assert_allclose(result.bse, np.r_[1.295155], rtol=1e-5)
+
+
+def test_logit_formula():
+    """Test that ConditionalLogit uses the right environment for formulas"""
+
+    def times_two(x):
+        return 2 * x
+
+    groups = np.repeat([0, 1], 50)
+    exog = np.linspace(-2, 2, len(groups))
+
+    error = np.linspace(-1, 1, len(groups))  # Needed for within-group variance
+    logit_link = 1 / (1 + np.exp(exog + groups)) + error
+    endog = (logit_link > 0.5).astype(int)
+
+    data = pd.DataFrame({"exog": exog, "groups": groups, "endog": endog})
+
+    result_direct = ConditionalLogit(endog, times_two(exog), groups=groups).fit()
+
+    result_formula = ConditionalLogit.from_formula(
+        "endog ~ 0 + times_two(exog)", groups="groups", data=data
+    ).fit()
+
+    assert_allclose(result_direct.params, result_formula.params)
+    assert_allclose(result_direct.bse, result_formula.bse)
+
+
+def test_logit_2d():
+
+    y = np.r_[0, 1, 0, 1, 0, 1, 0, 1, 1, 1]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+
+    x1 = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x2 = np.r_[0, 0, 1, 0, 0, 1, 0, 1, 1, 1]
+    x = np.empty((10, 2))
+    x[:, 0] = x1
+    x[:, 1] = x2
+
+    model = ConditionalLogit(y, x, groups=g)
+
+    # Check the gradient for the denominator of the partial likelihood
+    for x in -1, 0, 1, 2:
+        params = np.r_[x, -1.5 * x]
+        _, grad = model._denom_grad(0, params)
+        ngrad = approx_fprime(params, lambda x: model._denom(0, x))
+        assert_allclose(grad, ngrad, rtol=1e-5)
+
+    # Check the gradient for the loglikelihood
+    for x in -1, 0, 1, 2:
+        params = np.r_[-0.5 * x, 0.5 * x]
+        grad = approx_fprime(params, model.loglike)
+        score = model.score(params)
+        assert_allclose(grad, score, rtol=1e-4)
+
+    result = model.fit()
+
+    # From Stata
+    assert_allclose(result.params, np.r_[1.011074, 1.236758], rtol=1e-3)
+    assert_allclose(result.bse, np.r_[1.420784, 1.361738], rtol=1e-5)
+
+    result.summary()
+
+
+def test_formula():
+
+    for j in 0, 1:
+
+        rs = np.random.RandomState(34234)
+        n = 200
+        y = rs.randint(0, 2, size=n)
+        x1 = rs.normal(size=n)
+        x2 = rs.normal(size=n)
+        g = rs.randint(0, 25, size=n)
+
+        x = np.hstack((x1[:, None], x2[:, None]))
+        if j == 0:
+            model1 = ConditionalLogit(y, x, groups=g)
+        else:
+            model1 = ConditionalPoisson(y, x, groups=g)
+        result1 = model1.fit()
+
+        df = pd.DataFrame({"y": y, "x1": x1, "x2": x2, "g": g})
+        if j == 0:
+            model2 = ConditionalLogit.from_formula(
+                "y ~ 0 + x1 + x2", groups="g", data=df
+            )
+        else:
+            model2 = ConditionalPoisson.from_formula(
+                "y ~ 0 + x1 + x2", groups="g", data=df
+            )
+        result2 = model2.fit()
+
+        assert_allclose(result1.params, result2.params, rtol=1e-5)
+        assert_allclose(result1.bse, result2.bse, rtol=1e-5)
+        assert_allclose(result1.cov_params(), result2.cov_params(), rtol=1e-5)
+        assert_allclose(result1.tvalues, result2.tvalues, rtol=1e-5)
+
+
+def test_poisson_1d():
+
+    y = np.r_[3, 1, 1, 4, 5, 2, 0, 1, 6, 2]
+    g = np.r_[0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
+
+    x = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x = x[:, None]
+
+    model = ConditionalPoisson(y, x, groups=g)
+
+    # Check the gradient for the loglikelihood
+    for x in -1, 0, 1, 2:
+        grad = approx_fprime(np.r_[x,], model.loglike).squeeze()
+        score = model.score(np.r_[x,])
+        assert_allclose(grad, score, rtol=1e-4)
+
+    result = model.fit()
+
+    # From Stata
+    assert_allclose(result.params, np.r_[0.6466272], rtol=1e-4)
+    assert_allclose(result.bse, np.r_[0.4170918], rtol=1e-5)
+
+
+def test_poisson_2d():
+
+    y = np.r_[3, 1, 4, 8, 2, 5, 4, 7, 2, 6]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+
+    x1 = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x2 = np.r_[2, 1, 0, 0, 1, 2, 3, 2, 0, 1]
+    x = np.empty((10, 2))
+    x[:, 0] = x1
+    x[:, 1] = x2
+
+    model = ConditionalPoisson(y, x, groups=g)
+
+    # Check the gradient for the loglikelihood
+    for x in -1, 0, 1, 2:
+        params = np.r_[-0.5 * x, 0.5 * x]
+        grad = approx_fprime(params, model.loglike)
+        score = model.score(params)
+        assert_allclose(grad, score, rtol=1e-4)
+
+    result = model.fit()
+
+    # From Stata
+    assert_allclose(result.params, np.r_[-0.9478957, -0.0134279], rtol=1e-3)
+    assert_allclose(result.bse, np.r_[0.3874942, 0.1686712], rtol=1e-5)
+
+    result.summary()
+
+
+def test_lasso_logistic():
+
+    rs = np.random.RandomState(3423948)
+
+    n = 200
+    groups = np.arange(10)
+    groups = np.kron(groups, np.ones(n // 10))
+    group_effects = rs.normal(size=10)
+    group_effects = np.kron(group_effects, np.ones(n // 10))
+
+    x = rs.normal(size=(n, 4))
+    params = np.r_[0, 0, 1, 0]
+    lin_pred = np.dot(x, params) + group_effects
+
+    mean = 1 / (1 + np.exp(-lin_pred))
+    y = (rs.uniform(size=n) < mean).astype(int)
+
+    model0 = ConditionalLogit(y, x, groups=groups)
+    result0 = model0.fit()
+
+    # Should be the same as model0
+    model1 = ConditionalLogit(y, x, groups=groups)
+    result1 = model1.fit_regularized(L1_wt=0, alpha=0)
+
+    assert_allclose(result0.params, result1.params, rtol=1e-3)
+
+    model2 = ConditionalLogit(y, x, groups=groups)
+    result2 = model2.fit_regularized(L1_wt=1, alpha=0.05)
+
+    # Rxegression test
+    assert_allclose(result2.params, np.r_[0, 0, 0.55235152, 0], rtol=1e-4)
+
+    # Test with formula
+    df = pd.DataFrame(
+        {
+            "y": y,
+            "x1": x[:, 0],
+            "x2": x[:, 1],
+            "x3": x[:, 2],
+            "x4": x[:, 3],
+            "groups": groups,
+        }
+    )
+    fml = "y ~ 0 + x1 + x2 + x3 + x4"
+    model3 = ConditionalLogit.from_formula(fml, groups="groups", data=df)
+    result3 = model3.fit_regularized(L1_wt=1, alpha=0.05)
+    assert_allclose(result2.params, result3.params)
+
+
+def test_lasso_poisson():
+
+    rs = np.random.RandomState(342394)
+
+    n = 200
+    groups = np.arange(10)
+    groups = np.kron(groups, np.ones(n // 10))
+    group_effects = rs.normal(size=10)
+    group_effects = np.kron(group_effects, np.ones(n // 10))
+
+    x = rs.normal(size=(n, 4))
+    params = np.r_[0, 0, 1, 0]
+    lin_pred = np.dot(x, params) + group_effects
+
+    mean = np.exp(lin_pred)
+    y = rs.poisson(mean)
+
+    model0 = ConditionalPoisson(y, x, groups=groups)
+    result0 = model0.fit()
+
+    # Should be the same as model0
+    model1 = ConditionalPoisson(y, x, groups=groups)
+    result1 = model1.fit_regularized(L1_wt=0, alpha=0)
+
+    assert_allclose(result0.params, result1.params, rtol=1e-3)
+
+    model2 = ConditionalPoisson(y, x, groups=groups)
+    result2 = model2.fit_regularized(L1_wt=1, alpha=0.2)
+
+    # Regression test
+    assert_allclose(result2.params, np.r_[0, 0, 0.91697508, 0], rtol=1e-4)
+
+    # Test with formula
+    df = pd.DataFrame(
+        {
+            "y": y,
+            "x1": x[:, 0],
+            "x2": x[:, 1],
+            "x3": x[:, 2],
+            "x4": x[:, 3],
+            "groups": groups,
+        }
+    )
+    fml = "y ~ 0 + x1 + x2 + x3 + x4"
+    model3 = ConditionalPoisson.from_formula(fml, groups="groups", data=df)
+    result3 = model3.fit_regularized(L1_wt=1, alpha=0.2)
+    assert_allclose(result2.params, result3.params)
+
+
+def test_fit_regularized_invalid_method():
+    rs = np.random.RandomState(3423948)
+    n = 200
+    groups = np.kron(np.arange(10), np.ones(n // 10))
+    x = rs.normal(size=(n, 2))
+    y = (rs.uniform(size=n) < 0.5).astype(int)
+
+    model = ConditionalLogit(y, x, groups=groups)
+    with pytest.raises(ValueError, match="method"):
+        model.fit_regularized(method="not-a-method")
+
+
+def gen_mnlogit(n):
+
+    rs = np.random.RandomState(235)
+
+    g = np.kron(np.ones(5), np.arange(n // 5))
+    x1 = rs.normal(size=n)
+    x2 = rs.normal(size=n)
+    xm = np.concatenate((x1[:, None], x2[:, None]), axis=1)
+    pa = np.array([[0, 1, -1], [0, 2, -1]])
+    lpr = np.dot(xm, pa)
+    pr = np.exp(lpr)
+    pr /= pr.sum(1)[:, None]
+    cpr = pr.cumsum(1)
+    y = 2 * np.ones(n)
+    u = rs.uniform(size=n)
+    y[u < cpr[:, 2]] = 2
+    y[u < cpr[:, 1]] = 1
+    y[u < cpr[:, 0]] = 0
+
+    df = pd.DataFrame({"y": y, "x1": x1, "x2": x2, "g": g})
+    return df, rs
+
+
+def test_conditional_mnlogit_grad():
+    df, rs = gen_mnlogit(90)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+
+    # Compare the gradients to numeric gradients
+    for _ in range(5):
+        za = rs.normal(size=4)
+        grad = model.score(za)
+        ngrad = approx_fprime(za, model.loglike)
+        assert_allclose(grad, ngrad, rtol=1e-5, atol=1e-3)
+
+
+def test_conditional_mnlogit_2d():
+
+    df, rs = gen_mnlogit(90)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+    result = model.fit(rng=rs)
+
+    # Regression tests
+    assert_allclose(
+        result.params,
+        np.asarray([[0.75592035, -1.58565494], [1.82919869, -1.32594231]]),
+        rtol=1e-5,
+        atol=1e-5,
+    )
+    assert_allclose(
+        result.bse,
+        np.asarray([[0.68099698, 0.70142727], [0.65190315, 0.59653771]]),
+        rtol=1e-5,
+        atol=1e-5,
+    )
+
+
+def test_conditional_mnlogit_3d():
+    df, rs = gen_mnlogit(90)
+    df["x3"] = rs.normal(size=df.shape[0])
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2 + x3", groups="g", data=df)
+    result = model.fit(rng=rs)
+
+    # Regression tests
+    assert_allclose(
+        result.params,
+        np.asarray(
+            [[0.729629, -1.633673], [1.879019, -1.327163], [-0.114124, -0.109378]]
+        ),
+        atol=1e-5,
+        rtol=1e-5,
+    )
+
+    assert_allclose(
+        result.bse,
+        np.asarray([[0.682965, 0.60472], [0.672947, 0.42401], [0.722631, 0.33663]]),
+        atol=1e-5,
+        rtol=1e-5,
+    )
+
+    # Smoke test
+    result.summary()
+
+
+@pytest.mark.parametrize(
+    "rng",
+    [0, np.random.RandomState(0), np.random.default_rng(0)],
+    ids=["int", "randomstate", "generator"],
+)
+def test_conditional_mnlogit_fit_rng_types(recwarn, rng):
+    # GH: fit() used to special-case rng=None with an unconditional
+    # FutureWarning and global np.random state instead of routing
+    # through check_random_state like every other rng-accepting
+    # function, so the default call emitted a warning that no other
+    # canonical rng parameter does.
+    df, _ = gen_mnlogit(60)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+    model.fit(rng=rng, disp=False)
+    future_warnings = [w for w in recwarn.list if issubclass(w.category, FutureWarning)]
+    assert not future_warnings, [str(w.message) for w in future_warnings]
+
+
+@pytest.mark.singleton_randomstate
+def test_conditional_mnlogit_fit_warn():
+    # GH: fit() used to special-case rng=None with an unconditional
+    # FutureWarning and global np.random state instead of routing
+    # through check_random_state like every other rng-accepting
+    # function, so the default call emitted a warning that no other
+    # canonical rng parameter does.
+    df, _ = gen_mnlogit(60)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+    with pytest.warns(FutureWarning, match="When start_params is not specified,"):
+        model.fit(rng=None, disp=False)
+
+
+def test_conditional_mnlogit_fit_rng_reproducible():
+    df, _ = gen_mnlogit(60)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+    res1 = model.fit(rng=0, disp=False)
+    res2 = model.fit(rng=0, disp=False)
+    assert_allclose(res1.params, res2.params)
+
+
+def test_skip_hessian():
+    y = np.r_[0, 1, 0, 1, 0, 1, 0, 1, 1, 1]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+
+    x = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x = x[:, None]
+
+    model = ConditionalLogit(y, x, groups=g)
+    result_no_hess = model.fit(skip_hessian=True)
+    result_hess = model.fit(skip_hessian=False)
+    assert result_no_hess.normalized_cov_params is None
+    assert isinstance(result_hess.normalized_cov_params, np.ndarray)
+
+
+def _fit_conditional_logit_for_summary():
+    y = np.r_[0, 1, 0, 1, 0, 1, 0, 1, 1, 1]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+    x1 = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x2 = np.r_[0, 0, 1, 0, 0, 1, 0, 1, 1, 1]
+    x = np.column_stack((x1, x2))
+    return ConditionalLogit(y, x, groups=g).fit()
+
+
+def _fit_conditional_poisson_for_summary():
+    y = np.r_[3, 1, 4, 8, 2, 5, 4, 7, 2, 6]
+    g = np.r_[0, 0, 0, 1, 1, 1, 2, 2, 2, 2]
+    x1 = np.r_[0, 1, 0, 0, 1, 1, 0, 0, 1, 0]
+    x2 = np.r_[2, 1, 0, 0, 1, 2, 3, 2, 0, 1]
+    x = np.column_stack((x1, x2))
+    return ConditionalPoisson(y, x, groups=g).fit()
+
+
+def _fit_conditional_mnlogit_for_summary():
+    df, rs = gen_mnlogit(90)
+    model = ConditionalMNLogit.from_formula("y ~ 0 + x1 + x2", groups="g", data=df)
+    return model.fit(rng=rs)
+
+
+@pytest.mark.parametrize(
+    "fit_func",
+    [
+        _fit_conditional_logit_for_summary,
+        _fit_conditional_poisson_for_summary,
+        _fit_conditional_mnlogit_for_summary,
+    ],
+    ids=["ConditionalLogit", "ConditionalPoisson", "ConditionalMNLogit"],
+)
+def test_summary_after_remove_data(fit_func):
+    # summary() must still work after remove_data() has been called
+    res = fit_func()
+    assert isinstance(res.summary(), Summary)
+    res.remove_data()
+    assert isinstance(res.summary(), Summary)
diff --git a/shared/package-files/1b/1b75906b6844195020a669a8960b2179119b3f671f349a2a2865ae63e1e20e7b/test_summary2.py b/shared/package-files/1b/1b75906b6844195020a669a8960b2179119b3f671f349a2a2865ae63e1e20e7b/test_summary2.py
new file mode 100644
index 0000000000000000000000000000000000000000..72300c4034c32c5a760b48ff8f580700f6ce71f0
--- /dev/null
+++ b/shared/package-files/1b/1b75906b6844195020a669a8960b2179119b3f671f349a2a2865ae63e1e20e7b/test_summary2.py
@@ -0,0 +1,302 @@
+from statsmodels.compat.scipy import SP_LT_116
+
+import warnings
+
+import numpy as np
+from numpy.testing import assert_equal
+import pandas as pd
+import pytest
+
+from statsmodels.iolib.summary2 import summary_col
+from statsmodels.regression.linear_model import OLS
+from statsmodels.tools.tools import add_constant
+
+
+def _rstrip_txt(txt):
+    return "\n".join([line.rstrip() for line in txt.split("\n")])
+
+
+class TestSummaryLatex:
+
+    def test_summarycol(self):
+        # Test for latex output of summary_col object
+        desired = r"""
+\begin{table}
+\caption{}
+\label{}
+\begin{center}
+\begin{tabular}{lll}
+\hline
+               & y I      & y II      \\
+\hline
+const          & 7.7500   & 12.4231   \\
+               & (1.1058) & (3.1872)  \\
+x1             & -0.7500  & -1.5769   \\
+               & (0.2368) & (0.6826)  \\
+R-squared      & 0.7697   & 0.6401    \\
+R-squared Adj. & 0.6930   & 0.5202    \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+\bigskip
+Standard errors in parentheses.
+"""
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        y1 = [6, 4, 2, 7, 4]
+        y2 = [8, 5, 0, 12, 4]
+        reg1 = OLS(y1, x).fit()
+        reg2 = OLS(y2, x).fit()
+        actual = summary_col([reg1, reg2]).as_latex()
+        actual = f"\n{actual}\n"
+        assert_equal(desired, actual)
+
+    def test_summarycol_float_format(self):
+        # Test for latex output of summary_col object
+        desired = r"""
+==========================
+                y I   y II
+--------------------------
+const          7.7   12.4
+               (1.1) (3.2)
+x1             -0.7  -1.6
+               (0.2) (0.7)
+R-squared      0.8   0.6
+R-squared Adj. 0.7   0.5
+==========================
+Standard errors in
+parentheses.
+"""
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        y1 = [6, 4, 2, 7, 4]
+        y2 = [8, 5, 0, 12, 4]
+        reg1 = OLS(y1, x).fit()
+        reg2 = OLS(y2, x).fit()
+        actual = summary_col([reg1, reg2], float_format="%0.1f").as_text()
+        actual = f"{actual}\n"
+
+        starred = summary_col([reg1, reg2], stars=True, float_format="%0.1f")
+        assert "7.7***" in str(starred)
+        assert "12.4**" in str(starred)
+        assert "12.4***" not in str(starred)
+
+        assert_equal(_rstrip_txt(actual), desired)
+
+    def test_summarycol_drop_omitted(self):
+        # gh-3702
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        x2 = np.concatenate([x, np.array([[3], [9], [-1], [4], [0]])], 1)
+        y1 = [6, 4, 2, 7, 4]
+        y2 = [8, 5, 0, 12, 4]
+        reg1 = OLS(y1, x).fit()
+        reg2 = OLS(y2, x2).fit()
+        actual = summary_col(
+            [reg1, reg2], regressor_order=["const", "x1"], drop_omitted=True
+        )
+        assert "x2" not in str(actual)
+        actual = summary_col([reg1, reg2], regressor_order=["x1"], drop_omitted=False)
+        assert "const" in str(actual)
+        assert "x2" in str(actual)
+
+    def test_summary_col_ordering_preserved(self):
+        # gh-3767
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        x2 = np.concatenate([x, np.array([[3], [9], [-1], [4], [0]])], 1)
+        x2 = pd.DataFrame(x2, columns=["const", "b", "a"])
+        y1 = [6, 4, 2, 7, 4]
+        y2 = [8, 5, 0, 12, 4]
+        reg1 = OLS(y1, x2).fit()
+        reg2 = OLS(y2, x2).fit()
+
+        info_dict = {
+            "R2": lambda x: f"{int(x.rsquared):.3f}",
+            "N": lambda x: f"{int(x.nobs):d}",
+        }
+        original = actual = summary_col([reg1, reg2], float_format="%0.4f")
+        actual = summary_col(
+            [reg1, reg2],
+            regressor_order=["a", "b"],
+            float_format="%0.4f",
+            info_dict=info_dict,
+        )
+        variables = ("const", "b", "a")
+        for line in str(original).split("\n"):
+            for variable in variables:
+                if line.startswith(variable):
+                    assert line in str(actual)
+
+    def test__repr_latex_(self):
+        desired = r"""
+\begin{table}
+\caption{}
+\label{}
+\begin{center}
+\begin{tabular}{lll}
+\hline
+               & y I      & y II      \\
+\hline
+const          & 7.7500   & 12.4231   \\
+               & (1.1058) & (3.1872)  \\
+x1             & -0.7500  & -1.5769   \\
+               & (0.2368) & (0.6826)  \\
+R-squared      & 0.7697   & 0.6401    \\
+R-squared Adj. & 0.6930   & 0.5202    \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+\bigskip
+Standard errors in parentheses.
+"""
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        y1 = [6, 4, 2, 7, 4]
+        y2 = [8, 5, 0, 12, 4]
+        reg1 = OLS(y1, x).fit()
+        reg2 = OLS(y2, x).fit()
+
+        actual = summary_col([reg1, reg2])._repr_latex_()
+        actual = f"\n{actual}\n"
+        assert_equal(actual, desired)
+
+    def test_OLSsummary(self):
+        # Test that latex output of regular OLS output still contains
+        # multiple tables
+
+        x = [1, 5, 7, 3, 5]
+        x = add_constant(x)
+        y1 = [6, 4, 2, 7, 4]
+        reg1 = OLS(y1, x).fit()
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            actual = reg1.summary().as_latex()
+        string_to_find = r"""\end{tabular}
+\begin{tabular}"""
+        result = string_to_find in actual
+        assert result is True
+
+    def test_summarycol_fixed_effects(self):
+        desired1 = r"""
+======================================
+               model_0 model_1 model_2
+--------------------------------------
+Intercept      1.35    1.32    1.48
+               (0.19)  (0.42)  (0.73)
+yrs_married    -0.03   -0.02   -0.02
+               (0.00)  (0.00)  (0.00)
+educ           -0.03   -0.02   -0.02
+               (0.01)  (0.02)  (0.02)
+occupation FE          Yes     Yes
+religious FE           Yes     Yes
+R-squared      0.01    0.02    0.03
+R-squared Adj. 0.01    0.02    0.02
+======================================
+Standard errors in parentheses."""
+
+        desired2 = r"""
+========================================
+                     mod0   mod1   mod2
+----------------------------------------
+Intercept           1.35   1.32   1.48
+                    (0.19) (0.42) (0.73)
+yrs_married         -0.03  -0.02  -0.02
+                    (0.00) (0.00) (0.00)
+educ                -0.03  -0.02  -0.02
+                    (0.01) (0.02) (0.02)
+C(religious)[T.2.0]        -0.46  -0.86
+                           (0.08) (0.87)
+C(religious)[T.3.0]        -0.66  -0.71
+                           (0.08) (1.13)
+C(religious)[T.4.0]        -0.91  -0.92
+                           (0.11) (1.03)
+occupation FE              Yes    Yes
+R-squared           0.01   0.02   0.03
+R-squared Adj.      0.01   0.02   0.02
+========================================
+Standard errors in parentheses."""
+
+        from statsmodels.datasets.fair import load_pandas
+
+        df_fair = load_pandas().data
+
+        res0 = OLS.from_formula("affairs ~ yrs_married + educ", df_fair).fit()
+        form1 = "affairs ~ yrs_married + educ + C(occupation) + C(religious)"
+        res1 = OLS.from_formula(form1, df_fair).fit()
+        form2 = "affairs ~ yrs_married + educ + C(occupation) : C(religious)"
+        res2 = OLS.from_formula(form2, df_fair).fit()
+
+        regressions = [res0, res1, res2]
+        summary1 = summary_col(
+            regressions,
+            model_names=[f"model_{i}" for i, _ in enumerate(regressions)],
+            fixed_effects=["occupation", "religious"],
+            float_format="%0.2f",
+        )
+
+        assert_equal(_rstrip_txt(summary1.as_text()), desired1)
+
+        summary2 = summary_col(
+            regressions,
+            model_names=[f"mod{i}" for i, _ in enumerate(regressions)],
+            fixed_effects=["occupation"],
+            float_format="%0.2f",
+        )
+
+        assert_equal(_rstrip_txt(summary2.as_text()), desired2)
+
+
+def test_ols_summary_rsquared_label():
+    # Check that the "uncentered" label is correctly added after rsquared
+    x = [1, 5, 7, 3, 5, 2, 5, 3]
+    y = [6, 4, 2, 7, 4, 9, 10, 2]
+    reg_with_constant = OLS(y, add_constant(x)).fit()
+    r2_str = "R-squared:"
+    if SP_LT_116:
+        with pytest.warns(UserWarning, match=r"(.*)kurtosistest(.*)"):
+            assert r2_str in str(reg_with_constant.summary2())
+        with pytest.warns(UserWarning, match=r"(.*)kurtosistest(.*)"):
+            assert r2_str in str(reg_with_constant.summary())
+    else:
+        assert r2_str in str(reg_with_constant.summary2())
+        assert r2_str in str(reg_with_constant.summary())
+
+    reg_without_constant = OLS(y, x, hasconst=False).fit()
+    r2_str = "R-squared (uncentered):"
+    if SP_LT_116:
+        with pytest.warns(UserWarning, match=r"(.*)kurtosistest(.*)"):
+            assert r2_str in str(reg_without_constant.summary2())
+        with pytest.warns(UserWarning, match=r"(.*)kurtosistest(.*)"):
+            assert r2_str in str(reg_without_constant.summary())
+    else:
+        assert r2_str in str(reg_without_constant.summary2())
+        assert r2_str in str(reg_without_constant.summary())
+
+
+class TestSummaryLabels:
+    """
+    Test that the labels are correctly set in the summary table"""
+
+    @classmethod
+    def setup_class(cls):
+        y = [1, 1, 4, 2] * 4
+        x = add_constant([1, 2, 3, 4] * 4)
+        cls.mod = OLS(endog=y, exog=x).fit()
+
+    def test_summary_col_r2(
+        self,
+    ):
+        # GH 6578
+        table = summary_col(results=self.mod, include_r2=True)
+        assert "R-squared  " in str(table)
+        assert "R-squared Adj." in str(table)
+
+    def test_absence_of_r2(
+        self,
+    ):
+        table = summary_col(results=self.mod, include_r2=False)
+        assert "R-squared" not in str(table)
+        assert "R-squared Adj." not in str(table)
diff --git a/shared/package-files/1b/1b76b74d7bbc5435bfd4433128a2e718c6ef51284421412f7b779a9419492771/_machar.py b/shared/package-files/1b/1b76b74d7bbc5435bfd4433128a2e718c6ef51284421412f7b779a9419492771/_machar.py
new file mode 100644
index 0000000000000000000000000000000000000000..59d71014ff20053fecd69bf3448b152030842491
--- /dev/null
+++ b/shared/package-files/1b/1b76b74d7bbc5435bfd4433128a2e718c6ef51284421412f7b779a9419492771/_machar.py
@@ -0,0 +1,356 @@
+"""
+Machine arithmetic - determine the parameters of the
+floating-point arithmetic system
+
+Author: Pearu Peterson, September 2003
+
+"""
+__all__ = ['MachAr']
+
+from .fromnumeric import any
+from ._ufunc_config import errstate
+from .._utils import set_module
+
+# Need to speed this up...especially for longfloat
+
+# Deprecated 2021-10-20, NumPy 1.22
+class MachAr:
+    """
+    Diagnosing machine parameters.
+
+    Attributes
+    ----------
+    ibeta : int
+        Radix in which numbers are represented.
+    it : int
+        Number of base-`ibeta` digits in the floating point mantissa M.
+    machep : int
+        Exponent of the smallest (most negative) power of `ibeta` that,
+        added to 1.0, gives something different from 1.0
+    eps : float
+        Floating-point number ``beta**machep`` (floating point precision)
+    negep : int
+        Exponent of the smallest power of `ibeta` that, subtracted
+        from 1.0, gives something different from 1.0.
+    epsneg : float
+        Floating-point number ``beta**negep``.
+    iexp : int
+        Number of bits in the exponent (including its sign and bias).
+    minexp : int
+        Smallest (most negative) power of `ibeta` consistent with there
+        being no leading zeros in the mantissa.
+    xmin : float
+        Floating-point number ``beta**minexp`` (the smallest [in
+        magnitude] positive floating point number with full precision).
+    maxexp : int
+        Smallest (positive) power of `ibeta` that causes overflow.
+    xmax : float
+        ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
+        usable floating value).
+    irnd : int
+        In ``range(6)``, information on what kind of rounding is done
+        in addition, and on how underflow is handled.
+    ngrd : int
+        Number of 'guard digits' used when truncating the product
+        of two mantissas to fit the representation.
+    epsilon : float
+        Same as `eps`.
+    tiny : float
+        An alias for `smallest_normal`, kept for backwards compatibility.
+    huge : float
+        Same as `xmax`.
+    precision : float
+        ``- int(-log10(eps))``
+    resolution : float
+        ``- 10**(-precision)``
+    smallest_normal : float
+        The smallest positive floating point number with 1 as leading bit in
+        the mantissa following IEEE-754. Same as `xmin`.
+    smallest_subnormal : float
+        The smallest positive floating point number with 0 as leading bit in
+        the mantissa following IEEE-754.
+
+    Parameters
+    ----------
+    float_conv : function, optional
+        Function that converts an integer or integer array to a float
+        or float array. Default is `float`.
+    int_conv : function, optional
+        Function that converts a float or float array to an integer or
+        integer array. Default is `int`.
+    float_to_float : function, optional
+        Function that converts a float array to float. Default is `float`.
+        Note that this does not seem to do anything useful in the current
+        implementation.
+    float_to_str : function, optional
+        Function that converts a single float to a string. Default is
+        ``lambda v:'%24.16e' %v``.
+    title : str, optional
+        Title that is printed in the string representation of `MachAr`.
+
+    See Also
+    --------
+    finfo : Machine limits for floating point types.
+    iinfo : Machine limits for integer types.
+
+    References
+    ----------
+    .. [1] Press, Teukolsky, Vetterling and Flannery,
+           "Numerical Recipes in C++," 2nd ed,
+           Cambridge University Press, 2002, p. 31.
+
+    """
+
+    def __init__(self, float_conv=float,int_conv=int,
+                 float_to_float=float,
+                 float_to_str=lambda v:'%24.16e' % v,
+                 title='Python floating point number'):
+        """
+
+        float_conv - convert integer to float (array)
+        int_conv   - convert float (array) to integer
+        float_to_float - convert float array to float
+        float_to_str - convert array float to str
+        title        - description of used floating point numbers
+
+        """
+        # We ignore all errors here because we are purposely triggering
+        # underflow to detect the properties of the runninng arch.
+        with errstate(under='ignore'):
+            self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
+
+    def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
+        max_iterN = 10000
+        msg = "Did not converge after %d tries with %s"
+        one = float_conv(1)
+        two = one + one
+        zero = one - one
+
+        # Do we really need to do this?  Aren't they 2 and 2.0?
+        # Determine ibeta and beta
+        a = one
+        for _ in range(max_iterN):
+            a = a + a
+            temp = a + one
+            temp1 = temp - a
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        b = one
+        for _ in range(max_iterN):
+            b = b + b
+            temp = a + b
+            itemp = int_conv(temp-a)
+            if any(itemp != 0):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        ibeta = itemp
+        beta = float_conv(ibeta)
+
+        # Determine it and irnd
+        it = -1
+        b = one
+        for _ in range(max_iterN):
+            it = it + 1
+            b = b * beta
+            temp = b + one
+            temp1 = temp - b
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+
+        betah = beta / two
+        a = one
+        for _ in range(max_iterN):
+            a = a + a
+            temp = a + one
+            temp1 = temp - a
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        temp = a + betah
+        irnd = 0
+        if any(temp-a != zero):
+            irnd = 1
+        tempa = a + beta
+        temp = tempa + betah
+        if irnd == 0 and any(temp-tempa != zero):
+            irnd = 2
+
+        # Determine negep and epsneg
+        negep = it + 3
+        betain = one / beta
+        a = one
+        for i in range(negep):
+            a = a * betain
+        b = a
+        for _ in range(max_iterN):
+            temp = one - a
+            if any(temp-one != zero):
+                break
+            a = a * beta
+            negep = negep - 1
+            # Prevent infinite loop on PPC with gcc 4.0:
+            if negep < 0:
+                raise RuntimeError("could not determine machine tolerance "
+                                   "for 'negep', locals() -> %s" % (locals()))
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        negep = -negep
+        epsneg = a
+
+        # Determine machep and eps
+        machep = - it - 3
+        a = b
+
+        for _ in range(max_iterN):
+            temp = one + a
+            if any(temp-one != zero):
+                break
+            a = a * beta
+            machep = machep + 1
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        eps = a
+
+        # Determine ngrd
+        ngrd = 0
+        temp = one + eps
+        if irnd == 0 and any(temp*one - one != zero):
+            ngrd = 1
+
+        # Determine iexp
+        i = 0
+        k = 1
+        z = betain
+        t = one + eps
+        nxres = 0
+        for _ in range(max_iterN):
+            y = z
+            z = y*y
+            a = z*one  # Check here for underflow
+            temp = z*t
+            if any(a+a == zero) or any(abs(z) >= y):
+                break
+            temp1 = temp * betain
+            if any(temp1*beta == z):
+                break
+            i = i + 1
+            k = k + k
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        if ibeta != 10:
+            iexp = i + 1
+            mx = k + k
+        else:
+            iexp = 2
+            iz = ibeta
+            while k >= iz:
+                iz = iz * ibeta
+                iexp = iexp + 1
+            mx = iz + iz - 1
+
+        # Determine minexp and xmin
+        for _ in range(max_iterN):
+            xmin = y
+            y = y * betain
+            a = y * one
+            temp = y * t
+            if any((a + a) != zero) and any(abs(y) < xmin):
+                k = k + 1
+                temp1 = temp * betain
+                if any(temp1*beta == y) and any(temp != y):
+                    nxres = 3
+                    xmin = y
+                    break
+            else:
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        minexp = -k
+
+        # Determine maxexp, xmax
+        if mx <= k + k - 3 and ibeta != 10:
+            mx = mx + mx
+            iexp = iexp + 1
+        maxexp = mx + minexp
+        irnd = irnd + nxres
+        if irnd >= 2:
+            maxexp = maxexp - 2
+        i = maxexp + minexp
+        if ibeta == 2 and not i:
+            maxexp = maxexp - 1
+        if i > 20:
+            maxexp = maxexp - 1
+        if any(a != y):
+            maxexp = maxexp - 2
+        xmax = one - epsneg
+        if any(xmax*one != xmax):
+            xmax = one - beta*epsneg
+        xmax = xmax / (xmin*beta*beta*beta)
+        i = maxexp + minexp + 3
+        for j in range(i):
+            if ibeta == 2:
+                xmax = xmax + xmax
+            else:
+                xmax = xmax * beta
+
+        smallest_subnormal = abs(xmin / beta ** (it))
+
+        self.ibeta = ibeta
+        self.it = it
+        self.negep = negep
+        self.epsneg = float_to_float(epsneg)
+        self._str_epsneg = float_to_str(epsneg)
+        self.machep = machep
+        self.eps = float_to_float(eps)
+        self._str_eps = float_to_str(eps)
+        self.ngrd = ngrd
+        self.iexp = iexp
+        self.minexp = minexp
+        self.xmin = float_to_float(xmin)
+        self._str_xmin = float_to_str(xmin)
+        self.maxexp = maxexp
+        self.xmax = float_to_float(xmax)
+        self._str_xmax = float_to_str(xmax)
+        self.irnd = irnd
+
+        self.title = title
+        # Commonly used parameters
+        self.epsilon = self.eps
+        self.tiny = self.xmin
+        self.huge = self.xmax
+        self.smallest_normal = self.xmin
+        self._str_smallest_normal = float_to_str(self.xmin)
+        self.smallest_subnormal = float_to_float(smallest_subnormal)
+        self._str_smallest_subnormal = float_to_str(smallest_subnormal)
+
+        import math
+        self.precision = int(-math.log10(float_to_float(self.eps)))
+        ten = two + two + two + two + two
+        resolution = ten ** (-self.precision)
+        self.resolution = float_to_float(resolution)
+        self._str_resolution = float_to_str(resolution)
+
+    def __str__(self):
+        fmt = (
+           'Machine parameters for %(title)s\n'
+           '---------------------------------------------------------------------\n'
+           'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
+           'machep=%(machep)s     eps=%(_str_eps)s (beta**machep == epsilon)\n'
+           'negep =%(negep)s  epsneg=%(_str_epsneg)s (beta**epsneg)\n'
+           'minexp=%(minexp)s   xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
+           'maxexp=%(maxexp)s    xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
+           'smallest_normal=%(smallest_normal)s    '
+           'smallest_subnormal=%(smallest_subnormal)s\n'
+           '---------------------------------------------------------------------\n'
+           )
+        return fmt % self.__dict__
+
+
+if __name__ == '__main__':
+    print(MachAr())
diff --git a/shared/package-files/1b/1b7a055e7fa7e2e35e3e31a645b7b0e9888606022acf5e6fd15193fb9de227ba/cli.py b/shared/package-files/1b/1b7a055e7fa7e2e35e3e31a645b7b0e9888606022acf5e6fd15193fb9de227ba/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..3977a3baa24e462120aeb52dc28e24a73ddba642
--- /dev/null
+++ b/shared/package-files/1b/1b7a055e7fa7e2e35e3e31a645b7b0e9888606022acf5e6fd15193fb9de227ba/cli.py
@@ -0,0 +1,128 @@
+import os
+import argparse
+import logging
+from fontTools.misc.cliTools import makeOutputFileName
+from fontTools.ttLib import TTFont
+from fontTools.pens.qu2cuPen import Qu2CuPen
+from fontTools.pens.ttGlyphPen import TTGlyphPen
+import fontTools
+
+logger = logging.getLogger("fontTools.qu2cu")
+
+
+def _font_to_cubic(input_path, output_path=None, **kwargs):
+    font = TTFont(input_path)
+    logger.info("Converting curves for %s", input_path)
+
+    stats = {} if kwargs["dump_stats"] else None
+    qu2cu_kwargs = {
+        "stats": stats,
+        "max_err": kwargs["max_err_em"] * font["head"].unitsPerEm,
+        "all_cubic": kwargs["all_cubic"],
+    }
+
+    if "gvar" in font:
+        raise ValueError("Cannot convert variable font")
+    glyphSet = font.getGlyphSet()
+    glyphOrder = font.getGlyphOrder()
+    glyf = font["glyf"]
+    for glyphName in glyphOrder:
+        glyph = glyphSet[glyphName]
+        ttpen = TTGlyphPen(glyphSet)
+        pen = Qu2CuPen(ttpen, **qu2cu_kwargs)
+        glyph.draw(pen)
+        glyf[glyphName] = ttpen.glyph(dropImpliedOnCurves=True)
+
+    font["head"].glyphDataFormat = 1
+
+    if kwargs["dump_stats"]:
+        logger.info("Stats: %s", stats)
+
+    logger.info("Saving %s", output_path)
+    font.save(output_path)
+
+
+def _main(args=None):
+    """Convert an OpenType font from quadratic to cubic curves"""
+    parser = argparse.ArgumentParser(prog="qu2cu")
+    parser.add_argument("--version", action="version", version=fontTools.__version__)
+    parser.add_argument(
+        "infiles",
+        nargs="+",
+        metavar="INPUT",
+        help="one or more input TTF source file(s).",
+    )
+    parser.add_argument("-v", "--verbose", action="count", default=0)
+    parser.add_argument(
+        "-e",
+        "--conversion-error",
+        type=float,
+        metavar="ERROR",
+        default=0.001,
+        help="maxiumum approximation error measured in EM (default: 0.001)",
+    )
+    parser.add_argument(
+        "-c",
+        "--all-cubic",
+        default=False,
+        action="store_true",
+        help="whether to only use cubic curves",
+    )
+
+    output_parser = parser.add_mutually_exclusive_group()
+    output_parser.add_argument(
+        "-o",
+        "--output-file",
+        default=None,
+        metavar="OUTPUT",
+        help=("output filename for the converted TTF."),
+    )
+    output_parser.add_argument(
+        "-d",
+        "--output-dir",
+        default=None,
+        metavar="DIRECTORY",
+        help="output directory where to save converted TTFs",
+    )
+
+    options = parser.parse_args(args)
+
+    if options.conversion_error <= 0:
+        parser.error("--conversion-error must be greater than zero")
+
+    if not options.verbose:
+        level = "WARNING"
+    elif options.verbose == 1:
+        level = "INFO"
+    else:
+        level = "DEBUG"
+    logging.basicConfig(level=level)
+
+    if len(options.infiles) > 1 and options.output_file:
+        parser.error("-o/--output-file can't be used with multile inputs")
+
+    if options.output_dir:
+        output_dir = options.output_dir
+        if not os.path.exists(output_dir):
+            os.mkdir(output_dir)
+        elif not os.path.isdir(output_dir):
+            parser.error("'%s' is not a directory" % output_dir)
+        output_paths = [
+            os.path.join(output_dir, os.path.basename(p)) for p in options.infiles
+        ]
+    elif options.output_file:
+        output_paths = [options.output_file]
+    else:
+        output_paths = [
+            makeOutputFileName(p, overWrite=True, suffix=".cubic")
+            for p in options.infiles
+        ]
+
+    kwargs = dict(
+        dump_stats=options.verbose > 0,
+        max_err_em=options.conversion_error,
+        all_cubic=options.all_cubic,
+    )
+
+    for input_path, output_path in zip(options.infiles, output_paths):
+        _font_to_cubic(input_path, output_path, **kwargs)
diff --git a/shared/package-files/1b/1b7c0288d0a03624cb2c567118b7766459a7b1b891c616a949f656956482e74f/baseclasses.py b/shared/package-files/1b/1b7c0288d0a03624cb2c567118b7766459a7b1b891c616a949f656956482e74f/baseclasses.py
new file mode 100644
index 0000000000000000000000000000000000000000..452727566aa5e06c0b3933697dc104dd38288d6d
--- /dev/null
+++ b/shared/package-files/1b/1b7c0288d0a03624cb2c567118b7766459a7b1b891c616a949f656956482e74f/baseclasses.py
@@ -0,0 +1,979 @@
+from sympy.core import S, Basic, Dict, Symbol, Tuple, sympify
+from sympy.core.symbol import Str
+from sympy.sets import Set, FiniteSet, EmptySet
+from sympy.utilities.iterables import iterable
+
+
+class Class(Set):
+    r"""
+    The base class for any kind of class in the set-theoretic sense.
+
+    Explanation
+    ===========
+
+    In axiomatic set theories, everything is a class.  A class which
+    can be a member of another class is a set.  A class which is not a
+    member of another class is a proper class.  The class `\{1, 2\}`
+    is a set; the class of all sets is a proper class.
+
+    This class is essentially a synonym for :class:`sympy.core.Set`.
+    The goal of this class is to assure easier migration to the
+    eventual proper implementation of set theory.
+    """
+    is_proper = False
+
+
+class Object(Symbol):
+    """
+    The base class for any kind of object in an abstract category.
+
+    Explanation
+    ===========
+
+    While technically any instance of :class:`~.Basic` will do, this
+    class is the recommended way to create abstract objects in
+    abstract categories.
+    """
+
+
+class Morphism(Basic):
+    """
+    The base class for any morphism in an abstract category.
+
+    Explanation
+    ===========
+
+    In abstract categories, a morphism is an arrow between two
+    category objects.  The object where the arrow starts is called the
+    domain, while the object where the arrow ends is called the
+    codomain.
+
+    Two morphisms between the same pair of objects are considered to
+    be the same morphisms.  To distinguish between morphisms between
+    the same objects use :class:`NamedMorphism`.
+
+    It is prohibited to instantiate this class.  Use one of the
+    derived classes instead.
+
+    See Also
+    ========
+
+    IdentityMorphism, NamedMorphism, CompositeMorphism
+    """
+    def __new__(cls, domain, codomain):
+        raise(NotImplementedError(
+            "Cannot instantiate Morphism.  Use derived classes instead."))
+
+    @property
+    def domain(self):
+        """
+        Returns the domain of the morphism.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> f.domain
+        Object("A")
+
+        """
+        return self.args[0]
+
+    @property
+    def codomain(self):
+        """
+        Returns the codomain of the morphism.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> f.codomain
+        Object("B")
+
+        """
+        return self.args[1]
+
+    def compose(self, other):
+        r"""
+        Composes self with the supplied morphism.
+
+        The order of elements in the composition is the usual order,
+        i.e., to construct `g\circ f` use ``g.compose(f)``.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> g * f
+        CompositeMorphism((NamedMorphism(Object("A"), Object("B"), "f"),
+        NamedMorphism(Object("B"), Object("C"), "g")))
+        >>> (g * f).domain
+        Object("A")
+        >>> (g * f).codomain
+        Object("C")
+
+        """
+        return CompositeMorphism(other, self)
+
+    def __mul__(self, other):
+        r"""
+        Composes self with the supplied morphism.
+
+        The semantics of this operation is given by the following
+        equation: ``g * f == g.compose(f)`` for composable morphisms
+        ``g`` and ``f``.
+
+        See Also
+        ========
+
+        compose
+        """
+        return self.compose(other)
+
+
+class IdentityMorphism(Morphism):
+    """
+    Represents an identity morphism.
+
+    Explanation
+    ===========
+
+    An identity morphism is a morphism with equal domain and codomain,
+    which acts as an identity with respect to composition.
+
+    Examples
+    ========
+
+    >>> from sympy.categories import Object, NamedMorphism, IdentityMorphism
+    >>> A = Object("A")
+    >>> B = Object("B")
+    >>> f = NamedMorphism(A, B, "f")
+    >>> id_A = IdentityMorphism(A)
+    >>> id_B = IdentityMorphism(B)
+    >>> f * id_A == f
+    True
+    >>> id_B * f == f
+    True
+
+    See Also
+    ========
+
+    Morphism
+    """
+    def __new__(cls, domain):
+        return Basic.__new__(cls, domain)
+
+    @property
+    def codomain(self):
+        return self.domain
+
+
+class NamedMorphism(Morphism):
+    """
+    Represents a morphism which has a name.
+
+    Explanation
+    ===========
+
+    Names are used to distinguish between morphisms which have the
+    same domain and codomain: two named morphisms are equal if they
+    have the same domains, codomains, and names.
+
+    Examples
+    ========
+
+    >>> from sympy.categories import Object, NamedMorphism
+    >>> A = Object("A")
+    >>> B = Object("B")
+    >>> f = NamedMorphism(A, B, "f")
+    >>> f
+    NamedMorphism(Object("A"), Object("B"), "f")
+    >>> f.name
+    'f'
+
+    See Also
+    ========
+
+    Morphism
+    """
+    def __new__(cls, domain, codomain, name):
+        if not name:
+            raise ValueError("Empty morphism names not allowed.")
+
+        if not isinstance(name, Str):
+            name = Str(name)
+
+        return Basic.__new__(cls, domain, codomain, name)
+
+    @property
+    def name(self):
+        """
+        Returns the name of the morphism.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> f.name
+        'f'
+
+        """
+        return self.args[2].name
+
+
+class CompositeMorphism(Morphism):
+    r"""
+    Represents a morphism which is a composition of other morphisms.
+
+    Explanation
+    ===========
+
+    Two composite morphisms are equal if the morphisms they were
+    obtained from (components) are the same and were listed in the
+    same order.
+
+    The arguments to the constructor for this class should be listed
+    in diagram order: to obtain the composition `g\circ f` from the
+    instances of :class:`Morphism` ``g`` and ``f`` use
+    ``CompositeMorphism(f, g)``.
+
+    Examples
+    ========
+
+    >>> from sympy.categories import Object, NamedMorphism, CompositeMorphism
+    >>> A = Object("A")
+    >>> B = Object("B")
+    >>> C = Object("C")
+    >>> f = NamedMorphism(A, B, "f")
+    >>> g = NamedMorphism(B, C, "g")
+    >>> g * f
+    CompositeMorphism((NamedMorphism(Object("A"), Object("B"), "f"),
+    NamedMorphism(Object("B"), Object("C"), "g")))
+    >>> CompositeMorphism(f, g) == g * f
+    True
+
+    """
+    @staticmethod
+    def _add_morphism(t, morphism):
+        """
+        Intelligently adds ``morphism`` to tuple ``t``.
+
+        Explanation
+        ===========
+
+        If ``morphism`` is a composite morphism, its components are
+        added to the tuple.  If ``morphism`` is an identity, nothing
+        is added to the tuple.
+
+        No composability checks are performed.
+        """
+        if isinstance(morphism, CompositeMorphism):
+            # ``morphism`` is a composite morphism; we have to
+            # denest its components.
+            return t + morphism.components
+        elif isinstance(morphism, IdentityMorphism):
+            # ``morphism`` is an identity.  Nothing happens.
+            return t
+        else:
+            return t + Tuple(morphism)
+
+    def __new__(cls, *components):
+        if components and not isinstance(components[0], Morphism):
+            # Maybe the user has explicitly supplied a list of
+            # morphisms.
+            return CompositeMorphism.__new__(cls, *components[0])
+
+        normalised_components = Tuple()
+
+        for current, following in zip(components, components[1:]):
+            if not isinstance(current, Morphism) or \
+                    not isinstance(following, Morphism):
+                raise TypeError("All components must be morphisms.")
+
+            if current.codomain != following.domain:
+                raise ValueError("Uncomposable morphisms.")
+
+            normalised_components = CompositeMorphism._add_morphism(
+                normalised_components, current)
+
+        # We haven't added the last morphism to the list of normalised
+        # components.  Add it now.
+        normalised_components = CompositeMorphism._add_morphism(
+            normalised_components, components[-1])
+
+        if not normalised_components:
+            # If ``normalised_components`` is empty, only identities
+            # were supplied.  Since they all were composable, they are
+            # all the same identities.
+            return components[0]
+        elif len(normalised_components) == 1:
+            # No sense to construct a whole CompositeMorphism.
+            return normalised_components[0]
+
+        return Basic.__new__(cls, normalised_components)
+
+    @property
+    def components(self):
+        """
+        Returns the components of this composite morphism.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> (g * f).components
+        (NamedMorphism(Object("A"), Object("B"), "f"),
+        NamedMorphism(Object("B"), Object("C"), "g"))
+
+        """
+        return self.args[0]
+
+    @property
+    def domain(self):
+        """
+        Returns the domain of this composite morphism.
+
+        The domain of the composite morphism is the domain of its
+        first component.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> (g * f).domain
+        Object("A")
+
+        """
+        return self.components[0].domain
+
+    @property
+    def codomain(self):
+        """
+        Returns the codomain of this composite morphism.
+
+        The codomain of the composite morphism is the codomain of its
+        last component.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> (g * f).codomain
+        Object("C")
+
+        """
+        return self.components[-1].codomain
+
+    def flatten(self, new_name):
+        """
+        Forgets the composite structure of this morphism.
+
+        Explanation
+        ===========
+
+        If ``new_name`` is not empty, returns a :class:`NamedMorphism`
+        with the supplied name, otherwise returns a :class:`Morphism`.
+        In both cases the domain of the new morphism is the domain of
+        this composite morphism and the codomain of the new morphism
+        is the codomain of this composite morphism.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> (g * f).flatten("h")
+        NamedMorphism(Object("A"), Object("C"), "h")
+
+        """
+        return NamedMorphism(self.domain, self.codomain, new_name)
+
+
+class Category(Basic):
+    r"""
+    An (abstract) category.
+
+    Explanation
+    ===========
+
+    A category [JoyOfCats] is a quadruple `\mbox{K} = (O, \hom, id,
+    \circ)` consisting of
+
+    * a (set-theoretical) class `O`, whose members are called
+      `K`-objects,
+
+    * for each pair `(A, B)` of `K`-objects, a set `\hom(A, B)` whose
+      members are called `K`-morphisms from `A` to `B`,
+
+    * for a each `K`-object `A`, a morphism `id:A\rightarrow A`,
+      called the `K`-identity of `A`,
+
+    * a composition law `\circ` associating with every `K`-morphisms
+      `f:A\rightarrow B` and `g:B\rightarrow C` a `K`-morphism `g\circ
+      f:A\rightarrow C`, called the composite of `f` and `g`.
+
+    Composition is associative, `K`-identities are identities with
+    respect to composition, and the sets `\hom(A, B)` are pairwise
+    disjoint.
+
+    This class knows nothing about its objects and morphisms.
+    Concrete cases of (abstract) categories should be implemented as
+    classes derived from this one.
+
+    Certain instances of :class:`Diagram` can be asserted to be
+    commutative in a :class:`Category` by supplying the argument
+    ``commutative_diagrams`` in the constructor.
+
+    Examples
+    ========
+
+    >>> from sympy.categories import Object, NamedMorphism, Diagram, Category
+    >>> from sympy import FiniteSet
+    >>> A = Object("A")
+    >>> B = Object("B")
+    >>> C = Object("C")
+    >>> f = NamedMorphism(A, B, "f")
+    >>> g = NamedMorphism(B, C, "g")
+    >>> d = Diagram([f, g])
+    >>> K = Category("K", commutative_diagrams=[d])
+    >>> K.commutative_diagrams == FiniteSet(d)
+    True
+
+    See Also
+    ========
+
+    Diagram
+    """
+    def __new__(cls, name, objects=EmptySet, commutative_diagrams=EmptySet):
+        if not name:
+            raise ValueError("A Category cannot have an empty name.")
+
+        if not isinstance(name, Str):
+            name = Str(name)
+
+        if not isinstance(objects, Class):
+            objects = Class(objects)
+
+        new_category = Basic.__new__(cls, name, objects,
+                                     FiniteSet(*commutative_diagrams))
+        return new_category
+
+    @property
+    def name(self):
+        """
+        Returns the name of this category.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Category
+        >>> K = Category("K")
+        >>> K.name
+        'K'
+
+        """
+        return self.args[0].name
+
+    @property
+    def objects(self):
+        """
+        Returns the class of objects of this category.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, Category
+        >>> from sympy import FiniteSet
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> K = Category("K", FiniteSet(A, B))
+        >>> K.objects
+        Class({Object("A"), Object("B")})
+
+        """
+        return self.args[1]
+
+    @property
+    def commutative_diagrams(self):
+        """
+        Returns the :class:`~.FiniteSet` of diagrams which are known to
+        be commutative in this category.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism, Diagram, Category
+        >>> from sympy import FiniteSet
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g])
+        >>> K = Category("K", commutative_diagrams=[d])
+        >>> K.commutative_diagrams == FiniteSet(d)
+        True
+
+        """
+        return self.args[2]
+
+    def hom(self, A, B):
+        raise NotImplementedError(
+            "hom-sets are not implemented in Category.")
+
+    def all_morphisms(self):
+        raise NotImplementedError(
+            "Obtaining the class of morphisms is not implemented in Category.")
+
+
+class Diagram(Basic):
+    r"""
+    Represents a diagram in a certain category.
+
+    Explanation
+    ===========
+
+    Informally, a diagram is a collection of objects of a category and
+    certain morphisms between them.  A diagram is still a monoid with
+    respect to morphism composition; i.e., identity morphisms, as well
+    as all composites of morphisms included in the diagram belong to
+    the diagram.  For a more formal approach to this notion see
+    [Pare1970].
+
+    The components of composite morphisms are also added to the
+    diagram.  No properties are assigned to such morphisms by default.
+
+    A commutative diagram is often accompanied by a statement of the
+    following kind: "if such morphisms with such properties exist,
+    then such morphisms which such properties exist and the diagram is
+    commutative".  To represent this, an instance of :class:`Diagram`
+    includes a collection of morphisms which are the premises and
+    another collection of conclusions.  ``premises`` and
+    ``conclusions`` associate morphisms belonging to the corresponding
+    categories with the :class:`~.FiniteSet`'s of their properties.
+
+    The set of properties of a composite morphism is the intersection
+    of the sets of properties of its components.  The domain and
+    codomain of a conclusion morphism should be among the domains and
+    codomains of the morphisms listed as the premises of a diagram.
+
+    No checks are carried out of whether the supplied object and
+    morphisms do belong to one and the same category.
+
+    Examples
+    ========
+
+    >>> from sympy.categories import Object, NamedMorphism, Diagram
+    >>> from sympy import pprint, default_sort_key
+    >>> A = Object("A")
+    >>> B = Object("B")
+    >>> C = Object("C")
+    >>> f = NamedMorphism(A, B, "f")
+    >>> g = NamedMorphism(B, C, "g")
+    >>> d = Diagram([f, g])
+    >>> premises_keys = sorted(d.premises.keys(), key=default_sort_key)
+    >>> pprint(premises_keys, use_unicode=False)
+    [g*f:A-->C, id:A-->A, id:B-->B, id:C-->C, f:A-->B, g:B-->C]
+    >>> pprint(d.premises, use_unicode=False)
+    {g*f:A-->C: EmptySet, id:A-->A: EmptySet, id:B-->B: EmptySet, id:C-->C: EmptyS
+    et, f:A-->B: EmptySet, g:B-->C: EmptySet}
+    >>> d = Diagram([f, g], {g * f: "unique"})
+    >>> pprint(d.conclusions,use_unicode=False)
+    {g*f:A-->C: {unique}}
+
+    References
+    ==========
+
+    [Pare1970] B. Pareigis: Categories and functors.  Academic Press, 1970.
+
+    """
+    @staticmethod
+    def _set_dict_union(dictionary, key, value):
+        """
+        If ``key`` is in ``dictionary``, set the new value of ``key``
+        to be the union between the old value and ``value``.
+        Otherwise, set the value of ``key`` to ``value.
+
+        Returns ``True`` if the key already was in the dictionary and
+        ``False`` otherwise.
+        """
+        if key in dictionary:
+            dictionary[key] = dictionary[key] | value
+            return True
+        else:
+            dictionary[key] = value
+            return False
+
+    @staticmethod
+    def _add_morphism_closure(morphisms, morphism, props, add_identities=True,
+                              recurse_composites=True):
+        """
+        Adds a morphism and its attributes to the supplied dictionary
+        ``morphisms``.  If ``add_identities`` is True, also adds the
+        identity morphisms for the domain and the codomain of
+        ``morphism``.
+        """
+        if not Diagram._set_dict_union(morphisms, morphism, props):
+            # We have just added a new morphism.
+
+            if isinstance(morphism, IdentityMorphism):
+                if props:
+                    # Properties for identity morphisms don't really
+                    # make sense, because very much is known about
+                    # identity morphisms already, so much that they
+                    # are trivial.  Having properties for identity
+                    # morphisms would only be confusing.
+                    raise ValueError(
+                        "Instances of IdentityMorphism cannot have properties.")
+                return
+
+            if add_identities:
+                empty = EmptySet
+
+                id_dom = IdentityMorphism(morphism.domain)
+                id_cod = IdentityMorphism(morphism.codomain)
+
+                Diagram._set_dict_union(morphisms, id_dom, empty)
+                Diagram._set_dict_union(morphisms, id_cod, empty)
+
+            for existing_morphism, existing_props in list(morphisms.items()):
+                new_props = existing_props & props
+                if morphism.domain == existing_morphism.codomain:
+                    left = morphism * existing_morphism
+                    Diagram._set_dict_union(morphisms, left, new_props)
+                if morphism.codomain == existing_morphism.domain:
+                    right = existing_morphism * morphism
+                    Diagram._set_dict_union(morphisms, right, new_props)
+
+            if isinstance(morphism, CompositeMorphism) and recurse_composites:
+                # This is a composite morphism, add its components as
+                # well.
+                empty = EmptySet
+                for component in morphism.components:
+                    Diagram._add_morphism_closure(morphisms, component, empty,
+                                                  add_identities)
+
+    def __new__(cls, *args):
+        """
+        Construct a new instance of Diagram.
+
+        Explanation
+        ===========
+
+        If no arguments are supplied, an empty diagram is created.
+
+        If at least an argument is supplied, ``args[0]`` is
+        interpreted as the premises of the diagram.  If ``args[0]`` is
+        a list, it is interpreted as a list of :class:`Morphism`'s, in
+        which each :class:`Morphism` has an empty set of properties.
+        If ``args[0]`` is a Python dictionary or a :class:`Dict`, it
+        is interpreted as a dictionary associating to some
+        :class:`Morphism`'s some properties.
+
+        If at least two arguments are supplied ``args[1]`` is
+        interpreted as the conclusions of the diagram.  The type of
+        ``args[1]`` is interpreted in exactly the same way as the type
+        of ``args[0]``.  If only one argument is supplied, the diagram
+        has no conclusions.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> from sympy.categories import IdentityMorphism, Diagram
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g])
+        >>> IdentityMorphism(A) in d.premises.keys()
+        True
+        >>> g * f in d.premises.keys()
+        True
+        >>> d = Diagram([f, g], {g * f: "unique"})
+        >>> d.conclusions[g * f]
+        {unique}
+
+        """
+        premises = {}
+        conclusions = {}
+
+        # Here we will keep track of the objects which appear in the
+        # premises.
+        objects = EmptySet
+
+        if len(args) >= 1:
+            # We've got some premises in the arguments.
+            premises_arg = args[0]
+
+            if isinstance(premises_arg, list):
+                # The user has supplied a list of morphisms, none of
+                # which have any attributes.
+                empty = EmptySet
+
+                for morphism in premises_arg:
+                    objects |= FiniteSet(morphism.domain, morphism.codomain)
+                    Diagram._add_morphism_closure(premises, morphism, empty)
+            elif isinstance(premises_arg, (dict, Dict)):
+                # The user has supplied a dictionary of morphisms and
+                # their properties.
+                for morphism, props in premises_arg.items():
+                    objects |= FiniteSet(morphism.domain, morphism.codomain)
+                    Diagram._add_morphism_closure(
+                        premises, morphism, FiniteSet(*props) if iterable(props) else FiniteSet(props))
+
+        if len(args) >= 2:
+            # We also have some conclusions.
+            conclusions_arg = args[1]
+
+            if isinstance(conclusions_arg, list):
+                # The user has supplied a list of morphisms, none of
+                # which have any attributes.
+                empty = EmptySet
+
+                for morphism in conclusions_arg:
+                    # Check that no new objects appear in conclusions.
+                    if ((sympify(objects.contains(morphism.domain)) is S.true) and
+                        (sympify(objects.contains(morphism.codomain)) is S.true)):
+                        # No need to add identities and recurse
+                        # composites this time.
+                        Diagram._add_morphism_closure(
+                            conclusions, morphism, empty, add_identities=False,
+                            recurse_composites=False)
+            elif isinstance(conclusions_arg, dict) or \
+                    isinstance(conclusions_arg, Dict):
+                # The user has supplied a dictionary of morphisms and
+                # their properties.
+                for morphism, props in conclusions_arg.items():
+                    # Check that no new objects appear in conclusions.
+                    if (morphism.domain in objects) and \
+                       (morphism.codomain in objects):
+                        # No need to add identities and recurse
+                        # composites this time.
+                        Diagram._add_morphism_closure(
+                            conclusions, morphism, FiniteSet(*props) if iterable(props) else FiniteSet(props),
+                            add_identities=False, recurse_composites=False)
+
+        return Basic.__new__(cls, Dict(premises), Dict(conclusions), objects)
+
+    @property
+    def premises(self):
+        """
+        Returns the premises of this diagram.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> from sympy.categories import IdentityMorphism, Diagram
+        >>> from sympy import pretty
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> id_A = IdentityMorphism(A)
+        >>> id_B = IdentityMorphism(B)
+        >>> d = Diagram([f])
+        >>> print(pretty(d.premises, use_unicode=False))
+        {id:A-->A: EmptySet, id:B-->B: EmptySet, f:A-->B: EmptySet}
+
+        """
+        return self.args[0]
+
+    @property
+    def conclusions(self):
+        """
+        Returns the conclusions of this diagram.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism
+        >>> from sympy.categories import IdentityMorphism, Diagram
+        >>> from sympy import FiniteSet
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g])
+        >>> IdentityMorphism(A) in d.premises.keys()
+        True
+        >>> g * f in d.premises.keys()
+        True
+        >>> d = Diagram([f, g], {g * f: "unique"})
+        >>> d.conclusions[g * f] == FiniteSet("unique")
+        True
+
+        """
+        return self.args[1]
+
+    @property
+    def objects(self):
+        """
+        Returns the :class:`~.FiniteSet` of objects that appear in this
+        diagram.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism, Diagram
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g])
+        >>> d.objects
+        {Object("A"), Object("B"), Object("C")}
+
+        """
+        return self.args[2]
+
+    def hom(self, A, B):
+        """
+        Returns a 2-tuple of sets of morphisms between objects ``A`` and
+        ``B``: one set of morphisms listed as premises, and the other set
+        of morphisms listed as conclusions.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism, Diagram
+        >>> from sympy import pretty
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g], {g * f: "unique"})
+        >>> print(pretty(d.hom(A, C), use_unicode=False))
+        ({g*f:A-->C}, {g*f:A-->C})
+
+        See Also
+        ========
+        Object, Morphism
+        """
+        premises = EmptySet
+        conclusions = EmptySet
+
+        for morphism in self.premises.keys():
+            if (morphism.domain == A) and (morphism.codomain == B):
+                premises |= FiniteSet(morphism)
+        for morphism in self.conclusions.keys():
+            if (morphism.domain == A) and (morphism.codomain == B):
+                conclusions |= FiniteSet(morphism)
+
+        return (premises, conclusions)
+
+    def is_subdiagram(self, diagram):
+        """
+        Checks whether ``diagram`` is a subdiagram of ``self``.
+        Diagram `D'` is a subdiagram of `D` if all premises
+        (conclusions) of `D'` are contained in the premises
+        (conclusions) of `D`.  The morphisms contained
+        both in `D'` and `D` should have the same properties for `D'`
+        to be a subdiagram of `D`.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism, Diagram
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g], {g * f: "unique"})
+        >>> d1 = Diagram([f])
+        >>> d.is_subdiagram(d1)
+        True
+        >>> d1.is_subdiagram(d)
+        False
+        """
+        premises = all((m in self.premises) and
+                       (diagram.premises[m] == self.premises[m])
+                       for m in diagram.premises)
+        if not premises:
+            return False
+
+        conclusions = all((m in self.conclusions) and
+                          (diagram.conclusions[m] == self.conclusions[m])
+                          for m in diagram.conclusions)
+
+        # Premises is surely ``True`` here.
+        return conclusions
+
+    def subdiagram_from_objects(self, objects):
+        """
+        If ``objects`` is a subset of the objects of ``self``, returns
+        a diagram which has as premises all those premises of ``self``
+        which have a domains and codomains in ``objects``, likewise
+        for conclusions.  Properties are preserved.
+
+        Examples
+        ========
+
+        >>> from sympy.categories import Object, NamedMorphism, Diagram
+        >>> from sympy import FiniteSet
+        >>> A = Object("A")
+        >>> B = Object("B")
+        >>> C = Object("C")
+        >>> f = NamedMorphism(A, B, "f")
+        >>> g = NamedMorphism(B, C, "g")
+        >>> d = Diagram([f, g], {f: "unique", g*f: "veryunique"})
+        >>> d1 = d.subdiagram_from_objects(FiniteSet(A, B))
+        >>> d1 == Diagram([f], {f: "unique"})
+        True
+        """
+        if not objects.is_subset(self.objects):
+            raise ValueError(
+                "Supplied objects should all belong to the diagram.")
+
+        new_premises = {}
+        for morphism, props in self.premises.items():
+            if ((sympify(objects.contains(morphism.domain)) is S.true) and
+                (sympify(objects.contains(morphism.codomain)) is S.true)):
+                new_premises[morphism] = props
+
+        new_conclusions = {}
+        for morphism, props in self.conclusions.items():
+            if ((sympify(objects.contains(morphism.domain)) is S.true) and
+                (sympify(objects.contains(morphism.codomain)) is S.true)):
+                new_conclusions[morphism] = props
+
+        return Diagram(new_premises, new_conclusions)
diff --git a/shared/package-files/1b/1b7fde12abf309d5a7584092587ba1b4ee8cd4729191b8d356f2d7006152201b/dist.py b/shared/package-files/1b/1b7fde12abf309d5a7584092587ba1b4ee8cd4729191b8d356f2d7006152201b/dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3e8a167b693dcfec2becc5b274823210b60dea
--- /dev/null
+++ b/shared/package-files/1b/1b7fde12abf309d5a7584092587ba1b4ee8cd4729191b8d356f2d7006152201b/dist.py
@@ -0,0 +1,353 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+import re
+from collections import namedtuple
+from logging import getLogger
+
+from .. import CondaError
+from ..auxlib.entity import Entity, EntityType, IntegerField, StringField
+from ..base.constants import (
+    CONDA_PACKAGE_EXTENSIONS,
+    DEFAULTS_CHANNEL_NAME,
+    UNKNOWN_CHANNEL,
+)
+from ..base.context import context
+from ..common.compat import ensure_text_type
+from ..common.constants import NULL
+from ..common.url import has_platform, is_url, join_url
+from .channel import Channel
+from .package_info import PackageInfo
+from .records import PackageRecord
+
+log = getLogger(__name__)
+DistDetails = namedtuple(
+    "DistDetails",
+    ("name", "version", "build_string", "build_number", "dist_name", "fmt"),
+)
+
+
+IndexRecord = PackageRecord  # for conda-build backward compat
+
+
+class DistType(EntityType):
+    def __call__(cls, *args, **kwargs):
+        if len(args) == 1 and not kwargs:
+            value = args[0]
+            if value in Dist._cache_:
+                return Dist._cache_[value]
+            elif isinstance(value, Dist):
+                dist = value
+            elif isinstance(value, PackageRecord):
+                dist = Dist.from_string(
+                    value.fn, channel_override=value.channel.canonical_name
+                )
+            elif hasattr(value, "dist") and isinstance(value.dist, Dist):
+                dist = value.dist
+            elif isinstance(value, PackageInfo):
+                dist = Dist.from_string(
+                    value.repodata_record.fn,
+                    channel_override=value.channel.canonical_name,
+                )
+            elif isinstance(value, Channel):
+                dist = Dist.from_url(value.url())
+            else:
+                dist = Dist.from_string(value)
+            Dist._cache_[value] = dist
+            return dist
+        else:
+            return super().__call__(*args, **kwargs)
+
+
+def strip_extension(original_dist):
+    for ext in CONDA_PACKAGE_EXTENSIONS:
+        if original_dist.endswith(ext):
+            original_dist = original_dist[: -len(ext)]
+    return original_dist
+
+
+def split_extension(original_dist):
+    stripped = strip_extension(original_dist)
+    return stripped, original_dist[len(stripped) :]
+
+
+class Dist(Entity, metaclass=DistType):
+    _cache_ = {}
+    _lazy_validate = True
+
+    channel = StringField(required=False, nullable=True, immutable=True)
+
+    dist_name = StringField(immutable=True)
+    name = StringField(immutable=True)
+    fmt = StringField(immutable=True)
+    version = StringField(immutable=True)
+    build_string = StringField(immutable=True)
+    build_number = IntegerField(immutable=True)
+
+    base_url = StringField(required=False, nullable=True, immutable=True)
+    platform = StringField(required=False, nullable=True, immutable=True)
+
+    def __init__(
+        self,
+        channel,
+        dist_name=None,
+        name=None,
+        version=None,
+        build_string=None,
+        build_number=None,
+        base_url=None,
+        platform=None,
+        fmt=".tar.bz2",
+    ):
+        super().__init__(
+            channel=channel,
+            dist_name=dist_name,
+            name=name,
+            version=version,
+            build_string=build_string,
+            build_number=build_number,
+            base_url=base_url,
+            platform=platform,
+            fmt=fmt,
+        )
+
+    def to_package_ref(self):
+        return PackageRecord(
+            channel=self.channel,
+            subdir=self.platform,
+            name=self.name,
+            version=self.version,
+            build=self.build_string,
+            build_number=self.build_number,
+        )
+
+    @property
+    def full_name(self):
+        return self.__str__()
+
+    @property
+    def build(self):
+        return self.build_string
+
+    @property
+    def subdir(self):
+        return self.platform
+
+    @property
+    def pair(self):
+        return self.channel or DEFAULTS_CHANNEL_NAME, self.dist_name
+
+    @property
+    def quad(self):
+        # returns: name, version, build_string, channel
+        parts = self.dist_name.rsplit("-", 2) + ["", ""]
+        return parts[0], parts[1], parts[2], self.channel or DEFAULTS_CHANNEL_NAME
+
+    def __str__(self):
+        return f"{self.channel}::{self.dist_name}" if self.channel else self.dist_name
+
+    @property
+    def is_feature_package(self):
+        return self.dist_name.endswith("@")
+
+    @property
+    def is_channel(self):
+        return bool(self.base_url and self.platform)
+
+    def to_filename(self, extension=None):
+        if self.is_feature_package:
+            return self.dist_name
+        else:
+            return self.dist_name + self.fmt
+
+    def to_matchspec(self):
+        return " ".join(self.quad[:3])
+
+    def to_match_spec(self):
+        from .match_spec import MatchSpec
+
+        base = "=".join(self.quad[:3])
+        return MatchSpec(f"{self.channel}::{base}" if self.channel else base)
+
+    @classmethod
+    def from_string(cls, string, channel_override=NULL):
+        string = str(string)
+
+        if is_url(string) and channel_override == NULL:
+            return cls.from_url(string)
+
+        if string.endswith("@"):
+            return cls(
+                channel="@",
+                name=string,
+                version="",
+                build_string="",
+                build_number=0,
+                dist_name=string,
+            )
+
+        REGEX_STR = (
+            r"(?:([^\s\[\]]+)::)?"  # optional channel
+            r"([^\s\[\]]+)"  # 3.x dist
+            r"(?:\[([a-zA-Z0-9_-]+)\])?"  # with_features_depends
+        )
+        channel, original_dist, w_f_d = re.search(REGEX_STR, string).groups()
+
+        original_dist, fmt = split_extension(original_dist)
+
+        if channel_override != NULL:
+            channel = channel_override
+        if not channel:
+            channel = UNKNOWN_CHANNEL
+
+        # enforce dist format
+        dist_details = cls.parse_dist_name(original_dist)
+        return cls(
+            channel=channel,
+            name=dist_details.name,
+            version=dist_details.version,
+            build_string=dist_details.build_string,
+            build_number=dist_details.build_number,
+            dist_name=original_dist,
+            fmt=fmt,
+        )
+
+    @staticmethod
+    def parse_dist_name(string):
+        original_string = string
+        try:
+            string = ensure_text_type(string)
+            no_fmt_string, fmt = split_extension(string)
+
+            # remove any directory or channel information
+            if "::" in no_fmt_string:
+                dist_name = no_fmt_string.rsplit("::", 1)[-1]
+            else:
+                dist_name = no_fmt_string.rsplit("/", 1)[-1]
+
+            parts = dist_name.rsplit("-", 2)
+
+            name = parts[0]
+            version = parts[1]
+            build_string = parts[2] if len(parts) >= 3 else ""
+            build_number_as_string = "".join(
+                filter(
+                    lambda x: x.isdigit(),
+                    (build_string.rsplit("_")[-1] if build_string else "0"),
+                )
+            )
+            build_number = int(build_number_as_string) if build_number_as_string else 0
+
+            return DistDetails(
+                name, version, build_string, build_number, dist_name, fmt
+            )
+
+        except:
+            raise CondaError(
+                "dist_name is not a valid conda package: %s" % original_string
+            )
+
+    @classmethod
+    def from_url(cls, url):
+        assert is_url(url), url
+        if (
+            not any(url.endswith(ext) for ext in CONDA_PACKAGE_EXTENSIONS)
+            and "::" not in url
+        ):
+            raise CondaError("url '%s' is not a conda package" % url)
+
+        dist_details = cls.parse_dist_name(url)
+        if "::" in url:
+            url_no_tarball = url.rsplit("::", 1)[0]
+            platform = context.subdir
+            base_url = url_no_tarball.split("::")[0]
+            channel = str(Channel(base_url))
+        else:
+            url_no_tarball = url.rsplit("/", 1)[0]
+            platform = has_platform(url_no_tarball, context.known_subdirs)
+            base_url = url_no_tarball.rsplit("/", 1)[0] if platform else url_no_tarball
+            channel = Channel(base_url).canonical_name if platform else UNKNOWN_CHANNEL
+
+        return cls(
+            channel=channel,
+            name=dist_details.name,
+            version=dist_details.version,
+            build_string=dist_details.build_string,
+            build_number=dist_details.build_number,
+            dist_name=dist_details.dist_name,
+            base_url=base_url,
+            platform=platform,
+            fmt=dist_details.fmt,
+        )
+
+    def to_url(self):
+        if not self.base_url:
+            return None
+        filename = self.dist_name + self.fmt
+        return (
+            join_url(self.base_url, self.platform, filename)
+            if self.platform
+            else join_url(self.base_url, filename)
+        )
+
+    def __key__(self):
+        return self.channel, self.dist_name
+
+    def __lt__(self, other):
+        assert isinstance(other, self.__class__)
+        return self.__key__() < other.__key__()
+
+    def __gt__(self, other):
+        assert isinstance(other, self.__class__)
+        return self.__key__() > other.__key__()
+
+    def __le__(self, other):
+        assert isinstance(other, self.__class__)
+        return self.__key__() <= other.__key__()
+
+    def __ge__(self, other):
+        assert isinstance(other, self.__class__)
+        return self.__key__() >= other.__key__()
+
+    def __hash__(self):
+        # dists compare equal regardless of fmt, but fmt is taken into account for
+        #    object identity
+        return hash((self.__key__(), self.fmt))
+
+    def __eq__(self, other):
+        return isinstance(other, self.__class__) and self.__key__() == other.__key__()
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    # ############ conda-build compatibility ################
+
+    def split(self, sep=None, maxsplit=-1):
+        assert sep == "::"
+        return [self.channel, self.dist_name] if self.channel else [self.dist_name]
+
+    def rsplit(self, sep=None, maxsplit=-1):
+        assert sep == "-"
+        assert maxsplit == 2
+        name = f"{self.channel}::{self.quad[0]}" if self.channel else self.quad[0]
+        return name, self.quad[1], self.quad[2]
+
+    def startswith(self, match):
+        return self.dist_name.startswith(match)
+
+    def __contains__(self, item):
+        item = strip_extension(ensure_text_type(item))
+        return item in self.__str__()
+
+    @property
+    def fn(self):
+        return self.to_filename()
+
+
+def dist_str_to_quad(dist_str):
+    dist_str = strip_extension(dist_str)
+    if "::" in dist_str:
+        channel_str, dist_str = dist_str.split("::", 1)
+    else:
+        channel_str = UNKNOWN_CHANNEL
+    name, version, build = dist_str.rsplit("-", 2)
+    return name, version, build, channel_str
diff --git a/shared/package-files/1b/1b80f40c5447c07970ebe5e11e0c901d89134ce99a2ec5327145b445cce29594/bernoulli_native.h b/shared/package-files/1b/1b80f40c5447c07970ebe5e11e0c901d89134ce99a2ec5327145b445cce29594/bernoulli_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c6f6e60f2199de4a5bb6c14efe5dde78e5e4cffa
--- /dev/null
+++ b/shared/package-files/1b/1b80f40c5447c07970ebe5e11e0c901d89134ce99a2ec5327145b445cce29594/bernoulli_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor bernoulli(const at::Tensor & self, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & bernoulli_out(const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor bernoulli(const at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & bernoulli_Tensor_out(const at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & bernoulli_(at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & bernoulli_float_out(const at::Tensor & self, double p, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & bernoulli_(at::Tensor & self, double p=0.5, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor bernoulli(const at::Tensor & self, double p, c10::optional<at::Generator> generator=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/1b/1b818a0e56ea1064bc1e9bc138219326eb5012a0218d1c77761a078abc89a5f9/vwmterm b/shared/package-files/1b/1b818a0e56ea1064bc1e9bc138219326eb5012a0218d1c77761a078abc89a5f9/vwmterm
new file mode 100644
index 0000000000000000000000000000000000000000..180089608c9ad54927285775a069ca93264aaefe
Binary files /dev/null and b/shared/package-files/1b/1b818a0e56ea1064bc1e9bc138219326eb5012a0218d1c77761a078abc89a5f9/vwmterm differ
diff --git a/shared/package-files/1b/1b85dd185a7a948530349eaa9bbed7db864f2a019ccb1601b82bfad567913973/test_linearize.py b/shared/package-files/1b/1b85dd185a7a948530349eaa9bbed7db864f2a019ccb1601b82bfad567913973/test_linearize.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c9c2aeed6cc536a373ee2ede43978a38bbe81d6
--- /dev/null
+++ b/shared/package-files/1b/1b85dd185a7a948530349eaa9bbed7db864f2a019ccb1601b82bfad567913973/test_linearize.py
@@ -0,0 +1,334 @@
+from sympy.core.backend import (symbols, Matrix, cos, sin, atan, sqrt,
+    Rational, _simplify_matrix)
+from sympy.core.sympify import sympify
+from sympy.simplify.simplify import simplify
+from sympy.solvers.solvers import solve
+from sympy.physics.mechanics import dynamicsymbols, ReferenceFrame, Point,\
+    dot, cross, inertia, KanesMethod, Particle, RigidBody, Lagrangian,\
+    LagrangesMethod
+from sympy.testing.pytest import slow
+
+
+@slow
+def test_linearize_rolling_disc_kane():
+    # Symbols for time and constant parameters
+    t, r, m, g, v = symbols('t r m g v')
+
+    # Configuration variables and their time derivatives
+    q1, q2, q3, q4, q5, q6 = q = dynamicsymbols('q1:7')
+    q1d, q2d, q3d, q4d, q5d, q6d = qd = [qi.diff(t) for qi in q]
+
+    # Generalized speeds and their time derivatives
+    u = dynamicsymbols('u:6')
+    u1, u2, u3, u4, u5, u6 = u = dynamicsymbols('u1:7')
+    u1d, u2d, u3d, u4d, u5d, u6d = [ui.diff(t) for ui in u]
+
+    # Reference frames
+    N = ReferenceFrame('N')                   # Inertial frame
+    NO = Point('NO')                          # Inertial origin
+    A = N.orientnew('A', 'Axis', [q1, N.z])   # Yaw intermediate frame
+    B = A.orientnew('B', 'Axis', [q2, A.x])   # Lean intermediate frame
+    C = B.orientnew('C', 'Axis', [q3, B.y])   # Disc fixed frame
+    CO = NO.locatenew('CO', q4*N.x + q5*N.y + q6*N.z)      # Disc center
+
+    # Disc angular velocity in N expressed using time derivatives of coordinates
+    w_c_n_qd = C.ang_vel_in(N)
+    w_b_n_qd = B.ang_vel_in(N)
+
+    # Inertial angular velocity and angular acceleration of disc fixed frame
+    C.set_ang_vel(N, u1*B.x + u2*B.y + u3*B.z)
+
+    # Disc center velocity in N expressed using time derivatives of coordinates
+    v_co_n_qd = CO.pos_from(NO).dt(N)
+
+    # Disc center velocity in N expressed using generalized speeds
+    CO.set_vel(N, u4*C.x + u5*C.y + u6*C.z)
+
+    # Disc Ground Contact Point
+    P = CO.locatenew('P', r*B.z)
+    P.v2pt_theory(CO, N, C)
+
+    # Configuration constraint
+    f_c = Matrix([q6 - dot(CO.pos_from(P), N.z)])
+
+    # Velocity level constraints
+    f_v = Matrix([dot(P.vel(N), uv) for uv in C])
+
+    # Kinematic differential equations
+    kindiffs = Matrix([dot(w_c_n_qd - C.ang_vel_in(N), uv) for uv in B] +
+                        [dot(v_co_n_qd - CO.vel(N), uv) for uv in N])
+    qdots = solve(kindiffs, qd)
+
+    # Set angular velocity of remaining frames
+    B.set_ang_vel(N, w_b_n_qd.subs(qdots))
+    C.set_ang_acc(N, C.ang_vel_in(N).dt(B) + cross(B.ang_vel_in(N), C.ang_vel_in(N)))
+
+    # Active forces
+    F_CO = m*g*A.z
+
+    # Create inertia dyadic of disc C about point CO
+    I = (m * r**2) / 4
+    J = (m * r**2) / 2
+    I_C_CO = inertia(C, I, J, I)
+
+    Disc = RigidBody('Disc', CO, C, m, (I_C_CO, CO))
+    BL = [Disc]
+    FL = [(CO, F_CO)]
+    KM = KanesMethod(N, [q1, q2, q3, q4, q5], [u1, u2, u3], kd_eqs=kindiffs,
+            q_dependent=[q6], configuration_constraints=f_c,
+            u_dependent=[u4, u5, u6], velocity_constraints=f_v)
+    (fr, fr_star) = KM.kanes_equations(BL, FL)
+
+    # Test generalized form equations
+    linearizer = KM.to_linearizer()
+    assert linearizer.f_c == f_c
+    assert linearizer.f_v == f_v
+    assert linearizer.f_a == f_v.diff(t).subs(KM.kindiffdict())
+    sol = solve(linearizer.f_0 + linearizer.f_1, qd)
+    for qi in qdots.keys():
+        assert sol[qi] == qdots[qi]
+    assert simplify(linearizer.f_2 + linearizer.f_3 - fr - fr_star) == Matrix([0, 0, 0])
+
+    # Perform the linearization
+    # Precomputed operating point
+    q_op = {q6: -r*cos(q2)}
+    u_op = {u1: 0,
+            u2: sin(q2)*q1d + q3d,
+            u3: cos(q2)*q1d,
+            u4: -r*(sin(q2)*q1d + q3d)*cos(q3),
+            u5: 0,
+            u6: -r*(sin(q2)*q1d + q3d)*sin(q3)}
+    qd_op = {q2d: 0,
+             q4d: -r*(sin(q2)*q1d + q3d)*cos(q1),
+             q5d: -r*(sin(q2)*q1d + q3d)*sin(q1),
+             q6d: 0}
+    ud_op = {u1d: 4*g*sin(q2)/(5*r) + sin(2*q2)*q1d**2/2 + 6*cos(q2)*q1d*q3d/5,
+             u2d: 0,
+             u3d: 0,
+             u4d: r*(sin(q2)*sin(q3)*q1d*q3d + sin(q3)*q3d**2),
+             u5d: r*(4*g*sin(q2)/(5*r) + sin(2*q2)*q1d**2/2 + 6*cos(q2)*q1d*q3d/5),
+             u6d: -r*(sin(q2)*cos(q3)*q1d*q3d + cos(q3)*q3d**2)}
+
+    A, B = linearizer.linearize(op_point=[q_op, u_op, qd_op, ud_op], A_and_B=True, simplify=True)
+
+    upright_nominal = {q1d: 0, q2: 0, m: 1, r: 1, g: 1}
+
+    # Precomputed solution
+    A_sol = Matrix([[0, 0, 0, 0, 0, 0, 0, 1],
+                    [0, 0, 0, 0, 0, 1, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 1, 0],
+                    [sin(q1)*q3d, 0, 0, 0, 0, -sin(q1), -cos(q1), 0],
+                    [-cos(q1)*q3d, 0, 0, 0, 0, cos(q1), -sin(q1), 0],
+                    [0, Rational(4, 5), 0, 0, 0, 0, 0, 6*q3d/5],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, -2*q3d, 0, 0]])
+    B_sol = Matrix([])
+
+    # Check that linearization is correct
+    assert A.subs(upright_nominal) == A_sol
+    assert B.subs(upright_nominal) == B_sol
+
+    # Check eigenvalues at critical speed are all zero:
+    assert sympify(A.subs(upright_nominal).subs(q3d, 1/sqrt(3))).eigenvals() == {0: 8}
+
+def test_linearize_pendulum_kane_minimal():
+    q1 = dynamicsymbols('q1')                     # angle of pendulum
+    u1 = dynamicsymbols('u1')                     # Angular velocity
+    q1d = dynamicsymbols('q1', 1)                 # Angular velocity
+    L, m, t = symbols('L, m, t')
+    g = 9.8
+
+    # Compose world frame
+    N = ReferenceFrame('N')
+    pN = Point('N*')
+    pN.set_vel(N, 0)
+
+    # A.x is along the pendulum
+    A = N.orientnew('A', 'axis', [q1, N.z])
+    A.set_ang_vel(N, u1*N.z)
+
+    # Locate point P relative to the origin N*
+    P = pN.locatenew('P', L*A.x)
+    P.v2pt_theory(pN, N, A)
+    pP = Particle('pP', P, m)
+
+    # Create Kinematic Differential Equations
+    kde = Matrix([q1d - u1])
+
+    # Input the force resultant at P
+    R = m*g*N.x
+
+    # Solve for eom with kanes method
+    KM = KanesMethod(N, q_ind=[q1], u_ind=[u1], kd_eqs=kde)
+    (fr, frstar) = KM.kanes_equations([pP], [(P, R)])
+
+    # Linearize
+    A, B, inp_vec = KM.linearize(A_and_B=True, simplify=True)
+
+    assert A == Matrix([[0, 1], [-9.8*cos(q1)/L, 0]])
+    assert B == Matrix([])
+
+def test_linearize_pendulum_kane_nonminimal():
+    # Create generalized coordinates and speeds for this non-minimal realization
+    # q1, q2 = N.x and N.y coordinates of pendulum
+    # u1, u2 = N.x and N.y velocities of pendulum
+    q1, q2 = dynamicsymbols('q1:3')
+    q1d, q2d = dynamicsymbols('q1:3', level=1)
+    u1, u2 = dynamicsymbols('u1:3')
+    u1d, u2d = dynamicsymbols('u1:3', level=1)
+    L, m, t = symbols('L, m, t')
+    g = 9.8
+
+    # Compose world frame
+    N = ReferenceFrame('N')
+    pN = Point('N*')
+    pN.set_vel(N, 0)
+
+    # A.x is along the pendulum
+    theta1 = atan(q2/q1)
+    A = N.orientnew('A', 'axis', [theta1, N.z])
+
+    # Locate the pendulum mass
+    P = pN.locatenew('P1', q1*N.x + q2*N.y)
+    pP = Particle('pP', P, m)
+
+    # Calculate the kinematic differential equations
+    kde = Matrix([q1d - u1,
+                  q2d - u2])
+    dq_dict = solve(kde, [q1d, q2d])
+
+    # Set velocity of point P
+    P.set_vel(N, P.pos_from(pN).dt(N).subs(dq_dict))
+
+    # Configuration constraint is length of pendulum
+    f_c = Matrix([P.pos_from(pN).magnitude() - L])
+
+    # Velocity constraint is that the velocity in the A.x direction is
+    # always zero (the pendulum is never getting longer).
+    f_v = Matrix([P.vel(N).express(A).dot(A.x)])
+    f_v.simplify()
+
+    # Acceleration constraints is the time derivative of the velocity constraint
+    f_a = f_v.diff(t)
+    f_a.simplify()
+
+    # Input the force resultant at P
+    R = m*g*N.x
+
+    # Derive the equations of motion using the KanesMethod class.
+    KM = KanesMethod(N, q_ind=[q2], u_ind=[u2], q_dependent=[q1],
+            u_dependent=[u1], configuration_constraints=f_c,
+            velocity_constraints=f_v, acceleration_constraints=f_a, kd_eqs=kde)
+    (fr, frstar) = KM.kanes_equations([pP], [(P, R)])
+
+    # Set the operating point to be straight down, and non-moving
+    q_op = {q1: L, q2: 0}
+    u_op = {u1: 0, u2: 0}
+    ud_op = {u1d: 0, u2d: 0}
+
+    A, B, inp_vec = KM.linearize(op_point=[q_op, u_op, ud_op], A_and_B=True,
+                                 simplify=True)
+
+    assert A.expand() == Matrix([[0, 1], [-9.8/L, 0]])
+    assert B == Matrix([])
+
+def test_linearize_pendulum_lagrange_minimal():
+    q1 = dynamicsymbols('q1')                     # angle of pendulum
+    q1d = dynamicsymbols('q1', 1)                 # Angular velocity
+    L, m, t = symbols('L, m, t')
+    g = 9.8
+
+    # Compose world frame
+    N = ReferenceFrame('N')
+    pN = Point('N*')
+    pN.set_vel(N, 0)
+
+    # A.x is along the pendulum
+    A = N.orientnew('A', 'axis', [q1, N.z])
+    A.set_ang_vel(N, q1d*N.z)
+
+    # Locate point P relative to the origin N*
+    P = pN.locatenew('P', L*A.x)
+    P.v2pt_theory(pN, N, A)
+    pP = Particle('pP', P, m)
+
+    # Solve for eom with Lagranges method
+    Lag = Lagrangian(N, pP)
+    LM = LagrangesMethod(Lag, [q1], forcelist=[(P, m*g*N.x)], frame=N)
+    LM.form_lagranges_equations()
+
+    # Linearize
+    A, B, inp_vec = LM.linearize([q1], [q1d], A_and_B=True)
+
+    assert _simplify_matrix(A) == Matrix([[0, 1], [-9.8*cos(q1)/L, 0]])
+    assert B == Matrix([])
+
+def test_linearize_pendulum_lagrange_nonminimal():
+    q1, q2 = dynamicsymbols('q1:3')
+    q1d, q2d = dynamicsymbols('q1:3', level=1)
+    L, m, t = symbols('L, m, t')
+    g = 9.8
+    # Compose World Frame
+    N = ReferenceFrame('N')
+    pN = Point('N*')
+    pN.set_vel(N, 0)
+    # A.x is along the pendulum
+    theta1 = atan(q2/q1)
+    A = N.orientnew('A', 'axis', [theta1, N.z])
+    # Create point P, the pendulum mass
+    P = pN.locatenew('P1', q1*N.x + q2*N.y)
+    P.set_vel(N, P.pos_from(pN).dt(N))
+    pP = Particle('pP', P, m)
+    # Constraint Equations
+    f_c = Matrix([q1**2 + q2**2 - L**2])
+    # Calculate the lagrangian, and form the equations of motion
+    Lag = Lagrangian(N, pP)
+    LM = LagrangesMethod(Lag, [q1, q2], hol_coneqs=f_c, forcelist=[(P, m*g*N.x)], frame=N)
+    LM.form_lagranges_equations()
+    # Compose operating point
+    op_point = {q1: L, q2: 0, q1d: 0, q2d: 0, q1d.diff(t): 0, q2d.diff(t): 0}
+    # Solve for multiplier operating point
+    lam_op = LM.solve_multipliers(op_point=op_point)
+    op_point.update(lam_op)
+    # Perform the Linearization
+    A, B, inp_vec = LM.linearize([q2], [q2d], [q1], [q1d],
+            op_point=op_point, A_and_B=True)
+    assert _simplify_matrix(A) == Matrix([[0, 1], [-9.8/L, 0]])
+    assert B == Matrix([])
+
+def test_linearize_rolling_disc_lagrange():
+    q1, q2, q3 = q = dynamicsymbols('q1 q2 q3')
+    q1d, q2d, q3d = qd = dynamicsymbols('q1 q2 q3', 1)
+    r, m, g = symbols('r m g')
+
+    N = ReferenceFrame('N')
+    Y = N.orientnew('Y', 'Axis', [q1, N.z])
+    L = Y.orientnew('L', 'Axis', [q2, Y.x])
+    R = L.orientnew('R', 'Axis', [q3, L.y])
+
+    C = Point('C')
+    C.set_vel(N, 0)
+    Dmc = C.locatenew('Dmc', r * L.z)
+    Dmc.v2pt_theory(C, N, R)
+
+    I = inertia(L, m / 4 * r**2, m / 2 * r**2, m / 4 * r**2)
+    BodyD = RigidBody('BodyD', Dmc, R, m, (I, Dmc))
+    BodyD.potential_energy = - m * g * r * cos(q2)
+
+    Lag = Lagrangian(N, BodyD)
+    l = LagrangesMethod(Lag, q)
+    l.form_lagranges_equations()
+
+    # Linearize about steady-state upright rolling
+    op_point = {q1: 0, q2: 0, q3: 0,
+                q1d: 0, q2d: 0,
+                q1d.diff(): 0, q2d.diff(): 0, q3d.diff(): 0}
+    A = l.linearize(q_ind=q, qd_ind=qd, op_point=op_point, A_and_B=True)[0]
+    sol = Matrix([[0, 0, 0, 1, 0, 0],
+                  [0, 0, 0, 0, 1, 0],
+                  [0, 0, 0, 0, 0, 1],
+                  [0, 0, 0, 0, -6*q3d, 0],
+                  [0, -4*g/(5*r), 0, 6*q3d/5, 0, 0],
+                  [0, 0, 0, 0, 0, 0]])
+
+    assert A == sol
diff --git a/shared/package-files/1b/1b87df1df15e14c21752d408f0a77bc1976b7cd0abd6c271dbee07f7e9821731/test_constants.py b/shared/package-files/1b/1b87df1df15e14c21752d408f0a77bc1976b7cd0abd6c271dbee07f7e9821731/test_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b9dcd3b5355063cffb3b7a144937a95aab77955
--- /dev/null
+++ b/shared/package-files/1b/1b87df1df15e14c21752d408f0a77bc1976b7cd0abd6c271dbee07f7e9821731/test_constants.py
@@ -0,0 +1,90 @@
+import pytest
+
+import scipy.constants as sc
+from scipy.conftest import array_api_compatible
+from scipy._lib._array_api_no_0d import xp_assert_equal, xp_assert_close
+from numpy.testing import assert_allclose
+
+
+pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends")]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+class TestConvertTemperature:
+    def test_convert_temperature(self, xp):
+        xp_assert_equal(sc.convert_temperature(xp.asarray(32.), 'f', 'Celsius'),
+                        xp.asarray(0.0))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]),
+                                               'celsius', 'Kelvin'),
+                        xp.asarray([273.15, 273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]), 'kelvin', 'c'),
+                        xp.asarray([-273.15, -273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([32., 32.]), 'f', 'k'),
+                        xp.asarray([273.15, 273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([273.15, 273.15]),
+                                               'kelvin', 'F'),
+                        xp.asarray([32., 32.]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]), 'C', 'fahrenheit'),
+                        xp.asarray([32., 32.]))
+        xp_assert_close(sc.convert_temperature(xp.asarray([0., 0.], dtype=xp.float64),
+                                               'c', 'r'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 491.67],
+                                                        dtype=xp.float64),
+                                               'Rankine', 'C'),
+                        xp.asarray([0., 0.], dtype=xp.float64), rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 491.67],
+                                                        dtype=xp.float64),
+                                               'r', 'F'),
+                        xp.asarray([32., 32.], dtype=xp.float64), rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([32., 32.], dtype=xp.float64),
+                                               'fahrenheit', 'R'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([273.15, 273.15],
+                                                        dtype=xp.float64),
+                                               'K', 'R'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 0.],
+                                                          dtype=xp.float64),
+                                               'rankine', 'kelvin'),
+                        xp.asarray([273.15, 0.], dtype=xp.float64), rtol=0., atol=1e-13)
+
+    @skip_xp_backends(np_only=True, reason='Python list input uses NumPy backend')
+    def test_convert_temperature_array_like(self):
+        assert_allclose(sc.convert_temperature([491.67, 0.], 'rankine', 'kelvin'),
+                        [273.15, 0.], rtol=0., atol=1e-13)
+
+
+    @skip_xp_backends(np_only=True, reason='Python int input uses NumPy backend')
+    def test_convert_temperature_errors(self, xp):
+        with pytest.raises(NotImplementedError, match="old_scale="):
+            sc.convert_temperature(1, old_scale="cheddar", new_scale="kelvin")
+        with pytest.raises(NotImplementedError, match="new_scale="):
+            sc.convert_temperature(1, old_scale="kelvin", new_scale="brie")
+
+
+class TestLambdaToNu:
+    def test_lambda_to_nu(self, xp):
+        xp_assert_equal(sc.lambda2nu(xp.asarray([sc.speed_of_light, 1])),
+                        xp.asarray([1, sc.speed_of_light]))
+
+
+    @skip_xp_backends(np_only=True, reason='Python list input uses NumPy backend')
+    def test_lambda_to_nu_array_like(self, xp):
+        assert_allclose(sc.lambda2nu([sc.speed_of_light, 1]),
+                        [1, sc.speed_of_light])
+
+
+class TestNuToLambda:
+    def test_nu_to_lambda(self, xp):
+        xp_assert_equal(sc.nu2lambda(xp.asarray([sc.speed_of_light, 1])),
+                        xp.asarray([1, sc.speed_of_light]))
+
+    @skip_xp_backends(np_only=True, reason='Python list input uses NumPy backend')
+    def test_nu_to_lambda_array_like(self, xp):
+        assert_allclose(sc.nu2lambda([sc.speed_of_light, 1]),
+                        [1, sc.speed_of_light])
+
diff --git a/shared/package-files/1b/1b8815377e6669782698f59f6bda3dfef95c86a92eca3e7313018dadf660e51f/conv_utils.py b/shared/package-files/1b/1b8815377e6669782698f59f6bda3dfef95c86a92eca3e7313018dadf660e51f/conv_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b675e3b892bdb848f2599d566e6079427684e8e4
--- /dev/null
+++ b/shared/package-files/1b/1b8815377e6669782698f59f6bda3dfef95c86a92eca3e7313018dadf660e51f/conv_utils.py
@@ -0,0 +1,240 @@
+import torch
+import torch.nn.functional as F
+
+import numpy as np
+from typing import List, Optional
+
+from .expanded_weights_utils import \
+    set_grad_sample_if_exists, unpack_expanded_weight_or_tensor
+
+THRESHOLD = 32
+
+
+def conv_picker(func, conv1dOpt, conv2dOpt, conv3dOpt):
+    if func == F.conv1d:
+        return conv1dOpt
+    if func == F.conv2d:
+        return conv2dOpt
+    else:
+        assert func == F.conv3d
+        return conv3dOpt
+
+
+def conv_args_and_kwargs(kwarg_names, expanded_args_and_kwargs):
+    args = expanded_args_and_kwargs[:len(expanded_args_and_kwargs) - len(kwarg_names)]
+    kwargs = expanded_args_and_kwargs[len(expanded_args_and_kwargs) - len(kwarg_names):]
+    kwargs = dict(zip(kwarg_names, kwargs))
+
+    return conv_normalizer(*args, **kwargs)
+
+
+def conv_normalizer(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
+    return (input, weight), {'bias': bias, 'stride': stride, 'padding': padding, 'dilation': dilation, 'groups': groups}
+
+
+def conv_input_for_string_padding(func, padding_style, input, dilation, kernel_size):
+    if padding_style == "valid":
+        return input
+    else:
+        padding = int_padding_for_string_padding(func, padding_style, dilation, kernel_size)
+        return F.pad(input, padding)
+
+
+def int_padding_for_string_padding(func, padding_style, dilation, kernel_size):
+    def get_dilation(i):
+        return dilation[i] if isinstance(dilation, tuple) else dilation
+
+    if padding_style == "same":
+        padding: List[int] = []
+        # F.pad needs the padding in reverse order from what conv expects
+        for i in range(conv_picker(func, 0, 1, 2), -1, -1):
+            padding += conv_padding_for_same(get_dilation(i), kernel_size[i])
+        return padding
+    elif padding_style == "valid":
+        return conv_picker(func, 2, 4, 6) * (0,)
+    else:
+        raise RuntimeError(f"got padding type of {padding_style}, only accept 'same' or 'valid'")
+
+
+def conv_padding_for_same(dilation, kernel_size):
+    total_pad = dilation * (kernel_size - 1)
+    left_pad = total_pad // 2
+    right_pad = total_pad - left_pad
+    return left_pad, right_pad
+
+
+def conv_backward(func, ctx, grad_output):
+
+    def weight_grad_sample(weight):
+        if (batch_size < THRESHOLD and groups == 1):
+            return conv_group_weight_grad_sample(ctx.input, grad_output, weight_shape, stride, padding, dilation, batch_size, func)
+        else:
+            return conv_unfold_weight_grad_sample(ctx.input, grad_output, weight_shape, kernel_size,
+                                                  stride, padding, dilation, groups, func)
+
+    def expand(param):
+        if isinstance(param, int):
+            return conv_picker(func, (param,), (param, param), (param, param, param))
+        else:
+            return param
+
+    def calc_total_padding(func, was_same, padding, dilation, kernel_size):
+        if was_same:
+            all_padding = int_padding_for_string_padding(func, "same", dilation, kernel_size)
+            # F.pad needs the padding in reverse order from what conv expects
+            total_padding = tuple(all_padding[i] + all_padding[i - 1] for i in range(len(all_padding) - 1, -1, -2))
+            return total_padding
+        else:
+            return tuple(2 * pad for pad in padding)
+
+    weight_shape = ctx.weight.shape
+    stride, padding, dilation, groups = expand(ctx.stride), expand(ctx.padding), expand(ctx.dilation), ctx.groups
+
+    kernel_size = []
+    for i in range(2, conv_picker(func, 3, 4, 5)):
+        kernel_size.append(weight_shape[i])
+
+    batch_size = ctx.batch_size
+    results: List[Optional[torch.Tensor]] = []
+    results.append(None)  # for kwarg names
+    results.append(None)  # for op reference
+
+    # "same" padding may give uneven padding on either side so we need to separate the "padding" attr and total padding
+    total_padding = calc_total_padding(func, ctx.was_same_padding, padding, dilation, kernel_size)
+
+    if ctx.input_required_grad:
+        output_padding = []
+        input_dims = conv_picker(func, 1, 2, 3)
+        for i in range(input_dims):
+            input_dim = ctx.orig_input_shape[2 + i]
+            output_padding.append((total_padding[i] + input_dim - (kernel_size[i] * dilation[i] - dilation[i] + 1)) % stride[i])
+        weight_ = unpack_expanded_weight_or_tensor(ctx.weight)
+        transpose_func = conv_picker(func, F.conv_transpose1d, F.conv_transpose2d, F.conv_transpose3d)
+        out = transpose_func(grad_output, weight_, None, stride, padding, tuple(output_padding), groups, dilation)
+
+        if ctx.was_same_padding:
+            for i in range(len(total_padding)):
+                out = torch.narrow(out, 2 + i, total_padding[i] // 2, ctx.orig_input_shape[2 + i])
+
+        results.append(out)
+    else:
+        results.append(None)
+    # weight and bias don't compute batched gradients; no other arguments are differentiable
+    results = results + [None] * 6
+
+    # set grad_sample field for weight and bias with per sample gradients
+    set_grad_sample_if_exists(ctx.weight, weight_grad_sample)
+    set_grad_sample_if_exists(ctx.bias, lambda _: grad_output.reshape(*grad_output.shape[:2], -1).sum(dim=2))
+    return tuple(results)
+
+
+def conv_unfold_weight_grad_sample(input, grad_output, weight_shape, kernel_size, stride, padding, dilation, groups, func):
+    n = input.shape[0]
+    in_channels = input.shape[1]
+
+    unfold_func = conv_picker(
+        func,
+        lambda: F.unfold(input.unsqueeze(-2),
+                         kernel_size=(1, kernel_size[0]),
+                         dilation=(1, dilation[0]),
+                         padding=(0, padding[0]),
+                         stride=(1, stride[0])),
+        lambda: F.unfold(input, kernel_size, dilation=dilation, padding=padding, stride=stride),
+        lambda: unfold3d(input, kernel_size, padding, stride, dilation)
+    )
+
+    input = unfold_func()
+    grad_output = grad_output.reshape(n, -1, input.shape[-1])
+
+    # n=batch_sz; o=num_out_channels; p=(num_in_channels/groups)*kernel_sz
+    weight_grad_sample = torch.einsum("noq,npq->nop", grad_output, input)
+    # rearrange the above tensor and extract diagonals.
+    weight_grad_sample = weight_grad_sample.view(
+        n,
+        groups,
+        -1,
+        groups,
+        int(in_channels / groups),
+        np.prod(kernel_size),
+    )
+    weight_grad_sample = torch.einsum("ngrg...->ngr...", weight_grad_sample).contiguous()
+    shape = [n] + list(weight_shape)
+    weight_grad_sample = weight_grad_sample.view(shape)
+    return weight_grad_sample
+
+
+def conv_group_weight_grad_sample(input, grad_output, weight_shape, stride, padding, dilation, batch_size, func):
+    I = input.shape[1]
+    O = grad_output.shape[1]
+
+    input_ = input.transpose(0, 1)
+    grad_output_ = grad_output.view(grad_output.shape[0] * grad_output.shape[1], 1, *grad_output.shape[2:])
+
+    weight_grad_sample = func(input_, grad_output_, None, stride=dilation, padding=padding, dilation=stride, groups=batch_size)
+    input_dims = conv_picker(func, 3, 4, 5)
+    for i in range(2, input_dims):
+        weight_grad_sample = weight_grad_sample.narrow(i, 0, weight_shape[i])
+    weight_grad_sample = weight_grad_sample.view(I, batch_size, O, *weight_grad_sample.shape[2:])
+    weight_grad_sample = weight_grad_sample.movedim(0, 2)
+    return weight_grad_sample
+
+
+def unfold3d(
+    tensor,
+    kernel_size,
+    padding,
+    stride,
+    dilation,
+):
+    r"""
+    Extract sliding local blocks from an batched input tensor.
+
+    :class:`torch.nn.Unfold` only supports 4D inputs (batched image-like tensors).
+    This method implements the same action for 5D inputs
+    Args:
+        tensor: An input tensor of shape ``(B, C, D, H, W)``.
+        kernel_size: the size of the sliding blocks
+        padding: implicit zero padding to be added on both sides of input
+        stride: the stride of the sliding blocks in the input spatial dimensions
+        dilation: the spacing between the kernel points.
+    Returns:
+        A tensor of shape ``(B, C * np.prod(kernel_size), L)``, where L - output spatial dimensions.
+        See :class:`torch.nn.Unfold` for more details
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> B, C, D, H, W = 3, 4, 5, 6, 7
+        >>> tensor = torch.arange(1, B * C * D * H * W + 1.).view(B, C, D, H, W)
+        >>> unfold3d(tensor, kernel_size=2, padding=0, stride=1).shape
+        torch.Size([3, 32, 120])
+    """
+    if len(tensor.shape) != 5:
+        raise ValueError(
+            f"Input tensor must be of the shape [B, C, D, H, W]. Got{tensor.shape}"
+        )
+
+    if dilation != (1, 1, 1):
+        raise NotImplementedError(f"dilation={dilation} not supported.")
+
+    batch_size, channels, _, _, _ = tensor.shape
+
+    # Input shape: (B, C, D, H, W)
+    tensor = F.pad(
+        tensor, (padding[2], padding[2], padding[1], padding[1], padding[0], padding[0])
+    )
+    # Output shape: (B, C, D+2*padding[2], H+2*padding[1], W+2*padding[0])
+
+    tensor = tensor.unfold(dimension=2, size=kernel_size[0], step=stride[0])
+    tensor = tensor.unfold(dimension=3, size=kernel_size[1], step=stride[1])
+    tensor = tensor.unfold(dimension=4, size=kernel_size[2], step=stride[2])
+    # Output shape: (B, C, D_out, H_out, W_out, kernel_size[0], kernel_size[1], kernel_size[2])
+    # For D_out, H_out, W_out definitions see :class:`torch.nn.Unfold`
+
+    tensor = tensor.permute(0, 2, 3, 4, 1, 5, 6, 7)
+    # Output shape: (B, D_out, H_out, W_out, C, kernel_size[0], kernel_size[1], kernel_size[2])
+
+    tensor = tensor.reshape(batch_size, -1, channels * np.prod(kernel_size)).transpose(
+        1, 2
+    )
+    # Output shape: (B, D_out * H_out * W_out, C * kernel_size[0] * kernel_size[1] * kernel_size[2]
+
+    return tensor
diff --git a/shared/package-files/1b/1b88c26a20c3bfb81bef96fda4915530e55165f9c409a0e4d09627f23666c5a8/safestring.pyi b/shared/package-files/1b/1b88c26a20c3bfb81bef96fda4915530e55165f9c409a0e4d09627f23666c5a8/safestring.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ac52284a39db7f2132eba4280ab8f479f23f01b4
--- /dev/null
+++ b/shared/package-files/1b/1b88c26a20c3bfb81bef96fda4915530e55165f9c409a0e4d09627f23666c5a8/safestring.pyi
@@ -0,0 +1,26 @@
+from typing import TypeVar, overload, Callable, Any
+
+_SD = TypeVar("_SD", bound="SafeData")
+
+class SafeData:
+    def __html__(self: _SD) -> _SD: ...
+
+class SafeText(str, SafeData):
+    @overload
+    def __add__(self, rhs: SafeText) -> SafeText: ...
+    @overload
+    def __add__(self, rhs: str) -> str: ...
+    @overload
+    def __iadd__(self, rhs: SafeText) -> SafeText: ...
+    @overload
+    def __iadd__(self, rhs: str) -> str: ...
+
+SafeString = SafeText
+
+_C = TypeVar("_C", bound=Callable)
+@overload
+def mark_safe(s: _SD) -> _SD: ...
+@overload
+def mark_safe(s: _C) -> _C: ...
+@overload
+def mark_safe(s: Any) -> SafeText: ...
diff --git a/shared/package-files/1b/1b8b5fdb36ce3becc62a6115ed904a17083949ec8aaef5a80f7078cec232f43b/charmap.py b/shared/package-files/1b/1b8b5fdb36ce3becc62a6115ed904a17083949ec8aaef5a80f7078cec232f43b/charmap.py
new file mode 100644
index 0000000000000000000000000000000000000000..81189b161a64ad2edbe9172597d848e64b28b28c
--- /dev/null
+++ b/shared/package-files/1b/1b8b5fdb36ce3becc62a6115ed904a17083949ec8aaef5a80f7078cec232f43b/charmap.py
@@ -0,0 +1,69 @@
+""" Generic Python Character Mapping Codec.
+
+    Use this codec directly rather than through the automatic
+    conversion mechanisms supplied by unicode() and .encode().
+
+
+Written by Marc-Andre Lemburg (mal@lemburg.com).
+
+(c) Copyright CNRI, All Rights Reserved. NO WARRANTY.
+
+"""#"
+
+import codecs
+
+### Codec APIs
+
+class Codec(codecs.Codec):
+
+    # Note: Binding these as C functions will result in the class not
+    # converting them to methods. This is intended.
+    encode = codecs.charmap_encode
+    decode = codecs.charmap_decode
+
+class IncrementalEncoder(codecs.IncrementalEncoder):
+    def __init__(self, errors='strict', mapping=None):
+        codecs.IncrementalEncoder.__init__(self, errors)
+        self.mapping = mapping
+
+    def encode(self, input, final=False):
+        return codecs.charmap_encode(input, self.errors, self.mapping)[0]
+
+class IncrementalDecoder(codecs.IncrementalDecoder):
+    def __init__(self, errors='strict', mapping=None):
+        codecs.IncrementalDecoder.__init__(self, errors)
+        self.mapping = mapping
+
+    def decode(self, input, final=False):
+        return codecs.charmap_decode(input, self.errors, self.mapping)[0]
+
+class StreamWriter(Codec,codecs.StreamWriter):
+
+    def __init__(self,stream,errors='strict',mapping=None):
+        codecs.StreamWriter.__init__(self,stream,errors)
+        self.mapping = mapping
+
+    def encode(self,input,errors='strict'):
+        return Codec.encode(input,errors,self.mapping)
+
+class StreamReader(Codec,codecs.StreamReader):
+
+    def __init__(self,stream,errors='strict',mapping=None):
+        codecs.StreamReader.__init__(self,stream,errors)
+        self.mapping = mapping
+
+    def decode(self,input,errors='strict'):
+        return Codec.decode(input,errors,self.mapping)
+
+### encodings module API
+
+def getregentry():
+    return codecs.CodecInfo(
+        name='charmap',
+        encode=Codec.encode,
+        decode=Codec.decode,
+        incrementalencoder=IncrementalEncoder,
+        incrementaldecoder=IncrementalDecoder,
+        streamwriter=StreamWriter,
+        streamreader=StreamReader,
+    )
diff --git a/shared/package-files/1b/1b92a22a78a7285c98805ef9ebf2ed0c607af011a4ec8c0f89d50ecb01692d0a/odepack.py b/shared/package-files/1b/1b92a22a78a7285c98805ef9ebf2ed0c607af011a4ec8c0f89d50ecb01692d0a/odepack.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bb4c1a8c9be375df855abe6e1b30ca9711f2607
--- /dev/null
+++ b/shared/package-files/1b/1b92a22a78a7285c98805ef9ebf2ed0c607af011a4ec8c0f89d50ecb01692d0a/odepack.py
@@ -0,0 +1,17 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.integrate` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__ = ['odeint', 'ODEintWarning']  # noqa: F822
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="integrate", module="odepack",
+                                   private_modules=["_odepack_py"], all=__all__,
+                                   attribute=name)
diff --git a/shared/package-files/1b/1b92e264810dac2e852d33d90c683dbdda7ec2bac1757e14bad8f4bc91b555fd/fft_fftshift_ops.h b/shared/package-files/1b/1b92e264810dac2e852d33d90c683dbdda7ec2bac1757e14bad8f4bc91b555fd/fft_fftshift_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..edf2de936dbdc31a13ab0bf77124f8a3b8d5c3e9
--- /dev/null
+++ b/shared/package-files/1b/1b92e264810dac2e852d33d90c683dbdda7ec2bac1757e14bad8f4bc91b555fd/fft_fftshift_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API fft_fftshift {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fft_fftshift")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1b938ff2288b219380b6c7aa214751abb1db18845cac5e8316e78f198e00578d/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_compositeexplicitautograd_dispatch.h b/shared/package-files/1b/1b938ff2288b219380b6c7aa214751abb1db18845cac5e8316e78f198e00578d/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7cec2fedff17ebc8ea4b8113bdf6b27634edd9e7
--- /dev/null
+++ b/shared/package-files/1b/1b938ff2288b219380b6c7aa214751abb1db18845cac5e8316e78f198e00578d/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/1b/1b949218660c4911082c2e3792a2f97eaa3de1ce2d2a0ba48ad3b5e66db5eed6/adapter.h b/shared/package-files/1b/1b949218660c4911082c2e3792a2f97eaa3de1ce2d2a0ba48ad3b5e66db5eed6/adapter.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ffff81f355f1ddcdc19516746c61b8021477de4
--- /dev/null
+++ b/shared/package-files/1b/1b949218660c4911082c2e3792a2f97eaa3de1ce2d2a0ba48ad3b5e66db5eed6/adapter.h
@@ -0,0 +1,323 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <vector>
+
+#include "arrow/adapters/orc/options.h"
+#include "arrow/io/interfaces.h"
+#include "arrow/memory_pool.h"
+#include "arrow/record_batch.h"
+#include "arrow/status.h"
+#include "arrow/type.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/macros.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace adapters {
+namespace orc {
+
+/// \brief Information about an ORC stripe
+struct StripeInformation {
+  /// \brief Offset of the stripe from the start of the file, in bytes
+  int64_t offset;
+  /// \brief Length of the stripe, in bytes
+  int64_t length;
+  /// \brief Number of rows in the stripe
+  int64_t num_rows;
+  /// \brief Index of the first row of the stripe
+  int64_t first_row_id;
+};
+
+/// \class ORCFileReader
+/// \brief Read an Arrow Table or RecordBatch from an ORC file.
+class ARROW_EXPORT ORCFileReader {
+ public:
+  ~ORCFileReader();
+
+  /// \brief Creates a new ORC reader
+  ///
+  /// \param[in] file the data source
+  /// \param[in] pool a MemoryPool to use for buffer allocations
+  /// \return the returned reader object
+  static Result<std::unique_ptr<ORCFileReader>> Open(
+      const std::shared_ptr<io::RandomAccessFile>& file, MemoryPool* pool);
+
+  /// \brief Return the schema read from the ORC file
+  ///
+  /// \return the returned Schema object
+  Result<std::shared_ptr<Schema>> ReadSchema();
+
+  /// \brief Read the file as a Table
+  ///
+  /// The table will be composed of one record batch per stripe.
+  ///
+  /// \return the returned Table
+  Result<std::shared_ptr<Table>> Read();
+
+  /// \brief Read the file as a Table
+  ///
+  /// The table will be composed of one record batch per stripe.
+  ///
+  /// \param[in] schema the Table schema
+  /// \return the returned Table
+  Result<std::shared_ptr<Table>> Read(const std::shared_ptr<Schema>& schema);
+
+  /// \brief Read the file as a Table
+  ///
+  /// The table will be composed of one record batch per stripe.
+  ///
+  /// \param[in] include_indices the selected field indices to read
+  /// \return the returned Table
+  Result<std::shared_ptr<Table>> Read(const std::vector<int>& include_indices);
+
+  /// \brief Read the file as a Table
+  ///
+  /// The table will be composed of one record batch per stripe.
+  ///
+  /// \param[in] include_names the selected field names to read
+  /// \return the returned Table
+  Result<std::shared_ptr<Table>> Read(const std::vector<std::string>& include_names);
+
+  /// \brief Read the file as a Table
+  ///
+  /// The table will be composed of one record batch per stripe.
+  ///
+  /// \param[in] schema the Table schema
+  /// \param[in] include_indices the selected field indices to read
+  /// \return the returned Table
+  Result<std::shared_ptr<Table>> Read(const std::shared_ptr<Schema>& schema,
+                                      const std::vector<int>& include_indices);
+
+  /// \brief Read a single stripe as a RecordBatch
+  ///
+  /// \param[in] stripe the stripe index
+  /// \return the returned RecordBatch
+  Result<std::shared_ptr<RecordBatch>> ReadStripe(int64_t stripe);
+
+  /// \brief Read a single stripe as a RecordBatch
+  ///
+  /// \param[in] stripe the stripe index
+  /// \param[in] include_indices the selected field indices to read
+  /// \return the returned RecordBatch
+  Result<std::shared_ptr<RecordBatch>> ReadStripe(
+      int64_t stripe, const std::vector<int>& include_indices);
+
+  /// \brief Read a single stripe as a RecordBatch
+  ///
+  /// \param[in] stripe the stripe index
+  /// \param[in] include_names the selected field names to read
+  /// \return the returned RecordBatch
+  Result<std::shared_ptr<RecordBatch>> ReadStripe(
+      int64_t stripe, const std::vector<std::string>& include_names);
+
+  /// \brief Seek to designated row. Invoke NextStripeReader() after seek
+  ///        will return stripe reader starting from designated row.
+  ///
+  /// \param[in] row_number the rows number to seek
+  Status Seek(int64_t row_number);
+
+  /// \brief Get a stripe level record batch iterator.
+  ///
+  /// Each record batch will have up to `batch_size` rows.
+  /// NextStripeReader serves as a fine-grained alternative to ReadStripe
+  /// which may cause OOM issues by loading the whole stripe into memory.
+  ///
+  /// Note this will only read rows for the current stripe, not the entire
+  /// file.
+  ///
+  /// \param[in] batch_size the maximum number of rows in each record batch
+  /// \return the returned stripe reader
+  Result<std::shared_ptr<RecordBatchReader>> NextStripeReader(int64_t batch_size);
+
+  /// \brief Get a stripe level record batch iterator.
+  ///
+  /// Each record batch will have up to `batch_size` rows.
+  /// NextStripeReader serves as a fine-grained alternative to ReadStripe
+  /// which may cause OOM issues by loading the whole stripe into memory.
+  ///
+  /// Note this will only read rows for the current stripe, not the entire
+  /// file.
+  ///
+  /// \param[in] batch_size the maximum number of rows in each record batch
+  /// \param[in] include_indices the selected field indices to read
+  /// \return the stripe reader
+  Result<std::shared_ptr<RecordBatchReader>> NextStripeReader(
+      int64_t batch_size, const std::vector<int>& include_indices);
+
+  /// \brief Get a record batch iterator for the entire file.
+  ///
+  /// Each record batch will have up to `batch_size` rows.
+  ///
+  /// \param[in] batch_size the maximum number of rows in each record batch
+  /// \param[in] include_names the selected field names to read, if not empty
+  /// (otherwise all fields are read)
+  /// \return the record batch iterator
+  Result<std::shared_ptr<RecordBatchReader>> GetRecordBatchReader(
+      int64_t batch_size, const std::vector<std::string>& include_names);
+
+  /// \brief The number of stripes in the file
+  int64_t NumberOfStripes();
+
+  /// \brief The number of rows in the file
+  int64_t NumberOfRows();
+
+  /// \brief StripeInformation for each stripe.
+  StripeInformation GetStripeInformation(int64_t stripe);
+
+  /// \brief Get the format version of the file.
+  ///         Currently known values are 0.11 and 0.12.
+  ///
+  /// \return The FileVersion of the ORC file.
+  FileVersion GetFileVersion();
+
+  /// \brief Get the software instance and version that wrote this file.
+  ///
+  /// \return a user-facing string that specifies the software version
+  std::string GetSoftwareVersion();
+
+  /// \brief Get the compression kind of the file.
+  ///
+  /// \return The kind of compression in the ORC file.
+  Result<Compression::type> GetCompression();
+
+  /// \brief Get the buffer size for the compression.
+  ///
+  /// \return Number of bytes to buffer for the compression codec.
+  int64_t GetCompressionSize();
+
+  /// \brief Get the number of rows per an entry in the row index.
+  /// \return the number of rows per an entry in the row index or 0 if there
+  ///          is no row index.
+  int64_t GetRowIndexStride();
+
+  /// \brief Get ID of writer that generated the file.
+  ///
+  /// \return UNKNOWN_WRITER if the writer ID is undefined
+  WriterId GetWriterId();
+
+  /// \brief Get the writer id value when getWriterId() returns an unknown writer.
+  ///
+  /// \return the integer value of the writer ID.
+  int32_t GetWriterIdValue();
+
+  /// \brief Get the version of the writer.
+  ///
+  /// \return the version of the writer.
+
+  WriterVersion GetWriterVersion();
+
+  /// \brief Get the number of stripe statistics in the file.
+  ///
+  /// \return the number of stripe statistics
+  int64_t GetNumberOfStripeStatistics();
+
+  /// \brief Get the length of the data stripes in the file.
+  ///
+  /// \return return the number of bytes in stripes
+  int64_t GetContentLength();
+
+  /// \brief Get the length of the file stripe statistics.
+  ///
+  /// \return the number of compressed bytes in the file stripe statistics
+  int64_t GetStripeStatisticsLength();
+
+  /// \brief Get the length of the file footer.
+  ///
+  /// \return the number of compressed bytes in the file footer
+  int64_t GetFileFooterLength();
+
+  /// \brief Get the length of the file postscript.
+  ///
+  /// \return the number of bytes in the file postscript
+  int64_t GetFilePostscriptLength();
+
+  /// \brief Get the total length of the file.
+  ///
+  /// \return the number of bytes in the file
+  int64_t GetFileLength();
+
+  /// \brief Get the serialized file tail.
+  ///         Useful if another reader of the same file wants to avoid re-reading
+  ///         the file tail. See ReadOptions.SetSerializedFileTail().
+  ///
+  /// \return a string of bytes with the file tail
+  std::string GetSerializedFileTail();
+
+  /// \brief Return the metadata read from the ORC file
+  ///
+  /// \return A KeyValueMetadata object containing the ORC metadata
+  Result<std::shared_ptr<const KeyValueMetadata>> ReadMetadata();
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+  ORCFileReader();
+};
+
+/// \class ORCFileWriter
+/// \brief Write an Arrow Table or RecordBatch to an ORC file.
+class ARROW_EXPORT ORCFileWriter {
+ public:
+  ~ORCFileWriter();
+  /// \brief Creates a new ORC writer.
+  ///
+  /// \param[in] output_stream a pointer to the io::OutputStream to write into
+  /// \param[in] write_options the ORC writer options for Arrow
+  /// \return the returned writer object
+  static Result<std::unique_ptr<ORCFileWriter>> Open(
+      io::OutputStream* output_stream,
+      const WriteOptions& write_options = WriteOptions());
+
+  /// \brief Write a table. This can be called multiple times.
+  ///
+  /// Tables passed in subsequent calls must match the schema of the table that was
+  /// written first.
+  ///
+  /// \param[in] table the Arrow table from which data is extracted.
+  /// \return Status
+  Status Write(const Table& table);
+
+  /// \brief Write a RecordBatch. This can be called multiple times.
+  ///
+  /// RecordBatches passed in subsequent calls must match the schema of the
+  /// RecordBatch that was written first.
+  ///
+  /// \param[in] record_batch the Arrow RecordBatch from which data is extracted.
+  /// \return Status
+  Status Write(const RecordBatch& record_batch);
+
+  /// \brief Close an ORC writer (orc::Writer)
+  ///
+  /// \return Status
+  Status Close();
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+
+ private:
+  ORCFileWriter();
+};
+
+}  // namespace orc
+}  // namespace adapters
+}  // namespace arrow
diff --git a/shared/package-files/1b/1b95bfe04ba29c2019045f047e8888de0e0b04d2f646b73d73edfc375e950920/special_erfcx.h b/shared/package-files/1b/1b95bfe04ba29c2019045f047e8888de0e0b04d2f646b73d73edfc375e950920/special_erfcx.h
new file mode 100644
index 0000000000000000000000000000000000000000..060001e9e0f53f44690e65d09858c8f3c77e6850
--- /dev/null
+++ b/shared/package-files/1b/1b95bfe04ba29c2019045f047e8888de0e0b04d2f646b73d73edfc375e950920/special_erfcx.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/special_erfcx_ops.h>
+
+namespace at {
+
+
+// aten::special_erfcx(Tensor self) -> Tensor
+inline at::Tensor special_erfcx(const at::Tensor & self) {
+    return at::_ops::special_erfcx::call(self);
+}
+
+// aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_erfcx_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_erfcx_out::call(self, out);
+}
+// aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_erfcx_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_erfcx_out::call(self, out);
+}
+
+}
diff --git a/shared/package-files/1b/1b95cff34b941ecec094768a141111eba4828bacc91b8f7076ee2ff8a8b409d8/_prelu_kernel_cuda_dispatch.h b/shared/package-files/1b/1b95cff34b941ecec094768a141111eba4828bacc91b8f7076ee2ff8a8b409d8/_prelu_kernel_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5e60b7ffb3ce4d6995b796085283fb862a4e780
--- /dev/null
+++ b/shared/package-files/1b/1b95cff34b941ecec094768a141111eba4828bacc91b8f7076ee2ff8a8b409d8/_prelu_kernel_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor _prelu_kernel(const at::Tensor & self, const at::Tensor & weight);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/1b/1b98862278616e4931498da94b4f01346f78e66f66e1e665353d5a26eb46b793/bench_sympify.py b/shared/package-files/1b/1b98862278616e4931498da94b4f01346f78e66f66e1e665353d5a26eb46b793/bench_sympify.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cc0abc1e35439a1a495454abf87769d5b40d04
--- /dev/null
+++ b/shared/package-files/1b/1b98862278616e4931498da94b4f01346f78e66f66e1e665353d5a26eb46b793/bench_sympify.py
@@ -0,0 +1,11 @@
+from sympy.core import sympify, Symbol
+
+x = Symbol('x')
+
+
+def timeit_sympify_1():
+    sympify(1)
+
+
+def timeit_sympify_x():
+    sympify(x)
diff --git a/shared/package-files/1b/1b9b22c2f67eed4bf9289ea1ec00373872fa7a216c77c2e78f1e08728072cc25/test3dmatrix_6.5.1_GLNX86.mat b/shared/package-files/1b/1b9b22c2f67eed4bf9289ea1ec00373872fa7a216c77c2e78f1e08728072cc25/test3dmatrix_6.5.1_GLNX86.mat
new file mode 100644
index 0000000000000000000000000000000000000000..e04d27d30378655ed14634330c7a8ddcd0b98c10
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diff --git a/shared/package-files/1b/1b9c804825d2558f8455a9600286bb8b6bef9dfbcd835bf31ba49d62c9bf11aa/_efficient_attention_forward_ops.h b/shared/package-files/1b/1b9c804825d2558f8455a9600286bb8b6bef9dfbcd835bf31ba49d62c9bf11aa/_efficient_attention_forward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1c9986924edb51f038598bdfebb6422f3e909bd
--- /dev/null
+++ b/shared/package-files/1b/1b9c804825d2558f8455a9600286bb8b6bef9dfbcd835bf31ba49d62c9bf11aa/_efficient_attention_forward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _efficient_attention_forward {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, c10::optional<int64_t>, c10::optional<int64_t>, double, int64_t, bool, c10::optional<double>, const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_efficient_attention_forward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, int? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? causal_diagonal=None, Tensor? seqlen_k=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k)")
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & cu_seqlens_q, const c10::optional<at::Tensor> & cu_seqlens_k, c10::optional<int64_t> max_seqlen_q, c10::optional<int64_t> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp, c10::optional<double> scale, const c10::optional<at::Tensor> & causal_diagonal, const c10::optional<at::Tensor> & seqlen_k);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & cu_seqlens_q, const c10::optional<at::Tensor> & cu_seqlens_k, c10::optional<int64_t> max_seqlen_q, c10::optional<int64_t> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp, c10::optional<double> scale, const c10::optional<at::Tensor> & causal_diagonal, const c10::optional<at::Tensor> & seqlen_k);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1b9dcd2dd133da88db94ea861792b4228d3ba25acf63fde361f892879598280d/qvt101+ b/shared/package-files/1b/1b9dcd2dd133da88db94ea861792b4228d3ba25acf63fde361f892879598280d/qvt101+
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diff --git a/shared/package-files/1b/1b9dcd2dd133da88db94ea861792b4228d3ba25acf63fde361f892879598280d/qvt101p b/shared/package-files/1b/1b9dcd2dd133da88db94ea861792b4228d3ba25acf63fde361f892879598280d/qvt101p
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diff --git a/shared/package-files/1b/1b9e09147cd70378c12650afc02821bd0ddaed35f51159ac463645b247ac47cd/_upsample_nearest_exact2d_backward.h b/shared/package-files/1b/1b9e09147cd70378c12650afc02821bd0ddaed35f51159ac463645b247ac47cd/_upsample_nearest_exact2d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..412bd147a4845a6cddfb794bc8254b8e6c5fa694
--- /dev/null
+++ b/shared/package-files/1b/1b9e09147cd70378c12650afc02821bd0ddaed35f51159ac463645b247ac47cd/_upsample_nearest_exact2d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_upsample_nearest_exact2d_backward_ops.h>
+
+namespace at {
+
+
+// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & _upsample_nearest_exact2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & _upsample_nearest_exact2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
+  }
+}
+
+// aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor _upsample_nearest_exact2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt) {
+    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/shared/package-files/1b/1ba1c984555b84a9175c137f6556e8ad15860463ba3cd000439fb3a4e6285c10/chbevl.h b/shared/package-files/1b/1ba1c984555b84a9175c137f6556e8ad15860463ba3cd000439fb3a4e6285c10/chbevl.h
new file mode 100644
index 0000000000000000000000000000000000000000..caaa74fc7b81015784608cc38aed5987ca145526
--- /dev/null
+++ b/shared/package-files/1b/1ba1c984555b84a9175c137f6556e8ad15860463ba3cd000439fb3a4e6285c10/chbevl.h
@@ -0,0 +1,85 @@
+/*                                                     chbevl.c
+ *
+ *     Evaluate Chebyshev series
+ *
+ *
+ *
+ * SYNOPSIS:
+ *
+ * int N;
+ * double x, y, coef[N], chebevl();
+ *
+ * y = chbevl( x, coef, N );
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Evaluates the series
+ *
+ *        N-1
+ *         - '
+ *  y  =   >   coef[i] T (x/2)
+ *         -            i
+ *        i=0
+ *
+ * of Chebyshev polynomials Ti at argument x/2.
+ *
+ * Coefficients are stored in reverse order, i.e. the zero
+ * order term is last in the array.  Note N is the number of
+ * coefficients, not the order.
+ *
+ * If coefficients are for the interval a to b, x must
+ * have been transformed to x -> 2(2x - b - a)/(b-a) before
+ * entering the routine.  This maps x from (a, b) to (-1, 1),
+ * over which the Chebyshev polynomials are defined.
+ *
+ * If the coefficients are for the inverted interval, in
+ * which (a, b) is mapped to (1/b, 1/a), the transformation
+ * required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
+ * this becomes x -> 4a/x - 1.
+ *
+ *
+ *
+ * SPEED:
+ *
+ * Taking advantage of the recurrence properties of the
+ * Chebyshev polynomials, the routine requires one more
+ * addition per loop than evaluating a nested polynomial of
+ * the same degree.
+ *
+ */
+/*							chbevl.c	*/
+
+/*
+ * Cephes Math Library Release 2.0:  April, 1987
+ * Copyright 1985, 1987 by Stephen L. Moshier
+ * Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+ */
+#pragma once
+
+#include "../config.h"
+
+namespace xsf {
+namespace cephes {
+
+    XSF_HOST_DEVICE double chbevl(double x, const double array[], int n) {
+        double b0, b1, b2;
+        const double *p;
+        int i;
+
+        p = array;
+        b0 = *p++;
+        b1 = 0.0;
+        i = n - 1;
+
+        do {
+            b2 = b1;
+            b1 = b0;
+            b0 = x * b1 - b2 + *p++;
+        } while (--i);
+
+        return (0.5 * (b0 - b2));
+    }
+} // namespace cephes
+} // namespace xsf
diff --git a/shared/package-files/1b/1ba32abe659ffb2d92c21ba1345daa37c866a2fc40915d5324c6e0e0bcd4f5c3/backend_bases.pyi b/shared/package-files/1b/1ba32abe659ffb2d92c21ba1345daa37c866a2fc40915d5324c6e0e0bcd4f5c3/backend_bases.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..23a19b79d3be43f707ff02cf33540aa1102e06cc
--- /dev/null
+++ b/shared/package-files/1b/1ba32abe659ffb2d92c21ba1345daa37c866a2fc40915d5324c6e0e0bcd4f5c3/backend_bases.pyi
@@ -0,0 +1,482 @@
+from enum import Enum, IntEnum
+import os
+from matplotlib import (
+    cbook,
+    transforms,
+    widgets,
+    _api,
+)
+from matplotlib.artist import Artist
+from matplotlib.axes import Axes
+from matplotlib.backend_managers import ToolManager
+from matplotlib.backend_tools import Cursors, ToolBase
+from matplotlib.colorbar import Colorbar
+from matplotlib.figure import Figure
+from matplotlib.font_manager import FontProperties
+from matplotlib.path import Path
+from matplotlib.texmanager import TexManager
+from matplotlib.text import Text
+from matplotlib.transforms import Bbox, BboxBase, Transform, TransformedPath
+
+from collections.abc import Callable, Iterable, Sequence
+from typing import Any, IO, Literal, NamedTuple, TypeVar
+from numpy.typing import ArrayLike
+from .typing import ColorType, LineStyleType, CapStyleType, JoinStyleType
+
+def register_backend(
+    format: str, backend: str | type[FigureCanvasBase], description: str | None = ...
+) -> None: ...
+def get_registered_canvas_class(format: str) -> type[FigureCanvasBase]: ...
+
+class RendererBase:
+    def __init__(self) -> None: ...
+    def open_group(self, s: str, gid: str | None = ...) -> None: ...
+    def close_group(self, s: str) -> None: ...
+    def draw_path(
+        self,
+        gc: GraphicsContextBase,
+        path: Path,
+        transform: Transform,
+        rgbFace: ColorType | None = ...,
+    ) -> None: ...
+    def draw_markers(
+        self,
+        gc: GraphicsContextBase,
+        marker_path: Path,
+        marker_trans: Transform,
+        path: Path,
+        trans: Transform,
+        rgbFace: ColorType | None = ...,
+    ) -> None: ...
+    def draw_path_collection(
+        self,
+        gc: GraphicsContextBase,
+        master_transform: Transform,
+        paths: Sequence[Path],
+        all_transforms: Sequence[ArrayLike],
+        offsets: ArrayLike | Sequence[ArrayLike],
+        offset_trans: Transform,
+        facecolors: ColorType | Sequence[ColorType],
+        edgecolors: ColorType | Sequence[ColorType],
+        linewidths: float | Sequence[float],
+        linestyles: LineStyleType | Sequence[LineStyleType],
+        antialiaseds: bool | Sequence[bool],
+        urls: str | Sequence[str],
+        offset_position: Any,
+    ) -> None: ...
+    def draw_quad_mesh(
+        self,
+        gc: GraphicsContextBase,
+        master_transform: Transform,
+        meshWidth,
+        meshHeight,
+        coordinates: ArrayLike,
+        offsets: ArrayLike | Sequence[ArrayLike],
+        offsetTrans: Transform,
+        facecolors: Sequence[ColorType],
+        antialiased: bool,
+        edgecolors: Sequence[ColorType] | ColorType | None,
+    ) -> None: ...
+    def draw_gouraud_triangles(
+        self,
+        gc: GraphicsContextBase,
+        triangles_array: ArrayLike,
+        colors_array: ArrayLike,
+        transform: Transform,
+    ) -> None: ...
+    def get_image_magnification(self) -> float: ...
+    def draw_image(
+        self,
+        gc: GraphicsContextBase,
+        x: float,
+        y: float,
+        im: ArrayLike,
+        transform: transforms.Affine2DBase | None = ...,
+    ) -> None: ...
+    def option_image_nocomposite(self) -> bool: ...
+    def option_scale_image(self) -> bool: ...
+    def draw_tex(
+        self,
+        gc: GraphicsContextBase,
+        x: float,
+        y: float,
+        s: str,
+        prop: FontProperties,
+        angle: float,
+        *,
+        mtext: Text | None = ...
+    ) -> None: ...
+    def draw_text(
+        self,
+        gc: GraphicsContextBase,
+        x: float,
+        y: float,
+        s: str,
+        prop: FontProperties,
+        angle: float,
+        ismath: bool | Literal["TeX"] = ...,
+        mtext: Text | None = ...,
+    ) -> None: ...
+    def get_text_width_height_descent(
+        self, s: str, prop: FontProperties, ismath: bool | Literal["TeX"]
+    ) -> tuple[float, float, float]: ...
+    def flipy(self) -> bool: ...
+    def get_canvas_width_height(self) -> tuple[float, float]: ...
+    def get_texmanager(self) -> TexManager: ...
+    def new_gc(self) -> GraphicsContextBase: ...
+    def points_to_pixels(self, points: ArrayLike) -> ArrayLike: ...
+    def start_rasterizing(self) -> None: ...
+    def stop_rasterizing(self) -> None: ...
+    def start_filter(self) -> None: ...
+    def stop_filter(self, filter_func) -> None: ...
+
+class GraphicsContextBase:
+    def __init__(self) -> None: ...
+    def copy_properties(self, gc: GraphicsContextBase) -> None: ...
+    def restore(self) -> None: ...
+    def get_alpha(self) -> float: ...
+    def get_antialiased(self) -> int: ...
+    def get_capstyle(self) -> Literal["butt", "projecting", "round"]: ...
+    def get_clip_rectangle(self) -> Bbox | None: ...
+    def get_clip_path(
+        self,
+    ) -> tuple[TransformedPath, Transform] | tuple[None, None]: ...
+    def get_dashes(self) -> tuple[float, ArrayLike | None]: ...
+    def get_forced_alpha(self) -> bool: ...
+    def get_joinstyle(self) -> Literal["miter", "round", "bevel"]: ...
+    def get_linewidth(self) -> float: ...
+    def get_rgb(self) -> tuple[float, float, float, float]: ...
+    def get_url(self) -> str | None: ...
+    def get_gid(self) -> int | None: ...
+    def get_snap(self) -> bool | None: ...
+    def set_alpha(self, alpha: float) -> None: ...
+    def set_antialiased(self, b: bool) -> None: ...
+    def set_capstyle(self, cs: CapStyleType) -> None: ...
+    def set_clip_rectangle(self, rectangle: Bbox | None) -> None: ...
+    def set_clip_path(self, path: TransformedPath | None) -> None: ...
+    def set_dashes(self, dash_offset: float, dash_list: ArrayLike | None) -> None: ...
+    def set_foreground(self, fg: ColorType, isRGBA: bool = ...) -> None: ...
+    def set_joinstyle(self, js: JoinStyleType) -> None: ...
+    def set_linewidth(self, w: float) -> None: ...
+    def set_url(self, url: str | None) -> None: ...
+    def set_gid(self, id: int | None) -> None: ...
+    def set_snap(self, snap: bool | None) -> None: ...
+    def set_hatch(self, hatch: str | None) -> None: ...
+    def get_hatch(self) -> str | None: ...
+    def get_hatch_path(self, density: float = ...) -> Path: ...
+    def get_hatch_color(self) -> ColorType: ...
+    def set_hatch_color(self, hatch_color: ColorType) -> None: ...
+    def get_hatch_linewidth(self) -> float: ...
+    def set_hatch_linewidth(self, hatch_linewidth: float) -> None: ...
+    def get_sketch_params(self) -> tuple[float, float, float] | None: ...
+    def set_sketch_params(
+        self,
+        scale: float | None = ...,
+        length: float | None = ...,
+        randomness: float | None = ...,
+    ) -> None: ...
+
+class TimerBase:
+    callbacks: list[tuple[Callable, tuple, dict[str, Any]]]
+    def __init__(
+        self,
+        interval: int | None = ...,
+        callbacks: list[tuple[Callable, tuple, dict[str, Any]]] | None = ...,
+    ) -> None: ...
+    def __del__(self) -> None: ...
+    def start(self, interval: int | None = ...) -> None: ...
+    def stop(self) -> None: ...
+    @property
+    def interval(self) -> int: ...
+    @interval.setter
+    def interval(self, interval: int) -> None: ...
+    @property
+    def single_shot(self) -> bool: ...
+    @single_shot.setter
+    def single_shot(self, ss: bool) -> None: ...
+    def add_callback(self, func: Callable, *args, **kwargs) -> Callable: ...
+    def remove_callback(self, func: Callable, *args, **kwargs) -> None: ...
+
+class Event:
+    name: str
+    canvas: FigureCanvasBase
+    guiEvent: Any
+    def __init__(
+        self, name: str, canvas: FigureCanvasBase, guiEvent: Any | None = ...
+    ) -> None: ...
+
+class DrawEvent(Event):
+    renderer: RendererBase
+    def __init__(
+        self, name: str, canvas: FigureCanvasBase, renderer: RendererBase
+    ) -> None: ...
+
+class ResizeEvent(Event):
+    width: int
+    height: int
+    def __init__(self, name: str, canvas: FigureCanvasBase) -> None: ...
+
+class CloseEvent(Event): ...
+
+class LocationEvent(Event):
+    x: int
+    y: int
+    inaxes: Axes | None
+    xdata: float | None
+    ydata: float | None
+    def __init__(
+        self,
+        name: str,
+        canvas: FigureCanvasBase,
+        x: int,
+        y: int,
+        guiEvent: Any | None = ...,
+        *,
+        modifiers: Iterable[str] | None = ...,
+    ) -> None: ...
+
+class MouseButton(IntEnum):
+    LEFT = 1
+    MIDDLE = 2
+    RIGHT = 3
+    BACK = 8
+    FORWARD = 9
+
+class MouseEvent(LocationEvent):
+    button: MouseButton | Literal["up", "down"] | None
+    key: str | None
+    step: float
+    dblclick: bool
+    def __init__(
+        self,
+        name: str,
+        canvas: FigureCanvasBase,
+        x: int,
+        y: int,
+        button: MouseButton | Literal["up", "down"] | None = ...,
+        key: str | None = ...,
+        step: float = ...,
+        dblclick: bool = ...,
+        guiEvent: Any | None = ...,
+        *,
+        buttons: Iterable[MouseButton] | None = ...,
+        modifiers: Iterable[str] | None = ...,
+    ) -> None: ...
+
+class PickEvent(Event):
+    mouseevent: MouseEvent
+    artist: Artist
+    def __init__(
+        self,
+        name: str,
+        canvas: FigureCanvasBase,
+        mouseevent: MouseEvent,
+        artist: Artist,
+        guiEvent: Any | None = ...,
+        **kwargs
+    ) -> None: ...
+
+class KeyEvent(LocationEvent):
+    key: str | None
+    def __init__(
+        self,
+        name: str,
+        canvas: FigureCanvasBase,
+        key: str | None,
+        x: int = ...,
+        y: int = ...,
+        guiEvent: Any | None = ...,
+    ) -> None: ...
+
+class FigureCanvasBase:
+    required_interactive_framework: str | None
+
+    @_api.classproperty
+    def manager_class(cls) -> type[FigureManagerBase]: ...
+    events: list[str]
+    fixed_dpi: None | float
+    filetypes: dict[str, str]
+
+    @_api.classproperty
+    def supports_blit(cls) -> bool: ...
+
+    figure: Figure
+    manager: None | FigureManagerBase
+    widgetlock: widgets.LockDraw
+    mouse_grabber: None | Axes
+    toolbar: None | NavigationToolbar2
+    def __init__(self, figure: Figure | None = ...) -> None: ...
+    @property
+    def callbacks(self) -> cbook.CallbackRegistry: ...
+    @property
+    def button_pick_id(self) -> int: ...
+    @property
+    def scroll_pick_id(self) -> int: ...
+    @classmethod
+    def new_manager(cls, figure: Figure, num: int | str) -> FigureManagerBase: ...
+    def is_saving(self) -> bool: ...
+    def blit(self, bbox: BboxBase | None = ...) -> None: ...
+    def inaxes(self, xy: tuple[float, float]) -> Axes | None: ...
+    def grab_mouse(self, ax: Axes) -> None: ...
+    def release_mouse(self, ax: Axes) -> None: ...
+    def set_cursor(self, cursor: Cursors) -> None: ...
+    def draw(self, *args, **kwargs) -> None: ...
+    def draw_idle(self, *args, **kwargs) -> None: ...
+    @property
+    def device_pixel_ratio(self) -> float: ...
+    def get_width_height(self, *, physical: bool = ...) -> tuple[int, int]: ...
+    @classmethod
+    def get_supported_filetypes(cls) -> dict[str, str]: ...
+    @classmethod
+    def get_supported_filetypes_grouped(cls) -> dict[str, list[str]]: ...
+    def print_figure(
+        self,
+        filename: str | os.PathLike | IO,
+        dpi: float | None = ...,
+        facecolor: ColorType | Literal["auto"] | None = ...,
+        edgecolor: ColorType | Literal["auto"] | None = ...,
+        orientation: str = ...,
+        format: str | None = ...,
+        *,
+        bbox_inches: Literal["tight"] | Bbox | None = ...,
+        pad_inches: float | None = ...,
+        bbox_extra_artists: list[Artist] | None = ...,
+        backend: str | None = ...,
+        **kwargs
+    ) -> Any: ...
+    @classmethod
+    def get_default_filetype(cls) -> str: ...
+    def get_default_filename(self) -> str: ...
+    _T = TypeVar("_T", bound=FigureCanvasBase)
+    def mpl_connect(self, s: str, func: Callable[[Event], Any]) -> int: ...
+    def mpl_disconnect(self, cid: int) -> None: ...
+    def new_timer(
+        self,
+        interval: int | None = ...,
+        callbacks: list[tuple[Callable, tuple, dict[str, Any]]] | None = ...,
+    ) -> TimerBase: ...
+    def flush_events(self) -> None: ...
+    def start_event_loop(self, timeout: float = ...) -> None: ...
+    def stop_event_loop(self) -> None: ...
+
+def key_press_handler(
+    event: KeyEvent,
+    canvas: FigureCanvasBase | None = ...,
+    toolbar: NavigationToolbar2 | None = ...,
+) -> None: ...
+def button_press_handler(
+    event: MouseEvent,
+    canvas: FigureCanvasBase | None = ...,
+    toolbar: NavigationToolbar2 | None = ...,
+) -> None: ...
+
+class NonGuiException(Exception): ...
+
+class FigureManagerBase:
+    canvas: FigureCanvasBase
+    num: int | str
+    key_press_handler_id: int | None
+    button_press_handler_id: int | None
+    toolmanager: ToolManager | None
+    toolbar: NavigationToolbar2 | ToolContainerBase | None
+    def __init__(self, canvas: FigureCanvasBase, num: int | str) -> None: ...
+    @classmethod
+    def create_with_canvas(
+        cls, canvas_class: type[FigureCanvasBase], figure: Figure, num: int | str
+    ) -> FigureManagerBase: ...
+    @classmethod
+    def start_main_loop(cls) -> None: ...
+    @classmethod
+    def pyplot_show(cls, *, block: bool | None = ...) -> None: ...
+    def show(self) -> None: ...
+    def destroy(self) -> None: ...
+    def full_screen_toggle(self) -> None: ...
+    def resize(self, w: int, h: int) -> None: ...
+    def get_window_title(self) -> str: ...
+    def set_window_title(self, title: str) -> None: ...
+
+cursors = Cursors
+
+class _Mode(str, Enum):
+    NONE = ""
+    PAN = "pan/zoom"
+    ZOOM = "zoom rect"
+
+class NavigationToolbar2:
+    toolitems: tuple[tuple[str, ...] | tuple[None, ...], ...]
+    UNKNOWN_SAVED_STATUS: object
+    canvas: FigureCanvasBase
+    mode: _Mode
+    def __init__(self, canvas: FigureCanvasBase) -> None: ...
+    def set_message(self, s: str) -> None: ...
+    def draw_rubberband(
+        self, event: Event, x0: float, y0: float, x1: float, y1: float
+    ) -> None: ...
+    def remove_rubberband(self) -> None: ...
+    def home(self, *args) -> None: ...
+    def back(self, *args) -> None: ...
+    def forward(self, *args) -> None: ...
+    def mouse_move(self, event: MouseEvent) -> None: ...
+    def pan(self, *args) -> None: ...
+
+    class _PanInfo(NamedTuple):
+        button: MouseButton
+        axes: list[Axes]
+        cid: int
+    def press_pan(self, event: Event) -> None: ...
+    def drag_pan(self, event: Event) -> None: ...
+    def release_pan(self, event: Event) -> None: ...
+    def zoom(self, *args) -> None: ...
+
+    class _ZoomInfo(NamedTuple):
+        direction: Literal["in", "out"]
+        start_xy: tuple[float, float]
+        axes: list[Axes]
+        cid: int
+        cbar: Colorbar
+    def press_zoom(self, event: Event) -> None: ...
+    def drag_zoom(self, event: Event) -> None: ...
+    def release_zoom(self, event: Event) -> None: ...
+    def push_current(self) -> None: ...
+    subplot_tool: widgets.SubplotTool
+    def configure_subplots(self, *args): ...
+    def save_figure(self, *args) -> str | None | object: ...
+    def update(self) -> None: ...
+    def set_history_buttons(self) -> None: ...
+
+class ToolContainerBase:
+    toolmanager: ToolManager
+    def __init__(self, toolmanager: ToolManager) -> None: ...
+    def add_tool(self, tool: ToolBase, group: str, position: int = ...) -> None: ...
+    def trigger_tool(self, name: str) -> None: ...
+    def add_toolitem(
+        self,
+        name: str,
+        group: str,
+        position: int,
+        image: str,
+        description: str,
+        toggle: bool,
+    ) -> None: ...
+    def toggle_toolitem(self, name: str, toggled: bool) -> None: ...
+    def remove_toolitem(self, name: str) -> None: ...
+    def set_message(self, s: str) -> None: ...
+
+class _Backend:
+    backend_version: str
+    FigureCanvas: type[FigureCanvasBase] | None
+    FigureManager: type[FigureManagerBase]
+    mainloop: None | Callable[[], Any]
+    @classmethod
+    def new_figure_manager(cls, num: int | str, *args, **kwargs) -> FigureManagerBase: ...
+    @classmethod
+    def new_figure_manager_given_figure(cls, num: int | str, figure: Figure) -> FigureManagerBase: ...
+    @classmethod
+    def draw_if_interactive(cls) -> None: ...
+    @classmethod
+    def show(cls, *, block: bool | None = ...) -> None: ...
+    @staticmethod
+    def export(cls) -> type[_Backend]: ...
+
+class ShowBase(_Backend):
+    def __call__(self, block: bool | None = ...) -> None: ...
diff --git a/shared/package-files/1b/1ba4bb4b3794513b3f0062fdd7a418b9a10a4611b2ae909e90456dd093a84519/parser.pyi b/shared/package-files/1b/1ba4bb4b3794513b3f0062fdd7a418b9a10a4611b2ae909e90456dd093a84519/parser.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ae5962f64d088b9b7e8f65ccb1ffb501eeaa2591
--- /dev/null
+++ b/shared/package-files/1b/1ba4bb4b3794513b3f0062fdd7a418b9a10a4611b2ae909e90456dd093a84519/parser.pyi
@@ -0,0 +1,68 @@
+from typing import Any, Optional
+
+class Parser:
+    environment: Any
+    stream: Any
+    name: Any
+    filename: Any
+    closed: bool
+    extensions: Any
+    def __init__(
+        self, environment, source, name: Optional[Any] = ..., filename: Optional[Any] = ..., state: Optional[Any] = ...
+    ) -> None: ...
+    def fail(self, msg, lineno: Optional[Any] = ..., exc: Any = ...): ...
+    def fail_unknown_tag(self, name, lineno: Optional[Any] = ...): ...
+    def fail_eof(self, end_tokens: Optional[Any] = ..., lineno: Optional[Any] = ...): ...
+    def is_tuple_end(self, extra_end_rules: Optional[Any] = ...): ...
+    def free_identifier(self, lineno: Optional[Any] = ...): ...
+    def parse_statement(self): ...
+    def parse_statements(self, end_tokens, drop_needle: bool = ...): ...
+    def parse_set(self): ...
+    def parse_for(self): ...
+    def parse_if(self): ...
+    def parse_block(self): ...
+    def parse_extends(self): ...
+    def parse_import_context(self, node, default): ...
+    def parse_include(self): ...
+    def parse_import(self): ...
+    def parse_from(self): ...
+    def parse_signature(self, node): ...
+    def parse_call_block(self): ...
+    def parse_filter_block(self): ...
+    def parse_macro(self): ...
+    def parse_print(self): ...
+    def parse_assign_target(self, with_tuple: bool = ..., name_only: bool = ..., extra_end_rules: Optional[Any] = ...): ...
+    def parse_expression(self, with_condexpr: bool = ...): ...
+    def parse_condexpr(self): ...
+    def parse_or(self): ...
+    def parse_and(self): ...
+    def parse_not(self): ...
+    def parse_compare(self): ...
+    def parse_add(self): ...
+    def parse_sub(self): ...
+    def parse_concat(self): ...
+    def parse_mul(self): ...
+    def parse_div(self): ...
+    def parse_floordiv(self): ...
+    def parse_mod(self): ...
+    def parse_pow(self): ...
+    def parse_unary(self, with_filter: bool = ...): ...
+    def parse_primary(self): ...
+    def parse_tuple(
+        self,
+        simplified: bool = ...,
+        with_condexpr: bool = ...,
+        extra_end_rules: Optional[Any] = ...,
+        explicit_parentheses: bool = ...,
+    ): ...
+    def parse_list(self): ...
+    def parse_dict(self): ...
+    def parse_postfix(self, node): ...
+    def parse_filter_expr(self, node): ...
+    def parse_subscript(self, node): ...
+    def parse_subscribed(self): ...
+    def parse_call(self, node): ...
+    def parse_filter(self, node, start_inline: bool = ...): ...
+    def parse_test(self, node): ...
+    def subparse(self, end_tokens: Optional[Any] = ...): ...
+    def parse(self): ...
diff --git a/shared/package-files/1b/1bab64d20396301a106901a8e25ca9d0e2d292a119a8c841d33dbb328411deb5/public-api.pxi b/shared/package-files/1b/1bab64d20396301a106901a8e25ca9d0e2d292a119a8c841d33dbb328411deb5/public-api.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..d1fa1192debc3cd24b23a34e226761ec4aab02cc
--- /dev/null
+++ b/shared/package-files/1b/1bab64d20396301a106901a8e25ca9d0e2d292a119a8c841d33dbb328411deb5/public-api.pxi
@@ -0,0 +1,443 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from libcpp.memory cimport shared_ptr
+from pyarrow.includes.libarrow cimport (CArray, CDataType, CField,
+                                        CRecordBatch, CSchema,
+                                        CTable, CTensor,
+                                        CSparseCOOTensor, CSparseCSRMatrix,
+                                        CSparseCSCMatrix, CSparseCSFTensor)
+
+# You cannot assign something to a dereferenced pointer in Cython thus these
+# methods don't use Status to indicate a successful operation.
+
+
+cdef api bint pyarrow_is_buffer(object buffer):
+    return isinstance(buffer, Buffer)
+
+
+cdef api shared_ptr[CBuffer] pyarrow_unwrap_buffer(object buffer):
+    cdef Buffer buf
+    if pyarrow_is_buffer(buffer):
+        buf = <Buffer>(buffer)
+        return buf.buffer
+
+    return shared_ptr[CBuffer]()
+
+
+cdef api object pyarrow_wrap_buffer(const shared_ptr[CBuffer]& buf):
+    cdef Buffer result = Buffer.__new__(Buffer)
+    result.init(buf)
+    return result
+
+
+cdef api object pyarrow_wrap_resizable_buffer(
+        const shared_ptr[CResizableBuffer]& buf):
+    cdef ResizableBuffer result = ResizableBuffer.__new__(ResizableBuffer)
+    result.init_rz(buf)
+    return result
+
+
+cdef api bint pyarrow_is_data_type(object type_):
+    return isinstance(type_, DataType)
+
+
+cdef api shared_ptr[CDataType] pyarrow_unwrap_data_type(
+        object data_type):
+    cdef DataType type_
+    if pyarrow_is_data_type(data_type):
+        type_ = <DataType>(data_type)
+        return type_.sp_type
+
+    return shared_ptr[CDataType]()
+
+
+# Workaround for Cython parsing bug
+# https://github.com/cython/cython/issues/2143
+ctypedef const CPyExtensionType* _CPyExtensionTypePtr
+
+
+cdef api object pyarrow_wrap_data_type(
+        const shared_ptr[CDataType]& type):
+    cdef:
+        const CExtensionType* ext_type
+        const CPyExtensionType* cpy_ext_type
+        DataType out
+
+    if type.get() == NULL:
+        return None
+
+    if type.get().id() == _Type_DICTIONARY:
+        out = DictionaryType.__new__(DictionaryType)
+    elif type.get().id() == _Type_LIST:
+        out = ListType.__new__(ListType)
+    elif type.get().id() == _Type_LARGE_LIST:
+        out = LargeListType.__new__(LargeListType)
+    elif type.get().id() == _Type_LIST_VIEW:
+        out = ListViewType.__new__(ListViewType)
+    elif type.get().id() == _Type_LARGE_LIST_VIEW:
+        out = LargeListViewType.__new__(LargeListViewType)
+    elif type.get().id() == _Type_MAP:
+        out = MapType.__new__(MapType)
+    elif type.get().id() == _Type_FIXED_SIZE_LIST:
+        out = FixedSizeListType.__new__(FixedSizeListType)
+    elif type.get().id() == _Type_STRUCT:
+        out = StructType.__new__(StructType)
+    elif type.get().id() == _Type_SPARSE_UNION:
+        out = SparseUnionType.__new__(SparseUnionType)
+    elif type.get().id() == _Type_DENSE_UNION:
+        out = DenseUnionType.__new__(DenseUnionType)
+    elif type.get().id() == _Type_TIME32:
+        out = Time32Type.__new__(Time32Type)
+    elif type.get().id() == _Type_TIME64:
+        out = Time64Type.__new__(Time64Type)
+    elif type.get().id() == _Type_TIMESTAMP:
+        out = TimestampType.__new__(TimestampType)
+    elif type.get().id() == _Type_DURATION:
+        out = DurationType.__new__(DurationType)
+    elif type.get().id() == _Type_FIXED_SIZE_BINARY:
+        out = FixedSizeBinaryType.__new__(FixedSizeBinaryType)
+    elif type.get().id() == _Type_DECIMAL32:
+        out = Decimal32Type.__new__(Decimal32Type)
+    elif type.get().id() == _Type_DECIMAL64:
+        out = Decimal64Type.__new__(Decimal64Type)
+    elif type.get().id() == _Type_DECIMAL128:
+        out = Decimal128Type.__new__(Decimal128Type)
+    elif type.get().id() == _Type_DECIMAL256:
+        out = Decimal256Type.__new__(Decimal256Type)
+    elif type.get().id() == _Type_RUN_END_ENCODED:
+        out = RunEndEncodedType.__new__(RunEndEncodedType)
+    elif type.get().id() == _Type_EXTENSION:
+        ext_type = <const CExtensionType*> type.get()
+        cpy_ext_type = dynamic_cast[_CPyExtensionTypePtr](ext_type)
+        extension_name = ext_type.extension_name()
+        if cpy_ext_type != nullptr:
+            return cpy_ext_type.GetInstance()
+        elif extension_name == b"arrow.bool8":
+            out = Bool8Type.__new__(Bool8Type)
+        elif extension_name == b"arrow.fixed_shape_tensor":
+            out = FixedShapeTensorType.__new__(FixedShapeTensorType)
+        elif extension_name == b"arrow.opaque":
+            out = OpaqueType.__new__(OpaqueType)
+        elif extension_name == b"arrow.uuid":
+            out = UuidType.__new__(UuidType)
+        elif extension_name == b"arrow.json":
+            out = JsonType.__new__(JsonType)
+        else:
+            out = BaseExtensionType.__new__(BaseExtensionType)
+    else:
+        out = DataType.__new__(DataType)
+
+    out.init(type)
+    return out
+
+
+cdef object pyarrow_wrap_metadata(
+        const shared_ptr[const CKeyValueMetadata]& meta):
+    if meta.get() == nullptr:
+        return None
+    else:
+        return KeyValueMetadata.wrap(meta)
+
+
+cdef api bint pyarrow_is_metadata(object metadata):
+    return isinstance(metadata, KeyValueMetadata)
+
+
+cdef shared_ptr[const CKeyValueMetadata] pyarrow_unwrap_metadata(object meta):
+    cdef shared_ptr[const CKeyValueMetadata] c_meta
+    if pyarrow_is_metadata(meta):
+        c_meta = (<KeyValueMetadata>meta).unwrap()
+    return c_meta
+
+
+cdef api bint pyarrow_is_field(object field):
+    return isinstance(field, Field)
+
+
+cdef api shared_ptr[CField] pyarrow_unwrap_field(object field):
+    cdef Field field_
+    if pyarrow_is_field(field):
+        field_ = <Field>(field)
+        return field_.sp_field
+
+    return shared_ptr[CField]()
+
+
+cdef api object pyarrow_wrap_field(const shared_ptr[CField]& field):
+    if field.get() == NULL:
+        return None
+    cdef Field out = Field.__new__(Field)
+    out.init(field)
+    return out
+
+
+cdef api bint pyarrow_is_schema(object schema):
+    return isinstance(schema, Schema)
+
+
+cdef api shared_ptr[CSchema] pyarrow_unwrap_schema(object schema):
+    cdef Schema sch
+    if pyarrow_is_schema(schema):
+        sch = <Schema>(schema)
+        return sch.sp_schema
+
+    return shared_ptr[CSchema]()
+
+
+cdef api object pyarrow_wrap_schema(const shared_ptr[CSchema]& schema):
+    cdef Schema out = Schema.__new__(Schema)
+    out.init_schema(schema)
+    return out
+
+
+cdef api bint pyarrow_is_array(object array):
+    return isinstance(array, Array)
+
+
+cdef api shared_ptr[CArray] pyarrow_unwrap_array(object array):
+    cdef Array arr
+    if pyarrow_is_array(array):
+        arr = <Array>(array)
+        return arr.sp_array
+
+    return shared_ptr[CArray]()
+
+
+cdef api object pyarrow_wrap_array(const shared_ptr[CArray]& sp_array):
+    if sp_array.get() == NULL:
+        raise ValueError('Array was NULL')
+
+    klass = get_array_class_from_type(sp_array.get().type())
+
+    cdef Array arr = klass.__new__(klass)
+    arr.init(sp_array)
+    return arr
+
+
+cdef api bint pyarrow_is_chunked_array(object array):
+    return isinstance(array, ChunkedArray)
+
+
+cdef api shared_ptr[CChunkedArray] pyarrow_unwrap_chunked_array(object array):
+    cdef ChunkedArray arr
+    if pyarrow_is_chunked_array(array):
+        arr = <ChunkedArray>(array)
+        return arr.sp_chunked_array
+
+    return shared_ptr[CChunkedArray]()
+
+
+cdef api object pyarrow_wrap_chunked_array(
+        const shared_ptr[CChunkedArray]& sp_array):
+    if sp_array.get() == NULL:
+        raise ValueError('ChunkedArray was NULL')
+
+    cdef CDataType* data_type = sp_array.get().type().get()
+
+    if data_type == NULL:
+        raise ValueError('ChunkedArray data type was NULL')
+
+    cdef ChunkedArray arr = ChunkedArray.__new__(ChunkedArray)
+    arr.init(sp_array)
+    return arr
+
+
+cdef api bint pyarrow_is_scalar(object value):
+    return isinstance(value, Scalar)
+
+
+cdef api shared_ptr[CScalar] pyarrow_unwrap_scalar(object scalar):
+    if pyarrow_is_scalar(scalar):
+        return (<Scalar> scalar).unwrap()
+    return shared_ptr[CScalar]()
+
+
+cdef api object pyarrow_wrap_scalar(const shared_ptr[CScalar]& sp_scalar):
+    if sp_scalar.get() == NULL:
+        raise ValueError('Scalar was NULL')
+
+    cdef CDataType* data_type = sp_scalar.get().type.get()
+
+    if data_type == NULL:
+        raise ValueError('Scalar data type was NULL')
+
+    if data_type.id() == _Type_NA:
+        return _NULL
+
+    if data_type.id() not in _scalar_classes:
+        raise ValueError('Scalar type not supported')
+
+    klass = get_scalar_class_from_type(sp_scalar.get().type)
+
+    cdef Scalar scalar = klass.__new__(klass)
+    scalar.init(sp_scalar)
+    return scalar
+
+
+cdef api bint pyarrow_is_tensor(object tensor):
+    return isinstance(tensor, Tensor)
+
+
+cdef api shared_ptr[CTensor] pyarrow_unwrap_tensor(object tensor):
+    cdef Tensor ten
+    if pyarrow_is_tensor(tensor):
+        ten = <Tensor>(tensor)
+        return ten.sp_tensor
+
+    return shared_ptr[CTensor]()
+
+
+cdef api object pyarrow_wrap_tensor(
+        const shared_ptr[CTensor]& sp_tensor):
+    if sp_tensor.get() == NULL:
+        raise ValueError('Tensor was NULL')
+
+    cdef Tensor tensor = Tensor.__new__(Tensor)
+    tensor.init(sp_tensor)
+    return tensor
+
+
+cdef api bint pyarrow_is_sparse_coo_tensor(object sparse_tensor):
+    return isinstance(sparse_tensor, SparseCOOTensor)
+
+cdef api shared_ptr[CSparseCOOTensor] pyarrow_unwrap_sparse_coo_tensor(
+        object sparse_tensor):
+    cdef SparseCOOTensor sten
+    if pyarrow_is_sparse_coo_tensor(sparse_tensor):
+        sten = <SparseCOOTensor>(sparse_tensor)
+        return sten.sp_sparse_tensor
+
+    return shared_ptr[CSparseCOOTensor]()
+
+cdef api object pyarrow_wrap_sparse_coo_tensor(
+        const shared_ptr[CSparseCOOTensor]& sp_sparse_tensor):
+    if sp_sparse_tensor.get() == NULL:
+        raise ValueError('SparseCOOTensor was NULL')
+
+    cdef SparseCOOTensor sparse_tensor = SparseCOOTensor.__new__(
+        SparseCOOTensor)
+    sparse_tensor.init(sp_sparse_tensor)
+    return sparse_tensor
+
+
+cdef api bint pyarrow_is_sparse_csr_matrix(object sparse_tensor):
+    return isinstance(sparse_tensor, SparseCSRMatrix)
+
+cdef api shared_ptr[CSparseCSRMatrix] pyarrow_unwrap_sparse_csr_matrix(
+        object sparse_tensor):
+    cdef SparseCSRMatrix sten
+    if pyarrow_is_sparse_csr_matrix(sparse_tensor):
+        sten = <SparseCSRMatrix>(sparse_tensor)
+        return sten.sp_sparse_tensor
+
+    return shared_ptr[CSparseCSRMatrix]()
+
+cdef api object pyarrow_wrap_sparse_csr_matrix(
+        const shared_ptr[CSparseCSRMatrix]& sp_sparse_tensor):
+    if sp_sparse_tensor.get() == NULL:
+        raise ValueError('SparseCSRMatrix was NULL')
+
+    cdef SparseCSRMatrix sparse_tensor = SparseCSRMatrix.__new__(
+        SparseCSRMatrix)
+    sparse_tensor.init(sp_sparse_tensor)
+    return sparse_tensor
+
+
+cdef api bint pyarrow_is_sparse_csc_matrix(object sparse_tensor):
+    return isinstance(sparse_tensor, SparseCSCMatrix)
+
+cdef api shared_ptr[CSparseCSCMatrix] pyarrow_unwrap_sparse_csc_matrix(
+        object sparse_tensor):
+    cdef SparseCSCMatrix sten
+    if pyarrow_is_sparse_csc_matrix(sparse_tensor):
+        sten = <SparseCSCMatrix>(sparse_tensor)
+        return sten.sp_sparse_tensor
+
+    return shared_ptr[CSparseCSCMatrix]()
+
+cdef api object pyarrow_wrap_sparse_csc_matrix(
+        const shared_ptr[CSparseCSCMatrix]& sp_sparse_tensor):
+    if sp_sparse_tensor.get() == NULL:
+        raise ValueError('SparseCSCMatrix was NULL')
+
+    cdef SparseCSCMatrix sparse_tensor = SparseCSCMatrix.__new__(
+        SparseCSCMatrix)
+    sparse_tensor.init(sp_sparse_tensor)
+    return sparse_tensor
+
+
+cdef api bint pyarrow_is_sparse_csf_tensor(object sparse_tensor):
+    return isinstance(sparse_tensor, SparseCSFTensor)
+
+cdef api shared_ptr[CSparseCSFTensor] pyarrow_unwrap_sparse_csf_tensor(
+        object sparse_tensor):
+    cdef SparseCSFTensor sten
+    if pyarrow_is_sparse_csf_tensor(sparse_tensor):
+        sten = <SparseCSFTensor>(sparse_tensor)
+        return sten.sp_sparse_tensor
+
+    return shared_ptr[CSparseCSFTensor]()
+
+cdef api object pyarrow_wrap_sparse_csf_tensor(
+        const shared_ptr[CSparseCSFTensor]& sp_sparse_tensor):
+    if sp_sparse_tensor.get() == NULL:
+        raise ValueError('SparseCSFTensor was NULL')
+
+    cdef SparseCSFTensor sparse_tensor = SparseCSFTensor.__new__(
+        SparseCSFTensor)
+    sparse_tensor.init(sp_sparse_tensor)
+    return sparse_tensor
+
+
+cdef api bint pyarrow_is_table(object table):
+    return isinstance(table, Table)
+
+
+cdef api shared_ptr[CTable] pyarrow_unwrap_table(object table):
+    cdef Table tab
+    if pyarrow_is_table(table):
+        tab = <Table>(table)
+        return tab.sp_table
+
+    return shared_ptr[CTable]()
+
+
+cdef api object pyarrow_wrap_table(const shared_ptr[CTable]& ctable):
+    cdef Table table = Table.__new__(Table)
+    table.init(ctable)
+    return table
+
+
+cdef api bint pyarrow_is_batch(object batch):
+    return isinstance(batch, RecordBatch)
+
+
+cdef api shared_ptr[CRecordBatch] pyarrow_unwrap_batch(object batch):
+    cdef RecordBatch bat
+    if pyarrow_is_batch(batch):
+        bat = <RecordBatch>(batch)
+        return bat.sp_batch
+
+    return shared_ptr[CRecordBatch]()
+
+
+cdef api object pyarrow_wrap_batch(
+        const shared_ptr[CRecordBatch]& cbatch):
+    cdef RecordBatch batch = RecordBatch.__new__(RecordBatch)
+    batch.init(cbatch)
+    return batch
diff --git a/shared/package-files/1b/1babfdc18159f040785907c8b2a45b6e288a7766f6ee3ed9b797458c16f3c76a/Araguaina b/shared/package-files/1b/1babfdc18159f040785907c8b2a45b6e288a7766f6ee3ed9b797458c16f3c76a/Araguaina
new file mode 100644
index 0000000000000000000000000000000000000000..919723dc2ca581b4f95154d4192d8f732cc55a45
Binary files /dev/null and b/shared/package-files/1b/1babfdc18159f040785907c8b2a45b6e288a7766f6ee3ed9b797458c16f3c76a/Araguaina differ
diff --git a/shared/package-files/1b/1bac578bdc45e70878d1e76cfc7b3f5ec3c82f3ba0c663d0079c6d540cfd19fc/_embedding_bag_backward.h b/shared/package-files/1b/1bac578bdc45e70878d1e76cfc7b3f5ec3c82f3ba0c663d0079c6d540cfd19fc/_embedding_bag_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..a05795e149af4b001cfd6c01d802e412706b3e2d
--- /dev/null
+++ b/shared/package-files/1b/1bac578bdc45e70878d1e76cfc7b3f5ec3c82f3ba0c663d0079c6d540cfd19fc/_embedding_bag_backward.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_embedding_bag_backward_ops.h>
+
+namespace at {
+
+
+// aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
+inline at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
+    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
+    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
+  }
+}
+
+// aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
+inline at::Tensor _embedding_bag_backward_symint(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
+    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
+    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
+  }
+}
+
+}
diff --git a/shared/package-files/1b/1bad71c588fc916618c82e6f6285ecb63bd7c43e35d9140966879de34868879d/_values_ops.h b/shared/package-files/1b/1bad71c588fc916618c82e6f6285ecb63bd7c43e35d9140966879de34868879d/_values_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ccfe10585e53f0906e1160ab00aa42965dfba014
--- /dev/null
+++ b/shared/package-files/1b/1bad71c588fc916618c82e6f6285ecb63bd7c43e35d9140966879de34868879d/_values_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _values {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_values")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_values(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1bb290c9dbdbc60882973d6b7e1b419a030cbb713641fd4d772d8da12f7b30ee/lcm.h b/shared/package-files/1b/1bb290c9dbdbc60882973d6b7e1b419a030cbb713641fd4d772d8da12f7b30ee/lcm.h
new file mode 100644
index 0000000000000000000000000000000000000000..d253bd57a7c4be1e22879ea3659e0327f7231fb0
--- /dev/null
+++ b/shared/package-files/1b/1bb290c9dbdbc60882973d6b7e1b419a030cbb713641fd4d772d8da12f7b30ee/lcm.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/lcm_ops.h>
+
+namespace at {
+
+
+// aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & lcm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::lcm_out::call(self, other, out);
+}
+// aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & lcm_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::lcm_out::call(self, other, out);
+}
+
+// aten::lcm(Tensor self, Tensor other) -> Tensor
+inline at::Tensor lcm(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::lcm::call(self, other);
+}
+
+// aten::lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!)
+inline at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::lcm_::call(self, other);
+}
+
+}
diff --git a/shared/package-files/1b/1bb41d3c66637ace762ade92ee1401ca7a4beac7e258b2ca4c3f3a5a89e342a2/trapz_ops.h b/shared/package-files/1b/1bb41d3c66637ace762ade92ee1401ca7a4beac7e258b2ca4c3f3a5a89e342a2/trapz_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..63c2733ef14f9a567bbe9f108e2f4cbb8c323bd5
--- /dev/null
+++ b/shared/package-files/1b/1bb41d3c66637ace762ade92ee1401ca7a4beac7e258b2ca4c3f3a5a89e342a2/trapz_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trapz_x {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::trapz")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "x")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor")
+  static at::Tensor call(const at::Tensor & y, const at::Tensor & x, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim);
+};
+
+struct TORCH_API trapz_dx {
+  using schema = at::Tensor (const at::Tensor &, double, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::trapz")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dx")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor")
+  static at::Tensor call(const at::Tensor & y, double dx, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, double dx, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1bb5b353cbbd6c8b64aa5fad9524e745d7a4c341cf7820df39b098430715eb7c/test_common.py b/shared/package-files/1b/1bb5b353cbbd6c8b64aa5fad9524e745d7a4c341cf7820df39b098430715eb7c/test_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..afebc96ac035c4945c4084dc968323a602072066
--- /dev/null
+++ b/shared/package-files/1b/1bb5b353cbbd6c8b64aa5fad9524e745d7a4c341cf7820df39b098430715eb7c/test_common.py
@@ -0,0 +1,220 @@
+import numpy as np
+import pytest
+
+from sklearn.experimental import enable_iterative_imputer  # noqa: F401
+from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_allclose_dense_sparse,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+def imputers():
+    return [IterativeImputer(tol=0.1), KNNImputer(), SimpleImputer()]
+
+
+def sparse_imputers():
+    return [SimpleImputer()]
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_imputation_missing_value_in_test_array(imputer):
+    # [Non Regression Test for issue #13968] Missing value in test set should
+    # not throw an error and return a finite dataset
+    train = [[1], [2]]
+    test = [[3], [np.nan]]
+    imputer.set_params(add_indicator=True)
+    imputer.fit(train).transform(test)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("marker", [np.nan, -1, 0])
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_imputers_add_indicator(marker, imputer):
+    X = np.array(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    X_true_indicator = np.array(
+        [
+            [1.0, 0.0, 0.0, 1.0],
+            [0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    imputer.set_params(missing_values=marker, add_indicator=True)
+
+    X_trans = imputer.fit_transform(X)
+    assert_allclose(X_trans[:, -4:], X_true_indicator)
+    assert_array_equal(imputer.indicator_.features_, np.array([0, 1, 2, 3]))
+
+    imputer.set_params(add_indicator=False)
+    X_trans_no_indicator = imputer.fit_transform(X)
+    assert_allclose(X_trans[:, :-4], X_trans_no_indicator)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("marker", [np.nan, -1])
+@pytest.mark.parametrize(
+    "imputer", sparse_imputers(), ids=lambda x: x.__class__.__name__
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_imputers_add_indicator_sparse(imputer, marker, csr_container):
+    X = csr_container(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    X_true_indicator = csr_container(
+        [
+            [1.0, 0.0, 0.0, 1.0],
+            [0.0, 1.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    imputer.set_params(missing_values=marker, add_indicator=True)
+
+    X_trans = imputer.fit_transform(X)
+    assert_allclose_dense_sparse(X_trans[:, -4:], X_true_indicator)
+    assert_array_equal(imputer.indicator_.features_, np.array([0, 1, 2, 3]))
+
+    imputer.set_params(add_indicator=False)
+    X_trans_no_indicator = imputer.fit_transform(X)
+    assert_allclose_dense_sparse(X_trans[:, :-4], X_trans_no_indicator)
+
+
+# ConvergenceWarning will be raised by the IterativeImputer
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("add_indicator", [True, False])
+def test_imputers_pandas_na_integer_array_support(imputer, add_indicator):
+    # Test pandas IntegerArray with pd.NA
+    pd = pytest.importorskip("pandas")
+    marker = np.nan
+    imputer = imputer.set_params(add_indicator=add_indicator, missing_values=marker)
+
+    X = np.array(
+        [
+            [marker, 1, 5, marker, 1],
+            [2, marker, 1, marker, 2],
+            [6, 3, marker, marker, 3],
+            [1, 2, 9, marker, 4],
+        ]
+    )
+    # fit on numpy array
+    X_trans_expected = imputer.fit_transform(X)
+
+    # Creates dataframe with IntegerArrays with pd.NA
+    X_df = pd.DataFrame(X, dtype="Int16", columns=["a", "b", "c", "d", "e"])
+
+    # fit on pandas dataframe with IntegerArrays
+    X_trans = imputer.fit_transform(X_df)
+
+    assert_allclose(X_trans_expected, X_trans)
+
+
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("add_indicator", [True, False])
+def test_imputers_feature_names_out_pandas(imputer, add_indicator):
+    """Check feature names out for imputers."""
+    pd = pytest.importorskip("pandas")
+    marker = np.nan
+    imputer = imputer.set_params(add_indicator=add_indicator, missing_values=marker)
+
+    X = np.array(
+        [
+            [marker, 1, 5, 3, marker, 1],
+            [2, marker, 1, 4, marker, 2],
+            [6, 3, 7, marker, marker, 3],
+            [1, 2, 9, 8, marker, 4],
+        ]
+    )
+    X_df = pd.DataFrame(X, columns=["a", "b", "c", "d", "e", "f"])
+    imputer.fit(X_df)
+
+    names = imputer.get_feature_names_out()
+
+    if add_indicator:
+        expected_names = [
+            "a",
+            "b",
+            "c",
+            "d",
+            "f",
+            "missingindicator_a",
+            "missingindicator_b",
+            "missingindicator_d",
+            "missingindicator_e",
+        ]
+        assert_array_equal(expected_names, names)
+    else:
+        expected_names = ["a", "b", "c", "d", "f"]
+        assert_array_equal(expected_names, names)
+
+
+@pytest.mark.parametrize("keep_empty_features", [True, False])
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+def test_keep_empty_features(imputer, keep_empty_features):
+    """Check that the imputer keeps features with only missing values."""
+    X = np.array([[np.nan, 1], [np.nan, 2], [np.nan, 3]])
+    imputer = imputer.set_params(
+        add_indicator=False, keep_empty_features=keep_empty_features
+    )
+
+    for method in ["fit_transform", "transform"]:
+        X_imputed = getattr(imputer, method)(X)
+        if keep_empty_features:
+            assert X_imputed.shape == X.shape
+        else:
+            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
+
+
+@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__)
+@pytest.mark.parametrize("missing_value_test", [np.nan, 1])
+def test_imputation_adds_missing_indicator_if_add_indicator_is_true(
+    imputer, missing_value_test
+):
+    """Check that missing indicator always exists when add_indicator=True.
+
+    Non-regression test for gh-26590.
+    """
+    X_train = np.array([[0, np.nan], [1, 2]])
+
+    # Test data where missing_value_test variable can be set to np.nan or 1.
+    X_test = np.array([[0, missing_value_test], [1, 2]])
+
+    imputer.set_params(add_indicator=True)
+    imputer.fit(X_train)
+
+    X_test_imputed_with_indicator = imputer.transform(X_test)
+    assert X_test_imputed_with_indicator.shape == (2, 3)
+
+    imputer.set_params(add_indicator=False)
+    imputer.fit(X_train)
+    X_test_imputed_without_indicator = imputer.transform(X_test)
+    assert X_test_imputed_without_indicator.shape == (2, 2)
+
+    assert_allclose(
+        X_test_imputed_with_indicator[:, :-1], X_test_imputed_without_indicator
+    )
+    if np.isnan(missing_value_test):
+        expected_missing_indicator = [1, 0]
+    else:
+        expected_missing_indicator = [0, 0]
+
+    assert_allclose(X_test_imputed_with_indicator[:, -1], expected_missing_indicator)
diff --git a/shared/package-files/1b/1bbbb40c4fef8c4c06a7d66e6e30bafc30d5152ee579a4a1f3972fe64bb3ec17/macrodata_jmulti_cst_lagorder.txt b/shared/package-files/1b/1bbbb40c4fef8c4c06a7d66e6e30bafc30d5152ee579a4a1f3972fe64bb3ec17/macrodata_jmulti_cst_lagorder.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea14d63c7ea07da4d5d8d8200cc7bd0e6d4bea09
--- /dev/null
+++ b/shared/package-files/1b/1bbbb40c4fef8c4c06a7d66e6e30bafc30d5152ee579a4a1f3972fe64bb3ec17/macrodata_jmulti_cst_lagorder.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:33:45 ***
+OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA
+
+endogenous variables:     realcons realgdp realinv 
+deterministic variables:  CONST S1 S2 S3 TREND 
+sample range:             [1961 Q3, 2009 Q3], T = 193
+
+optimal number of lags (searched up to 10 lags of levels):
+Akaike Info Criterion:    4            
+Final Prediction Error:   4            
+Hannan-Quinn Criterion:   2            
+Schwarz Criterion:        2            
+
diff --git a/shared/package-files/1b/1bbf5bf7ed3f25e3adca01c7300af4e80d66d8da6afbabf065ff3053875d265d/dequantize_compositeexplicitautograd_dispatch.h b/shared/package-files/1b/1bbf5bf7ed3f25e3adca01c7300af4e80d66d8da6afbabf065ff3053875d265d/dequantize_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ebd880412244368efd83a11b39d9cbdc716ca652
--- /dev/null
+++ b/shared/package-files/1b/1bbf5bf7ed3f25e3adca01c7300af4e80d66d8da6afbabf065ff3053875d265d/dequantize_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & dequantize_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & dequantize_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API void dequantize_out(at::TensorList out, at::TensorList tensors);
+TORCH_API void dequantize_outf(at::TensorList tensors, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/1b/1bc076abf35ce87b8198751feeda82fc9437f0adfa0984d38acc828cfc5fd8be/direct_url.json b/shared/package-files/1b/1bc076abf35ce87b8198751feeda82fc9437f0adfa0984d38acc828cfc5fd8be/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d1140977279dabce5ebec7d2576559e71a120a4
--- /dev/null
+++ b/shared/package-files/1b/1bc076abf35ce87b8198751feeda82fc9437f0adfa0984d38acc828cfc5fd8be/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///opt/conda/conda-bld/psutil_1656431268089/work"}
\ No newline at end of file
diff --git a/shared/package-files/1b/1bc0c62c609aa47fda60217f3a168be50a277fb14e02000fc1e94ee61b425817/Glace_Bay b/shared/package-files/1b/1bc0c62c609aa47fda60217f3a168be50a277fb14e02000fc1e94ee61b425817/Glace_Bay
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index 0000000000000000000000000000000000000000..48412a4cbf9241ea83887876b1b8b22c367ff4fd
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diff --git a/shared/package-files/1b/1bc0d6e7b81a3eab66e77114c354c5bed08bd26a7846268eaebc3bca553c9f20/nan_to_num_cpu_dispatch.h b/shared/package-files/1b/1bc0d6e7b81a3eab66e77114c354c5bed08bd26a7846268eaebc3bca553c9f20/nan_to_num_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..17a27eec3e977d97548ff03a40ec30146e048efb
--- /dev/null
+++ b/shared/package-files/1b/1bc0d6e7b81a3eab66e77114c354c5bed08bd26a7846268eaebc3bca553c9f20/nan_to_num_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & nan_to_num_out(at::Tensor & out, const at::Tensor & self, c10::optional<double> nan=c10::nullopt, c10::optional<double> posinf=c10::nullopt, c10::optional<double> neginf=c10::nullopt);
+TORCH_API at::Tensor & nan_to_num_outf(const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/1b/1bc22af98267d635e3f07615a264a716940a2b1faa5caa3aff54d4c5a4a34370/es_gt.msg b/shared/package-files/1b/1bc22af98267d635e3f07615a264a716940a2b1faa5caa3aff54d4c5a4a34370/es_gt.msg
new file mode 100644
index 0000000000000000000000000000000000000000..ecd6faf9191c9eb348e8e620a8b264c32e1a25d8
--- /dev/null
+++ b/shared/package-files/1b/1bc22af98267d635e3f07615a264a716940a2b1faa5caa3aff54d4c5a4a34370/es_gt.msg
@@ -0,0 +1,6 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset es_GT DATE_FORMAT "%e/%m/%Y"
+    ::msgcat::mcset es_GT TIME_FORMAT_12 "%I:%M:%S %P"
+    ::msgcat::mcset es_GT DATE_TIME_FORMAT "%e/%m/%Y %I:%M:%S %P %z"
+}
diff --git a/shared/package-files/1b/1bc38b73ba57b0865ba14194b97acc2daf6ffd30d3c6d47cb82008950bfde690/d414-unix-sr b/shared/package-files/1b/1bc38b73ba57b0865ba14194b97acc2daf6ffd30d3c6d47cb82008950bfde690/d414-unix-sr
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diff --git a/shared/package-files/1b/1bc38b73ba57b0865ba14194b97acc2daf6ffd30d3c6d47cb82008950bfde690/d464-unix-sr b/shared/package-files/1b/1bc38b73ba57b0865ba14194b97acc2daf6ffd30d3c6d47cb82008950bfde690/d464-unix-sr
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diff --git a/shared/package-files/1b/1bc5409956b3471b0b3391ab44997009bb94e3ea45e92081ff8666bb834e9dda/laplacianmatrix.py b/shared/package-files/1b/1bc5409956b3471b0b3391ab44997009bb94e3ea45e92081ff8666bb834e9dda/laplacianmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..4448b3e072c8bccc36f935c0615a267d8268dd75
--- /dev/null
+++ b/shared/package-files/1b/1bc5409956b3471b0b3391ab44997009bb94e3ea45e92081ff8666bb834e9dda/laplacianmatrix.py
@@ -0,0 +1,429 @@
+"""Laplacian matrix of graphs.
+"""
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "laplacian_matrix",
+    "normalized_laplacian_matrix",
+    "total_spanning_tree_weight",
+    "directed_laplacian_matrix",
+    "directed_combinatorial_laplacian_matrix",
+]
+
+
+@not_implemented_for("directed")
+def laplacian_matrix(G, nodelist=None, weight="weight"):
+    """Returns the Laplacian matrix of G.
+
+    The graph Laplacian is the matrix L = D - A, where
+    A is the adjacency matrix and D is the diagonal matrix of node degrees.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    L : SciPy sparse array
+      The Laplacian matrix of G.
+
+    Notes
+    -----
+    For MultiGraph, the edges weights are summed.
+
+    See Also
+    --------
+    to_numpy_array
+    normalized_laplacian_matrix
+    laplacian_spectrum
+
+    Examples
+    --------
+    For graphs with multiple connected components, L is permutation-similar
+    to a block diagonal matrix where each block is the respective Laplacian
+    matrix for each component.
+
+    >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)])
+    >>> print(nx.laplacian_matrix(G).toarray())
+    [[ 1 -1  0  0  0]
+     [-1  2 -1  0  0]
+     [ 0 -1  1  0  0]
+     [ 0  0  0  1 -1]
+     [ 0  0  0 -1  1]]
+
+    """
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    n, m = A.shape
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, m, n, format="csr"))
+    return D - A
+
+
+@not_implemented_for("directed")
+def normalized_laplacian_matrix(G, nodelist=None, weight="weight"):
+    r"""Returns the normalized Laplacian matrix of G.
+
+    The normalized graph Laplacian is the matrix
+
+    .. math::
+
+        N = D^{-1/2} L D^{-1/2}
+
+    where `L` is the graph Laplacian and `D` is the diagonal matrix of
+    node degrees [1]_.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    N : SciPy sparse array
+      The normalized Laplacian matrix of G.
+
+    Notes
+    -----
+    For MultiGraph, the edges weights are summed.
+    See :func:`to_numpy_array` for other options.
+
+    If the Graph contains selfloops, D is defined as ``diag(sum(A, 1))``, where A is
+    the adjacency matrix [2]_.
+
+    See Also
+    --------
+    laplacian_matrix
+    normalized_laplacian_spectrum
+
+    References
+    ----------
+    .. [1] Fan Chung-Graham, Spectral Graph Theory,
+       CBMS Regional Conference Series in Mathematics, Number 92, 1997.
+    .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
+       Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
+       March 2007.
+    """
+    import numpy as np
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    n, m = A.shape
+    diags = A.sum(axis=1)
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    D = sp.sparse.csr_array(sp.sparse.spdiags(diags, 0, m, n, format="csr"))
+    L = D - A
+    with sp.errstate(divide="ignore"):
+        diags_sqrt = 1.0 / np.sqrt(diags)
+    diags_sqrt[np.isinf(diags_sqrt)] = 0
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format="csr"))
+    return DH @ (L @ DH)
+
+
+def total_spanning_tree_weight(G, weight=None):
+    """
+    Returns the total weight of all spanning trees of `G`.
+
+    Kirchoff's Tree Matrix Theorem states that the determinant of any cofactor of the
+    Laplacian matrix of a graph is the number of spanning trees in the graph. For a
+    weighted Laplacian matrix, it is the sum across all spanning trees of the
+    multiplicative weight of each tree. That is, the weight of each tree is the
+    product of its edge weights.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to use Kirchhoff's theorem on.
+
+    weight : string or None
+        The key for the edge attribute holding the edge weight. If `None`, then
+        each edge is assumed to have a weight of 1 and this function returns the
+        total number of spanning trees in `G`.
+
+    Returns
+    -------
+    float
+        The sum of the total multiplicative weights for all spanning trees in `G`
+    """
+    import numpy as np
+
+    G_laplacian = nx.laplacian_matrix(G, weight=weight).toarray()
+    # Determinant ignoring first row and column
+    return abs(np.linalg.det(G_laplacian[1:, 1:]))
+
+
+###############################################################################
+# Code based on work from https://github.com/bjedwards
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+def directed_laplacian_matrix(
+    G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
+):
+    r"""Returns the directed Laplacian matrix of G.
+
+    The graph directed Laplacian is the matrix
+
+    .. math::
+
+        L = I - (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} ) / 2
+
+    where `I` is the identity matrix, `P` is the transition matrix of the
+    graph, and `\Phi` a matrix with the Perron vector of `P` in the diagonal and
+    zeros elsewhere [1]_.
+
+    Depending on the value of walk_type, `P` can be the transition matrix
+    induced by a random walk, a lazy random walk, or a random walk with
+    teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+       If None, `P` is selected depending on the properties of the
+       graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    L : NumPy matrix
+      Normalized Laplacian of G.
+
+    Notes
+    -----
+    Only implemented for DiGraphs
+
+    See Also
+    --------
+    laplacian_matrix
+
+    References
+    ----------
+    .. [1] Fan Chung (2005).
+       Laplacians and the Cheeger inequality for directed graphs.
+       Annals of Combinatorics, 9(1), 2005
+    """
+    import numpy as np
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+    import scipy.sparse.linalg  # call as sp.sparse.linalg
+
+    # NOTE: P has type ndarray if walk_type=="pagerank", else csr_array
+    P = _transition_matrix(
+        G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
+    )
+
+    n, m = P.shape
+
+    evals, evecs = sp.sparse.linalg.eigs(P.T, k=1)
+    v = evecs.flatten().real
+    p = v / v.sum()
+    sqrtp = np.sqrt(p)
+    Q = (
+        # TODO: rm csr_array wrapper when spdiags creates arrays
+        sp.sparse.csr_array(sp.sparse.spdiags(sqrtp, 0, n, n))
+        @ P
+        # TODO: rm csr_array wrapper when spdiags creates arrays
+        @ sp.sparse.csr_array(sp.sparse.spdiags(1.0 / sqrtp, 0, n, n))
+    )
+    # NOTE: This could be sparsified for the non-pagerank cases
+    I = np.identity(len(G))
+
+    return I - (Q + Q.T) / 2.0
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+def directed_combinatorial_laplacian_matrix(
+    G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
+):
+    r"""Return the directed combinatorial Laplacian matrix of G.
+
+    The graph directed combinatorial Laplacian is the matrix
+
+    .. math::
+
+        L = \Phi - (\Phi P + P^T \Phi) / 2
+
+    where `P` is the transition matrix of the graph and `\Phi` a matrix
+    with the Perron vector of `P` in the diagonal and zeros elsewhere [1]_.
+
+    Depending on the value of walk_type, `P` can be the transition matrix
+    induced by a random walk, a lazy random walk, or a random walk with
+    teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+       If None, `P` is selected depending on the properties of the
+       graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    L : NumPy matrix
+      Combinatorial Laplacian of G.
+
+    Notes
+    -----
+    Only implemented for DiGraphs
+
+    See Also
+    --------
+    laplacian_matrix
+
+    References
+    ----------
+    .. [1] Fan Chung (2005).
+       Laplacians and the Cheeger inequality for directed graphs.
+       Annals of Combinatorics, 9(1), 2005
+    """
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+    import scipy.sparse.linalg  # call as sp.sparse.linalg
+
+    P = _transition_matrix(
+        G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
+    )
+
+    n, m = P.shape
+
+    evals, evecs = sp.sparse.linalg.eigs(P.T, k=1)
+    v = evecs.flatten().real
+    p = v / v.sum()
+    # NOTE: could be improved by not densifying
+    # TODO: Rm csr_array wrapper when spdiags array creation becomes available
+    Phi = sp.sparse.csr_array(sp.sparse.spdiags(p, 0, n, n)).toarray()
+
+    return Phi - (Phi @ P + P.T @ Phi) / 2.0
+
+
+def _transition_matrix(G, nodelist=None, weight="weight", walk_type=None, alpha=0.95):
+    """Returns the transition matrix of G.
+
+    This is a row stochastic giving the transition probabilities while
+    performing a random walk on the graph. Depending on the value of walk_type,
+    P can be the transition matrix induced by a random walk, a lazy random walk,
+    or a random walk with teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+       If None, `P` is selected depending on the properties of the
+       graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    P : numpy.ndarray
+      transition matrix of G.
+
+    Raises
+    ------
+    NetworkXError
+        If walk_type not specified or alpha not in valid range
+    """
+    import numpy as np
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+
+    if walk_type is None:
+        if nx.is_strongly_connected(G):
+            if nx.is_aperiodic(G):
+                walk_type = "random"
+            else:
+                walk_type = "lazy"
+        else:
+            walk_type = "pagerank"
+
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, dtype=float)
+    n, m = A.shape
+    if walk_type in ["random", "lazy"]:
+        # TODO: Rm csr_array wrapper when spdiags array creation becomes available
+        DI = sp.sparse.csr_array(sp.sparse.spdiags(1.0 / A.sum(axis=1), 0, n, n))
+        if walk_type == "random":
+            P = DI @ A
+        else:
+            # TODO: Rm csr_array wrapper when identity array creation becomes available
+            I = sp.sparse.csr_array(sp.sparse.identity(n))
+            P = (I + DI @ A) / 2.0
+
+    elif walk_type == "pagerank":
+        if not (0 < alpha < 1):
+            raise nx.NetworkXError("alpha must be between 0 and 1")
+        # this is using a dense representation. NOTE: This should be sparsified!
+        A = A.toarray()
+        # add constant to dangling nodes' row
+        A[A.sum(axis=1) == 0, :] = 1 / n
+        # normalize
+        A = A / A.sum(axis=1)[np.newaxis, :].T
+        P = alpha * A + (1 - alpha) / n
+    else:
+        raise nx.NetworkXError("walk_type must be random, lazy, or pagerank")
+
+    return P
diff --git a/shared/package-files/1b/1bc7ad7a4fcb36a384106327284c1a28a1d68395441da21d814e44535b16e03a/remainder_meta_dispatch.h b/shared/package-files/1b/1bc7ad7a4fcb36a384106327284c1a28a1d68395441da21d814e44535b16e03a/remainder_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ce9c77f6a0b33925bbb15e391a915e31891849e
--- /dev/null
+++ b/shared/package-files/1b/1bc7ad7a4fcb36a384106327284c1a28a1d68395441da21d814e44535b16e03a/remainder_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor remainder(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & remainder_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & remainder_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & remainder_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/1b/1bcaec2d94aaeb883956622afa507b51c209d608c0c48409993178444665790d/resolvers.py b/shared/package-files/1b/1bcaec2d94aaeb883956622afa507b51c209d608c0c48409993178444665790d/resolvers.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c3d0e306f91f9dfac1843b40babd223766bbf50
--- /dev/null
+++ b/shared/package-files/1b/1bcaec2d94aaeb883956622afa507b51c209d608c0c48409993178444665790d/resolvers.py
@@ -0,0 +1,547 @@
+import collections
+import itertools
+import operator
+
+from .providers import AbstractResolver
+from .structs import DirectedGraph, IteratorMapping, build_iter_view
+
+RequirementInformation = collections.namedtuple(
+    "RequirementInformation", ["requirement", "parent"]
+)
+
+
+class ResolverException(Exception):
+    """A base class for all exceptions raised by this module.
+
+    Exceptions derived by this class should all be handled in this module. Any
+    bubbling pass the resolver should be treated as a bug.
+    """
+
+
+class RequirementsConflicted(ResolverException):
+    def __init__(self, criterion):
+        super(RequirementsConflicted, self).__init__(criterion)
+        self.criterion = criterion
+
+    def __str__(self):
+        return "Requirements conflict: {}".format(
+            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
+        )
+
+
+class InconsistentCandidate(ResolverException):
+    def __init__(self, candidate, criterion):
+        super(InconsistentCandidate, self).__init__(candidate, criterion)
+        self.candidate = candidate
+        self.criterion = criterion
+
+    def __str__(self):
+        return "Provided candidate {!r} does not satisfy {}".format(
+            self.candidate,
+            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
+        )
+
+
+class Criterion(object):
+    """Representation of possible resolution results of a package.
+
+    This holds three attributes:
+
+    * `information` is a collection of `RequirementInformation` pairs.
+      Each pair is a requirement contributing to this criterion, and the
+      candidate that provides the requirement.
+    * `incompatibilities` is a collection of all known not-to-work candidates
+      to exclude from consideration.
+    * `candidates` is a collection containing all possible candidates deducted
+      from the union of contributing requirements and known incompatibilities.
+      It should never be empty, except when the criterion is an attribute of a
+      raised `RequirementsConflicted` (in which case it is always empty).
+
+    .. note::
+        This class is intended to be externally immutable. **Do not** mutate
+        any of its attribute containers.
+    """
+
+    def __init__(self, candidates, information, incompatibilities):
+        self.candidates = candidates
+        self.information = information
+        self.incompatibilities = incompatibilities
+
+    def __repr__(self):
+        requirements = ", ".join(
+            "({!r}, via={!r})".format(req, parent)
+            for req, parent in self.information
+        )
+        return "Criterion({})".format(requirements)
+
+    def iter_requirement(self):
+        return (i.requirement for i in self.information)
+
+    def iter_parent(self):
+        return (i.parent for i in self.information)
+
+
+class ResolutionError(ResolverException):
+    pass
+
+
+class ResolutionImpossible(ResolutionError):
+    def __init__(self, causes):
+        super(ResolutionImpossible, self).__init__(causes)
+        # causes is a list of RequirementInformation objects
+        self.causes = causes
+
+
+class ResolutionTooDeep(ResolutionError):
+    def __init__(self, round_count):
+        super(ResolutionTooDeep, self).__init__(round_count)
+        self.round_count = round_count
+
+
+# Resolution state in a round.
+State = collections.namedtuple("State", "mapping criteria backtrack_causes")
+
+
+class Resolution(object):
+    """Stateful resolution object.
+
+    This is designed as a one-off object that holds information to kick start
+    the resolution process, and holds the results afterwards.
+    """
+
+    def __init__(self, provider, reporter):
+        self._p = provider
+        self._r = reporter
+        self._states = []
+
+    @property
+    def state(self):
+        try:
+            return self._states[-1]
+        except IndexError:
+            raise AttributeError("state")
+
+    def _push_new_state(self):
+        """Push a new state into history.
+
+        This new state will be used to hold resolution results of the next
+        coming round.
+        """
+        base = self._states[-1]
+        state = State(
+            mapping=base.mapping.copy(),
+            criteria=base.criteria.copy(),
+            backtrack_causes=base.backtrack_causes[:],
+        )
+        self._states.append(state)
+
+    def _add_to_criteria(self, criteria, requirement, parent):
+        self._r.adding_requirement(requirement=requirement, parent=parent)
+
+        identifier = self._p.identify(requirement_or_candidate=requirement)
+        criterion = criteria.get(identifier)
+        if criterion:
+            incompatibilities = list(criterion.incompatibilities)
+        else:
+            incompatibilities = []
+
+        matches = self._p.find_matches(
+            identifier=identifier,
+            requirements=IteratorMapping(
+                criteria,
+                operator.methodcaller("iter_requirement"),
+                {identifier: [requirement]},
+            ),
+            incompatibilities=IteratorMapping(
+                criteria,
+                operator.attrgetter("incompatibilities"),
+                {identifier: incompatibilities},
+            ),
+        )
+
+        if criterion:
+            information = list(criterion.information)
+            information.append(RequirementInformation(requirement, parent))
+        else:
+            information = [RequirementInformation(requirement, parent)]
+
+        criterion = Criterion(
+            candidates=build_iter_view(matches),
+            information=information,
+            incompatibilities=incompatibilities,
+        )
+        if not criterion.candidates:
+            raise RequirementsConflicted(criterion)
+        criteria[identifier] = criterion
+
+    def _remove_information_from_criteria(self, criteria, parents):
+        """Remove information from parents of criteria.
+
+        Concretely, removes all values from each criterion's ``information``
+        field that have one of ``parents`` as provider of the requirement.
+
+        :param criteria: The criteria to update.
+        :param parents: Identifiers for which to remove information from all criteria.
+        """
+        if not parents:
+            return
+        for key, criterion in criteria.items():
+            criteria[key] = Criterion(
+                criterion.candidates,
+                [
+                    information
+                    for information in criterion.information
+                    if (
+                        information.parent is None
+                        or self._p.identify(information.parent) not in parents
+                    )
+                ],
+                criterion.incompatibilities,
+            )
+
+    def _get_preference(self, name):
+        return self._p.get_preference(
+            identifier=name,
+            resolutions=self.state.mapping,
+            candidates=IteratorMapping(
+                self.state.criteria,
+                operator.attrgetter("candidates"),
+            ),
+            information=IteratorMapping(
+                self.state.criteria,
+                operator.attrgetter("information"),
+            ),
+            backtrack_causes=self.state.backtrack_causes,
+        )
+
+    def _is_current_pin_satisfying(self, name, criterion):
+        try:
+            current_pin = self.state.mapping[name]
+        except KeyError:
+            return False
+        return all(
+            self._p.is_satisfied_by(requirement=r, candidate=current_pin)
+            for r in criterion.iter_requirement()
+        )
+
+    def _get_updated_criteria(self, candidate):
+        criteria = self.state.criteria.copy()
+        for requirement in self._p.get_dependencies(candidate=candidate):
+            self._add_to_criteria(criteria, requirement, parent=candidate)
+        return criteria
+
+    def _attempt_to_pin_criterion(self, name):
+        criterion = self.state.criteria[name]
+
+        causes = []
+        for candidate in criterion.candidates:
+            try:
+                criteria = self._get_updated_criteria(candidate)
+            except RequirementsConflicted as e:
+                self._r.rejecting_candidate(e.criterion, candidate)
+                causes.append(e.criterion)
+                continue
+
+            # Check the newly-pinned candidate actually works. This should
+            # always pass under normal circumstances, but in the case of a
+            # faulty provider, we will raise an error to notify the implementer
+            # to fix find_matches() and/or is_satisfied_by().
+            satisfied = all(
+                self._p.is_satisfied_by(requirement=r, candidate=candidate)
+                for r in criterion.iter_requirement()
+            )
+            if not satisfied:
+                raise InconsistentCandidate(candidate, criterion)
+
+            self._r.pinning(candidate=candidate)
+            self.state.criteria.update(criteria)
+
+            # Put newly-pinned candidate at the end. This is essential because
+            # backtracking looks at this mapping to get the last pin.
+            self.state.mapping.pop(name, None)
+            self.state.mapping[name] = candidate
+
+            return []
+
+        # All candidates tried, nothing works. This criterion is a dead
+        # end, signal for backtracking.
+        return causes
+
+    def _backjump(self, causes):
+        """Perform backjumping.
+
+        When we enter here, the stack is like this::
+
+            [ state Z ]
+            [ state Y ]
+            [ state X ]
+            .... earlier states are irrelevant.
+
+        1. No pins worked for Z, so it does not have a pin.
+        2. We want to reset state Y to unpinned, and pin another candidate.
+        3. State X holds what state Y was before the pin, but does not
+           have the incompatibility information gathered in state Y.
+
+        Each iteration of the loop will:
+
+        1.  Identify Z. The incompatibility is not always caused by the latest
+            state. For example, given three requirements A, B and C, with
+            dependencies A1, B1 and C1, where A1 and B1 are incompatible: the
+            last state might be related to C, so we want to discard the
+            previous state.
+        2.  Discard Z.
+        3.  Discard Y but remember its incompatibility information gathered
+            previously, and the failure we're dealing with right now.
+        4.  Push a new state Y' based on X, and apply the incompatibility
+            information from Y to Y'.
+        5a. If this causes Y' to conflict, we need to backtrack again. Make Y'
+            the new Z and go back to step 2.
+        5b. If the incompatibilities apply cleanly, end backtracking.
+        """
+        incompatible_reqs = itertools.chain(
+            (c.parent for c in causes if c.parent is not None),
+            (c.requirement for c in causes),
+        )
+        incompatible_deps = {self._p.identify(r) for r in incompatible_reqs}
+        while len(self._states) >= 3:
+            # Remove the state that triggered backtracking.
+            del self._states[-1]
+
+            # Ensure to backtrack to a state that caused the incompatibility
+            incompatible_state = False
+            while not incompatible_state:
+                # Retrieve the last candidate pin and known incompatibilities.
+                try:
+                    broken_state = self._states.pop()
+                    name, candidate = broken_state.mapping.popitem()
+                except (IndexError, KeyError):
+                    raise ResolutionImpossible(causes)
+                current_dependencies = {
+                    self._p.identify(d)
+                    for d in self._p.get_dependencies(candidate)
+                }
+                incompatible_state = not current_dependencies.isdisjoint(
+                    incompatible_deps
+                )
+
+            incompatibilities_from_broken = [
+                (k, list(v.incompatibilities))
+                for k, v in broken_state.criteria.items()
+            ]
+
+            # Also mark the newly known incompatibility.
+            incompatibilities_from_broken.append((name, [candidate]))
+
+            # Create a new state from the last known-to-work one, and apply
+            # the previously gathered incompatibility information.
+            def _patch_criteria():
+                for k, incompatibilities in incompatibilities_from_broken:
+                    if not incompatibilities:
+                        continue
+                    try:
+                        criterion = self.state.criteria[k]
+                    except KeyError:
+                        continue
+                    matches = self._p.find_matches(
+                        identifier=k,
+                        requirements=IteratorMapping(
+                            self.state.criteria,
+                            operator.methodcaller("iter_requirement"),
+                        ),
+                        incompatibilities=IteratorMapping(
+                            self.state.criteria,
+                            operator.attrgetter("incompatibilities"),
+                            {k: incompatibilities},
+                        ),
+                    )
+                    candidates = build_iter_view(matches)
+                    if not candidates:
+                        return False
+                    incompatibilities.extend(criterion.incompatibilities)
+                    self.state.criteria[k] = Criterion(
+                        candidates=candidates,
+                        information=list(criterion.information),
+                        incompatibilities=incompatibilities,
+                    )
+                return True
+
+            self._push_new_state()
+            success = _patch_criteria()
+
+            # It works! Let's work on this new state.
+            if success:
+                return True
+
+            # State does not work after applying known incompatibilities.
+            # Try the still previous state.
+
+        # No way to backtrack anymore.
+        return False
+
+    def resolve(self, requirements, max_rounds):
+        if self._states:
+            raise RuntimeError("already resolved")
+
+        self._r.starting()
+
+        # Initialize the root state.
+        self._states = [
+            State(
+                mapping=collections.OrderedDict(),
+                criteria={},
+                backtrack_causes=[],
+            )
+        ]
+        for r in requirements:
+            try:
+                self._add_to_criteria(self.state.criteria, r, parent=None)
+            except RequirementsConflicted as e:
+                raise ResolutionImpossible(e.criterion.information)
+
+        # The root state is saved as a sentinel so the first ever pin can have
+        # something to backtrack to if it fails. The root state is basically
+        # pinning the virtual "root" package in the graph.
+        self._push_new_state()
+
+        for round_index in range(max_rounds):
+            self._r.starting_round(index=round_index)
+
+            unsatisfied_names = [
+                key
+                for key, criterion in self.state.criteria.items()
+                if not self._is_current_pin_satisfying(key, criterion)
+            ]
+
+            # All criteria are accounted for. Nothing more to pin, we are done!
+            if not unsatisfied_names:
+                self._r.ending(state=self.state)
+                return self.state
+
+            # keep track of satisfied names to calculate diff after pinning
+            satisfied_names = set(self.state.criteria.keys()) - set(
+                unsatisfied_names
+            )
+
+            # Choose the most preferred unpinned criterion to try.
+            name = min(unsatisfied_names, key=self._get_preference)
+            failure_causes = self._attempt_to_pin_criterion(name)
+
+            if failure_causes:
+                causes = [i for c in failure_causes for i in c.information]
+                # Backjump if pinning fails. The backjump process puts us in
+                # an unpinned state, so we can work on it in the next round.
+                self._r.resolving_conflicts(causes=causes)
+                success = self._backjump(causes)
+                self.state.backtrack_causes[:] = causes
+
+                # Dead ends everywhere. Give up.
+                if not success:
+                    raise ResolutionImpossible(self.state.backtrack_causes)
+            else:
+                # discard as information sources any invalidated names
+                # (unsatisfied names that were previously satisfied)
+                newly_unsatisfied_names = {
+                    key
+                    for key, criterion in self.state.criteria.items()
+                    if key in satisfied_names
+                    and not self._is_current_pin_satisfying(key, criterion)
+                }
+                self._remove_information_from_criteria(
+                    self.state.criteria, newly_unsatisfied_names
+                )
+                # Pinning was successful. Push a new state to do another pin.
+                self._push_new_state()
+
+            self._r.ending_round(index=round_index, state=self.state)
+
+        raise ResolutionTooDeep(max_rounds)
+
+
+def _has_route_to_root(criteria, key, all_keys, connected):
+    if key in connected:
+        return True
+    if key not in criteria:
+        return False
+    for p in criteria[key].iter_parent():
+        try:
+            pkey = all_keys[id(p)]
+        except KeyError:
+            continue
+        if pkey in connected:
+            connected.add(key)
+            return True
+        if _has_route_to_root(criteria, pkey, all_keys, connected):
+            connected.add(key)
+            return True
+    return False
+
+
+Result = collections.namedtuple("Result", "mapping graph criteria")
+
+
+def _build_result(state):
+    mapping = state.mapping
+    all_keys = {id(v): k for k, v in mapping.items()}
+    all_keys[id(None)] = None
+
+    graph = DirectedGraph()
+    graph.add(None)  # Sentinel as root dependencies' parent.
+
+    connected = {None}
+    for key, criterion in state.criteria.items():
+        if not _has_route_to_root(state.criteria, key, all_keys, connected):
+            continue
+        if key not in graph:
+            graph.add(key)
+        for p in criterion.iter_parent():
+            try:
+                pkey = all_keys[id(p)]
+            except KeyError:
+                continue
+            if pkey not in graph:
+                graph.add(pkey)
+            graph.connect(pkey, key)
+
+    return Result(
+        mapping={k: v for k, v in mapping.items() if k in connected},
+        graph=graph,
+        criteria=state.criteria,
+    )
+
+
+class Resolver(AbstractResolver):
+    """The thing that performs the actual resolution work."""
+
+    base_exception = ResolverException
+
+    def resolve(self, requirements, max_rounds=100):
+        """Take a collection of constraints, spit out the resolution result.
+
+        The return value is a representation to the final resolution result. It
+        is a tuple subclass with three public members:
+
+        * `mapping`: A dict of resolved candidates. Each key is an identifier
+            of a requirement (as returned by the provider's `identify` method),
+            and the value is the resolved candidate.
+        * `graph`: A `DirectedGraph` instance representing the dependency tree.
+            The vertices are keys of `mapping`, and each edge represents *why*
+            a particular package is included. A special vertex `None` is
+            included to represent parents of user-supplied requirements.
+        * `criteria`: A dict of "criteria" that hold detailed information on
+            how edges in the graph are derived. Each key is an identifier of a
+            requirement, and the value is a `Criterion` instance.
+
+        The following exceptions may be raised if a resolution cannot be found:
+
+        * `ResolutionImpossible`: A resolution cannot be found for the given
+            combination of requirements. The `causes` attribute of the
+            exception is a list of (requirement, parent), giving the
+            requirements that could not be satisfied.
+        * `ResolutionTooDeep`: The dependency tree is too deeply nested and
+            the resolver gave up. This is usually caused by a circular
+            dependency, but you can try to resolve this by increasing the
+            `max_rounds` argument.
+        """
+        resolution = Resolution(self.provider, self.reporter)
+        state = resolution.resolve(requirements, max_rounds=max_rounds)
+        return _build_result(state)
diff --git a/shared/package-files/1b/1bcc2d9b2fad1901f3421a174eeecb5b8ccc6763283b87bbe0705b404c71904b/ndbm.py b/shared/package-files/1b/1bcc2d9b2fad1901f3421a174eeecb5b8ccc6763283b87bbe0705b404c71904b/ndbm.py
new file mode 100644
index 0000000000000000000000000000000000000000..23056a29ef2b6200c8bfd2b44fba832b6774a488
--- /dev/null
+++ b/shared/package-files/1b/1bcc2d9b2fad1901f3421a174eeecb5b8ccc6763283b87bbe0705b404c71904b/ndbm.py
@@ -0,0 +1,3 @@
+"""Provide the _dbm module as a dbm submodule."""
+
+from _dbm import *
diff --git a/shared/package-files/1b/1bd23c5dd8f4e078d0abec446edf361e5c329bb11ede89d0702cf013b1f84f5a/hp2624b-10p-p b/shared/package-files/1b/1bd23c5dd8f4e078d0abec446edf361e5c329bb11ede89d0702cf013b1f84f5a/hp2624b-10p-p
new file mode 100644
index 0000000000000000000000000000000000000000..3a846f106457bb702a9e7ad1e0bd8a86e7f5df45
Binary files /dev/null and b/shared/package-files/1b/1bd23c5dd8f4e078d0abec446edf361e5c329bb11ede89d0702cf013b1f84f5a/hp2624b-10p-p differ
diff --git a/shared/package-files/1b/1bd27a70146667454590043bdae19db2243dfb8665a936782ac5fc72cfd06578/_support_alternative_backends.py b/shared/package-files/1b/1bd27a70146667454590043bdae19db2243dfb8665a936782ac5fc72cfd06578/_support_alternative_backends.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbb913b14c76873202fce8eaabe2d08f778abe94
--- /dev/null
+++ b/shared/package-files/1b/1bd27a70146667454590043bdae19db2243dfb8665a936782ac5fc72cfd06578/_support_alternative_backends.py
@@ -0,0 +1,72 @@
+import functools
+from scipy._lib._array_api import (
+    is_cupy, is_jax, scipy_namespace_for, SCIPY_ARRAY_API
+)
+
+import numpy as np
+from ._ndimage_api import *   # noqa: F403
+from . import _ndimage_api
+from . import _delegators
+__all__ = _ndimage_api.__all__
+
+
+MODULE_NAME = 'ndimage'
+
+
+def delegate_xp(delegator, module_name):
+    def inner(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwds):
+            xp = delegator(*args, **kwds)
+
+            # try delegating to a cupyx/jax namesake
+            if is_cupy(xp):
+                # https://github.com/cupy/cupy/issues/8336
+                import importlib
+                cupyx_module = importlib.import_module(f"cupyx.scipy.{module_name}")
+                cupyx_func = getattr(cupyx_module, func.__name__)
+                return cupyx_func(*args, **kwds)
+            elif is_jax(xp) and func.__name__ == "map_coordinates":
+                spx = scipy_namespace_for(xp)
+                jax_module = getattr(spx, module_name)
+                jax_func = getattr(jax_module, func.__name__)
+                return jax_func(*args, **kwds)
+            else:
+                # the original function (does all np.asarray internally)
+                # XXX: output arrays
+                result = func(*args, **kwds)
+
+                if isinstance(result, (np.ndarray, np.generic)):
+                    # XXX: np.int32->np.array_0D
+                    return xp.asarray(result)
+                elif isinstance(result, int):
+                    return result
+                elif isinstance(result, dict):
+                    # value_indices: result is {np.int64(1): (array(0), array(1))} etc
+                    return {
+                        k.item(): tuple(xp.asarray(vv) for vv in v)
+                        for k,v in result.items()
+                    }
+                elif result is None:
+                    # inplace operations
+                    return result
+                else:
+                    # lists/tuples
+                    return type(result)(
+                        xp.asarray(x) if isinstance(x, np.ndarray) else x
+                        for x in result
+                    )
+        return wrapper
+    return inner
+
+# ### decorate ###
+for func_name in _ndimage_api.__all__:
+    bare_func = getattr(_ndimage_api, func_name)
+    delegator = getattr(_delegators, func_name + "_signature")
+
+    f = (delegate_xp(delegator, MODULE_NAME)(bare_func)
+         if SCIPY_ARRAY_API
+         else bare_func)
+
+    # add the decorated function to the namespace, to be imported in __init__.py
+    vars()[func_name] = f
diff --git a/shared/package-files/1b/1bd3422ab6f9301a14b21881fd00fceb0e8cc100312f03a673efc714efd95dd1/multivariate_resultants.py b/shared/package-files/1b/1bd3422ab6f9301a14b21881fd00fceb0e8cc100312f03a673efc714efd95dd1/multivariate_resultants.py
new file mode 100644
index 0000000000000000000000000000000000000000..06651f111f916935e6c933042da52e6aa62024f5
--- /dev/null
+++ b/shared/package-files/1b/1bd3422ab6f9301a14b21881fd00fceb0e8cc100312f03a673efc714efd95dd1/multivariate_resultants.py
@@ -0,0 +1,473 @@
+"""
+This module contains functions for two multivariate resultants. These
+are:
+
+- Dixon's resultant.
+- Macaulay's resultant.
+
+Multivariate resultants are used to identify whether a multivariate
+system has common roots. That is when the resultant is equal to zero.
+"""
+from math import prod
+
+from sympy.core.mul import Mul
+from sympy.matrices.dense import (Matrix, diag)
+from sympy.polys.polytools import (Poly, degree_list, rem)
+from sympy.simplify.simplify import simplify
+from sympy.tensor.indexed import IndexedBase
+from sympy.polys.monomials import itermonomials, monomial_deg
+from sympy.polys.orderings import monomial_key
+from sympy.polys.polytools import poly_from_expr, total_degree
+from sympy.functions.combinatorial.factorials import binomial
+from itertools import combinations_with_replacement
+from sympy.utilities.exceptions import sympy_deprecation_warning
+
+class DixonResultant():
+    """
+    A class for retrieving the Dixon's resultant of a multivariate
+    system.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols
+
+    >>> from sympy.polys.multivariate_resultants import DixonResultant
+    >>> x, y = symbols('x, y')
+
+    >>> p = x + y
+    >>> q = x ** 2 + y ** 3
+    >>> h = x ** 2 + y
+
+    >>> dixon = DixonResultant(variables=[x, y], polynomials=[p, q, h])
+    >>> poly = dixon.get_dixon_polynomial()
+    >>> matrix = dixon.get_dixon_matrix(polynomial=poly)
+    >>> matrix
+    Matrix([
+    [ 0,  0, -1,  0, -1],
+    [ 0, -1,  0, -1,  0],
+    [-1,  0,  1,  0,  0],
+    [ 0, -1,  0,  0,  1],
+    [-1,  0,  0,  1,  0]])
+    >>> matrix.det()
+    0
+
+    See Also
+    ========
+
+    Notebook in examples: sympy/example/notebooks.
+
+    References
+    ==========
+
+    .. [1] [Kapur1994]_
+    .. [2] [Palancz08]_
+
+    """
+
+    def __init__(self, polynomials, variables):
+        """
+        A class that takes two lists, a list of polynomials and list of
+        variables. Returns the Dixon matrix of the multivariate system.
+
+        Parameters
+        ----------
+        polynomials : list of polynomials
+            A  list of m n-degree polynomials
+        variables: list
+            A list of all n variables
+        """
+        self.polynomials = polynomials
+        self.variables = variables
+
+        self.n = len(self.variables)
+        self.m = len(self.polynomials)
+
+        a = IndexedBase("alpha")
+        # A list of n alpha variables (the replacing variables)
+        self.dummy_variables = [a[i] for i in range(self.n)]
+
+        # A list of the d_max of each variable.
+        self._max_degrees = [max(degree_list(poly)[i] for poly in self.polynomials)
+            for i in range(self.n)]
+
+    @property
+    def max_degrees(self):
+        sympy_deprecation_warning(
+            """
+            The max_degrees property of DixonResultant is deprecated.
+            """,
+            deprecated_since_version="1.5",
+            active_deprecations_target="deprecated-dixonresultant-properties",
+        )
+        return self._max_degrees
+
+    def get_dixon_polynomial(self):
+        r"""
+        Returns
+        =======
+
+        dixon_polynomial: polynomial
+            Dixon's polynomial is calculated as:
+
+            delta = Delta(A) / ((x_1 - a_1) ... (x_n - a_n)) where,
+
+            A =  |p_1(x_1,... x_n), ..., p_n(x_1,... x_n)|
+                 |p_1(a_1,... x_n), ..., p_n(a_1,... x_n)|
+                 |...             , ...,              ...|
+                 |p_1(a_1,... a_n), ..., p_n(a_1,... a_n)|
+        """
+        if self.m != (self.n + 1):
+            raise ValueError('Method invalid for given combination.')
+
+        # First row
+        rows = [self.polynomials]
+
+        temp = list(self.variables)
+
+        for idx in range(self.n):
+            temp[idx] = self.dummy_variables[idx]
+            substitution = {var: t for var, t in zip(self.variables, temp)}
+            rows.append([f.subs(substitution) for f in self.polynomials])
+
+        A = Matrix(rows)
+
+        terms = zip(self.variables, self.dummy_variables)
+        product_of_differences = Mul(*[a - b for a, b in terms])
+        dixon_polynomial = (A.det() / product_of_differences).factor()
+
+        return poly_from_expr(dixon_polynomial, self.dummy_variables)[0]
+
+    def get_upper_degree(self):
+        sympy_deprecation_warning(
+            """
+            The get_upper_degree() method of DixonResultant is deprecated. Use
+            get_max_degrees() instead.
+            """,
+            deprecated_since_version="1.5",
+            active_deprecations_target="deprecated-dixonresultant-properties"
+        )
+        list_of_products = [self.variables[i] ** self._max_degrees[i]
+                            for i in range(self.n)]
+        product = prod(list_of_products)
+        product = Poly(product).monoms()
+
+        return monomial_deg(*product)
+
+    def get_max_degrees(self, polynomial):
+        r"""
+        Returns a list of the maximum degree of each variable appearing
+        in the coefficients of the Dixon polynomial. The coefficients are
+        viewed as polys in $x_1, x_2, \dots, x_n$.
+        """
+        deg_lists = [degree_list(Poly(poly, self.variables))
+                     for poly in polynomial.coeffs()]
+
+        max_degrees = [max(degs) for degs in zip(*deg_lists)]
+
+        return max_degrees
+
+    def get_dixon_matrix(self, polynomial):
+        r"""
+        Construct the Dixon matrix from the coefficients of polynomial
+        \alpha. Each coefficient is viewed as a polynomial of x_1, ...,
+        x_n.
+        """
+
+        max_degrees = self.get_max_degrees(polynomial)
+
+        # list of column headers of the Dixon matrix.
+        monomials = itermonomials(self.variables, max_degrees)
+        monomials = sorted(monomials, reverse=True,
+                           key=monomial_key('lex', self.variables))
+
+        dixon_matrix = Matrix([[Poly(c, *self.variables).coeff_monomial(m)
+                                for m in monomials]
+                                for c in polynomial.coeffs()])
+
+        # remove columns if needed
+        if dixon_matrix.shape[0] != dixon_matrix.shape[1]:
+            keep = [column for column in range(dixon_matrix.shape[-1])
+                    if any(element != 0 for element
+                        in dixon_matrix[:, column])]
+
+            dixon_matrix = dixon_matrix[:, keep]
+
+        return dixon_matrix
+
+    def KSY_precondition(self, matrix):
+        """
+        Test for the validity of the Kapur-Saxena-Yang precondition.
+
+        The precondition requires that the column corresponding to the
+        monomial 1 = x_1 ^ 0 * x_2 ^ 0 * ... * x_n ^ 0 is not a linear
+        combination of the remaining ones. In SymPy notation this is
+        the last column. For the precondition to hold the last non-zero
+        row of the rref matrix should be of the form [0, 0, ..., 1].
+        """
+        if matrix.is_zero_matrix:
+            return False
+
+        m, n = matrix.shape
+
+        # simplify the matrix and keep only its non-zero rows
+        matrix = simplify(matrix.rref()[0])
+        rows = [i for i in range(m) if any(matrix[i, j] != 0 for j in range(n))]
+        matrix = matrix[rows,:]
+
+        condition = Matrix([[0]*(n-1) + [1]])
+
+        if matrix[-1,:] == condition:
+            return True
+        else:
+            return False
+
+    def delete_zero_rows_and_columns(self, matrix):
+        """Remove the zero rows and columns of the matrix."""
+        rows = [
+            i for i in range(matrix.rows) if not matrix.row(i).is_zero_matrix]
+        cols = [
+            j for j in range(matrix.cols) if not matrix.col(j).is_zero_matrix]
+
+        return matrix[rows, cols]
+
+    def product_leading_entries(self, matrix):
+        """Calculate the product of the leading entries of the matrix."""
+        res = 1
+        for row in range(matrix.rows):
+            for el in matrix.row(row):
+                if el != 0:
+                    res = res * el
+                    break
+        return res
+
+    def get_KSY_Dixon_resultant(self, matrix):
+        """Calculate the Kapur-Saxena-Yang approach to the Dixon Resultant."""
+        matrix = self.delete_zero_rows_and_columns(matrix)
+        _, U, _ = matrix.LUdecomposition()
+        matrix = self.delete_zero_rows_and_columns(simplify(U))
+
+        return self.product_leading_entries(matrix)
+
+class MacaulayResultant():
+    """
+    A class for calculating the Macaulay resultant. Note that the
+    polynomials must be homogenized and their coefficients must be
+    given as symbols.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols
+
+    >>> from sympy.polys.multivariate_resultants import MacaulayResultant
+    >>> x, y, z = symbols('x, y, z')
+
+    >>> a_0, a_1, a_2 = symbols('a_0, a_1, a_2')
+    >>> b_0, b_1, b_2 = symbols('b_0, b_1, b_2')
+    >>> c_0, c_1, c_2,c_3, c_4 = symbols('c_0, c_1, c_2, c_3, c_4')
+
+    >>> f = a_0 * y -  a_1 * x + a_2 * z
+    >>> g = b_1 * x ** 2 + b_0 * y ** 2 - b_2 * z ** 2
+    >>> h = c_0 * y * z ** 2 - c_1 * x ** 3 + c_2 * x ** 2 * z - c_3 * x * z ** 2 + c_4 * z ** 3
+
+    >>> mac = MacaulayResultant(polynomials=[f, g, h], variables=[x, y, z])
+    >>> mac.monomial_set
+    [x**4, x**3*y, x**3*z, x**2*y**2, x**2*y*z, x**2*z**2, x*y**3,
+    x*y**2*z, x*y*z**2, x*z**3, y**4, y**3*z, y**2*z**2, y*z**3, z**4]
+    >>> matrix = mac.get_matrix()
+    >>> submatrix = mac.get_submatrix(matrix)
+    >>> submatrix
+    Matrix([
+    [-a_1,  a_0,  a_2,    0],
+    [   0, -a_1,    0,    0],
+    [   0,    0, -a_1,    0],
+    [   0,    0,    0, -a_1]])
+
+    See Also
+    ========
+
+    Notebook in examples: sympy/example/notebooks.
+
+    References
+    ==========
+
+    .. [1] [Bruce97]_
+    .. [2] [Stiller96]_
+
+    """
+    def __init__(self, polynomials, variables):
+        """
+        Parameters
+        ==========
+
+        variables: list
+            A list of all n variables
+        polynomials : list of SymPy polynomials
+            A  list of m n-degree polynomials
+        """
+        self.polynomials = polynomials
+        self.variables = variables
+        self.n = len(variables)
+
+        # A list of the d_max of each polynomial.
+        self.degrees = [total_degree(poly, *self.variables) for poly
+                        in self.polynomials]
+
+        self.degree_m = self._get_degree_m()
+        self.monomials_size = self.get_size()
+
+        # The set T of all possible monomials of degree degree_m
+        self.monomial_set = self.get_monomials_of_certain_degree(self.degree_m)
+
+    def _get_degree_m(self):
+        r"""
+        Returns
+        =======
+
+        degree_m: int
+            The degree_m is calculated as  1 + \sum_1 ^ n (d_i - 1),
+            where d_i is the degree of the i polynomial
+        """
+        return 1 + sum(d - 1 for d in self.degrees)
+
+    def get_size(self):
+        r"""
+        Returns
+        =======
+
+        size: int
+            The size of set T. Set T is the set of all possible
+            monomials of the n variables for degree equal to the
+            degree_m
+        """
+        return binomial(self.degree_m + self.n - 1, self.n - 1)
+
+    def get_monomials_of_certain_degree(self, degree):
+        """
+        Returns
+        =======
+
+        monomials: list
+            A list of monomials of a certain degree.
+        """
+        monomials = [Mul(*monomial) for monomial
+                     in combinations_with_replacement(self.variables,
+                                                      degree)]
+
+        return sorted(monomials, reverse=True,
+                      key=monomial_key('lex', self.variables))
+
+    def get_row_coefficients(self):
+        """
+        Returns
+        =======
+
+        row_coefficients: list
+            The row coefficients of Macaulay's matrix
+        """
+        row_coefficients = []
+        divisible = []
+        for i in range(self.n):
+            if i == 0:
+                degree = self.degree_m - self.degrees[i]
+                monomial = self.get_monomials_of_certain_degree(degree)
+                row_coefficients.append(monomial)
+            else:
+                divisible.append(self.variables[i - 1] **
+                                 self.degrees[i - 1])
+                degree = self.degree_m - self.degrees[i]
+                poss_rows = self.get_monomials_of_certain_degree(degree)
+                for div in divisible:
+                    for p in poss_rows:
+                        if rem(p, div) == 0:
+                            poss_rows = [item for item in poss_rows
+                                         if item != p]
+                row_coefficients.append(poss_rows)
+        return row_coefficients
+
+    def get_matrix(self):
+        """
+        Returns
+        =======
+
+        macaulay_matrix: Matrix
+            The Macaulay numerator matrix
+        """
+        rows = []
+        row_coefficients = self.get_row_coefficients()
+        for i in range(self.n):
+            for multiplier in row_coefficients[i]:
+                coefficients = []
+                poly = Poly(self.polynomials[i] * multiplier,
+                            *self.variables)
+
+                for mono in self.monomial_set:
+                    coefficients.append(poly.coeff_monomial(mono))
+                rows.append(coefficients)
+
+        macaulay_matrix = Matrix(rows)
+        return macaulay_matrix
+
+    def get_reduced_nonreduced(self):
+        r"""
+        Returns
+        =======
+
+        reduced: list
+            A list of the reduced monomials
+        non_reduced: list
+            A list of the monomials that are not reduced
+
+        Definition
+        ==========
+
+        A polynomial is said to be reduced in x_i, if its degree (the
+        maximum degree of its monomials) in x_i is less than d_i. A
+        polynomial that is reduced in all variables but one is said
+        simply to be reduced.
+        """
+        divisible = []
+        for m in self.monomial_set:
+            temp = []
+            for i, v in enumerate(self.variables):
+                temp.append(bool(total_degree(m, v) >= self.degrees[i]))
+            divisible.append(temp)
+        reduced = [i for i, r in enumerate(divisible)
+                   if sum(r) < self.n - 1]
+        non_reduced = [i for i, r in enumerate(divisible)
+                       if sum(r) >= self.n -1]
+
+        return reduced, non_reduced
+
+    def get_submatrix(self, matrix):
+        r"""
+        Returns
+        =======
+
+        macaulay_submatrix: Matrix
+            The Macaulay denominator matrix. Columns that are non reduced are kept.
+            The row which contains one of the a_{i}s is dropped. a_{i}s
+            are the coefficients of x_i ^ {d_i}.
+        """
+        reduced, non_reduced = self.get_reduced_nonreduced()
+
+        # if reduced == [], then det(matrix) should be 1
+        if reduced == []:
+            return diag([1])
+
+        # reduced != []
+        reduction_set = [v ** self.degrees[i] for i, v
+                         in enumerate(self.variables)]
+
+        ais = [self.polynomials[i].coeff(reduction_set[i])
+               for i in range(self.n)]
+
+        reduced_matrix = matrix[:, reduced]
+        keep = []
+        for row in range(reduced_matrix.rows):
+            check = [ai in reduced_matrix[row, :] for ai in ais]
+            if True not in check:
+                keep.append(row)
+
+        return matrix[keep, non_reduced]
diff --git a/shared/package-files/1b/1bd6528b1cd12bbffdb59e6c4a3aa1a7e73b532e5ff2d7c8fd211053062749cc/os.pyi b/shared/package-files/1b/1bd6528b1cd12bbffdb59e6c4a3aa1a7e73b532e5ff2d7c8fd211053062749cc/os.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5144abe98cb42fe6eba2d592e2c23f5de900650f
--- /dev/null
+++ b/shared/package-files/1b/1bd6528b1cd12bbffdb59e6c4a3aa1a7e73b532e5ff2d7c8fd211053062749cc/os.pyi
@@ -0,0 +1,26 @@
+import sys
+from _typeshed import AnyPath
+from os import stat_result
+from typing import Optional, Sequence, Union, overload
+
+_FdOrAnyPath = Union[int, AnyPath]
+
+async def stat(path: _FdOrAnyPath, *, dir_fd: Optional[int] = ..., follow_symlinks: bool = ...) -> stat_result: ...
+async def rename(src: AnyPath, dst: AnyPath, *, src_dir_fd: Optional[int] = ..., dst_dir_fd: Optional[int] = ...) -> None: ...
+async def remove(path: AnyPath, *, dir_fd: Optional[int] = ...) -> None: ...
+async def mkdir(path: AnyPath, mode: int = ..., *, dir_fd: Optional[int] = ...) -> None: ...
+async def rmdir(path: AnyPath, *, dir_fd: Optional[int] = ...) -> None: ...
+
+if sys.platform != "win32":
+    @overload
+    async def sendfile(__out_fd: int, __in_fd: int, offset: Optional[int], count: int) -> int: ...
+    @overload
+    async def sendfile(
+        __out_fd: int,
+        __in_fd: int,
+        offset: int,
+        count: int,
+        headers: Sequence[bytes] = ...,
+        trailers: Sequence[bytes] = ...,
+        flags: int = ...,
+    ) -> int: ...
diff --git a/shared/package-files/1b/1bd7dd8545e6cf1eb9d419f267a57b00e60857d115e5a309326e3878968b2d9c/Bratislava b/shared/package-files/1b/1bd7dd8545e6cf1eb9d419f267a57b00e60857d115e5a309326e3878968b2d9c/Bratislava
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diff --git a/shared/package-files/1b/1bd7dd8545e6cf1eb9d419f267a57b00e60857d115e5a309326e3878968b2d9c/Prague b/shared/package-files/1b/1bd7dd8545e6cf1eb9d419f267a57b00e60857d115e5a309326e3878968b2d9c/Prague
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diff --git a/shared/package-files/1b/1bd910bda04ccdd354a02b38580a40731cd290ea33144c9f0d81458cbf199597/holiday.py b/shared/package-files/1b/1bd910bda04ccdd354a02b38580a40731cd290ea33144c9f0d81458cbf199597/holiday.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c429a960b45181c831055cb3e877dbc4cad3b30
--- /dev/null
+++ b/shared/package-files/1b/1bd910bda04ccdd354a02b38580a40731cd290ea33144c9f0d81458cbf199597/holiday.py
@@ -0,0 +1,634 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+import warnings
+
+from dateutil.relativedelta import (
+    FR,
+    MO,
+    SA,
+    SU,
+    TH,
+    TU,
+    WE,
+)
+import numpy as np
+
+from pandas.errors import PerformanceWarning
+
+from pandas import (
+    DateOffset,
+    DatetimeIndex,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+)
+
+from pandas.tseries.offsets import (
+    Day,
+    Easter,
+)
+
+
+def next_monday(dt: datetime) -> datetime:
+    """
+    If holiday falls on Saturday, use following Monday instead;
+    if holiday falls on Sunday, use Monday instead
+    """
+    if dt.weekday() == 5:
+        return dt + timedelta(2)
+    elif dt.weekday() == 6:
+        return dt + timedelta(1)
+    return dt
+
+
+def next_monday_or_tuesday(dt: datetime) -> datetime:
+    """
+    For second holiday of two adjacent ones!
+    If holiday falls on Saturday, use following Monday instead;
+    if holiday falls on Sunday or Monday, use following Tuesday instead
+    (because Monday is already taken by adjacent holiday on the day before)
+    """
+    dow = dt.weekday()
+    if dow in (5, 6):
+        return dt + timedelta(2)
+    if dow == 0:
+        return dt + timedelta(1)
+    return dt
+
+
+def previous_friday(dt: datetime) -> datetime:
+    """
+    If holiday falls on Saturday or Sunday, use previous Friday instead.
+    """
+    if dt.weekday() == 5:
+        return dt - timedelta(1)
+    elif dt.weekday() == 6:
+        return dt - timedelta(2)
+    return dt
+
+
+def sunday_to_monday(dt: datetime) -> datetime:
+    """
+    If holiday falls on Sunday, use day thereafter (Monday) instead.
+    """
+    if dt.weekday() == 6:
+        return dt + timedelta(1)
+    return dt
+
+
+def weekend_to_monday(dt: datetime) -> datetime:
+    """
+    If holiday falls on Sunday or Saturday,
+    use day thereafter (Monday) instead.
+    Needed for holidays such as Christmas observation in Europe
+    """
+    if dt.weekday() == 6:
+        return dt + timedelta(1)
+    elif dt.weekday() == 5:
+        return dt + timedelta(2)
+    return dt
+
+
+def nearest_workday(dt: datetime) -> datetime:
+    """
+    If holiday falls on Saturday, use day before (Friday) instead;
+    if holiday falls on Sunday, use day thereafter (Monday) instead.
+    """
+    if dt.weekday() == 5:
+        return dt - timedelta(1)
+    elif dt.weekday() == 6:
+        return dt + timedelta(1)
+    return dt
+
+
+def next_workday(dt: datetime) -> datetime:
+    """
+    returns next weekday used for observances
+    """
+    dt += timedelta(days=1)
+    while dt.weekday() > 4:
+        # Mon-Fri are 0-4
+        dt += timedelta(days=1)
+    return dt
+
+
+def previous_workday(dt: datetime) -> datetime:
+    """
+    returns previous weekday used for observances
+    """
+    dt -= timedelta(days=1)
+    while dt.weekday() > 4:
+        # Mon-Fri are 0-4
+        dt -= timedelta(days=1)
+    return dt
+
+
+def before_nearest_workday(dt: datetime) -> datetime:
+    """
+    returns previous workday after nearest workday
+    """
+    return previous_workday(nearest_workday(dt))
+
+
+def after_nearest_workday(dt: datetime) -> datetime:
+    """
+    returns next workday after nearest workday
+    needed for Boxing day or multiple holidays in a series
+    """
+    return next_workday(nearest_workday(dt))
+
+
+class Holiday:
+    """
+    Class that defines a holiday with start/end dates and rules
+    for observance.
+    """
+
+    start_date: Timestamp | None
+    end_date: Timestamp | None
+    days_of_week: tuple[int, ...] | None
+
+    def __init__(
+        self,
+        name: str,
+        year=None,
+        month=None,
+        day=None,
+        offset=None,
+        observance=None,
+        start_date=None,
+        end_date=None,
+        days_of_week=None,
+    ) -> None:
+        """
+        Parameters
+        ----------
+        name : str
+            Name of the holiday , defaults to class name
+        offset : array of pandas.tseries.offsets or
+                class from pandas.tseries.offsets
+            computes offset from date
+        observance: function
+            computes when holiday is given a pandas Timestamp
+        days_of_week:
+            provide a tuple of days e.g  (0,1,2,3,) for Monday Through Thursday
+            Monday=0,..,Sunday=6
+
+        Examples
+        --------
+        >>> from dateutil.relativedelta import MO
+
+        >>> USMemorialDay = pd.tseries.holiday.Holiday(
+        ...     "Memorial Day", month=5, day=31, offset=pd.DateOffset(weekday=MO(-1))
+        ... )
+        >>> USMemorialDay
+        Holiday: Memorial Day (month=5, day=31, offset=<DateOffset: weekday=MO(-1)>)
+
+        >>> USLaborDay = pd.tseries.holiday.Holiday(
+        ...     "Labor Day", month=9, day=1, offset=pd.DateOffset(weekday=MO(1))
+        ... )
+        >>> USLaborDay
+        Holiday: Labor Day (month=9, day=1, offset=<DateOffset: weekday=MO(+1)>)
+
+        >>> July3rd = pd.tseries.holiday.Holiday("July 3rd", month=7, day=3)
+        >>> July3rd
+        Holiday: July 3rd (month=7, day=3, )
+
+        >>> NewYears = pd.tseries.holiday.Holiday(
+        ...     "New Years Day", month=1,  day=1,
+        ...      observance=pd.tseries.holiday.nearest_workday
+        ... )
+        >>> NewYears  # doctest: +SKIP
+        Holiday: New Years Day (
+            month=1, day=1, observance=<function nearest_workday at 0x66545e9bc440>
+        )
+
+        >>> July3rd = pd.tseries.holiday.Holiday(
+        ...     "July 3rd", month=7, day=3,
+        ...     days_of_week=(0, 1, 2, 3)
+        ... )
+        >>> July3rd
+        Holiday: July 3rd (month=7, day=3, )
+        """
+        if offset is not None and observance is not None:
+            raise NotImplementedError("Cannot use both offset and observance.")
+
+        self.name = name
+        self.year = year
+        self.month = month
+        self.day = day
+        self.offset = offset
+        self.start_date = (
+            Timestamp(start_date) if start_date is not None else start_date
+        )
+        self.end_date = Timestamp(end_date) if end_date is not None else end_date
+        self.observance = observance
+        assert days_of_week is None or type(days_of_week) == tuple
+        self.days_of_week = days_of_week
+
+    def __repr__(self) -> str:
+        info = ""
+        if self.year is not None:
+            info += f"year={self.year}, "
+        info += f"month={self.month}, day={self.day}, "
+
+        if self.offset is not None:
+            info += f"offset={self.offset}"
+
+        if self.observance is not None:
+            info += f"observance={self.observance}"
+
+        repr = f"Holiday: {self.name} ({info})"
+        return repr
+
+    def dates(
+        self, start_date, end_date, return_name: bool = False
+    ) -> Series | DatetimeIndex:
+        """
+        Calculate holidays observed between start date and end date
+
+        Parameters
+        ----------
+        start_date : starting date, datetime-like, optional
+        end_date : ending date, datetime-like, optional
+        return_name : bool, optional, default=False
+            If True, return a series that has dates and holiday names.
+            False will only return dates.
+
+        Returns
+        -------
+        Series or DatetimeIndex
+            Series if return_name is True
+        """
+        start_date = Timestamp(start_date)
+        end_date = Timestamp(end_date)
+
+        filter_start_date = start_date
+        filter_end_date = end_date
+
+        if self.year is not None:
+            dt = Timestamp(datetime(self.year, self.month, self.day))
+            dti = DatetimeIndex([dt])
+            if return_name:
+                return Series(self.name, index=dti)
+            else:
+                return dti
+
+        dates = self._reference_dates(start_date, end_date)
+        holiday_dates = self._apply_rule(dates)
+        if self.days_of_week is not None:
+            holiday_dates = holiday_dates[
+                np.isin(
+                    # error: "DatetimeIndex" has no attribute "dayofweek"
+                    holiday_dates.dayofweek,  # type: ignore[attr-defined]
+                    self.days_of_week,
+                ).ravel()
+            ]
+
+        if self.start_date is not None:
+            filter_start_date = max(
+                self.start_date.tz_localize(filter_start_date.tz), filter_start_date
+            )
+        if self.end_date is not None:
+            filter_end_date = min(
+                self.end_date.tz_localize(filter_end_date.tz), filter_end_date
+            )
+        holiday_dates = holiday_dates[
+            (holiday_dates >= filter_start_date) & (holiday_dates <= filter_end_date)
+        ]
+        if return_name:
+            return Series(self.name, index=holiday_dates)
+        return holiday_dates
+
+    def _reference_dates(
+        self, start_date: Timestamp, end_date: Timestamp
+    ) -> DatetimeIndex:
+        """
+        Get reference dates for the holiday.
+
+        Return reference dates for the holiday also returning the year
+        prior to the start_date and year following the end_date.  This ensures
+        that any offsets to be applied will yield the holidays within
+        the passed in dates.
+        """
+        if self.start_date is not None:
+            start_date = self.start_date.tz_localize(start_date.tz)
+
+        if self.end_date is not None:
+            end_date = self.end_date.tz_localize(start_date.tz)
+
+        year_offset = DateOffset(years=1)
+        reference_start_date = Timestamp(
+            datetime(start_date.year - 1, self.month, self.day)
+        )
+
+        reference_end_date = Timestamp(
+            datetime(end_date.year + 1, self.month, self.day)
+        )
+        # Don't process unnecessary holidays
+        dates = date_range(
+            start=reference_start_date,
+            end=reference_end_date,
+            freq=year_offset,
+            tz=start_date.tz,
+        )
+
+        return dates
+
+    def _apply_rule(self, dates: DatetimeIndex) -> DatetimeIndex:
+        """
+        Apply the given offset/observance to a DatetimeIndex of dates.
+
+        Parameters
+        ----------
+        dates : DatetimeIndex
+            Dates to apply the given offset/observance rule
+
+        Returns
+        -------
+        Dates with rules applied
+        """
+        if dates.empty:
+            return dates.copy()
+
+        if self.observance is not None:
+            return dates.map(lambda d: self.observance(d))
+
+        if self.offset is not None:
+            if not isinstance(self.offset, list):
+                offsets = [self.offset]
+            else:
+                offsets = self.offset
+            for offset in offsets:
+                # if we are adding a non-vectorized value
+                # ignore the PerformanceWarnings:
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore", PerformanceWarning)
+                    dates += offset
+        return dates
+
+
+holiday_calendars = {}
+
+
+def register(cls) -> None:
+    try:
+        name = cls.name
+    except AttributeError:
+        name = cls.__name__
+    holiday_calendars[name] = cls
+
+
+def get_calendar(name: str):
+    """
+    Return an instance of a calendar based on its name.
+
+    Parameters
+    ----------
+    name : str
+        Calendar name to return an instance of
+    """
+    return holiday_calendars[name]()
+
+
+class HolidayCalendarMetaClass(type):
+    def __new__(cls, clsname: str, bases, attrs):
+        calendar_class = super().__new__(cls, clsname, bases, attrs)
+        register(calendar_class)
+        return calendar_class
+
+
+class AbstractHolidayCalendar(metaclass=HolidayCalendarMetaClass):
+    """
+    Abstract interface to create holidays following certain rules.
+    """
+
+    rules: list[Holiday] = []
+    start_date = Timestamp(datetime(1970, 1, 1))
+    end_date = Timestamp(datetime(2200, 12, 31))
+    _cache = None
+
+    def __init__(self, name: str = "", rules=None) -> None:
+        """
+        Initializes holiday object with a given set a rules.  Normally
+        classes just have the rules defined within them.
+
+        Parameters
+        ----------
+        name : str
+            Name of the holiday calendar, defaults to class name
+        rules : array of Holiday objects
+            A set of rules used to create the holidays.
+        """
+        super().__init__()
+        if not name:
+            name = type(self).__name__
+        self.name = name
+
+        if rules is not None:
+            self.rules = rules
+
+    def rule_from_name(self, name: str):
+        for rule in self.rules:
+            if rule.name == name:
+                return rule
+
+        return None
+
+    def holidays(self, start=None, end=None, return_name: bool = False):
+        """
+        Returns a curve with holidays between start_date and end_date
+
+        Parameters
+        ----------
+        start : starting date, datetime-like, optional
+        end : ending date, datetime-like, optional
+        return_name : bool, optional
+            If True, return a series that has dates and holiday names.
+            False will only return a DatetimeIndex of dates.
+
+        Returns
+        -------
+            DatetimeIndex of holidays
+        """
+        if self.rules is None:
+            raise Exception(
+                f"Holiday Calendar {self.name} does not have any rules specified"
+            )
+
+        if start is None:
+            start = AbstractHolidayCalendar.start_date
+
+        if end is None:
+            end = AbstractHolidayCalendar.end_date
+
+        start = Timestamp(start)
+        end = Timestamp(end)
+
+        # If we don't have a cache or the dates are outside the prior cache, we
+        # get them again
+        if self._cache is None or start < self._cache[0] or end > self._cache[1]:
+            pre_holidays = [
+                rule.dates(start, end, return_name=True) for rule in self.rules
+            ]
+            if pre_holidays:
+                # error: Argument 1 to "concat" has incompatible type
+                # "List[Union[Series, DatetimeIndex]]"; expected
+                # "Union[Iterable[DataFrame], Mapping[<nothing>, DataFrame]]"
+                holidays = concat(pre_holidays)  # type: ignore[arg-type]
+            else:
+                # error: Incompatible types in assignment (expression has type
+                # "Series", variable has type "DataFrame")
+                holidays = Series(
+                    index=DatetimeIndex([]), dtype=object
+                )  # type: ignore[assignment]
+
+            self._cache = (start, end, holidays.sort_index())
+
+        holidays = self._cache[2]
+        holidays = holidays[start:end]
+
+        if return_name:
+            return holidays
+        else:
+            return holidays.index
+
+    @staticmethod
+    def merge_class(base, other):
+        """
+        Merge holiday calendars together. The base calendar
+        will take precedence to other. The merge will be done
+        based on each holiday's name.
+
+        Parameters
+        ----------
+        base : AbstractHolidayCalendar
+          instance/subclass or array of Holiday objects
+        other : AbstractHolidayCalendar
+          instance/subclass or array of Holiday objects
+        """
+        try:
+            other = other.rules
+        except AttributeError:
+            pass
+
+        if not isinstance(other, list):
+            other = [other]
+        other_holidays = {holiday.name: holiday for holiday in other}
+
+        try:
+            base = base.rules
+        except AttributeError:
+            pass
+
+        if not isinstance(base, list):
+            base = [base]
+        base_holidays = {holiday.name: holiday for holiday in base}
+
+        other_holidays.update(base_holidays)
+        return list(other_holidays.values())
+
+    def merge(self, other, inplace: bool = False):
+        """
+        Merge holiday calendars together.  The caller's class
+        rules take precedence.  The merge will be done
+        based on each holiday's name.
+
+        Parameters
+        ----------
+        other : holiday calendar
+        inplace : bool (default=False)
+            If True set rule_table to holidays, else return array of Holidays
+        """
+        holidays = self.merge_class(self, other)
+        if inplace:
+            self.rules = holidays
+        else:
+            return holidays
+
+
+USMemorialDay = Holiday(
+    "Memorial Day", month=5, day=31, offset=DateOffset(weekday=MO(-1))
+)
+USLaborDay = Holiday("Labor Day", month=9, day=1, offset=DateOffset(weekday=MO(1)))
+USColumbusDay = Holiday(
+    "Columbus Day", month=10, day=1, offset=DateOffset(weekday=MO(2))
+)
+USThanksgivingDay = Holiday(
+    "Thanksgiving Day", month=11, day=1, offset=DateOffset(weekday=TH(4))
+)
+USMartinLutherKingJr = Holiday(
+    "Birthday of Martin Luther King, Jr.",
+    start_date=datetime(1986, 1, 1),
+    month=1,
+    day=1,
+    offset=DateOffset(weekday=MO(3)),
+)
+USPresidentsDay = Holiday(
+    "Washington's Birthday", month=2, day=1, offset=DateOffset(weekday=MO(3))
+)
+GoodFriday = Holiday("Good Friday", month=1, day=1, offset=[Easter(), Day(-2)])
+
+EasterMonday = Holiday("Easter Monday", month=1, day=1, offset=[Easter(), Day(1)])
+
+
+class USFederalHolidayCalendar(AbstractHolidayCalendar):
+    """
+    US Federal Government Holiday Calendar based on rules specified by:
+    https://www.opm.gov/policy-data-oversight/pay-leave/federal-holidays/
+    """
+
+    rules = [
+        Holiday("New Year's Day", month=1, day=1, observance=nearest_workday),
+        USMartinLutherKingJr,
+        USPresidentsDay,
+        USMemorialDay,
+        Holiday(
+            "Juneteenth National Independence Day",
+            month=6,
+            day=19,
+            start_date="2021-06-18",
+            observance=nearest_workday,
+        ),
+        Holiday("Independence Day", month=7, day=4, observance=nearest_workday),
+        USLaborDay,
+        USColumbusDay,
+        Holiday("Veterans Day", month=11, day=11, observance=nearest_workday),
+        USThanksgivingDay,
+        Holiday("Christmas Day", month=12, day=25, observance=nearest_workday),
+    ]
+
+
+def HolidayCalendarFactory(name: str, base, other, base_class=AbstractHolidayCalendar):
+    rules = AbstractHolidayCalendar.merge_class(base, other)
+    calendar_class = type(name, (base_class,), {"rules": rules, "name": name})
+    return calendar_class
+
+
+__all__ = [
+    "after_nearest_workday",
+    "before_nearest_workday",
+    "FR",
+    "get_calendar",
+    "HolidayCalendarFactory",
+    "MO",
+    "nearest_workday",
+    "next_monday",
+    "next_monday_or_tuesday",
+    "next_workday",
+    "previous_friday",
+    "previous_workday",
+    "register",
+    "SA",
+    "SU",
+    "sunday_to_monday",
+    "TH",
+    "TU",
+    "WE",
+    "weekend_to_monday",
+]
diff --git a/shared/package-files/1b/1bdc055e30464a3a8a6b221824ef184f6f1a4d2d745ddd5190919fe674fc8d74/slow_conv_transpose2d_cpu_dispatch.h b/shared/package-files/1b/1bdc055e30464a3a8a6b221824ef184f6f1a4d2d745ddd5190919fe674fc8d74/slow_conv_transpose2d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca0ff2956eecef067a2a4ba942cc60425788e8af
--- /dev/null
+++ b/shared/package-files/1b/1bdc055e30464a3a8a6b221824ef184f6f1a4d2d745ddd5190919fe674fc8d74/slow_conv_transpose2d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1);
+TORCH_API at::Tensor slow_conv_transpose2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1));
+TORCH_API at::Tensor & slow_conv_transpose2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1);
+TORCH_API at::Tensor & slow_conv_transpose2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out);
+TORCH_API at::Tensor & slow_conv_transpose2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1));
+TORCH_API at::Tensor & slow_conv_transpose2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/1b/1bdedde822dd8850a75a8d7c1ff387096b7e247792a980afeebb39aaea2c2375/test_fs.py b/shared/package-files/1b/1bdedde822dd8850a75a8d7c1ff387096b7e247792a980afeebb39aaea2c2375/test_fs.py
new file mode 100644
index 0000000000000000000000000000000000000000..376398baa07ea1bac64f865194bc5f1b5f839369
--- /dev/null
+++ b/shared/package-files/1b/1bdedde822dd8850a75a8d7c1ff387096b7e247792a980afeebb39aaea2c2375/test_fs.py
@@ -0,0 +1,2256 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from datetime import datetime, timezone, timedelta
+import gzip
+import os
+import pathlib
+from urllib.request import urlopen
+import subprocess
+import sys
+import time
+
+import pytest
+import weakref
+
+import pyarrow as pa
+from pyarrow.tests.test_io import assert_file_not_found
+from pyarrow.tests.util import (_filesystem_uri, ProxyHandler,
+                                _configure_s3_limited_user,
+                                running_on_musllinux)
+
+from pyarrow.fs import (FileType, FileInfo, FileSelector, FileSystem,
+                        LocalFileSystem, SubTreeFileSystem, _MockFileSystem,
+                        FileSystemHandler, PyFileSystem, FSSpecHandler,
+                        copy_files)
+from pyarrow.util import find_free_port
+
+
+here = os.path.dirname(os.path.abspath(__file__))
+
+
+class DummyHandler(FileSystemHandler):
+    def __init__(self, value=42):
+        self._value = value
+
+    def __eq__(self, other):
+        if isinstance(other, FileSystemHandler):
+            return self._value == other._value
+        return NotImplemented
+
+    def __ne__(self, other):
+        if isinstance(other, FileSystemHandler):
+            return self._value != other._value
+        return NotImplemented
+
+    def get_type_name(self):
+        return "dummy"
+
+    def normalize_path(self, path):
+        return path
+
+    def get_file_info(self, paths):
+        info = []
+        for path in paths:
+            if "file" in path:
+                info.append(FileInfo(path, FileType.File))
+            elif "dir" in path:
+                info.append(FileInfo(path, FileType.Directory))
+            elif "notfound" in path:
+                info.append(FileInfo(path, FileType.NotFound))
+            elif "badtype" in path:
+                # Will raise when converting
+                info.append(object())
+            else:
+                raise IOError
+        return info
+
+    def get_file_info_selector(self, selector):
+        if selector.base_dir != "somedir":
+            if selector.allow_not_found:
+                return []
+            else:
+                raise FileNotFoundError(selector.base_dir)
+        infos = [
+            FileInfo("somedir/file1", FileType.File, size=123),
+            FileInfo("somedir/subdir1", FileType.Directory),
+        ]
+        if selector.recursive:
+            infos += [
+                FileInfo("somedir/subdir1/file2", FileType.File, size=456),
+            ]
+        return infos
+
+    def create_dir(self, path, recursive):
+        if path == "recursive":
+            assert recursive is True
+        elif path == "non-recursive":
+            assert recursive is False
+        else:
+            raise IOError
+
+    def delete_dir(self, path):
+        assert path == "delete_dir"
+
+    def delete_dir_contents(self, path, missing_dir_ok):
+        if not path.strip("/"):
+            raise ValueError
+        assert path == "delete_dir_contents"
+
+    def delete_root_dir_contents(self):
+        pass
+
+    def delete_file(self, path):
+        assert path == "delete_file"
+
+    def move(self, src, dest):
+        assert src == "move_from"
+        assert dest == "move_to"
+
+    def copy_file(self, src, dest):
+        assert src == "copy_file_from"
+        assert dest == "copy_file_to"
+
+    def open_input_stream(self, path):
+        if "notfound" in path:
+            raise FileNotFoundError(path)
+        data = f"{path}:input_stream".encode('utf8')
+        return pa.BufferReader(data)
+
+    def open_input_file(self, path):
+        if "notfound" in path:
+            raise FileNotFoundError(path)
+        data = f"{path}:input_file".encode('utf8')
+        return pa.BufferReader(data)
+
+    def open_output_stream(self, path, metadata):
+        if "notfound" in path:
+            raise FileNotFoundError(path)
+        return pa.BufferOutputStream()
+
+    def open_append_stream(self, path, metadata):
+        if "notfound" in path:
+            raise FileNotFoundError(path)
+        return pa.BufferOutputStream()
+
+
+@pytest.fixture
+def localfs(request, tempdir):
+    return dict(
+        fs=LocalFileSystem(),
+        pathfn=lambda p: (tempdir / p).as_posix(),
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def py_localfs(request, tempdir):
+    return dict(
+        fs=PyFileSystem(ProxyHandler(LocalFileSystem())),
+        pathfn=lambda p: (tempdir / p).as_posix(),
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def mockfs(request):
+    return dict(
+        fs=_MockFileSystem(),
+        pathfn=lambda p: p,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def py_mockfs(request):
+    return dict(
+        fs=PyFileSystem(ProxyHandler(_MockFileSystem())),
+        pathfn=lambda p: p,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def localfs_with_mmap(request, tempdir):
+    return dict(
+        fs=LocalFileSystem(use_mmap=True),
+        pathfn=lambda p: (tempdir / p).as_posix(),
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def subtree_localfs(request, tempdir, localfs):
+    return dict(
+        fs=SubTreeFileSystem(str(tempdir), localfs['fs']),
+        pathfn=lambda p: p,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def gcsfs(request, gcs_server):
+    request.config.pyarrow.requires('gcs')
+    from pyarrow.fs import GcsFileSystem
+
+    host, port = gcs_server['connection']
+    bucket = 'pyarrow-filesystem/'
+
+    fs = GcsFileSystem(
+        endpoint_override=f'{host}:{port}',
+        scheme='http',
+        # Mock endpoint doesn't check credentials.
+        anonymous=True,
+        retry_time_limit=timedelta(seconds=45),
+        project_id='test-project-id'
+    )
+    try:
+        fs.create_dir(bucket)
+    except OSError as e:
+        pytest.skip(f"Could not create directory in {fs}: {e}")
+
+    yield dict(
+        fs=fs,
+        pathfn=bucket.__add__,
+        allow_move_dir=False,
+        allow_append_to_file=False,
+    )
+    fs.delete_dir(bucket)
+
+
+@pytest.fixture
+def s3fs(request, s3_server):
+    request.config.pyarrow.requires('s3')
+    from pyarrow.fs import S3FileSystem
+
+    host, port, access_key, secret_key = s3_server['connection']
+    bucket = 'pyarrow-filesystem/'
+
+    fs = S3FileSystem(
+        access_key=access_key,
+        secret_key=secret_key,
+        endpoint_override=f'{host}:{port}',
+        scheme='http',
+        allow_bucket_creation=True,
+        allow_bucket_deletion=True
+    )
+    fs.create_dir(bucket)
+
+    yield dict(
+        fs=fs,
+        pathfn=bucket.__add__,
+        allow_move_dir=False,
+        allow_append_to_file=False,
+    )
+    fs.delete_dir(bucket)
+
+
+@pytest.fixture
+def subtree_s3fs(request, s3fs):
+    prefix = 'pyarrow-filesystem/prefix/'
+    return dict(
+        fs=SubTreeFileSystem(prefix, s3fs['fs']),
+        pathfn=prefix.__add__,
+        allow_move_dir=False,
+        allow_append_to_file=False,
+    )
+
+
+_minio_limited_policy = """{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Action": [
+                "s3:ListAllMyBuckets",
+                "s3:PutObject",
+                "s3:GetObject",
+                "s3:ListBucket",
+                "s3:PutObjectTagging",
+                "s3:DeleteObject",
+                "s3:GetObjectVersion"
+            ],
+            "Resource": [
+                "arn:aws:s3:::*"
+            ]
+
+        },
+        {
+            "Effect": "Deny",
+            "Action": [
+                "s3:DeleteObject"
+            ],
+            "Resource": [
+                "arn:aws:s3:::no-delete-bucket*"
+            ]
+        }
+    ]
+}"""
+
+
+@pytest.fixture
+def azurefs(request, azure_server):
+    request.config.pyarrow.requires('azure')
+    from pyarrow.fs import AzureFileSystem
+
+    host, port, account_name, account_key = azure_server['connection']
+    azurite_authority = f"{host}:{port}"
+    azurite_scheme = "http"
+
+    container = 'pyarrow-filesystem/'
+
+    fs = AzureFileSystem(account_name=account_name,
+                         account_key=account_key,
+                         blob_storage_authority=azurite_authority,
+                         dfs_storage_authority=azurite_authority,
+                         blob_storage_scheme=azurite_scheme,
+                         dfs_storage_scheme=azurite_scheme)
+
+    fs.create_dir(container)
+
+    yield dict(
+        fs=fs,
+        pathfn=container.__add__,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+    fs.delete_dir(container)
+
+
+@pytest.fixture
+def hdfs(request, hdfs_connection):
+    request.config.pyarrow.requires('hdfs')
+    if not pa.have_libhdfs():
+        pytest.skip('Cannot locate libhdfs')
+
+    from pyarrow.fs import HadoopFileSystem
+
+    host, port, user = hdfs_connection
+    fs = HadoopFileSystem(host, port=port, user=user)
+
+    return dict(
+        fs=fs,
+        pathfn=lambda p: p,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def py_fsspec_localfs(request, tempdir):
+    fsspec = pytest.importorskip("fsspec")
+    fs = fsspec.filesystem('file')
+    return dict(
+        fs=PyFileSystem(FSSpecHandler(fs)),
+        pathfn=lambda p: (tempdir / p).as_posix(),
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def py_fsspec_memoryfs(request, tempdir):
+    fsspec = pytest.importorskip("fsspec", minversion="0.7.5")
+    if fsspec.__version__ == "0.8.5":
+        # see https://issues.apache.org/jira/browse/ARROW-10934
+        pytest.skip("Bug in fsspec 0.8.5 for in-memory filesystem")
+    fs = fsspec.filesystem('memory')
+    return dict(
+        fs=PyFileSystem(FSSpecHandler(fs)),
+        pathfn=lambda p: p,
+        allow_move_dir=True,
+        allow_append_to_file=True,
+    )
+
+
+@pytest.fixture
+def py_fsspec_s3fs(request, s3_server):
+    s3fs = pytest.importorskip("s3fs")
+    host, port, access_key, secret_key = s3_server['connection']
+    bucket = 'pyarrow-filesystem/'
+
+    fs = s3fs.S3FileSystem(
+        key=access_key,
+        secret=secret_key,
+        client_kwargs=dict(endpoint_url=f'http://{host}:{port}')
+    )
+    fs = PyFileSystem(FSSpecHandler(fs))
+    fs.create_dir(bucket)
+
+    yield dict(
+        fs=fs,
+        pathfn=bucket.__add__,
+        allow_move_dir=False,
+        allow_append_to_file=True,
+    )
+    fs.delete_dir(bucket)
+
+
+@pytest.fixture(params=[
+    pytest.param(
+        'localfs',
+        id='LocalFileSystem()'
+    ),
+    pytest.param(
+        'localfs_with_mmap',
+        id='LocalFileSystem(use_mmap=True)'
+    ),
+    pytest.param(
+        'subtree_localfs',
+        id='SubTreeFileSystem(LocalFileSystem())'
+    ),
+    pytest.param(
+        's3fs',
+        id='S3FileSystem',
+        marks=pytest.mark.s3
+    ),
+    pytest.param(
+        'gcsfs',
+        id='GcsFileSystem',
+        marks=pytest.mark.gcs
+    ),
+    pytest.param(
+        'azurefs',
+        id='AzureFileSystem',
+        marks=pytest.mark.azure
+    ),
+    pytest.param(
+        'hdfs',
+        id='HadoopFileSystem',
+        marks=pytest.mark.hdfs
+    ),
+    pytest.param(
+        'mockfs',
+        id='_MockFileSystem()'
+    ),
+    pytest.param(
+        'py_localfs',
+        id='PyFileSystem(ProxyHandler(LocalFileSystem()))'
+    ),
+    pytest.param(
+        'py_mockfs',
+        id='PyFileSystem(ProxyHandler(_MockFileSystem()))'
+    ),
+    pytest.param(
+        'py_fsspec_localfs',
+        id='PyFileSystem(FSSpecHandler(fsspec.LocalFileSystem()))'
+    ),
+    pytest.param(
+        'py_fsspec_memoryfs',
+        id='PyFileSystem(FSSpecHandler(fsspec.filesystem("memory")))'
+    ),
+    pytest.param(
+        'py_fsspec_s3fs',
+        id='PyFileSystem(FSSpecHandler(s3fs.S3FileSystem()))',
+        marks=pytest.mark.s3
+    ),
+])
+def filesystem_config(request):
+    return request.getfixturevalue(request.param)
+
+
+@pytest.fixture
+def fs(filesystem_config):
+    return filesystem_config['fs']
+
+
+@pytest.fixture
+def pathfn(filesystem_config):
+    return filesystem_config['pathfn']
+
+
+@pytest.fixture
+def allow_move_dir(filesystem_config):
+    return filesystem_config['allow_move_dir']
+
+
+@pytest.fixture
+def allow_append_to_file(filesystem_config):
+    return filesystem_config['allow_append_to_file']
+
+
+def check_mtime(file_info):
+    assert isinstance(file_info.mtime, datetime)
+    assert isinstance(file_info.mtime_ns, int)
+    assert file_info.mtime_ns >= 0
+    assert file_info.mtime_ns == pytest.approx(
+        file_info.mtime.timestamp() * 1e9)
+    # It's an aware UTC datetime
+    tzinfo = file_info.mtime.tzinfo
+    assert tzinfo is not None
+    assert tzinfo.utcoffset(None) == timedelta(0)
+
+
+def check_mtime_absent(file_info):
+    assert file_info.mtime is None
+    assert file_info.mtime_ns is None
+
+
+def check_mtime_or_absent(file_info):
+    if file_info.mtime is None:
+        check_mtime_absent(file_info)
+    else:
+        check_mtime(file_info)
+
+
+def skip_fsspec_s3fs(fs):
+    if fs.type_name == "py::fsspec+('s3', 's3a')":
+        pytest.xfail(reason="Not working with fsspec's s3fs")
+
+
+def skip_azure(fs, reason):
+    if fs.type_name == "abfs":
+        pytest.skip(reason=reason)
+
+
+@pytest.mark.s3
+def test_s3fs_limited_permissions_create_bucket(s3_server):
+    from pyarrow.fs import S3FileSystem
+    _configure_s3_limited_user(s3_server, _minio_limited_policy,
+                               'test_fs_limited_user', 'limited123')
+    host, port, _, _ = s3_server['connection']
+
+    fs = S3FileSystem(
+        access_key='test_fs_limited_user',
+        secret_key='limited123',
+        endpoint_override=f'{host}:{port}',
+        scheme='http'
+    )
+    fs.create_dir('existing-bucket/test')
+
+    with pytest.raises(pa.ArrowIOError, match="Bucket 'new-bucket' not found"):
+        fs.create_dir('new-bucket')
+
+    with pytest.raises(pa.ArrowIOError, match="Would delete bucket"):
+        fs.delete_dir('existing-bucket')
+
+    with pytest.raises(OSError, match="Request ID:"):
+        fs.copy_file("existing-bucket/test-file", "existing-bucket/test-file-copy")
+
+    with pytest.raises(pa.ArrowIOError, match="Request ID:"):
+        with fs.open_output_stream("non-existing-bucket/test-file") as f:
+            f.write(b"test")
+
+    # Create a file in the protected bucket then try to delete it
+    with fs.open_output_stream("no-delete-bucket/test-file") as f:
+        f.write(b"test")
+
+    with pytest.raises(OSError, match="Request ID:"):
+        fs.delete_file("no-delete-bucket/test-file")
+
+
+def test_file_info_constructor():
+    dt = datetime.fromtimestamp(1568799826, timezone.utc)
+
+    info = FileInfo("foo/bar")
+    assert info.path == "foo/bar"
+    assert info.base_name == "bar"
+    assert info.type == FileType.Unknown
+    assert info.size is None
+    check_mtime_absent(info)
+
+    info = FileInfo("foo/baz.txt", type=FileType.File, size=123,
+                    mtime=1568799826.5)
+    assert info.path == "foo/baz.txt"
+    assert info.base_name == "baz.txt"
+    assert info.type == FileType.File
+    assert info.size == 123
+    assert info.mtime_ns == 1568799826500000000
+    check_mtime(info)
+
+    info = FileInfo("foo", type=FileType.Directory, mtime=dt)
+    assert info.path == "foo"
+    assert info.base_name == "foo"
+    assert info.type == FileType.Directory
+    assert info.size is None
+    assert info.mtime == dt
+    assert info.mtime_ns == 1568799826000000000
+    check_mtime(info)
+
+
+def test_cannot_instantiate_base_filesystem():
+    with pytest.raises(TypeError):
+        FileSystem()
+
+
+def test_filesystem_equals():
+    fs0 = LocalFileSystem()
+    fs1 = LocalFileSystem()
+    fs2 = _MockFileSystem()
+
+    assert fs0.equals(fs0)
+    assert fs0.equals(fs1)
+    with pytest.raises(TypeError):
+        fs0.equals('string')
+    assert fs0 == fs0 == fs1
+    assert fs0 != 4
+
+    assert fs2 == fs2
+    assert fs2 != _MockFileSystem()
+
+    assert SubTreeFileSystem('/base', fs0) == SubTreeFileSystem('/base', fs0)
+    assert SubTreeFileSystem('/base', fs0) != SubTreeFileSystem('/base', fs2)
+    assert SubTreeFileSystem('/base', fs0) != SubTreeFileSystem('/other', fs0)
+
+
+def test_filesystem_equals_none(fs):
+    with pytest.raises(TypeError, match="got NoneType"):
+        fs.equals(None)
+
+    assert fs is not None
+
+
+def test_subtree_filesystem():
+    localfs = LocalFileSystem()
+
+    subfs = SubTreeFileSystem('/base', localfs)
+    assert subfs.base_path == '/base/'
+    assert subfs.base_fs == localfs
+    assert repr(subfs).startswith('SubTreeFileSystem(base_path=/base/, '
+                                  'base_fs=<pyarrow._fs.LocalFileSystem')
+
+    subfs = SubTreeFileSystem('/another/base/', LocalFileSystem())
+    assert subfs.base_path == '/another/base/'
+    assert subfs.base_fs == localfs
+    assert repr(subfs).startswith('SubTreeFileSystem(base_path=/another/base/,'
+                                  ' base_fs=<pyarrow._fs.LocalFileSystem')
+
+
+def test_filesystem_pickling(fs, pickle_module):
+    if fs.type_name.split('::')[-1] == 'mock':
+        pytest.xfail(reason='MockFileSystem is not serializable')
+
+    serialized = pickle_module.dumps(fs)
+    restored = pickle_module.loads(serialized)
+    assert isinstance(restored, FileSystem)
+    assert restored.equals(fs)
+
+
+def test_filesystem_is_functional_after_pickling(fs, pathfn, pickle_module):
+    if fs.type_name.split('::')[-1] == 'mock':
+        pytest.xfail(reason='MockFileSystem is not serializable')
+    skip_fsspec_s3fs(fs)
+
+    aaa = pathfn('a/aa/aaa/')
+    bb = pathfn('a/bb')
+    c = pathfn('c.txt')
+
+    fs.create_dir(aaa)
+    with fs.open_output_stream(bb):
+        pass  # touch
+    with fs.open_output_stream(c) as fp:
+        fp.write(b'test')
+
+    restored = pickle_module.loads(pickle_module.dumps(fs))
+    aaa_info, bb_info, c_info = restored.get_file_info([aaa, bb, c])
+    assert aaa_info.type == FileType.Directory
+    assert bb_info.type == FileType.File
+    assert c_info.type == FileType.File
+
+
+def test_type_name():
+    fs = LocalFileSystem()
+    assert fs.type_name == "local"
+    fs = _MockFileSystem()
+    assert fs.type_name == "mock"
+
+
+def test_normalize_path(fs):
+    # Trivial path names (without separators) should generally be
+    # already normalized.  Just a sanity check.
+    assert fs.normalize_path("foo") == "foo"
+
+
+def test_non_path_like_input_raises(fs):
+    class Path:
+        pass
+
+    invalid_paths = [1, 1.1, Path(), tuple(), {}, [], lambda: 1,
+                     pathlib.Path()]
+    for path in invalid_paths:
+        with pytest.raises(TypeError):
+            fs.create_dir(path)
+
+
+def test_get_file_info(fs, pathfn):
+    aaa = pathfn('a/aa/aaa/')
+    bb = pathfn('a/bb')
+    c = pathfn('c.txt')
+    zzz = pathfn('zzz')
+
+    fs.create_dir(aaa)
+    with fs.open_output_stream(bb):
+        pass  # touch
+    with fs.open_output_stream(c) as fp:
+        fp.write(b'test')
+
+    aaa_info, bb_info, c_info, zzz_info = fs.get_file_info([aaa, bb, c, zzz])
+
+    assert aaa_info.path == aaa
+    assert 'aaa' in repr(aaa_info)
+    assert aaa_info.extension == ''
+    if fs.type_name == "py::fsspec+('s3', 's3a')":
+        # s3fs doesn't create empty directories
+        assert aaa_info.type == FileType.NotFound
+    else:
+        assert aaa_info.type == FileType.Directory
+        assert 'FileType.Directory' in repr(aaa_info)
+    assert aaa_info.size is None
+    check_mtime_or_absent(aaa_info)
+
+    assert bb_info.path == str(bb)
+    assert bb_info.base_name == 'bb'
+    assert bb_info.extension == ''
+    assert bb_info.type == FileType.File
+    assert 'FileType.File' in repr(bb_info)
+    assert bb_info.size == 0
+    if fs.type_name not in ["py::fsspec+memory", "py::fsspec+('s3', 's3a')"]:
+        check_mtime(bb_info)
+
+    assert c_info.path == str(c)
+    assert c_info.base_name == 'c.txt'
+    assert c_info.extension == 'txt'
+    assert c_info.type == FileType.File
+    assert 'FileType.File' in repr(c_info)
+    assert c_info.size == 4
+    if fs.type_name not in ["py::fsspec+memory", "py::fsspec+('s3', 's3a')"]:
+        check_mtime(c_info)
+
+    assert zzz_info.path == str(zzz)
+    assert zzz_info.base_name == 'zzz'
+    assert zzz_info.extension == ''
+    assert zzz_info.type == FileType.NotFound
+    assert zzz_info.size is None
+    assert zzz_info.mtime is None
+    assert 'FileType.NotFound' in repr(zzz_info)
+    check_mtime_absent(zzz_info)
+
+    # with single path
+    aaa_info2 = fs.get_file_info(aaa)
+    assert aaa_info.path == aaa_info2.path
+    assert aaa_info.type == aaa_info2.type
+
+
+def test_get_file_info_with_selector(fs, pathfn):
+    base_dir = pathfn('selector-dir/')
+    file_a = pathfn('selector-dir/test_file_a')
+    file_b = pathfn('selector-dir/test_file_b')
+    dir_a = pathfn('selector-dir/test_dir_a')
+    file_c = pathfn('selector-dir/test_dir_a/test_file_c')
+    dir_b = pathfn('selector-dir/test_dir_b')
+
+    try:
+        fs.create_dir(base_dir)
+        with fs.open_output_stream(file_a):
+            pass
+        with fs.open_output_stream(file_b):
+            pass
+        fs.create_dir(dir_a)
+        with fs.open_output_stream(file_c):
+            pass
+        fs.create_dir(dir_b)
+
+        # recursive selector
+        selector = FileSelector(base_dir, allow_not_found=False,
+                                recursive=True)
+        assert selector.base_dir == base_dir
+
+        infos = fs.get_file_info(selector)
+        if fs.type_name == "py::fsspec+('s3', 's3a')":
+            # s3fs only lists directories if they are not empty
+            len(infos) == 4
+        else:
+            assert len(infos) == 5
+
+        for info in infos:
+            if (info.path.endswith(file_a) or info.path.endswith(file_b) or
+                    info.path.endswith(file_c)):
+                assert info.type == FileType.File
+            elif (info.path.rstrip("/").endswith(dir_a) or
+                  info.path.rstrip("/").endswith(dir_b)):
+                assert info.type == FileType.Directory
+            else:
+                raise ValueError(f'unexpected path {info.path}')
+            check_mtime_or_absent(info)
+
+        # non-recursive selector -> not selecting the nested file_c
+        selector = FileSelector(base_dir, recursive=False)
+
+        infos = fs.get_file_info(selector)
+        if fs.type_name == "py::fsspec+('s3', 's3a')":
+            # s3fs only lists directories if they are not empty
+            assert len(infos) == 3
+        else:
+            assert len(infos) == 4
+
+    finally:
+        fs.delete_dir(base_dir)
+
+
+def test_create_dir(fs, pathfn):
+    # s3fs fails deleting dir fails if it is empty
+    # (https://github.com/dask/s3fs/issues/317)
+    skip_fsspec_s3fs(fs)
+    d = pathfn('test-directory/')
+
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(d)
+
+    fs.create_dir(d)
+    fs.delete_dir(d)
+
+    d = pathfn('deeply/nested/test-directory/')
+    fs.create_dir(d, recursive=True)
+    fs.delete_dir(d)
+
+
+def test_delete_dir(fs, pathfn):
+    skip_fsspec_s3fs(fs)
+
+    d = pathfn('directory/')
+    nd = pathfn('directory/nested/')
+
+    fs.create_dir(nd)
+    fs.delete_dir(d)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(nd)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(d)
+
+
+def test_delete_dir_with_explicit_subdir(fs, pathfn):
+    # GH-38618: regression with AWS failing to delete directories,
+    # depending on whether they were created explicitly. Note that
+    # Minio doesn't reproduce the issue, so this test is not a regression
+    # test in itself.
+    skip_fsspec_s3fs(fs)
+
+    d = pathfn('directory/')
+    nd = pathfn('directory/nested/')
+
+    # deleting dir with explicit subdir
+    fs.create_dir(d)
+    fs.create_dir(nd)
+    fs.delete_dir(d)
+    dir_info = fs.get_file_info(d)
+    assert dir_info.type == FileType.NotFound
+
+    # deleting dir with blob in explicit subdir
+    d = pathfn('directory2')
+    nd = pathfn('directory2/nested')
+    f = pathfn('directory2/nested/target-file')
+
+    fs.create_dir(d)
+    fs.create_dir(nd)
+    with fs.open_output_stream(f) as s:
+        s.write(b'data')
+
+    fs.delete_dir(d)
+    dir_info = fs.get_file_info(d)
+    assert dir_info.type == FileType.NotFound
+
+
+def test_delete_dir_contents(fs, pathfn):
+    skip_fsspec_s3fs(fs)
+
+    d = pathfn('directory/')
+    nd = pathfn('directory/nested/')
+
+    fs.create_dir(nd)
+    fs.delete_dir_contents(d)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(nd)
+    fs.delete_dir_contents(nd, missing_dir_ok=True)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir_contents(nd)
+    fs.delete_dir(d)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(d)
+
+
+def _check_root_dir_contents(config):
+    fs = config['fs']
+    pathfn = config['pathfn']
+
+    d = pathfn('directory/')
+    nd = pathfn('directory/nested/')
+
+    fs.create_dir(nd)
+    with pytest.raises(pa.ArrowInvalid):
+        fs.delete_dir_contents("")
+    with pytest.raises(pa.ArrowInvalid):
+        fs.delete_dir_contents("/")
+    with pytest.raises(pa.ArrowInvalid):
+        fs.delete_dir_contents("//")
+
+    fs.delete_dir_contents("", accept_root_dir=True)
+    fs.delete_dir_contents("/", accept_root_dir=True)
+    fs.delete_dir_contents("//", accept_root_dir=True)
+
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_dir(d)
+
+
+def test_delete_root_dir_contents(mockfs, py_mockfs):
+    _check_root_dir_contents(mockfs)
+    _check_root_dir_contents(py_mockfs)
+
+
+def test_copy_file(fs, pathfn):
+    s = pathfn('test-copy-source-file')
+    t = pathfn('test-copy-target-file')
+
+    with fs.open_output_stream(s):
+        pass
+
+    fs.copy_file(s, t)
+    fs.delete_file(s)
+    fs.delete_file(t)
+
+
+def test_move_directory(fs, pathfn, allow_move_dir):
+    # TODO(GH-40025): Stop skipping this test
+    skip_azure(fs, "Not implemented yet in for Azure. See GH-40025")
+
+    # move directory (doesn't work with S3)
+    s = pathfn('source-dir/')
+    t = pathfn('target-dir/')
+
+    fs.create_dir(s)
+
+    if allow_move_dir:
+        fs.move(s, t)
+        with pytest.raises(pa.ArrowIOError):
+            fs.delete_dir(s)
+        fs.delete_dir(t)
+    else:
+        with pytest.raises(pa.ArrowIOError):
+            fs.move(s, t)
+
+
+def test_move_file(fs, pathfn):
+    # s3fs moving a file with recursive=True on latest 0.5 version
+    # (https://github.com/dask/s3fs/issues/394)
+    skip_fsspec_s3fs(fs)
+
+    # TODO(GH-40025): Stop skipping this test
+    skip_azure(fs, "Not implemented yet in for Azure. See GH-40025")
+
+    s = pathfn('test-move-source-file')
+    t = pathfn('test-move-target-file')
+
+    with fs.open_output_stream(s):
+        pass
+
+    fs.move(s, t)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_file(s)
+    fs.delete_file(t)
+
+
+def test_delete_file(fs, pathfn):
+    p = pathfn('test-delete-target-file')
+    with fs.open_output_stream(p):
+        pass
+
+    fs.delete_file(p)
+    with pytest.raises(pa.ArrowIOError):
+        fs.delete_file(p)
+
+    d = pathfn('test-delete-nested')
+    fs.create_dir(d)
+    f = pathfn('test-delete-nested/target-file')
+    with fs.open_output_stream(f) as s:
+        s.write(b'data')
+
+    fs.delete_dir(d)
+
+
+def identity(v):
+    return v
+
+
+@pytest.mark.gzip
+@pytest.mark.parametrize(
+    ('compression', 'buffer_size', 'compressor'),
+    [
+        (None, None, identity),
+        (None, 64, identity),
+        ('gzip', None, gzip.compress),
+        ('gzip', 256, gzip.compress),
+    ]
+)
+def test_open_input_stream(fs, pathfn, compression, buffer_size, compressor):
+    p = pathfn('open-input-stream')
+
+    data = b'some data for reading\n' * 512
+    with fs.open_output_stream(p) as s:
+        s.write(compressor(data))
+
+    with fs.open_input_stream(p, compression, buffer_size) as s:
+        result = s.read()
+
+    assert result == data
+
+
+def test_open_input_file(fs, pathfn):
+    p = pathfn('open-input-file')
+
+    data = b'some data' * 1024
+    with fs.open_output_stream(p) as s:
+        s.write(data)
+
+    read_from = len(b'some data') * 512
+    with fs.open_input_file(p) as f:
+        result = f.read()
+    assert result == data
+
+    with fs.open_input_file(p) as f:
+        f.seek(read_from)
+        result = f.read()
+
+    assert result == data[read_from:]
+
+
+def test_open_input_stream_not_found(fs, pathfn):
+    # The proper exception should be raised for this common case (ARROW-15896)
+    p = pathfn('open-input-stream-not-found')
+    with pytest.raises(FileNotFoundError):
+        fs.open_input_stream(p)
+
+
+@pytest.mark.gzip
+@pytest.mark.parametrize(
+    ('compression', 'buffer_size', 'decompressor'),
+    [
+        (None, None, identity),
+        (None, 64, identity),
+        ('gzip', None, gzip.decompress),
+        ('gzip', 256, gzip.decompress),
+    ]
+)
+def test_open_output_stream(fs, pathfn, compression, buffer_size,
+                            decompressor):
+    p = pathfn('open-output-stream')
+
+    data = b'some data for writing' * 1024
+    with fs.open_output_stream(p, compression, buffer_size) as f:
+        f.write(data)
+
+    with fs.open_input_stream(p, compression, buffer_size) as f:
+        assert f.read(len(data)) == data
+
+
+@pytest.mark.gzip
+@pytest.mark.parametrize(
+    ('compression', 'buffer_size', 'compressor', 'decompressor'),
+    [
+        (None, None, identity, identity),
+        (None, 64, identity, identity),
+        ('gzip', None, gzip.compress, gzip.decompress),
+        ('gzip', 256, gzip.compress, gzip.decompress),
+    ]
+)
+def test_open_append_stream(fs, pathfn, compression, buffer_size, compressor,
+                            decompressor, allow_append_to_file):
+    p = pathfn('open-append-stream')
+
+    initial = compressor(b'already existing')
+    with fs.open_output_stream(p) as s:
+        s.write(initial)
+
+    if allow_append_to_file:
+        with fs.open_append_stream(p, compression=compression,
+                                   buffer_size=buffer_size) as f:
+            f.write(b'\nnewly added')
+
+        with fs.open_input_stream(p) as f:
+            result = f.read()
+
+        result = decompressor(result)
+        assert result == b'already existing\nnewly added'
+    else:
+        with pytest.raises(pa.ArrowNotImplementedError):
+            fs.open_append_stream(p, compression=compression,
+                                  buffer_size=buffer_size)
+
+
+def test_open_output_stream_metadata(fs, pathfn):
+    p = pathfn('open-output-stream-metadata')
+    metadata = {'Content-Type': 'x-pyarrow/test'}
+
+    data = b'some data'
+    with fs.open_output_stream(p, metadata=metadata) as f:
+        f.write(data)
+
+    with fs.open_input_stream(p) as f:
+        assert f.read() == data
+        got_metadata = f.metadata()
+
+    if fs.type_name in ['s3', 'gcs', 'abfs'] or 'mock' in fs.type_name:
+        # TODO(GH-40026): Stop skipping this test
+        skip_azure(
+            fs, "Azure filesystem currently only returns system metadata not user "
+            "metadata. See GH-40026")
+        for k, v in metadata.items():
+            assert got_metadata[k] == v.encode()
+    else:
+        assert got_metadata == {}
+
+
+def test_localfs_options():
+    # LocalFileSystem instantiation
+    LocalFileSystem(use_mmap=False)
+
+    with pytest.raises(TypeError):
+        LocalFileSystem(xxx=False)
+
+
+def test_localfs_errors(localfs):
+    # Local filesystem errors should raise the right Python exceptions
+    # (e.g. FileNotFoundError)
+    fs = localfs['fs']
+    with assert_file_not_found():
+        fs.open_input_stream('/non/existent/file')
+    with assert_file_not_found():
+        fs.open_output_stream('/non/existent/file')
+    with assert_file_not_found():
+        fs.create_dir('/non/existent/dir', recursive=False)
+    with assert_file_not_found():
+        fs.delete_dir('/non/existent/dir')
+    with assert_file_not_found():
+        fs.delete_file('/non/existent/dir')
+    with assert_file_not_found():
+        fs.move('/non/existent', '/xxx')
+    with assert_file_not_found():
+        fs.copy_file('/non/existent', '/xxx')
+
+
+def test_localfs_file_info(localfs):
+    fs = localfs['fs']
+
+    file_path = pathlib.Path(__file__)
+    dir_path = file_path.parent
+    [file_info, dir_info] = fs.get_file_info([file_path.as_posix(),
+                                              dir_path.as_posix()])
+    assert file_info.size == file_path.stat().st_size
+    assert file_info.mtime_ns == file_path.stat().st_mtime_ns
+    check_mtime(file_info)
+    assert dir_info.mtime_ns == dir_path.stat().st_mtime_ns
+    check_mtime(dir_info)
+
+
+def test_mockfs_mtime_roundtrip(mockfs):
+    dt = datetime.fromtimestamp(1568799826, timezone.utc)
+    fs = _MockFileSystem(dt)
+
+    with fs.open_output_stream('foo'):
+        pass
+    [info] = fs.get_file_info(['foo'])
+    assert info.mtime == dt
+
+
+@pytest.mark.gcs
+def test_gcs_options(pickle_module):
+    from pyarrow.fs import GcsFileSystem
+    dt = datetime.now()
+    fs = GcsFileSystem(access_token='abc',
+                       target_service_account='service_account@apache',
+                       credential_token_expiration=dt,
+                       default_bucket_location='us-west2',
+                       scheme='https', endpoint_override='localhost:8999',
+                       project_id='test-project-id')
+    assert isinstance(fs, GcsFileSystem)
+    assert fs.default_bucket_location == 'us-west2'
+    assert fs.project_id == 'test-project-id'
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = GcsFileSystem()
+    assert isinstance(fs, GcsFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = GcsFileSystem(anonymous=True)
+    assert isinstance(fs, GcsFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = GcsFileSystem(default_metadata={"ACL": "authenticated-read",
+                                         "Content-Type": "text/plain"})
+    assert isinstance(fs, GcsFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    with pytest.raises(ValueError):
+        GcsFileSystem(access_token='access')
+    with pytest.raises(ValueError):
+        GcsFileSystem(anonymous=True, access_token='secret')
+    with pytest.raises(ValueError):
+        GcsFileSystem(anonymous=True, target_service_account='acct')
+    with pytest.raises(ValueError):
+        GcsFileSystem(credential_token_expiration=datetime.now())
+
+
+@pytest.mark.s3
+def test_s3_options(pickle_module):
+    from pyarrow.fs import (AwsDefaultS3RetryStrategy,
+                            AwsStandardS3RetryStrategy, S3FileSystem,
+                            S3RetryStrategy)
+
+    fs = S3FileSystem(access_key='access', secret_key='secret',
+                      session_token='token', region='us-east-2',
+                      scheme='https', endpoint_override='localhost:8999')
+    assert isinstance(fs, S3FileSystem)
+    assert fs.region == 'us-east-2'
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(role_arn='role', session_name='session',
+                      external_id='id', load_frequency=100)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    # Note that the retry strategy won't survive pickling for now
+    fs = S3FileSystem(
+        retry_strategy=AwsStandardS3RetryStrategy(max_attempts=5))
+    assert isinstance(fs, S3FileSystem)
+
+    fs = S3FileSystem(
+        retry_strategy=AwsDefaultS3RetryStrategy(max_attempts=5))
+    assert isinstance(fs, S3FileSystem)
+
+    fs2 = S3FileSystem(role_arn='role')
+    assert isinstance(fs2, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs2
+    assert fs2 != fs
+
+    fs = S3FileSystem(anonymous=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(background_writes=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs2 = S3FileSystem(background_writes=True,
+                       default_metadata={"ACL": "authenticated-read",
+                                         "Content-Type": "text/plain"})
+    assert isinstance(fs2, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs2
+    assert fs2 != fs
+
+    fs = S3FileSystem(allow_delayed_open=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+    assert pickle_module.loads(pickle_module.dumps(fs)) != S3FileSystem()
+
+    fs = S3FileSystem(allow_bucket_creation=True, allow_bucket_deletion=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(allow_bucket_creation=True, allow_bucket_deletion=True,
+                      check_directory_existence_before_creation=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(request_timeout=0.5, connect_timeout=0.25)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs2 = S3FileSystem(request_timeout=0.25, connect_timeout=0.5)
+    assert isinstance(fs2, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs2
+    assert fs2 != fs
+
+    fs = S3FileSystem(endpoint_override='localhost:8999', force_virtual_addressing=True)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(tls_ca_file_path="ca.pem")
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+    assert fs != S3FileSystem(tls_ca_file_path="other_ca.pem")
+    assert fs != S3FileSystem()
+
+    with pytest.raises(ValueError):
+        S3FileSystem(access_key='access')
+    with pytest.raises(ValueError):
+        S3FileSystem(secret_key='secret')
+    with pytest.raises(ValueError):
+        S3FileSystem(access_key='access', session_token='token')
+    with pytest.raises(ValueError):
+        S3FileSystem(secret_key='secret', session_token='token')
+    with pytest.raises(ValueError):
+        S3FileSystem(
+            access_key='access', secret_key='secret', role_arn='arn'
+        )
+    with pytest.raises(ValueError):
+        S3FileSystem(
+            access_key='access', secret_key='secret', anonymous=True
+        )
+    with pytest.raises(ValueError):
+        S3FileSystem(role_arn="arn", anonymous=True)
+    with pytest.raises(ValueError):
+        S3FileSystem(default_metadata=["foo", "bar"])
+    with pytest.raises(ValueError):
+        S3FileSystem(retry_strategy=S3RetryStrategy())
+
+
+@pytest.mark.s3
+def test_s3_proxy_options(monkeypatch, pickle_module):
+    from pyarrow.fs import S3FileSystem
+
+    # The following two are equivalent:
+    proxy_opts_1_dict = {'scheme': 'http', 'host': 'localhost', 'port': 8999}
+    proxy_opts_1_str = 'http://localhost:8999'
+    # The following two are equivalent:
+    proxy_opts_2_dict = {'scheme': 'https', 'host': 'localhost', 'port': 8080}
+    proxy_opts_2_str = 'https://localhost:8080'
+
+    # Check dict case for 'proxy_options'
+    fs = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    # Check str case for 'proxy_options'
+    fs = S3FileSystem(proxy_options=proxy_opts_1_str)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    fs = S3FileSystem(proxy_options=proxy_opts_2_str)
+    assert isinstance(fs, S3FileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    # Check that two FSs using the same proxy_options dict are equal
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    # Check that two FSs using the same proxy_options str are equal
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    # Check that two FSs using equivalent proxy_options
+    # (one dict, one str) are equal
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    assert fs1 == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs1
+
+    # Check that two FSs using nonequivalent proxy_options are not equal
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    fs2 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    # Check that two FSs (one using proxy_options and the other not)
+    # are not equal
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_dict)
+    fs2 = S3FileSystem()
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_1_str)
+    fs2 = S3FileSystem()
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_2_dict)
+    fs2 = S3FileSystem()
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    fs1 = S3FileSystem(proxy_options=proxy_opts_2_str)
+    fs2 = S3FileSystem()
+    assert fs1 != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs1)) != fs2
+    assert pickle_module.loads(pickle_module.dumps(fs2)) != fs1
+
+    # Only dict and str are supported
+    with pytest.raises(TypeError):
+        S3FileSystem(proxy_options=('http', 'localhost', 9090))
+    # Missing scheme
+    with pytest.raises(KeyError):
+        S3FileSystem(proxy_options={'host': 'localhost', 'port': 9090})
+    # Missing host
+    with pytest.raises(KeyError):
+        S3FileSystem(proxy_options={'scheme': 'https', 'port': 9090})
+    # Missing port
+    with pytest.raises(KeyError):
+        S3FileSystem(proxy_options={'scheme': 'http', 'host': 'localhost'})
+    # Invalid proxy URI (invalid scheme httpsB)
+    with pytest.raises(pa.ArrowInvalid):
+        S3FileSystem(proxy_options='httpsB://localhost:9000')
+    # Invalid proxy_options dict (invalid scheme httpA)
+    with pytest.raises(pa.ArrowInvalid):
+        S3FileSystem(proxy_options={'scheme': 'httpA', 'host': 'localhost',
+                                    'port': 8999})
+
+
+@pytest.mark.s3
+def test_s3fs_wrong_region():
+    from pyarrow.fs import S3FileSystem
+
+    # wrong region for bucket
+    # anonymous=True incase CI/etc has invalid credentials
+    fs = S3FileSystem(region='eu-north-1', anonymous=True)
+
+    msg = ("When getting information for bucket 'arrow-datasets': "
+           r"AWS Error UNKNOWN \(HTTP status 301\) during HeadBucket "
+           "operation: No response body. Looks like the configured region is "
+           "'eu-north-1' while the bucket is located in 'us-east-1'."
+           "|NETWORK_CONNECTION")
+    with pytest.raises(OSError, match=msg) as exc:
+        fs.get_file_info("arrow-datasets")
+
+    # Sometimes fails on unrelated network error, so next call would also fail.
+    if 'NETWORK_CONNECTION' in str(exc.value):
+        return
+
+    fs = S3FileSystem(region='us-east-1', anonymous=True)
+    fs.get_file_info("arrow-datasets")
+
+
+@pytest.mark.azure
+def test_azurefs_options(pickle_module):
+    from pyarrow.fs import AzureFileSystem
+
+    fs1 = AzureFileSystem(account_name='fake-account-name')
+    assert isinstance(fs1, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs1)) == fs1
+
+    fs2 = AzureFileSystem(account_name='fake-account-name',
+                          account_key='fakeaccountkey')
+    assert isinstance(fs2, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs2)) == fs2
+    assert fs2 != fs1
+
+    fs3 = AzureFileSystem(account_name='fake-account', account_key='fakeaccount',
+                          blob_storage_authority='fake-blob-authority',
+                          dfs_storage_authority='fake-dfs-authority',
+                          blob_storage_scheme='https',
+                          dfs_storage_scheme='https')
+    assert isinstance(fs3, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs3)) == fs3
+    assert fs3 != fs2
+
+    fs4 = AzureFileSystem(account_name='fake-account-name',
+                          sas_token='fakesastoken')
+    assert isinstance(fs4, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs4)) == fs4
+    assert fs4 != fs3
+
+    fs5 = AzureFileSystem(
+        account_name='fake-account-name',
+        tenant_id='fake-tenant-id',
+        client_id='fake-client-id',
+        client_secret='fake-client-secret'
+    )
+    assert isinstance(fs5, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs5)) == fs5
+    assert fs5 != fs4
+
+    fs6 = AzureFileSystem(
+        account_name='fake-account-name',
+        client_id='fake-client-id'
+    )
+    assert isinstance(fs6, AzureFileSystem)
+    assert pickle_module.loads(pickle_module.dumps(fs6)) == fs6
+    assert fs6 != fs5
+
+    with pytest.raises(ValueError, match="client_id must be specified"):
+        AzureFileSystem(
+            account_name='fake-account-name',
+            tenant_id='fake-tenant-id'
+        )
+
+    with pytest.raises(ValueError, match="client_id must be specified"):
+        AzureFileSystem(
+            account_name='fake-account-name',
+            client_secret='fake-client-secret'
+        )
+
+    invalid_msg = (
+        "Invalid Azure credential configuration: "
+        "For ManagedIdentityCredential, provide only client_id. "
+        "For ClientSecretCredential, provide tenant_id, client_id, and client_secret."
+    )
+
+    with pytest.raises(ValueError, match=invalid_msg):
+        AzureFileSystem(
+            account_name='fake-account-name',
+            client_id='fake-client-id',
+            client_secret='fake-client-secret'
+        )
+
+    with pytest.raises(ValueError, match="client_id must be specified"):
+        AzureFileSystem(
+            account_name='fake-account-name',
+            tenant_id='fake-tenant-id',
+            client_secret='fake-client-secret'
+        )
+
+    with pytest.raises(ValueError, match=invalid_msg):
+        AzureFileSystem(
+            account_name='fake-account-name',
+            tenant_id='fake-tenant-id',
+            client_id='fake-client-id'
+        )
+
+    with pytest.raises(ValueError):
+        AzureFileSystem(account_name='fake-account-name', account_key='fakeaccount',
+                        sas_token='fakesastoken')
+
+    with pytest.raises(TypeError):
+        AzureFileSystem()
+
+
+@pytest.mark.hdfs
+def test_hdfs_options(hdfs_connection, pickle_module):
+    from pyarrow.fs import HadoopFileSystem
+    if not pa.have_libhdfs():
+        pytest.skip('Cannot locate libhdfs')
+
+    host, port, user = hdfs_connection
+
+    replication = 2
+    buffer_size = 64*1024
+    default_block_size = 128*1024**2
+    uri = ('hdfs://{}:{}/?user={}&replication={}&buffer_size={}'
+           '&default_block_size={}')
+
+    hdfs1 = HadoopFileSystem(host, port, user='libhdfs',
+                             replication=replication, buffer_size=buffer_size,
+                             default_block_size=default_block_size)
+    hdfs2 = HadoopFileSystem.from_uri(uri.format(
+        host, port, 'libhdfs', replication, buffer_size, default_block_size
+    ))
+    hdfs3 = HadoopFileSystem.from_uri(uri.format(
+        host, port, 'me', replication, buffer_size, default_block_size
+    ))
+    hdfs4 = HadoopFileSystem.from_uri(uri.format(
+        host, port, 'me', replication + 1, buffer_size, default_block_size
+    ))
+    hdfs5 = HadoopFileSystem(host, port)
+    hdfs6 = HadoopFileSystem.from_uri(f'hdfs://{host}:{port}')
+    hdfs7 = HadoopFileSystem(host, port, user='localuser')
+    hdfs8 = HadoopFileSystem(host, port, user='localuser',
+                             kerb_ticket="cache_path")
+    hdfs9 = HadoopFileSystem(host, port, user='localuser',
+                             kerb_ticket=pathlib.Path("cache_path"))
+    hdfs10 = HadoopFileSystem(host, port, user='localuser',
+                              kerb_ticket="cache_path2")
+    hdfs11 = HadoopFileSystem(host, port, user='localuser',
+                              kerb_ticket="cache_path",
+                              extra_conf={'hdfs_token': 'abcd'})
+
+    assert hdfs1 == hdfs2
+    assert hdfs5 == hdfs6
+    assert hdfs6 != hdfs7
+    assert hdfs2 != hdfs3
+    assert hdfs3 != hdfs4
+    assert hdfs7 != hdfs5
+    assert hdfs2 != hdfs3
+    assert hdfs3 != hdfs4
+    assert hdfs7 != hdfs8
+    assert hdfs8 == hdfs9
+    assert hdfs10 != hdfs9
+    assert hdfs11 != hdfs8
+
+    with pytest.raises(TypeError):
+        HadoopFileSystem()
+    with pytest.raises(TypeError):
+        HadoopFileSystem.from_uri(3)
+
+    for fs in [hdfs1, hdfs2, hdfs3, hdfs4, hdfs5, hdfs6, hdfs7, hdfs8,
+               hdfs9, hdfs10, hdfs11]:
+        assert pickle_module.loads(pickle_module.dumps(fs)) == fs
+
+    host, port, user = hdfs_connection
+
+    hdfs = HadoopFileSystem(host, port, user=user)
+    assert hdfs.get_file_info(FileSelector('/'))
+
+    hdfs = HadoopFileSystem.from_uri(
+        f"hdfs://{host}:{port}/?user={user}"
+    )
+    assert hdfs.get_file_info(FileSelector('/'))
+
+
+@pytest.mark.parametrize(('uri', 'expected_klass', 'expected_path'), [
+    # leading slashes are removed intentionally, because MockFileSystem doesn't
+    # have a distinction between relative and absolute paths
+    ('mock:', _MockFileSystem, ''),
+    ('mock:foo/bar', _MockFileSystem, 'foo/bar'),
+    ('mock:/foo/bar', _MockFileSystem, 'foo/bar'),
+    ('mock:///foo/bar', _MockFileSystem, 'foo/bar'),
+    ('mock:///some%20path/%C3%A9', _MockFileSystem, 'some path/é'),
+    ('file:/', LocalFileSystem, '/'),
+    ('file:///', LocalFileSystem, '/'),
+    ('file:/foo/bar', LocalFileSystem, '/foo/bar'),
+    ('file:///foo/bar', LocalFileSystem, '/foo/bar'),
+    ('file:///some%20path/%C3%A9', LocalFileSystem, '/some path/é'),
+    # no %-decoding for non-URI inputs
+    ('/', LocalFileSystem, '/'),
+    ('/foo/bar', LocalFileSystem, '/foo/bar'),
+    ('/some path/%20é', LocalFileSystem, '/some path/%20é'),
+])
+def test_filesystem_from_uri(uri, expected_klass, expected_path):
+    fs, path = FileSystem.from_uri(uri)
+    assert isinstance(fs, expected_klass)
+    assert path == expected_path
+
+
+def test_filesystem_from_uri_calling():
+    # Call using class staticmethod
+    fs, path = FileSystem.from_uri("file:/")
+    assert isinstance(fs, LocalFileSystem)
+    assert path == "/"
+
+    # Call using class staticmethod with explicit arguments
+    fs, path = FileSystem.from_uri(uri="file:/")
+    assert isinstance(fs, LocalFileSystem)
+    assert path == "/"
+
+    # Call using instance method passthrough
+    fs, path = LocalFileSystem().from_uri(uri="file:/")
+    assert isinstance(fs, LocalFileSystem)
+    assert path == "/"
+
+
+@pytest.mark.parametrize(
+    'path',
+    ['', '/', 'foo/bar', '/foo/bar', __file__]
+)
+def test_filesystem_from_path_object(path):
+    p = pathlib.Path(path)
+    fs, path = FileSystem.from_uri(p)
+    assert isinstance(fs, LocalFileSystem)
+    assert path == p.resolve().absolute().as_posix()
+
+
+@pytest.mark.s3
+def test_filesystem_from_uri_s3(s3_server):
+    from pyarrow.fs import S3FileSystem
+
+    host, port, access_key, secret_key = s3_server['connection']
+
+    uri = f"s3://{access_key}:{secret_key}@mybucket/foo/bar?scheme=http&" \
+        f"endpoint_override={host}:{port}&allow_bucket_creation=True"
+
+    fs, path = FileSystem.from_uri(uri)
+    assert isinstance(fs, S3FileSystem)
+    assert path == "mybucket/foo/bar"
+
+    fs.create_dir(path)
+    [info] = fs.get_file_info([path])
+    assert info.path == path
+    assert info.type == FileType.Directory
+
+
+@pytest.mark.gcs
+def test_filesystem_from_uri_gcs(gcs_server):
+    from pyarrow.fs import GcsFileSystem
+
+    host, port = gcs_server['connection']
+
+    uri = ("gs://anonymous@" +
+           f"mybucket/foo/bar?scheme=http&endpoint_override={host}:{port}&" +
+           "retry_limit_seconds=5&project_id=test-project-id")
+
+    fs, path = FileSystem.from_uri(uri)
+    assert isinstance(fs, GcsFileSystem)
+    assert path == "mybucket/foo/bar"
+
+    fs.create_dir(path)
+    [info] = fs.get_file_info([path])
+    assert info.path == path
+    assert info.type == FileType.Directory
+
+
+def test_py_filesystem():
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+    assert isinstance(fs, PyFileSystem)
+    assert fs.type_name == "py::dummy"
+    assert fs.handler is handler
+
+    with pytest.raises(TypeError):
+        PyFileSystem(None)
+
+
+def test_py_filesystem_equality():
+    handler1 = DummyHandler(1)
+    handler2 = DummyHandler(2)
+    handler3 = DummyHandler(2)
+    fs1 = PyFileSystem(handler1)
+    fs2 = PyFileSystem(handler1)
+    fs3 = PyFileSystem(handler2)
+    fs4 = PyFileSystem(handler3)
+
+    assert fs2 is not fs1
+    assert fs3 is not fs2
+    assert fs4 is not fs3
+    assert fs2 == fs1  # Same handler
+    assert fs3 != fs2  # Unequal handlers
+    assert fs4 == fs3  # Equal handlers
+
+    assert fs1 != LocalFileSystem()
+    assert fs1 != object()
+
+
+def test_py_filesystem_pickling(pickle_module):
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+
+    serialized = pickle_module.dumps(fs)
+    restored = pickle_module.loads(serialized)
+    assert isinstance(restored, FileSystem)
+    assert restored == fs
+    assert restored.handler == handler
+    assert restored.type_name == "py::dummy"
+
+
+def test_py_filesystem_lifetime():
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+    assert isinstance(fs, PyFileSystem)
+    wr = weakref.ref(handler)
+    handler = None
+    assert wr() is not None
+    fs = None
+    assert wr() is None
+
+    # Taking the .handler attribute doesn't wreck reference counts
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+    wr = weakref.ref(handler)
+    handler = None
+    assert wr() is fs.handler
+    assert wr() is not None
+    fs = None
+    assert wr() is None
+
+
+def test_py_filesystem_get_file_info():
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+
+    [info] = fs.get_file_info(['some/dir'])
+    assert info.path == 'some/dir'
+    assert info.type == FileType.Directory
+
+    [info] = fs.get_file_info(['some/file'])
+    assert info.path == 'some/file'
+    assert info.type == FileType.File
+
+    [info] = fs.get_file_info(['notfound'])
+    assert info.path == 'notfound'
+    assert info.type == FileType.NotFound
+
+    with pytest.raises(TypeError):
+        fs.get_file_info(['badtype'])
+
+    with pytest.raises(IOError):
+        fs.get_file_info(['xxx'])
+
+
+def test_py_filesystem_get_file_info_selector():
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+
+    selector = FileSelector(base_dir="somedir")
+    infos = fs.get_file_info(selector)
+    assert len(infos) == 2
+    assert infos[0].path == "somedir/file1"
+    assert infos[0].type == FileType.File
+    assert infos[0].size == 123
+    assert infos[1].path == "somedir/subdir1"
+    assert infos[1].type == FileType.Directory
+    assert infos[1].size is None
+
+    selector = FileSelector(base_dir="somedir", recursive=True)
+    infos = fs.get_file_info(selector)
+    assert len(infos) == 3
+    assert infos[0].path == "somedir/file1"
+    assert infos[1].path == "somedir/subdir1"
+    assert infos[2].path == "somedir/subdir1/file2"
+
+    selector = FileSelector(base_dir="notfound")
+    with pytest.raises(FileNotFoundError):
+        fs.get_file_info(selector)
+
+    selector = FileSelector(base_dir="notfound", allow_not_found=True)
+    assert fs.get_file_info(selector) == []
+
+
+def test_py_filesystem_ops():
+    handler = DummyHandler()
+    fs = PyFileSystem(handler)
+
+    fs.create_dir("recursive", recursive=True)
+    fs.create_dir("non-recursive", recursive=False)
+    with pytest.raises(IOError):
+        fs.create_dir("foobar")
+
+    fs.delete_dir("delete_dir")
+    fs.delete_dir_contents("delete_dir_contents")
+    for path in ("", "/", "//"):
+        with pytest.raises(ValueError):
+            fs.delete_dir_contents(path)
+        fs.delete_dir_contents(path, accept_root_dir=True)
+    fs.delete_file("delete_file")
+    fs.move("move_from", "move_to")
+    fs.copy_file("copy_file_from", "copy_file_to")
+
+
+def test_py_open_input_stream():
+    fs = PyFileSystem(DummyHandler())
+
+    with fs.open_input_stream("somefile") as f:
+        assert f.read() == b"somefile:input_stream"
+    with pytest.raises(FileNotFoundError):
+        fs.open_input_stream("notfound")
+
+
+def test_py_open_input_file():
+    fs = PyFileSystem(DummyHandler())
+
+    with fs.open_input_file("somefile") as f:
+        assert f.read() == b"somefile:input_file"
+    with pytest.raises(FileNotFoundError):
+        fs.open_input_file("notfound")
+
+
+def test_py_open_output_stream():
+    fs = PyFileSystem(DummyHandler())
+
+    with fs.open_output_stream("somefile") as f:
+        f.write(b"data")
+
+
+def test_py_open_append_stream():
+    fs = PyFileSystem(DummyHandler())
+
+    with fs.open_append_stream("somefile") as f:
+        f.write(b"data")
+
+
+@pytest.mark.s3
+def test_s3_real_aws():
+    # Exercise connection code with an AWS-backed S3 bucket.
+    # This is a minimal integration check for ARROW-9261 and similar issues.
+    from pyarrow.fs import S3FileSystem
+    default_region = (os.environ.get('PYARROW_TEST_S3_REGION') or
+                      'us-east-1')
+    fs = S3FileSystem(anonymous=True)
+    assert fs.region == default_region
+
+    fs = S3FileSystem(anonymous=True, region='us-east-1')
+    entries = fs.get_file_info(FileSelector(
+        'arrow-datasets/nyc-taxi'))
+    assert len(entries) > 0
+    key = 'arrow-datasets/nyc-taxi/year=2019/month=6/part-0.parquet'
+    with fs.open_input_stream(key) as f:
+        md = f.metadata()
+        assert 'Content-Type' in md
+        assert md['Last-Modified'] == b'2025-11-26T10:28:55Z'
+        # For some reason, the header value is quoted
+        # (both with AWS and Minio)
+        assert md['ETag'] == b'"4c6a76826a695c6ac61592bc30cda3df-16"'
+
+
+@pytest.mark.s3
+def test_s3_real_aws_region_selection():
+    # Taken from a registry of open S3-hosted datasets
+    # at https://github.com/awslabs/open-data-registry
+    fs, path = FileSystem.from_uri('s3://mf-nwp-models/README.txt')
+    assert fs.region == 'eu-west-1'
+    with fs.open_input_stream(path) as f:
+        assert b"Meteo-France Atmospheric models on AWS" in f.read(50)
+
+    # Passing an explicit region disables auto-selection
+    fs, path = FileSystem.from_uri(
+        's3://mf-nwp-models/README.txt?region=us-east-2')
+    assert fs.region == 'us-east-2'
+    # Reading from the wrong region may still work for public buckets...
+
+    # Nonexistent bucket. This bucket can't exist as AWS imposes that
+    # Bucket names must not contain two adjacent periods.
+    # Hopefully this won't fail in the future if a new validation rule applies.
+    # See: https://github.com/apache/arrow/pull/47166
+    with pytest.raises(IOError, match="Bucket '.*' not found"):
+        FileSystem.from_uri('s3://x-arrow..nonexistent-bucket')
+    fs, path = FileSystem.from_uri('s3://x-arrow-nonexistent-bucket?region=us-east-3')
+    assert fs.region == 'us-east-3'
+
+    # allow_delayed_open has a side-effect of delaying errors until I/O is performed.
+    # region is required to bypass the lookup for a nonexistent bucket, allowing us to
+    # verify that errors are properly delayed until stream closure.
+    fs, path = FileSystem.from_uri(
+        's3://x-arrow-nonexistent-bucket/T.md?region=us-east-2&allow_delayed_open=true')
+    stream = fs.open_output_stream(path)
+    with pytest.raises(IOError):
+        stream.close()
+
+
+@pytest.mark.s3
+def test_resolve_s3_region():
+    from pyarrow.fs import resolve_s3_region
+    assert resolve_s3_region('arrow-datasets') == 'us-east-1'
+    assert resolve_s3_region('mf-nwp-models') == 'eu-west-1'
+
+    with pytest.raises(ValueError, match="Not a valid bucket name"):
+        resolve_s3_region('foo/bar')
+    with pytest.raises(ValueError, match="Not a valid bucket name"):
+        resolve_s3_region('s3:bucket')
+
+
+@pytest.mark.s3
+def test_copy_files(s3_connection, s3fs, tempdir):
+    fs = s3fs["fs"]
+    pathfn = s3fs["pathfn"]
+
+    # create test file on S3 filesystem
+    path = pathfn('c.txt')
+    with fs.open_output_stream(path) as f:
+        f.write(b'test')
+
+    # create URI for created file
+    host, port, access_key, secret_key = s3_connection
+    source_uri = (
+        f"s3://{access_key}:{secret_key}@{path}"
+        f"?scheme=http&endpoint_override={host}:{port}"
+    )
+    # copy from S3 URI to local file
+    local_path1 = str(tempdir / "c_copied1.txt")
+    copy_files(source_uri, local_path1)
+
+    localfs = LocalFileSystem()
+    with localfs.open_input_stream(local_path1) as f:
+        assert f.read() == b"test"
+
+    # copy from S3 path+filesystem to local file
+    local_path2 = str(tempdir / "c_copied2.txt")
+    copy_files(path, local_path2, source_filesystem=fs)
+    with localfs.open_input_stream(local_path2) as f:
+        assert f.read() == b"test"
+
+    # copy to local file with URI
+    local_path3 = str(tempdir / "c_copied3.txt")
+    destination_uri = _filesystem_uri(local_path3)  # file://
+    copy_files(source_uri, destination_uri)
+
+    with localfs.open_input_stream(local_path3) as f:
+        assert f.read() == b"test"
+
+    # copy to local file with path+filesystem
+    local_path4 = str(tempdir / "c_copied4.txt")
+    copy_files(source_uri, local_path4, destination_filesystem=localfs)
+
+    with localfs.open_input_stream(local_path4) as f:
+        assert f.read() == b"test"
+
+    # copy with additional options
+    local_path5 = str(tempdir / "c_copied5.txt")
+    copy_files(source_uri, local_path5, chunk_size=1, use_threads=False)
+
+    with localfs.open_input_stream(local_path5) as f:
+        assert f.read() == b"test"
+
+
+def test_copy_files_directory(tempdir):
+    localfs = LocalFileSystem()
+
+    # create source directory with 2 files
+    source_dir = tempdir / "source"
+    source_dir.mkdir()
+    with localfs.open_output_stream(str(source_dir / "file1")) as f:
+        f.write(b'test1')
+    with localfs.open_output_stream(str(source_dir / "file2")) as f:
+        f.write(b'test2')
+
+    def check_copied_files(destination_dir):
+        with localfs.open_input_stream(str(destination_dir / "file1")) as f:
+            assert f.read() == b"test1"
+        with localfs.open_input_stream(str(destination_dir / "file2")) as f:
+            assert f.read() == b"test2"
+
+    # Copy directory with local file paths
+    destination_dir1 = tempdir / "destination1"
+    destination_dir1.mkdir()
+    copy_files(str(source_dir), str(destination_dir1))
+    check_copied_files(destination_dir1)
+
+    # Copy directory with path+filesystem
+    destination_dir2 = tempdir / "destination2"
+    destination_dir2.mkdir()
+    copy_files(str(source_dir), str(destination_dir2),
+               source_filesystem=localfs, destination_filesystem=localfs)
+    check_copied_files(destination_dir2)
+
+    # Copy directory with URI
+    destination_dir3 = tempdir / "destination3"
+    destination_dir3.mkdir()
+    source_uri = _filesystem_uri(str(source_dir))  # file://
+    destination_uri = _filesystem_uri(str(destination_dir3))
+    copy_files(source_uri, destination_uri)
+    check_copied_files(destination_dir3)
+
+    # Copy directory with Path objects
+    destination_dir4 = tempdir / "destination4"
+    destination_dir4.mkdir()
+    copy_files(source_dir, destination_dir4)
+    check_copied_files(destination_dir4)
+
+    # copy with additional non-default options
+    destination_dir5 = tempdir / "destination5"
+    destination_dir5.mkdir()
+    copy_files(source_dir, destination_dir5, chunk_size=1, use_threads=False)
+    check_copied_files(destination_dir5)
+
+
+@pytest.mark.s3
+def test_s3_finalize():
+    # Once finalize_s3() was called, most/all operations on S3 filesystems
+    # should raise.
+    code = """if 1:
+        import pytest
+        from pyarrow.fs import (FileSystem, S3FileSystem,
+                                ensure_s3_initialized, finalize_s3)
+
+        fs, path = FileSystem.from_uri('s3://mf-nwp-models/README.txt')
+        assert fs.region == 'eu-west-1'
+        f = fs.open_input_stream(path)
+        f.read(50)
+
+        finalize_s3()
+
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            f.read(50)
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            fs.open_input_stream(path)
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            S3FileSystem(anonymous=True)
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            FileSystem.from_uri('s3://mf-nwp-models/README.txt')
+        """
+    subprocess.check_call([sys.executable, "-c", code])
+
+
+@pytest.mark.s3
+def test_s3_finalize_region_resolver():
+    # Same as test_s3_finalize(), but exercising region resolution
+    code = """if 1:
+        import pytest
+        from pyarrow.fs import resolve_s3_region, ensure_s3_initialized, finalize_s3
+
+        resolve_s3_region('mf-nwp-models')
+
+        finalize_s3()
+
+        # Testing both cached and uncached accesses
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            resolve_s3_region('mf-nwp-models')
+        with pytest.raises(ValueError, match="S3 .* finalized"):
+            resolve_s3_region('arrow-datasets')
+        """
+    subprocess.check_call([sys.executable, "-c", code])
+
+
+@pytest.mark.processes
+@pytest.mark.threading
+@pytest.mark.s3
+def test_concurrent_s3fs_init():
+    # GH-39897: lazy concurrent initialization of S3 subsystem should not crash
+    code = """if 1:
+        import threading
+        import pytest
+        from pyarrow.fs import (FileSystem, S3FileSystem,
+                                ensure_s3_initialized, finalize_s3)
+        threads = []
+        fn = lambda: FileSystem.from_uri('s3://mf-nwp-models/README.txt')
+        for i in range(4):
+            thread = threading.Thread(target = fn)
+            threads.append(thread)
+            thread.start()
+
+        for thread in threads:
+            thread.join()
+
+        finalize_s3()
+        """
+    subprocess.check_call([sys.executable, "-c", code])
+
+
+@pytest.mark.s3
+@pytest.mark.skip(reason="atexit(ensure_s3_finalized) will be called too late "
+                  "with bundled aws-sdk-cpp 1.11.587")
+@pytest.mark.skipif(running_on_musllinux(), reason="Leaking S3ClientFinalizer causes "
+                                                   "segfault on musl based systems")
+def test_uwsgi_integration():
+    # GH-44071: using S3FileSystem under uwsgi shouldn't lead to a crash at shutdown
+    try:
+        subprocess.check_call(["uwsgi", "--version"])
+    except FileNotFoundError:
+        pytest.skip("uwsgi not installed on this Python")
+
+    port = find_free_port()
+    args = ["uwsgi", "-i", "--http", f"127.0.0.1:{port}",
+            "--wsgi-file", os.path.join(here, "wsgi_examples.py")]
+    proc = subprocess.Popen(args, stdin=subprocess.DEVNULL)
+    # Try to fetch URL, it should return 200 Ok...
+    try:
+        url = f"http://127.0.0.1:{port}/s3/"
+        start_time = time.time()
+        error = None
+        while time.time() < start_time + 5:
+            try:
+                with urlopen(url) as resp:
+                    assert resp.status == 200
+                break
+            except OSError as e:
+                error = e
+                time.sleep(0.1)
+        else:
+            pytest.fail(f"Could not fetch {url!r}: {error}")
+    finally:
+        proc.terminate()
+    # ... and uwsgi should gracefully shutdown after it's been asked above
+    assert proc.wait() == 30  # UWSGI_END_CODE = 30
+
+
+def test_fsspec_filesystem_from_uri():
+    try:
+        from fsspec.implementations.local import LocalFileSystem
+        from fsspec.implementations.memory import MemoryFileSystem
+    except ImportError:
+        pytest.skip("fsspec not installed")
+
+    fs, path = FileSystem.from_uri("fsspec+memory://path/to/data.parquet")
+    expected_fs = PyFileSystem(FSSpecHandler(MemoryFileSystem()))
+    assert fs == expected_fs
+    assert path == "/path/to/data.parquet"
+
+    # check that if fsspec+ is specified than we don't coerce to the native
+    # arrow local filesystem
+    uri = "file:///tmp/my.file"
+    fs, _ = FileSystem.from_uri(f"fsspec+{uri}")
+    expected_fs = PyFileSystem(FSSpecHandler(LocalFileSystem()))
+    assert fs == expected_fs
+
+
+def test_fsspec_delete_root_dir_contents():
+    try:
+        from fsspec.implementations.memory import MemoryFileSystem
+    except ImportError:
+        pytest.skip("fsspec not installed")
+
+    fs = FSSpecHandler(MemoryFileSystem())
+
+    # Create some files and directories
+    fs.create_dir("test_dir", recursive=True)
+    fs.create_dir("test_dir/subdir", recursive=True)
+
+    with fs.open_output_stream("test_file.txt", metadata={}) as stream:
+        stream.write(b"test content")
+
+    with fs.open_output_stream("test_dir/nested_file.txt", metadata={}) as stream:
+        stream.write(b"nested content")
+
+    # Verify files exist before deletion
+    def get_type(path):
+        return fs.get_file_info([path])[0].type
+
+    assert get_type("test_file.txt") == FileType.File
+    assert get_type("test_dir") == FileType.Directory
+    assert get_type("test_dir/nested_file.txt") == FileType.File
+
+    # Delete root directory contents
+    fs.delete_root_dir_contents()
+
+    # Assert all files and directories are deleted
+    assert get_type("test_file.txt") == FileType.NotFound
+    assert get_type("test_dir") == FileType.NotFound
+    assert get_type("test_dir/nested_file.txt") == FileType.NotFound
+
+
+def test_huggingface_filesystem_from_uri():
+    pytest.importorskip("fsspec")
+    try:
+        from huggingface_hub import HfFileSystem
+    except ImportError:
+        pytest.skip("huggingface_hub not installed")
+
+    fs, path = FileSystem.from_uri(
+        "hf://datasets/stanfordnlp/imdb/plain_text/train-00000-of-00001.parquet"
+    )
+    expected_fs = PyFileSystem(FSSpecHandler(HfFileSystem()))
+    assert fs == expected_fs
+    assert path == "datasets/stanfordnlp/imdb/plain_text/train-00000-of-00001.parquet"
diff --git a/shared/package-files/1b/1be28d61b627f8bda0d32b6b08464f5c5bb963d5dddaf91106339eb97dd859bc/texmanager.py b/shared/package-files/1b/1be28d61b627f8bda0d32b6b08464f5c5bb963d5dddaf91106339eb97dd859bc/texmanager.py
new file mode 100644
index 0000000000000000000000000000000000000000..6856329931efb369afb2d251682f09b0e273bf50
--- /dev/null
+++ b/shared/package-files/1b/1be28d61b627f8bda0d32b6b08464f5c5bb963d5dddaf91106339eb97dd859bc/texmanager.py
@@ -0,0 +1,369 @@
+r"""
+Support for embedded TeX expressions in Matplotlib.
+
+Requirements:
+
+* LaTeX.
+* \*Agg backends: dvipng>=1.6.
+* PS backend: PSfrag, dvips, and Ghostscript>=9.0.
+* PDF and SVG backends: if LuaTeX is present, it will be used to speed up some
+  post-processing steps, but note that it is not used to parse the TeX string
+  itself (only LaTeX is supported).
+
+To enable TeX rendering of all text in your Matplotlib figure, set
+:rc:`text.usetex` to True.
+
+TeX and dvipng/dvips processing results are cached
+in ~/.matplotlib/tex.cache for reuse between sessions.
+
+`TexManager.get_rgba` can also be used to directly obtain raster output as RGBA
+NumPy arrays.
+"""
+
+import functools
+import hashlib
+import logging
+import os
+from pathlib import Path
+import subprocess
+from tempfile import TemporaryDirectory
+
+import numpy as np
+
+import matplotlib as mpl
+from matplotlib import cbook, dviread
+
+_log = logging.getLogger(__name__)
+
+
+def _usepackage_if_not_loaded(package, *, option=None):
+    """
+    Output LaTeX code that loads a package (possibly with an option) if it
+    hasn't been loaded yet.
+
+    LaTeX cannot load twice a package with different options, so this helper
+    can be used to protect against users loading arbitrary packages/options in
+    their custom preamble.
+    """
+    option = f"[{option}]" if option is not None else ""
+    return (
+        r"\makeatletter"
+        r"\@ifpackageloaded{%(package)s}{}{\usepackage%(option)s{%(package)s}}"
+        r"\makeatother"
+    ) % {"package": package, "option": option}
+
+
+class TexManager:
+    """
+    Convert strings to dvi files using TeX, caching the results to a directory.
+
+    The cache directory is called ``tex.cache`` and is located in the directory
+    returned by `.get_cachedir`.
+
+    Repeated calls to this constructor always return the same instance.
+    """
+
+    _texcache = os.path.join(mpl.get_cachedir(), 'tex.cache')
+    _grey_arrayd = {}
+
+    _font_families = ('serif', 'sans-serif', 'cursive', 'monospace')
+    _font_preambles = {
+        'new century schoolbook': r'\renewcommand{\rmdefault}{pnc}',
+        'bookman': r'\renewcommand{\rmdefault}{pbk}',
+        'times': r'\usepackage{mathptmx}',
+        'palatino': r'\usepackage{mathpazo}',
+        'zapf chancery': r'\usepackage{chancery}',
+        'cursive': r'\usepackage{chancery}',
+        'charter': r'\usepackage{charter}',
+        'serif': '',
+        'sans-serif': '',
+        'helvetica': r'\usepackage{helvet}',
+        'avant garde': r'\usepackage{avant}',
+        'courier': r'\usepackage{courier}',
+        # Loading the type1ec package ensures that cm-super is installed, which
+        # is necessary for Unicode computer modern.  (It also allows the use of
+        # computer modern at arbitrary sizes, but that's just a side effect.)
+        'monospace': r'\usepackage{type1ec}',
+        'computer modern roman': r'\usepackage{type1ec}',
+        'computer modern sans serif': r'\usepackage{type1ec}',
+        'computer modern typewriter': r'\usepackage{type1ec}',
+    }
+    _font_types = {
+        'new century schoolbook': 'serif',
+        'bookman': 'serif',
+        'times': 'serif',
+        'palatino': 'serif',
+        'zapf chancery': 'cursive',
+        'charter': 'serif',
+        'helvetica': 'sans-serif',
+        'avant garde': 'sans-serif',
+        'courier': 'monospace',
+        'computer modern roman': 'serif',
+        'computer modern sans serif': 'sans-serif',
+        'computer modern typewriter': 'monospace',
+    }
+
+    @functools.lru_cache  # Always return the same instance.
+    def __new__(cls):
+        Path(cls._texcache).mkdir(parents=True, exist_ok=True)
+        return object.__new__(cls)
+
+    @classmethod
+    def _get_font_family_and_reduced(cls):
+        """Return the font family name and whether the font is reduced."""
+        ff = mpl.rcParams['font.family']
+        ff_val = ff[0].lower() if len(ff) == 1 else None
+        if len(ff) == 1 and ff_val in cls._font_families:
+            return ff_val, False
+        elif len(ff) == 1 and ff_val in cls._font_preambles:
+            return cls._font_types[ff_val], True
+        else:
+            _log.info('font.family must be one of (%s) when text.usetex is '
+                      'True. serif will be used by default.',
+                      ', '.join(cls._font_families))
+            return 'serif', False
+
+    @classmethod
+    def _get_font_preamble_and_command(cls):
+        requested_family, is_reduced_font = cls._get_font_family_and_reduced()
+
+        preambles = {}
+        for font_family in cls._font_families:
+            if is_reduced_font and font_family == requested_family:
+                preambles[font_family] = cls._font_preambles[
+                    mpl.rcParams['font.family'][0].lower()]
+            else:
+                rcfonts = mpl.rcParams[f"font.{font_family}"]
+                for i, font in enumerate(map(str.lower, rcfonts)):
+                    if font in cls._font_preambles:
+                        preambles[font_family] = cls._font_preambles[font]
+                        _log.debug(
+                            'family: %s, package: %s, font: %s, skipped: %s',
+                            font_family, cls._font_preambles[font], rcfonts[i],
+                            ', '.join(rcfonts[:i]),
+                        )
+                        break
+                else:
+                    _log.info('No LaTeX-compatible font found for the %s font'
+                              'family in rcParams. Using default.',
+                              font_family)
+                    preambles[font_family] = cls._font_preambles[font_family]
+
+        # The following packages and commands need to be included in the latex
+        # file's preamble:
+        cmd = {preambles[family]
+               for family in ['serif', 'sans-serif', 'monospace']}
+        if requested_family == 'cursive':
+            cmd.add(preambles['cursive'])
+        cmd.add(r'\usepackage{type1cm}')
+        preamble = '\n'.join(sorted(cmd))
+        fontcmd = (r'\sffamily' if requested_family == 'sans-serif' else
+                   r'\ttfamily' if requested_family == 'monospace' else
+                   r'\rmfamily')
+        return preamble, fontcmd
+
+    @classmethod
+    def get_basefile(cls, tex, fontsize, dpi=None):
+        """
+        Return a filename based on a hash of the string, fontsize, and dpi.
+        """
+        src = cls._get_tex_source(tex, fontsize) + str(dpi)
+        filehash = hashlib.sha256(
+            src.encode('utf-8'),
+            usedforsecurity=False
+        ).hexdigest()
+        filepath = Path(cls._texcache)
+
+        num_letters, num_levels = 2, 2
+        for i in range(0, num_letters*num_levels, num_letters):
+            filepath = filepath / Path(filehash[i:i+2])
+
+        filepath.mkdir(parents=True, exist_ok=True)
+        return os.path.join(filepath, filehash)
+
+    @classmethod
+    def get_font_preamble(cls):
+        """
+        Return a string containing font configuration for the tex preamble.
+        """
+        font_preamble, command = cls._get_font_preamble_and_command()
+        return font_preamble
+
+    @classmethod
+    def get_custom_preamble(cls):
+        """Return a string containing user additions to the tex preamble."""
+        return mpl.rcParams['text.latex.preamble']
+
+    @classmethod
+    def _get_tex_source(cls, tex, fontsize):
+        """Return the complete TeX source for processing a TeX string."""
+        font_preamble, fontcmd = cls._get_font_preamble_and_command()
+        baselineskip = 1.25 * fontsize
+        return "\n".join([
+            r"\documentclass{article}",
+            r"% Pass-through \mathdefault, which is used in non-usetex mode",
+            r"% to use the default text font but was historically suppressed",
+            r"% in usetex mode.",
+            r"\newcommand{\mathdefault}[1]{#1}",
+            font_preamble,
+            r"\usepackage[utf8]{inputenc}",
+            r"\DeclareUnicodeCharacter{2212}{\ensuremath{-}}",
+            r"% geometry is loaded before the custom preamble as ",
+            r"% convert_psfrags relies on a custom preamble to change the ",
+            r"% geometry.",
+            r"\usepackage[papersize=72in, margin=1in]{geometry}",
+            cls.get_custom_preamble(),
+            r"% Use `underscore` package to take care of underscores in text.",
+            r"% The [strings] option allows to use underscores in file names.",
+            _usepackage_if_not_loaded("underscore", option="strings"),
+            r"% Custom packages (e.g. newtxtext) may already have loaded ",
+            r"% textcomp with different options.",
+            _usepackage_if_not_loaded("textcomp"),
+            r"\pagestyle{empty}",
+            r"\begin{document}",
+            r"% The empty hbox ensures that a page is printed even for empty",
+            r"% inputs, except when using psfrag which gets confused by it.",
+            r"% matplotlibbaselinemarker is used by dviread to detect the",
+            r"% last line's baseline.",
+            rf"\fontsize{{{fontsize}}}{{{baselineskip}}}%",
+            r"\ifdefined\psfrag\else\hbox{}\fi%",
+            rf"{{{fontcmd} {tex}}}%",
+            r"\end{document}",
+        ])
+
+    @classmethod
+    def make_tex(cls, tex, fontsize):
+        """
+        Generate a tex file to render the tex string at a specific font size.
+
+        Return the file name.
+        """
+        texfile = cls.get_basefile(tex, fontsize) + ".tex"
+        Path(texfile).write_text(cls._get_tex_source(tex, fontsize),
+                                 encoding='utf-8')
+        return texfile
+
+    @classmethod
+    def _run_checked_subprocess(cls, command, tex, *, cwd=None):
+        _log.debug(cbook._pformat_subprocess(command))
+        try:
+            report = subprocess.check_output(
+                command, cwd=cwd if cwd is not None else cls._texcache,
+                stderr=subprocess.STDOUT)
+        except FileNotFoundError as exc:
+            raise RuntimeError(
+                f'Failed to process string with tex because {command[0]} '
+                'could not be found') from exc
+        except subprocess.CalledProcessError as exc:
+            raise RuntimeError(
+                '{prog} was not able to process the following string:\n'
+                '{tex!r}\n\n'
+                'Here is the full command invocation and its output:\n\n'
+                '{format_command}\n\n'
+                '{exc}\n\n'.format(
+                    prog=command[0],
+                    format_command=cbook._pformat_subprocess(command),
+                    tex=tex.encode('unicode_escape'),
+                    exc=exc.output.decode('utf-8', 'backslashreplace'))
+                ) from None
+        _log.debug(report)
+        return report
+
+    @classmethod
+    def make_dvi(cls, tex, fontsize):
+        """
+        Generate a dvi file containing latex's layout of tex string.
+
+        Return the file name.
+        """
+        dvifile = Path(cls.get_basefile(tex, fontsize)).with_suffix(".dvi")
+        if not dvifile.exists():
+            # Generate the tex and dvi in a temporary directory to avoid race
+            # conditions e.g. if multiple processes try to process the same tex
+            # string at the same time.  Having tmpdir be a subdirectory of the
+            # final output dir ensures that they are on the same filesystem,
+            # and thus replace() works atomically.  It also allows referring to
+            # the texfile with a relative path (for pathological MPLCONFIGDIRs,
+            # the absolute path may contain characters (e.g. ~) that TeX does
+            # not support; n.b. relative paths cannot traverse parents, or it
+            # will be blocked when `openin_any = p` in texmf.cnf).
+            with TemporaryDirectory(dir=dvifile.parent) as tmpdir:
+                Path(tmpdir, "file.tex").write_text(
+                    cls._get_tex_source(tex, fontsize), encoding='utf-8')
+                cls._run_checked_subprocess(
+                    ["latex", "-interaction=nonstopmode", "-halt-on-error",
+                     "-no-shell-escape", "file.tex"], tex, cwd=tmpdir)
+                Path(tmpdir, "file.dvi").replace(dvifile)
+                # Also move the tex source to the main cache directory, but
+                # only for backcompat.
+                Path(tmpdir, "file.tex").replace(dvifile.with_suffix(".tex"))
+        return str(dvifile)
+
+    @classmethod
+    def make_png(cls, tex, fontsize, dpi):
+        """
+        Generate a png file containing latex's rendering of tex string.
+
+        Return the file name.
+        """
+        pngfile = Path(cls.get_basefile(tex, fontsize, dpi)).with_suffix(".png")
+        # see get_rgba for a discussion of the background
+        if not pngfile.exists():
+            dvifile = cls.make_dvi(tex, fontsize)
+            with TemporaryDirectory(dir=pngfile.parent) as tmpdir:
+                cmd = ["dvipng", "-bg", "Transparent", "-D", str(dpi),
+                       "-T", "tight", "-o", "file.png", dvifile]
+                # When testing, disable FreeType rendering for reproducibility;
+                # but dvipng 1.16 has a bug (fixed in f3ff241) that breaks
+                # --freetype0 mode, so for it we keep FreeType enabled; the
+                # image will be slightly off.
+                if (getattr(mpl, "_called_from_pytest", False) and
+                        mpl._get_executable_info("dvipng").raw_version != "1.16"):
+                    cmd.insert(1, "--freetype0")
+                cls._run_checked_subprocess(cmd, tex, cwd=tmpdir)
+                Path(tmpdir, "file.png").replace(pngfile)
+        return str(pngfile)
+
+    @classmethod
+    def get_grey(cls, tex, fontsize=None, dpi=None):
+        """Return the alpha channel."""
+        if not fontsize:
+            fontsize = mpl.rcParams['font.size']
+        if not dpi:
+            dpi = mpl.rcParams['savefig.dpi']
+        key = cls._get_tex_source(tex, fontsize), dpi
+        alpha = cls._grey_arrayd.get(key)
+        if alpha is None:
+            pngfile = cls.make_png(tex, fontsize, dpi)
+            rgba = mpl.image.imread(os.path.join(cls._texcache, pngfile))
+            cls._grey_arrayd[key] = alpha = rgba[:, :, -1]
+        return alpha
+
+    @classmethod
+    def get_rgba(cls, tex, fontsize=None, dpi=None, rgb=(0, 0, 0)):
+        r"""
+        Return latex's rendering of the tex string as an RGBA array.
+
+        Examples
+        --------
+        >>> texmanager = TexManager()
+        >>> s = r"\TeX\ is $\displaystyle\sum_n\frac{-e^{i\pi}}{2^n}$!"
+        >>> Z = texmanager.get_rgba(s, fontsize=12, dpi=80, rgb=(1, 0, 0))
+        """
+        alpha = cls.get_grey(tex, fontsize, dpi)
+        rgba = np.empty((*alpha.shape, 4))
+        rgba[..., :3] = mpl.colors.to_rgb(rgb)
+        rgba[..., -1] = alpha
+        return rgba
+
+    @classmethod
+    def get_text_width_height_descent(cls, tex, fontsize, renderer=None):
+        """Return width, height and descent of the text."""
+        if tex.strip() == '':
+            return 0, 0, 0
+        dvifile = cls.make_dvi(tex, fontsize)
+        dpi_fraction = renderer.points_to_pixels(1.) if renderer else 1
+        with dviread.Dvi(dvifile, 72 * dpi_fraction) as dvi:
+            page, = dvi
+        # A total height (including the descent) needs to be returned.
+        return page.width, page.height + page.descent, page.descent
diff --git a/shared/package-files/1b/1be4180965a6cc7a973adccb7e84e8be4303d3968fdba3af3149e178a0433685/logit_backward_ops.h b/shared/package-files/1b/1be4180965a6cc7a973adccb7e84e8be4303d3968fdba3af3149e178a0433685/logit_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf8ec41865205e75395500fb7255889af71bd82c
--- /dev/null
+++ b/shared/package-files/1b/1be4180965a6cc7a973adccb7e84e8be4303d3968fdba3af3149e178a0433685/logit_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API logit_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, c10::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::logit_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input);
+};
+
+struct TORCH_API logit_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::logit_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1be5c81c38fc5edbcd8ad07f7c31046894bd8a5591ca3f30eae1a5e3a6406315/cumprod_ops.h b/shared/package-files/1b/1be5c81c38fc5edbcd8ad07f7c31046894bd8a5591ca3f30eae1a5e3a6406315/cumprod_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e16cffd7a30d7c264365794294e1368cf60e1e9
--- /dev/null
+++ b/shared/package-files/1b/1be5c81c38fc5edbcd8ad07f7c31046894bd8a5591ca3f30eae1a5e3a6406315/cumprod_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API cumprod {
+  using schema = at::Tensor (const at::Tensor &, int64_t, c10::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API cumprod_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, c10::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API cumprod_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, c10::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+struct TORCH_API cumprod_dimname {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname, c10::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dimname")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API cumprod__dimname {
+  using schema = at::Tensor & (at::Tensor &, at::Dimname, c10::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dimname")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API cumprod_dimname_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Dimname, c10::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cumprod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dimname_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/1b/1be6b1c7e23be41041fe3fe44f2a1e4a6cad42566fb9b38b49ce816807f09f80/emitter.py b/shared/package-files/1b/1be6b1c7e23be41041fe3fe44f2a1e4a6cad42566fb9b38b49ce816807f09f80/emitter.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9611ee1918919550cb1f97a37c81098cb2bc518
--- /dev/null
+++ b/shared/package-files/1b/1be6b1c7e23be41041fe3fe44f2a1e4a6cad42566fb9b38b49ce816807f09f80/emitter.py
@@ -0,0 +1,1772 @@
+# coding: utf-8
+
+# Emitter expects events obeying the following grammar:
+# stream ::= STREAM-START document* STREAM-END
+# document ::= DOCUMENT-START node DOCUMENT-END
+# node ::= SCALAR | sequence | mapping
+# sequence ::= SEQUENCE-START node* SEQUENCE-END
+# mapping ::= MAPPING-START (node node)* MAPPING-END
+
+import sys
+from ruamel.yaml.error import YAMLError, YAMLStreamError
+from ruamel.yaml.events import *  # NOQA
+
+# fmt: off
+from ruamel.yaml.compat import _F, nprint, dbg, DBG_EVENT, \
+    check_anchorname_char, nprintf  # NOQA
+# fmt: on
+
+if False:  # MYPY
+    from typing import Any, Dict, List, Union, Text, Tuple, Optional  # NOQA
+    from ruamel.yaml.compat import StreamType  # NOQA
+
+__all__ = ['Emitter', 'EmitterError']
+
+
+class EmitterError(YAMLError):
+    pass
+
+
+class ScalarAnalysis:
+    def __init__(
+        self,
+        scalar,
+        empty,
+        multiline,
+        allow_flow_plain,
+        allow_block_plain,
+        allow_single_quoted,
+        allow_double_quoted,
+        allow_block,
+    ):
+        # type: (Any, Any, Any, bool, bool, bool, bool, bool) -> None
+        self.scalar = scalar
+        self.empty = empty
+        self.multiline = multiline
+        self.allow_flow_plain = allow_flow_plain
+        self.allow_block_plain = allow_block_plain
+        self.allow_single_quoted = allow_single_quoted
+        self.allow_double_quoted = allow_double_quoted
+        self.allow_block = allow_block
+
+
+class Indents:
+    # replacement for the list based stack of None/int
+    def __init__(self):
+        # type: () -> None
+        self.values = []  # type: List[Tuple[Any, bool]]
+
+    def append(self, val, seq):
+        # type: (Any, Any) -> None
+        self.values.append((val, seq))
+
+    def pop(self):
+        # type: () -> Any
+        return self.values.pop()[0]
+
+    def last_seq(self):
+        # type: () -> bool
+        # return the seq(uence) value for the element added before the last one
+        # in increase_indent()
+        try:
+            return self.values[-2][1]
+        except IndexError:
+            return False
+
+    def seq_flow_align(self, seq_indent, column, pre_comment=False):
+        # type: (int, int, Optional[bool]) -> int
+        # extra spaces because of dash
+        # nprint('seq_flow_align', self.values, pre_comment)
+        if len(self.values) < 2 or not self.values[-1][1]:
+            if len(self.values) == 0 or not pre_comment:
+                return 0
+        base = self.values[-1][0] if self.values[-1][0] is not None else 0
+        if pre_comment:
+            return base + seq_indent  # type: ignore
+            # return (len(self.values)) * seq_indent
+        # -1 for the dash
+        return base + seq_indent - column - 1  # type: ignore
+
+    def __len__(self):
+        # type: () -> int
+        return len(self.values)
+
+
+class Emitter:
+    # fmt: off
+    DEFAULT_TAG_PREFIXES = {
+        '!': '!',
+        'tag:yaml.org,2002:': '!!',
+    }
+    # fmt: on
+
+    MAX_SIMPLE_KEY_LENGTH = 128
+
+    def __init__(
+        self,
+        stream,
+        canonical=None,
+        indent=None,
+        width=None,
+        allow_unicode=None,
+        line_break=None,
+        block_seq_indent=None,
+        top_level_colon_align=None,
+        prefix_colon=None,
+        brace_single_entry_mapping_in_flow_sequence=None,
+        dumper=None,
+    ):
+        # type: (StreamType, Any, Optional[int], Optional[int], Optional[bool], Any, Optional[int], Optional[bool], Any, Optional[bool], Any) -> None  # NOQA
+        self.dumper = dumper
+        if self.dumper is not None and getattr(self.dumper, '_emitter', None) is None:
+            self.dumper._emitter = self
+        self.stream = stream
+
+        # Encoding can be overriden by STREAM-START.
+        self.encoding = None  # type: Optional[Text]
+        self.allow_space_break = None
+
+        # Emitter is a state machine with a stack of states to handle nested
+        # structures.
+        self.states = []  # type: List[Any]
+        self.state = self.expect_stream_start  # type: Any
+
+        # Current event and the event queue.
+        self.events = []  # type: List[Any]
+        self.event = None  # type: Any
+
+        # The current indentation level and the stack of previous indents.
+        self.indents = Indents()
+        self.indent = None  # type: Optional[int]
+
+        # flow_context is an expanding/shrinking list consisting of '{' and '['
+        # for each unclosed flow context. If empty list that means block context
+        self.flow_context = []  # type: List[Text]
+
+        # Contexts.
+        self.root_context = False
+        self.sequence_context = False
+        self.mapping_context = False
+        self.simple_key_context = False
+
+        # Characteristics of the last emitted character:
+        #  - current position.
+        #  - is it a whitespace?
+        #  - is it an indention character
+        #    (indentation space, '-', '?', or ':')?
+        self.line = 0
+        self.column = 0
+        self.whitespace = True
+        self.indention = True
+        self.compact_seq_seq = True  # dash after dash
+        self.compact_seq_map = True  # key after dash
+        # self.compact_ms = False   # dash after key, only when excplicit key with ?
+        self.no_newline = None  # type: Optional[bool]  # set if directly after `- `
+
+        # Whether the document requires an explicit document end indicator
+        self.open_ended = False
+
+        # colon handling
+        self.colon = ':'
+        self.prefixed_colon = self.colon if prefix_colon is None else prefix_colon + self.colon
+        # single entry mappings in flow sequence
+        self.brace_single_entry_mapping_in_flow_sequence = (
+            brace_single_entry_mapping_in_flow_sequence  # NOQA
+        )
+
+        # Formatting details.
+        self.canonical = canonical
+        self.allow_unicode = allow_unicode
+        # set to False to get "\Uxxxxxxxx" for non-basic unicode like emojis
+        self.unicode_supplementary = sys.maxunicode > 0xFFFF
+        self.sequence_dash_offset = block_seq_indent if block_seq_indent else 0
+        self.top_level_colon_align = top_level_colon_align
+        self.best_sequence_indent = 2
+        self.requested_indent = indent  # specific for literal zero indent
+        if indent and 1 < indent < 10:
+            self.best_sequence_indent = indent
+        self.best_map_indent = self.best_sequence_indent
+        # if self.best_sequence_indent < self.sequence_dash_offset + 1:
+        #     self.best_sequence_indent = self.sequence_dash_offset + 1
+        self.best_width = 80
+        if width and width > self.best_sequence_indent * 2:
+            self.best_width = width
+        self.best_line_break = '\n'  # type: Any
+        if line_break in ['\r', '\n', '\r\n']:
+            self.best_line_break = line_break
+
+        # Tag prefixes.
+        self.tag_prefixes = None  # type: Any
+
+        # Prepared anchor and tag.
+        self.prepared_anchor = None  # type: Any
+        self.prepared_tag = None  # type: Any
+
+        # Scalar analysis and style.
+        self.analysis = None  # type: Any
+        self.style = None  # type: Any
+
+        self.scalar_after_indicator = True  # write a scalar on the same line as `---`
+
+        self.alt_null = 'null'
+
+    @property
+    def stream(self):
+        # type: () -> Any
+        try:
+            return self._stream
+        except AttributeError:
+            raise YAMLStreamError('output stream needs to specified')
+
+    @stream.setter
+    def stream(self, val):
+        # type: (Any) -> None
+        if val is None:
+            return
+        if not hasattr(val, 'write'):
+            raise YAMLStreamError('stream argument needs to have a write() method')
+        self._stream = val
+
+    @property
+    def serializer(self):
+        # type: () -> Any
+        try:
+            if hasattr(self.dumper, 'typ'):
+                return self.dumper.serializer
+            return self.dumper._serializer
+        except AttributeError:
+            return self  # cyaml
+
+    @property
+    def flow_level(self):
+        # type: () -> int
+        return len(self.flow_context)
+
+    def dispose(self):
+        # type: () -> None
+        # Reset the state attributes (to clear self-references)
+        self.states = []
+        self.state = None
+
+    def emit(self, event):
+        # type: (Any) -> None
+        if dbg(DBG_EVENT):
+            nprint(event)
+        self.events.append(event)
+        while not self.need_more_events():
+            self.event = self.events.pop(0)
+            self.state()
+            self.event = None
+
+    # In some cases, we wait for a few next events before emitting.
+
+    def need_more_events(self):
+        # type: () -> bool
+        if not self.events:
+            return True
+        event = self.events[0]
+        if isinstance(event, DocumentStartEvent):
+            return self.need_events(1)
+        elif isinstance(event, SequenceStartEvent):
+            return self.need_events(2)
+        elif isinstance(event, MappingStartEvent):
+            return self.need_events(3)
+        else:
+            return False
+
+    def need_events(self, count):
+        # type: (int) -> bool
+        level = 0
+        for event in self.events[1:]:
+            if isinstance(event, (DocumentStartEvent, CollectionStartEvent)):
+                level += 1
+            elif isinstance(event, (DocumentEndEvent, CollectionEndEvent)):
+                level -= 1
+            elif isinstance(event, StreamEndEvent):
+                level = -1
+            if level < 0:
+                return False
+        return len(self.events) < count + 1
+
+    def increase_indent(self, flow=False, sequence=None, indentless=False):
+        # type: (bool, Optional[bool], bool) -> None
+        self.indents.append(self.indent, sequence)
+        if self.indent is None:  # top level
+            if flow:
+                # self.indent = self.best_sequence_indent if self.indents.last_seq() else \
+                #              self.best_map_indent
+                # self.indent = self.best_sequence_indent
+                self.indent = self.requested_indent
+            else:
+                self.indent = 0
+        elif not indentless:
+            self.indent += (
+                self.best_sequence_indent if self.indents.last_seq() else self.best_map_indent
+            )
+            # if self.indents.last_seq():
+            #     if self.indent == 0: # top level block sequence
+            #         self.indent = self.best_sequence_indent - self.sequence_dash_offset
+            #     else:
+            #         self.indent += self.best_sequence_indent
+            # else:
+            #     self.indent += self.best_map_indent
+
+    # States.
+
+    # Stream handlers.
+
+    def expect_stream_start(self):
+        # type: () -> None
+        if isinstance(self.event, StreamStartEvent):
+            if self.event.encoding and not hasattr(self.stream, 'encoding'):
+                self.encoding = self.event.encoding
+            self.write_stream_start()
+            self.state = self.expect_first_document_start
+        else:
+            raise EmitterError(
+                _F('expected StreamStartEvent, but got {self_event!s}', self_event=self.event)
+            )
+
+    def expect_nothing(self):
+        # type: () -> None
+        raise EmitterError(
+            _F('expected nothing, but got {self_event!s}', self_event=self.event)
+        )
+
+    # Document handlers.
+
+    def expect_first_document_start(self):
+        # type: () -> Any
+        return self.expect_document_start(first=True)
+
+    def expect_document_start(self, first=False):
+        # type: (bool) -> None
+        if isinstance(self.event, DocumentStartEvent):
+            if (self.event.version or self.event.tags) and self.open_ended:
+                self.write_indicator('...', True)
+                self.write_indent()
+            if self.event.version:
+                version_text = self.prepare_version(self.event.version)
+                self.write_version_directive(version_text)
+            self.tag_prefixes = self.DEFAULT_TAG_PREFIXES.copy()
+            if self.event.tags:
+                handles = sorted(self.event.tags.keys())
+                for handle in handles:
+                    prefix = self.event.tags[handle]
+                    self.tag_prefixes[prefix] = handle
+                    handle_text = self.prepare_tag_handle(handle)
+                    prefix_text = self.prepare_tag_prefix(prefix)
+                    self.write_tag_directive(handle_text, prefix_text)
+            implicit = (
+                first
+                and not self.event.explicit
+                and not self.canonical
+                and not self.event.version
+                and not self.event.tags
+                and not self.check_empty_document()
+            )
+            if not implicit:
+                self.write_indent()
+                self.write_indicator('---', True)
+                if self.canonical:
+                    self.write_indent()
+            self.state = self.expect_document_root
+        elif isinstance(self.event, StreamEndEvent):
+            if self.open_ended:
+                self.write_indicator('...', True)
+                self.write_indent()
+            self.write_stream_end()
+            self.state = self.expect_nothing
+        else:
+            raise EmitterError(
+                _F(
+                    'expected DocumentStartEvent, but got {self_event!s}',
+                    self_event=self.event,
+                )
+            )
+
+    def expect_document_end(self):
+        # type: () -> None
+        if isinstance(self.event, DocumentEndEvent):
+            self.write_indent()
+            if self.event.explicit:
+                self.write_indicator('...', True)
+                self.write_indent()
+            self.flush_stream()
+            self.state = self.expect_document_start
+        else:
+            raise EmitterError(
+                _F('expected DocumentEndEvent, but got {self_event!s}', self_event=self.event)
+            )
+
+    def expect_document_root(self):
+        # type: () -> None
+        self.states.append(self.expect_document_end)
+        self.expect_node(root=True)
+
+    # Node handlers.
+
+    def expect_node(self, root=False, sequence=False, mapping=False, simple_key=False):
+        # type: (bool, bool, bool, bool) -> None
+        self.root_context = root
+        self.sequence_context = sequence  # not used in PyYAML
+        force_flow_indent = False
+        self.mapping_context = mapping
+        self.simple_key_context = simple_key
+        if isinstance(self.event, AliasEvent):
+            self.expect_alias()
+        elif isinstance(self.event, (ScalarEvent, CollectionStartEvent)):
+            if (
+                self.process_anchor('&')
+                and isinstance(self.event, ScalarEvent)
+                and self.sequence_context
+            ):
+                self.sequence_context = False
+            if (
+                root
+                and isinstance(self.event, ScalarEvent)
+                and not self.scalar_after_indicator
+            ):
+                self.write_indent()
+            self.process_tag()
+            if isinstance(self.event, ScalarEvent):
+                # nprint('@', self.indention, self.no_newline, self.column)
+                self.expect_scalar()
+            elif isinstance(self.event, SequenceStartEvent):
+                # nprint('@', self.indention, self.no_newline, self.column)
+                i2, n2 = self.indention, self.no_newline  # NOQA
+                if self.event.comment:
+                    if self.event.flow_style is False:
+                        if self.write_post_comment(self.event):
+                            self.indention = False
+                            self.no_newline = True
+                    if self.event.flow_style:
+                        column = self.column
+                    if self.write_pre_comment(self.event):
+                        if self.event.flow_style:
+                            # force_flow_indent = True
+                            force_flow_indent = not self.indents.values[-1][1]
+                        self.indention = i2
+                        self.no_newline = not self.indention
+                    if self.event.flow_style:
+                        self.column = column
+                if (
+                    self.flow_level
+                    or self.canonical
+                    or self.event.flow_style
+                    or self.check_empty_sequence()
+                ):
+                    self.expect_flow_sequence(force_flow_indent)
+                else:
+                    self.expect_block_sequence()
+            elif isinstance(self.event, MappingStartEvent):
+                if self.event.flow_style is False and self.event.comment:
+                    self.write_post_comment(self.event)
+                if self.event.comment and self.event.comment[1]:
+                    self.write_pre_comment(self.event)
+                    if self.event.flow_style:
+                        force_flow_indent = not self.indents.values[-1][1]
+                if (
+                    self.flow_level
+                    or self.canonical
+                    or self.event.flow_style
+                    or self.check_empty_mapping()
+                ):
+                    self.expect_flow_mapping(single=self.event.nr_items == 1,
+                                             force_flow_indent=force_flow_indent)
+                else:
+                    self.expect_block_mapping()
+        else:
+            raise EmitterError(
+                _F('expected NodeEvent, but got {self_event!s}', self_event=self.event)
+            )
+
+    def expect_alias(self):
+        # type: () -> None
+        if self.event.anchor is None:
+            raise EmitterError('anchor is not specified for alias')
+        self.process_anchor('*')
+        self.state = self.states.pop()
+
+    def expect_scalar(self):
+        # type: () -> None
+        self.increase_indent(flow=True)
+        self.process_scalar()
+        self.indent = self.indents.pop()
+        self.state = self.states.pop()
+
+    # Flow sequence handlers.
+
+    def expect_flow_sequence(self, force_flow_indent=False):
+        # type: (Optional[bool]) -> None
+        if force_flow_indent:
+            self.increase_indent(flow=True, sequence=True)
+        ind = self.indents.seq_flow_align(self.best_sequence_indent, self.column,
+                                          force_flow_indent)
+        self.write_indicator(' ' * ind + '[', True, whitespace=True)
+        if not force_flow_indent:
+            self.increase_indent(flow=True, sequence=True)
+        self.flow_context.append('[')
+        self.state = self.expect_first_flow_sequence_item
+
+    def expect_first_flow_sequence_item(self):
+        # type: () -> None
+        if isinstance(self.event, SequenceEndEvent):
+            self.indent = self.indents.pop()
+            popped = self.flow_context.pop()
+            assert popped == '['
+            self.write_indicator(']', False)
+            if self.event.comment and self.event.comment[0]:
+                # eol comment on empty flow sequence
+                self.write_post_comment(self.event)
+            elif self.flow_level == 0:
+                self.write_line_break()
+            self.state = self.states.pop()
+        else:
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            self.states.append(self.expect_flow_sequence_item)
+            self.expect_node(sequence=True)
+
+    def expect_flow_sequence_item(self):
+        # type: () -> None
+        if isinstance(self.event, SequenceEndEvent):
+            self.indent = self.indents.pop()
+            popped = self.flow_context.pop()
+            assert popped == '['
+            if self.canonical:
+                self.write_indicator(',', False)
+                self.write_indent()
+            self.write_indicator(']', False)
+            if self.event.comment and self.event.comment[0]:
+                # eol comment on flow sequence
+                self.write_post_comment(self.event)
+            else:
+                self.no_newline = False
+            self.state = self.states.pop()
+        else:
+            self.write_indicator(',', False)
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            self.states.append(self.expect_flow_sequence_item)
+            self.expect_node(sequence=True)
+
+    # Flow mapping handlers.
+
+    def expect_flow_mapping(self, single=False, force_flow_indent=False):
+        # type: (Optional[bool], Optional[bool]) -> None
+        if force_flow_indent:
+            self.increase_indent(flow=True, sequence=False)
+        ind = self.indents.seq_flow_align(self.best_sequence_indent, self.column,
+                                          force_flow_indent)
+        map_init = '{'
+        if (
+            single
+            and self.flow_level
+            and self.flow_context[-1] == '['
+            and not self.canonical
+            and not self.brace_single_entry_mapping_in_flow_sequence
+        ):
+            # single map item with flow context, no curly braces necessary
+            map_init = ''
+        self.write_indicator(' ' * ind + map_init, True, whitespace=True)
+        self.flow_context.append(map_init)
+        if not force_flow_indent:
+            self.increase_indent(flow=True, sequence=False)
+        self.state = self.expect_first_flow_mapping_key
+
+    def expect_first_flow_mapping_key(self):
+        # type: () -> None
+        if isinstance(self.event, MappingEndEvent):
+            self.indent = self.indents.pop()
+            popped = self.flow_context.pop()
+            assert popped == '{'  # empty flow mapping
+            self.write_indicator('}', False)
+            if self.event.comment and self.event.comment[0]:
+                # eol comment on empty mapping
+                self.write_post_comment(self.event)
+            elif self.flow_level == 0:
+                self.write_line_break()
+            self.state = self.states.pop()
+        else:
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            if not self.canonical and self.check_simple_key():
+                self.states.append(self.expect_flow_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+            else:
+                self.write_indicator('?', True)
+                self.states.append(self.expect_flow_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_flow_mapping_key(self):
+        # type: () -> None
+        if isinstance(self.event, MappingEndEvent):
+            # if self.event.comment and self.event.comment[1]:
+            #     self.write_pre_comment(self.event)
+            self.indent = self.indents.pop()
+            popped = self.flow_context.pop()
+            assert popped in ['{', '']
+            if self.canonical:
+                self.write_indicator(',', False)
+                self.write_indent()
+            if popped != '':
+                self.write_indicator('}', False)
+            if self.event.comment and self.event.comment[0]:
+                # eol comment on flow mapping, never reached on empty mappings
+                self.write_post_comment(self.event)
+            else:
+                self.no_newline = False
+            self.state = self.states.pop()
+        else:
+            self.write_indicator(',', False)
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            if not self.canonical and self.check_simple_key():
+                self.states.append(self.expect_flow_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+            else:
+                self.write_indicator('?', True)
+                self.states.append(self.expect_flow_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_flow_mapping_simple_value(self):
+        # type: () -> None
+        self.write_indicator(self.prefixed_colon, False)
+        self.states.append(self.expect_flow_mapping_key)
+        self.expect_node(mapping=True)
+
+    def expect_flow_mapping_value(self):
+        # type: () -> None
+        if self.canonical or self.column > self.best_width:
+            self.write_indent()
+        self.write_indicator(self.prefixed_colon, True)
+        self.states.append(self.expect_flow_mapping_key)
+        self.expect_node(mapping=True)
+
+    # Block sequence handlers.
+
+    def expect_block_sequence(self):
+        # type: () -> None
+        if self.mapping_context:
+            indentless = not self.indention
+        else:
+            indentless = False
+            if not self.compact_seq_seq and self.column != 0:
+                self.write_line_break()
+        self.increase_indent(flow=False, sequence=True, indentless=indentless)
+        self.state = self.expect_first_block_sequence_item
+
+    def expect_first_block_sequence_item(self):
+        # type: () -> Any
+        return self.expect_block_sequence_item(first=True)
+
+    def expect_block_sequence_item(self, first=False):
+        # type: (bool) -> None
+        if not first and isinstance(self.event, SequenceEndEvent):
+            if self.event.comment and self.event.comment[1]:
+                # final comments on a block list e.g. empty line
+                self.write_pre_comment(self.event)
+            self.indent = self.indents.pop()
+            self.state = self.states.pop()
+            self.no_newline = False
+        else:
+            if self.event.comment and self.event.comment[1]:
+                self.write_pre_comment(self.event)
+            nonl = self.no_newline if self.column == 0 else False
+            self.write_indent()
+            ind = self.sequence_dash_offset  # if  len(self.indents) > 1 else 0
+            self.write_indicator(' ' * ind + '-', True, indention=True)
+            if nonl or self.sequence_dash_offset + 2 > self.best_sequence_indent:
+                self.no_newline = True
+            self.states.append(self.expect_block_sequence_item)
+            self.expect_node(sequence=True)
+
+    # Block mapping handlers.
+
+    def expect_block_mapping(self):
+        # type: () -> None
+        if not self.mapping_context and not (self.compact_seq_map or self.column == 0):
+            self.write_line_break()
+        self.increase_indent(flow=False, sequence=False)
+        self.state = self.expect_first_block_mapping_key
+
+    def expect_first_block_mapping_key(self):
+        # type: () -> None
+        return self.expect_block_mapping_key(first=True)
+
+    def expect_block_mapping_key(self, first=False):
+        # type: (Any) -> None
+        if not first and isinstance(self.event, MappingEndEvent):
+            if self.event.comment and self.event.comment[1]:
+                # final comments from a doc
+                self.write_pre_comment(self.event)
+            self.indent = self.indents.pop()
+            self.state = self.states.pop()
+        else:
+            if self.event.comment and self.event.comment[1]:
+                # final comments from a doc
+                self.write_pre_comment(self.event)
+            self.write_indent()
+            if self.check_simple_key():
+                if not isinstance(
+                    self.event, (SequenceStartEvent, MappingStartEvent)
+                ):  # sequence keys
+                    try:
+                        if self.event.style == '?':
+                            self.write_indicator('?', True, indention=True)
+                    except AttributeError:  # aliases have no style
+                        pass
+                self.states.append(self.expect_block_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+                # test on style for alias in !!set
+                if isinstance(self.event, AliasEvent) and not self.event.style == '?':
+                    self.stream.write(' ')
+            else:
+                self.write_indicator('?', True, indention=True)
+                self.states.append(self.expect_block_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_block_mapping_simple_value(self):
+        # type: () -> None
+        if getattr(self.event, 'style', None) != '?':
+            # prefix = ''
+            if self.indent == 0 and self.top_level_colon_align is not None:
+                # write non-prefixed colon
+                c = ' ' * (self.top_level_colon_align - self.column) + self.colon
+            else:
+                c = self.prefixed_colon
+            self.write_indicator(c, False)
+        self.states.append(self.expect_block_mapping_key)
+        self.expect_node(mapping=True)
+
+    def expect_block_mapping_value(self):
+        # type: () -> None
+        self.write_indent()
+        self.write_indicator(self.prefixed_colon, True, indention=True)
+        self.states.append(self.expect_block_mapping_key)
+        self.expect_node(mapping=True)
+
+    # Checkers.
+
+    def check_empty_sequence(self):
+        # type: () -> bool
+        return (
+            isinstance(self.event, SequenceStartEvent)
+            and bool(self.events)
+            and isinstance(self.events[0], SequenceEndEvent)
+        )
+
+    def check_empty_mapping(self):
+        # type: () -> bool
+        return (
+            isinstance(self.event, MappingStartEvent)
+            and bool(self.events)
+            and isinstance(self.events[0], MappingEndEvent)
+        )
+
+    def check_empty_document(self):
+        # type: () -> bool
+        if not isinstance(self.event, DocumentStartEvent) or not self.events:
+            return False
+        event = self.events[0]
+        return (
+            isinstance(event, ScalarEvent)
+            and event.anchor is None
+            and event.tag is None
+            and event.implicit
+            and event.value == ""
+        )
+
+    def check_simple_key(self):
+        # type: () -> bool
+        length = 0
+        if isinstance(self.event, NodeEvent) and self.event.anchor is not None:
+            if self.prepared_anchor is None:
+                self.prepared_anchor = self.prepare_anchor(self.event.anchor)
+            length += len(self.prepared_anchor)
+        if (
+            isinstance(self.event, (ScalarEvent, CollectionStartEvent))
+            and self.event.tag is not None
+        ):
+            if self.prepared_tag is None:
+                self.prepared_tag = self.prepare_tag(self.event.tag)
+            length += len(self.prepared_tag)
+        if isinstance(self.event, ScalarEvent):
+            if self.analysis is None:
+                self.analysis = self.analyze_scalar(self.event.value)
+            length += len(self.analysis.scalar)
+        return length < self.MAX_SIMPLE_KEY_LENGTH and (
+            isinstance(self.event, AliasEvent)
+            or (isinstance(self.event, SequenceStartEvent) and self.event.flow_style is True)
+            or (isinstance(self.event, MappingStartEvent) and self.event.flow_style is True)
+            or (
+                isinstance(self.event, ScalarEvent)
+                # if there is an explicit style for an empty string, it is a simple key
+                and not (self.analysis.empty and self.style and self.style not in '\'"')
+                and not self.analysis.multiline
+            )
+            or self.check_empty_sequence()
+            or self.check_empty_mapping()
+        )
+
+    # Anchor, Tag, and Scalar processors.
+
+    def process_anchor(self, indicator):
+        # type: (Any) -> bool
+        if self.event.anchor is None:
+            self.prepared_anchor = None
+            return False
+        if self.prepared_anchor is None:
+            self.prepared_anchor = self.prepare_anchor(self.event.anchor)
+        if self.prepared_anchor:
+            self.write_indicator(indicator + self.prepared_anchor, True)
+            # issue 288
+            self.no_newline = False
+        self.prepared_anchor = None
+        return True
+
+    def process_tag(self):
+        # type: () -> None
+        tag = self.event.tag
+        if isinstance(self.event, ScalarEvent):
+            if self.style is None:
+                self.style = self.choose_scalar_style()
+                if (
+                    self.event.value == ''
+                    and self.style == "'"
+                    and tag == 'tag:yaml.org,2002:null'
+                    and self.alt_null is not None
+                ):
+                    self.event.value = self.alt_null
+                    self.analysis = None
+                    self.style = self.choose_scalar_style()
+            if (not self.canonical or tag is None) and (
+                (self.style == "" and self.event.implicit[0])
+                or (self.style != "" and self.event.implicit[1])
+            ):
+                self.prepared_tag = None
+                return
+            if self.event.implicit[0] and tag is None:
+                tag = '!'
+                self.prepared_tag = None
+        else:
+            if (not self.canonical or tag is None) and self.event.implicit:
+                self.prepared_tag = None
+                return
+        if tag is None:
+            raise EmitterError('tag is not specified')
+        if self.prepared_tag is None:
+            self.prepared_tag = self.prepare_tag(tag)
+        if self.prepared_tag:
+            self.write_indicator(self.prepared_tag, True)
+            if (
+                self.sequence_context
+                and not self.flow_level
+                and isinstance(self.event, ScalarEvent)
+            ):
+                self.no_newline = True
+        self.prepared_tag = None
+
+    def choose_scalar_style(self):
+        # type: () -> Any
+        if self.analysis is None:
+            self.analysis = self.analyze_scalar(self.event.value)
+        if self.event.style == '"' or self.canonical:
+            return '"'
+        if (not self.event.style or self.event.style == '?') and (
+            self.event.implicit[0] or not self.event.implicit[2]
+        ):
+            if not (
+                self.simple_key_context and (self.analysis.empty or self.analysis.multiline)
+            ) and (
+                self.flow_level
+                and self.analysis.allow_flow_plain
+                or (not self.flow_level and self.analysis.allow_block_plain)
+            ):
+                return ""
+        self.analysis.allow_block = True
+        if self.event.style and self.event.style in '|>':
+            if (
+                not self.flow_level
+                and not self.simple_key_context
+                and self.analysis.allow_block
+            ):
+                return self.event.style
+        if not self.event.style and self.analysis.allow_double_quoted:
+            if "'" in self.event.value or '\n' in self.event.value:
+                return '"'
+        if not self.event.style or self.event.style == "'":
+            if self.analysis.allow_single_quoted and not (
+                self.simple_key_context and self.analysis.multiline
+            ):
+                return "'"
+        return '"'
+
+    def process_scalar(self):
+        # type: () -> None
+        if self.analysis is None:
+            self.analysis = self.analyze_scalar(self.event.value)
+        if self.style is None:
+            self.style = self.choose_scalar_style()
+        split = not self.simple_key_context
+        # if self.analysis.multiline and split    \
+        #         and (not self.style or self.style in '\'\"'):
+        #     self.write_indent()
+        # nprint('xx', self.sequence_context, self.flow_level)
+        if self.sequence_context and not self.flow_level:
+            self.write_indent()
+        if self.style == '"':
+            self.write_double_quoted(self.analysis.scalar, split)
+        elif self.style == "'":
+            self.write_single_quoted(self.analysis.scalar, split)
+        elif self.style == '>':
+            self.write_folded(self.analysis.scalar)
+            if (
+                self.event.comment
+                and self.event.comment[0]
+                and self.event.comment[0].column >= self.indent
+            ):
+                # comment following a folded scalar must dedent (issue 376)
+                self.event.comment[0].column = self.indent - 1  # type: ignore
+        elif self.style == '|':
+            # self.write_literal(self.analysis.scalar, self.event.comment)
+            try:
+                cmx = self.event.comment[1][0]
+            except (IndexError, TypeError):
+                cmx = ""
+            self.write_literal(self.analysis.scalar, cmx)
+            if (
+                self.event.comment
+                and self.event.comment[0]
+                and self.event.comment[0].column >= self.indent
+            ):
+                # comment following a literal scalar must dedent (issue 376)
+                self.event.comment[0].column = self.indent - 1  # type: ignore
+        else:
+            self.write_plain(self.analysis.scalar, split)
+        self.analysis = None
+        self.style = None
+        if self.event.comment:
+            self.write_post_comment(self.event)
+
+    # Analyzers.
+
+    def prepare_version(self, version):
+        # type: (Any) -> Any
+        major, minor = version
+        if major != 1:
+            raise EmitterError(
+                _F('unsupported YAML version: {major:d}.{minor:d}', major=major, minor=minor)
+            )
+        return _F('{major:d}.{minor:d}', major=major, minor=minor)
+
+    def prepare_tag_handle(self, handle):
+        # type: (Any) -> Any
+        if not handle:
+            raise EmitterError('tag handle must not be empty')
+        if handle[0] != '!' or handle[-1] != '!':
+            raise EmitterError(
+                _F("tag handle must start and end with '!': {handle!r}", handle=handle)
+            )
+        for ch in handle[1:-1]:
+            if not ('0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z' or ch in '-_'):
+                raise EmitterError(
+                    _F(
+                        'invalid character {ch!r} in the tag handle: {handle!r}',
+                        ch=ch,
+                        handle=handle,
+                    )
+                )
+        return handle
+
+    def prepare_tag_prefix(self, prefix):
+        # type: (Any) -> Any
+        if not prefix:
+            raise EmitterError('tag prefix must not be empty')
+        chunks = []  # type: List[Any]
+        start = end = 0
+        if prefix[0] == '!':
+            end = 1
+        ch_set = "-;/?:@&=+$,_.~*'()[]"
+        if self.dumper:
+            version = getattr(self.dumper, 'version', (1, 2))
+            if version is None or version >= (1, 2):
+                ch_set += '#'
+        while end < len(prefix):
+            ch = prefix[end]
+            if '0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z' or ch in ch_set:
+                end += 1
+            else:
+                if start < end:
+                    chunks.append(prefix[start:end])
+                start = end = end + 1
+                data = ch
+                for ch in data:
+                    chunks.append(_F('%{ord_ch:02X}', ord_ch=ord(ch)))
+        if start < end:
+            chunks.append(prefix[start:end])
+        return "".join(chunks)
+
+    def prepare_tag(self, tag):
+        # type: (Any) -> Any
+        if not tag:
+            raise EmitterError('tag must not be empty')
+        if tag == '!':
+            return tag
+        handle = None
+        suffix = tag
+        prefixes = sorted(self.tag_prefixes.keys())
+        for prefix in prefixes:
+            if tag.startswith(prefix) and (prefix == '!' or len(prefix) < len(tag)):
+                handle = self.tag_prefixes[prefix]
+                suffix = tag[len(prefix) :]
+        chunks = []  # type: List[Any]
+        start = end = 0
+        ch_set = "-;/?:@&=+$,_.~*'()[]"
+        if self.dumper:
+            version = getattr(self.dumper, 'version', (1, 2))
+            if version is None or version >= (1, 2):
+                ch_set += '#'
+        while end < len(suffix):
+            ch = suffix[end]
+            if (
+                '0' <= ch <= '9'
+                or 'A' <= ch <= 'Z'
+                or 'a' <= ch <= 'z'
+                or ch in ch_set
+                or (ch == '!' and handle != '!')
+            ):
+                end += 1
+            else:
+                if start < end:
+                    chunks.append(suffix[start:end])
+                start = end = end + 1
+                data = ch
+                for ch in data:
+                    chunks.append(_F('%{ord_ch:02X}', ord_ch=ord(ch)))
+        if start < end:
+            chunks.append(suffix[start:end])
+        suffix_text = "".join(chunks)
+        if handle:
+            return _F('{handle!s}{suffix_text!s}', handle=handle, suffix_text=suffix_text)
+        else:
+            return _F('!<{suffix_text!s}>', suffix_text=suffix_text)
+
+    def prepare_anchor(self, anchor):
+        # type: (Any) -> Any
+        if not anchor:
+            raise EmitterError('anchor must not be empty')
+        for ch in anchor:
+            if not check_anchorname_char(ch):
+                raise EmitterError(
+                    _F(
+                        'invalid character {ch!r} in the anchor: {anchor!r}',
+                        ch=ch,
+                        anchor=anchor,
+                    )
+                )
+        return anchor
+
+    def analyze_scalar(self, scalar):
+        # type: (Any) -> Any
+        # Empty scalar is a special case.
+        if not scalar:
+            return ScalarAnalysis(
+                scalar=scalar,
+                empty=True,
+                multiline=False,
+                allow_flow_plain=False,
+                allow_block_plain=True,
+                allow_single_quoted=True,
+                allow_double_quoted=True,
+                allow_block=False,
+            )
+
+        # Indicators and special characters.
+        block_indicators = False
+        flow_indicators = False
+        line_breaks = False
+        special_characters = False
+
+        # Important whitespace combinations.
+        leading_space = False
+        leading_break = False
+        trailing_space = False
+        trailing_break = False
+        break_space = False
+        space_break = False
+
+        # Check document indicators.
+        if scalar.startswith('---') or scalar.startswith('...'):
+            block_indicators = True
+            flow_indicators = True
+
+        # First character or preceded by a whitespace.
+        preceeded_by_whitespace = True
+
+        # Last character or followed by a whitespace.
+        followed_by_whitespace = len(scalar) == 1 or scalar[1] in '\0 \t\r\n\x85\u2028\u2029'
+
+        # The previous character is a space.
+        previous_space = False
+
+        # The previous character is a break.
+        previous_break = False
+
+        index = 0
+        while index < len(scalar):
+            ch = scalar[index]
+
+            # Check for indicators.
+            if index == 0:
+                # Leading indicators are special characters.
+                if ch in '#,[]{}&*!|>\'"%@`':
+                    flow_indicators = True
+                    block_indicators = True
+                if ch in '?:':  # ToDo
+                    if self.serializer.use_version == (1, 1):
+                        flow_indicators = True
+                    elif len(scalar) == 1:  # single character
+                        flow_indicators = True
+                    if followed_by_whitespace:
+                        block_indicators = True
+                if ch == '-' and followed_by_whitespace:
+                    flow_indicators = True
+                    block_indicators = True
+            else:
+                # Some indicators cannot appear within a scalar as well.
+                if ch in ',[]{}':  # http://yaml.org/spec/1.2/spec.html#id2788859
+                    flow_indicators = True
+                if ch == '?' and self.serializer.use_version == (1, 1):
+                    flow_indicators = True
+                if ch == ':':
+                    if followed_by_whitespace:
+                        flow_indicators = True
+                        block_indicators = True
+                if ch == '#' and preceeded_by_whitespace:
+                    flow_indicators = True
+                    block_indicators = True
+
+            # Check for line breaks, special, and unicode characters.
+            if ch in '\n\x85\u2028\u2029':
+                line_breaks = True
+            if not (ch == '\n' or '\x20' <= ch <= '\x7E'):
+                if (
+                    ch == '\x85'
+                    or '\xA0' <= ch <= '\uD7FF'
+                    or '\uE000' <= ch <= '\uFFFD'
+                    or (self.unicode_supplementary and ('\U00010000' <= ch <= '\U0010FFFF'))
+                ) and ch != '\uFEFF':
+                    # unicode_characters = True
+                    if not self.allow_unicode:
+                        special_characters = True
+                else:
+                    special_characters = True
+
+            # Detect important whitespace combinations.
+            if ch == ' ':
+                if index == 0:
+                    leading_space = True
+                if index == len(scalar) - 1:
+                    trailing_space = True
+                if previous_break:
+                    break_space = True
+                previous_space = True
+                previous_break = False
+            elif ch in '\n\x85\u2028\u2029':
+                if index == 0:
+                    leading_break = True
+                if index == len(scalar) - 1:
+                    trailing_break = True
+                if previous_space:
+                    space_break = True
+                previous_space = False
+                previous_break = True
+            else:
+                previous_space = False
+                previous_break = False
+
+            # Prepare for the next character.
+            index += 1
+            preceeded_by_whitespace = ch in '\0 \t\r\n\x85\u2028\u2029'
+            followed_by_whitespace = (
+                index + 1 >= len(scalar) or scalar[index + 1] in '\0 \t\r\n\x85\u2028\u2029'
+            )
+
+        # Let's decide what styles are allowed.
+        allow_flow_plain = True
+        allow_block_plain = True
+        allow_single_quoted = True
+        allow_double_quoted = True
+        allow_block = True
+
+        # Leading and trailing whitespaces are bad for plain scalars.
+        if leading_space or leading_break or trailing_space or trailing_break:
+            allow_flow_plain = allow_block_plain = False
+
+        # We do not permit trailing spaces for block scalars.
+        if trailing_space:
+            allow_block = False
+
+        # Spaces at the beginning of a new line are only acceptable for block
+        # scalars.
+        if break_space:
+            allow_flow_plain = allow_block_plain = allow_single_quoted = False
+
+        # Spaces followed by breaks, as well as special character are only
+        # allowed for double quoted scalars.
+        if special_characters:
+            allow_flow_plain = allow_block_plain = allow_single_quoted = allow_block = False
+        elif space_break:
+            allow_flow_plain = allow_block_plain = allow_single_quoted = False
+            if not self.allow_space_break:
+                allow_block = False
+
+        # Although the plain scalar writer supports breaks, we never emit
+        # multiline plain scalars.
+        if line_breaks:
+            allow_flow_plain = allow_block_plain = False
+
+        # Flow indicators are forbidden for flow plain scalars.
+        if flow_indicators:
+            allow_flow_plain = False
+
+        # Block indicators are forbidden for block plain scalars.
+        if block_indicators:
+            allow_block_plain = False
+
+        return ScalarAnalysis(
+            scalar=scalar,
+            empty=False,
+            multiline=line_breaks,
+            allow_flow_plain=allow_flow_plain,
+            allow_block_plain=allow_block_plain,
+            allow_single_quoted=allow_single_quoted,
+            allow_double_quoted=allow_double_quoted,
+            allow_block=allow_block,
+        )
+
+    # Writers.
+
+    def flush_stream(self):
+        # type: () -> None
+        if hasattr(self.stream, 'flush'):
+            self.stream.flush()
+
+    def write_stream_start(self):
+        # type: () -> None
+        # Write BOM if needed.
+        if self.encoding and self.encoding.startswith('utf-16'):
+            self.stream.write('\uFEFF'.encode(self.encoding))
+
+    def write_stream_end(self):
+        # type: () -> None
+        self.flush_stream()
+
+    def write_indicator(self, indicator, need_whitespace, whitespace=False, indention=False):
+        # type: (Any, Any, bool, bool) -> None
+        if self.whitespace or not need_whitespace:
+            data = indicator
+        else:
+            data = ' ' + indicator
+        self.whitespace = whitespace
+        self.indention = self.indention and indention
+        self.column += len(data)
+        self.open_ended = False
+        if bool(self.encoding):
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+
+    def write_indent(self):
+        # type: () -> None
+        indent = self.indent or 0
+        if (
+            not self.indention
+            or self.column > indent
+            or (self.column == indent and not self.whitespace)
+        ):
+            if bool(self.no_newline):
+                self.no_newline = False
+            else:
+                self.write_line_break()
+        if self.column < indent:
+            self.whitespace = True
+            data = ' ' * (indent - self.column)
+            self.column = indent
+            if self.encoding:
+                data = data.encode(self.encoding)  # type: ignore
+            self.stream.write(data)
+
+    def write_line_break(self, data=None):
+        # type: (Any) -> None
+        if data is None:
+            data = self.best_line_break
+        self.whitespace = True
+        self.indention = True
+        self.line += 1
+        self.column = 0
+        if bool(self.encoding):
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+
+    def write_version_directive(self, version_text):
+        # type: (Any) -> None
+        data = _F('%YAML {version_text!s}', version_text=version_text)
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+        self.write_line_break()
+
+    def write_tag_directive(self, handle_text, prefix_text):
+        # type: (Any, Any) -> None
+        data = _F(
+            '%TAG {handle_text!s} {prefix_text!s}',
+            handle_text=handle_text,
+            prefix_text=prefix_text,
+        )
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+        self.write_line_break()
+
+    # Scalar streams.
+
+    def write_single_quoted(self, text, split=True):
+        # type: (Any, Any) -> None
+        if self.root_context:
+            if self.requested_indent is not None:
+                self.write_line_break()
+                if self.requested_indent != 0:
+                    self.write_indent()
+        self.write_indicator("'", True)
+        spaces = False
+        breaks = False
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if spaces:
+                if ch is None or ch != ' ':
+                    if (
+                        start + 1 == end
+                        and self.column > self.best_width
+                        and split
+                        and start != 0
+                        and end != len(text)
+                    ):
+                        self.write_indent()
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if bool(self.encoding):
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                    start = end
+            elif breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029':
+                    if text[start] == '\n':
+                        self.write_line_break()
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    self.write_indent()
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029' or ch == "'":
+                    if start < end:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if bool(self.encoding):
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                        start = end
+            if ch == "'":
+                data = "''"
+                self.column += 2
+                if bool(self.encoding):
+                    data = data.encode(self.encoding)
+                self.stream.write(data)
+                start = end + 1
+            if ch is not None:
+                spaces = ch == ' '
+                breaks = ch in '\n\x85\u2028\u2029'
+            end += 1
+        self.write_indicator("'", False)
+
+    ESCAPE_REPLACEMENTS = {
+        '\0': '0',
+        '\x07': 'a',
+        '\x08': 'b',
+        '\x09': 't',
+        '\x0A': 'n',
+        '\x0B': 'v',
+        '\x0C': 'f',
+        '\x0D': 'r',
+        '\x1B': 'e',
+        '"': '"',
+        '\\': '\\',
+        '\x85': 'N',
+        '\xA0': '_',
+        '\u2028': 'L',
+        '\u2029': 'P',
+    }
+
+    def write_double_quoted(self, text, split=True):
+        # type: (Any, Any) -> None
+        if self.root_context:
+            if self.requested_indent is not None:
+                self.write_line_break()
+                if self.requested_indent != 0:
+                    self.write_indent()
+        self.write_indicator('"', True)
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if (
+                ch is None
+                or ch in '"\\\x85\u2028\u2029\uFEFF'
+                or not (
+                    '\x20' <= ch <= '\x7E'
+                    or (
+                        self.allow_unicode
+                        and ('\xA0' <= ch <= '\uD7FF' or '\uE000' <= ch <= '\uFFFD')
+                    )
+                )
+            ):
+                if start < end:
+                    data = text[start:end]
+                    self.column += len(data)
+                    if bool(self.encoding):
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    start = end
+                if ch is not None:
+                    if ch in self.ESCAPE_REPLACEMENTS:
+                        data = '\\' + self.ESCAPE_REPLACEMENTS[ch]
+                    elif ch <= '\xFF':
+                        data = _F('\\x{ord_ch:02X}', ord_ch=ord(ch))
+                    elif ch <= '\uFFFF':
+                        data = _F('\\u{ord_ch:04X}', ord_ch=ord(ch))
+                    else:
+                        data = _F('\\U{ord_ch:08X}', ord_ch=ord(ch))
+                    self.column += len(data)
+                    if bool(self.encoding):
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    start = end + 1
+            if (
+                0 < end < len(text) - 1
+                and (ch == ' ' or start >= end)
+                and self.column + (end - start) > self.best_width
+                and split
+            ):
+                data = text[start:end] + '\\'
+                if start < end:
+                    start = end
+                self.column += len(data)
+                if bool(self.encoding):
+                    data = data.encode(self.encoding)
+                self.stream.write(data)
+                self.write_indent()
+                self.whitespace = False
+                self.indention = False
+                if text[start] == ' ':
+                    data = '\\'
+                    self.column += len(data)
+                    if bool(self.encoding):
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+            end += 1
+        self.write_indicator('"', False)
+
+    def determine_block_hints(self, text):
+        # type: (Any) -> Any
+        indent = 0
+        indicator = ''
+        hints = ''
+        if text:
+            if text[0] in ' \n\x85\u2028\u2029':
+                indent = self.best_sequence_indent
+                hints += str(indent)
+            elif self.root_context:
+                for end in ['\n---', '\n...']:
+                    pos = 0
+                    while True:
+                        pos = text.find(end, pos)
+                        if pos == -1:
+                            break
+                        try:
+                            if text[pos + 4] in ' \r\n':
+                                break
+                        except IndexError:
+                            pass
+                        pos += 1
+                    if pos > -1:
+                        break
+                if pos > 0:
+                    indent = self.best_sequence_indent
+            if text[-1] not in '\n\x85\u2028\u2029':
+                indicator = '-'
+            elif len(text) == 1 or text[-2] in '\n\x85\u2028\u2029':
+                indicator = '+'
+        hints += indicator
+        return hints, indent, indicator
+
+    def write_folded(self, text):
+        # type: (Any) -> None
+        hints, _indent, _indicator = self.determine_block_hints(text)
+        self.write_indicator('>' + hints, True)
+        if _indicator == '+':
+            self.open_ended = True
+        self.write_line_break()
+        leading_space = True
+        spaces = False
+        breaks = True
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029\a':
+                    if (
+                        not leading_space
+                        and ch is not None
+                        and ch != ' '
+                        and text[start] == '\n'
+                    ):
+                        self.write_line_break()
+                    leading_space = ch == ' '
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    if ch is not None:
+                        self.write_indent()
+                    start = end
+            elif spaces:
+                if ch != ' ':
+                    if start + 1 == end and self.column > self.best_width:
+                        self.write_indent()
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if bool(self.encoding):
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029\a':
+                    data = text[start:end]
+                    self.column += len(data)
+                    if bool(self.encoding):
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    if ch == '\a':
+                        if end < (len(text) - 1) and not text[end + 2].isspace():
+                            self.write_line_break()
+                            self.write_indent()
+                            end += 2  # \a and the space that is inserted on the fold
+                        else:
+                            raise EmitterError('unexcpected fold indicator \\a before space')
+                    if ch is None:
+                        self.write_line_break()
+                    start = end
+            if ch is not None:
+                breaks = ch in '\n\x85\u2028\u2029'
+                spaces = ch == ' '
+            end += 1
+
+    def write_literal(self, text, comment=None):
+        # type: (Any, Any) -> None
+        hints, _indent, _indicator = self.determine_block_hints(text)
+        # if comment is not None:
+        #    try:
+        #        hints += comment[1][0]
+        #    except (TypeError, IndexError) as e:
+        #        pass
+        if not isinstance(comment, str):
+            comment = ''
+        self.write_indicator('|' + hints + comment, True)
+        # try:
+        #    nprintf('selfev', comment)
+        #    cmx = comment[1][0]
+        #    if cmx:
+        #        self.stream.write(cmx)
+        # except (TypeError, IndexError) as e:
+        #    pass
+        if _indicator == '+':
+            self.open_ended = True
+        self.write_line_break()
+        breaks = True
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029':
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    if ch is not None:
+                        if self.root_context:
+                            idnx = self.indent if self.indent is not None else 0
+                            self.stream.write(' ' * (_indent + idnx))
+                        else:
+                            self.write_indent()
+                    start = end
+            else:
+                if ch is None or ch in '\n\x85\u2028\u2029':
+                    data = text[start:end]
+                    if bool(self.encoding):
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    if ch is None:
+                        self.write_line_break()
+                    start = end
+            if ch is not None:
+                breaks = ch in '\n\x85\u2028\u2029'
+            end += 1
+
+    def write_plain(self, text, split=True):
+        # type: (Any, Any) -> None
+        if self.root_context:
+            if self.requested_indent is not None:
+                self.write_line_break()
+                if self.requested_indent != 0:
+                    self.write_indent()
+            else:
+                self.open_ended = True
+        if not text:
+            return
+        if not self.whitespace:
+            data = ' '
+            self.column += len(data)
+            if self.encoding:
+                data = data.encode(self.encoding)  # type: ignore
+            self.stream.write(data)
+        self.whitespace = False
+        self.indention = False
+        spaces = False
+        breaks = False
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if spaces:
+                if ch != ' ':
+                    if start + 1 == end and self.column > self.best_width and split:
+                        self.write_indent()
+                        self.whitespace = False
+                        self.indention = False
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if self.encoding:
+                            data = data.encode(self.encoding)  # type: ignore
+                        self.stream.write(data)
+                    start = end
+            elif breaks:
+                if ch not in '\n\x85\u2028\u2029':  # type: ignore
+                    if text[start] == '\n':
+                        self.write_line_break()
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    self.write_indent()
+                    self.whitespace = False
+                    self.indention = False
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029':
+                    data = text[start:end]
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)  # type: ignore
+                    try:
+                        self.stream.write(data)
+                    except:  # NOQA
+                        sys.stdout.write(repr(data) + '\n')
+                        raise
+                    start = end
+            if ch is not None:
+                spaces = ch == ' '
+                breaks = ch in '\n\x85\u2028\u2029'
+            end += 1
+
+    def write_comment(self, comment, pre=False):
+        # type: (Any, bool) -> None
+        value = comment.value
+        # nprintf('{:02d} {:02d} {!r}'.format(self.column, comment.start_mark.column, value))
+        if not pre and value[-1] == '\n':
+            value = value[:-1]
+        try:
+            # get original column position
+            col = comment.start_mark.column
+            if comment.value and comment.value.startswith('\n'):
+                # never inject extra spaces if the comment starts with a newline
+                # and not a real comment (e.g. if you have an empty line following a key-value
+                col = self.column
+            elif col < self.column + 1:
+                ValueError
+        except ValueError:
+            col = self.column + 1
+        # nprint('post_comment', self.line, self.column, value)
+        try:
+            # at least one space if the current column >= the start column of the comment
+            # but not at the start of a line
+            nr_spaces = col - self.column
+            if self.column and value.strip() and nr_spaces < 1 and value[0] != '\n':
+                nr_spaces = 1
+            value = ' ' * nr_spaces + value
+            try:
+                if bool(self.encoding):
+                    value = value.encode(self.encoding)
+            except UnicodeDecodeError:
+                pass
+            self.stream.write(value)
+        except TypeError:
+            raise
+        if not pre:
+            self.write_line_break()
+
+    def write_pre_comment(self, event):
+        # type: (Any) -> bool
+        comments = event.comment[1]
+        if comments is None:
+            return False
+        try:
+            start_events = (MappingStartEvent, SequenceStartEvent)
+            for comment in comments:
+                if isinstance(event, start_events) and getattr(comment, 'pre_done', None):
+                    continue
+                if self.column != 0:
+                    self.write_line_break()
+                self.write_comment(comment, pre=True)
+                if isinstance(event, start_events):
+                    comment.pre_done = True
+        except TypeError:
+            sys.stdout.write('eventtt {} {}'.format(type(event), event))
+            raise
+        return True
+
+    def write_post_comment(self, event):
+        # type: (Any) -> bool
+        if self.event.comment[0] is None:
+            return False
+        comment = event.comment[0]
+        self.write_comment(comment)
+        return True
diff --git a/shared/package-files/1b/1be71b394531b9cdfce7dbd59110cd8dc2d14a752ef3e4fe63c9f4752a6fc1ea/test11.arff b/shared/package-files/1b/1be71b394531b9cdfce7dbd59110cd8dc2d14a752ef3e4fe63c9f4752a6fc1ea/test11.arff
new file mode 100644
index 0000000000000000000000000000000000000000..fadfaee884e3e91cd59f691afd954a6a6d4042da
--- /dev/null
+++ b/shared/package-files/1b/1be71b394531b9cdfce7dbd59110cd8dc2d14a752ef3e4fe63c9f4752a6fc1ea/test11.arff
@@ -0,0 +1,11 @@
+@RELATION test11
+
+@ATTRIBUTE attr0	REAL
+@ATTRIBUTE attr1 	REAL
+@ATTRIBUTE attr2 	REAL
+@ATTRIBUTE attr3	REAL
+@ATTRIBUTE class 	{ class0, class1, class2, class3 }
+@DATA
+0.1, 0.2, 0.3, 0.4,class1
+-0.1, -0.2, -0.3, -0.4,class2
+1, 2, 3, 4,class3
diff --git a/shared/package-files/1b/1bea536a6e619eda34aefc5011bf3e0701b8b5ddf901d977366aa04fe497c689/xterm+sl-twm b/shared/package-files/1b/1bea536a6e619eda34aefc5011bf3e0701b8b5ddf901d977366aa04fe497c689/xterm+sl-twm
new file mode 100644
index 0000000000000000000000000000000000000000..67e954bb8dc0bd5418f035c38a941371626e5448
Binary files /dev/null and b/shared/package-files/1b/1bea536a6e619eda34aefc5011bf3e0701b8b5ddf901d977366aa04fe497c689/xterm+sl-twm differ
diff --git a/shared/package-files/1b/1bf10fa57c6fdb10d25275803eb440440f6f4e38831cf66ef02fe927f15cac65/instantiator.py b/shared/package-files/1b/1bf10fa57c6fdb10d25275803eb440440f6f4e38831cf66ef02fe927f15cac65/instantiator.py
new file mode 100644
index 0000000000000000000000000000000000000000..24f53c4f1a607c152fa670ff3591874bf5a896fa
--- /dev/null
+++ b/shared/package-files/1b/1bf10fa57c6fdb10d25275803eb440440f6f4e38831cf66ef02fe927f15cac65/instantiator.py
@@ -0,0 +1,153 @@
+#!/usr/bin/python3
+import importlib
+import logging
+import os
+import sys
+import tempfile
+from typing import Optional
+
+import torch
+from torch.distributed.nn.jit.templates.remote_module_template import (
+    get_remote_module_template,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+_FILE_PREFIX = "_remote_module_"
+_TEMP_DIR = tempfile.TemporaryDirectory()
+INSTANTIATED_TEMPLATE_DIR_PATH = _TEMP_DIR.name
+logger.info("Created a temporary directory at %s", INSTANTIATED_TEMPLATE_DIR_PATH)
+sys.path.append(INSTANTIATED_TEMPLATE_DIR_PATH)
+
+
+def get_arg_return_types_from_interface(module_interface):
+    assert getattr(
+        module_interface, "__torch_script_interface__", False
+    ), "Expect a TorchScript class interface decorated by @torch.jit.interface."
+    qualified_name = torch._jit_internal._qualified_name(module_interface)
+    cu = torch.jit._state._python_cu
+    module_interface_c = cu.get_interface(qualified_name)
+    assert (
+        "forward" in module_interface_c.getMethodNames()
+    ), f"Expect forward in interface methods, while it has {module_interface_c.getMethodNames()}"
+    method_schema = module_interface_c.getMethod("forward")
+
+    arg_str_list = []
+    arg_type_str_list = []
+    assert method_schema is not None
+    for argument in method_schema.arguments:
+        arg_str_list.append(argument.name)
+
+        if argument.has_default_value():
+            default_value_str = f" = {argument.default_value}"
+        else:
+            default_value_str = ""
+        arg_type_str = f"{argument.name}: {argument.type}{default_value_str}"
+        arg_type_str_list.append(arg_type_str)
+
+    arg_str_list = arg_str_list[1:]  # Remove "self".
+    args_str = ", ".join(arg_str_list)
+
+    arg_type_str_list = arg_type_str_list[1:]  # Remove "self".
+    arg_types_str = ", ".join(arg_type_str_list)
+
+    assert len(method_schema.returns) == 1
+    argument = method_schema.returns[0]
+    return_type_str = str(argument.type)
+
+    return args_str, arg_types_str, return_type_str
+
+
+def _write(out_path, text):
+    old_text: Optional[str]
+    try:
+        with open(out_path) as f:
+            old_text = f.read()
+    except OSError:
+        old_text = None
+    if old_text != text:
+        with open(out_path, "w") as f:
+            logger.info("Writing %s", out_path)
+            f.write(text)
+    else:
+        logger.info("Skipped writing %s", out_path)
+
+
+def _do_instantiate_remote_module_template(
+    generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+):
+    generated_code_text = get_remote_module_template(
+        enable_moving_cpu_tensors_to_cuda
+    ).format(**str_dict)
+    out_path = os.path.join(
+        INSTANTIATED_TEMPLATE_DIR_PATH, f"{generated_module_name}.py"
+    )
+    _write(out_path, generated_code_text)
+
+    # From importlib doc,
+    # > If you are dynamically importing a module that was created since
+    # the interpreter began execution (e.g., created a Python source file),
+    # you may need to call invalidate_caches() in order for the new module
+    # to be noticed by the import system.
+    importlib.invalidate_caches()
+    generated_module = importlib.import_module(f"{generated_module_name}")
+    return generated_module
+
+
+def instantiate_scriptable_remote_module_template(
+    module_interface_cls, enable_moving_cpu_tensors_to_cuda=True
+):
+    if not getattr(module_interface_cls, "__torch_script_interface__", False):
+        raise ValueError(
+            f"module_interface_cls {module_interface_cls} must be a type object decorated by "
+            "@torch.jit.interface"
+        )
+
+    # Generate the template instance name.
+    module_interface_cls_name = torch._jit_internal._qualified_name(
+        module_interface_cls
+    ).replace(".", "_")
+    generated_module_name = f"{_FILE_PREFIX}{module_interface_cls_name}"
+
+    # Generate type annotation strs.
+    assign_module_interface_cls_str = (
+        f"from {module_interface_cls.__module__} import "
+        f"{module_interface_cls.__name__} as module_interface_cls"
+    )
+    args_str, arg_types_str, return_type_str = get_arg_return_types_from_interface(
+        module_interface_cls
+    )
+    kwargs_str = ""
+    arrow_and_return_type_str = f" -> {return_type_str}"
+    arrow_and_future_return_type_str = f" -> Future[{return_type_str}]"
+
+    str_dict = dict(
+        assign_module_interface_cls=assign_module_interface_cls_str,
+        arg_types=arg_types_str,
+        arrow_and_return_type=arrow_and_return_type_str,
+        arrow_and_future_return_type=arrow_and_future_return_type_str,
+        args=args_str,
+        kwargs=kwargs_str,
+        jit_script_decorator="@torch.jit.script",
+    )
+    return _do_instantiate_remote_module_template(
+        generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+    )
+
+
+def instantiate_non_scriptable_remote_module_template():
+    generated_module_name = f"{_FILE_PREFIX}non_scriptable"
+    str_dict = dict(
+        assign_module_interface_cls="module_interface_cls = None",
+        args="*args",
+        kwargs="**kwargs",
+        arg_types="*args, **kwargs",
+        arrow_and_return_type="",
+        arrow_and_future_return_type="",
+        jit_script_decorator="",
+    )
+    # For a non-scriptable template, always enable moving CPU tensors to a cuda device,
+    # because there is no syntax limitation on the extra handling caused by the script.
+    return _do_instantiate_remote_module_template(generated_module_name, str_dict, True)
diff --git a/shared/package-files/1b/1bf221f8bd478bed9f201e971563fb45f29da44945069e2f4dd2be13f4b46593/exported_program.py b/shared/package-files/1b/1bf221f8bd478bed9f201e971563fb45f29da44945069e2f4dd2be13f4b46593/exported_program.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d28ea31549087f2b118e6c431d812da314e6497
--- /dev/null
+++ b/shared/package-files/1b/1bf221f8bd478bed9f201e971563fb45f29da44945069e2f4dd2be13f4b46593/exported_program.py
@@ -0,0 +1,50 @@
+import warnings
+
+
+import torch
+import torch.fx
+
+
+# TODO(ycao): This is added to avoid breaking existing code temporarily.
+# Remove when migration is done.
+from torch.export.graph_signature import (
+    ExportBackwardSignature,
+    ExportGraphSignature,
+)
+
+from torch.export.exported_program import (
+    ExportedProgram,
+    ModuleCallEntry,
+    ModuleCallSignature,
+)
+
+
+
+__all__ = [
+    "ExportBackwardSignature",
+    "ExportGraphSignature",
+    "ExportedProgram",
+    "ModuleCallEntry",
+    "ModuleCallSignature",
+]
+
+
+def _create_graph_module_for_export(root, graph):
+    try:
+        gm = torch.fx.GraphModule(root, graph)
+    except SyntaxError:
+        # If custom objects stored in memory are being used in the graph,
+        # the generated python code will result in a syntax error on the custom
+        # object, since it is unable to parse the in-memory object. However
+        # we can still run the graph eagerly through torch.fx.Interpreter,
+        # so we will bypass this error.
+        warnings.warn(
+            "Unable to execute the generated python source code from "
+            "the graph. The graph module will no longer be directly callable, "
+            "but you can still run the ExportedProgram, and if needed, you can "
+            "run the graph module eagerly using torch.fx.Interpreter."
+        )
+        gm = torch.fx.GraphModule(root, torch.fx.Graph())
+        gm._graph = graph
+
+    return gm
diff --git a/shared/package-files/1b/1bf6953f803f37eb6a99ea65cd88940b530436ee95ecec3bf8d2fc222af54a1b/adm+sgr b/shared/package-files/1b/1bf6953f803f37eb6a99ea65cd88940b530436ee95ecec3bf8d2fc222af54a1b/adm+sgr
new file mode 100644
index 0000000000000000000000000000000000000000..afbc99edc591a8ef453b49b0f6dda5b0ac491aa3
Binary files /dev/null and b/shared/package-files/1b/1bf6953f803f37eb6a99ea65cd88940b530436ee95ecec3bf8d2fc222af54a1b/adm+sgr differ
diff --git a/shared/package-files/1b/1bf8a6e5d4fa0da4442603001aed8c09dfad8f913e7a57399985f8b2e952616d/_vector_sentinel.pxd b/shared/package-files/1b/1bf8a6e5d4fa0da4442603001aed8c09dfad8f913e7a57399985f8b2e952616d/_vector_sentinel.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..64de6c18830b5e24c77bfed38cfffccc3b62955a
--- /dev/null
+++ b/shared/package-files/1b/1bf8a6e5d4fa0da4442603001aed8c09dfad8f913e7a57399985f8b2e952616d/_vector_sentinel.pxd
@@ -0,0 +1,12 @@
+cimport numpy as cnp
+
+from libcpp.vector cimport vector
+from ..utils._typedefs cimport intp_t, float64_t, int32_t, int64_t
+
+ctypedef fused vector_typed:
+    vector[float64_t]
+    vector[intp_t]
+    vector[int32_t]
+    vector[int64_t]
+
+cdef cnp.ndarray vector_to_nd_array(vector_typed * vect_ptr)
diff --git a/shared/package-files/1b/1bfb51912275d8e346dce0a40ab84316b15e3f142e66529a8c9cfd52210c1a1f/test_multicall.py b/shared/package-files/1b/1bfb51912275d8e346dce0a40ab84316b15e3f142e66529a8c9cfd52210c1a1f/test_multicall.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3a3bfb88f9c31fa025d112803991212261e2508
--- /dev/null
+++ b/shared/package-files/1b/1bfb51912275d8e346dce0a40ab84316b15e3f142e66529a8c9cfd52210c1a1f/test_multicall.py
@@ -0,0 +1,48 @@
+"Test multicall, coverage 33%."
+
+from idlelib import multicall
+import unittest
+from test.support import requires
+from tkinter import Tk, Text
+
+
+class MultiCallTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        requires('gui')
+        cls.root = Tk()
+        cls.root.withdraw()
+        cls.mc = multicall.MultiCallCreator(Text)
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.mc
+        cls.root.update_idletasks()
+##        for id in cls.root.tk.call('after', 'info'):
+##            cls.root.after_cancel(id)  # Need for EditorWindow.
+        cls.root.destroy()
+        del cls.root
+
+    def test_creator(self):
+        mc = self.mc
+        self.assertIs(multicall._multicall_dict[Text], mc)
+        self.assertTrue(issubclass(mc, Text))
+        mc2 = multicall.MultiCallCreator(Text)
+        self.assertIs(mc, mc2)
+
+    def test_init(self):
+        mctext = self.mc(self.root)
+        self.assertIsInstance(mctext._MultiCall__binders, list)
+
+    def test_yview(self):
+        # Added for tree.wheel_event
+        # (it depends on yview to not be overridden)
+        mc = self.mc
+        self.assertIs(mc.yview, Text.yview)
+        mctext = self.mc(self.root)
+        self.assertIs(mctext.yview.__func__, Text.yview)
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)
diff --git a/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python-3.10-embed.pc b/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python-3.10-embed.pc
new file mode 100644
index 0000000000000000000000000000000000000000..246426e721db61fadab78a669f3e6dc18d78f04d
--- /dev/null
+++ b/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python-3.10-embed.pc
@@ -0,0 +1,13 @@
+# See: man pkg-config
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Python
+Description: Embed Python into an application
+Requires:
+Version: 3.10
+Libs.private: -lcrypt -lpthread -ldl  -lutil -lm
+Libs: -L${libdir} -lpython3.10
+Cflags: -I${includedir}/python3.10
diff --git a/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python3-embed.pc b/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python3-embed.pc
new file mode 100644
index 0000000000000000000000000000000000000000..246426e721db61fadab78a669f3e6dc18d78f04d
--- /dev/null
+++ b/shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/python3-embed.pc
@@ -0,0 +1,13 @@
+# See: man pkg-config
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Python
+Description: Embed Python into an application
+Requires:
+Version: 3.10
+Libs.private: -lcrypt -lpthread -ldl  -lutil -lm
+Libs: -L${libdir} -lpython3.10
+Cflags: -I${includedir}/python3.10
diff --git a/shared/package-files/32/3204305af3d1b1a4fa532c1b68e8ec5d331c02add9f6d6a1772f39ff0ee47c16/_warnings.py b/shared/package-files/32/3204305af3d1b1a4fa532c1b68e8ec5d331c02add9f6d6a1772f39ff0ee47c16/_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..a38471842782b526e38c8f7602c40ec38e9416a6
--- /dev/null
+++ b/shared/package-files/32/3204305af3d1b1a4fa532c1b68e8ec5d331c02add9f6d6a1772f39ff0ee47c16/_warnings.py
@@ -0,0 +1,4 @@
+class ExperimentalWarning(Warning):
+    """Used to manage warning messages for any experimental integrations."""
+
+    pass
diff --git a/shared/package-files/32/3208279bebd7d83b14c1360f25f93cd40048e904280d52b3b8549b05f0e3ae71/_fused_sgd.h b/shared/package-files/32/3208279bebd7d83b14c1360f25f93cd40048e904280d52b3b8549b05f0e3ae71/_fused_sgd.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f16f621b345113a4e265abcd5de19e8095bc5e8
--- /dev/null
+++ b/shared/package-files/32/3208279bebd7d83b14c1360f25f93cd40048e904280d52b3b8549b05f0e3ae71/_fused_sgd.h
@@ -0,0 +1,63 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_fused_sgd_ops.h>
+
+namespace at {
+
+
+// aten::_fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
+inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
+inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd__tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) {
+    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+
+// aten::_fused_sgd(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
+inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) {
+    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+
+// aten::_fused_sgd.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
+inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+}
diff --git a/shared/package-files/32/3209ff37311d53ba8ccea59c7e6c7fe0579a3eb10c185ff6b115b15e859aa149/test_stack_unstack.py b/shared/package-files/32/3209ff37311d53ba8ccea59c7e6c7fe0579a3eb10c185ff6b115b15e859aa149/test_stack_unstack.py
new file mode 100644
index 0000000000000000000000000000000000000000..de470fcda18ed47417ff84b88c6b48bc888cec8f
--- /dev/null
+++ b/shared/package-files/32/3209ff37311d53ba8ccea59c7e6c7fe0579a3eb10c185ff6b115b15e859aa149/test_stack_unstack.py
@@ -0,0 +1,2684 @@
+from datetime import datetime
+import itertools
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Period,
+    Series,
+    Timedelta,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.reshape import reshape as reshape_lib
+
+
+@pytest.fixture(params=[True, False])
+def future_stack(request):
+    return request.param
+
+
+class TestDataFrameReshape:
+    def test_stack_unstack(self, float_frame, future_stack):
+        df = float_frame.copy()
+        df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)
+
+        stacked = df.stack(future_stack=future_stack)
+        stacked_df = DataFrame({"foo": stacked, "bar": stacked})
+
+        unstacked = stacked.unstack()
+        unstacked_df = stacked_df.unstack()
+
+        tm.assert_frame_equal(unstacked, df)
+        tm.assert_frame_equal(unstacked_df["bar"], df)
+
+        unstacked_cols = stacked.unstack(0)
+        unstacked_cols_df = stacked_df.unstack(0)
+        tm.assert_frame_equal(unstacked_cols.T, df)
+        tm.assert_frame_equal(unstacked_cols_df["bar"].T, df)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_level(self, future_stack):
+        # GH 18310
+        levels = [range(3), [3, "a", "b"], [1, 2]]
+
+        # flat columns:
+        df = DataFrame(1, index=levels[0], columns=levels[1])
+        result = df.stack(future_stack=future_stack)
+        expected = Series(1, index=MultiIndex.from_product(levels[:2]))
+        tm.assert_series_equal(result, expected)
+
+        # MultiIndex columns:
+        df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:]))
+        result = df.stack(1, future_stack=future_stack)
+        expected = DataFrame(
+            1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # as above, but used labels in level are actually of homogeneous type
+        result = df[["a", "b"]].stack(1, future_stack=future_stack)
+        expected = expected[["a", "b"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_not_consolidated(self, using_array_manager):
+        # Gh#34708
+        df = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]})
+        df2 = df[["x"]]
+        df2["y"] = df["y"]
+        if not using_array_manager:
+            assert len(df2._mgr.blocks) == 2
+
+        res = df2.unstack()
+        expected = df.unstack()
+        tm.assert_series_equal(res, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_fill(self, future_stack):
+        # GH #9746: fill_value keyword argument for Series
+        # and DataFrame unstack
+
+        # From a series
+        data = Series([1, 2, 4, 5], dtype=np.int16)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack(fill_value=-1)
+        expected = DataFrame(
+            {"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # From a series with incorrect data type for fill_value
+        result = data.unstack(fill_value=0.5)
+        expected = DataFrame(
+            {"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=float
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # GH #13971: fill_value when unstacking multiple levels:
+        df = DataFrame(
+            {"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]}
+        ).set_index(["x", "y", "z"])
+        unstacked = df.unstack(["x", "y"], fill_value=0)
+        key = ("w", "b", "j")
+        expected = unstacked[key]
+        result = Series([0, 0, 2], index=unstacked.index, name=key)
+        tm.assert_series_equal(result, expected)
+
+        stacked = unstacked.stack(["x", "y"], future_stack=future_stack)
+        stacked.index = stacked.index.reorder_levels(df.index.names)
+        # Workaround for GH #17886 (unnecessarily casts to float):
+        stacked = stacked.astype(np.int64)
+        result = stacked.loc[df.index]
+        tm.assert_frame_equal(result, df)
+
+        # From a series
+        s = df["w"]
+        result = s.unstack(["x", "y"], fill_value=0)
+        expected = unstacked["w"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame(self):
+        # From a dataframe
+        rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
+        df = DataFrame(rows, columns=list("AB"), dtype=np.int32)
+        df.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = df.unstack(fill_value=-1)
+
+        rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
+        expected = DataFrame(rows, index=list("xyz"), dtype=np.int32)
+        expected.columns = MultiIndex.from_tuples(
+            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # From a mixed type dataframe
+        df["A"] = df["A"].astype(np.int16)
+        df["B"] = df["B"].astype(np.float64)
+
+        result = df.unstack(fill_value=-1)
+        expected["A"] = expected["A"].astype(np.int16)
+        expected["B"] = expected["B"].astype(np.float64)
+        tm.assert_frame_equal(result, expected)
+
+        # From a dataframe with incorrect data type for fill_value
+        result = df.unstack(fill_value=0.5)
+
+        rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
+        expected = DataFrame(rows, index=list("xyz"), dtype=float)
+        expected.columns = MultiIndex.from_tuples(
+            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_datetime(self):
+        # Test unstacking with date times
+        dv = date_range("2012-01-01", periods=4).values
+        data = Series(dv)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=dv[0])
+        expected = DataFrame(
+            {"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_timedelta(self):
+        # Test unstacking with time deltas
+        td = [Timedelta(days=i) for i in range(4)]
+        data = Series(td)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=td[1])
+        expected = DataFrame(
+            {"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_period(self):
+        # Test unstacking with period
+        periods = [
+            Period("2012-01"),
+            Period("2012-02"),
+            Period("2012-03"),
+            Period("2012-04"),
+        ]
+        data = Series(periods)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=periods[1])
+        expected = DataFrame(
+            {
+                "a": [periods[0], periods[1], periods[3]],
+                "b": [periods[1], periods[2], periods[1]],
+            },
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_categorical(self):
+        # Test unstacking with categorical
+        data = Series(["a", "b", "c", "a"], dtype="category")
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        # By default missing values will be NaN
+        result = data.unstack()
+        expected = DataFrame(
+            {
+                "a": pd.Categorical(list("axa"), categories=list("abc")),
+                "b": pd.Categorical(list("bcx"), categories=list("abc")),
+            },
+            index=list("xyz"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Fill with non-category results in a ValueError
+        msg = r"Cannot setitem on a Categorical with a new category \(d\)"
+        with pytest.raises(TypeError, match=msg):
+            data.unstack(fill_value="d")
+
+        # Fill with category value replaces missing values as expected
+        result = data.unstack(fill_value="c")
+        expected = DataFrame(
+            {
+                "a": pd.Categorical(list("aca"), categories=list("abc")),
+                "b": pd.Categorical(list("bcc"), categories=list("abc")),
+            },
+            index=list("xyz"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_tuplename_in_multiindex(self):
+        # GH 19966
+        idx = MultiIndex.from_product(
+            [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
+        )
+        df = DataFrame({"d": [1] * 9, "e": [2] * 9}, index=idx)
+        result = df.unstack(("A", "a"))
+
+        expected = DataFrame(
+            [[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]],
+            columns=MultiIndex.from_tuples(
+                [
+                    ("d", "a"),
+                    ("d", "b"),
+                    ("d", "c"),
+                    ("e", "a"),
+                    ("e", "b"),
+                    ("e", "c"),
+                ],
+                names=[None, ("A", "a")],
+            ),
+            index=Index([1, 2, 3], name=("B", "b")),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "unstack_idx, expected_values, expected_index, expected_columns",
+        [
+            (
+                ("A", "a"),
+                [[1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2]],
+                MultiIndex.from_tuples(
+                    [(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]
+                ),
+                MultiIndex.from_tuples(
+                    [("d", "a"), ("d", "b"), ("e", "a"), ("e", "b")],
+                    names=[None, ("A", "a")],
+                ),
+            ),
+            (
+                (("A", "a"), "B"),
+                [[1, 1, 1, 1, 2, 2, 2, 2], [1, 1, 1, 1, 2, 2, 2, 2]],
+                Index([3, 4], name="C"),
+                MultiIndex.from_tuples(
+                    [
+                        ("d", "a", 1),
+                        ("d", "a", 2),
+                        ("d", "b", 1),
+                        ("d", "b", 2),
+                        ("e", "a", 1),
+                        ("e", "a", 2),
+                        ("e", "b", 1),
+                        ("e", "b", 2),
+                    ],
+                    names=[None, ("A", "a"), "B"],
+                ),
+            ),
+        ],
+    )
+    def test_unstack_mixed_type_name_in_multiindex(
+        self, unstack_idx, expected_values, expected_index, expected_columns
+    ):
+        # GH 19966
+        idx = MultiIndex.from_product(
+            [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
+        )
+        df = DataFrame({"d": [1] * 8, "e": [2] * 8}, index=idx)
+        result = df.unstack(unstack_idx)
+
+        expected = DataFrame(
+            expected_values, columns=expected_columns, index=expected_index
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_preserve_dtypes(self):
+        # Checks fix for #11847
+        df = DataFrame(
+            {
+                "state": ["IL", "MI", "NC"],
+                "index": ["a", "b", "c"],
+                "some_categories": Series(["a", "b", "c"]).astype("category"),
+                "A": np.random.default_rng(2).random(3),
+                "B": 1,
+                "C": "foo",
+                "D": pd.Timestamp("20010102"),
+                "E": Series([1.0, 50.0, 100.0]).astype("float32"),
+                "F": Series([3.0, 4.0, 5.0]).astype("float64"),
+                "G": False,
+                "H": Series([1, 200, 923442]).astype("int8"),
+            }
+        )
+
+        def unstack_and_compare(df, column_name):
+            unstacked1 = df.unstack([column_name])
+            unstacked2 = df.unstack(column_name)
+            tm.assert_frame_equal(unstacked1, unstacked2)
+
+        df1 = df.set_index(["state", "index"])
+        unstack_and_compare(df1, "index")
+
+        df1 = df.set_index(["state", "some_categories"])
+        unstack_and_compare(df1, "some_categories")
+
+        df1 = df.set_index(["F", "C"])
+        unstack_and_compare(df1, "F")
+
+        df1 = df.set_index(["G", "B", "state"])
+        unstack_and_compare(df1, "B")
+
+        df1 = df.set_index(["E", "A"])
+        unstack_and_compare(df1, "E")
+
+        df1 = df.set_index(["state", "index"])
+        s = df1["A"]
+        unstack_and_compare(s, "index")
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_ints(self, future_stack):
+        columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3)))
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((30, 27)), columns=columns
+        )
+
+        tm.assert_frame_equal(
+            df.stack(level=[1, 2], future_stack=future_stack),
+            df.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+        tm.assert_frame_equal(
+            df.stack(level=[-2, -1], future_stack=future_stack),
+            df.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+
+        df_named = df.copy()
+        return_value = df_named.columns.set_names(range(3), inplace=True)
+        assert return_value is None
+
+        tm.assert_frame_equal(
+            df_named.stack(level=[1, 2], future_stack=future_stack),
+            df_named.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_levels(self, future_stack):
+        columns = MultiIndex.from_tuples(
+            [
+                ("A", "cat", "long"),
+                ("B", "cat", "long"),
+                ("A", "dog", "short"),
+                ("B", "dog", "short"),
+            ],
+            names=["exp", "animal", "hair_length"],
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=columns
+        )
+
+        animal_hair_stacked = df.stack(
+            level=["animal", "hair_length"], future_stack=future_stack
+        )
+        exp_hair_stacked = df.stack(
+            level=["exp", "hair_length"], future_stack=future_stack
+        )
+
+        # GH #8584: Need to check that stacking works when a number
+        # is passed that is both a level name and in the range of
+        # the level numbers
+        df2 = df.copy()
+        df2.columns.names = ["exp", "animal", 1]
+        tm.assert_frame_equal(
+            df2.stack(level=["animal", 1], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=["exp", 1], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+        # When mixed types are passed and the ints are not level
+        # names, raise
+        msg = (
+            "level should contain all level names or all level numbers, not "
+            "a mixture of the two"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df2.stack(level=["animal", 0], future_stack=future_stack)
+
+        # GH #8584: Having 0 in the level names could raise a
+        # strange error about lexsort depth
+        df3 = df.copy()
+        df3.columns.names = ["exp", "animal", 0]
+        tm.assert_frame_equal(
+            df3.stack(level=["animal", 0], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_int_level_names(self, future_stack):
+        columns = MultiIndex.from_tuples(
+            [
+                ("A", "cat", "long"),
+                ("B", "cat", "long"),
+                ("A", "dog", "short"),
+                ("B", "dog", "short"),
+            ],
+            names=["exp", "animal", "hair_length"],
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=columns
+        )
+
+        exp_animal_stacked = df.stack(
+            level=["exp", "animal"], future_stack=future_stack
+        )
+        animal_hair_stacked = df.stack(
+            level=["animal", "hair_length"], future_stack=future_stack
+        )
+        exp_hair_stacked = df.stack(
+            level=["exp", "hair_length"], future_stack=future_stack
+        )
+
+        df2 = df.copy()
+        df2.columns.names = [0, 1, 2]
+        tm.assert_frame_equal(
+            df2.stack(level=[1, 2], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=[0, 1], future_stack=future_stack),
+            exp_animal_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=[0, 2], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+        # Out-of-order int column names
+        df3 = df.copy()
+        df3.columns.names = [2, 0, 1]
+        tm.assert_frame_equal(
+            df3.stack(level=[0, 1], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df3.stack(level=[2, 0], future_stack=future_stack),
+            exp_animal_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df3.stack(level=[2, 1], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+    def test_unstack_bool(self):
+        df = DataFrame(
+            [False, False],
+            index=MultiIndex.from_arrays([["a", "b"], ["c", "l"]]),
+            columns=["col"],
+        )
+        rs = df.unstack()
+        xp = DataFrame(
+            np.array([[False, np.nan], [np.nan, False]], dtype=object),
+            index=["a", "b"],
+            columns=MultiIndex.from_arrays([["col", "col"], ["c", "l"]]),
+        )
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_level_binding(self, future_stack):
+        # GH9856
+        mi = MultiIndex(
+            levels=[["foo", "bar"], ["one", "two"], ["a", "b"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]],
+            names=["first", "second", "third"],
+        )
+        s = Series(0, index=mi)
+        result = s.unstack([1, 2]).stack(0, future_stack=future_stack)
+
+        expected_mi = MultiIndex(
+            levels=[["foo", "bar"], ["one", "two"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+            names=["first", "second"],
+        )
+
+        expected = DataFrame(
+            np.array(
+                [[0, np.nan], [np.nan, 0], [0, np.nan], [np.nan, 0]], dtype=np.float64
+            ),
+            index=expected_mi,
+            columns=Index(["b", "a"], name="third"),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_to_series(self, float_frame):
+        # check reversibility
+        data = float_frame.unstack()
+
+        assert isinstance(data, Series)
+        undo = data.unstack().T
+        tm.assert_frame_equal(undo, float_frame)
+
+        # check NA handling
+        data = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]})
+        data.index = Index(["a", "b", "c"])
+        result = data.unstack()
+
+        midx = MultiIndex(
+            levels=[["x", "y"], ["a", "b", "c"]],
+            codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
+        )
+        expected = Series([1, 2, np.nan, 3, 4, np.nan], index=midx)
+
+        tm.assert_series_equal(result, expected)
+
+        # check composability of unstack
+        old_data = data.copy()
+        for _ in range(4):
+            data = data.unstack()
+        tm.assert_frame_equal(old_data, data)
+
+    def test_unstack_dtypes(self, using_infer_string):
+        # GH 2929
+        rows = [[1, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4], [2, 2, 3, 4]]
+
+        df = DataFrame(rows, columns=list("ABCD"))
+        result = df.dtypes
+        expected = Series([np.dtype("int64")] * 4, index=list("ABCD"))
+        tm.assert_series_equal(result, expected)
+
+        # single dtype
+        df2 = df.set_index(["A", "B"])
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        expected = Series(
+            [np.dtype("int64")] * 4,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+        # mixed
+        df2 = df.set_index(["A", "B"])
+        df2["C"] = 3.0
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        expected = Series(
+            [np.dtype("float64")] * 2 + [np.dtype("int64")] * 2,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+        df2["D"] = "foo"
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        dtype = (
+            pd.StringDtype(na_value=np.nan)
+            if using_infer_string
+            else np.dtype("object")
+        )
+        expected = Series(
+            [np.dtype("float64")] * 2 + [dtype] * 2,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "c, d",
+        (
+            (np.zeros(5), np.zeros(5)),
+            (np.arange(5, dtype="f8"), np.arange(5, 10, dtype="f8")),
+        ),
+    )
+    def test_unstack_dtypes_mixed_date(self, c, d):
+        # GH7405
+        df = DataFrame(
+            {
+                "A": ["a"] * 5,
+                "C": c,
+                "D": d,
+                "B": date_range("2012-01-01", periods=5),
+            }
+        )
+
+        right = df.iloc[:3].copy(deep=True)
+
+        df = df.set_index(["A", "B"])
+        df["D"] = df["D"].astype("int64")
+
+        left = df.iloc[:3].unstack(0)
+        right = right.set_index(["A", "B"]).unstack(0)
+        right[("D", "a")] = right[("D", "a")].astype("int64")
+
+        assert left.shape == (3, 2)
+        tm.assert_frame_equal(left, right)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_non_unique_index_names(self, future_stack):
+        idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"])
+        df = DataFrame([1, 2], index=idx)
+        msg = "The name c1 occurs multiple times, use a level number"
+        with pytest.raises(ValueError, match=msg):
+            df.unstack("c1")
+
+        with pytest.raises(ValueError, match=msg):
+            df.T.stack("c1", future_stack=future_stack)
+
+    def test_unstack_unused_levels(self):
+        # GH 17845: unused codes in index make unstack() cast int to float
+        idx = MultiIndex.from_product([["a"], ["A", "B", "C", "D"]])[:-1]
+        df = DataFrame([[1, 0]] * 3, index=idx)
+
+        result = df.unstack()
+        exp_col = MultiIndex.from_product([[0, 1], ["A", "B", "C"]])
+        expected = DataFrame([[1, 1, 1, 0, 0, 0]], index=["a"], columns=exp_col)
+        tm.assert_frame_equal(result, expected)
+        assert (result.columns.levels[1] == idx.levels[1]).all()
+
+        # Unused items on both levels
+        levels = [[0, 1, 7], [0, 1, 2, 3]]
+        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
+        idx = MultiIndex(levels, codes)
+        block = np.arange(4).reshape(2, 2)
+        df = DataFrame(np.concatenate([block, block + 4]), index=idx)
+        result = df.unstack()
+        expected = DataFrame(
+            np.concatenate([block * 2, block * 2 + 1], axis=1), columns=idx
+        )
+        tm.assert_frame_equal(result, expected)
+        assert (result.columns.levels[1] == idx.levels[1]).all()
+
+    @pytest.mark.parametrize(
+        "level, idces, col_level, idx_level",
+        (
+            (0, [13, 16, 6, 9, 2, 5, 8, 11], [np.nan, "a", 2], [np.nan, 5, 1]),
+            (1, [8, 11, 1, 4, 12, 15, 13, 16], [np.nan, 5, 1], [np.nan, "a", 2]),
+        ),
+    )
+    def test_unstack_unused_levels_mixed_with_nan(
+        self, level, idces, col_level, idx_level
+    ):
+        # With mixed dtype and NaN
+        levels = [["a", 2, "c"], [1, 3, 5, 7]]
+        codes = [[0, -1, 1, 1], [0, 2, -1, 2]]
+        idx = MultiIndex(levels, codes)
+        data = np.arange(8)
+        df = DataFrame(data.reshape(4, 2), index=idx)
+
+        result = df.unstack(level=level)
+        exp_data = np.zeros(18) * np.nan
+        exp_data[idces] = data
+        cols = MultiIndex.from_product([[0, 1], col_level])
+        expected = DataFrame(exp_data.reshape(3, 6), index=idx_level, columns=cols)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("cols", [["A", "C"], slice(None)])
+    def test_unstack_unused_level(self, cols):
+        # GH 18562 : unused codes on the unstacked level
+        df = DataFrame([[2010, "a", "I"], [2011, "b", "II"]], columns=["A", "B", "C"])
+
+        ind = df.set_index(["A", "B", "C"], drop=False)
+        selection = ind.loc[(slice(None), slice(None), "I"), cols]
+        result = selection.unstack()
+
+        expected = ind.iloc[[0]][cols]
+        expected.columns = MultiIndex.from_product(
+            [expected.columns, ["I"]], names=[None, "C"]
+        )
+        expected.index = expected.index.droplevel("C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_long_index(self):
+        # PH 32624: Error when using a lot of indices to unstack.
+        # The error occurred only, if a lot of indices are used.
+        df = DataFrame(
+            [[1]],
+            columns=MultiIndex.from_tuples([[0]], names=["c1"]),
+            index=MultiIndex.from_tuples(
+                [[0, 0, 1, 0, 0, 0, 1]],
+                names=["i1", "i2", "i3", "i4", "i5", "i6", "i7"],
+            ),
+        )
+        result = df.unstack(["i2", "i3", "i4", "i5", "i6", "i7"])
+        expected = DataFrame(
+            [[1]],
+            columns=MultiIndex.from_tuples(
+                [[0, 0, 1, 0, 0, 0, 1]],
+                names=["c1", "i2", "i3", "i4", "i5", "i6", "i7"],
+            ),
+            index=Index([0], name="i1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_multi_level_cols(self):
+        # PH 24729: Unstack a df with multi level columns
+        df = DataFrame(
+            [[0.0, 0.0], [0.0, 0.0]],
+            columns=MultiIndex.from_tuples(
+                [["B", "C"], ["B", "D"]], names=["c1", "c2"]
+            ),
+            index=MultiIndex.from_tuples(
+                [[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"]
+            ),
+        )
+        assert df.unstack(["i2", "i1"]).columns.names[-2:] == ["i2", "i1"]
+
+    def test_unstack_multi_level_rows_and_cols(self):
+        # PH 28306: Unstack df with multi level cols and rows
+        df = DataFrame(
+            [[1, 2], [3, 4], [-1, -2], [-3, -4]],
+            columns=MultiIndex.from_tuples([["a", "b", "c"], ["d", "e", "f"]]),
+            index=MultiIndex.from_tuples(
+                [
+                    ["m1", "P3", 222],
+                    ["m1", "A5", 111],
+                    ["m2", "P3", 222],
+                    ["m2", "A5", 111],
+                ],
+                names=["i1", "i2", "i3"],
+            ),
+        )
+        result = df.unstack(["i3", "i2"])
+        expected = df.unstack(["i3"]).unstack(["i2"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("idx", [("jim", "joe"), ("joe", "jim")])
+    @pytest.mark.parametrize("lev", list(range(2)))
+    def test_unstack_nan_index1(self, idx, lev):
+        # GH7466
+        def cast(val):
+            val_str = "" if val != val else val
+            return f"{val_str:1}"
+
+        df = DataFrame(
+            {
+                "jim": ["a", "b", np.nan, "d"],
+                "joe": ["w", "x", "y", "z"],
+                "jolie": ["a.w", "b.x", " .y", "d.z"],
+            }
+        )
+
+        left = df.set_index(["jim", "joe"]).unstack()["jolie"]
+        right = df.set_index(["joe", "jim"]).unstack()["jolie"].T
+        tm.assert_frame_equal(left, right)
+
+        mi = df.set_index(list(idx))
+        udf = mi.unstack(level=lev)
+        assert udf.notna().values.sum() == len(df)
+        mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
+        rows, cols = udf["jolie"].notna().values.nonzero()
+        for i, j in zip(rows, cols):
+            left = sorted(udf["jolie"].iloc[i, j].split("."))
+            right = mk_list(udf["jolie"].index[i]) + mk_list(udf["jolie"].columns[j])
+            right = sorted(map(cast, right))
+            assert left == right
+
+    @pytest.mark.parametrize("idx", itertools.permutations(["1st", "2nd", "3rd"]))
+    @pytest.mark.parametrize("lev", list(range(3)))
+    @pytest.mark.parametrize("col", ["4th", "5th"])
+    def test_unstack_nan_index_repeats(self, idx, lev, col):
+        def cast(val):
+            val_str = "" if val != val else val
+            return f"{val_str:1}"
+
+        df = DataFrame(
+            {
+                "1st": ["d"] * 3
+                + [np.nan] * 5
+                + ["a"] * 2
+                + ["c"] * 3
+                + ["e"] * 2
+                + ["b"] * 5,
+                "2nd": ["y"] * 2
+                + ["w"] * 3
+                + [np.nan] * 3
+                + ["z"] * 4
+                + [np.nan] * 3
+                + ["x"] * 3
+                + [np.nan] * 2,
+                "3rd": [
+                    67,
+                    39,
+                    53,
+                    72,
+                    57,
+                    80,
+                    31,
+                    18,
+                    11,
+                    30,
+                    59,
+                    50,
+                    62,
+                    59,
+                    76,
+                    52,
+                    14,
+                    53,
+                    60,
+                    51,
+                ],
+            }
+        )
+
+        df["4th"], df["5th"] = (
+            df.apply(lambda r: ".".join(map(cast, r)), axis=1),
+            df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1),
+        )
+
+        mi = df.set_index(list(idx))
+        udf = mi.unstack(level=lev)
+        assert udf.notna().values.sum() == 2 * len(df)
+        mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
+        rows, cols = udf[col].notna().values.nonzero()
+        for i, j in zip(rows, cols):
+            left = sorted(udf[col].iloc[i, j].split("."))
+            right = mk_list(udf[col].index[i]) + mk_list(udf[col].columns[j])
+            right = sorted(map(cast, right))
+            assert left == right
+
+    def test_unstack_nan_index2(self):
+        # GH7403
+        df = DataFrame({"A": list("aaaabbbb"), "B": range(8), "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [
+            [3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan],
+            [np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7],
+        ]
+        vals = list(map(list, zip(*vals)))
+        idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B")
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[2, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]]
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+        idx = Index([np.nan, 0, 1, 2, 3], name="B")
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]]
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+        idx = Index([np.nan, 0, 1, 2, 3], name="B")
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+    def test_unstack_nan_index3(self, using_array_manager):
+        # GH7401
+        df = DataFrame(
+            {
+                "A": list("aaaaabbbbb"),
+                "B": (date_range("2012-01-01", periods=5).tolist() * 2),
+                "C": np.arange(10),
+            }
+        )
+
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack()
+
+        vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]])
+        idx = Index(["a", "b"], name="A")
+        cols = MultiIndex(
+            levels=[["C"], date_range("2012-01-01", periods=5)],
+            codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
+            names=[None, "B"],
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        if using_array_manager:
+            # INFO(ArrayManager) with ArrayManager preserve dtype where possible
+            cols = right.columns[[1, 2, 3, 5]]
+            right[cols] = right[cols].astype(df["C"].dtype)
+        tm.assert_frame_equal(left, right)
+
+    def test_unstack_nan_index4(self):
+        # GH4862
+        vals = [
+            ["Hg", np.nan, np.nan, 680585148],
+            ["U", 0.0, np.nan, 680585148],
+            ["Pb", 7.07e-06, np.nan, 680585148],
+            ["Sn", 2.3614e-05, 0.0133, 680607017],
+            ["Ag", 0.0, 0.0133, 680607017],
+            ["Hg", -0.00015, 0.0133, 680607017],
+        ]
+        df = DataFrame(
+            vals,
+            columns=["agent", "change", "dosage", "s_id"],
+            index=[17263, 17264, 17265, 17266, 17267, 17268],
+        )
+
+        left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack()
+
+        vals = [
+            [np.nan, np.nan, 7.07e-06, np.nan, 0.0],
+            [0.0, -0.00015, np.nan, 2.3614e-05, np.nan],
+        ]
+
+        idx = MultiIndex(
+            levels=[[680585148, 680607017], [0.0133]],
+            codes=[[0, 1], [-1, 0]],
+            names=["s_id", "dosage"],
+        )
+
+        cols = MultiIndex(
+            levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]],
+            codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
+            names=[None, "agent"],
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
+        tm.assert_frame_equal(left.unstack(), right)
+
+    def test_unstack_nan_index5(self):
+        # GH9497 - multiple unstack with nulls
+        df = DataFrame(
+            {
+                "1st": [1, 2, 1, 2, 1, 2],
+                "2nd": date_range("2014-02-01", periods=6, freq="D"),
+                "jim": 100 + np.arange(6),
+                "joe": (np.random.default_rng(2).standard_normal(6) * 10).round(2),
+            }
+        )
+
+        df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02")
+        df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan
+        df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan
+
+        left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"])
+        assert left.notna().values.sum() == 2 * len(df)
+
+        for col in ["jim", "joe"]:
+            for _, r in df.iterrows():
+                key = r["1st"], (col, r["2nd"], r["3rd"])
+                assert r[col] == left.loc[key]
+
+    def test_stack_datetime_column_multiIndex(self, future_stack):
+        # GH 8039
+        t = datetime(2014, 1, 1)
+        df = DataFrame([1, 2, 3, 4], columns=MultiIndex.from_tuples([(t, "A", "B")]))
+        warn = None if future_stack else FutureWarning
+        msg = "The previous implementation of stack is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = df.stack(future_stack=future_stack)
+
+        eidx = MultiIndex.from_product([(0, 1, 2, 3), ("B",)])
+        ecols = MultiIndex.from_tuples([(t, "A")])
+        expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "multiindex_columns",
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3],
+            [0, 1, 2, 4],
+            [0, 1, 2],
+            [1, 2, 3],
+            [2, 3, 4],
+            [0, 1],
+            [0, 2],
+            [0, 3],
+            [0],
+            [2],
+            [4],
+            [4, 3, 2, 1, 0],
+            [3, 2, 1, 0],
+            [4, 2, 1, 0],
+            [2, 1, 0],
+            [3, 2, 1],
+            [4, 3, 2],
+            [1, 0],
+            [2, 0],
+            [3, 0],
+        ],
+    )
+    @pytest.mark.parametrize("level", (-1, 0, 1, [0, 1], [1, 0]))
+    def test_stack_partial_multiIndex(self, multiindex_columns, level, future_stack):
+        # GH 8844
+        dropna = False if not future_stack else lib.no_default
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
+        multiindex = full_multiindex[multiindex_columns]
+        df = DataFrame(
+            np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
+            columns=multiindex,
+        )
+        result = df.stack(level=level, dropna=dropna, future_stack=future_stack)
+
+        if isinstance(level, int) and not future_stack:
+            # Stacking a single level should not make any all-NaN rows,
+            # so df.stack(level=level, dropna=False) should be the same
+            # as df.stack(level=level, dropna=True).
+            expected = df.stack(level=level, dropna=True, future_stack=future_stack)
+            if isinstance(expected, Series):
+                tm.assert_series_equal(result, expected)
+            else:
+                tm.assert_frame_equal(result, expected)
+
+        df.columns = MultiIndex.from_tuples(
+            df.columns.to_numpy(), names=df.columns.names
+        )
+        expected = df.stack(level=level, dropna=dropna, future_stack=future_stack)
+        if isinstance(expected, Series):
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_full_multiIndex(self, future_stack):
+        # GH 8844
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
+        df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]])
+        dropna = False if not future_stack else lib.no_default
+        result = df.stack(dropna=dropna, future_stack=future_stack)
+        expected = DataFrame(
+            [[0, 2], [1, np.nan], [3, 5], [4, np.nan]],
+            index=MultiIndex(
+                levels=[[0, 1], ["u", "x", "y", "z"]],
+                codes=[[0, 0, 1, 1], [1, 3, 1, 3]],
+                names=[None, "Lower"],
+            ),
+            columns=Index(["B", "C"], name="Upper"),
+        )
+        expected["B"] = expected["B"].astype(df.dtypes.iloc[0])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [False, True])
+    def test_stack_preserve_categorical_dtype(self, ordered, future_stack):
+        # GH13854
+        cidx = pd.CategoricalIndex(list("yxz"), categories=list("xyz"), ordered=ordered)
+        df = DataFrame([[10, 11, 12]], columns=cidx)
+        result = df.stack(future_stack=future_stack)
+
+        # `MultiIndex.from_product` preserves categorical dtype -
+        # it's tested elsewhere.
+        midx = MultiIndex.from_product([df.index, cidx])
+        expected = Series([10, 11, 12], index=midx)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize("ordered", [False, True])
+    @pytest.mark.parametrize(
+        "labels,data",
+        [
+            (list("xyz"), [10, 11, 12, 13, 14, 15]),
+            (list("zyx"), [14, 15, 12, 13, 10, 11]),
+        ],
+    )
+    def test_stack_multi_preserve_categorical_dtype(
+        self, ordered, labels, data, future_stack
+    ):
+        # GH-36991
+        cidx = pd.CategoricalIndex(labels, categories=sorted(labels), ordered=ordered)
+        cidx2 = pd.CategoricalIndex(["u", "v"], ordered=ordered)
+        midx = MultiIndex.from_product([cidx, cidx2])
+        df = DataFrame([sorted(data)], columns=midx)
+        result = df.stack([0, 1], future_stack=future_stack)
+
+        labels = labels if future_stack else sorted(labels)
+        s_cidx = pd.CategoricalIndex(labels, ordered=ordered)
+        expected_data = sorted(data) if future_stack else data
+        expected = Series(
+            expected_data, index=MultiIndex.from_product([[0], s_cidx, cidx2])
+        )
+
+        tm.assert_series_equal(result, expected)
+
+    def test_stack_preserve_categorical_dtype_values(self, future_stack):
+        # GH-23077
+        cat = pd.Categorical(["a", "a", "b", "c"])
+        df = DataFrame({"A": cat, "B": cat})
+        result = df.stack(future_stack=future_stack)
+        index = MultiIndex.from_product([[0, 1, 2, 3], ["A", "B"]])
+        expected = Series(
+            pd.Categorical(["a", "a", "a", "a", "b", "b", "c", "c"]), index=index
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    @pytest.mark.parametrize(
+        "index, columns",
+        [
+            ([0, 0, 1, 1], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+            ([0, 0, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+            ([0, 1, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+        ],
+    )
+    def test_stack_multi_columns_non_unique_index(self, index, columns, future_stack):
+        # GH-28301
+
+        df = DataFrame(index=index, columns=columns).fillna(1)
+        stacked = df.stack(future_stack=future_stack)
+        new_index = MultiIndex.from_tuples(stacked.index.to_numpy())
+        expected = DataFrame(
+            stacked.to_numpy(), index=new_index, columns=stacked.columns
+        )
+        tm.assert_frame_equal(stacked, expected)
+        stacked_codes = np.asarray(stacked.index.codes)
+        expected_codes = np.asarray(new_index.codes)
+        tm.assert_numpy_array_equal(stacked_codes, expected_codes)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "vals1, vals2, dtype1, dtype2, expected_dtype",
+        [
+            ([1, 2], [3.0, 4.0], "Int64", "Float64", "Float64"),
+            ([1, 2], ["foo", "bar"], "Int64", "string", "object"),
+        ],
+    )
+    def test_stack_multi_columns_mixed_extension_types(
+        self, vals1, vals2, dtype1, dtype2, expected_dtype, future_stack
+    ):
+        # GH45740
+        df = DataFrame(
+            {
+                ("A", 1): Series(vals1, dtype=dtype1),
+                ("A", 2): Series(vals2, dtype=dtype2),
+            }
+        )
+        result = df.stack(future_stack=future_stack)
+        expected = (
+            df.astype(object).stack(future_stack=future_stack).astype(expected_dtype)
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("level", [0, 1])
+    def test_unstack_mixed_extension_types(self, level):
+        index = MultiIndex.from_tuples([("A", 0), ("A", 1), ("B", 1)], names=["a", "b"])
+        df = DataFrame(
+            {
+                "A": pd.array([0, 1, None], dtype="Int64"),
+                "B": pd.Categorical(["a", "a", "b"]),
+            },
+            index=index,
+        )
+
+        result = df.unstack(level=level)
+        expected = df.astype(object).unstack(level=level)
+        if level == 0:
+            expected[("A", "B")] = expected[("A", "B")].fillna(pd.NA)
+        else:
+            expected[("A", 0)] = expected[("A", 0)].fillna(pd.NA)
+
+        expected_dtypes = Series(
+            [df.A.dtype] * 2 + [df.B.dtype] * 2, index=result.columns
+        )
+        tm.assert_series_equal(result.dtypes, expected_dtypes)
+        tm.assert_frame_equal(result.astype(object), expected)
+
+    @pytest.mark.parametrize("level", [0, "baz"])
+    def test_unstack_swaplevel_sortlevel(self, level):
+        # GH 20994
+        mi = MultiIndex.from_product([[0], ["d", "c"]], names=["bar", "baz"])
+        df = DataFrame([[0, 2], [1, 3]], index=mi, columns=["B", "A"])
+        df.columns.name = "foo"
+
+        expected = DataFrame(
+            [[3, 1, 2, 0]],
+            columns=MultiIndex.from_tuples(
+                [("c", "A"), ("c", "B"), ("d", "A"), ("d", "B")], names=["baz", "foo"]
+            ),
+        )
+        expected.index.name = "bar"
+
+        result = df.unstack().swaplevel(axis=1).sort_index(axis=1, level=level)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+def test_unstack_sort_false(frame_or_series, dtype):
+    # GH 15105
+    index = MultiIndex.from_tuples(
+        [("two", "z", "b"), ("two", "y", "a"), ("one", "z", "b"), ("one", "y", "a")]
+    )
+    obj = frame_or_series(np.arange(1.0, 5.0), index=index, dtype=dtype)
+    result = obj.unstack(level=-1, sort=False)
+
+    if frame_or_series is DataFrame:
+        expected_columns = MultiIndex.from_tuples([(0, "b"), (0, "a")])
+    else:
+        expected_columns = ["b", "a"]
+    expected = DataFrame(
+        [[1.0, np.nan], [np.nan, 2.0], [3.0, np.nan], [np.nan, 4.0]],
+        columns=expected_columns,
+        index=MultiIndex.from_tuples(
+            [("two", "z"), ("two", "y"), ("one", "z"), ("one", "y")]
+        ),
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = obj.unstack(level=[1, 2], sort=False)
+
+    if frame_or_series is DataFrame:
+        expected_columns = MultiIndex.from_tuples([(0, "z", "b"), (0, "y", "a")])
+    else:
+        expected_columns = MultiIndex.from_tuples([("z", "b"), ("y", "a")])
+    expected = DataFrame(
+        [[1.0, 2.0], [3.0, 4.0]],
+        index=["two", "one"],
+        columns=expected_columns,
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_fill_frame_object():
+    # GH12815 Test unstacking with object.
+    data = Series(["a", "b", "c", "a"], dtype="object")
+    data.index = MultiIndex.from_tuples(
+        [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+    )
+
+    # By default missing values will be NaN
+    result = data.unstack()
+    expected = DataFrame(
+        {"a": ["a", np.nan, "a"], "b": ["b", "c", np.nan]},
+        index=list("xyz"),
+        dtype=object,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # Fill with any value replaces missing values as expected
+    result = data.unstack(fill_value="d")
+    expected = DataFrame(
+        {"a": ["a", "d", "a"], "b": ["b", "c", "d"]}, index=list("xyz"), dtype=object
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_timezone_aware_values():
+    # GH 18338
+    df = DataFrame(
+        {
+            "timestamp": [pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC")],
+            "a": ["a"],
+            "b": ["b"],
+            "c": ["c"],
+        },
+        columns=["timestamp", "a", "b", "c"],
+    )
+    result = df.set_index(["a", "b"]).unstack()
+    expected = DataFrame(
+        [[pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC"), "c"]],
+        index=Index(["a"], name="a"),
+        columns=MultiIndex(
+            levels=[["timestamp", "c"], ["b"]],
+            codes=[[0, 1], [0, 0]],
+            names=[None, "b"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_stack_timezone_aware_values(future_stack):
+    # GH 19420
+    ts = date_range(freq="D", start="20180101", end="20180103", tz="America/New_York")
+    df = DataFrame({"A": ts}, index=["a", "b", "c"])
+    result = df.stack(future_stack=future_stack)
+    expected = Series(
+        ts,
+        index=MultiIndex(levels=[["a", "b", "c"], ["A"]], codes=[[0, 1, 2], [0, 0, 0]]),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+@pytest.mark.parametrize("dropna", [True, False, lib.no_default])
+def test_stack_empty_frame(dropna, future_stack):
+    # GH 36113
+    levels = [np.array([], dtype=np.int64), np.array([], dtype=np.int64)]
+    expected = Series(dtype=np.float64, index=MultiIndex(levels=levels, codes=[[], []]))
+    if future_stack and dropna is not lib.no_default:
+        with pytest.raises(ValueError, match="dropna must be unspecified"):
+            DataFrame(dtype=np.float64).stack(dropna=dropna, future_stack=future_stack)
+    else:
+        result = DataFrame(dtype=np.float64).stack(
+            dropna=dropna, future_stack=future_stack
+        )
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+@pytest.mark.parametrize("dropna", [True, False, lib.no_default])
+@pytest.mark.parametrize("fill_value", [None, 0])
+def test_stack_unstack_empty_frame(dropna, fill_value, future_stack):
+    # GH 36113
+    if future_stack and dropna is not lib.no_default:
+        with pytest.raises(ValueError, match="dropna must be unspecified"):
+            DataFrame(dtype=np.int64).stack(
+                dropna=dropna, future_stack=future_stack
+            ).unstack(fill_value=fill_value)
+    else:
+        result = (
+            DataFrame(dtype=np.int64)
+            .stack(dropna=dropna, future_stack=future_stack)
+            .unstack(fill_value=fill_value)
+        )
+        expected = DataFrame(dtype=np.int64)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_single_index_series():
+    # GH 36113
+    msg = r"index must be a MultiIndex to unstack.*"
+    with pytest.raises(ValueError, match=msg):
+        Series(dtype=np.int64).unstack()
+
+
+def test_unstacking_multi_index_df():
+    # see gh-30740
+    df = DataFrame(
+        {
+            "name": ["Alice", "Bob"],
+            "score": [9.5, 8],
+            "employed": [False, True],
+            "kids": [0, 0],
+            "gender": ["female", "male"],
+        }
+    )
+    df = df.set_index(["name", "employed", "kids", "gender"])
+    df = df.unstack(["gender"], fill_value=0)
+    expected = df.unstack("employed", fill_value=0).unstack("kids", fill_value=0)
+    result = df.unstack(["employed", "kids"], fill_value=0)
+    expected = DataFrame(
+        [[9.5, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 8.0]],
+        index=Index(["Alice", "Bob"], name="name"),
+        columns=MultiIndex.from_tuples(
+            [
+                ("score", "female", False, 0),
+                ("score", "female", True, 0),
+                ("score", "male", False, 0),
+                ("score", "male", True, 0),
+            ],
+            names=[None, "gender", "employed", "kids"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_positional_level_duplicate_column_names(future_stack):
+    # https://github.com/pandas-dev/pandas/issues/36353
+    columns = MultiIndex.from_product([("x", "y"), ("y", "z")], names=["a", "a"])
+    df = DataFrame([[1, 1, 1, 1]], columns=columns)
+    result = df.stack(0, future_stack=future_stack)
+
+    new_columns = Index(["y", "z"], name="a")
+    new_index = MultiIndex.from_tuples([(0, "x"), (0, "y")], names=[None, "a"])
+    expected = DataFrame([[1, 1], [1, 1]], index=new_index, columns=new_columns)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_non_slice_like_blocks(using_array_manager):
+    # Case where the mgr_locs of a DataFrame's underlying blocks are not slice-like
+
+    mi = MultiIndex.from_product([range(5), ["A", "B", "C"]])
+    df = DataFrame(
+        {
+            0: np.random.default_rng(2).standard_normal(15),
+            1: np.random.default_rng(2).standard_normal(15).astype(np.int64),
+            2: np.random.default_rng(2).standard_normal(15),
+            3: np.random.default_rng(2).standard_normal(15),
+        },
+        index=mi,
+    )
+    if not using_array_manager:
+        assert any(not x.mgr_locs.is_slice_like for x in df._mgr.blocks)
+
+    res = df.unstack()
+
+    expected = pd.concat([df[n].unstack() for n in range(4)], keys=range(4), axis=1)
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_sort_false(future_stack):
+    # GH 15105
+    data = [[1, 2, 3.0, 4.0], [2, 3, 4.0, 5.0], [3, 4, np.nan, np.nan]]
+    df = DataFrame(
+        data,
+        columns=MultiIndex(
+            levels=[["B", "A"], ["x", "y"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]]
+        ),
+    )
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack(level=0, future_stack=future_stack, **kwargs)
+    if future_stack:
+        expected = DataFrame(
+            {
+                "x": [1.0, 3.0, 2.0, 4.0, 3.0, np.nan],
+                "y": [2.0, 4.0, 3.0, 5.0, 4.0, np.nan],
+            },
+            index=MultiIndex.from_arrays(
+                [[0, 0, 1, 1, 2, 2], ["B", "A", "B", "A", "B", "A"]]
+            ),
+        )
+    else:
+        expected = DataFrame(
+            {"x": [1.0, 3.0, 2.0, 4.0, 3.0], "y": [2.0, 4.0, 3.0, 5.0, 4.0]},
+            index=MultiIndex.from_arrays([[0, 0, 1, 1, 2], ["B", "A", "B", "A", "B"]]),
+        )
+    tm.assert_frame_equal(result, expected)
+
+    # Codes sorted in this call
+    df = DataFrame(
+        data,
+        columns=MultiIndex.from_arrays([["B", "B", "A", "A"], ["x", "y", "x", "y"]]),
+    )
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack(level=0, future_stack=future_stack, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_sort_false_multi_level(future_stack):
+    # GH 15105
+    idx = MultiIndex.from_tuples([("weight", "kg"), ("height", "m")])
+    df = DataFrame([[1.0, 2.0], [3.0, 4.0]], index=["cat", "dog"], columns=idx)
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack([0, 1], future_stack=future_stack, **kwargs)
+    expected_index = MultiIndex.from_tuples(
+        [
+            ("cat", "weight", "kg"),
+            ("cat", "height", "m"),
+            ("dog", "weight", "kg"),
+            ("dog", "height", "m"),
+        ]
+    )
+    expected = Series([1.0, 2.0, 3.0, 4.0], index=expected_index)
+    tm.assert_series_equal(result, expected)
+
+
+class TestStackUnstackMultiLevel:
+    def test_unstack(self, multiindex_year_month_day_dataframe_random_data):
+        # just check that it works for now
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack()
+        unstacked.unstack()
+
+        # test that ints work
+        ymd.astype(int).unstack()
+
+        # test that int32 work
+        ymd.astype(np.int32).unstack()
+
+    @pytest.mark.parametrize(
+        "result_rows,result_columns,index_product,expected_row",
+        [
+            (
+                [[1, 1, None, None, 30.0, None], [2, 2, None, None, 30.0, None]],
+                ["ix1", "ix2", "col1", "col2", "col3", "col4"],
+                2,
+                [None, None, 30.0, None],
+            ),
+            (
+                [[1, 1, None, None, 30.0], [2, 2, None, None, 30.0]],
+                ["ix1", "ix2", "col1", "col2", "col3"],
+                2,
+                [None, None, 30.0],
+            ),
+            (
+                [[1, 1, None, None, 30.0], [2, None, None, None, 30.0]],
+                ["ix1", "ix2", "col1", "col2", "col3"],
+                None,
+                [None, None, 30.0],
+            ),
+        ],
+    )
+    def test_unstack_partial(
+        self, result_rows, result_columns, index_product, expected_row
+    ):
+        # check for regressions on this issue:
+        # https://github.com/pandas-dev/pandas/issues/19351
+        # make sure DataFrame.unstack() works when its run on a subset of the DataFrame
+        # and the Index levels contain values that are not present in the subset
+        result = DataFrame(result_rows, columns=result_columns).set_index(
+            ["ix1", "ix2"]
+        )
+        result = result.iloc[1:2].unstack("ix2")
+        expected = DataFrame(
+            [expected_row],
+            columns=MultiIndex.from_product(
+                [result_columns[2:], [index_product]], names=[None, "ix2"]
+            ),
+            index=Index([2], name="ix1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_multiple_no_empty_columns(self):
+        index = MultiIndex.from_tuples(
+            [(0, "foo", 0), (0, "bar", 0), (1, "baz", 1), (1, "qux", 1)]
+        )
+
+        s = Series(np.random.default_rng(2).standard_normal(4), index=index)
+
+        unstacked = s.unstack([1, 2])
+        expected = unstacked.dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack(self, multiindex_year_month_day_dataframe_random_data, future_stack):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        # regular roundtrip
+        unstacked = ymd.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked, ymd)
+
+        unlexsorted = ymd.sort_index(level=2)
+
+        unstacked = unlexsorted.unstack(2)
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        unlexsorted = unlexsorted[::-1]
+        unstacked = unlexsorted.unstack(1)
+        restacked = unstacked.stack(future_stack=future_stack).swaplevel(1, 2)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        unlexsorted = unlexsorted.swaplevel(0, 1)
+        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
+        restacked = unstacked.stack(0, future_stack=future_stack).swaplevel(1, 2)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        # columns unsorted
+        unstacked = ymd.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked, ymd)
+
+        # more than 2 levels in the columns
+        unstacked = ymd.unstack(1).unstack(1)
+
+        result = unstacked.stack(1, future_stack=future_stack)
+        expected = ymd.unstack()
+        tm.assert_frame_equal(result, expected)
+
+        result = unstacked.stack(2, future_stack=future_stack)
+        expected = ymd.unstack(1)
+        tm.assert_frame_equal(result, expected)
+
+        result = unstacked.stack(0, future_stack=future_stack)
+        expected = ymd.stack(future_stack=future_stack).unstack(1).unstack(1)
+        tm.assert_frame_equal(result, expected)
+
+        # not all levels present in each echelon
+        unstacked = ymd.unstack(2).loc[:, ::3]
+        stacked = unstacked.stack(future_stack=future_stack).stack(
+            future_stack=future_stack
+        )
+        ymd_stacked = ymd.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            stacked = stacked.dropna(how="all")
+            ymd_stacked = ymd_stacked.dropna(how="all")
+        tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))
+
+        # stack with negative number
+        result = ymd.unstack(0).stack(-2, future_stack=future_stack)
+        expected = ymd.unstack(0).stack(0, future_stack=future_stack)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx, columns, exp_idx",
+        [
+            [
+                list("abab"),
+                ["1st", "2nd", "1st"],
+                MultiIndex(
+                    levels=[["a", "b"], ["1st", "2nd"]],
+                    codes=[np.tile(np.arange(2).repeat(3), 2), np.tile([0, 1, 0], 4)],
+                ),
+            ],
+            [
+                MultiIndex.from_tuples((("a", 2), ("b", 1), ("a", 1), ("b", 2))),
+                ["1st", "2nd", "1st"],
+                MultiIndex(
+                    levels=[["a", "b"], [1, 2], ["1st", "2nd"]],
+                    codes=[
+                        np.tile(np.arange(2).repeat(3), 2),
+                        np.repeat([1, 0, 1], [3, 6, 3]),
+                        np.tile([0, 1, 0], 4),
+                    ],
+                ),
+            ],
+        ],
+    )
+    def test_stack_duplicate_index(self, idx, columns, exp_idx, future_stack):
+        # GH10417
+        df = DataFrame(
+            np.arange(12).reshape(4, 3),
+            index=idx,
+            columns=columns,
+        )
+        if future_stack:
+            msg = "Columns with duplicate values are not supported in stack"
+            with pytest.raises(ValueError, match=msg):
+                df.stack(future_stack=future_stack)
+        else:
+            result = df.stack(future_stack=future_stack)
+            expected = Series(np.arange(12), index=exp_idx)
+            tm.assert_series_equal(result, expected)
+            assert result.index.is_unique is False
+            li, ri = result.index, expected.index
+            tm.assert_index_equal(li, ri)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_odd_failure(self, future_stack):
+        mi = MultiIndex.from_arrays(
+            [
+                ["Fri"] * 4 + ["Sat"] * 2 + ["Sun"] * 2 + ["Thu"] * 3,
+                ["Dinner"] * 2 + ["Lunch"] * 2 + ["Dinner"] * 5 + ["Lunch"] * 2,
+                ["No", "Yes"] * 4 + ["No", "No", "Yes"],
+            ],
+            names=["day", "time", "smoker"],
+        )
+        df = DataFrame(
+            {
+                "sum": np.arange(11, dtype="float64"),
+                "len": np.arange(11, dtype="float64"),
+            },
+            index=mi,
+        )
+        # it works, #2100
+        result = df.unstack(2)
+
+        recons = result.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            recons = recons.dropna(how="all")
+        tm.assert_frame_equal(recons, df)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_dtype(self, multiindex_dataframe_random_data, future_stack):
+        frame = multiindex_dataframe_random_data
+
+        df = frame.T
+        df["foo", "four"] = "foo"
+        df = df.sort_index(level=1, axis=1)
+
+        stacked = df.stack(future_stack=future_stack)
+        result = df["foo"].stack(future_stack=future_stack).sort_index()
+        tm.assert_series_equal(stacked["foo"], result, check_names=False)
+        assert result.name is None
+        assert stacked["bar"].dtype == np.float64
+
+    def test_unstack_bug(self, future_stack):
+        df = DataFrame(
+            {
+                "state": ["naive", "naive", "naive", "active", "active", "active"],
+                "exp": ["a", "b", "b", "b", "a", "a"],
+                "barcode": [1, 2, 3, 4, 1, 3],
+                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
+                "extra": np.arange(6.0),
+            }
+        )
+
+        msg = "DataFrameGroupBy.apply operated on the grouping columns"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
+
+        unstacked = result.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        tm.assert_series_equal(restacked, result.reindex(restacked.index).astype(float))
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_preserve_names(
+        self, multiindex_dataframe_random_data, future_stack
+    ):
+        frame = multiindex_dataframe_random_data
+
+        unstacked = frame.unstack()
+        assert unstacked.index.name == "first"
+        assert unstacked.columns.names == ["exp", "second"]
+
+        restacked = unstacked.stack(future_stack=future_stack)
+        assert restacked.index.names == frame.index.names
+
+    @pytest.mark.parametrize("method", ["stack", "unstack"])
+    def test_stack_unstack_wrong_level_name(
+        self, method, multiindex_dataframe_random_data, future_stack
+    ):
+        # GH 18303 - wrong level name should raise
+        frame = multiindex_dataframe_random_data
+
+        # A DataFrame with flat axes:
+        df = frame.loc["foo"]
+
+        kwargs = {"future_stack": future_stack} if method == "stack" else {}
+        with pytest.raises(KeyError, match="does not match index name"):
+            getattr(df, method)("mistake", **kwargs)
+
+        if method == "unstack":
+            # Same on a Series:
+            s = df.iloc[:, 0]
+            with pytest.raises(KeyError, match="does not match index name"):
+                getattr(s, method)("mistake", **kwargs)
+
+    def test_unstack_level_name(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        result = frame.unstack("second")
+        expected = frame.unstack(level=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_level_name(self, multiindex_dataframe_random_data, future_stack):
+        frame = multiindex_dataframe_random_data
+
+        unstacked = frame.unstack("second")
+        result = unstacked.stack("exp", future_stack=future_stack)
+        expected = frame.unstack().stack(0, future_stack=future_stack)
+        tm.assert_frame_equal(result, expected)
+
+        result = frame.stack("exp", future_stack=future_stack)
+        expected = frame.stack(future_stack=future_stack)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_multiple(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+        expected = ymd.unstack("year").unstack("month")
+        tm.assert_frame_equal(unstacked, expected)
+        assert unstacked.columns.names == expected.columns.names
+
+        # series
+        s = ymd["A"]
+        s_unstacked = s.unstack(["year", "month"])
+        tm.assert_frame_equal(s_unstacked, expected["A"])
+
+        restacked = unstacked.stack(["year", "month"], future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
+        restacked = restacked.sort_index(level=0)
+
+        tm.assert_frame_equal(restacked, ymd)
+        assert restacked.index.names == ymd.index.names
+
+        # GH #451
+        unstacked = ymd.unstack([1, 2])
+        expected = ymd.unstack(1).unstack(1).dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected)
+
+        unstacked = ymd.unstack([2, 1])
+        expected = ymd.unstack(2).unstack(1).dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_names_and_numbers(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+
+        # Can't use mixture of names and numbers to stack
+        with pytest.raises(ValueError, match="level should contain"):
+            unstacked.stack([0, "month"], future_stack=future_stack)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_multiple_out_of_bounds(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        # nlevels == 3
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+
+        with pytest.raises(IndexError, match="Too many levels"):
+            unstacked.stack([2, 3], future_stack=future_stack)
+        with pytest.raises(IndexError, match="not a valid level number"):
+            unstacked.stack([-4, -3], future_stack=future_stack)
+
+    def test_unstack_period_series(self):
+        # GH4342
+        idx1 = pd.PeriodIndex(
+            ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
+            freq="M",
+            name="period",
+        )
+        idx2 = Index(["A", "B"] * 3, name="str")
+        value = [1, 2, 3, 4, 5, 6]
+
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        s = Series(value, index=idx)
+
+        result1 = s.unstack()
+        result2 = s.unstack(level=1)
+        result3 = s.unstack(level=0)
+
+        e_idx = pd.PeriodIndex(
+            ["2013-01", "2013-02", "2013-03"], freq="M", name="period"
+        )
+        expected = DataFrame(
+            {"A": [1, 3, 5], "B": [2, 4, 6]}, index=e_idx, columns=["A", "B"]
+        )
+        expected.columns.name = "str"
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+        tm.assert_frame_equal(result3, expected.T)
+
+        idx1 = pd.PeriodIndex(
+            ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
+            freq="M",
+            name="period1",
+        )
+
+        idx2 = pd.PeriodIndex(
+            ["2013-12", "2013-11", "2013-10", "2013-09", "2013-08", "2013-07"],
+            freq="M",
+            name="period2",
+        )
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        s = Series(value, index=idx)
+
+        result1 = s.unstack()
+        result2 = s.unstack(level=1)
+        result3 = s.unstack(level=0)
+
+        e_idx = pd.PeriodIndex(
+            ["2013-01", "2013-02", "2013-03"], freq="M", name="period1"
+        )
+        e_cols = pd.PeriodIndex(
+            ["2013-07", "2013-08", "2013-09", "2013-10", "2013-11", "2013-12"],
+            freq="M",
+            name="period2",
+        )
+        expected = DataFrame(
+            [
+                [np.nan, np.nan, np.nan, np.nan, 2, 1],
+                [np.nan, np.nan, 4, 3, np.nan, np.nan],
+                [6, 5, np.nan, np.nan, np.nan, np.nan],
+            ],
+            index=e_idx,
+            columns=e_cols,
+        )
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+        tm.assert_frame_equal(result3, expected.T)
+
+    def test_unstack_period_frame(self):
+        # GH4342
+        idx1 = pd.PeriodIndex(
+            ["2014-01", "2014-02", "2014-02", "2014-02", "2014-01", "2014-01"],
+            freq="M",
+            name="period1",
+        )
+        idx2 = pd.PeriodIndex(
+            ["2013-12", "2013-12", "2014-02", "2013-10", "2013-10", "2014-02"],
+            freq="M",
+            name="period2",
+        )
+        value = {"A": [1, 2, 3, 4, 5, 6], "B": [6, 5, 4, 3, 2, 1]}
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        df = DataFrame(value, index=idx)
+
+        result1 = df.unstack()
+        result2 = df.unstack(level=1)
+        result3 = df.unstack(level=0)
+
+        e_1 = pd.PeriodIndex(["2014-01", "2014-02"], freq="M", name="period1")
+        e_2 = pd.PeriodIndex(
+            ["2013-10", "2013-12", "2014-02", "2013-10", "2013-12", "2014-02"],
+            freq="M",
+            name="period2",
+        )
+        e_cols = MultiIndex.from_arrays(["A A A B B B".split(), e_2])
+        expected = DataFrame(
+            [[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]], index=e_1, columns=e_cols
+        )
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+
+        e_1 = pd.PeriodIndex(
+            ["2014-01", "2014-02", "2014-01", "2014-02"], freq="M", name="period1"
+        )
+        e_2 = pd.PeriodIndex(
+            ["2013-10", "2013-12", "2014-02"], freq="M", name="period2"
+        )
+        e_cols = MultiIndex.from_arrays(["A A B B".split(), e_1])
+        expected = DataFrame(
+            [[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]], index=e_2, columns=e_cols
+        )
+
+        tm.assert_frame_equal(result3, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_multiple_bug(self, future_stack, using_infer_string):
+        # bug when some uniques are not present in the data GH#3170
+        id_col = ([1] * 3) + ([2] * 3)
+        name = (["a"] * 3) + (["b"] * 3)
+        date = pd.to_datetime(["2013-01-03", "2013-01-04", "2013-01-05"] * 2)
+        var1 = np.random.default_rng(2).integers(0, 100, 6)
+        df = DataFrame({"ID": id_col, "NAME": name, "DATE": date, "VAR1": var1})
+
+        multi = df.set_index(["DATE", "ID"])
+        multi.columns.name = "Params"
+        unst = multi.unstack("ID")
+        msg = re.escape("agg function failed [how->mean,dtype->")
+        if using_infer_string:
+            msg = "dtype 'str' does not support operation 'mean'"
+        with pytest.raises(TypeError, match=msg):
+            unst.resample("W-THU").mean()
+        down = unst.resample("W-THU").mean(numeric_only=True)
+        rs = down.stack("ID", future_stack=future_stack)
+        xp = (
+            unst.loc[:, ["VAR1"]]
+            .resample("W-THU")
+            .mean()
+            .stack("ID", future_stack=future_stack)
+        )
+        xp.columns.name = "Params"
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_dropna(self, future_stack):
+        # GH#3997
+        df = DataFrame({"A": ["a1", "a2"], "B": ["b1", "b2"], "C": [1, 1]})
+        df = df.set_index(["A", "B"])
+
+        dropna = False if not future_stack else lib.no_default
+        stacked = df.unstack().stack(dropna=dropna, future_stack=future_stack)
+        assert len(stacked) > len(stacked.dropna())
+
+        if future_stack:
+            with pytest.raises(ValueError, match="dropna must be unspecified"):
+                df.unstack().stack(dropna=True, future_stack=future_stack)
+        else:
+            stacked = df.unstack().stack(dropna=True, future_stack=future_stack)
+            tm.assert_frame_equal(stacked, stacked.dropna())
+
+    def test_unstack_multiple_hierarchical(self, future_stack):
+        df = DataFrame(
+            index=[
+                [0, 0, 0, 0, 1, 1, 1, 1],
+                [0, 0, 1, 1, 0, 0, 1, 1],
+                [0, 1, 0, 1, 0, 1, 0, 1],
+            ],
+            columns=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        )
+
+        df.index.names = ["a", "b", "c"]
+        df.columns.names = ["d", "e"]
+
+        # it works!
+        df.unstack(["b", "c"])
+
+    def test_unstack_sparse_keyspace(self):
+        # memory problems with naive impl GH#2278
+        # Generate Long File & Test Pivot
+        NUM_ROWS = 1000
+
+        df = DataFrame(
+            {
+                "A": np.random.default_rng(2).integers(100, size=NUM_ROWS),
+                "B": np.random.default_rng(3).integers(300, size=NUM_ROWS),
+                "C": np.random.default_rng(4).integers(-7, 7, size=NUM_ROWS),
+                "D": np.random.default_rng(5).integers(-19, 19, size=NUM_ROWS),
+                "E": np.random.default_rng(6).integers(3000, size=NUM_ROWS),
+                "F": np.random.default_rng(7).standard_normal(NUM_ROWS),
+            }
+        )
+
+        idf = df.set_index(["A", "B", "C", "D", "E"])
+
+        # it works! is sufficient
+        idf.unstack("E")
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_unobserved_keys(self, future_stack):
+        # related to GH#2278 refactoring
+        levels = [[0, 1], [0, 1, 2, 3]]
+        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
+
+        index = MultiIndex(levels, codes)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((4, 2)), index=index)
+
+        result = df.unstack()
+        assert len(result.columns) == 4
+
+        recons = result.stack(future_stack=future_stack)
+        tm.assert_frame_equal(recons, df)
+
+    @pytest.mark.slow
+    def test_unstack_number_of_levels_larger_than_int32(self, monkeypatch):
+        # GH#20601
+        # GH 26314: Change ValueError to PerformanceWarning
+
+        class MockUnstacker(reshape_lib._Unstacker):
+            def __init__(self, *args, **kwargs) -> None:
+                # __init__ will raise the warning
+                super().__init__(*args, **kwargs)
+                raise Exception("Don't compute final result.")
+
+        with monkeypatch.context() as m:
+            m.setattr(reshape_lib, "_Unstacker", MockUnstacker)
+            df = DataFrame(
+                np.zeros((2**16, 2)),
+                index=[np.arange(2**16), np.arange(2**16)],
+            )
+            msg = "The following operation may generate"
+            with tm.assert_produces_warning(PerformanceWarning, match=msg):
+                with pytest.raises(Exception, match="Don't compute final result."):
+                    df.unstack()
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "levels",
+        itertools.chain.from_iterable(
+            itertools.product(itertools.permutations([0, 1, 2], width), repeat=2)
+            for width in [2, 3]
+        ),
+    )
+    @pytest.mark.parametrize("stack_lev", range(2))
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_stack_order_with_unsorted_levels(
+        self, levels, stack_lev, sort, future_stack
+    ):
+        # GH#16323
+        # deep check for 1-row case
+        columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        df = DataFrame(columns=columns, data=[range(4)])
+        kwargs = {} if future_stack else {"sort": sort}
+        df_stacked = df.stack(stack_lev, future_stack=future_stack, **kwargs)
+        for row in df.index:
+            for col in df.columns:
+                expected = df.loc[row, col]
+                result_row = row, col[stack_lev]
+                result_col = col[1 - stack_lev]
+                result = df_stacked.loc[result_row, result_col]
+                assert result == expected
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_order_with_unsorted_levels_multi_row(self, future_stack):
+        # GH#16323
+
+        # check multi-row case
+        mi = MultiIndex(
+            levels=[["A", "C", "B"], ["B", "A", "C"]],
+            codes=[np.repeat(range(3), 3), np.tile(range(3), 3)],
+        )
+        df = DataFrame(
+            columns=mi, index=range(5), data=np.arange(5 * len(mi)).reshape(5, -1)
+        )
+        assert all(
+            df.loc[row, col]
+            == df.stack(0, future_stack=future_stack).loc[(row, col[0]), col[1]]
+            for row in df.index
+            for col in df.columns
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_order_with_unsorted_levels_multi_row_2(self, future_stack):
+        # GH#53636
+        levels = ((0, 1), (1, 0))
+        stack_lev = 1
+        columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        df = DataFrame(columns=columns, data=[range(4)], index=[1, 0, 2, 3])
+        kwargs = {} if future_stack else {"sort": True}
+        result = df.stack(stack_lev, future_stack=future_stack, **kwargs)
+        expected_index = MultiIndex(
+            levels=[[0, 1, 2, 3], [0, 1]],
+            codes=[[1, 1, 0, 0, 2, 2, 3, 3], [1, 0, 1, 0, 1, 0, 1, 0]],
+        )
+        expected = DataFrame(
+            {
+                0: [0, 1, 0, 1, 0, 1, 0, 1],
+                1: [2, 3, 2, 3, 2, 3, 2, 3],
+            },
+            index=expected_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_unordered_multiindex(self, future_stack):
+        # GH# 18265
+        values = np.arange(5)
+        data = np.vstack(
+            [
+                [f"b{x}" for x in values],  # b0, b1, ..
+                [f"a{x}" for x in values],  # a0, a1, ..
+            ]
+        )
+        df = DataFrame(data.T, columns=["b", "a"])
+        df.columns.name = "first"
+        second_level_dict = {"x": df}
+        multi_level_df = pd.concat(second_level_dict, axis=1)
+        multi_level_df.columns.names = ["second", "first"]
+        df = multi_level_df.reindex(sorted(multi_level_df.columns), axis=1)
+        result = df.stack(["first", "second"], future_stack=future_stack).unstack(
+            ["first", "second"]
+        )
+        expected = DataFrame(
+            [["a0", "b0"], ["a1", "b1"], ["a2", "b2"], ["a3", "b3"], ["a4", "b4"]],
+            index=[0, 1, 2, 3, 4],
+            columns=MultiIndex.from_tuples(
+                [("a", "x"), ("b", "x")], names=["first", "second"]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_preserve_types(
+        self, multiindex_year_month_day_dataframe_random_data, using_infer_string
+    ):
+        # GH#403
+        ymd = multiindex_year_month_day_dataframe_random_data
+        ymd["E"] = "foo"
+        ymd["F"] = 2
+
+        unstacked = ymd.unstack("month")
+        assert unstacked["A", 1].dtype == np.float64
+        assert (
+            unstacked["E", 1].dtype == np.object_
+            if not using_infer_string
+            else "string"
+        )
+        assert unstacked["F", 1].dtype == np.float64
+
+    def test_unstack_group_index_overflow(self, future_stack):
+        codes = np.tile(np.arange(500), 2)
+        level = np.arange(500)
+
+        index = MultiIndex(
+            levels=[level] * 8 + [[0, 1]],
+            codes=[codes] * 8 + [np.arange(2).repeat(500)],
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack()
+        assert result.shape == (500, 2)
+
+        # test roundtrip
+        stacked = result.stack(future_stack=future_stack)
+        tm.assert_series_equal(s, stacked.reindex(s.index))
+
+        # put it at beginning
+        index = MultiIndex(
+            levels=[[0, 1]] + [level] * 8,
+            codes=[np.arange(2).repeat(500)] + [codes] * 8,
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack(0)
+        assert result.shape == (500, 2)
+
+        # put it in middle
+        index = MultiIndex(
+            levels=[level] * 4 + [[0, 1]] + [level] * 4,
+            codes=([codes] * 4 + [np.arange(2).repeat(500)] + [codes] * 4),
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack(4)
+        assert result.shape == (500, 2)
+
+    def test_unstack_with_missing_int_cast_to_float(self, using_array_manager):
+        # https://github.com/pandas-dev/pandas/issues/37115
+        df = DataFrame(
+            {
+                "a": ["A", "A", "B"],
+                "b": ["ca", "cb", "cb"],
+                "v": [10] * 3,
+            }
+        ).set_index(["a", "b"])
+
+        # add another int column to get 2 blocks
+        df["is_"] = 1
+        if not using_array_manager:
+            assert len(df._mgr.blocks) == 2
+
+        result = df.unstack("b")
+        result[("is_", "ca")] = result[("is_", "ca")].fillna(0)
+
+        expected = DataFrame(
+            [[10.0, 10.0, 1.0, 1.0], [np.nan, 10.0, 0.0, 1.0]],
+            index=Index(["A", "B"], name="a"),
+            columns=MultiIndex.from_tuples(
+                [("v", "ca"), ("v", "cb"), ("is_", "ca"), ("is_", "cb")],
+                names=[None, "b"],
+            ),
+        )
+        if using_array_manager:
+            # INFO(ArrayManager) with ArrayManager preserve dtype where possible
+            expected[("v", "cb")] = expected[("v", "cb")].astype("int64")
+            expected[("is_", "cb")] = expected[("is_", "cb")].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_with_level_has_nan(self):
+        # GH 37510
+        df1 = DataFrame(
+            {
+                "L1": [1, 2, 3, 4],
+                "L2": [3, 4, 1, 2],
+                "L3": [1, 1, 1, 1],
+                "x": [1, 2, 3, 4],
+            }
+        )
+        df1 = df1.set_index(["L1", "L2", "L3"])
+        new_levels = ["n1", "n2", "n3", None]
+        df1.index = df1.index.set_levels(levels=new_levels, level="L1")
+        df1.index = df1.index.set_levels(levels=new_levels, level="L2")
+
+        result = df1.unstack("L3")[("x", 1)].sort_index().index
+        expected = MultiIndex(
+            levels=[["n1", "n2", "n3", None], ["n1", "n2", "n3", None]],
+            codes=[[0, 1, 2, 3], [2, 3, 0, 1]],
+            names=["L1", "L2"],
+        )
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nan_in_multiindex_columns(self, future_stack):
+        # GH#39481
+        df = DataFrame(
+            np.zeros([1, 5]),
+            columns=MultiIndex.from_tuples(
+                [
+                    (0, None, None),
+                    (0, 2, 0),
+                    (0, 2, 1),
+                    (0, 3, 0),
+                    (0, 3, 1),
+                ],
+            ),
+        )
+        result = df.stack(2, future_stack=future_stack)
+        if future_stack:
+            index = MultiIndex(levels=[[0], [0.0, 1.0]], codes=[[0, 0, 0], [-1, 0, 1]])
+            columns = MultiIndex(levels=[[0], [2, 3]], codes=[[0, 0, 0], [-1, 0, 1]])
+        else:
+            index = Index([(0, None), (0, 0), (0, 1)])
+            columns = Index([(0, None), (0, 2), (0, 3)])
+        expected = DataFrame(
+            [[0.0, np.nan, np.nan], [np.nan, 0.0, 0.0], [np.nan, 0.0, 0.0]],
+            index=index,
+            columns=columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_multi_level_stack_categorical(self, future_stack):
+        # GH 15239
+        midx = MultiIndex.from_arrays(
+            [
+                ["A"] * 2 + ["B"] * 2,
+                pd.Categorical(list("abab")),
+                pd.Categorical(list("ccdd")),
+            ]
+        )
+        df = DataFrame(np.arange(8).reshape(2, 4), columns=midx)
+        result = df.stack([1, 2], future_stack=future_stack)
+        if future_stack:
+            expected = DataFrame(
+                [
+                    [0, np.nan],
+                    [1, np.nan],
+                    [np.nan, 2],
+                    [np.nan, 3],
+                    [4, np.nan],
+                    [5, np.nan],
+                    [np.nan, 6],
+                    [np.nan, 7],
+                ],
+                columns=["A", "B"],
+                index=MultiIndex.from_arrays(
+                    [
+                        [0] * 4 + [1] * 4,
+                        pd.Categorical(list("abababab")),
+                        pd.Categorical(list("ccddccdd")),
+                    ]
+                ),
+            )
+        else:
+            expected = DataFrame(
+                [
+                    [0, np.nan],
+                    [np.nan, 2],
+                    [1, np.nan],
+                    [np.nan, 3],
+                    [4, np.nan],
+                    [np.nan, 6],
+                    [5, np.nan],
+                    [np.nan, 7],
+                ],
+                columns=["A", "B"],
+                index=MultiIndex.from_arrays(
+                    [
+                        [0] * 4 + [1] * 4,
+                        pd.Categorical(list("aabbaabb")),
+                        pd.Categorical(list("cdcdcdcd")),
+                    ]
+                ),
+            )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nan_level(self, future_stack):
+        # GH 9406
+        df_nan = DataFrame(
+            np.arange(4).reshape(2, 2),
+            columns=MultiIndex.from_tuples(
+                [("A", np.nan), ("B", "b")], names=["Upper", "Lower"]
+            ),
+            index=Index([0, 1], name="Num"),
+            dtype=np.float64,
+        )
+        result = df_nan.stack(future_stack=future_stack)
+        if future_stack:
+            index = MultiIndex(
+                levels=[[0, 1], [np.nan, "b"]],
+                codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+                names=["Num", "Lower"],
+            )
+        else:
+            index = MultiIndex.from_tuples(
+                [(0, np.nan), (0, "b"), (1, np.nan), (1, "b")], names=["Num", "Lower"]
+            )
+        expected = DataFrame(
+            [[0.0, np.nan], [np.nan, 1], [2.0, np.nan], [np.nan, 3.0]],
+            columns=Index(["A", "B"], name="Upper"),
+            index=index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_categorical_columns(self):
+        # GH 14018
+        idx = MultiIndex.from_product([["A"], [0, 1]])
+        df = DataFrame({"cat": pd.Categorical(["a", "b"])}, index=idx)
+        result = df.unstack()
+        expected = DataFrame(
+            {
+                0: pd.Categorical(["a"], categories=["a", "b"]),
+                1: pd.Categorical(["b"], categories=["a", "b"]),
+            },
+            index=["A"],
+        )
+        expected.columns = MultiIndex.from_tuples([("cat", 0), ("cat", 1)])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unsorted(self, future_stack):
+        # GH 16925
+        PAE = ["ITA", "FRA"]
+        VAR = ["A1", "A2"]
+        TYP = ["CRT", "DBT", "NET"]
+        MI = MultiIndex.from_product([PAE, VAR, TYP], names=["PAE", "VAR", "TYP"])
+
+        V = list(range(len(MI)))
+        DF = DataFrame(data=V, index=MI, columns=["VALUE"])
+
+        DF = DF.unstack(["VAR", "TYP"])
+        DF.columns = DF.columns.droplevel(0)
+        DF.loc[:, ("A0", "NET")] = 9999
+
+        result = DF.stack(["VAR", "TYP"], future_stack=future_stack).sort_index()
+        expected = (
+            DF.sort_index(axis=1)
+            .stack(["VAR", "TYP"], future_stack=future_stack)
+            .sort_index()
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nullable_dtype(self, future_stack):
+        # GH#43561
+        columns = MultiIndex.from_product(
+            [["54511", "54515"], ["r", "t_mean"]], names=["station", "element"]
+        )
+        index = Index([1, 2, 3], name="time")
+
+        arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]])
+        df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype())
+
+        result = df.stack("station", future_stack=future_stack)
+
+        expected = (
+            df.astype(np.int64)
+            .stack("station", future_stack=future_stack)
+            .astype(pd.Int64Dtype())
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # non-homogeneous case
+        df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype())
+        result = df.stack("station", future_stack=future_stack)
+
+        expected = DataFrame(
+            {
+                "r": pd.array(
+                    [50.0, 10.0, 10.0, 9.0, 305.0, 111.0], dtype=pd.Float64Dtype()
+                ),
+                "t_mean": pd.array(
+                    [226, 215, 215, 220, 232, 220], dtype=pd.Int64Dtype()
+                ),
+            },
+            index=MultiIndex.from_product([index, columns.levels[0]]),
+        )
+        expected.columns.name = "element"
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_mixed_level_names(self):
+        # GH#48763
+        arrays = [["a", "a"], [1, 2], ["red", "blue"]]
+        idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
+        df = DataFrame({"m": [1, 2]}, index=idx)
+        result = df.unstack("x")
+        expected = DataFrame(
+            [[1], [2]],
+            columns=MultiIndex.from_tuples([("m", "a")], names=[None, "x"]),
+            index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_stack_tuple_columns(future_stack):
+    # GH#54948 - test stack when the input has a non-MultiIndex with tuples
+    df = DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=[("a", 1), ("a", 2), ("b", 1)]
+    )
+    result = df.stack(future_stack=future_stack)
+    expected = Series(
+        [1, 2, 3, 4, 5, 6, 7, 8, 9],
+        index=MultiIndex(
+            levels=[[0, 1, 2], [("a", 1), ("a", 2), ("b", 1)]],
+            codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
+        ),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype, na_value",
+    [
+        ("float64", np.nan),
+        ("Float64", np.nan),
+        ("Float64", pd.NA),
+        ("Int64", pd.NA),
+    ],
+)
+@pytest.mark.parametrize("test_multiindex", [True, False])
+def test_stack_preserves_na(dtype, na_value, test_multiindex):
+    # GH#56573
+    if test_multiindex:
+        index = MultiIndex.from_arrays(2 * [Index([na_value], dtype=dtype)])
+    else:
+        index = Index([na_value], dtype=dtype)
+    df = DataFrame({"a": [1]}, index=index)
+    result = df.stack(future_stack=True)
+
+    if test_multiindex:
+        expected_index = MultiIndex.from_arrays(
+            [
+                Index([na_value], dtype=dtype),
+                Index([na_value], dtype=dtype),
+                Index(["a"]),
+            ]
+        )
+    else:
+        expected_index = MultiIndex.from_arrays(
+            [
+                Index([na_value], dtype=dtype),
+                Index(["a"]),
+            ]
+        )
+    expected = Series(1, index=expected_index)
+    tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/32/320b3d7ae0eb563e60d7a62387de282960b1ab1a94b376935691bb8cc4967ed0/optimal_transport.py b/shared/package-files/32/320b3d7ae0eb563e60d7a62387de282960b1ab1a94b376935691bb8cc4967ed0/optimal_transport.py
new file mode 100644
index 0000000000000000000000000000000000000000..48e1e3609a4605b5960de1547d3b19e95e06ef6a
--- /dev/null
+++ b/shared/package-files/32/320b3d7ae0eb563e60d7a62387de282960b1ab1a94b376935691bb8cc4967ed0/optimal_transport.py
@@ -0,0 +1,1194 @@
+#############################################################################
+# This code draws from the Python Optimal Transport version of the
+# network simplex algorithm, which in turn was adapted from the LEMON
+# library. The copyrights/comment blocks for those are preserved below.
+# The Python/Numba implementation was adapted by Leland McInnes (2020).
+#
+#  * This file has been adapted by Nicolas Bonneel (2013),
+#  * from network_simplex.h from LEMON, a generic C++ optimization library,
+#  * to implement a lightweight network simplex for mass transport, more
+#  * memory efficient that the original file. A previous version of this file
+#  * is used as part of the Displacement Interpolation project,
+#  * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/
+#  *
+#  *
+#  **** Original file Copyright Notice :
+#  *
+#  * Copyright (C) 2003-2010
+#  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
+#  * (Egervary Research Group on Combinatorial Optimization, EGRES).
+#  *
+#  * Permission to use, modify and distribute this software is granted
+#  * provided that this copyright notice appears in all copies. For
+#  * precise terms see the accompanying LICENSE file.
+#  *
+#  * This software is provided "AS IS" with no warranty of any kind,
+#  * express or implied, and with no claim as to its suitability for any
+#  * purpose.
+
+import numpy as np
+import numba
+from collections import namedtuple
+from enum import Enum, IntEnum
+
+_mock_identity = np.eye(2, dtype=np.float32)
+_mock_ones = np.ones(2, dtype=np.float32)
+_dummy_cost = np.zeros((2, 2), dtype=np.float64)
+
+# Accuracy tolerance and net supply tolerance
+EPSILON = 2.2204460492503131e-15
+NET_SUPPLY_ERROR_TOLERANCE = 1e-8
+
+## Defaults to double for everythig in POT
+INFINITY = np.finfo(np.float64).max
+MAX = np.finfo(np.float64).max
+
+dummy_cost = np.zeros((2, 2), dtype=np.float64)
+
+# Invalid Arc num
+INVALID = -1
+
+# Problem Status
+class ProblemStatus(Enum):
+    OPTIMAL = 0
+    MAX_ITER_REACHED = 1
+    UNBOUNDED = 2
+    INFEASIBLE = 3
+
+
+# Arc States
+class ArcState(IntEnum):
+    STATE_UPPER = -1
+    STATE_TREE = 0
+    STATE_LOWER = 1
+
+
+SpanningTree = namedtuple(
+    "SpanningTree",
+    [
+        "parent",  # int array
+        "pred",  # int array
+        "thread",  # int array
+        "rev_thread",  # int array
+        "succ_num",  # int array
+        "last_succ",  # int array
+        "forward",  # bool array
+        "state",  # state array
+        "root",  # int
+    ],
+)
+DiGraph = namedtuple(
+    "DiGraph",
+    [
+        "n_nodes",  # int
+        "n_arcs",  # int
+        "n",  # int
+        "m",  # int
+        "use_arc_mixing",  # bool
+        "num_total_big_subsequence_numbers",  # int
+        "subsequence_length",  # int
+        "num_big_subsequences",  # int
+        "mixing_coeff",
+    ],
+)
+NodeArcData = namedtuple(
+    "NodeArcData",
+    [
+        "cost",  # double array
+        "supply",  # double array
+        "flow",  # double array
+        "pi",  # double array
+        "source",  # unsigned int array
+        "target",  # unsigned int array
+    ],
+)
+LeavingArcData = namedtuple(
+    "LeavingArcData", ["u_in", "u_out", "v_in", "delta", "change"]
+)
+
+# Just reproduce a simpler version of numpy isclose (not numba supported yet)
+@numba.njit()
+def isclose(a, b, rtol=1.0e-5, atol=EPSILON):
+    diff = np.abs(a - b)
+    return diff <= (atol + rtol * np.abs(b))
+
+
+# locals: c, min, e, cnt, a
+# modifies _in_arc, _next_arc,
+@numba.njit(locals={"a": numba.uint32, "e": numba.uint32})
+def find_entering_arc(
+    pivot_block_size,
+    pivot_next_arc,
+    search_arc_num,
+    state_vector,
+    node_arc_data,
+    in_arc,
+):
+    min = 0
+    cnt = pivot_block_size
+
+    # Pull from tuple for quick reference
+    cost = node_arc_data.cost
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+
+    for e in range(pivot_next_arc, search_arc_num):
+        c = state_vector[e] * (cost[e] + pi[source[e]] - pi[target[e]])
+        if c < min:
+            min = c
+            in_arc = e
+
+        cnt -= 1
+        if cnt == 0:
+            if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+                a = np.fabs(pi[source[in_arc]])
+            else:
+                a = np.fabs(pi[target[in_arc]])
+
+            if a <= np.fabs(cost[in_arc]):
+                a = np.fabs(cost[in_arc])
+
+            if min < -(EPSILON * a):
+                pivot_next_arc = e
+                return in_arc, pivot_next_arc
+            else:
+                cnt = pivot_block_size
+
+    for e in range(pivot_next_arc):
+        c = state_vector[e] * (cost[e] + pi[source[e]] - pi[target[e]])
+        if c < min:
+            min = c
+            in_arc = e
+
+        cnt -= 1
+        if cnt == 0:
+            if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+                a = np.fabs(pi[source[in_arc]])
+            else:
+                a = np.fabs(pi[target[in_arc]])
+
+            if a <= np.fabs(cost[in_arc]):
+                a = np.fabs(cost[in_arc])
+
+            if min < -(EPSILON * a):
+                pivot_next_arc = e
+                return in_arc, pivot_next_arc
+            else:
+                cnt = pivot_block_size
+
+    # assert(pivot_block.next_arc[0] == 0 or e == pivot_block.next_arc[0] - 1)
+
+    if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+        a = np.fabs(pi[source[in_arc]])
+    else:
+        a = np.fabs(pi[target[in_arc]])
+
+    if a <= np.fabs(cost[in_arc]):
+        a = np.fabs(cost[in_arc])
+
+    if min >= -(EPSILON * a):
+        return -1, 0
+
+    return in_arc, pivot_next_arc
+
+
+# Find the join node
+# Operates with graph (_source, _target) and MST (_succ_num, _parent, in_arc) data
+# locals: u, v
+# modifies: join
+@numba.njit(locals={"u": numba.types.uint16, "v": numba.types.uint16})
+def find_join_node(source, target, succ_num, parent, in_arc):
+    u = source[in_arc]
+    v = target[in_arc]
+    while u != v:
+        if succ_num[u] < succ_num[v]:
+            u = parent[u]
+        else:
+            v = parent[v]
+
+    join = u
+
+    return join
+
+
+# Find the leaving arc of the cycle and returns true if the
+# leaving arc is not the same as the entering arc
+# locals: first, second, result, d, e
+# modifies: u_in, v_in, u_out, delta
+@numba.njit(
+    locals={
+        "u": numba.uint16,
+        "u_in": numba.uint16,
+        "u_out": numba.uint16,
+        "v_in": numba.uint16,
+        "first": numba.uint16,
+        "second": numba.uint16,
+        "result": numba.uint8,
+        "in_arc": numba.uint32,
+    }
+)
+def find_leaving_arc(join, in_arc, node_arc_data, spanning_tree):
+    source = node_arc_data.source
+    target = node_arc_data.target
+    flow = node_arc_data.flow
+
+    state = spanning_tree.state
+    forward = spanning_tree.forward
+    pred = spanning_tree.pred
+    parent = spanning_tree.parent
+
+    u_out = -1  # May not be set, but we need to return something?
+
+    # Initialize first and second nodes according to the direction
+    # of the cycle
+    if state[in_arc] == ArcState.STATE_LOWER:
+        first = source[in_arc]
+        second = target[in_arc]
+    else:
+        first = target[in_arc]
+        second = source[in_arc]
+
+    delta = INFINITY
+    result = 0
+
+    # Search the cycle along the path form the first node to the root
+    u = first
+    while u != join:
+        e = pred[u]
+        if forward[u]:
+            d = flow[e]
+        else:
+            d = INFINITY
+
+        if d < delta:
+            delta = d
+            u_out = u
+            result = 1
+
+        u = parent[u]
+
+    # Search the cycle along the path form the second node to the root
+    u = second
+    while u != join:
+        e = pred[u]
+        if forward[u]:
+            d = INFINITY
+        else:
+            d = flow[e]
+
+        if d <= delta:
+            delta = d
+            u_out = u
+            result = 2
+
+        u = parent[u]
+
+    if result == 1:
+        u_in = first
+        v_in = second
+    else:
+        u_in = second
+        v_in = first
+
+    return LeavingArcData(u_in, u_out, v_in, delta, result != 0)
+
+
+# Change _flow and _state vectors
+# locals: val, u
+# modifies: _state, _flow
+@numba.njit(locals={"u": numba.uint16, "in_arc": numba.uint32, "val": numba.float64})
+def update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc):
+    source = node_arc_data.source
+    target = node_arc_data.target
+    flow = node_arc_data.flow
+
+    state = spanning_tree.state
+    pred = spanning_tree.pred
+    parent = spanning_tree.parent
+    forward = spanning_tree.forward
+
+    # Augment along the cycle
+    if leaving_arc_data.delta > 0:
+        val = state[in_arc] * leaving_arc_data.delta
+        flow[in_arc] += val
+        u = source[in_arc]
+        while u != join:
+            if forward[u]:
+                flow[pred[u]] -= val
+            else:
+                flow[pred[u]] += val
+
+            u = parent[u]
+
+        u = target[in_arc]
+        while u != join:
+            if forward[u]:
+                flow[pred[u]] += val
+            else:
+                flow[pred[u]] -= val
+
+            u = parent[u]
+
+    # Update the state of the entering and leaving arcs
+    if leaving_arc_data.change:
+        state[in_arc] = ArcState.STATE_TREE
+        if flow[pred[leaving_arc_data.u_out]] == 0:
+            state[pred[leaving_arc_data.u_out]] = ArcState.STATE_LOWER
+        else:
+            state[pred[leaving_arc_data.u_out]] = ArcState.STATE_UPPER
+    else:
+        state[in_arc] = -state[in_arc]
+
+
+# Update the tree structure
+# locals: u, w, old_rev_thread, old_succ_num, old_last_succ, tmp_sc, tmp_ls
+# more locals: up_limit_in, up_limit_out, _dirty_revs
+# modifies: v_out, _thread, _rev_thread, _parent, _last_succ,
+# modifies: _pred, _forward, _succ_num
+@numba.njit(
+    locals={
+        "u": numba.int32,
+        "w": numba.int32,
+        "u_in": numba.uint16,
+        "u_out": numba.uint16,
+        "v_in": numba.uint16,
+        "right": numba.uint16,
+        "stem": numba.uint16,
+        "new_stem": numba.uint16,
+        "par_stem": numba.uint16,
+        "in_arc": numba.uint32,
+    }
+)
+def update_spanning_tree(spanning_tree, leaving_arc_data, join, in_arc, source):
+
+    parent = spanning_tree.parent
+    thread = spanning_tree.thread
+    rev_thread = spanning_tree.rev_thread
+    succ_num = spanning_tree.succ_num
+    last_succ = spanning_tree.last_succ
+    forward = spanning_tree.forward
+    pred = spanning_tree.pred
+
+    u_out = leaving_arc_data.u_out
+    u_in = leaving_arc_data.u_in
+    v_in = leaving_arc_data.v_in
+
+    old_rev_thread = rev_thread[u_out]
+    old_succ_num = succ_num[u_out]
+    old_last_succ = last_succ[u_out]
+    v_out = parent[u_out]
+
+    u = last_succ[u_in]  # the last successor of u_in
+    right = thread[u]  # the node after it
+
+    # Handle the case when old_rev_thread equals to v_in
+    # (it also means that join and v_out coincide)
+    if old_rev_thread == v_in:
+        last = thread[last_succ[u_out]]
+    else:
+        last = thread[v_in]
+
+    # Update _thread and _parent along the stem nodes (i.e. the nodes
+    # between u_in and u_out, whose parent have to be changed)
+    thread[v_in] = stem = u_in
+    dirty_revs = []
+    dirty_revs.append(v_in)
+    par_stem = v_in
+    while stem != u_out:
+        # Insert the next stem node into the thread list
+        new_stem = parent[stem]
+        thread[u] = new_stem
+        dirty_revs.append(u)
+
+        # Remove the subtree of stem from the thread list
+        w = rev_thread[stem]
+        thread[w] = right
+        rev_thread[right] = w
+
+        # Change the parent node and shift stem nodes
+        parent[stem] = par_stem
+        par_stem = stem
+        stem = new_stem
+
+        # Update u and right
+        if last_succ[stem] == last_succ[par_stem]:
+            u = rev_thread[par_stem]
+        else:
+            u = last_succ[stem]
+
+        right = thread[u]
+
+    parent[u_out] = par_stem
+    thread[u] = last
+    rev_thread[last] = u
+    last_succ[u_out] = u
+
+    # Remove the subtree of u_out from the thread list except for
+    # the case when old_rev_thread equals to v_in
+    # (it also means that join and v_out coincide)
+    if old_rev_thread != v_in:
+        thread[old_rev_thread] = right
+        rev_thread[right] = old_rev_thread
+
+    # Update _rev_thread using the new _thread values
+    for i in range(len(dirty_revs)):
+        u = dirty_revs[i]
+        rev_thread[thread[u]] = u
+
+    # Update _pred, _forward, _last_succ and _succ_num for the
+    # stem nodes from u_out to u_in
+    tmp_sc = 0
+    tmp_ls = last_succ[u_out]
+    u = u_out
+    while u != u_in:
+        w = parent[u]
+        pred[u] = pred[w]
+        forward[u] = not forward[w]
+        tmp_sc += succ_num[u] - succ_num[w]
+        succ_num[u] = tmp_sc
+        last_succ[w] = tmp_ls
+        u = w
+
+    pred[u_in] = in_arc
+    forward[u_in] = u_in == source[in_arc]
+    succ_num[u_in] = old_succ_num
+
+    # Set limits for updating _last_succ form v_in and v_out
+    # towards the root
+    up_limit_in = -1
+    up_limit_out = -1
+    if last_succ[join] == v_in:
+        up_limit_out = join
+    else:
+        up_limit_in = join
+
+    # Update _last_succ from v_in towards the root
+    u = v_in
+    while u != up_limit_in and last_succ[u] == v_in:
+        last_succ[u] = last_succ[u_out]
+        u = parent[u]
+
+    # Update _last_succ from v_out towards the root
+    if join != old_rev_thread and v_in != old_rev_thread:
+        u = v_out
+        while u != up_limit_out and last_succ[u] == old_last_succ:
+            last_succ[u] = old_rev_thread
+            u = parent[u]
+
+    else:
+        u = v_out
+        while u != up_limit_out and last_succ[u] == old_last_succ:
+            last_succ[u] = last_succ[u_out]
+            u = parent[u]
+
+    # Update _succ_num from v_in to join
+    u = v_in
+    while u != join:
+        succ_num[u] += old_succ_num
+        u = parent[u]
+
+    # Update _succ_num from v_out to join
+    u = v_out
+    while u != join:
+        succ_num[u] -= old_succ_num
+        u = parent[u]
+
+
+# Update potentials
+# locals: sigma, end
+# modifies: _pi
+@numba.njit(
+    fastmath=True,
+    inline="always",
+    locals={"u": numba.uint16, "u_in": numba.uint16, "v_in": numba.uint16},
+)
+def update_potential(leaving_arc_data, pi, cost, spanning_tree):
+
+    thread = spanning_tree.thread
+    pred = spanning_tree.pred
+    forward = spanning_tree.forward
+    last_succ = spanning_tree.last_succ
+
+    u_in = leaving_arc_data.u_in
+    v_in = leaving_arc_data.v_in
+
+    if forward[u_in]:
+        sigma = pi[v_in] - pi[u_in] - cost[pred[u_in]]
+    else:
+        sigma = pi[v_in] - pi[u_in] + cost[pred[u_in]]
+
+    # Update potentials in the subtree, which has been moved
+    end = thread[last_succ[u_in]]
+    u = u_in
+    while u != end:
+        pi[u] += sigma
+        u = thread[u]
+
+
+# If we have mixed arcs (for better random access)
+# we need a more complicated function to get the ID of a given arc
+@numba.njit()
+def arc_id(arc, graph):
+    k = graph.n_arcs - arc - 1
+    if graph.use_arc_mixing:
+        smallv = (k > graph.num_total_big_subsequence_numbers) & 1
+        k -= graph.num_total_big_subsequence_numbers * smallv
+        subsequence_length2 = graph.subsequence_length - smallv
+        subsequence_num = (
+            k // subsequence_length2
+        ) + graph.num_big_subsequences * smallv
+        subsequence_offset = (k % subsequence_length2) * graph.mixing_coeff
+
+        return subsequence_offset + subsequence_num
+    else:
+        return k
+
+
+# Heuristic initial pivots
+# locals: curr, total, supply_nodes, demand_nodes, u
+# modifies:
+@numba.njit(locals={"i": numba.uint16})
+def construct_initial_pivots(graph, node_arc_data, spanning_tree):
+
+    cost = node_arc_data.cost
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+    supply = node_arc_data.supply
+
+    n1 = graph.n
+    n2 = graph.m
+    n_nodes = graph.n_nodes
+    n_arcs = graph.n_arcs
+
+    state = spanning_tree.state
+
+    total = 0
+    supply_nodes = []
+    demand_nodes = []
+
+    for u in range(n_nodes):
+        curr = supply[n_nodes - u - 1]  # _node_id(u)
+        if curr > 0:
+            total += curr
+            supply_nodes.append(u)
+        elif curr < 0:
+            demand_nodes.append(u)
+
+    arc_vector = []
+    if len(supply_nodes) == 1 and len(demand_nodes) == 1:
+        # Perform a reverse graph search from the sink to the source
+        reached = np.zeros(n_nodes, dtype=np.bool_)
+        s = supply_nodes[0]
+        t = demand_nodes[0]
+        stack = []
+        reached[t] = True
+        stack.append(t)
+        while len(stack) > 0:
+            u = stack[-1]
+            v = stack[-1]
+            stack.pop(-1)
+            if v == s:
+                break
+
+            first_arc = n_arcs + v - n_nodes if v >= n1 else -1
+            for a in range(first_arc, -1, -n2):
+                u = a // n2
+                if reached[u]:
+                    continue
+
+                j = arc_id(a, graph)
+                if INFINITY >= total:
+                    arc_vector.append(j)
+                    reached[u] = True
+                    stack.append(u)
+
+    else:
+        # Find the min. cost incomming arc for each demand node
+        for i in range(len(demand_nodes)):
+            v = demand_nodes[i]
+            c = MAX
+            min_cost = MAX
+            min_arc = INVALID
+            first_arc = n_arcs + v - n_nodes if v >= n1 else -1
+            for a in range(first_arc, -1, -n2):
+                c = cost[arc_id(a, graph)]
+                if c < min_cost:
+                    min_cost = c
+                    min_arc = a
+
+            if min_arc != INVALID:
+                arc_vector.append(arc_id(min_arc, graph))
+
+    # Perform heuristic initial pivots
+    in_arc = -1
+    for i in range(len(arc_vector)):
+        in_arc = arc_vector[i]
+        # Bad arcs
+        if (
+            state[in_arc] * (cost[in_arc] + pi[source[in_arc]] - pi[target[in_arc]])
+            >= 0
+        ):
+            continue
+
+        join = find_join_node(
+            source, target, spanning_tree.succ_num, spanning_tree.parent, in_arc
+        )
+        leaving_arc_data = find_leaving_arc(join, in_arc, node_arc_data, spanning_tree)
+        if leaving_arc_data.delta >= MAX:
+            return False, in_arc
+
+        update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc)
+        if leaving_arc_data.change:
+            update_spanning_tree(spanning_tree, leaving_arc_data, join, in_arc, source)
+            update_potential(leaving_arc_data, pi, cost, spanning_tree)
+
+    return True, in_arc
+
+
+@numba.njit()
+def allocate_graph_structures(n, m, use_arc_mixing=True):
+
+    # Size bipartite graph
+    n_nodes = n + m
+    n_arcs = n * m
+
+    # Resize vectors
+    all_node_num = n_nodes + 1
+    max_arc_num = n_arcs + 2 * n_nodes
+    root = n_nodes
+
+    source = np.zeros(max_arc_num, dtype=np.uint16)
+    target = np.zeros(max_arc_num, dtype=np.uint16)
+    cost = np.ones(max_arc_num, dtype=np.float64)
+    supply = np.zeros(all_node_num, dtype=np.float64)
+    flow = np.zeros(max_arc_num, dtype=np.float64)
+    pi = np.zeros(all_node_num, dtype=np.float64)
+
+    parent = np.zeros(all_node_num, dtype=np.int32)
+    pred = np.zeros(all_node_num, dtype=np.int32)
+    forward = np.zeros(all_node_num, dtype=np.bool_)
+    thread = np.zeros(all_node_num, dtype=np.int32)
+    rev_thread = np.zeros(all_node_num, dtype=np.int32)
+    succ_num = np.zeros(all_node_num, dtype=np.int32)
+    last_succ = np.zeros(all_node_num, dtype=np.int32)
+    state = np.zeros(max_arc_num, dtype=np.int8)
+
+    if use_arc_mixing:
+        # Store the arcs in a mixed order
+        k = max(np.int32(np.sqrt(n_arcs)), 10)
+        mixing_coeff = k
+        subsequence_length = (n_arcs // mixing_coeff) + 1
+        num_big_subsequences = n_arcs % mixing_coeff
+        num_total_big_subsequence_numbers = subsequence_length * num_big_subsequences
+
+        i = 0
+        j = 0
+        for a in range(n_arcs - 1, -1, -1):
+            source[i] = n_nodes - (a // m) - 1
+            target[i] = n_nodes - ((a % m) + n) - 1
+            i += k
+            if i >= n_arcs:
+                j += 1
+                i = j
+
+    else:
+        # dummy values
+        subsequence_length = 0
+        mixing_coeff = 0
+        num_big_subsequences = 0
+        num_total_big_subsequence_numbers = 0
+        # Store the arcs in the original order
+        i = 0
+        for a in range(n_arcs - 1, -1, -1):
+            source[i] = n_nodes - (a // m) - 1
+            target[i] = n_nodes - ((a % m) + n) - 1
+            i += 1
+
+    node_arc_data = NodeArcData(cost, supply, flow, pi, source, target)
+    spanning_tree = SpanningTree(
+        parent, pred, thread, rev_thread, succ_num, last_succ, forward, state, root
+    )
+    graph = DiGraph(
+        n_nodes,
+        n_arcs,
+        n,
+        m,
+        use_arc_mixing,
+        num_total_big_subsequence_numbers,
+        subsequence_length,
+        num_big_subsequences,
+        mixing_coeff,
+    )
+
+    return node_arc_data, spanning_tree, graph
+
+
+@numba.njit(locals={"u": numba.uint16, "e": numba.uint32})
+def initialize_graph_structures(graph, node_arc_data, spanning_tree):
+
+    n_nodes = graph.n_nodes
+    n_arcs = graph.n_arcs
+
+    # unpack arrays
+    cost = node_arc_data.cost
+    supply = node_arc_data.supply
+    flow = node_arc_data.flow
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+
+    parent = spanning_tree.parent
+    pred = spanning_tree.pred
+    thread = spanning_tree.thread
+    rev_thread = spanning_tree.rev_thread
+    succ_num = spanning_tree.succ_num
+    last_succ = spanning_tree.last_succ
+    forward = spanning_tree.forward
+    state = spanning_tree.state
+
+    if n_nodes == 0:
+        return False
+
+    # Check the sum of supply values
+    net_supply = 0
+    for i in range(n_nodes):
+        net_supply += supply[i]
+
+    if np.fabs(net_supply) > NET_SUPPLY_ERROR_TOLERANCE:
+        return False
+
+    # Fix using doubles
+    # Initialize artifical cost
+    artificial_cost = 0.0
+    for i in range(n_arcs):
+        if cost[i] > artificial_cost:
+            artificial_cost = cost[i]
+        # reset flow and state vectors
+        if flow[i] != 0:
+            flow[i] = 0
+        state[i] = ArcState.STATE_LOWER
+
+    artificial_cost = (artificial_cost + 1) * n_nodes
+
+    # Set data for the artificial root node
+    root = n_nodes
+    parent[root] = -1
+    pred[root] = -1
+    thread[root] = 0
+    rev_thread[0] = root
+    succ_num[root] = n_nodes + 1
+    last_succ[root] = root - 1
+    supply[root] = -net_supply
+    pi[root] = 0
+
+    # Add artificial arcs and initialize the spanning tree data structure
+    # EQ supply constraints
+    e = n_arcs
+    for u in range(n_nodes):
+        parent[u] = root
+        pred[u] = e
+        thread[u] = u + 1
+        rev_thread[u + 1] = u
+        succ_num[u] = 1
+        last_succ[u] = u
+        state[e] = ArcState.STATE_TREE
+        if supply[u] >= 0:
+            forward[u] = True
+            pi[u] = 0
+            source[e] = u
+            target[e] = root
+            flow[e] = supply[u]
+            cost[e] = 0
+        else:
+            forward[u] = False
+            pi[u] = artificial_cost
+            source[e] = root
+            target[e] = u
+            flow[e] = -supply[u]
+            cost[e] = artificial_cost
+        e += 1
+
+    return True
+
+
+@numba.njit()
+def initialize_supply(left_node_supply, right_node_supply, graph, supply):
+    for n in range(graph.n_nodes):
+        if n < graph.n:
+            supply[graph.n_nodes - n - 1] = left_node_supply[n]
+        else:
+            supply[graph.n_nodes - n - 1] = right_node_supply[n - graph.n]
+
+
+@numba.njit(inline="always")
+def set_cost(arc, cost_val, cost, graph):
+    cost[arc_id(arc, graph)] = cost_val
+
+
+@numba.njit(locals={"i": numba.uint16, "j": numba.uint16})
+def initialize_cost(cost_matrix, graph, cost):
+    for i in range(cost_matrix.shape[0]):
+        for j in range(cost_matrix.shape[1]):
+            set_cost(i * cost_matrix.shape[1] + j, cost_matrix[i, j], cost, graph)
+
+
+@numba.njit(fastmath=True, locals={"i": numba.uint32})
+def total_cost(flow, cost):
+    c = 0.0
+    for i in range(flow.shape[0]):
+        c += flow[i] * cost[i]
+    return c
+
+
+@numba.njit(nogil=True)
+def network_simplex_core(node_arc_data, spanning_tree, graph, max_iter):
+
+    # pivot_block = PivotBlock(
+    #     max(np.int32(np.sqrt(graph.n_arcs)), 10),
+    #     np.zeros(1, dtype=np.int32),
+    #     graph.n_arcs,
+    # )
+    pivot_block_size = max(np.int32(np.sqrt(graph.n_arcs)), 10)
+    search_arc_num = graph.n_arcs
+    solution_status = ProblemStatus.OPTIMAL
+
+    # Perform heuristic initial pivots
+    bounded, in_arc = construct_initial_pivots(graph, node_arc_data, spanning_tree)
+    if not bounded:
+        return ProblemStatus.UNBOUNDED
+
+    iter_number = 0
+    # pivot.setDantzig(true);
+    # Execute the Network Simplex algorithm
+    in_arc, pivot_next_arc = find_entering_arc(
+        pivot_block_size, 0, search_arc_num, spanning_tree.state, node_arc_data, in_arc
+    )
+    while in_arc >= 0:
+        iter_number += 1
+        if max_iter > 0 and iter_number >= max_iter:
+            solution_status = ProblemStatus.MAX_ITER_REACHED
+            break
+
+        join = find_join_node(
+            node_arc_data.source,
+            node_arc_data.target,
+            spanning_tree.succ_num,
+            spanning_tree.parent,
+            in_arc,
+        )
+        leaving_arc_data = find_leaving_arc(join, in_arc, node_arc_data, spanning_tree)
+        if leaving_arc_data.delta >= MAX:
+            return ProblemStatus.UNBOUNDED
+
+        update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc)
+
+        if leaving_arc_data.change:
+            update_spanning_tree(
+                spanning_tree, leaving_arc_data, join, in_arc, node_arc_data.source
+            )
+            update_potential(
+                leaving_arc_data, node_arc_data.pi, node_arc_data.cost, spanning_tree
+            )
+
+        in_arc, pivot_next_arc = find_entering_arc(
+            pivot_block_size,
+            pivot_next_arc,
+            search_arc_num,
+            spanning_tree.state,
+            node_arc_data,
+            in_arc,
+        )
+
+    flow = node_arc_data.flow
+    pi = node_arc_data.pi
+
+    # Check feasibility
+    if solution_status == ProblemStatus.OPTIMAL:
+        for e in range(graph.n_arcs, graph.n_arcs + graph.n_nodes):
+            if flow[e] != 0:
+                if np.abs(flow[e]) > EPSILON:
+                    return ProblemStatus.INFEASIBLE
+                else:
+                    flow[e] = 0
+
+    # Shift potentials to meet the requirements of the GEQ/LEQ type
+    # optimality conditions
+    max_pot = -INFINITY
+    for i in range(graph.n_nodes):
+        if pi[i] > max_pot:
+            max_pot = pi[i]
+    if max_pot > 0:
+        for i in range(graph.n_nodes):
+            pi[i] -= max_pot
+
+    return solution_status
+
+
+#######################################################
+# SINKHORN distances in various variations
+#######################################################
+
+
+@numba.njit(
+    fastmath=True,
+    parallel=True,
+    locals={"diff": numba.float32, "result": numba.float32},
+    cache=False,
+)
+def right_marginal_error(u, K, v, y):
+    uK = u @ K
+    result = 0.0
+    for i in numba.prange(uK.shape[0]):
+        diff = y[i] - uK[i] * v[i]
+        result += diff * diff
+    return np.sqrt(result)
+
+
+@numba.njit(
+    fastmath=True,
+    parallel=True,
+    locals={"diff": numba.float32, "result": numba.float32},
+    cache=False,
+)
+def right_marginal_error_batch(u, K, v, y):
+    uK = K.T @ u
+    result = 0.0
+    for i in numba.prange(uK.shape[0]):
+        for j in range(uK.shape[1]):
+            diff = y[j, i] - uK[i, j] * v[i, j]
+            result += diff * diff
+    return np.sqrt(result)
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def transport_plan(K, u, v):
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.empty_like(K)
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            result[i, j] = u[i] * K[i, j] * v[j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, locals={"result": numba.float32}, cache=False)
+def relative_change_in_plan(old_u, old_v, new_u, new_v):
+    i_dim = old_u.shape[0]
+    j_dim = old_v.shape[0]
+    result = 0.0
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            old_uv = old_u[i] * old_v[j]
+            result += np.float32(np.abs(old_uv - new_u[i] * new_v[j]) / old_uv)
+
+    return result / (i_dim * j_dim)
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def precompute_K_prime(K, x):
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.empty_like(K)
+    for i in numba.prange(i_dim):
+        if x[i] > 0.0:
+            x_i_inverse = 1.0 / x[i]
+        else:
+            x_i_inverse = INFINITY
+        for j in range(j_dim):
+            result[i, j] = x_i_inverse * K[i, j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def K_from_cost(cost, regularization):
+    i_dim = cost.shape[0]
+    j_dim = cost.shape[1]
+    result = np.empty_like(cost)
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            scaled_cost = cost[i, j] / regularization
+            result[i, j] = np.exp(-scaled_cost)
+
+    return result
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_iterations(
+    x, y, u, v, K, max_iter=1000, error_tolerance=1e-9, change_tolerance=1e-9
+):
+    K_prime = precompute_K_prime(K, x)
+
+    prev_u = u
+    prev_v = v
+
+    for iteration in range(max_iter):
+
+        next_v = y / (K.T @ u)
+
+        if np.any(~np.isfinite(next_v)):
+            break
+
+        next_u = 1.0 / (K_prime @ next_v)
+
+        if np.any(~np.isfinite(next_u)):
+            break
+
+        u = next_u
+        v = next_v
+
+        if iteration % 20 == 0:
+            # Check if values in plan have changed significantly since last 20 iterations
+            relative_change = relative_change_in_plan(prev_u, prev_v, next_u, next_v)
+            if relative_change <= change_tolerance:
+                break
+
+            prev_u = u
+            prev_v = v
+
+        if iteration % 10 == 0:
+            # Check if right marginal error is less than tolerance every 10 iterations
+            err = right_marginal_error(u, K, v, y)
+            if err <= error_tolerance:
+                break
+
+    return u, v
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_iterations_batch(x, y, u, v, K, max_iter=1000, error_tolerance=1e-9):
+    K_prime = precompute_K_prime(K, x)
+
+    for iteration in range(max_iter):
+
+        next_v = y.T / (K.T @ u)
+
+        if np.any(~np.isfinite(next_v)):
+            break
+
+        next_u = 1.0 / (K_prime @ next_v)
+
+        if np.any(~np.isfinite(next_u)):
+            break
+
+        u = next_u
+        v = next_v
+
+        if iteration % 10 == 0:
+            # Check if right marginal error is less than tolerance every 10 iterations
+            err = right_marginal_error_batch(u, K, v, y)
+            if err <= error_tolerance:
+                break
+
+    return u, v
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_transport_plan(
+    x,
+    y,
+    cost=_dummy_cost,
+    regularization=1.0,
+    max_iter=1000,
+    error_tolerance=1e-9,
+    change_tolerance=1e-9,
+):
+    dim_x = x.shape[0]
+    dim_y = y.shape[0]
+    u = np.full(dim_x, 1.0 / dim_x, dtype=cost.dtype)
+    v = np.full(dim_y, 1.0 / dim_y, dtype=cost.dtype)
+
+    K = K_from_cost(cost, regularization)
+    u, v = sinkhorn_iterations(
+        x,
+        y,
+        u,
+        v,
+        K,
+        max_iter=max_iter,
+        error_tolerance=error_tolerance,
+        change_tolerance=change_tolerance,
+    )
+
+    return transport_plan(K, u, v)
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_distance(x, y, cost=_dummy_cost, regularization=1.0):
+    transport_plan = sinkhorn_transport_plan(
+        x, y, cost=cost, regularization=regularization
+    )
+    dim_i = transport_plan.shape[0]
+    dim_j = transport_plan.shape[1]
+    result = 0.0
+    for i in range(dim_i):
+        for j in range(dim_j):
+            result += transport_plan[i, j] * cost[i, j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def sinkhorn_distance_batch(x, y, cost=_dummy_cost, regularization=1.0):
+    dim_x = x.shape[0]
+    dim_y = y.shape[0]
+
+    batch_size = y.shape[1]
+
+    u = np.full((dim_x, batch_size), 1.0 / dim_x, dtype=cost.dtype)
+    v = np.full((dim_y, batch_size), 1.0 / dim_y, dtype=cost.dtype)
+
+    K = K_from_cost(cost, regularization)
+    u, v = sinkhorn_iterations_batch(
+        x,
+        y,
+        u,
+        v,
+        K,
+    )
+
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.zeros(batch_size)
+    for i in range(i_dim):
+        for j in range(j_dim):
+            K_times_cost = K[i, j] * cost[i, j]
+            for batch in range(batch_size):
+                result[batch] += u[i, batch] * K_times_cost * v[j, batch]
+
+    return result
+
+
+def make_fixed_cost_sinkhorn_distance(cost, regularization=1.0):
+
+    K = K_from_cost(cost, regularization)
+    dim_x = K.shape[0]
+    dim_y = K.shape[1]
+
+    @numba.njit(fastmath=True)
+    def closure(x, y):
+        u = np.full(dim_x, 1.0 / dim_x, dtype=cost.dtype)
+        v = np.full(dim_y, 1.0 / dim_y, dtype=cost.dtype)
+
+        K = K_from_cost(cost, regularization)
+        u, v = sinkhorn_iterations(
+            x,
+            y,
+            u,
+            v,
+            K,
+        )
+
+        current_plan = transport_plan(K, u, v)
+
+        result = 0.0
+        for i in range(dim_x):
+            for j in range(dim_y):
+                result += current_plan[i, j] * cost[i, j]
+
+        return result
+
+    return closure
diff --git a/shared/package-files/32/320cceeab3e51f6f4515bee7920fbb652418523bd0f769be82a892f9bab9676c/utils.pyi b/shared/package-files/32/320cceeab3e51f6f4515bee7920fbb652418523bd0f769be82a892f9bab9676c/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..92fc2115e9731cca3008b4ca0f5fbfe020196849
--- /dev/null
+++ b/shared/package-files/32/320cceeab3e51f6f4515bee7920fbb652418523bd0f769be82a892f9bab9676c/utils.pyi
@@ -0,0 +1,33 @@
+from collections import UserList
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+from django.core.exceptions import ValidationError
+from django.utils.safestring import SafeText
+
+def pretty_name(name: str) -> str: ...
+def flatatt(attrs: Dict[str, Any]) -> SafeText: ...
+
+class ErrorDict(dict):
+    def as_data(self) -> Dict[str, List[ValidationError]]: ...
+    def get_json_data(self, escape_html: bool = ...) -> Dict[str, Any]: ...
+    def as_json(self, escape_html: bool = ...) -> str: ...
+    def as_ul(self) -> str: ...
+    def as_text(self) -> str: ...
+
+class ErrorList(UserList):
+    data: List[Union[ValidationError, str]]
+    error_class: str = ...
+    def __init__(
+        self,
+        initlist: Optional[Union[ErrorList, Sequence[Union[str, Exception]]]] = ...,
+        error_class: Optional[str] = ...,
+    ) -> None: ...
+    def as_data(self) -> List[ValidationError]: ...
+    def get_json_data(self, escape_html: bool = ...) -> List[Dict[str, str]]: ...
+    def as_json(self, escape_html: bool = ...) -> str: ...
+    def as_ul(self) -> str: ...
+    def as_text(self) -> str: ...
+
+def from_current_timezone(value: datetime) -> datetime: ...
+def to_current_timezone(value: datetime) -> datetime: ...
diff --git a/shared/package-files/32/320de8e22b5d74767e87fabe35eacbdff562c3ae498f6fcec9628f7fc895e0e9/utils.py b/shared/package-files/32/320de8e22b5d74767e87fabe35eacbdff562c3ae498f6fcec9628f7fc895e0e9/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b3aa9aeb267455bc993057bec369c74cf807a96
--- /dev/null
+++ b/shared/package-files/32/320de8e22b5d74767e87fabe35eacbdff562c3ae498f6fcec9628f7fc895e0e9/utils.py
@@ -0,0 +1,493 @@
+import contextlib
+import fnmatch
+import hashlib
+import logging
+import os
+import re
+import shutil
+import sys
+import warnings as _warnings
+from concurrent.futures import Executor, ProcessPoolExecutor, ThreadPoolExecutor
+from errno import EACCES, ENOENT, EPERM, EROFS
+from itertools import chain
+from os.path import (
+    abspath,
+    basename,
+    dirname,
+    isdir,
+    isfile,
+    islink,
+    join,
+    lexists,
+    normpath,
+    split,
+)
+from stat import S_IEXEC, S_IMODE, S_ISDIR, S_ISREG, S_IWRITE
+from subprocess import STDOUT, CalledProcessError, check_output, list2cmdline
+from tempfile import NamedTemporaryFile, mkdtemp
+
+on_win = sys.platform == "win32"
+log = logging.getLogger(__file__)
+CONDA_TEMP_EXTENSION = ".c~"
+
+
+def which(executable):
+    from distutils.spawn import find_executable
+
+    return find_executable(executable)
+
+
+def make_writable(path):
+    try:
+        mode = os.lstat(path).st_mode
+        if S_ISDIR(mode):
+            os.chmod(path, S_IMODE(mode) | S_IWRITE | S_IEXEC)
+        elif S_ISREG(mode) or islink(path):
+            os.chmod(path, S_IMODE(mode) | S_IWRITE)
+        else:
+            log.debug("path cannot be made writable: %s", path)
+        return True
+    except Exception as e:
+        eno = getattr(e, "errno", None)
+        if eno in (ENOENT,):
+            log.debug("tried to make writable, but didn't exist: %s", path)
+            raise
+        elif eno in (EACCES, EPERM, EROFS):
+            log.debug("tried make writable but failed: %s\n%r", path, e)
+            return False
+        else:
+            log.warn("Error making path writable: %s\n%r", path, e)
+            raise
+
+
+class DummyExecutor(Executor):
+    def map(self, func, *iterables):
+        for iterable in iterables:
+            for thing in iterable:
+                yield func(thing)
+
+
+def get_executor(processes):
+    return DummyExecutor() if processes == 1 else ProcessPoolExecutor(max_workers=processes)
+
+
+def recursive_make_writable(path):
+    # The need for this function was pointed out at
+    #   https://github.com/conda/conda/issues/3266#issuecomment-239241915
+    # Especially on windows, file removal will often fail because it is marked read-only
+    if isdir(path):
+        for root, dirs, files in os.walk(path):
+            for path in chain.from_iterable((files, dirs)):
+                try:
+                    make_writable(join(root, path))
+                except:
+                    pass
+    else:
+        try:
+            make_writable(path)
+        except:
+            pass
+
+
+def quote_for_shell(arguments, shell=None):
+    if not shell:
+        shell = "cmd.exe" if on_win else "bash"
+    if shell == "cmd.exe":
+        return list2cmdline(arguments)
+    else:
+        # If any multiline argument gets mixed with any other argument (which is true if we've
+        # arrived in this function) then we just quote it. This assumes something like:
+        # ['python', '-c', 'a\nmultiline\nprogram\n']
+        # It may make sense to allow specifying a replacement character for '\n' too? e.g. ';'
+        quoted = []
+        # This could all be replaced with some regex wizardry but that is less readable and
+        # for code like this, readability is very important.
+        for arg in arguments:
+            if '"' in arg:
+                quote = "'"
+            elif "'" in arg:
+                quote = '"'
+            elif not any(_ in arg for _ in (" ", "\n")):
+                quote = ""
+            else:
+                quote = '"'
+            quoted.append(quote + arg + quote)
+        return " ".join(quoted)
+
+
+def rmtree(path, *args, **kwargs):
+    # subprocessing to delete large folders can be quite a bit faster
+    path = normpath(path)
+    if on_win:
+        try:
+            # the fastest way seems to be using DEL to recursively delete files
+            # https://www.ghacks.net/2017/07/18/how-to-delete-large-folders-in-windows-super-fast/
+            # However, this is not entirely safe, as it can end up following symlinks to folders
+            # https://superuser.com/a/306618/184799
+            # so, we stick with the slower, but hopefully safer way.  Maybe if we figured out how
+            #    to scan for any possible symlinks, we could do the faster way.
+            # out = check_output('DEL /F/Q/S *.* > NUL 2> NUL'.format(path), shell=True,
+            #                    stderr=STDOUT, cwd=path)
+
+            out = check_output(f'RD /S /Q "{path}" > NUL 2> NUL', shell=True, stderr=STDOUT)
+        except:
+            try:
+                # Try to delete in Unicode
+                name = None
+
+                with NamedTemporaryFile(suffix=".bat", delete=False) as batch_file:
+                    batch_file.write(f"RD /S {quote_for_shell([path])}\n")
+                    batch_file.write("chcp 65001\n")
+                    batch_file.write(f"RD /S {quote_for_shell([path])}\n")
+                    batch_file.write("EXIT 0\n")
+                    name = batch_file.name
+                # If the above is bugged we can end up deleting hard-drives, so we check
+                # that 'path' appears in it. This is not bulletproof but it could save you (me).
+                with open(name) as contents:
+                    content = contents.read()
+                    assert path in content
+                comspec = os.environ["COMSPEC"]
+                CREATE_NO_WINDOW = 0x08000000
+                # It is essential that we `pass stdout=None, stderr=None, stdin=None` here because
+                # if we do not, then the standard console handles get attached and chcp affects the
+                # parent process (and any which share those console handles!)
+                out = check_output(
+                    [comspec, "/d", "/c", name],
+                    shell=False,
+                    stdout=None,
+                    stderr=None,
+                    stdin=None,
+                    creationflags=CREATE_NO_WINDOW,
+                )
+
+            except CalledProcessError as e:
+                if e.returncode != 5:
+                    log.error(f"Removing folder {path} the fast way failed.  Output was: {out}")
+                    raise
+                else:
+                    log.debug(f"removing dir contents the fast way failed.  Output was: {out}")
+    else:
+        try:
+            os.makedirs(".empty")
+        except:
+            pass
+        # yes, this looks strange.  See
+        #    https://unix.stackexchange.com/a/79656/34459
+        #    https://web.archive.org/web/20130929001850/http://linuxnote.net/jianingy/en/linux/a-fast-way-to-remove-huge-number-of-files.html  # NOQA
+        rsync = which("rsync")
+        if rsync and isdir(".empty"):
+            try:
+                out = check_output(
+                    [
+                        rsync,
+                        "-a",
+                        "--force",
+                        "--delete",
+                        join(os.getcwd(), ".empty") + "/",
+                        path + "/",
+                    ],
+                    stderr=STDOUT,
+                )
+            except CalledProcessError:
+                log.debug(f"removing dir contents the fast way failed.  Output was: {out}")
+            shutil.rmtree(".empty")
+    shutil.rmtree(path)
+
+
+def unlink_or_rename_to_trash(path):
+    """If files are in use, especially on windows, we can't remove them.
+    The fallback path is to rename them (but keep their folder the same),
+    which maintains the file handle validity.  See comments at:
+    https://serverfault.com/a/503769
+    """
+    try:
+        make_writable(path)
+        os.unlink(path)
+    except OSError:
+        try:
+            os.rename(path, path + ".conda_trash")
+        except OSError:
+            if on_win:
+                # on windows, it is important to use the rename program, as just using python's
+                #    rename leads to permission errors when files are in use.
+                with NamedTemporaryFile(suffix=".bat") as trash_script:
+                    with open(trash_script, "w") as f:
+                        f.write('@pushd "%1"\n')
+                        f.write("@REM Rename src to dest")
+                        f.write('@ren "%2" "%3" > NUL 2> NUL")')
+
+                    _dirname, _fn = split(path)
+                    dest_fn = path + ".conda_trash"
+                    counter = 1
+                    while isfile(dest_fn):
+                        dest_fn = dest_fn.splitext[0] + f".conda_trash_{counter}"
+                        counter += 1
+                    out = "< empty >"
+                    try:
+                        out = check_output(
+                            [
+                                "cmd.exe",
+                                "/C",
+                                trash_script,
+                                _dirname,
+                                _fn,
+                                basename(dest_fn),
+                            ],
+                            stderr=STDOUT,
+                        )
+                    except CalledProcessError:
+                        log.warn(
+                            "renaming file path {} to trash failed.  Output was: {}".format(
+                                path, out
+                            )
+                        )
+
+            log.warn(
+                "Could not remove or rename {}.  Please remove this file manually (you "
+                "may need to reboot to free file handles)".format(path)
+            )
+
+
+def remove_empty_parent_paths(path):
+    # recurse to clean up empty folders that were created to have a nested hierarchy
+    parent_path = dirname(path)
+    while isdir(parent_path) and not os.listdir(parent_path):
+        rmdir(parent_path)
+        parent_path = dirname(parent_path)
+
+
+def rm_rf(path, clean_empty_parents=False, *args, **kw):
+    """
+    Completely delete path
+    max_retries is the number of times to retry on failure. The default is 5. This only applies
+    to deleting a directory.
+    If removing path fails and trash is True, files will be moved to the trash directory.
+    """
+    recursive_make_writable(path)
+    try:
+        path = abspath(path)
+        if isdir(path) and not islink(path):
+            rmdir(path)
+        elif lexists(path):
+            unlink_or_rename_to_trash(path)
+        else:
+            log.debug("rm_rf failed. Not a link, file, or directory: %s", path)
+    finally:
+        if lexists(path):
+            log.info("rm_rf failed for %s", path)
+            return False
+    if isdir(path):
+        delete_trash(path)
+    if clean_empty_parents:
+        remove_empty_parent_paths(path)
+    return True
+
+
+# aliases that all do the same thing (legacy compat)
+try_rmdir_all_empty = move_to_trash = move_path_to_trash = rm_rf
+
+
+def delete_trash(prefix):
+    if not prefix:
+        prefix = sys.prefix
+    exclude = {"envs"}
+    for root, dirs, files in os.walk(prefix, topdown=True):
+        dirs[:] = [d for d in dirs if d not in exclude]
+        for fn in files:
+            if fnmatch.fnmatch(fn, "*.conda_trash*") or fnmatch.fnmatch(
+                fn, "*" + CONDA_TEMP_EXTENSION
+            ):
+                filename = join(root, fn)
+                try:
+                    os.unlink(filename)
+                    remove_empty_parent_paths(filename)
+                except OSError as e:
+                    log.debug("%r errno %d\nCannot unlink %s.", e, e.errno, filename)
+
+
+def rmdir(dirpath):
+    if not isdir(dirpath):
+        return
+    try:
+        rmtree(dirpath)
+    # we don't really care about errors that much.  We'll catch remaining files
+    #    with slower python logic.
+    except:
+        pass
+
+    for root, dirs, files in os.walk(dirpath, topdown=False):
+        for f in files:
+            unlink_or_rename_to_trash(join(root, f))
+
+
+# we have our own TemporaryDirectory class because it's faster and handles disk issues better.
+class TemporaryDirectory:
+    """Create and return a temporary directory.  This has the same
+    behavior as mkdtemp but can be used as a context manager.  For
+    example:
+
+        with TemporaryDirectory() as tmpdir:
+            ...
+
+    Upon exiting the context, the directory and everything contained
+    in it are removed.
+    """
+
+    # Handle mkdtemp raising an exception
+    name = None
+    _closed = False
+
+    def __init__(self, suffix="", prefix=".cph_tmp", dir=os.getcwd()):
+        self.name = mkdtemp(suffix, prefix, dir)
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__} {self.name!r}>"
+
+    def __enter__(self):
+        return self.name
+
+    def cleanup(self, _warn=False, _warnings=_warnings):
+        if self.name and not self._closed:
+            try:
+                rm_rf(self.name)
+            except:
+                _warnings.warn(
+                    'Conda-package-handling says: "I tried to clean up, '
+                    "but I could not.  There is a mess in %s that you might "
+                    'want to clean up yourself.  Sorry..."' % self.name
+                )
+            self._closed = True
+            if _warn and _warnings.warn:
+                _warnings.warn(
+                    f"Implicitly cleaning up {self!r}",
+                    _warnings.ResourceWarning,
+                )
+
+    def __exit__(self, exc, value, tb):
+        self.cleanup()
+
+    def __del__(self):
+        # Issue a ResourceWarning if implicit cleanup needed
+        self.cleanup(_warn=True)
+
+
+@contextlib.contextmanager
+def tmp_chdir(dest):
+    curdir = os.getcwd()
+    try:
+        os.chdir(dest)
+        yield
+    finally:
+        os.chdir(curdir)
+
+
+def ensure_list(arg):
+    if isinstance(arg, str) or not hasattr(arg, "__iter__"):
+        if arg is not None:
+            arg = [arg]
+        else:
+            arg = []
+    return arg
+
+
+def filter_files(
+    files_list,
+    prefix,
+    filter_patterns=(
+        r"(.*[\\\\/])?\.git[\\\\/].*",
+        r"(.*[\\\\/])?\.git$",
+        r"(.*)?\.DS_Store.*",
+        r".*\.la$",
+        r"conda-meta.*",
+    ),
+):
+    """Remove things like the .git directory from the list of files to be copied"""
+    for pattern in filter_patterns:
+        r = re.compile(pattern)
+        files_list = set(files_list) - set(filter(r.match, files_list))
+    return [
+        f
+        for f in files_list
+        if
+        # `islink` prevents symlinks to directories from being removed
+        os.path.islink(os.path.join(prefix, f)) or not os.path.isdir(os.path.join(prefix, f))
+    ]
+
+
+def filter_info_files(files_list, prefix):
+    return filter_files(
+        files_list,
+        prefix,
+        filter_patterns=(
+            "info[\\\\/]index\\.json",
+            "info[\\\\/]files",
+            "info[\\\\/]paths\\.json",
+            "info[\\\\/]about\\.json",
+            "info[\\\\/]has_prefix",
+            "info[\\\\/]hash_input_files",  # legacy, not used anymore
+            "info[\\\\/]hash_input\\.json",
+            "info[\\\\/]run_exports\\.yaml",  # legacy
+            "info[\\\\/]run_exports\\.json",  # current
+            "info[\\\\/]git",
+            "info[\\\\/]recipe[\\\\/].*",
+            "info[\\\\/]recipe_log.json",
+            "info[\\\\/]recipe.tar",
+            "info[\\\\/]test[\\\\/].*",
+            "info[\\\\/]LICENSE.*",
+            "info[\\\\/]requires",
+            "info[\\\\/]meta",
+            "info[\\\\/]platform",
+            "info[\\\\/]no_link",
+            "info[\\\\/]link\\.json",
+            "info[\\\\/]icon\\.png",
+        ),
+    )
+
+
+def _checksum(fd, algorithm, buffersize=65536):
+    hash_impl = getattr(hashlib, algorithm)
+    if not hash_impl:
+        raise ValueError(f"Unrecognized hash algorithm: {algorithm}")
+    else:
+        hash_impl = hash_impl()
+    for block in iter(lambda: fd.read(buffersize), b""):
+        hash_impl.update(block)
+    return hash_impl.hexdigest()
+
+
+def sha256_checksum(fd):
+    return _checksum(fd, "sha256")
+
+
+def md5_checksum(fd):
+    return _checksum(fd, "md5")
+
+
+def checksum(fn, algorithm, buffersize=1 << 18):
+    """
+    Calculate a checksum for a filename (not an open file).
+    """
+    with open(fn, "rb") as fd:
+        return _checksum(fd, algorithm, buffersize)
+
+
+def checksums(fn, algorithms, buffersize=1 << 18):
+    """
+    Calculate multiple checksums for a filename in parallel.
+    """
+    with ThreadPoolExecutor(max_workers=len(algorithms)) as e:
+        # take care not to share hash_impl between threads
+        results = [e.submit(checksum, fn, algorithm, buffersize) for algorithm in algorithms]
+    return [result.result() for result in results]
+
+
+def anonymize_tarinfo(tarinfo):
+    """
+    Remove user id, name from tarinfo.
+    """
+    # also remove timestamps?
+    tarinfo.uid = 0
+    tarinfo.uname = ""
+    tarinfo.gid = 0
+    tarinfo.gname = ""
+    return tarinfo
diff --git a/shared/package-files/32/320e1d8e583d6c7c1f5a26bb913b1315d62a8ec594ca23ff52b9a517218214c9/_cslt_compress.h b/shared/package-files/32/320e1d8e583d6c7c1f5a26bb913b1315d62a8ec594ca23ff52b9a517218214c9/_cslt_compress.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a4f10ed85baabd4a10da33c7695abdb959a0eb6
--- /dev/null
+++ b/shared/package-files/32/320e1d8e583d6c7c1f5a26bb913b1315d62a8ec594ca23ff52b9a517218214c9/_cslt_compress.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_cslt_compress_ops.h>
+
+namespace at {
+
+
+// aten::_cslt_compress(Tensor input) -> Tensor
+inline at::Tensor _cslt_compress(const at::Tensor & input) {
+    return at::_ops::_cslt_compress::call(input);
+}
+
+}
diff --git a/shared/package-files/32/320f996f1f43e0a3bf44729c901672b9a07966798be64ff26f0f953aff41021a/cudnn_affine_grid_generator_cuda_dispatch.h b/shared/package-files/32/320f996f1f43e0a3bf44729c901672b9a07966798be64ff26f0f953aff41021a/cudnn_affine_grid_generator_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f3f8f0e1fdf4c230a2913b6274dbaf681fa4df17
--- /dev/null
+++ b/shared/package-files/32/320f996f1f43e0a3bf44729c901672b9a07966798be64ff26f0f953aff41021a/cudnn_affine_grid_generator_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor cudnn_affine_grid_generator(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/32/32108975cdae6500033fd995ff28642facdcfabeb25e22f82894d3f488d0c868/_sparse_csr_prod.h b/shared/package-files/32/32108975cdae6500033fd995ff28642facdcfabeb25e22f82894d3f488d0c868/_sparse_csr_prod.h
new file mode 100644
index 0000000000000000000000000000000000000000..b9290baae61fe8b96dffeeebb1e840c9d2949608
--- /dev/null
+++ b/shared/package-files/32/32108975cdae6500033fd995ff28642facdcfabeb25e22f82894d3f488d0c868/_sparse_csr_prod.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_sparse_csr_prod_ops.h>
+
+namespace at {
+
+
+// aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor _sparse_csr_prod(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt) {
+    return at::_ops::_sparse_csr_prod_dim_dtype::call(self, dim, keepdim, dtype);
+}
+
+// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _sparse_csr_prod_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt) {
+    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
+}
+// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _sparse_csr_prod_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
+}
+
+}
diff --git a/shared/package-files/32/321245e3a66997b61f2214325015e61b9a64398230c37092ae487220afc52f9f/plugin_pb2.pyi b/shared/package-files/32/321245e3a66997b61f2214325015e61b9a64398230c37092ae487220afc52f9f/plugin_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5c75e8bdab2a1c0c5ba574677301b316554ad321
--- /dev/null
+++ b/shared/package-files/32/321245e3a66997b61f2214325015e61b9a64398230c37092ae487220afc52f9f/plugin_pb2.pyi
@@ -0,0 +1,136 @@
+"""
+@generated by mypy-protobuf.  Do not edit manually!
+isort:skip_file
+"""
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
+    FileDescriptor as google___protobuf___descriptor___FileDescriptor,
+)
+
+from google.protobuf.descriptor_pb2 import (
+    FileDescriptorProto as google___protobuf___descriptor_pb2___FileDescriptorProto,
+    GeneratedCodeInfo as google___protobuf___descriptor_pb2___GeneratedCodeInfo,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.internal.enum_type_wrapper import (
+    _EnumTypeWrapper as google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    NewType as typing___NewType,
+    Optional as typing___Optional,
+    Text as typing___Text,
+    cast as typing___cast,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+DESCRIPTOR: google___protobuf___descriptor___FileDescriptor = ...
+
+class Version(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    major: builtin___int = ...
+    minor: builtin___int = ...
+    patch: builtin___int = ...
+    suffix: typing___Text = ...
+
+    def __init__(self,
+        *,
+        major : typing___Optional[builtin___int] = None,
+        minor : typing___Optional[builtin___int] = None,
+        patch : typing___Optional[builtin___int] = None,
+        suffix : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"major",b"major",u"minor",b"minor",u"patch",b"patch",u"suffix",b"suffix"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"major",b"major",u"minor",b"minor",u"patch",b"patch",u"suffix",b"suffix"]) -> None: ...
+type___Version = Version
+
+class CodeGeneratorRequest(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    file_to_generate: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ...
+    parameter: typing___Text = ...
+
+    @property
+    def proto_file(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[google___protobuf___descriptor_pb2___FileDescriptorProto]: ...
+
+    @property
+    def compiler_version(self) -> type___Version: ...
+
+    def __init__(self,
+        *,
+        file_to_generate : typing___Optional[typing___Iterable[typing___Text]] = None,
+        parameter : typing___Optional[typing___Text] = None,
+        proto_file : typing___Optional[typing___Iterable[google___protobuf___descriptor_pb2___FileDescriptorProto]] = None,
+        compiler_version : typing___Optional[type___Version] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"compiler_version",b"compiler_version",u"parameter",b"parameter"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"compiler_version",b"compiler_version",u"file_to_generate",b"file_to_generate",u"parameter",b"parameter",u"proto_file",b"proto_file"]) -> None: ...
+type___CodeGeneratorRequest = CodeGeneratorRequest
+
+class CodeGeneratorResponse(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    FeatureValue = typing___NewType('FeatureValue', builtin___int)
+    type___FeatureValue = FeatureValue
+    Feature: _Feature
+    class _Feature(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[CodeGeneratorResponse.FeatureValue]):
+        DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+        FEATURE_NONE = typing___cast(CodeGeneratorResponse.FeatureValue, 0)
+        FEATURE_PROTO3_OPTIONAL = typing___cast(CodeGeneratorResponse.FeatureValue, 1)
+    FEATURE_NONE = typing___cast(CodeGeneratorResponse.FeatureValue, 0)
+    FEATURE_PROTO3_OPTIONAL = typing___cast(CodeGeneratorResponse.FeatureValue, 1)
+
+    class File(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+        name: typing___Text = ...
+        insertion_point: typing___Text = ...
+        content: typing___Text = ...
+
+        @property
+        def generated_code_info(self) -> google___protobuf___descriptor_pb2___GeneratedCodeInfo: ...
+
+        def __init__(self,
+            *,
+            name : typing___Optional[typing___Text] = None,
+            insertion_point : typing___Optional[typing___Text] = None,
+            content : typing___Optional[typing___Text] = None,
+            generated_code_info : typing___Optional[google___protobuf___descriptor_pb2___GeneratedCodeInfo] = None,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions___Literal[u"content",b"content",u"generated_code_info",b"generated_code_info",u"insertion_point",b"insertion_point",u"name",b"name"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"content",b"content",u"generated_code_info",b"generated_code_info",u"insertion_point",b"insertion_point",u"name",b"name"]) -> None: ...
+    type___File = File
+
+    error: typing___Text = ...
+    supported_features: builtin___int = ...
+
+    @property
+    def file(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___CodeGeneratorResponse.File]: ...
+
+    def __init__(self,
+        *,
+        error : typing___Optional[typing___Text] = None,
+        supported_features : typing___Optional[builtin___int] = None,
+        file : typing___Optional[typing___Iterable[type___CodeGeneratorResponse.File]] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"error",b"error",u"supported_features",b"supported_features"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"error",b"error",u"file",b"file",u"supported_features",b"supported_features"]) -> None: ...
+type___CodeGeneratorResponse = CodeGeneratorResponse
diff --git a/shared/package-files/32/3212543684feef77fea9442b687e6b7f67d93e5144135aa44fd5f0180e4c0591/slice_backward.h b/shared/package-files/32/3212543684feef77fea9442b687e6b7f67d93e5144135aa44fd5f0180e4c0591/slice_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..2da48e78d2c8df7a5cb6b858b1e503406e4c220d
--- /dev/null
+++ b/shared/package-files/32/3212543684feef77fea9442b687e6b7f67d93e5144135aa44fd5f0180e4c0591/slice_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/slice_backward_ops.h>
+
+namespace at {
+
+
+// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
+inline at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
+  }
+}
+
+// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
+inline at::Tensor slice_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor slice_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+  }
+}
+
+}
diff --git a/shared/package-files/32/3212bc4d893d7ebbc7cddea573d04eb8b5a6d7319c5658bf1154d70c5f28a16e/libtclstub8.6.a b/shared/package-files/32/3212bc4d893d7ebbc7cddea573d04eb8b5a6d7319c5658bf1154d70c5f28a16e/libtclstub8.6.a
new file mode 100644
index 0000000000000000000000000000000000000000..1cf6efb0f1a10ddfc8b79f3dd3c042d6f944b8fc
Binary files /dev/null and b/shared/package-files/32/3212bc4d893d7ebbc7cddea573d04eb8b5a6d7319c5658bf1154d70c5f28a16e/libtclstub8.6.a differ
diff --git a/shared/package-files/32/321333b3eaad9ecbf633186bc625d4a60c4c736def0fa00665add2ab899eecb1/test_replace.py b/shared/package-files/32/321333b3eaad9ecbf633186bc625d4a60c4c736def0fa00665add2ab899eecb1/test_replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c07389b29ad4558077353dd65eccb32c722dcba
--- /dev/null
+++ b/shared/package-files/32/321333b3eaad9ecbf633186bc625d4a60c4c736def0fa00665add2ab899eecb1/test_replace.py
@@ -0,0 +1,294 @@
+"Test replace, coverage 78%."
+
+from idlelib.replace import ReplaceDialog
+import unittest
+from test.support import requires
+requires('gui')
+from tkinter import Tk, Text
+
+from unittest.mock import Mock
+from idlelib.idle_test.mock_tk import Mbox
+import idlelib.searchengine as se
+
+orig_mbox = se.messagebox
+showerror = Mbox.showerror
+
+
+class ReplaceDialogTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root = Tk()
+        cls.root.withdraw()
+        se.messagebox = Mbox
+        cls.engine = se.SearchEngine(cls.root)
+        cls.dialog = ReplaceDialog(cls.root, cls.engine)
+        cls.dialog.bell = lambda: None
+        cls.dialog.ok = Mock()
+        cls.text = Text(cls.root)
+        cls.text.undo_block_start = Mock()
+        cls.text.undo_block_stop = Mock()
+        cls.dialog.text = cls.text
+
+    @classmethod
+    def tearDownClass(cls):
+        se.messagebox = orig_mbox
+        del cls.text, cls.dialog, cls.engine
+        cls.root.destroy()
+        del cls.root
+
+    def setUp(self):
+        self.text.insert('insert', 'This is a sample sTring')
+
+    def tearDown(self):
+        self.engine.patvar.set('')
+        self.dialog.replvar.set('')
+        self.engine.wordvar.set(False)
+        self.engine.casevar.set(False)
+        self.engine.revar.set(False)
+        self.engine.wrapvar.set(True)
+        self.engine.backvar.set(False)
+        showerror.title = ''
+        showerror.message = ''
+        self.text.delete('1.0', 'end')
+
+    def test_replace_simple(self):
+        # Test replace function with all options at default setting.
+        # Wrap around - True
+        # Regular Expression - False
+        # Match case - False
+        # Match word - False
+        # Direction - Forwards
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+
+        # test accessor method
+        self.engine.setpat('asdf')
+        equal(self.engine.getpat(), pv.get())
+
+        # text found and replaced
+        pv.set('a')
+        rv.set('asdf')
+        replace()
+        equal(text.get('1.8', '1.12'), 'asdf')
+
+        # don't "match word" case
+        text.mark_set('insert', '1.0')
+        pv.set('is')
+        rv.set('hello')
+        replace()
+        equal(text.get('1.2', '1.7'), 'hello')
+
+        # don't "match case" case
+        pv.set('string')
+        rv.set('world')
+        replace()
+        equal(text.get('1.23', '1.28'), 'world')
+
+        # without "regular expression" case
+        text.mark_set('insert', 'end')
+        text.insert('insert', '\nline42:')
+        before_text = text.get('1.0', 'end')
+        pv.set(r'[a-z][\d]+')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # test with wrap around selected and complete a cycle
+        text.mark_set('insert', '1.9')
+        pv.set('i')
+        rv.set('j')
+        replace()
+        equal(text.get('1.8'), 'i')
+        equal(text.get('2.1'), 'j')
+        replace()
+        equal(text.get('2.1'), 'j')
+        equal(text.get('1.8'), 'j')
+        before_text = text.get('1.0', 'end')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # text not found
+        before_text = text.get('1.0', 'end')
+        pv.set('foobar')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # test access method
+        self.dialog.find_it(0)
+
+    def test_replace_wrap_around(self):
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.wrapvar.set(False)
+
+        # replace candidate found both after and before 'insert'
+        text.mark_set('insert', '1.4')
+        pv.set('i')
+        rv.set('j')
+        replace()
+        equal(text.get('1.2'), 'i')
+        equal(text.get('1.5'), 'j')
+        replace()
+        equal(text.get('1.2'), 'i')
+        equal(text.get('1.20'), 'j')
+        replace()
+        equal(text.get('1.2'), 'i')
+
+        # replace candidate found only before 'insert'
+        text.mark_set('insert', '1.8')
+        pv.set('is')
+        before_text = text.get('1.0', 'end')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+    def test_replace_whole_word(self):
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.wordvar.set(True)
+
+        pv.set('is')
+        rv.set('hello')
+        replace()
+        equal(text.get('1.0', '1.4'), 'This')
+        equal(text.get('1.5', '1.10'), 'hello')
+
+    def test_replace_match_case(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.casevar.set(True)
+
+        before_text = self.text.get('1.0', 'end')
+        pv.set('this')
+        rv.set('that')
+        replace()
+        after_text = self.text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        pv.set('This')
+        replace()
+        equal(text.get('1.0', '1.4'), 'that')
+
+    def test_replace_regex(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.revar.set(True)
+
+        before_text = text.get('1.0', 'end')
+        pv.set(r'[a-z][\d]+')
+        rv.set('hello')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        text.insert('insert', '\nline42')
+        replace()
+        equal(text.get('2.0', '2.8'), 'linhello')
+
+        pv.set('')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Empty', showerror.message)
+
+        pv.set(r'[\d')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Pattern', showerror.message)
+
+        showerror.title = ''
+        showerror.message = ''
+        pv.set('[a]')
+        rv.set('test\\')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Invalid Replace Expression', showerror.message)
+
+        # test access method
+        self.engine.setcookedpat("?")
+        equal(pv.get(), "\\?")
+
+    def test_replace_backwards(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.backvar.set(True)
+
+        text.insert('insert', '\nis as ')
+
+        pv.set('is')
+        rv.set('was')
+        replace()
+        equal(text.get('1.2', '1.4'), 'is')
+        equal(text.get('2.0', '2.3'), 'was')
+        replace()
+        equal(text.get('1.5', '1.8'), 'was')
+        replace()
+        equal(text.get('1.2', '1.5'), 'was')
+
+    def test_replace_all(self):
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace_all = self.dialog.replace_all
+
+        text.insert('insert', '\n')
+        text.insert('insert', text.get('1.0', 'end')*100)
+        pv.set('is')
+        rv.set('was')
+        replace_all()
+        self.assertNotIn('is', text.get('1.0', 'end'))
+
+        self.engine.revar.set(True)
+        pv.set('')
+        replace_all()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Empty', showerror.message)
+
+        pv.set('[s][T]')
+        rv.set('\\')
+        replace_all()
+
+        self.engine.revar.set(False)
+        pv.set('text which is not present')
+        rv.set('foobar')
+        replace_all()
+
+    def test_default_command(self):
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace_find = self.dialog.default_command
+        equal = self.assertEqual
+
+        pv.set('This')
+        rv.set('was')
+        replace_find()
+        equal(text.get('sel.first', 'sel.last'), 'was')
+
+        self.engine.revar.set(True)
+        pv.set('')
+        replace_find()
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)
diff --git a/shared/package-files/32/32142548019430c5f8baba4dab0f88e8d2b663c846343045d375f7749dce490b/nwp-517-w b/shared/package-files/32/32142548019430c5f8baba4dab0f88e8d2b663c846343045d375f7749dce490b/nwp-517-w
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diff --git a/shared/package-files/32/32142548019430c5f8baba4dab0f88e8d2b663c846343045d375f7749dce490b/nwp517-w b/shared/package-files/32/32142548019430c5f8baba4dab0f88e8d2b663c846343045d375f7749dce490b/nwp517-w
new file mode 100644
index 0000000000000000000000000000000000000000..ae2e2d3852f178a798dba66676d2e45cc3e904ef
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diff --git a/shared/package-files/32/3216fd20e5c0bf873a9e4936e10438cd135dbfc15c60a373707cc7d98e35312f/binary_cross_entropy_backward_ops.h b/shared/package-files/32/3216fd20e5c0bf873a9e4936e10438cd135dbfc15c60a373707cc7d98e35312f/binary_cross_entropy_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..603471784ac99a5a451b8f88e229d35cea875873
--- /dev/null
+++ b/shared/package-files/32/3216fd20e5c0bf873a9e4936e10438cd135dbfc15c60a373707cc7d98e35312f/binary_cross_entropy_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API binary_cross_entropy_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::binary_cross_entropy_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction);
+};
+
+struct TORCH_API binary_cross_entropy_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::binary_cross_entropy_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/32/32183bd6f9e38dbe1f9b48d98230e8596f2b0093017a1f1a0c233be608c4a4a4/test_special.py b/shared/package-files/32/32183bd6f9e38dbe1f9b48d98230e8596f2b0093017a1f1a0c233be608c4a4a4/test_special.py
new file mode 100644
index 0000000000000000000000000000000000000000..beeaf1d76a63673b6622709cda598dfcb295bba4
--- /dev/null
+++ b/shared/package-files/32/32183bd6f9e38dbe1f9b48d98230e8596f2b0093017a1f1a0c233be608c4a4a4/test_special.py
@@ -0,0 +1,228 @@
+from sympy.core.add import Add
+from sympy.core.expr import unchanged
+from sympy.core.mul import Mul
+from sympy.core.symbol import symbols
+from sympy.core.relational import Eq
+from sympy.concrete.summations import Sum
+from sympy.functions.elementary.complexes import im, re
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.matrices.immutable import ImmutableDenseMatrix
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions.matadd import MatAdd
+from sympy.matrices.expressions.special import (
+    ZeroMatrix, GenericZeroMatrix, Identity, GenericIdentity, OneMatrix)
+from sympy.matrices.expressions.matmul import MatMul
+from sympy.testing.pytest import raises
+
+
+def test_zero_matrix_creation():
+    assert unchanged(ZeroMatrix, 2, 2)
+    assert unchanged(ZeroMatrix, 0, 0)
+    raises(ValueError, lambda: ZeroMatrix(-1, 2))
+    raises(ValueError, lambda: ZeroMatrix(2.0, 2))
+    raises(ValueError, lambda: ZeroMatrix(2j, 2))
+    raises(ValueError, lambda: ZeroMatrix(2, -1))
+    raises(ValueError, lambda: ZeroMatrix(2, 2.0))
+    raises(ValueError, lambda: ZeroMatrix(2, 2j))
+
+    n = symbols('n')
+    assert unchanged(ZeroMatrix, n, n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: ZeroMatrix(n, n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: ZeroMatrix(n, n))
+
+
+def test_generic_zero_matrix():
+    z = GenericZeroMatrix()
+    n = symbols('n', integer=True)
+    A = MatrixSymbol("A", n, n)
+
+    assert z == z
+    assert z != A
+    assert A != z
+
+    assert z.is_ZeroMatrix
+
+    raises(TypeError, lambda: z.shape)
+    raises(TypeError, lambda: z.rows)
+    raises(TypeError, lambda: z.cols)
+
+    assert MatAdd() == z
+    assert MatAdd(z, A) == MatAdd(A)
+    # Make sure it is hashable
+    hash(z)
+
+
+def test_identity_matrix_creation():
+    assert Identity(2)
+    assert Identity(0)
+    raises(ValueError, lambda: Identity(-1))
+    raises(ValueError, lambda: Identity(2.0))
+    raises(ValueError, lambda: Identity(2j))
+
+    n = symbols('n')
+    assert Identity(n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: Identity(n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: Identity(n))
+
+
+def test_generic_identity():
+    I = GenericIdentity()
+    n = symbols('n', integer=True)
+    A = MatrixSymbol("A", n, n)
+
+    assert I == I
+    assert I != A
+    assert A != I
+
+    assert I.is_Identity
+    assert I**-1 == I
+
+    raises(TypeError, lambda: I.shape)
+    raises(TypeError, lambda: I.rows)
+    raises(TypeError, lambda: I.cols)
+
+    assert MatMul() == I
+    assert MatMul(I, A) == MatMul(A)
+    # Make sure it is hashable
+    hash(I)
+
+
+def test_one_matrix_creation():
+    assert OneMatrix(2, 2)
+    assert OneMatrix(0, 0)
+    assert Eq(OneMatrix(1, 1), Identity(1))
+    raises(ValueError, lambda: OneMatrix(-1, 2))
+    raises(ValueError, lambda: OneMatrix(2.0, 2))
+    raises(ValueError, lambda: OneMatrix(2j, 2))
+    raises(ValueError, lambda: OneMatrix(2, -1))
+    raises(ValueError, lambda: OneMatrix(2, 2.0))
+    raises(ValueError, lambda: OneMatrix(2, 2j))
+
+    n = symbols('n')
+    assert OneMatrix(n, n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: OneMatrix(n, n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: OneMatrix(n, n))
+
+
+def test_ZeroMatrix():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    Z = ZeroMatrix(n, m)
+
+    assert A + Z == A
+    assert A*Z.T == ZeroMatrix(n, n)
+    assert Z*A.T == ZeroMatrix(n, n)
+    assert A - A == ZeroMatrix(*A.shape)
+
+    assert Z
+
+    assert Z.transpose() == ZeroMatrix(m, n)
+    assert Z.conjugate() == Z
+    assert Z.adjoint() == ZeroMatrix(m, n)
+    assert re(Z) == Z
+    assert im(Z) == Z
+
+    assert ZeroMatrix(n, n)**0 == Identity(n)
+    assert ZeroMatrix(3, 3).as_explicit() == ImmutableDenseMatrix.zeros(3, 3)
+
+
+def test_ZeroMatrix_doit():
+    n = symbols('n', integer=True)
+    Znn = ZeroMatrix(Add(n, n, evaluate=False), n)
+    assert isinstance(Znn.rows, Add)
+    assert Znn.doit() == ZeroMatrix(2*n, n)
+    assert isinstance(Znn.doit().rows, Mul)
+
+
+def test_OneMatrix():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    U = OneMatrix(n, m)
+
+    assert U.shape == (n, m)
+    assert isinstance(A + U, Add)
+    assert U.transpose() == OneMatrix(m, n)
+    assert U.conjugate() == U
+    assert U.adjoint() == OneMatrix(m, n)
+    assert re(U) == U
+    assert im(U) == ZeroMatrix(n, m)
+
+    assert OneMatrix(n, n) ** 0 == Identity(n)
+
+    U = OneMatrix(n, n)
+    assert U[1, 2] == 1
+
+    U = OneMatrix(2, 3)
+    assert U.as_explicit() == ImmutableDenseMatrix.ones(2, 3)
+
+
+def test_OneMatrix_doit():
+    n = symbols('n', integer=True)
+    Unn = OneMatrix(Add(n, n, evaluate=False), n)
+    assert isinstance(Unn.rows, Add)
+    assert Unn.doit() == OneMatrix(2 * n, n)
+    assert isinstance(Unn.doit().rows, Mul)
+
+
+def test_OneMatrix_mul():
+    n, m, k = symbols('n m k', integer=True)
+    w = MatrixSymbol('w', n, 1)
+    assert OneMatrix(n, m) * OneMatrix(m, k) == OneMatrix(n, k) * m
+    assert w * OneMatrix(1, 1) == w
+    assert OneMatrix(1, 1) * w.T == w.T
+
+
+def test_Identity():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    i, j = symbols('i j')
+
+    In = Identity(n)
+    Im = Identity(m)
+
+    assert A*Im == A
+    assert In*A == A
+
+    assert In.transpose() == In
+    assert In.inverse() == In
+    assert In.conjugate() == In
+    assert In.adjoint() == In
+    assert re(In) == In
+    assert im(In) == ZeroMatrix(n, n)
+
+    assert In[i, j] != 0
+    assert Sum(In[i, j], (i, 0, n-1), (j, 0, n-1)).subs(n,3).doit() == 3
+    assert Sum(Sum(In[i, j], (i, 0, n-1)), (j, 0, n-1)).subs(n,3).doit() == 3
+
+    # If range exceeds the limit `(0, n-1)`, do not remove `Piecewise`:
+    expr = Sum(In[i, j], (i, 0, n-1))
+    assert expr.doit() == 1
+    expr = Sum(In[i, j], (i, 0, n-2))
+    assert expr.doit().dummy_eq(
+        Piecewise(
+            (1, (j >= 0) & (j <= n-2)),
+            (0, True)
+        )
+    )
+    expr = Sum(In[i, j], (i, 1, n-1))
+    assert expr.doit().dummy_eq(
+        Piecewise(
+            (1, (j >= 1) & (j <= n-1)),
+            (0, True)
+        )
+    )
+    assert Identity(3).as_explicit() == ImmutableDenseMatrix.eye(3)
+
+
+def test_Identity_doit():
+    n = symbols('n', integer=True)
+    Inn = Identity(Add(n, n, evaluate=False))
+    assert isinstance(Inn.rows, Add)
+    assert Inn.doit() == Identity(2*n)
+    assert isinstance(Inn.doit().rows, Mul)
diff --git a/shared/package-files/32/32191fb149da315122ea846b71f4e0a7a875ff54024bc6efc6dc2813a6ea652e/aead.pyi b/shared/package-files/32/32191fb149da315122ea846b71f4e0a7a875ff54024bc6efc6dc2813a6ea652e/aead.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..68b3dc19a965d0c30bdceba78599487b8589c392
--- /dev/null
+++ b/shared/package-files/32/32191fb149da315122ea846b71f4e0a7a875ff54024bc6efc6dc2813a6ea652e/aead.pyi
@@ -0,0 +1,22 @@
+from typing import Optional
+
+class AESCCM(object):
+    def __init__(self, key: bytes, tag_length: Optional[int]) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls, bit_length: int) -> bytes: ...
+
+class AESGCM(object):
+    def __init__(self, key: bytes) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls, bit_length: int) -> bytes: ...
+
+class ChaCha20Poly1305(object):
+    def __init__(self, key: bytes) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls) -> bytes: ...
diff --git a/shared/package-files/32/3219b2a5be56cf119d05a429c9269ef4d6e21499659047b3762b631da8dded10/solidity.py b/shared/package-files/32/3219b2a5be56cf119d05a429c9269ef4d6e21499659047b3762b631da8dded10/solidity.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1654e46fad0d391c24cab6dc921bbd912551f57
--- /dev/null
+++ b/shared/package-files/32/3219b2a5be56cf119d05a429c9269ef4d6e21499659047b3762b631da8dded10/solidity.py
@@ -0,0 +1,87 @@
+"""
+    pygments.lexers.solidity
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for Solidity.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, bygroups, include, words
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Whitespace
+
+__all__ = ['SolidityLexer']
+
+
+class SolidityLexer(RegexLexer):
+    """
+    For Solidity source code.
+
+    .. versionadded:: 2.5
+    """
+
+    name = 'Solidity'
+    aliases = ['solidity']
+    filenames = ['*.sol']
+    mimetypes = []
+
+    datatype = (
+        r'\b(address|bool|(?:(?:bytes|hash|int|string|uint)(?:8|16|24|32|40|48|56|64'
+        r'|72|80|88|96|104|112|120|128|136|144|152|160|168|176|184|192|200|208'
+        r'|216|224|232|240|248|256)?))\b'
+    )
+
+    tokens = {
+        'root': [
+            include('whitespace'),
+            include('comments'),
+            (r'\bpragma\s+solidity\b', Keyword, 'pragma'),
+            (r'\b(contract)(\s+)([a-zA-Z_]\w*)',
+             bygroups(Keyword, Whitespace, Name.Entity)),
+            (datatype + r'(\s+)((?:external|public|internal|private)\s+)?' +
+             r'([a-zA-Z_]\w*)',
+             bygroups(Keyword.Type, Whitespace, Keyword, Name.Variable)),
+            (r'\b(enum|event|function|struct)(\s+)([a-zA-Z_]\w*)',
+             bygroups(Keyword.Type, Whitespace, Name.Variable)),
+            (r'\b(msg|block|tx)\.([A-Za-z_][a-zA-Z0-9_]*)\b', Keyword),
+            (words((
+                'block', 'break', 'constant', 'constructor', 'continue',
+                'contract', 'do', 'else', 'external', 'false', 'for',
+                'function', 'if', 'import', 'inherited', 'internal', 'is',
+                'library', 'mapping', 'memory', 'modifier', 'msg', 'new',
+                'payable', 'private', 'public', 'require', 'return',
+                'returns', 'struct', 'suicide', 'throw', 'this', 'true',
+                'tx', 'var', 'while'), prefix=r'\b', suffix=r'\b'),
+             Keyword.Type),
+            (words(('keccak256',), prefix=r'\b', suffix=r'\b'), Name.Builtin),
+            (datatype, Keyword.Type),
+            include('constants'),
+            (r'[a-zA-Z_]\w*', Text),
+            (r'[~!%^&*+=|?:<>/-]', Operator),
+            (r'[.;{}(),\[\]]', Punctuation)
+        ],
+        'comments': [
+            (r'//(\n|[\w\W]*?[^\\]\n)', Comment.Single),
+            (r'/(\\\n)?[*][\w\W]*?[*](\\\n)?/', Comment.Multiline),
+            (r'/(\\\n)?[*][\w\W]*', Comment.Multiline)
+        ],
+        'constants': [
+            (r'("(\\"|.)*?")', String.Double),
+            (r"('(\\'|.)*?')", String.Single),
+            (r'\b0[xX][0-9a-fA-F]+\b', Number.Hex),
+            (r'\b\d+\b', Number.Decimal),
+        ],
+        'pragma': [
+            include('whitespace'),
+            include('comments'),
+            (r'(\^|>=|<)(\s*)(\d+\.\d+\.\d+)',
+             bygroups(Operator, Whitespace, Keyword)),
+            (r';', Punctuation, '#pop')
+        ],
+        'whitespace': [
+            (r'\s+', Whitespace),
+            (r'\n', Whitespace)
+        ]
+    }
diff --git a/shared/package-files/32/3221d56abebe6de504615129a97391217e1ff06dbd238ec3dfa92b7a5b5d7e84/pathf95.py b/shared/package-files/32/3221d56abebe6de504615129a97391217e1ff06dbd238ec3dfa92b7a5b5d7e84/pathf95.py
new file mode 100644
index 0000000000000000000000000000000000000000..0768cb12e87a54aa7fc0d10a04d97953eaa8aa41
--- /dev/null
+++ b/shared/package-files/32/3221d56abebe6de504615129a97391217e1ff06dbd238ec3dfa92b7a5b5d7e84/pathf95.py
@@ -0,0 +1,33 @@
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['PathScaleFCompiler']
+
+class PathScaleFCompiler(FCompiler):
+
+    compiler_type = 'pathf95'
+    description = 'PathScale Fortran Compiler'
+    version_pattern =  r'PathScale\(TM\) Compiler Suite: Version (?P<version>[\d.]+)'
+
+    executables = {
+        'version_cmd'  : ["pathf95", "-version"],
+        'compiler_f77' : ["pathf95", "-fixedform"],
+        'compiler_fix' : ["pathf95", "-fixedform"],
+        'compiler_f90' : ["pathf95"],
+        'linker_so'    : ["pathf95", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+    }
+    pic_flags = ['-fPIC']
+    module_dir_switch = '-module ' # Don't remove ending space!
+    module_include_switch = '-I'
+
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_flags_debug(self):
+        return ['-g']
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='pathf95').get_version())
diff --git a/shared/package-files/32/32258d53e28c9d2c075b977388a296199f18363315d016297765aac4f57cec8f/_has_same_storage_numel.h b/shared/package-files/32/32258d53e28c9d2c075b977388a296199f18363315d016297765aac4f57cec8f/_has_same_storage_numel.h
new file mode 100644
index 0000000000000000000000000000000000000000..34ac03e2cda11361558616a9afd01ac9946192d8
--- /dev/null
+++ b/shared/package-files/32/32258d53e28c9d2c075b977388a296199f18363315d016297765aac4f57cec8f/_has_same_storage_numel.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_has_same_storage_numel_ops.h>
+
+namespace at {
+
+
+// aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool
+inline bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::_has_same_storage_numel::call(self, other);
+}
+
+}
diff --git a/shared/package-files/32/322745165c7c739c69422e50ecd36bf6e1b991115dc19348c6dec887aba48817/min_compositeimplicitautograd_dispatch.h b/shared/package-files/32/322745165c7c739c69422e50ecd36bf6e1b991115dc19348c6dec887aba48817/min_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3385923bc75f293ea2dfeb5cf0f6a317f3d4419b
--- /dev/null
+++ b/shared/package-files/32/322745165c7c739c69422e50ecd36bf6e1b991115dc19348c6dec887aba48817/min_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, at::Dimname dim, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> min_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices);
+TORCH_API at::Tensor min(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & min_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & min_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/32/3227f4f6071f45f20edae842a8d37d382fcd5e080a8b36eec1acfa49c1b33141/test_factorize.py b/shared/package-files/32/3227f4f6071f45f20edae842a8d37d382fcd5e080a8b36eec1acfa49c1b33141/test_factorize.py
new file mode 100644
index 0000000000000000000000000000000000000000..41ecf9ee6b82317137b1a6accee14ad8c1b5a35a
--- /dev/null
+++ b/shared/package-files/32/3227f4f6071f45f20edae842a8d37d382fcd5e080a8b36eec1acfa49c1b33141/test_factorize.py
@@ -0,0 +1,125 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    Index,
+    date_range,
+    factorize,
+)
+import pandas._testing as tm
+
+
+class TestDatetimeIndexFactorize:
+    def test_factorize(self):
+        idx1 = DatetimeIndex(
+            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"]
+        )
+
+        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
+
+        arr, idx = idx1.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        arr, idx = idx1.factorize(sort=True)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        # tz must be preserved
+        idx1 = idx1.tz_localize("Asia/Tokyo")
+        exp_idx = exp_idx.tz_localize("Asia/Tokyo")
+
+        arr, idx = idx1.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        idx2 = DatetimeIndex(
+            ["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"]
+        )
+
+        exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
+        arr, idx = idx2.factorize(sort=True)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-03", "2014-02", "2014-01"])
+        arr, idx = idx2.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+    def test_factorize_preserves_freq(self):
+        # GH#38120 freq should be preserved
+        idx3 = date_range("2000-01", periods=4, freq="ME", tz="Asia/Tokyo")
+        exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
+
+        arr, idx = idx3.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
+
+        arr, idx = factorize(idx3)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
+
+    def test_factorize_tz(self, tz_naive_fixture, index_or_series):
+        tz = tz_naive_fixture
+        # GH#13750
+        base = date_range("2016-11-05", freq="h", periods=100, tz=tz)
+        idx = base.repeat(5)
+
+        exp_arr = np.arange(100, dtype=np.intp).repeat(5)
+
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        expected = base._with_freq(None)
+        tm.assert_index_equal(res, expected)
+        assert res.freq == expected.freq
+
+    def test_factorize_dst(self, index_or_series):
+        # GH#13750
+        idx = date_range("2016-11-06", freq="h", periods=12, tz="US/Eastern")
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
+        tm.assert_index_equal(res, idx)
+        if index_or_series is Index:
+            assert res.freq == idx.freq
+
+        idx = date_range("2016-06-13", freq="h", periods=12, tz="US/Eastern")
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
+        tm.assert_index_equal(res, idx)
+        if index_or_series is Index:
+            assert res.freq == idx.freq
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_factorize_no_freq_non_nano(self, tz_naive_fixture, sort):
+        # GH#51978 case that does not go through the fastpath based on
+        #  non-None freq
+        tz = tz_naive_fixture
+        idx = date_range("2016-11-06", freq="h", periods=5, tz=tz)[[0, 4, 1, 3, 2]]
+        exp_codes, exp_uniques = idx.factorize(sort=sort)
+
+        res_codes, res_uniques = idx.as_unit("s").factorize(sort=sort)
+
+        tm.assert_numpy_array_equal(res_codes, exp_codes)
+        tm.assert_index_equal(res_uniques, exp_uniques.as_unit("s"))
+
+        res_codes, res_uniques = idx.as_unit("s").to_series().factorize(sort=sort)
+        tm.assert_numpy_array_equal(res_codes, exp_codes)
+        tm.assert_index_equal(res_uniques, exp_uniques.as_unit("s"))
diff --git a/shared/package-files/32/322a98cc2207bcd6cc1fe2dd4582effb84f410803b8432ade462d1d084532dd2/rebuild.py b/shared/package-files/32/322a98cc2207bcd6cc1fe2dd4582effb84f410803b8432ade462d1d084532dd2/rebuild.py
new file mode 100644
index 0000000000000000000000000000000000000000..684c6586f091350c347f2b6150935f5214ffec27
--- /dev/null
+++ b/shared/package-files/32/322a98cc2207bcd6cc1fe2dd4582effb84f410803b8432ade462d1d084532dd2/rebuild.py
@@ -0,0 +1,75 @@
+import logging
+import os
+import tempfile
+import shutil
+import json
+from subprocess import check_call, check_output
+from tarfile import TarFile
+
+from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
+
+
+def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
+    """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
+
+    filename is the timezone tarball from ``ftp.iana.org/tz``.
+
+    """
+    tmpdir = tempfile.mkdtemp()
+    zonedir = os.path.join(tmpdir, "zoneinfo")
+    moduledir = os.path.dirname(__file__)
+    try:
+        with TarFile.open(filename) as tf:
+            for name in zonegroups:
+                tf.extract(name, tmpdir)
+            filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
+
+            _run_zic(zonedir, filepaths)
+
+        # write metadata file
+        with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
+            json.dump(metadata, f, indent=4, sort_keys=True)
+        target = os.path.join(moduledir, ZONEFILENAME)
+        with TarFile.open(target, "w:%s" % format) as tf:
+            for entry in os.listdir(zonedir):
+                entrypath = os.path.join(zonedir, entry)
+                tf.add(entrypath, entry)
+    finally:
+        shutil.rmtree(tmpdir)
+
+
+def _run_zic(zonedir, filepaths):
+    """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
+
+    Recent versions of ``zic`` default to ``-b slim``, while older versions
+    don't even have the ``-b`` option (but default to "fat" binaries). The
+    current version of dateutil does not support Version 2+ TZif files, which
+    causes problems when used in conjunction with "slim" binaries, so this
+    function is used to ensure that we always get a "fat" binary.
+    """
+
+    try:
+        help_text = check_output(["zic", "--help"])
+    except OSError as e:
+        _print_on_nosuchfile(e)
+        raise
+
+    if b"-b " in help_text:
+        bloat_args = ["-b", "fat"]
+    else:
+        bloat_args = []
+
+    check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
+
+
+def _print_on_nosuchfile(e):
+    """Print helpful troubleshooting message
+
+    e is an exception raised by subprocess.check_call()
+
+    """
+    if e.errno == 2:
+        logging.error(
+            "Could not find zic. Perhaps you need to install "
+            "libc-bin or some other package that provides it, "
+            "or it's not in your PATH?")
diff --git a/shared/package-files/32/322b66ac1f492a2dbdf42851ccbf072f4027493d420a4dd4b4dddc63b2483245/unsupported_nodes.py b/shared/package-files/32/322b66ac1f492a2dbdf42851ccbf072f4027493d420a4dd4b4dddc63b2483245/unsupported_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..5da0dbed3d919b953d75a0a62277122087b85ea3
--- /dev/null
+++ b/shared/package-files/32/322b66ac1f492a2dbdf42851ccbf072f4027493d420a4dd4b4dddc63b2483245/unsupported_nodes.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import dataclasses
+from typing import Dict
+
+from torch.onnx._internal.fx import _pass, diagnostics, registration
+
+
+@dataclasses.dataclass
+class UnsupportedFxNodesAnalysisResult(_pass.AnalysisResult):
+    unsupported_op_to_target_mapping: Dict[str, Dict[str, None]]
+
+
+class UnsupportedFxNodesAnalysis(_pass.Analysis):
+    """An analysis that detects unsupported FX nodes in the graph."""
+
+    def _lint(
+        self,
+        analysis_result: UnsupportedFxNodesAnalysisResult,
+        diagnostic_level: diagnostics.infra.Level,
+    ):
+        """Lint the graph and emit diagnostics if unsupported FX nodes are found."""
+        if not analysis_result.unsupported_op_to_target_mapping:
+            return
+
+        normalized_op_targets_map = {
+            op: list(targets.keys())
+            for op, targets in analysis_result.unsupported_op_to_target_mapping.items()
+        }
+
+        rule = diagnostics.rules.unsupported_fx_node_analysis
+        diagnostic = diagnostics.Diagnostic(
+            rule,
+            level=diagnostic_level,
+            message=rule.format_message(normalized_op_targets_map),
+        )
+        self.diagnostic_context.log_and_raise_if_error(diagnostic)
+
+    def analyze(
+        self, diagnostic_level: diagnostics.infra.Level
+    ) -> UnsupportedFxNodesAnalysisResult:
+        """Analyze the graph, emit diagnostics and return a result that contains unsupported FX nodes.
+
+        Args:
+            diagnostic_level: The diagnostic level to use when emitting diagnostics.
+
+        Returns:
+            An analysis result that contains unsupported FX nodes.
+
+        Raises:
+            RuntimeErrorWithDiagnostic: If diagnostics are emitted and the diagnostic
+                level is `ERROR`.
+        """
+
+        op_to_target_mapping: Dict[str, Dict[str, None]] = {}
+        for node in self.module.graph.nodes:
+            if node.op == "call_function":
+                # NOTE: OPSchema matcher is not in this analysis scope.
+                internal_opname: registration.OpName = (
+                    self.onnxfunction_dispatcher._get_aten_name(
+                        node=node, diagnostic_context=self.diagnostic_context
+                    )
+                )
+                overload_registration = (
+                    self.onnxfunction_dispatcher.onnx_registry.is_registered_op(
+                        namespace=internal_opname.namespace,
+                        op_name=internal_opname.op_name,
+                        overload=internal_opname.overload,
+                    )
+                )
+                # NOTE: Fall back to default overload if the ONNX registry doesn't have the overload.
+                default_registration = (
+                    self.onnxfunction_dispatcher.onnx_registry.is_registered_op(
+                        namespace=internal_opname.namespace,
+                        op_name=internal_opname.op_name,
+                        overload=None,
+                    )
+                )
+                if not overload_registration and not default_registration:
+                    op_to_target_mapping.setdefault(node.op, {}).setdefault(
+                        str(node.target), None
+                    )
+
+        analysis_result = UnsupportedFxNodesAnalysisResult(op_to_target_mapping)
+        self._lint(analysis_result, diagnostic_level)
+        return analysis_result
diff --git a/shared/package-files/32/322ce163824bf29a9cb8fdacb9494fd61324c0bd5f228c981e489a10ab8aa934/_norm.py b/shared/package-files/32/322ce163824bf29a9cb8fdacb9494fd61324c0bd5f228c981e489a10ab8aa934/_norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..821ed02bb1b1d7c2047d5e5dcb3049cc2bf8ad02
--- /dev/null
+++ b/shared/package-files/32/322ce163824bf29a9cb8fdacb9494fd61324c0bd5f228c981e489a10ab8aa934/_norm.py
@@ -0,0 +1,195 @@
+"""Sparse matrix norms.
+
+"""
+import numpy as np
+from scipy.sparse import issparse
+from scipy.sparse.linalg import svds
+from scipy.sparse._sputils import convert_pydata_sparse_to_scipy
+import scipy.sparse as sp
+
+from numpy import sqrt, abs
+
+__all__ = ['norm']
+
+
+def _sparse_frobenius_norm(x):
+    data = sp._sputils._todata(x)
+    return np.linalg.norm(data)
+
+
+def norm(x, ord=None, axis=None):
+    """
+    Norm of a sparse matrix
+
+    This function is able to return one of seven different matrix norms,
+    depending on the value of the ``ord`` parameter.
+
+    Parameters
+    ----------
+    x : a sparse array
+        Input sparse array.
+    ord : {non-zero int, inf, -inf, 'fro'}, optional
+        Order of the norm (see table under ``Notes``). inf means numpy's
+        `inf` object.
+    axis : {int, 2-tuple of ints, None}, optional
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None then either a vector norm (when `x`
+        is 1-D) or a matrix norm (when `x` is 2-D) is returned.
+
+    Returns
+    -------
+    n : float or ndarray
+
+    Notes
+    -----
+    Some of the ord are not implemented because some associated functions like,
+    _multi_svd_norm, are not yet available for sparse array.
+
+    This docstring is modified based on numpy.linalg.norm.
+    https://github.com/numpy/numpy/blob/main/numpy/linalg/linalg.py
+
+    The following norms can be calculated:
+
+    =====  ============================
+    ord    norm for sparse arrays
+    =====  ============================
+    None   Frobenius norm
+    'fro'  Frobenius norm
+    inf    max(sum(abs(x), axis=1))
+    -inf   min(sum(abs(x), axis=1))
+    0      abs(x).sum(axis=axis)
+    1      max(sum(abs(x), axis=0))
+    -1     min(sum(abs(x), axis=0))
+    2      Spectral norm (the largest singular value)
+    -2     Not implemented
+    other  Not implemented
+    =====  ============================
+
+    The Frobenius norm is given by [1]_:
+
+        :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+        Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+
+    Examples
+    --------
+    >>> from scipy.sparse import csr_array, diags_array
+    >>> import numpy as np
+    >>> from scipy.sparse.linalg import norm
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4, -3, -2],
+           [-1, 0, 1],
+           [ 2, 3, 4]])
+
+    >>> b = csr_array(b)
+    >>> norm(b)
+    7.745966692414834
+    >>> norm(b, 'fro')
+    7.745966692414834
+    >>> norm(b, np.inf)
+    9
+    >>> norm(b, -np.inf)
+    2
+    >>> norm(b, 1)
+    7
+    >>> norm(b, -1)
+    6
+
+    The matrix 2-norm or the spectral norm is the largest singular
+    value, computed approximately and with limitations.
+
+    >>> b = diags_array([-1, 1], [0, 1], shape=(9, 10))
+    >>> norm(b, 2)
+    1.9753...
+    """
+    x = convert_pydata_sparse_to_scipy(x, target_format="csr")
+    if not issparse(x):
+        raise TypeError("input is not sparse. use numpy.linalg.norm")
+
+    # Check the default case first and handle it immediately.
+    if axis is None and ord in (None, 'fro', 'f'):
+        return _sparse_frobenius_norm(x)
+
+    # Some norms require functions that are not implemented for all types.
+    x = x.tocsr()
+
+    if axis is None:
+        axis = tuple(range(x.ndim))
+    elif not isinstance(axis, tuple):
+        msg = "'axis' must be None, an integer or a tuple of integers"
+        try:
+            int_axis = int(axis)
+        except TypeError as e:
+            raise TypeError(msg) from e
+        if axis != int_axis:
+            raise TypeError(msg)
+        axis = (int_axis,)
+
+    nd = x.ndim
+    if len(axis) == 2:
+        row_axis, col_axis = axis
+        if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
+            message = f'Invalid axis {axis!r} for an array with shape {x.shape!r}'
+            raise ValueError(message)
+        if row_axis % nd == col_axis % nd:
+            raise ValueError('Duplicate axes given.')
+        if ord == 2:
+            # Only solver="lobpcg" supports all numpy dtypes
+            _, s, _ = svds(x, k=1, solver="lobpcg")
+            return s[0]
+        elif ord == -2:
+            raise NotImplementedError
+            #return _multi_svd_norm(x, row_axis, col_axis, amin)
+        elif ord == 1:
+            return abs(x).sum(axis=row_axis).max()
+        elif ord == np.inf:
+            return abs(x).sum(axis=col_axis).max()
+        elif ord == -1:
+            return abs(x).sum(axis=row_axis).min()
+        elif ord == -np.inf:
+            return abs(x).sum(axis=col_axis).min()
+        elif ord in (None, 'f', 'fro'):
+            # The axis order does not matter for this norm.
+            return _sparse_frobenius_norm(x)
+        else:
+            raise ValueError("Invalid norm order for matrices.")
+    elif len(axis) == 1:
+        a, = axis
+        if not (-nd <= a < nd):
+            message = f'Invalid axis {axis!r} for an array with shape {x.shape!r}'
+            raise ValueError(message)
+        if ord == np.inf:
+            M = abs(x).max(axis=a)
+        elif ord == -np.inf:
+            M = abs(x).min(axis=a)
+        elif ord == 0:
+            # Zero norm
+            M = (x != 0).sum(axis=a)
+        elif ord == 1:
+            # special case for speedup
+            M = abs(x).sum(axis=a)
+        elif ord in (2, None):
+            M = sqrt(abs(x).power(2).sum(axis=a))
+        else:
+            try:
+                ord + 1
+            except TypeError as e:
+                raise ValueError('Invalid norm order for vectors.') from e
+            M = np.power(abs(x).power(ord).sum(axis=a), 1 / ord)
+        if hasattr(M, 'toarray'):
+            return M.toarray().ravel()
+        elif hasattr(M, 'A'):
+            return M.A.ravel()
+        else:
+            return M.ravel()
+    else:
+        raise ValueError("Improper number of dimensions to norm.")
diff --git a/shared/package-files/32/322e7aba5ee2b4d4ba3436cca4a3890ae7e31d5a9a498e7fadbb0d44d2c4e7ad/schema.h b/shared/package-files/32/322e7aba5ee2b4d4ba3436cca4a3890ae7e31d5a9a498e7fadbb0d44d2c4e7ad/schema.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd60fde43422889c53ebd7cf86fbac99c8c6f282
--- /dev/null
+++ b/shared/package-files/32/322e7aba5ee2b4d4ba3436cca4a3890ae7e31d5a9a498e7fadbb0d44d2c4e7ad/schema.h
@@ -0,0 +1,184 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <cassert>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "arrow/result.h"
+#include "arrow/status.h"
+#include "arrow/type.h"
+#include "arrow/type_fwd.h"
+
+#include "parquet/level_conversion.h"
+#include "parquet/platform.h"
+#include "parquet/schema.h"
+
+namespace parquet {
+
+class ArrowReaderProperties;
+class ArrowWriterProperties;
+class WriterProperties;
+
+namespace arrow {
+
+/// \defgroup arrow-to-parquet-schema-conversion Functions to convert an Arrow
+/// schema into a Parquet schema.
+///
+/// @{
+
+PARQUET_EXPORT
+::arrow::Status FieldToNode(const std::shared_ptr<::arrow::Field>& field,
+                            const WriterProperties& properties,
+                            const ArrowWriterProperties& arrow_properties,
+                            schema::NodePtr* out);
+
+PARQUET_EXPORT
+::arrow::Status ToParquetSchema(const ::arrow::Schema* arrow_schema,
+                                const WriterProperties& properties,
+                                const ArrowWriterProperties& arrow_properties,
+                                std::shared_ptr<SchemaDescriptor>* out);
+
+PARQUET_EXPORT
+::arrow::Status ToParquetSchema(const ::arrow::Schema* arrow_schema,
+                                const WriterProperties& properties,
+                                std::shared_ptr<SchemaDescriptor>* out);
+
+/// @}
+
+/// \defgroup parquet-to-arrow-schema-conversion Functions to convert a Parquet
+/// schema into an Arrow schema.
+///
+/// @{
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(
+    const SchemaDescriptor* parquet_schema, const ArrowReaderProperties& properties,
+    const std::shared_ptr<const ::arrow::KeyValueMetadata>& key_value_metadata,
+    std::shared_ptr<::arrow::Schema>* out);
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(const SchemaDescriptor* parquet_schema,
+                                  const ArrowReaderProperties& properties,
+                                  std::shared_ptr<::arrow::Schema>* out);
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(const SchemaDescriptor* parquet_schema,
+                                  std::shared_ptr<::arrow::Schema>* out);
+
+/// @}
+
+/// \brief Bridge between an arrow::Field and parquet column indices.
+struct PARQUET_EXPORT SchemaField {
+  std::shared_ptr<::arrow::Field> field;
+  std::vector<SchemaField> children;
+
+  // Only set for leaf nodes
+  int column_index = -1;
+
+  parquet::internal::LevelInfo level_info;
+
+  bool is_leaf() const { return column_index != -1; }
+};
+
+/// \brief Bridge between a parquet Schema and an arrow Schema.
+///
+/// Expose parquet columns as a tree structure. Useful traverse and link
+/// between arrow's Schema and parquet's Schema.
+struct PARQUET_EXPORT SchemaManifest {
+  static ::arrow::Status Make(
+      const SchemaDescriptor* schema,
+      const std::shared_ptr<const ::arrow::KeyValueMetadata>& metadata,
+      const ArrowReaderProperties& properties, SchemaManifest* manifest);
+
+  const SchemaDescriptor* descr;
+  std::shared_ptr<::arrow::Schema> origin_schema;
+  std::shared_ptr<const ::arrow::KeyValueMetadata> schema_metadata;
+  std::vector<SchemaField> schema_fields;
+
+  std::unordered_map<int, const SchemaField*> column_index_to_field;
+  std::unordered_map<const SchemaField*, const SchemaField*> child_to_parent;
+
+  ::arrow::Status GetColumnField(int column_index, const SchemaField** out) const {
+    auto it = column_index_to_field.find(column_index);
+    if (it == column_index_to_field.end()) {
+      return ::arrow::Status::KeyError("Column index ", column_index,
+                                       " not found in schema manifest, may be malformed");
+    }
+    *out = it->second;
+    return ::arrow::Status::OK();
+  }
+
+  const SchemaField* GetParent(const SchemaField* field) const {
+    // Returns nullptr also if not found
+    auto it = child_to_parent.find(field);
+    if (it == child_to_parent.end()) {
+      return NULLPTR;
+    }
+    return it->second;
+  }
+
+  /// Coalesce a list of field indices (relative to the equivalent arrow::Schema) which
+  /// correspond to the column root (first node below the parquet schema's root group) of
+  /// each leaf referenced in column_indices.
+  ///
+  /// For example, for leaves `a.b.c`, `a.b.d.e`, and `i.j.k` (column_indices=[0,1,3])
+  /// the roots are `a` and `i` (return=[0,2]).
+  ///
+  /// root
+  /// -- a  <------
+  /// -- -- b  |  |
+  /// -- -- -- c  |
+  /// -- -- -- d  |
+  /// -- -- -- -- e
+  /// -- f
+  /// -- -- g
+  /// -- -- -- h
+  /// -- i  <---
+  /// -- -- j  |
+  /// -- -- -- k
+  ::arrow::Result<std::vector<int>> GetFieldIndices(
+      const std::vector<int>& column_indices) const {
+    const schema::GroupNode* group = descr->group_node();
+    std::unordered_set<int> already_added;
+
+    std::vector<int> out;
+    for (int column_idx : column_indices) {
+      if (column_idx < 0 || column_idx >= descr->num_columns()) {
+        return ::arrow::Status::IndexError("Column index ", column_idx, " is not valid");
+      }
+
+      auto field_node = descr->GetColumnRoot(column_idx);
+      auto field_idx = group->FieldIndex(*field_node);
+      if (field_idx == -1) {
+        return ::arrow::Status::IndexError("Column index ", column_idx, " is not valid");
+      }
+
+      if (already_added.insert(field_idx).second) {
+        out.push_back(field_idx);
+      }
+    }
+    return out;
+  }
+};
+
+}  // namespace arrow
+}  // namespace parquet
diff --git a/shared/package-files/32/32325d17fca654bea6b60431f9a4abfebb0d3716938b6a4714b8f268f081ab94/test_assumptions.py b/shared/package-files/32/32325d17fca654bea6b60431f9a4abfebb0d3716938b6a4714b8f268f081ab94/test_assumptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..574e90178fb489fe99c99ea0c72df57ceec4b249
--- /dev/null
+++ b/shared/package-files/32/32325d17fca654bea6b60431f9a4abfebb0d3716938b6a4714b8f268f081ab94/test_assumptions.py
@@ -0,0 +1,1335 @@
+from sympy.core.mod import Mod
+from sympy.core.numbers import (I, oo, pi)
+from sympy.functions.combinatorial.factorials import factorial
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (asin, sin)
+from sympy.simplify.simplify import simplify
+from sympy.core import Symbol, S, Rational, Integer, Dummy, Wild, Pow
+from sympy.core.assumptions import (assumptions, check_assumptions,
+    failing_assumptions, common_assumptions, _generate_assumption_rules,
+    _load_pre_generated_assumption_rules)
+from sympy.core.facts import InconsistentAssumptions
+from sympy.core.random import seed
+from sympy.combinatorics import Permutation
+from sympy.combinatorics.perm_groups import PermutationGroup
+
+from sympy.testing.pytest import raises, XFAIL
+
+
+def test_symbol_unset():
+    x = Symbol('x', real=True, integer=True)
+    assert x.is_real is True
+    assert x.is_integer is True
+    assert x.is_imaginary is False
+    assert x.is_noninteger is False
+    assert x.is_number is False
+
+
+def test_zero():
+    z = Integer(0)
+    assert z.is_commutative is True
+    assert z.is_integer is True
+    assert z.is_rational is True
+    assert z.is_algebraic is True
+    assert z.is_transcendental is False
+    assert z.is_real is True
+    assert z.is_complex is True
+    assert z.is_noninteger is False
+    assert z.is_irrational is False
+    assert z.is_imaginary is False
+    assert z.is_positive is False
+    assert z.is_negative is False
+    assert z.is_nonpositive is True
+    assert z.is_nonnegative is True
+    assert z.is_even is True
+    assert z.is_odd is False
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is True
+    assert z.is_prime is False
+    assert z.is_composite is False
+    assert z.is_number is True
+
+
+def test_one():
+    z = Integer(1)
+    assert z.is_commutative is True
+    assert z.is_integer is True
+    assert z.is_rational is True
+    assert z.is_algebraic is True
+    assert z.is_transcendental is False
+    assert z.is_real is True
+    assert z.is_complex is True
+    assert z.is_noninteger is False
+    assert z.is_irrational is False
+    assert z.is_imaginary is False
+    assert z.is_positive is True
+    assert z.is_negative is False
+    assert z.is_nonpositive is False
+    assert z.is_nonnegative is True
+    assert z.is_even is False
+    assert z.is_odd is True
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is True
+    assert z.is_prime is False
+    assert z.is_number is True
+    assert z.is_composite is False  # issue 8807
+
+
+def test_negativeone():
+    z = Integer(-1)
+    assert z.is_commutative is True
+    assert z.is_integer is True
+    assert z.is_rational is True
+    assert z.is_algebraic is True
+    assert z.is_transcendental is False
+    assert z.is_real is True
+    assert z.is_complex is True
+    assert z.is_noninteger is False
+    assert z.is_irrational is False
+    assert z.is_imaginary is False
+    assert z.is_positive is False
+    assert z.is_negative is True
+    assert z.is_nonpositive is True
+    assert z.is_nonnegative is False
+    assert z.is_even is False
+    assert z.is_odd is True
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is True
+    assert z.is_prime is False
+    assert z.is_composite is False
+    assert z.is_number is True
+
+
+def test_infinity():
+    oo = S.Infinity
+
+    assert oo.is_commutative is True
+    assert oo.is_integer is False
+    assert oo.is_rational is False
+    assert oo.is_algebraic is False
+    assert oo.is_transcendental is False
+    assert oo.is_extended_real is True
+    assert oo.is_real is False
+    assert oo.is_complex is False
+    assert oo.is_noninteger is True
+    assert oo.is_irrational is False
+    assert oo.is_imaginary is False
+    assert oo.is_nonzero is False
+    assert oo.is_positive is False
+    assert oo.is_negative is False
+    assert oo.is_nonpositive is False
+    assert oo.is_nonnegative is False
+    assert oo.is_extended_nonzero is True
+    assert oo.is_extended_positive is True
+    assert oo.is_extended_negative is False
+    assert oo.is_extended_nonpositive is False
+    assert oo.is_extended_nonnegative is True
+    assert oo.is_even is False
+    assert oo.is_odd is False
+    assert oo.is_finite is False
+    assert oo.is_infinite is True
+    assert oo.is_comparable is True
+    assert oo.is_prime is False
+    assert oo.is_composite is False
+    assert oo.is_number is True
+
+
+def test_neg_infinity():
+    mm = S.NegativeInfinity
+
+    assert mm.is_commutative is True
+    assert mm.is_integer is False
+    assert mm.is_rational is False
+    assert mm.is_algebraic is False
+    assert mm.is_transcendental is False
+    assert mm.is_extended_real is True
+    assert mm.is_real is False
+    assert mm.is_complex is False
+    assert mm.is_noninteger is True
+    assert mm.is_irrational is False
+    assert mm.is_imaginary is False
+    assert mm.is_nonzero is False
+    assert mm.is_positive is False
+    assert mm.is_negative is False
+    assert mm.is_nonpositive is False
+    assert mm.is_nonnegative is False
+    assert mm.is_extended_nonzero is True
+    assert mm.is_extended_positive is False
+    assert mm.is_extended_negative is True
+    assert mm.is_extended_nonpositive is True
+    assert mm.is_extended_nonnegative is False
+    assert mm.is_even is False
+    assert mm.is_odd is False
+    assert mm.is_finite is False
+    assert mm.is_infinite is True
+    assert mm.is_comparable is True
+    assert mm.is_prime is False
+    assert mm.is_composite is False
+    assert mm.is_number is True
+
+
+def test_zoo():
+    zoo = S.ComplexInfinity
+    assert zoo.is_complex is False
+    assert zoo.is_real is False
+    assert zoo.is_prime is False
+
+
+def test_nan():
+    nan = S.NaN
+
+    assert nan.is_commutative is True
+    assert nan.is_integer is None
+    assert nan.is_rational is None
+    assert nan.is_algebraic is None
+    assert nan.is_transcendental is None
+    assert nan.is_real is None
+    assert nan.is_complex is None
+    assert nan.is_noninteger is None
+    assert nan.is_irrational is None
+    assert nan.is_imaginary is None
+    assert nan.is_positive is None
+    assert nan.is_negative is None
+    assert nan.is_nonpositive is None
+    assert nan.is_nonnegative is None
+    assert nan.is_even is None
+    assert nan.is_odd is None
+    assert nan.is_finite is None
+    assert nan.is_infinite is None
+    assert nan.is_comparable is False
+    assert nan.is_prime is None
+    assert nan.is_composite is None
+    assert nan.is_number is True
+
+
+def test_pos_rational():
+    r = Rational(3, 4)
+    assert r.is_commutative is True
+    assert r.is_integer is False
+    assert r.is_rational is True
+    assert r.is_algebraic is True
+    assert r.is_transcendental is False
+    assert r.is_real is True
+    assert r.is_complex is True
+    assert r.is_noninteger is True
+    assert r.is_irrational is False
+    assert r.is_imaginary is False
+    assert r.is_positive is True
+    assert r.is_negative is False
+    assert r.is_nonpositive is False
+    assert r.is_nonnegative is True
+    assert r.is_even is False
+    assert r.is_odd is False
+    assert r.is_finite is True
+    assert r.is_infinite is False
+    assert r.is_comparable is True
+    assert r.is_prime is False
+    assert r.is_composite is False
+
+    r = Rational(1, 4)
+    assert r.is_nonpositive is False
+    assert r.is_positive is True
+    assert r.is_negative is False
+    assert r.is_nonnegative is True
+    r = Rational(5, 4)
+    assert r.is_negative is False
+    assert r.is_positive is True
+    assert r.is_nonpositive is False
+    assert r.is_nonnegative is True
+    r = Rational(5, 3)
+    assert r.is_nonnegative is True
+    assert r.is_positive is True
+    assert r.is_negative is False
+    assert r.is_nonpositive is False
+
+
+def test_neg_rational():
+    r = Rational(-3, 4)
+    assert r.is_positive is False
+    assert r.is_nonpositive is True
+    assert r.is_negative is True
+    assert r.is_nonnegative is False
+    r = Rational(-1, 4)
+    assert r.is_nonpositive is True
+    assert r.is_positive is False
+    assert r.is_negative is True
+    assert r.is_nonnegative is False
+    r = Rational(-5, 4)
+    assert r.is_negative is True
+    assert r.is_positive is False
+    assert r.is_nonpositive is True
+    assert r.is_nonnegative is False
+    r = Rational(-5, 3)
+    assert r.is_nonnegative is False
+    assert r.is_positive is False
+    assert r.is_negative is True
+    assert r.is_nonpositive is True
+
+
+def test_pi():
+    z = S.Pi
+    assert z.is_commutative is True
+    assert z.is_integer is False
+    assert z.is_rational is False
+    assert z.is_algebraic is False
+    assert z.is_transcendental is True
+    assert z.is_real is True
+    assert z.is_complex is True
+    assert z.is_noninteger is True
+    assert z.is_irrational is True
+    assert z.is_imaginary is False
+    assert z.is_positive is True
+    assert z.is_negative is False
+    assert z.is_nonpositive is False
+    assert z.is_nonnegative is True
+    assert z.is_even is False
+    assert z.is_odd is False
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is True
+    assert z.is_prime is False
+    assert z.is_composite is False
+
+
+def test_E():
+    z = S.Exp1
+    assert z.is_commutative is True
+    assert z.is_integer is False
+    assert z.is_rational is False
+    assert z.is_algebraic is False
+    assert z.is_transcendental is True
+    assert z.is_real is True
+    assert z.is_complex is True
+    assert z.is_noninteger is True
+    assert z.is_irrational is True
+    assert z.is_imaginary is False
+    assert z.is_positive is True
+    assert z.is_negative is False
+    assert z.is_nonpositive is False
+    assert z.is_nonnegative is True
+    assert z.is_even is False
+    assert z.is_odd is False
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is True
+    assert z.is_prime is False
+    assert z.is_composite is False
+
+
+def test_I():
+    z = S.ImaginaryUnit
+    assert z.is_commutative is True
+    assert z.is_integer is False
+    assert z.is_rational is False
+    assert z.is_algebraic is True
+    assert z.is_transcendental is False
+    assert z.is_real is False
+    assert z.is_complex is True
+    assert z.is_noninteger is False
+    assert z.is_irrational is False
+    assert z.is_imaginary is True
+    assert z.is_positive is False
+    assert z.is_negative is False
+    assert z.is_nonpositive is False
+    assert z.is_nonnegative is False
+    assert z.is_even is False
+    assert z.is_odd is False
+    assert z.is_finite is True
+    assert z.is_infinite is False
+    assert z.is_comparable is False
+    assert z.is_prime is False
+    assert z.is_composite is False
+
+
+def test_symbol_real_false():
+    # issue 3848
+    a = Symbol('a', real=False)
+
+    assert a.is_real is False
+    assert a.is_integer is False
+    assert a.is_zero is False
+
+    assert a.is_negative is False
+    assert a.is_positive is False
+    assert a.is_nonnegative is False
+    assert a.is_nonpositive is False
+    assert a.is_nonzero is False
+
+    assert a.is_extended_negative is None
+    assert a.is_extended_positive is None
+    assert a.is_extended_nonnegative is None
+    assert a.is_extended_nonpositive is None
+    assert a.is_extended_nonzero is None
+
+
+def test_symbol_extended_real_false():
+    # issue 3848
+    a = Symbol('a', extended_real=False)
+
+    assert a.is_real is False
+    assert a.is_integer is False
+    assert a.is_zero is False
+
+    assert a.is_negative is False
+    assert a.is_positive is False
+    assert a.is_nonnegative is False
+    assert a.is_nonpositive is False
+    assert a.is_nonzero is False
+
+    assert a.is_extended_negative is False
+    assert a.is_extended_positive is False
+    assert a.is_extended_nonnegative is False
+    assert a.is_extended_nonpositive is False
+    assert a.is_extended_nonzero is False
+
+
+def test_symbol_imaginary():
+    a = Symbol('a', imaginary=True)
+
+    assert a.is_real is False
+    assert a.is_integer is False
+    assert a.is_negative is False
+    assert a.is_positive is False
+    assert a.is_nonnegative is False
+    assert a.is_nonpositive is False
+    assert a.is_zero is False
+    assert a.is_nonzero is False  # since nonzero -> real
+
+
+def test_symbol_zero():
+    x = Symbol('x', zero=True)
+    assert x.is_positive is False
+    assert x.is_nonpositive
+    assert x.is_negative is False
+    assert x.is_nonnegative
+    assert x.is_zero is True
+    # TODO Change to x.is_nonzero is None
+    # See https://github.com/sympy/sympy/pull/9583
+    assert x.is_nonzero is False
+    assert x.is_finite is True
+
+
+def test_symbol_positive():
+    x = Symbol('x', positive=True)
+    assert x.is_positive is True
+    assert x.is_nonpositive is False
+    assert x.is_negative is False
+    assert x.is_nonnegative is True
+    assert x.is_zero is False
+    assert x.is_nonzero is True
+
+
+def test_neg_symbol_positive():
+    x = -Symbol('x', positive=True)
+    assert x.is_positive is False
+    assert x.is_nonpositive is True
+    assert x.is_negative is True
+    assert x.is_nonnegative is False
+    assert x.is_zero is False
+    assert x.is_nonzero is True
+
+
+def test_symbol_nonpositive():
+    x = Symbol('x', nonpositive=True)
+    assert x.is_positive is False
+    assert x.is_nonpositive is True
+    assert x.is_negative is None
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_neg_symbol_nonpositive():
+    x = -Symbol('x', nonpositive=True)
+    assert x.is_positive is None
+    assert x.is_nonpositive is None
+    assert x.is_negative is False
+    assert x.is_nonnegative is True
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_symbol_falsepositive():
+    x = Symbol('x', positive=False)
+    assert x.is_positive is False
+    assert x.is_nonpositive is None
+    assert x.is_negative is None
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_symbol_falsepositive_mul():
+    # To test pull request 9379
+    # Explicit handling of arg.is_positive=False was added to Mul._eval_is_positive
+    x = 2*Symbol('x', positive=False)
+    assert x.is_positive is False  # This was None before
+    assert x.is_nonpositive is None
+    assert x.is_negative is None
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+@XFAIL
+def test_symbol_infinitereal_mul():
+    ix = Symbol('ix', infinite=True, extended_real=True)
+    assert (-ix).is_extended_positive is None
+
+
+def test_neg_symbol_falsepositive():
+    x = -Symbol('x', positive=False)
+    assert x.is_positive is None
+    assert x.is_nonpositive is None
+    assert x.is_negative is False
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_neg_symbol_falsenegative():
+    # To test pull request 9379
+    # Explicit handling of arg.is_negative=False was added to Mul._eval_is_positive
+    x = -Symbol('x', negative=False)
+    assert x.is_positive is False  # This was None before
+    assert x.is_nonpositive is None
+    assert x.is_negative is None
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_symbol_falsepositive_real():
+    x = Symbol('x', positive=False, real=True)
+    assert x.is_positive is False
+    assert x.is_nonpositive is True
+    assert x.is_negative is None
+    assert x.is_nonnegative is None
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_neg_symbol_falsepositive_real():
+    x = -Symbol('x', positive=False, real=True)
+    assert x.is_positive is None
+    assert x.is_nonpositive is None
+    assert x.is_negative is False
+    assert x.is_nonnegative is True
+    assert x.is_zero is None
+    assert x.is_nonzero is None
+
+
+def test_symbol_falsenonnegative():
+    x = Symbol('x', nonnegative=False)
+    assert x.is_positive is False
+    assert x.is_nonpositive is None
+    assert x.is_negative is None
+    assert x.is_nonnegative is False
+    assert x.is_zero is False
+    assert x.is_nonzero is None
+
+
+@XFAIL
+def test_neg_symbol_falsenonnegative():
+    x = -Symbol('x', nonnegative=False)
+    assert x.is_positive is None
+    assert x.is_nonpositive is False  # this currently returns None
+    assert x.is_negative is False  # this currently returns None
+    assert x.is_nonnegative is None
+    assert x.is_zero is False  # this currently returns None
+    assert x.is_nonzero is True  # this currently returns None
+
+
+def test_symbol_falsenonnegative_real():
+    x = Symbol('x', nonnegative=False, real=True)
+    assert x.is_positive is False
+    assert x.is_nonpositive is True
+    assert x.is_negative is True
+    assert x.is_nonnegative is False
+    assert x.is_zero is False
+    assert x.is_nonzero is True
+
+
+def test_neg_symbol_falsenonnegative_real():
+    x = -Symbol('x', nonnegative=False, real=True)
+    assert x.is_positive is True
+    assert x.is_nonpositive is False
+    assert x.is_negative is False
+    assert x.is_nonnegative is True
+    assert x.is_zero is False
+    assert x.is_nonzero is True
+
+
+def test_prime():
+    assert S.NegativeOne.is_prime is False
+    assert S(-2).is_prime is False
+    assert S(-4).is_prime is False
+    assert S.Zero.is_prime is False
+    assert S.One.is_prime is False
+    assert S(2).is_prime is True
+    assert S(17).is_prime is True
+    assert S(4).is_prime is False
+
+
+def test_composite():
+    assert S.NegativeOne.is_composite is False
+    assert S(-2).is_composite is False
+    assert S(-4).is_composite is False
+    assert S.Zero.is_composite is False
+    assert S(2).is_composite is False
+    assert S(17).is_composite is False
+    assert S(4).is_composite is True
+    x = Dummy(integer=True, positive=True, prime=False)
+    assert x.is_composite is None # x could be 1
+    assert (x + 1).is_composite is None
+    x = Dummy(positive=True, even=True, prime=False)
+    assert x.is_integer is True
+    assert x.is_composite is True
+
+
+def test_prime_symbol():
+    x = Symbol('x', prime=True)
+    assert x.is_prime is True
+    assert x.is_integer is True
+    assert x.is_positive is True
+    assert x.is_negative is False
+    assert x.is_nonpositive is False
+    assert x.is_nonnegative is True
+
+    x = Symbol('x', prime=False)
+    assert x.is_prime is False
+    assert x.is_integer is None
+    assert x.is_positive is None
+    assert x.is_negative is None
+    assert x.is_nonpositive is None
+    assert x.is_nonnegative is None
+
+
+def test_symbol_noncommutative():
+    x = Symbol('x', commutative=True)
+    assert x.is_complex is None
+
+    x = Symbol('x', commutative=False)
+    assert x.is_integer is False
+    assert x.is_rational is False
+    assert x.is_algebraic is False
+    assert x.is_irrational is False
+    assert x.is_real is False
+    assert x.is_complex is False
+
+
+def test_other_symbol():
+    x = Symbol('x', integer=True)
+    assert x.is_integer is True
+    assert x.is_real is True
+    assert x.is_finite is True
+
+    x = Symbol('x', integer=True, nonnegative=True)
+    assert x.is_integer is True
+    assert x.is_nonnegative is True
+    assert x.is_negative is False
+    assert x.is_positive is None
+    assert x.is_finite is True
+
+    x = Symbol('x', integer=True, nonpositive=True)
+    assert x.is_integer is True
+    assert x.is_nonpositive is True
+    assert x.is_positive is False
+    assert x.is_negative is None
+    assert x.is_finite is True
+
+    x = Symbol('x', odd=True)
+    assert x.is_odd is True
+    assert x.is_even is False
+    assert x.is_integer is True
+    assert x.is_finite is True
+
+    x = Symbol('x', odd=False)
+    assert x.is_odd is False
+    assert x.is_even is None
+    assert x.is_integer is None
+    assert x.is_finite is None
+
+    x = Symbol('x', even=True)
+    assert x.is_even is True
+    assert x.is_odd is False
+    assert x.is_integer is True
+    assert x.is_finite is True
+
+    x = Symbol('x', even=False)
+    assert x.is_even is False
+    assert x.is_odd is None
+    assert x.is_integer is None
+    assert x.is_finite is None
+
+    x = Symbol('x', integer=True, nonnegative=True)
+    assert x.is_integer is True
+    assert x.is_nonnegative is True
+    assert x.is_finite is True
+
+    x = Symbol('x', integer=True, nonpositive=True)
+    assert x.is_integer is True
+    assert x.is_nonpositive is True
+    assert x.is_finite is True
+
+    x = Symbol('x', rational=True)
+    assert x.is_real is True
+    assert x.is_finite is True
+
+    x = Symbol('x', rational=False)
+    assert x.is_real is None
+    assert x.is_finite is None
+
+    x = Symbol('x', irrational=True)
+    assert x.is_real is True
+    assert x.is_finite is True
+
+    x = Symbol('x', irrational=False)
+    assert x.is_real is None
+    assert x.is_finite is None
+
+    with raises(AttributeError):
+        x.is_real = False
+
+    x = Symbol('x', algebraic=True)
+    assert x.is_transcendental is False
+    x = Symbol('x', transcendental=True)
+    assert x.is_algebraic is False
+    assert x.is_rational is False
+    assert x.is_integer is False
+
+
+def test_evaluate_false():
+    # Previously this failed because the assumptions query would make new
+    # expressions and some of the evaluation logic would fail under
+    # evaluate(False).
+    from sympy.core.parameters import evaluate
+    from sympy.abc import x, h
+    f = 2**x**7
+    with evaluate(False):
+        fh = f.xreplace({x: x+h})
+        assert fh.exp.is_rational is None
+
+
+def test_issue_3825():
+    """catch: hash instability"""
+    x = Symbol("x")
+    y = Symbol("y")
+    a1 = x + y
+    a2 = y + x
+    a2.is_comparable
+
+    h1 = hash(a1)
+    h2 = hash(a2)
+    assert h1 == h2
+
+
+def test_issue_4822():
+    z = (-1)**Rational(1, 3)*(1 - I*sqrt(3))
+    assert z.is_real in [True, None]
+
+
+def test_hash_vs_typeinfo():
+    """seemingly different typeinfo, but in fact equal"""
+
+    # the following two are semantically equal
+    x1 = Symbol('x', even=True)
+    x2 = Symbol('x', integer=True, odd=False)
+
+    assert hash(x1) == hash(x2)
+    assert x1 == x2
+
+
+def test_hash_vs_typeinfo_2():
+    """different typeinfo should mean !eq"""
+    # the following two are semantically different
+    x = Symbol('x')
+    x1 = Symbol('x', even=True)
+
+    assert x != x1
+    assert hash(x) != hash(x1)  # This might fail with very low probability
+
+
+def test_hash_vs_eq():
+    """catch: different hash for equal objects"""
+    a = 1 + S.Pi    # important: do not fold it into a Number instance
+    ha = hash(a)  # it should be Add/Mul/... to trigger the bug
+
+    a.is_positive   # this uses .evalf() and deduces it is positive
+    assert a.is_positive is True
+
+    # be sure that hash stayed the same
+    assert ha == hash(a)
+
+    # now b should be the same expression
+    b = a.expand(trig=True)
+    hb = hash(b)
+
+    assert a == b
+    assert ha == hb
+
+
+def test_Add_is_pos_neg():
+    # these cover lines not covered by the rest of tests in core
+    n = Symbol('n', extended_negative=True, infinite=True)
+    nn = Symbol('n', extended_nonnegative=True, infinite=True)
+    np = Symbol('n', extended_nonpositive=True, infinite=True)
+    p = Symbol('p', extended_positive=True, infinite=True)
+    r = Dummy(extended_real=True, finite=False)
+    x = Symbol('x')
+    xf = Symbol('xf', finite=True)
+    assert (n + p).is_extended_positive is None
+    assert (n + x).is_extended_positive is None
+    assert (p + x).is_extended_positive is None
+    assert (n + p).is_extended_negative is None
+    assert (n + x).is_extended_negative is None
+    assert (p + x).is_extended_negative is None
+
+    assert (n + xf).is_extended_positive is False
+    assert (p + xf).is_extended_positive is True
+    assert (n + xf).is_extended_negative is True
+    assert (p + xf).is_extended_negative is False
+
+    assert (x - S.Infinity).is_extended_negative is None  # issue 7798
+    # issue 8046, 16.2
+    assert (p + nn).is_extended_positive
+    assert (n + np).is_extended_negative
+    assert (p + r).is_extended_positive is None
+
+
+def test_Add_is_imaginary():
+    nn = Dummy(nonnegative=True)
+    assert (I*nn + I).is_imaginary  # issue 8046, 17
+
+
+def test_Add_is_algebraic():
+    a = Symbol('a', algebraic=True)
+    b = Symbol('a', algebraic=True)
+    na = Symbol('na', algebraic=False)
+    nb = Symbol('nb', algebraic=False)
+    x = Symbol('x')
+    assert (a + b).is_algebraic
+    assert (na + nb).is_algebraic is None
+    assert (a + na).is_algebraic is False
+    assert (a + x).is_algebraic is None
+    assert (na + x).is_algebraic is None
+
+
+def test_Mul_is_algebraic():
+    a = Symbol('a', algebraic=True)
+    b = Symbol('b', algebraic=True)
+    na = Symbol('na', algebraic=False)
+    an = Symbol('an', algebraic=True, nonzero=True)
+    nb = Symbol('nb', algebraic=False)
+    x = Symbol('x')
+    assert (a*b).is_algebraic is True
+    assert (na*nb).is_algebraic is None
+    assert (a*na).is_algebraic is None
+    assert (an*na).is_algebraic is False
+    assert (a*x).is_algebraic is None
+    assert (na*x).is_algebraic is None
+
+
+def test_Pow_is_algebraic():
+    e = Symbol('e', algebraic=True)
+
+    assert Pow(1, e, evaluate=False).is_algebraic
+    assert Pow(0, e, evaluate=False).is_algebraic
+
+    a = Symbol('a', algebraic=True)
+    azf = Symbol('azf', algebraic=True, zero=False)
+    na = Symbol('na', algebraic=False)
+    ia = Symbol('ia', algebraic=True, irrational=True)
+    ib = Symbol('ib', algebraic=True, irrational=True)
+    r = Symbol('r', rational=True)
+    x = Symbol('x')
+    assert (a**2).is_algebraic is True
+    assert (a**r).is_algebraic is None
+    assert (azf**r).is_algebraic is True
+    assert (a**x).is_algebraic is None
+    assert (na**r).is_algebraic is None
+    assert (ia**r).is_algebraic is True
+    assert (ia**ib).is_algebraic is False
+
+    assert (a**e).is_algebraic is None
+
+    # Gelfond-Schneider constant:
+    assert Pow(2, sqrt(2), evaluate=False).is_algebraic is False
+
+    assert Pow(S.GoldenRatio, sqrt(3), evaluate=False).is_algebraic is False
+
+    # issue 8649
+    t = Symbol('t', real=True, transcendental=True)
+    n = Symbol('n', integer=True)
+    assert (t**n).is_algebraic is None
+    assert (t**n).is_integer is None
+
+    assert (pi**3).is_algebraic is False
+    r = Symbol('r', zero=True)
+    assert (pi**r).is_algebraic is True
+
+
+def test_Mul_is_prime_composite():
+    x = Symbol('x', positive=True, integer=True)
+    y = Symbol('y', positive=True, integer=True)
+    assert (x*y).is_prime is None
+    assert ( (x+1)*(y+1) ).is_prime is False
+    assert ( (x+1)*(y+1) ).is_composite is True
+
+    x = Symbol('x', positive=True)
+    assert ( (x+1)*(y+1) ).is_prime is None
+    assert ( (x+1)*(y+1) ).is_composite is None
+
+
+def test_Pow_is_pos_neg():
+    z = Symbol('z', real=True)
+    w = Symbol('w', nonpositive=True)
+
+    assert (S.NegativeOne**S(2)).is_positive is True
+    assert (S.One**z).is_positive is True
+    assert (S.NegativeOne**S(3)).is_positive is False
+    assert (S.Zero**S.Zero).is_positive is True  # 0**0 is 1
+    assert (w**S(3)).is_positive is False
+    assert (w**S(2)).is_positive is None
+    assert (I**2).is_positive is False
+    assert (I**4).is_positive is True
+
+    # tests emerging from #16332 issue
+    p = Symbol('p', zero=True)
+    q = Symbol('q', zero=False, real=True)
+    j = Symbol('j', zero=False, even=True)
+    x = Symbol('x', zero=True)
+    y = Symbol('y', zero=True)
+    assert (p**q).is_positive is False
+    assert (p**q).is_negative is False
+    assert (p**j).is_positive is False
+    assert (x**y).is_positive is True   # 0**0
+    assert (x**y).is_negative is False
+
+
+def test_Pow_is_prime_composite():
+    x = Symbol('x', positive=True, integer=True)
+    y = Symbol('y', positive=True, integer=True)
+    assert (x**y).is_prime is None
+    assert ( x**(y+1) ).is_prime is False
+    assert ( x**(y+1) ).is_composite is None
+    assert ( (x+1)**(y+1) ).is_composite is True
+    assert ( (-x-1)**(2*y) ).is_composite is True
+
+    x = Symbol('x', positive=True)
+    assert (x**y).is_prime is None
+
+
+def test_Mul_is_infinite():
+    x = Symbol('x')
+    f = Symbol('f', finite=True)
+    i = Symbol('i', infinite=True)
+    z = Dummy(zero=True)
+    nzf = Dummy(finite=True, zero=False)
+    from sympy.core.mul import Mul
+    assert (x*f).is_finite is None
+    assert (x*i).is_finite is None
+    assert (f*i).is_finite is None
+    assert (x*f*i).is_finite is None
+    assert (z*i).is_finite is None
+    assert (nzf*i).is_finite is False
+    assert (z*f).is_finite is True
+    assert Mul(0, f, evaluate=False).is_finite is True
+    assert Mul(0, i, evaluate=False).is_finite is None
+
+    assert (x*f).is_infinite is None
+    assert (x*i).is_infinite is None
+    assert (f*i).is_infinite is None
+    assert (x*f*i).is_infinite is None
+    assert (z*i).is_infinite is S.NaN.is_infinite
+    assert (nzf*i).is_infinite is True
+    assert (z*f).is_infinite is False
+    assert Mul(0, f, evaluate=False).is_infinite is False
+    assert Mul(0, i, evaluate=False).is_infinite is S.NaN.is_infinite
+
+
+def test_Add_is_infinite():
+    x = Symbol('x')
+    f = Symbol('f', finite=True)
+    i = Symbol('i', infinite=True)
+    i2 = Symbol('i2', infinite=True)
+    z = Dummy(zero=True)
+    nzf = Dummy(finite=True, zero=False)
+    from sympy.core.add import Add
+    assert (x+f).is_finite is None
+    assert (x+i).is_finite is None
+    assert (f+i).is_finite is False
+    assert (x+f+i).is_finite is None
+    assert (z+i).is_finite is False
+    assert (nzf+i).is_finite is False
+    assert (z+f).is_finite is True
+    assert (i+i2).is_finite is None
+    assert Add(0, f, evaluate=False).is_finite is True
+    assert Add(0, i, evaluate=False).is_finite is False
+
+    assert (x+f).is_infinite is None
+    assert (x+i).is_infinite is None
+    assert (f+i).is_infinite is True
+    assert (x+f+i).is_infinite is None
+    assert (z+i).is_infinite is True
+    assert (nzf+i).is_infinite is True
+    assert (z+f).is_infinite is False
+    assert (i+i2).is_infinite is None
+    assert Add(0, f, evaluate=False).is_infinite is False
+    assert Add(0, i, evaluate=False).is_infinite is True
+
+
+def test_special_is_rational():
+    i = Symbol('i', integer=True)
+    i2 = Symbol('i2', integer=True)
+    ni = Symbol('ni', integer=True, nonzero=True)
+    r = Symbol('r', rational=True)
+    rn = Symbol('r', rational=True, nonzero=True)
+    nr = Symbol('nr', irrational=True)
+    x = Symbol('x')
+    assert sqrt(3).is_rational is False
+    assert (3 + sqrt(3)).is_rational is False
+    assert (3*sqrt(3)).is_rational is False
+    assert exp(3).is_rational is False
+    assert exp(ni).is_rational is False
+    assert exp(rn).is_rational is False
+    assert exp(x).is_rational is None
+    assert exp(log(3), evaluate=False).is_rational is True
+    assert log(exp(3), evaluate=False).is_rational is True
+    assert log(3).is_rational is False
+    assert log(ni + 1).is_rational is False
+    assert log(rn + 1).is_rational is False
+    assert log(x).is_rational is None
+    assert (sqrt(3) + sqrt(5)).is_rational is None
+    assert (sqrt(3) + S.Pi).is_rational is False
+    assert (x**i).is_rational is None
+    assert (i**i).is_rational is True
+    assert (i**i2).is_rational is None
+    assert (r**i).is_rational is None
+    assert (r**r).is_rational is None
+    assert (r**x).is_rational is None
+    assert (nr**i).is_rational is None  # issue 8598
+    assert (nr**Symbol('z', zero=True)).is_rational
+    assert sin(1).is_rational is False
+    assert sin(ni).is_rational is False
+    assert sin(rn).is_rational is False
+    assert sin(x).is_rational is None
+    assert asin(r).is_rational is False
+    assert sin(asin(3), evaluate=False).is_rational is True
+
+
+@XFAIL
+def test_issue_6275():
+    x = Symbol('x')
+    # both zero or both Muls...but neither "change would be very appreciated.
+    # This is similar to x/x => 1 even though if x = 0, it is really nan.
+    assert isinstance(x*0, type(0*S.Infinity))
+    if 0*S.Infinity is S.NaN:
+        b = Symbol('b', finite=None)
+        assert (b*0).is_zero is None
+
+
+def test_sanitize_assumptions():
+    # issue 6666
+    for cls in (Symbol, Dummy, Wild):
+        x = cls('x', real=1, positive=0)
+        assert x.is_real is True
+        assert x.is_positive is False
+        assert cls('', real=True, positive=None).is_positive is None
+        raises(ValueError, lambda: cls('', commutative=None))
+    raises(ValueError, lambda: Symbol._sanitize({"commutative": None}))
+
+
+def test_special_assumptions():
+    e = -3 - sqrt(5) + (-sqrt(10)/2 - sqrt(2)/2)**2
+    assert simplify(e < 0) is S.false
+    assert simplify(e > 0) is S.false
+    assert (e == 0) is False  # it's not a literal 0
+    assert e.equals(0) is True
+
+
+def test_inconsistent():
+    # cf. issues 5795 and 5545
+    raises(InconsistentAssumptions, lambda: Symbol('x', real=True,
+           commutative=False))
+
+
+def test_issue_6631():
+    assert ((-1)**(I)).is_real is True
+    assert ((-1)**(I*2)).is_real is True
+    assert ((-1)**(I/2)).is_real is True
+    assert ((-1)**(I*S.Pi)).is_real is True
+    assert (I**(I + 2)).is_real is True
+
+
+def test_issue_2730():
+    assert (1/(1 + I)).is_real is False
+
+
+def test_issue_4149():
+    assert (3 + I).is_complex
+    assert (3 + I).is_imaginary is False
+    assert (3*I + S.Pi*I).is_imaginary
+    # as Zero.is_imaginary is False, see issue 7649
+    y = Symbol('y', real=True)
+    assert (3*I + S.Pi*I + y*I).is_imaginary is None
+    p = Symbol('p', positive=True)
+    assert (3*I + S.Pi*I + p*I).is_imaginary
+    n = Symbol('n', negative=True)
+    assert (-3*I - S.Pi*I + n*I).is_imaginary
+
+    i = Symbol('i', imaginary=True)
+    assert ([(i**a).is_imaginary for a in range(4)] ==
+            [False, True, False, True])
+
+    # tests from the PR #7887:
+    e = S("-sqrt(3)*I/2 + 0.866025403784439*I")
+    assert e.is_real is False
+    assert e.is_imaginary
+
+
+def test_issue_2920():
+    n = Symbol('n', negative=True)
+    assert sqrt(n).is_imaginary
+
+
+def test_issue_7899():
+    x = Symbol('x', real=True)
+    assert (I*x).is_real is None
+    assert ((x - I)*(x - 1)).is_zero is None
+    assert ((x - I)*(x - 1)).is_real is None
+
+
+@XFAIL
+def test_issue_7993():
+    x = Dummy(integer=True)
+    y = Dummy(noninteger=True)
+    assert (x - y).is_zero is False
+
+
+def test_issue_8075():
+    raises(InconsistentAssumptions, lambda: Dummy(zero=True, finite=False))
+    raises(InconsistentAssumptions, lambda: Dummy(zero=True, infinite=True))
+
+
+def test_issue_8642():
+    x = Symbol('x', real=True, integer=False)
+    assert (x*2).is_integer is None, (x*2).is_integer
+
+
+def test_issues_8632_8633_8638_8675_8992():
+    p = Dummy(integer=True, positive=True)
+    nn = Dummy(integer=True, nonnegative=True)
+    assert (p - S.Half).is_positive
+    assert (p - 1).is_nonnegative
+    assert (nn + 1).is_positive
+    assert (-p + 1).is_nonpositive
+    assert (-nn - 1).is_negative
+    prime = Dummy(prime=True)
+    assert (prime - 2).is_nonnegative
+    assert (prime - 3).is_nonnegative is None
+    even = Dummy(positive=True, even=True)
+    assert (even - 2).is_nonnegative
+
+    p = Dummy(positive=True)
+    assert (p/(p + 1) - 1).is_negative
+    assert ((p + 2)**3 - S.Half).is_positive
+    n = Dummy(negative=True)
+    assert (n - 3).is_nonpositive
+
+
+def test_issue_9115_9150():
+    n = Dummy('n', integer=True, nonnegative=True)
+    assert (factorial(n) >= 1) == True
+    assert (factorial(n) < 1) == False
+
+    assert factorial(n + 1).is_even is None
+    assert factorial(n + 2).is_even is True
+    assert factorial(n + 2) >= 2
+
+
+def test_issue_9165():
+    z = Symbol('z', zero=True)
+    f = Symbol('f', finite=False)
+    assert 0/z is S.NaN
+    assert 0*(1/z) is S.NaN
+    assert 0*f is S.NaN
+
+
+def test_issue_10024():
+    x = Dummy('x')
+    assert Mod(x, 2*pi).is_zero is None
+
+
+def test_issue_10302():
+    x = Symbol('x')
+    r = Symbol('r', real=True)
+    u = -(3*2**pi)**(1/pi) + 2*3**(1/pi)
+    i = u + u*I
+
+    assert i.is_real is None  # w/o simplification this should fail
+    assert (u + i).is_zero is None
+    assert (1 + i).is_zero is False
+
+    a = Dummy('a', zero=True)
+    assert (a + I).is_zero is False
+    assert (a + r*I).is_zero is None
+    assert (a + I).is_imaginary
+    assert (a + x + I).is_imaginary is None
+    assert (a + r*I + I).is_imaginary is None
+
+
+def test_complex_reciprocal_imaginary():
+    assert (1 / (4 + 3*I)).is_imaginary is False
+
+
+def test_issue_16313():
+    x = Symbol('x', extended_real=False)
+    k = Symbol('k', real=True)
+    l = Symbol('l', real=True, zero=False)
+    assert (-x).is_real is False
+    assert (k*x).is_real is None  # k can be zero also
+    assert (l*x).is_real is False
+    assert (l*x*x).is_real is None  # since x*x can be a real number
+    assert (-x).is_positive is False
+
+
+def test_issue_16579():
+    # extended_real -> finite | infinite
+    x = Symbol('x', extended_real=True, infinite=False)
+    y = Symbol('y', extended_real=True, finite=False)
+    assert x.is_finite is True
+    assert y.is_infinite is True
+
+    # With PR 16978, complex now implies finite
+    c = Symbol('c', complex=True)
+    assert c.is_finite is True
+    raises(InconsistentAssumptions, lambda: Dummy(complex=True, finite=False))
+
+    # Now infinite == !finite
+    nf = Symbol('nf', finite=False)
+    assert nf.is_infinite is True
+
+
+def test_issue_17556():
+    z = I*oo
+    assert z.is_imaginary is False
+    assert z.is_finite is False
+
+
+def test_issue_21651():
+    k = Symbol('k', positive=True, integer=True)
+    exp = 2*2**(-k)
+    assert exp.is_integer is None
+
+
+def test_assumptions_copy():
+    assert assumptions(Symbol('x'), {"commutative": True}
+        ) == {'commutative': True}
+    assert assumptions(Symbol('x'), ['integer']) == {}
+    assert assumptions(Symbol('x'), ['commutative']
+        ) == {'commutative': True}
+    assert assumptions(Symbol('x')) == {'commutative': True}
+    assert assumptions(1)['positive']
+    assert assumptions(3 + I) == {
+        'algebraic': True,
+        'commutative': True,
+        'complex': True,
+        'composite': False,
+        'even': False,
+        'extended_negative': False,
+        'extended_nonnegative': False,
+        'extended_nonpositive': False,
+        'extended_nonzero': False,
+        'extended_positive': False,
+        'extended_real': False,
+        'finite': True,
+        'imaginary': False,
+        'infinite': False,
+        'integer': False,
+        'irrational': False,
+        'negative': False,
+        'noninteger': False,
+        'nonnegative': False,
+        'nonpositive': False,
+        'nonzero': False,
+        'odd': False,
+        'positive': False,
+        'prime': False,
+        'rational': False,
+        'real': False,
+        'transcendental': False,
+        'zero': False}
+
+
+def test_check_assumptions():
+    assert check_assumptions(1, 0) is False
+    x = Symbol('x', positive=True)
+    assert check_assumptions(1, x) is True
+    assert check_assumptions(1, 1) is True
+    assert check_assumptions(-1, 1) is False
+    i = Symbol('i', integer=True)
+    # don't know if i is positive (or prime, etc...)
+    assert check_assumptions(i, 1) is None
+    assert check_assumptions(Dummy(integer=None), integer=True) is None
+    assert check_assumptions(Dummy(integer=None), integer=False) is None
+    assert check_assumptions(Dummy(integer=False), integer=True) is False
+    assert check_assumptions(Dummy(integer=True), integer=False) is False
+    # no T/F assumptions to check
+    assert check_assumptions(Dummy(integer=False), integer=None) is True
+    raises(ValueError, lambda: check_assumptions(2*x, x, positive=True))
+
+
+def test_failing_assumptions():
+    x = Symbol('x', positive=True)
+    y = Symbol('y')
+    assert failing_assumptions(6*x + y, **x.assumptions0) == \
+    {'real': None, 'imaginary': None, 'complex': None, 'hermitian': None,
+    'positive': None, 'nonpositive': None, 'nonnegative': None, 'nonzero': None,
+    'negative': None, 'zero': None, 'extended_real': None, 'finite': None,
+    'infinite': None, 'extended_negative': None, 'extended_nonnegative': None,
+    'extended_nonpositive': None, 'extended_nonzero': None,
+    'extended_positive': None }
+
+
+def test_common_assumptions():
+    assert common_assumptions([0, 1, 2]
+        ) == {'algebraic': True, 'irrational': False, 'hermitian':
+        True, 'extended_real': True, 'real': True, 'extended_negative':
+        False, 'extended_nonnegative': True, 'integer': True,
+        'rational': True, 'imaginary': False, 'complex': True,
+        'commutative': True,'noninteger': False, 'composite': False,
+        'infinite': False, 'nonnegative': True, 'finite': True,
+        'transcendental': False,'negative': False}
+    assert common_assumptions([0, 1, 2], 'positive integer'.split()
+        ) == {'integer': True}
+    assert common_assumptions([0, 1, 2], []) == {}
+    assert common_assumptions([], ['integer']) == {}
+    assert common_assumptions([0], ['integer']) == {'integer': True}
+
+def test_pre_generated_assumption_rules_are_valid():
+    # check the pre-generated assumptions match freshly generated assumptions
+    # if this check fails, consider updating the assumptions
+    # see sympy.core.assumptions._generate_assumption_rules
+    pre_generated_assumptions =_load_pre_generated_assumption_rules()
+    generated_assumptions =_generate_assumption_rules()
+    assert pre_generated_assumptions._to_python() == generated_assumptions._to_python(), "pre-generated assumptions are invalid, see sympy.core.assumptions._generate_assumption_rules"
+
+
+def test_ask_shuffle():
+    grp = PermutationGroup(Permutation(1, 0, 2), Permutation(2, 1, 3))
+
+    seed(123)
+    first = grp.random()
+    seed(123)
+    simplify(I)
+    second = grp.random()
+    seed(123)
+    simplify(-I)
+    third = grp.random()
+
+    assert first == second == third
diff --git a/shared/package-files/32/3233a2802ef9cfaaa844c9201e21eaa2dedeab17f00d94ab11f94930f9be6c71/hashes.py b/shared/package-files/32/3233a2802ef9cfaaa844c9201e21eaa2dedeab17f00d94ab11f94930f9be6c71/hashes.py
new file mode 100644
index 0000000000000000000000000000000000000000..843cffc6b3ddd6eb01483bcf1b5c33c717e027b6
--- /dev/null
+++ b/shared/package-files/32/3233a2802ef9cfaaa844c9201e21eaa2dedeab17f00d94ab11f94930f9be6c71/hashes.py
@@ -0,0 +1,151 @@
+import hashlib
+from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional
+
+from pip._internal.exceptions import HashMismatch, HashMissing, InstallationError
+from pip._internal.utils.misc import read_chunks
+
+if TYPE_CHECKING:
+    from hashlib import _Hash
+
+    # NoReturn introduced in 3.6.2; imported only for type checking to maintain
+    # pip compatibility with older patch versions of Python 3.6
+    from typing import NoReturn
+
+
+# The recommended hash algo of the moment. Change this whenever the state of
+# the art changes; it won't hurt backward compatibility.
+FAVORITE_HASH = "sha256"
+
+
+# Names of hashlib algorithms allowed by the --hash option and ``pip hash``
+# Currently, those are the ones at least as collision-resistant as sha256.
+STRONG_HASHES = ["sha256", "sha384", "sha512"]
+
+
+class Hashes:
+    """A wrapper that builds multiple hashes at once and checks them against
+    known-good values
+
+    """
+
+    def __init__(self, hashes: Optional[Dict[str, List[str]]] = None) -> None:
+        """
+        :param hashes: A dict of algorithm names pointing to lists of allowed
+            hex digests
+        """
+        allowed = {}
+        if hashes is not None:
+            for alg, keys in hashes.items():
+                # Make sure values are always sorted (to ease equality checks)
+                allowed[alg] = sorted(keys)
+        self._allowed = allowed
+
+    def __and__(self, other: "Hashes") -> "Hashes":
+        if not isinstance(other, Hashes):
+            return NotImplemented
+
+        # If either of the Hashes object is entirely empty (i.e. no hash
+        # specified at all), all hashes from the other object are allowed.
+        if not other:
+            return self
+        if not self:
+            return other
+
+        # Otherwise only hashes that present in both objects are allowed.
+        new = {}
+        for alg, values in other._allowed.items():
+            if alg not in self._allowed:
+                continue
+            new[alg] = [v for v in values if v in self._allowed[alg]]
+        return Hashes(new)
+
+    @property
+    def digest_count(self) -> int:
+        return sum(len(digests) for digests in self._allowed.values())
+
+    def is_hash_allowed(self, hash_name: str, hex_digest: str) -> bool:
+        """Return whether the given hex digest is allowed."""
+        return hex_digest in self._allowed.get(hash_name, [])
+
+    def check_against_chunks(self, chunks: Iterable[bytes]) -> None:
+        """Check good hashes against ones built from iterable of chunks of
+        data.
+
+        Raise HashMismatch if none match.
+
+        """
+        gots = {}
+        for hash_name in self._allowed.keys():
+            try:
+                gots[hash_name] = hashlib.new(hash_name)
+            except (ValueError, TypeError):
+                raise InstallationError(f"Unknown hash name: {hash_name}")
+
+        for chunk in chunks:
+            for hash in gots.values():
+                hash.update(chunk)
+
+        for hash_name, got in gots.items():
+            if got.hexdigest() in self._allowed[hash_name]:
+                return
+        self._raise(gots)
+
+    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
+        raise HashMismatch(self._allowed, gots)
+
+    def check_against_file(self, file: BinaryIO) -> None:
+        """Check good hashes against a file-like object
+
+        Raise HashMismatch if none match.
+
+        """
+        return self.check_against_chunks(read_chunks(file))
+
+    def check_against_path(self, path: str) -> None:
+        with open(path, "rb") as file:
+            return self.check_against_file(file)
+
+    def has_one_of(self, hashes: Dict[str, str]) -> bool:
+        """Return whether any of the given hashes are allowed."""
+        for hash_name, hex_digest in hashes.items():
+            if self.is_hash_allowed(hash_name, hex_digest):
+                return True
+        return False
+
+    def __bool__(self) -> bool:
+        """Return whether I know any known-good hashes."""
+        return bool(self._allowed)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Hashes):
+            return NotImplemented
+        return self._allowed == other._allowed
+
+    def __hash__(self) -> int:
+        return hash(
+            ",".join(
+                sorted(
+                    ":".join((alg, digest))
+                    for alg, digest_list in self._allowed.items()
+                    for digest in digest_list
+                )
+            )
+        )
+
+
+class MissingHashes(Hashes):
+    """A workalike for Hashes used when we're missing a hash for a requirement
+
+    It computes the actual hash of the requirement and raises a HashMissing
+    exception showing it to the user.
+
+    """
+
+    def __init__(self) -> None:
+        """Don't offer the ``hashes`` kwarg."""
+        # Pass our favorite hash in to generate a "gotten hash". With the
+        # empty list, it will never match, so an error will always raise.
+        super().__init__(hashes={FAVORITE_HASH: []})
+
+    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
+        raise HashMissing(gots[FAVORITE_HASH].hexdigest())
diff --git a/shared/package-files/32/323560dd7d8305a117ab3ee787395bf5a26076de4ec4e75b182114a65ad562ad/elastic_net_generate_tests.py b/shared/package-files/32/323560dd7d8305a117ab3ee787395bf5a26076de4ec4e75b182114a65ad562ad/elastic_net_generate_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..753803e798d8fd06c3820c3b557b44b6fd9336ca
--- /dev/null
+++ b/shared/package-files/32/323560dd7d8305a117ab3ee787395bf5a26076de4ec4e75b182114a65ad562ad/elastic_net_generate_tests.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+"""
+Generate data sets for testing elastic net fits of GLMs.
+"""
+
+n = 200
+p = 5
+
+# Logistic
+exog = np.random.normal(size=(n, p))
+lin_pred = exog.sum(1) * 0.2
+exp_val = 1 / (1 + np.exp(-lin_pred))
+endog = 1 * (np.random.uniform(size=n) < exp_val)
+mat = np.concatenate((endog[:, None], exog), axis=1)
+np.savetxt("enet_binomial.csv", mat, fmt="%.2f", delimiter=",")
+
+# Poisson
+exog = np.random.normal(size=(n, p))
+lin_pred = exog.sum(1) * 0.2
+exp_val = np.exp(lin_pred)
+endog = np.random.poisson(exp_val)
+mat = np.concatenate((endog[:, None], exog), axis=1)
+np.savetxt("enet_poisson.csv", mat, fmt="%.2f", delimiter=",")
diff --git a/shared/package-files/32/3236a309ba2bdd7fc90877810d424fd3f9326a3e65b1ba25b28bc247e98d7f1b/functions.pyi b/shared/package-files/32/3236a309ba2bdd7fc90877810d424fd3f9326a3e65b1ba25b28bc247e98d7f1b/functions.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b1fbdc733ca8f5ea881e861ec15ef87aae26aef2
--- /dev/null
+++ b/shared/package-files/32/3236a309ba2bdd7fc90877810d424fd3f9326a3e65b1ba25b28bc247e98d7f1b/functions.pyi
@@ -0,0 +1,239 @@
+import weakref
+from collections.abc import Callable as _Callable
+from collections.abc import Mapping, Sequence
+from typing import Any, Final, Hashable, Iterable, NamedTuple, Self, TypeAlias
+
+from _typeshed import Incomplete
+from typing_extensions import Generic, TypeVar, override
+
+from numba.core import dispatcher
+from numba.core.typing.context import Context
+from numba.core.typing.templates import FunctionTemplate, Signature
+
+from .abstract import Callable, DTypeSpec, Dummy, Literal, Type
+from .common import Opaque
+
+_T_co = TypeVar("_T_co", covariant=True)
+_ThisT = TypeVar("_ThisT", bound=Type)
+_ThisT_co = TypeVar("_ThisT_co", bound=Type, default=Type, covariant=True)
+_TemplateTypeT = TypeVar("_TemplateTypeT", bound=_TemplateType)
+_DispatcherT_co = TypeVar(
+    "_DispatcherT_co",
+    bound=dispatcher.Dispatcher,
+    default=dispatcher.Dispatcher,
+    covariant=True,
+)
+_TupleT_co = TypeVar(
+    "_TupleT_co",
+    bound=tuple[Any, ...],
+    default=tuple[Any, ...],
+    covariant=True,
+)
+
+_TemplateType: TypeAlias = type[FunctionTemplate]
+_AnyCallable: TypeAlias = _Callable[..., Any]
+_BoundFunctionKey: TypeAlias = tuple[Hashable, _ThisT, _Callable[..., object] | None]
+_GetPointerFn: TypeAlias = _Callable[[Any], int]
+
+###
+
+def argsnkwargs_to_str(args: Iterable[object], kwargs: Mapping[str, object]) -> str: ...
+
+class BaseFunction(Callable):
+    templates: Final[tuple[_TemplateType, ...]]
+    typing_key: Final[Hashable]
+
+    # this free `_TemplateT` is needed because `list` is invariant
+    def __init__(
+        self,
+        template: tuple[_TemplateTypeT, ...] | list[_TemplateTypeT] | _TemplateTypeT,
+    ) -> None: ...
+
+    #
+    @property
+    @override
+    def key(self) -> tuple[Incomplete, tuple[_TemplateType, ...]]: ...
+
+    #
+    @override
+    def get_impl_key(self, sig: Signature) -> Hashable: ...
+    @override
+    def get_call_type(
+        self,
+        context: Context,
+        args: tuple[Type, ...],
+        kws: Mapping[str, Type],
+    ) -> Signature | None: ...
+    @override
+    def get_call_signatures(self) -> tuple[Sequence[Signature], bool]: ...
+
+class Function(BaseFunction, Opaque): ...
+
+class BoundFunction(Callable, Opaque, Generic[_ThisT_co]):
+    template: Final[_TemplateType]
+    typing_key: Final[Hashable]
+    this: _ThisT_co
+
+    def __init__(self, template: _TemplateType, this: _ThisT_co) -> None: ...
+
+    #
+    @override
+    def unify(
+        self,
+        typingctx: Context,
+        other: _ThisT,
+    ) -> BoundFunction[_ThisT_co | _ThisT] | None: ...
+
+    #
+    def copy(self, this: _ThisT) -> BoundFunction[_ThisT]: ...
+
+    #
+    @property
+    @override
+    def key(self) -> _BoundFunctionKey[_ThisT_co]: ...
+    @override
+    def get_impl_key(self, sig: Signature) -> Hashable: ...
+    @override
+    def get_call_type(
+        self,
+        context: Context,
+        args: tuple[Type, ...],
+        kws: Mapping[str, Type],
+    ) -> Signature | None: ...
+    @override
+    def get_call_signatures(self) -> tuple[Sequence[Signature], bool]: ...
+
+class MakeFunctionLiteral(Literal[_T_co], Opaque): ...
+
+class WeakType(Type, Generic[_T_co]):
+    @property
+    @override
+    def key(self) -> weakref.ref[_T_co]: ...
+
+    #
+    @override
+    def __eq__(self, other: Self) -> bool: ...  # type: ignore[override]  # pyrefly:ignore[bad-override]
+    @override
+    def __hash__(self) -> int: ...
+
+class Dispatcher(WeakType[_DispatcherT_co], Callable, Dummy, Generic[_DispatcherT_co]):
+    def __init__(self, dispatcher: _DispatcherT_co) -> None: ...
+
+    #
+    @property
+    def dispatcher(self) -> _DispatcherT_co: ...
+
+    #
+    @override
+    def get_call_type(
+        self,
+        context: Context,
+        args: tuple[Type, ...],
+        kws: Mapping[str, Type],
+    ) -> Signature | None: ...
+    @override
+    def get_call_signatures(self) -> tuple[Sequence[Signature], bool]: ...
+    @override
+    def get_impl_key(self, sig: Signature) -> _AnyCallable: ...
+    def get_overload(self, sig: Signature) -> _AnyCallable: ...
+
+class ObjModeDispatcher(Dispatcher[_DispatcherT_co], Generic[_DispatcherT_co]): ...
+
+class ExternalFunctionPointer(BaseFunction):
+    sig: Final[Signature]
+    requires_gil: Final[bool]
+    get_pointer: Final[_GetPointerFn]
+    cconv: Final[str | None]
+
+    def __init__(
+        self,
+        sig: Signature,
+        get_pointer: _GetPointerFn,
+        cconv: str | None = None,
+    ) -> None: ...
+
+    #
+    @property
+    @override
+    def key(self) -> tuple[Signature, str | None, _GetPointerFn]: ...  # type: ignore[override] # pyrefly:ignore[bad-override]
+
+class ExternalFunction(Function):
+    symbol: Final[str]
+    sig: Final[Signature]
+
+    def __init__(self, symbol: str, sig: Signature) -> None: ...
+
+    #
+    @property
+    @override
+    def key(self) -> tuple[str, Signature]: ...  # type: ignore[override] # pyrefly:ignore[bad-override]
+
+class NamedTupleClass(Callable, Opaque, Generic[_TupleT_co]):
+    instance_class: type[_TupleT_co]
+
+    def __init__(self, instance_class: type[_TupleT_co]) -> None: ...
+
+    #
+    @property
+    @override
+    def key(self) -> type[_TupleT_co]: ...
+
+    #
+    @override
+    def get_call_type(
+        self,
+        context: Context,
+        args: tuple[Type, ...],
+        kws: Mapping[str, Type],
+    ) -> None: ...
+    @override
+    def get_call_signatures(self) -> tuple[tuple[()], bool]: ...
+    @override
+    def get_impl_key(self, sig: Signature) -> type[Self]: ...
+
+class NumberClass(Callable, DTypeSpec, Opaque, Generic[_T_co]):
+    instance_type: _T_co
+
+    def __init__(self, instance_type: _T_co) -> None: ...
+
+    #
+    @property
+    @override
+    def key(self) -> _T_co: ...
+    @property
+    @override
+    def dtype(self) -> _T_co: ...
+
+    #
+    @override
+    def get_call_type(
+        self,
+        context: Context,
+        args: tuple[Type, ...],
+        kws: Mapping[str, Type],
+    ) -> None: ...
+    @override
+    def get_call_signatures(self) -> tuple[tuple[()], bool]: ...
+    @override
+    def get_impl_key(self, sig: Signature) -> type[Self]: ...
+
+class _RecursiveCallOverloads(NamedTuple):
+    qualname: str
+    uid: int
+
+class RecursiveCall(Opaque, Generic[_DispatcherT_co]):
+    dispatcher_type: Dispatcher[_DispatcherT_co]
+
+    def __init__(self, dispatcher_type: Dispatcher[_DispatcherT_co]) -> None: ...
+    @property
+    @override
+    def key(self) -> Dispatcher[_DispatcherT_co]: ...
+
+    #
+    def add_overloads(
+        self,
+        args: tuple[Type, ...],
+        qualname: str,
+        uid: int,
+    ) -> None: ...
+    def get_overloads(self, args: tuple[Type, ...]) -> _RecursiveCallOverloads: ...
diff --git a/shared/package-files/32/323cd1f3e4ea7d5f01f80b5509681de168dbf7e34e0cd68991eb2dc710dacd5d/enet_binomial.csv b/shared/package-files/32/323cd1f3e4ea7d5f01f80b5509681de168dbf7e34e0cd68991eb2dc710dacd5d/enet_binomial.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3884d20d4603a6d1d12db498820113181ae687d4
--- /dev/null
+++ b/shared/package-files/32/323cd1f3e4ea7d5f01f80b5509681de168dbf7e34e0cd68991eb2dc710dacd5d/enet_binomial.csv
@@ -0,0 +1,200 @@
+0.00,0.42,0.45,0.24,-0.28,0.68
+0.00,0.62,0.12,0.94,-1.49,-0.53
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diff --git a/shared/package-files/32/323e6f214cd09790edb0f7b788ccd2eeb47ef3c53d1ca5b672c42a51d6b824d3/San_Juan b/shared/package-files/32/323e6f214cd09790edb0f7b788ccd2eeb47ef3c53d1ca5b672c42a51d6b824d3/San_Juan
new file mode 100644
index 0000000000000000000000000000000000000000..7eba33c1c5b13cbd9b7566f450d524a45cf8b65e
Binary files /dev/null and b/shared/package-files/32/323e6f214cd09790edb0f7b788ccd2eeb47ef3c53d1ca5b672c42a51d6b824d3/San_Juan differ
diff --git a/shared/package-files/32/323ebfb193d521090740fab408b3e04efc55b56893194b4f2b1408d276440086/arctan2_compositeimplicitautograd_dispatch.h b/shared/package-files/32/323ebfb193d521090740fab408b3e04efc55b56893194b4f2b1408d276440086/arctan2_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b10f365d03b774d0904a0defab63a07f693f1090
--- /dev/null
+++ b/shared/package-files/32/323ebfb193d521090740fab408b3e04efc55b56893194b4f2b1408d276440086/arctan2_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor arctan2(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & arctan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & arctan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & arctan2_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/32/323fdf8d2d1c55173b1dff999637c7c7beed99fd94d2f0820ef9cf262df3d335/graph_task.h b/shared/package-files/32/323fdf8d2d1c55173b1dff999637c7c7beed99fd94d2f0820ef9cf262df3d335/graph_task.h
new file mode 100644
index 0000000000000000000000000000000000000000..03a9647cad833479e4fd2ac75987aca21f1e0633
--- /dev/null
+++ b/shared/package-files/32/323fdf8d2d1c55173b1dff999637c7c7beed99fd94d2f0820ef9cf262df3d335/graph_task.h
@@ -0,0 +1,226 @@
+#pragma once
+#include <ATen/ThreadLocalState.h>
+#include <ATen/core/Tensor.h>
+#include <c10/util/ThreadLocal.h>
+#include <torch/csrc/autograd/input_buffer.h>
+#include <torch/csrc/autograd/utils/warnings.h>
+#include <vector>
+
+namespace torch::autograd {
+
+using edge_list = std::vector<Edge>;
+struct ReadyQueue;
+
+static constexpr int NO_DEVICE = -2;
+static constexpr int CPU_DEVICE = -1;
+
+// GraphTask holds metadata needed for a single execution of backward()
+struct GraphTask : std::enable_shared_from_this<GraphTask> {
+  std::atomic<uint64_t> outstanding_tasks_{0};
+  // Indicates if an error occurred while executing any task.  When this is
+  // true, it signals all threads to stop executing.
+  std::atomic_bool has_error_{false};
+  std::atomic_bool future_completed_{false};
+  // It is safe to read keep_graph_ without synchronization
+  bool keep_graph_;
+
+  // To protect reads/writes to not_ready_, dependencies_, captured_vars_,
+  // has_error_, future_result_, cpu_ready_queue_, and leaf_streams.
+  std::mutex mutex_;
+  std::unordered_map<Node*, InputBuffer> not_ready_;
+  std::unordered_map<Node*, int> dependencies_;
+
+  // Records the nodes that are in the graph
+  std::unordered_set<Node*> nodes_in_graph_;
+  c10::SmallVector<Node*, 4> graph_roots_;
+  // Note [Exec info]
+  // Exec info is created for each GraphTask, which allows filtering paths on
+  // the graph that are not needed. It has a bit complicated semantics. If it's
+  // empty, it means the task is run in a "default" mode, which means that all
+  // next_edges we encounter should get executed. If it's not empty, only
+  // functions that have an entry and this entry has needed == True should be
+  // executed. exec_info is only empty when the graph is executed via
+  // .backward() and the inputs parameter is not passed. Otherwise, when
+  // executed through .grad(), or when inputs arg is specified for .backward(),
+  // exec_info will be non-empty.
+  //
+  struct ExecInfo {
+    struct Capture {
+      Capture(const Capture&) = delete;
+      Capture(Capture&&) = default;
+
+      Capture(int input_idx, int output_idx)
+          : input_idx_(input_idx), output_idx_(output_idx) {}
+      int input_idx_; // within Node inputs
+      int output_idx_; // within the output vector of a GraphTask
+
+      // This hook will be executed after a grad is captured. The captured
+      // grad will be replaced by the return value of the hook.
+      struct GradCaptureHook {
+        virtual ~GradCaptureHook() = default;
+        virtual at::Tensor operator()(const at::Tensor& grad) = 0;
+      };
+      // NOTE [Deprecated capture hooks]
+      //
+      // The current status of capture hooks is that we continue to support
+      // the single usage of it by distributed in the dist_engine. If anyone
+      // else needs to use it for other purposes, they should file an issue.
+      //
+      // Capture hooks were originally created because there did not exist
+      // any way to register pre/post hooks to grad_fn in a way such that it
+      // would still be executed even if that is the grad_fn of a Tensor
+      // passed as input= of .grad. As far as I know, only dist_engine uses
+      // this hook.
+      //
+      // However, there are other alternatives today like tensor hooks that can
+      // replace the usage that originally motivated its creation. Also,
+      // Captures hooks are an outlier in terms of the types of hook that
+      // autograd offers in how it is registered and behaves, e.g. it is a hook
+      // registered not to the graph, but to a particular graph_task! This makes
+      // it a burden to maintain.
+      //
+      // It would be very nice to clean up/do a migration from pre/post
+      // hooks used in distributed to use tensor hooks, but for now we just
+      // mark this method as deprecated to prevent additional usage.
+      //
+      // If you still think you really need to capture hooks, please file an
+      // issue (and tag autograd).
+      const std::vector<std::unique_ptr<GradCaptureHook>>&
+      DO_NOT_USE_DEPRECATED_get_capture_hooks() const {
+        return hooks_;
+      }
+      // See NOTE [deprecated capture hooks]
+      void DO_NOT_USE_DEPRECATED_register_capture_hook(
+          std::unique_ptr<GradCaptureHook> hook) {
+        hooks_.push_back(std::move(hook));
+      }
+
+     private:
+      // The hooks will be called one by one in the order as they were added.
+      // The input grad of a hook will be the output of its preceding hook. The
+      // first hook will take the captured grad as the input. The output of the
+      // last hook will replace the captured grad.
+      std::vector<std::unique_ptr<GradCaptureHook>> hooks_;
+    };
+
+    bool should_execute() const {
+      return needed_ || captures_;
+    }
+
+    bool needed_ = false;
+    std::unique_ptr<std::vector<Capture>> captures_;
+  };
+  // exec_info_ is safe to read without synchronization
+  std::unordered_map<Node*, ExecInfo> exec_info_;
+  // Captures variables are grads captured that we return to the user. After
+  // execution of the GraphTask is completed, the captured_vars_ are moved
+  // out of the GraphTask and are no longer valid.
+  std::vector<Variable> captured_vars_;
+
+  // Note: this field is not ready to be used until the proper
+  // `thread_locals_.set_grad_mode()` call in the constructor.
+  at::ThreadLocalState thread_locals_ = at::ThreadLocalState();
+
+  std::unordered_set<c10::Stream> leaf_streams;
+
+  // Per-device current streams of the execute() that called this GraphTask.
+  // These will be synced with leaf_streams in exec_post_processing.
+  std::vector<c10::optional<c10::Stream>> caller_current_streams_;
+
+  // Collects caller_current_streams_ for the accelerator device.
+  void stash_current_streams();
+
+  void init_to_execute(
+      Node& graph_root,
+      const edge_list& outputs,
+      bool accumulate_grad,
+      uint64_t min_topo_nr);
+
+  // The value of worker_device in the thread that created this task.
+  // See Note [Reentrant backwards]
+  // Safe to read owner_ and reentrant_depth_ without synchronization
+  int owner_;
+  // The number of parent graph tasks for this graph task
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
+  const int reentrant_depth_;
+
+  bool can_checkpoint() const {
+    return exec_info_.empty();
+  }
+
+  // check if the GraphTask is completed or not
+  bool completed();
+  // mark the graph task as completed and trigger post processing
+  void mark_as_completed_and_run_post_processing();
+
+  // Set an appropriate exception on this graph_task which was encountered while
+  // running the provided function.
+  void set_exception(std::exception_ptr eptr, const std::shared_ptr<Node>& fn);
+
+  // Set an appropriate exception on this graph_task which was encountered while
+  // running the provided function. But doesn't signal completion on
+  // 'future_result_' right away. The user needs to explicitly mark
+  // 'future_result_' completed with an appropriate exception.
+  void set_exception_without_signal(const std::shared_ptr<Node>& fn);
+
+  // Whether or not to stop execution for this GraphTask when an error is
+  // encountered. When set to true, this would cause Engine::execute() to throw
+  // an exception as soon as the autograd engine receives an exception.
+  bool exit_on_error_;
+
+  // CPU threads are dedicated to processing CPU work for the backward they
+  // invoked. So any given graph task maintains its own cpu_ready_queue_ where
+  // you should send work for it to be done. We memoize the cpu_ready_queue_ per
+  // GraphTask so that we know which ready queue we should push to if we are on
+  // device thread (i.e. GPU) and but next NodeTask should be run on CPU.
+  std::shared_ptr<ReadyQueue> cpu_ready_queue_;
+
+  // Future representing the completion of the graph task. Notified when all
+  // tasks are done.
+  c10::intrusive_ptr<at::ivalue::Future> future_result_;
+
+  // Final callbacks installed during execution of this GraphTask
+  std::vector<std::function<void()>> final_callbacks_;
+  // To protect reads and writes to final_callbacks_. Intentionally no reusing
+  // mutex_ as the two are protecting different data structures.
+  std::mutex final_callbacks_lock_;
+
+  utils::DelayWarningHandler warning_handler_;
+
+  uint64_t id_;
+
+  GraphTask(
+      bool keep_graph,
+      bool grad_mode,
+      int reentrant_depth,
+      std::shared_ptr<ReadyQueue> cpu_ready_queue,
+      c10::SmallVector<Node*, 4> graph_roots,
+      bool exit_on_error = false);
+
+ private:
+  // run GraphTask post processing
+  void exec_post_processing();
+};
+
+// The guard that sets and restores current_graph_task.
+class GraphTaskGuard {
+ public:
+  explicit GraphTaskGuard(std::shared_ptr<GraphTask> graph_task);
+  ~GraphTaskGuard();
+
+  void restore_current_graph_task();
+
+ private:
+  std::shared_ptr<GraphTask> last_graph_task_;
+};
+
+TORCH_API const std::unordered_map<Node*, GraphTask::ExecInfo>*
+get_current_graph_task_exec_info();
+TORCH_API const std::unordered_set<Node*>*
+get_current_graph_task_nodes_in_graph();
+TORCH_API bool get_current_graph_task_keep_graph();
+TORCH_API std::vector<Node*> get_current_graph_task_execution_order();
+TORCH_API int get_current_graph_task_id();
+void add_node_to_current_graph_task_exec_info(Node* fn);
+
+} // namespace torch::autograd
diff --git a/shared/package-files/32/32409f05d42f6c2b622e00c5688029d23725c23da6221c202f727402cbeadf54/test_odr.py b/shared/package-files/32/32409f05d42f6c2b622e00c5688029d23725c23da6221c202f727402cbeadf54/test_odr.py
new file mode 100644
index 0000000000000000000000000000000000000000..971cce6c55a84e08a182e3b25bf9a7e362937e01
--- /dev/null
+++ b/shared/package-files/32/32409f05d42f6c2b622e00c5688029d23725c23da6221c202f727402cbeadf54/test_odr.py
@@ -0,0 +1,607 @@
+import pickle
+import tempfile
+import shutil
+import os
+
+import numpy as np
+from numpy import pi
+from numpy.testing import (assert_array_almost_equal,
+                           assert_equal, assert_warns,
+                           assert_allclose)
+import pytest
+from pytest import raises as assert_raises
+
+from scipy.odr import (Data, Model, ODR, RealData, OdrStop, OdrWarning,
+                       multilinear, exponential, unilinear, quadratic,
+                       polynomial)
+
+
+class TestODR:
+
+    # Bad Data for 'x'
+
+    def test_bad_data(self):
+        assert_raises(ValueError, Data, 2, 1)
+        assert_raises(ValueError, RealData, 2, 1)
+
+    # Empty Data for 'x'
+    def empty_data_func(self, B, x):
+        return B[0]*x + B[1]
+
+    @pytest.mark.thread_unsafe
+    def test_empty_data(self):
+        beta0 = [0.02, 0.0]
+        linear = Model(self.empty_data_func)
+
+        empty_dat = Data([], [])
+        assert_warns(OdrWarning, ODR,
+                     empty_dat, linear, beta0=beta0)
+
+        empty_dat = RealData([], [])
+        assert_warns(OdrWarning, ODR,
+                     empty_dat, linear, beta0=beta0)
+
+    # Explicit Example
+
+    def explicit_fcn(self, B, x):
+        ret = B[0] + B[1] * np.power(np.exp(B[2]*x) - 1.0, 2)
+        return ret
+
+    def explicit_fjd(self, B, x):
+        eBx = np.exp(B[2]*x)
+        ret = B[1] * 2.0 * (eBx-1.0) * B[2] * eBx
+        return ret
+
+    def explicit_fjb(self, B, x):
+        eBx = np.exp(B[2]*x)
+        res = np.vstack([np.ones(x.shape[-1]),
+                         np.power(eBx-1.0, 2),
+                         B[1]*2.0*(eBx-1.0)*eBx*x])
+        return res
+
+    def test_explicit(self):
+        explicit_mod = Model(
+            self.explicit_fcn,
+            fjacb=self.explicit_fjb,
+            fjacd=self.explicit_fjd,
+            meta=dict(name='Sample Explicit Model',
+                      ref='ODRPACK UG, pg. 39'),
+        )
+        explicit_dat = Data([0.,0.,5.,7.,7.5,10.,16.,26.,30.,34.,34.5,100.],
+                        [1265.,1263.6,1258.,1254.,1253.,1249.8,1237.,1218.,1220.6,
+                         1213.8,1215.5,1212.])
+        explicit_odr = ODR(explicit_dat, explicit_mod, beta0=[1500.0, -50.0, -0.1],
+                       ifixx=[0,0,1,1,1,1,1,1,1,1,1,0])
+        explicit_odr.set_job(deriv=2)
+        explicit_odr.set_iprint(init=0, iter=0, final=0)
+
+        out = explicit_odr.run()
+        assert_array_almost_equal(
+            out.beta,
+            np.array([1.2646548050648876e+03, -5.4018409956678255e+01,
+                -8.7849712165253724e-02]),
+        )
+        assert_array_almost_equal(
+            out.sd_beta,
+            np.array([1.0349270280543437, 1.583997785262061, 0.0063321988657267]),
+        )
+        assert_array_almost_equal(
+            out.cov_beta,
+            np.array([[4.4949592379003039e-01, -3.7421976890364739e-01,
+                 -8.0978217468468912e-04],
+               [-3.7421976890364739e-01, 1.0529686462751804e+00,
+                 -1.9453521827942002e-03],
+               [-8.0978217468468912e-04, -1.9453521827942002e-03,
+                  1.6827336938454476e-05]]),
+        )
+
+    # Implicit Example
+
+    def implicit_fcn(self, B, x):
+        return (B[2]*np.power(x[0]-B[0], 2) +
+                2.0*B[3]*(x[0]-B[0])*(x[1]-B[1]) +
+                B[4]*np.power(x[1]-B[1], 2) - 1.0)
+
+    def test_implicit(self):
+        implicit_mod = Model(
+            self.implicit_fcn,
+            implicit=1,
+            meta=dict(name='Sample Implicit Model',
+                      ref='ODRPACK UG, pg. 49'),
+        )
+        implicit_dat = Data([
+            [0.5,1.2,1.6,1.86,2.12,2.36,2.44,2.36,2.06,1.74,1.34,0.9,-0.28,
+             -0.78,-1.36,-1.9,-2.5,-2.88,-3.18,-3.44],
+            [-0.12,-0.6,-1.,-1.4,-2.54,-3.36,-4.,-4.75,-5.25,-5.64,-5.97,-6.32,
+             -6.44,-6.44,-6.41,-6.25,-5.88,-5.5,-5.24,-4.86]],
+            1,
+        )
+        implicit_odr = ODR(implicit_dat, implicit_mod,
+            beta0=[-1.0, -3.0, 0.09, 0.02, 0.08])
+
+        out = implicit_odr.run()
+        assert_array_almost_equal(
+            out.beta,
+            np.array([-0.9993809167281279, -2.9310484652026476, 0.0875730502693354,
+                0.0162299708984738, 0.0797537982976416]),
+        )
+        assert_array_almost_equal(
+            out.sd_beta,
+            np.array([0.1113840353364371, 0.1097673310686467, 0.0041060738314314,
+                0.0027500347539902, 0.0034962501532468]),
+        )
+        assert_allclose(
+            out.cov_beta,
+            np.array([[2.1089274602333052e+00, -1.9437686411979040e+00,
+                  7.0263550868344446e-02, -4.7175267373474862e-02,
+                  5.2515575927380355e-02],
+               [-1.9437686411979040e+00, 2.0481509222414456e+00,
+                 -6.1600515853057307e-02, 4.6268827806232933e-02,
+                 -5.8822307501391467e-02],
+               [7.0263550868344446e-02, -6.1600515853057307e-02,
+                  2.8659542561579308e-03, -1.4628662260014491e-03,
+                  1.4528860663055824e-03],
+               [-4.7175267373474862e-02, 4.6268827806232933e-02,
+                 -1.4628662260014491e-03, 1.2855592885514335e-03,
+                 -1.2692942951415293e-03],
+               [5.2515575927380355e-02, -5.8822307501391467e-02,
+                  1.4528860663055824e-03, -1.2692942951415293e-03,
+                  2.0778813389755596e-03]]),
+            rtol=1e-6, atol=2e-6,
+        )
+
+    # Multi-variable Example
+
+    def multi_fcn(self, B, x):
+        if (x < 0.0).any():
+            raise OdrStop
+        theta = pi*B[3]/2.
+        ctheta = np.cos(theta)
+        stheta = np.sin(theta)
+        omega = np.power(2.*pi*x*np.exp(-B[2]), B[3])
+        phi = np.arctan2((omega*stheta), (1.0 + omega*ctheta))
+        r = (B[0] - B[1]) * np.power(np.sqrt(np.power(1.0 + omega*ctheta, 2) +
+             np.power(omega*stheta, 2)), -B[4])
+        ret = np.vstack([B[1] + r*np.cos(B[4]*phi),
+                         r*np.sin(B[4]*phi)])
+        return ret
+
+    def test_multi(self):
+        multi_mod = Model(
+            self.multi_fcn,
+            meta=dict(name='Sample Multi-Response Model',
+                      ref='ODRPACK UG, pg. 56'),
+        )
+
+        multi_x = np.array([30.0, 50.0, 70.0, 100.0, 150.0, 200.0, 300.0, 500.0,
+            700.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0,
+            15000.0, 20000.0, 30000.0, 50000.0, 70000.0, 100000.0, 150000.0])
+        multi_y = np.array([
+            [4.22, 4.167, 4.132, 4.038, 4.019, 3.956, 3.884, 3.784, 3.713,
+             3.633, 3.54, 3.433, 3.358, 3.258, 3.193, 3.128, 3.059, 2.984,
+             2.934, 2.876, 2.838, 2.798, 2.759],
+            [0.136, 0.167, 0.188, 0.212, 0.236, 0.257, 0.276, 0.297, 0.309,
+             0.311, 0.314, 0.311, 0.305, 0.289, 0.277, 0.255, 0.24, 0.218,
+             0.202, 0.182, 0.168, 0.153, 0.139],
+        ])
+        n = len(multi_x)
+        multi_we = np.zeros((2, 2, n), dtype=float)
+        multi_ifixx = np.ones(n, dtype=int)
+        multi_delta = np.zeros(n, dtype=float)
+
+        multi_we[0,0,:] = 559.6
+        multi_we[1,0,:] = multi_we[0,1,:] = -1634.0
+        multi_we[1,1,:] = 8397.0
+
+        for i in range(n):
+            if multi_x[i] < 100.0:
+                multi_ifixx[i] = 0
+            elif multi_x[i] <= 150.0:
+                pass  # defaults are fine
+            elif multi_x[i] <= 1000.0:
+                multi_delta[i] = 25.0
+            elif multi_x[i] <= 10000.0:
+                multi_delta[i] = 560.0
+            elif multi_x[i] <= 100000.0:
+                multi_delta[i] = 9500.0
+            else:
+                multi_delta[i] = 144000.0
+            if multi_x[i] == 100.0 or multi_x[i] == 150.0:
+                multi_we[:,:,i] = 0.0
+
+        multi_dat = Data(multi_x, multi_y, wd=1e-4/np.power(multi_x, 2),
+            we=multi_we)
+        multi_odr = ODR(multi_dat, multi_mod, beta0=[4.,2.,7.,.4,.5],
+            delta0=multi_delta, ifixx=multi_ifixx)
+        multi_odr.set_job(deriv=1, del_init=1)
+
+        out = multi_odr.run()
+        assert_array_almost_equal(
+            out.beta,
+            np.array([4.3799880305938963, 2.4333057577497703, 8.0028845899503978,
+                0.5101147161764654, 0.5173902330489161]),
+        )
+        assert_array_almost_equal(
+            out.sd_beta,
+            np.array([0.0130625231081944, 0.0130499785273277, 0.1167085962217757,
+                0.0132642749596149, 0.0288529201353984]),
+        )
+        assert_array_almost_equal(
+            out.cov_beta,
+            np.array([[0.0064918418231375, 0.0036159705923791, 0.0438637051470406,
+                -0.0058700836512467, 0.011281212888768],
+               [0.0036159705923791, 0.0064793789429006, 0.0517610978353126,
+                -0.0051181304940204, 0.0130726943624117],
+               [0.0438637051470406, 0.0517610978353126, 0.5182263323095322,
+                -0.0563083340093696, 0.1269490939468611],
+               [-0.0058700836512467, -0.0051181304940204, -0.0563083340093696,
+                 0.0066939246261263, -0.0140184391377962],
+               [0.011281212888768, 0.0130726943624117, 0.1269490939468611,
+                -0.0140184391377962, 0.0316733013820852]]),
+        )
+
+    # Pearson's Data
+    # K. Pearson, Philosophical Magazine, 2, 559 (1901)
+
+    def pearson_fcn(self, B, x):
+        return B[0] + B[1]*x
+
+    def test_pearson(self):
+        p_x = np.array([0.,.9,1.8,2.6,3.3,4.4,5.2,6.1,6.5,7.4])
+        p_y = np.array([5.9,5.4,4.4,4.6,3.5,3.7,2.8,2.8,2.4,1.5])
+        p_sx = np.array([.03,.03,.04,.035,.07,.11,.13,.22,.74,1.])
+        p_sy = np.array([1.,.74,.5,.35,.22,.22,.12,.12,.1,.04])
+
+        p_dat = RealData(p_x, p_y, sx=p_sx, sy=p_sy)
+
+        # Reverse the data to test invariance of results
+        pr_dat = RealData(p_y, p_x, sx=p_sy, sy=p_sx)
+
+        p_mod = Model(self.pearson_fcn, meta=dict(name='Uni-linear Fit'))
+
+        p_odr = ODR(p_dat, p_mod, beta0=[1.,1.])
+        pr_odr = ODR(pr_dat, p_mod, beta0=[1.,1.])
+
+        out = p_odr.run()
+        assert_array_almost_equal(
+            out.beta,
+            np.array([5.4767400299231674, -0.4796082367610305]),
+        )
+        assert_array_almost_equal(
+            out.sd_beta,
+            np.array([0.3590121690702467, 0.0706291186037444]),
+        )
+        assert_array_almost_equal(
+            out.cov_beta,
+            np.array([[0.0854275622946333, -0.0161807025443155],
+               [-0.0161807025443155, 0.003306337993922]]),
+        )
+
+        rout = pr_odr.run()
+        assert_array_almost_equal(
+            rout.beta,
+            np.array([11.4192022410781231, -2.0850374506165474]),
+        )
+        assert_array_almost_equal(
+            rout.sd_beta,
+            np.array([0.9820231665657161, 0.3070515616198911]),
+        )
+        assert_array_almost_equal(
+            rout.cov_beta,
+            np.array([[0.6391799462548782, -0.1955657291119177],
+               [-0.1955657291119177, 0.0624888159223392]]),
+        )
+
+    # Lorentz Peak
+    # The data is taken from one of the undergraduate physics labs I performed.
+
+    def lorentz(self, beta, x):
+        return (beta[0]*beta[1]*beta[2] / np.sqrt(np.power(x*x -
+            beta[2]*beta[2], 2.0) + np.power(beta[1]*x, 2.0)))
+
+    def test_lorentz(self):
+        l_sy = np.array([.29]*18)
+        l_sx = np.array([.000972971,.000948268,.000707632,.000706679,
+            .000706074, .000703918,.000698955,.000456856,
+            .000455207,.000662717,.000654619,.000652694,
+            .000000859202,.00106589,.00106378,.00125483, .00140818,.00241839])
+
+        l_dat = RealData(
+            [3.9094, 3.85945, 3.84976, 3.84716, 3.84551, 3.83964, 3.82608,
+             3.78847, 3.78163, 3.72558, 3.70274, 3.6973, 3.67373, 3.65982,
+             3.6562, 3.62498, 3.55525, 3.41886],
+            [652, 910.5, 984, 1000, 1007.5, 1053, 1160.5, 1409.5, 1430, 1122,
+             957.5, 920, 777.5, 709.5, 698, 578.5, 418.5, 275.5],
+            sx=l_sx,
+            sy=l_sy,
+        )
+        l_mod = Model(self.lorentz, meta=dict(name='Lorentz Peak'))
+        l_odr = ODR(l_dat, l_mod, beta0=(1000., .1, 3.8))
+
+        out = l_odr.run()
+        assert_array_almost_equal(
+            out.beta,
+            np.array([1.4306780846149925e+03, 1.3390509034538309e-01,
+                 3.7798193600109009e+00]),
+        )
+        assert_array_almost_equal(
+            out.sd_beta,
+            np.array([7.3621186811330963e-01, 3.5068899941471650e-04,
+                 2.4451209281408992e-04]),
+        )
+        assert_array_almost_equal(
+            out.cov_beta,
+            np.array([[2.4714409064597873e-01, -6.9067261911110836e-05,
+                 -3.1236953270424990e-05],
+               [-6.9067261911110836e-05, 5.6077531517333009e-08,
+                  3.6133261832722601e-08],
+               [-3.1236953270424990e-05, 3.6133261832722601e-08,
+                  2.7261220025171730e-08]]),
+        )
+
+    def test_ticket_1253(self):
+        def linear(c, x):
+            return c[0]*x+c[1]
+
+        c = [2.0, 3.0]
+        x = np.linspace(0, 10)
+        y = linear(c, x)
+
+        model = Model(linear)
+        data = Data(x, y, wd=1.0, we=1.0)
+        job = ODR(data, model, beta0=[1.0, 1.0])
+        result = job.run()
+        assert_equal(result.info, 2)
+
+    # Verify fix for gh-9140
+
+    def test_ifixx(self):
+        x1 = [-2.01, -0.99, -0.001, 1.02, 1.98]
+        x2 = [3.98, 1.01, 0.001, 0.998, 4.01]
+        fix = np.vstack((np.zeros_like(x1, dtype=int), np.ones_like(x2, dtype=int)))
+        data = Data(np.vstack((x1, x2)), y=1, fix=fix)
+        model = Model(lambda beta, x: x[1, :] - beta[0] * x[0, :]**2., implicit=True)
+
+        odr1 = ODR(data, model, beta0=np.array([1.]))
+        sol1 = odr1.run()
+        odr2 = ODR(data, model, beta0=np.array([1.]), ifixx=fix)
+        sol2 = odr2.run()
+        assert_equal(sol1.beta, sol2.beta)
+
+    # verify bugfix for #11800 in #11802
+    def test_ticket_11800(self):
+        # parameters
+        beta_true = np.array([1.0, 2.3, 1.1, -1.0, 1.3, 0.5])
+        nr_measurements = 10
+
+        std_dev_x = 0.01
+        x_error = np.array([[0.00063445, 0.00515731, 0.00162719, 0.01022866,
+            -0.01624845, 0.00482652, 0.00275988, -0.00714734, -0.00929201, -0.00687301],
+            [-0.00831623, -0.00821211, -0.00203459, 0.00938266, -0.00701829,
+            0.0032169, 0.00259194, -0.00581017, -0.0030283, 0.01014164]])
+
+        std_dev_y = 0.05
+        y_error = np.array([[0.05275304, 0.04519563, -0.07524086, 0.03575642,
+            0.04745194, 0.03806645, 0.07061601, -0.00753604, -0.02592543, -0.02394929],
+            [0.03632366, 0.06642266, 0.08373122, 0.03988822, -0.0092536,
+            -0.03750469, -0.03198903, 0.01642066, 0.01293648, -0.05627085]])
+
+        beta_solution = np.array([
+            2.62920235756665876536e+00, -1.26608484996299608838e+02,
+            1.29703572775403074502e+02, -1.88560985401185465804e+00,
+            7.83834160771274923718e+01, -7.64124076838087091801e+01])
+
+        # model's function and Jacobians
+        def func(beta, x):
+            y0 = beta[0] + beta[1] * x[0, :] + beta[2] * x[1, :]
+            y1 = beta[3] + beta[4] * x[0, :] + beta[5] * x[1, :]
+
+            return np.vstack((y0, y1))
+
+        def df_dbeta_odr(beta, x):
+            nr_meas = np.shape(x)[1]
+            zeros = np.zeros(nr_meas)
+            ones = np.ones(nr_meas)
+
+            dy0 = np.array([ones, x[0, :], x[1, :], zeros, zeros, zeros])
+            dy1 = np.array([zeros, zeros, zeros, ones, x[0, :], x[1, :]])
+
+            return np.stack((dy0, dy1))
+
+        def df_dx_odr(beta, x):
+            nr_meas = np.shape(x)[1]
+            ones = np.ones(nr_meas)
+
+            dy0 = np.array([beta[1] * ones, beta[2] * ones])
+            dy1 = np.array([beta[4] * ones, beta[5] * ones])
+            return np.stack((dy0, dy1))
+
+        # do measurements with errors in independent and dependent variables
+        x0_true = np.linspace(1, 10, nr_measurements)
+        x1_true = np.linspace(1, 10, nr_measurements)
+        x_true = np.array([x0_true, x1_true])
+
+        y_true = func(beta_true, x_true)
+
+        x_meas = x_true + x_error
+        y_meas = y_true + y_error
+
+        # estimate model's parameters
+        model_f = Model(func, fjacb=df_dbeta_odr, fjacd=df_dx_odr)
+
+        data = RealData(x_meas, y_meas, sx=std_dev_x, sy=std_dev_y)
+
+        odr_obj = ODR(data, model_f, beta0=0.9 * beta_true, maxit=100)
+        #odr_obj.set_iprint(init=2, iter=0, iter_step=1, final=1)
+        odr_obj.set_job(deriv=3)
+
+        odr_out = odr_obj.run()
+
+        # check results
+        assert_equal(odr_out.info, 1)
+        assert_array_almost_equal(odr_out.beta, beta_solution)
+
+    def test_multilinear_model(self):
+        x = np.linspace(0.0, 5.0)
+        y = 10.0 + 5.0 * x
+        data = Data(x, y)
+        odr_obj = ODR(data, multilinear)
+        output = odr_obj.run()
+        assert_array_almost_equal(output.beta, [10.0, 5.0])
+
+    def test_exponential_model(self):
+        x = np.linspace(0.0, 5.0)
+        y = -10.0 + np.exp(0.5*x)
+        data = Data(x, y)
+        odr_obj = ODR(data, exponential)
+        output = odr_obj.run()
+        assert_array_almost_equal(output.beta, [-10.0, 0.5])
+
+    def test_polynomial_model(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 + 2.0 * x + 3.0 * x ** 2 + 4.0 * x ** 3
+        poly_model = polynomial(3)
+        data = Data(x, y)
+        odr_obj = ODR(data, poly_model)
+        output = odr_obj.run()
+        assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0, 4.0])
+
+    def test_unilinear_model(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x + 2.0
+        data = Data(x, y)
+        odr_obj = ODR(data, unilinear)
+        output = odr_obj.run()
+        assert_array_almost_equal(output.beta, [1.0, 2.0])
+
+    def test_quadratic_model(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x ** 2 + 2.0 * x + 3.0
+        data = Data(x, y)
+        odr_obj = ODR(data, quadratic)
+        output = odr_obj.run()
+        assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0])
+
+    def test_work_ind(self):
+
+        def func(par, x):
+            b0, b1 = par
+            return b0 + b1 * x
+
+        # generate some data
+        n_data = 4
+        x = np.arange(n_data)
+        y = np.where(x % 2, x + 0.1, x - 0.1)
+        x_err = np.full(n_data, 0.1)
+        y_err = np.full(n_data, 0.1)
+
+        # do the fitting
+        linear_model = Model(func)
+        real_data = RealData(x, y, sx=x_err, sy=y_err)
+        odr_obj = ODR(real_data, linear_model, beta0=[0.4, 0.4])
+        odr_obj.set_job(fit_type=0)
+        out = odr_obj.run()
+
+        sd_ind = out.work_ind['sd']
+        assert_array_almost_equal(out.sd_beta,
+                                  out.work[sd_ind:sd_ind + len(out.sd_beta)])
+
+    @pytest.mark.skipif(True, reason="Fortran I/O prone to crashing so better "
+                                     "not to run this test, see gh-13127")
+    def test_output_file_overwrite(self):
+        """
+        Verify fix for gh-1892
+        """
+        def func(b, x):
+            return b[0] + b[1] * x
+
+        p = Model(func)
+        data = Data(np.arange(10), 12 * np.arange(10))
+        tmp_dir = tempfile.mkdtemp()
+        error_file_path = os.path.join(tmp_dir, "error.dat")
+        report_file_path = os.path.join(tmp_dir, "report.dat")
+        try:
+            ODR(data, p, beta0=[0.1, 13], errfile=error_file_path,
+                rptfile=report_file_path).run()
+            ODR(data, p, beta0=[0.1, 13], errfile=error_file_path,
+                rptfile=report_file_path, overwrite=True).run()
+        finally:
+            # remove output files for clean up
+            shutil.rmtree(tmp_dir)
+
+    def test_odr_model_default_meta(self):
+        def func(b, x):
+            return b[0] + b[1] * x
+
+        p = Model(func)
+        p.set_meta(name='Sample Model Meta', ref='ODRPACK')
+        assert_equal(p.meta, {'name': 'Sample Model Meta', 'ref': 'ODRPACK'})
+
+    def test_work_array_del_init(self):
+        """
+        Verify fix for gh-18739 where del_init=1 fails.
+        """
+        def func(b, x):
+            return b[0] + b[1] * x
+
+        # generate some data
+        n_data = 4
+        x = np.arange(n_data)
+        y = np.where(x % 2, x + 0.1, x - 0.1)
+        x_err = np.full(n_data, 0.1)
+        y_err = np.full(n_data, 0.1)
+
+        linear_model = Model(func)
+        # Try various shapes of the `we` array from various `sy` and `covy`
+        rd0 = RealData(x, y, sx=x_err, sy=y_err)
+        rd1 = RealData(x, y, sx=x_err, sy=0.1)
+        rd2 = RealData(x, y, sx=x_err, sy=[0.1])
+        rd3 = RealData(x, y, sx=x_err, sy=np.full((1, n_data), 0.1))
+        rd4 = RealData(x, y, sx=x_err, covy=[[0.01]])
+        rd5 = RealData(x, y, sx=x_err, covy=np.full((1, 1, n_data), 0.01))
+        for rd in [rd0, rd1, rd2, rd3, rd4, rd5]:
+            odr_obj = ODR(rd, linear_model, beta0=[0.4, 0.4],
+                          delta0=np.full(n_data, -0.1))
+            odr_obj.set_job(fit_type=0, del_init=1)
+            # Just make sure that it runs without raising an exception.
+            odr_obj.run()
+
+    def test_pickling_data(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x + 2.0
+        data = Data(x, y)
+
+        obj_pickle = pickle.dumps(data)
+        del data
+        pickle.loads(obj_pickle)
+
+    def test_pickling_real_data(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x + 2.0
+        data = RealData(x, y)
+
+        obj_pickle = pickle.dumps(data)
+        del data
+        pickle.loads(obj_pickle)
+
+    def test_pickling_model(self):
+        obj_pickle = pickle.dumps(unilinear)
+        pickle.loads(obj_pickle)
+
+    def test_pickling_odr(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x + 2.0
+        odr_obj = ODR(Data(x, y), unilinear)
+
+        obj_pickle = pickle.dumps(odr_obj)
+        del odr_obj
+        pickle.loads(obj_pickle)
+
+    def test_pickling_output(self):
+        x = np.linspace(0.0, 5.0)
+        y = 1.0 * x + 2.0
+        output = ODR(Data(x, y), unilinear).run
+
+        obj_pickle = pickle.dumps(output)
+        del output
+        pickle.loads(obj_pickle)
diff --git a/shared/package-files/32/32417db3cbb65fef7f09e8bdb098b19636f6fc8fc1ff850ea95c7489178a214d/table.py b/shared/package-files/32/32417db3cbb65fef7f09e8bdb098b19636f6fc8fc1ff850ea95c7489178a214d/table.py
new file mode 100644
index 0000000000000000000000000000000000000000..370ce9fe922fab333b8acc418e36d57a228d74e5
--- /dev/null
+++ b/shared/package-files/32/32417db3cbb65fef7f09e8bdb098b19636f6fc8fc1ff850ea95c7489178a214d/table.py
@@ -0,0 +1,846 @@
+# Original code by:
+#    John Gill <jng@europe.renre.com>
+#    Copyright 2004 John Gill and John Hunter
+#
+# Subsequent changes:
+#    The Matplotlib development team
+#    Copyright The Matplotlib development team
+
+"""
+Tables drawing.
+
+.. note::
+    The table implementation in Matplotlib is lightly maintained. For a more
+    featureful table implementation, you may wish to try `blume
+    <https://github.com/swfiua/blume>`_.
+
+Use the factory function `~matplotlib.table.table` to create a ready-made
+table from texts. If you need more control, use the `.Table` class and its
+methods.
+
+The table consists of a grid of cells, which are indexed by (row, column).
+The cell (0, 0) is positioned at the top left.
+
+Thanks to John Gill for providing the class and table.
+"""
+
+import numpy as np
+
+from . import _api, _docstring
+from .artist import Artist, allow_rasterization
+from .patches import Rectangle
+from .text import Text
+from .transforms import Bbox
+from .path import Path
+
+from .cbook import _is_pandas_dataframe
+
+
+class Cell(Rectangle):
+    """
+    A cell is a `.Rectangle` with some associated `.Text`.
+
+    As a user, you'll most likely not creates cells yourself. Instead, you
+    should use either the `~matplotlib.table.table` factory function or
+    `.Table.add_cell`.
+    """
+
+    PAD = 0.1
+    """Padding between text and rectangle."""
+
+    _edges = 'BRTL'
+    _edge_aliases = {'open':         '',
+                     'closed':       _edges,  # default
+                     'horizontal':   'BT',
+                     'vertical':     'RL'
+                     }
+
+    def __init__(self, xy, width, height, *,
+                 edgecolor='k', facecolor='w',
+                 fill=True,
+                 text='',
+                 loc='right',
+                 fontproperties=None,
+                 visible_edges='closed',
+                 ):
+        """
+        Parameters
+        ----------
+        xy : 2-tuple
+            The position of the bottom left corner of the cell.
+        width : float
+            The cell width.
+        height : float
+            The cell height.
+        edgecolor : :mpltype:`color`, default: 'k'
+            The color of the cell border.
+        facecolor : :mpltype:`color`, default: 'w'
+            The cell facecolor.
+        fill : bool, default: True
+            Whether the cell background is filled.
+        text : str, optional
+            The cell text.
+        loc : {'right', 'center', 'left'}
+            The alignment of the text within the cell.
+        fontproperties : dict, optional
+            A dict defining the font properties of the text. Supported keys and
+            values are the keyword arguments accepted by `.FontProperties`.
+        visible_edges : {'closed', 'open', 'horizontal', 'vertical'} or \
+substring of 'BRTL'
+            The cell edges to be drawn with a line: a substring of 'BRTL'
+            (bottom, right, top, left), or one of 'open' (no edges drawn),
+            'closed' (all edges drawn), 'horizontal' (bottom and top),
+            'vertical' (right and left).
+        """
+
+        # Call base
+        super().__init__(xy, width=width, height=height, fill=fill,
+                         edgecolor=edgecolor, facecolor=facecolor)
+        self.set_clip_on(False)
+        self.visible_edges = visible_edges
+
+        # Create text object
+        self._loc = loc
+        self._text = Text(x=xy[0], y=xy[1], clip_on=False,
+                          text=text, fontproperties=fontproperties,
+                          horizontalalignment=loc, verticalalignment='center')
+
+    def set_transform(self, t):
+        super().set_transform(t)
+        # the text does not get the transform!
+        self.stale = True
+
+    def set_figure(self, fig):
+        super().set_figure(fig)
+        self._text.set_figure(fig)
+
+    def get_text(self):
+        """Return the cell `.Text` instance."""
+        return self._text
+
+    def set_fontsize(self, size):
+        """Set the text fontsize."""
+        self._text.set_fontsize(size)
+        self.stale = True
+
+    def get_fontsize(self):
+        """Return the cell fontsize."""
+        return self._text.get_fontsize()
+
+    def auto_set_font_size(self, renderer):
+        """Shrink font size until the text fits into the cell width."""
+        fontsize = self.get_fontsize()
+        required = self.get_required_width(renderer)
+        while fontsize > 1 and required > self.get_width():
+            fontsize -= 1
+            self.set_fontsize(fontsize)
+            required = self.get_required_width(renderer)
+
+        return fontsize
+
+    @allow_rasterization
+    def draw(self, renderer):
+        if not self.get_visible():
+            return
+        # draw the rectangle
+        super().draw(renderer)
+        # position the text
+        self._set_text_position(renderer)
+        self._text.draw(renderer)
+        self.stale = False
+
+    def _set_text_position(self, renderer):
+        """Set text up so it is drawn in the right place."""
+        bbox = self.get_window_extent(renderer)
+        # center vertically
+        y = bbox.y0 + bbox.height / 2
+        # position horizontally
+        loc = self._text.get_horizontalalignment()
+        if loc == 'center':
+            x = bbox.x0 + bbox.width / 2
+        elif loc == 'left':
+            x = bbox.x0 + bbox.width * self.PAD
+        else:  # right.
+            x = bbox.x0 + bbox.width * (1 - self.PAD)
+        self._text.set_position((x, y))
+
+    def get_text_bounds(self, renderer):
+        """
+        Return the text bounds as *(x, y, width, height)* in table coordinates.
+        """
+        return (self._text.get_window_extent(renderer)
+                .transformed(self.get_data_transform().inverted())
+                .bounds)
+
+    def get_required_width(self, renderer):
+        """Return the minimal required width for the cell."""
+        l, b, w, h = self.get_text_bounds(renderer)
+        return w * (1.0 + (2.0 * self.PAD))
+
+    @_docstring.interpd
+    def set_text_props(self, **kwargs):
+        """
+        Update the text properties.
+
+        Valid keyword arguments are:
+
+        %(Text:kwdoc)s
+        """
+        self._text._internal_update(kwargs)
+        self.stale = True
+
+    @property
+    def visible_edges(self):
+        """
+        The cell edges to be drawn with a line.
+
+        Reading this property returns a substring of 'BRTL' (bottom, right,
+        top, left').
+
+        When setting this property, you can use a substring of 'BRTL' or one
+        of {'open', 'closed', 'horizontal', 'vertical'}.
+        """
+        return self._visible_edges
+
+    @visible_edges.setter
+    def visible_edges(self, value):
+        if value is None:
+            self._visible_edges = self._edges
+        elif value in self._edge_aliases:
+            self._visible_edges = self._edge_aliases[value]
+        else:
+            if any(edge not in self._edges for edge in value):
+                raise ValueError('Invalid edge param {}, must only be one of '
+                                 '{} or string of {}'.format(
+                                     value,
+                                     ", ".join(self._edge_aliases),
+                                     ", ".join(self._edges)))
+            self._visible_edges = value
+        self.stale = True
+
+    def get_path(self):
+        """Return a `.Path` for the `.visible_edges`."""
+        codes = [Path.MOVETO]
+        codes.extend(
+            Path.LINETO if edge in self._visible_edges else Path.MOVETO
+            for edge in self._edges)
+        if Path.MOVETO not in codes[1:]:  # All sides are visible
+            codes[-1] = Path.CLOSEPOLY
+        return Path(
+            [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]],
+            codes,
+            readonly=True
+            )
+
+
+CustomCell = Cell  # Backcompat. alias.
+
+
+class Table(Artist):
+    """
+    A table of cells.
+
+    The table consists of a grid of cells, which are indexed by (row, column).
+
+    For a simple table, you'll have a full grid of cells with indices from
+    (0, 0) to (num_rows-1, num_cols-1), in which the cell (0, 0) is positioned
+    at the top left. However, you can also add cells with negative indices.
+    You don't have to add a cell to every grid position, so you can create
+    tables that have holes.
+
+    *Note*: You'll usually not create an empty table from scratch. Instead use
+    `~matplotlib.table.table` to create a table from data.
+    """
+    codes = {'best': 0,
+             'upper right':  1,  # default
+             'upper left':   2,
+             'lower left':   3,
+             'lower right':  4,
+             'center left':  5,
+             'center right': 6,
+             'lower center': 7,
+             'upper center': 8,
+             'center':       9,
+             'top right':    10,
+             'top left':     11,
+             'bottom left':  12,
+             'bottom right': 13,
+             'right':        14,
+             'left':         15,
+             'top':          16,
+             'bottom':       17,
+             }
+    """Possible values where to place the table relative to the Axes."""
+
+    FONTSIZE = 10
+
+    AXESPAD = 0.02
+    """The border between the Axes and the table edge in Axes units."""
+
+    def __init__(self, ax, loc=None, bbox=None, **kwargs):
+        """
+        Parameters
+        ----------
+        ax : `~matplotlib.axes.Axes`
+            The `~.axes.Axes` to plot the table into.
+        loc : str, optional
+            The position of the cell with respect to *ax*. This must be one of
+            the `~.Table.codes`.
+        bbox : `.Bbox` or [xmin, ymin, width, height], optional
+            A bounding box to draw the table into. If this is not *None*, this
+            overrides *loc*.
+
+        Other Parameters
+        ----------------
+        **kwargs
+            `.Artist` properties.
+        """
+
+        super().__init__()
+
+        if isinstance(loc, str):
+            if loc not in self.codes:
+                raise ValueError(
+                    "Unrecognized location {!r}. Valid locations are\n\t{}"
+                    .format(loc, '\n\t'.join(self.codes)))
+            loc = self.codes[loc]
+        self.set_figure(ax.get_figure(root=False))
+        self._axes = ax
+        self._loc = loc
+        self._bbox = bbox
+
+        # use axes coords
+        ax._unstale_viewLim()
+        self.set_transform(ax.transAxes)
+
+        self._cells = {}
+        self._edges = None
+        self._autoColumns = []
+        self._autoFontsize = True
+        self._internal_update(kwargs)
+
+        self.set_clip_on(False)
+
+    def add_cell(self, row, col, *args, **kwargs):
+        """
+        Create a cell and add it to the table.
+
+        Parameters
+        ----------
+        row : int
+            Row index.
+        col : int
+            Column index.
+        *args, **kwargs
+            All other parameters are passed on to `Cell`.
+
+        Returns
+        -------
+        `.Cell`
+            The created cell.
+
+        """
+        xy = (0, 0)
+        cell = Cell(xy, visible_edges=self.edges, *args, **kwargs)
+        self[row, col] = cell
+        return cell
+
+    def __setitem__(self, position, cell):
+        """
+        Set a custom cell in a given position.
+        """
+        _api.check_isinstance(Cell, cell=cell)
+        try:
+            row, col = position[0], position[1]
+        except Exception as err:
+            raise KeyError('Only tuples length 2 are accepted as '
+                           'coordinates') from err
+        cell.set_figure(self.get_figure(root=False))
+        cell.set_transform(self.get_transform())
+        cell.set_clip_on(False)
+        self._cells[row, col] = cell
+        self.stale = True
+
+    def __getitem__(self, position):
+        """Retrieve a custom cell from a given position."""
+        return self._cells[position]
+
+    @property
+    def edges(self):
+        """
+        The default value of `~.Cell.visible_edges` for newly added
+        cells using `.add_cell`.
+
+        Notes
+        -----
+        This setting does currently only affect newly created cells using
+        `.add_cell`.
+
+        To change existing cells, you have to set their edges explicitly::
+
+            for c in tab.get_celld().values():
+                c.visible_edges = 'horizontal'
+
+        """
+        return self._edges
+
+    @edges.setter
+    def edges(self, value):
+        self._edges = value
+        self.stale = True
+
+    def _approx_text_height(self):
+        return (self.FONTSIZE / 72.0 * self.get_figure(root=True).dpi /
+                self._axes.bbox.height * 1.2)
+
+    @allow_rasterization
+    def draw(self, renderer):
+        # docstring inherited
+
+        # Need a renderer to do hit tests on mouseevent; assume the last one
+        # will do
+        if renderer is None:
+            renderer = self.get_figure(root=True)._get_renderer()
+        if renderer is None:
+            raise RuntimeError('No renderer defined')
+
+        if not self.get_visible():
+            return
+        renderer.open_group('table', gid=self.get_gid())
+        self._update_positions(renderer)
+
+        for key in sorted(self._cells):
+            self._cells[key].draw(renderer)
+
+        renderer.close_group('table')
+        self.stale = False
+
+    def _get_grid_bbox(self, renderer):
+        """
+        Get a bbox, in axes coordinates for the cells.
+
+        Only include those in the range (0, 0) to (maxRow, maxCol).
+        """
+        boxes = [cell.get_window_extent(renderer)
+                 for (row, col), cell in self._cells.items()
+                 if row >= 0 and col >= 0]
+        bbox = Bbox.union(boxes)
+        return bbox.transformed(self.get_transform().inverted())
+
+    def contains(self, mouseevent):
+        # docstring inherited
+        if self._different_canvas(mouseevent):
+            return False, {}
+        # TODO: Return index of the cell containing the cursor so that the user
+        # doesn't have to bind to each one individually.
+        renderer = self.get_figure(root=True)._get_renderer()
+        if renderer is not None:
+            boxes = [cell.get_window_extent(renderer)
+                     for (row, col), cell in self._cells.items()
+                     if row >= 0 and col >= 0]
+            bbox = Bbox.union(boxes)
+            return bbox.contains(mouseevent.x, mouseevent.y), {}
+        else:
+            return False, {}
+
+    def get_children(self):
+        """Return the Artists contained by the table."""
+        return list(self._cells.values())
+
+    def get_window_extent(self, renderer=None):
+        # docstring inherited
+        if renderer is None:
+            renderer = self.get_figure(root=True)._get_renderer()
+        self._update_positions(renderer)
+        boxes = [cell.get_window_extent(renderer)
+                 for cell in self._cells.values()]
+        return Bbox.union(boxes)
+
+    def _do_cell_alignment(self):
+        """
+        Calculate row heights and column widths; position cells accordingly.
+        """
+        # Calculate row/column widths
+        widths = {}
+        heights = {}
+        for (row, col), cell in self._cells.items():
+            height = heights.setdefault(row, 0.0)
+            heights[row] = max(height, cell.get_height())
+            width = widths.setdefault(col, 0.0)
+            widths[col] = max(width, cell.get_width())
+
+        # work out left position for each column
+        xpos = 0
+        lefts = {}
+        for col in sorted(widths):
+            lefts[col] = xpos
+            xpos += widths[col]
+
+        ypos = 0
+        bottoms = {}
+        for row in sorted(heights, reverse=True):
+            bottoms[row] = ypos
+            ypos += heights[row]
+
+        # set cell positions
+        for (row, col), cell in self._cells.items():
+            cell.set_x(lefts[col])
+            cell.set_y(bottoms[row])
+
+    def auto_set_column_width(self, col):
+        """
+        Automatically set the widths of given columns to optimal sizes.
+
+        Parameters
+        ----------
+        col : int or sequence of ints
+            The indices of the columns to auto-scale.
+        """
+        col1d = np.atleast_1d(col)
+        if not np.issubdtype(col1d.dtype, np.integer):
+            raise TypeError("col must be an int or sequence of ints.")
+        for cell in col1d:
+            self._autoColumns.append(cell)
+
+        self.stale = True
+
+    def _auto_set_column_width(self, col, renderer):
+        """Automatically set width for column."""
+        cells = [cell for key, cell in self._cells.items() if key[1] == col]
+        max_width = max((cell.get_required_width(renderer) for cell in cells),
+                        default=0)
+        for cell in cells:
+            cell.set_width(max_width)
+
+    def auto_set_font_size(self, value=True):
+        """Automatically set font size."""
+        self._autoFontsize = value
+        self.stale = True
+
+    def _auto_set_font_size(self, renderer):
+
+        if len(self._cells) == 0:
+            return
+        fontsize = next(iter(self._cells.values())).get_fontsize()
+        cells = []
+        for key, cell in self._cells.items():
+            # ignore auto-sized columns
+            if key[1] in self._autoColumns:
+                continue
+            size = cell.auto_set_font_size(renderer)
+            fontsize = min(fontsize, size)
+            cells.append(cell)
+
+        # now set all fontsizes equal
+        for cell in self._cells.values():
+            cell.set_fontsize(fontsize)
+
+    def scale(self, xscale, yscale):
+        """Scale column widths by *xscale* and row heights by *yscale*."""
+        for c in self._cells.values():
+            c.set_width(c.get_width() * xscale)
+            c.set_height(c.get_height() * yscale)
+
+    def set_fontsize(self, size):
+        """
+        Set the font size, in points, of the cell text.
+
+        Parameters
+        ----------
+        size : float
+
+        Notes
+        -----
+        As long as auto font size has not been disabled, the value will be
+        clipped such that the text fits horizontally into the cell.
+
+        You can disable this behavior using `.auto_set_font_size`.
+
+        >>> the_table.auto_set_font_size(False)
+        >>> the_table.set_fontsize(20)
+
+        However, there is no automatic scaling of the row height so that the
+        text may exceed the cell boundary.
+        """
+        for cell in self._cells.values():
+            cell.set_fontsize(size)
+        self.stale = True
+
+    def _offset(self, ox, oy):
+        """Move all the artists by ox, oy (axes coords)."""
+        for c in self._cells.values():
+            x, y = c.get_x(), c.get_y()
+            c.set_x(x + ox)
+            c.set_y(y + oy)
+
+    def _update_positions(self, renderer):
+        # called from renderer to allow more precise estimates of
+        # widths and heights with get_window_extent
+
+        # Do any auto width setting
+        for col in self._autoColumns:
+            self._auto_set_column_width(col, renderer)
+
+        if self._autoFontsize:
+            self._auto_set_font_size(renderer)
+
+        # Align all the cells
+        self._do_cell_alignment()
+
+        bbox = self._get_grid_bbox(renderer)
+        l, b, w, h = bbox.bounds
+
+        if self._bbox is not None:
+            # Position according to bbox
+            if isinstance(self._bbox, Bbox):
+                rl, rb, rw, rh = self._bbox.bounds
+            else:
+                rl, rb, rw, rh = self._bbox
+            self.scale(rw / w, rh / h)
+            ox = rl - l
+            oy = rb - b
+            self._do_cell_alignment()
+        else:
+            # Position using loc
+            (BEST, UR, UL, LL, LR, CL, CR, LC, UC, C,
+             TR, TL, BL, BR, R, L, T, B) = range(len(self.codes))
+            # defaults for center
+            ox = (0.5 - w / 2) - l
+            oy = (0.5 - h / 2) - b
+            if self._loc in (UL, LL, CL):   # left
+                ox = self.AXESPAD - l
+            if self._loc in (BEST, UR, LR, R, CR):  # right
+                ox = 1 - (l + w + self.AXESPAD)
+            if self._loc in (BEST, UR, UL, UC):     # upper
+                oy = 1 - (b + h + self.AXESPAD)
+            if self._loc in (LL, LR, LC):           # lower
+                oy = self.AXESPAD - b
+            if self._loc in (LC, UC, C):            # center x
+                ox = (0.5 - w / 2) - l
+            if self._loc in (CL, CR, C):            # center y
+                oy = (0.5 - h / 2) - b
+
+            if self._loc in (TL, BL, L):            # out left
+                ox = - (l + w)
+            if self._loc in (TR, BR, R):            # out right
+                ox = 1.0 - l
+            if self._loc in (TR, TL, T):            # out top
+                oy = 1.0 - b
+            if self._loc in (BL, BR, B):           # out bottom
+                oy = - (b + h)
+
+        self._offset(ox, oy)
+
+    def get_celld(self):
+        r"""
+        Return a dict of cells in the table mapping *(row, column)* to
+        `.Cell`\s.
+
+        Notes
+        -----
+        You can also directly index into the Table object to access individual
+        cells::
+
+            cell = table[row, col]
+
+        """
+        return self._cells
+
+
+@_docstring.interpd
+def table(ax,
+          cellText=None, cellColours=None,
+          cellLoc='right', colWidths=None,
+          rowLabels=None, rowColours=None, rowLoc='left',
+          colLabels=None, colColours=None, colLoc='center',
+          loc='bottom', bbox=None, edges='closed',
+          **kwargs):
+    """
+    Add a table to an `~.axes.Axes`.
+
+    At least one of *cellText* or *cellColours* must be specified. These
+    parameters must be 2D lists, in which the outer lists define the rows and
+    the inner list define the column values per row. Each row must have the
+    same number of elements.
+
+    The table can optionally have row and column headers, which are configured
+    using *rowLabels*, *rowColours*, *rowLoc* and *colLabels*, *colColours*,
+    *colLoc* respectively.
+
+    For finer grained control over tables, use the `.Table` class and add it to
+    the Axes with `.Axes.add_table`.
+
+    Parameters
+    ----------
+    cellText : 2D list of str or pandas.DataFrame, optional
+        The texts to place into the table cells.
+
+        *Note*: Line breaks in the strings are currently not accounted for and
+        will result in the text exceeding the cell boundaries.
+
+    cellColours : 2D list of :mpltype:`color`, optional
+        The background colors of the cells.
+
+    cellLoc : {'right', 'center', 'left'}
+        The alignment of the text within the cells.
+
+    colWidths : list of float, optional
+        The column widths in units of the axes. If not given, all columns will
+        have a width of *1 / ncols*.
+
+    rowLabels : list of str, optional
+        The text of the row header cells.
+
+    rowColours : list of :mpltype:`color`, optional
+        The colors of the row header cells.
+
+    rowLoc : {'left', 'center', 'right'}
+        The text alignment of the row header cells.
+
+    colLabels : list of str, optional
+        The text of the column header cells.
+
+    colColours : list of :mpltype:`color`, optional
+        The colors of the column header cells.
+
+    colLoc : {'center', 'left', 'right'}
+        The text alignment of the column header cells.
+
+    loc : str, default: 'bottom'
+        The position of the cell with respect to *ax*. This must be one of
+        the `~.Table.codes`.
+
+    bbox : `.Bbox` or [xmin, ymin, width, height], optional
+        A bounding box to draw the table into. If this is not *None*, this
+        overrides *loc*.
+
+    edges : {'closed', 'open', 'horizontal', 'vertical'} or substring of 'BRTL'
+        The cell edges to be drawn with a line. See also
+        `~.Cell.visible_edges`.
+
+    Returns
+    -------
+    `~matplotlib.table.Table`
+        The created table.
+
+    Other Parameters
+    ----------------
+    **kwargs
+        `.Table` properties.
+
+    %(Table:kwdoc)s
+    """
+
+    if cellColours is None and cellText is None:
+        raise ValueError('At least one argument from "cellColours" or '
+                         '"cellText" must be provided to create a table.')
+
+    # Check we have some cellText
+    if cellText is None:
+        # assume just colours are needed
+        rows = len(cellColours)
+        cols = len(cellColours[0])
+        cellText = [[''] * cols] * rows
+
+    # Check if we have a Pandas DataFrame
+    if _is_pandas_dataframe(cellText):
+        # if rowLabels/colLabels are empty, use DataFrame entries.
+        # Otherwise, throw an error.
+        if rowLabels is None:
+            rowLabels = cellText.index
+        else:
+            raise ValueError("rowLabels cannot be used alongside Pandas DataFrame")
+        if colLabels is None:
+            colLabels = cellText.columns
+        else:
+            raise ValueError("colLabels cannot be used alongside Pandas DataFrame")
+        # Update cellText with only values
+        cellText = cellText.values
+
+    rows = len(cellText)
+    cols = len(cellText[0])
+    for row in cellText:
+        if len(row) != cols:
+            raise ValueError(f"Each row in 'cellText' must have {cols} "
+                             "columns")
+
+    if cellColours is not None:
+        if len(cellColours) != rows:
+            raise ValueError(f"'cellColours' must have {rows} rows")
+        for row in cellColours:
+            if len(row) != cols:
+                raise ValueError("Each row in 'cellColours' must have "
+                                 f"{cols} columns")
+    else:
+        cellColours = ['w' * cols] * rows
+
+    # Set colwidths if not given
+    if colWidths is None:
+        colWidths = [1.0 / cols] * cols
+
+    # Fill in missing information for column
+    # and row labels
+    rowLabelWidth = 0
+    if rowLabels is None:
+        if rowColours is not None:
+            rowLabels = [''] * rows
+            rowLabelWidth = colWidths[0]
+    elif rowColours is None:
+        rowColours = 'w' * rows
+
+    if rowLabels is not None:
+        if len(rowLabels) != rows:
+            raise ValueError(f"'rowLabels' must be of length {rows}")
+
+    # If we have column labels, need to shift
+    # the text and colour arrays down 1 row
+    offset = 1
+    if colLabels is None:
+        if colColours is not None:
+            colLabels = [''] * cols
+        else:
+            offset = 0
+    elif colColours is None:
+        colColours = 'w' * cols
+
+    # Set up cell colours if not given
+    if cellColours is None:
+        cellColours = ['w' * cols] * rows
+
+    # Now create the table
+    table = Table(ax, loc, bbox, **kwargs)
+    table.edges = edges
+    height = table._approx_text_height()
+
+    # Add the cells
+    for row in range(rows):
+        for col in range(cols):
+            table.add_cell(row + offset, col,
+                           width=colWidths[col], height=height,
+                           text=cellText[row][col],
+                           facecolor=cellColours[row][col],
+                           loc=cellLoc)
+    # Do column labels
+    if colLabels is not None:
+        for col in range(cols):
+            table.add_cell(0, col,
+                           width=colWidths[col], height=height,
+                           text=colLabels[col], facecolor=colColours[col],
+                           loc=colLoc)
+
+    # Do row labels
+    if rowLabels is not None:
+        for row in range(rows):
+            table.add_cell(row + offset, -1,
+                           width=rowLabelWidth or 1e-15, height=height,
+                           text=rowLabels[row], facecolor=rowColours[row],
+                           loc=rowLoc)
+        if rowLabelWidth == 0:
+            table.auto_set_column_width(-1)
+
+    # set_fontsize is only effective after cells are added
+    if "fontsize" in kwargs:
+        table.set_fontsize(kwargs["fontsize"])
+
+    ax.add_table(table)
+    return table
diff --git a/shared/package-files/32/3242055be14104744f53fc54196919feb1888a429d30a0281f5eca633c3bbc8b/_sfdp_pattern_11.py b/shared/package-files/32/3242055be14104744f53fc54196919feb1888a429d30a0281f5eca633c3bbc8b/_sfdp_pattern_11.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cfb091cbf1569b21cc812edf62a8f9e60ab3fef
--- /dev/null
+++ b/shared/package-files/32/3242055be14104744f53fc54196919feb1888a429d30a0281f5eca633c3bbc8b/_sfdp_pattern_11.py
@@ -0,0 +1,212 @@
+# mypy: ignore-errors
+
+# noqa: F401, E501
+# This is an auto-generated file. Please do not modify it by hand.
+# To re-generate, run:
+# cd ~/pytorch && python
+# torchgen/fuse_attention_patterns/gen_attention_patterns.py
+
+import torch
+import torch._inductor
+
+aten = torch.ops.aten
+prims = torch.ops.prims
+
+from torch._inductor.pattern_matcher import (
+   Arg,
+   CallFunction,
+   CallFunctionVarArgs,
+   CallMethod,
+   CallMethodVarArgs,
+   CallModule,
+   CallModuleVarArgs,
+   ExclusiveKeywordArg,
+   Ignored,
+   KeywordArg,
+   ListOf,
+   MultiOutputPattern,
+   PatternExpr,
+   RepeatedExpr,
+   _TargetArgsExpr,
+   _TargetExpr,
+   _TargetExprVarArgs,
+)
+permute_default = CallFunction(aten.permute.default, KeywordArg('query'), Ignored())
+expand_default = CallFunction(aten.expand.default, permute_default, Ignored())
+clone_default = CallFunction(aten.clone.default, expand_default, memory_format=torch.contiguous_format)
+view_default = CallFunction(aten.view.default, clone_default, Ignored(), _users=2)
+permute_default_1 = CallFunction(aten.permute.default, KeywordArg('key'), Ignored())
+permute_default_2 = CallFunction(aten.permute.default, permute_default_1, Ignored())
+expand_default_1 = CallFunction(aten.expand.default, permute_default_2, Ignored())
+clone_default_1 = CallFunction(aten.clone.default, expand_default_1, memory_format=torch.contiguous_format)
+view_default_1 = CallFunction(aten.view.default, clone_default_1, Ignored(), _users=2)
+bmm_default = CallFunction(aten.bmm.default, view_default, view_default_1)
+view_default_2 = CallFunction(aten.view.default, bmm_default, Ignored())
+div_Tensor = CallFunction(aten.div.Tensor, view_default_2, KeywordArg('inv_scale'), _users=2)
+amax_default = CallFunction(aten.amax.default, div_Tensor, Ignored(), True)
+sub_Tensor = CallFunction(aten.sub.Tensor, div_Tensor, amax_default)
+exp_default = CallFunction(aten.exp.default, sub_Tensor, _users=2)
+sum_dim_IntList = CallFunction(aten.sum.dim_IntList, exp_default, Ignored(), True)
+div_Tensor_1 = CallFunction(aten.div.Tensor, exp_default, sum_dim_IntList, _users=2)
+expand_default_2 = CallFunction(aten.expand.default, div_Tensor_1, Ignored())
+view_default_3 = CallFunction(aten.view.default, expand_default_2, Ignored(), _users=2)
+permute_default_3 = CallFunction(aten.permute.default, KeywordArg('value'), Ignored())
+expand_default_3 = CallFunction(aten.expand.default, permute_default_3, Ignored())
+clone_default_2 = CallFunction(aten.clone.default, expand_default_3, memory_format=torch.contiguous_format)
+view_default_4 = CallFunction(aten.view.default, clone_default_2, Ignored(), _users=2)
+bmm_default_1 = CallFunction(aten.bmm.default, view_default_3, view_default_4)
+view_default_5 = CallFunction(aten.view.default, bmm_default_1, Ignored())
+view_default_6 = CallFunction(aten.view.default, KeywordArg('tangents_1'), Ignored(), _users=2)
+permute_default_4 = CallFunction(aten.permute.default, view_default_4, Ignored())
+bmm_default_2 = CallFunction(aten.bmm.default, view_default_6, permute_default_4)
+view_default_7 = CallFunction(aten.view.default, bmm_default_2, Ignored())
+alias_default = CallFunction(aten.alias.default, div_Tensor_1)
+alias_default_1 = CallFunction(aten.alias.default, alias_default)
+alias_default_2 = CallFunction(aten.alias.default, alias_default_1)
+alias_default_3 = CallFunction(aten.alias.default, alias_default_2, _users=2)
+mul_Tensor = CallFunction(aten.mul.Tensor, view_default_7, alias_default_3, _users=2)
+sum_dim_IntList_1 = CallFunction(aten.sum.dim_IntList, mul_Tensor, Ignored(), True)
+mul_Tensor_1 = CallFunction(aten.mul.Tensor, alias_default_3, sum_dim_IntList_1)
+sub_Tensor_1 = CallFunction(aten.sub.Tensor, mul_Tensor, mul_Tensor_1)
+div_Tensor_2 = CallFunction(aten.div.Tensor, sub_Tensor_1, KeywordArg('inv_scale'))
+view_default_8 = CallFunction(aten.view.default, div_Tensor_2, Ignored(), _users=2)
+permute_default_5 = CallFunction(aten.permute.default, view_default_1, Ignored())
+bmm_default_3 = CallFunction(aten.bmm.default, view_default_8, permute_default_5)
+view_default_9 = CallFunction(aten.view.default, bmm_default_3, Ignored())
+permute_default_6 = CallFunction(aten.permute.default, view_default_9, Ignored())
+permute_default_7 = CallFunction(aten.permute.default, view_default, Ignored())
+bmm_default_4 = CallFunction(aten.bmm.default, permute_default_7, view_default_8)
+view_default_10 = CallFunction(aten.view.default, bmm_default_4, Ignored())
+permute_default_8 = CallFunction(aten.permute.default, view_default_10, Ignored())
+permute_default_9 = CallFunction(aten.permute.default, permute_default_8, Ignored())
+permute_default_10 = CallFunction(aten.permute.default, view_default_3, Ignored())
+bmm_default_5 = CallFunction(aten.bmm.default, permute_default_10, view_default_6)
+view_default_11 = CallFunction(aten.view.default, bmm_default_5, Ignored())
+permute_default_11 = CallFunction(aten.permute.default, view_default_11, Ignored())
+_sfdp_pattern_11_training = MultiOutputPattern([view_default_5,
+  permute_default_6,
+  permute_default_9,
+  permute_default_11,
+  None
+])
+
+
+permute_default = CallFunction(aten.permute.default, KeywordArg('query'), Ignored())
+expand_default = CallFunction(aten.expand.default, permute_default, Ignored())
+clone_default = CallFunction(aten.clone.default, expand_default, memory_format=torch.contiguous_format)
+view_default = CallFunction(aten.view.default, clone_default, Ignored())
+permute_default_1 = CallFunction(aten.permute.default, KeywordArg('key'), Ignored())
+permute_default_2 = CallFunction(aten.permute.default, permute_default_1, Ignored())
+expand_default_1 = CallFunction(aten.expand.default, permute_default_2, Ignored())
+clone_default_1 = CallFunction(aten.clone.default, expand_default_1, memory_format=torch.contiguous_format)
+view_default_1 = CallFunction(aten.view.default, clone_default_1, Ignored())
+bmm_default = CallFunction(aten.bmm.default, view_default, view_default_1)
+view_default_2 = CallFunction(aten.view.default, bmm_default, Ignored())
+div_Tensor = CallFunction(aten.div.Tensor, view_default_2, KeywordArg('inv_scale'), _users=2)
+amax_default = CallFunction(aten.amax.default, div_Tensor, Ignored(), True)
+sub_Tensor = CallFunction(aten.sub.Tensor, div_Tensor, amax_default)
+exp_default = CallFunction(aten.exp.default, sub_Tensor, _users=2)
+sum_dim_IntList = CallFunction(aten.sum.dim_IntList, exp_default, Ignored(), True)
+div_Tensor_1 = CallFunction(aten.div.Tensor, exp_default, sum_dim_IntList)
+expand_default_2 = CallFunction(aten.expand.default, div_Tensor_1, Ignored())
+view_default_3 = CallFunction(aten.view.default, expand_default_2, Ignored())
+permute_default_3 = CallFunction(aten.permute.default, KeywordArg('value'), Ignored())
+expand_default_3 = CallFunction(aten.expand.default, permute_default_3, Ignored())
+clone_default_2 = CallFunction(aten.clone.default, expand_default_3, memory_format=torch.contiguous_format)
+view_default_4 = CallFunction(aten.view.default, clone_default_2, Ignored())
+bmm_default_1 = CallFunction(aten.bmm.default, view_default_3, view_default_4)
+_sfdp_pattern_11_inference = CallFunction(aten.view.default, bmm_default_1, Ignored())
+
+
+permute_default = CallFunction(aten.permute.default, KeywordArg('query'), Ignored())
+expand_default = CallFunction(aten.expand.default, permute_default, Ignored())
+clone_default = CallFunction(aten.clone.default, expand_default, memory_format=torch.contiguous_format)
+view_default = CallFunction(aten.view.default, clone_default, Ignored(), _users=2)
+permute_default_1 = CallFunction(aten.permute.default, KeywordArg('key'), Ignored())
+permute_default_2 = CallFunction(aten.permute.default, permute_default_1, Ignored())
+expand_default_1 = CallFunction(aten.expand.default, permute_default_2, Ignored())
+clone_default_1 = CallFunction(aten.clone.default, expand_default_1, memory_format=torch.contiguous_format)
+view_default_1 = CallFunction(aten.view.default, clone_default_1, Ignored(), _users=2)
+bmm_default = CallFunction(aten.bmm.default, view_default, view_default_1)
+view_default_2 = CallFunction(aten.view.default, bmm_default, Ignored())
+div_Tensor = CallFunction(aten.div.Tensor, view_default_2, KeywordArg('inv_scale'))
+convert_element_type_default = CallFunction(prims.convert_element_type.default, div_Tensor, Ignored(), _users=2)
+amax_default = CallFunction(aten.amax.default, convert_element_type_default, Ignored(), True)
+sub_Tensor = CallFunction(aten.sub.Tensor, convert_element_type_default, amax_default)
+exp_default = CallFunction(aten.exp.default, sub_Tensor, _users=2)
+sum_dim_IntList = CallFunction(aten.sum.dim_IntList, exp_default, Ignored(), True)
+div_Tensor_1 = CallFunction(aten.div.Tensor, exp_default, sum_dim_IntList)
+convert_element_type_default_1 = CallFunction(prims.convert_element_type.default, div_Tensor_1, Ignored(), _users=2)
+expand_default_2 = CallFunction(aten.expand.default, convert_element_type_default_1, Ignored())
+view_default_3 = CallFunction(aten.view.default, expand_default_2, Ignored(), _users=2)
+permute_default_3 = CallFunction(aten.permute.default, KeywordArg('value'), Ignored())
+expand_default_3 = CallFunction(aten.expand.default, permute_default_3, Ignored())
+clone_default_2 = CallFunction(aten.clone.default, expand_default_3, memory_format=torch.contiguous_format)
+view_default_4 = CallFunction(aten.view.default, clone_default_2, Ignored(), _users=2)
+bmm_default_1 = CallFunction(aten.bmm.default, view_default_3, view_default_4)
+view_default_5 = CallFunction(aten.view.default, bmm_default_1, Ignored())
+view_default_6 = CallFunction(aten.view.default, KeywordArg('tangents_1'), Ignored(), _users=2)
+permute_default_4 = CallFunction(aten.permute.default, view_default_4, Ignored())
+bmm_default_2 = CallFunction(aten.bmm.default, view_default_6, permute_default_4)
+view_default_7 = CallFunction(aten.view.default, bmm_default_2, Ignored())
+convert_element_type_default_2 = CallFunction(prims.convert_element_type.default, view_default_7, Ignored())
+alias_default = CallFunction(aten.alias.default, convert_element_type_default_1)
+alias_default_1 = CallFunction(aten.alias.default, alias_default)
+alias_default_2 = CallFunction(aten.alias.default, alias_default_1)
+alias_default_3 = CallFunction(aten.alias.default, alias_default_2)
+convert_element_type_default_3 = CallFunction(prims.convert_element_type.default, alias_default_3, Ignored(), _users=2)
+mul_Tensor = CallFunction(aten.mul.Tensor, convert_element_type_default_2, convert_element_type_default_3, _users=2)
+sum_dim_IntList_1 = CallFunction(aten.sum.dim_IntList, mul_Tensor, Ignored(), True)
+mul_Tensor_1 = CallFunction(aten.mul.Tensor, convert_element_type_default_3, sum_dim_IntList_1)
+sub_Tensor_1 = CallFunction(aten.sub.Tensor, mul_Tensor, mul_Tensor_1)
+convert_element_type_default_4 = CallFunction(prims.convert_element_type.default, sub_Tensor_1, Ignored())
+div_Tensor_2 = CallFunction(aten.div.Tensor, convert_element_type_default_4, KeywordArg('inv_scale'))
+view_default_8 = CallFunction(aten.view.default, div_Tensor_2, Ignored(), _users=2)
+permute_default_5 = CallFunction(aten.permute.default, view_default_1, Ignored())
+bmm_default_3 = CallFunction(aten.bmm.default, view_default_8, permute_default_5)
+view_default_9 = CallFunction(aten.view.default, bmm_default_3, Ignored())
+permute_default_6 = CallFunction(aten.permute.default, view_default_9, Ignored())
+permute_default_7 = CallFunction(aten.permute.default, view_default, Ignored())
+bmm_default_4 = CallFunction(aten.bmm.default, permute_default_7, view_default_8)
+view_default_10 = CallFunction(aten.view.default, bmm_default_4, Ignored())
+permute_default_8 = CallFunction(aten.permute.default, view_default_10, Ignored())
+permute_default_9 = CallFunction(aten.permute.default, permute_default_8, Ignored())
+permute_default_10 = CallFunction(aten.permute.default, view_default_3, Ignored())
+bmm_default_5 = CallFunction(aten.bmm.default, permute_default_10, view_default_6)
+view_default_11 = CallFunction(aten.view.default, bmm_default_5, Ignored())
+permute_default_11 = CallFunction(aten.permute.default, view_default_11, Ignored())
+_sfdp_pattern_11_half_training = MultiOutputPattern([view_default_5,
+  permute_default_6,
+  permute_default_9,
+  permute_default_11,
+  None
+])
+
+
+permute_default = CallFunction(aten.permute.default, KeywordArg('query'), Ignored())
+expand_default = CallFunction(aten.expand.default, permute_default, Ignored())
+clone_default = CallFunction(aten.clone.default, expand_default, memory_format=torch.contiguous_format)
+view_default = CallFunction(aten.view.default, clone_default, Ignored())
+permute_default_1 = CallFunction(aten.permute.default, KeywordArg('key'), Ignored())
+permute_default_2 = CallFunction(aten.permute.default, permute_default_1, Ignored())
+expand_default_1 = CallFunction(aten.expand.default, permute_default_2, Ignored())
+clone_default_1 = CallFunction(aten.clone.default, expand_default_1, memory_format=torch.contiguous_format)
+view_default_1 = CallFunction(aten.view.default, clone_default_1, Ignored())
+bmm_default = CallFunction(aten.bmm.default, view_default, view_default_1)
+view_default_2 = CallFunction(aten.view.default, bmm_default, Ignored())
+div_Tensor = CallFunction(aten.div.Tensor, view_default_2, KeywordArg('inv_scale'))
+convert_element_type_default = CallFunction(prims.convert_element_type.default, div_Tensor, Ignored(), _users=2)
+amax_default = CallFunction(aten.amax.default, convert_element_type_default, Ignored(), True)
+sub_Tensor = CallFunction(aten.sub.Tensor, convert_element_type_default, amax_default)
+exp_default = CallFunction(aten.exp.default, sub_Tensor, _users=2)
+sum_dim_IntList = CallFunction(aten.sum.dim_IntList, exp_default, Ignored(), True)
+div_Tensor_1 = CallFunction(aten.div.Tensor, exp_default, sum_dim_IntList)
+convert_element_type_default_1 = CallFunction(prims.convert_element_type.default, div_Tensor_1, Ignored())
+expand_default_2 = CallFunction(aten.expand.default, convert_element_type_default_1, Ignored())
+view_default_3 = CallFunction(aten.view.default, expand_default_2, Ignored())
+permute_default_3 = CallFunction(aten.permute.default, KeywordArg('value'), Ignored())
+expand_default_3 = CallFunction(aten.expand.default, permute_default_3, Ignored())
+clone_default_2 = CallFunction(aten.clone.default, expand_default_3, memory_format=torch.contiguous_format)
+view_default_4 = CallFunction(aten.view.default, clone_default_2, Ignored())
+bmm_default_1 = CallFunction(aten.bmm.default, view_default_3, view_default_4)
+_sfdp_pattern_11_half_inference = CallFunction(aten.view.default, bmm_default_1, Ignored())
diff --git a/shared/package-files/32/32459867484ab8dcaea50f07e42cfc3294f69546165ab418bf0c7b48b90edb71/test_resistant_linear_model.py b/shared/package-files/32/32459867484ab8dcaea50f07e42cfc3294f69546165ab418bf0c7b48b90edb71/test_resistant_linear_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..38dafef8bc64a3917b0d5630d9a8cee439e8f9ff
--- /dev/null
+++ b/shared/package-files/32/32459867484ab8dcaea50f07e42cfc3294f69546165ab418bf0c7b48b90edb71/test_resistant_linear_model.py
@@ -0,0 +1,221 @@
+"""
+Tests for RLMDetS and RLMDetSMM (statsmodels.robust.resistant_linear_model).
+
+Neither estimator has a closed-form solution or a reference implementation
+in another package, so correctness is checked two independent ways:
+
+* the M-scale defining equation ``mean(rho(resid / raw_scale)) ==
+  scale_bias`` must hold at the returned ``(params, scale)`` -- this is
+  the algebraic condition an S-estimator solution must satisfy by
+  definition, derived directly from ``RLMDetS.__init__`` and
+  ``RLM._estimate_scale``, independent of how ``fit`` gets there;
+* under gross contamination, both estimators must recover the true
+  regression coefficients far better than OLS, which is the entire
+  reason to use them.
+"""
+from pathlib import Path
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+
+import statsmodels.api as sm
+from statsmodels.robust.resistant_linear_model import (
+    DetSStartResult,
+    RLMDetS,
+    RLMDetSMM,
+)
+
+cur_dir = Path(__file__).parent.resolve()
+
+
+def _moment_condition(mod, res, rtol=1e-4):
+    """(params, scale) satisfy the S-estimator's M-scale equation.
+
+    Only valid where params and scale were determined jointly by a single
+    S-type fixed point using ``mod.norm``/``mod.mscale`` -- i.e. for
+    ``RLMDetS.fit``, not for ``RLMDetSMM.fit``'s second (M) stage, which
+    reweights with a different norm and, in the fixed-scale branch, does
+    not re-solve the scale equation at all.
+
+    ``res.scale`` carries RLM's extra ``sqrt(nobs / df_resid)`` factor
+    (see ``RLM._estimate_scale``), so back it out before checking against
+    ``mscale.scale_bias``.
+    """
+    endog, exog = mod.endog, mod.exog
+    resid = endog - exog @ res.params
+    df_resid = exog.shape[0] - exog.shape[1]
+    raw_scale = res.scale * np.sqrt(df_resid / exog.shape[0])
+    lhs = np.mean(mod.norm.rho(resid / raw_scale))
+    assert_allclose(lhs, mod.mscale.scale_bias, rtol=rtol)
+
+
+def _normal_equations(mod, res, norm, atol=1e-4):
+    """The weighted normal equations X' W(resid/scale) resid == 0 hold.
+
+    This is the first-order condition any converged (weighted) IRLS fit
+    satisfies for the norm that produced its final weights, regardless of
+    how the scale itself was obtained -- so unlike ``_moment_condition``
+    it applies uniformly to every RLMDetS/RLMDetSMM code path.
+    """
+    endog, exog = mod.endog, mod.exog
+    resid = endog - exog @ res.params
+    weights = norm.weights(resid / res.scale)
+    grad = exog.T @ (weights * resid)
+    assert_allclose(grad, 0, atol=atol)
+
+
+def _stackloss():
+    from statsmodels.datasets.stackloss import load
+    data = load()
+    endog = np.asarray(data.endog)
+    exog = sm.add_constant(np.asarray(data.exog))
+    return endog, exog
+
+
+def _contaminated(n=200, k=3, frac=0.2, seed=12345):
+    rng = np.random.default_rng(seed)
+    exog = sm.add_constant(rng.standard_normal((n, k)))
+    beta = np.array([1.0, 2.0, -1.5, 0.5])
+    endog = exog @ beta + rng.standard_normal(n) * 0.5
+    n_out = int(frac * n)
+    idx = rng.choice(n, n_out, replace=False)
+    endog[idx] += rng.choice([-1, 1], n_out) * rng.uniform(15, 25, n_out)
+    return endog, exog, beta
+
+
+class TestRLMDetS:
+
+    def test_fit_moment_condition_and_start_results(self):
+        endog, exog = _stackloss()
+        mod = RLMDetS(endog, exog, breakdown_point=0.5)
+        res = mod.fit(h=14)
+        _moment_condition(mod, res)
+
+        assert res.model_dets is mod
+        results_iter = res._results.results_iter
+        assert len(results_iter) > 0
+        for entry in results_iter.values():
+            assert isinstance(entry, DetSStartResult)
+            assert entry.scale > 0
+            assert entry.params.shape == (exog.shape[1],)
+
+    def test_fit_beats_ols_under_contamination(self):
+        endog, exog, beta = _contaminated()
+        mod = RLMDetS(endog, exog)
+        res = mod.fit(h=120)
+        ols = sm.OLS(endog, exog).fit()
+
+        err_dets = np.linalg.norm(res.params - beta)
+        err_ols = np.linalg.norm(ols.params - beta)
+        assert err_dets < 0.3
+        assert err_dets < 0.3 * err_ols
+        _moment_condition(mod, res)
+        _normal_equations(mod, res, mod.norm)
+
+    def test_start_params_extra(self):
+        endog, exog = _stackloss()
+        mod = RLMDetS(endog, exog)
+        ols_params = sm.OLS(endog, exog).fit().params
+        res = mod.fit(h=14, start_params_extra=[np.asarray(ols_params)])
+        _moment_condition(mod, res)
+
+    def test_univariate_quantile_start(self):
+        # exog is constant-only, so data_start is empty and
+        # _get_start_params falls back to endog quantiles.
+        endog, _ = _stackloss()
+        exog = np.ones((len(endog), 1))
+        mod = RLMDetS(endog, exog)
+
+        starts = mod._get_start_params(h=14)
+        assert len(starts) == 3
+        assert_allclose(sorted(s[0] for s in starts),
+                        np.quantile(endog, [0.25, 0.5, 0.75]))
+
+        res = mod.fit(h=14)
+        _moment_condition(mod, res)
+
+    def test_include_endog_and_col_indices(self):
+        endog, exog = _stackloss()
+        n_extra = exog.shape[1] - 1
+
+        mod = RLMDetS(endog, exog, include_endog=True)
+        assert mod.data_start.shape == (len(endog), n_extra + 1)
+        res = mod.fit(h=14)
+        _moment_condition(mod, res)
+
+        mod2 = RLMDetS(endog, exog, col_indices=[1, 2])
+        assert mod2.data_start.shape == (len(endog), 2)
+        res2 = mod2.fit(h=14)
+        _moment_condition(mod2, res2)
+
+
+class TestRLMDetSMM:
+
+    def test_fit_default_h(self):
+        # Regression test: h=None previously reached np.argpartition(d,
+        # None) inside _get_detcov_startidx and raised a bare
+        # "TypeError: Partition index must be integer" -- fit()'s
+        # documented default was unreachable.
+        endog, exog = _stackloss()
+        mod = RLMDetSMM(endog, exog)
+        res = mod.fit()
+        assert np.isfinite(res.scale)
+        assert res.scale > 0
+        assert res._results.results_dets is not None
+        _normal_equations(mod, res, mod.norm_mean)
+
+    def test_fit_beats_ols_under_contamination(self):
+        endog, exog, beta = _contaminated()
+        mod = RLMDetSMM(endog, exog, efficiency=0.95)
+        res = mod.fit(h=120)
+        ols = sm.OLS(endog, exog).fit()
+
+        err_detsmm = np.linalg.norm(res.params - beta)
+        err_ols = np.linalg.norm(ols.params - beta)
+        assert err_detsmm < 0.3
+        assert err_detsmm < 0.3 * err_ols
+        _normal_equations(mod, res, mod.norm_mean)
+
+    def test_efficiency_close_to_ols_on_clean_data(self):
+        rng = np.random.default_rng(999)
+        n, k = 300, 3
+        exog = sm.add_constant(rng.standard_normal((n, k)))
+        beta = np.array([1.0, 2.0, -1.5, 0.5])
+        endog = exog @ beta + rng.standard_normal(n) * 0.5
+
+        mod = RLMDetSMM(endog, exog, efficiency=0.95)
+        res = mod.fit(h=200)
+        ols = sm.OLS(endog, exog).fit()
+        assert_allclose(res.params, ols.params, atol=0.25)
+        _normal_equations(mod, res, mod.norm_mean)
+
+    def test_scale_binding(self):
+        endog, exog = _stackloss()
+        mod = RLMDetSMM(endog, exog)
+        res = mod.fit(h=14, scale_binding=True)
+        assert np.isfinite(res.scale)
+        assert res.scale > 0
+        assert res._results.results_dets is not None
+        _normal_equations(mod, res, mod.norm_mean)
+
+    def test_start_tuple_skips_s_stage(self):
+        endog, exog = _stackloss()
+        res_s = RLMDetS(endog, exog).fit(h=14)
+
+        mod = RLMDetSMM(endog, exog)
+        res = mod.fit(start=(np.asarray(res_s.params), res_s.scale))
+        assert res._results.results_dets is None
+        assert np.isfinite(res.scale)
+        assert res.scale > 0
+        _normal_equations(mod, res, mod.norm_mean)
+
+    @pytest.mark.parametrize("breakdown_point", [0.25, 0.5])
+    def test_breakdown_point_passthrough(self, breakdown_point):
+        endog, exog = _stackloss()
+        mod = RLMDetSMM(endog, exog, breakdown_point=breakdown_point)
+        assert mod.breakdown_point == breakdown_point
+        res = mod.fit(h=14)
+        assert np.isfinite(res.scale)
+        assert res.scale > 0
+        _normal_equations(mod, res, mod.norm_mean)
diff --git a/shared/package-files/32/3245c26feced6b48a616cfeb03e0b35565dadebaa1e1c679abc8d9ae2b88c603/Copy.h b/shared/package-files/32/3245c26feced6b48a616cfeb03e0b35565dadebaa1e1c679abc8d9ae2b88c603/Copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..5639567d666686dd81ca5b4b032fb44f039eb782
--- /dev/null
+++ b/shared/package-files/32/3245c26feced6b48a616cfeb03e0b35565dadebaa1e1c679abc8d9ae2b88c603/Copy.h
@@ -0,0 +1,10 @@
+#pragma once
+
+namespace at {
+struct TensorIteratorBase;
+
+namespace native {
+
+void direct_copy_kernel_cuda(TensorIteratorBase &iter);
+
+}}  // namespace at::native
diff --git a/shared/package-files/32/32480f004cc641df91ab4c343d95d25f62da7515a150409c8ac258f254ab9b84/__init__.py b/shared/package-files/32/32480f004cc641df91ab4c343d95d25f62da7515a150409c8ac258f254ab9b84/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c5661e93a205bf4fb22404d4fc50f902cc31369
--- /dev/null
+++ b/shared/package-files/32/32480f004cc641df91ab4c343d95d25f62da7515a150409c8ac258f254ab9b84/__init__.py
@@ -0,0 +1 @@
+# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
diff --git a/shared/package-files/32/324abef1dbe9719d97854e4656decbd489269e3254532dce331ee11ca8d04813/slice_scatter.h b/shared/package-files/32/324abef1dbe9719d97854e4656decbd489269e3254532dce331ee11ca8d04813/slice_scatter.h
new file mode 100644
index 0000000000000000000000000000000000000000..08e58a347e86f6a50c79b79b3322d29b082fa9c9
--- /dev/null
+++ b/shared/package-files/32/324abef1dbe9719d97854e4656decbd489269e3254532dce331ee11ca8d04813/slice_scatter.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/slice_scatter_ops.h>
+
+namespace at {
+
+
+// aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
+inline at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<int64_t> start=c10::nullopt, c10::optional<int64_t> end=c10::nullopt, int64_t step=1) {
+    return at::_ops::slice_scatter::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<int64_t> start=c10::nullopt, c10::optional<int64_t> end=c10::nullopt, int64_t step=1) {
+    return at::_ops::slice_scatter::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step);
+  }
+}
+
+// aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
+inline at::Tensor slice_scatter_symint(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<c10::SymInt> start=c10::nullopt, c10::optional<c10::SymInt> end=c10::nullopt, c10::SymInt step=1) {
+    return at::_ops::slice_scatter::call(self, src, dim, start, end, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<c10::SymInt> start=c10::nullopt, c10::optional<c10::SymInt> end=c10::nullopt, c10::SymInt step=1) {
+    return at::_ops::slice_scatter::call(self, src, dim, start, end, step);
+  }
+}
+
+// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<int64_t> start=c10::nullopt, c10::optional<int64_t> end=c10::nullopt, int64_t step=1) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<int64_t> start=c10::nullopt, c10::optional<int64_t> end=c10::nullopt, int64_t step=1) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out);
+  }
+}
+
+// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<int64_t> start, c10::optional<int64_t> end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<int64_t> start, c10::optional<int64_t> end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out);
+  }
+}
+
+// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_scatter_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<c10::SymInt> start=c10::nullopt, c10::optional<c10::SymInt> end=c10::nullopt, c10::SymInt step=1) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional<c10::SymInt> start=c10::nullopt, c10::optional<c10::SymInt> end=c10::nullopt, c10::SymInt step=1) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
+  }
+}
+
+// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_scatter_symint_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
+  }
+}
+
+}
diff --git a/shared/package-files/32/324b38c7e4a3f573236c5fdddcc8b3939e3e79838b994352cb32df6c8950a8c9/test_exceptions.py b/shared/package-files/32/324b38c7e4a3f573236c5fdddcc8b3939e3e79838b994352cb32df6c8950a8c9/test_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..76da4ee5236e935303a92bc74e746613bb8cffe5
--- /dev/null
+++ b/shared/package-files/32/324b38c7e4a3f573236c5fdddcc8b3939e3e79838b994352cb32df6c8950a8c9/test_exceptions.py
@@ -0,0 +1,528 @@
+import numpy as np
+import sys
+import traceback
+
+from numba import jit, njit
+from numba.core import types, errors, utils
+from numba.tests.support import (TestCase, expected_failure_py311,
+                                 expected_failure_py312,
+                                 expected_failure_py313,
+                                 expected_failure_py314,
+                                 expected_failure_py315,
+                                 )
+import unittest
+
+
+force_pyobj_flags = {'nopython': False, 'forceobj': True}
+no_pyobj_flags = {'nopython': True, '_nrt': False}
+no_pyobj_flags_w_nrt = {'nopython': True, '_nrt': True}
+no_gil_flags = {'nopython': True, 'nogil': True, '_nrt': True}
+
+
+class MyError(Exception):
+    pass
+
+
+class OtherError(Exception):
+    pass
+
+
+class UDEArgsToSuper(Exception):
+    def __init__(self, arg, value0):
+        super(UDEArgsToSuper, self).__init__(arg)
+        self.value0 = value0
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return False
+        same = True
+        same |= self.args == other.args
+        same |= self.value0 == other.value0
+        return same
+
+    def __hash__(self):
+        return hash((super(UDEArgsToSuper).__hash__(), self.value0))
+
+
+class UDENoArgSuper(Exception):
+    def __init__(self, arg, value0):
+        super(UDENoArgSuper, self).__init__()
+        self.deferarg = arg
+        self.value0 = value0
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return False
+        same = True
+        same |= self.args == other.args
+        same |= self.deferarg == other.deferarg
+        same |= self.value0 == other.value0
+        return same
+
+    def __hash__(self):
+        return hash((super(UDENoArgSuper).__hash__(), self.deferarg,
+                     self.value0))
+
+
+def raise_class(exc):
+    def raiser(i):
+        if i == 1:
+            raise exc
+        elif i == 2:
+            raise ValueError
+        elif i == 3:
+            # The exception type is looked up on a module (issue #1624)
+            raise np.linalg.LinAlgError
+        return i
+    return raiser
+
+
+def raise_instance(exc, arg):
+    def raiser(i):
+        if i == 1:
+            raise exc(arg, 1)
+        elif i == 2:
+            raise ValueError(arg, 2)
+        elif i == 3:
+            raise np.linalg.LinAlgError(arg, 3)
+        return i
+    return raiser
+
+
+def raise_instance_runtime_args(exc):
+    def raiser(i, arg):
+        if i == 1:
+            raise exc(arg, 1)
+        elif i == 2:
+            raise ValueError(arg, 2)
+        elif i == 3:
+            raise np.linalg.LinAlgError(arg, 3)
+        return i
+    return raiser
+
+
+def reraise():
+    raise
+
+
+def outer_function(inner):
+    def outer(i):
+        if i == 3:
+            raise OtherError("bar", 3)
+        return inner(i)
+    return outer
+
+
+def assert_usecase(i):
+    assert i == 1, "bar"
+
+
+def ude_bug_usecase():
+    raise UDEArgsToSuper()  # oops user forgot args to exception ctor
+
+
+def raise_runtime_value(arg):
+    raise ValueError(arg)
+
+
+class TestRaising(TestCase):
+
+    def test_unituple_index_error(self):
+        def pyfunc(a, i):
+            return a.shape[i]
+
+        cfunc = njit((types.Array(types.int32, 1, 'A'), types.int32),)(pyfunc)
+
+        a = np.empty(2, dtype=np.int32)
+
+        self.assertEqual(cfunc(a, 0), pyfunc(a, 0))
+
+        with self.assertRaises(IndexError) as cm:
+            cfunc(a, 2)
+        self.assertEqual(str(cm.exception), "tuple index out of range")
+
+    def check_against_python(self, exec_mode, pyfunc, cfunc,
+                             expected_error_class, *args):
+
+        assert exec_mode in (force_pyobj_flags, no_pyobj_flags,
+                             no_pyobj_flags_w_nrt, no_gil_flags)
+
+        # invariant of mode, check the error class and args are the same
+        with self.assertRaises(expected_error_class) as pyerr:
+            pyfunc(*args)
+        with self.assertRaises(expected_error_class) as jiterr:
+            cfunc(*args)
+        self.assertEqual(pyerr.exception.args, jiterr.exception.args)
+
+        # special equality check for UDEs
+        if isinstance(pyerr.exception, (UDEArgsToSuper, UDENoArgSuper)):
+            self.assertTrue(pyerr.exception == jiterr.exception)
+
+        # in npm check bottom of traceback matches as frame injection with
+        # location info should ensure this
+        if exec_mode is no_pyobj_flags:
+
+            # we only care about the bottom two frames, the error and the
+            # location it was raised.
+            try:
+                pyfunc(*args)
+            except Exception:
+                py_frames = traceback.format_exception(*sys.exc_info())
+                expected_frames = py_frames[-2:]
+
+            try:
+                cfunc(*args)
+            except Exception:
+                c_frames = traceback.format_exception(*sys.exc_info())
+                got_frames = c_frames[-2:]
+
+            # check exception and the injected frame are the same
+            for expf, gotf in zip(expected_frames, got_frames):
+                # Note use of assertIn not assertEqual, Py 3.11 has markers (^)
+                # that point to the variable causing the problem, Numba doesn't
+                # do this so only the start of the string will match.
+                self.assertIn(gotf, expf)
+
+    def check_raise_class(self, flags):
+        pyfunc = raise_class(MyError)
+        cfunc = jit((types.int32,), **flags)(pyfunc)
+        self.assertEqual(cfunc(0), 0)
+        self.check_against_python(flags, pyfunc, cfunc, MyError, 1)
+        self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+        self.check_against_python(flags, pyfunc, cfunc,
+                                  np.linalg.LinAlgError, 3)
+
+    def test_raise_class_nopython(self):
+        self.check_raise_class(flags=no_pyobj_flags)
+
+    def test_raise_class_objmode(self):
+        self.check_raise_class(flags=force_pyobj_flags)
+
+    def check_raise_instance(self, flags):
+        for clazz in [MyError, UDEArgsToSuper,
+                      UDENoArgSuper]:
+            pyfunc = raise_instance(clazz, "some message")
+            cfunc = jit((types.int32,), **flags)(pyfunc)
+
+            self.assertEqual(cfunc(0), 0)
+            self.check_against_python(flags, pyfunc, cfunc, clazz, 1)
+            self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+            self.check_against_python(flags, pyfunc, cfunc,
+                                      np.linalg.LinAlgError, 3)
+
+    def test_raise_instance_objmode(self):
+        self.check_raise_instance(flags=force_pyobj_flags)
+
+    def test_raise_instance_nopython(self):
+        self.check_raise_instance(flags=no_pyobj_flags)
+
+    def check_raise_nested(self, flags, **jit_args):
+        """
+        Check exception propagation from nested functions.
+        """
+        for clazz in [MyError, UDEArgsToSuper,
+                      UDENoArgSuper]:
+            inner_pyfunc = raise_instance(clazz, "some message")
+            pyfunc = outer_function(inner_pyfunc)
+            inner_cfunc = jit(**jit_args)(inner_pyfunc)
+            cfunc = jit(**jit_args)(outer_function(inner_cfunc))
+
+            self.check_against_python(flags, pyfunc, cfunc, clazz, 1)
+            self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+            self.check_against_python(flags, pyfunc, cfunc, OtherError, 3)
+
+    def test_raise_nested_objmode(self):
+        self.check_raise_nested(force_pyobj_flags, forceobj=True)
+
+    def test_raise_nested_nopython(self):
+        self.check_raise_nested(no_pyobj_flags, nopython=True)
+
+    def check_reraise(self, flags):
+        def raise_exc(exc):
+            raise exc
+        pyfunc = reraise
+        cfunc = jit((), **flags)(pyfunc)
+        for op, err in [(lambda : raise_exc(ZeroDivisionError),
+                         ZeroDivisionError),
+                        (lambda : raise_exc(UDEArgsToSuper("msg", 1)),
+                         UDEArgsToSuper),
+                        (lambda : raise_exc(UDENoArgSuper("msg", 1)),
+                         UDENoArgSuper)]:
+            def gen_impl(fn):
+                def impl():
+                    try:
+                        op()
+                    except err:
+                        fn()
+                return impl
+            pybased = gen_impl(pyfunc)
+            cbased = gen_impl(cfunc)
+            self.check_against_python(flags, pybased, cbased, err,)
+
+    def test_reraise_objmode(self):
+        self.check_reraise(flags=force_pyobj_flags)
+
+    def test_reraise_nopython(self):
+        self.check_reraise(flags=no_pyobj_flags)
+
+    def check_raise_invalid_class(self, cls, flags):
+        pyfunc = raise_class(cls)
+        cfunc = jit((types.int32,), **flags)(pyfunc)
+        with self.assertRaises(TypeError) as cm:
+            cfunc(1)
+        self.assertEqual(str(cm.exception),
+                         "exceptions must derive from BaseException")
+
+    def test_raise_invalid_class_objmode(self):
+        self.check_raise_invalid_class(int, flags=force_pyobj_flags)
+        self.check_raise_invalid_class(1, flags=force_pyobj_flags)
+
+    def test_raise_invalid_class_nopython(self):
+        msg = "Encountered unsupported constant type used for exception"
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            self.check_raise_invalid_class(int, flags=no_pyobj_flags)
+        self.assertIn(msg, str(raises.exception))
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            self.check_raise_invalid_class(1, flags=no_pyobj_flags)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_raise_bare_string_nopython(self):
+        @njit
+        def foo():
+            raise "illegal"
+        msg = ("Directly raising a string constant as an exception is not "
+               "supported")
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+        self.assertIn(msg, str(raises.exception))
+
+    def check_assert_statement(self, flags):
+        pyfunc = assert_usecase
+        cfunc = jit((types.int32,), **flags)(pyfunc)
+        cfunc(1)
+        self.check_against_python(flags, pyfunc, cfunc, AssertionError, 2)
+
+    def test_assert_statement_objmode(self):
+        self.check_assert_statement(flags=force_pyobj_flags)
+
+    def test_assert_statement_nopython(self):
+        self.check_assert_statement(flags=no_pyobj_flags)
+
+    def check_raise_from_exec_string(self, flags):
+        # issue #3428
+        simple_raise = "def f(a):\n  raise exc('msg', 10)"
+        assert_raise = "def f(a):\n  assert a != 1"
+        py312_pep695_raise = "def f[T: int](a: T) -> T:\n  assert a != 1"
+        py312_pep695_raise_2 = "def f[T: int\n](a: T) -> T:\n  assert a != 1"
+        test_cases = [
+            (assert_raise, AssertionError),
+            (simple_raise, UDEArgsToSuper),
+            (simple_raise, UDENoArgSuper),
+        ]
+        if utils.PYVERSION >= (3, 12):
+            # Added for https://github.com/numba/numba/issues/9443
+            test_cases.append((py312_pep695_raise, AssertionError))
+            test_cases.append((py312_pep695_raise_2, AssertionError))
+        for f_text, exc in test_cases:
+            loc = {}
+            exec(f_text, {'exc': exc}, loc)
+            pyfunc = loc['f']
+            cfunc = jit((types.int32,), **flags)(pyfunc)
+            self.check_against_python(flags, pyfunc, cfunc, exc, 1)
+
+    def test_assert_from_exec_string_objmode(self):
+        self.check_raise_from_exec_string(flags=force_pyobj_flags)
+
+    def test_assert_from_exec_string_nopython(self):
+        self.check_raise_from_exec_string(flags=no_pyobj_flags)
+
+    def check_user_code_error_traceback(self, flags):
+        # this test checks that if a user tries to compile code that contains
+        # a bug in exception initialisation (e.g. missing arg) then this also
+        # has a frame injected with the location information.
+        pyfunc = ude_bug_usecase
+        cfunc = jit((), **flags)(pyfunc)
+        self.check_against_python(flags, pyfunc, cfunc, TypeError)
+
+    def test_user_code_error_traceback_objmode(self):
+        self.check_user_code_error_traceback(flags=force_pyobj_flags)
+
+    def test_user_code_error_traceback_nopython(self):
+        self.check_user_code_error_traceback(flags=no_pyobj_flags)
+
+    def check_raise_runtime_value(self, flags):
+        pyfunc = raise_runtime_value
+        cfunc = jit((types.string,), **flags)(pyfunc)
+        self.check_against_python(flags, pyfunc, cfunc, ValueError, 'hello')
+
+    def test_raise_runtime_value_objmode(self):
+        self.check_raise_runtime_value(flags=force_pyobj_flags)
+
+    def test_raise_runtime_value_nopython(self):
+        self.check_raise_runtime_value(flags=no_pyobj_flags_w_nrt)
+
+    def test_raise_runtime_value_nogil(self):
+        self.check_raise_runtime_value(flags=no_gil_flags)
+
+    def check_raise_instance_with_runtime_args(self, flags):
+        for clazz in [MyError, UDEArgsToSuper,
+                      UDENoArgSuper]:
+            pyfunc = raise_instance_runtime_args(clazz)
+            cfunc = jit((types.int32, types.string), **flags)(pyfunc)
+
+            self.assertEqual(cfunc(0, 'test'), 0)
+            self.check_against_python(flags, pyfunc, cfunc, clazz, 1, 'hello')
+            self.check_against_python(flags, pyfunc, cfunc, ValueError, 2,
+                                      'world')
+            self.check_against_python(flags, pyfunc, cfunc,
+                                      np.linalg.LinAlgError, 3, 'linalg')
+
+    def test_raise_instance_with_runtime_args_objmode(self):
+        self.check_raise_instance_with_runtime_args(flags=force_pyobj_flags)
+
+    def test_raise_instance_with_runtime_args_nopython(self):
+        self.check_raise_instance_with_runtime_args(flags=no_pyobj_flags_w_nrt)
+
+    def test_raise_instance_with_runtime_args_nogil(self):
+        self.check_raise_instance_with_runtime_args(flags=no_gil_flags)
+
+    def test_dynamic_raise_bad_args(self):
+        def raise_literal_dict():
+            raise ValueError({'a': 1, 'b': np.ones(4)})
+
+        def raise_range():
+            raise ValueError(range(3))
+
+        def raise_rng(rng):
+            raise ValueError(rng.bit_generator)
+
+        funcs = [
+            (raise_literal_dict, ()),
+            (raise_range, ()),
+            (raise_rng, (types.npy_rng,)),
+        ]
+
+        for pyfunc, argtypes in funcs:
+            msg = '.*Cannot convert native .* to a Python object.*'
+            with self.assertRaisesRegex(errors.TypingError, msg):
+                njit(argtypes)(pyfunc)
+
+    def test_dynamic_raise_dict(self):
+        @njit
+        def raise_literal_dict2():
+            raise ValueError({'a': 1, 'b': 3})
+
+        msg = "{a: 1, b: 3}"
+        with self.assertRaisesRegex(ValueError, msg):
+            raise_literal_dict2()
+
+    def test_dynamic_raise_literal_message(self):
+        @njit
+        def raise_literal_message(a):
+            if a == 0:
+                msg = "A was 0"
+                raise ValueError(msg)
+            if a == 1:
+                msg = "A was 1"
+                raise ValueError(msg)
+            if a == 2:
+                msg = "A was 2"
+                raise ValueError(msg)
+
+        for i in range(3):
+            with self.subTest(i=i):
+                with self.assertRaises(ValueError) as raises:
+                    raise_literal_message(i)
+                self.assertEqual(str(raises.exception), f"A was {i}")
+
+    def test_dynamic_raise_single_literal_message(self):
+        @njit
+        def raise_single_literal_message(arg):
+            msg = "False"
+            if arg:
+                msg = "True"
+            raise ValueError(msg)
+
+        for arg in (False, True):
+            with self.subTest(arg=arg):
+                with self.assertRaises(ValueError) as raises:
+                    raise_single_literal_message(arg)
+                self.assertEqual(str(raises.exception), str(arg))
+
+    def test_dynamic_raise_mixed_int_width(self):
+        @njit
+        def raise_mixed_int_width(a):
+            if a == 0:
+                value = np.int64(-7)
+                raise ValueError(value)
+            value = np.uint64(2 ** 63)
+            raise ValueError(value)
+
+        cases = [(0, "-7"), (1, str(2 ** 63))]
+        for arg, expected in cases:
+            with self.subTest(arg=arg):
+                with self.assertRaises(ValueError) as raises:
+                    raise_mixed_int_width(arg)
+                self.assertEqual(str(raises.exception), expected)
+
+    def test_disable_nrt(self):
+        @njit(_nrt=False)
+        def raise_with_no_nrt(i):
+            raise ValueError(i)
+
+        msg = 'NRT required but not enabled'
+        with self.assertRaisesRegex(errors.NumbaRuntimeError, msg):
+            raise_with_no_nrt(123)
+
+    def test_try_raise(self):
+
+        @njit
+        def raise_(a):
+            raise ValueError(a)
+
+        @njit
+        def try_raise(a):
+            try:
+                raise_(a)
+            except Exception:
+                pass
+            return a + 1
+
+        self.assertEqual(try_raise.py_func(3), try_raise(3))
+
+    @expected_failure_py311
+    @expected_failure_py312
+    @expected_failure_py313
+    @expected_failure_py314
+    @expected_failure_py315
+    def test_dynamic_raise(self):
+
+        @njit
+        def raise_(a):
+            raise ValueError(a)
+
+        @njit
+        def try_raise_(a):
+            try:
+                raise_(a)
+            except Exception:
+                raise ValueError(a)
+
+        args = [
+            1,
+            1.1,
+            'hello',
+            np.ones(3),
+            [1, 2],
+            (1, 2),
+            set([1, 2]),
+        ]
+        for fn in (raise_, try_raise_):
+            for arg in args:
+                with self.assertRaises(ValueError) as e:
+                    fn(arg)
+                self.assertEqual((arg,), e.exception.args)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/32/324b55e45fa06b8bcf5f8a185d949a16c35222804de38b8e3e366df986ba1bb0/fmt.pc b/shared/package-files/32/324b55e45fa06b8bcf5f8a185d949a16c35222804de38b8e3e366df986ba1bb0/fmt.pc
new file mode 100644
index 0000000000000000000000000000000000000000..33bbdb86b8125ae28498f0d807854c12d47d9b7e
--- /dev/null
+++ b/shared/package-files/32/324b55e45fa06b8bcf5f8a185d949a16c35222804de38b8e3e366df986ba1bb0/fmt.pc
@@ -0,0 +1,11 @@
+prefix=/opt/conda
+exec_prefix=/opt/conda
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: fmt
+Description: A modern formatting library
+Version: 9.1.0
+Libs: -L${libdir} -lfmt
+Cflags: -I${includedir}
+
diff --git a/shared/package-files/32/324bc7df8f8707e7b1a3955e92bafefc71c0b40ed40d6e151c07fc8f50d8672e/python_arg_parser.h b/shared/package-files/32/324bc7df8f8707e7b1a3955e92bafefc71c0b40ed40d6e151c07fc8f50d8672e/python_arg_parser.h
new file mode 100644
index 0000000000000000000000000000000000000000..cec99a843301c3d0c80e3746ac5e565b5cf634d9
--- /dev/null
+++ b/shared/package-files/32/324bc7df8f8707e7b1a3955e92bafefc71c0b40ed40d6e151c07fc8f50d8672e/python_arg_parser.h
@@ -0,0 +1,1286 @@
+#pragma once
+
+// Parse arguments to Python functions implemented in C++
+// This is similar to PyArg_ParseTupleAndKeywords(), but specifically handles
+// the types relevant to PyTorch and distinguishes between overloaded function
+// signatures.
+//
+// Example:
+//
+//   static PythonArgParser parser({
+//     "norm(Scalar p, int64_t dim, bool keepdim=False)",
+//     "norm(Scalar p=2)",
+//   });
+//   ParsedArgs<3> parsed_args;
+//   auto r = parser.parse(args, kwargs, parsed_args);
+//   if (r.idx == 0) {
+//     norm(r.scalar(0), r.int64(1), r.bool(0));
+//   } else {
+//     norm(r.scalar(0));
+//   }
+//
+// We auto-generate most uses of PythonArgParser; the generated files
+// are torch/csrc/autograd/generated/python_*.cpp
+//
+// Some gotchas that you should watch out for:
+//
+//    - Note [Order of overloads matters]
+//      Order of overloads matters.  A set of input arguments may
+//      bind to multiple argument specs; we will always pick the
+//      first one in PythonArgParser.  However, when you are writing
+//      overloads in, e.g., native_functions.yaml, you don't have to
+//      worry about what order you write them, because the code
+//      generation logic always gives the overloads a canonical
+//      order, where Tensor overloads come first, before Scalar overloads.
+//      This logic is in sort_declarations in
+//      tools/autograd/gen_python_functions.py
+//
+//    - Zero-dim tensors (e.g., torch.tensor(2)) bind to both
+//      Scalar and Tensor, UNLESS they require grad (in which case
+//      they only bind to Tensor).
+
+#include <pybind11/pytypes.h>
+#include <torch/csrc/python_headers.h>
+
+#include <torch/csrc/Device.h>
+#include <torch/csrc/Dtype.h>
+#include <torch/csrc/DynamicTypes.h>
+#include <torch/csrc/Exceptions.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/Generator.h>
+#include <torch/csrc/Layout.h>
+#include <torch/csrc/MemoryFormat.h>
+#include <torch/csrc/QScheme.h>
+#include <torch/csrc/Stream.h>
+#include <torch/csrc/autograd/python_variable.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/dynamo/eval_frame.h>
+#include <torch/csrc/jit/frontend/tracer.h>
+#include <torch/csrc/python_dimname.h>
+#include <torch/csrc/tensor/python_tensor.h>
+#include <torch/csrc/utils/disable_torch_function.h>
+#include <torch/csrc/utils/object_ptr.h>
+#include <torch/csrc/utils/pybind.h>
+#include <torch/csrc/utils/python_numbers.h>
+#include <torch/csrc/utils/python_strings.h>
+#include <torch/csrc/utils/python_symnode.h>
+#include <torch/csrc/utils/six.h>
+
+#include <ATen/PythonTorchFunctionTLS.h>
+#include <ATen/core/Tensor.h>
+#include <c10/util/Exception.h>
+#include <c10/util/irange.h>
+
+#include <c10/core/SymFloat.h>
+#include <c10/core/SymNodeImpl.h>
+
+#include <c10/core/DispatchKeySet.h>
+#include <array>
+#include <cstddef>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <vector>
+
+inline bool THPUtils_checkScalar(PyObject* obj) {
+#ifdef USE_NUMPY
+  if (torch::utils::is_numpy_scalar(obj)) {
+    return true;
+  }
+#endif
+  return PyFloat_Check(obj) || PyLong_Check(obj) || PyComplex_Check(obj) ||
+      torch::is_symint(py::handle(obj)) ||
+      torch::is_symfloat(py::handle(obj)) || torch::is_symbool(py::handle(obj));
+}
+
+namespace torch {
+
+bool should_allow_numbers_as_tensors(const std::string& name);
+
+enum class ParameterType {
+  TENSOR,
+  SCALAR,
+  INT64,
+  SYM_INT,
+  DOUBLE,
+  COMPLEX,
+  TENSOR_LIST,
+  INT_LIST,
+  GENERATOR,
+  BOOL,
+  STORAGE,
+  PYOBJECT,
+  SCALARTYPE,
+  LAYOUT,
+  MEMORY_FORMAT,
+  DEVICE,
+  STREAM,
+  STRING,
+  DIMNAME,
+  DIMNAME_LIST,
+  QSCHEME,
+  FLOAT_LIST,
+  SCALAR_LIST,
+  SYM_INT_LIST,
+  DISPATCH_KEY_SET
+};
+
+struct FunctionParameter;
+struct FunctionSignature;
+struct PythonArgs;
+
+// Contains bound Python arguments in declaration order
+template <int N>
+struct ParsedArgs {
+  ParsedArgs() : args() {}
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+  PyObject* args[N];
+};
+
+// A PythonArgParser contains a list of valid signatures. Instances are
+// typically global variables and should be immutable.
+struct PYBIND11_EXPORT PythonArgParser {
+  explicit PythonArgParser(
+      const std::vector<std::string>& fmts,
+      bool traceable = false);
+
+  // meant only for `torch` functions.
+  template <int N>
+  inline PythonArgs parse(
+      PyObject* self,
+      PyObject* args,
+      PyObject* kwargs,
+      ParsedArgs<N>& dst);
+
+  template <int N>
+  inline PythonArgs parse(PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst);
+
+  inline PythonArgs parse(PyObject* self, ParsedArgs<0>& dst);
+
+  // Formatted strings of non-hidden signatures
+  std::vector<std::string> get_signatures() const;
+
+ private:
+  [[noreturn]] void print_error(
+      PyObject* self,
+      PyObject* args,
+      PyObject* kwargs,
+      // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+      PyObject* parsed_args[]);
+  void check_deprecated(const FunctionSignature& signature);
+  PythonArgs raw_parse(
+      PyObject* self,
+      PyObject* args,
+      PyObject* kwargs,
+      // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+      PyObject* parsed_args[]);
+
+  std::vector<FunctionSignature> signatures_;
+  std::string function_name;
+  size_t max_args;
+  bool traceable;
+};
+
+// FunctionSignature represents a single valid signature for a Python function.
+// It is immutable once constructed. The contained data can be concurrently
+// accessed by multiple calls.
+struct FunctionSignature {
+  explicit FunctionSignature(const std::string& fmt, int index);
+
+  bool parse(
+      PyObject* self,
+      PyObject* args,
+      PyObject* kwargs,
+      // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+      PyObject* dst[],
+      std::vector<PyObject*>& overloaded_args,
+      bool raise_exception);
+
+  std::string toString() const;
+
+  std::string name;
+  std::vector<FunctionParameter> params;
+  size_t min_args;
+  size_t max_args;
+  size_t max_pos_args;
+  int index;
+  bool hidden;
+  bool deprecated;
+};
+
+// PythonArgs contains bound Python arguments for an actual invocation
+// along with references to the matched signature.
+struct PythonArgs {
+  PythonArgs(
+      bool traceable,
+      const FunctionSignature& signature,
+      PyObject** args,
+      std::vector<PyObject*> overloaded_args)
+      : idx(signature.index),
+        traceable(traceable),
+        signature(signature),
+        args(args),
+        overloaded_args(std::move(overloaded_args)) {}
+
+  int idx;
+  bool traceable;
+  const FunctionSignature& signature;
+  PyObject** args;
+  std::vector<PyObject*> overloaded_args; // NOTE: borrowed references
+
+  inline bool has_torch_function();
+  inline std::string get_func_name();
+  inline at::Tensor tensor(int i);
+  inline c10::optional<at::Tensor> optionalTensor(int i);
+  inline at::Scalar scalar(int i);
+  inline at::Scalar scalarWithDefault(int i, const at::Scalar& default_scalar);
+  inline std::vector<at::Scalar> scalarlist(int i);
+  inline std::vector<at::Tensor> tensorlist(int i);
+  inline torch::List<c10::optional<at::Tensor>> list_of_optional_tensors(int i);
+  template <int N>
+  inline std::array<at::Tensor, N> tensorlist_n(int i);
+  inline std::vector<int64_t> intlist(int i);
+  inline std::vector<c10::SymInt> symintlist(int i);
+  inline c10::OptionalArray<int64_t> intlistOptional(int i);
+  inline c10::OptionalArray<c10::SymInt> symintlistOptional(int i);
+  inline std::vector<int64_t> intlistWithDefault(
+      int i,
+      std::vector<int64_t> default_intlist);
+  inline c10::optional<at::Generator> generator(int i);
+  inline at::Storage storage(int i);
+  inline at::Storage storage(
+      int i,
+      at::ScalarType& storage_scalar_type,
+      bool& is_typed_storage);
+  inline c10::Stream stream(int i);
+  inline at::ScalarType scalartype(int i);
+  inline at::ScalarType scalartypeWithDefault(
+      int i,
+      at::ScalarType default_scalartype);
+  inline c10::optional<at::ScalarType> scalartypeOptional(int i);
+  inline c10::optional<at::Scalar> scalarOptional(int i);
+  inline c10::optional<int64_t> toInt64Optional(int i);
+  inline c10::optional<c10::SymInt> toSymIntOptional(int i);
+  inline c10::optional<bool> toBoolOptional(int i);
+  inline c10::optional<double> toDoubleOptional(int i);
+  inline c10::OptionalArray<double> doublelistOptional(int i);
+  inline std::vector<double> doublelist(int i);
+  inline std::vector<double> getDoublelist(int i);
+  inline at::Layout layout(int i);
+  inline at::Layout layoutWithDefault(int i, at::Layout default_layout);
+  inline c10::optional<at::Layout> layoutOptional(int i);
+  inline at::Device device(int i);
+  inline at::Device deviceWithDefault(int i, const at::Device& default_device);
+  inline c10::optional<at::Device> deviceOptional(int i);
+  inline at::Dimname dimname(int i);
+  inline std::vector<at::Dimname> dimnamelist(int i);
+  inline c10::optional<std::vector<at::Dimname>> toDimnameListOptional(int i);
+  inline at::MemoryFormat memoryformat(int i);
+  inline c10::optional<at::MemoryFormat> memoryformatOptional(int i);
+  inline at::QScheme toQScheme(int i);
+  inline std::string string(int i);
+  inline std::string stringWithDefault(int i, const std::string& default_str);
+  inline c10::optional<std::string> stringOptional(int i);
+  inline c10::string_view stringView(int i);
+  inline c10::string_view stringViewWithDefault(
+      int i,
+      const c10::string_view default_str);
+  inline c10::optional<c10::string_view> stringViewOptional(int i);
+  inline PyObject* pyobject(int i);
+  inline int64_t toInt64(int i);
+  inline c10::SymInt toSymInt(int i);
+  inline c10::SymBool toSymBool(int i);
+  inline int64_t toInt64WithDefault(int i, int64_t default_int);
+  inline double toDouble(int i);
+  inline double toDoubleWithDefault(int i, double default_double);
+  inline c10::complex<double> toComplex(int i);
+  inline c10::complex<double> toComplexWithDefault(
+      int i,
+      c10::complex<double> default_complex);
+  inline bool toBool(int i);
+  inline bool toBoolWithDefault(int i, bool default_bool);
+  inline bool isNone(int i);
+  inline c10::optional<c10::DispatchKeySet> toDispatchKeySetOptional(int i);
+
+ private:
+  at::Tensor tensor_slow(int i);
+  at::Scalar scalar_slow(int i);
+  at::Scalar scalar_slow(PyObject* arg);
+};
+
+// FunctionParameter is a single formal parameter of a Python function.
+// It is immutable once constructed.
+struct FunctionParameter {
+  FunctionParameter(const std::string& fmt, bool keyword_only);
+
+  bool check(
+      PyObject* obj,
+      std::vector<PyObject*>& overloaded_args,
+      int argnum,
+      int64_t* failed_idx = nullptr);
+
+  void set_default_str(const std::string& str);
+  std::string type_name() const;
+
+  ParameterType type_;
+  bool optional;
+  bool allow_none;
+  bool keyword_only;
+  bool allow_numbers_as_tensors = false;
+  int size;
+  std::string name;
+  // having this as a raw PyObject * will presumably leak it, but these are only
+  // held by static objects anyway, and Py_Finalize can already be called when
+  // this is destructed.
+  PyObject* python_name;
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
+  at::SmallVector<PyObject*, 5> numpy_python_names;
+  at::Scalar default_scalar;
+  std::vector<int64_t> default_intlist;
+  std::string default_string;
+  union {
+    bool default_bool;
+    int64_t default_int;
+    double default_double;
+    // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+    double default_complex[2]; // see Scalar
+    at::ScalarType default_scalartype;
+    at::Layout default_layout;
+  };
+};
+
+template <int N>
+inline PythonArgs PythonArgParser::parse(
+    PyObject* self,
+    PyObject* args,
+    PyObject* kwargs,
+    ParsedArgs<N>& dst) {
+  TORCH_CHECK_VALUE(
+      N >= max_args,
+      "PythonArgParser: dst ParsedArgs buffer does not have enough capacity, expected ",
+      max_args,
+      " (got ",
+      N,
+      ")");
+  return raw_parse(self, args, kwargs, dst.args);
+}
+
+template <int N>
+inline PythonArgs PythonArgParser::parse(
+    PyObject* args,
+    PyObject* kwargs,
+    ParsedArgs<N>& dst) {
+  return parse(nullptr, args, kwargs, dst);
+}
+
+inline PythonArgs PythonArgParser::parse(PyObject* self, ParsedArgs<0>& dst) {
+  return parse(self, nullptr, nullptr, dst);
+}
+
+inline bool PythonArgs::has_torch_function() {
+  return !overloaded_args.empty() || at::impl::torch_function_mode_enabled();
+}
+
+inline std::string PythonArgs::get_func_name() {
+  return signature.name;
+}
+
+// TODO: this can return MaybeOwned
+inline at::Tensor PythonArgs::tensor(int i) {
+  if (args[i] && THPVariable_CheckExact(args[i])) {
+    return THPVariable_Unpack(args[i]);
+  }
+  return tensor_slow(i);
+}
+
+inline c10::optional<at::Tensor> PythonArgs::optionalTensor(int i) {
+  at::Tensor t = tensor(i);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  if (t.defined()) {
+    return t;
+  } else {
+    return c10::nullopt;
+  }
+}
+
+inline at::Scalar PythonArgs::scalar(int i) {
+  if (!args[i])
+    return signature.params[i].default_scalar;
+  return scalar_slow(i);
+}
+
+inline std::vector<at::Scalar> PythonArgs::scalarlist(int i) {
+  if (!args[i])
+    return std::vector<at::Scalar>();
+  auto tuple = six::isTuple(args[i]);
+  THPObjectPtr arg = six::maybeAsTuple(args[i]);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
+  std::vector<at::Scalar> res(size);
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx)
+                          : PyList_GET_ITEM(arg.get(), idx);
+    res[idx] = scalar_slow(obj);
+  }
+  return res;
+}
+
+inline at::Scalar PythonArgs::scalarWithDefault(
+    int i,
+    const at::Scalar& default_scalar) {
+  if (!args[i])
+    return default_scalar;
+  return scalar_slow(i);
+}
+
+inline c10::optional<at::Scalar> PythonArgs::scalarOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return scalar_slow(i);
+}
+
+inline std::vector<at::Tensor> PythonArgs::tensorlist(int i) {
+  if (!args[i])
+    return std::vector<at::Tensor>();
+  auto tuple = six::isTuple(args[i]);
+  THPObjectPtr arg = six::maybeAsTuple(args[i]);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
+  std::vector<at::Tensor> res(size);
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx)
+                          : PyList_GET_ITEM(arg.get(), idx);
+    // This is checked by the argument parser so it's safe to cast without
+    // checking if this is a tensor first
+    res[idx] = THPVariable_Unpack(obj);
+  }
+  return res;
+}
+
+inline torch::List<c10::optional<at::Tensor>> PythonArgs::
+    list_of_optional_tensors(int i) {
+  if (!args[i])
+    return torch::List<c10::optional<at::Tensor>>();
+  auto tuple = six::isTuple(args[i]);
+  THPObjectPtr arg = six::maybeAsTuple(args[i]);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
+  torch::List<c10::optional<at::Tensor>> res;
+  res.reserve(size);
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx)
+                          : PyList_GET_ITEM(arg.get(), idx);
+    // This is checked by the argument parser so it's safe to cast without
+    // checking if this is a tensor first
+    res.push_back(THPVariable_Unpack(obj));
+  }
+  return res;
+}
+
+template <int N>
+inline std::array<at::Tensor, N> PythonArgs::tensorlist_n(int i) {
+  auto res = std::array<at::Tensor, N>();
+  if (!args[i])
+    return res;
+  auto tuple = six::isTuple(args[i]);
+  THPObjectPtr arg = six::maybeAsTuple(args[i]);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
+  if (size != N) {
+    throw TypeError("expected tuple of %d elements but got %d", N, (int)size);
+  }
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx)
+                          : PyList_GET_ITEM(arg.get(), idx);
+    // This is checked by the argument parser so it's safe to cast without
+    // checking if this is a tensor first
+    res[idx] = THPVariable_Unpack(obj);
+  }
+  return res;
+}
+
+inline std::vector<int64_t> PythonArgs::intlist(int i) {
+  return intlistWithDefault(i, signature.params[i].default_intlist);
+}
+
+inline PyObject* toPyObject(c10::SymInt symint) {
+  if (symint.is_symbolic()) {
+    auto r = py::cast(symint).release().ptr();
+    TORCH_INTERNAL_ASSERT(r);
+    return r;
+  } else {
+    auto m = symint.maybe_as_int();
+    return THPUtils_packInt64(*m);
+  }
+}
+
+inline void throw_intlist_exception(
+    const torch::PythonArgs* args,
+    size_t i,
+    PyObject* obj,
+    size_t idx,
+    const std::exception& e = python_error()) {
+  std::string error = strlen(e.what())
+      ? e.what()
+      : std::string("type must be ") + args->signature.params[i].type_name() +
+          ",but got " + Py_TYPE(obj)->tp_name;
+  throw TypeError(
+      "%s(): argument '%s' failed to unpack the object at pos %zu with error \"%s\"",
+      args->signature.name.c_str(),
+      args->signature.params[i].name.c_str(),
+      idx + 1,
+      error.c_str());
+}
+
+inline std::vector<c10::SymInt> PythonArgs::symintlist(int i) {
+  if (!args[i]) {
+    return c10::fmap(signature.params[i].default_intlist, [](int64_t di) {
+      return c10::SymInt(di);
+    });
+  }
+
+  const auto size1 = signature.params[i].size;
+  if (size1 > 0 && THPUtils_checkLong(args[i])) {
+    return std::vector<c10::SymInt>(
+        size1, c10::SymInt(THPUtils_unpackLong(args[i])));
+  }
+
+  if (size1 > 0 && torch::is_symint(py::handle(args[i]))) {
+    auto si = py::handle(args[i]).cast<c10::SymInt>();
+    return std::vector<c10::SymInt>(size1, si);
+  }
+
+  PyObject* arg = args[i];
+  auto tuple = PyTuple_Check(arg);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  const auto size2 = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
+  std::vector<c10::SymInt> res;
+  res.reserve(size2);
+  for (const auto idx : c10::irange(size2)) {
+    PyObject* obj =
+        tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
+
+    // Elements of torch.Size are tensors during tracing, and we need to
+    // record extra information before they are turned into an IntArrayRef
+    if (traceable && jit::tracer::isTracing() && THPVariable_Check(obj)) {
+      auto& var = THPVariable_Unpack(obj);
+      jit::tracer::ArgumentStash::stashIntArrayRefElem(
+          signature.params[i].name, size2, idx, var);
+      try {
+        res.emplace_back(var.item<int64_t>());
+        continue;
+      } catch (std::exception& e) {
+        throw_intlist_exception(this, i, obj, idx, e);
+      }
+      continue;
+    } else {
+      // convert tensor to scalar outside of try / catch,
+      // so that Tensor subclass exceptions will not be caught.
+      if (THPUtils_checkLongExact(obj)) {
+        // Fast path for plain numbers
+        try {
+          res.emplace_back(THPUtils_unpackLong(obj));
+        } catch (std::exception& e) {
+          throw_intlist_exception(this, i, obj, idx, e);
+        }
+      } else if (THPVariable_Check(obj)) {
+        auto& var = THPVariable_Unpack(obj);
+        if (var.numel() != 1 ||
+            !at::isIntegralType(
+                var.dtype().toScalarType(), /*include_bool*/ true)) {
+          throw_intlist_exception(this, i, obj, idx);
+        }
+        auto scalar = var.item();
+        TORCH_CHECK(scalar.isIntegral(/*include bool*/ false));
+        res.push_back(scalar.toSymInt());
+      } else {
+        try {
+          if (is_symint(py::handle(obj))) {
+            res.push_back(py::handle(obj).cast<c10::SymInt>());
+          } else {
+            res.emplace_back(THPUtils_unpackIndex(obj));
+          }
+        } catch (std::exception& e) {
+          throw_intlist_exception(this, i, obj, idx, e);
+        }
+      }
+    }
+  }
+
+  return res;
+}
+
+inline std::vector<int64_t> PythonArgs::intlistWithDefault(
+    int i,
+    std::vector<int64_t> default_intlist) {
+  if (!args[i])
+    return default_intlist;
+  PyObject* arg = args[i];
+  const auto size1 = signature.params[i].size;
+  if (size1 > 0 && THPUtils_checkLong(arg)) {
+    return std::vector<int64_t>(size1, THPUtils_unpackLong(arg));
+  }
+  if (size1 > 0 && torch::is_symint(py::handle(arg))) {
+    return std::vector<int64_t>(
+        size1,
+        py::handle(arg).cast<c10::SymInt>().guard_int(__FILE__, __LINE__));
+  }
+  auto tuple = PyTuple_Check(arg);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  const auto size2 = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
+  std::vector<int64_t> res(size2);
+  for (const auto idx : c10::irange(size2)) {
+    PyObject* obj =
+        tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
+    // Elements of torch.Size are tensors during tracing, and we need to
+    // record extra information before they are turned into an IntArrayRef
+    if (traceable && jit::tracer::isTracing() && THPVariable_Check(obj)) {
+      auto& var = THPVariable_Unpack(obj);
+      jit::tracer::ArgumentStash::stashIntArrayRefElem(
+          signature.params[i].name, size2, idx, var);
+      try {
+        res[idx] = var.item<int64_t>();
+        continue;
+      } catch (std::exception& e) {
+        throw_intlist_exception(this, i, obj, idx, e);
+      }
+    } else {
+      // convert tensor to scalar outside of try / catch,
+      // so that Tensor subclass exceptions will not be caught.
+      if (THPUtils_checkLongExact(obj)) {
+        // Fast path for plain numbers
+        try {
+          res[idx] = THPUtils_unpackLong(obj);
+        } catch (std::exception& e) {
+          throw_intlist_exception(this, i, obj, idx, e);
+        }
+      } else if (torch::is_symint(py::handle(obj))) {
+        res[idx] = py::cast<c10::SymInt>(py::handle(obj))
+                       .guard_int(__FILE__, __LINE__);
+      } else if (THPVariable_Check(obj)) {
+        auto& var = THPVariable_Unpack(obj);
+        if (var.numel() != 1 ||
+            !at::isIntegralType(
+                var.dtype().toScalarType(), /*include_bool*/ true)) {
+          throw_intlist_exception(this, i, obj, idx);
+        }
+        res[idx] = var.item<int64_t>();
+      } else {
+        try {
+          res[idx] = THPUtils_unpackIndex(obj);
+        } catch (std::exception& e) {
+          throw_intlist_exception(this, i, obj, idx, e);
+        }
+      }
+    }
+  }
+  return res;
+}
+
+inline c10::OptionalArray<int64_t> PythonArgs::intlistOptional(int i) {
+  if (!args[i]) {
+    return {};
+  }
+  return intlist(i);
+}
+
+inline c10::OptionalArray<c10::SymInt> PythonArgs::symintlistOptional(int i) {
+  if (!args[i]) {
+    return {};
+  }
+  return symintlist(i);
+}
+
+inline std::vector<double> PythonArgs::getDoublelist(int i) {
+  PyObject* arg = args[i];
+  auto tuple = PyTuple_Check(arg);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
+  std::vector<double> res(size);
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj =
+        tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
+    try {
+      res[idx] = THPUtils_unpackDouble(obj);
+    } catch (const std::exception& e) {
+      throw TypeError(
+          "%s(): argument '%s' must be %s, but found element of type %s at pos %zu",
+          signature.name.c_str(),
+          signature.params[i].name.c_str(),
+          signature.params[i].type_name().c_str(),
+          Py_TYPE(obj)->tp_name,
+          idx + 1);
+    }
+  }
+  return res;
+}
+
+inline c10::OptionalArray<double> PythonArgs::doublelistOptional(int i) {
+  if (!args[i]) {
+    return {};
+  }
+  return this->getDoublelist(i);
+}
+
+inline std::vector<double> PythonArgs::doublelist(int i) {
+  if (!args[i]) {
+    return {};
+  }
+  return this->getDoublelist(i);
+}
+
+inline c10::optional<c10::DispatchKeySet> PythonArgs::toDispatchKeySetOptional(
+    int i) {
+  if (!args[i]) {
+    return {};
+  }
+  return py::cast<c10::DispatchKeySet>(py::handle(args[i]));
+}
+
+inline at::ScalarType PythonArgs::scalartypeWithDefault(
+    int i,
+    at::ScalarType default_scalartype) {
+  if (!args[i])
+    return default_scalartype;
+  return scalartype(i);
+}
+
+inline at::ScalarType toScalarType(PyObject* obj) {
+  if (obj == (PyObject*)&PyFloat_Type) {
+    return at::ScalarType::Double;
+  }
+  if (obj == (PyObject*)&PyBool_Type) {
+    return at::ScalarType::Bool;
+  }
+  if (obj == (PyObject*)&PyLong_Type) {
+    return at::ScalarType::Long;
+  }
+  return reinterpret_cast<THPDtype*>(obj)->scalar_type;
+}
+
+inline at::ScalarType PythonArgs::scalartype(int i) {
+  if (!args[i]) {
+    auto scalartype = signature.params[i].default_scalartype;
+    return (scalartype == at::ScalarType::Undefined)
+        ? torch::tensors::get_default_scalar_type()
+        : scalartype;
+  }
+  PyObject* obj = args[i];
+  return toScalarType(obj);
+}
+
+inline c10::optional<at::ScalarType> PythonArgs::scalartypeOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return scalartype(i);
+}
+
+inline at::Layout toLayout(PyObject* obj) {
+  const auto layout = reinterpret_cast<THPLayout*>(obj);
+  return layout->layout;
+}
+
+inline at::Layout PythonArgs::layout(int i) {
+  if (!args[i])
+    return signature.params[i].default_layout;
+  return toLayout(args[i]);
+}
+
+inline at::Layout PythonArgs::layoutWithDefault(
+    int i,
+    at::Layout default_layout) {
+  if (!args[i])
+    return default_layout;
+  return layout(i);
+}
+
+inline c10::optional<at::Layout> PythonArgs::layoutOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return layout(i);
+}
+
+inline at::Device toDevice(PyObject* obj) {
+  if (THPDevice_Check(obj)) {
+    const auto device = reinterpret_cast<THPDevice*>(obj);
+    return device->device;
+  }
+  if (THPUtils_checkLong(obj)) {
+    const auto device_index = THPUtils_unpackLong(obj);
+    TORCH_CHECK(device_index >= 0, "Device index must not be negative");
+    if (c10::is_privateuse1_backend_registered()) {
+      return at::Device(
+          c10::DeviceType::PrivateUse1,
+          static_cast<c10::DeviceIndex>(device_index));
+    }
+    return at::Device(
+        c10::DeviceType::CUDA, static_cast<c10::DeviceIndex>(device_index));
+  }
+  const std::string& device_str = THPUtils_unpackString(obj);
+  return at::Device(device_str);
+}
+
+inline at::Device PythonArgs::device(int i) {
+  if (!args[i]) {
+    return torch::tensors::get_default_device();
+  }
+  return toDevice(args[i]);
+}
+
+inline at::Device PythonArgs::deviceWithDefault(
+    int i,
+    const at::Device& default_device) {
+  if (!args[i])
+    return default_device;
+  return device(i);
+}
+
+inline c10::optional<at::Device> PythonArgs::deviceOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return device(i);
+}
+
+inline at::Dimname PythonArgs::dimname(int i) {
+  TORCH_INTERNAL_ASSERT(args[i] != nullptr);
+  return THPDimname_parse(args[i]);
+}
+
+inline std::vector<at::Dimname> parseDimnameList(PyObject* arg) {
+  auto tuple = PyTuple_Check(arg);
+  // NOLINTNEXTLINE(bugprone-branch-clone)
+  auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
+  std::vector<at::Dimname> res;
+  res.reserve(size);
+  for (const auto idx : c10::irange(size)) {
+    PyObject* obj =
+        tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
+    res.push_back(THPDimname_parse(obj));
+  }
+  return res;
+}
+
+inline c10::optional<std::vector<at::Dimname>> PythonArgs::
+    toDimnameListOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return parseDimnameList(args[i]);
+}
+
+inline std::vector<at::Dimname> PythonArgs::dimnamelist(int i) {
+  TORCH_INTERNAL_ASSERT(args[i]);
+  PyObject* arg = args[i];
+  auto size = signature.params[i].size;
+  TORCH_INTERNAL_ASSERT(size == 0 || size == 1);
+  if (size == 1 && THPUtils_checkDimname(arg)) {
+    return {THPDimname_parse(arg)};
+  }
+  return parseDimnameList(arg);
+}
+
+inline at::MemoryFormat PythonArgs::memoryformat(int i) {
+  if (!args[i])
+    return at::MemoryFormat::Contiguous;
+  TORCH_CHECK(
+      THPMemoryFormat_Check(args[i]),
+      "memory_format arg must be an instance of the torch.memory_format");
+  const auto memory_format = reinterpret_cast<THPMemoryFormat*>(args[i]);
+  return memory_format->memory_format;
+}
+
+inline c10::optional<at::MemoryFormat> PythonArgs::memoryformatOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return memoryformat(i);
+}
+
+inline at::QScheme PythonArgs::toQScheme(int i) {
+  if (!args[i])
+    return at::kPerTensorAffine;
+  TORCH_CHECK(
+      THPQScheme_Check(args[i]),
+      "qscheme arg must be an instance of the torch.qscheme");
+  const auto qscheme = reinterpret_cast<THPQScheme*>(args[i]);
+  return qscheme->qscheme;
+}
+
+inline std::string PythonArgs::string(int i) {
+  return stringWithDefault(i, signature.params[i].default_string);
+}
+
+inline std::string PythonArgs::stringWithDefault(
+    int i,
+    const std::string& default_str) {
+  if (!args[i])
+    return default_str;
+  return THPUtils_unpackString(args[i]);
+}
+
+inline c10::optional<std::string> PythonArgs::stringOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return THPUtils_unpackString(args[i]);
+}
+
+inline c10::string_view PythonArgs::stringView(int i) {
+  return stringViewWithDefault(i, signature.params[i].default_string);
+}
+
+inline c10::string_view PythonArgs::stringViewWithDefault(
+    int i,
+    const c10::string_view default_str) {
+  if (!args[i])
+    return default_str;
+  return THPUtils_unpackStringView(args[i]);
+}
+
+inline c10::optional<c10::string_view> PythonArgs::stringViewOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return THPUtils_unpackStringView(args[i]);
+}
+
+inline int64_t PythonArgs::toInt64(int i) {
+  if (!args[i])
+    return signature.params[i].default_int;
+  if (traceable && jit::tracer::isTracing() && THPVariable_Check(args[i])) {
+    auto& var = THPVariable_Unpack(args[i]);
+    jit::tracer::ArgumentStash::stashValue(
+        signature.params[i].name, idx, var, c10::IntType::get());
+  }
+  if (torch::is_symint(py::handle(args[i]))) {
+    return py::cast<c10::SymInt>(py::handle(args[i]))
+        .guard_int(__FILE__, __LINE__);
+  }
+  return THPUtils_unpackLong(args[i]);
+}
+
+inline c10::SymInt PythonArgs::toSymInt(int i) {
+  if (!args[i]) {
+    return c10::SymInt(signature.params[i].default_int);
+  }
+
+  if (traceable && jit::tracer::isTracing() && THPVariable_Check(args[i])) {
+    auto& var = THPVariable_Unpack(args[i]);
+    jit::tracer::ArgumentStash::stashValue(
+        signature.params[i].name, idx, var, c10::IntType::get());
+  }
+
+  return py::cast<c10::SymInt>(py::handle(args[i]));
+}
+
+inline c10::SymBool PythonArgs::toSymBool(int i) {
+  if (!args[i]) {
+    return c10::SymBool(signature.params[i].default_bool);
+  }
+  if (traceable && jit::tracer::isTracing() && THPVariable_Check(args[i])) {
+    auto& var = THPVariable_Unpack(args[i]);
+    jit::tracer::ArgumentStash::stashValue(
+        signature.params[i].name, idx, var, c10::BoolType::get());
+  }
+
+  return py::cast<c10::SymBool>(py::handle(args[i]));
+}
+
+inline int64_t PythonArgs::toInt64WithDefault(int i, int64_t default_int) {
+  if (!args[i])
+    return default_int;
+  return toInt64(i);
+}
+
+inline c10::optional<int64_t> PythonArgs::toInt64Optional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return toInt64(i);
+}
+
+inline c10::optional<c10::SymInt> PythonArgs::toSymIntOptional(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return toSymInt(i);
+}
+
+inline c10::optional<bool> PythonArgs::toBoolOptional(int i) {
+  if (!args[i]) {
+    return c10::nullopt;
+  }
+  return toBool(i);
+}
+
+inline c10::optional<double> PythonArgs::toDoubleOptional(int i) {
+  if (!args[i]) {
+    return c10::nullopt;
+  }
+  return toDouble(i);
+}
+
+inline double PythonArgs::toDouble(int i) {
+  if (!args[i])
+    return signature.params[i].default_double;
+  if (torch::is_symfloat(py::handle(args[i]))) {
+    return py::cast<c10::SymFloat>(py::handle(args[i]))
+        .guard_float(__FILE__, __LINE__);
+  }
+  if (torch::is_symint(py::handle(args[i]))) {
+    return static_cast<double>(py::cast<c10::SymInt>(py::handle(args[i]))
+                                   .guard_int(__FILE__, __LINE__));
+  }
+  return THPUtils_unpackDouble(args[i]);
+}
+
+inline bool PythonArgs::toBool(int i) {
+  if (!args[i])
+    return signature.params[i].default_bool;
+  if (torch::is_symbool(py::handle(args[i]))) {
+    return py::cast<c10::SymBool>(py::handle(args[i]))
+        .guard_bool(__FILE__, __LINE__);
+  }
+  return args[i] == Py_True;
+}
+
+inline double PythonArgs::toDoubleWithDefault(int i, double default_double) {
+  if (!args[i])
+    return default_double;
+  return toDouble(i);
+}
+
+inline c10::complex<double> PythonArgs::toComplex(int i) {
+  if (!args[i])
+    return *(reinterpret_cast<const c10::complex<double>*>(
+        signature.params[i].default_complex));
+  return THPUtils_unpackComplexDouble(args[i]);
+}
+
+inline c10::complex<double> PythonArgs::toComplexWithDefault(
+    int i,
+    c10::complex<double> default_value) {
+  if (!args[i])
+    return default_value;
+  return toComplex(i);
+}
+
+inline bool PythonArgs::toBoolWithDefault(int i, bool default_bool) {
+  if (!args[i])
+    return default_bool;
+  return toBool(i);
+}
+
+inline bool PythonArgs::isNone(int i) {
+  return args[i] == nullptr;
+}
+
+inline c10::optional<at::Generator> PythonArgs::generator(int i) {
+  if (!args[i])
+    return c10::nullopt;
+  return reinterpret_cast<THPGenerator*>(args[i])->cdata;
+}
+
+inline at::Storage PythonArgs::storage(int i) {
+  if (!args[i])
+    return at::Storage();
+  return createStorage(args[i]);
+}
+
+inline at::Storage PythonArgs::storage(
+    int i,
+    at::ScalarType& storage_scalar_type,
+    bool& is_typed_storage) {
+  at::Storage storage;
+  if (!args[i]) {
+    storage = at::Storage();
+    is_typed_storage = false;
+    storage_scalar_type = at::ScalarType::Undefined;
+  } else {
+    std::tie(storage, storage_scalar_type, is_typed_storage) =
+        createStorageGetType(args[i]);
+  }
+  return storage;
+}
+
+inline c10::Stream PythonArgs::stream(int i) {
+  if (!args[i])
+    return c10::Stream(
+        c10::Stream::Default::DEFAULT, c10::Device(c10::DeviceType::CPU, -1));
+  if (!THPStream_Check(args[i])) {
+    throw TypeError(
+        "expected Stream object. Got '%s'", Py_TYPE(args[i])->tp_name);
+  }
+  return c10::Stream::unpack3(
+      ((THPStream*)args[i])->stream_id,
+      static_cast<c10::DeviceIndex>(((THPStream*)args[i])->device_index),
+      static_cast<c10::DeviceType>(((THPStream*)args[i])->device_type));
+}
+
+inline PyObject* PythonArgs::pyobject(int i) {
+  if (!args[i])
+    return Py_None;
+  return args[i];
+}
+
+/*
+ *
+ * Handle __torch_function__ overrides if we know that there are overloaded
+ * arguments.  All objects stored in r.overloaded_args must have a
+ * __torch_function__ implementation and the arguments must be ordered in order
+ * of precedence. Precedence goes from left to right in the order of the
+ * signature of the function the overloaded arguments were passed to, except
+ * subclasses are always considered before superclasses.
+ *
+ * If the result of calling __torch_function__ is NotImplemented, the
+ * next implementation in the precedence order is called. If all
+ * arguments return NotImplemented from their __torch_function__
+ * implementation, a TypeError is raised in Python.
+ *
+ * Assumes overloaded_args has at least one entry. All entries must have
+ * a __torch_function__ attribute that resolves to a callable that
+ * accepts a torch API function, a tuple of arguments, and a dict of
+ * keyword arguments for the torch API function.
+ *
+ * It is sufficient to call PythonArgs::has_torch_function before
+ * calling this function to verify that there are valid arguments
+ * present. If that is not done then special care must be taken to
+ * ensure there are arguments that are overloaded with
+ * __torch_function__.
+ *
+ * See torch._overrides.handle_torch_function for the equivalent
+ * code in the pure-python implementation.
+ *
+ * 'r' is a parsed PythonArgs instance, returned from
+ * PythonArgParser::parse.
+ *
+ * 'args' is a reference to the python tuple of arguments to the torch
+ * API function.
+ *
+ * 'kwargs' is a reference to the python dict of keyword arguments to
+ * the torch API function.
+ *
+ * 'torch_api' is a reference to a python torch API namespace.
+ *
+ * 'torch_api_function' is the reference to the original torch method, usually,
+ * we can use torch_api and func_name to get torch_api_function. In some cases,
+ * e.g., torch custom op, we create the function in C++, if we still use
+ * torch_api and func_name to fetch original api, a cyclic call will happen.
+ *
+ * 'overloaded_args' is the args which have overloaded __torch_function__.
+ *
+ * 'func_name' is the named of the original torch method.
+ *
+ * TODO: we could use different names for the following 'handle_torch_function'
+ * instead of overloading.
+ *
+ */
+// Used for Tensor methods with arguments.
+auto handle_torch_function(
+    PythonArgs& r,
+    PyObject* self,
+    PyObject* args,
+    PyObject* kwargs,
+    PyObject* torch_api,
+    const char* module_name,
+    const char* func_name_override = nullptr) -> PyObject*;
+
+// Used for functions which needs to parse python args.
+auto handle_torch_function(
+    PythonArgs& r,
+    PyObject* args,
+    PyObject* kwargs,
+    PyObject* torch_api,
+    const char* module_name,
+    const char* func_name_override = nullptr) -> PyObject*;
+
+// Used for functions that have no argument parsing.
+auto handle_torch_function(
+    PyObject* self,
+    const std::string& func_name,
+    PyObject* args = nullptr,
+    PyObject* kwargs = nullptr,
+    PyObject* torch_api = THPVariableClass,
+    const std::string& module_name = "torch.Tensor") -> PyObject*;
+
+// Used for functions created in C++, e.g., C++ custom op, which doesn't use
+// PythonArgParser to get overloaded_args.
+enum class TorchFunctionName { TorchFunction, TorchDispatch };
+
+auto TORCH_PYTHON_API handle_torch_function_no_python_arg_parser(
+    at::ArrayRef<PyObject*> overloaded_args,
+    PyObject* args,
+    PyObject* kwargs,
+    const char* func_name,
+    PyObject* torch_api_function,
+    const char* module_name,
+    TorchFunctionName torch_function_name = TorchFunctionName::TorchFunction)
+    -> PyObject*;
+
+// Used for getters of Tensor properties
+auto handle_torch_function_getter(
+    THPVariable* self,
+    const std::string& property_name) -> PyObject*;
+
+// Used for setters of Tensor properties.
+auto handle_torch_function_setter(
+    THPVariable* self,
+    const std::string& property_name,
+    PyObject* value) -> int;
+
+// Used for __getitem__ and __setitem__
+auto handle_torch_function_indexing(
+    PyObject* self,
+    PyObject* index,
+    PyObject* val = nullptr) -> PyObject*;
+
+/*
+ * Check if the input obj is Tensor type, including its subclass, or overloaded
+ * type. If the type defines __torch_function__, it also returns true.
+ * Otherwise returns flase. If the class is not torch.Tensor, and it defines
+ * __torch_function__, we append obj to overloaded_args.
+ *
+ * 'obj': the input argument to be checked
+ * 'overloaded_args': the vector to append the overloaded args.
+ */
+bool is_tensor_and_append_overloaded(
+    PyObject* obj,
+    std::vector<PyObject*>* overloaded_args);
+
+/*
+ * Check if the input obj is Tensor List or Tensor Tuple type. First check
+ * whether obj is Tuple or List type, if true, iterate over each element and
+ * check whether it is Tensor type, including its subclass or overloaded type.
+ * At the same time, the overloaded arg is appended to the overloaded_args.
+ *
+ * 'obj': the input argument to be checked
+ * 'overloaded_args': the vector to append the overloaded args.
+ * 'argnum': the number of total arguments of the function being checked.
+ * 'throw_error': whether throw error if any element in the list or tuple is
+ *                not tensor type or overloaded.
+ */
+bool is_tensor_list_and_append_overloaded(
+    PyObject* obj,
+    std::vector<PyObject*>* overloaded_args,
+    int argnum,
+    bool throw_error);
+
+/* Given an argument that is definitely a tensor and is definitely overloaded,
+ * append it to the overloaded arguments list.  Use this instead of
+ * is_tensor_and_append_overloaded in situations where you have a PyObject
+ * and you know it definitely is a Tensor and it is definitely overloaded.
+ *
+ * 'overloaded_args': the vector to append the overloaded args
+ * 'obj': the input tensor that is overloaded
+ */
+void append_overloaded_tensor(
+    std::vector<PyObject*>* overloaded_args,
+    PyObject* obj);
+
+/* Given an argument that is definitely a type and is definitely overloaded,
+ * append it to the overloaded arguments list. Use this only with
+ * __torch_dispatch__, where we operate on classes that have a
+ * __torch_dispatch__ classmethod.
+ *
+ * 'overloaded_args': the vector to append the overloaded type
+ * 'obj': the input class that has a __torch_dispatch__ classmethod.
+ */
+void append_overloaded_type(
+    std::vector<PyObject*>* overloaded_args,
+    PyObject* obj);
+
+} // namespace torch
diff --git a/shared/package-files/32/32528f32242acc327ab543207af44eaf99f4cb236e6e3226b91614a8cf231756/__init__.py b/shared/package-files/32/32528f32242acc327ab543207af44eaf99f4cb236e6e3226b91614a8cf231756/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..09ac19f97671e40ff5bd2ccbc20838cde962bcaf
--- /dev/null
+++ b/shared/package-files/32/32528f32242acc327ab543207af44eaf99f4cb236e6e3226b91614a8cf231756/__init__.py
@@ -0,0 +1,70 @@
+# This file also re-exports symbols for wider use. We configure mypy and flake8
+# to be aware that this file does this.
+
+from jedi.inference.compiled.value import CompiledValue, CompiledName, \
+    CompiledValueFilter, CompiledValueName, create_from_access_path
+from jedi.inference.base_value import LazyValueWrapper
+
+
+def builtin_from_name(inference_state, string):
+    typing_builtins_module = inference_state.builtins_module
+    if string in ('None', 'True', 'False'):
+        builtins, = typing_builtins_module.non_stub_value_set
+        filter_ = next(builtins.get_filters())
+    else:
+        filter_ = next(typing_builtins_module.get_filters())
+    name, = filter_.get(string)
+    value, = name.infer()
+    return value
+
+
+class ExactValue(LazyValueWrapper):
+    """
+    This class represents exact values, that makes operations like additions
+    and exact boolean values possible, while still being a "normal" stub.
+    """
+    def __init__(self, compiled_value):
+        self.inference_state = compiled_value.inference_state
+        self._compiled_value = compiled_value
+
+    def __getattribute__(self, name):
+        if name in ('get_safe_value', 'execute_operation', 'access_handle',
+                    'negate', 'py__bool__', 'is_compiled'):
+            return getattr(self._compiled_value, name)
+        return super().__getattribute__(name)
+
+    def _get_wrapped_value(self):
+        instance, = builtin_from_name(
+            self.inference_state, self._compiled_value.name.string_name).execute_with_values()
+        return instance
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._compiled_value)
+
+
+def create_simple_object(inference_state, obj):
+    """
+    Only allows creations of objects that are easily picklable across Python
+    versions.
+    """
+    assert type(obj) in (int, float, str, bytes, slice, complex, bool), repr(obj)
+    compiled_value = create_from_access_path(
+        inference_state,
+        inference_state.compiled_subprocess.create_simple_object(obj)
+    )
+    return ExactValue(compiled_value)
+
+
+def get_string_value_set(inference_state):
+    return builtin_from_name(inference_state, 'str').execute_with_values()
+
+
+def load_module(inference_state, dotted_name, **kwargs):
+    # Temporary, some tensorflow builtins cannot be loaded, so it's tried again
+    # and again and it's really slow.
+    if dotted_name.startswith('tensorflow.'):
+        return None
+    access_path = inference_state.compiled_subprocess.load_module(dotted_name=dotted_name, **kwargs)
+    if access_path is None:
+        return None
+    return create_from_access_path(inference_state, access_path)
diff --git a/shared/package-files/32/3255c26491aa76e261f8c934a472f0d3aa0dc5e5b57ef3cfde2c5fa3648cf1a9/__init__.pyi b/shared/package-files/32/3255c26491aa76e261f8c934a472f0d3aa0dc5e5b57ef3cfde2c5fa3648cf1a9/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..334ca4825094f1bb181fcea9a37d40cf5206d3b8
--- /dev/null
+++ b/shared/package-files/32/3255c26491aa76e261f8c934a472f0d3aa0dc5e5b57ef3cfde2c5fa3648cf1a9/__init__.pyi
@@ -0,0 +1,15 @@
+from .tz import (
+    datetime_ambiguous as datetime_ambiguous,
+    datetime_exists as datetime_exists,
+    gettz as gettz,
+    resolve_imaginary as resolve_imaginary,
+    tzfile as tzfile,
+    tzical as tzical,
+    tzlocal as tzlocal,
+    tzoffset as tzoffset,
+    tzrange as tzrange,
+    tzstr as tzstr,
+    tzutc as tzutc,
+)
+
+UTC: tzutc
diff --git a/shared/package-files/32/32579ffdd8269f4c338c5c59037a016ed1eb420adf759c3ff24d662d83593f80/base.py b/shared/package-files/32/32579ffdd8269f4c338c5c59037a016ed1eb420adf759c3ff24d662d83593f80/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..46db9a69dfb3e7aee5c150ac6795234cd455dfe5
--- /dev/null
+++ b/shared/package-files/32/32579ffdd8269f4c338c5c59037a016ed1eb420adf759c3ff24d662d83593f80/base.py
@@ -0,0 +1,290 @@
+import numpy as np
+
+
+def check_arguments(fun, y0, support_complex):
+    """Helper function for checking arguments common to all solvers."""
+    y0 = np.asarray(y0)
+    if np.issubdtype(y0.dtype, np.complexfloating):
+        if not support_complex:
+            raise ValueError("`y0` is complex, but the chosen solver does "
+                             "not support integration in a complex domain.")
+        dtype = complex
+    else:
+        dtype = float
+    y0 = y0.astype(dtype, copy=False)
+
+    if y0.ndim != 1:
+        raise ValueError("`y0` must be 1-dimensional.")
+
+    if not np.isfinite(y0).all():
+        raise ValueError("All components of the initial state `y0` must be finite.")
+
+    def fun_wrapped(t, y):
+        return np.asarray(fun(t, y), dtype=dtype)
+
+    return fun_wrapped, y0
+
+
+class OdeSolver:
+    """Base class for ODE solvers.
+
+    In order to implement a new solver you need to follow the guidelines:
+
+        1. A constructor must accept parameters presented in the base class
+           (listed below) along with any other parameters specific to a solver.
+        2. A constructor must accept arbitrary extraneous arguments
+           ``**extraneous``, but warn that these arguments are irrelevant
+           using `common.warn_extraneous` function. Do not pass these
+           arguments to the base class.
+        3. A solver must implement a private method `_step_impl(self)` which
+           propagates a solver one step further. It must return tuple
+           ``(success, message)``, where ``success`` is a boolean indicating
+           whether a step was successful, and ``message`` is a string
+           containing description of a failure if a step failed or None
+           otherwise.
+        4. A solver must implement a private method `_dense_output_impl(self)`,
+           which returns a `DenseOutput` object covering the last successful
+           step.
+        5. A solver must have attributes listed below in Attributes section.
+           Note that ``t_old`` and ``step_size`` are updated automatically.
+        6. Use `fun(self, t, y)` method for the system rhs evaluation, this
+           way the number of function evaluations (`nfev`) will be tracked
+           automatically.
+        7. For convenience, a base class provides `fun_single(self, t, y)` and
+           `fun_vectorized(self, t, y)` for evaluating the rhs in
+           non-vectorized and vectorized fashions respectively (regardless of
+           how `fun` from the constructor is implemented). These calls don't
+           increment `nfev`.
+        8. If a solver uses a Jacobian matrix and LU decompositions, it should
+           track the number of Jacobian evaluations (`njev`) and the number of
+           LU decompositions (`nlu`).
+        9. By convention, the function evaluations used to compute a finite
+           difference approximation of the Jacobian should not be counted in
+           `nfev`, thus use `fun_single(self, t, y)` or
+           `fun_vectorized(self, t, y)` when computing a finite difference
+           approximation of the Jacobian.
+
+    Parameters
+    ----------
+    fun : callable
+        Right-hand side of the system: the time derivative of the state ``y``
+        at time ``t``. The calling signature is ``fun(t, y)``, where ``t`` is a
+        scalar and ``y`` is an ndarray with ``len(y) = len(y0)``. ``fun`` must
+        return an array of the same shape as ``y``. See `vectorized` for more
+        information.
+    t0 : float
+        Initial time.
+    y0 : array_like, shape (n,)
+        Initial state.
+    t_bound : float
+        Boundary time --- the integration won't continue beyond it. It also
+        determines the direction of the integration.
+    vectorized : bool
+        Whether `fun` can be called in a vectorized fashion. Default is False.
+
+        If ``vectorized`` is False, `fun` will always be called with ``y`` of
+        shape ``(n,)``, where ``n = len(y0)``.
+
+        If ``vectorized`` is True, `fun` may be called with ``y`` of shape
+        ``(n, k)``, where ``k`` is an integer. In this case, `fun` must behave
+        such that ``fun(t, y)[:, i] == fun(t, y[:, i])`` (i.e. each column of
+        the returned array is the time derivative of the state corresponding
+        with a column of ``y``).
+
+        Setting ``vectorized=True`` allows for faster finite difference
+        approximation of the Jacobian by methods 'Radau' and 'BDF', but
+        will result in slower execution for other methods. It can also
+        result in slower overall execution for 'Radau' and 'BDF' in some
+        circumstances (e.g. small ``len(y0)``).
+    support_complex : bool, optional
+        Whether integration in a complex domain should be supported.
+        Generally determined by a derived solver class capabilities.
+        Default is False.
+
+    Attributes
+    ----------
+    n : int
+        Number of equations.
+    status : string
+        Current status of the solver: 'running', 'finished' or 'failed'.
+    t_bound : float
+        Boundary time.
+    direction : float
+        Integration direction: +1 or -1.
+    t : float
+        Current time.
+    y : ndarray
+        Current state.
+    t_old : float
+        Previous time. None if no steps were made yet.
+    step_size : float
+        Size of the last successful step. None if no steps were made yet.
+    nfev : int
+        Number of the system's rhs evaluations.
+    njev : int
+        Number of the Jacobian evaluations.
+    nlu : int
+        Number of LU decompositions.
+    """
+    TOO_SMALL_STEP = "Required step size is less than spacing between numbers."
+
+    def __init__(self, fun, t0, y0, t_bound, vectorized,
+                 support_complex=False):
+        self.t_old = None
+        self.t = t0
+        self._fun, self.y = check_arguments(fun, y0, support_complex)
+        self.t_bound = t_bound
+        self.vectorized = vectorized
+
+        if vectorized:
+            def fun_single(t, y):
+                return self._fun(t, y[:, None]).ravel()
+            fun_vectorized = self._fun
+        else:
+            fun_single = self._fun
+
+            def fun_vectorized(t, y):
+                f = np.empty_like(y)
+                for i, yi in enumerate(y.T):
+                    f[:, i] = self._fun(t, yi)
+                return f
+
+        def fun(t, y):
+            self.nfev += 1
+            return self.fun_single(t, y)
+
+        self.fun = fun
+        self.fun_single = fun_single
+        self.fun_vectorized = fun_vectorized
+
+        self.direction = np.sign(t_bound - t0) if t_bound != t0 else 1
+        self.n = self.y.size
+        self.status = 'running'
+
+        self.nfev = 0
+        self.njev = 0
+        self.nlu = 0
+
+    @property
+    def step_size(self):
+        if self.t_old is None:
+            return None
+        else:
+            return np.abs(self.t - self.t_old)
+
+    def step(self):
+        """Perform one integration step.
+
+        Returns
+        -------
+        message : string or None
+            Report from the solver. Typically a reason for a failure if
+            `self.status` is 'failed' after the step was taken or None
+            otherwise.
+        """
+        if self.status != 'running':
+            raise RuntimeError("Attempt to step on a failed or finished "
+                               "solver.")
+
+        if self.n == 0 or self.t == self.t_bound:
+            # Handle corner cases of empty solver or no integration.
+            self.t_old = self.t
+            self.t = self.t_bound
+            message = None
+            self.status = 'finished'
+        else:
+            t = self.t
+            success, message = self._step_impl()
+
+            if not success:
+                self.status = 'failed'
+            else:
+                self.t_old = t
+                if self.direction * (self.t - self.t_bound) >= 0:
+                    self.status = 'finished'
+
+        return message
+
+    def dense_output(self):
+        """Compute a local interpolant over the last successful step.
+
+        Returns
+        -------
+        sol : `DenseOutput`
+            Local interpolant over the last successful step.
+        """
+        if self.t_old is None:
+            raise RuntimeError("Dense output is available after a successful "
+                               "step was made.")
+
+        if self.n == 0 or self.t == self.t_old:
+            # Handle corner cases of empty solver and no integration.
+            return ConstantDenseOutput(self.t_old, self.t, self.y)
+        else:
+            return self._dense_output_impl()
+
+    def _step_impl(self):
+        raise NotImplementedError
+
+    def _dense_output_impl(self):
+        raise NotImplementedError
+
+
+class DenseOutput:
+    """Base class for local interpolant over step made by an ODE solver.
+
+    It interpolates between `t_min` and `t_max` (see Attributes below).
+    Evaluation outside this interval is not forbidden, but the accuracy is not
+    guaranteed.
+
+    Attributes
+    ----------
+    t_min, t_max : float
+        Time range of the interpolation.
+    """
+    def __init__(self, t_old, t):
+        self.t_old = t_old
+        self.t = t
+        self.t_min = min(t, t_old)
+        self.t_max = max(t, t_old)
+
+    def __call__(self, t):
+        """Evaluate the interpolant.
+
+        Parameters
+        ----------
+        t : float or array_like with shape (n_points,)
+            Points to evaluate the solution at.
+
+        Returns
+        -------
+        y : ndarray, shape (n,) or (n, n_points)
+            Computed values. Shape depends on whether `t` was a scalar or a
+            1-D array.
+        """
+        t = np.asarray(t)
+        if t.ndim > 1:
+            raise ValueError("`t` must be a float or a 1-D array.")
+        return self._call_impl(t)
+
+    def _call_impl(self, t):
+        raise NotImplementedError
+
+
+class ConstantDenseOutput(DenseOutput):
+    """Constant value interpolator.
+
+    This class used for degenerate integration cases: equal integration limits
+    or a system with 0 equations.
+    """
+    def __init__(self, t_old, t, value):
+        super().__init__(t_old, t)
+        self.value = value
+
+    def _call_impl(self, t):
+        if t.ndim == 0:
+            return self.value
+        else:
+            ret = np.empty((self.value.shape[0], t.shape[0]))
+            ret[:] = self.value[:, None]
+            return ret
diff --git a/shared/package-files/32/325baaa5dfd8c1575c76065f4aefbfa1575a764fc98d7f45b8f1858e7fd2e990/_tester.py b/shared/package-files/32/325baaa5dfd8c1575c76065f4aefbfa1575a764fc98d7f45b8f1858e7fd2e990/_tester.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cfddef7ddff87275ebf31eb7ec10e65d26f8668
--- /dev/null
+++ b/shared/package-files/32/325baaa5dfd8c1575c76065f4aefbfa1575a764fc98d7f45b8f1858e7fd2e990/_tester.py
@@ -0,0 +1,53 @@
+"""
+Entrypoint for testing from the top-level namespace.
+"""
+from __future__ import annotations
+
+import os
+import sys
+
+from pandas.compat._optional import import_optional_dependency
+
+PKG = os.path.dirname(os.path.dirname(__file__))
+
+
+def test(extra_args: list[str] | None = None, run_doctests: bool = False) -> None:
+    """
+    Run the pandas test suite using pytest.
+
+    By default, runs with the marks -m "not slow and not network and not db"
+
+    Parameters
+    ----------
+    extra_args : list[str], default None
+        Extra marks to run the tests.
+    run_doctests : bool, default False
+        Whether to only run the Python and Cython doctests. If you would like to run
+        both doctests/regular tests, just append "--doctest-modules"/"--doctest-cython"
+        to extra_args.
+
+    Examples
+    --------
+    >>> pd.test()  # doctest: +SKIP
+    running: pytest...
+    """
+    pytest = import_optional_dependency("pytest")
+    import_optional_dependency("hypothesis")
+    cmd = ["-m not slow and not network and not db"]
+    if extra_args:
+        if not isinstance(extra_args, list):
+            extra_args = [extra_args]
+        cmd = extra_args
+    if run_doctests:
+        cmd = [
+            "--doctest-modules",
+            "--doctest-cython",
+            f"--ignore={os.path.join(PKG, 'tests')}",
+        ]
+    cmd += [PKG]
+    joined = " ".join(cmd)
+    print(f"running: pytest {joined}")
+    sys.exit(pytest.main(cmd))
+
+
+__all__ = ["test"]
diff --git a/shared/package-files/32/325be43a6adf155859f3b9ea0c83fddd8302efb7535bf9ff49c0b6f6f3096c11/upsample_nearest2d_cuda_dispatch.h b/shared/package-files/32/325be43a6adf155859f3b9ea0c83fddd8302efb7535bf9ff49c0b6f6f3096c11/upsample_nearest2d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..632f69b67466ce199eeffb326eb350e4815454d7
--- /dev/null
+++ b/shared/package-files/32/325be43a6adf155859f3b9ea0c83fddd8302efb7535bf9ff49c0b6f6f3096c11/upsample_nearest2d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/32/325c454b26039c1311808ab3a44029b08985c725094a0e8476e6c35402a94da9/print_version.py b/shared/package-files/32/325c454b26039c1311808ab3a44029b08985c725094a0e8476e6c35402a94da9/print_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..0efef1f08b0c554711e52d63d9477e86a9d04bb2
--- /dev/null
+++ b/shared/package-files/32/325c454b26039c1311808ab3a44029b08985c725094a0e8476e6c35402a94da9/print_version.py
@@ -0,0 +1,357 @@
+#!/usr/bin/env python
+"""Print installed dependency versions for diagnostics"""
+
+from functools import reduce
+import os
+from pathlib import Path
+import platform
+import sys
+
+
+def safe_version(module, attr="__version__", *others):
+    """
+    Return a version attribute from a module, or a fallback message
+
+    Parameters
+    ----------
+    module : module
+        The imported module to inspect.
+    attr : str or list of str, optional
+        Name of the attribute (or chain of nested attribute names) to
+        look up on ``module``, e.g., ``"__version__"`` or
+        ``["version", "version"]``. Default is ``"__version__"``.
+    *others : str
+        Additional attribute names to try, in order, if ``attr`` is not
+        found on ``module``.
+
+    Returns
+    -------
+    str
+        The value of the located version attribute, or
+        ``"Cannot detect version"`` if none of the attributes exist.
+    """
+    if not isinstance(attr, list):
+        attr = [attr]
+    try:
+        return reduce(getattr, [module, *attr])
+    except AttributeError:
+        if others:
+            return safe_version(module, others[0], *others[1:])
+        return "Cannot detect version"
+
+
+def _show_versions_only():
+    print("\nINSTALLED VERSIONS")
+    print("------------------")
+    print("Python: {:d}.{:d}.{:d}.{}.{}".format(*sys.version_info[:]))
+
+    uname = platform.uname()
+    sysname = uname.system
+    release = uname.release
+    version = uname.version
+    machine = uname.machine
+    print(f"OS: {sysname} {release} {version} {machine}")
+    print(f"byteorder: {sys.byteorder}")
+    print("LC_ALL: {}".format(os.environ.get("LC_ALL", "None")))
+    print("LANG: {}".format(os.environ.get("LANG", "None")))
+    try:
+        import statsmodels
+
+        has_sm = True
+    except ImportError:
+        has_sm = False
+
+    print("\nstatsmodels\n===========\n")
+    if has_sm:
+        print(f"Installed: {safe_version(statsmodels)}")
+    else:
+        print("Not installed")
+
+    print("\nRequired Dependencies\n=====================\n")
+    try:
+        import Cython
+
+        print(f"cython: {safe_version(Cython)}")
+    except ImportError:
+        print("cython: Not installed")
+
+    try:
+        import numpy as np
+
+        print(f"numpy: {safe_version(np)}")
+    except ImportError:
+        print("numpy: Not installed")
+
+    try:
+        import scipy
+
+        print(f"scipy: {safe_version(scipy)}")
+    except ImportError:
+        print("scipy: Not installed")
+
+    try:
+        import pandas as pd
+
+        print(f"pandas: {safe_version(pd)}")
+    except ImportError:
+        print("pandas: Not installed")
+
+    try:
+        import dateutil
+
+        print(f"    dateutil: {safe_version(dateutil)}")
+    except ImportError:
+        print("    dateutil: not installed")
+
+    try:
+        import patsy
+
+        print(f"patsy: {safe_version(patsy)}")
+    except ImportError:
+        print("patsy: Not installed")
+
+    try:
+        import formulaic
+
+        print(f"formulaic: {safe_version(formulaic)}")
+    except ImportError:
+        print("formulaic: Not installed")
+
+    print("\nOptional Dependencies\n=====================\n")
+
+    try:
+        import matplotlib as mpl
+
+        print(f"matplotlib: {safe_version(mpl)}")
+    except ImportError:
+        print("matplotlib: Not installed")
+
+    try:
+        from cvxopt import info
+
+        print("cvxopt: {}".format(safe_version(info, "version")))
+    except ImportError:
+        print("cvxopt: Not installed")
+
+    try:
+        import joblib
+
+        print(f"joblib: {safe_version(joblib)} ")
+    except ImportError:
+        print("joblib: Not installed")
+
+    print("\nDeveloper Tools\n================\n")
+
+    try:
+        import IPython
+
+        print(f"IPython: {safe_version(IPython)}")
+    except ImportError:
+        print("IPython: Not installed")
+    try:
+        import jinja2
+
+        print(f"    jinja2: {safe_version(jinja2)}")
+    except ImportError:
+        print("    jinja2: Not installed")
+
+    try:
+        import sphinx
+
+        print(f"sphinx: {safe_version(sphinx)}")
+    except ImportError:
+        print("sphinx: Not installed")
+
+    try:
+        import pygments
+
+        print(f"    pygments: {safe_version(pygments)}")
+    except ImportError:
+        print("    pygments: Not installed")
+
+    try:
+        import pytest
+
+        print(f"pytest: {safe_version(pytest)} ({Path(pytest.__file__).parent})")
+    except ImportError:
+        print("pytest: Not installed")
+
+    try:
+        import virtualenv
+
+        print(f"virtualenv: {safe_version(virtualenv)}")
+    except ImportError:
+        print("virtualenv: Not installed")
+
+    print("\n")
+
+
+def show_versions(show_dirs=True):
+    """
+    List the versions of statsmodels and any installed dependencies
+
+    Parameters
+    ----------
+    show_dirs : bool, optional
+        Flag indicating to show module locations. Default is True.
+
+    """
+    if not show_dirs:
+        _show_versions_only()
+        return
+    print("\nINSTALLED VERSIONS")
+    print("------------------")
+    print("Python: {:d}.{:d}.{:d}.{}.{}".format(*sys.version_info[:]))
+    uname = platform.uname()
+    sysname = uname.system
+    release = uname.release
+    version = uname.version
+    machine = uname.machine
+    print(f"OS: {sysname} {release} {version} {machine}")
+    print(f"byteorder: {sys.byteorder}")
+    print("LC_ALL: {}".format(os.environ.get("LC_ALL", "None")))
+    print("LANG: {}".format(os.environ.get("LANG", "None")))
+
+    try:
+        import statsmodels
+
+        has_sm = True
+    except ImportError:
+        has_sm = False
+
+    print("\nstatsmodels\n===========\n")
+    if has_sm:
+        print(
+            f"Installed: {safe_version(statsmodels)} ({Path(statsmodels.__file__).parent})"
+            )
+    else:
+        print("Not installed")
+
+    print("\nRequired Dependencies\n=====================\n")
+    try:
+        import Cython
+
+        print(f"cython: {safe_version(Cython)} ({Path(Cython.__file__).parent})")
+    except ImportError:
+        print("cython: Not installed")
+
+    try:
+        import numpy as np
+
+        print(f"numpy: {safe_version(np)} ({Path(np.__file__).parent})")
+    except ImportError:
+        print("numpy: Not installed")
+
+    try:
+        import scipy
+
+        print(f"scipy: {safe_version(scipy)} ({Path(scipy.__file__).parent})")
+    except ImportError:
+        print("scipy: Not installed")
+
+    try:
+        import pandas as pd
+
+        print(
+            "pandas: {} ({})".format(
+                safe_version(pd, "__version__"),
+                Path(pd.__file__).parent,
+            )
+        )
+    except ImportError:
+        print("pandas: Not installed")
+
+    try:
+        import dateutil
+
+        print(
+            f"    dateutil: {safe_version(dateutil)} ({Path(dateutil.__file__).parent})"
+            )
+    except ImportError:
+        print("    dateutil: not installed")
+
+    try:
+        import patsy
+
+        print(f"patsy: {safe_version(patsy)} ({Path(patsy.__file__).parent})")
+    except ImportError:
+        print("patsy: Not installed")
+
+    print("\nOptional Dependencies\n=====================\n")
+
+    try:
+        import matplotlib as mpl
+
+        print(f"matplotlib: {safe_version(mpl)} ({Path(mpl.__file__).parent})")
+        print("    backend: {} ".format(mpl.rcParams["backend"]))
+    except ImportError:
+        print("matplotlib: Not installed")
+
+    try:
+        from cvxopt import info
+
+        print(
+            "cvxopt: {} ({})".format(
+                safe_version(info, "version"), Path(info.__file__).parent
+            )
+        )
+    except ImportError:
+        print("cvxopt: Not installed")
+
+    try:
+        import joblib
+
+        print(f"joblib: {safe_version(joblib)} ({Path(joblib.__file__).parent})")
+    except ImportError:
+        print("joblib: Not installed")
+
+    print("\nDeveloper Tools\n================\n")
+
+    try:
+        import IPython
+
+        print(f"IPython: {safe_version(IPython)} ({Path(IPython.__file__).parent})")
+    except ImportError:
+        print("IPython: Not installed")
+    try:
+        import jinja2
+
+        print(f"    jinja2: {safe_version(jinja2)} ({Path(jinja2.__file__).parent})")
+    except ImportError:
+        print("    jinja2: Not installed")
+
+    try:
+        import sphinx
+
+        print(f"sphinx: {safe_version(sphinx)} ({Path(sphinx.__file__).parent})")
+    except ImportError:
+        print("sphinx: Not installed")
+
+    try:
+        import pygments
+
+        print(
+            f"    pygments: {safe_version(pygments)} ({Path(pygments.__file__).parent})"
+        )
+    except ImportError:
+        print("    pygments: Not installed")
+
+    try:
+        import pytest
+
+        print(f"pytest: {safe_version(pytest)} ({Path(pytest.__file__).parent})")
+    except ImportError:
+        print("pytest: Not installed")
+
+    try:
+        import virtualenv
+
+        print(f"virtualenv: {safe_version(virtualenv)} ({Path(virtualenv.__file__).parent})")
+    except ImportError:
+        print("virtualenv: Not installed")
+
+    print("\n")
+
+
+if __name__ == "__main__":
+    show_versions()
diff --git a/shared/package-files/32/326015b294fc0c34ea0ee2fc6498712bada0d2572ca38bea23a40d43d0ac7e84/setup.py b/shared/package-files/32/326015b294fc0c34ea0ee2fc6498712bada0d2572ca38bea23a40d43d0ac7e84/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..05bef3000147a1137731c25b6d07d9491b461112
--- /dev/null
+++ b/shared/package-files/32/326015b294fc0c34ea0ee2fc6498712bada0d2572ca38bea23a40d43d0ac7e84/setup.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""
+setup.py for installing F2PY
+
+Usage:
+   pip install .
+
+Copyright 2001-2005 Pearu Peterson all rights reserved,
+Pearu Peterson <pearu@cens.ioc.ee>
+Permission to use, modify, and distribute this software is given under the
+terms of the NumPy License.
+
+NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
+$Revision: 1.32 $
+$Date: 2005/01/30 17:22:14 $
+Pearu Peterson
+
+"""
+from numpy.distutils.core import setup
+from numpy.distutils.misc_util import Configuration
+
+
+from __version__ import version
+
+
+def configuration(parent_package='', top_path=None):
+    config = Configuration('f2py', parent_package, top_path)
+    config.add_subpackage('tests')
+    config.add_subpackage('_backends')
+    config.add_data_dir('tests/src')
+    config.add_data_files(
+        'src/fortranobject.c',
+        'src/fortranobject.h',
+        '_backends/meson.build.template',
+    )
+    config.add_data_files('*.pyi')
+    return config
+
+
+if __name__ == "__main__":
+
+    config = configuration(top_path='')
+    config = config.todict()
+
+    config['classifiers'] = [
+        'Development Status :: 5 - Production/Stable',
+        'Intended Audience :: Developers',
+        'Intended Audience :: Science/Research',
+        'License :: OSI Approved :: NumPy License',
+        'Natural Language :: English',
+        'Operating System :: OS Independent',
+        'Programming Language :: C',
+        'Programming Language :: Fortran',
+        'Programming Language :: Python',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Software Development :: Code Generators',
+    ]
+    setup(version=version,
+          description="F2PY - Fortran to Python Interface Generator",
+          author="Pearu Peterson",
+          author_email="pearu@cens.ioc.ee",
+          maintainer="Pearu Peterson",
+          maintainer_email="pearu@cens.ioc.ee",
+          license="BSD",
+          platforms="Unix, Windows (mingw|cygwin), Mac OSX",
+          long_description="""\
+The Fortran to Python Interface Generator, or F2PY for short, is a
+command line tool (f2py) for generating Python C/API modules for
+wrapping Fortran 77/90/95 subroutines, accessing common blocks from
+Python, and calling Python functions from Fortran (call-backs).
+Interfacing subroutines/data from Fortran 90/95 modules is supported.""",
+          url="https://numpy.org/doc/stable/f2py/",
+          keywords=['Fortran', 'f2py'],
+          **config)
diff --git a/shared/package-files/32/3261b3fe6c85f84613e668eb86cccbd80d61965d88b7605a76503f9cecd97bc3/test_tmodel.py b/shared/package-files/32/3261b3fe6c85f84613e668eb86cccbd80d61965d88b7605a76503f9cecd97bc3/test_tmodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fc16a4691abf3d3ff1e5b4ca8795e05f6ccc6ea
--- /dev/null
+++ b/shared/package-files/32/3261b3fe6c85f84613e668eb86cccbd80d61965d88b7605a76503f9cecd97bc3/test_tmodel.py
@@ -0,0 +1,194 @@
+"""
+
+Created on Sun Jun 30 20:25:22 2013
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+
+from statsmodels.miscmodels.tmodel import TLinearModel
+from statsmodels.tools.testing import Holder
+from statsmodels.tools.tools import add_constant
+
+mm = Holder()
+mm.date_label = ["Apr.1982",  "Apr.1983", "Apr.1984", "Apr.1985", "Apr.1986",
+                 "Aug.1982", "Aug.1983",  "Aug.1984", "Aug.1985", "Aug.1986",
+                 "Dec.1982", "Dec.1983", "Dec.1984",  "Dec.1985", "Dec.1986",
+                 "Feb.1284", "Feb.1982", "Feb.1983", "Feb.1985",  "Feb.1986",
+                 "Jan.1982", "Jan.1983", "Jan.1984", "Jan.1985", "Jan.1986",
+                 "Jul.1982", "July1983", "July1984", "July1985", "July1986",
+                 "June1982",  "June1983", "June1984", "June1985", "June1986",
+                 "Mar.1982", "Mar.1983",  "Mar.1984", "Mar.1985", "Mar.1986",
+                 "May1982", "May1983", "May1984",  "May1985", "May1986",
+                 "Nov.1982", "Nov.1983", "Nov.1984", "Nov.1985",  "Nov.1986",
+                 "Oct.1982", "Oct.1983", "Oct.1984", "Oct.1985", "Oct.1986",
+                 "Sept.1982", "Sept.1983", "Sept.1984", "Sept.1985",
+                 "Sept.1986"]
+
+mm.m_marietta = np.array([
+     -0.1365, -0.0769, -0.0575, 0.0526, -0.0449, -0.0859, -0.0742, 0.6879,
+     -0.077, 0.085, 0.003, 0.0754, -0.0412, -0.089, 0.2319, 0.1087, 0.0375,
+     0.0958, 0.0174, -0.0724, 0.075, -0.0588, -0.062, -0.0378, 0.0169,
+     -0.0799, -0.0147, 0.0106, -0.0421, -0.0036, 0.0876, 0.1025, -0.0499,
+     0.1953, -0.0714, 0.0469, 0.1311, 0.0461, -0.0328, -0.0096, 0.1272,
+     -0.0077, 0.0165, -0.015, -0.1479, -0.0065, 0.039, 0.0223, -0.069,
+     0.1338, 0.1458, 0.0063, 0.0692, -0.0239, -0.0568, 0.0814, -0.0889,
+     -0.0887, 0.1037, -0.1163
+    ])
+mm.CRSP = np.array([
+     -0.03, -0.0584, -0.0181, 0.0306, -0.0397, -0.0295, -0.0316, 0.1176,
+     0.0075, 0.1098, 0.0408, 0.0095, 0.0301, 0.0221, 0.0269, 0.0655,
+     -0.003, 0.0325, -0.0374, 0.0049, 0.0105, -0.0257, 0.0186, -0.0155,
+     -0.0165, -0.044, 0.0094, -0.0028, -0.0591, 0.0158, -0.0238, 0.1031,
+     -0.0065, -0.0067, -0.0167, 0.0188, 0.0733, 0.0105, -0.007, -0.0099,
+     0.0521, 0.0117, -0.0099, -0.0102, -0.0428, 0.0376, 0.0628, 0.0391,
+     2e-04, 0.0688, 0.0486, -0.0174, 0.046, 0.01, -0.0594, 0.068, -0.0839,
+     0.0481, 0.0136, -0.0322
+    ])
+mm.am_can = np.array([
+     -0.0596, -0.17, 0.0276, 0.0058, -0.0106, 0.045, -0.0243, 0.1135,
+     -0.0331, 0.0468, -0.0223, -0.0026, 0.0166, 0.0343, 0.0443, 0.1477,
+     0.1728, -0.0372, -0.0451, -0.0257, 0.0509, 0.0035, 0.1334, -0.0458,
+     0.1199, -0.0766, -0.0511, -0.0194, -0.0687, 0.0928, -0.0704, 0.0905,
+     0.0232, -0.0054, 0.0082, 0.0242, 0.0153, 0.0016, 0.028, 0.0088,
+     0.0734, 0.0315, -0.0276, 0.0162, -0.0975, 0.0563, 0.1368, -0.069,
+     0.1044, 0.1636, -0.019, -0.0746, 0.0433, 0.0306, 0.0636, 0.0917,
+     -0.0796, 0.0778, -0.0353, -0.0137
+    ])
+mm.date = np.array([
+     21, 17, 36, 1, 41, 31, 26, 6, 56, 51, 46, 11, 22, 18, 37, 2, 42, 32,
+     27, 7, 57, 52, 47, 12, 23, 16, 38, 3, 43, 33, 28, 8, 58, 53, 48, 13,
+     24, 19, 39, 4, 44, 34, 29, 9, 59, 54, 49, 14, 25, 20, 40, 5, 45, 35,
+     30, 10, 60, 55, 50, 15
+    ])
+
+
+class CheckTLinearModelMixin:
+
+    def test_basic(self):
+        res1 = self.res1
+        res2 = self.res2
+        # location
+        assert_allclose(res1.params[:-2], res2.loc_fit.coefficients, atol=3e-5)
+        assert_allclose(res1.bse[:-2], res2.loc_fit.table[:, 1], rtol=0.003, atol=1e-5)
+        assert_allclose(res1.tvalues[:-2], res2.loc_fit.table[:, 2], rtol=0.003, atol=1e-5)
+        assert_allclose(res1.pvalues[:-2], res2.loc_fit.table[:, 3], rtol=0.009, atol=1e-5)
+
+        # df
+        assert_allclose(res1.params[-2], res2.dof, rtol=5e-5)
+        assert_allclose(res1.bse[-2], res2.dofse, rtol=0.16, atol=1e-5)
+        # scale
+        scale_est = np.sqrt(res2.scale_fit.fitted_values.mean())
+        assert_allclose(res1.params[-1], scale_est, atol=1e-5)
+
+        assert_allclose(res1.llf, res2.logLik, atol=1e-5)
+
+    def test_bse(self):
+        # check that they are roughly the same
+        res1 = self.res1
+        assert_allclose(res1.bsejac, res1.bse, rtol=0.15, atol=0.002)
+        assert_allclose(res1.bsejac, res1.bse, rtol=0.1, atol=0.004)
+
+    def test_fitted(self):
+        res1 = self.res1
+        res2 = self.res2
+
+        fittedvalues = res1.predict()
+        resid = res1.model.endog - fittedvalues
+        assert_allclose(fittedvalues, res2.loc_fit.fitted_values, rtol=0.00025)
+        assert_allclose(resid, res2.loc_fit.residuals, atol=2e-6)  # rtol=0.00036)
+        # TODO: no resid available as attribute
+        # assert_allclose(res1.resid, res2.loc_fit.residuals)
+        # assert_allclose(res1.fittedvalues, res2.loc_fit.fitted_values)
+
+    def test_formula(self):
+        res1 = self.res1
+        resf = self.resf
+        # converges slightly differently why?
+        assert_allclose(res1.params, resf.params,  atol=1e-4)  # rtol=2e-5,
+        assert_allclose(res1.bse, resf.bse, rtol=5e-5)
+
+        assert_allclose(res1.model.endog, resf.model.endog, rtol=1e-10)
+        assert_allclose(res1.model.exog, resf.model.exog, rtol=1e-10)
+
+    def test_df(self):
+        res = self.res1
+        k_extra = getattr(self, "k_extra", 0)
+        nobs, k_vars = res.model.exog.shape
+        assert res.df_resid == nobs - k_vars - k_extra
+        assert res.df_model == k_vars - 1  # -1 for constant
+        assert len(res.params) == k_vars + k_extra
+
+    @pytest.mark.smoke
+    def test_smoke(self):  # TODO: break into well-scoped tests
+        res1 = self.res1
+        resf = self.resf
+        contr = np.eye(len(res1.params))
+
+        # smoke test for summary and t_test, f_test
+        res1.summary()
+        res1.t_test(contr)
+        res1.f_test(contr)
+
+        resf.summary()
+        resf.t_test(contr)
+        resf.f_test(contr)
+
+
+class TestTModel(CheckTLinearModelMixin):
+
+    @classmethod
+    def setup_class(cls):
+        endog = mm.m_marietta
+        exog = add_constant(mm.CRSP)
+        mod = TLinearModel(endog, exog)
+        res = mod.fit(method="bfgs", disp=False)
+        with pytest.warns(DeprecationWarning, match="Using"):
+            modf = TLinearModel.from_formula(
+                "price ~ CRSP", data={"price": mm.m_marietta, "CRSP": mm.CRSP}
+            )
+        resf = modf.fit(method="bfgs", disp=False)
+        from .results_tmodel import res_t_dfest as res2
+        cls.res2 = res2
+        cls.res1 = res  # take from module scope temporarily
+        cls.resf = resf
+        cls.k_extra = 2
+
+
+class TestTModelFixed:
+
+    @classmethod
+    def setup_class(cls):
+        endog = mm.m_marietta
+        exog = add_constant(mm.CRSP)
+        mod = TLinearModel(endog, exog, fix_df=3)
+        res = mod.fit(method="bfgs", disp=False)
+        with pytest.warns(DeprecationWarning, match="Using"):
+            modf = TLinearModel.from_formula(
+                "price ~ CRSP", data={"price": mm.m_marietta, "CRSP": mm.CRSP}, fix_df=3
+            )
+        resf = modf.fit(method="bfgs", disp=False)
+        # TODO: no reference results yet
+        # from results_tmodel import res_t_dfest as res2
+        # cls.res2 = res2
+        cls.res1 = res  # take from module scope temporarily
+        cls.resf = resf
+        cls.k_extra = 1
+
+    @pytest.mark.smoke
+    def test_smoke(self):  # TODO: break into well-scoped tests
+        res1 = self.res1
+        resf = self.resf
+        contr = np.eye(len(res1.params))
+
+        # smoke test for summary and t_test, f_test
+        res1.summary()
+        res1.t_test(contr)
+        res1.f_test(contr)
+
+        resf.summary()
+        resf.t_test(contr)
+        resf.f_test(contr)
diff --git a/shared/package-files/32/3265cff9e0f5336766e448b8108c045224927c63a259a850f9412247f58138f4/arima112_results.py b/shared/package-files/32/3265cff9e0f5336766e448b8108c045224927c63a259a850f9412247f58138f4/arima112_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cbecbcae84b574d57c82c9ac6b0d27e0bfd36aa
--- /dev/null
+++ b/shared/package-files/32/3265cff9e0f5336766e448b8108c045224927c63a259a850f9412247f58138f4/arima112_results.py
@@ -0,0 +1,1315 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+llf = np.array([-245.40783909604])
+
+nobs = np.array([202])
+
+k = np.array([5])
+
+k_exog = np.array([1])
+
+sigma = np.array([.8100467417583])
+
+chi2 = np.array([2153.20304012])
+
+df_model = np.array([3])
+
+k_ar = np.array([1])
+
+k_ma = np.array([2])
+
+params = np.array([
+    .92817025087557,
+    -.89593490671979,
+    1.3025011610587,
+    .30250063082791,
+    .8100467417583])
+
+cov_params = np.array([
+    .00638581549851,
+    .0001858475428,
+    2.8222806545671,
+    .8538806860364,
+    -1.1429127085819,
+    .0001858475428,
+    .00132037832566,
+    -.14420925344502,
+    -.04447007102804,
+    .0576156187095,
+    2.8222806545671,
+    -.14420925344502,
+    40397.568324803,
+    12222.977216556,
+    -16359.547340433,
+    .8538806860364,
+    -.04447007102804,
+    12222.977216556,
+    3698.2722243412,
+    -4949.8609964351,
+    -1.1429127085819,
+    .0576156187095,
+    -16359.547340433,
+    -4949.8609964351,
+    6625.0231409853]).reshape(5, 5)
+
+xb = np.array([
+    .92817026376724,
+    .92817026376724,
+    .69511789083481,
+    .77192437648773,
+    .66135895252228,
+    .77525061368942,
+    .64687132835388,
+    .79659670591354,
+    .65842008590698,
+    .71215486526489,
+    .69971066713333,
+    .72092038393021,
+    .68201982975006,
+    .76510280370712,
+    .64253836870193,
+    .78239262104034,
+    .64609551429749,
+    .74087703227997,
+    .71774411201477,
+    .7119727730751,
+    .73067259788513,
+    .67785596847534,
+    .70898467302322,
+    .71334755420685,
+    .72984194755554,
+    .7017787694931,
+    .75292426347733,
+    .67507487535477,
+    .78219056129456,
+    .78040039539337,
+    .71250075101852,
+    .82028061151505,
+    .63505899906158,
+    .79452306032181,
+    .72773635387421,
+    .79555094242096,
+    .76685506105423,
+    .77427339553833,
+    .82101213932037,
+    .77917188405991,
+    .78917801380157,
+    .86641925573349,
+    .78457218408585,
+    .83697980642319,
+    .83281791210175,
+    .85224026441574,
+    .75030690431595,
+    .8551008105278,
+    .78025943040848,
+    .72790426015854,
+    .84552866220474,
+    .72061747312546,
+    .78669738769531,
+    .73868823051453,
+    .78071022033691,
+    .78002023696899,
+    .83737623691559,
+    .98988044261932,
+    .72882527112961,
+    1.2245427370071,
+    .85331875085831,
+    1.1637357473373,
+    .86477434635162,
+    1.3248475790024,
+    .81245219707489,
+    .98008638620377,
+    .85591268539429,
+    1.0162551403046,
+    .8165408372879,
+    .78947591781616,
+    .94166398048401,
+    .93266606330872,
+    .85924750566483,
+    1.1245046854019,
+    .75576168298721,
+    1.0030617713928,
+    .91267073154449,
+    1.0848042964935,
+    1.0778224468231,
+    1.1551086902618,
+    .97817331552505,
+    1.4012540578842,
+    1.2360861301422,
+    1.3335381746292,
+    1.4352362155914,
+    1.4941285848618,
+    .9415163397789,
+    1.437669634819,
+    1.2404690980911,
+    1.2285294532776,
+    1.3219480514526,
+    1.1560415029526,
+    .83524394035339,
+    .87116771936417,
+    1.5561962127686,
+    .47358739376068,
+    .78093349933624,
+    .90549737215042,
+    1.0217791795731,
+    .86397403478622,
+    1.1526786088943,
+    .87662625312805,
+    .95803648233414,
+    .89513635635376,
+    .85281348228455,
+    1.0852742195129,
+    .76808404922485,
+    .96872144937515,
+    1.0732915401459,
+    .02145584858954,
+    1.3687089681625,
+    .50049883127213,
+    1.3895837068558,
+    .6889950633049,
+    1.2795144319534,
+    .7050421833992,
+    1.2218985557556,
+    .74481928348541,
+    1.3074514865875,
+    .7919961810112,
+    1.2807723283768,
+    1.0120536088943,
+    1.1938916444778,
+    .68923074007034,
+    1.6174983978271,
+    .64740318059921,
+    1.4949930906296,
+    1.2678960561752,
+    1.0586776733398,
+    .55762887001038,
+    1.2790743112564,
+    .66515874862671,
+    1.2538269758224,
+    .70554333925247,
+    1.2391568422318,
+    .75241559743881,
+    1.2129040956497,
+    .69235223531723,
+    1.0785228013992,
+    .8043577671051,
+    1.0037930011749,
+    .78750842809677,
+    1.1880930662155,
+    .74399447441101,
+    1.1791603565216,
+    .85870295763016,
+    1.0032330751419,
+    .8019300699234,
+    1.1696527004242,
+    .92376220226288,
+    .99186056852341,
+    .94733852148056,
+    1.0748032331467,
+    .64247089624405,
+    .95419937372208,
+    .92043441534042,
+    .8104555606842,
+    .66252142190933,
+    1.1178470849991,
+    .69223344326019,
+    1.0570795536041,
+    .90239083766937,
+    .95320242643356,
+    1.0541093349457,
+    1.0082466602325,
+    1.1376332044601,
+    1.1841852664948,
+    .90440809726715,
+    1.2733660936356,
+    .66835701465607,
+    1.1515763998032,
+    .44600257277489,
+    .93500959873199,
+    1.0847823619843,
+    .83353632688522,
+    1.0442448854446,
+    1.077241897583,
+    .71010553836823,
+    .89557945728302,
+    1.0163468122482,
+    1.094814658165,
+    .89641278982162,
+    1.2808450460434,
+    1.0223702192307,
+    .96094745397568,
+    1.309353351593,
+    .73499941825867,
+    2.4902238845825,
+    -.2579345703125,
+    1.9272556304932,
+    .53125941753387,
+    .7708500623703,
+    1.0312130451202,
+    1.6360099315643,
+    .6022145152092,
+    1.6338716745377,
+    1.3494771718979,
+    1.1322995424271,
+    2.1901025772095,
+    -.72639065980911,
+    -.37026473879814,
+    1.2391144037247,
+    1.1353877782822])
+
+y = np.array([
+    np.nan,
+    29.908170700073,
+    29.84511756897,
+    30.121925354004,
+    30.031360626221,
+    30.315252304077,
+    30.196870803833,
+    30.5465965271,
+    30.498420715332,
+    30.52215385437,
+    30.619710922241,
+    30.70092010498,
+    30.722021102905,
+    30.975101470947,
+    30.862537384033,
+    31.162391662598,
+    31.086095809937,
+    31.220876693726,
+    31.407745361328,
+    31.461973190308,
+    31.670673370361,
+    31.627857208252,
+    31.728984832764,
+    31.833349227905,
+    32.009841918945,
+    32.08177947998,
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+    .66191446781158,
+    .6618646979332,
+    .66181582212448,
+    .66176778078079,
+    .66172051429749,
+    .66167408227921,
+    .66162836551666,
+    .66158348321915,
+    .66153925657272,
+    .66149580478668,
+    .66145300865173,
+    .66141092777252,
+    .6613695025444,
+    .66132873296738,
+    .6612885594368,
+    .66124904155731,
+    .66121011972427,
+    .66117179393768,
+    .66113406419754,
+    .6610968708992,
+    .66106027364731,
+    .66102415323257,
+    .66098862886429,
+    .66095358133316,
+    .66091907024384,
+    .66088503599167,
+    .66085147857666,
+    .66081839799881,
+    .66078579425812,
+    .66075360774994,
+    .66072189807892,
+    .66069066524506,
+    .66065979003906,
+    .66062939167023,
+    .66059935092926,
+    .66056972742081,
+    .66054052114487,
+    .6605116724968,
+    .66048324108124,
+    .6604551076889,
+    .66042739152908,
+    .66040003299713,
+    .66037303209305,
+    .66034632921219,
+    .66032004356384,
+    .66029399633408,
+    .66026836633682,
+    .66024297475815,
+    .66021794080734,
+    .66019320487976,
+    .6601687669754,
+    .66014462709427,
+    .66012072563171,
+    .66009718179703,
+    .66007387638092,
+    .66005086898804,
+    .66002810001373,
+    .66000562906265,
+    .65998339653015,
+    .65996146202087,
+    .65993976593018,
+    .65991830825806,
+    .65989708900452,
+    .65987610816956,
+    .65985536575317,
+    .65983480215073,
+    .6598145365715,
+    .65979450941086,
+    .65977466106415,
+    .65975499153137,
+    .65973562002182,
+    .6597164273262,
+    .65969741344452,
+    .65967857837677,
+    .6596599817276,
+    .65964162349701,
+    .65962338447571,
+    .65960538387299,
+    .6595875620842,
+    .65956991910934,
+    .65955245494843,
+    .65953516960144,
+    .65951806306839,
+    .65950113534927,
+    .65948438644409,
+    .6594677567482,
+    .65945136547089,
+    .65943509340286,
+    .65941900014877,
+    .65940302610397,
+    .65938723087311,
+    .65937161445618,
+    .65935611724854,
+    .65934079885483,
+    .65932559967041,
+    .65931057929993,
+    .65929567813873,
+    .65928089618683,
+    .65926629304886,
+    .65925180912018,
+    .65923744440079,
+    .65922319889069,
+    .65920913219452,
+    .65919518470764,
+    .65918135643005])
+
+stdp = np.array([
+    .92817026376724,
+    .92817026376724,
+    .69511789083481,
+    .77192437648773,
+    .66135895252228,
+    .77525061368942,
+    .64687132835388,
+    .79659670591354,
+    .65842008590698,
+    .71215486526489,
+    .69971066713333,
+    .72092038393021,
+    .68201982975006,
+    .76510280370712,
+    .64253836870193,
+    .78239262104034,
+    .64609551429749,
+    .74087703227997,
+    .71774411201477,
+    .7119727730751,
+    .73067259788513,
+    .67785596847534,
+    .70898467302322,
+    .71334755420685,
+    .72984194755554,
+    .7017787694931,
+    .75292426347733,
+    .67507487535477,
+    .78219056129456,
+    .78040039539337,
+    .71250075101852,
+    .82028061151505,
+    .63505899906158,
+    .79452306032181,
+    .72773635387421,
+    .79555094242096,
+    .76685506105423,
+    .77427339553833,
+    .82101213932037,
+    .77917188405991,
+    .78917801380157,
+    .86641925573349,
+    .78457218408585,
+    .83697980642319,
+    .83281791210175,
+    .85224026441574,
+    .75030690431595,
+    .8551008105278,
+    .78025943040848,
+    .72790426015854,
+    .84552866220474,
+    .72061747312546,
+    .78669738769531,
+    .73868823051453,
+    .78071022033691,
+    .78002023696899,
+    .83737623691559,
+    .98988044261932,
+    .72882527112961,
+    1.2245427370071,
+    .85331875085831,
+    1.1637357473373,
+    .86477434635162,
+    1.3248475790024,
+    .81245219707489,
+    .98008638620377,
+    .85591268539429,
+    1.0162551403046,
+    .8165408372879,
+    .78947591781616,
+    .94166398048401,
+    .93266606330872,
+    .85924750566483,
+    1.1245046854019,
+    .75576168298721,
+    1.0030617713928,
+    .91267073154449,
+    1.0848042964935,
+    1.0778224468231,
+    1.1551086902618,
+    .97817331552505,
+    1.4012540578842,
+    1.2360861301422,
+    1.3335381746292,
+    1.4352362155914,
+    1.4941285848618,
+    .9415163397789,
+    1.437669634819,
+    1.2404690980911,
+    1.2285294532776,
+    1.3219480514526,
+    1.1560415029526,
+    .83524394035339,
+    .87116771936417,
+    1.5561962127686,
+    .47358739376068,
+    .78093349933624,
+    .90549737215042,
+    1.0217791795731,
+    .86397403478622,
+    1.1526786088943,
+    .87662625312805,
+    .95803648233414,
+    .89513635635376,
+    .85281348228455,
+    1.0852742195129,
+    .76808404922485,
+    .96872144937515,
+    1.0732915401459,
+    .02145584858954,
+    1.3687089681625,
+    .50049883127213,
+    1.3895837068558,
+    .6889950633049,
+    1.2795144319534,
+    .7050421833992,
+    1.2218985557556,
+    .74481928348541,
+    1.3074514865875,
+    .7919961810112,
+    1.2807723283768,
+    1.0120536088943,
+    1.1938916444778,
+    .68923074007034,
+    1.6174983978271,
+    .64740318059921,
+    1.4949930906296,
+    1.2678960561752,
+    1.0586776733398,
+    .55762887001038,
+    1.2790743112564,
+    .66515874862671,
+    1.2538269758224,
+    .70554333925247,
+    1.2391568422318,
+    .75241559743881,
+    1.2129040956497,
+    .69235223531723,
+    1.0785228013992,
+    .8043577671051,
+    1.0037930011749,
+    .78750842809677,
+    1.1880930662155,
+    .74399447441101,
+    1.1791603565216,
+    .85870295763016,
+    1.0032330751419,
+    .8019300699234,
+    1.1696527004242,
+    .92376220226288,
+    .99186056852341,
+    .94733852148056,
+    1.0748032331467,
+    .64247089624405,
+    .95419937372208,
+    .92043441534042,
+    .8104555606842,
+    .66252142190933,
+    1.1178470849991,
+    .69223344326019,
+    1.0570795536041,
+    .90239083766937,
+    .95320242643356,
+    1.0541093349457,
+    1.0082466602325,
+    1.1376332044601,
+    1.1841852664948,
+    .90440809726715,
+    1.2733660936356,
+    .66835701465607,
+    1.1515763998032,
+    .44600257277489,
+    .93500959873199,
+    1.0847823619843,
+    .83353632688522,
+    1.0442448854446,
+    1.077241897583,
+    .71010553836823,
+    .89557945728302,
+    1.0163468122482,
+    1.094814658165,
+    .89641278982162,
+    1.2808450460434,
+    1.0223702192307,
+    .96094745397568,
+    1.309353351593,
+    .73499941825867,
+    2.4902238845825,
+    -.2579345703125,
+    1.9272556304932,
+    .53125941753387,
+    .7708500623703,
+    1.0312130451202,
+    1.6360099315643,
+    .6022145152092,
+    1.6338716745377,
+    1.3494771718979,
+    1.1322995424271,
+    2.1901025772095,
+    -.72639065980911,
+    -.37026473879814,
+    1.2391144037247,
+    1.1353877782822])
+
+icstats = np.array([
+    202,
+    np.nan,
+    -245.40783909604,
+    5,
+    500.81567819208,
+    517.35701667909])
+
+
+results = Bunch(
+    llf=llf,
+    nobs=nobs,
+    k=k,
+    k_exog=k_exog,
+    sigma=sigma,
+    chi2=chi2,
+    df_model=df_model,
+    k_ar=k_ar,
+    k_ma=k_ma,
+    params=params,
+    cov_params=cov_params,
+    xb=xb,
+    y=y,
+    resid=resid,
+    yr=yr,
+    mse=mse,
+    stdp=stdp,
+    icstats=icstats
+)
diff --git a/shared/package-files/32/3266647fd7bcf4fe8bed249e4942cfa167f61882c76caf36f27930e89ede2b19/common.pyi b/shared/package-files/32/3266647fd7bcf4fe8bed249e4942cfa167f61882c76caf36f27930e89ede2b19/common.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..430543ca8dbb396242ee92fb4c8c4030cf40e3af
--- /dev/null
+++ b/shared/package-files/32/3266647fd7bcf4fe8bed249e4942cfa167f61882c76caf36f27930e89ede2b19/common.pyi
@@ -0,0 +1,68 @@
+from typing import ClassVar, Literal, TypeAlias
+
+from typing_extensions import Generic, Self, TypeVar, override
+
+from .abstract import ArrayCompatible, Dummy, IterableType, IteratorType, Type
+from .iterators import ArrayIterator
+
+_TypeT_co = TypeVar("_TypeT_co", bound=Type, default=Type, covariant=True)
+
+_Layout: TypeAlias = Literal["C", "F", "CS", "FS", "A"]
+
+###
+
+class Opaque(Dummy): ...
+
+class SimpleIterableType(IterableType[_TypeT_co], Generic[_TypeT_co]):
+    def __init__(self, name: str, iterator_type: IteratorType[_TypeT_co]) -> None: ...
+    @property
+    @override
+    def iterator_type(self) -> IteratorType[_TypeT_co]: ...
+
+class SimpleIteratorType(IteratorType[_TypeT_co], Generic[_TypeT_co]):
+    def __init__(self, name: str, yield_type: _TypeT_co) -> None: ...
+    @property
+    @override
+    def yield_type(self) -> _TypeT_co: ...
+
+class Buffer(IterableType, ArrayCompatible):
+    LAYOUTS: ClassVar[frozenset[_Layout]] = ...
+
+    slice_is_copy: ClassVar[bool] = False
+    
+    aligned: bool = True
+    mutable: bool = True
+
+    def __init__(
+        self,
+        dtype: Type,
+        ndim: int,
+        layout: _Layout,
+        readonly: bool = False,
+        name: str | None = None,
+    ) -> None: ...
+    @property  # TODO: Use generic `ArrayIterator` once we have `iterators.pyi`
+    @override
+    def iterator_type(self) -> ArrayIterator: ...
+    @property
+    @override
+    def as_array(self) -> Self: ...
+    @property
+    @override
+    def key(self) -> tuple[Type, int, _Layout, bool]: ...
+
+    #
+    @property
+    def is_c_contig(self) -> bool: ...
+    @property
+    def is_f_contig(self) -> bool: ...
+    @property
+    def is_contig(self) -> bool: ...
+
+    #
+    def copy(
+        self,
+        dtype: Type | None = None,
+        ndim: int | None = None,
+        layout: _Layout | None = None,
+    ) -> Self: ...
diff --git a/shared/package-files/32/3266c5a45817e321b2e8f4aff109897f1f497376b113bfd8b5ef02d7130b30e9/_cdist_backward_ops.h b/shared/package-files/32/3266c5a45817e321b2e8f4aff109897f1f497376b113bfd8b5ef02d7130b30e9/_cdist_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1caa0989425a2a374613794d0044f6edbbb60c29
--- /dev/null
+++ b/shared/package-files/32/3266c5a45817e321b2e8f4aff109897f1f497376b113bfd8b5ef02d7130b30e9/_cdist_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _cdist_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, double, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_cdist_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist);
+};
+
+struct TORCH_API _cdist_backward_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, double, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_cdist_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/32/3267e6992bdbaba3b80d5dda998db1a1c9cbb04d8236c01933ec33c2c14666e0/_conventional.pyx.in b/shared/package-files/32/3267e6992bdbaba3b80d5dda998db1a1c9cbb04d8236c01933ec33c2c14666e0/_conventional.pyx.in
new file mode 100644
index 0000000000000000000000000000000000000000..c053d20d3a9f24f760ce9195f00f42c65ad382b2
--- /dev/null
+++ b/shared/package-files/32/3267e6992bdbaba3b80d5dda998db1a1c9cbb04d8236c01933ec33c2c14666e0/_conventional.pyx.in
@@ -0,0 +1,455 @@
+#cython: boundscheck=False
+#cython: wraparound=False
+#cython: cdivision=False
+"""
+State Space Models
+
+Author: Chad Fulton
+License: Simplified-BSD
+
+Notes
+-----
+
+The dimensions used in all the BLAS / LAPACK calls below use the following
+convention:
+
+- The dimensions of the arrays *as they are to be manipulated* are all defined
+  as model._k_*
+- If the array in question is defined in the Statespace object
+  (obs, obs_intercept, design, obs_cov, state_intercept, transition, selection,
+  state_cov, selected_state_cov), then the dimension in-memory is defined as
+  model._k_*
+  This is because the in-memory shape of matrices changes according to whether
+  or not data is missing and whether or not the generalized collapse transform
+  is applied.
+- If the array in question is defined in the Kalman filter object
+  (forecast_*, filtered_*, predicted_*, etc.), then the dimension in-memory is
+  defined as kfilter.k_*
+  This is because the in-memory shape of matrices only changes according to
+  filter_method.
+- If the array in question is defined in the Kalman smoother object
+  (smoothed_*, etc.), then the dimension in-memory is defined as kfilter.k_*
+  This is because the in-memory shape of matrices only changes according to
+  filter_method.
+
+Thus, for example, a ?gemm call has the following signature:
+
+dgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc)
+
+- m, n, and k are the dimensions *as they are to be manipulated*, and are
+  always defined as model._k_*
+- lda, ldb, and ldc are the *in-memory* dimension, and they are set as
+  model._k_* if the array is defined in the Statespace object, otherwise
+  (in either the filter or smoother cases) they are set as kfilter.k_*
+
+Note that for ?copy calls, the number of elements to be copied is defined to be
+the dimension in memory of the array that is being copied *from*.
+"""
+
+{{py:
+
+TYPES = {
+    "s": ("np.float32_t", "np.float32", "np.NPY_FLOAT32"),
+    "d": ("np.float64_t", "float", "np.NPY_FLOAT64"),
+    "c": ("np.complex64_t", "np.complex64", "np.NPY_COMPLEX64"),
+    "z": ("np.complex128_t", "complex", "np.NPY_COMPLEX128"),
+}
+
+}}
+
+# Typical imports
+import numpy as np
+cimport numpy as np
+from statsmodels.includes.math cimport *
+cimport scipy.linalg.cython_blas as blas
+include "statsmodels/tsa/statespace/_blas_int.pxi"
+
+from statsmodels.tsa.statespace._kalman_smoother cimport (
+    SMOOTHER_STATE, SMOOTHER_STATE_COV, SMOOTHER_DISTURBANCE,
+    SMOOTHER_DISTURBANCE_COV
+)
+
+{{for prefix, types in TYPES.items()}}
+{{py:cython_type, dtype, typenum = types}}
+{{py:
+combined_prefix = prefix
+combined_cython_type = cython_type
+if prefix == 'c':
+    combined_prefix = 'z'
+    combined_cython_type = 'np.complex128_t'
+if prefix == 's':
+    combined_prefix = 'd'
+    combined_cython_type = 'np.float64_t'
+}}
+
+# ### Missing Observation Conventional Kalman smoother
+#
+# See Durbin and Koopman (2012) Chapter 4.10
+#
+# Here k_endog is the same as usual, but the design matrix and observation
+# covariance matrix are enforced to be zero matrices.
+
+cdef int {{prefix}}smoothed_estimators_missing_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model) except *:
+    cdef:
+        blas_int inc = 1
+        blas_int kf_k_states = kfilter.k_states
+        blas_int m_k_endog = model._k_endog
+        blas_int m_k_states = model._k_states
+        blas_int m_k_states2 = model._k_states2
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+
+    # Scaled smoothed estimator
+    # $r_{t-1} = T_t' r_t$
+    # $(m \times 1) = (m \times m) (m \times 1)$
+    # Note: save $r_{t-1}$ as scaled_smoothed_estimator[t] rather than
+    # as scaled_smoothed_estimator[t-1] because we actually need to store
+    # T+1 of them (r_{T-1} to r_{-1} -> r_T to r_0)
+    if smoother.smoother_output & (SMOOTHER_STATE | SMOOTHER_DISTURBANCE):
+        if model.identity_transition:
+            blas.{{prefix}}copy(&m_k_states, smoother._input_scaled_smoothed_estimator, &inc, smoother._scaled_smoothed_estimator, &inc)
+        else:
+            blas.{{prefix}}gemv("T", &m_k_states, &m_k_states,
+                    &alpha, model._transition, &m_k_states,
+                            smoother._input_scaled_smoothed_estimator, &inc,
+                    &beta, smoother._scaled_smoothed_estimator, &inc)
+
+    # Scaled smoothed estimator covariance matrix
+    # $N_{t-1} = T_t' N_t T_t$
+    # $(m \times m) = (m \times m) (m \times m) (m \times m)$
+    # Note: save $N_{t-1}$ as scaled_smoothed_estimator_cov[t] rather
+    # than as scaled_smoothed_estimator_cov[t-1] because we actually
+    # need to store T+1 of them (N_{T-1} to N_{-1} -> N_T to N_0)
+    if smoother.smoother_output & (SMOOTHER_STATE_COV | SMOOTHER_DISTURBANCE_COV):
+        if model.identity_transition:
+            blas.{{prefix}}copy(&m_k_states2, smoother._input_scaled_smoothed_estimator_cov, &inc, smoother._scaled_smoothed_estimator_cov, &inc)
+        else:
+            blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_states, &m_k_states,
+                      &alpha, smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                              model._transition, &m_k_states,
+                      &beta, smoother._tmp0, &kf_k_states)
+            blas.{{prefix}}gemm("T", "N", &kf_k_states, &kf_k_states, &kf_k_states,
+                      &alpha, model._transition, &m_k_states,
+                              smoother._tmp0, &kf_k_states,
+                      &beta, smoother._scaled_smoothed_estimator_cov, &kf_k_states)
+
+    # $L_t = T_t$
+    blas.{{prefix}}copy(&m_k_states2, model._transition, &inc, smoother._tmpL, &inc)
+
+    # Smoothing error
+    # It is undefined here, since F_t^{-1} is nan
+    # for i in range(kfilter.k_endog):
+    #     smoother._smoothing_error[i] = 0
+
+    # Smoothing error
+    # $u_t = - K_t' r_t$
+    # $(p \times 1) = (p \times 1) - (p \times m) (m \times 1)$
+    # TODO in the missing case, I think K_t is defined to be zeros, so this
+    # would be unnecessary
+    if smoother.smoother_output & (SMOOTHER_DISTURBANCE):
+        blas.{{prefix}}gemv("T", &m_k_states, &m_k_endog,
+                  &gamma, kfilter._kalman_gain, &kf_k_states,
+                          smoother._input_scaled_smoothed_estimator, &inc,
+                  &beta, smoother._smoothing_error, &inc)
+
+cdef int {{prefix}}smoothed_disturbances_missing_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model):
+    cdef:
+        blas_int inc = 1
+        blas_int kf_k_posdef = kfilter.k_posdef
+        blas_int kf_k_states = kfilter.k_states
+        blas_int m_k_endog = model._k_endog
+        blas_int m_k_endog2 = model._k_endog2
+        blas_int m_k_posdef = model._k_posdef
+        blas_int m_k_posdef2 = model._k_posdef2
+        blas_int m_k_states = model._k_states
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+
+    # Temporary arrays
+
+    # $\\#_0 = R_t Q_t$
+    # $(m \times r) = (m \times r) (r \times r)$
+    if smoother.smoother_output & (SMOOTHER_DISTURBANCE | SMOOTHER_DISTURBANCE_COV):
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_posdef, &m_k_posdef,
+                  &alpha, model._selection, &m_k_states,
+                          model._state_cov, &m_k_posdef,
+                  &beta, smoother._tmp0, &kf_k_states)
+
+    if smoother.smoother_output & SMOOTHER_DISTURBANCE:
+        # Smoothed state disturbance
+        # $\hat \eta_t = \\#_0' r_t$
+        # $(r \times 1) = (r \times m) (m \times 1)$
+        blas.{{prefix}}gemv("T", &kf_k_states, &kf_k_posdef,
+                      &alpha, smoother._tmp0, &kf_k_states,
+                              smoother._input_scaled_smoothed_estimator, &inc,
+                      &beta, smoother._smoothed_state_disturbance, &inc)
+
+    if smoother.smoother_output & SMOOTHER_DISTURBANCE_COV:
+      # Smoothed state disturbance covariance matrix
+        # $Var(\eta_t | Y_n) = Q_t - \\#_0' N_t \\#_0$
+        # $(r \times r) = (r \times r) - (r \times m) (m \times m) (m \times r)$
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_posdef, &m_k_states,
+                  &alpha, smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                          smoother._tmp0, &kf_k_states,
+                  &beta, smoother._tmpL, &kf_k_states)
+        blas.{{prefix}}copy(&m_k_posdef2, model._state_cov, &inc, smoother._smoothed_state_disturbance_cov, &inc)
+        blas.{{prefix}}gemm("T", "N", &m_k_posdef, &m_k_posdef, &m_k_states,
+                  &gamma, smoother._tmp0, &kf_k_states,
+                          smoother._tmpL, &kf_k_states,
+                  &alpha, smoother._smoothed_state_disturbance_cov, &kf_k_posdef)
+
+
+    # Just return the unconditional distribution for the measurement
+    # disturbances corresponding to a missing observation
+
+    # TODO this is not explicitly addressed in Durbin and Koopman Chapter 4
+    # or in Koopman (1993) - need to find a source for if this is correct
+    # Note: this is what the MATLAB ssm toolbox does, also
+
+    # Smoothed measurement disturbances have unconditional expected
+    # value of 0, so no need to do anything
+
+    # Smoothed measurement and state disturbances have unconditional covariance
+    # matrix of $H_t, Q_t$, respectively
+    blas.{{prefix}}copy(&m_k_endog2, model._obs_cov, &inc, smoother._smoothed_measurement_disturbance_cov, &inc)
+
+
+# ### Conventional Kalman smoother
+#
+# The following are the above routines as defined in the conventional Kalman
+# smoother.
+#
+# See Durbin and Koopman (2012) Chapter 4
+
+cdef int {{prefix}}smoothed_estimators_measurement_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model) except *:
+    cdef:
+        int i
+        blas_int inc = 1
+        blas_int m_k_endog = model._k_endog
+        blas_int m_k_states = model._k_states
+        blas_int m_k_states2 = model._k_states2
+        blas_int kf_k_endog = kfilter.k_endog
+        blas_int kf_k_states = kfilter.k_states
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+
+    # Smoothing error
+    # $u_t = \\#_2 - K_t' r_t$
+    # $(p \times 1) = (p \times 1) - (p \times m) (m \times 1)$
+    if smoother.smoother_output & (SMOOTHER_DISTURBANCE):
+        blas.{{prefix}}copy(&m_k_endog, kfilter._tmp2, &inc, smoother._smoothing_error, &inc)
+        blas.{{prefix}}gemv("T", &m_k_states, &m_k_endog,
+                  &gamma, kfilter._kalman_gain, &kf_k_states,
+                          smoother._input_scaled_smoothed_estimator, &inc,
+                  &alpha, smoother._smoothing_error, &inc)
+
+    # $L_t = (T_t - K_t Z_t)$  = (T_t - T_t P_t Z_t' F_t^{-1} Z_t)
+    # $(m \times m) = (m \times m) + (m \times p) (p \times m)$
+    # (this is required for any type of smoothing)
+    blas.{{prefix}}copy(&m_k_states2, model._transition, &inc, smoother._tmpL, &inc)
+    blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_states, &m_k_endog,
+              &gamma, kfilter._kalman_gain, &kf_k_states,
+                      model._design, &m_k_endog,
+              &alpha, smoother._tmpL, &kf_k_states)
+
+    # Scaled smoothed estimator
+    # $r_{t-1} = Z_t' \\#_2 + L_t' r_t$
+    # $(m \times 1) = (m \times p) (p \times 1) + (m \times m) (m \times 1)$
+    # Note: save $r_{t-1}$ as scaled_smoothed_estimator[t] rather than
+    # as scaled_smoothed_estimator[t-1] because we actually need to store
+    # T+1 of them (r_{T-1} to r_{-1} -> r_T to r_0)
+    if smoother.smoother_output & (SMOOTHER_STATE | SMOOTHER_DISTURBANCE):
+        blas.{{prefix}}gemv("T", &m_k_states, &m_k_states,
+                  &alpha, smoother._tmpL, &kf_k_states,
+                          smoother._input_scaled_smoothed_estimator, &inc,
+                  &beta, smoother._scaled_smoothed_estimator, &inc)
+
+        blas.{{prefix}}gemv("T", &m_k_endog, &m_k_states,
+                  &alpha, model._design, &m_k_endog,
+                          kfilter._tmp2, &inc,
+                  &alpha, smoother._scaled_smoothed_estimator, &inc)
+
+    # Scaled smoothed estimator covariance matrix
+    # $N_{t-1} = Z_t' \\#_3 + L_t' N_t L_t$
+    # $(m \times m) = (m \times p) (p \times m) + (m \times m) (m \times m) (m \times m)$
+    # Note: save $N_{t-1}$ as scaled_smoothed_estimator_cov[t] rather
+    # than as scaled_smoothed_estimator_cov[t-1] because we actually
+    # need to store T+1 of them (N_{T-1} to N_{-1} -> N_T to N_0)
+    if smoother.smoother_output & (SMOOTHER_STATE_COV | SMOOTHER_DISTURBANCE_COV):
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_states, &m_k_states,
+                  &alpha, smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                          smoother._tmpL, &kf_k_states,
+                  &beta, smoother._tmp0, &kf_k_states)
+        blas.{{prefix}}gemm("T", "N", &m_k_states, &m_k_states, &m_k_states,
+                  &alpha, smoother._tmpL, &kf_k_states,
+                          smoother._tmp0, &kf_k_states,
+                  &beta, smoother._scaled_smoothed_estimator_cov, &kf_k_states)
+        blas.{{prefix}}gemm("T", "N", &m_k_states, &m_k_states, &m_k_endog,
+                  &alpha, model._design, &m_k_endog,
+                          kfilter._tmp3, &kf_k_endog,
+                  &alpha, smoother._scaled_smoothed_estimator_cov, &kf_k_states)
+
+cdef int {{prefix}}smoothed_estimators_time_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model):
+  pass
+
+cdef int {{prefix}}smoothed_state_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model):
+    cdef int i, j
+    cdef:
+        blas_int inc = 1
+        blas_int kf_k_states = kfilter.k_states
+        blas_int m_k_states = model._k_states
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+
+    # Smoothed state
+    if smoother.smoother_output & SMOOTHER_STATE:
+        # $\hat \alpha_t = a_t + P_t r_{t-1}$
+        # $(m \times 1) = (m \times 1) + (m \times m) (m \times 1)$
+        blas.{{prefix}}copy(&kf_k_states, &kfilter.predicted_state[0,smoother.t], &inc, smoother._smoothed_state, &inc)
+        blas.{{prefix}}gemv("N", &m_k_states, &m_k_states,
+                  &alpha, &kfilter.predicted_state_cov[0,0,smoother.t], &kf_k_states,
+                          smoother._scaled_smoothed_estimator, &inc,
+                  &alpha, smoother._smoothed_state, &inc)
+
+    # Smoothed state covariance
+    if smoother.smoother_output & SMOOTHER_STATE_COV:
+        # $V_t = P_t [I - N_{t-1} P_t]$
+        # $(m \times m) = (m \times m) [(m \times m) - (m \times m) (m \times m)]$
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_states, &m_k_states,
+              &gamma, smoother._scaled_smoothed_estimator_cov, &kf_k_states,
+                      &kfilter.predicted_state_cov[0,0,smoother.t], &kf_k_states,
+              &beta, smoother._tmp0, &kf_k_states)
+        for i in range(kfilter.k_states):
+            smoother.tmp0[i,i] = 1 + smoother.tmp0[i,i]
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_states, &m_k_states,
+              &alpha, &kfilter.predicted_state_cov[0,0,smoother.t], &kf_k_states,
+                      smoother._tmp0, &kf_k_states,
+              &beta, smoother._smoothed_state_cov, &kf_k_states)
+
+
+cdef int {{prefix}}smoothed_state_autocov_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model):
+    cdef:
+        int i
+        blas_int inc = 1
+        blas_int kf_k_states = kfilter.k_states
+        blas_int mod_k_states = model.k_states
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+    # This function computes Cov(alpha_{t+1}, alpha_t) = Cov(alpha_t, alpha_{t+1})'
+    # From Durbin and Koopman, 2012, Chapter 4.7
+    # Cov(alpha_{t+1}, alpha_t) = (I - P_{t+1} N_{t}) L_t P_t
+
+    blas.{{prefix}}gemm("N", "N", &mod_k_states, &mod_k_states, &mod_k_states,
+                  &gamma, &kfilter.predicted_state_cov[0,0,smoother.t+1], &kf_k_states,
+                          smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                  &beta, smoother._tmp0, &kf_k_states)
+    for i in range(kfilter.k_states):
+        smoother.tmp0[i,i] = 1 + smoother.tmp0[i,i]
+
+    blas.{{prefix}}gemm("N", "N", &mod_k_states, &mod_k_states, &mod_k_states,
+                  &alpha, smoother._tmpL, &kf_k_states,
+                          &kfilter.predicted_state_cov[0,0,smoother.t], &kf_k_states,
+                  &beta, smoother._tmp_autocov, &kf_k_states)
+
+    blas.{{prefix}}gemm("N", "N", &mod_k_states, &mod_k_states, &mod_k_states,
+                  &alpha, smoother._tmp0, &kf_k_states,
+                          smoother._tmp_autocov, &kf_k_states,
+                  &beta, smoother._smoothed_state_autocov, &kf_k_states)
+
+cdef int {{prefix}}smoothed_disturbances_conventional({{prefix}}KalmanSmoother smoother, {{prefix}}KalmanFilter kfilter, {{prefix}}Statespace model):
+    cdef int i, j
+    cdef:
+        blas_int inc = 1
+        blas_int kf_k_endog = kfilter.k_endog
+        blas_int kf_k_posdef = kfilter.k_posdef
+        blas_int kf_k_states = kfilter.k_states
+        blas_int m_k_endog = model._k_endog
+        blas_int m_k_posdef = model._k_posdef
+        blas_int m_k_posdef2 = model._k_posdef2
+        blas_int m_k_states = model._k_states
+        {{cython_type}} alpha = 1.0
+        {{cython_type}} beta = 0.0
+        {{cython_type}} gamma = -1.0
+
+    # Temporary arrays
+
+    # $\\#_0 = R_t Q_t$
+    # $(m \times r) = (m \times r) (r \times r)$
+    if smoother.smoother_output & (SMOOTHER_DISTURBANCE | SMOOTHER_DISTURBANCE_COV):
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_posdef, &m_k_posdef,
+                  &alpha, model._selection, &m_k_states,
+                          model._state_cov, &m_k_posdef,
+                  &beta, smoother._tmp0, &kf_k_states)
+
+    if smoother.smoother_output & SMOOTHER_DISTURBANCE:
+        # Smoothed measurement disturbance
+        # $\hat \varepsilon_t = H_t u_t$
+        # $(p \times 1) = (p \times p) (p \times 1)$
+        blas.{{prefix}}gemv("N", &m_k_endog, &m_k_endog,
+                      &alpha, model._obs_cov, &m_k_endog,
+                              smoother._smoothing_error, &inc,
+                      &beta, smoother._smoothed_measurement_disturbance, &inc)
+
+        # Smoothed state disturbance
+        # $\hat \eta_t = \\#_0' r_t$
+        # $(r \times 1) = (r \times m) (m \times 1)$
+        blas.{{prefix}}gemv("T", &m_k_states, &m_k_posdef,
+                      &alpha, smoother._tmp0, &kf_k_states,
+                              smoother._input_scaled_smoothed_estimator, &inc,
+                      &beta, smoother._smoothed_state_disturbance, &inc)
+
+    if smoother.smoother_output & SMOOTHER_DISTURBANCE_COV:
+        # $\\#_00 = K_t H_t$
+        # $(m \times p) = (m \times p) (p \times p)$
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_endog, &m_k_endog,
+                  &alpha, kfilter._kalman_gain, &kf_k_states,
+                          model._obs_cov, &m_k_endog,
+                  &beta, smoother._tmp00, &kf_k_states)
+
+        # Smoothed measurement disturbance covariance matrix
+        # $Var(\varepsilon_t | Y_n) = H_t - H_t \\#_4 - \\#_00' N_t \\#_00$
+        # $(p \times p) = (p \times p) - (p \times p) (p \times p) - (p \times m) (m \times m) (m \times p)$
+        blas.{{prefix}}gemm("N", "N", &m_k_endog, &m_k_endog, &m_k_endog,
+                  &gamma, model._obs_cov, &m_k_endog,
+                          kfilter._tmp4, &kf_k_endog,
+                  &beta, smoother._smoothed_measurement_disturbance_cov, &kf_k_endog)
+
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_endog, &m_k_states,
+                  &alpha, smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                          smoother._tmp00, &kf_k_states,
+                  &beta, smoother._tmp000, &kf_k_states)
+
+        blas.{{prefix}}gemm("T", "N", &m_k_endog, &m_k_endog, &m_k_states,
+                  &gamma, smoother._tmp00, &kf_k_states,
+                          smoother._tmp000, &kf_k_states,
+                  &alpha, smoother._smoothed_measurement_disturbance_cov, &kf_k_endog)
+
+        # blas.{{prefix}}axpy(&model._k_endog2, &alpha,
+        #        model._obs_cov, &inc,
+        #        smoother._smoothed_measurement_disturbance_cov, &inc)
+        for i in range(kfilter.k_endog):
+            for j in range(i+1):
+                smoother._smoothed_measurement_disturbance_cov[i + j*kfilter.k_endog] = model._obs_cov[i + j*model._k_endog] + smoother._smoothed_measurement_disturbance_cov[i + j*kfilter.k_endog]
+                if not i == j:
+                    smoother._smoothed_measurement_disturbance_cov[j + i*kfilter.k_endog] = model._obs_cov[j + i*model._k_endog] + smoother._smoothed_measurement_disturbance_cov[j + i*kfilter.k_endog]
+
+        # Smoothed state disturbance covariance matrix
+        # $Var(\eta_t | Y_n) = Q_t - \\#_0' N_t \\#_0$
+        # $(r \times r) = (r \times r) - (r \times m) (m \times m) (m \times r)$
+        blas.{{prefix}}gemm("N", "N", &m_k_states, &m_k_posdef, &m_k_states,
+                  &alpha, smoother._input_scaled_smoothed_estimator_cov, &kf_k_states,
+                          smoother._tmp0, &kf_k_states,
+                  &beta, smoother._tmpL, &kf_k_states)
+
+        blas.{{prefix}}copy(&m_k_posdef2, model._state_cov, &inc, smoother._smoothed_state_disturbance_cov, &inc)
+        blas.{{prefix}}gemm("T", "N", &m_k_posdef, &m_k_posdef, &m_k_states,
+                  &gamma, smoother._tmp0, &kf_k_states,
+                          smoother._tmpL, &kf_k_states,
+                  &alpha, smoother._smoothed_state_disturbance_cov, &kf_k_posdef)
+
+{{endfor}}
\ No newline at end of file
diff --git a/shared/package-files/32/326b8a1ee706ee7b1a033172666c2c6b1ededfaef6d97a811c2fc26ade3aed9f/q_per_channel_axis_native.h b/shared/package-files/32/326b8a1ee706ee7b1a033172666c2c6b1ededfaef6d97a811c2fc26ade3aed9f/q_per_channel_axis_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b053651d163059d12c1a1fd2ca8c8ef0c4b1fd3
--- /dev/null
+++ b/shared/package-files/32/326b8a1ee706ee7b1a033172666c2c6b1ededfaef6d97a811c2fc26ade3aed9f/q_per_channel_axis_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API int64_t q_per_channel_axis(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/3271b08d2ebcc4be44ac1c0049ebba3ebe19a47420c23a971a7babeb76fc6207/python.py b/shared/package-files/32/3271b08d2ebcc4be44ac1c0049ebba3ebe19a47420c23a971a7babeb76fc6207/python.py
new file mode 100644
index 0000000000000000000000000000000000000000..d80839dc7e4729b948914991c9ddf8e7f3e01cb6
--- /dev/null
+++ b/shared/package-files/32/3271b08d2ebcc4be44ac1c0049ebba3ebe19a47420c23a971a7babeb76fc6207/python.py
@@ -0,0 +1,178 @@
+import itertools
+import unittest.mock
+from contextlib import contextmanager
+from typing import Iterator
+
+import torch
+import torch._C
+import torch._ops
+import torch.utils._python_dispatch
+import torch.utils._pytree as pytree
+
+__all__ = ["enable_python_dispatcher", "no_python_dispatcher", "enable_pre_dispatch"]
+
+no_python_dispatcher = torch._C._DisablePythonDispatcher
+enable_python_dispatcher = torch._C._EnablePythonDispatcher
+enable_pre_dispatch = torch._C._EnablePreDispatch
+
+CROSSREF_FUNCTIONALIZE = False
+
+
+def all_py_loaded_overloads() -> Iterator[torch._ops.OpOverload]:
+    """
+    Warning: the set of overloads this will report is very subtle.  It is precisely
+    the set of torch.ops functions that have actually been accessed from Python
+    (e.g., we actually called torch.ops.aten.blah at some point.  This is DIFFERENT
+    from the set of registered operators, which will in general be a larger set,
+    as this would include all operators which we ran C++ static initializers or
+    Python operator registration on.  This does not eagerly populate the list on
+    torch.ops.aten; this list is lazy!
+
+    In other words, this is good for traversing over everything that has an
+    OpOverload object allocated in Python.  We use it for cache invalidation, but
+    don't rely on this list being complete.
+
+    Note that even if we did report all C++ registered overloads, this isn't guaranteed
+    to be complete either, as a subsequent lazy load of a library which triggers more
+    registrations could add more things to the set.
+    """
+    for ns in torch.ops:
+        packets = getattr(torch.ops, ns)
+        for op_name in packets:
+            packet = getattr(packets, op_name)
+            for overload in packet:
+                yield getattr(packet, overload)
+
+
+@contextmanager
+def suspend_functionalization():
+    f_tls = torch._C._dispatch_tls_is_dispatch_key_included(
+        torch._C.DispatchKey.Functionalize
+    )
+    f_rv = torch._C._functionalization_reapply_views_tls()
+    if f_tls:
+        torch._disable_functionalization()
+    try:
+        yield
+    finally:
+        if f_tls:
+            torch._enable_functionalization(reapply_views=f_rv)
+
+
+def check_tensor_metadata_matches(nv, rv, desc):
+    assert callable(desc)
+    assert nv.size() == rv.size(), f"{desc()}: sizes {nv.size()} != {rv.size()}"
+    assert nv.dtype == rv.dtype, f"{desc()}: dtype {nv.dtype} != {rv.dtype}"
+    same_strides, idx = torch._prims_common.check_significant_strides(
+        nv, rv, only_cuda=False
+    )
+    assert (
+        same_strides
+    ), f"{desc()}: strides {nv.stride()} != {rv.stride()} (mismatch at index {idx})"
+
+
+def check_metadata_matches(n, r, desc):
+    assert callable(desc)
+    n_vals, n_spec = pytree.tree_flatten(n)
+    r_vals, r_spec = pytree.tree_flatten(r)
+    # TODO: test the specs match; empirically  sometimes we have a tuple
+    # on one side and a list on the other
+    assert len(n_vals) == len(r_vals), f"{len(n_vals)} != {len(r_vals)}"
+    for i, nv, rv in zip(range(len(n_vals)), n_vals, r_vals):
+        if not isinstance(rv, torch.Tensor):
+            continue
+        check_tensor_metadata_matches(nv, rv, lambda: f"{desc()} output {i}")
+
+
+class Lit:
+    def __init__(self, s):
+        self.s = s
+
+    def __repr__(self):
+        return self.s
+
+
+def _fmt(a: object) -> object:
+    if isinstance(a, torch.Tensor):
+        return Lit(
+            f"torch.empty_strided({tuple(a.size())}, {a.stride()}, dtype={a.dtype})"
+        )
+    else:
+        return a
+
+
+def make_crossref_functionalize(op, final_key):
+    from torch._subclasses.fake_tensor import FakeTensorMode
+
+    # This case is pretty weird, suppress it for now
+    if op == torch.ops.aten.lift_fresh.default:
+        return final_key
+
+    def handler(*args, **kwargs):
+        fake_mode = FakeTensorMode()
+
+        def fakeify_defun(t):
+            if isinstance(t, torch.Tensor):
+                if torch._is_functional_tensor(t):
+                    r = torch._from_functional_tensor(t)
+                    # NB: This assumes that the inner tensor sizes/strides match
+                    # the outer tensor sizes/strides.  This doesn't necessarily have to
+                    # be the case, see discussion at
+                    # https://github.com/pytorch/pytorch/pull/87610/files/401ddeda1d769bedc88a12de332c7357b60e51a4#r1007264456
+                    assert t.size() == r.size()
+                    assert t.stride() == r.stride()
+                else:
+                    r = t
+                # TODO: suppress guards
+                return fake_mode.from_tensor(r)
+            return t
+
+        def maybe_detach(t):
+            if isinstance(t, torch.Tensor):
+                return t.detach()
+            else:
+                return t
+
+        # TODO: This probably does the wrong thing if you're running other
+        # substantive modes with the normal op outside here
+        with torch.utils._python_dispatch._disable_current_modes(), suspend_functionalization():
+            f_args, f_kwargs = pytree.tree_map(fakeify_defun, (args, kwargs))
+            orig_f_args, orig_f_kwargs = pytree.tree_map(
+                maybe_detach, (f_args, f_kwargs)
+            )
+            with fake_mode:
+                f_r = op(*f_args, **f_kwargs)
+        r = op._op_dk(final_key, *args, **kwargs)
+
+        def desc():
+            fmt_args = ", ".join(
+                itertools.chain(
+                    (repr(pytree.tree_map(_fmt, a)) for a in orig_f_args),
+                    (
+                        f"{k}={pytree.tree_map(_fmt, v)}"
+                        for k, v in orig_f_kwargs.items()
+                    ),
+                )
+            )
+            return f"{op}({fmt_args})"
+
+        check_metadata_matches(f_r, r, desc)
+        return r
+
+    return handler
+
+
+# NB: enabling this is slow, don't do it in a hot loop.  This is purely
+# for debugging purposes.
+@contextmanager
+def enable_crossref_functionalize():
+    for op in all_py_loaded_overloads():
+        op._uncache_dispatch(torch._C.DispatchKey.Functionalize)
+    try:
+        with enable_python_dispatcher(), unittest.mock.patch(
+            "torch._dispatch.python.CROSSREF_FUNCTIONALIZE", True
+        ):
+            yield
+    finally:
+        for op in all_py_loaded_overloads():
+            op._uncache_dispatch(torch._C.DispatchKey.Functionalize)
diff --git a/shared/package-files/32/32727362022261ce8aef23c6033ccaa243366301dba98c16cd5a447d0c1d61ff/main.pyi b/shared/package-files/32/32727362022261ce8aef23c6033ccaa243366301dba98c16cd5a447d0c1d61ff/main.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ad2deebbdc4fcaa02a75b658ea607869a3b79f19
--- /dev/null
+++ b/shared/package-files/32/32727362022261ce8aef23c6033ccaa243366301dba98c16cd5a447d0c1d61ff/main.pyi
@@ -0,0 +1,89 @@
+from collections import OrderedDict
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+
+from django.contrib.admin.filters import ListFilter, SimpleListFilter
+from django.contrib.admin.options import (  # noqa: F401
+    ModelAdmin,
+    IS_POPUP_VAR as IS_POPUP_VAR,
+    TO_FIELD_VAR as TO_FIELD_VAR,
+)
+from django.core.handlers.wsgi import WSGIRequest
+from django.db.models.base import Model
+from django.db.models.expressions import Combinable, CombinedExpression, OrderBy
+from django.db.models.query import QuerySet
+
+from django.db.models.options import Options
+from django.forms.formsets import BaseFormSet
+
+ALL_VAR: str
+ORDER_VAR: str
+ORDER_TYPE_VAR: str
+PAGE_VAR: str
+SEARCH_VAR: str
+ERROR_FLAG: str
+IGNORED_PARAMS: Any
+
+class ChangeList:
+    model: Type[Model] = ...
+    opts: Options = ...
+    lookup_opts: Options = ...
+    root_queryset: QuerySet = ...
+    list_display: List[str] = ...
+    list_display_links: List[str] = ...
+    list_filter: Tuple = ...
+    date_hierarchy: None = ...
+    search_fields: Tuple = ...
+    list_select_related: bool = ...
+    list_per_page: int = ...
+    list_max_show_all: int = ...
+    model_admin: ModelAdmin = ...
+    preserved_filters: str = ...
+    sortable_by: Tuple[str] = ...
+    page_num: int = ...
+    show_all: bool = ...
+    is_popup: bool = ...
+    to_field: None = ...
+    params: Dict[Any, Any] = ...
+    list_editable: Tuple = ...
+    query: str = ...
+    queryset: Any = ...
+    title: Any = ...
+    pk_attname: Any = ...
+    formset: Optional[BaseFormSet]
+    def __init__(
+        self,
+        request: WSGIRequest,
+        model: Type[Model],
+        list_display: Union[List[Union[Callable, str]], Tuple[str]],
+        list_display_links: Optional[Union[List[Callable], List[str], Tuple[str]]],
+        list_filter: Union[List[Type[SimpleListFilter]], List[str], Tuple],
+        date_hierarchy: Optional[str],
+        search_fields: Union[List[str], Tuple],
+        list_select_related: Union[Tuple, bool],
+        list_per_page: int,
+        list_max_show_all: int,
+        list_editable: Union[List[str], Tuple],
+        model_admin: ModelAdmin,
+        sortable_by: Union[List[Callable], List[str], Tuple],
+    ) -> None: ...
+    def get_filters_params(self, params: None = ...) -> Dict[str, str]: ...
+    def get_filters(self, request: WSGIRequest) -> Tuple[List[ListFilter], bool, Dict[str, Union[bool, str]], bool]: ...
+    def get_query_string(
+        self, new_params: Optional[Dict[str, None]] = ..., remove: Optional[List[str]] = ...
+    ) -> str: ...
+    result_count: Any = ...
+    show_full_result_count: Any = ...
+    show_admin_actions: Any = ...
+    full_result_count: Any = ...
+    result_list: Any = ...
+    can_show_all: Any = ...
+    multi_page: Any = ...
+    paginator: Any = ...
+    def get_results(self, request: WSGIRequest) -> None: ...
+    def get_ordering_field(self, field_name: Union[Callable, str]) -> Optional[Union[CombinedExpression, str]]: ...
+    def get_ordering(self, request: WSGIRequest, queryset: QuerySet) -> List[Union[OrderBy, Combinable, str]]: ...
+    def get_ordering_field_columns(self) -> OrderedDict: ...
+    def get_queryset(self, request: WSGIRequest) -> QuerySet: ...
+    def apply_select_related(self, qs: QuerySet) -> QuerySet: ...
+    def has_related_field_in_list_display(self) -> bool: ...
+    def url_for_result(self, result: Model) -> str: ...
diff --git a/shared/package-files/32/3274c9b8ef51d63ad66c52f2efd0d4e0c2005d05e016f9abf1c487416b283644/geqrf_cpu_dispatch.h b/shared/package-files/32/3274c9b8ef51d63ad66c52f2efd0d4e0c2005d05e016f9abf1c487416b283644/geqrf_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2a6f18f26a9b422ba359fdbf1dbb9e6acfdb5e35
--- /dev/null
+++ b/shared/package-files/32/3274c9b8ef51d63ad66c52f2efd0d4e0c2005d05e016f9abf1c487416b283644/geqrf_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> geqrf(const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> geqrf_out(at::Tensor & a, at::Tensor & tau, const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> geqrf_outf(const at::Tensor & self, at::Tensor & a, at::Tensor & tau);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/32/3275a7d95bc6b63c412f8f9f7c4c4bc1d6e8d1927b38e155f7ef99dda936ef74/rpc_test_faulty.py b/shared/package-files/32/3275a7d95bc6b63c412f8f9f7c4c4bc1d6e8d1927b38e155f7ef99dda936ef74/rpc_test_faulty.py
new file mode 100644
index 0000000000000000000000000000000000000000..89ff7309fb08d685f4e7858eeb6036b218ecd7f6
--- /dev/null
+++ b/shared/package-files/32/3275a7d95bc6b63c412f8f9f7c4c4bc1d6e8d1927b38e155f7ef99dda936ef74/rpc_test_faulty.py
@@ -0,0 +1,218 @@
+# mypy: ignore-errors
+
+from typing import Dict, Tuple
+
+import torch
+import torch.distributed.rpc as rpc
+from torch import Tensor
+from torch.distributed.rpc import RRef
+from torch.testing._internal.dist_utils import (
+    dist_init,
+    worker_name,
+    wait_until_pending_futures_and_users_flushed
+)
+from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
+    RpcAgentTestFixture,
+)
+
+
+@torch.jit.script
+def two_args_two_kwargs(
+    first_arg,
+    second_arg,
+    first_kwarg=torch.tensor([3, 3]),
+    second_kwarg=torch.tensor([4, 4]),
+):
+    return first_arg + second_arg + first_kwarg + second_kwarg
+
+
+@torch.jit.script
+def script_rpc_async_call(
+    dst_worker_name: str, args: Tuple[Tensor, Tensor], kwargs: Dict[str, Tensor]
+):
+    fut = rpc.rpc_async(dst_worker_name, two_args_two_kwargs, args, kwargs)
+    ret = fut.wait()
+    return ret
+
+
+@torch.jit.script
+def rpc_async_call_with_timeout(
+    dst_worker_name: str,
+    args: Tuple[Tensor, Tensor],
+    kwargs: Dict[str, Tensor],
+    timeout: float,
+):
+    fut = rpc.rpc_async(dst_worker_name, two_args_two_kwargs, args, kwargs, timeout)
+    ret = fut.wait()
+    return ret
+
+
+@torch.jit.script
+def rpc_async_call_with_timeout_future_ret(
+    dst_worker_name: str,
+    args: Tuple[Tensor, Tensor],
+    kwargs: Dict[str, Tensor],
+    timeout: float,
+):
+    fut = rpc.rpc_async(dst_worker_name, two_args_two_kwargs, args, kwargs, timeout)
+    return fut
+
+
+@torch.jit.script
+def rpc_async_call_future_ret(
+    dst_worker_name: str, args: Tuple[Tensor, Tensor], kwargs: Dict[str, Tensor]
+):
+    fut = rpc.rpc_async(dst_worker_name, two_args_two_kwargs, args, kwargs)
+    return fut
+
+@torch.jit.script
+def rref_to_here(rref_var: RRef[Tensor]) -> Tensor:
+    return rref_var.to_here()
+
+@torch.jit.script
+def rref_to_here_with_timeout(rref_var: RRef[Tensor], timeout: float) -> Tensor:
+    return rref_var.to_here(timeout)
+
+@torch.jit.script
+def rpc_async_with_rref_arg(dst_worker_name: str, args: Tuple[RRef[Tensor]]) -> Tensor:
+    fut = rpc.rpc_async(dst_worker_name, rref_to_here, args)
+    ret = fut.wait()
+    return ret
+
+
+class JitFaultyAgentRpcTest(RpcAgentTestFixture):
+    """
+    Run tests for rpc_async in JIT under the faulty agent test fixture to test
+    arbitrary timeouts.
+    """
+    @dist_init(faulty_messages=[], messages_to_delay={"SCRIPT_CALL": 1.5})
+    def test_timeout_in_torchscript_function(self):
+        # Call rpc_async + fut.wait() in torchscript function and ensure that
+        # timeout is raised.
+        if self.rank != 0:
+            return
+
+        dst_worker_name = worker_name((self.rank + 1) % self.world_size)
+
+        args = (torch.tensor([1, 1]), torch.tensor([2, 2]))
+        kwargs = {
+            "first_kwarg": torch.tensor([2, 2]),
+            "second_kwarg": torch.tensor([3, 3]),
+        }
+        expected_error = self.get_timeout_error_regex()
+        # Ensure that we get a timeout if we override the default timeout and
+        # the RPC takes longer to execute.
+        with self.assertRaisesRegex(RuntimeError, expected_error):
+            rpc_async_call_with_timeout(dst_worker_name, args, kwargs, 0.5)
+
+        # Ensure that we timeout if we don't specify a timeout but the default
+        # is less than the RPC takes to execute.
+        rpc._set_rpc_timeout(0.001)
+        with self.assertRaisesRegex(RuntimeError, expected_error):
+            script_rpc_async_call(
+                dst_worker_name, args, kwargs
+            )
+
+        # Ensure that we run to completion if zero timeout is specified.
+        ret = rpc_async_call_with_timeout(dst_worker_name, args, kwargs, 0)
+        self.assertEqual(ret, torch.tensor([8, 8]))
+        # reset for clean shutdown
+        rpc._set_rpc_timeout(rpc.constants.DEFAULT_RPC_TIMEOUT_SEC)
+
+    @dist_init(faulty_messages=[], messages_to_delay={"SCRIPT_CALL": 1.5})
+    def test_timeout_in_python(self):
+        # Ensures timeouts are raised if we call rpc_async from within a
+        # torchscript function, but wait on the future in python.
+        if self.rank != 0:
+            return
+
+        dst_worker_name = worker_name((self.rank + 1) % self.world_size)
+        args = (torch.tensor([1, 1]), torch.tensor([2, 2]))
+        kwargs = {
+            "first_kwarg": torch.tensor([2, 2]),
+            "second_kwarg": torch.tensor([3, 3]),
+        }
+        expected_error = self.get_timeout_error_regex()
+
+        fut = rpc_async_call_with_timeout_future_ret(dst_worker_name, args, kwargs, 0.5)
+        with self.assertRaisesRegex(RuntimeError, expected_error):
+            fut.wait()
+
+        # Ensure timeout if we don't specify but the default is less than the
+        # RPC takes to execute.
+        rpc._set_rpc_timeout(0.001)
+        fut = rpc_async_call_future_ret(dst_worker_name, args, kwargs)
+        with self.assertRaisesRegex(RuntimeError, expected_error):
+            fut.wait()
+
+        # Ensure run to completion if zero timeout is specified
+        fut = rpc_async_call_with_timeout_future_ret(dst_worker_name, args, kwargs, 0)
+        result = fut.wait()
+        self.assertEqual(result, torch.tensor([8, 8]))
+        # reset for clean shutdown
+        rpc._set_rpc_timeout(rpc.constants.DEFAULT_RPC_TIMEOUT_SEC)
+
+    @dist_init(faulty_messages=["SCRIPT_REMOTE_CALL"])
+    def test_remote_timeout_to_here_in_jit(self):
+        # Test that calling to_here() in JIT will raise timeout error if
+        # rpc.remote failed.
+        if self.rank != 0:
+            return
+        dst_rank = (self.rank + 1) % self.world_size
+        dst_worker = f"worker{dst_rank}"
+        rref = rpc.remote(
+            dst_worker, torch.add, args=(torch.tensor(1), torch.tensor(1))
+        )
+        # Will ensure error handling callbacks are run.
+        wait_until_pending_futures_and_users_flushed()
+        # Call to_here() within a ScriptFunction and ensure it raises
+        with self.assertRaisesRegex(RuntimeError, "RRef creation"):
+            rref_to_here(rref)
+
+    @dist_init(faulty_messages=[], messages_to_delay={"SCRIPT_RREF_FETCH_CALL": 1})
+    def test_rref_to_here_timeout_in_jit(self):
+        if self.rank != 0:
+            return
+
+        dst_rank = (self.rank + 1) % self.world_size
+        dst_worker = f"worker{dst_rank}"
+        rref = rpc.remote(
+            dst_worker, torch.add, args=(torch.tensor(1), torch.tensor(1))
+        )
+        expected_error = self.get_timeout_error_regex()
+        with self.assertRaisesRegex(RuntimeError, expected_error):
+            rref_to_here_with_timeout(rref, 0.01)
+
+        rref_to_here_with_timeout(rref, 100)
+
+    @dist_init(faulty_messages=["SCRIPT_REMOTE_CALL"])
+    def test_rref_timeout_pickle_in_jit(self):
+        if self.rank != 0:
+            return
+        dst_rank = (self.rank + 1) % self.world_size
+        dst_worker = f"worker{dst_rank}"
+        rref = rpc.remote(
+            dst_worker, torch.add, args=(torch.tensor(1), torch.tensor(1))
+        )
+        # Will ensure error handling callbacks are run.
+        wait_until_pending_futures_and_users_flushed()
+        # Call RPC with RRef arg in JIT, which will go through JIT pickling and
+        # ensure error is raised.
+        with self.assertRaisesRegex(RuntimeError, "RRef creation"):
+            rpc_async_with_rref_arg(dst_worker, (rref, ))
+
+    @dist_init(faulty_messages=["SCRIPT_REMOTE_CALL"])
+    def test_rref_timeout_pickle_script_func(self):
+        # Similar to above test, but calls python rpc with script function.
+        if self.rank != 0:
+            return
+        dst_rank = (self.rank + 1) % self.world_size
+        dst_worker = f"worker{dst_rank}"
+        rref = rpc.remote(
+            dst_worker, torch.add, args=(torch.tensor(1), torch.tensor(1))
+        )
+        # Will ensure error handling callbacks are run.
+        wait_until_pending_futures_and_users_flushed()
+        # Call RPC with script function that takes RRef, ensure timeout during pickling
+        with self.assertRaisesRegex(RuntimeError, "RRef creation"):
+            rpc.rpc_sync(dst_worker, rref_to_here, args=(rref, ))
diff --git a/shared/package-files/32/327712548fe5f75e2a116d82520fe58af42b27a01c3b1bf399a88d39dce53a80/test_indexing.py b/shared/package-files/32/327712548fe5f75e2a116d82520fe58af42b27a01c3b1bf399a88d39dce53a80/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfbcdcff51ee6e7f50325962a44209a5c5bf9653
--- /dev/null
+++ b/shared/package-files/32/327712548fe5f75e2a116d82520fe58af42b27a01c3b1bf399a88d39dce53a80/test_indexing.py
@@ -0,0 +1,717 @@
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+from pandas.compat.numpy import np_long
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    Timestamp,
+    bdate_range,
+    date_range,
+    notna,
+)
+import pandas._testing as tm
+
+from pandas.tseries.frequencies import to_offset
+
+START, END = datetime(2009, 1, 1), datetime(2010, 1, 1)
+
+
+class TestGetItem:
+    def test_getitem_slice_keeps_name(self):
+        # GH4226
+        st = Timestamp("2013-07-01 00:00:00", tz="America/Los_Angeles")
+        et = Timestamp("2013-07-02 00:00:00", tz="America/Los_Angeles")
+        dr = date_range(st, et, freq="h", name="timebucket")
+        assert dr[1:].name == dr.name
+
+    @pytest.mark.parametrize("tz", [None, "Asia/Tokyo"])
+    def test_getitem(self, tz):
+        idx = date_range("2011-01-01", "2011-01-31", freq="D", tz=tz, name="idx")
+
+        result = idx[0]
+        assert result == Timestamp("2011-01-01", tz=idx.tz)
+
+        result = idx[0:5]
+        expected = date_range(
+            "2011-01-01", "2011-01-05", freq="D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx[0:10:2]
+        expected = date_range(
+            "2011-01-01", "2011-01-09", freq="2D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx[-20:-5:3]
+        expected = date_range(
+            "2011-01-12", "2011-01-24", freq="3D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx[4::-1]
+        expected = DatetimeIndex(
+            ["2011-01-05", "2011-01-04", "2011-01-03", "2011-01-02", "2011-01-01"],
+            dtype=idx.dtype,
+            freq="-1D",
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+    @pytest.mark.parametrize("freq", ["B", "C"])
+    def test_dti_business_getitem(self, freq):
+        rng = bdate_range(START, END, freq=freq)
+        smaller = rng[:5]
+        exp = DatetimeIndex(rng.view(np.ndarray)[:5], freq=freq)
+        tm.assert_index_equal(smaller, exp)
+        assert smaller.freq == exp.freq
+        assert smaller.freq == rng.freq
+
+        sliced = rng[::5]
+        assert sliced.freq == to_offset(freq) * 5
+
+        fancy_indexed = rng[[4, 3, 2, 1, 0]]
+        assert len(fancy_indexed) == 5
+        assert isinstance(fancy_indexed, DatetimeIndex)
+        assert fancy_indexed.freq is None
+
+        # 32-bit vs. 64-bit platforms
+        assert rng[4] == rng[np_long(4)]
+
+    @pytest.mark.parametrize("freq", ["B", "C"])
+    def test_dti_business_getitem_matplotlib_hackaround(self, freq):
+        rng = bdate_range(START, END, freq=freq)
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            # GH#30588 multi-dimensional indexing deprecated
+            rng[:, None]
+
+    def test_getitem_int_list(self):
+        dti = date_range(start="1/1/2005", end="12/1/2005", freq="ME")
+        dti2 = dti[[1, 3, 5]]
+
+        v1 = dti2[0]
+        v2 = dti2[1]
+        v3 = dti2[2]
+
+        assert v1 == Timestamp("2/28/2005")
+        assert v2 == Timestamp("4/30/2005")
+        assert v3 == Timestamp("6/30/2005")
+
+        # getitem with non-slice drops freq
+        assert dti2.freq is None
+
+
+class TestWhere:
+    def test_where_doesnt_retain_freq(self):
+        dti = date_range("20130101", periods=3, freq="D", name="idx")
+        cond = [True, True, False]
+        expected = DatetimeIndex([dti[0], dti[1], dti[0]], freq=None, name="idx")
+
+        result = dti.where(cond, dti[::-1])
+        tm.assert_index_equal(result, expected)
+
+    def test_where_other(self):
+        # other is ndarray or Index
+        i = date_range("20130101", periods=3, tz="US/Eastern")
+
+        for arr in [np.nan, pd.NaT]:
+            result = i.where(notna(i), other=arr)
+            expected = i
+            tm.assert_index_equal(result, expected)
+
+        i2 = i.copy()
+        i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
+        result = i.where(notna(i2), i2)
+        tm.assert_index_equal(result, i2)
+
+        i2 = i.copy()
+        i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
+        result = i.where(notna(i2), i2._values)
+        tm.assert_index_equal(result, i2)
+
+    def test_where_invalid_dtypes(self):
+        dti = date_range("20130101", periods=3, tz="US/Eastern")
+
+        tail = dti[2:].tolist()
+        i2 = Index([pd.NaT, pd.NaT] + tail)
+
+        mask = notna(i2)
+
+        # passing tz-naive ndarray to tzaware DTI
+        result = dti.where(mask, i2.values)
+        expected = Index([pd.NaT.asm8, pd.NaT.asm8] + tail, dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        # passing tz-aware DTI to tznaive DTI
+        naive = dti.tz_localize(None)
+        result = naive.where(mask, i2)
+        expected = Index([i2[0], i2[1]] + naive[2:].tolist(), dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        pi = i2.tz_localize(None).to_period("D")
+        result = dti.where(mask, pi)
+        expected = Index([pi[0], pi[1]] + tail, dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        tda = i2.asi8.view("timedelta64[ns]")
+        result = dti.where(mask, tda)
+        expected = Index([tda[0], tda[1]] + tail, dtype=object)
+        assert isinstance(expected[0], np.timedelta64)
+        tm.assert_index_equal(result, expected)
+
+        result = dti.where(mask, i2.asi8)
+        expected = Index([pd.NaT._value, pd.NaT._value] + tail, dtype=object)
+        assert isinstance(expected[0], int)
+        tm.assert_index_equal(result, expected)
+
+        # non-matching scalar
+        td = pd.Timedelta(days=4)
+        result = dti.where(mask, td)
+        expected = Index([td, td] + tail, dtype=object)
+        assert expected[0] is td
+        tm.assert_index_equal(result, expected)
+
+    def test_where_mismatched_nat(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        dti = date_range("2013-01-01", periods=3, tz=tz)
+        cond = np.array([True, False, True])
+
+        tdnat = np.timedelta64("NaT", "ns")
+        expected = Index([dti[0], tdnat, dti[2]], dtype=object)
+        assert expected[1] is tdnat
+
+        result = dti.where(cond, tdnat)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_tz(self):
+        i = date_range("20130101", periods=3, tz="US/Eastern")
+        result = i.where(notna(i))
+        expected = i
+        tm.assert_index_equal(result, expected)
+
+        i2 = i.copy()
+        i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
+        result = i.where(notna(i2))
+        expected = i2
+        tm.assert_index_equal(result, expected)
+
+
+class TestTake:
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_dti_take_dont_lose_meta(self, tzstr):
+        rng = date_range("1/1/2000", periods=20, tz=tzstr)
+
+        result = rng.take(range(5))
+        assert result.tz == rng.tz
+        assert result.freq == rng.freq
+
+    def test_take_nan_first_datetime(self):
+        index = DatetimeIndex([pd.NaT, Timestamp("20130101"), Timestamp("20130102")])
+        result = index.take([-1, 0, 1])
+        expected = DatetimeIndex([index[-1], index[0], index[1]])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "Asia/Tokyo"])
+    def test_take(self, tz):
+        # GH#10295
+        idx = date_range("2011-01-01", "2011-01-31", freq="D", name="idx", tz=tz)
+
+        result = idx.take([0])
+        assert result == Timestamp("2011-01-01", tz=idx.tz)
+
+        result = idx.take([0, 1, 2])
+        expected = date_range(
+            "2011-01-01", "2011-01-03", freq="D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx.take([0, 2, 4])
+        expected = date_range(
+            "2011-01-01", "2011-01-05", freq="2D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx.take([7, 4, 1])
+        expected = date_range(
+            "2011-01-08", "2011-01-02", freq="-3D", tz=idx.tz, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+        result = idx.take([3, 2, 5])
+        expected = DatetimeIndex(
+            ["2011-01-04", "2011-01-03", "2011-01-06"],
+            dtype=idx.dtype,
+            freq=None,
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq is None
+
+        result = idx.take([-3, 2, 5])
+        expected = DatetimeIndex(
+            ["2011-01-29", "2011-01-03", "2011-01-06"],
+            dtype=idx.dtype,
+            freq=None,
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.freq is None
+
+    def test_take_invalid_kwargs(self):
+        idx = date_range("2011-01-01", "2011-01-31", freq="D", name="idx")
+        indices = [1, 6, 5, 9, 10, 13, 15, 3]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            idx.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, mode="clip")
+
+    # TODO: This method came from test_datetime; de-dup with version above
+    @pytest.mark.parametrize("tz", [None, "US/Eastern", "Asia/Tokyo"])
+    def test_take2(self, tz):
+        dates = [
+            datetime(2010, 1, 1, 14),
+            datetime(2010, 1, 1, 15),
+            datetime(2010, 1, 1, 17),
+            datetime(2010, 1, 1, 21),
+        ]
+
+        idx = date_range(
+            start="2010-01-01 09:00",
+            end="2010-02-01 09:00",
+            freq="h",
+            tz=tz,
+            name="idx",
+        )
+        expected = DatetimeIndex(dates, freq=None, name="idx", dtype=idx.dtype)
+
+        taken1 = idx.take([5, 6, 8, 12])
+        taken2 = idx[[5, 6, 8, 12]]
+
+        for taken in [taken1, taken2]:
+            tm.assert_index_equal(taken, expected)
+            assert isinstance(taken, DatetimeIndex)
+            assert taken.freq is None
+            assert taken.tz == expected.tz
+            assert taken.name == expected.name
+
+    def test_take_fill_value(self):
+        # GH#12631
+        idx = DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"], name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = DatetimeIndex(["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = DatetimeIndex(["2011-02-01", "2011-01-01", "NaT"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = DatetimeIndex(["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "out of bounds"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+    def test_take_fill_value_with_timezone(self):
+        idx = DatetimeIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01"], name="xxx", tz="US/Eastern"
+        )
+        result = idx.take(np.array([1, 0, -1]))
+        expected = DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx", tz="US/Eastern"
+        )
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "NaT"], name="xxx", tz="US/Eastern"
+        )
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx", tz="US/Eastern"
+        )
+        tm.assert_index_equal(result, expected)
+
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "out of bounds"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+
+class TestGetLoc:
+    def test_get_loc_key_unit_mismatch(self):
+        idx = date_range("2000-01-01", periods=3)
+        key = idx[1].as_unit("ms")
+        loc = idx.get_loc(key)
+        assert loc == 1
+        assert key in idx
+
+    def test_get_loc_key_unit_mismatch_not_castable(self):
+        dta = date_range("2000-01-01", periods=3)._data.astype("M8[s]")
+        dti = DatetimeIndex(dta)
+        key = dta[0].as_unit("ns") + pd.Timedelta(1)
+
+        with pytest.raises(
+            KeyError, match=r"Timestamp\('2000-01-01 00:00:00.000000001'\)"
+        ):
+            dti.get_loc(key)
+
+        assert key not in dti
+
+    def test_get_loc_time_obj(self):
+        # time indexing
+        idx = date_range("2000-01-01", periods=24, freq="h")
+
+        result = idx.get_loc(time(12))
+        expected = np.array([12])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+        result = idx.get_loc(time(12, 30))
+        expected = np.array([])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+    @pytest.mark.parametrize("offset", [-10, 10])
+    def test_get_loc_time_obj2(self, monkeypatch, offset):
+        # GH#8667
+        size_cutoff = 50
+        n = size_cutoff + offset
+        key = time(15, 11, 30)
+        start = key.hour * 3600 + key.minute * 60 + key.second
+        step = 24 * 3600
+
+        with monkeypatch.context():
+            monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff)
+            idx = date_range("2014-11-26", periods=n, freq="s")
+            ts = pd.Series(np.random.default_rng(2).standard_normal(n), index=idx)
+            locs = np.arange(start, n, step, dtype=np.intp)
+
+            result = ts.index.get_loc(key)
+            tm.assert_numpy_array_equal(result, locs)
+            tm.assert_series_equal(ts[key], ts.iloc[locs])
+
+            left, right = ts.copy(), ts.copy()
+            left[key] *= -10
+            right.iloc[locs] *= -10
+            tm.assert_series_equal(left, right)
+
+    def test_get_loc_time_nat(self):
+        # GH#35114
+        # Case where key's total microseconds happens to match iNaT % 1e6 // 1000
+        tic = time(minute=12, second=43, microsecond=145224)
+        dti = DatetimeIndex([pd.NaT])
+
+        loc = dti.get_loc(tic)
+        expected = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(loc, expected)
+
+    def test_get_loc_nat(self):
+        # GH#20464
+        index = DatetimeIndex(["1/3/2000", "NaT"])
+        assert index.get_loc(pd.NaT) == 1
+
+        assert index.get_loc(None) == 1
+
+        assert index.get_loc(np.nan) == 1
+
+        assert index.get_loc(pd.NA) == 1
+
+        assert index.get_loc(np.datetime64("NaT")) == 1
+
+        with pytest.raises(KeyError, match="NaT"):
+            index.get_loc(np.timedelta64("NaT"))
+
+    @pytest.mark.parametrize("key", [pd.Timedelta(0), pd.Timedelta(1), timedelta(0)])
+    def test_get_loc_timedelta_invalid_key(self, key):
+        # GH#20464
+        dti = date_range("1970-01-01", periods=10)
+        msg = "Cannot index DatetimeIndex with [Tt]imedelta"
+        with pytest.raises(TypeError, match=msg):
+            dti.get_loc(key)
+
+    def test_get_loc_reasonable_key_error(self):
+        # GH#1062
+        index = DatetimeIndex(["1/3/2000"])
+        with pytest.raises(KeyError, match="2000"):
+            index.get_loc("1/1/2000")
+
+    def test_get_loc_year_str(self):
+        rng = date_range("1/1/2000", "1/1/2010")
+
+        result = rng.get_loc("2009")
+        expected = slice(3288, 3653)
+        assert result == expected
+
+
+class TestContains:
+    def test_dti_contains_with_duplicates(self):
+        d = datetime(2011, 12, 5, 20, 30)
+        ix = DatetimeIndex([d, d])
+        assert d in ix
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [0, 1, 0],
+            [0, 0, -1],
+            [0, -1, -1],
+            ["2015", "2015", "2016"],
+            ["2015", "2015", "2014"],
+        ],
+    )
+    def test_contains_nonunique(self, vals):
+        # GH#9512
+        idx = DatetimeIndex(vals)
+        assert idx[0] in idx
+
+
+class TestGetIndexer:
+    def test_get_indexer_date_objs(self):
+        rng = date_range("1/1/2000", periods=20)
+
+        result = rng.get_indexer(rng.map(lambda x: x.date()))
+        expected = rng.get_indexer(rng)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer(self):
+        idx = date_range("2000-01-01", periods=3)
+        exp = np.array([0, 1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(idx.get_indexer(idx), exp)
+
+        target = idx[0] + pd.to_timedelta(["-1 hour", "12 hours", "1 day 1 hour"])
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "nearest", tolerance=pd.Timedelta("1 hour")),
+            np.array([0, -1, 1], dtype=np.intp),
+        )
+        tol_raw = [
+            pd.Timedelta("1 hour"),
+            pd.Timedelta("1 hour"),
+            pd.Timedelta("1 hour").to_timedelta64(),
+        ]
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(
+                target, "nearest", tolerance=[np.timedelta64(x) for x in tol_raw]
+            ),
+            np.array([0, -1, 1], dtype=np.intp),
+        )
+        tol_bad = [
+            pd.Timedelta("2 hour").to_timedelta64(),
+            pd.Timedelta("1 hour").to_timedelta64(),
+            "foo",
+        ]
+        msg = "Could not convert 'foo' to NumPy timedelta"
+        with pytest.raises(ValueError, match=msg):
+            idx.get_indexer(target, "nearest", tolerance=tol_bad)
+        with pytest.raises(ValueError, match="abbreviation w/o a number"):
+            idx.get_indexer(idx[[0]], method="nearest", tolerance="foo")
+
+    @pytest.mark.parametrize(
+        "target",
+        [
+            [date(2020, 1, 1), Timestamp("2020-01-02")],
+            [Timestamp("2020-01-01"), date(2020, 1, 2)],
+        ],
+    )
+    def test_get_indexer_mixed_dtypes(self, target):
+        # https://github.com/pandas-dev/pandas/issues/33741
+        values = DatetimeIndex([Timestamp("2020-01-01"), Timestamp("2020-01-02")])
+        result = values.get_indexer(target)
+        expected = np.array([0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "target, positions",
+        [
+            ([date(9999, 1, 1), Timestamp("2020-01-01")], [-1, 0]),
+            ([Timestamp("2020-01-01"), date(9999, 1, 1)], [0, -1]),
+            ([date(9999, 1, 1), date(9999, 1, 1)], [-1, -1]),
+        ],
+    )
+    def test_get_indexer_out_of_bounds_date(self, target, positions):
+        values = DatetimeIndex([Timestamp("2020-01-01"), Timestamp("2020-01-02")])
+
+        result = values.get_indexer(target)
+        expected = np.array(positions, dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_pad_requires_monotonicity(self):
+        rng = date_range("1/1/2000", "3/1/2000", freq="B")
+
+        # neither monotonic increasing or decreasing
+        rng2 = rng[[1, 0, 2]]
+
+        msg = "index must be monotonic increasing or decreasing"
+        with pytest.raises(ValueError, match=msg):
+            rng2.get_indexer(rng, method="pad")
+
+
+class TestMaybeCastSliceBound:
+    def test_maybe_cast_slice_bounds_empty(self):
+        # GH#14354
+        empty_idx = date_range(freq="1h", periods=0, end="2015")
+
+        right = empty_idx._maybe_cast_slice_bound("2015-01-02", "right")
+        exp = Timestamp("2015-01-02 23:59:59.999999999")
+        assert right == exp
+
+        left = empty_idx._maybe_cast_slice_bound("2015-01-02", "left")
+        exp = Timestamp("2015-01-02 00:00:00")
+        assert left == exp
+
+    def test_maybe_cast_slice_duplicate_monotonic(self):
+        # https://github.com/pandas-dev/pandas/issues/16515
+        idx = DatetimeIndex(["2017", "2017"])
+        result = idx._maybe_cast_slice_bound("2017-01-01", "left")
+        expected = Timestamp("2017-01-01")
+        assert result == expected
+
+
+class TestGetSliceBounds:
+    @pytest.mark.parametrize("box", [date, datetime, Timestamp])
+    @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
+    def test_get_slice_bounds_datetime_within(
+        self, box, side, expected, tz_aware_fixture
+    ):
+        # GH 35690
+        tz = tz_aware_fixture
+        index = bdate_range("2000-01-03", "2000-02-11").tz_localize(tz)
+        key = box(year=2000, month=1, day=7)
+
+        if tz is not None:
+            with pytest.raises(TypeError, match="Cannot compare tz-naive"):
+                # GH#36148 we require tzawareness-compat as of 2.0
+                index.get_slice_bound(key, side=side)
+        else:
+            result = index.get_slice_bound(key, side=side)
+            assert result == expected
+
+    @pytest.mark.parametrize("box", [datetime, Timestamp])
+    @pytest.mark.parametrize("side", ["left", "right"])
+    @pytest.mark.parametrize("year, expected", [(1999, 0), (2020, 30)])
+    def test_get_slice_bounds_datetime_outside(
+        self, box, side, year, expected, tz_aware_fixture
+    ):
+        # GH 35690
+        tz = tz_aware_fixture
+        index = bdate_range("2000-01-03", "2000-02-11").tz_localize(tz)
+        key = box(year=year, month=1, day=7)
+
+        if tz is not None:
+            with pytest.raises(TypeError, match="Cannot compare tz-naive"):
+                # GH#36148 we require tzawareness-compat as of 2.0
+                index.get_slice_bound(key, side=side)
+        else:
+            result = index.get_slice_bound(key, side=side)
+            assert result == expected
+
+    @pytest.mark.parametrize("box", [datetime, Timestamp])
+    def test_slice_datetime_locs(self, box, tz_aware_fixture):
+        # GH 34077
+        tz = tz_aware_fixture
+        index = DatetimeIndex(["2010-01-01", "2010-01-03"]).tz_localize(tz)
+        key = box(2010, 1, 1)
+
+        if tz is not None:
+            with pytest.raises(TypeError, match="Cannot compare tz-naive"):
+                # GH#36148 we require tzawareness-compat as of 2.0
+                index.slice_locs(key, box(2010, 1, 2))
+        else:
+            result = index.slice_locs(key, box(2010, 1, 2))
+            expected = (0, 1)
+            assert result == expected
+
+
+class TestIndexerBetweenTime:
+    def test_indexer_between_time(self):
+        # GH#11818
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        msg = r"Cannot convert arg \[datetime\.datetime\(2010, 1, 2, 1, 0\)\] to a time"
+        with pytest.raises(ValueError, match=msg):
+            rng.indexer_between_time(datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5))
+
+    @pytest.mark.parametrize("unit", ["us", "ms", "s"])
+    def test_indexer_between_time_non_nano(self, unit):
+        # For simple cases like this, the non-nano indexer_between_time
+        #  should match the nano result
+
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        arr_nano = rng._data._ndarray
+
+        arr = arr_nano.astype(f"M8[{unit}]")
+
+        dta = type(rng._data)._simple_new(arr, dtype=arr.dtype)
+        dti = DatetimeIndex(dta)
+        assert dti.dtype == arr.dtype
+
+        tic = time(1, 25)
+        toc = time(2, 29)
+
+        result = dti.indexer_between_time(tic, toc)
+        expected = rng.indexer_between_time(tic, toc)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # case with non-zero micros in arguments
+        tic = time(1, 25, 0, 45678)
+        toc = time(2, 29, 0, 1234)
+
+        result = dti.indexer_between_time(tic, toc)
+        expected = rng.indexer_between_time(tic, toc)
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/shared/package-files/32/3277a99a9ade798d75397f674078f94f181876738f248442f82af73320e70b7f/expanding.py b/shared/package-files/32/3277a99a9ade798d75397f674078f94f181876738f248442f82af73320e70b7f/expanding.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac10596ffc699c2b229f959b9c1b26393384b03
--- /dev/null
+++ b/shared/package-files/32/3277a99a9ade798d75397f674078f94f181876738f248442f82af73320e70b7f/expanding.py
@@ -0,0 +1,964 @@
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+from pandas.util._decorators import (
+    deprecate_kwarg,
+    doc,
+)
+
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    ExpandingIndexer,
+    GroupbyIndexer,
+)
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+    window_apply_parameters,
+)
+from pandas.core.window.rolling import (
+    BaseWindowGroupby,
+    RollingAndExpandingMixin,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        QuantileInterpolation,
+        WindowingRankType,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+
+
+class Expanding(RollingAndExpandingMixin):
+    """
+    Provide expanding window calculations.
+
+    Parameters
+    ----------
+    min_periods : int, default 1
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+    axis : int or str, default 0
+        If ``0`` or ``'index'``, roll across the rows.
+
+        If ``1`` or ``'columns'``, roll across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+    method : str {'single', 'table'}, default 'single'
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+        .. versionadded:: 1.3.0
+
+    Returns
+    -------
+    pandas.api.typing.Expanding
+
+    See Also
+    --------
+    rolling : Provides rolling window calculations.
+    ewm : Provides exponential weighted functions.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations <window.expanding>` for further usage details
+    and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    **min_periods**
+
+    Expanding sum with 1 vs 3 observations needed to calculate a value.
+
+    >>> df.expanding(1).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  3.0
+    4  7.0
+    >>> df.expanding(3).sum()
+         B
+    0  NaN
+    1  NaN
+    2  3.0
+    3  3.0
+    4  7.0
+    """
+
+    _attributes: list[str] = ["min_periods", "axis", "method"]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        min_periods: int = 1,
+        axis: Axis = 0,
+        method: str = "single",
+        selection=None,
+    ) -> None:
+        super().__init__(
+            obj=obj,
+            min_periods=min_periods,
+            axis=axis,
+            method=method,
+            selection=selection,
+        )
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        return ExpandingIndexer()
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.aggregate : Similar DataFrame method.
+        pandas.Series.aggregate : Similar Series method.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.ewm(alpha=0.5).mean()
+                  A         B         C
+        0  1.000000  4.000000  7.000000
+        1  1.666667  4.666667  7.666667
+        2  2.428571  5.428571  8.428571
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().count()
+        a    1.0
+        b    2.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="count of non NaN observations",
+        agg_method="count",
+    )
+    def count(self, numeric_only: bool = False):
+        return super().count(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        window_apply_parameters,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
+        a   -1.0
+        b    0.0
+        c    1.0
+        d    2.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="custom aggregation function",
+        agg_method="apply",
+    )
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        return super().apply(
+            func,
+            raw=raw,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().sum()
+        a     1.0
+        b     3.0
+        c     6.0
+        d    10.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().sum(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([3, 2, 1, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().max()
+        a    3.0
+        b    3.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="maximum",
+        agg_method="max",
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().max(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([2, 3, 4, 1], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().min()
+        a    2.0
+        b    2.0
+        c    2.0
+        d    1.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="minimum",
+        agg_method="min",
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().min(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().mean()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().mean(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().median()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="median",
+        agg_method="median",
+    )
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().median(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.std : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.std` is different
+        than the default ``ddof`` of 0 in :func:`numpy.std`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).std()
+        0         NaN
+        1         NaN
+        2    0.577350
+        3    0.957427
+        4    0.894427
+        5    0.836660
+        6    0.786796
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard deviation",
+        agg_method="std",
+    )
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().std(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.var : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.var` is different
+        than the default ``ddof`` of 0 in :func:`numpy.var`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).var()
+        0         NaN
+        1         NaN
+        2    0.333333
+        3    0.916667
+        4    0.800000
+        5    0.700000
+        6    0.619048
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="variance",
+        agg_method="var",
+    )
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().var(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of one period is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([0, 1, 2, 3])
+
+        >>> s.expanding().sem()
+        0         NaN
+        1    0.707107
+        2    0.707107
+        3    0.745356
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard error of mean",
+        agg_method="sem",
+    )
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        return super().sem(ddof=ddof, numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.skew : Third moment of a probability density.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of three periods is required for the rolling calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([-1, 0, 2, -1, 2], index=['a', 'b', 'c', 'd', 'e'])
+        >>> ser.expanding().skew()
+        a         NaN
+        b         NaN
+        c    0.935220
+        d    1.414214
+        e    0.315356
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="unbiased skewness",
+        agg_method="skew",
+    )
+    def skew(self, numeric_only: bool = False):
+        return super().skew(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.kurtosis : Reference SciPy method.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of four periods is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        The example below will show a rolling calculation with a window size of
+        four matching the equivalent function call using `scipy.stats`.
+
+        >>> arr = [1, 2, 3, 4, 999]
+        >>> import scipy.stats
+        >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
+        -1.200000
+        >>> print(f"{{scipy.stats.kurtosis(arr, bias=False):.6f}}")
+        4.999874
+        >>> s = pd.Series(arr)
+        >>> s.expanding(4).kurt()
+        0         NaN
+        1         NaN
+        2         NaN
+        3   -1.200000
+        4    4.999874
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="Fisher's definition of kurtosis without bias",
+        agg_method="kurt",
+    )
+    def kurt(self, numeric_only: bool = False):
+        return super().kurt(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        quantile : float
+            Quantile to compute. 0 <= quantile <= 1.
+
+            .. deprecated:: 2.1.0
+                This will be renamed to 'q' in a future version.
+        interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
+        >>> ser.expanding(min_periods=4).quantile(.25)
+        a     NaN
+        b     NaN
+        c     NaN
+        d    1.75
+        e    2.00
+        f    2.25
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="quantile",
+        agg_method="quantile",
+    )
+    @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q")
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        return super().quantile(
+            q=q,
+            interpolation=interpolation,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        ".. versionadded:: 1.4.0 \n\n",
+        create_section_header("Parameters"),
+        dedent(
+            """
+        method : {{'average', 'min', 'max'}}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+        >>> s.expanding().rank()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.5
+        dtype: float64
+
+        >>> s.expanding().rank(method="max")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    4.0
+        dtype: float64
+
+        >>> s.expanding().rank(method="min")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="rank",
+        agg_method="rank",
+    )
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        return super().rank(
+            method=method,
+            ascending=ascending,
+            pct=pct,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().cov(ser2)
+        a         NaN
+        b    0.500000
+        c    1.500000
+        d    3.333333
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().cov(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        dedent(
+            """
+        cov : Similar method to calculate covariance.
+        numpy.corrcoef : NumPy Pearson's correlation calculation.
+        """
+        ).replace("\n", "", 1),
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        This function uses Pearson's definition of correlation
+        (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+
+        When `other` is not specified, the output will be self correlation (e.g.
+        all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
+        set to `True`.
+
+        Function will return ``NaN`` for correlations of equal valued sequences;
+        this is the result of a 0/0 division error.
+
+        When `pairwise` is set to `False`, only matching columns between `self` and
+        `other` will be used.
+
+        When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
+        with the original index on the first level, and the `other` DataFrame
+        columns on the second level.
+
+        In the case of missing elements, only complete pairwise observations
+        will be used.\n
+        """
+        ),
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().corr(ser2)
+        a         NaN
+        b    1.000000
+        c    0.981981
+        d    0.975900
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().corr(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+
+class ExpandingGroupby(BaseWindowGroupby, Expanding):
+    """
+    Provide a expanding groupby implementation.
+    """
+
+    _attributes = Expanding._attributes + BaseWindowGroupby._attributes
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        window_indexer = GroupbyIndexer(
+            groupby_indices=self._grouper.indices,
+            window_indexer=ExpandingIndexer,
+        )
+        return window_indexer
diff --git a/shared/package-files/32/32799ec2d30e2792f8ddbd4b33ac83d5d410142c71199078c2036c6503ef29d2/codeop.py b/shared/package-files/32/32799ec2d30e2792f8ddbd4b33ac83d5d410142c71199078c2036c6503ef29d2/codeop.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe8713ecf46bfa4a60bed8e110e4c0ab3b8c8d73
--- /dev/null
+++ b/shared/package-files/32/32799ec2d30e2792f8ddbd4b33ac83d5d410142c71199078c2036c6503ef29d2/codeop.py
@@ -0,0 +1,153 @@
+r"""Utilities to compile possibly incomplete Python source code.
+
+This module provides two interfaces, broadly similar to the builtin
+function compile(), which take program text, a filename and a 'mode'
+and:
+
+- Return code object if the command is complete and valid
+- Return None if the command is incomplete
+- Raise SyntaxError, ValueError or OverflowError if the command is a
+  syntax error (OverflowError and ValueError can be produced by
+  malformed literals).
+
+The two interfaces are:
+
+compile_command(source, filename, symbol):
+
+    Compiles a single command in the manner described above.
+
+CommandCompiler():
+
+    Instances of this class have __call__ methods identical in
+    signature to compile_command; the difference is that if the
+    instance compiles program text containing a __future__ statement,
+    the instance 'remembers' and compiles all subsequent program texts
+    with the statement in force.
+
+The module also provides another class:
+
+Compile():
+
+    Instances of this class act like the built-in function compile,
+    but with 'memory' in the sense described above.
+"""
+
+import __future__
+import warnings
+
+_features = [getattr(__future__, fname)
+             for fname in __future__.all_feature_names]
+
+__all__ = ["compile_command", "Compile", "CommandCompiler"]
+
+# The following flags match the values from Include/cpython/compile.h
+# Caveat emptor: These flags are undocumented on purpose and depending
+# on their effect outside the standard library is **unsupported**.
+PyCF_DONT_IMPLY_DEDENT = 0x200          
+PyCF_ALLOW_INCOMPLETE_INPUT = 0x4000
+
+def _maybe_compile(compiler, source, filename, symbol):
+    # Check for source consisting of only blank lines and comments.
+    for line in source.split("\n"):
+        line = line.strip()
+        if line and line[0] != '#':
+            break               # Leave it alone.
+    else:
+        if symbol != "eval":
+            source = "pass"     # Replace it with a 'pass' statement
+
+    # Disable compiler warnings when checking for incomplete input.
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", (SyntaxWarning, DeprecationWarning))
+        try:
+            compiler(source, filename, symbol)
+        except SyntaxError:  # Let other compile() errors propagate.
+            try:
+                compiler(source + "\n", filename, symbol)
+                return None
+            except SyntaxError as e:
+                if "incomplete input" in str(e):
+                    return None
+                # fallthrough
+
+    return compiler(source, filename, symbol)
+
+
+def _is_syntax_error(err1, err2):
+    rep1 = repr(err1)
+    rep2 = repr(err2)
+    if "was never closed" in rep1 and "was never closed" in rep2:
+        return False
+    if rep1 == rep2:
+        return True
+    return False
+
+def _compile(source, filename, symbol):
+    return compile(source, filename, symbol, PyCF_DONT_IMPLY_DEDENT | PyCF_ALLOW_INCOMPLETE_INPUT)
+
+def compile_command(source, filename="<input>", symbol="single"):
+    r"""Compile a command and determine whether it is incomplete.
+
+    Arguments:
+
+    source -- the source string; may contain \n characters
+    filename -- optional filename from which source was read; default
+                "<input>"
+    symbol -- optional grammar start symbol; "single" (default), "exec"
+              or "eval"
+
+    Return value / exceptions raised:
+
+    - Return a code object if the command is complete and valid
+    - Return None if the command is incomplete
+    - Raise SyntaxError, ValueError or OverflowError if the command is a
+      syntax error (OverflowError and ValueError can be produced by
+      malformed literals).
+    """
+    return _maybe_compile(_compile, source, filename, symbol)
+
+class Compile:
+    """Instances of this class behave much like the built-in compile
+    function, but if one is used to compile text containing a future
+    statement, it "remembers" and compiles all subsequent program texts
+    with the statement in force."""
+    def __init__(self):
+        self.flags = PyCF_DONT_IMPLY_DEDENT | PyCF_ALLOW_INCOMPLETE_INPUT
+
+    def __call__(self, source, filename, symbol):
+        codeob = compile(source, filename, symbol, self.flags, True)
+        for feature in _features:
+            if codeob.co_flags & feature.compiler_flag:
+                self.flags |= feature.compiler_flag
+        return codeob
+
+class CommandCompiler:
+    """Instances of this class have __call__ methods identical in
+    signature to compile_command; the difference is that if the
+    instance compiles program text containing a __future__ statement,
+    the instance 'remembers' and compiles all subsequent program texts
+    with the statement in force."""
+
+    def __init__(self,):
+        self.compiler = Compile()
+
+    def __call__(self, source, filename="<input>", symbol="single"):
+        r"""Compile a command and determine whether it is incomplete.
+
+        Arguments:
+
+        source -- the source string; may contain \n characters
+        filename -- optional filename from which source was read;
+                    default "<input>"
+        symbol -- optional grammar start symbol; "single" (default) or
+                  "eval"
+
+        Return value / exceptions raised:
+
+        - Return a code object if the command is complete and valid
+        - Return None if the command is incomplete
+        - Raise SyntaxError, ValueError or OverflowError if the command is a
+          syntax error (OverflowError and ValueError can be produced by
+          malformed literals).
+        """
+        return _maybe_compile(self.compiler, source, filename, symbol)
diff --git a/shared/package-files/32/327d8242564b9017b5161587eeb332b82ac0a7d3732f26b2ddee198030f0e014/linalg_lu_factor_ex_cuda_dispatch.h b/shared/package-files/32/327d8242564b9017b5161587eeb332b82ac0a7d3732f26b2ddee198030f0e014/linalg_lu_factor_ex_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f1386c061174e068cc35eb79d540532a9d4c6c6e
--- /dev/null
+++ b/shared/package-files/32/327d8242564b9017b5161587eeb332b82ac0a7d3732f26b2ddee198030f0e014/linalg_lu_factor_ex_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex(const at::Tensor & A, bool pivot=true, bool check_errors=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out(at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, bool pivot=true, bool check_errors=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_outf(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/32/327e4fab88034e2087cf2191c3b9b9e2c8016d7c7bab299b83959227cc48c22b/_add_relu.h b/shared/package-files/32/327e4fab88034e2087cf2191c3b9b9e2c8016d7c7bab299b83959227cc48c22b/_add_relu.h
new file mode 100644
index 0000000000000000000000000000000000000000..21230134079caf8c1919949d88ccf5a985defdff
--- /dev/null
+++ b/shared/package-files/32/327e4fab88034e2087cf2191c3b9b9e2c8016d7c7bab299b83959227cc48c22b/_add_relu.h
@@ -0,0 +1,63 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_add_relu_ops.h>
+
+namespace at {
+
+
+// aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
+inline at::Tensor _add_relu(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu_Tensor::call(self, other, alpha);
+}
+
+// aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
+inline at::Tensor & _add_relu_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu__Tensor::call(self, other, alpha);
+}
+
+// aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu_out::call(self, other, alpha, out);
+}
+// aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::_add_relu_out::call(self, other, alpha, out);
+}
+
+// aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
+inline at::Tensor _add_relu(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu_Scalar::call(self, other, alpha);
+}
+
+// aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
+inline at::Tensor & _add_relu_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu__Scalar::call(self, other, alpha);
+}
+
+// aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::_add_relu_Scalar_out::call(self, other, alpha, out);
+}
+// aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::_add_relu_Scalar_out::call(self, other, alpha, out);
+}
+
+}
diff --git a/shared/package-files/32/32806640e01151678f6af55de6f562502e0dd2ec9c172654e8ba90e3bf8d3a1a/scheduler.py b/shared/package-files/32/32806640e01151678f6af55de6f562502e0dd2ec9c172654e8ba90e3bf8d3a1a/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..5091f69000bc2974c62ddae3b8f610aa06f87ce1
--- /dev/null
+++ b/shared/package-files/32/32806640e01151678f6af55de6f562502e0dd2ec9c172654e8ba90e3bf8d3a1a/scheduler.py
@@ -0,0 +1,2445 @@
+import collections
+import dataclasses
+import functools
+import itertools
+import logging
+import math
+import operator
+import os
+import pprint
+import textwrap
+from typing import (
+    Any,
+    Counter,
+    DefaultDict,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+import sympy
+
+import torch
+from torch._dynamo.utils import dynamo_timed
+from torch._inductor.metrics import get_metric_table, is_metric_table_enabled
+from torch.utils._triton import has_triton
+
+from . import comms, config, dependencies, ir, metrics
+from .codegen.common import get_scheduling_for_device, Kernel
+from .comm_analysis import estimate_nccl_collective_runtime
+from .dependencies import Dep, MemoryDep, StarDep, WeakDep
+from .ir import ComputedBuffer, MultiOutput, MultiOutputLayout
+from .sizevars import SimplifyIndexing
+from .utils import (
+    cache_on_self,
+    cmp,
+    free_symbol_has,
+    get_device_tflops,
+    get_dtype_size,
+    get_gpu_dram_gbps,
+    green_text,
+    is_collective,
+    is_wait,
+    red_text,
+    sympy_product,
+)
+from .virtualized import V
+
+
+log = logging.getLogger(__name__)
+fusion_log = torch._logging.getArtifactLogger(__name__, "fusion")
+
+
+class WhyNoFuse:
+    # TODO when we drop support for Python < 3.10, we can use
+    # @dataclass(slots=True) instead of manually specifying __slots__.
+    __slots__ = ["node1", "node2", "reason", "args"]
+    reason: str
+    args: Tuple[Any, ...]
+
+    def __init__(self, node1: "BaseSchedulerNode", node2: "BaseSchedulerNode"):
+        self.node1 = node1
+        self.node2 = node2
+
+    def __call__(self, reason, *args):
+        self.reason = reason
+        self.args = args
+        fusion_log.debug(self)
+
+    def __str__(self):
+        return f"cannot fuse {self.node1.get_name()} with {self.node2.get_name()}: " + (
+            self.reason % self.args
+        )
+
+
+def pformat(obj):
+    if isinstance(obj, set):
+        # pformat has trouble with sets of sympy exprs
+        obj = sorted(obj, key=str)
+    result = pprint.pformat(obj, indent=4)
+    if "\n" in result:
+        return f"\n{textwrap.indent(result, ' '*4)}"
+    return result
+
+
+class OutputNode:
+    def __init__(self, dep):
+        self.unmet_dependencies = {dep}
+        self.inverse_users = []
+
+    def is_reduction(self):
+        return False
+
+    def get_alias_names(self):
+        return ()
+
+    def get_name(self):
+        return "OUTPUT"
+
+    __repr__ = get_name
+
+
+def _prune_redundant_deps(node, name_to_fused_node):
+    """
+    Prunes weakdeps intended for mutation ordering
+    on an upstream fused node if after fusion there is another dependency
+    on the fused upstream node, making the weakdep redundant
+
+    In essence this enforces an ordering on fusions. As fusions occur, weakdeps will
+    be incrementally removed, enabling other fusions, ensuring they are fused in order.
+    """
+    name_to_dep_count: Counter[str] = collections.Counter()
+
+    for dep in node.unmet_dependencies:
+        if not isinstance(dep, WeakDep):
+            name_to_dep_count[name_to_fused_node[dep.name].get_name()] += 1
+
+    def should_prune(dep):
+        if isinstance(dep, WeakDep):
+            is_redundant = (
+                name_to_dep_count[name_to_fused_node[dep.name].get_name()] > 0
+            )
+            # These can occur because fused nodes always gather deps from their snodes
+            # If B has a weakdep on A
+            # B gets fused with C, then any time BC is fused, the weakdep will reappear
+            is_self_dep = name_to_fused_node[dep.name] == node
+            return is_redundant or is_self_dep
+        else:
+            return False
+
+    deps_to_prune = {dep for dep in node.unmet_dependencies if should_prune(dep)}
+
+    if deps_to_prune:
+        node.unmet_dependencies = node.unmet_dependencies - deps_to_prune
+        node.set_read_writes(node.read_writes.remove_reads(deps_to_prune))
+
+
+# TODO(xmfan): reuse an existing mapping for this if it exists, or formalize this into ir.py:ExternKernel
+kernel_name_to_op = {
+    "extern_kernels.convolution": torch.ops.aten.convolution,
+    "extern_kernels.mm": torch.ops.aten.mm,
+    "extern_kernels.bmm": torch.ops.aten.bmm,
+    "extern_kernels.addmm": torch.ops.aten.addmm,
+}
+
+
+class BaseSchedulerNode:
+    def __init__(self, scheduler: "Scheduler", node: ir.Buffer):
+        self.scheduler: Scheduler = scheduler
+        self.node: ir.Buffer = node
+        self.users: List[NodeUser] = []
+        self.inverse_users: List[BaseSchedulerNode] = []
+        self.node_users: List[BaseSchedulerNode] = []
+        self.set_read_writes(node.get_read_writes())
+        self.ancestors: Set[str] = set()
+        self.min_order: int
+        self.max_order: int
+        self.last_usage: Set[
+            str
+        ] = set()  # buffers that won't be used after this kernel
+        self.written = False
+
+    def __repr__(self):
+        return f"{type(self).__name__}(name={self.get_name()!r})"
+
+    def debug_str(self) -> str:
+        """Longer form printout for trace logs"""
+        name = self.get_name()
+        lines = [
+            f"{name}: {type(self).__name__}({type(getattr(self, 'node', None)).__name__})",
+            f"{name}.writes = {pformat(self.read_writes.writes)}",
+            f"{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}",
+            f"{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}",
+            f"{name}.users = {self.users}",
+        ]
+        try:
+            lines += [
+                self.debug_str_extra(),
+            ]
+        except Exception:
+            log.warning("Ignoring error in debug_str()", exc_info=True)
+
+        return "\n".join(lines).rstrip()
+
+    def debug_str_extra(self) -> str:
+        return ""
+
+    def log_details(self):
+        log.info(
+            "%s: unmet_dependencies = %s, writes = %s",
+            self,
+            self.unmet_dependencies,
+            self.read_writes.writes,
+        )
+
+    def update_mutated_names(self, renames: Dict[str, str]):
+        self.set_read_writes(self.read_writes.rename(renames))
+
+    def add_mutation_dep(self, dep):
+        self.set_read_writes(self.read_writes.with_read(dep))
+
+    def add_fake_dep(self, dep):
+        self.set_read_writes(self.read_writes.with_read(dep))
+
+    def set_users(self, users: List["NodeUser"]):
+        # deduplicate
+        result: Dict[int, NodeUser] = {}
+        for use in users:
+            if id(use.node) in result:
+                result[id(use.node)] = use.merge(result[id(use.node)])
+            else:
+                result[id(use.node)] = use
+        self.users = list(result.values())
+
+    def set_last_usage(
+        self, future_used_buffers: Set[str], mutation_real_name: Dict[str, str]
+    ):
+        used_buffers = self.used_or_aliased_buffer_names()
+        used_buffers = {mutation_real_name.get(k, k) for k in used_buffers}
+        self.last_usage = used_buffers - future_used_buffers
+
+    def get_aliases(self):
+        return self.node.get_alias_names()
+
+    def get_mutations(self):
+        return self.node.get_mutation_names()
+
+    def has_aliasing_or_mutation(self):
+        return bool(self.get_aliases() or self.get_mutations())
+
+    def set_read_writes(self, rw: dependencies.ReadWrites):
+        self.read_writes: dependencies.ReadWrites = rw
+        self.unmet_dependencies = self.read_writes.reads
+        self.prune_deps()
+
+    def op_counts(self):
+        return self.read_writes.op_counts
+
+    def used_buffer_names(self) -> Set[str]:
+        return {
+            dep.name
+            for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes)
+        }
+
+    def used_or_aliased_buffer_names(self) -> Set[str]:
+        used_names = set()
+
+        for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes):
+            used_names.add(dep.name)
+            if V.graph.name_to_buffer.get(dep.name):
+                layout = V.graph.name_to_buffer[dep.name].get_layout()
+                # needed to avoid deallocating aliased buffer
+                # if there are still uses of aliases ahead
+                if isinstance(layout, ir.AliasedLayout):
+                    used_names.add(layout.view.data.get_name())
+        return used_names
+
+    def prune_deps(self):
+        self.unmet_dependencies = {
+            dep
+            for dep in self.unmet_dependencies
+            if dep.name not in self.scheduler.available_buffer_names
+        }
+
+    def prune_weak_deps(self):
+        # Prune weak dependencies on buffers that have been removed
+        def should_prune(dep):
+            return isinstance(dep, WeakDep) and dep.name in V.graph.removed_buffers
+
+        to_remove = {dep for dep in self.read_writes.reads if should_prune(dep)}
+        self.set_read_writes(self.read_writes.remove_reads(to_remove))
+
+    def prune_redundant_deps(self, name_to_fused_node):
+        _prune_redundant_deps(self, name_to_fused_node)
+
+    def get_name(self) -> str:
+        return self.node.get_name()
+
+    def get_first_name(self) -> str:
+        return self.get_name()
+
+    def get_names(self) -> Set[str]:
+        return {self.get_name()}
+
+    def get_nodes(self) -> Sequence["BaseSchedulerNode"]:
+        return [self]
+
+    def get_device(self):
+        return self.node.get_device()
+
+    def is_reduction(self):
+        return False
+
+    def is_split_scan(self):
+        return False
+
+    def is_template(self):
+        return False
+
+    def is_extern(self):
+        return False
+
+    def is_foreach(self):
+        return False
+
+    def can_inplace(self, read_dep: dependencies.MemoryDep):
+        return False
+
+    def has_side_effects(self):
+        return False
+
+    def decide_inplace_update(self):
+        """
+        Decide if there should be inplace updates for the node
+        and record the decision in the active kernel.
+        """
+        if not self.node.should_allocate():
+            return
+
+        if isinstance(self, (SchedulerNode,)) and (
+            self.node.get_alias_names() or self.node.get_mutation_names()
+        ):
+            return
+
+        if (
+            (
+                isinstance(self, (SchedulerNode,))
+                # o what have i done.  lets make this an api
+                or (
+                    isinstance(self, ExternKernelSchedulerNode)
+                    and isinstance(self.node, (ir.AllReduce, ir.InPlaceHint))
+                )
+            )
+            and config.inplace_buffers
+            and (
+                not isinstance(V.kernel, torch._inductor.codegen.triton.TritonKernel)
+                or getattr(V.kernel, "mutations", None) is not None
+            )
+        ):
+            from .codegen.wrapper import buffer_reuse_key
+
+            ordered_reads = sorted(self.read_writes.reads, key=lambda x: x.name)
+
+            for read in ordered_reads:
+                input_node: Optional[
+                    BaseSchedulerNode
+                ] = self.scheduler.name_to_node.get(read.name)
+                if input_node and V.graph.wrapper_code.can_reuse(input_node, self):
+                    assert input_node.users is not None
+                    remaining_uses = [
+                        x
+                        for x in input_node.users
+                        if x.node.get_name()
+                        not in self.scheduler.available_buffer_names
+                    ]
+                    if (
+                        len(remaining_uses) == 1
+                        and remaining_uses[0].can_inplace
+                        and remaining_uses[0].node is self
+                        and not isinstance(
+                            input_node.node.get_layout(),
+                            (
+                                ir.MultiOutputLayout,
+                                ir.MutationLayout,
+                                ir.AliasedLayout,
+                            ),
+                        )
+                        and not (
+                            isinstance(
+                                input_node.node, (ir.FallbackKernel, ir.MultiOutput)
+                            )
+                            and len(input_node.node.get_alias_names()) > 0
+                        )
+                        and buffer_reuse_key(input_node.node)
+                        == buffer_reuse_key(self.node)
+                    ):
+                        # hacky check for if V.kernel is a real kernel or NullHandler
+                        if hasattr(V.kernel, "args"):
+                            # if there isn't a triton kernel, then we don't need to call triton-specific things.
+                            # but TODO this might be a convenient place to signal to the Collective kernels to inplace
+                            # (and, can we make "kernel" less generic of a name?)
+                            V.kernel.args.make_inplace(
+                                input_node.get_name(), self.get_name()
+                            )
+                            # mutations not tracked in cpp kernels
+                            if isinstance(
+                                V.kernel, torch._inductor.codegen.triton.TritonKernel
+                            ):
+                                V.kernel.mutations.add(input_node.get_name())
+                                V.kernel.mutations.add(self.get_name())
+
+                            # update last usage of reused node
+                            self.last_usage.discard(input_node.get_name())
+
+                            V.kernel.inplace_update_buffers[
+                                self.get_name()
+                            ] = input_node.get_name()
+                        break
+
+    def allocate(self):
+        if not self.node.should_allocate():
+            return
+
+        if isinstance(self, (SchedulerNode,)) and (
+            self.node.get_alias_names() or self.node.get_mutation_names()
+        ):
+            V.graph.wrapper_code.codegen_allocation(self.node)
+            return
+
+        # hacky check for if V.kernel is a real kernel or NullHandler
+        if (
+            hasattr(V.kernel, "args")
+            and self.get_name() in V.kernel.inplace_update_buffers
+        ):
+            V.graph.wrapper_code.codegen_inplace_reuse(
+                self.scheduler.name_to_node[
+                    V.kernel.inplace_update_buffers[self.get_name()]
+                ].node,
+                self.node,
+            )
+        else:
+            V.graph.wrapper_code.codegen_allocation(self.node)
+
+    def can_free(self):
+        # There's no real allocated buffer, no need to free it
+        if isinstance(self.node.layout, ir.NoneLayout):
+            return False
+        for use in self.users:
+            if isinstance(use.node, OutputNode):
+                return False
+        return True
+
+    def codegen_originating_info(self, buffer, only_once=True):
+        if not config.comment_origin:
+            return
+
+        if only_once and self.written:
+            return
+        origins = self.node.origins
+        out_lines = []
+
+        for o in origins:
+            if o.op == "output":
+                # These are boring and samey
+                continue
+
+            out_lines.append("")
+            # TODO(voz): Should the pragma be constant somewhere?
+            out_lines.append("#pragma CMT ORIGIN:")
+            op_info_str = f"#pragma CMT {o.op} {o.target}"
+            if "seq_nr" in o.meta:
+                op_info_str = op_info_str + f" seq_nr:{o.meta['seq_nr']}"
+            out_lines.append(op_info_str)
+            if "stack_trace" in o.meta:
+                stack_trace = f"{o.meta['stack_trace']}"
+                stack_trace_last_line = stack_trace.split("|")[-1]
+                out_lines.append(
+                    "#pragma CMT "
+                    + stack_trace_last_line.replace("{", "{{")
+                    .replace("}", "}}")
+                    .replace("\n", "\\")
+                )
+                out_lines.append("#pragma CMT END ORIGIN")
+                out_lines.append("")
+
+        if len(out_lines) == 0:
+            return
+
+        # TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does
+        # not use BracesBuffer, so we have no good indicator of a C++ buffer atm.
+        buffer.writelines(out_lines)
+        self.written = True
+
+    def get_read_write_buffers_sizes(self) -> int:
+        """
+        Counting the number of bytes accessed for a kernel is
+        surprisingly tricky. In particular, there is a differentiation
+        between 'theoretical' memory accesses and practical memory
+        accesses. For example, a layernorm kernel may actually access an
+        input 3 times, but in theory, it only needs to access its input
+        once (and may be optimized to do so through say, persistent
+        reductions)
+
+        Another example is that even though a buffer is passed in, we may
+        not access the entire buffer. This may occur if we are accessing
+        a slice of the buffer. Another tricky case is for indirect
+        indexing, where the amount of bytes accessed depends on the
+        values of the input.
+
+        What this function aims to compute is the memory accesses for
+        worst-case inputs, best-case optimization. What this means is
+        that for each buffer we compute the amount of potential accesses in two ways and take the minimum.
+
+        1. Numel in ranges multiplied by number of deps the buffer has
+        2. The buffer size
+        """
+        if isinstance(self, NopKernelSchedulerNode):
+            return 0
+        if isinstance(self, ExternKernelSchedulerNode) and isinstance(
+            self.node, MultiOutput
+        ):
+            return 0
+
+        if isinstance(self, SchedulerNode):
+            node_numel = V.graph.sizevars.size_hint(
+                sympy_product(self.get_ranges()[0])
+                * sympy_product(self.get_ranges()[1])
+            )
+        else:
+            node_numel = int(1e9)
+        buf_accesses = collections.defaultdict(list)
+        for dep in self.read_writes.reads | self.read_writes.writes:
+            buf_accesses[dep.name].append(dep)
+
+        reads = {dep.name for dep in self.read_writes.reads}
+        writes = {dep.name for dep in self.read_writes.writes}
+
+        def is_materialized(buf, snodes):
+            users = self.scheduler.name_to_node[buf].users
+            buf_uses = {user.node for user in users}
+            return len(buf_uses - set(snodes)) > 0
+
+        if isinstance(self, FusedSchedulerNode):
+            removed_buffers = {
+                dep for dep in writes if not is_materialized(dep, self.snodes)
+            }
+            writes = writes - removed_buffers
+            reads = reads - removed_buffers
+        node_bytes = 0
+
+        for buf_name in reads | writes:
+            buf_accessed_elems = sum([node_numel for dep in buf_accesses[buf_name]])
+            buf: Union[ir.Buffer, ir.TensorBox]
+            if buf_name in V.graph.name_to_buffer:
+                buf = V.graph.name_to_buffer[buf_name]
+            elif buf_name in V.graph.graph_inputs:
+                buf = V.graph.graph_inputs[buf_name]
+            else:
+                continue
+
+            def get_buf_elems(buf):
+                return V.graph.sizevars.size_hint(sympy_product(buf.get_size()))
+
+            # Kind of a lazy way to get the MultiOutput nodes corresponding to
+            # a MultiOutputLayout
+            if isinstance(buf.layout, MultiOutputLayout):
+                users = self.scheduler.name_to_node[buf.get_name()].users
+                buf_elems = sum(get_buf_elems(user.node.node) for user in users)
+            else:
+                buf_elems = get_buf_elems(buf)
+
+            node_bytes += min(buf_elems, buf_accessed_elems) * get_dtype_size(
+                buf.get_dtype()
+            )
+
+        return node_bytes
+
+    def get_estimated_runtime(self) -> float:
+        """
+        Returns estimated op runtime in nanoseconds (ns)
+        """
+        layout = None
+        dtype = None
+        if not hasattr(self, "node") or not self.node:
+            assert isinstance(
+                self, (FusedSchedulerNode, ForeachKernelSchedulerNode)
+            ), f"{type(self)=}"
+            assert self.snodes
+            if not self.snodes[0].node:
+                return 0
+            layout = self.snodes[0].node.get_layout()
+            dtype = self.snodes[0].node.get_dtype()
+        else:
+            layout = self.node.get_layout()
+            dtype = self.node.get_dtype()
+
+        if "cuda" != layout.device.type:
+            # default to no reordering based on runtime
+            return 0
+
+        # Collective kernels
+        if is_collective(self.node):
+            return estimate_nccl_collective_runtime(self.node)
+        elif is_wait(self.node):
+            # ir.Wait is only used for collective ops.
+            # The time needed for the collective op is already estimated and considered
+            # when we are processing the collective op IR node, so ir.Wait takes 0 time
+            # since it doesn't take extra time to get the result after the collective is completed.
+            return 0
+
+        try:
+            gpu_memory_bandwidth = get_gpu_dram_gbps()
+            gpu_flops = get_device_tflops(dtype) * 10**12
+        except Exception:
+            return 0
+
+        if isinstance(self, ExternKernelSchedulerNode):
+            assert isinstance(self.node, ir.ExternKernel), f"{type(self.node)=}"
+            op = kernel_name_to_op.get(
+                getattr(self.node, "python_kernel_name", ""), None
+            )
+
+            # if there is a resolved op, dry-run using fake mode and record flop count
+            if op is not None:
+                from torch._subclasses.fake_tensor import FakeTensorMode
+                from torch.utils.flop_counter import FlopCounterMode
+
+                with FakeTensorMode(), FlopCounterMode(
+                    display=False
+                ) as flop_counter_mode:
+                    from .ir import ir_node_to_tensor
+
+                    fake_inputs = [
+                        ir_node_to_tensor(input, guard_shape=False)
+                        for input in self.node.inputs
+                    ]
+                    cls = self.node.__class__
+                    cls.process_kernel(op, *fake_inputs, **self.node.kwargs)
+
+                    # TODO(xmfan): find a better heuristic to model FLOPS/latency relationship
+                    factor = 1.0
+                    counted_flops = flop_counter_mode.get_total_flops()
+                    counted_bytes = self.get_read_write_buffers_sizes()
+                    compute_time = (factor * counted_flops / gpu_flops) * 1e9
+                    transfer_time = counted_bytes / gpu_memory_bandwidth
+
+                    # Return estimated runtime in nanoseconds
+                    return max(compute_time, transfer_time)
+
+        elif isinstance(self, FusedSchedulerNode) or isinstance(
+            self.node, ComputedBuffer
+        ):
+            # Return estimated runtime in nanoseconds (bytes / gbps)
+            return self.get_read_write_buffers_sizes() / gpu_memory_bandwidth
+
+        return 0
+
+
+class ExternKernelSchedulerNode(BaseSchedulerNode):
+    def debug_str_extra(self) -> str:
+        return f"{self.get_name()}.node.kernel = {getattr(self.node, 'python_kernel_name', None)}"
+
+    def is_extern(self):
+        return True
+
+    def has_side_effects(self):
+        return hasattr(self.node, "has_side_effects") and self.node.has_side_effects()
+
+    def can_inplace(self, read_dep: dependencies.MemoryDep):
+        if self.get_aliases() or self.is_template():
+            return False
+
+        if read_dep.name not in self.scheduler.name_to_node:
+            # don't allow reuse of an 'input' buffer, we don't own it
+            # (would this have been fixed if I tracked mutations properly above?)
+            return False
+        if not isinstance(
+            self.node, (torch._inductor.ir.AllReduce, torch._inductor.ir.InPlaceHint)
+        ):
+            # TODO make this a property of the IR
+            return False
+
+        if len(self.read_writes.writes) == 1:
+            write_dep = next(iter(self.read_writes.writes))
+            numel_diff = read_dep.get_numel() - write_dep.get_numel()
+            return V.graph.sizevars.simplify(numel_diff) == 0
+
+        return False
+
+
+class NopKernelSchedulerNode(BaseSchedulerNode):
+    pass
+
+
+class SchedulerNode(BaseSchedulerNode):
+    def __init__(
+        self,
+        scheduler: "Scheduler",
+        node: Union[ir.ComputedBuffer, ir.TemplateBuffer],
+    ):
+        super().__init__(scheduler, node)
+        self._compute_attrs()
+
+    def _compute_attrs(
+        self,
+        extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None,
+    ):
+        assert isinstance(self.node, (ir.ComputedBuffer, ir.TemplateBuffer))
+        self._sizes, self._body = self.node.simplify_and_reorder(
+            extra_indexing_constraints=extra_indexing_constraints
+        )
+
+        group_fn = self.scheduler.get_backend(self.node.get_device()).group_fn
+        self.group = (self.node.get_device(), group_fn(self._sizes))
+
+        if isinstance(self.node, ir.TemplateBuffer):
+            self.set_read_writes(self.node.normalized_read_writes())
+        else:
+            self.set_read_writes(
+                dependencies.extract_read_writes(
+                    self._body, *self._sizes, normalize=True
+                )
+            )
+
+    def recompute_size_and_body(
+        self, extra_indexing_constraints: Tuple[Dict[Any, Any], List[Any]]
+    ):
+        self._compute_attrs(extra_indexing_constraints=extra_indexing_constraints)
+
+    def debug_str_extra(self) -> str:
+        name = self.get_name()
+        lines = [
+            f"{name}.group.device = {self.group[0]}",
+            f"{name}.group.iteration = {self.group[1]}",
+            f"{name}.sizes = {self._sizes}",
+        ]
+        if self.get_aliases():
+            lines.append(f"{name}.aliases = {pformat(self.get_aliases())}")
+        if self.get_mutations():
+            lines.append(f"{name}.mutations = {pformat(self.get_mutations())}")
+        if isinstance(self._body, ir.LoopBody):
+            lines.append(f"class {name}_loop_body:")
+            lines.append(textwrap.indent(self._body.debug_str(), "    "))
+        return "\n".join(lines)
+
+    def get_ranges(self):
+        return self._sizes
+
+    def is_reduction(self):
+        assert isinstance(
+            self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
+        ), f"{type(self.node)=}"
+        return bool(self.node.get_reduction_type())
+
+    def is_split_scan(self):
+        assert isinstance(
+            self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
+        ), f"{type(self.node)=}"
+        return isinstance(self.node, ir.ComputedBuffer) and isinstance(
+            self.node.data, ir.SplitScan
+        )
+
+    def is_template(self):
+        return isinstance(self.node, ir.TemplateBuffer)
+
+    def get_template_node(self):
+        return self.node if self.is_template() else None
+
+    def run(self, *index_vars):
+        self.decide_inplace_update()
+        self.mark_run()
+        self.codegen(index_vars)
+
+    def mark_run(self):
+        self.allocate()
+
+    def ranges_from_index_vars(self, index_vars):
+        sizes = self._sizes
+        assert sum(map(len, sizes)) == sum(map(len, index_vars))
+        var_ranges = dict(
+            zip(
+                itertools.chain.from_iterable(index_vars),
+                itertools.chain.from_iterable(sizes),
+            )
+        )
+        return var_ranges
+
+    def codegen(self, index_vars):
+        var_ranges = self.ranges_from_index_vars(index_vars)
+        try:
+            with V.set_ops_handler(
+                SimplifyIndexing(V.get_ops_handler(), var_ranges)
+            ), V.kernel.set_current_node(self):
+                self._body(*index_vars)
+        except Exception:
+            log.fatal("Error in codegen for %s", self.node)
+            raise
+
+    def pointwise_read_writes(self):
+        """
+        Get the memory dependencies in the non-reduction axis.
+        """
+        sizes, reduction_sizes = self._sizes
+
+        def fn(index):
+            return self._body(index, [sympy.Integer(0) for _ in reduction_sizes])
+
+        return dependencies.extract_read_writes(fn, sizes)
+
+    def can_inplace(self, read_dep: dependencies.MemoryDep):
+        if self.get_aliases() or self.is_template():
+            return False
+        if len(self.read_writes.writes) == 1 and isinstance(
+            read_dep, dependencies.MemoryDep
+        ):
+            write_dep = next(iter(self.read_writes.writes))
+            assert isinstance(write_dep, dependencies.MemoryDep), f"{type(write_dep)=}"
+            return read_dep.index == write_dep.index and read_dep.size == write_dep.size
+        return False
+
+    @cache_on_self
+    def _get_atomic_add_buffers(self) -> Set[str]:
+        buffers_store_as_atomic_add = set()
+        if isinstance(self._body, ir.LoopBody):
+            for node in self._body.get_nodes():
+                if (
+                    node.op == "call_method"
+                    and node.target == "store"
+                    and (
+                        ("mode" in node.kwargs and node.kwargs["mode"] == "atomic_add")
+                        or (len(node.args) == 5 and node.args[4] == "atomic_add")
+                    )
+                ):
+                    buffers_store_as_atomic_add.add(
+                        node.kwargs["name"]
+                        if "name" in node.kwargs
+                        else (node.args[1] if len(node.args) >= 2 else "")
+                    )
+        return buffers_store_as_atomic_add
+
+    def has_atomic_add(self, check_buf):
+        return check_buf in self._get_atomic_add_buffers()
+
+
+class FusedSchedulerNode(BaseSchedulerNode):
+    """
+    This is a "fake" scheduler node that represents a group of scheduler nodes
+    that are meant to be fused together. The way it does this is by maintaining
+    its unmet dependencies as the union of its constituent nodes.
+    """
+
+    @classmethod
+    def fuse(cls, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        assert node1.scheduler is node2.scheduler
+        assert isinstance(node1, (SchedulerNode, FusedSchedulerNode)) and isinstance(
+            node2, (SchedulerNode, FusedSchedulerNode)
+        )
+        return cls(node1.scheduler, list(node1.get_nodes()) + list(node2.get_nodes()))  # type: ignore[arg-type]
+
+    def __init__(self, scheduler: "Scheduler", snodes: List[SchedulerNode]):
+        # NB: No need to call super().__init__() because we don't need to re-use any of its logic.
+        self.snodes = snodes
+        self.scheduler = scheduler
+        self.node: ir.Buffer = None  # type: ignore[assignment]
+        self.users: List[NodeUser] = []
+        self.inverse_users = []
+        self.node_users = []
+        self.group = max(snodes, key=lambda x: int(x.is_reduction())).group
+        self.ancestors = set.union(
+            *[x.ancestors for x in snodes if x.ancestors is not None]
+        )
+
+        self.set_read_writes(
+            dependencies.ReadWrites.merge_list([x.read_writes for x in snodes])
+        )
+
+        self.unmet_dependencies = {
+            dep
+            for dep in set.union(*[x.unmet_dependencies for x in snodes])
+            if dep.name not in self.get_names()
+        } - self.read_writes.writes
+        self.min_order = min([x.min_order for x in self.snodes])
+        self.max_order = max([x.max_order for x in self.snodes])
+
+    @cache_on_self
+    def get_name(self) -> str:
+        return "_".join([x.get_name() for x in self.snodes])
+
+    def get_first_name(self) -> str:
+        return self.snodes[0].get_name()
+
+    @cache_on_self
+    def get_names(self) -> Set[str]:
+        return set.union(*[x.get_names() for x in self.snodes])
+
+    def debug_str_extra(self) -> str:
+        lines = [
+            f"{self.get_name()}.snodes[{i}] =\n{node.debug_str()}"
+            for i, node in enumerate(self.snodes)
+        ]
+        return textwrap.indent("\n".join(lines).rstrip(), "    ")
+
+    def set_last_usage(
+        self, future_used_buffers: Set[str], mutation_real_name: Dict[str, str]
+    ):
+        # Set self.last_usage using the global information
+        # This will be used for inter-kernel optimisations
+        super().set_last_usage(future_used_buffers, mutation_real_name)
+        # Set self.last_usage on the snodes
+        # This will be used for optimisations within the kernel
+        future_used_buffers: Set[str] = set()
+        for node in reversed(self.snodes):
+            node.set_last_usage(future_used_buffers, mutation_real_name)
+            future_used_buffers.update(node.last_usage)  # type: ignore[arg-type]
+
+    @cache_on_self
+    def used_buffer_names(self) -> Set[str]:
+        return set.union(*[x.used_buffer_names() for x in self.snodes])
+
+    @cache_on_self
+    def used_or_aliased_buffer_names(self) -> Set[str]:
+        return set.union(*[x.used_or_aliased_buffer_names() for x in self.snodes])
+
+    def get_nodes(self) -> List[SchedulerNode]:
+        return self.snodes
+
+    def __repr__(self):
+        return f"{type(self).__name__}(nodes={self.get_name()})"
+
+    @cache_on_self
+    def is_reduction(self):
+        return any(x.is_reduction() for x in self.snodes)
+
+    @cache_on_self
+    def is_split_scan(self):
+        return any(x.is_split_scan() for x in self.snodes)
+
+    @cache_on_self
+    def is_template(self):
+        return any(x.is_template() for x in self.snodes)
+
+    @cache_on_self
+    def get_template_node(self):
+        for node in self.snodes:
+            if node.is_template():
+                return node
+        return None
+
+    def get_device(self):
+        return self.group[0]
+
+    @cache_on_self
+    def has_aliasing_or_mutation(self):
+        return any(x.has_aliasing_or_mutation() for x in self.snodes)
+
+    @cache_on_self
+    def op_counts(self):
+        op_counts: Counter[str] = collections.Counter()
+        for node in self.snodes:
+            op_counts.update(node.op_counts())
+        return op_counts
+
+    def has_atomic_add(self, check_buf):
+        return any(
+            (
+                isinstance(sub_schedule_node1, SchedulerNode)
+                and sub_schedule_node1.has_atomic_add(check_buf)
+            )
+            for sub_schedule_node1 in self.get_nodes()
+        )
+
+    # None of these need to be implemented, as a FusedSchedulerNode is just an
+    # abstraction for scheduling purposes
+    def update_mutated_names(self, renames: Dict[str, str]):
+        raise NotImplementedError
+
+    def add_mutation_dep(self, name):
+        raise NotImplementedError
+
+    def set_users(self, users: List["NodeUser"]):
+        raise NotImplementedError
+
+    def get_aliases(self):
+        raise NotImplementedError
+
+    def get_mutations(self):
+        raise NotImplementedError
+
+    def can_inplace(self, read_dep: dependencies.MemoryDep):
+        raise NotImplementedError
+
+    def allocate(self):
+        raise NotImplementedError
+
+    def can_free(self):
+        raise NotImplementedError
+
+    def debug_str(self) -> str:
+        """Longer form printout for trace logs"""
+        name = self.get_name()
+        node_typestr = ",".join(type(n).__name__ for n in self.snodes)
+        lines = [
+            f"{name}: {type(self).__name__}({node_typestr})",
+            f"{name}.writes = {pformat(self.read_writes.writes)}",
+            f"{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}",
+            f"{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}",
+            f"{name}.users = {self.users}",
+        ]
+        try:
+            lines += [
+                self.debug_str_extra(),
+            ]
+        except Exception:
+            log.warning("Ignoring error in debug_str()", exc_info=True)
+
+        return "\n".join(lines).rstrip()
+
+
+class ForeachKernelSchedulerNode(FusedSchedulerNode):
+    """Scheduler node which consists of a list of scheduler nodes that each operate on a
+    distinct tensor in a list of tensors."""
+
+    def get_consumer_subnode_for(self, producer):
+        if producer.get_name() in self.read_to_node:
+            return self.read_to_node[producer.get_name()]
+
+        return None
+
+    def get_producer_subnode_for(self, consumer):
+        for rd in consumer.read_writes.reads:
+            if rd.name in self.name_to_node:
+                return self.name_to_node[rd.name]
+
+        return None
+
+    @classmethod
+    def can_fuse(cls, producer, consumer):
+        why = WhyNoFuse(producer, consumer)
+        if producer.is_foreach() and consumer.is_foreach():
+            foreach_match = len(producer.snodes) == len(consumer.snodes)
+            if not foreach_match:
+                why("foreach do not have same length")
+            return foreach_match and all(
+                producer.scheduler.can_fuse(l, r)
+                for l, r in zip(producer.snodes, consumer.snodes)
+            )
+        elif consumer.is_foreach():
+            consumer_subnode = consumer.get_consumer_subnode_for(producer)
+            if consumer_subnode is not None:
+                return consumer.scheduler.can_fuse(producer, consumer_subnode)
+
+            why("candidate producer is not dep of any foreach consumer")
+            return False
+
+        elif producer.is_foreach():
+            producer_subnode = producer.get_producer_subnode_for(consumer)
+            if producer_subnode is not None:
+                return producer.scheduler.can_fuse(producer_subnode, consumer)
+
+            why("candidate consumer has no dep in any foreach producer")
+            return False
+
+        raise AssertionError(
+            "At least one node passed to ForeachKernelSchedulerNode.can_fuse should be a foreach node"
+        )
+
+    @classmethod
+    def fuse(cls, producer, consumer):
+        assert producer.is_foreach() or consumer.is_foreach()
+        prev_node_1 = None
+        prev_node_2 = None
+        if producer.is_foreach() and consumer.is_foreach():
+            fused_nodes = [
+                FusedSchedulerNode.fuse(l, r)
+                for l, r in zip(producer.snodes, consumer.snodes)
+            ]
+        elif producer.is_foreach():
+            producer_subnode = producer.get_producer_subnode_for(consumer)
+            fused_nodes = []
+            prev_node_1 = producer
+            prev_node_2 = None
+            for node in producer.snodes:
+                if node is producer_subnode:
+                    new_node = FusedSchedulerNode.fuse(node, consumer)
+                    prev_node_2 = new_node
+                    fused_nodes.append(new_node)
+                else:
+                    fused_nodes.append(node)
+
+        elif consumer.is_foreach():
+            consumer_subnode = consumer.get_consumer_subnode_for(producer)
+            fused_nodes = []
+            prev_node_1 = consumer
+            prev_node_2 = None
+
+            for node in consumer.snodes:
+                if node is consumer_subnode:
+                    new_node = FusedSchedulerNode.fuse(producer, node)
+                    prev_node_2 = new_node
+                    fused_nodes.append(new_node)
+                else:
+                    fused_nodes.append(node)
+
+        return cls(producer.scheduler, fused_nodes, prev_node_1, prev_node_2)  # type: ignore[possibly-undefined]
+
+    def __init__(
+        self,
+        scheduler: "Scheduler",
+        nodes: List[SchedulerNode],
+        prev_node_1=None,
+        prev_node_2=None,
+    ):
+        self.read_to_node = {}
+        self.name_to_node = {}
+
+        if prev_node_1 is None or prev_node_2 is None:
+            super().__init__(scheduler, nodes)
+
+            for node in nodes:
+                for read in node.read_writes.reads:
+                    self.read_to_node[read.name] = node
+
+                for name in node.get_names():
+                    self.name_to_node[name] = node
+        else:
+            self.scheduler = scheduler
+            self.snodes = nodes
+            self.node: ir.Buffer = None  # type: ignore[assignment]
+            self.users: List[NodeUser] = []
+
+            self.set_read_writes(
+                dependencies.ReadWrites.merge_list(
+                    [prev_node_1.read_writes, prev_node_2.read_writes]
+                )
+            )
+
+            self.unmet_dependencies = {
+                dep
+                for dep in set.union(
+                    prev_node_1.unmet_dependencies, prev_node_2.unmet_dependencies
+                )
+                if dep.name not in self.get_names()
+            } - self.read_writes.writes
+
+            self.min_order = min([prev_node_1.min_order, prev_node_2.min_order])
+            self.max_order = max([prev_node_1.max_order, prev_node_2.max_order])
+
+            foreach_node = prev_node_1 if prev_node_1.is_foreach() else prev_node_2
+            other_node = prev_node_2 if prev_node_1.is_foreach() else prev_node_1
+
+            self.ancestors = foreach_node.ancestors
+            self.ancestors.update(other_node.ancestors)
+
+            self.name_to_node = foreach_node.name_to_node
+            for name in other_node.get_names():
+                self.name_to_node[name] = other_node
+
+        self.group = (nodes[0].get_device(), "foreach")
+
+        self.origins: Set[torch.fx.Node] = set()
+
+    def mark_run(self):
+        raise NotImplementedError
+
+    def codegen(self):
+        assert isinstance(self.node, ir.ComputedBuffer), f"{type(self.node)=}"
+        self.node.get_store_function()(self.node.make_loader()())
+
+    def can_free(self):
+        return NotImplementedError
+
+    def is_foreach(self):
+        return True
+
+    def get_subkernel_nodes(self):
+        """Returns a list of nodes which comprise the foreach kernel, operating on corresponding elements of our input lists.
+        These nodes may be vertically fused."""
+        return list(self.snodes)
+
+    def get_nodes(self):
+        """Returns all nodes contained in this kernel, unpacking fused nodes into their constituent scheduler nodes."""
+        return list(itertools.chain.from_iterable(x.get_nodes() for x in self.snodes))
+
+    def get_first_name(self):
+        return self.snodes[0].get_first_name()
+
+    def prune_redundant_deps(self, name_to_fused_node):
+        _prune_redundant_deps(self, name_to_fused_node)
+
+        for node in self.snodes:
+            node.prune_redundant_deps(name_to_fused_node)
+
+
+def pick_loop_order(stride_lengths, sizes, priority_idx=()):
+    """
+    A heuristic to decide loop iteration orders.  This has not been well
+    tuned and may be something we should autotune.
+    """
+
+    @functools.cmp_to_key
+    def index_cmp(a, b):
+        if sizes[a] == 1 or sizes[b] == 1:
+            # 1-sizes don't matter, just move them to the end
+            return cmp(sizes[a] == 1, sizes[b] == 1)
+
+        stride_len_a = [sl[a] for sl in stride_lengths]
+        stride_len_b = [sl[b] for sl in stride_lengths]
+
+        # equivalent to
+        # np.logical_or(stride_lengths[:, b] == 0, stride_lengths[:, a] < stride_lengths[:, b]).all()
+        a_first = sum(
+            sl_b == 0 or sl_a < sl_b for sl_a, sl_b in zip(stride_len_a, stride_len_b)
+        )
+        b_first = sum(
+            sl_a == 0 or sl_b < sl_a for sl_a, sl_b in zip(stride_len_a, stride_len_b)
+        )
+        if a_first > b_first:
+            return -1
+        if b_first > a_first:
+            return 1
+
+        # otherwise contiguous
+        return cmp(b, a)
+
+    order = list(reversed(range(len(stride_lengths[0]))))
+    if len(priority_idx) > 0:
+        # if we have priority node, only use that node's order
+        stride_lengths = [stride_lengths[pi] for pi in priority_idx]
+    if config.pick_loop_orders:
+        order.sort(key=index_cmp)
+    return order
+
+
+@dataclasses.dataclass
+class NodeUser:
+    node: BaseSchedulerNode
+    can_inplace: bool = False
+
+    # A weak user must be scheduled after a given node, but doesn't actually
+    # use the result
+    is_weak: bool = False
+
+    def __hash__(self):
+        return hash((self.node.get_name(), self.can_inplace, self.is_weak))
+
+    def __eq__(self, other):
+        return (
+            self.get_name() == other.get_name()
+            and self.can_inplace == other.can_inplace
+            and self.is_weak == other.is_weak
+        )
+
+    def get_name(self):
+        return self.node.get_name()
+
+    def merge(self, other: "NodeUser") -> "NodeUser":
+        assert self.node is other.node
+        return NodeUser(
+            self.node,
+            self.can_inplace and other.can_inplace,
+            self.is_weak and other.is_weak,
+        )
+
+
+_post_grad_graph_counter = itertools.count()
+
+
+class Scheduler:
+    @dynamo_timed
+    def __init__(self, nodes):
+        super().__init__()
+        self.backends = {}
+        self.fuse_cache = {}
+        self.post_grad_graph_id = next(_post_grad_graph_counter)
+
+        self.nodes = []
+        self.available_buffer_names = {
+            *V.graph.graph_inputs.keys(),
+            *V.graph.constants.keys(),
+        }
+
+        self.nodes = [self.create_scheduler_node(n) for n in nodes]
+
+        # some new constants could have been created above
+        self.available_buffer_names.update(V.graph.constants.keys())
+        for node in self.nodes:
+            node.prune_deps()
+
+        self.name_to_node: Dict[str, BaseSchedulerNode] = {
+            n.get_name(): n for n in self.nodes
+        }
+        self.name_to_fused_node: Dict[
+            str, BaseSchedulerNode
+        ] = dict()  # set in fuse_nodes()
+
+        # mutation_real_name: Maps back to the original name for codegen
+        # Example:
+        # If you mutate buf0 inside of buf1's kernel, then:
+        # mutation_real_name = {"buf0" : "buf1"}
+        # all subsequent uses of buf0 become buf1's usage in dependency graph
+        self.mutation_real_name = {}
+
+        # We handle mutation by renaming modified versions of the same
+        # buffer in the dependency graph to prevent cycles.
+        # mutation_renames: tracks the current name for a given buffer
+        #                   (changed once per mutation)
+        # Example:
+        # If you mutate buf0 inside of buf1's kernel, then:
+        # mutation_renames = {"buf1" : "buf0"}
+        # in codegen we only use buf0, never buf1
+        self.mutation_renames = {}
+
+        self.compute_dependencies()
+        self.topological_sort_schedule()
+        self.dead_node_elimination()
+        if config.reorder_for_compute_comm_overlap:
+            comms.decide_global_ordering_of_comms(self.nodes)
+        self.compute_ancestors()
+
+        metrics.ir_nodes_pre_fusion += len(self.nodes)
+        V.debug.ir_pre_fusion(self.nodes)
+        self.num_orig_nodes = len(self.nodes)
+        self.name_to_fused_node = {n.get_name(): n for n in self.nodes}
+        self.create_foreach_nodes()
+        self.topological_sort_schedule()
+        self.logged_slow_fusion = set()
+        self.fuse_nodes()
+        if config.reorder_for_compute_comm_overlap:
+            # Refresh node_users and inverse_users to reflect fused nodes
+            self.compute_node_users()
+            self.nodes = comms.reorder_compute_and_comm_for_overlap(self.nodes)
+        self.compute_last_usage()
+        V.debug.ir_post_fusion(self.nodes)
+        V.debug.graph_diagram(self.nodes)
+        self.debug_draw_graph()
+
+        # used during codegen:
+        self.current_device: torch.device = None  # type: ignore[assignment]
+        self.buffer_names_to_free = set()
+
+        # fx graph node to the position it appears in the graph
+        # for debug attribution
+        self.origin_to_index = {}
+
+        get_metric_table("graph_stats").add_row(
+            lambda: {
+                "graph_id": self.post_grad_graph_id,
+                "num_nodes_before_fusion": self.num_orig_nodes,
+                "num_nodes_after_fusion": len(self.nodes),
+            }
+        )
+
+    def debug_draw_graph(self):
+        """Generate an image of the graph for debugging"""
+        if os.environ.get("INDUCTOR_WRITE_SCHEDULER_GRAPH", None) == "1":
+            from .debug import draw_buffers
+
+            draw_buffers(self.nodes, print_graph=True)
+
+    def debug_print_nodes(self, label):
+        if log.isEnabledFor(logging.INFO):
+            log.info("%s:", label)
+            for node in self.nodes:
+                node.log_details()
+
+    def create_scheduler_node(self, node):
+        assert (
+            node.origins is not None
+        ), "All nodes passed to scheduling must have an origin"
+        if node.is_no_op():
+            return NopKernelSchedulerNode(self, node)
+        elif isinstance(node, (ir.ComputedBuffer, ir.TemplateBuffer)):
+            return SchedulerNode(self, node)
+        elif isinstance(node, ir.ExternKernel):
+            return ExternKernelSchedulerNode(self, node)
+        else:
+            raise NotImplementedError(node)
+
+    def create_foreach_nodes(self):
+        removed_node_names = set()
+        fe_nodes = []
+        kept_node_names = self.name_to_fused_node.keys()
+
+        for names in V.graph.lists.values():
+            names = [
+                name
+                for name in names
+                if name in kept_node_names
+                and not isinstance(self.name_to_node[name], NopKernelSchedulerNode)
+            ]
+            if not names:
+                # All nodes eliminated
+                continue
+
+            removed_node_names.update(names)
+            snodes = [self.name_to_node[name] for name in names]
+
+            fe_node = ForeachKernelSchedulerNode(self, snodes)  # type: ignore[arg-type]
+
+            fe_nodes.append(fe_node)
+
+            for name in names:
+                self.name_to_fused_node[name] = fe_node
+
+        self.nodes = [
+            node for node in self.nodes if node.get_name() not in removed_node_names
+        ] + fe_nodes
+
+    def compute_dependencies(self):
+        """
+        Create dependency edges between nodes, handling aliasing and
+        mutation properly.
+        """
+
+        T = TypeVar("T")
+
+        class DedupList(Generic[T]):
+            """
+            This data structure behaves like a list except it makes sure the
+            elements remain unique.
+            Normally one could use a set/dict for this purpose however
+            the list in question gets elements appended as it is being
+            iterated over which means that we need to keep the list
+            semantics.
+            """
+
+            def __init__(self, items=None, membership=None):
+                self.items = items or list()
+                self.membership = membership or set()
+
+            def append(self, node_user: T) -> None:
+                if node_user in self.membership:
+                    return
+                self.items.append(node_user)
+                self.membership.add(node_user)
+
+            def __add__(self, other: "DedupList[T]") -> "DedupList[T]":
+                new_membership = set.union(self.membership, other.membership)
+                new_items = self.items + [
+                    x for x in other.items if x not in self.membership
+                ]
+                return DedupList(new_items, new_membership)
+
+        name_to_users: DefaultDict[str, DedupList[NodeUser]] = collections.defaultdict(
+            DedupList
+        )
+
+        # handle aliasing by using python aliasing in name_to_users
+        # if foo aliases bar then we will make name_to_users["foo"] point
+        # to the same python list as name_to_users["bar"]
+        for node1 in self.nodes:
+            node1_name = node1.get_name()
+            for node2_name in node1.get_aliases():
+                if node1_name in name_to_users and node2_name in name_to_users:
+                    # merge the two
+                    list1 = name_to_users[node1_name]
+                    list2 = name_to_users[node2_name]
+                    combined = list1 + list2
+                    for key in name_to_users.keys():
+                        if name_to_users[key] is list1 or name_to_users[key] is list2:
+                            name_to_users[key] = combined
+                elif node1_name in name_to_users:
+                    name_to_users[node2_name] = name_to_users[node1_name]
+                else:
+                    name_to_users[node1_name] = name_to_users[node2_name]
+
+        def rename(n):
+            if n in self.mutation_renames:
+                return rename(self.mutation_renames[n])
+            return n
+
+        def dep_closure(node_name):
+            reachable_names = {node_name}
+            node = self.name_to_node[node_name]
+            write_dep = next(iter(node.read_writes.writes))
+            for read_dep in node.read_writes.reads:
+                if (
+                    read_dep.name in self.name_to_node
+                    and isinstance(read_dep, dependencies.MemoryDep)
+                    and isinstance(write_dep, dependencies.MemoryDep)
+                    and read_dep.index == write_dep.index
+                    and read_dep.size == write_dep.size
+                ):
+                    reachable_names.update(dep_closure(read_dep.name))
+            return reachable_names
+
+        def add_user(used_by_name, user_node, can_inplace=False, is_weak=False):
+            name_to_users[rename(used_by_name)].append(
+                NodeUser(user_node, can_inplace, is_weak)
+            )
+
+        unbacked_symbol_to_origin_node = {}
+
+        for node in self.nodes:
+            log.debug("scheduling %s", node.node)
+
+            # unbacked symbols don't follow ordinary buffer dependencies, so
+            # we track their def/uses separately
+            unbacked_symbol_defs = sorted(
+                node.node.get_unbacked_symbol_defs(), key=lambda x: x.name
+            )
+            for s in unbacked_symbol_defs:
+                assert isinstance(s, sympy.Symbol)
+                # Pick the first definer as canonical.  There may be multiple
+                # because if a MultiOutputLayout buffer propagates an unbacked
+                # symint to multiple outputs, they will all claim to def it.
+                if s not in unbacked_symbol_to_origin_node:
+                    unbacked_symbol_to_origin_node[s] = node
+
+            unbacked_symbol_uses = sorted(
+                node.node.get_unbacked_symbol_uses(), key=lambda x: x.name
+            )
+            # if a kernel takes unbacked symints, register dependencies
+            for s in unbacked_symbol_uses:
+                assert (
+                    s in unbacked_symbol_to_origin_node
+                ), f"{s} not in {unbacked_symbol_to_origin_node}"
+                node.add_fake_dep(StarDep(unbacked_symbol_to_origin_node[s].get_name()))
+
+            # a node will mutate either 0 or 1 buffers
+            assert len(node.get_mutations()) <= 1
+            for alt_name in node.get_mutations():
+                alt_name = rename(alt_name)
+                # this node must run after the prior writer
+                add_user(alt_name, node)
+                node.add_mutation_dep(StarDep(alt_name))
+                for other_node in name_to_users[alt_name].items:
+                    # this node must run after all prior readers
+                    other_name = rename(other_node.get_name())
+                    known_dep_node_names = dep_closure(node.get_name())
+                    if other_name not in known_dep_node_names:
+                        # If this node already directly or indirectly depends on other_node,
+                        # we don't need to insert an extra dep.
+                        node.add_mutation_dep(WeakDep(other_name))
+                        add_user(other_name, node, is_weak=True)
+
+            # add normal non-mutation dependencies
+            for read in node.read_writes.reads:
+                is_weak = isinstance(read, WeakDep)
+                add_user(read.name, node, node.can_inplace(read), is_weak)
+
+            node.update_mutated_names(self.mutation_renames)
+
+            # update our renaming scheme for the next iteration
+            for alt_name in node.get_mutations():
+                self.mutation_renames[rename(alt_name)] = node.get_name()
+                self.mutation_renames[alt_name] = node.get_name()
+                self.mutation_real_name[node.get_name()] = self.mutation_real_name.get(
+                    alt_name, alt_name
+                )
+
+        # make sure outputs aren't dead-code-eliminated
+        for node_name in V.graph.get_output_names():
+            log.debug("scheduling output %s", node_name)
+            add_user(node_name, OutputNode(StarDep(node_name)))
+
+        # make sure unbacked symints aren't dead-code-eliminated
+        for node in V.graph.graph_outputs:
+            for s in node.get_unbacked_symbol_uses():
+                assert (
+                    s in unbacked_symbol_to_origin_node
+                ), f"{s} not in {unbacked_symbol_to_origin_node.keys()}"
+                node_name = unbacked_symbol_to_origin_node[s].node.name
+                log.debug("scheduling output %s for unbacked symint %s", node_name, s)
+                add_user(node_name, OutputNode(StarDep(node_name)))
+
+        # make sure input mutation isn't dead-code-eliminated
+        for name in self.mutation_renames:
+            if name in V.graph.graph_inputs:
+                add_user(name, OutputNode(StarDep(name)))
+                V.graph.mutated_inputs.add(name)
+
+        inp_names = {
+            name: index for index, name in enumerate(V.graph.graph_inputs.keys())
+        }
+        V.graph.mutated_input_idxs = [
+            inp_names[name] for name in V.graph.mutated_inputs
+        ]
+
+        # copy users information onto the nodes
+        for node in self.nodes:
+            node.set_users(name_to_users[node.get_name()].items)
+
+        # populate inverse_users
+        for node in self.nodes:
+            for user in node.users:
+                user.node.inverse_users.append(node)
+
+    def compute_node_users(self):
+        # set up buffer name to (fused)snode mapping
+        buf_to_snode = {}
+        for node in self.nodes:
+            if isinstance(node, FusedSchedulerNode):
+                for x in node.snodes:
+                    buf_to_snode[x.get_name()] = node
+            buf_to_snode[node.get_name()] = node
+
+        for node in self.nodes:
+            node.node_users = []
+            node.inverse_users = []
+
+        # compute inverse_users
+        for node in self.nodes:
+            inverse_users = []
+            for dep in node.unmet_dependencies:
+                assert dep.name in buf_to_snode
+                dep_node = buf_to_snode[dep.name]
+                inverse_users.append(dep_node)
+            node.inverse_users = inverse_users
+
+        # compute node_users
+        # TODO: ideally, we should deduplicate .users and .node_users,
+        # but currently .users contains extra information that's difficult to
+        # extract into a standalone container.
+        node_to_users: Dict[BaseSchedulerNode, List[BaseSchedulerNode]] = {}
+        for node in self.nodes:
+            for inverse_user in node.inverse_users:
+                node_to_users.setdefault(inverse_user, []).append(node)
+        for node, users in node_to_users.items():
+            node.node_users = users
+
+    def dead_node_elimination(self):
+        """
+        Remove any nodes without users
+        """
+        again = True  # repeat until a fixed point
+        while again:
+            updated_nodes = []
+            for node in self.nodes:
+
+                def can_eliminate_user(user: NodeUser):
+                    return user.is_weak or user.get_name() in V.graph.removed_buffers
+
+                can_eliminate = not node.has_side_effects() and all(
+                    can_eliminate_user(u) for u in node.users
+                )
+
+                if not can_eliminate:
+                    updated_nodes.append(node)
+                else:
+                    # dead code
+                    log.debug("removed dead node: %s", node.get_name())
+                    V.graph.removed_buffers.add(node.get_name())
+
+            again = len(self.nodes) > len(updated_nodes)
+            self.nodes = updated_nodes
+
+        # Prune any WeakDeps no longer needed
+        for node in self.nodes:
+            node.prune_weak_deps()
+
+    def topological_sort_schedule(self):
+        """
+        Ensure self.nodes is in topologically sorted order
+        """
+        seen: Set[ir.Buffer] = set()
+        name_to_node: Dict[str, ir.Buffer] = dict()
+        result: List[ir.Buffer] = []
+
+        def visit(n):
+            if n not in seen:
+                seen.add(n)
+                for dep in sorted(n.unmet_dependencies, key=lambda d: d.name):
+                    visit(name_to_node[dep.name])
+                result.append(n)
+
+        for node in self.nodes:
+            for name in node.get_names():
+                name_to_node[name] = node
+        for node in self.nodes:
+            visit(node)
+        self.nodes = result
+
+    def compute_ancestors(self):
+        """
+        Populate each node.ancestors
+        """
+        # note self.nodes is topologically sorted
+        name_to_ancestors: Dict[str, Set[str]] = {}
+        for node in self.nodes:
+            ancestors = set()
+            for dep in node.unmet_dependencies:
+                ancestors.add(dep.name)
+                ancestors |= name_to_ancestors[dep.name]
+            name_to_ancestors[node.get_name()] = ancestors
+            node.ancestors = ancestors
+
+        for order, node in enumerate(self.nodes):
+            node.min_order = order
+            node.max_order = order
+
+    def fuse_nodes(self):
+        """
+        Mutates self.nodes to combine nodes into FusedSchedulerNodes.
+        """
+        for i in range(10):
+            old_len = len(self.nodes)
+            fusion_log.debug(
+                "===== attempting fusion (%d/10): %d nodes =====", i + 1, old_len
+            )
+            self.fuse_nodes_once()
+            new_len = len(self.nodes)
+            fusion_log.debug(
+                "completed fusion round (%d/10): fused %d nodes into %d nodes\n",
+                i + 1,
+                old_len,
+                new_len,
+            )
+            if new_len == old_len or new_len == 1:
+                fusion_log.debug("===== fusion complete (%d iterations) =====", i + 1)
+                break
+
+    def benchmark_fused_nodes(self, nodes):
+        """
+        Benchmark fused list of nodes and return the execution time
+        in milliseconds on randomly generated inputs.
+        """
+        assert len(nodes) > 0
+        device = nodes[0].get_device()
+        V.graph.scheduler = self
+        self.current_device = device
+        backend = self.get_backend(device)
+        return backend.benchmark_fused_nodes(nodes)
+
+    def speedup_by_fusion(self, node1, node2):
+        """
+        If config.benchmark_fusion is False, always return True.
+        Otherwise, return True if fusion can brings speedup.
+        """
+        if not config.benchmark_fusion:
+            return True
+
+        if (
+            node1.is_template()
+            and not isinstance(node1.get_template_node(), ir.TritonTemplateBuffer)
+            or node1.is_foreach()
+            or node2.is_foreach()
+        ):
+            # TODO support benchmarking epilogue fusion
+            return True
+
+        node_list_1 = node1.get_nodes()
+        device = node_list_1[0].get_device()
+
+        # don't support benchmark fusion for CPU right now.
+        if device.type == "cpu":
+            return True
+
+        node_list_2 = node2.get_nodes()
+        node_list_fused = node_list_1 + node_list_2
+
+        # We can not accurately benchmark kernel using atomic_add
+        # due to how we generate random integer inputs.
+        # Skip benchmarking them by allowing fusion.
+        if any(
+            hasattr(n.node, "data")
+            and hasattr(n.node.data, "scatter_mode")
+            and n.node.data.scatter_mode == "atomic_add"
+            for n in node_list_fused
+        ):
+            return True
+
+        from triton.compiler.errors import CompilationError
+
+        why = WhyNoFuse(node1, node2)
+
+        try:
+            ms1, path1 = self.benchmark_fused_nodes(node_list_1)
+            if math.isinf(ms1):
+                why("register spilling of the first kernel")
+                return False
+            ms2, path2 = self.benchmark_fused_nodes(node_list_2)
+            if math.isinf(ms2):
+                why("register spilling of the second kernel")
+                return False
+            ms_fused, path_fused = self.benchmark_fused_nodes(node_list_fused)
+            if math.isinf(ms_fused):
+                why("register spilling of the fused kernel")
+                return False
+        except CompilationError as e:
+            # workaround triton issue: https://github.com/openai/triton/issues/2151
+            if "Loop-carried variable" in str(e):
+                return True  # allow fusion
+            else:
+                raise
+
+        if fusion_log.isEnabledFor(logging.DEBUG):
+            if ms_fused < ms1 + ms2:
+                fusion_log.debug(
+                    "can fuse (benchmark): fusing %s with %s cause %sx speedup",
+                    node1.get_names(),
+                    node2.get_names(),
+                    green_text(f"{(ms1 + ms2) / ms_fused:.3f}"),
+                )
+            else:
+                fusion_log.debug(
+                    "cannot fuse (benchmark): fusing %s with %s cause %sx slowdown",
+                    node1.get_names(),
+                    node2.get_names(),
+                    red_text(f"{ms_fused / (ms1 + ms2):.3f}"),
+                )
+
+        if (
+            is_metric_table_enabled("slow_fusion")
+            and ms_fused >= ms1 + ms2
+            and (path1, path2) not in self.logged_slow_fusion
+        ):
+            self.logged_slow_fusion.add((path1, path2))
+            get_metric_table("slow_fusion").add_row(
+                lambda: {
+                    "kernel1_path": path1,
+                    "kernel1_latency": ms1,
+                    "kernel2_path": path2,
+                    "kernel2_latency": ms2,
+                    "fused_kernel_path": path_fused,
+                    "fused_kernel_latency": ms_fused,
+                    "slow_down_ratio": ms_fused / (ms1 + ms2),
+                }
+            )
+        return ms_fused < ms1 + ms2
+
+    def fuse_nodes_once(self):
+        """
+        Mutates self.nodes to combine nodes into FusedSchedulerNodes.
+
+        This relies on two key functions to control the logic:
+            - self.can_fuse(): checks if a fusion is legal
+            - self.score_fusion(): assigns priority to a given fusion
+        """
+        fused_nodes = set(self.nodes)
+        for node1, node2 in self.get_possible_fusions():
+            node1 = self.name_to_fused_node[node1.get_first_name()]
+            node2 = self.name_to_fused_node[node2.get_first_name()]
+            if self.can_fuse(node1, node2) and not self.will_fusion_create_cycle(
+                node1, node2
+            ):
+                if not self.speedup_by_fusion(node1, node2):
+                    continue
+                fusion_log.debug(
+                    "fusing %s with %s", node1.get_name(), node2.get_name()
+                )
+
+                # above can_fuse asserts that node2 has the same device
+                device = node1.get_device()
+                node3 = self.get_backend(device).fuse(node1, node2)
+                fused_nodes.remove(node1)
+                fused_nodes.remove(node2)
+                fused_nodes.add(node3)
+                self.name_to_fused_node.update(
+                    {n.get_name(): node3 for n in node3.get_nodes()}
+                )
+        self.nodes = sorted(fused_nodes, key=lambda x: x.min_order)
+        self.topological_sort_schedule()
+        self.prune_redundant_deps()
+
+    def prune_redundant_deps(self):
+        for node in self.nodes:
+            node.prune_redundant_deps(self.name_to_fused_node)
+
+    def get_possible_fusions(self):
+        """
+        Helper to find all legal fusion opportunities, sorted by self.score_fusion()
+        """
+        possible_fusions = []
+        seen = set()
+
+        def check_all_pairs(nodes):
+            for node1_index, node1 in enumerate(nodes):
+                for node2 in nodes[node1_index + 1 :]:
+                    key = (node1, node2)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+
+                    if self.can_fuse(node1, node2):
+                        possible_fusions.append(key)
+                    elif (node2.is_template() or node2.is_foreach()) and self.can_fuse(
+                        node2, node1
+                    ):
+                        # foreach fusions and epilogue fusions are order dependent
+                        possible_fusions.append((node2, node1))
+
+        buffer_names_grouping = collections.defaultdict(list)
+        for node in self.nodes:
+            for buf in node.used_buffer_names():
+                buffer_names_grouping[buf].append(node)
+        for node_grouping in buffer_names_grouping.values():
+            check_all_pairs(node_grouping)
+
+        if config.aggressive_fusion:
+            group_grouping = collections.defaultdict(list)
+            for node in self.nodes:
+                group = getattr(node, "group", None)
+                if group:
+                    group_grouping[group].append(node)
+            for node_grouping in group_grouping.values():
+                check_all_pairs(node_grouping)
+
+        possible_fusions.sort(key=self.score_fusion_key, reverse=True)
+        fusion_log.debug("found %d possible fusions", len(possible_fusions))
+        return possible_fusions
+
+    def will_fusion_create_cycle(self, node1, node2):
+        """
+        Finds whether there's a path from node1 to node2 (or vice-versa)
+        caused indirectly by other fusions.
+        """
+
+        def found_path(node):
+            # only fused nodes can introduce new ancestors.
+            if isinstance(node, FusedSchedulerNode) and node not in visited:
+                visited.add(node)
+                if node.get_names().issubset(combined_ancestors):
+                    # All fusion outputs are in ancestors of node1 and node2, thus
+                    # cannot introduce new path:
+                    #
+                    # 1. if output is neither descendent of node1 or node2, the
+                    #        output cannot introduce a path
+                    # 2. due to [can_fuse]: if WLOG output is descendent of node1, it cannot be
+                    #        on path(node1->node2), hence it cannot be ancestor of node2
+                    # 3. due to [acyclic]: if WLOG output is descendent of node1, it cannot be
+                    #        ancestor of node1
+                    return False
+                else:
+                    # continue DFS of new ancestors introduced by the fusion
+                    return bool(combined_names & node.ancestors) or any(
+                        found_path(self.name_to_fused_node[n])
+                        for n in node.ancestors - combined_ancestors
+                    )
+            return False
+
+        visited = set()
+        combined_names = node1.get_names() | node2.get_names()
+        combined_ancestors = (node1.ancestors | node2.ancestors) - combined_names
+        cycle = any(found_path(self.name_to_fused_node[n]) for n in combined_ancestors)
+        if cycle:
+            WhyNoFuse(node1, node2)("will create cycle")
+        return cycle
+
+    def can_fusion_increase_peak_memory(
+        self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
+    ):
+        """
+        This function prevents fusion for nodes that can increase memory
+        footprint. This problem is more common in horizontal fusion, where nodes
+        that are far apart in the original order get fused, lengthening the live
+        intervals of tensors. This is very evident in models with activation
+        checkpointing, where the recomputed nodes from different checkpointed
+        regions get fused and significantly increase the memory footprint.
+
+        The current attempt is a quick, possibly hacky, heuristic to prevent the
+        fusion of nodes that are far away in the original order.
+
+        A better but difficult to implement heurisitic would be to use live
+        intervals of the buffers, find region of peak pressure in the original
+        program and prevent fusion that crosses that peak region. We might need
+        special care or good approximation in this implementation, as fusion of
+        node changes live intervals, and re-computing live intervals and peak
+        memory after each fusion can introduce large compilation overhead.
+        """
+        proximity_score = max(
+            abs(node1.min_order - node2.max_order),
+            abs(node2.min_order - node1.max_order),
+        )
+        return proximity_score > 64
+
+    def can_fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        """
+        Determine if it is possible to combine node1 and node2 into a
+        single fused node.
+        """
+
+        if node1 is node2:
+            return False
+
+        why = WhyNoFuse(node1, node2)
+
+        if (
+            isinstance(node1, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
+            and not node1.is_template()
+        ):
+            why("node1 is extern or nop")
+            return False
+        if (
+            isinstance(node2, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
+            and not node2.is_template()
+        ):
+            why("node2 is extern or nop")
+            return False
+
+        if node2.get_names() & node1.ancestors:
+            why("node1 must go before node2")
+            return False
+
+        if (
+            isinstance(node1, (FusedSchedulerNode, SchedulerNode))
+            and isinstance(node2, SchedulerNode)
+            and isinstance(node2._body, ir.LoopBody)
+        ):
+            # Fix issue: https://github.com/pytorch/pytorch/issues/108963
+            # Check:
+            #   If node2 reads a buf which is a mutation buf of node1(SchedulerNode) or among nodes in node1(FusedSchedulerNode),
+            #   we will get the corresponding mutation buf and check if this mutation buf is stored by atomic_add mode.
+            # If True, we will disable the fusion of node1 and node2.
+            if any(
+                (
+                    node2_used_buf in self.mutation_renames
+                    and node1.has_atomic_add(self.mutation_renames[node2_used_buf])
+                )
+                for node2_used_buf in node2._body.reads_name2expr.keys()
+            ):
+                return False
+
+        if node2.is_template():
+            why("templates can only fuse epilogues")
+            return False
+        if node1.is_template() and (
+            node2.has_aliasing_or_mutation()
+            or node2.is_reduction()
+            or not config.epilogue_fusion
+        ):
+            why("template epilogue not satisfied")
+            return False
+
+        device = node1.get_device()
+        device2 = node2.get_device()
+        if device != device2:
+            why("device mismatch (%s vs %s)", device, device2)
+            return False
+        del device2
+
+        no_shared_data = self.score_fusion_memory(node1, node2) == 0
+        if no_shared_data and (
+            not config.aggressive_fusion or node1.is_reduction() or node2.is_reduction()
+        ):
+            why("no shared data")
+            return False  # heuristic not needed for correctness
+
+        if (
+            not node1.is_foreach()
+            and not node2.is_foreach()
+            and len(node1.get_nodes()) + len(node2.get_nodes()) > config.max_fusion_size
+        ):
+            why("exceeds max fusion")
+            return False  # heuristic not needed for correctness
+
+        if node1.get_names() & node2.ancestors:
+            # node2 depends on node1 outputs
+            if not self.can_fuse_vertical(node1, node2):
+                return False
+            return self.get_backend(device).can_fuse_vertical(node1, node2)
+        else:  # nodes don't depend on each other, but may have common reads
+            if self.can_fusion_increase_peak_memory(node1, node2):
+                why("will increase peak memory")
+                return False
+            return self.get_backend(device).can_fuse_horizontal(node1, node2)
+
+    def can_fuse_vertical(self, node1, node2):
+        """
+        Check if it is legal to fuse a consumer (node2) into a producer (node1).
+
+        We can fuse them if all the reads of node2 either match
+        corresponding writes in node1, or are written by nodes that can
+        be scheduled before the fusion of node1 and node2.
+
+        We also disable fusion of a write subsequent to a read if the reads
+        and writes do not align.
+        """
+        node1_names = node1.get_names()
+        computed_deps = set()
+        why = WhyNoFuse(node1, node2)
+
+        # StarDep doesn't match MemoryDep, different indices don't match
+        # However, broadcasting sometimes strips dimensions, and if that's the case
+        # we still can match unmet dep
+        # if there's indirect indexing, don't match it
+        def fusable_read_and_write(read: Dep, write: Dep):
+            return (
+                self.mutation_renames.get(read.name, read.name) == write.name
+                and (isinstance(read, MemoryDep) and isinstance(write, MemoryDep))
+                and not free_symbol_has(read.index, "tmp")
+                and not free_symbol_has(write.index, "tmp")
+                and read.index == write.index
+                and len(read.size) >= len(write.size)
+                and read.size[: len(write.size)] == write.size
+            )
+
+        for rd in node2.unmet_dependencies:
+            for cd in node1.read_writes.writes:
+                if fusable_read_and_write(rd, cd):
+                    computed_deps.add(rd)
+
+        remaining_deps = {dep.name for dep in node2.unmet_dependencies - computed_deps}
+        if remaining_deps & node1_names:
+            # MemoryDeps didn't match and read different locations of the same buffer.
+            # Examples here include:
+            #   - MemoryDep("foo", x) != MemoryDep("foo", x + 1)
+            #   - MemoryDep("foo", x) != StarDep("foo")
+            why("memory deps did not match")
+            return False
+        for name in remaining_deps:
+            if node1_names & self.name_to_fused_node[name].ancestors:
+                why("intermediate nodes between node1 & node2")
+                return False
+
+        # similar to can_inplace, if we are going to fuse a write subsequent to a read
+        # require that the indexing and size is the same
+        for write in node2.read_writes.writes:
+            for read in node1.read_writes.reads:
+                if write.name != self.mutation_renames.get(read.name, read.name):
+                    continue
+
+                # bail on StarDep
+                if not fusable_read_and_write(read=read, write=write):
+                    why("fusing a write into a read with different indexing formula")
+                    return False
+
+        return True
+
+    def score_fusion(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        """
+        Assign a score (higher comes first) to the fusion of node1
+        and node2.  When different fusions conflict with each other,
+        this is the way we decide what order to run them in.
+
+        Our current score is based on:
+        - Estimate of the saved memory operations
+        - Fusions closer together in original order
+        """
+        memory_score = self.score_fusion_memory(node1, node2)
+        proximity_score = -max(
+            abs(node1.min_order - node2.max_order),
+            abs(node2.min_order - node1.max_order),
+        )
+        return (
+            node1.is_template() == config.epilogue_fusion_first and memory_score > 0,
+            node1.is_reduction() == node2.is_reduction() and memory_score > 0,
+            memory_score,
+            proximity_score,
+        )
+
+    def score_fusion_memory(self, node1, node2):
+        """
+        The first term in our fusion score that estimates number of saved memory operations.
+        """
+        common_memory_deps = (node1.read_writes.reads | node1.read_writes.writes) & (
+            node2.read_writes.reads | node2.read_writes.writes
+        )
+        common_memory_deps = {
+            dep for dep in common_memory_deps if not dep.has_unbacked_symbols()
+        }
+        return sum(dep.numbytes_hint() for dep in common_memory_deps)
+
+    def score_fusion_key(self, nodes):
+        """
+        Shim for list.sort(key=...)
+        """
+        node1, node2 = nodes
+        return self.score_fusion(node1, node2)
+
+    def compute_last_usage(self):
+        """
+        Populate node.last_usage recursively (also for the nodes within a FusedSchedulerNode)
+        """
+
+        future_used_buffers = set()
+        for node_name in V.graph.get_output_names():
+            future_used_buffers.add(node_name)
+
+        for node in reversed(self.nodes):
+            node.set_last_usage(future_used_buffers, self.mutation_real_name)
+            future_used_buffers.update(node.last_usage)
+
+    def free_buffers(self):
+        """Free any buffers that are no longer needed"""
+        for name in sorted(
+            self.buffer_names_to_free
+            - V.graph.removed_buffers
+            - V.graph.wrapper_code.freed
+        ):
+            if name in self.name_to_node:
+                node = self.name_to_node[name]
+                if node.can_free():
+                    V.graph.wrapper_code.codegen_free(node.node)
+            elif name in V.graph.graph_inputs:
+                storage = V.graph.graph_inputs[name].data
+                assert isinstance(storage, ir.StorageBox) and storage.is_input_buffer()
+                V.graph.wrapper_code.codegen_free(storage.data)
+
+        self.buffer_names_to_free.clear()
+
+    def remove_kernel_local_buffers(self):
+        """
+        Any buffers that are both created and have a last use in the
+        same kernel can be removed.
+        """
+
+        # V.kernel.store_buffer_names should represent the set of nodes
+        # get fused
+        fused_node_names = V.kernel.store_buffer_names
+        names_to_remove = []
+        for out_buf in V.kernel.store_buffer_names:
+            users = self.name_to_node[out_buf].users
+            assert users is not None
+            users = {user.get_name() for user in users if not user.is_weak}
+            if users.issubset(fused_node_names):
+                names_to_remove.append(out_buf)
+
+        def remove_filter(n):
+            return (
+                n not in V.kernel.must_keep_buffers
+                and n not in V.kernel.args.input_buffers
+                and n not in self.mutation_renames
+                and n not in self.mutation_real_name
+            )
+
+        names_to_remove = list(filter(remove_filter, names_to_remove))
+
+        for name in names_to_remove:
+            if name in V.kernel.args.inplace_buffers:
+                buf = V.kernel.args.inplace_buffers[name]
+                if isinstance(buf, str) and buf.startswith("REMOVED"):
+                    continue
+                remove = all(n in names_to_remove for n in buf.other_names)
+                if remove:
+                    self.remove_inplace_buffer(name)
+                V.kernel.inplaced_to_remove.add(name)
+            else:
+                self.remove_buffer(name)
+
+    def remove_buffer(self, name):
+        # Assign a special value instead of deleting the entry
+        # because we still rely on output_buffers's length to
+        # generate unique arg name.
+        log.debug("remove_buffer(%r)", name)
+        V.kernel.args.output_buffers[name] = "REMOVED"
+        V.kernel.removed_buffers.add(name)
+
+    def remove_inplace_buffer(self, name):
+        log.debug("removing_inplace_buffer(%r)", name)
+        inner_name = V.kernel.args.inplace_buffers[name].inner_name
+        V.kernel.args.inplace_buffers[name] = inner_name.replace(
+            "in_out_ptr", "REMOVED"
+        )
+        V.kernel.removed_buffers.add(name)
+
+    def flush(self):
+        for backend in self.backends.values():
+            backend.flush()
+        self.free_buffers()
+
+    def codegen_extern_call(self, scheduler_node: ExternKernelSchedulerNode):
+        assert isinstance(scheduler_node, ExternKernelSchedulerNode)
+        # 'decide_inplace_update' stores the inplace update decisions in
+        # the current kernel from where 'allocate' retrieve those decisions.
+        # We have to make sure there is a non-NULL kernel handler to store
+        # those inplace update decisions.
+        with V.set_kernel_handler(Kernel(increase_kernel_count=False)):
+            scheduler_node.decide_inplace_update()
+            scheduler_node.allocate()
+        node = scheduler_node.node
+        assert isinstance(node, ir.ExternKernel), f"{type(node)=}"
+        node.codegen(V.graph.wrapper_code)
+        self.free_buffers()
+
+    def create_backend(self, device: torch.device):
+        assert (
+            device.type != "cuda" or device.index is not None
+        ), f"{device} should have been normalized in lowering"
+        V.graph.add_device_info(device)
+
+        device_scheduling = get_scheduling_for_device(device.type)
+        if device_scheduling is None:
+            raise RuntimeError(f"Unsupported device type: {device.type}")
+
+        if device.type == "cuda" and not has_triton():
+            device_props = torch.cuda.get_device_properties(device)
+            if device_props.major < 7:
+                raise RuntimeError(
+                    f"Found {device_props.name} which is too old to be supported by the triton GPU compiler, which is used as the backend. Triton only supports devices of CUDA Capability >= 7.0, but your device is of CUDA capability {device_props.major}.{device_props.minor}"  # noqa: B950
+                )
+            else:
+                raise RuntimeError(
+                    "Cannot find a working triton installation. More information on installing Triton can be found at https://github.com/openai/triton"  # noqa: B950
+                )
+
+        return device_scheduling(self)
+
+    def get_backend(self, device: torch.device):
+        if device not in self.backends:
+            self.backends[device] = self.create_backend(device)
+        return self.backends[device]
+
+    def enter_context(self, node):
+        def get_order(n):
+            if n not in self.origin_to_index:
+                self.origin_to_index.update({n: i for i, n in enumerate(n.graph.nodes)})
+            return self.origin_to_index[n]
+
+        # Use a dict to have ordering
+        origins = {
+            (get_order(e), e): None for n in node.get_nodes() for e in n.node.origins
+        }
+        origins = list(origins.keys())
+        if origins:
+            _, last = max(origins, key=operator.itemgetter(0))
+            V.graph.wrapper_code.enter_context(last)
+
+    @dynamo_timed
+    def codegen(self):
+        for node in self.nodes:
+            try:
+                log.debug(
+                    "Generating code for node %s with estimated runtime %f",
+                    node.get_name(),
+                    node.get_estimated_runtime(),
+                )
+            except Exception as e:
+                log.debug(
+                    "Generating code for node %s with estimated runtime 0.0",
+                    node.get_name(),
+                )
+
+            self.enter_context(node)
+
+            if not isinstance(node, NopKernelSchedulerNode):
+                device = node.get_device()
+                if (
+                    device != self.current_device
+                    or node.is_extern()
+                    or node.is_template()
+                ):
+                    self.flush()
+                if device != self.current_device:
+                    if device.type == "cuda":
+                        if self.current_device and self.current_device.type == "cuda":
+                            V.graph.wrapper_code.codegen_device_guard_exit()
+                        assert device.index is not None, "device should have an index"
+                        V.graph.wrapper_code.codegen_device_guard_enter(device.index)
+                    elif self.current_device and self.current_device.type == "cuda":
+                        V.graph.wrapper_code.codegen_device_guard_exit()
+                    self.current_device = device
+
+            self.buffer_names_to_free.update(node.last_usage)
+
+            if node.is_template():
+                node, *epilogue = node.get_nodes()
+                self.get_backend(device).codegen_template(node, epilogue)  # type: ignore[possibly-undefined]
+            elif node.is_extern():
+                self.codegen_extern_call(node)
+            elif node.is_foreach():
+                self.get_backend(device).codegen_foreach(node)  # type: ignore[possibly-undefined]
+            elif isinstance(node, (FusedSchedulerNode, SchedulerNode)):
+                self.get_backend(device).codegen_nodes(node.get_nodes())  # type: ignore[possibly-undefined]
+            else:
+                assert isinstance(node, NopKernelSchedulerNode)
+                node.allocate()
+
+            if config.debug_check_inf_and_nan:
+                V.graph.wrapper_code.generate_inf_and_nan_checker(node)
+
+            if config.triton.debug_sync_kernel:
+                self.get_backend(device).codegen_sync()  # type: ignore[possibly-undefined]
+
+            self.available_buffer_names.update(node.get_names())
+
+            if not isinstance(node, NopKernelSchedulerNode):
+                device = node.get_device()
+                if self.get_backend(device).ready_to_flush():
+                    self.flush()
+
+        if self.current_device and self.current_device.type == "cuda":
+            # exit the outermost CUDA device guard. this is
+            # important for nested indentation codegen-ing.
+            V.graph.wrapper_code.codegen_device_guard_exit()
+
+        self.flush()
+
+    def is_unaligned_buffer(self, buf_name):
+        if buf_name in V.graph.graph_inputs or buf_name in V.graph.constants:
+            # all graph inputs or constants are assumed to be aligned
+            return False
+        node = self.name_to_node[buf_name]
+        layout = node.node.get_layout()
+        if isinstance(layout, ir.AliasedLayout):
+            return not layout.maybe_guard_aligned()
+        else:
+            return False
+
+
+class BaseScheduling:
+    def can_fuse_vertical(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        """
+        Check whether node1 and node2 can be vertically fused or not.
+        """
+        raise NotImplementedError()
+
+    def can_fuse_horizontal(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        """
+        Check whether node1 and node2 can be horizontally fused or not.
+        """
+        raise NotImplementedError()
+
+    def fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
+        """
+        Fuse two nodes
+        """
+        if node1.is_foreach() or node2.is_foreach():
+            return ForeachKernelSchedulerNode.fuse(node1, node2)
+        else:
+            return FusedSchedulerNode.fuse(node1, node2)
+
+    def group_fn(self, sizes):
+        """
+        Process the iteration sizes in case a transformation needs to be applied.
+        """
+        raise NotImplementedError()
+
+    def codegen_template(
+        self, template_node: SchedulerNode, epilogue_nodes: List[SchedulerNode]
+    ):
+        """
+        Given a template node, generate a kernel.
+
+        This function is only available for triton now. If the third-party backend behaves as a sub-class
+        of TritonScheduling, it can override it or reuse it.
+        """
+        raise NotImplementedError()
+
+    def codegen_nodes(self, nodes: List[SchedulerNode]):
+        """
+        Generate a kernel given a list of pre-fused nodes.
+        """
+        raise NotImplementedError()
+
+    def codegen_sync(self):
+        """
+        Generate synchronization code for the kernel. This method depends on the hardware characteristics.
+        """
+        raise NotImplementedError()
+
+    def ready_to_flush(self) -> bool:
+        """
+        Check whether the backend is requesting the scheduler to flush the generated kernel.
+        If not supported, please return False.
+        """
+        return False
+
+    def flush(self):
+        """
+        Flush the generated kernel and python wrapper code to the source code file.
+        """
+        raise NotImplementedError()
+
+    def benchmark_fused_nodes(self, nodes):
+        """
+        Benchmark fused list of nodes and return the execution time
+        in milliseconds on randomly generated inputs.
+        """
+        raise NotImplementedError()
diff --git a/shared/package-files/32/3281f56c5323866af89441df67c4e51eeee541f9df3d7dbb17c552facf852dae/MPSGeneratorImpl.h b/shared/package-files/32/3281f56c5323866af89441df67c4e51eeee541f9df3d7dbb17c552facf852dae/MPSGeneratorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..31cfaba98004e83c0aba84ead7f66b4e62552771
--- /dev/null
+++ b/shared/package-files/32/3281f56c5323866af89441df67c4e51eeee541f9df3d7dbb17c552facf852dae/MPSGeneratorImpl.h
@@ -0,0 +1,52 @@
+//  Copyright © 2022 Apple Inc.
+
+#pragma once
+
+#include <ATen/core/Generator.h>
+#include <ATen/core/PhiloxRNGEngine.h>
+#include <c10/core/GeneratorImpl.h>
+#include <c10/util/Optional.h>
+
+namespace at {
+namespace mps::detail {
+
+static const uint32_t PHILOX_STATE_N = 7;
+struct rng_data_pod {
+  std::array<uint32_t, PHILOX_STATE_N> state{1};
+  uint64_t seed = default_rng_seed_val;
+};
+
+TORCH_API const Generator& getDefaultMPSGenerator();
+TORCH_API Generator createMPSGenerator(uint64_t seed_val = default_rng_seed_val);
+
+} // namespace mps::detail
+
+struct TORCH_API MPSGeneratorImpl : public c10::GeneratorImpl {
+  // Constructors
+  MPSGeneratorImpl(uint64_t seed_in = default_rng_seed_val);
+  ~MPSGeneratorImpl() override = default;
+
+  // MPSGeneratorImpl methods
+  std::shared_ptr<MPSGeneratorImpl> clone() const;
+  void set_current_seed(uint64_t seed) override;
+  void set_offset(uint64_t offset) override;
+  uint64_t get_offset() const override;
+  uint64_t current_seed() const override;
+  uint64_t seed() override;
+  void set_state(const c10::TensorImpl& new_state) override;
+  c10::intrusive_ptr<c10::TensorImpl> get_state() const override;
+  void update_philox_counters();
+
+  void set_engine(at::Philox4_32 engine) { engine_ = engine; };
+  at::Philox4_32 engine() { return engine_; };
+  uint32_t* state_data() { return data_.state.data(); }
+  static DeviceType device_type() { return DeviceType::MPS; };
+
+private:
+  mps::detail::rng_data_pod data_;
+  at::Philox4_32 engine_;
+
+  MPSGeneratorImpl* clone_impl() const override;
+};
+
+} // namespace at
diff --git a/shared/package-files/32/3283f4c6088d5bfa5354cfb7c38635f1f39d84d1516c4c7c21a36bf3ac147b46/hp236 b/shared/package-files/32/3283f4c6088d5bfa5354cfb7c38635f1f39d84d1516c4c7c21a36bf3ac147b46/hp236
new file mode 100644
index 0000000000000000000000000000000000000000..713202d6fa315829d8e7f64249308a1b45e46c78
Binary files /dev/null and b/shared/package-files/32/3283f4c6088d5bfa5354cfb7c38635f1f39d84d1516c4c7c21a36bf3ac147b46/hp236 differ
diff --git a/shared/package-files/32/3287cf5a6748adb00f2b4aed50d30af8121731e18b621671cd41ff26350d198d/_thnn_fused_gru_cell.h b/shared/package-files/32/3287cf5a6748adb00f2b4aed50d30af8121731e18b621671cd41ff26350d198d/_thnn_fused_gru_cell.h
new file mode 100644
index 0000000000000000000000000000000000000000..3da681329cc202a55983ce121f9f0efec4b7df68
--- /dev/null
+++ b/shared/package-files/32/3287cf5a6748adb00f2b4aed50d30af8121731e18b621671cd41ff26350d198d/_thnn_fused_gru_cell.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_thnn_fused_gru_cell_ops.h>
+
+namespace at {
+
+
+// aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional<at::Tensor> & input_bias={}, const c10::optional<at::Tensor> & hidden_bias={}) {
+    return at::_ops::_thnn_fused_gru_cell::call(input_gates, hidden_gates, hx, input_bias, hidden_bias);
+}
+
+// aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional<at::Tensor> & input_bias={}, const c10::optional<at::Tensor> & hidden_bias={}) {
+    return at::_ops::_thnn_fused_gru_cell_out::call(input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1);
+}
+// aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_outf(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1) {
+    return at::_ops::_thnn_fused_gru_cell_out::call(input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1);
+}
+
+}
diff --git a/shared/package-files/32/3287e42c001c3be0310c140b556e30ae11f023cc6c9d11055f51e914735c7d1a/_trilinear_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/32/3287e42c001c3be0310c140b556e30ae11f023cc6c9d11055f51e914735c7d1a/_trilinear_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2ea991c7452b1511f08f0c2d44cac3c286050108
--- /dev/null
+++ b/shared/package-files/32/3287e42c001c3be0310c140b556e30ae11f023cc6c9d11055f51e914735c7d1a/_trilinear_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor _trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim=1);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/32/328ad6eaf4cb2b1aa947de8d841e51c6490158ec6b093caf27be88967c1becb6/series_struct.py b/shared/package-files/32/328ad6eaf4cb2b1aa947de8d841e51c6490158ec6b093caf27be88967c1becb6/series_struct.py
new file mode 100644
index 0000000000000000000000000000000000000000..906725ba7b9a3745abcb09d5cf8509cfa4917449
--- /dev/null
+++ b/shared/package-files/32/328ad6eaf4cb2b1aa947de8d841e51c6490158ec6b093caf27be88967c1becb6/series_struct.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pyarrow.compute as pc
+
+from narwhals._arrow.utils import ArrowSeriesNamespace
+from narwhals._compliant.any_namespace import StructNamespace
+
+if TYPE_CHECKING:
+    from narwhals._arrow.series import ArrowSeries
+
+
+class ArrowSeriesStructNamespace(ArrowSeriesNamespace, StructNamespace["ArrowSeries"]):
+    def field(self, name: str) -> ArrowSeries:
+        return self.with_native(pc.struct_field(self.native, name)).alias(name)
diff --git a/shared/package-files/32/328c17581c5524b9a4001a26ff3a01b8b28cec8b0e254e655ae30de0f52c3d56/__init__.py b/shared/package-files/32/328c17581c5524b9a4001a26ff3a01b8b28cec8b0e254e655ae30de0f52c3d56/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a2572cd4a187ff52cf3b38bfeb10e0c4b9308f1
--- /dev/null
+++ b/shared/package-files/32/328c17581c5524b9a4001a26ff3a01b8b28cec8b0e254e655ae30de0f52c3d56/__init__.py
@@ -0,0 +1,57 @@
+# coding: utf-8
+
+if False:  # MYPY
+    from typing import Dict, Any  # NOQA
+
+_package_data = dict(
+    full_package_name='ruamel.yaml',
+    version_info=(0, 17, 21),
+    __version__='0.17.21',
+    version_timestamp='2022-02-12 09:49:22',
+    author='Anthon van der Neut',
+    author_email='a.van.der.neut@ruamel.eu',
+    description='ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order',  # NOQA
+    entry_points=None,
+    since=2014,
+    extras_require={
+        ':platform_python_implementation=="CPython" and python_version<"3.11"': ['ruamel.yaml.clib>=0.2.6'],  # NOQA
+        'jinja2': ['ruamel.yaml.jinja2>=0.2'],
+        'docs': ['ryd'],
+    },
+    classifiers=[
+        'Programming Language :: Python :: 3 :: Only',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: Implementation :: CPython',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+        'Topic :: Text Processing :: Markup',
+        'Typing :: Typed',
+    ],
+    keywords='yaml 1.2 parser round-trip preserve quotes order config',
+    read_the_docs='yaml',
+    supported=[(3, 5)],  # minimum
+    tox=dict(
+        env='*f',  # f for 3.5
+        fl8excl='_test/lib',
+    ),
+    # universal=True,
+    python_requires='>=3',
+    rtfd='yaml',
+)  # type: Dict[Any, Any]
+
+
+version_info = _package_data['version_info']
+__version__ = _package_data['__version__']
+
+try:
+    from .cyaml import *  # NOQA
+
+    __with_libyaml__ = True
+except (ImportError, ValueError):  # for Jython
+    __with_libyaml__ = False
+
+from ruamel.yaml.main import *  # NOQA
diff --git a/shared/package-files/32/328e753ef0425f4725c2330f192fd26d6269243090c1560abb56be424a59bc7c/_functional.py b/shared/package-files/32/328e753ef0425f4725c2330f192fd26d6269243090c1560abb56be424a59bc7c/_functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ea361d8efba10c59e3ecf3b7353035ccb995f4e
--- /dev/null
+++ b/shared/package-files/32/328e753ef0425f4725c2330f192fd26d6269243090c1560abb56be424a59bc7c/_functional.py
@@ -0,0 +1,79 @@
+r"""Functional interface."""
+import math
+from torch import Tensor
+from typing import List
+
+from .adadelta import adadelta  # type: ignore[attr-defined] # noqa: F401
+from .adagrad import adagrad, _make_sparse  # type: ignore[attr-defined] # noqa: F401
+from .adam import adam  # type: ignore[attr-defined] # noqa: F401
+from .adamw import adamw  # type: ignore[attr-defined] # noqa: F401
+from .adamax import adamax  # type: ignore[attr-defined] # noqa: F401
+from .asgd import asgd  # type: ignore[attr-defined] # noqa: F401
+from .nadam import nadam  # type: ignore[attr-defined] # noqa: F401
+from .radam import radam  # type: ignore[attr-defined] # noqa: F401
+from .rmsprop import rmsprop  # type: ignore[attr-defined] # noqa: F401
+from .rprop import rprop  # type: ignore[attr-defined] # noqa: F401
+from .sgd import sgd  # type: ignore[attr-defined] # noqa: F401
+
+
+# TODO: use foreach API in optim._functional to do all the computation
+
+
+def sparse_adam(params: List[Tensor],
+                grads: List[Tensor],
+                exp_avgs: List[Tensor],
+                exp_avg_sqs: List[Tensor],
+                state_steps: List[int],
+                *,
+                eps: float,
+                beta1: float,
+                beta2: float,
+                lr: float,
+                maximize: bool):
+    r"""Functional API that performs Sparse Adam algorithm computation.
+
+    See :class:`~torch.optim.SparseAdam` for details.
+    """
+    for i, param in enumerate(params):
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        grad = grad.coalesce()  # the update is non-linear so indices must be unique
+        grad_indices = grad._indices()
+        grad_values = grad._values()
+        if grad_values.numel() == 0:
+            # Skip update for empty grad
+            continue
+        size = grad.size()
+
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step = state_steps[i]
+
+
+        def make_sparse(values):
+            constructor = grad.new
+            if grad_indices.dim() == 0 or values.dim() == 0:
+                return constructor().resize_as_(grad)
+            return constructor(grad_indices, values, size)
+
+        # Decay the first and second moment running average coefficient
+        #      old <- b * old + (1 - b) * new
+        # <==> old += (1 - b) * (new - old)
+        old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
+        exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
+        exp_avg.add_(make_sparse(exp_avg_update_values))
+        old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
+        exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
+        exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))
+
+        # Dense addition again is intended, avoiding another sparse_mask
+        numer = exp_avg_update_values.add_(old_exp_avg_values)
+        exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
+        denom = exp_avg_sq_update_values.sqrt_().add_(eps)
+        del exp_avg_update_values, exp_avg_sq_update_values
+
+        bias_correction1 = 1 - beta1 ** step
+        bias_correction2 = 1 - beta2 ** step
+        step_size = lr * math.sqrt(bias_correction2) / bias_correction1
+
+        param.add_(make_sparse(-step_size * numer.div_(denom)))
diff --git a/shared/package-files/32/328f3c2609b2dc2b92433db95921f7e049d94acfc9058ec4f8e28f3b64a384f7/test_vector.py b/shared/package-files/32/328f3c2609b2dc2b92433db95921f7e049d94acfc9058ec4f8e28f3b64a384f7/test_vector.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68fb9fb3efb1f11f1d5a8908aa80dc1f9d7e46e
--- /dev/null
+++ b/shared/package-files/32/328f3c2609b2dc2b92433db95921f7e049d94acfc9058ec4f8e28f3b64a384f7/test_vector.py
@@ -0,0 +1,266 @@
+from sympy.core import Rational, S
+from sympy.simplify import simplify, trigsimp
+from sympy.core.function import (Derivative, Function, diff)
+from sympy.core.numbers import pi
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.integrals.integrals import Integral
+from sympy.matrices.immutable import ImmutableDenseMatrix as Matrix
+from sympy.vector.vector import Vector, BaseVector, VectorAdd, \
+     VectorMul, VectorZero
+from sympy.vector.coordsysrect import CoordSys3D
+from sympy.vector.vector import Cross, Dot, cross
+from sympy.testing.pytest import raises
+
+C = CoordSys3D('C')
+
+i, j, k = C.base_vectors()
+a, b, c = symbols('a b c')
+
+
+def test_cross():
+    v1 = C.x * i + C.z * C.z * j
+    v2 = C.x * i + C.y * j + C.z * k
+    assert Cross(v1, v2) == Cross(C.x*C.i + C.z**2*C.j, C.x*C.i + C.y*C.j + C.z*C.k)
+    assert Cross(v1, v2).doit() == C.z**3*C.i + (-C.x*C.z)*C.j + (C.x*C.y - C.x*C.z**2)*C.k
+    assert cross(v1, v2) == C.z**3*C.i + (-C.x*C.z)*C.j + (C.x*C.y - C.x*C.z**2)*C.k
+    assert Cross(v1, v2) == -Cross(v2, v1)
+    assert Cross(v1, v2) + Cross(v2, v1) == Vector.zero
+
+
+def test_dot():
+    v1 = C.x * i + C.z * C.z * j
+    v2 = C.x * i + C.y * j + C.z * k
+    assert Dot(v1, v2) == Dot(C.x*C.i + C.z**2*C.j, C.x*C.i + C.y*C.j + C.z*C.k)
+    assert Dot(v1, v2).doit() == C.x**2 + C.y*C.z**2
+    assert Dot(v1, v2).doit() == C.x**2 + C.y*C.z**2
+    assert Dot(v1, v2) == Dot(v2, v1)
+
+
+def test_vector_sympy():
+    """
+    Test whether the Vector framework confirms to the hashing
+    and equality testing properties of SymPy.
+    """
+    v1 = 3*j
+    assert v1 == j*3
+    assert v1.components == {j: 3}
+    v2 = 3*i + 4*j + 5*k
+    v3 = 2*i + 4*j + i + 4*k + k
+    assert v3 == v2
+    assert v3.__hash__() == v2.__hash__()
+
+
+def test_vector():
+    assert isinstance(i, BaseVector)
+    assert i != j
+    assert j != k
+    assert k != i
+    assert i - i == Vector.zero
+    assert i + Vector.zero == i
+    assert i - Vector.zero == i
+    assert Vector.zero != 0
+    assert -Vector.zero == Vector.zero
+
+    v1 = a*i + b*j + c*k
+    v2 = a**2*i + b**2*j + c**2*k
+    v3 = v1 + v2
+    v4 = 2 * v1
+    v5 = a * i
+
+    assert isinstance(v1, VectorAdd)
+    assert v1 - v1 == Vector.zero
+    assert v1 + Vector.zero == v1
+    assert v1.dot(i) == a
+    assert v1.dot(j) == b
+    assert v1.dot(k) == c
+    assert i.dot(v2) == a**2
+    assert j.dot(v2) == b**2
+    assert k.dot(v2) == c**2
+    assert v3.dot(i) == a**2 + a
+    assert v3.dot(j) == b**2 + b
+    assert v3.dot(k) == c**2 + c
+
+    assert v1 + v2 == v2 + v1
+    assert v1 - v2 == -1 * (v2 - v1)
+    assert a * v1 == v1 * a
+
+    assert isinstance(v5, VectorMul)
+    assert v5.base_vector == i
+    assert v5.measure_number == a
+    assert isinstance(v4, Vector)
+    assert isinstance(v4, VectorAdd)
+    assert isinstance(v4, Vector)
+    assert isinstance(Vector.zero, VectorZero)
+    assert isinstance(Vector.zero, Vector)
+    assert isinstance(v1 * 0, VectorZero)
+
+    assert v1.to_matrix(C) == Matrix([[a], [b], [c]])
+
+    assert i.components == {i: 1}
+    assert v5.components == {i: a}
+    assert v1.components == {i: a, j: b, k: c}
+
+    assert VectorAdd(v1, Vector.zero) == v1
+    assert VectorMul(a, v1) == v1*a
+    assert VectorMul(1, i) == i
+    assert VectorAdd(v1, Vector.zero) == v1
+    assert VectorMul(0, Vector.zero) == Vector.zero
+    raises(TypeError, lambda: v1.outer(1))
+    raises(TypeError, lambda: v1.dot(1))
+
+
+def test_vector_magnitude_normalize():
+    assert Vector.zero.magnitude() == 0
+    assert Vector.zero.normalize() == Vector.zero
+
+    assert i.magnitude() == 1
+    assert j.magnitude() == 1
+    assert k.magnitude() == 1
+    assert i.normalize() == i
+    assert j.normalize() == j
+    assert k.normalize() == k
+
+    v1 = a * i
+    assert v1.normalize() == (a/sqrt(a**2))*i
+    assert v1.magnitude() == sqrt(a**2)
+
+    v2 = a*i + b*j + c*k
+    assert v2.magnitude() == sqrt(a**2 + b**2 + c**2)
+    assert v2.normalize() == v2 / v2.magnitude()
+
+    v3 = i + j
+    assert v3.normalize() == (sqrt(2)/2)*C.i + (sqrt(2)/2)*C.j
+
+
+def test_vector_simplify():
+    A, s, k, m = symbols('A, s, k, m')
+
+    test1 = (1 / a + 1 / b) * i
+    assert (test1 & i) != (a + b) / (a * b)
+    test1 = simplify(test1)
+    assert (test1 & i) == (a + b) / (a * b)
+    assert test1.simplify() == simplify(test1)
+
+    test2 = (A**2 * s**4 / (4 * pi * k * m**3)) * i
+    test2 = simplify(test2)
+    assert (test2 & i) == (A**2 * s**4 / (4 * pi * k * m**3))
+
+    test3 = ((4 + 4 * a - 2 * (2 + 2 * a)) / (2 + 2 * a)) * i
+    test3 = simplify(test3)
+    assert (test3 & i) == 0
+
+    test4 = ((-4 * a * b**2 - 2 * b**3 - 2 * a**2 * b) / (a + b)**2) * i
+    test4 = simplify(test4)
+    assert (test4 & i) == -2 * b
+
+    v = (sin(a)+cos(a))**2*i - j
+    assert trigsimp(v) == (2*sin(a + pi/4)**2)*i + (-1)*j
+    assert trigsimp(v) == v.trigsimp()
+
+    assert simplify(Vector.zero) == Vector.zero
+
+
+def test_vector_dot():
+    assert i.dot(Vector.zero) == 0
+    assert Vector.zero.dot(i) == 0
+    assert i & Vector.zero == 0
+
+    assert i.dot(i) == 1
+    assert i.dot(j) == 0
+    assert i.dot(k) == 0
+    assert i & i == 1
+    assert i & j == 0
+    assert i & k == 0
+
+    assert j.dot(i) == 0
+    assert j.dot(j) == 1
+    assert j.dot(k) == 0
+    assert j & i == 0
+    assert j & j == 1
+    assert j & k == 0
+
+    assert k.dot(i) == 0
+    assert k.dot(j) == 0
+    assert k.dot(k) == 1
+    assert k & i == 0
+    assert k & j == 0
+    assert k & k == 1
+
+    raises(TypeError, lambda: k.dot(1))
+
+
+def test_vector_cross():
+    assert i.cross(Vector.zero) == Vector.zero
+    assert Vector.zero.cross(i) == Vector.zero
+
+    assert i.cross(i) == Vector.zero
+    assert i.cross(j) == k
+    assert i.cross(k) == -j
+    assert i ^ i == Vector.zero
+    assert i ^ j == k
+    assert i ^ k == -j
+
+    assert j.cross(i) == -k
+    assert j.cross(j) == Vector.zero
+    assert j.cross(k) == i
+    assert j ^ i == -k
+    assert j ^ j == Vector.zero
+    assert j ^ k == i
+
+    assert k.cross(i) == j
+    assert k.cross(j) == -i
+    assert k.cross(k) == Vector.zero
+    assert k ^ i == j
+    assert k ^ j == -i
+    assert k ^ k == Vector.zero
+
+    assert k.cross(1) == Cross(k, 1)
+
+
+def test_projection():
+    v1 = i + j + k
+    v2 = 3*i + 4*j
+    v3 = 0*i + 0*j
+    assert v1.projection(v1) == i + j + k
+    assert v1.projection(v2) == Rational(7, 3)*C.i + Rational(7, 3)*C.j + Rational(7, 3)*C.k
+    assert v1.projection(v1, scalar=True) == S.One
+    assert v1.projection(v2, scalar=True) == Rational(7, 3)
+    assert v3.projection(v1) == Vector.zero
+    assert v3.projection(v1, scalar=True) == S.Zero
+
+
+def test_vector_diff_integrate():
+    f = Function('f')
+    v = f(a)*C.i + a**2*C.j - C.k
+    assert Derivative(v, a) == Derivative((f(a))*C.i +
+                                          a**2*C.j + (-1)*C.k, a)
+    assert (diff(v, a) == v.diff(a) == Derivative(v, a).doit() ==
+            (Derivative(f(a), a))*C.i + 2*a*C.j)
+    assert (Integral(v, a) == (Integral(f(a), a))*C.i +
+            (Integral(a**2, a))*C.j + (Integral(-1, a))*C.k)
+
+
+def test_vector_args():
+    raises(ValueError, lambda: BaseVector(3, C))
+    raises(TypeError, lambda: BaseVector(0, Vector.zero))
+
+
+def test_srepr():
+    from sympy.printing.repr import srepr
+    res = "CoordSys3D(Str('C'), Tuple(ImmutableDenseMatrix([[Integer(1), "\
+            "Integer(0), Integer(0)], [Integer(0), Integer(1), Integer(0)], "\
+            "[Integer(0), Integer(0), Integer(1)]]), VectorZero())).i"
+    assert srepr(C.i) == res
+
+
+def test_scalar():
+    from sympy.vector import CoordSys3D
+    C = CoordSys3D('C')
+    v1 = 3*C.i + 4*C.j + 5*C.k
+    v2 = 3*C.i - 4*C.j + 5*C.k
+    assert v1.is_Vector is True
+    assert v1.is_scalar is False
+    assert (v1.dot(v2)).is_scalar is True
+    assert (v1.cross(v2)).is_scalar is False
diff --git a/shared/package-files/32/328f813f0a1a73fbc6fdbac73926746398e3eef7831fe9c5e2791f3d753bba91/anomaly_mode.py b/shared/package-files/32/328f813f0a1a73fbc6fdbac73926746398e3eef7831fe9c5e2791f3d753bba91/anomaly_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..80a2526a81ded1c9776ca9c8cea1879465ff3066
--- /dev/null
+++ b/shared/package-files/32/328f813f0a1a73fbc6fdbac73926746398e3eef7831fe9c5e2791f3d753bba91/anomaly_mode.py
@@ -0,0 +1,119 @@
+import warnings
+
+import torch
+
+__all__ = ["detect_anomaly", "set_detect_anomaly"]
+
+
+class detect_anomaly:
+    r"""Context-manager that enable anomaly detection for the autograd engine.
+
+    This does two things:
+
+    - Running the forward pass with detection enabled will allow the backward
+      pass to print the traceback of the forward operation that created the failing
+      backward function.
+    - If ``check_nan`` is ``True``, any backward computation that generate "nan"
+      value will raise an error. Default ``True``.
+
+    .. warning::
+        This mode should be enabled only for debugging as the different tests
+        will slow down your program execution.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
+        >>> import torch
+        >>> from torch import autograd
+        >>> class MyFunc(autograd.Function):
+        ...     @staticmethod
+        ...     def forward(ctx, inp):
+        ...         return inp.clone()
+        ...     @staticmethod
+        ...     def backward(ctx, gO):
+        ...         # Error during the backward pass
+        ...         raise RuntimeError("Some error in backward")
+        ...         return gO.clone()
+        >>> def run_fn(a):
+        ...     out = MyFunc.apply(a)
+        ...     return out.sum()
+        >>> inp = torch.rand(10, 10, requires_grad=True)
+        >>> out = run_fn(inp)
+        >>> out.backward()
+            Traceback (most recent call last):
+              File "<stdin>", line 1, in <module>
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "<stdin>", line 8, in backward
+            RuntimeError: Some error in backward
+        >>> with autograd.detect_anomaly():
+        ...     inp = torch.rand(10, 10, requires_grad=True)
+        ...     out = run_fn(inp)
+        ...     out.backward()
+            Traceback of forward call that caused the error:
+              File "tmp.py", line 53, in <module>
+                out = run_fn(inp)
+              File "tmp.py", line 44, in run_fn
+                out = MyFunc.apply(a)
+            Traceback (most recent call last):
+              File "<stdin>", line 4, in <module>
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "<stdin>", line 8, in backward
+            RuntimeError: Some error in backward
+
+    """
+
+    def __init__(self, check_nan=True) -> None:
+        self.prev = torch.is_anomaly_enabled()
+        self.check_nan = check_nan
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        warnings.warn(
+            "Anomaly Detection has been enabled. "
+            "This mode will increase the runtime "
+            "and should only be enabled for debugging.",
+            stacklevel=2,
+        )
+
+    def __enter__(self) -> None:
+        torch.set_anomaly_enabled(True, self.check_nan)
+
+    def __exit__(self, *args: object) -> None:
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
+
+
+class set_detect_anomaly:
+    r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
+
+    ``set_detect_anomaly`` will enable or disable the autograd anomaly detection
+    based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    See ``detect_anomaly`` above for details of the anomaly detection behaviour.
+
+    Args:
+        mode (bool): Flag whether to enable anomaly detection (``True``),
+                     or disable (``False``).
+        check_nan (bool): Flag whether to raise an error when the backward
+                          generate "nan"
+
+    """
+
+    def __init__(self, mode: bool, check_nan: bool = True) -> None:
+        self.prev = torch.is_anomaly_enabled()
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        torch.set_anomaly_enabled(mode, check_nan)
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, *args: object) -> None:
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
diff --git a/shared/package-files/32/328f93ef417d6217d2bf128e2414cbabd51b12a10d4782d6444432d6de414d7d/reflection_pad3d_cpu_dispatch.h b/shared/package-files/32/328f93ef417d6217d2bf128e2414cbabd51b12a10d4782d6444432d6de414d7d/reflection_pad3d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..268fcd9ab0852a8228b33204dd4d6c19a84fe40a
--- /dev/null
+++ b/shared/package-files/32/328f93ef417d6217d2bf128e2414cbabd51b12a10d4782d6444432d6de414d7d/reflection_pad3d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding);
+TORCH_API at::Tensor reflection_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding);
+TORCH_API at::Tensor & reflection_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding);
+TORCH_API at::Tensor & reflection_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out);
+TORCH_API at::Tensor & reflection_pad3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding);
+TORCH_API at::Tensor & reflection_pad3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/32/3290332227e6222ff7831da83fa3c2dd1da5bb7e84c2caf6f96d6b5e3b33d948/_a_n_k_r.py b/shared/package-files/32/3290332227e6222ff7831da83fa3c2dd1da5bb7e84c2caf6f96d6b5e3b33d948/_a_n_k_r.py
new file mode 100644
index 0000000000000000000000000000000000000000..5466d42d4117f9935b54def71e5351fa1a0ee6ab
--- /dev/null
+++ b/shared/package-files/32/3290332227e6222ff7831da83fa3c2dd1da5bb7e84c2caf6f96d6b5e3b33d948/_a_n_k_r.py
@@ -0,0 +1,15 @@
+from .otBase import BaseTTXConverter
+
+
+class table__a_n_k_r(BaseTTXConverter):
+    """Anchor Point table
+
+    The anchor point table provides a way to define anchor points.
+    These are points within the coordinate space of a given glyph,
+    independent of the control points used to render the glyph.
+    Anchor points are used in conjunction with the ``kerx`` table.
+
+    See also https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6ankr.html
+    """
+
+    pass
diff --git a/shared/package-files/32/3293bb5a1666aeb8cd5f8119115b36fe8cedd759927b2b6f0c7ef25534a9a532/asinh_meta.h b/shared/package-files/32/3293bb5a1666aeb8cd5f8119115b36fe8cedd759927b2b6f0c7ef25534a9a532/asinh_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..f4d33d7dc783c565acaae1e314176299d0b2f381
--- /dev/null
+++ b/shared/package-files/32/3293bb5a1666aeb8cd5f8119115b36fe8cedd759927b2b6f0c7ef25534a9a532/asinh_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_asinh : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32943d3b921c1c3f0d3776d19e5120806990b817bc99a7e22799847abfda1f63/fix_has_key.py b/shared/package-files/32/32943d3b921c1c3f0d3776d19e5120806990b817bc99a7e22799847abfda1f63/fix_has_key.py
new file mode 100644
index 0000000000000000000000000000000000000000..439708c9923404312dfabb964615062ea32c0aea
--- /dev/null
+++ b/shared/package-files/32/32943d3b921c1c3f0d3776d19e5120806990b817bc99a7e22799847abfda1f63/fix_has_key.py
@@ -0,0 +1,109 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Fixer for has_key().
+
+Calls to .has_key() methods are expressed in terms of the 'in'
+operator:
+
+    d.has_key(k) -> k in d
+
+CAVEATS:
+1) While the primary target of this fixer is dict.has_key(), the
+   fixer will change any has_key() method call, regardless of its
+   class.
+
+2) Cases like this will not be converted:
+
+    m = d.has_key
+    if m(k):
+        ...
+
+   Only *calls* to has_key() are converted. While it is possible to
+   convert the above to something like
+
+    m = d.__contains__
+    if m(k):
+        ...
+
+   this is currently not done.
+"""
+
+# Local imports
+from .. import pytree
+from .. import fixer_base
+from ..fixer_util import Name, parenthesize
+
+
+class FixHasKey(fixer_base.BaseFix):
+    BM_compatible = True
+
+    PATTERN = """
+    anchor=power<
+        before=any+
+        trailer< '.' 'has_key' >
+        trailer<
+            '('
+            ( not(arglist | argument<any '=' any>) arg=any
+            | arglist<(not argument<any '=' any>) arg=any ','>
+            )
+            ')'
+        >
+        after=any*
+    >
+    |
+    negation=not_test<
+        'not'
+        anchor=power<
+            before=any+
+            trailer< '.' 'has_key' >
+            trailer<
+                '('
+                ( not(arglist | argument<any '=' any>) arg=any
+                | arglist<(not argument<any '=' any>) arg=any ','>
+                )
+                ')'
+            >
+        >
+    >
+    """
+
+    def transform(self, node, results):
+        assert results
+        syms = self.syms
+        if (node.parent.type == syms.not_test and
+            self.pattern.match(node.parent)):
+            # Don't transform a node matching the first alternative of the
+            # pattern when its parent matches the second alternative
+            return None
+        negation = results.get("negation")
+        anchor = results["anchor"]
+        prefix = node.prefix
+        before = [n.clone() for n in results["before"]]
+        arg = results["arg"].clone()
+        after = results.get("after")
+        if after:
+            after = [n.clone() for n in after]
+        if arg.type in (syms.comparison, syms.not_test, syms.and_test,
+                        syms.or_test, syms.test, syms.lambdef, syms.argument):
+            arg = parenthesize(arg)
+        if len(before) == 1:
+            before = before[0]
+        else:
+            before = pytree.Node(syms.power, before)
+        before.prefix = " "
+        n_op = Name("in", prefix=" ")
+        if negation:
+            n_not = Name("not", prefix=" ")
+            n_op = pytree.Node(syms.comp_op, (n_not, n_op))
+        new = pytree.Node(syms.comparison, (arg, n_op, before))
+        if after:
+            new = parenthesize(new)
+            new = pytree.Node(syms.power, (new,) + tuple(after))
+        if node.parent.type in (syms.comparison, syms.expr, syms.xor_expr,
+                                syms.and_expr, syms.shift_expr,
+                                syms.arith_expr, syms.term,
+                                syms.factor, syms.power):
+            new = parenthesize(new)
+        new.prefix = prefix
+        return new
diff --git a/shared/package-files/32/329b9cc4d158103cad5e1eefad908ca939117ee5d3bef3294365bf024d8e9cf9/_warnings_errors.py b/shared/package-files/32/329b9cc4d158103cad5e1eefad908ca939117ee5d3bef3294365bf024d8e9cf9/_warnings_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..38385b862c9d642b41af8d74279f98c6a427208a
--- /dev/null
+++ b/shared/package-files/32/329b9cc4d158103cad5e1eefad908ca939117ee5d3bef3294365bf024d8e9cf9/_warnings_errors.py
@@ -0,0 +1,38 @@
+# Warnings
+
+
+class DegenerateDataWarning(RuntimeWarning):
+    """Warns when data is degenerate and results may not be reliable."""
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("Degenerate data encountered; results may not be reliable.")
+        self.args = (msg,)
+
+
+class ConstantInputWarning(DegenerateDataWarning):
+    """Warns when all values in data are exactly equal."""
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("All values in data are exactly equal; "
+                   "results may not be reliable.")
+        self.args = (msg,)
+
+
+class NearConstantInputWarning(DegenerateDataWarning):
+    """Warns when all values in data are nearly equal."""
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("All values in data are nearly equal; "
+                   "results may not be reliable.")
+        self.args = (msg,)
+
+
+# Errors
+
+
+class FitError(RuntimeError):
+    """Represents an error condition when fitting a distribution to data."""
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("An error occurred when fitting a distribution to data.")
+        self.args = (msg,)
diff --git a/shared/package-files/32/32a0ca0ba630bfa8cd916b11f2b74bb9a6f31d4728e70d4eca50c711dc6f7bf2/dtype.py b/shared/package-files/32/32a0ca0ba630bfa8cd916b11f2b74bb9a6f31d4728e70d4eca50c711dc6f7bf2/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec44e6b06cdc443b1dd930d8f06fe70be577e97
--- /dev/null
+++ b/shared/package-files/32/32a0ca0ba630bfa8cd916b11f2b74bb9a6f31d4728e70d4eca50c711dc6f7bf2/dtype.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+dtype_obj = np.dtype(np.str_)
+void_dtype_obj = np.dtype([("f0", np.float64), ("f1", np.float32)])
+
+np.dtype(dtype=np.int64)
+np.dtype(int)
+np.dtype("int")
+np.dtype(None)
+
+np.dtype((int, 2))
+np.dtype((int, (1,)))
+
+np.dtype({"names": ["a", "b"], "formats": [int, float]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object()]})
+
+np.dtype([("name", np.str_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
+
+np.dtype(
+    {
+        "names": ["a", "b"],
+        "formats": [int, float],
+        "itemsize": 9,
+        "aligned": False,
+        "titles": ["x", "y"],
+        "offsets": [0, 1],
+    }
+)
+
+np.dtype((np.float_, float))
+
+
+class Test:
+    dtype = np.dtype(float)
+
+
+np.dtype(Test())
+
+# Methods and attributes
+dtype_obj.base
+dtype_obj.subdtype
+dtype_obj.newbyteorder()
+dtype_obj.type
+dtype_obj.name
+dtype_obj.names
+
+dtype_obj * 0
+dtype_obj * 2
+
+0 * dtype_obj
+2 * dtype_obj
+
+void_dtype_obj["f0"]
+void_dtype_obj[0]
+void_dtype_obj[["f0", "f1"]]
+void_dtype_obj[["f0"]]
diff --git a/shared/package-files/32/32a14c4d05f15b81dbcc8a59f652831c1dc637c48fe328877a74e67fc83f3f16/euckrprober.py b/shared/package-files/32/32a14c4d05f15b81dbcc8a59f652831c1dc637c48fe328877a74e67fc83f3f16/euckrprober.py
new file mode 100644
index 0000000000000000000000000000000000000000..345a060d0230ada58f769b0f6b55f43a428fc3bd
--- /dev/null
+++ b/shared/package-files/32/32a14c4d05f15b81dbcc8a59f652831c1dc637c48fe328877a74e67fc83f3f16/euckrprober.py
@@ -0,0 +1,47 @@
+######################## BEGIN LICENSE BLOCK ########################
+# The Original Code is mozilla.org code.
+#
+# The Initial Developer of the Original Code is
+# Netscape Communications Corporation.
+# Portions created by the Initial Developer are Copyright (C) 1998
+# the Initial Developer. All Rights Reserved.
+#
+# Contributor(s):
+#   Mark Pilgrim - port to Python
+#
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
+# 02110-1301  USA
+######################### END LICENSE BLOCK #########################
+
+from .mbcharsetprober import MultiByteCharSetProber
+from .codingstatemachine import CodingStateMachine
+from .chardistribution import EUCKRDistributionAnalysis
+from .mbcssm import EUCKR_SM_MODEL
+
+
+class EUCKRProber(MultiByteCharSetProber):
+    def __init__(self):
+        super(EUCKRProber, self).__init__()
+        self.coding_sm = CodingStateMachine(EUCKR_SM_MODEL)
+        self.distribution_analyzer = EUCKRDistributionAnalysis()
+        self.reset()
+
+    @property
+    def charset_name(self):
+        return "EUC-KR"
+
+    @property
+    def language(self):
+        return "Korean"
diff --git a/shared/package-files/32/32a2dd25a6c1664cc38e249c690ee27fd766a001af5d3d906af1a81a4868de55/cpu_info.h b/shared/package-files/32/32a2dd25a6c1664cc38e249c690ee27fd766a001af5d3d906af1a81a4868de55/cpu_info.h
new file mode 100644
index 0000000000000000000000000000000000000000..949719b97ed84da6277139a70e22203706ed6055
--- /dev/null
+++ b/shared/package-files/32/32a2dd25a6c1664cc38e249c690ee27fd766a001af5d3d906af1a81a4868de55/cpu_info.h
@@ -0,0 +1,114 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+// From Apache Impala (incubating) as of 2016-01-29. Pared down to a minimal
+// set of functions needed for Apache Arrow / Apache parquet-cpp
+
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <string>
+
+#include "arrow/util/macros.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace internal {
+
+/// CpuInfo is an interface to query for cpu information at runtime.  The caller can
+/// ask for the sizes of the caches and what hardware features are supported.
+/// On Linux, this information is pulled from a couple of sys files (/proc/cpuinfo and
+/// /sys/devices)
+class ARROW_EXPORT CpuInfo {
+ public:
+  ~CpuInfo();
+
+  /// x86 features
+  static constexpr int64_t SSSE3 = (1LL << 0);
+  static constexpr int64_t SSE4_1 = (1LL << 1);
+  static constexpr int64_t SSE4_2 = (1LL << 2);
+  static constexpr int64_t POPCNT = (1LL << 3);
+  static constexpr int64_t AVX = (1LL << 4);
+  static constexpr int64_t AVX2 = (1LL << 5);
+  static constexpr int64_t AVX512F = (1LL << 6);
+  static constexpr int64_t AVX512CD = (1LL << 7);
+  static constexpr int64_t AVX512VL = (1LL << 8);
+  static constexpr int64_t AVX512DQ = (1LL << 9);
+  static constexpr int64_t AVX512BW = (1LL << 10);
+  static constexpr int64_t AVX512 = AVX512F | AVX512CD | AVX512VL | AVX512DQ | AVX512BW;
+  static constexpr int64_t BMI1 = (1LL << 11);
+  static constexpr int64_t BMI2 = (1LL << 12);
+
+  /// Arm features
+  static constexpr int64_t ASIMD = (1LL << 32);
+
+  /// Cache enums for L1 (data), L2 and L3
+  enum class CacheLevel { L1 = 0, L2, L3, Last = L3 };
+
+  /// CPU vendors
+  enum class Vendor { Unknown, Intel, AMD };
+
+  static const CpuInfo* GetInstance();
+
+  /// Returns all the flags for this cpu
+  int64_t hardware_flags() const;
+
+  /// Returns the number of cores (including hyper-threaded) on this machine.
+  int num_cores() const;
+
+  /// Returns the vendor of the cpu.
+  Vendor vendor() const;
+
+  /// Returns the model name of the cpu (e.g. Intel i7-2600)
+  const std::string& model_name() const;
+
+  /// Returns the size of the cache in KB at this cache level
+  int64_t CacheSize(CacheLevel level) const;
+
+  /// \brief Returns whether or not the given feature is enabled.
+  ///
+  /// IsSupported() is true iff IsDetected() is also true and the feature
+  /// wasn't disabled by the user (for example by setting the ARROW_USER_SIMD_LEVEL
+  /// environment variable).
+  bool IsSupported(int64_t flags) const;
+
+  /// Returns whether or not the given feature is available on the CPU.
+  bool IsDetected(int64_t flags) const;
+
+  /// Determine if the CPU meets the minimum CPU requirements and if not, issue an error
+  /// and terminate.
+  void VerifyCpuRequirements() const;
+
+  /// Toggle a hardware feature on and off.  It is not valid to turn on a feature
+  /// that the underlying hardware cannot support. This is useful for testing.
+  void EnableFeature(int64_t flag, bool enable);
+
+  bool HasEfficientBmi2() const {
+    // BMI2 (pext, pdep) is only efficient on Intel X86 processors.
+    return vendor() == Vendor::Intel && IsSupported(BMI2);
+  }
+
+ private:
+  CpuInfo();
+
+  struct Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace internal
+}  // namespace arrow
diff --git a/shared/package-files/32/32a3f7c0bde6a8ff59654e3cb5b31d5ded1176025c826c8663d1e65508689b4c/special_log1p_native.h b/shared/package-files/32/32a3f7c0bde6a8ff59654e3cb5b31d5ded1176025c826c8663d1e65508689b4c/special_log1p_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..af28e043d4a1af25b46d2fcc92aa8b5fb19522ad
--- /dev/null
+++ b/shared/package-files/32/32a3f7c0bde6a8ff59654e3cb5b31d5ded1176025c826c8663d1e65508689b4c/special_log1p_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_log1p(const at::Tensor & self);
+TORCH_API at::Tensor & special_log1p_out(const at::Tensor & self, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32a5509013fcd656c1d98ef358e3f12a315c6d63dfec0c74e0ab2a78fc3befc9/t_ops.h b/shared/package-files/32/32a5509013fcd656c1d98ef358e3f12a315c6d63dfec0c74e0ab2a78fc3befc9/t_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..716aa691739115faa77f02a8f5ceb07613731fea
--- /dev/null
+++ b/shared/package-files/32/32a5509013fcd656c1d98ef358e3f12a315c6d63dfec0c74e0ab2a78fc3befc9/t_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API t {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "t(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API t_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::t_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "t_(Tensor(a!) self) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/32/32a6463f32edd39bec3cfdc6daa94cfafd78a1d1c4f69fe1141868da9854099e/_coalesce_compositeexplicitautograd_dispatch.h b/shared/package-files/32/32a6463f32edd39bec3cfdc6daa94cfafd78a1d1c4f69fe1141868da9854099e/_coalesce_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2a41c2286e93e8946ecf69248355846b14c761d1
--- /dev/null
+++ b/shared/package-files/32/32a6463f32edd39bec3cfdc6daa94cfafd78a1d1c4f69fe1141868da9854099e/_coalesce_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _coalesce_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & _coalesce_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/32/32a6a6ebb58ef4891399417223aeaf4ba2284974e9f46dfcf0369d1f62c230b6/__init__.py b/shared/package-files/32/32a6a6ebb58ef4891399417223aeaf4ba2284974e9f46dfcf0369d1f62c230b6/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2c19c06fe14476a9bfa4f1f60de7a997a41191c
--- /dev/null
+++ b/shared/package-files/32/32a6a6ebb58ef4891399417223aeaf4ba2284974e9f46dfcf0369d1f62c230b6/__init__.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+import sys
+
+try:
+    from ._version import version as __version__
+except ImportError:
+    __version__ = 'unknown'
+
+__all__ = ['easter', 'parser', 'relativedelta', 'rrule', 'tz',
+           'utils', 'zoneinfo']
+
+def __getattr__(name):
+    import importlib
+
+    if name in __all__:
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(
+        "module {!r} has not attribute {!r}".format(__name__, name)
+    )
+
+
+def __dir__():
+    # __dir__ should include all the lazy-importable modules as well.
+    return [x for x in globals() if x not in sys.modules] + __all__
diff --git a/shared/package-files/32/32a789d4f0f583deed28bf6cbaf1352d60f670ef8c58cc28f3e6a0dc55c06ae6/helpers.cc b/shared/package-files/32/32a789d4f0f583deed28bf6cbaf1352d60f670ef8c58cc28f3e6a0dc55c06ae6/helpers.cc
new file mode 100644
index 0000000000000000000000000000000000000000..0a24b259310e585a34f69c00c2b2a5609894ee73
--- /dev/null
+++ b/shared/package-files/32/32a789d4f0f583deed28bf6cbaf1352d60f670ef8c58cc28f3e6a0dc55c06ae6/helpers.cc
@@ -0,0 +1,504 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+// helpers.h includes a NumPy header, so we include this first
+#include "arrow/python/numpy_init.h"
+#include "arrow/python/numpy_interop.h"
+
+#include "arrow/python/helpers.h"
+
+#include <cmath>
+#include <limits>
+#include <mutex>
+#include <sstream>
+#include <type_traits>
+
+#include "arrow/python/common.h"
+#include "arrow/python/decimal.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/checked_cast.h"
+#include "arrow/util/config.h"
+#include "arrow/util/float16.h"
+#include "arrow/util/logging.h"
+
+namespace arrow {
+
+using internal::checked_cast;
+
+namespace py {
+
+#define GET_PRIMITIVE_TYPE(NAME, FACTORY) \
+  case Type::NAME:                        \
+    return FACTORY()
+
+std::shared_ptr<DataType> GetPrimitiveType(Type::type type) {
+  switch (type) {
+    case Type::NA:
+      return null();
+      GET_PRIMITIVE_TYPE(UINT8, uint8);
+      GET_PRIMITIVE_TYPE(INT8, int8);
+      GET_PRIMITIVE_TYPE(UINT16, uint16);
+      GET_PRIMITIVE_TYPE(INT16, int16);
+      GET_PRIMITIVE_TYPE(UINT32, uint32);
+      GET_PRIMITIVE_TYPE(INT32, int32);
+      GET_PRIMITIVE_TYPE(UINT64, uint64);
+      GET_PRIMITIVE_TYPE(INT64, int64);
+      GET_PRIMITIVE_TYPE(DATE32, date32);
+      GET_PRIMITIVE_TYPE(DATE64, date64);
+      GET_PRIMITIVE_TYPE(BOOL, boolean);
+      GET_PRIMITIVE_TYPE(HALF_FLOAT, float16);
+      GET_PRIMITIVE_TYPE(FLOAT, float32);
+      GET_PRIMITIVE_TYPE(DOUBLE, float64);
+      GET_PRIMITIVE_TYPE(BINARY, binary);
+      GET_PRIMITIVE_TYPE(STRING, utf8);
+      GET_PRIMITIVE_TYPE(LARGE_BINARY, large_binary);
+      GET_PRIMITIVE_TYPE(LARGE_STRING, large_utf8);
+      GET_PRIMITIVE_TYPE(BINARY_VIEW, binary_view);
+      GET_PRIMITIVE_TYPE(STRING_VIEW, utf8_view);
+      GET_PRIMITIVE_TYPE(INTERVAL_MONTH_DAY_NANO, month_day_nano_interval);
+    default:
+      return nullptr;
+  }
+}
+
+PyObject* PyFloat_FromHalf(uint16_t value) {
+  // Convert the uint16_t Float16 value to a PyFloat object
+  arrow::util::Float16 half_val = arrow::util::Float16::FromBits(value);
+  return PyFloat_FromDouble(half_val.ToDouble());
+}
+
+Result<uint16_t> PyFloat_AsHalf(PyObject* obj) {
+  if (PyFloat_Check(obj)) {
+    arrow::util::Float16 half_val =
+        arrow::util::Float16::FromDouble(PyFloat_AsDouble(obj));
+    return half_val.bits();
+  } else if (has_numpy() && PyArray_IsScalar(obj, Half)) {
+    return PyArrayScalar_VAL(obj, Half);
+  } else {
+    return Status::TypeError("conversion to float16 expects a `float` or ",
+                             "`np.float16` object, got ", Py_TYPE(obj)->tp_name);
+  }
+}
+
+namespace internal {
+
+std::string PyBytes_AsStdString(PyObject* obj) {
+  ARROW_DCHECK(PyBytes_Check(obj));
+  return std::string(PyBytes_AS_STRING(obj), PyBytes_GET_SIZE(obj));
+}
+
+Status PyUnicode_AsStdString(PyObject* obj, std::string* out) {
+  ARROW_DCHECK(PyUnicode_Check(obj));
+  Py_ssize_t size;
+  // The utf-8 representation is cached on the unicode object
+  const char* data = PyUnicode_AsUTF8AndSize(obj, &size);
+  RETURN_IF_PYERROR();
+  *out = std::string(data, size);
+  return Status::OK();
+}
+
+std::string PyObject_StdStringRepr(PyObject* obj) {
+  OwnedRef unicode_ref(PyObject_Repr(obj));
+  OwnedRef bytes_ref;
+
+  if (unicode_ref) {
+    bytes_ref.reset(
+        PyUnicode_AsEncodedString(unicode_ref.obj(), "utf8", "backslashreplace"));
+  }
+  if (!bytes_ref) {
+    PyErr_Clear();
+    std::stringstream ss;
+    ss << "<object of type '" << Py_TYPE(obj)->tp_name << "' repr() failed>";
+    return ss.str();
+  }
+  return PyBytes_AsStdString(bytes_ref.obj());
+}
+
+Status PyObject_StdStringStr(PyObject* obj, std::string* out) {
+  OwnedRef string_ref(PyObject_Str(obj));
+  RETURN_IF_PYERROR();
+  return PyUnicode_AsStdString(string_ref.obj(), out);
+}
+
+Result<bool> IsModuleImported(const std::string& module_name) {
+  // PyImport_GetModuleDict returns with a borrowed reference
+  OwnedRef key(PyUnicode_FromString(module_name.c_str()));
+  auto is_imported = PyDict_Contains(PyImport_GetModuleDict(), key.obj());
+  RETURN_IF_PYERROR();
+  return is_imported;
+}
+
+Status ImportModule(const std::string& module_name, OwnedRef* ref) {
+  PyObject* module = PyImport_ImportModule(module_name.c_str());
+  RETURN_IF_PYERROR();
+  ref->reset(module);
+  return Status::OK();
+}
+
+Status ImportFromModule(PyObject* module, const std::string& name, OwnedRef* ref) {
+  PyObject* attr = PyObject_GetAttrString(module, name.c_str());
+  RETURN_IF_PYERROR();
+  ref->reset(attr);
+  return Status::OK();
+}
+
+namespace {
+
+Status IntegerOverflowStatus(PyObject* obj, const std::string& overflow_message) {
+  if (overflow_message.empty()) {
+    std::string obj_as_stdstring;
+    RETURN_NOT_OK(PyObject_StdStringStr(obj, &obj_as_stdstring));
+    return Status::Invalid("Value ", obj_as_stdstring,
+                           " too large to fit in C integer type");
+  } else {
+    return Status::Invalid(overflow_message);
+  }
+}
+
+Result<OwnedRef> PyObjectToPyInt(PyObject* obj) {
+  // Try to call __index__ or __int__ on `obj`
+  // (starting from Python 3.10, the latter isn't done anymore by PyLong_AsLong*).
+  OwnedRef ref(PyNumber_Index(obj));
+  if (ref) {
+    return std::move(ref);
+  }
+  PyErr_Clear();
+  const auto nb = Py_TYPE(obj)->tp_as_number;
+  if (nb && nb->nb_int) {
+    ref.reset(nb->nb_int(obj));
+    if (!ref) {
+      RETURN_IF_PYERROR();
+    }
+    ARROW_DCHECK(ref);
+    return std::move(ref);
+  }
+  return Status::TypeError(
+      "object of type ",
+      PyObject_StdStringRepr(reinterpret_cast<PyObject*>(Py_TYPE(obj))),
+      " cannot be converted to int");
+}
+
+// Extract C signed int from Python object
+template <typename Int, enable_if_t<std::is_signed<Int>::value, Int> = 0>
+Status CIntFromPythonImpl(PyObject* obj, Int* out, const std::string& overflow_message) {
+  static_assert(sizeof(Int) <= sizeof(long long),  // NOLINT
+                "integer type larger than long long");
+
+  OwnedRef ref;
+  if (!PyLong_Check(obj)) {
+    ARROW_ASSIGN_OR_RAISE(ref, PyObjectToPyInt(obj));
+    obj = ref.obj();
+  }
+
+  if (sizeof(Int) > sizeof(long)) {  // NOLINT
+    const auto value = PyLong_AsLongLong(obj);
+    if (ARROW_PREDICT_FALSE(value == -1)) {
+      RETURN_IF_PYERROR();
+    }
+    if (ARROW_PREDICT_FALSE(value < std::numeric_limits<Int>::min() ||
+                            value > std::numeric_limits<Int>::max())) {
+      return IntegerOverflowStatus(obj, overflow_message);
+    }
+    *out = static_cast<Int>(value);
+  } else {
+    const auto value = PyLong_AsLong(obj);
+    if (ARROW_PREDICT_FALSE(value == -1)) {
+      RETURN_IF_PYERROR();
+    }
+    if (ARROW_PREDICT_FALSE(value < std::numeric_limits<Int>::min() ||
+                            value > std::numeric_limits<Int>::max())) {
+      return IntegerOverflowStatus(obj, overflow_message);
+    }
+    *out = static_cast<Int>(value);
+  }
+  return Status::OK();
+}
+
+// Extract C unsigned int from Python object
+template <typename Int, enable_if_t<std::is_unsigned<Int>::value, Int> = 0>
+Status CIntFromPythonImpl(PyObject* obj, Int* out, const std::string& overflow_message) {
+  static_assert(sizeof(Int) <= sizeof(unsigned long long),  // NOLINT
+                "integer type larger than unsigned long long");
+
+  OwnedRef ref;
+  if (!PyLong_Check(obj)) {
+    ARROW_ASSIGN_OR_RAISE(ref, PyObjectToPyInt(obj));
+    obj = ref.obj();
+  }
+
+  if (sizeof(Int) > sizeof(unsigned long)) {  // NOLINT
+    const auto value = PyLong_AsUnsignedLongLong(obj);
+    if (ARROW_PREDICT_FALSE(value == static_cast<decltype(value)>(-1))) {
+      RETURN_IF_PYERROR();
+    }
+    if (ARROW_PREDICT_FALSE(value > std::numeric_limits<Int>::max())) {
+      return IntegerOverflowStatus(obj, overflow_message);
+    }
+    *out = static_cast<Int>(value);
+  } else {
+    const auto value = PyLong_AsUnsignedLong(obj);
+    if (ARROW_PREDICT_FALSE(value == static_cast<decltype(value)>(-1))) {
+      RETURN_IF_PYERROR();
+    }
+    if (ARROW_PREDICT_FALSE(value > std::numeric_limits<Int>::max())) {
+      return IntegerOverflowStatus(obj, overflow_message);
+    }
+    *out = static_cast<Int>(value);
+  }
+  return Status::OK();
+}
+
+}  // namespace
+
+template <typename Int>
+Status CIntFromPython(PyObject* obj, Int* out, const std::string& overflow_message) {
+  if (PyBool_Check(obj)) {
+    return Status::TypeError("Expected integer, got bool");
+  }
+  return CIntFromPythonImpl(obj, out, overflow_message);
+}
+
+template Status CIntFromPython(PyObject*, int8_t*, const std::string&);
+template Status CIntFromPython(PyObject*, int16_t*, const std::string&);
+template Status CIntFromPython(PyObject*, int32_t*, const std::string&);
+template Status CIntFromPython(PyObject*, int64_t*, const std::string&);
+template Status CIntFromPython(PyObject*, uint8_t*, const std::string&);
+template Status CIntFromPython(PyObject*, uint16_t*, const std::string&);
+template Status CIntFromPython(PyObject*, uint32_t*, const std::string&);
+template Status CIntFromPython(PyObject*, uint64_t*, const std::string&);
+
+inline bool MayHaveNaN(PyObject* obj) {
+  // Some core types can be very quickly type-checked and do not allow NaN values
+  const int64_t non_nan_tpflags = Py_TPFLAGS_LONG_SUBCLASS | Py_TPFLAGS_LIST_SUBCLASS |
+                                  Py_TPFLAGS_TUPLE_SUBCLASS | Py_TPFLAGS_BYTES_SUBCLASS |
+                                  Py_TPFLAGS_UNICODE_SUBCLASS | Py_TPFLAGS_DICT_SUBCLASS |
+                                  Py_TPFLAGS_BASE_EXC_SUBCLASS | Py_TPFLAGS_TYPE_SUBCLASS;
+  return !PyType_HasFeature(Py_TYPE(obj), non_nan_tpflags);
+}
+
+bool PyFloat_IsNaN(PyObject* obj) {
+  return PyFloat_Check(obj) && std::isnan(PyFloat_AsDouble(obj));
+}
+
+namespace {
+
+// This needs a conditional, because using std::once_flag could introduce
+// a deadlock when the GIL is enabled. See
+// https://github.com/apache/arrow/commit/f69061935e92e36e25bb891177ca8bc4f463b272 for
+// more info.
+#ifdef Py_GIL_DISABLED
+static std::once_flag pandas_static_initialized;
+#else
+static bool pandas_static_initialized = false;
+#endif
+
+// Once initialized, these variables hold borrowed references to Pandas static data.
+// We should not use OwnedRef here because Python destructors would be
+// called on a finalized interpreter.
+static PyObject* pandas_NA = nullptr;
+static PyObject* pandas_NaT = nullptr;
+static PyObject* pandas_Timedelta = nullptr;
+static PyObject* pandas_Timestamp = nullptr;
+static PyTypeObject* pandas_NaTType = nullptr;
+static PyObject* pandas_DateOffset = nullptr;
+
+void GetPandasStaticSymbols() {
+  OwnedRef pandas;
+
+  // Import pandas
+  Status s = ImportModule("pandas", &pandas);
+  if (!s.ok()) {
+    return;
+  }
+
+#ifndef Py_GIL_DISABLED
+  // Since ImportModule can release the GIL, another thread could have
+  // already initialized the static data.
+  if (pandas_static_initialized) {
+    return;
+  }
+#endif
+
+  OwnedRef ref;
+
+  // set NaT sentinel and its type
+  if (ImportFromModule(pandas.obj(), "NaT", &ref).ok()) {
+    pandas_NaT = ref.obj();
+    // PyObject_Type returns a new reference but we trust that pandas.NaT will
+    // outlive our use of this PyObject*
+    pandas_NaTType = Py_TYPE(ref.obj());
+  }
+
+  // retain a reference to Timedelta
+  if (ImportFromModule(pandas.obj(), "Timedelta", &ref).ok()) {
+    pandas_Timedelta = ref.obj();
+  }
+
+  // retain a reference to Timestamp
+  if (ImportFromModule(pandas.obj(), "Timestamp", &ref).ok()) {
+    pandas_Timestamp = ref.obj();
+  }
+
+  // if pandas.NA exists, retain a reference to it
+  if (ImportFromModule(pandas.obj(), "NA", &ref).ok()) {
+    pandas_NA = ref.obj();
+  }
+
+  // Import DateOffset type
+  if (ImportFromModule(pandas.obj(), "DateOffset", &ref).ok()) {
+    pandas_DateOffset = ref.obj();
+  }
+}
+
+}  // namespace
+
+#ifdef Py_GIL_DISABLED
+void InitPandasStaticData() {
+  std::call_once(pandas_static_initialized, GetPandasStaticSymbols);
+}
+#else
+void InitPandasStaticData() {
+  // NOTE: This is called with the GIL held.  We needn't (and shouldn't,
+  // to avoid deadlocks) use an additional C++ lock (ARROW-10519).
+  if (pandas_static_initialized) {
+    return;
+  }
+  GetPandasStaticSymbols();
+  pandas_static_initialized = true;
+}
+#endif
+
+bool PandasObjectIsNull(PyObject* obj) {
+  if (!MayHaveNaN(obj)) {
+    return false;
+  }
+  if (obj == Py_None) {
+    return true;
+  }
+  if (PyFloat_IsNaN(obj) || (pandas_NA && obj == pandas_NA) ||
+      (pandas_NaTType && PyObject_TypeCheck(obj, pandas_NaTType)) ||
+      (internal::PyDecimal_Check(obj) && internal::PyDecimal_ISNAN(obj))) {
+    return true;
+  }
+  return false;
+}
+
+bool IsPandasTimedelta(PyObject* obj) {
+  return pandas_Timedelta && PyObject_IsInstance(obj, pandas_Timedelta);
+}
+
+bool IsPandasTimestamp(PyObject* obj) {
+  return pandas_Timestamp && PyObject_IsInstance(obj, pandas_Timestamp);
+}
+
+PyObject* BorrowPandasDataOffsetType() { return pandas_DateOffset; }
+
+Status InvalidValue(PyObject* obj, const std::string& why) {
+  auto obj_as_str = PyObject_StdStringRepr(obj);
+  return Status::Invalid("Could not convert ", std::move(obj_as_str), " with type ",
+                         Py_TYPE(obj)->tp_name, ": ", why);
+}
+
+Status InvalidType(PyObject* obj, const std::string& why) {
+  auto obj_as_str = PyObject_StdStringRepr(obj);
+  return Status::TypeError("Could not convert ", std::move(obj_as_str), " with type ",
+                           Py_TYPE(obj)->tp_name, ": ", why);
+}
+
+Status UnboxIntegerAsInt64(PyObject* obj, int64_t* out) {
+  if (PyLong_Check(obj)) {
+    int overflow = 0;
+    *out = PyLong_AsLongLongAndOverflow(obj, &overflow);
+    if (overflow) {
+      return Status::Invalid("PyLong is too large to fit int64");
+    }
+  } else if (PyArray_IsScalar(obj, Byte)) {
+    *out = reinterpret_cast<PyByteScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, UByte)) {
+    *out = reinterpret_cast<PyUByteScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, Short)) {
+    *out = reinterpret_cast<PyShortScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, UShort)) {
+    *out = reinterpret_cast<PyUShortScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, Int)) {
+    *out = reinterpret_cast<PyIntScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, UInt)) {
+    *out = reinterpret_cast<PyUIntScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, Long)) {
+    *out = reinterpret_cast<PyLongScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, ULong)) {
+    *out = reinterpret_cast<PyULongScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, LongLong)) {
+    *out = reinterpret_cast<PyLongLongScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, Int64)) {
+    *out = reinterpret_cast<PyInt64ScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, ULongLong)) {
+    *out = reinterpret_cast<PyULongLongScalarObject*>(obj)->obval;
+  } else if (PyArray_IsScalar(obj, UInt64)) {
+    *out = reinterpret_cast<PyUInt64ScalarObject*>(obj)->obval;
+  } else {
+    return Status::Invalid("Integer scalar type not recognized");
+  }
+  return Status::OK();
+}
+
+Status IntegerScalarToDoubleSafe(PyObject* obj, double* out) {
+  int64_t value = 0;
+  RETURN_NOT_OK(UnboxIntegerAsInt64(obj, &value));
+
+  constexpr int64_t kDoubleMax = 1LL << 53;
+  constexpr int64_t kDoubleMin = -(1LL << 53);
+
+  if (value < kDoubleMin || value > kDoubleMax) {
+    return Status::Invalid("Integer value ", value, " is outside of the range exactly",
+                           " representable by a IEEE 754 double precision value");
+  }
+  *out = static_cast<double>(value);
+  return Status::OK();
+}
+
+Status IntegerScalarToFloat32Safe(PyObject* obj, float* out) {
+  int64_t value = 0;
+  RETURN_NOT_OK(UnboxIntegerAsInt64(obj, &value));
+
+  constexpr int64_t kFloatMax = 1LL << 24;
+  constexpr int64_t kFloatMin = -(1LL << 24);
+
+  if (value < kFloatMin || value > kFloatMax) {
+    return Status::Invalid("Integer value ", value, " is outside of the range exactly",
+                           " representable by a IEEE 754 single precision value");
+  }
+  *out = static_cast<float>(value);
+  return Status::OK();
+}
+
+void DebugPrint(PyObject* obj) {
+  std::string repr = PyObject_StdStringRepr(obj);
+  PySys_WriteStderr("%s\n", repr.c_str());
+}
+
+bool IsThreadingEnabled() {
+#ifdef ARROW_ENABLE_THREADING
+  return true;
+#else
+  return false;
+#endif
+}
+
+}  // namespace internal
+}  // namespace py
+}  // namespace arrow
diff --git a/shared/package-files/32/32a824bfcdf64bec1d60c8532c414b439fb932cd37ec4d325238bd28c1260f57/__init__.py b/shared/package-files/32/32a824bfcdf64bec1d60c8532c414b439fb932cd37ec4d325238bd28c1260f57/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2b5cfac6290430ffb267084f79e82718dd71123
--- /dev/null
+++ b/shared/package-files/32/32a824bfcdf64bec1d60c8532c414b439fb932cd37ec4d325238bd28c1260f57/__init__.py
@@ -0,0 +1,218 @@
+"""isort:skip_file"""
+# Import order is significant here.
+
+from . import math
+from . import extra
+from .standard import (
+    argmax,
+    argmin,
+    cdiv,
+    cumprod,
+    cumsum,
+    max,
+    maximum,
+    min,
+    minimum,
+    sigmoid,
+    softmax,
+    sort,
+    sum,
+    ravel,
+    swizzle2d,
+    xor_sum,
+    zeros,
+    zeros_like,
+)
+from .core import (
+    TRITON_MAX_TENSOR_NUMEL,
+    abs,
+    advance,
+    arange,
+    associative_scan,
+    atomic_add,
+    atomic_and,
+    atomic_cas,
+    atomic_max,
+    atomic_min,
+    atomic_or,
+    atomic_xchg,
+    atomic_xor,
+    bfloat16,
+    block_type,
+    broadcast,
+    broadcast_to,
+    cat,
+    constexpr,
+    cos,
+    debug_barrier,
+    device_assert,
+    device_print,
+    dot,
+    dtype,
+    exp,
+    expand_dims,
+    full,
+    fdiv,
+    float16,
+    float32,
+    float64,
+    float8e4b15,
+    float8e4b15x4,
+    float8e4nv,
+    float8e5,
+    function_type,
+    inline_asm_elementwise,
+    int1,
+    int16,
+    int32,
+    int64,
+    int8,
+    load,
+    log,
+    make_block_ptr,
+    max_constancy,
+    max_contiguous,
+    multiple_of,
+    num_programs,
+    pi32_t,
+    pointer_type,
+    program_id,
+    reduce,
+    reshape,
+    sin,
+    sqrt,
+    static_assert,
+    static_print,
+    store,
+    static_range,
+    tensor,
+    trans,
+    # triton,
+    uint16,
+    uint32,
+    uint64,
+    uint8,
+    umulhi,
+    view,
+    void,
+    where,
+)
+from .random import (
+    pair_uniform_to_normal,
+    philox,
+    philox_impl,
+    rand,
+    rand4x,
+    randint,
+    randint4x,
+    randn,
+    randn4x,
+    uint_to_uniform_float,
+)
+
+__all__ = [
+    "TRITON_MAX_TENSOR_NUMEL",
+    "abs",
+    "advance",
+    "arange",
+    "argmin",
+    "argmax",
+    "associative_scan",
+    "atomic_add",
+    "atomic_and",
+    "atomic_cas",
+    "atomic_max",
+    "atomic_min",
+    "atomic_or",
+    "atomic_xchg",
+    "atomic_xor",
+    "bfloat16",
+    "block_type",
+    "broadcast",
+    "broadcast_to",
+    "builtin",
+    "cat",
+    "cdiv",
+    "constexpr",
+    "cos",
+    "cumprod",
+    "cumsum",
+    "debug_barrier",
+    "device_assert",
+    "device_print",
+    "dot",
+    "dtype",
+    "exp",
+    "expand_dims",
+    "extra",
+    "fdiv",
+    "float16",
+    "float32",
+    "float64",
+    "float8e4b15",
+    "float8e4b15x4",
+    "float8e4nv",
+    "float8e5",
+    "full",
+    "function_type",
+    "inline_asm_elementwise",
+    "int1",
+    "int16",
+    "int32",
+    "int64",
+    "int8",
+    "ir",
+    "math",
+    "load",
+    "log",
+    "make_block_ptr",
+    "max",
+    "max_constancy",
+    "max_contiguous",
+    "maximum",
+    "min",
+    "minimum",
+    "multiple_of",
+    "num_programs",
+    "pair_uniform_to_normal",
+    "philox",
+    "philox_impl",
+    "pi32_t",
+    "pointer_type",
+    "program_id",
+    "rand",
+    "rand4x",
+    "randint",
+    "randint4x",
+    "randn",
+    "randn4x",
+    "ravel",
+    "reduce",
+    "reshape",
+    "sigmoid",
+    "sin",
+    "softmax",
+    "sort",
+    "sqrt",
+    "static_range",
+    "static_assert",
+    "static_print",
+    "store",
+    "sum",
+    "swizzle2d",
+    "tensor",
+    "trans",
+    "triton",
+    "uint16",
+    "uint32",
+    "uint_to_uniform_float",
+    "uint64",
+    "uint8",
+    "umulhi",
+    "view",
+    "void",
+    "where",
+    "xor_sum",
+    "zeros",
+    "zeros_like",
+]
diff --git a/shared/package-files/32/32a98c89caffc479001a81be4fb349e1875464824b3b8645a7a0261090e5ca1c/setup.py b/shared/package-files/32/32a98c89caffc479001a81be4fb349e1875464824b3b8645a7a0261090e5ca1c/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..018d38cdd5003103adec60cbfd844f49ca18c932
--- /dev/null
+++ b/shared/package-files/32/32a98c89caffc479001a81be4fb349e1875464824b3b8645a7a0261090e5ca1c/setup.py
@@ -0,0 +1,12 @@
+#!/usr/bin/env python3
+def configuration(parent_package='',top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    config = Configuration('ma', parent_package, top_path)
+    config.add_subpackage('tests')
+    config.add_data_files('*.pyi')
+    return config
+
+if __name__ == "__main__":
+    from numpy.distutils.core import setup
+    config = configuration(top_path='').todict()
+    setup(**config)
diff --git a/shared/package-files/32/32acc1bc543116cbe2cff10cb867772df2f254ff2634c870aef0b46c4b696fdb/cli-32.exe b/shared/package-files/32/32acc1bc543116cbe2cff10cb867772df2f254ff2634c870aef0b46c4b696fdb/cli-32.exe
new file mode 100644
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Binary files /dev/null and b/shared/package-files/32/32acc1bc543116cbe2cff10cb867772df2f254ff2634c870aef0b46c4b696fdb/cli-32.exe differ
diff --git a/shared/package-files/32/32acc1bc543116cbe2cff10cb867772df2f254ff2634c870aef0b46c4b696fdb/cli.exe b/shared/package-files/32/32acc1bc543116cbe2cff10cb867772df2f254ff2634c870aef0b46c4b696fdb/cli.exe
new file mode 100644
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diff --git a/shared/package-files/32/32b37b19593bff3156c0349f221b43dc58281609851a54e98ce9e723374fd35d/test_holonomic.py b/shared/package-files/32/32b37b19593bff3156c0349f221b43dc58281609851a54e98ce9e723374fd35d/test_holonomic.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5069c89a84ab89c46c95a026c8998089b73fac5
--- /dev/null
+++ b/shared/package-files/32/32b37b19593bff3156c0349f221b43dc58281609851a54e98ce9e723374fd35d/test_holonomic.py
@@ -0,0 +1,830 @@
+from sympy.holonomic import (DifferentialOperator, HolonomicFunction,
+                             DifferentialOperators, from_hyper,
+                             from_meijerg, expr_to_holonomic)
+from sympy.holonomic.recurrence import RecurrenceOperators, HolonomicSequence
+from sympy.core import EulerGamma
+from sympy.core.numbers import (I, Rational, pi)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.hyperbolic import (asinh, cosh)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.functions.special.bessel import besselj
+from sympy.functions.special.beta_functions import beta
+from sympy.functions.special.error_functions import (Ci, Si, erf, erfc)
+from sympy.functions.special.gamma_functions import gamma
+from sympy.functions.special.hyper import (hyper, meijerg)
+from sympy.printing.str import sstr
+from sympy.series.order import O
+from sympy.simplify.hyperexpand import hyperexpand
+from sympy.polys.domains.integerring import ZZ
+from sympy.polys.domains.rationalfield import QQ
+from sympy.polys.domains.realfield import RR
+
+
+def test_DifferentialOperator():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    assert Dx == R.derivative_operator
+    assert Dx == DifferentialOperator([R.base.zero, R.base.one], R)
+    assert x * Dx + x**2 * Dx**2 == DifferentialOperator([0, x, x**2], R)
+    assert (x**2 + 1) + Dx + x * \
+        Dx**5 == DifferentialOperator([x**2 + 1, 1, 0, 0, 0, x], R)
+    assert (x * Dx + x**2 + 1 - Dx * (x**3 + x))**3 == (-48 * x**6) + \
+        (-57 * x**7) * Dx + (-15 * x**8) * Dx**2 + (-x**9) * Dx**3
+    p = (x * Dx**2 + (x**2 + 3) * Dx**5) * (Dx + x**2)
+    q = (2 * x) + (4 * x**2) * Dx + (x**3) * Dx**2 + \
+        (20 * x**2 + x + 60) * Dx**3 + (10 * x**3 + 30 * x) * Dx**4 + \
+        (x**4 + 3 * x**2) * Dx**5 + (x**2 + 3) * Dx**6
+    assert p == q
+
+
+def test_HolonomicFunction_addition():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx**2 * x, x)
+    q = HolonomicFunction((2) * Dx + (x) * Dx**2, x)
+    assert p == q
+    p = HolonomicFunction(x * Dx + 1, x)
+    q = HolonomicFunction(Dx + 1, x)
+    r = HolonomicFunction((x - 2) + (x**2 - 2) * Dx + (x**2 - x) * Dx**2, x)
+    assert p + q == r
+    p = HolonomicFunction(x * Dx + Dx**2 * (x**2 + 2), x)
+    q = HolonomicFunction(Dx - 3, x)
+    r = HolonomicFunction((-54 * x**2 - 126 * x - 150) + (-135 * x**3 - 252 * x**2 - 270 * x + 140) * Dx +\
+                 (-27 * x**4 - 24 * x**2 + 14 * x - 150) * Dx**2 + \
+                 (9 * x**4 + 15 * x**3 + 38 * x**2 + 30 * x +40) * Dx**3, x)
+    assert p + q == r
+    p = HolonomicFunction(Dx**5 - 1, x)
+    q = HolonomicFunction(x**3 + Dx, x)
+    r = HolonomicFunction((-x**18 + 45*x**14 - 525*x**10 + 1575*x**6 - x**3 - 630*x**2) + \
+        (-x**15 + 30*x**11 - 195*x**7 + 210*x**3 - 1)*Dx + (x**18 - 45*x**14 + 525*x**10 - \
+        1575*x**6 + x**3 + 630*x**2)*Dx**5 + (x**15 - 30*x**11 + 195*x**7 - 210*x**3 + \
+        1)*Dx**6, x)
+    assert p+q == r
+
+    p = x**2 + 3*x + 8
+    q = x**3 - 7*x + 5
+    p = p*Dx - p.diff()
+    q = q*Dx - q.diff()
+    r = HolonomicFunction(p, x) + HolonomicFunction(q, x)
+    s = HolonomicFunction((6*x**2 + 18*x + 14) + (-4*x**3 - 18*x**2 - 62*x + 10)*Dx +\
+        (x**4 + 6*x**3 + 31*x**2 - 10*x - 71)*Dx**2, x)
+    assert r == s
+
+
+def test_HolonomicFunction_multiplication():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx+x+x*Dx**2, x)
+    q = HolonomicFunction(x*Dx+Dx*x+Dx**2, x)
+    r = HolonomicFunction((8*x**6 + 4*x**4 + 6*x**2 + 3) + (24*x**5 - 4*x**3 + 24*x)*Dx + \
+        (8*x**6 + 20*x**4 + 12*x**2 + 2)*Dx**2 + (8*x**5 + 4*x**3 + 4*x)*Dx**3 + \
+        (2*x**4 + x**2)*Dx**4, x)
+    assert p*q == r
+    p = HolonomicFunction(Dx**2+1, x)
+    q = HolonomicFunction(Dx-1, x)
+    r = HolonomicFunction((2) + (-2)*Dx + (1)*Dx**2, x)
+    assert p*q == r
+    p = HolonomicFunction(Dx**2+1+x+Dx, x)
+    q = HolonomicFunction((Dx*x-1)**2, x)
+    r = HolonomicFunction((4*x**7 + 11*x**6 + 16*x**5 + 4*x**4 - 6*x**3 - 7*x**2 - 8*x - 2) + \
+        (8*x**6 + 26*x**5 + 24*x**4 - 3*x**3 - 11*x**2 - 6*x - 2)*Dx + \
+        (8*x**6 + 18*x**5 + 15*x**4 - 3*x**3 - 6*x**2 - 6*x - 2)*Dx**2 + (8*x**5 + \
+            10*x**4 + 6*x**3 - 2*x**2 - 4*x)*Dx**3 + (4*x**5 + 3*x**4 - x**2)*Dx**4, x)
+    assert p*q == r
+    p = HolonomicFunction(x*Dx**2-1, x)
+    q = HolonomicFunction(Dx*x-x, x)
+    r = HolonomicFunction((x - 3) + (-2*x + 2)*Dx + (x)*Dx**2, x)
+    assert p*q == r
+
+
+def test_addition_initial_condition():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx-1, x, 0, [3])
+    q = HolonomicFunction(Dx**2+1, x, 0, [1, 0])
+    r = HolonomicFunction(-1 + Dx - Dx**2 + Dx**3, x, 0, [4, 3, 2])
+    assert p + q == r
+    p = HolonomicFunction(Dx - x + Dx**2, x, 0, [1, 2])
+    q = HolonomicFunction(Dx**2 + x, x, 0, [1, 0])
+    r = HolonomicFunction((-x**4 - x**3/4 - x**2 + Rational(1, 4)) + (x**3 + x**2/4 + x*Rational(3, 4) + 1)*Dx + \
+        (x*Rational(-3, 2) + Rational(7, 4))*Dx**2 + (x**2 - x*Rational(7, 4) + Rational(1, 4))*Dx**3 + (x**2 + x/4 + S.Half)*Dx**4, x, 0, [2, 2, -2, 2])
+    assert p + q == r
+    p = HolonomicFunction(Dx**2 + 4*x*Dx + x**2, x, 0, [3, 4])
+    q = HolonomicFunction(Dx**2 + 1, x, 0, [1, 1])
+    r = HolonomicFunction((x**6 + 2*x**4 - 5*x**2 - 6) + (4*x**5 + 36*x**3 - 32*x)*Dx + \
+         (x**6 + 3*x**4 + 5*x**2 - 9)*Dx**2 + (4*x**5 + 36*x**3 - 32*x)*Dx**3 + (x**4 + \
+            10*x**2 - 3)*Dx**4, x, 0, [4, 5, -1, -17])
+    assert p + q == r
+    q = HolonomicFunction(Dx**3 + x, x, 2, [3, 0, 1])
+    p = HolonomicFunction(Dx - 1, x, 2, [1])
+    r = HolonomicFunction((-x**2 - x + 1) + (x**2 + x)*Dx + (-x - 2)*Dx**3 + \
+        (x + 1)*Dx**4, x, 2, [4, 1, 2, -5 ])
+    assert p + q == r
+    p = expr_to_holonomic(sin(x))
+    q = expr_to_holonomic(1/x, x0=1)
+    r = HolonomicFunction((x**2 + 6) + (x**3 + 2*x)*Dx + (x**2 + 6)*Dx**2 + (x**3 + 2*x)*Dx**3, \
+        x, 1, [sin(1) + 1, -1 + cos(1), -sin(1) + 2])
+    assert p + q == r
+    C_1 = symbols('C_1')
+    p = expr_to_holonomic(sqrt(x))
+    q = expr_to_holonomic(sqrt(x**2-x))
+    r = (p + q).to_expr().subs(C_1, -I/2).expand()
+    assert r == I*sqrt(x)*sqrt(-x + 1) + sqrt(x)
+
+
+def test_multiplication_initial_condition():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx**2 + x*Dx - 1, x, 0, [3, 1])
+    q = HolonomicFunction(Dx**2 + 1, x, 0, [1, 1])
+    r = HolonomicFunction((x**4 + 14*x**2 + 60) + 4*x*Dx + (x**4 + 9*x**2 + 20)*Dx**2 + \
+        (2*x**3 + 18*x)*Dx**3 + (x**2 + 10)*Dx**4, x, 0, [3, 4, 2, 3])
+    assert p * q == r
+    p = HolonomicFunction(Dx**2 + x, x, 0, [1, 0])
+    q = HolonomicFunction(Dx**3 - x**2, x, 0, [3, 3, 3])
+    r = HolonomicFunction((x**8 - 37*x**7/27 - 10*x**6/27 - 164*x**5/9 - 184*x**4/9 + \
+        160*x**3/27 + 404*x**2/9 + 8*x + Rational(40, 3)) + (6*x**7 - 128*x**6/9 - 98*x**5/9 - 28*x**4/9 + \
+        8*x**3/9 + 28*x**2 + x*Rational(40, 9) - 40)*Dx + (3*x**6 - 82*x**5/9 + 76*x**4/9 + 4*x**3/3 + \
+        220*x**2/9 - x*Rational(80, 3))*Dx**2 + (-2*x**6 + 128*x**5/27 - 2*x**4/3 -80*x**2/9 + Rational(200, 9))*Dx**3 + \
+        (3*x**5 - 64*x**4/9 - 28*x**3/9 + 6*x**2 - x*Rational(20, 9) - Rational(20, 3))*Dx**4 + (-4*x**3 + 64*x**2/9 + \
+            x*Rational(8, 3))*Dx**5 + (x**4 - 64*x**3/27 - 4*x**2/3 + Rational(20, 9))*Dx**6, x, 0, [3, 3, 3, -3, -12, -24])
+    assert p * q == r
+    p = HolonomicFunction(Dx - 1, x, 0, [2])
+    q = HolonomicFunction(Dx**2 + 1, x, 0, [0, 1])
+    r = HolonomicFunction(2 -2*Dx + Dx**2, x, 0, [0, 2])
+    assert p * q == r
+    q = HolonomicFunction(x*Dx**2 + 1 + 2*Dx, x, 0,[0, 1])
+    r = HolonomicFunction((x - 1) + (-2*x + 2)*Dx + x*Dx**2, x, 0, [0, 2])
+    assert p * q == r
+    p = HolonomicFunction(Dx**2 - 1, x, 0, [1, 3])
+    q = HolonomicFunction(Dx**3 + 1, x, 0, [1, 2, 1])
+    r = HolonomicFunction(6*Dx + 3*Dx**2 + 2*Dx**3 - 3*Dx**4 + Dx**6, x, 0, [1, 5, 14, 17, 17, 2])
+    assert p * q == r
+    p = expr_to_holonomic(sin(x))
+    q = expr_to_holonomic(1/x, x0=1)
+    r = HolonomicFunction(x + 2*Dx + x*Dx**2, x, 1, [sin(1), -sin(1) + cos(1)])
+    assert p * q == r
+    p = expr_to_holonomic(sqrt(x))
+    q = expr_to_holonomic(sqrt(x**2-x))
+    r = (p * q).to_expr()
+    assert r == I*x*sqrt(-x + 1)
+
+
+def test_HolonomicFunction_composition():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx-1, x).composition(x**2+x)
+    r = HolonomicFunction((-2*x - 1) + Dx, x)
+    assert p == r
+    p = HolonomicFunction(Dx**2+1, x).composition(x**5+x**2+1)
+    r = HolonomicFunction((125*x**12 + 150*x**9 + 60*x**6 + 8*x**3) + (-20*x**3 - 2)*Dx + \
+        (5*x**4 + 2*x)*Dx**2, x)
+    assert p == r
+    p = HolonomicFunction(Dx**2*x+x, x).composition(2*x**3+x**2+1)
+    r = HolonomicFunction((216*x**9 + 324*x**8 + 180*x**7 + 152*x**6 + 112*x**5 + \
+        36*x**4 + 4*x**3) + (24*x**4 + 16*x**3 + 3*x**2 - 6*x - 1)*Dx + (6*x**5 + 5*x**4 + \
+        x**3 + 3*x**2 + x)*Dx**2, x)
+    assert p == r
+    p = HolonomicFunction(Dx**2+1, x).composition(1-x**2)
+    r = HolonomicFunction((4*x**3) - Dx + x*Dx**2, x)
+    assert p == r
+    p = HolonomicFunction(Dx**2+1, x).composition(x - 2/(x**2 + 1))
+    r = HolonomicFunction((x**12 + 6*x**10 + 12*x**9 + 15*x**8 + 48*x**7 + 68*x**6 + \
+        72*x**5 + 111*x**4 + 112*x**3 + 54*x**2 + 12*x + 1) + (12*x**8 + 32*x**6 + \
+        24*x**4 - 4)*Dx + (x**12 + 6*x**10 + 4*x**9 + 15*x**8 + 16*x**7 + 20*x**6 + 24*x**5+ \
+        15*x**4 + 16*x**3 + 6*x**2 + 4*x + 1)*Dx**2, x)
+    assert p == r
+
+
+def test_from_hyper():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = hyper([1, 1], [Rational(3, 2)], x**2/4)
+    q = HolonomicFunction((4*x) + (5*x**2 - 8)*Dx + (x**3 - 4*x)*Dx**2, x, 1, [2*sqrt(3)*pi/9, -4*sqrt(3)*pi/27 + Rational(4, 3)])
+    r = from_hyper(p)
+    assert r == q
+    p = from_hyper(hyper([1], [Rational(3, 2)], x**2/4))
+    q = HolonomicFunction(-x + (-x**2/2 + 2)*Dx + x*Dx**2, x)
+    # x0 = 1
+    y0 = '[sqrt(pi)*exp(1/4)*erf(1/2), -sqrt(pi)*exp(1/4)*erf(1/2)/2 + 1]'
+    assert sstr(p.y0) == y0
+    assert q.annihilator == p.annihilator
+
+
+def test_from_meijerg():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = from_meijerg(meijerg(([], [Rational(3, 2)]), ([S.Half], [S.Half, 1]), x))
+    q = HolonomicFunction(x/2 - Rational(1, 4) + (-x**2 + x/4)*Dx + x**2*Dx**2 + x**3*Dx**3, x, 1, \
+        [1/sqrt(pi), 1/(2*sqrt(pi)), -1/(4*sqrt(pi))])
+    assert p == q
+    p = from_meijerg(meijerg(([], []), ([0], []), x))
+    q = HolonomicFunction(1 + Dx, x, 0, [1])
+    assert p == q
+    p = from_meijerg(meijerg(([1], []), ([S.Half], [0]), x))
+    q = HolonomicFunction((x + S.Half)*Dx + x*Dx**2, x, 1, [sqrt(pi)*erf(1), exp(-1)])
+    assert p == q
+    p = from_meijerg(meijerg(([0], [1]), ([0], []), 2*x**2))
+    q = HolonomicFunction((3*x**2 - 1)*Dx + x**3*Dx**2, x, 1, [-exp(Rational(-1, 2)) + 1, -exp(Rational(-1, 2))])
+    assert p == q
+
+
+def test_to_Sequence():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    n = symbols('n', integer=True)
+    _, Sn = RecurrenceOperators(ZZ.old_poly_ring(n), 'Sn')
+    p = HolonomicFunction(x**2*Dx**4 + x + Dx, x).to_sequence()
+    q = [(HolonomicSequence(1 + (n + 2)*Sn**2 + (n**4 + 6*n**3 + 11*n**2 + 6*n)*Sn**3), 0, 1)]
+    assert p == q
+    p = HolonomicFunction(x**2*Dx**4 + x**3 + Dx**2, x).to_sequence()
+    q = [(HolonomicSequence(1 + (n**4 + 14*n**3 + 72*n**2 + 163*n + 140)*Sn**5), 0, 0)]
+    assert p == q
+    p = HolonomicFunction(x**3*Dx**4 + 1 + Dx**2, x).to_sequence()
+    q = [(HolonomicSequence(1 + (n**4 - 2*n**3 - n**2 + 2*n)*Sn + (n**2 + 3*n + 2)*Sn**2), 0, 0)]
+    assert p == q
+    p = HolonomicFunction(3*x**3*Dx**4 + 2*x*Dx + x*Dx**3, x).to_sequence()
+    q = [(HolonomicSequence(2*n + (3*n**4 - 6*n**3 - 3*n**2 + 6*n)*Sn + (n**3 + 3*n**2 + 2*n)*Sn**2), 0, 1)]
+    assert p == q
+
+
+def test_to_Sequence_Initial_Coniditons():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    n = symbols('n', integer=True)
+    _, Sn = RecurrenceOperators(QQ.old_poly_ring(n), 'Sn')
+    p = HolonomicFunction(Dx - 1, x, 0, [1]).to_sequence()
+    q = [(HolonomicSequence(-1 + (n + 1)*Sn, 1), 0)]
+    assert p == q
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [0, 1]).to_sequence()
+    q = [(HolonomicSequence(1 + (n**2 + 3*n + 2)*Sn**2, [0, 1]), 0)]
+    assert p == q
+    p = HolonomicFunction(Dx**2 + 1 + x**3*Dx, x, 0, [2, 3]).to_sequence()
+    q = [(HolonomicSequence(n + Sn**2 + (n**2 + 7*n + 12)*Sn**4, [2, 3, -1, Rational(-1, 2), Rational(1, 12)]), 1)]
+    assert p == q
+    p = HolonomicFunction(x**3*Dx**5 + 1 + Dx, x).to_sequence()
+    q = [(HolonomicSequence(1 + (n + 1)*Sn + (n**5 - 5*n**3 + 4*n)*Sn**2), 0, 3)]
+    assert p == q
+    C_0, C_1, C_2, C_3 = symbols('C_0, C_1, C_2, C_3')
+    p = expr_to_holonomic(log(1+x**2))
+    q = [(HolonomicSequence(n**2 + (n**2 + 2*n)*Sn**2, [0, 0, C_2]), 0, 1)]
+    assert p.to_sequence() == q
+    p = p.diff()
+    q = [(HolonomicSequence((n + 2) + (n + 2)*Sn**2, [C_0, 0]), 1, 0)]
+    assert p.to_sequence() == q
+    p = expr_to_holonomic(erf(x) + x).to_sequence()
+    q = [(HolonomicSequence((2*n**2 - 2*n) + (n**3 + 2*n**2 - n - 2)*Sn**2, [0, 1 + 2/sqrt(pi), 0, C_3]), 0, 2)]
+    assert p == q
+
+def test_series():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx**2 + 2*x*Dx, x, 0, [0, 1]).series(n=10)
+    q = x - x**3/3 + x**5/10 - x**7/42 + x**9/216 + O(x**10)
+    assert p == q
+    p = HolonomicFunction(Dx - 1, x).composition(x**2, 0, [1])  # e^(x**2)
+    q = HolonomicFunction(Dx**2 + 1, x, 0, [1, 0])  # cos(x)
+    r = (p * q).series(n=10)  # expansion of cos(x) * exp(x**2)
+    s = 1 + x**2/2 + x**4/24 - 31*x**6/720 - 179*x**8/8064 + O(x**10)
+    assert r == s
+    t = HolonomicFunction((1 + x)*Dx**2 + Dx, x, 0, [0, 1])  # log(1 + x)
+    r = (p * t + q).series(n=10)
+    s = 1 + x - x**2 + 4*x**3/3 - 17*x**4/24 + 31*x**5/30 - 481*x**6/720 +\
+     71*x**7/105 - 20159*x**8/40320 + 379*x**9/840 + O(x**10)
+    assert r == s
+    p = HolonomicFunction((6+6*x-3*x**2) - (10*x-3*x**2-3*x**3)*Dx + \
+        (4-6*x**3+2*x**4)*Dx**2, x, 0, [0, 1]).series(n=7)
+    q = x + x**3/6 - 3*x**4/16 + x**5/20 - 23*x**6/960 + O(x**7)
+    assert p == q
+    p = HolonomicFunction((6+6*x-3*x**2) - (10*x-3*x**2-3*x**3)*Dx + \
+        (4-6*x**3+2*x**4)*Dx**2, x, 0, [1, 0]).series(n=7)
+    q = 1 - 3*x**2/4 - x**3/4 - 5*x**4/32 - 3*x**5/40 - 17*x**6/384 + O(x**7)
+    assert p == q
+    p = expr_to_holonomic(erf(x) + x).series(n=10)
+    C_3 = symbols('C_3')
+    q = (erf(x) + x).series(n=10)
+    assert p.subs(C_3, -2/(3*sqrt(pi))) == q
+    assert expr_to_holonomic(sqrt(x**3 + x)).series(n=10) == sqrt(x**3 + x).series(n=10)
+    assert expr_to_holonomic((2*x - 3*x**2)**Rational(1, 3)).series() == ((2*x - 3*x**2)**Rational(1, 3)).series()
+    assert  expr_to_holonomic(sqrt(x**2-x)).series() == (sqrt(x**2-x)).series()
+    assert expr_to_holonomic(cos(x)**2/x**2, y0={-2: [1, 0, -1]}).series(n=10) == (cos(x)**2/x**2).series(n=10)
+    assert expr_to_holonomic(cos(x)**2/x**2, x0=1).series(n=10).together() == (cos(x)**2/x**2).series(n=10, x0=1).together()
+    assert expr_to_holonomic(cos(x-1)**2/(x-1)**2, x0=1, y0={-2: [1, 0, -1]}).series(n=10) \
+        == (cos(x-1)**2/(x-1)**2).series(x0=1, n=10)
+
+def test_evalf_euler():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+
+    # log(1+x)
+    p = HolonomicFunction((1 + x)*Dx**2 + Dx, x, 0, [0, 1])
+
+    # path taken is a straight line from 0 to 1, on the real axis
+    r = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
+    s = '0.699525841805253'  # approx. equal to log(2) i.e. 0.693147180559945
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # path taken is a triangle 0-->1+i-->2
+    r = [0.1 + 0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1+0.1*I)
+    for i in range(10):
+        r.append(r[-1]+0.1-0.1*I)
+
+    # close to the exact solution 1.09861228866811
+    # imaginary part also close to zero
+    s = '1.07530466271334 - 0.0251200594793912*I'
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # sin(x)
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [0, 1])
+    s = '0.905546532085401 - 6.93889390390723e-18*I'
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # computing sin(pi/2) using this method
+    # using a linear path from 0 to pi/2
+    r = [0.1]
+    for i in range(14):
+        r.append(r[-1] + 0.1)
+    r.append(pi/2)
+    s = '1.08016557252834' # close to 1.0 (exact solution)
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # trying different path, a rectangle (0-->i-->pi/2 + i-->pi/2)
+    # computing the same value sin(pi/2) using different path
+    r = [0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1*I)
+    for i in range(15):
+        r.append(r[-1]+0.1)
+    r.append(pi/2+I)
+    for i in range(10):
+        r.append(r[-1]-0.1*I)
+
+    # close to 1.0
+    s = '0.976882381836257 - 1.65557671738537e-16*I'
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # cos(x)
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [1, 0])
+    # compute cos(pi) along 0-->pi
+    r = [0.05]
+    for i in range(61):
+        r.append(r[-1]+0.05)
+    r.append(pi)
+    # close to -1 (exact answer)
+    s = '-1.08140824719196'
+    assert sstr(p.evalf(r, method='Euler')[-1]) == s
+
+    # a rectangular path (0 -> i -> 2+i -> 2)
+    r = [0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1*I)
+    for i in range(20):
+        r.append(r[-1]+0.1)
+    for i in range(10):
+        r.append(r[-1]-0.1*I)
+
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [1,1]).evalf(r, method='Euler')
+    s = '0.501421652861245 - 3.88578058618805e-16*I'
+    assert sstr(p[-1]) == s
+
+def test_evalf_rk4():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+
+    # log(1+x)
+    p = HolonomicFunction((1 + x)*Dx**2 + Dx, x, 0, [0, 1])
+
+    # path taken is a straight line from 0 to 1, on the real axis
+    r = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
+    s = '0.693146363174626'  # approx. equal to log(2) i.e. 0.693147180559945
+    assert sstr(p.evalf(r)[-1]) == s
+
+    # path taken is a triangle 0-->1+i-->2
+    r = [0.1 + 0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1+0.1*I)
+    for i in range(10):
+        r.append(r[-1]+0.1-0.1*I)
+
+    # close to the exact solution 1.09861228866811
+    # imaginary part also close to zero
+    s = '1.098616 + 1.36083e-7*I'
+    assert sstr(p.evalf(r)[-1].n(7)) == s
+
+    # sin(x)
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [0, 1])
+    s = '0.90929463522785 + 1.52655665885959e-16*I'
+    assert sstr(p.evalf(r)[-1]) == s
+
+    # computing sin(pi/2) using this method
+    # using a linear path from 0 to pi/2
+    r = [0.1]
+    for i in range(14):
+        r.append(r[-1] + 0.1)
+    r.append(pi/2)
+    s = '0.999999895088917' # close to 1.0 (exact solution)
+    assert sstr(p.evalf(r)[-1]) == s
+
+    # trying different path, a rectangle (0-->i-->pi/2 + i-->pi/2)
+    # computing the same value sin(pi/2) using different path
+    r = [0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1*I)
+    for i in range(15):
+        r.append(r[-1]+0.1)
+    r.append(pi/2+I)
+    for i in range(10):
+        r.append(r[-1]-0.1*I)
+
+    # close to 1.0
+    s = '1.00000003415141 + 6.11940487991086e-16*I'
+    assert sstr(p.evalf(r)[-1]) == s
+
+    # cos(x)
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [1, 0])
+    # compute cos(pi) along 0-->pi
+    r = [0.05]
+    for i in range(61):
+        r.append(r[-1]+0.05)
+    r.append(pi)
+    # close to -1 (exact answer)
+    s = '-0.999999993238714'
+    assert sstr(p.evalf(r)[-1]) == s
+
+    # a rectangular path (0 -> i -> 2+i -> 2)
+    r = [0.1*I]
+    for i in range(9):
+        r.append(r[-1]+0.1*I)
+    for i in range(20):
+        r.append(r[-1]+0.1)
+    for i in range(10):
+        r.append(r[-1]-0.1*I)
+
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [1,1]).evalf(r)
+    s = '0.493152791638442 - 1.41553435639707e-15*I'
+    assert sstr(p[-1]) == s
+
+
+def test_expr_to_holonomic():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = expr_to_holonomic((sin(x)/x)**2)
+    q = HolonomicFunction(8*x + (4*x**2 + 6)*Dx + 6*x*Dx**2 + x**2*Dx**3, x, 0, \
+        [1, 0, Rational(-2, 3)])
+    assert p == q
+    p = expr_to_holonomic(1/(1+x**2)**2)
+    q = HolonomicFunction(4*x + (x**2 + 1)*Dx, x, 0, [1])
+    assert p == q
+    p = expr_to_holonomic(exp(x)*sin(x)+x*log(1+x))
+    q = HolonomicFunction((2*x**3 + 10*x**2 + 20*x + 18) + (-2*x**4 - 10*x**3 - 20*x**2 \
+        - 18*x)*Dx + (2*x**5 + 6*x**4 + 7*x**3 + 8*x**2 + 10*x - 4)*Dx**2 + \
+        (-2*x**5 - 5*x**4 - 2*x**3 + 2*x**2 - x + 4)*Dx**3 + (x**5 + 2*x**4 - x**3 - \
+        7*x**2/2 + x + Rational(5, 2))*Dx**4, x, 0, [0, 1, 4, -1])
+    assert p == q
+    p = expr_to_holonomic(x*exp(x)+cos(x)+1)
+    q = HolonomicFunction((-x - 3)*Dx + (x + 2)*Dx**2 + (-x - 3)*Dx**3 + (x + 2)*Dx**4, x, \
+        0, [2, 1, 1, 3])
+    assert p == q
+    assert (x*exp(x)+cos(x)+1).series(n=10) == p.series(n=10)
+    p = expr_to_holonomic(log(1 + x)**2 + 1)
+    q = HolonomicFunction(Dx + (3*x + 3)*Dx**2 + (x**2 + 2*x + 1)*Dx**3, x, 0, [1, 0, 2])
+    assert p == q
+    p = expr_to_holonomic(erf(x)**2 + x)
+    q = HolonomicFunction((8*x**4 - 2*x**2 + 2)*Dx**2 + (6*x**3 - x/2)*Dx**3 + \
+        (x**2+ Rational(1, 4))*Dx**4, x, 0, [0, 1, 8/pi, 0])
+    assert p == q
+    p = expr_to_holonomic(cosh(x)*x)
+    q = HolonomicFunction((-x**2 + 2) -2*x*Dx + x**2*Dx**2, x, 0, [0, 1])
+    assert p == q
+    p = expr_to_holonomic(besselj(2, x))
+    q = HolonomicFunction((x**2 - 4) + x*Dx + x**2*Dx**2, x, 0, [0, 0])
+    assert p == q
+    p = expr_to_holonomic(besselj(0, x) + exp(x))
+    q = HolonomicFunction((-x**2 - x/2 + S.Half) + (x**2 - x/2 - Rational(3, 2))*Dx + (-x**2 + x/2 + 1)*Dx**2 +\
+        (x**2 + x/2)*Dx**3, x, 0, [2, 1, S.Half])
+    assert p == q
+    p = expr_to_holonomic(sin(x)**2/x)
+    q = HolonomicFunction(4 + 4*x*Dx + 3*Dx**2 + x*Dx**3, x, 0, [0, 1, 0])
+    assert p == q
+    p = expr_to_holonomic(sin(x)**2/x, x0=2)
+    q = HolonomicFunction((4) + (4*x)*Dx + (3)*Dx**2 + (x)*Dx**3, x, 2, [sin(2)**2/2,
+        sin(2)*cos(2) - sin(2)**2/4, -3*sin(2)**2/4 + cos(2)**2 - sin(2)*cos(2)])
+    assert p == q
+    p = expr_to_holonomic(log(x)/2 - Ci(2*x)/2 + Ci(2)/2)
+    q = HolonomicFunction(4*Dx + 4*x*Dx**2 + 3*Dx**3 + x*Dx**4, x, 0, \
+        [-log(2)/2 - EulerGamma/2 + Ci(2)/2, 0, 1, 0])
+    assert p == q
+    p = p.to_expr()
+    q = log(x)/2 - Ci(2*x)/2 + Ci(2)/2
+    assert p == q
+    p = expr_to_holonomic(x**S.Half, x0=1)
+    q = HolonomicFunction(x*Dx - S.Half, x, 1, [1])
+    assert p == q
+    p = expr_to_holonomic(sqrt(1 + x**2))
+    q = HolonomicFunction((-x) + (x**2 + 1)*Dx, x, 0, [1])
+    assert p == q
+    assert (expr_to_holonomic(sqrt(x) + sqrt(2*x)).to_expr()-\
+        (sqrt(x) + sqrt(2*x))).simplify() == 0
+    assert expr_to_holonomic(3*x+2*sqrt(x)).to_expr() == 3*x+2*sqrt(x)
+    p = expr_to_holonomic((x**4+x**3+5*x**2+3*x+2)/x**2, lenics=3)
+    q = HolonomicFunction((-2*x**4 - x**3 + 3*x + 4) + (x**5 + x**4 + 5*x**3 + 3*x**2 + \
+        2*x)*Dx, x, 0, {-2: [2, 3, 5]})
+    assert p == q
+    p = expr_to_holonomic(1/(x-1)**2, lenics=3, x0=1)
+    q = HolonomicFunction((2) + (x - 1)*Dx, x, 1, {-2: [1, 0, 0]})
+    assert p == q
+    a = symbols("a")
+    p = expr_to_holonomic(sqrt(a*x), x=x)
+    assert p.to_expr() == sqrt(a)*sqrt(x)
+
+def test_to_hyper():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx - 2, x, 0, [3]).to_hyper()
+    q = 3 * hyper([], [], 2*x)
+    assert p == q
+    p = hyperexpand(HolonomicFunction((1 + x) * Dx - 3, x, 0, [2]).to_hyper()).expand()
+    q = 2*x**3 + 6*x**2 + 6*x + 2
+    assert p == q
+    p = HolonomicFunction((1 + x)*Dx**2 + Dx, x, 0, [0, 1]).to_hyper()
+    q = -x**2*hyper((2, 2, 1), (3, 2), -x)/2 + x
+    assert p == q
+    p = HolonomicFunction(2*x*Dx + Dx**2, x, 0, [0, 2/sqrt(pi)]).to_hyper()
+    q = 2*x*hyper((S.Half,), (Rational(3, 2),), -x**2)/sqrt(pi)
+    assert p == q
+    p = hyperexpand(HolonomicFunction(2*x*Dx + Dx**2, x, 0, [1, -2/sqrt(pi)]).to_hyper())
+    q = erfc(x)
+    assert p.rewrite(erfc) == q
+    p =  hyperexpand(HolonomicFunction((x**2 - 1) + x*Dx + x**2*Dx**2,
+        x, 0, [0, S.Half]).to_hyper())
+    q = besselj(1, x)
+    assert p == q
+    p = hyperexpand(HolonomicFunction(x*Dx**2 + Dx + x, x, 0, [1, 0]).to_hyper())
+    q = besselj(0, x)
+    assert p == q
+
+def test_to_expr():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(Dx - 1, x, 0, [1]).to_expr()
+    q = exp(x)
+    assert p == q
+    p = HolonomicFunction(Dx**2 + 1, x, 0, [1, 0]).to_expr()
+    q = cos(x)
+    assert p == q
+    p = HolonomicFunction(Dx**2 - 1, x, 0, [1, 0]).to_expr()
+    q = cosh(x)
+    assert p == q
+    p = HolonomicFunction(2 + (4*x - 1)*Dx + \
+        (x**2 - x)*Dx**2, x, 0, [1, 2]).to_expr().expand()
+    q = 1/(x**2 - 2*x + 1)
+    assert p == q
+    p = expr_to_holonomic(sin(x)**2/x).integrate((x, 0, x)).to_expr()
+    q = (sin(x)**2/x).integrate((x, 0, x))
+    assert p == q
+    C_0, C_1, C_2, C_3 = symbols('C_0, C_1, C_2, C_3')
+    p = expr_to_holonomic(log(1+x**2)).to_expr()
+    q = C_2*log(x**2 + 1)
+    assert p == q
+    p = expr_to_holonomic(log(1+x**2)).diff().to_expr()
+    q = C_0*x/(x**2 + 1)
+    assert p == q
+    p = expr_to_holonomic(erf(x) + x).to_expr()
+    q = 3*C_3*x - 3*sqrt(pi)*C_3*erf(x)/2 + x + 2*x/sqrt(pi)
+    assert p == q
+    p = expr_to_holonomic(sqrt(x), x0=1).to_expr()
+    assert p == sqrt(x)
+    assert expr_to_holonomic(sqrt(x)).to_expr() == sqrt(x)
+    p = expr_to_holonomic(sqrt(1 + x**2)).to_expr()
+    assert p == sqrt(1+x**2)
+    p = expr_to_holonomic((2*x**2 + 1)**Rational(2, 3)).to_expr()
+    assert p == (2*x**2 + 1)**Rational(2, 3)
+    p = expr_to_holonomic(sqrt(-x**2+2*x)).to_expr()
+    assert p == sqrt(x)*sqrt(-x + 2)
+    p = expr_to_holonomic((-2*x**3+7*x)**Rational(2, 3)).to_expr()
+    q = x**Rational(2, 3)*(-2*x**2 + 7)**Rational(2, 3)
+    assert p == q
+    p = from_hyper(hyper((-2, -3), (S.Half, ), x))
+    s = hyperexpand(hyper((-2, -3), (S.Half, ), x))
+    D_0 = Symbol('D_0')
+    C_0 = Symbol('C_0')
+    assert (p.to_expr().subs({C_0:1, D_0:0}) - s).simplify() == 0
+    p.y0 = {0: [1], S.Half: [0]}
+    assert p.to_expr() == s
+    assert expr_to_holonomic(x**5).to_expr() == x**5
+    assert expr_to_holonomic(2*x**3-3*x**2).to_expr().expand() == \
+        2*x**3-3*x**2
+    a = symbols("a")
+    p = (expr_to_holonomic(1.4*x)*expr_to_holonomic(a*x, x)).to_expr()
+    q = 1.4*a*x**2
+    assert p == q
+    p = (expr_to_holonomic(1.4*x)+expr_to_holonomic(a*x, x)).to_expr()
+    q = x*(a + 1.4)
+    assert p == q
+    p = (expr_to_holonomic(1.4*x)+expr_to_holonomic(x)).to_expr()
+    assert p == 2.4*x
+
+
+def test_integrate():
+    x = symbols('x')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = expr_to_holonomic(sin(x)**2/x, x0=1).integrate((x, 2, 3))
+    q = '0.166270406994788'
+    assert sstr(p) == q
+    p = expr_to_holonomic(sin(x)).integrate((x, 0, x)).to_expr()
+    q = 1 - cos(x)
+    assert p == q
+    p = expr_to_holonomic(sin(x)).integrate((x, 0, 3))
+    q = 1 - cos(3)
+    assert p == q
+    p = expr_to_holonomic(sin(x)/x, x0=1).integrate((x, 1, 2))
+    q = '0.659329913368450'
+    assert sstr(p) == q
+    p = expr_to_holonomic(sin(x)**2/x, x0=1).integrate((x, 1, 0))
+    q = '-0.423690480850035'
+    assert sstr(p) == q
+    p = expr_to_holonomic(sin(x)/x)
+    assert p.integrate(x).to_expr() == Si(x)
+    assert p.integrate((x, 0, 2)) == Si(2)
+    p = expr_to_holonomic(sin(x)**2/x)
+    q = p.to_expr()
+    assert p.integrate(x).to_expr() == q.integrate((x, 0, x))
+    assert p.integrate((x, 0, 1)) == q.integrate((x, 0, 1))
+    assert expr_to_holonomic(1/x, x0=1).integrate(x).to_expr() == log(x)
+    p = expr_to_holonomic((x + 1)**3*exp(-x), x0=-1).integrate(x).to_expr()
+    q = (-x**3 - 6*x**2 - 15*x + 6*exp(x + 1) - 16)*exp(-x)
+    assert p == q
+    p = expr_to_holonomic(cos(x)**2/x**2, y0={-2: [1, 0, -1]}).integrate(x).to_expr()
+    q = -Si(2*x) - cos(x)**2/x
+    assert p == q
+    p = expr_to_holonomic(sqrt(x**2+x)).integrate(x).to_expr()
+    q = (x**Rational(3, 2)*(2*x**2 + 3*x + 1) - x*sqrt(x + 1)*asinh(sqrt(x)))/(4*x*sqrt(x + 1))
+    assert p == q
+    p = expr_to_holonomic(sqrt(x**2+1)).integrate(x).to_expr()
+    q = (sqrt(x**2+1)).integrate(x)
+    assert (p-q).simplify() == 0
+    p = expr_to_holonomic(1/x**2, y0={-2:[1, 0, 0]})
+    r = expr_to_holonomic(1/x**2, lenics=3)
+    assert p == r
+    q = expr_to_holonomic(cos(x)**2)
+    assert (r*q).integrate(x).to_expr() == -Si(2*x) - cos(x)**2/x
+
+
+def test_diff():
+    x, y = symbols('x, y')
+    R, Dx = DifferentialOperators(ZZ.old_poly_ring(x), 'Dx')
+    p = HolonomicFunction(x*Dx**2 + 1, x, 0, [0, 1])
+    assert p.diff().to_expr() == p.to_expr().diff().simplify()
+    p = HolonomicFunction(Dx**2 - 1, x, 0, [1, 0])
+    assert p.diff(x, 2).to_expr() == p.to_expr()
+    p = expr_to_holonomic(Si(x))
+    assert p.diff().to_expr() == sin(x)/x
+    assert p.diff(y) == 0
+    C_0, C_1, C_2, C_3 = symbols('C_0, C_1, C_2, C_3')
+    q = Si(x)
+    assert p.diff(x).to_expr() == q.diff()
+    assert p.diff(x, 2).to_expr().subs(C_0, Rational(-1, 3)).cancel() == q.diff(x, 2).cancel()
+    assert p.diff(x, 3).series().subs({C_3: Rational(-1, 3), C_0: 0}) == q.diff(x, 3).series()
+
+
+def test_extended_domain_in_expr_to_holonomic():
+    x = symbols('x')
+    p = expr_to_holonomic(1.2*cos(3.1*x))
+    assert p.to_expr() == 1.2*cos(3.1*x)
+    assert sstr(p.integrate(x).to_expr()) == '0.387096774193548*sin(3.1*x)'
+    _, Dx = DifferentialOperators(RR.old_poly_ring(x), 'Dx')
+    p = expr_to_holonomic(1.1329138213*x)
+    q = HolonomicFunction((-1.1329138213) + (1.1329138213*x)*Dx, x, 0, {1: [1.1329138213]})
+    assert p == q
+    assert p.to_expr() == 1.1329138213*x
+    assert sstr(p.integrate((x, 1, 2))) == sstr((1.1329138213*x).integrate((x, 1, 2)))
+    y, z = symbols('y, z')
+    p = expr_to_holonomic(sin(x*y*z), x=x)
+    assert p.to_expr() == sin(x*y*z)
+    assert p.integrate(x).to_expr() == (-cos(x*y*z) + 1)/(y*z)
+    p = expr_to_holonomic(sin(x*y + z), x=x).integrate(x).to_expr()
+    q = (cos(z) - cos(x*y + z))/y
+    assert p == q
+    a = symbols('a')
+    p = expr_to_holonomic(a*x, x)
+    assert p.to_expr() == a*x
+    assert p.integrate(x).to_expr() == a*x**2/2
+    D_2, C_1 = symbols("D_2, C_1")
+    p = expr_to_holonomic(x) + expr_to_holonomic(1.2*cos(x))
+    p = p.to_expr().subs(D_2, 0)
+    assert p - x - 1.2*cos(1.0*x) == 0
+    p = expr_to_holonomic(x) * expr_to_holonomic(1.2*cos(x))
+    p = p.to_expr().subs(C_1, 0)
+    assert p - 1.2*x*cos(1.0*x) == 0
+
+
+def test_to_meijerg():
+    x = symbols('x')
+    assert hyperexpand(expr_to_holonomic(sin(x)).to_meijerg()) == sin(x)
+    assert hyperexpand(expr_to_holonomic(cos(x)).to_meijerg()) == cos(x)
+    assert hyperexpand(expr_to_holonomic(exp(x)).to_meijerg()) == exp(x)
+    assert hyperexpand(expr_to_holonomic(log(x)).to_meijerg()).simplify() == log(x)
+    assert expr_to_holonomic(4*x**2/3 + 7).to_meijerg() == 4*x**2/3 + 7
+    assert hyperexpand(expr_to_holonomic(besselj(2, x), lenics=3).to_meijerg()) == besselj(2, x)
+    p = hyper((Rational(-1, 2), -3), (), x)
+    assert from_hyper(p).to_meijerg() == hyperexpand(p)
+    p = hyper((S.One, S(3)), (S(2), ), x)
+    assert (hyperexpand(from_hyper(p).to_meijerg()) - hyperexpand(p)).expand() == 0
+    p = from_hyper(hyper((-2, -3), (S.Half, ), x))
+    s = hyperexpand(hyper((-2, -3), (S.Half, ), x))
+    C_0 = Symbol('C_0')
+    C_1 = Symbol('C_1')
+    D_0 = Symbol('D_0')
+    assert (hyperexpand(p.to_meijerg()).subs({C_0:1, D_0:0}) - s).simplify() == 0
+    p.y0 = {0: [1], S.Half: [0]}
+    assert (hyperexpand(p.to_meijerg()) - s).simplify() == 0
+    p = expr_to_holonomic(besselj(S.Half, x), initcond=False)
+    assert (p.to_expr() - (D_0*sin(x) + C_0*cos(x) + C_1*sin(x))/sqrt(x)).simplify() == 0
+    p = expr_to_holonomic(besselj(S.Half, x), y0={Rational(-1, 2): [sqrt(2)/sqrt(pi), sqrt(2)/sqrt(pi)]})
+    assert (p.to_expr() - besselj(S.Half, x) - besselj(Rational(-1, 2), x)).simplify() == 0
+
+
+def test_gaussian():
+    mu, x = symbols("mu x")
+    sd = symbols("sd", positive=True)
+    Q = QQ[mu, sd].get_field()
+    e = sqrt(2)*exp(-(-mu + x)**2/(2*sd**2))/(2*sqrt(pi)*sd)
+    h1 = expr_to_holonomic(e, x, domain=Q)
+
+    _, Dx = DifferentialOperators(Q.old_poly_ring(x), 'Dx')
+    h2 = HolonomicFunction((-mu/sd**2 + x/sd**2) + (1)*Dx, x)
+
+    assert h1 == h2
+
+
+def test_beta():
+    a, b, x = symbols("a b x", positive=True)
+    e = x**(a - 1)*(-x + 1)**(b - 1)/beta(a, b)
+    Q = QQ[a, b].get_field()
+    h1 = expr_to_holonomic(e, x, domain=Q)
+
+    _, Dx = DifferentialOperators(Q.old_poly_ring(x), 'Dx')
+    h2 = HolonomicFunction((a + x*(-a - b + 2) - 1) + (x**2 - x)*Dx, x)
+
+    assert h1 == h2
+
+
+def test_gamma():
+    a, b, x = symbols("a b x", positive=True)
+    e = b**(-a)*x**(a - 1)*exp(-x/b)/gamma(a)
+    Q = QQ[a, b].get_field()
+    h1 = expr_to_holonomic(e, x, domain=Q)
+
+    _, Dx = DifferentialOperators(Q.old_poly_ring(x), 'Dx')
+    h2 = HolonomicFunction((-a + 1 + x/b) + (x)*Dx, x)
+
+    assert h1 == h2
+
+
+def test_symbolic_power():
+    x, n = symbols("x n")
+    Q = QQ[n].get_field()
+    _, Dx = DifferentialOperators(Q.old_poly_ring(x), 'Dx')
+    h1 = HolonomicFunction((-1) + (x)*Dx, x) ** -n
+    h2 = HolonomicFunction((n) + (x)*Dx, x)
+
+    assert h1 == h2
+
+
+def test_negative_power():
+    x = symbols("x")
+    _, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    h1 = HolonomicFunction((-1) + (x)*Dx, x) ** -2
+    h2 = HolonomicFunction((2) + (x)*Dx, x)
+
+    assert h1 == h2
+
+
+def test_expr_in_power():
+    x, n = symbols("x n")
+    Q = QQ[n].get_field()
+    _, Dx = DifferentialOperators(Q.old_poly_ring(x), 'Dx')
+    h1 = HolonomicFunction((-1) + (x)*Dx, x) ** (n - 3)
+    h2 = HolonomicFunction((-n + 3) + (x)*Dx, x)
+
+    assert h1 == h2
+
+def test_DifferentialOperatorEqPoly():
+    x = symbols('x', integer=True)
+    R, Dx = DifferentialOperators(QQ.old_poly_ring(x), 'Dx')
+    do = DifferentialOperator([x**2, R.base.zero, R.base.zero], R)
+    do2 = DifferentialOperator([x**2, 1, x], R)
+    assert not do == do2
+
+    # polynomial comparison issue, see https://github.com/sympy/sympy/pull/15799
+    # should work once that is solved
+    # p = do.listofpoly[0]
+    # assert do == p
+
+    p2 = do2.listofpoly[0]
+    assert not do2 == p2
diff --git a/shared/package-files/32/32b5cd24c6aafcdd73a2835e99f3bdf8c4f53d04de0011b3cb04f786ae60b994/install.py b/shared/package-files/32/32b5cd24c6aafcdd73a2835e99f3bdf8c4f53d04de0011b3cb04f786ae60b994/install.py
new file mode 100644
index 0000000000000000000000000000000000000000..01d5331a63069b14241c6edad5682bc14f77ca48
--- /dev/null
+++ b/shared/package-files/32/32b5cd24c6aafcdd73a2835e99f3bdf8c4f53d04de0011b3cb04f786ae60b994/install.py
@@ -0,0 +1,679 @@
+"""distutils.command.install
+
+Implements the Distutils 'install' command."""
+
+import sys
+import sysconfig
+import os
+import re
+
+from distutils import log
+from distutils.core import Command
+from distutils.debug import DEBUG
+from distutils.sysconfig import get_config_vars
+from distutils.errors import DistutilsPlatformError
+from distutils.file_util import write_file
+from distutils.util import convert_path, subst_vars, change_root
+from distutils.util import get_platform
+from distutils.errors import DistutilsOptionError
+
+from site import USER_BASE
+from site import USER_SITE
+
+HAS_USER_SITE = (USER_SITE is not None)
+
+# The keys to an installation scheme; if any new types of files are to be
+# installed, be sure to add an entry to every scheme in
+# sysconfig._INSTALL_SCHEMES, and to SCHEME_KEYS here.
+SCHEME_KEYS = ('purelib', 'platlib', 'headers', 'scripts', 'data')
+
+# The following code provides backward-compatible INSTALL_SCHEMES
+# while making the sysconfig module the single point of truth.
+# This makes it easier for OS distributions where they need to
+# alter locations for packages installations in a single place.
+# Note that this module is deprecated (PEP 632); all consumers
+# of this information should switch to using sysconfig directly.
+INSTALL_SCHEMES = {"unix_prefix": {}, "unix_home": {}, "nt": {}}
+
+# Copy from sysconfig._INSTALL_SCHEMES
+for key in SCHEME_KEYS:
+    for distutils_scheme_name, sys_scheme_name in (
+            ("unix_prefix", "posix_prefix"), ("unix_home", "posix_home"),
+            ("nt", "nt")):
+        sys_key = key
+        sys_scheme = sysconfig._INSTALL_SCHEMES[sys_scheme_name]
+        if key == "headers" and key not in sys_scheme:
+            # On POSIX-y platforms, Python will:
+            # - Build from .h files in 'headers' (only there when
+            #   building CPython)
+            # - Install .h files to 'include'
+            # When 'headers' is missing, fall back to 'include'
+            sys_key = 'include'
+        INSTALL_SCHEMES[distutils_scheme_name][key] = sys_scheme[sys_key]
+
+# Transformation to different template format
+for main_key in INSTALL_SCHEMES:
+    for key, value in INSTALL_SCHEMES[main_key].items():
+        # Change all ocurences of {variable} to $variable
+        value = re.sub(r"\{(.+?)\}", r"$\g<1>", value)
+        value = value.replace("$installed_base", "$base")
+        value = value.replace("$py_version_nodot_plat", "$py_version_nodot")
+        if key == "headers":
+            value += "/$dist_name"
+        if sys.version_info >= (3, 9) and key == "platlib":
+            # platlibdir is available since 3.9: bpo-1294959
+            value = value.replace("/lib/", "/$platlibdir/")
+        INSTALL_SCHEMES[main_key][key] = value
+
+# The following part of INSTALL_SCHEMES has a different definition
+# than the one in sysconfig, but because both depend on the site module,
+# the outcomes should be the same.
+if HAS_USER_SITE:
+    INSTALL_SCHEMES['nt_user'] = {
+        'purelib': '$usersite',
+        'platlib': '$usersite',
+        'headers': '$userbase/Python$py_version_nodot/Include/$dist_name',
+        'scripts': '$userbase/Python$py_version_nodot/Scripts',
+        'data'   : '$userbase',
+        }
+
+    INSTALL_SCHEMES['unix_user'] = {
+        'purelib': '$usersite',
+        'platlib': '$usersite',
+        'headers':
+            '$userbase/include/python$py_version_short$abiflags/$dist_name',
+        'scripts': '$userbase/bin',
+        'data'   : '$userbase',
+        }
+
+
+class install(Command):
+
+    description = "install everything from build directory"
+
+    user_options = [
+        # Select installation scheme and set base director(y|ies)
+        ('prefix=', None,
+         "installation prefix"),
+        ('exec-prefix=', None,
+         "(Unix only) prefix for platform-specific files"),
+        ('home=', None,
+         "(Unix only) home directory to install under"),
+
+        # Or, just set the base director(y|ies)
+        ('install-base=', None,
+         "base installation directory (instead of --prefix or --home)"),
+        ('install-platbase=', None,
+         "base installation directory for platform-specific files " +
+         "(instead of --exec-prefix or --home)"),
+        ('root=', None,
+         "install everything relative to this alternate root directory"),
+
+        # Or, explicitly set the installation scheme
+        ('install-purelib=', None,
+         "installation directory for pure Python module distributions"),
+        ('install-platlib=', None,
+         "installation directory for non-pure module distributions"),
+        ('install-lib=', None,
+         "installation directory for all module distributions " +
+         "(overrides --install-purelib and --install-platlib)"),
+
+        ('install-headers=', None,
+         "installation directory for C/C++ headers"),
+        ('install-scripts=', None,
+         "installation directory for Python scripts"),
+        ('install-data=', None,
+         "installation directory for data files"),
+
+        # Byte-compilation options -- see install_lib.py for details, as
+        # these are duplicated from there (but only install_lib does
+        # anything with them).
+        ('compile', 'c', "compile .py to .pyc [default]"),
+        ('no-compile', None, "don't compile .py files"),
+        ('optimize=', 'O',
+         "also compile with optimization: -O1 for \"python -O\", "
+         "-O2 for \"python -OO\", and -O0 to disable [default: -O0]"),
+
+        # Miscellaneous control options
+        ('force', 'f',
+         "force installation (overwrite any existing files)"),
+        ('skip-build', None,
+         "skip rebuilding everything (for testing/debugging)"),
+
+        # Where to install documentation (eventually!)
+        #('doc-format=', None, "format of documentation to generate"),
+        #('install-man=', None, "directory for Unix man pages"),
+        #('install-html=', None, "directory for HTML documentation"),
+        #('install-info=', None, "directory for GNU info files"),
+
+        ('record=', None,
+         "filename in which to record list of installed files"),
+        ]
+
+    boolean_options = ['compile', 'force', 'skip-build']
+
+    if HAS_USER_SITE:
+        user_options.append(('user', None,
+                             "install in user site-package '%s'" % USER_SITE))
+        boolean_options.append('user')
+
+    negative_opt = {'no-compile' : 'compile'}
+
+
+    def initialize_options(self):
+        """Initializes options."""
+        # High-level options: these select both an installation base
+        # and scheme.
+        self.prefix = None
+        self.exec_prefix = None
+        self.home = None
+        self.user = 0
+
+        # These select only the installation base; it's up to the user to
+        # specify the installation scheme (currently, that means supplying
+        # the --install-{platlib,purelib,scripts,data} options).
+        self.install_base = None
+        self.install_platbase = None
+        self.root = None
+
+        # These options are the actual installation directories; if not
+        # supplied by the user, they are filled in using the installation
+        # scheme implied by prefix/exec-prefix/home and the contents of
+        # that installation scheme.
+        self.install_purelib = None     # for pure module distributions
+        self.install_platlib = None     # non-pure (dists w/ extensions)
+        self.install_headers = None     # for C/C++ headers
+        self.install_lib = None         # set to either purelib or platlib
+        self.install_scripts = None
+        self.install_data = None
+        if HAS_USER_SITE:
+            self.install_userbase = USER_BASE
+            self.install_usersite = USER_SITE
+
+        self.compile = None
+        self.optimize = None
+
+        # Deprecated
+        # These two are for putting non-packagized distributions into their
+        # own directory and creating a .pth file if it makes sense.
+        # 'extra_path' comes from the setup file; 'install_path_file' can
+        # be turned off if it makes no sense to install a .pth file.  (But
+        # better to install it uselessly than to guess wrong and not
+        # install it when it's necessary and would be used!)  Currently,
+        # 'install_path_file' is always true unless some outsider meddles
+        # with it.
+        self.extra_path = None
+        self.install_path_file = 1
+
+        # 'force' forces installation, even if target files are not
+        # out-of-date.  'skip_build' skips running the "build" command,
+        # handy if you know it's not necessary.  'warn_dir' (which is *not*
+        # a user option, it's just there so the bdist_* commands can turn
+        # it off) determines whether we warn about installing to a
+        # directory not in sys.path.
+        self.force = 0
+        self.skip_build = 0
+        self.warn_dir = 1
+
+        # These are only here as a conduit from the 'build' command to the
+        # 'install_*' commands that do the real work.  ('build_base' isn't
+        # actually used anywhere, but it might be useful in future.)  They
+        # are not user options, because if the user told the install
+        # command where the build directory is, that wouldn't affect the
+        # build command.
+        self.build_base = None
+        self.build_lib = None
+
+        # Not defined yet because we don't know anything about
+        # documentation yet.
+        #self.install_man = None
+        #self.install_html = None
+        #self.install_info = None
+
+        self.record = None
+
+
+    # -- Option finalizing methods -------------------------------------
+    # (This is rather more involved than for most commands,
+    # because this is where the policy for installing third-
+    # party Python modules on various platforms given a wide
+    # array of user input is decided.  Yes, it's quite complex!)
+
+    def finalize_options(self):
+        """Finalizes options."""
+        # This method (and its helpers, like 'finalize_unix()',
+        # 'finalize_other()', and 'select_scheme()') is where the default
+        # installation directories for modules, extension modules, and
+        # anything else we care to install from a Python module
+        # distribution.  Thus, this code makes a pretty important policy
+        # statement about how third-party stuff is added to a Python
+        # installation!  Note that the actual work of installation is done
+        # by the relatively simple 'install_*' commands; they just take
+        # their orders from the installation directory options determined
+        # here.
+
+        # Check for errors/inconsistencies in the options; first, stuff
+        # that's wrong on any platform.
+
+        if ((self.prefix or self.exec_prefix or self.home) and
+            (self.install_base or self.install_platbase)):
+            raise DistutilsOptionError(
+                   "must supply either prefix/exec-prefix/home or " +
+                   "install-base/install-platbase -- not both")
+
+        if self.home and (self.prefix or self.exec_prefix):
+            raise DistutilsOptionError(
+                  "must supply either home or prefix/exec-prefix -- not both")
+
+        if self.user and (self.prefix or self.exec_prefix or self.home or
+                self.install_base or self.install_platbase):
+            raise DistutilsOptionError("can't combine user with prefix, "
+                                       "exec_prefix/home, or install_(plat)base")
+
+        # Next, stuff that's wrong (or dubious) only on certain platforms.
+        if os.name != "posix":
+            if self.exec_prefix:
+                self.warn("exec-prefix option ignored on this platform")
+                self.exec_prefix = None
+
+        # Now the interesting logic -- so interesting that we farm it out
+        # to other methods.  The goal of these methods is to set the final
+        # values for the install_{lib,scripts,data,...}  options, using as
+        # input a heady brew of prefix, exec_prefix, home, install_base,
+        # install_platbase, user-supplied versions of
+        # install_{purelib,platlib,lib,scripts,data,...}, and the
+        # INSTALL_SCHEME dictionary above.  Phew!
+
+        self.dump_dirs("pre-finalize_{unix,other}")
+
+        if os.name == 'posix':
+            self.finalize_unix()
+        else:
+            self.finalize_other()
+
+        self.dump_dirs("post-finalize_{unix,other}()")
+
+        # Expand configuration variables, tilde, etc. in self.install_base
+        # and self.install_platbase -- that way, we can use $base or
+        # $platbase in the other installation directories and not worry
+        # about needing recursive variable expansion (shudder).
+
+        py_version = sys.version.split()[0]
+        (prefix, exec_prefix) = get_config_vars('prefix', 'exec_prefix')
+        try:
+            abiflags = sys.abiflags
+        except AttributeError:
+            # sys.abiflags may not be defined on all platforms.
+            abiflags = ''
+        self.config_vars = {'dist_name': self.distribution.get_name(),
+                            'dist_version': self.distribution.get_version(),
+                            'dist_fullname': self.distribution.get_fullname(),
+                            'py_version': py_version,
+                            'py_version_short': '%d.%d' % sys.version_info[:2],
+                            'py_version_nodot': '%d%d' % sys.version_info[:2],
+                            'sys_prefix': prefix,
+                            'prefix': prefix,
+                            'sys_exec_prefix': exec_prefix,
+                            'exec_prefix': exec_prefix,
+                            'abiflags': abiflags,
+                            'platlibdir': sys.platlibdir,
+                           }
+
+        if HAS_USER_SITE:
+            self.config_vars['userbase'] = self.install_userbase
+            self.config_vars['usersite'] = self.install_usersite
+
+        if sysconfig.is_python_build(True):
+            self.config_vars['srcdir'] = sysconfig.get_config_var('srcdir')
+
+        self.expand_basedirs()
+
+        self.dump_dirs("post-expand_basedirs()")
+
+        # Now define config vars for the base directories so we can expand
+        # everything else.
+        self.config_vars['base'] = self.install_base
+        self.config_vars['platbase'] = self.install_platbase
+
+        if DEBUG:
+            from pprint import pprint
+            print("config vars:")
+            pprint(self.config_vars)
+
+        # Expand "~" and configuration variables in the installation
+        # directories.
+        self.expand_dirs()
+
+        self.dump_dirs("post-expand_dirs()")
+
+        # Create directories in the home dir:
+        if self.user:
+            self.create_home_path()
+
+        # Pick the actual directory to install all modules to: either
+        # install_purelib or install_platlib, depending on whether this
+        # module distribution is pure or not.  Of course, if the user
+        # already specified install_lib, use their selection.
+        if self.install_lib is None:
+            if self.distribution.ext_modules: # has extensions: non-pure
+                self.install_lib = self.install_platlib
+            else:
+                self.install_lib = self.install_purelib
+
+
+        # Convert directories from Unix /-separated syntax to the local
+        # convention.
+        self.convert_paths('lib', 'purelib', 'platlib',
+                           'scripts', 'data', 'headers')
+        if HAS_USER_SITE:
+            self.convert_paths('userbase', 'usersite')
+
+        # Deprecated
+        # Well, we're not actually fully completely finalized yet: we still
+        # have to deal with 'extra_path', which is the hack for allowing
+        # non-packagized module distributions (hello, Numerical Python!) to
+        # get their own directories.
+        self.handle_extra_path()
+        self.install_libbase = self.install_lib # needed for .pth file
+        self.install_lib = os.path.join(self.install_lib, self.extra_dirs)
+
+        # If a new root directory was supplied, make all the installation
+        # dirs relative to it.
+        if self.root is not None:
+            self.change_roots('libbase', 'lib', 'purelib', 'platlib',
+                              'scripts', 'data', 'headers')
+
+        self.dump_dirs("after prepending root")
+
+        # Find out the build directories, ie. where to install from.
+        self.set_undefined_options('build',
+                                   ('build_base', 'build_base'),
+                                   ('build_lib', 'build_lib'))
+
+        # Punt on doc directories for now -- after all, we're punting on
+        # documentation completely!
+
+    def dump_dirs(self, msg):
+        """Dumps the list of user options."""
+        if not DEBUG:
+            return
+        from distutils.fancy_getopt import longopt_xlate
+        log.debug(msg + ":")
+        for opt in self.user_options:
+            opt_name = opt[0]
+            if opt_name[-1] == "=":
+                opt_name = opt_name[0:-1]
+            if opt_name in self.negative_opt:
+                opt_name = self.negative_opt[opt_name]
+                opt_name = opt_name.translate(longopt_xlate)
+                val = not getattr(self, opt_name)
+            else:
+                opt_name = opt_name.translate(longopt_xlate)
+                val = getattr(self, opt_name)
+            log.debug("  %s: %s", opt_name, val)
+
+    def finalize_unix(self):
+        """Finalizes options for posix platforms."""
+        if self.install_base is not None or self.install_platbase is not None:
+            if ((self.install_lib is None and
+                 self.install_purelib is None and
+                 self.install_platlib is None) or
+                self.install_headers is None or
+                self.install_scripts is None or
+                self.install_data is None):
+                raise DistutilsOptionError(
+                      "install-base or install-platbase supplied, but "
+                      "installation scheme is incomplete")
+            return
+
+        if self.user:
+            if self.install_userbase is None:
+                raise DistutilsPlatformError(
+                    "User base directory is not specified")
+            self.install_base = self.install_platbase = self.install_userbase
+            self.select_scheme("unix_user")
+        elif self.home is not None:
+            self.install_base = self.install_platbase = self.home
+            self.select_scheme("unix_home")
+        else:
+            if self.prefix is None:
+                if self.exec_prefix is not None:
+                    raise DistutilsOptionError(
+                          "must not supply exec-prefix without prefix")
+
+                self.prefix = os.path.normpath(sys.prefix)
+                self.exec_prefix = os.path.normpath(sys.exec_prefix)
+
+            else:
+                if self.exec_prefix is None:
+                    self.exec_prefix = self.prefix
+
+            self.install_base = self.prefix
+            self.install_platbase = self.exec_prefix
+            self.select_scheme("unix_prefix")
+
+    def finalize_other(self):
+        """Finalizes options for non-posix platforms"""
+        if self.user:
+            if self.install_userbase is None:
+                raise DistutilsPlatformError(
+                    "User base directory is not specified")
+            self.install_base = self.install_platbase = self.install_userbase
+            self.select_scheme(os.name + "_user")
+        elif self.home is not None:
+            self.install_base = self.install_platbase = self.home
+            self.select_scheme("unix_home")
+        else:
+            if self.prefix is None:
+                self.prefix = os.path.normpath(sys.prefix)
+
+            self.install_base = self.install_platbase = self.prefix
+            try:
+                self.select_scheme(os.name)
+            except KeyError:
+                raise DistutilsPlatformError(
+                      "I don't know how to install stuff on '%s'" % os.name)
+
+    def select_scheme(self, name):
+        """Sets the install directories by applying the install schemes."""
+        # it's the caller's problem if they supply a bad name!
+        scheme = INSTALL_SCHEMES[name]
+        for key in SCHEME_KEYS:
+            attrname = 'install_' + key
+            if getattr(self, attrname) is None:
+                setattr(self, attrname, scheme[key])
+
+    def _expand_attrs(self, attrs):
+        for attr in attrs:
+            val = getattr(self, attr)
+            if val is not None:
+                if os.name == 'posix' or os.name == 'nt':
+                    val = os.path.expanduser(val)
+                val = subst_vars(val, self.config_vars)
+                setattr(self, attr, val)
+
+    def expand_basedirs(self):
+        """Calls `os.path.expanduser` on install_base, install_platbase and
+        root."""
+        self._expand_attrs(['install_base', 'install_platbase', 'root'])
+
+    def expand_dirs(self):
+        """Calls `os.path.expanduser` on install dirs."""
+        self._expand_attrs(['install_purelib', 'install_platlib',
+                            'install_lib', 'install_headers',
+                            'install_scripts', 'install_data',])
+
+    def convert_paths(self, *names):
+        """Call `convert_path` over `names`."""
+        for name in names:
+            attr = "install_" + name
+            setattr(self, attr, convert_path(getattr(self, attr)))
+
+    def handle_extra_path(self):
+        """Set `path_file` and `extra_dirs` using `extra_path`."""
+        if self.extra_path is None:
+            self.extra_path = self.distribution.extra_path
+
+        if self.extra_path is not None:
+            log.warn(
+                "Distribution option extra_path is deprecated. "
+                "See issue27919 for details."
+            )
+            if isinstance(self.extra_path, str):
+                self.extra_path = self.extra_path.split(',')
+
+            if len(self.extra_path) == 1:
+                path_file = extra_dirs = self.extra_path[0]
+            elif len(self.extra_path) == 2:
+                path_file, extra_dirs = self.extra_path
+            else:
+                raise DistutilsOptionError(
+                      "'extra_path' option must be a list, tuple, or "
+                      "comma-separated string with 1 or 2 elements")
+
+            # convert to local form in case Unix notation used (as it
+            # should be in setup scripts)
+            extra_dirs = convert_path(extra_dirs)
+        else:
+            path_file = None
+            extra_dirs = ''
+
+        # XXX should we warn if path_file and not extra_dirs? (in which
+        # case the path file would be harmless but pointless)
+        self.path_file = path_file
+        self.extra_dirs = extra_dirs
+
+    def change_roots(self, *names):
+        """Change the install directories pointed by name using root."""
+        for name in names:
+            attr = "install_" + name
+            setattr(self, attr, change_root(self.root, getattr(self, attr)))
+
+    def create_home_path(self):
+        """Create directories under ~."""
+        if not self.user:
+            return
+        home = convert_path(os.path.expanduser("~"))
+        for name, path in self.config_vars.items():
+            if path.startswith(home) and not os.path.isdir(path):
+                self.debug_print("os.makedirs('%s', 0o700)" % path)
+                os.makedirs(path, 0o700)
+
+    # -- Command execution methods -------------------------------------
+
+    def run(self):
+        """Runs the command."""
+        # Obviously have to build before we can install
+        if not self.skip_build:
+            self.run_command('build')
+            # If we built for any other platform, we can't install.
+            build_plat = self.distribution.get_command_obj('build').plat_name
+            # check warn_dir - it is a clue that the 'install' is happening
+            # internally, and not to sys.path, so we don't check the platform
+            # matches what we are running.
+            if self.warn_dir and build_plat != get_platform():
+                raise DistutilsPlatformError("Can't install when "
+                                             "cross-compiling")
+
+        # Run all sub-commands (at least those that need to be run)
+        for cmd_name in self.get_sub_commands():
+            self.run_command(cmd_name)
+
+        if self.path_file:
+            self.create_path_file()
+
+        # write list of installed files, if requested.
+        if self.record:
+            outputs = self.get_outputs()
+            if self.root:               # strip any package prefix
+                root_len = len(self.root)
+                for counter in range(len(outputs)):
+                    outputs[counter] = outputs[counter][root_len:]
+            self.execute(write_file,
+                         (self.record, outputs),
+                         "writing list of installed files to '%s'" %
+                         self.record)
+
+        sys_path = map(os.path.normpath, sys.path)
+        sys_path = map(os.path.normcase, sys_path)
+        install_lib = os.path.normcase(os.path.normpath(self.install_lib))
+        if (self.warn_dir and
+            not (self.path_file and self.install_path_file) and
+            install_lib not in sys_path):
+            log.debug(("modules installed to '%s', which is not in "
+                       "Python's module search path (sys.path) -- "
+                       "you'll have to change the search path yourself"),
+                       self.install_lib)
+
+    def create_path_file(self):
+        """Creates the .pth file"""
+        filename = os.path.join(self.install_libbase,
+                                self.path_file + ".pth")
+        if self.install_path_file:
+            self.execute(write_file,
+                         (filename, [self.extra_dirs]),
+                         "creating %s" % filename)
+        else:
+            self.warn("path file '%s' not created" % filename)
+
+
+    # -- Reporting methods ---------------------------------------------
+
+    def get_outputs(self):
+        """Assembles the outputs of all the sub-commands."""
+        outputs = []
+        for cmd_name in self.get_sub_commands():
+            cmd = self.get_finalized_command(cmd_name)
+            # Add the contents of cmd.get_outputs(), ensuring
+            # that outputs doesn't contain duplicate entries
+            for filename in cmd.get_outputs():
+                if filename not in outputs:
+                    outputs.append(filename)
+
+        if self.path_file and self.install_path_file:
+            outputs.append(os.path.join(self.install_libbase,
+                                        self.path_file + ".pth"))
+
+        return outputs
+
+    def get_inputs(self):
+        """Returns the inputs of all the sub-commands"""
+        # XXX gee, this looks familiar ;-(
+        inputs = []
+        for cmd_name in self.get_sub_commands():
+            cmd = self.get_finalized_command(cmd_name)
+            inputs.extend(cmd.get_inputs())
+
+        return inputs
+
+    # -- Predicates for sub-command list -------------------------------
+
+    def has_lib(self):
+        """Returns true if the current distribution has any Python
+        modules to install."""
+        return (self.distribution.has_pure_modules() or
+                self.distribution.has_ext_modules())
+
+    def has_headers(self):
+        """Returns true if the current distribution has any headers to
+        install."""
+        return self.distribution.has_headers()
+
+    def has_scripts(self):
+        """Returns true if the current distribution has any scripts to.
+        install."""
+        return self.distribution.has_scripts()
+
+    def has_data(self):
+        """Returns true if the current distribution has any data to.
+        install."""
+        return self.distribution.has_data_files()
+
+    # 'sub_commands': a list of commands this command might have to run to
+    # get its work done.  See cmd.py for more info.
+    sub_commands = [('install_lib',     has_lib),
+                    ('install_headers', has_headers),
+                    ('install_scripts', has_scripts),
+                    ('install_data',    has_data),
+                    ('install_egg_info', lambda self:True),
+                   ]
diff --git a/shared/package-files/32/32ba092523c26c10d3f201acfc676aa55fca1ec96530d269c71d0c537bca255f/plot_controller.py b/shared/package-files/32/32ba092523c26c10d3f201acfc676aa55fca1ec96530d269c71d0c537bca255f/plot_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa7e01e6fd17fddf07b733442208a0a4c9d87d5b
--- /dev/null
+++ b/shared/package-files/32/32ba092523c26c10d3f201acfc676aa55fca1ec96530d269c71d0c537bca255f/plot_controller.py
@@ -0,0 +1,218 @@
+from pyglet.window import key
+from pyglet.window.mouse import LEFT, RIGHT, MIDDLE
+from sympy.plotting.pygletplot.util import get_direction_vectors, get_basis_vectors
+
+
+class PlotController:
+
+    normal_mouse_sensitivity = 4.0
+    modified_mouse_sensitivity = 1.0
+
+    normal_key_sensitivity = 160.0
+    modified_key_sensitivity = 40.0
+
+    keymap = {
+        key.LEFT: 'left',
+        key.A: 'left',
+        key.NUM_4: 'left',
+
+        key.RIGHT: 'right',
+        key.D: 'right',
+        key.NUM_6: 'right',
+
+        key.UP: 'up',
+        key.W: 'up',
+        key.NUM_8: 'up',
+
+        key.DOWN: 'down',
+        key.S: 'down',
+        key.NUM_2: 'down',
+
+        key.Z: 'rotate_z_neg',
+        key.NUM_1: 'rotate_z_neg',
+
+        key.C: 'rotate_z_pos',
+        key.NUM_3: 'rotate_z_pos',
+
+        key.Q: 'spin_left',
+        key.NUM_7: 'spin_left',
+        key.E: 'spin_right',
+        key.NUM_9: 'spin_right',
+
+        key.X: 'reset_camera',
+        key.NUM_5: 'reset_camera',
+
+        key.NUM_ADD: 'zoom_in',
+        key.PAGEUP: 'zoom_in',
+        key.R: 'zoom_in',
+
+        key.NUM_SUBTRACT: 'zoom_out',
+        key.PAGEDOWN: 'zoom_out',
+        key.F: 'zoom_out',
+
+        key.RSHIFT: 'modify_sensitivity',
+        key.LSHIFT: 'modify_sensitivity',
+
+        key.F1: 'rot_preset_xy',
+        key.F2: 'rot_preset_xz',
+        key.F3: 'rot_preset_yz',
+        key.F4: 'rot_preset_perspective',
+
+        key.F5: 'toggle_axes',
+        key.F6: 'toggle_axe_colors',
+
+        key.F8: 'save_image'
+    }
+
+    def __init__(self, window, *, invert_mouse_zoom=False, **kwargs):
+        self.invert_mouse_zoom = invert_mouse_zoom
+        self.window = window
+        self.camera = window.camera
+        self.action = {
+            # Rotation around the view Y (up) vector
+            'left': False,
+            'right': False,
+            # Rotation around the view X vector
+            'up': False,
+            'down': False,
+            # Rotation around the view Z vector
+            'spin_left': False,
+            'spin_right': False,
+            # Rotation around the model Z vector
+            'rotate_z_neg': False,
+            'rotate_z_pos': False,
+            # Reset to the default rotation
+            'reset_camera': False,
+            # Performs camera z-translation
+            'zoom_in': False,
+            'zoom_out': False,
+            # Use alternative sensitivity (speed)
+            'modify_sensitivity': False,
+            # Rotation presets
+            'rot_preset_xy': False,
+            'rot_preset_xz': False,
+            'rot_preset_yz': False,
+            'rot_preset_perspective': False,
+            # axes
+            'toggle_axes': False,
+            'toggle_axe_colors': False,
+            # screenshot
+            'save_image': False
+        }
+
+    def update(self, dt):
+        z = 0
+        if self.action['zoom_out']:
+            z -= 1
+        if self.action['zoom_in']:
+            z += 1
+        if z != 0:
+            self.camera.zoom_relative(z/10.0, self.get_key_sensitivity()/10.0)
+
+        dx, dy, dz = 0, 0, 0
+        if self.action['left']:
+            dx -= 1
+        if self.action['right']:
+            dx += 1
+        if self.action['up']:
+            dy -= 1
+        if self.action['down']:
+            dy += 1
+        if self.action['spin_left']:
+            dz += 1
+        if self.action['spin_right']:
+            dz -= 1
+
+        if not self.is_2D():
+            if dx != 0:
+                self.camera.euler_rotate(dx*dt*self.get_key_sensitivity(),
+                                         *(get_direction_vectors()[1]))
+            if dy != 0:
+                self.camera.euler_rotate(dy*dt*self.get_key_sensitivity(),
+                                         *(get_direction_vectors()[0]))
+            if dz != 0:
+                self.camera.euler_rotate(dz*dt*self.get_key_sensitivity(),
+                                         *(get_direction_vectors()[2]))
+        else:
+            self.camera.mouse_translate(0, 0, dx*dt*self.get_key_sensitivity(),
+                                        -dy*dt*self.get_key_sensitivity())
+
+        rz = 0
+        if self.action['rotate_z_neg'] and not self.is_2D():
+            rz -= 1
+        if self.action['rotate_z_pos'] and not self.is_2D():
+            rz += 1
+
+        if rz != 0:
+            self.camera.euler_rotate(rz*dt*self.get_key_sensitivity(),
+                                     *(get_basis_vectors()[2]))
+
+        if self.action['reset_camera']:
+            self.camera.reset()
+
+        if self.action['rot_preset_xy']:
+            self.camera.set_rot_preset('xy')
+        if self.action['rot_preset_xz']:
+            self.camera.set_rot_preset('xz')
+        if self.action['rot_preset_yz']:
+            self.camera.set_rot_preset('yz')
+        if self.action['rot_preset_perspective']:
+            self.camera.set_rot_preset('perspective')
+
+        if self.action['toggle_axes']:
+            self.action['toggle_axes'] = False
+            self.camera.axes.toggle_visible()
+
+        if self.action['toggle_axe_colors']:
+            self.action['toggle_axe_colors'] = False
+            self.camera.axes.toggle_colors()
+
+        if self.action['save_image']:
+            self.action['save_image'] = False
+            self.window.plot.saveimage()
+
+        return True
+
+    def get_mouse_sensitivity(self):
+        if self.action['modify_sensitivity']:
+            return self.modified_mouse_sensitivity
+        else:
+            return self.normal_mouse_sensitivity
+
+    def get_key_sensitivity(self):
+        if self.action['modify_sensitivity']:
+            return self.modified_key_sensitivity
+        else:
+            return self.normal_key_sensitivity
+
+    def on_key_press(self, symbol, modifiers):
+        if symbol in self.keymap:
+            self.action[self.keymap[symbol]] = True
+
+    def on_key_release(self, symbol, modifiers):
+        if symbol in self.keymap:
+            self.action[self.keymap[symbol]] = False
+
+    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):
+        if buttons & LEFT:
+            if self.is_2D():
+                self.camera.mouse_translate(x, y, dx, dy)
+            else:
+                self.camera.spherical_rotate((x - dx, y - dy), (x, y),
+                                             self.get_mouse_sensitivity())
+        if buttons & MIDDLE:
+            self.camera.zoom_relative([1, -1][self.invert_mouse_zoom]*dy,
+                                      self.get_mouse_sensitivity()/20.0)
+        if buttons & RIGHT:
+            self.camera.mouse_translate(x, y, dx, dy)
+
+    def on_mouse_scroll(self, x, y, dx, dy):
+        self.camera.zoom_relative([1, -1][self.invert_mouse_zoom]*dy,
+                                  self.get_mouse_sensitivity())
+
+    def is_2D(self):
+        functions = self.window.plot._functions
+        for i in functions:
+            if len(functions[i].i_vars) > 1 or len(functions[i].d_vars) > 2:
+                return False
+        return True
diff --git a/shared/package-files/32/32bc7b191d3be854fb4055c707307abb4fc504be5a690d0ccf2e99d8d1ee4fa2/q_per_channel_axis.h b/shared/package-files/32/32bc7b191d3be854fb4055c707307abb4fc504be5a690d0ccf2e99d8d1ee4fa2/q_per_channel_axis.h
new file mode 100644
index 0000000000000000000000000000000000000000..3bdde935ee880f79dbccd59d9b0669b526b23ddd
--- /dev/null
+++ b/shared/package-files/32/32bc7b191d3be854fb4055c707307abb4fc504be5a690d0ccf2e99d8d1ee4fa2/q_per_channel_axis.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/q_per_channel_axis_ops.h>
+
+namespace at {
+
+
+// aten::q_per_channel_axis(Tensor self) -> int
+inline int64_t q_per_channel_axis(const at::Tensor & self) {
+    return at::_ops::q_per_channel_axis::call(self);
+}
+
+}
diff --git a/shared/package-files/32/32bebb705fcc4da4acf606460fba5a690144f1dabad9d5edea2c44234168edd3/__main__.py b/shared/package-files/32/32bebb705fcc4da4acf606460fba5a690144f1dabad9d5edea2c44234168edd3/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e85bf372d86cb67d41d024ff5495236469dcae4
--- /dev/null
+++ b/shared/package-files/32/32bebb705fcc4da4acf606460fba5a690144f1dabad9d5edea2c44234168edd3/__main__.py
@@ -0,0 +1,6 @@
+"""Expose Numba command via ``python -m numba``."""
+import sys
+from numba.misc.numba_entry import main
+
+if __name__ == '__main__':
+    sys.exit(main())
diff --git a/shared/package-files/32/32c05c7c9c8c535e655b579fbb9c3b4f377831e9eb6078ae6a2c5b1cc7c0a1ca/top_level.txt b/shared/package-files/32/32c05c7c9c8c535e655b579fbb9c3b4f377831e9eb6078ae6a2c5b1cc7c0a1ca/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..90d81bec1d35eb3996334268974885a5398b6c6c
--- /dev/null
+++ b/shared/package-files/32/32c05c7c9c8c535e655b579fbb9c3b4f377831e9eb6078ae6a2c5b1cc7c0a1ca/top_level.txt
@@ -0,0 +1,3 @@
+functorch
+torch
+torchgen
diff --git a/shared/package-files/32/32c1ca9105fba22a78bf4905dbc2b5463b4aab148d5729224a383cc13db46321/test_eigenvector_centrality.py b/shared/package-files/32/32c1ca9105fba22a78bf4905dbc2b5463b4aab148d5729224a383cc13db46321/test_eigenvector_centrality.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8620056a94995100fae72a66bb7e0558aae953b
--- /dev/null
+++ b/shared/package-files/32/32c1ca9105fba22a78bf4905dbc2b5463b4aab148d5729224a383cc13db46321/test_eigenvector_centrality.py
@@ -0,0 +1,175 @@
+import math
+
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+import networkx as nx
+
+
+class TestEigenvectorCentrality:
+    def test_K5(self):
+        """Eigenvector centrality: K5"""
+        G = nx.complete_graph(5)
+        b = nx.eigenvector_centrality(G)
+        v = math.sqrt(1 / 5.0)
+        b_answer = dict.fromkeys(G, v)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+        nstart = {n: 1 for n in G}
+        b = nx.eigenvector_centrality(G, nstart=nstart)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+        b = nx.eigenvector_centrality_numpy(G)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-3)
+
+    def test_P3(self):
+        """Eigenvector centrality: P3"""
+        G = nx.path_graph(3)
+        b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
+        b = nx.eigenvector_centrality_numpy(G)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
+        b = nx.eigenvector_centrality(G)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
+
+    def test_P3_unweighted(self):
+        """Eigenvector centrality: P3"""
+        G = nx.path_graph(3)
+        b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
+        b = nx.eigenvector_centrality_numpy(G, weight=None)
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
+
+    def test_maxiter(self):
+        with pytest.raises(nx.PowerIterationFailedConvergence):
+            G = nx.path_graph(3)
+            nx.eigenvector_centrality(G, max_iter=0)
+
+
+class TestEigenvectorCentralityDirected:
+    @classmethod
+    def setup_class(cls):
+        G = nx.DiGraph()
+
+        edges = [
+            (1, 2),
+            (1, 3),
+            (2, 4),
+            (3, 2),
+            (3, 5),
+            (4, 2),
+            (4, 5),
+            (4, 6),
+            (5, 6),
+            (5, 7),
+            (5, 8),
+            (6, 8),
+            (7, 1),
+            (7, 5),
+            (7, 8),
+            (8, 6),
+            (8, 7),
+        ]
+
+        G.add_edges_from(edges, weight=2.0)
+        cls.G = G.reverse()
+        cls.G.evc = [
+            0.25368793,
+            0.19576478,
+            0.32817092,
+            0.40430835,
+            0.48199885,
+            0.15724483,
+            0.51346196,
+            0.32475403,
+        ]
+
+        H = nx.DiGraph()
+
+        edges = [
+            (1, 2),
+            (1, 3),
+            (2, 4),
+            (3, 2),
+            (3, 5),
+            (4, 2),
+            (4, 5),
+            (4, 6),
+            (5, 6),
+            (5, 7),
+            (5, 8),
+            (6, 8),
+            (7, 1),
+            (7, 5),
+            (7, 8),
+            (8, 6),
+            (8, 7),
+        ]
+
+        G.add_edges_from(edges)
+        cls.H = G.reverse()
+        cls.H.evc = [
+            0.25368793,
+            0.19576478,
+            0.32817092,
+            0.40430835,
+            0.48199885,
+            0.15724483,
+            0.51346196,
+            0.32475403,
+        ]
+
+    def test_eigenvector_centrality_weighted(self):
+        G = self.G
+        p = nx.eigenvector_centrality(G)
+        for a, b in zip(list(p.values()), self.G.evc):
+            assert a == pytest.approx(b, abs=1e-4)
+
+    def test_eigenvector_centrality_weighted_numpy(self):
+        G = self.G
+        p = nx.eigenvector_centrality_numpy(G)
+        for a, b in zip(list(p.values()), self.G.evc):
+            assert a == pytest.approx(b, abs=1e-7)
+
+    def test_eigenvector_centrality_unweighted(self):
+        G = self.H
+        p = nx.eigenvector_centrality(G)
+        for a, b in zip(list(p.values()), self.G.evc):
+            assert a == pytest.approx(b, abs=1e-4)
+
+    def test_eigenvector_centrality_unweighted_numpy(self):
+        G = self.H
+        p = nx.eigenvector_centrality_numpy(G)
+        for a, b in zip(list(p.values()), self.G.evc):
+            assert a == pytest.approx(b, abs=1e-7)
+
+
+class TestEigenvectorCentralityExceptions:
+    def test_multigraph(self):
+        with pytest.raises(nx.NetworkXException):
+            nx.eigenvector_centrality(nx.MultiGraph())
+
+    def test_multigraph_numpy(self):
+        with pytest.raises(nx.NetworkXException):
+            nx.eigenvector_centrality_numpy(nx.MultiGraph())
+
+    def test_empty(self):
+        with pytest.raises(nx.NetworkXException):
+            nx.eigenvector_centrality(nx.Graph())
+
+    def test_empty_numpy(self):
+        with pytest.raises(nx.NetworkXException):
+            nx.eigenvector_centrality_numpy(nx.Graph())
+
+    def test_zero_nstart(self):
+        G = nx.Graph([(1, 2), (1, 3), (2, 3)])
+        with pytest.raises(
+            nx.NetworkXException, match="initial vector cannot have all zero values"
+        ):
+            nx.eigenvector_centrality(G, nstart={v: 0 for v in G})
diff --git a/shared/package-files/32/32c40e5eed16461760de68cbe75841fd5613498b29c200b251fc0aee9c848320/session.pyi b/shared/package-files/32/32c40e5eed16461760de68cbe75841fd5613498b29c200b251fc0aee9c848320/session.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8ba628194dfede876bd50106a0c5e811e44b508b
--- /dev/null
+++ b/shared/package-files/32/32c40e5eed16461760de68cbe75841fd5613498b29c200b251fc0aee9c848320/session.pyi
@@ -0,0 +1,10 @@
+from typing import Any, List, Optional, Sequence, Union
+
+from django.contrib.messages.storage.base import BaseStorage
+from django.http.request import HttpRequest
+
+class SessionStorage(BaseStorage):
+    session_key: str = ...
+    def __init__(self, request: HttpRequest, *args: Any, **kwargs: Any) -> None: ...
+    def serialize_messages(self, messages: Sequence[Any]) -> str: ...
+    def deserialize_messages(self, data: Optional[Union[List[Any], str]]) -> Optional[List[Any]]: ...
diff --git a/shared/package-files/32/32c6627e8d77ccb3dc8a900101d95a6d0c5041474d4b63b24a4649984ce13561/generator_methods.py b/shared/package-files/32/32c6627e8d77ccb3dc8a900101d95a6d0c5041474d4b63b24a4649984ce13561/generator_methods.py
new file mode 100644
index 0000000000000000000000000000000000000000..03968e8699b49e9a6478be188fcf9738a1ab9330
--- /dev/null
+++ b/shared/package-files/32/32c6627e8d77ccb3dc8a900101d95a6d0c5041474d4b63b24a4649984ce13561/generator_methods.py
@@ -0,0 +1,971 @@
+"""
+Implementation of method overloads for Generator objects.
+"""
+
+import numpy as np
+from numba.core import types
+from numba.core.extending import overload_method, register_jitable
+from numba.np.numpy_support import as_dtype, from_dtype
+from numba.np.random.generator_core import next_float, next_double
+from numba.np.numpy_support import is_nonelike
+from numba.core.errors import TypingError
+from numba.core.types.containers import Tuple, UniTuple
+from numba.np.random.distributions import \
+    (random_standard_exponential_inv_f, random_standard_exponential_inv,
+     random_standard_exponential, random_standard_normal_f,
+     random_standard_gamma, random_standard_normal, random_uniform,
+     random_standard_exponential_f, random_standard_gamma_f, random_normal,
+     random_exponential, random_gamma, random_beta, random_power,
+     random_f,random_chisquare,random_standard_cauchy,random_pareto,
+     random_weibull, random_laplace, random_logistic,
+     random_lognormal, random_rayleigh, random_standard_t, random_wald,
+     random_geometric, random_zipf, random_triangular,
+     random_poisson, random_negative_binomial, random_logseries,
+     random_noncentral_chisquare, random_noncentral_f, random_binomial)
+from numba.np.random import random_methods
+
+
+def _get_proper_func(func_32, func_64, dtype, dist_name="the given"):
+    """
+        Most of the standard NumPy distributions that accept dtype argument
+        only support either np.float32 or np.float64 as dtypes.
+
+        This is a helper function that helps Numba select the proper underlying
+        implementation according to provided dtype.
+    """
+    if isinstance(dtype, types.Omitted):
+        dtype = dtype.value
+
+    np_dt = dtype
+    if isinstance(dtype, type):
+        nb_dt = from_dtype(np.dtype(dtype))
+    elif isinstance(dtype, types.NumberClass):
+        nb_dt = dtype
+        np_dt = as_dtype(nb_dt)
+
+    if np_dt not in [np.float32, np.float64]:
+        raise TypingError("Argument dtype is not one of the" +
+                          " expected type(s): " +
+                          " np.float32 or np.float64")
+
+    if np_dt == np.float32:
+        next_func = func_32
+    else:
+        next_func = func_64
+
+    return next_func, nb_dt
+
+
+def check_size(size):
+    if not any([isinstance(size, UniTuple) and
+                isinstance(size.dtype, types.Integer),
+                isinstance(size, Tuple) and size.count == 0,
+                isinstance(size, types.Integer)]):
+        raise TypingError("Argument size is not one of the" +
+                          " expected type(s): " +
+                          " an integer, an empty tuple or a tuple of integers")
+
+
+def check_types(obj, type_list, arg_name):
+    """
+    Check if given object is one of the provided types.
+    If not raises an TypeError
+    """
+    if isinstance(obj, types.Omitted):
+        obj = obj.value
+
+    if not isinstance(type_list, (list, tuple)):
+        type_list = [type_list]
+
+    if not any([isinstance(obj, _type) for _type in type_list]):
+        raise TypingError(f"Argument {arg_name} is not one of the" +
+                          f" expected type(s): {type_list}")
+
+
+# Overload the Generator().integers()
+@overload_method(types.NumPyRandomGeneratorType, 'integers')
+def NumPyRandomGeneratorType_integers(inst, low, high, size=None,
+                                      dtype=np.int64, endpoint=False):
+    check_types(low, [types.Integer,
+                      types.Boolean, bool, int], 'low')
+    check_types(high, [types.Integer, types.Boolean,
+                       bool, int], 'high')
+    check_types(endpoint, [types.Boolean, bool], 'endpoint')
+
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if isinstance(dtype, types.Omitted):
+        dtype = dtype.value
+
+    if isinstance(dtype, type):
+        nb_dt = from_dtype(np.dtype(dtype))
+        _dtype = dtype
+    elif isinstance(dtype, types.NumberClass):
+        nb_dt = dtype
+        _dtype = as_dtype(nb_dt)
+    else:
+        raise TypingError("Argument dtype is not one of the" +
+                          " expected type(s): " +
+                          "np.int32, np.int64, np.int16, np.int8, "
+                          "np.uint32, np.uint64, np.uint16, np.uint8, "
+                          "np.bool_")
+
+    if _dtype == np.bool_:
+        int_func = random_methods.random_bounded_bool_fill
+        lower_bound = -1
+        upper_bound = 2
+    else:
+        try:
+            i_info = np.iinfo(_dtype)
+        except ValueError:
+            raise TypingError("Argument dtype is not one of the" +
+                              " expected type(s): " +
+                              "np.int32, np.int64, np.int16, np.int8, "
+                              "np.uint32, np.uint64, np.uint16, np.uint8, "
+                              "np.bool_")
+        int_func = getattr(random_methods,
+                           f'random_bounded_uint{i_info.bits}_fill')
+        lower_bound = i_info.min
+        upper_bound = i_info.max
+
+    if is_nonelike(size):
+        def impl(inst, low, high, size=None,
+                 dtype=np.int64, endpoint=False):
+            random_methods._randint_arg_check(low, high, endpoint,
+                                              lower_bound, upper_bound)
+            if not endpoint:
+                high -= dtype(1)
+                low = dtype(low)
+                high = dtype(high)
+                rng = high - low
+                return int_func(inst.bit_generator, low, rng, 1, dtype)[0]
+            else:
+                low = dtype(low)
+                high = dtype(high)
+                rng = high - low
+                return int_func(inst.bit_generator, low, rng, 1, dtype)[0]
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, low, high, size=None,
+                 dtype=np.int64, endpoint=False):
+            random_methods._randint_arg_check(low, high, endpoint,
+                                              lower_bound, upper_bound)
+            if not endpoint:
+                high -= dtype(1)
+                low = dtype(low)
+                high = dtype(high)
+                rng = high - low
+                return int_func(inst.bit_generator, low, rng, size, dtype)
+            else:
+                low = dtype(low)
+                high = dtype(high)
+                rng = high - low
+                return int_func(inst.bit_generator, low, rng, size, dtype)
+        return impl
+
+
+# The following `shuffle` implementation is a direct translation from:
+# https://github.com/numpy/numpy/blob/95e3e7f445407e4f355b23d6a9991d8774f0eb0c/numpy/random/_generator.pyx#L4578
+
+# Overload the Generator().shuffle()
+@overload_method(types.NumPyRandomGeneratorType, 'shuffle')
+def NumPyRandomGeneratorType_shuffle(inst, x, axis=0):
+    check_types(x, [types.Array], 'x')
+    check_types(axis, [int, types.Integer], 'axis')
+
+    def impl(inst, x, axis=0):
+        if axis < 0:
+            axis = axis + x.ndim
+        if axis > x.ndim - 1 or axis < 0:
+            raise IndexError("Axis is out of bounds for the given array")
+
+        z = np.swapaxes(x, 0, axis)
+        buf = np.empty_like(z[0, ...])
+
+        for i in range(len(z) - 1, 0, -1):
+            j = types.intp(random_methods.random_interval(inst.bit_generator,
+                                                          i))
+            if i == j:
+                continue
+            buf[...] = z[j, ...]
+            z[j, ...] = z[i, ...]
+            z[i, ...] = buf
+
+    return impl
+
+
+# The following `permutation` implementation is a direct translation from:
+# https://github.com/numpy/numpy/blob/95e3e7f445407e4f355b23d6a9991d8774f0eb0c/numpy/random/_generator.pyx#L4710
+# Overload the Generator().permutation()
+@overload_method(types.NumPyRandomGeneratorType, 'permutation')
+def NumPyRandomGeneratorType_permutation(inst, x, axis=0):
+    check_types(x, [types.Array, types.Integer], 'x')
+    check_types(axis, [int, types.Integer], 'axis')
+
+    IS_INT = isinstance(x, types.Integer)
+
+    def impl(inst, x, axis=0):
+        if IS_INT:
+            new_arr = np.arange(x)
+            # NumPy ignores the axis argument when x is an integer
+            inst.shuffle(new_arr)
+        else:
+            new_arr = x.copy()
+            inst.shuffle(new_arr, axis=axis)
+        return new_arr
+
+    return impl
+
+
+# Overload the Generator().random()
+@overload_method(types.NumPyRandomGeneratorType, 'random')
+def NumPyRandomGeneratorType_random(inst, size=None, dtype=np.float64):
+    dist_func, nb_dt = _get_proper_func(next_float, next_double,
+                                        dtype, "random")
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, size=None, dtype=np.float64):
+            return nb_dt(dist_func(inst.bit_generator))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, size=None, dtype=np.float64):
+            out = np.empty(size, dtype=dtype)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = dist_func(inst.bit_generator)
+            return out
+        return impl
+
+
+# Overload the Generator().standard_exponential() method
+@overload_method(types.NumPyRandomGeneratorType, 'standard_exponential')
+def NumPyRandomGeneratorType_standard_exponential(inst, size=None,
+                                                  dtype=np.float64,
+                                                  method='zig'):
+    check_types(method, [types.UnicodeType, str], 'method')
+    dist_func_inv, nb_dt = _get_proper_func(
+        random_standard_exponential_inv_f,
+        random_standard_exponential_inv,
+        dtype
+    )
+
+    dist_func, nb_dt = _get_proper_func(random_standard_exponential_f,
+                                        random_standard_exponential,
+                                        dtype)
+
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, size=None, dtype=np.float64, method='zig'):
+            if method == 'zig':
+                return nb_dt(dist_func(inst.bit_generator))
+            elif method == 'inv':
+                return nb_dt(dist_func_inv(inst.bit_generator))
+            else:
+                raise ValueError("Method must be either 'zig' or 'inv'")
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, size=None, dtype=np.float64, method='zig'):
+            out = np.empty(size, dtype=dtype)
+            out_f = out.flat
+            if method == 'zig':
+                for i in range(out.size):
+                    out_f[i] = dist_func(inst.bit_generator)
+            elif method == 'inv':
+                for i in range(out.size):
+                    out_f[i] = dist_func_inv(inst.bit_generator)
+            else:
+                raise ValueError("Method must be either 'zig' or 'inv'")
+            return out
+        return impl
+
+
+# Overload the Generator().standard_normal() method
+@overload_method(types.NumPyRandomGeneratorType, 'standard_normal')
+def NumPyRandomGeneratorType_standard_normal(inst, size=None, dtype=np.float64):
+    dist_func, nb_dt = _get_proper_func(random_standard_normal_f,
+                                        random_standard_normal,
+                                        dtype)
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, size=None, dtype=np.float64):
+            return nb_dt(dist_func(inst.bit_generator))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, size=None, dtype=np.float64):
+            out = np.empty(size, dtype=dtype)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = dist_func(inst.bit_generator)
+            return out
+        return impl
+
+
+# Overload the Generator().standard_gamma() method
+@overload_method(types.NumPyRandomGeneratorType, 'standard_gamma')
+def NumPyRandomGeneratorType_standard_gamma(inst, shape, size=None,
+                                            dtype=np.float64):
+    check_types(shape, [types.Float, types.Integer, int, float], 'shape')
+    dist_func, nb_dt = _get_proper_func(random_standard_gamma_f,
+                                        random_standard_gamma,
+                                        dtype)
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, shape, size=None, dtype=np.float64):
+            return nb_dt(dist_func(inst.bit_generator, shape))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, shape, size=None, dtype=np.float64):
+            out = np.empty(size, dtype=dtype)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = dist_func(inst.bit_generator, shape)
+            return out
+        return impl
+
+
+# Overload the Generator().normal() method
+@overload_method(types.NumPyRandomGeneratorType, 'normal')
+def NumPyRandomGeneratorType_normal(inst, loc=0.0, scale=1.0,
+                                    size=None):
+    check_types(loc, [types.Float, types.Integer, int, float], 'loc')
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            return random_normal(inst.bit_generator, loc, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_normal(inst.bit_generator, loc, scale)
+            return out
+        return impl
+
+
+# Overload the Generator().uniform() method
+@overload_method(types.NumPyRandomGeneratorType, 'uniform')
+def NumPyRandomGeneratorType_uniform(inst, low=0.0, high=1.0,
+                                     size=None):
+    check_types(low, [types.Float, types.Integer, int, float], 'low')
+    check_types(high, [types.Float, types.Integer, int, float], 'high')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, low=0.0, high=1.0, size=None):
+            return random_uniform(inst.bit_generator, low, high - low)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, low=0.0, high=1.0, size=None):
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_uniform(inst.bit_generator, low, high - low)
+            return out
+        return impl
+
+
+# Overload the Generator().exponential() method
+@overload_method(types.NumPyRandomGeneratorType, 'exponential')
+def NumPyRandomGeneratorType_exponential(inst, scale=1.0, size=None):
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, scale=1.0, size=None):
+            return random_exponential(inst.bit_generator, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, scale=1.0, size=None):
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_exponential(inst.bit_generator, scale)
+            return out
+        return impl
+
+
+# Overload the Generator().gamma() method
+@overload_method(types.NumPyRandomGeneratorType, 'gamma')
+def NumPyRandomGeneratorType_gamma(inst, shape, scale=1.0, size=None):
+    check_types(shape, [types.Float, types.Integer, int, float], 'shape')
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, shape, scale=1.0, size=None):
+            return random_gamma(inst.bit_generator, shape, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, shape, scale=1.0, size=None):
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_gamma(inst.bit_generator, shape, scale)
+            return out
+        return impl
+
+
+# Overload the Generator().beta() method
+@overload_method(types.NumPyRandomGeneratorType, 'beta')
+def NumPyRandomGeneratorType_beta(inst, a, b, size=None):
+    check_types(a, [types.Float, types.Integer, int, float], 'a')
+    check_types(b, [types.Float, types.Integer, int, float], 'b')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, a, b, size=None):
+            return random_beta(inst.bit_generator, a, b)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, a, b, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_beta(inst.bit_generator, a, b)
+            return out
+        return impl
+
+
+# Overload the Generator().f() method
+@overload_method(types.NumPyRandomGeneratorType, 'f')
+def NumPyRandomGeneratorType_f(inst, dfnum, dfden, size=None):
+    check_types(dfnum, [types.Float, types.Integer, int, float], 'dfnum')
+    check_types(dfden, [types.Float, types.Integer, int, float], 'dfden')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, dfnum, dfden, size=None):
+            return random_f(inst.bit_generator, dfnum, dfden)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, dfnum, dfden, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_f(inst.bit_generator, dfnum, dfden)
+            return out
+        return impl
+
+
+# Overload the Generator().chisquare() method
+@overload_method(types.NumPyRandomGeneratorType, 'chisquare')
+def NumPyRandomGeneratorType_chisquare(inst, df, size=None):
+    check_types(df, [types.Float, types.Integer, int, float], 'df')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, df, size=None):
+            return random_chisquare(inst.bit_generator, df)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, df, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_chisquare(inst.bit_generator, df)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'standard_cauchy')
+def NumPyRandomGeneratorType_standard_cauchy(inst, size=None):
+
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, size=None):
+            return random_standard_cauchy(inst.bit_generator)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_standard_cauchy(inst.bit_generator)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'pareto')
+def NumPyRandomGeneratorType_pareto(inst, a, size=None):
+    check_types(a, [types.Float, types.Integer, int, float], 'a')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, a, size=None):
+            return random_pareto(inst.bit_generator, a)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, a, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_pareto(inst.bit_generator, a)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'weibull')
+def NumPyRandomGeneratorType_weibull(inst, a, size=None):
+    check_types(a, [types.Float, types.Integer, int, float], 'a')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, a, size=None):
+            return random_weibull(inst.bit_generator, a)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, a, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_weibull(inst.bit_generator, a)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'power')
+def NumPyRandomGeneratorType_power(inst, a, size=None):
+    check_types(a, [types.Float, types.Integer, int, float], 'a')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, a, size=None):
+            return random_power(inst.bit_generator, a)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, a, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_power(inst.bit_generator, a)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'laplace')
+def NumPyRandomGeneratorType_laplace(inst, loc=0.0, scale=1.0, size=None):
+    check_types(loc, [types.Float, types.Integer, int, float], 'loc')
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            return random_laplace(inst.bit_generator, loc, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_laplace(inst.bit_generator, loc, scale)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'logistic')
+def NumPyRandomGeneratorType_logistic(inst, loc=0.0, scale=1.0, size=None):
+    check_types(loc, [types.Float, types.Integer, int, float], 'loc')
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            return random_logistic(inst.bit_generator, loc, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, loc=0.0, scale=1.0, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_logistic(inst.bit_generator, loc, scale)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'lognormal')
+def NumPyRandomGeneratorType_lognormal(inst, mean=0.0, sigma=1.0, size=None):
+    check_types(mean, [types.Float, types.Integer, int, float], 'mean')
+    check_types(sigma, [types.Float, types.Integer, int, float], 'sigma')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, mean=0.0, sigma=1.0, size=None):
+            return random_lognormal(inst.bit_generator, mean, sigma)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, mean=0.0, sigma=1.0, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_lognormal(inst.bit_generator, mean, sigma)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'rayleigh')
+def NumPyRandomGeneratorType_rayleigh(inst, scale=1.0, size=None):
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, scale=1.0, size=None):
+            return random_rayleigh(inst.bit_generator, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, scale=1.0, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_rayleigh(inst.bit_generator, scale)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'standard_t')
+def NumPyRandomGeneratorType_standard_t(inst, df, size=None):
+    check_types(df, [types.Float, types.Integer, int, float], 'df')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, df, size=None):
+            return random_standard_t(inst.bit_generator, df)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, df, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_standard_t(inst.bit_generator, df)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'wald')
+def NumPyRandomGeneratorType_wald(inst, mean, scale, size=None):
+    check_types(mean, [types.Float, types.Integer, int, float], 'mean')
+    check_types(scale, [types.Float, types.Integer, int, float], 'scale')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, mean, scale, size=None):
+            return random_wald(inst.bit_generator, mean, scale)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, mean, scale, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_wald(inst.bit_generator, mean, scale)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'geometric')
+def NumPyRandomGeneratorType_geometric(inst, p, size=None):
+    check_types(p, [types.Float, types.Integer, int, float], 'p')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, p, size=None):
+            return np.int64(random_geometric(inst.bit_generator, p))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, p, size=None):
+            out = np.empty(size, dtype=np.int64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_geometric(inst.bit_generator, p)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'zipf')
+def NumPyRandomGeneratorType_zipf(inst, a, size=None):
+    check_types(a, [types.Float, types.Integer, int, float], 'a')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, a, size=None):
+            return np.int64(random_zipf(inst.bit_generator, a))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, a, size=None):
+            out = np.empty(size, dtype=np.int64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_zipf(inst.bit_generator, a)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'triangular')
+def NumPyRandomGeneratorType_triangular(inst, left, mode, right, size=None):
+    check_types(left, [types.Float, types.Integer, int, float], 'left')
+    check_types(mode, [types.Float, types.Integer, int, float], 'mode')
+    check_types(right, [types.Float, types.Integer, int, float], 'right')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, left, mode, right, size=None):
+            return random_triangular(inst.bit_generator, left, mode, right)
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, left, mode, right, size=None):
+            out = np.empty(size)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_triangular(inst.bit_generator,
+                                             left, mode, right)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'poisson')
+def NumPyRandomGeneratorType_poisson(inst, lam , size=None):
+    check_types(lam, [types.Float, types.Integer, int, float], 'lam')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, lam , size=None):
+            return np.int64(random_poisson(inst.bit_generator, lam))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, lam , size=None):
+            out = np.empty(size, dtype=np.int64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_poisson(inst.bit_generator, lam)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'negative_binomial')
+def NumPyRandomGeneratorType_negative_binomial(inst, n, p, size=None):
+    check_types(n, [types.Float, types.Integer, int, float], 'n')
+    check_types(p, [types.Float, types.Integer, int, float], 'p')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst,  n, p , size=None):
+            return np.int64(random_negative_binomial(inst.bit_generator, n, p))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, n, p , size=None):
+            out = np.empty(size, dtype=np.int64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_negative_binomial(inst.bit_generator, n, p)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'noncentral_chisquare')
+def NumPyRandomGeneratorType_noncentral_chisquare(inst, df, nonc, size=None):
+    check_types(df, [types.Float, types.Integer, int, float], 'df')
+    check_types(nonc, [types.Float, types.Integer, int, float], 'nonc')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    @register_jitable
+    def check_arg_bounds(df, nonc):
+        if df <= 0:
+            raise ValueError("df <= 0")
+        if nonc < 0:
+            raise ValueError("nonc < 0")
+
+    if is_nonelike(size):
+        def impl(inst, df, nonc, size=None):
+            check_arg_bounds(df, nonc)
+            return np.float64(random_noncentral_chisquare(inst.bit_generator,
+                                                          df, nonc))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, df, nonc, size=None):
+            check_arg_bounds(df, nonc)
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_noncentral_chisquare(inst.bit_generator,
+                                                       df, nonc)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'noncentral_f')
+def NumPyRandomGeneratorType_noncentral_f(inst, dfnum, dfden, nonc, size=None):
+    check_types(dfnum, [types.Float, types.Integer, int, float], 'dfnum')
+    check_types(dfden, [types.Float, types.Integer, int, float], 'dfden')
+    check_types(nonc, [types.Float, types.Integer, int, float], 'nonc')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    @register_jitable
+    def check_arg_bounds(dfnum, dfden, nonc):
+        if dfnum <= 0:
+            raise ValueError("dfnum <= 0")
+        if dfden <= 0:
+            raise ValueError("dfden <= 0")
+        if nonc < 0:
+            raise ValueError("nonc < 0")
+
+    if is_nonelike(size):
+        def impl(inst,  dfnum, dfden, nonc, size=None):
+            check_arg_bounds(dfnum, dfden, nonc)
+            return np.float64(random_noncentral_f(inst.bit_generator,
+                                                  dfnum, dfden, nonc))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, dfnum, dfden, nonc, size=None):
+            check_arg_bounds(dfnum, dfden, nonc)
+            out = np.empty(size, dtype=np.float64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_noncentral_f(inst.bit_generator,
+                                               dfnum, dfden, nonc)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'logseries')
+def NumPyRandomGeneratorType_logseries(inst, p, size=None):
+    check_types(p, [types.Float, types.Integer, int, float], 'p')
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    @register_jitable
+    def check_arg_bounds(p):
+        if p < 0 or p >= 1 or np.isnan(p):
+            raise ValueError("p < 0, p >= 1 or p is NaN")
+
+    if is_nonelike(size):
+        def impl(inst, p, size=None):
+            check_arg_bounds(p)
+            return np.int64(random_logseries(inst.bit_generator, p))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, p, size=None):
+            check_arg_bounds(p)
+            out = np.empty(size, dtype=np.int64)
+            out_f = out.flat
+            for i in range(out.size):
+                out_f[i] = random_logseries(inst.bit_generator, p)
+            return out
+        return impl
+
+
+@overload_method(types.NumPyRandomGeneratorType, 'binomial')
+def NumPyRandomGeneratorType_binomial(inst, n, p, size=None):
+    check_types(n, [types.Float, types.Integer, int, float], 'n')
+    check_types(p, [types.Float, types.Integer, int, float], 'p')
+
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, n, p, size=None):
+            return np.int64(random_binomial(inst.bit_generator, n, p))
+        return impl
+    else:
+        check_size(size)
+
+        def impl(inst, n, p, size=None):
+            out = np.empty(size, dtype=np.int64)
+            for i in np.ndindex(size):
+                out[i] = random_binomial(inst.bit_generator, n, p)
+            return out
+        return impl
diff --git a/shared/package-files/32/32c68d31596d1d97ffc157db0d1eb8973ebcd470c13daec11e2e7a29e161ea88/test_extension.py b/shared/package-files/32/32c68d31596d1d97ffc157db0d1eb8973ebcd470c13daec11e2e7a29e161ea88/test_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..24ecf7e97e3e56ea51327cc4704ff1fa749c15aa
--- /dev/null
+++ b/shared/package-files/32/32c68d31596d1d97ffc157db0d1eb8973ebcd470c13daec11e2e7a29e161ea88/test_extension.py
@@ -0,0 +1,95 @@
+import os.path
+
+from tempfile import TemporaryDirectory
+
+import IPython.testing.tools as tt
+from IPython.utils.syspathcontext import prepended_to_syspath
+
+ext1_content = """
+def load_ipython_extension(ip):
+    print("Running ext1 load")
+
+def unload_ipython_extension(ip):
+    print("Running ext1 unload")
+"""
+
+ext2_content = """
+def load_ipython_extension(ip):
+    print("Running ext2 load")
+"""
+
+ext3_content = """
+def load_ipython_extension(ip):
+    ip2 = get_ipython()
+    print(ip is ip2)
+"""
+
+def test_extension_loading():
+    em = get_ipython().extension_manager
+    with TemporaryDirectory() as td:
+        ext1 = os.path.join(td, "ext1.py")
+        with open(ext1, "w", encoding="utf-8") as f:
+            f.write(ext1_content)
+
+        ext2 = os.path.join(td, "ext2.py")
+        with open(ext2, "w", encoding="utf-8") as f:
+            f.write(ext2_content)
+        
+        with prepended_to_syspath(td):
+            assert 'ext1' not in em.loaded
+            assert 'ext2' not in em.loaded
+            
+            # Load extension
+            with tt.AssertPrints("Running ext1 load"):
+                assert em.load_extension('ext1') is None
+            assert 'ext1' in em.loaded
+            
+            # Should refuse to load it again
+            with tt.AssertNotPrints("Running ext1 load"):
+                assert em.load_extension('ext1') == 'already loaded'
+            
+            # Reload
+            with tt.AssertPrints("Running ext1 unload"):
+                with tt.AssertPrints("Running ext1 load", suppress=False):
+                    em.reload_extension('ext1')
+            
+            # Unload
+            with tt.AssertPrints("Running ext1 unload"):
+                assert em.unload_extension('ext1') is None
+            
+            # Can't unload again
+            with tt.AssertNotPrints("Running ext1 unload"):
+                assert em.unload_extension('ext1') == 'not loaded'
+            assert em.unload_extension('ext2') == 'not loaded'
+            
+            # Load extension 2
+            with tt.AssertPrints("Running ext2 load"):
+                assert em.load_extension('ext2') is None
+            
+            # Can't unload this
+            assert em.unload_extension('ext2') == 'no unload function'
+            
+            # But can reload it
+            with tt.AssertPrints("Running ext2 load"):
+                em.reload_extension('ext2')
+
+
+def test_extension_builtins():
+    em = get_ipython().extension_manager
+    with TemporaryDirectory() as td:
+        ext3 = os.path.join(td, "ext3.py")
+        with open(ext3, "w", encoding="utf-8") as f:
+            f.write(ext3_content)
+        
+        assert 'ext3' not in em.loaded
+        
+        with prepended_to_syspath(td):
+            # Load extension
+            with tt.AssertPrints("True"):
+                assert em.load_extension('ext3') is None
+            assert 'ext3' in em.loaded
+
+
+def test_non_extension():
+    em = get_ipython().extension_manager
+    assert em.load_extension("sys") == "no load function"
diff --git a/shared/package-files/32/32c76b43f527ebbe41f77313c8567570b734f5a2c62e95e1c2f25366147f8f46/autograd.h b/shared/package-files/32/32c76b43f527ebbe41f77313c8567570b734f5a2c62e95e1c2f25366147f8f46/autograd.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf0608fa01bbf5549d81c6edc1b3e4cd82de379b
--- /dev/null
+++ b/shared/package-files/32/32c76b43f527ebbe41f77313c8567570b734f5a2c62e95e1c2f25366147f8f46/autograd.h
@@ -0,0 +1,5 @@
+#pragma once
+
+#include <torch/csrc/autograd/autograd.h>
+#include <torch/csrc/autograd/autograd_not_implemented_fallback.h>
+#include <torch/csrc/autograd/custom_function.h>
diff --git a/shared/package-files/32/32c99a196c24e81fcf80bf6a1f8add21acba32f82b98bc4e39194e812915ec7b/batch_norm_native.h b/shared/package-files/32/32c99a196c24e81fcf80bf6a1f8add21acba32f82b98bc4e39194e812915ec7b/batch_norm_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..972857213afc30d3b3ca68c49e37d8ee806083c0
--- /dev/null
+++ b/shared/package-files/32/32c99a196c24e81fcf80bf6a1f8add21acba32f82b98bc4e39194e812915ec7b/batch_norm_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor batch_norm(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32d587e6579b9a97e1d58032f0030d8a3b081b34faff761213694b45240c1d81/_native_batch_norm_legit_no_training.h b/shared/package-files/32/32d587e6579b9a97e1d58032f0030d8a3b081b34faff761213694b45240c1d81/_native_batch_norm_legit_no_training.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b026ee87193a5e45023e584993f6c3b15bf7687
--- /dev/null
+++ b/shared/package-files/32/32d587e6579b9a97e1d58032f0030d8a3b081b34faff761213694b45240c1d81/_native_batch_norm_legit_no_training.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_native_batch_norm_legit_no_training_ops.h>
+
+namespace at {
+
+
+// aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit_no_training(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
+    return at::_ops::_native_batch_norm_legit_no_training::call(input, weight, bias, running_mean, running_var, momentum, eps);
+}
+
+// aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_no_training_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
+    return at::_ops::_native_batch_norm_legit_no_training_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2);
+}
+// aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_no_training_outf(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
+    return at::_ops::_native_batch_norm_legit_no_training_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2);
+}
+
+}
diff --git a/shared/package-files/32/32d635f1bcfcaffc9e5235056e5d37854210193f2585ba986dc071ad9bbf1405/lgamma_cuda_dispatch.h b/shared/package-files/32/32d635f1bcfcaffc9e5235056e5d37854210193f2585ba986dc071ad9bbf1405/lgamma_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5895ca6f884253d6d07434b43e8276e7baf979a1
--- /dev/null
+++ b/shared/package-files/32/32d635f1bcfcaffc9e5235056e5d37854210193f2585ba986dc071ad9bbf1405/lgamma_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor lgamma(const at::Tensor & self);
+TORCH_API at::Tensor & lgamma_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & lgamma_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & lgamma_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/32/32d6c5ca78875c43c5d4d18daa7c771aefa2a881f9196c9faf309b25413b0908/managers.py b/shared/package-files/32/32d6c5ca78875c43c5d4d18daa7c771aefa2a881f9196c9faf309b25413b0908/managers.py
new file mode 100644
index 0000000000000000000000000000000000000000..22292c78b7b5b3a253c1d231ad12b225acd6f390
--- /dev/null
+++ b/shared/package-files/32/32d6c5ca78875c43c5d4d18daa7c771aefa2a881f9196c9faf309b25413b0908/managers.py
@@ -0,0 +1,1378 @@
+#
+# Module providing manager classes for dealing
+# with shared objects
+#
+# multiprocessing/managers.py
+#
+# Copyright (c) 2006-2008, R Oudkerk
+# Licensed to PSF under a Contributor Agreement.
+#
+
+__all__ = [ 'BaseManager', 'SyncManager', 'BaseProxy', 'Token' ]
+
+#
+# Imports
+#
+
+import sys
+import threading
+import signal
+import array
+import queue
+import time
+import types
+import os
+from os import getpid
+
+from traceback import format_exc
+
+from . import connection
+from .context import reduction, get_spawning_popen, ProcessError
+from . import pool
+from . import process
+from . import util
+from . import get_context
+try:
+    from . import shared_memory
+except ImportError:
+    HAS_SHMEM = False
+else:
+    HAS_SHMEM = True
+    __all__.append('SharedMemoryManager')
+
+#
+# Register some things for pickling
+#
+
+def reduce_array(a):
+    return array.array, (a.typecode, a.tobytes())
+reduction.register(array.array, reduce_array)
+
+view_types = [type(getattr({}, name)()) for name in ('items','keys','values')]
+if view_types[0] is not list:       # only needed in Py3.0
+    def rebuild_as_list(obj):
+        return list, (list(obj),)
+    for view_type in view_types:
+        reduction.register(view_type, rebuild_as_list)
+
+#
+# Type for identifying shared objects
+#
+
+class Token(object):
+    '''
+    Type to uniquely identify a shared object
+    '''
+    __slots__ = ('typeid', 'address', 'id')
+
+    def __init__(self, typeid, address, id):
+        (self.typeid, self.address, self.id) = (typeid, address, id)
+
+    def __getstate__(self):
+        return (self.typeid, self.address, self.id)
+
+    def __setstate__(self, state):
+        (self.typeid, self.address, self.id) = state
+
+    def __repr__(self):
+        return '%s(typeid=%r, address=%r, id=%r)' % \
+               (self.__class__.__name__, self.typeid, self.address, self.id)
+
+#
+# Function for communication with a manager's server process
+#
+
+def dispatch(c, id, methodname, args=(), kwds={}):
+    '''
+    Send a message to manager using connection `c` and return response
+    '''
+    c.send((id, methodname, args, kwds))
+    kind, result = c.recv()
+    if kind == '#RETURN':
+        return result
+    raise convert_to_error(kind, result)
+
+def convert_to_error(kind, result):
+    if kind == '#ERROR':
+        return result
+    elif kind in ('#TRACEBACK', '#UNSERIALIZABLE'):
+        if not isinstance(result, str):
+            raise TypeError(
+                "Result {0!r} (kind '{1}') type is {2}, not str".format(
+                    result, kind, type(result)))
+        if kind == '#UNSERIALIZABLE':
+            return RemoteError('Unserializable message: %s\n' % result)
+        else:
+            return RemoteError(result)
+    else:
+        return ValueError('Unrecognized message type {!r}'.format(kind))
+
+class RemoteError(Exception):
+    def __str__(self):
+        return ('\n' + '-'*75 + '\n' + str(self.args[0]) + '-'*75)
+
+#
+# Functions for finding the method names of an object
+#
+
+def all_methods(obj):
+    '''
+    Return a list of names of methods of `obj`
+    '''
+    temp = []
+    for name in dir(obj):
+        func = getattr(obj, name)
+        if callable(func):
+            temp.append(name)
+    return temp
+
+def public_methods(obj):
+    '''
+    Return a list of names of methods of `obj` which do not start with '_'
+    '''
+    return [name for name in all_methods(obj) if name[0] != '_']
+
+#
+# Server which is run in a process controlled by a manager
+#
+
+class Server(object):
+    '''
+    Server class which runs in a process controlled by a manager object
+    '''
+    public = ['shutdown', 'create', 'accept_connection', 'get_methods',
+              'debug_info', 'number_of_objects', 'dummy', 'incref', 'decref']
+
+    def __init__(self, registry, address, authkey, serializer):
+        if not isinstance(authkey, bytes):
+            raise TypeError(
+                "Authkey {0!r} is type {1!s}, not bytes".format(
+                    authkey, type(authkey)))
+        self.registry = registry
+        self.authkey = process.AuthenticationString(authkey)
+        Listener, Client = listener_client[serializer]
+
+        # do authentication later
+        self.listener = Listener(address=address, backlog=16)
+        self.address = self.listener.address
+
+        self.id_to_obj = {'0': (None, ())}
+        self.id_to_refcount = {}
+        self.id_to_local_proxy_obj = {}
+        self.mutex = threading.Lock()
+
+    def serve_forever(self):
+        '''
+        Run the server forever
+        '''
+        self.stop_event = threading.Event()
+        process.current_process()._manager_server = self
+        try:
+            accepter = threading.Thread(target=self.accepter)
+            accepter.daemon = True
+            accepter.start()
+            try:
+                while not self.stop_event.is_set():
+                    self.stop_event.wait(1)
+            except (KeyboardInterrupt, SystemExit):
+                pass
+        finally:
+            if sys.stdout != sys.__stdout__: # what about stderr?
+                util.debug('resetting stdout, stderr')
+                sys.stdout = sys.__stdout__
+                sys.stderr = sys.__stderr__
+            sys.exit(0)
+
+    def accepter(self):
+        while True:
+            try:
+                c = self.listener.accept()
+            except OSError:
+                continue
+            t = threading.Thread(target=self.handle_request, args=(c,))
+            t.daemon = True
+            t.start()
+
+    def _handle_request(self, c):
+        request = None
+        try:
+            connection.deliver_challenge(c, self.authkey)
+            connection.answer_challenge(c, self.authkey)
+            request = c.recv()
+            ignore, funcname, args, kwds = request
+            assert funcname in self.public, '%r unrecognized' % funcname
+            func = getattr(self, funcname)
+        except Exception:
+            msg = ('#TRACEBACK', format_exc())
+        else:
+            try:
+                result = func(c, *args, **kwds)
+            except Exception:
+                msg = ('#TRACEBACK', format_exc())
+            else:
+                msg = ('#RETURN', result)
+
+        try:
+            c.send(msg)
+        except Exception as e:
+            try:
+                c.send(('#TRACEBACK', format_exc()))
+            except Exception:
+                pass
+            util.info('Failure to send message: %r', msg)
+            util.info(' ... request was %r', request)
+            util.info(' ... exception was %r', e)
+
+    def handle_request(self, conn):
+        '''
+        Handle a new connection
+        '''
+        try:
+            self._handle_request(conn)
+        except SystemExit:
+            # Server.serve_client() calls sys.exit(0) on EOF
+            pass
+        finally:
+            conn.close()
+
+    def serve_client(self, conn):
+        '''
+        Handle requests from the proxies in a particular process/thread
+        '''
+        util.debug('starting server thread to service %r',
+                   threading.current_thread().name)
+
+        recv = conn.recv
+        send = conn.send
+        id_to_obj = self.id_to_obj
+
+        while not self.stop_event.is_set():
+
+            try:
+                methodname = obj = None
+                request = recv()
+                ident, methodname, args, kwds = request
+                try:
+                    obj, exposed, gettypeid = id_to_obj[ident]
+                except KeyError as ke:
+                    try:
+                        obj, exposed, gettypeid = \
+                            self.id_to_local_proxy_obj[ident]
+                    except KeyError:
+                        raise ke
+
+                if methodname not in exposed:
+                    raise AttributeError(
+                        'method %r of %r object is not in exposed=%r' %
+                        (methodname, type(obj), exposed)
+                        )
+
+                function = getattr(obj, methodname)
+
+                try:
+                    res = function(*args, **kwds)
+                except Exception as e:
+                    msg = ('#ERROR', e)
+                else:
+                    typeid = gettypeid and gettypeid.get(methodname, None)
+                    if typeid:
+                        rident, rexposed = self.create(conn, typeid, res)
+                        token = Token(typeid, self.address, rident)
+                        msg = ('#PROXY', (rexposed, token))
+                    else:
+                        msg = ('#RETURN', res)
+
+            except AttributeError:
+                if methodname is None:
+                    msg = ('#TRACEBACK', format_exc())
+                else:
+                    try:
+                        fallback_func = self.fallback_mapping[methodname]
+                        result = fallback_func(
+                            self, conn, ident, obj, *args, **kwds
+                            )
+                        msg = ('#RETURN', result)
+                    except Exception:
+                        msg = ('#TRACEBACK', format_exc())
+
+            except EOFError:
+                util.debug('got EOF -- exiting thread serving %r',
+                           threading.current_thread().name)
+                sys.exit(0)
+
+            except Exception:
+                msg = ('#TRACEBACK', format_exc())
+
+            try:
+                try:
+                    send(msg)
+                except Exception:
+                    send(('#UNSERIALIZABLE', format_exc()))
+            except Exception as e:
+                util.info('exception in thread serving %r',
+                        threading.current_thread().name)
+                util.info(' ... message was %r', msg)
+                util.info(' ... exception was %r', e)
+                conn.close()
+                sys.exit(1)
+
+    def fallback_getvalue(self, conn, ident, obj):
+        return obj
+
+    def fallback_str(self, conn, ident, obj):
+        return str(obj)
+
+    def fallback_repr(self, conn, ident, obj):
+        return repr(obj)
+
+    fallback_mapping = {
+        '__str__':fallback_str,
+        '__repr__':fallback_repr,
+        '#GETVALUE':fallback_getvalue
+        }
+
+    def dummy(self, c):
+        pass
+
+    def debug_info(self, c):
+        '''
+        Return some info --- useful to spot problems with refcounting
+        '''
+        # Perhaps include debug info about 'c'?
+        with self.mutex:
+            result = []
+            keys = list(self.id_to_refcount.keys())
+            keys.sort()
+            for ident in keys:
+                if ident != '0':
+                    result.append('  %s:       refcount=%s\n    %s' %
+                                  (ident, self.id_to_refcount[ident],
+                                   str(self.id_to_obj[ident][0])[:75]))
+            return '\n'.join(result)
+
+    def number_of_objects(self, c):
+        '''
+        Number of shared objects
+        '''
+        # Doesn't use (len(self.id_to_obj) - 1) as we shouldn't count ident='0'
+        return len(self.id_to_refcount)
+
+    def shutdown(self, c):
+        '''
+        Shutdown this process
+        '''
+        try:
+            util.debug('manager received shutdown message')
+            c.send(('#RETURN', None))
+        except:
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.stop_event.set()
+
+    def create(self, c, typeid, /, *args, **kwds):
+        '''
+        Create a new shared object and return its id
+        '''
+        with self.mutex:
+            callable, exposed, method_to_typeid, proxytype = \
+                      self.registry[typeid]
+
+            if callable is None:
+                if kwds or (len(args) != 1):
+                    raise ValueError(
+                        "Without callable, must have one non-keyword argument")
+                obj = args[0]
+            else:
+                obj = callable(*args, **kwds)
+
+            if exposed is None:
+                exposed = public_methods(obj)
+            if method_to_typeid is not None:
+                if not isinstance(method_to_typeid, dict):
+                    raise TypeError(
+                        "Method_to_typeid {0!r}: type {1!s}, not dict".format(
+                            method_to_typeid, type(method_to_typeid)))
+                exposed = list(exposed) + list(method_to_typeid)
+
+            ident = '%x' % id(obj)  # convert to string because xmlrpclib
+                                    # only has 32 bit signed integers
+            util.debug('%r callable returned object with id %r', typeid, ident)
+
+            self.id_to_obj[ident] = (obj, set(exposed), method_to_typeid)
+            if ident not in self.id_to_refcount:
+                self.id_to_refcount[ident] = 0
+
+        self.incref(c, ident)
+        return ident, tuple(exposed)
+
+    def get_methods(self, c, token):
+        '''
+        Return the methods of the shared object indicated by token
+        '''
+        return tuple(self.id_to_obj[token.id][1])
+
+    def accept_connection(self, c, name):
+        '''
+        Spawn a new thread to serve this connection
+        '''
+        threading.current_thread().name = name
+        c.send(('#RETURN', None))
+        self.serve_client(c)
+
+    def incref(self, c, ident):
+        with self.mutex:
+            try:
+                self.id_to_refcount[ident] += 1
+            except KeyError as ke:
+                # If no external references exist but an internal (to the
+                # manager) still does and a new external reference is created
+                # from it, restore the manager's tracking of it from the
+                # previously stashed internal ref.
+                if ident in self.id_to_local_proxy_obj:
+                    self.id_to_refcount[ident] = 1
+                    self.id_to_obj[ident] = \
+                        self.id_to_local_proxy_obj[ident]
+                    obj, exposed, gettypeid = self.id_to_obj[ident]
+                    util.debug('Server re-enabled tracking & INCREF %r', ident)
+                else:
+                    raise ke
+
+    def decref(self, c, ident):
+        if ident not in self.id_to_refcount and \
+            ident in self.id_to_local_proxy_obj:
+            util.debug('Server DECREF skipping %r', ident)
+            return
+
+        with self.mutex:
+            if self.id_to_refcount[ident] <= 0:
+                raise AssertionError(
+                    "Id {0!s} ({1!r}) has refcount {2:n}, not 1+".format(
+                        ident, self.id_to_obj[ident],
+                        self.id_to_refcount[ident]))
+            self.id_to_refcount[ident] -= 1
+            if self.id_to_refcount[ident] == 0:
+                del self.id_to_refcount[ident]
+
+        if ident not in self.id_to_refcount:
+            # Two-step process in case the object turns out to contain other
+            # proxy objects (e.g. a managed list of managed lists).
+            # Otherwise, deleting self.id_to_obj[ident] would trigger the
+            # deleting of the stored value (another managed object) which would
+            # in turn attempt to acquire the mutex that is already held here.
+            self.id_to_obj[ident] = (None, (), None)  # thread-safe
+            util.debug('disposing of obj with id %r', ident)
+            with self.mutex:
+                del self.id_to_obj[ident]
+
+
+#
+# Class to represent state of a manager
+#
+
+class State(object):
+    __slots__ = ['value']
+    INITIAL = 0
+    STARTED = 1
+    SHUTDOWN = 2
+
+#
+# Mapping from serializer name to Listener and Client types
+#
+
+listener_client = {
+    'pickle' : (connection.Listener, connection.Client),
+    'xmlrpclib' : (connection.XmlListener, connection.XmlClient)
+    }
+
+#
+# Definition of BaseManager
+#
+
+class BaseManager(object):
+    '''
+    Base class for managers
+    '''
+    _registry = {}
+    _Server = Server
+
+    def __init__(self, address=None, authkey=None, serializer='pickle',
+                 ctx=None):
+        if authkey is None:
+            authkey = process.current_process().authkey
+        self._address = address     # XXX not final address if eg ('', 0)
+        self._authkey = process.AuthenticationString(authkey)
+        self._state = State()
+        self._state.value = State.INITIAL
+        self._serializer = serializer
+        self._Listener, self._Client = listener_client[serializer]
+        self._ctx = ctx or get_context()
+
+    def get_server(self):
+        '''
+        Return server object with serve_forever() method and address attribute
+        '''
+        if self._state.value != State.INITIAL:
+            if self._state.value == State.STARTED:
+                raise ProcessError("Already started server")
+            elif self._state.value == State.SHUTDOWN:
+                raise ProcessError("Manager has shut down")
+            else:
+                raise ProcessError(
+                    "Unknown state {!r}".format(self._state.value))
+        return Server(self._registry, self._address,
+                      self._authkey, self._serializer)
+
+    def connect(self):
+        '''
+        Connect manager object to the server process
+        '''
+        Listener, Client = listener_client[self._serializer]
+        conn = Client(self._address, authkey=self._authkey)
+        dispatch(conn, None, 'dummy')
+        self._state.value = State.STARTED
+
+    def start(self, initializer=None, initargs=()):
+        '''
+        Spawn a server process for this manager object
+        '''
+        if self._state.value != State.INITIAL:
+            if self._state.value == State.STARTED:
+                raise ProcessError("Already started server")
+            elif self._state.value == State.SHUTDOWN:
+                raise ProcessError("Manager has shut down")
+            else:
+                raise ProcessError(
+                    "Unknown state {!r}".format(self._state.value))
+
+        if initializer is not None and not callable(initializer):
+            raise TypeError('initializer must be a callable')
+
+        # pipe over which we will retrieve address of server
+        reader, writer = connection.Pipe(duplex=False)
+
+        # spawn process which runs a server
+        self._process = self._ctx.Process(
+            target=type(self)._run_server,
+            args=(self._registry, self._address, self._authkey,
+                  self._serializer, writer, initializer, initargs),
+            )
+        ident = ':'.join(str(i) for i in self._process._identity)
+        self._process.name = type(self).__name__  + '-' + ident
+        self._process.start()
+
+        # get address of server
+        writer.close()
+        self._address = reader.recv()
+        reader.close()
+
+        # register a finalizer
+        self._state.value = State.STARTED
+        self.shutdown = util.Finalize(
+            self, type(self)._finalize_manager,
+            args=(self._process, self._address, self._authkey,
+                  self._state, self._Client),
+            exitpriority=0
+            )
+
+    @classmethod
+    def _run_server(cls, registry, address, authkey, serializer, writer,
+                    initializer=None, initargs=()):
+        '''
+        Create a server, report its address and run it
+        '''
+        # bpo-36368: protect server process from KeyboardInterrupt signals
+        signal.signal(signal.SIGINT, signal.SIG_IGN)
+
+        if initializer is not None:
+            initializer(*initargs)
+
+        # create server
+        server = cls._Server(registry, address, authkey, serializer)
+
+        # inform parent process of the server's address
+        writer.send(server.address)
+        writer.close()
+
+        # run the manager
+        util.info('manager serving at %r', server.address)
+        server.serve_forever()
+
+    def _create(self, typeid, /, *args, **kwds):
+        '''
+        Create a new shared object; return the token and exposed tuple
+        '''
+        assert self._state.value == State.STARTED, 'server not yet started'
+        conn = self._Client(self._address, authkey=self._authkey)
+        try:
+            id, exposed = dispatch(conn, None, 'create', (typeid,)+args, kwds)
+        finally:
+            conn.close()
+        return Token(typeid, self._address, id), exposed
+
+    def join(self, timeout=None):
+        '''
+        Join the manager process (if it has been spawned)
+        '''
+        if self._process is not None:
+            self._process.join(timeout)
+            if not self._process.is_alive():
+                self._process = None
+
+    def _debug_info(self):
+        '''
+        Return some info about the servers shared objects and connections
+        '''
+        conn = self._Client(self._address, authkey=self._authkey)
+        try:
+            return dispatch(conn, None, 'debug_info')
+        finally:
+            conn.close()
+
+    def _number_of_objects(self):
+        '''
+        Return the number of shared objects
+        '''
+        conn = self._Client(self._address, authkey=self._authkey)
+        try:
+            return dispatch(conn, None, 'number_of_objects')
+        finally:
+            conn.close()
+
+    def __enter__(self):
+        if self._state.value == State.INITIAL:
+            self.start()
+        if self._state.value != State.STARTED:
+            if self._state.value == State.INITIAL:
+                raise ProcessError("Unable to start server")
+            elif self._state.value == State.SHUTDOWN:
+                raise ProcessError("Manager has shut down")
+            else:
+                raise ProcessError(
+                    "Unknown state {!r}".format(self._state.value))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.shutdown()
+
+    @staticmethod
+    def _finalize_manager(process, address, authkey, state, _Client):
+        '''
+        Shutdown the manager process; will be registered as a finalizer
+        '''
+        if process.is_alive():
+            util.info('sending shutdown message to manager')
+            try:
+                conn = _Client(address, authkey=authkey)
+                try:
+                    dispatch(conn, None, 'shutdown')
+                finally:
+                    conn.close()
+            except Exception:
+                pass
+
+            process.join(timeout=1.0)
+            if process.is_alive():
+                util.info('manager still alive')
+                if hasattr(process, 'terminate'):
+                    util.info('trying to `terminate()` manager process')
+                    process.terminate()
+                    process.join(timeout=1.0)
+                    if process.is_alive():
+                        util.info('manager still alive after terminate')
+
+        state.value = State.SHUTDOWN
+        try:
+            del BaseProxy._address_to_local[address]
+        except KeyError:
+            pass
+
+    @property
+    def address(self):
+        return self._address
+
+    @classmethod
+    def register(cls, typeid, callable=None, proxytype=None, exposed=None,
+                 method_to_typeid=None, create_method=True):
+        '''
+        Register a typeid with the manager type
+        '''
+        if '_registry' not in cls.__dict__:
+            cls._registry = cls._registry.copy()
+
+        if proxytype is None:
+            proxytype = AutoProxy
+
+        exposed = exposed or getattr(proxytype, '_exposed_', None)
+
+        method_to_typeid = method_to_typeid or \
+                           getattr(proxytype, '_method_to_typeid_', None)
+
+        if method_to_typeid:
+            for key, value in list(method_to_typeid.items()): # isinstance?
+                assert type(key) is str, '%r is not a string' % key
+                assert type(value) is str, '%r is not a string' % value
+
+        cls._registry[typeid] = (
+            callable, exposed, method_to_typeid, proxytype
+            )
+
+        if create_method:
+            def temp(self, /, *args, **kwds):
+                util.debug('requesting creation of a shared %r object', typeid)
+                token, exp = self._create(typeid, *args, **kwds)
+                proxy = proxytype(
+                    token, self._serializer, manager=self,
+                    authkey=self._authkey, exposed=exp
+                    )
+                conn = self._Client(token.address, authkey=self._authkey)
+                dispatch(conn, None, 'decref', (token.id,))
+                return proxy
+            temp.__name__ = typeid
+            setattr(cls, typeid, temp)
+
+#
+# Subclass of set which get cleared after a fork
+#
+
+class ProcessLocalSet(set):
+    def __init__(self):
+        util.register_after_fork(self, lambda obj: obj.clear())
+    def __reduce__(self):
+        return type(self), ()
+
+#
+# Definition of BaseProxy
+#
+
+class BaseProxy(object):
+    '''
+    A base for proxies of shared objects
+    '''
+    _address_to_local = {}
+    _mutex = util.ForkAwareThreadLock()
+
+    def __init__(self, token, serializer, manager=None,
+                 authkey=None, exposed=None, incref=True, manager_owned=False):
+        with BaseProxy._mutex:
+            tls_idset = BaseProxy._address_to_local.get(token.address, None)
+            if tls_idset is None:
+                tls_idset = util.ForkAwareLocal(), ProcessLocalSet()
+                BaseProxy._address_to_local[token.address] = tls_idset
+
+        # self._tls is used to record the connection used by this
+        # thread to communicate with the manager at token.address
+        self._tls = tls_idset[0]
+
+        # self._idset is used to record the identities of all shared
+        # objects for which the current process owns references and
+        # which are in the manager at token.address
+        self._idset = tls_idset[1]
+
+        self._token = token
+        self._id = self._token.id
+        self._manager = manager
+        self._serializer = serializer
+        self._Client = listener_client[serializer][1]
+
+        # Should be set to True only when a proxy object is being created
+        # on the manager server; primary use case: nested proxy objects.
+        # RebuildProxy detects when a proxy is being created on the manager
+        # and sets this value appropriately.
+        self._owned_by_manager = manager_owned
+
+        if authkey is not None:
+            self._authkey = process.AuthenticationString(authkey)
+        elif self._manager is not None:
+            self._authkey = self._manager._authkey
+        else:
+            self._authkey = process.current_process().authkey
+
+        if incref:
+            self._incref()
+
+        util.register_after_fork(self, BaseProxy._after_fork)
+
+    def _connect(self):
+        util.debug('making connection to manager')
+        name = process.current_process().name
+        if threading.current_thread().name != 'MainThread':
+            name += '|' + threading.current_thread().name
+        conn = self._Client(self._token.address, authkey=self._authkey)
+        dispatch(conn, None, 'accept_connection', (name,))
+        self._tls.connection = conn
+
+    def _callmethod(self, methodname, args=(), kwds={}):
+        '''
+        Try to call a method of the referent and return a copy of the result
+        '''
+        try:
+            conn = self._tls.connection
+        except AttributeError:
+            util.debug('thread %r does not own a connection',
+                       threading.current_thread().name)
+            self._connect()
+            conn = self._tls.connection
+
+        conn.send((self._id, methodname, args, kwds))
+        kind, result = conn.recv()
+
+        if kind == '#RETURN':
+            return result
+        elif kind == '#PROXY':
+            exposed, token = result
+            proxytype = self._manager._registry[token.typeid][-1]
+            token.address = self._token.address
+            proxy = proxytype(
+                token, self._serializer, manager=self._manager,
+                authkey=self._authkey, exposed=exposed
+                )
+            conn = self._Client(token.address, authkey=self._authkey)
+            dispatch(conn, None, 'decref', (token.id,))
+            return proxy
+        raise convert_to_error(kind, result)
+
+    def _getvalue(self):
+        '''
+        Get a copy of the value of the referent
+        '''
+        return self._callmethod('#GETVALUE')
+
+    def _incref(self):
+        if self._owned_by_manager:
+            util.debug('owned_by_manager skipped INCREF of %r', self._token.id)
+            return
+
+        conn = self._Client(self._token.address, authkey=self._authkey)
+        dispatch(conn, None, 'incref', (self._id,))
+        util.debug('INCREF %r', self._token.id)
+
+        self._idset.add(self._id)
+
+        state = self._manager and self._manager._state
+
+        self._close = util.Finalize(
+            self, BaseProxy._decref,
+            args=(self._token, self._authkey, state,
+                  self._tls, self._idset, self._Client),
+            exitpriority=10
+            )
+
+    @staticmethod
+    def _decref(token, authkey, state, tls, idset, _Client):
+        idset.discard(token.id)
+
+        # check whether manager is still alive
+        if state is None or state.value == State.STARTED:
+            # tell manager this process no longer cares about referent
+            try:
+                util.debug('DECREF %r', token.id)
+                conn = _Client(token.address, authkey=authkey)
+                dispatch(conn, None, 'decref', (token.id,))
+            except Exception as e:
+                util.debug('... decref failed %s', e)
+
+        else:
+            util.debug('DECREF %r -- manager already shutdown', token.id)
+
+        # check whether we can close this thread's connection because
+        # the process owns no more references to objects for this manager
+        if not idset and hasattr(tls, 'connection'):
+            util.debug('thread %r has no more proxies so closing conn',
+                       threading.current_thread().name)
+            tls.connection.close()
+            del tls.connection
+
+    def _after_fork(self):
+        self._manager = None
+        try:
+            self._incref()
+        except Exception as e:
+            # the proxy may just be for a manager which has shutdown
+            util.info('incref failed: %s' % e)
+
+    def __reduce__(self):
+        kwds = {}
+        if get_spawning_popen() is not None:
+            kwds['authkey'] = self._authkey
+
+        if getattr(self, '_isauto', False):
+            kwds['exposed'] = self._exposed_
+            return (RebuildProxy,
+                    (AutoProxy, self._token, self._serializer, kwds))
+        else:
+            return (RebuildProxy,
+                    (type(self), self._token, self._serializer, kwds))
+
+    def __deepcopy__(self, memo):
+        return self._getvalue()
+
+    def __repr__(self):
+        return '<%s object, typeid %r at %#x>' % \
+               (type(self).__name__, self._token.typeid, id(self))
+
+    def __str__(self):
+        '''
+        Return representation of the referent (or a fall-back if that fails)
+        '''
+        try:
+            return self._callmethod('__repr__')
+        except Exception:
+            return repr(self)[:-1] + "; '__str__()' failed>"
+
+#
+# Function used for unpickling
+#
+
+def RebuildProxy(func, token, serializer, kwds):
+    '''
+    Function used for unpickling proxy objects.
+    '''
+    server = getattr(process.current_process(), '_manager_server', None)
+    if server and server.address == token.address:
+        util.debug('Rebuild a proxy owned by manager, token=%r', token)
+        kwds['manager_owned'] = True
+        if token.id not in server.id_to_local_proxy_obj:
+            server.id_to_local_proxy_obj[token.id] = \
+                server.id_to_obj[token.id]
+    incref = (
+        kwds.pop('incref', True) and
+        not getattr(process.current_process(), '_inheriting', False)
+        )
+    return func(token, serializer, incref=incref, **kwds)
+
+#
+# Functions to create proxies and proxy types
+#
+
+def MakeProxyType(name, exposed, _cache={}):
+    '''
+    Return a proxy type whose methods are given by `exposed`
+    '''
+    exposed = tuple(exposed)
+    try:
+        return _cache[(name, exposed)]
+    except KeyError:
+        pass
+
+    dic = {}
+
+    for meth in exposed:
+        exec('''def %s(self, /, *args, **kwds):
+        return self._callmethod(%r, args, kwds)''' % (meth, meth), dic)
+
+    ProxyType = type(name, (BaseProxy,), dic)
+    ProxyType._exposed_ = exposed
+    _cache[(name, exposed)] = ProxyType
+    return ProxyType
+
+
+def AutoProxy(token, serializer, manager=None, authkey=None,
+              exposed=None, incref=True, manager_owned=False):
+    '''
+    Return an auto-proxy for `token`
+    '''
+    _Client = listener_client[serializer][1]
+
+    if exposed is None:
+        conn = _Client(token.address, authkey=authkey)
+        try:
+            exposed = dispatch(conn, None, 'get_methods', (token,))
+        finally:
+            conn.close()
+
+    if authkey is None and manager is not None:
+        authkey = manager._authkey
+    if authkey is None:
+        authkey = process.current_process().authkey
+
+    ProxyType = MakeProxyType('AutoProxy[%s]' % token.typeid, exposed)
+    proxy = ProxyType(token, serializer, manager=manager, authkey=authkey,
+                      incref=incref, manager_owned=manager_owned)
+    proxy._isauto = True
+    return proxy
+
+#
+# Types/callables which we will register with SyncManager
+#
+
+class Namespace(object):
+    def __init__(self, /, **kwds):
+        self.__dict__.update(kwds)
+    def __repr__(self):
+        items = list(self.__dict__.items())
+        temp = []
+        for name, value in items:
+            if not name.startswith('_'):
+                temp.append('%s=%r' % (name, value))
+        temp.sort()
+        return '%s(%s)' % (self.__class__.__name__, ', '.join(temp))
+
+class Value(object):
+    def __init__(self, typecode, value, lock=True):
+        self._typecode = typecode
+        self._value = value
+    def get(self):
+        return self._value
+    def set(self, value):
+        self._value = value
+    def __repr__(self):
+        return '%s(%r, %r)'%(type(self).__name__, self._typecode, self._value)
+    value = property(get, set)
+
+def Array(typecode, sequence, lock=True):
+    return array.array(typecode, sequence)
+
+#
+# Proxy types used by SyncManager
+#
+
+class IteratorProxy(BaseProxy):
+    _exposed_ = ('__next__', 'send', 'throw', 'close')
+    def __iter__(self):
+        return self
+    def __next__(self, *args):
+        return self._callmethod('__next__', args)
+    def send(self, *args):
+        return self._callmethod('send', args)
+    def throw(self, *args):
+        return self._callmethod('throw', args)
+    def close(self, *args):
+        return self._callmethod('close', args)
+
+
+class AcquirerProxy(BaseProxy):
+    _exposed_ = ('acquire', 'release')
+    def acquire(self, blocking=True, timeout=None):
+        args = (blocking,) if timeout is None else (blocking, timeout)
+        return self._callmethod('acquire', args)
+    def release(self):
+        return self._callmethod('release')
+    def __enter__(self):
+        return self._callmethod('acquire')
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        return self._callmethod('release')
+
+
+class ConditionProxy(AcquirerProxy):
+    _exposed_ = ('acquire', 'release', 'wait', 'notify', 'notify_all')
+    def wait(self, timeout=None):
+        return self._callmethod('wait', (timeout,))
+    def notify(self, n=1):
+        return self._callmethod('notify', (n,))
+    def notify_all(self):
+        return self._callmethod('notify_all')
+    def wait_for(self, predicate, timeout=None):
+        result = predicate()
+        if result:
+            return result
+        if timeout is not None:
+            endtime = time.monotonic() + timeout
+        else:
+            endtime = None
+            waittime = None
+        while not result:
+            if endtime is not None:
+                waittime = endtime - time.monotonic()
+                if waittime <= 0:
+                    break
+            self.wait(waittime)
+            result = predicate()
+        return result
+
+
+class EventProxy(BaseProxy):
+    _exposed_ = ('is_set', 'set', 'clear', 'wait')
+    def is_set(self):
+        return self._callmethod('is_set')
+    def set(self):
+        return self._callmethod('set')
+    def clear(self):
+        return self._callmethod('clear')
+    def wait(self, timeout=None):
+        return self._callmethod('wait', (timeout,))
+
+
+class BarrierProxy(BaseProxy):
+    _exposed_ = ('__getattribute__', 'wait', 'abort', 'reset')
+    def wait(self, timeout=None):
+        return self._callmethod('wait', (timeout,))
+    def abort(self):
+        return self._callmethod('abort')
+    def reset(self):
+        return self._callmethod('reset')
+    @property
+    def parties(self):
+        return self._callmethod('__getattribute__', ('parties',))
+    @property
+    def n_waiting(self):
+        return self._callmethod('__getattribute__', ('n_waiting',))
+    @property
+    def broken(self):
+        return self._callmethod('__getattribute__', ('broken',))
+
+
+class NamespaceProxy(BaseProxy):
+    _exposed_ = ('__getattribute__', '__setattr__', '__delattr__')
+    def __getattr__(self, key):
+        if key[0] == '_':
+            return object.__getattribute__(self, key)
+        callmethod = object.__getattribute__(self, '_callmethod')
+        return callmethod('__getattribute__', (key,))
+    def __setattr__(self, key, value):
+        if key[0] == '_':
+            return object.__setattr__(self, key, value)
+        callmethod = object.__getattribute__(self, '_callmethod')
+        return callmethod('__setattr__', (key, value))
+    def __delattr__(self, key):
+        if key[0] == '_':
+            return object.__delattr__(self, key)
+        callmethod = object.__getattribute__(self, '_callmethod')
+        return callmethod('__delattr__', (key,))
+
+
+class ValueProxy(BaseProxy):
+    _exposed_ = ('get', 'set')
+    def get(self):
+        return self._callmethod('get')
+    def set(self, value):
+        return self._callmethod('set', (value,))
+    value = property(get, set)
+
+    __class_getitem__ = classmethod(types.GenericAlias)
+
+
+BaseListProxy = MakeProxyType('BaseListProxy', (
+    '__add__', '__contains__', '__delitem__', '__getitem__', '__len__',
+    '__mul__', '__reversed__', '__rmul__', '__setitem__',
+    'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove',
+    'reverse', 'sort', '__imul__'
+    ))
+class ListProxy(BaseListProxy):
+    def __iadd__(self, value):
+        self._callmethod('extend', (value,))
+        return self
+    def __imul__(self, value):
+        self._callmethod('__imul__', (value,))
+        return self
+
+
+DictProxy = MakeProxyType('DictProxy', (
+    '__contains__', '__delitem__', '__getitem__', '__iter__', '__len__',
+    '__setitem__', 'clear', 'copy', 'get', 'items',
+    'keys', 'pop', 'popitem', 'setdefault', 'update', 'values'
+    ))
+DictProxy._method_to_typeid_ = {
+    '__iter__': 'Iterator',
+    }
+
+
+ArrayProxy = MakeProxyType('ArrayProxy', (
+    '__len__', '__getitem__', '__setitem__'
+    ))
+
+
+BasePoolProxy = MakeProxyType('PoolProxy', (
+    'apply', 'apply_async', 'close', 'imap', 'imap_unordered', 'join',
+    'map', 'map_async', 'starmap', 'starmap_async', 'terminate',
+    ))
+BasePoolProxy._method_to_typeid_ = {
+    'apply_async': 'AsyncResult',
+    'map_async': 'AsyncResult',
+    'starmap_async': 'AsyncResult',
+    'imap': 'Iterator',
+    'imap_unordered': 'Iterator'
+    }
+class PoolProxy(BasePoolProxy):
+    def __enter__(self):
+        return self
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.terminate()
+
+#
+# Definition of SyncManager
+#
+
+class SyncManager(BaseManager):
+    '''
+    Subclass of `BaseManager` which supports a number of shared object types.
+
+    The types registered are those intended for the synchronization
+    of threads, plus `dict`, `list` and `Namespace`.
+
+    The `multiprocessing.Manager()` function creates started instances of
+    this class.
+    '''
+
+SyncManager.register('Queue', queue.Queue)
+SyncManager.register('JoinableQueue', queue.Queue)
+SyncManager.register('Event', threading.Event, EventProxy)
+SyncManager.register('Lock', threading.Lock, AcquirerProxy)
+SyncManager.register('RLock', threading.RLock, AcquirerProxy)
+SyncManager.register('Semaphore', threading.Semaphore, AcquirerProxy)
+SyncManager.register('BoundedSemaphore', threading.BoundedSemaphore,
+                     AcquirerProxy)
+SyncManager.register('Condition', threading.Condition, ConditionProxy)
+SyncManager.register('Barrier', threading.Barrier, BarrierProxy)
+SyncManager.register('Pool', pool.Pool, PoolProxy)
+SyncManager.register('list', list, ListProxy)
+SyncManager.register('dict', dict, DictProxy)
+SyncManager.register('Value', Value, ValueProxy)
+SyncManager.register('Array', Array, ArrayProxy)
+SyncManager.register('Namespace', Namespace, NamespaceProxy)
+
+# types returned by methods of PoolProxy
+SyncManager.register('Iterator', proxytype=IteratorProxy, create_method=False)
+SyncManager.register('AsyncResult', create_method=False)
+
+#
+# Definition of SharedMemoryManager and SharedMemoryServer
+#
+
+if HAS_SHMEM:
+    class _SharedMemoryTracker:
+        "Manages one or more shared memory segments."
+
+        def __init__(self, name, segment_names=[]):
+            self.shared_memory_context_name = name
+            self.segment_names = segment_names
+
+        def register_segment(self, segment_name):
+            "Adds the supplied shared memory block name to tracker."
+            util.debug(f"Register segment {segment_name!r} in pid {getpid()}")
+            self.segment_names.append(segment_name)
+
+        def destroy_segment(self, segment_name):
+            """Calls unlink() on the shared memory block with the supplied name
+            and removes it from the list of blocks being tracked."""
+            util.debug(f"Destroy segment {segment_name!r} in pid {getpid()}")
+            self.segment_names.remove(segment_name)
+            segment = shared_memory.SharedMemory(segment_name)
+            segment.close()
+            segment.unlink()
+
+        def unlink(self):
+            "Calls destroy_segment() on all tracked shared memory blocks."
+            for segment_name in self.segment_names[:]:
+                self.destroy_segment(segment_name)
+
+        def __del__(self):
+            util.debug(f"Call {self.__class__.__name__}.__del__ in {getpid()}")
+            self.unlink()
+
+        def __getstate__(self):
+            return (self.shared_memory_context_name, self.segment_names)
+
+        def __setstate__(self, state):
+            self.__init__(*state)
+
+
+    class SharedMemoryServer(Server):
+
+        public = Server.public + \
+                 ['track_segment', 'release_segment', 'list_segments']
+
+        def __init__(self, *args, **kwargs):
+            Server.__init__(self, *args, **kwargs)
+            address = self.address
+            # The address of Linux abstract namespaces can be bytes
+            if isinstance(address, bytes):
+                address = os.fsdecode(address)
+            self.shared_memory_context = \
+                _SharedMemoryTracker(f"shm_{address}_{getpid()}")
+            util.debug(f"SharedMemoryServer started by pid {getpid()}")
+
+        def create(self, c, typeid, /, *args, **kwargs):
+            """Create a new distributed-shared object (not backed by a shared
+            memory block) and return its id to be used in a Proxy Object."""
+            # Unless set up as a shared proxy, don't make shared_memory_context
+            # a standard part of kwargs.  This makes things easier for supplying
+            # simple functions.
+            if hasattr(self.registry[typeid][-1], "_shared_memory_proxy"):
+                kwargs['shared_memory_context'] = self.shared_memory_context
+            return Server.create(self, c, typeid, *args, **kwargs)
+
+        def shutdown(self, c):
+            "Call unlink() on all tracked shared memory, terminate the Server."
+            self.shared_memory_context.unlink()
+            return Server.shutdown(self, c)
+
+        def track_segment(self, c, segment_name):
+            "Adds the supplied shared memory block name to Server's tracker."
+            self.shared_memory_context.register_segment(segment_name)
+
+        def release_segment(self, c, segment_name):
+            """Calls unlink() on the shared memory block with the supplied name
+            and removes it from the tracker instance inside the Server."""
+            self.shared_memory_context.destroy_segment(segment_name)
+
+        def list_segments(self, c):
+            """Returns a list of names of shared memory blocks that the Server
+            is currently tracking."""
+            return self.shared_memory_context.segment_names
+
+
+    class SharedMemoryManager(BaseManager):
+        """Like SyncManager but uses SharedMemoryServer instead of Server.
+
+        It provides methods for creating and returning SharedMemory instances
+        and for creating a list-like object (ShareableList) backed by shared
+        memory.  It also provides methods that create and return Proxy Objects
+        that support synchronization across processes (i.e. multi-process-safe
+        locks and semaphores).
+        """
+
+        _Server = SharedMemoryServer
+
+        def __init__(self, *args, **kwargs):
+            if os.name == "posix":
+                # bpo-36867: Ensure the resource_tracker is running before
+                # launching the manager process, so that concurrent
+                # shared_memory manipulation both in the manager and in the
+                # current process does not create two resource_tracker
+                # processes.
+                from . import resource_tracker
+                resource_tracker.ensure_running()
+            BaseManager.__init__(self, *args, **kwargs)
+            util.debug(f"{self.__class__.__name__} created by pid {getpid()}")
+
+        def __del__(self):
+            util.debug(f"{self.__class__.__name__}.__del__ by pid {getpid()}")
+            pass
+
+        def get_server(self):
+            'Better than monkeypatching for now; merge into Server ultimately'
+            if self._state.value != State.INITIAL:
+                if self._state.value == State.STARTED:
+                    raise ProcessError("Already started SharedMemoryServer")
+                elif self._state.value == State.SHUTDOWN:
+                    raise ProcessError("SharedMemoryManager has shut down")
+                else:
+                    raise ProcessError(
+                        "Unknown state {!r}".format(self._state.value))
+            return self._Server(self._registry, self._address,
+                                self._authkey, self._serializer)
+
+        def SharedMemory(self, size):
+            """Returns a new SharedMemory instance with the specified size in
+            bytes, to be tracked by the manager."""
+            with self._Client(self._address, authkey=self._authkey) as conn:
+                sms = shared_memory.SharedMemory(None, create=True, size=size)
+                try:
+                    dispatch(conn, None, 'track_segment', (sms.name,))
+                except BaseException as e:
+                    sms.unlink()
+                    raise e
+            return sms
+
+        def ShareableList(self, sequence):
+            """Returns a new ShareableList instance populated with the values
+            from the input sequence, to be tracked by the manager."""
+            with self._Client(self._address, authkey=self._authkey) as conn:
+                sl = shared_memory.ShareableList(sequence)
+                try:
+                    dispatch(conn, None, 'track_segment', (sl.shm.name,))
+                except BaseException as e:
+                    sl.shm.unlink()
+                    raise e
+            return sl
diff --git a/shared/package-files/32/32d7b39c6d2ac9779b4db6f614a27f512ae15781dc3da39011b9a4da003f24fd/_aminmax_compositeexplicitautograd_dispatch.h b/shared/package-files/32/32d7b39c6d2ac9779b4db6f614a27f512ae15781dc3da39011b9a4da003f24fd/_aminmax_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ddcb0dc25ad9d91e2069a7d5edcddccbfcf3df59
--- /dev/null
+++ b/shared/package-files/32/32d7b39c6d2ac9779b4db6f614a27f512ae15781dc3da39011b9a4da003f24fd/_aminmax_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_outf(const at::Tensor & self, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, int64_t dim, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/32/32dbb792fde36c675a0ba941c5f3a69effbf53e5abf20792bf0a76aeeda899ff/tensor_ops.py b/shared/package-files/32/32dbb792fde36c675a0ba941c5f3a69effbf53e5abf20792bf0a76aeeda899ff/tensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bfc4c60e91026f35bc87ba5ed791c3e1d6abd6b
--- /dev/null
+++ b/shared/package-files/32/32dbb792fde36c675a0ba941c5f3a69effbf53e5abf20792bf0a76aeeda899ff/tensor_ops.py
@@ -0,0 +1,826 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import itertools
+from typing import cast, List, Optional, Sequence, Tuple
+
+import torch
+
+from torch.distributed._tensor._utils import compute_local_shape
+from torch.distributed._tensor.op_schema import (
+    OpSchema,
+    OpStrategy,
+    OutputSharding,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed._tensor.ops.common_rules import pointwise_rule
+from torch.distributed._tensor.ops.embedding_ops import _MaskPartial
+from torch.distributed._tensor.ops.utils import (
+    generate_redistribute_costs,
+    is_tensor_dim_sharded,
+    is_tensor_partial,
+    is_tensor_shardable,
+    normalize_dim,
+    prod,
+    register_op_strategy,
+    register_prop_rule,
+)
+from torch.distributed._tensor.placement_types import (
+    _Partial,
+    DTensorSpec,
+    Placement,
+    Replicate,
+    Shard,
+)
+from torch.distributed.device_mesh import DeviceMesh
+
+
+aten = torch.ops.aten
+
+
+def default_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    # Default strategy by default just propagate the first input strategy
+    select_strategy = op_schema.args_schema[0]
+    assert isinstance(select_strategy, OpStrategy)
+    default_strategy = []
+    for strategy in select_strategy.strategies:
+        # we create new DTensorSpecs even for default strategy to assure that
+        # the tensor metas are distinct between the arguments and outputs
+        default_strategy.append(
+            PlacementStrategy(
+                output_specs=DTensorSpec(
+                    mesh=strategy.output_spec.mesh,
+                    placements=strategy.output_spec.placements,
+                )
+            )
+        )
+    return OpStrategy(default_strategy)
+
+
+register_op_strategy(
+    [
+        aten.clone.default,
+        aten.contiguous.default,
+        aten.copy_.default,
+        aten.detach.default,
+        aten.fill_.Scalar,
+        aten.zero_.default,
+    ]
+)(default_strategy)
+
+register_op_strategy(
+    aten._to_copy.default, schema_info=RuntimeSchemaInfo(static_kwargkey=["dtype"])
+)(default_strategy)
+
+
+@register_op_strategy(
+    [
+        aten.equal.default,
+        aten.is_same_size.default,
+    ]
+)
+def equal_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    # equal_strategy deals with ops that comparing two tensor, we need to make sure
+    # sharding layout the same with two operands, we choose to follow the arg with max
+    # num of shards, still keep is_same_size here for completeness as they share the
+    # same strategy in theory.
+    self_strategy, other_strategy = op_schema.args_schema
+    assert isinstance(self_strategy, OpStrategy)
+    assert isinstance(other_strategy, OpStrategy)
+
+    select_strategy = (
+        self_strategy
+        if self_strategy.max_num_shards() >= other_strategy.max_num_shards()
+        else other_strategy
+    )
+    equal_strategy = OpStrategy([])
+
+    for arg_strategy in select_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if is_tensor_partial(arg_spec):
+            # if the arg_spec have partial, reshard to replicate
+            # otherwise local shard tensor comparison would be invalid
+            output_spec = DTensorSpec(
+                mesh=arg_spec.mesh,
+                placements=tuple(
+                    Replicate() if isinstance(p, _Partial) else p
+                    for p in arg_spec.placements
+                ),
+            )
+            equal_strategy.strategies.append(
+                PlacementStrategy(output_specs=output_spec)
+            )
+        else:
+            equal_strategy.strategies.append(PlacementStrategy(arg_spec))
+    return equal_strategy
+
+
+@register_op_strategy(
+    [
+        aten.empty_like.default,
+        aten.ones_like.default,
+        aten.rand_like.default,
+        aten.randn_like.default,
+        aten.zeros_like.default,
+    ],
+    schema_info=RuntimeSchemaInfo(1, ["dtype"]),
+)
+@register_op_strategy(
+    [aten.full_like.default],
+    schema_info=RuntimeSchemaInfo(2, ["dtype"]),
+)
+@register_op_strategy(
+    [
+        aten.randint_like.default,
+        aten.randint_like.low_dtype,
+        aten.randint_like.low_dtype_out,
+    ],
+    schema_info=RuntimeSchemaInfo(3, ["dtype"]),
+)
+def create_like_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    # create_like_strategy deals with ops that creating tensors with same
+    # shape as input, but with specific content that does not depend on
+    # the input, we can propagate sharding, but we have to make sure we
+    # move from partial to replicated.
+    select_strategy = op_schema.args_schema[0]
+    create_like_strategy = OpStrategy([])
+    assert isinstance(select_strategy, OpStrategy)
+    for arg_strategy in select_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if is_tensor_partial(arg_spec):
+            # if the arg_spec have partial, accept partial
+            # in the input_specs but output replicate for
+            # those corresponding mesh dims
+            output_spec = DTensorSpec(
+                mesh=arg_spec.mesh,
+                placements=tuple(
+                    Replicate() if isinstance(p, _Partial) else p
+                    for p in arg_spec.placements
+                ),
+            )
+            create_like_strategy.strategies.append(
+                PlacementStrategy(output_specs=output_spec, input_specs=(arg_spec,))
+            )
+
+        else:
+            create_like_strategy.strategies.append(PlacementStrategy(arg_spec))
+
+    return create_like_strategy
+
+
+@register_op_strategy(
+    [
+        aten.new_empty.default,
+        aten.new_full.default,
+        aten.new_ones.default,
+        aten.new_zeros.default,
+        aten.new_empty_strided.default,  # TODO: re-think new_empty_strided
+    ],
+    schema_info=RuntimeSchemaInfo(1, ["dtype"]),
+)
+def new_factory_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    # TODO: maybe we should generate all possible shardings intead of just stay
+    # replicated for new factory methods
+    input_strategy = op_schema.args_schema[0]
+    new_factory_strategy = OpStrategy([])
+    assert isinstance(input_strategy, OpStrategy)
+    for arg_strategy in input_strategy.strategies:
+        input_spec = arg_strategy.output_spec
+        replica_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+        new_factory_strategy.strategies.append(
+            PlacementStrategy(output_specs=replica_spec, input_specs=(input_spec,))
+        )
+
+    return new_factory_strategy
+
+
+@register_op_strategy(aten.bucketize.Tensor)
+def gen_bucketize_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    """Just propagate input sharding, but expect replicated for boundaries input."""
+    input_strategy = op_schema.args_schema[0]
+    bucketize_strategy = OpStrategy([])
+    assert isinstance(input_strategy, OpStrategy)
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = DTensorSpec(mesh, arg_strategy.output_spec.placements)
+        replica_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+        bucketize_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=arg_spec, input_specs=(arg_spec, replica_spec)
+            )
+        )
+
+    return bucketize_strategy
+
+
+@register_op_strategy(aten.slice.Tensor, schema_info=RuntimeSchemaInfo(1))
+def gen_slice_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    """Forward all shardings except the slice dimension."""
+    defaults = (None, 0, None, None, 1)
+    input_strategy, dim, start, end, step = (
+        op_schema.args_schema + defaults[len(op_schema.args_schema) :]
+    )
+    assert isinstance(input_strategy, OpStrategy)
+    input_shape = input_strategy.output_shape
+    input_ndim = input_strategy.output_ndim
+    assert isinstance(dim, int)
+    if start is None:
+        start = 0
+    if end is None or end > input_shape[dim]:
+        end = input_shape[dim]
+    assert isinstance(start, int)
+    assert isinstance(end, int)
+    assert isinstance(step, int)
+
+    # normalize args
+    slice_dim = normalize_dim(dim, input_ndim)
+    start = normalize_dim(start, input_shape[dim])
+    end = normalize_dim(end, input_shape[dim])
+
+    redundant_slice = start == 0 and end == input_shape[dim] and step == 1
+
+    slice_strategy = OpStrategy([])
+
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if not is_tensor_dim_sharded(arg_spec, dim=slice_dim) or redundant_slice:
+            # only add the strategy if the slice dim is not sharded
+            out_spec = DTensorSpec(mesh, arg_spec.placements)
+            slice_strategy.strategies.append(PlacementStrategy(output_specs=out_spec))
+    if not slice_strategy.strategies:
+        # if all strategies are filtered out, unsharding all specs on slice dim
+        # of the input strategy, and use that as the op strategy
+        for arg_strategy in input_strategy.strategies:
+            arg_spec = arg_strategy.output_spec
+            unshard_spec = DTensorSpec(
+                mesh, unshard_tensor_dim(arg_spec.placements, dim=slice_dim)
+            )
+            slice_strategy.strategies.append(
+                PlacementStrategy(output_specs=unshard_spec)
+            )
+    return slice_strategy
+
+
+def unshard_tensor_dim(
+    placements: Sequence[Placement], dim: int
+) -> Tuple[Placement, ...]:
+    """Disallow the given tensor dimension to be sharded."""
+    return tuple(
+        p if (not isinstance(p, Shard) or p.dim != dim) else Replicate()
+        for p in placements
+    )
+
+
+def replicate_tensor_dim(
+    placements: Sequence[Placement], dim: int
+) -> Tuple[Placement, ...]:
+    """Force the given tensor dimension to be replicated."""
+    # Not using p.is_shard() to avoid mypy complain about Placement not having
+    # attribute dim.
+    return tuple(
+        Replicate() if p.is_partial() or isinstance(p, Shard) and p.dim == dim else p
+        for p in placements
+    )
+
+
+@register_op_strategy(aten.slice_scatter.default, schema_info=RuntimeSchemaInfo(2))
+def gen_slice_scatter_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    # 1. number of dimensions in input and src need to match.
+    # 2. number of elements on all non-dim need to match between input and src.
+    # 3. numer of elements in src in dim need to match the slice size.
+    # Given the above:
+    # - We suggest for src to follow the sharding of input, except on the scatter dimension,
+    #   where our best bet for now is to make them replicated as a fall-back.
+    #   TODO: Ideally we'd like to make sure the output is re-sharded afterwards to keep input sharding.
+
+    input_strategy = op_schema.args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+    input_ndim = input_strategy.output_ndim
+    slice_dim = (
+        cast(int, op_schema.args_schema[2]) if len(op_schema.args_schema) > 2 else 0
+    )
+    slice_dim = normalize_dim(slice_dim, input_ndim)
+
+    slice_scatter_strategy = OpStrategy([])
+    # by default follow the input strategy for both input and src
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if not (
+            is_tensor_dim_sharded(arg_spec, dim=slice_dim)
+            or is_tensor_partial(arg_spec)
+        ):
+            # only add the strategy if the slice_scatter dim is not sharded or partial
+            slice_scatter_strategy.strategies.append(
+                PlacementStrategy(output_specs=arg_spec)
+            )
+
+    if not slice_scatter_strategy.strategies:
+        # if all strategies are filtered out, replicating all specs on slice_scatter dim
+        # of the input strategy, and use that as the op strategy
+        for arg_strategy in input_strategy.strategies:
+            arg_spec = arg_strategy.output_spec
+            replicate_spec = DTensorSpec(
+                mesh, replicate_tensor_dim(arg_spec.placements, dim=slice_dim)
+            )
+            slice_scatter_strategy.strategies.append(
+                PlacementStrategy(output_specs=replicate_spec)
+            )
+    return slice_scatter_strategy
+
+
+@register_op_strategy(aten._local_scalar_dense.default)
+def replica_only_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    """Only allow replication on the input/output."""
+    replicate_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+    return OpStrategy([PlacementStrategy(replicate_spec)])
+
+
+@register_op_strategy(aten.gather.default)
+def gather_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    input_strategy = cast(OpStrategy, op_schema.args_schema[0])
+    dim = cast(int, op_schema.args_schema[1])
+    index_strategy = cast(OpStrategy, op_schema.args_schema[2])
+
+    input_shape = input_strategy.output_shape
+    index_shape = index_strategy.output_shape
+
+    all_mesh_dim_strategies = []
+
+    for mesh_dim in range(mesh.ndim):
+        single_mesh_dim_strategies = []
+
+        # placement list stores placements of [output, input, index]
+        # first we always have replicate all for inputs and output
+        all_replicate: List[Placement] = [Replicate()] * 3
+        single_mesh_dim_strategies.append(all_replicate)
+
+        # input sharding, input sharded, index accepts mask partial, output follows index
+        # this only works when the input is sharded on the gather dimension, and
+        # index has size 1 on the gather dimension
+        if index_shape[dim] == 1:
+            index_partial_placement = _MaskPartial(logical_dim_size=input_shape[dim])
+            input_sharding = [
+                index_partial_placement,
+                Shard(dim),
+                index_partial_placement,
+            ]
+            single_mesh_dim_strategies.append(input_sharding)
+
+        # index sharding, input replicated, index sharded, output follows index
+        # this only works when the sharding dimension is the gather dimension
+        index_sharding = [Shard(dim), Replicate(), Shard(dim)]
+        single_mesh_dim_strategies.append(index_sharding)
+
+        all_mesh_dim_strategies.append(single_mesh_dim_strategies)
+
+    strategy_combs = itertools.product(*all_mesh_dim_strategies)
+
+    all_strategies = []
+    for strategy_comb in strategy_combs:
+        spec_list = []
+        for specs in zip(*strategy_comb):
+            spec_list.append(DTensorSpec(mesh, tuple(specs)))
+
+        if is_tensor_shardable(input_shape, spec_list[1]) and is_tensor_shardable(
+            index_shape, spec_list[2]
+        ):
+            input_spec, index_spec = spec_list[1:]
+            redistribute_cost = [
+                generate_redistribute_costs(input_strategy, input_spec),
+                generate_redistribute_costs(index_strategy, index_spec),
+            ]
+            strat = PlacementStrategy(
+                output_specs=spec_list[0],
+                input_specs=spec_list[1:],
+                redistribute_cost=redistribute_cost,
+            )
+            all_strategies.append(strat)
+
+    return OpStrategy(all_strategies)
+
+
+@register_op_strategy(aten.stack.default, RuntimeSchemaInfo(1, needs_pytree=True))
+def stack_strategy(mesh: DeviceMesh, op_schema: OpSchema) -> StrategyType:
+    args_schema = op_schema.args_schema
+    input_tuple_strategy = args_schema[0]
+    assert isinstance(input_tuple_strategy, TupleStrategy), f"{input_tuple_strategy}"
+    dim = cast(int, args_schema[1]) if len(args_schema) > 1 else 0
+
+    # Follow the 1st child strategy's placement strategies
+    child_strategy = input_tuple_strategy.childs[0]
+    assert isinstance(child_strategy, OpStrategy), f"{child_strategy}"
+    strategies: List[PlacementStrategy] = []
+
+    # For each arg strategy of the child to follow, we check if every other
+    # child has an equal strategy. If so, then that is a valid strategy. If
+    # there are no such valid strategies, then we replicate.
+    for arg_strategy in child_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        # For each arg strategy (whether the one to follow or other), we
+        # replicate the stack dim since we cannot stack on a sharded dim
+        if is_tensor_dim_sharded(arg_spec, dim):
+            arg_spec = DTensorSpec(
+                mesh, unshard_tensor_dim(arg_spec.placements, dim=dim)
+            )
+        all_compatible = True
+        for other_child_strategy in input_tuple_strategy.childs[1:]:
+            has_compatible_strategy = False
+            assert isinstance(
+                other_child_strategy, OpStrategy
+            ), f"{other_child_strategy}"
+            for other_arg_strategy in other_child_strategy.strategies:
+                other_arg_spec = other_arg_strategy.output_spec
+                if is_tensor_dim_sharded(other_arg_spec, dim):
+                    other_arg_spec = DTensorSpec(
+                        mesh, unshard_tensor_dim(other_arg_spec.placements, dim=dim)
+                    )
+                if other_arg_spec.placements == arg_spec.placements:
+                    has_compatible_strategy = True
+                    break
+            if not has_compatible_strategy:
+                all_compatible = False
+                break
+        if all_compatible:
+            input_specs = tuple(
+                arg_spec for _ in range(len(input_tuple_strategy.childs))
+            )
+            strategies.append(
+                PlacementStrategy(
+                    output_specs=DTensorSpec(mesh, arg_spec.placements),
+                    input_specs=input_specs,
+                )
+            )
+    if not strategies:
+        # Arbitrarily use each child strategy's 0th strategy's output spec
+        input_specs = tuple(
+            cast(OpStrategy, child_strategy).strategies[0].output_spec
+            for child_strategy in input_tuple_strategy.childs
+        )
+        replicate_spec = DTensorSpec(mesh, tuple(Replicate() for _ in range(mesh.ndim)))
+        strategies.append(PlacementStrategy(output_specs=replicate_spec))
+    return OpStrategy(strategies)
+
+
+@register_prop_rule(aten.index_select.default, schema_info=RuntimeSchemaInfo(1))
+def prop_index_select(op_schema: OpSchema) -> OutputSharding:
+    values_spec, dim, indices_spec = op_schema.args_schema
+
+    assert isinstance(values_spec, DTensorSpec)
+    assert isinstance(dim, int)
+    assert isinstance(indices_spec, DTensorSpec)
+
+    all_indices_spec: List[Optional[DTensorSpec]] = [
+        indices_spec if dim == i else None for i in range(values_spec.ndim)
+    ]
+
+    result = prop_index(
+        OpSchema(
+            op=op_schema.op,
+            args_schema=(values_spec, all_indices_spec),
+            kwargs_schema=op_schema.kwargs_schema,
+        )
+    )
+    if result.schema_suggestions:
+        result.schema_suggestions = [
+            OpSchema(
+                op=op_schema.op,
+                args_schema=(s.args_schema[0], dim, s.args_schema[1][dim]),
+                kwargs_schema=op_schema.kwargs_schema,
+            )
+            for s in result.schema_suggestions
+        ]
+    return result
+
+
+@register_prop_rule(aten.index.Tensor, schema_info=RuntimeSchemaInfo(needs_pytree=True))
+def prop_index(op_schema: OpSchema) -> OutputSharding:
+    """
+    Expect replicated on the first input; _mostly_ pointwise on the second input.
+
+    TODO: exception: when the dtype of second input is "bool", then a torch.nonzero needs to be triggered first.
+    """
+    # Current sharding constraints:
+    # For values:
+    #   1. We currently require that the dimension of values_spec be replicated or partial
+    #      if they are being indexed on.
+    #   2. Other dimensions of values_spec can remain sharded if they are so.
+    # For indices:
+    #   Indices can be either sharded or replicated. All index tensors need to be sharded
+    #   in a compatible way, following the pointwise rule (including resolving _Partial
+    #   into either sharded or replicated)
+
+    values_spec, multi_indices_spec = op_schema.args_schema
+    assert isinstance(values_spec, DTensorSpec)
+    assert isinstance(multi_indices_spec, list)
+    multi_indices_spec = cast(List[Optional[DTensorSpec]], multi_indices_spec)
+    valid_indices_spec: List[Tuple[int, DTensorSpec]] = [
+        (i, a) for i, a in enumerate(multi_indices_spec) if a is not None
+    ]
+
+    # 1. All indices have to be sharded equally. Moreover, indices can be broadcast.
+    #    Here, we piggyback on the pointwise sharding rule for indices.
+    indices_out = pointwise_rule(
+        OpSchema(
+            op=op_schema.op,
+            args_schema=tuple(v[1] for v in valid_indices_spec),
+            kwargs_schema={},
+        )
+    )
+    need_reshard_on_indices = indices_out.output_spec is None
+
+    if not need_reshard_on_indices:
+        # this means that our inputs are already sharded properly and we will use that as our indices_spec
+        assert isinstance(indices_out.output_spec, DTensorSpec)
+        indices_spec: DTensorSpec = indices_out.output_spec
+    else:
+        assert indices_out.schema_suggestions is not None
+        valid_indices_suggestion = indices_out.schema_suggestions[0]
+        for i, v in enumerate(valid_indices_suggestion.args_spec):
+            multi_indices_spec[valid_indices_spec[i][0]] = v
+        # we'll need to call pointwise_rule again to see what's our ideal indices_spec and then
+        # use that to compute our ideal values_spec
+        indices_output_spec = pointwise_rule(valid_indices_suggestion).output_spec
+        assert isinstance(indices_output_spec, DTensorSpec)
+        indices_spec = indices_output_spec
+
+    lookup_dims = {v[0] for v in valid_indices_spec}
+
+    need_reshard_on_values = tuple(
+        (isinstance(vp, Shard) and (vp.dim in lookup_dims or isinstance(ip, Shard)))
+        for vp, ip in zip(values_spec.placements, indices_spec.placements)
+    )
+
+    if not need_reshard_on_indices and not any(need_reshard_on_values):
+        value_placements = values_spec.placements
+
+        all_dims_consecutive = all(
+            b[0] - a[0] == 1
+            for b, a in zip(valid_indices_spec[1:], valid_indices_spec[:-1])
+        )
+        if all_dims_consecutive:
+            # if all index vectors are consecutives, insert at the dimension of the first index
+            insert_dim: int = valid_indices_spec[0][0]
+        else:
+            # else, insert on the first dimension
+            insert_dim = 0
+
+        def place(vp: Placement, ip: Placement) -> Placement:
+            if isinstance(vp, Shard):
+                return Shard(
+                    vp.dim
+                    if vp.dim < insert_dim
+                    # accounts for the offset in output dimensions
+                    else vp.dim
+                    + indices_spec.ndim
+                    - sum(1 if vp.dim > v[0] else 0 for v in valid_indices_spec)
+                )
+            if isinstance(ip, Shard):
+                return Shard(ip.dim + insert_dim)
+            # _Partial or Replicated
+            return vp
+
+        value_placements = tuple(
+            place(vp, ip)
+            for vp, ip in zip(values_spec.placements, indices_spec.placements)
+        )
+        result = OutputSharding(
+            output_spec=DTensorSpec(
+                mesh=values_spec.mesh,
+                placements=value_placements,
+            )
+        )
+        return result
+    else:
+        result = OutputSharding(
+            output_spec=None,
+            schema_suggestions=[
+                OpSchema(
+                    op=op_schema.op,
+                    args_schema=(
+                        DTensorSpec(
+                            mesh=values_spec.mesh,
+                            placements=tuple(
+                                [
+                                    Replicate() if need_reshard_on_values[i] else v
+                                    for i, v in enumerate(values_spec.placements)
+                                ]
+                            ),
+                            tensor_meta=values_spec.tensor_meta,
+                        ),
+                        multi_indices_spec,
+                    ),
+                    kwargs_schema=op_schema.kwargs_schema,
+                )
+            ],
+        )
+        return result
+
+
+@register_prop_rule(
+    aten.cat.default, schema_info=RuntimeSchemaInfo(1, needs_pytree=True)
+)
+def cat_rule(op_schema: OpSchema) -> OutputSharding:
+    # torch.cat requires all tensors must either have the same shape (except
+    # in the concatenating dimension) or be "empty". "Empty" here strictly means
+    # tensor.shape is torch.Size([0]). When tensor.ndim > 1, it will be treated
+    # as a non-empty tensor and the shape must match on non-cat dimensions.
+    def is_empty(spec: DTensorSpec) -> bool:
+        return list(spec.shape) == [0]
+
+    # the first arg is a list of input tensor specs
+    tensor_list_specs = cast(List[DTensorSpec], op_schema.args_schema[0])
+    assert len(tensor_list_specs) > 0, "torch.cat expects a non-empty list of tensors"
+    non_empty_specs = [spec for spec in tensor_list_specs if not is_empty(spec)]
+
+    if len(non_empty_specs) == 0:
+        # all tensors are empty, we can return any output sharding
+        return OutputSharding(
+            output_spec=DTensorSpec(
+                mesh=tensor_list_specs[0].mesh,
+                placements=tensor_list_specs[0].placements,
+            )
+        )
+
+    assert all(
+        spec.ndim == non_empty_specs[0].ndim for spec in non_empty_specs
+    ), f"Expect all tensors to have same shape or empty, but got {tensor_list_specs}"
+    assert all(
+        spec.mesh == tensor_list_specs[0].mesh for spec in tensor_list_specs
+    ), f"Expect all tensors to have same mesh, but got {tensor_list_specs}"
+
+    # ndim will also be the result's ndim
+    ndim = 1
+    for spec in tensor_list_specs:
+        ndim = max(ndim, spec.ndim)
+
+    dim = 0  # default dim = 0
+    if len(op_schema.args_schema) > 1:
+        dim = cast(int, op_schema.args_schema[1])
+    dim = normalize_dim(dim, ndim)
+
+    # Make sure all tensors are replicated on cat dimension
+    need_reshard = False
+    tensor_list_specs_after: List[DTensorSpec] = []
+    for spec in tensor_list_specs:
+        if not is_empty(spec) and (
+            is_tensor_dim_sharded(spec, dim=dim) or is_tensor_partial(spec)
+        ):
+            need_reshard = True
+            tensor_list_specs_after.append(
+                DTensorSpec(
+                    mesh=spec.mesh,
+                    placements=replicate_tensor_dim(spec.placements, dim=dim),
+                    tensor_meta=spec.tensor_meta,
+                )
+            )
+        else:
+            tensor_list_specs_after.append(spec)
+
+    tensor_list_specs = tensor_list_specs_after
+
+    # align non-cat dimensions placements based on reshard cost
+    non_empty_specs = [spec for spec in tensor_list_specs if not is_empty(spec)]
+    mesh = non_empty_specs[0].mesh
+    ndim = non_empty_specs[0].ndim
+    new_placements: List[Placement] = []
+    for mesh_dim in range(mesh.ndim):
+        # compute the minimum cost of resharding on this mesh_dim
+        if any(
+            spec.placements[mesh_dim] != non_empty_specs[0].placements[mesh_dim]
+            for spec in non_empty_specs
+        ):
+            # only reshard if there is a mismatch
+            need_reshard = True
+            reshard_cost = []
+            for shard_dim in range(ndim):
+                # compute the cost of resharding on this shard_dim
+                cost: float = 0.0
+                for spec in non_empty_specs:
+                    global_shape = spec.shape
+                    if global_shape[shard_dim] < mesh.size(mesh_dim):
+                        # found one tensor where the shard_dim is smaller than
+                        # mesh_dim. In this case, we cannot shard on this shard_dim,
+                        # and hence set cost to infinity.
+                        cost = +float("inf")
+                    elif (
+                        is_tensor_dim_sharded(spec, dim=shard_dim)
+                        or prod(global_shape) == 0
+                    ):
+                        continue
+                    else:
+                        local_shape = compute_local_shape(
+                            global_shape, spec.mesh, spec.placements
+                        )
+                        cost += prod(local_shape) * spec.mesh.size(mesh_dim)
+                reshard_cost.append(cost)
+            best_dim = reshard_cost.index(min(reshard_cost))
+            new_placements.append(Shard(best_dim))
+        else:
+            # no mismatch, keep the original placement
+            new_placements.append(non_empty_specs[0].placements[mesh_dim])
+
+    if need_reshard:
+        tensor_list_specs_after = []
+        for spec in tensor_list_specs:
+            if is_empty(spec):
+                tensor_list_specs_after.append(spec)
+            else:
+                tensor_list_specs_after.append(
+                    DTensorSpec(
+                        mesh=spec.mesh,
+                        placements=tuple(new_placements),
+                        tensor_meta=spec.tensor_meta,
+                    )
+                )
+
+        return OutputSharding(
+            output_spec=None,
+            schema_suggestions=[
+                OpSchema(
+                    op=op_schema.op,
+                    args_schema=(
+                        tuple(tensor_list_specs_after),
+                        *op_schema.args_schema[1:],
+                    ),
+                    kwargs_schema=op_schema.kwargs_schema,
+                ),
+            ],
+        )
+    else:
+        # at this point, the cat dim is not sharded,
+        return OutputSharding(
+            output_spec=DTensorSpec(
+                mesh=non_empty_specs[0].mesh,
+                placements=non_empty_specs[0].placements,
+            ),
+        )
+
+
+@register_prop_rule(
+    [
+        aten.split.Tensor,
+        aten.split_with_sizes.default,
+        aten.split_with_sizes_copy.default,
+    ],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def split_rule(op_schema: OpSchema) -> OutputSharding:
+    output_spec_list: List[DTensorSpec] = []
+    input_spec = cast(DTensorSpec, op_schema.args_schema[0])
+    ndim = input_spec.ndim
+    split_size_or_sections = op_schema.args_schema[1]
+    dim = cast(int, op_schema.args_schema[2]) if len(op_schema.args_schema) > 2 else 0
+    dim = normalize_dim(dim, ndim)
+
+    # TODO: tensor to split cannot have _Partial
+    # in its placements for now. Will need to
+    # support in future.
+    if input_spec.sums:
+        raise NotImplementedError(
+            f"splitting distributed tensor with "
+            f"_Partial placement is not implemented!\n"
+            f"DTensorSpec={input_spec}"
+        )
+
+    # TODO: just like slice op, split replicates before
+    # splitting on a sharded dimension
+    need_reshard = False
+    if is_tensor_dim_sharded(input_spec, dim=dim):
+        need_reshard = True
+        input_spec = DTensorSpec(
+            mesh=input_spec.mesh,
+            placements=unshard_tensor_dim(input_spec.placements, dim=dim),
+            tensor_meta=input_spec.tensor_meta,
+        )
+
+    if need_reshard:
+        return OutputSharding(
+            None,
+            schema_suggestions=[
+                OpSchema(
+                    op=op_schema.op,
+                    args_schema=(input_spec,) + op_schema.args_schema[1:],
+                    kwargs_schema=op_schema.kwargs_schema,
+                ),
+            ],
+        )
+
+    def size_split(N, i):
+        # Last chunk will be smaller if the tensor size N
+        # along the given dimension dim is not divisible by i.
+        assert i > 0
+        return [i] * (N // i) + ([N % i] if N % i != 0 else [])
+
+    output_size_list = (
+        size_split(input_spec.shape[dim], split_size_or_sections)
+        if isinstance(split_size_or_sections, int)
+        else split_size_or_sections
+    )
+    output_spec_list = [
+        DTensorSpec(
+            mesh=input_spec.mesh,
+            placements=input_spec.placements,
+        )
+        for _ in range(len(output_size_list))
+    ]
+    return OutputSharding(output_spec_list)
diff --git a/shared/package-files/32/32de5ee25f7844229b5c8e66106b6d6719be546cb4e27e822fd09f97359a923d/_grid_sampler_2d_cpu_fallback_native.h b/shared/package-files/32/32de5ee25f7844229b5c8e66106b6d6719be546cb4e27e822fd09f97359a923d/_grid_sampler_2d_cpu_fallback_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..729802d12c2b3800e16b3bda2cf4e94103ae6190
--- /dev/null
+++ b/shared/package-files/32/32de5ee25f7844229b5c8e66106b6d6719be546cb4e27e822fd09f97359a923d/_grid_sampler_2d_cpu_fallback_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor _grid_sampler_2d_cpu_fallback(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners);
+TORCH_API at::Tensor & _grid_sampler_2d_cpu_fallback_out(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32e04197156a3fa7beb848525b8c5ff82f10cb185190c4f9bb1e6013f256e84a/atan2_meta.h b/shared/package-files/32/32e04197156a3fa7beb848525b8c5ff82f10cb185190c4f9bb1e6013f256e84a/atan2_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..40a12ecd8d81e8dd872987775c81e8315eb7f963
--- /dev/null
+++ b/shared/package-files/32/32e04197156a3fa7beb848525b8c5ff82f10cb185190c4f9bb1e6013f256e84a/atan2_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_atan2 : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32e10562a1687b7884c3eb25c46fc0af809de6ddb1f4e249217f3f4a23c1c411/variable.py b/shared/package-files/32/32e10562a1687b7884c3eb25c46fc0af809de6ddb1f4e249217f3f4a23c1c411/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed841d4da7d42e53b7143a1bd7fc3a3af979f630
--- /dev/null
+++ b/shared/package-files/32/32e10562a1687b7884c3eb25c46fc0af809de6ddb1f4e249217f3f4a23c1c411/variable.py
@@ -0,0 +1,14 @@
+import torch
+from torch._C import _ImperativeEngine as ImperativeEngine
+
+
+__all__ = ["VariableMeta", "Variable"]
+
+
+class VariableMeta(type):
+    def __instancecheck__(cls, other):
+        return isinstance(other, torch.Tensor)
+
+
+class Variable(torch._C._LegacyVariableBase, metaclass=VariableMeta):  # type: ignore[misc]
+    _execution_engine = ImperativeEngine()
diff --git a/shared/package-files/32/32e1228d940d3e77ddc361207c80f8c996eff6b2ad9b5070331b0a31820727d4/digamma_meta_dispatch.h b/shared/package-files/32/32e1228d940d3e77ddc361207c80f8c996eff6b2ad9b5070331b0a31820727d4/digamma_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..27b20e4ebb0046bb4dbe189d212ad2c5099dd946
--- /dev/null
+++ b/shared/package-files/32/32e1228d940d3e77ddc361207c80f8c996eff6b2ad9b5070331b0a31820727d4/digamma_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor digamma(const at::Tensor & self);
+TORCH_API at::Tensor & digamma_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & digamma_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & digamma_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/32/32e308ccffa8ba2b91b935393e606d53e5e4ff406dbb89b29be0b6d0cf25379f/test_matmul.py b/shared/package-files/32/32e308ccffa8ba2b91b935393e606d53e5e4ff406dbb89b29be0b6d0cf25379f/test_matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..cca168bb0db5014db095ae398ac888580a056850
--- /dev/null
+++ b/shared/package-files/32/32e308ccffa8ba2b91b935393e606d53e5e4ff406dbb89b29be0b6d0cf25379f/test_matmul.py
@@ -0,0 +1,186 @@
+from sympy.core import I, symbols, Basic, Mul, S
+from sympy.core.mul import mul
+from sympy.functions import adjoint, transpose
+from sympy.matrices.common import ShapeError
+from sympy.matrices import (Identity, Inverse, Matrix, MatrixSymbol, ZeroMatrix,
+        eye, ImmutableMatrix)
+from sympy.matrices.expressions import Adjoint, Transpose, det, MatPow
+from sympy.matrices.expressions.special import GenericIdentity
+from sympy.matrices.expressions.matmul import (factor_in_front, remove_ids,
+        MatMul, combine_powers, any_zeros, unpack, only_squares)
+from sympy.strategies import null_safe
+from sympy.assumptions.ask import Q
+from sympy.assumptions.refine import refine
+from sympy.core.symbol import Symbol
+
+from sympy.testing.pytest import XFAIL, raises
+
+n, m, l, k = symbols('n m l k', integer=True)
+x = symbols('x')
+A = MatrixSymbol('A', n, m)
+B = MatrixSymbol('B', m, l)
+C = MatrixSymbol('C', n, n)
+D = MatrixSymbol('D', n, n)
+E = MatrixSymbol('E', m, n)
+
+def test_evaluate():
+    assert MatMul(C, C, evaluate=True) == MatMul(C, C).doit()
+
+def test_adjoint():
+    assert adjoint(A*B) == Adjoint(B)*Adjoint(A)
+    assert adjoint(2*A*B) == 2*Adjoint(B)*Adjoint(A)
+    assert adjoint(2*I*C) == -2*I*Adjoint(C)
+
+    M = Matrix(2, 2, [1, 2 + I, 3, 4])
+    MA = Matrix(2, 2, [1, 3, 2 - I, 4])
+    assert adjoint(M) == MA
+    assert adjoint(2*M) == 2*MA
+    assert adjoint(MatMul(2, M)) == MatMul(2, MA).doit()
+
+
+def test_transpose():
+    assert transpose(A*B) == Transpose(B)*Transpose(A)
+    assert transpose(2*A*B) == 2*Transpose(B)*Transpose(A)
+    assert transpose(2*I*C) == 2*I*Transpose(C)
+
+    M = Matrix(2, 2, [1, 2 + I, 3, 4])
+    MT = Matrix(2, 2, [1, 3, 2 + I, 4])
+    assert transpose(M) == MT
+    assert transpose(2*M) == 2*MT
+    assert transpose(x*M) == x*MT
+    assert transpose(MatMul(2, M)) == MatMul(2, MT).doit()
+
+
+def test_factor_in_front():
+    assert factor_in_front(MatMul(A, 2, B, evaluate=False)) ==\
+                           MatMul(2, A, B, evaluate=False)
+
+
+def test_remove_ids():
+    assert remove_ids(MatMul(A, Identity(m), B, evaluate=False)) == \
+                      MatMul(A, B, evaluate=False)
+    assert null_safe(remove_ids)(MatMul(Identity(n), evaluate=False)) == \
+                                 MatMul(Identity(n), evaluate=False)
+
+
+def test_combine_powers():
+    assert combine_powers(MatMul(D, Inverse(D), D, evaluate=False)) == \
+                 MatMul(Identity(n), D, evaluate=False)
+    assert combine_powers(MatMul(B.T, Inverse(E*A), E, A, B, evaluate=False)) == \
+        MatMul(B.T, Identity(m), B, evaluate=False)
+    assert combine_powers(MatMul(A, E, Inverse(A*E), D, evaluate=False)) == \
+        MatMul(Identity(n), D, evaluate=False)
+
+
+def test_any_zeros():
+    assert any_zeros(MatMul(A, ZeroMatrix(m, k), evaluate=False)) == \
+                     ZeroMatrix(n, k)
+
+
+def test_unpack():
+    assert unpack(MatMul(A, evaluate=False)) == A
+    x = MatMul(A, B)
+    assert unpack(x) == x
+
+
+def test_only_squares():
+    assert only_squares(C) == [C]
+    assert only_squares(C, D) == [C, D]
+    assert only_squares(C, A, A.T, D) == [C, A*A.T, D]
+
+
+def test_determinant():
+    assert det(2*C) == 2**n*det(C)
+    assert det(2*C*D) == 2**n*det(C)*det(D)
+    assert det(3*C*A*A.T*D) == 3**n*det(C)*det(A*A.T)*det(D)
+
+
+def test_doit():
+    assert MatMul(C, 2, D).args == (C, 2, D)
+    assert MatMul(C, 2, D).doit().args == (2, C, D)
+    assert MatMul(C, Transpose(D*C)).args == (C, Transpose(D*C))
+    assert MatMul(C, Transpose(D*C)).doit(deep=True).args == (C, C.T, D.T)
+
+
+def test_doit_drills_down():
+    X = ImmutableMatrix([[1, 2], [3, 4]])
+    Y = ImmutableMatrix([[2, 3], [4, 5]])
+    assert MatMul(X, MatPow(Y, 2)).doit() == X*Y**2
+    assert MatMul(C, Transpose(D*C)).doit().args == (C, C.T, D.T)
+
+
+def test_doit_deep_false_still_canonical():
+    assert (MatMul(C, Transpose(D*C), 2).doit(deep=False).args ==
+            (2, C, Transpose(D*C)))
+
+
+def test_matmul_scalar_Matrix_doit():
+    # Issue 9053
+    X = Matrix([[1, 2], [3, 4]])
+    assert MatMul(2, X).doit() == 2*X
+
+
+def test_matmul_sympify():
+    assert isinstance(MatMul(eye(1), eye(1)).args[0], Basic)
+
+
+def test_collapse_MatrixBase():
+    A = Matrix([[1, 1], [1, 1]])
+    B = Matrix([[1, 2], [3, 4]])
+    assert MatMul(A, B).doit() == ImmutableMatrix([[4, 6], [4, 6]])
+
+
+def test_refine():
+    assert refine(C*C.T*D, Q.orthogonal(C)).doit() == D
+
+    kC = k*C
+    assert refine(kC*C.T, Q.orthogonal(C)).doit() == k*Identity(n)
+    assert refine(kC* kC.T, Q.orthogonal(C)).doit() == (k**2)*Identity(n)
+
+def test_matmul_no_matrices():
+    assert MatMul(1) == 1
+    assert MatMul(n, m) == n*m
+    assert not isinstance(MatMul(n, m), MatMul)
+
+def test_matmul_args_cnc():
+    assert MatMul(n, A, A.T).args_cnc() == [[n], [A, A.T]]
+    assert MatMul(A, A.T).args_cnc() == [[], [A, A.T]]
+
+@XFAIL
+def test_matmul_args_cnc_symbols():
+    # Not currently supported
+    a, b = symbols('a b', commutative=False)
+    assert MatMul(n, a, b, A, A.T).args_cnc() == [[n], [a, b, A, A.T]]
+    assert MatMul(n, a, A, b, A.T).args_cnc() == [[n], [a, A, b, A.T]]
+
+def test_issue_12950():
+    M = Matrix([[Symbol("x")]]) * MatrixSymbol("A", 1, 1)
+    assert MatrixSymbol("A", 1, 1).as_explicit()[0]*Symbol('x') == M.as_explicit()[0]
+
+def test_construction_with_Mul():
+    assert Mul(C, D) == MatMul(C, D)
+    assert Mul(D, C) == MatMul(D, C)
+
+def test_construction_with_mul():
+    assert mul(C, D) == MatMul(C, D)
+    assert mul(D, C) == MatMul(D, C)
+    assert mul(C, D) != MatMul(D, C)
+
+def test_generic_identity():
+    assert MatMul.identity == GenericIdentity()
+    assert MatMul.identity != S.One
+
+
+def test_issue_23519():
+    N = Symbol("N", integer=True)
+    M1 = MatrixSymbol("M1", N, N)
+    M2 = MatrixSymbol("M2", N, N)
+    I = Identity(N)
+    z = (M2 + 2 * (M2 + I) * M1 + I)
+    assert z.coeff(M1) == 2*I + 2*M2
+
+
+def test_shape_error():
+    A = MatrixSymbol('A', 2, 2)
+    B = MatrixSymbol('B', 3, 3)
+    raises(ShapeError, lambda: MatMul(A, B))
diff --git a/shared/package-files/32/32e366a95fef11c1ccc54a3ba099f80585f9cf648264462e8baba69e48f13692/outer_native.h b/shared/package-files/32/32e366a95fef11c1ccc54a3ba099f80585f9cf648264462e8baba69e48f13692/outer_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..58c1c21dbc9c766481392c15f51772187763caa6
--- /dev/null
+++ b/shared/package-files/32/32e366a95fef11c1ccc54a3ba099f80585f9cf648264462e8baba69e48f13692/outer_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor outer(const at::Tensor & self, const at::Tensor & vec2);
+TORCH_API at::Tensor & outer_out(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32e48fe6fd6414bede1ff092a8e3e197ac437751aec110567194120b0072e818/test_inequalities.py b/shared/package-files/32/32e48fe6fd6414bede1ff092a8e3e197ac437751aec110567194120b0072e818/test_inequalities.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9eb6ec6b3a09a8ea683ad99bc975227b4e8184c
--- /dev/null
+++ b/shared/package-files/32/32e48fe6fd6414bede1ff092a8e3e197ac437751aec110567194120b0072e818/test_inequalities.py
@@ -0,0 +1,488 @@
+"""Tests for tools for solving inequalities and systems of inequalities. """
+
+from sympy.concrete.summations import Sum
+from sympy.core.function import Function
+from sympy.core.numbers import (I, Rational, oo, pi)
+from sympy.core.relational import (Eq, Ge, Gt, Le, Lt, Ne)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Dummy, Symbol)
+from sympy.functions.elementary.complexes import Abs
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import (root, sqrt)
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.elementary.trigonometric import (cos, sin, tan)
+from sympy.integrals.integrals import Integral
+from sympy.logic.boolalg import (And, Or)
+from sympy.polys.polytools import (Poly, PurePoly)
+from sympy.sets.sets import (FiniteSet, Interval, Union)
+from sympy.solvers.inequalities import (reduce_inequalities,
+                                        solve_poly_inequality as psolve,
+                                        reduce_rational_inequalities,
+                                        solve_univariate_inequality as isolve,
+                                        reduce_abs_inequality,
+                                        _solve_inequality)
+from sympy.polys.rootoftools import rootof
+from sympy.solvers.solvers import solve
+from sympy.solvers.solveset import solveset
+from sympy.abc import x, y
+
+from sympy.core.mod import Mod
+
+from sympy.testing.pytest import raises, XFAIL
+
+
+inf = oo.evalf()
+
+
+def test_solve_poly_inequality():
+    assert psolve(Poly(0, x), '==') == [S.Reals]
+    assert psolve(Poly(1, x), '==') == [S.EmptySet]
+    assert psolve(PurePoly(x + 1, x), ">") == [Interval(-1, oo, True, False)]
+
+
+def test_reduce_poly_inequalities_real_interval():
+    assert reduce_rational_inequalities(
+        [[Eq(x**2, 0)]], x, relational=False) == FiniteSet(0)
+    assert reduce_rational_inequalities(
+        [[Le(x**2, 0)]], x, relational=False) == FiniteSet(0)
+    assert reduce_rational_inequalities(
+        [[Lt(x**2, 0)]], x, relational=False) == S.EmptySet
+    assert reduce_rational_inequalities(
+        [[Ge(x**2, 0)]], x, relational=False) == \
+        S.Reals if x.is_real else Interval(-oo, oo)
+    assert reduce_rational_inequalities(
+        [[Gt(x**2, 0)]], x, relational=False) == \
+        FiniteSet(0).complement(S.Reals)
+    assert reduce_rational_inequalities(
+        [[Ne(x**2, 0)]], x, relational=False) == \
+        FiniteSet(0).complement(S.Reals)
+
+    assert reduce_rational_inequalities(
+        [[Eq(x**2, 1)]], x, relational=False) == FiniteSet(-1, 1)
+    assert reduce_rational_inequalities(
+        [[Le(x**2, 1)]], x, relational=False) == Interval(-1, 1)
+    assert reduce_rational_inequalities(
+        [[Lt(x**2, 1)]], x, relational=False) == Interval(-1, 1, True, True)
+    assert reduce_rational_inequalities(
+        [[Ge(x**2, 1)]], x, relational=False) == \
+        Union(Interval(-oo, -1), Interval(1, oo))
+    assert reduce_rational_inequalities(
+        [[Gt(x**2, 1)]], x, relational=False) == \
+        Interval(-1, 1).complement(S.Reals)
+    assert reduce_rational_inequalities(
+        [[Ne(x**2, 1)]], x, relational=False) == \
+        FiniteSet(-1, 1).complement(S.Reals)
+    assert reduce_rational_inequalities([[Eq(
+        x**2, 1.0)]], x, relational=False) == FiniteSet(-1.0, 1.0).evalf()
+    assert reduce_rational_inequalities(
+        [[Le(x**2, 1.0)]], x, relational=False) == Interval(-1.0, 1.0)
+    assert reduce_rational_inequalities([[Lt(
+        x**2, 1.0)]], x, relational=False) == Interval(-1.0, 1.0, True, True)
+    assert reduce_rational_inequalities(
+        [[Ge(x**2, 1.0)]], x, relational=False) == \
+        Union(Interval(-inf, -1.0), Interval(1.0, inf))
+    assert reduce_rational_inequalities(
+        [[Gt(x**2, 1.0)]], x, relational=False) == \
+        Union(Interval(-inf, -1.0, right_open=True),
+        Interval(1.0, inf, left_open=True))
+    assert reduce_rational_inequalities([[Ne(
+        x**2, 1.0)]], x, relational=False) == \
+        FiniteSet(-1.0, 1.0).complement(S.Reals)
+
+    s = sqrt(2)
+
+    assert reduce_rational_inequalities([[Lt(
+        x**2 - 1, 0), Gt(x**2 - 1, 0)]], x, relational=False) == S.EmptySet
+    assert reduce_rational_inequalities([[Le(x**2 - 1, 0), Ge(
+        x**2 - 1, 0)]], x, relational=False) == FiniteSet(-1, 1)
+    assert reduce_rational_inequalities(
+        [[Le(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False
+        ) == Union(Interval(-s, -1, False, False), Interval(1, s, False, False))
+    assert reduce_rational_inequalities(
+        [[Le(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False
+        ) == Union(Interval(-s, -1, False, True), Interval(1, s, True, False))
+    assert reduce_rational_inequalities(
+        [[Lt(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False
+        ) == Union(Interval(-s, -1, True, False), Interval(1, s, False, True))
+    assert reduce_rational_inequalities(
+        [[Lt(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False
+        ) == Union(Interval(-s, -1, True, True), Interval(1, s, True, True))
+    assert reduce_rational_inequalities(
+        [[Lt(x**2 - 2, 0), Ne(x**2 - 1, 0)]], x, relational=False
+        ) == Union(Interval(-s, -1, True, True), Interval(-1, 1, True, True),
+        Interval(1, s, True, True))
+
+    assert reduce_rational_inequalities([[Lt(x**2, -1.)]], x) is S.false
+
+
+def test_reduce_poly_inequalities_complex_relational():
+    assert reduce_rational_inequalities(
+        [[Eq(x**2, 0)]], x, relational=True) == Eq(x, 0)
+    assert reduce_rational_inequalities(
+        [[Le(x**2, 0)]], x, relational=True) == Eq(x, 0)
+    assert reduce_rational_inequalities(
+        [[Lt(x**2, 0)]], x, relational=True) == False
+    assert reduce_rational_inequalities(
+        [[Ge(x**2, 0)]], x, relational=True) == And(Lt(-oo, x), Lt(x, oo))
+    assert reduce_rational_inequalities(
+        [[Gt(x**2, 0)]], x, relational=True) == \
+        And(Gt(x, -oo), Lt(x, oo), Ne(x, 0))
+    assert reduce_rational_inequalities(
+        [[Ne(x**2, 0)]], x, relational=True) == \
+        And(Gt(x, -oo), Lt(x, oo), Ne(x, 0))
+
+    for one in (S.One, S(1.0)):
+        inf = one*oo
+        assert reduce_rational_inequalities(
+            [[Eq(x**2, one)]], x, relational=True) == \
+            Or(Eq(x, -one), Eq(x, one))
+        assert reduce_rational_inequalities(
+            [[Le(x**2, one)]], x, relational=True) == \
+            And(And(Le(-one, x), Le(x, one)))
+        assert reduce_rational_inequalities(
+            [[Lt(x**2, one)]], x, relational=True) == \
+            And(And(Lt(-one, x), Lt(x, one)))
+        assert reduce_rational_inequalities(
+            [[Ge(x**2, one)]], x, relational=True) == \
+            And(Or(And(Le(one, x), Lt(x, inf)), And(Le(x, -one), Lt(-inf, x))))
+        assert reduce_rational_inequalities(
+            [[Gt(x**2, one)]], x, relational=True) == \
+            And(Or(And(Lt(-inf, x), Lt(x, -one)), And(Lt(one, x), Lt(x, inf))))
+        assert reduce_rational_inequalities(
+            [[Ne(x**2, one)]], x, relational=True) == \
+            Or(And(Lt(-inf, x), Lt(x, -one)),
+               And(Lt(-one, x), Lt(x, one)),
+               And(Lt(one, x), Lt(x, inf)))
+
+
+def test_reduce_rational_inequalities_real_relational():
+    assert reduce_rational_inequalities([], x) == False
+    assert reduce_rational_inequalities(
+        [[(x**2 + 3*x + 2)/(x**2 - 16) >= 0]], x, relational=False) == \
+        Union(Interval.open(-oo, -4), Interval(-2, -1), Interval.open(4, oo))
+
+    assert reduce_rational_inequalities(
+        [[((-2*x - 10)*(3 - x))/((x**2 + 5)*(x - 2)**2) < 0]], x,
+        relational=False) == \
+        Union(Interval.open(-5, 2), Interval.open(2, 3))
+
+    assert reduce_rational_inequalities([[(x + 1)/(x - 5) <= 0]], x,
+        relational=False) == \
+        Interval.Ropen(-1, 5)
+
+    assert reduce_rational_inequalities([[(x**2 + 4*x + 3)/(x - 1) > 0]], x,
+        relational=False) == \
+        Union(Interval.open(-3, -1), Interval.open(1, oo))
+
+    assert reduce_rational_inequalities([[(x**2 - 16)/(x - 1)**2 < 0]], x,
+        relational=False) == \
+        Union(Interval.open(-4, 1), Interval.open(1, 4))
+
+    assert reduce_rational_inequalities([[(3*x + 1)/(x + 4) >= 1]], x,
+        relational=False) == \
+        Union(Interval.open(-oo, -4), Interval.Ropen(Rational(3, 2), oo))
+
+    assert reduce_rational_inequalities([[(x - 8)/x <= 3 - x]], x,
+        relational=False) == \
+        Union(Interval.Lopen(-oo, -2), Interval.Lopen(0, 4))
+
+    # issue sympy/sympy#10237
+    assert reduce_rational_inequalities(
+        [[x < oo, x >= 0, -oo < x]], x, relational=False) == Interval(0, oo)
+
+
+def test_reduce_abs_inequalities():
+    e = abs(x - 5) < 3
+    ans = And(Lt(2, x), Lt(x, 8))
+    assert reduce_inequalities(e) == ans
+    assert reduce_inequalities(e, x) == ans
+    assert reduce_inequalities(abs(x - 5)) == Eq(x, 5)
+    assert reduce_inequalities(
+        abs(2*x + 3) >= 8) == Or(And(Le(Rational(5, 2), x), Lt(x, oo)),
+        And(Le(x, Rational(-11, 2)), Lt(-oo, x)))
+    assert reduce_inequalities(abs(x - 4) + abs(
+        3*x - 5) < 7) == And(Lt(S.Half, x), Lt(x, 4))
+    assert reduce_inequalities(abs(x - 4) + abs(3*abs(x) - 5) < 7) == \
+        Or(And(S(-2) < x, x < -1), And(S.Half < x, x < 4))
+
+    nr = Symbol('nr', extended_real=False)
+    raises(TypeError, lambda: reduce_inequalities(abs(nr - 5) < 3))
+    assert reduce_inequalities(x < 3, symbols=[x, nr]) == And(-oo < x, x < 3)
+
+
+def test_reduce_inequalities_general():
+    assert reduce_inequalities(Ge(sqrt(2)*x, 1)) == And(sqrt(2)/2 <= x, x < oo)
+    assert reduce_inequalities(x + 1 > 0) == And(S.NegativeOne < x, x < oo)
+
+
+def test_reduce_inequalities_boolean():
+    assert reduce_inequalities(
+        [Eq(x**2, 0), True]) == Eq(x, 0)
+    assert reduce_inequalities([Eq(x**2, 0), False]) == False
+    assert reduce_inequalities(x**2 >= 0) is S.true  # issue 10196
+
+
+def test_reduce_inequalities_multivariate():
+    assert reduce_inequalities([Ge(x**2, 1), Ge(y**2, 1)]) == And(
+        Or(And(Le(S.One, x), Lt(x, oo)), And(Le(x, -1), Lt(-oo, x))),
+        Or(And(Le(S.One, y), Lt(y, oo)), And(Le(y, -1), Lt(-oo, y))))
+
+
+def test_reduce_inequalities_errors():
+    raises(NotImplementedError, lambda: reduce_inequalities(Ge(sin(x) + x, 1)))
+    raises(NotImplementedError, lambda: reduce_inequalities(Ge(x**2*y + y, 1)))
+
+
+def test__solve_inequalities():
+    assert reduce_inequalities(x + y < 1, symbols=[x]) == (x < 1 - y)
+    assert reduce_inequalities(x + y >= 1, symbols=[x]) == (x < oo) & (x >= -y + 1)
+    assert reduce_inequalities(Eq(0, x - y), symbols=[x]) == Eq(x, y)
+    assert reduce_inequalities(Ne(0, x - y), symbols=[x]) == Ne(x, y)
+
+
+def test_issue_6343():
+    eq = -3*x**2/2 - x*Rational(45, 4) + Rational(33, 2) > 0
+    assert reduce_inequalities(eq) == \
+        And(x < Rational(-15, 4) + sqrt(401)/4, -sqrt(401)/4 - Rational(15, 4) < x)
+
+
+def test_issue_8235():
+    assert reduce_inequalities(x**2 - 1 < 0) == \
+        And(S.NegativeOne < x, x < 1)
+    assert reduce_inequalities(x**2 - 1 <= 0) == \
+        And(S.NegativeOne <= x, x <= 1)
+    assert reduce_inequalities(x**2 - 1 > 0) == \
+        Or(And(-oo < x, x < -1), And(x < oo, S.One < x))
+    assert reduce_inequalities(x**2 - 1 >= 0) == \
+        Or(And(-oo < x, x <= -1), And(S.One <= x, x < oo))
+
+    eq = x**8 + x - 9  # we want CRootOf solns here
+    sol = solve(eq >= 0)
+    tru = Or(And(rootof(eq, 1) <= x, x < oo), And(-oo < x, x <= rootof(eq, 0)))
+    assert sol == tru
+
+    # recast vanilla as real
+    assert solve(sqrt((-x + 1)**2) < 1) == And(S.Zero < x, x < 2)
+
+
+def test_issue_5526():
+    assert reduce_inequalities(0 <=
+        x + Integral(y**2, (y, 1, 3)) - 1, [x]) == \
+        (x >= -Integral(y**2, (y, 1, 3)) + 1)
+    f = Function('f')
+    e = Sum(f(x), (x, 1, 3))
+    assert reduce_inequalities(0 <= x + e + y**2, [x]) == \
+        (x >= -y**2 - Sum(f(x), (x, 1, 3)))
+
+
+def test_solve_univariate_inequality():
+    assert isolve(x**2 >= 4, x, relational=False) == Union(Interval(-oo, -2),
+        Interval(2, oo))
+    assert isolve(x**2 >= 4, x) == Or(And(Le(2, x), Lt(x, oo)), And(Le(x, -2),
+        Lt(-oo, x)))
+    assert isolve((x - 1)*(x - 2)*(x - 3) >= 0, x, relational=False) == \
+        Union(Interval(1, 2), Interval(3, oo))
+    assert isolve((x - 1)*(x - 2)*(x - 3) >= 0, x) == \
+        Or(And(Le(1, x), Le(x, 2)), And(Le(3, x), Lt(x, oo)))
+    assert isolve((x - 1)*(x - 2)*(x - 4) < 0, x, domain = FiniteSet(0, 3)) == \
+        Or(Eq(x, 0), Eq(x, 3))
+    # issue 2785:
+    assert isolve(x**3 - 2*x - 1 > 0, x, relational=False) == \
+        Union(Interval(-1, -sqrt(5)/2 + S.Half, True, True),
+              Interval(S.Half + sqrt(5)/2, oo, True, True))
+    # issue 2794:
+    assert isolve(x**3 - x**2 + x - 1 > 0, x, relational=False) == \
+        Interval(1, oo, True)
+    #issue 13105
+    assert isolve((x + I)*(x + 2*I) < 0, x) == Eq(x, 0)
+    assert isolve(((x - 1)*(x - 2) + I)*((x - 1)*(x - 2) + 2*I) < 0, x) == Or(Eq(x, 1), Eq(x, 2))
+    assert isolve((((x - 1)*(x - 2) + I)*((x - 1)*(x - 2) + 2*I))/(x - 2) > 0, x) == Eq(x, 1)
+    raises (ValueError, lambda: isolve((x**2 - 3*x*I + 2)/x < 0, x))
+
+    # numerical testing in valid() is needed
+    assert isolve(x**7 - x - 2 > 0, x) == \
+        And(rootof(x**7 - x - 2, 0) < x, x < oo)
+
+    # handle numerator and denominator; although these would be handled as
+    # rational inequalities, these test confirm that the right thing is done
+    # when the domain is EX (e.g. when 2 is replaced with sqrt(2))
+    assert isolve(1/(x - 2) > 0, x) == And(S(2) < x, x < oo)
+    den = ((x - 1)*(x - 2)).expand()
+    assert isolve((x - 1)/den <= 0, x) == \
+        (x > -oo) & (x < 2) & Ne(x, 1)
+
+    n = Dummy('n')
+    raises(NotImplementedError, lambda: isolve(Abs(x) <= n, x, relational=False))
+    c1 = Dummy("c1", positive=True)
+    raises(NotImplementedError, lambda: isolve(n/c1 < 0, c1))
+    n = Dummy('n', negative=True)
+    assert isolve(n/c1 > -2, c1) == (-n/2 < c1)
+    assert isolve(n/c1 < 0, c1) == True
+    assert isolve(n/c1 > 0, c1) == False
+
+    zero = cos(1)**2 + sin(1)**2 - 1
+    raises(NotImplementedError, lambda: isolve(x**2 < zero, x))
+    raises(NotImplementedError, lambda: isolve(
+        x**2 < zero*I, x))
+    raises(NotImplementedError, lambda: isolve(1/(x - y) < 2, x))
+    raises(NotImplementedError, lambda: isolve(1/(x - y) < 0, x))
+    raises(TypeError, lambda: isolve(x - I < 0, x))
+
+    zero = x**2 + x - x*(x + 1)
+    assert isolve(zero < 0, x, relational=False) is S.EmptySet
+    assert isolve(zero <= 0, x, relational=False) is S.Reals
+
+    # make sure iter_solutions gets a default value
+    raises(NotImplementedError, lambda: isolve(
+        Eq(cos(x)**2 + sin(x)**2, 1), x))
+
+
+def test_trig_inequalities():
+    # all the inequalities are solved in a periodic interval.
+    assert isolve(sin(x) < S.Half, x, relational=False) == \
+        Union(Interval(0, pi/6, False, True), Interval.open(pi*Rational(5, 6), 2*pi))
+    assert isolve(sin(x) > S.Half, x, relational=False) == \
+        Interval(pi/6, pi*Rational(5, 6), True, True)
+    assert isolve(cos(x) < S.Zero, x, relational=False) == \
+        Interval(pi/2, pi*Rational(3, 2), True, True)
+    assert isolve(cos(x) >= S.Zero, x, relational=False) == \
+        Union(Interval(0, pi/2), Interval.Ropen(pi*Rational(3, 2), 2*pi))
+
+    assert isolve(tan(x) < S.One, x, relational=False) == \
+        Union(Interval.Ropen(0, pi/4), Interval.open(pi/2, pi))
+
+    assert isolve(sin(x) <= S.Zero, x, relational=False) == \
+        Union(FiniteSet(S.Zero), Interval.Ropen(pi, 2*pi))
+
+    assert isolve(sin(x) <= S.One, x, relational=False) == S.Reals
+    assert isolve(cos(x) < S(-2), x, relational=False) == S.EmptySet
+    assert isolve(sin(x) >= S.NegativeOne, x, relational=False) == S.Reals
+    assert isolve(cos(x) > S.One, x, relational=False) == S.EmptySet
+
+
+def test_issue_9954():
+    assert isolve(x**2 >= 0, x, relational=False) == S.Reals
+    assert isolve(x**2 >= 0, x, relational=True) == S.Reals.as_relational(x)
+    assert isolve(x**2 < 0, x, relational=False) == S.EmptySet
+    assert isolve(x**2 < 0, x, relational=True) == S.EmptySet.as_relational(x)
+
+
+@XFAIL
+def test_slow_general_univariate():
+    r = rootof(x**5 - x**2 + 1, 0)
+    assert solve(sqrt(x) + 1/root(x, 3) > 1) == \
+        Or(And(0 < x, x < r**6), And(r**6 < x, x < oo))
+
+
+def test_issue_8545():
+    eq = 1 - x - abs(1 - x)
+    ans = And(Lt(1, x), Lt(x, oo))
+    assert reduce_abs_inequality(eq, '<', x) == ans
+    eq = 1 - x - sqrt((1 - x)**2)
+    assert reduce_inequalities(eq < 0) == ans
+
+
+def test_issue_8974():
+    assert isolve(-oo < x, x) == And(-oo < x, x < oo)
+    assert isolve(oo > x, x) == And(-oo < x, x < oo)
+
+
+def test_issue_10198():
+    assert reduce_inequalities(
+        -1 + 1/abs(1/x - 1) < 0) == (x > -oo) & (x < S(1)/2) & Ne(x, 0)
+
+    assert reduce_inequalities(abs(1/sqrt(x)) - 1, x) == Eq(x, 1)
+    assert reduce_abs_inequality(-3 + 1/abs(1 - 1/x), '<', x) == \
+        Or(And(-oo < x, x < 0),
+        And(S.Zero < x, x < Rational(3, 4)), And(Rational(3, 2) < x, x < oo))
+    raises(ValueError,lambda: reduce_abs_inequality(-3 + 1/abs(
+        1 - 1/sqrt(x)), '<', x))
+
+
+def test_issue_10047():
+    # issue 10047: this must remain an inequality, not True, since if x
+    # is not real the inequality is invalid
+    # assert solve(sin(x) < 2) == (x <= oo)
+
+    # with PR 16956, (x <= oo) autoevaluates when x is extended_real
+    # which is assumed in the current implementation of inequality solvers
+    assert solve(sin(x) < 2) == True
+    assert solveset(sin(x) < 2, domain=S.Reals) == S.Reals
+
+
+def test_issue_10268():
+    assert solve(log(x) < 1000) == And(S.Zero < x, x < exp(1000))
+
+
+@XFAIL
+def test_isolve_Sets():
+    n = Dummy('n')
+    assert isolve(Abs(x) <= n, x, relational=False) == \
+        Piecewise((S.EmptySet, n < 0), (Interval(-n, n), True))
+
+
+def test_integer_domain_relational_isolve():
+
+    dom = FiniteSet(0, 3)
+    x = Symbol('x',zero=False)
+    assert isolve((x - 1)*(x - 2)*(x - 4) < 0, x, domain=dom) == Eq(x, 3)
+
+    x = Symbol('x')
+    assert isolve(x + 2 < 0, x, domain=S.Integers) == \
+           (x <= -3) & (x > -oo) & Eq(Mod(x, 1), 0)
+    assert isolve(2 * x + 3 > 0, x, domain=S.Integers) == \
+           (x >= -1) & (x < oo)  & Eq(Mod(x, 1), 0)
+    assert isolve((x ** 2 + 3 * x - 2) < 0, x, domain=S.Integers) == \
+           (x >= -3) & (x <= 0)  & Eq(Mod(x, 1), 0)
+    assert isolve((x ** 2 + 3 * x - 2) > 0, x, domain=S.Integers) == \
+           ((x >= 1) & (x < oo)  & Eq(Mod(x, 1), 0)) | (
+               (x <= -4) & (x > -oo)  & Eq(Mod(x, 1), 0))
+
+
+def test_issue_10671_12466():
+    assert solveset(sin(y), y, Interval(0, pi)) == FiniteSet(0, pi)
+    i = Interval(1, 10)
+    assert solveset((1/x).diff(x) < 0, x, i) == i
+    assert solveset((log(x - 6)/x) <= 0, x, S.Reals) == \
+        Interval.Lopen(6, 7)
+
+
+def test__solve_inequality():
+    for op in (Gt, Lt, Le, Ge, Eq, Ne):
+        assert _solve_inequality(op(x, 1), x).lhs == x
+        assert _solve_inequality(op(S.One, x), x).lhs == x
+    # don't get tricked by symbol on right: solve it
+    assert _solve_inequality(Eq(2*x - 1, x), x) == Eq(x, 1)
+    ie = Eq(S.One, y)
+    assert _solve_inequality(ie, x) == ie
+    for fx in (x**2, exp(x), sin(x) + cos(x), x*(1 + x)):
+        for c in (0, 1):
+            e = 2*fx - c > 0
+            assert _solve_inequality(e, x, linear=True) == (
+                fx > c/S(2))
+    assert _solve_inequality(2*x**2 + 2*x - 1 < 0, x, linear=True) == (
+        x*(x + 1) < S.Half)
+    assert _solve_inequality(Eq(x*y, 1), x) == Eq(x*y, 1)
+    nz = Symbol('nz', nonzero=True)
+    assert _solve_inequality(Eq(x*nz, 1), x) == Eq(x, 1/nz)
+    assert _solve_inequality(x*nz < 1, x) == (x*nz < 1)
+    a = Symbol('a', positive=True)
+    assert _solve_inequality(a/x > 1, x) == (S.Zero < x) & (x < a)
+    assert _solve_inequality(a/x > 1, x, linear=True) == (1/x > 1/a)
+    # make sure to include conditions under which solution is valid
+    e = Eq(1 - x, x*(1/x - 1))
+    assert _solve_inequality(e, x) == Ne(x, 0)
+    assert _solve_inequality(x < x*(1/x - 1), x) == (x < S.Half) & Ne(x, 0)
+
+
+def test__pt():
+    from sympy.solvers.inequalities import _pt
+    assert _pt(-oo, oo) == 0
+    assert _pt(S.One, S(3)) == 2
+    assert _pt(S.One, oo) == _pt(oo, S.One) == 2
+    assert _pt(S.One, -oo) == _pt(-oo, S.One) == S.Half
+    assert _pt(S.NegativeOne, oo) == _pt(oo, S.NegativeOne) == Rational(-1, 2)
+    assert _pt(S.NegativeOne, -oo) == _pt(-oo, S.NegativeOne) == -2
+    assert _pt(x, oo) == _pt(oo, x) == x + 1
+    assert _pt(x, -oo) == _pt(-oo, x) == x - 1
+    raises(ValueError, lambda: _pt(Dummy('i', infinite=True), S.One))
diff --git a/shared/package-files/32/32e55c08ef2da92fecea793b761abe67ad88e518be2111375164907ce48e5a93/fbgemm_pack_quantized_matrix.h b/shared/package-files/32/32e55c08ef2da92fecea793b761abe67ad88e518be2111375164907ce48e5a93/fbgemm_pack_quantized_matrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8086fad61eb91b21943bb125ccf0f2cbd9ab3c2
--- /dev/null
+++ b/shared/package-files/32/32e55c08ef2da92fecea793b761abe67ad88e518be2111375164907ce48e5a93/fbgemm_pack_quantized_matrix.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/fbgemm_pack_quantized_matrix_ops.h>
+
+namespace at {
+
+
+// aten::fbgemm_pack_quantized_matrix(Tensor input) -> Tensor
+inline at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input) {
+    return at::_ops::fbgemm_pack_quantized_matrix::call(input);
+}
+
+// aten::fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor
+inline at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input, int64_t K, int64_t N) {
+    return at::_ops::fbgemm_pack_quantized_matrix_KN::call(input, K, N);
+}
+
+}
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diff --git a/shared/package-files/32/32ee2738ef2cd1bab43b497b357501b8d32175a6a5f6d5d8731951ceaa2d61bd/_index.py b/shared/package-files/32/32ee2738ef2cd1bab43b497b357501b8d32175a6a5f6d5d8731951ceaa2d61bd/_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..451df8b242c73b35036878feaf1f7ad302dc1f91
--- /dev/null
+++ b/shared/package-files/32/32ee2738ef2cd1bab43b497b357501b8d32175a6a5f6d5d8731951ceaa2d61bd/_index.py
@@ -0,0 +1,444 @@
+"""Indexing mixin for sparse array/matrix classes.
+"""
+import numpy as np
+from ._sputils import isintlike
+from ._base import sparray, issparse
+
+INT_TYPES = (int, np.integer)
+
+
+def _broadcast_arrays(a, b):
+    """
+    Same as np.broadcast_arrays(a, b) but old writeability rules.
+
+    NumPy >= 1.17.0 transitions broadcast_arrays to return
+    read-only arrays. Set writeability explicitly to avoid warnings.
+    Retain the old writeability rules, as our Cython code assumes
+    the old behavior.
+    """
+    x, y = np.broadcast_arrays(a, b)
+    x.flags.writeable = a.flags.writeable
+    y.flags.writeable = b.flags.writeable
+    return x, y
+
+
+class IndexMixin:
+    """
+    This class provides common dispatching and validation logic for indexing.
+    """
+    def __getitem__(self, key):
+        index, new_shape = self._validate_indices(key)
+
+        # 1D array
+        if len(index) == 1:
+            idx = index[0]
+            if isinstance(idx, np.ndarray):
+                if idx.shape == ():
+                    idx = idx.item()
+            if isinstance(idx, INT_TYPES):
+                res = self._get_int(idx)
+            elif isinstance(idx, slice):
+                res = self._get_slice(idx)
+            else:  # assume array idx
+                res = self._get_array(idx)
+
+            # package the result and return
+            if not isinstance(self, sparray):
+                return res
+            # handle np.newaxis in idx when result would otherwise be a scalar
+            if res.shape == () and new_shape != ():
+                if len(new_shape) == 1:
+                    return self.__class__([res], shape=new_shape, dtype=self.dtype)
+                if len(new_shape) == 2:
+                    return self.__class__([[res]], shape=new_shape, dtype=self.dtype)
+            return res.reshape(new_shape)
+
+        # 2D array
+        row, col = index
+
+        # Dispatch to specialized methods.
+        if isinstance(row, INT_TYPES):
+            if isinstance(col, INT_TYPES):
+                res = self._get_intXint(row, col)
+            elif isinstance(col, slice):
+                res = self._get_intXslice(row, col)
+            elif col.ndim == 1:
+                res = self._get_intXarray(row, col)
+            elif col.ndim == 2:
+                res = self._get_intXarray(row, col)
+            else:
+                raise IndexError('index results in >2 dimensions')
+        elif isinstance(row, slice):
+            if isinstance(col, INT_TYPES):
+                res = self._get_sliceXint(row, col)
+            elif isinstance(col, slice):
+                if row == slice(None) and row == col:
+                    res = self.copy()
+                else:
+                    res = self._get_sliceXslice(row, col)
+            elif col.ndim == 1:
+                res = self._get_sliceXarray(row, col)
+            else:
+                raise IndexError('index results in >2 dimensions')
+        else:
+            if isinstance(col, INT_TYPES):
+                res = self._get_arrayXint(row, col)
+            elif isinstance(col, slice):
+                res = self._get_arrayXslice(row, col)
+            # arrayXarray preprocess
+            elif (row.ndim == 2 and row.shape[1] == 1
+                and (col.ndim == 1 or col.shape[0] == 1)):
+                # outer indexing
+                res = self._get_columnXarray(row[:, 0], col.ravel())
+            else:
+                # inner indexing
+                row, col = _broadcast_arrays(row, col)
+                if row.shape != col.shape:
+                    raise IndexError('number of row and column indices differ')
+                if row.size == 0:
+                    res = self.__class__(np.atleast_2d(row).shape, dtype=self.dtype)
+                else:
+                    res = self._get_arrayXarray(row, col)
+
+        # handle spmatrix (must be 2d, dont let 1d new_shape start reshape)
+        if not isinstance(self, sparray):
+            if new_shape == () or (len(new_shape) == 1 and res.ndim != 0):
+                # res handles cases not inflated by None
+                return res
+            if len(new_shape) == 1:
+                # shape inflated to 1D by None in index. Make 2D
+                new_shape = (1,) + new_shape
+            # reshape if needed (when None changes shape, e.g. A[1,:,None])
+            return res if new_shape == res.shape else res.reshape(new_shape)
+
+        # package the result and return
+        if res.shape != new_shape:
+            # handle formats that support indexing but not 1D (lil for now)
+            if self.format == "lil" and len(new_shape) != 2:
+                if res.shape == ():
+                    return self._coo_container([res], shape = new_shape)
+                return res.tocoo().reshape(new_shape)
+            return res.reshape(new_shape)
+        return res
+
+    def __setitem__(self, key, x):
+        index, _ = self._validate_indices(key)
+
+        # 1D array
+        if len(index) == 1:
+            idx = index[0]
+
+            if issparse(x):
+                x = x.toarray()
+            else:
+                x = np.asarray(x, dtype=self.dtype)
+
+            if isinstance(idx, INT_TYPES):
+                if x.size != 1:
+                    raise ValueError('Trying to assign a sequence to an item')
+                self._set_int(idx, x.flat[0])
+                return
+
+            if isinstance(idx, slice):
+                # check for simple case of slice that gives 1 item
+                # Note: Python `range` does not use lots of memory
+                idx_range = range(*idx.indices(self.shape[0]))
+                N = len(idx_range)
+                if N == 1 and x.size == 1:
+                    self._set_int(idx_range[0], x.flat[0])
+                    return
+                idx = np.arange(*idx.indices(self.shape[0]))
+                idx_shape = idx.shape
+            else:
+                idx_shape = idx.squeeze().shape
+            # broadcast scalar to full 1d
+            if x.squeeze().shape != idx_shape:
+                x = np.broadcast_to(x, idx.shape)
+            if x.size != 0:
+                self._set_array(idx, x)
+            return
+
+        # 2D array
+        row, col = index
+
+        if isinstance(row, INT_TYPES) and isinstance(col, INT_TYPES):
+            x = np.asarray(x, dtype=self.dtype)
+            if x.size != 1:
+                raise ValueError('Trying to assign a sequence to an item')
+            self._set_intXint(row, col, x.flat[0])
+            return
+
+        if isinstance(row, slice):
+            row = np.arange(*row.indices(self.shape[0]))[:, None]
+        else:
+            row = np.atleast_1d(row)
+
+        if isinstance(col, slice):
+            col = np.arange(*col.indices(self.shape[1]))[None, :]
+            if row.ndim == 1:
+                row = row[:, None]
+        else:
+            col = np.atleast_1d(col)
+
+        i, j = _broadcast_arrays(row, col)
+        if i.shape != j.shape:
+            raise IndexError('number of row and column indices differ')
+
+        if issparse(x):
+            if 0 in x.shape:
+                return
+            if i.ndim == 1:
+                # Inner indexing, so treat them like row vectors.
+                i = i[None]
+                j = j[None]
+            x = x.tocoo(copy=False).reshape(x._shape_as_2d, copy=True)
+            broadcast_row = x.shape[0] == 1 and i.shape[0] != 1
+            broadcast_col = x.shape[1] == 1 and i.shape[1] != 1
+            if not ((broadcast_row or x.shape[0] == i.shape[0]) and
+                    (broadcast_col or x.shape[1] == i.shape[1])):
+                raise ValueError('shape mismatch in assignment')
+            x.sum_duplicates()
+            self._set_arrayXarray_sparse(i, j, x)
+        else:
+            # Make x and i into the same shape
+            x = np.asarray(x, dtype=self.dtype)
+            if x.squeeze().shape != i.squeeze().shape:
+                x = np.broadcast_to(x, i.shape)
+            if x.size == 0:
+                return
+            x = x.reshape(i.shape)
+            self._set_arrayXarray(i, j, x)
+
+    def _validate_indices(self, key):
+        """Returns two tuples: (index tuple, requested shape tuple)"""
+        # single ellipsis
+        if key is Ellipsis:
+            return (slice(None),) * self.ndim, self.shape
+
+        if not isinstance(key, tuple):
+            key = [key]
+
+        ellps_pos = None
+        index_1st = []
+        prelim_ndim = 0
+        for i, idx in enumerate(key):
+            if idx is Ellipsis:
+                if ellps_pos is not None:
+                    raise IndexError('an index can only have a single ellipsis')
+                ellps_pos = i
+            elif idx is None:
+                index_1st.append(idx)
+            elif isinstance(idx, slice) or isintlike(idx):
+                index_1st.append(idx)
+                prelim_ndim += 1
+            elif (ix := _compatible_boolean_index(idx, self.ndim)) is not None:
+                index_1st.append(ix)
+                prelim_ndim += ix.ndim
+            elif issparse(idx):
+                # TODO: make sparse matrix indexing work for sparray
+                raise IndexError(
+                    'Indexing with sparse matrices is not supported '
+                    'except boolean indexing where matrix and index '
+                    'are equal shapes.')
+            else:  # dense array
+                index_1st.append(np.asarray(idx))
+                prelim_ndim += 1
+        ellip_slices = (self.ndim - prelim_ndim) * [slice(None)]
+        if ellip_slices:
+            if ellps_pos is None:
+                index_1st.extend(ellip_slices)
+            else:
+                index_1st = index_1st[:ellps_pos] + ellip_slices + index_1st[ellps_pos:]
+
+        # second pass (have processed ellipsis and preprocessed arrays)
+        idx_shape = []
+        index_ndim = 0
+        index = []
+        array_indices = []
+        for i, idx in enumerate(index_1st):
+            if idx is None:
+                idx_shape.append(1)
+            elif isinstance(idx, slice):
+                index.append(idx)
+                Ms = self._shape[index_ndim]
+                len_slice = len(range(*idx.indices(Ms)))
+                idx_shape.append(len_slice)
+                index_ndim += 1
+            elif isintlike(idx):
+                N = self._shape[index_ndim]
+                if not (-N <= idx < N):
+                    raise IndexError(f'index ({idx}) out of range')
+                idx = int(idx + N if idx < 0 else idx)
+                index.append(idx)
+                index_ndim += 1
+            # bool array (checked in first pass)
+            elif idx.dtype.kind == 'b':
+                ix = idx
+                tmp_ndim = index_ndim + ix.ndim
+                mid_shape = self._shape[index_ndim:tmp_ndim]
+                if ix.shape != mid_shape:
+                    raise IndexError(
+                        f"bool index {i} has shape {mid_shape} instead of {ix.shape}"
+                    )
+                index.extend(ix.nonzero())
+                array_indices.extend(range(index_ndim, tmp_ndim))
+                index_ndim = tmp_ndim
+            else:  # dense array
+                N = self._shape[index_ndim]
+                idx = self._asindices(idx, N)
+                index.append(idx)
+                array_indices.append(index_ndim)
+                index_ndim += 1
+        if index_ndim > self.ndim:
+            raise IndexError(
+                f'invalid index ndim. Array is {self.ndim}D. Index needs {index_ndim}D'
+            )
+        if len(array_indices) > 1:
+            idx_arrays = _broadcast_arrays(*(index[i] for i in array_indices))
+            if any(idx_arrays[0].shape != ix.shape for ix in idx_arrays[1:]):
+                shapes = " ".join(str(ix.shape) for ix in idx_arrays)
+                msg = (f'shape mismatch: indexing arrays could not be broadcast '
+                       f'together with shapes {shapes}')
+                raise IndexError(msg)
+            # TODO: handle this for nD (adjacent arrays stay, separated move to start)
+            idx_shape = list(idx_arrays[0].shape) + idx_shape
+        elif len(array_indices) == 1:
+            arr_index = array_indices[0]
+            arr_shape = list(index[arr_index].shape)
+            idx_shape = idx_shape[:arr_index] + arr_shape + idx_shape[arr_index:]
+        if (ndim := len(idx_shape)) > 2:
+            raise IndexError(f'Only 1D or 2D arrays allowed. Index makes {ndim}D')
+        return tuple(index), tuple(idx_shape)
+
+    def _asindices(self, idx, length):
+        """Convert `idx` to a valid index for an axis with a given length.
+
+        Subclasses that need special validation can override this method.
+        """
+        try:
+            x = np.asarray(idx)
+        except (ValueError, TypeError, MemoryError) as e:
+            raise IndexError('invalid index') from e
+
+        if x.ndim not in (1, 2):
+            raise IndexError('Index dimension must be 1 or 2')
+
+        if x.size == 0:
+            return x
+
+        # Check bounds
+        max_indx = x.max()
+        if max_indx >= length:
+            raise IndexError('index (%d) out of range' % max_indx)
+
+        min_indx = x.min()
+        if min_indx < 0:
+            if min_indx < -length:
+                raise IndexError('index (%d) out of range' % min_indx)
+            if x is idx or not x.flags.owndata:
+                x = x.copy()
+            x[x < 0] += length
+        return x
+
+    def _getrow(self, i):
+        """Return a copy of row i of the matrix, as a (1 x n) row vector.
+        """
+        M, N = self.shape
+        i = int(i)
+        if i < -M or i >= M:
+            raise IndexError('index (%d) out of range' % i)
+        if i < 0:
+            i += M
+        return self._get_intXslice(i, slice(None))
+
+    def _getcol(self, i):
+        """Return a copy of column i of the matrix, as a (m x 1) column vector.
+        """
+        M, N = self.shape
+        i = int(i)
+        if i < -N or i >= N:
+            raise IndexError('index (%d) out of range' % i)
+        if i < 0:
+            i += N
+        return self._get_sliceXint(slice(None), i)
+
+    def _get_int(self, idx):
+        raise NotImplementedError()
+
+    def _get_slice(self, idx):
+        raise NotImplementedError()
+
+    def _get_array(self, idx):
+        raise NotImplementedError()
+
+    def _get_intXint(self, row, col):
+        raise NotImplementedError()
+
+    def _get_intXarray(self, row, col):
+        raise NotImplementedError()
+
+    def _get_intXslice(self, row, col):
+        raise NotImplementedError()
+
+    def _get_sliceXint(self, row, col):
+        raise NotImplementedError()
+
+    def _get_sliceXslice(self, row, col):
+        raise NotImplementedError()
+
+    def _get_sliceXarray(self, row, col):
+        raise NotImplementedError()
+
+    def _get_arrayXint(self, row, col):
+        raise NotImplementedError()
+
+    def _get_arrayXslice(self, row, col):
+        raise NotImplementedError()
+
+    def _get_columnXarray(self, row, col):
+        raise NotImplementedError()
+
+    def _get_arrayXarray(self, row, col):
+        raise NotImplementedError()
+
+    def _set_int(self, idx, x):
+        raise NotImplementedError()
+
+    def _set_array(self, idx, x):
+        raise NotImplementedError()
+
+    def _set_intXint(self, row, col, x):
+        raise NotImplementedError()
+
+    def _set_arrayXarray(self, row, col, x):
+        raise NotImplementedError()
+
+    def _set_arrayXarray_sparse(self, row, col, x):
+        # Fall back to densifying x
+        x = np.asarray(x.toarray(), dtype=self.dtype)
+        x, _ = _broadcast_arrays(x, row)
+        self._set_arrayXarray(row, col, x)
+
+
+def _compatible_boolean_index(idx, desired_ndim):
+    """Check for boolean array or array-like. peek before asarray for array-like"""
+    # use attribute ndim to indicate a compatible array and check dtype
+    # if not, look at 1st element as quick rejection of bool, else slower asanyarray
+    if not hasattr(idx, 'ndim'):
+        # is first element boolean?
+        try:
+            ix = next(iter(idx), None)
+            for _ in range(desired_ndim):
+                if isinstance(ix, bool):
+                    break
+                ix = next(iter(ix), None)
+            else:
+                return None
+        except TypeError:
+            return None
+        # since first is boolean, construct array and check all elements
+        idx = np.asanyarray(idx)
+
+    if idx.dtype.kind == 'b':
+        return idx
+    return None
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diff --git a/shared/package-files/32/32f37a84eec01577f84b071972f54840dd4b726ba04f8c49e4f3f5f3a5284b19/xterm+keypad b/shared/package-files/32/32f37a84eec01577f84b071972f54840dd4b726ba04f8c49e4f3f5f3a5284b19/xterm+keypad
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diff --git a/shared/package-files/32/32fbebb50f6ff07b1846588e6d491b262557b20f171d7e7474e5138daff0add3/_mixed_dtypes_linear_native.h b/shared/package-files/32/32fbebb50f6ff07b1846588e6d491b262557b20f171d7e7474e5138daff0add3/_mixed_dtypes_linear_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d85eed9b0873a13e775973fdcff7a4ac04be929
--- /dev/null
+++ b/shared/package-files/32/32fbebb50f6ff07b1846588e6d491b262557b20f171d7e7474e5138daff0add3/_mixed_dtypes_linear_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor _mixed_dtypes_linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const c10::optional<at::Tensor> & bias={}, c10::optional<c10::string_view> activation=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/32/32fc48197f01bdd21dddc2b17624ea11deae5aee6773e7e1297513260b625e35/__init__.py b/shared/package-files/32/32fc48197f01bdd21dddc2b17624ea11deae5aee6773e7e1297513260b625e35/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8df5389fbe8cb9055d8c4c74ed6d9c60bfbf17c9
--- /dev/null
+++ b/shared/package-files/32/32fc48197f01bdd21dddc2b17624ea11deae5aee6773e7e1297513260b625e35/__init__.py
@@ -0,0 +1,19 @@
+# mypy: ignore-errors
+
+from .utils import (
+    _gen_alignment_data,
+    assert_,
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    assert_array_less,
+    assert_equal,
+    assert_raises_regex,
+    assert_warns,
+    HAS_REFCOUNT,
+    IS_WASM,
+    suppress_warnings,
+)
+
+# from .testing import assert_allclose    # FIXME
diff --git a/shared/package-files/32/32fce7fcfc928486fd9bb9c98c9e087ad771c262d812c08f1ef2682bb9f6be25/twodim_base.py b/shared/package-files/32/32fce7fcfc928486fd9bb9c98c9e087ad771c262d812c08f1ef2682bb9f6be25/twodim_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dcb656519342e516a6f1be0bf283bb1f326214f
--- /dev/null
+++ b/shared/package-files/32/32fce7fcfc928486fd9bb9c98c9e087ad771c262d812c08f1ef2682bb9f6be25/twodim_base.py
@@ -0,0 +1,1183 @@
+""" Basic functions for manipulating 2d arrays
+
+"""
+import functools
+import operator
+
+from numpy.core.numeric import (
+    asanyarray, arange, zeros, greater_equal, multiply, ones,
+    asarray, where, int8, int16, int32, int64, intp, empty, promote_types,
+    diagonal, nonzero, indices
+    )
+from numpy.core.overrides import set_array_function_like_doc, set_module
+from numpy.core import overrides
+from numpy.core import iinfo
+from numpy.lib.stride_tricks import broadcast_to
+
+
+__all__ = [
+    'diag', 'diagflat', 'eye', 'fliplr', 'flipud', 'tri', 'triu',
+    'tril', 'vander', 'histogram2d', 'mask_indices', 'tril_indices',
+    'tril_indices_from', 'triu_indices', 'triu_indices_from', ]
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+i1 = iinfo(int8)
+i2 = iinfo(int16)
+i4 = iinfo(int32)
+
+
+def _min_int(low, high):
+    """ get small int that fits the range """
+    if high <= i1.max and low >= i1.min:
+        return int8
+    if high <= i2.max and low >= i2.min:
+        return int16
+    if high <= i4.max and low >= i4.min:
+        return int32
+    return int64
+
+
+def _flip_dispatcher(m):
+    return (m,)
+
+
+@array_function_dispatch(_flip_dispatcher)
+def fliplr(m):
+    """
+    Reverse the order of elements along axis 1 (left/right).
+
+    For a 2-D array, this flips the entries in each row in the left/right
+    direction. Columns are preserved, but appear in a different order than
+    before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array, must be at least 2-D.
+
+    Returns
+    -------
+    f : ndarray
+        A view of `m` with the columns reversed.  Since a view
+        is returned, this operation is :math:`\\mathcal O(1)`.
+
+    See Also
+    --------
+    flipud : Flip array in the up/down direction.
+    flip : Flip array in one or more dimensions.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[:,::-1]`` or ``np.flip(m, axis=1)``.
+    Requires the array to be at least 2-D.
+
+    Examples
+    --------
+    >>> A = np.diag([1.,2.,3.])
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.fliplr(A)
+    array([[0.,  0.,  1.],
+           [0.,  2.,  0.],
+           [3.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.fliplr(A) == A[:,::-1,...])
+    True
+
+    """
+    m = asanyarray(m)
+    if m.ndim < 2:
+        raise ValueError("Input must be >= 2-d.")
+    return m[:, ::-1]
+
+
+@array_function_dispatch(_flip_dispatcher)
+def flipud(m):
+    """
+    Reverse the order of elements along axis 0 (up/down).
+
+    For a 2-D array, this flips the entries in each column in the up/down
+    direction. Rows are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the rows reversed.  Since a view is
+        returned, this operation is :math:`\\mathcal O(1)`.
+
+    See Also
+    --------
+    fliplr : Flip array in the left/right direction.
+    flip : Flip array in one or more dimensions.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[::-1, ...]`` or ``np.flip(m, axis=0)``.
+    Requires the array to be at least 1-D.
+
+    Examples
+    --------
+    >>> A = np.diag([1.0, 2, 3])
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.flipud(A)
+    array([[0.,  0.,  3.],
+           [0.,  2.,  0.],
+           [1.,  0.,  0.]])
+
+    >>> A = np.random.randn(2,3,5)
+    >>> np.all(np.flipud(A) == A[::-1,...])
+    True
+
+    >>> np.flipud([1,2])
+    array([2, 1])
+
+    """
+    m = asanyarray(m)
+    if m.ndim < 1:
+        raise ValueError("Input must be >= 1-d.")
+    return m[::-1, ...]
+
+
+@set_array_function_like_doc
+@set_module('numpy')
+def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+      Number of rows in the output.
+    M : int, optional
+      Number of columns in the output. If None, defaults to `N`.
+    k : int, optional
+      Index of the diagonal: 0 (the default) refers to the main diagonal,
+      a positive value refers to an upper diagonal, and a negative value
+      to a lower diagonal.
+    dtype : data-type, optional
+      Data-type of the returned array.
+    order : {'C', 'F'}, optional
+        Whether the output should be stored in row-major (C-style) or
+        column-major (Fortran-style) order in memory.
+
+        .. versionadded:: 1.14.0
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+      An array where all elements are equal to zero, except for the `k`-th
+      diagonal, whose values are equal to one.
+
+    See Also
+    --------
+    identity : (almost) equivalent function
+    diag : diagonal 2-D array from a 1-D array specified by the user.
+
+    Examples
+    --------
+    >>> np.eye(2, dtype=int)
+    array([[1, 0],
+           [0, 1]])
+    >>> np.eye(3, k=1)
+    array([[0.,  1.,  0.],
+           [0.,  0.,  1.],
+           [0.,  0.,  0.]])
+
+    """
+    if like is not None:
+        return _eye_with_like(like, N, M=M, k=k, dtype=dtype, order=order)
+    if M is None:
+        M = N
+    m = zeros((N, M), dtype=dtype, order=order)
+    if k >= M:
+        return m
+    # Ensure M and k are integers, so we don't get any surprise casting
+    # results in the expressions `M-k` and `M+1` used below.  This avoids
+    # a problem with inputs with type (for example) np.uint64.
+    M = operator.index(M)
+    k = operator.index(k)
+    if k >= 0:
+        i = k
+    else:
+        i = (-k) * M
+    m[:M-k].flat[i::M+1] = 1
+    return m
+
+
+_eye_with_like = array_function_dispatch()(eye)
+
+
+def _diag_dispatcher(v, k=None):
+    return (v,)
+
+
+@array_function_dispatch(_diag_dispatcher)
+def diag(v, k=0):
+    """
+    Extract a diagonal or construct a diagonal array.
+
+    See the more detailed documentation for ``numpy.diagonal`` if you use this
+    function to extract a diagonal and wish to write to the resulting array;
+    whether it returns a copy or a view depends on what version of numpy you
+    are using.
+
+    Parameters
+    ----------
+    v : array_like
+        If `v` is a 2-D array, return a copy of its `k`-th diagonal.
+        If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th
+        diagonal.
+    k : int, optional
+        Diagonal in question. The default is 0. Use `k>0` for diagonals
+        above the main diagonal, and `k<0` for diagonals below the main
+        diagonal.
+
+    Returns
+    -------
+    out : ndarray
+        The extracted diagonal or constructed diagonal array.
+
+    See Also
+    --------
+    diagonal : Return specified diagonals.
+    diagflat : Create a 2-D array with the flattened input as a diagonal.
+    trace : Sum along diagonals.
+    triu : Upper triangle of an array.
+    tril : Lower triangle of an array.
+
+    Examples
+    --------
+    >>> x = np.arange(9).reshape((3,3))
+    >>> x
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+
+    >>> np.diag(x)
+    array([0, 4, 8])
+    >>> np.diag(x, k=1)
+    array([1, 5])
+    >>> np.diag(x, k=-1)
+    array([3, 7])
+
+    >>> np.diag(np.diag(x))
+    array([[0, 0, 0],
+           [0, 4, 0],
+           [0, 0, 8]])
+
+    """
+    v = asanyarray(v)
+    s = v.shape
+    if len(s) == 1:
+        n = s[0]+abs(k)
+        res = zeros((n, n), v.dtype)
+        if k >= 0:
+            i = k
+        else:
+            i = (-k) * n
+        res[:n-k].flat[i::n+1] = v
+        return res
+    elif len(s) == 2:
+        return diagonal(v, k)
+    else:
+        raise ValueError("Input must be 1- or 2-d.")
+
+
+@array_function_dispatch(_diag_dispatcher)
+def diagflat(v, k=0):
+    """
+    Create a two-dimensional array with the flattened input as a diagonal.
+
+    Parameters
+    ----------
+    v : array_like
+        Input data, which is flattened and set as the `k`-th
+        diagonal of the output.
+    k : int, optional
+        Diagonal to set; 0, the default, corresponds to the "main" diagonal,
+        a positive (negative) `k` giving the number of the diagonal above
+        (below) the main.
+
+    Returns
+    -------
+    out : ndarray
+        The 2-D output array.
+
+    See Also
+    --------
+    diag : MATLAB work-alike for 1-D and 2-D arrays.
+    diagonal : Return specified diagonals.
+    trace : Sum along diagonals.
+
+    Examples
+    --------
+    >>> np.diagflat([[1,2], [3,4]])
+    array([[1, 0, 0, 0],
+           [0, 2, 0, 0],
+           [0, 0, 3, 0],
+           [0, 0, 0, 4]])
+
+    >>> np.diagflat([1,2], 1)
+    array([[0, 1, 0],
+           [0, 0, 2],
+           [0, 0, 0]])
+
+    """
+    try:
+        wrap = v.__array_wrap__
+    except AttributeError:
+        wrap = None
+    v = asarray(v).ravel()
+    s = len(v)
+    n = s + abs(k)
+    res = zeros((n, n), v.dtype)
+    if (k >= 0):
+        i = arange(0, n-k, dtype=intp)
+        fi = i+k+i*n
+    else:
+        i = arange(0, n+k, dtype=intp)
+        fi = i+(i-k)*n
+    res.flat[fi] = v
+    if not wrap:
+        return res
+    return wrap(res)
+
+
+@set_array_function_like_doc
+@set_module('numpy')
+def tri(N, M=None, k=0, dtype=float, *, like=None):
+    """
+    An array with ones at and below the given diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in the array.
+    M : int, optional
+        Number of columns in the array.
+        By default, `M` is taken equal to `N`.
+    k : int, optional
+        The sub-diagonal at and below which the array is filled.
+        `k` = 0 is the main diagonal, while `k` < 0 is below it,
+        and `k` > 0 is above.  The default is 0.
+    dtype : dtype, optional
+        Data type of the returned array.  The default is float.
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    tri : ndarray of shape (N, M)
+        Array with its lower triangle filled with ones and zero elsewhere;
+        in other words ``T[i,j] == 1`` for ``j <= i + k``, 0 otherwise.
+
+    Examples
+    --------
+    >>> np.tri(3, 5, 2, dtype=int)
+    array([[1, 1, 1, 0, 0],
+           [1, 1, 1, 1, 0],
+           [1, 1, 1, 1, 1]])
+
+    >>> np.tri(3, 5, -1)
+    array([[0.,  0.,  0.,  0.,  0.],
+           [1.,  0.,  0.,  0.,  0.],
+           [1.,  1.,  0.,  0.,  0.]])
+
+    """
+    if like is not None:
+        return _tri_with_like(like, N, M=M, k=k, dtype=dtype)
+
+    if M is None:
+        M = N
+
+    m = greater_equal.outer(arange(N, dtype=_min_int(0, N)),
+                            arange(-k, M-k, dtype=_min_int(-k, M - k)))
+
+    # Avoid making a copy if the requested type is already bool
+    m = m.astype(dtype, copy=False)
+
+    return m
+
+
+_tri_with_like = array_function_dispatch()(tri)
+
+
+def _trilu_dispatcher(m, k=None):
+    return (m,)
+
+
+@array_function_dispatch(_trilu_dispatcher)
+def tril(m, k=0):
+    """
+    Lower triangle of an array.
+
+    Return a copy of an array with elements above the `k`-th diagonal zeroed.
+    For arrays with ``ndim`` exceeding 2, `tril` will apply to the final two
+    axes.
+
+    Parameters
+    ----------
+    m : array_like, shape (..., M, N)
+        Input array.
+    k : int, optional
+        Diagonal above which to zero elements.  `k = 0` (the default) is the
+        main diagonal, `k < 0` is below it and `k > 0` is above.
+
+    Returns
+    -------
+    tril : ndarray, shape (..., M, N)
+        Lower triangle of `m`, of same shape and data-type as `m`.
+
+    See Also
+    --------
+    triu : same thing, only for the upper triangle
+
+    Examples
+    --------
+    >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
+    array([[ 0,  0,  0],
+           [ 4,  0,  0],
+           [ 7,  8,  0],
+           [10, 11, 12]])
+
+    >>> np.tril(np.arange(3*4*5).reshape(3, 4, 5))
+    array([[[ 0,  0,  0,  0,  0],
+            [ 5,  6,  0,  0,  0],
+            [10, 11, 12,  0,  0],
+            [15, 16, 17, 18,  0]],
+           [[20,  0,  0,  0,  0],
+            [25, 26,  0,  0,  0],
+            [30, 31, 32,  0,  0],
+            [35, 36, 37, 38,  0]],
+           [[40,  0,  0,  0,  0],
+            [45, 46,  0,  0,  0],
+            [50, 51, 52,  0,  0],
+            [55, 56, 57, 58,  0]]])
+
+    """
+    m = asanyarray(m)
+    mask = tri(*m.shape[-2:], k=k, dtype=bool)
+
+    return where(mask, m, zeros(1, m.dtype))
+
+
+@array_function_dispatch(_trilu_dispatcher)
+def triu(m, k=0):
+    """
+    Upper triangle of an array.
+
+    Return a copy of an array with the elements below the `k`-th diagonal
+    zeroed. For arrays with ``ndim`` exceeding 2, `triu` will apply to the
+    final two axes.
+
+    Please refer to the documentation for `tril` for further details.
+
+    See Also
+    --------
+    tril : lower triangle of an array
+
+    Examples
+    --------
+    >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
+    array([[ 1,  2,  3],
+           [ 4,  5,  6],
+           [ 0,  8,  9],
+           [ 0,  0, 12]])
+
+    >>> np.triu(np.arange(3*4*5).reshape(3, 4, 5))
+    array([[[ 0,  1,  2,  3,  4],
+            [ 0,  6,  7,  8,  9],
+            [ 0,  0, 12, 13, 14],
+            [ 0,  0,  0, 18, 19]],
+           [[20, 21, 22, 23, 24],
+            [ 0, 26, 27, 28, 29],
+            [ 0,  0, 32, 33, 34],
+            [ 0,  0,  0, 38, 39]],
+           [[40, 41, 42, 43, 44],
+            [ 0, 46, 47, 48, 49],
+            [ 0,  0, 52, 53, 54],
+            [ 0,  0,  0, 58, 59]]])
+
+    """
+    m = asanyarray(m)
+    mask = tri(*m.shape[-2:], k=k-1, dtype=bool)
+
+    return where(mask, zeros(1, m.dtype), m)
+
+
+def _vander_dispatcher(x, N=None, increasing=None):
+    return (x,)
+
+
+# Originally borrowed from John Hunter and matplotlib
+@array_function_dispatch(_vander_dispatcher)
+def vander(x, N=None, increasing=False):
+    """
+    Generate a Vandermonde matrix.
+
+    The columns of the output matrix are powers of the input vector. The
+    order of the powers is determined by the `increasing` boolean argument.
+    Specifically, when `increasing` is False, the `i`-th output column is
+    the input vector raised element-wise to the power of ``N - i - 1``. Such
+    a matrix with a geometric progression in each row is named for Alexandre-
+    Theophile Vandermonde.
+
+    Parameters
+    ----------
+    x : array_like
+        1-D input array.
+    N : int, optional
+        Number of columns in the output.  If `N` is not specified, a square
+        array is returned (``N = len(x)``).
+    increasing : bool, optional
+        Order of the powers of the columns.  If True, the powers increase
+        from left to right, if False (the default) they are reversed.
+
+        .. versionadded:: 1.9.0
+
+    Returns
+    -------
+    out : ndarray
+        Vandermonde matrix.  If `increasing` is False, the first column is
+        ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is
+        True, the columns are ``x^0, x^1, ..., x^(N-1)``.
+
+    See Also
+    --------
+    polynomial.polynomial.polyvander
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 3, 5])
+    >>> N = 3
+    >>> np.vander(x, N)
+    array([[ 1,  1,  1],
+           [ 4,  2,  1],
+           [ 9,  3,  1],
+           [25,  5,  1]])
+
+    >>> np.column_stack([x**(N-1-i) for i in range(N)])
+    array([[ 1,  1,  1],
+           [ 4,  2,  1],
+           [ 9,  3,  1],
+           [25,  5,  1]])
+
+    >>> x = np.array([1, 2, 3, 5])
+    >>> np.vander(x)
+    array([[  1,   1,   1,   1],
+           [  8,   4,   2,   1],
+           [ 27,   9,   3,   1],
+           [125,  25,   5,   1]])
+    >>> np.vander(x, increasing=True)
+    array([[  1,   1,   1,   1],
+           [  1,   2,   4,   8],
+           [  1,   3,   9,  27],
+           [  1,   5,  25, 125]])
+
+    The determinant of a square Vandermonde matrix is the product
+    of the differences between the values of the input vector:
+
+    >>> np.linalg.det(np.vander(x))
+    48.000000000000043 # may vary
+    >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
+    48
+
+    """
+    x = asarray(x)
+    if x.ndim != 1:
+        raise ValueError("x must be a one-dimensional array or sequence.")
+    if N is None:
+        N = len(x)
+
+    v = empty((len(x), N), dtype=promote_types(x.dtype, int))
+    tmp = v[:, ::-1] if not increasing else v
+
+    if N > 0:
+        tmp[:, 0] = 1
+    if N > 1:
+        tmp[:, 1:] = x[:, None]
+        multiply.accumulate(tmp[:, 1:], out=tmp[:, 1:], axis=1)
+
+    return v
+
+
+def _histogram2d_dispatcher(x, y, bins=None, range=None, density=None,
+                            weights=None):
+    yield x
+    yield y
+
+    # This terrible logic is adapted from the checks in histogram2d
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+    if N == 2:
+        yield from bins  # bins=[x, y]
+    else:
+        yield bins
+
+    yield weights
+
+
+@array_function_dispatch(_histogram2d_dispatcher)
+def histogram2d(x, y, bins=10, range=None, density=None, weights=None):
+    """
+    Compute the bi-dimensional histogram of two data samples.
+
+    Parameters
+    ----------
+    x : array_like, shape (N,)
+        An array containing the x coordinates of the points to be
+        histogrammed.
+    y : array_like, shape (N,)
+        An array containing the y coordinates of the points to be
+        histogrammed.
+    bins : int or array_like or [int, int] or [array, array], optional
+        The bin specification:
+
+          * If int, the number of bins for the two dimensions (nx=ny=bins).
+          * If array_like, the bin edges for the two dimensions
+            (x_edges=y_edges=bins).
+          * If [int, int], the number of bins in each dimension
+            (nx, ny = bins).
+          * If [array, array], the bin edges in each dimension
+            (x_edges, y_edges = bins).
+          * A combination [int, array] or [array, int], where int
+            is the number of bins and array is the bin edges.
+
+    range : array_like, shape(2,2), optional
+        The leftmost and rightmost edges of the bins along each dimension
+        (if not specified explicitly in the `bins` parameters):
+        ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
+        will be considered outliers and not tallied in the histogram.
+    density : bool, optional
+        If False, the default, returns the number of samples in each bin.
+        If True, returns the probability *density* function at the bin,
+        ``bin_count / sample_count / bin_area``.
+    weights : array_like, shape(N,), optional
+        An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
+        Weights are normalized to 1 if `density` is True. If `density` is
+        False, the values of the returned histogram are equal to the sum of
+        the weights belonging to the samples falling into each bin.
+
+    Returns
+    -------
+    H : ndarray, shape(nx, ny)
+        The bi-dimensional histogram of samples `x` and `y`. Values in `x`
+        are histogrammed along the first dimension and values in `y` are
+        histogrammed along the second dimension.
+    xedges : ndarray, shape(nx+1,)
+        The bin edges along the first dimension.
+    yedges : ndarray, shape(ny+1,)
+        The bin edges along the second dimension.
+
+    See Also
+    --------
+    histogram : 1D histogram
+    histogramdd : Multidimensional histogram
+
+    Notes
+    -----
+    When `density` is True, then the returned histogram is the sample
+    density, defined such that the sum over bins of the product
+    ``bin_value * bin_area`` is 1.
+
+    Please note that the histogram does not follow the Cartesian convention
+    where `x` values are on the abscissa and `y` values on the ordinate
+    axis.  Rather, `x` is histogrammed along the first dimension of the
+    array (vertical), and `y` along the second dimension of the array
+    (horizontal).  This ensures compatibility with `histogramdd`.
+
+    Examples
+    --------
+    >>> from matplotlib.image import NonUniformImage
+    >>> import matplotlib.pyplot as plt
+
+    Construct a 2-D histogram with variable bin width. First define the bin
+    edges:
+
+    >>> xedges = [0, 1, 3, 5]
+    >>> yedges = [0, 2, 3, 4, 6]
+
+    Next we create a histogram H with random bin content:
+
+    >>> x = np.random.normal(2, 1, 100)
+    >>> y = np.random.normal(1, 1, 100)
+    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
+    >>> # Histogram does not follow Cartesian convention (see Notes),
+    >>> # therefore transpose H for visualization purposes.
+    >>> H = H.T
+
+    :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:
+
+    >>> fig = plt.figure(figsize=(7, 3))
+    >>> ax = fig.add_subplot(131, title='imshow: square bins')
+    >>> plt.imshow(H, interpolation='nearest', origin='lower',
+    ...         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
+    <matplotlib.image.AxesImage object at 0x...>
+
+    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:
+
+    >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
+    ...         aspect='equal')
+    >>> X, Y = np.meshgrid(xedges, yedges)
+    >>> ax.pcolormesh(X, Y, H)
+    <matplotlib.collections.QuadMesh object at 0x...>
+
+    :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
+    display actual bin edges with interpolation:
+
+    >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
+    ...         aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
+    >>> im = NonUniformImage(ax, interpolation='bilinear')
+    >>> xcenters = (xedges[:-1] + xedges[1:]) / 2
+    >>> ycenters = (yedges[:-1] + yedges[1:]) / 2
+    >>> im.set_data(xcenters, ycenters, H)
+    >>> ax.add_image(im)
+    >>> plt.show()
+
+    It is also possible to construct a 2-D histogram without specifying bin
+    edges:
+
+    >>> # Generate non-symmetric test data
+    >>> n = 10000
+    >>> x = np.linspace(1, 100, n)
+    >>> y = 2*np.log(x) + np.random.rand(n) - 0.5
+    >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges
+    >>> H, yedges, xedges = np.histogram2d(y, x, bins=20)
+
+    Now we can plot the histogram using
+    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>`, and a
+    :func:`hexbin <matplotlib.pyplot.hexbin>` for comparison.
+
+    >>> # Plot histogram using pcolormesh
+    >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
+    >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow')
+    >>> ax1.plot(x, 2*np.log(x), 'k-')
+    >>> ax1.set_xlim(x.min(), x.max())
+    >>> ax1.set_ylim(y.min(), y.max())
+    >>> ax1.set_xlabel('x')
+    >>> ax1.set_ylabel('y')
+    >>> ax1.set_title('histogram2d')
+    >>> ax1.grid()
+
+    >>> # Create hexbin plot for comparison
+    >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow')
+    >>> ax2.plot(x, 2*np.log(x), 'k-')
+    >>> ax2.set_title('hexbin')
+    >>> ax2.set_xlim(x.min(), x.max())
+    >>> ax2.set_xlabel('x')
+    >>> ax2.grid()
+
+    >>> plt.show()
+    """
+    from numpy import histogramdd
+
+    if len(x) != len(y):
+        raise ValueError('x and y must have the same length.')
+
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+
+    if N != 1 and N != 2:
+        xedges = yedges = asarray(bins)
+        bins = [xedges, yedges]
+    hist, edges = histogramdd([x, y], bins, range, density, weights)
+    return hist, edges[0], edges[1]
+
+
+@set_module('numpy')
+def mask_indices(n, mask_func, k=0):
+    """
+    Return the indices to access (n, n) arrays, given a masking function.
+
+    Assume `mask_func` is a function that, for a square array a of size
+    ``(n, n)`` with a possible offset argument `k`, when called as
+    ``mask_func(a, k)`` returns a new array with zeros in certain locations
+    (functions like `triu` or `tril` do precisely this). Then this function
+    returns the indices where the non-zero values would be located.
+
+    Parameters
+    ----------
+    n : int
+        The returned indices will be valid to access arrays of shape (n, n).
+    mask_func : callable
+        A function whose call signature is similar to that of `triu`, `tril`.
+        That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.
+        `k` is an optional argument to the function.
+    k : scalar
+        An optional argument which is passed through to `mask_func`. Functions
+        like `triu`, `tril` take a second argument that is interpreted as an
+        offset.
+
+    Returns
+    -------
+    indices : tuple of arrays.
+        The `n` arrays of indices corresponding to the locations where
+        ``mask_func(np.ones((n, n)), k)`` is True.
+
+    See Also
+    --------
+    triu, tril, triu_indices, tril_indices
+
+    Notes
+    -----
+    .. versionadded:: 1.4.0
+
+    Examples
+    --------
+    These are the indices that would allow you to access the upper triangular
+    part of any 3x3 array:
+
+    >>> iu = np.mask_indices(3, np.triu)
+
+    For example, if `a` is a 3x3 array:
+
+    >>> a = np.arange(9).reshape(3, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> a[iu]
+    array([0, 1, 2, 4, 5, 8])
+
+    An offset can be passed also to the masking function.  This gets us the
+    indices starting on the first diagonal right of the main one:
+
+    >>> iu1 = np.mask_indices(3, np.triu, 1)
+
+    with which we now extract only three elements:
+
+    >>> a[iu1]
+    array([1, 2, 5])
+
+    """
+    m = ones((n, n), int)
+    a = mask_func(m, k)
+    return nonzero(a != 0)
+
+
+@set_module('numpy')
+def tril_indices(n, k=0, m=None):
+    """
+    Return the indices for the lower-triangle of an (n, m) array.
+
+    Parameters
+    ----------
+    n : int
+        The row dimension of the arrays for which the returned
+        indices will be valid.
+    k : int, optional
+        Diagonal offset (see `tril` for details).
+    m : int, optional
+        .. versionadded:: 1.9.0
+
+        The column dimension of the arrays for which the returned
+        arrays will be valid.
+        By default `m` is taken equal to `n`.
+
+
+    Returns
+    -------
+    inds : tuple of arrays
+        The indices for the triangle. The returned tuple contains two arrays,
+        each with the indices along one dimension of the array.
+
+    See also
+    --------
+    triu_indices : similar function, for upper-triangular.
+    mask_indices : generic function accepting an arbitrary mask function.
+    tril, triu
+
+    Notes
+    -----
+    .. versionadded:: 1.4.0
+
+    Examples
+    --------
+    Compute two different sets of indices to access 4x4 arrays, one for the
+    lower triangular part starting at the main diagonal, and one starting two
+    diagonals further right:
+
+    >>> il1 = np.tril_indices(4)
+    >>> il2 = np.tril_indices(4, 2)
+
+    Here is how they can be used with a sample array:
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Both for indexing:
+
+    >>> a[il1]
+    array([ 0,  4,  5, ..., 13, 14, 15])
+
+    And for assigning values:
+
+    >>> a[il1] = -1
+    >>> a
+    array([[-1,  1,  2,  3],
+           [-1, -1,  6,  7],
+           [-1, -1, -1, 11],
+           [-1, -1, -1, -1]])
+
+    These cover almost the whole array (two diagonals right of the main one):
+
+    >>> a[il2] = -10
+    >>> a
+    array([[-10, -10, -10,   3],
+           [-10, -10, -10, -10],
+           [-10, -10, -10, -10],
+           [-10, -10, -10, -10]])
+
+    """
+    tri_ = tri(n, m, k=k, dtype=bool)
+
+    return tuple(broadcast_to(inds, tri_.shape)[tri_]
+                 for inds in indices(tri_.shape, sparse=True))
+
+
+def _trilu_indices_form_dispatcher(arr, k=None):
+    return (arr,)
+
+
+@array_function_dispatch(_trilu_indices_form_dispatcher)
+def tril_indices_from(arr, k=0):
+    """
+    Return the indices for the lower-triangle of arr.
+
+    See `tril_indices` for full details.
+
+    Parameters
+    ----------
+    arr : array_like
+        The indices will be valid for square arrays whose dimensions are
+        the same as arr.
+    k : int, optional
+        Diagonal offset (see `tril` for details).
+
+    Examples
+    --------
+
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the lower triangular elements.
+
+    >>> trili = np.tril_indices_from(a)
+    >>> trili
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    >>> a[trili]
+    array([ 0,  4,  5,  8,  9, 10, 12, 13, 14, 15])
+
+    This is syntactic sugar for tril_indices().
+
+    >>> np.tril_indices(a.shape[0])
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    Use the `k` parameter to return the indices for the lower triangular array
+    up to the k-th diagonal.
+
+    >>> trili1 = np.tril_indices_from(a, k=1)
+    >>> a[trili1]
+    array([ 0,  1,  4,  5,  6,  8,  9, 10, 11, 12, 13, 14, 15])
+
+    See Also
+    --------
+    tril_indices, tril, triu_indices_from
+
+    Notes
+    -----
+    .. versionadded:: 1.4.0
+
+    """
+    if arr.ndim != 2:
+        raise ValueError("input array must be 2-d")
+    return tril_indices(arr.shape[-2], k=k, m=arr.shape[-1])
+
+
+@set_module('numpy')
+def triu_indices(n, k=0, m=None):
+    """
+    Return the indices for the upper-triangle of an (n, m) array.
+
+    Parameters
+    ----------
+    n : int
+        The size of the arrays for which the returned indices will
+        be valid.
+    k : int, optional
+        Diagonal offset (see `triu` for details).
+    m : int, optional
+        .. versionadded:: 1.9.0
+
+        The column dimension of the arrays for which the returned
+        arrays will be valid.
+        By default `m` is taken equal to `n`.
+
+
+    Returns
+    -------
+    inds : tuple, shape(2) of ndarrays, shape(`n`)
+        The indices for the triangle. The returned tuple contains two arrays,
+        each with the indices along one dimension of the array.  Can be used
+        to slice a ndarray of shape(`n`, `n`).
+
+    See also
+    --------
+    tril_indices : similar function, for lower-triangular.
+    mask_indices : generic function accepting an arbitrary mask function.
+    triu, tril
+
+    Notes
+    -----
+    .. versionadded:: 1.4.0
+
+    Examples
+    --------
+    Compute two different sets of indices to access 4x4 arrays, one for the
+    upper triangular part starting at the main diagonal, and one starting two
+    diagonals further right:
+
+    >>> iu1 = np.triu_indices(4)
+    >>> iu2 = np.triu_indices(4, 2)
+
+    Here is how they can be used with a sample array:
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Both for indexing:
+
+    >>> a[iu1]
+    array([ 0,  1,  2, ..., 10, 11, 15])
+
+    And for assigning values:
+
+    >>> a[iu1] = -1
+    >>> a
+    array([[-1, -1, -1, -1],
+           [ 4, -1, -1, -1],
+           [ 8,  9, -1, -1],
+           [12, 13, 14, -1]])
+
+    These cover only a small part of the whole array (two diagonals right
+    of the main one):
+
+    >>> a[iu2] = -10
+    >>> a
+    array([[ -1,  -1, -10, -10],
+           [  4,  -1,  -1, -10],
+           [  8,   9,  -1,  -1],
+           [ 12,  13,  14,  -1]])
+
+    """
+    tri_ = ~tri(n, m, k=k - 1, dtype=bool)
+
+    return tuple(broadcast_to(inds, tri_.shape)[tri_]
+                 for inds in indices(tri_.shape, sparse=True))
+
+
+@array_function_dispatch(_trilu_indices_form_dispatcher)
+def triu_indices_from(arr, k=0):
+    """
+    Return the indices for the upper-triangle of arr.
+
+    See `triu_indices` for full details.
+
+    Parameters
+    ----------
+    arr : ndarray, shape(N, N)
+        The indices will be valid for square arrays.
+    k : int, optional
+        Diagonal offset (see `triu` for details).
+
+    Returns
+    -------
+    triu_indices_from : tuple, shape(2) of ndarray, shape(N)
+        Indices for the upper-triangle of `arr`.
+
+    Examples
+    --------
+
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the upper triangular elements.
+
+    >>> triui = np.triu_indices_from(a)
+    >>> triui
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    >>> a[triui]
+    array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
+
+    This is syntactic sugar for triu_indices().
+
+    >>> np.triu_indices(a.shape[0])
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    Use the `k` parameter to return the indices for the upper triangular array
+    from the k-th diagonal.
+
+    >>> triuim1 = np.triu_indices_from(a, k=1)
+    >>> a[triuim1]
+    array([ 1,  2,  3,  6,  7, 11])
+
+
+    See Also
+    --------
+    triu_indices, triu, tril_indices_from
+
+    Notes
+    -----
+    .. versionadded:: 1.4.0
+
+    """
+    if arr.ndim != 2:
+        raise ValueError("input array must be 2-d")
+    return triu_indices(arr.shape[-2], k=k, m=arr.shape[-1])
diff --git a/shared/package-files/32/32fe4507eae2452bb0c2f54527ad06c314466ee16453f2d4e433d6f0a63c3539/test_drop.py b/shared/package-files/32/32fe4507eae2452bb0c2f54527ad06c314466ee16453f2d4e433d6f0a63c3539/test_drop.py
new file mode 100644
index 0000000000000000000000000000000000000000..99c8ebb1e57b22059d5a545a79de7b8348d73b14
--- /dev/null
+++ b/shared/package-files/32/32fe4507eae2452bb0c2f54527ad06c314466ee16453f2d4e433d6f0a63c3539/test_drop.py
@@ -0,0 +1,190 @@
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_drop(idx):
+    dropped = idx.drop([("foo", "two"), ("qux", "one")])
+
+    index = MultiIndex.from_tuples([("foo", "two"), ("qux", "one")])
+    dropped2 = idx.drop(index)
+
+    expected = idx[[0, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+    tm.assert_index_equal(dropped2, expected)
+
+    dropped = idx.drop(["bar"])
+    expected = idx[[0, 1, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop("foo")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    index = MultiIndex.from_tuples([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(index)
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(["foo", "two"])
+
+    # partially correct argument
+    mixed_index = MultiIndex.from_tuples([("qux", "one"), ("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(mixed_index)
+
+    # error='ignore'
+    dropped = idx.drop(index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(["foo", "two"], errors="ignore")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop
+    dropped = idx.drop(["foo", ("qux", "one")])
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop / error='ignore'
+    mixed_index = ["foo", ("qux", "one"), "two"]
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(mixed_index)
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+
+def test_droplevel_with_names(idx):
+    index = idx[idx.get_loc("foo")]
+    dropped = index.droplevel(0)
+    assert dropped.name == "second"
+
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[
+            np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+            np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+            np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+        ],
+        names=["one", "two", "three"],
+    )
+    dropped = index.droplevel(0)
+    assert dropped.names == ("two", "three")
+
+    dropped = index.droplevel("two")
+    expected = index.droplevel(1)
+    assert dropped.equals(expected)
+
+
+def test_droplevel_list():
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[
+            np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+            np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+            np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+        ],
+        names=["one", "two", "three"],
+    )
+
+    dropped = index[:2].droplevel(["three", "one"])
+    expected = index[:2].droplevel(2).droplevel(0)
+    assert dropped.equals(expected)
+
+    dropped = index[:2].droplevel([])
+    expected = index[:2]
+    assert dropped.equals(expected)
+
+    msg = (
+        "Cannot remove 3 levels from an index with 3 levels: "
+        "at least one level must be left"
+    )
+    with pytest.raises(ValueError, match=msg):
+        index[:2].droplevel(["one", "two", "three"])
+
+    with pytest.raises(KeyError, match="'Level four not found'"):
+        index[:2].droplevel(["one", "four"])
+
+
+def test_drop_not_lexsorted():
+    # GH 12078
+
+    # define the lexsorted version of the multi-index
+    tuples = [("a", ""), ("b1", "c1"), ("b2", "c2")]
+    lexsorted_mi = MultiIndex.from_tuples(tuples, names=["b", "c"])
+    assert lexsorted_mi._is_lexsorted()
+
+    # and the not-lexsorted version
+    df = pd.DataFrame(
+        columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
+    )
+    df = df.pivot_table(index="a", columns=["b", "c"], values="d")
+    df = df.reset_index()
+    not_lexsorted_mi = df.columns
+    assert not not_lexsorted_mi._is_lexsorted()
+
+    # compare the results
+    tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi)
+    with tm.assert_produces_warning(PerformanceWarning):
+        tm.assert_index_equal(lexsorted_mi.drop("a"), not_lexsorted_mi.drop("a"))
+
+
+def test_drop_with_nan_in_index(nulls_fixture):
+    # GH#18853
+    mi = MultiIndex.from_tuples([("blah", nulls_fixture)], names=["name", "date"])
+    msg = r"labels \[Timestamp\('2001-01-01 00:00:00'\)\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(pd.Timestamp("2001"), level="date")
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+def test_drop_with_non_monotonic_duplicates():
+    # GH#33494
+    mi = MultiIndex.from_tuples([(1, 2), (2, 3), (1, 2)])
+    result = mi.drop((1, 2))
+    expected = MultiIndex.from_tuples([(2, 3)])
+    tm.assert_index_equal(result, expected)
+
+
+def test_single_level_drop_partially_missing_elements():
+    # GH 37820
+
+    mi = MultiIndex.from_tuples([(1, 2), (2, 2), (3, 2)])
+    msg = r"labels \[4\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(4, level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([1, 4], level=0)
+    msg = r"labels \[nan\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan], level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, 2, 3], level=0)
+
+    mi = MultiIndex.from_tuples([(np.nan, 1), (1, 2)])
+    msg = r"labels \['a'\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, "a"], level=0)
+
+
+def test_droplevel_multiindex_one_level():
+    # GH#37208
+    index = MultiIndex.from_tuples([(2,)], names=("b",))
+    result = index.droplevel([])
+    expected = Index([2], name="b")
+    tm.assert_index_equal(result, expected)
diff --git a/shared/package-files/40/4006bb644ed2dd416d53f568cf7a22dd872521885b86f88991a7cb398822b968/xxhasher.h b/shared/package-files/40/4006bb644ed2dd416d53f568cf7a22dd872521885b86f88991a7cb398822b968/xxhasher.h
new file mode 100644
index 0000000000000000000000000000000000000000..a54f287883e006e9cd6d9aeeb2efeb1d6f9db2df
--- /dev/null
+++ b/shared/package-files/40/4006bb644ed2dd416d53f568cf7a22dd872521885b86f88991a7cb398822b968/xxhasher.h
@@ -0,0 +1,50 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <cstdint>
+
+#include "parquet/hasher.h"
+#include "parquet/platform.h"
+#include "parquet/types.h"
+
+namespace parquet {
+
+class PARQUET_EXPORT XxHasher : public Hasher {
+ public:
+  uint64_t Hash(int32_t value) const override;
+  uint64_t Hash(int64_t value) const override;
+  uint64_t Hash(float value) const override;
+  uint64_t Hash(double value) const override;
+  uint64_t Hash(const Int96* value) const override;
+  uint64_t Hash(const ByteArray* value) const override;
+  uint64_t Hash(const FLBA* val, uint32_t len) const override;
+
+  void Hashes(const int32_t* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const int64_t* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const float* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const double* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const Int96* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const ByteArray* values, int num_values, uint64_t* hashes) const override;
+  void Hashes(const FLBA* values, uint32_t type_len, int num_values,
+              uint64_t* hashes) const override;
+
+  static constexpr int kParquetBloomXxHashSeed = 0;
+};
+
+}  // namespace parquet
diff --git a/shared/package-files/40/4007b1b9796ff4471991e72181d23ed4d7577bd0fb01ba7f1bedb96463c157a7/model_report_visualizer.py b/shared/package-files/40/4007b1b9796ff4471991e72181d23ed4d7577bd0fb01ba7f1bedb96463c157a7/model_report_visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5463862aa1cd41dde5b2713ee391429824d13bb2
--- /dev/null
+++ b/shared/package-files/40/4007b1b9796ff4471991e72181d23ed4d7577bd0fb01ba7f1bedb96463c157a7/model_report_visualizer.py
@@ -0,0 +1,666 @@
+import torch
+from typing import Any, Set, Dict, List, Tuple, OrderedDict
+from collections import OrderedDict as OrdDict
+
+# try to import tablate
+got_tabulate = True
+try:
+    from tabulate import tabulate
+except ImportError:
+    got_tabulate = False
+
+
+# var to see if we could import matplotlib
+got_matplotlib = True
+try:
+    import matplotlib.pyplot as plt
+except ImportError:
+    got_matplotlib = False
+
+class ModelReportVisualizer:
+    r"""
+    The ModelReportVisualizer class aims to provide users a way to visualize some of the statistics
+    that were generated by the ModelReport API. However, at a higher level, the class aims to provide
+    some level of visualization of statistics to PyTorch in order to make it easier to parse data and
+    diagnose any potential issues with data or a specific model. With respect to the visualizations,
+    the ModelReportVisualizer class currently supports several methods of visualizing data.
+
+    Supported Visualization Methods Include:
+    - Table format
+    - Plot format (line graph)
+    - Histogram format
+
+    For all of the existing visualization methods, there is the option to filter data based on:
+    - A module fqn prefix
+    - Feature [required for the plot and histogram]
+
+    * :attr:`generated_reports` The reports generated by the ModelReport class in the structure below
+        Ensure sure that features that are the same across different report contain the same name
+        Ensure that objects representing the same features are the same type / dimension (where applicable)
+
+    Note:
+        Currently, the ModelReportVisualizer class supports visualization of data generated by the
+        ModelReport class. However, this structure is extensible and should allow the visualization of
+        other information as long as the information is structured in the following general format:
+
+        Report Structure
+        -- module_fqn [module with attached detectors]
+            |
+            -- feature keys [not every detector extracts same information]
+                                    [same collected info has same keys, unless can be specific to detector]
+
+
+    The goal behind the class is that the generated visualizations can be used in conjunction with the generated
+    report for people to get a better understanding of issues and what the fix might be. It is also just to provide
+    a good visualization platform, since it might be hard to parse through the ModelReport returned dictionary as
+    that grows in size.
+
+    General Use Flow Expected
+    1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects
+    2.) Prepare your model with prepare_fx
+    3.) Call model_report.prepare_detailed_calibration on your model to add relevant observers
+    4.) Callibrate your model with data
+    5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
+    6.) Use output of model_report.generate_report to initialize ModelReportVisualizer instance
+    7.) Use instance to view different views of data as desired, applying filters as needed
+        8.) Either see the super detailed information or just the actual printed or shown table / plot / histogram
+
+    """
+
+    # keys for table dict
+    TABLE_TENSOR_KEY = "tensor_level_info"
+    TABLE_CHANNEL_KEY = "channel_level_info"
+
+    # Constants for header vals
+    NUM_NON_FEATURE_TENSOR_HEADERS = 2
+    NUM_NON_FEATURE_CHANNEL_HEADERS = 3
+
+    # Constants for row index in header
+    CHANNEL_NUM_INDEX = 2
+
+    def __init__(self, generated_reports: OrderedDict[str, Any]):
+        r"""
+        Initializes the ModelReportVisualizer instance with the necessary reports.
+
+        Args:
+            generated_reports (Dict[str, Any]): The reports generated by the ModelReport class
+                can also be a dictionary generated in another manner, as long as format is same
+        """
+        self.generated_reports = generated_reports
+
+    def get_all_unique_module_fqns(self) -> Set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all module_fqns so that if
+        they wish to use some of the filtering capabilities of the ModelReportVisualizer class,
+        they don't need to manually parse the generated_reports dictionary to get this information.
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        # returns the keys of the ordered dict
+        return set(self.generated_reports.keys())
+
+    def get_all_unique_feature_names(self, plottable_features_only: bool = True) -> Set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all feature names so that if
+        they wish to use the filtering capabilities of the generate_table_view(), or use either of
+        the generate_plot_view() or generate_histogram_view(), they don't need to manually parse
+        the generated_reports dictionary to get this information.
+
+        Args:
+            plottable_features_only (bool): True if the user is only looking for plottable features,
+                False otherwise
+                plottable features are those that are tensor values
+                Default: True (only return those feature names that are plottable)
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        unique_feature_names = set()
+        for module_fqn in self.generated_reports:
+            # get dict of the features
+            feature_dict: Dict[str, Any] = self.generated_reports[module_fqn]
+
+            # loop through features
+            for feature_name in feature_dict:
+                # if we need plottable, ensure type of val is tensor
+                if not plottable_features_only or type(feature_dict[feature_name]) == torch.Tensor:
+                    unique_feature_names.add(feature_name)
+
+        # return our compiled set of unique feature names
+        return unique_feature_names
+
+    def _get_filtered_data(self, feature_filter: str, module_fqn_filter: str) -> OrderedDict[str, Any]:
+        r"""
+        Filters the data and returns it in the same ordered dictionary format so the relevant views can be displayed.
+
+        Args:
+            feature_filter (str): The feature filter, if we want to filter the set of data to only include
+                a certain set of features that include feature_filter
+                If feature = "", then we do not filter based on any features
+            module_fqn_filter (str): The filter on prefix for the module fqn. All modules that have fqn with
+                this prefix will be included
+                If module_fqn_filter = "" we do not filter based on module fqn, and include all modules
+
+        First, the data is filtered based on module_fqn, and then filtered based on feature
+        Returns an OrderedDict (sorted in order of model) mapping:
+            module_fqns -> feature_names -> values
+        """
+        # create return dict
+        filtered_dict: OrderedDict[str, Any] = OrdDict()
+
+        for module_fqn in self.generated_reports:
+            # first filter based on module
+            if module_fqn_filter == "" or module_fqn_filter in module_fqn:
+                # create entry for module and loop through features
+                filtered_dict[module_fqn] = {}
+                module_reports = self.generated_reports[module_fqn]
+                for feature_name in module_reports:
+                    # check if filtering on features and do so if desired
+                    if feature_filter == "" or feature_filter in feature_name:
+                        filtered_dict[module_fqn][feature_name] = module_reports[feature_name]
+
+        # we have populated the filtered dict, and must return it
+
+        return filtered_dict
+
+    def _generate_tensor_table(
+        self,
+        filtered_data: OrderedDict[str, Dict[str, Any]],
+        tensor_features: List[str]
+    ) -> Tuple[List, List]:
+        r"""
+        Takes in the filtered data and features list and generates the tensor headers and table
+
+        Currently meant to generate the headers and table for both the tensor information.
+
+        Args:
+            filtered_data (OrderedDict[str, Dict[str, Any]]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            tensor_features (List[str]): A list of the tensor level features
+
+        Returns a tuple with:
+            A list of the headers of the tensor table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the tensor information table
+        tensor_table: List[List[Any]] = []
+        tensor_headers: List[str] = []
+
+        # append the table row to the table only if we have features
+        if len(tensor_features) > 0:
+            # now we add all the data
+            for index, module_fqn in enumerate(filtered_data):
+                # we make a new row for the tensor table
+                tensor_table_row = [index, module_fqn]
+                for feature in tensor_features:
+                    # we iterate in same order of added features
+
+                    if feature in filtered_data[module_fqn]:
+                        # add value if applicable to module
+                        feature_val = filtered_data[module_fqn][feature]
+                    else:
+                        # add that it is not applicable
+                        feature_val = "Not Applicable"
+
+                    # if it's a tensor we want to extract val
+                    if isinstance(feature_val, torch.Tensor):
+                        feature_val = feature_val.item()
+
+                    # we add to our list of values
+                    tensor_table_row.append(feature_val)
+
+                tensor_table.append(tensor_table_row)
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(tensor_table) != 0:
+            tensor_headers = ["idx", "layer_fqn"] + tensor_features
+
+        return (tensor_headers, tensor_table)
+
+    def _generate_channels_table(
+        self,
+        filtered_data: OrderedDict[str, Any],
+        channel_features: List[str],
+        num_channels: int
+    ) -> Tuple[List, List]:
+        r"""
+        Takes in the filtered data and features list and generates the channels headers and table
+
+        Currently meant to generate the headers and table for both the channels information.
+
+        Args:
+            filtered_data (OrderedDict[str, Any]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            channel_features (List[str]): A list of the channel level features
+            num_channels (int): Number of channels in the channel data
+
+        Returns a tuple with:
+            A list of the headers of the channel table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the table for the channel information table
+        channel_table: List[List[Any]] = []
+        channel_headers: List[str] = []
+
+        # counter to keep track of number of entries in
+        channel_table_entry_counter: int = 0
+
+        if len(channel_features) > 0:
+            # now we add all channel data
+            for module_fqn in filtered_data:
+                # we iterate over all channels
+                for channel in range(num_channels):
+                    # we make a new row for the channel
+                    new_channel_row = [channel_table_entry_counter, module_fqn, channel]
+                    for feature in channel_features:
+                        if feature in filtered_data[module_fqn]:
+                            # add value if applicable to module
+                            feature_val = filtered_data[module_fqn][feature][channel]
+                        else:
+                            # add that it is not applicable
+                            feature_val = "Not Applicable"
+
+                        # if it's a tensor we want to extract val
+                        if type(feature_val) is torch.Tensor:
+                            feature_val = feature_val.item()
+
+                        # add value to channel specific row
+                        new_channel_row.append(feature_val)
+
+                    # add to table and increment row index counter
+                    channel_table.append(new_channel_row)
+                    channel_table_entry_counter += 1
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(channel_table) != 0:
+            channel_headers = ["idx", "layer_fqn", "channel"] + channel_features
+
+        return (channel_headers, channel_table)
+
+    def generate_filtered_tables(self, feature_filter: str = "", module_fqn_filter: str = "") -> Dict[str, Tuple[List, List]]:
+        r"""
+        Takes in optional filter values and generates two tables with desired information.
+
+        The generated tables are presented in both a list-of-lists format
+
+        The reason for the two tables are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+            idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+            idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Returns a dictionary with two keys:
+            (Dict[str, Tuple[List, List]]) A dict containing two keys:
+            "tensor_level_info", "channel_level_info"
+                Each key maps to a tuple with:
+                    A list of the headers of each table
+                    A list of lists containing the table information row by row
+                    The 0th index row will contain the headers of the columns
+                    The rest of the rows will contain data
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_filtered_tables(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... ) # generates table with per_channel_min info for all modules in block 1 of the model
+        """
+        # first get the filtered data
+        filtered_data: OrderedDict[str, Any] = self._get_filtered_data(feature_filter, module_fqn_filter)
+
+        # now we split into tensor and per-channel data
+        tensor_features: Set[str] = set()
+        channel_features: Set[str] = set()
+
+        # keep track of the number of channels we have
+        num_channels: int = 0
+
+        for module_fqn in filtered_data:
+            for feature_name in filtered_data[module_fqn]:
+                # get the data for that specific feature
+                feature_data = filtered_data[module_fqn][feature_name]
+
+                # check if not zero dim tensor
+                is_tensor: bool = isinstance(feature_data, torch.Tensor)
+                is_not_zero_dim: bool = is_tensor and len(feature_data.shape) != 0
+
+                if is_not_zero_dim or isinstance(feature_data, list):
+                    # works means per channel
+                    channel_features.add(feature_name)
+                    num_channels = len(feature_data)
+                else:
+                    # means is per-tensor
+                    tensor_features.add(feature_name)
+
+        # we make them lists for iteration purposes
+        tensor_features_list: List[str] = sorted(tensor_features)
+        channel_features_list: List[str] = sorted(channel_features)
+
+        # get the tensor info
+        tensor_headers, tensor_table = self._generate_tensor_table(filtered_data, tensor_features_list)
+
+        # get the channel info
+        channel_headers, channel_table = self._generate_channels_table(
+            filtered_data, channel_features_list, num_channels
+        )
+
+        # let's now create the dictionary to return
+        table_dict = {
+            self.TABLE_TENSOR_KEY : (tensor_headers, tensor_table),
+            self.TABLE_CHANNEL_KEY : (channel_headers, channel_table)
+        }
+
+        # return the two tables
+        return table_dict
+
+    def generate_table_visualization(self, feature_filter: str = "", module_fqn_filter: str = ""):
+        r"""
+        Takes in optional filter values and prints out formatted tables of the information.
+
+        The reason for the two tables printed out instead of one large one are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+
+         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_table_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            >>> # prints out neatly formatted table with per_channel_min info
+            >>> # for all modules in block 1 of the model
+        """
+        # see if we got tabulate
+        if not got_tabulate:
+            print("Make sure to install tabulate and try again.")
+            return None
+
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # get the table string and print it out
+        # now we have populated the tables for each one
+        # let's create the strings to be returned
+        table_str = ""
+        # the tables will have some headers columns that are non-feature
+        # ex. table index, module name, channel index, etc.
+        # we want to look at header columns for features, that come after those headers
+        if len(tensor_headers) > self.NUM_NON_FEATURE_TENSOR_HEADERS:
+            # if we have at least one tensor level feature to be added we add tensor table
+            table_str += "Tensor Level Information \n"
+            table_str += tabulate(tensor_table, headers=tensor_headers)
+        if len(channel_headers) > self.NUM_NON_FEATURE_CHANNEL_HEADERS:
+            # if we have at least one channel level feature to be added we add tensor table
+            table_str += "\n\n Channel Level Information \n"
+            table_str += tabulate(channel_table, headers=channel_headers)
+
+        # if no features at all, let user know
+        if table_str == "":
+            table_str = "No data points to generate table with."
+
+        print(table_str)
+
+    def _get_plottable_data(self, feature_filter: str, module_fqn_filter: str) -> Tuple[List, List[List], bool]:
+        r"""
+        Takes in the feature filters and module filters and outputs the x and y data for plotting
+
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str): Only includes modules that contains this string
+
+        Returns a tuple of three elements
+            The first is a list containing relevant x-axis data
+            The second is a list containing the corresponding y-axis data
+            If the data is per channel
+        """
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # make sure it is only 1 feature that is being plotted
+        # get the number of features in each of these
+        tensor_info_features_count = len(tensor_headers) - ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        channel_info_features_count = len(channel_headers) - ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+
+        # see if valid tensor or channel plot
+        is_valid_per_tensor_plot: bool = tensor_info_features_count == 1
+        is_valid_per_channel_plot: bool = channel_info_features_count == 1
+
+        # offset should either be one of tensor or channel table or neither
+        feature_column_offset = ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        table = tensor_table
+
+        # if a per_channel plot, we have different offset and table
+        if is_valid_per_channel_plot:
+            feature_column_offset = ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+            table = channel_table
+
+        x_data: List = []
+        y_data: List[List] = []
+        # the feature will either be a tensor feature or channel feature
+        if is_valid_per_tensor_plot:
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if not type(row_value) == str:
+                    x_data.append(x_val_to_append)
+                    y_data.append(row_value)
+        elif is_valid_per_channel_plot:
+            # gather the x_data and multiple y_data
+            # calculate the number of channels
+            num_channels: int = max(row[self.CHANNEL_NUM_INDEX] for row in table) + 1
+            for channel in range(num_channels):
+                y_data.append([])  # separate data list per channel
+
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                current_channel = row[self.CHANNEL_NUM_INDEX]  # initially chose current channel
+                new_module_index: int = table_row_num // num_channels
+                x_val_to_append = new_module_index
+
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if not type(row_value) == str:
+                    # only append if new index we are appending
+                    if len(x_data) == 0 or x_data[-1] != x_val_to_append:
+                        x_data.append(x_val_to_append)
+
+                    # append value for that channel
+                    y_data[current_channel].append(row_value)
+        else:
+            # more than one feature was chosen
+            error_str = "Make sure to pick only a single feature with your filter to plot a graph."
+            error_str += " We recommend calling get_all_unique_feature_names() to find unique feature names."
+            error_str += " Pick one of those features to plot."
+            raise ValueError(error_str)
+
+        # return x, y values, and if data is per-channel
+        return (x_data, y_data, is_valid_per_channel_plot)
+
+    def generate_plot_visualization(self, feature_filter: str, module_fqn_filter: str = ""):
+        r"""
+        Takes in a feature and optional module_filter and plots of the desired data.
+
+        For per channel features, it averages the value across the channels and plots a point
+        per module. The reason for this is that for models with hundreds of channels, it can
+        be hard to differentiate one channel line from another, and so the point of generating
+        a single average point per module is to give a sense of general trends that encourage
+        further deep dives.
+
+        Note:
+            Only features in the report that have tensor value data are plottable by this class
+            When the tensor information is plotted, it will plot:
+                idx as the x val, feature value as the y_val
+            When the channel information is plotted, it will plot:
+                the first idx of each module as the x val, feature value as the y_val [for each channel]
+                The reason for this is that we want to be able to compare values across the
+                channels for same layer, and it will be hard if values are staggered by idx
+                This means each module is represented by only 1 x value
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_plot_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            >>> # outputs line plot of per_channel_min information for all
+            >>> # modules in block1 of model each channel gets it's own line,
+            >>> # and it's plotted across the in-order modules on the x-axis
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        x_data, y_data, data_per_channel = self._get_plottable_data(feature_filter, module_fqn_filter)
+
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_ylabel(feature_filter)
+        ax.set_title(feature_filter + " Plot")
+        plt.xticks(x_data)  # only show ticks for actual points
+
+        if data_per_channel:
+            ax.set_xlabel("First idx of module")
+            # set the legend as well
+            # plot a single line that is average of the channel values
+            num_modules = len(y_data[0])  # all y_data have same length, so get num modules
+            num_channels = len(y_data)  # we want num channels to be able to calculate average later
+
+            avg_vals = [sum(y_data[:][index]) / num_channels for index in range(num_modules)]
+
+            # plot the three things we measured
+            ax.plot(x_data, avg_vals, label=f"Average Value Across {num_channels} Channels")
+            ax.legend(loc='upper right')
+        else:
+            ax.set_xlabel("idx")
+            ax.plot(x_data, y_data)
+
+        # actually show the plot
+        plt.show()
+
+    def generate_histogram_visualization(self, feature_filter: str, module_fqn_filter: str = "", num_bins: int = 10):
+        r"""
+        Takes in a feature and optional module_filter and plots the histogram of desired data.
+
+        Note:
+            Only features in the report that have tensor value data can be viewed as a histogram
+            If you want to plot a histogram from all the channel values of a specific feature for
+                a specific model, make sure to specify both the model and the feature properly
+                in the filters and you should be able to see a distribution of the channel data
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+            num_bins (int, optional): The number of bins to create the histogram with
+                Default = 10, the values will be split into 10 equal sized bins
+
+        Example Use:
+            >>> # xdoctest: +SKIP
+            >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            # outputs histogram of per_channel_min information for all modules in block1 of model
+                information is gathered across all channels for all modules in block 1 for the
+                per_channel_min and is displayed in a histogram of equally sized bins
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        x_data, y_data, data_per_channel = self._get_plottable_data(feature_filter, module_fqn_filter)
+
+        # for histogram, we just care about plotting the y data
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_xlabel(feature_filter)
+        ax.set_ylabel("Frequency")
+        ax.set_title(feature_filter + " Histogram")
+
+        if data_per_channel:
+            # set the legend as well
+            # combine all the data
+            all_data = []
+            for channel_info in y_data:
+                all_data.extend(channel_info)
+
+            val, bins, _ = plt.hist(
+                all_data,
+                bins=num_bins,
+                stacked=True,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+        else:
+            val, bins, _ = plt.hist(
+                y_data,
+                bins=num_bins,
+                stacked=False,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+
+        plt.show()
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diff --git a/shared/package-files/40/400f53447670a90f24a9f38f03811e1df2b3d5d0aaa41a6946d55313ad48a4ed/sympy_parser.py b/shared/package-files/40/400f53447670a90f24a9f38f03811e1df2b3d5d0aaa41a6946d55313ad48a4ed/sympy_parser.py
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--- /dev/null
+++ b/shared/package-files/40/400f53447670a90f24a9f38f03811e1df2b3d5d0aaa41a6946d55313ad48a4ed/sympy_parser.py
@@ -0,0 +1,1264 @@
+"""Transform a string with Python-like source code into SymPy expression. """
+
+from tokenize import (generate_tokens, untokenize, TokenError,
+    NUMBER, STRING, NAME, OP, ENDMARKER, ERRORTOKEN, NEWLINE)
+
+from keyword import iskeyword
+
+import ast
+import unicodedata
+from io import StringIO
+import builtins
+import types
+from typing import Tuple as tTuple, Dict as tDict, Any, Callable, \
+    List, Optional, Union as tUnion
+
+from sympy.assumptions.ask import AssumptionKeys
+from sympy.core.basic import Basic
+from sympy.core import Symbol
+from sympy.core.function import Function
+from sympy.utilities.misc import func_name
+from sympy.functions.elementary.miscellaneous import Max, Min
+
+
+null = ''
+
+TOKEN = tTuple[int, str]
+DICT = tDict[str, Any]
+TRANS = Callable[[List[TOKEN], DICT, DICT], List[TOKEN]]
+
+def _token_splittable(token_name: str) -> bool:
+    """
+    Predicate for whether a token name can be split into multiple tokens.
+
+    A token is splittable if it does not contain an underscore character and
+    it is not the name of a Greek letter. This is used to implicitly convert
+    expressions like 'xyz' into 'x*y*z'.
+    """
+    if '_' in token_name:
+        return False
+    try:
+        return not unicodedata.lookup('GREEK SMALL LETTER ' + token_name)
+    except KeyError:
+        return len(token_name) > 1
+
+
+def _token_callable(token: TOKEN, local_dict: DICT, global_dict: DICT, nextToken=None):
+    """
+    Predicate for whether a token name represents a callable function.
+
+    Essentially wraps ``callable``, but looks up the token name in the
+    locals and globals.
+    """
+    func = local_dict.get(token[1])
+    if not func:
+        func = global_dict.get(token[1])
+    return callable(func) and not isinstance(func, Symbol)
+
+
+def _add_factorial_tokens(name: str, result: List[TOKEN]) -> List[TOKEN]:
+    if result == [] or result[-1][1] == '(':
+        raise TokenError()
+
+    beginning = [(NAME, name), (OP, '(')]
+    end = [(OP, ')')]
+
+    diff = 0
+    length = len(result)
+
+    for index, token in enumerate(result[::-1]):
+        toknum, tokval = token
+        i = length - index - 1
+
+        if tokval == ')':
+            diff += 1
+        elif tokval == '(':
+            diff -= 1
+
+        if diff == 0:
+            if i - 1 >= 0 and result[i - 1][0] == NAME:
+                return result[:i - 1] + beginning + result[i - 1:] + end
+            else:
+                return result[:i] + beginning + result[i:] + end
+
+    return result
+
+
+class ParenthesisGroup(List[TOKEN]):
+    """List of tokens representing an expression in parentheses."""
+    pass
+
+
+class AppliedFunction:
+    """
+    A group of tokens representing a function and its arguments.
+
+    `exponent` is for handling the shorthand sin^2, ln^2, etc.
+    """
+    def __init__(self, function: TOKEN, args: ParenthesisGroup, exponent=None):
+        if exponent is None:
+            exponent = []
+        self.function = function
+        self.args = args
+        self.exponent = exponent
+        self.items = ['function', 'args', 'exponent']
+
+    def expand(self) -> List[TOKEN]:
+        """Return a list of tokens representing the function"""
+        return [self.function, *self.args]
+
+    def __getitem__(self, index):
+        return getattr(self, self.items[index])
+
+    def __repr__(self):
+        return "AppliedFunction(%s, %s, %s)" % (self.function, self.args,
+                                                self.exponent)
+
+
+def _flatten(result: List[tUnion[TOKEN, AppliedFunction]]):
+    result2: List[TOKEN] = []
+    for tok in result:
+        if isinstance(tok, AppliedFunction):
+            result2.extend(tok.expand())
+        else:
+            result2.append(tok)
+    return result2
+
+
+def _group_parentheses(recursor: TRANS):
+    def _inner(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+        """Group tokens between parentheses with ParenthesisGroup.
+
+        Also processes those tokens recursively.
+
+        """
+        result: List[tUnion[TOKEN, ParenthesisGroup]] = []
+        stacks: List[ParenthesisGroup] = []
+        stacklevel = 0
+        for token in tokens:
+            if token[0] == OP:
+                if token[1] == '(':
+                    stacks.append(ParenthesisGroup([]))
+                    stacklevel += 1
+                elif token[1] == ')':
+                    stacks[-1].append(token)
+                    stack = stacks.pop()
+
+                    if len(stacks) > 0:
+                        # We don't recurse here since the upper-level stack
+                        # would reprocess these tokens
+                        stacks[-1].extend(stack)
+                    else:
+                        # Recurse here to handle nested parentheses
+                        # Strip off the outer parentheses to avoid an infinite loop
+                        inner = stack[1:-1]
+                        inner = recursor(inner,
+                                         local_dict,
+                                         global_dict)
+                        parenGroup = [stack[0]] + inner + [stack[-1]]
+                        result.append(ParenthesisGroup(parenGroup))
+                    stacklevel -= 1
+                    continue
+            if stacklevel:
+                stacks[-1].append(token)
+            else:
+                result.append(token)
+        if stacklevel:
+            raise TokenError("Mismatched parentheses")
+        return result
+    return _inner
+
+
+def _apply_functions(tokens: List[tUnion[TOKEN, ParenthesisGroup]], local_dict: DICT, global_dict: DICT):
+    """Convert a NAME token + ParenthesisGroup into an AppliedFunction.
+
+    Note that ParenthesisGroups, if not applied to any function, are
+    converted back into lists of tokens.
+
+    """
+    result: List[tUnion[TOKEN, AppliedFunction]] = []
+    symbol = None
+    for tok in tokens:
+        if isinstance(tok, ParenthesisGroup):
+            if symbol and _token_callable(symbol, local_dict, global_dict):
+                result[-1] = AppliedFunction(symbol, tok)
+                symbol = None
+            else:
+                result.extend(tok)
+        elif tok[0] == NAME:
+            symbol = tok
+            result.append(tok)
+        else:
+            symbol = None
+            result.append(tok)
+    return result
+
+
+def _implicit_multiplication(tokens: List[tUnion[TOKEN, AppliedFunction]], local_dict: DICT, global_dict: DICT):
+    """Implicitly adds '*' tokens.
+
+    Cases:
+
+    - Two AppliedFunctions next to each other ("sin(x)cos(x)")
+
+    - AppliedFunction next to an open parenthesis ("sin x (cos x + 1)")
+
+    - A close parenthesis next to an AppliedFunction ("(x+2)sin x")\
+
+    - A close parenthesis next to an open parenthesis ("(x+2)(x+3)")
+
+    - AppliedFunction next to an implicitly applied function ("sin(x)cos x")
+
+    """
+    result: List[tUnion[TOKEN, AppliedFunction]] = []
+    skip = False
+    for tok, nextTok in zip(tokens, tokens[1:]):
+        result.append(tok)
+        if skip:
+            skip = False
+            continue
+        if tok[0] == OP and tok[1] == '.' and nextTok[0] == NAME:
+            # Dotted name. Do not do implicit multiplication
+            skip = True
+            continue
+        if isinstance(tok, AppliedFunction):
+            if isinstance(nextTok, AppliedFunction):
+                result.append((OP, '*'))
+            elif nextTok == (OP, '('):
+                # Applied function followed by an open parenthesis
+                if tok.function[1] == "Function":
+                    tok.function = (tok.function[0], 'Symbol')
+                result.append((OP, '*'))
+            elif nextTok[0] == NAME:
+                # Applied function followed by implicitly applied function
+                result.append((OP, '*'))
+        else:
+            if tok == (OP, ')'):
+                if isinstance(nextTok, AppliedFunction):
+                    # Close parenthesis followed by an applied function
+                    result.append((OP, '*'))
+                elif nextTok[0] == NAME:
+                    # Close parenthesis followed by an implicitly applied function
+                    result.append((OP, '*'))
+                elif nextTok == (OP, '('):
+                    # Close parenthesis followed by an open parenthesis
+                    result.append((OP, '*'))
+            elif tok[0] == NAME and not _token_callable(tok, local_dict, global_dict):
+                if isinstance(nextTok, AppliedFunction) or \
+                    (nextTok[0] == NAME and _token_callable(nextTok, local_dict, global_dict)):
+                    # Constant followed by (implicitly applied) function
+                    result.append((OP, '*'))
+                elif nextTok == (OP, '('):
+                    # Constant followed by parenthesis
+                    result.append((OP, '*'))
+                elif nextTok[0] == NAME:
+                    # Constant followed by constant
+                    result.append((OP, '*'))
+    if tokens:
+        result.append(tokens[-1])
+    return result
+
+
+def _implicit_application(tokens: List[tUnion[TOKEN, AppliedFunction]], local_dict: DICT, global_dict: DICT):
+    """Adds parentheses as needed after functions."""
+    result: List[tUnion[TOKEN, AppliedFunction]] = []
+    appendParen = 0  # number of closing parentheses to add
+    skip = 0  # number of tokens to delay before adding a ')' (to
+              # capture **, ^, etc.)
+    exponentSkip = False  # skipping tokens before inserting parentheses to
+                          # work with function exponentiation
+    for tok, nextTok in zip(tokens, tokens[1:]):
+        result.append(tok)
+        if (tok[0] == NAME and nextTok[0] not in [OP, ENDMARKER, NEWLINE]):
+            if _token_callable(tok, local_dict, global_dict, nextTok):  # type: ignore
+                result.append((OP, '('))
+                appendParen += 1
+        # name followed by exponent - function exponentiation
+        elif (tok[0] == NAME and nextTok[0] == OP and nextTok[1] == '**'):
+            if _token_callable(tok, local_dict, global_dict):  # type: ignore
+                exponentSkip = True
+        elif exponentSkip:
+            # if the last token added was an applied function (i.e. the
+            # power of the function exponent) OR a multiplication (as
+            # implicit multiplication would have added an extraneous
+            # multiplication)
+            if (isinstance(tok, AppliedFunction)
+                or (tok[0] == OP and tok[1] == '*')):
+                # don't add anything if the next token is a multiplication
+                # or if there's already a parenthesis (if parenthesis, still
+                # stop skipping tokens)
+                if not (nextTok[0] == OP and nextTok[1] == '*'):
+                    if not(nextTok[0] == OP and nextTok[1] == '('):
+                        result.append((OP, '('))
+                        appendParen += 1
+                    exponentSkip = False
+        elif appendParen:
+            if nextTok[0] == OP and nextTok[1] in ('^', '**', '*'):
+                skip = 1
+                continue
+            if skip:
+                skip -= 1
+                continue
+            result.append((OP, ')'))
+            appendParen -= 1
+
+    if tokens:
+        result.append(tokens[-1])
+
+    if appendParen:
+        result.extend([(OP, ')')] * appendParen)
+    return result
+
+
+def function_exponentiation(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Allows functions to be exponentiated, e.g. ``cos**2(x)``.
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr,
+    ... standard_transformations, function_exponentiation)
+    >>> transformations = standard_transformations + (function_exponentiation,)
+    >>> parse_expr('sin**4(x)', transformations=transformations)
+    sin(x)**4
+    """
+    result: List[TOKEN] = []
+    exponent: List[TOKEN] = []
+    consuming_exponent = False
+    level = 0
+    for tok, nextTok in zip(tokens, tokens[1:]):
+        if tok[0] == NAME and nextTok[0] == OP and nextTok[1] == '**':
+            if _token_callable(tok, local_dict, global_dict):
+                consuming_exponent = True
+        elif consuming_exponent:
+            if tok[0] == NAME and tok[1] == 'Function':
+                tok = (NAME, 'Symbol')
+            exponent.append(tok)
+
+            # only want to stop after hitting )
+            if tok[0] == nextTok[0] == OP and tok[1] == ')' and nextTok[1] == '(':
+                consuming_exponent = False
+            # if implicit multiplication was used, we may have )*( instead
+            if tok[0] == nextTok[0] == OP and tok[1] == '*' and nextTok[1] == '(':
+                consuming_exponent = False
+                del exponent[-1]
+            continue
+        elif exponent and not consuming_exponent:
+            if tok[0] == OP:
+                if tok[1] == '(':
+                    level += 1
+                elif tok[1] == ')':
+                    level -= 1
+            if level == 0:
+                result.append(tok)
+                result.extend(exponent)
+                exponent = []
+                continue
+        result.append(tok)
+    if tokens:
+        result.append(tokens[-1])
+    if exponent:
+        result.extend(exponent)
+    return result
+
+
+def split_symbols_custom(predicate: Callable[[str], bool]):
+    """Creates a transformation that splits symbol names.
+
+    ``predicate`` should return True if the symbol name is to be split.
+
+    For instance, to retain the default behavior but avoid splitting certain
+    symbol names, a predicate like this would work:
+
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr, _token_splittable,
+    ... standard_transformations, implicit_multiplication,
+    ... split_symbols_custom)
+    >>> def can_split(symbol):
+    ...     if symbol not in ('list', 'of', 'unsplittable', 'names'):
+    ...             return _token_splittable(symbol)
+    ...     return False
+    ...
+    >>> transformation = split_symbols_custom(can_split)
+    >>> parse_expr('unsplittable', transformations=standard_transformations +
+    ... (transformation, implicit_multiplication))
+    unsplittable
+    """
+    def _split_symbols(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+        result: List[TOKEN] = []
+        split = False
+        split_previous=False
+
+        for tok in tokens:
+            if split_previous:
+                # throw out closing parenthesis of Symbol that was split
+                split_previous=False
+                continue
+            split_previous=False
+
+            if tok[0] == NAME and tok[1] in ['Symbol', 'Function']:
+                split = True
+
+            elif split and tok[0] == NAME:
+                symbol = tok[1][1:-1]
+
+                if predicate(symbol):
+                    tok_type = result[-2][1]  # Symbol or Function
+                    del result[-2:]  # Get rid of the call to Symbol
+
+                    i = 0
+                    while i < len(symbol):
+                        char = symbol[i]
+                        if char in local_dict or char in global_dict:
+                            result.append((NAME, "%s" % char))
+                        elif char.isdigit():
+                            chars = [char]
+                            for i in range(i + 1, len(symbol)):
+                                if not symbol[i].isdigit():
+                                  i -= 1
+                                  break
+                                chars.append(symbol[i])
+                            char = ''.join(chars)
+                            result.extend([(NAME, 'Number'), (OP, '('),
+                                           (NAME, "'%s'" % char), (OP, ')')])
+                        else:
+                            use = tok_type if i == len(symbol) else 'Symbol'
+                            result.extend([(NAME, use), (OP, '('),
+                                           (NAME, "'%s'" % char), (OP, ')')])
+                        i += 1
+
+                    # Set split_previous=True so will skip
+                    # the closing parenthesis of the original Symbol
+                    split = False
+                    split_previous = True
+                    continue
+
+                else:
+                    split = False
+
+            result.append(tok)
+
+        return result
+
+    return _split_symbols
+
+
+#: Splits symbol names for implicit multiplication.
+#:
+#: Intended to let expressions like ``xyz`` be parsed as ``x*y*z``. Does not
+#: split Greek character names, so ``theta`` will *not* become
+#: ``t*h*e*t*a``. Generally this should be used with
+#: ``implicit_multiplication``.
+split_symbols = split_symbols_custom(_token_splittable)
+
+
+def implicit_multiplication(tokens: List[TOKEN], local_dict: DICT,
+                            global_dict: DICT) -> List[TOKEN]:
+    """Makes the multiplication operator optional in most cases.
+
+    Use this before :func:`implicit_application`, otherwise expressions like
+    ``sin 2x`` will be parsed as ``x * sin(2)`` rather than ``sin(2*x)``.
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr,
+    ... standard_transformations, implicit_multiplication)
+    >>> transformations = standard_transformations + (implicit_multiplication,)
+    >>> parse_expr('3 x y', transformations=transformations)
+    3*x*y
+    """
+    # These are interdependent steps, so we don't expose them separately
+    res1 = _group_parentheses(implicit_multiplication)(tokens, local_dict, global_dict)
+    res2 = _apply_functions(res1, local_dict, global_dict)
+    res3 = _implicit_multiplication(res2, local_dict, global_dict)
+    result = _flatten(res3)
+    return result
+
+
+def implicit_application(tokens: List[TOKEN], local_dict: DICT,
+                         global_dict: DICT) -> List[TOKEN]:
+    """Makes parentheses optional in some cases for function calls.
+
+    Use this after :func:`implicit_multiplication`, otherwise expressions
+    like ``sin 2x`` will be parsed as ``x * sin(2)`` rather than
+    ``sin(2*x)``.
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr,
+    ... standard_transformations, implicit_application)
+    >>> transformations = standard_transformations + (implicit_application,)
+    >>> parse_expr('cot z + csc z', transformations=transformations)
+    cot(z) + csc(z)
+    """
+    res1 = _group_parentheses(implicit_application)(tokens, local_dict, global_dict)
+    res2 = _apply_functions(res1, local_dict, global_dict)
+    res3 = _implicit_application(res2, local_dict, global_dict)
+    result = _flatten(res3)
+    return result
+
+
+def implicit_multiplication_application(result: List[TOKEN], local_dict: DICT,
+                                        global_dict: DICT) -> List[TOKEN]:
+    """Allows a slightly relaxed syntax.
+
+    - Parentheses for single-argument method calls are optional.
+
+    - Multiplication is implicit.
+
+    - Symbol names can be split (i.e. spaces are not needed between
+      symbols).
+
+    - Functions can be exponentiated.
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr,
+    ... standard_transformations, implicit_multiplication_application)
+    >>> parse_expr("10sin**2 x**2 + 3xyz + tan theta",
+    ... transformations=(standard_transformations +
+    ... (implicit_multiplication_application,)))
+    3*x*y*z + 10*sin(x**2)**2 + tan(theta)
+
+    """
+    for step in (split_symbols, implicit_multiplication,
+                 implicit_application, function_exponentiation):
+        result = step(result, local_dict, global_dict)
+
+    return result
+
+
+def auto_symbol(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Inserts calls to ``Symbol``/``Function`` for undefined variables."""
+    result: List[TOKEN] = []
+    prevTok = (-1, '')
+
+    tokens.append((-1, ''))  # so zip traverses all tokens
+    for tok, nextTok in zip(tokens, tokens[1:]):
+        tokNum, tokVal = tok
+        nextTokNum, nextTokVal = nextTok
+        if tokNum == NAME:
+            name = tokVal
+
+            if (name in ['True', 'False', 'None']
+                    or iskeyword(name)
+                    # Don't convert attribute access
+                    or (prevTok[0] == OP and prevTok[1] == '.')
+                    # Don't convert keyword arguments
+                    or (prevTok[0] == OP and prevTok[1] in ('(', ',')
+                        and nextTokNum == OP and nextTokVal == '=')
+                    # the name has already been defined
+                    or name in local_dict and local_dict[name] is not null):
+                result.append((NAME, name))
+                continue
+            elif name in local_dict:
+                local_dict.setdefault(null, set()).add(name)
+                if nextTokVal == '(':
+                    local_dict[name] = Function(name)
+                else:
+                    local_dict[name] = Symbol(name)
+                result.append((NAME, name))
+                continue
+            elif name in global_dict:
+                obj = global_dict[name]
+                if isinstance(obj, (AssumptionKeys, Basic, type)) or callable(obj):
+                    result.append((NAME, name))
+                    continue
+
+            result.extend([
+                (NAME, 'Symbol' if nextTokVal != '(' else 'Function'),
+                (OP, '('),
+                (NAME, repr(str(name))),
+                (OP, ')'),
+            ])
+        else:
+            result.append((tokNum, tokVal))
+
+        prevTok = (tokNum, tokVal)
+
+    return result
+
+
+def lambda_notation(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Substitutes "lambda" with its SymPy equivalent Lambda().
+    However, the conversion does not take place if only "lambda"
+    is passed because that is a syntax error.
+
+    """
+    result: List[TOKEN] = []
+    flag = False
+    toknum, tokval = tokens[0]
+    tokLen = len(tokens)
+
+    if toknum == NAME and tokval == 'lambda':
+        if tokLen == 2 or tokLen == 3 and tokens[1][0] == NEWLINE:
+            # In Python 3.6.7+, inputs without a newline get NEWLINE added to
+            # the tokens
+            result.extend(tokens)
+        elif tokLen > 2:
+            result.extend([
+                (NAME, 'Lambda'),
+                (OP, '('),
+                (OP, '('),
+                (OP, ')'),
+                (OP, ')'),
+            ])
+            for tokNum, tokVal in tokens[1:]:
+                if tokNum == OP and tokVal == ':':
+                    tokVal = ','
+                    flag = True
+                if not flag and tokNum == OP and tokVal in ('*', '**'):
+                    raise TokenError("Starred arguments in lambda not supported")
+                if flag:
+                    result.insert(-1, (tokNum, tokVal))
+                else:
+                    result.insert(-2, (tokNum, tokVal))
+    else:
+        result.extend(tokens)
+
+    return result
+
+
+def factorial_notation(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Allows standard notation for factorial."""
+    result: List[TOKEN] = []
+    nfactorial = 0
+    for toknum, tokval in tokens:
+        if toknum == ERRORTOKEN:
+            op = tokval
+            if op == '!':
+                nfactorial += 1
+            else:
+                nfactorial = 0
+                result.append((OP, op))
+        else:
+            if nfactorial == 1:
+                result = _add_factorial_tokens('factorial', result)
+            elif nfactorial == 2:
+                result = _add_factorial_tokens('factorial2', result)
+            elif nfactorial > 2:
+                raise TokenError
+            nfactorial = 0
+            result.append((toknum, tokval))
+    return result
+
+
+def convert_xor(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Treats XOR, ``^``, as exponentiation, ``**``."""
+    result: List[TOKEN] = []
+    for toknum, tokval in tokens:
+        if toknum == OP:
+            if tokval == '^':
+                result.append((OP, '**'))
+            else:
+                result.append((toknum, tokval))
+        else:
+            result.append((toknum, tokval))
+
+    return result
+
+
+def repeated_decimals(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """
+    Allows 0.2[1] notation to represent the repeated decimal 0.2111... (19/90)
+
+    Run this before auto_number.
+
+    """
+    result: List[TOKEN] = []
+
+    def is_digit(s):
+        return all(i in '0123456789_' for i in s)
+
+    # num will running match any DECIMAL [ INTEGER ]
+    num: List[TOKEN] = []
+    for toknum, tokval in tokens:
+        if toknum == NUMBER:
+            if (not num and '.' in tokval and 'e' not in tokval.lower() and
+                'j' not in tokval.lower()):
+                num.append((toknum, tokval))
+            elif is_digit(tokval)and  len(num) == 2:
+                num.append((toknum, tokval))
+            elif is_digit(tokval) and len(num) == 3 and is_digit(num[-1][1]):
+                # Python 2 tokenizes 00123 as '00', '123'
+                # Python 3 tokenizes 01289 as '012', '89'
+                num.append((toknum, tokval))
+            else:
+                num = []
+        elif toknum == OP:
+            if tokval == '[' and len(num) == 1:
+                num.append((OP, tokval))
+            elif tokval == ']' and len(num) >= 3:
+                num.append((OP, tokval))
+            elif tokval == '.' and not num:
+                # handle .[1]
+                num.append((NUMBER, '0.'))
+            else:
+                num = []
+        else:
+            num = []
+
+        result.append((toknum, tokval))
+
+        if num and num[-1][1] == ']':
+            # pre.post[repetend] = a + b/c + d/e where a = pre, b/c = post,
+            # and d/e = repetend
+            result = result[:-len(num)]
+            pre, post = num[0][1].split('.')
+            repetend = num[2][1]
+            if len(num) == 5:
+                repetend += num[3][1]
+
+            pre = pre.replace('_', '')
+            post = post.replace('_', '')
+            repetend = repetend.replace('_', '')
+
+            zeros = '0'*len(post)
+            post, repetends = [w.lstrip('0') for w in [post, repetend]]
+                                        # or else interpreted as octal
+
+            a = pre or '0'
+            b, c = post or '0', '1' + zeros
+            d, e = repetends, ('9'*len(repetend)) + zeros
+
+            seq = [
+                (OP, '('),
+                    (NAME, 'Integer'),
+                    (OP, '('),
+                        (NUMBER, a),
+                    (OP, ')'),
+                    (OP, '+'),
+                    (NAME, 'Rational'),
+                    (OP, '('),
+                        (NUMBER, b),
+                        (OP, ','),
+                        (NUMBER, c),
+                    (OP, ')'),
+                    (OP, '+'),
+                    (NAME, 'Rational'),
+                    (OP, '('),
+                        (NUMBER, d),
+                        (OP, ','),
+                        (NUMBER, e),
+                    (OP, ')'),
+                (OP, ')'),
+            ]
+            result.extend(seq)
+            num = []
+
+    return result
+
+
+def auto_number(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """
+    Converts numeric literals to use SymPy equivalents.
+
+    Complex numbers use ``I``, integer literals use ``Integer``, and float
+    literals use ``Float``.
+
+    """
+    result: List[TOKEN] = []
+
+    for toknum, tokval in tokens:
+        if toknum == NUMBER:
+            number = tokval
+            postfix = []
+
+            if number.endswith('j') or number.endswith('J'):
+                number = number[:-1]
+                postfix = [(OP, '*'), (NAME, 'I')]
+
+            if '.' in number or (('e' in number or 'E' in number) and
+                    not (number.startswith('0x') or number.startswith('0X'))):
+                seq = [(NAME, 'Float'), (OP, '('),
+                    (NUMBER, repr(str(number))), (OP, ')')]
+            else:
+                seq = [(NAME, 'Integer'), (OP, '('), (
+                    NUMBER, number), (OP, ')')]
+
+            result.extend(seq + postfix)
+        else:
+            result.append((toknum, tokval))
+
+    return result
+
+
+def rationalize(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Converts floats into ``Rational``. Run AFTER ``auto_number``."""
+    result: List[TOKEN] = []
+    passed_float = False
+    for toknum, tokval in tokens:
+        if toknum == NAME:
+            if tokval == 'Float':
+                passed_float = True
+                tokval = 'Rational'
+            result.append((toknum, tokval))
+        elif passed_float == True and toknum == NUMBER:
+            passed_float = False
+            result.append((STRING, tokval))
+        else:
+            result.append((toknum, tokval))
+
+    return result
+
+
+def _transform_equals_sign(tokens: List[TOKEN], local_dict: DICT, global_dict: DICT):
+    """Transforms the equals sign ``=`` to instances of Eq.
+
+    This is a helper function for ``convert_equals_signs``.
+    Works with expressions containing one equals sign and no
+    nesting. Expressions like ``(1=2)=False`` will not work with this
+    and should be used with ``convert_equals_signs``.
+
+    Examples: 1=2     to Eq(1,2)
+              1*2=x   to Eq(1*2, x)
+
+    This does not deal with function arguments yet.
+
+    """
+    result: List[TOKEN] = []
+    if (OP, "=") in tokens:
+        result.append((NAME, "Eq"))
+        result.append((OP, "("))
+        for token in tokens:
+            if token == (OP, "="):
+                result.append((OP, ","))
+                continue
+            result.append(token)
+        result.append((OP, ")"))
+    else:
+        result = tokens
+    return result
+
+
+def convert_equals_signs(tokens: List[TOKEN], local_dict: DICT,
+                         global_dict: DICT) -> List[TOKEN]:
+    """ Transforms all the equals signs ``=`` to instances of Eq.
+
+    Parses the equals signs in the expression and replaces them with
+    appropriate Eq instances. Also works with nested equals signs.
+
+    Does not yet play well with function arguments.
+    For example, the expression ``(x=y)`` is ambiguous and can be interpreted
+    as x being an argument to a function and ``convert_equals_signs`` will not
+    work for this.
+
+    See also
+    ========
+    convert_equality_operators
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import (parse_expr,
+    ... standard_transformations, convert_equals_signs)
+    >>> parse_expr("1*2=x", transformations=(
+    ... standard_transformations + (convert_equals_signs,)))
+    Eq(2, x)
+    >>> parse_expr("(1*2=x)=False", transformations=(
+    ... standard_transformations + (convert_equals_signs,)))
+    Eq(Eq(2, x), False)
+
+    """
+    res1 = _group_parentheses(convert_equals_signs)(tokens, local_dict, global_dict)
+    res2 = _apply_functions(res1, local_dict, global_dict)
+    res3 = _transform_equals_sign(res2, local_dict, global_dict)
+    result = _flatten(res3)
+    return result
+
+
+#: Standard transformations for :func:`parse_expr`.
+#: Inserts calls to :class:`~.Symbol`, :class:`~.Integer`, and other SymPy
+#: datatypes and allows the use of standard factorial notation (e.g. ``x!``).
+standard_transformations: tTuple[TRANS, ...] \
+    = (lambda_notation, auto_symbol, repeated_decimals, auto_number,
+       factorial_notation)
+
+
+def stringify_expr(s: str, local_dict: DICT, global_dict: DICT,
+        transformations: tTuple[TRANS, ...]) -> str:
+    """
+    Converts the string ``s`` to Python code, in ``local_dict``
+
+    Generally, ``parse_expr`` should be used.
+    """
+
+    tokens = []
+    input_code = StringIO(s.strip())
+    for toknum, tokval, _, _, _ in generate_tokens(input_code.readline):
+        tokens.append((toknum, tokval))
+
+    for transform in transformations:
+        tokens = transform(tokens, local_dict, global_dict)
+
+    return untokenize(tokens)
+
+
+def eval_expr(code, local_dict: DICT, global_dict: DICT):
+    """
+    Evaluate Python code generated by ``stringify_expr``.
+
+    Generally, ``parse_expr`` should be used.
+    """
+    expr = eval(
+        code, global_dict, local_dict)  # take local objects in preference
+    return expr
+
+
+def parse_expr(s: str, local_dict: Optional[DICT] = None,
+               transformations: tUnion[tTuple[TRANS, ...], str] \
+                   = standard_transformations,
+               global_dict: Optional[DICT] = None, evaluate=True):
+    """Converts the string ``s`` to a SymPy expression, in ``local_dict``.
+
+    Parameters
+    ==========
+
+    s : str
+        The string to parse.
+
+    local_dict : dict, optional
+        A dictionary of local variables to use when parsing.
+
+    global_dict : dict, optional
+        A dictionary of global variables. By default, this is initialized
+        with ``from sympy import *``; provide this parameter to override
+        this behavior (for instance, to parse ``"Q & S"``).
+
+    transformations : tuple or str
+        A tuple of transformation functions used to modify the tokens of the
+        parsed expression before evaluation. The default transformations
+        convert numeric literals into their SymPy equivalents, convert
+        undefined variables into SymPy symbols, and allow the use of standard
+        mathematical factorial notation (e.g. ``x!``). Selection via
+        string is available (see below).
+
+    evaluate : bool, optional
+        When False, the order of the arguments will remain as they were in the
+        string and automatic simplification that would normally occur is
+        suppressed. (see examples)
+
+    Examples
+    ========
+
+    >>> from sympy.parsing.sympy_parser import parse_expr
+    >>> parse_expr("1/2")
+    1/2
+    >>> type(_)
+    <class 'sympy.core.numbers.Half'>
+    >>> from sympy.parsing.sympy_parser import standard_transformations,\\
+    ... implicit_multiplication_application
+    >>> transformations = (standard_transformations +
+    ...     (implicit_multiplication_application,))
+    >>> parse_expr("2x", transformations=transformations)
+    2*x
+
+    When evaluate=False, some automatic simplifications will not occur:
+
+    >>> parse_expr("2**3"), parse_expr("2**3", evaluate=False)
+    (8, 2**3)
+
+    In addition the order of the arguments will not be made canonical.
+    This feature allows one to tell exactly how the expression was entered:
+
+    >>> a = parse_expr('1 + x', evaluate=False)
+    >>> b = parse_expr('x + 1', evaluate=0)
+    >>> a == b
+    False
+    >>> a.args
+    (1, x)
+    >>> b.args
+    (x, 1)
+
+    Note, however, that when these expressions are printed they will
+    appear the same:
+
+    >>> assert str(a) == str(b)
+
+    As a convenience, transformations can be seen by printing ``transformations``:
+
+    >>> from sympy.parsing.sympy_parser import transformations
+
+    >>> print(transformations)
+    0: lambda_notation
+    1: auto_symbol
+    2: repeated_decimals
+    3: auto_number
+    4: factorial_notation
+    5: implicit_multiplication_application
+    6: convert_xor
+    7: implicit_application
+    8: implicit_multiplication
+    9: convert_equals_signs
+    10: function_exponentiation
+    11: rationalize
+
+    The ``T`` object provides a way to select these transformations:
+
+    >>> from sympy.parsing.sympy_parser import T
+
+    If you print it, you will see the same list as shown above.
+
+    >>> str(T) == str(transformations)
+    True
+
+    Standard slicing will return a tuple of transformations:
+
+    >>> T[:5] == standard_transformations
+    True
+
+    So ``T`` can be used to specify the parsing transformations:
+
+    >>> parse_expr("2x", transformations=T[:5])
+    Traceback (most recent call last):
+    ...
+    SyntaxError: invalid syntax
+    >>> parse_expr("2x", transformations=T[:6])
+    2*x
+    >>> parse_expr('.3', transformations=T[3, 11])
+    3/10
+    >>> parse_expr('.3x', transformations=T[:])
+    3*x/10
+
+    As a further convenience, strings 'implicit' and 'all' can be used
+    to select 0-5 and all the transformations, respectively.
+
+    >>> parse_expr('.3x', transformations='all')
+    3*x/10
+
+    See Also
+    ========
+
+    stringify_expr, eval_expr, standard_transformations,
+    implicit_multiplication_application
+
+    """
+
+    if local_dict is None:
+        local_dict = {}
+    elif not isinstance(local_dict, dict):
+        raise TypeError('expecting local_dict to be a dict')
+    elif null in local_dict:
+        raise ValueError('cannot use "" in local_dict')
+
+    if global_dict is None:
+        global_dict = {}
+        exec('from sympy import *', global_dict)
+
+        builtins_dict = vars(builtins)
+        for name, obj in builtins_dict.items():
+            if isinstance(obj, types.BuiltinFunctionType):
+                global_dict[name] = obj
+        global_dict['max'] = Max
+        global_dict['min'] = Min
+
+    elif not isinstance(global_dict, dict):
+        raise TypeError('expecting global_dict to be a dict')
+
+    transformations = transformations or ()
+    if isinstance(transformations, str):
+        if transformations == 'all':
+            _transformations = T[:]
+        elif transformations == 'implicit':
+            _transformations = T[:6]
+        else:
+            raise ValueError('unknown transformation group name')
+    else:
+        _transformations = transformations
+
+    code = stringify_expr(s, local_dict, global_dict, _transformations)
+
+    if not evaluate:
+        code = compile(evaluateFalse(code), '<string>', 'eval') # type: ignore
+
+    try:
+        rv = eval_expr(code, local_dict, global_dict)
+        # restore neutral definitions for names
+        for i in local_dict.pop(null, ()):
+            local_dict[i] = null
+        return rv
+    except Exception as e:
+        # restore neutral definitions for names
+        for i in local_dict.pop(null, ()):
+            local_dict[i] = null
+        raise e from ValueError(f"Error from parse_expr with transformed code: {code!r}")
+
+
+def evaluateFalse(s: str):
+    """
+    Replaces operators with the SymPy equivalent and sets evaluate=False.
+    """
+    node = ast.parse(s)
+    transformed_node = EvaluateFalseTransformer().visit(node)
+    # node is a Module, we want an Expression
+    transformed_node = ast.Expression(transformed_node.body[0].value)
+
+    return ast.fix_missing_locations(transformed_node)
+
+
+class EvaluateFalseTransformer(ast.NodeTransformer):
+    operators = {
+        ast.Add: 'Add',
+        ast.Mult: 'Mul',
+        ast.Pow: 'Pow',
+        ast.Sub: 'Add',
+        ast.Div: 'Mul',
+        ast.BitOr: 'Or',
+        ast.BitAnd: 'And',
+        ast.BitXor: 'Not',
+    }
+    functions = (
+        'Abs', 'im', 're', 'sign', 'arg', 'conjugate',
+        'acos', 'acot', 'acsc', 'asec', 'asin', 'atan',
+        'acosh', 'acoth', 'acsch', 'asech', 'asinh', 'atanh',
+        'cos', 'cot', 'csc', 'sec', 'sin', 'tan',
+        'cosh', 'coth', 'csch', 'sech', 'sinh', 'tanh',
+        'exp', 'ln', 'log', 'sqrt', 'cbrt',
+    )
+
+    relational_operators = {
+        ast.NotEq: 'Ne',
+        ast.Lt: 'Lt',
+        ast.LtE: 'Le',
+        ast.Gt: 'Gt',
+        ast.GtE: 'Ge',
+        ast.Eq: 'Eq'
+    }
+    def visit_Compare(self, node):
+        if node.ops[0].__class__ in self.relational_operators:
+            sympy_class = self.relational_operators[node.ops[0].__class__]
+            right = self.visit(node.comparators[0])
+            left = self.visit(node.left)
+            new_node = ast.Call(
+                func=ast.Name(id=sympy_class, ctx=ast.Load()),
+                args=[left, right],
+                keywords=[ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load()))],
+                starargs=None,
+                kwargs=None
+            )
+            return new_node
+        return node
+
+    def flatten(self, args, func):
+        result = []
+        for arg in args:
+            if isinstance(arg, ast.Call):
+                arg_func = arg.func
+                if isinstance(arg_func, ast.Call):
+                    arg_func = arg_func.func
+                if arg_func.id == func:
+                    result.extend(self.flatten(arg.args, func))
+                else:
+                    result.append(arg)
+            else:
+                result.append(arg)
+        return result
+
+    def visit_BinOp(self, node):
+        if node.op.__class__ in self.operators:
+            sympy_class = self.operators[node.op.__class__]
+            right = self.visit(node.right)
+            left = self.visit(node.left)
+
+            rev = False
+            if isinstance(node.op, ast.Sub):
+                right = ast.Call(
+                    func=ast.Name(id='Mul', ctx=ast.Load()),
+                    args=[ast.UnaryOp(op=ast.USub(), operand=ast.Num(1)), right],
+                    keywords=[ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load()))],
+                    starargs=None,
+                    kwargs=None
+                )
+            elif isinstance(node.op, ast.Div):
+                if isinstance(node.left, ast.UnaryOp):
+                    left, right = right, left
+                    rev = True
+                    left = ast.Call(
+                    func=ast.Name(id='Pow', ctx=ast.Load()),
+                    args=[left, ast.UnaryOp(op=ast.USub(), operand=ast.Num(1))],
+                    keywords=[ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load()))],
+                    starargs=None,
+                    kwargs=None
+                )
+                else:
+                    right = ast.Call(
+                    func=ast.Name(id='Pow', ctx=ast.Load()),
+                    args=[right, ast.UnaryOp(op=ast.USub(), operand=ast.Num(1))],
+                    keywords=[ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load()))],
+                    starargs=None,
+                    kwargs=None
+                )
+
+            if rev:  # undo reversal
+                left, right = right, left
+            new_node = ast.Call(
+                func=ast.Name(id=sympy_class, ctx=ast.Load()),
+                args=[left, right],
+                keywords=[ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load()))],
+                starargs=None,
+                kwargs=None
+            )
+
+            if sympy_class in ('Add', 'Mul'):
+                # Denest Add or Mul as appropriate
+                new_node.args = self.flatten(new_node.args, sympy_class)
+
+            return new_node
+        return node
+
+    def visit_Call(self, node):
+        new_node = self.generic_visit(node)
+        if isinstance(node.func, ast.Name) and node.func.id in self.functions:
+            new_node.keywords.append(ast.keyword(arg='evaluate', value=ast.NameConstant(value=False, ctx=ast.Load())))
+        return new_node
+
+
+_transformation = {  # items can be added but never re-ordered
+0: lambda_notation,
+1: auto_symbol,
+2: repeated_decimals,
+3: auto_number,
+4: factorial_notation,
+5: implicit_multiplication_application,
+6: convert_xor,
+7: implicit_application,
+8: implicit_multiplication,
+9: convert_equals_signs,
+10: function_exponentiation,
+11: rationalize}
+
+transformations = '\n'.join('%s: %s' % (i, func_name(f)) for i, f in _transformation.items())
+
+
+class _T():
+    """class to retrieve transformations from a given slice
+
+    EXAMPLES
+    ========
+
+    >>> from sympy.parsing.sympy_parser import T, standard_transformations
+    >>> assert T[:5] == standard_transformations
+    """
+    def __init__(self):
+        self.N = len(_transformation)
+
+    def __str__(self):
+        return transformations
+
+    def __getitem__(self, t):
+        if not type(t) is tuple:
+            t = (t,)
+        i = []
+        for ti in t:
+            if type(ti) is int:
+                i.append(range(self.N)[ti])
+            elif type(ti) is slice:
+                i.extend(range(*ti.indices(self.N)))
+            else:
+                raise TypeError('unexpected slice arg')
+        return tuple([_transformation[_] for _ in i])
+
+T = _T()
diff --git a/shared/package-files/40/400f8bd01a075d2fdb05462aed24750a5798f28a471309b5ff81c35b33de7b27/special_round_native.h b/shared/package-files/40/400f8bd01a075d2fdb05462aed24750a5798f28a471309b5ff81c35b33de7b27/special_round_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb16492c2d07b81a6c5f6721ccb30cd6c1711370
--- /dev/null
+++ b/shared/package-files/40/400f8bd01a075d2fdb05462aed24750a5798f28a471309b5ff81c35b33de7b27/special_round_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_round(const at::Tensor & self, int64_t decimals=0);
+TORCH_API at::Tensor & special_round_out(const at::Tensor & self, int64_t decimals, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/40/400fe97b31d87aeb0e25e2ccfb916af6a23bdf616f1c600868dc205b9e5f2a8e/fixedTools.py b/shared/package-files/40/400fe97b31d87aeb0e25e2ccfb916af6a23bdf616f1c600868dc205b9e5f2a8e/fixedTools.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23f6733adeebc88b139cf59dc5b8823966693f8
--- /dev/null
+++ b/shared/package-files/40/400fe97b31d87aeb0e25e2ccfb916af6a23bdf616f1c600868dc205b9e5f2a8e/fixedTools.py
@@ -0,0 +1,253 @@
+"""
+The `OpenType specification <https://docs.microsoft.com/en-us/typography/opentype/spec/otff#data-types>`_
+defines two fixed-point data types:
+
+``Fixed``
+        A 32-bit signed fixed-point number with a 16 bit twos-complement
+        magnitude component and 16 fractional bits.
+``F2DOT14``
+        A 16-bit signed fixed-point number with a 2 bit twos-complement
+        magnitude component and 14 fractional bits.
+
+To support reading and writing data with these data types, this module provides
+functions for converting between fixed-point, float and string representations.
+
+.. data:: MAX_F2DOT14
+
+        The maximum value that can still fit in an F2Dot14. (1.99993896484375)
+"""
+
+from .roundTools import otRound, nearestMultipleShortestRepr
+import logging
+
+log = logging.getLogger(__name__)
+
+__all__ = [
+    "MAX_F2DOT14",
+    "fixedToFloat",
+    "floatToFixed",
+    "floatToFixedToFloat",
+    "floatToFixedToStr",
+    "fixedToStr",
+    "strToFixed",
+    "strToFixedToFloat",
+    "ensureVersionIsLong",
+    "versionToFixed",
+]
+
+
+MAX_F2DOT14 = 0x7FFF / (1 << 14)
+
+
+def fixedToFloat(value: float, precisionBits: int) -> float:
+    """Converts a fixed-point number to a float given the number of
+    precision bits.
+
+    Args:
+            value (int): Number in fixed-point format.
+            precisionBits (int): Number of precision bits.
+
+    Returns:
+            Floating point value.
+
+    Examples::
+
+            >>> import math
+            >>> f = fixedToFloat(-10139, precisionBits=14)
+            >>> math.isclose(f, -0.61883544921875)
+            True
+    """
+    return value / (1 << precisionBits)
+
+
+def floatToFixed(value, precisionBits):
+    """Converts a float to a fixed-point number given the number of
+    precision bits.
+
+    Args:
+            value (float): Floating point value.
+            precisionBits (int): Number of precision bits.
+
+    Returns:
+            int: Fixed-point representation.
+
+    Examples::
+
+            >>> floatToFixed(-0.61883544921875, precisionBits=14)
+            -10139
+            >>> floatToFixed(-0.61884, precisionBits=14)
+            -10139
+    """
+    return otRound(value * (1 << precisionBits))
+
+
+def floatToFixedToFloat(value, precisionBits):
+    """Converts a float to a fixed-point number and back again.
+
+    By converting the float to fixed, rounding it, and converting it back
+    to float again, this returns a floating point values which is exactly
+    representable in fixed-point format.
+
+    Note: this **is** equivalent to ``fixedToFloat(floatToFixed(value))``.
+
+    Args:
+            value (float): The input floating point value.
+            precisionBits (int): Number of precision bits.
+
+    Returns:
+            float: The transformed and rounded value.
+
+    Examples::
+            >>> import math
+            >>> f1 = -0.61884
+            >>> f2 = floatToFixedToFloat(-0.61884, precisionBits=14)
+            >>> f1 != f2
+            True
+            >>> math.isclose(f2, -0.61883544921875)
+            True
+    """
+    scale = 1 << precisionBits
+    return otRound(value * scale) / scale
+
+
+def fixedToStr(value, precisionBits):
+    """Converts a fixed-point number to a string representing a decimal float.
+
+    This chooses the float that has the shortest decimal representation (the least
+    number of fractional decimal digits).
+
+    For example, to convert a fixed-point number in a 2.14 format, use
+    ``precisionBits=14``::
+
+            >>> fixedToStr(-10139, precisionBits=14)
+            '-0.61884'
+
+    This is pretty slow compared to the simple division used in ``fixedToFloat``.
+    Use sporadically when you need to serialize or print the fixed-point number in
+    a human-readable form.
+    It uses nearestMultipleShortestRepr under the hood.
+
+    Args:
+            value (int): The fixed-point value to convert.
+            precisionBits (int): Number of precision bits, *up to a maximum of 16*.
+
+    Returns:
+            str: A string representation of the value.
+    """
+    scale = 1 << precisionBits
+    return nearestMultipleShortestRepr(value / scale, factor=1.0 / scale)
+
+
+def strToFixed(string, precisionBits):
+    """Converts a string representing a decimal float to a fixed-point number.
+
+    Args:
+            string (str): A string representing a decimal float.
+            precisionBits (int): Number of precision bits, *up to a maximum of 16*.
+
+    Returns:
+            int: Fixed-point representation.
+
+    Examples::
+
+            >>> ## to convert a float string to a 2.14 fixed-point number:
+            >>> strToFixed('-0.61884', precisionBits=14)
+            -10139
+    """
+    value = float(string)
+    return otRound(value * (1 << precisionBits))
+
+
+def strToFixedToFloat(string, precisionBits):
+    """Convert a string to a decimal float with fixed-point rounding.
+
+    This first converts string to a float, then turns it into a fixed-point
+    number with ``precisionBits`` fractional binary digits, then back to a
+    float again.
+
+    This is simply a shorthand for fixedToFloat(floatToFixed(float(s))).
+
+    Args:
+            string (str): A string representing a decimal float.
+            precisionBits (int): Number of precision bits.
+
+    Returns:
+            float: The transformed and rounded value.
+
+    Examples::
+
+            >>> import math
+            >>> s = '-0.61884'
+            >>> bits = 14
+            >>> f = strToFixedToFloat(s, precisionBits=bits)
+            >>> math.isclose(f, -0.61883544921875)
+            True
+            >>> f == fixedToFloat(floatToFixed(float(s), precisionBits=bits), precisionBits=bits)
+            True
+    """
+    value = float(string)
+    scale = 1 << precisionBits
+    return otRound(value * scale) / scale
+
+
+def floatToFixedToStr(value, precisionBits):
+    """Convert float to string with fixed-point rounding.
+
+    This uses the shortest decimal representation (ie. the least
+    number of fractional decimal digits) to represent the equivalent
+    fixed-point number with ``precisionBits`` fractional binary digits.
+    It uses nearestMultipleShortestRepr under the hood.
+
+    >>> floatToFixedToStr(-0.61883544921875, precisionBits=14)
+    '-0.61884'
+
+    Args:
+            value (float): The float value to convert.
+            precisionBits (int): Number of precision bits, *up to a maximum of 16*.
+
+    Returns:
+            str: A string representation of the value.
+
+    """
+    scale = 1 << precisionBits
+    return nearestMultipleShortestRepr(value, factor=1.0 / scale)
+
+
+def ensureVersionIsLong(value):
+    """Ensure a table version is an unsigned long.
+
+    OpenType table version numbers are expressed as a single unsigned long
+    comprising of an unsigned short major version and unsigned short minor
+    version. This function detects if the value to be used as a version number
+    looks too small (i.e. is less than ``0x10000``), and converts it to
+    fixed-point using :func:`floatToFixed` if so.
+
+    Args:
+            value (Number): a candidate table version number.
+
+    Returns:
+            int: A table version number, possibly corrected to fixed-point.
+    """
+    if value < 0x10000:
+        newValue = floatToFixed(value, 16)
+        log.warning(
+            "Table version value is a float: %.4f; " "fix to use hex instead: 0x%08x",
+            value,
+            newValue,
+        )
+        value = newValue
+    return value
+
+
+def versionToFixed(value):
+    """Ensure a table version number is fixed-point.
+
+    Args:
+            value (str): a candidate table version number.
+
+    Returns:
+            int: A table version number, possibly corrected to fixed-point.
+    """
+    value = int(value, 0) if value.startswith("0") else float(value)
+    value = ensureVersionIsLong(value)
+    return value
diff --git a/shared/package-files/40/4010926f3319c7545f015257e360ff40c3f0eac903f99a33bfa41636577271d9/MtxOrb162 b/shared/package-files/40/4010926f3319c7545f015257e360ff40c3f0eac903f99a33bfa41636577271d9/MtxOrb162
new file mode 100644
index 0000000000000000000000000000000000000000..e009b47da51b1249a96b066b7bdbfbcb083884ac
Binary files /dev/null and b/shared/package-files/40/4010926f3319c7545f015257e360ff40c3f0eac903f99a33bfa41636577271d9/MtxOrb162 differ
diff --git a/shared/package-files/40/4011a3b7e9308639b3537f9766d9e42525f2e0a0ba88893b0defe4dd364215fe/_continuous_distns.py b/shared/package-files/40/4011a3b7e9308639b3537f9766d9e42525f2e0a0ba88893b0defe4dd364215fe/_continuous_distns.py
new file mode 100644
index 0000000000000000000000000000000000000000..d391a946205c975101a9e329fb35052f64be7287
--- /dev/null
+++ b/shared/package-files/40/4011a3b7e9308639b3537f9766d9e42525f2e0a0ba88893b0defe4dd364215fe/_continuous_distns.py
@@ -0,0 +1,12516 @@
+#
+# Author:  Travis Oliphant  2002-2011 with contributions from
+#          SciPy Developers 2004-2011
+#
+import warnings
+from collections.abc import Iterable
+from functools import wraps, cached_property
+import ctypes
+
+import numpy as np
+from numpy.polynomial import Polynomial
+from scipy.interpolate import BSpline
+from scipy._lib.doccer import (extend_notes_in_docstring,
+                               replace_notes_in_docstring,
+                               inherit_docstring_from)
+from scipy._lib._ccallback import LowLevelCallable
+from scipy import optimize
+from scipy import integrate
+import scipy.special as sc
+
+import scipy.special._ufuncs as scu
+from scipy._lib._util import _lazyselect, _lazywhere
+
+from . import _stats
+from ._tukeylambda_stats import (tukeylambda_variance as _tlvar,
+                                 tukeylambda_kurtosis as _tlkurt)
+from ._distn_infrastructure import (_vectorize_rvs_over_shapes,
+    get_distribution_names, _kurtosis, _isintegral,
+    rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo)
+from scipy.stats._distribution_infrastructure import _log1mexp
+from ._ksstats import kolmogn, kolmognp, kolmogni
+from ._constants import (_XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI,
+                         _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI)
+from ._censored_data import CensoredData
+from scipy.optimize import root_scalar
+from scipy.stats._warnings_errors import FitError
+import scipy.stats as stats
+
+def _remove_optimizer_parameters(kwds):
+    """
+    Remove the optimizer-related keyword arguments 'loc', 'scale' and
+    'optimizer' from `kwds`.  Then check that `kwds` is empty, and
+    raise `TypeError("Unknown arguments: %s." % kwds)` if it is not.
+
+    This function is used in the fit method of distributions that override
+    the default method and do not use the default optimization code.
+
+    `kwds` is modified in-place.
+    """
+    kwds.pop('loc', None)
+    kwds.pop('scale', None)
+    kwds.pop('optimizer', None)
+    kwds.pop('method', None)
+    if kwds:
+        raise TypeError(f"Unknown arguments: {kwds}.")
+
+
+def _call_super_mom(fun):
+    # If fit method is overridden only for MLE and doesn't specify what to do
+    # if method == 'mm' or with censored data, this decorator calls the generic
+    # implementation.
+    @wraps(fun)
+    def wrapper(self, data, *args, **kwds):
+        method = kwds.get('method', 'mle').lower()
+        censored = isinstance(data, CensoredData)
+        if method == 'mm' or (censored and data.num_censored() > 0):
+            return super(type(self), self).fit(data, *args, **kwds)
+        else:
+            if censored:
+                # data is an instance of CensoredData, but actually holds
+                # no censored values, so replace it with the array of
+                # uncensored values.
+                data = data._uncensored
+            return fun(self, data, *args, **kwds)
+
+    return wrapper
+
+
+def _get_left_bracket(fun, rbrack, lbrack=None):
+    # find left bracket for `root_scalar`. A guess for lbrack may be provided.
+    lbrack = lbrack or rbrack - 1
+    diff = rbrack - lbrack
+
+    # if there is no sign change in `fun` between the brackets, expand
+    # rbrack - lbrack until a sign change occurs
+    def interval_contains_root(lbrack, rbrack):
+        # return true if the signs disagree.
+        return np.sign(fun(lbrack)) != np.sign(fun(rbrack))
+
+    while not interval_contains_root(lbrack, rbrack):
+        diff *= 2
+        lbrack = rbrack - diff
+
+        msg = ("The solver could not find a bracket containing a "
+               "root to an MLE first order condition.")
+        if np.isinf(lbrack):
+            raise FitSolverError(msg)
+
+    return lbrack
+
+
+class ksone_gen(rv_continuous):
+    r"""Kolmogorov-Smirnov one-sided test statistic distribution.
+
+    This is the distribution of the one-sided Kolmogorov-Smirnov (KS)
+    statistics :math:`D_n^+` and :math:`D_n^-`
+    for a finite sample size ``n >= 1`` (the shape parameter).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    kstwobign, kstwo, kstest
+
+    Notes
+    -----
+    :math:`D_n^+` and :math:`D_n^-` are given by
+
+    .. math::
+
+        D_n^+ &= \text{sup}_x (F_n(x) - F(x)),\\
+        D_n^- &= \text{sup}_x (F(x) - F_n(x)),\\
+
+    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
+    `ksone` describes the distribution under the null hypothesis of the KS test
+    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
+    with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Birnbaum, Z. W. and Tingey, F.H. "One-sided confidence contours
+       for probability distribution functions", The Annals of Mathematical
+       Statistics, 22(4), pp 592-596 (1951).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import ksone
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> n = 1e+03
+    >>> x = np.linspace(ksone.ppf(0.01, n),
+    ...                 ksone.ppf(0.99, n), 100)
+    >>> ax.plot(x, ksone.pdf(x, n),
+    ...         'r-', lw=5, alpha=0.6, label='ksone pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = ksone(n)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = ksone.ppf([0.001, 0.5, 0.999], n)
+    >>> np.allclose([0.001, 0.5, 0.999], ksone.cdf(vals, n))
+    True
+
+    """
+    def _argcheck(self, n):
+        return (n >= 1) & (n == np.round(n))
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _pdf(self, x, n):
+        return -scu._smirnovp(n, x)
+
+    def _cdf(self, x, n):
+        return scu._smirnovc(n, x)
+
+    def _sf(self, x, n):
+        return sc.smirnov(n, x)
+
+    def _ppf(self, q, n):
+        return scu._smirnovci(n, q)
+
+    def _isf(self, q, n):
+        return sc.smirnovi(n, q)
+
+
+ksone = ksone_gen(a=0.0, b=1.0, name='ksone')
+
+
+class kstwo_gen(rv_continuous):
+    r"""Kolmogorov-Smirnov two-sided test statistic distribution.
+
+    This is the distribution of the two-sided Kolmogorov-Smirnov (KS)
+    statistic :math:`D_n` for a finite sample size ``n >= 1``
+    (the shape parameter).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    kstwobign, ksone, kstest
+
+    Notes
+    -----
+    :math:`D_n` is given by
+
+    .. math::
+
+        D_n = \text{sup}_x |F_n(x) - F(x)|
+
+    where :math:`F` is a (continuous) CDF and :math:`F_n` is an empirical CDF.
+    `kstwo` describes the distribution under the null hypothesis of the KS test
+    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
+    with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Simard, R., L'Ecuyer, P. "Computing the Two-Sided
+       Kolmogorov-Smirnov Distribution",  Journal of Statistical Software,
+       Vol 39, 11, 1-18 (2011).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import kstwo
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> n = 10
+    >>> x = np.linspace(kstwo.ppf(0.01, n),
+    ...                 kstwo.ppf(0.99, n), 100)
+    >>> ax.plot(x, kstwo.pdf(x, n),
+    ...         'r-', lw=5, alpha=0.6, label='kstwo pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = kstwo(n)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = kstwo.ppf([0.001, 0.5, 0.999], n)
+    >>> np.allclose([0.001, 0.5, 0.999], kstwo.cdf(vals, n))
+    True
+
+    """
+    def _argcheck(self, n):
+        return (n >= 1) & (n == np.round(n))
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _get_support(self, n):
+        return (0.5/(n if not isinstance(n, Iterable) else np.asanyarray(n)),
+                1.0)
+
+    def _pdf(self, x, n):
+        return kolmognp(n, x)
+
+    def _cdf(self, x, n):
+        return kolmogn(n, x)
+
+    def _sf(self, x, n):
+        return kolmogn(n, x, cdf=False)
+
+    def _ppf(self, q, n):
+        return kolmogni(n, q, cdf=True)
+
+    def _isf(self, q, n):
+        return kolmogni(n, q, cdf=False)
+
+
+# Use the pdf, (not the ppf) to compute moments
+kstwo = kstwo_gen(momtype=0, a=0.0, b=1.0, name='kstwo')
+
+
+class kstwobign_gen(rv_continuous):
+    r"""Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
+
+    This is the asymptotic distribution of the two-sided Kolmogorov-Smirnov
+    statistic :math:`\sqrt{n} D_n` that measures the maximum absolute
+    distance of the theoretical (continuous) CDF from the empirical CDF.
+    (see `kstest`).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ksone, kstwo, kstest
+
+    Notes
+    -----
+    :math:`\sqrt{n} D_n` is given by
+
+    .. math::
+
+        D_n = \text{sup}_x |F_n(x) - F(x)|
+
+    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
+    `kstwobign`  describes the asymptotic distribution (i.e. the limit of
+    :math:`\sqrt{n} D_n`) under the null hypothesis of the KS test that the
+    empirical CDF corresponds to i.i.d. random variates with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Feller, W. "On the Kolmogorov-Smirnov Limit Theorems for Empirical
+       Distributions",  Ann. Math. Statist. Vol 19, 177-189 (1948).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        return -scu._kolmogp(x)
+
+    def _cdf(self, x):
+        return scu._kolmogc(x)
+
+    def _sf(self, x):
+        return sc.kolmogorov(x)
+
+    def _ppf(self, q):
+        return scu._kolmogci(q)
+
+    def _isf(self, q):
+        return sc.kolmogi(q)
+
+
+kstwobign = kstwobign_gen(a=0.0, name='kstwobign')
+
+
+## Normal distribution
+
+# loc = mu, scale = std
+# Keep these implementations out of the class definition so they can be reused
+# by other distributions.
+_norm_pdf_C = np.sqrt(2*np.pi)
+_norm_pdf_logC = np.log(_norm_pdf_C)
+
+
+def _norm_pdf(x):
+    return np.exp(-x**2/2.0) / _norm_pdf_C
+
+
+def _norm_logpdf(x):
+    return -x**2 / 2.0 - _norm_pdf_logC
+
+
+def _norm_cdf(x):
+    return sc.ndtr(x)
+
+
+def _norm_logcdf(x):
+    return sc.log_ndtr(x)
+
+
+def _norm_ppf(q):
+    return sc.ndtri(q)
+
+
+def _norm_sf(x):
+    return _norm_cdf(-x)
+
+
+def _norm_logsf(x):
+    return _norm_logcdf(-x)
+
+
+def _norm_isf(q):
+    return -_norm_ppf(q)
+
+
+class norm_gen(rv_continuous):
+    r"""A normal continuous random variable.
+
+    The location (``loc``) keyword specifies the mean.
+    The scale (``scale``) keyword specifies the standard deviation.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `norm` is:
+
+    .. math::
+
+        f(x) = \frac{\exp(-x^2/2)}{\sqrt{2\pi}}
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.standard_normal(size)
+
+    def _pdf(self, x):
+        # norm.pdf(x) = exp(-x**2/2)/sqrt(2*pi)
+        return _norm_pdf(x)
+
+    def _logpdf(self, x):
+        return _norm_logpdf(x)
+
+    def _cdf(self, x):
+        return _norm_cdf(x)
+
+    def _logcdf(self, x):
+        return _norm_logcdf(x)
+
+    def _sf(self, x):
+        return _norm_sf(x)
+
+    def _logsf(self, x):
+        return _norm_logsf(x)
+
+    def _ppf(self, q):
+        return _norm_ppf(q)
+
+    def _isf(self, q):
+        return _norm_isf(q)
+
+    def _stats(self):
+        return 0.0, 1.0, 0.0, 0.0
+
+    def _entropy(self):
+        return 0.5*(np.log(2*np.pi)+1)
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        For the normal distribution, method of moments and maximum likelihood
+        estimation give identical fits, and explicit formulas for the estimates
+        are available.
+        This function uses these explicit formulas for the maximum likelihood
+        estimation of the normal distribution parameters, so the
+        `optimizer` and `method` arguments are ignored.\n\n""")
+    def fit(self, data, **kwds):
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            # Without this check, this function would just return the
+            # parameters that were given.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        if floc is None:
+            loc = data.mean()
+        else:
+            loc = floc
+
+        if fscale is None:
+            scale = np.sqrt(((data - loc)**2).mean())
+        else:
+            scale = fscale
+
+        return loc, scale
+
+    def _munp(self, n):
+        """
+        @returns Moments of standard normal distribution for integer n >= 0
+
+        See eq. 16 of https://arxiv.org/abs/1209.4340v2
+        """
+        if n == 0:
+            return 1.
+        if n % 2 == 0:
+            return sc.factorial2(int(n) - 1)
+        else:
+            return 0.
+
+
+norm = norm_gen(name='norm')
+
+
+class alpha_gen(rv_continuous):
+    r"""An alpha continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `alpha` ([1]_, [2]_) is:
+
+    .. math::
+
+        f(x, a) = \frac{1}{x^2 \Phi(a) \sqrt{2\pi}} *
+                  \exp(-\frac{1}{2} (a-1/x)^2)
+
+    where :math:`\Phi` is the normal CDF, :math:`x > 0`, and :math:`a > 0`.
+
+    `alpha` takes ``a`` as a shape parameter.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
+           Distributions, Volume 1", Second Edition, John Wiley and Sons,
+           p. 173 (1994).
+    .. [2] Anthony A. Salvia, "Reliability applications of the Alpha
+           Distribution", IEEE Transactions on Reliability, Vol. R-34,
+           No. 3, pp. 251-252 (1985).
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # alpha.pdf(x, a) = 1/(x**2*Phi(a)*sqrt(2*pi)) * exp(-1/2 * (a-1/x)**2)
+        return 1.0/(x**2)/_norm_cdf(a)*_norm_pdf(a-1.0/x)
+
+    def _logpdf(self, x, a):
+        return -2*np.log(x) + _norm_logpdf(a-1.0/x) - np.log(_norm_cdf(a))
+
+    def _cdf(self, x, a):
+        return _norm_cdf(a-1.0/x) / _norm_cdf(a)
+
+    def _ppf(self, q, a):
+        return 1.0/np.asarray(a - _norm_ppf(q*_norm_cdf(a)))
+
+    def _stats(self, a):
+        return [np.inf]*2 + [np.nan]*2
+
+
+alpha = alpha_gen(a=0.0, name='alpha')
+
+
+class anglit_gen(rv_continuous):
+    r"""An anglit continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `anglit` is:
+
+    .. math::
+
+        f(x) = \sin(2x + \pi/2) = \cos(2x)
+
+    for :math:`-\pi/4 \le x \le \pi/4`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # anglit.pdf(x) = sin(2*x + \pi/2) = cos(2*x)
+        return np.cos(2*x)
+
+    def _cdf(self, x):
+        return np.sin(x+np.pi/4)**2.0
+
+    def _sf(self, x):
+        return np.cos(x + np.pi / 4) ** 2.0
+
+    def _ppf(self, q):
+        return np.arcsin(np.sqrt(q))-np.pi/4
+
+    def _stats(self):
+        return 0.0, np.pi*np.pi/16-0.5, 0.0, -2*(np.pi**4 - 96)/(np.pi*np.pi-8)**2
+
+    def _entropy(self):
+        return 1-np.log(2)
+
+
+anglit = anglit_gen(a=-np.pi/4, b=np.pi/4, name='anglit')
+
+
+class arcsine_gen(rv_continuous):
+    r"""An arcsine continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `arcsine` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi \sqrt{x (1-x)}}
+
+    for :math:`0 < x < 1`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # arcsine.pdf(x) = 1/(pi*sqrt(x*(1-x)))
+        with np.errstate(divide='ignore'):
+            return 1.0/np.pi/np.sqrt(x*(1-x))
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arcsin(np.sqrt(x))
+
+    def _ppf(self, q):
+        return np.sin(np.pi/2.0*q)**2.0
+
+    def _stats(self):
+        mu = 0.5
+        mu2 = 1.0/8
+        g1 = 0
+        g2 = -3.0/2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self):
+        return -0.24156447527049044468
+
+
+arcsine = arcsine_gen(a=0.0, b=1.0, name='arcsine')
+
+
+class FitDataError(ValueError):
+    """Raised when input data is inconsistent with fixed parameters."""
+    # This exception is raised by, for example, beta_gen.fit when both floc
+    # and fscale are fixed and there are values in the data not in the open
+    # interval (floc, floc+fscale).
+    def __init__(self, distr, lower, upper):
+        self.args = (
+            "Invalid values in `data`.  Maximum likelihood "
+            f"estimation with {distr!r} requires that {lower!r} < "
+            f"(x - loc)/scale  < {upper!r} for each x in `data`.",
+        )
+
+
+class FitSolverError(FitError):
+    """
+    Raised when a solver fails to converge while fitting a distribution.
+    """
+    # This exception is raised by, for example, beta_gen.fit when
+    # optimize.fsolve returns with ier != 1.
+    def __init__(self, mesg):
+        emsg = "Solver for the MLE equations failed to converge: "
+        emsg += mesg.replace('\n', '')
+        self.args = (emsg,)
+
+
+def _beta_mle_a(a, b, n, s1):
+    # The zeros of this function give the MLE for `a`, with
+    # `b`, `n` and `s1` given.  `s1` is the sum of the logs of
+    # the data. `n` is the number of data points.
+    psiab = sc.psi(a + b)
+    func = s1 - n * (-psiab + sc.psi(a))
+    return func
+
+
+def _beta_mle_ab(theta, n, s1, s2):
+    # Zeros of this function are critical points of
+    # the maximum likelihood function.  Solving this system
+    # for theta (which contains a and b) gives the MLE for a and b
+    # given `n`, `s1` and `s2`.  `s1` is the sum of the logs of the data,
+    # and `s2` is the sum of the logs of 1 - data.  `n` is the number
+    # of data points.
+    a, b = theta
+    psiab = sc.psi(a + b)
+    func = [s1 - n * (-psiab + sc.psi(a)),
+            s2 - n * (-psiab + sc.psi(b))]
+    return func
+
+
+class beta_gen(rv_continuous):
+    r"""A beta continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `beta` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{\Gamma(a+b) x^{a-1} (1-x)^{b-1}}
+                          {\Gamma(a) \Gamma(b)}
+
+    for :math:`0 <= x <= 1`, :math:`a > 0`, :math:`b > 0`, where
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `beta` takes :math:`a` and :math:`b` as shape parameters.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        return random_state.beta(a, b, size)
+
+    def _pdf(self, x, a, b):
+        #                     gamma(a+b) * x**(a-1) * (1-x)**(b-1)
+        # beta.pdf(x, a, b) = ------------------------------------
+        #                              gamma(a)*gamma(b)
+        with np.errstate(over='ignore'):
+            return scu._beta_pdf(x, a, b)
+
+    def _logpdf(self, x, a, b):
+        lPx = sc.xlog1py(b - 1.0, -x) + sc.xlogy(a - 1.0, x)
+        lPx -= sc.betaln(a, b)
+        return lPx
+
+    def _cdf(self, x, a, b):
+        return sc.betainc(a, b, x)
+
+    def _sf(self, x, a, b):
+        return sc.betaincc(a, b, x)
+
+    def _isf(self, x, a, b):
+        return sc.betainccinv(a, b, x)
+
+    def _ppf(self, q, a, b):
+        return scu._beta_ppf(q, a, b)
+
+    def _stats(self, a, b):
+        a_plus_b = a + b
+        _beta_mean = a/a_plus_b
+        _beta_variance = a*b / (a_plus_b**2 * (a_plus_b + 1))
+        _beta_skewness = ((2 * (b - a) * np.sqrt(a_plus_b + 1)) /
+                          ((a_plus_b + 2) * np.sqrt(a * b)))
+        _beta_kurtosis_excess_n = 6 * ((a - b)**2 * (a_plus_b + 1) -
+                                       a * b * (a_plus_b + 2))
+        _beta_kurtosis_excess_d = a * b * (a_plus_b + 2) * (a_plus_b + 3)
+        _beta_kurtosis_excess = _beta_kurtosis_excess_n / _beta_kurtosis_excess_d
+        return (
+            _beta_mean,
+            _beta_variance,
+            _beta_skewness,
+            _beta_kurtosis_excess)
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+
+        g1 = _skew(data)
+        g2 = _kurtosis(data)
+
+        def func(x):
+            a, b = x
+            sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)
+            ku = a**3 - a**2*(2*b-1) + b**2*(b+1) - 2*a*b*(b+2)
+            ku /= a*b*(a+b+2)*(a+b+3)
+            ku *= 6
+            return [sk-g1, ku-g2]
+        a, b = optimize.fsolve(func, (1.0, 1.0))
+        return super()._fitstart(data, args=(a, b))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        In the special case where `method="MLE"` and
+        both `floc` and `fscale` are given, a
+        `ValueError` is raised if any value `x` in `data` does not satisfy
+        `floc < x < floc + fscale`.\n\n""")
+    def fit(self, data, *args, **kwds):
+        # Override rv_continuous.fit, so we can more efficiently handle the
+        # case where floc and fscale are given.
+
+        floc = kwds.get('floc', None)
+        fscale = kwds.get('fscale', None)
+
+        if floc is None or fscale is None:
+            # do general fit
+            return super().fit(data, *args, **kwds)
+
+        # We already got these from kwds, so just pop them.
+        kwds.pop('floc', None)
+        kwds.pop('fscale', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        f1 = _get_fixed_fit_value(kwds, ['f1', 'fb', 'fix_b'])
+
+        _remove_optimizer_parameters(kwds)
+
+        if f0 is not None and f1 is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        # Special case: loc and scale are constrained, so we are fitting
+        # just the shape parameters.  This can be done much more efficiently
+        # than the method used in `rv_continuous.fit`.  (See the subsection
+        # "Two unknown parameters" in the section "Maximum likelihood" of
+        # the Wikipedia article on the Beta distribution for the formulas.)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # Normalize the data to the interval [0, 1].
+        data = (np.ravel(data) - floc) / fscale
+        if np.any(data <= 0) or np.any(data >= 1):
+            raise FitDataError("beta", lower=floc, upper=floc + fscale)
+
+        xbar = data.mean()
+
+        if f0 is not None or f1 is not None:
+            # One of the shape parameters is fixed.
+
+            if f0 is not None:
+                # The shape parameter a is fixed, so swap the parameters
+                # and flip the data.  We always solve for `a`.  The result
+                # will be swapped back before returning.
+                b = f0
+                data = 1 - data
+                xbar = 1 - xbar
+            else:
+                b = f1
+
+            # Initial guess for a.  Use the formula for the mean of the beta
+            # distribution, E[x] = a / (a + b), to generate a reasonable
+            # starting point based on the mean of the data and the given
+            # value of b.
+            a = b * xbar / (1 - xbar)
+
+            # Compute the MLE for `a` by solving _beta_mle_a.
+            theta, info, ier, mesg = optimize.fsolve(
+                _beta_mle_a, a,
+                args=(b, len(data), np.log(data).sum()),
+                full_output=True
+            )
+            if ier != 1:
+                raise FitSolverError(mesg=mesg)
+            a = theta[0]
+
+            if f0 is not None:
+                # The shape parameter a was fixed, so swap back the
+                # parameters.
+                a, b = b, a
+
+        else:
+            # Neither of the shape parameters is fixed.
+
+            # s1 and s2 are used in the extra arguments passed to _beta_mle_ab
+            # by optimize.fsolve.
+            s1 = np.log(data).sum()
+            s2 = sc.log1p(-data).sum()
+
+            # Use the "method of moments" to estimate the initial
+            # guess for a and b.
+            fac = xbar * (1 - xbar) / data.var(ddof=0) - 1
+            a = xbar * fac
+            b = (1 - xbar) * fac
+
+            # Compute the MLE for a and b by solving _beta_mle_ab.
+            theta, info, ier, mesg = optimize.fsolve(
+                _beta_mle_ab, [a, b],
+                args=(len(data), s1, s2),
+                full_output=True
+            )
+            if ier != 1:
+                raise FitSolverError(mesg=mesg)
+            a, b = theta
+
+        return a, b, floc, fscale
+
+    def _entropy(self, a, b):
+        def regular(a, b):
+            return (sc.betaln(a, b) - (a - 1) * sc.psi(a) -
+                    (b - 1) * sc.psi(b) + (a + b - 2) * sc.psi(a + b))
+
+        def asymptotic_ab_large(a, b):
+            sum_ab = a + b
+            log_term = 0.5 * (
+                np.log(2*np.pi) + np.log(a) + np.log(b) - 3*np.log(sum_ab) + 1
+            )
+            t1 = 110/sum_ab + 20*sum_ab**-2.0 + sum_ab**-3.0 - 2*sum_ab**-4.0
+            t2 = -50/a - 10*a**-2.0 - a**-3.0 + a**-4.0
+            t3 = -50/b - 10*b**-2.0 - b**-3.0 + b**-4.0
+            return log_term + (t1 + t2 + t3) / 120
+
+        def asymptotic_b_large(a, b):
+            sum_ab = a + b
+            t1 = sc.gammaln(a) - (a - 1) * sc.psi(a)
+            t2 = (
+                - 1/(2*b) + 1/(12*b) - b**-2.0/12 - b**-3.0/120 + b**-4.0/120
+                + b**-5.0/252 - b**-6.0/252 + 1/sum_ab - 1/(12*sum_ab)
+                + sum_ab**-2.0/6 + sum_ab**-3.0/120 - sum_ab**-4.0/60
+                - sum_ab**-5.0/252 + sum_ab**-6.0/126
+            )
+            log_term = sum_ab*np.log1p(a/b) + np.log(b) - 2*np.log(sum_ab)
+            return t1 + t2 + log_term
+
+        def threshold_large(v):
+            if v == 1.0:
+                return 1000
+
+            j = np.log10(v)
+            digits = int(j)
+            d = int(v / 10 ** digits) + 2
+            return d*10**(7 + j)
+
+        if a >= 4.96e6 and b >= 4.96e6:
+            return asymptotic_ab_large(a, b)
+        elif a <= 4.9e6 and b - a >= 1e6 and b >= threshold_large(a):
+            return asymptotic_b_large(a, b)
+        elif b <= 4.9e6 and a - b >= 1e6 and a >= threshold_large(b):
+            return asymptotic_b_large(b, a)
+        else:
+            return regular(a, b)
+
+
+beta = beta_gen(a=0.0, b=1.0, name='beta')
+
+
+class betaprime_gen(rv_continuous):
+    r"""A beta prime continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `betaprime` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{x^{a-1} (1+x)^{-a-b}}{\beta(a, b)}
+
+    for :math:`x >= 0`, :math:`a > 0`, :math:`b > 0`, where
+    :math:`\beta(a, b)` is the beta function (see `scipy.special.beta`).
+
+    `betaprime` takes ``a`` and ``b`` as shape parameters.
+
+    The distribution is related to the `beta` distribution as follows:
+    If :math:`X` follows a beta distribution with parameters :math:`a, b`,
+    then :math:`Y = X/(1-X)` has a beta prime distribution with
+    parameters :math:`a, b` ([1]_).
+
+    The beta prime distribution is a reparametrized version of the
+    F distribution.  The beta prime distribution with shape parameters
+    ``a`` and ``b`` and ``scale = s`` is equivalent to the F distribution
+    with parameters ``d1 = 2*a``, ``d2 = 2*b`` and ``scale = (a/b)*s``.
+    For example,
+
+    >>> from scipy.stats import betaprime, f
+    >>> x = [1, 2, 5, 10]
+    >>> a = 12
+    >>> b = 5
+    >>> betaprime.pdf(x, a, b, scale=2)
+    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])
+    >>> f.pdf(x, 2*a, 2*b, scale=(a/b)*2)
+    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Beta prime distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Beta_prime_distribution
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        u1 = gamma.rvs(a, size=size, random_state=random_state)
+        u2 = gamma.rvs(b, size=size, random_state=random_state)
+        return u1 / u2
+
+    def _pdf(self, x, a, b):
+        # betaprime.pdf(x, a, b) = x**(a-1) * (1+x)**(-a-b) / beta(a, b)
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return sc.xlogy(a - 1.0, x) - sc.xlog1py(a + b, x) - sc.betaln(a, b)
+
+    def _cdf(self, x, a, b):
+        # note: f2 is the direct way to compute the cdf if the relationship
+        # to the beta distribution is used.
+        # however, for very large x, x/(1+x) == 1. since the distribution
+        # has very fat tails if b is small, this can cause inaccurate results
+        # use the following relationship of the incomplete beta function:
+        # betainc(x, a, b) = 1 - betainc(1-x, b, a)
+        # see gh-17631
+        return _lazywhere(
+            x > 1, [x, a, b],
+            lambda x_, a_, b_: beta._sf(1/(1+x_), b_, a_),
+            f2=lambda x_, a_, b_: beta._cdf(x_/(1+x_), a_, b_))
+
+    def _sf(self, x, a, b):
+        return _lazywhere(
+            x > 1, [x, a, b],
+            lambda x_, a_, b_: beta._cdf(1/(1+x_), b_, a_),
+            f2=lambda x_, a_, b_: beta._sf(x_/(1+x_), a_, b_)
+        )
+
+    def _ppf(self, p, a, b):
+        p, a, b = np.broadcast_arrays(p, a, b)
+        # By default, compute the ppf by solving the following:
+        # p = beta._cdf(x/(1+x), a, b). This implies x = r/(1-r) with
+        # r = beta._ppf(p, a, b). This can cause numerical issues if r is
+        # very close to 1. In that case, invert the alternative expression of
+        # the cdf: p = beta._sf(1/(1+x), b, a).
+        r = stats.beta._ppf(p, a, b)
+        with np.errstate(divide='ignore'):
+            out = r / (1 - r)
+        rnear1 = r > 0.9999
+        if np.isscalar(r):
+            if rnear1:
+                out = 1/stats.beta._isf(p, b, a) - 1
+        else:
+            out[rnear1] = 1/stats.beta._isf(p[rnear1], b[rnear1], a[rnear1]) - 1
+        return out
+
+    def _munp(self, n, a, b):
+        return _lazywhere(
+            b > n, (a, b),
+            lambda a, b: np.prod([(a+i-1)/(b-i) for i in range(1, int(n)+1)], axis=0),
+            fillvalue=np.inf)
+
+
+betaprime = betaprime_gen(a=0.0, name='betaprime')
+
+
+class bradford_gen(rv_continuous):
+    r"""A Bradford continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `bradford` is:
+
+    .. math::
+
+        f(x, c) = \frac{c}{\log(1+c) (1+cx)}
+
+    for :math:`0 <= x <= 1` and :math:`c > 0`.
+
+    `bradford` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # bradford.pdf(x, c) = c / (k * (1+c*x))
+        return c / (c*x + 1.0) / sc.log1p(c)
+
+    def _cdf(self, x, c):
+        return sc.log1p(c*x) / sc.log1p(c)
+
+    def _ppf(self, q, c):
+        return sc.expm1(q * sc.log1p(c)) / c
+
+    def _stats(self, c, moments='mv'):
+        k = np.log(1.0+c)
+        mu = (c-k)/(c*k)
+        mu2 = ((c+2.0)*k-2.0*c)/(2*c*k*k)
+        g1 = None
+        g2 = None
+        if 's' in moments:
+            g1 = np.sqrt(2)*(12*c*c-9*c*k*(c+2)+2*k*k*(c*(c+3)+3))
+            g1 /= np.sqrt(c*(c*(k-2)+2*k))*(3*c*(k-2)+6*k)
+        if 'k' in moments:
+            g2 = (c**3*(k-3)*(k*(3*k-16)+24)+12*k*c*c*(k-4)*(k-3) +
+                  6*c*k*k*(3*k-14) + 12*k**3)
+            g2 /= 3*c*(c*(k-2)+2*k)**2
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        k = np.log(1+c)
+        return k/2.0 - np.log(c/k)
+
+
+bradford = bradford_gen(a=0.0, b=1.0, name='bradford')
+
+
+class burr_gen(rv_continuous):
+    r"""A Burr (Type III) continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    fisk : a special case of either `burr` or `burr12` with ``d=1``
+    burr12 : Burr Type XII distribution
+    mielke : Mielke Beta-Kappa / Dagum distribution
+
+    Notes
+    -----
+    The probability density function for `burr` is:
+
+    .. math::
+
+        f(x; c, d) = c d \frac{x^{-c - 1}}
+                              {{(1 + x^{-c})}^{d + 1}}
+
+    for :math:`x >= 0` and :math:`c, d > 0`.
+
+    `burr` takes ``c`` and ``d`` as shape parameters for :math:`c` and
+    :math:`d`.
+
+    This is the PDF corresponding to the third CDF given in Burr's list;
+    specifically, it is equation (11) in Burr's paper [1]_. The distribution
+    is also commonly referred to as the Dagum distribution [2]_. If the
+    parameter :math:`c < 1` then the mean of the distribution does not
+    exist and if :math:`c < 2` the variance does not exist [2]_.
+    The PDF is finite at the left endpoint :math:`x = 0` if :math:`c * d >= 1`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
+       Mathematical Statistics, 13(2), pp 215-232 (1942).
+    .. [2] https://en.wikipedia.org/wiki/Dagum_distribution
+    .. [3] Kleiber, Christian. "A guide to the Dagum distributions."
+       Modeling Income Distributions and Lorenz Curves  pp 97-117 (2008).
+
+    %(example)s
+
+    """
+    # Do not set _support_mask to rv_continuous._open_support_mask
+    # Whether the left-hand endpoint is suitable for pdf evaluation is dependent
+    # on the values of c and d: if c*d >= 1, the pdf is finite, otherwise infinite.
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        id = _ShapeInfo("d", False, (0, np.inf), (False, False))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        # burr.pdf(x, c, d) = c * d * x**(-c-1) * (1+x**(-c))**(-d-1)
+        output = _lazywhere(
+            x == 0, [x, c, d],
+            lambda x_, c_, d_: c_ * d_ * (x_**(c_*d_-1)) / (1 + x_**c_),
+            f2=lambda x_, c_, d_: (c_ * d_ * (x_ ** (-c_ - 1.0)) /
+                                   ((1 + x_ ** (-c_)) ** (d_ + 1.0))))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def _logpdf(self, x, c, d):
+        output = _lazywhere(
+            x == 0, [x, c, d],
+            lambda x_, c_, d_: (np.log(c_) + np.log(d_) + sc.xlogy(c_*d_ - 1, x_)
+                                - (d_+1) * sc.log1p(x_**(c_))),
+            f2=lambda x_, c_, d_: (np.log(c_) + np.log(d_)
+                                   + sc.xlogy(-c_ - 1, x_)
+                                   - sc.xlog1py(d_+1, x_**(-c_))))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def _cdf(self, x, c, d):
+        return (1 + x**(-c))**(-d)
+
+    def _logcdf(self, x, c, d):
+        return sc.log1p(x**(-c)) * (-d)
+
+    def _sf(self, x, c, d):
+        return np.exp(self._logsf(x, c, d))
+
+    def _logsf(self, x, c, d):
+        return np.log1p(- (1 + x**(-c))**(-d))
+
+    def _ppf(self, q, c, d):
+        return (q**(-1.0/d) - 1)**(-1.0/c)
+
+    def _isf(self, q, c, d):
+        _q = sc.xlog1py(-1.0 / d, -q)
+        return sc.expm1(_q) ** (-1.0 / c)
+
+    def _stats(self, c, d):
+        nc = np.arange(1, 5).reshape(4,1) / c
+        # ek is the kth raw moment, e1 is the mean e2-e1**2 variance etc.
+        e1, e2, e3, e4 = sc.beta(d + nc, 1. - nc) * d
+        mu = np.where(c > 1.0, e1, np.nan)
+        mu2_if_c = e2 - mu**2
+        mu2 = np.where(c > 2.0, mu2_if_c, np.nan)
+        g1 = _lazywhere(
+            c > 3.0,
+            (c, e1, e2, e3, mu2_if_c),
+            lambda c, e1, e2, e3, mu2_if_c: ((e3 - 3*e2*e1 + 2*e1**3)
+                                             / np.sqrt((mu2_if_c)**3)),
+            fillvalue=np.nan)
+        g2 = _lazywhere(
+            c > 4.0,
+            (c, e1, e2, e3, e4, mu2_if_c),
+            lambda c, e1, e2, e3, e4, mu2_if_c: (
+                ((e4 - 4*e3*e1 + 6*e2*e1**2 - 3*e1**4) / mu2_if_c**2) - 3),
+            fillvalue=np.nan)
+        if np.ndim(c) == 0:
+            return mu.item(), mu2.item(), g1.item(), g2.item()
+        return mu, mu2, g1, g2
+
+    def _munp(self, n, c, d):
+        def __munp(n, c, d):
+            nc = 1. * n / c
+            return d * sc.beta(1.0 - nc, d + nc)
+        n, c, d = np.asarray(n), np.asarray(c), np.asarray(d)
+        return _lazywhere((c > n) & (n == n) & (d == d), (c, d, n),
+                          lambda c, d, n: __munp(n, c, d),
+                          np.nan)
+
+
+burr = burr_gen(a=0.0, name='burr')
+
+
+class burr12_gen(rv_continuous):
+    r"""A Burr (Type XII) continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    fisk : a special case of either `burr` or `burr12` with ``d=1``
+    burr : Burr Type III distribution
+
+    Notes
+    -----
+    The probability density function for `burr12` is:
+
+    .. math::
+
+        f(x; c, d) = c d \frac{x^{c-1}}
+                              {(1 + x^c)^{d + 1}}
+
+    for :math:`x >= 0` and :math:`c, d > 0`.
+
+    `burr12` takes ``c`` and ``d`` as shape parameters for :math:`c`
+    and :math:`d`.
+
+    This is the PDF corresponding to the twelfth CDF given in Burr's list;
+    specifically, it is equation (20) in Burr's paper [1]_.
+
+    %(after_notes)s
+
+    The Burr type 12 distribution is also sometimes referred to as
+    the Singh-Maddala distribution from NIST [2]_.
+
+    References
+    ----------
+    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
+       Mathematical Statistics, 13(2), pp 215-232 (1942).
+
+    .. [2] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/b12pdf.htm
+
+    .. [3] "Burr distribution",
+       https://en.wikipedia.org/wiki/Burr_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        id = _ShapeInfo("d", False, (0, np.inf), (False, False))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        # burr12.pdf(x, c, d) = c * d * x**(c-1) * (1+x**(c))**(-d-1)
+        return np.exp(self._logpdf(x, c, d))
+
+    def _logpdf(self, x, c, d):
+        return np.log(c) + np.log(d) + sc.xlogy(c - 1, x) + sc.xlog1py(-d-1, x**c)
+
+    def _cdf(self, x, c, d):
+        return -sc.expm1(self._logsf(x, c, d))
+
+    def _logcdf(self, x, c, d):
+        return sc.log1p(-(1 + x**c)**(-d))
+
+    def _sf(self, x, c, d):
+        return np.exp(self._logsf(x, c, d))
+
+    def _logsf(self, x, c, d):
+        return sc.xlog1py(-d, x**c)
+
+    def _ppf(self, q, c, d):
+        # The following is an implementation of
+        #   ((1 - q)**(-1.0/d) - 1)**(1.0/c)
+        # that does a better job handling small values of q.
+        return sc.expm1(-1/d * sc.log1p(-q))**(1/c)
+
+    def _isf(self, p, c, d):
+        return sc.expm1(-1/d * np.log(p))**(1/c)
+
+    def _munp(self, n, c, d):
+        def moment_if_exists(n, c, d):
+            nc = 1. * n / c
+            return d * sc.beta(1.0 + nc, d - nc)
+
+        return _lazywhere(c * d > n, (n, c, d), moment_if_exists,
+                          fillvalue=np.nan)
+
+
+burr12 = burr12_gen(a=0.0, name='burr12')
+
+
+class fisk_gen(burr_gen):
+    r"""A Fisk continuous random variable.
+
+    The Fisk distribution is also known as the log-logistic distribution.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    burr
+
+    Notes
+    -----
+    The probability density function for `fisk` is:
+
+    .. math::
+
+        f(x, c) = \frac{c x^{c-1}}
+                       {(1 + x^c)^2}
+
+    for :math:`x >= 0` and :math:`c > 0`.
+
+    Please note that the above expression can be transformed into the following
+    one, which is also commonly used:
+
+    .. math::
+
+        f(x, c) = \frac{c x^{-c-1}}
+                       {(1 + x^{-c})^2}
+
+    `fisk` takes ``c`` as a shape parameter for :math:`c`.
+
+    `fisk` is a special case of `burr` or `burr12` with ``d=1``.
+
+    Suppose ``X`` is a logistic random variable with location ``l``
+    and scale ``s``. Then ``Y = exp(X)`` is a Fisk (log-logistic)
+    random variable with ``scale = exp(l)`` and shape ``c = 1/s``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # fisk.pdf(x, c) = c * x**(-c-1) * (1 + x**(-c))**(-2)
+        return burr._pdf(x, c, 1.0)
+
+    def _cdf(self, x, c):
+        return burr._cdf(x, c, 1.0)
+
+    def _sf(self, x, c):
+        return burr._sf(x, c, 1.0)
+
+    def _logpdf(self, x, c):
+        # fisk.pdf(x, c) = c * x**(-c-1) * (1 + x**(-c))**(-2)
+        return burr._logpdf(x, c, 1.0)
+
+    def _logcdf(self, x, c):
+        return burr._logcdf(x, c, 1.0)
+
+    def _logsf(self, x, c):
+        return burr._logsf(x, c, 1.0)
+
+    def _ppf(self, x, c):
+        return burr._ppf(x, c, 1.0)
+
+    def _isf(self, q, c):
+        return burr._isf(q, c, 1.0)
+
+    def _munp(self, n, c):
+        return burr._munp(n, c, 1.0)
+
+    def _stats(self, c):
+        return burr._stats(c, 1.0)
+
+    def _entropy(self, c):
+        return 2 - np.log(c)
+
+
+fisk = fisk_gen(a=0.0, name='fisk')
+
+
+class cauchy_gen(rv_continuous):
+    r"""A Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `cauchy` is
+
+    .. math::
+
+        f(x) = \frac{1}{\pi (1 + x^2)}
+
+    for a real number :math:`x`.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``ppf` and ``isf`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # cauchy.pdf(x) = 1 / (pi * (1 + x**2))
+        with np.errstate(over='ignore'):
+            return 1.0/np.pi/(1.0+x*x)
+
+    def _logpdf(self, x):
+        # The formulas
+        #     log(1/(pi*(1 + x**2))) = -log(pi) - log(1 + x**2)
+        #                            = -log(pi) - log(x**2*(1 + 1/x**2))
+        #                            = -log(pi) - (2log(|x|) + log1p(1/x**2))
+        # are used here.
+        absx = np.abs(x)
+        # In the following _lazywhere, `f` provides better precision than `f2`
+        # for small and moderate x, while `f2` avoids the overflow that can
+        # occur with absx**2.
+        y = _lazywhere(absx < 1, (absx,),
+                       f=lambda absx: -_LOG_PI - np.log1p(absx**2),
+                       f2=lambda absx: (-_LOG_PI -
+                                        (2*np.log(absx) + np.log1p((1/absx)**2))))
+        return y
+
+    def _cdf(self, x):
+        return np.arctan2(1, -x)/np.pi
+
+    def _ppf(self, q):
+        return scu._cauchy_ppf(q, 0, 1)
+
+    def _sf(self, x):
+        return np.arctan2(1, x)/np.pi
+
+    def _isf(self, q):
+        return scu._cauchy_isf(q, 0, 1)
+
+    def _stats(self):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _entropy(self):
+        return np.log(4*np.pi)
+
+    def _fitstart(self, data, args=None):
+        # Initialize ML guesses using quartiles instead of moments.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return p50, (p75 - p25)/2
+
+
+cauchy = cauchy_gen(name='cauchy')
+
+
+class chi_gen(rv_continuous):
+    r"""A chi continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `chi` is:
+
+    .. math::
+
+        f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
+                   x^{k-1} \exp \left( -x^2/2 \right)
+
+    for :math:`x >= 0` and :math:`k > 0` (degrees of freedom, denoted ``df``
+    in the implementation). :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    Special cases of `chi` are:
+
+        - ``chi(1, loc, scale)`` is equivalent to `halfnorm`
+        - ``chi(2, 0, scale)`` is equivalent to `rayleigh`
+        - ``chi(3, 0, scale)`` is equivalent to `maxwell`
+
+    `chi` takes ``df`` as a shape parameter.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return np.sqrt(chi2.rvs(df, size=size, random_state=random_state))
+
+    def _pdf(self, x, df):
+        #                   x**(df-1) * exp(-x**2/2)
+        # chi.pdf(x, df) =  -------------------------
+        #                   2**(df/2-1) * gamma(df/2)
+        return np.exp(self._logpdf(x, df))
+
+    def _logpdf(self, x, df):
+        l = np.log(2) - .5*np.log(2)*df - sc.gammaln(.5*df)
+        return l + sc.xlogy(df - 1., x) - .5*x**2
+
+    def _cdf(self, x, df):
+        return sc.gammainc(.5*df, .5*x**2)
+
+    def _sf(self, x, df):
+        return sc.gammaincc(.5*df, .5*x**2)
+
+    def _ppf(self, q, df):
+        return np.sqrt(2*sc.gammaincinv(.5*df, q))
+
+    def _isf(self, q, df):
+        return np.sqrt(2*sc.gammainccinv(.5*df, q))
+
+    def _stats(self, df):
+        # poch(df/2, 1/2) = gamma(df/2 + 1/2) / gamma(df/2)
+        mu = np.sqrt(2) * sc.poch(0.5 * df, 0.5)
+        mu2 = df - mu*mu
+        g1 = (2*mu**3.0 + mu*(1-2*df))/np.asarray(np.power(mu2, 1.5))
+        g2 = 2*df*(1.0-df)-6*mu**4 + 4*mu**2 * (2*df-1)
+        g2 /= np.asarray(mu2**2.0)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+
+        def regular_formula(df):
+            return (sc.gammaln(.5 * df)
+                    + 0.5 * (df - np.log(2) - (df - 1) * sc.digamma(0.5 * df)))
+
+        def asymptotic_formula(df):
+            return (0.5 + np.log(np.pi)/2 - (df**-1)/6 - (df**-2)/6
+                    - 4/45*(df**-3) + (df**-4)/15)
+
+        return _lazywhere(df < 3e2, (df, ), regular_formula,
+                          f2=asymptotic_formula)
+
+
+chi = chi_gen(a=0.0, name='chi')
+
+
+class chi2_gen(rv_continuous):
+    r"""A chi-squared continuous random variable.
+
+    For the noncentral chi-square distribution, see `ncx2`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ncx2
+
+    Notes
+    -----
+    The probability density function for `chi2` is:
+
+    .. math::
+
+        f(x, k) = \frac{1}{2^{k/2} \Gamma \left( k/2 \right)}
+                   x^{k/2-1} \exp \left( -x/2 \right)
+
+    for :math:`x > 0`  and :math:`k > 0` (degrees of freedom, denoted ``df``
+    in the implementation).
+
+    `chi2` takes ``df`` as a shape parameter.
+
+    The chi-squared distribution is a special case of the gamma
+    distribution, with gamma parameters ``a = df/2``, ``loc = 0`` and
+    ``scale = 2``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return random_state.chisquare(df, size)
+
+    def _pdf(self, x, df):
+        # chi2.pdf(x, df) = 1 / (2*gamma(df/2)) * (x/2)**(df/2-1) * exp(-x/2)
+        return np.exp(self._logpdf(x, df))
+
+    def _logpdf(self, x, df):
+        return sc.xlogy(df/2.-1, x) - x/2. - sc.gammaln(df/2.) - (np.log(2)*df)/2.
+
+    def _cdf(self, x, df):
+        return sc.chdtr(df, x)
+
+    def _sf(self, x, df):
+        return sc.chdtrc(df, x)
+
+    def _isf(self, p, df):
+        return sc.chdtri(df, p)
+
+    def _ppf(self, p, df):
+        return 2*sc.gammaincinv(df/2, p)
+
+    def _stats(self, df):
+        mu = df
+        mu2 = 2*df
+        g1 = 2*np.sqrt(2.0/df)
+        g2 = 12.0/df
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+        half_df = 0.5 * df
+
+        def regular_formula(half_df):
+            return (half_df + np.log(2) + sc.gammaln(half_df) +
+                    (1 - half_df) * sc.psi(half_df))
+
+        def asymptotic_formula(half_df):
+            # plug in the above formula the following asymptotic
+            # expansions:
+            # ln(gamma(a)) ~ (a - 0.5) * ln(a) - a + 0.5 * ln(2 * pi) +
+            #                 1/(12 * a) - 1/(360 * a**3)
+            # psi(a) ~ ln(a) - 1/(2 * a) - 1/(3 * a**2) + 1/120 * a**4)
+            c = np.log(2) + 0.5*(1 + np.log(2*np.pi))
+            h = 0.5/half_df
+            return (h*(-2/3 + h*(-1/3 + h*(-4/45 + h/7.5))) +
+                    0.5*np.log(half_df) + c)
+
+        return _lazywhere(half_df < 125, (half_df, ),
+                          regular_formula,
+                          f2=asymptotic_formula)
+
+
+chi2 = chi2_gen(a=0.0, name='chi2')
+
+
+class cosine_gen(rv_continuous):
+    r"""A cosine continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The cosine distribution is an approximation to the normal distribution.
+    The probability density function for `cosine` is:
+
+    .. math::
+
+        f(x) = \frac{1}{2\pi} (1+\cos(x))
+
+    for :math:`-\pi \le x \le \pi`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # cosine.pdf(x) = 1/(2*pi) * (1+cos(x))
+        return 1.0/2/np.pi*(1+np.cos(x))
+
+    def _logpdf(self, x):
+        c = np.cos(x)
+        return _lazywhere(c != -1, (c,),
+                          lambda c: np.log1p(c) - np.log(2*np.pi),
+                          fillvalue=-np.inf)
+
+    def _cdf(self, x):
+        return scu._cosine_cdf(x)
+
+    def _sf(self, x):
+        return scu._cosine_cdf(-x)
+
+    def _ppf(self, p):
+        return scu._cosine_invcdf(p)
+
+    def _isf(self, p):
+        return -scu._cosine_invcdf(p)
+
+    def _stats(self):
+        v = (np.pi * np.pi / 3.0) - 2.0
+        k = -6.0 * (np.pi**4 - 90) / (5.0 * (np.pi * np.pi - 6)**2)
+        return 0.0, v, 0.0, k
+
+    def _entropy(self):
+        return np.log(4*np.pi)-1.0
+
+
+cosine = cosine_gen(a=-np.pi, b=np.pi, name='cosine')
+
+
+class dgamma_gen(rv_continuous):
+    r"""A double gamma continuous random variable.
+
+    The double gamma distribution is also known as the reflected gamma
+    distribution [1]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dgamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{1}{2\Gamma(a)} |x|^{a-1} \exp(-|x|)
+
+    for a real number :math:`x` and :math:`a > 0`. :math:`\Gamma` is the
+    gamma function (`scipy.special.gamma`).
+
+    `dgamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
+           Distributions, Volume 1", Second Edition, John Wiley and Sons
+           (1994).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, a, size=None, random_state=None):
+        u = random_state.uniform(size=size)
+        gm = gamma.rvs(a, size=size, random_state=random_state)
+        return gm * np.where(u >= 0.5, 1, -1)
+
+    def _pdf(self, x, a):
+        # dgamma.pdf(x, a) = 1 / (2*gamma(a)) * abs(x)**(a-1) * exp(-abs(x))
+        ax = abs(x)
+        return 1.0/(2*sc.gamma(a))*ax**(a-1.0) * np.exp(-ax)
+
+    def _logpdf(self, x, a):
+        ax = abs(x)
+        return sc.xlogy(a - 1.0, ax) - ax - np.log(2) - sc.gammaln(a)
+
+    def _cdf(self, x, a):
+        return np.where(x > 0,
+                        0.5 + 0.5*sc.gammainc(a, x),
+                        0.5*sc.gammaincc(a, -x))
+
+    def _sf(self, x, a):
+        return np.where(x > 0,
+                        0.5*sc.gammaincc(a, x),
+                        0.5 + 0.5*sc.gammainc(a, -x))
+
+    def _entropy(self, a):
+        return stats.gamma._entropy(a) - np.log(0.5)
+
+    def _ppf(self, q, a):
+        return np.where(q > 0.5,
+                        sc.gammaincinv(a, 2*q - 1),
+                        -sc.gammainccinv(a, 2*q))
+
+    def _isf(self, q, a):
+        return np.where(q > 0.5,
+                        -sc.gammaincinv(a, 2*q - 1),
+                        sc.gammainccinv(a, 2*q))
+
+    def _stats(self, a):
+        mu2 = a*(a+1.0)
+        return 0.0, mu2, 0.0, (a+2.0)*(a+3.0)/mu2-3.0
+
+
+dgamma = dgamma_gen(name='dgamma')
+
+
+class dpareto_lognorm_gen(rv_continuous):
+    r"""A double Pareto lognormal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dpareto_lognorm` is:
+
+    .. math::
+
+        f(x, \mu, \sigma, \alpha, \beta) =
+        \frac{\alpha \beta}{(\alpha + \beta) x}
+        \phi\left( \frac{\log x - \mu}{\sigma} \right)
+        \left( R(y_1) + R(y_2) \right)
+
+    where :math:`R(t) = \frac{1 - \Phi(t)}{\phi(t)}`,
+    :math:`\phi` and :math:`\Phi` are the normal PDF and CDF, respectively,
+    :math:`y_1 = \alpha \sigma - \frac{\log x - \mu}{\sigma}`,
+    and :math:`y_2 = \beta \sigma + \frac{\log x - \mu}{\sigma}`
+    for real numbers :math:`x` and :math:`\mu`, :math:`\sigma > 0`,
+    :math:`\alpha > 0`, and :math:`\beta > 0` [1]_.
+
+    `dpareto_lognorm` takes
+    ``u`` as a shape parameter for :math:`\mu`,
+    ``s`` as a shape parameter for :math:`\sigma`,
+    ``a`` as a shape parameter for :math:`\alpha`, and
+    ``b`` as a shape parameter for :math:`\beta`.
+
+    A random variable :math:`X` distributed according to the PDF above
+    can be represented as :math:`X = U \frac{V_1}{V_2}` where :math:`U`,
+    :math:`V_1`, and :math:`V_2` are independent, :math:`U` is lognormally
+    distributed such that :math:`\log U \sim N(\mu, \sigma^2)`, and
+    :math:`V_1` and :math:`V_2` follow Pareto distributions with parameters
+    :math:`\alpha` and :math:`\beta`, respectively [2]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Hajargasht, Gholamreza, and William E. Griffiths. "Pareto-lognormal
+           distributions: Inequality, poverty, and estimation from grouped income
+           data." Economic Modelling 33 (2013): 593-604.
+    .. [2] Reed, William J., and Murray Jorgensen. "The double Pareto-lognormal
+           distribution - a new parametric model for size distributions."
+           Communications in Statistics - Theory and Methods 33.8 (2004): 1733-1753.
+
+    %(example)s
+
+    """
+    _logphi = norm._logpdf
+    _logPhi = norm._logcdf
+    _logPhic = norm._logsf
+    _phi = norm._pdf
+    _Phi = norm._cdf
+    _Phic = norm._sf
+
+    def _R(self, z):
+        return self._Phic(z) / self._phi(z)
+
+    def _logR(self, z):
+        return self._logPhic(z) - self._logphi(z)
+
+    def _shape_info(self):
+        return [_ShapeInfo("u", False, (-np.inf, np.inf), (False, False)),
+                _ShapeInfo("s", False, (0, np.inf), (False, False)),
+                _ShapeInfo("a", False, (0, np.inf), (False, False)),
+                _ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _argcheck(self, u, s, a, b):
+        return (s > 0) & (a > 0) & (b > 0)
+
+    def _rvs(self, u, s, a, b, size=None, random_state=None):
+        # From [1] after Equation (12): "To generate pseudo-random
+        # deviates from the dPlN distribution, one can exponentiate
+        # pseudo-random deviates from NL generated using (6)."
+        Z = random_state.normal(u, s, size=size)
+        E1 = random_state.standard_exponential(size=size)
+        E2 = random_state.standard_exponential(size=size)
+        return np.exp(Z + E1 / a - E2 / b)
+
+    def _logpdf(self, x, u, s, a, b):
+        with np.errstate(invalid='ignore', divide='ignore'):
+            log_y, m = np.log(x), u  # compare against [1] Eq. 1
+            z = (log_y - m) / s
+            x1 = a * s - z
+            x2 = b * s + z
+            out = np.asarray(np.log(a) + np.log(b) - np.log(a + b) - log_y)
+            out += self._logphi(z)
+            out += np.logaddexp(self._logR(x1), self._logR(x2))
+        out[(x == 0) | np.isinf(x)] = -np.inf
+        return out[()]
+
+    def _logcdf(self, x, u, s, a, b):
+        with np.errstate(invalid='ignore', divide='ignore'):
+            log_y, m = np.log(x), u  # compare against [1] Eq. 2
+            z = (log_y - m) / s
+            x1 = a * s - z
+            x2 = b * s + z
+            t1 = self._logPhi(z)
+            t2 = self._logphi(z)
+            t3 = (np.log(b) + self._logR(x1))
+            t4 = (np.log(a) + self._logR(x2))
+            t1, t2, t3, t4, one = np.broadcast_arrays(t1, t2, t3, t4, 1)
+            # t3 can be smaller than t4, so we have to consider log of negative number
+            # This would be much simpler, but `return_sign` is available, so use it?
+            # t5 =  sc.logsumexp([t3, t4 + np.pi*1j])
+            t5, sign =  sc.logsumexp([t3, t4], b=[one, -one], axis=0, return_sign=True)
+            temp = [t1, t2 + t5 - np.log(a + b)]
+            out = np.asarray(sc.logsumexp(temp, b=[one, -one*sign], axis=0))
+        out[x == 0] = -np.inf
+        return out[()]
+
+    def _logsf(self, x, u, s, a, b):
+        return _log1mexp(self._logcdf(x, u, s, a, b))
+
+    # Infrastructure doesn't seem to do this, so...
+
+    def _pdf(self, x, u, s, a, b):
+        return np.exp(self._logpdf(x, u, s, a, b))
+
+    def _cdf(self, x, u, s, a, b):
+        return np.exp(self._logcdf(x, u, s, a, b))
+
+    def _sf(self, x, u, s, a, b):
+        return np.exp(self._logsf(x, u, s, a, b))
+
+    def _munp(self, n, u, s, a, b):
+        m, k = u, float(n)  # compare against [1] Eq. 6
+        out = (a * b) / ((a - k) * (b + k)) * np.exp(k * m + k ** 2 * s ** 2 / 2)
+        out = np.asarray(out)
+        out[a <= k] = np.nan
+        return out
+
+
+dpareto_lognorm = dpareto_lognorm_gen(a=0, name='dpareto_lognorm')
+
+
+class dweibull_gen(rv_continuous):
+    r"""A double Weibull continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dweibull` is given by
+
+    .. math::
+
+        f(x, c) = c / 2 |x|^{c-1} \exp(-|x|^c)
+
+    for a real number :math:`x` and :math:`c > 0`.
+
+    `dweibull` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        u = random_state.uniform(size=size)
+        w = weibull_min.rvs(c, size=size, random_state=random_state)
+        return w * (np.where(u >= 0.5, 1, -1))
+
+    def _pdf(self, x, c):
+        # dweibull.pdf(x, c) = c / 2 * abs(x)**(c-1) * exp(-abs(x)**c)
+        ax = abs(x)
+        Px = c / 2.0 * ax**(c-1.0) * np.exp(-ax**c)
+        return Px
+
+    def _logpdf(self, x, c):
+        ax = abs(x)
+        return np.log(c) - np.log(2.0) + sc.xlogy(c - 1.0, ax) - ax**c
+
+    def _cdf(self, x, c):
+        Cx1 = 0.5 * np.exp(-abs(x)**c)
+        return np.where(x > 0, 1 - Cx1, Cx1)
+
+    def _ppf(self, q, c):
+        fac = 2. * np.where(q <= 0.5, q, 1. - q)
+        fac = np.power(-np.log(fac), 1.0 / c)
+        return np.where(q > 0.5, fac, -fac)
+
+    def _sf(self, x, c):
+        half_weibull_min_sf = 0.5 * stats.weibull_min._sf(np.abs(x), c)
+        return np.where(x > 0, half_weibull_min_sf, 1 - half_weibull_min_sf)
+
+    def _isf(self, q, c):
+        double_q = 2. * np.where(q <= 0.5, q, 1. - q)
+        weibull_min_isf = stats.weibull_min._isf(double_q, c)
+        return np.where(q > 0.5, -weibull_min_isf, weibull_min_isf)
+
+    def _munp(self, n, c):
+        return (1 - (n % 2)) * sc.gamma(1.0 + 1.0 * n / c)
+
+    # since we know that all odd moments are zeros, return them at once.
+    # returning Nones from _stats makes the public stats call _munp
+    # so overall we're saving one or two gamma function evaluations here.
+    def _stats(self, c):
+        return 0, None, 0, None
+
+    def _entropy(self, c):
+        h = stats.weibull_min._entropy(c) - np.log(0.5)
+        return h
+
+
+dweibull = dweibull_gen(name='dweibull')
+
+
+class expon_gen(rv_continuous):
+    r"""An exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `expon` is:
+
+    .. math::
+
+        f(x) = \exp(-x)
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    A common parameterization for `expon` is in terms of the rate parameter
+    ``lambda``, such that ``pdf = lambda * exp(-lambda * x)``. This
+    parameterization corresponds to using ``scale = 1 / lambda``.
+
+    The exponential distribution is a special case of the gamma
+    distributions, with gamma shape parameter ``a = 1``.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.standard_exponential(size)
+
+    def _pdf(self, x):
+        # expon.pdf(x) = exp(-x)
+        return np.exp(-x)
+
+    def _logpdf(self, x):
+        return -x
+
+    def _cdf(self, x):
+        return -sc.expm1(-x)
+
+    def _ppf(self, q):
+        return -sc.log1p(-q)
+
+    def _sf(self, x):
+        return np.exp(-x)
+
+    def _logsf(self, x):
+        return -x
+
+    def _isf(self, q):
+        return -np.log(q)
+
+    def _stats(self):
+        return 1.0, 1.0, 2.0, 6.0
+
+    def _entropy(self):
+        return 1.0
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        When `method='MLE'`,
+        this function uses explicit formulas for the maximum likelihood
+        estimation of the exponential distribution parameters, so the
+        `optimizer`, `loc` and `scale` keyword arguments are
+        ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if len(args) > 0:
+            raise TypeError("Too many arguments.")
+
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        data_min = data.min()
+
+        if floc is None:
+            # ML estimate of the location is the minimum of the data.
+            loc = data_min
+        else:
+            loc = floc
+            if data_min < loc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("expon", lower=floc, upper=np.inf)
+
+        if fscale is None:
+            # ML estimate of the scale is the shifted mean.
+            scale = data.mean() - loc
+        else:
+            scale = fscale
+
+        # We expect the return values to be floating point, so ensure it
+        # by explicitly converting to float.
+        return float(loc), float(scale)
+
+
+expon = expon_gen(a=0.0, name='expon')
+
+
+class exponnorm_gen(rv_continuous):
+    r"""An exponentially modified Normal continuous random variable.
+
+    Also known as the exponentially modified Gaussian distribution [1]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `exponnorm` is:
+
+    .. math::
+
+        f(x, K) = \frac{1}{2K} \exp\left(\frac{1}{2 K^2} - x / K \right)
+                  \text{erfc}\left(-\frac{x - 1/K}{\sqrt{2}}\right)
+
+    where :math:`x` is a real number and :math:`K > 0`.
+
+    It can be thought of as the sum of a standard normal random variable
+    and an independent exponentially distributed random variable with rate
+    ``1/K``.
+
+    %(after_notes)s
+
+    An alternative parameterization of this distribution (for example, in
+    the Wikipedia article [1]_) involves three parameters, :math:`\mu`,
+    :math:`\lambda` and :math:`\sigma`.
+
+    In the present parameterization this corresponds to having ``loc`` and
+    ``scale`` equal to :math:`\mu` and :math:`\sigma`, respectively, and
+    shape parameter :math:`K = 1/(\sigma\lambda)`.
+
+    .. versionadded:: 0.16.0
+
+    References
+    ----------
+    .. [1] Exponentially modified Gaussian distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Exponentially_modified_Gaussian_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("K", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, K, size=None, random_state=None):
+        expval = random_state.standard_exponential(size) * K
+        gval = random_state.standard_normal(size)
+        return expval + gval
+
+    def _pdf(self, x, K):
+        return np.exp(self._logpdf(x, K))
+
+    def _logpdf(self, x, K):
+        invK = 1.0 / K
+        exparg = invK * (0.5 * invK - x)
+        return exparg + _norm_logcdf(x - invK) - np.log(K)
+
+    def _cdf(self, x, K):
+        invK = 1.0 / K
+        expval = invK * (0.5 * invK - x)
+        logprod = expval + _norm_logcdf(x - invK)
+        return _norm_cdf(x) - np.exp(logprod)
+
+    def _sf(self, x, K):
+        invK = 1.0 / K
+        expval = invK * (0.5 * invK - x)
+        logprod = expval + _norm_logcdf(x - invK)
+        return _norm_cdf(-x) + np.exp(logprod)
+
+    def _stats(self, K):
+        K2 = K * K
+        opK2 = 1.0 + K2
+        skw = 2 * K**3 * opK2**(-1.5)
+        krt = 6.0 * K2 * K2 * opK2**(-2)
+        return K, opK2, skw, krt
+
+
+exponnorm = exponnorm_gen(name='exponnorm')
+
+
+def _pow1pm1(x, y):
+    """
+    Compute (1 + x)**y - 1.
+
+    Uses expm1 and xlog1py to avoid loss of precision when
+    (1 + x)**y is close to 1.
+
+    Note that the inverse of this function with respect to x is
+    ``_pow1pm1(x, 1/y)``.  That is, if
+
+        t = _pow1pm1(x, y)
+
+    then
+
+        x = _pow1pm1(t, 1/y)
+    """
+    return np.expm1(sc.xlog1py(y, x))
+
+
+class exponweib_gen(rv_continuous):
+    r"""An exponentiated Weibull continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min, numpy.random.Generator.weibull
+
+    Notes
+    -----
+    The probability density function for `exponweib` is:
+
+    .. math::
+
+        f(x, a, c) = a c [1-\exp(-x^c)]^{a-1} \exp(-x^c) x^{c-1}
+
+    and its cumulative distribution function is:
+
+    .. math::
+
+        F(x, a, c) = [1-\exp(-x^c)]^a
+
+    for :math:`x > 0`, :math:`a > 0`, :math:`c > 0`.
+
+    `exponweib` takes :math:`a` and :math:`c` as shape parameters:
+
+    * :math:`a` is the exponentiation parameter,
+      with the special case :math:`a=1` corresponding to the
+      (non-exponentiated) Weibull distribution `weibull_min`.
+    * :math:`c` is the shape parameter of the non-exponentiated Weibull law.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Exponentiated_Weibull_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        return [ia, ic]
+
+    def _pdf(self, x, a, c):
+        # exponweib.pdf(x, a, c) =
+        #     a * c * (1-exp(-x**c))**(a-1) * exp(-x**c)*x**(c-1)
+        return np.exp(self._logpdf(x, a, c))
+
+    def _logpdf(self, x, a, c):
+        negxc = -x**c
+        exm1c = -sc.expm1(negxc)
+        logp = (np.log(a) + np.log(c) + sc.xlogy(a - 1.0, exm1c) +
+                negxc + sc.xlogy(c - 1.0, x))
+        return logp
+
+    def _cdf(self, x, a, c):
+        exm1c = -sc.expm1(-x**c)
+        return exm1c**a
+
+    def _ppf(self, q, a, c):
+        return (-sc.log1p(-q**(1.0/a)))**np.asarray(1.0/c)
+
+    def _sf(self, x, a, c):
+        return -_pow1pm1(-np.exp(-x**c), a)
+
+    def _isf(self, p, a, c):
+        return (-np.log(-_pow1pm1(-p, 1/a)))**(1/c)
+
+
+exponweib = exponweib_gen(a=0.0, name='exponweib')
+
+
+class exponpow_gen(rv_continuous):
+    r"""An exponential power continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `exponpow` is:
+
+    .. math::
+
+        f(x, b) = b x^{b-1} \exp(1 + x^b - \exp(x^b))
+
+    for :math:`x \ge 0`, :math:`b > 0`.  Note that this is a different
+    distribution from the exponential power distribution that is also known
+    under the names "generalized normal" or "generalized Gaussian".
+
+    `exponpow` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Exponentialpower.pdf
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, b):
+        # exponpow.pdf(x, b) = b * x**(b-1) * exp(1 + x**b - exp(x**b))
+        return np.exp(self._logpdf(x, b))
+
+    def _logpdf(self, x, b):
+        xb = x**b
+        f = 1 + np.log(b) + sc.xlogy(b - 1.0, x) + xb - np.exp(xb)
+        return f
+
+    def _cdf(self, x, b):
+        return -sc.expm1(-sc.expm1(x**b))
+
+    def _sf(self, x, b):
+        return np.exp(-sc.expm1(x**b))
+
+    def _isf(self, x, b):
+        return (sc.log1p(-np.log(x)))**(1./b)
+
+    def _ppf(self, q, b):
+        return pow(sc.log1p(-sc.log1p(-q)), 1.0/b)
+
+
+exponpow = exponpow_gen(a=0.0, name='exponpow')
+
+
+class fatiguelife_gen(rv_continuous):
+    r"""A fatigue-life (Birnbaum-Saunders) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `fatiguelife` is:
+
+    .. math::
+
+        f(x, c) = \frac{x+1}{2c\sqrt{2\pi x^3}} \exp(-\frac{(x-1)^2}{2x c^2})
+
+    for :math:`x >= 0` and :math:`c > 0`.
+
+    `fatiguelife` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Birnbaum-Saunders distribution",
+           https://en.wikipedia.org/wiki/Birnbaum-Saunders_distribution
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        z = random_state.standard_normal(size)
+        x = 0.5*c*z
+        x2 = x*x
+        t = 1.0 + 2*x2 + 2*x*np.sqrt(1 + x2)
+        return t
+
+    def _pdf(self, x, c):
+        # fatiguelife.pdf(x, c) =
+        #     (x+1) / (2*c*sqrt(2*pi*x**3)) * exp(-(x-1)**2/(2*x*c**2))
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return (np.log(x+1) - (x-1)**2 / (2.0*x*c**2) - np.log(2*c) -
+                0.5*(np.log(2*np.pi) + 3*np.log(x)))
+
+    def _cdf(self, x, c):
+        return _norm_cdf(1.0 / c * (np.sqrt(x) - 1.0/np.sqrt(x)))
+
+    def _ppf(self, q, c):
+        tmp = c * _norm_ppf(q)
+        return 0.25 * (tmp + np.sqrt(tmp**2 + 4))**2
+
+    def _sf(self, x, c):
+        return _norm_sf(1.0 / c * (np.sqrt(x) - 1.0/np.sqrt(x)))
+
+    def _isf(self, q, c):
+        tmp = -c * _norm_ppf(q)
+        return 0.25 * (tmp + np.sqrt(tmp**2 + 4))**2
+
+    def _stats(self, c):
+        # NB: the formula for kurtosis in wikipedia seems to have an error:
+        # it's 40, not 41. At least it disagrees with the one from Wolfram
+        # Alpha.  And the latter one, below, passes the tests, while the wiki
+        # one doesn't So far I didn't have the guts to actually check the
+        # coefficients from the expressions for the raw moments.
+        c2 = c*c
+        mu = c2 / 2.0 + 1.0
+        den = 5.0 * c2 + 4.0
+        mu2 = c2*den / 4.0
+        g1 = 4 * c * (11*c2 + 6.0) / np.power(den, 1.5)
+        g2 = 6 * c2 * (93*c2 + 40.0) / den**2.0
+        return mu, mu2, g1, g2
+
+
+fatiguelife = fatiguelife_gen(a=0.0, name='fatiguelife')
+
+
+class foldcauchy_gen(rv_continuous):
+    r"""A folded Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `foldcauchy` is:
+
+    .. math::
+
+        f(x, c) = \frac{1}{\pi (1+(x-c)^2)} + \frac{1}{\pi (1+(x+c)^2)}
+
+    for :math:`x \ge 0` and :math:`c \ge 0`.
+
+    `foldcauchy` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return c >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        return abs(cauchy.rvs(loc=c, size=size,
+                              random_state=random_state))
+
+    def _pdf(self, x, c):
+        # foldcauchy.pdf(x, c) = 1/(pi*(1+(x-c)**2)) + 1/(pi*(1+(x+c)**2))
+        return 1.0/np.pi*(1.0/(1+(x-c)**2) + 1.0/(1+(x+c)**2))
+
+    def _cdf(self, x, c):
+        return 1.0/np.pi*(np.arctan(x-c) + np.arctan(x+c))
+
+    def _sf(self, x, c):
+        # 1 - CDF(x, c) = 1 - (atan(x - c) + atan(x + c))/pi
+        #               = ((pi/2 - atan(x - c)) + (pi/2 - atan(x + c)))/pi
+        #               = (acot(x - c) + acot(x + c))/pi
+        #               = (atan2(1, x - c) + atan2(1, x + c))/pi
+        return (np.arctan2(1, x - c) + np.arctan2(1, x + c))/np.pi
+
+    def _stats(self, c):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+foldcauchy = foldcauchy_gen(a=0.0, name='foldcauchy')
+
+
+class f_gen(rv_continuous):
+    r"""An F continuous random variable.
+
+    For the noncentral F distribution, see `ncf`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ncf
+
+    Notes
+    -----
+    The F distribution with :math:`df_1 > 0` and :math:`df_2 > 0` degrees of freedom is
+    the distribution of the ratio of two independent chi-squared distributions with
+    :math:`df_1` and :math:`df_2` degrees of freedom, after rescaling by
+    :math:`df_2 / df_1`.
+
+    The probability density function for `f` is:
+
+    .. math::
+
+        f(x, df_1, df_2) = \frac{df_2^{df_2/2} df_1^{df_1/2} x^{df_1 / 2-1}}
+                                {(df_2+df_1 x)^{(df_1+df_2)/2}
+                                 B(df_1/2, df_2/2)}
+
+    for :math:`x > 0`.
+
+    `f` accepts shape parameters ``dfn`` and ``dfd`` for :math:`df_1`, the degrees of
+    freedom of the chi-squared distribution in the numerator, and :math:`df_2`, the
+    degrees of freedom of the chi-squared distribution in the denominator, respectively.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        idfn = _ShapeInfo("dfn", False, (0, np.inf), (False, False))
+        idfd = _ShapeInfo("dfd", False, (0, np.inf), (False, False))
+        return [idfn, idfd]
+
+    def _rvs(self, dfn, dfd, size=None, random_state=None):
+        return random_state.f(dfn, dfd, size)
+
+    def _pdf(self, x, dfn, dfd):
+        #                      df2**(df2/2) * df1**(df1/2) * x**(df1/2-1)
+        # F.pdf(x, df1, df2) = --------------------------------------------
+        #                      (df2+df1*x)**((df1+df2)/2) * B(df1/2, df2/2)
+        return np.exp(self._logpdf(x, dfn, dfd))
+
+    def _logpdf(self, x, dfn, dfd):
+        n = 1.0 * dfn
+        m = 1.0 * dfd
+        lPx = (m/2 * np.log(m) + n/2 * np.log(n) + sc.xlogy(n/2 - 1, x)
+               - (((n+m)/2) * np.log(m + n*x) + sc.betaln(n/2, m/2)))
+        return lPx
+
+    def _cdf(self, x, dfn, dfd):
+        return sc.fdtr(dfn, dfd, x)
+
+    def _sf(self, x, dfn, dfd):
+        return sc.fdtrc(dfn, dfd, x)
+
+    def _ppf(self, q, dfn, dfd):
+        return sc.fdtri(dfn, dfd, q)
+
+    def _stats(self, dfn, dfd):
+        v1, v2 = 1. * dfn, 1. * dfd
+        v2_2, v2_4, v2_6, v2_8 = v2 - 2., v2 - 4., v2 - 6., v2 - 8.
+
+        mu = _lazywhere(
+            v2 > 2, (v2, v2_2),
+            lambda v2, v2_2: v2 / v2_2,
+            np.inf)
+
+        mu2 = _lazywhere(
+            v2 > 4, (v1, v2, v2_2, v2_4),
+            lambda v1, v2, v2_2, v2_4:
+            2 * v2 * v2 * (v1 + v2_2) / (v1 * v2_2**2 * v2_4),
+            np.inf)
+
+        g1 = _lazywhere(
+            v2 > 6, (v1, v2_2, v2_4, v2_6),
+            lambda v1, v2_2, v2_4, v2_6:
+            (2 * v1 + v2_2) / v2_6 * np.sqrt(v2_4 / (v1 * (v1 + v2_2))),
+            np.nan)
+        g1 *= np.sqrt(8.)
+
+        g2 = _lazywhere(
+            v2 > 8, (g1, v2_6, v2_8),
+            lambda g1, v2_6, v2_8: (8 + g1 * g1 * v2_6) / v2_8,
+            np.nan)
+        g2 *= 3. / 2.
+
+        return mu, mu2, g1, g2
+
+    def _entropy(self, dfn, dfd):
+        # the formula found in literature is incorrect. This one yields the
+        # same result as numerical integration using the generic entropy
+        # definition. This is also tested in tests/test_conntinous_basic
+        half_dfn = 0.5 * dfn
+        half_dfd = 0.5 * dfd
+        half_sum = 0.5 * (dfn + dfd)
+
+        return (np.log(dfd) - np.log(dfn) + sc.betaln(half_dfn, half_dfd) +
+                (1 - half_dfn) * sc.psi(half_dfn) - (1 + half_dfd) *
+                sc.psi(half_dfd) + half_sum * sc.psi(half_sum))
+
+
+f = f_gen(a=0.0, name='f')
+
+
+## Folded Normal
+##   abs(Z) where (Z is normal with mu=L and std=S so that c=abs(L)/S)
+##
+##  note: regress docs have scale parameter correct, but first parameter
+##    he gives is a shape parameter A = c * scale
+
+##  Half-normal is folded normal with shape-parameter c=0.
+
+class foldnorm_gen(rv_continuous):
+    r"""A folded normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `foldnorm` is:
+
+    .. math::
+
+        f(x, c) = \sqrt{2/\pi} cosh(c x) \exp(-\frac{x^2+c^2}{2})
+
+    for :math:`x \ge 0` and :math:`c \ge 0`.
+
+    `foldnorm` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return c >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        return abs(random_state.standard_normal(size) + c)
+
+    def _pdf(self, x, c):
+        # foldnormal.pdf(x, c) = sqrt(2/pi) * cosh(c*x) * exp(-(x**2+c**2)/2)
+        return _norm_pdf(x + c) + _norm_pdf(x-c)
+
+    def _cdf(self, x, c):
+        sqrt_two = np.sqrt(2)
+        return 0.5 * (sc.erf((x - c)/sqrt_two) + sc.erf((x + c)/sqrt_two))
+
+    def _sf(self, x, c):
+        return _norm_sf(x - c) + _norm_sf(x + c)
+
+    def _stats(self, c):
+        # Regina C. Elandt, Technometrics 3, 551 (1961)
+        # https://www.jstor.org/stable/1266561
+        #
+        c2 = c*c
+        expfac = np.exp(-0.5*c2) / np.sqrt(2.*np.pi)
+
+        mu = 2.*expfac + c * sc.erf(c/np.sqrt(2))
+        mu2 = c2 + 1 - mu*mu
+
+        g1 = 2. * (mu*mu*mu - c2*mu - expfac)
+        g1 /= np.power(mu2, 1.5)
+
+        g2 = c2 * (c2 + 6.) + 3 + 8.*expfac*mu
+        g2 += (2. * (c2 - 3.) - 3. * mu**2) * mu**2
+        g2 = g2 / mu2**2.0 - 3.
+
+        return mu, mu2, g1, g2
+
+
+foldnorm = foldnorm_gen(a=0.0, name='foldnorm')
+
+
+class weibull_min_gen(rv_continuous):
+    r"""Weibull minimum continuous random variable.
+
+    The Weibull Minimum Extreme Value distribution, from extreme value theory
+    (Fisher-Gnedenko theorem), is also often simply called the Weibull
+    distribution. It arises as the limiting distribution of the rescaled
+    minimum of iid random variables.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_max, numpy.random.Generator.weibull, exponweib
+
+    Notes
+    -----
+    The probability density function for `weibull_min` is:
+
+    .. math::
+
+        f(x, c) = c x^{c-1} \exp(-x^c)
+
+    for :math:`x > 0`, :math:`c > 0`.
+
+    `weibull_min` takes ``c`` as a shape parameter for :math:`c`.
+    (named :math:`k` in Wikipedia article and :math:`a` in
+    ``numpy.random.weibull``).  Special shape values are :math:`c=1` and
+    :math:`c=2` where Weibull distribution reduces to the `expon` and
+    `rayleigh` distributions respectively.
+
+    Suppose ``X`` is an exponentially distributed random variable with
+    scale ``s``. Then ``Y = X**k`` is `weibull_min` distributed with shape
+    ``c = 1/k`` and scale ``s**k``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Weibull_distribution
+
+    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # weibull_min.pdf(x, c) = c * x**(c-1) * exp(-x**c)
+        return c*pow(x, c-1)*np.exp(-pow(x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + sc.xlogy(c - 1, x) - pow(x, c)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-pow(x, c))
+
+    def _ppf(self, q, c):
+        return pow(-sc.log1p(-q), 1.0/c)
+
+    def _sf(self, x, c):
+        return np.exp(self._logsf(x, c))
+
+    def _logsf(self, x, c):
+        return -pow(x, c)
+
+    def _isf(self, q, c):
+        return (-np.log(q))**(1/c)
+
+    def _munp(self, n, c):
+        return sc.gamma(1.0+n*1.0/c)
+
+    def _entropy(self, c):
+        return -_EULER / c - np.log(c) + _EULER + 1
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        If ``method='mm'``, parameters fixed by the user are respected, and the
+        remaining parameters are used to match distribution and sample moments
+        where possible. For example, if the user fixes the location with
+        ``floc``, the parameters will only match the distribution skewness and
+        variance to the sample skewness and variance; no attempt will be made
+        to match the means or minimize a norm of the errors.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+
+        if isinstance(data, CensoredData):
+            if data.num_censored() == 0:
+                data = data._uncensor()
+            else:
+                return super().fit(data, *args, **kwds)
+
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        # this extracts fixed shape, location, and scale however they
+        # are specified, and also leaves them in `kwds`
+        data, fc, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+        method = kwds.get("method", "mle").lower()
+
+        # See https://en.wikipedia.org/wiki/Weibull_distribution#Moments for
+        # moment formulas.
+        def skew(c):
+            gamma1 = sc.gamma(1+1/c)
+            gamma2 = sc.gamma(1+2/c)
+            gamma3 = sc.gamma(1+3/c)
+            num = 2 * gamma1**3 - 3*gamma1*gamma2 + gamma3
+            den = (gamma2 - gamma1**2)**(3/2)
+            return num/den
+
+        # For c in [1e2, 3e4], population skewness appears to approach
+        # asymptote near -1.139, but past c > 3e4, skewness begins to vary
+        # wildly, and MoM won't provide a good guess. Get out early.
+        s = stats.skew(data)
+        max_c = 1e4
+        s_min = skew(max_c)
+        if s < s_min and method != "mm" and fc is None and not args:
+            return super().fit(data, *args, **kwds)
+
+        # If method is method of moments, we don't need the user's guesses.
+        # Otherwise, extract the guesses from args and kwds.
+        if method == "mm":
+            c, loc, scale = None, None, None
+        else:
+            c = args[0] if len(args) else None
+            loc = kwds.pop('loc', None)
+            scale = kwds.pop('scale', None)
+
+        if fc is None and c is None:  # not fixed and no guess: use MoM
+            # Solve for c that matches sample distribution skewness to sample
+            # skewness.
+            # we start having numerical issues with `weibull_min` with
+            # parameters outside this range - and not just in this method.
+            # We could probably improve the situation by doing everything
+            # in the log space, but that is for another time.
+            c = root_scalar(lambda c: skew(c) - s, bracket=[0.02, max_c],
+                            method='bisect').root
+        elif fc is not None:  # fixed: use it
+            c = fc
+
+        if fscale is None and scale is None:
+            v = np.var(data)
+            scale = np.sqrt(v / (sc.gamma(1+2/c) - sc.gamma(1+1/c)**2))
+        elif fscale is not None:
+            scale = fscale
+
+        if floc is None and loc is None:
+            m = np.mean(data)
+            loc = m - scale*sc.gamma(1 + 1/c)
+        elif floc is not None:
+            loc = floc
+
+        if method == 'mm':
+            return c, loc, scale
+        else:
+            # At this point, parameter "guesses" may equal the fixed parameters
+            # in kwds. No harm in passing them as guesses, too.
+            return super().fit(data, c, loc=loc, scale=scale, **kwds)
+
+
+weibull_min = weibull_min_gen(a=0.0, name='weibull_min')
+
+
+class truncweibull_min_gen(rv_continuous):
+    r"""A doubly truncated Weibull minimum continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min, truncexpon
+
+    Notes
+    -----
+    The probability density function for `truncweibull_min` is:
+
+    .. math::
+
+        f(x, a, b, c) = \frac{c x^{c-1} \exp(-x^c)}{\exp(-a^c) - \exp(-b^c)}
+
+    for :math:`a < x <= b`, :math:`0 \le a < b` and :math:`c > 0`.
+
+    `truncweibull_min` takes :math:`a`, :math:`b`, and :math:`c` as shape
+    parameters.
+
+    Notice that the truncation values, :math:`a` and :math:`b`, are defined in
+    standardized form:
+
+    .. math::
+
+        a = (u_l - loc)/scale
+        b = (u_r - loc)/scale
+
+    where :math:`u_l` and :math:`u_r` are the specific left and right
+    truncation values, respectively. In other words, the support of the
+    distribution becomes :math:`(a*scale + loc) < x <= (b*scale + loc)` when
+    :math:`loc` and/or :math:`scale` are provided.
+
+    %(after_notes)s
+
+    References
+    ----------
+
+    .. [1] Rinne, H. "The Weibull Distribution: A Handbook". CRC Press (2009).
+
+    %(example)s
+
+    """
+    def _argcheck(self, c, a, b):
+        return (a >= 0.) & (b > a) & (c > 0.)
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        ia = _ShapeInfo("a", False, (0, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ic, ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but default a=b=c=1 is not valid
+        return super()._fitstart(data, args=(1, 0, 1))
+
+    def _get_support(self, c, a, b):
+        return a, b
+
+    def _pdf(self, x, c, a, b):
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return (c * pow(x, c-1) * np.exp(-pow(x, c))) / denum
+
+    def _logpdf(self, x, c, a, b):
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return np.log(c) + sc.xlogy(c - 1, x) - pow(x, c) - logdenum
+
+    def _cdf(self, x, c, a, b):
+        num = (np.exp(-pow(a, c)) - np.exp(-pow(x, c)))
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return num / denum
+
+    def _logcdf(self, x, c, a, b):
+        lognum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(x, c)))
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return lognum - logdenum
+
+    def _sf(self, x, c, a, b):
+        num = (np.exp(-pow(x, c)) - np.exp(-pow(b, c)))
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return num / denum
+
+    def _logsf(self, x, c, a, b):
+        lognum = np.log(np.exp(-pow(x, c)) - np.exp(-pow(b, c)))
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return lognum - logdenum
+
+    def _isf(self, q, c, a, b):
+        return pow(
+            -np.log((1 - q) * np.exp(-pow(b, c)) + q * np.exp(-pow(a, c))), 1/c
+            )
+
+    def _ppf(self, q, c, a, b):
+        return pow(
+            -np.log((1 - q) * np.exp(-pow(a, c)) + q * np.exp(-pow(b, c))), 1/c
+            )
+
+    def _munp(self, n, c, a, b):
+        gamma_fun = sc.gamma(n/c + 1.) * (
+            sc.gammainc(n/c + 1., pow(b, c)) - sc.gammainc(n/c + 1., pow(a, c))
+            )
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return gamma_fun / denum
+
+
+truncweibull_min = truncweibull_min_gen(name='truncweibull_min')
+truncweibull_min._support = ('a', 'b')
+
+
+class weibull_max_gen(rv_continuous):
+    r"""Weibull maximum continuous random variable.
+
+    The Weibull Maximum Extreme Value distribution, from extreme value theory
+    (Fisher-Gnedenko theorem), is the limiting distribution of rescaled
+    maximum of iid random variables. This is the distribution of -X
+    if X is from the `weibull_min` function.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min
+
+    Notes
+    -----
+    The probability density function for `weibull_max` is:
+
+    .. math::
+
+        f(x, c) = c (-x)^{c-1} \exp(-(-x)^c)
+
+    for :math:`x < 0`, :math:`c > 0`.
+
+    `weibull_max` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Weibull_distribution
+
+    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # weibull_max.pdf(x, c) = c * (-x)**(c-1) * exp(-(-x)**c)
+        return c*pow(-x, c-1)*np.exp(-pow(-x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + sc.xlogy(c-1, -x) - pow(-x, c)
+
+    def _cdf(self, x, c):
+        return np.exp(-pow(-x, c))
+
+    def _logcdf(self, x, c):
+        return -pow(-x, c)
+
+    def _sf(self, x, c):
+        return -sc.expm1(-pow(-x, c))
+
+    def _ppf(self, q, c):
+        return -pow(-np.log(q), 1.0/c)
+
+    def _munp(self, n, c):
+        val = sc.gamma(1.0+n*1.0/c)
+        if int(n) % 2:
+            sgn = -1
+        else:
+            sgn = 1
+        return sgn * val
+
+    def _entropy(self, c):
+        return -_EULER / c - np.log(c) + _EULER + 1
+
+
+weibull_max = weibull_max_gen(b=0.0, name='weibull_max')
+
+
+class genlogistic_gen(rv_continuous):
+    r"""A generalized logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genlogistic` is:
+
+    .. math::
+
+        f(x, c) = c \frac{\exp(-x)}
+                         {(1 + \exp(-x))^{c+1}}
+
+    for real :math:`x` and :math:`c > 0`. In literature, different
+    generalizations of the logistic distribution can be found. This is the type 1
+    generalized logistic distribution according to [1]_. It is also referred to
+    as the skew-logistic distribution [2]_.
+
+    `genlogistic` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson et al. "Continuous Univariate Distributions", Volume 2,
+           Wiley. 1995.
+    .. [2] "Generalized Logistic Distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Generalized_logistic_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # genlogistic.pdf(x, c) = c * exp(-x) / (1 + exp(-x))**(c+1)
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        # Two mathematically equivalent expressions for log(pdf(x, c)):
+        #     log(pdf(x, c)) = log(c) - x - (c + 1)*log(1 + exp(-x))
+        #                    = log(c) + c*x - (c + 1)*log(1 + exp(x))
+        mult = -(c - 1) * (x < 0) - 1
+        absx = np.abs(x)
+        return np.log(c) + mult*absx - (c+1) * sc.log1p(np.exp(-absx))
+
+    def _cdf(self, x, c):
+        Cx = (1+np.exp(-x))**(-c)
+        return Cx
+
+    def _logcdf(self, x, c):
+        return -c * np.log1p(np.exp(-x))
+
+    def _ppf(self, q, c):
+        return -np.log(sc.powm1(q, -1.0/c))
+
+    def _sf(self, x, c):
+        return -sc.expm1(self._logcdf(x, c))
+
+    def _isf(self, q, c):
+        return self._ppf(1 - q, c)
+
+    def _stats(self, c):
+        mu = _EULER + sc.psi(c)
+        mu2 = np.pi*np.pi/6.0 + sc.zeta(2, c)
+        g1 = -2*sc.zeta(3, c) + 2*_ZETA3
+        g1 /= np.power(mu2, 1.5)
+        g2 = np.pi**4/15.0 + 6*sc.zeta(4, c)
+        g2 /= mu2**2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        return _lazywhere(c < 8e6, (c, ),
+                          lambda c: -np.log(c) + sc.psi(c + 1) + _EULER + 1,
+                          # asymptotic expansion: psi(c) ~ log(c) - 1/(2 * c)
+                          # a = -log(c) + psi(c + 1)
+                          #   = -log(c) + psi(c) + 1/c
+                          #   ~ -log(c) + log(c) - 1/(2 * c) + 1/c
+                          #   = 1/(2 * c)
+                          f2=lambda c: 1/(2 * c) + _EULER + 1)
+
+
+genlogistic = genlogistic_gen(name='genlogistic')
+
+
+class genpareto_gen(rv_continuous):
+    r"""A generalized Pareto continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genpareto` is:
+
+    .. math::
+
+        f(x, c) = (1 + c x)^{-1 - 1/c}
+
+    defined for :math:`x \ge 0` if :math:`c \ge 0`, and for
+    :math:`0 \le x \le -1/c` if :math:`c < 0`.
+
+    `genpareto` takes ``c`` as a shape parameter for :math:`c`.
+
+    For :math:`c=0`, `genpareto` reduces to the exponential
+    distribution, `expon`:
+
+    .. math::
+
+        f(x, 0) = \exp(-x)
+
+    For :math:`c=-1`, `genpareto` is uniform on ``[0, 1]``:
+
+    .. math::
+
+        f(x, -1) = 1
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return np.isfinite(c)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        c = np.asarray(c)
+        b = _lazywhere(c < 0, (c,),
+                       lambda c: -1. / c,
+                       np.inf)
+        a = np.where(c >= 0, self.a, self.a)
+        return a, b
+
+    def _pdf(self, x, c):
+        # genpareto.pdf(x, c) = (1 + c * x)**(-1 - 1/c)
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return _lazywhere((x == x) & (c != 0), (x, c),
+                          lambda x, c: -sc.xlog1py(c + 1., c*x) / c,
+                          -x)
+
+    def _cdf(self, x, c):
+        return -sc.inv_boxcox1p(-x, -c)
+
+    def _sf(self, x, c):
+        return sc.inv_boxcox(-x, -c)
+
+    def _logsf(self, x, c):
+        return _lazywhere((x == x) & (c != 0), (x, c),
+                          lambda x, c: -sc.log1p(c*x) / c,
+                          -x)
+
+    def _ppf(self, q, c):
+        return -sc.boxcox1p(-q, -c)
+
+    def _isf(self, q, c):
+        return -sc.boxcox(q, -c)
+
+    def _stats(self, c, moments='mv'):
+        if 'm' not in moments:
+            m = None
+        else:
+            m = _lazywhere(c < 1, (c,),
+                           lambda xi: 1/(1 - xi),
+                           np.inf)
+        if 'v' not in moments:
+            v = None
+        else:
+            v = _lazywhere(c < 1/2, (c,),
+                           lambda xi: 1 / (1 - xi)**2 / (1 - 2*xi),
+                           np.nan)
+        if 's' not in moments:
+            s = None
+        else:
+            s = _lazywhere(c < 1/3, (c,),
+                           lambda xi: (2 * (1 + xi) * np.sqrt(1 - 2*xi) /
+                                       (1 - 3*xi)),
+                           np.nan)
+        if 'k' not in moments:
+            k = None
+        else:
+            k = _lazywhere(c < 1/4, (c,),
+                           lambda xi: (3 * (1 - 2*xi) * (2*xi**2 + xi + 3) /
+                                       (1 - 3*xi) / (1 - 4*xi) - 3),
+                           np.nan)
+        return m, v, s, k
+
+    def _munp(self, n, c):
+        def __munp(n, c):
+            val = 0.0
+            k = np.arange(0, n + 1)
+            for ki, cnk in zip(k, sc.comb(n, k)):
+                val = val + cnk * (-1) ** ki / (1.0 - c * ki)
+            return np.where(c * n < 1, val * (-1.0 / c) ** n, np.inf)
+        return _lazywhere(c != 0, (c,),
+                          lambda c: __munp(n, c),
+                          sc.gamma(n + 1))
+
+    def _entropy(self, c):
+        return 1. + c
+
+
+genpareto = genpareto_gen(a=0.0, name='genpareto')
+
+
+class genexpon_gen(rv_continuous):
+    r"""A generalized exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genexpon` is:
+
+    .. math::
+
+        f(x, a, b, c) = (a + b (1 - \exp(-c x)))
+                        \exp(-a x - b x + \frac{b}{c}  (1-\exp(-c x)))
+
+    for :math:`x \ge 0`, :math:`a, b, c > 0`.
+
+    `genexpon` takes :math:`a`, :math:`b` and :math:`c` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    H.K. Ryu, "An Extension of Marshall and Olkin's Bivariate Exponential
+    Distribution", Journal of the American Statistical Association, 1993.
+
+    N. Balakrishnan, Asit P. Basu (editors), *The Exponential Distribution:
+    Theory, Methods and Applications*, Gordon and Breach, 1995.
+    ISBN 10: 2884491929
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        return [ia, ib, ic]
+
+    def _pdf(self, x, a, b, c):
+        # genexpon.pdf(x, a, b, c) = (a + b * (1 - exp(-c*x))) * \
+        #                            exp(-a*x - b*x + b/c * (1-exp(-c*x)))
+        return (a + b*(-sc.expm1(-c*x)))*np.exp((-a-b)*x +
+                                                b*(-sc.expm1(-c*x))/c)
+
+    def _logpdf(self, x, a, b, c):
+        return np.log(a+b*(-sc.expm1(-c*x))) + (-a-b)*x+b*(-sc.expm1(-c*x))/c
+
+    def _cdf(self, x, a, b, c):
+        return -sc.expm1((-a-b)*x + b*(-sc.expm1(-c*x))/c)
+
+    def _ppf(self, p, a, b, c):
+        s = a + b
+        t = (b - c*np.log1p(-p))/s
+        return (t + sc.lambertw(-b/s * np.exp(-t)).real)/c
+
+    def _sf(self, x, a, b, c):
+        return np.exp((-a-b)*x + b*(-sc.expm1(-c*x))/c)
+
+    def _isf(self, p, a, b, c):
+        s = a + b
+        t = (b - c*np.log(p))/s
+        return (t + sc.lambertw(-b/s * np.exp(-t)).real)/c
+
+
+genexpon = genexpon_gen(a=0.0, name='genexpon')
+
+
+class genextreme_gen(rv_continuous):
+    r"""A generalized extreme value continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_r
+
+    Notes
+    -----
+    For :math:`c=0`, `genextreme` is equal to `gumbel_r` with
+    probability density function
+
+    .. math::
+
+        f(x) = \exp(-\exp(-x)) \exp(-x),
+
+    where :math:`-\infty < x < \infty`.
+
+    For :math:`c \ne 0`, the probability density function for `genextreme` is:
+
+    .. math::
+
+        f(x, c) = \exp(-(1-c x)^{1/c}) (1-c x)^{1/c-1},
+
+    where :math:`-\infty < x \le 1/c` if :math:`c > 0` and
+    :math:`1/c \le x < \infty` if :math:`c < 0`.
+
+    Note that several sources and software packages use the opposite
+    convention for the sign of the shape parameter :math:`c`.
+
+    `genextreme` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return np.isfinite(c)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        _b = np.where(c > 0, 1.0 / np.maximum(c, _XMIN), np.inf)
+        _a = np.where(c < 0, 1.0 / np.minimum(c, -_XMIN), -np.inf)
+        return _a, _b
+
+    def _loglogcdf(self, x, c):
+        # Returns log(-log(cdf(x, c)))
+        return _lazywhere((x == x) & (c != 0), (x, c),
+                          lambda x, c: sc.log1p(-c*x)/c, -x)
+
+    def _pdf(self, x, c):
+        # genextreme.pdf(x, c) =
+        #     exp(-exp(-x))*exp(-x),                    for c==0
+        #     exp(-(1-c*x)**(1/c))*(1-c*x)**(1/c-1),    for x \le 1/c, c > 0
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        cx = _lazywhere((x == x) & (c != 0), (x, c), lambda x, c: c*x, 0.0)
+        logex2 = sc.log1p(-cx)
+        logpex2 = self._loglogcdf(x, c)
+        pex2 = np.exp(logpex2)
+        # Handle special cases
+        np.putmask(logpex2, (c == 0) & (x == -np.inf), 0.0)
+        logpdf = _lazywhere(~((cx == 1) | (cx == -np.inf)),
+                            (pex2, logpex2, logex2),
+                            lambda pex2, lpex2, lex2: -pex2 + lpex2 - lex2,
+                            fillvalue=-np.inf)
+        np.putmask(logpdf, (c == 1) & (x == 1), 0.0)
+        return logpdf
+
+    def _logcdf(self, x, c):
+        return -np.exp(self._loglogcdf(x, c))
+
+    def _cdf(self, x, c):
+        return np.exp(self._logcdf(x, c))
+
+    def _sf(self, x, c):
+        return -sc.expm1(self._logcdf(x, c))
+
+    def _ppf(self, q, c):
+        x = -np.log(-np.log(q))
+        return _lazywhere((x == x) & (c != 0), (x, c),
+                          lambda x, c: -sc.expm1(-c * x) / c, x)
+
+    def _isf(self, q, c):
+        x = -np.log(-sc.log1p(-q))
+        return _lazywhere((x == x) & (c != 0), (x, c),
+                          lambda x, c: -sc.expm1(-c * x) / c, x)
+
+    def _stats(self, c):
+        def g(n):
+            return sc.gamma(n * c + 1)
+        g1 = g(1)
+        g2 = g(2)
+        g3 = g(3)
+        g4 = g(4)
+        g2mg12 = np.where(abs(c) < 1e-7, (c*np.pi)**2.0/6.0, g2-g1**2.0)
+        def gam2k_f(c):
+            return sc.expm1(sc.gammaln(2.0*c+1.0)-2*sc.gammaln(c + 1.0))/c**2.0
+        gam2k = _lazywhere(abs(c) >= 1e-7, (c,), f=gam2k_f, fillvalue=np.pi**2.0/6.0)
+        eps = 1e-14
+        def gamk_f(c):
+            return sc.expm1(sc.gammaln(c + 1))/c
+        gamk = _lazywhere(abs(c) >= eps, (c,), f=gamk_f, fillvalue=-_EULER)
+
+        # mean
+        m = np.where(c < -1.0, np.nan, -gamk)
+        
+        # variance
+        v = np.where(c < -0.5, np.nan, g1**2.0*gam2k)
+
+        # skewness
+        def sk1_eval(c, *args):
+            def sk1_eval_f(c, g1, g2, g3, g2mg12):
+                return np.sign(c)*(-g3 + (g2 + 2*g2mg12)*g1)/g2mg12**1.5
+            return _lazywhere(c >= -1./3, (c,)+args, f=sk1_eval_f, fillvalue=np.nan)
+
+        sk_fill = 12*np.sqrt(6)*_ZETA3/np.pi**3
+        args = (g1, g2, g3, g2mg12)
+        sk = _lazywhere(abs(c) > eps**0.29, (c,)+args, f=sk1_eval, fillvalue=sk_fill)
+
+        # kurtosis
+        def ku1_eval(c, *args):
+            def ku1_eval_f(g1, g2, g3, g4, g2mg12):
+                return (g4 + (-4*g3 + 3*(g2 + g2mg12)*g1)*g1)/g2mg12**2 - 3
+            return _lazywhere(c >= -1./4, args, ku1_eval_f, fillvalue=np.nan)
+
+        args = (g1, g2, g3, g4, g2mg12)
+        ku = _lazywhere(abs(c) > eps**0.23, (c,)+args, f=ku1_eval, fillvalue=12.0/5.0)
+
+        return m, v, sk, ku
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        # This is better than the default shape of (1,).
+        g = _skew(data)
+        if g < 0:
+            a = 0.5
+        else:
+            a = -0.5
+        return super()._fitstart(data, args=(a,))
+
+    def _munp(self, n, c):
+        k = np.arange(0, n+1)
+        vals = 1.0/c**n * np.sum(
+            sc.comb(n, k) * (-1)**k * sc.gamma(c*k + 1),
+            axis=0)
+        return np.where(c*n > -1, vals, np.inf)
+
+    def _entropy(self, c):
+        return _EULER*(1 - c) + 1
+
+
+genextreme = genextreme_gen(name='genextreme')
+
+
+def _digammainv(y):
+    """Inverse of the digamma function (real positive arguments only).
+
+    This function is used in the `fit` method of `gamma_gen`.
+    The function uses either optimize.fsolve or optimize.newton
+    to solve `sc.digamma(x) - y = 0`.  There is probably room for
+    improvement, but currently it works over a wide range of y:
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> y = 64*rng.standard_normal(1000000)
+    >>> y.min(), y.max()
+    (-311.43592651416662, 351.77388222276869)
+    >>> x = [_digammainv(t) for t in y]
+    >>> np.abs(sc.digamma(x) - y).max()
+    1.1368683772161603e-13
+
+    """
+    _em = 0.5772156649015328606065120
+
+    def func(x):
+        return sc.digamma(x) - y
+
+    if y > -0.125:
+        x0 = np.exp(y) + 0.5
+        if y < 10:
+            # Some experimentation shows that newton reliably converges
+            # must faster than fsolve in this y range.  For larger y,
+            # newton sometimes fails to converge.
+            value = optimize.newton(func, x0, tol=1e-10)
+            return value
+    elif y > -3:
+        x0 = np.exp(y/2.332) + 0.08661
+    else:
+        x0 = 1.0 / (-y - _em)
+
+    value, info, ier, mesg = optimize.fsolve(func, x0, xtol=1e-11,
+                                             full_output=True)
+    if ier != 1:
+        raise RuntimeError(f"_digammainv: fsolve failed, y = {y!r}")
+
+    return value[0]
+
+
+## Gamma (Use MATLAB and MATHEMATICA (b=theta=scale, a=alpha=shape) definition)
+
+## gamma(a, loc, scale)  with a an integer is the Erlang distribution
+## gamma(1, loc, scale)  is the Exponential distribution
+## gamma(df/2, 0, 2) is the chi2 distribution with df degrees of freedom.
+
+class gamma_gen(rv_continuous):
+    r"""A gamma continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    erlang, expon
+
+    Notes
+    -----
+    The probability density function for `gamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{x^{a-1} e^{-x}}{\Gamma(a)}
+
+    for :math:`x \ge 0`, :math:`a > 0`. Here :math:`\Gamma(a)` refers to the
+    gamma function.
+
+    `gamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    When :math:`a` is an integer, `gamma` reduces to the Erlang
+    distribution, and when :math:`a=1` to the exponential distribution.
+
+    Gamma distributions are sometimes parameterized with two variables,
+    with a probability density function of:
+
+    .. math::
+
+        f(x, \alpha, \beta) =
+        \frac{\beta^\alpha x^{\alpha - 1} e^{-\beta x }}{\Gamma(\alpha)}
+
+    Note that this parameterization is equivalent to the above, with
+    ``scale = 1 / beta``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, a, size=None, random_state=None):
+        return random_state.standard_gamma(a, size)
+
+    def _pdf(self, x, a):
+        # gamma.pdf(x, a) = x**(a-1) * exp(-x) / gamma(a)
+        return np.exp(self._logpdf(x, a))
+
+    def _logpdf(self, x, a):
+        return sc.xlogy(a-1.0, x) - x - sc.gammaln(a)
+
+    def _cdf(self, x, a):
+        return sc.gammainc(a, x)
+
+    def _sf(self, x, a):
+        return sc.gammaincc(a, x)
+
+    def _ppf(self, q, a):
+        return sc.gammaincinv(a, q)
+
+    def _isf(self, q, a):
+        return sc.gammainccinv(a, q)
+
+    def _stats(self, a):
+        return a, a, 2.0/np.sqrt(a), 6.0/a
+
+    def _munp(self, n, a):
+        return sc.poch(a, n)
+
+    def _entropy(self, a):
+
+        def regular_formula(a):
+            return sc.psi(a) * (1-a) + a + sc.gammaln(a)
+
+        def asymptotic_formula(a):
+            # plug in above formula the expansions:
+            # psi(a) ~ ln(a) - 1/2a - 1/12a^2 + 1/120a^4
+            # gammaln(a) ~ a * ln(a) - a - 1/2 * ln(a) + 1/2 ln(2 * pi) +
+            #              1/12a - 1/360a^3
+            return (0.5 * (1. + np.log(2*np.pi) + np.log(a)) - 1/(3 * a)
+                    - (a**-2.)/12 - (a**-3.)/90 + (a**-4.)/120)
+
+        return _lazywhere(a < 250, (a, ), regular_formula,
+                          f2=asymptotic_formula)
+
+    def _fitstart(self, data):
+        # The skewness of the gamma distribution is `2 / np.sqrt(a)`.
+        # We invert that to estimate the shape `a` using the skewness
+        # of the data.  The formula is regularized with 1e-8 in the
+        # denominator to allow for degenerate data where the skewness
+        # is close to 0.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        sk = _skew(data)
+        a = 4 / (1e-8 + sk**2)
+        return super()._fitstart(data, args=(a,))
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        When the location is fixed by using the argument `floc`
+        and `method='MLE'`, this
+        function uses explicit formulas or solves a simpler numerical
+        problem than the full ML optimization problem.  So in that case,
+        the `optimizer`, `loc` and `scale` arguments are ignored.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        floc = kwds.get('floc', None)
+        method = kwds.get('method', 'mle')
+
+        if (isinstance(data, CensoredData) or
+                floc is None and method.lower() != 'mm'):
+            # loc is not fixed or we're not doing standard MLE.
+            # Use the default fit method.
+            return super().fit(data, *args, **kwds)
+
+        # We already have this value, so just pop it from kwds.
+        kwds.pop('floc', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if f0 is not None and floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            # Without this check, this function would just return the
+            # parameters that were given.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        # Fixed location is handled by shifting the data.
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # Use explicit formulas for mm (gh-19884)
+        if method.lower() == 'mm':
+            m1 = np.mean(data)
+            m2 = np.var(data)
+            m3 = np.mean((data - m1) ** 3)
+            a, loc, scale = f0, floc, fscale
+            # Three unknowns
+            if a is None and loc is None and scale is None:
+                scale = m3 / (2 * m2)
+            # Two unknowns
+            if loc is None and scale is None:
+                scale = np.sqrt(m2 / a)
+            if a is None and scale is None:
+                scale = m2 / (m1 - loc)
+            if a is None and loc is None:
+                a = m2 / (scale ** 2)
+            # One unknown
+            if a is None:
+                a = (m1 - loc) / scale
+            if loc is None:
+                loc = m1 - a * scale
+            if scale is None:
+                scale = (m1 - loc) / a
+            return a, loc, scale
+
+        # Special case: loc is fixed.
+
+        # NB: data == loc is ok if a >= 1; the below check is more strict.
+        if np.any(data <= floc):
+            raise FitDataError("gamma", lower=floc, upper=np.inf)
+
+        if floc != 0:
+            # Don't do the subtraction in-place, because `data` might be a
+            # view of the input array.
+            data = data - floc
+        xbar = data.mean()
+
+        # Three cases to handle:
+        # * shape and scale both free
+        # * shape fixed, scale free
+        # * shape free, scale fixed
+
+        if fscale is None:
+            # scale is free
+            if f0 is not None:
+                # shape is fixed
+                a = f0
+            else:
+                # shape and scale are both free.
+                # The MLE for the shape parameter `a` is the solution to:
+                # np.log(a) - sc.digamma(a) - np.log(xbar) +
+                #                             np.log(data).mean() = 0
+                s = np.log(xbar) - np.log(data).mean()
+                aest = (3-s + np.sqrt((s-3)**2 + 24*s)) / (12*s)
+                xa = aest*(1-0.4)
+                xb = aest*(1+0.4)
+                a = optimize.brentq(lambda a: np.log(a) - sc.digamma(a) - s,
+                                    xa, xb, disp=0)
+
+            # The MLE for the scale parameter is just the data mean
+            # divided by the shape parameter.
+            scale = xbar / a
+        else:
+            # scale is fixed, shape is free
+            # The MLE for the shape parameter `a` is the solution to:
+            # sc.digamma(a) - np.log(data).mean() + np.log(fscale) = 0
+            c = np.log(data).mean() - np.log(fscale)
+            a = _digammainv(c)
+            scale = fscale
+
+        return a, floc, scale
+
+
+gamma = gamma_gen(a=0.0, name='gamma')
+
+
+class erlang_gen(gamma_gen):
+    """An Erlang continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gamma
+
+    Notes
+    -----
+    The Erlang distribution is a special case of the Gamma distribution, with
+    the shape parameter `a` an integer.  Note that this restriction is not
+    enforced by `erlang`. It will, however, generate a warning the first time
+    a non-integer value is used for the shape parameter.
+
+    Refer to `gamma` for examples.
+
+    """
+
+    def _argcheck(self, a):
+        allint = np.all(np.floor(a) == a)
+        if not allint:
+            # An Erlang distribution shouldn't really have a non-integer
+            # shape parameter, so warn the user.
+            message = ('The shape parameter of the erlang distribution '
+                       f'has been given a non-integer value {a!r}.')
+            warnings.warn(message, RuntimeWarning, stacklevel=3)
+        return a > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", True, (1, np.inf), (True, False))]
+
+    def _fitstart(self, data):
+        # Override gamma_gen_fitstart so that an integer initial value is
+        # used.  (Also regularize the division, to avoid issues when
+        # _skew(data) is 0 or close to 0.)
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        a = int(4.0 / (1e-8 + _skew(data)**2))
+        return super(gamma_gen, self)._fitstart(data, args=(a,))
+
+    # Trivial override of the fit method, so we can monkey-patch its
+    # docstring.
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        The Erlang distribution is generally defined to have integer values
+        for the shape parameter.  This is not enforced by the `erlang` class.
+        When fitting the distribution, it will generally return a non-integer
+        value for the shape parameter.  By using the keyword argument
+        `f0=<integer>`, the fit method can be constrained to fit the data to
+        a specific integer shape parameter.""")
+    def fit(self, data, *args, **kwds):
+        return super().fit(data, *args, **kwds)
+
+
+erlang = erlang_gen(a=0.0, name='erlang')
+
+
+class gengamma_gen(rv_continuous):
+    r"""A generalized gamma continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gamma, invgamma, weibull_min
+
+    Notes
+    -----
+    The probability density function for `gengamma` is ([1]_):
+
+    .. math::
+
+        f(x, a, c) = \frac{|c| x^{c a-1} \exp(-x^c)}{\Gamma(a)}
+
+    for :math:`x \ge 0`, :math:`a > 0`, and :math:`c \ne 0`.
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `gengamma` takes :math:`a` and :math:`c` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] E.W. Stacy, "A Generalization of the Gamma Distribution",
+       Annals of Mathematical Statistics, Vol 33(3), pp. 1187--1192.
+
+    %(example)s
+
+    """
+    def _argcheck(self, a, c):
+        return (a > 0) & (c != 0)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (-np.inf, np.inf), (False, False))
+        return [ia, ic]
+
+    def _pdf(self, x, a, c):
+        return np.exp(self._logpdf(x, a, c))
+
+    def _logpdf(self, x, a, c):
+        return _lazywhere((x != 0) | (c > 0), (x, c),
+                          lambda x, c: (np.log(abs(c)) + sc.xlogy(c*a - 1, x)
+                                        - x**c - sc.gammaln(a)),
+                          fillvalue=-np.inf)
+
+    def _cdf(self, x, a, c):
+        xc = x**c
+        val1 = sc.gammainc(a, xc)
+        val2 = sc.gammaincc(a, xc)
+        return np.where(c > 0, val1, val2)
+
+    def _rvs(self, a, c, size=None, random_state=None):
+        r = random_state.standard_gamma(a, size=size)
+        return r**(1./c)
+
+    def _sf(self, x, a, c):
+        xc = x**c
+        val1 = sc.gammainc(a, xc)
+        val2 = sc.gammaincc(a, xc)
+        return np.where(c > 0, val2, val1)
+
+    def _ppf(self, q, a, c):
+        val1 = sc.gammaincinv(a, q)
+        val2 = sc.gammainccinv(a, q)
+        return np.where(c > 0, val1, val2)**(1.0/c)
+
+    def _isf(self, q, a, c):
+        val1 = sc.gammaincinv(a, q)
+        val2 = sc.gammainccinv(a, q)
+        return np.where(c > 0, val2, val1)**(1.0/c)
+
+    def _munp(self, n, a, c):
+        # Pochhammer symbol: sc.pocha,n) = gamma(a+n)/gamma(a)
+        return sc.poch(a, n*1.0/c)
+
+    def _entropy(self, a, c):
+        def regular(a, c):
+            val = sc.psi(a)
+            A = a * (1 - val) + val / c
+            B = sc.gammaln(a) - np.log(abs(c))
+            h = A + B
+            return h
+
+        def asymptotic(a, c):
+            # using asymptotic expansions for gammaln and psi (see gh-18093)
+            return (norm._entropy() - np.log(a)/2
+                    - np.log(np.abs(c)) + (a**-1.)/6 - (a**-3.)/90
+                    + (np.log(a) - (a**-1.)/2 - (a**-2.)/12 + (a**-4.)/120)/c)
+
+        h = _lazywhere(a >= 2e2, (a, c), f=asymptotic, f2=regular)
+        return h
+
+
+gengamma = gengamma_gen(a=0.0, name='gengamma')
+
+
+class genhalflogistic_gen(rv_continuous):
+    r"""A generalized half-logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genhalflogistic` is:
+
+    .. math::
+
+        f(x, c) = \frac{2 (1 - c x)^{1/(c-1)}}{[1 + (1 - c x)^{1/c}]^2}
+
+    for :math:`0 \le x \le 1/c`, and :math:`c > 0`.
+
+    `genhalflogistic` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        return self.a, 1.0/c
+
+    def _pdf(self, x, c):
+        # genhalflogistic.pdf(x, c) =
+        #    2 * (1-c*x)**(1/c-1) / (1+(1-c*x)**(1/c))**2
+        limit = 1.0/c
+        tmp = np.asarray(1-c*x)
+        tmp0 = tmp**(limit-1)
+        tmp2 = tmp0*tmp
+        return 2*tmp0 / (1+tmp2)**2
+
+    def _cdf(self, x, c):
+        limit = 1.0/c
+        tmp = np.asarray(1-c*x)
+        tmp2 = tmp**(limit)
+        return (1.0-tmp2) / (1+tmp2)
+
+    def _ppf(self, q, c):
+        return 1.0/c*(1-((1.0-q)/(1.0+q))**c)
+
+    def _entropy(self, c):
+        return 2 - (2*c+1)*np.log(2)
+
+
+genhalflogistic = genhalflogistic_gen(a=0.0, name='genhalflogistic')
+
+
+class genhyperbolic_gen(rv_continuous):
+    r"""A generalized hyperbolic continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    t, norminvgauss, geninvgauss, laplace, cauchy
+
+    Notes
+    -----
+    The probability density function for `genhyperbolic` is:
+
+    .. math::
+
+        f(x, p, a, b) =
+            \frac{(a^2 - b^2)^{p/2}}
+            {\sqrt{2\pi}a^{p-1/2}
+            K_p\Big(\sqrt{a^2 - b^2}\Big)}
+            e^{bx} \times \frac{K_{p - 1/2}
+            (a \sqrt{1 + x^2})}
+            {(\sqrt{1 + x^2})^{1/2 - p}}
+
+    for :math:`x, p \in ( - \infty; \infty)`,
+    :math:`|b| < a` if :math:`p \ge 0`,
+    :math:`|b| \le a` if :math:`p < 0`.
+    :math:`K_{p}(.)` denotes the modified Bessel function of the second
+    kind and order :math:`p` (`scipy.special.kv`)
+
+    `genhyperbolic` takes ``p`` as a tail parameter,
+    ``a`` as a shape parameter,
+    ``b`` as a skewness parameter.
+
+    %(after_notes)s
+
+    The original parameterization of the Generalized Hyperbolic Distribution
+    is found in [1]_ as follows
+
+    .. math::
+
+        f(x, \lambda, \alpha, \beta, \delta, \mu) =
+           \frac{(\gamma/\delta)^\lambda}{\sqrt{2\pi}K_\lambda(\delta \gamma)}
+           e^{\beta (x - \mu)} \times \frac{K_{\lambda - 1/2}
+           (\alpha \sqrt{\delta^2 + (x - \mu)^2})}
+           {(\sqrt{\delta^2 + (x - \mu)^2} / \alpha)^{1/2 - \lambda}}
+
+    for :math:`x \in ( - \infty; \infty)`,
+    :math:`\gamma := \sqrt{\alpha^2 - \beta^2}`,
+    :math:`\lambda, \mu \in ( - \infty; \infty)`,
+    :math:`\delta \ge 0, |\beta| < \alpha` if :math:`\lambda \ge 0`,
+    :math:`\delta > 0, |\beta| \le \alpha` if :math:`\lambda < 0`.
+
+    The location-scale-based parameterization implemented in
+    SciPy is based on [2]_, where :math:`a = \alpha\delta`,
+    :math:`b = \beta\delta`, :math:`p = \lambda`,
+    :math:`scale=\delta` and :math:`loc=\mu`
+
+    Moments are implemented based on [3]_ and [4]_.
+
+    For the distributions that are a special case such as Student's t,
+    it is not recommended to rely on the implementation of genhyperbolic.
+    To avoid potential numerical problems and for performance reasons,
+    the methods of the specific distributions should be used.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions
+       on Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
+       pp. 151-157, 1978. https://www.jstor.org/stable/4615705
+
+    .. [2] Eberlein E., Prause K. (2002) The Generalized Hyperbolic Model:
+        Financial Derivatives and Risk Measures. In: Geman H., Madan D.,
+        Pliska S.R., Vorst T. (eds) Mathematical Finance - Bachelier
+        Congress 2000. Springer Finance. Springer, Berlin, Heidelberg.
+        :doi:`10.1007/978-3-662-12429-1_12`
+
+    .. [3] Scott, David J, Würtz, Diethelm, Dong, Christine and Tran,
+       Thanh Tam, (2009), Moments of the generalized hyperbolic
+       distribution, MPRA Paper, University Library of Munich, Germany,
+       https://EconPapers.repec.org/RePEc:pra:mprapa:19081.
+
+    .. [4] E. Eberlein and E. A. von Hammerstein. Generalized hyperbolic
+       and inverse Gaussian distributions: Limiting cases and approximation
+       of processes. FDM Preprint 80, April 2003. University of Freiburg.
+       https://freidok.uni-freiburg.de/fedora/objects/freidok:7974/datastreams/FILE1/content
+
+    %(example)s
+
+    """
+
+    def _argcheck(self, p, a, b):
+        return (np.logical_and(np.abs(b) < a, p >= 0)
+                | np.logical_and(np.abs(b) <= a, p < 0))
+
+    def _shape_info(self):
+        ip = _ShapeInfo("p", False, (-np.inf, np.inf), (False, False))
+        ia = _ShapeInfo("a", False, (0, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, False))
+        return [ip, ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default p = a = b = 1 is not valid; the
+        # distribution requires |b| < a if p >= 0.
+        return super()._fitstart(data, args=(1, 1, 0.5))
+
+    def _logpdf(self, x, p, a, b):
+        # kve instead of kv works better for large values of p
+        # and smaller values of sqrt(a^2  - b^2)
+        @np.vectorize
+        def _logpdf_single(x, p, a, b):
+            return _stats.genhyperbolic_logpdf(x, p, a, b)
+
+        return _logpdf_single(x, p, a, b)
+
+    def _pdf(self, x, p, a, b):
+        # kve instead of kv works better for large values of p
+        # and smaller values of sqrt(a^2  - b^2)
+        @np.vectorize
+        def _pdf_single(x, p, a, b):
+            return _stats.genhyperbolic_pdf(x, p, a, b)
+
+        return _pdf_single(x, p, a, b)
+
+    # np.vectorize isn't currently designed to be used as a decorator,
+    # so use a lambda instead.  This allows us to decorate the function
+    # with `np.vectorize` and still provide the `otypes` parameter.
+    @lambda func: np.vectorize(func, otypes=[np.float64])
+    @staticmethod
+    def _integrate_pdf(x0, x1, p, a, b):
+        """
+        Integrate the pdf of the genhyberbolic distribution from x0 to x1.
+        This is a private function used by _cdf() and _sf() only; either x0
+        will be -inf or x1 will be inf.
+        """
+        user_data = np.array([p, a, b], float).ctypes.data_as(ctypes.c_void_p)
+        llc = LowLevelCallable.from_cython(_stats, '_genhyperbolic_pdf',
+                                           user_data)
+        d = np.sqrt((a + b)*(a - b))
+        mean = b/d * sc.kv(p + 1, d) / sc.kv(p, d)
+        epsrel = 1e-10
+        epsabs = 0
+        if x0 < mean < x1:
+            # If the interval includes the mean, integrate over the two
+            # intervals [x0, mean] and [mean, x1] and add. If we try to do
+            # the integral in one call of quad and the non-infinite endpoint
+            # is far in the tail, quad might return an incorrect result
+            # because it does not "see" the peak of the PDF.
+            intgrl = (integrate.quad(llc, x0, mean,
+                                     epsrel=epsrel, epsabs=epsabs)[0]
+                      + integrate.quad(llc, mean, x1,
+                                       epsrel=epsrel, epsabs=epsabs)[0])
+        else:
+            intgrl = integrate.quad(llc, x0, x1,
+                                    epsrel=epsrel, epsabs=epsabs)[0]
+        if np.isnan(intgrl):
+            msg = ("Infinite values encountered in scipy.special.kve. "
+                   "Values replaced by NaN to avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+        return max(0.0, min(1.0, intgrl))
+
+    def _cdf(self, x, p, a, b):
+        return self._integrate_pdf(-np.inf, x, p, a, b)
+
+    def _sf(self, x, p, a, b):
+        return self._integrate_pdf(x, np.inf, p, a, b)
+
+    def _rvs(self, p, a, b, size=None, random_state=None):
+        # note: X = b * V + sqrt(V) * X  has a
+        # generalized hyperbolic distribution
+        # if X is standard normal and V is
+        # geninvgauss(p = p, b = t2, loc = loc, scale = t3)
+        t1 = np.float_power(a, 2) - np.float_power(b, 2)
+        # b in the GIG
+        t2 = np.float_power(t1, 0.5)
+        # scale in the GIG
+        t3 = np.float_power(t1, - 0.5)
+        gig = geninvgauss.rvs(
+            p=p,
+            b=t2,
+            scale=t3,
+            size=size,
+            random_state=random_state
+            )
+        normst = norm.rvs(size=size, random_state=random_state)
+
+        return b * gig + np.sqrt(gig) * normst
+
+    def _stats(self, p, a, b):
+        # https://mpra.ub.uni-muenchen.de/19081/1/MPRA_paper_19081.pdf
+        # https://freidok.uni-freiburg.de/fedora/objects/freidok:7974/datastreams/FILE1/content
+        # standardized moments
+        p, a, b = np.broadcast_arrays(p, a, b)
+        t1 = np.float_power(a, 2) - np.float_power(b, 2)
+        t1 = np.float_power(t1, 0.5)
+        t2 = np.float_power(1, 2) * np.float_power(t1, - 1)
+        integers = np.linspace(0, 4, 5)
+        # make integers perpendicular to existing dimensions
+        integers = integers.reshape(integers.shape + (1,) * p.ndim)
+        b0, b1, b2, b3, b4 = sc.kv(p + integers, t1)
+        r1, r2, r3, r4 = (b / b0 for b in (b1, b2, b3, b4))
+
+        m = b * t2 * r1
+        v = (
+            t2 * r1 + np.float_power(b, 2) * np.float_power(t2, 2) *
+            (r2 - np.float_power(r1, 2))
+        )
+        m3e = (
+            np.float_power(b, 3) * np.float_power(t2, 3) *
+            (r3 - 3 * b2 * b1 * np.float_power(b0, -2) +
+             2 * np.float_power(r1, 3)) +
+            3 * b * np.float_power(t2, 2) *
+            (r2 - np.float_power(r1, 2))
+        )
+        s = m3e * np.float_power(v, - 3 / 2)
+        m4e = (
+            np.float_power(b, 4) * np.float_power(t2, 4) *
+            (r4 - 4 * b3 * b1 * np.float_power(b0, - 2) +
+             6 * b2 * np.float_power(b1, 2) * np.float_power(b0, - 3) -
+             3 * np.float_power(r1, 4)) +
+            np.float_power(b, 2) * np.float_power(t2, 3) *
+            (6 * r3 - 12 * b2 * b1 * np.float_power(b0, - 2) +
+             6 * np.float_power(r1, 3)) +
+            3 * np.float_power(t2, 2) * r2
+        )
+        k = m4e * np.float_power(v, -2) - 3
+
+        return m, v, s, k
+
+
+genhyperbolic = genhyperbolic_gen(name='genhyperbolic')
+
+
+class gompertz_gen(rv_continuous):
+    r"""A Gompertz (or truncated Gumbel) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gompertz` is:
+
+    .. math::
+
+        f(x, c) = c \exp(x) \exp(-c (e^x-1))
+
+    for :math:`x \ge 0`, :math:`c > 0`.
+
+    `gompertz` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # gompertz.pdf(x, c) = c * exp(x) * exp(-c*(exp(x)-1))
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + x - c * sc.expm1(x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-c * sc.expm1(x))
+
+    def _ppf(self, q, c):
+        return sc.log1p(-1.0 / c * sc.log1p(-q))
+
+    def _sf(self, x, c):
+        return np.exp(-c * sc.expm1(x))
+
+    def _isf(self, p, c):
+        return sc.log1p(-np.log(p)/c)
+
+    def _entropy(self, c):
+        return 1.0 - np.log(c) - sc._ufuncs._scaled_exp1(c)/c
+
+
+gompertz = gompertz_gen(a=0.0, name='gompertz')
+
+
+def _average_with_log_weights(x, logweights):
+    x = np.asarray(x)
+    logweights = np.asarray(logweights)
+    maxlogw = logweights.max()
+    weights = np.exp(logweights - maxlogw)
+    return np.average(x, weights=weights)
+
+
+class gumbel_r_gen(rv_continuous):
+    r"""A right-skewed Gumbel continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_l, gompertz, genextreme
+
+    Notes
+    -----
+    The probability density function for `gumbel_r` is:
+
+    .. math::
+
+        f(x) = \exp(-(x + e^{-x}))
+
+    for real :math:`x`.
+
+    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
+    distribution.  It is also related to the extreme value distribution,
+    log-Weibull and Gompertz distributions.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # gumbel_r.pdf(x) = exp(-(x + exp(-x)))
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return -x - np.exp(-x)
+
+    def _cdf(self, x):
+        return np.exp(-np.exp(-x))
+
+    def _logcdf(self, x):
+        return -np.exp(-x)
+
+    def _ppf(self, q):
+        return -np.log(-np.log(q))
+
+    def _sf(self, x):
+        return -sc.expm1(-np.exp(-x))
+
+    def _isf(self, p):
+        return -np.log(-np.log1p(-p))
+
+    def _stats(self):
+        return _EULER, np.pi*np.pi/6.0, 12*np.sqrt(6)/np.pi**3 * _ZETA3, 12.0/5
+
+    def _entropy(self):
+        # https://en.wikipedia.org/wiki/Gumbel_distribution
+        return _EULER + 1.
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # By the method of maximum likelihood, the estimators of the
+        # location and scale are the roots of the equations defined in
+        # `func` and the value of the expression for `loc` that follows.
+        # The first `func` is a first order derivative of the log-likelihood
+        # equation and the second is from Source: Statistical Distributions,
+        # 3rd Edition. Evans, Hastings, and Peacock (2000), Page 101.
+
+        def get_loc_from_scale(scale):
+            return -scale * (sc.logsumexp(-data / scale) - np.log(len(data)))
+
+        if fscale is not None:
+            # if the scale is fixed, the location can be analytically
+            # determined.
+            scale = fscale
+            loc = get_loc_from_scale(scale)
+        else:
+            # A different function is solved depending on whether the location
+            # is fixed.
+            if floc is not None:
+                loc = floc
+
+                # equation to use if the location is fixed.
+                # note that one cannot use the equation in Evans, Hastings,
+                # and Peacock (2000) (since it assumes that the derivative
+                # w.r.t. the log-likelihood is zero). however, it is easy to
+                # derive the MLE condition directly if loc is fixed
+                def func(scale):
+                    term1 = (loc - data) * np.exp((loc - data) / scale) + data
+                    term2 = len(data) * (loc + scale)
+                    return term1.sum() - term2
+            else:
+
+                # equation to use if both location and scale are free
+                def func(scale):
+                    sdata = -data / scale
+                    wavg = _average_with_log_weights(data, logweights=sdata)
+                    return data.mean() - wavg - scale
+
+            # set brackets for `root_scalar` to use when optimizing over the
+            # scale such that a root is likely between them. Use user supplied
+            # guess or default 1.
+            brack_start = kwds.get('scale', 1)
+            lbrack, rbrack = brack_start / 2, brack_start * 2
+
+            # if a root is not between the brackets, iteratively expand them
+            # until they include a sign change, checking after each bracket is
+            # modified.
+            def interval_contains_root(lbrack, rbrack):
+                # return true if the signs disagree.
+                return (np.sign(func(lbrack)) !=
+                        np.sign(func(rbrack)))
+            while (not interval_contains_root(lbrack, rbrack)
+                   and (lbrack > 0 or rbrack < np.inf)):
+                lbrack /= 2
+                rbrack *= 2
+
+            res = optimize.root_scalar(func, bracket=(lbrack, rbrack),
+                                       rtol=1e-14, xtol=1e-14)
+            scale = res.root
+            loc = floc if floc is not None else get_loc_from_scale(scale)
+        return loc, scale
+
+
+gumbel_r = gumbel_r_gen(name='gumbel_r')
+
+
+class gumbel_l_gen(rv_continuous):
+    r"""A left-skewed Gumbel continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_r, gompertz, genextreme
+
+    Notes
+    -----
+    The probability density function for `gumbel_l` is:
+
+    .. math::
+
+        f(x) = \exp(x - e^x)
+
+    for real :math:`x`.
+
+    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
+    distribution.  It is also related to the extreme value distribution,
+    log-Weibull and Gompertz distributions.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # gumbel_l.pdf(x) = exp(x - exp(x))
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return x - np.exp(x)
+
+    def _cdf(self, x):
+        return -sc.expm1(-np.exp(x))
+
+    def _ppf(self, q):
+        return np.log(-sc.log1p(-q))
+
+    def _logsf(self, x):
+        return -np.exp(x)
+
+    def _sf(self, x):
+        return np.exp(-np.exp(x))
+
+    def _isf(self, x):
+        return np.log(-np.log(x))
+
+    def _stats(self):
+        return -_EULER, np.pi*np.pi/6.0, \
+               -12*np.sqrt(6)/np.pi**3 * _ZETA3, 12.0/5
+
+    def _entropy(self):
+        return _EULER + 1.
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        # The fit method of `gumbel_r` can be used for this distribution with
+        # small modifications. The process to do this is
+        # 1. pass the sign negated data into `gumbel_r.fit`
+        #    - if the location is fixed, it should also be negated.
+        # 2. negate the sign of the resulting location, leaving the scale
+        #    unmodified.
+        # `gumbel_r.fit` holds necessary input checks.
+
+        if kwds.get('floc') is not None:
+            kwds['floc'] = -kwds['floc']
+        loc_r, scale_r, = gumbel_r.fit(-np.asarray(data), *args, **kwds)
+        return -loc_r, scale_r
+
+
+gumbel_l = gumbel_l_gen(name='gumbel_l')
+
+
+class halfcauchy_gen(rv_continuous):
+    r"""A Half-Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halfcauchy` is:
+
+    .. math::
+
+        f(x) = \frac{2}{\pi (1 + x^2)}
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # halfcauchy.pdf(x) = 2 / (pi * (1 + x**2))
+        return 2.0/np.pi/(1.0+x*x)
+
+    def _logpdf(self, x):
+        return np.log(2.0/np.pi) - sc.log1p(x*x)
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arctan(x)
+
+    def _ppf(self, q):
+        return np.tan(np.pi/2*q)
+
+    def _sf(self, x):
+        return 2.0/np.pi * np.arctan2(1, x)
+
+    def _isf(self, p):
+        return 1.0/np.tan(np.pi*p/2)
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+    def _entropy(self):
+        return np.log(2*np.pi)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # location is independent from the scale
+        data_min = np.min(data)
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halfcauchy", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            # if not provided, location MLE is the minimal data point
+            loc = data_min
+
+        # find scale
+        def find_scale(loc, data):
+            shifted_data = data - loc
+            n = data.size
+            shifted_data_squared = np.square(shifted_data)
+
+            def fun_to_solve(scale):
+                denominator = scale**2 + shifted_data_squared
+                return 2 * np.sum(shifted_data_squared/denominator) - n
+
+            small = np.finfo(1.0).tiny**0.5  # avoid underflow
+            res = root_scalar(fun_to_solve, bracket=(small, np.max(shifted_data)))
+            return res.root
+
+        if fscale is not None:
+            scale = fscale
+        else:
+            scale = find_scale(loc, data)
+
+        return loc, scale
+
+
+halfcauchy = halfcauchy_gen(a=0.0, name='halfcauchy')
+
+
+class halflogistic_gen(rv_continuous):
+    r"""A half-logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halflogistic` is:
+
+    .. math::
+
+        f(x) = \frac{ 2 e^{-x} }{ (1+e^{-x})^2 }
+             = \frac{1}{2} \text{sech}(x/2)^2
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Asgharzadeh et al (2011). "Comparisons of Methods of Estimation for the
+           Half-Logistic Distribution". Selcuk J. Appl. Math. 93-108.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # halflogistic.pdf(x) = 2 * exp(-x) / (1+exp(-x))**2
+        #                     = 1/2 * sech(x/2)**2
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return np.log(2) - x - 2. * sc.log1p(np.exp(-x))
+
+    def _cdf(self, x):
+        return np.tanh(x/2.0)
+
+    def _ppf(self, q):
+        return 2*np.arctanh(q)
+
+    def _sf(self, x):
+        return 2 * sc.expit(-x)
+
+    def _isf(self, q):
+        return _lazywhere(q < 0.5, (q, ),
+                          lambda q: -sc.logit(0.5 * q),
+                          f2=lambda q: 2*np.arctanh(1 - q))
+
+    def _munp(self, n):
+        if n == 0:
+            return 1  # otherwise returns NaN
+        if n == 1:
+            return 2*np.log(2)
+        if n == 2:
+            return np.pi*np.pi/3.0
+        if n == 3:
+            return 9*_ZETA3
+        if n == 4:
+            return 7*np.pi**4 / 15.0
+        return 2*(1-pow(2.0, 1-n))*sc.gamma(n+1)*sc.zeta(n, 1)
+
+    def _entropy(self):
+        return 2-np.log(2)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        def find_scale(data, loc):
+            # scale is solution to a fix point problem ([1] 2.6)
+            # use approximate MLE as starting point ([1] 3.1)
+            n_observations = data.shape[0]
+            sorted_data = np.sort(data, axis=0)
+            p = np.arange(1, n_observations + 1)/(n_observations + 1)
+            q = 1 - p
+            pp1 = 1 + p
+            alpha = p - 0.5 * q * pp1 * np.log(pp1 / q)
+            beta = 0.5 * q * pp1
+            sorted_data = sorted_data - loc
+            B = 2 * np.sum(alpha[1:] * sorted_data[1:])
+            C = 2 * np.sum(beta[1:] * sorted_data[1:]**2)
+            # starting guess
+            scale = ((B + np.sqrt(B**2 + 8 * n_observations * C))
+                    /(4 * n_observations))
+
+            # relative tolerance of fix point iterator
+            rtol = 1e-8
+            relative_residual = 1
+            shifted_mean = sorted_data.mean()  # y_mean - y_min
+
+            # find fix point by repeated application of eq. (2.6)
+            # simplify as
+            # exp(-x) / (1 + exp(-x)) = 1 / (1 + exp(x))
+            #                         = expit(-x))
+            while relative_residual > rtol:
+                sum_term = sorted_data * sc.expit(-sorted_data/scale)
+                scale_new = shifted_mean - 2/n_observations * sum_term.sum()
+                relative_residual = abs((scale - scale_new)/scale)
+                scale = scale_new
+            return scale
+
+        # location is independent from the scale
+        data_min = np.min(data)
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halflogistic", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            # if not provided, location MLE is the minimal data point
+            loc = data_min
+
+        # scale depends on location
+        scale = fscale if fscale is not None else find_scale(data, loc)
+
+        return loc, scale
+
+
+halflogistic = halflogistic_gen(a=0.0, name='halflogistic')
+
+
+class halfnorm_gen(rv_continuous):
+    r"""A half-normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halfnorm` is:
+
+    .. math::
+
+        f(x) = \sqrt{2/\pi} \exp(-x^2 / 2)
+
+    for :math:`x >= 0`.
+
+    `halfnorm` is a special case of `chi` with ``df=1``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return abs(random_state.standard_normal(size=size))
+
+    def _pdf(self, x):
+        # halfnorm.pdf(x) = sqrt(2/pi) * exp(-x**2/2)
+        return np.sqrt(2.0/np.pi)*np.exp(-x*x/2.0)
+
+    def _logpdf(self, x):
+        return 0.5 * np.log(2.0/np.pi) - x*x/2.0
+
+    def _cdf(self, x):
+        return sc.erf(x / np.sqrt(2))
+
+    def _ppf(self, q):
+        return _norm_ppf((1+q)/2.0)
+
+    def _sf(self, x):
+        return 2 * _norm_sf(x)
+
+    def _isf(self, p):
+        return _norm_isf(p/2)
+
+    def _stats(self):
+        return (np.sqrt(2.0/np.pi),
+                1-2.0/np.pi,
+                np.sqrt(2)*(4-np.pi)/(np.pi-2)**1.5,
+                8*(np.pi-3)/(np.pi-2)**2)
+
+    def _entropy(self):
+        return 0.5*np.log(np.pi/2.0)+0.5
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        data_min = np.min(data)
+
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halfnorm", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            loc = data_min
+
+        if fscale is not None:
+            scale = fscale
+        else:
+            scale = stats.moment(data, order=2, center=loc)**0.5
+
+        return loc, scale
+
+
+halfnorm = halfnorm_gen(a=0.0, name='halfnorm')
+
+
+class hypsecant_gen(rv_continuous):
+    r"""A hyperbolic secant continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `hypsecant` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi} \text{sech}(x)
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # hypsecant.pdf(x) = 1/pi * sech(x)
+        return 1.0/(np.pi*np.cosh(x))
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arctan(np.exp(x))
+
+    def _ppf(self, q):
+        return np.log(np.tan(np.pi*q/2.0))
+
+    def _sf(self, x):
+        return 2.0/np.pi*np.arctan(np.exp(-x))
+
+    def _isf(self, q):
+        return -np.log(np.tan(np.pi*q/2.0))
+
+    def _stats(self):
+        return 0, np.pi*np.pi/4, 0, 2
+
+    def _entropy(self):
+        return np.log(2*np.pi)
+
+
+hypsecant = hypsecant_gen(name='hypsecant')
+
+
+class gausshyper_gen(rv_continuous):
+    r"""A Gauss hypergeometric continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gausshyper` is:
+
+    .. math::
+
+        f(x, a, b, c, z) = C x^{a-1} (1-x)^{b-1} (1+zx)^{-c}
+
+    for :math:`0 \le x \le 1`, :math:`a,b > 0`, :math:`c` a real number,
+    :math:`z > -1`, and :math:`C = \frac{1}{B(a, b) F[2, 1](c, a; a+b; -z)}`.
+    :math:`F[2, 1]` is the Gauss hypergeometric function
+    `scipy.special.hyp2f1`.
+
+    `gausshyper` takes :math:`a`, :math:`b`, :math:`c` and :math:`z` as shape
+    parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Armero, C., and M. J. Bayarri. "Prior Assessments for Prediction in
+           Queues." *Journal of the Royal Statistical Society*. Series D (The
+           Statistician) 43, no. 1 (1994): 139-53. doi:10.2307/2348939
+
+    %(example)s
+
+    """
+
+    def _argcheck(self, a, b, c, z):
+        # z > -1 per gh-10134
+        return (a > 0) & (b > 0) & (c == c) & (z > -1)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (-np.inf, np.inf), (False, False))
+        iz = _ShapeInfo("z", False, (-1, np.inf), (False, False))
+        return [ia, ib, ic, iz]
+
+    def _pdf(self, x, a, b, c, z):
+        normalization_constant = sc.beta(a, b) * sc.hyp2f1(c, a, a + b, -z)
+        return (1./normalization_constant * x**(a - 1.) * (1. - x)**(b - 1.0)
+                / (1.0 + z*x)**c)
+
+    def _munp(self, n, a, b, c, z):
+        fac = sc.beta(n+a, b) / sc.beta(a, b)
+        num = sc.hyp2f1(c, a+n, a+b+n, -z)
+        den = sc.hyp2f1(c, a, a+b, -z)
+        return fac*num / den
+
+
+gausshyper = gausshyper_gen(a=0.0, b=1.0, name='gausshyper')
+
+
+class invgamma_gen(rv_continuous):
+    r"""An inverted gamma continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invgamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{x^{-a-1}}{\Gamma(a)} \exp(-\frac{1}{x})
+
+    for :math:`x >= 0`, :math:`a > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    `invgamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    `invgamma` is a special case of `gengamma` with ``c=-1``, and it is a
+    different parameterization of the scaled inverse chi-squared distribution.
+    Specifically, if the scaled inverse chi-squared distribution is
+    parameterized with degrees of freedom :math:`\nu` and scaling parameter
+    :math:`\tau^2`, then it can be modeled using `invgamma` with
+    ``a=`` :math:`\nu/2` and ``scale=`` :math:`\nu \tau^2/2`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # invgamma.pdf(x, a) = x**(-a-1) / gamma(a) * exp(-1/x)
+        return np.exp(self._logpdf(x, a))
+
+    def _logpdf(self, x, a):
+        return -(a+1) * np.log(x) - sc.gammaln(a) - 1.0/x
+
+    def _cdf(self, x, a):
+        return sc.gammaincc(a, 1.0 / x)
+
+    def _ppf(self, q, a):
+        return 1.0 / sc.gammainccinv(a, q)
+
+    def _sf(self, x, a):
+        return sc.gammainc(a, 1.0 / x)
+
+    def _isf(self, q, a):
+        return 1.0 / sc.gammaincinv(a, q)
+
+    def _stats(self, a, moments='mvsk'):
+        m1 = _lazywhere(a > 1, (a,), lambda x: 1. / (x - 1.), np.inf)
+        m2 = _lazywhere(a > 2, (a,), lambda x: 1. / (x - 1.)**2 / (x - 2.),
+                        np.inf)
+
+        g1, g2 = None, None
+        if 's' in moments:
+            g1 = _lazywhere(
+                a > 3, (a,),
+                lambda x: 4. * np.sqrt(x - 2.) / (x - 3.), np.nan)
+        if 'k' in moments:
+            g2 = _lazywhere(
+                a > 4, (a,),
+                lambda x: 6. * (5. * x - 11.) / (x - 3.) / (x - 4.), np.nan)
+        return m1, m2, g1, g2
+
+    def _entropy(self, a):
+        def regular(a):
+            h = a - (a + 1.0) * sc.psi(a) + sc.gammaln(a)
+            return h
+
+        def asymptotic(a):
+            # gammaln(a) ~ a * ln(a) - a - 0.5 * ln(a) + 0.5 * ln(2 * pi)
+            # psi(a) ~ ln(a) - 1 / (2 * a)
+            h = ((1 - 3*np.log(a) + np.log(2) + np.log(np.pi))/2
+                 + 2/3*a**-1. + a**-2./12 - a**-3./90 - a**-4./120)
+            return h
+
+        h = _lazywhere(a >= 2e2, (a,), f=asymptotic, f2=regular)
+        return h
+
+
+invgamma = invgamma_gen(a=0.0, name='invgamma')
+
+
+class invgauss_gen(rv_continuous):
+    r"""An inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invgauss` is:
+
+    .. math::
+
+        f(x; \mu) = \frac{1}{\sqrt{2 \pi x^3}}
+                    \exp\left(-\frac{(x-\mu)^2}{2 \mu^2 x}\right)
+
+    for :math:`x \ge 0` and :math:`\mu > 0`.
+
+    `invgauss` takes ``mu`` as a shape parameter for :math:`\mu`.
+
+    %(after_notes)s
+
+    A common shape-scale parameterization of the inverse Gaussian distribution
+    has density
+
+    .. math::
+
+        f(x; \nu, \lambda) = \sqrt{\frac{\lambda}{2 \pi x^3}}
+                    \exp\left( -\frac{\lambda(x-\nu)^2}{2 \nu^2 x}\right)
+
+    Using ``nu`` for :math:`\nu` and ``lam`` for :math:`\lambda`, this
+    parameterization is equivalent to the one above with ``mu = nu/lam``,
+    ``loc = 0``, and ``scale = lam``.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``ppf`` and ``isf`` methods. [1]_
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("mu", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, mu, size=None, random_state=None):
+        return random_state.wald(mu, 1.0, size=size)
+
+    def _pdf(self, x, mu):
+        # invgauss.pdf(x, mu) =
+        #                  1 / sqrt(2*pi*x**3) * exp(-(x-mu)**2/(2*x*mu**2))
+        return 1.0/np.sqrt(2*np.pi*x**3.0)*np.exp(-1.0/(2*x)*((x-mu)/mu)**2)
+
+    def _logpdf(self, x, mu):
+        return -0.5*np.log(2*np.pi) - 1.5*np.log(x) - ((x-mu)/mu)**2/(2*x)
+
+    # approach adapted from equations in
+    # https://journal.r-project.org/archive/2016-1/giner-smyth.pdf,
+    # not R code. see gh-13616
+
+    def _logcdf(self, x, mu):
+        fac = 1 / np.sqrt(x)
+        a = _norm_logcdf(fac * ((x / mu) - 1))
+        b = 2 / mu + _norm_logcdf(-fac * ((x / mu) + 1))
+        return a + np.log1p(np.exp(b - a))
+
+    def _logsf(self, x, mu):
+        fac = 1 / np.sqrt(x)
+        a = _norm_logsf(fac * ((x / mu) - 1))
+        b = 2 / mu + _norm_logcdf(-fac * (x + mu) / mu)
+        return a + np.log1p(-np.exp(b - a))
+
+    def _sf(self, x, mu):
+        return np.exp(self._logsf(x, mu))
+
+    def _cdf(self, x, mu):
+        return np.exp(self._logcdf(x, mu))
+
+    def _ppf(self, x, mu):
+        with np.errstate(divide='ignore', over='ignore', invalid='ignore'):
+            x, mu = np.broadcast_arrays(x, mu)
+            ppf = np.asarray(scu._invgauss_ppf(x, mu, 1))
+            i_wt = x > 0.5  # "wrong tail" - sometimes too inaccurate
+            ppf[i_wt] = scu._invgauss_isf(1-x[i_wt], mu[i_wt], 1)
+            i_nan = np.isnan(ppf)
+            ppf[i_nan] = super()._ppf(x[i_nan], mu[i_nan])
+        return ppf
+
+    def _isf(self, x, mu):
+        with np.errstate(divide='ignore', over='ignore', invalid='ignore'):
+            x, mu = np.broadcast_arrays(x, mu)
+            isf = scu._invgauss_isf(x, mu, 1)
+            i_wt = x > 0.5  # "wrong tail" - sometimes too inaccurate
+            isf[i_wt] = scu._invgauss_ppf(1-x[i_wt], mu[i_wt], 1)
+            i_nan = np.isnan(isf)
+            isf[i_nan] = super()._isf(x[i_nan], mu[i_nan])
+        return isf
+
+    def _stats(self, mu):
+        return mu, mu**3.0, 3*np.sqrt(mu), 15*mu
+
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        method = kwds.get('method', 'mle')
+
+        if (isinstance(data, CensoredData) or isinstance(self, wald_gen)
+                or method.lower() == 'mm'):
+            return super().fit(data, *args, **kwds)
+
+        data, fshape_s, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                   args, kwds)
+        '''
+        Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+        and Peacock (2000), Page 121. Their shape parameter is equivalent to
+        SciPy's with the conversion `fshape_s = fshape / scale`.
+
+        MLE formulas are not used in 3 conditions:
+        - `loc` is not fixed
+        - `mu` is fixed
+        These cases fall back on the superclass fit method.
+        - `loc` is fixed but translation results in negative data raises
+          a `FitDataError`.
+        '''
+        if floc is None or fshape_s is not None:
+            return super().fit(data, *args, **kwds)
+        elif np.any(data - floc < 0):
+            raise FitDataError("invgauss", lower=0, upper=np.inf)
+        else:
+            data = data - floc
+            fshape_n = np.mean(data)
+            if fscale is None:
+                fscale = len(data) / (np.sum(data ** -1 - fshape_n ** -1))
+            fshape_s = fshape_n / fscale
+        return fshape_s, floc, fscale
+
+    def _entropy(self, mu):
+        """
+        Ref.: https://moser-isi.ethz.ch/docs/papers/smos-2012-10.pdf (eq. 9)
+        """
+        # a = log(2*pi*e*mu**3)
+        #   = 1 + log(2*pi) + 3 * log(mu)
+        a = 1. + np.log(2 * np.pi) + 3 * np.log(mu)
+        # b = exp(2/mu) * exp1(2/mu)
+        #   = _scaled_exp1(2/mu) / (2/mu)
+        r = 2/mu
+        b = sc._ufuncs._scaled_exp1(r)/r
+        return 0.5 * a - 1.5 * b
+
+
+invgauss = invgauss_gen(a=0.0, name='invgauss')
+
+
+class geninvgauss_gen(rv_continuous):
+    r"""A Generalized Inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `geninvgauss` is:
+
+    .. math::
+
+        f(x, p, b) = x^{p-1} \exp(-b (x + 1/x) / 2) / (2 K_p(b))
+
+    where ``x > 0``, `p` is a real number and ``b > 0``\([1]_).
+    :math:`K_p` is the modified Bessel function of second kind of order `p`
+    (`scipy.special.kv`).
+
+    %(after_notes)s
+
+    The inverse Gaussian distribution `stats.invgauss(mu)` is a special case of
+    `geninvgauss` with ``p = -1/2``, ``b = 1 / mu`` and ``scale = mu``.
+
+    Generating random variates is challenging for this distribution. The
+    implementation is based on [2]_.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, P. Blaesild, C. Halgreen, "First hitting time
+       models for the generalized inverse gaussian distribution",
+       Stochastic Processes and their Applications 7, pp. 49--54, 1978.
+
+    .. [2] W. Hoermann and J. Leydold, "Generating generalized inverse Gaussian
+       random variates", Statistics and Computing, 24(4), p. 547--557, 2014.
+
+    %(example)s
+
+    """
+    def _argcheck(self, p, b):
+        return (p == p) & (b > 0)
+
+    def _shape_info(self):
+        ip = _ShapeInfo("p", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ip, ib]
+
+    def _logpdf(self, x, p, b):
+        # kve instead of kv works better for large values of b
+        # warn if kve produces infinite values and replace by nan
+        # otherwise c = -inf and the results are often incorrect
+        def logpdf_single(x, p, b):
+            return _stats.geninvgauss_logpdf(x, p, b)
+
+        logpdf_single = np.vectorize(logpdf_single, otypes=[np.float64])
+
+        z = logpdf_single(x, p, b)
+        if np.isnan(z).any():
+            msg = ("Infinite values encountered in scipy.special.kve(p, b). "
+                   "Values replaced by NaN to avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+        return z
+
+    def _pdf(self, x, p, b):
+        # relying on logpdf avoids overflow of x**(p-1) for large x and p
+        return np.exp(self._logpdf(x, p, b))
+
+    def _cdf(self, x, p, b):
+        _a, _b = self._get_support(p, b)
+
+        def _cdf_single(x, p, b):
+            user_data = np.array([p, b], float).ctypes.data_as(ctypes.c_void_p)
+            llc = LowLevelCallable.from_cython(_stats, '_geninvgauss_pdf',
+                                               user_data)
+
+            return integrate.quad(llc, _a, x)[0]
+
+        _cdf_single = np.vectorize(_cdf_single, otypes=[np.float64])
+
+        return _cdf_single(x, p, b)
+
+    def _logquasipdf(self, x, p, b):
+        # log of the quasi-density (w/o normalizing constant) used in _rvs
+        return _lazywhere(x > 0, (x, p, b),
+                          lambda x, p, b: (p - 1)*np.log(x) - b*(x + 1/x)/2,
+                          -np.inf)
+
+    def _rvs(self, p, b, size=None, random_state=None):
+        # if p and b are scalar, use _rvs_scalar, otherwise need to create
+        # output by iterating over parameters
+        if np.isscalar(p) and np.isscalar(b):
+            out = self._rvs_scalar(p, b, size, random_state)
+        elif p.size == 1 and b.size == 1:
+            out = self._rvs_scalar(p.item(), b.item(), size, random_state)
+        else:
+            # When this method is called, size will be a (possibly empty)
+            # tuple of integers.  It will not be None; if `size=None` is passed
+            # to `rvs()`, size will be the empty tuple ().
+
+            p, b = np.broadcast_arrays(p, b)
+            # p and b now have the same shape.
+
+            # `shp` is the shape of the blocks of random variates that are
+            # generated for each combination of parameters associated with
+            # broadcasting p and b.
+            # bc is a tuple the same length as size.  The values
+            # in bc are bools.  If bc[j] is True, it means that
+            # entire axis is filled in for a given combination of the
+            # broadcast arguments.
+            shp, bc = _check_shape(p.shape, size)
+
+            # `numsamples` is the total number of variates to be generated
+            # for each combination of the input arguments.
+            numsamples = int(np.prod(shp))
+
+            # `out` is the array to be returned.  It is filled in the
+            # loop below.
+            out = np.empty(size)
+
+            it = np.nditer([p, b],
+                           flags=['multi_index'],
+                           op_flags=[['readonly'], ['readonly']])
+            while not it.finished:
+                # Convert the iterator's multi_index into an index into the
+                # `out` array where the call to _rvs_scalar() will be stored.
+                # Where bc is True, we use a full slice; otherwise we use the
+                # index value from it.multi_index.  len(it.multi_index) might
+                # be less than len(bc), and in that case we want to align these
+                # two sequences to the right, so the loop variable j runs from
+                # -len(size) to 0.  This doesn't cause an IndexError, as
+                # bc[j] will be True in those cases where it.multi_index[j]
+                # would cause an IndexError.
+                idx = tuple((it.multi_index[j] if not bc[j] else slice(None))
+                            for j in range(-len(size), 0))
+                out[idx] = self._rvs_scalar(it[0], it[1], numsamples,
+                                            random_state).reshape(shp)
+                it.iternext()
+
+        if size == ():
+            out = out.item()
+        return out
+
+    def _rvs_scalar(self, p, b, numsamples, random_state):
+        # following [2], the quasi-pdf is used instead of the pdf for the
+        # generation of rvs
+        invert_res = False
+        if not numsamples:
+            numsamples = 1
+        if p < 0:
+            # note: if X is geninvgauss(p, b), then 1/X is geninvgauss(-p, b)
+            p = -p
+            invert_res = True
+        m = self._mode(p, b)
+
+        # determine method to be used following [2]
+        ratio_unif = True
+        if p >= 1 or b > 1:
+            # ratio of uniforms with mode shift below
+            mode_shift = True
+        elif b >= min(0.5, 2 * np.sqrt(1 - p) / 3):
+            # ratio of uniforms without mode shift below
+            mode_shift = False
+        else:
+            # new algorithm in [2]
+            ratio_unif = False
+
+        # prepare sampling of rvs
+        size1d = tuple(np.atleast_1d(numsamples))
+        N = np.prod(size1d)  # number of rvs needed, reshape upon return
+        x = np.zeros(N)
+        simulated = 0
+
+        if ratio_unif:
+            # use ratio of uniforms method
+            if mode_shift:
+                a2 = -2 * (p + 1) / b - m
+                a1 = 2 * m * (p - 1) / b - 1
+                # find roots of x**3 + a2*x**2 + a1*x + m (Cardano's formula)
+                p1 = a1 - a2**2 / 3
+                q1 = 2 * a2**3 / 27 - a2 * a1 / 3 + m
+                phi = np.arccos(-q1 * np.sqrt(-27 / p1**3) / 2)
+                s1 = -np.sqrt(-4 * p1 / 3)
+                root1 = s1 * np.cos(phi / 3 + np.pi / 3) - a2 / 3
+                root2 = -s1 * np.cos(phi / 3) - a2 / 3
+                # root3 = s1 * np.cos(phi / 3 - np.pi / 3) - a2 / 3
+
+                # if g is the quasipdf, rescale: g(x) / g(m) which we can write
+                # as exp(log(g(x)) - log(g(m))). This is important
+                # since for large values of p and b, g cannot be evaluated.
+                # denote the rescaled quasipdf by h
+                lm = self._logquasipdf(m, p, b)
+                d1 = self._logquasipdf(root1, p, b) - lm
+                d2 = self._logquasipdf(root2, p, b) - lm
+                # compute the bounding rectangle w.r.t. h. Note that
+                # np.exp(0.5*d1) = np.sqrt(g(root1)/g(m)) = np.sqrt(h(root1))
+                vmin = (root1 - m) * np.exp(0.5 * d1)
+                vmax = (root2 - m) * np.exp(0.5 * d2)
+                umax = 1  # umax = sqrt(h(m)) = 1
+
+                def logqpdf(x):
+                    return self._logquasipdf(x, p, b) - lm
+
+                c = m
+            else:
+                # ratio of uniforms without mode shift
+                # compute np.sqrt(quasipdf(m))
+                umax = np.exp(0.5*self._logquasipdf(m, p, b))
+                xplus = ((1 + p) + np.sqrt((1 + p)**2 + b**2))/b
+                vmin = 0
+                # compute xplus * np.sqrt(quasipdf(xplus))
+                vmax = xplus * np.exp(0.5 * self._logquasipdf(xplus, p, b))
+                c = 0
+
+                def logqpdf(x):
+                    return self._logquasipdf(x, p, b)
+
+            if vmin >= vmax:
+                raise ValueError("vmin must be smaller than vmax.")
+            if umax <= 0:
+                raise ValueError("umax must be positive.")
+
+            i = 1
+            while simulated < N:
+                k = N - simulated
+                # simulate uniform rvs on [0, umax] and [vmin, vmax]
+                u = umax * random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                v = vmin + (vmax - vmin) * v
+                rvs = v / u + c
+                # rewrite acceptance condition u**2 <= pdf(rvs) by taking logs
+                accept = (2*np.log(u) <= logqpdf(rvs))
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = rvs[accept]
+                    simulated += num_accept
+
+                if (simulated == 0) and (i*N >= 50000):
+                    msg = ("Not a single random variate could be generated "
+                           f"in {i*N} attempts. Sampling does not appear to "
+                           "work for the provided parameters.")
+                    raise RuntimeError(msg)
+                i += 1
+        else:
+            # use new algorithm in [2]
+            x0 = b / (1 - p)
+            xs = np.max((x0, 2 / b))
+            k1 = np.exp(self._logquasipdf(m, p, b))
+            A1 = k1 * x0
+            if x0 < 2 / b:
+                k2 = np.exp(-b)
+                if p > 0:
+                    A2 = k2 * ((2 / b)**p - x0**p) / p
+                else:
+                    A2 = k2 * np.log(2 / b**2)
+            else:
+                k2, A2 = 0, 0
+            k3 = xs**(p - 1)
+            A3 = 2 * k3 * np.exp(-xs * b / 2) / b
+            A = A1 + A2 + A3
+
+            # [2]: rejection constant is < 2.73; so expected runtime is finite
+            while simulated < N:
+                k = N - simulated
+                h, rvs = np.zeros(k), np.zeros(k)
+                # simulate uniform rvs on [x1, x2] and [0, y2]
+                u = random_state.uniform(size=k)
+                v = A * random_state.uniform(size=k)
+                cond1 = v <= A1
+                cond2 = np.logical_not(cond1) & (v <= A1 + A2)
+                cond3 = np.logical_not(cond1 | cond2)
+                # subdomain (0, x0)
+                rvs[cond1] = x0 * v[cond1] / A1
+                h[cond1] = k1
+                # subdomain (x0, 2 / b)
+                if p > 0:
+                    rvs[cond2] = (x0**p + (v[cond2] - A1) * p / k2)**(1 / p)
+                else:
+                    rvs[cond2] = b * np.exp((v[cond2] - A1) * np.exp(b))
+                h[cond2] = k2 * rvs[cond2]**(p - 1)
+                # subdomain (xs, infinity)
+                z = np.exp(-xs * b / 2) - b * (v[cond3] - A1 - A2) / (2 * k3)
+                rvs[cond3] = -2 / b * np.log(z)
+                h[cond3] = k3 * np.exp(-rvs[cond3] * b / 2)
+                # apply rejection method
+                accept = (np.log(u * h) <= self._logquasipdf(rvs, p, b))
+                num_accept = sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = rvs[accept]
+                    simulated += num_accept
+
+        rvs = np.reshape(x, size1d)
+        if invert_res:
+            rvs = 1 / rvs
+        return rvs
+
+    def _mode(self, p, b):
+        # distinguish cases to avoid catastrophic cancellation (see [2])
+        if p < 1:
+            return b / (np.sqrt((p - 1)**2 + b**2) + 1 - p)
+        else:
+            return (np.sqrt((1 - p)**2 + b**2) - (1 - p)) / b
+
+    def _munp(self, n, p, b):
+        num = sc.kve(p + n, b)
+        denom = sc.kve(p, b)
+        inf_vals = np.isinf(num) | np.isinf(denom)
+        if inf_vals.any():
+            msg = ("Infinite values encountered in the moment calculation "
+                   "involving scipy.special.kve. Values replaced by NaN to "
+                   "avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+            m = np.full_like(num, np.nan, dtype=np.float64)
+            m[~inf_vals] = num[~inf_vals] / denom[~inf_vals]
+        else:
+            m = num / denom
+        return m
+
+
+geninvgauss = geninvgauss_gen(a=0.0, name="geninvgauss")
+
+
+class norminvgauss_gen(rv_continuous):
+    r"""A Normal Inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `norminvgauss` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{a \, K_1(a \sqrt{1 + x^2})}{\pi \sqrt{1 + x^2}} \,
+                     \exp(\sqrt{a^2 - b^2} + b x)
+
+    where :math:`x` is a real number, the parameter :math:`a` is the tail
+    heaviness and :math:`b` is the asymmetry parameter satisfying
+    :math:`a > 0` and :math:`|b| <= a`.
+    :math:`K_1` is the modified Bessel function of second kind
+    (`scipy.special.k1`).
+
+    %(after_notes)s
+
+    A normal inverse Gaussian random variable `Y` with parameters `a` and `b`
+    can be expressed as a normal mean-variance mixture:
+    ``Y = b * V + sqrt(V) * X`` where `X` is ``norm(0,1)`` and `V` is
+    ``invgauss(mu=1/sqrt(a**2 - b**2))``. This representation is used
+    to generate random variates.
+
+    Another common parametrization of the distribution (see Equation 2.1 in
+    [2]_) is given by the following expression of the pdf:
+
+    .. math::
+
+        g(x, \alpha, \beta, \delta, \mu) =
+        \frac{\alpha\delta K_1\left(\alpha\sqrt{\delta^2 + (x - \mu)^2}\right)}
+        {\pi \sqrt{\delta^2 + (x - \mu)^2}} \,
+        e^{\delta \sqrt{\alpha^2 - \beta^2} + \beta (x - \mu)}
+
+    In SciPy, this corresponds to
+    `a = alpha * delta, b = beta * delta, loc = mu, scale=delta`.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions on
+           Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
+           pp. 151-157, 1978.
+
+    .. [2] O. Barndorff-Nielsen, "Normal Inverse Gaussian Distributions and
+           Stochastic Volatility Modelling", Scandinavian Journal of
+           Statistics, Vol. 24, pp. 1-13, 1997.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, a, b):
+        return (a > 0) & (np.absolute(b) < a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, False))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default a = b = 1 is not valid; the distribution
+        # requires |b| < a.
+        return super()._fitstart(data, args=(1, 0.5))
+
+    def _pdf(self, x, a, b):
+        gamma = np.sqrt(a**2 - b**2)
+        fac1 = a / np.pi
+        sq = np.hypot(1, x)  # reduce overflows
+        return fac1 * sc.k1e(a * sq) * np.exp(b*x - a*sq + gamma) / sq
+
+    def _sf(self, x, a, b):
+        if np.isscalar(x):
+            # If x is a scalar, then so are a and b.
+            return integrate.quad(self._pdf, x, np.inf, args=(a, b))[0]
+        else:
+            a = np.atleast_1d(a)
+            b = np.atleast_1d(b)
+            result = []
+            for (x0, a0, b0) in zip(x, a, b):
+                result.append(integrate.quad(self._pdf, x0, np.inf,
+                                             args=(a0, b0))[0])
+            return np.array(result)
+
+    def _isf(self, q, a, b):
+        def _isf_scalar(q, a, b):
+
+            def eq(x, a, b, q):
+                # Solve eq(x, a, b, q) = 0 to obtain isf(x, a, b) = q.
+                return self._sf(x, a, b) - q
+
+            # Find a bracketing interval for the root.
+            # Start at the mean, and grow the length of the interval
+            # by 2 each iteration until there is a sign change in eq.
+            xm = self.mean(a, b)
+            em = eq(xm, a, b, q)
+            if em == 0:
+                # Unlikely, but might as well check.
+                return xm
+            if em > 0:
+                delta = 1
+                left = xm
+                right = xm + delta
+                while eq(right, a, b, q) > 0:
+                    delta = 2*delta
+                    right = xm + delta
+            else:
+                # em < 0
+                delta = 1
+                right = xm
+                left = xm - delta
+                while eq(left, a, b, q) < 0:
+                    delta = 2*delta
+                    left = xm - delta
+            result = optimize.brentq(eq, left, right, args=(a, b, q),
+                                     xtol=self.xtol)
+            return result
+
+        if np.isscalar(q):
+            return _isf_scalar(q, a, b)
+        else:
+            result = []
+            for (q0, a0, b0) in zip(q, a, b):
+                result.append(_isf_scalar(q0, a0, b0))
+            return np.array(result)
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        # note: X = b * V + sqrt(V) * X is norminvgaus(a,b) if X is standard
+        # normal and V is invgauss(mu=1/sqrt(a**2 - b**2))
+        gamma = np.sqrt(a**2 - b**2)
+        ig = invgauss.rvs(mu=1/gamma, size=size, random_state=random_state)
+        return b * ig + np.sqrt(ig) * norm.rvs(size=size,
+                                               random_state=random_state)
+
+    def _stats(self, a, b):
+        gamma = np.sqrt(a**2 - b**2)
+        mean = b / gamma
+        variance = a**2 / gamma**3
+        skewness = 3.0 * b / (a * np.sqrt(gamma))
+        kurtosis = 3.0 * (1 + 4 * b**2 / a**2) / gamma
+        return mean, variance, skewness, kurtosis
+
+
+norminvgauss = norminvgauss_gen(name="norminvgauss")
+
+
+class invweibull_gen(rv_continuous):
+    """An inverted Weibull continuous random variable.
+
+    This distribution is also known as the Fréchet distribution or the
+    type II extreme value distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invweibull` is:
+
+    .. math::
+
+        f(x, c) = c x^{-c-1} \\exp(-x^{-c})
+
+    for :math:`x > 0`, :math:`c > 0`.
+
+    `invweibull` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    F.R.S. de Gusmao, E.M.M Ortega and G.M. Cordeiro, "The generalized inverse
+    Weibull distribution", Stat. Papers, vol. 52, pp. 591-619, 2011.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # invweibull.pdf(x, c) = c * x**(-c-1) * exp(-x**(-c))
+        xc1 = np.power(x, -c - 1.0)
+        xc2 = np.power(x, -c)
+        xc2 = np.exp(-xc2)
+        return c * xc1 * xc2
+
+    def _cdf(self, x, c):
+        xc1 = np.power(x, -c)
+        return np.exp(-xc1)
+
+    def _sf(self, x, c):
+        return -np.expm1(-x**-c)
+
+    def _ppf(self, q, c):
+        return np.power(-np.log(q), -1.0/c)
+
+    def _isf(self, p, c):
+        return (-np.log1p(-p))**(-1/c)
+
+    def _munp(self, n, c):
+        return sc.gamma(1 - n / c)
+
+    def _entropy(self, c):
+        return 1+_EULER + _EULER / c - np.log(c)
+
+    def _fitstart(self, data, args=None):
+        # invweibull requires c > 1 for the first moment to exist, so use 2.0
+        args = (2.0,) if args is None else args
+        return super()._fitstart(data, args=args)
+
+
+invweibull = invweibull_gen(a=0, name='invweibull')
+
+
+class jf_skew_t_gen(rv_continuous):
+    r"""Jones and Faddy skew-t distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `jf_skew_t` is:
+
+    .. math::
+
+        f(x; a, b) = C_{a,b}^{-1}
+                    \left(1+\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{a+1/2}
+                    \left(1-\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{b+1/2}
+
+    for real numbers :math:`a>0` and :math:`b>0`, where
+    :math:`C_{a,b} = 2^{a+b-1}B(a,b)(a+b)^{1/2}`, and :math:`B` denotes the
+    beta function (`scipy.special.beta`).
+
+    When :math:`a<b`, the distribution is negatively skewed, and when
+    :math:`a>b`, the distribution is positively skewed. If :math:`a=b`, then
+    we recover the `t` distribution with :math:`2a` degrees of freedom.
+
+    `jf_skew_t` takes :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] M.C. Jones and M.J. Faddy. "A skew extension of the t distribution,
+           with applications" *Journal of the Royal Statistical Society*.
+           Series B (Statistical Methodology) 65, no. 1 (2003): 159-174.
+           :doi:`10.1111/1467-9868.00378`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        c = 2 ** (a + b - 1) * sc.beta(a, b) * np.sqrt(a + b)
+        d1 = (1 + x / np.sqrt(a + b + x ** 2)) ** (a + 0.5)
+        d2 = (1 - x / np.sqrt(a + b + x ** 2)) ** (b + 0.5)
+        return d1 * d2 / c
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        d1 = random_state.beta(a, b, size)
+        d2 = (2 * d1 - 1) * np.sqrt(a + b)
+        d3 = 2 * np.sqrt(d1 * (1 - d1))
+        return d2 / d3
+
+    def _cdf(self, x, a, b):
+        y = (1 + x / np.sqrt(a + b + x ** 2)) * 0.5
+        return sc.betainc(a, b, y)
+
+    def _sf(self, x, a, b):
+        y = (1 + x / np.sqrt(a + b + x ** 2)) * 0.5
+        return sc.betaincc(a, b, y)
+
+    def _ppf(self, q, a, b):
+        d1 = beta.ppf(q, a, b)
+        d2 = (2 * d1 - 1) * np.sqrt(a + b)
+        d3 = 2 * np.sqrt(d1 * (1 - d1))
+        return d2 / d3
+
+    def _munp(self, n, a, b):
+        """Returns the n-th moment(s) where all the following hold:
+
+        - n >= 0
+        - a > n / 2
+        - b > n / 2
+
+        The result is np.nan in all other cases.
+        """
+        def nth_moment(n_k, a_k, b_k):
+            """Computes E[T^(n_k)] where T is skew-t distributed with
+            parameters a_k and b_k.
+            """
+            num = (a_k + b_k) ** (0.5 * n_k)
+            denom = 2 ** n_k * sc.beta(a_k, b_k)
+
+            indices = np.arange(n_k + 1)
+            sgn = np.where(indices % 2 > 0, -1, 1)
+            d = sc.beta(a_k + 0.5 * n_k - indices, b_k - 0.5 * n_k + indices)
+            sum_terms = sc.comb(n_k, indices) * sgn * d
+
+            return num / denom * sum_terms.sum()
+
+        nth_moment_valid = (a > 0.5 * n) & (b > 0.5 * n) & (n >= 0)
+        return _lazywhere(
+            nth_moment_valid,
+            (n, a, b),
+            np.vectorize(nth_moment, otypes=[np.float64]),
+            np.nan,
+        )
+
+
+jf_skew_t = jf_skew_t_gen(name='jf_skew_t')
+
+
+class johnsonsb_gen(rv_continuous):
+    r"""A Johnson SB continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    johnsonsu
+
+    Notes
+    -----
+    The probability density function for `johnsonsb` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{b}{x(1-x)}  \phi(a + b \log \frac{x}{1-x} )
+
+    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`
+    and :math:`x \in [0,1]`.  :math:`\phi` is the pdf of the normal
+    distribution.
+
+    `johnsonsb` takes :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, a, b):
+        return (b > 0) & (a == a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        # johnsonsb.pdf(x, a, b) = b / (x*(1-x)) * phi(a + b * log(x/(1-x)))
+        trm = _norm_pdf(a + b*sc.logit(x))
+        return b*1.0/(x*(1-x))*trm
+
+    def _cdf(self, x, a, b):
+        return _norm_cdf(a + b*sc.logit(x))
+
+    def _ppf(self, q, a, b):
+        return sc.expit(1.0 / b * (_norm_ppf(q) - a))
+
+    def _sf(self, x, a, b):
+        return _norm_sf(a + b*sc.logit(x))
+
+    def _isf(self, q, a, b):
+        return sc.expit(1.0 / b * (_norm_isf(q) - a))
+
+
+johnsonsb = johnsonsb_gen(a=0.0, b=1.0, name='johnsonsb')
+
+
+class johnsonsu_gen(rv_continuous):
+    r"""A Johnson SU continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    johnsonsb
+
+    Notes
+    -----
+    The probability density function for `johnsonsu` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{b}{\sqrt{x^2 + 1}}
+                     \phi(a + b \log(x + \sqrt{x^2 + 1}))
+
+    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`.
+    :math:`\phi` is the pdf of the normal distribution.
+
+    `johnsonsu` takes :math:`a` and :math:`b` as shape parameters.
+
+    The first four central moments are calculated according to the formulas
+    in [1]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Taylor Enterprises. "Johnson Family of Distributions".
+       https://variation.com/wp-content/distribution_analyzer_help/hs126.htm
+
+    %(example)s
+
+    """
+    def _argcheck(self, a, b):
+        return (b > 0) & (a == a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        # johnsonsu.pdf(x, a, b) = b / sqrt(x**2 + 1) *
+        #                          phi(a + b * log(x + sqrt(x**2 + 1)))
+        x2 = x*x
+        trm = _norm_pdf(a + b * np.arcsinh(x))
+        return b*1.0/np.sqrt(x2+1.0)*trm
+
+    def _cdf(self, x, a, b):
+        return _norm_cdf(a + b * np.arcsinh(x))
+
+    def _ppf(self, q, a, b):
+        return np.sinh((_norm_ppf(q) - a) / b)
+
+    def _sf(self, x, a, b):
+        return _norm_sf(a + b * np.arcsinh(x))
+
+    def _isf(self, x, a, b):
+        return np.sinh((_norm_isf(x) - a) / b)
+
+    def _stats(self, a, b, moments='mv'):
+        # Naive implementation of first and second moment to address gh-18071.
+        # https://variation.com/wp-content/distribution_analyzer_help/hs126.htm
+        # Numerical improvements left to future enhancements.
+        mu, mu2, g1, g2 = None, None, None, None
+
+        bn2 = b**-2.
+        expbn2 = np.exp(bn2)
+        a_b = a / b
+
+        if 'm' in moments:
+            mu = -expbn2**0.5 * np.sinh(a_b)
+        if 'v' in moments:
+            mu2 = 0.5*sc.expm1(bn2)*(expbn2*np.cosh(2*a_b) + 1)
+        if 's' in moments:
+            t1 = expbn2**.5 * sc.expm1(bn2)**0.5
+            t2 = 3*np.sinh(a_b)
+            t3 = expbn2 * (expbn2 + 2) * np.sinh(3*a_b)
+            denom = np.sqrt(2) * (1 + expbn2 * np.cosh(2*a_b))**(3/2)
+            g1 = -t1 * (t2 + t3) / denom
+        if 'k' in moments:
+            t1 = 3 + 6*expbn2
+            t2 = 4*expbn2**2 * (expbn2 + 2) * np.cosh(2*a_b)
+            t3 = expbn2**2 * np.cosh(4*a_b)
+            t4 = -3 + 3*expbn2**2 + 2*expbn2**3 + expbn2**4
+            denom = 2*(1 + expbn2*np.cosh(2*a_b))**2
+            g2 = (t1 + t2 + t3*t4) / denom - 3
+        return mu, mu2, g1, g2
+
+
+johnsonsu = johnsonsu_gen(name='johnsonsu')
+
+
+class landau_gen(rv_continuous):
+    r"""A Landau continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `landau` ([1]_, [2]_) is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi}\int_0^\infty \exp(-t \log t - xt)\sin(\pi t) dt
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    Often (e.g. [2]_), the Landau distribution is parameterized in terms of a
+    location parameter :math:`\mu` and scale parameter :math:`c`, the latter of
+    which *also* introduces a location shift. If ``mu`` and ``c`` are used to
+    represent these parameters, this corresponds with SciPy's parameterization
+    with ``loc = mu + 2*c / np.pi * np.log(c)`` and ``scale = c``.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    References
+    ----------
+    .. [1] Landau, L. (1944). "On the energy loss of fast particles by
+           ionization". J. Phys. (USSR). 8: 201.
+    .. [2] "Landau Distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Landau_distribution
+    .. [3] Chambers, J. M., Mallows, C. L., & Stuck, B. (1976).
+           "A method for simulating stable random variables."
+           Journal of the American Statistical Association, 71(354), 340-344.
+    .. [4] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+    .. [5] Yoshimura, T. "Numerical Evaluation and High Precision Approximation
+           Formula for Landau Distribution".
+           :doi:`10.36227/techrxiv.171822215.53612870/v2`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _entropy(self):
+        # Computed with mpmath - see gh-19145
+        return 2.37263644000448182
+
+    def _pdf(self, x):
+        return scu._landau_pdf(x, 0, 1)
+
+    def _cdf(self, x):
+        return scu._landau_cdf(x, 0, 1)
+
+    def _sf(self, x):
+        return scu._landau_sf(x, 0, 1)
+
+    def _ppf(self, p):
+        return scu._landau_ppf(p, 0, 1)
+
+    def _isf(self, p):
+        return scu._landau_isf(p, 0, 1)
+
+    def _stats(self):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _munp(self, n):
+        return np.nan if n > 0 else 1
+
+    def _fitstart(self, data, args=None):
+        # Initialize ML guesses using quartiles instead of moments.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return p50, (p75 - p25)/2
+
+    def _rvs(self, size=None, random_state=None):
+        # Method from https://www.jstor.org/stable/2285309 Eq. 2.4
+        pi_2 = np.pi / 2
+        U = random_state.uniform(-np.pi / 2, np.pi / 2, size=size)
+        W = random_state.standard_exponential(size=size)
+        S = 2 / np.pi * ((pi_2 + U) * np.tan(U)
+                         - np.log((pi_2 * W * np.cos(U)) / (pi_2 + U)))
+        return S
+
+
+landau = landau_gen(name='landau')
+
+
+class laplace_gen(rv_continuous):
+    r"""A Laplace continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `laplace` is
+
+    .. math::
+
+        f(x) = \frac{1}{2} \exp(-|x|)
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.laplace(0, 1, size=size)
+
+    def _pdf(self, x):
+        # laplace.pdf(x) = 1/2 * exp(-abs(x))
+        return 0.5*np.exp(-abs(x))
+
+    def _cdf(self, x):
+        with np.errstate(over='ignore'):
+            return np.where(x > 0, 1.0 - 0.5*np.exp(-x), 0.5*np.exp(x))
+
+    def _sf(self, x):
+        # By symmetry...
+        return self._cdf(-x)
+
+    def _ppf(self, q):
+        return np.where(q > 0.5, -np.log(2*(1-q)), np.log(2*q))
+
+    def _isf(self, q):
+        # By symmetry...
+        return -self._ppf(q)
+
+    def _stats(self):
+        return 0, 2, 0, 3
+
+    def _entropy(self):
+        return np.log(2)+1
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        This function uses explicit formulas for the maximum likelihood
+        estimation of the Laplace distribution parameters, so the keyword
+        arguments `loc`, `scale`, and `optimizer` are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+        # and Peacock (2000), Page 124
+
+        if floc is None:
+            floc = np.median(data)
+
+        if fscale is None:
+            fscale = (np.sum(np.abs(data - floc))) / len(data)
+
+        return floc, fscale
+
+
+laplace = laplace_gen(name='laplace')
+
+
+class laplace_asymmetric_gen(rv_continuous):
+    r"""An asymmetric Laplace continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    laplace : Laplace distribution
+
+    Notes
+    -----
+    The probability density function for `laplace_asymmetric` is
+
+    .. math::
+
+       f(x, \kappa) &= \frac{1}{\kappa+\kappa^{-1}}\exp(-x\kappa),\quad x\ge0\\
+                    &= \frac{1}{\kappa+\kappa^{-1}}\exp(x/\kappa),\quad x<0\\
+
+    for :math:`-\infty < x < \infty`, :math:`\kappa > 0`.
+
+    `laplace_asymmetric` takes ``kappa`` as a shape parameter for
+    :math:`\kappa`. For :math:`\kappa = 1`, it is identical to a
+    Laplace distribution.
+
+    %(after_notes)s
+
+    Note that the scale parameter of some references is the reciprocal of
+    SciPy's ``scale``. For example, :math:`\lambda = 1/2` in the
+    parameterization of [1]_ is equivalent to ``scale = 2`` with
+    `laplace_asymmetric`.
+
+    References
+    ----------
+    .. [1] "Asymmetric Laplace distribution", Wikipedia
+            https://en.wikipedia.org/wiki/Asymmetric_Laplace_distribution
+
+    .. [2] Kozubowski TJ and Podgórski K. A Multivariate and
+           Asymmetric Generalization of Laplace Distribution,
+           Computational Statistics 15, 531--540 (2000).
+           :doi:`10.1007/PL00022717`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("kappa", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, kappa):
+        return np.exp(self._logpdf(x, kappa))
+
+    def _logpdf(self, x, kappa):
+        kapinv = 1/kappa
+        lPx = x * np.where(x >= 0, -kappa, kapinv)
+        lPx -= np.log(kappa+kapinv)
+        return lPx
+
+    def _cdf(self, x, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(x >= 0,
+                        1 - np.exp(-x*kappa)*(kapinv/kappkapinv),
+                        np.exp(x*kapinv)*(kappa/kappkapinv))
+
+    def _sf(self, x, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(x >= 0,
+                        np.exp(-x*kappa)*(kapinv/kappkapinv),
+                        1 - np.exp(x*kapinv)*(kappa/kappkapinv))
+
+    def _ppf(self, q, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(q >= kappa/kappkapinv,
+                        -np.log((1 - q)*kappkapinv*kappa)*kapinv,
+                        np.log(q*kappkapinv/kappa)*kappa)
+
+    def _isf(self, q, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(q <= kapinv/kappkapinv,
+                        -np.log(q*kappkapinv*kappa)*kapinv,
+                        np.log((1 - q)*kappkapinv/kappa)*kappa)
+
+    def _stats(self, kappa):
+        kapinv = 1/kappa
+        mn = kapinv - kappa
+        var = kapinv*kapinv + kappa*kappa
+        g1 = 2.0*(1-np.power(kappa, 6))/np.power(1+np.power(kappa, 4), 1.5)
+        g2 = 6.0*(1+np.power(kappa, 8))/np.power(1+np.power(kappa, 4), 2)
+        return mn, var, g1, g2
+
+    def _entropy(self, kappa):
+        return 1 + np.log(kappa+1/kappa)
+
+
+laplace_asymmetric = laplace_asymmetric_gen(name='laplace_asymmetric')
+
+
+def _check_fit_input_parameters(dist, data, args, kwds):
+    if not isinstance(data, CensoredData):
+        data = np.asarray(data)
+
+    floc = kwds.get('floc', None)
+    fscale = kwds.get('fscale', None)
+
+    num_shapes = len(dist.shapes.split(",")) if dist.shapes else 0
+    fshape_keys = []
+    fshapes = []
+
+    # user has many options for fixing the shape, so here we standardize it
+    # into 'f' + the number of the shape.
+    # Adapted from `_reduce_func` in `_distn_infrastructure.py`:
+    if dist.shapes:
+        shapes = dist.shapes.replace(',', ' ').split()
+        for j, s in enumerate(shapes):
+            key = 'f' + str(j)
+            names = [key, 'f' + s, 'fix_' + s]
+            val = _get_fixed_fit_value(kwds, names)
+            fshape_keys.append(key)
+            fshapes.append(val)
+            if val is not None:
+                kwds[key] = val
+
+    # determine if there are any unknown arguments in kwds
+    known_keys = {'loc', 'scale', 'optimizer', 'method',
+                  'floc', 'fscale', *fshape_keys}
+    unknown_keys = set(kwds).difference(known_keys)
+    if unknown_keys:
+        raise TypeError(f"Unknown keyword arguments: {unknown_keys}.")
+
+    if len(args) > num_shapes:
+        raise TypeError("Too many positional arguments.")
+
+    if None not in {floc, fscale, *fshapes}:
+        # This check is for consistency with `rv_continuous.fit`.
+        # Without this check, this function would just return the
+        # parameters that were given.
+        raise RuntimeError("All parameters fixed. There is nothing to "
+                           "optimize.")
+
+    uncensored = data._uncensor() if isinstance(data, CensoredData) else data
+    if not np.isfinite(uncensored).all():
+        raise ValueError("The data contains non-finite values.")
+
+    return (data, *fshapes, floc, fscale)
+
+
+class levy_gen(rv_continuous):
+    r"""A Levy continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    levy_stable, levy_l
+
+    Notes
+    -----
+    The probability density function for `levy` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp\left(-\frac{1}{2x}\right)
+
+    for :math:`x > 0`.
+
+    This is the same as the Levy-stable distribution with :math:`a=1/2` and
+    :math:`b=1`.
+
+    %(after_notes)s
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import levy
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Calculate the first four moments:
+
+    >>> mean, var, skew, kurt = levy.stats(moments='mvsk')
+
+    Display the probability density function (``pdf``):
+
+    >>> # `levy` is very heavy-tailed.
+    >>> # To show a nice plot, let's cut off the upper 40 percent.
+    >>> a, b = levy.ppf(0), levy.ppf(0.6)
+    >>> x = np.linspace(a, b, 100)
+    >>> ax.plot(x, levy.pdf(x),
+    ...        'r-', lw=5, alpha=0.6, label='levy pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = levy()
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = levy.ppf([0.001, 0.5, 0.999])
+    >>> np.allclose([0.001, 0.5, 0.999], levy.cdf(vals))
+    True
+
+    Generate random numbers:
+
+    >>> r = levy.rvs(size=1000)
+
+    And compare the histogram:
+
+    >>> # manual binning to ignore the tail
+    >>> bins = np.concatenate((np.linspace(a, b, 20), [np.max(r)]))
+    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim([x[0], x[-1]])
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # levy.pdf(x) = 1 / (x * sqrt(2*pi*x)) * exp(-1/(2*x))
+        return 1 / np.sqrt(2*np.pi*x) / x * np.exp(-1/(2*x))
+
+    def _cdf(self, x):
+        # Equivalent to 2*norm.sf(np.sqrt(1/x))
+        return sc.erfc(np.sqrt(0.5 / x))
+
+    def _sf(self, x):
+        return sc.erf(np.sqrt(0.5 / x))
+
+    def _ppf(self, q):
+        # Equivalent to 1.0/(norm.isf(q/2)**2) or 0.5/(erfcinv(q)**2)
+        val = _norm_isf(q/2)
+        return 1.0 / (val * val)
+
+    def _isf(self, p):
+        return 1/(2*sc.erfinv(p)**2)
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+levy = levy_gen(a=0.0, name="levy")
+
+
+class levy_l_gen(rv_continuous):
+    r"""A left-skewed Levy continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    levy, levy_stable
+
+    Notes
+    -----
+    The probability density function for `levy_l` is:
+
+    .. math::
+        f(x) = \frac{1}{|x| \sqrt{2\pi |x|}} \exp{ \left(-\frac{1}{2|x|} \right)}
+
+    for :math:`x < 0`.
+
+    This is the same as the Levy-stable distribution with :math:`a=1/2` and
+    :math:`b=-1`.
+
+    %(after_notes)s
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import levy_l
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Calculate the first four moments:
+
+    >>> mean, var, skew, kurt = levy_l.stats(moments='mvsk')
+
+    Display the probability density function (``pdf``):
+
+    >>> # `levy_l` is very heavy-tailed.
+    >>> # To show a nice plot, let's cut off the lower 40 percent.
+    >>> a, b = levy_l.ppf(0.4), levy_l.ppf(1)
+    >>> x = np.linspace(a, b, 100)
+    >>> ax.plot(x, levy_l.pdf(x),
+    ...        'r-', lw=5, alpha=0.6, label='levy_l pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = levy_l()
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = levy_l.ppf([0.001, 0.5, 0.999])
+    >>> np.allclose([0.001, 0.5, 0.999], levy_l.cdf(vals))
+    True
+
+    Generate random numbers:
+
+    >>> r = levy_l.rvs(size=1000)
+
+    And compare the histogram:
+
+    >>> # manual binning to ignore the tail
+    >>> bins = np.concatenate(([np.min(r)], np.linspace(a, b, 20)))
+    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim([x[0], x[-1]])
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # levy_l.pdf(x) = 1 / (abs(x) * sqrt(2*pi*abs(x))) * exp(-1/(2*abs(x)))
+        ax = abs(x)
+        return 1/np.sqrt(2*np.pi*ax)/ax*np.exp(-1/(2*ax))
+
+    def _cdf(self, x):
+        ax = abs(x)
+        return 2 * _norm_cdf(1 / np.sqrt(ax)) - 1
+
+    def _sf(self, x):
+        ax = abs(x)
+        return 2 * _norm_sf(1 / np.sqrt(ax))
+
+    def _ppf(self, q):
+        val = _norm_ppf((q + 1.0) / 2)
+        return -1.0 / (val * val)
+
+    def _isf(self, p):
+        return -1/_norm_isf(p/2)**2
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+levy_l = levy_l_gen(b=0.0, name="levy_l")
+
+
+class logistic_gen(rv_continuous):
+    r"""A logistic (or Sech-squared) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `logistic` is:
+
+    .. math::
+
+        f(x) = \frac{\exp(-x)}
+                    {(1+\exp(-x))^2}
+
+    `logistic` is a special case of `genlogistic` with ``c=1``.
+
+    Remark that the survival function (``logistic.sf``) is equal to the
+    Fermi-Dirac distribution describing fermionic statistics.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.logistic(size=size)
+
+    def _pdf(self, x):
+        # logistic.pdf(x) = exp(-x) / (1+exp(-x))**2
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        y = -np.abs(x)
+        return y - 2. * sc.log1p(np.exp(y))
+
+    def _cdf(self, x):
+        return sc.expit(x)
+
+    def _logcdf(self, x):
+        return sc.log_expit(x)
+
+    def _ppf(self, q):
+        return sc.logit(q)
+
+    def _sf(self, x):
+        return sc.expit(-x)
+
+    def _logsf(self, x):
+        return sc.log_expit(-x)
+
+    def _isf(self, q):
+        return -sc.logit(q)
+
+    def _stats(self):
+        return 0, np.pi*np.pi/3.0, 0, 6.0/5.0
+
+    def _entropy(self):
+        # https://en.wikipedia.org/wiki/Logistic_distribution
+        return 2.0
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+        n = len(data)
+
+        # rv_continuous provided guesses
+        loc, scale = self._fitstart(data)
+        # these are trumped by user-provided guesses
+        loc, scale = kwds.get('loc', loc), kwds.get('scale', scale)
+
+        # the maximum likelihood estimators `a` and `b` of the location and
+        # scale parameters are roots of the two equations described in `func`.
+        # Source: Statistical Distributions, 3rd Edition. Evans, Hastings, and
+        # Peacock (2000), Page 130
+
+        def dl_dloc(loc, scale=fscale):
+            c = (data - loc) / scale
+            return np.sum(sc.expit(c)) - n/2
+
+        def dl_dscale(scale, loc=floc):
+            c = (data - loc) / scale
+            return np.sum(c*np.tanh(c/2)) - n
+
+        def func(params):
+            loc, scale = params
+            return dl_dloc(loc, scale), dl_dscale(scale, loc)
+
+        if fscale is not None and floc is None:
+            res = optimize.root(dl_dloc, (loc,))
+            loc = res.x[0]
+            scale = fscale
+        elif floc is not None and fscale is None:
+            res = optimize.root(dl_dscale, (scale,))
+            scale = res.x[0]
+            loc = floc
+        else:
+            res = optimize.root(func, (loc, scale))
+            loc, scale = res.x
+
+        # Note: gh-18176 reported data for which the reported MLE had
+        # `scale < 0`. To fix the bug, we return abs(scale). This is OK because
+        # `dl_dscale` and `dl_dloc` are even and odd functions of `scale`,
+        # respectively, so if `-scale` is a solution, so is `scale`.
+        scale = abs(scale)
+        return ((loc, scale) if res.success
+                else super().fit(data, *args, **kwds))
+
+
+logistic = logistic_gen(name='logistic')
+
+
+class loggamma_gen(rv_continuous):
+    r"""A log gamma continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `loggamma` is:
+
+    .. math::
+
+        f(x, c) = \frac{\exp(c x - \exp(x))}
+                       {\Gamma(c)}
+
+    for all :math:`x, c > 0`. Here, :math:`\Gamma` is the
+    gamma function (`scipy.special.gamma`).
+
+    `loggamma` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        # Use the property of the gamma distribution Gamma(c)
+        #    Gamma(c) ~ Gamma(c + 1)*U**(1/c),
+        # where U is uniform on [0, 1]. (See, e.g.,
+        # G. Marsaglia and W.W. Tsang, "A simple method for generating gamma
+        # variables", https://doi.org/10.1145/358407.358414)
+        # So
+        #    log(Gamma(c)) ~ log(Gamma(c + 1)) + log(U)/c
+        # Generating a sample with this formulation is a bit slower
+        # than the more obvious log(Gamma(c)), but it avoids loss
+        # of precision when c << 1.
+        return (np.log(random_state.gamma(c + 1, size=size))
+                + np.log(random_state.uniform(size=size))/c)
+
+    def _pdf(self, x, c):
+        # loggamma.pdf(x, c) = exp(c*x-exp(x)) / gamma(c)
+        return np.exp(c*x-np.exp(x)-sc.gammaln(c))
+
+    def _logpdf(self, x, c):
+        return c*x - np.exp(x) - sc.gammaln(c)
+
+    def _cdf(self, x, c):
+        # This function is gammainc(c, exp(x)), where gammainc(c, z) is
+        # the regularized incomplete gamma function.
+        # The first term in a series expansion of gamminc(c, z) is
+        # z**c/Gamma(c+1); see 6.5.29 of Abramowitz & Stegun (and refer
+        # back to 6.5.1, 6.5.2 and 6.5.4 for the relevant notation).
+        # This can also be found in the wikipedia article
+        # https://en.wikipedia.org/wiki/Incomplete_gamma_function.
+        # Here we use that formula when x is sufficiently negative that
+        # exp(x) will result in subnormal numbers and lose precision.
+        # We evaluate the log of the expression first to allow the possible
+        # cancellation of the terms in the division, and then exponentiate.
+        # That is,
+        #     exp(x)**c/Gamma(c+1) = exp(log(exp(x)**c/Gamma(c+1)))
+        #                          = exp(c*x - gammaln(c+1))
+        return _lazywhere(x < _LOGXMIN, (x, c),
+                          lambda x, c: np.exp(c*x - sc.gammaln(c+1)),
+                          f2=lambda x, c: sc.gammainc(c, np.exp(x)))
+
+    def _ppf(self, q, c):
+        # The expression used when g < _XMIN inverts the one term expansion
+        # given in the comments of _cdf().
+        g = sc.gammaincinv(c, q)
+        return _lazywhere(g < _XMIN, (g, q, c),
+                          lambda g, q, c: (np.log(q) + sc.gammaln(c+1))/c,
+                          f2=lambda g, q, c: np.log(g))
+
+    def _sf(self, x, c):
+        # See the comments for _cdf() for how x < _LOGXMIN is handled.
+        return _lazywhere(x < _LOGXMIN, (x, c),
+                          lambda x, c: -np.expm1(c*x - sc.gammaln(c+1)),
+                          f2=lambda x, c: sc.gammaincc(c, np.exp(x)))
+
+    def _isf(self, q, c):
+        # The expression used when g < _XMIN inverts the complement of
+        # the one term expansion given in the comments of _cdf().
+        g = sc.gammainccinv(c, q)
+        return _lazywhere(g < _XMIN, (g, q, c),
+                          lambda g, q, c: (np.log1p(-q) + sc.gammaln(c+1))/c,
+                          f2=lambda g, q, c: np.log(g))
+
+    def _stats(self, c):
+        # See, for example, "A Statistical Study of Log-Gamma Distribution", by
+        # Ping Shing Chan (thesis, McMaster University, 1993).
+        mean = sc.digamma(c)
+        var = sc.polygamma(1, c)
+        skewness = sc.polygamma(2, c) / np.power(var, 1.5)
+        excess_kurtosis = sc.polygamma(3, c) / (var*var)
+        return mean, var, skewness, excess_kurtosis
+
+    def _entropy(self, c):
+        def regular(c):
+            h = sc.gammaln(c) - c * sc.digamma(c) + c
+            return h
+
+        def asymptotic(c):
+            # using asymptotic expansions for gammaln and psi (see gh-18093)
+            term = -0.5*np.log(c) + c**-1./6 - c**-3./90 + c**-5./210
+            h = norm._entropy() + term
+            return h
+
+        h = _lazywhere(c >= 45, (c, ), f=asymptotic, f2=regular)
+        return h
+
+
+loggamma = loggamma_gen(name='loggamma')
+
+
+class loglaplace_gen(rv_continuous):
+    r"""A log-Laplace continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `loglaplace` is:
+
+    .. math::
+
+        f(x, c) = \begin{cases}\frac{c}{2} x^{ c-1}  &\text{for } 0 < x < 1\\
+                               \frac{c}{2} x^{-c-1}  &\text{for } x \ge 1
+                  \end{cases}
+
+    for :math:`c > 0`.
+
+    `loglaplace` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    Suppose a random variable ``X`` follows the Laplace distribution with
+    location ``a`` and scale ``b``.  Then ``Y = exp(X)`` follows the
+    log-Laplace distribution with ``c = 1 / b`` and ``scale = exp(a)``.
+
+    References
+    ----------
+    T.J. Kozubowski and K. Podgorski, "A log-Laplace growth rate model",
+    The Mathematical Scientist, vol. 28, pp. 49-60, 2003.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # loglaplace.pdf(x, c) = c / 2 * x**(c-1),   for 0 < x < 1
+        #                      = c / 2 * x**(-c-1),  for x >= 1
+        cd2 = c/2.0
+        c = np.where(x < 1, c, -c)
+        return cd2*x**(c-1)
+
+    def _cdf(self, x, c):
+        return np.where(x < 1, 0.5*x**c, 1-0.5*x**(-c))
+
+    def _sf(self, x, c):
+        return np.where(x < 1, 1 - 0.5*x**c, 0.5*x**(-c))
+
+    def _ppf(self, q, c):
+        return np.where(q < 0.5, (2.0*q)**(1.0/c), (2*(1.0-q))**(-1.0/c))
+
+    def _isf(self, q, c):
+        return np.where(q > 0.5, (2.0*(1.0 - q))**(1.0/c), (2*q)**(-1.0/c))
+
+    def _munp(self, n, c):
+        with np.errstate(divide='ignore'):
+            c2, n2 = c**2, n**2
+            return np.where(n2 < c2, c2 / (c2 - n2), np.inf)
+
+    def _entropy(self, c):
+        return np.log(2.0/c) + 1.0
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        data, fc, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+
+        # Specialize MLE only when location is known.
+        if floc is None:
+            return super(type(self), self).fit(data, *args, **kwds)
+
+        # Raise an error if any observation has zero likelihood.
+        if np.any(data <= floc):
+            raise FitDataError("loglaplace", lower=floc, upper=np.inf)
+
+        # Remove location from data.
+        if floc != 0:
+            data = data - floc
+
+        # When location is zero, the log-Laplace distribution is related to
+        # the Laplace distribution in that if X ~ Laplace(loc=a, scale=b),
+        # then Y = exp(X) ~ LogLaplace(c=1/b, loc=0, scale=exp(a)).  It can
+        # be shown that the MLE for Y is the same as the MLE for X = ln(Y).
+        # Therefore, we reuse the formulas from laplace.fit() and transform
+        # the result back into log-laplace's parameter space.
+        a, b = laplace.fit(np.log(data),
+                           floc=np.log(fscale) if fscale is not None else None,
+                           fscale=1/fc if fc is not None else None,
+                           method='mle')
+        loc = floc
+        scale = np.exp(a) if fscale is None else fscale
+        c = 1 / b if fc is None else fc
+        return c, loc, scale
+
+loglaplace = loglaplace_gen(a=0.0, name='loglaplace')
+
+
+def _lognorm_logpdf(x, s):
+    return _lazywhere(x != 0, (x, s),
+                      lambda x, s: (-np.log(x)**2 / (2 * s**2)
+                                    - np.log(s * x * np.sqrt(2 * np.pi))),
+                      -np.inf)
+
+
+class lognorm_gen(rv_continuous):
+    r"""A lognormal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `lognorm` is:
+
+    .. math::
+
+        f(x, s) = \frac{1}{s x \sqrt{2\pi}}
+                  \exp\left(-\frac{\log^2(x)}{2s^2}\right)
+
+    for :math:`x > 0`, :math:`s > 0`.
+
+    `lognorm` takes ``s`` as a shape parameter for :math:`s`.
+
+    %(after_notes)s
+
+    Suppose a normally distributed random variable ``X`` has  mean ``mu`` and
+    standard deviation ``sigma``. Then ``Y = exp(X)`` is lognormally
+    distributed with ``s = sigma`` and ``scale = exp(mu)``.
+
+    %(example)s
+
+    The logarithm of a log-normally distributed random variable is
+    normally distributed:
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> mu, sigma = 2, 0.5
+    >>> X = stats.norm(loc=mu, scale=sigma)
+    >>> Y = stats.lognorm(s=sigma, scale=np.exp(mu))
+    >>> x = np.linspace(*X.interval(0.999))
+    >>> y = Y.rvs(size=10000)
+    >>> ax.plot(x, X.pdf(x), label='X (pdf)')
+    >>> ax.hist(np.log(y), density=True, bins=x, label='log(Y) (histogram)')
+    >>> ax.legend()
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("s", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, s, size=None, random_state=None):
+        return np.exp(s * random_state.standard_normal(size))
+
+    def _pdf(self, x, s):
+        # lognorm.pdf(x, s) = 1 / (s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2)
+        return np.exp(self._logpdf(x, s))
+
+    def _logpdf(self, x, s):
+        return _lognorm_logpdf(x, s)
+
+    def _cdf(self, x, s):
+        return _norm_cdf(np.log(x) / s)
+
+    def _logcdf(self, x, s):
+        return _norm_logcdf(np.log(x) / s)
+
+    def _ppf(self, q, s):
+        return np.exp(s * _norm_ppf(q))
+
+    def _sf(self, x, s):
+        return _norm_sf(np.log(x) / s)
+
+    def _logsf(self, x, s):
+        return _norm_logsf(np.log(x) / s)
+
+    def _isf(self, q, s):
+        return np.exp(s * _norm_isf(q))
+
+    def _stats(self, s):
+        p = np.exp(s*s)
+        mu = np.sqrt(p)
+        mu2 = p*(p-1)
+        g1 = np.sqrt(p-1)*(2+p)
+        g2 = np.polyval([1, 2, 3, 0, -6.0], p)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, s):
+        return 0.5 * (1 + np.log(2*np.pi) + 2 * np.log(s))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        When `method='MLE'` and
+        the location parameter is fixed by using the `floc` argument,
+        this function uses explicit formulas for the maximum likelihood
+        estimation of the log-normal shape and scale parameters, so the
+        `optimizer`, `loc` and `scale` keyword arguments are ignored.
+        If the location is free, a likelihood maximum is found by
+        setting its partial derivative wrt to location to 0, and
+        solving by substituting the analytical expressions of shape
+        and scale (or provided parameters).
+        See, e.g., equation 3.1 in
+        A. Clifford Cohen & Betty Jones Whitten (1980)
+        Estimation in the Three-Parameter Lognormal Distribution,
+        Journal of the American Statistical Association, 75:370, 399-404
+        https://doi.org/10.2307/2287466
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fshape, floc, fscale = parameters
+        data_min = np.min(data)
+
+        def get_shape_scale(loc):
+            # Calculate maximum likelihood scale and shape with analytical
+            # formulas unless provided by the user
+            if fshape is None or fscale is None:
+                lndata = np.log(data - loc)
+            scale = fscale or np.exp(lndata.mean())
+            shape = fshape or np.sqrt(np.mean((lndata - np.log(scale))**2))
+            return shape, scale
+
+        def dL_dLoc(loc):
+            # Derivative of (positive) LL w.r.t. loc
+            shape, scale = get_shape_scale(loc)
+            shifted = data - loc
+            return np.sum((1 + np.log(shifted/scale)/shape**2)/shifted)
+
+        def ll(loc):
+            # (Positive) log-likelihood
+            shape, scale = get_shape_scale(loc)
+            return -self.nnlf((shape, loc, scale), data)
+
+        if floc is None:
+            # The location must be less than the minimum of the data.
+            # Back off a bit to avoid numerical issues.
+            spacing = np.spacing(data_min)
+            rbrack = data_min - spacing
+
+            # Find the right end of the bracket by successive doubling of the
+            # distance to data_min. We're interested in a maximum LL, so the
+            # slope dL_dLoc_rbrack should be negative at the right end.
+            # optimization for later: share shape, scale
+            dL_dLoc_rbrack = dL_dLoc(rbrack)
+            ll_rbrack = ll(rbrack)
+            delta = 2 * spacing  # 2 * (data_min - rbrack)
+            while dL_dLoc_rbrack >= -1e-6:
+                rbrack = data_min - delta
+                dL_dLoc_rbrack = dL_dLoc(rbrack)
+                delta *= 2
+
+            if not np.isfinite(rbrack) or not np.isfinite(dL_dLoc_rbrack):
+                # If we never find a negative slope, either we missed it or the
+                # slope is always positive. It's usually the latter,
+                # which means
+                # loc = data_min - spacing
+                # But sometimes when shape and/or scale are fixed there are
+                # other issues, so be cautious.
+                return super().fit(data, *args, **kwds)
+
+            # Now find the left end of the bracket. Guess is `rbrack-1`
+            # unless that is too small of a difference to resolve. Double
+            # the size of the interval until the left end is found.
+            lbrack = np.minimum(np.nextafter(rbrack, -np.inf), rbrack-1)
+            dL_dLoc_lbrack = dL_dLoc(lbrack)
+            delta = 2 * (rbrack - lbrack)
+            while (np.isfinite(lbrack) and np.isfinite(dL_dLoc_lbrack)
+                   and np.sign(dL_dLoc_lbrack) == np.sign(dL_dLoc_rbrack)):
+                lbrack = rbrack - delta
+                dL_dLoc_lbrack = dL_dLoc(lbrack)
+                delta *= 2
+
+            # I don't recall observing this, but just in case...
+            if not np.isfinite(lbrack) or not np.isfinite(dL_dLoc_lbrack):
+                return super().fit(data, *args, **kwds)
+
+            # If we have a valid bracket, find the root
+            res = root_scalar(dL_dLoc, bracket=(lbrack, rbrack))
+            if not res.converged:
+                return super().fit(data, *args, **kwds)
+
+            # If the slope was positive near the minimum of the data,
+            # the maximum LL could be there instead of at the root. Compare
+            # the LL of the two points to decide.
+            ll_root = ll(res.root)
+            loc = res.root if ll_root > ll_rbrack else data_min-spacing
+
+        else:
+            if floc >= data_min:
+                raise FitDataError("lognorm", lower=0., upper=np.inf)
+            loc = floc
+
+        shape, scale = get_shape_scale(loc)
+        if not (self._argcheck(shape) and scale > 0):
+            return super().fit(data, *args, **kwds)
+        return shape, loc, scale
+
+
+lognorm = lognorm_gen(a=0.0, name='lognorm')
+
+
+class gibrat_gen(rv_continuous):
+    r"""A Gibrat continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gibrat` is:
+
+    .. math::
+
+        f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)
+
+    for :math:`x >= 0`.
+
+    `gibrat` is a special case of `lognorm` with ``s=1``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return np.exp(random_state.standard_normal(size))
+
+    def _pdf(self, x):
+        # gibrat.pdf(x) = 1/(x*sqrt(2*pi)) * exp(-1/2*(log(x))**2)
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return _lognorm_logpdf(x, 1.0)
+
+    def _cdf(self, x):
+        return _norm_cdf(np.log(x))
+
+    def _ppf(self, q):
+        return np.exp(_norm_ppf(q))
+
+    def _sf(self, x):
+        return _norm_sf(np.log(x))
+
+    def _isf(self, p):
+        return np.exp(_norm_isf(p))
+
+    def _stats(self):
+        p = np.e
+        mu = np.sqrt(p)
+        mu2 = p * (p - 1)
+        g1 = np.sqrt(p - 1) * (2 + p)
+        g2 = np.polyval([1, 2, 3, 0, -6.0], p)
+        return mu, mu2, g1, g2
+
+    def _entropy(self):
+        return 0.5 * np.log(2 * np.pi) + 0.5
+
+
+gibrat = gibrat_gen(a=0.0, name='gibrat')
+
+
+class maxwell_gen(rv_continuous):
+    r"""A Maxwell continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    A special case of a `chi` distribution,  with ``df=3``, ``loc=0.0``,
+    and given ``scale = a``, where ``a`` is the parameter used in the
+    Mathworld description [1]_.
+
+    The probability density function for `maxwell` is:
+
+    .. math::
+
+        f(x) = \sqrt{2/\pi}x^2 \exp(-x^2/2)
+
+    for :math:`x >= 0`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] http://mathworld.wolfram.com/MaxwellDistribution.html
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return chi.rvs(3.0, size=size, random_state=random_state)
+
+    def _pdf(self, x):
+        # maxwell.pdf(x) = sqrt(2/pi)x**2 * exp(-x**2/2)
+        return _SQRT_2_OVER_PI*x*x*np.exp(-x*x/2.0)
+
+    def _logpdf(self, x):
+        # Allow x=0 without 'divide by zero' warnings
+        with np.errstate(divide='ignore'):
+            return _LOG_SQRT_2_OVER_PI + 2*np.log(x) - 0.5*x*x
+
+    def _cdf(self, x):
+        return sc.gammainc(1.5, x*x/2.0)
+
+    def _ppf(self, q):
+        return np.sqrt(2*sc.gammaincinv(1.5, q))
+
+    def _sf(self, x):
+        return sc.gammaincc(1.5, x*x/2.0)
+
+    def _isf(self, q):
+        return np.sqrt(2*sc.gammainccinv(1.5, q))
+
+    def _stats(self):
+        val = 3*np.pi-8
+        return (2*np.sqrt(2.0/np.pi),
+                3-8/np.pi,
+                np.sqrt(2)*(32-10*np.pi)/val**1.5,
+                (-12*np.pi*np.pi + 160*np.pi - 384) / val**2.0)
+
+    def _entropy(self):
+        return _EULER + 0.5*np.log(2*np.pi)-0.5
+
+
+maxwell = maxwell_gen(a=0.0, name='maxwell')
+
+
+class mielke_gen(rv_continuous):
+    r"""A Mielke Beta-Kappa / Dagum continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `mielke` is:
+
+    .. math::
+
+        f(x, k, s) = \frac{k x^{k-1}}{(1+x^s)^{1+k/s}}
+
+    for :math:`x > 0` and :math:`k, s > 0`. The distribution is sometimes
+    called Dagum distribution ([2]_). It was already defined in [3]_, called
+    a Burr Type III distribution (`burr` with parameters ``c=s`` and
+    ``d=k/s``).
+
+    `mielke` takes ``k`` and ``s`` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Mielke, P.W., 1973 "Another Family of Distributions for Describing
+           and Analyzing Precipitation Data." J. Appl. Meteor., 12, 275-280
+    .. [2] Dagum, C., 1977 "A new model for personal income distribution."
+           Economie Appliquee, 33, 327-367.
+    .. [3] Burr, I. W. "Cumulative frequency functions", Annals of
+           Mathematical Statistics, 13(2), pp 215-232 (1942).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ik = _ShapeInfo("k", False, (0, np.inf), (False, False))
+        i_s = _ShapeInfo("s", False, (0, np.inf), (False, False))
+        return [ik, i_s]
+
+    def _pdf(self, x, k, s):
+        return k*x**(k-1.0) / (1.0+x**s)**(1.0+k*1.0/s)
+
+    def _logpdf(self, x, k, s):
+        # Allow x=0 without 'divide by zero' warnings.
+        with np.errstate(divide='ignore'):
+            return np.log(k) + np.log(x)*(k - 1) - np.log1p(x**s)*(1 + k/s)
+
+    def _cdf(self, x, k, s):
+        return x**k / (1.0+x**s)**(k*1.0/s)
+
+    def _ppf(self, q, k, s):
+        qsk = pow(q, s*1.0/k)
+        return pow(qsk/(1.0-qsk), 1.0/s)
+
+    def _munp(self, n, k, s):
+        def nth_moment(n, k, s):
+            # n-th moment is defined for -k < n < s
+            return sc.gamma((k+n)/s)*sc.gamma(1-n/s)/sc.gamma(k/s)
+
+        return _lazywhere(n < s, (n, k, s), nth_moment, np.inf)
+
+
+mielke = mielke_gen(a=0.0, name='mielke')
+
+
+class kappa4_gen(rv_continuous):
+    r"""Kappa 4 parameter distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for kappa4 is:
+
+    .. math::
+
+        f(x, h, k) = (1 - k x)^{1/k - 1} (1 - h (1 - k x)^{1/k})^{1/h-1}
+
+    if :math:`h` and :math:`k` are not equal to 0.
+
+    If :math:`h` or :math:`k` are zero then the pdf can be simplified:
+
+    h = 0 and k != 0::
+
+        kappa4.pdf(x, h, k) = (1.0 - k*x)**(1.0/k - 1.0)*
+                              exp(-(1.0 - k*x)**(1.0/k))
+
+    h != 0 and k = 0::
+
+        kappa4.pdf(x, h, k) = exp(-x)*(1.0 - h*exp(-x))**(1.0/h - 1.0)
+
+    h = 0 and k = 0::
+
+        kappa4.pdf(x, h, k) = exp(-x)*exp(-exp(-x))
+
+    kappa4 takes :math:`h` and :math:`k` as shape parameters.
+
+    The kappa4 distribution returns other distributions when certain
+    :math:`h` and :math:`k` values are used.
+
+    +------+-------------+----------------+------------------+
+    | h    | k=0.0       | k=1.0          | -inf<=k<=inf     |
+    +======+=============+================+==================+
+    | -1.0 | Logistic    |                | Generalized      |
+    |      |             |                | Logistic(1)      |
+    |      |             |                |                  |
+    |      | logistic(x) |                |                  |
+    +------+-------------+----------------+------------------+
+    |  0.0 | Gumbel      | Reverse        | Generalized      |
+    |      |             | Exponential(2) | Extreme Value    |
+    |      |             |                |                  |
+    |      | gumbel_r(x) |                | genextreme(x, k) |
+    +------+-------------+----------------+------------------+
+    |  1.0 | Exponential | Uniform        | Generalized      |
+    |      |             |                | Pareto           |
+    |      |             |                |                  |
+    |      | expon(x)    | uniform(x)     | genpareto(x, -k) |
+    +------+-------------+----------------+------------------+
+
+    (1) There are at least five generalized logistic distributions.
+        Four are described here:
+        https://en.wikipedia.org/wiki/Generalized_logistic_distribution
+        The "fifth" one is the one kappa4 should match which currently
+        isn't implemented in scipy:
+        https://en.wikipedia.org/wiki/Talk:Generalized_logistic_distribution
+        https://www.mathwave.com/help/easyfit/html/analyses/distributions/gen_logistic.html
+    (2) This distribution is currently not in scipy.
+
+    References
+    ----------
+    J.C. Finney, "Optimization of a Skewed Logistic Distribution With Respect
+    to the Kolmogorov-Smirnov Test", A Dissertation Submitted to the Graduate
+    Faculty of the Louisiana State University and Agricultural and Mechanical
+    College, (August, 2004),
+    https://digitalcommons.lsu.edu/gradschool_dissertations/3672
+
+    J.R.M. Hosking, "The four-parameter kappa distribution". IBM J. Res.
+    Develop. 38 (3), 25 1-258 (1994).
+
+    B. Kumphon, A. Kaew-Man, P. Seenoi, "A Rainfall Distribution for the Lampao
+    Site in the Chi River Basin, Thailand", Journal of Water Resource and
+    Protection, vol. 4, 866-869, (2012).
+    :doi:`10.4236/jwarp.2012.410101`
+
+    C. Winchester, "On Estimation of the Four-Parameter Kappa Distribution", A
+    Thesis Submitted to Dalhousie University, Halifax, Nova Scotia, (March
+    2000).
+    http://www.nlc-bnc.ca/obj/s4/f2/dsk2/ftp01/MQ57336.pdf
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, h, k):
+        shape = np.broadcast_arrays(h, k)[0].shape
+        return np.full(shape, fill_value=True)
+
+    def _shape_info(self):
+        ih = _ShapeInfo("h", False, (-np.inf, np.inf), (False, False))
+        ik = _ShapeInfo("k", False, (-np.inf, np.inf), (False, False))
+        return [ih, ik]
+
+    def _get_support(self, h, k):
+        condlist = [np.logical_and(h > 0, k > 0),
+                    np.logical_and(h > 0, k == 0),
+                    np.logical_and(h > 0, k < 0),
+                    np.logical_and(h <= 0, k > 0),
+                    np.logical_and(h <= 0, k == 0),
+                    np.logical_and(h <= 0, k < 0)]
+
+        def f0(h, k):
+            return (1.0 - np.float_power(h, -k))/k
+
+        def f1(h, k):
+            return np.log(h)
+
+        def f3(h, k):
+            a = np.empty(np.shape(h))
+            a[:] = -np.inf
+            return a
+
+        def f5(h, k):
+            return 1.0/k
+
+        _a = _lazyselect(condlist,
+                         [f0, f1, f0, f3, f3, f5],
+                         [h, k],
+                         default=np.nan)
+
+        def f0(h, k):
+            return 1.0/k
+
+        def f1(h, k):
+            a = np.empty(np.shape(h))
+            a[:] = np.inf
+            return a
+
+        _b = _lazyselect(condlist,
+                         [f0, f1, f1, f0, f1, f1],
+                         [h, k],
+                         default=np.nan)
+        return _a, _b
+
+    def _pdf(self, x, h, k):
+        # kappa4.pdf(x, h, k) = (1.0 - k*x)**(1.0/k - 1.0)*
+        #                       (1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h-1)
+        return np.exp(self._logpdf(x, h, k))
+
+    def _logpdf(self, x, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(x, h, k):
+            '''pdf = (1.0 - k*x)**(1.0/k - 1.0)*(
+                      1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h-1.0)
+               logpdf = ...
+            '''
+            return (sc.xlog1py(1.0/k - 1.0, -k*x) +
+                    sc.xlog1py(1.0/h - 1.0, -h*(1.0 - k*x)**(1.0/k)))
+
+        def f1(x, h, k):
+            '''pdf = (1.0 - k*x)**(1.0/k - 1.0)*np.exp(-(
+                      1.0 - k*x)**(1.0/k))
+               logpdf = ...
+            '''
+            return sc.xlog1py(1.0/k - 1.0, -k*x) - (1.0 - k*x)**(1.0/k)
+
+        def f2(x, h, k):
+            '''pdf = np.exp(-x)*(1.0 - h*np.exp(-x))**(1.0/h - 1.0)
+               logpdf = ...
+            '''
+            return -x + sc.xlog1py(1.0/h - 1.0, -h*np.exp(-x))
+
+        def f3(x, h, k):
+            '''pdf = np.exp(-x-np.exp(-x))
+               logpdf = ...
+            '''
+            return -x - np.exp(-x)
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [x, h, k],
+                           default=np.nan)
+
+    def _cdf(self, x, h, k):
+        return np.exp(self._logcdf(x, h, k))
+
+    def _logcdf(self, x, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(x, h, k):
+            '''cdf = (1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h)
+               logcdf = ...
+            '''
+            return (1.0/h)*sc.log1p(-h*(1.0 - k*x)**(1.0/k))
+
+        def f1(x, h, k):
+            '''cdf = np.exp(-(1.0 - k*x)**(1.0/k))
+               logcdf = ...
+            '''
+            return -(1.0 - k*x)**(1.0/k)
+
+        def f2(x, h, k):
+            '''cdf = (1.0 - h*np.exp(-x))**(1.0/h)
+               logcdf = ...
+            '''
+            return (1.0/h)*sc.log1p(-h*np.exp(-x))
+
+        def f3(x, h, k):
+            '''cdf = np.exp(-np.exp(-x))
+               logcdf = ...
+            '''
+            return -np.exp(-x)
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [x, h, k],
+                           default=np.nan)
+
+    def _ppf(self, q, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(q, h, k):
+            return 1.0/k*(1.0 - ((1.0 - (q**h))/h)**k)
+
+        def f1(q, h, k):
+            return 1.0/k*(1.0 - (-np.log(q))**k)
+
+        def f2(q, h, k):
+            '''ppf = -np.log((1.0 - (q**h))/h)
+            '''
+            return -sc.log1p(-(q**h)) + np.log(h)
+
+        def f3(q, h, k):
+            return -np.log(-np.log(q))
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [q, h, k],
+                           default=np.nan)
+
+    def _get_stats_info(self, h, k):
+        condlist = [
+            np.logical_and(h < 0, k >= 0),
+            k < 0,
+        ]
+
+        def f0(h, k):
+            return (-1.0/h*k).astype(int)
+
+        def f1(h, k):
+            return (-1.0/k).astype(int)
+
+        return _lazyselect(condlist, [f0, f1], [h, k], default=5)
+
+    def _stats(self, h, k):
+        maxr = self._get_stats_info(h, k)
+        outputs = [None if np.any(r < maxr) else np.nan for r in range(1, 5)]
+        return outputs[:]
+
+    def _mom1_sc(self, m, *args):
+        maxr = self._get_stats_info(args[0], args[1])
+        if m >= maxr:
+            return np.nan
+        return integrate.quad(self._mom_integ1, 0, 1, args=(m,)+args)[0]
+
+
+kappa4 = kappa4_gen(name='kappa4')
+
+
+class kappa3_gen(rv_continuous):
+    r"""Kappa 3 parameter distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `kappa3` is:
+
+    .. math::
+
+        f(x, a) = a (a + x^a)^{-(a + 1)/a}
+
+    for :math:`x > 0` and :math:`a > 0`.
+
+    `kappa3` takes ``a`` as a shape parameter for :math:`a`.
+
+    References
+    ----------
+    P.W. Mielke and E.S. Johnson, "Three-Parameter Kappa Distribution Maximum
+    Likelihood and Likelihood Ratio Tests", Methods in Weather Research,
+    701-707, (September, 1973),
+    :doi:`10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2`
+
+    B. Kumphon, "Maximum Entropy and Maximum Likelihood Estimation for the
+    Three-Parameter Kappa Distribution", Open Journal of Statistics, vol 2,
+    415-419 (2012), :doi:`10.4236/ojs.2012.24050`
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # kappa3.pdf(x, a) = a*(a + x**a)**(-(a + 1)/a),     for x > 0
+        return a*(a + x**a)**(-1.0/a-1)
+
+    def _cdf(self, x, a):
+        return x*(a + x**a)**(-1.0/a)
+
+    def _sf(self, x, a):
+        x, a = np.broadcast_arrays(x, a)  # some code paths pass scalars
+        sf = super()._sf(x, a)
+
+        # When the SF is small, another formulation is typically more accurate.
+        # However, it blows up for large `a`, so use it only if it also returns
+        # a small value of the SF.
+        cutoff = 0.01
+        i = sf < cutoff
+        sf2 = -sc.expm1(sc.xlog1py(-1.0 / a[i], a[i] * x[i]**-a[i]))
+        i2 = sf2 > cutoff
+        sf2[i2] = sf[i][i2]  # replace bad values with original values
+
+        sf[i] = sf2
+        return sf
+
+    def _ppf(self, q, a):
+        return (a/(q**-a - 1.0))**(1.0/a)
+
+    def _isf(self, q, a):
+        lg = sc.xlog1py(-a, -q)
+        denom = sc.expm1(lg)
+        return (a / denom)**(1.0 / a)
+
+    def _stats(self, a):
+        outputs = [None if np.any(i < a) else np.nan for i in range(1, 5)]
+        return outputs[:]
+
+    def _mom1_sc(self, m, *args):
+        if np.any(m >= args[0]):
+            return np.nan
+        return integrate.quad(self._mom_integ1, 0, 1, args=(m,)+args)[0]
+
+
+kappa3 = kappa3_gen(a=0.0, name='kappa3')
+
+
+class moyal_gen(rv_continuous):
+    r"""A Moyal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `moyal` is:
+
+    .. math::
+
+        f(x) = \exp(-(x + \exp(-x))/2) / \sqrt{2\pi}
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    This distribution has utility in high-energy physics and radiation
+    detection. It describes the energy loss of a charged relativistic
+    particle due to ionization of the medium [1]_. It also provides an
+    approximation for the Landau distribution. For an in depth description
+    see [2]_. For additional description, see [3]_.
+
+    References
+    ----------
+    .. [1] J.E. Moyal, "XXX. Theory of ionization fluctuations",
+           The London, Edinburgh, and Dublin Philosophical Magazine
+           and Journal of Science, vol 46, 263-280, (1955).
+           :doi:`10.1080/14786440308521076` (gated)
+    .. [2] G. Cordeiro et al., "The beta Moyal: a useful skew distribution",
+           International Journal of Research and Reviews in Applied Sciences,
+           vol 10, 171-192, (2012).
+           http://www.arpapress.com/Volumes/Vol10Issue2/IJRRAS_10_2_02.pdf
+    .. [3] C. Walck, "Handbook on Statistical Distributions for
+           Experimentalists; International Report SUF-PFY/96-01", Chapter 26,
+           University of Stockholm: Stockholm, Sweden, (2007).
+           http://www.stat.rice.edu/~dobelman/textfiles/DistributionsHandbook.pdf
+
+    .. versionadded:: 1.1.0
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        u1 = gamma.rvs(a=0.5, scale=2, size=size,
+                       random_state=random_state)
+        return -np.log(u1)
+
+    def _pdf(self, x):
+        return np.exp(-0.5 * (x + np.exp(-x))) / np.sqrt(2*np.pi)
+
+    def _cdf(self, x):
+        return sc.erfc(np.exp(-0.5 * x) / np.sqrt(2))
+
+    def _sf(self, x):
+        return sc.erf(np.exp(-0.5 * x) / np.sqrt(2))
+
+    def _ppf(self, x):
+        return -np.log(2 * sc.erfcinv(x)**2)
+
+    def _stats(self):
+        mu = np.log(2) + np.euler_gamma
+        mu2 = np.pi**2 / 2
+        g1 = 28 * np.sqrt(2) * sc.zeta(3) / np.pi**3
+        g2 = 4.
+        return mu, mu2, g1, g2
+
+    def _munp(self, n):
+        if n == 1.0:
+            return np.log(2) + np.euler_gamma
+        elif n == 2.0:
+            return np.pi**2 / 2 + (np.log(2) + np.euler_gamma)**2
+        elif n == 3.0:
+            tmp1 = 1.5 * np.pi**2 * (np.log(2)+np.euler_gamma)
+            tmp2 = (np.log(2)+np.euler_gamma)**3
+            tmp3 = 14 * sc.zeta(3)
+            return tmp1 + tmp2 + tmp3
+        elif n == 4.0:
+            tmp1 = 4 * 14 * sc.zeta(3) * (np.log(2) + np.euler_gamma)
+            tmp2 = 3 * np.pi**2 * (np.log(2) + np.euler_gamma)**2
+            tmp3 = (np.log(2) + np.euler_gamma)**4
+            tmp4 = 7 * np.pi**4 / 4
+            return tmp1 + tmp2 + tmp3 + tmp4
+        else:
+            # return generic for higher moments
+            # return rv_continuous._mom1_sc(self, n, b)
+            return self._mom1_sc(n)
+
+
+moyal = moyal_gen(name="moyal")
+
+
+class nakagami_gen(rv_continuous):
+    r"""A Nakagami continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `nakagami` is:
+
+    .. math::
+
+        f(x, \nu) = \frac{2 \nu^\nu}{\Gamma(\nu)} x^{2\nu-1} \exp(-\nu x^2)
+
+    for :math:`x >= 0`, :math:`\nu > 0`. The distribution was introduced in
+    [2]_, see also [1]_ for further information.
+
+    `nakagami` takes ``nu`` as a shape parameter for :math:`\nu`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Nakagami distribution", Wikipedia
+           https://en.wikipedia.org/wiki/Nakagami_distribution
+    .. [2] M. Nakagami, "The m-distribution - A general formula of intensity
+           distribution of rapid fading", Statistical methods in radio wave
+           propagation, Pergamon Press, 1960, 3-36.
+           :doi:`10.1016/B978-0-08-009306-2.50005-4`
+
+    %(example)s
+
+    """
+    def _argcheck(self, nu):
+        return nu > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("nu", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, nu):
+        return np.exp(self._logpdf(x, nu))
+
+    def _logpdf(self, x, nu):
+        # nakagami.pdf(x, nu) = 2 * nu**nu / gamma(nu) *
+        #                       x**(2*nu-1) * exp(-nu*x**2)
+        return (np.log(2) + sc.xlogy(nu, nu) - sc.gammaln(nu) +
+                sc.xlogy(2*nu - 1, x) - nu*x**2)
+
+    def _cdf(self, x, nu):
+        return sc.gammainc(nu, nu*x*x)
+
+    def _ppf(self, q, nu):
+        return np.sqrt(1.0/nu*sc.gammaincinv(nu, q))
+
+    def _sf(self, x, nu):
+        return sc.gammaincc(nu, nu*x*x)
+
+    def _isf(self, p, nu):
+        return np.sqrt(1/nu * sc.gammainccinv(nu, p))
+
+    def _stats(self, nu):
+        mu = sc.poch(nu, 0.5)/np.sqrt(nu)
+        mu2 = 1.0-mu*mu
+        g1 = mu * (1 - 4*nu*mu2) / 2.0 / nu / np.power(mu2, 1.5)
+        g2 = -6*mu**4*nu + (8*nu-2)*mu**2-2*nu + 1
+        g2 /= nu*mu2**2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self, nu):
+        shape = np.shape(nu)
+        # because somehow this isn't taken care of by the infrastructure...
+        nu = np.atleast_1d(nu)
+        A = sc.gammaln(nu)
+        B = nu - (nu - 0.5) * sc.digamma(nu)
+        C = -0.5 * np.log(nu) - np.log(2)
+        h = A + B + C
+        # This is the asymptotic sum of A and B (see gh-17868)
+        norm_entropy = stats.norm._entropy()
+        # Above, this is lost to rounding error for large nu, so use the
+        # asymptotic sum when the approximation becomes accurate
+        i = nu > 5e4  # roundoff error ~ approximation error
+        # -1 / (12 * nu) is the O(1/nu) term; see gh-17929
+        h[i] = C[i] + norm_entropy - 1/(12*nu[i])
+        return h.reshape(shape)[()]
+
+    def _rvs(self, nu, size=None, random_state=None):
+        # this relationship can be found in [1] or by a direct calculation
+        return np.sqrt(random_state.standard_gamma(nu, size=size) / nu)
+
+    def _fitstart(self, data, args=None):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        if args is None:
+            args = (1.0,) * self.numargs
+        # Analytical justified estimates
+        # see: https://docs.scipy.org/doc/scipy/reference/tutorial/stats/continuous_nakagami.html
+        loc = np.min(data)
+        scale = np.sqrt(np.sum((data - loc)**2) / len(data))
+        return args + (loc, scale)
+
+
+nakagami = nakagami_gen(a=0.0, name="nakagami")
+
+
+# The function name ncx2 is an abbreviation for noncentral chi squared.
+def _ncx2_log_pdf(x, df, nc):
+    # We use (xs**2 + ns**2)/2 = (xs - ns)**2/2  + xs*ns, and include the
+    # factor of exp(-xs*ns) into the ive function to improve numerical
+    # stability at large values of xs. See also `rice.pdf`.
+    df2 = df/2.0 - 1.0
+    xs, ns = np.sqrt(x), np.sqrt(nc)
+    res = sc.xlogy(df2/2.0, x/nc) - 0.5*(xs - ns)**2
+    corr = sc.ive(df2, xs*ns) / 2.0
+    # Return res + np.log(corr) avoiding np.log(0)
+    return _lazywhere(
+        corr > 0,
+        (res, corr),
+        f=lambda r, c: r + np.log(c),
+        fillvalue=-np.inf)
+
+
+class ncx2_gen(rv_continuous):
+    r"""A non-central chi-squared continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `ncx2` is:
+
+    .. math::
+
+        f(x, k, \lambda) = \frac{1}{2} \exp(-(\lambda+x)/2)
+            (x/\lambda)^{(k-2)/4}  I_{(k-2)/2}(\sqrt{\lambda x})
+
+    for :math:`x >= 0`, :math:`k > 0` and :math:`\lambda \ge 0`.
+    :math:`k` specifies the degrees of freedom (denoted ``df`` in the
+    implementation) and :math:`\lambda` is the non-centrality parameter
+    (denoted ``nc`` in the implementation). :math:`I_\nu` denotes the
+    modified Bessel function of first order of degree :math:`\nu`
+    (`scipy.special.iv`).
+
+    `ncx2` takes ``df`` and ``nc`` as shape parameters.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, df, nc):
+        return (df > 0) & np.isfinite(df) & (nc >= 0)
+
+    def _shape_info(self):
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (0, np.inf), (True, False))
+        return [idf, inc]
+
+    def _rvs(self, df, nc, size=None, random_state=None):
+        return random_state.noncentral_chisquare(df, nc, size)
+
+    def _logpdf(self, x, df, nc):
+        cond = np.ones_like(x, dtype=bool) & (nc != 0)
+        return _lazywhere(cond, (x, df, nc), f=_ncx2_log_pdf,
+                          f2=lambda x, df, _: chi2._logpdf(x, df))
+
+    def _pdf(self, x, df, nc):
+        cond = np.ones_like(x, dtype=bool) & (nc != 0)
+        with np.errstate(over='ignore'):  # see gh-17432
+            return _lazywhere(cond, (x, df, nc), f=scu._ncx2_pdf,
+                              f2=lambda x, df, _: chi2._pdf(x, df))
+
+    def _cdf(self, x, df, nc):
+        cond = np.ones_like(x, dtype=bool) & (nc != 0)
+        with np.errstate(over='ignore'):  # see gh-17432
+            return _lazywhere(cond, (x, df, nc), f=scu._ncx2_cdf,
+                              f2=lambda x, df, _: chi2._cdf(x, df))
+
+    def _ppf(self, q, df, nc):
+        cond = np.ones_like(q, dtype=bool) & (nc != 0)
+        with np.errstate(over='ignore'):  # see gh-17432
+            return _lazywhere(cond, (q, df, nc), f=scu._ncx2_ppf,
+                              f2=lambda x, df, _: chi2._ppf(x, df))
+
+    def _sf(self, x, df, nc):
+        cond = np.ones_like(x, dtype=bool) & (nc != 0)
+        with np.errstate(over='ignore'):  # see gh-17432
+            return _lazywhere(cond, (x, df, nc), f=scu._ncx2_sf,
+                              f2=lambda x, df, _: chi2._sf(x, df))
+
+    def _isf(self, x, df, nc):
+        cond = np.ones_like(x, dtype=bool) & (nc != 0)
+        with np.errstate(over='ignore'):  # see gh-17432
+            return _lazywhere(cond, (x, df, nc), f=scu._ncx2_isf,
+                              f2=lambda x, df, _: chi2._isf(x, df))
+
+    def _stats(self, df, nc):
+        _ncx2_mean = df + nc
+        def k_plus_cl(k, l, c):
+            return k + c*l
+        _ncx2_variance =  2.0 * k_plus_cl(df, nc, 2.0)
+        _ncx2_skewness = (np.sqrt(8.0) * k_plus_cl(df, nc, 3) /
+                          np.sqrt(k_plus_cl(df, nc, 2.0)**3))
+        _ncx2_kurtosis_excess = (12.0 * k_plus_cl(df, nc, 4.0) /
+                                 k_plus_cl(df, nc, 2.0)**2)
+        return (
+            _ncx2_mean,
+            _ncx2_variance,
+            _ncx2_skewness,
+            _ncx2_kurtosis_excess,
+        )
+
+
+ncx2 = ncx2_gen(a=0.0, name='ncx2')
+
+
+class ncf_gen(rv_continuous):
+    r"""A non-central F distribution continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    scipy.stats.f : Fisher distribution
+
+    Notes
+    -----
+    The probability density function for `ncf` is:
+
+    .. math::
+
+        f(x, n_1, n_2, \lambda) =
+            \exp\left(\frac{\lambda}{2} +
+                      \lambda n_1 \frac{x}{2(n_1 x + n_2)}
+                \right)
+            n_1^{n_1/2} n_2^{n_2/2} x^{n_1/2 - 1} \\
+            (n_2 + n_1 x)^{-(n_1 + n_2)/2}
+            \gamma(n_1/2) \gamma(1 + n_2/2) \\
+            \frac{L^{\frac{n_1}{2}-1}_{n_2/2}
+                \left(-\lambda n_1 \frac{x}{2(n_1 x + n_2)}\right)}
+            {B(n_1/2, n_2/2)
+                \gamma\left(\frac{n_1 + n_2}{2}\right)}
+
+    for :math:`n_1, n_2 > 0`, :math:`\lambda \ge 0`.  Here :math:`n_1` is the
+    degrees of freedom in the numerator, :math:`n_2` the degrees of freedom in
+    the denominator, :math:`\lambda` the non-centrality parameter,
+    :math:`\gamma` is the logarithm of the Gamma function, :math:`L_n^k` is a
+    generalized Laguerre polynomial and :math:`B` is the beta function.
+
+    `ncf` takes ``dfn``, ``dfd`` and ``nc`` as shape parameters. If ``nc=0``,
+    the distribution becomes equivalent to the Fisher distribution.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``stats``, ``sf`` and
+    ``isf`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, dfn, dfd, nc):
+        return (dfn > 0) & (dfd > 0) & (nc >= 0)
+
+    def _shape_info(self):
+        idf1 = _ShapeInfo("dfn", False, (0, np.inf), (False, False))
+        idf2 = _ShapeInfo("dfd", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (0, np.inf), (True, False))
+        return [idf1, idf2, inc]
+
+    def _rvs(self, dfn, dfd, nc, size=None, random_state=None):
+        return random_state.noncentral_f(dfn, dfd, nc, size)
+
+    def _pdf(self, x, dfn, dfd, nc):
+        return scu._ncf_pdf(x, dfn, dfd, nc)
+
+    def _cdf(self, x, dfn, dfd, nc):
+        return sc.ncfdtr(dfn, dfd, nc, x)
+
+    def _ppf(self, q, dfn, dfd, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return sc.ncfdtri(dfn, dfd, nc, q)
+
+    def _sf(self, x, dfn, dfd, nc):
+        return scu._ncf_sf(x, dfn, dfd, nc)
+
+    def _isf(self, x, dfn, dfd, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._ncf_isf(x, dfn, dfd, nc)
+
+    # # Produces bogus values as written - maybe it's close, though?
+    # def _munp(self, n, dfn, dfd, nc):
+    #     val = (dfn * 1.0/dfd)**n
+    #     term = sc.gammaln(n+0.5*dfn) + sc.gammaln(0.5*dfd-n) - sc.gammaln(dfd*0.5)
+    #     val *= np.exp(-nc / 2.0+term)
+    #     val *= sc.hyp1f1(n+0.5*dfn, 0.5*dfn, 0.5*nc)
+    #     return val
+
+    def _stats(self, dfn, dfd, nc, moments='mv'):
+        mu = scu._ncf_mean(dfn, dfd, nc)
+        mu2 = scu._ncf_variance(dfn, dfd, nc)
+        g1 = scu._ncf_skewness(dfn, dfd, nc) if 's' in moments else None
+        g2 = scu._ncf_kurtosis_excess(  # isn't really excess kurtosis!
+            dfn, dfd, nc) - 3 if 'k' in moments else None
+        # Mathematica: Kurtosis[NoncentralFRatioDistribution[27, 27, 0.415784417992261]]
+        return mu, mu2, g1, g2
+
+
+ncf = ncf_gen(a=0.0, name='ncf')
+
+
+class t_gen(rv_continuous):
+    r"""A Student's t continuous random variable.
+
+    For the noncentral t distribution, see `nct`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    nct
+
+    Notes
+    -----
+    The probability density function for `t` is:
+
+    .. math::
+
+        f(x, \nu) = \frac{\Gamma((\nu+1)/2)}
+                        {\sqrt{\pi \nu} \Gamma(\nu/2)}
+                    (1+x^2/\nu)^{-(\nu+1)/2}
+
+    where :math:`x` is a real number and the degrees of freedom parameter
+    :math:`\nu` (denoted ``df`` in the implementation) satisfies
+    :math:`\nu > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return random_state.standard_t(df, size=size)
+
+    def _pdf(self, x, df):
+        return _lazywhere(
+            df == np.inf, (x, df),
+            f=lambda x, df: norm._pdf(x),
+            f2=lambda x, df: (
+                np.exp(self._logpdf(x, df))
+            )
+        )
+
+    def _logpdf(self, x, df):
+
+        def t_logpdf(x, df):
+            return (np.log(sc.poch(0.5 * df, 0.5))
+                    - 0.5 * (np.log(df) + np.log(np.pi))
+                    - (df + 1)/2*np.log1p(x * x/df))
+
+        def norm_logpdf(x, df):
+            return norm._logpdf(x)
+
+        return _lazywhere(df == np.inf, (x, df, ), f=norm_logpdf, f2=t_logpdf)
+
+    def _cdf(self, x, df):
+        return sc.stdtr(df, x)
+
+    def _sf(self, x, df):
+        return sc.stdtr(df, -x)
+
+    def _ppf(self, q, df):
+        return sc.stdtrit(df, q)
+
+    def _isf(self, q, df):
+        return -sc.stdtrit(df, q)
+
+    def _stats(self, df):
+        # infinite df -> normal distribution (0.0, 1.0, 0.0, 0.0)
+        infinite_df = np.isposinf(df)
+
+        mu = np.where(df > 1, 0.0, np.inf)
+
+        condlist = ((df > 1) & (df <= 2),
+                    (df > 2) & np.isfinite(df),
+                    infinite_df)
+        choicelist = (lambda df: np.broadcast_to(np.inf, df.shape),
+                      lambda df: df / (df-2.0),
+                      lambda df: np.broadcast_to(1, df.shape))
+        mu2 = _lazyselect(condlist, choicelist, (df,), np.nan)
+
+        g1 = np.where(df > 3, 0.0, np.nan)
+
+        condlist = ((df > 2) & (df <= 4),
+                    (df > 4) & np.isfinite(df),
+                    infinite_df)
+        choicelist = (lambda df: np.broadcast_to(np.inf, df.shape),
+                      lambda df: 6.0 / (df-4.0),
+                      lambda df: np.broadcast_to(0, df.shape))
+        g2 = _lazyselect(condlist, choicelist, (df,), np.nan)
+
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+        if df == np.inf:
+            return norm._entropy()
+
+        def regular(df):
+            half = df/2
+            half1 = (df + 1)/2
+            return (half1*(sc.digamma(half1) - sc.digamma(half))
+                    + np.log(np.sqrt(df)*sc.beta(half, 0.5)))
+
+        def asymptotic(df):
+            # Formula from Wolfram Alpha:
+            # "asymptotic expansion (d+1)/2 * (digamma((d+1)/2) - digamma(d/2))
+            #  + log(sqrt(d) * beta(d/2, 1/2))"
+            h = (norm._entropy() + 1/df + (df**-2.)/4 - (df**-3.)/6
+                 - (df**-4.)/8 + 3/10*(df**-5.) + (df**-6.)/4)
+            return h
+
+        h = _lazywhere(df >= 100, (df, ), f=asymptotic, f2=regular)
+        return h
+
+
+t = t_gen(name='t')
+
+
+class nct_gen(rv_continuous):
+    r"""A non-central Student's t continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    If :math:`Y` is a standard normal random variable and :math:`V` is
+    an independent chi-square random variable (`chi2`) with :math:`k` degrees
+    of freedom, then
+
+    .. math::
+
+        X = \frac{Y + c}{\sqrt{V/k}}
+
+    has a non-central Student's t distribution on the real line.
+    The degrees of freedom parameter :math:`k` (denoted ``df`` in the
+    implementation) satisfies :math:`k > 0` and the noncentrality parameter
+    :math:`c` (denoted ``nc`` in the implementation) is a real number.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, df, nc):
+        return (df > 0) & (nc == nc)
+
+    def _shape_info(self):
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (-np.inf, np.inf), (False, False))
+        return [idf, inc]
+
+    def _rvs(self, df, nc, size=None, random_state=None):
+        n = norm.rvs(loc=nc, size=size, random_state=random_state)
+        c2 = chi2.rvs(df, size=size, random_state=random_state)
+        return n * np.sqrt(df) / np.sqrt(c2)
+
+    def _pdf(self, x, df, nc):
+        return scu._nct_pdf(x, df, nc)
+
+    def _cdf(self, x, df, nc):
+        return sc.nctdtr(df, nc, x)
+
+    def _ppf(self, q, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._nct_ppf(q, df, nc)
+
+    def _sf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return np.clip(scu._nct_sf(x, df, nc), 0, 1)
+
+    def _isf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._nct_isf(x, df, nc)
+
+    def _stats(self, df, nc, moments='mv'):
+        mu = scu._nct_mean(df, nc)
+        mu2 = scu._nct_variance(df, nc)
+        g1 = scu._nct_skewness(df, nc) if 's' in moments else None
+        g2 = scu._nct_kurtosis_excess(df, nc) if 'k' in moments else None
+        return mu, mu2, g1, g2
+
+
+nct = nct_gen(name="nct")
+
+
+class pareto_gen(rv_continuous):
+    r"""A Pareto continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `pareto` is:
+
+    .. math::
+
+        f(x, b) = \frac{b}{x^{b+1}}
+
+    for :math:`x \ge 1`, :math:`b > 0`.
+
+    `pareto` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, b):
+        # pareto.pdf(x, b) = b / x**(b+1)
+        return b * x**(-b-1)
+
+    def _cdf(self, x, b):
+        return 1 - x**(-b)
+
+    def _ppf(self, q, b):
+        return pow(1-q, -1.0/b)
+
+    def _sf(self, x, b):
+        return x**(-b)
+
+    def _isf(self, q, b):
+        return np.power(q, -1.0 / b)
+
+    def _stats(self, b, moments='mv'):
+        mu, mu2, g1, g2 = None, None, None, None
+        if 'm' in moments:
+            mask = b > 1
+            bt = np.extract(mask, b)
+            mu = np.full(np.shape(b), fill_value=np.inf)
+            np.place(mu, mask, bt / (bt-1.0))
+        if 'v' in moments:
+            mask = b > 2
+            bt = np.extract(mask, b)
+            mu2 = np.full(np.shape(b), fill_value=np.inf)
+            np.place(mu2, mask, bt / (bt-2.0) / (bt-1.0)**2)
+        if 's' in moments:
+            mask = b > 3
+            bt = np.extract(mask, b)
+            g1 = np.full(np.shape(b), fill_value=np.nan)
+            vals = 2 * (bt + 1.0) * np.sqrt(bt - 2.0) / ((bt - 3.0) * np.sqrt(bt))
+            np.place(g1, mask, vals)
+        if 'k' in moments:
+            mask = b > 4
+            bt = np.extract(mask, b)
+            g2 = np.full(np.shape(b), fill_value=np.nan)
+            vals = (6.0*np.polyval([1.0, 1.0, -6, -2], bt) /
+                    np.polyval([1.0, -7.0, 12.0, 0.0], bt))
+            np.place(g2, mask, vals)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, b):
+        return 1 + 1.0/b - np.log(b)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fshape, floc, fscale = parameters
+
+        # ensure that any fixed parameters don't violate constraints of the
+        # distribution before continuing.
+        if floc is not None and np.min(data) - floc < (fscale or 0):
+            raise FitDataError("pareto", lower=1, upper=np.inf)
+
+        ndata = data.shape[0]
+
+        def get_shape(scale, location):
+            # The first-order necessary condition on `shape` can be solved in
+            # closed form
+            return ndata / np.sum(np.log((data - location) / scale))
+
+        if floc is fscale is None:
+            # The support of the distribution is `(x - loc)/scale > 0`.
+            # The method of Lagrange multipliers turns this constraint
+            # into an equation that can be solved numerically.
+            # See gh-12545 for details.
+
+            def dL_dScale(shape, scale):
+                # The partial derivative of the log-likelihood function w.r.t.
+                # the scale.
+                return ndata * shape / scale
+
+            def dL_dLocation(shape, location):
+                # The partial derivative of the log-likelihood function w.r.t.
+                # the location.
+                return (shape + 1) * np.sum(1 / (data - location))
+
+            def fun_to_solve(scale):
+                # optimize the scale by setting the partial derivatives
+                # w.r.t. to location and scale equal and solving.
+                location = np.min(data) - scale
+                shape = fshape or get_shape(scale, location)
+                return dL_dLocation(shape, location) - dL_dScale(shape, scale)
+
+            def interval_contains_root(lbrack, rbrack):
+                # return true if the signs disagree.
+                return (np.sign(fun_to_solve(lbrack)) !=
+                        np.sign(fun_to_solve(rbrack)))
+
+            # set brackets for `root_scalar` to use when optimizing over the
+            # scale such that a root is likely between them. Use user supplied
+            # guess or default 1.
+            brack_start = float(kwds.get('scale', 1))
+            lbrack, rbrack = brack_start / 2, brack_start * 2
+            # if a root is not between the brackets, iteratively expand them
+            # until they include a sign change, checking after each bracket is
+            # modified.
+            while (not interval_contains_root(lbrack, rbrack)
+                   and (lbrack > 0 or rbrack < np.inf)):
+                lbrack /= 2
+                rbrack *= 2
+            res = root_scalar(fun_to_solve, bracket=[lbrack, rbrack])
+            if res.converged:
+                scale = res.root
+                loc = np.min(data) - scale
+                shape = fshape or get_shape(scale, loc)
+
+                # The Pareto distribution requires that its parameters satisfy
+                # the condition `fscale + floc <= min(data)`. However, to
+                # avoid numerical issues, we require that `fscale + floc`
+                # is strictly less than `min(data)`. If this condition
+                # is not satisfied, reduce the scale with `np.nextafter` to
+                # ensure that data does not fall outside of the support.
+                if not (scale + loc) < np.min(data):
+                    scale = np.min(data) - loc
+                    scale = np.nextafter(scale, 0)
+                return shape, loc, scale
+            else:
+                return super().fit(data, **kwds)
+        elif floc is None:
+            loc = np.min(data) - fscale
+        else:
+            loc = floc
+        # Source: Evans, Hastings, and Peacock (2000), Statistical
+        # Distributions, 3rd. Ed., John Wiley and Sons. Page 149.
+        scale = fscale or np.min(data) - loc
+        shape = fshape or get_shape(scale, loc)
+        return shape, loc, scale
+
+
+pareto = pareto_gen(a=1.0, name="pareto")
+
+
+class lomax_gen(rv_continuous):
+    r"""A Lomax (Pareto of the second kind) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `lomax` is:
+
+    .. math::
+
+        f(x, c) = \frac{c}{(1+x)^{c+1}}
+
+    for :math:`x \ge 0`, :math:`c > 0`.
+
+    `lomax` takes ``c`` as a shape parameter for :math:`c`.
+
+    `lomax` is a special case of `pareto` with ``loc=-1.0``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # lomax.pdf(x, c) = c / (1+x)**(c+1)
+        return c*1.0/(1.0+x)**(c+1.0)
+
+    def _logpdf(self, x, c):
+        return np.log(c) - (c+1)*sc.log1p(x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-c*sc.log1p(x))
+
+    def _sf(self, x, c):
+        return np.exp(-c*sc.log1p(x))
+
+    def _logsf(self, x, c):
+        return -c*sc.log1p(x)
+
+    def _ppf(self, q, c):
+        return sc.expm1(-sc.log1p(-q)/c)
+
+    def _isf(self, q, c):
+        return q**(-1.0 / c) - 1
+
+    def _stats(self, c):
+        mu, mu2, g1, g2 = pareto.stats(c, loc=-1.0, moments='mvsk')
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        return 1+1.0/c-np.log(c)
+
+
+lomax = lomax_gen(a=0.0, name="lomax")
+
+
+class pearson3_gen(rv_continuous):
+    r"""A pearson type III continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `pearson3` is:
+
+    .. math::
+
+        f(x, \kappa) = \frac{|\beta|}{\Gamma(\alpha)}
+                       (\beta (x - \zeta))^{\alpha - 1}
+                       \exp(-\beta (x - \zeta))
+
+    where:
+
+    .. math::
+
+            \beta = \frac{2}{\kappa}
+
+            \alpha = \beta^2 = \frac{4}{\kappa^2}
+
+            \zeta = -\frac{\alpha}{\beta} = -\beta
+
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+    Pass the skew :math:`\kappa` into `pearson3` as the shape parameter
+    ``skew``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    References
+    ----------
+    R.W. Vogel and D.E. McMartin, "Probability Plot Goodness-of-Fit and
+    Skewness Estimation Procedures for the Pearson Type 3 Distribution", Water
+    Resources Research, Vol.27, 3149-3158 (1991).
+
+    L.R. Salvosa, "Tables of Pearson's Type III Function", Ann. Math. Statist.,
+    Vol.1, 191-198 (1930).
+
+    "Using Modern Computing Tools to Fit the Pearson Type III Distribution to
+    Aviation Loads Data", Office of Aviation Research (2003).
+
+    """
+    def _preprocess(self, x, skew):
+        # The real 'loc' and 'scale' are handled in the calling pdf(...). The
+        # local variables 'loc' and 'scale' within pearson3._pdf are set to
+        # the defaults just to keep them as part of the equations for
+        # documentation.
+        loc = 0.0
+        scale = 1.0
+
+        # If skew is small, return _norm_pdf. The divide between pearson3
+        # and norm was found by brute force and is approximately a skew of
+        # 0.000016.  No one, I hope, would actually use a skew value even
+        # close to this small.
+        norm2pearson_transition = 0.000016
+
+        ans, x, skew = np.broadcast_arrays(1.0, x, skew)
+        ans = ans.copy()
+
+        # mask is True where skew is small enough to use the normal approx.
+        mask = np.absolute(skew) < norm2pearson_transition
+        invmask = ~mask
+
+        beta = 2.0 / (skew[invmask] * scale)
+        alpha = (scale * beta)**2
+        zeta = loc - alpha / beta
+
+        transx = beta * (x[invmask] - zeta)
+        return ans, x, transx, mask, invmask, beta, alpha, zeta
+
+    def _argcheck(self, skew):
+        # The _argcheck function in rv_continuous only allows positive
+        # arguments.  The skew argument for pearson3 can be zero (which I want
+        # to handle inside pearson3._pdf) or negative.  So just return True
+        # for all skew args.
+        return np.isfinite(skew)
+
+    def _shape_info(self):
+        return [_ShapeInfo("skew", False, (-np.inf, np.inf), (False, False))]
+
+    def _stats(self, skew):
+        m = 0.0
+        v = 1.0
+        s = skew
+        k = 1.5*skew**2
+        return m, v, s, k
+
+    def _pdf(self, x, skew):
+        # pearson3.pdf(x, skew) = abs(beta) / gamma(alpha) *
+        #     (beta * (x - zeta))**(alpha - 1) * exp(-beta*(x - zeta))
+        # Do the calculation in _logpdf since helps to limit
+        # overflow/underflow problems
+        ans = np.exp(self._logpdf(x, skew))
+        if ans.ndim == 0:
+            if np.isnan(ans):
+                return 0.0
+            return ans
+        ans[np.isnan(ans)] = 0.0
+        return ans
+
+    def _logpdf(self, x, skew):
+        #   PEARSON3 logpdf                           GAMMA logpdf
+        #   np.log(abs(beta))
+        # + (alpha - 1)*np.log(beta*(x - zeta))          + (a - 1)*np.log(x)
+        # - beta*(x - zeta)                           - x
+        # - sc.gammalnalpha)                              - sc.gammalna)
+        ans, x, transx, mask, invmask, beta, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = np.log(_norm_pdf(x[mask]))
+        # use logpdf instead of _logpdf to fix issue mentioned in gh-12640
+        # (_logpdf does not return correct result for alpha = 1)
+        ans[invmask] = np.log(abs(beta)) + gamma.logpdf(transx, alpha)
+        return ans
+
+    def _cdf(self, x, skew):
+        ans, x, transx, mask, invmask, _, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = _norm_cdf(x[mask])
+
+        skew = np.broadcast_to(skew, invmask.shape)
+        invmask1a = np.logical_and(invmask, skew > 0)
+        invmask1b = skew[invmask] > 0
+        # use cdf instead of _cdf to fix issue mentioned in gh-12640
+        # (_cdf produces NaNs for inputs outside support)
+        ans[invmask1a] = gamma.cdf(transx[invmask1b], alpha[invmask1b])
+
+        # The gamma._cdf approach wasn't working with negative skew.
+        # Note that multiplying the skew by -1 reflects about x=0.
+        # So instead of evaluating the CDF with negative skew at x,
+        # evaluate the SF with positive skew at -x.
+        invmask2a = np.logical_and(invmask, skew < 0)
+        invmask2b = skew[invmask] < 0
+        # gamma._sf produces NaNs when transx < 0, so use gamma.sf
+        ans[invmask2a] = gamma.sf(transx[invmask2b], alpha[invmask2b])
+
+        return ans
+
+    def _sf(self, x, skew):
+        ans, x, transx, mask, invmask, _, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = _norm_sf(x[mask])
+
+        skew = np.broadcast_to(skew, invmask.shape)
+        invmask1a = np.logical_and(invmask, skew > 0)
+        invmask1b = skew[invmask] > 0
+        ans[invmask1a] = gamma.sf(transx[invmask1b], alpha[invmask1b])
+
+        invmask2a = np.logical_and(invmask, skew < 0)
+        invmask2b = skew[invmask] < 0
+        ans[invmask2a] = gamma.cdf(transx[invmask2b], alpha[invmask2b])
+
+        return ans
+
+    def _rvs(self, skew, size=None, random_state=None):
+        skew = np.broadcast_to(skew, size)
+        ans, _, _, mask, invmask, beta, alpha, zeta = (
+            self._preprocess([0], skew))
+
+        nsmall = mask.sum()
+        nbig = mask.size - nsmall
+        ans[mask] = random_state.standard_normal(nsmall)
+        ans[invmask] = random_state.standard_gamma(alpha, nbig)/beta + zeta
+
+        if size == ():
+            ans = ans[0]
+        return ans
+
+    def _ppf(self, q, skew):
+        ans, q, _, mask, invmask, beta, alpha, zeta = (
+            self._preprocess(q, skew))
+        ans[mask] = _norm_ppf(q[mask])
+        q = q[invmask]
+        q[beta < 0] = 1 - q[beta < 0]  # for negative skew; see gh-17050
+        ans[invmask] = sc.gammaincinv(alpha, q)/beta + zeta
+        return ans
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Note that method of moments (`method='MM'`) is not
+        available for this distribution.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.get("method", None) == 'MM':
+            raise NotImplementedError("Fit `method='MM'` is not available for "
+                                      "the Pearson3 distribution. Please try "
+                                      "the default `method='MLE'`.")
+        else:
+            return super(type(self), self).fit(data, *args, **kwds)
+
+
+pearson3 = pearson3_gen(name="pearson3")
+
+
+class powerlaw_gen(rv_continuous):
+    r"""A power-function continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    pareto
+
+    Notes
+    -----
+    The probability density function for `powerlaw` is:
+
+    .. math::
+
+        f(x, a) = a x^{a-1}
+
+    for :math:`0 \le x \le 1`, :math:`a > 0`.
+
+    `powerlaw` takes ``a`` as a shape parameter for :math:`a`.
+
+    %(after_notes)s
+
+    For example, the support of `powerlaw` can be adjusted from the default
+    interval ``[0, 1]`` to the interval ``[c, c+d]`` by setting ``loc=c`` and
+    ``scale=d``. For a power-law distribution with infinite support, see
+    `pareto`.
+
+    `powerlaw` is a special case of `beta` with ``b=1``.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # powerlaw.pdf(x, a) = a * x**(a-1)
+        return a*x**(a-1.0)
+
+    def _logpdf(self, x, a):
+        return np.log(a) + sc.xlogy(a - 1, x)
+
+    def _cdf(self, x, a):
+        return x**(a*1.0)
+
+    def _logcdf(self, x, a):
+        return a*np.log(x)
+
+    def _ppf(self, q, a):
+        return pow(q, 1.0/a)
+
+    def _sf(self, p, a):
+        return -sc.powm1(p, a)
+
+    def _munp(self, n, a):
+        # The following expression is correct for all real n (provided a > 0).
+        return a / (a + n)
+
+    def _stats(self, a):
+        return (a / (a + 1.0),
+                a / (a + 2.0) / (a + 1.0) ** 2,
+                -2.0 * ((a - 1.0) / (a + 3.0)) * np.sqrt((a + 2.0) / a),
+                6 * np.polyval([1, -1, -6, 2], a) / (a * (a + 3.0) * (a + 4)))
+
+    def _entropy(self, a):
+        return 1 - 1.0/a - np.log(a)
+
+    def _support_mask(self, x, a):
+        return (super()._support_mask(x, a)
+                & ((x != 0) | (a >= 1)))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Notes specifically for ``powerlaw.fit``: If the location is a free
+        parameter and the value returned for the shape parameter is less than
+        one, the true maximum likelihood approaches infinity. This causes
+        numerical difficulties, and the resulting estimates are approximate.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        # Summary of the strategy:
+        #
+        # 1) If the scale and location are fixed, return the shape according
+        #    to a formula.
+        #
+        # 2) If the scale is fixed, there are two possibilities for the other
+        #    parameters - one corresponding with shape less than one, and
+        #    another with shape greater than one. Calculate both, and return
+        #    whichever has the better log-likelihood.
+        #
+        # At this point, the scale is known to be free.
+        #
+        # 3) If the location is fixed, return the scale and shape according to
+        #    formulas (or, if the shape is fixed, the fixed shape).
+        #
+        # At this point, the location and scale are both free. There are
+        # separate equations depending on whether the shape is less than one or
+        # greater than one.
+        #
+        # 4a) If the shape is less than one, there are formulas for shape,
+        #     location, and scale.
+        # 4b) If the shape is greater than one, there are formulas for shape
+        #     and scale, but there is a condition for location to be solved
+        #     numerically.
+        #
+        # If the shape is fixed and less than one, we use 4a.
+        # If the shape is fixed and greater than one, we use 4b.
+        # If the shape is also free, we calculate fits using both 4a and 4b
+        # and choose the one that results a better log-likelihood.
+        #
+        # In many cases, the use of `np.nextafter` is used to avoid numerical
+        # issues.
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        if len(np.unique(data)) == 1:
+            return super().fit(data, *args, **kwds)
+
+        data, fshape, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                 args, kwds)
+        penalized_nllf_args = [data, (self._fitstart(data),)]
+        penalized_nllf = self._reduce_func(penalized_nllf_args, {})[1]
+
+        # ensure that any fixed parameters don't violate constraints of the
+        # distribution before continuing. The support of the distribution
+        # is `0 < (x - loc)/scale < 1`.
+        if floc is not None:
+            if not data.min() > floc:
+                raise FitDataError('powerlaw', 0, 1)
+            if fscale is not None and not data.max() <= floc + fscale:
+                raise FitDataError('powerlaw', 0, 1)
+
+        if fscale is not None:
+            if fscale <= 0:
+                raise ValueError("Negative or zero `fscale` is outside the "
+                                 "range allowed by the distribution.")
+            if fscale <= np.ptp(data):
+                msg = "`fscale` must be greater than the range of data."
+                raise ValueError(msg)
+
+        def get_shape(data, loc, scale):
+            # The first-order necessary condition on `shape` can be solved in
+            # closed form. It can be used no matter the assumption of the
+            # value of the shape.
+            N = len(data)
+            return - N / (np.sum(np.log(data - loc)) - N*np.log(scale))
+
+        def get_scale(data, loc):
+            # analytical solution for `scale` based on the location.
+            # It can be used no matter the assumption of the value of the
+            # shape.
+            return data.max() - loc
+
+        # 1) The location and scale are both fixed. Analytically determine the
+        # shape.
+        if fscale is not None and floc is not None:
+            return get_shape(data, floc, fscale), floc, fscale
+
+        # 2) The scale is fixed. There are two possibilities for the other
+        # parameters. Choose the option with better log-likelihood.
+        if fscale is not None:
+            # using `data.min()` as the optimal location
+            loc_lt1 = np.nextafter(data.min(), -np.inf)
+            shape_lt1 = fshape or get_shape(data, loc_lt1, fscale)
+            ll_lt1 = penalized_nllf((shape_lt1, loc_lt1, fscale), data)
+
+            # using `data.max() - scale` as the optimal location
+            loc_gt1 = np.nextafter(data.max() - fscale, np.inf)
+            shape_gt1 = fshape or get_shape(data, loc_gt1, fscale)
+            ll_gt1 = penalized_nllf((shape_gt1, loc_gt1, fscale), data)
+
+            if ll_lt1 < ll_gt1:
+                return shape_lt1, loc_lt1, fscale
+            else:
+                return shape_gt1, loc_gt1, fscale
+
+        # 3) The location is fixed. Return the analytical scale and the
+        # analytical (or fixed) shape.
+        if floc is not None:
+            scale = get_scale(data, floc)
+            shape = fshape or get_shape(data, floc, scale)
+            return shape, floc, scale
+
+        # 4) Location and scale are both free
+        # 4a) Use formulas that assume `shape <= 1`.
+
+        def fit_loc_scale_w_shape_lt_1():
+            loc = np.nextafter(data.min(), -np.inf)
+            if np.abs(loc) < np.finfo(loc.dtype).tiny:
+                loc = np.sign(loc) * np.finfo(loc.dtype).tiny
+            scale = np.nextafter(get_scale(data, loc), np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return shape, loc, scale
+
+        # 4b) Fit under the assumption that `shape > 1`. The support
+        # of the distribution is `(x - loc)/scale <= 1`. The method of Lagrange
+        # multipliers turns this constraint into the condition that
+        # dL_dScale - dL_dLocation must be zero, which is solved numerically.
+        # (Alternatively, substitute the constraint into the objective
+        # function before deriving the likelihood equation for location.)
+
+        def dL_dScale(data, shape, scale):
+            # The partial derivative of the log-likelihood function w.r.t.
+            # the scale.
+            return -data.shape[0] * shape / scale
+
+        def dL_dLocation(data, shape, loc):
+            # The partial derivative of the log-likelihood function w.r.t.
+            # the location.
+            return (shape - 1) * np.sum(1 / (loc - data))  # -1/(data-loc)
+
+        def dL_dLocation_star(loc):
+            # The derivative of the log-likelihood function w.r.t.
+            # the location, given optimal shape and scale
+            scale = np.nextafter(get_scale(data, loc), -np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return dL_dLocation(data, shape, loc)
+
+        def fun_to_solve(loc):
+            # optimize the location by setting the partial derivatives
+            # w.r.t. to location and scale equal and solving.
+            scale = np.nextafter(get_scale(data, loc), -np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return (dL_dScale(data, shape, scale)
+                    - dL_dLocation(data, shape, loc))
+
+        def fit_loc_scale_w_shape_gt_1():
+            # set brackets for `root_scalar` to use when optimizing over the
+            # location such that a root is likely between them.
+            rbrack = np.nextafter(data.min(), -np.inf)
+
+            # if the sign of `dL_dLocation_star` is positive at rbrack,
+            # we're not going to find the root we're looking for
+            delta = (data.min() - rbrack)
+            while dL_dLocation_star(rbrack) > 0:
+                rbrack = data.min() - delta
+                delta *= 2
+
+            def interval_contains_root(lbrack, rbrack):
+                # Check if the interval (lbrack, rbrack) contains the root.
+                return (np.sign(fun_to_solve(lbrack))
+                        != np.sign(fun_to_solve(rbrack)))
+
+            lbrack = rbrack - 1
+
+            # if the sign doesn't change between the brackets, move the left
+            # bracket until it does. (The right bracket remains fixed at the
+            # maximum permissible value.)
+            i = 1.0
+            while (not interval_contains_root(lbrack, rbrack)
+                   and lbrack != -np.inf):
+                lbrack = (data.min() - i)
+                i *= 2
+
+            root = optimize.root_scalar(fun_to_solve, bracket=(lbrack, rbrack))
+
+            loc = np.nextafter(root.root, -np.inf)
+            scale = np.nextafter(get_scale(data, loc), np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return shape, loc, scale
+
+        # Shape is fixed - choose 4a or 4b accordingly.
+        if fshape is not None and fshape <= 1:
+            return fit_loc_scale_w_shape_lt_1()
+        elif fshape is not None and fshape > 1:
+            return fit_loc_scale_w_shape_gt_1()
+
+        # Shape is free
+        fit_shape_lt1 = fit_loc_scale_w_shape_lt_1()
+        ll_lt1 = self.nnlf(fit_shape_lt1, data)
+
+        fit_shape_gt1 = fit_loc_scale_w_shape_gt_1()
+        ll_gt1 = self.nnlf(fit_shape_gt1, data)
+
+        if ll_lt1 <= ll_gt1 and fit_shape_lt1[0] <= 1:
+            return fit_shape_lt1
+        elif ll_lt1 > ll_gt1 and fit_shape_gt1[0] > 1:
+            return fit_shape_gt1
+        else:
+            return super().fit(data, *args, **kwds)
+
+
+powerlaw = powerlaw_gen(a=0.0, b=1.0, name="powerlaw")
+
+
+class powerlognorm_gen(rv_continuous):
+    r"""A power log-normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `powerlognorm` is:
+
+    .. math::
+
+        f(x, c, s) = \frac{c}{x s} \phi(\log(x)/s)
+                     (\Phi(-\log(x)/s))^{c-1}
+
+    where :math:`\phi` is the normal pdf, and :math:`\Phi` is the normal cdf,
+    and :math:`x > 0`, :math:`s, c > 0`.
+
+    `powerlognorm` takes :math:`c` and :math:`s` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        i_s = _ShapeInfo("s", False, (0, np.inf), (False, False))
+        return [ic, i_s]
+
+    def _pdf(self, x, c, s):
+        return np.exp(self._logpdf(x, c, s))
+
+    def _logpdf(self, x, c, s):
+        return (np.log(c) - np.log(x) - np.log(s) +
+                _norm_logpdf(np.log(x) / s) +
+                _norm_logcdf(-np.log(x) / s) * (c - 1.))
+
+    def _cdf(self, x, c, s):
+        return -sc.expm1(self._logsf(x, c, s))
+
+    def _ppf(self, q, c, s):
+        return self._isf(1 - q, c, s)
+
+    def _sf(self, x, c, s):
+        return np.exp(self._logsf(x, c, s))
+
+    def _logsf(self, x, c, s):
+        return _norm_logcdf(-np.log(x) / s) * c
+
+    def _isf(self, q, c, s):
+        return np.exp(-_norm_ppf(q**(1/c)) * s)
+
+
+powerlognorm = powerlognorm_gen(a=0.0, name="powerlognorm")
+
+
+class powernorm_gen(rv_continuous):
+    r"""A power normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `powernorm` is:
+
+    .. math::
+
+        f(x, c) = c \phi(x) (\Phi(-x))^{c-1}
+
+    where :math:`\phi` is the normal pdf, :math:`\Phi` is the normal cdf,
+    :math:`x` is any real, and :math:`c > 0` [1]_.
+
+    `powernorm` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] NIST Engineering Statistics Handbook, Section 1.3.6.6.13,
+           https://www.itl.nist.gov/div898/handbook//eda/section3/eda366d.htm
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # powernorm.pdf(x, c) = c * phi(x) * (Phi(-x))**(c-1)
+        return c*_norm_pdf(x) * (_norm_cdf(-x)**(c-1.0))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + _norm_logpdf(x) + (c-1)*_norm_logcdf(-x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(self._logsf(x, c))
+
+    def _ppf(self, q, c):
+        return -_norm_ppf(pow(1.0 - q, 1.0 / c))
+
+    def _sf(self, x, c):
+        return np.exp(self._logsf(x, c))
+
+    def _logsf(self, x, c):
+        return c * _norm_logcdf(-x)
+
+    def _isf(self, q, c):
+        return -_norm_ppf(np.exp(np.log(q) / c))
+
+
+powernorm = powernorm_gen(name='powernorm')
+
+
+class rdist_gen(rv_continuous):
+    r"""An R-distributed (symmetric beta) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rdist` is:
+
+    .. math::
+
+        f(x, c) = \frac{(1-x^2)^{c/2-1}}{B(1/2, c/2)}
+
+    for :math:`-1 \le x \le 1`, :math:`c > 0`. `rdist` is also called the
+    symmetric beta distribution: if B has a `beta` distribution with
+    parameters (c/2, c/2), then X = 2*B - 1 follows a R-distribution with
+    parameter c.
+
+    `rdist` takes ``c`` as a shape parameter for :math:`c`.
+
+    This distribution includes the following distribution kernels as
+    special cases::
+
+        c = 2:  uniform
+        c = 3:  `semicircular`
+        c = 4:  Epanechnikov (parabolic)
+        c = 6:  quartic (biweight)
+        c = 8:  triweight
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    # use relation to the beta distribution for pdf, cdf, etc
+    def _pdf(self, x, c):
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return -np.log(2) + beta._logpdf((x + 1)/2, c/2, c/2)
+
+    def _cdf(self, x, c):
+        return beta._cdf((x + 1)/2, c/2, c/2)
+
+    def _sf(self, x, c):
+        return beta._sf((x + 1)/2, c/2, c/2)
+
+    def _ppf(self, q, c):
+        return 2*beta._ppf(q, c/2, c/2) - 1
+
+    def _rvs(self, c, size=None, random_state=None):
+        return 2 * random_state.beta(c/2, c/2, size) - 1
+
+    def _munp(self, n, c):
+        numerator = (1 - (n % 2)) * sc.beta((n + 1.0) / 2, c / 2.0)
+        return numerator / sc.beta(1. / 2, c / 2.)
+
+
+rdist = rdist_gen(a=-1.0, b=1.0, name="rdist")
+
+
+class rayleigh_gen(rv_continuous):
+    r"""A Rayleigh continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rayleigh` is:
+
+    .. math::
+
+        f(x) = x \exp(-x^2/2)
+
+    for :math:`x \ge 0`.
+
+    `rayleigh` is a special case of `chi` with ``df=2``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return chi.rvs(2, size=size, random_state=random_state)
+
+    def _pdf(self, r):
+        # rayleigh.pdf(r) = r * exp(-r**2/2)
+        return np.exp(self._logpdf(r))
+
+    def _logpdf(self, r):
+        return np.log(r) - 0.5 * r * r
+
+    def _cdf(self, r):
+        return -sc.expm1(-0.5 * r**2)
+
+    def _ppf(self, q):
+        return np.sqrt(-2 * sc.log1p(-q))
+
+    def _sf(self, r):
+        return np.exp(self._logsf(r))
+
+    def _logsf(self, r):
+        return -0.5 * r * r
+
+    def _isf(self, q):
+        return np.sqrt(-2 * np.log(q))
+
+    def _stats(self):
+        val = 4 - np.pi
+        return (np.sqrt(np.pi/2),
+                val/2,
+                2*(np.pi-3)*np.sqrt(np.pi)/val**1.5,
+                6*np.pi/val-16/val**2)
+
+    def _entropy(self):
+        return _EULER/2.0 + 1 - 0.5*np.log(2)
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Notes specifically for ``rayleigh.fit``: If the location is fixed with
+        the `floc` parameter, this method uses an analytical formula to find
+        the scale.  Otherwise, this function uses a numerical root finder on
+        the first order conditions of the log-likelihood function to find the
+        MLE.  Only the (optional) `loc` parameter is used as the initial guess
+        for the root finder; the `scale` parameter and any other parameters
+        for the optimizer are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        def scale_mle(loc):
+            # Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+            # and Peacock (2000), Page 175
+            return (np.sum((data - loc) ** 2) / (2 * len(data))) ** .5
+
+        def loc_mle(loc):
+            # This implicit equation for `loc` is used when
+            # both `loc` and `scale` are free.
+            xm = data - loc
+            s1 = xm.sum()
+            s2 = (xm**2).sum()
+            s3 = (1/xm).sum()
+            return s1 - s2/(2*len(data))*s3
+
+        def loc_mle_scale_fixed(loc, scale=fscale):
+            # This implicit equation for `loc` is used when
+            # `scale` is fixed but `loc` is not.
+            xm = data - loc
+            return xm.sum() - scale**2 * (1/xm).sum()
+
+        if floc is not None:
+            # `loc` is fixed, analytically determine `scale`.
+            if np.any(data - floc <= 0):
+                raise FitDataError("rayleigh", lower=1, upper=np.inf)
+            else:
+                return floc, scale_mle(floc)
+
+        # Account for user provided guess of `loc`.
+        loc0 = kwds.get('loc')
+        if loc0 is None:
+            # Use _fitstart to estimate loc; ignore the returned scale.
+            loc0 = self._fitstart(data)[0]
+
+        fun = loc_mle if fscale is None else loc_mle_scale_fixed
+        rbrack = np.nextafter(np.min(data), -np.inf)
+        lbrack = _get_left_bracket(fun, rbrack)
+        res = optimize.root_scalar(fun, bracket=(lbrack, rbrack))
+        if not res.converged:
+            raise FitSolverError(res.flag)
+        loc = res.root
+        scale = fscale or scale_mle(loc)
+        return loc, scale
+
+
+rayleigh = rayleigh_gen(a=0.0, name="rayleigh")
+
+
+class reciprocal_gen(rv_continuous):
+    r"""A loguniform or reciprocal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for this class is:
+
+    .. math::
+
+        f(x, a, b) = \frac{1}{x \log(b/a)}
+
+    for :math:`a \le x \le b`, :math:`b > a > 0`. This class takes
+    :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    This doesn't show the equal probability of ``0.01``, ``0.1`` and
+    ``1``. This is best when the x-axis is log-scaled:
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.hist(np.log10(r))
+    >>> ax.set_ylabel("Frequency")
+    >>> ax.set_xlabel("Value of random variable")
+    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
+    >>> ticks = ["$10^{{ {} }}$".format(i) for i in [-2, -1, 0]]
+    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
+    >>> plt.show()
+
+    This random variable will be log-uniform regardless of the base chosen for
+    ``a`` and ``b``. Let's specify with base ``2`` instead:
+
+    >>> rvs = %(name)s(2**-2, 2**0).rvs(size=1000)
+
+    Values of ``1/4``, ``1/2`` and ``1`` are equally likely with this random
+    variable.  Here's the histogram:
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.hist(np.log2(rvs))
+    >>> ax.set_ylabel("Frequency")
+    >>> ax.set_xlabel("Value of random variable")
+    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
+    >>> ticks = ["$2^{{ {} }}$".format(i) for i in [-2, -1, 0]]
+    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
+    >>> plt.show()
+
+    """
+    def _argcheck(self, a, b):
+        return (a > 0) & (b > a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        # Reasonable, since support is [a, b]
+        return super()._fitstart(data, args=(np.min(data), np.max(data)))
+
+    def _get_support(self, a, b):
+        return a, b
+
+    def _pdf(self, x, a, b):
+        # reciprocal.pdf(x, a, b) = 1 / (x*(log(b) - log(a)))
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return -np.log(x) - np.log(np.log(b) - np.log(a))
+
+    def _cdf(self, x, a, b):
+        return (np.log(x)-np.log(a)) / (np.log(b) - np.log(a))
+
+    def _ppf(self, q, a, b):
+        return np.exp(np.log(a) + q*(np.log(b) - np.log(a)))
+
+    def _munp(self, n, a, b):
+        if n == 0:
+            return 1.0
+        t1 = 1 / (np.log(b) - np.log(a)) / n
+        t2 = np.real(np.exp(_log_diff(n * np.log(b), n*np.log(a))))
+        return t1 * t2
+
+    def _entropy(self, a, b):
+        return 0.5*(np.log(a) + np.log(b)) + np.log(np.log(b) - np.log(a))
+
+    fit_note = """\
+        `loguniform`/`reciprocal` is over-parameterized. `fit` automatically
+         fixes `scale` to 1 unless `fscale` is provided by the user.\n\n"""
+
+    @extend_notes_in_docstring(rv_continuous, notes=fit_note)
+    def fit(self, data, *args, **kwds):
+        fscale = kwds.pop('fscale', 1)
+        return super().fit(data, *args, fscale=fscale, **kwds)
+
+    # Details related to the decision of not defining
+    # the survival function for this distribution can be
+    # found in the PR: https://github.com/scipy/scipy/pull/18614
+
+
+loguniform = reciprocal_gen(name="loguniform")
+reciprocal = reciprocal_gen(name="reciprocal")
+loguniform._support = ('a', 'b')
+reciprocal._support = ('a', 'b')
+
+
+class rice_gen(rv_continuous):
+    r"""A Rice continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rice` is:
+
+    .. math::
+
+        f(x, b) = x \exp(- \frac{x^2 + b^2}{2}) I_0(x b)
+
+    for :math:`x >= 0`, :math:`b > 0`. :math:`I_0` is the modified Bessel
+    function of order zero (`scipy.special.i0`).
+
+    `rice` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    The Rice distribution describes the length, :math:`r`, of a 2-D vector with
+    components :math:`(U+u, V+v)`, where :math:`U, V` are constant, :math:`u,
+    v` are independent Gaussian random variables with standard deviation
+    :math:`s`.  Let :math:`R = \sqrt{U^2 + V^2}`. Then the pdf of :math:`r` is
+    ``rice.pdf(x, R/s, scale=s)``.
+
+    %(example)s
+
+    """
+    def _argcheck(self, b):
+        return b >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, b, size=None, random_state=None):
+        # https://en.wikipedia.org/wiki/Rice_distribution
+        t = b/np.sqrt(2) + random_state.standard_normal(size=(2,) + size)
+        return np.sqrt((t*t).sum(axis=0))
+
+    def _cdf(self, x, b):
+        return sc.chndtr(np.square(x), 2, np.square(b))
+
+    def _ppf(self, q, b):
+        return np.sqrt(sc.chndtrix(q, 2, np.square(b)))
+
+    def _pdf(self, x, b):
+        # rice.pdf(x, b) = x * exp(-(x**2+b**2)/2) * I[0](x*b)
+        #
+        # We use (x**2 + b**2)/2 = ((x-b)**2)/2 + xb.
+        # The factor of np.exp(-xb) is then included in the i0e function
+        # in place of the modified Bessel function, i0, improving
+        # numerical stability for large values of xb.
+        return x * np.exp(-(x-b)*(x-b)/2.0) * sc.i0e(x*b)
+
+    def _munp(self, n, b):
+        nd2 = n/2.0
+        n1 = 1 + nd2
+        b2 = b*b/2.0
+        return (2.0**(nd2) * np.exp(-b2) * sc.gamma(n1) *
+                sc.hyp1f1(n1, 1, b2))
+
+
+rice = rice_gen(a=0.0, name="rice")
+
+class irwinhall_gen(rv_continuous):
+    r"""An Irwin-Hall (Uniform Sum) continuous random variable.
+
+    An `Irwin-Hall <https://en.wikipedia.org/wiki/Irwin-Hall_distribution/>`_
+    continuous random variable is the sum of :math:`n` independent
+    standard uniform random variables [1]_ [2]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    Applications include `Rao's Spacing Test
+    <https://jammalam.faculty.pstat.ucsb.edu/html/favorite/test.htm>`_,
+    a more powerful alternative to the Rayleigh test
+    when the data are not unimodal, and radar [3]_.
+
+    Conveniently, the pdf and cdf are the :math:`n`-fold convolution of
+    the ones for the standard uniform distribution, which is also the
+    definition of the cardinal B-splines of degree :math:`n-1`
+    having knots evenly spaced from :math:`1` to :math:`n` [4]_ [5]_.
+
+    The Bates distribution, which represents the *mean* of statistically
+    independent, uniformly distributed random variables, is simply the
+    Irwin-Hall distribution scaled by :math:`1/n`. For example, the frozen
+    distribution ``bates = irwinhall(10, scale=1/10)`` represents the
+    distribution of the mean of 10 uniformly distributed random variables.
+    
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] P. Hall, "The distribution of means for samples of size N drawn
+            from a population in which the variate takes values between 0 and 1,
+            all such values being equally probable",
+            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 240-244,
+            :doi:`10.1093/biomet/19.3-4.240`.
+    .. [2] J. O. Irwin, "On the frequency distribution of the means of samples
+            from a population having any law of frequency with finite moments,
+            with special reference to Pearson's Type II,
+            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 225-239,
+            :doi:`0.1093/biomet/19.3-4.225`.
+    .. [3] K. Buchanan, T. Adeyemi, C. Flores-Molina, S. Wheeland and D. Overturf, 
+            "Sidelobe behavior and bandwidth characteristics
+            of distributed antenna arrays,"
+            2018 United States National Committee of
+            URSI National Radio Science Meeting (USNC-URSI NRSM),
+            Boulder, CO, USA, 2018, pp. 1-2.
+            https://www.usnc-ursi-archive.org/nrsm/2018/papers/B15-9.pdf.
+    .. [4] Amos Ron, "Lecture 1: Cardinal B-splines and convolution operators", p. 1
+            https://pages.cs.wisc.edu/~deboor/887/lec1new.pdf.
+    .. [5] Trefethen, N. (2012, July). B-splines and convolution. Chebfun. 
+            Retrieved April 30, 2024, from http://www.chebfun.org/examples/approx/BSplineConv.html.
+
+    %(example)s
+    """  # noqa: E501
+
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        Raises a ``NotImplementedError`` for the Irwin-Hall distribution because
+        the generic `fit` implementation is unreliable and no custom implementation
+        is available. Consider using `scipy.stats.fit`.\n\n""")
+    def fit(self, data, *args, **kwds):
+        fit_notes = ("The generic `fit` implementation is unreliable for this "
+                     "distribution, and no custom implementation is available. "
+                     "Consider using `scipy.stats.fit`.")
+        raise NotImplementedError(fit_notes)
+
+    def _argcheck(self, n):
+        return (n > 0) & _isintegral(n) & np.isrealobj(n)
+
+    def _get_support(self, n):
+        return 0, n
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _munp(self, order, n):
+        # see https://link.springer.com/content/pdf/10.1007/s10959-020-01050-9.pdf
+        # page 640, with m=n, j=n+order
+        def vmunp(order, n):
+            n = np.asarray(n, dtype=np.int64)
+            return (sc.stirling2(n+order, n, exact=True)
+                    / sc.comb(n+order, n, exact=True))
+
+        # exact rationals, but we convert to float anyway
+        return np.vectorize(vmunp, otypes=[np.float64])(order, n)
+
+    @staticmethod
+    def _cardbspl(n):
+        t = np.arange(n+1)
+        return BSpline.basis_element(t)
+
+    def _pdf(self, x, n):
+        def vpdf(x, n):
+            return self._cardbspl(n)(x)
+        return np.vectorize(vpdf, otypes=[np.float64])(x, n)
+
+    def _cdf(self, x, n):
+        def vcdf(x, n):
+            return self._cardbspl(n).antiderivative()(x)
+        return np.vectorize(vcdf, otypes=[np.float64])(x, n)
+
+    def _sf(self, x, n):
+        def vsf(x, n):
+            return self._cardbspl(n).antiderivative()(n-x)
+        return np.vectorize(vsf, otypes=[np.float64])(x, n)
+
+    def _rvs(self, n, size=None, random_state=None, *args):
+        @_vectorize_rvs_over_shapes
+        def _rvs1(n, size=None, random_state=None):
+            n = np.floor(n).astype(int)
+            usize = (n,) if size is None else (n, *size)
+            return random_state.uniform(size=usize).sum(axis=0)
+        return _rvs1(n, size=size, random_state=random_state)
+
+    def _stats(self, n):
+        # mgf = ((exp(t) - 1)/t)**n
+        # m'th derivative follows from the generalized Leibniz rule
+        # Moments follow directly from the definition as the sum of n iid unif(0,1)
+        # and the summation rules for moments of a sum of iid random variables
+        # E(IH((n))) = n*E(U(0,1)) = n/2
+        # Var(IH((n))) = n*Var(U(0,1)) = n/12
+        # Skew(IH((n))) = Skew(U(0,1))/sqrt(n) = 0
+        # Kurt(IH((n))) = Kurt(U(0,1))/n = -6/(5*n) -- Fisher's excess kurtosis
+        # See e.g. https://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution
+
+        return n/2, n/12, 0, -6/(5*n)
+
+
+irwinhall = irwinhall_gen(name="irwinhall")
+irwinhall._support = (0.0, 'n')
+
+
+class recipinvgauss_gen(rv_continuous):
+    r"""A reciprocal inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `recipinvgauss` is:
+
+    .. math::
+
+        f(x, \mu) = \frac{1}{\sqrt{2\pi x}}
+                    \exp\left(\frac{-(1-\mu x)^2}{2\mu^2x}\right)
+
+    for :math:`x \ge 0`.
+
+    `recipinvgauss` takes ``mu`` as a shape parameter for :math:`\mu`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("mu", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, mu):
+        # recipinvgauss.pdf(x, mu) =
+        #                     1/sqrt(2*pi*x) * exp(-(1-mu*x)**2/(2*x*mu**2))
+        return np.exp(self._logpdf(x, mu))
+
+    def _logpdf(self, x, mu):
+        return _lazywhere(x > 0, (x, mu),
+                          lambda x, mu: (-(1 - mu*x)**2.0 / (2*x*mu**2.0)
+                                         - 0.5*np.log(2*np.pi*x)),
+                          fillvalue=-np.inf)
+
+    def _cdf(self, x, mu):
+        trm1 = 1.0/mu - x
+        trm2 = 1.0/mu + x
+        isqx = 1.0/np.sqrt(x)
+        return _norm_cdf(-isqx*trm1) - np.exp(2.0/mu)*_norm_cdf(-isqx*trm2)
+
+    def _sf(self, x, mu):
+        trm1 = 1.0/mu - x
+        trm2 = 1.0/mu + x
+        isqx = 1.0/np.sqrt(x)
+        return _norm_cdf(isqx*trm1) + np.exp(2.0/mu)*_norm_cdf(-isqx*trm2)
+
+    def _rvs(self, mu, size=None, random_state=None):
+        return 1.0/random_state.wald(mu, 1.0, size=size)
+
+
+recipinvgauss = recipinvgauss_gen(a=0.0, name='recipinvgauss')
+
+
+class semicircular_gen(rv_continuous):
+    r"""A semicircular continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    rdist
+
+    Notes
+    -----
+    The probability density function for `semicircular` is:
+
+    .. math::
+
+        f(x) = \frac{2}{\pi} \sqrt{1-x^2}
+
+    for :math:`-1 \le x \le 1`.
+
+    The distribution is a special case of `rdist` with ``c = 3``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Wigner semicircle distribution",
+           https://en.wikipedia.org/wiki/Wigner_semicircle_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        return 2.0/np.pi*np.sqrt(1-x*x)
+
+    def _logpdf(self, x):
+        return np.log(2/np.pi) + 0.5*sc.log1p(-x*x)
+
+    def _cdf(self, x):
+        return 0.5+1.0/np.pi*(x*np.sqrt(1-x*x) + np.arcsin(x))
+
+    def _ppf(self, q):
+        return rdist._ppf(q, 3)
+
+    def _rvs(self, size=None, random_state=None):
+        # generate values uniformly distributed on the area under the pdf
+        # (semi-circle) by randomly generating the radius and angle
+        r = np.sqrt(random_state.uniform(size=size))
+        a = np.cos(np.pi * random_state.uniform(size=size))
+        return r * a
+
+    def _stats(self):
+        return 0, 0.25, 0, -1.0
+
+    def _entropy(self):
+        return 0.64472988584940017414
+
+
+semicircular = semicircular_gen(a=-1.0, b=1.0, name="semicircular")
+
+
+class skewcauchy_gen(rv_continuous):
+    r"""A skewed Cauchy random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    cauchy : Cauchy distribution
+
+    Notes
+    -----
+
+    The probability density function for `skewcauchy` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi \left(\frac{x^2}{\left(a\, \text{sign}(x) + 1
+                                                   \right)^2} + 1 \right)}
+
+    for a real number :math:`x` and skewness parameter :math:`-1 < a < 1`.
+
+    When :math:`a=0`, the distribution reduces to the usual Cauchy
+    distribution.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Skewed generalized *t* distribution", Wikipedia
+       https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution#Skewed_Cauchy_distribution
+
+    %(example)s
+
+    """
+    def _argcheck(self, a):
+        return np.abs(a) < 1
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (-1.0, 1.0), (False, False))]
+
+    def _pdf(self, x, a):
+        return 1 / (np.pi * (x**2 / (a * np.sign(x) + 1)**2 + 1))
+
+    def _cdf(self, x, a):
+        return np.where(x <= 0,
+                        (1 - a) / 2 + (1 - a) / np.pi * np.arctan(x / (1 - a)),
+                        (1 - a) / 2 + (1 + a) / np.pi * np.arctan(x / (1 + a)))
+
+    def _ppf(self, x, a):
+        i = x < self._cdf(0, a)
+        return np.where(i,
+                        np.tan(np.pi / (1 - a) * (x - (1 - a) / 2)) * (1 - a),
+                        np.tan(np.pi / (1 + a) * (x - (1 - a) / 2)) * (1 + a))
+
+    def _stats(self, a, moments='mvsk'):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _fitstart(self, data):
+        # Use 0 as the initial guess of the skewness shape parameter.
+        # For the location and scale, estimate using the median and
+        # quartiles.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return 0.0, p50, (p75 - p25)/2
+
+
+skewcauchy = skewcauchy_gen(name='skewcauchy')
+
+
+class skewnorm_gen(rv_continuous):
+    r"""A skew-normal random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The pdf is::
+
+        skewnorm.pdf(x, a) = 2 * norm.pdf(x) * norm.cdf(a*x)
+
+    `skewnorm` takes a real number :math:`a` as a skewness parameter
+    When ``a = 0`` the distribution is identical to a normal distribution
+    (`norm`). `rvs` implements the method of [1]_.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of ``cdf``, ``ppf`` and ``isf`` methods. [2]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] A. Azzalini and A. Capitanio (1999). Statistical applications of
+        the multivariate skew-normal distribution. J. Roy. Statist. Soc.,
+        B 61, 579-602. :arxiv:`0911.2093`
+    .. [2] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, a):
+        return np.isfinite(a)
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (-np.inf, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        return _lazywhere(
+            a == 0, (x, a), lambda x, a: _norm_pdf(x),
+            f2=lambda x, a: 2.*_norm_pdf(x)*_norm_cdf(a*x)
+        )
+
+    def _logpdf(self, x, a):
+        return _lazywhere(
+            a == 0, (x, a), lambda x, a: _norm_logpdf(x),
+            f2=lambda x, a: np.log(2)+_norm_logpdf(x)+_norm_logcdf(a*x),
+        )
+
+    def _cdf(self, x, a):
+        a = np.atleast_1d(a)
+        cdf = scu._skewnorm_cdf(x, 0.0, 1.0, a)
+        # for some reason, a isn't broadcasted if some of x are invalid
+        a = np.broadcast_to(a, cdf.shape)
+        # Boost is not accurate in left tail when a > 0
+        i_small_cdf = (cdf < 1e-6) & (a > 0)
+        cdf[i_small_cdf] = super()._cdf(x[i_small_cdf], a[i_small_cdf])
+        return np.clip(cdf, 0, 1)
+
+    def _ppf(self, x, a):
+        return scu._skewnorm_ppf(x, 0.0, 1.0, a)
+
+    def _sf(self, x, a):
+        # Boost's SF is implemented this way. Use whatever customizations
+        # we made in the _cdf.
+        return self._cdf(-x, -a)
+
+    def _isf(self, x, a):
+        return scu._skewnorm_isf(x, 0.0, 1.0, a)
+
+    def _rvs(self, a, size=None, random_state=None):
+        u0 = random_state.normal(size=size)
+        v = random_state.normal(size=size)
+        d = a/np.sqrt(1 + a**2)
+        u1 = d*u0 + v*np.sqrt(1 - d**2)
+        return np.where(u0 >= 0, u1, -u1)
+
+    def _stats(self, a, moments='mvsk'):
+        output = [None, None, None, None]
+        const = np.sqrt(2/np.pi) * a/np.sqrt(1 + a**2)
+
+        if 'm' in moments:
+            output[0] = const
+        if 'v' in moments:
+            output[1] = 1 - const**2
+        if 's' in moments:
+            output[2] = ((4 - np.pi)/2) * (const/np.sqrt(1 - const**2))**3
+        if 'k' in moments:
+            output[3] = (2*(np.pi - 3)) * (const**4/(1 - const**2)**2)
+
+        return output
+
+    # For odd order, the each noncentral moment of the skew-normal distribution
+    # with location 0 and scale 1 can be expressed as a polynomial in delta,
+    # where delta = a/sqrt(1 + a**2) and `a` is the skew-normal shape
+    # parameter.  The dictionary _skewnorm_odd_moments defines those
+    # polynomials for orders up to 19.  The dict is implemented as a cached
+    # property to reduce the impact of the creation of the dict on import time.
+    @cached_property
+    def _skewnorm_odd_moments(self):
+        skewnorm_odd_moments = {
+            1: Polynomial([1]),
+            3: Polynomial([3, -1]),
+            5: Polynomial([15, -10, 3]),
+            7: Polynomial([105, -105, 63, -15]),
+            9: Polynomial([945, -1260, 1134, -540, 105]),
+            11: Polynomial([10395, -17325, 20790, -14850, 5775, -945]),
+            13: Polynomial([135135, -270270, 405405, -386100, 225225, -73710,
+                            10395]),
+            15: Polynomial([2027025, -4729725, 8513505, -10135125, 7882875,
+                            -3869775, 1091475, -135135]),
+            17: Polynomial([34459425, -91891800, 192972780, -275675400,
+                            268017750, -175429800, 74220300, -18378360,
+                            2027025]),
+            19: Polynomial([654729075, -1964187225, 4714049340, -7856748900,
+                            9166207050, -7499623950, 4230557100, -1571349780,
+                            346621275, -34459425]),
+        }
+        return skewnorm_odd_moments
+
+    def _munp(self, order, a):
+        if order % 2:
+            if order > 19:
+                raise NotImplementedError("skewnorm noncentral moments not "
+                                          "implemented for odd orders greater "
+                                          "than 19.")
+            # Use the precomputed polynomials that were derived from the
+            # moment generating function.
+            delta = a/np.sqrt(1 + a**2)
+            return (delta * self._skewnorm_odd_moments[order](delta**2)
+                    * _SQRT_2_OVER_PI)
+        else:
+            # For even order, the moment is just (order-1)!!, where !! is the
+            # notation for the double factorial; for an odd integer m, m!! is
+            # m*(m-2)*...*3*1.
+            # We could use special.factorial2, but we know the argument is odd,
+            # so avoid the overhead of that function and compute the result
+            # directly here.
+            return sc.gamma((order + 1)/2) * 2**(order/2) / _SQRT_PI
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        If ``method='mm'``, parameters fixed by the user are respected, and the
+        remaining parameters are used to match distribution and sample moments
+        where possible. For example, if the user fixes the location with
+        ``floc``, the parameters will only match the distribution skewness and
+        variance to the sample skewness and variance; no attempt will be made
+        to match the means or minimize a norm of the errors.
+        Note that the maximum possible skewness magnitude of a
+        `scipy.stats.skewnorm` distribution is approximately 0.9952717; if the
+        magnitude of the data's sample skewness exceeds this, the returned
+        shape parameter ``a`` will be infinite.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop("superfit", False):
+            return super().fit(data, *args, **kwds)
+        if isinstance(data, CensoredData):
+            if data.num_censored() == 0:
+                data = data._uncensor()
+            else:
+                return super().fit(data, *args, **kwds)
+
+        # this extracts fixed shape, location, and scale however they
+        # are specified, and also leaves them in `kwds`
+        data, fa, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+        method = kwds.get("method", "mle").lower()
+
+        # See https://en.wikipedia.org/wiki/Skew_normal_distribution for
+        # moment formulas.
+        def skew_d(d):  # skewness in terms of delta
+            return (4-np.pi)/2 * ((d * np.sqrt(2 / np.pi))**3
+                                  / (1 - 2*d**2 / np.pi)**(3/2))
+
+        def d_skew(skew):  # delta in terms of skewness
+            s_23 = np.abs(skew)**(2/3)
+            return np.sign(skew) * np.sqrt(
+                np.pi/2 * s_23 / (s_23 + ((4 - np.pi)/2)**(2/3))
+            )
+
+        # If method is method of moments, we don't need the user's guesses.
+        # Otherwise, extract the guesses from args and kwds.
+        if method == "mm":
+            a, loc, scale = None, None, None
+        else:
+            a = args[0] if len(args) else None
+            loc = kwds.pop('loc', None)
+            scale = kwds.pop('scale', None)
+
+        if fa is None and a is None:  # not fixed and no guess: use MoM
+            # Solve for a that matches sample distribution skewness to sample
+            # skewness.
+            s = stats.skew(data)
+            if method == 'mle':
+                # For MLE initial conditions, clip skewness to a large but
+                # reasonable value in case the data skewness is out-of-range.
+                s = np.clip(s, -0.99, 0.99)
+            else:
+                s_max = skew_d(1)
+                s = np.clip(s, -s_max, s_max)
+            d = d_skew(s)
+            with np.errstate(divide='ignore'):
+                a = np.sqrt(np.divide(d**2, (1-d**2)))*np.sign(s)
+        else:
+            a = fa if fa is not None else a
+            d = a / np.sqrt(1 + a**2)
+
+        if fscale is None and scale is None:
+            v = np.var(data)
+            scale = np.sqrt(v / (1 - 2*d**2/np.pi))
+        elif fscale is not None:
+            scale = fscale
+
+        if floc is None and loc is None:
+            m = np.mean(data)
+            loc = m - scale*d*np.sqrt(2/np.pi)
+        elif floc is not None:
+            loc = floc
+
+        if method == 'mm':
+            return a, loc, scale
+        else:
+            # At this point, parameter "guesses" may equal the fixed parameters
+            # in kwds. No harm in passing them as guesses, too.
+            return super().fit(data, a, loc=loc, scale=scale, **kwds)
+
+
+skewnorm = skewnorm_gen(name='skewnorm')
+
+
+class trapezoid_gen(rv_continuous):
+    r"""A trapezoidal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The trapezoidal distribution can be represented with an up-sloping line
+    from ``loc`` to ``(loc + c*scale)``, then constant to ``(loc + d*scale)``
+    and then downsloping from ``(loc + d*scale)`` to ``(loc+scale)``.  This
+    defines the trapezoid base from ``loc`` to ``(loc+scale)`` and the flat
+    top from ``c`` to ``d`` proportional to the position along the base
+    with ``0 <= c <= d <= 1``.  When ``c=d``, this is equivalent to `triang`
+    with the same values for `loc`, `scale` and `c`.
+    The method of [1]_ is used for computing moments.
+
+    `trapezoid` takes :math:`c` and :math:`d` as shape parameters.
+
+    %(after_notes)s
+
+    The standard form is in the range [0, 1] with c the mode.
+    The location parameter shifts the start to `loc`.
+    The scale parameter changes the width from 1 to `scale`.
+
+    %(example)s
+
+    References
+    ----------
+    .. [1] Kacker, R.N. and Lawrence, J.F. (2007). Trapezoidal and triangular
+       distributions for Type B evaluation of standard uncertainty.
+       Metrologia 44, 117-127. :doi:`10.1088/0026-1394/44/2/003`
+
+
+    """
+    def _argcheck(self, c, d):
+        return (c >= 0) & (c <= 1) & (d >= 0) & (d <= 1) & (d >= c)
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, 1.0), (True, True))
+        id = _ShapeInfo("d", False, (0, 1.0), (True, True))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        u = 2 / (d-c+1)
+
+        return _lazyselect([x < c,
+                            (c <= x) & (x <= d),
+                            x > d],
+                           [lambda x, c, d, u: u * x / c,
+                            lambda x, c, d, u: u,
+                            lambda x, c, d, u: u * (1-x) / (1-d)],
+                           (x, c, d, u))
+
+    def _cdf(self, x, c, d):
+        return _lazyselect([x < c,
+                            (c <= x) & (x <= d),
+                            x > d],
+                           [lambda x, c, d: x**2 / c / (d-c+1),
+                            lambda x, c, d: (c + 2 * (x-c)) / (d-c+1),
+                            lambda x, c, d: 1-((1-x) ** 2
+                                               / (d-c+1) / (1-d))],
+                           (x, c, d))
+
+    def _ppf(self, q, c, d):
+        qc, qd = self._cdf(c, c, d), self._cdf(d, c, d)
+        condlist = [q < qc, q <= qd, q > qd]
+        choicelist = [np.sqrt(q * c * (1 + d - c)),
+                      0.5 * q * (1 + d - c) + 0.5 * c,
+                      1 - np.sqrt((1 - q) * (d - c + 1) * (1 - d))]
+        return np.select(condlist, choicelist)
+
+    def _munp(self, n, c, d):
+        # Using the parameterization from Kacker, 2007, with
+        # a=bottom left, c=top left, d=top right, b=bottom right, then
+        #     E[X^n] = h/(n+1)/(n+2) [(b^{n+2}-d^{n+2})/(b-d)
+        #                             - ((c^{n+2} - a^{n+2})/(c-a)]
+        # with h = 2/((b-a) - (d-c)). The corresponding parameterization
+        # in scipy, has a'=loc, c'=loc+c*scale, d'=loc+d*scale, b'=loc+scale,
+        # which for standard form reduces to a'=0, b'=1, c'=c, d'=d.
+        # Substituting into E[X^n] gives the bd' term as (1 - d^{n+2})/(1 - d)
+        # and the ac' term as c^{n-1} for the standard form. The bd' term has
+        # numerical difficulties near d=1, so replace (1 - d^{n+2})/(1-d)
+        # with expm1((n+2)*log(d))/(d-1).
+        # Testing with n=18 for c=(1e-30,1-eps) shows that this is stable.
+        # We still require an explicit test for d=1 to prevent divide by zero,
+        # and now a test for d=0 to prevent log(0).
+        ab_term = c**(n+1)
+        dc_term = _lazyselect(
+            [d == 0.0, (0.0 < d) & (d < 1.0), d == 1.0],
+            [lambda d: 1.0,
+             lambda d: np.expm1((n+2) * np.log(d)) / (d-1.0),
+             lambda d: n+2],
+            [d])
+        val = 2.0 / (1.0+d-c) * (dc_term - ab_term) / ((n+1) * (n+2))
+        return val
+
+    def _entropy(self, c, d):
+        # Using the parameterization from Wikipedia (van Dorp, 2003)
+        # with a=bottom left, c=top left, d=top right, b=bottom right
+        # gives a'=loc, b'=loc+c*scale, c'=loc+d*scale, d'=loc+scale,
+        # which for loc=0, scale=1 is a'=0, b'=c, c'=d, d'=1.
+        # Substituting into the entropy formula from Wikipedia gives
+        # the following result.
+        return 0.5 * (1.0-d+c) / (1.0+d-c) + np.log(0.5 * (1.0+d-c))
+
+
+# deprecation of trapz, see #20486
+deprmsg = ("`trapz` is deprecated in favour of `trapezoid` "
+           "and will be removed in SciPy 1.16.0.")
+
+
+class trapz_gen(trapezoid_gen):
+    # override __call__ protocol from rv_generic to also
+    # deprecate instantiation of frozen distributions
+    """
+
+    .. deprecated:: 1.14.0
+        `trapz` is deprecated and will be removed in SciPy 1.16.
+        Plese use `trapezoid` instead!
+    """
+    def __call__(self, *args, **kwds):
+        warnings.warn(deprmsg, DeprecationWarning, stacklevel=2)
+        return self.freeze(*args, **kwds)
+
+
+trapezoid = trapezoid_gen(a=0.0, b=1.0, name="trapezoid")
+trapz = trapz_gen(a=0.0, b=1.0, name="trapz")
+
+# since the deprecated class gets intantiated upon import (and we only want to
+# warn upon use), add the deprecation to each class method
+_method_names = [
+    "cdf", "entropy", "expect", "fit", "interval", "isf", "logcdf", "logpdf",
+    "logsf", "mean", "median", "moment", "pdf", "ppf", "rvs", "sf", "stats",
+    "std", "var"
+]
+
+
+class _DeprecationWrapper:
+    def __init__(self, method):
+        self.msg = (f"`trapz.{method}` is deprecated in favour of trapezoid.{method}. "
+                     "Please replace all uses of the distribution class "
+                     "`trapz` with `trapezoid`. `trapz` will be removed in SciPy 1.16.")
+        self.method = getattr(trapezoid, method)
+
+    def __call__(self, *args, **kwargs):
+        warnings.warn(self.msg, DeprecationWarning, stacklevel=2)
+        return self.method(*args, **kwargs)
+
+
+for m in _method_names:
+    setattr(trapz, m, _DeprecationWrapper(m))
+
+
+class triang_gen(rv_continuous):
+    r"""A triangular continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The triangular distribution can be represented with an up-sloping line from
+    ``loc`` to ``(loc + c*scale)`` and then downsloping for ``(loc + c*scale)``
+    to ``(loc + scale)``.
+
+    `triang` takes ``c`` as a shape parameter for :math:`0 \le c \le 1`.
+
+    %(after_notes)s
+
+    The standard form is in the range [0, 1] with c the mode.
+    The location parameter shifts the start to `loc`.
+    The scale parameter changes the width from 1 to `scale`.
+
+    %(example)s
+
+    """
+    def _rvs(self, c, size=None, random_state=None):
+        return random_state.triangular(0, c, 1, size)
+
+    def _argcheck(self, c):
+        return (c >= 0) & (c <= 1)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, 1.0), (True, True))]
+
+    def _pdf(self, x, c):
+        # 0: edge case where c=0
+        # 1: generalised case for x < c, don't use x <= c, as it doesn't cope
+        #    with c = 0.
+        # 2: generalised case for x >= c, but doesn't cope with c = 1
+        # 3: edge case where c=1
+        r = _lazyselect([c == 0,
+                         x < c,
+                         (x >= c) & (c != 1),
+                         c == 1],
+                        [lambda x, c: 2 - 2 * x,
+                         lambda x, c: 2 * x / c,
+                         lambda x, c: 2 * (1 - x) / (1 - c),
+                         lambda x, c: 2 * x],
+                        (x, c))
+        return r
+
+    def _cdf(self, x, c):
+        r = _lazyselect([c == 0,
+                         x < c,
+                         (x >= c) & (c != 1),
+                         c == 1],
+                        [lambda x, c: 2*x - x*x,
+                         lambda x, c: x * x / c,
+                         lambda x, c: (x*x - 2*x + c) / (c-1),
+                         lambda x, c: x * x],
+                        (x, c))
+        return r
+
+    def _ppf(self, q, c):
+        return np.where(q < c, np.sqrt(c * q), 1-np.sqrt((1-c) * (1-q)))
+
+    def _stats(self, c):
+        return ((c+1.0)/3.0,
+                (1.0-c+c*c)/18,
+                np.sqrt(2)*(2*c-1)*(c+1)*(c-2) / (5*np.power((1.0-c+c*c), 1.5)),
+                -3.0/5.0)
+
+    def _entropy(self, c):
+        return 0.5-np.log(2)
+
+
+triang = triang_gen(a=0.0, b=1.0, name="triang")
+
+
+class truncexpon_gen(rv_continuous):
+    r"""A truncated exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `truncexpon` is:
+
+    .. math::
+
+        f(x, b) = \frac{\exp(-x)}{1 - \exp(-b)}
+
+    for :math:`0 <= x <= b`.
+
+    `truncexpon` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _get_support(self, b):
+        return self.a, b
+
+    def _pdf(self, x, b):
+        # truncexpon.pdf(x, b) = exp(-x) / (1-exp(-b))
+        return np.exp(-x)/(-sc.expm1(-b))
+
+    def _logpdf(self, x, b):
+        return -x - np.log(-sc.expm1(-b))
+
+    def _cdf(self, x, b):
+        return sc.expm1(-x)/sc.expm1(-b)
+
+    def _ppf(self, q, b):
+        return -sc.log1p(q*sc.expm1(-b))
+
+    def _sf(self, x, b):
+        return (np.exp(-b) - np.exp(-x))/sc.expm1(-b)
+
+    def _isf(self, q, b):
+        return -np.log(np.exp(-b) - q * sc.expm1(-b))
+
+    def _munp(self, n, b):
+        # wrong answer with formula, same as in continuous.pdf
+        # return sc.gamman+1)-sc.gammainc1+n, b)
+        if n == 1:
+            return (1-(b+1)*np.exp(-b))/(-sc.expm1(-b))
+        elif n == 2:
+            return 2*(1-0.5*(b*b+2*b+2)*np.exp(-b))/(-sc.expm1(-b))
+        else:
+            # return generic for higher moments
+            return super()._munp(n, b)
+
+    def _entropy(self, b):
+        eB = np.exp(b)
+        return np.log(eB-1)+(1+eB*(b-1.0))/(1.0-eB)
+
+
+truncexpon = truncexpon_gen(a=0.0, name='truncexpon')
+truncexpon._support = (0.0, 'b')
+
+
+# logsumexp trick for log(p + q) with only log(p) and log(q)
+def _log_sum(log_p, log_q):
+    return sc.logsumexp([log_p, log_q], axis=0)
+
+
+# same as above, but using -exp(x) = exp(x + πi)
+def _log_diff(log_p, log_q):
+    return sc.logsumexp([log_p, log_q+np.pi*1j], axis=0)
+
+
+def _log_gauss_mass(a, b):
+    """Log of Gaussian probability mass within an interval"""
+    a, b = np.broadcast_arrays(a, b)
+
+    # Calculations in right tail are inaccurate, so we'll exploit the
+    # symmetry and work only in the left tail
+    case_left = b <= 0
+    case_right = a > 0
+    case_central = ~(case_left | case_right)
+
+    def mass_case_left(a, b):
+        return _log_diff(_norm_logcdf(b), _norm_logcdf(a))
+
+    def mass_case_right(a, b):
+        return mass_case_left(-b, -a)
+
+    def mass_case_central(a, b):
+        # Previously, this was implemented as:
+        # left_mass = mass_case_left(a, 0)
+        # right_mass = mass_case_right(0, b)
+        # return _log_sum(left_mass, right_mass)
+        # Catastrophic cancellation occurs as np.exp(log_mass) approaches 1.
+        # Correct for this with an alternative formulation.
+        # We're not concerned with underflow here: if only one term
+        # underflows, it was insignificant; if both terms underflow,
+        # the result can't accurately be represented in logspace anyway
+        # because sc.log1p(x) ~ x for small x.
+        return sc.log1p(-_norm_cdf(a) - _norm_cdf(-b))
+
+    # _lazyselect not working; don't care to debug it
+    out = np.full_like(a, fill_value=np.nan, dtype=np.complex128)
+    if a[case_left].size:
+        out[case_left] = mass_case_left(a[case_left], b[case_left])
+    if a[case_right].size:
+        out[case_right] = mass_case_right(a[case_right], b[case_right])
+    if a[case_central].size:
+        out[case_central] = mass_case_central(a[case_central], b[case_central])
+    return np.real(out)  # discard ~0j
+
+
+class truncnorm_gen(rv_continuous):
+    r"""A truncated normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    This distribution is the normal distribution centered on ``loc`` (default
+    0), with standard deviation ``scale`` (default 1), and truncated at ``a``
+    and ``b`` *standard deviations* from ``loc``. For arbitrary ``loc`` and
+    ``scale``, ``a`` and ``b`` are *not* the abscissae at which the shifted
+    and scaled distribution is truncated.
+
+    .. note::
+        If ``a_trunc`` and ``b_trunc`` are the abscissae at which we wish
+        to truncate the distribution (as opposed to the number of standard
+        deviations from ``loc``), then we can calculate the distribution
+        parameters ``a`` and ``b`` as follows::
+
+            a, b = (a_trunc - loc) / scale, (b_trunc - loc) / scale
+
+        This is a common point of confusion. For additional clarification,
+        please see the example below.
+
+    %(example)s
+
+    In the examples above, ``loc=0`` and ``scale=1``, so the plot is truncated
+    at ``a`` on the left and ``b`` on the right. However, suppose we were to
+    produce the same histogram with ``loc = 1`` and ``scale=0.5``.
+
+    >>> loc, scale = 1, 0.5
+    >>> rv = truncnorm(a, b, loc=loc, scale=scale)
+    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
+    ...                 truncnorm.ppf(0.99, a, b), 100)
+    >>> r = rv.rvs(size=1000)
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim(a, b)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Note that the distribution is no longer appears to be truncated at
+    abscissae ``a`` and ``b``. That is because the *standard* normal
+    distribution is first truncated at ``a`` and ``b``, *then* the resulting
+    distribution is scaled by ``scale`` and shifted by ``loc``. If we instead
+    want the shifted and scaled distribution to be truncated at ``a`` and
+    ``b``, we need to transform these values before passing them as the
+    distribution parameters.
+
+    >>> a_transformed, b_transformed = (a - loc) / scale, (b - loc) / scale
+    >>> rv = truncnorm(a_transformed, b_transformed, loc=loc, scale=scale)
+    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
+    ...                 truncnorm.ppf(0.99, a, b), 100)
+    >>> r = rv.rvs(size=10000)
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim(a-0.1, b+0.1)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+    """
+
+    def _argcheck(self, a, b):
+        return a < b
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, True))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        # Reasonable, since support is [a, b]
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        return super()._fitstart(data, args=(np.min(data), np.max(data)))
+
+    def _get_support(self, a, b):
+        return a, b
+
+    def _pdf(self, x, a, b):
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return _norm_logpdf(x) - _log_gauss_mass(a, b)
+
+    def _cdf(self, x, a, b):
+        return np.exp(self._logcdf(x, a, b))
+
+    def _logcdf(self, x, a, b):
+        x, a, b = np.broadcast_arrays(x, a, b)
+        logcdf = np.asarray(_log_gauss_mass(a, x) - _log_gauss_mass(a, b))
+        i = logcdf > -0.1  # avoid catastrophic cancellation
+        if np.any(i):
+            logcdf[i] = np.log1p(-np.exp(self._logsf(x[i], a[i], b[i])))
+        return logcdf
+
+    def _sf(self, x, a, b):
+        return np.exp(self._logsf(x, a, b))
+
+    def _logsf(self, x, a, b):
+        x, a, b = np.broadcast_arrays(x, a, b)
+        logsf = np.asarray(_log_gauss_mass(x, b) - _log_gauss_mass(a, b))
+        i = logsf > -0.1  # avoid catastrophic cancellation
+        if np.any(i):
+            logsf[i] = np.log1p(-np.exp(self._logcdf(x[i], a[i], b[i])))
+        return logsf
+
+    def _entropy(self, a, b):
+        A = _norm_cdf(a)
+        B = _norm_cdf(b)
+        Z = B - A
+        C = np.log(np.sqrt(2 * np.pi * np.e) * Z)
+        D = (a * _norm_pdf(a) - b * _norm_pdf(b)) / (2 * Z)
+        h = C + D
+        return h
+
+    def _ppf(self, q, a, b):
+        q, a, b = np.broadcast_arrays(q, a, b)
+
+        case_left = a < 0
+        case_right = ~case_left
+
+        def ppf_left(q, a, b):
+            log_Phi_x = _log_sum(_norm_logcdf(a),
+                                 np.log(q) + _log_gauss_mass(a, b))
+            return sc.ndtri_exp(log_Phi_x)
+
+        def ppf_right(q, a, b):
+            log_Phi_x = _log_sum(_norm_logcdf(-b),
+                                 np.log1p(-q) + _log_gauss_mass(a, b))
+            return -sc.ndtri_exp(log_Phi_x)
+
+        out = np.empty_like(q)
+
+        q_left = q[case_left]
+        q_right = q[case_right]
+
+        if q_left.size:
+            out[case_left] = ppf_left(q_left, a[case_left], b[case_left])
+        if q_right.size:
+            out[case_right] = ppf_right(q_right, a[case_right], b[case_right])
+
+        return out
+
+    def _isf(self, q, a, b):
+        # Mostly copy-paste of _ppf, but I think this is simpler than combining
+        q, a, b = np.broadcast_arrays(q, a, b)
+
+        case_left = b < 0
+        case_right = ~case_left
+
+        def isf_left(q, a, b):
+            log_Phi_x = _log_diff(_norm_logcdf(b),
+                                  np.log(q) + _log_gauss_mass(a, b))
+            return sc.ndtri_exp(np.real(log_Phi_x))
+
+        def isf_right(q, a, b):
+            log_Phi_x = _log_diff(_norm_logcdf(-a),
+                                  np.log1p(-q) + _log_gauss_mass(a, b))
+            return -sc.ndtri_exp(np.real(log_Phi_x))
+
+        out = np.empty_like(q)
+
+        q_left = q[case_left]
+        q_right = q[case_right]
+
+        if q_left.size:
+            out[case_left] = isf_left(q_left, a[case_left], b[case_left])
+        if q_right.size:
+            out[case_right] = isf_right(q_right, a[case_right], b[case_right])
+
+        return out
+
+    def _munp(self, n, a, b):
+        def n_th_moment(n, a, b):
+            """
+            Returns n-th moment. Defined only if n >= 0.
+            Function cannot broadcast due to the loop over n
+            """
+            pA, pB = self._pdf(np.asarray([a, b]), a, b)
+            probs = [pA, -pB]
+            moments = [0, 1]
+            for k in range(1, n+1):
+                # a or b might be infinite, and the corresponding pdf value
+                # is 0 in that case, but nan is returned for the
+                # multiplication.  However, as b->infinity,  pdf(b)*b**k -> 0.
+                # So it is safe to use _lazywhere to avoid the nan.
+                vals = _lazywhere(probs, [probs, [a, b]],
+                                  lambda x, y: x * y**(k-1), fillvalue=0)
+                mk = np.sum(vals) + (k-1) * moments[-2]
+                moments.append(mk)
+            return moments[-1]
+
+        return _lazywhere((n >= 0) & (a == a) & (b == b), (n, a, b),
+                          np.vectorize(n_th_moment, otypes=[np.float64]),
+                          np.nan)
+
+    def _stats(self, a, b, moments='mv'):
+        pA, pB = self.pdf(np.array([a, b]), a, b)
+
+        def _truncnorm_stats_scalar(a, b, pA, pB, moments):
+            m1 = pA - pB
+            mu = m1
+            # use _lazywhere to avoid nan (See detailed comment in _munp)
+            probs = [pA, -pB]
+            vals = _lazywhere(probs, [probs, [a, b]], lambda x, y: x*y,
+                              fillvalue=0)
+            m2 = 1 + np.sum(vals)
+            vals = _lazywhere(probs, [probs, [a-mu, b-mu]], lambda x, y: x*y,
+                              fillvalue=0)
+            # mu2 = m2 - mu**2, but not as numerically stable as:
+            # mu2 = (a-mu)*pA - (b-mu)*pB + 1
+            mu2 = 1 + np.sum(vals)
+            vals = _lazywhere(probs, [probs, [a, b]], lambda x, y: x*y**2,
+                              fillvalue=0)
+            m3 = 2*m1 + np.sum(vals)
+            vals = _lazywhere(probs, [probs, [a, b]], lambda x, y: x*y**3,
+                              fillvalue=0)
+            m4 = 3*m2 + np.sum(vals)
+
+            mu3 = m3 + m1 * (-3*m2 + 2*m1**2)
+            g1 = mu3 / np.power(mu2, 1.5)
+            mu4 = m4 + m1*(-4*m3 + 3*m1*(2*m2 - m1**2))
+            g2 = mu4 / mu2**2 - 3
+            return mu, mu2, g1, g2
+
+        _truncnorm_stats = np.vectorize(_truncnorm_stats_scalar,
+                                        excluded=('moments',))
+        return _truncnorm_stats(a, b, pA, pB, moments)
+
+
+truncnorm = truncnorm_gen(name='truncnorm', momtype=1)
+truncnorm._support = ('a', 'b')
+
+
+class truncpareto_gen(rv_continuous):
+    r"""An upper truncated Pareto continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    pareto : Pareto distribution
+
+    Notes
+    -----
+    The probability density function for `truncpareto` is:
+
+    .. math::
+
+        f(x, b, c) = \frac{b}{1 - c^{-b}} \frac{1}{x^{b+1}}
+
+    for :math:`b > 0`, :math:`c > 1` and :math:`1 \le x \le c`.
+
+    `truncpareto` takes `b` and `c` as shape parameters for :math:`b` and
+    :math:`c`.
+
+    Notice that the upper truncation value :math:`c` is defined in
+    standardized form so that random values of an unscaled, unshifted variable
+    are within the range ``[1, c]``.
+    If ``u_r`` is the upper bound to a scaled and/or shifted variable,
+    then ``c = (u_r - loc) / scale``. In other words, the support of the
+    distribution becomes ``(scale + loc) <= x <= (c*scale + loc)`` when
+    `scale` and/or `loc` are provided.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Burroughs, S. M., and Tebbens S. F.
+        "Upper-truncated power laws in natural systems."
+        Pure and Applied Geophysics 158.4 (2001): 741-757.
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        ib = _ShapeInfo("b", False, (0.0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (1.0, np.inf), (False, False))
+        return [ib, ic]
+
+    def _argcheck(self, b, c):
+        return (b > 0.) & (c > 1.)
+
+    def _get_support(self, b, c):
+        return self.a, c
+
+    def _pdf(self, x, b, c):
+        return b * x**-(b+1) / (1 - 1/c**b)
+
+    def _logpdf(self, x, b, c):
+        return np.log(b) - np.log(-np.expm1(-b*np.log(c))) - (b+1)*np.log(x)
+
+    def _cdf(self, x, b, c):
+        return (1 - x**-b) / (1 - 1/c**b)
+
+    def _logcdf(self, x, b, c):
+        return np.log1p(-x**-b) - np.log1p(-1/c**b)
+
+    def _ppf(self, q, b, c):
+        return pow(1 - (1 - 1/c**b)*q, -1/b)
+
+    def _sf(self, x, b, c):
+        return (x**-b - 1/c**b) / (1 - 1/c**b)
+
+    def _logsf(self, x, b, c):
+        return np.log(x**-b - 1/c**b) - np.log1p(-1/c**b)
+
+    def _isf(self, q, b, c):
+        return pow(1/c**b + (1 - 1/c**b)*q, -1/b)
+
+    def _entropy(self, b, c):
+        return -(np.log(b/(1 - 1/c**b))
+                 + (b+1)*(np.log(c)/(c**b - 1) - 1/b))
+
+    def _munp(self, n, b, c):
+        if (n == b).all():
+            return b*np.log(c) / (1 - 1/c**b)
+        else:
+            return b / (b-n) * (c**b - c**n) / (c**b - 1)
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        b, loc, scale = pareto.fit(data)
+        c = (max(data) - loc)/scale
+        return b, c, loc, scale
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop("superfit", False):
+            return super().fit(data, *args, **kwds)
+
+        def log_mean(x):
+            return np.mean(np.log(x))
+
+        def harm_mean(x):
+            return 1/np.mean(1/x)
+
+        def get_b(c, loc, scale):
+            u = (data-loc)/scale
+            harm_m = harm_mean(u)
+            log_m = log_mean(u)
+            quot = (harm_m-1)/log_m
+            return (1 - (quot-1) / (quot - (1 - 1/c)*harm_m/np.log(c)))/log_m
+
+        def get_c(loc, scale):
+            return (mx - loc)/scale
+
+        def get_loc(fc, fscale):
+            if fscale:  # (fscale and fc) or (fscale and not fc)
+                loc = mn - fscale
+                return loc
+            if fc:
+                loc = (fc*mn - mx)/(fc - 1)
+                return loc
+
+        def get_scale(loc):
+            return mn - loc
+
+        # Functions used for optimisation; partial derivatives of
+        # the Lagrangian, set to equal 0.
+
+        def dL_dLoc(loc, b_=None):
+            # Partial derivative wrt location.
+            # Optimised upon when no parameters, or only b, are fixed.
+            scale = get_scale(loc)
+            c = get_c(loc, scale)
+            b = get_b(c, loc, scale) if b_ is None else b_
+            harm_m = harm_mean((data - loc)/scale)
+            return 1 - (1 + (c - 1)/(c**(b+1) - c)) * (1 - 1/(b+1)) * harm_m
+
+        def dL_dB(b, logc, logm):
+            # Partial derivative wrt b.
+            # Optimised upon whenever at least one parameter but b is fixed,
+            # and b is free.
+            return b - np.log1p(b*logc / (1 - b*logm)) / logc
+
+        def fallback(data, *args, **kwargs):
+            # Should any issue arise, default to the general fit method.
+            return super(truncpareto_gen, self).fit(data, *args, **kwargs)
+
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fb, fc, floc, fscale = parameters
+        mn, mx = data.min(), data.max()
+        mn_inf = np.nextafter(mn, -np.inf)
+
+        if (fb is not None
+                and fc is not None
+                and floc is not None
+                and fscale is not None):
+            raise ValueError("All parameters fixed."
+                             "There is nothing to optimize.")
+        elif fc is None and floc is None and fscale is None:
+            if fb is None:
+                def cond_b(loc):
+                    # b is positive only if this function is positive
+                    scale = get_scale(loc)
+                    c = get_c(loc, scale)
+                    harm_m = harm_mean((data - loc)/scale)
+                    return (1 + 1/(c-1)) * np.log(c) / harm_m - 1
+
+                # This gives an upper bound on loc allowing for a positive b.
+                # Iteratively look for a bracket for root_scalar.
+                mn_inf = np.nextafter(mn, -np.inf)
+                rbrack = mn_inf
+                i = 0
+                lbrack = rbrack - 1
+                while ((lbrack > -np.inf)
+                       and (cond_b(lbrack)*cond_b(rbrack) >= 0)):
+                    i += 1
+                    lbrack = rbrack - np.power(2., i)
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(cond_b, bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+
+                # Determine the MLE for loc.
+                # Iteratively look for a bracket for root_scalar.
+                rbrack = res.root - 1e-3  # grad_loc is numerically ill-behaved
+                lbrack = rbrack - 1
+                i = 0
+                while ((lbrack > -np.inf)
+                       and (dL_dLoc(lbrack)*dL_dLoc(rbrack) >= 0)):
+                    i += 1
+                    lbrack = rbrack - np.power(2., i)
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(dL_dLoc, bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+                loc = res.root
+                scale = get_scale(loc)
+                c = get_c(loc, scale)
+                b = get_b(c, loc, scale)
+
+                std_data = (data - loc)/scale
+                # The expression of b relies on b being bounded above.
+                up_bound_b = min(1/log_mean(std_data),
+                                 1/(harm_mean(std_data)-1))
+                if not (b < up_bound_b):
+                    return fallback(data, *args, **kwds)
+            else:
+                # We know b is positive (or a FitError will be triggered)
+                # so we let loc get close to min(data).
+                rbrack = mn_inf
+                lbrack = mn_inf - 1
+                i = 0
+                # Iteratively look for a bracket for root_scalar.
+                while (lbrack > -np.inf
+                       and (dL_dLoc(lbrack, fb)
+                            * dL_dLoc(rbrack, fb) >= 0)):
+                    i += 1
+                    lbrack = rbrack - 2**i
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(dL_dLoc, (fb,),
+                                  bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+                loc = res.root
+                scale = get_scale(loc)
+                c = get_c(loc, scale)
+                b = fb
+        else:
+            # At least one of the parameters determining the support is fixed;
+            # the others then have analytical expressions from the constraints.
+            # The completely determined case (fixed c, loc and scale)
+            # has to be checked for not overflowing the support.
+            # If not fixed, b has to be determined numerically.
+            loc = floc if floc is not None else get_loc(fc, fscale)
+            scale = fscale or get_scale(loc)
+            c = fc or get_c(loc, scale)
+
+            # Unscaled, translated values should be positive when the location
+            # is fixed. If it is not the case, we end up with negative `scale`
+            # and `c`, which would trigger a FitError before exiting the
+            # method.
+            if floc is not None and data.min() - floc < 0:
+                raise FitDataError("truncpareto", lower=1, upper=c)
+
+            # Standardised values should be within the distribution support
+            # when all parameters controlling it are fixed. If it not the case,
+            # `fc` is overridden by `c` determined from `floc` and `fscale` when
+            # raising the exception.
+            if fc and (floc is not None) and fscale:
+                if data.max() > fc*fscale + floc:
+                    raise FitDataError("truncpareto", lower=1,
+                                       upper=get_c(loc, scale))
+
+            # The other constraints should be automatically satisfied
+            # from the analytical expressions of the parameters.
+            # If fc or fscale are respectively less than one or less than 0,
+            # a FitError is triggered before exiting the method.
+
+            if fb is None:
+                std_data = (data - loc)/scale
+                logm = log_mean(std_data)
+                logc = np.log(c)
+                # Condition for a positive root to exist.
+                if not (2*logm < logc):
+                    return fallback(data, *args, **kwds)
+
+                lbrack = 1/logm + 1/(logm - logc)
+                rbrack = np.nextafter(1/logm, 0)
+                try:
+                    res = root_scalar(dL_dB, (logc, logm),
+                                      bracket=(lbrack, rbrack))
+                    # we should then never get there
+                    if not res.converged:
+                        return fallback(data, *args, **kwds)
+                    b = res.root
+                except ValueError:
+                    b = rbrack
+            else:
+                b = fb
+
+        # The distribution requires that `scale+loc <= data <= c*scale+loc`.
+        # To avoid numerical issues, some tuning may be necessary.
+        # We adjust `scale` to satisfy the lower bound, and we adjust
+        # `c` to satisfy the upper bound.
+        if not (scale+loc) < mn:
+            if fscale:
+                loc = np.nextafter(loc, -np.inf)
+            else:
+                scale = get_scale(loc)
+                scale = np.nextafter(scale, 0)
+        if not (c*scale+loc) > mx:
+            c = get_c(loc, scale)
+            c = np.nextafter(c, np.inf)
+
+        if not (np.all(self._argcheck(b, c)) and (scale > 0)):
+            return fallback(data, *args, **kwds)
+
+        params_override = b, c, loc, scale
+        if floc is None and fscale is None:
+            # Based on testing in gh-16782, the following methods are only
+            # reliable if either `floc` or `fscale` are provided. They are
+            # fast, though, so might as well see if they are better than the
+            # generic method.
+            params_super = fallback(data, *args, **kwds)
+            nllf_override = self.nnlf(params_override, data)
+            nllf_super = self.nnlf(params_super, data)
+            if nllf_super < nllf_override:
+                return params_super
+
+        return params_override
+
+
+truncpareto = truncpareto_gen(a=1.0, name='truncpareto')
+truncpareto._support = (1.0, 'c')
+
+
+class tukeylambda_gen(rv_continuous):
+    r"""A Tukey-Lamdba continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    A flexible distribution, able to represent and interpolate between the
+    following distributions:
+
+    - Cauchy                (:math:`lambda = -1`)
+    - logistic              (:math:`lambda = 0`)
+    - approx Normal         (:math:`lambda = 0.14`)
+    - uniform from -1 to 1  (:math:`lambda = 1`)
+
+    `tukeylambda` takes a real number :math:`lambda` (denoted ``lam``
+    in the implementation) as a shape parameter.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, lam):
+        return np.isfinite(lam)
+
+    def _shape_info(self):
+        return [_ShapeInfo("lam", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, lam):
+        b = _lazywhere(lam > 0, (lam,),
+                       f=lambda lam: 1/lam,
+                       fillvalue=np.inf)
+        return -b, b
+
+    def _pdf(self, x, lam):
+        Fx = np.asarray(sc.tklmbda(x, lam))
+        Px = Fx**(lam-1.0) + (np.asarray(1-Fx))**(lam-1.0)
+        with np.errstate(divide='ignore'):
+            Px = 1.0/np.asarray(Px)
+            return np.where((lam <= 0) | (abs(x) < 1.0/np.asarray(lam)), Px, 0.0)
+
+    def _cdf(self, x, lam):
+        return sc.tklmbda(x, lam)
+
+    def _ppf(self, q, lam):
+        return sc.boxcox(q, lam) - sc.boxcox1p(-q, lam)
+
+    def _stats(self, lam):
+        return 0, _tlvar(lam), 0, _tlkurt(lam)
+
+    def _entropy(self, lam):
+        def integ(p):
+            return np.log(pow(p, lam-1)+pow(1-p, lam-1))
+        return integrate.quad(integ, 0, 1)[0]
+
+
+tukeylambda = tukeylambda_gen(name='tukeylambda')
+
+
+class FitUniformFixedScaleDataError(FitDataError):
+    def __init__(self, ptp, fscale):
+        self.args = (
+            "Invalid values in `data`.  Maximum likelihood estimation with "
+            "the uniform distribution and fixed scale requires that "
+            f"np.ptp(data) <= fscale, but np.ptp(data) = {ptp} and "
+            f"fscale = {fscale}."
+        )
+
+
+class uniform_gen(rv_continuous):
+    r"""A uniform continuous random variable.
+
+    In the standard form, the distribution is uniform on ``[0, 1]``. Using
+    the parameters ``loc`` and ``scale``, one obtains the uniform distribution
+    on ``[loc, loc + scale]``.
+
+    %(before_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.uniform(0.0, 1.0, size)
+
+    def _pdf(self, x):
+        return 1.0*(x == x)
+
+    def _cdf(self, x):
+        return x
+
+    def _ppf(self, q):
+        return q
+
+    def _stats(self):
+        return 0.5, 1.0/12, 0, -1.2
+
+    def _entropy(self):
+        return 0.0
+
+    @_call_super_mom
+    def fit(self, data, *args, **kwds):
+        """
+        Maximum likelihood estimate for the location and scale parameters.
+
+        `uniform.fit` uses only the following parameters.  Because exact
+        formulas are used, the parameters related to optimization that are
+        available in the `fit` method of other distributions are ignored
+        here.  The only positional argument accepted is `data`.
+
+        Parameters
+        ----------
+        data : array_like
+            Data to use in calculating the maximum likelihood estimate.
+        floc : float, optional
+            Hold the location parameter fixed to the specified value.
+        fscale : float, optional
+            Hold the scale parameter fixed to the specified value.
+
+        Returns
+        -------
+        loc, scale : float
+            Maximum likelihood estimates for the location and scale.
+
+        Notes
+        -----
+        An error is raised if `floc` is given and any values in `data` are
+        less than `floc`, or if `fscale` is given and `fscale` is less
+        than ``data.max() - data.min()``.  An error is also raised if both
+        `floc` and `fscale` are given.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy.stats import uniform
+
+        We'll fit the uniform distribution to `x`:
+
+        >>> x = np.array([2, 2.5, 3.1, 9.5, 13.0])
+
+        For a uniform distribution MLE, the location is the minimum of the
+        data, and the scale is the maximum minus the minimum.
+
+        >>> loc, scale = uniform.fit(x)
+        >>> loc
+        2.0
+        >>> scale
+        11.0
+
+        If we know the data comes from a uniform distribution where the support
+        starts at 0, we can use ``floc=0``:
+
+        >>> loc, scale = uniform.fit(x, floc=0)
+        >>> loc
+        0.0
+        >>> scale
+        13.0
+
+        Alternatively, if we know the length of the support is 12, we can use
+        ``fscale=12``:
+
+        >>> loc, scale = uniform.fit(x, fscale=12)
+        >>> loc
+        1.5
+        >>> scale
+        12.0
+
+        In that last example, the support interval is [1.5, 13.5].  This
+        solution is not unique.  For example, the distribution with ``loc=2``
+        and ``scale=12`` has the same likelihood as the one above.  When
+        `fscale` is given and it is larger than ``data.max() - data.min()``,
+        the parameters returned by the `fit` method center the support over
+        the interval ``[data.min(), data.max()]``.
+
+        """
+        if len(args) > 0:
+            raise TypeError("Too many arguments.")
+
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # MLE for the uniform distribution
+        # --------------------------------
+        # The PDF is
+        #
+        #     f(x, loc, scale) = {1/scale  for loc <= x <= loc + scale
+        #                        {0        otherwise}
+        #
+        # The likelihood function is
+        #     L(x, loc, scale) = (1/scale)**n
+        # where n is len(x), assuming loc <= x <= loc + scale for all x.
+        # The log-likelihood is
+        #     l(x, loc, scale) = -n*log(scale)
+        # The log-likelihood is maximized by making scale as small as possible,
+        # while keeping loc <= x <= loc + scale.   So if neither loc nor scale
+        # are fixed, the log-likelihood is maximized by choosing
+        #     loc = x.min()
+        #     scale = np.ptp(x)
+        # If loc is fixed, it must be less than or equal to x.min(), and then
+        # the scale is
+        #     scale = x.max() - loc
+        # If scale is fixed, it must not be less than np.ptp(x).  If scale is
+        # greater than np.ptp(x), the solution is not unique.  Note that the
+        # likelihood does not depend on loc, except for the requirement that
+        # loc <= x <= loc + scale.  All choices of loc for which
+        #     x.max() - scale <= loc <= x.min()
+        # have the same log-likelihood.  In this case, we choose loc such that
+        # the support is centered over the interval [data.min(), data.max()]:
+        #     loc = x.min() = 0.5*(scale - np.ptp(x))
+
+        if fscale is None:
+            # scale is not fixed.
+            if floc is None:
+                # loc is not fixed, scale is not fixed.
+                loc = data.min()
+                scale = np.ptp(data)
+            else:
+                # loc is fixed, scale is not fixed.
+                loc = floc
+                scale = data.max() - loc
+                if data.min() < loc:
+                    raise FitDataError("uniform", lower=loc, upper=loc + scale)
+        else:
+            # loc is not fixed, scale is fixed.
+            ptp = np.ptp(data)
+            if ptp > fscale:
+                raise FitUniformFixedScaleDataError(ptp=ptp, fscale=fscale)
+            # If ptp < fscale, the ML estimate is not unique; see the comments
+            # above.  We choose the distribution for which the support is
+            # centered over the interval [data.min(), data.max()].
+            loc = data.min() - 0.5*(fscale - ptp)
+            scale = fscale
+
+        # We expect the return values to be floating point, so ensure it
+        # by explicitly converting to float.
+        return float(loc), float(scale)
+
+
+uniform = uniform_gen(a=0.0, b=1.0, name='uniform')
+
+
+class vonmises_gen(rv_continuous):
+    r"""A Von Mises continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    scipy.stats.vonmises_fisher : Von-Mises Fisher distribution on a
+                                  hypersphere
+
+    Notes
+    -----
+    The probability density function for `vonmises` and `vonmises_line` is:
+
+    .. math::
+
+        f(x, \kappa) = \frac{ \exp(\kappa \cos(x)) }{ 2 \pi I_0(\kappa) }
+
+    for :math:`-\pi \le x \le \pi`, :math:`\kappa \ge 0`. :math:`I_0` is the
+    modified Bessel function of order zero (`scipy.special.i0`).
+
+    `vonmises` is a circular distribution which does not restrict the
+    distribution to a fixed interval. Currently, there is no circular
+    distribution framework in SciPy. The ``cdf`` is implemented such that
+    ``cdf(x + 2*np.pi) == cdf(x) + 1``.
+
+    `vonmises_line` is the same distribution, defined on :math:`[-\pi, \pi]`
+    on the real line. This is a regular (i.e. non-circular) distribution.
+
+    Note about distribution parameters: `vonmises` and `vonmises_line` take
+    ``kappa`` as a shape parameter (concentration) and ``loc`` as the location
+    (circular mean). A ``scale`` parameter is accepted but does not have any
+    effect.
+
+    Examples
+    --------
+    Import the necessary modules.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import vonmises
+
+    Define distribution parameters.
+
+    >>> loc = 0.5 * np.pi  # circular mean
+    >>> kappa = 1  # concentration
+
+    Compute the probability density at ``x=0`` via the ``pdf`` method.
+
+    >>> vonmises.pdf(0, loc=loc, kappa=kappa)
+    0.12570826359722018
+
+    Verify that the percentile function ``ppf`` inverts the cumulative
+    distribution function ``cdf`` up to floating point accuracy.
+
+    >>> x = 1
+    >>> cdf_value = vonmises.cdf(x, loc=loc, kappa=kappa)
+    >>> ppf_value = vonmises.ppf(cdf_value, loc=loc, kappa=kappa)
+    >>> x, cdf_value, ppf_value
+    (1, 0.31489339900904967, 1.0000000000000004)
+
+    Draw 1000 random variates by calling the ``rvs`` method.
+
+    >>> sample_size = 1000
+    >>> sample = vonmises(loc=loc, kappa=kappa).rvs(sample_size)
+
+    Plot the von Mises density on a Cartesian and polar grid to emphasize
+    that it is a circular distribution.
+
+    >>> fig = plt.figure(figsize=(12, 6))
+    >>> left = plt.subplot(121)
+    >>> right = plt.subplot(122, projection='polar')
+    >>> x = np.linspace(-np.pi, np.pi, 500)
+    >>> vonmises_pdf = vonmises.pdf(x, loc=loc, kappa=kappa)
+    >>> ticks = [0, 0.15, 0.3]
+
+    The left image contains the Cartesian plot.
+
+    >>> left.plot(x, vonmises_pdf)
+    >>> left.set_yticks(ticks)
+    >>> number_of_bins = int(np.sqrt(sample_size))
+    >>> left.hist(sample, density=True, bins=number_of_bins)
+    >>> left.set_title("Cartesian plot")
+    >>> left.set_xlim(-np.pi, np.pi)
+    >>> left.grid(True)
+
+    The right image contains the polar plot.
+
+    >>> right.plot(x, vonmises_pdf, label="PDF")
+    >>> right.set_yticks(ticks)
+    >>> right.hist(sample, density=True, bins=number_of_bins,
+    ...            label="Histogram")
+    >>> right.set_title("Polar plot")
+    >>> right.legend(bbox_to_anchor=(0.15, 1.06))
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("kappa", False, (0, np.inf), (True, False))]
+
+    def _argcheck(self, kappa):
+        return kappa >= 0
+
+    def _rvs(self, kappa, size=None, random_state=None):
+        return random_state.vonmises(0.0, kappa, size=size)
+
+    @inherit_docstring_from(rv_continuous)
+    def rvs(self, *args, **kwds):
+        rvs = super().rvs(*args, **kwds)
+        return np.mod(rvs + np.pi, 2*np.pi) - np.pi
+
+    def _pdf(self, x, kappa):
+        # vonmises.pdf(x, kappa) = exp(kappa * cos(x)) / (2*pi*I[0](kappa))
+        #                        = exp(kappa * (cos(x) - 1)) /
+        #                          (2*pi*exp(-kappa)*I[0](kappa))
+        #                        = exp(kappa * cosm1(x)) / (2*pi*i0e(kappa))
+        return np.exp(kappa*sc.cosm1(x)) / (2*np.pi*sc.i0e(kappa))
+
+    def _logpdf(self, x, kappa):
+        # vonmises.pdf(x, kappa) = exp(kappa * cosm1(x)) / (2*pi*i0e(kappa))
+        return kappa * sc.cosm1(x) - np.log(2*np.pi) - np.log(sc.i0e(kappa))
+
+    def _cdf(self, x, kappa):
+        return _stats.von_mises_cdf(kappa, x)
+
+    def _stats_skip(self, kappa):
+        return 0, None, 0, None
+
+    def _entropy(self, kappa):
+        # vonmises.entropy(kappa) = -kappa * I[1](kappa) / I[0](kappa) +
+        #                           log(2 * np.pi * I[0](kappa))
+        #                         = -kappa * I[1](kappa) * exp(-kappa) /
+        #                           (I[0](kappa) * exp(-kappa)) +
+        #                           log(2 * np.pi *
+        #                           I[0](kappa) * exp(-kappa) / exp(-kappa))
+        #                         = -kappa * sc.i1e(kappa) / sc.i0e(kappa) +
+        #                           log(2 * np.pi * i0e(kappa)) + kappa
+        return (-kappa * sc.i1e(kappa) / sc.i0e(kappa) +
+                np.log(2 * np.pi * sc.i0e(kappa)) + kappa)
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        The default limits of integration are endpoints of the interval
+        of width ``2*pi`` centered at `loc` (e.g. ``[-pi, pi]`` when
+        ``loc=0``).\n\n""")
+    def expect(self, func=None, args=(), loc=0, scale=1, lb=None, ub=None,
+               conditional=False, **kwds):
+        _a, _b = -np.pi, np.pi
+
+        if lb is None:
+            lb = loc + _a
+        if ub is None:
+            ub = loc + _b
+
+        return super().expect(func, args, loc,
+                              scale, lb, ub, conditional, **kwds)
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Fit data is assumed to represent angles and will be wrapped onto the
+        unit circle. `f0` and `fscale` are ignored; the returned shape is
+        always the maximum likelihood estimate and the scale is always
+        1. Initial guesses are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, fshape, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                 args, kwds)
+        if self.a == -np.pi:
+            # vonmises line case, here the default fit method will be used
+            return super().fit(data, *args, **kwds)
+
+        # wrap data to interval [0, 2*pi]
+        data = np.mod(data, 2 * np.pi)
+
+        def find_mu(data):
+            return stats.circmean(data)
+
+        def find_kappa(data, loc):
+            # Usually, sources list the following as the equation to solve for
+            # the MLE of the shape parameter:
+            # r = I[1](kappa)/I[0](kappa), where r = mean resultant length
+            # This is valid when the location is the MLE of location.
+            # More generally, when the location may be fixed at an arbitrary
+            # value, r should be defined as follows:
+            r = np.sum(np.cos(loc - data))/len(data)
+            # See gh-18128 for more information.
+
+            # The function r[0](kappa) := I[1](kappa)/I[0](kappa) is monotonic
+            # increasing from r[0](0) = 0 to r[0](+inf) = 1.  The partial
+            # derivative of the log likelihood function with respect to kappa
+            # is monotonic decreasing in kappa.
+            if r == 1:
+                # All observations are (almost) equal to the mean.  Return
+                # some large kappa such that r[0](kappa) = 1.0 numerically.
+                return 1e16
+            elif r > 0:
+                def solve_for_kappa(kappa):
+                    return sc.i1e(kappa)/sc.i0e(kappa) - r
+
+                # The bounds of the root of r[0](kappa) = r are derived from
+                # selected bounds of r[0](x) given in [1, Eq. 11 & 16].  See
+                # gh-20102 for details.
+                #
+                # [1] Amos, D. E. (1973).  Computation of Modified Bessel
+                #     Functions and Their Ratios.  Mathematics of Computation,
+                #     28(125): 239-251.
+                lower_bound = r/(1-r)/(1+r)
+                upper_bound = 2*lower_bound
+
+                # The bounds are violated numerically for certain values of r,
+                # where solve_for_kappa evaluated at the bounds have the same
+                # sign.  This indicates numerical imprecision of i1e()/i0e().
+                # Return the violated bound in this case as it's more accurate.
+                if solve_for_kappa(lower_bound) >= 0:
+                    return lower_bound
+                elif solve_for_kappa(upper_bound) <= 0:
+                    return upper_bound
+                else:
+                    root_res = root_scalar(solve_for_kappa, method="brentq",
+                                           bracket=(lower_bound, upper_bound))
+                    return root_res.root
+            else:
+                # if the provided floc is very far from the circular mean,
+                # the mean resultant length r can become negative.
+                # In that case, the equation
+                # I[1](kappa)/I[0](kappa) = r does not have a solution.
+                # The maximum likelihood kappa is then 0 which practically
+                # results in the uniform distribution on the circle. As
+                # vonmises is defined for kappa > 0, return instead the
+                # smallest floating point value.
+                # See gh-18190 for more information
+                return np.finfo(float).tiny
+
+        # location likelihood equation has a solution independent of kappa
+        loc = floc if floc is not None else find_mu(data)
+        # shape likelihood equation depends on location
+        shape = fshape if fshape is not None else find_kappa(data, loc)
+
+        loc = np.mod(loc + np.pi, 2 * np.pi) - np.pi  # ensure in [-pi, pi]
+        return shape, loc, 1  # scale is not handled
+
+
+vonmises = vonmises_gen(name='vonmises')
+vonmises_line = vonmises_gen(a=-np.pi, b=np.pi, name='vonmises_line')
+
+
+class wald_gen(invgauss_gen):
+    r"""A Wald continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `wald` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp(- \frac{ (x-1)^2 }{ 2x })
+
+    for :math:`x >= 0`.
+
+    `wald` is a special case of `invgauss` with ``mu=1``.
+
+    %(after_notes)s
+
+    %(example)s
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.wald(1.0, 1.0, size=size)
+
+    def _pdf(self, x):
+        # wald.pdf(x) = 1/sqrt(2*pi*x**3) * exp(-(x-1)**2/(2*x))
+        return invgauss._pdf(x, 1.0)
+
+    def _cdf(self, x):
+        return invgauss._cdf(x, 1.0)
+
+    def _sf(self, x):
+        return invgauss._sf(x, 1.0)
+
+    def _ppf(self, x):
+        return invgauss._ppf(x, 1.0)
+
+    def _isf(self, x):
+        return invgauss._isf(x, 1.0)
+
+    def _logpdf(self, x):
+        return invgauss._logpdf(x, 1.0)
+
+    def _logcdf(self, x):
+        return invgauss._logcdf(x, 1.0)
+
+    def _logsf(self, x):
+        return invgauss._logsf(x, 1.0)
+
+    def _stats(self):
+        return 1.0, 1.0, 3.0, 15.0
+
+    def _entropy(self):
+        return invgauss._entropy(1.0)
+
+
+wald = wald_gen(a=0.0, name="wald")
+
+
+class wrapcauchy_gen(rv_continuous):
+    r"""A wrapped Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `wrapcauchy` is:
+
+    .. math::
+
+        f(x, c) = \frac{1-c^2}{2\pi (1+c^2 - 2c \cos(x))}
+
+    for :math:`0 \le x \le 2\pi`, :math:`0 < c < 1`.
+
+    `wrapcauchy` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return (c > 0) & (c < 1)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, 1), (False, False))]
+
+    def _pdf(self, x, c):
+        # wrapcauchy.pdf(x, c) = (1-c**2) / (2*pi*(1+c**2-2*c*cos(x)))
+        return (1.0-c*c)/(2*np.pi*(1+c*c-2*c*np.cos(x)))
+
+    def _cdf(self, x, c):
+
+        def f1(x, cr):
+            # CDF for 0 <= x < pi
+            return 1/np.pi * np.arctan(cr*np.tan(x/2))
+
+        def f2(x, cr):
+            # CDF for pi <= x <= 2*pi
+            return 1 - 1/np.pi * np.arctan(cr*np.tan((2*np.pi - x)/2))
+
+        cr = (1 + c)/(1 - c)
+        return _lazywhere(x < np.pi, (x, cr), f=f1, f2=f2)
+
+    def _ppf(self, q, c):
+        val = (1.0-c)/(1.0+c)
+        rcq = 2*np.arctan(val*np.tan(np.pi*q))
+        rcmq = 2*np.pi-2*np.arctan(val*np.tan(np.pi*(1-q)))
+        return np.where(q < 1.0/2, rcq, rcmq)
+
+    def _entropy(self, c):
+        return np.log(2*np.pi*(1-c*c))
+
+    def _fitstart(self, data):
+        # Use 0.5 as the initial guess of the shape parameter.
+        # For the location and scale, use the minimum and
+        # peak-to-peak/(2*pi), respectively.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        return 0.5, np.min(data), np.ptp(data)/(2*np.pi)
+
+
+wrapcauchy = wrapcauchy_gen(a=0.0, b=2*np.pi, name='wrapcauchy')
+
+
+class gennorm_gen(rv_continuous):
+    r"""A generalized normal continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    laplace : Laplace distribution
+    norm : normal distribution
+
+    Notes
+    -----
+    The probability density function for `gennorm` is [1]_:
+
+    .. math::
+
+        f(x, \beta) = \frac{\beta}{2 \Gamma(1/\beta)} \exp(-|x|^\beta),
+
+    where :math:`x` is a real number, :math:`\beta > 0` and
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `gennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
+    For :math:`\beta = 1`, it is identical to a Laplace distribution.
+    For :math:`\beta = 2`, it is identical to a normal distribution
+    (with ``scale=1/sqrt(2)``).
+
+    References
+    ----------
+
+    .. [1] "Generalized normal distribution, Version 1",
+           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1
+
+    .. [2] Nardon, Martina, and Paolo Pianca. "Simulation techniques for
+           generalized Gaussian densities." Journal of Statistical
+           Computation and Simulation 79.11 (2009): 1317-1329
+
+    .. [3] Wicklin, Rick. "Simulate data from a generalized Gaussian
+           distribution" in The DO Loop blog, September 21, 2016,
+           https://blogs.sas.com/content/iml/2016/09/21/simulate-generalized-gaussian-sas.html
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("beta", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, beta):
+        return np.exp(self._logpdf(x, beta))
+
+    def _logpdf(self, x, beta):
+        return np.log(0.5*beta) - sc.gammaln(1.0/beta) - abs(x)**beta
+
+    def _cdf(self, x, beta):
+        c = 0.5 * np.sign(x)
+        # evaluating (.5 + c) first prevents numerical cancellation
+        return (0.5 + c) - c * sc.gammaincc(1.0/beta, abs(x)**beta)
+
+    def _ppf(self, x, beta):
+        c = np.sign(x - 0.5)
+        # evaluating (1. + c) first prevents numerical cancellation
+        return c * sc.gammainccinv(1.0/beta, (1.0 + c) - 2.0*c*x)**(1.0/beta)
+
+    def _sf(self, x, beta):
+        return self._cdf(-x, beta)
+
+    def _isf(self, x, beta):
+        return -self._ppf(x, beta)
+
+    def _stats(self, beta):
+        c1, c3, c5 = sc.gammaln([1.0/beta, 3.0/beta, 5.0/beta])
+        return 0., np.exp(c3 - c1), 0., np.exp(c5 + c1 - 2.0*c3) - 3.
+
+    def _entropy(self, beta):
+        return 1. / beta - np.log(.5 * beta) + sc.gammaln(1. / beta)
+
+    def _rvs(self, beta, size=None, random_state=None):
+        # see [2]_ for the algorithm
+        # see [3]_ for reference implementation in SAS
+        z = random_state.gamma(1/beta, size=size)
+        y = z ** (1/beta)
+        # convert y to array to ensure masking support
+        y = np.asarray(y)
+        mask = random_state.random(size=y.shape) < 0.5
+        y[mask] = -y[mask]
+        return y
+
+
+gennorm = gennorm_gen(name='gennorm')
+
+
+class halfgennorm_gen(rv_continuous):
+    r"""The upper half of a generalized normal continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gennorm : generalized normal distribution
+    expon : exponential distribution
+    halfnorm : half normal distribution
+
+    Notes
+    -----
+    The probability density function for `halfgennorm` is:
+
+    .. math::
+
+        f(x, \beta) = \frac{\beta}{\Gamma(1/\beta)} \exp(-|x|^\beta)
+
+    for :math:`x, \beta > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    `halfgennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
+    For :math:`\beta = 1`, it is identical to an exponential distribution.
+    For :math:`\beta = 2`, it is identical to a half normal distribution
+    (with ``scale=1/sqrt(2)``).
+
+    References
+    ----------
+
+    .. [1] "Generalized normal distribution, Version 1",
+           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("beta", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, beta):
+        #                                 beta
+        # halfgennorm.pdf(x, beta) =  -------------  exp(-|x|**beta)
+        #                             gamma(1/beta)
+        return np.exp(self._logpdf(x, beta))
+
+    def _logpdf(self, x, beta):
+        return np.log(beta) - sc.gammaln(1.0/beta) - x**beta
+
+    def _cdf(self, x, beta):
+        return sc.gammainc(1.0/beta, x**beta)
+
+    def _ppf(self, x, beta):
+        return sc.gammaincinv(1.0/beta, x)**(1.0/beta)
+
+    def _sf(self, x, beta):
+        return sc.gammaincc(1.0/beta, x**beta)
+
+    def _isf(self, x, beta):
+        return sc.gammainccinv(1.0/beta, x)**(1.0/beta)
+
+    def _entropy(self, beta):
+        return 1.0/beta - np.log(beta) + sc.gammaln(1.0/beta)
+
+
+halfgennorm = halfgennorm_gen(a=0, name='halfgennorm')
+
+
+class crystalball_gen(rv_continuous):
+    r"""
+    Crystalball distribution
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `crystalball` is:
+
+    .. math::
+
+        f(x, \beta, m) =  \begin{cases}
+                            N \exp(-x^2 / 2),  &\text{for } x > -\beta\\
+                            N A (B - x)^{-m}  &\text{for } x \le -\beta
+                          \end{cases}
+
+    where :math:`A = (m / |\beta|)^m  \exp(-\beta^2 / 2)`,
+    :math:`B = m/|\beta| - |\beta|` and :math:`N` is a normalisation constant.
+
+    `crystalball` takes :math:`\beta > 0` and :math:`m > 1` as shape
+    parameters.  :math:`\beta` defines the point where the pdf changes
+    from a power-law to a Gaussian distribution.  :math:`m` is the power
+    of the power-law tail.
+
+    %(after_notes)s
+
+    .. versionadded:: 0.19.0
+
+    References
+    ----------
+    .. [1] "Crystal Ball Function",
+           https://en.wikipedia.org/wiki/Crystal_Ball_function
+
+    %(example)s
+    """
+    def _argcheck(self, beta, m):
+        """
+        Shape parameter bounds are m > 1 and beta > 0.
+        """
+        return (m > 1) & (beta > 0)
+
+    def _shape_info(self):
+        ibeta = _ShapeInfo("beta", False, (0, np.inf), (False, False))
+        im = _ShapeInfo("m", False, (1, np.inf), (False, False))
+        return [ibeta, im]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default m=1 is not valid
+        return super()._fitstart(data, args=(1, 1.5))
+
+    def _pdf(self, x, beta, m):
+        """
+        Return PDF of the crystalball function.
+
+                                            --
+                                           | exp(-x**2 / 2),  for x > -beta
+        crystalball.pdf(x, beta, m) =  N * |
+                                           | A * (B - x)**(-m), for x <= -beta
+                                            --
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return np.exp(-x**2 / 2)
+
+        def lhs(x, beta, m):
+            return ((m/beta)**m * np.exp(-beta**2 / 2.0) *
+                    (m/beta - beta - x)**(-m))
+
+        return N * _lazywhere(x > -beta, (x, beta, m), f=rhs, f2=lhs)
+
+    def _logpdf(self, x, beta, m):
+        """
+        Return the log of the PDF of the crystalball function.
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return -x**2/2
+
+        def lhs(x, beta, m):
+            return m*np.log(m/beta) - beta**2/2 - m*np.log(m/beta - beta - x)
+
+        return np.log(N) + _lazywhere(x > -beta, (x, beta, m), f=rhs, f2=lhs)
+
+    def _cdf(self, x, beta, m):
+        """
+        Return CDF of the crystalball function
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return ((m/beta) * np.exp(-beta**2 / 2.0) / (m-1) +
+                    _norm_pdf_C * (_norm_cdf(x) - _norm_cdf(-beta)))
+
+        def lhs(x, beta, m):
+            return ((m/beta)**m * np.exp(-beta**2 / 2.0) *
+                    (m/beta - beta - x)**(-m+1) / (m-1))
+
+        return N * _lazywhere(x > -beta, (x, beta, m), f=rhs, f2=lhs)
+
+    def _sf(self, x, beta, m):
+        """
+        Survival function of the crystalball distribution.
+        """
+
+        def rhs(x, beta, m):
+            # M is the same as 1/N used elsewhere.
+            M = m/beta/(m - 1)*np.exp(-beta**2/2) + _norm_pdf_C*_norm_cdf(beta)
+            return _norm_pdf_C*_norm_sf(x)/M
+
+        def lhs(x, beta, m):
+            # Default behavior is OK in the left tail of the SF.
+            return 1 - self._cdf(x, beta, m)
+
+        return _lazywhere(x > -beta, (x, beta, m), f=rhs, f2=lhs)
+
+    def _ppf(self, p, beta, m):
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+        pbeta = N * (m/beta) * np.exp(-beta**2/2) / (m - 1)
+
+        def ppf_less(p, beta, m):
+            eb2 = np.exp(-beta**2/2)
+            C = (m/beta) * eb2 / (m-1)
+            N = 1/(C + _norm_pdf_C * _norm_cdf(beta))
+            return (m/beta - beta -
+                    ((m - 1)*(m/beta)**(-m)/eb2*p/N)**(1/(1-m)))
+
+        def ppf_greater(p, beta, m):
+            eb2 = np.exp(-beta**2/2)
+            C = (m/beta) * eb2 / (m-1)
+            N = 1/(C + _norm_pdf_C * _norm_cdf(beta))
+            return _norm_ppf(_norm_cdf(-beta) + (1/_norm_pdf_C)*(p/N - C))
+
+        return _lazywhere(p < pbeta, (p, beta, m), f=ppf_less, f2=ppf_greater)
+
+    def _munp(self, n, beta, m):
+        """
+        Returns the n-th non-central moment of the crystalball function.
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def n_th_moment(n, beta, m):
+            """
+            Returns n-th moment. Defined only if n+1 < m
+            Function cannot broadcast due to the loop over n
+            """
+            A = (m/beta)**m * np.exp(-beta**2 / 2.0)
+            B = m/beta - beta
+            rhs = (2**((n-1)/2.0) * sc.gamma((n+1)/2) *
+                   (1.0 + (-1)**n * sc.gammainc((n+1)/2, beta**2 / 2)))
+            lhs = np.zeros(rhs.shape)
+            for k in range(int(n) + 1):
+                lhs += (sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) *
+                        (m/beta)**(-m + k + 1))
+            return A * lhs + rhs
+
+        return N * _lazywhere(n + 1 < m, (n, beta, m),
+                              np.vectorize(n_th_moment, otypes=[np.float64]),
+                              np.inf)
+
+
+crystalball = crystalball_gen(name='crystalball', longname="A Crystalball Function")
+
+
+def _argus_phi(chi):
+    """
+    Utility function for the argus distribution used in the pdf, sf and
+    moment calculation.
+    Note that for all x > 0:
+    gammainc(1.5, x**2/2) = 2 * (_norm_cdf(x) - x * _norm_pdf(x) - 0.5).
+    This can be verified directly by noting that the cdf of Gamma(1.5) can
+    be written as erf(sqrt(x)) - 2*sqrt(x)*exp(-x)/sqrt(Pi).
+    We use gammainc instead of the usual definition because it is more precise
+    for small chi.
+    """
+    return sc.gammainc(1.5, chi**2/2) / 2
+
+
+class argus_gen(rv_continuous):
+    r"""
+    Argus distribution
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `argus` is:
+
+    .. math::
+
+        f(x, \chi) = \frac{\chi^3}{\sqrt{2\pi} \Psi(\chi)} x \sqrt{1-x^2}
+                     \exp(-\chi^2 (1 - x^2)/2)
+
+    for :math:`0 < x < 1` and :math:`\chi > 0`, where
+
+    .. math::
+
+        \Psi(\chi) = \Phi(\chi) - \chi \phi(\chi) - 1/2
+
+    with :math:`\Phi` and :math:`\phi` being the CDF and PDF of a standard
+    normal distribution, respectively.
+
+    `argus` takes :math:`\chi` as shape a parameter. Details about sampling
+    from the ARGUS distribution can be found in [2]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "ARGUS distribution",
+           https://en.wikipedia.org/wiki/ARGUS_distribution
+    .. [2] Christoph Baumgarten "Random variate generation by fast numerical
+           inversion in the varying parameter case." Research in Statistics,
+           vol. 1, 2023, doi:10.1080/27684520.2023.2279060.
+
+    .. versionadded:: 0.19.0
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("chi", False, (0, np.inf), (False, False))]
+
+    def _logpdf(self, x, chi):
+        # for x = 0 or 1, logpdf returns -np.inf
+        with np.errstate(divide='ignore'):
+            y = 1.0 - x*x
+            A = 3*np.log(chi) - _norm_pdf_logC - np.log(_argus_phi(chi))
+            return A + np.log(x) + 0.5*np.log1p(-x*x) - chi**2 * y / 2
+
+    def _pdf(self, x, chi):
+        return np.exp(self._logpdf(x, chi))
+
+    def _cdf(self, x, chi):
+        return 1.0 - self._sf(x, chi)
+
+    def _sf(self, x, chi):
+        return _argus_phi(chi * np.sqrt((1 - x)*(1 + x))) / _argus_phi(chi)
+
+    def _rvs(self, chi, size=None, random_state=None):
+        chi = np.asarray(chi)
+        if chi.size == 1:
+            out = self._rvs_scalar(chi, numsamples=size,
+                                   random_state=random_state)
+        else:
+            shp, bc = _check_shape(chi.shape, size)
+            numsamples = int(np.prod(shp))
+            out = np.empty(size)
+            it = np.nditer([chi],
+                           flags=['multi_index'],
+                           op_flags=[['readonly']])
+            while not it.finished:
+                idx = tuple((it.multi_index[j] if not bc[j] else slice(None))
+                            for j in range(-len(size), 0))
+                r = self._rvs_scalar(it[0], numsamples=numsamples,
+                                     random_state=random_state)
+                out[idx] = r.reshape(shp)
+                it.iternext()
+
+        if size == ():
+            out = out[()]
+        return out
+
+    def _rvs_scalar(self, chi, numsamples=None, random_state=None):
+        # if chi <= 1.8:
+        # use rejection method, see Devroye:
+        # Non-Uniform Random Variate Generation, 1986, section II.3.2.
+        # write: PDF f(x) = c * g(x) * h(x), where
+        # h is [0,1]-valued and g is a density
+        # we use two ways to write f
+        #
+        # Case 1:
+        # write g(x) = 3*x*sqrt(1-x**2), h(x) = exp(-chi**2 (1-x**2) / 2)
+        # If X has a distribution with density g its ppf G_inv is given by:
+        # G_inv(u) = np.sqrt(1 - u**(2/3))
+        #
+        # Case 2:
+        # g(x) = chi**2 * x * exp(-chi**2 * (1-x**2)/2) / (1 - exp(-chi**2 /2))
+        # h(x) = sqrt(1 - x**2), 0 <= x <= 1
+        # one can show that
+        # G_inv(u) = np.sqrt(2*np.log(u*(np.exp(chi**2/2)-1)+1))/chi
+        #          = np.sqrt(1 + 2*np.log(np.exp(-chi**2/2)*(1-u)+u)/chi**2)
+        # the latter expression is used for precision with small chi
+        #
+        # In both cases, the inverse cdf of g can be written analytically, and
+        # we can apply the rejection method:
+        #
+        # REPEAT
+        #    Generate U uniformly distributed on [0, 1]
+        #    Generate X with density g (e.g. via inverse transform sampling:
+        #    X = G_inv(V) with V uniformly distributed on [0, 1])
+        # UNTIL X <= h(X)
+        # RETURN X
+        #
+        # We use case 1 for chi <= 0.5 as it maintains precision for small chi
+        # and case 2 for 0.5 < chi <= 1.8 due to its speed for moderate chi.
+        #
+        # if chi > 1.8:
+        # use relation to the Gamma distribution: if X is ARGUS with parameter
+        # chi), then Y = chi**2 * (1 - X**2) / 2 has density proportional to
+        # sqrt(u) * exp(-u) on [0, chi**2 / 2], i.e. a Gamma(3/2) distribution
+        # conditioned on [0, chi**2 / 2]). Therefore, to sample X from the
+        # ARGUS distribution, we sample Y from the gamma distribution, keeping
+        # only samples on [0, chi**2 / 2], and apply the inverse
+        # transformation X = (1 - 2*Y/chi**2)**(1/2). Since we only
+        # look at chi > 1.8, gamma(1.5).cdf(chi**2/2) is large enough such
+        # Y falls in the interval [0, chi**2 / 2] with a high probability:
+        # stats.gamma(1.5).cdf(1.8**2/2) = 0.644...
+        #
+        # The points to switch between the different methods are determined
+        # by a comparison of the runtime of the different methods. However,
+        # the runtime is platform-dependent. The implemented values should
+        # ensure a good overall performance and are supported by an analysis
+        # of the rejection constants of different methods.
+
+        size1d = tuple(np.atleast_1d(numsamples))
+        N = int(np.prod(size1d))
+        x = np.zeros(N)
+        simulated = 0
+        chi2 = chi * chi
+        if chi <= 0.5:
+            d = -chi2 / 2
+            while simulated < N:
+                k = N - simulated
+                u = random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                z = v**(2/3)
+                # acceptance condition: u <= h(G_inv(v)). This simplifies to
+                accept = (np.log(u) <= d * z)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    # we still need to transform z=v**(2/3) to X = G_inv(v)
+                    rvs = np.sqrt(1 - z[accept])
+                    x[simulated:(simulated + num_accept)] = rvs
+                    simulated += num_accept
+        elif chi <= 1.8:
+            echi = np.exp(-chi2 / 2)
+            while simulated < N:
+                k = N - simulated
+                u = random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                z = 2 * np.log(echi * (1 - v) + v) / chi2
+                # as in case one, simplify u <= h(G_inv(v)) and then transform
+                # z to the target distribution X = G_inv(v)
+                accept = (u**2 + z <= 0)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    rvs = np.sqrt(1 + z[accept])
+                    x[simulated:(simulated + num_accept)] = rvs
+                    simulated += num_accept
+        else:
+            # conditional Gamma for chi > 1.8
+            while simulated < N:
+                k = N - simulated
+                g = random_state.standard_gamma(1.5, size=k)
+                accept = (g <= chi2 / 2)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = g[accept]
+                    simulated += num_accept
+            x = np.sqrt(1 - 2 * x / chi2)
+
+        return np.reshape(x, size1d)
+
+    def _stats(self, chi):
+        # need to ensure that dtype is float
+        # otherwise the mask below does not work for integers
+        chi = np.asarray(chi, dtype=float)
+        phi = _argus_phi(chi)
+        m = np.sqrt(np.pi/8) * chi * sc.ive(1, chi**2/4) / phi
+        # compute second moment, use Taylor expansion for small chi (<= 0.1)
+        mu2 = np.empty_like(chi)
+        mask = chi > 0.1
+        c = chi[mask]
+        mu2[mask] = 1 - 3 / c**2 + c * _norm_pdf(c) / phi[mask]
+        c = chi[~mask]
+        coef = [-358/65690625, 0, -94/1010625, 0, 2/2625, 0, 6/175, 0, 0.4]
+        mu2[~mask] = np.polyval(coef, c)
+        return m, mu2 - m**2, None, None
+
+
+argus = argus_gen(name='argus', longname="An Argus Function", a=0.0, b=1.0)
+
+
+class rv_histogram(rv_continuous):
+    """
+    Generates a distribution given by a histogram.
+    This is useful to generate a template distribution from a binned
+    datasample.
+
+    As a subclass of the `rv_continuous` class, `rv_histogram` inherits from it
+    a collection of generic methods (see `rv_continuous` for the full list),
+    and implements them based on the properties of the provided binned
+    datasample.
+
+    Parameters
+    ----------
+    histogram : tuple of array_like
+        Tuple containing two array_like objects.
+        The first containing the content of n bins,
+        the second containing the (n+1) bin boundaries.
+        In particular, the return value of `numpy.histogram` is accepted.
+
+    density : bool, optional
+        If False, assumes the histogram is proportional to counts per bin;
+        otherwise, assumes it is proportional to a density.
+        For constant bin widths, these are equivalent, but the distinction
+        is important when bin widths vary (see Notes).
+        If None (default), sets ``density=True`` for backwards compatibility,
+        but warns if the bin widths are variable. Set `density` explicitly
+        to silence the warning.
+
+        .. versionadded:: 1.10.0
+
+    Notes
+    -----
+    When a histogram has unequal bin widths, there is a distinction between
+    histograms that are proportional to counts per bin and histograms that are
+    proportional to probability density over a bin. If `numpy.histogram` is
+    called with its default ``density=False``, the resulting histogram is the
+    number of counts per bin, so ``density=False`` should be passed to
+    `rv_histogram`. If `numpy.histogram` is called with ``density=True``, the
+    resulting histogram is in terms of probability density, so ``density=True``
+    should be passed to `rv_histogram`. To avoid warnings, always pass
+    ``density`` explicitly when the input histogram has unequal bin widths.
+
+    There are no additional shape parameters except for the loc and scale.
+    The pdf is defined as a stepwise function from the provided histogram.
+    The cdf is a linear interpolation of the pdf.
+
+    .. versionadded:: 0.19.0
+
+    Examples
+    --------
+
+    Create a scipy.stats distribution from a numpy histogram
+
+    >>> import scipy.stats
+    >>> import numpy as np
+    >>> data = scipy.stats.norm.rvs(size=100000, loc=0, scale=1.5,
+    ...                             random_state=123)
+    >>> hist = np.histogram(data, bins=100)
+    >>> hist_dist = scipy.stats.rv_histogram(hist, density=False)
+
+    Behaves like an ordinary scipy rv_continuous distribution
+
+    >>> hist_dist.pdf(1.0)
+    0.20538577847618705
+    >>> hist_dist.cdf(2.0)
+    0.90818568543056499
+
+    PDF is zero above (below) the highest (lowest) bin of the histogram,
+    defined by the max (min) of the original dataset
+
+    >>> hist_dist.pdf(np.max(data))
+    0.0
+    >>> hist_dist.cdf(np.max(data))
+    1.0
+    >>> hist_dist.pdf(np.min(data))
+    7.7591907244498314e-05
+    >>> hist_dist.cdf(np.min(data))
+    0.0
+
+    PDF and CDF follow the histogram
+
+    >>> import matplotlib.pyplot as plt
+    >>> X = np.linspace(-5.0, 5.0, 100)
+    >>> fig, ax = plt.subplots()
+    >>> ax.set_title("PDF from Template")
+    >>> ax.hist(data, density=True, bins=100)
+    >>> ax.plot(X, hist_dist.pdf(X), label='PDF')
+    >>> ax.plot(X, hist_dist.cdf(X), label='CDF')
+    >>> ax.legend()
+    >>> fig.show()
+
+    """
+    _support_mask = rv_continuous._support_mask
+
+    def __init__(self, histogram, *args, density=None, **kwargs):
+        """
+        Create a new distribution using the given histogram
+
+        Parameters
+        ----------
+        histogram : tuple of array_like
+            Tuple containing two array_like objects.
+            The first containing the content of n bins,
+            the second containing the (n+1) bin boundaries.
+            In particular, the return value of np.histogram is accepted.
+        density : bool, optional
+            If False, assumes the histogram is proportional to counts per bin;
+            otherwise, assumes it is proportional to a density.
+            For constant bin widths, these are equivalent.
+            If None (default), sets ``density=True`` for backward
+            compatibility, but warns if the bin widths are variable. Set
+            `density` explicitly to silence the warning.
+        """
+        self._histogram = histogram
+        self._density = density
+        if len(histogram) != 2:
+            raise ValueError("Expected length 2 for parameter histogram")
+        self._hpdf = np.asarray(histogram[0])
+        self._hbins = np.asarray(histogram[1])
+        if len(self._hpdf) + 1 != len(self._hbins):
+            raise ValueError("Number of elements in histogram content "
+                             "and histogram boundaries do not match, "
+                             "expected n and n+1.")
+        self._hbin_widths = self._hbins[1:] - self._hbins[:-1]
+        bins_vary = not np.allclose(self._hbin_widths, self._hbin_widths[0])
+        if density is None and bins_vary:
+            message = ("Bin widths are not constant. Assuming `density=True`."
+                       "Specify `density` explicitly to silence this warning.")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+            density = True
+        elif not density:
+            self._hpdf = self._hpdf / self._hbin_widths
+
+        self._hpdf = self._hpdf / float(np.sum(self._hpdf * self._hbin_widths))
+        self._hcdf = np.cumsum(self._hpdf * self._hbin_widths)
+        self._hpdf = np.hstack([0.0, self._hpdf, 0.0])
+        self._hcdf = np.hstack([0.0, self._hcdf])
+        # Set support
+        kwargs['a'] = self.a = self._hbins[0]
+        kwargs['b'] = self.b = self._hbins[-1]
+        super().__init__(*args, **kwargs)
+
+    def _pdf(self, x):
+        """
+        PDF of the histogram
+        """
+        return self._hpdf[np.searchsorted(self._hbins, x, side='right')]
+
+    def _cdf(self, x):
+        """
+        CDF calculated from the histogram
+        """
+        return np.interp(x, self._hbins, self._hcdf)
+
+    def _ppf(self, x):
+        """
+        Percentile function calculated from the histogram
+        """
+        return np.interp(x, self._hcdf, self._hbins)
+
+    def _munp(self, n):
+        """Compute the n-th non-central moment."""
+        integrals = (self._hbins[1:]**(n+1) - self._hbins[:-1]**(n+1)) / (n+1)
+        return np.sum(self._hpdf[1:-1] * integrals)
+
+    def _entropy(self):
+        """Compute entropy of distribution"""
+        res = _lazywhere(self._hpdf[1:-1] > 0.0,
+                         (self._hpdf[1:-1],),
+                         np.log,
+                         0.0)
+        return -np.sum(self._hpdf[1:-1] * res * self._hbin_widths)
+
+    def _updated_ctor_param(self):
+        """
+        Set the histogram as additional constructor argument
+        """
+        dct = super()._updated_ctor_param()
+        dct['histogram'] = self._histogram
+        dct['density'] = self._density
+        return dct
+
+
+class studentized_range_gen(rv_continuous):
+    r"""A studentized range continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    t: Student's t distribution
+
+    Notes
+    -----
+    The probability density function for `studentized_range` is:
+
+    .. math::
+
+         f(x; k, \nu) = \frac{k(k-1)\nu^{\nu/2}}{\Gamma(\nu/2)
+                        2^{\nu/2-1}} \int_{0}^{\infty} \int_{-\infty}^{\infty}
+                        s^{\nu} e^{-\nu s^2/2} \phi(z) \phi(sx + z)
+                        [\Phi(sx + z) - \Phi(z)]^{k-2} \,dz \,ds
+
+    for :math:`x ≥ 0`, :math:`k > 1`, and :math:`\nu > 0`.
+
+    `studentized_range` takes ``k`` for :math:`k` and ``df`` for :math:`\nu`
+    as shape parameters.
+
+    When :math:`\nu` exceeds 100,000, an asymptotic approximation (infinite
+    degrees of freedom) is used to compute the cumulative distribution
+    function [4]_ and probability distribution function.
+
+    %(after_notes)s
+
+    References
+    ----------
+
+    .. [1] "Studentized range distribution",
+           https://en.wikipedia.org/wiki/Studentized_range_distribution
+    .. [2] Batista, Ben Dêivide, et al. "Externally Studentized Normal Midrange
+           Distribution." Ciência e Agrotecnologia, vol. 41, no. 4, 2017, pp.
+           378-389., doi:10.1590/1413-70542017414047716.
+    .. [3] Harter, H. Leon. "Tables of Range and Studentized Range." The Annals
+           of Mathematical Statistics, vol. 31, no. 4, 1960, pp. 1122-1147.
+           JSTOR, www.jstor.org/stable/2237810. Accessed 18 Feb. 2021.
+    .. [4] Lund, R. E., and J. R. Lund. "Algorithm AS 190: Probabilities and
+           Upper Quantiles for the Studentized Range." Journal of the Royal
+           Statistical Society. Series C (Applied Statistics), vol. 32, no. 2,
+           1983, pp. 204-210. JSTOR, www.jstor.org/stable/2347300. Accessed 18
+           Feb. 2021.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import studentized_range
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> k, df = 3, 10
+    >>> x = np.linspace(studentized_range.ppf(0.01, k, df),
+    ...                 studentized_range.ppf(0.99, k, df), 100)
+    >>> ax.plot(x, studentized_range.pdf(x, k, df),
+    ...         'r-', lw=5, alpha=0.6, label='studentized_range pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = studentized_range(k, df)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = studentized_range.ppf([0.001, 0.5, 0.999], k, df)
+    >>> np.allclose([0.001, 0.5, 0.999], studentized_range.cdf(vals, k, df))
+    True
+
+    Rather than using (``studentized_range.rvs``) to generate random variates,
+    which is very slow for this distribution, we can approximate the inverse
+    CDF using an interpolator, and then perform inverse transform sampling
+    with this approximate inverse CDF.
+
+    This distribution has an infinite but thin right tail, so we focus our
+    attention on the leftmost 99.9 percent.
+
+    >>> a, b = studentized_range.ppf([0, .999], k, df)
+    >>> a, b
+    0, 7.41058083802274
+
+    >>> from scipy.interpolate import interp1d
+    >>> rng = np.random.default_rng()
+    >>> xs = np.linspace(a, b, 50)
+    >>> cdf = studentized_range.cdf(xs, k, df)
+    # Create an interpolant of the inverse CDF
+    >>> ppf = interp1d(cdf, xs, fill_value='extrapolate')
+    # Perform inverse transform sampling using the interpolant
+    >>> r = ppf(rng.uniform(size=1000))
+
+    And compare the histogram:
+
+    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+
+    def _argcheck(self, k, df):
+        return (k > 1) & (df > 0)
+
+    def _shape_info(self):
+        ik = _ShapeInfo("k", False, (1, np.inf), (False, False))
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        return [ik, idf]
+
+    def _fitstart(self, data):
+        # Default is k=1, but that is not a valid value of the parameter.
+        return super()._fitstart(data, args=(2, 1))
+
+    def _munp(self, K, k, df):
+        cython_symbol = '_studentized_range_moment'
+        _a, _b = self._get_support()
+        # all three of these are used to create a numpy array so they must
+        # be the same shape.
+
+        def _single_moment(K, k, df):
+            log_const = _stats._studentized_range_pdf_logconst(k, df)
+            arg = [K, k, df, log_const]
+            usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+
+            ranges = [(-np.inf, np.inf), (0, np.inf), (_a, _b)]
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_moment, 3, 1)
+        return np.asarray(ufunc(K, k, df), dtype=np.float64)[()]
+
+    def _pdf(self, x, k, df):
+
+        def _single_pdf(q, k, df):
+            # The infinite form of the PDF is derived from the infinite
+            # CDF.
+            if df < 100000:
+                cython_symbol = '_studentized_range_pdf'
+                log_const = _stats._studentized_range_pdf_logconst(k, df)
+                arg = [q, k, df, log_const]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf), (0, np.inf)]
+
+            else:
+                cython_symbol = '_studentized_range_pdf_asymptotic'
+                arg = [q, k]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf)]
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_pdf, 3, 1)
+        return np.asarray(ufunc(x, k, df), dtype=np.float64)[()]
+
+    def _cdf(self, x, k, df):
+
+        def _single_cdf(q, k, df):
+            # "When the degrees of freedom V are infinite the probability
+            # integral takes [on a] simpler form," and a single asymptotic
+            # integral is evaluated rather than the standard double integral.
+            # (Lund, Lund, page 205)
+            if df < 100000:
+                cython_symbol = '_studentized_range_cdf'
+                log_const = _stats._studentized_range_cdf_logconst(k, df)
+                arg = [q, k, df, log_const]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf), (0, np.inf)]
+
+            else:
+                cython_symbol = '_studentized_range_cdf_asymptotic'
+                arg = [q, k]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf)]
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_cdf, 3, 1)
+
+        # clip p-values to ensure they are in [0, 1].
+        return np.clip(np.asarray(ufunc(x, k, df), dtype=np.float64)[()], 0, 1)
+
+
+studentized_range = studentized_range_gen(name='studentized_range', a=0,
+                                          b=np.inf)
+
+
+class rel_breitwigner_gen(rv_continuous):
+    r"""A relativistic Breit-Wigner random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    cauchy: Cauchy distribution, also known as the Breit-Wigner distribution.
+
+    Notes
+    -----
+
+    The probability density function for `rel_breitwigner` is
+
+    .. math::
+
+        f(x, \rho) = \frac{k}{(x^2 - \rho^2)^2 + \rho^2}
+
+    where
+
+    .. math::
+        k = \frac{2\sqrt{2}\rho^2\sqrt{\rho^2 + 1}}
+            {\pi\sqrt{\rho^2 + \rho\sqrt{\rho^2 + 1}}}
+
+    The relativistic Breit-Wigner distribution is used in high energy physics
+    to model resonances [1]_. It gives the uncertainty in the invariant mass,
+    :math:`M` [2]_, of a resonance with characteristic mass :math:`M_0` and
+    decay-width :math:`\Gamma`, where :math:`M`, :math:`M_0` and :math:`\Gamma`
+    are expressed in natural units. In SciPy's parametrization, the shape
+    parameter :math:`\rho` is equal to :math:`M_0/\Gamma` and takes values in
+    :math:`(0, \infty)`.
+
+    Equivalently, the relativistic Breit-Wigner distribution is said to give
+    the uncertainty in the center-of-mass energy :math:`E_{\text{cm}}`. In
+    natural units, the speed of light :math:`c` is equal to 1 and the invariant
+    mass :math:`M` is equal to the rest energy :math:`Mc^2`. In the
+    center-of-mass frame, the rest energy is equal to the total energy [3]_.
+
+    %(after_notes)s
+
+    :math:`\rho = M/\Gamma` and :math:`\Gamma` is the scale parameter. For
+    example, if one seeks to model the :math:`Z^0` boson with :math:`M_0
+    \approx 91.1876 \text{ GeV}` and :math:`\Gamma \approx 2.4952\text{ GeV}`
+    [4]_ one can set ``rho=91.1876/2.4952`` and ``scale=2.4952``.
+
+    To ensure a physically meaningful result when using the `fit` method, one
+    should set ``floc=0`` to fix the location parameter to 0.
+
+    References
+    ----------
+    .. [1] Relativistic Breit-Wigner distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Relativistic_Breit-Wigner_distribution
+    .. [2] Invariant mass, Wikipedia,
+           https://en.wikipedia.org/wiki/Invariant_mass
+    .. [3] Center-of-momentum frame, Wikipedia,
+           https://en.wikipedia.org/wiki/Center-of-momentum_frame
+    .. [4] M. Tanabashi et al. (Particle Data Group) Phys. Rev. D 98, 030001 -
+           Published 17 August 2018
+
+    %(example)s
+
+    """
+    def _argcheck(self, rho):
+        return rho > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("rho", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, rho):
+        # C = k / rho**2
+        C = np.sqrt(
+            2 * (1 + 1/rho**2) / (1 + np.sqrt(1 + 1/rho**2))
+        ) * 2 / np.pi
+        with np.errstate(over='ignore'):
+            return C / (((x - rho)*(x + rho)/rho)**2 + 1)
+
+    def _cdf(self, x, rho):
+        # C = k / (2 * rho**2) / np.sqrt(1 + 1/rho**2)
+        C = np.sqrt(2/(1 + np.sqrt(1 + 1/rho**2)))/np.pi
+        result = (
+            np.sqrt(-1 + 1j/rho)
+            * np.arctan(x/np.sqrt(-rho*(rho + 1j)))
+        )
+        result = C * 2 * np.imag(result)
+        # Sometimes above formula produces values greater than 1.
+        return np.clip(result, None, 1)
+
+    def _munp(self, n, rho):
+        if n == 0:
+            return 1.
+        if n == 1:
+            # C = k / (2 * rho)
+            C = np.sqrt(
+                2 * (1 + 1/rho**2) / (1 + np.sqrt(1 + 1/rho**2))
+            ) / np.pi * rho
+            return C * (np.pi/2 + np.arctan(rho))
+        if n == 2:
+            # C = pi * k / (4 * rho)
+            C = np.sqrt(
+                (1 + 1/rho**2) / (2 * (1 + np.sqrt(1 + 1/rho**2)))
+            ) * rho
+            result = (1 - rho * 1j) / np.sqrt(-1 - 1j/rho)
+            return 2 * C * np.real(result)
+        else:
+            return np.inf
+
+    def _stats(self, rho):
+        # Returning None from stats makes public stats use _munp.
+        # nan values will be omitted from public stats. Skew and
+        # kurtosis are actually infinite.
+        return None, None, np.nan, np.nan
+
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        # Override rv_continuous.fit to better handle case where floc is set.
+        data, _, floc, fscale = _check_fit_input_parameters(
+            self, data, args, kwds
+        )
+
+        censored = isinstance(data, CensoredData)
+        if censored:
+            if data.num_censored() == 0:
+                # There are no censored values in data, so replace the
+                # CensoredData instance with a regular array.
+                data = data._uncensored
+                censored = False
+
+        if floc is None or censored:
+            return super().fit(data, *args, **kwds)
+
+        if fscale is None:
+            # The interquartile range approximates the scale parameter gamma.
+            # The median approximates rho * gamma.
+            p25, p50, p75 = np.quantile(data - floc, [0.25, 0.5, 0.75])
+            scale_0 = p75 - p25
+            rho_0 = p50 / scale_0
+            if not args:
+                args = [rho_0]
+            if "scale" not in kwds:
+                kwds["scale"] = scale_0
+        else:
+            M_0 = np.median(data - floc)
+            rho_0 = M_0 / fscale
+            if not args:
+                args = [rho_0]
+        return super().fit(data, *args, **kwds)
+
+
+rel_breitwigner = rel_breitwigner_gen(a=0.0, name="rel_breitwigner")
+
+
+# Collect names of classes and objects in this module.
+pairs = list(globals().copy().items())
+_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_continuous)
+
+__all__ = _distn_names + _distn_gen_names + ['rv_histogram']
diff --git a/shared/package-files/40/4012f9b98ee6f90ec2e3a98edead588a8f6cef7c354e723dcbffe3e44fa3e386/config.py b/shared/package-files/40/4012f9b98ee6f90ec2e3a98edead588a8f6cef7c354e723dcbffe3e44fa3e386/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e1cb38c254f412cae88890bdd2da92da5232908
--- /dev/null
+++ b/shared/package-files/40/4012f9b98ee6f90ec2e3a98edead588a8f6cef7c354e723dcbffe3e44fa3e386/config.py
@@ -0,0 +1,140 @@
+"""Implementation of configuration-related magic functions.
+"""
+#-----------------------------------------------------------------------------
+#  Copyright (c) 2012 The IPython Development Team.
+#
+#  Distributed under the terms of the Modified BSD License.
+#
+#  The full license is in the file COPYING.txt, distributed with this software.
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+# Stdlib
+import re
+
+# Our own packages
+from IPython.core.error import UsageError
+from IPython.core.magic import Magics, magics_class, line_magic
+from logging import error
+
+#-----------------------------------------------------------------------------
+# Magic implementation classes
+#-----------------------------------------------------------------------------
+
+reg = re.compile(r'^\w+\.\w+$')
+@magics_class
+class ConfigMagics(Magics):
+
+    def __init__(self, shell):
+        super(ConfigMagics, self).__init__(shell)
+        self.configurables = []
+
+    @line_magic
+    def config(self, s):
+        """configure IPython
+
+            %config Class[.trait=value]
+
+        This magic exposes most of the IPython config system. Any
+        Configurable class should be able to be configured with the simple
+        line::
+
+            %config Class.trait=value
+
+        Where `value` will be resolved in the user's namespace, if it is an
+        expression or variable name.
+
+        Examples
+        --------
+
+        To see what classes are available for config, pass no arguments::
+
+            In [1]: %config
+            Available objects for config:
+                AliasManager
+                DisplayFormatter
+                HistoryManager
+                IPCompleter
+                LoggingMagics
+                MagicsManager
+                OSMagics
+                PrefilterManager
+                ScriptMagics
+                TerminalInteractiveShell
+
+        To view what is configurable on a given class, just pass the class
+        name::
+
+            In [2]: %config LoggingMagics
+            LoggingMagics(Magics) options
+            ---------------------------
+            LoggingMagics.quiet=<Bool>
+                Suppress output of log state when logging is enabled
+                Current: False
+
+        but the real use is in setting values::
+
+            In [3]: %config LoggingMagics.quiet = True
+
+        and these values are read from the user_ns if they are variables::
+
+            In [4]: feeling_quiet=False
+
+            In [5]: %config LoggingMagics.quiet = feeling_quiet
+
+        """
+        from traitlets.config.loader import Config
+        # some IPython objects are Configurable, but do not yet have
+        # any configurable traits.  Exclude them from the effects of
+        # this magic, as their presence is just noise:
+        configurables = sorted(set([ c for c in self.shell.configurables
+                                     if c.__class__.class_traits(config=True)
+                                     ]), key=lambda x: x.__class__.__name__)
+        classnames = [ c.__class__.__name__ for c in configurables ]
+
+        line = s.strip()
+        if not line:
+            # print available configurable names
+            print("Available objects for config:")
+            for name in classnames:
+                print("   ", name)
+            return
+        elif line in classnames:
+            # `%config TerminalInteractiveShell` will print trait info for
+            # TerminalInteractiveShell
+            c = configurables[classnames.index(line)]
+            cls = c.__class__
+            help = cls.class_get_help(c)
+            # strip leading '--' from cl-args:
+            help = re.sub(re.compile(r'^--', re.MULTILINE), '', help)
+            print(help)
+            return
+        elif reg.match(line):
+            cls, attr = line.split('.')
+            return getattr(configurables[classnames.index(cls)],attr)
+        elif '=' not in line:
+            msg = "Invalid config statement: %r, "\
+                  "should be `Class.trait = value`."
+            
+            ll = line.lower()
+            for classname in classnames:
+                if ll == classname.lower():
+                    msg = msg + '\nDid you mean %s (note the case)?' % classname
+                    break
+
+            raise UsageError( msg % line)
+
+        # otherwise, assume we are setting configurables.
+        # leave quotes on args when splitting, because we want
+        # unquoted args to eval in user_ns
+        cfg = Config()
+        exec("cfg."+line, self.shell.user_ns, locals())
+
+        for configurable in configurables:
+            try:
+                configurable.update_config(cfg)
+            except Exception as e:
+                error(e)
diff --git a/shared/package-files/40/4013503d4f9d4303f39a1c461f701cc6181c279ac2c2d2de5896de956dcbc371/features.pyi b/shared/package-files/40/4013503d4f9d4303f39a1c461f701cc6181c279ac2c2d2de5896de956dcbc371/features.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bb77363578c98763964b35dde345ee8e354e7e00
--- /dev/null
+++ b/shared/package-files/40/4013503d4f9d4303f39a1c461f701cc6181c279ac2c2d2de5896de956dcbc371/features.pyi
@@ -0,0 +1,3 @@
+from django.db.backends.base.features import BaseDatabaseFeatures
+
+class DatabaseFeatures(BaseDatabaseFeatures): ...
diff --git a/shared/package-files/40/401390afe8a6e2d332681c872f70001cb71ad0041febfb06e7573cd511ab1a58/DistributionTemplates.h b/shared/package-files/40/401390afe8a6e2d332681c872f70001cb71ad0041febfb06e7573cd511ab1a58/DistributionTemplates.h
new file mode 100644
index 0000000000000000000000000000000000000000..a5ed9526c270dcedd93aba16ab28854d94b584fb
--- /dev/null
+++ b/shared/package-files/40/401390afe8a6e2d332681c872f70001cb71ad0041febfb06e7573cd511ab1a58/DistributionTemplates.h
@@ -0,0 +1,394 @@
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
+#include <ATen/Generator.h>
+#include <ATen/ExpandUtils.h>
+#include <ATen/Tensor.h>
+#include <ATen/MemoryOverlap.h>
+#include <ATen/NamedTensorUtils.h>
+#include <ATen/native/Resize.h>
+#include <ATen/native/TensorIterator.h>
+#include <c10/util/Optional.h>
+#include <limits>
+#include <cmath>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/empty_like.h>
+#include <ATen/ops/empty.h>
+#include <ATen/ops/full.h>
+#include <ATen/ops/view_as_real.h>
+#endif
+
+namespace at::native::templates {
+
+// ==================================================== Random ========================================================
+
+// The purpose of `update_from` and `update_to` is to find the closest valid int64_t number that can be used as actual `from`.
+// The current implementation of `random_` uses uint64_t arithmetics and casts the result to the target dtype(scalar_t).
+// This casting can result in generating numbers that happen to be greater or equal to `to` value. For instance:
+//
+//    auto actual = torch::empty({3, 3}, torch::half);
+//    actual.random_(0, 65504);
+//
+// If random's uint64_t arithmetics produces 65503 as a random value after casting to torch::half it becomes 65504
+// and violates the requirement that random value must be less than `to`. To resolve this issue `update_from` and `update_to`
+// moves `from` to the right and `to` to the left to the next closest value that won't go outside [from, to) after casting to
+// the target dtype. For `to` = 65504 it moves left for (1 << (log2(to) - 11 + 1)) = 32 and becomes 65472, which is previous
+// available number for torch::half dtype.
+template<typename scalar_t>
+int64_t update_from(int64_t from) {
+  static_assert(
+    std::is_floating_point<scalar_t>::value ||
+    std::is_same<scalar_t, at::Half>::value ||
+    std::is_same<scalar_t, at::BFloat16>::value, "scalar_t must be floating-point type");
+  const auto from_plus_1 = static_cast<int64_t>(static_cast<scalar_t>(from + 1));
+  if (from_plus_1 < from) {
+    int64_t from_ = std::abs(from + 1);
+    int n = 0;
+    while (from_ >>= 1) ++n;
+    // NOLINTNEXTLINE(clang-analyzer-core.UndefinedBinaryOperatorResult)
+    from = from_plus_1 + (1LL << (n - std::numeric_limits<scalar_t>::digits + 1));
+  }
+  return from;
+}
+
+template<typename scalar_t>
+int64_t update_to(int64_t to) {
+  static_assert(
+    std::is_floating_point<scalar_t>::value ||
+    std::is_same<scalar_t, at::Half>::value ||
+    std::is_same<scalar_t, at::BFloat16>::value, "scalar_t must be floating-point type");
+  const auto to_minus_1 = static_cast<int64_t>(static_cast<scalar_t>(to - 1));
+  if (to_minus_1 >= to) {
+    int64_t to_ = std::abs(to - 1);
+    int n = 0;
+    while (to_ >>= 1) ++n;
+    // NOLINTNEXTLINE(clang-analyzer-core.UndefinedBinaryOperatorResult)
+    to = to_minus_1 - (1LL << (n - std::numeric_limits<scalar_t>::digits + 1));
+  }
+  return to;
+}
+
+// Return earlier for not invoking kernel.
+// See https://github.com/pytorch/pytorch/issues/103418 for more details
+#define CHECK_EMPTY_AND_RETURN(tensor) \
+  if (tensor.numel() == 0) {  \
+    return tensor;  \
+  }
+
+template<template<typename> class random_kernel, typename RNG>
+at::Tensor& random_impl(at::Tensor& self, c10::optional<Generator> generator) {
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = at::TensorIterator::borrowing_nullary_op(self);
+  random_kernel<RNG>()(iter, generator);
+  return self;
+}
+
+#define CHECK_OUT_OF_BOUNDS(var, name, min, max, dtype) \
+  TORCH_CHECK(var >= min && var <= max, name , " is out of bounds for ", dtype); \
+
+#define WARN_OUT_OF_BOUNDS(var, name, digits, dtype) \
+  if (var < -(1LL << digits) || var > (1LL << digits)) { \
+    TORCH_WARN(name , " is out of bounds [-(2^", digits, "), 2^", digits, "]. ", \
+      "Due to precision limitations ", dtype, " can support discrete uniform distribution only within this range. ", \
+      "This warning will become an error in version 1.7 release, please fix the code in advance"); \
+  }
+
+static void check_from_to_in_range(int64_t from, int64_t to_inc, caffe2::TypeMeta dtype) {
+  const auto scalar_type = typeMetaToScalarType(dtype);
+  if (isFloatingType(scalar_type)) {
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, scalar_type, "check_random_fp_bounds", [&] {
+      const auto min = static_cast<double>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<double>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", min, max, dtype);
+
+      constexpr auto digits = std::numeric_limits<scalar_t>::digits;
+      WARN_OUT_OF_BOUNDS(from, "from", digits, dtype);
+      WARN_OUT_OF_BOUNDS(to_inc, "to - 1", digits, dtype);
+    });
+  } else if (scalar_type == kUInt64) {
+    // When you do a comparison between int64_t and uint64_t, the usual
+    // arithmetic conversions say that the int64_t value is promoted to
+    // unsigned. But this conversion wraps around: if I had -1 as my int64_t,
+    // then it will promote to 0xFFFFFFFFFFFFFFFF in uint64_t. This is never
+    // the right thing to do.
+    CHECK_OUT_OF_BOUNDS(from, "from", 0, INT64_MAX, dtype);
+    CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", 0, INT64_MAX, dtype);
+  } else if (isIntegralType(scalar_type, /*includeBool=*/true)) {
+    AT_DISPATCH_V2(scalar_type, "check_random_integral_bounds", AT_WRAP([&]() {
+      const auto min = static_cast<int64_t>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<int64_t>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", min, max, dtype);
+    }), AT_EXPAND(AT_INTEGRAL_TYPES), kUInt16, kUInt32, kBool);
+  } else {
+    TORCH_CHECK(false, "check_random_bounds handles only integral, floating-point and boolean types");
+  }
+}
+
+template<template<typename> class random_from_to_kernel, typename RNG>
+at::Tensor& random_from_to_impl(at::Tensor& self, int64_t from, c10::optional<int64_t> to_opt, c10::optional<Generator> generator) {
+  uint64_t range = 0;
+  auto iter = at::TensorIterator::borrowing_nullary_op(self);
+  if (to_opt.has_value()) {
+    // [from, to)
+    int64_t to = *to_opt;
+    TORCH_CHECK(from < to, "random_ expects 'from' to be less than 'to', but got from=", from, " >= to=", to);
+    if (isFloatingType(iter.dtype())) {
+      AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "random_update_from_to", [&] {
+        from = update_from<scalar_t>(from);
+        to = update_to<scalar_t>(to);
+        TORCH_CHECK(from < to, "random_ expects 'from' casted to dtype to be less than 'to' casted to dtype, but got from=", from, " >= to=", to);
+      });
+    }
+    check_from_to_in_range(from, to - 1, self.dtype());
+    CHECK_EMPTY_AND_RETURN(self);
+    range = static_cast<uint64_t>(to) - static_cast<uint64_t>(from);
+    random_from_to_kernel<RNG>()(iter, range, from, generator);
+  } else if (from != std::numeric_limits<int64_t>::lowest()) {
+    // [from, std::numeric_limits<int64_t>::max()]
+    int64_t to_inc = 0;
+    if (isFloatingType(iter.dtype())) {
+      AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "random_from_to_range_calc", [&] {
+        constexpr int64_t scalar_t_max = static_cast<int64_t>(1) << std::numeric_limits<scalar_t>::digits;
+        to_inc = scalar_t_max > std::numeric_limits<int64_t>::max() ? std::numeric_limits<int64_t>::max() : static_cast<int64_t>(scalar_t_max);
+        from = update_from<scalar_t>(from);
+        TORCH_CHECK(from < to_inc, "random_ expects 'from' casted to dtype to be less than or equal to 'to_inc' casted to dtype, but got from=", from, " > to_inc=", to_inc);
+      });
+    } else if (isIntegralType(iter.dtype(), /*includeBool=*/true)) {
+      AT_DISPATCH_V2(self.scalar_type(), "random_from_to_range_calc", AT_WRAP([&] {
+        if constexpr (std::is_same_v<scalar_t, bool>) {
+          to_inc = static_cast<int64_t>(true);
+        } else {
+          to_inc = static_cast<int64_t>(std::numeric_limits<scalar_t>::max());
+        }
+      }), AT_EXPAND(AT_INTEGRAL_TYPES_V2), kBool);
+    } else {
+      TORCH_CHECK(false, "random_from_to_impl handles only integral, floating-point and boolean types");
+    }
+    check_from_to_in_range(from, to_inc, self.dtype());
+    CHECK_EMPTY_AND_RETURN(self);
+    range = static_cast<uint64_t>(to_inc) - static_cast<uint64_t>(from) + 1;
+    random_from_to_kernel<RNG>()(iter, range, from, generator);
+  } else {
+    // [std::numeric_limits<int64_t>::lowest(), std::numeric_limits<int64_t>::max()]
+    // range = 2^64
+    CHECK_EMPTY_AND_RETURN(self);
+    random_from_to_kernel<RNG>()(iter, generator);
+  }
+  return self;
+}
+
+// ==================================================== Normal ========================================================
+
+#define CHECK_NORMAL_TENSOR_STD(std) \
+  do { \
+    TORCH_CHECK( \
+      !std.is_complex(), \
+      "normal expects standard deviation to be non-complex"); \
+    TORCH_CHECK( \
+      std.numel() == 0 || std.is_meta() || std.min().ge(0).item<bool>(), \
+      "normal expects all elements of std >= 0.0"); \
+  } while (0)
+
+#define CHECK_NORMAL_STD(std) \
+  TORCH_CHECK(std >= 0.0, "normal expects std >= 0.0, but found std ", std);
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_impl_(Tensor& self, double mean, double std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  CHECK_EMPTY_AND_RETURN(self);
+
+  if (self.is_complex()) {
+    auto float_tensor = at::view_as_real(self);
+    // variance for normal distribution of the real and imaginary values
+    // is half of the input variance
+    normal_kernel<RNG>()(float_tensor, mean, std/(std::sqrt(2)), gen);
+  } else {
+    normal_kernel<RNG>()(self, mean, std, gen);
+  }
+  return self;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, const Tensor& mean, double std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  auto std_tensor = at::empty_like(output, MemoryFormat::Contiguous);
+  auto shape = at::infer_size(mean.sizes(), std_tensor.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, std, gen);
+  output.add_(mean);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, double mean, const Tensor& std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto mean_tensor = at::full({}, mean, output.options());
+  auto shape = at::infer_size(mean_tensor.sizes(), std.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, 1, gen);
+  // CUDA NB: addcmul_out copies the tensor to be added into the output.
+  // The previous function here was addcmul_out(output, mean_tensor, output, std, 1);
+  // The third argument is not a constant reference and hence the samples in output are overwritten.
+  // Consequently, the computation performed is mean_tensor + mean_tensor * std instead of mean_tensor + output * std
+  output.mul_(std).add_(mean_tensor);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, const Tensor& mean, const Tensor& std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto shape = at::infer_size(mean.sizes(), std.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, 1, gen);
+  // CUDA NB: addcmul_out copies the tensor to be added into the output.
+  // The previous function here was addcmul_out(output, mean, output, std, 1);
+  // The third argument is not a constant reference and hence the samples in output are overwritten.
+  // Consequently, the computation performed is mean + mean * std instead of mean + output * std
+  output.mul_(std).add_(mean);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(const Tensor& mean, double std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  Tensor ret = at::empty_like(mean, MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(double mean, const Tensor& std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  Tensor ret = at::empty_like(std, MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(const Tensor& mean, const Tensor& std, c10::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto shape = at::infer_size(mean.sizes(), std.sizes());
+  Tensor ret = at::empty(shape, mean.options(), MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+// ==================================================== Uniform =======================================================
+
+template<template<typename> class uniform_kernel, typename RNG>
+at::Tensor& uniform_impl_(at::Tensor& self, double from, double to, c10::optional<Generator> generator) {
+  if (self.is_complex()) {
+    CHECK_EMPTY_AND_RETURN(self);
+    auto float_tensor = at::view_as_real(self);
+    uniform_impl_<uniform_kernel, RNG>(float_tensor, from, to, generator);
+  } else {
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "check_uniform_bounds", [&] {
+      const auto dtype = self.dtype();
+      const auto min = static_cast<double>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<double>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to, "to", min, max, dtype);
+      TORCH_CHECK(from <= to, "uniform_ expects to return a [from, to) range, but found from=", from, " > to=", to);
+      TORCH_CHECK((to - from) <= std::numeric_limits<scalar_t>::max(),
+            "uniform_ expects to-from <= std::numeric_limits<", toString(self.scalar_type()),
+            ">::max(), but found to=", to, " and from=", from,
+            " which result in to-from to exceed the limit");
+      from = std::min(std::max(from, min), max);
+      to = std::max(std::min(to, max), min);
+    });
+    CHECK_EMPTY_AND_RETURN(self);
+    auto iter = at::TensorIterator::borrowing_nullary_op(self);
+    uniform_kernel<RNG>()(iter, from, to, generator);
+  }
+  return self;
+}
+
+// ================================================== LogNormal =======================================================
+
+template<template<typename> class log_normal_kernel, typename RNG>
+at::Tensor& log_normal_impl_(at::Tensor& self, double mean, double std, c10::optional<Generator> gen) {
+  TORCH_CHECK(std > 0.0, "log_normal_ expects std > 0.0, but found std=", std);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  log_normal_kernel<RNG>()(iter, mean, std, gen);
+  return self;
+}
+
+// =================================================== Geometric ======================================================
+
+template<template<typename> class geometric_kernel, typename RNG>
+Tensor& geometric_impl_(Tensor& self, double p, c10::optional<Generator> gen) {
+  TORCH_CHECK(0 < p && p < 1, "geometric_ expects p to be in (0, 1), but got p=", p);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  geometric_kernel<RNG>()(iter, p, gen);
+  return self;
+}
+
+// ================================================== Exponential =====================================================
+
+template<template<typename> class exponential_kernel, typename RNG>
+Tensor& exponential_impl_(Tensor& self, double lambda, c10::optional<Generator> gen) {
+  TORCH_CHECK(lambda > 0.0, "exponential_ expects lambda > 0.0, but found lambda=", lambda);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  exponential_kernel<RNG>()(iter, lambda, gen);
+  return self;
+}
+
+// ==================================================== Cauchy ========================================================
+
+template<template<typename> class cauchy_kernel, typename RNG>
+Tensor& cauchy_impl_(Tensor& self, double median, double sigma, c10::optional<Generator> gen) {
+  // TODO: instead of variable name 'sigma', use 'gamma' or 'scale'
+  // the variance, squared sigma, is undefined for cauchy distribution
+  TORCH_CHECK(sigma > 0.0, "cauchy_ expects sigma > 0.0, but found sigma=", sigma);
+  TORCH_CHECK(at::isFloatingType(self.scalar_type()), "Cauchy distribution is a continuous probability distribution. dtype must be a floating point but you specified ", self.dtype());
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  cauchy_kernel<RNG>()(iter, median, sigma, gen);
+  return self;
+}
+
+// ==================================================== Bernoulli =====================================================
+
+template<template<typename> class bernoulli_tensor_kernel, typename RNG>
+Tensor& bernoulli_impl_(Tensor& self, const Tensor& p_, c10::optional<Generator> gen) {
+  CHECK_EMPTY_AND_RETURN(self);
+  NoNamesGuard guard;
+  at::assert_no_internal_overlap(self);
+  bernoulli_tensor_kernel<RNG>()(self, p_, gen);
+  return self;
+}
+
+template<template<typename> class bernoulli_scalar_kernel, typename RNG>
+Tensor& bernoulli_impl_(Tensor& self, double p, c10::optional<Generator> gen) {
+  TORCH_CHECK(0 <= p && p <= 1, "bernoulli_ expects p to be in [0, 1], but got p=", p);
+  CHECK_EMPTY_AND_RETURN(self);
+  at::assert_no_internal_overlap(self);
+  bernoulli_scalar_kernel<RNG>()(self, p, gen);
+  return self;
+}
+
+template<template<typename> class bernoulli_tensor_kernel, typename RNG>
+Tensor& bernoulli_out_impl(Tensor& result, const Tensor& self, c10::optional<Generator> gen) {
+  // result.resize_as_(self) requires self to have same dtype as result, so we
+  // use resize_ instead.
+  // TODO: Fix resize_as_. See pytorch/pytorch#11665.
+  result.resize_(self.sizes());
+  bernoulli_impl_<bernoulli_tensor_kernel, RNG>(result, self, gen);
+  namedinference::propagate_names(result, self);
+  return result;
+}
+
+#undef CHECK_OUT_OF_BOUNDS
+#undef WARN_OUT_OF_BOUNDS
+
+} // namespace at::native::templates
diff --git a/shared/package-files/40/401f948ad86ede3660a3a105126858e4836c2e57dce9cc77927c97482eaa116a/plot.tcl b/shared/package-files/40/401f948ad86ede3660a3a105126858e4836c2e57dce9cc77927c97482eaa116a/plot.tcl
new file mode 100644
index 0000000000000000000000000000000000000000..453b7a6653f64548a36ddd91852683b77845b040
--- /dev/null
+++ b/shared/package-files/40/401f948ad86ede3660a3a105126858e4836c2e57dce9cc77927c97482eaa116a/plot.tcl
@@ -0,0 +1,97 @@
+# plot.tcl --
+#
+# This demonstration script creates a canvas widget showing a 2-D
+# plot with data points that can be dragged with the mouse.
+
+if {![info exists widgetDemo]} {
+    error "This script should be run from the \"widget\" demo."
+}
+
+package require Tk
+
+set w .plot
+catch {destroy $w}
+toplevel $w
+wm title $w "Plot Demonstration"
+wm iconname $w "Plot"
+positionWindow $w
+set c $w.c
+
+label $w.msg -font $font -wraplength 4i -justify left -text "This window displays a canvas widget containing a simple 2-dimensional plot.  You can doctor the data by dragging any of the points with mouse button 1."
+pack $w.msg -side top
+
+## See Code / Dismiss buttons
+set btns [addSeeDismiss $w.buttons $w]
+pack $btns -side bottom -fill x
+
+canvas $c -relief raised -width 450 -height 300
+pack $w.c -side top -fill x
+
+set plotFont {Helvetica 18}
+
+$c create line 100 250 400 250 -width 2
+$c create line 100 250 100 50 -width 2
+$c create text 225 20 -text "A Simple Plot" -font $plotFont -fill brown
+
+for {set i 0} {$i <= 10} {incr i} {
+    set x [expr {100 + ($i*30)}]
+    $c create line $x 250 $x 245 -width 2
+    $c create text $x 254 -text [expr {10*$i}] -anchor n -font $plotFont
+}
+for {set i 0} {$i <= 5} {incr i} {
+    set y [expr {250 - ($i*40)}]
+    $c create line 100 $y 105 $y -width 2
+    $c create text 96 $y -text [expr {$i*50}].0 -anchor e -font $plotFont
+}
+
+foreach point {
+    {12 56} {20 94} {33 98} {32 120} {61 180} {75 160} {98 223}
+} {
+    set x [expr {100 + (3*[lindex $point 0])}]
+    set y [expr {250 - (4*[lindex $point 1])/5}]
+    set item [$c create oval [expr {$x-6}] [expr {$y-6}] \
+	    [expr {$x+6}] [expr {$y+6}] -width 1 -outline black \
+	    -fill SkyBlue2]
+    $c addtag point withtag $item
+}
+
+$c bind point <Enter> "$c itemconfig current -fill red"
+$c bind point <Leave> "$c itemconfig current -fill SkyBlue2"
+$c bind point <Button-1> "plotDown $c %x %y"
+$c bind point <ButtonRelease-1> "$c dtag selected"
+bind $c <B1-Motion> "plotMove $c %x %y"
+
+set plot(lastX) 0
+set plot(lastY) 0
+
+# plotDown --
+# This procedure is invoked when the mouse is pressed over one of the
+# data points.  It sets up state to allow the point to be dragged.
+#
+# Arguments:
+# w -		The canvas window.
+# x, y -	The coordinates of the mouse press.
+
+proc plotDown {w x y} {
+    global plot
+    $w dtag selected
+    $w addtag selected withtag current
+    $w raise current
+    set plot(lastX) $x
+    set plot(lastY) $y
+}
+
+# plotMove --
+# This procedure is invoked during mouse motion events.  It drags the
+# current item.
+#
+# Arguments:
+# w -		The canvas window.
+# x, y -	The coordinates of the mouse.
+
+proc plotMove {w x y} {
+    global plot
+    $w move selected [expr {$x-$plot(lastX)}] [expr {$y-$plot(lastY)}]
+    set plot(lastX) $x
+    set plot(lastY) $y
+}
diff --git a/shared/package-files/40/4020cb5ac1d4314dbcc95919e836eaa0abe1e09c20f5b3fada448c15573dd50d/RECORD b/shared/package-files/40/4020cb5ac1d4314dbcc95919e836eaa0abe1e09c20f5b3fada448c15573dd50d/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..c443610e40d792b7ba8eb49fa883ba969b210583
--- /dev/null
+++ b/shared/package-files/40/4020cb5ac1d4314dbcc95919e836eaa0abe1e09c20f5b3fada448c15573dd50d/RECORD
@@ -0,0 +1,155 @@
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diff --git a/shared/package-files/40/4020f168ead62ae04c7f2b7dda45a04ec207c86bfe078532199b134543145290/as_strided_ops.h b/shared/package-files/40/4020f168ead62ae04c7f2b7dda45a04ec207c86bfe078532199b134543145290/as_strided_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..071ad94112b0eadd228b43dbb264d4dab892b797
--- /dev/null
+++ b/shared/package-files/40/4020f168ead62ae04c7f2b7dda45a04ec207c86bfe078532199b134543145290/as_strided_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API as_strided {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::optional<c10::SymInt>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::as_strided")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset);
+};
+
+struct TORCH_API as_strided_ {
+  using schema = const at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::optional<c10::SymInt>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::as_strided_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)")
+  static const at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset);
+  static const at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/402208973ae1251f4aff858b9e2c11ec9b9a632037ff78cfb52701271e0d5377/test_decomp_cossin.py b/shared/package-files/40/402208973ae1251f4aff858b9e2c11ec9b9a632037ff78cfb52701271e0d5377/test_decomp_cossin.py
new file mode 100644
index 0000000000000000000000000000000000000000..df112f0e4cf75d03d2787c24306665b7027967ee
--- /dev/null
+++ b/shared/package-files/40/402208973ae1251f4aff858b9e2c11ec9b9a632037ff78cfb52701271e0d5377/test_decomp_cossin.py
@@ -0,0 +1,300 @@
+import pytest
+import numpy as np
+from numpy.random import default_rng
+from numpy.testing import assert_allclose
+
+from scipy import linalg
+from scipy.linalg.lapack import _compute_lwork
+from scipy.stats import ortho_group, unitary_group
+from scipy.linalg import cossin, get_lapack_funcs
+
+REAL_DTYPES = (np.float32, np.float64)
+COMPLEX_DTYPES = (np.complex64, np.complex128)
+DTYPES = REAL_DTYPES + COMPLEX_DTYPES
+
+
+@pytest.mark.parametrize('dtype_', DTYPES)
+@pytest.mark.parametrize('m, p, q',
+                         [
+                             (2, 1, 1),
+                             (3, 2, 1),
+                             (3, 1, 2),
+                             (4, 2, 2),
+                             (4, 1, 2),
+                             (40, 12, 20),
+                             (40, 30, 1),
+                             (40, 1, 30),
+                             (100, 50, 1),
+                             (100, 50, 50),
+                         ])
+@pytest.mark.parametrize('swap_sign', [True, False])
+def test_cossin(dtype_, m, p, q, swap_sign):
+    rng = default_rng(1708093570726217)
+    if dtype_ in COMPLEX_DTYPES:
+        x = np.array(unitary_group.rvs(m, random_state=rng), dtype=dtype_)
+    else:
+        x = np.array(ortho_group.rvs(m, random_state=rng), dtype=dtype_)
+
+    u, cs, vh = cossin(x, p, q,
+                       swap_sign=swap_sign)
+    assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
+    assert u.dtype == dtype_
+    # Test for float32 or float 64
+    assert cs.dtype == np.real(u).dtype
+    assert vh.dtype == dtype_
+
+    u, cs, vh = cossin([x[:p, :q], x[:p, q:], x[p:, :q], x[p:, q:]],
+                       swap_sign=swap_sign)
+    assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
+    assert u.dtype == dtype_
+    assert cs.dtype == np.real(u).dtype
+    assert vh.dtype == dtype_
+
+    _, cs2, vh2 = cossin(x, p, q,
+                         compute_u=False,
+                         swap_sign=swap_sign)
+    assert_allclose(cs, cs2, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(vh, vh2, rtol=0., atol=10*np.finfo(dtype_).eps)
+
+    u2, cs2, _ = cossin(x, p, q,
+                        compute_vh=False,
+                        swap_sign=swap_sign)
+    assert_allclose(u, u2, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(cs, cs2, rtol=0., atol=10*np.finfo(dtype_).eps)
+
+    _, cs2, _ = cossin(x, p, q,
+                       compute_u=False,
+                       compute_vh=False,
+                       swap_sign=swap_sign)
+    assert_allclose(cs, cs2, rtol=0., atol=10*np.finfo(dtype_).eps)
+
+
+def test_cossin_mixed_types():
+    rng = default_rng(1708093736390459)
+    x = np.array(ortho_group.rvs(4, random_state=rng), dtype=np.float64)
+    u, cs, vh = cossin([x[:2, :2],
+                        np.array(x[:2, 2:], dtype=np.complex128),
+                        x[2:, :2],
+                        x[2:, 2:]])
+
+    assert u.dtype == np.complex128
+    assert cs.dtype == np.float64
+    assert vh.dtype == np.complex128
+    assert_allclose(x, u @ cs @ vh, rtol=0.,
+                    atol=1e4 * np.finfo(np.complex128).eps)
+
+
+def test_cossin_error_incorrect_subblocks():
+    with pytest.raises(ValueError, match="be due to missing p, q arguments."):
+        cossin(([1, 2], [3, 4, 5], [6, 7], [8, 9, 10]))
+
+
+def test_cossin_error_empty_subblocks():
+    with pytest.raises(ValueError, match="x11.*empty"):
+        cossin(([], [], [], []))
+    with pytest.raises(ValueError, match="x12.*empty"):
+        cossin(([1, 2], [], [6, 7], [8, 9, 10]))
+    with pytest.raises(ValueError, match="x21.*empty"):
+        cossin(([1, 2], [3, 4, 5], [], [8, 9, 10]))
+    with pytest.raises(ValueError, match="x22.*empty"):
+        cossin(([1, 2], [3, 4, 5], [2], []))
+
+
+def test_cossin_error_missing_partitioning():
+    with pytest.raises(ValueError, match=".*exactly four arrays.* got 2"):
+        cossin(unitary_group.rvs(2))
+
+    with pytest.raises(ValueError, match=".*might be due to missing p, q"):
+        cossin(unitary_group.rvs(4))
+
+
+def test_cossin_error_non_iterable():
+    with pytest.raises(ValueError, match="containing the subblocks of X"):
+        cossin(12j)
+
+
+def test_cossin_error_non_square():
+    with pytest.raises(ValueError, match="only supports square"):
+        cossin(np.array([[1, 2]]), 1, 1)
+
+
+def test_cossin_error_partitioning():
+    x = np.array(ortho_group.rvs(4), dtype=np.float64)
+    with pytest.raises(ValueError, match="invalid p=0.*0<p<4.*"):
+        cossin(x, 0, 1)
+    with pytest.raises(ValueError, match="invalid p=4.*0<p<4.*"):
+        cossin(x, 4, 1)
+    with pytest.raises(ValueError, match="invalid q=-2.*0<q<4.*"):
+        cossin(x, 1, -2)
+    with pytest.raises(ValueError, match="invalid q=5.*0<q<4.*"):
+        cossin(x, 1, 5)
+
+
+@pytest.mark.parametrize("dtype_", DTYPES)
+def test_cossin_separate(dtype_):
+    rng = default_rng(1708093590167096)
+    m, p, q = 98, 37, 61
+
+    pfx = 'or' if dtype_ in REAL_DTYPES else 'un'
+    X = (ortho_group.rvs(m, random_state=rng) if pfx == 'or'
+         else unitary_group.rvs(m, random_state=rng))
+    X = np.array(X, dtype=dtype_)
+
+    drv, dlw = get_lapack_funcs((pfx + 'csd', pfx + 'csd_lwork'), [X])
+    lwval = _compute_lwork(dlw, m, p, q)
+    lwvals = {'lwork': lwval} if pfx == 'or' else dict(zip(['lwork',
+                                                            'lrwork'],
+                                                           lwval))
+
+    *_, theta, u1, u2, v1t, v2t, _ = \
+        drv(X[:p, :q], X[:p, q:], X[p:, :q], X[p:, q:], **lwvals)
+
+    (u1_2, u2_2), theta2, (v1t_2, v2t_2) = cossin(X, p, q, separate=True)
+
+    assert_allclose(u1_2, u1, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(u2_2, u2, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(v1t_2, v1t, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(v2t_2, v2t, rtol=0., atol=10*np.finfo(dtype_).eps)
+    assert_allclose(theta2, theta, rtol=0., atol=10*np.finfo(dtype_).eps)
+
+
+@pytest.mark.parametrize("m", [2, 5, 10, 15, 20])
+@pytest.mark.parametrize("p", [1, 4, 9, 14, 19])
+@pytest.mark.parametrize("q", [1, 4, 9, 14, 19])
+@pytest.mark.parametrize("swap_sign", [True, False])
+def test_properties(m, p, q, swap_sign):
+    # Test all the properties advertised in `linalg.cossin` documentation.
+    # There may be some overlap with tests above, but this is sensitive to
+    # the bug reported in gh-19365 and more.
+    if (p >= m) or (q >= m):
+        pytest.skip("`0 < p < m` and `0 < q < m` must hold")
+
+    # Generate unitary input
+    rng = np.random.default_rng(329548272348596421)
+    X = unitary_group.rvs(m, random_state=rng)
+    np.testing.assert_allclose(X @ X.conj().T, np.eye(m), atol=1e-15)
+
+    # Perform the decomposition
+    u0, cs0, vh0 = linalg.cossin(X, p=p, q=q, separate=True, swap_sign=swap_sign)
+    u1, u2 = u0
+    v1, v2 = vh0
+    v1, v2 = v1.conj().T, v2.conj().T
+
+    # "U1, U2, V1, V2 are square orthogonal/unitary matrices
+    # of dimensions (p,p), (m-p,m-p), (q,q), and (m-q,m-q) respectively"
+    np.testing.assert_allclose(u1 @ u1.conj().T, np.eye(p), atol=1e-13)
+    np.testing.assert_allclose(u2 @ u2.conj().T, np.eye(m-p), atol=1e-13)
+    np.testing.assert_allclose(v1 @ v1.conj().T, np.eye(q), atol=1e-13)
+    np.testing.assert_allclose(v2 @ v2.conj().T, np.eye(m-q), atol=1e-13)
+
+    # "and C and S are (r, r) nonnegative diagonal matrices..."
+    C = np.diag(np.cos(cs0))
+    S = np.diag(np.sin(cs0))
+    # "...satisfying C^2 + S^2 = I where r = min(p, m-p, q, m-q)."
+    r = min(p, m-p, q, m-q)
+    np.testing.assert_allclose(C**2 + S**2, np.eye(r))
+
+    # "Moreover, the rank of the identity matrices are
+    # min(p, q) - r, min(p, m - q) - r, min(m - p, q) - r,
+    # and min(m - p, m - q) - r respectively."
+    I11 = np.eye(min(p, q) - r)
+    I12 = np.eye(min(p, m - q) - r)
+    I21 = np.eye(min(m - p, q) - r)
+    I22 = np.eye(min(m - p, m - q) - r)
+
+    # From:
+    #                            ┌                   ┐
+    #                            │ I  0  0 │ 0  0  0 │
+    # ┌           ┐   ┌         ┐│ 0  C  0 │ 0 -S  0 │┌         ┐*
+    # │ X11 │ X12 │   │ U1 │    ││ 0  0  0 │ 0  0 -I ││ V1 │    │
+    # │ ────┼──── │ = │────┼────││─────────┼─────────││────┼────│
+    # │ X21 │ X22 │   │    │ U2 ││ 0  0  0 │ I  0  0 ││    │ V2 │
+    # └           ┘   └         ┘│ 0  S  0 │ 0  C  0 │└         ┘
+    #                            │ 0  0  I │ 0  0  0 │
+    #                            └                   ┘
+
+    # We can see that U and V are block diagonal matrices like so:
+    U = linalg.block_diag(u1, u2)
+    V = linalg.block_diag(v1, v2)
+
+    # And the center matrix, which we'll call Q here, must be:
+    Q11 = np.zeros((u1.shape[1], v1.shape[0]))
+    IC11 = linalg.block_diag(I11, C)
+    Q11[:IC11.shape[0], :IC11.shape[1]] = IC11
+
+    Q12 = np.zeros((u1.shape[1], v2.shape[0]))
+    SI12 = linalg.block_diag(S, I12) if swap_sign else linalg.block_diag(-S, -I12)
+    Q12[-SI12.shape[0]:, -SI12.shape[1]:] = SI12
+
+    Q21 = np.zeros((u2.shape[1], v1.shape[0]))
+    SI21 = linalg.block_diag(-S, -I21) if swap_sign else linalg.block_diag(S, I21)
+    Q21[-SI21.shape[0]:, -SI21.shape[1]:] = SI21
+
+    Q22 = np.zeros((u2.shape[1], v2.shape[0]))
+    IC22 = linalg.block_diag(I22, C)
+    Q22[:IC22.shape[0], :IC22.shape[1]] = IC22
+
+    Q = np.block([[Q11, Q12], [Q21, Q22]])
+
+    # Confirm that `cossin` decomposes `X` as shown
+    np.testing.assert_allclose(X, U @ Q @ V.conj().T)
+
+    # And check that `separate=False` agrees
+    U0, CS0, Vh0 = linalg.cossin(X, p=p, q=q, swap_sign=swap_sign)
+    np.testing.assert_allclose(U, U0)
+    np.testing.assert_allclose(Q, CS0)
+    np.testing.assert_allclose(V, Vh0.conj().T)
+
+    # Confirm that `compute_u`/`compute_vh` don't affect the results
+    kwargs = dict(p=p, q=q, swap_sign=swap_sign)
+
+    # `compute_u=False`
+    u, cs, vh = linalg.cossin(X, separate=True, compute_u=False, **kwargs)
+    assert u[0].shape == (0, 0)  # probably not ideal, but this is what it does
+    assert u[1].shape == (0, 0)
+    assert_allclose(cs, cs0, rtol=1e-15)
+    assert_allclose(vh[0], vh0[0], rtol=1e-15)
+    assert_allclose(vh[1], vh0[1], rtol=1e-15)
+
+    U, CS, Vh = linalg.cossin(X, compute_u=False, **kwargs)
+    assert U.shape == (0, 0)
+    assert_allclose(CS, CS0, rtol=1e-15)
+    assert_allclose(Vh, Vh0, rtol=1e-15)
+
+    # `compute_vh=False`
+    u, cs, vh = linalg.cossin(X, separate=True, compute_vh=False, **kwargs)
+    assert_allclose(u[0], u[0], rtol=1e-15)
+    assert_allclose(u[1], u[1], rtol=1e-15)
+    assert_allclose(cs, cs0, rtol=1e-15)
+    assert vh[0].shape == (0, 0)
+    assert vh[1].shape == (0, 0)
+
+    U, CS, Vh = linalg.cossin(X, compute_vh=False, **kwargs)
+    assert_allclose(U, U0, rtol=1e-15)
+    assert_allclose(CS, CS0, rtol=1e-15)
+    assert Vh.shape == (0, 0)
+
+    # `compute_u=False, compute_vh=False`
+    u, cs, vh = linalg.cossin(X, separate=True, compute_u=False,
+                              compute_vh=False, **kwargs)
+    assert u[0].shape == (0, 0)
+    assert u[1].shape == (0, 0)
+    assert_allclose(cs, cs0, rtol=1e-15)
+    assert vh[0].shape == (0, 0)
+    assert vh[1].shape == (0, 0)
+
+    U, CS, Vh = linalg.cossin(X, compute_u=False, compute_vh=False, **kwargs)
+    assert U.shape == (0, 0)
+    assert_allclose(CS, CS0, rtol=1e-15)
+    assert Vh.shape == (0, 0)
+
+
+def test_indexing_bug_gh19365():
+    # Regression test for gh-19365, which reported a bug with `separate=False`
+    rng = np.random.default_rng(32954827234421)
+    m = rng.integers(50, high=100)
+    p = rng.integers(10, 40)  # always p < m
+    q = rng.integers(m - p + 1, m - 1)  # always m-p < q < m
+    X = unitary_group.rvs(m, random_state=rng)  # random unitary matrix
+    U, D, Vt = linalg.cossin(X, p=p, q=q, separate=False)
+    assert np.allclose(U @ D @ Vt, X)
diff --git a/shared/package-files/40/402438746c3730ce2b8702a62a7b722c9ec907e9de31b9d1b91ae6dac7296601/_reloader.pyi b/shared/package-files/40/402438746c3730ce2b8702a62a7b722c9ec907e9de31b9d1b91ae6dac7296601/_reloader.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..be23222f6252fcfdda5eb50d272f17f42fcd621a
--- /dev/null
+++ b/shared/package-files/40/402438746c3730ce2b8702a62a7b722c9ec907e9de31b9d1b91ae6dac7296601/_reloader.pyi
@@ -0,0 +1,29 @@
+from typing import Any, Optional
+
+class ReloaderLoop:
+    name: Any
+    extra_files: Any
+    interval: float
+    def __init__(self, extra_files: Optional[Any] = ..., interval: float = ...): ...
+    def run(self): ...
+    def restart_with_reloader(self): ...
+    def trigger_reload(self, filename): ...
+    def log_reload(self, filename): ...
+
+class StatReloaderLoop(ReloaderLoop):
+    name: Any
+    def run(self): ...
+
+class WatchdogReloaderLoop(ReloaderLoop):
+    observable_paths: Any
+    name: Any
+    observer_class: Any
+    event_handler: Any
+    should_reload: Any
+    def __init__(self, *args, **kwargs): ...
+    def trigger_reload(self, filename): ...
+    def run(self): ...
+
+reloader_loops: Any
+
+def run_with_reloader(main_func, extra_files: Optional[Any] = ..., interval: float = ..., reloader_type: str = ...): ...
diff --git a/shared/package-files/40/40268f99572b2c64294a8553fe4871a9ea660f4dabd53823398330c4580f34d0/_copy_from_and_resize_ops.h b/shared/package-files/40/40268f99572b2c64294a8553fe4871a9ea660f4dabd53823398330c4580f34d0/_copy_from_and_resize_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..173a0558a04211badc4213b7f1cce5d96cb5d48c
--- /dev/null
+++ b/shared/package-files/40/40268f99572b2c64294a8553fe4871a9ea660f4dabd53823398330c4580f34d0/_copy_from_and_resize_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _copy_from_and_resize {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_copy_from_and_resize")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & dst);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst);
+};
+
+struct TORCH_API _copy_from_and_resize_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_copy_from_and_resize")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & dst, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.csv b/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4b423d168730ac6c111a8badc00bb6b1e7e6c3db
--- /dev/null
+++ b/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.csv
@@ -0,0 +1,33 @@
+"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42"
+"Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3
+"Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3
+"Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4
+"Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57
+"Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7
+"Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8
+"Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5
+"East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5
+"East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1
+"East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7
+"East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6
+"Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62
+"Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4
+"Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2
+"Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7
+"Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75
+"Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1
+"Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2
+"Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7
+"Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7
+"North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7
+"North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2
+"Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4
+"Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3
+"Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6
+"Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7
+"Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6
+"South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2
+"South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6
+"Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9
+"West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7
+"West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3
diff --git a/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.dat b/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.dat
new file mode 100644
index 0000000000000000000000000000000000000000..4b423d168730ac6c111a8badc00bb6b1e7e6c3db
--- /dev/null
+++ b/shared/package-files/40/40272566bd9e11b30b9f3ce3075fc420f0b7b8736a12c810160da7276dd2544a/scotvote.dat
@@ -0,0 +1,33 @@
+"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42"
+"Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3
+"Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3
+"Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4
+"Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57
+"Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7
+"Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8
+"Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5
+"East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5
+"East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1
+"East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7
+"East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6
+"Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62
+"Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4
+"Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2
+"Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7
+"Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75
+"Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1
+"Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2
+"Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7
+"Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7
+"North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7
+"North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2
+"Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4
+"Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3
+"Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6
+"Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7
+"Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6
+"South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2
+"South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6
+"Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9
+"West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7
+"West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3
diff --git a/shared/package-files/40/4027d967fb6891c7b7524039bd1680379c5cf3e76e488a9995491471b8b09a15/cuda.py b/shared/package-files/40/4027d967fb6891c7b7524039bd1680379c5cf3e76e488a9995491471b8b09a15/cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..17d5f44af6a570d6c94626820e341a462931bdfc
--- /dev/null
+++ b/shared/package-files/40/4027d967fb6891c7b7524039bd1680379c5cf3e76e488a9995491471b8b09a15/cuda.py
@@ -0,0 +1,150 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+import ctypes
+import functools
+import itertools
+import multiprocessing
+import os
+import platform
+from contextlib import suppress
+
+from ...common.decorators import env_override
+from .. import CondaVirtualPackage, hookimpl
+
+
+@env_override("CONDA_OVERRIDE_CUDA", convert_empty_to_none=True)
+def cuda_version():
+    """
+    Attempt to detect the version of CUDA present in the operating system.
+
+    On Windows and Linux, the CUDA library is installed by the NVIDIA
+    driver package, and is typically found in the standard library path,
+    rather than with the CUDA SDK (which is optional for running CUDA apps).
+
+    On macOS, the CUDA library is only installed with the CUDA SDK, and
+    might not be in the library path.
+
+    Returns: version string (e.g., '9.2') or None if CUDA is not found.
+    """
+    # Do not inherit file descriptors and handles from the parent process.
+    # The `fork` start method should be considered unsafe as it can lead to
+    # crashes of the subprocess. The `spawn` start method is preferred.
+    context = multiprocessing.get_context("spawn")
+    queue = context.SimpleQueue()
+    try:
+        # Spawn a subprocess to detect the CUDA version
+        detector = context.Process(
+            target=_cuda_driver_version_detector_target,
+            args=(queue,),
+            name="CUDA driver version detector",
+            daemon=True,
+        )
+        detector.start()
+        detector.join(timeout=60.0)
+    finally:
+        # Always cleanup the subprocess
+        detector.kill()  # requires Python 3.7+
+
+    if queue.empty():
+        return None
+
+    result = queue.get()
+    return result
+
+
+@functools.lru_cache(maxsize=None)
+def cached_cuda_version():
+    """A cached version of the cuda detection system."""
+    return cuda_version()
+
+
+@hookimpl
+def conda_virtual_packages():
+    cuda_version = cached_cuda_version()
+    if cuda_version is not None:
+        yield CondaVirtualPackage("cuda", cuda_version, None)
+
+
+def _cuda_driver_version_detector_target(queue):
+    """
+    Attempt to detect the version of CUDA present in the operating system in a
+    subprocess.
+
+    On Windows and Linux, the CUDA library is installed by the NVIDIA
+    driver package, and is typically found in the standard library path,
+    rather than with the CUDA SDK (which is optional for running CUDA apps).
+
+    On macOS, the CUDA library is only installed with the CUDA SDK, and
+    might not be in the library path.
+
+    Returns: version string (e.g., '9.2') or None if CUDA is not found.
+             The result is put in the queue rather than a return value.
+    """
+    # Platform-specific libcuda location
+    system = platform.system()
+    if system == "Darwin":
+        lib_filenames = [
+            "libcuda.1.dylib",  # check library path first
+            "libcuda.dylib",
+            "/usr/local/cuda/lib/libcuda.1.dylib",
+            "/usr/local/cuda/lib/libcuda.dylib",
+        ]
+    elif system == "Linux":
+        lib_filenames = [
+            "libcuda.so",  # check library path first
+            "/usr/lib64/nvidia/libcuda.so",  # RHEL/Centos/Fedora
+            "/usr/lib/x86_64-linux-gnu/libcuda.so",  # Ubuntu
+            "/usr/lib/wsl/lib/libcuda.so",  # WSL
+        ]
+        # Also add libraries with version suffix `.1`
+        lib_filenames = list(
+            itertools.chain.from_iterable((f"{lib}.1", lib) for lib in lib_filenames)
+        )
+    elif system == "Windows":
+        bits = platform.architecture()[0].replace("bit", "")  # e.g. "64" or "32"
+        lib_filenames = [f"nvcuda{bits}.dll", "nvcuda.dll"]
+    else:
+        queue.put(None)  # CUDA not available for other operating systems
+        return
+
+    # Open library
+    if system == "Windows":
+        dll = ctypes.windll
+    else:
+        dll = ctypes.cdll
+    for lib_filename in lib_filenames:
+        with suppress(Exception):
+            libcuda = dll.LoadLibrary(lib_filename)
+            break
+    else:
+        queue.put(None)
+        return
+
+    # Empty `CUDA_VISIBLE_DEVICES` can cause `cuInit()` returns `CUDA_ERROR_NO_DEVICE`
+    # Invalid `CUDA_VISIBLE_DEVICES` can cause `cuInit()` returns `CUDA_ERROR_INVALID_DEVICE`
+    # Unset this environment variable to avoid these errors
+    os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+
+    # Get CUDA version
+    try:
+        cuInit = libcuda.cuInit
+        flags = ctypes.c_uint(0)
+        ret = cuInit(flags)
+        if ret != 0:
+            queue.put(None)
+            return
+
+        cuDriverGetVersion = libcuda.cuDriverGetVersion
+        version_int = ctypes.c_int(0)
+        ret = cuDriverGetVersion(ctypes.byref(version_int))
+        if ret != 0:
+            queue.put(None)
+            return
+
+        # Convert version integer to version string
+        value = version_int.value
+        queue.put(f"{value // 1000}.{(value % 1000) // 10}")
+        return
+    except Exception:
+        queue.put(None)
+        return
diff --git a/shared/package-files/40/40285e1c40c5ef0688485e36fda6ae9aabd5388713c3eca1e5ee7e09b3ef436a/dep_util.pyi b/shared/package-files/40/40285e1c40c5ef0688485e36fda6ae9aabd5388713c3eca1e5ee7e09b3ef436a/dep_util.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6f779d540e5e9a4f02f1a1917949322b1d29cfc1
--- /dev/null
+++ b/shared/package-files/40/40285e1c40c5ef0688485e36fda6ae9aabd5388713c3eca1e5ee7e09b3ef436a/dep_util.pyi
@@ -0,0 +1,5 @@
+from typing import List, Tuple
+
+def newer(source: str, target: str) -> bool: ...
+def newer_pairwise(sources: List[str], targets: List[str]) -> List[Tuple[str, str]]: ...
+def newer_group(sources: List[str], target: str, missing: str = ...) -> bool: ...
diff --git a/shared/package-files/40/402edcdbe1f86c0c9006589968dccce6b3ddbddad07cf7b972dffd72efb444a4/linalg_norm_compositeimplicitautograd_dispatch.h b/shared/package-files/40/402edcdbe1f86c0c9006589968dccce6b3ddbddad07cf7b972dffd72efb444a4/linalg_norm_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3684c2dde5e902f7cfd6c09844e66c4eebcce5fd
--- /dev/null
+++ b/shared/package-files/40/402edcdbe1f86c0c9006589968dccce6b3ddbddad07cf7b972dffd72efb444a4/linalg_norm_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor linalg_norm(const at::Tensor & self, const c10::optional<at::Scalar> & ord=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & linalg_norm_out(at::Tensor & out, const at::Tensor & self, const c10::optional<at::Scalar> & ord=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & linalg_norm_outf(const at::Tensor & self, const c10::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+TORCH_API at::Tensor linalg_norm(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & linalg_norm_out(at::Tensor & out, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & linalg_norm_outf(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/40/4031fa0b69309a47b00bccf0ee91019dd5d1d90be0b153120730a9bc4f4fce8c/safe_numerics.h b/shared/package-files/40/4031fa0b69309a47b00bccf0ee91019dd5d1d90be0b153120730a9bc4f4fce8c/safe_numerics.h
new file mode 100644
index 0000000000000000000000000000000000000000..e242cfaf8449a36cfd6df0f710e4f507705c0063
--- /dev/null
+++ b/shared/package-files/40/4031fa0b69309a47b00bccf0ee91019dd5d1d90be0b153120730a9bc4f4fce8c/safe_numerics.h
@@ -0,0 +1,90 @@
+#pragma once
+#include <c10/macros/Macros.h>
+
+#include <cstdint>
+
+// GCC has __builtin_mul_overflow from before it supported __has_builtin
+#ifdef _MSC_VER
+#define C10_HAS_BUILTIN_OVERFLOW() (0)
+#include <c10/util/llvmMathExtras.h>
+#include <intrin.h>
+#else
+#define C10_HAS_BUILTIN_OVERFLOW() (1)
+#endif
+
+namespace c10 {
+
+C10_ALWAYS_INLINE bool add_overflows(uint64_t a, uint64_t b, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_add_overflow(a, b, out);
+#else
+  unsigned long long tmp;
+#if defined(_M_IX86) || defined(_M_X64)
+  auto carry = _addcarry_u64(0, a, b, &tmp);
+#else
+  tmp = a + b;
+  unsigned long long vector = (a & b) ^ ((a ^ b) & ~tmp);
+  auto carry = vector >> 63;
+#endif
+  *out = tmp;
+  return carry;
+#endif
+}
+
+C10_ALWAYS_INLINE bool mul_overflows(uint64_t a, uint64_t b, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_mul_overflow(a, b, out);
+#else
+  *out = a * b;
+  // This test isnt exact, but avoids doing integer division
+  return (
+      (c10::llvm::countLeadingZeros(a) + c10::llvm::countLeadingZeros(b)) < 64);
+#endif
+}
+
+C10_ALWAYS_INLINE bool mul_overflows(int64_t a, int64_t b, int64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_mul_overflow(a, b, out);
+#else
+  volatile int64_t tmp = a * b;
+  *out = tmp;
+  if (a == 0 || b == 0) {
+    return false;
+  }
+  return !(a == tmp / b);
+#endif
+}
+
+template <typename It>
+bool safe_multiplies_u64(It first, It last, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  uint64_t prod = 1;
+  bool overflow = false;
+  for (; first != last; ++first) {
+    overflow |= c10::mul_overflows(prod, *first, &prod);
+  }
+  *out = prod;
+  return overflow;
+#else
+  uint64_t prod = 1;
+  uint64_t prod_log2 = 0;
+  bool is_zero = false;
+  for (; first != last; ++first) {
+    auto x = static_cast<uint64_t>(*first);
+    prod *= x;
+    // log2(0) isn't valid, so need to track it specially
+    is_zero |= (x == 0);
+    prod_log2 += c10::llvm::Log2_64_Ceil(x);
+  }
+  *out = prod;
+  // This test isnt exact, but avoids doing integer division
+  return !is_zero && (prod_log2 >= 64);
+#endif
+}
+
+template <typename Container>
+bool safe_multiplies_u64(const Container& c, uint64_t* out) {
+  return safe_multiplies_u64(c.begin(), c.end(), out);
+}
+
+} // namespace c10
diff --git a/shared/package-files/40/40343d83a97e7bbde94c9a7e0e8b22ca76fea246ea8bdd43e0a8ab8e122372fe/ndarray_shape_manipulation.pyi b/shared/package-files/40/40343d83a97e7bbde94c9a7e0e8b22ca76fea246ea8bdd43e0a8ab8e122372fe/ndarray_shape_manipulation.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9a41a90f1ee92baa07605d5d202530623369bd8a
--- /dev/null
+++ b/shared/package-files/40/40343d83a97e7bbde94c9a7e0e8b22ca76fea246ea8bdd43e0a8ab8e122372fe/ndarray_shape_manipulation.pyi
@@ -0,0 +1,44 @@
+import sys
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+if sys.version_info >= (3, 11):
+    from typing import assert_type
+else:
+    from typing_extensions import assert_type
+
+nd: npt.NDArray[np.int64]
+
+# reshape
+assert_type(nd.reshape(), npt.NDArray[np.int64])
+assert_type(nd.reshape(4), npt.NDArray[np.int64])
+assert_type(nd.reshape(2, 2), npt.NDArray[np.int64])
+assert_type(nd.reshape((2, 2)), npt.NDArray[np.int64])
+
+assert_type(nd.reshape((2, 2), order="C"), npt.NDArray[np.int64])
+assert_type(nd.reshape(4, order="C"), npt.NDArray[np.int64])
+
+# resize does not return a value
+
+# transpose
+assert_type(nd.transpose(), npt.NDArray[np.int64])
+assert_type(nd.transpose(1, 0), npt.NDArray[np.int64])
+assert_type(nd.transpose((1, 0)), npt.NDArray[np.int64])
+
+# swapaxes
+assert_type(nd.swapaxes(0, 1), npt.NDArray[np.int64])
+
+# flatten
+assert_type(nd.flatten(), npt.NDArray[np.int64])
+assert_type(nd.flatten("C"), npt.NDArray[np.int64])
+
+# ravel
+assert_type(nd.ravel(), npt.NDArray[np.int64])
+assert_type(nd.ravel("C"), npt.NDArray[np.int64])
+
+# squeeze
+assert_type(nd.squeeze(), npt.NDArray[np.int64])
+assert_type(nd.squeeze(0), npt.NDArray[np.int64])
+assert_type(nd.squeeze((0, 2)), npt.NDArray[np.int64])
diff --git a/shared/package-files/40/403a7e7accb7d78401aff96f9cf2619900b5aa8b0a1c93ed8732edd7db9e2f6b/wy160-vb b/shared/package-files/40/403a7e7accb7d78401aff96f9cf2619900b5aa8b0a1c93ed8732edd7db9e2f6b/wy160-vb
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diff --git a/shared/package-files/40/403f9843ff205e299b6a143c19e876c6f37e4cd5ff393545d7137186f327189f/linalg_cross_ops.h b/shared/package-files/40/403f9843ff205e299b6a143c19e876c6f37e4cd5ff393545d7137186f327189f/linalg_cross_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..45c8abbf96b847e8ca43d19edca846c96d310d0c
--- /dev/null
+++ b/shared/package-files/40/403f9843ff205e299b6a143c19e876c6f37e4cd5ff393545d7137186f327189f/linalg_cross_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API linalg_cross {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_cross")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim);
+};
+
+struct TORCH_API linalg_cross_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_cross")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/4040ee7c1b328c77419b4ad83fec93008422eec45d44b50011cd14116326523c/finalize.h b/shared/package-files/40/4040ee7c1b328c77419b4ad83fec93008422eec45d44b50011cd14116326523c/finalize.h
new file mode 100644
index 0000000000000000000000000000000000000000..d73addbc387f6b4d55360480b3c20fc1e0b84d3c
--- /dev/null
+++ b/shared/package-files/40/4040ee7c1b328c77419b4ad83fec93008422eec45d44b50011cd14116326523c/finalize.h
@@ -0,0 +1,63 @@
+#pragma once
+
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/passes/quantization/quantization_type.h>
+
+namespace torch {
+namespace jit {
+
+/** \brief Backend specific pass to fuse dequantize - op - quantize calls
+ * as quantized_op calls.
+ *
+ * Right now this is a fusion for fbgemm backend and only works for quantized
+ * conv op, we'll extend to more ops and more backends in the future.
+ *
+ * Currently supported fusion:
+ * q(conv2d(dq(a), dq(w), dq(b))) --> to_nchw(fbgemm_conv2d(prepack(to_nhwc(a)),
+ *                                                          prepack(to_nhwc(w)),
+ *                                                          prepack(to_nhwc(b))))
+ *
+ * q(linear(dq(a), dq(w), dq(b))) --> to_nchw(fbgemm_linear(prepack(to_nhwc(a)),
+ *                                                          prepack(to_nhwc(w)),
+ *                                                          prepack(to_nhwc(b))))
+ *
+ * \param graph the graph we want to apply fusion
+ */
+TORCH_API void QuantFusion(
+    std::shared_ptr<Graph>& graph,
+    QuantType quant_type = QuantType::STATIC);
+
+/** \brief Insert prepack and unpack function in graph
+ *  We want add pack/unpack functions for quantized weight because later we want
+ * to fold the packed weight as an attribute of the module, in order to reduce
+ * the cost of packing the weight on the fly in quantized models.
+ *
+ *  Each quantized op has it's corresponding prepack/unpack function,
+ *  right now, we only need to do prepack/unpack for quantized::linear
+ * and quantized::conv2d.
+ */
+TORCH_API void InsertPrepackUnpack(std::shared_ptr<Graph>& graph);
+
+/** \brief Insert pack and unpack function in all graphs
+ *   of module
+ *
+ *   Go through graphs of all the methods of all child modules
+ *   and call InsertPrepackUnpack on the graph.
+ */
+TORCH_API void InsertPrepackUnpack(Module& module);
+
+TORCH_API script::Module Finalize(
+    script::Module& module,
+    QuantType quant_type = QuantType::STATIC,
+    const std::vector<std::string>& preserved_attrs =
+        std::vector<std::string>());
+
+TORCH_API void FoldQuantizedPrepackingOps(Module& module);
+
+TORCH_API Module FinalizeOnDevicePTQ(
+    Module& module,
+    QuantType quant_type,
+    const std::string& method_name);
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/40/4043f36c599e311331008a9f911ac92e7223519812ba96cff185047b35be3dcc/sample.py b/shared/package-files/40/4043f36c599e311331008a9f911ac92e7223519812ba96cff185047b35be3dcc/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..eebbed3512c4eca42e401c40605838c6f69011df
--- /dev/null
+++ b/shared/package-files/40/4043f36c599e311331008a9f911ac92e7223519812ba96cff185047b35be3dcc/sample.py
@@ -0,0 +1,154 @@
+"""
+Module containing utilities for NDFrame.sample() and .GroupBy.sample()
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AxisInt
+
+    from pandas.core.generic import NDFrame
+
+
+def preprocess_weights(obj: NDFrame, weights, axis: AxisInt) -> np.ndarray:
+    """
+    Process and validate the `weights` argument to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns `weights` as an ndarray[np.float64], validated except for normalizing
+    weights (because that must be done groupwise in groupby sampling).
+    """
+    # If a series, align with frame
+    if isinstance(weights, ABCSeries):
+        weights = weights.reindex(obj.axes[axis])
+
+    # Strings acceptable if a dataframe and axis = 0
+    if isinstance(weights, str):
+        if isinstance(obj, ABCDataFrame):
+            if axis == 0:
+                try:
+                    weights = obj[weights]
+                except KeyError as err:
+                    raise KeyError(
+                        "String passed to weights not a valid column"
+                    ) from err
+            else:
+                raise ValueError(
+                    "Strings can only be passed to "
+                    "weights when sampling from rows on "
+                    "a DataFrame"
+                )
+        else:
+            raise ValueError(
+                "Strings cannot be passed as weights when sampling from a Series."
+            )
+
+    if isinstance(obj, ABCSeries):
+        func = obj._constructor
+    else:
+        func = obj._constructor_sliced
+
+    weights = func(weights, dtype="float64")._values
+
+    if len(weights) != obj.shape[axis]:
+        raise ValueError("Weights and axis to be sampled must be of same length")
+
+    if lib.has_infs(weights):
+        raise ValueError("weight vector may not include `inf` values")
+
+    if (weights < 0).any():
+        raise ValueError("weight vector many not include negative values")
+
+    missing = np.isnan(weights)
+    if missing.any():
+        # Don't modify weights in place
+        weights = weights.copy()
+        weights[missing] = 0
+    return weights
+
+
+def process_sampling_size(
+    n: int | None, frac: float | None, replace: bool
+) -> int | None:
+    """
+    Process and validate the `n` and `frac` arguments to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns None if `frac` should be used (variable sampling sizes), otherwise returns
+    the constant sampling size.
+    """
+    # If no frac or n, default to n=1.
+    if n is None and frac is None:
+        n = 1
+    elif n is not None and frac is not None:
+        raise ValueError("Please enter a value for `frac` OR `n`, not both")
+    elif n is not None:
+        if n < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `n` >= 0."
+            )
+        if n % 1 != 0:
+            raise ValueError("Only integers accepted as `n` values")
+    else:
+        assert frac is not None  # for mypy
+        if frac > 1 and not replace:
+            raise ValueError(
+                "Replace has to be set to `True` when "
+                "upsampling the population `frac` > 1."
+            )
+        if frac < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `frac` >= 0."
+            )
+
+    return n
+
+
+def sample(
+    obj_len: int,
+    size: int,
+    replace: bool,
+    weights: np.ndarray | None,
+    random_state: np.random.RandomState | np.random.Generator,
+) -> np.ndarray:
+    """
+    Randomly sample `size` indices in `np.arange(obj_len)`
+
+    Parameters
+    ----------
+    obj_len : int
+        The length of the indices being considered
+    size : int
+        The number of values to choose
+    replace : bool
+        Allow or disallow sampling of the same row more than once.
+    weights : np.ndarray[np.float64] or None
+        If None, equal probability weighting, otherwise weights according
+        to the vector normalized
+    random_state: np.random.RandomState or np.random.Generator
+        State used for the random sampling
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    if weights is not None:
+        weight_sum = weights.sum()
+        if weight_sum != 0:
+            weights = weights / weight_sum
+        else:
+            raise ValueError("Invalid weights: weights sum to zero")
+
+    return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(
+        np.intp, copy=False
+    )
diff --git a/shared/package-files/40/4044d6ea2ef7fd8caf98cfdb0a6fb9dc0de59ff615f76e82ab60f3298e76e97f/sparsifiers.py b/shared/package-files/40/4044d6ea2ef7fd8caf98cfdb0a6fb9dc0de59ff615f76e82ab60f3298e76e97f/sparsifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c425d1e25eb155affce35665305527a893a8984
--- /dev/null
+++ b/shared/package-files/40/4044d6ea2ef7fd8caf98cfdb0a6fb9dc0de59ff615f76e82ab60f3298e76e97f/sparsifiers.py
@@ -0,0 +1,294 @@
+"""Functions for computing sparsifiers of graphs."""
+import math
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["spanner"]
+
+
+@py_random_state(3)
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def spanner(G, stretch, weight=None, seed=None):
+    """Returns a spanner of the given graph with the given stretch.
+
+    A spanner of a graph G = (V, E) with stretch t is a subgraph
+    H = (V, E_S) such that E_S is a subset of E and the distance between
+    any pair of nodes in H is at most t times the distance between the
+    nodes in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected simple graph.
+
+    stretch : float
+        The stretch of the spanner.
+
+    weight : object
+        The edge attribute to use as distance.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    NetworkX graph
+        A spanner of the given graph with the given stretch.
+
+    Raises
+    ------
+    ValueError
+        If a stretch less than 1 is given.
+
+    Notes
+    -----
+    This function implements the spanner algorithm by Baswana and Sen,
+    see [1].
+
+    This algorithm is a randomized las vegas algorithm: The expected
+    running time is O(km) where k = (stretch + 1) // 2 and m is the
+    number of edges in G. The returned graph is always a spanner of the
+    given graph with the specified stretch. For weighted graphs the
+    number of edges in the spanner is O(k * n^(1 + 1 / k)) where k is
+    defined as above and n is the number of nodes in G. For unweighted
+    graphs the number of edges is O(n^(1 + 1 / k) + kn).
+
+    References
+    ----------
+    [1] S. Baswana, S. Sen. A Simple and Linear Time Randomized
+    Algorithm for Computing Sparse Spanners in Weighted Graphs.
+    Random Struct. Algorithms 30(4): 532-563 (2007).
+    """
+    if stretch < 1:
+        raise ValueError("stretch must be at least 1")
+
+    k = (stretch + 1) // 2
+
+    # initialize spanner H with empty edge set
+    H = nx.empty_graph()
+    H.add_nodes_from(G.nodes)
+
+    # phase 1: forming the clusters
+    # the residual graph has V' from the paper as its node set
+    # and E' from the paper as its edge set
+    residual_graph = _setup_residual_graph(G, weight)
+    # clustering is a dictionary that maps nodes in a cluster to the
+    # cluster center
+    clustering = {v: v for v in G.nodes}
+    sample_prob = math.pow(G.number_of_nodes(), -1 / k)
+    size_limit = 2 * math.pow(G.number_of_nodes(), 1 + 1 / k)
+
+    i = 0
+    while i < k - 1:
+        # step 1: sample centers
+        sampled_centers = set()
+        for center in set(clustering.values()):
+            if seed.random() < sample_prob:
+                sampled_centers.add(center)
+
+        # combined loop for steps 2 and 3
+        edges_to_add = set()
+        edges_to_remove = set()
+        new_clustering = {}
+        for v in residual_graph.nodes:
+            if clustering[v] in sampled_centers:
+                continue
+
+            # step 2: find neighboring (sampled) clusters and
+            # lightest edges to them
+            lightest_edge_neighbor, lightest_edge_weight = _lightest_edge_dicts(
+                residual_graph, clustering, v
+            )
+            neighboring_sampled_centers = (
+                set(lightest_edge_weight.keys()) & sampled_centers
+            )
+
+            # step 3: add edges to spanner
+            if not neighboring_sampled_centers:
+                # connect to each neighboring center via lightest edge
+                for neighbor in lightest_edge_neighbor.values():
+                    edges_to_add.add((v, neighbor))
+                # remove all incident edges
+                for neighbor in residual_graph.adj[v]:
+                    edges_to_remove.add((v, neighbor))
+
+            else:  # there is a neighboring sampled center
+                closest_center = min(
+                    neighboring_sampled_centers, key=lightest_edge_weight.get
+                )
+                closest_center_weight = lightest_edge_weight[closest_center]
+                closest_center_neighbor = lightest_edge_neighbor[closest_center]
+
+                edges_to_add.add((v, closest_center_neighbor))
+                new_clustering[v] = closest_center
+
+                # connect to centers with edge weight less than
+                # closest_center_weight
+                for center, edge_weight in lightest_edge_weight.items():
+                    if edge_weight < closest_center_weight:
+                        neighbor = lightest_edge_neighbor[center]
+                        edges_to_add.add((v, neighbor))
+
+                # remove edges to centers with edge weight less than
+                # closest_center_weight
+                for neighbor in residual_graph.adj[v]:
+                    neighbor_cluster = clustering[neighbor]
+                    neighbor_weight = lightest_edge_weight[neighbor_cluster]
+                    if (
+                        neighbor_cluster == closest_center
+                        or neighbor_weight < closest_center_weight
+                    ):
+                        edges_to_remove.add((v, neighbor))
+
+        # check whether iteration added too many edges to spanner,
+        # if so repeat
+        if len(edges_to_add) > size_limit:
+            # an iteration is repeated O(1) times on expectation
+            continue
+
+        # iteration succeeded
+        i = i + 1
+
+        # actually add edges to spanner
+        for u, v in edges_to_add:
+            _add_edge_to_spanner(H, residual_graph, u, v, weight)
+
+        # actually delete edges from residual graph
+        residual_graph.remove_edges_from(edges_to_remove)
+
+        # copy old clustering data to new_clustering
+        for node, center in clustering.items():
+            if center in sampled_centers:
+                new_clustering[node] = center
+        clustering = new_clustering
+
+        # step 4: remove intra-cluster edges
+        for u in residual_graph.nodes:
+            for v in list(residual_graph.adj[u]):
+                if clustering[u] == clustering[v]:
+                    residual_graph.remove_edge(u, v)
+
+        # update residual graph node set
+        for v in list(residual_graph.nodes):
+            if v not in clustering:
+                residual_graph.remove_node(v)
+
+    # phase 2: vertex-cluster joining
+    for v in residual_graph.nodes:
+        lightest_edge_neighbor, _ = _lightest_edge_dicts(residual_graph, clustering, v)
+        for neighbor in lightest_edge_neighbor.values():
+            _add_edge_to_spanner(H, residual_graph, v, neighbor, weight)
+
+    return H
+
+
+def _setup_residual_graph(G, weight):
+    """Setup residual graph as a copy of G with unique edges weights.
+
+    The node set of the residual graph corresponds to the set V' from
+    the Baswana-Sen paper and the edge set corresponds to the set E'
+    from the paper.
+
+    This function associates distinct weights to the edges of the
+    residual graph (even for unweighted input graphs), as required by
+    the algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected simple graph.
+
+    weight : object
+        The edge attribute to use as distance.
+
+    Returns
+    -------
+    NetworkX graph
+        The residual graph used for the Baswana-Sen algorithm.
+    """
+    residual_graph = G.copy()
+
+    # establish unique edge weights, even for unweighted graphs
+    for u, v in G.edges():
+        if not weight:
+            residual_graph[u][v]["weight"] = (id(u), id(v))
+        else:
+            residual_graph[u][v]["weight"] = (G[u][v][weight], id(u), id(v))
+
+    return residual_graph
+
+
+def _lightest_edge_dicts(residual_graph, clustering, node):
+    """Find the lightest edge to each cluster.
+
+    Searches for the minimum-weight edge to each cluster adjacent to
+    the given node.
+
+    Parameters
+    ----------
+    residual_graph : NetworkX graph
+        The residual graph used by the Baswana-Sen algorithm.
+
+    clustering : dictionary
+        The current clustering of the nodes.
+
+    node : node
+        The node from which the search originates.
+
+    Returns
+    -------
+    lightest_edge_neighbor, lightest_edge_weight : dictionary, dictionary
+        lightest_edge_neighbor is a dictionary that maps a center C to
+        a node v in the corresponding cluster such that the edge from
+        the given node to v is the lightest edge from the given node to
+        any node in cluster. lightest_edge_weight maps a center C to the
+        weight of the aforementioned edge.
+
+    Notes
+    -----
+    If a cluster has no node that is adjacent to the given node in the
+    residual graph then the center of the cluster is not a key in the
+    returned dictionaries.
+    """
+    lightest_edge_neighbor = {}
+    lightest_edge_weight = {}
+    for neighbor in residual_graph.adj[node]:
+        neighbor_center = clustering[neighbor]
+        weight = residual_graph[node][neighbor]["weight"]
+        if (
+            neighbor_center not in lightest_edge_weight
+            or weight < lightest_edge_weight[neighbor_center]
+        ):
+            lightest_edge_neighbor[neighbor_center] = neighbor
+            lightest_edge_weight[neighbor_center] = weight
+    return lightest_edge_neighbor, lightest_edge_weight
+
+
+def _add_edge_to_spanner(H, residual_graph, u, v, weight):
+    """Add the edge {u, v} to the spanner H and take weight from
+    the residual graph.
+
+    Parameters
+    ----------
+    H : NetworkX graph
+        The spanner under construction.
+
+    residual_graph : NetworkX graph
+        The residual graph used by the Baswana-Sen algorithm. The weight
+        for the edge is taken from this graph.
+
+    u : node
+        One endpoint of the edge.
+
+    v : node
+        The other endpoint of the edge.
+
+    weight : object
+        The edge attribute to use as distance.
+    """
+    H.add_edge(u, v)
+    if weight:
+        H[u][v][weight] = residual_graph[u][v]["weight"][0]
diff --git a/shared/package-files/40/4048143c96e2e53ba792442883ea3ace8223a4b159e95147418c4a7bb054022c/python_torch_functions.cpp b/shared/package-files/40/4048143c96e2e53ba792442883ea3ace8223a4b159e95147418c4a7bb054022c/python_torch_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c17d1040e1892b6a215a8c4264fe5a5345265bc7
--- /dev/null
+++ b/shared/package-files/40/4048143c96e2e53ba792442883ea3ace8223a4b159e95147418c4a7bb054022c/python_torch_functions.cpp
@@ -0,0 +1,93 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+// Python bindings for torch.* functions implemented through ATen.
+//
+// The functions are bound as static methods on a class
+// torch._C._VariableFunctions which is also aliased as Variable._torch
+// and also copied into 'torch' module.
+
+#include <Python.h>
+
+// Undefine the copysign macro so that at::copysign works as intended with MSVC
+// https://github.com/python/cpython/blob/c60394c7fc9cc09b16e9675a3eeb5844b6d8523f/PC/pyconfig.h#L196
+#ifdef _MSC_VER
+#undef copysign
+#endif // _MSC_VER
+
+#include "torch/csrc/autograd/python_torch_functions.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/Dtype.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/utils/out_types.h"
+#include "torch/csrc/utils/pybind.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/tensor_layouts.h"
+#include "torch/csrc/utils/tensor_new.h"
+#include "torch/csrc/utils/tensor_numpy.h"
+#include "torch/csrc/jit/frontend/tracer.h"
+#include "torch/csrc/autograd/generated/variable_factories.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/utils/device_lazy_init.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+
+#include <ATen/core/Tensor.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+#include <functional>
+#include <initializer_list>
+#include <stdexcept>
+#include <utility>
+
+using at::Tensor;
+using at::Device;
+using at::Layout;
+using at::Scalar;
+using at::ScalarType;
+using at::Backend;
+using at::OptionalDeviceGuard;
+using at::DeviceGuard;
+using at::TensorOptions;
+using at::IntArrayRef;
+using at::Generator;
+using at::TensorList;
+using at::Dimname;
+using at::DimnameList;
+using at::ArrayRef;
+
+using torch::utils::check_out_type_matches;
+using namespace torch::autograd::utils;
+
+// NOTE: See [Sharded File] comment in VariableType
+
+namespace torch::autograd {
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef torch_functions_shard[] = {
+  ${py_method_defs}
+};
+
+void gatherTorchFunctions${shard_id}(std::vector<PyMethodDef> &torch_functions) {
+  constexpr size_t num_functions = sizeof(torch_functions_shard) / sizeof(torch_functions_shard[0]);
+  torch_functions.insert(
+    torch_functions.end(),
+    torch_functions_shard,
+    torch_functions_shard + num_functions);
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/shared/package-files/40/4049218827061ced8224b6891d772ae082c379db48a65784b37010750f8c1396/test__numdiff.py b/shared/package-files/40/4049218827061ced8224b6891d772ae082c379db48a65784b37010750f8c1396/test__numdiff.py
new file mode 100644
index 0000000000000000000000000000000000000000..21fcf36b01f480ea23e15b67e4bebb1270d63c3b
--- /dev/null
+++ b/shared/package-files/40/4049218827061ced8224b6891d772ae082c379db48a65784b37010750f8c1396/test__numdiff.py
@@ -0,0 +1,841 @@
+import math
+from itertools import product
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal, assert_
+from pytest import raises as assert_raises
+
+from scipy.sparse import csr_matrix, csc_matrix, lil_matrix
+
+from scipy.optimize._numdiff import (
+    _adjust_scheme_to_bounds, approx_derivative, check_derivative,
+    group_columns, _eps_for_method, _compute_absolute_step)
+
+
+def test_group_columns():
+    structure = [
+        [1, 1, 0, 0, 0, 0],
+        [1, 1, 1, 0, 0, 0],
+        [0, 1, 1, 1, 0, 0],
+        [0, 0, 1, 1, 1, 0],
+        [0, 0, 0, 1, 1, 1],
+        [0, 0, 0, 0, 1, 1],
+        [0, 0, 0, 0, 0, 0]
+    ]
+    for transform in [np.asarray, csr_matrix, csc_matrix, lil_matrix]:
+        A = transform(structure)
+        order = np.arange(6)
+        groups_true = np.array([0, 1, 2, 0, 1, 2])
+        groups = group_columns(A, order)
+        assert_equal(groups, groups_true)
+
+        order = [1, 2, 4, 3, 5, 0]
+        groups_true = np.array([2, 0, 1, 2, 0, 1])
+        groups = group_columns(A, order)
+        assert_equal(groups, groups_true)
+
+    # Test repeatability.
+    groups_1 = group_columns(A)
+    groups_2 = group_columns(A)
+    assert_equal(groups_1, groups_2)
+
+
+def test_correct_fp_eps():
+    # check that relative step size is correct for FP size
+    EPS = np.finfo(np.float64).eps
+    relative_step = {"2-point": EPS**0.5,
+                    "3-point": EPS**(1/3),
+                     "cs": EPS**0.5}
+    for method in ['2-point', '3-point', 'cs']:
+        assert_allclose(
+            _eps_for_method(np.float64, np.float64, method),
+            relative_step[method])
+        assert_allclose(
+            _eps_for_method(np.complex128, np.complex128, method),
+            relative_step[method]
+        )
+
+    # check another FP size
+    EPS = np.finfo(np.float32).eps
+    relative_step = {"2-point": EPS**0.5,
+                    "3-point": EPS**(1/3),
+                     "cs": EPS**0.5}
+
+    for method in ['2-point', '3-point', 'cs']:
+        assert_allclose(
+            _eps_for_method(np.float64, np.float32, method),
+            relative_step[method]
+        )
+        assert_allclose(
+            _eps_for_method(np.float32, np.float64, method),
+            relative_step[method]
+        )
+        assert_allclose(
+            _eps_for_method(np.float32, np.float32, method),
+            relative_step[method]
+        )
+
+
+class TestAdjustSchemeToBounds:
+    def test_no_bounds(self):
+        x0 = np.zeros(3)
+        h = np.full(3, 1e-2)
+        inf_lower = np.empty_like(x0)
+        inf_upper = np.empty_like(x0)
+        inf_lower.fill(-np.inf)
+        inf_upper.fill(np.inf)
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 1, '1-sided', inf_lower, inf_upper)
+        assert_allclose(h_adjusted, h)
+        assert_(np.all(one_sided))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 2, '1-sided', inf_lower, inf_upper)
+        assert_allclose(h_adjusted, h)
+        assert_(np.all(one_sided))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 1, '2-sided', inf_lower, inf_upper)
+        assert_allclose(h_adjusted, h)
+        assert_(np.all(~one_sided))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 2, '2-sided', inf_lower, inf_upper)
+        assert_allclose(h_adjusted, h)
+        assert_(np.all(~one_sided))
+
+    def test_with_bound(self):
+        x0 = np.array([0.0, 0.85, -0.85])
+        lb = -np.ones(3)
+        ub = np.ones(3)
+        h = np.array([1, 1, -1]) * 1e-1
+
+        h_adjusted, _ = _adjust_scheme_to_bounds(x0, h, 1, '1-sided', lb, ub)
+        assert_allclose(h_adjusted, h)
+
+        h_adjusted, _ = _adjust_scheme_to_bounds(x0, h, 2, '1-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([1, -1, 1]) * 1e-1)
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 1, '2-sided', lb, ub)
+        assert_allclose(h_adjusted, np.abs(h))
+        assert_(np.all(~one_sided))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 2, '2-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([1, -1, 1]) * 1e-1)
+        assert_equal(one_sided, np.array([False, True, True]))
+
+    def test_tight_bounds(self):
+        lb = np.array([-0.03, -0.03])
+        ub = np.array([0.05, 0.05])
+        x0 = np.array([0.0, 0.03])
+        h = np.array([-0.1, -0.1])
+
+        h_adjusted, _ = _adjust_scheme_to_bounds(x0, h, 1, '1-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([0.05, -0.06]))
+
+        h_adjusted, _ = _adjust_scheme_to_bounds(x0, h, 2, '1-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([0.025, -0.03]))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 1, '2-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([0.03, -0.03]))
+        assert_equal(one_sided, np.array([False, True]))
+
+        h_adjusted, one_sided = _adjust_scheme_to_bounds(
+            x0, h, 2, '2-sided', lb, ub)
+        assert_allclose(h_adjusted, np.array([0.015, -0.015]))
+        assert_equal(one_sided, np.array([False, True]))
+
+
+class TestApproxDerivativesDense:
+    def fun_scalar_scalar(self, x):
+        return np.sinh(x)
+
+    def jac_scalar_scalar(self, x):
+        return np.cosh(x)
+
+    def fun_scalar_vector(self, x):
+        return np.array([x[0]**2, np.tan(x[0]), np.exp(x[0])])
+
+    def jac_scalar_vector(self, x):
+        return np.array(
+            [2 * x[0], np.cos(x[0]) ** -2, np.exp(x[0])]).reshape(-1, 1)
+
+    def fun_vector_scalar(self, x):
+        return np.sin(x[0] * x[1]) * np.log(x[0])
+
+    def wrong_dimensions_fun(self, x):
+        return np.array([x**2, np.tan(x), np.exp(x)])
+
+    def jac_vector_scalar(self, x):
+        return np.array([
+            x[1] * np.cos(x[0] * x[1]) * np.log(x[0]) +
+            np.sin(x[0] * x[1]) / x[0],
+            x[0] * np.cos(x[0] * x[1]) * np.log(x[0])
+        ])
+
+    def fun_vector_vector(self, x):
+        return np.array([
+            x[0] * np.sin(x[1]),
+            x[1] * np.cos(x[0]),
+            x[0] ** 3 * x[1] ** -0.5
+        ])
+
+    def fun_vector_vector_with_arg(self, x, arg):
+        """Used to test passing custom arguments with check_derivative()"""
+        assert arg == 42
+        return np.array([
+            x[0] * np.sin(x[1]),
+            x[1] * np.cos(x[0]),
+            x[0] ** 3 * x[1] ** -0.5
+        ])
+
+    def jac_vector_vector(self, x):
+        return np.array([
+            [np.sin(x[1]), x[0] * np.cos(x[1])],
+            [-x[1] * np.sin(x[0]), np.cos(x[0])],
+            [3 * x[0] ** 2 * x[1] ** -0.5, -0.5 * x[0] ** 3 * x[1] ** -1.5]
+        ])
+
+    def jac_vector_vector_with_arg(self, x, arg):
+        """Used to test passing custom arguments with check_derivative()"""
+        assert arg == 42
+        return np.array([
+            [np.sin(x[1]), x[0] * np.cos(x[1])],
+            [-x[1] * np.sin(x[0]), np.cos(x[0])],
+            [3 * x[0] ** 2 * x[1] ** -0.5, -0.5 * x[0] ** 3 * x[1] ** -1.5]
+        ])
+
+    def fun_parametrized(self, x, c0, c1=1.0):
+        return np.array([np.exp(c0 * x[0]), np.exp(c1 * x[1])])
+
+    def jac_parametrized(self, x, c0, c1=0.1):
+        return np.array([
+            [c0 * np.exp(c0 * x[0]), 0],
+            [0, c1 * np.exp(c1 * x[1])]
+        ])
+
+    def fun_with_nan(self, x):
+        return x if np.abs(x) <= 1e-8 else np.nan
+
+    def jac_with_nan(self, x):
+        return 1.0 if np.abs(x) <= 1e-8 else np.nan
+
+    def fun_zero_jacobian(self, x):
+        return np.array([x[0] * x[1], np.cos(x[0] * x[1])])
+
+    def jac_zero_jacobian(self, x):
+        return np.array([
+            [x[1], x[0]],
+            [-x[1] * np.sin(x[0] * x[1]), -x[0] * np.sin(x[0] * x[1])]
+        ])
+
+    def jac_non_numpy(self, x):
+        # x can be a scalar or an array [val].
+        # Cast to true scalar before handing over to math.exp
+        xp = np.asarray(x).item()
+        return math.exp(xp)
+
+    def test_scalar_scalar(self):
+        x0 = 1.0
+        jac_diff_2 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='2-point')
+        jac_diff_3 = approx_derivative(self.fun_scalar_scalar, x0)
+        jac_diff_4 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='cs')
+        jac_true = self.jac_scalar_scalar(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_scalar_scalar_abs_step(self):
+        # can approx_derivative use abs_step?
+        x0 = 1.0
+        jac_diff_2 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='2-point', abs_step=1.49e-8)
+        jac_diff_3 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       abs_step=1.49e-8)
+        jac_diff_4 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='cs', abs_step=1.49e-8)
+        jac_true = self.jac_scalar_scalar(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_scalar_vector(self):
+        x0 = 0.5
+        jac_diff_2 = approx_derivative(self.fun_scalar_vector, x0,
+                                       method='2-point')
+        jac_diff_3 = approx_derivative(self.fun_scalar_vector, x0)
+        jac_diff_4 = approx_derivative(self.fun_scalar_vector, x0,
+                                       method='cs')
+        jac_true = self.jac_scalar_vector(np.atleast_1d(x0))
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_vector_scalar(self):
+        x0 = np.array([100.0, -0.5])
+        jac_diff_2 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='2-point')
+        jac_diff_3 = approx_derivative(self.fun_vector_scalar, x0)
+        jac_diff_4 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='cs')
+        jac_true = self.jac_vector_scalar(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-7)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_vector_scalar_abs_step(self):
+        # can approx_derivative use abs_step?
+        x0 = np.array([100.0, -0.5])
+        jac_diff_2 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='2-point', abs_step=1.49e-8)
+        jac_diff_3 = approx_derivative(self.fun_vector_scalar, x0,
+                                       abs_step=1.49e-8, rel_step=np.inf)
+        jac_diff_4 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='cs', abs_step=1.49e-8)
+        jac_true = self.jac_vector_scalar(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=3e-9)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_vector_vector(self):
+        x0 = np.array([-100.0, 0.2])
+        jac_diff_2 = approx_derivative(self.fun_vector_vector, x0,
+                                       method='2-point')
+        jac_diff_3 = approx_derivative(self.fun_vector_vector, x0)
+        jac_diff_4 = approx_derivative(self.fun_vector_vector, x0,
+                                       method='cs')
+        jac_true = self.jac_vector_vector(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-5)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_4, jac_true, rtol=1e-12)
+
+    def test_wrong_dimensions(self):
+        x0 = 1.0
+        assert_raises(RuntimeError, approx_derivative,
+                      self.wrong_dimensions_fun, x0)
+        f0 = self.wrong_dimensions_fun(np.atleast_1d(x0))
+        assert_raises(ValueError, approx_derivative,
+                      self.wrong_dimensions_fun, x0, f0=f0)
+
+    def test_custom_rel_step(self):
+        x0 = np.array([-0.1, 0.1])
+        jac_diff_2 = approx_derivative(self.fun_vector_vector, x0,
+                                       method='2-point', rel_step=1e-4)
+        jac_diff_3 = approx_derivative(self.fun_vector_vector, x0,
+                                       rel_step=1e-4)
+        jac_true = self.jac_vector_vector(x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-2)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-4)
+
+    def test_options(self):
+        x0 = np.array([1.0, 1.0])
+        c0 = -1.0
+        c1 = 1.0
+        lb = 0.0
+        ub = 2.0
+        f0 = self.fun_parametrized(x0, c0, c1=c1)
+        rel_step = np.array([-1e-6, 1e-7])
+        jac_true = self.jac_parametrized(x0, c0, c1)
+        jac_diff_2 = approx_derivative(
+            self.fun_parametrized, x0, method='2-point', rel_step=rel_step,
+            f0=f0, args=(c0,), kwargs=dict(c1=c1), bounds=(lb, ub))
+        jac_diff_3 = approx_derivative(
+            self.fun_parametrized, x0, rel_step=rel_step,
+            f0=f0, args=(c0,), kwargs=dict(c1=c1), bounds=(lb, ub))
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+
+    def test_with_bounds_2_point(self):
+        lb = -np.ones(2)
+        ub = np.ones(2)
+
+        x0 = np.array([-2.0, 0.2])
+        assert_raises(ValueError, approx_derivative,
+                      self.fun_vector_vector, x0, bounds=(lb, ub))
+
+        x0 = np.array([-1.0, 1.0])
+        jac_diff = approx_derivative(self.fun_vector_vector, x0,
+                                     method='2-point', bounds=(lb, ub))
+        jac_true = self.jac_vector_vector(x0)
+        assert_allclose(jac_diff, jac_true, rtol=1e-6)
+
+    def test_with_bounds_3_point(self):
+        lb = np.array([1.0, 1.0])
+        ub = np.array([2.0, 2.0])
+
+        x0 = np.array([1.0, 2.0])
+        jac_true = self.jac_vector_vector(x0)
+
+        jac_diff = approx_derivative(self.fun_vector_vector, x0)
+        assert_allclose(jac_diff, jac_true, rtol=1e-9)
+
+        jac_diff = approx_derivative(self.fun_vector_vector, x0,
+                                     bounds=(lb, np.inf))
+        assert_allclose(jac_diff, jac_true, rtol=1e-9)
+
+        jac_diff = approx_derivative(self.fun_vector_vector, x0,
+                                     bounds=(-np.inf, ub))
+        assert_allclose(jac_diff, jac_true, rtol=1e-9)
+
+        jac_diff = approx_derivative(self.fun_vector_vector, x0,
+                                     bounds=(lb, ub))
+        assert_allclose(jac_diff, jac_true, rtol=1e-9)
+
+    def test_tight_bounds(self):
+        x0 = np.array([10.0, 10.0])
+        lb = x0 - 3e-9
+        ub = x0 + 2e-9
+        jac_true = self.jac_vector_vector(x0)
+        jac_diff = approx_derivative(
+            self.fun_vector_vector, x0, method='2-point', bounds=(lb, ub))
+        assert_allclose(jac_diff, jac_true, rtol=1e-6)
+        jac_diff = approx_derivative(
+            self.fun_vector_vector, x0, method='2-point',
+            rel_step=1e-6, bounds=(lb, ub))
+        assert_allclose(jac_diff, jac_true, rtol=1e-6)
+
+        jac_diff = approx_derivative(
+            self.fun_vector_vector, x0, bounds=(lb, ub))
+        assert_allclose(jac_diff, jac_true, rtol=1e-6)
+        jac_diff = approx_derivative(
+            self.fun_vector_vector, x0, rel_step=1e-6, bounds=(lb, ub))
+        assert_allclose(jac_true, jac_diff, rtol=1e-6)
+
+    def test_bound_switches(self):
+        lb = -1e-8
+        ub = 1e-8
+        x0 = 0.0
+        jac_true = self.jac_with_nan(x0)
+        jac_diff_2 = approx_derivative(
+            self.fun_with_nan, x0, method='2-point', rel_step=1e-6,
+            bounds=(lb, ub))
+        jac_diff_3 = approx_derivative(
+            self.fun_with_nan, x0, rel_step=1e-6, bounds=(lb, ub))
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+
+        x0 = 1e-8
+        jac_true = self.jac_with_nan(x0)
+        jac_diff_2 = approx_derivative(
+            self.fun_with_nan, x0, method='2-point', rel_step=1e-6,
+            bounds=(lb, ub))
+        jac_diff_3 = approx_derivative(
+            self.fun_with_nan, x0, rel_step=1e-6, bounds=(lb, ub))
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-9)
+
+    def test_non_numpy(self):
+        x0 = 1.0
+        jac_true = self.jac_non_numpy(x0)
+        jac_diff_2 = approx_derivative(self.jac_non_numpy, x0,
+                                       method='2-point')
+        jac_diff_3 = approx_derivative(self.jac_non_numpy, x0)
+        assert_allclose(jac_diff_2, jac_true, rtol=1e-6)
+        assert_allclose(jac_diff_3, jac_true, rtol=1e-8)
+
+        # math.exp cannot handle complex arguments, hence this raises
+        assert_raises(TypeError, approx_derivative, self.jac_non_numpy, x0,
+                      **dict(method='cs'))
+
+    def test_fp(self):
+        # checks that approx_derivative works for FP size other than 64.
+        # Example is derived from the minimal working example in gh12991.
+        np.random.seed(1)
+
+        def func(p, x):
+            return p[0] + p[1] * x
+
+        def err(p, x, y):
+            return func(p, x) - y
+
+        x = np.linspace(0, 1, 100, dtype=np.float64)
+        y = np.random.random(100).astype(np.float64)
+        p0 = np.array([-1.0, -1.0])
+
+        jac_fp64 = approx_derivative(err, p0, method='2-point', args=(x, y))
+
+        # parameter vector is float32, func output is float64
+        jac_fp = approx_derivative(err, p0.astype(np.float32),
+                                   method='2-point', args=(x, y))
+        assert err(p0, x, y).dtype == np.float64
+        assert_allclose(jac_fp, jac_fp64, atol=1e-3)
+
+        # parameter vector is float64, func output is float32
+        def err_fp32(p):
+            assert p.dtype == np.float32
+            return err(p, x, y).astype(np.float32)
+
+        jac_fp = approx_derivative(err_fp32, p0.astype(np.float32),
+                                   method='2-point')
+        assert_allclose(jac_fp, jac_fp64, atol=1e-3)
+
+        # check upper bound of error on the derivative for 2-point
+        def f(x):
+            return np.sin(x)
+        def g(x):
+            return np.cos(x)
+        def hess(x):
+            return -np.sin(x)
+
+        def calc_atol(h, x0, f, hess, EPS):
+            # truncation error
+            t0 = h / 2 * max(np.abs(hess(x0)), np.abs(hess(x0 + h)))
+            # roundoff error. There may be a divisor (>1) missing from
+            # the following line, so this contribution is possibly
+            # overestimated
+            t1 = EPS / h * max(np.abs(f(x0)), np.abs(f(x0 + h)))
+            return t0 + t1
+
+        for dtype in [np.float16, np.float32, np.float64]:
+            EPS = np.finfo(dtype).eps
+            x0 = np.array(1.0).astype(dtype)
+            h = _compute_absolute_step(None, x0, f(x0), '2-point')
+            atol = calc_atol(h, x0, f, hess, EPS)
+            err = approx_derivative(f, x0, method='2-point',
+                                    abs_step=h) - g(x0)
+            assert abs(err) < atol
+
+    def test_check_derivative(self):
+        x0 = np.array([-10.0, 10])
+        accuracy = check_derivative(self.fun_vector_vector,
+                                    self.jac_vector_vector, x0)
+        assert_(accuracy < 1e-9)
+        accuracy = check_derivative(self.fun_vector_vector,
+                                    self.jac_vector_vector, x0)
+        assert_(accuracy < 1e-6)
+
+        x0 = np.array([0.0, 0.0])
+        accuracy = check_derivative(self.fun_zero_jacobian,
+                                    self.jac_zero_jacobian, x0)
+        assert_(accuracy == 0)
+        accuracy = check_derivative(self.fun_zero_jacobian,
+                                    self.jac_zero_jacobian, x0)
+        assert_(accuracy == 0)
+
+    def test_check_derivative_with_kwargs(self):
+        x0 = np.array([-10.0, 10])
+        accuracy = check_derivative(self.fun_vector_vector_with_arg,
+                                    self.jac_vector_vector_with_arg,
+                                    x0,
+                                    kwargs={'arg': 42})
+        assert_(accuracy < 1e-9)
+
+
+class TestApproxDerivativeSparse:
+    # Example from Numerical Optimization 2nd edition, p. 198.
+    def setup_method(self):
+        np.random.seed(0)
+        self.n = 50
+        self.lb = -0.1 * (1 + np.arange(self.n))
+        self.ub = 0.1 * (1 + np.arange(self.n))
+        self.x0 = np.empty(self.n)
+        self.x0[::2] = (1 - 1e-7) * self.lb[::2]
+        self.x0[1::2] = (1 - 1e-7) * self.ub[1::2]
+
+        self.J_true = self.jac(self.x0)
+
+    def fun(self, x):
+        e = x[1:]**3 - x[:-1]**2
+        return np.hstack((0, 3 * e)) + np.hstack((2 * e, 0))
+
+    def jac(self, x):
+        n = x.size
+        J = np.zeros((n, n))
+        J[0, 0] = -4 * x[0]
+        J[0, 1] = 6 * x[1]**2
+        for i in range(1, n - 1):
+            J[i, i - 1] = -6 * x[i-1]
+            J[i, i] = 9 * x[i]**2 - 4 * x[i]
+            J[i, i + 1] = 6 * x[i+1]**2
+        J[-1, -1] = 9 * x[-1]**2
+        J[-1, -2] = -6 * x[-2]
+
+        return J
+
+    def structure(self, n):
+        A = np.zeros((n, n), dtype=int)
+        A[0, 0] = 1
+        A[0, 1] = 1
+        for i in range(1, n - 1):
+            A[i, i - 1: i + 2] = 1
+        A[-1, -1] = 1
+        A[-1, -2] = 1
+
+        return A
+
+    def test_all(self):
+        A = self.structure(self.n)
+        order = np.arange(self.n)
+        groups_1 = group_columns(A, order)
+        np.random.shuffle(order)
+        groups_2 = group_columns(A, order)
+
+        for method, groups, l, u in product(
+                ['2-point', '3-point', 'cs'], [groups_1, groups_2],
+                [-np.inf, self.lb], [np.inf, self.ub]):
+            J = approx_derivative(self.fun, self.x0, method=method,
+                                  bounds=(l, u), sparsity=(A, groups))
+            assert_(isinstance(J, csr_matrix))
+            assert_allclose(J.toarray(), self.J_true, rtol=1e-6)
+
+            rel_step = np.full_like(self.x0, 1e-8)
+            rel_step[::2] *= -1
+            J = approx_derivative(self.fun, self.x0, method=method,
+                                  rel_step=rel_step, sparsity=(A, groups))
+            assert_allclose(J.toarray(), self.J_true, rtol=1e-5)
+
+    def test_no_precomputed_groups(self):
+        A = self.structure(self.n)
+        J = approx_derivative(self.fun, self.x0, sparsity=A)
+        assert_allclose(J.toarray(), self.J_true, rtol=1e-6)
+
+    def test_equivalence(self):
+        structure = np.ones((self.n, self.n), dtype=int)
+        groups = np.arange(self.n)
+        for method in ['2-point', '3-point', 'cs']:
+            J_dense = approx_derivative(self.fun, self.x0, method=method)
+            J_sparse = approx_derivative(
+                self.fun, self.x0, sparsity=(structure, groups), method=method)
+            assert_allclose(J_dense, J_sparse.toarray(),
+                            rtol=5e-16, atol=7e-15)
+
+    def test_check_derivative(self):
+        def jac(x):
+            return csr_matrix(self.jac(x))
+
+        accuracy = check_derivative(self.fun, jac, self.x0,
+                                    bounds=(self.lb, self.ub))
+        assert_(accuracy < 1e-9)
+
+        accuracy = check_derivative(self.fun, jac, self.x0,
+                                    bounds=(self.lb, self.ub))
+        assert_(accuracy < 1e-9)
+
+
+class TestApproxDerivativeLinearOperator:
+
+    def fun_scalar_scalar(self, x):
+        return np.sinh(x)
+
+    def jac_scalar_scalar(self, x):
+        return np.cosh(x)
+
+    def fun_scalar_vector(self, x):
+        return np.array([x[0]**2, np.tan(x[0]), np.exp(x[0])])
+
+    def jac_scalar_vector(self, x):
+        return np.array(
+            [2 * x[0], np.cos(x[0]) ** -2, np.exp(x[0])]).reshape(-1, 1)
+
+    def fun_vector_scalar(self, x):
+        return np.sin(x[0] * x[1]) * np.log(x[0])
+
+    def jac_vector_scalar(self, x):
+        return np.array([
+            x[1] * np.cos(x[0] * x[1]) * np.log(x[0]) +
+            np.sin(x[0] * x[1]) / x[0],
+            x[0] * np.cos(x[0] * x[1]) * np.log(x[0])
+        ])
+
+    def fun_vector_vector(self, x):
+        return np.array([
+            x[0] * np.sin(x[1]),
+            x[1] * np.cos(x[0]),
+            x[0] ** 3 * x[1] ** -0.5
+        ])
+
+    def jac_vector_vector(self, x):
+        return np.array([
+            [np.sin(x[1]), x[0] * np.cos(x[1])],
+            [-x[1] * np.sin(x[0]), np.cos(x[0])],
+            [3 * x[0] ** 2 * x[1] ** -0.5, -0.5 * x[0] ** 3 * x[1] ** -1.5]
+        ])
+
+    def test_scalar_scalar(self):
+        x0 = 1.0
+        jac_diff_2 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='2-point',
+                                       as_linear_operator=True)
+        jac_diff_3 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       as_linear_operator=True)
+        jac_diff_4 = approx_derivative(self.fun_scalar_scalar, x0,
+                                       method='cs',
+                                       as_linear_operator=True)
+        jac_true = self.jac_scalar_scalar(x0)
+        np.random.seed(1)
+        for i in range(10):
+            p = np.random.uniform(-10, 10, size=(1,))
+            assert_allclose(jac_diff_2.dot(p), jac_true*p,
+                            rtol=1e-5)
+            assert_allclose(jac_diff_3.dot(p), jac_true*p,
+                            rtol=5e-6)
+            assert_allclose(jac_diff_4.dot(p), jac_true*p,
+                            rtol=5e-6)
+
+    def test_scalar_vector(self):
+        x0 = 0.5
+        jac_diff_2 = approx_derivative(self.fun_scalar_vector, x0,
+                                       method='2-point',
+                                       as_linear_operator=True)
+        jac_diff_3 = approx_derivative(self.fun_scalar_vector, x0,
+                                       as_linear_operator=True)
+        jac_diff_4 = approx_derivative(self.fun_scalar_vector, x0,
+                                       method='cs',
+                                       as_linear_operator=True)
+        jac_true = self.jac_scalar_vector(np.atleast_1d(x0))
+        np.random.seed(1)
+        for i in range(10):
+            p = np.random.uniform(-10, 10, size=(1,))
+            assert_allclose(jac_diff_2.dot(p), jac_true.dot(p),
+                            rtol=1e-5)
+            assert_allclose(jac_diff_3.dot(p), jac_true.dot(p),
+                            rtol=5e-6)
+            assert_allclose(jac_diff_4.dot(p), jac_true.dot(p),
+                            rtol=5e-6)
+
+    def test_vector_scalar(self):
+        x0 = np.array([100.0, -0.5])
+        jac_diff_2 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='2-point',
+                                       as_linear_operator=True)
+        jac_diff_3 = approx_derivative(self.fun_vector_scalar, x0,
+                                       as_linear_operator=True)
+        jac_diff_4 = approx_derivative(self.fun_vector_scalar, x0,
+                                       method='cs',
+                                       as_linear_operator=True)
+        jac_true = self.jac_vector_scalar(x0)
+        np.random.seed(1)
+        for i in range(10):
+            p = np.random.uniform(-10, 10, size=x0.shape)
+            assert_allclose(jac_diff_2.dot(p), np.atleast_1d(jac_true.dot(p)),
+                            rtol=1e-5)
+            assert_allclose(jac_diff_3.dot(p), np.atleast_1d(jac_true.dot(p)),
+                            rtol=5e-6)
+            assert_allclose(jac_diff_4.dot(p), np.atleast_1d(jac_true.dot(p)),
+                            rtol=1e-7)
+
+    def test_vector_vector(self):
+        x0 = np.array([-100.0, 0.2])
+        jac_diff_2 = approx_derivative(self.fun_vector_vector, x0,
+                                       method='2-point',
+                                       as_linear_operator=True)
+        jac_diff_3 = approx_derivative(self.fun_vector_vector, x0,
+                                       as_linear_operator=True)
+        jac_diff_4 = approx_derivative(self.fun_vector_vector, x0,
+                                       method='cs',
+                                       as_linear_operator=True)
+        jac_true = self.jac_vector_vector(x0)
+        np.random.seed(1)
+        for i in range(10):
+            p = np.random.uniform(-10, 10, size=x0.shape)
+            assert_allclose(jac_diff_2.dot(p), jac_true.dot(p), rtol=1e-5)
+            assert_allclose(jac_diff_3.dot(p), jac_true.dot(p), rtol=1e-6)
+            assert_allclose(jac_diff_4.dot(p), jac_true.dot(p), rtol=1e-7)
+
+    def test_exception(self):
+        x0 = np.array([-100.0, 0.2])
+        assert_raises(ValueError, approx_derivative,
+                      self.fun_vector_vector, x0,
+                      method='2-point', bounds=(1, np.inf))
+
+
+def test_absolute_step_sign():
+    # test for gh12487
+    # if an absolute step is specified for 2-point differences make sure that
+    # the side corresponds to the step. i.e. if step is positive then forward
+    # differences should be used, if step is negative then backwards
+    # differences should be used.
+
+    # function has double discontinuity at x = [-1, -1]
+    # first component is \/, second component is /\
+    def f(x):
+        return -np.abs(x[0] + 1) + np.abs(x[1] + 1)
+
+    # check that the forward difference is used
+    grad = approx_derivative(f, [-1, -1], method='2-point', abs_step=1e-8)
+    assert_allclose(grad, [-1.0, 1.0])
+
+    # check that the backwards difference is used
+    grad = approx_derivative(f, [-1, -1], method='2-point', abs_step=-1e-8)
+    assert_allclose(grad, [1.0, -1.0])
+
+    # check that the forwards difference is used with a step for both
+    # parameters
+    grad = approx_derivative(
+        f, [-1, -1], method='2-point', abs_step=[1e-8, 1e-8]
+    )
+    assert_allclose(grad, [-1.0, 1.0])
+
+    # check that we can mix forward/backwards steps.
+    grad = approx_derivative(
+        f, [-1, -1], method='2-point', abs_step=[1e-8, -1e-8]
+     )
+    assert_allclose(grad, [-1.0, -1.0])
+    grad = approx_derivative(
+        f, [-1, -1], method='2-point', abs_step=[-1e-8, 1e-8]
+    )
+    assert_allclose(grad, [1.0, 1.0])
+
+    # the forward step should reverse to a backwards step if it runs into a
+    # bound
+    # This is kind of tested in TestAdjustSchemeToBounds, but only for a lower level
+    # function.
+    grad = approx_derivative(
+        f, [-1, -1], method='2-point', abs_step=1e-8,
+        bounds=(-np.inf, -1)
+    )
+    assert_allclose(grad, [1.0, -1.0])
+
+    grad = approx_derivative(
+        f, [-1, -1], method='2-point', abs_step=-1e-8, bounds=(-1, np.inf)
+    )
+    assert_allclose(grad, [-1.0, 1.0])
+
+
+def test__compute_absolute_step():
+    # tests calculation of absolute step from rel_step
+    methods = ['2-point', '3-point', 'cs']
+
+    x0 = np.array([1e-5, 0, 1, 1e5])
+
+    EPS = np.finfo(np.float64).eps
+    relative_step = {
+        "2-point": EPS**0.5,
+        "3-point": EPS**(1/3),
+        "cs": EPS**0.5
+    }
+    f0 = np.array(1.0)
+
+    for method in methods:
+        rel_step = relative_step[method]
+        correct_step = np.array([rel_step,
+                                 rel_step * 1.,
+                                 rel_step * 1.,
+                                 rel_step * np.abs(x0[3])])
+
+        abs_step = _compute_absolute_step(None, x0, f0, method)
+        assert_allclose(abs_step, correct_step)
+
+        sign_x0 = (-x0 >= 0).astype(float) * 2 - 1
+        abs_step = _compute_absolute_step(None, -x0, f0, method)
+        assert_allclose(abs_step, sign_x0 * correct_step)
+
+    # if a relative step is provided it should be used
+    rel_step = np.array([0.1, 1, 10, 100])
+    correct_step = np.array([rel_step[0] * x0[0],
+                             relative_step['2-point'],
+                             rel_step[2] * 1.,
+                             rel_step[3] * np.abs(x0[3])])
+
+    abs_step = _compute_absolute_step(rel_step, x0, f0, '2-point')
+    assert_allclose(abs_step, correct_step)
+
+    sign_x0 = (-x0 >= 0).astype(float) * 2 - 1
+    abs_step = _compute_absolute_step(rel_step, -x0, f0, '2-point')
+    assert_allclose(abs_step, sign_x0 * correct_step)
diff --git a/shared/package-files/40/4049ab24a941fd6d950bad350a38f27ccbb1b60ccf430c042859703021876d3d/_embedding.h b/shared/package-files/40/4049ab24a941fd6d950bad350a38f27ccbb1b60ccf430c042859703021876d3d/_embedding.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cb66f2352cd0ac9dc506bcdb7b333f3d70b92ed
--- /dev/null
+++ b/shared/package-files/40/4049ab24a941fd6d950bad350a38f27ccbb1b60ccf430c042859703021876d3d/_embedding.h
@@ -0,0 +1,550 @@
+
+/***** Support code for embedding *****/
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+
+#if defined(_WIN32)
+#  define CFFI_DLLEXPORT  __declspec(dllexport)
+#elif defined(__GNUC__)
+#  define CFFI_DLLEXPORT  __attribute__((visibility("default")))
+#else
+#  define CFFI_DLLEXPORT  /* nothing */
+#endif
+
+
+/* There are two global variables of type _cffi_call_python_fnptr:
+
+   * _cffi_call_python, which we declare just below, is the one called
+     by ``extern "Python"`` implementations.
+
+   * _cffi_call_python_org, which on CPython is actually part of the
+     _cffi_exports[] array, is the function pointer copied from
+     _cffi_backend.  If _cffi_start_python() fails, then this is set
+     to NULL; otherwise, it should never be NULL.
+
+   After initialization is complete, both are equal.  However, the
+   first one remains equal to &_cffi_start_and_call_python until the
+   very end of initialization, when we are (or should be) sure that
+   concurrent threads also see a completely initialized world, and
+   only then is it changed.
+*/
+#undef _cffi_call_python
+typedef void (*_cffi_call_python_fnptr)(struct _cffi_externpy_s *, char *);
+static void _cffi_start_and_call_python(struct _cffi_externpy_s *, char *);
+static _cffi_call_python_fnptr _cffi_call_python = &_cffi_start_and_call_python;
+
+
+#ifndef _MSC_VER
+   /* --- Assuming a GCC not infinitely old --- */
+# define cffi_compare_and_swap(l,o,n)  __sync_bool_compare_and_swap(l,o,n)
+# define cffi_write_barrier()          __sync_synchronize()
+# if !defined(__amd64__) && !defined(__x86_64__) &&   \
+     !defined(__i386__) && !defined(__i386)
+#   define cffi_read_barrier()         __sync_synchronize()
+# else
+#   define cffi_read_barrier()         (void)0
+# endif
+#else
+   /* --- Windows threads version --- */
+# include <Windows.h>
+# define cffi_compare_and_swap(l,o,n) \
+                               (InterlockedCompareExchangePointer(l,n,o) == (o))
+# define cffi_write_barrier()       InterlockedCompareExchange(&_cffi_dummy,0,0)
+# define cffi_read_barrier()           (void)0
+static volatile LONG _cffi_dummy;
+#endif
+
+#ifdef WITH_THREAD
+# ifndef _MSC_VER
+#  include <pthread.h>
+   static pthread_mutex_t _cffi_embed_startup_lock;
+# else
+   static CRITICAL_SECTION _cffi_embed_startup_lock;
+# endif
+  static char _cffi_embed_startup_lock_ready = 0;
+#endif
+
+static void _cffi_acquire_reentrant_mutex(void)
+{
+    static void *volatile lock = NULL;
+
+    while (!cffi_compare_and_swap(&lock, NULL, (void *)1)) {
+        /* should ideally do a spin loop instruction here, but
+           hard to do it portably and doesn't really matter I
+           think: pthread_mutex_init() should be very fast, and
+           this is only run at start-up anyway. */
+    }
+
+#ifdef WITH_THREAD
+    if (!_cffi_embed_startup_lock_ready) {
+# ifndef _MSC_VER
+        pthread_mutexattr_t attr;
+        pthread_mutexattr_init(&attr);
+        pthread_mutexattr_settype(&attr, PTHREAD_MUTEX_RECURSIVE);
+        pthread_mutex_init(&_cffi_embed_startup_lock, &attr);
+# else
+        InitializeCriticalSection(&_cffi_embed_startup_lock);
+# endif
+        _cffi_embed_startup_lock_ready = 1;
+    }
+#endif
+
+    while (!cffi_compare_and_swap(&lock, (void *)1, NULL))
+        ;
+
+#ifndef _MSC_VER
+    pthread_mutex_lock(&_cffi_embed_startup_lock);
+#else
+    EnterCriticalSection(&_cffi_embed_startup_lock);
+#endif
+}
+
+static void _cffi_release_reentrant_mutex(void)
+{
+#ifndef _MSC_VER
+    pthread_mutex_unlock(&_cffi_embed_startup_lock);
+#else
+    LeaveCriticalSection(&_cffi_embed_startup_lock);
+#endif
+}
+
+
+/**********  CPython-specific section  **********/
+#ifndef PYPY_VERSION
+
+#include "_cffi_errors.h"
+
+
+#define _cffi_call_python_org  _cffi_exports[_CFFI_CPIDX]
+
+PyMODINIT_FUNC _CFFI_PYTHON_STARTUP_FUNC(void);   /* forward */
+
+static void _cffi_py_initialize(void)
+{
+    /* XXX use initsigs=0, which "skips initialization registration of
+       signal handlers, which might be useful when Python is
+       embedded" according to the Python docs.  But review and think
+       if it should be a user-controllable setting.
+
+       XXX we should also give a way to write errors to a buffer
+       instead of to stderr.
+
+       XXX if importing 'site' fails, CPython (any version) calls
+       exit().  Should we try to work around this behavior here?
+    */
+    Py_InitializeEx(0);
+}
+
+static int _cffi_initialize_python(void)
+{
+    /* This initializes Python, imports _cffi_backend, and then the
+       present .dll/.so is set up as a CPython C extension module.
+    */
+    int result;
+    PyGILState_STATE state;
+    PyObject *pycode=NULL, *global_dict=NULL, *x;
+    PyObject *builtins;
+
+    state = PyGILState_Ensure();
+
+    /* Call the initxxx() function from the present module.  It will
+       create and initialize us as a CPython extension module, instead
+       of letting the startup Python code do it---it might reimport
+       the same .dll/.so and get maybe confused on some platforms.
+       It might also have troubles locating the .dll/.so again for all
+       I know.
+    */
+    (void)_CFFI_PYTHON_STARTUP_FUNC();
+    if (PyErr_Occurred())
+        goto error;
+
+    /* Now run the Python code provided to ffi.embedding_init_code().
+     */
+    pycode = Py_CompileString(_CFFI_PYTHON_STARTUP_CODE,
+                              "<init code for '" _CFFI_MODULE_NAME "'>",
+                              Py_file_input);
+    if (pycode == NULL)
+        goto error;
+    global_dict = PyDict_New();
+    if (global_dict == NULL)
+        goto error;
+    builtins = PyEval_GetBuiltins();
+    if (builtins == NULL)
+        goto error;
+    if (PyDict_SetItemString(global_dict, "__builtins__", builtins) < 0)
+        goto error;
+    x = PyEval_EvalCode(
+#if PY_MAJOR_VERSION < 3
+                        (PyCodeObject *)
+#endif
+                        pycode, global_dict, global_dict);
+    if (x == NULL)
+        goto error;
+    Py_DECREF(x);
+
+    /* Done!  Now if we've been called from
+       _cffi_start_and_call_python() in an ``extern "Python"``, we can
+       only hope that the Python code did correctly set up the
+       corresponding @ffi.def_extern() function.  Otherwise, the
+       general logic of ``extern "Python"`` functions (inside the
+       _cffi_backend module) will find that the reference is still
+       missing and print an error.
+     */
+    result = 0;
+ done:
+    Py_XDECREF(pycode);
+    Py_XDECREF(global_dict);
+    PyGILState_Release(state);
+    return result;
+
+ error:;
+    {
+        /* Print as much information as potentially useful.
+           Debugging load-time failures with embedding is not fun
+        */
+        PyObject *ecap;
+        PyObject *exception, *v, *tb, *f, *modules, *mod;
+        PyErr_Fetch(&exception, &v, &tb);
+        ecap = _cffi_start_error_capture();
+        f = PySys_GetObject((char *)"stderr");
+        if (f != NULL && f != Py_None) {
+            PyFile_WriteString(
+                "Failed to initialize the Python-CFFI embedding logic:\n\n", f);
+        }
+
+        if (exception != NULL) {
+            PyErr_NormalizeException(&exception, &v, &tb);
+            PyErr_Display(exception, v, tb);
+        }
+        Py_XDECREF(exception);
+        Py_XDECREF(v);
+        Py_XDECREF(tb);
+
+        if (f != NULL && f != Py_None) {
+            PyFile_WriteString("\nFrom: " _CFFI_MODULE_NAME
+                               "\ncompiled with cffi version: 1.16.0"
+                               "\n_cffi_backend module: ", f);
+            modules = PyImport_GetModuleDict();
+            mod = PyDict_GetItemString(modules, "_cffi_backend");
+            if (mod == NULL) {
+                PyFile_WriteString("not loaded", f);
+            }
+            else {
+                v = PyObject_GetAttrString(mod, "__file__");
+                PyFile_WriteObject(v, f, 0);
+                Py_XDECREF(v);
+            }
+            PyFile_WriteString("\nsys.path: ", f);
+            PyFile_WriteObject(PySys_GetObject((char *)"path"), f, 0);
+            PyFile_WriteString("\n\n", f);
+        }
+        _cffi_stop_error_capture(ecap);
+    }
+    result = -1;
+    goto done;
+}
+
+#if PY_VERSION_HEX < 0x03080000
+PyAPI_DATA(char *) _PyParser_TokenNames[];  /* from CPython */
+#endif
+
+static int _cffi_carefully_make_gil(void)
+{
+    /* This does the basic initialization of Python.  It can be called
+       completely concurrently from unrelated threads.  It assumes
+       that we don't hold the GIL before (if it exists), and we don't
+       hold it afterwards.
+
+       (What it really does used to be completely different in Python 2
+       and Python 3, with the Python 2 solution avoiding the spin-lock
+       around the Py_InitializeEx() call.  However, after recent changes
+       to CPython 2.7 (issue #358) it no longer works.  So we use the
+       Python 3 solution everywhere.)
+
+       This initializes Python by calling Py_InitializeEx().
+       Important: this must not be called concurrently at all.
+       So we use a global variable as a simple spin lock.  This global
+       variable must be from 'libpythonX.Y.so', not from this
+       cffi-based extension module, because it must be shared from
+       different cffi-based extension modules.
+
+       In Python < 3.8, we choose
+       _PyParser_TokenNames[0] as a completely arbitrary pointer value
+       that is never written to.  The default is to point to the
+       string "ENDMARKER".  We change it temporarily to point to the
+       next character in that string.  (Yes, I know it's REALLY
+       obscure.)
+
+       In Python >= 3.8, this string array is no longer writable, so
+       instead we pick PyCapsuleType.tp_version_tag.  We can't change
+       Python < 3.8 because someone might use a mixture of cffi
+       embedded modules, some of which were compiled before this file
+       changed.
+
+       In Python >= 3.12, this stopped working because that particular
+       tp_version_tag gets modified during interpreter startup.  It's
+       arguably a bad idea before 3.12 too, but again we can't change
+       that because someone might use a mixture of cffi embedded
+       modules, and no-one reported a bug so far.  In Python >= 3.12
+       we go instead for PyCapsuleType.tp_as_buffer, which is supposed
+       to always be NULL.  We write to it temporarily a pointer to
+       a struct full of NULLs, which is semantically the same.
+    */
+
+#ifdef WITH_THREAD
+# if PY_VERSION_HEX < 0x03080000
+    char *volatile *lock = (char *volatile *)_PyParser_TokenNames;
+    char *old_value, *locked_value;
+
+    while (1) {    /* spin loop */
+        old_value = *lock;
+        locked_value = old_value + 1;
+        if (old_value[0] == 'E') {
+            assert(old_value[1] == 'N');
+            if (cffi_compare_and_swap(lock, old_value, locked_value))
+                break;
+        }
+        else {
+            assert(old_value[0] == 'N');
+            /* should ideally do a spin loop instruction here, but
+               hard to do it portably and doesn't really matter I
+               think: PyEval_InitThreads() should be very fast, and
+               this is only run at start-up anyway. */
+        }
+    }
+# else
+#  if PY_VERSION_HEX < 0x030C0000
+    int volatile *lock = (int volatile *)&PyCapsule_Type.tp_version_tag;
+    int old_value, locked_value = -42;
+    assert(!(PyCapsule_Type.tp_flags & Py_TPFLAGS_HAVE_VERSION_TAG));
+#  else
+    static struct ebp_s { PyBufferProcs buf; int mark; } empty_buffer_procs;
+    empty_buffer_procs.mark = -42;
+    PyBufferProcs *volatile *lock = (PyBufferProcs *volatile *)
+        &PyCapsule_Type.tp_as_buffer;
+    PyBufferProcs *old_value, *locked_value = &empty_buffer_procs.buf;
+#  endif
+
+    while (1) {    /* spin loop */
+        old_value = *lock;
+        if (old_value == 0) {
+            if (cffi_compare_and_swap(lock, old_value, locked_value))
+                break;
+        }
+        else {
+#  if PY_VERSION_HEX < 0x030C0000
+            assert(old_value == locked_value);
+#  else
+            /* The pointer should point to a possibly different
+               empty_buffer_procs from another C extension module */
+            assert(((struct ebp_s *)old_value)->mark == -42);
+#  endif
+            /* should ideally do a spin loop instruction here, but
+               hard to do it portably and doesn't really matter I
+               think: PyEval_InitThreads() should be very fast, and
+               this is only run at start-up anyway. */
+        }
+    }
+# endif
+#endif
+
+    /* call Py_InitializeEx() */
+    if (!Py_IsInitialized()) {
+        _cffi_py_initialize();
+#if PY_VERSION_HEX < 0x03070000
+        PyEval_InitThreads();
+#endif
+        PyEval_SaveThread();  /* release the GIL */
+        /* the returned tstate must be the one that has been stored into the
+           autoTLSkey by _PyGILState_Init() called from Py_Initialize(). */
+    }
+    else {
+#if PY_VERSION_HEX < 0x03070000
+        /* PyEval_InitThreads() is always a no-op from CPython 3.7 */
+        PyGILState_STATE state = PyGILState_Ensure();
+        PyEval_InitThreads();
+        PyGILState_Release(state);
+#endif
+    }
+
+#ifdef WITH_THREAD
+    /* release the lock */
+    while (!cffi_compare_and_swap(lock, locked_value, old_value))
+        ;
+#endif
+
+    return 0;
+}
+
+/**********  end CPython-specific section  **********/
+
+
+#else
+
+
+/**********  PyPy-specific section  **********/
+
+PyMODINIT_FUNC _CFFI_PYTHON_STARTUP_FUNC(const void *[]);   /* forward */
+
+static struct _cffi_pypy_init_s {
+    const char *name;
+    void *func;    /* function pointer */
+    const char *code;
+} _cffi_pypy_init = {
+    _CFFI_MODULE_NAME,
+    _CFFI_PYTHON_STARTUP_FUNC,
+    _CFFI_PYTHON_STARTUP_CODE,
+};
+
+extern int pypy_carefully_make_gil(const char *);
+extern int pypy_init_embedded_cffi_module(int, struct _cffi_pypy_init_s *);
+
+static int _cffi_carefully_make_gil(void)
+{
+    return pypy_carefully_make_gil(_CFFI_MODULE_NAME);
+}
+
+static int _cffi_initialize_python(void)
+{
+    return pypy_init_embedded_cffi_module(0xB011, &_cffi_pypy_init);
+}
+
+/**********  end PyPy-specific section  **********/
+
+
+#endif
+
+
+#ifdef __GNUC__
+__attribute__((noinline))
+#endif
+static _cffi_call_python_fnptr _cffi_start_python(void)
+{
+    /* Delicate logic to initialize Python.  This function can be
+       called multiple times concurrently, e.g. when the process calls
+       its first ``extern "Python"`` functions in multiple threads at
+       once.  It can also be called recursively, in which case we must
+       ignore it.  We also have to consider what occurs if several
+       different cffi-based extensions reach this code in parallel
+       threads---it is a different copy of the code, then, and we
+       can't have any shared global variable unless it comes from
+       'libpythonX.Y.so'.
+
+       Idea:
+
+       * _cffi_carefully_make_gil(): "carefully" call
+         PyEval_InitThreads() (possibly with Py_InitializeEx() first).
+
+       * then we use a (local) custom lock to make sure that a call to this
+         cffi-based extension will wait if another call to the *same*
+         extension is running the initialization in another thread.
+         It is reentrant, so that a recursive call will not block, but
+         only one from a different thread.
+
+       * then we grab the GIL and (Python 2) we call Py_InitializeEx().
+         At this point, concurrent calls to Py_InitializeEx() are not
+         possible: we have the GIL.
+
+       * do the rest of the specific initialization, which may
+         temporarily release the GIL but not the custom lock.
+         Only release the custom lock when we are done.
+    */
+    static char called = 0;
+
+    if (_cffi_carefully_make_gil() != 0)
+        return NULL;
+
+    _cffi_acquire_reentrant_mutex();
+
+    /* Here the GIL exists, but we don't have it.  We're only protected
+       from concurrency by the reentrant mutex. */
+
+    /* This file only initializes the embedded module once, the first
+       time this is called, even if there are subinterpreters. */
+    if (!called) {
+        called = 1;  /* invoke _cffi_initialize_python() only once,
+                        but don't set '_cffi_call_python' right now,
+                        otherwise concurrent threads won't call
+                        this function at all (we need them to wait) */
+        if (_cffi_initialize_python() == 0) {
+            /* now initialization is finished.  Switch to the fast-path. */
+
+            /* We would like nobody to see the new value of
+               '_cffi_call_python' without also seeing the rest of the
+               data initialized.  However, this is not possible.  But
+               the new value of '_cffi_call_python' is the function
+               'cffi_call_python()' from _cffi_backend.  So:  */
+            cffi_write_barrier();
+            /* ^^^ we put a write barrier here, and a corresponding
+               read barrier at the start of cffi_call_python().  This
+               ensures that after that read barrier, we see everything
+               done here before the write barrier.
+            */
+
+            assert(_cffi_call_python_org != NULL);
+            _cffi_call_python = (_cffi_call_python_fnptr)_cffi_call_python_org;
+        }
+        else {
+            /* initialization failed.  Reset this to NULL, even if it was
+               already set to some other value.  Future calls to
+               _cffi_start_python() are still forced to occur, and will
+               always return NULL from now on. */
+            _cffi_call_python_org = NULL;
+        }
+    }
+
+    _cffi_release_reentrant_mutex();
+
+    return (_cffi_call_python_fnptr)_cffi_call_python_org;
+}
+
+static
+void _cffi_start_and_call_python(struct _cffi_externpy_s *externpy, char *args)
+{
+    _cffi_call_python_fnptr fnptr;
+    int current_err = errno;
+#ifdef _MSC_VER
+    int current_lasterr = GetLastError();
+#endif
+    fnptr = _cffi_start_python();
+    if (fnptr == NULL) {
+        fprintf(stderr, "function %s() called, but initialization code "
+                        "failed.  Returning 0.\n", externpy->name);
+        memset(args, 0, externpy->size_of_result);
+    }
+#ifdef _MSC_VER
+    SetLastError(current_lasterr);
+#endif
+    errno = current_err;
+
+    if (fnptr != NULL)
+        fnptr(externpy, args);
+}
+
+
+/* The cffi_start_python() function makes sure Python is initialized
+   and our cffi module is set up.  It can be called manually from the
+   user C code.  The same effect is obtained automatically from any
+   dll-exported ``extern "Python"`` function.  This function returns
+   -1 if initialization failed, 0 if all is OK.  */
+_CFFI_UNUSED_FN
+static int cffi_start_python(void)
+{
+    if (_cffi_call_python == &_cffi_start_and_call_python) {
+        if (_cffi_start_python() == NULL)
+            return -1;
+    }
+    cffi_read_barrier();
+    return 0;
+}
+
+#undef cffi_compare_and_swap
+#undef cffi_write_barrier
+#undef cffi_read_barrier
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/shared/package-files/40/404c429c1486f845dd54a9e80e426334593a91b5f4143786791e74c2d2c78aca/test_numpy_pickle.py b/shared/package-files/40/404c429c1486f845dd54a9e80e426334593a91b5f4143786791e74c2d2c78aca/test_numpy_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed320497b42eec7a5d1c09c530ff33b28d944653
--- /dev/null
+++ b/shared/package-files/40/404c429c1486f845dd54a9e80e426334593a91b5f4143786791e74c2d2c78aca/test_numpy_pickle.py
@@ -0,0 +1,1225 @@
+"""Test the numpy pickler as a replacement of the standard pickler."""
+
+import bz2
+import copy
+import gzip
+import io
+import mmap
+import os
+import pickle
+import random
+import re
+import socket
+import sys
+import warnings
+import zlib
+from contextlib import closing
+from pathlib import Path
+
+try:
+    import lzma
+except ImportError:
+    lzma = None
+
+import pytest
+
+# numpy_pickle is not a drop-in replacement of pickle, as it takes
+# filenames instead of open files as arguments.
+from joblib import numpy_pickle, register_compressor
+from joblib.compressor import (
+    _COMPRESSORS,
+    _LZ4_PREFIX,
+    LZ4_NOT_INSTALLED_ERROR,
+    BinaryZlibFile,
+    CompressorWrapper,
+)
+from joblib.numpy_pickle_utils import (
+    _IO_BUFFER_SIZE,
+    _detect_compressor,
+    _ensure_native_byte_order,
+    _is_numpy_array_byte_order_mismatch,
+)
+from joblib.test import data
+from joblib.test.common import (
+    memory_used,
+    np,
+    with_lz4,
+    with_memory_profiler,
+    with_numpy,
+    without_lz4,
+)
+from joblib.testing import parametrize, raises, warns
+
+###############################################################################
+# Define a list of standard types.
+# Borrowed from dill, initial author: Micheal McKerns:
+# http://dev.danse.us/trac/pathos/browser/dill/dill_test2.py
+
+typelist = []
+
+# testing types
+_none = None
+typelist.append(_none)
+_type = type
+typelist.append(_type)
+_bool = bool(1)
+typelist.append(_bool)
+_int = int(1)
+typelist.append(_int)
+_float = float(1)
+typelist.append(_float)
+_complex = complex(1)
+typelist.append(_complex)
+_string = str(1)
+typelist.append(_string)
+_tuple = ()
+typelist.append(_tuple)
+_list = []
+typelist.append(_list)
+_dict = {}
+typelist.append(_dict)
+_builtin = len
+typelist.append(_builtin)
+
+
+def _function(x):
+    yield x
+
+
+class _class:
+    def _method(self):
+        pass
+
+
+class _newclass(object):
+    def _method(self):
+        pass
+
+
+typelist.append(_function)
+typelist.append(_class)
+typelist.append(_newclass)  # <type 'type'>
+_instance = _class()
+typelist.append(_instance)
+_object = _newclass()
+typelist.append(_object)  # <type 'class'>
+
+
+###############################################################################
+# Tests
+
+
+@parametrize("compress", [0, 1])
+@parametrize("member", typelist)
+def test_standard_types(tmpdir, compress, member):
+    # Test pickling and saving with standard types.
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump(member, filename, compress=compress)
+    _member = numpy_pickle.load(filename)
+    # We compare the pickled instance to the reloaded one only if it
+    # can be compared to a copied one
+    if member == copy.deepcopy(member):
+        assert member == _member
+
+
+def test_value_error():
+    # Test inverting the input arguments to dump
+    with raises(ValueError):
+        numpy_pickle.dump("foo", dict())
+
+
+@parametrize("wrong_compress", [-1, 10, dict()])
+def test_compress_level_error(wrong_compress):
+    # Verify that passing an invalid compress argument raises an error.
+    exception_msg = 'Non valid compress level given: "{0}"'.format(wrong_compress)
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump("dummy", "foo", compress=wrong_compress)
+    excinfo.match(exception_msg)
+
+
+@with_numpy
+@parametrize("compress", [False, True, 0, 3, "zlib"])
+def test_numpy_persistence(tmpdir, compress):
+    filename = tmpdir.join("test.pkl").strpath
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample((10, 2))
+    # We use 'a.T' to have a non C-contiguous array.
+    for index, obj in enumerate(((a,), (a.T,), (a, a), [a, a, a])):
+        filenames = numpy_pickle.dump(obj, filename, compress=compress)
+
+        # All is cached in one file
+        assert len(filenames) == 1
+        # Check that only one file was created
+        assert filenames[0] == filename
+        # Check that this file does exist
+        assert os.path.exists(filenames[0])
+
+        # Unpickle the object
+        obj_ = numpy_pickle.load(filename)
+        # Check that the items are indeed arrays
+        for item in obj_:
+            assert isinstance(item, np.ndarray)
+        # And finally, check that all the values are equal.
+        np.testing.assert_array_equal(np.array(obj), np.array(obj_))
+
+    # Now test with an array subclass
+    obj = np.memmap(filename + "mmap", mode="w+", shape=4, dtype=np.float64)
+    filenames = numpy_pickle.dump(obj, filename, compress=compress)
+    # All is cached in one file
+    assert len(filenames) == 1
+
+    obj_ = numpy_pickle.load(filename)
+    if type(obj) is not np.memmap and hasattr(obj, "__array_prepare__"):
+        # We don't reconstruct memmaps
+        assert isinstance(obj_, type(obj))
+
+    np.testing.assert_array_equal(obj_, obj)
+
+    # Test with an object containing multiple numpy arrays
+    obj = ComplexTestObject()
+    filenames = numpy_pickle.dump(obj, filename, compress=compress)
+    # All is cached in one file
+    assert len(filenames) == 1
+
+    obj_loaded = numpy_pickle.load(filename)
+    assert isinstance(obj_loaded, type(obj))
+    np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
+    np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
+    np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)
+
+
+@with_numpy
+def test_numpy_persistence_bufferred_array_compression(tmpdir):
+    big_array = np.ones((_IO_BUFFER_SIZE + 100), dtype=np.uint8)
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump(big_array, filename, compress=True)
+    arr_reloaded = numpy_pickle.load(filename)
+
+    np.testing.assert_array_equal(big_array, arr_reloaded)
+
+
+@with_numpy
+def test_memmap_persistence(tmpdir):
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    filename = tmpdir.join("test1.pkl").strpath
+    numpy_pickle.dump(a, filename)
+    b = numpy_pickle.load(filename, mmap_mode="r")
+
+    assert isinstance(b, np.memmap)
+
+    # Test with an object containing multiple numpy arrays
+    filename = tmpdir.join("test2.pkl").strpath
+    obj = ComplexTestObject()
+    numpy_pickle.dump(obj, filename)
+    obj_loaded = numpy_pickle.load(filename, mmap_mode="r")
+    assert isinstance(obj_loaded, type(obj))
+    assert isinstance(obj_loaded.array_float, np.memmap)
+    assert not obj_loaded.array_float.flags.writeable
+    assert isinstance(obj_loaded.array_int, np.memmap)
+    assert not obj_loaded.array_int.flags.writeable
+    # Memory map not allowed for numpy object arrays
+    assert not isinstance(obj_loaded.array_obj, np.memmap)
+    np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
+    np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
+    np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)
+
+    # Test we can write in memmapped arrays
+    obj_loaded = numpy_pickle.load(filename, mmap_mode="r+")
+    assert obj_loaded.array_float.flags.writeable
+    obj_loaded.array_float[0:10] = 10.0
+    assert obj_loaded.array_int.flags.writeable
+    obj_loaded.array_int[0:10] = 10
+
+    obj_reloaded = numpy_pickle.load(filename, mmap_mode="r")
+    np.testing.assert_array_equal(obj_reloaded.array_float, obj_loaded.array_float)
+    np.testing.assert_array_equal(obj_reloaded.array_int, obj_loaded.array_int)
+
+    # Test w+ mode is caught and the mode has switched to r+
+    numpy_pickle.load(filename, mmap_mode="w+")
+    assert obj_loaded.array_int.flags.writeable
+    assert obj_loaded.array_int.mode == "r+"
+    assert obj_loaded.array_float.flags.writeable
+    assert obj_loaded.array_float.mode == "r+"
+
+
+@with_numpy
+def test_memmap_persistence_mixed_dtypes(tmpdir):
+    # loading datastructures that have sub-arrays with dtype=object
+    # should not prevent memmapping on fixed size dtype sub-arrays.
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    b = np.array([1, "b"], dtype=object)
+    construct = (a, b)
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump(construct, filename)
+    a_clone, b_clone = numpy_pickle.load(filename, mmap_mode="r")
+
+    # the floating point array has been memory mapped
+    assert isinstance(a_clone, np.memmap)
+
+    # the object-dtype array has been loaded in memory
+    assert not isinstance(b_clone, np.memmap)
+
+
+@with_numpy
+def test_masked_array_persistence(tmpdir):
+    # The special-case picker fails, because saving masked_array
+    # not implemented, but it just delegates to the standard pickler.
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    a = np.ma.masked_greater(a, 0.5)
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump(a, filename)
+    b = numpy_pickle.load(filename, mmap_mode="r")
+    assert isinstance(b, np.ma.masked_array)
+
+
+@with_numpy
+def test_compress_mmap_mode_warning(tmpdir):
+    # Test the warning in case of compress + mmap_mode
+    rnd = np.random.RandomState(0)
+    obj = rnd.random_sample(10)
+    this_filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump(obj, this_filename, compress=1)
+    with warns(UserWarning) as warninfo:
+        reloaded_obj = numpy_pickle.load(this_filename, mmap_mode="r+")
+    debug_msg = "\n".join([str(w) for w in warninfo])
+    warninfo = [w.message for w in warninfo]
+    assert not isinstance(reloaded_obj, np.memmap)
+    np.testing.assert_array_equal(obj, reloaded_obj)
+    assert len(warninfo) == 1, debug_msg
+    assert (
+        str(warninfo[0]) == 'mmap_mode "r+" is not compatible with compressed '
+        f'file {this_filename}. "r+" flag will be ignored.'
+    )
+
+
+@with_numpy
+@with_memory_profiler
+@parametrize("compress", [True, False])
+def test_memory_usage(tmpdir, compress):
+    # Verify memory stays within expected bounds.
+    filename = tmpdir.join("test.pkl").strpath
+    small_array = np.ones((10, 10))
+    big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
+
+    for obj in (small_array, big_array):
+        size = obj.nbytes / 1e6
+        obj_filename = filename + str(np.random.randint(0, 1000))
+        mem_used = memory_used(numpy_pickle.dump, obj, obj_filename, compress=compress)
+
+        # The memory used to dump the object shouldn't exceed the buffer
+        # size used to write array chunks (16MB).
+        write_buf_size = _IO_BUFFER_SIZE + 16 * 1024**2 / 1e6
+        assert mem_used <= write_buf_size
+
+        mem_used = memory_used(numpy_pickle.load, obj_filename)
+        # memory used should be less than array size + buffer size used to
+        # read the array chunk by chunk.
+        read_buf_size = 32 + _IO_BUFFER_SIZE  # MiB
+        assert mem_used < size + read_buf_size
+
+
+@with_numpy
+def test_compressed_pickle_dump_and_load(tmpdir):
+    expected_list = [
+        np.arange(5, dtype=np.dtype("<i8")),
+        np.arange(5, dtype=np.dtype(">i8")),
+        np.arange(5, dtype=np.dtype("<f8")),
+        np.arange(5, dtype=np.dtype(">f8")),
+        np.array([1, "abc", {"a": 1, "b": 2}], dtype="O"),
+        np.arange(256, dtype=np.uint8).tobytes(),
+        "C'est l'\xe9t\xe9 !",
+    ]
+
+    fname = tmpdir.join("temp.pkl.gz").strpath
+
+    dumped_filenames = numpy_pickle.dump(expected_list, fname, compress=1)
+    assert len(dumped_filenames) == 1
+    result_list = numpy_pickle.load(fname)
+    for result, expected in zip(result_list, expected_list):
+        if isinstance(expected, np.ndarray):
+            expected = _ensure_native_byte_order(expected)
+            assert result.dtype == expected.dtype
+            np.testing.assert_equal(result, expected)
+        else:
+            assert result == expected
+
+
+@with_numpy
+def test_memmap_load(tmpdir):
+    little_endian_dtype = np.dtype("<i8")
+    big_endian_dtype = np.dtype(">i8")
+    all_dtypes = (little_endian_dtype, big_endian_dtype)
+
+    le_array = np.arange(5, dtype=little_endian_dtype)
+    be_array = np.arange(5, dtype=big_endian_dtype)
+
+    fname = tmpdir.join("temp.pkl").strpath
+
+    numpy_pickle.dump([le_array, be_array], fname)
+
+    le_array_native_load, be_array_native_load = numpy_pickle.load(
+        fname, ensure_native_byte_order=True
+    )
+
+    assert le_array_native_load.dtype == be_array_native_load.dtype
+    assert le_array_native_load.dtype in all_dtypes
+
+    le_array_nonnative_load, be_array_nonnative_load = numpy_pickle.load(
+        fname, ensure_native_byte_order=False
+    )
+
+    assert le_array_nonnative_load.dtype == le_array.dtype
+    assert be_array_nonnative_load.dtype == be_array.dtype
+
+
+def test_invalid_parameters_raise():
+    expected_msg = (
+        "Native byte ordering can only be enforced if 'mmap_mode' parameter "
+        "is set to None, but got 'mmap_mode=r+' instead."
+    )
+
+    with raises(ValueError, match=re.escape(expected_msg)):
+        numpy_pickle.load(
+            "/path/to/some/dump.pkl", ensure_native_byte_order=True, mmap_mode="r+"
+        )
+
+
+def _check_pickle(filename, expected_list, mmap_mode=None):
+    """Helper function to test joblib pickle content.
+
+    Note: currently only pickles containing an iterable are supported
+    by this function.
+    """
+    version_match = re.match(r".+py(\d)(\d).+", filename)
+    py_version_used_for_writing = int(version_match.group(1))
+
+    py_version_to_default_pickle_protocol = {2: 2, 3: 3}
+    pickle_reading_protocol = py_version_to_default_pickle_protocol.get(3, 4)
+    pickle_writing_protocol = py_version_to_default_pickle_protocol.get(
+        py_version_used_for_writing, 4
+    )
+    if pickle_reading_protocol >= pickle_writing_protocol:
+        try:
+            with warnings.catch_warnings(record=True) as warninfo:
+                warnings.simplefilter("always")
+                result_list = numpy_pickle.load(filename, mmap_mode=mmap_mode)
+            filename_base = os.path.basename(filename)
+            expected_nb_deprecation_warnings = (
+                1 if ("_0.9" in filename_base or "_0.8.4" in filename_base) else 0
+            )
+
+            expected_nb_user_warnings = (
+                3
+                if (re.search("_0.1.+.pkl$", filename_base) and mmap_mode is not None)
+                else 0
+            )
+            expected_nb_warnings = (
+                expected_nb_deprecation_warnings + expected_nb_user_warnings
+            )
+            assert len(warninfo) == expected_nb_warnings, (
+                "Did not get the expected number of warnings. Expected "
+                f"{expected_nb_warnings} but got warnings: "
+                f"{[w.message for w in warninfo]}"
+            )
+
+            deprecation_warnings = [
+                w for w in warninfo if issubclass(w.category, DeprecationWarning)
+            ]
+            user_warnings = [w for w in warninfo if issubclass(w.category, UserWarning)]
+            for w in deprecation_warnings:
+                assert (
+                    str(w.message)
+                    == "The file '{0}' has been generated with a joblib "
+                    "version less than 0.10. Please regenerate this "
+                    "pickle file.".format(filename)
+                )
+
+            for w in user_warnings:
+                escaped_filename = re.escape(filename)
+                assert re.search(
+                    f"memmapped.+{escaped_filename}.+segmentation fault", str(w.message)
+                )
+
+            for result, expected in zip(result_list, expected_list):
+                if isinstance(expected, np.ndarray):
+                    expected = _ensure_native_byte_order(expected)
+                    assert result.dtype == expected.dtype
+                    np.testing.assert_equal(result, expected)
+                else:
+                    assert result == expected
+        except Exception as exc:
+            # When trying to read with python 3 a pickle generated
+            # with python 2 we expect a user-friendly error
+            if py_version_used_for_writing == 2:
+                assert isinstance(exc, ValueError)
+                message = (
+                    "You may be trying to read with "
+                    "python 3 a joblib pickle generated with python 2."
+                )
+                assert message in str(exc)
+            elif filename.endswith(".lz4") and with_lz4.args[0]:
+                assert isinstance(exc, ValueError)
+                assert LZ4_NOT_INSTALLED_ERROR in str(exc)
+            else:
+                raise
+    else:
+        # Pickle protocol used for writing is too high. We expect a
+        # "unsupported pickle protocol" error message
+        try:
+            numpy_pickle.load(filename)
+            raise AssertionError(
+                "Numpy pickle loading should have raised a ValueError exception"
+            )
+        except ValueError as e:
+            message = "unsupported pickle protocol: {0}".format(pickle_writing_protocol)
+            assert message in str(e.args)
+
+
+@with_numpy
+def test_joblib_pickle_across_python_versions():
+    # We need to be specific about dtypes in particular endianness
+    # because the pickles can be generated on one architecture and
+    # the tests run on another one. See
+    # https://github.com/joblib/joblib/issues/279.
+    expected_list = [
+        np.arange(5, dtype=np.dtype("<i8")),
+        np.arange(5, dtype=np.dtype("<f8")),
+        np.array([1, "abc", {"a": 1, "b": 2}], dtype="O"),
+        np.arange(256, dtype=np.uint8).tobytes(),
+        # np.matrix is a subclass of np.ndarray, here we want
+        # to verify this type of object is correctly unpickled
+        # among versions.
+        np.matrix([0, 1, 2], dtype=np.dtype("<i8")),
+        "C'est l'\xe9t\xe9 !",
+    ]
+
+    # Testing all the compressed and non compressed
+    # pickles in joblib/test/data. These pickles were generated by
+    # the joblib/test/data/create_numpy_pickle.py script for the
+    # relevant python, joblib and numpy versions.
+    test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
+
+    pickle_extensions = (".pkl", ".gz", ".gzip", ".bz2", "lz4")
+    if lzma is not None:
+        pickle_extensions += (".xz", ".lzma")
+    pickle_filenames = [
+        os.path.join(test_data_dir, fn)
+        for fn in os.listdir(test_data_dir)
+        if any(fn.endswith(ext) for ext in pickle_extensions)
+    ]
+
+    for fname in pickle_filenames:
+        _check_pickle(fname, expected_list)
+
+
+@with_numpy
+def test_joblib_pickle_across_python_versions_with_mmap():
+    expected_list = [
+        np.arange(5, dtype=np.dtype("<i8")),
+        np.arange(5, dtype=np.dtype("<f8")),
+        np.array([1, "abc", {"a": 1, "b": 2}], dtype="O"),
+        np.arange(256, dtype=np.uint8).tobytes(),
+        # np.matrix is a subclass of np.ndarray, here we want
+        # to verify this type of object is correctly unpickled
+        # among versions.
+        np.matrix([0, 1, 2], dtype=np.dtype("<i8")),
+        "C'est l'\xe9t\xe9 !",
+    ]
+
+    test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
+
+    pickle_filenames = [
+        os.path.join(test_data_dir, fn)
+        for fn in os.listdir(test_data_dir)
+        if fn.endswith(".pkl")
+    ]
+    for fname in pickle_filenames:
+        _check_pickle(fname, expected_list, mmap_mode="r")
+
+
+@with_numpy
+def test_numpy_array_byte_order_mismatch_detection():
+    # List of numpy arrays with big endian byteorder.
+    be_arrays = [
+        np.array([(1, 2.0), (3, 4.0)], dtype=[("", ">i8"), ("", ">f8")]),
+        np.arange(3, dtype=np.dtype(">i8")),
+        np.arange(3, dtype=np.dtype(">f8")),
+    ]
+
+    # Verify the byteorder mismatch is correctly detected.
+    for array in be_arrays:
+        if sys.byteorder == "big":
+            assert not _is_numpy_array_byte_order_mismatch(array)
+        else:
+            assert _is_numpy_array_byte_order_mismatch(array)
+        converted = _ensure_native_byte_order(array)
+        if converted.dtype.fields:
+            for f in converted.dtype.fields.values():
+                f[0].byteorder == "="
+        else:
+            assert converted.dtype.byteorder == "="
+
+    # List of numpy arrays with little endian byteorder.
+    le_arrays = [
+        np.array([(1, 2.0), (3, 4.0)], dtype=[("", "<i8"), ("", "<f8")]),
+        np.arange(3, dtype=np.dtype("<i8")),
+        np.arange(3, dtype=np.dtype("<f8")),
+    ]
+
+    # Verify the byteorder mismatch is correctly detected.
+    for array in le_arrays:
+        if sys.byteorder == "little":
+            assert not _is_numpy_array_byte_order_mismatch(array)
+        else:
+            assert _is_numpy_array_byte_order_mismatch(array)
+        converted = _ensure_native_byte_order(array)
+        if converted.dtype.fields:
+            for f in converted.dtype.fields.values():
+                f[0].byteorder == "="
+        else:
+            assert converted.dtype.byteorder == "="
+
+
+@parametrize("compress_tuple", [("zlib", 3), ("gzip", 3)])
+def test_compress_tuple_argument(tmpdir, compress_tuple):
+    # Verify the tuple is correctly taken into account.
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump("dummy", filename, compress=compress_tuple)
+    # Verify the file contains the right magic number
+    with open(filename, "rb") as f:
+        assert _detect_compressor(f) == compress_tuple[0]
+
+
+@parametrize(
+    "compress_tuple,message",
+    [
+        (
+            ("zlib", 3, "extra"),  # wrong compress tuple
+            "Compress argument tuple should contain exactly 2 elements",
+        ),
+        (
+            ("wrong", 3),  # wrong compress method
+            'Non valid compression method given: "{}"'.format("wrong"),
+        ),
+        (
+            ("zlib", "wrong"),  # wrong compress level
+            'Non valid compress level given: "{}"'.format("wrong"),
+        ),
+    ],
+)
+def test_compress_tuple_argument_exception(tmpdir, compress_tuple, message):
+    filename = tmpdir.join("test.pkl").strpath
+    # Verify setting a wrong compress tuple raises a ValueError.
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump("dummy", filename, compress=compress_tuple)
+    excinfo.match(message)
+
+
+@parametrize("compress_string", ["zlib", "gzip"])
+def test_compress_string_argument(tmpdir, compress_string):
+    # Verify the string is correctly taken into account.
+    filename = tmpdir.join("test.pkl").strpath
+    numpy_pickle.dump("dummy", filename, compress=compress_string)
+    # Verify the file contains the right magic number
+    with open(filename, "rb") as f:
+        assert _detect_compressor(f) == compress_string
+
+
+@with_numpy
+@parametrize("compress", [1, 3, 6])
+@parametrize("cmethod", _COMPRESSORS)
+def test_joblib_compression_formats(tmpdir, compress, cmethod):
+    filename = tmpdir.join("test.pkl").strpath
+    objects = (
+        np.ones(shape=(100, 100), dtype="f8"),
+        range(10),
+        {"a": 1, 2: "b"},
+        [],
+        (),
+        {},
+        0,
+        1.0,
+    )
+
+    if cmethod in ("lzma", "xz") and lzma is None:
+        pytest.skip("lzma is support not available")
+
+    elif cmethod == "lz4" and with_lz4.args[0]:
+        # Skip the test if lz4 is not installed. We here use the with_lz4
+        # skipif fixture whose argument is True when lz4 is not installed
+        pytest.skip("lz4 is not installed.")
+
+    dump_filename = filename + "." + cmethod
+    for obj in objects:
+        numpy_pickle.dump(obj, dump_filename, compress=(cmethod, compress))
+        # Verify the file contains the right magic number
+        with open(dump_filename, "rb") as f:
+            assert _detect_compressor(f) == cmethod
+        # Verify the reloaded object is correct
+        obj_reloaded = numpy_pickle.load(dump_filename)
+        assert isinstance(obj_reloaded, type(obj))
+        if isinstance(obj, np.ndarray):
+            np.testing.assert_array_equal(obj_reloaded, obj)
+        else:
+            assert obj_reloaded == obj
+
+
+def _gzip_file_decompress(source_filename, target_filename):
+    """Decompress a gzip file."""
+    with closing(gzip.GzipFile(source_filename, "rb")) as fo:
+        buf = fo.read()
+
+    with open(target_filename, "wb") as fo:
+        fo.write(buf)
+
+
+def _zlib_file_decompress(source_filename, target_filename):
+    """Decompress a zlib file."""
+    with open(source_filename, "rb") as fo:
+        buf = zlib.decompress(fo.read())
+
+    with open(target_filename, "wb") as fo:
+        fo.write(buf)
+
+
+@parametrize(
+    "extension,decompress",
+    [(".z", _zlib_file_decompress), (".gz", _gzip_file_decompress)],
+)
+def test_load_externally_decompressed_files(tmpdir, extension, decompress):
+    # Test that BinaryZlibFile generates valid gzip and zlib compressed files.
+    obj = "a string to persist"
+    filename_raw = tmpdir.join("test.pkl").strpath
+
+    filename_compressed = filename_raw + extension
+    # Use automatic extension detection to compress with the right method.
+    numpy_pickle.dump(obj, filename_compressed)
+
+    # Decompress with the corresponding method
+    decompress(filename_compressed, filename_raw)
+
+    # Test that the uncompressed pickle can be loaded and
+    # that the result is correct.
+    obj_reloaded = numpy_pickle.load(filename_raw)
+    assert obj == obj_reloaded
+
+
+@parametrize(
+    "extension,cmethod",
+    # valid compressor extensions
+    [
+        (".z", "zlib"),
+        (".gz", "gzip"),
+        (".bz2", "bz2"),
+        (".lzma", "lzma"),
+        (".xz", "xz"),
+        # invalid compressor extensions
+        (".pkl", "not-compressed"),
+        ("", "not-compressed"),
+    ],
+)
+def test_compression_using_file_extension(tmpdir, extension, cmethod):
+    if cmethod in ("lzma", "xz") and lzma is None:
+        pytest.skip("lzma is missing")
+    # test that compression method corresponds to the given filename extension.
+    filename = tmpdir.join("test.pkl").strpath
+    obj = "object to dump"
+
+    dump_fname = filename + extension
+    numpy_pickle.dump(obj, dump_fname)
+    # Verify the file contains the right magic number
+    with open(dump_fname, "rb") as f:
+        assert _detect_compressor(f) == cmethod
+    # Verify the reloaded object is correct
+    obj_reloaded = numpy_pickle.load(dump_fname)
+    assert isinstance(obj_reloaded, type(obj))
+    assert obj_reloaded == obj
+
+
+@with_numpy
+def test_file_handle_persistence(tmpdir):
+    objs = [np.random.random((10, 10)), "some data"]
+    fobjs = [bz2.BZ2File, gzip.GzipFile]
+    if lzma is not None:
+        fobjs += [lzma.LZMAFile]
+    filename = tmpdir.join("test.pkl").strpath
+
+    for obj in objs:
+        for fobj in fobjs:
+            with fobj(filename, "wb") as f:
+                numpy_pickle.dump(obj, f)
+
+            # using the same decompressor prevents from internally
+            # decompress again.
+            with fobj(filename, "rb") as f:
+                obj_reloaded = numpy_pickle.load(f)
+
+            # when needed, the correct decompressor should be used when
+            # passing a raw file handle.
+            with open(filename, "rb") as f:
+                obj_reloaded_2 = numpy_pickle.load(f)
+
+            if isinstance(obj, np.ndarray):
+                np.testing.assert_array_equal(obj_reloaded, obj)
+                np.testing.assert_array_equal(obj_reloaded_2, obj)
+            else:
+                assert obj_reloaded == obj
+                assert obj_reloaded_2 == obj
+
+
+@with_numpy
+def test_in_memory_persistence():
+    objs = [np.random.random((10, 10)), "some data"]
+    for obj in objs:
+        f = io.BytesIO()
+        numpy_pickle.dump(obj, f)
+        obj_reloaded = numpy_pickle.load(f)
+        if isinstance(obj, np.ndarray):
+            np.testing.assert_array_equal(obj_reloaded, obj)
+        else:
+            assert obj_reloaded == obj
+
+
+@with_numpy
+def test_file_handle_persistence_mmap(tmpdir):
+    obj = np.random.random((10, 10))
+    filename = tmpdir.join("test.pkl").strpath
+
+    with open(filename, "wb") as f:
+        numpy_pickle.dump(obj, f)
+
+    with open(filename, "rb") as f:
+        obj_reloaded = numpy_pickle.load(f, mmap_mode="r+")
+
+    np.testing.assert_array_equal(obj_reloaded, obj)
+
+
+@with_numpy
+def test_file_handle_persistence_compressed_mmap(tmpdir):
+    obj = np.random.random((10, 10))
+    filename = tmpdir.join("test.pkl").strpath
+
+    with open(filename, "wb") as f:
+        numpy_pickle.dump(obj, f, compress=("gzip", 3))
+
+    with closing(gzip.GzipFile(filename, "rb")) as f:
+        with warns(UserWarning) as warninfo:
+            numpy_pickle.load(f, mmap_mode="r+")
+        assert len(warninfo) == 1
+        assert (
+            str(warninfo[0].message)
+            == '"%(fileobj)r" is not a raw file, mmap_mode "%(mmap_mode)s" '
+            "flag will be ignored." % {"fileobj": f, "mmap_mode": "r+"}
+        )
+
+
+@with_numpy
+def test_file_handle_persistence_in_memory_mmap():
+    obj = np.random.random((10, 10))
+    buf = io.BytesIO()
+
+    numpy_pickle.dump(obj, buf)
+
+    with warns(UserWarning) as warninfo:
+        numpy_pickle.load(buf, mmap_mode="r+")
+    assert len(warninfo) == 1
+    assert (
+        str(warninfo[0].message)
+        == "In memory persistence is not compatible with mmap_mode "
+        '"%(mmap_mode)s" flag passed. mmap_mode option will be '
+        "ignored." % {"mmap_mode": "r+"}
+    )
+
+
+@parametrize(
+    "data",
+    [
+        b"a little data as bytes.",
+        # More bytes
+        10000 * "{}".format(random.randint(0, 1000) * 1000).encode("latin-1"),
+    ],
+    ids=["a little data as bytes.", "a large data as bytes."],
+)
+@parametrize("compress_level", [1, 3, 9])
+def test_binary_zlibfile(tmpdir, data, compress_level):
+    filename = tmpdir.join("test.pkl").strpath
+    # Regular cases
+    with open(filename, "wb") as f:
+        with BinaryZlibFile(f, "wb", compresslevel=compress_level) as fz:
+            assert fz.writable()
+            fz.write(data)
+            assert fz.fileno() == f.fileno()
+            with raises(io.UnsupportedOperation):
+                fz._check_can_read()
+
+            with raises(io.UnsupportedOperation):
+                fz._check_can_seek()
+        assert fz.closed
+        with raises(ValueError):
+            fz._check_not_closed()
+
+    with open(filename, "rb") as f:
+        with BinaryZlibFile(f) as fz:
+            assert fz.readable()
+            assert fz.seekable()
+            assert fz.fileno() == f.fileno()
+            assert fz.read() == data
+            with raises(io.UnsupportedOperation):
+                fz._check_can_write()
+            assert fz.seekable()
+            fz.seek(0)
+            assert fz.tell() == 0
+        assert fz.closed
+
+    # Test with a filename as input
+    with BinaryZlibFile(filename, "wb", compresslevel=compress_level) as fz:
+        assert fz.writable()
+        fz.write(data)
+
+    with BinaryZlibFile(filename, "rb") as fz:
+        assert fz.read() == data
+        assert fz.seekable()
+
+    # Test without context manager
+    fz = BinaryZlibFile(filename, "wb", compresslevel=compress_level)
+    assert fz.writable()
+    fz.write(data)
+    fz.close()
+
+    fz = BinaryZlibFile(filename, "rb")
+    assert fz.read() == data
+    fz.close()
+
+
+@parametrize("bad_value", [-1, 10, 15, "a", (), {}])
+def test_binary_zlibfile_bad_compression_levels(tmpdir, bad_value):
+    filename = tmpdir.join("test.pkl").strpath
+    with raises(ValueError) as excinfo:
+        BinaryZlibFile(filename, "wb", compresslevel=bad_value)
+    pattern = re.escape(
+        "'compresslevel' must be an integer between 1 and 9. "
+        "You provided 'compresslevel={}'".format(bad_value)
+    )
+    excinfo.match(pattern)
+
+
+@parametrize("bad_mode", ["a", "x", "r", "w", 1, 2])
+def test_binary_zlibfile_invalid_modes(tmpdir, bad_mode):
+    filename = tmpdir.join("test.pkl").strpath
+    with raises(ValueError) as excinfo:
+        BinaryZlibFile(filename, bad_mode)
+    excinfo.match("Invalid mode")
+
+
+@parametrize("bad_file", [1, (), {}])
+def test_binary_zlibfile_invalid_filename_type(bad_file):
+    with raises(TypeError) as excinfo:
+        BinaryZlibFile(bad_file, "rb")
+    excinfo.match("filename must be a str or bytes object, or a file")
+
+
+###############################################################################
+# Test dumping array subclasses
+if np is not None:
+
+    class SubArray(np.ndarray):
+        def __reduce__(self):
+            return _load_sub_array, (np.asarray(self),)
+
+    def _load_sub_array(arr):
+        d = SubArray(arr.shape)
+        d[:] = arr
+        return d
+
+    class ComplexTestObject:
+        """A complex object containing numpy arrays as attributes."""
+
+        def __init__(self):
+            self.array_float = np.arange(100, dtype="float64")
+            self.array_int = np.ones(100, dtype="int32")
+            self.array_obj = np.array(["a", 10, 20.0], dtype="object")
+
+
+@with_numpy
+def test_numpy_subclass(tmpdir):
+    filename = tmpdir.join("test.pkl").strpath
+    a = SubArray((10,))
+    numpy_pickle.dump(a, filename)
+    c = numpy_pickle.load(filename)
+    assert isinstance(c, SubArray)
+    np.testing.assert_array_equal(c, a)
+
+
+def test_pathlib(tmpdir):
+    filename = tmpdir.join("test.pkl").strpath
+    value = 123
+    numpy_pickle.dump(value, Path(filename))
+    assert numpy_pickle.load(filename) == value
+    numpy_pickle.dump(value, filename)
+    assert numpy_pickle.load(Path(filename)) == value
+
+
+@with_numpy
+def test_non_contiguous_array_pickling(tmpdir):
+    filename = tmpdir.join("test.pkl").strpath
+
+    for array in [  # Array that triggers a contiguousness issue with nditer,
+        # see https://github.com/joblib/joblib/pull/352 and see
+        # https://github.com/joblib/joblib/pull/353
+        np.asfortranarray([[1, 2], [3, 4]])[1:],
+        # Non contiguous array with works fine with nditer
+        np.ones((10, 50, 20), order="F")[:, :1, :],
+    ]:
+        assert not array.flags.c_contiguous
+        assert not array.flags.f_contiguous
+        numpy_pickle.dump(array, filename)
+        array_reloaded = numpy_pickle.load(filename)
+        np.testing.assert_array_equal(array_reloaded, array)
+
+
+@with_numpy
+def test_pickle_highest_protocol(tmpdir):
+    # ensure persistence of a numpy array is valid even when using
+    # the pickle HIGHEST_PROTOCOL.
+    # see https://github.com/joblib/joblib/issues/362
+
+    filename = tmpdir.join("test.pkl").strpath
+    test_array = np.zeros(10)
+
+    numpy_pickle.dump(test_array, filename, protocol=pickle.HIGHEST_PROTOCOL)
+    array_reloaded = numpy_pickle.load(filename)
+
+    np.testing.assert_array_equal(array_reloaded, test_array)
+
+
+@with_numpy
+def test_pickle_in_socket():
+    # test that joblib can pickle in sockets
+    test_array = np.arange(10)
+    _ADDR = ("localhost", 12345)
+    listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    listener.bind(_ADDR)
+    listener.listen(1)
+
+    with socket.create_connection(_ADDR) as client:
+        server, client_addr = listener.accept()
+
+        with server.makefile("wb") as sf:
+            numpy_pickle.dump(test_array, sf)
+
+        with client.makefile("rb") as cf:
+            array_reloaded = numpy_pickle.load(cf)
+
+        np.testing.assert_array_equal(array_reloaded, test_array)
+
+        # Check that a byte-aligned numpy array written in a file can be send
+        # over a socket and then read on the other side
+        bytes_to_send = io.BytesIO()
+        numpy_pickle.dump(test_array, bytes_to_send)
+        server.send(bytes_to_send.getvalue())
+
+        with client.makefile("rb") as cf:
+            array_reloaded = numpy_pickle.load(cf)
+
+        np.testing.assert_array_equal(array_reloaded, test_array)
+
+
+@with_numpy
+def test_load_memmap_with_big_offset(tmpdir):
+    # Test that numpy memmap offset is set correctly if greater than
+    # mmap.ALLOCATIONGRANULARITY, see
+    # https://github.com/joblib/joblib/issues/451 and
+    # https://github.com/numpy/numpy/pull/8443 for more details.
+    fname = tmpdir.join("test.mmap").strpath
+    size = mmap.ALLOCATIONGRANULARITY
+    obj = [np.zeros(size, dtype="uint8"), np.ones(size, dtype="uint8")]
+    numpy_pickle.dump(obj, fname)
+    memmaps = numpy_pickle.load(fname, mmap_mode="r")
+    assert isinstance(memmaps[1], np.memmap)
+    assert memmaps[1].offset > size
+    np.testing.assert_array_equal(obj, memmaps)
+
+
+def test_register_compressor(tmpdir):
+    # Check that registering compressor file works.
+    compressor_name = "test-name"
+    compressor_prefix = "test-prefix"
+
+    class BinaryCompressorTestFile(io.BufferedIOBase):
+        pass
+
+    class BinaryCompressorTestWrapper(CompressorWrapper):
+        def __init__(self):
+            CompressorWrapper.__init__(
+                self, obj=BinaryCompressorTestFile, prefix=compressor_prefix
+            )
+
+    register_compressor(compressor_name, BinaryCompressorTestWrapper())
+
+    assert _COMPRESSORS[compressor_name].fileobj_factory == BinaryCompressorTestFile
+    assert _COMPRESSORS[compressor_name].prefix == compressor_prefix
+
+    # Remove this dummy compressor file from extra compressors because other
+    # tests might fail because of this
+    _COMPRESSORS.pop(compressor_name)
+
+
+@parametrize("invalid_name", [1, (), {}])
+def test_register_compressor_invalid_name(invalid_name):
+    # Test that registering an invalid compressor name is not allowed.
+    with raises(ValueError) as excinfo:
+        register_compressor(invalid_name, None)
+    excinfo.match("Compressor name should be a string")
+
+
+def test_register_compressor_invalid_fileobj():
+    # Test that registering an invalid file object is not allowed.
+
+    class InvalidFileObject:
+        pass
+
+    class InvalidFileObjectWrapper(CompressorWrapper):
+        def __init__(self):
+            CompressorWrapper.__init__(self, obj=InvalidFileObject, prefix=b"prefix")
+
+    with raises(ValueError) as excinfo:
+        register_compressor("invalid", InvalidFileObjectWrapper())
+
+    excinfo.match(
+        "Compressor 'fileobj_factory' attribute should implement "
+        "the file object interface"
+    )
+
+
+class AnotherZlibCompressorWrapper(CompressorWrapper):
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=BinaryZlibFile, prefix=b"prefix")
+
+
+class StandardLibGzipCompressorWrapper(CompressorWrapper):
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=gzip.GzipFile, prefix=b"prefix")
+
+
+def test_register_compressor_already_registered():
+    # Test registration of existing compressor files.
+    compressor_name = "test-name"
+
+    # register a test compressor
+    register_compressor(compressor_name, AnotherZlibCompressorWrapper())
+
+    with raises(ValueError) as excinfo:
+        register_compressor(compressor_name, StandardLibGzipCompressorWrapper())
+    excinfo.match("Compressor '{}' already registered.".format(compressor_name))
+
+    register_compressor(compressor_name, StandardLibGzipCompressorWrapper(), force=True)
+
+    assert compressor_name in _COMPRESSORS
+    assert _COMPRESSORS[compressor_name].fileobj_factory == gzip.GzipFile
+
+    # Remove this dummy compressor file from extra compressors because other
+    # tests might fail because of this
+    _COMPRESSORS.pop(compressor_name)
+
+
+@with_lz4
+def test_lz4_compression(tmpdir):
+    # Check that lz4 can be used when dependency is available.
+    import lz4.frame
+
+    compressor = "lz4"
+    assert compressor in _COMPRESSORS
+    assert _COMPRESSORS[compressor].fileobj_factory == lz4.frame.LZ4FrameFile
+
+    fname = tmpdir.join("test.pkl").strpath
+    data = "test data"
+    numpy_pickle.dump(data, fname, compress=compressor)
+
+    with open(fname, "rb") as f:
+        assert f.read(len(_LZ4_PREFIX)) == _LZ4_PREFIX
+    assert numpy_pickle.load(fname) == data
+
+    # Test that LZ4 is applied based on file extension
+    numpy_pickle.dump(data, fname + ".lz4")
+    with open(fname, "rb") as f:
+        assert f.read(len(_LZ4_PREFIX)) == _LZ4_PREFIX
+    assert numpy_pickle.load(fname) == data
+
+
+@without_lz4
+def test_lz4_compression_without_lz4(tmpdir):
+    # Check that lz4 cannot be used when dependency is not available.
+    fname = tmpdir.join("test.nolz4").strpath
+    data = "test data"
+    msg = LZ4_NOT_INSTALLED_ERROR
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump(data, fname, compress="lz4")
+    excinfo.match(msg)
+
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump(data, fname + ".lz4")
+    excinfo.match(msg)
+
+
+protocols = [pickle.DEFAULT_PROTOCOL]
+if pickle.HIGHEST_PROTOCOL != pickle.DEFAULT_PROTOCOL:
+    protocols.append(pickle.HIGHEST_PROTOCOL)
+
+
+@with_numpy
+@parametrize("protocol", protocols)
+def test_memmap_alignment_padding(tmpdir, protocol):
+    # Test that memmaped arrays returned by numpy.load are correctly aligned
+    fname = tmpdir.join("test.mmap").strpath
+
+    a = np.random.randn(2)
+    numpy_pickle.dump(a, fname, protocol=protocol)
+    memmap = numpy_pickle.load(fname, mmap_mode="r")
+    assert isinstance(memmap, np.memmap)
+    np.testing.assert_array_equal(a, memmap)
+    assert memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0
+    assert memmap.flags.aligned
+
+    array_list = [
+        np.random.randn(2),
+        np.random.randn(2),
+        np.random.randn(2),
+        np.random.randn(2),
+    ]
+
+    # On Windows OSError 22 if reusing the same path for memmap ...
+    fname = tmpdir.join("test1.mmap").strpath
+    numpy_pickle.dump(array_list, fname, protocol=protocol)
+    l_reloaded = numpy_pickle.load(fname, mmap_mode="r")
+
+    for idx, memmap in enumerate(l_reloaded):
+        assert isinstance(memmap, np.memmap)
+        np.testing.assert_array_equal(array_list[idx], memmap)
+        assert memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0
+        assert memmap.flags.aligned
+
+    array_dict = {
+        "a0": np.arange(2, dtype=np.uint8),
+        "a1": np.arange(3, dtype=np.uint8),
+        "a2": np.arange(5, dtype=np.uint8),
+        "a3": np.arange(7, dtype=np.uint8),
+        "a4": np.arange(11, dtype=np.uint8),
+        "a5": np.arange(13, dtype=np.uint8),
+        "a6": np.arange(17, dtype=np.uint8),
+        "a7": np.arange(19, dtype=np.uint8),
+        "a8": np.arange(23, dtype=np.uint8),
+    }
+
+    # On Windows OSError 22 if reusing the same path for memmap ...
+    fname = tmpdir.join("test2.mmap").strpath
+    numpy_pickle.dump(array_dict, fname, protocol=protocol)
+    d_reloaded = numpy_pickle.load(fname, mmap_mode="r")
+
+    for key, memmap in d_reloaded.items():
+        assert isinstance(memmap, np.memmap)
+        np.testing.assert_array_equal(array_dict[key], memmap)
+        assert memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0
+        assert memmap.flags.aligned
diff --git a/shared/package-files/40/404d22fa5344e291b67988c1da6674bb0ee33c3fbb4dd2fb63da7bdc9a16426a/test__init.py b/shared/package-files/40/404d22fa5344e291b67988c1da6674bb0ee33c3fbb4dd2fb63da7bdc9a16426a/test__init.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecbcce36df7cd37781dd45879f63f7d6f55e5567
--- /dev/null
+++ b/shared/package-files/40/404d22fa5344e291b67988c1da6674bb0ee33c3fbb4dd2fb63da7bdc9a16426a/test__init.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+def test_utils_namespace():
+    """Ensure objects are not unintentionally exposed in utils namespace."""
+    with pytest.raises(ImportError):
+        from networkx.utils import nx
+    with pytest.raises(ImportError):
+        from networkx.utils import sys
+    with pytest.raises(ImportError):
+        from networkx.utils import defaultdict, deque
diff --git a/shared/package-files/40/404dc1507bb0e8c20c58eb002106463b009020c772c8e7a8bda6c1114151f7a9/apl b/shared/package-files/40/404dc1507bb0e8c20c58eb002106463b009020c772c8e7a8bda6c1114151f7a9/apl
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diff --git a/shared/package-files/40/4050c683957d6f366ae7001463dd6585929875d4b9f9bf8a4d11721fe52b694f/smooth_l1_loss_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/40/4050c683957d6f366ae7001463dd6585929875d4b9f9bf8a4d11721fe52b694f/smooth_l1_loss_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e296027ffd18e14317ef76becc45261ef91a7e3
--- /dev/null
+++ b/shared/package-files/40/4050c683957d6f366ae7001463dd6585929875d4b9f9bf8a4d11721fe52b694f/smooth_l1_loss_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double beta=1.0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/40/40513297ec75275e1a0a780b249e42abf33dcf552c5b5c9f0d2b14d4fd68ffb2/stream.h b/shared/package-files/40/40513297ec75275e1a0a780b249e42abf33dcf552c5b5c9f0d2b14d4fd68ffb2/stream.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d30c9e2e0d08fcdff31cbb73b10899d1f1ca8db
--- /dev/null
+++ b/shared/package-files/40/40513297ec75275e1a0a780b249e42abf33dcf552c5b5c9f0d2b14d4fd68ffb2/stream.h
@@ -0,0 +1,63 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <torch/data/samplers/base.h>
+#include <torch/data/samplers/custom_batch_request.h>
+#include <torch/types.h>
+
+#include <cstddef>
+
+namespace torch {
+namespace serialize {
+class InputArchive;
+class OutputArchive;
+} // namespace serialize
+} // namespace torch
+
+namespace torch {
+namespace data {
+namespace samplers {
+
+/// A wrapper around a batch size value, which implements the
+/// `CustomBatchRequest` interface.
+struct TORCH_API BatchSize : public CustomBatchRequest {
+  explicit BatchSize(size_t size);
+  size_t size() const noexcept override;
+  operator size_t() const noexcept;
+  size_t size_;
+};
+
+/// A sampler for (potentially infinite) streams of data.
+///
+/// The major feature of the `StreamSampler` is that it does not return
+/// particular indices, but instead only the number of elements to fetch from
+/// the dataset. The dataset has to decide how to produce those elements.
+class TORCH_API StreamSampler : public Sampler<BatchSize> {
+ public:
+  /// Constructs the `StreamSampler` with the number of individual examples that
+  /// should be fetched until the sampler is exhausted.
+  explicit StreamSampler(size_t epoch_size);
+
+  /// Resets the internal state of the sampler.
+  void reset(optional<size_t> new_size = nullopt) override;
+
+  /// Returns a `BatchSize` object with the number of elements to fetch in the
+  /// next batch. This number is the minimum of the supplied `batch_size` and
+  /// the difference between the `epoch_size` and the current index. If the
+  /// `epoch_size` has been reached, returns an empty optional.
+  optional<BatchSize> next(size_t batch_size) override;
+
+  /// Serializes the `StreamSampler` to the `archive`.
+  void save(serialize::OutputArchive& archive) const override;
+
+  /// Deserializes the `StreamSampler` from the `archive`.
+  void load(serialize::InputArchive& archive) override;
+
+ private:
+  size_t examples_retrieved_so_far_ = 0;
+  size_t epoch_size_;
+};
+
+} // namespace samplers
+} // namespace data
+} // namespace torch
diff --git a/shared/package-files/40/40566aa5231ccd85c623b02116a82c2770b2d10f877f5f58f219189faac0dbf2/_thnn_fused_lstm_cell_backward_native.h b/shared/package-files/40/40566aa5231ccd85c623b02116a82c2770b2d10f877f5f58f219189faac0dbf2/_thnn_fused_lstm_cell_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..28df35acf3ea896914f97d216d060c360e8d25b3
--- /dev/null
+++ b/shared/package-files/40/40566aa5231ccd85c623b02116a82c2770b2d10f877f5f58f219189faac0dbf2/_thnn_fused_lstm_cell_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward(const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/40/40586c20a3e83f56529f9817c25a1a2d1e7e959a48f783790b6c7400a67fe710/softplus.h b/shared/package-files/40/40586c20a3e83f56529f9817c25a1a2d1e7e959a48f783790b6c7400a67fe710/softplus.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d449fa9f88a03a2cb4cb246b857695a0a06ec67
--- /dev/null
+++ b/shared/package-files/40/40586c20a3e83f56529f9817c25a1a2d1e7e959a48f783790b6c7400a67fe710/softplus.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/softplus_ops.h>
+
+namespace at {
+
+
+// aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & softplus_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) {
+    return at::_ops::softplus_out::call(self, beta, threshold, out);
+}
+// aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & softplus_outf(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out) {
+    return at::_ops::softplus_out::call(self, beta, threshold, out);
+}
+
+// aten::softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor
+inline at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) {
+    return at::_ops::softplus::call(self, beta, threshold);
+}
+
+}
diff --git a/shared/package-files/40/405e7539960ac4b9a83d3a5a9900d009106c108935f443f1c278f4da96aae917/pixelshuffle.py b/shared/package-files/40/405e7539960ac4b9a83d3a5a9900d009106c108935f443f1c278f4da96aae917/pixelshuffle.py
new file mode 100644
index 0000000000000000000000000000000000000000..6050b7eaea60cf6ef655d1219c5f2869cac11615
--- /dev/null
+++ b/shared/package-files/40/405e7539960ac4b9a83d3a5a9900d009106c108935f443f1c278f4da96aae917/pixelshuffle.py
@@ -0,0 +1,113 @@
+from .module import Module
+from .. import functional as F
+
+from torch import Tensor
+
+__all__ = ['PixelShuffle', 'PixelUnshuffle']
+
+class PixelShuffle(Module):
+    r"""Rearrange elements in a tensor according to an upscaling factor.
+
+    Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
+    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.
+
+    This is useful for implementing efficient sub-pixel convolution
+    with a stride of :math:`1/r`.
+
+    See the paper:
+    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
+    by Shi et. al (2016) for more details.
+
+    Args:
+        upscale_factor (int): factor to increase spatial resolution by
+
+    Shape:
+        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
+        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
+
+    .. math::
+        C_{out} = C_{in} \div \text{upscale\_factor}^2
+
+    .. math::
+        H_{out} = H_{in} \times \text{upscale\_factor}
+
+    .. math::
+        W_{out} = W_{in} \times \text{upscale\_factor}
+
+    Examples::
+
+        >>> pixel_shuffle = nn.PixelShuffle(3)
+        >>> input = torch.randn(1, 9, 4, 4)
+        >>> output = pixel_shuffle(input)
+        >>> print(output.size())
+        torch.Size([1, 1, 12, 12])
+
+    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
+        https://arxiv.org/abs/1609.05158
+    """
+
+    __constants__ = ['upscale_factor']
+    upscale_factor: int
+
+    def __init__(self, upscale_factor: int) -> None:
+        super().__init__()
+        self.upscale_factor = upscale_factor
+
+    def forward(self, input: Tensor) -> Tensor:
+        return F.pixel_shuffle(input, self.upscale_factor)
+
+    def extra_repr(self) -> str:
+        return f'upscale_factor={self.upscale_factor}'
+
+
+class PixelUnshuffle(Module):
+    r"""Reverse the PixelShuffle operation.
+
+    Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements
+    in a tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
+    :math:`(*, C \times r^2, H, W)`, where r is a downscale factor.
+
+    See the paper:
+    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
+    by Shi et. al (2016) for more details.
+
+    Args:
+        downscale_factor (int): factor to decrease spatial resolution by
+
+    Shape:
+        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
+        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
+
+    .. math::
+        C_{out} = C_{in} \times \text{downscale\_factor}^2
+
+    .. math::
+        H_{out} = H_{in} \div \text{downscale\_factor}
+
+    .. math::
+        W_{out} = W_{in} \div \text{downscale\_factor}
+
+    Examples::
+
+        >>> pixel_unshuffle = nn.PixelUnshuffle(3)
+        >>> input = torch.randn(1, 1, 12, 12)
+        >>> output = pixel_unshuffle(input)
+        >>> print(output.size())
+        torch.Size([1, 9, 4, 4])
+
+    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
+        https://arxiv.org/abs/1609.05158
+    """
+
+    __constants__ = ['downscale_factor']
+    downscale_factor: int
+
+    def __init__(self, downscale_factor: int) -> None:
+        super().__init__()
+        self.downscale_factor = downscale_factor
+
+    def forward(self, input: Tensor) -> Tensor:
+        return F.pixel_unshuffle(input, self.downscale_factor)
+
+    def extra_repr(self) -> str:
+        return f'downscale_factor={self.downscale_factor}'
diff --git a/shared/package-files/40/4063daa1ab9c24ff4a202ebc83cce6938762bc1ff0e4fb507380cc207329700f/test_legend3d.py b/shared/package-files/40/4063daa1ab9c24ff4a202ebc83cce6938762bc1ff0e4fb507380cc207329700f/test_legend3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fd676df1e31591f343a4a1bd27c27b98d564949
--- /dev/null
+++ b/shared/package-files/40/4063daa1ab9c24ff4a202ebc83cce6938762bc1ff0e4fb507380cc207329700f/test_legend3d.py
@@ -0,0 +1,117 @@
+import platform
+
+import numpy as np
+
+import matplotlib as mpl
+from matplotlib.colors import same_color
+from matplotlib.testing.decorators import image_comparison
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import art3d
+
+
+@image_comparison(['legend_plot.png'], remove_text=True, style='mpl20')
+def test_legend_plot():
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    x = np.arange(10)
+    ax.plot(x, 5 - x, 'o', zdir='y', label='z=1')
+    ax.plot(x, x - 5, 'o', zdir='y', label='z=-1')
+    ax.legend()
+
+
+@image_comparison(['legend_bar.png'], remove_text=True, style='mpl20')
+def test_legend_bar():
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    x = np.arange(10)
+    b1 = ax.bar(x, x, zdir='y', align='edge', color='m')
+    b2 = ax.bar(x, x[::-1], zdir='x', align='edge', color='g')
+    ax.legend([b1[0], b2[0]], ['up', 'down'])
+
+
+@image_comparison(['fancy.png'], remove_text=True, style='mpl20',
+                  tol=0 if platform.machine() == 'x86_64' else 0.011)
+def test_fancy():
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    ax.plot(np.arange(10), np.full(10, 5), np.full(10, 5), 'o--', label='line')
+    ax.scatter(np.arange(10), np.arange(10, 0, -1), label='scatter')
+    ax.errorbar(np.full(10, 5), np.arange(10), np.full(10, 10),
+                xerr=0.5, zerr=0.5, label='errorbar')
+    ax.legend(loc='lower left', ncols=2, title='My legend', numpoints=1)
+
+
+def test_linecollection_scaled_dashes():
+    lines1 = [[(0, .5), (.5, 1)], [(.3, .6), (.2, .2)]]
+    lines2 = [[[0.7, .2], [.8, .4]], [[.5, .7], [.6, .1]]]
+    lines3 = [[[0.6, .2], [.8, .4]], [[.5, .7], [.1, .1]]]
+    lc1 = art3d.Line3DCollection(lines1, linestyles="--", lw=3)
+    lc2 = art3d.Line3DCollection(lines2, linestyles="-.")
+    lc3 = art3d.Line3DCollection(lines3, linestyles=":", lw=.5)
+
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    ax.add_collection(lc1)
+    ax.add_collection(lc2)
+    ax.add_collection(lc3)
+
+    leg = ax.legend([lc1, lc2, lc3], ['line1', 'line2', 'line 3'])
+    h1, h2, h3 = leg.legend_handles
+
+    for oh, lh in zip((lc1, lc2, lc3), (h1, h2, h3)):
+        assert oh.get_linestyles()[0] == lh._dash_pattern
+
+
+def test_handlerline3d():
+    # Test marker consistency for monolithic Line3D legend handler.
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    ax.scatter([0, 1], [0, 1], marker="v")
+    handles = [art3d.Line3D([0], [0], [0], marker="v")]
+    leg = ax.legend(handles, ["Aardvark"], numpoints=1)
+    assert handles[0].get_marker() == leg.legend_handles[0].get_marker()
+
+
+def test_contour_legend_elements():
+    x, y = np.mgrid[1:10, 1:10]
+    h = x * y
+    colors = ['blue', '#00FF00', 'red']
+
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    cs = ax.contour(x, y, h, levels=[10, 30, 50], colors=colors, extend='both')
+
+    artists, labels = cs.legend_elements()
+    assert labels == ['$x = 10.0$', '$x = 30.0$', '$x = 50.0$']
+    assert all(isinstance(a, mpl.lines.Line2D) for a in artists)
+    assert all(same_color(a.get_color(), c)
+               for a, c in zip(artists, colors))
+
+
+def test_contourf_legend_elements():
+    x, y = np.mgrid[1:10, 1:10]
+    h = x * y
+
+    fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
+    cs = ax.contourf(x, y, h, levels=[10, 30, 50],
+                     colors=['#FFFF00', '#FF00FF', '#00FFFF'],
+                     extend='both')
+    cs.cmap.set_over('red')
+    cs.cmap.set_under('blue')
+    cs.changed()
+    artists, labels = cs.legend_elements()
+    assert labels == ['$x \\leq -1e+250s$',
+                      '$10.0 < x \\leq 30.0$',
+                      '$30.0 < x \\leq 50.0$',
+                      '$x > 1e+250s$']
+    expected_colors = ('blue', '#FFFF00', '#FF00FF', 'red')
+    assert all(isinstance(a, mpl.patches.Rectangle) for a in artists)
+    assert all(same_color(a.get_facecolor(), c)
+               for a, c in zip(artists, expected_colors))
+
+
+def test_legend_Poly3dCollection():
+
+    verts = np.asarray([[0, 0, 0], [0, 1, 1], [1, 0, 1]])
+    mesh = art3d.Poly3DCollection([verts], label="surface")
+
+    fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
+    mesh.set_edgecolor('k')
+    handle = ax.add_collection3d(mesh)
+    leg = ax.legend()
+    assert (leg.legend_handles[0].get_facecolor()
+            == handle.get_facecolor()).all()
diff --git a/shared/package-files/40/4069890f261d75e29f1fc66b4eb810b6a157b1b2dff2c9d31a153efe0516f052/RECORD b/shared/package-files/40/4069890f261d75e29f1fc66b4eb810b6a157b1b2dff2c9d31a153efe0516f052/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..184a1ceca9b623c7be187fd8091bb725e94b2756
--- /dev/null
+++ b/shared/package-files/40/4069890f261d75e29f1fc66b4eb810b6a157b1b2dff2c9d31a153efe0516f052/RECORD
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diff --git a/shared/package-files/40/406a18ac4d386d427e3b32f7eddb763194f917158d2e92433d55e025bb2d6190/GMT+3 b/shared/package-files/40/406a18ac4d386d427e3b32f7eddb763194f917158d2e92433d55e025bb2d6190/GMT+3
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diff --git a/shared/package-files/40/406d77f9959ea87b19e478e8a09b6a2aa3001f664b31e08bbaf34feeabbcffe2/completerlib.py b/shared/package-files/40/406d77f9959ea87b19e478e8a09b6a2aa3001f664b31e08bbaf34feeabbcffe2/completerlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..29ddab185608ebe2ad6d4ffb5a5a79bde5815c5c
--- /dev/null
+++ b/shared/package-files/40/406d77f9959ea87b19e478e8a09b6a2aa3001f664b31e08bbaf34feeabbcffe2/completerlib.py
@@ -0,0 +1,374 @@
+# encoding: utf-8
+"""Implementations for various useful completers.
+
+These are all loaded by default by IPython.
+"""
+#-----------------------------------------------------------------------------
+#  Copyright (C) 2010-2011 The IPython Development Team.
+#
+#  Distributed under the terms of the BSD License.
+#
+#  The full license is in the file COPYING.txt, distributed with this software.
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+# Stdlib imports
+import glob
+import inspect
+import os
+import re
+import sys
+from importlib import import_module
+from importlib.machinery import all_suffixes
+
+
+# Third-party imports
+from time import time
+from zipimport import zipimporter
+
+# Our own imports
+from .completer import expand_user, compress_user
+from .error import TryNext
+from ..utils._process_common import arg_split
+
+# FIXME: this should be pulled in with the right call via the component system
+from IPython import get_ipython
+
+from typing import List
+
+#-----------------------------------------------------------------------------
+# Globals and constants
+#-----------------------------------------------------------------------------
+_suffixes = all_suffixes()
+
+# Time in seconds after which the rootmodules will be stored permanently in the
+# ipython ip.db database (kept in the user's .ipython dir).
+TIMEOUT_STORAGE = 2
+
+# Time in seconds after which we give up
+TIMEOUT_GIVEUP = 20
+
+# Regular expression for the python import statement
+import_re = re.compile(r'(?P<name>[^\W\d]\w*?)'
+                       r'(?P<package>[/\\]__init__)?'
+                       r'(?P<suffix>%s)$' %
+                       r'|'.join(re.escape(s) for s in _suffixes))
+
+# RE for the ipython %run command (python + ipython scripts)
+magic_run_re = re.compile(r'.*(\.ipy|\.ipynb|\.py[w]?)$')
+
+#-----------------------------------------------------------------------------
+# Local utilities
+#-----------------------------------------------------------------------------
+
+def module_list(path):
+    """
+    Return the list containing the names of the modules available in the given
+    folder.
+    """
+    # sys.path has the cwd as an empty string, but isdir/listdir need it as '.'
+    if path == '':
+        path = '.'
+
+    # A few local constants to be used in loops below
+    pjoin = os.path.join
+
+    if os.path.isdir(path):
+        # Build a list of all files in the directory and all files
+        # in its subdirectories. For performance reasons, do not
+        # recurse more than one level into subdirectories.
+        files = []
+        for root, dirs, nondirs in os.walk(path, followlinks=True):
+            subdir = root[len(path)+1:]
+            if subdir:
+                files.extend(pjoin(subdir, f) for f in nondirs)
+                dirs[:] = [] # Do not recurse into additional subdirectories.
+            else:
+                files.extend(nondirs)
+
+    else:
+        try:
+            files = list(zipimporter(path)._files.keys())
+        except:
+            files = []
+
+    # Build a list of modules which match the import_re regex.
+    modules = []
+    for f in files:
+        m = import_re.match(f)
+        if m:
+            modules.append(m.group('name'))
+    return list(set(modules))
+
+
+def get_root_modules():
+    """
+    Returns a list containing the names of all the modules available in the
+    folders of the pythonpath.
+
+    ip.db['rootmodules_cache'] maps sys.path entries to list of modules.
+    """
+    ip = get_ipython()
+    if ip is None:
+        # No global shell instance to store cached list of modules.
+        # Don't try to scan for modules every time.
+        return list(sys.builtin_module_names)
+
+    if getattr(ip.db, "_mock", False):
+        rootmodules_cache = {}
+    else:
+        rootmodules_cache = ip.db.get("rootmodules_cache", {})
+    rootmodules = list(sys.builtin_module_names)
+    start_time = time()
+    store = False
+    for path in sys.path:
+        try:
+            modules = rootmodules_cache[path]
+        except KeyError:
+            modules = module_list(path)
+            try:
+                modules.remove('__init__')
+            except ValueError:
+                pass
+            if path not in ('', '.'): # cwd modules should not be cached
+                rootmodules_cache[path] = modules
+            if time() - start_time > TIMEOUT_STORAGE and not store:
+                store = True
+                print("\nCaching the list of root modules, please wait!")
+                print("(This will only be done once - type '%rehashx' to "
+                      "reset cache!)\n")
+                sys.stdout.flush()
+            if time() - start_time > TIMEOUT_GIVEUP:
+                print("This is taking too long, we give up.\n")
+                return []
+        rootmodules.extend(modules)
+    if store:
+        ip.db['rootmodules_cache'] = rootmodules_cache
+    rootmodules = list(set(rootmodules))
+    return rootmodules
+
+
+def is_importable(module, attr, only_modules):
+    if only_modules:
+        return inspect.ismodule(getattr(module, attr))
+    else:
+        return not(attr[:2] == '__' and attr[-2:] == '__')
+
+def is_possible_submodule(module, attr):
+    try:
+        obj = getattr(module, attr)
+    except AttributeError:
+        # Is possilby an unimported submodule
+        return True
+    except TypeError:
+        # https://github.com/ipython/ipython/issues/9678
+        return False
+    return inspect.ismodule(obj)
+
+
+def try_import(mod: str, only_modules=False) -> List[str]:
+    """
+    Try to import given module and return list of potential completions.
+    """
+    mod = mod.rstrip('.')
+    try:
+        m = import_module(mod)
+    except:
+        return []
+
+    m_is_init = '__init__' in (getattr(m, '__file__', '') or '')
+
+    completions = []
+    if (not hasattr(m, '__file__')) or (not only_modules) or m_is_init:
+        completions.extend( [attr for attr in dir(m) if
+                             is_importable(m, attr, only_modules)])
+
+    m_all = getattr(m, "__all__", [])
+    if only_modules:
+        completions.extend(attr for attr in m_all if is_possible_submodule(m, attr))
+    else:
+        completions.extend(m_all)
+
+    if m_is_init:
+        file_ = m.__file__
+        completions.extend(module_list(os.path.dirname(file_)))
+    completions_set = {c for c in completions if isinstance(c, str)}
+    completions_set.discard('__init__')
+    return list(completions_set)
+
+
+#-----------------------------------------------------------------------------
+# Completion-related functions.
+#-----------------------------------------------------------------------------
+
+def quick_completer(cmd, completions):
+    r""" Easily create a trivial completer for a command.
+
+    Takes either a list of completions, or all completions in string (that will
+    be split on whitespace).
+
+    Example::
+
+        [d:\ipython]|1> import ipy_completers
+        [d:\ipython]|2> ipy_completers.quick_completer('foo', ['bar','baz'])
+        [d:\ipython]|3> foo b<TAB>
+        bar baz
+        [d:\ipython]|3> foo ba
+    """
+
+    if isinstance(completions, str):
+        completions = completions.split()
+
+    def do_complete(self, event):
+        return completions
+
+    get_ipython().set_hook('complete_command',do_complete, str_key = cmd)
+
+def module_completion(line):
+    """
+    Returns a list containing the completion possibilities for an import line.
+
+    The line looks like this :
+    'import xml.d'
+    'from xml.dom import'
+    """
+
+    words = line.split(' ')
+    nwords = len(words)
+
+    # from whatever <tab> -> 'import '
+    if nwords == 3 and words[0] == 'from':
+        return ['import ']
+
+    # 'from xy<tab>' or 'import xy<tab>'
+    if nwords < 3 and (words[0] in {'%aimport', 'import', 'from'}) :
+        if nwords == 1:
+            return get_root_modules()
+        mod = words[1].split('.')
+        if len(mod) < 2:
+            return get_root_modules()
+        completion_list = try_import('.'.join(mod[:-1]), True)
+        return ['.'.join(mod[:-1] + [el]) for el in completion_list]
+
+    # 'from xyz import abc<tab>'
+    if nwords >= 3 and words[0] == 'from':
+        mod = words[1]
+        return try_import(mod)
+
+#-----------------------------------------------------------------------------
+# Completers
+#-----------------------------------------------------------------------------
+# These all have the func(self, event) signature to be used as custom
+# completers
+
+def module_completer(self,event):
+    """Give completions after user has typed 'import ...' or 'from ...'"""
+
+    # This works in all versions of python.  While 2.5 has
+    # pkgutil.walk_packages(), that particular routine is fairly dangerous,
+    # since it imports *EVERYTHING* on sys.path.  That is: a) very slow b) full
+    # of possibly problematic side effects.
+    # This search the folders in the sys.path for available modules.
+
+    return module_completion(event.line)
+
+# FIXME: there's a lot of logic common to the run, cd and builtin file
+# completers, that is currently reimplemented in each.
+
+def magic_run_completer(self, event):
+    """Complete files that end in .py or .ipy or .ipynb for the %run command.
+    """
+    comps = arg_split(event.line, strict=False)
+    # relpath should be the current token that we need to complete.
+    if (len(comps) > 1) and (not event.line.endswith(' ')):
+        relpath = comps[-1].strip("'\"")
+    else:
+        relpath = ''
+
+    #print("\nev=", event)  # dbg
+    #print("rp=", relpath)  # dbg
+    #print('comps=', comps)  # dbg
+
+    lglob = glob.glob
+    isdir = os.path.isdir
+    relpath, tilde_expand, tilde_val = expand_user(relpath)
+
+    # Find if the user has already typed the first filename, after which we
+    # should complete on all files, since after the first one other files may
+    # be arguments to the input script.
+
+    if any(magic_run_re.match(c) for c in comps):
+        matches =  [f.replace('\\','/') + ('/' if isdir(f) else '')
+                            for f in lglob(relpath+'*')]
+    else:
+        dirs = [f.replace('\\','/') + "/" for f in lglob(relpath+'*') if isdir(f)]
+        pys =  [f.replace('\\','/')
+                for f in lglob(relpath+'*.py') + lglob(relpath+'*.ipy') +
+                lglob(relpath+'*.ipynb') + lglob(relpath + '*.pyw')]
+
+        matches = dirs + pys
+
+    #print('run comp:', dirs+pys) # dbg
+    return [compress_user(p, tilde_expand, tilde_val) for p in matches]
+
+
+def cd_completer(self, event):
+    """Completer function for cd, which only returns directories."""
+    ip = get_ipython()
+    relpath = event.symbol
+
+    #print(event) # dbg
+    if event.line.endswith('-b') or ' -b ' in event.line:
+        # return only bookmark completions
+        bkms = self.db.get('bookmarks', None)
+        if bkms:
+            return bkms.keys()
+        else:
+            return []
+
+    if event.symbol == '-':
+        width_dh = str(len(str(len(ip.user_ns['_dh']) + 1)))
+        # jump in directory history by number
+        fmt = '-%0' + width_dh +'d [%s]'
+        ents = [ fmt % (i,s) for i,s in enumerate(ip.user_ns['_dh'])]
+        if len(ents) > 1:
+            return ents
+        return []
+
+    if event.symbol.startswith('--'):
+        return ["--" + os.path.basename(d) for d in ip.user_ns['_dh']]
+
+    # Expand ~ in path and normalize directory separators.
+    relpath, tilde_expand, tilde_val = expand_user(relpath)
+    relpath = relpath.replace('\\','/')
+
+    found = []
+    for d in [f.replace('\\','/') + '/' for f in glob.glob(relpath+'*')
+              if os.path.isdir(f)]:
+        if ' ' in d:
+            # we don't want to deal with any of that, complex code
+            # for this is elsewhere
+            raise TryNext
+
+        found.append(d)
+
+    if not found:
+        if os.path.isdir(relpath):
+            return [compress_user(relpath, tilde_expand, tilde_val)]
+
+        # if no completions so far, try bookmarks
+        bks = self.db.get('bookmarks',{})
+        bkmatches = [s for s in bks if s.startswith(event.symbol)]
+        if bkmatches:
+            return bkmatches
+
+        raise TryNext
+
+    return [compress_user(p, tilde_expand, tilde_val) for p in found]
+
+def reset_completer(self, event):
+    "A completer for %reset magic"
+    return '-f -s in out array dhist'.split()
diff --git a/shared/package-files/40/40721b8e5d10dfd7f16ace0369bb8bcad5e38c166b7ea3647edd07fe75b05ebe/binding.pyi b/shared/package-files/40/40721b8e5d10dfd7f16ace0369bb8bcad5e38c166b7ea3647edd07fe75b05ebe/binding.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9fd46444e55fa319d6d37a481384c3e844f065c9
--- /dev/null
+++ b/shared/package-files/40/40721b8e5d10dfd7f16ace0369bb8bcad5e38c166b7ea3647edd07fe75b05ebe/binding.pyi
@@ -0,0 +1,6 @@
+from typing import Any, Optional
+
+class Binding(object):
+    ffi: Optional[Any]
+    lib: Optional[Any]
+    def init_static_locks(self) -> None: ...
diff --git a/shared/package-files/40/4072ef53cc346f934a7f9ae44ce3e8bcbdb6886dc75e7dcb29d9071e46523ff2/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt b/shared/package-files/40/4072ef53cc346f934a7f9ae44ce3e8bcbdb6886dc75e7dcb29d9071e46523ff2/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9f229e11e539d87964ced43a7ef0153f81b7370b
--- /dev/null
+++ b/shared/package-files/40/4072ef53cc346f934a7f9ae44ce3e8bcbdb6886dc75e7dcb29d9071e46523ff2/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:24:00 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realcons, realgdp" do not Granger-cause "realinv"
+
+Test statistic l = 16.6303
+pval-F( l; 8, 558) = 0.0000 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realcons, realgdp" and "realinv"
+
+Test statistic: c = 79.5492
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/40/407440bbbc7041cba4ef5a846ff35378841d540cab3f0adea6cdd1468599fcc6/ElementTree.py b/shared/package-files/40/407440bbbc7041cba4ef5a846ff35378841d540cab3f0adea6cdd1468599fcc6/ElementTree.py
new file mode 100644
index 0000000000000000000000000000000000000000..516656b69e3dfef076825ad47472d8265ca6312c
--- /dev/null
+++ b/shared/package-files/40/407440bbbc7041cba4ef5a846ff35378841d540cab3f0adea6cdd1468599fcc6/ElementTree.py
@@ -0,0 +1,2097 @@
+"""Lightweight XML support for Python.
+
+ XML is an inherently hierarchical data format, and the most natural way to
+ represent it is with a tree.  This module has two classes for this purpose:
+
+    1. ElementTree represents the whole XML document as a tree and
+
+    2. Element represents a single node in this tree.
+
+ Interactions with the whole document (reading and writing to/from files) are
+ usually done on the ElementTree level.  Interactions with a single XML element
+ and its sub-elements are done on the Element level.
+
+ Element is a flexible container object designed to store hierarchical data
+ structures in memory. It can be described as a cross between a list and a
+ dictionary.  Each Element has a number of properties associated with it:
+
+    'tag' - a string containing the element's name.
+
+    'attributes' - a Python dictionary storing the element's attributes.
+
+    'text' - a string containing the element's text content.
+
+    'tail' - an optional string containing text after the element's end tag.
+
+    And a number of child elements stored in a Python sequence.
+
+ To create an element instance, use the Element constructor,
+ or the SubElement factory function.
+
+ You can also use the ElementTree class to wrap an element structure
+ and convert it to and from XML.
+
+"""
+
+#---------------------------------------------------------------------
+# Licensed to PSF under a Contributor Agreement.
+# See https://www.python.org/psf/license for licensing details.
+#
+# ElementTree
+# Copyright (c) 1999-2008 by Fredrik Lundh.  All rights reserved.
+#
+# fredrik@pythonware.com
+# http://www.pythonware.com
+# --------------------------------------------------------------------
+# The ElementTree toolkit is
+#
+# Copyright (c) 1999-2008 by Fredrik Lundh
+#
+# By obtaining, using, and/or copying this software and/or its
+# associated documentation, you agree that you have read, understood,
+# and will comply with the following terms and conditions:
+#
+# Permission to use, copy, modify, and distribute this software and
+# its associated documentation for any purpose and without fee is
+# hereby granted, provided that the above copyright notice appears in
+# all copies, and that both that copyright notice and this permission
+# notice appear in supporting documentation, and that the name of
+# Secret Labs AB or the author not be used in advertising or publicity
+# pertaining to distribution of the software without specific, written
+# prior permission.
+#
+# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
+# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
+# ABILITY AND FITNESS.  IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
+# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
+# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+# OF THIS SOFTWARE.
+# --------------------------------------------------------------------
+
+__all__ = [
+    # public symbols
+    "Comment",
+    "dump",
+    "Element", "ElementTree",
+    "fromstring", "fromstringlist",
+    "indent", "iselement", "iterparse",
+    "parse", "ParseError",
+    "PI", "ProcessingInstruction",
+    "QName",
+    "SubElement",
+    "tostring", "tostringlist",
+    "TreeBuilder",
+    "VERSION",
+    "XML", "XMLID",
+    "XMLParser", "XMLPullParser",
+    "register_namespace",
+    "canonicalize", "C14NWriterTarget",
+    ]
+
+VERSION = "1.3.0"
+
+import sys
+import re
+import warnings
+import io
+import collections
+import collections.abc
+import contextlib
+
+from . import ElementPath
+
+
+class ParseError(SyntaxError):
+    """An error when parsing an XML document.
+
+    In addition to its exception value, a ParseError contains
+    two extra attributes:
+        'code'     - the specific exception code
+        'position' - the line and column of the error
+
+    """
+    pass
+
+# --------------------------------------------------------------------
+
+
+def iselement(element):
+    """Return True if *element* appears to be an Element."""
+    return hasattr(element, 'tag')
+
+
+class Element:
+    """An XML element.
+
+    This class is the reference implementation of the Element interface.
+
+    An element's length is its number of subelements.  That means if you
+    want to check if an element is truly empty, you should check BOTH
+    its length AND its text attribute.
+
+    The element tag, attribute names, and attribute values can be either
+    bytes or strings.
+
+    *tag* is the element name.  *attrib* is an optional dictionary containing
+    element attributes. *extra* are additional element attributes given as
+    keyword arguments.
+
+    Example form:
+        <tag attrib>text<child/>...</tag>tail
+
+    """
+
+    tag = None
+    """The element's name."""
+
+    attrib = None
+    """Dictionary of the element's attributes."""
+
+    text = None
+    """
+    Text before first subelement. This is either a string or the value None.
+    Note that if there is no text, this attribute may be either
+    None or the empty string, depending on the parser.
+
+    """
+
+    tail = None
+    """
+    Text after this element's end tag, but before the next sibling element's
+    start tag.  This is either a string or the value None.  Note that if there
+    was no text, this attribute may be either None or an empty string,
+    depending on the parser.
+
+    """
+
+    def __init__(self, tag, attrib={}, **extra):
+        if not isinstance(attrib, dict):
+            raise TypeError("attrib must be dict, not %s" % (
+                attrib.__class__.__name__,))
+        self.tag = tag
+        self.attrib = {**attrib, **extra}
+        self._children = []
+
+    def __repr__(self):
+        return "<%s %r at %#x>" % (self.__class__.__name__, self.tag, id(self))
+
+    def makeelement(self, tag, attrib):
+        """Create a new element with the same type.
+
+        *tag* is a string containing the element name.
+        *attrib* is a dictionary containing the element attributes.
+
+        Do not call this method, use the SubElement factory function instead.
+
+        """
+        return self.__class__(tag, attrib)
+
+    def copy(self):
+        """Return copy of current element.
+
+        This creates a shallow copy. Subelements will be shared with the
+        original tree.
+
+        """
+        warnings.warn(
+            "elem.copy() is deprecated. Use copy.copy(elem) instead.",
+            DeprecationWarning
+            )
+        return self.__copy__()
+
+    def __copy__(self):
+        elem = self.makeelement(self.tag, self.attrib)
+        elem.text = self.text
+        elem.tail = self.tail
+        elem[:] = self
+        return elem
+
+    def __len__(self):
+        return len(self._children)
+
+    def __bool__(self):
+        warnings.warn(
+            "The behavior of this method will change in future versions.  "
+            "Use specific 'len(elem)' or 'elem is not None' test instead.",
+            FutureWarning, stacklevel=2
+            )
+        return len(self._children) != 0 # emulate old behaviour, for now
+
+    def __getitem__(self, index):
+        return self._children[index]
+
+    def __setitem__(self, index, element):
+        if isinstance(index, slice):
+            for elt in element:
+                self._assert_is_element(elt)
+        else:
+            self._assert_is_element(element)
+        self._children[index] = element
+
+    def __delitem__(self, index):
+        del self._children[index]
+
+    def append(self, subelement):
+        """Add *subelement* to the end of this element.
+
+        The new element will appear in document order after the last existing
+        subelement (or directly after the text, if it's the first subelement),
+        but before the end tag for this element.
+
+        """
+        self._assert_is_element(subelement)
+        self._children.append(subelement)
+
+    def extend(self, elements):
+        """Append subelements from a sequence.
+
+        *elements* is a sequence with zero or more elements.
+
+        """
+        for element in elements:
+            self._assert_is_element(element)
+            self._children.append(element)
+
+    def insert(self, index, subelement):
+        """Insert *subelement* at position *index*."""
+        self._assert_is_element(subelement)
+        self._children.insert(index, subelement)
+
+    def _assert_is_element(self, e):
+        # Need to refer to the actual Python implementation, not the
+        # shadowing C implementation.
+        if not isinstance(e, _Element_Py):
+            raise TypeError('expected an Element, not %s' % type(e).__name__)
+
+    def remove(self, subelement):
+        """Remove matching subelement.
+
+        Unlike the find methods, this method compares elements based on
+        identity, NOT ON tag value or contents.  To remove subelements by
+        other means, the easiest way is to use a list comprehension to
+        select what elements to keep, and then use slice assignment to update
+        the parent element.
+
+        ValueError is raised if a matching element could not be found.
+
+        """
+        # assert iselement(element)
+        self._children.remove(subelement)
+
+    def find(self, path, namespaces=None):
+        """Find first matching element by tag name or path.
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return the first matching element, or None if no element was found.
+
+        """
+        return ElementPath.find(self, path, namespaces)
+
+    def findtext(self, path, default=None, namespaces=None):
+        """Find text for first matching element by tag name or path.
+
+        *path* is a string having either an element tag or an XPath,
+        *default* is the value to return if the element was not found,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return text content of first matching element, or default value if
+        none was found.  Note that if an element is found having no text
+        content, the empty string is returned.
+
+        """
+        return ElementPath.findtext(self, path, default, namespaces)
+
+    def findall(self, path, namespaces=None):
+        """Find all matching subelements by tag name or path.
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Returns list containing all matching elements in document order.
+
+        """
+        return ElementPath.findall(self, path, namespaces)
+
+    def iterfind(self, path, namespaces=None):
+        """Find all matching subelements by tag name or path.
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return an iterable yielding all matching elements in document order.
+
+        """
+        return ElementPath.iterfind(self, path, namespaces)
+
+    def clear(self):
+        """Reset element.
+
+        This function removes all subelements, clears all attributes, and sets
+        the text and tail attributes to None.
+
+        """
+        self.attrib.clear()
+        self._children = []
+        self.text = self.tail = None
+
+    def get(self, key, default=None):
+        """Get element attribute.
+
+        Equivalent to attrib.get, but some implementations may handle this a
+        bit more efficiently.  *key* is what attribute to look for, and
+        *default* is what to return if the attribute was not found.
+
+        Returns a string containing the attribute value, or the default if
+        attribute was not found.
+
+        """
+        return self.attrib.get(key, default)
+
+    def set(self, key, value):
+        """Set element attribute.
+
+        Equivalent to attrib[key] = value, but some implementations may handle
+        this a bit more efficiently.  *key* is what attribute to set, and
+        *value* is the attribute value to set it to.
+
+        """
+        self.attrib[key] = value
+
+    def keys(self):
+        """Get list of attribute names.
+
+        Names are returned in an arbitrary order, just like an ordinary
+        Python dict.  Equivalent to attrib.keys()
+
+        """
+        return self.attrib.keys()
+
+    def items(self):
+        """Get element attributes as a sequence.
+
+        The attributes are returned in arbitrary order.  Equivalent to
+        attrib.items().
+
+        Return a list of (name, value) tuples.
+
+        """
+        return self.attrib.items()
+
+    def iter(self, tag=None):
+        """Create tree iterator.
+
+        The iterator loops over the element and all subelements in document
+        order, returning all elements with a matching tag.
+
+        If the tree structure is modified during iteration, new or removed
+        elements may or may not be included.  To get a stable set, use the
+        list() function on the iterator, and loop over the resulting list.
+
+        *tag* is what tags to look for (default is to return all elements)
+
+        Return an iterator containing all the matching elements.
+
+        """
+        if tag == "*":
+            tag = None
+        if tag is None or self.tag == tag:
+            yield self
+        for e in self._children:
+            yield from e.iter(tag)
+
+    def itertext(self):
+        """Create text iterator.
+
+        The iterator loops over the element and all subelements in document
+        order, returning all inner text.
+
+        """
+        tag = self.tag
+        if not isinstance(tag, str) and tag is not None:
+            return
+        t = self.text
+        if t:
+            yield t
+        for e in self:
+            yield from e.itertext()
+            t = e.tail
+            if t:
+                yield t
+
+
+def SubElement(parent, tag, attrib={}, **extra):
+    """Subelement factory which creates an element instance, and appends it
+    to an existing parent.
+
+    The element tag, attribute names, and attribute values can be either
+    bytes or Unicode strings.
+
+    *parent* is the parent element, *tag* is the subelements name, *attrib* is
+    an optional directory containing element attributes, *extra* are
+    additional attributes given as keyword arguments.
+
+    """
+    attrib = {**attrib, **extra}
+    element = parent.makeelement(tag, attrib)
+    parent.append(element)
+    return element
+
+
+def Comment(text=None):
+    """Comment element factory.
+
+    This function creates a special element which the standard serializer
+    serializes as an XML comment.
+
+    *text* is a string containing the comment string.
+
+    """
+    element = Element(Comment)
+    element.text = text
+    return element
+
+
+def ProcessingInstruction(target, text=None):
+    """Processing Instruction element factory.
+
+    This function creates a special element which the standard serializer
+    serializes as an XML comment.
+
+    *target* is a string containing the processing instruction, *text* is a
+    string containing the processing instruction contents, if any.
+
+    """
+    element = Element(ProcessingInstruction)
+    element.text = target
+    if text:
+        element.text = element.text + " " + text
+    return element
+
+PI = ProcessingInstruction
+
+
+class QName:
+    """Qualified name wrapper.
+
+    This class can be used to wrap a QName attribute value in order to get
+    proper namespace handing on output.
+
+    *text_or_uri* is a string containing the QName value either in the form
+    {uri}local, or if the tag argument is given, the URI part of a QName.
+
+    *tag* is an optional argument which if given, will make the first
+    argument (text_or_uri) be interpreted as a URI, and this argument (tag)
+    be interpreted as a local name.
+
+    """
+    def __init__(self, text_or_uri, tag=None):
+        if tag:
+            text_or_uri = "{%s}%s" % (text_or_uri, tag)
+        self.text = text_or_uri
+    def __str__(self):
+        return self.text
+    def __repr__(self):
+        return '<%s %r>' % (self.__class__.__name__, self.text)
+    def __hash__(self):
+        return hash(self.text)
+    def __le__(self, other):
+        if isinstance(other, QName):
+            return self.text <= other.text
+        return self.text <= other
+    def __lt__(self, other):
+        if isinstance(other, QName):
+            return self.text < other.text
+        return self.text < other
+    def __ge__(self, other):
+        if isinstance(other, QName):
+            return self.text >= other.text
+        return self.text >= other
+    def __gt__(self, other):
+        if isinstance(other, QName):
+            return self.text > other.text
+        return self.text > other
+    def __eq__(self, other):
+        if isinstance(other, QName):
+            return self.text == other.text
+        return self.text == other
+
+# --------------------------------------------------------------------
+
+
+class ElementTree:
+    """An XML element hierarchy.
+
+    This class also provides support for serialization to and from
+    standard XML.
+
+    *element* is an optional root element node,
+    *file* is an optional file handle or file name of an XML file whose
+    contents will be used to initialize the tree with.
+
+    """
+    def __init__(self, element=None, file=None):
+        # assert element is None or iselement(element)
+        self._root = element # first node
+        if file:
+            self.parse(file)
+
+    def getroot(self):
+        """Return root element of this tree."""
+        return self._root
+
+    def _setroot(self, element):
+        """Replace root element of this tree.
+
+        This will discard the current contents of the tree and replace it
+        with the given element.  Use with care!
+
+        """
+        # assert iselement(element)
+        self._root = element
+
+    def parse(self, source, parser=None):
+        """Load external XML document into element tree.
+
+        *source* is a file name or file object, *parser* is an optional parser
+        instance that defaults to XMLParser.
+
+        ParseError is raised if the parser fails to parse the document.
+
+        Returns the root element of the given source document.
+
+        """
+        close_source = False
+        if not hasattr(source, "read"):
+            source = open(source, "rb")
+            close_source = True
+        try:
+            if parser is None:
+                # If no parser was specified, create a default XMLParser
+                parser = XMLParser()
+                if hasattr(parser, '_parse_whole'):
+                    # The default XMLParser, when it comes from an accelerator,
+                    # can define an internal _parse_whole API for efficiency.
+                    # It can be used to parse the whole source without feeding
+                    # it with chunks.
+                    self._root = parser._parse_whole(source)
+                    return self._root
+            while True:
+                data = source.read(65536)
+                if not data:
+                    break
+                parser.feed(data)
+            self._root = parser.close()
+            return self._root
+        finally:
+            if close_source:
+                source.close()
+
+    def iter(self, tag=None):
+        """Create and return tree iterator for the root element.
+
+        The iterator loops over all elements in this tree, in document order.
+
+        *tag* is a string with the tag name to iterate over
+        (default is to return all elements).
+
+        """
+        # assert self._root is not None
+        return self._root.iter(tag)
+
+    def find(self, path, namespaces=None):
+        """Find first matching element by tag name or path.
+
+        Same as getroot().find(path), which is Element.find()
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return the first matching element, or None if no element was found.
+
+        """
+        # assert self._root is not None
+        if path[:1] == "/":
+            path = "." + path
+            warnings.warn(
+                "This search is broken in 1.3 and earlier, and will be "
+                "fixed in a future version.  If you rely on the current "
+                "behaviour, change it to %r" % path,
+                FutureWarning, stacklevel=2
+                )
+        return self._root.find(path, namespaces)
+
+    def findtext(self, path, default=None, namespaces=None):
+        """Find first matching element by tag name or path.
+
+        Same as getroot().findtext(path),  which is Element.findtext()
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return the first matching element, or None if no element was found.
+
+        """
+        # assert self._root is not None
+        if path[:1] == "/":
+            path = "." + path
+            warnings.warn(
+                "This search is broken in 1.3 and earlier, and will be "
+                "fixed in a future version.  If you rely on the current "
+                "behaviour, change it to %r" % path,
+                FutureWarning, stacklevel=2
+                )
+        return self._root.findtext(path, default, namespaces)
+
+    def findall(self, path, namespaces=None):
+        """Find all matching subelements by tag name or path.
+
+        Same as getroot().findall(path), which is Element.findall().
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return list containing all matching elements in document order.
+
+        """
+        # assert self._root is not None
+        if path[:1] == "/":
+            path = "." + path
+            warnings.warn(
+                "This search is broken in 1.3 and earlier, and will be "
+                "fixed in a future version.  If you rely on the current "
+                "behaviour, change it to %r" % path,
+                FutureWarning, stacklevel=2
+                )
+        return self._root.findall(path, namespaces)
+
+    def iterfind(self, path, namespaces=None):
+        """Find all matching subelements by tag name or path.
+
+        Same as getroot().iterfind(path), which is element.iterfind()
+
+        *path* is a string having either an element tag or an XPath,
+        *namespaces* is an optional mapping from namespace prefix to full name.
+
+        Return an iterable yielding all matching elements in document order.
+
+        """
+        # assert self._root is not None
+        if path[:1] == "/":
+            path = "." + path
+            warnings.warn(
+                "This search is broken in 1.3 and earlier, and will be "
+                "fixed in a future version.  If you rely on the current "
+                "behaviour, change it to %r" % path,
+                FutureWarning, stacklevel=2
+                )
+        return self._root.iterfind(path, namespaces)
+
+    def write(self, file_or_filename,
+              encoding=None,
+              xml_declaration=None,
+              default_namespace=None,
+              method=None, *,
+              short_empty_elements=True):
+        """Write element tree to a file as XML.
+
+        Arguments:
+          *file_or_filename* -- file name or a file object opened for writing
+
+          *encoding* -- the output encoding (default: US-ASCII)
+
+          *xml_declaration* -- bool indicating if an XML declaration should be
+                               added to the output. If None, an XML declaration
+                               is added if encoding IS NOT either of:
+                               US-ASCII, UTF-8, or Unicode
+
+          *default_namespace* -- sets the default XML namespace (for "xmlns")
+
+          *method* -- either "xml" (default), "html, "text", or "c14n"
+
+          *short_empty_elements* -- controls the formatting of elements
+                                    that contain no content. If True (default)
+                                    they are emitted as a single self-closed
+                                    tag, otherwise they are emitted as a pair
+                                    of start/end tags
+
+        """
+        if not method:
+            method = "xml"
+        elif method not in _serialize:
+            raise ValueError("unknown method %r" % method)
+        if not encoding:
+            if method == "c14n":
+                encoding = "utf-8"
+            else:
+                encoding = "us-ascii"
+        with _get_writer(file_or_filename, encoding) as (write, declared_encoding):
+            if method == "xml" and (xml_declaration or
+                    (xml_declaration is None and
+                     encoding.lower() != "unicode" and
+                     declared_encoding.lower() not in ("utf-8", "us-ascii"))):
+                write("<?xml version='1.0' encoding='%s'?>\n" % (
+                    declared_encoding,))
+            if method == "text":
+                _serialize_text(write, self._root)
+            else:
+                qnames, namespaces = _namespaces(self._root, default_namespace)
+                serialize = _serialize[method]
+                serialize(write, self._root, qnames, namespaces,
+                          short_empty_elements=short_empty_elements)
+
+    def write_c14n(self, file):
+        # lxml.etree compatibility.  use output method instead
+        return self.write(file, method="c14n")
+
+# --------------------------------------------------------------------
+# serialization support
+
+@contextlib.contextmanager
+def _get_writer(file_or_filename, encoding):
+    # returns text write method and release all resources after using
+    try:
+        write = file_or_filename.write
+    except AttributeError:
+        # file_or_filename is a file name
+        if encoding.lower() == "unicode":
+            encoding="utf-8"
+        with open(file_or_filename, "w", encoding=encoding,
+                  errors="xmlcharrefreplace") as file:
+            yield file.write, encoding
+    else:
+        # file_or_filename is a file-like object
+        # encoding determines if it is a text or binary writer
+        if encoding.lower() == "unicode":
+            # use a text writer as is
+            yield write, getattr(file_or_filename, "encoding", None) or "utf-8"
+        else:
+            # wrap a binary writer with TextIOWrapper
+            with contextlib.ExitStack() as stack:
+                if isinstance(file_or_filename, io.BufferedIOBase):
+                    file = file_or_filename
+                elif isinstance(file_or_filename, io.RawIOBase):
+                    file = io.BufferedWriter(file_or_filename)
+                    # Keep the original file open when the BufferedWriter is
+                    # destroyed
+                    stack.callback(file.detach)
+                else:
+                    # This is to handle passed objects that aren't in the
+                    # IOBase hierarchy, but just have a write method
+                    file = io.BufferedIOBase()
+                    file.writable = lambda: True
+                    file.write = write
+                    try:
+                        # TextIOWrapper uses this methods to determine
+                        # if BOM (for UTF-16, etc) should be added
+                        file.seekable = file_or_filename.seekable
+                        file.tell = file_or_filename.tell
+                    except AttributeError:
+                        pass
+                file = io.TextIOWrapper(file,
+                                        encoding=encoding,
+                                        errors="xmlcharrefreplace",
+                                        newline="\n")
+                # Keep the original file open when the TextIOWrapper is
+                # destroyed
+                stack.callback(file.detach)
+                yield file.write, encoding
+
+def _namespaces(elem, default_namespace=None):
+    # identify namespaces used in this tree
+
+    # maps qnames to *encoded* prefix:local names
+    qnames = {None: None}
+
+    # maps uri:s to prefixes
+    namespaces = {}
+    if default_namespace:
+        namespaces[default_namespace] = ""
+
+    def add_qname(qname):
+        # calculate serialized qname representation
+        try:
+            if qname[:1] == "{":
+                uri, tag = qname[1:].rsplit("}", 1)
+                prefix = namespaces.get(uri)
+                if prefix is None:
+                    prefix = _namespace_map.get(uri)
+                    if prefix is None:
+                        prefix = "ns%d" % len(namespaces)
+                    if prefix != "xml":
+                        namespaces[uri] = prefix
+                if prefix:
+                    qnames[qname] = "%s:%s" % (prefix, tag)
+                else:
+                    qnames[qname] = tag # default element
+            else:
+                if default_namespace:
+                    # FIXME: can this be handled in XML 1.0?
+                    raise ValueError(
+                        "cannot use non-qualified names with "
+                        "default_namespace option"
+                        )
+                qnames[qname] = qname
+        except TypeError:
+            _raise_serialization_error(qname)
+
+    # populate qname and namespaces table
+    for elem in elem.iter():
+        tag = elem.tag
+        if isinstance(tag, QName):
+            if tag.text not in qnames:
+                add_qname(tag.text)
+        elif isinstance(tag, str):
+            if tag not in qnames:
+                add_qname(tag)
+        elif tag is not None and tag is not Comment and tag is not PI:
+            _raise_serialization_error(tag)
+        for key, value in elem.items():
+            if isinstance(key, QName):
+                key = key.text
+            if key not in qnames:
+                add_qname(key)
+            if isinstance(value, QName) and value.text not in qnames:
+                add_qname(value.text)
+        text = elem.text
+        if isinstance(text, QName) and text.text not in qnames:
+            add_qname(text.text)
+    return qnames, namespaces
+
+def _serialize_xml(write, elem, qnames, namespaces,
+                   short_empty_elements, **kwargs):
+    tag = elem.tag
+    text = elem.text
+    if tag is Comment:
+        write("<!--%s-->" % text)
+    elif tag is ProcessingInstruction:
+        write("<?%s?>" % text)
+    else:
+        tag = qnames[tag]
+        if tag is None:
+            if text:
+                write(_escape_cdata(text))
+            for e in elem:
+                _serialize_xml(write, e, qnames, None,
+                               short_empty_elements=short_empty_elements)
+        else:
+            write("<" + tag)
+            items = list(elem.items())
+            if items or namespaces:
+                if namespaces:
+                    for v, k in sorted(namespaces.items(),
+                                       key=lambda x: x[1]):  # sort on prefix
+                        if k:
+                            k = ":" + k
+                        write(" xmlns%s=\"%s\"" % (
+                            k,
+                            _escape_attrib(v)
+                            ))
+                for k, v in items:
+                    if isinstance(k, QName):
+                        k = k.text
+                    if isinstance(v, QName):
+                        v = qnames[v.text]
+                    else:
+                        v = _escape_attrib(v)
+                    write(" %s=\"%s\"" % (qnames[k], v))
+            if text or len(elem) or not short_empty_elements:
+                write(">")
+                if text:
+                    write(_escape_cdata(text))
+                for e in elem:
+                    _serialize_xml(write, e, qnames, None,
+                                   short_empty_elements=short_empty_elements)
+                write("</" + tag + ">")
+            else:
+                write(" />")
+    if elem.tail:
+        write(_escape_cdata(elem.tail))
+
+HTML_EMPTY = ("area", "base", "basefont", "br", "col", "frame", "hr",
+              "img", "input", "isindex", "link", "meta", "param")
+
+try:
+    HTML_EMPTY = set(HTML_EMPTY)
+except NameError:
+    pass
+
+def _serialize_html(write, elem, qnames, namespaces, **kwargs):
+    tag = elem.tag
+    text = elem.text
+    if tag is Comment:
+        write("<!--%s-->" % _escape_cdata(text))
+    elif tag is ProcessingInstruction:
+        write("<?%s?>" % _escape_cdata(text))
+    else:
+        tag = qnames[tag]
+        if tag is None:
+            if text:
+                write(_escape_cdata(text))
+            for e in elem:
+                _serialize_html(write, e, qnames, None)
+        else:
+            write("<" + tag)
+            items = list(elem.items())
+            if items or namespaces:
+                if namespaces:
+                    for v, k in sorted(namespaces.items(),
+                                       key=lambda x: x[1]):  # sort on prefix
+                        if k:
+                            k = ":" + k
+                        write(" xmlns%s=\"%s\"" % (
+                            k,
+                            _escape_attrib(v)
+                            ))
+                for k, v in items:
+                    if isinstance(k, QName):
+                        k = k.text
+                    if isinstance(v, QName):
+                        v = qnames[v.text]
+                    else:
+                        v = _escape_attrib_html(v)
+                    # FIXME: handle boolean attributes
+                    write(" %s=\"%s\"" % (qnames[k], v))
+            write(">")
+            ltag = tag.lower()
+            if text:
+                if ltag == "script" or ltag == "style":
+                    write(text)
+                else:
+                    write(_escape_cdata(text))
+            for e in elem:
+                _serialize_html(write, e, qnames, None)
+            if ltag not in HTML_EMPTY:
+                write("</" + tag + ">")
+    if elem.tail:
+        write(_escape_cdata(elem.tail))
+
+def _serialize_text(write, elem):
+    for part in elem.itertext():
+        write(part)
+    if elem.tail:
+        write(elem.tail)
+
+_serialize = {
+    "xml": _serialize_xml,
+    "html": _serialize_html,
+    "text": _serialize_text,
+# this optional method is imported at the end of the module
+#   "c14n": _serialize_c14n,
+}
+
+
+def register_namespace(prefix, uri):
+    """Register a namespace prefix.
+
+    The registry is global, and any existing mapping for either the
+    given prefix or the namespace URI will be removed.
+
+    *prefix* is the namespace prefix, *uri* is a namespace uri. Tags and
+    attributes in this namespace will be serialized with prefix if possible.
+
+    ValueError is raised if prefix is reserved or is invalid.
+
+    """
+    if re.match(r"ns\d+$", prefix):
+        raise ValueError("Prefix format reserved for internal use")
+    for k, v in list(_namespace_map.items()):
+        if k == uri or v == prefix:
+            del _namespace_map[k]
+    _namespace_map[uri] = prefix
+
+_namespace_map = {
+    # "well-known" namespace prefixes
+    "http://www.w3.org/XML/1998/namespace": "xml",
+    "http://www.w3.org/1999/xhtml": "html",
+    "http://www.w3.org/1999/02/22-rdf-syntax-ns#": "rdf",
+    "http://schemas.xmlsoap.org/wsdl/": "wsdl",
+    # xml schema
+    "http://www.w3.org/2001/XMLSchema": "xs",
+    "http://www.w3.org/2001/XMLSchema-instance": "xsi",
+    # dublin core
+    "http://purl.org/dc/elements/1.1/": "dc",
+}
+# For tests and troubleshooting
+register_namespace._namespace_map = _namespace_map
+
+def _raise_serialization_error(text):
+    raise TypeError(
+        "cannot serialize %r (type %s)" % (text, type(text).__name__)
+        )
+
+def _escape_cdata(text):
+    # escape character data
+    try:
+        # it's worth avoiding do-nothing calls for strings that are
+        # shorter than 500 characters, or so.  assume that's, by far,
+        # the most common case in most applications.
+        if "&" in text:
+            text = text.replace("&", "&amp;")
+        if "<" in text:
+            text = text.replace("<", "&lt;")
+        if ">" in text:
+            text = text.replace(">", "&gt;")
+        return text
+    except (TypeError, AttributeError):
+        _raise_serialization_error(text)
+
+def _escape_attrib(text):
+    # escape attribute value
+    try:
+        if "&" in text:
+            text = text.replace("&", "&amp;")
+        if "<" in text:
+            text = text.replace("<", "&lt;")
+        if ">" in text:
+            text = text.replace(">", "&gt;")
+        if "\"" in text:
+            text = text.replace("\"", "&quot;")
+        # Although section 2.11 of the XML specification states that CR or
+        # CR LN should be replaced with just LN, it applies only to EOLNs
+        # which take part of organizing file into lines. Within attributes,
+        # we are replacing these with entity numbers, so they do not count.
+        # http://www.w3.org/TR/REC-xml/#sec-line-ends
+        # The current solution, contained in following six lines, was
+        # discussed in issue 17582 and 39011.
+        if "\r" in text:
+            text = text.replace("\r", "&#13;")
+        if "\n" in text:
+            text = text.replace("\n", "&#10;")
+        if "\t" in text:
+            text = text.replace("\t", "&#09;")
+        return text
+    except (TypeError, AttributeError):
+        _raise_serialization_error(text)
+
+def _escape_attrib_html(text):
+    # escape attribute value
+    try:
+        if "&" in text:
+            text = text.replace("&", "&amp;")
+        if ">" in text:
+            text = text.replace(">", "&gt;")
+        if "\"" in text:
+            text = text.replace("\"", "&quot;")
+        return text
+    except (TypeError, AttributeError):
+        _raise_serialization_error(text)
+
+# --------------------------------------------------------------------
+
+def tostring(element, encoding=None, method=None, *,
+             xml_declaration=None, default_namespace=None,
+             short_empty_elements=True):
+    """Generate string representation of XML element.
+
+    All subelements are included.  If encoding is "unicode", a string
+    is returned. Otherwise a bytestring is returned.
+
+    *element* is an Element instance, *encoding* is an optional output
+    encoding defaulting to US-ASCII, *method* is an optional output which can
+    be one of "xml" (default), "html", "text" or "c14n", *default_namespace*
+    sets the default XML namespace (for "xmlns").
+
+    Returns an (optionally) encoded string containing the XML data.
+
+    """
+    stream = io.StringIO() if encoding == 'unicode' else io.BytesIO()
+    ElementTree(element).write(stream, encoding,
+                               xml_declaration=xml_declaration,
+                               default_namespace=default_namespace,
+                               method=method,
+                               short_empty_elements=short_empty_elements)
+    return stream.getvalue()
+
+class _ListDataStream(io.BufferedIOBase):
+    """An auxiliary stream accumulating into a list reference."""
+    def __init__(self, lst):
+        self.lst = lst
+
+    def writable(self):
+        return True
+
+    def seekable(self):
+        return True
+
+    def write(self, b):
+        self.lst.append(b)
+
+    def tell(self):
+        return len(self.lst)
+
+def tostringlist(element, encoding=None, method=None, *,
+                 xml_declaration=None, default_namespace=None,
+                 short_empty_elements=True):
+    lst = []
+    stream = _ListDataStream(lst)
+    ElementTree(element).write(stream, encoding,
+                               xml_declaration=xml_declaration,
+                               default_namespace=default_namespace,
+                               method=method,
+                               short_empty_elements=short_empty_elements)
+    return lst
+
+
+def dump(elem):
+    """Write element tree or element structure to sys.stdout.
+
+    This function should be used for debugging only.
+
+    *elem* is either an ElementTree, or a single Element.  The exact output
+    format is implementation dependent.  In this version, it's written as an
+    ordinary XML file.
+
+    """
+    # debugging
+    if not isinstance(elem, ElementTree):
+        elem = ElementTree(elem)
+    elem.write(sys.stdout, encoding="unicode")
+    tail = elem.getroot().tail
+    if not tail or tail[-1] != "\n":
+        sys.stdout.write("\n")
+
+
+def indent(tree, space="  ", level=0):
+    """Indent an XML document by inserting newlines and indentation space
+    after elements.
+
+    *tree* is the ElementTree or Element to modify.  The (root) element
+    itself will not be changed, but the tail text of all elements in its
+    subtree will be adapted.
+
+    *space* is the whitespace to insert for each indentation level, two
+    space characters by default.
+
+    *level* is the initial indentation level. Setting this to a higher
+    value than 0 can be used for indenting subtrees that are more deeply
+    nested inside of a document.
+    """
+    if isinstance(tree, ElementTree):
+        tree = tree.getroot()
+    if level < 0:
+        raise ValueError(f"Initial indentation level must be >= 0, got {level}")
+    if not len(tree):
+        return
+
+    # Reduce the memory consumption by reusing indentation strings.
+    indentations = ["\n" + level * space]
+
+    def _indent_children(elem, level):
+        # Start a new indentation level for the first child.
+        child_level = level + 1
+        try:
+            child_indentation = indentations[child_level]
+        except IndexError:
+            child_indentation = indentations[level] + space
+            indentations.append(child_indentation)
+
+        if not elem.text or not elem.text.strip():
+            elem.text = child_indentation
+
+        for child in elem:
+            if len(child):
+                _indent_children(child, child_level)
+            if not child.tail or not child.tail.strip():
+                child.tail = child_indentation
+
+        # Dedent after the last child by overwriting the previous indentation.
+        if not child.tail.strip():
+            child.tail = indentations[level]
+
+    _indent_children(tree, 0)
+
+
+# --------------------------------------------------------------------
+# parsing
+
+
+def parse(source, parser=None):
+    """Parse XML document into element tree.
+
+    *source* is a filename or file object containing XML data,
+    *parser* is an optional parser instance defaulting to XMLParser.
+
+    Return an ElementTree instance.
+
+    """
+    tree = ElementTree()
+    tree.parse(source, parser)
+    return tree
+
+
+def iterparse(source, events=None, parser=None):
+    """Incrementally parse XML document into ElementTree.
+
+    This class also reports what's going on to the user based on the
+    *events* it is initialized with.  The supported events are the strings
+    "start", "end", "start-ns" and "end-ns" (the "ns" events are used to get
+    detailed namespace information).  If *events* is omitted, only
+    "end" events are reported.
+
+    *source* is a filename or file object containing XML data, *events* is
+    a list of events to report back, *parser* is an optional parser instance.
+
+    Returns an iterator providing (event, elem) pairs.
+
+    """
+    # Use the internal, undocumented _parser argument for now; When the
+    # parser argument of iterparse is removed, this can be killed.
+    pullparser = XMLPullParser(events=events, _parser=parser)
+
+    def iterator(source):
+        close_source = False
+        try:
+            if not hasattr(source, "read"):
+                source = open(source, "rb")
+                close_source = True
+            yield None
+            while True:
+                yield from pullparser.read_events()
+                # load event buffer
+                data = source.read(16 * 1024)
+                if not data:
+                    break
+                pullparser.feed(data)
+            root = pullparser._close_and_return_root()
+            yield from pullparser.read_events()
+            it.root = root
+        finally:
+            if close_source:
+                source.close()
+
+    class IterParseIterator(collections.abc.Iterator):
+        __next__ = iterator(source).__next__
+    it = IterParseIterator()
+    it.root = None
+    del iterator, IterParseIterator
+
+    next(it)
+    return it
+
+
+class XMLPullParser:
+
+    def __init__(self, events=None, *, _parser=None):
+        # The _parser argument is for internal use only and must not be relied
+        # upon in user code. It will be removed in a future release.
+        # See https://bugs.python.org/issue17741 for more details.
+
+        self._events_queue = collections.deque()
+        self._parser = _parser or XMLParser(target=TreeBuilder())
+        # wire up the parser for event reporting
+        if events is None:
+            events = ("end",)
+        self._parser._setevents(self._events_queue, events)
+
+    def feed(self, data):
+        """Feed encoded data to parser."""
+        if self._parser is None:
+            raise ValueError("feed() called after end of stream")
+        if data:
+            try:
+                self._parser.feed(data)
+            except SyntaxError as exc:
+                self._events_queue.append(exc)
+
+    def _close_and_return_root(self):
+        # iterparse needs this to set its root attribute properly :(
+        root = self._parser.close()
+        self._parser = None
+        return root
+
+    def close(self):
+        """Finish feeding data to parser.
+
+        Unlike XMLParser, does not return the root element. Use
+        read_events() to consume elements from XMLPullParser.
+        """
+        self._close_and_return_root()
+
+    def read_events(self):
+        """Return an iterator over currently available (event, elem) pairs.
+
+        Events are consumed from the internal event queue as they are
+        retrieved from the iterator.
+        """
+        events = self._events_queue
+        while events:
+            event = events.popleft()
+            if isinstance(event, Exception):
+                raise event
+            else:
+                yield event
+
+    def flush(self):
+        if self._parser is None:
+            raise ValueError("flush() called after end of stream")
+        self._parser.flush()
+
+
+def XML(text, parser=None):
+    """Parse XML document from string constant.
+
+    This function can be used to embed "XML Literals" in Python code.
+
+    *text* is a string containing XML data, *parser* is an
+    optional parser instance, defaulting to the standard XMLParser.
+
+    Returns an Element instance.
+
+    """
+    if not parser:
+        parser = XMLParser(target=TreeBuilder())
+    parser.feed(text)
+    return parser.close()
+
+
+def XMLID(text, parser=None):
+    """Parse XML document from string constant for its IDs.
+
+    *text* is a string containing XML data, *parser* is an
+    optional parser instance, defaulting to the standard XMLParser.
+
+    Returns an (Element, dict) tuple, in which the
+    dict maps element id:s to elements.
+
+    """
+    if not parser:
+        parser = XMLParser(target=TreeBuilder())
+    parser.feed(text)
+    tree = parser.close()
+    ids = {}
+    for elem in tree.iter():
+        id = elem.get("id")
+        if id:
+            ids[id] = elem
+    return tree, ids
+
+# Parse XML document from string constant.  Alias for XML().
+fromstring = XML
+
+def fromstringlist(sequence, parser=None):
+    """Parse XML document from sequence of string fragments.
+
+    *sequence* is a list of other sequence, *parser* is an optional parser
+    instance, defaulting to the standard XMLParser.
+
+    Returns an Element instance.
+
+    """
+    if not parser:
+        parser = XMLParser(target=TreeBuilder())
+    for text in sequence:
+        parser.feed(text)
+    return parser.close()
+
+# --------------------------------------------------------------------
+
+
+class TreeBuilder:
+    """Generic element structure builder.
+
+    This builder converts a sequence of start, data, and end method
+    calls to a well-formed element structure.
+
+    You can use this class to build an element structure using a custom XML
+    parser, or a parser for some other XML-like format.
+
+    *element_factory* is an optional element factory which is called
+    to create new Element instances, as necessary.
+
+    *comment_factory* is a factory to create comments to be used instead of
+    the standard factory.  If *insert_comments* is false (the default),
+    comments will not be inserted into the tree.
+
+    *pi_factory* is a factory to create processing instructions to be used
+    instead of the standard factory.  If *insert_pis* is false (the default),
+    processing instructions will not be inserted into the tree.
+    """
+    def __init__(self, element_factory=None, *,
+                 comment_factory=None, pi_factory=None,
+                 insert_comments=False, insert_pis=False):
+        self._data = [] # data collector
+        self._elem = [] # element stack
+        self._last = None # last element
+        self._root = None # root element
+        self._tail = None # true if we're after an end tag
+        if comment_factory is None:
+            comment_factory = Comment
+        self._comment_factory = comment_factory
+        self.insert_comments = insert_comments
+        if pi_factory is None:
+            pi_factory = ProcessingInstruction
+        self._pi_factory = pi_factory
+        self.insert_pis = insert_pis
+        if element_factory is None:
+            element_factory = Element
+        self._factory = element_factory
+
+    def close(self):
+        """Flush builder buffers and return toplevel document Element."""
+        assert len(self._elem) == 0, "missing end tags"
+        assert self._root is not None, "missing toplevel element"
+        return self._root
+
+    def _flush(self):
+        if self._data:
+            if self._last is not None:
+                text = "".join(self._data)
+                if self._tail:
+                    assert self._last.tail is None, "internal error (tail)"
+                    self._last.tail = text
+                else:
+                    assert self._last.text is None, "internal error (text)"
+                    self._last.text = text
+            self._data = []
+
+    def data(self, data):
+        """Add text to current element."""
+        self._data.append(data)
+
+    def start(self, tag, attrs):
+        """Open new element and return it.
+
+        *tag* is the element name, *attrs* is a dict containing element
+        attributes.
+
+        """
+        self._flush()
+        self._last = elem = self._factory(tag, attrs)
+        if self._elem:
+            self._elem[-1].append(elem)
+        elif self._root is None:
+            self._root = elem
+        self._elem.append(elem)
+        self._tail = 0
+        return elem
+
+    def end(self, tag):
+        """Close and return current Element.
+
+        *tag* is the element name.
+
+        """
+        self._flush()
+        self._last = self._elem.pop()
+        assert self._last.tag == tag,\
+               "end tag mismatch (expected %s, got %s)" % (
+                   self._last.tag, tag)
+        self._tail = 1
+        return self._last
+
+    def comment(self, text):
+        """Create a comment using the comment_factory.
+
+        *text* is the text of the comment.
+        """
+        return self._handle_single(
+            self._comment_factory, self.insert_comments, text)
+
+    def pi(self, target, text=None):
+        """Create a processing instruction using the pi_factory.
+
+        *target* is the target name of the processing instruction.
+        *text* is the data of the processing instruction, or ''.
+        """
+        return self._handle_single(
+            self._pi_factory, self.insert_pis, target, text)
+
+    def _handle_single(self, factory, insert, *args):
+        elem = factory(*args)
+        if insert:
+            self._flush()
+            self._last = elem
+            if self._elem:
+                self._elem[-1].append(elem)
+            self._tail = 1
+        return elem
+
+
+# also see ElementTree and TreeBuilder
+class XMLParser:
+    """Element structure builder for XML source data based on the expat parser.
+
+    *target* is an optional target object which defaults to an instance of the
+    standard TreeBuilder class, *encoding* is an optional encoding string
+    which if given, overrides the encoding specified in the XML file:
+    http://www.iana.org/assignments/character-sets
+
+    """
+
+    def __init__(self, *, target=None, encoding=None):
+        try:
+            from xml.parsers import expat
+        except ImportError:
+            try:
+                import pyexpat as expat
+            except ImportError:
+                raise ImportError(
+                    "No module named expat; use SimpleXMLTreeBuilder instead"
+                    )
+        parser = expat.ParserCreate(encoding, "}")
+        if target is None:
+            target = TreeBuilder()
+        # underscored names are provided for compatibility only
+        self.parser = self._parser = parser
+        self.target = self._target = target
+        self._error = expat.error
+        self._names = {} # name memo cache
+        # main callbacks
+        parser.DefaultHandlerExpand = self._default
+        if hasattr(target, 'start'):
+            parser.StartElementHandler = self._start
+        if hasattr(target, 'end'):
+            parser.EndElementHandler = self._end
+        if hasattr(target, 'start_ns'):
+            parser.StartNamespaceDeclHandler = self._start_ns
+        if hasattr(target, 'end_ns'):
+            parser.EndNamespaceDeclHandler = self._end_ns
+        if hasattr(target, 'data'):
+            parser.CharacterDataHandler = target.data
+        # miscellaneous callbacks
+        if hasattr(target, 'comment'):
+            parser.CommentHandler = target.comment
+        if hasattr(target, 'pi'):
+            parser.ProcessingInstructionHandler = target.pi
+        # Configure pyexpat: buffering, new-style attribute handling.
+        parser.buffer_text = 1
+        parser.ordered_attributes = 1
+        self._doctype = None
+        self.entity = {}
+        try:
+            self.version = "Expat %d.%d.%d" % expat.version_info
+        except AttributeError:
+            pass # unknown
+
+    def _setevents(self, events_queue, events_to_report):
+        # Internal API for XMLPullParser
+        # events_to_report: a list of events to report during parsing (same as
+        # the *events* of XMLPullParser's constructor.
+        # events_queue: a list of actual parsing events that will be populated
+        # by the underlying parser.
+        #
+        parser = self._parser
+        append = events_queue.append
+        for event_name in events_to_report:
+            if event_name == "start":
+                parser.ordered_attributes = 1
+                def handler(tag, attrib_in, event=event_name, append=append,
+                            start=self._start):
+                    append((event, start(tag, attrib_in)))
+                parser.StartElementHandler = handler
+            elif event_name == "end":
+                def handler(tag, event=event_name, append=append,
+                            end=self._end):
+                    append((event, end(tag)))
+                parser.EndElementHandler = handler
+            elif event_name == "start-ns":
+                # TreeBuilder does not implement .start_ns()
+                if hasattr(self.target, "start_ns"):
+                    def handler(prefix, uri, event=event_name, append=append,
+                                start_ns=self._start_ns):
+                        append((event, start_ns(prefix, uri)))
+                else:
+                    def handler(prefix, uri, event=event_name, append=append):
+                        append((event, (prefix or '', uri or '')))
+                parser.StartNamespaceDeclHandler = handler
+            elif event_name == "end-ns":
+                # TreeBuilder does not implement .end_ns()
+                if hasattr(self.target, "end_ns"):
+                    def handler(prefix, event=event_name, append=append,
+                                end_ns=self._end_ns):
+                        append((event, end_ns(prefix)))
+                else:
+                    def handler(prefix, event=event_name, append=append):
+                        append((event, None))
+                parser.EndNamespaceDeclHandler = handler
+            elif event_name == 'comment':
+                def handler(text, event=event_name, append=append, self=self):
+                    append((event, self.target.comment(text)))
+                parser.CommentHandler = handler
+            elif event_name == 'pi':
+                def handler(pi_target, data, event=event_name, append=append,
+                            self=self):
+                    append((event, self.target.pi(pi_target, data)))
+                parser.ProcessingInstructionHandler = handler
+            else:
+                raise ValueError("unknown event %r" % event_name)
+
+    def _raiseerror(self, value):
+        err = ParseError(value)
+        err.code = value.code
+        err.position = value.lineno, value.offset
+        raise err
+
+    def _fixname(self, key):
+        # expand qname, and convert name string to ascii, if possible
+        try:
+            name = self._names[key]
+        except KeyError:
+            name = key
+            if "}" in name:
+                name = "{" + name
+            self._names[key] = name
+        return name
+
+    def _start_ns(self, prefix, uri):
+        return self.target.start_ns(prefix or '', uri or '')
+
+    def _end_ns(self, prefix):
+        return self.target.end_ns(prefix or '')
+
+    def _start(self, tag, attr_list):
+        # Handler for expat's StartElementHandler. Since ordered_attributes
+        # is set, the attributes are reported as a list of alternating
+        # attribute name,value.
+        fixname = self._fixname
+        tag = fixname(tag)
+        attrib = {}
+        if attr_list:
+            for i in range(0, len(attr_list), 2):
+                attrib[fixname(attr_list[i])] = attr_list[i+1]
+        return self.target.start(tag, attrib)
+
+    def _end(self, tag):
+        return self.target.end(self._fixname(tag))
+
+    def _default(self, text):
+        prefix = text[:1]
+        if prefix == "&":
+            # deal with undefined entities
+            try:
+                data_handler = self.target.data
+            except AttributeError:
+                return
+            try:
+                data_handler(self.entity[text[1:-1]])
+            except KeyError:
+                from xml.parsers import expat
+                err = expat.error(
+                    "undefined entity %s: line %d, column %d" %
+                    (text, self.parser.ErrorLineNumber,
+                    self.parser.ErrorColumnNumber)
+                    )
+                err.code = 11 # XML_ERROR_UNDEFINED_ENTITY
+                err.lineno = self.parser.ErrorLineNumber
+                err.offset = self.parser.ErrorColumnNumber
+                raise err
+        elif prefix == "<" and text[:9] == "<!DOCTYPE":
+            self._doctype = [] # inside a doctype declaration
+        elif self._doctype is not None:
+            # parse doctype contents
+            if prefix == ">":
+                self._doctype = None
+                return
+            text = text.strip()
+            if not text:
+                return
+            self._doctype.append(text)
+            n = len(self._doctype)
+            if n > 2:
+                type = self._doctype[1]
+                if type == "PUBLIC" and n == 4:
+                    name, type, pubid, system = self._doctype
+                    if pubid:
+                        pubid = pubid[1:-1]
+                elif type == "SYSTEM" and n == 3:
+                    name, type, system = self._doctype
+                    pubid = None
+                else:
+                    return
+                if hasattr(self.target, "doctype"):
+                    self.target.doctype(name, pubid, system[1:-1])
+                elif hasattr(self, "doctype"):
+                    warnings.warn(
+                        "The doctype() method of XMLParser is ignored.  "
+                        "Define doctype() method on the TreeBuilder target.",
+                        RuntimeWarning)
+
+                self._doctype = None
+
+    def feed(self, data):
+        """Feed encoded data to parser."""
+        try:
+            self.parser.Parse(data, False)
+        except self._error as v:
+            self._raiseerror(v)
+
+    def close(self):
+        """Finish feeding data to parser and return element structure."""
+        try:
+            self.parser.Parse(b"", True) # end of data
+        except self._error as v:
+            self._raiseerror(v)
+        try:
+            close_handler = self.target.close
+        except AttributeError:
+            pass
+        else:
+            return close_handler()
+        finally:
+            # get rid of circular references
+            del self.parser, self._parser
+            del self.target, self._target
+
+    def flush(self):
+        was_enabled = self.parser.GetReparseDeferralEnabled()
+        try:
+            self.parser.SetReparseDeferralEnabled(False)
+            self.parser.Parse(b"", False)
+        except self._error as v:
+            self._raiseerror(v)
+        finally:
+            self.parser.SetReparseDeferralEnabled(was_enabled)
+
+# --------------------------------------------------------------------
+# C14N 2.0
+
+def canonicalize(xml_data=None, *, out=None, from_file=None, **options):
+    """Convert XML to its C14N 2.0 serialised form.
+
+    If *out* is provided, it must be a file or file-like object that receives
+    the serialised canonical XML output (text, not bytes) through its ``.write()``
+    method.  To write to a file, open it in text mode with encoding "utf-8".
+    If *out* is not provided, this function returns the output as text string.
+
+    Either *xml_data* (an XML string) or *from_file* (a file path or
+    file-like object) must be provided as input.
+
+    The configuration options are the same as for the ``C14NWriterTarget``.
+    """
+    if xml_data is None and from_file is None:
+        raise ValueError("Either 'xml_data' or 'from_file' must be provided as input")
+    sio = None
+    if out is None:
+        sio = out = io.StringIO()
+
+    parser = XMLParser(target=C14NWriterTarget(out.write, **options))
+
+    if xml_data is not None:
+        parser.feed(xml_data)
+        parser.close()
+    elif from_file is not None:
+        parse(from_file, parser=parser)
+
+    return sio.getvalue() if sio is not None else None
+
+
+_looks_like_prefix_name = re.compile(r'^\w+:\w+$', re.UNICODE).match
+
+
+class C14NWriterTarget:
+    """
+    Canonicalization writer target for the XMLParser.
+
+    Serialises parse events to XML C14N 2.0.
+
+    The *write* function is used for writing out the resulting data stream
+    as text (not bytes).  To write to a file, open it in text mode with encoding
+    "utf-8" and pass its ``.write`` method.
+
+    Configuration options:
+
+    - *with_comments*: set to true to include comments
+    - *strip_text*: set to true to strip whitespace before and after text content
+    - *rewrite_prefixes*: set to true to replace namespace prefixes by "n{number}"
+    - *qname_aware_tags*: a set of qname aware tag names in which prefixes
+                          should be replaced in text content
+    - *qname_aware_attrs*: a set of qname aware attribute names in which prefixes
+                           should be replaced in text content
+    - *exclude_attrs*: a set of attribute names that should not be serialised
+    - *exclude_tags*: a set of tag names that should not be serialised
+    """
+    def __init__(self, write, *,
+                 with_comments=False, strip_text=False, rewrite_prefixes=False,
+                 qname_aware_tags=None, qname_aware_attrs=None,
+                 exclude_attrs=None, exclude_tags=None):
+        self._write = write
+        self._data = []
+        self._with_comments = with_comments
+        self._strip_text = strip_text
+        self._exclude_attrs = set(exclude_attrs) if exclude_attrs else None
+        self._exclude_tags = set(exclude_tags) if exclude_tags else None
+
+        self._rewrite_prefixes = rewrite_prefixes
+        if qname_aware_tags:
+            self._qname_aware_tags = set(qname_aware_tags)
+        else:
+            self._qname_aware_tags = None
+        if qname_aware_attrs:
+            self._find_qname_aware_attrs = set(qname_aware_attrs).intersection
+        else:
+            self._find_qname_aware_attrs = None
+
+        # Stack with globally and newly declared namespaces as (uri, prefix) pairs.
+        self._declared_ns_stack = [[
+            ("http://www.w3.org/XML/1998/namespace", "xml"),
+        ]]
+        # Stack with user declared namespace prefixes as (uri, prefix) pairs.
+        self._ns_stack = []
+        if not rewrite_prefixes:
+            self._ns_stack.append(list(_namespace_map.items()))
+        self._ns_stack.append([])
+        self._prefix_map = {}
+        self._preserve_space = [False]
+        self._pending_start = None
+        self._root_seen = False
+        self._root_done = False
+        self._ignored_depth = 0
+
+    def _iter_namespaces(self, ns_stack, _reversed=reversed):
+        for namespaces in _reversed(ns_stack):
+            if namespaces:  # almost no element declares new namespaces
+                yield from namespaces
+
+    def _resolve_prefix_name(self, prefixed_name):
+        prefix, name = prefixed_name.split(':', 1)
+        for uri, p in self._iter_namespaces(self._ns_stack):
+            if p == prefix:
+                return f'{{{uri}}}{name}'
+        raise ValueError(f'Prefix {prefix} of QName "{prefixed_name}" is not declared in scope')
+
+    def _qname(self, qname, uri=None):
+        if uri is None:
+            uri, tag = qname[1:].rsplit('}', 1) if qname[:1] == '{' else ('', qname)
+        else:
+            tag = qname
+
+        prefixes_seen = set()
+        for u, prefix in self._iter_namespaces(self._declared_ns_stack):
+            if u == uri and prefix not in prefixes_seen:
+                return f'{prefix}:{tag}' if prefix else tag, tag, uri
+            prefixes_seen.add(prefix)
+
+        # Not declared yet => add new declaration.
+        if self._rewrite_prefixes:
+            if uri in self._prefix_map:
+                prefix = self._prefix_map[uri]
+            else:
+                prefix = self._prefix_map[uri] = f'n{len(self._prefix_map)}'
+            self._declared_ns_stack[-1].append((uri, prefix))
+            return f'{prefix}:{tag}', tag, uri
+
+        if not uri and '' not in prefixes_seen:
+            # No default namespace declared => no prefix needed.
+            return tag, tag, uri
+
+        for u, prefix in self._iter_namespaces(self._ns_stack):
+            if u == uri:
+                self._declared_ns_stack[-1].append((uri, prefix))
+                return f'{prefix}:{tag}' if prefix else tag, tag, uri
+
+        if not uri:
+            # As soon as a default namespace is defined,
+            # anything that has no namespace (and thus, no prefix) goes there.
+            return tag, tag, uri
+
+        raise ValueError(f'Namespace "{uri}" is not declared in scope')
+
+    def data(self, data):
+        if not self._ignored_depth:
+            self._data.append(data)
+
+    def _flush(self, _join_text=''.join):
+        data = _join_text(self._data)
+        del self._data[:]
+        if self._strip_text and not self._preserve_space[-1]:
+            data = data.strip()
+        if self._pending_start is not None:
+            args, self._pending_start = self._pending_start, None
+            qname_text = data if data and _looks_like_prefix_name(data) else None
+            self._start(*args, qname_text)
+            if qname_text is not None:
+                return
+        if data and self._root_seen:
+            self._write(_escape_cdata_c14n(data))
+
+    def start_ns(self, prefix, uri):
+        if self._ignored_depth:
+            return
+        # we may have to resolve qnames in text content
+        if self._data:
+            self._flush()
+        self._ns_stack[-1].append((uri, prefix))
+
+    def start(self, tag, attrs):
+        if self._exclude_tags is not None and (
+                self._ignored_depth or tag in self._exclude_tags):
+            self._ignored_depth += 1
+            return
+        if self._data:
+            self._flush()
+
+        new_namespaces = []
+        self._declared_ns_stack.append(new_namespaces)
+
+        if self._qname_aware_tags is not None and tag in self._qname_aware_tags:
+            # Need to parse text first to see if it requires a prefix declaration.
+            self._pending_start = (tag, attrs, new_namespaces)
+            return
+        self._start(tag, attrs, new_namespaces)
+
+    def _start(self, tag, attrs, new_namespaces, qname_text=None):
+        if self._exclude_attrs is not None and attrs:
+            attrs = {k: v for k, v in attrs.items() if k not in self._exclude_attrs}
+
+        qnames = {tag, *attrs}
+        resolved_names = {}
+
+        # Resolve prefixes in attribute and tag text.
+        if qname_text is not None:
+            qname = resolved_names[qname_text] = self._resolve_prefix_name(qname_text)
+            qnames.add(qname)
+        if self._find_qname_aware_attrs is not None and attrs:
+            qattrs = self._find_qname_aware_attrs(attrs)
+            if qattrs:
+                for attr_name in qattrs:
+                    value = attrs[attr_name]
+                    if _looks_like_prefix_name(value):
+                        qname = resolved_names[value] = self._resolve_prefix_name(value)
+                        qnames.add(qname)
+            else:
+                qattrs = None
+        else:
+            qattrs = None
+
+        # Assign prefixes in lexicographical order of used URIs.
+        parse_qname = self._qname
+        parsed_qnames = {n: parse_qname(n) for n in sorted(
+            qnames, key=lambda n: n.split('}', 1))}
+
+        # Write namespace declarations in prefix order ...
+        if new_namespaces:
+            attr_list = [
+                ('xmlns:' + prefix if prefix else 'xmlns', uri)
+                for uri, prefix in new_namespaces
+            ]
+            attr_list.sort()
+        else:
+            # almost always empty
+            attr_list = []
+
+        # ... followed by attributes in URI+name order
+        if attrs:
+            for k, v in sorted(attrs.items()):
+                if qattrs is not None and k in qattrs and v in resolved_names:
+                    v = parsed_qnames[resolved_names[v]][0]
+                attr_qname, attr_name, uri = parsed_qnames[k]
+                # No prefix for attributes in default ('') namespace.
+                attr_list.append((attr_qname if uri else attr_name, v))
+
+        # Honour xml:space attributes.
+        space_behaviour = attrs.get('{http://www.w3.org/XML/1998/namespace}space')
+        self._preserve_space.append(
+            space_behaviour == 'preserve' if space_behaviour
+            else self._preserve_space[-1])
+
+        # Write the tag.
+        write = self._write
+        write('<' + parsed_qnames[tag][0])
+        if attr_list:
+            write(''.join([f' {k}="{_escape_attrib_c14n(v)}"' for k, v in attr_list]))
+        write('>')
+
+        # Write the resolved qname text content.
+        if qname_text is not None:
+            write(_escape_cdata_c14n(parsed_qnames[resolved_names[qname_text]][0]))
+
+        self._root_seen = True
+        self._ns_stack.append([])
+
+    def end(self, tag):
+        if self._ignored_depth:
+            self._ignored_depth -= 1
+            return
+        if self._data:
+            self._flush()
+        self._write(f'</{self._qname(tag)[0]}>')
+        self._preserve_space.pop()
+        self._root_done = len(self._preserve_space) == 1
+        self._declared_ns_stack.pop()
+        self._ns_stack.pop()
+
+    def comment(self, text):
+        if not self._with_comments:
+            return
+        if self._ignored_depth:
+            return
+        if self._root_done:
+            self._write('\n')
+        elif self._root_seen and self._data:
+            self._flush()
+        self._write(f'<!--{_escape_cdata_c14n(text)}-->')
+        if not self._root_seen:
+            self._write('\n')
+
+    def pi(self, target, data):
+        if self._ignored_depth:
+            return
+        if self._root_done:
+            self._write('\n')
+        elif self._root_seen and self._data:
+            self._flush()
+        self._write(
+            f'<?{target} {_escape_cdata_c14n(data)}?>' if data else f'<?{target}?>')
+        if not self._root_seen:
+            self._write('\n')
+
+
+def _escape_cdata_c14n(text):
+    # escape character data
+    try:
+        # it's worth avoiding do-nothing calls for strings that are
+        # shorter than 500 character, or so.  assume that's, by far,
+        # the most common case in most applications.
+        if '&' in text:
+            text = text.replace('&', '&amp;')
+        if '<' in text:
+            text = text.replace('<', '&lt;')
+        if '>' in text:
+            text = text.replace('>', '&gt;')
+        if '\r' in text:
+            text = text.replace('\r', '&#xD;')
+        return text
+    except (TypeError, AttributeError):
+        _raise_serialization_error(text)
+
+
+def _escape_attrib_c14n(text):
+    # escape attribute value
+    try:
+        if '&' in text:
+            text = text.replace('&', '&amp;')
+        if '<' in text:
+            text = text.replace('<', '&lt;')
+        if '"' in text:
+            text = text.replace('"', '&quot;')
+        if '\t' in text:
+            text = text.replace('\t', '&#x9;')
+        if '\n' in text:
+            text = text.replace('\n', '&#xA;')
+        if '\r' in text:
+            text = text.replace('\r', '&#xD;')
+        return text
+    except (TypeError, AttributeError):
+        _raise_serialization_error(text)
+
+
+# --------------------------------------------------------------------
+
+# Import the C accelerators
+try:
+    # Element is going to be shadowed by the C implementation. We need to keep
+    # the Python version of it accessible for some "creative" by external code
+    # (see tests)
+    _Element_Py = Element
+
+    # Element, SubElement, ParseError, TreeBuilder, XMLParser, _set_factories
+    from _elementtree import *
+    from _elementtree import _set_factories
+except ImportError:
+    pass
+else:
+    _set_factories(Comment, ProcessingInstruction)
diff --git a/shared/package-files/40/407ac4b31c719b56cf779765b4f79370d3ac301bb411cc5ada87ba1885fe9ef9/_transforms.py b/shared/package-files/40/407ac4b31c719b56cf779765b4f79370d3ac301bb411cc5ada87ba1885fe9ef9/_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..802cbd3d7783d6117fa2d5361d55db567aba43d5
--- /dev/null
+++ b/shared/package-files/40/407ac4b31c719b56cf779765b4f79370d3ac301bb411cc5ada87ba1885fe9ef9/_transforms.py
@@ -0,0 +1,2137 @@
+# -*- coding: utf-8 -*-
+
+import math
+import warnings
+from typing import Callable, Optional, Sequence, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.nn.modules.lazy import LazyModuleMixin
+from torch.nn.parameter import UninitializedParameter
+
+from torchaudio import functional as F
+from torchaudio.functional.functional import (
+    _apply_sinc_resample_kernel,
+    _check_convolve_mode,
+    _fix_waveform_shape,
+    _get_sinc_resample_kernel,
+    _stretch_waveform,
+)
+
+__all__ = []
+
+
+class Spectrogram(torch.nn.Module):
+    r"""Create a spectrogram from a audio signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
+        win_length (int or None, optional): Window size. (Default: ``n_fft``)
+        hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
+        pad (int, optional): Two sided padding of signal. (Default: ``0``)
+        window_fn (Callable[..., Tensor], optional): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        power (float or None, optional): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
+            If None, then the complex spectrum is returned instead. (Default: ``2``)
+        normalized (bool or str, optional): Whether to normalize by magnitude after stft. If input is str, choices are
+            ``"window"`` and ``"frame_length"``, if specific normalization type is desirable. ``True`` maps to
+            ``"window"``. (Default: ``False``)
+        wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
+        center (bool, optional): whether to pad :attr:`waveform` on both sides so
+            that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
+            (Default: ``True``)
+        pad_mode (string, optional): controls the padding method used when
+            :attr:`center` is ``True``. (Default: ``"reflect"``)
+        onesided (bool, optional): controls whether to return half of results to
+            avoid redundancy (Default: ``True``)
+        return_complex (bool, optional):
+            Deprecated and not used.
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = torchaudio.transforms.Spectrogram(n_fft=800)
+        >>> spectrogram = transform(waveform)
+
+    """
+    __constants__ = ["n_fft", "win_length", "hop_length", "pad", "power", "normalized"]
+
+    def __init__(
+        self,
+        n_fft: int = 400,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        pad: int = 0,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        power: Optional[float] = 2.0,
+        normalized: Union[bool, str] = False,
+        wkwargs: Optional[dict] = None,
+        center: bool = True,
+        pad_mode: str = "reflect",
+        onesided: bool = True,
+        return_complex: Optional[bool] = None,
+    ) -> None:
+        super(Spectrogram, self).__init__()
+        torch._C._log_api_usage_once("torchaudio.transforms.Spectrogram")
+        self.n_fft = n_fft
+        # number of FFT bins. the returned STFT result will have n_fft // 2 + 1
+        # number of frequencies due to onesided=True in torch.stft
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer("window", window)
+        self.pad = pad
+        self.power = power
+        self.normalized = normalized
+        self.center = center
+        self.pad_mode = pad_mode
+        self.onesided = onesided
+        if return_complex is not None:
+            warnings.warn(
+                "`return_complex` argument is now deprecated and is not effective."
+                "`torchaudio.transforms.Spectrogram(power=None)` always returns a tensor with "
+                "complex dtype. Please remove the argument in the function call."
+            )
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension (..., time).
+
+        Returns:
+            Tensor: Dimension (..., freq, time), where freq is
+            ``n_fft // 2 + 1`` where ``n_fft`` is the number of
+            Fourier bins, and time is the number of window hops (n_frame).
+        """
+        return F.spectrogram(
+            waveform,
+            self.pad,
+            self.window,
+            self.n_fft,
+            self.hop_length,
+            self.win_length,
+            self.power,
+            self.normalized,
+            self.center,
+            self.pad_mode,
+            self.onesided,
+        )
+
+
+class InverseSpectrogram(torch.nn.Module):
+    r"""Create an inverse spectrogram to recover an audio signal from a spectrogram.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
+        win_length (int or None, optional): Window size. (Default: ``n_fft``)
+        hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
+        pad (int, optional): Two sided padding of signal. (Default: ``0``)
+        window_fn (Callable[..., Tensor], optional): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        normalized (bool or str, optional): Whether the stft output was normalized by magnitude. If input is str,
+            choices are ``"window"`` and ``"frame_length"``, dependent on normalization mode. ``True`` maps to
+            ``"window"``. (Default: ``False``)
+        wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
+        center (bool, optional): whether the signal in spectrogram was padded on both sides so
+            that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
+            (Default: ``True``)
+        pad_mode (string, optional): controls the padding method used when
+            :attr:`center` is ``True``. (Default: ``"reflect"``)
+        onesided (bool, optional): controls whether spectrogram was used to return half of results to
+            avoid redundancy (Default: ``True``)
+
+    Example
+        >>> batch, freq, time = 2, 257, 100
+        >>> length = 25344
+        >>> spectrogram = torch.randn(batch, freq, time, dtype=torch.cdouble)
+        >>> transform = transforms.InverseSpectrogram(n_fft=512)
+        >>> waveform = transform(spectrogram, length)
+    """
+    __constants__ = ["n_fft", "win_length", "hop_length", "pad", "power", "normalized"]
+
+    def __init__(
+        self,
+        n_fft: int = 400,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        pad: int = 0,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        normalized: Union[bool, str] = False,
+        wkwargs: Optional[dict] = None,
+        center: bool = True,
+        pad_mode: str = "reflect",
+        onesided: bool = True,
+    ) -> None:
+        super(InverseSpectrogram, self).__init__()
+        self.n_fft = n_fft
+        # number of FFT bins. the returned STFT result will have n_fft // 2 + 1
+        # number of frequencies due to onesided=True in torch.stft
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer("window", window)
+        self.pad = pad
+        self.normalized = normalized
+        self.center = center
+        self.pad_mode = pad_mode
+        self.onesided = onesided
+
+    def forward(self, spectrogram: Tensor, length: Optional[int] = None) -> Tensor:
+        r"""
+        Args:
+            spectrogram (Tensor): Complex tensor of audio of dimension (..., freq, time).
+            length (int or None, optional): The output length of the waveform.
+
+        Returns:
+            Tensor: Dimension (..., time), Least squares estimation of the original signal.
+        """
+        return F.inverse_spectrogram(
+            spectrogram,
+            length,
+            self.pad,
+            self.window,
+            self.n_fft,
+            self.hop_length,
+            self.win_length,
+            self.normalized,
+            self.center,
+            self.pad_mode,
+            self.onesided,
+        )
+
+
+class GriffinLim(torch.nn.Module):
+    r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Implementation ported from
+    *librosa* :cite:`brian_mcfee-proc-scipy-2015`, *A fast Griffin-Lim algorithm* :cite:`6701851`
+    and *Signal estimation from modified short-time Fourier transform* :cite:`1172092`.
+
+    Args:
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
+        n_iter (int, optional): Number of iteration for phase recovery process. (Default: ``32``)
+        win_length (int or None, optional): Window size. (Default: ``n_fft``)
+        hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
+        window_fn (Callable[..., Tensor], optional): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        power (float, optional): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc. (Default: ``2``)
+        wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
+        momentum (float, optional): The momentum parameter for fast Griffin-Lim.
+            Setting this to 0 recovers the original Griffin-Lim method.
+            Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: ``0.99``)
+        length (int, optional): Array length of the expected output. (Default: ``None``)
+        rand_init (bool, optional): Initializes phase randomly if True and to zero otherwise. (Default: ``True``)
+
+    Example
+        >>> batch, freq, time = 2, 257, 100
+        >>> spectrogram = torch.randn(batch, freq, time)
+        >>> transform = transforms.GriffinLim(n_fft=512)
+        >>> waveform = transform(spectrogram)
+    """
+    __constants__ = ["n_fft", "n_iter", "win_length", "hop_length", "power", "length", "momentum", "rand_init"]
+
+    def __init__(
+        self,
+        n_fft: int = 400,
+        n_iter: int = 32,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        power: float = 2.0,
+        wkwargs: Optional[dict] = None,
+        momentum: float = 0.99,
+        length: Optional[int] = None,
+        rand_init: bool = True,
+    ) -> None:
+        super(GriffinLim, self).__init__()
+
+        if not (0 <= momentum < 1):
+            raise ValueError("momentum must be in the range [0, 1). Found: {}".format(momentum))
+
+        self.n_fft = n_fft
+        self.n_iter = n_iter
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer("window", window)
+        self.length = length
+        self.power = power
+        self.momentum = momentum
+        self.rand_init = rand_init
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor):
+                A magnitude-only STFT spectrogram of dimension (..., freq, frames)
+                where freq is ``n_fft // 2 + 1``.
+
+        Returns:
+            Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
+        """
+        return F.griffinlim(
+            specgram,
+            self.window,
+            self.n_fft,
+            self.hop_length,
+            self.win_length,
+            self.power,
+            self.n_iter,
+            self.momentum,
+            self.length,
+            self.rand_init,
+        )
+
+
+class AmplitudeToDB(torch.nn.Module):
+    r"""Turn a tensor from the power/amplitude scale to the decibel scale.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    This output depends on the maximum value in the input tensor, and so
+    may return different values for an audio clip split into snippets vs. a
+    a full clip.
+
+    Args:
+        stype (str, optional): scale of input tensor (``"power"`` or ``"magnitude"``). The
+            power being the elementwise square of the magnitude. (Default: ``"power"``)
+        top_db (float or None, optional): minimum negative cut-off in decibels.  A reasonable
+            number is 80. (Default: ``None``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.AmplitudeToDB(stype="amplitude", top_db=80)
+        >>> waveform_db = transform(waveform)
+    """
+    __constants__ = ["multiplier", "amin", "ref_value", "db_multiplier"]
+
+    def __init__(self, stype: str = "power", top_db: Optional[float] = None) -> None:
+        super(AmplitudeToDB, self).__init__()
+        self.stype = stype
+        if top_db is not None and top_db < 0:
+            raise ValueError("top_db must be positive value")
+        self.top_db = top_db
+        self.multiplier = 10.0 if stype == "power" else 20.0
+        self.amin = 1e-10
+        self.ref_value = 1.0
+        self.db_multiplier = math.log10(max(self.amin, self.ref_value))
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""Numerically stable implementation from Librosa.
+
+        https://librosa.org/doc/latest/generated/librosa.amplitude_to_db.html
+
+        Args:
+            x (Tensor): Input tensor before being converted to decibel scale.
+
+        Returns:
+            Tensor: Output tensor in decibel scale.
+        """
+        return F.amplitude_to_DB(x, self.multiplier, self.amin, self.db_multiplier, self.top_db)
+
+
+class MelScale(torch.nn.Module):
+    r"""Turn a normal STFT into a mel frequency STFT with triangular filter banks.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
+        sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        f_min (float, optional): Minimum frequency. (Default: ``0.``)
+        f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
+        n_stft (int, optional): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`. (Default: ``201``)
+        norm (str or None, optional): If ``"slaney"``, divide the triangular mel weights by the width of the mel band
+            (area normalization). (Default: ``None``)
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> spectrogram_transform = transforms.Spectrogram(n_fft=1024)
+        >>> spectrogram = spectrogram_transform(waveform)
+        >>> melscale_transform = transforms.MelScale(sample_rate=sample_rate, n_stft=1024 // 2 + 1)
+        >>> melscale_spectrogram = melscale_transform(spectrogram)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
+    """
+    __constants__ = ["n_mels", "sample_rate", "f_min", "f_max"]
+
+    def __init__(
+        self,
+        n_mels: int = 128,
+        sample_rate: int = 16000,
+        f_min: float = 0.0,
+        f_max: Optional[float] = None,
+        n_stft: int = 201,
+        norm: Optional[str] = None,
+        mel_scale: str = "htk",
+    ) -> None:
+        super(MelScale, self).__init__()
+        self.n_mels = n_mels
+        self.sample_rate = sample_rate
+        self.f_max = f_max if f_max is not None else float(sample_rate // 2)
+        self.f_min = f_min
+        self.norm = norm
+        self.mel_scale = mel_scale
+
+        if f_min > self.f_max:
+            raise ValueError("Require f_min: {} <= f_max: {}".format(f_min, self.f_max))
+
+        fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm, self.mel_scale)
+        self.register_buffer("fb", fb)
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor): A spectrogram STFT of dimension (..., freq, time).
+
+        Returns:
+            Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
+        """
+
+        # (..., time, freq) dot (freq, n_mels) -> (..., n_mels, time)
+        mel_specgram = torch.matmul(specgram.transpose(-1, -2), self.fb).transpose(-1, -2)
+
+        return mel_specgram
+
+
+class InverseMelScale(torch.nn.Module):
+    r"""Estimate a STFT in normal frequency domain from mel frequency domain.
+
+    .. devices:: CPU CUDA
+
+    It minimizes the euclidian norm between the input mel-spectrogram and the product between
+    the estimated spectrogram and the filter banks using `torch.linalg.lstsq`.
+
+    Args:
+        n_stft (int): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.
+        n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
+        sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        f_min (float, optional): Minimum frequency. (Default: ``0.``)
+        f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
+        norm (str or None, optional): If "slaney", divide the triangular mel weights by the width of the mel band
+            (area normalization). (Default: ``None``)
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+        driver (str, optional): Name of the LAPACK/MAGMA method to be used for `torch.lstsq`.
+            For CPU inputs the valid values are ``"gels"``, ``"gelsy"``, ``"gelsd"``, ``"gelss"``.
+            For CUDA input, the only valid driver is ``"gels"``, which assumes that A is full-rank.
+            (Default: ``"gels``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> mel_spectrogram_transform = transforms.MelSpectrogram(sample_rate, n_fft=1024)
+        >>> mel_spectrogram = mel_spectrogram_transform(waveform)
+        >>> inverse_melscale_transform = transforms.InverseMelScale(n_stft=1024 // 2 + 1)
+        >>> spectrogram = inverse_melscale_transform(mel_spectrogram)
+    """
+    __constants__ = [
+        "n_stft",
+        "n_mels",
+        "sample_rate",
+        "f_min",
+        "f_max",
+    ]
+
+    def __init__(
+        self,
+        n_stft: int,
+        n_mels: int = 128,
+        sample_rate: int = 16000,
+        f_min: float = 0.0,
+        f_max: Optional[float] = None,
+        norm: Optional[str] = None,
+        mel_scale: str = "htk",
+        driver: str = "gels",
+    ) -> None:
+        super(InverseMelScale, self).__init__()
+        self.n_mels = n_mels
+        self.sample_rate = sample_rate
+        self.f_max = f_max or float(sample_rate // 2)
+        self.f_min = f_min
+        self.driver = driver
+
+        if f_min > self.f_max:
+            raise ValueError("Require f_min: {} <= f_max: {}".format(f_min, self.f_max))
+
+        if driver not in ["gels", "gelsy", "gelsd", "gelss"]:
+            raise ValueError(f'driver must be one of ["gels", "gelsy", "gelsd", "gelss"]. Found {driver}.')
+
+        fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm, mel_scale)
+        self.register_buffer("fb", fb)
+
+    def forward(self, melspec: Tensor) -> Tensor:
+        r"""
+        Args:
+            melspec (Tensor): A Mel frequency spectrogram of dimension (..., ``n_mels``, time)
+
+        Returns:
+            Tensor: Linear scale spectrogram of size (..., freq, time)
+        """
+        # pack batch
+        shape = melspec.size()
+        melspec = melspec.view(-1, shape[-2], shape[-1])
+
+        n_mels, time = shape[-2], shape[-1]
+        freq, _ = self.fb.size()  # (freq, n_mels)
+        if self.n_mels != n_mels:
+            raise ValueError("Expected an input with {} mel bins. Found: {}".format(self.n_mels, n_mels))
+
+        specgram = torch.relu(torch.linalg.lstsq(self.fb.transpose(-1, -2)[None], melspec, driver=self.driver).solution)
+
+        # unpack batch
+        specgram = specgram.view(shape[:-2] + (freq, time))
+        return specgram
+
+
+class MelSpectrogram(torch.nn.Module):
+    r"""Create MelSpectrogram for a raw audio signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    This is a composition of :py:func:`torchaudio.transforms.Spectrogram`
+    and :py:func:`torchaudio.transforms.MelScale`.
+
+    Sources
+        * https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
+        * https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
+        * http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
+
+    Args:
+        sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
+        win_length (int or None, optional): Window size. (Default: ``n_fft``)
+        hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
+        f_min (float, optional): Minimum frequency. (Default: ``0.``)
+        f_max (float or None, optional): Maximum frequency. (Default: ``None``)
+        pad (int, optional): Two sided padding of signal. (Default: ``0``)
+        n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
+        window_fn (Callable[..., Tensor], optional): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        power (float, optional): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc. (Default: ``2``)
+        normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
+        wkwargs (Dict[..., ...] or None, optional): Arguments for window function. (Default: ``None``)
+        center (bool, optional): whether to pad :attr:`waveform` on both sides so
+            that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
+            (Default: ``True``)
+        pad_mode (string, optional): controls the padding method used when
+            :attr:`center` is ``True``. (Default: ``"reflect"``)
+        onesided: Deprecated and unused.
+        norm (str or None, optional): If "slaney", divide the triangular mel weights by the width of the mel band
+            (area normalization). (Default: ``None``)
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.MelSpectrogram(sample_rate)
+        >>> mel_specgram = transform(waveform)  # (channel, n_mels, time)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
+    """
+    __constants__ = ["sample_rate", "n_fft", "win_length", "hop_length", "pad", "n_mels", "f_min"]
+
+    def __init__(
+        self,
+        sample_rate: int = 16000,
+        n_fft: int = 400,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        f_min: float = 0.0,
+        f_max: Optional[float] = None,
+        pad: int = 0,
+        n_mels: int = 128,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        power: float = 2.0,
+        normalized: bool = False,
+        wkwargs: Optional[dict] = None,
+        center: bool = True,
+        pad_mode: str = "reflect",
+        onesided: Optional[bool] = None,
+        norm: Optional[str] = None,
+        mel_scale: str = "htk",
+    ) -> None:
+        super(MelSpectrogram, self).__init__()
+        torch._C._log_api_usage_once("torchaudio.transforms.MelSpectrogram")
+
+        if onesided is not None:
+            warnings.warn(
+                "Argument 'onesided' has been deprecated and has no influence on the behavior of this module."
+            )
+
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        self.pad = pad
+        self.power = power
+        self.normalized = normalized
+        self.n_mels = n_mels  # number of mel frequency bins
+        self.f_max = f_max
+        self.f_min = f_min
+        self.spectrogram = Spectrogram(
+            n_fft=self.n_fft,
+            win_length=self.win_length,
+            hop_length=self.hop_length,
+            pad=self.pad,
+            window_fn=window_fn,
+            power=self.power,
+            normalized=self.normalized,
+            wkwargs=wkwargs,
+            center=center,
+            pad_mode=pad_mode,
+            onesided=True,
+        )
+        self.mel_scale = MelScale(
+            self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1, norm, mel_scale
+        )
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension (..., time).
+
+        Returns:
+            Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
+        """
+        specgram = self.spectrogram(waveform)
+        mel_specgram = self.mel_scale(specgram)
+        return mel_specgram
+
+
+class MFCC(torch.nn.Module):
+    r"""Create the Mel-frequency cepstrum coefficients from an audio signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    By default, this calculates the MFCC on the DB-scaled Mel spectrogram.
+    This is not the textbook implementation, but is implemented here to
+    give consistency with librosa.
+
+    This output depends on the maximum value in the input spectrogram, and so
+    may return different values for an audio clip split into snippets vs. a
+    a full clip.
+
+    Args:
+        sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        n_mfcc (int, optional): Number of mfc coefficients to retain. (Default: ``40``)
+        dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``)
+        norm (str, optional): norm to use. (Default: ``"ortho"``)
+        log_mels (bool, optional): whether to use log-mel spectrograms instead of db-scaled. (Default: ``False``)
+        melkwargs (dict or None, optional): arguments for MelSpectrogram. (Default: ``None``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.MFCC(
+        >>>     sample_rate=sample_rate,
+        >>>     n_mfcc=13,
+        >>>     melkwargs={"n_fft": 400, "hop_length": 160, "n_mels": 23, "center": False},
+        >>> )
+        >>> mfcc = transform(waveform)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
+    """
+    __constants__ = ["sample_rate", "n_mfcc", "dct_type", "top_db", "log_mels"]
+
+    def __init__(
+        self,
+        sample_rate: int = 16000,
+        n_mfcc: int = 40,
+        dct_type: int = 2,
+        norm: str = "ortho",
+        log_mels: bool = False,
+        melkwargs: Optional[dict] = None,
+    ) -> None:
+        super(MFCC, self).__init__()
+        supported_dct_types = [2]
+        if dct_type not in supported_dct_types:
+            raise ValueError("DCT type not supported: {}".format(dct_type))
+        self.sample_rate = sample_rate
+        self.n_mfcc = n_mfcc
+        self.dct_type = dct_type
+        self.norm = norm
+        self.top_db = 80.0
+        self.amplitude_to_DB = AmplitudeToDB("power", self.top_db)
+
+        melkwargs = melkwargs or {}
+        self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs)
+
+        if self.n_mfcc > self.MelSpectrogram.n_mels:
+            raise ValueError("Cannot select more MFCC coefficients than # mel bins")
+        dct_mat = F.create_dct(self.n_mfcc, self.MelSpectrogram.n_mels, self.norm)
+        self.register_buffer("dct_mat", dct_mat)
+        self.log_mels = log_mels
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension (..., time).
+
+        Returns:
+            Tensor: specgram_mel_db of size (..., ``n_mfcc``, time).
+        """
+        mel_specgram = self.MelSpectrogram(waveform)
+        if self.log_mels:
+            log_offset = 1e-6
+            mel_specgram = torch.log(mel_specgram + log_offset)
+        else:
+            mel_specgram = self.amplitude_to_DB(mel_specgram)
+
+        # (..., time, n_mels) dot (n_mels, n_mfcc) -> (..., n_nfcc, time)
+        mfcc = torch.matmul(mel_specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2)
+        return mfcc
+
+
+class LFCC(torch.nn.Module):
+    r"""Create the linear-frequency cepstrum coefficients from an audio signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    By default, this calculates the LFCC on the DB-scaled linear filtered spectrogram.
+    This is not the textbook implementation, but is implemented here to
+    give consistency with librosa.
+
+    This output depends on the maximum value in the input spectrogram, and so
+    may return different values for an audio clip split into snippets vs. a
+    a full clip.
+
+    Args:
+        sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        n_filter (int, optional): Number of linear filters to apply. (Default: ``128``)
+        n_lfcc (int, optional): Number of lfc coefficients to retain. (Default: ``40``)
+        f_min (float, optional): Minimum frequency. (Default: ``0.``)
+        f_max (float or None, optional): Maximum frequency. (Default: ``None``)
+        dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``)
+        norm (str, optional): norm to use. (Default: ``"ortho"``)
+        log_lf (bool, optional): whether to use log-lf spectrograms instead of db-scaled. (Default: ``False``)
+        speckwargs (dict or None, optional): arguments for Spectrogram. (Default: ``None``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.LFCC(
+        >>>     sample_rate=sample_rate,
+        >>>     n_lfcc=13,
+        >>>     speckwargs={"n_fft": 400, "hop_length": 160, "center": False},
+        >>> )
+        >>> lfcc = transform(waveform)
+
+    See also:
+        :py:func:`torchaudio.functional.linear_fbanks` - The function used to
+        generate the filter banks.
+    """
+    __constants__ = ["sample_rate", "n_filter", "n_lfcc", "dct_type", "top_db", "log_lf"]
+
+    def __init__(
+        self,
+        sample_rate: int = 16000,
+        n_filter: int = 128,
+        f_min: float = 0.0,
+        f_max: Optional[float] = None,
+        n_lfcc: int = 40,
+        dct_type: int = 2,
+        norm: str = "ortho",
+        log_lf: bool = False,
+        speckwargs: Optional[dict] = None,
+    ) -> None:
+        super(LFCC, self).__init__()
+        supported_dct_types = [2]
+        if dct_type not in supported_dct_types:
+            raise ValueError("DCT type not supported: {}".format(dct_type))
+        self.sample_rate = sample_rate
+        self.f_min = f_min
+        self.f_max = f_max if f_max is not None else float(sample_rate // 2)
+        self.n_filter = n_filter
+        self.n_lfcc = n_lfcc
+        self.dct_type = dct_type
+        self.norm = norm
+        self.top_db = 80.0
+        self.amplitude_to_DB = AmplitudeToDB("power", self.top_db)
+
+        speckwargs = speckwargs or {}
+        self.Spectrogram = Spectrogram(**speckwargs)
+
+        if self.n_lfcc > self.Spectrogram.n_fft:
+            raise ValueError("Cannot select more LFCC coefficients than # fft bins")
+
+        filter_mat = F.linear_fbanks(
+            n_freqs=self.Spectrogram.n_fft // 2 + 1,
+            f_min=self.f_min,
+            f_max=self.f_max,
+            n_filter=self.n_filter,
+            sample_rate=self.sample_rate,
+        )
+        self.register_buffer("filter_mat", filter_mat)
+
+        dct_mat = F.create_dct(self.n_lfcc, self.n_filter, self.norm)
+        self.register_buffer("dct_mat", dct_mat)
+        self.log_lf = log_lf
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension (..., time).
+
+        Returns:
+            Tensor: Linear Frequency Cepstral Coefficients of size (..., ``n_lfcc``, time).
+        """
+        specgram = self.Spectrogram(waveform)
+
+        # (..., time, freq) dot (freq, n_filter) -> (..., n_filter, time)
+        specgram = torch.matmul(specgram.transpose(-1, -2), self.filter_mat).transpose(-1, -2)
+
+        if self.log_lf:
+            log_offset = 1e-6
+            specgram = torch.log(specgram + log_offset)
+        else:
+            specgram = self.amplitude_to_DB(specgram)
+
+        # (..., time, n_filter) dot (n_filter, n_lfcc) -> (..., n_lfcc, time)
+        lfcc = torch.matmul(specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2)
+        return lfcc
+
+
+class MuLawEncoding(torch.nn.Module):
+    r"""Encode signal based on mu-law companding.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    For more info see the
+    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
+
+    This algorithm assumes the signal has been scaled to between -1 and 1 and
+    returns a signal encoded with values from 0 to quantization_channels - 1
+
+    Args:
+        quantization_channels (int, optional): Number of channels. (Default: ``256``)
+
+    Example
+       >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+       >>> transform = torchaudio.transforms.MuLawEncoding(quantization_channels=512)
+       >>> mulawtrans = transform(waveform)
+
+    """
+    __constants__ = ["quantization_channels"]
+
+    def __init__(self, quantization_channels: int = 256) -> None:
+        super(MuLawEncoding, self).__init__()
+        self.quantization_channels = quantization_channels
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""
+        Args:
+            x (Tensor): A signal to be encoded.
+
+        Returns:
+            Tensor: An encoded signal.
+        """
+        return F.mu_law_encoding(x, self.quantization_channels)
+
+
+class MuLawDecoding(torch.nn.Module):
+    r"""Decode mu-law encoded signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    For more info see the
+    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
+
+    This expects an input with values between 0 and ``quantization_channels - 1``
+    and returns a signal scaled between -1 and 1.
+
+    Args:
+        quantization_channels (int, optional): Number of channels. (Default: ``256``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = torchaudio.transforms.MuLawDecoding(quantization_channels=512)
+        >>> mulawtrans = transform(waveform)
+    """
+    __constants__ = ["quantization_channels"]
+
+    def __init__(self, quantization_channels: int = 256) -> None:
+        super(MuLawDecoding, self).__init__()
+        self.quantization_channels = quantization_channels
+
+    def forward(self, x_mu: Tensor) -> Tensor:
+        r"""
+        Args:
+            x_mu (Tensor): A mu-law encoded signal which needs to be decoded.
+
+        Returns:
+            Tensor: The signal decoded.
+        """
+        return F.mu_law_decoding(x_mu, self.quantization_channels)
+
+
+class Resample(torch.nn.Module):
+    r"""Resample a signal from one frequency to another. A resampling method can be given.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Note:
+        If resampling on waveforms of higher precision than float32, there may be a small loss of precision
+        because the kernel is cached once as float32. If high precision resampling is important for your application,
+        the functional form will retain higher precision, but run slower because it does not cache the kernel.
+        Alternatively, you could rewrite a transform that caches a higher precision kernel.
+
+    Args:
+        orig_freq (int, optional): The original frequency of the signal. (Default: ``16000``)
+        new_freq (int, optional): The desired frequency. (Default: ``16000``)
+        resampling_method (str, optional): The resampling method to use.
+            Options: [``sinc_interp_hann``, ``sinc_interp_kaiser``] (Default: ``"sinc_interp_hann"``)
+        lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper
+            but less efficient. (Default: ``6``)
+        rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist.
+            Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``)
+        beta (float or None, optional): The shape parameter used for kaiser window.
+        dtype (torch.device, optional):
+            Determnines the precision that resampling kernel is pre-computed and cached. If not provided,
+            kernel is computed with ``torch.float64`` then cached as ``torch.float32``.
+            If you need higher precision, provide ``torch.float64``, and the pre-computed kernel is computed and
+            cached as ``torch.float64``. If you use resample with lower precision, then instead of providing this
+            providing this argument, please use ``Resample.to(dtype)``, so that the kernel generation is still
+            carried out on ``torch.float64``.
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.Resample(sample_rate, sample_rate/10)
+        >>> waveform = transform(waveform)
+    """
+
+    def __init__(
+        self,
+        orig_freq: int = 16000,
+        new_freq: int = 16000,
+        resampling_method: str = "sinc_interp_hann",
+        lowpass_filter_width: int = 6,
+        rolloff: float = 0.99,
+        beta: Optional[float] = None,
+        *,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        super().__init__()
+
+        self.orig_freq = orig_freq
+        self.new_freq = new_freq
+        self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq))
+        self.resampling_method = resampling_method
+        self.lowpass_filter_width = lowpass_filter_width
+        self.rolloff = rolloff
+        self.beta = beta
+
+        if self.orig_freq != self.new_freq:
+            kernel, self.width = _get_sinc_resample_kernel(
+                self.orig_freq,
+                self.new_freq,
+                self.gcd,
+                self.lowpass_filter_width,
+                self.rolloff,
+                self.resampling_method,
+                beta,
+                dtype=dtype,
+            )
+            self.register_buffer("kernel", kernel)
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension (..., time).
+
+        Returns:
+            Tensor: Output signal of dimension (..., time).
+        """
+        if self.orig_freq == self.new_freq:
+            return waveform
+        return _apply_sinc_resample_kernel(waveform, self.orig_freq, self.new_freq, self.gcd, self.kernel, self.width)
+
+
+class ComputeDeltas(torch.nn.Module):
+    r"""Compute delta coefficients of a tensor, usually a spectrogram.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    See `torchaudio.functional.compute_deltas` for more details.
+
+    Args:
+        win_length (int, optional): The window length used for computing delta. (Default: ``5``)
+        mode (str, optional): Mode parameter passed to padding. (Default: ``"replicate"``)
+    """
+    __constants__ = ["win_length"]
+
+    def __init__(self, win_length: int = 5, mode: str = "replicate") -> None:
+        super(ComputeDeltas, self).__init__()
+        self.win_length = win_length
+        self.mode = mode
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor): Tensor of audio of dimension (..., freq, time).
+
+        Returns:
+            Tensor: Tensor of deltas of dimension (..., freq, time).
+        """
+        return F.compute_deltas(specgram, win_length=self.win_length, mode=self.mode)
+
+
+class TimeStretch(torch.nn.Module):
+    r"""Stretch stft in time without modifying pitch for a given rate.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Proposed in *SpecAugment* :cite:`specaugment`.
+
+    Args:
+        hop_length (int or None, optional): Length of hop between STFT windows.
+            (Default: ``n_fft // 2``, where ``n_fft == (n_freq - 1) * 2``)
+        n_freq (int, optional): number of filter banks from stft. (Default: ``201``)
+        fixed_rate (float or None, optional): rate to speed up or slow down by.
+            If None is provided, rate must be passed to the forward method. (Default: ``None``)
+
+    .. note::
+
+       The expected input is raw, complex-valued spectrogram.
+
+    Example
+        >>> spectrogram = torchaudio.transforms.Spectrogram(power=None)
+        >>> stretch = torchaudio.transforms.TimeStretch()
+        >>>
+        >>> original = spectrogram(waveform)
+        >>> stretched_1_2 = stretch(original, 1.2)
+        >>> stretched_0_9 = stretch(original, 0.9)
+
+        .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_stretch.png
+           :width: 600
+           :alt: The visualization of stretched spectrograms.
+    """
+    __constants__ = ["fixed_rate"]
+
+    def __init__(self, hop_length: Optional[int] = None, n_freq: int = 201, fixed_rate: Optional[float] = None) -> None:
+        super(TimeStretch, self).__init__()
+
+        self.fixed_rate = fixed_rate
+
+        n_fft = (n_freq - 1) * 2
+        hop_length = hop_length if hop_length is not None else n_fft // 2
+        self.register_buffer("phase_advance", torch.linspace(0, math.pi * hop_length, n_freq)[..., None])
+
+    def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] = None) -> Tensor:
+        r"""
+        Args:
+            complex_specgrams (Tensor):
+                A tensor of dimension `(..., freq, num_frame)` with complex dtype.
+            overriding_rate (float or None, optional): speed up to apply to this batch.
+                If no rate is passed, use ``self.fixed_rate``. (Default: ``None``)
+
+        Returns:
+            Tensor:
+                Stretched spectrogram. The resulting tensor is of the corresponding complex dtype
+                as the input spectrogram, and the number of frames is changed to ``ceil(num_frame / rate)``.
+        """
+        if not torch.is_complex(complex_specgrams):
+            warnings.warn(
+                "The input to TimeStretch must be complex type. "
+                "Providing non-complex tensor produces invalid results.",
+                stacklevel=4,
+            )
+
+        if overriding_rate is None:
+            if self.fixed_rate is None:
+                raise ValueError("If no fixed_rate is specified, must pass a valid rate to the forward method.")
+            rate = self.fixed_rate
+        else:
+            rate = overriding_rate
+        return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
+
+
+class Fade(torch.nn.Module):
+    r"""Add a fade in and/or fade out to an waveform.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        fade_in_len (int, optional): Length of fade-in (time frames). (Default: ``0``)
+        fade_out_len (int, optional): Length of fade-out (time frames). (Default: ``0``)
+        fade_shape (str, optional): Shape of fade. Must be one of: "quarter_sine",
+            ``"half_sine"``, ``"linear"``, ``"logarithmic"``, ``"exponential"``.
+            (Default: ``"linear"``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.Fade(fade_in_len=sample_rate, fade_out_len=2 * sample_rate, fade_shape="linear")
+        >>> faded_waveform = transform(waveform)
+    """
+
+    def __init__(self, fade_in_len: int = 0, fade_out_len: int = 0, fade_shape: str = "linear") -> None:
+        super(Fade, self).__init__()
+        self.fade_in_len = fade_in_len
+        self.fade_out_len = fade_out_len
+        self.fade_shape = fade_shape
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension `(..., time)`.
+
+        Returns:
+            Tensor: Tensor of audio of dimension `(..., time)`.
+        """
+        waveform_length = waveform.size()[-1]
+        device = waveform.device
+        return self._fade_in(waveform_length, device) * self._fade_out(waveform_length, device) * waveform
+
+    def _fade_in(self, waveform_length: int, device: torch.device) -> Tensor:
+        fade = torch.linspace(0, 1, self.fade_in_len, device=device)
+        ones = torch.ones(waveform_length - self.fade_in_len, device=device)
+
+        if self.fade_shape == "linear":
+            fade = fade
+
+        if self.fade_shape == "exponential":
+            fade = torch.pow(2, (fade - 1)) * fade
+
+        if self.fade_shape == "logarithmic":
+            fade = torch.log10(0.1 + fade) + 1
+
+        if self.fade_shape == "quarter_sine":
+            fade = torch.sin(fade * math.pi / 2)
+
+        if self.fade_shape == "half_sine":
+            fade = torch.sin(fade * math.pi - math.pi / 2) / 2 + 0.5
+
+        return torch.cat((fade, ones)).clamp_(0, 1)
+
+    def _fade_out(self, waveform_length: int, device: torch.device) -> Tensor:
+        fade = torch.linspace(0, 1, self.fade_out_len, device=device)
+        ones = torch.ones(waveform_length - self.fade_out_len, device=device)
+
+        if self.fade_shape == "linear":
+            fade = -fade + 1
+
+        if self.fade_shape == "exponential":
+            fade = torch.pow(2, -fade) * (1 - fade)
+
+        if self.fade_shape == "logarithmic":
+            fade = torch.log10(1.1 - fade) + 1
+
+        if self.fade_shape == "quarter_sine":
+            fade = torch.sin(fade * math.pi / 2 + math.pi / 2)
+
+        if self.fade_shape == "half_sine":
+            fade = torch.sin(fade * math.pi + math.pi / 2) / 2 + 0.5
+
+        return torch.cat((ones, fade)).clamp_(0, 1)
+
+
+class _AxisMasking(torch.nn.Module):
+    r"""Apply masking to a spectrogram.
+
+    Args:
+        mask_param (int): Maximum possible length of the mask.
+        axis (int): What dimension the mask is applied on (assuming the tensor is 3D).
+            For frequency masking, axis = 1.
+            For time masking, axis = 2.
+        iid_masks (bool): Applies iid masks to each of the examples in the batch dimension.
+            This option is applicable only when the dimension of the input tensor is >= 3.
+        p (float, optional): maximum proportion of columns that can be masked. (Default: 1.0)
+    """
+    __constants__ = ["mask_param", "axis", "iid_masks", "p"]
+
+    def __init__(self, mask_param: int, axis: int, iid_masks: bool, p: float = 1.0) -> None:
+        super(_AxisMasking, self).__init__()
+        self.mask_param = mask_param
+        self.axis = axis
+        self.iid_masks = iid_masks
+        self.p = p
+
+    def forward(self, specgram: Tensor, mask_value: float = 0.0) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor): Tensor of dimension `(..., freq, time)`.
+            mask_value (float): Value to assign to the masked columns.
+
+        Returns:
+            Tensor: Masked spectrogram of dimensions `(..., freq, time)`.
+        """
+        # if iid_masks flag marked and specgram has a batch dimension
+        # self.axis + specgram.dim() - 3 gives the time/frequency dimension (last two dimensions)
+        # for input tensor for which the dimension is not 3.
+        if self.iid_masks:
+            return F.mask_along_axis_iid(
+                specgram, self.mask_param, mask_value, self.axis + specgram.dim() - 3, p=self.p
+            )
+        else:
+            return F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis + specgram.dim() - 3, p=self.p)
+
+
+class FrequencyMasking(_AxisMasking):
+    r"""Apply masking to a spectrogram in the frequency domain.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Proposed in *SpecAugment* :cite:`specaugment`.
+
+    Args:
+        freq_mask_param (int): maximum possible length of the mask.
+            Indices uniformly sampled from [0, freq_mask_param).
+        iid_masks (bool, optional): whether to apply different masks to each
+            example/channel in the batch. (Default: ``False``)
+            This option is applicable only when the input tensor >= 3D.
+
+    Example
+        >>> spectrogram = torchaudio.transforms.Spectrogram()
+        >>> masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=80)
+        >>>
+        >>> original = spectrogram(waveform)
+        >>> masked = masking(original)
+
+        .. image::  https://download.pytorch.org/torchaudio/doc-assets/specaugment_freq_masking1.png
+           :alt: The original spectrogram
+
+        .. image::  https://download.pytorch.org/torchaudio/doc-assets/specaugment_freq_masking2.png
+           :alt: The spectrogram masked along frequency axis
+    """
+
+    def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None:
+        super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks)
+
+
+class TimeMasking(_AxisMasking):
+    r"""Apply masking to a spectrogram in the time domain.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Proposed in *SpecAugment* :cite:`specaugment`.
+
+    Args:
+        time_mask_param (int): maximum possible length of the mask.
+            Indices uniformly sampled from [0, time_mask_param).
+        iid_masks (bool, optional): whether to apply different masks to each
+            example/channel in the batch. (Default: ``False``)
+            This option is applicable only when the input tensor >= 3D.
+        p (float, optional): maximum proportion of time steps that can be masked.
+            Must be within range [0.0, 1.0]. (Default: 1.0)
+
+    Example
+        >>> spectrogram = torchaudio.transforms.Spectrogram()
+        >>> masking = torchaudio.transforms.TimeMasking(time_mask_param=80)
+        >>>
+        >>> original = spectrogram(waveform)
+        >>> masked = masking(original)
+
+        .. image::  https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_masking1.png
+           :alt: The original spectrogram
+
+        .. image::  https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_masking2.png
+           :alt: The spectrogram masked along time axis
+    """
+
+    def __init__(self, time_mask_param: int, iid_masks: bool = False, p: float = 1.0) -> None:
+        if not 0.0 <= p <= 1.0:
+            raise ValueError(f"The value of p must be between 0.0 and 1.0 ({p} given).")
+        super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks, p=p)
+
+
+class SpecAugment(torch.nn.Module):
+    r"""Apply time and frequency masking to a spectrogram.
+    Args:
+        n_time_masks (int): Number of time masks. If its value is zero, no time masking will be applied.
+        time_mask_param (int): Maximum possible length of the time mask.
+        n_freq_masks (int): Number of frequency masks. If its value is zero, no frequency masking will be applied.
+        freq_mask_param (int): Maximum possible length of the frequency mask.
+        iid_masks (bool, optional): Applies iid masks to each of the examples in the batch dimension.
+            This option is applicable only when the input tensor is 4D. (Default: ``True``)
+        p (float, optional): maximum proportion of time steps that can be masked.
+            Must be within range [0.0, 1.0]. (Default: 1.0)
+        zero_masking (bool, optional): If ``True``, use 0 as the mask value,
+            else use mean of the input tensor. (Default: ``False``)
+    """
+    __constants__ = [
+        "n_time_masks",
+        "time_mask_param",
+        "n_freq_masks",
+        "freq_mask_param",
+        "iid_masks",
+        "p",
+        "zero_masking",
+    ]
+
+    def __init__(
+        self,
+        n_time_masks: int,
+        time_mask_param: int,
+        n_freq_masks: int,
+        freq_mask_param: int,
+        iid_masks: bool = True,
+        p: float = 1.0,
+        zero_masking: bool = False,
+    ) -> None:
+        super(SpecAugment, self).__init__()
+        self.n_time_masks = n_time_masks
+        self.time_mask_param = time_mask_param
+        self.n_freq_masks = n_freq_masks
+        self.freq_mask_param = freq_mask_param
+        self.iid_masks = iid_masks
+        self.p = p
+        self.zero_masking = zero_masking
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor): Tensor of shape `(..., freq, time)`.
+        Returns:
+            Tensor: Masked spectrogram of shape `(..., freq, time)`.
+        """
+        if self.zero_masking:
+            mask_value = 0.0
+        else:
+            mask_value = specgram.mean()
+        time_dim = specgram.dim() - 1
+        freq_dim = time_dim - 1
+
+        if specgram.dim() > 2 and self.iid_masks is True:
+            for _ in range(self.n_time_masks):
+                specgram = F.mask_along_axis_iid(specgram, self.time_mask_param, mask_value, time_dim, p=self.p)
+            for _ in range(self.n_freq_masks):
+                specgram = F.mask_along_axis_iid(specgram, self.freq_mask_param, mask_value, freq_dim, p=self.p)
+        else:
+            for _ in range(self.n_time_masks):
+                specgram = F.mask_along_axis(specgram, self.time_mask_param, mask_value, time_dim, p=self.p)
+            for _ in range(self.n_freq_masks):
+                specgram = F.mask_along_axis(specgram, self.freq_mask_param, mask_value, freq_dim, p=self.p)
+
+        return specgram
+
+
+class Loudness(torch.nn.Module):
+    r"""Measure audio loudness according to the ITU-R BS.1770-4 recommendation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        sample_rate (int): Sample rate of audio signal.
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.Loudness(sample_rate)
+        >>> loudness = transform(waveform)
+
+    Reference:
+        - https://www.itu.int/rec/R-REC-BS.1770-4-201510-I/en
+    """
+    __constants__ = ["sample_rate"]
+
+    def __init__(self, sample_rate: int):
+        super(Loudness, self).__init__()
+        self.sample_rate = sample_rate
+
+    def forward(self, wavefrom: Tensor):
+        r"""
+        Args:
+            waveform(torch.Tensor): audio waveform of dimension `(..., channels, time)`
+
+        Returns:
+            Tensor: loudness estimates (LKFS)
+        """
+        return F.loudness(wavefrom, self.sample_rate)
+
+
+class Vol(torch.nn.Module):
+    r"""Adjust volume of waveform.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        gain (float): Interpreted according to the given gain_type:
+            If ``gain_type`` = ``amplitude``, ``gain`` is a positive amplitude ratio.
+            If ``gain_type`` = ``power``, ``gain`` is a power (voltage squared).
+            If ``gain_type`` = ``db``, ``gain`` is in decibels.
+        gain_type (str, optional): Type of gain. One of: ``amplitude``, ``power``, ``db`` (Default: ``amplitude``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.Vol(gain=0.5, gain_type="amplitude")
+        >>> quieter_waveform = transform(waveform)
+    """
+
+    def __init__(self, gain: float, gain_type: str = "amplitude"):
+        super(Vol, self).__init__()
+        self.gain = gain
+        self.gain_type = gain_type
+
+        if gain_type in ["amplitude", "power"] and gain < 0:
+            raise ValueError("If gain_type = amplitude or power, gain must be positive.")
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension `(..., time)`.
+
+        Returns:
+            Tensor: Tensor of audio of dimension `(..., time)`.
+        """
+        if self.gain_type == "amplitude":
+            waveform = waveform * self.gain
+
+        if self.gain_type == "db":
+            waveform = F.gain(waveform, self.gain)
+
+        if self.gain_type == "power":
+            waveform = F.gain(waveform, 10 * math.log10(self.gain))
+
+        return torch.clamp(waveform, -1, 1)
+
+
+class SlidingWindowCmn(torch.nn.Module):
+    r"""
+    Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600)
+        min_cmn_window (int, optional):  Minimum CMN window used at start of decoding (adds latency only at start).
+            Only applicable if center == false, ignored if center==true (int, default = 100)
+        center (bool, optional): If true, use a window centered on the current frame
+            (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false)
+        norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.SlidingWindowCmn(cmn_window=1000)
+        >>> cmn_waveform = transform(waveform)
+    """
+
+    def __init__(
+        self, cmn_window: int = 600, min_cmn_window: int = 100, center: bool = False, norm_vars: bool = False
+    ) -> None:
+        super().__init__()
+        self.cmn_window = cmn_window
+        self.min_cmn_window = min_cmn_window
+        self.center = center
+        self.norm_vars = norm_vars
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""
+        Args:
+            specgram (Tensor): Tensor of spectrogram of dimension `(..., time, freq)`.
+
+        Returns:
+            Tensor: Tensor of spectrogram of dimension `(..., time, freq)`.
+        """
+        cmn_specgram = F.sliding_window_cmn(specgram, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars)
+        return cmn_specgram
+
+
+class Vad(torch.nn.Module):
+    r"""Voice Activity Detector. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Attempts to trim silence and quiet background sounds from the ends of recordings of speech.
+    The algorithm currently uses a simple cepstral power measurement to detect voice,
+    so may be fooled by other things, especially music.
+
+    The effect can trim only from the front of the audio,
+    so in order to trim from the back, the reverse effect must also be used.
+
+    Args:
+        sample_rate (int): Sample rate of audio signal.
+        trigger_level (float, optional): The measurement level used to trigger activity detection.
+            This may need to be changed depending on the noise level, signal level,
+            and other characteristics of the input audio. (Default: 7.0)
+        trigger_time (float, optional): The time constant (in seconds)
+            used to help ignore short bursts of sound. (Default: 0.25)
+        search_time (float, optional): The amount of audio (in seconds)
+            to search for quieter/shorter bursts of audio to include prior
+            to the detected trigger point. (Default: 1.0)
+        allowed_gap (float, optional): The allowed gap (in seconds) between
+            quiteter/shorter bursts of audio to include prior
+            to the detected trigger point. (Default: 0.25)
+        pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve
+            before the trigger point and any found quieter/shorter bursts. (Default: 0.0)
+        boot_time (float, optional) The algorithm (internally) uses adaptive noise
+            estimation/reduction in order to detect the start of the wanted audio.
+            This option sets the time for the initial noise estimate. (Default: 0.35)
+        noise_up_time (float, optional) Time constant used by the adaptive noise estimator
+            for when the noise level is increasing. (Default: 0.1)
+        noise_down_time (float, optional) Time constant used by the adaptive noise estimator
+            for when the noise level is decreasing. (Default: 0.01)
+        noise_reduction_amount (float, optional) Amount of noise reduction to use in
+            the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35)
+        measure_freq (float, optional) Frequency of the algorithm’s
+            processing/measurements. (Default: 20.0)
+        measure_duration: (float or None, optional) Measurement duration.
+            (Default: Twice the measurement period; i.e. with overlap.)
+        measure_smooth_time (float, optional) Time constant used to smooth
+            spectral measurements. (Default: 0.4)
+        hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied
+            at the input to the detector algorithm. (Default: 50.0)
+        lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied
+            at the input to the detector algorithm. (Default: 6000.0)
+        hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used
+            in the detector algorithm. (Default: 150.0)
+        lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used
+            in the detector algorithm. (Default: 2000.0)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> waveform_reversed, sample_rate = apply_effects_tensor(waveform, sample_rate, [["reverse"]])
+        >>> transform = transforms.Vad(sample_rate=sample_rate, trigger_level=7.5)
+        >>> waveform_reversed_front_trim = transform(waveform_reversed)
+        >>> waveform_end_trim, sample_rate = apply_effects_tensor(
+        >>>     waveform_reversed_front_trim, sample_rate, [["reverse"]]
+        >>> )
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+    """
+
+    def __init__(
+        self,
+        sample_rate: int,
+        trigger_level: float = 7.0,
+        trigger_time: float = 0.25,
+        search_time: float = 1.0,
+        allowed_gap: float = 0.25,
+        pre_trigger_time: float = 0.0,
+        boot_time: float = 0.35,
+        noise_up_time: float = 0.1,
+        noise_down_time: float = 0.01,
+        noise_reduction_amount: float = 1.35,
+        measure_freq: float = 20.0,
+        measure_duration: Optional[float] = None,
+        measure_smooth_time: float = 0.4,
+        hp_filter_freq: float = 50.0,
+        lp_filter_freq: float = 6000.0,
+        hp_lifter_freq: float = 150.0,
+        lp_lifter_freq: float = 2000.0,
+    ) -> None:
+        super().__init__()
+
+        self.sample_rate = sample_rate
+        self.trigger_level = trigger_level
+        self.trigger_time = trigger_time
+        self.search_time = search_time
+        self.allowed_gap = allowed_gap
+        self.pre_trigger_time = pre_trigger_time
+        self.boot_time = boot_time
+        self.noise_up_time = noise_up_time
+        self.noise_down_time = noise_down_time
+        self.noise_reduction_amount = noise_reduction_amount
+        self.measure_freq = measure_freq
+        self.measure_duration = measure_duration
+        self.measure_smooth_time = measure_smooth_time
+        self.hp_filter_freq = hp_filter_freq
+        self.lp_filter_freq = lp_filter_freq
+        self.hp_lifter_freq = hp_lifter_freq
+        self.lp_lifter_freq = lp_lifter_freq
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)`
+                Tensor of shape `(channels, time)` is treated as a multi-channel recording
+                of the same event and the resulting output will be trimmed to the earliest
+                voice activity in any channel.
+        """
+        return F.vad(
+            waveform=waveform,
+            sample_rate=self.sample_rate,
+            trigger_level=self.trigger_level,
+            trigger_time=self.trigger_time,
+            search_time=self.search_time,
+            allowed_gap=self.allowed_gap,
+            pre_trigger_time=self.pre_trigger_time,
+            boot_time=self.boot_time,
+            noise_up_time=self.noise_up_time,
+            noise_down_time=self.noise_down_time,
+            noise_reduction_amount=self.noise_reduction_amount,
+            measure_freq=self.measure_freq,
+            measure_duration=self.measure_duration,
+            measure_smooth_time=self.measure_smooth_time,
+            hp_filter_freq=self.hp_filter_freq,
+            lp_filter_freq=self.lp_filter_freq,
+            hp_lifter_freq=self.hp_lifter_freq,
+            lp_lifter_freq=self.lp_lifter_freq,
+        )
+
+
+class SpectralCentroid(torch.nn.Module):
+    r"""Compute the spectral centroid for each channel along the time axis.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    The spectral centroid is defined as the weighted average of the
+    frequency values, weighted by their magnitude.
+
+    Args:
+        sample_rate (int): Sample rate of audio signal.
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
+        win_length (int or None, optional): Window size. (Default: ``n_fft``)
+        hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
+        pad (int, optional): Two sided padding of signal. (Default: ``0``)
+        window_fn (Callable[..., Tensor], optional): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.SpectralCentroid(sample_rate)
+        >>> spectral_centroid = transform(waveform)  # (channel, time)
+    """
+    __constants__ = ["sample_rate", "n_fft", "win_length", "hop_length", "pad"]
+
+    def __init__(
+        self,
+        sample_rate: int,
+        n_fft: int = 400,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        pad: int = 0,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        wkwargs: Optional[dict] = None,
+    ) -> None:
+        super(SpectralCentroid, self).__init__()
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer("window", window)
+        self.pad = pad
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension `(..., time)`.
+
+        Returns:
+            Tensor: Spectral Centroid of size `(..., time)`.
+        """
+
+        return F.spectral_centroid(
+            waveform, self.sample_rate, self.pad, self.window, self.n_fft, self.hop_length, self.win_length
+        )
+
+
+class PitchShift(LazyModuleMixin, torch.nn.Module):
+    r"""Shift the pitch of a waveform by ``n_steps`` steps.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        waveform (Tensor): The input waveform of shape `(..., time)`.
+        sample_rate (int): Sample rate of `waveform`.
+        n_steps (int): The (fractional) steps to shift `waveform`.
+        bins_per_octave (int, optional): The number of steps per octave (Default : ``12``).
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins (Default: ``512``).
+        win_length (int or None, optional): Window size. If None, then ``n_fft`` is used. (Default: ``None``).
+        hop_length (int or None, optional): Length of hop between STFT windows. If None, then ``win_length // 4``
+            is used (Default: ``None``).
+        window (Tensor or None, optional): Window tensor that is applied/multiplied to each frame/window.
+            If None, then ``torch.hann_window(win_length)`` is used (Default: ``None``).
+
+    Example
+        >>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
+        >>> transform = transforms.PitchShift(sample_rate, 4)
+        >>> waveform_shift = transform(waveform)  # (channel, time)
+    """
+    __constants__ = ["sample_rate", "n_steps", "bins_per_octave", "n_fft", "win_length", "hop_length"]
+
+    kernel: UninitializedParameter
+    width: int
+
+    def __init__(
+        self,
+        sample_rate: int,
+        n_steps: int,
+        bins_per_octave: int = 12,
+        n_fft: int = 512,
+        win_length: Optional[int] = None,
+        hop_length: Optional[int] = None,
+        window_fn: Callable[..., Tensor] = torch.hann_window,
+        wkwargs: Optional[dict] = None,
+    ) -> None:
+        super().__init__()
+        self.n_steps = n_steps
+        self.bins_per_octave = bins_per_octave
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 4
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer("window", window)
+        rate = 2.0 ** (-float(n_steps) / bins_per_octave)
+        self.orig_freq = int(sample_rate / rate)
+        self.gcd = math.gcd(int(self.orig_freq), int(sample_rate))
+
+        if self.orig_freq != sample_rate:
+            self.width = -1
+            self.kernel = UninitializedParameter(device=None, dtype=None)
+
+    def initialize_parameters(self, input):
+        if self.has_uninitialized_params():
+            if self.orig_freq != self.sample_rate:
+                with torch.no_grad():
+                    kernel, self.width = _get_sinc_resample_kernel(
+                        self.orig_freq,
+                        self.sample_rate,
+                        self.gcd,
+                        dtype=input.dtype,
+                        device=input.device,
+                    )
+                    self.kernel.materialize(kernel.shape)
+                    self.kernel.copy_(kernel)
+
+    def forward(self, waveform: Tensor) -> Tensor:
+        r"""
+        Args:
+            waveform (Tensor): Tensor of audio of dimension `(..., time)`.
+
+        Returns:
+            Tensor: The pitch-shifted audio of shape `(..., time)`.
+        """
+        shape = waveform.size()
+
+        waveform_stretch = _stretch_waveform(
+            waveform,
+            self.n_steps,
+            self.bins_per_octave,
+            self.n_fft,
+            self.win_length,
+            self.hop_length,
+            self.window,
+        )
+
+        if self.orig_freq != self.sample_rate:
+            waveform_shift = _apply_sinc_resample_kernel(
+                waveform_stretch,
+                self.orig_freq,
+                self.sample_rate,
+                self.gcd,
+                self.kernel,
+                self.width,
+            )
+        else:
+            waveform_shift = waveform_stretch
+
+        return _fix_waveform_shape(
+            waveform_shift,
+            shape,
+        )
+
+
+class RNNTLoss(torch.nn.Module):
+    """Compute the RNN Transducer loss from *Sequence Transduction with Recurrent Neural Networks*
+    :cite:`graves2012sequence`.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    The RNN Transducer loss extends the CTC loss by defining a distribution over output
+    sequences of all lengths, and by jointly modelling both input-output and output-output
+    dependencies.
+
+    Args:
+        blank (int, optional): blank label (Default: ``-1``)
+        clamp (float, optional): clamp for gradients (Default: ``-1``)
+        reduction (string, optional): Specifies the reduction to apply to the output:
+            ``"none"`` | ``"mean"`` | ``"sum"``. (Default: ``"mean"``)
+        fused_log_softmax (bool): set to False if calling log_softmax outside of loss (Default: ``True``)
+
+    Example
+        >>> # Hypothetical values
+        >>> logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1],
+        >>>                          [0.1, 0.1, 0.6, 0.1, 0.1],
+        >>>                          [0.1, 0.1, 0.2, 0.8, 0.1]],
+        >>>                         [[0.1, 0.6, 0.1, 0.1, 0.1],
+        >>>                          [0.1, 0.1, 0.2, 0.1, 0.1],
+        >>>                          [0.7, 0.1, 0.2, 0.1, 0.1]]]],
+        >>>                       dtype=torch.float32,
+        >>>                       requires_grad=True)
+        >>> targets = torch.tensor([[1, 2]], dtype=torch.int)
+        >>> logit_lengths = torch.tensor([2], dtype=torch.int)
+        >>> target_lengths = torch.tensor([2], dtype=torch.int)
+        >>> transform = transforms.RNNTLoss(blank=0)
+        >>> loss = transform(logits, targets, logit_lengths, target_lengths)
+        >>> loss.backward()
+    """
+
+    def __init__(
+        self,
+        blank: int = -1,
+        clamp: float = -1.0,
+        reduction: str = "mean",
+        fused_log_softmax: bool = True,
+    ):
+        super().__init__()
+        self.blank = blank
+        self.clamp = clamp
+        self.reduction = reduction
+        self.fused_log_softmax = fused_log_softmax
+
+    def forward(
+        self,
+        logits: Tensor,
+        targets: Tensor,
+        logit_lengths: Tensor,
+        target_lengths: Tensor,
+    ):
+        """
+        Args:
+            logits (Tensor): Tensor of dimension `(batch, max seq length, max target length + 1, class)`
+                containing output from joiner
+            targets (Tensor): Tensor of dimension `(batch, max target length)` containing targets with zero padded
+            logit_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of each sequence from encoder
+            target_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of targets for each sequence
+        Returns:
+            Tensor: Loss with the reduction option applied. If ``reduction`` is  ``"none"``, then size (batch),
+            otherwise scalar.
+        """
+        return F.rnnt_loss(
+            logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            self.blank,
+            self.clamp,
+            self.reduction,
+            self.fused_log_softmax,
+        )
+
+
+class Convolve(torch.nn.Module):
+    r"""
+    Convolves inputs along their last dimension using the direct method.
+    Note that, in contrast to :class:`torch.nn.Conv1d`, which actually applies the valid cross-correlation
+    operator, this module applies the true `convolution`_ operator.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        mode (str, optional): Must be one of ("full", "valid", "same").
+
+            * "full": Returns the full convolution result, with shape `(..., N + M - 1)`, where
+              `N` and `M` are the trailing dimensions of the two inputs. (Default)
+            * "valid": Returns the segment of the full convolution result corresponding to where
+              the two inputs overlap completely, with shape `(..., max(N, M) - min(N, M) + 1)`.
+            * "same": Returns the center segment of the full convolution result, with shape `(..., N)`.
+
+    .. _convolution:
+        https://en.wikipedia.org/wiki/Convolution
+    """
+
+    def __init__(self, mode: str = "full") -> None:
+        _check_convolve_mode(mode)
+
+        super().__init__()
+        self.mode = mode
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            x (torch.Tensor): First convolution operand, with shape `(..., N)`.
+            y (torch.Tensor): Second convolution operand, with shape `(..., M)`
+                (leading dimensions must be broadcast-able with those of ``x``).
+
+        Returns:
+            torch.Tensor: Result of convolving ``x`` and ``y``, with shape `(..., L)`, where
+            the leading dimensions match those of ``x`` and `L` is dictated by ``mode``.
+        """
+        return F.convolve(x, y, mode=self.mode)
+
+
+class FFTConvolve(torch.nn.Module):
+    r"""
+    Convolves inputs along their last dimension using FFT. For inputs with large last dimensions, this module
+    is generally much faster than :class:`Convolve`.
+    Note that, in contrast to :class:`torch.nn.Conv1d`, which actually applies the valid cross-correlation
+    operator, this module applies the true `convolution`_ operator.
+    Also note that this module can only output float tensors (int tensor inputs will be cast to float).
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        mode (str, optional): Must be one of ("full", "valid", "same").
+
+            * "full": Returns the full convolution result, with shape `(..., N + M - 1)`, where
+              `N` and `M` are the trailing dimensions of the two inputs. (Default)
+            * "valid": Returns the segment of the full convolution result corresponding to where
+              the two inputs overlap completely, with shape `(..., max(N, M) - min(N, M) + 1)`.
+            * "same": Returns the center segment of the full convolution result, with shape `(..., N)`.
+
+    .. _convolution:
+        https://en.wikipedia.org/wiki/Convolution
+    """
+
+    def __init__(self, mode: str = "full") -> None:
+        _check_convolve_mode(mode)
+
+        super().__init__()
+        self.mode = mode
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            x (torch.Tensor): First convolution operand, with shape `(..., N)`.
+            y (torch.Tensor): Second convolution operand, with shape `(..., M)`
+                (leading dimensions must be broadcast-able with those of ``x``).
+
+        Returns:
+            torch.Tensor: Result of convolving ``x`` and ``y``, with shape `(..., L)`, where
+            the leading dimensions match those of ``x`` and `L` is dictated by ``mode``.
+        """
+        return F.fftconvolve(x, y, mode=self.mode)
+
+
+def _source_target_sample_rate(orig_freq: int, speed: float) -> Tuple[int, int]:
+    source_sample_rate = int(speed * orig_freq)
+    target_sample_rate = int(orig_freq)
+    gcd = math.gcd(source_sample_rate, target_sample_rate)
+    return source_sample_rate // gcd, target_sample_rate // gcd
+
+
+class Speed(torch.nn.Module):
+    r"""Adjusts waveform speed.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        orig_freq (int): Original frequency of the signals in ``waveform``.
+        factor (float): Factor by which to adjust speed of input. Values greater than 1.0
+            compress ``waveform`` in time, whereas values less than 1.0 stretch ``waveform`` in time.
+    """
+
+    def __init__(self, orig_freq, factor) -> None:
+        super().__init__()
+
+        self.orig_freq = orig_freq
+        self.factor = factor
+
+        self.source_sample_rate, self.target_sample_rate = _source_target_sample_rate(orig_freq, factor)
+        self.resampler = Resample(orig_freq=self.source_sample_rate, new_freq=self.target_sample_rate)
+
+    def forward(self, waveform, lengths: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        r"""
+        Args:
+            waveform (torch.Tensor): Input signals, with shape `(..., time)`.
+            lengths (torch.Tensor or None, optional): Valid lengths of signals in ``waveform``, with shape `(...)`.
+                If ``None``, all elements in ``waveform`` are treated as valid. (Default: ``None``)
+
+        Returns:
+            (torch.Tensor, torch.Tensor or None):
+                torch.Tensor
+                    Speed-adjusted waveform, with shape `(..., new_time).`
+                torch.Tensor or None
+                    If ``lengths`` is not ``None``, valid lengths of signals in speed-adjusted waveform,
+                    with shape `(...)`; otherwise, ``None``.
+        """
+
+        if lengths is None:
+            out_lengths = None
+        else:
+            out_lengths = torch.ceil(lengths * self.target_sample_rate / self.source_sample_rate).to(lengths.dtype)
+
+        return self.resampler(waveform), out_lengths
+
+
+class SpeedPerturbation(torch.nn.Module):
+    r"""Applies the speed perturbation augmentation introduced in
+    *Audio augmentation for speech recognition* :cite:`ko15_interspeech`. For a given input,
+    the module samples a speed-up factor from ``factors`` uniformly at random and adjusts
+    the speed of the input by that factor.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        orig_freq (int): Original frequency of the signals in ``waveform``.
+        factors (Sequence[float]): Factors by which to adjust speed of input. Values greater than 1.0
+            compress ``waveform`` in time, whereas values less than 1.0 stretch ``waveform`` in time.
+
+    Example
+        >>> speed_perturb = SpeedPerturbation(16000, [0.9, 1.1, 1.0, 1.0, 1.0])
+        >>> # waveform speed will be adjusted by factor 0.9 with 20% probability,
+        >>> # 1.1 with 20% probability, and 1.0 (i.e. kept the same) with 60% probability.
+        >>> speed_perturbed_waveform = speed_perturb(waveform, lengths)
+    """
+
+    def __init__(self, orig_freq: int, factors: Sequence[float]) -> None:
+        super().__init__()
+
+        self.speeders = torch.nn.ModuleList([Speed(orig_freq=orig_freq, factor=factor) for factor in factors])
+
+    def forward(
+        self, waveform: torch.Tensor, lengths: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        r"""
+        Args:
+            waveform (torch.Tensor): Input signals, with shape `(..., time)`.
+            lengths (torch.Tensor or None, optional): Valid lengths of signals in ``waveform``, with shape `(...)`.
+                If ``None``, all elements in ``waveform`` are treated as valid. (Default: ``None``)
+
+        Returns:
+            (torch.Tensor, torch.Tensor or None):
+                torch.Tensor
+                    Speed-adjusted waveform, with shape `(..., new_time).`
+                torch.Tensor or None
+                    If ``lengths`` is not ``None``, valid lengths of signals in speed-adjusted waveform,
+                    with shape `(...)`; otherwise, ``None``.
+        """
+
+        idx = int(torch.randint(len(self.speeders), ()))
+        # NOTE: we do this because TorchScript doesn't allow for
+        # indexing ModuleList instances with non-literals.
+        for speeder_idx, speeder in enumerate(self.speeders):
+            if idx == speeder_idx:
+                return speeder(waveform, lengths)
+        raise RuntimeError("Speeder not found; execution should have never reached here.")
+
+
+class AddNoise(torch.nn.Module):
+    r"""Scales and adds noise to waveform per signal-to-noise ratio.
+    See :meth:`torchaudio.functional.add_noise` for more details.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+    """
+
+    def forward(
+        self, waveform: torch.Tensor, noise: torch.Tensor, snr: torch.Tensor, lengths: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            waveform (torch.Tensor): Input waveform, with shape `(..., L)`.
+            noise (torch.Tensor): Noise, with shape `(..., L)` (same shape as ``waveform``).
+            snr (torch.Tensor): Signal-to-noise ratios in dB, with shape `(...,)`.
+            lengths (torch.Tensor or None, optional): Valid lengths of signals in ``waveform`` and ``noise``,
+            with shape `(...,)` (leading dimensions must match those of ``waveform``). If ``None``, all
+            elements in ``waveform`` and ``noise`` are treated as valid. (Default: ``None``)
+
+        Returns:
+            torch.Tensor: Result of scaling and adding ``noise`` to ``waveform``, with shape `(..., L)`
+            (same shape as ``waveform``).
+        """
+        return F.add_noise(waveform, noise, snr, lengths)
+
+
+class Preemphasis(torch.nn.Module):
+    r"""Pre-emphasizes a waveform along its last dimension.
+    See :meth:`torchaudio.functional.preemphasis` for more details.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        coeff (float, optional): Pre-emphasis coefficient. Typically between 0.0 and 1.0.
+            (Default: 0.97)
+    """
+
+    def __init__(self, coeff: float = 0.97) -> None:
+        super().__init__()
+        self.coeff = coeff
+
+    def forward(self, waveform: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            waveform (torch.Tensor): Waveform, with shape `(..., N)`.
+
+        Returns:
+            torch.Tensor: Pre-emphasized waveform, with shape `(..., N)`.
+        """
+        return F.preemphasis(waveform, coeff=self.coeff)
+
+
+class Deemphasis(torch.nn.Module):
+    r"""De-emphasizes a waveform along its last dimension.
+    See :meth:`torchaudio.functional.deemphasis` for more details.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        coeff (float, optional): De-emphasis coefficient. Typically between 0.0 and 1.0.
+            (Default: 0.97)
+    """
+
+    def __init__(self, coeff: float = 0.97) -> None:
+        super().__init__()
+        self.coeff = coeff
+
+    def forward(self, waveform: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            waveform (torch.Tensor): Waveform, with shape `(..., N)`.
+
+        Returns:
+            torch.Tensor: De-emphasized waveform, with shape `(..., N)`.
+        """
+        return F.deemphasis(waveform, coeff=self.coeff)
diff --git a/shared/package-files/40/407b856943a5f67f12c8d18f67e202308d4d78345404cc7fa2607177e53772ad/session.py b/shared/package-files/40/407b856943a5f67f12c8d18f67e202308d4d78345404cc7fa2607177e53772ad/session.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8e8cf22643174415f50147881101776be83ce9e
--- /dev/null
+++ b/shared/package-files/40/407b856943a5f67f12c8d18f67e202308d4d78345404cc7fa2607177e53772ad/session.py
@@ -0,0 +1,220 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+from logging import getLogger
+from threading import local
+
+from ...auxlib.ish import dals
+from ...base.constants import CONDA_HOMEPAGE_URL
+from ...base.context import context
+from ...common.url import (
+    add_username_and_password,
+    get_proxy_username_and_pass,
+    split_anaconda_token,
+    urlparse,
+)
+from ...exceptions import ProxyError
+from ..anaconda_client import read_binstar_tokens
+from . import (
+    AuthBase,
+    BaseAdapter,
+    HTTPAdapter,
+    Retry,
+    Session,
+    _basic_auth_str,
+    extract_cookies_to_jar,
+    get_auth_from_url,
+    get_netrc_auth,
+)
+from .adapters.ftp import FTPAdapter
+from .adapters.localfs import LocalFSAdapter
+from .adapters.s3 import S3Adapter
+
+log = getLogger(__name__)
+RETRIES = 3
+
+
+CONDA_SESSION_SCHEMES = frozenset(
+    (
+        "http",
+        "https",
+        "ftp",
+        "s3",
+        "file",
+    )
+)
+
+
+class EnforceUnusedAdapter(BaseAdapter):
+    def send(self, request, *args, **kwargs):
+        message = dals(
+            """
+        EnforceUnusedAdapter called with url %s
+        This command is using a remote connection in offline mode.
+        """
+            % request.url
+        )
+        raise RuntimeError(message)
+
+    def close(self):
+        raise NotImplementedError()
+
+
+class CondaSessionType(type):
+    """
+    Takes advice from https://github.com/requests/requests/issues/1871#issuecomment-33327847
+    and creates one Session instance per thread.
+    """
+
+    def __new__(mcs, name, bases, dct):
+        dct["_thread_local"] = local()
+        return super().__new__(mcs, name, bases, dct)
+
+    def __call__(cls):
+        try:
+            return cls._thread_local.session
+        except AttributeError:
+            session = cls._thread_local.session = super().__call__()
+            return session
+
+
+class CondaSession(Session, metaclass=CondaSessionType):
+    def __init__(self):
+        super().__init__()
+
+        self.auth = (
+            CondaHttpAuth()
+        )  # TODO: should this just be for certain protocol adapters?
+
+        self.proxies.update(context.proxy_servers)
+
+        if context.offline:
+            unused_adapter = EnforceUnusedAdapter()
+            self.mount("http://", unused_adapter)
+            self.mount("https://", unused_adapter)
+            self.mount("ftp://", unused_adapter)
+            self.mount("s3://", unused_adapter)
+
+        else:
+            # Configure retries
+            retry = Retry(
+                total=context.remote_max_retries,
+                backoff_factor=context.remote_backoff_factor,
+                status_forcelist=[413, 429, 500, 503],
+                raise_on_status=False,
+            )
+            http_adapter = HTTPAdapter(max_retries=retry)
+            self.mount("http://", http_adapter)
+            self.mount("https://", http_adapter)
+            self.mount("ftp://", FTPAdapter())
+            self.mount("s3://", S3Adapter())
+
+        self.mount("file://", LocalFSAdapter())
+
+        self.headers["User-Agent"] = context.user_agent
+
+        self.verify = context.ssl_verify
+
+        if context.client_ssl_cert_key:
+            self.cert = (context.client_ssl_cert, context.client_ssl_cert_key)
+        elif context.client_ssl_cert:
+            self.cert = context.client_ssl_cert
+
+
+class CondaHttpAuth(AuthBase):
+    # TODO: make this class thread-safe by adding some of the requests.auth.HTTPDigestAuth() code
+
+    def __call__(self, request):
+        request.url = CondaHttpAuth.add_binstar_token(request.url)
+        self._apply_basic_auth(request)
+        request.register_hook("response", self.handle_407)
+        return request
+
+    @staticmethod
+    def _apply_basic_auth(request):
+        # this logic duplicated from Session.prepare_request and PreparedRequest.prepare_auth
+        url_auth = get_auth_from_url(request.url)
+        auth = url_auth if any(url_auth) else None
+
+        if auth is None:
+            # look for auth information in a .netrc file
+            auth = get_netrc_auth(request.url)
+
+        if isinstance(auth, tuple) and len(auth) == 2:
+            request.headers["Authorization"] = _basic_auth_str(*auth)
+
+        return request
+
+    @staticmethod
+    def add_binstar_token(url):
+        clean_url, token = split_anaconda_token(url)
+        if not token and context.add_anaconda_token:
+            for binstar_url, token in read_binstar_tokens().items():
+                if clean_url.startswith(binstar_url):
+                    log.debug("Adding anaconda token for url <%s>", clean_url)
+                    from ...models.channel import Channel
+
+                    channel = Channel(clean_url)
+                    channel.token = token
+                    return channel.url(with_credentials=True)
+        return url
+
+    @staticmethod
+    def handle_407(response, **kwargs):  # pragma: no cover
+        """
+        Prompts the user for the proxy username and password and modifies the
+        proxy in the session object to include it.
+
+        This method is modeled after
+          * requests.auth.HTTPDigestAuth.handle_401()
+          * requests.auth.HTTPProxyAuth
+          * the previous conda.fetch.handle_proxy_407()
+
+        It both adds 'username:password' to the proxy URL, as well as adding a
+        'Proxy-Authorization' header.  If any of this is incorrect, please file an issue.
+
+        """
+        # kwargs = {'verify': True, 'cert': None, 'proxies': {}, 'stream': False,
+        #           'timeout': (3.05, 60)}
+
+        if response.status_code != 407:
+            return response
+
+        # Consume content and release the original connection
+        # to allow our new request to reuse the same one.
+        response.content
+        response.close()
+
+        proxies = kwargs.pop("proxies")
+
+        proxy_scheme = urlparse(response.url).scheme
+        if proxy_scheme not in proxies:
+            raise ProxyError(
+                dals(
+                    """
+            Could not find a proxy for {!r}. See
+            {}/docs/html#configure-conda-for-use-behind-a-proxy-server
+            for more information on how to configure proxies.
+            """.format(
+                        proxy_scheme, CONDA_HOMEPAGE_URL
+                    )
+                )
+            )
+
+        # fix-up proxy_url with username & password
+        proxy_url = proxies[proxy_scheme]
+        username, password = get_proxy_username_and_pass(proxy_scheme)
+        proxy_url = add_username_and_password(proxy_url, username, password)
+        proxy_authorization_header = _basic_auth_str(username, password)
+        proxies[proxy_scheme] = proxy_url
+        kwargs["proxies"] = proxies
+
+        prep = response.request.copy()
+        extract_cookies_to_jar(prep._cookies, response.request, response.raw)
+        prep.prepare_cookies(prep._cookies)
+        prep.headers["Proxy-Authorization"] = proxy_authorization_header
+
+        _response = response.connection.send(prep, **kwargs)
+        _response.history.append(response)
+        _response.request = prep
+
+        return _response
diff --git a/shared/package-files/40/407eda2694286c535e1a76263d451ef0509266e8b5569a5e3e43a58145b376d1/macrodata_jmulti_cst_Sigmau.txt b/shared/package-files/40/407eda2694286c535e1a76263d451ef0509266e8b5569a5e3e43a58145b376d1/macrodata_jmulti_cst_Sigmau.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5b2c5eb9ae0fbb3065499848644c17370e4e27df
--- /dev/null
+++ b/shared/package-files/40/407eda2694286c535e1a76263d451ef0509266e8b5569a5e3e43a58145b376d1/macrodata_jmulti_cst_Sigmau.txt
@@ -0,0 +1,20 @@
+*** Sat, 3 Sep 2016 22:34:05 ***
+VAR MODEL STATISTICS
+sample range:   [1960 Q1, 2009 Q3], T = 199
+
+Log Likelihood:      -2.773442e+03 
+Determinant (Cov):    2.945441e+08 
+
+Covariance:   7.162689e+02  6.729414e+02 -1.526202e+00 
+              6.729414e+02  2.107000e+03  1.045390e+03 
+             -1.526202e+00  1.045390e+03  1.107563e+03 
+             
+Correlation:  1.000000e+00  5.477809e-01 -1.713522e-03 
+              5.477809e-01  1.000000e+00  6.843241e-01 
+             -1.713522e-03  6.843241e-01  1.000000e+00 
+             
+AIC:          2.001350e+01
+FPE:          4.923766e+08
+SC:           2.085751e+01
+HQ:           2.035510e+01
+
diff --git a/shared/package-files/40/407fc0fe06d2a057e9ba0109ea9356cab38f27756d135ef3b06a85705b616f50/cp865.enc b/shared/package-files/40/407fc0fe06d2a057e9ba0109ea9356cab38f27756d135ef3b06a85705b616f50/cp865.enc
new file mode 100644
index 0000000000000000000000000000000000000000..543da9c5f381a51b616b8d327a100d53f4e00cf0
--- /dev/null
+++ b/shared/package-files/40/407fc0fe06d2a057e9ba0109ea9356cab38f27756d135ef3b06a85705b616f50/cp865.enc
@@ -0,0 +1,20 @@
+# Encoding file: cp865, single-byte
+S
+003F 0 1
+00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 --git a/shared/package-files/40/4080474c95286c493072a9ca313fc610abb902bce978698afbcbfbcf8a861c84/interfaces.h b/shared/package-files/40/4080474c95286c493072a9ca313fc610abb902bce978698afbcbfbcf8a861c84/interfaces.h
new file mode 100644
index 0000000000000000000000000000000000000000..b36c38c6d48688a793c2588477f97648a8b550c6
--- /dev/null
+++ b/shared/package-files/40/4080474c95286c493072a9ca313fc610abb902bce978698afbcbfbcf8a861c84/interfaces.h
@@ -0,0 +1,362 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <string>
+#include <string_view>
+#include <vector>
+
+#include "arrow/io/type_fwd.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/cancel.h"
+#include "arrow/util/macros.h"
+#include "arrow/util/type_fwd.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace io {
+
+struct ReadRange {
+  int64_t offset;
+  int64_t length;
+
+  friend bool operator==(const ReadRange& left, const ReadRange& right) {
+    return (left.offset == right.offset && left.length == right.length);
+  }
+  friend bool operator!=(const ReadRange& left, const ReadRange& right) {
+    return !(left == right);
+  }
+
+  bool Contains(const ReadRange& other) const {
+    return (offset <= other.offset && offset + length >= other.offset + other.length);
+  }
+};
+
+/// EXPERIMENTAL: options provider for IO tasks
+///
+/// Includes an Executor (which will be used to execute asynchronous reads),
+/// a MemoryPool (which will be used to allocate buffers when zero copy reads
+/// are not possible), and an external id (in case the executor receives tasks from
+/// multiple sources and must distinguish tasks associated with this IOContext).
+struct ARROW_EXPORT IOContext {
+  // No specified executor: will use a global IO thread pool
+  IOContext() : IOContext(default_memory_pool(), StopToken::Unstoppable()) {}
+
+  explicit IOContext(StopToken stop_token)
+      : IOContext(default_memory_pool(), std::move(stop_token)) {}
+
+  explicit IOContext(MemoryPool* pool, StopToken stop_token = StopToken::Unstoppable());
+
+  explicit IOContext(MemoryPool* pool, ::arrow::internal::Executor* executor,
+                     StopToken stop_token = StopToken::Unstoppable(),
+                     int64_t external_id = -1)
+      : pool_(pool),
+        executor_(executor),
+        external_id_(external_id),
+        stop_token_(std::move(stop_token)) {}
+
+  explicit IOContext(::arrow::internal::Executor* executor,
+                     StopToken stop_token = StopToken::Unstoppable(),
+                     int64_t external_id = -1)
+      : pool_(default_memory_pool()),
+        executor_(executor),
+        external_id_(external_id),
+        stop_token_(std::move(stop_token)) {}
+
+  MemoryPool* pool() const { return pool_; }
+
+  ::arrow::internal::Executor* executor() const { return executor_; }
+
+  // An application-specific ID, forwarded to executor task submissions
+  int64_t external_id() const { return external_id_; }
+
+  StopToken stop_token() const { return stop_token_; }
+
+ private:
+  MemoryPool* pool_;
+  ::arrow::internal::Executor* executor_;
+  int64_t external_id_;
+  StopToken stop_token_;
+};
+
+class ARROW_EXPORT FileInterface : public std::enable_shared_from_this<FileInterface> {
+ public:
+  virtual ~FileInterface() = 0;
+
+  /// \brief Close the stream cleanly
+  ///
+  /// For writable streams, this will attempt to flush any pending data
+  /// before releasing the underlying resource.
+  ///
+  /// After Close() is called, closed() returns true and the stream is not
+  /// available for further operations.
+  virtual Status Close() = 0;
+
+  /// \brief Close the stream asynchronously
+  ///
+  /// By default, this will just submit the synchronous Close() to the
+  /// default I/O thread pool. Subclasses may implement this in a more
+  /// efficient manner.
+  virtual Future<> CloseAsync();
+
+  /// \brief Close the stream abruptly
+  ///
+  /// This method does not guarantee that any pending data is flushed.
+  /// It merely releases any underlying resource used by the stream for
+  /// its operation.
+  ///
+  /// After Abort() is called, closed() returns true and the stream is not
+  /// available for further operations.
+  virtual Status Abort();
+
+  /// \brief Return the position in this stream
+  virtual Result<int64_t> Tell() const = 0;
+
+  /// \brief Return whether the stream is closed
+  virtual bool closed() const = 0;
+
+  FileMode::type mode() const { return mode_; }
+
+ protected:
+  FileInterface() : mode_(FileMode::READ) {}
+  FileMode::type mode_;
+  void set_mode(FileMode::type mode) { mode_ = mode; }
+
+ private:
+  ARROW_DISALLOW_COPY_AND_ASSIGN(FileInterface);
+};
+
+class ARROW_EXPORT Seekable {
+ public:
+  virtual ~Seekable() = default;
+  virtual Status Seek(int64_t position) = 0;
+};
+
+class ARROW_EXPORT Writable {
+ public:
+  virtual ~Writable() = default;
+
+  /// \brief Write the given data to the stream
+  ///
+  /// This method always processes the bytes in full.  Depending on the
+  /// semantics of the stream, the data may be written out immediately,
+  /// held in a buffer, or written asynchronously.  In the case where
+  /// the stream buffers the data, it will be copied.  To avoid potentially
+  /// large copies, use the Write variant that takes an owned Buffer.
+  virtual Status Write(const void* data, int64_t nbytes) = 0;
+
+  /// \brief Write the given data to the stream
+  ///
+  /// Since the Buffer owns its memory, this method can avoid a copy if
+  /// buffering is required.  See Write(const void*, int64_t) for details.
+  virtual Status Write(const std::shared_ptr<Buffer>& data);
+
+  /// \brief Flush buffered bytes, if any
+  virtual Status Flush();
+
+  Status Write(std::string_view data);
+};
+
+class ARROW_EXPORT Readable {
+ public:
+  virtual ~Readable() = default;
+
+  /// \brief Read data from current file position.
+  ///
+  /// Read at most `nbytes` from the current file position into `out`.
+  /// The number of bytes read is returned.
+  virtual Result<int64_t> Read(int64_t nbytes, void* out) = 0;
+
+  /// \brief Read data from current file position.
+  ///
+  /// Read at most `nbytes` from the current file position. Less bytes may
+  /// be read if EOF is reached. This method updates the current file position.
+  ///
+  /// In some cases (e.g. a memory-mapped file), this method may avoid a
+  /// memory copy.
+  virtual Result<std::shared_ptr<Buffer>> Read(int64_t nbytes) = 0;
+
+  /// EXPERIMENTAL: The IOContext associated with this file.
+  ///
+  /// By default, this is the same as default_io_context(), but it may be
+  /// overridden by subclasses.
+  virtual const IOContext& io_context() const;
+};
+
+class ARROW_EXPORT OutputStream : virtual public FileInterface, public Writable {
+ protected:
+  OutputStream() = default;
+};
+
+class ARROW_EXPORT InputStream : virtual public FileInterface, virtual public Readable {
+ public:
+  /// \brief Advance or skip stream indicated number of bytes
+  /// \param[in] nbytes the number to move forward
+  /// \return Status
+  Status Advance(int64_t nbytes);
+
+  /// \brief Return zero-copy string_view to upcoming bytes.
+  ///
+  /// Do not modify the stream position.  The view becomes invalid after
+  /// any operation on the stream.  May trigger buffering if the requested
+  /// size is larger than the number of buffered bytes.
+  ///
+  /// May return NotImplemented on streams that don't support it.
+  ///
+  /// \param[in] nbytes the maximum number of bytes to see
+  virtual Result<std::string_view> Peek(int64_t nbytes);
+
+  /// \brief Return true if InputStream is capable of zero copy Buffer reads
+  ///
+  /// Zero copy reads imply the use of Buffer-returning Read() overloads.
+  virtual bool supports_zero_copy() const;
+
+  /// \brief Read and return stream metadata
+  ///
+  /// If the stream implementation doesn't support metadata, empty metadata
+  /// is returned.  Note that it is allowed to return a null pointer rather
+  /// than an allocated empty metadata.
+  virtual Result<std::shared_ptr<const KeyValueMetadata>> ReadMetadata();
+
+  /// \brief Read stream metadata asynchronously
+  virtual Future<std::shared_ptr<const KeyValueMetadata>> ReadMetadataAsync(
+      const IOContext& io_context);
+  Future<std::shared_ptr<const KeyValueMetadata>> ReadMetadataAsync();
+
+ protected:
+  InputStream() = default;
+};
+
+class ARROW_EXPORT RandomAccessFile : public InputStream, public Seekable {
+ public:
+  /// Necessary because we hold a std::unique_ptr
+  ~RandomAccessFile() override;
+
+  /// \brief Create an isolated InputStream that reads a segment of a
+  /// RandomAccessFile. Multiple such stream can be created and used
+  /// independently without interference
+  /// \param[in] file a file instance
+  /// \param[in] file_offset the starting position in the file
+  /// \param[in] nbytes the extent of bytes to read. The file should have
+  /// sufficient bytes available
+  static Result<std::shared_ptr<InputStream>> GetStream(
+      std::shared_ptr<RandomAccessFile> file, int64_t file_offset, int64_t nbytes);
+
+  /// \brief Return the total file size in bytes.
+  ///
+  /// This method does not read or move the current file position, so is safe
+  /// to call concurrently with e.g. ReadAt().
+  virtual Result<int64_t> GetSize() = 0;
+
+  /// \brief Read data from given file position.
+  ///
+  /// At most `nbytes` bytes are read.  The number of bytes read is returned
+  /// (it can be less than `nbytes` if EOF is reached).
+  ///
+  /// This method can be safely called from multiple threads concurrently.
+  /// It is unspecified whether this method updates the file position or not.
+  ///
+  /// The default RandomAccessFile-provided implementation uses Seek() and Read(),
+  /// but subclasses may override it with a more efficient implementation
+  /// that doesn't depend on implicit file positioning.
+  ///
+  /// \param[in] position Where to read bytes from
+  /// \param[in] nbytes The number of bytes to read
+  /// \param[out] out The buffer to read bytes into
+  /// \return The number of bytes read, or an error
+  virtual Result<int64_t> ReadAt(int64_t position, int64_t nbytes, void* out);
+
+  /// \brief Read data from given file position.
+  ///
+  /// At most `nbytes` bytes are read, but it can be less if EOF is reached.
+  ///
+  /// \param[in] position Where to read bytes from
+  /// \param[in] nbytes The number of bytes to read
+  /// \return A buffer containing the bytes read, or an error
+  virtual Result<std::shared_ptr<Buffer>> ReadAt(int64_t position, int64_t nbytes);
+
+  /// EXPERIMENTAL: Read data asynchronously.
+  virtual Future<std::shared_ptr<Buffer>> ReadAsync(const IOContext&, int64_t position,
+                                                    int64_t nbytes);
+
+  /// EXPERIMENTAL: Read data asynchronously, using the file's IOContext.
+  Future<std::shared_ptr<Buffer>> ReadAsync(int64_t position, int64_t nbytes);
+
+  /// EXPERIMENTAL: Explicit multi-read.
+  /// \brief Request multiple reads at once
+  ///
+  /// The underlying filesystem may optimize these reads by coalescing small reads into
+  /// large reads or by breaking up large reads into multiple parallel smaller reads.  The
+  /// reads should be issued in parallel if it makes sense for the filesystem.
+  ///
+  /// One future will be returned for each input read range.  Multiple returned futures
+  /// may correspond to a single read.  Or, a single returned future may be a combined
+  /// result of several individual reads.
+  ///
+  /// \param[in] ranges The ranges to read
+  /// \return A future that will complete with the data from the requested range is
+  /// available
+  virtual std::vector<Future<std::shared_ptr<Buffer>>> ReadManyAsync(
+      const IOContext&, const std::vector<ReadRange>& ranges);
+
+  /// EXPERIMENTAL: Explicit multi-read, using the file's IOContext.
+  std::vector<Future<std::shared_ptr<Buffer>>> ReadManyAsync(
+      const std::vector<ReadRange>& ranges);
+
+  /// EXPERIMENTAL: Inform that the given ranges may be read soon.
+  ///
+  /// Some implementations might arrange to prefetch some of the data.
+  /// However, no guarantee is made and the default implementation does nothing.
+  /// For robust prefetching, use ReadAt() or ReadAsync().
+  virtual Status WillNeed(const std::vector<ReadRange>& ranges);
+
+ protected:
+  RandomAccessFile();
+
+ private:
+  struct ARROW_NO_EXPORT Impl;
+  std::unique_ptr<Impl> interface_impl_;
+};
+
+class ARROW_EXPORT WritableFile : public OutputStream, public Seekable {
+ public:
+  virtual Status WriteAt(int64_t position, const void* data, int64_t nbytes) = 0;
+
+ protected:
+  WritableFile() = default;
+};
+
+class ARROW_EXPORT ReadWriteFileInterface : public RandomAccessFile, public WritableFile {
+ protected:
+  ReadWriteFileInterface() { RandomAccessFile::set_mode(FileMode::READWRITE); }
+};
+
+/// \brief Return an iterator on an input stream
+///
+/// The iterator yields a fixed-size block on each Next() call, except the
+/// last block in the stream which may be smaller.
+/// Once the end of stream is reached, Next() returns nullptr
+/// (unlike InputStream::Read() which returns an empty buffer).
+ARROW_EXPORT
+Result<Iterator<std::shared_ptr<Buffer>>> MakeInputStreamIterator(
+    std::shared_ptr<InputStream> stream, int64_t block_size);
+
+}  // namespace io
+}  // namespace arrow
diff --git a/shared/package-files/40/4084852b92d2584059925e84a4e6bbad64ad68aac2860671dfdc6db24bc7a044/options.py b/shared/package-files/40/4084852b92d2584059925e84a4e6bbad64ad68aac2860671dfdc6db24bc7a044/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e99c8daad1e9cdbec479aba13bcd069e5148f9
--- /dev/null
+++ b/shared/package-files/40/4084852b92d2584059925e84a4e6bbad64ad68aac2860671dfdc6db24bc7a044/options.py
@@ -0,0 +1,108 @@
+"""
+Target Options
+"""
+import operator
+
+from numba.core import config, utils
+from numba.core.targetconfig import TargetConfig, Option
+
+
+class TargetOptions:
+    """Target options maps user options from decorators to the
+    ``numba.core.compiler.Flags`` used by lowering and target context.
+    """
+    class Mapping:
+        def __init__(self, flag_name, apply=lambda x: x):
+            self.flag_name = flag_name
+            self.apply = apply
+
+    def finalize(self, flags, options):
+        """Subclasses can override this method to make target specific
+        customizations of default flags.
+
+        Parameters
+        ----------
+        flags : Flags
+        options : dict
+        """
+        pass
+
+    @classmethod
+    def parse_as_flags(cls, flags, options):
+        """Parse target options defined in ``options`` and set ``flags``
+        accordingly.
+
+        Parameters
+        ----------
+        flags : Flags
+        options : dict
+        """
+        opt = cls()
+        opt._apply(flags, options)
+        opt.finalize(flags, options)
+        return flags
+
+    def _apply(self, flags, options):
+        # Find all Mapping instances in the class
+        mappings = {}
+        cls = type(self)
+        for k in dir(cls):
+            v = getattr(cls, k)
+            if isinstance(v, cls.Mapping):
+                mappings[k] = v
+
+        used = set()
+        for k, mapping in mappings.items():
+            if k in options:
+                v = mapping.apply(options[k])
+                setattr(flags, mapping.flag_name, v)
+                used.add(k)
+
+        unused = set(options) - used
+        if unused:
+            # Unread options?
+            m = (f"Unrecognized options: {unused}. "
+                 f"Known options are {mappings.keys()}")
+            raise KeyError(m)
+
+
+_mapping = TargetOptions.Mapping
+
+
+class DefaultOptions:
+    """Defines how user-level target options are mapped to the target flags.
+    """
+    nopython = _mapping("enable_pyobject", operator.not_)
+    forceobj = _mapping("force_pyobject")
+    looplift = _mapping("enable_looplift")
+    _nrt = _mapping("nrt")
+    debug = _mapping("debuginfo")
+    boundscheck = _mapping("boundscheck")
+    nogil = _mapping("release_gil")
+    writable_args = _mapping("writable_args")
+
+    no_rewrites = _mapping("no_rewrites")
+    no_cpython_wrapper = _mapping("no_cpython_wrapper")
+    no_cfunc_wrapper = _mapping("no_cfunc_wrapper")
+
+    parallel = _mapping("auto_parallel")
+    fastmath = _mapping("fastmath")
+    error_model = _mapping("error_model")
+    inline = _mapping("inline")
+    forceinline = _mapping("forceinline")
+
+    _dbg_extend_lifetimes = _mapping("dbg_extend_lifetimes")
+    _dbg_optnone = _mapping("dbg_optnone")
+
+
+def include_default_options(*args):
+    """Returns a mixin class with a subset of the options
+
+    Parameters
+    ----------
+    *args : str
+        Option names to include.
+    """
+    glbs = {k: getattr(DefaultOptions, k) for k in args}
+
+    return type("OptionMixins", (), glbs)
diff --git a/shared/package-files/40/4085fce5f7c2c0cbedcd7b4b91678f15092ca3f995b487df5a96e0cb8ca4133d/test_comparison.py b/shared/package-files/40/4085fce5f7c2c0cbedcd7b4b91678f15092ca3f995b487df5a96e0cb8ca4133d/test_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eeb9da574b1e7973d98390ada40f23f57526203
--- /dev/null
+++ b/shared/package-files/40/4085fce5f7c2c0cbedcd7b4b91678f15092ca3f995b487df5a96e0cb8ca4133d/test_comparison.py
@@ -0,0 +1,60 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import BooleanArray
+from pandas.tests.arrays.masked_shared import ComparisonOps
+
+
+@pytest.fixture
+def data():
+    """Fixture returning boolean array with valid and missing data"""
+    return pd.array(
+        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
+        dtype="boolean",
+    )
+
+
+@pytest.fixture
+def dtype():
+    """Fixture returning BooleanDtype"""
+    return pd.BooleanDtype()
+
+
+class TestComparisonOps(ComparisonOps):
+    def test_compare_scalar(self, data, comparison_op):
+        self._compare_other(data, comparison_op, True)
+
+    def test_compare_array(self, data, comparison_op):
+        other = pd.array([True] * len(data), dtype="boolean")
+        self._compare_other(data, comparison_op, other)
+        other = np.array([True] * len(data))
+        self._compare_other(data, comparison_op, other)
+        other = pd.Series([True] * len(data))
+        self._compare_other(data, comparison_op, other)
+
+    @pytest.mark.parametrize("other", [True, False, pd.NA])
+    def test_scalar(self, other, comparison_op, dtype):
+        ComparisonOps.test_scalar(self, other, comparison_op, dtype)
+
+    def test_array(self, comparison_op):
+        op = comparison_op
+        a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        b = pd.array([True, False, None] * 3, dtype="boolean")
+
+        result = op(a, b)
+
+        values = op(a._data, b._data)
+        mask = a._mask | b._mask
+        expected = BooleanArray(values, mask)
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        result[0] = None
+        tm.assert_extension_array_equal(
+            a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        )
+        tm.assert_extension_array_equal(
+            b, pd.array([True, False, None] * 3, dtype="boolean")
+        )
diff --git a/shared/package-files/40/408abff3d754077d26f7d0fb5db4b612a862538f2ca8365dd7f414cffdfa261f/test_tree.py b/shared/package-files/40/408abff3d754077d26f7d0fb5db4b612a862538f2ca8365dd7f414cffdfa261f/test_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..790ebdcea11279bf46a1f0e90ab08b5c890f71f9
--- /dev/null
+++ b/shared/package-files/40/408abff3d754077d26f7d0fb5db4b612a862538f2ca8365dd7f414cffdfa261f/test_tree.py
@@ -0,0 +1,2839 @@
+"""
+Testing for the tree module (sklearn.tree).
+"""
+
+import copy
+import copyreg
+import io
+import pickle
+import re
+import struct
+from itertools import chain, pairwise, product
+
+import joblib
+import numpy as np
+import pytest
+from joblib.numpy_pickle import NumpyPickler
+from numpy.testing import assert_allclose
+
+from sklearn import clone, datasets, tree
+from sklearn.dummy import DummyRegressor
+from sklearn.exceptions import NotFittedError
+from sklearn.impute import SimpleImputer
+from sklearn.metrics import accuracy_score, mean_poisson_deviance, mean_squared_error
+from sklearn.model_selection import cross_val_score, train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.random_projection import _sparse_random_matrix
+from sklearn.tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    ExtraTreeClassifier,
+    ExtraTreeRegressor,
+)
+from sklearn.tree._classes import (
+    CRITERIA_CLF,
+    CRITERIA_REG,
+    DENSE_SPLITTERS,
+    SPARSE_SPLITTERS,
+)
+from sklearn.tree._partitioner import _py_sort
+from sklearn.tree._tree import (
+    NODE_DTYPE,
+    TREE_LEAF,
+    TREE_UNDEFINED,
+    _build_pruned_tree_py,
+    _check_n_classes,
+    _check_node_ndarray,
+    _check_value_ndarray,
+)
+from sklearn.tree._tree import Tree as CythonTree
+from sklearn.utils import compute_sample_weight
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    create_memmap_backed_data,
+    ignore_warnings,
+    skip_if_32bit,
+)
+from sklearn.utils.fixes import (
+    _IS_32BIT,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+)
+from sklearn.utils.validation import check_random_state
+
+CLF_CRITERIONS = ("gini", "log_loss")
+REG_CRITERIONS = ("squared_error", "absolute_error", "friedman_mse", "poisson")
+
+CLF_TREES = {
+    "DecisionTreeClassifier": DecisionTreeClassifier,
+    "ExtraTreeClassifier": ExtraTreeClassifier,
+}
+
+REG_TREES = {
+    "DecisionTreeRegressor": DecisionTreeRegressor,
+    "ExtraTreeRegressor": ExtraTreeRegressor,
+}
+
+ALL_TREES: dict = dict()
+ALL_TREES.update(CLF_TREES)
+ALL_TREES.update(REG_TREES)
+
+SPARSE_TREES = [
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "ExtraTreeClassifier",
+    "ExtraTreeRegressor",
+]
+
+
+X_small = np.array(
+    [
+        [0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0],
+        [0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1],
+        [-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1],
+        [-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1],
+        [-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1],
+        [-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1],
+        [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1],
+        [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1],
+        [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1],
+        [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0],
+        [2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0],
+        [2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0],
+        [2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0],
+        [1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0],
+        [3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1],
+        [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1],
+        [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1],
+        [2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1],
+        [2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1],
+        [2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1],
+        [2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1],
+        [1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1],
+        [3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0],
+    ]
+)
+
+y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
+y_small_reg = [
+    1.0,
+    2.1,
+    1.2,
+    0.05,
+    10,
+    2.4,
+    3.1,
+    1.01,
+    0.01,
+    2.98,
+    3.1,
+    1.1,
+    0.0,
+    1.2,
+    2,
+    11,
+    0,
+    0,
+    4.5,
+    0.201,
+    1.06,
+    0.9,
+    0,
+]
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+T = [[-1, -1], [2, 2], [3, 2]]
+true_result = [-1, 1, 1]
+
+# also load the iris dataset
+# and randomly permute it
+iris = datasets.load_iris()
+rng = np.random.RandomState(1)
+perm = rng.permutation(iris.target.size)
+iris.data = iris.data[perm]
+iris.target = iris.target[perm]
+
+# also load the diabetes dataset
+# and randomly permute it
+diabetes = datasets.load_diabetes()
+perm = rng.permutation(diabetes.target.size)
+diabetes.data = diabetes.data[perm]
+diabetes.target = diabetes.target[perm]
+
+digits = datasets.load_digits()
+perm = rng.permutation(digits.target.size)
+digits.data = digits.data[perm]
+digits.target = digits.target[perm]
+
+random_state = check_random_state(0)
+X_multilabel, y_multilabel = datasets.make_multilabel_classification(
+    random_state=0, n_samples=30, n_features=10
+)
+
+# NB: despite their names X_sparse_* are numpy arrays (and not sparse matrices)
+X_sparse_pos = random_state.uniform(size=(20, 5))
+X_sparse_pos[X_sparse_pos <= 0.8] = 0.0
+y_random = random_state.randint(0, 4, size=(20,))
+X_sparse_mix = _sparse_random_matrix(20, 10, density=0.25, random_state=0).toarray()
+
+
+DATASETS = {
+    "iris": {"X": iris.data, "y": iris.target},
+    "diabetes": {"X": diabetes.data, "y": diabetes.target},
+    "digits": {"X": digits.data, "y": digits.target},
+    "toy": {"X": X, "y": y},
+    "clf_small": {"X": X_small, "y": y_small},
+    "reg_small": {"X": X_small, "y": y_small_reg},
+    "multilabel": {"X": X_multilabel, "y": y_multilabel},
+    "sparse-pos": {"X": X_sparse_pos, "y": y_random},
+    "sparse-neg": {"X": -X_sparse_pos, "y": y_random},
+    "sparse-mix": {"X": X_sparse_mix, "y": y_random},
+    "zeros": {"X": np.zeros((20, 3)), "y": y_random},
+}
+
+
+def assert_tree_equal(d, s, message):
+    assert s.node_count == d.node_count, (
+        "{0}: inequal number of node ({1} != {2})".format(
+            message, s.node_count, d.node_count
+        )
+    )
+
+    assert_array_equal(
+        d.children_right, s.children_right, message + ": inequal children_right"
+    )
+    assert_array_equal(
+        d.children_left, s.children_left, message + ": inequal children_left"
+    )
+
+    external = d.children_right == TREE_LEAF
+    internal = np.logical_not(external)
+
+    assert_array_equal(
+        d.feature[internal], s.feature[internal], message + ": inequal features"
+    )
+    assert_array_equal(
+        d.threshold[internal], s.threshold[internal], message + ": inequal threshold"
+    )
+    assert_array_equal(
+        d.n_node_samples.sum(),
+        s.n_node_samples.sum(),
+        message + ": inequal sum(n_node_samples)",
+    )
+    assert_array_equal(
+        d.n_node_samples, s.n_node_samples, message + ": inequal n_node_samples"
+    )
+
+    assert_almost_equal(d.impurity, s.impurity, err_msg=message + ": inequal impurity")
+
+    assert_array_almost_equal(
+        d.value[external], s.value[external], err_msg=message + ": inequal value"
+    )
+
+
+def test_classification_toy():
+    # Check classification on a toy dataset.
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+        clf = Tree(max_features=1, random_state=1)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+
+def test_weighted_classification_toy():
+    # Check classification on a weighted toy dataset.
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+
+        clf.fit(X, y, sample_weight=np.ones(len(X)))
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+        clf.fit(X, y, sample_weight=np.full(len(X), 0.5))
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+@pytest.mark.parametrize("criterion", REG_CRITERIONS)
+def test_regression_toy(Tree, criterion):
+    # Check regression on a toy dataset.
+    if criterion == "poisson":
+        # make target positive while not touching the original y and
+        # true_result
+        a = np.abs(np.min(y)) + 1
+        y_train = np.array(y) + a
+        y_test = np.array(true_result) + a
+    else:
+        y_train = y
+        y_test = true_result
+
+    reg = Tree(criterion=criterion, random_state=1)
+    reg.fit(X, y_train)
+    assert_allclose(reg.predict(T), y_test)
+
+    clf = Tree(criterion=criterion, max_features=1, random_state=1)
+    clf.fit(X, y_train)
+    assert_allclose(reg.predict(T), y_test)
+
+
+def test_xor():
+    # Check on a XOR problem
+    y = np.zeros((10, 10))
+    y[:5, :5] = 1
+    y[5:, 5:] = 1
+
+    gridx, gridy = np.indices(y.shape)
+
+    X = np.vstack([gridx.ravel(), gridy.ravel()]).T
+    y = y.ravel()
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+        clf.fit(X, y)
+        assert clf.score(X, y) == 1.0, "Failed with {0}".format(name)
+
+        clf = Tree(random_state=0, max_features=1)
+        clf.fit(X, y)
+        assert clf.score(X, y) == 1.0, "Failed with {0}".format(name)
+
+
+def test_iris():
+    # Check consistency on dataset iris.
+    for (name, Tree), criterion in product(CLF_TREES.items(), CLF_CRITERIONS):
+        clf = Tree(criterion=criterion, random_state=0)
+        clf.fit(iris.data, iris.target)
+        score = accuracy_score(clf.predict(iris.data), iris.target)
+        assert score > 0.9, "Failed with {0}, criterion = {1} and score = {2}".format(
+            name, criterion, score
+        )
+
+        clf = Tree(criterion=criterion, max_features=2, random_state=0)
+        clf.fit(iris.data, iris.target)
+        score = accuracy_score(clf.predict(iris.data), iris.target)
+        assert score > 0.5, "Failed with {0}, criterion = {1} and score = {2}".format(
+            name, criterion, score
+        )
+
+
+@pytest.mark.parametrize("name, Tree", REG_TREES.items())
+@pytest.mark.parametrize("criterion", REG_CRITERIONS)
+def test_diabetes_overfit(name, Tree, criterion):
+    # check consistency of overfitted trees on the diabetes dataset
+    # since the trees will overfit, we expect an MSE of 0
+    reg = Tree(criterion=criterion, random_state=0)
+    reg.fit(diabetes.data, diabetes.target)
+    score = mean_squared_error(diabetes.target, reg.predict(diabetes.data))
+    assert score == pytest.approx(0), (
+        f"Failed with {name}, criterion = {criterion} and score = {score}"
+    )
+
+
+@skip_if_32bit
+@pytest.mark.parametrize("name, Tree", REG_TREES.items())
+@pytest.mark.parametrize(
+    "criterion, max_depth, metric, max_loss",
+    [
+        ("squared_error", 15, mean_squared_error, 60),
+        ("absolute_error", 20, mean_squared_error, 60),
+        ("friedman_mse", 15, mean_squared_error, 60),
+        ("poisson", 15, mean_poisson_deviance, 30),
+    ],
+)
+def test_diabetes_underfit(name, Tree, criterion, max_depth, metric, max_loss):
+    # check consistency of trees when the depth and the number of features are
+    # limited
+
+    reg = Tree(criterion=criterion, max_depth=max_depth, max_features=6, random_state=0)
+    reg.fit(diabetes.data, diabetes.target)
+    loss = metric(diabetes.target, reg.predict(diabetes.data))
+    assert 0 < loss < max_loss
+
+
+def test_probability():
+    # Predict probabilities using DecisionTreeClassifier.
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(max_depth=1, max_features=1, random_state=42)
+        clf.fit(iris.data, iris.target)
+
+        prob_predict = clf.predict_proba(iris.data)
+        assert_array_almost_equal(
+            np.sum(prob_predict, 1),
+            np.ones(iris.data.shape[0]),
+            err_msg="Failed with {0}".format(name),
+        )
+        assert_array_equal(
+            np.argmax(prob_predict, 1),
+            clf.predict(iris.data),
+            err_msg="Failed with {0}".format(name),
+        )
+        assert_almost_equal(
+            clf.predict_proba(iris.data),
+            np.exp(clf.predict_log_proba(iris.data)),
+            8,
+            err_msg="Failed with {0}".format(name),
+        )
+
+
+def test_arrayrepr():
+    # Check the array representation.
+    # Check resize
+    X = np.arange(10000)[:, np.newaxis]
+    y = np.arange(10000)
+
+    for name, Tree in REG_TREES.items():
+        reg = Tree(max_depth=None, random_state=0)
+        reg.fit(X, y)
+
+
+def test_pure_set():
+    # Check when y is pure.
+    X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+    y = [1, 1, 1, 1, 1, 1]
+
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(X), y, err_msg="Failed with {0}".format(name))
+
+    for name, TreeRegressor in REG_TREES.items():
+        reg = TreeRegressor(random_state=0)
+        reg.fit(X, y)
+        assert_almost_equal(reg.predict(X), y, err_msg="Failed with {0}".format(name))
+
+
+def test_numerical_stability():
+    # Check numerical stability.
+    X = np.array(
+        [
+            [152.08097839, 140.40744019, 129.75102234, 159.90493774],
+            [142.50700378, 135.81935120, 117.82884979, 162.75781250],
+            [127.28772736, 140.40744019, 129.75102234, 159.90493774],
+            [132.37025452, 143.71923828, 138.35694885, 157.84558105],
+            [103.10237122, 143.71928406, 138.35696411, 157.84559631],
+            [127.71276855, 143.71923828, 138.35694885, 157.84558105],
+            [120.91514587, 140.40744019, 129.75102234, 159.90493774],
+        ]
+    )
+
+    y = np.array([1.0, 0.70209277, 0.53896582, 0.0, 0.90914464, 0.48026916, 0.49622521])
+
+    with np.errstate(all="raise"):
+        for name, Tree in REG_TREES.items():
+            reg = Tree(random_state=0)
+            reg.fit(X, y)
+            reg.fit(X, -y)
+            reg.fit(-X, y)
+            reg.fit(-X, -y)
+
+
+def test_importances():
+    # Check variable importances.
+    X, y = datasets.make_classification(
+        n_samples=5000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        shuffle=False,
+        random_state=0,
+    )
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+
+        clf.fit(X, y)
+        importances = clf.feature_importances_
+        n_important = np.sum(importances > 0.1)
+
+        assert importances.shape[0] == 10, "Failed with {0}".format(name)
+        assert n_important == 3, "Failed with {0}".format(name)
+
+    # Check on iris that importances are the same for all builders
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(iris.data, iris.target)
+    clf2 = DecisionTreeClassifier(random_state=0, max_leaf_nodes=len(iris.data))
+    clf2.fit(iris.data, iris.target)
+
+    assert_array_equal(clf.feature_importances_, clf2.feature_importances_)
+
+
+def test_importances_raises():
+    # Check if variable importance before fit raises ValueError.
+    clf = DecisionTreeClassifier()
+    with pytest.raises(ValueError):
+        getattr(clf, "feature_importances_")
+
+
+def test_importances_gini_equal_squared_error():
+    # Check that gini is equivalent to squared_error for binary output variable
+
+    X, y = datasets.make_classification(
+        n_samples=2000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        shuffle=False,
+        random_state=0,
+    )
+
+    # The gini index and the mean square error (variance) might differ due
+    # to numerical instability. Since those instabilities mainly occurs at
+    # high tree depth, we restrict this maximal depth.
+    clf = DecisionTreeClassifier(criterion="gini", max_depth=5, random_state=0).fit(
+        X, y
+    )
+    reg = DecisionTreeRegressor(
+        criterion="squared_error", max_depth=5, random_state=0
+    ).fit(X, y)
+
+    assert_almost_equal(clf.feature_importances_, reg.feature_importances_)
+    assert_array_equal(clf.tree_.feature, reg.tree_.feature)
+    assert_array_equal(clf.tree_.children_left, reg.tree_.children_left)
+    assert_array_equal(clf.tree_.children_right, reg.tree_.children_right)
+    assert_array_equal(clf.tree_.n_node_samples, reg.tree_.n_node_samples)
+
+
+def test_max_features():
+    # Check max_features.
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_features="sqrt")
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(np.sqrt(iris.data.shape[1]))
+
+        est = TreeEstimator(max_features="log2")
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(np.log2(iris.data.shape[1]))
+
+        est = TreeEstimator(max_features=1)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 1
+
+        est = TreeEstimator(max_features=3)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 3
+
+        est = TreeEstimator(max_features=0.01)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 1
+
+        est = TreeEstimator(max_features=0.5)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(0.5 * iris.data.shape[1])
+
+        est = TreeEstimator(max_features=1.0)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == iris.data.shape[1]
+
+        est = TreeEstimator(max_features=None)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == iris.data.shape[1]
+
+
+def test_error():
+    # Test that it gives proper exception on deficient input.
+    for name, TreeEstimator in CLF_TREES.items():
+        # predict before fit
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.predict_proba(X)
+
+        est.fit(X, y)
+        X2 = [[-2, -1, 1]]  # wrong feature shape for sample
+        with pytest.raises(ValueError):
+            est.predict_proba(X2)
+
+        # Wrong dimensions
+        est = TreeEstimator()
+        y2 = y[:-1]
+        with pytest.raises(ValueError):
+            est.fit(X, y2)
+
+        # Test with arrays that are non-contiguous.
+        Xf = np.asfortranarray(X)
+        est = TreeEstimator()
+        est.fit(Xf, y)
+        assert_almost_equal(est.predict(T), true_result)
+
+        # predict before fitting
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.predict(T)
+
+        # predict on vector with different dims
+        est.fit(X, y)
+        t = np.asarray(T)
+        with pytest.raises(ValueError):
+            est.predict(t[:, 1:])
+
+        # wrong sample shape
+        Xt = np.array(X).T
+
+        est = TreeEstimator()
+        est.fit(np.dot(X, Xt), y)
+        with pytest.raises(ValueError):
+            est.predict(X)
+        with pytest.raises(ValueError):
+            est.apply(X)
+
+        clf = TreeEstimator()
+        clf.fit(X, y)
+        with pytest.raises(ValueError):
+            clf.predict(Xt)
+        with pytest.raises(ValueError):
+            clf.apply(Xt)
+
+        # apply before fitting
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.apply(T)
+
+    # non positive target for Poisson splitting Criterion
+    est = DecisionTreeRegressor(criterion="poisson")
+    with pytest.raises(ValueError, match="y is not positive.*Poisson"):
+        est.fit([[0, 1, 2]], [0, 0, 0])
+    with pytest.raises(ValueError, match="Some.*y are negative.*Poisson"):
+        est.fit([[0, 1, 2]], [5, -0.1, 2])
+
+
+def test_min_samples_split():
+    """Test min_samples_split parameter"""
+    X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE)
+    y = iris.target
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # test for integer parameter
+        est = TreeEstimator(
+            min_samples_split=10, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        # count samples on nodes, -1 means it is a leaf
+        node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1]
+
+        assert np.min(node_samples) > 9, "Failed with {0}".format(name)
+
+        # test for float parameter
+        est = TreeEstimator(
+            min_samples_split=0.2, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        # count samples on nodes, -1 means it is a leaf
+        node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1]
+
+        assert np.min(node_samples) > 9, "Failed with {0}".format(name)
+
+
+def test_min_samples_leaf():
+    # Test if leaves contain more than leaf_count training examples
+    X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE)
+    y = iris.target
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # test integer parameter
+        est = TreeEstimator(
+            min_samples_leaf=5, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        out = est.tree_.apply(X)
+        node_counts = np.bincount(out)
+        # drop inner nodes
+        leaf_count = node_counts[node_counts != 0]
+        assert np.min(leaf_count) > 4, "Failed with {0}".format(name)
+
+        # test float parameter
+        est = TreeEstimator(
+            min_samples_leaf=0.1, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        out = est.tree_.apply(X)
+        node_counts = np.bincount(out)
+        # drop inner nodes
+        leaf_count = node_counts[node_counts != 0]
+        assert np.min(leaf_count) > 4, "Failed with {0}".format(name)
+
+
+def check_min_weight_fraction_leaf(name, datasets, sparse_container=None):
+    """Test if leaves contain at least min_weight_fraction_leaf of the
+    training set"""
+    X = DATASETS[datasets]["X"].astype(np.float32)
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = DATASETS[datasets]["y"]
+
+    weights = rng.rand(X.shape[0])
+    total_weight = np.sum(weights)
+
+    TreeEstimator = ALL_TREES[name]
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)):
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y, sample_weight=weights)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out, weights=weights)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf, (
+            "Failed with {0} min_weight_fraction_leaf={1}".format(
+                name, est.min_weight_fraction_leaf
+            )
+        )
+
+    # test case with no weights passed in
+    total_weight = X.shape[0]
+
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)):
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf, (
+            "Failed with {0} min_weight_fraction_leaf={1}".format(
+                name, est.min_weight_fraction_leaf
+            )
+        )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_min_weight_fraction_leaf_on_dense_input(name):
+    check_min_weight_fraction_leaf(name, "iris")
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_min_weight_fraction_leaf_on_sparse_input(name, csc_container):
+    check_min_weight_fraction_leaf(name, "multilabel", sparse_container=csc_container)
+
+
+def check_min_weight_fraction_leaf_with_min_samples_leaf(
+    name, datasets, sparse_container=None
+):
+    """Test the interaction between min_weight_fraction_leaf and
+    min_samples_leaf when sample_weights is not provided in fit."""
+    X = DATASETS[datasets]["X"].astype(np.float32)
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = DATASETS[datasets]["y"]
+
+    total_weight = X.shape[0]
+    TreeEstimator = ALL_TREES[name]
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)):
+        # test integer min_samples_leaf
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac,
+            max_leaf_nodes=max_leaf_nodes,
+            min_samples_leaf=5,
+            random_state=0,
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= max(
+            (total_weight * est.min_weight_fraction_leaf), 5
+        ), "Failed with {0} min_weight_fraction_leaf={1}, min_samples_leaf={2}".format(
+            name, est.min_weight_fraction_leaf, est.min_samples_leaf
+        )
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)):
+        # test float min_samples_leaf
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac,
+            max_leaf_nodes=max_leaf_nodes,
+            min_samples_leaf=0.1,
+            random_state=0,
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= max(
+            (total_weight * est.min_weight_fraction_leaf),
+            (total_weight * est.min_samples_leaf),
+        ), "Failed with {0} min_weight_fraction_leaf={1}, min_samples_leaf={2}".format(
+            name, est.min_weight_fraction_leaf, est.min_samples_leaf
+        )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_min_weight_fraction_leaf_with_min_samples_leaf_on_dense_input(name):
+    check_min_weight_fraction_leaf_with_min_samples_leaf(name, "iris")
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input(
+    name, csc_container
+):
+    check_min_weight_fraction_leaf_with_min_samples_leaf(
+        name, "multilabel", sparse_container=csc_container
+    )
+
+
+def test_min_impurity_decrease(global_random_seed):
+    # test if min_impurity_decrease ensure that a split is made only if
+    # if the impurity decrease is at least that value
+    X, y = datasets.make_classification(n_samples=100, random_state=global_random_seed)
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # Check default value of min_impurity_decrease, 1e-7
+        est1 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0)
+        # Check with explicit value of 0.05
+        est2 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.05, random_state=0
+        )
+        # Check with a much lower value of 0.0001
+        est3 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.0001, random_state=0
+        )
+        # Check with a much lower value of 0.1
+        est4 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.1, random_state=0
+        )
+
+        for est, expected_decrease in (
+            (est1, 1e-7),
+            (est2, 0.05),
+            (est3, 0.0001),
+            (est4, 0.1),
+        ):
+            assert est.min_impurity_decrease <= expected_decrease, (
+                "Failed, min_impurity_decrease = {0} > {1}".format(
+                    est.min_impurity_decrease, expected_decrease
+                )
+            )
+            est.fit(X, y)
+            for node in range(est.tree_.node_count):
+                # If current node is a not leaf node, check if the split was
+                # justified w.r.t the min_impurity_decrease
+                if est.tree_.children_left[node] != TREE_LEAF:
+                    imp_parent = est.tree_.impurity[node]
+                    wtd_n_node = est.tree_.weighted_n_node_samples[node]
+
+                    left = est.tree_.children_left[node]
+                    wtd_n_left = est.tree_.weighted_n_node_samples[left]
+                    imp_left = est.tree_.impurity[left]
+                    wtd_imp_left = wtd_n_left * imp_left
+
+                    right = est.tree_.children_right[node]
+                    wtd_n_right = est.tree_.weighted_n_node_samples[right]
+                    imp_right = est.tree_.impurity[right]
+                    wtd_imp_right = wtd_n_right * imp_right
+
+                    wtd_avg_left_right_imp = wtd_imp_right + wtd_imp_left
+                    wtd_avg_left_right_imp /= wtd_n_node
+
+                    fractional_node_weight = (
+                        est.tree_.weighted_n_node_samples[node] / X.shape[0]
+                    )
+
+                    actual_decrease = fractional_node_weight * (
+                        imp_parent - wtd_avg_left_right_imp
+                    )
+
+                    assert actual_decrease >= expected_decrease, (
+                        "Failed with {0} expected min_impurity_decrease={1}".format(
+                            actual_decrease, expected_decrease
+                        )
+                    )
+
+
+def test_pickle():
+    """Test pickling preserves Tree properties and performance."""
+    for name, TreeEstimator in ALL_TREES.items():
+        if "Classifier" in name:
+            X, y = iris.data, iris.target
+        else:
+            X, y = diabetes.data, diabetes.target
+
+        est = TreeEstimator(random_state=0)
+        est.fit(X, y)
+        score = est.score(X, y)
+
+        # test that all class properties are maintained
+        attributes = [
+            "max_depth",
+            "node_count",
+            "capacity",
+            "n_classes",
+            "children_left",
+            "children_right",
+            "n_leaves",
+            "feature",
+            "threshold",
+            "impurity",
+            "n_node_samples",
+            "weighted_n_node_samples",
+            "value",
+        ]
+        fitted_attribute = {
+            attribute: getattr(est.tree_, attribute) for attribute in attributes
+        }
+
+        serialized_object = pickle.dumps(est)
+        est2 = pickle.loads(serialized_object)
+        assert type(est2) == est.__class__
+
+        score2 = est2.score(X, y)
+        assert score == score2, (
+            "Failed to generate same score  after pickling with {0}".format(name)
+        )
+        for attribute in fitted_attribute:
+            assert_array_equal(
+                getattr(est2.tree_, attribute),
+                fitted_attribute[attribute],
+                err_msg=(
+                    f"Failed to generate same attribute {attribute} after pickling with"
+                    f" {name}"
+                ),
+            )
+
+
+def test_multioutput():
+    # Check estimators on multi-output problems.
+    X = [
+        [-2, -1],
+        [-1, -1],
+        [-1, -2],
+        [1, 1],
+        [1, 2],
+        [2, 1],
+        [-2, 1],
+        [-1, 1],
+        [-1, 2],
+        [2, -1],
+        [1, -1],
+        [1, -2],
+    ]
+
+    y = [
+        [-1, 0],
+        [-1, 0],
+        [-1, 0],
+        [1, 1],
+        [1, 1],
+        [1, 1],
+        [-1, 2],
+        [-1, 2],
+        [-1, 2],
+        [1, 3],
+        [1, 3],
+        [1, 3],
+    ]
+
+    T = [[-1, -1], [1, 1], [-1, 1], [1, -1]]
+    y_true = [[-1, 0], [1, 1], [-1, 2], [1, 3]]
+
+    # toy classification problem
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        y_hat = clf.fit(X, y).predict(T)
+        assert_array_equal(y_hat, y_true)
+        assert y_hat.shape == (4, 2)
+
+        proba = clf.predict_proba(T)
+        assert len(proba) == 2
+        assert proba[0].shape == (4, 2)
+        assert proba[1].shape == (4, 4)
+
+        log_proba = clf.predict_log_proba(T)
+        assert len(log_proba) == 2
+        assert log_proba[0].shape == (4, 2)
+        assert log_proba[1].shape == (4, 4)
+
+    # toy regression problem
+    for name, TreeRegressor in REG_TREES.items():
+        reg = TreeRegressor(random_state=0)
+        y_hat = reg.fit(X, y).predict(T)
+        assert_almost_equal(y_hat, y_true)
+        assert y_hat.shape == (4, 2)
+
+
+def test_classes_shape():
+    # Test that n_classes_ and classes_ have proper shape.
+    for name, TreeClassifier in CLF_TREES.items():
+        # Classification, single output
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, y)
+
+        assert clf.n_classes_ == 2
+        assert_array_equal(clf.classes_, [-1, 1])
+
+        # Classification, multi-output
+        _y = np.vstack((y, np.array(y) * 2)).T
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, _y)
+        assert len(clf.n_classes_) == 2
+        assert len(clf.classes_) == 2
+        assert_array_equal(clf.n_classes_, [2, 2])
+        assert_array_equal(clf.classes_, [[-1, 1], [-2, 2]])
+
+
+def test_unbalanced_iris():
+    # Check class rebalancing.
+    unbalanced_X = iris.data[:125]
+    unbalanced_y = iris.target[:125]
+    sample_weight = compute_sample_weight("balanced", unbalanced_y)
+
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        clf.fit(unbalanced_X, unbalanced_y, sample_weight=sample_weight)
+        assert_almost_equal(clf.predict(unbalanced_X), unbalanced_y)
+
+
+def test_memory_layout():
+    # Check that it works no matter the memory layout
+    for (name, TreeEstimator), dtype in product(
+        ALL_TREES.items(), [np.float64, np.float32]
+    ):
+        est = TreeEstimator(random_state=0)
+
+        # Nothing
+        X = np.asarray(iris.data, dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # C-order
+        X = np.asarray(iris.data, order="C", dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # F-order
+        X = np.asarray(iris.data, order="F", dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # Contiguous
+        X = np.ascontiguousarray(iris.data, dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # csr
+        for csr_container in CSR_CONTAINERS:
+            X = csr_container(iris.data, dtype=dtype)
+            y = iris.target
+            assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # csc
+        for csc_container in CSC_CONTAINERS:
+            X = csc_container(iris.data, dtype=dtype)
+            y = iris.target
+            assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # Strided
+        X = np.asarray(iris.data[::3], dtype=dtype)
+        y = iris.target[::3]
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+
+def test_sample_weight():
+    # Check sample weighting.
+    # Test that zero-weighted samples are not taken into account
+    X = np.arange(100)[:, np.newaxis]
+    y = np.ones(100)
+    y[:50] = 0.0
+
+    sample_weight = np.ones(100)
+    sample_weight[y == 0] = 0.0
+
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert_array_equal(clf.predict(X), np.ones(100))
+
+    # Test that low weighted samples are not taken into account at low depth
+    X = np.arange(200)[:, np.newaxis]
+    y = np.zeros(200)
+    y[50:100] = 1
+    y[100:200] = 2
+    X[100:200, 0] = 200
+
+    sample_weight = np.ones(200)
+
+    sample_weight[y == 2] = 0.51  # Samples of class '2' are still weightier
+    clf = DecisionTreeClassifier(max_depth=1, random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert clf.tree_.threshold[0] == 149.5
+
+    sample_weight[y == 2] = 0.5  # Samples of class '2' are no longer weightier
+    clf = DecisionTreeClassifier(max_depth=1, random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert clf.tree_.threshold[0] == 49.5  # Threshold should have moved
+
+    # Test that sample weighting is the same as having duplicates
+    X = iris.data
+    y = iris.target
+
+    duplicates = rng.randint(0, X.shape[0], 100)
+
+    clf = DecisionTreeClassifier(random_state=1)
+    clf.fit(X[duplicates], y[duplicates])
+
+    sample_weight = np.bincount(duplicates, minlength=X.shape[0])
+    clf2 = DecisionTreeClassifier(random_state=1)
+    clf2.fit(X, y, sample_weight=sample_weight)
+
+    internal = clf.tree_.children_left != tree._tree.TREE_LEAF
+    assert_array_almost_equal(
+        clf.tree_.threshold[internal], clf2.tree_.threshold[internal]
+    )
+
+
+def test_sample_weight_invalid():
+    # Check sample weighting raises errors.
+    X = np.arange(100)[:, np.newaxis]
+    y = np.ones(100)
+    y[:50] = 0.0
+
+    clf = DecisionTreeClassifier(random_state=0)
+
+    sample_weight = np.random.rand(100, 1)
+    with pytest.raises(ValueError):
+        clf.fit(X, y, sample_weight=sample_weight)
+
+    sample_weight = np.array(0)
+
+    expected_err = re.escape(
+        (
+            "Input should have at least 1 dimension i.e. satisfy "
+            "`len(x.shape) > 0`, got scalar `array(0.)` instead."
+        )
+    )
+    with pytest.raises(TypeError, match=expected_err):
+        clf.fit(X, y, sample_weight=sample_weight)
+
+
+@pytest.mark.parametrize("name", CLF_TREES)
+def test_class_weights(name):
+    # Test that class_weights resemble sample_weights behavior.
+    TreeClassifier = CLF_TREES[name]
+
+    # Iris is balanced, so no effect expected for using 'balanced' weights
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target)
+    clf2 = TreeClassifier(class_weight="balanced", random_state=0)
+    clf2.fit(iris.data, iris.target)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+    # Make a multi-output problem with three copies of Iris
+    iris_multi = np.vstack((iris.target, iris.target, iris.target)).T
+    # Create user-defined weights that should balance over the outputs
+    clf3 = TreeClassifier(
+        class_weight=[
+            {0: 2.0, 1: 2.0, 2: 1.0},
+            {0: 2.0, 1: 1.0, 2: 2.0},
+            {0: 1.0, 1: 2.0, 2: 2.0},
+        ],
+        random_state=0,
+    )
+    clf3.fit(iris.data, iris_multi)
+    assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_)
+    # Check against multi-output "auto" which should also have no effect
+    clf4 = TreeClassifier(class_weight="balanced", random_state=0)
+    clf4.fit(iris.data, iris_multi)
+    assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_)
+
+    # Inflate importance of class 1, check against user-defined weights
+    sample_weight = np.ones(iris.target.shape)
+    sample_weight[iris.target == 1] *= 100
+    class_weight = {0: 1.0, 1: 100.0, 2: 1.0}
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target, sample_weight)
+    clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
+    clf2.fit(iris.data, iris.target)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+    # Check that sample_weight and class_weight are multiplicative
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target, sample_weight**2)
+    clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
+    clf2.fit(iris.data, iris.target, sample_weight)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+
+@pytest.mark.parametrize("name", CLF_TREES)
+def test_class_weight_errors(name):
+    # Test if class_weight raises errors and warnings when expected.
+    TreeClassifier = CLF_TREES[name]
+    _y = np.vstack((y, np.array(y) * 2)).T
+
+    # Incorrect length list for multi-output
+    clf = TreeClassifier(class_weight=[{-1: 0.5, 1: 1.0}], random_state=0)
+    err_msg = "number of elements in class_weight should match number of outputs."
+    with pytest.raises(ValueError, match=err_msg):
+        clf.fit(X, _y)
+
+
+def test_max_leaf_nodes():
+    # Test greedy trees with max_depth + 1 leafs.
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    k = 4
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_depth=None, max_leaf_nodes=k + 1).fit(X, y)
+        assert est.get_n_leaves() == k + 1
+
+
+def test_max_leaf_nodes_max_depth():
+    # Test precedence of max_leaf_nodes over max_depth.
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    k = 4
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
+        assert est.get_depth() == 1
+
+
+def test_arrays_persist():
+    # Ensure property arrays' memory stays alive when tree disappears
+    # non-regression for #2726
+    for attr in [
+        "n_classes",
+        "value",
+        "children_left",
+        "children_right",
+        "threshold",
+        "impurity",
+        "feature",
+        "n_node_samples",
+    ]:
+        value = getattr(DecisionTreeClassifier().fit([[0], [1]], [0, 1]).tree_, attr)
+        # if pointing to freed memory, contents may be arbitrary
+        assert -3 <= value.flat[0] < 3, "Array points to arbitrary memory"
+
+
+def test_only_constant_features():
+    random_state = check_random_state(0)
+    X = np.zeros((10, 20))
+    y = random_state.randint(0, 2, (10,))
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(random_state=0)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 0
+
+
+def test_behaviour_constant_feature_after_splits():
+    X = np.transpose(
+        np.vstack(([[0, 0, 0, 0, 0, 1, 2, 4, 5, 6, 7]], np.zeros((4, 11))))
+    )
+    y = [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3]
+    for name, TreeEstimator in ALL_TREES.items():
+        # do not check extra random trees
+        if "ExtraTree" not in name:
+            est = TreeEstimator(random_state=0, max_features=1)
+            est.fit(X, y)
+            assert est.tree_.max_depth == 2
+            assert est.tree_.node_count == 5
+
+
+def test_with_only_one_non_constant_features():
+    X = np.hstack([np.array([[1.0], [1.0], [0.0], [0.0]]), np.zeros((4, 1000))])
+
+    y = np.array([0.0, 1.0, 0.0, 1.0])
+    for name, TreeEstimator in CLF_TREES.items():
+        est = TreeEstimator(random_state=0, max_features=1)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 1
+        assert_array_equal(est.predict_proba(X), np.full((4, 2), 0.5))
+
+    for name, TreeEstimator in REG_TREES.items():
+        est = TreeEstimator(random_state=0, max_features=1)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 1
+        assert_array_equal(est.predict(X), np.full((4,), 0.5))
+
+
+def test_big_input():
+    # Test if the warning for too large inputs is appropriate.
+    X = np.repeat(10**40.0, 4).astype(np.float64).reshape(-1, 1)
+    clf = DecisionTreeClassifier()
+    with pytest.raises(ValueError, match="float32"):
+        clf.fit(X, [0, 1, 0, 1])
+
+
+def test_realloc():
+    from sklearn.tree._utils import _realloc_test
+
+    with pytest.raises(MemoryError):
+        _realloc_test()
+
+
+def test_huge_allocations():
+    n_bits = 8 * struct.calcsize("P")
+
+    X = np.random.randn(10, 2)
+    y = np.random.randint(0, 2, 10)
+
+    # Sanity check: we cannot request more memory than the size of the address
+    # space. Currently raises OverflowError.
+    huge = 2 ** (n_bits + 1)
+    clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge)
+    with pytest.raises(Exception):
+        clf.fit(X, y)
+
+    # Non-regression test: MemoryError used to be dropped by Cython
+    # because of missing "except *".
+    huge = 2 ** (n_bits - 1) - 1
+    clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge)
+    with pytest.raises(MemoryError):
+        clf.fit(X, y)
+
+
+def check_sparse_input(tree, dataset, max_depth=None):
+    TreeEstimator = ALL_TREES[tree]
+    X = DATASETS[dataset]["X"]
+    y = DATASETS[dataset]["y"]
+
+    # Gain testing time
+    if dataset in ["digits", "diabetes"]:
+        n_samples = X.shape[0] // 5
+        X = X[:n_samples]
+        y = y[:n_samples]
+
+    for sparse_container in COO_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS:
+        X_sparse = sparse_container(X)
+
+        # Check the default (depth first search)
+        d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
+        s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
+
+        assert_tree_equal(
+            d.tree_,
+            s.tree_,
+            "{0} with dense and sparse format gave different trees".format(tree),
+        )
+
+        y_pred = d.predict(X)
+        if tree in CLF_TREES:
+            y_proba = d.predict_proba(X)
+            y_log_proba = d.predict_log_proba(X)
+
+        for sparse_container_test in COO_CONTAINERS + CSR_CONTAINERS + CSC_CONTAINERS:
+            X_sparse_test = sparse_container_test(X_sparse, dtype=np.float32)
+
+            assert_array_almost_equal(s.predict(X_sparse_test), y_pred)
+
+            if tree in CLF_TREES:
+                assert_array_almost_equal(s.predict_proba(X_sparse_test), y_proba)
+                assert_array_almost_equal(
+                    s.predict_log_proba(X_sparse_test), y_log_proba
+                )
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize(
+    "dataset",
+    (
+        "clf_small",
+        "toy",
+        "digits",
+        "multilabel",
+        "sparse-pos",
+        "sparse-neg",
+        "sparse-mix",
+        "zeros",
+    ),
+)
+def test_sparse_input(tree_type, dataset):
+    max_depth = 3 if dataset == "digits" else None
+    check_sparse_input(tree_type, dataset, max_depth)
+
+
+@pytest.mark.parametrize("tree_type", sorted(set(SPARSE_TREES).intersection(REG_TREES)))
+@pytest.mark.parametrize("dataset", ["diabetes", "reg_small"])
+def test_sparse_input_reg_trees(tree_type, dataset):
+    # Due to numerical instability of MSE and too strict test, we limit the
+    # maximal depth
+    check_sparse_input(tree_type, dataset, 2)
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"])
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_sparse_parameters(tree_type, dataset, csc_container):
+    TreeEstimator = ALL_TREES[tree_type]
+    X = DATASETS[dataset]["X"]
+    X_sparse = csc_container(X)
+    y = DATASETS[dataset]["y"]
+
+    # Check max_features
+    d = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X, y)
+    s = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X_sparse, y)
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check min_samples_split
+    d = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(X, y)
+    s = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(
+        X_sparse, y
+    )
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check min_samples_leaf
+    d = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y)
+    s = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(
+        X_sparse, y
+    )
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check best-first search
+    d = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X, y)
+    s = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X_sparse, y)
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+
+@pytest.mark.parametrize(
+    "tree_type, criterion",
+    list(product([tree for tree in SPARSE_TREES if tree in REG_TREES], REG_CRITERIONS))
+    + list(
+        product([tree for tree in SPARSE_TREES if tree in CLF_TREES], CLF_CRITERIONS)
+    ),
+)
+@pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"])
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_sparse_criteria(tree_type, dataset, csc_container, criterion):
+    TreeEstimator = ALL_TREES[tree_type]
+    X = DATASETS[dataset]["X"]
+    X_sparse = csc_container(X)
+    y = DATASETS[dataset]["y"]
+
+    d = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X, y)
+    s = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X_sparse, y)
+
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize(
+    "csc_container,csr_container", zip(CSC_CONTAINERS, CSR_CONTAINERS)
+)
+def test_explicit_sparse_zeros(tree_type, csc_container, csr_container):
+    TreeEstimator = ALL_TREES[tree_type]
+    max_depth = 3
+    n_features = 10
+
+    # n_samples set n_feature to ease construction of a simultaneous
+    # construction of a csr and csc matrix
+    n_samples = n_features
+    samples = np.arange(n_samples)
+
+    # Generate X, y
+    random_state = check_random_state(0)
+    indices = []
+    data = []
+    offset = 0
+    indptr = [offset]
+    for i in range(n_features):
+        n_nonzero_i = random_state.binomial(n_samples, 0.5)
+        indices_i = random_state.permutation(samples)[:n_nonzero_i]
+        indices.append(indices_i)
+        data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i,)) - 1
+        data.append(data_i)
+        offset += n_nonzero_i
+        indptr.append(offset)
+
+    indices = np.concatenate(indices).astype(np.int32)
+    indptr = np.array(indptr, dtype=np.int32)
+    data = np.array(np.concatenate(data), dtype=np.float32)
+    X_sparse = csc_container((data, indices, indptr), shape=(n_samples, n_features))
+    X = X_sparse.toarray()
+    X_sparse_test = csr_container(
+        (data, indices, indptr), shape=(n_samples, n_features)
+    )
+    X_test = X_sparse_test.toarray()
+    y = random_state.randint(0, 3, size=(n_samples,))
+
+    # Ensure that X_sparse_test owns its data, indices and indptr array
+    X_sparse_test = X_sparse_test.copy()
+
+    # Ensure that we have explicit zeros
+    assert (X_sparse.data == 0.0).sum() > 0
+    assert (X_sparse_test.data == 0.0).sum() > 0
+
+    # Perform the comparison
+    d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
+    s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
+
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree),
+    )
+
+    Xs = (X_test, X_sparse_test)
+    for X1, X2 in product(Xs, Xs):
+        assert_array_almost_equal(s.tree_.apply(X1), d.tree_.apply(X2))
+        assert_array_almost_equal(s.apply(X1), d.apply(X2))
+        assert_array_almost_equal(s.apply(X1), s.tree_.apply(X1))
+
+        assert_array_almost_equal(
+            s.tree_.decision_path(X1).toarray(), d.tree_.decision_path(X2).toarray()
+        )
+        assert_array_almost_equal(
+            s.decision_path(X1).toarray(), d.decision_path(X2).toarray()
+        )
+        assert_array_almost_equal(
+            s.decision_path(X1).toarray(), s.tree_.decision_path(X1).toarray()
+        )
+
+        assert_array_almost_equal(s.predict(X1), d.predict(X2))
+
+        if tree in CLF_TREES:
+            assert_array_almost_equal(s.predict_proba(X1), d.predict_proba(X2))
+
+
+def check_raise_error_on_1d_input(name):
+    TreeEstimator = ALL_TREES[name]
+
+    X = iris.data[:, 0].ravel()
+    X_2d = iris.data[:, 0].reshape((-1, 1))
+    y = iris.target
+
+    with pytest.raises(ValueError):
+        TreeEstimator(random_state=0).fit(X, y)
+
+    est = TreeEstimator(random_state=0)
+    est.fit(X_2d, y)
+    with pytest.raises(ValueError):
+        est.predict([X])
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_1d_input(name):
+    with ignore_warnings():
+        check_raise_error_on_1d_input(name)
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
+def test_min_weight_leaf_split_level(name, sparse_container):
+    TreeEstimator = ALL_TREES[name]
+
+    X = np.array([[0], [0], [0], [0], [1]])
+    y = [0, 0, 0, 0, 1]
+    sample_weight = [0.2, 0.2, 0.2, 0.2, 0.2]
+    if sparse_container is not None:
+        X = sparse_container(X)
+
+    est = TreeEstimator(random_state=0)
+    est.fit(X, y, sample_weight=sample_weight)
+    assert est.tree_.max_depth == 1
+
+    est = TreeEstimator(random_state=0, min_weight_fraction_leaf=0.4)
+    est.fit(X, y, sample_weight=sample_weight)
+    assert est.tree_.max_depth == 0
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_public_apply_all_trees(name):
+    X_small32 = X_small.astype(tree._tree.DTYPE, copy=False)
+
+    est = ALL_TREES[name]()
+    est.fit(X_small, y_small)
+    assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32))
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_public_apply_sparse_trees(name, csr_container):
+    X_small32 = csr_container(X_small.astype(tree._tree.DTYPE, copy=False))
+
+    est = ALL_TREES[name]()
+    est.fit(X_small, y_small)
+    assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32))
+
+
+def test_decision_path_hardcoded():
+    X = iris.data
+    y = iris.target
+    est = DecisionTreeClassifier(random_state=0, max_depth=1).fit(X, y)
+    node_indicator = est.decision_path(X[:2]).toarray()
+    assert_array_equal(node_indicator, [[1, 1, 0], [1, 0, 1]])
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_decision_path(name):
+    X = iris.data
+    y = iris.target
+    n_samples = X.shape[0]
+
+    TreeEstimator = ALL_TREES[name]
+    est = TreeEstimator(random_state=0, max_depth=2)
+    est.fit(X, y)
+
+    node_indicator_csr = est.decision_path(X)
+    node_indicator = node_indicator_csr.toarray()
+    assert node_indicator.shape == (n_samples, est.tree_.node_count)
+
+    # Assert that leaves index are correct
+    leaves = est.apply(X)
+    leave_indicator = [node_indicator[i, j] for i, j in enumerate(leaves)]
+    assert_array_almost_equal(leave_indicator, np.ones(shape=n_samples))
+
+    # Ensure only one leave node per sample
+    all_leaves = est.tree_.children_left == TREE_LEAF
+    assert_array_almost_equal(
+        np.dot(node_indicator, all_leaves), np.ones(shape=n_samples)
+    )
+
+    # Ensure max depth is consistent with sum of indicator
+    max_depth = node_indicator.sum(axis=1).max()
+    assert est.tree_.max_depth <= max_depth
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_no_sparse_y_support(name, csr_container):
+    # Currently we don't support sparse y
+    X, y = X_multilabel, csr_container(y_multilabel)
+    TreeEstimator = ALL_TREES[name]
+    with pytest.raises(TypeError):
+        TreeEstimator(random_state=0).fit(X, y)
+
+
+def test_mae():
+    """Check MAE criterion produces correct results on small toy dataset:
+
+    ------------------
+    | X | y | weight |
+    ------------------
+    | 3 | 3 |  0.1   |
+    | 5 | 3 |  0.3   |
+    | 8 | 4 |  1.0   |
+    | 3 | 6 |  0.6   |
+    | 5 | 7 |  0.3   |
+    ------------------
+    |sum wt:|  2.3   |
+    ------------------
+
+    Because we are dealing with sample weights, we cannot find the median by
+    simply choosing/averaging the centre value(s), instead we consider the
+    median where 50% of the cumulative weight is found (in a y sorted data set)
+    . Therefore with regards to this test data, the cumulative weight is >= 50%
+    when y = 4.  Therefore:
+    Median = 4
+
+    For all the samples, we can get the total error by summing:
+    Absolute(Median - y) * weight
+
+    I.e., total error = (Absolute(4 - 3) * 0.1)
+                      + (Absolute(4 - 3) * 0.3)
+                      + (Absolute(4 - 4) * 1.0)
+                      + (Absolute(4 - 6) * 0.6)
+                      + (Absolute(4 - 7) * 0.3)
+                      = 2.5
+
+    Impurity = Total error / total weight
+             = 2.5 / 2.3
+             = 1.08695652173913
+             ------------------
+
+    From this root node, the next best split is between X values of 3 and 5.
+    Thus, we have left and right child nodes:
+
+    LEFT                    RIGHT
+    ------------------      ------------------
+    | X | y | weight |      | X | y | weight |
+    ------------------      ------------------
+    | 3 | 3 |  0.1   |      | 5 | 3 |  0.3   |
+    | 3 | 6 |  0.6   |      | 8 | 4 |  1.0   |
+    ------------------      | 5 | 7 |  0.3   |
+    |sum wt:|  0.7   |      ------------------
+    ------------------      |sum wt:|  1.6   |
+                            ------------------
+
+    Impurity is found in the same way:
+    Left node Median = 6
+    Total error = (Absolute(6 - 3) * 0.1)
+                + (Absolute(6 - 6) * 0.6)
+                = 0.3
+
+    Left Impurity = Total error / total weight
+            = 0.3 / 0.7
+            = 0.428571428571429
+            -------------------
+
+    Likewise for Right node:
+    Right node Median = 4
+    Total error = (Absolute(4 - 3) * 0.3)
+                + (Absolute(4 - 4) * 1.0)
+                + (Absolute(4 - 7) * 0.3)
+                = 1.2
+
+    Right Impurity = Total error / total weight
+            = 1.2 / 1.6
+            = 0.75
+            ------
+    """
+    dt_mae = DecisionTreeRegressor(
+        random_state=0, criterion="absolute_error", max_leaf_nodes=2
+    )
+
+    # Test MAE where sample weights are non-uniform (as illustrated above):
+    dt_mae.fit(
+        X=[[3], [5], [3], [8], [5]],
+        y=[6, 7, 3, 4, 3],
+        sample_weight=[0.6, 0.3, 0.1, 1.0, 0.3],
+    )
+    assert_allclose(dt_mae.tree_.impurity, [2.5 / 2.3, 0.3 / 0.7, 1.2 / 1.6])
+    assert_array_equal(dt_mae.tree_.value.flat, [4.0, 6.0, 4.0])
+
+    # Test MAE where all sample weights are uniform:
+    dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], sample_weight=np.ones(5))
+    assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0])
+    assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0])
+
+    # Test MAE where a `sample_weight` is not explicitly provided.
+    # This is equivalent to providing uniform sample weights, though
+    # the internal logic is different:
+    dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3])
+    assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0])
+    assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0])
+
+
+def test_criterion_copy():
+    # Let's check whether copy of our criterion has the same type
+    # and properties as original
+    n_outputs = 3
+    n_classes = np.arange(3, dtype=np.intp)
+    n_samples = 100
+
+    def _pickle_copy(obj):
+        return pickle.loads(pickle.dumps(obj))
+
+    for copy_func in [copy.copy, copy.deepcopy, _pickle_copy]:
+        for _, typename in CRITERIA_CLF.items():
+            criteria = typename(n_outputs, n_classes)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_classes_), _ = result
+            assert typename == typename_
+            assert n_outputs == n_outputs_
+            assert_array_equal(n_classes, n_classes_)
+
+        for _, typename in CRITERIA_REG.items():
+            criteria = typename(n_outputs, n_samples)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_samples_), _ = result
+            assert typename == typename_
+            assert n_outputs == n_outputs_
+            assert n_samples == n_samples_
+
+
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
+def test_empty_leaf_infinite_threshold(sparse_container):
+    # try to make empty leaf by using near infinite value.
+    data = np.random.RandomState(0).randn(100, 11) * 2e38
+    data = np.nan_to_num(data.astype("float32"))
+    X = data[:, :-1]
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = data[:, -1]
+
+    tree = DecisionTreeRegressor(random_state=0).fit(X, y)
+    terminal_regions = tree.apply(X)
+    left_leaf = set(np.where(tree.tree_.children_left == TREE_LEAF)[0])
+    empty_leaf = left_leaf.difference(terminal_regions)
+    infinite_threshold = np.where(~np.isfinite(tree.tree_.threshold))[0]
+    assert len(infinite_threshold) == 0
+    assert len(empty_leaf) == 0
+
+
+@pytest.mark.parametrize(
+    "dataset", sorted(set(DATASETS.keys()) - {"reg_small", "diabetes"})
+)
+@pytest.mark.parametrize("tree_cls", [DecisionTreeClassifier, ExtraTreeClassifier])
+def test_prune_tree_classifier_are_subtrees(dataset, tree_cls):
+    dataset = DATASETS[dataset]
+    X, y = dataset["X"], dataset["y"]
+    est = tree_cls(max_leaf_nodes=20, random_state=0)
+    info = est.cost_complexity_pruning_path(X, y)
+
+    pruning_path = info.ccp_alphas
+    impurities = info.impurities
+    assert np.all(np.diff(pruning_path) >= 0)
+    assert np.all(np.diff(impurities) >= 0)
+
+    assert_pruning_creates_subtree(tree_cls, X, y, pruning_path)
+
+
+@pytest.mark.parametrize("dataset", DATASETS.keys())
+@pytest.mark.parametrize("tree_cls", [DecisionTreeRegressor, ExtraTreeRegressor])
+def test_prune_tree_regression_are_subtrees(dataset, tree_cls):
+    dataset = DATASETS[dataset]
+    X, y = dataset["X"], dataset["y"]
+
+    est = tree_cls(max_leaf_nodes=20, random_state=0)
+    info = est.cost_complexity_pruning_path(X, y)
+
+    pruning_path = info.ccp_alphas
+    impurities = info.impurities
+    assert np.all(np.diff(pruning_path) >= 0)
+    assert np.all(np.diff(impurities) >= 0)
+
+    assert_pruning_creates_subtree(tree_cls, X, y, pruning_path)
+
+
+def test_prune_single_node_tree():
+    # single node tree
+    clf1 = DecisionTreeClassifier(random_state=0)
+    clf1.fit([[0], [1]], [0, 0])
+
+    # pruned single node tree
+    clf2 = DecisionTreeClassifier(random_state=0, ccp_alpha=10)
+    clf2.fit([[0], [1]], [0, 0])
+
+    assert_is_subtree(clf1.tree_, clf2.tree_)
+
+
+def assert_pruning_creates_subtree(estimator_cls, X, y, pruning_path):
+    # generate trees with increasing alphas
+    estimators = []
+    for ccp_alpha in pruning_path:
+        est = estimator_cls(max_leaf_nodes=20, ccp_alpha=ccp_alpha, random_state=0).fit(
+            X, y
+        )
+        estimators.append(est)
+
+    # A pruned tree must be a subtree of the previous tree (which had a
+    # smaller ccp_alpha)
+    for prev_est, next_est in pairwise(estimators):
+        assert_is_subtree(prev_est.tree_, next_est.tree_)
+
+
+def assert_is_subtree(tree, subtree):
+    assert tree.node_count >= subtree.node_count
+    assert tree.max_depth >= subtree.max_depth
+
+    tree_c_left = tree.children_left
+    tree_c_right = tree.children_right
+    subtree_c_left = subtree.children_left
+    subtree_c_right = subtree.children_right
+
+    stack = [(0, 0)]
+    while stack:
+        tree_node_idx, subtree_node_idx = stack.pop()
+        assert_array_almost_equal(
+            tree.value[tree_node_idx], subtree.value[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.impurity[tree_node_idx], subtree.impurity[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.n_node_samples[tree_node_idx], subtree.n_node_samples[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.weighted_n_node_samples[tree_node_idx],
+            subtree.weighted_n_node_samples[subtree_node_idx],
+        )
+
+        if subtree_c_left[subtree_node_idx] == subtree_c_right[subtree_node_idx]:
+            # is a leaf
+            assert_almost_equal(TREE_UNDEFINED, subtree.threshold[subtree_node_idx])
+        else:
+            # not a leaf
+            assert_almost_equal(
+                tree.threshold[tree_node_idx], subtree.threshold[subtree_node_idx]
+            )
+            stack.append((tree_c_left[tree_node_idx], subtree_c_left[subtree_node_idx]))
+            stack.append(
+                (tree_c_right[tree_node_idx], subtree_c_right[subtree_node_idx])
+            )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("splitter", ["best", "random"])
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
+def test_apply_path_readonly_all_trees(name, splitter, sparse_container):
+    dataset = DATASETS["clf_small"]
+    X_small = dataset["X"].astype(tree._tree.DTYPE, copy=False)
+    if sparse_container is None:
+        X_readonly = create_memmap_backed_data(X_small)
+    else:
+        X_readonly = sparse_container(dataset["X"])
+
+        X_readonly.data = np.array(X_readonly.data, dtype=tree._tree.DTYPE)
+        (
+            X_readonly.data,
+            X_readonly.indices,
+            X_readonly.indptr,
+        ) = create_memmap_backed_data(
+            (X_readonly.data, X_readonly.indices, X_readonly.indptr)
+        )
+
+    y_readonly = create_memmap_backed_data(np.array(y_small, dtype=tree._tree.DTYPE))
+    est = ALL_TREES[name](splitter=splitter)
+    est.fit(X_readonly, y_readonly)
+    assert_array_equal(est.predict(X_readonly), est.predict(X_small))
+    assert_array_equal(
+        est.decision_path(X_readonly).todense(), est.decision_path(X_small).todense()
+    )
+
+
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse", "poisson"])
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+def test_balance_property(criterion, Tree):
+    # Test that sum(y_pred)=sum(y_true) on training set.
+    # This works if the mean is predicted (should even be true for each leaf).
+    # MAE predicts the median and is therefore excluded from this test.
+
+    # Choose a training set with non-negative targets (for poisson)
+    X, y = diabetes.data, diabetes.target
+    reg = Tree(criterion=criterion)
+    reg.fit(X, y)
+    assert np.sum(reg.predict(X)) == pytest.approx(np.sum(y))
+
+
+@pytest.mark.parametrize("seed", range(3))
+def test_poisson_zero_nodes(seed):
+    # Test that sum(y)=0 and therefore y_pred=0 is forbidden on nodes.
+    X = [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 2], [1, 2], [1, 3]]
+    y = [0, 0, 0, 0, 1, 2, 3, 4]
+    # Note that X[:, 0] == 0 is a 100% indicator for y == 0. The tree can
+    # easily learn that:
+    reg = DecisionTreeRegressor(criterion="squared_error", random_state=seed)
+    reg.fit(X, y)
+    assert np.amin(reg.predict(X)) == 0
+    # whereas Poisson must predict strictly positive numbers
+    reg = DecisionTreeRegressor(criterion="poisson", random_state=seed)
+    reg.fit(X, y)
+    assert np.all(reg.predict(X) > 0)
+
+    # Test additional dataset where something could go wrong.
+    n_features = 10
+    X, y = datasets.make_regression(
+        effective_rank=n_features * 2 // 3,
+        tail_strength=0.6,
+        n_samples=1_000,
+        n_features=n_features,
+        n_informative=n_features * 2 // 3,
+        random_state=seed,
+    )
+    # some excess zeros
+    y[(-1 < y) & (y < 0)] = 0
+    # make sure the target is positive
+    y = np.abs(y)
+    reg = DecisionTreeRegressor(criterion="poisson", random_state=seed)
+    reg.fit(X, y)
+    assert np.all(reg.predict(X) > 0)
+
+
+def test_poisson_vs_mse():
+    # For a Poisson distributed target, Poisson loss should give better results
+    # than squared error measured in Poisson deviance as metric.
+    # We have a similar test, test_poisson(), in
+    # sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+    rng = np.random.RandomState(42)
+    n_train, n_test, n_features = 500, 500, 10
+    X = datasets.make_low_rank_matrix(
+        n_samples=n_train + n_test, n_features=n_features, random_state=rng
+    )
+    # We create a log-linear Poisson model and downscale coef as it will get
+    # exponentiated.
+    coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
+    y = rng.poisson(lam=np.exp(X @ coef))
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=n_test, random_state=rng
+    )
+    # We prevent some overfitting by setting min_samples_split=10.
+    tree_poi = DecisionTreeRegressor(
+        criterion="poisson", min_samples_split=10, random_state=rng
+    )
+    tree_mse = DecisionTreeRegressor(
+        criterion="squared_error", min_samples_split=10, random_state=rng
+    )
+
+    tree_poi.fit(X_train, y_train)
+    tree_mse.fit(X_train, y_train)
+    dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)
+
+    for X, y, val in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
+        metric_poi = mean_poisson_deviance(y, tree_poi.predict(X))
+        # squared_error might produce non-positive predictions => clip
+        metric_mse = mean_poisson_deviance(y, np.clip(tree_mse.predict(X), 1e-15, None))
+        metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
+        # As squared_error might correctly predict 0 in train set, its train
+        # score can be better than Poisson. This is no longer the case for the
+        # test set.
+        if val == "test":
+            assert metric_poi < 0.5 * metric_mse
+        assert metric_poi < 0.75 * metric_dummy
+
+
+@pytest.mark.parametrize("Tree", [DecisionTreeClassifier, ExtraTreeClassifier])
+@pytest.mark.parametrize("n_classes", [2, 4])
+def test_criterion_entropy_same_as_log_loss(Tree, n_classes):
+    """Test that criterion=entropy gives same as log_loss."""
+    n_samples, n_features = 50, 5
+    X, y = datasets.make_classification(
+        n_classes=n_classes,
+        n_samples=n_samples,
+        n_features=n_features,
+        n_informative=n_features,
+        n_redundant=0,
+        random_state=42,
+    )
+    tree_log_loss = Tree(criterion="log_loss", random_state=43).fit(X, y)
+    tree_entropy = Tree(criterion="entropy", random_state=43).fit(X, y)
+
+    assert_tree_equal(
+        tree_log_loss.tree_,
+        tree_entropy.tree_,
+        f"{Tree!r} with criterion 'entropy' and 'log_loss' gave different trees.",
+    )
+    assert_allclose(tree_log_loss.predict(X), tree_entropy.predict(X))
+
+
+def test_different_endianness_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def reduce_ndarray(arr):
+        return arr.byteswap().view(arr.dtype.newbyteorder()).__reduce__()
+
+    def get_pickle_non_native_endianness():
+        f = io.BytesIO()
+        p = pickle.Pickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[np.ndarray] = reduce_ndarray
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = pickle.load(get_pickle_non_native_endianness())
+    new_score = new_clf.score(X, y)
+    assert np.isclose(score, new_score)
+
+
+def test_different_endianness_joblib_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    class NonNativeEndiannessNumpyPickler(NumpyPickler):
+        def save(self, obj):
+            if isinstance(obj, np.ndarray):
+                obj = obj.byteswap().view(obj.dtype.newbyteorder())
+            super().save(obj)
+
+    def get_joblib_pickle_non_native_endianness():
+        f = io.BytesIO()
+        p = NonNativeEndiannessNumpyPickler(f)
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = joblib.load(get_joblib_pickle_non_native_endianness())
+    new_score = new_clf.score(X, y)
+    assert np.isclose(score, new_score)
+
+
+def get_different_bitness_node_ndarray(node_ndarray):
+    new_dtype_for_indexing_fields = np.int64 if _IS_32BIT else np.int32
+
+    # field names in Node struct with SIZE_t types (see sklearn/tree/_tree.pxd)
+    indexing_field_names = ["left_child", "right_child", "feature", "n_node_samples"]
+
+    new_dtype_dict = {
+        name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()
+    }
+    for name in indexing_field_names:
+        new_dtype_dict[name] = new_dtype_for_indexing_fields
+
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+    return node_ndarray.astype(new_dtype, casting="same_kind")
+
+
+def get_different_alignment_node_ndarray(node_ndarray):
+    new_dtype_dict = {
+        name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()
+    }
+    offsets = [offset for dtype, offset in node_ndarray.dtype.fields.values()]
+    shifted_offsets = [8 + offset for offset in offsets]
+
+    new_dtype = np.dtype(
+        {
+            "names": list(new_dtype_dict.keys()),
+            "formats": list(new_dtype_dict.values()),
+            "offsets": shifted_offsets,
+        }
+    )
+    return node_ndarray.astype(new_dtype, casting="same_kind")
+
+
+def reduce_tree_with_different_bitness(tree):
+    new_dtype = np.int64 if _IS_32BIT else np.int32
+    tree_cls, (n_features, n_classes, n_outputs), state = tree.__reduce__()
+    new_n_classes = n_classes.astype(new_dtype, casting="same_kind")
+
+    new_state = state.copy()
+    new_state["nodes"] = get_different_bitness_node_ndarray(new_state["nodes"])
+
+    return (tree_cls, (n_features, new_n_classes, n_outputs), new_state)
+
+
+def test_different_bitness_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def pickle_dump_with_different_bitness():
+        f = io.BytesIO()
+        p = pickle.Pickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[CythonTree] = reduce_tree_with_different_bitness
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = pickle.load(pickle_dump_with_different_bitness())
+    new_score = new_clf.score(X, y)
+    assert score == pytest.approx(new_score)
+
+
+def test_different_bitness_joblib_pickle():
+    # Make sure that a platform specific pickle generated on a 64 bit
+    # platform can be converted at pickle load time into an estimator
+    # with Cython code that works with the host's native integer precision
+    # to index nodes in the tree data structure when the host is a 32 bit
+    # platform (and vice versa).
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def joblib_dump_with_different_bitness():
+        f = io.BytesIO()
+        p = NumpyPickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[CythonTree] = reduce_tree_with_different_bitness
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = joblib.load(joblib_dump_with_different_bitness())
+    new_score = new_clf.score(X, y)
+    assert score == pytest.approx(new_score)
+
+
+def test_check_n_classes():
+    expected_dtype = np.dtype(np.int32) if _IS_32BIT else np.dtype(np.int64)
+    allowed_dtypes = [np.dtype(np.int32), np.dtype(np.int64)]
+    allowed_dtypes += [dt.newbyteorder() for dt in allowed_dtypes]
+
+    n_classes = np.array([0, 1], dtype=expected_dtype)
+    for dt in allowed_dtypes:
+        _check_n_classes(n_classes.astype(dt), expected_dtype)
+
+    with pytest.raises(ValueError, match="Wrong dimensions.+n_classes"):
+        wrong_dim_n_classes = np.array([[0, 1]], dtype=expected_dtype)
+        _check_n_classes(wrong_dim_n_classes, expected_dtype)
+
+    with pytest.raises(ValueError, match="n_classes.+incompatible dtype"):
+        wrong_dtype_n_classes = n_classes.astype(np.float64)
+        _check_n_classes(wrong_dtype_n_classes, expected_dtype)
+
+
+def test_check_value_ndarray():
+    expected_dtype = np.dtype(np.float64)
+    expected_shape = (5, 1, 2)
+    value_ndarray = np.zeros(expected_shape, dtype=expected_dtype)
+
+    allowed_dtypes = [expected_dtype, expected_dtype.newbyteorder()]
+
+    for dt in allowed_dtypes:
+        _check_value_ndarray(
+            value_ndarray, expected_dtype=dt, expected_shape=expected_shape
+        )
+
+    with pytest.raises(ValueError, match="Wrong shape.+value array"):
+        _check_value_ndarray(
+            value_ndarray, expected_dtype=expected_dtype, expected_shape=(1, 2)
+        )
+
+    for problematic_arr in [value_ndarray[:, :, :1], np.asfortranarray(value_ndarray)]:
+        with pytest.raises(ValueError, match="value array.+C-contiguous"):
+            _check_value_ndarray(
+                problematic_arr,
+                expected_dtype=expected_dtype,
+                expected_shape=problematic_arr.shape,
+            )
+
+    with pytest.raises(ValueError, match="value array.+incompatible dtype"):
+        _check_value_ndarray(
+            value_ndarray.astype(np.float32),
+            expected_dtype=expected_dtype,
+            expected_shape=expected_shape,
+        )
+
+
+def test_check_node_ndarray():
+    expected_dtype = NODE_DTYPE
+
+    node_ndarray = np.zeros((5,), dtype=expected_dtype)
+
+    valid_node_ndarrays = [
+        node_ndarray,
+        get_different_bitness_node_ndarray(node_ndarray),
+        get_different_alignment_node_ndarray(node_ndarray),
+    ]
+    valid_node_ndarrays += [
+        arr.astype(arr.dtype.newbyteorder()) for arr in valid_node_ndarrays
+    ]
+
+    for arr in valid_node_ndarrays:
+        _check_node_ndarray(node_ndarray, expected_dtype=expected_dtype)
+
+    with pytest.raises(ValueError, match="Wrong dimensions.+node array"):
+        problematic_node_ndarray = np.zeros((5, 2), dtype=expected_dtype)
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    with pytest.raises(ValueError, match="node array.+C-contiguous"):
+        problematic_node_ndarray = node_ndarray[::2]
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    dtype_dict = {name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()}
+
+    # array with wrong 'threshold' field dtype (int64 rather than float64)
+    new_dtype_dict = dtype_dict.copy()
+    new_dtype_dict["threshold"] = np.int64
+
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+    problematic_node_ndarray = node_ndarray.astype(new_dtype)
+
+    with pytest.raises(ValueError, match="node array.+incompatible dtype"):
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    # array with wrong 'left_child' field dtype (float64 rather than int64 or int32)
+    new_dtype_dict = dtype_dict.copy()
+    new_dtype_dict["left_child"] = np.float64
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+
+    problematic_node_ndarray = node_ndarray.astype(new_dtype)
+
+    with pytest.raises(ValueError, match="node array.+incompatible dtype"):
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+
+@pytest.mark.parametrize(
+    "Splitter", chain(DENSE_SPLITTERS.values(), SPARSE_SPLITTERS.values())
+)
+def test_splitter_serializable(Splitter):
+    """Check that splitters are serializable."""
+    rng = np.random.RandomState(42)
+    max_features = 10
+    n_outputs, n_classes = 2, np.array([3, 2], dtype=np.intp)
+
+    criterion = CRITERIA_CLF["gini"](n_outputs, n_classes)
+    splitter = Splitter(criterion, max_features, 5, 0.5, rng, monotonic_cst=None)
+    splitter_serialize = pickle.dumps(splitter)
+
+    splitter_back = pickle.loads(splitter_serialize)
+    assert splitter_back.max_features == max_features
+    assert isinstance(splitter_back, Splitter)
+
+
+def test_tree_deserialization_from_read_only_buffer(tmpdir):
+    """Check that Trees can be deserialized with read only buffers.
+
+    Non-regression test for gh-25584.
+    """
+    pickle_path = str(tmpdir.join("clf.joblib"))
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(X_small, y_small)
+
+    joblib.dump(clf, pickle_path)
+    loaded_clf = joblib.load(pickle_path, mmap_mode="r")
+
+    assert_tree_equal(
+        loaded_clf.tree_,
+        clf.tree_,
+        "The trees of the original and loaded classifiers are not equal.",
+    )
+
+
+@pytest.mark.parametrize("Tree", ALL_TREES.values())
+def test_min_sample_split_1_error(Tree):
+    """Check that an error is raised when min_sample_split=1.
+
+    non-regression test for issue gh-25481.
+    """
+    X = np.array([[0, 0], [1, 1]])
+    y = np.array([0, 1])
+
+    # min_samples_split=1.0 is valid
+    Tree(min_samples_split=1.0).fit(X, y)
+
+    # min_samples_split=1 is invalid
+    tree = Tree(min_samples_split=1)
+    msg = (
+        r"'min_samples_split' .* must be an int in the range \[2, inf\) "
+        r"or a float in the range \(0.0, 1.0\]"
+    )
+    with pytest.raises(ValueError, match=msg):
+        tree.fit(X, y)
+
+
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_missing_values_best_splitter_on_equal_nodes_no_missing(criterion):
+    """Check missing values goes to correct node during predictions."""
+    X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T
+    y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6])
+
+    dtc = DecisionTreeRegressor(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X, y)
+
+    # Goes to right node because it has the most data points
+    y_pred = dtc.predict([[np.nan]])
+    assert_allclose(y_pred, [np.mean(y[-5:])])
+
+    # equal number of elements in both nodes
+    X_equal = X[:-1]
+    y_equal = y[:-1]
+
+    dtc = DecisionTreeRegressor(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X_equal, y_equal)
+
+    # Goes to right node because the implementation sets:
+    # missing_go_to_left = n_left > n_right, which is False
+    y_pred = dtc.predict([[np.nan]])
+    assert_allclose(y_pred, [np.mean(y_equal[-4:])])
+
+
+@pytest.mark.parametrize("seed", range(3))
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_missing_values_random_splitter_on_equal_nodes_no_missing(criterion, seed):
+    """Check missing values go to the correct node during predictions for ExtraTree.
+
+    Since ETC use random splits, we use different seeds to verify that the
+    left/right node is chosen correctly when the splits occur.
+    """
+    X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T
+    y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6])
+
+    etr = ExtraTreeRegressor(random_state=seed, max_depth=1, criterion=criterion)
+    etr.fit(X, y)
+
+    # Get the left and right children of the root node
+    left_child = etr.tree_.children_left[0]
+    right_child = etr.tree_.children_right[0]
+
+    # Get the number of samples for the left and right children
+    left_samples = etr.tree_.weighted_n_node_samples[left_child]
+    right_samples = etr.tree_.weighted_n_node_samples[right_child]
+    went_left = left_samples > right_samples
+
+    # predictions
+    y_pred_left = etr.tree_.value[left_child][0]
+    y_pred_right = etr.tree_.value[right_child][0]
+
+    # Goes to node with the most data points
+    y_pred = etr.predict([[np.nan]])
+    if went_left:
+        assert_allclose(y_pred_left, y_pred)
+    else:
+        assert_allclose(y_pred_right, y_pred)
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_three_classes(criterion):
+    """Test when missing values are uniquely present in a class among 3 classes."""
+    missing_values_class = 0
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 8, 9, 11, 12]]).T
+    y = np.array([missing_values_class] * 4 + [1] * 4 + [2] * 4)
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 3, 12]]).T
+    y_nan_pred = dtc.predict(X_test)
+    # Missing values necessarily are associated to the observed class.
+    assert_array_equal(y_nan_pred, [missing_values_class, 1, 2])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_to_left(criterion):
+    """Missing values spanning only one class at fit-time must make missing
+    values at predict-time be classified has belonging to this class."""
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 4, 5]]).T
+    y = np.array([0] * 4 + [1] * 6)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 5, np.nan]]).T
+    y_pred = dtc.predict(X_test)
+
+    assert_array_equal(y_pred, [0, 1, 0])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_to_right(criterion):
+    """Missing values and non-missing values sharing one class at fit-time
+    must make missing values at predict-time be classified has belonging
+    to this class."""
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 4, 5]]).T
+    y = np.array([1] * 4 + [0] * 4 + [1] * 2)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 1.2, 4.8]]).T
+    y_pred = dtc.predict(X_test)
+
+    assert_array_equal(y_pred, [1, 0, 1])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_missing_both_classes_has_nan(criterion):
+    """Check behavior of missing value when there is one missing value in each class."""
+    X = np.array([[1, 2, 3, 5, np.nan, 10, 20, 30, 60, np.nan]]).T
+    y = np.array([0] * 5 + [1] * 5)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X, y)
+    X_test = np.array([[np.nan, 2.3, 34.2]]).T
+    y_pred = dtc.predict(X_test)
+
+    # Missing value goes to the class at the right (here 1) because the implementation
+    # searches right first.
+    assert_array_equal(y_pred, [1, 0, 1])
+
+
+@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
+@pytest.mark.parametrize(
+    "tree",
+    [
+        DecisionTreeRegressor(criterion="absolute_error"),
+        ExtraTreeRegressor(criterion="absolute_error"),
+    ],
+)
+def test_missing_value_errors(sparse_container, tree):
+    """Check unsupported configurations for missing values."""
+
+    X = np.array([[1, 2, 3, 5, np.nan, 10, 20, 30, 60, np.nan]]).T
+    y = np.array([0] * 5 + [1] * 5)
+
+    if sparse_container is not None:
+        X = sparse_container(X)
+
+    with pytest.raises(ValueError, match="Input X contains NaN"):
+        tree.fit(X, y)
+
+
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+def test_missing_values_poisson(Tree):
+    """Smoke test for poisson regression and missing values."""
+    X, y = diabetes.data.copy(), diabetes.target
+
+    # Set some values missing
+    X[::5, 0] = np.nan
+    X[::6, -1] = np.nan
+
+    reg = Tree(criterion="poisson", random_state=42)
+    reg.fit(X, y)
+
+    y_pred = reg.predict(X)
+    assert (y_pred >= 0.0).all()
+
+
+def make_friedman1_classification(*args, **kwargs):
+    X, y = datasets.make_friedman1(*args, **kwargs)
+    y = y > 14
+    return X, y
+
+
+@pytest.mark.parametrize(
+    "make_data, Tree, tolerance",
+    [
+        # Due to the sine link between X and y, we expect the native handling of
+        # missing values to always be better than the naive mean imputation in the
+        # regression case.
+        #
+        # Due to randomness in ExtraTree, we expect the native handling of missing
+        # values to be sometimes better than the naive mean imputation, but not always
+        (datasets.make_friedman1, DecisionTreeRegressor, 0),
+        (datasets.make_friedman1, ExtraTreeRegressor, 0.07),
+        (make_friedman1_classification, DecisionTreeClassifier, 0.03),
+        (make_friedman1_classification, ExtraTreeClassifier, 0.12),
+    ],
+)
+@pytest.mark.parametrize("sample_weight_train", [None, "ones"])
+def test_missing_values_is_resilience(
+    make_data, Tree, sample_weight_train, global_random_seed, tolerance
+):
+    """Check that trees can deal with missing values have decent performance."""
+    n_samples, n_features = 5_000, 10
+    X, y = make_data(
+        n_samples=n_samples,
+        n_features=n_features,
+        noise=1.0,
+        random_state=global_random_seed,
+    )
+
+    X_missing = X.copy()
+    rng = np.random.RandomState(global_random_seed)
+    X_missing[rng.choice([False, True], size=X.shape, p=[0.9, 0.1])] = np.nan
+    X_missing_train, X_missing_test, y_train, y_test = train_test_split(
+        X_missing, y, random_state=global_random_seed
+    )
+    if sample_weight_train == "ones":
+        sample_weight = np.ones(X_missing_train.shape[0])
+    else:
+        sample_weight = None
+
+    # max_depth is used to avoid overfitting and also improve the runtime
+    # of the test.
+    max_depth = 10
+    native_tree = Tree(max_depth=max_depth, random_state=global_random_seed)
+    native_tree.fit(X_missing_train, y_train, sample_weight=sample_weight)
+    score_native_tree = native_tree.score(X_missing_test, y_test)
+
+    tree_with_imputer = make_pipeline(
+        SimpleImputer(), Tree(max_depth=max_depth, random_state=global_random_seed)
+    )
+    tree_with_imputer.fit(X_missing_train, y_train)
+    score_tree_with_imputer = tree_with_imputer.score(X_missing_test, y_test)
+
+    assert score_native_tree + tolerance > score_tree_with_imputer, (
+        f"{score_native_tree=} + {tolerance} should be strictly greater than"
+        f" {score_tree_with_imputer}"
+    )
+
+
+# A single ExtraTree will randomly send missing values down the left, or right child,
+# and therefore will not necessarily have the same performance as the greedy
+# handling of missing values.
+@pytest.mark.parametrize("Tree, expected_score", zip(CLF_TREES.values(), [0.85, 0.53]))
+def test_missing_value_is_predictive(Tree, expected_score, global_random_seed):
+    """Check the tree learns when only the missing value is predictive."""
+    rng = np.random.RandomState(0)
+    n_samples = 500
+
+    X = rng.standard_normal(size=(n_samples, 20))
+    y = np.concatenate([np.zeros(n_samples // 2), np.ones(n_samples // 2)])
+    # y = rng.randint(0, high=2, size=n_samples)
+
+    # Create a predictive feature using `y` and with some noise
+    X_random_mask = rng.choice([False, True], size=n_samples, p=[0.95, 0.05])
+    y_mask = y.copy().astype(bool)
+    y_mask[X_random_mask] = ~y_mask[X_random_mask]
+
+    X_predictive = rng.standard_normal(size=n_samples)
+    X_predictive[y_mask] = np.nan
+
+    X[:, 5] = X_predictive
+
+    tree = Tree(random_state=global_random_seed)
+
+    # Check that the tree can learn the predictive feature
+    # over an average of cross-validation fits.
+    tree_cv_score = cross_val_score(tree, X, y, cv=5).mean()
+    assert tree_cv_score >= expected_score, (
+        f"Expected CV score: {expected_score} but got {tree_cv_score}"
+    )
+
+
+@pytest.mark.parametrize(
+    "make_data, Tree",
+    [
+        (datasets.make_regression, DecisionTreeRegressor),
+        (datasets.make_classification, DecisionTreeClassifier),
+    ],
+)
+def test_sample_weight_non_uniform(make_data, Tree):
+    """Check sample weight is correctly handled with missing values."""
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 1000, 10
+    X, y = make_data(n_samples=n_samples, n_features=n_features, random_state=rng)
+
+    # Create dataset with missing values
+    X[rng.choice([False, True], size=X.shape, p=[0.9, 0.1])] = np.nan
+
+    # Zero sample weight is the same as removing the sample
+    sample_weight = np.ones(X.shape[0])
+    sample_weight[::2] = 0.0
+
+    tree_with_sw = Tree(random_state=0)
+    tree_with_sw.fit(X, y, sample_weight=sample_weight)
+
+    tree_samples_removed = Tree(random_state=0)
+    tree_samples_removed.fit(X[1::2, :], y[1::2])
+
+    assert_allclose(tree_samples_removed.predict(X), tree_with_sw.predict(X))
+
+
+def test_deterministic_pickle():
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/27268
+    # Uninitialised memory would lead to the two pickle strings being different.
+    tree1 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+    tree2 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+
+    pickle1 = pickle.dumps(tree1)
+    pickle2 = pickle.dumps(tree2)
+
+    assert pickle1 == pickle2
+
+
+@pytest.mark.parametrize("Tree", [DecisionTreeRegressor, ExtraTreeRegressor])
+@pytest.mark.parametrize(
+    "X",
+    [
+        # missing values will go left for greedy splits
+        np.array([np.nan, 2, np.nan, 4, 5, 6]),
+        np.array([np.nan, np.nan, 3, 4, 5, 6]),
+        # missing values will go right for greedy splits
+        np.array([1, 2, 3, 4, np.nan, np.nan]),
+        np.array([1, 2, 3, np.nan, 6, np.nan]),
+    ],
+)
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_regression_tree_missing_values_toy(Tree, X, criterion):
+    """Check that we properly handle missing values in regression trees using a toy
+    dataset.
+
+    The regression targeted by this test was that we were not reinitializing the
+    criterion when it comes to the number of missing values. Therefore, the value
+    of the critetion (i.e. MSE) was completely wrong.
+
+    This test check that the MSE is null when there is a single sample in the leaf.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28254
+    https://github.com/scikit-learn/scikit-learn/issues/28316
+    """
+    X = X.reshape(-1, 1)
+    y = np.arange(6)
+
+    tree = Tree(criterion=criterion, random_state=0).fit(X, y)
+    tree_ref = clone(tree).fit(y.reshape(-1, 1), y)
+
+    impurity = tree.tree_.impurity
+    assert all(impurity >= 0), impurity.min()  # MSE should always be positive
+
+    # Check the impurity match after the first split
+    assert_allclose(tree.tree_.impurity[:2], tree_ref.tree_.impurity[:2])
+
+    # Find the leaves with a single sample where the MSE should be 0
+    leaves_idx = np.flatnonzero(
+        (tree.tree_.children_left == -1) & (tree.tree_.n_node_samples == 1)
+    )
+    assert_allclose(tree.tree_.impurity[leaves_idx], 0.0)
+
+
+def test_regression_extra_tree_missing_values_toy(global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    X = np.arange(n_samples, dtype=np.float64).reshape(-1, 1)
+    X[-20:, :] = np.nan
+    rng.shuffle(X)
+    y = np.arange(n_samples)
+
+    tree = ExtraTreeRegressor(random_state=global_random_seed, max_depth=5).fit(X, y)
+
+    impurity = tree.tree_.impurity
+    assert all(impurity >= 0), impurity  # MSE should always be positive
+
+
+def test_classification_tree_missing_values_toy():
+    """Check that we properly handle missing values in classification trees using a toy
+    dataset.
+
+    The test is more involved because we use a case where we detected a regression
+    in a random forest. We therefore define the seed and bootstrap indices to detect
+    one of the non-frequent regression.
+
+    Here, we check that the impurity is null or positive in the leaves.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28254
+    """
+    X, y = datasets.load_iris(return_X_y=True)
+
+    rng = np.random.RandomState(42)
+    X_missing = X.copy()
+    mask = rng.binomial(
+        n=np.ones(shape=(1, 4), dtype=np.int32), p=X[:, [2]] / 8
+    ).astype(bool)
+    X_missing[mask] = np.nan
+    X_train, _, y_train, _ = train_test_split(X_missing, y, random_state=13)
+
+    # fmt: off
+    # no black reformatting for this specific array
+    indices = np.array([
+        2, 81, 39, 97, 91, 38, 46, 31, 101, 13, 89, 82, 100, 42, 69, 27, 81, 16, 73, 74,
+        51, 47, 107, 17, 75, 110, 20, 15, 104, 57, 26, 15, 75, 79, 35, 77, 90, 51, 46,
+        13, 94, 91, 23, 8, 93, 93, 73, 77, 12, 13, 74, 109, 110, 24, 10, 23, 104, 27,
+        92, 52, 20, 109, 8, 8, 28, 27, 35, 12, 12, 7, 43, 0, 30, 31, 78, 12, 24, 105,
+        50, 0, 73, 12, 102, 105, 13, 31, 1, 69, 11, 32, 75, 90, 106, 94, 60, 56, 35, 17,
+        62, 85, 81, 39, 80, 16, 63, 6, 80, 84, 3, 3, 76, 78
+    ], dtype=np.int32)
+    # fmt: on
+
+    tree = DecisionTreeClassifier(
+        max_depth=3, max_features="sqrt", random_state=1857819720
+    )
+    tree.fit(X_train[indices], y_train[indices])
+    assert all(tree.tree_.impurity >= 0)
+
+    leaves_idx = np.flatnonzero(
+        (tree.tree_.children_left == -1) & (tree.tree_.n_node_samples == 1)
+    )
+    assert_allclose(tree.tree_.impurity[leaves_idx], 0.0)
+
+
+def test_build_pruned_tree_py():
+    """Test pruning a tree with the Python caller of the Cythonized prune tree."""
+    tree = DecisionTreeClassifier(random_state=0, max_depth=1)
+    tree.fit(iris.data, iris.target)
+
+    n_classes = np.atleast_1d(tree.n_classes_)
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+
+    # only keep the root note
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[0] = 1
+    _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+
+    assert tree.tree_.node_count == 3
+    assert pruned_tree.node_count == 1
+    with pytest.raises(AssertionError):
+        assert_array_equal(tree.tree_.value, pruned_tree.value)
+    assert_array_equal(tree.tree_.value[0], pruned_tree.value[0])
+
+    # now keep all the leaves
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[1:] = 1
+
+    # Prune the tree
+    _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+    assert tree.tree_.node_count == 3
+    assert pruned_tree.node_count == 3, pruned_tree.node_count
+    assert_array_equal(tree.tree_.value, pruned_tree.value)
+
+
+def test_build_pruned_tree_infinite_loop():
+    """Test pruning a tree does not result in an infinite loop."""
+
+    # Create a tree with root and two children
+    tree = DecisionTreeClassifier(random_state=0, max_depth=1)
+    tree.fit(iris.data, iris.target)
+    n_classes = np.atleast_1d(tree.n_classes_)
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+
+    # only keeping one child as a leaf results in an improper tree
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[1] = 1
+    with pytest.raises(
+        ValueError, match="Node has reached a leaf in the original tree"
+    ):
+        _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+
+
+def test_sort_log2_build():
+    """Non-regression test for gh-30554.
+
+    Using log2 and log in sort correctly sorts feature_values, but the tie breaking is
+    different which can results in placing samples in a different order.
+    """
+    rng = np.random.default_rng(75)
+    some = rng.normal(loc=0.0, scale=10.0, size=10).astype(np.float32)
+    feature_values = np.concatenate([some] * 5)
+    samples = np.arange(50, dtype=np.intp)
+    _py_sort(feature_values, samples, 50)
+    # fmt: off
+    # no black reformatting for this specific array
+    expected_samples = [
+        0, 40, 30, 20, 10, 29, 39, 19, 49,  9, 45, 15, 35,  5, 25, 11, 31,
+        41,  1, 21, 22, 12,  2, 42, 32, 23, 13, 43,  3, 33,  6, 36, 46, 16,
+        26,  4, 14, 24, 34, 44, 27, 47,  7, 37, 17,  8, 38, 48, 28, 18
+    ]
+    # fmt: on
+    assert_array_equal(samples, expected_samples)
diff --git a/shared/package-files/40/408e204ad1ca151d83a39c21b74901bad648e222e1ce0f3285c88720f4002aad/new_empty_strided_ops.h b/shared/package-files/40/408e204ad1ca151d83a39c21b74901bad648e222e1ce0f3285c88720f4002aad/new_empty_strided_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..27087b6af4671a3c4082ddb2cd7d8675c0ab27c1
--- /dev/null
+++ b/shared/package-files/40/408e204ad1ca151d83a39c21b74901bad648e222e1ce0f3285c88720f4002aad/new_empty_strided_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API new_empty_strided {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::new_empty_strided")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+};
+
+struct TORCH_API new_empty_strided_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::new_empty_strided")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/4090adce0b76eb7c703607999646b3baa0932b17c7eba01ce818fc90d4dea5e9/direct_url.json b/shared/package-files/40/4090adce0b76eb7c703607999646b3baa0932b17c7eba01ce818fc90d4dea5e9/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..23480843d975d7b7cb38b2ab93cad61f671fca93
--- /dev/null
+++ b/shared/package-files/40/4090adce0b76eb7c703607999646b3baa0932b17c7eba01ce818fc90d4dea5e9/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///croot/referencing_1699012038513/work"}
\ No newline at end of file
diff --git a/shared/package-files/40/40916d5e40590cfae114f70ddf4a7a1f66225fdb62137e1a7d0c505b2e356e27/inputsplitter.py b/shared/package-files/40/40916d5e40590cfae114f70ddf4a7a1f66225fdb62137e1a7d0c505b2e356e27/inputsplitter.py
new file mode 100644
index 0000000000000000000000000000000000000000..33ed563221b9304ec5201ee6b52f86e37849b1a1
--- /dev/null
+++ b/shared/package-files/40/40916d5e40590cfae114f70ddf4a7a1f66225fdb62137e1a7d0c505b2e356e27/inputsplitter.py
@@ -0,0 +1,795 @@
+"""DEPRECATED: Input handling and transformation machinery.
+
+This module was deprecated in IPython 7.0, in favour of inputtransformer2.
+
+The first class in this module, :class:`InputSplitter`, is designed to tell when
+input from a line-oriented frontend is complete and should be executed, and when
+the user should be prompted for another line of code instead. The name 'input
+splitter' is largely for historical reasons.
+
+A companion, :class:`IPythonInputSplitter`, provides the same functionality but
+with full support for the extended IPython syntax (magics, system calls, etc).
+The code to actually do these transformations is in :mod:`IPython.core.inputtransformer`.
+:class:`IPythonInputSplitter` feeds the raw code to the transformers in order
+and stores the results.
+
+For more details, see the class docstrings below.
+"""
+
+from warnings import warn
+
+warn('IPython.core.inputsplitter is deprecated since IPython 7 in favor of `IPython.core.inputtransformer2`',
+     DeprecationWarning)
+
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+import ast
+import codeop
+import io
+import re
+import sys
+import tokenize
+import warnings
+
+from typing import List, Tuple, Union, Optional
+from types import CodeType
+
+from IPython.core.inputtransformer import (leading_indent,
+                                           classic_prompt,
+                                           ipy_prompt,
+                                           cellmagic,
+                                           assemble_logical_lines,
+                                           help_end,
+                                           escaped_commands,
+                                           assign_from_magic,
+                                           assign_from_system,
+                                           assemble_python_lines,
+                                           )
+from IPython.utils import tokenutil
+
+# These are available in this module for backwards compatibility.
+from IPython.core.inputtransformer import (ESC_SHELL, ESC_SH_CAP, ESC_HELP,
+                                        ESC_HELP2, ESC_MAGIC, ESC_MAGIC2,
+                                        ESC_QUOTE, ESC_QUOTE2, ESC_PAREN, ESC_SEQUENCES)
+
+#-----------------------------------------------------------------------------
+# Utilities
+#-----------------------------------------------------------------------------
+
+# FIXME: These are general-purpose utilities that later can be moved to the
+# general ward.  Kept here for now because we're being very strict about test
+# coverage with this code, and this lets us ensure that we keep 100% coverage
+# while developing.
+
+# compiled regexps for autoindent management
+dedent_re = re.compile('|'.join([
+    r'^\s+raise(\s.*)?$', # raise statement (+ space + other stuff, maybe)
+    r'^\s+raise\([^\)]*\).*$', # wacky raise with immediate open paren
+    r'^\s+return(\s.*)?$', # normal return (+ space + other stuff, maybe)
+    r'^\s+return\([^\)]*\).*$', # wacky return with immediate open paren
+    r'^\s+pass\s*$', # pass (optionally followed by trailing spaces)
+    r'^\s+break\s*$', # break (optionally followed by trailing spaces)
+    r'^\s+continue\s*$', # continue (optionally followed by trailing spaces)
+]))
+ini_spaces_re = re.compile(r'^([ \t\r\f\v]+)')
+
+# regexp to match pure comment lines so we don't accidentally insert 'if 1:'
+# before pure comments
+comment_line_re = re.compile(r'^\s*\#')
+
+
+def num_ini_spaces(s):
+    """Return the number of initial spaces in a string.
+
+    Note that tabs are counted as a single space.  For now, we do *not* support
+    mixing of tabs and spaces in the user's input.
+
+    Parameters
+    ----------
+    s : string
+
+    Returns
+    -------
+    n : int
+    """
+    warnings.warn(
+        "`num_ini_spaces` is Pending Deprecation since IPython 8.17."
+        "It is considered fro removal in in future version. "
+        "Please open an issue if you believe it should be kept.",
+        stacklevel=2,
+        category=PendingDeprecationWarning,
+    )
+    ini_spaces = ini_spaces_re.match(s)
+    if ini_spaces:
+        return ini_spaces.end()
+    else:
+        return 0
+
+# Fake token types for partial_tokenize:
+INCOMPLETE_STRING = tokenize.N_TOKENS
+IN_MULTILINE_STATEMENT = tokenize.N_TOKENS + 1
+
+# The 2 classes below have the same API as TokenInfo, but don't try to look up
+# a token type name that they won't find.
+class IncompleteString:
+    type = exact_type = INCOMPLETE_STRING
+    def __init__(self, s, start, end, line):
+        self.s = s
+        self.start = start
+        self.end = end
+        self.line = line
+
+class InMultilineStatement:
+    type = exact_type = IN_MULTILINE_STATEMENT
+    def __init__(self, pos, line):
+        self.s = ''
+        self.start = self.end = pos
+        self.line = line
+
+def partial_tokens(s):
+    """Iterate over tokens from a possibly-incomplete string of code.
+
+    This adds two special token types: INCOMPLETE_STRING and
+    IN_MULTILINE_STATEMENT. These can only occur as the last token yielded, and
+    represent the two main ways for code to be incomplete.
+    """
+    readline = io.StringIO(s).readline
+    token = tokenize.TokenInfo(tokenize.NEWLINE, '', (1, 0), (1, 0), '')
+    try:
+        for token in tokenutil.generate_tokens_catch_errors(readline):
+            yield token
+    except tokenize.TokenError as e:
+        # catch EOF error
+        lines = s.splitlines(keepends=True)
+        end = len(lines), len(lines[-1])
+        if 'multi-line string' in e.args[0]:
+            l, c = start = token.end
+            s = lines[l-1][c:] + ''.join(lines[l:])
+            yield IncompleteString(s, start, end, lines[-1])
+        elif 'multi-line statement' in e.args[0]:
+            yield InMultilineStatement(end, lines[-1])
+        else:
+            raise
+
+def find_next_indent(code) -> int:
+    """Find the number of spaces for the next line of indentation"""
+    tokens = list(partial_tokens(code))
+    if tokens[-1].type == tokenize.ENDMARKER:
+        tokens.pop()
+    if not tokens:
+        return 0
+
+    while tokens[-1].type in {
+        tokenize.DEDENT,
+        tokenize.NEWLINE,
+        tokenize.COMMENT,
+        tokenize.ERRORTOKEN,
+    }:
+        tokens.pop()
+
+    # Starting in Python 3.12, the tokenize module adds implicit newlines at the end
+    # of input. We need to remove those if we're in a multiline statement
+    if tokens[-1].type == IN_MULTILINE_STATEMENT:
+        while tokens[-2].type in {tokenize.NL}:
+            tokens.pop(-2)
+
+
+    if tokens[-1].type == INCOMPLETE_STRING:
+        # Inside a multiline string
+        return 0
+
+    # Find the indents used before
+    prev_indents = [0]
+    def _add_indent(n):
+        if n != prev_indents[-1]:
+            prev_indents.append(n)
+
+    tokiter = iter(tokens)
+    for tok in tokiter:
+        if tok.type in {tokenize.INDENT, tokenize.DEDENT}:
+            _add_indent(tok.end[1])
+        elif (tok.type == tokenize.NL):
+            try:
+                _add_indent(next(tokiter).start[1])
+            except StopIteration:
+                break
+
+    last_indent = prev_indents.pop()
+
+    # If we've just opened a multiline statement (e.g. 'a = ['), indent more
+    if tokens[-1].type == IN_MULTILINE_STATEMENT:
+        if tokens[-2].exact_type in {tokenize.LPAR, tokenize.LSQB, tokenize.LBRACE}:
+            return last_indent + 4
+        return last_indent
+
+    if tokens[-1].exact_type == tokenize.COLON:
+        # Line ends with colon - indent
+        return last_indent + 4
+
+    if last_indent:
+        # Examine the last line for dedent cues - statements like return or
+        # raise which normally end a block of code.
+        last_line_starts = 0
+        for i, tok in enumerate(tokens):
+            if tok.type == tokenize.NEWLINE:
+                last_line_starts = i + 1
+
+        last_line_tokens = tokens[last_line_starts:]
+        names = [t.string for t in last_line_tokens if t.type == tokenize.NAME]
+        if names and names[0] in {'raise', 'return', 'pass', 'break', 'continue'}:
+            # Find the most recent indentation less than the current level
+            for indent in reversed(prev_indents):
+                if indent < last_indent:
+                    return indent
+
+    return last_indent
+
+
+def last_blank(src):
+    """Determine if the input source ends in a blank.
+
+    A blank is either a newline or a line consisting of whitespace.
+
+    Parameters
+    ----------
+    src : string
+        A single or multiline string.
+    """
+    if not src: return False
+    ll  = src.splitlines()[-1]
+    return (ll == '') or ll.isspace()
+
+
+last_two_blanks_re = re.compile(r'\n\s*\n\s*$', re.MULTILINE)
+last_two_blanks_re2 = re.compile(r'.+\n\s*\n\s+$', re.MULTILINE)
+
+def last_two_blanks(src):
+    """Determine if the input source ends in two blanks.
+
+    A blank is either a newline or a line consisting of whitespace.
+
+    Parameters
+    ----------
+    src : string
+        A single or multiline string.
+    """
+    if not src: return False
+    # The logic here is tricky: I couldn't get a regexp to work and pass all
+    # the tests, so I took a different approach: split the source by lines,
+    # grab the last two and prepend '###\n' as a stand-in for whatever was in
+    # the body before the last two lines.  Then, with that structure, it's
+    # possible to analyze with two regexps.  Not the most elegant solution, but
+    # it works.  If anyone tries to change this logic, make sure to validate
+    # the whole test suite first!
+    new_src = '\n'.join(['###\n'] + src.splitlines()[-2:])
+    return (bool(last_two_blanks_re.match(new_src)) or
+            bool(last_two_blanks_re2.match(new_src)) )
+
+
+def remove_comments(src):
+    """Remove all comments from input source.
+
+    Note: comments are NOT recognized inside of strings!
+
+    Parameters
+    ----------
+    src : string
+        A single or multiline input string.
+
+    Returns
+    -------
+    String with all Python comments removed.
+    """
+
+    return re.sub('#.*', '', src)
+
+
+def get_input_encoding():
+    """Return the default standard input encoding.
+
+    If sys.stdin has no encoding, 'ascii' is returned."""
+    # There are strange environments for which sys.stdin.encoding is None. We
+    # ensure that a valid encoding is returned.
+    encoding = getattr(sys.stdin, 'encoding', None)
+    if encoding is None:
+        encoding = 'ascii'
+    return encoding
+
+#-----------------------------------------------------------------------------
+# Classes and functions for normal Python syntax handling
+#-----------------------------------------------------------------------------
+
+class InputSplitter(object):
+    r"""An object that can accumulate lines of Python source before execution.
+
+    This object is designed to be fed python source line-by-line, using
+    :meth:`push`. It will return on each push whether the currently pushed
+    code could be executed already. In addition, it provides a method called
+    :meth:`push_accepts_more` that can be used to query whether more input
+    can be pushed into a single interactive block.
+
+    This is a simple example of how an interactive terminal-based client can use
+    this tool::
+
+        isp = InputSplitter()
+        while isp.push_accepts_more():
+            indent = ' '*isp.indent_spaces
+            prompt = '>>> ' + indent
+            line = indent + raw_input(prompt)
+            isp.push(line)
+        print 'Input source was:\n', isp.source_reset(),
+    """
+    # A cache for storing the current indentation
+    # The first value stores the most recently processed source input
+    # The second value is the number of spaces for the current indentation 
+    # If self.source matches the first value, the second value is a valid
+    # current indentation. Otherwise, the cache is invalid and the indentation
+    # must be recalculated.
+    _indent_spaces_cache: Union[Tuple[None, None], Tuple[str, int]] = None, None
+    # String, indicating the default input encoding.  It is computed by default
+    # at initialization time via get_input_encoding(), but it can be reset by a
+    # client with specific knowledge of the encoding.
+    encoding = ''
+    # String where the current full source input is stored, properly encoded.
+    # Reading this attribute is the normal way of querying the currently pushed
+    # source code, that has been properly encoded.
+    source: str = ""
+    # Code object corresponding to the current source.  It is automatically
+    # synced to the source, so it can be queried at any time to obtain the code
+    # object; it will be None if the source doesn't compile to valid Python.
+    code: Optional[CodeType] = None
+
+    # Private attributes
+
+    # List with lines of input accumulated so far
+    _buffer: List[str]
+    # Command compiler
+    _compile: codeop.CommandCompiler
+    # Boolean indicating whether the current block is complete
+    _is_complete: Optional[bool] = None
+    # Boolean indicating whether the current block has an unrecoverable syntax error
+    _is_invalid: bool = False
+
+    def __init__(self) -> None:
+        """Create a new InputSplitter instance."""
+        self._buffer = []
+        self._compile = codeop.CommandCompiler()
+        self.encoding = get_input_encoding()
+
+    def reset(self):
+        """Reset the input buffer and associated state."""
+        self._buffer[:] = []
+        self.source = ''
+        self.code = None
+        self._is_complete = False
+        self._is_invalid = False
+
+    def source_reset(self):
+        """Return the input source and perform a full reset.
+        """
+        out = self.source
+        self.reset()
+        return out
+
+    def check_complete(self, source):
+        """Return whether a block of code is ready to execute, or should be continued
+
+        This is a non-stateful API, and will reset the state of this InputSplitter.
+
+        Parameters
+        ----------
+        source : string
+            Python input code, which can be multiline.
+
+        Returns
+        -------
+        status : str
+            One of 'complete', 'incomplete', or 'invalid' if source is not a
+            prefix of valid code.
+        indent_spaces : int or None
+            The number of spaces by which to indent the next line of code. If
+            status is not 'incomplete', this is None.
+        """
+        self.reset()
+        try:
+            self.push(source)
+        except SyntaxError:
+            # Transformers in IPythonInputSplitter can raise SyntaxError,
+            # which push() will not catch.
+            return 'invalid', None
+        else:
+            if self._is_invalid:
+                return 'invalid', None
+            elif self.push_accepts_more():
+                return 'incomplete', self.get_indent_spaces()
+            else:
+                return 'complete', None
+        finally:
+            self.reset()
+
+    def push(self, lines:str) -> bool:
+        """Push one or more lines of input.
+
+        This stores the given lines and returns a status code indicating
+        whether the code forms a complete Python block or not.
+
+        Any exceptions generated in compilation are swallowed, but if an
+        exception was produced, the method returns True.
+
+        Parameters
+        ----------
+        lines : string
+            One or more lines of Python input.
+
+        Returns
+        -------
+        is_complete : boolean
+            True if the current input source (the result of the current input
+            plus prior inputs) forms a complete Python execution block.  Note that
+            this value is also stored as a private attribute (``_is_complete``), so it
+            can be queried at any time.
+        """
+        assert isinstance(lines, str)
+        self._store(lines)
+        source = self.source
+
+        # Before calling _compile(), reset the code object to None so that if an
+        # exception is raised in compilation, we don't mislead by having
+        # inconsistent code/source attributes.
+        self.code, self._is_complete = None, None
+        self._is_invalid = False
+
+        # Honor termination lines properly
+        if source.endswith('\\\n'):
+            return False
+
+        try:
+            with warnings.catch_warnings():
+                warnings.simplefilter('error', SyntaxWarning)
+                self.code = self._compile(source, symbol="exec")
+        # Invalid syntax can produce any of a number of different errors from
+        # inside the compiler, so we have to catch them all.  Syntax errors
+        # immediately produce a 'ready' block, so the invalid Python can be
+        # sent to the kernel for evaluation with possible ipython
+        # special-syntax conversion.
+        except (SyntaxError, OverflowError, ValueError, TypeError,
+                MemoryError, SyntaxWarning):
+            self._is_complete = True
+            self._is_invalid = True
+        else:
+            # Compilation didn't produce any exceptions (though it may not have
+            # given a complete code object)
+            self._is_complete = self.code is not None
+
+        return self._is_complete
+
+    def push_accepts_more(self):
+        """Return whether a block of interactive input can accept more input.
+
+        This method is meant to be used by line-oriented frontends, who need to
+        guess whether a block is complete or not based solely on prior and
+        current input lines.  The InputSplitter considers it has a complete
+        interactive block and will not accept more input when either:
+
+        * A SyntaxError is raised
+
+        * The code is complete and consists of a single line or a single
+          non-compound statement
+
+        * The code is complete and has a blank line at the end
+
+        If the current input produces a syntax error, this method immediately
+        returns False but does *not* raise the syntax error exception, as
+        typically clients will want to send invalid syntax to an execution
+        backend which might convert the invalid syntax into valid Python via
+        one of the dynamic IPython mechanisms.
+        """
+
+        # With incomplete input, unconditionally accept more
+        # A syntax error also sets _is_complete to True - see push()
+        if not self._is_complete:
+            #print("Not complete")  # debug
+            return True
+        
+        # The user can make any (complete) input execute by leaving a blank line
+        last_line = self.source.splitlines()[-1]
+        if (not last_line) or last_line.isspace():
+            #print("Blank line")  # debug
+            return False
+        
+        # If there's just a single line or AST node, and we're flush left, as is
+        # the case after a simple statement such as 'a=1', we want to execute it
+        # straight away.
+        if self.get_indent_spaces() == 0:
+            if len(self.source.splitlines()) <= 1:
+                return False
+            
+            try:
+                code_ast = ast.parse("".join(self._buffer))
+            except Exception:
+                #print("Can't parse AST")  # debug
+                return False
+            else:
+                if len(code_ast.body) == 1 and \
+                                    not hasattr(code_ast.body[0], 'body'):
+                    #print("Simple statement")  # debug
+                    return False
+
+        # General fallback - accept more code
+        return True
+
+    def get_indent_spaces(self) -> int:
+        sourcefor, n = self._indent_spaces_cache
+        if sourcefor == self.source:
+            assert n is not None
+            return n
+
+        # self.source always has a trailing newline
+        n = find_next_indent(self.source[:-1])
+        self._indent_spaces_cache = (self.source, n)
+        return n
+
+    # Backwards compatibility. I think all code that used .indent_spaces was
+    # inside IPython, but we can leave this here until IPython 7 in case any
+    # other modules are using it. -TK, November 2017
+    indent_spaces = property(get_indent_spaces)
+
+    def _store(self, lines, buffer=None, store='source'):
+        """Store one or more lines of input.
+
+        If input lines are not newline-terminated, a newline is automatically
+        appended."""
+
+        if buffer is None:
+            buffer = self._buffer
+
+        if lines.endswith('\n'):
+            buffer.append(lines)
+        else:
+            buffer.append(lines+'\n')
+        setattr(self, store, self._set_source(buffer))
+
+    def _set_source(self, buffer):
+        return u''.join(buffer)
+
+
+class IPythonInputSplitter(InputSplitter):
+    """An input splitter that recognizes all of IPython's special syntax."""
+
+    # String with raw, untransformed input.
+    source_raw = ''
+    
+    # Flag to track when a transformer has stored input that it hasn't given
+    # back yet.
+    transformer_accumulating = False
+    
+    # Flag to track when assemble_python_lines has stored input that it hasn't
+    # given back yet.
+    within_python_line = False
+
+    # Private attributes
+
+    # List with lines of raw input accumulated so far.
+    _buffer_raw: List[str]
+
+    def __init__(self, line_input_checker=True, physical_line_transforms=None,
+                    logical_line_transforms=None, python_line_transforms=None):
+        super(IPythonInputSplitter, self).__init__()
+        self._buffer_raw = []
+        self._validate = True
+        
+        if physical_line_transforms is not None:
+            self.physical_line_transforms = physical_line_transforms
+        else:
+            self.physical_line_transforms = [
+                                             leading_indent(),
+                                             classic_prompt(),
+                                             ipy_prompt(),
+                                             cellmagic(end_on_blank_line=line_input_checker),
+                                            ]
+        
+        self.assemble_logical_lines = assemble_logical_lines()
+        if logical_line_transforms is not None:
+            self.logical_line_transforms = logical_line_transforms
+        else:
+            self.logical_line_transforms = [
+                                            help_end(),
+                                            escaped_commands(),
+                                            assign_from_magic(),
+                                            assign_from_system(),
+                                           ]
+        
+        self.assemble_python_lines = assemble_python_lines()
+        if python_line_transforms is not None:
+            self.python_line_transforms = python_line_transforms
+        else:
+            # We don't use any of these at present
+            self.python_line_transforms = []
+    
+    @property
+    def transforms(self):
+        "Quick access to all transformers."
+        return self.physical_line_transforms + \
+            [self.assemble_logical_lines] + self.logical_line_transforms + \
+            [self.assemble_python_lines]  + self.python_line_transforms
+    
+    @property
+    def transforms_in_use(self):
+        """Transformers, excluding logical line transformers if we're in a
+        Python line."""
+        t = self.physical_line_transforms[:]
+        if not self.within_python_line:
+            t += [self.assemble_logical_lines] + self.logical_line_transforms
+        return t + [self.assemble_python_lines] + self.python_line_transforms
+
+    def reset(self):
+        """Reset the input buffer and associated state."""
+        super(IPythonInputSplitter, self).reset()
+        self._buffer_raw[:] = []
+        self.source_raw = ''
+        self.transformer_accumulating = False
+        self.within_python_line = False
+
+        for t in self.transforms:
+            try:
+                t.reset()
+            except SyntaxError:
+                # Nothing that calls reset() expects to handle transformer
+                # errors
+                pass
+    
+    def flush_transformers(self):
+        def _flush(transform, outs):
+            """yield transformed lines
+
+            always strings, never None
+
+            transform: the current transform
+            outs: an iterable of previously transformed inputs.
+                 Each may be multiline, which will be passed
+                 one line at a time to transform.
+            """
+            for out in outs:
+                for line in out.splitlines():
+                    # push one line at a time
+                    tmp = transform.push(line)
+                    if tmp is not None:
+                        yield tmp
+            
+            # reset the transform
+            tmp = transform.reset()
+            if tmp is not None:
+                yield tmp
+
+        out: List[str] = []
+        for t in self.transforms_in_use:
+            out = _flush(t, out)
+        
+        out = list(out)
+        if out:
+            self._store('\n'.join(out))
+
+    def raw_reset(self):
+        """Return raw input only and perform a full reset.
+        """
+        out = self.source_raw
+        self.reset()
+        return out
+    
+    def source_reset(self):
+        try:
+            self.flush_transformers()
+            return self.source
+        finally:
+            self.reset()
+
+    def push_accepts_more(self):
+        if self.transformer_accumulating:
+            return True
+        else:
+            return super(IPythonInputSplitter, self).push_accepts_more()
+
+    def transform_cell(self, cell):
+        """Process and translate a cell of input.
+        """
+        self.reset()
+        try:
+            self.push(cell)
+            self.flush_transformers()
+            return self.source
+        finally:
+            self.reset()
+
+    def push(self, lines:str) -> bool:
+        """Push one or more lines of IPython input.
+
+        This stores the given lines and returns a status code indicating
+        whether the code forms a complete Python block or not, after processing
+        all input lines for special IPython syntax.
+
+        Any exceptions generated in compilation are swallowed, but if an
+        exception was produced, the method returns True.
+
+        Parameters
+        ----------
+        lines : string
+            One or more lines of Python input.
+
+        Returns
+        -------
+        is_complete : boolean
+            True if the current input source (the result of the current input
+            plus prior inputs) forms a complete Python execution block.  Note that
+            this value is also stored as a private attribute (_is_complete), so it
+            can be queried at any time.
+        """
+        assert isinstance(lines, str)
+        # We must ensure all input is pure unicode
+        # ''.splitlines() --> [], but we need to push the empty line to transformers
+        lines_list = lines.splitlines()
+        if not lines_list:
+            lines_list = ['']
+
+        # Store raw source before applying any transformations to it.  Note
+        # that this must be done *after* the reset() call that would otherwise
+        # flush the buffer.
+        self._store(lines, self._buffer_raw, 'source_raw')
+
+        transformed_lines_list = []
+        for line in lines_list:
+            transformed = self._transform_line(line)
+            if transformed is not None:
+                transformed_lines_list.append(transformed)
+
+        if transformed_lines_list:
+            transformed_lines = '\n'.join(transformed_lines_list)
+            return super(IPythonInputSplitter, self).push(transformed_lines)
+        else:
+            # Got nothing back from transformers - they must be waiting for
+            # more input.
+            return False
+
+    def _transform_line(self, line):
+        """Push a line of input code through the various transformers.
+
+        Returns any output from the transformers, or None if a transformer
+        is accumulating lines.
+
+        Sets self.transformer_accumulating as a side effect.
+        """
+        def _accumulating(dbg):
+            #print(dbg)
+            self.transformer_accumulating = True
+            return None
+
+        for transformer in self.physical_line_transforms:
+            line = transformer.push(line)
+            if line is None:
+                return _accumulating(transformer)
+
+        if not self.within_python_line:
+            line = self.assemble_logical_lines.push(line)
+            if line is None:
+                return _accumulating('acc logical line')
+
+            for transformer in self.logical_line_transforms:
+                line = transformer.push(line)
+                if line is None:
+                    return _accumulating(transformer)
+
+        line = self.assemble_python_lines.push(line)
+        if line is None:
+            self.within_python_line = True
+            return _accumulating('acc python line')
+        else:
+            self.within_python_line = False
+
+        for transformer in self.python_line_transforms:
+            line = transformer.push(line)
+            if line is None:
+                return _accumulating(transformer)
+
+        #print("transformers clear") #debug
+        self.transformer_accumulating = False
+        return line
+
diff --git a/shared/package-files/40/40974c2d5bd03a23e6f1962be0e11257bd5de4083d41c2b574c52462fde9639a/test_sqfreetools.py b/shared/package-files/40/40974c2d5bd03a23e6f1962be0e11257bd5de4083d41c2b574c52462fde9639a/test_sqfreetools.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe229e713d06134638c54740a2f4da8d48de05b
--- /dev/null
+++ b/shared/package-files/40/40974c2d5bd03a23e6f1962be0e11257bd5de4083d41c2b574c52462fde9639a/test_sqfreetools.py
@@ -0,0 +1,149 @@
+"""Tests for square-free decomposition algorithms and related tools. """
+
+from sympy.polys.rings import ring
+from sympy.polys.domains import FF, ZZ, QQ
+from sympy.polys.specialpolys import f_polys
+
+from sympy.testing.pytest import raises
+
+f_0, f_1, f_2, f_3, f_4, f_5, f_6 = f_polys()
+
+def test_dup_sqf():
+    R, x = ring("x", ZZ)
+
+    assert R.dup_sqf_part(0) == 0
+    assert R.dup_sqf_p(0) is True
+
+    assert R.dup_sqf_part(7) == 1
+    assert R.dup_sqf_p(7) is True
+
+    assert R.dup_sqf_part(2*x + 2) == x + 1
+    assert R.dup_sqf_p(2*x + 2) is True
+
+    assert R.dup_sqf_part(x**3 + x + 1) == x**3 + x + 1
+    assert R.dup_sqf_p(x**3 + x + 1) is True
+
+    assert R.dup_sqf_part(-x**3 + x + 1) == x**3 - x - 1
+    assert R.dup_sqf_p(-x**3 + x + 1) is True
+
+    assert R.dup_sqf_part(2*x**3 + 3*x**2) == 2*x**2 + 3*x
+    assert R.dup_sqf_p(2*x**3 + 3*x**2) is False
+
+    assert R.dup_sqf_part(-2*x**3 + 3*x**2) == 2*x**2 - 3*x
+    assert R.dup_sqf_p(-2*x**3 + 3*x**2) is False
+
+    assert R.dup_sqf_list(0) == (0, [])
+    assert R.dup_sqf_list(1) == (1, [])
+
+    assert R.dup_sqf_list(x) == (1, [(x, 1)])
+    assert R.dup_sqf_list(2*x**2) == (2, [(x, 2)])
+    assert R.dup_sqf_list(3*x**3) == (3, [(x, 3)])
+
+    assert R.dup_sqf_list(-x**5 + x**4 + x - 1) == \
+        (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)])
+    assert R.dup_sqf_list(x**8 + 6*x**6 + 12*x**4 + 8*x**2) == \
+        ( 1, [(x, 2), (x**2 + 2, 3)])
+
+    assert R.dup_sqf_list(2*x**2 + 4*x + 2) == (2, [(x + 1, 2)])
+
+    R, x = ring("x", QQ)
+    assert R.dup_sqf_list(2*x**2 + 4*x + 2) == (2, [(x + 1, 2)])
+
+    R, x = ring("x", FF(2))
+    assert R.dup_sqf_list(x**2 + 1) == (1, [(x + 1, 2)])
+
+    R, x = ring("x", FF(3))
+    assert R.dup_sqf_list(x**10 + 2*x**7 + 2*x**4 + x) == \
+        (1, [(x, 1),
+             (x + 1, 3),
+             (x + 2, 6)])
+
+    R1, x = ring("x", ZZ)
+    R2, y = ring("y", FF(3))
+
+    f = x**3 + 1
+    g = y**3 + 1
+
+    assert R1.dup_sqf_part(f) == f
+    assert R2.dup_sqf_part(g) == y + 1
+
+    assert R1.dup_sqf_p(f) is True
+    assert R2.dup_sqf_p(g) is False
+
+    R, x, y = ring("x,y", ZZ)
+
+    A = x**4 - 3*x**2 + 6
+    D = x**6 - 5*x**4 + 5*x**2 + 4
+
+    f, g = D, R.dmp_sub(A, R.dmp_mul(R.dmp_diff(D, 1), y))
+    res = R.dmp_resultant(f, g)
+    h = (4*y**2 + 1).drop(x)
+
+    assert R.drop(x).dup_sqf_list(res) == (45796, [(h, 3)])
+
+    Rt, t = ring("t", ZZ)
+    R, x = ring("x", Rt)
+    assert R.dup_sqf_list_include(t**3*x**2) == [(t**3, 1), (x, 2)]
+
+
+def test_dmp_sqf():
+    R, x, y = ring("x,y", ZZ)
+    assert R.dmp_sqf_part(0) == 0
+    assert R.dmp_sqf_p(0) is True
+
+    assert R.dmp_sqf_part(7) == 1
+    assert R.dmp_sqf_p(7) is True
+
+    assert R.dmp_sqf_list(3) == (3, [])
+    assert R.dmp_sqf_list_include(3) == [(3, 1)]
+
+    R, x, y, z = ring("x,y,z", ZZ)
+    assert R.dmp_sqf_p(f_0) is True
+    assert R.dmp_sqf_p(f_0**2) is False
+    assert R.dmp_sqf_p(f_1) is True
+    assert R.dmp_sqf_p(f_1**2) is False
+    assert R.dmp_sqf_p(f_2) is True
+    assert R.dmp_sqf_p(f_2**2) is False
+    assert R.dmp_sqf_p(f_3) is True
+    assert R.dmp_sqf_p(f_3**2) is False
+    assert R.dmp_sqf_p(f_5) is False
+    assert R.dmp_sqf_p(f_5**2) is False
+
+    assert R.dmp_sqf_p(f_4) is True
+    assert R.dmp_sqf_part(f_4) == -f_4
+
+    assert R.dmp_sqf_part(f_5) == x + y - z
+
+    R, x, y, z, t = ring("x,y,z,t", ZZ)
+    assert R.dmp_sqf_p(f_6) is True
+    assert R.dmp_sqf_part(f_6) == f_6
+
+    R, x = ring("x", ZZ)
+    f = -x**5 + x**4 + x - 1
+
+    assert R.dmp_sqf_list(f) == (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)])
+    assert R.dmp_sqf_list_include(f) == [(-x**3 - x**2 - x - 1, 1), (x - 1, 2)]
+
+    R, x, y = ring("x,y", ZZ)
+    f = -x**5 + x**4 + x - 1
+
+    assert R.dmp_sqf_list(f) == (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)])
+    assert R.dmp_sqf_list_include(f) == [(-x**3 - x**2 - x - 1, 1), (x - 1, 2)]
+
+    f = -x**2 + 2*x - 1
+    assert R.dmp_sqf_list_include(f) == [(-1, 1), (x - 1, 2)]
+
+    R, x, y = ring("x,y", FF(2))
+    raises(NotImplementedError, lambda: R.dmp_sqf_list(y**2 + 1))
+
+
+def test_dup_gff_list():
+    R, x = ring("x", ZZ)
+
+    f = x**5 + 2*x**4 - x**3 - 2*x**2
+    assert R.dup_gff_list(f) == [(x, 1), (x + 2, 4)]
+
+    g = x**9 - 20*x**8 + 166*x**7 - 744*x**6 + 1965*x**5 - 3132*x**4 + 2948*x**3 - 1504*x**2 + 320*x
+    assert R.dup_gff_list(g) == [(x**2 - 5*x + 4, 1), (x**2 - 5*x + 4, 2), (x, 3)]
+
+    raises(ValueError, lambda: R.dup_gff_list(0))
diff --git a/shared/package-files/40/40975aa3b175bf4318b0f3ac0d5b629860851523781e2b1c2b526ad732b5a83b/linear.py b/shared/package-files/40/40975aa3b175bf4318b0f3ac0d5b629860851523781e2b1c2b526ad732b5a83b/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6c720e9b6094f0f44ef74d3f50f5c282f1738c7
--- /dev/null
+++ b/shared/package-files/40/40975aa3b175bf4318b0f3ac0d5b629860851523781e2b1c2b526ad732b5a83b/linear.py
@@ -0,0 +1,303 @@
+from collections.abc import Iterable
+import torch
+
+import torch.nn as nn
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from typing import Optional
+
+from .utils import _quantize_weight, _hide_packed_params_repr, WeightedQuantizedModule
+
+__all__ = ['LinearPackedParams', 'Linear']
+
+
+class LinearPackedParams(torch.nn.Module):
+    _version = 3
+
+    def __init__(self, dtype=torch.qint8):
+        super().__init__()
+        self.dtype = dtype
+        if self.dtype == torch.qint8:
+            wq = torch._empty_affine_quantized([1, 1], scale=1.0, zero_point=0, dtype=torch.qint8)
+        elif self.dtype == torch.float16:
+            wq = torch.zeros([1, 1], dtype=torch.float)
+        self.set_weight_bias(wq, None)  # type: ignore[possibly-undefined]
+
+    @torch.jit.export
+    def set_weight_bias(self, weight: torch.Tensor, bias: Optional[torch.Tensor]) -> None:
+        if self.dtype == torch.qint8:
+            self._packed_params = torch.ops.quantized.linear_prepack(weight, bias)
+        elif self.dtype == torch.float16:
+            self._packed_params = torch.ops.quantized.linear_prepack_fp16(weight, bias)
+        else:
+            raise RuntimeError('Unsupported dtype on dynamic quantized linear!')
+
+
+    @torch.jit.export
+    def _weight_bias(self):
+        if self.dtype == torch.qint8:
+            return torch.ops.quantized.linear_unpack(self._packed_params)
+        elif self.dtype == torch.float16:
+            return torch.ops.quantized.linear_unpack_fp16(self._packed_params)
+        else:
+            raise RuntimeError('Unsupported dtype on dynamic quantized linear!')
+
+    def forward(self, x):
+        return x
+
+    # Version 1
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- dtype : torch.dtype
+    #
+    # Version 3
+    #   self
+    #   |--- _packed_params : (Tensor, Tensor) representing (weight, bias)
+    #                         of LinearPackedParams
+    #   |--- dtype : torch.dtype
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + 'dtype'] = self.dtype
+        destination[prefix + '_packed_params'] = self._weight_bias()
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version < 2:
+            self.dtype = torch.qint8
+        else:
+            self.dtype = state_dict[prefix + 'dtype']
+            state_dict.pop(prefix + 'dtype')
+
+        if version is None or version < 3:
+            self.set_weight_bias(state_dict[prefix + 'weight'], state_dict[prefix + 'bias'])
+            state_dict.pop(prefix + 'weight')
+            state_dict.pop(prefix + 'bias')
+
+        if version == 3:
+            weight, bias = state_dict[prefix + '_packed_params']
+            state_dict.pop(prefix + '_packed_params')
+            self.set_weight_bias(weight, bias)
+
+        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
+                                      missing_keys, unexpected_keys, error_msgs)
+
+
+    def __repr__(self):
+        return self._weight_bias().__repr__()
+
+
+class Linear(WeightedQuantizedModule):
+    r"""
+    A quantized linear module with quantized tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
+
+    Similar to :class:`~torch.nn.Linear`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{out\_features}, \text{in\_features})`.
+        bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
+                If :attr:`bias` is ``True``, the values are initialized to zero.
+        scale: `scale` parameter of output Quantized Tensor, type: double
+        zero_point: `zero_point` parameter for output Quantized Tensor, type: long
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> m = nn.quantized.Linear(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> # xdoctest: +SKIP
+        >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _version = 3
+    _FLOAT_MODULE = (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear)
+
+    def __init__(self, in_features, out_features, bias_=True,
+                 dtype=torch.qint8):
+        super().__init__()
+        # We don't muck around with buffers or attributes or anything here
+        # to keep the module simple. *everything* is simply a Python attribute.
+        # Serialization logic is explicitly handled in the below serialization and
+        # deserialization modules
+        self.in_features = in_features
+        self.out_features = out_features
+        bias = None
+        if bias_:
+            bias = torch.zeros(out_features, dtype=torch.float)
+
+        if dtype == torch.qint8:
+            qweight = torch._empty_affine_quantized(
+                [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8)
+        elif dtype == torch.float16:
+            qweight = torch.zeros([out_features, in_features], dtype=torch.float)
+        else:
+            raise RuntimeError('Unsupported dtype specified for quantized Linear!')
+
+        self._packed_params = LinearPackedParams(dtype)
+        self._packed_params.set_weight_bias(qweight, bias)
+        self.scale = 1.0
+        self.zero_point = 0
+
+    def _get_name(self):
+        return 'QuantizedLinear'
+
+    def extra_repr(self):
+        return 'in_features={}, out_features={}, scale={}, zero_point={}, qscheme={}'.format(
+            self.in_features, self.out_features, self.scale, self.zero_point, self.weight().qscheme()
+        )
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear(
+            x, self._packed_params._packed_params, self.scale, self.zero_point)
+
+    # ===== Serialization methods =====
+    # The special consideration here is that we have to unpack the weights into their
+    # regular QTensor form for serialization. Packed weights should not live
+    # outside the process in which they were created, rather they should be derived
+    # from the QTensor weight.
+    #
+    # Version 1
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- weight : Tensor
+    #        |--- bias : Tensor
+    #
+    # Version 3
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- _packed_params : (Tensor, Tensor) representing weight, bias
+    #                              of LinearPackedParams C++ struct
+    #
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + 'scale'] = torch.tensor(self.scale)
+        destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)
+
+    # ===== Deserialization methods =====
+    # Counterpart to the serialization methods, we must pack the serialized QTensor
+    # weight into its packed format for use by the FBGEMM ops.
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        self.scale = float(state_dict[prefix + 'scale'])
+        state_dict.pop(prefix + 'scale')
+
+        self.zero_point = int(state_dict[prefix + 'zero_point'])
+        state_dict.pop(prefix + 'zero_point')
+
+        version = local_metadata.get('version', None)
+
+        if version is None or version == 1:
+            # We moved the parameters into a LinearPackedParameters submodule
+            weight = state_dict.pop(prefix + 'weight')
+            bias = state_dict.pop(prefix + 'bias')
+            state_dict.update({prefix + '_packed_params.weight': weight,
+                               prefix + '_packed_params.bias': bias})
+
+        super()._load_from_state_dict(
+            state_dict, prefix, local_metadata, False,
+            missing_keys, unexpected_keys, error_msgs)
+
+    # Function rather than property to make sure that JIT serialization doesn't
+    # register this as an attribute
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params.set_weight_bias(w, b)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a quantized module from an observed float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+        """
+        if hasattr(mod, 'weight_fake_quant'):
+            if type_before_parametrizations(mod) == nniqat.LinearBn1d:
+                mod.weight, mod.bias = fuse_linear_bn_weights(
+                    mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
+                    mod.bn.eps, mod.bn.weight, mod.bn.bias)
+            weight_post_process = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            # This function does not participate in JIT, so it is OK to ignore
+            # the type mismatch in assignment. Also, mypy has an issue with
+            # iterables not being implemented, so we are ignoring those too.
+            if not isinstance(cls._FLOAT_MODULE, Iterable):
+                cls._FLOAT_MODULE = [cls._FLOAT_MODULE]  # type: ignore[assignment]
+            supported_modules = ', '.join([float_mod.__name__ for float_mod in cls._FLOAT_MODULE])  # type: ignore[attr-defined]
+            error_msg = f'nnq.{cls.__name__}.from_float only works for {supported_modules}, but got: {type(mod)}'
+            assert type_before_parametrizations(mod) in cls._FLOAT_MODULE, error_msg.format()  # type: ignore[attr-defined]
+            assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+            activation_post_process = mod.activation_post_process
+            if type_before_parametrizations(mod) == nni.LinearReLU:
+                mod = mod[0]
+            weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()
+        assert dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear = cls(mod.in_features,
+                      mod.out_features,
+                      dtype=dtype)
+        qlinear.set_weight_bias(qweight, mod.bias)
+        qlinear.scale = float(act_scale)
+        qlinear.zero_point = int(act_zp)
+        return qlinear
+
+    @classmethod
+    def from_reference(cls, ref_qlinear, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+
+        Args:
+            ref_qlinear (Module): a reference quantized linear module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qlinear = cls(
+            ref_qlinear.in_features,
+            ref_qlinear.out_features)
+        qweight = ref_qlinear.get_quantized_weight()
+        qlinear.set_weight_bias(qweight, ref_qlinear.bias)
+
+        qlinear.scale = float(output_scale)
+        qlinear.zero_point = int(output_zero_point)
+        return qlinear
diff --git a/shared/package-files/40/409b4cdeee6a0180baf9f6921c6c359f4fd026141708690e227622390ad265a0/matmul_compositeimplicitautograd_dispatch.h b/shared/package-files/40/409b4cdeee6a0180baf9f6921c6c359f4fd026141708690e227622390ad265a0/matmul_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ed9a0f0c5deee9f6990b053c7610f6e2562dde2
--- /dev/null
+++ b/shared/package-files/40/409b4cdeee6a0180baf9f6921c6c359f4fd026141708690e227622390ad265a0/matmul_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor matmul(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & matmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & matmul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/40/409d2e9d60ff2cea00180a38adad08bd87a78f9e88614b62cf57c424499363d4/test_mem_overlap.py b/shared/package-files/40/409d2e9d60ff2cea00180a38adad08bd87a78f9e88614b62cf57c424499363d4/test_mem_overlap.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fd4c4d412078108f26dc1803b025d37da3329ac
--- /dev/null
+++ b/shared/package-files/40/409d2e9d60ff2cea00180a38adad08bd87a78f9e88614b62cf57c424499363d4/test_mem_overlap.py
@@ -0,0 +1,931 @@
+import itertools
+import pytest
+
+import numpy as np
+from numpy.core._multiarray_tests import solve_diophantine, internal_overlap
+from numpy.core import _umath_tests
+from numpy.lib.stride_tricks import as_strided
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_array_equal
+    )
+
+
+ndims = 2
+size = 10
+shape = tuple([size] * ndims)
+
+MAY_SHARE_BOUNDS = 0
+MAY_SHARE_EXACT = -1
+
+
+def _indices_for_nelems(nelems):
+    """Returns slices of length nelems, from start onwards, in direction sign."""
+
+    if nelems == 0:
+        return [size // 2]  # int index
+
+    res = []
+    for step in (1, 2):
+        for sign in (-1, 1):
+            start = size // 2 - nelems * step * sign // 2
+            stop = start + nelems * step * sign
+            res.append(slice(start, stop, step * sign))
+
+    return res
+
+
+def _indices_for_axis():
+    """Returns (src, dst) pairs of indices."""
+
+    res = []
+    for nelems in (0, 2, 3):
+        ind = _indices_for_nelems(nelems)
+        res.extend(itertools.product(ind, ind))  # all assignments of size "nelems"
+
+    return res
+
+
+def _indices(ndims):
+    """Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs."""
+
+    ind = _indices_for_axis()
+    return itertools.product(ind, repeat=ndims)
+
+
+def _check_assignment(srcidx, dstidx):
+    """Check assignment arr[dstidx] = arr[srcidx] works."""
+
+    arr = np.arange(np.prod(shape)).reshape(shape)
+
+    cpy = arr.copy()
+
+    cpy[dstidx] = arr[srcidx]
+    arr[dstidx] = arr[srcidx]
+
+    assert_(np.all(arr == cpy),
+            'assigning arr[%s] = arr[%s]' % (dstidx, srcidx))
+
+
+def test_overlapping_assignments():
+    # Test automatically generated assignments which overlap in memory.
+
+    inds = _indices(ndims)
+
+    for ind in inds:
+        srcidx = tuple([a[0] for a in ind])
+        dstidx = tuple([a[1] for a in ind])
+
+        _check_assignment(srcidx, dstidx)
+
+
+@pytest.mark.slow
+def test_diophantine_fuzz():
+    # Fuzz test the diophantine solver
+    rng = np.random.RandomState(1234)
+
+    max_int = np.iinfo(np.intp).max
+
+    for ndim in range(10):
+        feasible_count = 0
+        infeasible_count = 0
+
+        min_count = 500//(ndim + 1)
+
+        while min(feasible_count, infeasible_count) < min_count:
+            # Ensure big and small integer problems
+            A_max = 1 + rng.randint(0, 11, dtype=np.intp)**6
+            U_max = rng.randint(0, 11, dtype=np.intp)**6
+
+            A_max = min(max_int, A_max)
+            U_max = min(max_int-1, U_max)
+
+            A = tuple(int(rng.randint(1, A_max+1, dtype=np.intp))
+                      for j in range(ndim))
+            U = tuple(int(rng.randint(0, U_max+2, dtype=np.intp))
+                      for j in range(ndim))
+
+            b_ub = min(max_int-2, sum(a*ub for a, ub in zip(A, U)))
+            b = int(rng.randint(-1, b_ub+2, dtype=np.intp))
+
+            if ndim == 0 and feasible_count < min_count:
+                b = 0
+
+            X = solve_diophantine(A, U, b)
+
+            if X is None:
+                # Check the simplified decision problem agrees
+                X_simplified = solve_diophantine(A, U, b, simplify=1)
+                assert_(X_simplified is None, (A, U, b, X_simplified))
+
+                # Check no solution exists (provided the problem is
+                # small enough so that brute force checking doesn't
+                # take too long)
+                ranges = tuple(range(0, a*ub+1, a) for a, ub in zip(A, U))
+
+                size = 1
+                for r in ranges:
+                    size *= len(r)
+                if size < 100000:
+                    assert_(not any(sum(w) == b for w in itertools.product(*ranges)))
+                    infeasible_count += 1
+            else:
+                # Check the simplified decision problem agrees
+                X_simplified = solve_diophantine(A, U, b, simplify=1)
+                assert_(X_simplified is not None, (A, U, b, X_simplified))
+
+                # Check validity
+                assert_(sum(a*x for a, x in zip(A, X)) == b)
+                assert_(all(0 <= x <= ub for x, ub in zip(X, U)))
+                feasible_count += 1
+
+
+def test_diophantine_overflow():
+    # Smoke test integer overflow detection
+    max_intp = np.iinfo(np.intp).max
+    max_int64 = np.iinfo(np.int64).max
+
+    if max_int64 <= max_intp:
+        # Check that the algorithm works internally in 128-bit;
+        # solving this problem requires large intermediate numbers
+        A = (max_int64//2, max_int64//2 - 10)
+        U = (max_int64//2, max_int64//2 - 10)
+        b = 2*(max_int64//2) - 10
+
+        assert_equal(solve_diophantine(A, U, b), (1, 1))
+
+
+def check_may_share_memory_exact(a, b):
+    got = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
+
+    assert_equal(np.may_share_memory(a, b),
+                 np.may_share_memory(a, b, max_work=MAY_SHARE_BOUNDS))
+
+    a.fill(0)
+    b.fill(0)
+    a.fill(1)
+    exact = b.any()
+
+    err_msg = ""
+    if got != exact:
+        err_msg = "    " + "\n    ".join([
+            "base_a - base_b = %r" % (a.__array_interface__['data'][0] - b.__array_interface__['data'][0],),
+            "shape_a = %r" % (a.shape,),
+            "shape_b = %r" % (b.shape,),
+            "strides_a = %r" % (a.strides,),
+            "strides_b = %r" % (b.strides,),
+            "size_a = %r" % (a.size,),
+            "size_b = %r" % (b.size,)
+        ])
+
+    assert_equal(got, exact, err_msg=err_msg)
+
+
+def test_may_share_memory_manual():
+    # Manual test cases for may_share_memory
+
+    # Base arrays
+    xs0 = [
+        np.zeros([13, 21, 23, 22], dtype=np.int8),
+        np.zeros([13, 21, 23*2, 22], dtype=np.int8)[:,:,::2,:]
+    ]
+
+    # Generate all negative stride combinations
+    xs = []
+    for x in xs0:
+        for ss in itertools.product(*(([slice(None), slice(None, None, -1)],)*4)):
+            xp = x[ss]
+            xs.append(xp)
+
+    for x in xs:
+        # The default is a simple extent check
+        assert_(np.may_share_memory(x[:,0,:], x[:,1,:]))
+        assert_(np.may_share_memory(x[:,0,:], x[:,1,:], max_work=None))
+
+        # Exact checks
+        check_may_share_memory_exact(x[:,0,:], x[:,1,:])
+        check_may_share_memory_exact(x[:,::7], x[:,3::3])
+
+        try:
+            xp = x.ravel()
+            if xp.flags.owndata:
+                continue
+            xp = xp.view(np.int16)
+        except ValueError:
+            continue
+
+        # 0-size arrays cannot overlap
+        check_may_share_memory_exact(x.ravel()[6:6],
+                                     xp.reshape(13, 21, 23, 11)[:,::7])
+
+        # Test itemsize is dealt with
+        check_may_share_memory_exact(x[:,::7],
+                                     xp.reshape(13, 21, 23, 11))
+        check_may_share_memory_exact(x[:,::7],
+                                     xp.reshape(13, 21, 23, 11)[:,3::3])
+        check_may_share_memory_exact(x.ravel()[6:7],
+                                     xp.reshape(13, 21, 23, 11)[:,::7])
+
+    # Check unit size
+    x = np.zeros([1], dtype=np.int8)
+    check_may_share_memory_exact(x, x)
+    check_may_share_memory_exact(x, x.copy())
+
+
+def iter_random_view_pairs(x, same_steps=True, equal_size=False):
+    rng = np.random.RandomState(1234)
+
+    if equal_size and same_steps:
+        raise ValueError()
+
+    def random_slice(n, step):
+        start = rng.randint(0, n+1, dtype=np.intp)
+        stop = rng.randint(start, n+1, dtype=np.intp)
+        if rng.randint(0, 2, dtype=np.intp) == 0:
+            stop, start = start, stop
+            step *= -1
+        return slice(start, stop, step)
+
+    def random_slice_fixed_size(n, step, size):
+        start = rng.randint(0, n+1 - size*step)
+        stop = start + (size-1)*step + 1
+        if rng.randint(0, 2) == 0:
+            stop, start = start-1, stop-1
+            if stop < 0:
+                stop = None
+            step *= -1
+        return slice(start, stop, step)
+
+    # First a few regular views
+    yield x, x
+    for j in range(1, 7, 3):
+        yield x[j:], x[:-j]
+        yield x[...,j:], x[...,:-j]
+
+    # An array with zero stride internal overlap
+    strides = list(x.strides)
+    strides[0] = 0
+    xp = as_strided(x, shape=x.shape, strides=strides)
+    yield x, xp
+    yield xp, xp
+
+    # An array with non-zero stride internal overlap
+    strides = list(x.strides)
+    if strides[0] > 1:
+        strides[0] = 1
+    xp = as_strided(x, shape=x.shape, strides=strides)
+    yield x, xp
+    yield xp, xp
+
+    # Then discontiguous views
+    while True:
+        steps = tuple(rng.randint(1, 11, dtype=np.intp)
+                      if rng.randint(0, 5, dtype=np.intp) == 0 else 1
+                      for j in range(x.ndim))
+        s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
+
+        t1 = np.arange(x.ndim)
+        rng.shuffle(t1)
+
+        if equal_size:
+            t2 = t1
+        else:
+            t2 = np.arange(x.ndim)
+            rng.shuffle(t2)
+
+        a = x[s1]
+
+        if equal_size:
+            if a.size == 0:
+                continue
+
+            steps2 = tuple(rng.randint(1, max(2, p//(1+pa)))
+                           if rng.randint(0, 5) == 0 else 1
+                           for p, s, pa in zip(x.shape, s1, a.shape))
+            s2 = tuple(random_slice_fixed_size(p, s, pa)
+                       for p, s, pa in zip(x.shape, steps2, a.shape))
+        elif same_steps:
+            steps2 = steps
+        else:
+            steps2 = tuple(rng.randint(1, 11, dtype=np.intp)
+                           if rng.randint(0, 5, dtype=np.intp) == 0 else 1
+                           for j in range(x.ndim))
+
+        if not equal_size:
+            s2 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps2))
+
+        a = a.transpose(t1)
+        b = x[s2].transpose(t2)
+
+        yield a, b
+
+
+def check_may_share_memory_easy_fuzz(get_max_work, same_steps, min_count):
+    # Check that overlap problems with common strides are solved with
+    # little work.
+    x = np.zeros([17,34,71,97], dtype=np.int16)
+
+    feasible = 0
+    infeasible = 0
+
+    pair_iter = iter_random_view_pairs(x, same_steps)
+
+    while min(feasible, infeasible) < min_count:
+        a, b = next(pair_iter)
+
+        bounds_overlap = np.may_share_memory(a, b)
+        may_share_answer = np.may_share_memory(a, b)
+        easy_answer = np.may_share_memory(a, b, max_work=get_max_work(a, b))
+        exact_answer = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
+
+        if easy_answer != exact_answer:
+            # assert_equal is slow...
+            assert_equal(easy_answer, exact_answer)
+
+        if may_share_answer != bounds_overlap:
+            assert_equal(may_share_answer, bounds_overlap)
+
+        if bounds_overlap:
+            if exact_answer:
+                feasible += 1
+            else:
+                infeasible += 1
+
+
+@pytest.mark.slow
+def test_may_share_memory_easy_fuzz():
+    # Check that overlap problems with common strides are always
+    # solved with little work.
+
+    check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: 1,
+                                     same_steps=True,
+                                     min_count=2000)
+
+
+@pytest.mark.slow
+def test_may_share_memory_harder_fuzz():
+    # Overlap problems with not necessarily common strides take more
+    # work.
+    #
+    # The work bound below can't be reduced much. Harder problems can
+    # also exist but not be detected here, as the set of problems
+    # comes from RNG.
+
+    check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: max(a.size, b.size)//2,
+                                     same_steps=False,
+                                     min_count=2000)
+
+
+def test_shares_memory_api():
+    x = np.zeros([4, 5, 6], dtype=np.int8)
+
+    assert_equal(np.shares_memory(x, x), True)
+    assert_equal(np.shares_memory(x, x.copy()), False)
+
+    a = x[:,::2,::3]
+    b = x[:,::3,::2]
+    assert_equal(np.shares_memory(a, b), True)
+    assert_equal(np.shares_memory(a, b, max_work=None), True)
+    assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=1)
+
+
+def test_may_share_memory_bad_max_work():
+    x = np.zeros([1])
+    assert_raises(OverflowError, np.may_share_memory, x, x, max_work=10**100)
+    assert_raises(OverflowError, np.shares_memory, x, x, max_work=10**100)
+
+
+def test_internal_overlap_diophantine():
+    def check(A, U, exists=None):
+        X = solve_diophantine(A, U, 0, require_ub_nontrivial=1)
+
+        if exists is None:
+            exists = (X is not None)
+
+        if X is not None:
+            assert_(sum(a*x for a, x in zip(A, X)) == sum(a*u//2 for a, u in zip(A, U)))
+            assert_(all(0 <= x <= u for x, u in zip(X, U)))
+            assert_(any(x != u//2 for x, u in zip(X, U)))
+
+        if exists:
+            assert_(X is not None, repr(X))
+        else:
+            assert_(X is None, repr(X))
+
+    # Smoke tests
+    check((3, 2), (2*2, 3*2), exists=True)
+    check((3*2, 2), (15*2, (3-1)*2), exists=False)
+
+
+def test_internal_overlap_slices():
+    # Slicing an array never generates internal overlap
+
+    x = np.zeros([17,34,71,97], dtype=np.int16)
+
+    rng = np.random.RandomState(1234)
+
+    def random_slice(n, step):
+        start = rng.randint(0, n+1, dtype=np.intp)
+        stop = rng.randint(start, n+1, dtype=np.intp)
+        if rng.randint(0, 2, dtype=np.intp) == 0:
+            stop, start = start, stop
+            step *= -1
+        return slice(start, stop, step)
+
+    cases = 0
+    min_count = 5000
+
+    while cases < min_count:
+        steps = tuple(rng.randint(1, 11, dtype=np.intp)
+                      if rng.randint(0, 5, dtype=np.intp) == 0 else 1
+                      for j in range(x.ndim))
+        t1 = np.arange(x.ndim)
+        rng.shuffle(t1)
+        s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
+        a = x[s1].transpose(t1)
+
+        assert_(not internal_overlap(a))
+        cases += 1
+
+
+def check_internal_overlap(a, manual_expected=None):
+    got = internal_overlap(a)
+
+    # Brute-force check
+    m = set()
+    ranges = tuple(range(n) for n in a.shape)
+    for v in itertools.product(*ranges):
+        offset = sum(s*w for s, w in zip(a.strides, v))
+        if offset in m:
+            expected = True
+            break
+        else:
+            m.add(offset)
+    else:
+        expected = False
+
+    # Compare
+    if got != expected:
+        assert_equal(got, expected, err_msg=repr((a.strides, a.shape)))
+    if manual_expected is not None and expected != manual_expected:
+        assert_equal(expected, manual_expected)
+    return got
+
+
+def test_internal_overlap_manual():
+    # Stride tricks can construct arrays with internal overlap
+
+    # We don't care about memory bounds, the array is not
+    # read/write accessed
+    x = np.arange(1).astype(np.int8)
+
+    # Check low-dimensional special cases
+
+    check_internal_overlap(x, False) # 1-dim
+    check_internal_overlap(x.reshape([]), False) # 0-dim
+
+    a = as_strided(x, strides=(3, 4), shape=(4, 4))
+    check_internal_overlap(a, False)
+
+    a = as_strided(x, strides=(3, 4), shape=(5, 4))
+    check_internal_overlap(a, True)
+
+    a = as_strided(x, strides=(0,), shape=(0,))
+    check_internal_overlap(a, False)
+
+    a = as_strided(x, strides=(0,), shape=(1,))
+    check_internal_overlap(a, False)
+
+    a = as_strided(x, strides=(0,), shape=(2,))
+    check_internal_overlap(a, True)
+
+    a = as_strided(x, strides=(0, -9993), shape=(87, 22))
+    check_internal_overlap(a, True)
+
+    a = as_strided(x, strides=(0, -9993), shape=(1, 22))
+    check_internal_overlap(a, False)
+
+    a = as_strided(x, strides=(0, -9993), shape=(0, 22))
+    check_internal_overlap(a, False)
+
+
+def test_internal_overlap_fuzz():
+    # Fuzz check; the brute-force check is fairly slow
+
+    x = np.arange(1).astype(np.int8)
+
+    overlap = 0
+    no_overlap = 0
+    min_count = 100
+
+    rng = np.random.RandomState(1234)
+
+    while min(overlap, no_overlap) < min_count:
+        ndim = rng.randint(1, 4, dtype=np.intp)
+
+        strides = tuple(rng.randint(-1000, 1000, dtype=np.intp)
+                        for j in range(ndim))
+        shape = tuple(rng.randint(1, 30, dtype=np.intp)
+                      for j in range(ndim))
+
+        a = as_strided(x, strides=strides, shape=shape)
+        result = check_internal_overlap(a)
+
+        if result:
+            overlap += 1
+        else:
+            no_overlap += 1
+
+
+def test_non_ndarray_inputs():
+    # Regression check for gh-5604
+
+    class MyArray:
+        def __init__(self, data):
+            self.data = data
+
+        @property
+        def __array_interface__(self):
+            return self.data.__array_interface__
+
+    class MyArray2:
+        def __init__(self, data):
+            self.data = data
+
+        def __array__(self):
+            return self.data
+
+    for cls in [MyArray, MyArray2]:
+        x = np.arange(5)
+
+        assert_(np.may_share_memory(cls(x[::2]), x[1::2]))
+        assert_(not np.shares_memory(cls(x[::2]), x[1::2]))
+
+        assert_(np.shares_memory(cls(x[1::3]), x[::2]))
+        assert_(np.may_share_memory(cls(x[1::3]), x[::2]))
+
+
+def view_element_first_byte(x):
+    """Construct an array viewing the first byte of each element of `x`"""
+    from numpy.lib.stride_tricks import DummyArray
+    interface = dict(x.__array_interface__)
+    interface['typestr'] = '|b1'
+    interface['descr'] = [('', '|b1')]
+    return np.asarray(DummyArray(interface, x))
+
+
+def assert_copy_equivalent(operation, args, out, **kwargs):
+    """
+    Check that operation(*args, out=out) produces results
+    equivalent to out[...] = operation(*args, out=out.copy())
+    """
+
+    kwargs['out'] = out
+    kwargs2 = dict(kwargs)
+    kwargs2['out'] = out.copy()
+
+    out_orig = out.copy()
+    out[...] = operation(*args, **kwargs2)
+    expected = out.copy()
+    out[...] = out_orig
+
+    got = operation(*args, **kwargs).copy()
+
+    if (got != expected).any():
+        assert_equal(got, expected)
+
+
+class TestUFunc:
+    """
+    Test ufunc call memory overlap handling
+    """
+
+    def check_unary_fuzz(self, operation, get_out_axis_size, dtype=np.int16,
+                             count=5000):
+        shapes = [7, 13, 8, 21, 29, 32]
+
+        rng = np.random.RandomState(1234)
+
+        for ndim in range(1, 6):
+            x = rng.randint(0, 2**16, size=shapes[:ndim]).astype(dtype)
+
+            it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
+
+            min_count = count // (ndim + 1)**2
+
+            overlapping = 0
+            while overlapping < min_count:
+                a, b = next(it)
+
+                a_orig = a.copy()
+                b_orig = b.copy()
+
+                if get_out_axis_size is None:
+                    assert_copy_equivalent(operation, [a], out=b)
+
+                    if np.shares_memory(a, b):
+                        overlapping += 1
+                else:
+                    for axis in itertools.chain(range(ndim), [None]):
+                        a[...] = a_orig
+                        b[...] = b_orig
+
+                        # Determine size for reduction axis (None if scalar)
+                        outsize, scalarize = get_out_axis_size(a, b, axis)
+                        if outsize == 'skip':
+                            continue
+
+                        # Slice b to get an output array of the correct size
+                        sl = [slice(None)] * ndim
+                        if axis is None:
+                            if outsize is None:
+                                sl = [slice(0, 1)] + [0]*(ndim - 1)
+                            else:
+                                sl = [slice(0, outsize)] + [0]*(ndim - 1)
+                        else:
+                            if outsize is None:
+                                k = b.shape[axis]//2
+                                if ndim == 1:
+                                    sl[axis] = slice(k, k + 1)
+                                else:
+                                    sl[axis] = k
+                            else:
+                                assert b.shape[axis] >= outsize
+                                sl[axis] = slice(0, outsize)
+                        b_out = b[tuple(sl)]
+
+                        if scalarize:
+                            b_out = b_out.reshape([])
+
+                        if np.shares_memory(a, b_out):
+                            overlapping += 1
+
+                        # Check result
+                        assert_copy_equivalent(operation, [a], out=b_out, axis=axis)
+
+    @pytest.mark.slow
+    def test_unary_ufunc_call_fuzz(self):
+        self.check_unary_fuzz(np.invert, None, np.int16)
+
+    @pytest.mark.slow
+    def test_unary_ufunc_call_complex_fuzz(self):
+        # Complex typically has a smaller alignment than itemsize
+        self.check_unary_fuzz(np.negative, None, np.complex128, count=500)
+
+    def test_binary_ufunc_accumulate_fuzz(self):
+        def get_out_axis_size(a, b, axis):
+            if axis is None:
+                if a.ndim == 1:
+                    return a.size, False
+                else:
+                    return 'skip', False  # accumulate doesn't support this
+            else:
+                return a.shape[axis], False
+
+        self.check_unary_fuzz(np.add.accumulate, get_out_axis_size,
+                              dtype=np.int16, count=500)
+
+    def test_binary_ufunc_reduce_fuzz(self):
+        def get_out_axis_size(a, b, axis):
+            return None, (axis is None or a.ndim == 1)
+
+        self.check_unary_fuzz(np.add.reduce, get_out_axis_size,
+                              dtype=np.int16, count=500)
+
+    def test_binary_ufunc_reduceat_fuzz(self):
+        def get_out_axis_size(a, b, axis):
+            if axis is None:
+                if a.ndim == 1:
+                    return a.size, False
+                else:
+                    return 'skip', False  # reduceat doesn't support this
+            else:
+                return a.shape[axis], False
+
+        def do_reduceat(a, out, axis):
+            if axis is None:
+                size = len(a)
+                step = size//len(out)
+            else:
+                size = a.shape[axis]
+                step = a.shape[axis] // out.shape[axis]
+            idx = np.arange(0, size, step)
+            return np.add.reduceat(a, idx, out=out, axis=axis)
+
+        self.check_unary_fuzz(do_reduceat, get_out_axis_size,
+                              dtype=np.int16, count=500)
+
+    def test_binary_ufunc_reduceat_manual(self):
+        def check(ufunc, a, ind, out):
+            c1 = ufunc.reduceat(a.copy(), ind.copy(), out=out.copy())
+            c2 = ufunc.reduceat(a, ind, out=out)
+            assert_array_equal(c1, c2)
+
+        # Exactly same input/output arrays
+        a = np.arange(10000, dtype=np.int16)
+        check(np.add, a, a[::-1].copy(), a)
+
+        # Overlap with index
+        a = np.arange(10000, dtype=np.int16)
+        check(np.add, a, a[::-1], a)
+
+    @pytest.mark.slow
+    def test_unary_gufunc_fuzz(self):
+        shapes = [7, 13, 8, 21, 29, 32]
+        gufunc = _umath_tests.euclidean_pdist
+
+        rng = np.random.RandomState(1234)
+
+        for ndim in range(2, 6):
+            x = rng.rand(*shapes[:ndim])
+
+            it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
+
+            min_count = 500 // (ndim + 1)**2
+
+            overlapping = 0
+            while overlapping < min_count:
+                a, b = next(it)
+
+                if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:
+                    continue
+
+                # Ensure the shapes are so that euclidean_pdist is happy
+                if b.shape[-1] > b.shape[-2]:
+                    b = b[...,0,:]
+                else:
+                    b = b[...,:,0]
+
+                n = a.shape[-2]
+                p = n * (n - 1) // 2
+                if p <= b.shape[-1] and p > 0:
+                    b = b[...,:p]
+                else:
+                    n = max(2, int(np.sqrt(b.shape[-1]))//2)
+                    p = n * (n - 1) // 2
+                    a = a[...,:n,:]
+                    b = b[...,:p]
+
+                # Call
+                if np.shares_memory(a, b):
+                    overlapping += 1
+
+                with np.errstate(over='ignore', invalid='ignore'):
+                    assert_copy_equivalent(gufunc, [a], out=b)
+
+    def test_ufunc_at_manual(self):
+        def check(ufunc, a, ind, b=None):
+            a0 = a.copy()
+            if b is None:
+                ufunc.at(a0, ind.copy())
+                c1 = a0.copy()
+                ufunc.at(a, ind)
+                c2 = a.copy()
+            else:
+                ufunc.at(a0, ind.copy(), b.copy())
+                c1 = a0.copy()
+                ufunc.at(a, ind, b)
+                c2 = a.copy()
+            assert_array_equal(c1, c2)
+
+        # Overlap with index
+        a = np.arange(10000, dtype=np.int16)
+        check(np.invert, a[::-1], a)
+
+        # Overlap with second data array
+        a = np.arange(100, dtype=np.int16)
+        ind = np.arange(0, 100, 2, dtype=np.int16)
+        check(np.add, a, ind, a[25:75])
+
+    def test_unary_ufunc_1d_manual(self):
+        # Exercise ufunc fast-paths (that avoid creation of an `np.nditer`)
+
+        def check(a, b):
+            a_orig = a.copy()
+            b_orig = b.copy()
+
+            b0 = b.copy()
+            c1 = ufunc(a, out=b0)
+            c2 = ufunc(a, out=b)
+            assert_array_equal(c1, c2)
+
+            # Trigger "fancy ufunc loop" code path
+            mask = view_element_first_byte(b).view(np.bool_)
+
+            a[...] = a_orig
+            b[...] = b_orig
+            c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()
+
+            a[...] = a_orig
+            b[...] = b_orig
+            c2 = ufunc(a, out=b, where=mask.copy()).copy()
+
+            # Also, mask overlapping with output
+            a[...] = a_orig
+            b[...] = b_orig
+            c3 = ufunc(a, out=b, where=mask).copy()
+
+            assert_array_equal(c1, c2)
+            assert_array_equal(c1, c3)
+
+        dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,
+                  np.float64, np.complex64, np.complex128]
+        dtypes = [np.dtype(x) for x in dtypes]
+
+        for dtype in dtypes:
+            if np.issubdtype(dtype, np.integer):
+                ufunc = np.invert
+            else:
+                ufunc = np.reciprocal
+
+            n = 1000
+            k = 10
+            indices = [
+                np.index_exp[:n],
+                np.index_exp[k:k+n],
+                np.index_exp[n-1::-1],
+                np.index_exp[k+n-1:k-1:-1],
+                np.index_exp[:2*n:2],
+                np.index_exp[k:k+2*n:2],
+                np.index_exp[2*n-1::-2],
+                np.index_exp[k+2*n-1:k-1:-2],
+            ]
+
+            for xi, yi in itertools.product(indices, indices):
+                v = np.arange(1, 1 + n*2 + k, dtype=dtype)
+                x = v[xi]
+                y = v[yi]
+
+                with np.errstate(all='ignore'):
+                    check(x, y)
+
+                    # Scalar cases
+                    check(x[:1], y)
+                    check(x[-1:], y)
+                    check(x[:1].reshape([]), y)
+                    check(x[-1:].reshape([]), y)
+
+    def test_unary_ufunc_where_same(self):
+        # Check behavior at wheremask overlap
+        ufunc = np.invert
+
+        def check(a, out, mask):
+            c1 = ufunc(a, out=out.copy(), where=mask.copy())
+            c2 = ufunc(a, out=out, where=mask)
+            assert_array_equal(c1, c2)
+
+        # Check behavior with same input and output arrays
+        x = np.arange(100).astype(np.bool_)
+        check(x, x, x)
+        check(x, x.copy(), x)
+        check(x, x, x.copy())
+
+    @pytest.mark.slow
+    def test_binary_ufunc_1d_manual(self):
+        ufunc = np.add
+
+        def check(a, b, c):
+            c0 = c.copy()
+            c1 = ufunc(a, b, out=c0)
+            c2 = ufunc(a, b, out=c)
+            assert_array_equal(c1, c2)
+
+        for dtype in [np.int8, np.int16, np.int32, np.int64,
+                      np.float32, np.float64, np.complex64, np.complex128]:
+            # Check different data dependency orders
+
+            n = 1000
+            k = 10
+
+            indices = []
+            for p in [1, 2]:
+                indices.extend([
+                    np.index_exp[:p*n:p],
+                    np.index_exp[k:k+p*n:p],
+                    np.index_exp[p*n-1::-p],
+                    np.index_exp[k+p*n-1:k-1:-p],
+                ])
+
+            for x, y, z in itertools.product(indices, indices, indices):
+                v = np.arange(6*n).astype(dtype)
+                x = v[x]
+                y = v[y]
+                z = v[z]
+
+                check(x, y, z)
+
+                # Scalar cases
+                check(x[:1], y, z)
+                check(x[-1:], y, z)
+                check(x[:1].reshape([]), y, z)
+                check(x[-1:].reshape([]), y, z)
+                check(x, y[:1], z)
+                check(x, y[-1:], z)
+                check(x, y[:1].reshape([]), z)
+                check(x, y[-1:].reshape([]), z)
+
+    def test_inplace_op_simple_manual(self):
+        rng = np.random.RandomState(1234)
+        x = rng.rand(200, 200)  # bigger than bufsize
+
+        x += x.T
+        assert_array_equal(x - x.T, 0)
diff --git a/shared/package-files/40/40a0db648dc5b6ebbf61f5935fc78289bee3481a6119209f08e8fdf7a1a4c0e7/mkldnn_convolution_native.h b/shared/package-files/40/40a0db648dc5b6ebbf61f5935fc78289bee3481a6119209f08e8fdf7a1a4c0e7/mkldnn_convolution_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..05e95501aa4aab6146f5221feb74c7317c3e5372
--- /dev/null
+++ b/shared/package-files/40/40a0db648dc5b6ebbf61f5935fc78289bee3481a6119209f08e8fdf7a1a4c0e7/mkldnn_convolution_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups);
+TORCH_API at::Tensor & mkldnn_convolution_out_symint(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/40/40a58cbf28c21b5072b7ea0da7b473feac6755e4fbed9936ee5af3235a87d260/test_efficiency.py b/shared/package-files/40/40a58cbf28c21b5072b7ea0da7b473feac6755e4fbed9936ee5af3235a87d260/test_efficiency.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a2e7d0463b3a0abeb8395df4ab870456faa64b7
--- /dev/null
+++ b/shared/package-files/40/40a58cbf28c21b5072b7ea0da7b473feac6755e4fbed9936ee5af3235a87d260/test_efficiency.py
@@ -0,0 +1,58 @@
+"""Unit tests for the :mod:`networkx.algorithms.efficiency` module."""
+
+import networkx as nx
+
+
+class TestEfficiency:
+    def setup_method(self):
+        # G1 is a disconnected graph
+        self.G1 = nx.Graph()
+        self.G1.add_nodes_from([1, 2, 3])
+        # G2 is a cycle graph
+        self.G2 = nx.cycle_graph(4)
+        # G3 is the triangle graph with one additional edge
+        self.G3 = nx.lollipop_graph(3, 1)
+
+    def test_efficiency_disconnected_nodes(self):
+        """
+        When nodes are disconnected, efficiency is 0
+        """
+        assert nx.efficiency(self.G1, 1, 2) == 0
+
+    def test_local_efficiency_disconnected_graph(self):
+        """
+        In a disconnected graph the efficiency is 0
+        """
+        assert nx.local_efficiency(self.G1) == 0
+
+    def test_efficiency(self):
+        assert nx.efficiency(self.G2, 0, 1) == 1
+        assert nx.efficiency(self.G2, 0, 2) == 1 / 2
+
+    def test_global_efficiency(self):
+        assert nx.global_efficiency(self.G2) == 5 / 6
+
+    def test_global_efficiency_complete_graph(self):
+        """
+        Tests that the average global efficiency of the complete graph is one.
+        """
+        for n in range(2, 10):
+            G = nx.complete_graph(n)
+            assert nx.global_efficiency(G) == 1
+
+    def test_local_efficiency_complete_graph(self):
+        """
+        Test that the local efficiency for a complete graph with at least 3
+        nodes should be one. For a graph with only 2 nodes, the induced
+        subgraph has no edges.
+        """
+        for n in range(3, 10):
+            G = nx.complete_graph(n)
+            assert nx.local_efficiency(G) == 1
+
+    def test_using_ego_graph(self):
+        """
+        Test that the ego graph is used when computing local efficiency.
+        For more information, see GitHub issue #2710.
+        """
+        assert nx.local_efficiency(self.G3) == 7 / 12
diff --git a/shared/package-files/40/40a5e7d330ae3bc180dd66d040c43838429daf1a795b96128ffcc4a50898a8a9/_validate_sparse_compressed_tensor_args.h b/shared/package-files/40/40a5e7d330ae3bc180dd66d040c43838429daf1a795b96128ffcc4a50898a8a9/_validate_sparse_compressed_tensor_args.h
new file mode 100644
index 0000000000000000000000000000000000000000..87e18cd0e393c71ee98c18d2ddccc57fbbd21bce
--- /dev/null
+++ b/shared/package-files/40/40a5e7d330ae3bc180dd66d040c43838429daf1a795b96128ffcc4a50898a8a9/_validate_sparse_compressed_tensor_args.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_validate_sparse_compressed_tensor_args_ops.h>
+
+namespace at {
+
+
+// aten::_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout) -> ()
+inline void _validate_sparse_compressed_tensor_args(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout) {
+    return at::_ops::_validate_sparse_compressed_tensor_args::call(compressed_indices, plain_indices, values, size, layout);
+}
+
+}
diff --git a/shared/package-files/40/40a88170ccc25148c5ea3d2e3a58afd8615f0dcd9549b92d9b38597fdeefea2d/zone1970.tab b/shared/package-files/40/40a88170ccc25148c5ea3d2e3a58afd8615f0dcd9549b92d9b38597fdeefea2d/zone1970.tab
new file mode 100644
index 0000000000000000000000000000000000000000..1f1cecb848560c0ccff116090e23b0b811925496
--- /dev/null
+++ b/shared/package-files/40/40a88170ccc25148c5ea3d2e3a58afd8615f0dcd9549b92d9b38597fdeefea2d/zone1970.tab
@@ -0,0 +1,374 @@
+# tzdb timezone descriptions
+#
+# This file is in the public domain.
+#
+# From Paul Eggert (2018-06-27):
+# This file contains a table where each row stands for a timezone where
+# civil timestamps have agreed since 1970.  Columns are separated by
+# a single tab.  Lines beginning with '#' are comments.  All text uses
+# UTF-8 encoding.  The columns of the table are as follows:
+#
+# 1.  The countries that overlap the timezone, as a comma-separated list
+#     of ISO 3166 2-character country codes.  See the file 'iso3166.tab'.
+# 2.  Latitude and longitude of the timezone's principal location
+#     in ISO 6709 sign-degrees-minutes-seconds format,
+#     either ±DDMM±DDDMM or ±DDMMSS±DDDMMSS,
+#     first latitude (+ is north), then longitude (+ is east).
+# 3.  Timezone name used in value of TZ environment variable.
+#     Please see the theory.html file for how these names are chosen.
+#     If multiple timezones overlap a country, each has a row in the
+#     table, with each column 1 containing the country code.
+# 4.  Comments; present if and only if countries have multiple timezones,
+#     and useful only for those countries.  For example, the comments
+#     for the row with countries CH,DE,LI and name Europe/Zurich
+#     are useful only for DE, since CH and LI have no other timezones.
+#
+# If a timezone covers multiple countries, the most-populous city is used,
+# and that country is listed first in column 1; any other countries
+# are listed alphabetically by country code.  The table is sorted
+# first by country code, then (if possible) by an order within the
+# country that (1) makes some geographical sense, and (2) puts the
+# most populous timezones first, where that does not contradict (1).
+#
+# This table is intended as an aid for users, to help them select timezones
+# appropriate for their practical needs.  It is not intended to take or
+# endorse any position on legal or territorial claims.
+#
+#country-
+#codes	coordinates	TZ	comments
+AD	+4230+00131	Europe/Andorra
+AE,OM,RE,SC,TF	+2518+05518	Asia/Dubai	Crozet, Scattered Is
+AF	+3431+06912	Asia/Kabul
+AL	+4120+01950	Europe/Tirane
+AM	+4011+04430	Asia/Yerevan
+AQ	-6617+11031	Antarctica/Casey	Casey
+AQ	-6835+07758	Antarctica/Davis	Davis
+AQ	-6736+06253	Antarctica/Mawson	Mawson
+AQ	-6448-06406	Antarctica/Palmer	Palmer
+AQ	-6734-06808	Antarctica/Rothera	Rothera
+AQ	-720041+0023206	Antarctica/Troll	Troll
+AR	-3436-05827	America/Argentina/Buenos_Aires	Buenos Aires (BA, CF)
+AR	-3124-06411	America/Argentina/Cordoba	most areas: CB, CC, CN, ER, FM, MN, SE, SF
+AR	-2447-06525	America/Argentina/Salta	Salta (SA, LP, NQ, RN)
+AR	-2411-06518	America/Argentina/Jujuy	Jujuy (JY)
+AR	-2649-06513	America/Argentina/Tucuman	Tucumán (TM)
+AR	-2828-06547	America/Argentina/Catamarca	Catamarca (CT); Chubut (CH)
+AR	-2926-06651	America/Argentina/La_Rioja	La Rioja (LR)
+AR	-3132-06831	America/Argentina/San_Juan	San Juan (SJ)
+AR	-3253-06849	America/Argentina/Mendoza	Mendoza (MZ)
+AR	-3319-06621	America/Argentina/San_Luis	San Luis (SL)
+AR	-5138-06913	America/Argentina/Rio_Gallegos	Santa Cruz (SC)
+AR	-5448-06818	America/Argentina/Ushuaia	Tierra del Fuego (TF)
+AS,UM	-1416-17042	Pacific/Pago_Pago	Midway
+AT	+4813+01620	Europe/Vienna
+AU	-3133+15905	Australia/Lord_Howe	Lord Howe Island
+AU	-5430+15857	Antarctica/Macquarie	Macquarie Island
+AU	-4253+14719	Australia/Hobart	Tasmania
+AU	-3749+14458	Australia/Melbourne	Victoria
+AU	-3352+15113	Australia/Sydney	New South Wales (most areas)
+AU	-3157+14127	Australia/Broken_Hill	New South Wales (Yancowinna)
+AU	-2728+15302	Australia/Brisbane	Queensland (most areas)
+AU	-2016+14900	Australia/Lindeman	Queensland (Whitsunday Islands)
+AU	-3455+13835	Australia/Adelaide	South Australia
+AU	-1228+13050	Australia/Darwin	Northern Territory
+AU	-3157+11551	Australia/Perth	Western Australia (most areas)
+AU	-3143+12852	Australia/Eucla	Western Australia (Eucla)
+AZ	+4023+04951	Asia/Baku
+BB	+1306-05937	America/Barbados
+BD	+2343+09025	Asia/Dhaka
+BE,LU,NL	+5050+00420	Europe/Brussels
+BG	+4241+02319	Europe/Sofia
+BM	+3217-06446	Atlantic/Bermuda
+BO	-1630-06809	America/La_Paz
+BR	-0351-03225	America/Noronha	Atlantic islands
+BR	-0127-04829	America/Belem	Pará (east); Amapá
+BR	-0343-03830	America/Fortaleza	Brazil (northeast: MA, PI, CE, RN, PB)
+BR	-0803-03454	America/Recife	Pernambuco
+BR	-0712-04812	America/Araguaina	Tocantins
+BR	-0940-03543	America/Maceio	Alagoas, Sergipe
+BR	-1259-03831	America/Bahia	Bahia
+BR	-2332-04637	America/Sao_Paulo	Brazil (southeast: GO, DF, MG, ES, RJ, SP, PR, SC, RS)
+BR	-2027-05437	America/Campo_Grande	Mato Grosso do Sul
+BR	-1535-05605	America/Cuiaba	Mato Grosso
+BR	-0226-05452	America/Santarem	Pará (west)
+BR	-0846-06354	America/Porto_Velho	Rondônia
+BR	+0249-06040	America/Boa_Vista	Roraima
+BR	-0308-06001	America/Manaus	Amazonas (east)
+BR	-0640-06952	America/Eirunepe	Amazonas (west)
+BR	-0958-06748	America/Rio_Branco	Acre
+BT	+2728+08939	Asia/Thimphu
+BY	+5354+02734	Europe/Minsk
+BZ	+1730-08812	America/Belize
+CA	+4734-05243	America/St_Johns	Newfoundland; Labrador (southeast)
+CA	+4439-06336	America/Halifax	Atlantic - NS (most areas); PE
+CA	+4612-05957	America/Glace_Bay	Atlantic - NS (Cape Breton)
+CA	+4606-06447	America/Moncton	Atlantic - New Brunswick
+CA	+5320-06025	America/Goose_Bay	Atlantic - Labrador (most areas)
+CA,BS	+4339-07923	America/Toronto	Eastern - ON, QC (most areas)
+CA	+6344-06828	America/Iqaluit	Eastern - NU (most areas)
+CA	+4953-09709	America/Winnipeg	Central - ON (west); Manitoba
+CA	+744144-0944945	America/Resolute	Central - NU (Resolute)
+CA	+624900-0920459	America/Rankin_Inlet	Central - NU (central)
+CA	+5024-10439	America/Regina	CST - SK (most areas)
+CA	+5017-10750	America/Swift_Current	CST - SK (midwest)
+CA	+5333-11328	America/Edmonton	Mountain - AB; BC (E); NT (E); SK (W)
+CA	+690650-1050310	America/Cambridge_Bay	Mountain - NU (west)
+CA	+682059-1334300	America/Inuvik	Mountain - NT (west)
+CA	+5546-12014	America/Dawson_Creek	MST - BC (Dawson Cr, Ft St John)
+CA	+5848-12242	America/Fort_Nelson	MST - BC (Ft Nelson)
+CA	+6043-13503	America/Whitehorse	MST - Yukon (east)
+CA	+6404-13925	America/Dawson	MST - Yukon (west)
+CA	+4916-12307	America/Vancouver	Pacific - BC (most areas)
+CH,DE,LI	+4723+00832	Europe/Zurich	Büsingen
+CI,BF,GH,GM,GN,IS,ML,MR,SH,SL,SN,TG	+0519-00402	Africa/Abidjan
+CK	-2114-15946	Pacific/Rarotonga
+CL	-3327-07040	America/Santiago	most of Chile
+CL	-5309-07055	America/Punta_Arenas	Region of Magallanes
+CL	-2709-10926	Pacific/Easter	Easter Island
+CN	+3114+12128	Asia/Shanghai	Beijing Time
+CN,AQ	+4348+08735	Asia/Urumqi	Xinjiang Time, Vostok
+CO	+0436-07405	America/Bogota
+CR	+0956-08405	America/Costa_Rica
+CU	+2308-08222	America/Havana
+CV	+1455-02331	Atlantic/Cape_Verde
+CY	+3510+03322	Asia/Nicosia	most of Cyprus
+CY	+3507+03357	Asia/Famagusta	Northern Cyprus
+CZ,SK	+5005+01426	Europe/Prague
+DE,DK,NO,SE,SJ	+5230+01322	Europe/Berlin	most of Germany
+DO	+1828-06954	America/Santo_Domingo
+DZ	+3647+00303	Africa/Algiers
+EC	-0210-07950	America/Guayaquil	Ecuador (mainland)
+EC	-0054-08936	Pacific/Galapagos	Galápagos Islands
+EE	+5925+02445	Europe/Tallinn
+EG	+3003+03115	Africa/Cairo
+EH	+2709-01312	Africa/El_Aaiun
+ES	+4024-00341	Europe/Madrid	Spain (mainland)
+ES	+3553-00519	Africa/Ceuta	Ceuta, Melilla
+ES	+2806-01524	Atlantic/Canary	Canary Islands
+FI,AX	+6010+02458	Europe/Helsinki
+FJ	-1808+17825	Pacific/Fiji
+FK	-5142-05751	Atlantic/Stanley
+FM	+0519+16259	Pacific/Kosrae	Kosrae
+FO	+6201-00646	Atlantic/Faroe
+FR,MC	+4852+00220	Europe/Paris
+GB,GG,IM,JE	+513030-0000731	Europe/London
+GE	+4143+04449	Asia/Tbilisi
+GF	+0456-05220	America/Cayenne
+GI	+3608-00521	Europe/Gibraltar
+GL	+6411-05144	America/Nuuk	most of Greenland
+GL	+7646-01840	America/Danmarkshavn	National Park (east coast)
+GL	+7029-02158	America/Scoresbysund	Scoresbysund/Ittoqqortoormiit
+GL	+7634-06847	America/Thule	Thule/Pituffik
+GR	+3758+02343	Europe/Athens
+GS	-5416-03632	Atlantic/South_Georgia
+GT	+1438-09031	America/Guatemala
+GU,MP	+1328+14445	Pacific/Guam
+GW	+1151-01535	Africa/Bissau
+GY	+0648-05810	America/Guyana
+HK	+2217+11409	Asia/Hong_Kong
+HN	+1406-08713	America/Tegucigalpa
+HT	+1832-07220	America/Port-au-Prince
+HU	+4730+01905	Europe/Budapest
+ID	-0610+10648	Asia/Jakarta	Java, Sumatra
+ID	-0002+10920	Asia/Pontianak	Borneo (west, central)
+ID	-0507+11924	Asia/Makassar	Borneo (east, south); Sulawesi/Celebes, Bali, Nusa Tengarra; Timor (west)
+ID	-0232+14042	Asia/Jayapura	New Guinea (West Papua / Irian Jaya); Malukus/Moluccas
+IE	+5320-00615	Europe/Dublin
+IL	+314650+0351326	Asia/Jerusalem
+IN	+2232+08822	Asia/Kolkata
+IO	-0720+07225	Indian/Chagos
+IQ	+3321+04425	Asia/Baghdad
+IR	+3540+05126	Asia/Tehran
+IT,SM,VA	+4154+01229	Europe/Rome
+JM	+175805-0764736	America/Jamaica
+JO	+3157+03556	Asia/Amman
+JP	+353916+1394441	Asia/Tokyo
+KE,DJ,ER,ET,KM,MG,SO,TZ,UG,YT	-0117+03649	Africa/Nairobi
+KG	+4254+07436	Asia/Bishkek
+KI,MH,TV,UM,WF	+0125+17300	Pacific/Tarawa	Gilberts, Marshalls, Wake
+KI	-0247-17143	Pacific/Kanton	Phoenix Islands
+KI	+0152-15720	Pacific/Kiritimati	Line Islands
+KP	+3901+12545	Asia/Pyongyang
+KR	+3733+12658	Asia/Seoul
+KZ	+4315+07657	Asia/Almaty	most of Kazakhstan
+KZ	+4448+06528	Asia/Qyzylorda	Qyzylorda/Kyzylorda/Kzyl-Orda
+KZ	+5312+06337	Asia/Qostanay	Qostanay/Kostanay/Kustanay
+KZ	+5017+05710	Asia/Aqtobe	Aqtöbe/Aktobe
+KZ	+4431+05016	Asia/Aqtau	Mangghystaū/Mankistau
+KZ	+4707+05156	Asia/Atyrau	Atyraū/Atirau/Gur'yev
+KZ	+5113+05121	Asia/Oral	West Kazakhstan
+LB	+3353+03530	Asia/Beirut
+LK	+0656+07951	Asia/Colombo
+LR	+0618-01047	Africa/Monrovia
+LT	+5441+02519	Europe/Vilnius
+LV	+5657+02406	Europe/Riga
+LY	+3254+01311	Africa/Tripoli
+MA	+3339-00735	Africa/Casablanca
+MD	+4700+02850	Europe/Chisinau
+MH	+0905+16720	Pacific/Kwajalein	Kwajalein
+MM,CC	+1647+09610	Asia/Yangon
+MN	+4755+10653	Asia/Ulaanbaatar	most of Mongolia
+MN	+4801+09139	Asia/Hovd	Bayan-Ölgii, Govi-Altai, Hovd, Uvs, Zavkhan
+MN	+4804+11430	Asia/Choibalsan	Dornod, Sükhbaatar
+MO	+221150+1133230	Asia/Macau
+MQ	+1436-06105	America/Martinique
+MT	+3554+01431	Europe/Malta
+MU	-2010+05730	Indian/Mauritius
+MV,TF	+0410+07330	Indian/Maldives	Kerguelen, St Paul I, Amsterdam I
+MX	+1924-09909	America/Mexico_City	Central Mexico
+MX	+2105-08646	America/Cancun	Quintana Roo
+MX	+2058-08937	America/Merida	Campeche, Yucatán
+MX	+2540-10019	America/Monterrey	Durango; Coahuila, Nuevo León, Tamaulipas (most areas)
+MX	+2550-09730	America/Matamoros	Coahuila, Nuevo León, Tamaulipas (US border)
+MX	+2838-10605	America/Chihuahua	Chihuahua (most areas)
+MX	+3144-10629	America/Ciudad_Juarez	Chihuahua (US border - west)
+MX	+2934-10425	America/Ojinaga	Chihuahua (US border - east)
+MX	+2313-10625	America/Mazatlan	Baja California Sur, Nayarit (most areas), Sinaloa
+MX	+2048-10515	America/Bahia_Banderas	Bahía de Banderas
+MX	+2904-11058	America/Hermosillo	Sonora
+MX	+3232-11701	America/Tijuana	Baja California
+MY,BN	+0133+11020	Asia/Kuching	Sabah, Sarawak
+MZ,BI,BW,CD,MW,RW,ZM,ZW	-2558+03235	Africa/Maputo	Central Africa Time
+NA	-2234+01706	Africa/Windhoek
+NC	-2216+16627	Pacific/Noumea
+NF	-2903+16758	Pacific/Norfolk
+NG,AO,BJ,CD,CF,CG,CM,GA,GQ,NE	+0627+00324	Africa/Lagos	West Africa Time
+NI	+1209-08617	America/Managua
+NP	+2743+08519	Asia/Kathmandu
+NR	-0031+16655	Pacific/Nauru
+NU	-1901-16955	Pacific/Niue
+NZ,AQ	-3652+17446	Pacific/Auckland	New Zealand time
+NZ	-4357-17633	Pacific/Chatham	Chatham Islands
+PA,CA,KY	+0858-07932	America/Panama	EST - ON (Atikokan), NU (Coral H)
+PE	-1203-07703	America/Lima
+PF	-1732-14934	Pacific/Tahiti	Society Islands
+PF	-0900-13930	Pacific/Marquesas	Marquesas Islands
+PF	-2308-13457	Pacific/Gambier	Gambier Islands
+PG,AQ,FM	-0930+14710	Pacific/Port_Moresby	Papua New Guinea (most areas), Chuuk, Yap, Dumont d'Urville
+PG	-0613+15534	Pacific/Bougainville	Bougainville
+PH	+1435+12100	Asia/Manila
+PK	+2452+06703	Asia/Karachi
+PL	+5215+02100	Europe/Warsaw
+PM	+4703-05620	America/Miquelon
+PN	-2504-13005	Pacific/Pitcairn
+PR,AG,CA,AI,AW,BL,BQ,CW,DM,GD,GP,KN,LC,MF,MS,SX,TT,VC,VG,VI	+182806-0660622	America/Puerto_Rico	AST
+PS	+3130+03428	Asia/Gaza	Gaza Strip
+PS	+313200+0350542	Asia/Hebron	West Bank
+PT	+3843-00908	Europe/Lisbon	Portugal (mainland)
+PT	+3238-01654	Atlantic/Madeira	Madeira Islands
+PT	+3744-02540	Atlantic/Azores	Azores
+PW	+0720+13429	Pacific/Palau
+PY	-2516-05740	America/Asuncion
+QA,BH	+2517+05132	Asia/Qatar
+RO	+4426+02606	Europe/Bucharest
+RS,BA,HR,ME,MK,SI	+4450+02030	Europe/Belgrade
+RU	+5443+02030	Europe/Kaliningrad	MSK-01 - Kaliningrad
+RU	+554521+0373704	Europe/Moscow	MSK+00 - Moscow area
+# Mention RU and UA alphabetically.  See "territorial claims" above.
+RU,UA	+4457+03406	Europe/Simferopol	Crimea
+RU	+5836+04939	Europe/Kirov	MSK+00 - Kirov
+RU	+4844+04425	Europe/Volgograd	MSK+00 - Volgograd
+RU	+4621+04803	Europe/Astrakhan	MSK+01 - Astrakhan
+RU	+5134+04602	Europe/Saratov	MSK+01 - Saratov
+RU	+5420+04824	Europe/Ulyanovsk	MSK+01 - Ulyanovsk
+RU	+5312+05009	Europe/Samara	MSK+01 - Samara, Udmurtia
+RU	+5651+06036	Asia/Yekaterinburg	MSK+02 - Urals
+RU	+5500+07324	Asia/Omsk	MSK+03 - Omsk
+RU	+5502+08255	Asia/Novosibirsk	MSK+04 - Novosibirsk
+RU	+5322+08345	Asia/Barnaul	MSK+04 - Altai
+RU	+5630+08458	Asia/Tomsk	MSK+04 - Tomsk
+RU	+5345+08707	Asia/Novokuznetsk	MSK+04 - Kemerovo
+RU	+5601+09250	Asia/Krasnoyarsk	MSK+04 - Krasnoyarsk area
+RU	+5216+10420	Asia/Irkutsk	MSK+05 - Irkutsk, Buryatia
+RU	+5203+11328	Asia/Chita	MSK+06 - Zabaykalsky
+RU	+6200+12940	Asia/Yakutsk	MSK+06 - Lena River
+RU	+623923+1353314	Asia/Khandyga	MSK+06 - Tomponsky, Ust-Maysky
+RU	+4310+13156	Asia/Vladivostok	MSK+07 - Amur River
+RU	+643337+1431336	Asia/Ust-Nera	MSK+07 - Oymyakonsky
+RU	+5934+15048	Asia/Magadan	MSK+08 - Magadan
+RU	+4658+14242	Asia/Sakhalin	MSK+08 - Sakhalin Island
+RU	+6728+15343	Asia/Srednekolymsk	MSK+08 - Sakha (E); N Kuril Is
+RU	+5301+15839	Asia/Kamchatka	MSK+09 - Kamchatka
+RU	+6445+17729	Asia/Anadyr	MSK+09 - Bering Sea
+SA,AQ,KW,YE	+2438+04643	Asia/Riyadh	Syowa
+SB,FM	-0932+16012	Pacific/Guadalcanal	Pohnpei
+SD	+1536+03232	Africa/Khartoum
+SG,MY	+0117+10351	Asia/Singapore	peninsular Malaysia
+SR	+0550-05510	America/Paramaribo
+SS	+0451+03137	Africa/Juba
+ST	+0020+00644	Africa/Sao_Tome
+SV	+1342-08912	America/El_Salvador
+SY	+3330+03618	Asia/Damascus
+TC	+2128-07108	America/Grand_Turk
+TD	+1207+01503	Africa/Ndjamena
+TH,CX,KH,LA,VN	+1345+10031	Asia/Bangkok	north Vietnam
+TJ	+3835+06848	Asia/Dushanbe
+TK	-0922-17114	Pacific/Fakaofo
+TL	-0833+12535	Asia/Dili
+TM	+3757+05823	Asia/Ashgabat
+TN	+3648+01011	Africa/Tunis
+TO	-210800-1751200	Pacific/Tongatapu
+TR	+4101+02858	Europe/Istanbul
+TW	+2503+12130	Asia/Taipei
+UA	+5026+03031	Europe/Kyiv	most of Ukraine
+US	+404251-0740023	America/New_York	Eastern (most areas)
+US	+421953-0830245	America/Detroit	Eastern - MI (most areas)
+US	+381515-0854534	America/Kentucky/Louisville	Eastern - KY (Louisville area)
+US	+364947-0845057	America/Kentucky/Monticello	Eastern - KY (Wayne)
+US	+394606-0860929	America/Indiana/Indianapolis	Eastern - IN (most areas)
+US	+384038-0873143	America/Indiana/Vincennes	Eastern - IN (Da, Du, K, Mn)
+US	+410305-0863611	America/Indiana/Winamac	Eastern - IN (Pulaski)
+US	+382232-0862041	America/Indiana/Marengo	Eastern - IN (Crawford)
+US	+382931-0871643	America/Indiana/Petersburg	Eastern - IN (Pike)
+US	+384452-0850402	America/Indiana/Vevay	Eastern - IN (Switzerland)
+US	+415100-0873900	America/Chicago	Central (most areas)
+US	+375711-0864541	America/Indiana/Tell_City	Central - IN (Perry)
+US	+411745-0863730	America/Indiana/Knox	Central - IN (Starke)
+US	+450628-0873651	America/Menominee	Central - MI (Wisconsin border)
+US	+470659-1011757	America/North_Dakota/Center	Central - ND (Oliver)
+US	+465042-1012439	America/North_Dakota/New_Salem	Central - ND (Morton rural)
+US	+471551-1014640	America/North_Dakota/Beulah	Central - ND (Mercer)
+US	+394421-1045903	America/Denver	Mountain (most areas)
+US	+433649-1161209	America/Boise	Mountain - ID (south); OR (east)
+US,CA	+332654-1120424	America/Phoenix	MST - AZ (most areas), Creston BC
+US	+340308-1181434	America/Los_Angeles	Pacific
+US	+611305-1495401	America/Anchorage	Alaska (most areas)
+US	+581807-1342511	America/Juneau	Alaska - Juneau area
+US	+571035-1351807	America/Sitka	Alaska - Sitka area
+US	+550737-1313435	America/Metlakatla	Alaska - Annette Island
+US	+593249-1394338	America/Yakutat	Alaska - Yakutat
+US	+643004-1652423	America/Nome	Alaska (west)
+US	+515248-1763929	America/Adak	Alaska - western Aleutians
+US	+211825-1575130	Pacific/Honolulu	Hawaii
+UY	-345433-0561245	America/Montevideo
+UZ	+3940+06648	Asia/Samarkand	Uzbekistan (west)
+UZ	+4120+06918	Asia/Tashkent	Uzbekistan (east)
+VE	+1030-06656	America/Caracas
+VN	+1045+10640	Asia/Ho_Chi_Minh	south Vietnam
+VU	-1740+16825	Pacific/Efate
+WS	-1350-17144	Pacific/Apia
+ZA,LS,SZ	-2615+02800	Africa/Johannesburg
+#
+# The next section contains experimental tab-separated comments for
+# use by user agents like tzselect that identify continents and oceans.
+#
+# For example, the comment "#@AQ<tab>Antarctica/" means the country code
+# AQ is in the continent Antarctica regardless of the Zone name,
+# so Pacific/Auckland should be listed under Antarctica as well as
+# under the Pacific because its line's country codes include AQ.
+#
+# If more than one country code is affected each is listed separated
+# by commas, e.g., #@IS,SH<tab>Atlantic/".  If a country code is in
+# more than one continent or ocean, each is listed separated by
+# commas, e.g., the second column of "#@CY,TR<tab>Asia/,Europe/".
+#
+# These experimental comments are present only for country codes where
+# the continent or ocean is not already obvious from the Zone name.
+# For example, there is no such comment for RU since it already
+# corresponds to Zone names starting with both "Europe/" and "Asia/".
+#
+#@AQ	Antarctica/
+#@IS,SH	Atlantic/
+#@CY,TR	Asia/,Europe/
+#@SJ	Arctic/
+#@CC,CX,KM,MG,YT	Indian/
diff --git a/shared/package-files/40/40a98187cb96eada422e847e3d1a9147c0007f3070a9b5e057ea7b9d05e4c213/libpcre2-8.pc b/shared/package-files/40/40a98187cb96eada422e847e3d1a9147c0007f3070a9b5e057ea7b9d05e4c213/libpcre2-8.pc
new file mode 100644
index 0000000000000000000000000000000000000000..22b7d76eee2497a9022b97f97e9fd2402ee91481
--- /dev/null
+++ b/shared/package-files/40/40a98187cb96eada422e847e3d1a9147c0007f3070a9b5e057ea7b9d05e4c213/libpcre2-8.pc
@@ -0,0 +1,13 @@
+# Package Information for pkg-config
+
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: libpcre2-8
+Description: PCRE2 - Perl compatible regular expressions C library (2nd API) with 8 bit character support
+Version: 10.42
+Libs: -L${libdir} -lpcre2-8
+Libs.private:  
+Cflags: -I${includedir} 
diff --git a/shared/package-files/40/40ab1d491b0d1e634b64c34ceb8f2497824886d6e5c44cbda94096894d16a1db/swapaxes_compositeimplicitautograd_dispatch.h b/shared/package-files/40/40ab1d491b0d1e634b64c34ceb8f2497824886d6e5c44cbda94096894d16a1db/swapaxes_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f05972c32a4a7aa87f61f55cf2c2bbbd8d7301a2
--- /dev/null
+++ b/shared/package-files/40/40ab1d491b0d1e634b64c34ceb8f2497824886d6e5c44cbda94096894d16a1db/swapaxes_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1);
+TORCH_API at::Tensor & swapaxes_(at::Tensor & self, int64_t axis0, int64_t axis1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/40/40ae1e3f283bd87a3ca3ae69fde81e2beba39eb75713aa1b549aee45481f0004/channel_shuffle_cpu_dispatch.h b/shared/package-files/40/40ae1e3f283bd87a3ca3ae69fde81e2beba39eb75713aa1b549aee45481f0004/channel_shuffle_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c69cf90f5cffc521fe91079fefec1182d007562c
--- /dev/null
+++ b/shared/package-files/40/40ae1e3f283bd87a3ca3ae69fde81e2beba39eb75713aa1b549aee45481f0004/channel_shuffle_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor channel_shuffle(const at::Tensor & self, int64_t groups);
+TORCH_API at::Tensor channel_shuffle_symint(const at::Tensor & self, c10::SymInt groups);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/40/40b423f568c478e6da2b1dd5d73ec7287bb016fa239961c797e70d4460b68b32/swapdims_ops.h b/shared/package-files/40/40b423f568c478e6da2b1dd5d73ec7287bb016fa239961c797e70d4460b68b32/swapdims_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..31869c9f20dd321e3fcf6e7cfa40b2c7ead9a8b5
--- /dev/null
+++ b/shared/package-files/40/40b423f568c478e6da2b1dd5d73ec7287bb016fa239961c797e70d4460b68b32/swapdims_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API swapdims {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::swapdims")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+struct TORCH_API swapdims_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::swapdims_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/40b447785c7c19bcf5532c259c38b66a457a0c1b6a18745e404b8d976bd26dc1/test_jvm.py b/shared/package-files/40/40b447785c7c19bcf5532c259c38b66a457a0c1b6a18745e404b8d976bd26dc1/test_jvm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2ba780efc7fb8d46bd54c34e5d5ae11b07592db
--- /dev/null
+++ b/shared/package-files/40/40b447785c7c19bcf5532c259c38b66a457a0c1b6a18745e404b8d976bd26dc1/test_jvm.py
@@ -0,0 +1,435 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import json
+import os
+import pyarrow as pa
+import pyarrow.jvm as pa_jvm
+import pytest
+import sys
+import xml.etree.ElementTree as ET
+
+
+jpype = pytest.importorskip("jpype")
+
+pytestmark = pytest.mark.processes
+
+
+@pytest.fixture(scope="session")
+def root_allocator():
+    # This test requires Arrow Java to be built in the same source tree
+    try:
+        arrow_dir = os.environ["ARROW_SOURCE_DIR"]
+    except KeyError:
+        arrow_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..')
+    pom_path = os.path.join(arrow_dir, 'java', 'pom.xml')
+    tree = ET.parse(pom_path)
+    version = tree.getroot().find(
+        'POM:version',
+        namespaces={
+            'POM': 'http://maven.apache.org/POM/4.0.0'
+        }).text
+    jar_path = os.path.join(
+        arrow_dir, 'java', 'tools', 'target',
+        f'arrow-tools-{version}-jar-with-dependencies.jar')
+    jar_path = os.getenv("ARROW_TOOLS_JAR", jar_path)
+    kwargs = {}
+    # This will be the default behaviour in jpype 0.8+
+    kwargs['convertStrings'] = False
+    jpype.startJVM(jpype.getDefaultJVMPath(), "-Djava.class.path=" + jar_path,
+                   **kwargs)
+    return jpype.JPackage("org").apache.arrow.memory.RootAllocator(sys.maxsize)
+
+
+def test_jvm_buffer(root_allocator):
+    # Create a Java buffer
+    jvm_buffer = root_allocator.buffer(8)
+    for i in range(8):
+        jvm_buffer.setByte(i, 8 - i)
+
+    orig_refcnt = jvm_buffer.refCnt()
+
+    # Convert to Python
+    buf = pa_jvm.jvm_buffer(jvm_buffer)
+
+    # Check its content
+    assert buf.to_pybytes() == b'\x08\x07\x06\x05\x04\x03\x02\x01'
+
+    # Check Java buffer lifetime is tied to PyArrow buffer lifetime
+    assert jvm_buffer.refCnt() == orig_refcnt + 1
+    del buf
+    assert jvm_buffer.refCnt() == orig_refcnt
+
+
+def test_jvm_buffer_released(root_allocator):
+    import jpype.imports  # noqa
+    from java.lang import IllegalArgumentException
+
+    jvm_buffer = root_allocator.buffer(8)
+    jvm_buffer.release()
+
+    with pytest.raises(IllegalArgumentException):
+        pa_jvm.jvm_buffer(jvm_buffer)
+
+
+def _jvm_field(jvm_spec):
+    om = jpype.JClass('com.fasterxml.jackson.databind.ObjectMapper')()
+    pojo_Field = jpype.JClass('org.apache.arrow.vector.types.pojo.Field')
+    return om.readValue(jvm_spec, pojo_Field)
+
+
+def _jvm_schema(jvm_spec, metadata=None):
+    field = _jvm_field(jvm_spec)
+    schema_cls = jpype.JClass('org.apache.arrow.vector.types.pojo.Schema')
+    fields = jpype.JClass('java.util.ArrayList')()
+    fields.add(field)
+    if metadata:
+        dct = jpype.JClass('java.util.HashMap')()
+        for k, v in metadata.items():
+            dct.put(k, v)
+        return schema_cls(fields, dct)
+    else:
+        return schema_cls(fields)
+
+
+# In the following, we use the JSON serialization of the Field objects in Java.
+# This ensures that we neither rely on the exact mechanics on how to construct
+# them using Java code as well as enables us to define them as parameters
+# without to invoke the JVM.
+#
+# The specifications were created using:
+#
+#   om = jpype.JClass('com.fasterxml.jackson.databind.ObjectMapper')()
+#   field = …  # Code to instantiate the field
+#   jvm_spec = om.writeValueAsString(field)
+@pytest.mark.parametrize('pa_type,jvm_spec', [
+    (pa.null(), '{"name":"null"}'),
+    (pa.bool_(), '{"name":"bool"}'),
+    (pa.int8(), '{"name":"int","bitWidth":8,"isSigned":true}'),
+    (pa.int16(), '{"name":"int","bitWidth":16,"isSigned":true}'),
+    (pa.int32(), '{"name":"int","bitWidth":32,"isSigned":true}'),
+    (pa.int64(), '{"name":"int","bitWidth":64,"isSigned":true}'),
+    (pa.uint8(), '{"name":"int","bitWidth":8,"isSigned":false}'),
+    (pa.uint16(), '{"name":"int","bitWidth":16,"isSigned":false}'),
+    (pa.uint32(), '{"name":"int","bitWidth":32,"isSigned":false}'),
+    (pa.uint64(), '{"name":"int","bitWidth":64,"isSigned":false}'),
+    (pa.float16(), '{"name":"floatingpoint","precision":"HALF"}'),
+    (pa.float32(), '{"name":"floatingpoint","precision":"SINGLE"}'),
+    (pa.float64(), '{"name":"floatingpoint","precision":"DOUBLE"}'),
+    (pa.time32('s'), '{"name":"time","unit":"SECOND","bitWidth":32}'),
+    (pa.time32('ms'), '{"name":"time","unit":"MILLISECOND","bitWidth":32}'),
+    (pa.time64('us'), '{"name":"time","unit":"MICROSECOND","bitWidth":64}'),
+    (pa.time64('ns'), '{"name":"time","unit":"NANOSECOND","bitWidth":64}'),
+    (pa.timestamp('s'), '{"name":"timestamp","unit":"SECOND",'
+        '"timezone":null}'),
+    (pa.timestamp('ms'), '{"name":"timestamp","unit":"MILLISECOND",'
+        '"timezone":null}'),
+    (pa.timestamp('us'), '{"name":"timestamp","unit":"MICROSECOND",'
+        '"timezone":null}'),
+    (pa.timestamp('ns'), '{"name":"timestamp","unit":"NANOSECOND",'
+        '"timezone":null}'),
+    (pa.timestamp('ns', tz='UTC'), '{"name":"timestamp","unit":"NANOSECOND"'
+        ',"timezone":"UTC"}'),
+    (pa.timestamp('ns', tz='Europe/Paris'), '{"name":"timestamp",'
+        '"unit":"NANOSECOND","timezone":"Europe/Paris"}'),
+    (pa.date32(), '{"name":"date","unit":"DAY"}'),
+    (pa.date64(), '{"name":"date","unit":"MILLISECOND"}'),
+    (pa.decimal128(19, 4), '{"name":"decimal","precision":19,"scale":4}'),
+    (pa.string(), '{"name":"utf8"}'),
+    (pa.binary(), '{"name":"binary"}'),
+    (pa.binary(10), '{"name":"fixedsizebinary","byteWidth":10}'),
+    # TODO(ARROW-2609): complex types that have children
+    # pa.list_(pa.int32()),
+    # pa.struct([pa.field('a', pa.int32()),
+    #            pa.field('b', pa.int8()),
+    #            pa.field('c', pa.string())]),
+    # pa.union([pa.field('a', pa.binary(10)),
+    #           pa.field('b', pa.string())], mode=pa.lib.UnionMode_DENSE),
+    # pa.union([pa.field('a', pa.binary(10)),
+    #           pa.field('b', pa.string())], mode=pa.lib.UnionMode_SPARSE),
+    # TODO: DictionaryType requires a vector in the type
+    # pa.dictionary(pa.int32(), pa.array(['a', 'b', 'c'])),
+])
+@pytest.mark.parametrize('nullable', [True, False])
+def test_jvm_types(root_allocator, pa_type, jvm_spec, nullable):
+    if pa_type == pa.null() and not nullable:
+        return
+    spec = {
+        'name': 'field_name',
+        'nullable': nullable,
+        'type': json.loads(jvm_spec),
+        # TODO: This needs to be set for complex types
+        'children': []
+    }
+    jvm_field = _jvm_field(json.dumps(spec))
+    result = pa_jvm.field(jvm_field)
+    expected_field = pa.field('field_name', pa_type, nullable=nullable)
+    assert result == expected_field
+
+    jvm_schema = _jvm_schema(json.dumps(spec))
+    result = pa_jvm.schema(jvm_schema)
+    assert result == pa.schema([expected_field])
+
+    # Schema with custom metadata
+    jvm_schema = _jvm_schema(json.dumps(spec), {'meta': 'data'})
+    result = pa_jvm.schema(jvm_schema)
+    assert result == pa.schema([expected_field], {'meta': 'data'})
+
+    # Schema with custom field metadata
+    spec['metadata'] = [{'key': 'field meta', 'value': 'field data'}]
+    jvm_schema = _jvm_schema(json.dumps(spec))
+    result = pa_jvm.schema(jvm_schema)
+    expected_field = expected_field.with_metadata(
+        {'field meta': 'field data'})
+    assert result == pa.schema([expected_field])
+
+
+# These test parameters mostly use an integer range as an input as this is
+# often the only type that is understood by both Python and Java
+# implementations of Arrow.
+@pytest.mark.parametrize('pa_type,py_data,jvm_type', [
+    (pa.bool_(), [True, False, True, True], 'BitVector'),
+    (pa.uint8(), list(range(128)), 'UInt1Vector'),
+    (pa.uint16(), list(range(128)), 'UInt2Vector'),
+    (pa.int32(), list(range(128)), 'IntVector'),
+    (pa.int64(), list(range(128)), 'BigIntVector'),
+    (pa.float32(), list(range(128)), 'Float4Vector'),
+    (pa.float64(), list(range(128)), 'Float8Vector'),
+    (pa.timestamp('s'), list(range(128)), 'TimeStampSecVector'),
+    (pa.timestamp('ms'), list(range(128)), 'TimeStampMilliVector'),
+    (pa.timestamp('us'), list(range(128)), 'TimeStampMicroVector'),
+    (pa.timestamp('ns'), list(range(128)), 'TimeStampNanoVector'),
+    # TODO(ARROW-2605): These types miss a conversion from pure Python objects
+    #  * pa.time32('s')
+    #  * pa.time32('ms')
+    #  * pa.time64('us')
+    #  * pa.time64('ns')
+    (pa.date32(), list(range(128)), 'DateDayVector'),
+    (pa.date64(), list(range(128)), 'DateMilliVector'),
+    # TODO(ARROW-2606): pa.decimal128(19, 4)
+])
+def test_jvm_array(root_allocator, pa_type, py_data, jvm_type):
+    # Create vector
+    cls = f"org.apache.arrow.vector.{jvm_type}"
+    jvm_vector = jpype.JClass(cls)("vector", root_allocator)
+    jvm_vector.allocateNew(len(py_data))
+    for i, val in enumerate(py_data):
+        # char and int are ambiguous overloads for these two setSafe calls
+        if jvm_type in {'UInt1Vector', 'UInt2Vector'}:
+            val = jpype.JInt(val)
+        jvm_vector.setSafe(i, val)
+    jvm_vector.setValueCount(len(py_data))
+
+    py_array = pa.array(py_data, type=pa_type)
+    jvm_array = pa_jvm.array(jvm_vector)
+
+    assert py_array.equals(jvm_array)
+
+
+def test_jvm_array_empty(root_allocator):
+    cls = f"org.apache.arrow.vector.{'IntVector'}"
+    jvm_vector = jpype.JClass(cls)("vector", root_allocator)
+    jvm_vector.allocateNew()
+    jvm_array = pa_jvm.array(jvm_vector)
+    assert len(jvm_array) == 0
+    assert jvm_array.type == pa.int32()
+
+
+# These test parameters mostly use an integer range as an input as this is
+# often the only type that is understood by both Python and Java
+# implementations of Arrow.
+@pytest.mark.parametrize('pa_type,py_data,jvm_type,jvm_spec', [
+    # TODO: null
+    (pa.bool_(), [True, False, True, True], 'BitVector', '{"name":"bool"}'),
+    (
+        pa.uint8(),
+        list(range(128)),
+        'UInt1Vector',
+        '{"name":"int","bitWidth":8,"isSigned":false}'
+    ),
+    (
+        pa.uint16(),
+        list(range(128)),
+        'UInt2Vector',
+        '{"name":"int","bitWidth":16,"isSigned":false}'
+    ),
+    (
+        pa.uint32(),
+        list(range(128)),
+        'UInt4Vector',
+        '{"name":"int","bitWidth":32,"isSigned":false}'
+    ),
+    (
+        pa.uint64(),
+        list(range(128)),
+        'UInt8Vector',
+        '{"name":"int","bitWidth":64,"isSigned":false}'
+    ),
+    (
+        pa.int8(),
+        list(range(128)),
+        'TinyIntVector',
+        '{"name":"int","bitWidth":8,"isSigned":true}'
+    ),
+    (
+        pa.int16(),
+        list(range(128)),
+        'SmallIntVector',
+        '{"name":"int","bitWidth":16,"isSigned":true}'
+    ),
+    (
+        pa.int32(),
+        list(range(128)),
+        'IntVector',
+        '{"name":"int","bitWidth":32,"isSigned":true}'
+    ),
+    (
+        pa.int64(),
+        list(range(128)),
+        'BigIntVector',
+        '{"name":"int","bitWidth":64,"isSigned":true}'
+    ),
+    # TODO: float16
+    (
+        pa.float32(),
+        list(range(128)),
+        'Float4Vector',
+        '{"name":"floatingpoint","precision":"SINGLE"}'
+    ),
+    (
+        pa.float64(),
+        list(range(128)),
+        'Float8Vector',
+        '{"name":"floatingpoint","precision":"DOUBLE"}'
+    ),
+    (
+        pa.timestamp('s'),
+        list(range(128)),
+        'TimeStampSecVector',
+        '{"name":"timestamp","unit":"SECOND","timezone":null}'
+    ),
+    (
+        pa.timestamp('ms'),
+        list(range(128)),
+        'TimeStampMilliVector',
+        '{"name":"timestamp","unit":"MILLISECOND","timezone":null}'
+    ),
+    (
+        pa.timestamp('us'),
+        list(range(128)),
+        'TimeStampMicroVector',
+        '{"name":"timestamp","unit":"MICROSECOND","timezone":null}'
+    ),
+    (
+        pa.timestamp('ns'),
+        list(range(128)),
+        'TimeStampNanoVector',
+        '{"name":"timestamp","unit":"NANOSECOND","timezone":null}'
+    ),
+    # TODO(ARROW-2605): These types miss a conversion from pure Python objects
+    #  * pa.time32('s')
+    #  * pa.time32('ms')
+    #  * pa.time64('us')
+    #  * pa.time64('ns')
+    (
+        pa.date32(),
+        list(range(128)),
+        'DateDayVector',
+        '{"name":"date","unit":"DAY"}'
+    ),
+    (
+        pa.date64(),
+        list(range(128)),
+        'DateMilliVector',
+        '{"name":"date","unit":"MILLISECOND"}'
+    ),
+    # TODO(ARROW-2606): pa.decimal128(19, 4)
+])
+def test_jvm_record_batch(root_allocator, pa_type, py_data, jvm_type,
+                          jvm_spec):
+    # Create vector
+    cls = f"org.apache.arrow.vector.{jvm_type}"
+    jvm_vector = jpype.JClass(cls)("vector", root_allocator)
+    jvm_vector.allocateNew(len(py_data))
+    for i, val in enumerate(py_data):
+        if jvm_type in {'UInt1Vector', 'UInt2Vector'}:
+            val = jpype.JInt(val)
+        jvm_vector.setSafe(i, val)
+    jvm_vector.setValueCount(len(py_data))
+
+    # Create field
+    spec = {
+        'name': 'field_name',
+        'nullable': False,
+        'type': json.loads(jvm_spec),
+        # TODO: This needs to be set for complex types
+        'children': []
+    }
+    jvm_field = _jvm_field(json.dumps(spec))
+
+    # Create VectorSchemaRoot
+    jvm_fields = jpype.JClass('java.util.ArrayList')()
+    jvm_fields.add(jvm_field)
+    jvm_vectors = jpype.JClass('java.util.ArrayList')()
+    jvm_vectors.add(jvm_vector)
+    jvm_vsr = jpype.JClass('org.apache.arrow.vector.VectorSchemaRoot')
+    jvm_vsr = jvm_vsr(jvm_fields, jvm_vectors, len(py_data))
+
+    py_record_batch = pa.RecordBatch.from_arrays(
+        [pa.array(py_data, type=pa_type)],
+        ['col']
+    )
+    jvm_record_batch = pa_jvm.record_batch(jvm_vsr)
+
+    assert py_record_batch.equals(jvm_record_batch)
+
+
+def _string_to_varchar_holder(ra, string):
+    nvch_cls = "org.apache.arrow.vector.holders.NullableVarCharHolder"
+    holder = jpype.JClass(nvch_cls)()
+    if string is None:
+        holder.isSet = 0
+    else:
+        holder.isSet = 1
+        value = jpype.JClass("java.lang.String")("string")
+        std_charsets = jpype.JClass("java.nio.charset.StandardCharsets")
+        bytes_ = value.getBytes(std_charsets.UTF_8)
+        holder.buffer = ra.buffer(len(bytes_))
+        holder.buffer.setBytes(0, bytes_, 0, len(bytes_))
+        holder.start = 0
+        holder.end = len(bytes_)
+    return holder
+
+
+# TODO(ARROW-2607)
+@pytest.mark.xfail(reason="from_buffers is only supported for "
+                          "primitive arrays yet")
+def test_jvm_string_array(root_allocator):
+    data = ["string", None, "töst"]
+    cls = "org.apache.arrow.vector.VarCharVector"
+    jvm_vector = jpype.JClass(cls)("vector", root_allocator)
+    jvm_vector.allocateNew()
+
+    for i, string in enumerate(data):
+        holder = _string_to_varchar_holder(root_allocator, "string")
+        jvm_vector.setSafe(i, holder)
+        jvm_vector.setValueCount(i + 1)
+
+    py_array = pa.array(data, type=pa.string())
+    jvm_array = pa_jvm.array(jvm_vector)
+
+    assert py_array.equals(jvm_array)
diff --git a/shared/package-files/40/40b4a1689544ec49d6b5d7ef80aa584bb6e3f173a41164257945ef9560dac564/_unsafe_view.h b/shared/package-files/40/40b4a1689544ec49d6b5d7ef80aa584bb6e3f173a41164257945ef9560dac564/_unsafe_view.h
new file mode 100644
index 0000000000000000000000000000000000000000..32c11a99de4030f4c0347f9313b665f43cdfad22
--- /dev/null
+++ b/shared/package-files/40/40b4a1689544ec49d6b5d7ef80aa584bb6e3f173a41164257945ef9560dac564/_unsafe_view.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_unsafe_view_ops.h>
+
+namespace at {
+
+
+// aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor
+inline at::Tensor _unsafe_view(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::_unsafe_view::call(self, c10::fromIntArrayRefSlow(size));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _unsafe_view(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::_unsafe_view::call(self, c10::fromIntArrayRefSlow(size));
+  }
+}
+
+// aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor
+inline at::Tensor _unsafe_view_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::_unsafe_view::call(self, size);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _unsafe_view(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::_unsafe_view::call(self, size);
+  }
+}
+
+// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _unsafe_view_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _unsafe_view_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _unsafe_view_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::_unsafe_view_out::call(self, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::_unsafe_view_out::call(self, size, out);
+  }
+}
+
+// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _unsafe_view_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::_unsafe_view_out::call(self, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _unsafe_view_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::_unsafe_view_out::call(self, size, out);
+  }
+}
+
+}
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diff --git a/shared/package-files/40/40bbd7cd69a8dbfc6a1cccb9e4e4d7501d790a893240b3331ef997bb0323eb3f/hexdump.py b/shared/package-files/40/40bbd7cd69a8dbfc6a1cccb9e4e4d7501d790a893240b3331ef997bb0323eb3f/hexdump.py
new file mode 100644
index 0000000000000000000000000000000000000000..9672fd4dcf5ab5399458e66ff72325384fd06027
--- /dev/null
+++ b/shared/package-files/40/40bbd7cd69a8dbfc6a1cccb9e4e4d7501d790a893240b3331ef997bb0323eb3f/hexdump.py
@@ -0,0 +1,102 @@
+"""
+    pygments.lexers.hexdump
+    ~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for hexadecimal dumps.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, bygroups, include
+from pygments.token import Name, Number, String, Punctuation, Whitespace
+
+__all__ = ['HexdumpLexer']
+
+
+class HexdumpLexer(RegexLexer):
+    """
+    For typical hex dump output formats by the UNIX and GNU/Linux tools ``hexdump``,
+    ``hd``, ``hexcat``, ``od`` and ``xxd``, and the DOS tool ``DEBUG``. For example:
+
+    .. sourcecode:: hexdump
+
+        00000000  7f 45 4c 46 02 01 01 00  00 00 00 00 00 00 00 00  |.ELF............|
+        00000010  02 00 3e 00 01 00 00 00  c5 48 40 00 00 00 00 00  |..>......H@.....|
+
+    The specific supported formats are the outputs of:
+
+    * ``hexdump FILE``
+    * ``hexdump -C FILE`` -- the `canonical` format used in the example.
+    * ``hd FILE`` -- same as ``hexdump -C FILE``.
+    * ``hexcat FILE``
+    * ``od -t x1z FILE``
+    * ``xxd FILE``
+    * ``DEBUG.EXE FILE.COM`` and entering ``d`` to the prompt.
+
+    .. versionadded:: 2.1
+    """
+    name = 'Hexdump'
+    aliases = ['hexdump']
+
+    hd = r'[0-9A-Ha-h]'
+
+    tokens = {
+        'root': [
+            (r'\n', Whitespace),
+            include('offset'),
+            (r'('+hd+r'{2})(\-)('+hd+r'{2})',
+             bygroups(Number.Hex, Punctuation, Number.Hex)),
+            (hd+r'{2}', Number.Hex),
+            (r'(\s{2,3})(\>)(.{16})(\<)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation), 'bracket-strings'),
+            (r'(\s{2,3})(\|)(.{16})(\|)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation), 'piped-strings'),
+            (r'(\s{2,3})(\>)(.{1,15})(\<)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation)),
+            (r'(\s{2,3})(\|)(.{1,15})(\|)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation)),
+            (r'(\s{2,3})(.{1,15})$', bygroups(Whitespace, String)),
+            (r'(\s{2,3})(.{16}|.{20})$', bygroups(Whitespace, String), 'nonpiped-strings'),
+            (r'\s', Whitespace),
+            (r'^\*', Punctuation),
+        ],
+        'offset': [
+            (r'^('+hd+'+)(:)', bygroups(Name.Label, Punctuation), 'offset-mode'),
+            (r'^'+hd+'+', Name.Label),
+        ],
+        'offset-mode': [
+            (r'\s', Whitespace, '#pop'),
+            (hd+'+', Name.Label),
+            (r':', Punctuation)
+        ],
+        'piped-strings': [
+            (r'\n', Whitespace),
+            include('offset'),
+            (hd+r'{2}', Number.Hex),
+            (r'(\s{2,3})(\|)(.{1,16})(\|)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation)),
+            (r'\s', Whitespace),
+            (r'^\*', Punctuation),
+        ],
+        'bracket-strings': [
+            (r'\n', Whitespace),
+            include('offset'),
+            (hd+r'{2}', Number.Hex),
+            (r'(\s{2,3})(\>)(.{1,16})(\<)$',
+             bygroups(Whitespace, Punctuation, String, Punctuation)),
+            (r'\s', Whitespace),
+            (r'^\*', Punctuation),
+        ],
+        'nonpiped-strings': [
+            (r'\n', Whitespace),
+            include('offset'),
+            (r'('+hd+r'{2})(\-)('+hd+r'{2})',
+             bygroups(Number.Hex, Punctuation, Number.Hex)),
+            (hd+r'{2}', Number.Hex),
+            (r'(\s{19,})(.{1,20}?)$', bygroups(Whitespace, String)),
+            (r'(\s{2,3})(.{1,20})$', bygroups(Whitespace, String)),
+            (r'\s', Whitespace),
+            (r'^\*', Punctuation),
+        ],
+    }
diff --git a/shared/package-files/40/40bf74e2eac6caa545e4e39fcaad6e2e7027fae86045c6dba1a4258f6a1b68c5/__init__.py b/shared/package-files/40/40bf74e2eac6caa545e4e39fcaad6e2e7027fae86045c6dba1a4258f6a1b68c5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4eb89a92fcb80765a6f54b11bebde867ced37aa4
--- /dev/null
+++ b/shared/package-files/40/40bf74e2eac6caa545e4e39fcaad6e2e7027fae86045c6dba1a4258f6a1b68c5/__init__.py
@@ -0,0 +1,61 @@
+import os
+import sys
+import functools
+import unittest
+import traceback
+from fnmatch import fnmatch
+from os.path import join, isfile, relpath, normpath, splitext
+
+from .main import NumbaTestProgram, SerialSuite, make_tag_decorator
+from numba.core import config
+
+
+def load_testsuite(loader, dir):
+    """Find tests in 'dir'."""
+    try:
+        suite = unittest.TestSuite()
+        files = []
+        for f in os.listdir(dir):
+            path = join(dir, f)
+            if isfile(path) and fnmatch(f, 'test_*.py'):
+                files.append(f)
+            elif isfile(join(path, '__init__.py')):
+                suite.addTests(loader.discover(path))
+        for f in files:
+            # turn 'f' into a filename relative to the toplevel dir...
+            f = relpath(join(dir, f), loader._top_level_dir)
+            # ...and translate it to a module name.
+            f = splitext(normpath(f.replace(os.path.sep, '.')))[0]
+            suite.addTests(loader.loadTestsFromName(f))
+        return suite
+    except Exception:
+        traceback.print_exc(file=sys.stderr)
+        sys.exit(-1)
+
+
+def run_tests(argv=None, defaultTest=None, topleveldir=None,
+              xmloutput=None, verbosity=1, nomultiproc=False):
+    """
+    args
+    ----
+    - xmloutput [str or None]
+        Path of XML output directory (optional)
+    - verbosity [int]
+        Verbosity level of tests output
+
+    Returns the TestResult object after running the test *suite*.
+    """
+
+    if xmloutput is not None:
+        import xmlrunner
+        runner = xmlrunner.XMLTestRunner(output=xmloutput)
+    else:
+        runner = None
+    prog = NumbaTestProgram(argv=argv,
+                            module=None,
+                            defaultTest=defaultTest,
+                            topleveldir=topleveldir,
+                            testRunner=runner, exit=False,
+                            verbosity=verbosity,
+                            nomultiproc=nomultiproc)
+    return prog.result
diff --git a/shared/package-files/40/40c148c8e8766c6c1ab47ab7804d4e65622021d7c9da2e548e13533740d52b95/test_feather.py b/shared/package-files/40/40c148c8e8766c6c1ab47ab7804d4e65622021d7c9da2e548e13533740d52b95/test_feather.py
new file mode 100644
index 0000000000000000000000000000000000000000..054bf920b269bff7b54ace033b3ca80408e4406f
--- /dev/null
+++ b/shared/package-files/40/40c148c8e8766c6c1ab47ab7804d4e65622021d7c9da2e548e13533740d52b95/test_feather.py
@@ -0,0 +1,884 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import io
+import os
+import sys
+import tempfile
+import pytest
+import hypothesis as h
+import hypothesis.strategies as st
+
+try:
+    import numpy as np
+except ImportError:
+    np = None
+
+import pyarrow as pa
+import pyarrow.tests.strategies as past
+from pyarrow.feather import (read_feather, write_feather, read_table,
+                             FeatherDataset)
+
+try:
+    from pandas.testing import assert_frame_equal
+    import pandas as pd
+    import pyarrow.pandas_compat
+except ImportError:
+    pass
+
+
+@pytest.fixture(scope='module')
+def datadir(base_datadir):
+    return base_datadir / 'feather'
+
+
+def random_path(prefix='feather_'):
+    return tempfile.mktemp(prefix=prefix)
+
+
+@pytest.fixture(scope="module", params=[1, 2])
+def version(request):
+    yield request.param
+
+
+@pytest.fixture(scope="module", params=[None, "uncompressed", "lz4", "zstd"])
+def compression(request):
+    if request.param in ['lz4', 'zstd'] and not pa.Codec.is_available(
+            request.param):
+        pytest.skip(f'{request.param} is not available')
+    yield request.param
+
+
+TEST_FILES = None
+
+
+def setup_module(module):
+    global TEST_FILES
+    TEST_FILES = []
+
+
+def teardown_module(module):
+    for path in TEST_FILES:
+        try:
+            os.remove(path)
+        except os.error:
+            pass
+
+
+@pytest.mark.pandas
+def test_file_not_exist():
+    with pytest.raises(pa.ArrowIOError):
+        read_feather('test_invalid_file')
+
+
+def _check_pandas_roundtrip(df, expected=None, path=None,
+                            columns=None, use_threads=False,
+                            version=None, compression=None,
+                            compression_level=None):
+    if path is None:
+        path = random_path()
+
+    if version is None:
+        version = 2
+
+    TEST_FILES.append(path)
+    write_feather(df, path, compression=compression,
+                  compression_level=compression_level, version=version)
+
+    if not os.path.exists(path):
+        raise Exception('file not written')
+
+    result = read_feather(path, columns, use_threads=use_threads)
+
+    if expected is None:
+        expected = df
+
+    assert_frame_equal(result, expected)
+
+
+def _check_arrow_roundtrip(table, path=None, compression=None):
+    if path is None:
+        path = random_path()
+
+    TEST_FILES.append(path)
+    write_feather(table, path, compression=compression)
+    if not os.path.exists(path):
+        raise Exception('file not written')
+
+    result = read_table(path)
+    assert result.equals(table)
+
+
+def _assert_error_on_write(df, exc, path=None, version=2):
+    # check that we are raising the exception
+    # on writing
+
+    if path is None:
+        path = random_path()
+
+    TEST_FILES.append(path)
+
+    def f():
+        write_feather(df, path, version=version)
+
+    pytest.raises(exc, f)
+
+
+@pytest.mark.numpy
+def test_dataset(version):
+    num_values = (100, 100)
+    num_files = 5
+    paths = [random_path() for i in range(num_files)]
+    data = {
+        "col_" + str(i): np.random.randn(num_values[0])
+        for i in range(num_values[1])
+    }
+    table = pa.table(data)
+
+    TEST_FILES.extend(paths)
+    for index, path in enumerate(paths):
+        rows = (
+            index * (num_values[0] // num_files),
+            (index + 1) * (num_values[0] // num_files),
+        )
+
+        write_feather(table[rows[0]: rows[1]], path, version=version)
+
+    data = FeatherDataset(paths).read_table()
+    assert data.equals(table)
+
+
+@pytest.mark.pandas
+def test_float_no_nulls(version):
+    data = {}
+    numpy_dtypes = ['f4', 'f8']
+    num_values = 100
+
+    for dtype in numpy_dtypes:
+        values = np.random.randn(num_values)
+        data[dtype] = values.astype(dtype)
+
+    df = pd.DataFrame(data)
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_read_table(version):
+    num_values = (100, 100)
+    path = random_path()
+
+    TEST_FILES.append(path)
+
+    values = np.random.randint(0, 100, size=num_values)
+    columns = ['col_' + str(i) for i in range(100)]
+    table = pa.Table.from_arrays(values, columns)
+
+    write_feather(table, path, version=version)
+
+    result = read_table(path)
+    assert result.equals(table)
+
+    # Test without memory mapping
+    result = read_table(path, memory_map=False)
+    assert result.equals(table)
+
+    result = read_feather(path, memory_map=False)
+    assert_frame_equal(table.to_pandas(), result)
+
+
+@pytest.mark.pandas
+def test_use_threads(version):
+    # ARROW-14470
+    num_values = (10, 10)
+    path = random_path()
+
+    TEST_FILES.append(path)
+
+    values = np.random.randint(0, 10, size=num_values)
+    columns = ['col_' + str(i) for i in range(10)]
+    table = pa.Table.from_arrays(values, columns)
+
+    write_feather(table, path, version=version)
+
+    result = read_feather(path)
+    assert_frame_equal(table.to_pandas(), result)
+
+    # Test read_feather with use_threads=False
+    result = read_feather(path, use_threads=False)
+    assert_frame_equal(table.to_pandas(), result)
+
+    # Test read_table with use_threads=False
+    result = read_table(path, use_threads=False)
+    assert result.equals(table)
+
+
+@pytest.mark.pandas
+def test_float_nulls(version):
+    num_values = 100
+
+    path = random_path()
+    TEST_FILES.append(path)
+
+    null_mask = np.random.randint(0, 10, size=num_values) < 3
+    dtypes = ['f4', 'f8']
+    expected_cols = []
+
+    arrays = []
+    for name in dtypes:
+        values = np.random.randn(num_values).astype(name)
+        arrays.append(pa.array(values, mask=null_mask))
+
+        values[null_mask] = np.nan
+
+        expected_cols.append(values)
+
+    table = pa.table(arrays, names=dtypes)
+    _check_arrow_roundtrip(table)
+
+    df = table.to_pandas()
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_integer_no_nulls(version):
+    data, arr = {}, []
+
+    numpy_dtypes = ['i1', 'i2', 'i4', 'i8',
+                    'u1', 'u2', 'u4', 'u8']
+    num_values = 100
+
+    for dtype in numpy_dtypes:
+        values = np.random.randint(0, 100, size=num_values)
+        data[dtype] = values.astype(dtype)
+        arr.append(values.astype(dtype))
+
+    df = pd.DataFrame(data)
+    _check_pandas_roundtrip(df, version=version)
+
+    table = pa.table(arr, names=numpy_dtypes)
+    _check_arrow_roundtrip(table)
+
+
+@pytest.mark.pandas
+def test_platform_numpy_integers(version):
+    data = {}
+
+    numpy_dtypes = ['longlong']
+    num_values = 100
+
+    for dtype in numpy_dtypes:
+        values = np.random.randint(0, 100, size=num_values)
+        data[dtype] = values.astype(dtype)
+
+    df = pd.DataFrame(data)
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_integer_with_nulls(version):
+    # pandas requires upcast to float dtype
+    path = random_path()
+    TEST_FILES.append(path)
+
+    int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']
+    num_values = 100
+
+    arrays = []
+    null_mask = np.random.randint(0, 10, size=num_values) < 3
+    expected_cols = []
+    for name in int_dtypes:
+        values = np.random.randint(0, 100, size=num_values)
+        arrays.append(pa.array(values, mask=null_mask))
+
+        expected = values.astype('f8')
+        expected[null_mask] = np.nan
+
+        expected_cols.append(expected)
+
+    table = pa.table(arrays, names=int_dtypes)
+    _check_arrow_roundtrip(table)
+
+    df = table.to_pandas()
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_boolean_no_nulls(version):
+    num_values = 100
+
+    np.random.seed(0)
+
+    df = pd.DataFrame({'bools': np.random.randn(num_values) > 0})
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_boolean_nulls(version):
+    # pandas requires upcast to object dtype
+    path = random_path()
+    TEST_FILES.append(path)
+
+    num_values = 100
+    np.random.seed(0)
+
+    mask = np.random.randint(0, 10, size=num_values) < 3
+    values = np.random.randint(0, 10, size=num_values) < 5
+
+    table = pa.table([pa.array(values, mask=mask)], names=['bools'])
+    _check_arrow_roundtrip(table)
+
+    df = table.to_pandas()
+    _check_pandas_roundtrip(df, version=version)
+
+
+def test_buffer_bounds_error(version):
+    # ARROW-1676
+    path = random_path()
+    TEST_FILES.append(path)
+
+    for i in range(16, 256):
+        table = pa.Table.from_arrays(
+            [pa.array([None] + list(range(i)), type=pa.float64())],
+            names=["arr"]
+        )
+        _check_arrow_roundtrip(table)
+
+
+@pytest.mark.numpy
+def test_boolean_object_nulls(version):
+    repeats = 100
+    table = pa.Table.from_arrays(
+        [np.array([False, None, True] * repeats, dtype=object)],
+        names=["arr"]
+    )
+    _check_arrow_roundtrip(table)
+
+
+@pytest.mark.pandas
+def test_delete_partial_file_on_error(version):
+    if sys.platform == 'win32':
+        pytest.skip('Windows hangs on to file handle for some reason')
+
+    class CustomClass:
+        pass
+
+    # strings will fail
+    df = pd.DataFrame(
+        {
+            'numbers': range(5),
+            'strings': [b'foo', None, 'bar', CustomClass(), np.nan]},
+        columns=['numbers', 'strings'])
+
+    path = random_path()
+    try:
+        write_feather(df, path, version=version)
+    except Exception:
+        pass
+
+    assert not os.path.exists(path)
+
+
+@pytest.mark.pandas
+def test_strings(version):
+    repeats = 1000
+
+    # Mixed bytes, unicode, strings coerced to binary
+    values = [b'foo', None, 'bar', 'qux', np.nan]
+    df = pd.DataFrame({'strings': values * repeats})
+
+    ex_values = [b'foo', None, b'bar', b'qux', None]
+    expected = pd.DataFrame({'strings': ex_values * repeats})
+    _check_pandas_roundtrip(df, expected, version=version)
+
+    # embedded nulls are ok
+    values = ['foo', None, 'bar', 'qux', None]
+    df = pd.DataFrame({'strings': values * repeats})
+    expected = pd.DataFrame({'strings': values * repeats})
+    _check_pandas_roundtrip(df, expected, version=version)
+
+    values = ['foo', None, 'bar', 'qux', np.nan]
+    df = pd.DataFrame({'strings': values * repeats})
+    ex_values = ['foo', None, 'bar', 'qux', None]
+    expected = pd.DataFrame({'strings': ex_values * repeats})
+    _check_pandas_roundtrip(df, expected, version=version)
+
+
+@pytest.mark.pandas
+def test_empty_strings(version):
+    df = pd.DataFrame({'strings': [''] * 10})
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_all_none(version):
+    df = pd.DataFrame({'all_none': [None] * 10})
+    if version == 1 and pa.pandas_compat._pandas_api.uses_string_dtype():
+        expected = df.astype("str")
+    else:
+        expected = df
+    _check_pandas_roundtrip(df, version=version, expected=expected)
+
+
+@pytest.mark.pandas
+def test_all_null_category(version):
+    # ARROW-1188
+    df = pd.DataFrame({"A": (1, 2, 3), "B": (None, None, None)})
+    df = df.assign(B=df.B.astype("category"))
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_multithreaded_read(version):
+    data = {f'c{i}': [''] * 10
+            for i in range(100)}
+    df = pd.DataFrame(data)
+    _check_pandas_roundtrip(df, use_threads=True, version=version)
+
+
+@pytest.mark.pandas
+def test_nan_as_null(version):
+    # Create a nan that is not numpy.nan
+    values = np.array(['foo', np.nan, np.nan * 2, 'bar'] * 10)
+    df = pd.DataFrame({'strings': values})
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_category(version):
+    repeats = 1000
+    values = ['foo', None, 'bar', 'qux', np.nan]
+    df = pd.DataFrame({'strings': values * repeats})
+    df['strings'] = df['strings'].astype('category')
+
+    values = ['foo', None, 'bar', 'qux', None]
+    expected = pd.DataFrame({'strings': pd.Categorical(values * repeats)})
+    _check_pandas_roundtrip(df, expected, version=version)
+
+
+@pytest.mark.pandas
+def test_timestamp(version):
+    df = pd.DataFrame({'naive': pd.date_range('2016-03-28', periods=10)})
+    df['with_tz'] = (df.naive.dt.tz_localize('utc')
+                     .dt.tz_convert('America/Los_Angeles'))
+
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_timestamp_with_nulls(version):
+    df = pd.DataFrame({'test': [pd.Timestamp(2016, 1, 1),
+                                None,
+                                pd.Timestamp(2016, 1, 3)]})
+    df['with_tz'] = df.test.dt.tz_localize('utc')
+
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+@pytest.mark.xfail(reason="not supported", raises=TypeError)
+def test_timedelta_with_nulls_v1():
+    df = pd.DataFrame({'test': [pd.Timedelta('1 day'),
+                                None,
+                                pd.Timedelta('3 day')]})
+    _check_pandas_roundtrip(df, version=1)
+
+
+@pytest.mark.pandas
+def test_timedelta_with_nulls():
+    df = pd.DataFrame({'test': [pd.Timedelta('1 day'),
+                                None,
+                                pd.Timedelta('3 day')]})
+    _check_pandas_roundtrip(df, version=2)
+
+
+@pytest.mark.pandas
+def test_out_of_float64_timestamp_with_nulls(version):
+    df = pd.DataFrame(
+        {'test': pd.DatetimeIndex([1451606400000000001,
+                                   None, 14516064000030405])})
+    df['with_tz'] = df.test.dt.tz_localize('utc')
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.pandas
+def test_non_string_columns(version):
+    df = pd.DataFrame({0: [1, 2, 3, 4],
+                       1: [True, False, True, False]})
+    expected = df
+
+    if version == 1:
+        expected = df.rename(columns=str)
+    _check_pandas_roundtrip(df, expected, version=version)
+
+
+@pytest.mark.pandas
+@pytest.mark.skipif(not os.path.supports_unicode_filenames,
+                    reason='unicode filenames not supported')
+def test_unicode_filename(version):
+    # GH #209
+    name = (b'Besa_Kavaj\xc3\xab.feather').decode('utf-8')
+    df = pd.DataFrame({'foo': [1, 2, 3, 4]})
+    _check_pandas_roundtrip(df, path=random_path(prefix=name),
+                            version=version)
+
+
+@pytest.mark.pandas
+def test_read_columns(version):
+    df = pd.DataFrame({
+        'foo': [1, 2, 3, 4],
+        'boo': [5, 6, 7, 8],
+        'woo': [1, 3, 5, 7]
+    })
+    expected = df[['boo', 'woo']]
+
+    _check_pandas_roundtrip(df, expected, version=version,
+                            columns=['boo', 'woo'])
+
+
+@pytest.mark.numpy
+def test_overwritten_file(version):
+    path = random_path()
+    TEST_FILES.append(path)
+
+    num_values = 100
+    np.random.seed(0)
+
+    values = np.random.randint(0, 10, size=num_values)
+
+    table = pa.table({'ints': values})
+    write_feather(table, path)
+
+    table = pa.table({'more_ints': values[0:num_values//2]})
+    _check_arrow_roundtrip(table, path=path)
+
+
+@pytest.mark.pandas
+def test_filelike_objects(version):
+    buf = io.BytesIO()
+
+    # the copy makes it non-strided
+    df = pd.DataFrame(np.arange(12).reshape(4, 3),
+                      columns=['a', 'b', 'c']).copy()
+    write_feather(df, buf, version=version)
+
+    buf.seek(0)
+
+    result = read_feather(buf)
+    assert_frame_equal(result, df)
+
+
+@pytest.mark.pandas
+@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
+@pytest.mark.filterwarnings("ignore:DataFrame.to_sparse:FutureWarning")
+def test_sparse_dataframe(version):
+    if not pa.pandas_compat._pandas_api.has_sparse:
+        pytest.skip("version of pandas does not support SparseDataFrame")
+    # GH #221
+    data = {'A': [0, 1, 2],
+            'B': [1, 0, 1]}
+    df = pd.DataFrame(data).to_sparse(fill_value=1)
+    expected = df.to_dense()
+    _check_pandas_roundtrip(df, expected, version=version)
+
+
+@pytest.mark.pandas
+def test_duplicate_columns_pandas():
+
+    # https://github.com/wesm/feather/issues/53
+    # not currently able to handle duplicate columns
+    df = pd.DataFrame(np.arange(12).reshape(4, 3),
+                      columns=list('aaa')).copy()
+    _assert_error_on_write(df, ValueError)
+
+
+def test_duplicate_columns():
+    # only works for version 2
+    table = pa.table([[1, 2, 3], [4, 5, 6], [7, 8, 9]], names=['a', 'a', 'b'])
+    _check_arrow_roundtrip(table)
+    _assert_error_on_write(table, ValueError, version=1)
+
+
+@pytest.mark.pandas
+def test_unsupported():
+    # https://github.com/wesm/feather/issues/240
+    # serializing actual python objects
+
+    # custom python objects
+    class A:
+        pass
+
+    df = pd.DataFrame({'a': [A(), A()]})
+    _assert_error_on_write(df, ValueError)
+
+    # non-strings
+    df = pd.DataFrame({'a': ['a', 1, 2.0]})
+    _assert_error_on_write(df, TypeError)
+
+
+@pytest.mark.pandas
+def test_v2_set_chunksize():
+    df = pd.DataFrame({'A': np.arange(1000)})
+    table = pa.table(df)
+
+    buf = io.BytesIO()
+    write_feather(table, buf, chunksize=250, version=2)
+
+    result = buf.getvalue()
+
+    ipc_file = pa.ipc.open_file(pa.BufferReader(result))
+    assert ipc_file.num_record_batches == 4
+    assert len(ipc_file.get_batch(0)) == 250
+
+
+@pytest.mark.pandas
+@pytest.mark.lz4
+@pytest.mark.snappy
+@pytest.mark.zstd
+def test_v2_compression_options():
+    df = pd.DataFrame({'A': np.arange(1000)})
+
+    cases = [
+        # compression, compression_level
+        ('uncompressed', None),
+        ('lz4', None),
+        ('lz4', 1),
+        ('lz4', 12),
+        ('zstd', 1),
+        ('zstd', 10)
+    ]
+
+    for compression, compression_level in cases:
+        _check_pandas_roundtrip(df, compression=compression,
+                                compression_level=compression_level)
+
+    buf = io.BytesIO()
+
+    # Trying to compress with V1
+    with pytest.raises(
+            ValueError,
+            match="Feather V1 files do not support compression option"):
+        write_feather(df, buf, compression='lz4', version=1)
+
+    # Trying to set chunksize with V1
+    with pytest.raises(
+            ValueError,
+            match="Feather V1 files do not support chunksize option"):
+        write_feather(df, buf, chunksize=4096, version=1)
+
+    # Unsupported compressor
+    with pytest.raises(ValueError,
+                       match='compression="snappy" not supported'):
+        write_feather(df, buf, compression='snappy')
+
+
+@pytest.mark.numpy
+def test_v2_lz4_default_compression():
+    # ARROW-8750: Make sure that the compression=None option selects lz4 if
+    # it's available
+    if not pa.Codec.is_available('lz4_frame'):
+        pytest.skip("LZ4 compression support is not built in C++")
+
+    # some highly compressible data
+    t = pa.table([np.repeat(0, 100000)], names=['f0'])
+
+    buf = io.BytesIO()
+    write_feather(t, buf)
+    default_result = buf.getvalue()
+
+    buf = io.BytesIO()
+    write_feather(t, buf, compression='uncompressed')
+    uncompressed_result = buf.getvalue()
+
+    assert len(default_result) < len(uncompressed_result)
+
+
+def test_v1_unsupported_types():
+    table = pa.table([pa.array([[1, 2, 3], [], None])], names=['f0'])
+
+    buf = io.BytesIO()
+    with pytest.raises(TypeError,
+                       match=("Unsupported Feather V1 type: "
+                              "list<item: int64>. "
+                              "Use V2 format to serialize all Arrow types.")):
+        write_feather(table, buf, version=1)
+
+
+@pytest.mark.slow
+@pytest.mark.pandas
+def test_large_dataframe(version):
+    df = pd.DataFrame({'A': np.arange(400000000)})
+    _check_pandas_roundtrip(df, version=version)
+
+
+@pytest.mark.large_memory
+@pytest.mark.pandas
+def test_chunked_binary_error_message():
+    # ARROW-3058: As Feather does not yet support chunked columns, we at least
+    # make sure it's clear to the user what is going on
+
+    # 2^31 + 1 bytes
+    values = [b'x'] + [
+        b'x' * (1 << 20)
+    ] * 2 * (1 << 10)
+    df = pd.DataFrame({'byte_col': values})
+
+    # Works fine with version 2
+    buf = io.BytesIO()
+    write_feather(df, buf, version=2)
+    result = read_feather(pa.BufferReader(buf.getvalue()))
+    assert_frame_equal(result, df)
+
+    with pytest.raises(ValueError, match="'byte_col' exceeds 2GB maximum "
+                       "capacity of a Feather binary column. This restriction "
+                       "may be lifted in the future"):
+        write_feather(df, io.BytesIO(), version=1)
+
+
+def test_feather_without_pandas(tempdir, version):
+    # ARROW-8345
+    table = pa.table([pa.array([1, 2, 3])], names=['f0'])
+    path = str(tempdir / "data.feather")
+    _check_arrow_roundtrip(table, path)
+
+
+@pytest.mark.pandas
+def test_read_column_selection(version):
+    # ARROW-8641
+    df = pd.DataFrame(np.arange(12).reshape(4, 3), columns=['a', 'b', 'c'])
+
+    # select columns as string names or integer indices
+    _check_pandas_roundtrip(
+        df, columns=['a', 'c'], expected=df[['a', 'c']], version=version)
+    _check_pandas_roundtrip(
+        df, columns=[0, 2], expected=df[['a', 'c']], version=version)
+
+    # different order is followed
+    _check_pandas_roundtrip(
+        df, columns=['b', 'a'], expected=df[['b', 'a']], version=version)
+    _check_pandas_roundtrip(
+        df, columns=[1, 0], expected=df[['b', 'a']], version=version)
+
+
+def test_read_column_duplicated_selection(tempdir, version):
+    # duplicated columns in the column selection
+    table = pa.table([[1, 2, 3], [4, 5, 6], [7, 8, 9]], names=['a', 'b', 'c'])
+    path = str(tempdir / "data.feather")
+    write_feather(table, path, version=version)
+
+    expected = pa.table([[1, 2, 3], [4, 5, 6], [1, 2, 3]],
+                        names=['a', 'b', 'a'])
+    for col_selection in [['a', 'b', 'a'], [0, 1, 0]]:
+        result = read_table(path, columns=col_selection)
+        assert result.equals(expected)
+
+
+def test_read_column_duplicated_in_file(tempdir):
+    # duplicated columns in feather file (only works for feather v2)
+    table = pa.table([[1, 2, 3], [4, 5, 6], [7, 8, 9]], names=['a', 'b', 'a'])
+    path = str(tempdir / "data.feather")
+    write_feather(table, path, version=2)
+
+    # no selection works fine
+    result = read_table(path)
+    assert result.equals(table)
+
+    # selection with indices works
+    result = read_table(path, columns=[0, 2])
+    assert result.column_names == ['a', 'a']
+
+    # selection with column names errors
+    with pytest.raises(ValueError):
+        read_table(path, columns=['a', 'b'])
+
+
+def test_read_column_with_generator(tempdir, version):
+    table = pa.table([[1, 2, 3], [4, 5, 6], [7, 8, 9]], names=['a', 'b', 'c'])
+    path = str(tempdir / "data.feather")
+    write_feather(table, path, version=version)
+    columns_gen = (x for x in ['a', 'b', 'c'])
+    with pytest.raises(TypeError, match="Columns must be a sequence"):
+        read_table(path, columns=columns_gen)
+
+
+def test_nested_types(compression):
+    # https://issues.apache.org/jira/browse/ARROW-8860
+    table = pa.table({'col': pa.StructArray.from_arrays(
+        [[0, 1, 2], [1, 2, 3]], names=["f1", "f2"])})
+    _check_arrow_roundtrip(table, compression=compression)
+
+    table = pa.table({'col': pa.array([[1, 2], [3, 4]])})
+    _check_arrow_roundtrip(table, compression=compression)
+
+    table = pa.table({'col': pa.array([[[1, 2], [3, 4]], [[5, 6], None]])})
+    _check_arrow_roundtrip(table, compression=compression)
+
+
+@pytest.mark.numpy
+@h.given(past.all_tables, st.sampled_from(["uncompressed", "lz4", "zstd"]))
+def test_roundtrip(table, compression):
+    _check_arrow_roundtrip(table, compression=compression)
+
+
+@pytest.mark.lz4
+def test_feather_v017_experimental_compression_backward_compatibility(datadir):
+    # ARROW-11163 - ensure newer pyarrow versions can read the old feather
+    # files from version 0.17.0 with experimental compression support (before
+    # it was officially added to IPC format in 1.0.0)
+
+    # file generated with:
+    #     table = pa.table({'a': range(5)})
+    #     from pyarrow import feather
+    #     feather.write_feather(
+    #         table, "v0.17.0.version.2-compression.lz4.feather",
+    #         compression="lz4", version=2)
+    expected = pa.table({'a': range(5)})
+    result = read_table(datadir / "v0.17.0.version.2-compression.lz4.feather")
+    assert result.equals(expected)
+
+
+@pytest.mark.pandas
+def test_preserve_index_pandas(version):
+    df = pd.DataFrame({'a': [1, 2, 3]}, index=['a', 'b', 'c'])
+
+    if version == 1:
+        expected = df.reset_index(drop=True).rename(columns=str)
+    else:
+        expected = df
+
+    _check_pandas_roundtrip(df, expected, version=version)
+
+
+@pytest.mark.pandas
+def test_feather_datetime_resolution_arrow_to_pandas(tempdir):
+    # ARROW-17192 - ensure timestamp_as_object=True (together with other
+    # **kwargs) can be passed in read_feather to to_pandas.
+
+    from datetime import datetime
+    df = pd.DataFrame({"date": [
+        datetime.fromisoformat("1654-01-01"),
+        datetime.fromisoformat("1920-01-01"), ],
+    })
+    write_feather(df, tempdir / "test_resolution.feather")
+
+    expected_0 = datetime.fromisoformat("1654-01-01")
+    expected_1 = datetime.fromisoformat("1920-01-01")
+
+    result = read_feather(tempdir / "test_resolution.feather",
+                          timestamp_as_object=True)
+
+    assert expected_0 == result['date'][0]
+    assert expected_1 == result['date'][1]
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diff --git a/shared/package-files/40/40c356d076b1851de03ff2eac5839906cca90c3a6e0a3bb214fbfb48968c8fd8/_tanhsinh.py b/shared/package-files/40/40c356d076b1851de03ff2eac5839906cca90c3a6e0a3bb214fbfb48968c8fd8/_tanhsinh.py
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@@ -0,0 +1,1384 @@
+# mypy: disable-error-code="attr-defined"
+import math
+import numpy as np
+from scipy import special
+import scipy._lib._elementwise_iterative_method as eim
+from scipy._lib._util import _RichResult
+from scipy._lib._array_api import (array_namespace, xp_copy, xp_ravel,
+                                   xp_real, xp_take_along_axis)
+
+
+__all__ = ['nsum']
+
+
+# todo:
+#  figure out warning situation
+#  address https://github.com/scipy/scipy/pull/18650#discussion_r1233032521
+#  without `minweight`, we are also suppressing infinities within the interval.
+#    Is that OK? If so, we can probably get rid of `status=3`.
+#  Add heuristic to stop when improvement is too slow / antithrashing
+#  support singularities? interval subdivision? this feature will be added
+#    eventually, but do we adjust the interface now?
+#  When doing log-integration, should the tolerances control the error of the
+#    log-integral or the error of the integral?  The trouble is that `log`
+#    inherently looses some precision so it may not be possible to refine
+#    the integral further. Example: 7th moment of stats.f(15, 20)
+#  respect function evaluation limit?
+#  make public?
+
+
+def tanhsinh(f, a, b, *, args=(), log=False, maxlevel=None, minlevel=2,
+             atol=None, rtol=None, preserve_shape=False, callback=None):
+    """Evaluate a convergent integral numerically using tanh-sinh quadrature.
+
+    In practice, tanh-sinh quadrature achieves quadratic convergence for
+    many integrands: the number of accurate *digits* scales roughly linearly
+    with the number of function evaluations [1]_.
+
+    Either or both of the limits of integration may be infinite, and
+    singularities at the endpoints are acceptable. Divergent integrals and
+    integrands with non-finite derivatives or singularities within an interval
+    are out of scope, but the latter may be evaluated be calling `tanhsinh` on
+    each sub-interval separately.
+
+    Parameters
+    ----------
+    f : callable
+        The function to be integrated. The signature must be::
+
+            f(xi: ndarray, *argsi) -> ndarray
+
+        where each element of ``xi`` is a finite real number and ``argsi`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``xi``. `f` must be an elementwise function: see documentation of parameter
+        `preserve_shape` for details. It must not mutate the array ``xi`` or the arrays
+        in ``argsi``.
+        If ``f`` returns a value with complex dtype when evaluated at
+        either endpoint, subsequent arguments ``x`` will have complex dtype
+        (but zero imaginary part).
+    a, b : float array_like
+        Real lower and upper limits of integration. Must be broadcastable with one
+        another and with arrays in `args`. Elements may be infinite.
+    args : tuple of array_like, optional
+        Additional positional array arguments to be passed to `f`. Arrays
+        must be broadcastable with one another and the arrays of `a` and `b`.
+        If the callable for which the root is desired requires arguments that are
+        not broadcastable with `x`, wrap that callable with `f` such that `f`
+        accepts only `x` and broadcastable ``*args``.
+    log : bool, default: False
+        Setting to True indicates that `f` returns the log of the integrand
+        and that `atol` and `rtol` are expressed as the logs of the absolute
+        and relative errors. In this case, the result object will contain the
+        log of the integral and error. This is useful for integrands for which
+        numerical underflow or overflow would lead to inaccuracies.
+        When ``log=True``, the integrand (the exponential of `f`) must be real,
+        but it may be negative, in which case the log of the integrand is a
+        complex number with an imaginary part that is an odd multiple of π.
+    maxlevel : int, default: 10
+        The maximum refinement level of the algorithm.
+
+        At the zeroth level, `f` is called once, performing 16 function
+        evaluations. At each subsequent level, `f` is called once more,
+        approximately doubling the number of function evaluations that have
+        been performed. Accordingly, for many integrands, each successive level
+        will double the number of accurate digits in the result (up to the
+        limits of floating point precision).
+
+        The algorithm will terminate after completing level `maxlevel` or after
+        another termination condition is satisfied, whichever comes first.
+    minlevel : int, default: 2
+        The level at which to begin iteration (default: 2). This does not
+        change the total number of function evaluations or the abscissae at
+        which the function is evaluated; it changes only the *number of times*
+        `f` is called. If ``minlevel=k``, then the integrand is evaluated at
+        all abscissae from levels ``0`` through ``k`` in a single call.
+        Note that if `minlevel` exceeds `maxlevel`, the provided `minlevel` is
+        ignored, and `minlevel` is set equal to `maxlevel`.
+    atol, rtol : float, optional
+        Absolute termination tolerance (default: 0) and relative termination
+        tolerance (default: ``eps**0.75``, where ``eps`` is the precision of
+        the result dtype), respectively.  Iteration will stop when
+        ``res.error < atol`` or  ``res.error < res.integral * rtol``. The error
+        estimate is as described in [1]_ Section 5 but with a lower bound of
+        ``eps * res.integral``. While not theoretically rigorous or
+        conservative, it is said to work well in practice. Must be non-negative
+        and finite if `log` is False, and must be expressed as the log of a
+        non-negative and finite number if `log` is True.
+    preserve_shape : bool, default: False
+        In the following, "arguments of `f`" refers to the array ``xi`` and
+        any arrays within ``argsi``. Let ``shape`` be the broadcasted shape
+        of `a`, `b`, and all elements of `args` (which is conceptually
+        distinct from ``xi` and ``argsi`` passed into `f`).
+
+        - When ``preserve_shape=False`` (default), `f` must accept arguments
+          of *any* broadcastable shapes.
+
+        - When ``preserve_shape=True``, `f` must accept arguments of shape
+          ``shape`` *or* ``shape + (n,)``, where ``(n,)`` is the number of
+          abscissae at which the function is being evaluated.
+
+        In either case, for each scalar element ``xi[j]`` within ``xi``, the array
+        returned by `f` must include the scalar ``f(xi[j])`` at the same index.
+        Consequently, the shape of the output is always the shape of the input
+        ``xi``.
+
+        See Examples.
+
+    callback : callable, optional
+        An optional user-supplied function to be called before the first
+        iteration and after each iteration.
+        Called as ``callback(res)``, where ``res`` is a ``_RichResult``
+        similar to that returned by `_differentiate` (but containing the
+        current iterate's values of all variables). If `callback` raises a
+        ``StopIteration``, the algorithm will terminate immediately and
+        `tanhsinh` will return a result object. `callback` must not mutate
+        `res` or its attributes.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. (The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.)
+
+        success : bool array
+            ``True`` when the algorithm terminated successfully (status ``0``).
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            ``0`` : The algorithm converged to the specified tolerances.
+            ``-1`` : (unused)
+            ``-2`` : The maximum number of iterations was reached.
+            ``-3`` : A non-finite value was encountered.
+            ``-4`` : Iteration was terminated by `callback`.
+            ``1`` : The algorithm is proceeding normally (in `callback` only).
+
+        integral : float array
+            An estimate of the integral.
+        error : float array
+            An estimate of the error. Only available if level two or higher
+            has been completed; otherwise NaN.
+        maxlevel : int array
+            The maximum refinement level used.
+        nfev : int array
+            The number of points at which `f` was evaluated.
+
+    See Also
+    --------
+    quad
+
+    Notes
+    -----
+    Implements the algorithm as described in [1]_ with minor adaptations for
+    finite-precision arithmetic, including some described by [2]_ and [3]_. The
+    tanh-sinh scheme was originally introduced in [4]_.
+
+    Due to floating-point error in the abscissae, the function may be evaluated
+    at the endpoints of the interval during iterations, but the values returned by
+    the function at the endpoints will be ignored.
+
+    References
+    ----------
+    .. [1] Bailey, David H., Karthik Jeyabalan, and Xiaoye S. Li. "A comparison of
+           three high-precision quadrature schemes." Experimental Mathematics 14.3
+           (2005): 317-329.
+    .. [2] Vanherck, Joren, Bart Sorée, and Wim Magnus. "Tanh-sinh quadrature for
+           single and multiple integration using floating-point arithmetic."
+           arXiv preprint arXiv:2007.15057 (2020).
+    .. [3] van Engelen, Robert A.  "Improving the Double Exponential Quadrature
+           Tanh-Sinh, Sinh-Sinh and Exp-Sinh Formulas."
+           https://www.genivia.com/files/qthsh.pdf
+    .. [4] Takahasi, Hidetosi, and Masatake Mori. "Double exponential formulas for
+           numerical integration." Publications of the Research Institute for
+           Mathematical Sciences 9.3 (1974): 721-741.
+
+    Examples
+    --------
+    Evaluate the Gaussian integral:
+
+    >>> import numpy as np
+    >>> from scipy.integrate import tanhsinh
+    >>> def f(x):
+    ...     return np.exp(-x**2)
+    >>> res = tanhsinh(f, -np.inf, np.inf)
+    >>> res.integral  # true value is np.sqrt(np.pi), 1.7724538509055159
+    1.7724538509055159
+    >>> res.error  # actual error is 0
+    4.0007963937534104e-16
+
+    The value of the Gaussian function (bell curve) is nearly zero for
+    arguments sufficiently far from zero, so the value of the integral
+    over a finite interval is nearly the same.
+
+    >>> tanhsinh(f, -20, 20).integral
+    1.772453850905518
+
+    However, with unfavorable integration limits, the integration scheme
+    may not be able to find the important region.
+
+    >>> tanhsinh(f, -np.inf, 1000).integral
+    4.500490856616431
+
+    In such cases, or when there are singularities within the interval,
+    break the integral into parts with endpoints at the important points.
+
+    >>> tanhsinh(f, -np.inf, 0).integral + tanhsinh(f, 0, 1000).integral
+    1.772453850905404
+
+    For integration involving very large or very small magnitudes, use
+    log-integration. (For illustrative purposes, the following example shows a
+    case in which both regular and log-integration work, but for more extreme
+    limits of integration, log-integration would avoid the underflow
+    experienced when evaluating the integral normally.)
+
+    >>> res = tanhsinh(f, 20, 30, rtol=1e-10)
+    >>> res.integral, res.error
+    (4.7819613911309014e-176, 4.670364401645202e-187)
+    >>> def log_f(x):
+    ...     return -x**2
+    >>> res = tanhsinh(log_f, 20, 30, log=True, rtol=np.log(1e-10))
+    >>> np.exp(res.integral), np.exp(res.error)
+    (4.7819613911306924e-176, 4.670364401645093e-187)
+
+    The limits of integration and elements of `args` may be broadcastable
+    arrays, and integration is performed elementwise.
+
+    >>> from scipy import stats
+    >>> dist = stats.gausshyper(13.8, 3.12, 2.51, 5.18)
+    >>> a, b = dist.support()
+    >>> x = np.linspace(a, b, 100)
+    >>> res = tanhsinh(dist.pdf, a, x)
+    >>> ref = dist.cdf(x)
+    >>> np.allclose(res.integral, ref)
+    True
+
+    By default, `preserve_shape` is False, and therefore the callable
+    `f` may be called with arrays of any broadcastable shapes.
+    For example:
+
+    >>> shapes = []
+    >>> def f(x, c):
+    ...    shape = np.broadcast_shapes(x.shape, c.shape)
+    ...    shapes.append(shape)
+    ...    return np.sin(c*x)
+    >>>
+    >>> c = [1, 10, 30, 100]
+    >>> res = tanhsinh(f, 0, 1, args=(c,), minlevel=1)
+    >>> shapes
+    [(4,), (4, 34), (4, 32), (3, 64), (2, 128), (1, 256)]
+
+    To understand where these shapes are coming from - and to better
+    understand how `tanhsinh` computes accurate results - note that
+    higher values of ``c`` correspond with higher frequency sinusoids.
+    The higher frequency sinusoids make the integrand more complicated,
+    so more function evaluations are required to achieve the target
+    accuracy:
+
+    >>> res.nfev
+    array([ 67, 131, 259, 515], dtype=int32)
+
+    The initial ``shape``, ``(4,)``, corresponds with evaluating the
+    integrand at a single abscissa and all four frequencies; this is used
+    for input validation and to determine the size and dtype of the arrays
+    that store results. The next shape corresponds with evaluating the
+    integrand at an initial grid of abscissae and all four frequencies.
+    Successive calls to the function double the total number of abscissae at
+    which the function has been evaluated. However, in later function
+    evaluations, the integrand is evaluated at fewer frequencies because
+    the corresponding integral has already converged to the required
+    tolerance. This saves function evaluations to improve performance, but
+    it requires the function to accept arguments of any shape.
+
+    "Vector-valued" integrands, such as those written for use with
+    `scipy.integrate.quad_vec`, are unlikely to satisfy this requirement.
+    For example, consider
+
+    >>> def f(x):
+    ...    return [x, np.sin(10*x), np.cos(30*x), x*np.sin(100*x)**2]
+
+    This integrand is not compatible with `tanhsinh` as written; for instance,
+    the shape of the output will not be the same as the shape of ``x``. Such a
+    function *could* be converted to a compatible form with the introduction of
+    additional parameters, but this would be inconvenient. In such cases,
+    a simpler solution would be to use `preserve_shape`.
+
+    >>> shapes = []
+    >>> def f(x):
+    ...     shapes.append(x.shape)
+    ...     x0, x1, x2, x3 = x
+    ...     return [x0, np.sin(10*x1), np.cos(30*x2), x3*np.sin(100*x3)]
+    >>>
+    >>> a = np.zeros(4)
+    >>> res = tanhsinh(f, a, 1, preserve_shape=True)
+    >>> shapes
+    [(4,), (4, 66), (4, 64), (4, 128), (4, 256)]
+
+    Here, the broadcasted shape of `a` and `b` is ``(4,)``. With
+    ``preserve_shape=True``, the function may be called with argument
+    ``x`` of shape ``(4,)`` or ``(4, n)``, and this is what we observe.
+
+    """
+    maxfun = None  # unused right now
+    (f, a, b, log, maxfun, maxlevel, minlevel,
+     atol, rtol, args, preserve_shape, callback, xp) = _tanhsinh_iv(
+        f, a, b, log, maxfun, maxlevel, minlevel, atol,
+        rtol, args, preserve_shape, callback)
+
+    # Initialization
+    # `eim._initialize` does several important jobs, including
+    # ensuring that limits, each of the `args`, and the output of `f`
+    # broadcast correctly and are of consistent types. To save a function
+    # evaluation, I pass the midpoint of the integration interval. This comes
+    # at a cost of some gymnastics to ensure that the midpoint has the right
+    # shape and dtype. Did you know that 0d and >0d arrays follow different
+    # type promotion rules?
+    with np.errstate(over='ignore', invalid='ignore', divide='ignore'):
+        c = xp.reshape((xp_ravel(a) + xp_ravel(b))/2, a.shape)
+        inf_a, inf_b = xp.isinf(a), xp.isinf(b)
+        c[inf_a] = b[inf_a] - 1.  # takes care of infinite a
+        c[inf_b] = a[inf_b] + 1.  # takes care of infinite b
+        c[inf_a & inf_b] = 0.  # takes care of infinite a and b
+        temp = eim._initialize(f, (c,), args, complex_ok=True,
+                               preserve_shape=preserve_shape, xp=xp)
+    f, xs, fs, args, shape, dtype, xp = temp
+    a = xp_ravel(xp.astype(xp.broadcast_to(a, shape), dtype))
+    b = xp_ravel(xp.astype(xp.broadcast_to(b, shape), dtype))
+
+    # Transform improper integrals
+    a, b, a0, negative, abinf, ainf, binf = _transform_integrals(a, b, xp)
+
+    # Define variables we'll need
+    nit, nfev = 0, 1  # one function evaluation performed above
+    zero = -xp.inf if log else 0
+    pi = xp.asarray(xp.pi, dtype=dtype)[()]
+    maxiter = maxlevel - minlevel + 1
+    eps = xp.finfo(dtype).eps
+    if rtol is None:
+        rtol = 0.75*math.log(eps) if log else eps**0.75
+
+    Sn = xp_ravel(xp.full(shape, zero, dtype=dtype))  # latest integral estimate
+    Sn[xp.isnan(a) | xp.isnan(b) | xp.isnan(fs[0])] = xp.nan
+    Sk = xp.reshape(xp.empty_like(Sn), (-1, 1))[:, 0:0]  # all integral estimates
+    aerr = xp_ravel(xp.full(shape, xp.nan, dtype=dtype))  # absolute error
+    status = xp_ravel(xp.full(shape, eim._EINPROGRESS, dtype=xp.int32))
+    h0 = _get_base_step(dtype, xp)
+    h0 = xp_real(h0) # base step
+
+    # For term `d4` of error estimate ([1] Section 5), we need to keep the
+    # most extreme abscissae and corresponding `fj`s, `wj`s in Euler-Maclaurin
+    # sum. Here, we initialize these variables.
+    xr0 = xp_ravel(xp.full(shape, -xp.inf, dtype=dtype))
+    fr0 = xp_ravel(xp.full(shape, xp.nan, dtype=dtype))
+    wr0 = xp_ravel(xp.zeros(shape, dtype=dtype))
+    xl0 = xp_ravel(xp.full(shape, xp.inf, dtype=dtype))
+    fl0 = xp_ravel(xp.full(shape, xp.nan, dtype=dtype))
+    wl0 = xp_ravel(xp.zeros(shape, dtype=dtype))
+    d4 = xp_ravel(xp.zeros(shape, dtype=dtype))
+
+    work = _RichResult(
+        Sn=Sn, Sk=Sk, aerr=aerr, h=h0, log=log, dtype=dtype, pi=pi, eps=eps,
+        a=xp.reshape(a, (-1, 1)), b=xp.reshape(b, (-1, 1)),  # integration limits
+        n=minlevel, nit=nit, nfev=nfev, status=status,  # iter/eval counts
+        xr0=xr0, fr0=fr0, wr0=wr0, xl0=xl0, fl0=fl0, wl0=wl0, d4=d4,  # err est
+        ainf=ainf, binf=binf, abinf=abinf, a0=xp.reshape(a0, (-1, 1)),  # transforms
+        # Store the xjc/wj pair cache in an object so they can't get compressed
+        # Using RichResult to allow dot notation, but a dictionary would suffice
+        pair_cache=_RichResult(xjc=None, wj=None, indices=[0], h0=None))  # pair cache
+
+    # Constant scalars don't need to be put in `work` unless they need to be
+    # passed outside `tanhsinh`. Examples: atol, rtol, h0, minlevel.
+
+    # Correspondence between terms in the `work` object and the result
+    res_work_pairs = [('status', 'status'), ('integral', 'Sn'),
+                      ('error', 'aerr'), ('nit', 'nit'), ('nfev', 'nfev')]
+
+    def pre_func_eval(work):
+        # Determine abscissae at which to evaluate `f`
+        work.h = h0 / 2**work.n
+        xjc, wj = _get_pairs(work.n, h0, dtype=work.dtype,
+                             inclusive=(work.n == minlevel), xp=xp, work=work)
+        work.xj, work.wj = _transform_to_limits(xjc, wj, work.a, work.b, xp)
+
+        # Perform abscissae substitutions for infinite limits of integration
+        xj = xp_copy(work.xj)
+        # use xp_real here to avoid cupy/cupy#8434
+        xj[work.abinf] = xj[work.abinf] / (1 - xp_real(xj[work.abinf])**2)
+        xj[work.binf] = 1/xj[work.binf] - 1 + work.a0[work.binf]
+        xj[work.ainf] *= -1
+        return xj
+
+    def post_func_eval(x, fj, work):
+        # Weight integrand as required by substitutions for infinite limits
+        if work.log:
+            fj[work.abinf] += (xp.log(1 + work.xj[work.abinf]**2)
+                               - 2*xp.log(1 - work.xj[work.abinf]**2))
+            fj[work.binf] -= 2 * xp.log(work.xj[work.binf])
+        else:
+            fj[work.abinf] *= ((1 + work.xj[work.abinf]**2) /
+                               (1 - work.xj[work.abinf]**2)**2)
+            fj[work.binf] *= work.xj[work.binf]**-2.
+
+        # Estimate integral with Euler-Maclaurin Sum
+        fjwj, Sn = _euler_maclaurin_sum(fj, work, xp)
+        if work.Sk.shape[-1]:
+            Snm1 = work.Sk[:, -1]
+            Sn = (special.logsumexp(xp.stack([Snm1 - math.log(2), Sn]), axis=0) if log
+                  else Snm1 / 2 + Sn)
+
+        work.fjwj = fjwj
+        work.Sn = Sn
+
+    def check_termination(work):
+        """Terminate due to convergence or encountering non-finite values"""
+        stop = xp.zeros(work.Sn.shape, dtype=bool)
+
+        # Terminate before first iteration if integration limits are equal
+        if work.nit == 0:
+            i = xp_ravel(work.a == work.b)  # ravel singleton dimension
+            zero = xp.asarray(-xp.inf if log else 0.)
+            zero = xp.full(work.Sn.shape, zero, dtype=Sn.dtype)
+            zero[xp.isnan(Sn)] = xp.nan
+            work.Sn[i] = zero[i]
+            work.aerr[i] = zero[i]
+            work.status[i] = eim._ECONVERGED
+            stop[i] = True
+        else:
+            # Terminate if convergence criterion is met
+            rerr, aerr = _estimate_error(work, xp)
+            i = (rerr < rtol) | (aerr < atol)
+            work.aerr =  xp.reshape(xp.astype(aerr, work.dtype), work.Sn.shape)
+            work.status[i] = eim._ECONVERGED
+            stop[i] = True
+
+        # Terminate if integral estimate becomes invalid
+        if log:
+            Sn_real = xp_real(work.Sn)
+            Sn_pos_inf = xp.isinf(Sn_real) & (Sn_real > 0)
+            i = (Sn_pos_inf | xp.isnan(work.Sn)) & ~stop
+        else:
+            i = ~xp.isfinite(work.Sn) & ~stop
+        work.status[i] = eim._EVALUEERR
+        stop[i] = True
+
+        return stop
+
+    def post_termination_check(work):
+        work.n += 1
+        work.Sk = xp.concat((work.Sk, work.Sn[:, xp.newaxis]), axis=-1)
+        return
+
+    def customize_result(res, shape):
+        # If the integration limits were such that b < a, we reversed them
+        # to perform the calculation, and the final result needs to be negated.
+        if log and xp.any(negative):
+            dtype = res['integral'].dtype
+            pi = xp.asarray(xp.pi, dtype=dtype)[()]
+            j = xp.asarray(1j, dtype=xp.complex64)[()]  # minimum complex type
+            res['integral'] = res['integral'] + negative*pi*j
+        else:
+            res['integral'][negative] *= -1
+
+        # For this algorithm, it seems more appropriate to report the maximum
+        # level rather than the number of iterations in which it was performed.
+        res['maxlevel'] = minlevel + res['nit'] - 1
+        res['maxlevel'][res['nit'] == 0] = -1
+        del res['nit']
+        return shape
+
+    # Suppress all warnings initially, since there are many places in the code
+    # for which this is expected behavior.
+    with np.errstate(over='ignore', invalid='ignore', divide='ignore'):
+        res = eim._loop(work, callback, shape, maxiter, f, args, dtype, pre_func_eval,
+                        post_func_eval, check_termination, post_termination_check,
+                        customize_result, res_work_pairs, xp, preserve_shape)
+    return res
+
+
+def _get_base_step(dtype, xp):
+    # Compute the base step length for the provided dtype. Theoretically, the
+    # Euler-Maclaurin sum is infinite, but it gets cut off when either the
+    # weights underflow or the abscissae cannot be distinguished from the
+    # limits of integration. The latter happens to occur first for float32 and
+    # float64, and it occurs when `xjc` (the abscissa complement)
+    # in `_compute_pair` underflows. We can solve for the argument `tmax` at
+    # which it will underflow using [2] Eq. 13.
+    fmin = 4*xp.finfo(dtype).smallest_normal  # stay a little away from the limit
+    tmax = math.asinh(math.log(2/fmin - 1) / xp.pi)
+
+    # Based on this, we can choose a base step size `h` for level 0.
+    # The number of function evaluations will be `2 + m*2^(k+1)`, where `k` is
+    # the level and `m` is an integer we get to choose. I choose
+    # m = _N_BASE_STEPS = `8` somewhat arbitrarily, but a rationale is that a
+    # power of 2 makes floating point arithmetic more predictable. It also
+    # results in a base step size close to `1`, which is what [1] uses (and I
+    # used here until I found [2] and these ideas settled).
+    h0 = tmax / _N_BASE_STEPS
+    return xp.asarray(h0, dtype=dtype)[()]
+
+
+_N_BASE_STEPS = 8
+
+
+def _compute_pair(k, h0, xp):
+    # Compute the abscissa-weight pairs for each level k. See [1] page 9.
+
+    # For now, we compute and store in 64-bit precision. If higher-precision
+    # data types become better supported, it would be good to compute these
+    # using the highest precision available. Or, once there is an Array API-
+    # compatible arbitrary precision array, we can compute at the required
+    # precision.
+
+    # "....each level k of abscissa-weight pairs uses h = 2 **-k"
+    # We adapt to floating point arithmetic using ideas of [2].
+    h = h0 / 2**k
+    max = _N_BASE_STEPS * 2**k
+
+    # For iterations after the first, "....the integrand function needs to be
+    # evaluated only at the odd-indexed abscissas at each level."
+    j = xp.arange(max+1) if k == 0 else xp.arange(1, max+1, 2)
+    jh = j * h
+
+    # "In this case... the weights wj = u1/cosh(u2)^2, where..."
+    pi_2 = xp.pi / 2
+    u1 = pi_2*xp.cosh(jh)
+    u2 = pi_2*xp.sinh(jh)
+    # Denominators get big here. Overflow then underflow doesn't need warning.
+    # with np.errstate(under='ignore', over='ignore'):
+    wj = u1 / xp.cosh(u2)**2
+    # "We actually store 1-xj = 1/(...)."
+    xjc = 1 / (xp.exp(u2) * xp.cosh(u2))  # complement of xj = xp.tanh(u2)
+
+    # When level k == 0, the zeroth xj corresponds with xj = 0. To simplify
+    # code, the function will be evaluated there twice; each gets half weight.
+    wj[0] = wj[0] / 2 if k == 0 else wj[0]
+
+    return xjc, wj  # store at full precision
+
+
+def _pair_cache(k, h0, xp, work):
+    # Cache the abscissa-weight pairs up to a specified level.
+    # Abscissae and weights of consecutive levels are concatenated.
+    # `index` records the indices that correspond with each level:
+    # `xjc[index[k]:index[k+1]` extracts the level `k` abscissae.
+    if not isinstance(h0, type(work.pair_cache.h0)) or h0 != work.pair_cache.h0:
+        work.pair_cache.xjc = xp.empty(0)
+        work.pair_cache.wj = xp.empty(0)
+        work.pair_cache.indices = [0]
+
+    xjcs = [work.pair_cache.xjc]
+    wjs = [work.pair_cache.wj]
+
+    for i in range(len(work.pair_cache.indices)-1, k + 1):
+        xjc, wj = _compute_pair(i, h0, xp)
+        xjcs.append(xjc)
+        wjs.append(wj)
+        work.pair_cache.indices.append(work.pair_cache.indices[-1] + xjc.shape[0])
+
+    work.pair_cache.xjc = xp.concat(xjcs)
+    work.pair_cache.wj = xp.concat(wjs)
+    work.pair_cache.h0 = h0
+
+
+def _get_pairs(k, h0, inclusive, dtype, xp, work):
+    # Retrieve the specified abscissa-weight pairs from the cache
+    # If `inclusive`, return all up to and including the specified level
+    if (len(work.pair_cache.indices) <= k+2
+        or not isinstance (h0, type(work.pair_cache.h0))
+        or h0 != work.pair_cache.h0):
+            _pair_cache(k, h0, xp, work)
+
+    xjc = work.pair_cache.xjc
+    wj = work.pair_cache.wj
+    indices = work.pair_cache.indices
+
+    start = 0 if inclusive else indices[k]
+    end = indices[k+1]
+
+    return xp.astype(xjc[start:end], dtype), xp.astype(wj[start:end], dtype)
+
+
+def _transform_to_limits(xjc, wj, a, b, xp):
+    # Transform integral according to user-specified limits. This is just
+    # math that follows from the fact that the standard limits are (-1, 1).
+    # Note: If we had stored xj instead of xjc, we would have
+    # xj = alpha * xj + beta, where beta = (a + b)/2
+    alpha = (b - a) / 2
+    xj = xp.concat((-alpha * xjc + b, alpha * xjc + a), axis=-1)
+    wj = wj*alpha  # arguments get broadcasted, so we can't use *=
+    wj = xp.concat((wj, wj), axis=-1)
+
+    # Points at the boundaries can be generated due to finite precision
+    # arithmetic, but these function values aren't supposed to be included in
+    # the Euler-Maclaurin sum. Ideally we wouldn't evaluate the function at
+    # these points; however, we can't easily filter out points since this
+    # function is vectorized. Instead, zero the weights.
+    # Note: values may have complex dtype, but have zero imaginary part
+    xj_real, a_real, b_real = xp_real(xj), xp_real(a), xp_real(b)
+    invalid = (xj_real <= a_real) | (xj_real >= b_real)
+    wj[invalid] = 0
+    return xj, wj
+
+
+def _euler_maclaurin_sum(fj, work, xp):
+    # Perform the Euler-Maclaurin Sum, [1] Section 4
+
+    # The error estimate needs to know the magnitude of the last term
+    # omitted from the Euler-Maclaurin sum. This is a bit involved because
+    # it may have been computed at a previous level. I sure hope it's worth
+    # all the trouble.
+    xr0, fr0, wr0 = work.xr0, work.fr0, work.wr0
+    xl0, fl0, wl0 = work.xl0, work.fl0, work.wl0
+
+    # It is much more convenient to work with the transposes of our work
+    # variables here.
+    xj, fj, wj = work.xj.T, fj.T, work.wj.T
+    n_x, n_active = xj.shape  # number of abscissae, number of active elements
+
+    # We'll work with the left and right sides separately
+    xr, xl = xp_copy(xp.reshape(xj, (2, n_x // 2, n_active)))  # this gets modified
+    fr, fl = xp.reshape(fj, (2, n_x // 2, n_active))
+    wr, wl = xp.reshape(wj, (2, n_x // 2, n_active))
+
+    invalid_r = ~xp.isfinite(fr) | (wr == 0)
+    invalid_l = ~xp.isfinite(fl) | (wl == 0)
+
+    # integer index of the maximum abscissa at this level
+    xr[invalid_r] = -xp.inf
+    ir = xp.argmax(xp_real(xr), axis=0, keepdims=True)
+    # abscissa, function value, and weight at this index
+    ### Not Array API Compatible... yet ###
+    xr_max = xp_take_along_axis(xr, ir, axis=0)[0]
+    fr_max = xp_take_along_axis(fr, ir, axis=0)[0]
+    wr_max = xp_take_along_axis(wr, ir, axis=0)[0]
+    # boolean indices at which maximum abscissa at this level exceeds
+    # the incumbent maximum abscissa (from all previous levels)
+    # note: abscissa may have complex dtype, but will have zero imaginary part
+    j = xp_real(xr_max) > xp_real(xr0)
+    # Update record of the incumbent abscissa, function value, and weight
+    xr0[j] = xr_max[j]
+    fr0[j] = fr_max[j]
+    wr0[j] = wr_max[j]
+
+    # integer index of the minimum abscissa at this level
+    xl[invalid_l] = xp.inf
+    il = xp.argmin(xp_real(xl), axis=0, keepdims=True)
+    # abscissa, function value, and weight at this index
+    xl_min = xp_take_along_axis(xl, il, axis=0)[0]
+    fl_min = xp_take_along_axis(fl, il, axis=0)[0]
+    wl_min = xp_take_along_axis(wl, il, axis=0)[0]
+    # boolean indices at which minimum abscissa at this level is less than
+    # the incumbent minimum abscissa (from all previous levels)
+    # note: abscissa may have complex dtype, but will have zero imaginary part
+    j = xp_real(xl_min) < xp_real(xl0)
+    # Update record of the incumbent abscissa, function value, and weight
+    xl0[j] = xl_min[j]
+    fl0[j] = fl_min[j]
+    wl0[j] = wl_min[j]
+    fj = fj.T
+
+    # Compute the error estimate `d4` - the magnitude of the leftmost or
+    # rightmost term, whichever is greater.
+    flwl0 = fl0 + xp.log(wl0) if work.log else fl0 * wl0  # leftmost term
+    frwr0 = fr0 + xp.log(wr0) if work.log else fr0 * wr0  # rightmost term
+    magnitude = xp_real if work.log else xp.abs
+    work.d4 = xp.maximum(magnitude(flwl0), magnitude(frwr0))
+
+    # There are two approaches to dealing with function values that are
+    # numerically infinite due to approaching a singularity - zero them, or
+    # replace them with the function value at the nearest non-infinite point.
+    # [3] pg. 22 suggests the latter, so let's do that given that we have the
+    # information.
+    fr0b = xp.broadcast_to(fr0[xp.newaxis, :], fr.shape)
+    fl0b = xp.broadcast_to(fl0[xp.newaxis, :], fl.shape)
+    fr[invalid_r] = fr0b[invalid_r]
+    fl[invalid_l] = fl0b[invalid_l]
+
+    # When wj is zero, log emits a warning
+    # with np.errstate(divide='ignore'):
+    fjwj = fj + xp.log(work.wj) if work.log else fj * work.wj
+
+    # update integral estimate
+    Sn = (special.logsumexp(fjwj + xp.log(work.h), axis=-1) if work.log
+          else xp.sum(fjwj, axis=-1) * work.h)
+
+    work.xr0, work.fr0, work.wr0 = xr0, fr0, wr0
+    work.xl0, work.fl0, work.wl0 = xl0, fl0, wl0
+
+    return fjwj, Sn
+
+
+def _estimate_error(work, xp):
+    # Estimate the error according to [1] Section 5
+
+    if work.n == 0 or work.nit == 0:
+        # The paper says to use "one" as the error before it can be calculated.
+        # NaN seems to be more appropriate.
+        nan = xp.full_like(work.Sn, xp.nan)
+        return nan, nan
+
+    indices = work.pair_cache.indices
+
+    n_active = work.Sn.shape[0]  # number of active elements
+    axis_kwargs = dict(axis=-1, keepdims=True)
+
+    # With a jump start (starting at level higher than 0), we haven't
+    # explicitly calculated the integral estimate at lower levels. But we have
+    # all the function value-weight products, so we can compute the
+    # lower-level estimates.
+    if work.Sk.shape[-1] == 0:
+        h = 2 * work.h  # step size at this level
+        n_x = indices[work.n]  # number of abscissa up to this level
+        # The right and left fjwj terms from all levels are concatenated along
+        # the last axis. Get out only the terms up to this level.
+        fjwj_rl = xp.reshape(work.fjwj, (n_active, 2, -1))
+        fjwj = xp.reshape(fjwj_rl[:, :, :n_x], (n_active, 2*n_x))
+        # Compute the Euler-Maclaurin sum at this level
+        Snm1 = (special.logsumexp(fjwj, **axis_kwargs) + xp.log(h) if work.log
+                else xp.sum(fjwj, **axis_kwargs) * h)
+        work.Sk = xp.concat((Snm1, work.Sk), axis=-1)
+
+    if work.n == 1:
+        nan = xp.full_like(work.Sn, xp.nan)
+        return nan, nan
+
+    # The paper says not to calculate the error for n<=2, but it's not clear
+    # about whether it starts at level 0 or level 1. We start at level 0, so
+    # why not compute the error beginning in level 2?
+    if work.Sk.shape[-1] < 2:
+        h = 4 * work.h  # step size at this level
+        n_x = indices[work.n-1]  # number of abscissa up to this level
+        # The right and left fjwj terms from all levels are concatenated along
+        # the last axis. Get out only the terms up to this level.
+        fjwj_rl = xp.reshape(work.fjwj, (work.Sn.shape[0], 2, -1))
+        fjwj = xp.reshape(fjwj_rl[..., :n_x], (n_active, 2*n_x))
+        # Compute the Euler-Maclaurin sum at this level
+        Snm2 = (special.logsumexp(fjwj, **axis_kwargs) + xp.log(h) if work.log
+                else xp.sum(fjwj, **axis_kwargs) * h)
+        work.Sk = xp.concat((Snm2, work.Sk), axis=-1)
+
+    Snm2 = work.Sk[..., -2]
+    Snm1 = work.Sk[..., -1]
+
+    e1 = xp.asarray(work.eps)[()]
+
+    if work.log:
+        log_e1 = xp.log(e1)
+        # Currently, only real integrals are supported in log-scale. All
+        # complex values have imaginary part in increments of pi*j, which just
+        # carries sign information of the original integral, so use of
+        # `xp.real` here is equivalent to absolute value in real scale.
+        d1 = xp_real(special.logsumexp(xp.stack([work.Sn, Snm1 + work.pi*1j]), axis=0))
+        d2 = xp_real(special.logsumexp(xp.stack([work.Sn, Snm2 + work.pi*1j]), axis=0))
+        d3 = log_e1 + xp.max(xp_real(work.fjwj), axis=-1)
+        d4 = work.d4
+        d5 = log_e1 + xp.real(work.Sn)
+        temp = xp.where(d1 > -xp.inf, d1 ** 2 / d2, -xp.inf)
+        ds = xp.stack([temp, 2 * d1, d3, d4, d5])
+        aerr = xp.max(ds, axis=0)
+        rerr = aerr - xp.real(work.Sn)
+    else:
+        # Note: explicit computation of log10 of each of these is unnecessary.
+        d1 = xp.abs(work.Sn - Snm1)
+        d2 = xp.abs(work.Sn - Snm2)
+        d3 = e1 * xp.max(xp.abs(work.fjwj), axis=-1)
+        d4 = work.d4
+        d5 = e1 * xp.abs(work.Sn)
+        temp = xp.where(d1 > 0, d1**(xp.log(d1)/xp.log(d2)), 0)
+        ds = xp.stack([temp, d1**2, d3, d4, d5])
+        aerr = xp.max(ds, axis=0)
+        rerr = aerr/xp.abs(work.Sn)
+
+    return rerr, aerr
+
+
+def _transform_integrals(a, b, xp):
+    # Transform integrals to a form with finite a <= b
+    # For b == a (even infinite), we ensure that the limits remain equal
+    # For b < a, we reverse the limits and will multiply the final result by -1
+    # For infinite limit on the right, we use the substitution x = 1/t - 1 + a
+    # For infinite limit on the left, we substitute x = -x and treat as above
+    # For infinite limits, we substitute x = t / (1-t**2)
+    ab_same = (a == b)
+    a[ab_same], b[ab_same] = 1, 1
+
+    # `a, b` may have complex dtype but have zero imaginary part
+    negative = xp_real(b) < xp_real(a)
+    a[negative], b[negative] = b[negative], a[negative]
+
+    abinf = xp.isinf(a) & xp.isinf(b)
+    a[abinf], b[abinf] = -1, 1
+
+    ainf = xp.isinf(a)
+    a[ainf], b[ainf] = -b[ainf], -a[ainf]
+
+    binf = xp.isinf(b)
+    a0 = xp_copy(a)
+    a[binf], b[binf] = 0, 1
+
+    return a, b, a0, negative, abinf, ainf, binf
+
+
+def _tanhsinh_iv(f, a, b, log, maxfun, maxlevel, minlevel,
+                 atol, rtol, args, preserve_shape, callback):
+    # Input validation and standardization
+
+    xp = array_namespace(a, b)
+
+    message = '`f` must be callable.'
+    if not callable(f):
+        raise ValueError(message)
+
+    message = 'All elements of `a` and `b` must be real numbers.'
+    a, b = xp.asarray(a), xp.asarray(b)
+    a, b = xp.broadcast_arrays(a, b)
+    if (xp.isdtype(a.dtype, 'complex floating')
+            or xp.isdtype(b.dtype, 'complex floating')):
+        raise ValueError(message)
+
+    message = '`log` must be True or False.'
+    if log not in {True, False}:
+        raise ValueError(message)
+    log = bool(log)
+
+    if atol is None:
+        atol = -xp.inf if log else 0
+
+    rtol_temp = rtol if rtol is not None else 0.
+
+    # using NumPy for convenience here; these are just floats, not arrays
+    params = np.asarray([atol, rtol_temp, 0.])
+    message = "`atol` and `rtol` must be real numbers."
+    if not np.issubdtype(params.dtype, np.floating):
+        raise ValueError(message)
+
+    if log:
+        message = '`atol` and `rtol` may not be positive infinity.'
+        if np.any(np.isposinf(params)):
+            raise ValueError(message)
+    else:
+        message = '`atol` and `rtol` must be non-negative and finite.'
+        if np.any(params < 0) or np.any(np.isinf(params)):
+            raise ValueError(message)
+    atol = params[0]
+    rtol = rtol if rtol is None else params[1]
+
+    BIGINT = float(2**62)
+    if maxfun is None and maxlevel is None:
+        maxlevel = 10
+
+    maxfun = BIGINT if maxfun is None else maxfun
+    maxlevel = BIGINT if maxlevel is None else maxlevel
+
+    message = '`maxfun`, `maxlevel`, and `minlevel` must be integers.'
+    params = np.asarray([maxfun, maxlevel, minlevel])
+    if not (np.issubdtype(params.dtype, np.number)
+            and np.all(np.isreal(params))
+            and np.all(params.astype(np.int64) == params)):
+        raise ValueError(message)
+    message = '`maxfun`, `maxlevel`, and `minlevel` must be non-negative.'
+    if np.any(params < 0):
+        raise ValueError(message)
+    maxfun, maxlevel, minlevel = params.astype(np.int64)
+    minlevel = min(minlevel, maxlevel)
+
+    if not np.iterable(args):
+        args = (args,)
+    args = (xp.asarray(arg) for arg in args)
+
+    message = '`preserve_shape` must be True or False.'
+    if preserve_shape not in {True, False}:
+        raise ValueError(message)
+
+    if callback is not None and not callable(callback):
+        raise ValueError('`callback` must be callable.')
+
+    return (f, a, b, log, maxfun, maxlevel, minlevel,
+            atol, rtol, args, preserve_shape, callback, xp)
+
+
+def _nsum_iv(f, a, b, step, args, log, maxterms, tolerances):
+    # Input validation and standardization
+
+    xp = array_namespace(a, b)
+
+    message = '`f` must be callable.'
+    if not callable(f):
+        raise ValueError(message)
+
+    message = 'All elements of `a`, `b`, and `step` must be real numbers.'
+    a, b, step = xp.broadcast_arrays(xp.asarray(a), xp.asarray(b), xp.asarray(step))
+    dtype = xp.result_type(a.dtype, b.dtype, step.dtype)
+    if not xp.isdtype(dtype, 'numeric') or xp.isdtype(dtype, 'complex floating'):
+        raise ValueError(message)
+
+    valid_b = b >= a  # NaNs will be False
+    valid_step = xp.isfinite(step) & (step > 0)
+    valid_abstep = valid_b & valid_step
+
+    message = '`log` must be True or False.'
+    if log not in {True, False}:
+        raise ValueError(message)
+
+    tolerances = {} if tolerances is None else tolerances
+
+    atol = tolerances.get('atol', None)
+    if atol is None:
+        atol = -xp.inf if log else 0
+
+    rtol = tolerances.get('rtol', None)
+    rtol_temp = rtol if rtol is not None else 0.
+
+    # using NumPy for convenience here; these are just floats, not arrays
+    params = np.asarray([atol, rtol_temp, 0.])
+    message = "`atol` and `rtol` must be real numbers."
+    if not np.issubdtype(params.dtype, np.floating):
+        raise ValueError(message)
+
+    if log:
+        message = '`atol`, `rtol` may not be positive infinity or NaN.'
+        if np.any(np.isposinf(params) | np.isnan(params)):
+            raise ValueError(message)
+    else:
+        message = '`atol`, and `rtol` must be non-negative and finite.'
+        if np.any((params < 0) | (~np.isfinite(params))):
+            raise ValueError(message)
+    atol = params[0]
+    rtol = rtol if rtol is None else params[1]
+
+    maxterms_int = int(maxterms)
+    if maxterms_int != maxterms or maxterms < 0:
+        message = "`maxterms` must be a non-negative integer."
+        raise ValueError(message)
+
+    if not np.iterable(args):
+        args = (args,)
+
+    return f, a, b, step, valid_abstep, args, log, maxterms_int, atol, rtol, xp
+
+
+def nsum(f, a, b, *, step=1, args=(), log=False, maxterms=int(2**20), tolerances=None):
+    r"""Evaluate a convergent finite or infinite series.
+
+    For finite `a` and `b`, this evaluates::
+
+        f(a + np.arange(n)*step).sum()
+
+    where ``n = int((b - a) / step) + 1``, where `f` is smooth, positive, and
+    unimodal. The number of terms in the sum may be very large or infinite,
+    in which case a partial sum is evaluated directly and the remainder is
+    approximated using integration.
+
+    Parameters
+    ----------
+    f : callable
+        The function that evaluates terms to be summed. The signature must be::
+
+            f(x: ndarray, *args) -> ndarray
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+
+        `f` must be an elementwise function: each element ``f(x)[i]``
+        must equal ``f(x[i])`` for all indices ``i``. It must not mutate the
+        array ``x`` or the arrays in ``args``, and it must return NaN where
+        the argument is NaN.
+
+        `f` must represent a smooth, positive, unimodal function of `x` defined at
+        *all reals* between `a` and `b`.
+    a, b : float array_like
+        Real lower and upper limits of summed terms. Must be broadcastable.
+        Each element of `a` must be less than the corresponding element in `b`.
+    step : float array_like
+        Finite, positive, real step between summed terms. Must be broadcastable
+        with `a` and `b`. Note that the number of terms included in the sum will
+        be ``floor((b - a) / step)`` + 1; adjust `b` accordingly to ensure
+        that ``f(b)`` is included if intended.
+    args : tuple of array_like, optional
+        Additional positional arguments to be passed to `f`. Must be arrays
+        broadcastable with `a`, `b`, and `step`. If the callable to be summed
+        requires arguments that are not broadcastable with `a`, `b`, and `step`,
+        wrap that callable with `f` such that `f` accepts only `x` and
+        broadcastable ``*args``. See Examples.
+    log : bool, default: False
+        Setting to True indicates that `f` returns the log of the terms
+        and that `atol` and `rtol` are expressed as the logs of the absolute
+        and relative errors. In this case, the result object will contain the
+        log of the sum and error. This is useful for summands for which
+        numerical underflow or overflow would lead to inaccuracies.
+    maxterms : int, default: 2**20
+        The maximum number of terms to evaluate for direct summation.
+        Additional function evaluations may be performed for input
+        validation and integral evaluation.
+    atol, rtol : float, optional
+        Absolute termination tolerance (default: 0) and relative termination
+        tolerance (default: ``eps**0.5``, where ``eps`` is the precision of
+        the result dtype), respectively. Must be non-negative
+        and finite if `log` is False, and must be expressed as the log of a
+        non-negative and finite number if `log` is True.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. (The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.)
+
+        success : bool
+            ``True`` when the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm converged to the specified tolerances.
+            - ``-1`` : Element(s) of `a`, `b`, or `step` are invalid
+            - ``-2`` : Numerical integration reached its iteration limit;
+              the sum may be divergent.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : The magnitude of the last term of the partial sum exceeds
+              the tolerances, so the error estimate exceeds the tolerances.
+              Consider increasing `maxterms` or loosening `tolerances`.
+              Alternatively, the callable may not be unimodal, or the limits of
+              summation may be too far from the function maximum. Consider
+              increasing `maxterms` or breaking the sum into pieces.
+
+        sum : float array
+            An estimate of the sum.
+        error : float array
+            An estimate of the absolute error, assuming all terms are non-negative,
+            the function is computed exactly, and direct summation is accurate to
+            the precision of the result dtype.
+        nfev : int array
+            The number of points at which `f` was evaluated.
+
+    See Also
+    --------
+    mpmath.nsum
+
+    Notes
+    -----
+    The method implemented for infinite summation is related to the integral
+    test for convergence of an infinite series: assuming `step` size 1 for
+    simplicity of exposition, the sum of a monotone decreasing function is bounded by
+
+    .. math::
+
+        \int_u^\infty f(x) dx \leq \sum_{k=u}^\infty f(k) \leq \int_u^\infty f(x) dx + f(u)
+
+    Let :math:`a` represent  `a`, :math:`n` represent `maxterms`, :math:`\epsilon_a`
+    represent `atol`, and :math:`\epsilon_r` represent `rtol`.
+    The implementation first evaluates the integral :math:`S_l=\int_a^\infty f(x) dx`
+    as a lower bound of the infinite sum. Then, it seeks a value :math:`c > a` such
+    that :math:`f(c) < \epsilon_a + S_l \epsilon_r`, if it exists; otherwise,
+    let :math:`c = a + n`. Then the infinite sum is approximated as
+
+    .. math::
+
+        \sum_{k=a}^{c-1} f(k) + \int_c^\infty f(x) dx + f(c)/2
+
+    and the reported error is :math:`f(c)/2` plus the error estimate of
+    numerical integration. Note that the integral approximations may require
+    evaluation of the function at points besides those that appear in the sum,
+    so `f` must be a continuous and monotonically decreasing function defined
+    for all reals within the integration interval. However, due to the nature
+    of the integral approximation, the shape of the function between points
+    that appear in the sum has little effect. If there is not a natural
+    extension of the function to all reals, consider using linear interpolation,
+    which is easy to evaluate and preserves monotonicity.
+
+    The approach described above is generalized for non-unit
+    `step` and finite `b` that is too large for direct evaluation of the sum,
+    i.e. ``b - a + 1 > maxterms``. It is further generalized to unimodal
+    functions by directly summing terms surrounding the maximum.
+    This strategy may fail:
+
+    - If the left limit is finite and the maximum is far from it.
+    - If the right limit is finite and the maximum is far from it.
+    - If both limits are finite and the maximum is far from the origin.
+
+    In these cases, accuracy may be poor, and `nsum` may return status code ``4``.
+
+    Although the callable `f` must be non-negative and unimodal,
+    `nsum` can be used to evaluate more general forms of series. For instance, to
+    evaluate an alternating series, pass a callable that returns the difference
+    between pairs of adjacent terms, and adjust `step` accordingly. See Examples.
+
+    References
+    ----------
+    .. [1] Wikipedia. "Integral test for convergence."
+           https://en.wikipedia.org/wiki/Integral_test_for_convergence
+
+    Examples
+    --------
+    Compute the infinite sum of the reciprocals of squared integers.
+
+    >>> import numpy as np
+    >>> from scipy.integrate import nsum
+    >>> res = nsum(lambda k: 1/k**2, 1, np.inf)
+    >>> ref = np.pi**2/6  # true value
+    >>> res.error  # estimated error
+    np.float64(7.448762306416137e-09)
+    >>> (res.sum - ref)/ref  # true error
+    np.float64(-1.839871898894426e-13)
+    >>> res.nfev  # number of points at which callable was evaluated
+    np.int32(8561)
+
+    Compute the infinite sums of the reciprocals of integers raised to powers ``p``,
+    where ``p`` is an array.
+
+    >>> from scipy import special
+    >>> p = np.arange(3, 10)
+    >>> res = nsum(lambda k, p: 1/k**p, 1, np.inf, maxterms=1e3, args=(p,))
+    >>> ref = special.zeta(p, 1)
+    >>> np.allclose(res.sum, ref)
+    True
+
+    Evaluate the alternating harmonic series.
+
+    >>> res = nsum(lambda x: 1/x - 1/(x+1), 1, np.inf, step=2)
+    >>> res.sum, res.sum - np.log(2)  # result, difference vs analytical sum
+    (np.float64(0.6931471805598691), np.float64(-7.616129948928574e-14))
+
+    """ # noqa: E501
+    # Potential future work:
+    # - improve error estimate of `_direct` sum
+    # - add other methods for convergence acceleration (Richardson, epsilon)
+    # - support negative monotone increasing functions?
+    # - b < a / negative step?
+    # - complex-valued function?
+    # - check for violations of monotonicity?
+
+    # Function-specific input validation / standardization
+    tmp = _nsum_iv(f, a, b, step, args, log, maxterms, tolerances)
+    f, a, b, step, valid_abstep, args, log, maxterms, atol, rtol, xp = tmp
+
+    # Additional elementwise algorithm input validation / standardization
+    tmp = eim._initialize(f, (a,), args, complex_ok=False, xp=xp)
+    f, xs, fs, args, shape, dtype, xp = tmp
+
+    # Finish preparing `a`, `b`, and `step` arrays
+    a = xs[0]
+    b = xp.astype(xp_ravel(xp.broadcast_to(b, shape)), dtype)
+    step = xp.astype(xp_ravel(xp.broadcast_to(step, shape)), dtype)
+    valid_abstep = xp_ravel(xp.broadcast_to(valid_abstep, shape))
+    nterms = xp.floor((b - a) / step)
+    finite_terms = xp.isfinite(nterms)
+    b[finite_terms] = a[finite_terms] + nterms[finite_terms]*step[finite_terms]
+
+    # Define constants
+    eps = xp.finfo(dtype).eps
+    zero = xp.asarray(-xp.inf if log else 0, dtype=dtype)[()]
+    if rtol is None:
+        rtol = 0.5*math.log(eps) if log else eps**0.5
+    constants = (dtype, log, eps, zero, rtol, atol, maxterms)
+
+    # Prepare result arrays
+    S = xp.empty_like(a)
+    E = xp.empty_like(a)
+    status = xp.zeros(len(a), dtype=xp.int32)
+    nfev = xp.ones(len(a), dtype=xp.int32)  # one function evaluation above
+
+    # Branch for direct sum evaluation / integral approximation / invalid input
+    i0 = ~valid_abstep                     # invalid
+    i1 = (nterms + 1 <= maxterms) & ~i0    # direct sum evaluation
+    i2 = xp.isfinite(a) & ~i1 & ~i0        # infinite sum to the right
+    i3 = xp.isfinite(b) & ~i2 & ~i1 & ~i0  # infinite sum to the left
+    i4 = ~i3 & ~i2 & ~i1 & ~i0             # infinite sum on both sides
+
+    if xp.any(i0):
+        S[i0], E[i0] = xp.nan, xp.nan
+        status[i0] = -1
+
+    if xp.any(i1):
+        args_direct = [arg[i1] for arg in args]
+        tmp = _direct(f, a[i1], b[i1], step[i1], args_direct, constants, xp)
+        S[i1], E[i1] = tmp[:-1]
+        nfev[i1] += tmp[-1]
+        status[i1] = -3 * xp.asarray(~xp.isfinite(S[i1]), dtype=xp.int32)
+
+    if xp.any(i2):
+        args_indirect = [arg[i2] for arg in args]
+        tmp = _integral_bound(f, a[i2], b[i2], step[i2],
+                              args_indirect, constants, xp)
+        S[i2], E[i2], status[i2] = tmp[:-1]
+        nfev[i2] += tmp[-1]
+
+    if xp.any(i3):
+        args_indirect = [arg[i3] for arg in args]
+        def _f(x, *args): return f(-x, *args)
+        tmp = _integral_bound(_f, -b[i3], -a[i3], step[i3],
+                              args_indirect, constants, xp)
+        S[i3], E[i3], status[i3] = tmp[:-1]
+        nfev[i3] += tmp[-1]
+
+    if xp.any(i4):
+        args_indirect = [arg[i4] for arg in args]
+
+        # There are two obvious high-level strategies:
+        # - Do two separate half-infinite sums (e.g. from -inf to 0 and 1 to inf)
+        # - Make a callable that returns f(x) + f(-x) and do a single half-infinite sum
+        # I thought the latter would have about half the overhead, so I went that way.
+        # Then there are two ways of ensuring that f(0) doesn't get counted twice.
+        # - Evaluate the sum from 1 to inf and add f(0)
+        # - Evaluate the sum from 0 to inf and subtract f(0)
+        # - Evaluate the sum from 0 to inf, but apply a weight of 0.5 when `x = 0`
+        # The last option has more overhead, but is simpler to implement correctly
+        # (especially getting the status message right)
+        if log:
+            def _f(x, *args):
+                log_factor = xp.where(x==0, math.log(0.5), 0)
+                out = xp.stack([f(x, *args), f(-x, *args)], axis=0)
+                return special.logsumexp(out, axis=0) + log_factor
+
+        else:
+            def _f(x, *args):
+                factor = xp.where(x==0, 0.5, 1)
+                return (f(x, *args) + f(-x, *args)) * factor
+
+        zero = xp.zeros_like(a[i4])
+        tmp = _integral_bound(_f, zero, b[i4], step[i4], args_indirect, constants, xp)
+        S[i4], E[i4], status[i4] = tmp[:-1]
+        nfev[i4] += 2*tmp[-1]
+
+    # Return results
+    S, E = S.reshape(shape)[()], E.reshape(shape)[()]
+    status, nfev = status.reshape(shape)[()], nfev.reshape(shape)[()]
+    return _RichResult(sum=S, error=E, status=status, success=status == 0,
+                       nfev=nfev)
+
+
+def _direct(f, a, b, step, args, constants, xp, inclusive=True):
+    # Directly evaluate the sum.
+
+    # When used in the context of distributions, `args` would contain the
+    # distribution parameters. We have broadcasted for simplicity, but we could
+    # reduce function evaluations when distribution parameters are the same but
+    # sum limits differ. Roughly:
+    # - compute the function at all points between min(a) and max(b),
+    # - compute the cumulative sum,
+    # - take the difference between elements of the cumulative sum
+    #   corresponding with b and a.
+    # This is left to future enhancement
+
+    dtype, log, eps, zero, _, _, _ = constants
+
+    # To allow computation in a single vectorized call, find the maximum number
+    # of points (over all slices) at which the function needs to be evaluated.
+    # Note: if `inclusive` is `True`, then we want `1` more term in the sum.
+    # I didn't think it was great style to use `True` as `1` in Python, so I
+    # explicitly converted it to an `int` before using it.
+    inclusive_adjustment = int(inclusive)
+    steps = xp.round((b - a) / step) + inclusive_adjustment
+    # Equivalently, steps = xp.round((b - a) / step) + inclusive
+    max_steps = int(xp.max(steps))
+
+    # In each slice, the function will be evaluated at the same number of points,
+    # but excessive points (those beyond the right sum limit `b`) are replaced
+    # with NaN to (potentially) reduce the time of these unnecessary calculations.
+    # Use a new last axis for these calculations for consistency with other
+    # elementwise algorithms.
+    a2, b2, step2 = a[:, xp.newaxis], b[:, xp.newaxis], step[:, xp.newaxis]
+    args2 = [arg[:, xp.newaxis] for arg in args]
+    ks = a2 + xp.arange(max_steps, dtype=dtype) * step2
+    i_nan = ks >= (b2 + inclusive_adjustment*step2/2)
+    ks[i_nan] = xp.nan
+    fs = f(ks, *args2)
+
+    # The function evaluated at NaN is NaN, and NaNs are zeroed in the sum.
+    # In some cases it may be faster to loop over slices than to vectorize
+    # like this. This is an optimization that can be added later.
+    fs[i_nan] = zero
+    nfev = max_steps - i_nan.sum(axis=-1)
+    S = special.logsumexp(fs, axis=-1) if log else xp.sum(fs, axis=-1)
+    # Rough, non-conservative error estimate. See gh-19667 for improvement ideas.
+    E = xp_real(S) + math.log(eps) if log else eps * abs(S)
+    return S, E, nfev
+
+
+def _integral_bound(f, a, b, step, args, constants, xp):
+    # Estimate the sum with integral approximation
+    dtype, log, _, _, rtol, atol, maxterms = constants
+    log2 = xp.asarray(math.log(2), dtype=dtype)
+
+    # Get a lower bound on the sum and compute effective absolute tolerance
+    lb = tanhsinh(f, a, b, args=args, atol=atol, rtol=rtol, log=log)
+    tol = xp.broadcast_to(xp.asarray(atol), lb.integral.shape)
+    if log:
+        tol = special.logsumexp(xp.stack((tol, rtol + lb.integral)), axis=0)
+    else:
+        tol = tol + rtol*lb.integral
+    i_skip = lb.status < 0  # avoid unnecessary f_evals if integral is divergent
+    tol[i_skip] = xp.nan
+    status = lb.status
+
+    # As in `_direct`, we'll need a temporary new axis for points
+    # at which to evaluate the function. Append axis at the end for
+    # consistency with other elementwise algorithms.
+    a2 = a[..., xp.newaxis]
+    step2 = step[..., xp.newaxis]
+    args2 = [arg[..., xp.newaxis] for arg in args]
+
+    # Find the location of a term that is less than the tolerance (if possible)
+    log2maxterms = math.floor(math.log2(maxterms)) if maxterms else 0
+    n_steps = xp.concat((2**xp.arange(0, log2maxterms), xp.asarray([maxterms])))
+    n_steps = xp.astype(n_steps, dtype)
+    nfev = len(n_steps) * 2
+    ks = a2 + n_steps * step2
+    fks = f(ks, *args2)
+    fksp1 = f(ks + step2, *args2)  # check that the function is decreasing
+    fk_insufficient = (fks > tol[:, xp.newaxis]) | (fksp1 > fks)
+    n_fk_insufficient = xp.sum(fk_insufficient, axis=-1)
+    nt = xp.minimum(n_fk_insufficient, xp.asarray(n_steps.shape[-1]-1))
+    n_steps = n_steps[nt]
+
+    # If `maxterms` is insufficient (i.e. either the magnitude of the last term of the
+    # partial sum exceeds the tolerance or the function is not decreasing), finish the
+    # calculation, but report nonzero status. (Improvement: separate the status codes
+    # for these two cases.)
+    i_fk_insufficient = (n_fk_insufficient == nfev//2)
+
+    # Directly evaluate the sum up to this term
+    k = a + n_steps * step
+    left, left_error, left_nfev = _direct(f, a, k, step, args,
+                                          constants, xp, inclusive=False)
+    left_is_pos_inf = xp.isinf(left) & (left > 0)
+    i_skip |= left_is_pos_inf  # if sum is infinite, no sense in continuing
+    status[left_is_pos_inf] = -3
+    k[i_skip] = xp.nan
+
+    # Use integration to estimate the remaining sum
+    # Possible optimization for future work: if there were no terms less than
+    # the tolerance, there is no need to compute the integral to better accuracy.
+    # Something like:
+    # atol = xp.maximum(atol, xp.minimum(fk/2 - fb/2))
+    # rtol = xp.maximum(rtol, xp.minimum((fk/2 - fb/2)/left))
+    # where `fk`/`fb` are currently calculated below.
+    right = tanhsinh(f, k, b, args=args, atol=atol, rtol=rtol, log=log)
+
+    # Calculate the full estimate and error from the pieces
+    fk = fks[xp.arange(len(fks)), nt]
+
+    # fb = f(b, *args), but some functions return NaN at infinity.
+    # instead of 0 like they must (for the sum to be convergent).
+    fb = xp.full_like(fk, -xp.inf) if log else xp.zeros_like(fk)
+    i = xp.isfinite(b)
+    if xp.any(i):  # better not call `f` with empty arrays
+        fb[i] = f(b[i], *[arg[i] for arg in args])
+    nfev = nfev + xp.asarray(i, dtype=left_nfev.dtype)
+
+    if log:
+        log_step = xp.log(step)
+        S_terms = (left, right.integral - log_step, fk - log2, fb - log2)
+        S = special.logsumexp(xp.stack(S_terms), axis=0)
+        E_terms = (left_error, right.error - log_step, fk-log2, fb-log2+xp.pi*1j)
+        E = xp_real(special.logsumexp(xp.stack(E_terms), axis=0))
+    else:
+        S = left + right.integral/step + fk/2 + fb/2
+        E = left_error + right.error/step + fk/2 - fb/2
+    status[~i_skip] = right.status[~i_skip]
+
+    status[(status == 0) & i_fk_insufficient] = -4
+    return S, E, status, left_nfev + right.nfev + nfev + lb.nfev
diff --git a/shared/package-files/40/40c3e29452b64240e3f3de62e1faabb44e5bcf23b9e46264578b7810cff5433c/__init__.py b/shared/package-files/40/40c3e29452b64240e3f3de62e1faabb44e5bcf23b9e46264578b7810cff5433c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7c9b29a750593a812907ce2cf4c800d7d1435bb
--- /dev/null
+++ b/shared/package-files/40/40c3e29452b64240e3f3de62e1faabb44e5bcf23b9e46264578b7810cff5433c/__init__.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+
+Torchelastic agent and user worker failover contract:
+
+**TL;DR;**:
+
+* TE(torchelastic) expects user workers to finish with the 5 minutes drift
+* It is better to design DDP app to fail for all workers, rather than a single one.
+* TE does not synchronize number of restarts between agents
+* TE re-rendezvous does not trigger restart decrease
+* When a single agent finishes its job(successfully or not), it will close rendezvous.
+  If other agents still have workers in progress, they will be terminated.
+* Based on above, scale down does not work if at least single agent finishes the job.
+* When Scale up is detected by agents, it will not decrease ``max_restarts``
+
+
+In general TE(torchelastic) can launch arbitrary user code, but there is some
+clarifications need to be done around what failover mechanism torchelastic
+provides and what failover mechanism it expects from user workers.
+
+Torchelastic currently supports DDP style applications.  That means that
+TE expects *ALL* workers finish approximately at the same time. In practice,
+it is nearly to impossible to guarantee that all workers in arbitrary
+DDP application finish at the time, so TE provides a finalization barrier
+that waits for TIMEOUT(5 minutes) for worker finalization.
+
+**Worker Failure**
+
+When worker fails, TE will check the number of restarts
+available, if there is more than 0 restarts, TE will start a new rendezvous
+round and restart the worker process. New rendezvous round will other
+TE agents to terminate their workers.
+
+.. note:: The TE agent does not synchronize restarts between themselves.
+          When a single agent performs restart, it will trigger a local ``max_restarts``
+          decrease, other agent will not decrease their ``max_restarts``.
+          the user to run the distributed application locally on a dev host.
+
+A single worker failure can cause the whole cluster to fail:
+If a single worker is constantly failing, it will cause the TE agent
+``max_restarts``  to go to zero. This will cause an agent to finish its
+work and close rendezvous. If there are any other workers on different
+agents, they will be terminated.
+
+
+**Re-Rendezvous**
+
+Re-rendezvous occurs when TE agents detect a new node
+trying to joint a cluster. TE will not decrease ``max_restarts``. TE agents
+will terminate its workers and start a new rendezvous round.
+
+Note about DynamicRendezvous(etcd-v2, c10d-experimental): If the rendezvous
+has already max_nodes, the new node won't be added to the wait list right
+away since there is no need to tear down a rendezvous that is already fully
+utilized. The new node will wait until its timeout (600 secs by default)
+and periodically check the number of participants. If the number becomes
+less than max_nodes, it will be added to the wait list; otherwise, it will time out after 600 secs.
+
+*Scale up event*. When scale up event happens, torchelastic rendezvous
+will detect that there are new nodes trying to join. Torchelastic agent
+will stop all workers and perform re-rendezvous. Note: when scale up event
+happens, *``max_restarts``* will *not* decrease.
+
+*Scale down event*. When scale down event happens, rendezvous will not
+notify the torchelastic agent about it. If TE agent launched with ``max_restarts=0`` ,
+it relies on the underlying scheduler to handle job restart. If the ``max_restarts>0`` ,
+TE agent will terminate workers and start a new rdzv round, which is a *Scale up event*.
+
+"""
diff --git a/shared/package-files/40/40c4e830b7227f54b848d3ce33132d04ba9cd6c9146272216d40232847407fdc/GMT-2 b/shared/package-files/40/40c4e830b7227f54b848d3ce33132d04ba9cd6c9146272216d40232847407fdc/GMT-2
new file mode 100644
index 0000000000000000000000000000000000000000..97b44a9baecfd5bd30393142c63323160353bf2f
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diff --git a/shared/package-files/40/40c56805757297d4411af0232e8896f399005d827e847a6b50c7a0106ebd039c/testsparsecomplex_4.2c_SOL2.mat b/shared/package-files/40/40c56805757297d4411af0232e8896f399005d827e847a6b50c7a0106ebd039c/testsparsecomplex_4.2c_SOL2.mat
new file mode 100644
index 0000000000000000000000000000000000000000..81c536d0b067b92cae1b7a2ee71824e2c5e730d9
Binary files /dev/null and b/shared/package-files/40/40c56805757297d4411af0232e8896f399005d827e847a6b50c7a0106ebd039c/testsparsecomplex_4.2c_SOL2.mat differ
diff --git a/shared/package-files/40/40c5c9c3fea61420b7f75f1bfa973fe1bff380ef07b24eff481bccba96c48b92/test_gaussopt.py b/shared/package-files/40/40c5c9c3fea61420b7f75f1bfa973fe1bff380ef07b24eff481bccba96c48b92/test_gaussopt.py
new file mode 100644
index 0000000000000000000000000000000000000000..5271f3cbb69cf5de861ff332d36418b79daeb1b5
--- /dev/null
+++ b/shared/package-files/40/40c5c9c3fea61420b7f75f1bfa973fe1bff380ef07b24eff481bccba96c48b92/test_gaussopt.py
@@ -0,0 +1,102 @@
+from sympy.core.evalf import N
+from sympy.core.numbers import (Float, I, oo, pi)
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import atan2
+from sympy.matrices.dense import Matrix
+from sympy.polys.polytools import factor
+
+from sympy.physics.optics import (BeamParameter, CurvedMirror,
+  CurvedRefraction, FlatMirror, FlatRefraction, FreeSpace, GeometricRay,
+  RayTransferMatrix, ThinLens, conjugate_gauss_beams,
+  gaussian_conj, geometric_conj_ab, geometric_conj_af, geometric_conj_bf,
+  rayleigh2waist, waist2rayleigh)
+
+
+def streq(a, b):
+    return str(a) == str(b)
+
+
+def test_gauss_opt():
+    mat = RayTransferMatrix(1, 2, 3, 4)
+    assert mat == Matrix([[1, 2], [3, 4]])
+    assert mat == RayTransferMatrix( Matrix([[1, 2], [3, 4]]) )
+    assert [mat.A, mat.B, mat.C, mat.D] == [1, 2, 3, 4]
+
+    d, f, h, n1, n2, R = symbols('d f h n1 n2 R')
+    lens = ThinLens(f)
+    assert lens == Matrix([[   1, 0], [-1/f, 1]])
+    assert lens.C == -1/f
+    assert FreeSpace(d) == Matrix([[ 1, d], [0, 1]])
+    assert FlatRefraction(n1, n2) == Matrix([[1, 0], [0, n1/n2]])
+    assert CurvedRefraction(
+        R, n1, n2) == Matrix([[1, 0], [(n1 - n2)/(R*n2), n1/n2]])
+    assert FlatMirror() == Matrix([[1, 0], [0, 1]])
+    assert CurvedMirror(R) == Matrix([[   1, 0], [-2/R, 1]])
+    assert ThinLens(f) == Matrix([[   1, 0], [-1/f, 1]])
+
+    mul = CurvedMirror(R)*FreeSpace(d)
+    mul_mat = Matrix([[   1, 0], [-2/R, 1]])*Matrix([[ 1, d], [0, 1]])
+    assert mul.A == mul_mat[0, 0]
+    assert mul.B == mul_mat[0, 1]
+    assert mul.C == mul_mat[1, 0]
+    assert mul.D == mul_mat[1, 1]
+
+    angle = symbols('angle')
+    assert GeometricRay(h, angle) == Matrix([[    h], [angle]])
+    assert FreeSpace(
+        d)*GeometricRay(h, angle) == Matrix([[angle*d + h], [angle]])
+    assert GeometricRay( Matrix( ((h,), (angle,)) ) ) == Matrix([[h], [angle]])
+    assert (FreeSpace(d)*GeometricRay(h, angle)).height == angle*d + h
+    assert (FreeSpace(d)*GeometricRay(h, angle)).angle == angle
+
+    p = BeamParameter(530e-9, 1, w=1e-3)
+    assert streq(p.q, 1 + 1.88679245283019*I*pi)
+    assert streq(N(p.q), 1.0 + 5.92753330865999*I)
+    assert streq(N(p.w_0), Float(0.00100000000000000))
+    assert streq(N(p.z_r), Float(5.92753330865999))
+    fs = FreeSpace(10)
+    p1 = fs*p
+    assert streq(N(p.w), Float(0.00101413072159615))
+    assert streq(N(p1.w), Float(0.00210803120913829))
+
+    w, wavelen = symbols('w wavelen')
+    assert waist2rayleigh(w, wavelen) == pi*w**2/wavelen
+    z_r, wavelen = symbols('z_r wavelen')
+    assert rayleigh2waist(z_r, wavelen) == sqrt(wavelen*z_r)/sqrt(pi)
+
+    a, b, f = symbols('a b f')
+    assert geometric_conj_ab(a, b) == a*b/(a + b)
+    assert geometric_conj_af(a, f) == a*f/(a - f)
+    assert geometric_conj_bf(b, f) == b*f/(b - f)
+    assert geometric_conj_ab(oo, b) == b
+    assert geometric_conj_ab(a, oo) == a
+
+    s_in, z_r_in, f = symbols('s_in z_r_in f')
+    assert gaussian_conj(
+        s_in, z_r_in, f)[0] == 1/(-1/(s_in + z_r_in**2/(-f + s_in)) + 1/f)
+    assert gaussian_conj(
+        s_in, z_r_in, f)[1] == z_r_in/(1 - s_in**2/f**2 + z_r_in**2/f**2)
+    assert gaussian_conj(
+        s_in, z_r_in, f)[2] == 1/sqrt(1 - s_in**2/f**2 + z_r_in**2/f**2)
+
+    l, w_i, w_o, f = symbols('l w_i w_o f')
+    assert conjugate_gauss_beams(l, w_i, w_o, f=f)[0] == f*(
+        -sqrt(w_i**2/w_o**2 - pi**2*w_i**4/(f**2*l**2)) + 1)
+    assert factor(conjugate_gauss_beams(l, w_i, w_o, f=f)[1]) == f*w_o**2*(
+        w_i**2/w_o**2 - sqrt(w_i**2/w_o**2 - pi**2*w_i**4/(f**2*l**2)))/w_i**2
+    assert conjugate_gauss_beams(l, w_i, w_o, f=f)[2] == f
+
+    z, l, w_0 = symbols('z l w_0', positive=True)
+    p = BeamParameter(l, z, w=w_0)
+    assert p.radius == z*(pi**2*w_0**4/(l**2*z**2) + 1)
+    assert p.w == w_0*sqrt(l**2*z**2/(pi**2*w_0**4) + 1)
+    assert p.w_0 == w_0
+    assert p.divergence == l/(pi*w_0)
+    assert p.gouy == atan2(z, pi*w_0**2/l)
+    assert p.waist_approximation_limit == 2*l/pi
+
+    p = BeamParameter(530e-9, 1, w=1e-3, n=2)
+    assert streq(p.q, 1 + 3.77358490566038*I*pi)
+    assert streq(N(p.z_r), Float(11.8550666173200))
+    assert streq(N(p.w_0), Float(0.00100000000000000))
diff --git a/shared/package-files/40/40cb4b5cab6bb42911ccba0c53bbda08dbc2de4ec5ec9a15d44519d88d6ad6d8/crashhandler.py b/shared/package-files/40/40cb4b5cab6bb42911ccba0c53bbda08dbc2de4ec5ec9a15d44519d88d6ad6d8/crashhandler.py
new file mode 100644
index 0000000000000000000000000000000000000000..f60a75bbc5bfce7bd3aa8568fb3274d44cb05f90
--- /dev/null
+++ b/shared/package-files/40/40cb4b5cab6bb42911ccba0c53bbda08dbc2de4ec5ec9a15d44519d88d6ad6d8/crashhandler.py
@@ -0,0 +1,236 @@
+# encoding: utf-8
+"""sys.excepthook for IPython itself, leaves a detailed report on disk.
+
+Authors:
+
+* Fernando Perez
+* Brian E. Granger
+"""
+
+#-----------------------------------------------------------------------------
+#  Copyright (C) 2001-2007 Fernando Perez. <fperez@colorado.edu>
+#  Copyright (C) 2008-2011  The IPython Development Team
+#
+#  Distributed under the terms of the BSD License.  The full license is in
+#  the file COPYING, distributed as part of this software.
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+import sys
+import traceback
+from pprint import pformat
+from pathlib import Path
+
+from IPython.core import ultratb
+from IPython.core.release import author_email
+from IPython.utils.sysinfo import sys_info
+from IPython.utils.py3compat import input
+
+from IPython.core.release import __version__ as version
+
+from typing import Optional
+
+#-----------------------------------------------------------------------------
+# Code
+#-----------------------------------------------------------------------------
+
+# Template for the user message.
+_default_message_template = """\
+Oops, {app_name} crashed. We do our best to make it stable, but...
+
+A crash report was automatically generated with the following information:
+  - A verbatim copy of the crash traceback.
+  - A copy of your input history during this session.
+  - Data on your current {app_name} configuration.
+
+It was left in the file named:
+\t'{crash_report_fname}'
+If you can email this file to the developers, the information in it will help
+them in understanding and correcting the problem.
+
+You can mail it to: {contact_name} at {contact_email}
+with the subject '{app_name} Crash Report'.
+
+If you want to do it now, the following command will work (under Unix):
+mail -s '{app_name} Crash Report' {contact_email} < {crash_report_fname}
+
+In your email, please also include information about:
+- The operating system under which the crash happened: Linux, macOS, Windows,
+  other, and which exact version (for example: Ubuntu 16.04.3, macOS 10.13.2,
+  Windows 10 Pro), and whether it is 32-bit or 64-bit;
+- How {app_name} was installed: using pip or conda, from GitHub, as part of
+  a Docker container, or other, providing more detail if possible;
+- How to reproduce the crash: what exact sequence of instructions can one
+  input to get the same crash? Ideally, find a minimal yet complete sequence
+  of instructions that yields the crash.
+
+To ensure accurate tracking of this issue, please file a report about it at:
+{bug_tracker}
+"""
+
+_lite_message_template = """
+If you suspect this is an IPython {version} bug, please report it at:
+    https://github.com/ipython/ipython/issues
+or send an email to the mailing list at {email}
+
+You can print a more detailed traceback right now with "%tb", or use "%debug"
+to interactively debug it.
+
+Extra-detailed tracebacks for bug-reporting purposes can be enabled via:
+    {config}Application.verbose_crash=True
+"""
+
+
+class CrashHandler(object):
+    """Customizable crash handlers for IPython applications.
+
+    Instances of this class provide a :meth:`__call__` method which can be
+    used as a ``sys.excepthook``.  The :meth:`__call__` signature is::
+
+        def __call__(self, etype, evalue, etb)
+    """
+
+    message_template = _default_message_template
+    section_sep = '\n\n'+'*'*75+'\n\n'
+
+    def __init__(
+        self,
+        app,
+        contact_name: Optional[str] = None,
+        contact_email: Optional[str] = None,
+        bug_tracker: Optional[str] = None,
+        show_crash_traceback: bool = True,
+        call_pdb: bool = False,
+    ):
+        """Create a new crash handler
+
+        Parameters
+        ----------
+        app : Application
+            A running :class:`Application` instance, which will be queried at
+            crash time for internal information.
+        contact_name : str
+            A string with the name of the person to contact.
+        contact_email : str
+            A string with the email address of the contact.
+        bug_tracker : str
+            A string with the URL for your project's bug tracker.
+        show_crash_traceback : bool
+            If false, don't print the crash traceback on stderr, only generate
+            the on-disk report
+        call_pdb
+            Whether to call pdb on crash
+
+        Attributes
+        ----------
+        These instances contain some non-argument attributes which allow for
+        further customization of the crash handler's behavior. Please see the
+        source for further details.
+
+        """
+        self.crash_report_fname = "Crash_report_%s.txt" % app.name
+        self.app = app
+        self.call_pdb = call_pdb
+        #self.call_pdb = True # dbg
+        self.show_crash_traceback = show_crash_traceback
+        self.info = dict(app_name = app.name,
+                    contact_name = contact_name,
+                    contact_email = contact_email,
+                    bug_tracker = bug_tracker,
+                    crash_report_fname = self.crash_report_fname)
+
+
+    def __call__(self, etype, evalue, etb):
+        """Handle an exception, call for compatible with sys.excepthook"""
+        
+        # do not allow the crash handler to be called twice without reinstalling it
+        # this prevents unlikely errors in the crash handling from entering an
+        # infinite loop.
+        sys.excepthook = sys.__excepthook__
+        
+        # Report tracebacks shouldn't use color in general (safer for users)
+        color_scheme = 'NoColor'
+
+        # Use this ONLY for developer debugging (keep commented out for release)
+        #color_scheme = 'Linux'   # dbg
+        try:
+            rptdir = self.app.ipython_dir
+        except:
+            rptdir = Path.cwd()
+        if rptdir is None or not Path.is_dir(rptdir):
+            rptdir = Path.cwd()
+        report_name = rptdir / self.crash_report_fname
+        # write the report filename into the instance dict so it can get
+        # properly expanded out in the user message template
+        self.crash_report_fname = report_name
+        self.info['crash_report_fname'] = report_name
+        TBhandler = ultratb.VerboseTB(
+            color_scheme=color_scheme,
+            long_header=1,
+            call_pdb=self.call_pdb,
+        )
+        if self.call_pdb:
+            TBhandler(etype,evalue,etb)
+            return
+        else:
+            traceback = TBhandler.text(etype,evalue,etb,context=31)
+
+        # print traceback to screen
+        if self.show_crash_traceback:
+            print(traceback, file=sys.stderr)
+
+        # and generate a complete report on disk
+        try:
+            report = open(report_name, "w", encoding="utf-8")
+        except:
+            print('Could not create crash report on disk.', file=sys.stderr)
+            return
+
+        with report:
+            # Inform user on stderr of what happened
+            print('\n'+'*'*70+'\n', file=sys.stderr)
+            print(self.message_template.format(**self.info), file=sys.stderr)
+
+            # Construct report on disk
+            report.write(self.make_report(traceback))
+
+        input("Hit <Enter> to quit (your terminal may close):")
+
+    def make_report(self,traceback):
+        """Return a string containing a crash report."""
+
+        sec_sep = self.section_sep
+
+        report = ['*'*75+'\n\n'+'IPython post-mortem report\n\n']
+        rpt_add = report.append
+        rpt_add(sys_info())
+
+        try:
+            config = pformat(self.app.config)
+            rpt_add(sec_sep)
+            rpt_add('Application name: %s\n\n' % self.app_name)
+            rpt_add('Current user configuration structure:\n\n')
+            rpt_add(config)
+        except:
+            pass
+        rpt_add(sec_sep+'Crash traceback:\n\n' + traceback)
+
+        return ''.join(report)
+
+
+def crash_handler_lite(etype, evalue, tb):
+    """a light excepthook, adding a small message to the usual traceback"""
+    traceback.print_exception(etype, evalue, tb)
+    
+    from IPython.core.interactiveshell import InteractiveShell
+    if InteractiveShell.initialized():
+        # we are in a Shell environment, give %magic example
+        config = "%config "
+    else:
+        # we are not in a shell, show generic config
+        config = "c."
+    print(_lite_message_template.format(email=author_email, config=config, version=version), file=sys.stderr)
+
diff --git a/shared/package-files/40/40cc65de6ffb5c07195c4c70a94e2f91add3f73793ea2c0c638f10788eb2fc3a/ordered_set.py b/shared/package-files/40/40cc65de6ffb5c07195c4c70a94e2f91add3f73793ea2c0c638f10788eb2fc3a/ordered_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe246f125f9c443fa4ca2603b92cb82d8df173db
--- /dev/null
+++ b/shared/package-files/40/40cc65de6ffb5c07195c4c70a94e2f91add3f73793ea2c0c638f10788eb2fc3a/ordered_set.py
@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Iterator, Set
+from typing import Any, Generic, TypeVar
+
+ItemType = TypeVar("ItemType")
+
+
+class OrderedSet(Set, Generic[ItemType]):
+    """
+    A set-like container that retains the order in which item were added to the
+    set.
+    """
+
+    def __init__(self, values: Iterable[ItemType] = ()) -> None:
+        self.values = dict.fromkeys(values)
+
+    def __contains__(self, item: Any) -> bool:
+        return item in self.values
+
+    def __iter__(self) -> Iterator[ItemType]:
+        return iter(self.values)
+
+    def __len__(self) -> int:
+        return len(self.values)
+
+    def __repr__(self) -> str:
+        return f"{{{', '.join(repr(v) for v in self.values)}}}"
diff --git a/shared/package-files/40/40cd58b7484e1c19d44ef767d7868545475f31013d3f6ab51e9cdeb9dfa7f9db/test_densetools.py b/shared/package-files/40/40cd58b7484e1c19d44ef767d7868545475f31013d3f6ab51e9cdeb9dfa7f9db/test_densetools.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8efb52f52309187a43bb5b9cd352dc821dcecb6
--- /dev/null
+++ b/shared/package-files/40/40cd58b7484e1c19d44ef767d7868545475f31013d3f6ab51e9cdeb9dfa7f9db/test_densetools.py
@@ -0,0 +1,668 @@
+"""Tests for dense recursive polynomials' tools. """
+
+from sympy.polys.densebasic import (
+    dup_normal, dmp_normal,
+    dup_from_raw_dict,
+    dmp_convert, dmp_swap,
+)
+
+from sympy.polys.densearith import dmp_mul_ground
+
+from sympy.polys.densetools import (
+    dup_clear_denoms, dmp_clear_denoms,
+    dup_integrate, dmp_integrate, dmp_integrate_in,
+    dup_diff, dmp_diff, dmp_diff_in,
+    dup_eval, dmp_eval, dmp_eval_in,
+    dmp_eval_tail, dmp_diff_eval_in,
+    dup_trunc, dmp_trunc, dmp_ground_trunc,
+    dup_monic, dmp_ground_monic,
+    dup_content, dmp_ground_content,
+    dup_primitive, dmp_ground_primitive,
+    dup_extract, dmp_ground_extract,
+    dup_real_imag,
+    dup_mirror, dup_scale, dup_shift,
+    dup_transform,
+    dup_compose, dmp_compose,
+    dup_decompose,
+    dmp_lift,
+    dup_sign_variations,
+    dup_revert, dmp_revert,
+)
+
+from sympy.polys.polyclasses import ANP
+
+from sympy.polys.polyerrors import (
+    MultivariatePolynomialError,
+    ExactQuotientFailed,
+    NotReversible,
+    DomainError,
+)
+
+from sympy.polys.specialpolys import f_polys
+
+from sympy.polys.domains import FF, ZZ, QQ, EX
+from sympy.polys.rings import ring
+
+from sympy.core.numbers import I
+from sympy.core.singleton import S
+from sympy.functions.elementary.trigonometric import sin
+
+from sympy.abc import x
+
+from sympy.testing.pytest import raises
+
+f_0, f_1, f_2, f_3, f_4, f_5, f_6 = [ f.to_dense() for f in f_polys() ]
+
+def test_dup_integrate():
+    assert dup_integrate([], 1, QQ) == []
+    assert dup_integrate([], 2, QQ) == []
+
+    assert dup_integrate([QQ(1)], 1, QQ) == [QQ(1), QQ(0)]
+    assert dup_integrate([QQ(1)], 2, QQ) == [QQ(1, 2), QQ(0), QQ(0)]
+
+    assert dup_integrate([QQ(1), QQ(2), QQ(3)], 0, QQ) == \
+        [QQ(1), QQ(2), QQ(3)]
+    assert dup_integrate([QQ(1), QQ(2), QQ(3)], 1, QQ) == \
+        [QQ(1, 3), QQ(1), QQ(3), QQ(0)]
+    assert dup_integrate([QQ(1), QQ(2), QQ(3)], 2, QQ) == \
+        [QQ(1, 12), QQ(1, 3), QQ(3, 2), QQ(0), QQ(0)]
+    assert dup_integrate([QQ(1), QQ(2), QQ(3)], 3, QQ) == \
+        [QQ(1, 60), QQ(1, 12), QQ(1, 2), QQ(0), QQ(0), QQ(0)]
+
+    assert dup_integrate(dup_from_raw_dict({29: QQ(17)}, QQ), 3, QQ) == \
+        dup_from_raw_dict({32: QQ(17, 29760)}, QQ)
+
+    assert dup_integrate(dup_from_raw_dict({29: QQ(17), 5: QQ(1, 2)}, QQ), 3, QQ) == \
+        dup_from_raw_dict({32: QQ(17, 29760), 8: QQ(1, 672)}, QQ)
+
+
+def test_dmp_integrate():
+    assert dmp_integrate([[[]]], 1, 2, QQ) == [[[]]]
+    assert dmp_integrate([[[]]], 2, 2, QQ) == [[[]]]
+
+    assert dmp_integrate([[[QQ(1)]]], 1, 2, QQ) == [[[QQ(1)]], [[]]]
+    assert dmp_integrate([[[QQ(1)]]], 2, 2, QQ) == [[[QQ(1, 2)]], [[]], [[]]]
+
+    assert dmp_integrate([[QQ(1)], [QQ(2)], [QQ(3)]], 0, 1, QQ) == \
+        [[QQ(1)], [QQ(2)], [QQ(3)]]
+    assert dmp_integrate([[QQ(1)], [QQ(2)], [QQ(3)]], 1, 1, QQ) == \
+        [[QQ(1, 3)], [QQ(1)], [QQ(3)], []]
+    assert dmp_integrate([[QQ(1)], [QQ(2)], [QQ(3)]], 2, 1, QQ) == \
+        [[QQ(1, 12)], [QQ(1, 3)], [QQ(3, 2)], [], []]
+    assert dmp_integrate([[QQ(1)], [QQ(2)], [QQ(3)]], 3, 1, QQ) == \
+        [[QQ(1, 60)], [QQ(1, 12)], [QQ(1, 2)], [], [], []]
+
+
+def test_dmp_integrate_in():
+    f = dmp_convert(f_6, 3, ZZ, QQ)
+
+    assert dmp_integrate_in(f, 2, 1, 3, QQ) == \
+        dmp_swap(
+            dmp_integrate(dmp_swap(f, 0, 1, 3, QQ), 2, 3, QQ), 0, 1, 3, QQ)
+    assert dmp_integrate_in(f, 3, 1, 3, QQ) == \
+        dmp_swap(
+            dmp_integrate(dmp_swap(f, 0, 1, 3, QQ), 3, 3, QQ), 0, 1, 3, QQ)
+    assert dmp_integrate_in(f, 2, 2, 3, QQ) == \
+        dmp_swap(
+            dmp_integrate(dmp_swap(f, 0, 2, 3, QQ), 2, 3, QQ), 0, 2, 3, QQ)
+    assert dmp_integrate_in(f, 3, 2, 3, QQ) == \
+        dmp_swap(
+            dmp_integrate(dmp_swap(f, 0, 2, 3, QQ), 3, 3, QQ), 0, 2, 3, QQ)
+
+
+def test_dup_diff():
+    assert dup_diff([], 1, ZZ) == []
+    assert dup_diff([7], 1, ZZ) == []
+    assert dup_diff([2, 7], 1, ZZ) == [2]
+    assert dup_diff([1, 2, 1], 1, ZZ) == [2, 2]
+    assert dup_diff([1, 2, 3, 4], 1, ZZ) == [3, 4, 3]
+    assert dup_diff([1, -1, 0, 0, 2], 1, ZZ) == [4, -3, 0, 0]
+
+    f = dup_normal([17, 34, 56, -345, 23, 76, 0, 0, 12, 3, 7], ZZ)
+
+    assert dup_diff(f, 0, ZZ) == f
+    assert dup_diff(f, 1, ZZ) == [170, 306, 448, -2415, 138, 380, 0, 0, 24, 3]
+    assert dup_diff(f, 2, ZZ) == dup_diff(dup_diff(f, 1, ZZ), 1, ZZ)
+    assert dup_diff(
+        f, 3, ZZ) == dup_diff(dup_diff(dup_diff(f, 1, ZZ), 1, ZZ), 1, ZZ)
+
+    K = FF(3)
+    f = dup_normal([17, 34, 56, -345, 23, 76, 0, 0, 12, 3, 7], K)
+
+    assert dup_diff(f, 1, K) == dup_normal([2, 0, 1, 0, 0, 2, 0, 0, 0, 0], K)
+    assert dup_diff(f, 2, K) == dup_normal([1, 0, 0, 2, 0, 0, 0], K)
+    assert dup_diff(f, 3, K) == dup_normal([], K)
+
+    assert dup_diff(f, 0, K) == f
+    assert dup_diff(f, 2, K) == dup_diff(dup_diff(f, 1, K), 1, K)
+    assert dup_diff(
+        f, 3, K) == dup_diff(dup_diff(dup_diff(f, 1, K), 1, K), 1, K)
+
+
+def test_dmp_diff():
+    assert dmp_diff([], 1, 0, ZZ) == []
+    assert dmp_diff([[]], 1, 1, ZZ) == [[]]
+    assert dmp_diff([[[]]], 1, 2, ZZ) == [[[]]]
+
+    assert dmp_diff([[[1], [2]]], 1, 2, ZZ) == [[[]]]
+
+    assert dmp_diff([[[1]], [[]]], 1, 2, ZZ) == [[[1]]]
+    assert dmp_diff([[[3]], [[1]], [[]]], 1, 2, ZZ) == [[[6]], [[1]]]
+
+    assert dmp_diff([1, -1, 0, 0, 2], 1, 0, ZZ) == \
+        dup_diff([1, -1, 0, 0, 2], 1, ZZ)
+
+    assert dmp_diff(f_6, 0, 3, ZZ) == f_6
+    assert dmp_diff(f_6, 1, 3, ZZ) == [[[[8460]], [[]]],
+                       [[[135, 0, 0], [], [], [-135, 0, 0]]],
+                       [[[]]],
+                       [[[-423]], [[-47]], [[]], [[141], [], [94, 0], []], [[]]]]
+    assert dmp_diff(
+        f_6, 2, 3, ZZ) == dmp_diff(dmp_diff(f_6, 1, 3, ZZ), 1, 3, ZZ)
+    assert dmp_diff(f_6, 3, 3, ZZ) == dmp_diff(
+        dmp_diff(dmp_diff(f_6, 1, 3, ZZ), 1, 3, ZZ), 1, 3, ZZ)
+
+    K = FF(23)
+    F_6 = dmp_normal(f_6, 3, K)
+
+    assert dmp_diff(F_6, 0, 3, K) == F_6
+    assert dmp_diff(F_6, 1, 3, K) == dmp_diff(F_6, 1, 3, K)
+    assert dmp_diff(F_6, 2, 3, K) == dmp_diff(dmp_diff(F_6, 1, 3, K), 1, 3, K)
+    assert dmp_diff(F_6, 3, 3, K) == dmp_diff(
+        dmp_diff(dmp_diff(F_6, 1, 3, K), 1, 3, K), 1, 3, K)
+
+
+def test_dmp_diff_in():
+    assert dmp_diff_in(f_6, 2, 1, 3, ZZ) == \
+        dmp_swap(dmp_diff(dmp_swap(f_6, 0, 1, 3, ZZ), 2, 3, ZZ), 0, 1, 3, ZZ)
+    assert dmp_diff_in(f_6, 3, 1, 3, ZZ) == \
+        dmp_swap(dmp_diff(dmp_swap(f_6, 0, 1, 3, ZZ), 3, 3, ZZ), 0, 1, 3, ZZ)
+    assert dmp_diff_in(f_6, 2, 2, 3, ZZ) == \
+        dmp_swap(dmp_diff(dmp_swap(f_6, 0, 2, 3, ZZ), 2, 3, ZZ), 0, 2, 3, ZZ)
+    assert dmp_diff_in(f_6, 3, 2, 3, ZZ) == \
+        dmp_swap(dmp_diff(dmp_swap(f_6, 0, 2, 3, ZZ), 3, 3, ZZ), 0, 2, 3, ZZ)
+
+
+def test_dup_eval():
+    assert dup_eval([], 7, ZZ) == 0
+    assert dup_eval([1, 2], 0, ZZ) == 2
+    assert dup_eval([1, 2, 3], 7, ZZ) == 66
+
+
+def test_dmp_eval():
+    assert dmp_eval([], 3, 0, ZZ) == 0
+
+    assert dmp_eval([[]], 3, 1, ZZ) == []
+    assert dmp_eval([[[]]], 3, 2, ZZ) == [[]]
+
+    assert dmp_eval([[1, 2]], 0, 1, ZZ) == [1, 2]
+
+    assert dmp_eval([[[1]]], 3, 2, ZZ) == [[1]]
+    assert dmp_eval([[[1, 2]]], 3, 2, ZZ) == [[1, 2]]
+
+    assert dmp_eval([[3, 2], [1, 2]], 3, 1, ZZ) == [10, 8]
+    assert dmp_eval([[[3, 2]], [[1, 2]]], 3, 2, ZZ) == [[10, 8]]
+
+
+def test_dmp_eval_in():
+    assert dmp_eval_in(
+        f_6, -2, 1, 3, ZZ) == dmp_eval(dmp_swap(f_6, 0, 1, 3, ZZ), -2, 3, ZZ)
+    assert dmp_eval_in(
+        f_6, 7, 1, 3, ZZ) == dmp_eval(dmp_swap(f_6, 0, 1, 3, ZZ), 7, 3, ZZ)
+    assert dmp_eval_in(f_6, -2, 2, 3, ZZ) == dmp_swap(
+        dmp_eval(dmp_swap(f_6, 0, 2, 3, ZZ), -2, 3, ZZ), 0, 1, 2, ZZ)
+    assert dmp_eval_in(f_6, 7, 2, 3, ZZ) == dmp_swap(
+        dmp_eval(dmp_swap(f_6, 0, 2, 3, ZZ), 7, 3, ZZ), 0, 1, 2, ZZ)
+
+    f = [[[int(45)]], [[]], [[]], [[int(-9)], [-1], [], [int(3), int(0), int(10), int(0)]]]
+
+    assert dmp_eval_in(f, -2, 2, 2, ZZ) == \
+        [[45], [], [], [-9, -1, 0, -44]]
+
+
+def test_dmp_eval_tail():
+    assert dmp_eval_tail([[]], [1], 1, ZZ) == []
+    assert dmp_eval_tail([[[]]], [1], 2, ZZ) == [[]]
+    assert dmp_eval_tail([[[]]], [1, 2], 2, ZZ) == []
+
+    assert dmp_eval_tail(f_0, [], 2, ZZ) == f_0
+
+    assert dmp_eval_tail(f_0, [1, -17, 8], 2, ZZ) == 84496
+    assert dmp_eval_tail(f_0, [-17, 8], 2, ZZ) == [-1409, 3, 85902]
+    assert dmp_eval_tail(f_0, [8], 2, ZZ) == [[83, 2], [3], [302, 81, 1]]
+
+    assert dmp_eval_tail(f_1, [-17, 8], 2, ZZ) == [-136, 15699, 9166, -27144]
+
+    assert dmp_eval_tail(
+        f_2, [-12, 3], 2, ZZ) == [-1377, 0, -702, -1224, 0, -624]
+    assert dmp_eval_tail(
+        f_3, [-12, 3], 2, ZZ) == [144, 82, -5181, -28872, -14868, -540]
+
+    assert dmp_eval_tail(
+        f_4, [25, -1], 2, ZZ) == [152587890625, 9765625, -59605407714843750,
+        -3839159765625, -1562475, 9536712644531250, 610349546750, -4, 24414375000, 1562520]
+    assert dmp_eval_tail(f_5, [25, -1], 2, ZZ) == [-1, -78, -2028, -17576]
+
+    assert dmp_eval_tail(f_6, [0, 2, 4], 3, ZZ) == [5040, 0, 0, 4480]
+
+
+def test_dmp_diff_eval_in():
+    assert dmp_diff_eval_in(f_6, 2, 7, 1, 3, ZZ) == \
+        dmp_eval(dmp_diff(dmp_swap(f_6, 0, 1, 3, ZZ), 2, 3, ZZ), 7, 3, ZZ)
+
+
+def test_dup_revert():
+    f = [-QQ(1, 720), QQ(0), QQ(1, 24), QQ(0), -QQ(1, 2), QQ(0), QQ(1)]
+    g = [QQ(61, 720), QQ(0), QQ(5, 24), QQ(0), QQ(1, 2), QQ(0), QQ(1)]
+
+    assert dup_revert(f, 8, QQ) == g
+
+    raises(NotReversible, lambda: dup_revert([QQ(1), QQ(0)], 3, QQ))
+
+
+def test_dmp_revert():
+    f = [-QQ(1, 720), QQ(0), QQ(1, 24), QQ(0), -QQ(1, 2), QQ(0), QQ(1)]
+    g = [QQ(61, 720), QQ(0), QQ(5, 24), QQ(0), QQ(1, 2), QQ(0), QQ(1)]
+
+    assert dmp_revert(f, 8, 0, QQ) == g
+
+    raises(MultivariatePolynomialError, lambda: dmp_revert([[1]], 2, 1, QQ))
+
+
+def test_dup_trunc():
+    assert dup_trunc([1, 2, 3, 4, 5, 6], ZZ(3), ZZ) == [1, -1, 0, 1, -1, 0]
+    assert dup_trunc([6, 5, 4, 3, 2, 1], ZZ(3), ZZ) == [-1, 1, 0, -1, 1]
+
+
+def test_dmp_trunc():
+    assert dmp_trunc([[]], [1, 2], 2, ZZ) == [[]]
+    assert dmp_trunc([[1, 2], [1, 4, 1], [1]], [1, 2], 1, ZZ) == [[-3], [1]]
+
+
+def test_dmp_ground_trunc():
+    assert dmp_ground_trunc(f_0, ZZ(3), 2, ZZ) == \
+        dmp_normal(
+            [[[1, -1, 0], [-1]], [[]], [[1, -1, 0], [1, -1, 1], [1]]], 2, ZZ)
+
+
+def test_dup_monic():
+    assert dup_monic([3, 6, 9], ZZ) == [1, 2, 3]
+
+    raises(ExactQuotientFailed, lambda: dup_monic([3, 4, 5], ZZ))
+
+    assert dup_monic([], QQ) == []
+    assert dup_monic([QQ(1)], QQ) == [QQ(1)]
+    assert dup_monic([QQ(7), QQ(1), QQ(21)], QQ) == [QQ(1), QQ(1, 7), QQ(3)]
+
+
+def test_dmp_ground_monic():
+    assert dmp_ground_monic([[3], [6], [9]], 1, ZZ) == [[1], [2], [3]]
+
+    raises(
+        ExactQuotientFailed, lambda: dmp_ground_monic([[3], [4], [5]], 1, ZZ))
+
+    assert dmp_ground_monic([[]], 1, QQ) == [[]]
+    assert dmp_ground_monic([[QQ(1)]], 1, QQ) == [[QQ(1)]]
+    assert dmp_ground_monic(
+        [[QQ(7)], [QQ(1)], [QQ(21)]], 1, QQ) == [[QQ(1)], [QQ(1, 7)], [QQ(3)]]
+
+
+def test_dup_content():
+    assert dup_content([], ZZ) == ZZ(0)
+    assert dup_content([1], ZZ) == ZZ(1)
+    assert dup_content([-1], ZZ) == ZZ(1)
+    assert dup_content([1, 1], ZZ) == ZZ(1)
+    assert dup_content([2, 2], ZZ) == ZZ(2)
+    assert dup_content([1, 2, 1], ZZ) == ZZ(1)
+    assert dup_content([2, 4, 2], ZZ) == ZZ(2)
+
+    assert dup_content([QQ(2, 3), QQ(4, 9)], QQ) == QQ(2, 9)
+    assert dup_content([QQ(2, 3), QQ(4, 5)], QQ) == QQ(2, 15)
+
+
+def test_dmp_ground_content():
+    assert dmp_ground_content([[]], 1, ZZ) == ZZ(0)
+    assert dmp_ground_content([[]], 1, QQ) == QQ(0)
+    assert dmp_ground_content([[1]], 1, ZZ) == ZZ(1)
+    assert dmp_ground_content([[-1]], 1, ZZ) == ZZ(1)
+    assert dmp_ground_content([[1], [1]], 1, ZZ) == ZZ(1)
+    assert dmp_ground_content([[2], [2]], 1, ZZ) == ZZ(2)
+    assert dmp_ground_content([[1], [2], [1]], 1, ZZ) == ZZ(1)
+    assert dmp_ground_content([[2], [4], [2]], 1, ZZ) == ZZ(2)
+
+    assert dmp_ground_content([[QQ(2, 3)], [QQ(4, 9)]], 1, QQ) == QQ(2, 9)
+    assert dmp_ground_content([[QQ(2, 3)], [QQ(4, 5)]], 1, QQ) == QQ(2, 15)
+
+    assert dmp_ground_content(f_0, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_0, ZZ(2), 2, ZZ), 2, ZZ) == ZZ(2)
+
+    assert dmp_ground_content(f_1, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_1, ZZ(3), 2, ZZ), 2, ZZ) == ZZ(3)
+
+    assert dmp_ground_content(f_2, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_2, ZZ(4), 2, ZZ), 2, ZZ) == ZZ(4)
+
+    assert dmp_ground_content(f_3, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_3, ZZ(5), 2, ZZ), 2, ZZ) == ZZ(5)
+
+    assert dmp_ground_content(f_4, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_4, ZZ(6), 2, ZZ), 2, ZZ) == ZZ(6)
+
+    assert dmp_ground_content(f_5, 2, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_5, ZZ(7), 2, ZZ), 2, ZZ) == ZZ(7)
+
+    assert dmp_ground_content(f_6, 3, ZZ) == ZZ(1)
+    assert dmp_ground_content(
+        dmp_mul_ground(f_6, ZZ(8), 3, ZZ), 3, ZZ) == ZZ(8)
+
+
+def test_dup_primitive():
+    assert dup_primitive([], ZZ) == (ZZ(0), [])
+    assert dup_primitive([ZZ(1)], ZZ) == (ZZ(1), [ZZ(1)])
+    assert dup_primitive([ZZ(1), ZZ(1)], ZZ) == (ZZ(1), [ZZ(1), ZZ(1)])
+    assert dup_primitive([ZZ(2), ZZ(2)], ZZ) == (ZZ(2), [ZZ(1), ZZ(1)])
+    assert dup_primitive(
+        [ZZ(1), ZZ(2), ZZ(1)], ZZ) == (ZZ(1), [ZZ(1), ZZ(2), ZZ(1)])
+    assert dup_primitive(
+        [ZZ(2), ZZ(4), ZZ(2)], ZZ) == (ZZ(2), [ZZ(1), ZZ(2), ZZ(1)])
+
+    assert dup_primitive([], QQ) == (QQ(0), [])
+    assert dup_primitive([QQ(1)], QQ) == (QQ(1), [QQ(1)])
+    assert dup_primitive([QQ(1), QQ(1)], QQ) == (QQ(1), [QQ(1), QQ(1)])
+    assert dup_primitive([QQ(2), QQ(2)], QQ) == (QQ(2), [QQ(1), QQ(1)])
+    assert dup_primitive(
+        [QQ(1), QQ(2), QQ(1)], QQ) == (QQ(1), [QQ(1), QQ(2), QQ(1)])
+    assert dup_primitive(
+        [QQ(2), QQ(4), QQ(2)], QQ) == (QQ(2), [QQ(1), QQ(2), QQ(1)])
+
+    assert dup_primitive(
+        [QQ(2, 3), QQ(4, 9)], QQ) == (QQ(2, 9), [QQ(3), QQ(2)])
+    assert dup_primitive(
+        [QQ(2, 3), QQ(4, 5)], QQ) == (QQ(2, 15), [QQ(5), QQ(6)])
+
+
+def test_dmp_ground_primitive():
+    assert dmp_ground_primitive([[]], 1, ZZ) == (ZZ(0), [[]])
+
+    assert dmp_ground_primitive(f_0, 2, ZZ) == (ZZ(1), f_0)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_0, ZZ(2), 2, ZZ), 2, ZZ) == (ZZ(2), f_0)
+
+    assert dmp_ground_primitive(f_1, 2, ZZ) == (ZZ(1), f_1)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_1, ZZ(3), 2, ZZ), 2, ZZ) == (ZZ(3), f_1)
+
+    assert dmp_ground_primitive(f_2, 2, ZZ) == (ZZ(1), f_2)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_2, ZZ(4), 2, ZZ), 2, ZZ) == (ZZ(4), f_2)
+
+    assert dmp_ground_primitive(f_3, 2, ZZ) == (ZZ(1), f_3)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_3, ZZ(5), 2, ZZ), 2, ZZ) == (ZZ(5), f_3)
+
+    assert dmp_ground_primitive(f_4, 2, ZZ) == (ZZ(1), f_4)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_4, ZZ(6), 2, ZZ), 2, ZZ) == (ZZ(6), f_4)
+
+    assert dmp_ground_primitive(f_5, 2, ZZ) == (ZZ(1), f_5)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_5, ZZ(7), 2, ZZ), 2, ZZ) == (ZZ(7), f_5)
+
+    assert dmp_ground_primitive(f_6, 3, ZZ) == (ZZ(1), f_6)
+    assert dmp_ground_primitive(
+        dmp_mul_ground(f_6, ZZ(8), 3, ZZ), 3, ZZ) == (ZZ(8), f_6)
+
+    assert dmp_ground_primitive([[ZZ(2)]], 1, ZZ) == (ZZ(2), [[ZZ(1)]])
+    assert dmp_ground_primitive([[QQ(2)]], 1, QQ) == (QQ(2), [[QQ(1)]])
+
+    assert dmp_ground_primitive(
+        [[QQ(2, 3)], [QQ(4, 9)]], 1, QQ) == (QQ(2, 9), [[QQ(3)], [QQ(2)]])
+    assert dmp_ground_primitive(
+        [[QQ(2, 3)], [QQ(4, 5)]], 1, QQ) == (QQ(2, 15), [[QQ(5)], [QQ(6)]])
+
+
+def test_dup_extract():
+    f = dup_normal([2930944, 0, 2198208, 0, 549552, 0, 45796], ZZ)
+    g = dup_normal([17585664, 0, 8792832, 0, 1099104, 0], ZZ)
+
+    F = dup_normal([64, 0, 48, 0, 12, 0, 1], ZZ)
+    G = dup_normal([384, 0, 192, 0, 24, 0], ZZ)
+
+    assert dup_extract(f, g, ZZ) == (45796, F, G)
+
+
+def test_dmp_ground_extract():
+    f = dmp_normal(
+        [[2930944], [], [2198208], [], [549552], [], [45796]], 1, ZZ)
+    g = dmp_normal([[17585664], [], [8792832], [], [1099104], []], 1, ZZ)
+
+    F = dmp_normal([[64], [], [48], [], [12], [], [1]], 1, ZZ)
+    G = dmp_normal([[384], [], [192], [], [24], []], 1, ZZ)
+
+    assert dmp_ground_extract(f, g, 1, ZZ) == (45796, F, G)
+
+
+def test_dup_real_imag():
+    assert dup_real_imag([], ZZ) == ([[]], [[]])
+    assert dup_real_imag([1], ZZ) == ([[1]], [[]])
+
+    assert dup_real_imag([1, 1], ZZ) == ([[1], [1]], [[1, 0]])
+    assert dup_real_imag([1, 2], ZZ) == ([[1], [2]], [[1, 0]])
+
+    assert dup_real_imag(
+        [1, 2, 3], ZZ) == ([[1], [2], [-1, 0, 3]], [[2, 0], [2, 0]])
+
+    raises(DomainError, lambda: dup_real_imag([EX(1), EX(2)], EX))
+
+
+def test_dup_mirror():
+    assert dup_mirror([], ZZ) == []
+    assert dup_mirror([1], ZZ) == [1]
+
+    assert dup_mirror([1, 2, 3, 4, 5], ZZ) == [1, -2, 3, -4, 5]
+    assert dup_mirror([1, 2, 3, 4, 5, 6], ZZ) == [-1, 2, -3, 4, -5, 6]
+
+
+def test_dup_scale():
+    assert dup_scale([], -1, ZZ) == []
+    assert dup_scale([1], -1, ZZ) == [1]
+
+    assert dup_scale([1, 2, 3, 4, 5], -1, ZZ) == [1, -2, 3, -4, 5]
+    assert dup_scale([1, 2, 3, 4, 5], -7, ZZ) == [2401, -686, 147, -28, 5]
+
+
+def test_dup_shift():
+    assert dup_shift([], 1, ZZ) == []
+    assert dup_shift([1], 1, ZZ) == [1]
+
+    assert dup_shift([1, 2, 3, 4, 5], 1, ZZ) == [1, 6, 15, 20, 15]
+    assert dup_shift([1, 2, 3, 4, 5], 7, ZZ) == [1, 30, 339, 1712, 3267]
+
+
+def test_dup_transform():
+    assert dup_transform([], [], [1, 1], ZZ) == []
+    assert dup_transform([], [1], [1, 1], ZZ) == []
+    assert dup_transform([], [1, 2], [1, 1], ZZ) == []
+
+    assert dup_transform([6, -5, 4, -3, 17], [1, -3, 4], [2, -3], ZZ) == \
+        [6, -82, 541, -2205, 6277, -12723, 17191, -13603, 4773]
+
+
+def test_dup_compose():
+    assert dup_compose([], [], ZZ) == []
+    assert dup_compose([], [1], ZZ) == []
+    assert dup_compose([], [1, 2], ZZ) == []
+
+    assert dup_compose([1], [], ZZ) == [1]
+
+    assert dup_compose([1, 2, 0], [], ZZ) == []
+    assert dup_compose([1, 2, 1], [], ZZ) == [1]
+
+    assert dup_compose([1, 2, 1], [1], ZZ) == [4]
+    assert dup_compose([1, 2, 1], [7], ZZ) == [64]
+
+    assert dup_compose([1, 2, 1], [1, -1], ZZ) == [1, 0, 0]
+    assert dup_compose([1, 2, 1], [1, 1], ZZ) == [1, 4, 4]
+    assert dup_compose([1, 2, 1], [1, 2, 1], ZZ) == [1, 4, 8, 8, 4]
+
+
+def test_dmp_compose():
+    assert dmp_compose([1, 2, 1], [1, 2, 1], 0, ZZ) == [1, 4, 8, 8, 4]
+
+    assert dmp_compose([[[]]], [[[]]], 2, ZZ) == [[[]]]
+    assert dmp_compose([[[]]], [[[1]]], 2, ZZ) == [[[]]]
+    assert dmp_compose([[[]]], [[[1]], [[2]]], 2, ZZ) == [[[]]]
+
+    assert dmp_compose([[[1]]], [], 2, ZZ) == [[[1]]]
+
+    assert dmp_compose([[1], [2], [ ]], [[]], 1, ZZ) == [[]]
+    assert dmp_compose([[1], [2], [1]], [[]], 1, ZZ) == [[1]]
+
+    assert dmp_compose([[1], [2], [1]], [[1]], 1, ZZ) == [[4]]
+    assert dmp_compose([[1], [2], [1]], [[7]], 1, ZZ) == [[64]]
+
+    assert dmp_compose([[1], [2], [1]], [[1], [-1]], 1, ZZ) == [[1], [ ], [ ]]
+    assert dmp_compose([[1], [2], [1]], [[1], [ 1]], 1, ZZ) == [[1], [4], [4]]
+
+    assert dmp_compose(
+        [[1], [2], [1]], [[1], [2], [1]], 1, ZZ) == [[1], [4], [8], [8], [4]]
+
+
+def test_dup_decompose():
+    assert dup_decompose([1], ZZ) == [[1]]
+
+    assert dup_decompose([1, 0], ZZ) == [[1, 0]]
+    assert dup_decompose([1, 0, 0, 0], ZZ) == [[1, 0, 0, 0]]
+
+    assert dup_decompose([1, 0, 0, 0, 0], ZZ) == [[1, 0, 0], [1, 0, 0]]
+    assert dup_decompose(
+        [1, 0, 0, 0, 0, 0, 0], ZZ) == [[1, 0, 0, 0], [1, 0, 0]]
+
+    assert dup_decompose([7, 0, 0, 0, 1], ZZ) == [[7, 0, 1], [1, 0, 0]]
+    assert dup_decompose([4, 0, 3, 0, 2], ZZ) == [[4, 3, 2], [1, 0, 0]]
+
+    f = [1, 0, 20, 0, 150, 0, 500, 0, 625, -2, 0, -10, 9]
+
+    assert dup_decompose(f, ZZ) == [[1, 0, 0, -2, 9], [1, 0, 5, 0]]
+
+    f = [2, 0, 40, 0, 300, 0, 1000, 0, 1250, -4, 0, -20, 18]
+
+    assert dup_decompose(f, ZZ) == [[2, 0, 0, -4, 18], [1, 0, 5, 0]]
+
+    f = [1, 0, 20, -8, 150, -120, 524, -600, 865, -1034, 600, -170, 29]
+
+    assert dup_decompose(f, ZZ) == [[1, -8, 24, -34, 29], [1, 0, 5, 0]]
+
+    R, t = ring("t", ZZ)
+    f = [6*t**2 - 42,
+         48*t**2 + 96,
+         144*t**2 + 648*t + 288,
+         624*t**2 + 864*t + 384,
+         108*t**3 + 312*t**2 + 432*t + 192]
+
+    assert dup_decompose(f, R.to_domain()) == [f]
+
+
+def test_dmp_lift():
+    q = [QQ(1, 1), QQ(0, 1), QQ(1, 1)]
+
+    f = [ANP([QQ(1, 1)], q, QQ), ANP([], q, QQ), ANP([], q, QQ),
+         ANP([QQ(1, 1), QQ(0, 1)], q, QQ), ANP([QQ(17, 1), QQ(0, 1)], q, QQ)]
+
+    assert dmp_lift(f, 0, QQ.algebraic_field(I)) == \
+        [QQ(1), QQ(0), QQ(0), QQ(0), QQ(0), QQ(0), QQ(2), QQ(0), QQ(578),
+         QQ(0), QQ(0), QQ(0), QQ(1), QQ(0), QQ(-578), QQ(0), QQ(83521)]
+
+    raises(DomainError, lambda: dmp_lift([EX(1), EX(2)], 0, EX))
+
+
+def test_dup_sign_variations():
+    assert dup_sign_variations([], ZZ) == 0
+    assert dup_sign_variations([1, 0], ZZ) == 0
+    assert dup_sign_variations([1, 0, 2], ZZ) == 0
+    assert dup_sign_variations([1, 0, 3, 0], ZZ) == 0
+    assert dup_sign_variations([1, 0, 4, 0, 5], ZZ) == 0
+
+    assert dup_sign_variations([-1, 0, 2], ZZ) == 1
+    assert dup_sign_variations([-1, 0, 3, 0], ZZ) == 1
+    assert dup_sign_variations([-1, 0, 4, 0, 5], ZZ) == 1
+
+    assert dup_sign_variations([-1, -4, -5], ZZ) == 0
+    assert dup_sign_variations([ 1, -4, -5], ZZ) == 1
+    assert dup_sign_variations([ 1, 4, -5], ZZ) == 1
+    assert dup_sign_variations([ 1, -4, 5], ZZ) == 2
+    assert dup_sign_variations([-1, 4, -5], ZZ) == 2
+    assert dup_sign_variations([-1, 4, 5], ZZ) == 1
+    assert dup_sign_variations([-1, -4, 5], ZZ) == 1
+    assert dup_sign_variations([ 1, 4, 5], ZZ) == 0
+
+    assert dup_sign_variations([-1, 0, -4, 0, -5], ZZ) == 0
+    assert dup_sign_variations([ 1, 0, -4, 0, -5], ZZ) == 1
+    assert dup_sign_variations([ 1, 0, 4, 0, -5], ZZ) == 1
+    assert dup_sign_variations([ 1, 0, -4, 0, 5], ZZ) == 2
+    assert dup_sign_variations([-1, 0, 4, 0, -5], ZZ) == 2
+    assert dup_sign_variations([-1, 0, 4, 0, 5], ZZ) == 1
+    assert dup_sign_variations([-1, 0, -4, 0, 5], ZZ) == 1
+    assert dup_sign_variations([ 1, 0, 4, 0, 5], ZZ) == 0
+
+
+def test_dup_clear_denoms():
+    assert dup_clear_denoms([], QQ, ZZ) == (ZZ(1), [])
+
+    assert dup_clear_denoms([QQ(1)], QQ, ZZ) == (ZZ(1), [QQ(1)])
+    assert dup_clear_denoms([QQ(7)], QQ, ZZ) == (ZZ(1), [QQ(7)])
+
+    assert dup_clear_denoms([QQ(7, 3)], QQ) == (ZZ(3), [QQ(7)])
+    assert dup_clear_denoms([QQ(7, 3)], QQ, ZZ) == (ZZ(3), [QQ(7)])
+
+    assert dup_clear_denoms(
+        [QQ(3), QQ(1), QQ(0)], QQ, ZZ) == (ZZ(1), [QQ(3), QQ(1), QQ(0)])
+    assert dup_clear_denoms(
+        [QQ(1), QQ(1, 2), QQ(0)], QQ, ZZ) == (ZZ(2), [QQ(2), QQ(1), QQ(0)])
+
+    assert dup_clear_denoms([QQ(3), QQ(
+        1), QQ(0)], QQ, ZZ, convert=True) == (ZZ(1), [ZZ(3), ZZ(1), ZZ(0)])
+    assert dup_clear_denoms([QQ(1), QQ(
+        1, 2), QQ(0)], QQ, ZZ, convert=True) == (ZZ(2), [ZZ(2), ZZ(1), ZZ(0)])
+
+    assert dup_clear_denoms(
+        [EX(S(3)/2), EX(S(9)/4)], EX) == (EX(4), [EX(6), EX(9)])
+
+    assert dup_clear_denoms([EX(7)], EX) == (EX(1), [EX(7)])
+    assert dup_clear_denoms([EX(sin(x)/x), EX(0)], EX) == (EX(x), [EX(sin(x)), EX(0)])
+
+
+def test_dmp_clear_denoms():
+    assert dmp_clear_denoms([[]], 1, QQ, ZZ) == (ZZ(1), [[]])
+
+    assert dmp_clear_denoms([[QQ(1)]], 1, QQ, ZZ) == (ZZ(1), [[QQ(1)]])
+    assert dmp_clear_denoms([[QQ(7)]], 1, QQ, ZZ) == (ZZ(1), [[QQ(7)]])
+
+    assert dmp_clear_denoms([[QQ(7, 3)]], 1, QQ) == (ZZ(3), [[QQ(7)]])
+    assert dmp_clear_denoms([[QQ(7, 3)]], 1, QQ, ZZ) == (ZZ(3), [[QQ(7)]])
+
+    assert dmp_clear_denoms(
+        [[QQ(3)], [QQ(1)], []], 1, QQ, ZZ) == (ZZ(1), [[QQ(3)], [QQ(1)], []])
+    assert dmp_clear_denoms([[QQ(
+        1)], [QQ(1, 2)], []], 1, QQ, ZZ) == (ZZ(2), [[QQ(2)], [QQ(1)], []])
+
+    assert dmp_clear_denoms([QQ(3), QQ(
+        1), QQ(0)], 0, QQ, ZZ, convert=True) == (ZZ(1), [ZZ(3), ZZ(1), ZZ(0)])
+    assert dmp_clear_denoms([QQ(1), QQ(1, 2), QQ(
+        0)], 0, QQ, ZZ, convert=True) == (ZZ(2), [ZZ(2), ZZ(1), ZZ(0)])
+
+    assert dmp_clear_denoms([[QQ(3)], [QQ(
+        1)], []], 1, QQ, ZZ, convert=True) == (ZZ(1), [[QQ(3)], [QQ(1)], []])
+    assert dmp_clear_denoms([[QQ(1)], [QQ(1, 2)], []], 1, QQ, ZZ,
+                            convert=True) == (ZZ(2), [[QQ(2)], [QQ(1)], []])
+
+    assert dmp_clear_denoms(
+        [[EX(S(3)/2)], [EX(S(9)/4)]], 1, EX) == (EX(4), [[EX(6)], [EX(9)]])
+    assert dmp_clear_denoms([[EX(7)]], 1, EX) == (EX(1), [[EX(7)]])
+    assert dmp_clear_denoms([[EX(sin(x)/x), EX(0)]], 1, EX) == (EX(x), [[EX(sin(x)), EX(0)]])
diff --git a/shared/package-files/40/40cd6ff1c19e5f074fde93949d0dcf8ca00f27d357ea0f53f8cb2e15205a72c9/grower.py b/shared/package-files/40/40cd6ff1c19e5f074fde93949d0dcf8ca00f27d357ea0f53f8cb2e15205a72c9/grower.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3dbbe7d82948412cf0c567cbc672cb018ee9817
--- /dev/null
+++ b/shared/package-files/40/40cd6ff1c19e5f074fde93949d0dcf8ca00f27d357ea0f53f8cb2e15205a72c9/grower.py
@@ -0,0 +1,821 @@
+"""
+This module contains the TreeGrower class.
+
+TreeGrower builds a regression tree fitting a Newton-Raphson step, based on
+the gradients and hessians of the training data.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numbers
+from heapq import heappop, heappush
+from timeit import default_timer as time
+
+import numpy as np
+
+from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
+
+from ._bitset import set_raw_bitset_from_binned_bitset
+from .common import (
+    PREDICTOR_RECORD_DTYPE,
+    X_BITSET_INNER_DTYPE,
+    MonotonicConstraint,
+)
+from .histogram import HistogramBuilder
+from .predictor import TreePredictor
+from .splitting import Splitter
+
+
+class TreeNode:
+    """Tree Node class used in TreeGrower.
+
+    This isn't used for prediction purposes, only for training (see
+    TreePredictor).
+
+    Parameters
+    ----------
+    depth : int
+        The depth of the node, i.e. its distance from the root.
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint32
+        The indices of the samples at the node.
+    partition_start : int
+        start position of the node's sample_indices in splitter.partition.
+    partition_stop : int
+        stop position of the node's sample_indices in splitter.partition.
+    sum_gradients : float
+        The sum of the gradients of the samples at the node.
+    sum_hessians : float
+        The sum of the hessians of the samples at the node.
+
+    Attributes
+    ----------
+    depth : int
+        The depth of the node, i.e. its distance from the root.
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint32
+        The indices of the samples at the node.
+    sum_gradients : float
+        The sum of the gradients of the samples at the node.
+    sum_hessians : float
+        The sum of the hessians of the samples at the node.
+    split_info : SplitInfo or None
+        The result of the split evaluation.
+    is_leaf : bool
+        True if node is a leaf
+    left_child : TreeNode or None
+        The left child of the node. None for leaves.
+    right_child : TreeNode or None
+        The right child of the node. None for leaves.
+    value : float or None
+        The value of the leaf, as computed in finalize_leaf(). None for
+        non-leaf nodes.
+    partition_start : int
+        start position of the node's sample_indices in splitter.partition.
+    partition_stop : int
+        stop position of the node's sample_indices in splitter.partition.
+    allowed_features : None or ndarray, dtype=int
+        Indices of features allowed to split for children.
+    interaction_cst_indices : None or list of ints
+        Indices of the interaction sets that have to be applied on splits of
+        child nodes. The fewer sets the stronger the constraint as fewer sets
+        contain fewer features.
+    children_lower_bound : float
+    children_upper_bound : float
+    """
+
+    def __init__(
+        self,
+        *,
+        depth,
+        sample_indices,
+        partition_start,
+        partition_stop,
+        sum_gradients,
+        sum_hessians,
+        value=None,
+    ):
+        self.depth = depth
+        self.sample_indices = sample_indices
+        self.n_samples = sample_indices.shape[0]
+        self.sum_gradients = sum_gradients
+        self.sum_hessians = sum_hessians
+        self.value = value
+        self.is_leaf = False
+        self.allowed_features = None
+        self.interaction_cst_indices = None
+        self.set_children_bounds(float("-inf"), float("+inf"))
+        self.split_info = None
+        self.left_child = None
+        self.right_child = None
+        self.histograms = None
+        # start and stop indices of the node in the splitter.partition
+        # array. Concretely,
+        # self.sample_indices = view(self.splitter.partition[start:stop])
+        # Please see the comments about splitter.partition and
+        # splitter.split_indices for more info about this design.
+        # These 2 attributes are only used in _update_raw_prediction, because we
+        # need to iterate over the leaves and I don't know how to efficiently
+        # store the sample_indices views because they're all of different sizes.
+        self.partition_start = partition_start
+        self.partition_stop = partition_stop
+
+    def set_children_bounds(self, lower, upper):
+        """Set children values bounds to respect monotonic constraints."""
+
+        # These are bounds for the node's *children* values, not the node's
+        # value. The bounds are used in the splitter when considering potential
+        # left and right child.
+        self.children_lower_bound = lower
+        self.children_upper_bound = upper
+
+    def __lt__(self, other_node):
+        """Comparison for priority queue.
+
+        Nodes with high gain are higher priority than nodes with low gain.
+
+        heapq.heappush only need the '<' operator.
+        heapq.heappop take the smallest item first (smaller is higher
+        priority).
+
+        Parameters
+        ----------
+        other_node : TreeNode
+            The node to compare with.
+        """
+        return self.split_info.gain > other_node.split_info.gain
+
+
+class TreeGrower:
+    """Tree grower class used to build a tree.
+
+    The tree is fitted to predict the values of a Newton-Raphson step. The
+    splits are considered in a best-first fashion, and the quality of a
+    split is defined in splitting._split_gain.
+
+    Parameters
+    ----------
+    X_binned : ndarray of shape (n_samples, n_features), dtype=np.uint8
+        The binned input samples. Must be Fortran-aligned.
+    gradients : ndarray of shape (n_samples,)
+        The gradients of each training sample. Those are the gradients of the
+        loss w.r.t the predictions, evaluated at iteration ``i - 1``.
+    hessians : ndarray of shape (n_samples,)
+        The hessians of each training sample. Those are the hessians of the
+        loss w.r.t the predictions, evaluated at iteration ``i - 1``.
+    max_leaf_nodes : int, default=None
+        The maximum number of leaves for each tree. If None, there is no
+        maximum limit.
+    max_depth : int, default=None
+        The maximum depth of each tree. The depth of a tree is the number of
+        edges to go from the root to the deepest leaf.
+        Depth isn't constrained by default.
+    min_samples_leaf : int, default=20
+        The minimum number of samples per leaf.
+    min_gain_to_split : float, default=0.
+        The minimum gain needed to split a node. Splits with lower gain will
+        be ignored.
+    min_hessian_to_split : float, default=1e-3
+        The minimum sum of hessians needed in each node. Splits that result in
+        at least one child having a sum of hessians less than
+        ``min_hessian_to_split`` are discarded.
+    n_bins : int, default=256
+        The total number of bins, including the bin for missing values. Used
+        to define the shape of the histograms.
+    n_bins_non_missing : ndarray, dtype=np.uint32, default=None
+        For each feature, gives the number of bins actually used for
+        non-missing values. For features with a lot of unique values, this
+        is equal to ``n_bins - 1``. If it's an int, all features are
+        considered to have the same number of bins. If None, all features
+        are considered to have ``n_bins - 1`` bins.
+    has_missing_values : bool or ndarray, dtype=bool, default=False
+        Whether each feature contains missing values (in the training data).
+        If it's a bool, the same value is used for all features.
+    is_categorical : ndarray of bool of shape (n_features,), default=None
+        Indicates categorical features.
+    monotonic_cst : array-like of int of shape (n_features,), dtype=int, default=None
+        Indicates the monotonic constraint to enforce on each feature.
+          - 1: monotonic increase
+          - 0: no constraint
+          - -1: monotonic decrease
+
+        Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
+    interaction_cst : list of sets of integers, default=None
+        List of interaction constraints.
+    l2_regularization : float, default=0.
+        The L2 regularization parameter penalizing leaves with small hessians.
+        Use ``0`` for no regularization (default).
+    feature_fraction_per_split : float, default=1
+        Proportion of randomly chosen features in each and every node split.
+        This is a form of regularization, smaller values make the trees weaker
+        learners and might prevent overfitting.
+    rng : Generator
+        Numpy random Generator used for feature subsampling.
+    shrinkage : float, default=1.
+        The shrinkage parameter to apply to the leaves values, also known as
+        learning rate.
+    n_threads : int, default=None
+        Number of OpenMP threads to use. `_openmp_effective_n_threads` is called
+        to determine the effective number of threads use, which takes cgroups CPU
+        quotes into account. See the docstring of `_openmp_effective_n_threads`
+        for details.
+
+    Attributes
+    ----------
+    histogram_builder : HistogramBuilder
+    splitter : Splitter
+    root : TreeNode
+    finalized_leaves : list of TreeNode
+    splittable_nodes : list of TreeNode
+    missing_values_bin_idx : int
+        Equals n_bins - 1
+    n_categorical_splits : int
+    n_features : int
+    n_nodes : int
+    total_find_split_time : float
+        Time spent finding the best splits
+    total_compute_hist_time : float
+        Time spent computing histograms
+    total_apply_split_time : float
+        Time spent splitting nodes
+    with_monotonic_cst : bool
+        Whether there are monotonic constraints that apply. False iff monotonic_cst is
+        None.
+    """
+
+    def __init__(
+        self,
+        X_binned,
+        gradients,
+        hessians,
+        max_leaf_nodes=None,
+        max_depth=None,
+        min_samples_leaf=20,
+        min_gain_to_split=0.0,
+        min_hessian_to_split=1e-3,
+        n_bins=256,
+        n_bins_non_missing=None,
+        has_missing_values=False,
+        is_categorical=None,
+        monotonic_cst=None,
+        interaction_cst=None,
+        l2_regularization=0.0,
+        feature_fraction_per_split=1.0,
+        rng=np.random.default_rng(),
+        shrinkage=1.0,
+        n_threads=None,
+    ):
+        self._validate_parameters(
+            X_binned,
+            min_gain_to_split,
+            min_hessian_to_split,
+        )
+        n_threads = _openmp_effective_n_threads(n_threads)
+
+        if n_bins_non_missing is None:
+            n_bins_non_missing = n_bins - 1
+
+        if isinstance(n_bins_non_missing, numbers.Integral):
+            n_bins_non_missing = np.array(
+                [n_bins_non_missing] * X_binned.shape[1], dtype=np.uint32
+            )
+        else:
+            n_bins_non_missing = np.asarray(n_bins_non_missing, dtype=np.uint32)
+
+        if isinstance(has_missing_values, bool):
+            has_missing_values = [has_missing_values] * X_binned.shape[1]
+        has_missing_values = np.asarray(has_missing_values, dtype=np.uint8)
+
+        # `monotonic_cst` validation is done in _validate_monotonic_cst
+        # at the estimator level and therefore the following should not be
+        # needed when using the public API.
+        if monotonic_cst is None:
+            monotonic_cst = np.full(
+                shape=X_binned.shape[1],
+                fill_value=MonotonicConstraint.NO_CST,
+                dtype=np.int8,
+            )
+        else:
+            monotonic_cst = np.asarray(monotonic_cst, dtype=np.int8)
+        self.with_monotonic_cst = np.any(monotonic_cst != MonotonicConstraint.NO_CST)
+
+        if is_categorical is None:
+            is_categorical = np.zeros(shape=X_binned.shape[1], dtype=np.uint8)
+        else:
+            is_categorical = np.asarray(is_categorical, dtype=np.uint8)
+
+        if np.any(
+            np.logical_and(
+                is_categorical == 1, monotonic_cst != MonotonicConstraint.NO_CST
+            )
+        ):
+            raise ValueError("Categorical features cannot have monotonic constraints.")
+
+        hessians_are_constant = hessians.shape[0] == 1
+        self.histogram_builder = HistogramBuilder(
+            X_binned, n_bins, gradients, hessians, hessians_are_constant, n_threads
+        )
+        missing_values_bin_idx = n_bins - 1
+        self.splitter = Splitter(
+            X_binned=X_binned,
+            n_bins_non_missing=n_bins_non_missing,
+            missing_values_bin_idx=missing_values_bin_idx,
+            has_missing_values=has_missing_values,
+            is_categorical=is_categorical,
+            monotonic_cst=monotonic_cst,
+            l2_regularization=l2_regularization,
+            min_hessian_to_split=min_hessian_to_split,
+            min_samples_leaf=min_samples_leaf,
+            min_gain_to_split=min_gain_to_split,
+            hessians_are_constant=hessians_are_constant,
+            feature_fraction_per_split=feature_fraction_per_split,
+            rng=rng,
+            n_threads=n_threads,
+        )
+        self.X_binned = X_binned
+        self.max_leaf_nodes = max_leaf_nodes
+        self.max_depth = max_depth
+        self.min_samples_leaf = min_samples_leaf
+        self.min_gain_to_split = min_gain_to_split
+        self.n_bins_non_missing = n_bins_non_missing
+        self.missing_values_bin_idx = missing_values_bin_idx
+        self.has_missing_values = has_missing_values
+        self.is_categorical = is_categorical
+        self.monotonic_cst = monotonic_cst
+        self.interaction_cst = interaction_cst
+        self.l2_regularization = l2_regularization
+        self.shrinkage = shrinkage
+        self.n_features = X_binned.shape[1]
+        self.n_threads = n_threads
+        self.splittable_nodes = []
+        self.finalized_leaves = []
+        self.total_find_split_time = 0.0  # time spent finding the best splits
+        self.total_compute_hist_time = 0.0  # time spent computing histograms
+        self.total_apply_split_time = 0.0  # time spent splitting nodes
+        self.n_categorical_splits = 0
+        self._initialize_root()
+        self.n_nodes = 1
+
+    def _validate_parameters(
+        self,
+        X_binned,
+        min_gain_to_split,
+        min_hessian_to_split,
+    ):
+        """Validate parameters passed to __init__.
+
+        Also validate parameters passed to splitter.
+        """
+        if X_binned.dtype != np.uint8:
+            raise NotImplementedError("X_binned must be of type uint8.")
+        if not X_binned.flags.f_contiguous:
+            raise ValueError(
+                "X_binned should be passed as Fortran contiguous "
+                "array for maximum efficiency."
+            )
+        if min_gain_to_split < 0:
+            raise ValueError(
+                "min_gain_to_split={} must be positive.".format(min_gain_to_split)
+            )
+        if min_hessian_to_split < 0:
+            raise ValueError(
+                "min_hessian_to_split={} must be positive.".format(min_hessian_to_split)
+            )
+
+    def grow(self):
+        """Grow the tree, from root to leaves."""
+        while self.splittable_nodes:
+            self.split_next()
+
+        self._apply_shrinkage()
+
+    def _apply_shrinkage(self):
+        """Multiply leaves values by shrinkage parameter.
+
+        This must be done at the very end of the growing process. If this were
+        done during the growing process e.g. in finalize_leaf(), then a leaf
+        would be shrunk but its sibling would potentially not be (if it's a
+        non-leaf), which would lead to a wrong computation of the 'middle'
+        value needed to enforce the monotonic constraints.
+        """
+        for leaf in self.finalized_leaves:
+            leaf.value *= self.shrinkage
+
+    def _initialize_root(self):
+        """Initialize root node and finalize it if needed."""
+        tic = time()
+        if self.interaction_cst is not None:
+            allowed_features = set().union(*self.interaction_cst)
+            allowed_features = np.fromiter(
+                allowed_features, dtype=np.uint32, count=len(allowed_features)
+            )
+            arbitrary_feature = allowed_features[0]
+        else:
+            allowed_features = None
+            arbitrary_feature = 0
+
+        # TreeNode init needs the total sum of gradients and hessians. Therefore, we
+        # first compute the histograms and then compute the total grad/hess on an
+        # arbitrary feature histogram. This way we replace a loop over n_samples by a
+        # loop over n_bins.
+        histograms = self.histogram_builder.compute_histograms_brute(
+            self.splitter.partition,  # =self.root.sample_indices
+            allowed_features,
+        )
+        self.total_compute_hist_time += time() - tic
+
+        tic = time()
+        n_samples = self.X_binned.shape[0]
+        depth = 0
+        histogram_array = np.asarray(histograms[arbitrary_feature])
+        sum_gradients = histogram_array["sum_gradients"].sum()
+        if self.histogram_builder.hessians_are_constant:
+            sum_hessians = self.histogram_builder.hessians[0] * n_samples
+        else:
+            sum_hessians = histogram_array["sum_hessians"].sum()
+        self.root = TreeNode(
+            depth=depth,
+            sample_indices=self.splitter.partition,
+            partition_start=0,
+            partition_stop=n_samples,
+            sum_gradients=sum_gradients,
+            sum_hessians=sum_hessians,
+            value=0,
+        )
+
+        if self.root.n_samples < 2 * self.min_samples_leaf:
+            # Do not even bother computing any splitting statistics.
+            self._finalize_leaf(self.root)
+            return
+        if sum_hessians < self.splitter.min_hessian_to_split:
+            self._finalize_leaf(self.root)
+            return
+
+        if self.interaction_cst is not None:
+            self.root.interaction_cst_indices = range(len(self.interaction_cst))
+            self.root.allowed_features = allowed_features
+
+        self.root.histograms = histograms
+
+        self._compute_best_split_and_push(self.root)
+        self.total_find_split_time += time() - tic
+
+    def _compute_best_split_and_push(self, node):
+        """Compute the best possible split (SplitInfo) of a given node.
+
+        Also push it in the heap of splittable nodes if gain isn't zero.
+        The gain of a node is 0 if either all the leaves are pure
+        (best gain = 0), or if no split would satisfy the constraints,
+        (min_hessians_to_split, min_gain_to_split, min_samples_leaf)
+        """
+
+        node.split_info = self.splitter.find_node_split(
+            n_samples=node.n_samples,
+            histograms=node.histograms,
+            sum_gradients=node.sum_gradients,
+            sum_hessians=node.sum_hessians,
+            value=node.value,
+            lower_bound=node.children_lower_bound,
+            upper_bound=node.children_upper_bound,
+            allowed_features=node.allowed_features,
+        )
+
+        if node.split_info.gain <= 0:  # no valid split
+            self._finalize_leaf(node)
+        else:
+            heappush(self.splittable_nodes, node)
+
+    def split_next(self):
+        """Split the node with highest potential gain.
+
+        Returns
+        -------
+        left : TreeNode
+            The resulting left child.
+        right : TreeNode
+            The resulting right child.
+        """
+        # Consider the node with the highest loss reduction (a.k.a. gain)
+        node = heappop(self.splittable_nodes)
+
+        tic = time()
+        (
+            sample_indices_left,
+            sample_indices_right,
+            right_child_pos,
+        ) = self.splitter.split_indices(node.split_info, node.sample_indices)
+        self.total_apply_split_time += time() - tic
+
+        depth = node.depth + 1
+        n_leaf_nodes = len(self.finalized_leaves) + len(self.splittable_nodes)
+        n_leaf_nodes += 2
+
+        left_child_node = TreeNode(
+            depth=depth,
+            sample_indices=sample_indices_left,
+            partition_start=node.partition_start,
+            partition_stop=node.partition_start + right_child_pos,
+            sum_gradients=node.split_info.sum_gradient_left,
+            sum_hessians=node.split_info.sum_hessian_left,
+            value=node.split_info.value_left,
+        )
+        right_child_node = TreeNode(
+            depth=depth,
+            sample_indices=sample_indices_right,
+            partition_start=left_child_node.partition_stop,
+            partition_stop=node.partition_stop,
+            sum_gradients=node.split_info.sum_gradient_right,
+            sum_hessians=node.split_info.sum_hessian_right,
+            value=node.split_info.value_right,
+        )
+
+        node.right_child = right_child_node
+        node.left_child = left_child_node
+
+        # set interaction constraints (the indices of the constraints sets)
+        if self.interaction_cst is not None:
+            # Calculate allowed_features and interaction_cst_indices only once. Child
+            # nodes inherit them before they get split.
+            (
+                left_child_node.allowed_features,
+                left_child_node.interaction_cst_indices,
+            ) = self._compute_interactions(node)
+            right_child_node.interaction_cst_indices = (
+                left_child_node.interaction_cst_indices
+            )
+            right_child_node.allowed_features = left_child_node.allowed_features
+
+        if not self.has_missing_values[node.split_info.feature_idx]:
+            # If no missing values are encountered at fit time, then samples
+            # with missing values during predict() will go to whichever child
+            # has the most samples.
+            node.split_info.missing_go_to_left = (
+                left_child_node.n_samples > right_child_node.n_samples
+            )
+
+        self.n_nodes += 2
+        self.n_categorical_splits += node.split_info.is_categorical
+
+        if self.max_leaf_nodes is not None and n_leaf_nodes == self.max_leaf_nodes:
+            self._finalize_leaf(left_child_node)
+            self._finalize_leaf(right_child_node)
+            self._finalize_splittable_nodes()
+            return left_child_node, right_child_node
+
+        if self.max_depth is not None and depth == self.max_depth:
+            self._finalize_leaf(left_child_node)
+            self._finalize_leaf(right_child_node)
+            return left_child_node, right_child_node
+
+        if left_child_node.n_samples < self.min_samples_leaf * 2:
+            self._finalize_leaf(left_child_node)
+        if right_child_node.n_samples < self.min_samples_leaf * 2:
+            self._finalize_leaf(right_child_node)
+
+        if self.with_monotonic_cst:
+            # Set value bounds for respecting monotonic constraints
+            # See test_nodes_values() for details
+            if (
+                self.monotonic_cst[node.split_info.feature_idx]
+                == MonotonicConstraint.NO_CST
+            ):
+                lower_left = lower_right = node.children_lower_bound
+                upper_left = upper_right = node.children_upper_bound
+            else:
+                mid = (left_child_node.value + right_child_node.value) / 2
+                if (
+                    self.monotonic_cst[node.split_info.feature_idx]
+                    == MonotonicConstraint.POS
+                ):
+                    lower_left, upper_left = node.children_lower_bound, mid
+                    lower_right, upper_right = mid, node.children_upper_bound
+                else:  # NEG
+                    lower_left, upper_left = mid, node.children_upper_bound
+                    lower_right, upper_right = node.children_lower_bound, mid
+            left_child_node.set_children_bounds(lower_left, upper_left)
+            right_child_node.set_children_bounds(lower_right, upper_right)
+
+        # Compute histograms of children, and compute their best possible split
+        # (if needed)
+        should_split_left = not left_child_node.is_leaf
+        should_split_right = not right_child_node.is_leaf
+        if should_split_left or should_split_right:
+            # We will compute the histograms of both nodes even if one of them
+            # is a leaf, since computing the second histogram is very cheap
+            # (using histogram subtraction).
+            n_samples_left = left_child_node.sample_indices.shape[0]
+            n_samples_right = right_child_node.sample_indices.shape[0]
+            if n_samples_left < n_samples_right:
+                smallest_child = left_child_node
+                largest_child = right_child_node
+            else:
+                smallest_child = right_child_node
+                largest_child = left_child_node
+
+            # We use the brute O(n_samples) method on the child that has the
+            # smallest number of samples, and the subtraction trick O(n_bins)
+            # on the other one.
+            # Note that both left and right child have the same allowed_features.
+            tic = time()
+            smallest_child.histograms = self.histogram_builder.compute_histograms_brute(
+                smallest_child.sample_indices, smallest_child.allowed_features
+            )
+            largest_child.histograms = (
+                self.histogram_builder.compute_histograms_subtraction(
+                    node.histograms,
+                    smallest_child.histograms,
+                    smallest_child.allowed_features,
+                )
+            )
+            # node.histograms is reused in largest_child.histograms. To break cyclic
+            # memory references and help garbage collection, we set it to None.
+            node.histograms = None
+            self.total_compute_hist_time += time() - tic
+
+            tic = time()
+            if should_split_left:
+                self._compute_best_split_and_push(left_child_node)
+            if should_split_right:
+                self._compute_best_split_and_push(right_child_node)
+            self.total_find_split_time += time() - tic
+
+            # Release memory used by histograms as they are no longer needed
+            # for leaf nodes since they won't be split.
+            for child in (left_child_node, right_child_node):
+                if child.is_leaf:
+                    del child.histograms
+
+        # Release memory used by histograms as they are no longer needed for
+        # internal nodes once children histograms have been computed.
+        del node.histograms
+
+        return left_child_node, right_child_node
+
+    def _compute_interactions(self, node):
+        r"""Compute features allowed by interactions to be inherited by child nodes.
+
+        Example: Assume constraints [{0, 1}, {1, 2}].
+           1      <- Both constraint groups could be applied from now on
+          / \
+         1   2    <- Left split still fulfills both constraint groups.
+        / \ / \      Right split at feature 2 has only group {1, 2} from now on.
+
+        LightGBM uses the same logic for overlapping groups. See
+        https://github.com/microsoft/LightGBM/issues/4481 for details.
+
+        Parameters:
+        ----------
+        node : TreeNode
+            A node that might have children. Based on its feature_idx, the interaction
+            constraints for possible child nodes are computed.
+
+        Returns
+        -------
+        allowed_features : ndarray, dtype=uint32
+            Indices of features allowed to split for children.
+        interaction_cst_indices : list of ints
+            Indices of the interaction sets that have to be applied on splits of
+            child nodes. The fewer sets the stronger the constraint as fewer sets
+            contain fewer features.
+        """
+        # Note:
+        #  - Case of no interactions is already captured before function call.
+        #  - This is for nodes that are already split and have a
+        #    node.split_info.feature_idx.
+        allowed_features = set()
+        interaction_cst_indices = []
+        for i in node.interaction_cst_indices:
+            if node.split_info.feature_idx in self.interaction_cst[i]:
+                interaction_cst_indices.append(i)
+                allowed_features.update(self.interaction_cst[i])
+        return (
+            np.fromiter(allowed_features, dtype=np.uint32, count=len(allowed_features)),
+            interaction_cst_indices,
+        )
+
+    def _finalize_leaf(self, node):
+        """Make node a leaf of the tree being grown."""
+
+        node.is_leaf = True
+        self.finalized_leaves.append(node)
+
+    def _finalize_splittable_nodes(self):
+        """Transform all splittable nodes into leaves.
+
+        Used when some constraint is met e.g. maximum number of leaves or
+        maximum depth."""
+        while len(self.splittable_nodes) > 0:
+            node = self.splittable_nodes.pop()
+            self._finalize_leaf(node)
+
+    def make_predictor(self, binning_thresholds):
+        """Make a TreePredictor object out of the current tree.
+
+        Parameters
+        ----------
+        binning_thresholds : array-like of floats
+            Corresponds to the bin_thresholds_ attribute of the BinMapper.
+            For each feature, this stores:
+
+            - the bin frontiers for continuous features
+            - the unique raw category values for categorical features
+
+        Returns
+        -------
+        A TreePredictor object.
+        """
+        predictor_nodes = np.zeros(self.n_nodes, dtype=PREDICTOR_RECORD_DTYPE)
+        binned_left_cat_bitsets = np.zeros(
+            (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE
+        )
+        raw_left_cat_bitsets = np.zeros(
+            (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE
+        )
+        _fill_predictor_arrays(
+            predictor_nodes,
+            binned_left_cat_bitsets,
+            raw_left_cat_bitsets,
+            self.root,
+            binning_thresholds,
+            self.n_bins_non_missing,
+        )
+        return TreePredictor(
+            predictor_nodes, binned_left_cat_bitsets, raw_left_cat_bitsets
+        )
+
+
+def _fill_predictor_arrays(
+    predictor_nodes,
+    binned_left_cat_bitsets,
+    raw_left_cat_bitsets,
+    grower_node,
+    binning_thresholds,
+    n_bins_non_missing,
+    next_free_node_idx=0,
+    next_free_bitset_idx=0,
+):
+    """Helper used in make_predictor to set the TreePredictor fields."""
+    node = predictor_nodes[next_free_node_idx]
+    node["count"] = grower_node.n_samples
+    node["depth"] = grower_node.depth
+    if grower_node.split_info is not None:
+        node["gain"] = grower_node.split_info.gain
+    else:
+        node["gain"] = -1
+
+    node["value"] = grower_node.value
+
+    if grower_node.is_leaf:
+        # Leaf node
+        node["is_leaf"] = True
+        return next_free_node_idx + 1, next_free_bitset_idx
+
+    split_info = grower_node.split_info
+    feature_idx, bin_idx = split_info.feature_idx, split_info.bin_idx
+    node["feature_idx"] = feature_idx
+    node["bin_threshold"] = bin_idx
+    node["missing_go_to_left"] = split_info.missing_go_to_left
+    node["is_categorical"] = split_info.is_categorical
+
+    if split_info.bin_idx == n_bins_non_missing[feature_idx] - 1:
+        # Split is on the last non-missing bin: it's a "split on nans".
+        # All nans go to the right, the rest go to the left.
+        # Note: for categorical splits, bin_idx is 0 and we rely on the bitset
+        node["num_threshold"] = np.inf
+    elif split_info.is_categorical:
+        categories = binning_thresholds[feature_idx]
+        node["bitset_idx"] = next_free_bitset_idx
+        binned_left_cat_bitsets[next_free_bitset_idx] = split_info.left_cat_bitset
+        set_raw_bitset_from_binned_bitset(
+            raw_left_cat_bitsets[next_free_bitset_idx],
+            split_info.left_cat_bitset,
+            categories,
+        )
+        next_free_bitset_idx += 1
+    else:
+        node["num_threshold"] = binning_thresholds[feature_idx][bin_idx]
+
+    next_free_node_idx += 1
+
+    node["left"] = next_free_node_idx
+    next_free_node_idx, next_free_bitset_idx = _fill_predictor_arrays(
+        predictor_nodes,
+        binned_left_cat_bitsets,
+        raw_left_cat_bitsets,
+        grower_node.left_child,
+        binning_thresholds=binning_thresholds,
+        n_bins_non_missing=n_bins_non_missing,
+        next_free_node_idx=next_free_node_idx,
+        next_free_bitset_idx=next_free_bitset_idx,
+    )
+
+    node["right"] = next_free_node_idx
+    return _fill_predictor_arrays(
+        predictor_nodes,
+        binned_left_cat_bitsets,
+        raw_left_cat_bitsets,
+        grower_node.right_child,
+        binning_thresholds=binning_thresholds,
+        n_bins_non_missing=n_bins_non_missing,
+        next_free_node_idx=next_free_node_idx,
+        next_free_bitset_idx=next_free_bitset_idx,
+    )
diff --git a/shared/package-files/40/40cdafeb56fd118ebf0e62f2808ad8bfefd9d53037bed50d0fcb464923cd5fb8/mkldnn_linear_backward_weights_compositeexplicitautograd_dispatch.h b/shared/package-files/40/40cdafeb56fd118ebf0e62f2808ad8bfefd9d53037bed50d0fcb464923cd5fb8/mkldnn_linear_backward_weights_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2c82029f0839c00fd1c2be32092ab83307a1a434
--- /dev/null
+++ b/shared/package-files/40/40cdafeb56fd118ebf0e62f2808ad8bfefd9d53037bed50d0fcb464923cd5fb8/mkldnn_linear_backward_weights_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/40/40d06c64e9e4cf56eea21d08d8784a1b27f7bb11a1f0d19c1eaf80b400de8fb5/LegacyBatchedFallback.h b/shared/package-files/40/40d06c64e9e4cf56eea21d08d8784a1b27f7bb11a1f0d19c1eaf80b400de8fb5/LegacyBatchedFallback.h
new file mode 100644
index 0000000000000000000000000000000000000000..beef24a6ed9c5b8373a0db5bcc16f268b8c18726
--- /dev/null
+++ b/shared/package-files/40/40d06c64e9e4cf56eea21d08d8784a1b27f7bb11a1f0d19c1eaf80b400de8fb5/LegacyBatchedFallback.h
@@ -0,0 +1,25 @@
+#pragma once
+#include <ATen/ATen.h>
+#include <ATen/core/op_registration/op_registration.h>
+#include <torch/library.h>
+
+namespace at {
+
+// If an operator doesn't have a batching rule implemented then we fallback
+// to this implementation. The fallback only works on out-of-place operators
+// that return only tensors with new memory. (e.g., no in-place operators, no
+// view operations).
+//
+// The fallback effectively takes all of the BatchedTensors in `stack`, slices
+// them, and runs `op` on all of the corresponding slices to produce slices
+// of the outputs. The output slices then get `torch.stack`ed to create the
+// final returns.
+//
+// The performance of the fallback is not very good because it introduces an
+// extra copy from stacking the sliced outputs. Because of this, we prefer to
+// write batching rules for operators whenever possible.
+void batchedTensorForLoopFallback(
+    const c10::OperatorHandle& op,
+    torch::jit::Stack* stack);
+
+} // namespace at
diff --git a/shared/package-files/40/40d129fe4503f438f420216204675aa5f9f5d1897a61149a258320313c33e32a/libpcre2-32.pc b/shared/package-files/40/40d129fe4503f438f420216204675aa5f9f5d1897a61149a258320313c33e32a/libpcre2-32.pc
new file mode 100644
index 0000000000000000000000000000000000000000..25501b5a96d23fea67939423eca02ef4bb00d1ad
--- /dev/null
+++ b/shared/package-files/40/40d129fe4503f438f420216204675aa5f9f5d1897a61149a258320313c33e32a/libpcre2-32.pc
@@ -0,0 +1,13 @@
+# Package Information for pkg-config
+
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: libpcre2-32
+Description: PCRE2 - Perl compatible regular expressions C library (2nd API) with 32 bit character support
+Version: 10.42
+Libs: -L${libdir} -lpcre2-32
+Libs.private:  
+Cflags: -I${includedir} 
diff --git a/shared/package-files/40/40d26eea570ebc32cee70d521e4de354d3525287a82c63107c3c45a091a9fc04/initialization.py b/shared/package-files/40/40d26eea570ebc32cee70d521e4de354d3525287a82c63107c3c45a091a9fc04/initialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dcaebb0b405a5c94eecd7bb3b10f8f66666b25b
--- /dev/null
+++ b/shared/package-files/40/40d26eea570ebc32cee70d521e4de354d3525287a82c63107c3c45a091a9fc04/initialization.py
@@ -0,0 +1,866 @@
+"""
+State Space Representation - Initialization
+
+Author: Chad Fulton
+License: Simplified-BSD
+"""
+
+import warnings
+
+import numpy as np
+
+from statsmodels.tools.sm_exceptions import SpecificationWarning
+
+from . import tools
+
+
+class Initialization:
+    r"""
+    State space initialization
+
+    Parameters
+    ----------
+    k_states : int
+        Number of states in the state space model.
+    initialization_type : str, optional
+        The type of initialization to use globally for all of the states. Must
+        be one of 'known', 'diffuse', 'approximate_diffuse', or 'stationary'.
+        If not specified, no global initialization is performed and blocks of
+        states must instead be initialized using the `set` method.
+    initialization_classes : dict of str to type, optional
+        Dictionary with BLAS prefixes as keys and the associated Cython
+        initialization classes as values. If not specified, the default
+        mapping is used.
+    approximate_diffuse_variance : float, optional
+        If using approximate diffuse initialization, the initial variance used.
+        Default is 1e6.
+    constant : array_like, optional
+        A vector of constant values, denoted :math:`a`. Only used if
+        `initialization_type` is specified.
+    stationary_cov : array_like, optional
+        The covariance matrix of the stationary part, denoted :math:`Q_0`.
+        Only used if `initialization_type` is specified.
+
+    Notes
+    -----
+    As developed in Durbin and Koopman (2012), the state space recursions
+    must be initialized for the first time period. The general form of this
+    initialization is:
+
+    .. math::
+
+        \alpha_1 & = a + A \delta + R_0 \eta_0 \\
+        \delta & \sim N(0, \kappa I), \kappa \to \infty \\
+        \eta_0 & \sim N(0, Q_0)
+
+    Thus the state vector can be initialized with a known constant part
+    (elements of :math:`a`), with part modeled as a diffuse initial
+    distribution (as a part of :math:`\delta`), and with a part modeled as a
+    known (proper) initial distribution (as a part of :math:`\eta_0`).
+
+    There are two important restrictions:
+
+    1. An element of the state vector initialized as diffuse cannot be also
+       modeled with a stationary component. In the `validate` method,
+       violations of this cause an exception to be raised.
+    2. If an element of the state vector is initialized with both a known
+       constant part and with a diffuse initial distribution, the effect of
+       the diffuse initialization will essentially ignore the given known
+       constant value. In the `validate` method, violations of this cause a
+       warning to be given, since it is not technically invalid but may
+       indicate user error.
+
+    The :math:`\eta_0` component is also referred to as the stationary part
+    because it is often set to the unconditional distribution of a stationary
+    process.
+
+    Initialization is specified for blocks (consecutive only, for now) of the
+    state vector, with the entire state vector and individual elements as
+    special cases. Denote the block in question as :math:`\alpha_1^{(i)}`. It
+    can be initialized in the following ways:
+
+    - 'known'
+    - 'diffuse' or 'exact_diffuse' or 'approximate_diffuse'
+    - 'stationary'
+    - 'mixed'
+
+    In the first three cases, the block's initialization is specified as an
+    instance of the `Initialization` class, with the `initialization_type`
+    attribute set to one of those three string values. In the 'mixed' cases,
+    the initialization is also an instance of the `Initialization` class, but
+    it will itself contain sub-blocks. Details of each type follow.
+
+    Regardless of the type, for each block, the following must be defined:
+    the `constant` array, an array `diffuse` with indices corresponding to
+    diffuse elements, an array `stationary` with indices corresponding to
+    stationary elements, and `stationary_cov`, a matrix with order equal to the
+    number of stationary elements in the block.
+
+    **Known**
+
+    If a block is initialized as known, then a known (possibly degenerate)
+    distribution is used; in particular, the block of states is understood to
+    be distributed
+    :math:`\alpha_1^{(i)} \sim N(a^{(i)}, Q_0^{(i)})`. Here, it is possible to
+    set :math:`a^{(i)} = 0`, and it is also possible that
+    :math:`Q_0^{(i)}` is only positive-semidefinite; i.e.
+    :math:`\alpha_1^{(i)}` may be degenerate. One particular example is
+    that if the entire block's initial values are known, then
+    :math:`R_0^{(i)} = 0`, and so `Var(\alpha_1^{(i)}) = 0`.
+
+    Here, `constant` must be provided (although it can be zeros), and
+    `stationary_cov` is optional (by default it is a matrix of zeros).
+
+    **Diffuse**
+
+    If a block is initialized as diffuse, then set
+    :math:`\alpha_1^{(i)} \sim N(a^{(i)}, \kappa^{(i)} I)`. If the block is
+    initialized using the exact diffuse initialization procedure, then it is
+    understood that :math:`\kappa^{(i)} \to \infty`.
+
+    If the block is initialized using the approximate diffuse initialization
+    procedure, then `\kappa^{(i)}` is set to some large value rather than
+    driven to infinity.
+
+    In the approximate diffuse initialization case, it is possible, although
+    unlikely, that a known constant value may have some effect on
+    initialization if :math:`\kappa^{(i)}` is not set large enough.
+
+    Here, `constant` may be provided, and `approximate_diffuse_variance` may be
+    provided.
+
+    **Stationary**
+
+    If a block is initialized as stationary, then the block of states is
+    understood to have the distribution
+    :math:`\alpha_1^{(i)} \sim N(a^{(i)}, Q_0^{(i)})`. :math:`a^{(i)}` is
+    the unconditional mean of the block, computed as
+    :math:`(I - T^{(i)})^{-1} c_t`. :math:`Q_0^{(i)}` is the unconditional
+    variance of the block, computed as the solution to the discrete Lyapunov
+    equation:
+
+    .. math::
+
+        T^{(i)} Q_0^{(i)} T^{(i)} + (R Q R')^{(i)} = Q_0^{(i)}
+
+    :math:`T^{(i)}` and :math:`(R Q R')^{(i)}` are the submatrices of
+    the corresponding state space system matrices corresponding to the given
+    block of states.
+
+    Here, no values can be provided.
+
+    **Mixed**
+
+    In this case, the block can be further broken down into sub-blocks.
+    Usually, only the top-level `Initialization` instance will be of 'mixed'
+    type, and in many cases, even the top-level instance will be purely
+    'known', 'diffuse', or 'stationary'.
+
+    For a block of type mixed, suppose that it has `J` sub-blocks,
+    :math:`\alpha_1^{(i,j)}`. Then
+    :math:`\alpha_1^{(i)} = a^{(i)} + A^{(i)} \delta + R_0^{(i)} \eta_0^{(i)}`.
+
+    Examples
+    --------
+
+    Basic examples have one specification for all of the states:
+
+    >>> Initialization(2, 'known', constant=[0, 1])
+    >>> Initialization(2, 'known', stationary_cov=np.eye(2))
+    >>> Initialization(2, 'known', constant=[0, 1],
+                       stationary_cov=np.eye(2))
+    >>> Initialization(2, 'diffuse')
+    >>> Initialization(2, 'approximate_diffuse',
+                       approximate_diffuse_variance=1e6)
+    >>> Initialization(2, 'stationary')
+
+    More complex examples initialize different blocks of states separately
+
+    >>> init = Initialization(k_states=3)
+    >>> init.set((0, 1), 'known', constant=[0, 1])
+    >>> init.set((0, 1), 'known', stationary_cov=np.eye(2))
+    >>> init.set((0, 1), 'known', constant=[0, 1],
+                 stationary_cov=np.eye(2))
+    >>> init.set((0, 1), 'diffuse')
+    >>> init.set((0, 1), 'approximate_diffuse',
+                 approximate_diffuse_variance=1e6)
+    >>> init.set((0, 1), 'stationary')
+
+    A still more complex example initializes a block using a previously
+    created `Initialization` object:
+
+    >>> init1 = Initialization(2, 'known', constant=[0, 1])
+    >>> init2 = Initialization(k_states=3)
+    >>> init2.set((1, 2), init1)
+    """
+
+    def __init__(
+        self,
+        k_states,
+        initialization_type=None,
+        initialization_classes=None,
+        approximate_diffuse_variance=1e6,
+        constant=None,
+        stationary_cov=None,
+    ):
+        # Parameters
+        self.k_states = k_states
+
+        # Attributes handling blocks of states with different initializations
+        self._states = tuple(np.arange(k_states))
+        self._initialization = np.array([None] * k_states)
+        self.blocks = {}
+
+        # Attributes handling initialization of the entire set of states
+        # `constant` is a vector of constant values (i.e., it is the vector
+        # a from DK)
+        self.initialization_type = None
+        self.constant = np.zeros(self.k_states)
+        self.stationary_cov = np.zeros((self.k_states, self.k_states))
+        self.approximate_diffuse_variance = approximate_diffuse_variance
+
+        # Cython interface attributes
+        self.prefix_initialization_map = (
+            initialization_classes
+            if initialization_classes is not None
+            else tools.prefix_initialization_map.copy()
+        )
+        self._representations = {}
+        self._initializations = {}
+
+        # If given a global initialization, use it now
+        if initialization_type is not None:
+            self.set(
+                None,
+                initialization_type,
+                constant=constant,
+                stationary_cov=stationary_cov,
+            )
+
+    @classmethod
+    def from_components(
+        cls, k_states, a=None, Pstar=None, Pinf=None, A=None, R0=None, Q0=None
+    ):
+        r"""
+        Construct initialization object from component matrices
+
+        Parameters
+        ----------
+        k_states : int
+            Number of states in the time series process.
+        a : array_like, optional
+            Vector of constant values describing the mean of the stationary
+            component of the initial state.
+        Pstar : array_like, optional
+            Stationary component of the initial state covariance matrix. If
+            given, should be a matrix shaped `k_states x k_states`. The
+            submatrix associated with the diffuse states should contain zeros.
+            Note that by definition, `Pstar = R0 @ Q0 @ R0.T`, so either
+            `R0,Q0` or `Pstar` may be given, but not both.
+        Pinf : array_like, optional
+            Diffuse component of the initial state covariance matrix. If given,
+            should be a matrix shaped `k_states x k_states` with ones in the
+            diagonal positions corresponding to states with diffuse
+            initialization and zeros otherwise. Note that by definition,
+            `Pinf = A @ A.T`, so either `A` or `Pinf` may be given, but not
+            both.
+        A : array_like, optional
+            Diffuse selection matrix, used in the definition of the diffuse
+            initial state covariance matrix. If given, should be a
+            `k_states x k_diffuse_states` matrix that contains the subset of
+            the columns of the identity matrix that correspond to states with
+            diffuse initialization. Note that by definition, `Pinf = A @ A.T`,
+            so either `A` or `Pinf` may be given, but not both.
+        R0 : array_like, optional
+            Stationary selection matrix, used in the definition of the
+            stationary initial state covariance matrix. If given, should be a
+            `k_states x k_nondiffuse_states` matrix that contains the subset of
+            the columns of the identity matrix that correspond to states with a
+            non-diffuse initialization. Note that by definition,
+            `Pstar = R0 @ Q0 @ R0.T`, so either `R0,Q0` or `Pstar` may be
+            given, but not both.
+        Q0 : array_like, optional
+            Covariance matrix associated with stationary initial states. If
+            given, should be a matrix shaped
+            `k_nondiffuse_states x k_nondiffuse_states`.
+            Note that by definition, `Pstar = R0 @ Q0 @ R0.T`, so either
+            `R0,Q0` or `Pstar` may be given, but not both.
+
+        Returns
+        -------
+        Initialization
+            Initialization object.
+
+        Notes
+        -----
+        The matrices `a, Pstar, Pinf, A, R0, Q0` and the process for
+        initializing the state space model is as given in Chapter 5 of [1]_.
+        For the definitions of these matrices, see equation (5.2) and the
+        subsequent discussion there.
+
+        References
+        ----------
+        .. [1] Durbin, James, and Siem Jan Koopman. 2012.
+           Time Series Analysis by State Space Methods: Second Edition.
+           Oxford University Press.
+        """
+        # Standardize the input
+        a = tools._atleast_1d(a)
+        Pstar, Pinf, A, R0, Q0 = tools._atleast_2d(Pstar, Pinf, A, R0, Q0)
+
+        # Validate the diffuse component
+        if Pstar is not None and (R0 is not None or Q0 is not None):
+            raise ValueError(
+                "Cannot specify the initial state covariance both"
+                " as `Pstar` and as the components R0 and Q0"
+                "  (because `Pstar` is defined such that"
+                " `Pstar=R0 Q0 R0'`)."
+            )
+        if Pinf is not None and A is not None:
+            raise ValueError(
+                "Cannot specify both the diffuse covariance"
+                " matrix `Pinf` and the selection matrix for"
+                " diffuse elements, A, (because Pinf is defined"
+                " such that `Pinf=A A'`)."
+            )
+        elif A is not None:
+            Pinf = np.dot(A, A.T)
+
+        # Validate the non-diffuse component
+        if a is None:
+            a = np.zeros(k_states)
+        if len(a) != k_states:
+            raise ValueError(
+                "Must provide constant initialization vector for"
+                " the entire state vector."
+            )
+        if R0 is not None or Q0 is not None:
+            if R0 is None or Q0 is None:
+                raise ValueError(
+                    "If specifying either of R0 or Q0 then you"
+                    " must specify both R0 and Q0."
+                )
+            Pstar = R0.dot(Q0).dot(R0.T)
+
+        # Handle the diffuse component
+        diffuse_ix = []
+        if Pinf is not None:
+            diffuse_ix = np.where(np.diagonal(Pinf))[0].tolist()
+
+            if Pstar is not None:
+                for i in diffuse_ix:
+                    if not (np.all(Pstar[i] == 0) and np.all(Pstar[:, i] == 0)):
+                        raise ValueError(
+                            f"The state at position {i} was"
+                            " specified as diffuse in Pinf, but"
+                            " also contains a non-diffuse"
+                            " diagonal or off-diagonal in Pstar."
+                        )
+        k_diffuse_states = len(diffuse_ix)
+
+        nondiffuse_ix = [i for i in np.arange(k_states) if i not in diffuse_ix]
+        k_nondiffuse_states = k_states - k_diffuse_states
+
+        # If there are non-diffuse states, require Pstar
+        if Pstar is None and k_nondiffuse_states > 0:
+            raise ValueError(
+                "Must provide initial covariance matrix for non-diffuse states."
+            )
+
+        # Construct the initialization
+        init = cls(k_states)
+        if nondiffuse_ix:
+            nondiffuse_groups = np.split(
+                nondiffuse_ix, np.where(np.diff(nondiffuse_ix) != 1)[0] + 1
+            )
+        else:
+            nondiffuse_groups = []
+        for group in nondiffuse_groups:
+            s = slice(group[0], group[-1] + 1)
+            init.set(s, "known", constant=a[s], stationary_cov=Pstar[s, s])
+        for i in diffuse_ix:
+            init.set(i, "diffuse")
+
+        return init
+
+    @classmethod
+    def from_results(cls, filter_results):
+        """
+        Construct initialization object from filter results
+
+        Parameters
+        ----------
+        filter_results : FilterResults
+            Filter results object from which to take the initial state and
+            initial state covariance matrices.
+
+        Returns
+        -------
+        Initialization
+            Initialization object.
+        """
+        a = filter_results.initial_state
+        Pstar = filter_results.initial_state_cov
+        Pinf = filter_results.initial_diffuse_state_cov
+
+        return cls.from_components(
+            filter_results.model.k_states, a=a, Pstar=Pstar, Pinf=Pinf
+        )
+
+    def __setitem__(self, index, initialization_type):
+        self.set(index, initialization_type)
+
+    def _initialize_initialization(self, prefix):
+        dtype = tools.prefix_dtype_map[prefix]
+
+        # If the dtype-specific representation matrices do not exist, create
+        # them
+        if prefix not in self._representations:
+            # Copy the statespace representation matrices
+            self._representations[prefix] = {
+                "constant": self.constant.astype(dtype),
+                "stationary_cov": np.asfortranarray(self.stationary_cov.astype(dtype)),
+            }
+        # If they do exist, update them
+        else:
+            self._representations[prefix]["constant"][:] = self.constant.astype(dtype)[
+                :
+            ]
+            self._representations[prefix]["stationary_cov"][:] = (
+                self.stationary_cov.astype(dtype)[:]
+            )
+
+        # Create if necessary
+        if prefix not in self._initializations:
+            # Setup the base statespace object
+            cls = self.prefix_initialization_map[prefix]
+            self._initializations[prefix] = cls(
+                self.k_states,
+                self._representations[prefix]["constant"],
+                self._representations[prefix]["stationary_cov"],
+                self.approximate_diffuse_variance,
+            )
+        # Otherwise update
+        else:
+            self._initializations[prefix].approximate_diffuse_variance = (
+                self.approximate_diffuse_variance
+            )
+
+        return prefix, dtype
+
+    def set(
+        self,
+        index,
+        initialization_type,
+        constant=None,
+        stationary_cov=None,
+        approximate_diffuse_variance=None,
+    ):
+        r"""
+        Set initialization for states, either globally or for a block
+
+        Parameters
+        ----------
+        index : tuple or int or None
+            Arguments used to create a `slice` of states. Can be a tuple with
+            `(start, stop)` (note that for `slice`, stop is not inclusive), or
+            an integer (to select a specific state), or None (to select all the
+            states).
+        initialization_type : str or Initialization
+            The type of initialization used for the states selected by `index`.
+            If a string, must be one of 'known', 'diffuse',
+            'approximate_diffuse', or 'stationary'. Alternatively, a
+            previously created `Initialization` object may be given, in which
+            case it is used directly as the initialization for the selected
+            block of states.
+        constant : array_like, optional
+            A vector of constant values, denoted :math:`a`. Most often used
+            with 'known' initialization, but may also be used with
+            'approximate_diffuse' (although it will then likely have little
+            effect).
+        stationary_cov : array_like, optional
+            The covariance matrix of the stationary part, denoted :math:`Q_0`.
+            Only used with 'known' initialization.
+        approximate_diffuse_variance : float, optional
+            The approximate diffuse variance, denoted :math:`\kappa`. Only
+            applicable with 'approximate_diffuse' initialization. Default is
+            1e6.
+        """
+        # Construct the index, using a slice object as an intermediate step
+        # to enforce regularity
+        if not isinstance(index, slice):
+            if isinstance(index, (int, np.integer)):
+                index = int(index)
+                if index < 0 or index >= self.k_states:
+                    raise ValueError("Invalid index.")
+                index = (index, index + 1)
+            elif index is None:
+                index = (index,)
+            elif not isinstance(index, tuple):
+                raise ValueError("Invalid index.")
+            if len(index) > 2:
+                raise ValueError("Cannot include a slice step in `index`.")
+            index = slice(*index)
+        index = self._states[index]
+
+        # Compatibility with zero-length slices (can make it easier to set up
+        # initialization without lots of if statements)
+        if len(index) == 0:
+            return
+
+        # Make sure that we are not setting a block when global initialization
+        # was previously set
+        if self.initialization_type is not None and not index == self._states:
+            raise ValueError(
+                "Cannot set initialization for the block of"
+                f"  states {index!s} because initialization was"
+                " previously performed globally. You must either"
+                " re-initialize globally or"
+                " else unset the global initialization before"
+                " initializing specific blocks of states."
+            )
+        # Make sure that we are not setting a block that *overlaps* with
+        # another block (although we are free to *replace* an entire block)
+        uninitialized = np.equal(self._initialization[index,], None)
+        if index not in self.blocks and not np.all(uninitialized):
+            raise ValueError(
+                f"Cannot set initialization for the state(s) {np.array(index)[~uninitialized]!s}"
+                " because they are a subset of a previously"
+                " initialized block. You must either"
+                " re-initialize the entire block as a whole or"
+                " else unset the entire block before"
+                " re-initializing the subset."
+            )
+
+        # If setting for all states, set this object's initialization
+        # attributes
+        k_states = len(index)
+        if k_states == self.k_states:
+            self.initialization_type = initialization_type
+
+            # General validation
+            if (
+                approximate_diffuse_variance is not None
+                and not initialization_type == "approximate_diffuse"
+            ):
+                raise ValueError(
+                    "`approximate_diffuse_variance` can only be"
+                    " provided when using approximate diffuse"
+                    " initialization."
+                )
+            if stationary_cov is not None and not initialization_type == "known":
+                raise ValueError(
+                    "`stationary_cov` can only be provided when"
+                    " using known initialization."
+                )
+
+            # Specific initialization handling
+            if initialization_type == "known":
+                # Make sure we were given some known initialization
+                if constant is None and stationary_cov is None:
+                    raise ValueError(
+                        "Must specify either the constant vector"
+                        " or the stationary covariance matrix"
+                        " (or both) if using known"
+                        " initialization."
+                    )
+                # Defaults
+                if stationary_cov is None:
+                    stationary_cov = np.zeros((k_states, k_states))
+                else:
+                    stationary_cov = np.array(stationary_cov)
+
+                # Validate
+                if not stationary_cov.shape == (k_states, k_states):
+                    raise ValueError(
+                        "Invalid stationary covariance matrix;"
+                        f" given shape {stationary_cov.shape!s} but require shape {(k_states, k_states)!s}."
+                    )
+
+                # Set values
+                self.stationary_cov = stationary_cov
+            elif initialization_type == "diffuse":
+                if constant is not None:
+                    warnings.warn(
+                        "Constant values provided, but they are"
+                        " ignored in exact diffuse initialization.",
+                        SpecificationWarning,
+                        stacklevel=2,
+                    )
+            elif initialization_type == "approximate_diffuse":
+                if approximate_diffuse_variance is not None:
+                    self.approximate_diffuse_variance = approximate_diffuse_variance
+            elif initialization_type == "stationary":
+                if constant is not None:
+                    raise ValueError(
+                        "Constant values cannot be provided for"
+                        " stationary initialization."
+                    )
+            else:
+                raise ValueError("Invalid initialization type.")
+
+            # Handle constant
+            if constant is None:
+                constant = np.zeros(k_states)
+            else:
+                constant = np.array(constant)
+            if not constant.shape == (k_states,):
+                raise ValueError(
+                    f"Invalid constant vector; given shape {constant.shape!s}"
+                    f" but require shape {(k_states,)!s}."
+                )
+            self.constant = constant
+        # Otherwise, if setting a sub-block, construct the new initialization
+        # object
+        else:
+            if isinstance(initialization_type, Initialization):
+                init = initialization_type
+            else:
+                if approximate_diffuse_variance is None:
+                    approximate_diffuse_variance = self.approximate_diffuse_variance
+                init = Initialization(
+                    k_states,
+                    initialization_type,
+                    constant=constant,
+                    stationary_cov=stationary_cov,
+                    approximate_diffuse_variance=approximate_diffuse_variance,
+                )
+
+            self.blocks[index] = init
+            for i in index:
+                self._initialization[i] = index
+
+    def unset(self, index):
+        """
+        Unset initialization for states, either globally or for a block
+
+        Parameters
+        ----------
+        index : tuple or int or None
+            Arguments used to create a `slice` of states. Can be a tuple with
+            `(start, stop)` (note that for `slice`, stop is not inclusive), or
+            an integer (to select a specific state), or None (to select all the
+            states).
+
+        Notes
+        -----
+        Note that this specifically unsets initializations previously created
+        using `set` with this same index. Thus you cannot use `index=None` to
+        unset all initializations, but only to unset a previously set global
+        initialization. To unset all initializations (including both global and
+        block level), use the `clear` method.
+        """
+        if isinstance(index, (int, np.integer)):
+            index = int(index)
+            if index < 0 or index > self.k_states:
+                raise ValueError("Invalid index.")
+            index = (index, index + 1)
+        elif index is None:
+            index = (index,)
+        elif not isinstance(index, tuple):
+            raise ValueError("Invalid index.")
+        if len(index) > 2:
+            raise ValueError("Cannot include a slice step in `index`.")
+        index = self._states[slice(*index)]
+
+        # Compatibility with zero-length slices (can make it easier to set up
+        # initialization without lots of if statements)
+        if len(index) == 0:
+            return
+
+        # Unset the values
+        k_states = len(index)
+        if k_states == self.k_states and self.initialization_type is not None:
+            self.initialization_type = None
+            self.constant[:] = 0
+            self.stationary_cov[:] = 0
+        elif index in self.blocks:
+            for i in index:
+                self._initialization[i] = None
+            del self.blocks[index]
+        else:
+            raise ValueError(
+                "The given index does not correspond to a"
+                " previously initialized block."
+            )
+
+    def clear(self):
+        """Clear all previously set initializations, either global or block level"""
+        # Clear initializations
+        for i in self._states:
+            self._initialization[i] = None
+
+        # Delete block initializations
+        keys = list(self.blocks.keys())
+        for key in keys:
+            del self.blocks[key]
+
+        # Clear global attributes
+        self.initialization_type = None
+        self.constant[:] = 0
+        self.stationary_cov[:] = 0
+
+    @property
+    def initialized(self):
+        """bool, whether or not all states have been initialized"""
+        return not (
+            self.initialization_type is None
+            and np.any(np.equal(self._initialization, None))
+        )
+
+    def __call__(
+        self,
+        index=None,
+        model=None,
+        initial_state_mean=None,
+        initial_diffuse_state_cov=None,
+        initial_stationary_state_cov=None,
+        complex_step=False,
+    ):
+        r"""
+        Construct initialization representation
+
+        Parameters
+        ----------
+        index : array_like of int, optional
+            The base index of the block of states being initialized within the
+            full state vector. If not specified, all states are used.
+        model : Representation, optional
+            A state space model representation object. Required if
+            'stationary' initialization is used (either globally or for any
+            block of states); ignored otherwise. See Notes for details.
+        initial_state_mean : ndarray, optional
+            An array (or more usually view) in which to place the initial state
+            mean.
+        initial_diffuse_state_cov : ndarray, optional
+            An array (or more usually view) in which to place the diffuse
+            component of initial state covariance matrix.
+        initial_stationary_state_cov : ndarray, optional
+            An array (or more usually view) in which to place the stationary
+            component of initial state covariance matrix.
+        complex_step : bool, optional
+            Whether or not the model is being evaluated for the purposes of
+            computing a derivative using a complex-step differentiation
+            approach. Default is False.
+
+        Returns
+        -------
+        initial_state_mean : ndarray
+            Initial state mean, :math:`a_1^{(0)} = a`
+        initial_diffuse_state_cov : ndarray
+            Diffuse component of initial state covariance matrix,
+            :math:`P_\infty = A A'`
+        initial_stationary_state_cov : ndarray
+            Stationary component of initial state covariance matrix,
+            :math:`P_* = R_0 Q_0 R_0'`
+
+        Notes
+        -----
+        If stationary initialization is used either globally or for any block
+        of states, then `model` must be provided.
+        """
+        # Check that all states are initialized somehow
+        if self.initialization_type is None and np.any(
+            np.equal(self._initialization, None)
+        ):
+            raise ValueError(
+                "Cannot construct initialization representation"
+                " because not all states have been initialized."
+            )
+
+        # Setup indexes
+        if index is None:
+            index = self._states
+            ix1 = np.s_[:]
+            ix2 = np.s_[:, :]
+        else:
+            ix1 = np.s_[index[0] : index[-1] + 1]
+            ix2 = np.ix_(index, index)
+
+        # Retrieve state_intercept, etc. if `model` was given
+        if model is not None:
+            state_intercept = model["state_intercept", ix1, 0]
+            transition = model[("transition",) + ix2 + (0,)]
+            selection = model["selection", ix1, :, 0]
+            state_cov = model["state_cov", :, :, 0]
+            selected_state_cov = np.dot(selection, state_cov).dot(selection.T)
+
+        # Create output arrays if not given
+        if initial_state_mean is None:
+            initial_state_mean = np.zeros(self.k_states)
+        cov_shape = (self.k_states, self.k_states)
+        if initial_diffuse_state_cov is None:
+            initial_diffuse_state_cov = np.zeros(cov_shape)
+        if initial_stationary_state_cov is None:
+            initial_stationary_state_cov = np.zeros(cov_shape)
+
+        # If using global initialization, compute the actual elements and
+        # return them
+        if self.initialization_type is not None:
+            eye = np.eye(self.k_states)
+            zeros = np.zeros((self.k_states, self.k_states))
+
+            # General validation
+            if self.initialization_type == "stationary" and model is None:
+                raise ValueError(
+                    "Stationary initialization requires passing"
+                    " either the `model` argument or all of the"
+                    " individual transition equation arguments."
+                )
+            if self.initialization_type == "stationary":
+                # TODO performance
+                eigvals = np.linalg.eigvals(transition)
+                threshold = 1.0 - 1e-10
+                if not np.max(np.abs(eigvals)) < threshold:
+                    raise ValueError(
+                        "Transition equation is not stationary,"
+                        " and so stationary initialization cannot"
+                        " be used."
+                    )
+
+            # Set the initial state mean
+            if self.initialization_type == "stationary":
+                # TODO performance
+                initial_state_mean[ix1] = np.linalg.solve(
+                    eye - transition, state_intercept
+                )
+            else:
+                initial_state_mean[ix1] = self.constant
+
+            # Set the diffuse component
+            if self.initialization_type == "diffuse":
+                initial_diffuse_state_cov[ix2] = np.eye(self.k_states)
+            else:
+                initial_diffuse_state_cov[ix2] = zeros
+
+            # Set the stationary component
+            if self.initialization_type == "known":
+                initial_stationary_state_cov[ix2] = self.stationary_cov
+            elif self.initialization_type == "diffuse":
+                initial_stationary_state_cov[ix2] = zeros
+            elif self.initialization_type == "approximate_diffuse":
+                initial_stationary_state_cov[ix2] = (
+                    eye * self.approximate_diffuse_variance
+                )
+            elif self.initialization_type == "stationary":
+                # TODO performance
+                initial_stationary_state_cov[ix2] = tools.solve_discrete_lyapunov(
+                    transition, selected_state_cov, complex_step=complex_step
+                )
+        else:
+            # Otherwise, if using blocks, recursively initialize
+            # them (values will be set in-place)
+            for block_index, init in self.blocks.items():
+                init(
+                    index=tuple(np.array(index)[block_index,]),
+                    model=model,
+                    initial_state_mean=initial_state_mean,
+                    initial_diffuse_state_cov=initial_diffuse_state_cov,
+                    initial_stationary_state_cov=initial_stationary_state_cov,
+                )
+
+        return (
+            initial_state_mean,
+            initial_diffuse_state_cov,
+            initial_stationary_state_cov,
+        )
diff --git a/shared/package-files/40/40d424bb8bf7f0670e71cb4718d6d156862fe6654c55c7b19e60b187d7eaa46c/bdist_rpm.py b/shared/package-files/40/40d424bb8bf7f0670e71cb4718d6d156862fe6654c55c7b19e60b187d7eaa46c/bdist_rpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ed608b479dbbaa4a0fc92e1f7d9b593188bc0b9
--- /dev/null
+++ b/shared/package-files/40/40d424bb8bf7f0670e71cb4718d6d156862fe6654c55c7b19e60b187d7eaa46c/bdist_rpm.py
@@ -0,0 +1,614 @@
+"""distutils.command.bdist_rpm
+
+Implements the Distutils 'bdist_rpm' command (create RPM source and binary
+distributions)."""
+
+import subprocess
+import sys
+import os
+
+from ..core import Command
+from ..debug import DEBUG
+from ..file_util import write_file
+from ..errors import (
+    DistutilsOptionError,
+    DistutilsPlatformError,
+    DistutilsFileError,
+    DistutilsExecError,
+)
+from ..sysconfig import get_python_version
+from distutils._log import log
+
+
+class bdist_rpm(Command):
+    description = "create an RPM distribution"
+
+    user_options = [
+        ('bdist-base=', None, "base directory for creating built distributions"),
+        (
+            'rpm-base=',
+            None,
+            "base directory for creating RPMs (defaults to \"rpm\" under "
+            "--bdist-base; must be specified for RPM 2)",
+        ),
+        (
+            'dist-dir=',
+            'd',
+            "directory to put final RPM files in " "(and .spec files if --spec-only)",
+        ),
+        (
+            'python=',
+            None,
+            "path to Python interpreter to hard-code in the .spec file "
+            "(default: \"python\")",
+        ),
+        (
+            'fix-python',
+            None,
+            "hard-code the exact path to the current Python interpreter in "
+            "the .spec file",
+        ),
+        ('spec-only', None, "only regenerate spec file"),
+        ('source-only', None, "only generate source RPM"),
+        ('binary-only', None, "only generate binary RPM"),
+        ('use-bzip2', None, "use bzip2 instead of gzip to create source distribution"),
+        # More meta-data: too RPM-specific to put in the setup script,
+        # but needs to go in the .spec file -- so we make these options
+        # to "bdist_rpm".  The idea is that packagers would put this
+        # info in setup.cfg, although they are of course free to
+        # supply it on the command line.
+        (
+            'distribution-name=',
+            None,
+            "name of the (Linux) distribution to which this "
+            "RPM applies (*not* the name of the module distribution!)",
+        ),
+        ('group=', None, "package classification [default: \"Development/Libraries\"]"),
+        ('release=', None, "RPM release number"),
+        ('serial=', None, "RPM serial number"),
+        (
+            'vendor=',
+            None,
+            "RPM \"vendor\" (eg. \"Joe Blow <joe@example.com>\") "
+            "[default: maintainer or author from setup script]",
+        ),
+        (
+            'packager=',
+            None,
+            "RPM packager (eg. \"Jane Doe <jane@example.net>\") " "[default: vendor]",
+        ),
+        ('doc-files=', None, "list of documentation files (space or comma-separated)"),
+        ('changelog=', None, "RPM changelog"),
+        ('icon=', None, "name of icon file"),
+        ('provides=', None, "capabilities provided by this package"),
+        ('requires=', None, "capabilities required by this package"),
+        ('conflicts=', None, "capabilities which conflict with this package"),
+        ('build-requires=', None, "capabilities required to build this package"),
+        ('obsoletes=', None, "capabilities made obsolete by this package"),
+        ('no-autoreq', None, "do not automatically calculate dependencies"),
+        # Actions to take when building RPM
+        ('keep-temp', 'k', "don't clean up RPM build directory"),
+        ('no-keep-temp', None, "clean up RPM build directory [default]"),
+        (
+            'use-rpm-opt-flags',
+            None,
+            "compile with RPM_OPT_FLAGS when building from source RPM",
+        ),
+        ('no-rpm-opt-flags', None, "do not pass any RPM CFLAGS to compiler"),
+        ('rpm3-mode', None, "RPM 3 compatibility mode (default)"),
+        ('rpm2-mode', None, "RPM 2 compatibility mode"),
+        # Add the hooks necessary for specifying custom scripts
+        ('prep-script=', None, "Specify a script for the PREP phase of RPM building"),
+        ('build-script=', None, "Specify a script for the BUILD phase of RPM building"),
+        (
+            'pre-install=',
+            None,
+            "Specify a script for the pre-INSTALL phase of RPM building",
+        ),
+        (
+            'install-script=',
+            None,
+            "Specify a script for the INSTALL phase of RPM building",
+        ),
+        (
+            'post-install=',
+            None,
+            "Specify a script for the post-INSTALL phase of RPM building",
+        ),
+        (
+            'pre-uninstall=',
+            None,
+            "Specify a script for the pre-UNINSTALL phase of RPM building",
+        ),
+        (
+            'post-uninstall=',
+            None,
+            "Specify a script for the post-UNINSTALL phase of RPM building",
+        ),
+        ('clean-script=', None, "Specify a script for the CLEAN phase of RPM building"),
+        (
+            'verify-script=',
+            None,
+            "Specify a script for the VERIFY phase of the RPM build",
+        ),
+        # Allow a packager to explicitly force an architecture
+        ('force-arch=', None, "Force an architecture onto the RPM build process"),
+        ('quiet', 'q', "Run the INSTALL phase of RPM building in quiet mode"),
+    ]
+
+    boolean_options = [
+        'keep-temp',
+        'use-rpm-opt-flags',
+        'rpm3-mode',
+        'no-autoreq',
+        'quiet',
+    ]
+
+    negative_opt = {
+        'no-keep-temp': 'keep-temp',
+        'no-rpm-opt-flags': 'use-rpm-opt-flags',
+        'rpm2-mode': 'rpm3-mode',
+    }
+
+    def initialize_options(self):
+        self.bdist_base = None
+        self.rpm_base = None
+        self.dist_dir = None
+        self.python = None
+        self.fix_python = None
+        self.spec_only = None
+        self.binary_only = None
+        self.source_only = None
+        self.use_bzip2 = None
+
+        self.distribution_name = None
+        self.group = None
+        self.release = None
+        self.serial = None
+        self.vendor = None
+        self.packager = None
+        self.doc_files = None
+        self.changelog = None
+        self.icon = None
+
+        self.prep_script = None
+        self.build_script = None
+        self.install_script = None
+        self.clean_script = None
+        self.verify_script = None
+        self.pre_install = None
+        self.post_install = None
+        self.pre_uninstall = None
+        self.post_uninstall = None
+        self.prep = None
+        self.provides = None
+        self.requires = None
+        self.conflicts = None
+        self.build_requires = None
+        self.obsoletes = None
+
+        self.keep_temp = 0
+        self.use_rpm_opt_flags = 1
+        self.rpm3_mode = 1
+        self.no_autoreq = 0
+
+        self.force_arch = None
+        self.quiet = 0
+
+    def finalize_options(self):
+        self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
+        if self.rpm_base is None:
+            if not self.rpm3_mode:
+                raise DistutilsOptionError("you must specify --rpm-base in RPM 2 mode")
+            self.rpm_base = os.path.join(self.bdist_base, "rpm")
+
+        if self.python is None:
+            if self.fix_python:
+                self.python = sys.executable
+            else:
+                self.python = "python3"
+        elif self.fix_python:
+            raise DistutilsOptionError(
+                "--python and --fix-python are mutually exclusive options"
+            )
+
+        if os.name != 'posix':
+            raise DistutilsPlatformError(
+                "don't know how to create RPM " "distributions on platform %s" % os.name
+            )
+        if self.binary_only and self.source_only:
+            raise DistutilsOptionError(
+                "cannot supply both '--source-only' and '--binary-only'"
+            )
+
+        # don't pass CFLAGS to pure python distributions
+        if not self.distribution.has_ext_modules():
+            self.use_rpm_opt_flags = 0
+
+        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
+        self.finalize_package_data()
+
+    def finalize_package_data(self):
+        self.ensure_string('group', "Development/Libraries")
+        self.ensure_string(
+            'vendor',
+            "%s <%s>"
+            % (self.distribution.get_contact(), self.distribution.get_contact_email()),
+        )
+        self.ensure_string('packager')
+        self.ensure_string_list('doc_files')
+        if isinstance(self.doc_files, list):
+            for readme in ('README', 'README.txt'):
+                if os.path.exists(readme) and readme not in self.doc_files:
+                    self.doc_files.append(readme)
+
+        self.ensure_string('release', "1")
+        self.ensure_string('serial')  # should it be an int?
+
+        self.ensure_string('distribution_name')
+
+        self.ensure_string('changelog')
+        # Format changelog correctly
+        self.changelog = self._format_changelog(self.changelog)
+
+        self.ensure_filename('icon')
+
+        self.ensure_filename('prep_script')
+        self.ensure_filename('build_script')
+        self.ensure_filename('install_script')
+        self.ensure_filename('clean_script')
+        self.ensure_filename('verify_script')
+        self.ensure_filename('pre_install')
+        self.ensure_filename('post_install')
+        self.ensure_filename('pre_uninstall')
+        self.ensure_filename('post_uninstall')
+
+        # XXX don't forget we punted on summaries and descriptions -- they
+        # should be handled here eventually!
+
+        # Now *this* is some meta-data that belongs in the setup script...
+        self.ensure_string_list('provides')
+        self.ensure_string_list('requires')
+        self.ensure_string_list('conflicts')
+        self.ensure_string_list('build_requires')
+        self.ensure_string_list('obsoletes')
+
+        self.ensure_string('force_arch')
+
+    def run(self):  # noqa: C901
+        if DEBUG:
+            print("before _get_package_data():")
+            print("vendor =", self.vendor)
+            print("packager =", self.packager)
+            print("doc_files =", self.doc_files)
+            print("changelog =", self.changelog)
+
+        # make directories
+        if self.spec_only:
+            spec_dir = self.dist_dir
+            self.mkpath(spec_dir)
+        else:
+            rpm_dir = {}
+            for d in ('SOURCES', 'SPECS', 'BUILD', 'RPMS', 'SRPMS'):
+                rpm_dir[d] = os.path.join(self.rpm_base, d)
+                self.mkpath(rpm_dir[d])
+            spec_dir = rpm_dir['SPECS']
+
+        # Spec file goes into 'dist_dir' if '--spec-only specified',
+        # build/rpm.<plat> otherwise.
+        spec_path = os.path.join(spec_dir, "%s.spec" % self.distribution.get_name())
+        self.execute(
+            write_file, (spec_path, self._make_spec_file()), "writing '%s'" % spec_path
+        )
+
+        if self.spec_only:  # stop if requested
+            return
+
+        # Make a source distribution and copy to SOURCES directory with
+        # optional icon.
+        saved_dist_files = self.distribution.dist_files[:]
+        sdist = self.reinitialize_command('sdist')
+        if self.use_bzip2:
+            sdist.formats = ['bztar']
+        else:
+            sdist.formats = ['gztar']
+        self.run_command('sdist')
+        self.distribution.dist_files = saved_dist_files
+
+        source = sdist.get_archive_files()[0]
+        source_dir = rpm_dir['SOURCES']
+        self.copy_file(source, source_dir)
+
+        if self.icon:
+            if os.path.exists(self.icon):
+                self.copy_file(self.icon, source_dir)
+            else:
+                raise DistutilsFileError("icon file '%s' does not exist" % self.icon)
+
+        # build package
+        log.info("building RPMs")
+        rpm_cmd = ['rpmbuild']
+
+        if self.source_only:  # what kind of RPMs?
+            rpm_cmd.append('-bs')
+        elif self.binary_only:
+            rpm_cmd.append('-bb')
+        else:
+            rpm_cmd.append('-ba')
+        rpm_cmd.extend(['--define', '__python %s' % self.python])
+        if self.rpm3_mode:
+            rpm_cmd.extend(['--define', '_topdir %s' % os.path.abspath(self.rpm_base)])
+        if not self.keep_temp:
+            rpm_cmd.append('--clean')
+
+        if self.quiet:
+            rpm_cmd.append('--quiet')
+
+        rpm_cmd.append(spec_path)
+        # Determine the binary rpm names that should be built out of this spec
+        # file
+        # Note that some of these may not be really built (if the file
+        # list is empty)
+        nvr_string = "%{name}-%{version}-%{release}"
+        src_rpm = nvr_string + ".src.rpm"
+        non_src_rpm = "%{arch}/" + nvr_string + ".%{arch}.rpm"
+        q_cmd = r"rpm -q --qf '{} {}\n' --specfile '{}'".format(
+            src_rpm,
+            non_src_rpm,
+            spec_path,
+        )
+
+        out = os.popen(q_cmd)
+        try:
+            binary_rpms = []
+            source_rpm = None
+            while True:
+                line = out.readline()
+                if not line:
+                    break
+                ell = line.strip().split()
+                assert len(ell) == 2
+                binary_rpms.append(ell[1])
+                # The source rpm is named after the first entry in the spec file
+                if source_rpm is None:
+                    source_rpm = ell[0]
+
+            status = out.close()
+            if status:
+                raise DistutilsExecError("Failed to execute: %s" % repr(q_cmd))
+
+        finally:
+            out.close()
+
+        self.spawn(rpm_cmd)
+
+        if not self.dry_run:
+            if self.distribution.has_ext_modules():
+                pyversion = get_python_version()
+            else:
+                pyversion = 'any'
+
+            if not self.binary_only:
+                srpm = os.path.join(rpm_dir['SRPMS'], source_rpm)
+                assert os.path.exists(srpm)
+                self.move_file(srpm, self.dist_dir)
+                filename = os.path.join(self.dist_dir, source_rpm)
+                self.distribution.dist_files.append(('bdist_rpm', pyversion, filename))
+
+            if not self.source_only:
+                for rpm in binary_rpms:
+                    rpm = os.path.join(rpm_dir['RPMS'], rpm)
+                    if os.path.exists(rpm):
+                        self.move_file(rpm, self.dist_dir)
+                        filename = os.path.join(self.dist_dir, os.path.basename(rpm))
+                        self.distribution.dist_files.append(
+                            ('bdist_rpm', pyversion, filename)
+                        )
+
+    def _dist_path(self, path):
+        return os.path.join(self.dist_dir, os.path.basename(path))
+
+    def _make_spec_file(self):  # noqa: C901
+        """Generate the text of an RPM spec file and return it as a
+        list of strings (one per line).
+        """
+        # definitions and headers
+        spec_file = [
+            '%define name ' + self.distribution.get_name(),
+            '%define version ' + self.distribution.get_version().replace('-', '_'),
+            '%define unmangled_version ' + self.distribution.get_version(),
+            '%define release ' + self.release.replace('-', '_'),
+            '',
+            'Summary: ' + (self.distribution.get_description() or "UNKNOWN"),
+        ]
+
+        # Workaround for #14443 which affects some RPM based systems such as
+        # RHEL6 (and probably derivatives)
+        vendor_hook = subprocess.getoutput('rpm --eval %{__os_install_post}')
+        # Generate a potential replacement value for __os_install_post (whilst
+        # normalizing the whitespace to simplify the test for whether the
+        # invocation of brp-python-bytecompile passes in __python):
+        vendor_hook = '\n'.join(
+            ['  %s \\' % line.strip() for line in vendor_hook.splitlines()]
+        )
+        problem = "brp-python-bytecompile \\\n"
+        fixed = "brp-python-bytecompile %{__python} \\\n"
+        fixed_hook = vendor_hook.replace(problem, fixed)
+        if fixed_hook != vendor_hook:
+            spec_file.append('# Workaround for http://bugs.python.org/issue14443')
+            spec_file.append('%define __os_install_post ' + fixed_hook + '\n')
+
+        # put locale summaries into spec file
+        # XXX not supported for now (hard to put a dictionary
+        # in a config file -- arg!)
+        # for locale in self.summaries.keys():
+        #    spec_file.append('Summary(%s): %s' % (locale,
+        #                                          self.summaries[locale]))
+
+        spec_file.extend(
+            [
+                'Name: %{name}',
+                'Version: %{version}',
+                'Release: %{release}',
+            ]
+        )
+
+        # XXX yuck! this filename is available from the "sdist" command,
+        # but only after it has run: and we create the spec file before
+        # running "sdist", in case of --spec-only.
+        if self.use_bzip2:
+            spec_file.append('Source0: %{name}-%{unmangled_version}.tar.bz2')
+        else:
+            spec_file.append('Source0: %{name}-%{unmangled_version}.tar.gz')
+
+        spec_file.extend(
+            [
+                'License: ' + (self.distribution.get_license() or "UNKNOWN"),
+                'Group: ' + self.group,
+                'BuildRoot: %{_tmppath}/%{name}-%{version}-%{release}-buildroot',
+                'Prefix: %{_prefix}',
+            ]
+        )
+
+        if not self.force_arch:
+            # noarch if no extension modules
+            if not self.distribution.has_ext_modules():
+                spec_file.append('BuildArch: noarch')
+        else:
+            spec_file.append('BuildArch: %s' % self.force_arch)
+
+        for field in (
+            'Vendor',
+            'Packager',
+            'Provides',
+            'Requires',
+            'Conflicts',
+            'Obsoletes',
+        ):
+            val = getattr(self, field.lower())
+            if isinstance(val, list):
+                spec_file.append('{}: {}'.format(field, ' '.join(val)))
+            elif val is not None:
+                spec_file.append('{}: {}'.format(field, val))
+
+        if self.distribution.get_url():
+            spec_file.append('Url: ' + self.distribution.get_url())
+
+        if self.distribution_name:
+            spec_file.append('Distribution: ' + self.distribution_name)
+
+        if self.build_requires:
+            spec_file.append('BuildRequires: ' + ' '.join(self.build_requires))
+
+        if self.icon:
+            spec_file.append('Icon: ' + os.path.basename(self.icon))
+
+        if self.no_autoreq:
+            spec_file.append('AutoReq: 0')
+
+        spec_file.extend(
+            [
+                '',
+                '%description',
+                self.distribution.get_long_description() or "",
+            ]
+        )
+
+        # put locale descriptions into spec file
+        # XXX again, suppressed because config file syntax doesn't
+        # easily support this ;-(
+        # for locale in self.descriptions.keys():
+        #    spec_file.extend([
+        #        '',
+        #        '%description -l ' + locale,
+        #        self.descriptions[locale],
+        #        ])
+
+        # rpm scripts
+        # figure out default build script
+        def_setup_call = "{} {}".format(self.python, os.path.basename(sys.argv[0]))
+        def_build = "%s build" % def_setup_call
+        if self.use_rpm_opt_flags:
+            def_build = 'env CFLAGS="$RPM_OPT_FLAGS" ' + def_build
+
+        # insert contents of files
+
+        # XXX this is kind of misleading: user-supplied options are files
+        # that we open and interpolate into the spec file, but the defaults
+        # are just text that we drop in as-is.  Hmmm.
+
+        install_cmd = (
+            '%s install -O1 --root=$RPM_BUILD_ROOT ' '--record=INSTALLED_FILES'
+        ) % def_setup_call
+
+        script_options = [
+            ('prep', 'prep_script', "%setup -n %{name}-%{unmangled_version}"),
+            ('build', 'build_script', def_build),
+            ('install', 'install_script', install_cmd),
+            ('clean', 'clean_script', "rm -rf $RPM_BUILD_ROOT"),
+            ('verifyscript', 'verify_script', None),
+            ('pre', 'pre_install', None),
+            ('post', 'post_install', None),
+            ('preun', 'pre_uninstall', None),
+            ('postun', 'post_uninstall', None),
+        ]
+
+        for rpm_opt, attr, default in script_options:
+            # Insert contents of file referred to, if no file is referred to
+            # use 'default' as contents of script
+            val = getattr(self, attr)
+            if val or default:
+                spec_file.extend(
+                    [
+                        '',
+                        '%' + rpm_opt,
+                    ]
+                )
+                if val:
+                    with open(val) as f:
+                        spec_file.extend(f.read().split('\n'))
+                else:
+                    spec_file.append(default)
+
+        # files section
+        spec_file.extend(
+            [
+                '',
+                '%files -f INSTALLED_FILES',
+                '%defattr(-,root,root)',
+            ]
+        )
+
+        if self.doc_files:
+            spec_file.append('%doc ' + ' '.join(self.doc_files))
+
+        if self.changelog:
+            spec_file.extend(
+                [
+                    '',
+                    '%changelog',
+                ]
+            )
+            spec_file.extend(self.changelog)
+
+        return spec_file
+
+    def _format_changelog(self, changelog):
+        """Format the changelog correctly and convert it to a list of strings"""
+        if not changelog:
+            return changelog
+        new_changelog = []
+        for line in changelog.strip().split('\n'):
+            line = line.strip()
+            if line[0] == '*':
+                new_changelog.extend(['', line])
+            elif line[0] == '-':
+                new_changelog.append(line)
+            else:
+                new_changelog.append('  ' + line)
+
+        # strip trailing newline inserted by first changelog entry
+        if not new_changelog[0]:
+            del new_changelog[0]
+
+        return new_changelog
diff --git a/shared/package-files/40/40d4e101a64b75361f763479b01207ae71535337e79ce6e162265842f6471eed/focus.tcl b/shared/package-files/40/40d4e101a64b75361f763479b01207ae71535337e79ce6e162265842f6471eed/focus.tcl
new file mode 100644
index 0000000000000000000000000000000000000000..640406e4dddd421a59be2718b13b28142415f22a
--- /dev/null
+++ b/shared/package-files/40/40d4e101a64b75361f763479b01207ae71535337e79ce6e162265842f6471eed/focus.tcl
@@ -0,0 +1,178 @@
+# focus.tcl --
+#
+# This file defines several procedures for managing the input
+# focus.
+#
+# Copyright (c) 1994-1995 Sun Microsystems, Inc.
+#
+# See the file "license.terms" for information on usage and redistribution
+# of this file, and for a DISCLAIMER OF ALL WARRANTIES.
+#
+
+# ::tk_focusNext --
+# This procedure returns the name of the next window after "w" in
+# "focus order" (the window that should receive the focus next if
+# Tab is typed in w).  "Next" is defined by a pre-order search
+# of a top-level and its non-top-level descendants, with the stacking
+# order determining the order of siblings.  The "-takefocus" options
+# on windows determine whether or not they should be skipped.
+#
+# Arguments:
+# w -		Name of a window.
+
+proc ::tk_focusNext w {
+    set cur $w
+    while {1} {
+
+	# Descend to just before the first child of the current widget.
+
+	set parent $cur
+	set children [winfo children $cur]
+	set i -1
+
+	# Look for the next sibling that isn't a top-level.
+
+	while {1} {
+	    incr i
+	    if {$i < [llength $children]} {
+		set cur [lindex $children $i]
+		if {[winfo toplevel $cur] eq $cur} {
+		    continue
+		} else {
+		    break
+		}
+	    }
+
+	    # No more siblings, so go to the current widget's parent.
+	    # If it's a top-level, break out of the loop, otherwise
+	    # look for its next sibling.
+
+	    set cur $parent
+	    if {[winfo toplevel $cur] eq $cur} {
+		break
+	    }
+	    set parent [winfo parent $parent]
+	    set children [winfo children $parent]
+	    set i [lsearch -exact $children $cur]
+	}
+	if {$w eq $cur || [tk::FocusOK $cur]} {
+	    return $cur
+	}
+    }
+}
+
+# ::tk_focusPrev --
+# This procedure returns the name of the previous window before "w" in
+# "focus order" (the window that should receive the focus next if
+# Shift-Tab is typed in w).  "Next" is defined by a pre-order search
+# of a top-level and its non-top-level descendants, with the stacking
+# order determining the order of siblings.  The "-takefocus" options
+# on windows determine whether or not they should be skipped.
+#
+# Arguments:
+# w -		Name of a window.
+
+proc ::tk_focusPrev w {
+    set cur $w
+    while {1} {
+
+	# Collect information about the current window's position
+	# among its siblings.  Also, if the window is a top-level,
+	# then reposition to just after the last child of the window.
+
+	if {[winfo toplevel $cur] eq $cur}  {
+	    set parent $cur
+	    set children [winfo children $cur]
+	    set i [llength $children]
+	} else {
+	    set parent [winfo parent $cur]
+	    set children [winfo children $parent]
+	    set i [lsearch -exact $children $cur]
+	}
+
+	# Go to the previous sibling, then descend to its last descendant
+	# (highest in stacking order.  While doing this, ignore top-levels
+	# and their descendants.  When we run out of descendants, go up
+	# one level to the parent.
+
+	while {$i > 0} {
+	    incr i -1
+	    set cur [lindex $children $i]
+	    if {[winfo toplevel $cur] eq $cur} {
+		continue
+	    }
+	    set parent $cur
+	    set children [winfo children $parent]
+	    set i [llength $children]
+	}
+	set cur $parent
+	if {$w eq $cur || [tk::FocusOK $cur]} {
+	    return $cur
+	}
+    }
+}
+
+# ::tk::FocusOK --
+#
+# This procedure is invoked to decide whether or not to focus on
+# a given window.  It returns 1 if it's OK to focus on the window,
+# 0 if it's not OK.  The code first checks whether the window is
+# viewable.  If not, then it never focuses on the window.  Then it
+# checks the -takefocus option for the window and uses it if it's
+# set.  If there's no -takefocus option, the procedure checks to
+# see if (a) the widget isn't disabled, and (b) it has some key
+# bindings.  If all of these are true, then 1 is returned.
+#
+# Arguments:
+# w -		Name of a window.
+
+proc ::tk::FocusOK w {
+    set code [catch {$w cget -takefocus} value]
+    if {($code == 0) && ($value ne "")} {
+	if {$value == 0} {
+	    return 0
+	} elseif {$value == 1} {
+	    return [winfo viewable $w]
+	} else {
+	    set value [uplevel #0 $value [list $w]]
+	    if {$value ne ""} {
+		return $value
+	    }
+	}
+    }
+    if {![winfo viewable $w]} {
+	return 0
+    }
+    set code [catch {$w cget -state} value]
+    if {($code == 0) && $value eq "disabled"} {
+	return 0
+    }
+    regexp Key|Focus "[bind $w] [bind [winfo class $w]]"
+}
+
+# ::tk_focusFollowsMouse --
+#
+# If this procedure is invoked, Tk will enter "focus-follows-mouse"
+# mode, where the focus is always on whatever window contains the
+# mouse.  If this procedure isn't invoked, then the user typically
+# has to click on a window to give it the focus.
+#
+# Arguments:
+# None.
+
+proc ::tk_focusFollowsMouse {} {
+    set old [bind all <Enter>]
+    set script {
+	if {"%d" eq "NotifyAncestor" || "%d" eq "NotifyNonlinear" \
+		|| "%d" eq "NotifyInferior"} {
+	    if {[tk::FocusOK %W]} {
+		focus %W
+	    }
+	}
+    }
+    if {$old ne ""} {
+	bind all <Enter> "$old; $script"
+    } else {
+	bind all <Enter> $script
+    }
+}
diff --git a/shared/package-files/40/40dc2ca87f930fbba964911d63e6609a9d956c629c4a2e702c60acb50c1fd6dc/ansi+sgrbold b/shared/package-files/40/40dc2ca87f930fbba964911d63e6609a9d956c629c4a2e702c60acb50c1fd6dc/ansi+sgrbold
new file mode 100644
index 0000000000000000000000000000000000000000..0a10f6a658b07e79965af3a8c75e4635b97482fb
Binary files /dev/null and b/shared/package-files/40/40dc2ca87f930fbba964911d63e6609a9d956c629c4a2e702c60acb50c1fd6dc/ansi+sgrbold differ
diff --git a/shared/package-files/40/40de1219bb710d23f6305bf2625d3445ab18632345e00d60f18ebfd528a3ed84/test_array_tools.py b/shared/package-files/40/40de1219bb710d23f6305bf2625d3445ab18632345e00d60f18ebfd528a3ed84/test_array_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bda9716e0bc4b6ad3ed0c3954147043b74c421a
--- /dev/null
+++ b/shared/package-files/40/40de1219bb710d23f6305bf2625d3445ab18632345e00d60f18ebfd528a3ed84/test_array_tools.py
@@ -0,0 +1,111 @@
+import numpy as np
+
+from scipy._lib._array_api import xp_assert_equal
+from pytest import raises as assert_raises
+
+from scipy.signal._arraytools import (axis_slice, axis_reverse,
+     odd_ext, even_ext, const_ext, zero_ext)
+
+
+class TestArrayTools:
+
+    def test_axis_slice(self):
+        a = np.arange(12).reshape(3, 4)
+
+        s = axis_slice(a, start=0, stop=1, axis=0)
+        xp_assert_equal(s, a[0:1, :])
+
+        s = axis_slice(a, start=-1, axis=0)
+        xp_assert_equal(s, a[-1:, :])
+
+        s = axis_slice(a, start=0, stop=1, axis=1)
+        xp_assert_equal(s, a[:, 0:1])
+
+        s = axis_slice(a, start=-1, axis=1)
+        xp_assert_equal(s, a[:, -1:])
+
+        s = axis_slice(a, start=0, step=2, axis=0)
+        xp_assert_equal(s, a[::2, :])
+
+        s = axis_slice(a, start=0, step=2, axis=1)
+        xp_assert_equal(s, a[:, ::2])
+
+    def test_axis_reverse(self):
+        a = np.arange(12).reshape(3, 4)
+
+        r = axis_reverse(a, axis=0)
+        xp_assert_equal(r, a[::-1, :])
+
+        r = axis_reverse(a, axis=1)
+        xp_assert_equal(r, a[:, ::-1])
+
+    def test_odd_ext(self):
+        a = np.array([[1, 2, 3, 4, 5],
+                      [9, 8, 7, 6, 5]])
+
+        odd = odd_ext(a, 2, axis=1)
+        expected = np.array([[-1, 0, 1, 2, 3, 4, 5, 6, 7],
+                             [11, 10, 9, 8, 7, 6, 5, 4, 3]])
+        xp_assert_equal(odd, expected)
+
+        odd = odd_ext(a, 1, axis=0)
+        expected = np.array([[-7, -4, -1, 2, 5],
+                             [1, 2, 3, 4, 5],
+                             [9, 8, 7, 6, 5],
+                             [17, 14, 11, 8, 5]])
+        xp_assert_equal(odd, expected)
+
+        assert_raises(ValueError, odd_ext, a, 2, axis=0)
+        assert_raises(ValueError, odd_ext, a, 5, axis=1)
+
+    def test_even_ext(self):
+        a = np.array([[1, 2, 3, 4, 5],
+                      [9, 8, 7, 6, 5]])
+
+        even = even_ext(a, 2, axis=1)
+        expected = np.array([[3, 2, 1, 2, 3, 4, 5, 4, 3],
+                             [7, 8, 9, 8, 7, 6, 5, 6, 7]])
+        xp_assert_equal(even, expected)
+
+        even = even_ext(a, 1, axis=0)
+        expected = np.array([[9, 8, 7, 6, 5],
+                             [1, 2, 3, 4, 5],
+                             [9, 8, 7, 6, 5],
+                             [1, 2, 3, 4, 5]])
+        xp_assert_equal(even, expected)
+
+        assert_raises(ValueError, even_ext, a, 2, axis=0)
+        assert_raises(ValueError, even_ext, a, 5, axis=1)
+
+    def test_const_ext(self):
+        a = np.array([[1, 2, 3, 4, 5],
+                      [9, 8, 7, 6, 5]])
+
+        const = const_ext(a, 2, axis=1)
+        expected = np.array([[1, 1, 1, 2, 3, 4, 5, 5, 5],
+                             [9, 9, 9, 8, 7, 6, 5, 5, 5]])
+        xp_assert_equal(const, expected)
+
+        const = const_ext(a, 1, axis=0)
+        expected = np.array([[1, 2, 3, 4, 5],
+                             [1, 2, 3, 4, 5],
+                             [9, 8, 7, 6, 5],
+                             [9, 8, 7, 6, 5]])
+        xp_assert_equal(const, expected)
+
+    def test_zero_ext(self):
+        a = np.array([[1, 2, 3, 4, 5],
+                      [9, 8, 7, 6, 5]])
+
+        zero = zero_ext(a, 2, axis=1)
+        expected = np.array([[0, 0, 1, 2, 3, 4, 5, 0, 0],
+                             [0, 0, 9, 8, 7, 6, 5, 0, 0]])
+        xp_assert_equal(zero, expected)
+
+        zero = zero_ext(a, 1, axis=0)
+        expected = np.array([[0, 0, 0, 0, 0],
+                             [1, 2, 3, 4, 5],
+                             [9, 8, 7, 6, 5],
+                             [0, 0, 0, 0, 0]])
+        xp_assert_equal(zero, expected)
+
diff --git a/shared/package-files/40/40e1132bfd1ecc9cf50077b0c7e147888dadf6734d6adf9b8a09ffc97d61a6fa/init.py b/shared/package-files/40/40e1132bfd1ecc9cf50077b0c7e147888dadf6734d6adf9b8a09ffc97d61a6fa/init.py
new file mode 100644
index 0000000000000000000000000000000000000000..416ad0db8ef7ef64301614184f611a52c1a01e31
--- /dev/null
+++ b/shared/package-files/40/40e1132bfd1ecc9cf50077b0c7e147888dadf6734d6adf9b8a09ffc97d61a6fa/init.py
@@ -0,0 +1,53 @@
+import inspect
+import torch
+
+
+def skip_init(module_cls, *args, **kwargs):
+    r"""
+    Given a module class object and args / kwargs, instantiate the module without initializing parameters / buffers.
+
+    This can be useful if initialization is slow or if custom initialization will
+    be performed, making the default initialization unnecessary. There are some caveats to this, due to
+    the way this function is implemented:
+
+    1. The module must accept a `device` arg in its constructor that is passed to any parameters
+    or buffers created during construction.
+
+    2. The module must not perform any computation on parameters in its constructor except
+    initialization (i.e. functions from :mod:`torch.nn.init`).
+
+    If these conditions are satisfied, the module can be instantiated with parameter / buffer values
+    uninitialized, as if having been created using :func:`torch.empty`.
+
+    Args:
+        module_cls: Class object; should be a subclass of :class:`torch.nn.Module`
+        args: args to pass to the module's constructor
+        kwargs: kwargs to pass to the module's constructor
+
+    Returns:
+        Instantiated module with uninitialized parameters / buffers
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> import torch
+        >>> m = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1)
+        >>> m.weight
+        Parameter containing:
+        tensor([[0.0000e+00, 1.5846e+29, 7.8307e+00, 2.5250e-29, 1.1210e-44]],
+               requires_grad=True)
+        >>> m2 = torch.nn.utils.skip_init(torch.nn.Linear, in_features=6, out_features=1)
+        >>> m2.weight
+        Parameter containing:
+        tensor([[-1.4677e+24,  4.5915e-41,  1.4013e-45,  0.0000e+00, -1.4677e+24,
+                  4.5915e-41]], requires_grad=True)
+
+    """
+    if not issubclass(module_cls, torch.nn.Module):
+        raise RuntimeError(f'Expected a Module; got {module_cls}')
+    if 'device' not in inspect.signature(module_cls).parameters:
+        raise RuntimeError('Module must support a \'device\' arg to skip initialization')
+
+    final_device = kwargs.pop('device', 'cpu')
+    kwargs['device'] = 'meta'
+    return module_cls(*args, **kwargs).to_empty(device=final_device)
diff --git a/shared/package-files/40/40e60477924d9f1b8671619f3228d92bc05b3fe1ee056207d058ef3a8fdc7f24/positive_ops.h b/shared/package-files/40/40e60477924d9f1b8671619f3228d92bc05b3fe1ee056207d058ef3a8fdc7f24/positive_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3910cb044aeb6635ce0c7420134cd76b3e152dda
--- /dev/null
+++ b/shared/package-files/40/40e60477924d9f1b8671619f3228d92bc05b3fe1ee056207d058ef3a8fdc7f24/positive_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API positive {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::positive")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "positive(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/40/40e7470b516e0e413ad58c801d773685702ac71bed4a5d117b3ec43ac2cbc188/data.py b/shared/package-files/40/40e7470b516e0e413ad58c801d773685702ac71bed4a5d117b3ec43ac2cbc188/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0717e55cd283980e4d2e1027a1ef145d2340877
--- /dev/null
+++ b/shared/package-files/40/40e7470b516e0e413ad58c801d773685702ac71bed4a5d117b3ec43ac2cbc188/data.py
@@ -0,0 +1,68 @@
+"""U.S. Strike Duration Data"""
+from statsmodels.datasets import utils as du
+
+__docformat__ = "restructuredtext"
+
+COPYRIGHT = """This is public domain."""
+TITLE = __doc__
+SOURCE = """
+This is a subset of the data used in Kennan (1985). It was originally
+published by the Bureau of Labor Statistics.
+
+::
+
+    Kennan, J. 1985. "The duration of contract strikes in US manufacturing.
+        *Journal of Econometrics* 28.1, 5-28.
+"""
+
+DESCRSHORT = """Contains data on the length of strikes in US manufacturing and
+unanticipated industrial production."""
+
+DESCRLONG = """Contains data on the length of strikes in US manufacturing and
+unanticipated industrial production. The data is a subset of the data originally
+used by Kennan. The data here is data for the months of June only to avoid
+seasonal issues."""
+
+# suggested notes
+NOTE = """::
+
+    Number of observations - 62
+
+    Number of variables - 2
+
+    Variable name definitions::
+
+                duration - duration of the strike in days
+                iprod - unanticipated industrial production
+"""
+
+
+def load_pandas():
+    """
+    Load the strikes data and return a Dataset class instance.
+
+    Returns
+    -------
+    Dataset
+        A `Dataset` instance with ``data``, ``names``, ``endog``, ``exog``,
+        ``endog_name`` and ``exog_name`` attributes.
+    """
+    data = _get_data()
+    return du.process_pandas(data, endog_idx=0)
+
+
+def load():
+    """
+    Load the strikes data and return a Dataset class instance.
+
+    Returns
+    -------
+    Dataset
+        A `Dataset` instance with ``data``, ``names``, ``endog``, ``exog``,
+        ``endog_name`` and ``exog_name`` attributes.
+    """
+    return load_pandas()
+
+
+def _get_data():
+    return du.load_csv(__file__, "strikes.csv").astype(float)
diff --git a/shared/package-files/40/40e7fd681f5c637b11e923f90f526654b3e78540be9369841de7e0aac5a2422e/inverse_gamma.py b/shared/package-files/40/40e7fd681f5c637b11e923f90f526654b3e78540be9369841de7e0aac5a2422e/inverse_gamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a66138b6f04ac42bff7bef23395aa472a3b3a6d
--- /dev/null
+++ b/shared/package-files/40/40e7fd681f5c637b11e923f90f526654b3e78540be9369841de7e0aac5a2422e/inverse_gamma.py
@@ -0,0 +1,80 @@
+import torch
+from torch.distributions import constraints
+from torch.distributions.gamma import Gamma
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import PowerTransform
+
+
+__all__ = ["InverseGamma"]
+
+
+class InverseGamma(TransformedDistribution):
+    r"""
+    Creates an inverse gamma distribution parameterized by :attr:`concentration` and :attr:`rate`
+    where::
+
+        X ~ Gamma(concentration, rate)
+        Y = 1 / X ~ InverseGamma(concentration, rate)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> m = InverseGamma(torch.tensor([2.0]), torch.tensor([3.0]))
+        >>> m.sample()
+        tensor([ 1.2953])
+
+    Args:
+        concentration (float or Tensor): shape parameter of the distribution
+            (often referred to as alpha)
+        rate (float or Tensor): rate = 1 / scale of the distribution
+            (often referred to as beta)
+    """
+    arg_constraints = {
+        "concentration": constraints.positive,
+        "rate": constraints.positive,
+    }
+    support = constraints.positive
+    has_rsample = True
+
+    def __init__(self, concentration, rate, validate_args=None):
+        base_dist = Gamma(concentration, rate, validate_args=validate_args)
+        neg_one = -base_dist.rate.new_ones(())
+        super().__init__(
+            base_dist, PowerTransform(neg_one), validate_args=validate_args
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(InverseGamma, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def concentration(self):
+        return self.base_dist.concentration
+
+    @property
+    def rate(self):
+        return self.base_dist.rate
+
+    @property
+    def mean(self):
+        result = self.rate / (self.concentration - 1)
+        return torch.where(self.concentration > 1, result, torch.inf)
+
+    @property
+    def mode(self):
+        return self.rate / (self.concentration + 1)
+
+    @property
+    def variance(self):
+        result = self.rate.square() / (
+            (self.concentration - 1).square() * (self.concentration - 2)
+        )
+        return torch.where(self.concentration > 2, result, torch.inf)
+
+    def entropy(self):
+        return (
+            self.concentration
+            + self.rate.log()
+            + self.concentration.lgamma()
+            - (1 + self.concentration) * self.concentration.digamma()
+        )
diff --git a/shared/package-files/40/40e8d2a1c3b572284da39f6f4245b1bc814f452c44f5aa73d0a011571d5ccc43/Dublin b/shared/package-files/40/40e8d2a1c3b572284da39f6f4245b1bc814f452c44f5aa73d0a011571d5ccc43/Dublin
new file mode 100644
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diff --git a/shared/package-files/40/40e8d2a1c3b572284da39f6f4245b1bc814f452c44f5aa73d0a011571d5ccc43/Eire b/shared/package-files/40/40e8d2a1c3b572284da39f6f4245b1bc814f452c44f5aa73d0a011571d5ccc43/Eire
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diff --git a/shared/package-files/40/40ebc018d1f1d7f16121f2dec0cede039fea8a89bf18862d7ed3489adb934be8/expatbuilder.py b/shared/package-files/40/40ebc018d1f1d7f16121f2dec0cede039fea8a89bf18862d7ed3489adb934be8/expatbuilder.py
new file mode 100644
index 0000000000000000000000000000000000000000..199c22d0af347e15bf53005f74b5359ce0f0e904
--- /dev/null
+++ b/shared/package-files/40/40ebc018d1f1d7f16121f2dec0cede039fea8a89bf18862d7ed3489adb934be8/expatbuilder.py
@@ -0,0 +1,965 @@
+"""Facility to use the Expat parser to load a minidom instance
+from a string or file.
+
+This avoids all the overhead of SAX and pulldom to gain performance.
+"""
+
+# Warning!
+#
+# This module is tightly bound to the implementation details of the
+# minidom DOM and can't be used with other DOM implementations.  This
+# is due, in part, to a lack of appropriate methods in the DOM (there is
+# no way to create Entity and Notation nodes via the DOM Level 2
+# interface), and for performance.  The latter is the cause of some fairly
+# cryptic code.
+#
+# Performance hacks:
+#
+#   -  .character_data_handler() has an extra case in which continuing
+#      data is appended to an existing Text node; this can be a
+#      speedup since pyexpat can break up character data into multiple
+#      callbacks even though we set the buffer_text attribute on the
+#      parser.  This also gives us the advantage that we don't need a
+#      separate normalization pass.
+#
+#   -  Determining that a node exists is done using an identity comparison
+#      with None rather than a truth test; this avoids searching for and
+#      calling any methods on the node object if it exists.  (A rather
+#      nice speedup is achieved this way as well!)
+
+from xml.dom import xmlbuilder, minidom, Node
+from xml.dom import EMPTY_NAMESPACE, EMPTY_PREFIX, XMLNS_NAMESPACE
+from xml.parsers import expat
+from xml.dom.minidom import _append_child, _set_attribute_node
+from xml.dom.NodeFilter import NodeFilter
+
+TEXT_NODE = Node.TEXT_NODE
+CDATA_SECTION_NODE = Node.CDATA_SECTION_NODE
+DOCUMENT_NODE = Node.DOCUMENT_NODE
+
+FILTER_ACCEPT = xmlbuilder.DOMBuilderFilter.FILTER_ACCEPT
+FILTER_REJECT = xmlbuilder.DOMBuilderFilter.FILTER_REJECT
+FILTER_SKIP = xmlbuilder.DOMBuilderFilter.FILTER_SKIP
+FILTER_INTERRUPT = xmlbuilder.DOMBuilderFilter.FILTER_INTERRUPT
+
+theDOMImplementation = minidom.getDOMImplementation()
+
+# Expat typename -> TypeInfo
+_typeinfo_map = {
+    "CDATA":    minidom.TypeInfo(None, "cdata"),
+    "ENUM":     minidom.TypeInfo(None, "enumeration"),
+    "ENTITY":   minidom.TypeInfo(None, "entity"),
+    "ENTITIES": minidom.TypeInfo(None, "entities"),
+    "ID":       minidom.TypeInfo(None, "id"),
+    "IDREF":    minidom.TypeInfo(None, "idref"),
+    "IDREFS":   minidom.TypeInfo(None, "idrefs"),
+    "NMTOKEN":  minidom.TypeInfo(None, "nmtoken"),
+    "NMTOKENS": minidom.TypeInfo(None, "nmtokens"),
+    }
+
+class ElementInfo(object):
+    __slots__ = '_attr_info', '_model', 'tagName'
+
+    def __init__(self, tagName, model=None):
+        self.tagName = tagName
+        self._attr_info = []
+        self._model = model
+
+    def __getstate__(self):
+        return self._attr_info, self._model, self.tagName
+
+    def __setstate__(self, state):
+        self._attr_info, self._model, self.tagName = state
+
+    def getAttributeType(self, aname):
+        for info in self._attr_info:
+            if info[1] == aname:
+                t = info[-2]
+                if t[0] == "(":
+                    return _typeinfo_map["ENUM"]
+                else:
+                    return _typeinfo_map[info[-2]]
+        return minidom._no_type
+
+    def getAttributeTypeNS(self, namespaceURI, localName):
+        return minidom._no_type
+
+    def isElementContent(self):
+        if self._model:
+            type = self._model[0]
+            return type not in (expat.model.XML_CTYPE_ANY,
+                                expat.model.XML_CTYPE_MIXED)
+        else:
+            return False
+
+    def isEmpty(self):
+        if self._model:
+            return self._model[0] == expat.model.XML_CTYPE_EMPTY
+        else:
+            return False
+
+    def isId(self, aname):
+        for info in self._attr_info:
+            if info[1] == aname:
+                return info[-2] == "ID"
+        return False
+
+    def isIdNS(self, euri, ename, auri, aname):
+        # not sure this is meaningful
+        return self.isId((auri, aname))
+
+def _intern(builder, s):
+    return builder._intern_setdefault(s, s)
+
+def _parse_ns_name(builder, name):
+    assert ' ' in name
+    parts = name.split(' ')
+    intern = builder._intern_setdefault
+    if len(parts) == 3:
+        uri, localname, prefix = parts
+        prefix = intern(prefix, prefix)
+        qname = "%s:%s" % (prefix, localname)
+        qname = intern(qname, qname)
+        localname = intern(localname, localname)
+    elif len(parts) == 2:
+        uri, localname = parts
+        prefix = EMPTY_PREFIX
+        qname = localname = intern(localname, localname)
+    else:
+        raise ValueError("Unsupported syntax: spaces in URIs not supported: %r" % name)
+    return intern(uri, uri), localname, prefix, qname
+
+
+class ExpatBuilder:
+    """Document builder that uses Expat to build a ParsedXML.DOM document
+    instance."""
+
+    def __init__(self, options=None):
+        if options is None:
+            options = xmlbuilder.Options()
+        self._options = options
+        if self._options.filter is not None:
+            self._filter = FilterVisibilityController(self._options.filter)
+        else:
+            self._filter = None
+            # This *really* doesn't do anything in this case, so
+            # override it with something fast & minimal.
+            self._finish_start_element = id
+        self._parser = None
+        self.reset()
+
+    def createParser(self):
+        """Create a new parser object."""
+        return expat.ParserCreate()
+
+    def getParser(self):
+        """Return the parser object, creating a new one if needed."""
+        if not self._parser:
+            self._parser = self.createParser()
+            self._intern_setdefault = self._parser.intern.setdefault
+            self._parser.buffer_text = True
+            self._parser.ordered_attributes = True
+            self._parser.specified_attributes = True
+            self.install(self._parser)
+        return self._parser
+
+    def reset(self):
+        """Free all data structures used during DOM construction."""
+        self.document = theDOMImplementation.createDocument(
+            EMPTY_NAMESPACE, None, None)
+        self.curNode = self.document
+        self._elem_info = self.document._elem_info
+        self._cdata = False
+
+    def install(self, parser):
+        """Install the callbacks needed to build the DOM into the parser."""
+        # This creates circular references!
+        parser.StartDoctypeDeclHandler = self.start_doctype_decl_handler
+        parser.StartElementHandler = self.first_element_handler
+        parser.EndElementHandler = self.end_element_handler
+        parser.ProcessingInstructionHandler = self.pi_handler
+        if self._options.entities:
+            parser.EntityDeclHandler = self.entity_decl_handler
+        parser.NotationDeclHandler = self.notation_decl_handler
+        if self._options.comments:
+            parser.CommentHandler = self.comment_handler
+        if self._options.cdata_sections:
+            parser.StartCdataSectionHandler = self.start_cdata_section_handler
+            parser.EndCdataSectionHandler = self.end_cdata_section_handler
+            parser.CharacterDataHandler = self.character_data_handler_cdata
+        else:
+            parser.CharacterDataHandler = self.character_data_handler
+        parser.ExternalEntityRefHandler = self.external_entity_ref_handler
+        parser.XmlDeclHandler = self.xml_decl_handler
+        parser.ElementDeclHandler = self.element_decl_handler
+        parser.AttlistDeclHandler = self.attlist_decl_handler
+
+    def parseFile(self, file):
+        """Parse a document from a file object, returning the document
+        node."""
+        parser = self.getParser()
+        first_buffer = True
+        try:
+            while 1:
+                buffer = file.read(16*1024)
+                if not buffer:
+                    break
+                parser.Parse(buffer, False)
+                if first_buffer and self.document.documentElement:
+                    self._setup_subset(buffer)
+                first_buffer = False
+            parser.Parse(b"", True)
+        except ParseEscape:
+            pass
+        doc = self.document
+        self.reset()
+        self._parser = None
+        return doc
+
+    def parseString(self, string):
+        """Parse a document from a string, returning the document node."""
+        parser = self.getParser()
+        try:
+            parser.Parse(string, True)
+            self._setup_subset(string)
+        except ParseEscape:
+            pass
+        doc = self.document
+        self.reset()
+        self._parser = None
+        return doc
+
+    def _setup_subset(self, buffer):
+        """Load the internal subset if there might be one."""
+        if self.document.doctype:
+            extractor = InternalSubsetExtractor()
+            extractor.parseString(buffer)
+            subset = extractor.getSubset()
+            self.document.doctype.internalSubset = subset
+
+    def start_doctype_decl_handler(self, doctypeName, systemId, publicId,
+                                   has_internal_subset):
+        doctype = self.document.implementation.createDocumentType(
+            doctypeName, publicId, systemId)
+        doctype.ownerDocument = self.document
+        _append_child(self.document, doctype)
+        self.document.doctype = doctype
+        if self._filter and self._filter.acceptNode(doctype) == FILTER_REJECT:
+            self.document.doctype = None
+            del self.document.childNodes[-1]
+            doctype = None
+            self._parser.EntityDeclHandler = None
+            self._parser.NotationDeclHandler = None
+        if has_internal_subset:
+            if doctype is not None:
+                doctype.entities._seq = []
+                doctype.notations._seq = []
+            self._parser.CommentHandler = None
+            self._parser.ProcessingInstructionHandler = None
+            self._parser.EndDoctypeDeclHandler = self.end_doctype_decl_handler
+
+    def end_doctype_decl_handler(self):
+        if self._options.comments:
+            self._parser.CommentHandler = self.comment_handler
+        self._parser.ProcessingInstructionHandler = self.pi_handler
+        if not (self._elem_info or self._filter):
+            self._finish_end_element = id
+
+    def pi_handler(self, target, data):
+        node = self.document.createProcessingInstruction(target, data)
+        _append_child(self.curNode, node)
+        if self._filter and self._filter.acceptNode(node) == FILTER_REJECT:
+            self.curNode.removeChild(node)
+
+    def character_data_handler_cdata(self, data):
+        childNodes = self.curNode.childNodes
+        if self._cdata:
+            if (  self._cdata_continue
+                  and childNodes[-1].nodeType == CDATA_SECTION_NODE):
+                childNodes[-1].appendData(data)
+                return
+            node = self.document.createCDATASection(data)
+            self._cdata_continue = True
+        elif childNodes and childNodes[-1].nodeType == TEXT_NODE:
+            node = childNodes[-1]
+            value = node.data + data
+            node.data = value
+            return
+        else:
+            node = minidom.Text()
+            node.data = data
+            node.ownerDocument = self.document
+        _append_child(self.curNode, node)
+
+    def character_data_handler(self, data):
+        childNodes = self.curNode.childNodes
+        if childNodes and childNodes[-1].nodeType == TEXT_NODE:
+            node = childNodes[-1]
+            node.data = node.data + data
+            return
+        node = minidom.Text()
+        node.data = node.data + data
+        node.ownerDocument = self.document
+        _append_child(self.curNode, node)
+
+    def entity_decl_handler(self, entityName, is_parameter_entity, value,
+                            base, systemId, publicId, notationName):
+        if is_parameter_entity:
+            # we don't care about parameter entities for the DOM
+            return
+        if not self._options.entities:
+            return
+        node = self.document._create_entity(entityName, publicId,
+                                            systemId, notationName)
+        if value is not None:
+            # internal entity
+            # node *should* be readonly, but we'll cheat
+            child = self.document.createTextNode(value)
+            node.childNodes.append(child)
+        self.document.doctype.entities._seq.append(node)
+        if self._filter and self._filter.acceptNode(node) == FILTER_REJECT:
+            del self.document.doctype.entities._seq[-1]
+
+    def notation_decl_handler(self, notationName, base, systemId, publicId):
+        node = self.document._create_notation(notationName, publicId, systemId)
+        self.document.doctype.notations._seq.append(node)
+        if self._filter and self._filter.acceptNode(node) == FILTER_ACCEPT:
+            del self.document.doctype.notations._seq[-1]
+
+    def comment_handler(self, data):
+        node = self.document.createComment(data)
+        _append_child(self.curNode, node)
+        if self._filter and self._filter.acceptNode(node) == FILTER_REJECT:
+            self.curNode.removeChild(node)
+
+    def start_cdata_section_handler(self):
+        self._cdata = True
+        self._cdata_continue = False
+
+    def end_cdata_section_handler(self):
+        self._cdata = False
+        self._cdata_continue = False
+
+    def external_entity_ref_handler(self, context, base, systemId, publicId):
+        return 1
+
+    def first_element_handler(self, name, attributes):
+        if self._filter is None and not self._elem_info:
+            self._finish_end_element = id
+        self.getParser().StartElementHandler = self.start_element_handler
+        self.start_element_handler(name, attributes)
+
+    def start_element_handler(self, name, attributes):
+        node = self.document.createElement(name)
+        _append_child(self.curNode, node)
+        self.curNode = node
+
+        if attributes:
+            for i in range(0, len(attributes), 2):
+                a = minidom.Attr(attributes[i], EMPTY_NAMESPACE,
+                                 None, EMPTY_PREFIX)
+                value = attributes[i+1]
+                a.value = value
+                a.ownerDocument = self.document
+                _set_attribute_node(node, a)
+
+        if node is not self.document.documentElement:
+            self._finish_start_element(node)
+
+    def _finish_start_element(self, node):
+        if self._filter:
+            # To be general, we'd have to call isSameNode(), but this
+            # is sufficient for minidom:
+            if node is self.document.documentElement:
+                return
+            filt = self._filter.startContainer(node)
+            if filt == FILTER_REJECT:
+                # ignore this node & all descendents
+                Rejecter(self)
+            elif filt == FILTER_SKIP:
+                # ignore this node, but make it's children become
+                # children of the parent node
+                Skipper(self)
+            else:
+                return
+            self.curNode = node.parentNode
+            node.parentNode.removeChild(node)
+            node.unlink()
+
+    # If this ever changes, Namespaces.end_element_handler() needs to
+    # be changed to match.
+    #
+    def end_element_handler(self, name):
+        curNode = self.curNode
+        self.curNode = curNode.parentNode
+        self._finish_end_element(curNode)
+
+    def _finish_end_element(self, curNode):
+        info = self._elem_info.get(curNode.tagName)
+        if info:
+            self._handle_white_text_nodes(curNode, info)
+        if self._filter:
+            if curNode is self.document.documentElement:
+                return
+            if self._filter.acceptNode(curNode) == FILTER_REJECT:
+                self.curNode.removeChild(curNode)
+                curNode.unlink()
+
+    def _handle_white_text_nodes(self, node, info):
+        if (self._options.whitespace_in_element_content
+            or not info.isElementContent()):
+            return
+
+        # We have element type information and should remove ignorable
+        # whitespace; identify for text nodes which contain only
+        # whitespace.
+        L = []
+        for child in node.childNodes:
+            if child.nodeType == TEXT_NODE and not child.data.strip():
+                L.append(child)
+
+        # Remove ignorable whitespace from the tree.
+        for child in L:
+            node.removeChild(child)
+
+    def element_decl_handler(self, name, model):
+        info = self._elem_info.get(name)
+        if info is None:
+            self._elem_info[name] = ElementInfo(name, model)
+        else:
+            assert info._model is None
+            info._model = model
+
+    def attlist_decl_handler(self, elem, name, type, default, required):
+        info = self._elem_info.get(elem)
+        if info is None:
+            info = ElementInfo(elem)
+            self._elem_info[elem] = info
+        info._attr_info.append(
+            [None, name, None, None, default, 0, type, required])
+
+    def xml_decl_handler(self, version, encoding, standalone):
+        self.document.version = version
+        self.document.encoding = encoding
+        # This is still a little ugly, thanks to the pyexpat API. ;-(
+        if standalone >= 0:
+            if standalone:
+                self.document.standalone = True
+            else:
+                self.document.standalone = False
+
+
+# Don't include FILTER_INTERRUPT, since that's checked separately
+# where allowed.
+_ALLOWED_FILTER_RETURNS = (FILTER_ACCEPT, FILTER_REJECT, FILTER_SKIP)
+
+class FilterVisibilityController(object):
+    """Wrapper around a DOMBuilderFilter which implements the checks
+    to make the whatToShow filter attribute work."""
+
+    __slots__ = 'filter',
+
+    def __init__(self, filter):
+        self.filter = filter
+
+    def startContainer(self, node):
+        mask = self._nodetype_mask[node.nodeType]
+        if self.filter.whatToShow & mask:
+            val = self.filter.startContainer(node)
+            if val == FILTER_INTERRUPT:
+                raise ParseEscape
+            if val not in _ALLOWED_FILTER_RETURNS:
+                raise ValueError(
+                      "startContainer() returned illegal value: " + repr(val))
+            return val
+        else:
+            return FILTER_ACCEPT
+
+    def acceptNode(self, node):
+        mask = self._nodetype_mask[node.nodeType]
+        if self.filter.whatToShow & mask:
+            val = self.filter.acceptNode(node)
+            if val == FILTER_INTERRUPT:
+                raise ParseEscape
+            if val == FILTER_SKIP:
+                # move all child nodes to the parent, and remove this node
+                parent = node.parentNode
+                for child in node.childNodes[:]:
+                    parent.appendChild(child)
+                # node is handled by the caller
+                return FILTER_REJECT
+            if val not in _ALLOWED_FILTER_RETURNS:
+                raise ValueError(
+                      "acceptNode() returned illegal value: " + repr(val))
+            return val
+        else:
+            return FILTER_ACCEPT
+
+    _nodetype_mask = {
+        Node.ELEMENT_NODE:                NodeFilter.SHOW_ELEMENT,
+        Node.ATTRIBUTE_NODE:              NodeFilter.SHOW_ATTRIBUTE,
+        Node.TEXT_NODE:                   NodeFilter.SHOW_TEXT,
+        Node.CDATA_SECTION_NODE:          NodeFilter.SHOW_CDATA_SECTION,
+        Node.ENTITY_REFERENCE_NODE:       NodeFilter.SHOW_ENTITY_REFERENCE,
+        Node.ENTITY_NODE:                 NodeFilter.SHOW_ENTITY,
+        Node.PROCESSING_INSTRUCTION_NODE: NodeFilter.SHOW_PROCESSING_INSTRUCTION,
+        Node.COMMENT_NODE:                NodeFilter.SHOW_COMMENT,
+        Node.DOCUMENT_NODE:               NodeFilter.SHOW_DOCUMENT,
+        Node.DOCUMENT_TYPE_NODE:          NodeFilter.SHOW_DOCUMENT_TYPE,
+        Node.DOCUMENT_FRAGMENT_NODE:      NodeFilter.SHOW_DOCUMENT_FRAGMENT,
+        Node.NOTATION_NODE:               NodeFilter.SHOW_NOTATION,
+        }
+
+
+class FilterCrutch(object):
+    __slots__ = '_builder', '_level', '_old_start', '_old_end'
+
+    def __init__(self, builder):
+        self._level = 0
+        self._builder = builder
+        parser = builder._parser
+        self._old_start = parser.StartElementHandler
+        self._old_end = parser.EndElementHandler
+        parser.StartElementHandler = self.start_element_handler
+        parser.EndElementHandler = self.end_element_handler
+
+class Rejecter(FilterCrutch):
+    __slots__ = ()
+
+    def __init__(self, builder):
+        FilterCrutch.__init__(self, builder)
+        parser = builder._parser
+        for name in ("ProcessingInstructionHandler",
+                     "CommentHandler",
+                     "CharacterDataHandler",
+                     "StartCdataSectionHandler",
+                     "EndCdataSectionHandler",
+                     "ExternalEntityRefHandler",
+                     ):
+            setattr(parser, name, None)
+
+    def start_element_handler(self, *args):
+        self._level = self._level + 1
+
+    def end_element_handler(self, *args):
+        if self._level == 0:
+            # restore the old handlers
+            parser = self._builder._parser
+            self._builder.install(parser)
+            parser.StartElementHandler = self._old_start
+            parser.EndElementHandler = self._old_end
+        else:
+            self._level = self._level - 1
+
+class Skipper(FilterCrutch):
+    __slots__ = ()
+
+    def start_element_handler(self, *args):
+        node = self._builder.curNode
+        self._old_start(*args)
+        if self._builder.curNode is not node:
+            self._level = self._level + 1
+
+    def end_element_handler(self, *args):
+        if self._level == 0:
+            # We're popping back out of the node we're skipping, so we
+            # shouldn't need to do anything but reset the handlers.
+            self._builder._parser.StartElementHandler = self._old_start
+            self._builder._parser.EndElementHandler = self._old_end
+            self._builder = None
+        else:
+            self._level = self._level - 1
+            self._old_end(*args)
+
+
+# framework document used by the fragment builder.
+# Takes a string for the doctype, subset string, and namespace attrs string.
+
+_FRAGMENT_BUILDER_INTERNAL_SYSTEM_ID = \
+    "http://xml.python.org/entities/fragment-builder/internal"
+
+_FRAGMENT_BUILDER_TEMPLATE = (
+    '''\
+<!DOCTYPE wrapper
+  %%s [
+  <!ENTITY fragment-builder-internal
+    SYSTEM "%s">
+%%s
+]>
+<wrapper %%s
+>&fragment-builder-internal;</wrapper>'''
+    % _FRAGMENT_BUILDER_INTERNAL_SYSTEM_ID)
+
+
+class FragmentBuilder(ExpatBuilder):
+    """Builder which constructs document fragments given XML source
+    text and a context node.
+
+    The context node is expected to provide information about the
+    namespace declarations which are in scope at the start of the
+    fragment.
+    """
+
+    def __init__(self, context, options=None):
+        if context.nodeType == DOCUMENT_NODE:
+            self.originalDocument = context
+            self.context = context
+        else:
+            self.originalDocument = context.ownerDocument
+            self.context = context
+        ExpatBuilder.__init__(self, options)
+
+    def reset(self):
+        ExpatBuilder.reset(self)
+        self.fragment = None
+
+    def parseFile(self, file):
+        """Parse a document fragment from a file object, returning the
+        fragment node."""
+        return self.parseString(file.read())
+
+    def parseString(self, string):
+        """Parse a document fragment from a string, returning the
+        fragment node."""
+        self._source = string
+        parser = self.getParser()
+        doctype = self.originalDocument.doctype
+        ident = ""
+        if doctype:
+            subset = doctype.internalSubset or self._getDeclarations()
+            if doctype.publicId:
+                ident = ('PUBLIC "%s" "%s"'
+                         % (doctype.publicId, doctype.systemId))
+            elif doctype.systemId:
+                ident = 'SYSTEM "%s"' % doctype.systemId
+        else:
+            subset = ""
+        nsattrs = self._getNSattrs() # get ns decls from node's ancestors
+        document = _FRAGMENT_BUILDER_TEMPLATE % (ident, subset, nsattrs)
+        try:
+            parser.Parse(document, True)
+        except:
+            self.reset()
+            raise
+        fragment = self.fragment
+        self.reset()
+##         self._parser = None
+        return fragment
+
+    def _getDeclarations(self):
+        """Re-create the internal subset from the DocumentType node.
+
+        This is only needed if we don't already have the
+        internalSubset as a string.
+        """
+        doctype = self.context.ownerDocument.doctype
+        s = ""
+        if doctype:
+            for i in range(doctype.notations.length):
+                notation = doctype.notations.item(i)
+                if s:
+                    s = s + "\n  "
+                s = "%s<!NOTATION %s" % (s, notation.nodeName)
+                if notation.publicId:
+                    s = '%s PUBLIC "%s"\n             "%s">' \
+                        % (s, notation.publicId, notation.systemId)
+                else:
+                    s = '%s SYSTEM "%s">' % (s, notation.systemId)
+            for i in range(doctype.entities.length):
+                entity = doctype.entities.item(i)
+                if s:
+                    s = s + "\n  "
+                s = "%s<!ENTITY %s" % (s, entity.nodeName)
+                if entity.publicId:
+                    s = '%s PUBLIC "%s"\n             "%s"' \
+                        % (s, entity.publicId, entity.systemId)
+                elif entity.systemId:
+                    s = '%s SYSTEM "%s"' % (s, entity.systemId)
+                else:
+                    s = '%s "%s"' % (s, entity.firstChild.data)
+                if entity.notationName:
+                    s = "%s NOTATION %s" % (s, entity.notationName)
+                s = s + ">"
+        return s
+
+    def _getNSattrs(self):
+        return ""
+
+    def external_entity_ref_handler(self, context, base, systemId, publicId):
+        if systemId == _FRAGMENT_BUILDER_INTERNAL_SYSTEM_ID:
+            # this entref is the one that we made to put the subtree
+            # in; all of our given input is parsed in here.
+            old_document = self.document
+            old_cur_node = self.curNode
+            parser = self._parser.ExternalEntityParserCreate(context)
+            # put the real document back, parse into the fragment to return
+            self.document = self.originalDocument
+            self.fragment = self.document.createDocumentFragment()
+            self.curNode = self.fragment
+            try:
+                parser.Parse(self._source, True)
+            finally:
+                self.curNode = old_cur_node
+                self.document = old_document
+                self._source = None
+            return -1
+        else:
+            return ExpatBuilder.external_entity_ref_handler(
+                self, context, base, systemId, publicId)
+
+
+class Namespaces:
+    """Mix-in class for builders; adds support for namespaces."""
+
+    def _initNamespaces(self):
+        # list of (prefix, uri) ns declarations.  Namespace attrs are
+        # constructed from this and added to the element's attrs.
+        self._ns_ordered_prefixes = []
+
+    def createParser(self):
+        """Create a new namespace-handling parser."""
+        parser = expat.ParserCreate(namespace_separator=" ")
+        parser.namespace_prefixes = True
+        return parser
+
+    def install(self, parser):
+        """Insert the namespace-handlers onto the parser."""
+        ExpatBuilder.install(self, parser)
+        if self._options.namespace_declarations:
+            parser.StartNamespaceDeclHandler = (
+                self.start_namespace_decl_handler)
+
+    def start_namespace_decl_handler(self, prefix, uri):
+        """Push this namespace declaration on our storage."""
+        self._ns_ordered_prefixes.append((prefix, uri))
+
+    def start_element_handler(self, name, attributes):
+        if ' ' in name:
+            uri, localname, prefix, qname = _parse_ns_name(self, name)
+        else:
+            uri = EMPTY_NAMESPACE
+            qname = name
+            localname = None
+            prefix = EMPTY_PREFIX
+        node = minidom.Element(qname, uri, prefix, localname)
+        node.ownerDocument = self.document
+        _append_child(self.curNode, node)
+        self.curNode = node
+
+        if self._ns_ordered_prefixes:
+            for prefix, uri in self._ns_ordered_prefixes:
+                if prefix:
+                    a = minidom.Attr(_intern(self, 'xmlns:' + prefix),
+                                     XMLNS_NAMESPACE, prefix, "xmlns")
+                else:
+                    a = minidom.Attr("xmlns", XMLNS_NAMESPACE,
+                                     "xmlns", EMPTY_PREFIX)
+                a.value = uri
+                a.ownerDocument = self.document
+                _set_attribute_node(node, a)
+            del self._ns_ordered_prefixes[:]
+
+        if attributes:
+            node._ensure_attributes()
+            _attrs = node._attrs
+            _attrsNS = node._attrsNS
+            for i in range(0, len(attributes), 2):
+                aname = attributes[i]
+                value = attributes[i+1]
+                if ' ' in aname:
+                    uri, localname, prefix, qname = _parse_ns_name(self, aname)
+                    a = minidom.Attr(qname, uri, localname, prefix)
+                    _attrs[qname] = a
+                    _attrsNS[(uri, localname)] = a
+                else:
+                    a = minidom.Attr(aname, EMPTY_NAMESPACE,
+                                     aname, EMPTY_PREFIX)
+                    _attrs[aname] = a
+                    _attrsNS[(EMPTY_NAMESPACE, aname)] = a
+                a.ownerDocument = self.document
+                a.value = value
+                a.ownerElement = node
+
+    if __debug__:
+        # This only adds some asserts to the original
+        # end_element_handler(), so we only define this when -O is not
+        # used.  If changing one, be sure to check the other to see if
+        # it needs to be changed as well.
+        #
+        def end_element_handler(self, name):
+            curNode = self.curNode
+            if ' ' in name:
+                uri, localname, prefix, qname = _parse_ns_name(self, name)
+                assert (curNode.namespaceURI == uri
+                        and curNode.localName == localname
+                        and curNode.prefix == prefix), \
+                        "element stack messed up! (namespace)"
+            else:
+                assert curNode.nodeName == name, \
+                       "element stack messed up - bad nodeName"
+                assert curNode.namespaceURI == EMPTY_NAMESPACE, \
+                       "element stack messed up - bad namespaceURI"
+            self.curNode = curNode.parentNode
+            self._finish_end_element(curNode)
+
+
+class ExpatBuilderNS(Namespaces, ExpatBuilder):
+    """Document builder that supports namespaces."""
+
+    def reset(self):
+        ExpatBuilder.reset(self)
+        self._initNamespaces()
+
+
+class FragmentBuilderNS(Namespaces, FragmentBuilder):
+    """Fragment builder that supports namespaces."""
+
+    def reset(self):
+        FragmentBuilder.reset(self)
+        self._initNamespaces()
+
+    def _getNSattrs(self):
+        """Return string of namespace attributes from this element and
+        ancestors."""
+        # XXX This needs to be re-written to walk the ancestors of the
+        # context to build up the namespace information from
+        # declarations, elements, and attributes found in context.
+        # Otherwise we have to store a bunch more data on the DOM
+        # (though that *might* be more reliable -- not clear).
+        attrs = ""
+        context = self.context
+        L = []
+        while context:
+            if hasattr(context, '_ns_prefix_uri'):
+                for prefix, uri in context._ns_prefix_uri.items():
+                    # add every new NS decl from context to L and attrs string
+                    if prefix in L:
+                        continue
+                    L.append(prefix)
+                    if prefix:
+                        declname = "xmlns:" + prefix
+                    else:
+                        declname = "xmlns"
+                    if attrs:
+                        attrs = "%s\n    %s='%s'" % (attrs, declname, uri)
+                    else:
+                        attrs = " %s='%s'" % (declname, uri)
+            context = context.parentNode
+        return attrs
+
+
+class ParseEscape(Exception):
+    """Exception raised to short-circuit parsing in InternalSubsetExtractor."""
+    pass
+
+class InternalSubsetExtractor(ExpatBuilder):
+    """XML processor which can rip out the internal document type subset."""
+
+    subset = None
+
+    def getSubset(self):
+        """Return the internal subset as a string."""
+        return self.subset
+
+    def parseFile(self, file):
+        try:
+            ExpatBuilder.parseFile(self, file)
+        except ParseEscape:
+            pass
+
+    def parseString(self, string):
+        try:
+            ExpatBuilder.parseString(self, string)
+        except ParseEscape:
+            pass
+
+    def install(self, parser):
+        parser.StartDoctypeDeclHandler = self.start_doctype_decl_handler
+        parser.StartElementHandler = self.start_element_handler
+
+    def start_doctype_decl_handler(self, name, publicId, systemId,
+                                   has_internal_subset):
+        if has_internal_subset:
+            parser = self.getParser()
+            self.subset = []
+            parser.DefaultHandler = self.subset.append
+            parser.EndDoctypeDeclHandler = self.end_doctype_decl_handler
+        else:
+            raise ParseEscape()
+
+    def end_doctype_decl_handler(self):
+        s = ''.join(self.subset).replace('\r\n', '\n').replace('\r', '\n')
+        self.subset = s
+        raise ParseEscape()
+
+    def start_element_handler(self, name, attrs):
+        raise ParseEscape()
+
+
+def parse(file, namespaces=True):
+    """Parse a document, returning the resulting Document node.
+
+    'file' may be either a file name or an open file object.
+    """
+    if namespaces:
+        builder = ExpatBuilderNS()
+    else:
+        builder = ExpatBuilder()
+
+    if isinstance(file, str):
+        with open(file, 'rb') as fp:
+            result = builder.parseFile(fp)
+    else:
+        result = builder.parseFile(file)
+    return result
+
+
+def parseString(string, namespaces=True):
+    """Parse a document from a string, returning the resulting
+    Document node.
+    """
+    if namespaces:
+        builder = ExpatBuilderNS()
+    else:
+        builder = ExpatBuilder()
+    return builder.parseString(string)
+
+
+def parseFragment(file, context, namespaces=True):
+    """Parse a fragment of a document, given the context from which it
+    was originally extracted.  context should be the parent of the
+    node(s) which are in the fragment.
+
+    'file' may be either a file name or an open file object.
+    """
+    if namespaces:
+        builder = FragmentBuilderNS(context)
+    else:
+        builder = FragmentBuilder(context)
+
+    if isinstance(file, str):
+        with open(file, 'rb') as fp:
+            result = builder.parseFile(fp)
+    else:
+        result = builder.parseFile(file)
+    return result
+
+
+def parseFragmentString(string, context, namespaces=True):
+    """Parse a fragment of a document from a string, given the context
+    from which it was originally extracted.  context should be the
+    parent of the node(s) which are in the fragment.
+    """
+    if namespaces:
+        builder = FragmentBuilderNS(context)
+    else:
+        builder = FragmentBuilder(context)
+    return builder.parseString(string)
+
+
+def makeBuilder(options):
+    """Create a builder based on an Options object."""
+    if options.namespaces:
+        return ExpatBuilderNS(options)
+    else:
+        return ExpatBuilder(options)
diff --git a/shared/package-files/40/40ed04dbada9e6d9cea66f68c6a4ced6bc25d0a53e98a30ffe3c9d95a87214c3/test_comment.py b/shared/package-files/40/40ed04dbada9e6d9cea66f68c6a4ced6bc25d0a53e98a30ffe3c9d95a87214c3/test_comment.py
new file mode 100644
index 0000000000000000000000000000000000000000..abaeeb86476da183630b58708741bdad2bc5330d
--- /dev/null
+++ b/shared/package-files/40/40ed04dbada9e6d9cea66f68c6a4ced6bc25d0a53e98a30ffe3c9d95a87214c3/test_comment.py
@@ -0,0 +1,227 @@
+"""
+Tests that comments are properly handled during parsing
+for all of the parsers defined in parsers.py
+"""
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("na_values", [None, ["NaN"]])
+def test_comment(all_parsers, na_values):
+    parser = all_parsers
+    data = """A,B,C
+1,2.,4.#hello world
+5.,NaN,10.0
+"""
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", na_values=na_values)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", na_values=na_values)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "read_kwargs", [{}, {"lineterminator": "*"}, {"delim_whitespace": True}]
+)
+def test_line_comment(all_parsers, read_kwargs, request):
+    parser = all_parsers
+    data = """# empty
+A,B,C
+1,2.,4.#hello world
+#ignore this line
+5.,NaN,10.0
+"""
+    warn = None
+    depr_msg = "The 'delim_whitespace' keyword in pd.read_csv is deprecated"
+
+    if read_kwargs.get("delim_whitespace"):
+        data = data.replace(",", " ")
+        warn = FutureWarning
+    elif read_kwargs.get("lineterminator"):
+        data = data.replace("\n", read_kwargs.get("lineterminator"))
+
+    read_kwargs["comment"] = "#"
+    if parser.engine == "pyarrow":
+        if "lineterminator" in read_kwargs:
+            msg = (
+                "The 'lineterminator' option is not supported with the 'pyarrow' engine"
+            )
+        else:
+            msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(
+                warn, match=depr_msg, check_stacklevel=False
+            ):
+                parser.read_csv(StringIO(data), **read_kwargs)
+        return
+    elif parser.engine == "python" and read_kwargs.get("lineterminator"):
+        msg = r"Custom line terminators not supported in python parser \(yet\)"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(
+                warn, match=depr_msg, check_stacklevel=False
+            ):
+                parser.read_csv(StringIO(data), **read_kwargs)
+        return
+
+    with tm.assert_produces_warning(warn, match=depr_msg, check_stacklevel=False):
+        result = parser.read_csv(StringIO(data), **read_kwargs)
+
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_skiprows(all_parsers):
+    parser = all_parsers
+    data = """# empty
+random line
+# second empty line
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # This should ignore the first four lines (including comments).
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", skiprows=4)
+        return
+
+    result = parser.read_csv(StringIO(data), comment="#", skiprows=4)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_header(all_parsers):
+    parser = all_parsers
+    data = """# empty
+# second empty line
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # Header should begin at the second non-comment line.
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", header=1)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", header=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_skiprows_header(all_parsers):
+    parser = all_parsers
+    data = """# empty
+# second empty line
+# third empty line
+X,Y,Z
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # Skiprows should skip the first 4 lines (including comments),
+    # while header should start from the second non-commented line,
+    # starting with line 5.
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", skiprows=4, header=1)
+        return
+
+    result = parser.read_csv(StringIO(data), comment="#", skiprows=4, header=1)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("comment_char", ["#", "~", "&", "^", "*", "@"])
+def test_custom_comment_char(all_parsers, comment_char):
+    parser = all_parsers
+    data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo"
+
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(
+                StringIO(data.replace("#", comment_char)), comment=comment_char
+            )
+        return
+    result = parser.read_csv(
+        StringIO(data.replace("#", comment_char)), comment=comment_char
+    )
+
+    expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("header", ["infer", None])
+def test_comment_first_line(all_parsers, header):
+    # see gh-4623
+    parser = all_parsers
+    data = "# notes\na,b,c\n# more notes\n1,2,3"
+
+    if header is None:
+        expected = DataFrame({0: ["a", "1"], 1: ["b", "2"], 2: ["c", "3"]})
+    else:
+        expected = DataFrame([[1, 2, 3]], columns=["a", "b", "c"])
+
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", header=header)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", header=header)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_char_in_default_value(all_parsers, request):
+    # GH#34002
+    if all_parsers.engine == "c":
+        reason = "see gh-34002: works on the python engine but not the c engine"
+        # NA value containing comment char is interpreted as comment
+        request.applymarker(pytest.mark.xfail(reason=reason, raises=AssertionError))
+    parser = all_parsers
+
+    data = (
+        "# this is a comment\n"
+        "col1,col2,col3,col4\n"
+        "1,2,3,4#inline comment\n"
+        "4,5#,6,10\n"
+        "7,8,#N/A,11\n"
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", na_values="#N/A")
+        return
+    result = parser.read_csv(StringIO(data), comment="#", na_values="#N/A")
+    expected = DataFrame(
+        {
+            "col1": [1, 4, 7],
+            "col2": [2, 5, 8],
+            "col3": [3.0, np.nan, np.nan],
+            "col4": [4.0, np.nan, 11.0],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/40/40ee3dabecfc7638e4088d0d6d78ae68acba0fc4c9f3a6621cceeff073b84b7e/jaixterm b/shared/package-files/40/40ee3dabecfc7638e4088d0d6d78ae68acba0fc4c9f3a6621cceeff073b84b7e/jaixterm
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diff --git a/shared/package-files/40/40ee953dc7ca155840b639a1881ff84578075187fce0ac20eeda55a7fa0d9a90/METADATA b/shared/package-files/40/40ee953dc7ca155840b639a1881ff84578075187fce0ac20eeda55a7fa0d9a90/METADATA
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--- /dev/null
+++ b/shared/package-files/40/40ee953dc7ca155840b639a1881ff84578075187fce0ac20eeda55a7fa0d9a90/METADATA
@@ -0,0 +1,1574 @@
+Metadata-Version: 2.1
+Name: pandas
+Version: 2.3.3
+Summary: Powerful data structures for data analysis, time series, and statistics
+Author-Email: The Pandas Development Team <pandas-dev@python.org>
+License: BSD 3-Clause License
+         
+         Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
+         All rights reserved.
+         
+         Copyright (c) 2011-2023, Open source contributors.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+         * Redistributions of source code must retain the above copyright notice, this
+           list of conditions and the following disclaimer.
+         
+         * Redistributions in binary form must reproduce the above copyright notice,
+           this list of conditions and the following disclaimer in the documentation
+           and/or other materials provided with the distribution.
+         
+         * Neither the name of the copyright holder nor the names of its
+           contributors may be used to endorse or promote products derived from
+           this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+         AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+         IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+         DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+         SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+         OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+         Copyright (c) 2010-2019 Keith Goodman
+         Copyright (c) 2019 Bottleneck Developers
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+             * Redistributions of source code must retain the above copyright notice,
+               this list of conditions and the following disclaimer.
+         
+             * Redistributions in binary form must reproduce the above copyright
+               notice, this list of conditions and the following disclaimer in the
+               documentation and/or other materials provided with the distribution.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+         AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+         IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+         ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+         LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+         CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+         SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+         INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+         CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+         ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+         POSSIBILITY OF SUCH DAMAGE.Copyright 2017- Paul Ganssle <paul@ganssle.io>
+         Copyright 2017- dateutil contributors (see AUTHORS file)
+         
+            Licensed under the Apache License, Version 2.0 (the "License");
+            you may not use this file except in compliance with the License.
+            You may obtain a copy of the License at
+         
+                http://www.apache.org/licenses/LICENSE-2.0
+         
+            Unless required by applicable law or agreed to in writing, software
+            distributed under the License is distributed on an "AS IS" BASIS,
+            WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+            See the License for the specific language governing permissions and
+            limitations under the License.
+         
+         The above license applies to all contributions after 2017-12-01, as well as
+         all contributions that have been re-licensed (see AUTHORS file for the list of
+         contributors who have re-licensed their code).
+         --------------------------------------------------------------------------------
+         dateutil - Extensions to the standard Python datetime module.
+         
+         Copyright (c) 2003-2011 - Gustavo Niemeyer <gustavo@niemeyer.net>
+         Copyright (c) 2012-2014 - Tomi Pieviläinen <tomi.pievilainen@iki.fi>
+         Copyright (c) 2014-2016 - Yaron de Leeuw <me@jarondl.net>
+         Copyright (c) 2015-     - Paul Ganssle <paul@ganssle.io>
+         Copyright (c) 2015-     - dateutil contributors (see AUTHORS file)
+         
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+             * Redistributions of source code must retain the above copyright notice,
+               this list of conditions and the following disclaimer.
+             * Redistributions in binary form must reproduce the above copyright notice,
+               this list of conditions and the following disclaimer in the documentation
+               and/or other materials provided with the distribution.
+             * Neither the name of the copyright holder nor the names of its
+               contributors may be used to endorse or promote products derived from
+               this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+         "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+         LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+         A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+         CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+         EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+         PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+         PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+         LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+         NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+         SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+         
+         The above BSD License Applies to all code, even that also covered by Apache 2.0.# MIT License
+         
+         Copyright (c) 2019 Hadley Wickham; RStudio; and Evan Miller
+         
+         Permission is hereby granted, free of charge, to any person obtaining a copy
+         of this software and associated documentation files (the "Software"), to deal
+         in the Software without restriction, including without limitation the rights
+         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+         copies of the Software, and to permit persons to whom the Software is
+         furnished to do so, subject to the following conditions:
+         
+         The above copyright notice and this permission notice shall be included in all
+         copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+         SOFTWARE.
+         Based on http://opensource.org/licenses/MIT
+         
+         This is a template. Complete and ship as file LICENSE the following 2
+         lines (only)
+         
+         YEAR:
+         COPYRIGHT HOLDER:
+         
+         and specify as
+         
+         License: MIT + file LICENSE
+         
+         Copyright (c) <YEAR>, <COPYRIGHT HOLDER>
+         
+         Permission is hereby granted, free of charge, to any person obtaining
+         a copy of this software and associated documentation files (the
+         "Software"), to deal in the Software without restriction, including
+         without limitation the rights to use, copy, modify, merge, publish,
+         distribute, sublicense, and/or sell copies of the Software, and to
+         permit persons to whom the Software is furnished to do so, subject to
+         the following conditions:
+         
+         The above copyright notice and this permission notice shall be
+         included in all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+         NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+         LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+         OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+         WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+         The MIT License
+         
+         Copyright (c) 2008-     Attractive Chaos <attractor@live.co.uk>
+         
+         Permission is hereby granted, free of charge, to any person obtaining
+         a copy of this software and associated documentation files (the
+         "Software"), to deal in the Software without restriction, including
+         without limitation the rights to use, copy, modify, merge, publish,
+         distribute, sublicense, and/or sell copies of the Software, and to
+         permit persons to whom the Software is furnished to do so, subject to
+         the following conditions:
+         
+         The above copyright notice and this permission notice shall be
+         included in all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+         NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+         BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+         ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+         CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+         SOFTWARE.musl as a whole is licensed under the following standard MIT license:
+         
+         ----------------------------------------------------------------------
+         Copyright © 2005-2020 Rich Felker, et al.
+         
+         Permission is hereby granted, free of charge, to any person obtaining
+         a copy of this software and associated documentation files (the
+         "Software"), to deal in the Software without restriction, including
+         without limitation the rights to use, copy, modify, merge, publish,
+         distribute, sublicense, and/or sell copies of the Software, and to
+         permit persons to whom the Software is furnished to do so, subject to
+         the following conditions:
+         
+         The above copyright notice and this permission notice shall be
+         included in all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+         IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+         CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+         TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+         SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+         ----------------------------------------------------------------------
+         
+         Authors/contributors include:
+         
+         A. Wilcox
+         Ada Worcester
+         Alex Dowad
+         Alex Suykov
+         Alexander Monakov
+         Andre McCurdy
+         Andrew Kelley
+         Anthony G. Basile
+         Aric Belsito
+         Arvid Picciani
+         Bartosz Brachaczek
+         Benjamin Peterson
+         Bobby Bingham
+         Boris Brezillon
+         Brent Cook
+         Chris Spiegel
+         Clément Vasseur
+         Daniel Micay
+         Daniel Sabogal
+         Daurnimator
+         David Carlier
+         David Edelsohn
+         Denys Vlasenko
+         Dmitry Ivanov
+         Dmitry V. Levin
+         Drew DeVault
+         Emil Renner Berthing
+         Fangrui Song
+         Felix Fietkau
+         Felix Janda
+         Gianluca Anzolin
+         Hauke Mehrtens
+         He X
+         Hiltjo Posthuma
+         Isaac Dunham
+         Jaydeep Patil
+         Jens Gustedt
+         Jeremy Huntwork
+         Jo-Philipp Wich
+         Joakim Sindholt
+         John Spencer
+         Julien Ramseier
+         Justin Cormack
+         Kaarle Ritvanen
+         Khem Raj
+         Kylie McClain
+         Leah Neukirchen
+         Luca Barbato
+         Luka Perkov
+         M Farkas-Dyck (Strake)
+         Mahesh Bodapati
+         Markus Wichmann
+         Masanori Ogino
+         Michael Clark
+         Michael Forney
+         Mikhail Kremnyov
+         Natanael Copa
+         Nicholas J. Kain
+         orc
+         Pascal Cuoq
+         Patrick Oppenlander
+         Petr Hosek
+         Petr Skocik
+         Pierre Carrier
+         Reini Urban
+         Rich Felker
+         Richard Pennington
+         Ryan Fairfax
+         Samuel Holland
+         Segev Finer
+         Shiz
+         sin
+         Solar Designer
+         Stefan Kristiansson
+         Stefan O'Rear
+         Szabolcs Nagy
+         Timo Teräs
+         Trutz Behn
+         Valentin Ochs
+         Will Dietz
+         William Haddon
+         William Pitcock
+         
+         Portions of this software are derived from third-party works licensed
+         under terms compatible with the above MIT license:
+         
+         The TRE regular expression implementation (src/regex/reg* and
+         src/regex/tre*) is Copyright © 2001-2008 Ville Laurikari and licensed
+         under a 2-clause BSD license (license text in the source files). The
+         included version has been heavily modified by Rich Felker in 2012, in
+         the interests of size, simplicity, and namespace cleanliness.
+         
+         Much of the math library code (src/math/* and src/complex/*) is
+         Copyright © 1993,2004 Sun Microsystems or
+         Copyright © 2003-2011 David Schultz or
+         Copyright © 2003-2009 Steven G. Kargl or
+         Copyright © 2003-2009 Bruce D. Evans or
+         Copyright © 2008 Stephen L. Moshier or
+         Copyright © 2017-2018 Arm Limited
+         and labelled as such in comments in the individual source files. All
+         have been licensed under extremely permissive terms.
+         
+         The ARM memcpy code (src/string/arm/memcpy.S) is Copyright © 2008
+         The Android Open Source Project and is licensed under a two-clause BSD
+         license. It was taken from Bionic libc, used on Android.
+         
+         The AArch64 memcpy and memset code (src/string/aarch64/*) are
+         Copyright © 1999-2019, Arm Limited.
+         
+         The implementation of DES for crypt (src/crypt/crypt_des.c) is
+         Copyright © 1994 David Burren. It is licensed under a BSD license.
+         
+         The implementation of blowfish crypt (src/crypt/crypt_blowfish.c) was
+         originally written by Solar Designer and placed into the public
+         domain. The code also comes with a fallback permissive license for use
+         in jurisdictions that may not recognize the public domain.
+         
+         The smoothsort implementation (src/stdlib/qsort.c) is Copyright © 2011
+         Valentin Ochs and is licensed under an MIT-style license.
+         
+         The x86_64 port was written by Nicholas J. Kain and is licensed under
+         the standard MIT terms.
+         
+         The mips and microblaze ports were originally written by Richard
+         Pennington for use in the ellcc project. The original code was adapted
+         by Rich Felker for build system and code conventions during upstream
+         integration. It is licensed under the standard MIT terms.
+         
+         The mips64 port was contributed by Imagination Technologies and is
+         licensed under the standard MIT terms.
+         
+         The powerpc port was also originally written by Richard Pennington,
+         and later supplemented and integrated by John Spencer. It is licensed
+         under the standard MIT terms.
+         
+         All other files which have no copyright comments are original works
+         produced specifically for use as part of this library, written either
+         by Rich Felker, the main author of the library, or by one or more
+         contibutors listed above. Details on authorship of individual files
+         can be found in the git version control history of the project. The
+         omission of copyright and license comments in each file is in the
+         interest of source tree size.
+         
+         In addition, permission is hereby granted for all public header files
+         (include/* and arch/*/bits/*) and crt files intended to be linked into
+         applications (crt/*, ldso/dlstart.c, and arch/*/crt_arch.h) to omit
+         the copyright notice and permission notice otherwise required by the
+         license, and to use these files without any requirement of
+         attribution. These files include substantial contributions from:
+         
+         Bobby Bingham
+         John Spencer
+         Nicholas J. Kain
+         Rich Felker
+         Richard Pennington
+         Stefan Kristiansson
+         Szabolcs Nagy
+         
+         all of whom have explicitly granted such permission.
+         
+         This file previously contained text expressing a belief that most of
+         the files covered by the above exception were sufficiently trivial not
+         to be subject to copyright, resulting in confusion over whether it
+         negated the permissions granted in the license. In the spirit of
+         permissive licensing, and of not having licensing issues being an
+         obstacle to adoption, that text has been removed.Copyright (c) 2005-2023, NumPy Developers.
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are
+         met:
+         
+             * Redistributions of source code must retain the above copyright
+                notice, this list of conditions and the following disclaimer.
+         
+             * Redistributions in binary form must reproduce the above
+                copyright notice, this list of conditions and the following
+                disclaimer in the documentation and/or other materials provided
+                with the distribution.
+         
+             * Neither the name of the NumPy Developers nor the names of any
+                contributors may be used to endorse or promote products derived
+                from this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+         "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+         LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+         A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+         OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+         SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+         LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+         DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+         THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+         (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+                                          Apache License
+                                    Version 2.0, January 2004
+                                 http://www.apache.org/licenses/
+         
+            TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+         
+            1. Definitions.
+         
+               "License" shall mean the terms and conditions for use, reproduction,
+               and distribution as defined by Sections 1 through 9 of this document.
+         
+               "Licensor" shall mean the copyright owner or entity authorized by
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+         
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+         
+               "You" (or "Your") shall mean an individual or Legal Entity
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+         
+               "Source" form shall mean the preferred form for making modifications,
+               including but not limited to software source code, documentation
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+         
+               "Object" form shall mean any form resulting from mechanical
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+         
+               "Work" shall mean the work of authorship, whether in Source or
+               Object form, made available under the License, as indicated by a
+               copyright notice that is included in or attached to the work
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+         
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+         
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+         
+            5. Submission of Contributions. Unless You explicitly state otherwise,
+               any Contribution intentionally submitted for inclusion in the Work
+               by You to the Licensor shall be under the terms and conditions of
+               this License, without any additional terms or conditions.
+               Notwithstanding the above, nothing herein shall supersede or modify
+               the terms of any separate license agreement you may have executed
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+         
+            6. Trademarks. This License does not grant permission to use the trade
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+               appropriateness of using or redistributing the Work and assume any
+               risks associated with Your exercise of permissions under this License.
+         
+            8. Limitation of Liability. In no event and under no legal theory,
+               whether in tort (including negligence), contract, or otherwise,
+               unless required by applicable law (such as deliberate and grossly
+               negligent acts) or agreed to in writing, shall any Contributor be
+               liable to You for damages, including any direct, indirect, special,
+               incidental, or consequential damages of any character arising as a
+               result of this License or out of the use or inability to use the
+               Work (including but not limited to damages for loss of goodwill,
+               work stoppage, computer failure or malfunction, or any and all
+               other commercial damages or losses), even if such Contributor
+               has been advised of the possibility of such damages.
+         
+            9. Accepting Warranty or Additional Liability. While redistributing
+               the Work or Derivative Works thereof, You may choose to offer,
+               and charge a fee for, acceptance of support, warranty, indemnity,
+               or other liability obligations and/or rights consistent with this
+               License. However, in accepting such obligations, You may act only
+               on Your own behalf and on Your sole responsibility, not on behalf
+               of any other Contributor, and only if You agree to indemnify,
+               defend, and hold each Contributor harmless for any liability
+               incurred by, or claims asserted against, such Contributor by reason
+               of your accepting any such warranty or additional liability.
+         
+            END OF TERMS AND CONDITIONS
+         
+         
+         Copyright (c) Donald Stufft and individual contributors.
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+             1. Redistributions of source code must retain the above copyright notice,
+                this list of conditions and the following disclaimer.
+         
+             2. Redistributions in binary form must reproduce the above copyright
+                notice, this list of conditions and the following disclaimer in the
+                documentation and/or other materials provided with the distribution.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+         ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+         WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+         DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+         SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+         OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.A. HISTORY OF THE SOFTWARE
+         ==========================
+         
+         Python was created in the early 1990s by Guido van Rossum at Stichting
+         Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands
+         as a successor of a language called ABC.  Guido remains Python's
+         principal author, although it includes many contributions from others.
+         
+         In 1995, Guido continued his work on Python at the Corporation for
+         National Research Initiatives (CNRI, see https://www.cnri.reston.va.us)
+         in Reston, Virginia where he released several versions of the
+         software.
+         
+         In May 2000, Guido and the Python core development team moved to
+         BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+         year, the PythonLabs team moved to Digital Creations, which became
+         Zope Corporation.  In 2001, the Python Software Foundation (PSF, see
+         https://www.python.org/psf/) was formed, a non-profit organization
+         created specifically to own Python-related Intellectual Property.
+         Zope Corporation was a sponsoring member of the PSF.
+         
+         All Python releases are Open Source (see https://opensource.org for
+         the Open Source Definition).  Historically, most, but not all, Python
+         releases have also been GPL-compatible; the table below summarizes
+         the various releases.
+         
+             Release         Derived     Year        Owner       GPL-
+                             from                                compatible? (1)
+         
+             0.9.0 thru 1.2              1991-1995   CWI         yes
+             1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+             1.6             1.5.2       2000        CNRI        no
+             2.0             1.6         2000        BeOpen.com  no
+             1.6.1           1.6         2001        CNRI        yes (2)
+             2.1             2.0+1.6.1   2001        PSF         no
+             2.0.1           2.0+1.6.1   2001        PSF         yes
+             2.1.1           2.1+2.0.1   2001        PSF         yes
+             2.1.2           2.1.1       2002        PSF         yes
+             2.1.3           2.1.2       2002        PSF         yes
+             2.2 and above   2.1.1       2001-now    PSF         yes
+         
+         Footnotes:
+         
+         (1) GPL-compatible doesn't mean that we're distributing Python under
+             the GPL.  All Python licenses, unlike the GPL, let you distribute
+             a modified version without making your changes open source.  The
+             GPL-compatible licenses make it possible to combine Python with
+             other software that is released under the GPL; the others don't.
+         
+         (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+             because its license has a choice of law clause.  According to
+             CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+             is "not incompatible" with the GPL.
+         
+         Thanks to the many outside volunteers who have worked under Guido's
+         direction to make these releases possible.
+         
+         
+         B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+         ===============================================================
+         
+         Python software and documentation are licensed under the
+         Python Software Foundation License Version 2.
+         
+         Starting with Python 3.8.6, examples, recipes, and other code in
+         the documentation are dual licensed under the PSF License Version 2
+         and the Zero-Clause BSD license.
+         
+         Some software incorporated into Python is under different licenses.
+         The licenses are listed with code falling under that license.
+         
+         
+         PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+         --------------------------------------------
+         
+         1. This LICENSE AGREEMENT is between the Python Software Foundation
+         ("PSF"), and the Individual or Organization ("Licensee") accessing and
+         otherwise using this software ("Python") in source or binary form and
+         its associated documentation.
+         
+         2. Subject to the terms and conditions of this License Agreement, PSF hereby
+         grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+         analyze, test, perform and/or display publicly, prepare derivative works,
+         distribute, and otherwise use Python alone or in any derivative version,
+         provided, however, that PSF's License Agreement and PSF's notice of copyright,
+         i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+         2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 Python Software Foundation;
+         All Rights Reserved" are retained in Python alone or in any derivative version
+         prepared by Licensee.
+         
+         3. In the event Licensee prepares a derivative work that is based on
+         or incorporates Python or any part thereof, and wants to make
+         the derivative work available to others as provided herein, then
+         Licensee hereby agrees to include in any such work a brief summary of
+         the changes made to Python.
+         
+         4. PSF is making Python available to Licensee on an "AS IS"
+         basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+         IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+         DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+         FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+         INFRINGE ANY THIRD PARTY RIGHTS.
+         
+         5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+         FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+         A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+         OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+         
+         6. This License Agreement will automatically terminate upon a material
+         breach of its terms and conditions.
+         
+         7. Nothing in this License Agreement shall be deemed to create any
+         relationship of agency, partnership, or joint venture between PSF and
+         Licensee.  This License Agreement does not grant permission to use PSF
+         trademarks or trade name in a trademark sense to endorse or promote
+         products or services of Licensee, or any third party.
+         
+         8. By copying, installing or otherwise using Python, Licensee
+         agrees to be bound by the terms and conditions of this License
+         Agreement.
+         
+         
+         BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+         -------------------------------------------
+         
+         BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+         
+         1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+         office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+         Individual or Organization ("Licensee") accessing and otherwise using
+         this software in source or binary form and its associated
+         documentation ("the Software").
+         
+         2. Subject to the terms and conditions of this BeOpen Python License
+         Agreement, BeOpen hereby grants Licensee a non-exclusive,
+         royalty-free, world-wide license to reproduce, analyze, test, perform
+         and/or display publicly, prepare derivative works, distribute, and
+         otherwise use the Software alone or in any derivative version,
+         provided, however, that the BeOpen Python License is retained in the
+         Software, alone or in any derivative version prepared by Licensee.
+         
+         3. BeOpen is making the Software available to Licensee on an "AS IS"
+         basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+         IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+         DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+         FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+         INFRINGE ANY THIRD PARTY RIGHTS.
+         
+         4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+         SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+         AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+         DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+         
+         5. This License Agreement will automatically terminate upon a material
+         breach of its terms and conditions.
+         
+         6. This License Agreement shall be governed by and interpreted in all
+         respects by the law of the State of California, excluding conflict of
+         law provisions.  Nothing in this License Agreement shall be deemed to
+         create any relationship of agency, partnership, or joint venture
+         between BeOpen and Licensee.  This License Agreement does not grant
+         permission to use BeOpen trademarks or trade names in a trademark
+         sense to endorse or promote products or services of Licensee, or any
+         third party.  As an exception, the "BeOpen Python" logos available at
+         http://www.pythonlabs.com/logos.html may be used according to the
+         permissions granted on that web page.
+         
+         7. By copying, installing or otherwise using the software, Licensee
+         agrees to be bound by the terms and conditions of this License
+         Agreement.
+         
+         
+         CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+         ---------------------------------------
+         
+         1. This LICENSE AGREEMENT is between the Corporation for National
+         Research Initiatives, having an office at 1895 Preston White Drive,
+         Reston, VA 20191 ("CNRI"), and the Individual or Organization
+         ("Licensee") accessing and otherwise using Python 1.6.1 software in
+         source or binary form and its associated documentation.
+         
+         2. Subject to the terms and conditions of this License Agreement, CNRI
+         hereby grants Licensee a nonexclusive, royalty-free, world-wide
+         license to reproduce, analyze, test, perform and/or display publicly,
+         prepare derivative works, distribute, and otherwise use Python 1.6.1
+         alone or in any derivative version, provided, however, that CNRI's
+         License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+         1995-2001 Corporation for National Research Initiatives; All Rights
+         Reserved" are retained in Python 1.6.1 alone or in any derivative
+         version prepared by Licensee.  Alternately, in lieu of CNRI's License
+         Agreement, Licensee may substitute the following text (omitting the
+         quotes): "Python 1.6.1 is made available subject to the terms and
+         conditions in CNRI's License Agreement.  This Agreement together with
+         Python 1.6.1 may be located on the internet using the following
+         unique, persistent identifier (known as a handle): 1895.22/1013.  This
+         Agreement may also be obtained from a proxy server on the internet
+         using the following URL: http://hdl.handle.net/1895.22/1013".
+         
+         3. In the event Licensee prepares a derivative work that is based on
+         or incorporates Python 1.6.1 or any part thereof, and wants to make
+         the derivative work available to others as provided herein, then
+         Licensee hereby agrees to include in any such work a brief summary of
+         the changes made to Python 1.6.1.
+         
+         4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
+         basis.  CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+         IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
+         DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+         FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
+         INFRINGE ANY THIRD PARTY RIGHTS.
+         
+         5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+         1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+         A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
+         OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+         
+         6. This License Agreement will automatically terminate upon a material
+         breach of its terms and conditions.
+         
+         7. This License Agreement shall be governed by the federal
+         intellectual property law of the United States, including without
+         limitation the federal copyright law, and, to the extent such
+         U.S. federal law does not apply, by the law of the Commonwealth of
+         Virginia, excluding Virginia's conflict of law provisions.
+         Notwithstanding the foregoing, with regard to derivative works based
+         on Python 1.6.1 that incorporate non-separable material that was
+         previously distributed under the GNU General Public License (GPL), the
+         law of the Commonwealth of Virginia shall govern this License
+         Agreement only as to issues arising under or with respect to
+         Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in this
+         License Agreement shall be deemed to create any relationship of
+         agency, partnership, or joint venture between CNRI and Licensee.  This
+         License Agreement does not grant permission to use CNRI trademarks or
+         trade name in a trademark sense to endorse or promote products or
+         services of Licensee, or any third party.
+         
+         8. By clicking on the "ACCEPT" button where indicated, or by copying,
+         installing or otherwise using Python 1.6.1, Licensee agrees to be
+         bound by the terms and conditions of this License Agreement.
+         
+                 ACCEPT
+         
+         
+         CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+         --------------------------------------------------
+         
+         Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+         The Netherlands.  All rights reserved.
+         
+         Permission to use, copy, modify, and distribute this software and its
+         documentation for any purpose and without fee is hereby granted,
+         provided that the above copyright notice appear in all copies and that
+         both that copyright notice and this permission notice appear in
+         supporting documentation, and that the name of Stichting Mathematisch
+         Centrum or CWI not be used in advertising or publicity pertaining to
+         distribution of the software without specific, written prior
+         permission.
+         
+         STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+         THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+         FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+         FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+         WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+         ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+         OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+         
+         ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION
+         ----------------------------------------------------------------------
+         
+         Permission to use, copy, modify, and/or distribute this software for any
+         purpose with or without fee is hereby granted.
+         
+         THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+         REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+         AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+         INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+         LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+         OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+         PERFORMANCE OF THIS SOFTWARE.
+         Copyright (c) 2014, Al Sweigart
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+         * Redistributions of source code must retain the above copyright notice, this
+           list of conditions and the following disclaimer.
+         
+         * Redistributions in binary form must reproduce the above copyright notice,
+           this list of conditions and the following disclaimer in the documentation
+           and/or other materials provided with the distribution.
+         
+         * Neither the name of the {organization} nor the names of its
+           contributors may be used to endorse or promote products derived from
+           this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+         AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+         IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+         DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+         SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+         OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Copyright (c) 2017 Anthony Sottile
+         
+         Permission is hereby granted, free of charge, to any person obtaining a copy
+         of this software and associated documentation files (the "Software"), to deal
+         in the Software without restriction, including without limitation the rights
+         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+         copies of the Software, and to permit persons to whom the Software is
+         furnished to do so, subject to the following conditions:
+         
+         The above copyright notice and this permission notice shall be included in
+         all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+         THE SOFTWARE.Copyright (c) 2015-2019 Jared Hobbs
+         
+         Permission is hereby granted, free of charge, to any person obtaining a copy of
+         this software and associated documentation files (the "Software"), to deal in
+         the Software without restriction, including without limitation the rights to
+         use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+         of the Software, and to permit persons to whom the Software is furnished to do
+         so, subject to the following conditions:
+         
+         The above copyright notice and this permission notice shall be included in all
+         copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+         SOFTWARE.Developed by ESN, an Electronic Arts Inc. studio.
+         Copyright (c) 2014, Electronic Arts Inc.
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         * Redistributions of source code must retain the above copyright
+         notice, this list of conditions and the following disclaimer.
+         * Redistributions in binary form must reproduce the above copyright
+         notice, this list of conditions and the following disclaimer in the
+         documentation and/or other materials provided with the distribution.
+         * Neither the name of ESN, Electronic Arts Inc. nor the
+         names of its contributors may be used to endorse or promote products
+         derived from this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+         ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+         WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL ELECTRONIC ARTS INC. BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+         (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+         LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+         ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+         (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+         SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+         
+         ----
+         
+         Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
+         https://github.com/client9/stringencoders
+         
+           Copyright 2005, 2006, 2007
+           Nick Galbreath -- nickg [at] modp [dot] com
+           All rights reserved.
+         
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+Provides-Extra: excel
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+Requires-Dist: fastparquet>=2022.12.0; extra == "all"
+Requires-Dist: fsspec>=2022.11.0; extra == "all"
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+Requires-Dist: pyarrow>=10.0.1; extra == "all"
+Requires-Dist: pymysql>=1.0.2; extra == "all"
+Requires-Dist: PyQt5>=5.15.9; extra == "all"
+Requires-Dist: pyreadstat>=1.2.0; extra == "all"
+Requires-Dist: pytest>=7.3.2; extra == "all"
+Requires-Dist: pytest-xdist>=2.2.0; extra == "all"
+Requires-Dist: python-calamine>=0.1.7; extra == "all"
+Requires-Dist: pyxlsb>=1.0.10; extra == "all"
+Requires-Dist: qtpy>=2.3.0; extra == "all"
+Requires-Dist: scipy>=1.10.0; extra == "all"
+Requires-Dist: s3fs>=2022.11.0; extra == "all"
+Requires-Dist: SQLAlchemy>=2.0.0; extra == "all"
+Requires-Dist: tables>=3.8.0; extra == "all"
+Requires-Dist: tabulate>=0.9.0; extra == "all"
+Requires-Dist: xarray>=2022.12.0; extra == "all"
+Requires-Dist: xlrd>=2.0.1; extra == "all"
+Requires-Dist: xlsxwriter>=3.0.5; extra == "all"
+Requires-Dist: zstandard>=0.19.0; extra == "all"
+Description-Content-Type: text/markdown
+
+<div align="center">
+  <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
+</div>
+
+-----------------
+
+# pandas: powerful Python data analysis toolkit
+
+| | |
+| --- | --- |
+| Testing | [![CI - Test](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) |
+| Package | [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![PyPI Downloads](https://img.shields.io/pypi/dm/pandas.svg?label=PyPI%20downloads)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/conda-forge/pandas) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandas.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/pandas) |
+| Meta | [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![License - BSD 3-Clause](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) |
+
+
+## What is it?
+
+**pandas** is a Python package that provides fast, flexible, and expressive data
+structures designed to make working with "relational" or "labeled" data both
+easy and intuitive. It aims to be the fundamental high-level building block for
+doing practical, **real world** data analysis in Python. Additionally, it has
+the broader goal of becoming **the most powerful and flexible open source data
+analysis / manipulation tool available in any language**. It is already well on
+its way towards this goal.
+
+## Table of Contents
+
+- [Main Features](#main-features)
+- [Where to get it](#where-to-get-it)
+- [Dependencies](#dependencies)
+- [Installation from sources](#installation-from-sources)
+- [License](#license)
+- [Documentation](#documentation)
+- [Background](#background)
+- [Getting Help](#getting-help)
+- [Discussion and Development](#discussion-and-development)
+- [Contributing to pandas](#contributing-to-pandas)
+
+## Main Features
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of [**missing data**][missing-data] (represented as
+    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
+  - Size mutability: columns can be [**inserted and
+    deleted**][insertion-deletion] from DataFrame and higher dimensional
+    objects
+  - Automatic and explicit [**data alignment**][alignment]: objects can
+    be explicitly aligned to a set of labels, or the user can simply
+    ignore the labels and let `Series`, `DataFrame`, etc. automatically
+    align the data for you in computations
+  - Powerful, flexible [**group by**][groupby] functionality to perform
+    split-apply-combine operations on data sets, for both aggregating
+    and transforming data
+  - Make it [**easy to convert**][conversion] ragged,
+    differently-indexed data in other Python and NumPy data structures
+    into DataFrame objects
+  - Intelligent label-based [**slicing**][slicing], [**fancy
+    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
+    large data sets
+  - Intuitive [**merging**][merging] and [**joining**][joining] data
+    sets
+  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
+    data sets
+  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
+    labels per tick)
+  - Robust IO tools for loading data from [**flat files**][flat-files]
+    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
+  - [**Time series**][timeseries]-specific functionality: date range
+    generation and frequency conversion, moving window statistics,
+    date shifting and lagging
+
+
+   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
+   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
+   [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
+   [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
+   [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
+   [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
+   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
+   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
+   [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
+   [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
+   [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
+   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
+   [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
+   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
+   [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
+   [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
+   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
+   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
+
+## Where to get it
+The source code is currently hosted on GitHub at:
+https://github.com/pandas-dev/pandas
+
+Binary installers for the latest released version are available at the [Python
+Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
+
+```sh
+# conda
+conda install -c conda-forge pandas
+```
+
+```sh
+# or PyPI
+pip install pandas
+```
+
+The list of changes to pandas between each release can be found
+[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full
+details, see the commit logs at https://github.com/pandas-dev/pandas.
+
+## Dependencies
+- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
+- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
+- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
+
+See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
+
+## Installation from sources
+To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
+dependencies above. Cython can be installed from PyPI:
+
+```sh
+pip install cython
+```
+
+In the `pandas` directory (same one where you found this file after
+cloning the git repo), execute:
+
+```sh
+pip install .
+```
+
+or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
+
+
+```sh
+python -m pip install -ve . --no-build-isolation --config-settings=editable-verbose=true
+```
+
+See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).
+
+## License
+[BSD 3](LICENSE)
+
+## Documentation
+The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).
+
+## Background
+Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
+has been under active development since then.
+
+## Getting Help
+
+For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
+Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
+
+## Discussion and Development
+Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).
+
+Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.
+
+There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors.
+
+Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.
+
+## Contributing to pandas
+
+[![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)
+
+All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
+
+A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
+
+If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.
+
+You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).
+
+Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
+
+Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack).
+
+As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
+
+<hr>
+
+[Go to Top](#table-of-contents)
diff --git a/shared/package-files/40/40eed12794d4540eb982d9092de9e320e116d9e21b3ee28bc3e9c6583f681d16/libarrow_flight.pxd b/shared/package-files/40/40eed12794d4540eb982d9092de9e320e116d9e21b3ee28bc3e9c6583f681d16/libarrow_flight.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..a89137b8455ee3ae25630ce0f125654610f8c351
--- /dev/null
+++ b/shared/package-files/40/40eed12794d4540eb982d9092de9e320e116d9e21b3ee28bc3e9c6583f681d16/libarrow_flight.pxd
@@ -0,0 +1,621 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# distutils: language = c++
+
+from pyarrow.includes.common cimport *
+from pyarrow.includes.libarrow cimport *
+from pyarrow.includes.libarrow_python cimport CTimePoint
+
+from libcpp.map cimport multimap
+
+
+cdef extern from "arrow/flight/api.h" namespace "arrow" nogil:
+    cdef char* CTracingServerMiddlewareName\
+        " arrow::flight::TracingServerMiddleware::kMiddlewareName"
+
+    cdef cppclass CActionType" arrow::flight::ActionType":
+        c_string type
+        c_string description
+        bint operator==(CActionType)
+        CResult[c_string] SerializeToString()
+
+        @staticmethod
+        CResult[CActionType] Deserialize(const c_string& serialized)
+
+    cdef cppclass CAction" arrow::flight::Action":
+        c_string type
+        shared_ptr[CBuffer] body
+        bint operator==(CAction)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CAction] Deserialize(const c_string& serialized)
+
+    cdef cppclass CFlightResult" arrow::flight::Result":
+        CFlightResult()
+        CFlightResult(CFlightResult)
+        shared_ptr[CBuffer] body
+        bint operator==(CFlightResult)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CFlightResult] Deserialize(const c_string& serialized)
+
+    cdef cppclass CBasicAuth" arrow::flight::BasicAuth":
+        CBasicAuth()
+        CBasicAuth(CBuffer)
+        CBasicAuth(CBasicAuth)
+        c_string username
+        c_string password
+        bint operator==(CBasicAuth)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CBasicAuth] Deserialize(const c_string& serialized)
+
+    cdef cppclass CResultStream" arrow::flight::ResultStream":
+        CResult[unique_ptr[CFlightResult]] Next()
+
+    cdef cppclass CDescriptorType \
+            " arrow::flight::FlightDescriptor::DescriptorType":
+        bint operator==(CDescriptorType)
+
+    CDescriptorType CDescriptorTypeUnknown\
+        " arrow::flight::FlightDescriptor::UNKNOWN"
+    CDescriptorType CDescriptorTypePath\
+        " arrow::flight::FlightDescriptor::PATH"
+    CDescriptorType CDescriptorTypeCmd\
+        " arrow::flight::FlightDescriptor::CMD"
+
+    cdef cppclass CFlightDescriptor" arrow::flight::FlightDescriptor":
+        CDescriptorType type
+        c_string cmd
+        vector[c_string] path
+        bint operator==(CFlightDescriptor)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CFlightDescriptor] Deserialize(const c_string& serialized)
+
+    cdef cppclass CTicket" arrow::flight::Ticket":
+        CTicket()
+        c_string ticket
+        bint operator==(CTicket)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CTicket] Deserialize(const c_string& serialized)
+
+    cdef cppclass CCriteria" arrow::flight::Criteria":
+        CCriteria()
+        c_string expression
+        bint operator==(CCriteria)
+        CResult[c_string] SerializeToString()
+
+        @staticmethod
+        CResult[CCriteria] Deserialize(const c_string& serialized)
+
+    cdef cppclass CLocation" arrow::flight::Location":
+        CLocation()
+        c_string ToString()
+        c_bool Equals(const CLocation& other)
+
+        @staticmethod
+        CResult[CLocation] Parse(const c_string& uri_string)
+
+        @staticmethod
+        CResult[CLocation] ForGrpcTcp(const c_string& host, int port)
+
+        @staticmethod
+        CResult[CLocation] ForGrpcTls(const c_string& host, int port)
+
+        @staticmethod
+        CResult[CLocation] ForGrpcUnix(const c_string& path)
+
+    cdef cppclass CFlightEndpoint" arrow::flight::FlightEndpoint":
+        CFlightEndpoint()
+
+        CTicket ticket
+        vector[CLocation] locations
+        optional[CTimePoint] expiration_time
+        c_string app_metadata
+
+        bint operator==(CFlightEndpoint)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CFlightEndpoint] Deserialize(const c_string& serialized)
+
+    cdef cppclass CFlightInfo" arrow::flight::FlightInfo":
+        CFlightInfo(CFlightInfo info)
+        int64_t total_records()
+        int64_t total_bytes()
+        c_bool ordered()
+        c_string app_metadata()
+        CResult[shared_ptr[CSchema]] GetSchema(CDictionaryMemo* memo)
+        CFlightDescriptor& descriptor()
+        const vector[CFlightEndpoint]& endpoints()
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+        bint operator==(CFlightInfo)
+
+        @staticmethod
+        CResult[unique_ptr[CFlightInfo]] Deserialize(
+            const c_string& serialized)
+
+    cdef cppclass CSchemaResult" arrow::flight::SchemaResult":
+        CSchemaResult()
+        CSchemaResult(CSchemaResult result)
+        CResult[shared_ptr[CSchema]] GetSchema(CDictionaryMemo* memo)
+        bint operator==(CSchemaResult)
+        CResult[c_string] SerializeToString()
+        c_string ToString()
+
+        @staticmethod
+        CResult[CSchemaResult] Deserialize(const c_string& serialized)
+
+    cdef cppclass CFlightListing" arrow::flight::FlightListing":
+        CResult[unique_ptr[CFlightInfo]] Next()
+
+    cdef cppclass CSimpleFlightListing" arrow::flight::SimpleFlightListing":
+        # This doesn't work with Cython >= 3
+        # CSimpleFlightListing(vector[CFlightInfo]&& info)
+        CSimpleFlightListing(const vector[CFlightInfo]& info)
+
+    cdef cppclass CFlightPayload" arrow::flight::FlightPayload":
+        shared_ptr[CBuffer] descriptor
+        shared_ptr[CBuffer] app_metadata
+        CIpcPayload ipc_message
+
+    cdef cppclass CFlightDataStream" arrow::flight::FlightDataStream":
+        shared_ptr[CSchema] schema()
+        CResult[CFlightPayload] Next()
+
+    cdef cppclass CFlightStreamChunk" arrow::flight::FlightStreamChunk":
+        CFlightStreamChunk()
+        shared_ptr[CRecordBatch] data
+        shared_ptr[CBuffer] app_metadata
+
+    cdef cppclass CMetadataRecordBatchReader \
+            " arrow::flight::MetadataRecordBatchReader":
+        CResult[shared_ptr[CSchema]] GetSchema()
+        CResult[CFlightStreamChunk] Next()
+        CResult[shared_ptr[CTable]] ToTable()
+        CIpcReadStats stats() const
+
+    CResult[shared_ptr[CRecordBatchReader]] MakeRecordBatchReader\
+        " arrow::flight::MakeRecordBatchReader"(
+            shared_ptr[CMetadataRecordBatchReader])
+
+    cdef cppclass CMetadataRecordBatchWriter \
+            " arrow::flight::MetadataRecordBatchWriter"(CRecordBatchWriter):
+        CStatus Begin(shared_ptr[CSchema] schema,
+                      const CIpcWriteOptions& options)
+        CStatus WriteMetadata(shared_ptr[CBuffer] app_metadata)
+        CStatus WriteWithMetadata(const CRecordBatch& batch,
+                                  shared_ptr[CBuffer] app_metadata)
+
+    cdef cppclass CFlightStreamReader \
+            " arrow::flight::FlightStreamReader"(CMetadataRecordBatchReader):
+        void Cancel()
+        CResult[shared_ptr[CTable]] ToTableWithStopToken" ToTable"\
+            (const CStopToken& stop_token)
+
+    cdef cppclass CFlightMessageReader \
+            " arrow::flight::FlightMessageReader"(CMetadataRecordBatchReader):
+        CFlightDescriptor& descriptor()
+
+    cdef cppclass CFlightMessageWriter \
+            " arrow::flight::FlightMessageWriter"(CMetadataRecordBatchWriter):
+        pass
+
+    cdef cppclass CFlightStreamWriter \
+            " arrow::flight::FlightStreamWriter"(CMetadataRecordBatchWriter):
+        CStatus DoneWriting()
+
+    cdef cppclass CRecordBatchStream \
+            " arrow::flight::RecordBatchStream"(CFlightDataStream):
+        CRecordBatchStream(shared_ptr[CRecordBatchReader]& reader,
+                           const CIpcWriteOptions& options)
+
+    cdef cppclass CFlightMetadataReader" arrow::flight::FlightMetadataReader":
+        CStatus ReadMetadata(shared_ptr[CBuffer]* out)
+
+    cdef cppclass CFlightMetadataWriter" arrow::flight::FlightMetadataWriter":
+        CStatus WriteMetadata(const CBuffer& message)
+
+    cdef cppclass CServerAuthReader" arrow::flight::ServerAuthReader":
+        CStatus Read(c_string* token)
+
+    cdef cppclass CServerAuthSender" arrow::flight::ServerAuthSender":
+        CStatus Write(c_string& token)
+
+    cdef cppclass CClientAuthReader" arrow::flight::ClientAuthReader":
+        CStatus Read(c_string* token)
+
+    cdef cppclass CClientAuthSender" arrow::flight::ClientAuthSender":
+        CStatus Write(c_string& token)
+
+    cdef cppclass CServerAuthHandler" arrow::flight::ServerAuthHandler":
+        pass
+
+    cdef cppclass CClientAuthHandler" arrow::flight::ClientAuthHandler":
+        pass
+
+    cdef cppclass CServerCallContext" arrow::flight::ServerCallContext":
+        c_string& peer_identity()
+        c_string& peer()
+        c_bool is_cancelled()
+        void AddHeader(const c_string& key, const c_string& value)
+        void AddTrailer(const c_string& key, const c_string& value)
+        CServerMiddleware* GetMiddleware(const c_string& key)
+
+    cdef cppclass CTimeoutDuration" arrow::flight::TimeoutDuration":
+        CTimeoutDuration(double)
+        double count()
+
+    cdef cppclass CFlightCallOptions" arrow::flight::FlightCallOptions":
+        CFlightCallOptions()
+        CTimeoutDuration timeout
+        CIpcWriteOptions write_options
+        CIpcReadOptions read_options
+        vector[pair[c_string, c_string]] headers
+        CStopToken stop_token
+
+    cdef cppclass CCertKeyPair" arrow::flight::CertKeyPair":
+        CCertKeyPair()
+        c_string pem_cert
+        c_string pem_key
+
+    cdef cppclass CFlightMethod" arrow::flight::FlightMethod":
+        bint operator==(CFlightMethod)
+
+    CFlightMethod CFlightMethodInvalid\
+        " arrow::flight::FlightMethod::Invalid"
+    CFlightMethod CFlightMethodHandshake\
+        " arrow::flight::FlightMethod::Handshake"
+    CFlightMethod CFlightMethodListFlights\
+        " arrow::flight::FlightMethod::ListFlights"
+    CFlightMethod CFlightMethodGetFlightInfo\
+        " arrow::flight::FlightMethod::GetFlightInfo"
+    CFlightMethod CFlightMethodGetSchema\
+        " arrow::flight::FlightMethod::GetSchema"
+    CFlightMethod CFlightMethodDoGet\
+        " arrow::flight::FlightMethod::DoGet"
+    CFlightMethod CFlightMethodDoPut\
+        " arrow::flight::FlightMethod::DoPut"
+    CFlightMethod CFlightMethodDoAction\
+        " arrow::flight::FlightMethod::DoAction"
+    CFlightMethod CFlightMethodListActions\
+        " arrow::flight::FlightMethod::ListActions"
+    CFlightMethod CFlightMethodDoExchange\
+        " arrow::flight::FlightMethod::DoExchange"
+
+    cdef cppclass CCallInfo" arrow::flight::CallInfo":
+        CFlightMethod method
+
+    ctypedef multimap[cpp_string_view, cpp_string_view] CCallHeaders\
+        " arrow::flight::CallHeaders"
+
+    cdef cppclass CAddCallHeaders" arrow::flight::AddCallHeaders":
+        void AddHeader(const c_string& key, const c_string& value)
+
+    cdef cppclass CServerMiddleware" arrow::flight::ServerMiddleware":
+        c_string name()
+
+    cdef cppclass CServerMiddlewareFactory\
+            " arrow::flight::ServerMiddlewareFactory":
+        pass
+
+    cdef cppclass CClientMiddleware" arrow::flight::ClientMiddleware":
+        pass
+
+    cdef cppclass CClientMiddlewareFactory\
+            " arrow::flight::ClientMiddlewareFactory":
+        pass
+
+    cpdef cppclass CTracingServerMiddlewareTraceKey\
+            " arrow::flight::TracingServerMiddleware::TraceKey":
+        CTracingServerMiddlewareTraceKey()
+        c_string key
+        c_string value
+
+    cdef cppclass CTracingServerMiddleware\
+            " arrow::flight::TracingServerMiddleware"(CServerMiddleware):
+        vector[CTracingServerMiddlewareTraceKey] GetTraceContext()
+
+    cdef shared_ptr[CServerMiddlewareFactory] \
+        MakeTracingServerMiddlewareFactory\
+        " arrow::flight::MakeTracingServerMiddlewareFactory"()
+
+    cdef cppclass CFlightServerOptions" arrow::flight::FlightServerOptions":
+        CFlightServerOptions(const CLocation& location)
+        CLocation location
+        unique_ptr[CServerAuthHandler] auth_handler
+        vector[CCertKeyPair] tls_certificates
+        c_bool verify_client
+        c_string root_certificates
+        vector[pair[c_string, shared_ptr[CServerMiddlewareFactory]]] middleware
+
+    cdef cppclass CFlightClientOptions" arrow::flight::FlightClientOptions":
+        c_string tls_root_certs
+        c_string cert_chain
+        c_string private_key
+        c_string override_hostname
+        vector[shared_ptr[CClientMiddlewareFactory]] middleware
+        int64_t write_size_limit_bytes
+        vector[pair[c_string, CIntStringVariant]] generic_options
+        c_bool disable_server_verification
+
+        @staticmethod
+        CFlightClientOptions Defaults()
+
+    cdef cppclass CDoPutResult" arrow::flight::FlightClient::DoPutResult":
+        unique_ptr[CFlightStreamWriter] writer
+        unique_ptr[CFlightMetadataReader] reader
+
+    cdef cppclass CDoExchangeResult" arrow::flight::FlightClient::DoExchangeResult":
+        unique_ptr[CFlightStreamWriter] writer
+        unique_ptr[CFlightStreamReader] reader
+
+    cdef cppclass CFlightClient" arrow::flight::FlightClient":
+        @staticmethod
+        CResult[unique_ptr[CFlightClient]] Connect(const CLocation& location,
+                                                   const CFlightClientOptions& options)
+
+        c_bool supports_async()
+        CStatus CheckAsyncSupport()
+
+        CStatus Authenticate(CFlightCallOptions& options,
+                             unique_ptr[CClientAuthHandler] auth_handler)
+
+        CResult[pair[c_string, c_string]] AuthenticateBasicToken(
+            CFlightCallOptions& options,
+            const c_string& username,
+            const c_string& password)
+
+        CResult[unique_ptr[CResultStream]] DoAction(CFlightCallOptions& options, CAction& action)
+        CResult[vector[CActionType]] ListActions(CFlightCallOptions& options)
+
+        CResult[unique_ptr[CFlightListing]] ListFlights(CFlightCallOptions& options, CCriteria criteria)
+        CResult[unique_ptr[CFlightInfo]] GetFlightInfo(CFlightCallOptions& options,
+                                                       CFlightDescriptor& descriptor)
+        CFuture[CFlightInfo] GetFlightInfoAsync(CFlightCallOptions& options,
+                                                CFlightDescriptor& descriptor)
+        CResult[unique_ptr[CSchemaResult]] GetSchema(CFlightCallOptions& options,
+                                                     CFlightDescriptor& descriptor)
+        CResult[unique_ptr[CFlightStreamReader]] DoGet(CFlightCallOptions& options, CTicket& ticket)
+        CResult[CDoPutResult] DoPut(CFlightCallOptions& options,
+                                    CFlightDescriptor& descriptor,
+                                    shared_ptr[CSchema]& schema)
+        CResult[CDoExchangeResult] DoExchange(CFlightCallOptions& options,
+                                              CFlightDescriptor& descriptor)
+        CStatus Close()
+
+    cdef cppclass CFlightStatusCode" arrow::flight::FlightStatusCode":
+        bint operator==(CFlightStatusCode)
+
+    CFlightStatusCode CFlightStatusInternal \
+        " arrow::flight::FlightStatusCode::Internal"
+    CFlightStatusCode CFlightStatusTimedOut \
+        " arrow::flight::FlightStatusCode::TimedOut"
+    CFlightStatusCode CFlightStatusCancelled \
+        " arrow::flight::FlightStatusCode::Cancelled"
+    CFlightStatusCode CFlightStatusUnauthenticated \
+        " arrow::flight::FlightStatusCode::Unauthenticated"
+    CFlightStatusCode CFlightStatusUnauthorized \
+        " arrow::flight::FlightStatusCode::Unauthorized"
+    CFlightStatusCode CFlightStatusUnavailable \
+        " arrow::flight::FlightStatusCode::Unavailable"
+    CFlightStatusCode CFlightStatusFailed \
+        " arrow::flight::FlightStatusCode::Failed"
+
+    cdef cppclass FlightStatusDetail" arrow::flight::FlightStatusDetail":
+        CFlightStatusCode code()
+        c_string extra_info()
+
+        @staticmethod
+        shared_ptr[FlightStatusDetail] UnwrapStatus(const CStatus& status)
+
+    cdef cppclass FlightWriteSizeStatusDetail\
+            " arrow::flight::FlightWriteSizeStatusDetail":
+        int64_t limit()
+        int64_t actual()
+
+        @staticmethod
+        shared_ptr[FlightWriteSizeStatusDetail] UnwrapStatus(
+            const CStatus& status)
+
+    cdef CStatus MakeFlightError" arrow::flight::MakeFlightError" \
+        (CFlightStatusCode code, const c_string& message)
+
+    cdef CStatus MakeFlightError" arrow::flight::MakeFlightError" \
+        (CFlightStatusCode code,
+         const c_string& message,
+         const c_string& extra_info)
+
+# Callbacks for implementing Flight servers
+# Use typedef to emulate syntax for std::function<void(..)>
+ctypedef CStatus cb_list_flights(object, const CServerCallContext&,
+                                 const CCriteria*,
+                                 unique_ptr[CFlightListing]*)
+ctypedef CStatus cb_get_flight_info(object, const CServerCallContext&,
+                                    const CFlightDescriptor&,
+                                    unique_ptr[CFlightInfo]*)
+ctypedef CStatus cb_get_schema(object, const CServerCallContext&,
+                               const CFlightDescriptor&,
+                               unique_ptr[CSchemaResult]*)
+ctypedef CStatus cb_do_put(object, const CServerCallContext&,
+                           unique_ptr[CFlightMessageReader],
+                           unique_ptr[CFlightMetadataWriter])
+ctypedef CStatus cb_do_get(object, const CServerCallContext&,
+                           const CTicket&,
+                           unique_ptr[CFlightDataStream]*)
+ctypedef CStatus cb_do_exchange(object, const CServerCallContext&,
+                                unique_ptr[CFlightMessageReader],
+                                unique_ptr[CFlightMessageWriter])
+ctypedef CStatus cb_do_action(object, const CServerCallContext&,
+                              const CAction&,
+                              unique_ptr[CResultStream]*)
+ctypedef CStatus cb_list_actions(object, const CServerCallContext&,
+                                 vector[CActionType]*)
+ctypedef CStatus cb_result_next(object, unique_ptr[CFlightResult]*)
+ctypedef CStatus cb_data_stream_next(object, CFlightPayload*)
+ctypedef CStatus cb_server_authenticate(object, CServerAuthSender*,
+                                        CServerAuthReader*)
+ctypedef CStatus cb_is_valid(object, const c_string&, c_string*)
+ctypedef CStatus cb_client_authenticate(object, CClientAuthSender*,
+                                        CClientAuthReader*)
+ctypedef CStatus cb_get_token(object, c_string*)
+
+ctypedef CStatus cb_middleware_sending_headers(object, CAddCallHeaders*)
+ctypedef CStatus cb_middleware_call_completed(object, const CStatus&)
+ctypedef CStatus cb_client_middleware_received_headers(
+    object, const CCallHeaders&)
+ctypedef CStatus cb_server_middleware_start_call(
+    object,
+    const CCallInfo&,
+    const CCallHeaders&,
+    shared_ptr[CServerMiddleware]*)
+ctypedef CStatus cb_client_middleware_start_call(
+    object,
+    const CCallInfo&,
+    unique_ptr[CClientMiddleware]*)
+
+cdef extern from "arrow/python/flight.h" namespace "arrow::py::flight" nogil:
+    cdef char* CPyServerMiddlewareName\
+        " arrow::py::flight::kPyServerMiddlewareName"
+
+    cdef cppclass PyFlightServerVtable:
+        PyFlightServerVtable()
+        function[cb_list_flights] list_flights
+        function[cb_get_flight_info] get_flight_info
+        function[cb_get_schema] get_schema
+        function[cb_do_put] do_put
+        function[cb_do_get] do_get
+        function[cb_do_exchange] do_exchange
+        function[cb_do_action] do_action
+        function[cb_list_actions] list_actions
+
+    cdef cppclass PyServerAuthHandlerVtable:
+        PyServerAuthHandlerVtable()
+        function[cb_server_authenticate] authenticate
+        function[cb_is_valid] is_valid
+
+    cdef cppclass PyClientAuthHandlerVtable:
+        PyClientAuthHandlerVtable()
+        function[cb_client_authenticate] authenticate
+        function[cb_get_token] get_token
+
+    cdef cppclass PyFlightServer:
+        PyFlightServer(object server, PyFlightServerVtable vtable)
+
+        CStatus Init(CFlightServerOptions& options)
+        int port()
+        CStatus ServeWithSignals() except *
+        CStatus Shutdown()
+        CStatus Wait()
+
+    cdef cppclass PyServerAuthHandler\
+            " arrow::py::flight::PyServerAuthHandler"(CServerAuthHandler):
+        PyServerAuthHandler(object handler, PyServerAuthHandlerVtable vtable)
+
+    cdef cppclass PyClientAuthHandler\
+            " arrow::py::flight::PyClientAuthHandler"(CClientAuthHandler):
+        PyClientAuthHandler(object handler, PyClientAuthHandlerVtable vtable)
+
+    cdef cppclass CPyFlightResultStream\
+            " arrow::py::flight::PyFlightResultStream"(CResultStream):
+        CPyFlightResultStream(object generator,
+                              function[cb_result_next] callback)
+
+    cdef cppclass CPyFlightDataStream\
+            " arrow::py::flight::PyFlightDataStream"(CFlightDataStream):
+        CPyFlightDataStream(object data_source,
+                            unique_ptr[CFlightDataStream] stream)
+
+    cdef cppclass CPyGeneratorFlightDataStream\
+            " arrow::py::flight::PyGeneratorFlightDataStream"\
+            (CFlightDataStream):
+        CPyGeneratorFlightDataStream(object generator,
+                                     shared_ptr[CSchema] schema,
+                                     function[cb_data_stream_next] callback,
+                                     const CIpcWriteOptions& options)
+
+    cdef cppclass PyServerMiddlewareVtable\
+            " arrow::py::flight::PyServerMiddleware::Vtable":
+        PyServerMiddlewareVtable()
+        function[cb_middleware_sending_headers] sending_headers
+        function[cb_middleware_call_completed] call_completed
+
+    cdef cppclass PyClientMiddlewareVtable\
+            " arrow::py::flight::PyClientMiddleware::Vtable":
+        PyClientMiddlewareVtable()
+        function[cb_middleware_sending_headers] sending_headers
+        function[cb_client_middleware_received_headers] received_headers
+        function[cb_middleware_call_completed] call_completed
+
+    cdef cppclass CPyServerMiddleware\
+            " arrow::py::flight::PyServerMiddleware"(CServerMiddleware):
+        CPyServerMiddleware(object middleware, PyServerMiddlewareVtable vtable)
+        void* py_object()
+
+    cdef cppclass CPyServerMiddlewareFactory\
+            " arrow::py::flight::PyServerMiddlewareFactory"\
+            (CServerMiddlewareFactory):
+        CPyServerMiddlewareFactory(
+            object factory,
+            function[cb_server_middleware_start_call] start_call)
+
+    cdef cppclass CPyClientMiddleware\
+            " arrow::py::flight::PyClientMiddleware"(CClientMiddleware):
+        CPyClientMiddleware(object middleware, PyClientMiddlewareVtable vtable)
+
+    cdef cppclass CPyClientMiddlewareFactory\
+            " arrow::py::flight::PyClientMiddlewareFactory"\
+            (CClientMiddlewareFactory):
+        CPyClientMiddlewareFactory(
+            object factory,
+            function[cb_client_middleware_start_call] start_call)
+
+    cdef CStatus CreateFlightInfo" arrow::py::flight::CreateFlightInfo"(
+        shared_ptr[CSchema] schema,
+        CFlightDescriptor& descriptor,
+        vector[CFlightEndpoint] endpoints,
+        int64_t total_records,
+        int64_t total_bytes,
+        c_bool ordered,
+        const c_string& app_metadata,
+        unique_ptr[CFlightInfo]* out)
+
+    cdef CStatus CreateSchemaResult" arrow::py::flight::CreateSchemaResult"(
+        shared_ptr[CSchema] schema,
+        unique_ptr[CSchemaResult]* out)
+
+
+cdef extern from "<variant>" namespace "std" nogil:
+    cdef cppclass CIntStringVariant" std::variant<int, std::string>":
+        CIntStringVariant()
+        CIntStringVariant(int)
+        CIntStringVariant(c_string)
diff --git a/shared/package-files/40/40f04c964f48fe5ab6b01634ba8665e90702abd2dfb08a4c056edcdfbe207a7a/_cudnn_rnn.h b/shared/package-files/40/40f04c964f48fe5ab6b01634ba8665e90702abd2dfb08a4c056edcdfbe207a7a/_cudnn_rnn.h
new file mode 100644
index 0000000000000000000000000000000000000000..076ee0eb3d5a2b6cdf9c8154093c4fe46e4c9990
--- /dev/null
+++ b/shared/package-files/40/40f04c964f48fe5ab6b01634ba8665e90702abd2dfb08a4c056edcdfbe207a7a/_cudnn_rnn.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_cudnn_rnn_ops.h>
+
+namespace at {
+
+
+// aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state);
+  }
+}
+
+// aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn_symint(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state);
+  }
+}
+
+// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
+  }
+}
+
+// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
+  }
+}
+
+// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
+  }
+}
+
+// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_symint_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
+    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
+  }
+}
+
+}
diff --git a/shared/package-files/40/40f48a7add0e0805abea0ff67c7c0fb78ca341a009b237de25814f88a852f511/process_executor.py b/shared/package-files/40/40f48a7add0e0805abea0ff67c7c0fb78ca341a009b237de25814f88a852f511/process_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f32994e1c305862763051e9b84514b0130b9a0b
--- /dev/null
+++ b/shared/package-files/40/40f48a7add0e0805abea0ff67c7c0fb78ca341a009b237de25814f88a852f511/process_executor.py
@@ -0,0 +1,1344 @@
+###############################################################################
+# Re-implementation of the ProcessPoolExecutor more robust to faults
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from concurrent/futures/process_pool_executor.py (17/02/2017)
+#  * Add an extra management thread to detect executor_manager_thread failures,
+#  * Improve the shutdown process to avoid deadlocks,
+#  * Add timeout for workers,
+#  * More robust pickling process.
+#
+# Copyright 2009 Brian Quinlan. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Implements ProcessPoolExecutor.
+
+The follow diagram and text describe the data-flow through the system:
+
+|======================= In-process =====================|== Out-of-process ==|
+
++----------+     +----------+       +--------+     +-----------+    +---------+
+|          |  => | Work Ids |       |        |     | Call Q    |    | Process |
+|          |     +----------+       |        |     +-----------+    |  Pool   |
+|          |     | ...      |       |        |     | ...       |    +---------+
+|          |     | 6        |    => |        |  => | 5, call() | => |         |
+|          |     | 7        |       |        |     | ...       |    |         |
+| Process  |     | ...      |       | Local  |     +-----------+    | Process |
+|  Pool    |     +----------+       | Worker |                      |  #1..n  |
+| Executor |                        | Thread |                      |         |
+|          |     +----------- +     |        |     +-----------+    |         |
+|          | <=> | Work Items | <=> |        | <=  | Result Q  | <= |         |
+|          |     +------------+     |        |     +-----------+    |         |
+|          |     | 6: call()  |     |        |     | ...       |    |         |
+|          |     |    future  |     +--------+     | 4, result |    |         |
+|          |     | ...        |                    | 3, except |    |         |
++----------+     +------------+                    +-----------+    +---------+
+
+Executor.submit() called:
+- creates a uniquely numbered _WorkItem and adds it to the "Work Items" dict
+- adds the id of the _WorkItem to the "Work Ids" queue
+
+Local worker thread:
+- reads work ids from the "Work Ids" queue and looks up the corresponding
+  WorkItem from the "Work Items" dict: if the work item has been cancelled then
+  it is simply removed from the dict, otherwise it is repackaged as a
+  _CallItem and put in the "Call Q". New _CallItems are put in the "Call Q"
+  until "Call Q" is full. NOTE: the size of the "Call Q" is kept small because
+  calls placed in the "Call Q" can no longer be cancelled with Future.cancel().
+- reads _ResultItems from "Result Q", updates the future stored in the
+  "Work Items" dict and deletes the dict entry
+
+Process #1..n:
+- reads _CallItems from "Call Q", executes the calls, and puts the resulting
+  _ResultItems in "Result Q"
+"""
+
+
+__author__ = "Thomas Moreau (thomas.moreau.2010@gmail.com)"
+
+
+import faulthandler
+import os
+import gc
+import sys
+import queue
+import struct
+import weakref
+import warnings
+import itertools
+import traceback
+import threading
+from time import time, sleep
+import multiprocessing as mp
+from functools import partial
+from pickle import PicklingError
+from concurrent.futures import Executor
+from concurrent.futures._base import LOGGER
+from concurrent.futures.process import BrokenProcessPool as _BPPException
+from multiprocessing.connection import wait
+
+from ._base import Future
+from .backend import get_context
+from .backend.context import cpu_count, _MAX_WINDOWS_WORKERS
+from .backend.queues import Queue, SimpleQueue
+from .backend.reduction import set_loky_pickler, get_loky_pickler_name
+from .backend.utils import kill_process_tree, get_exitcodes_terminated_worker
+from .initializers import _prepare_initializer
+
+
+# Mechanism to prevent infinite process spawning. When a worker of a
+# ProcessPoolExecutor nested in MAX_DEPTH Executor tries to create a new
+# Executor, a LokyRecursionError is raised
+MAX_DEPTH = int(os.environ.get("LOKY_MAX_DEPTH", 10))
+_CURRENT_DEPTH = 0
+
+# Minimum time interval between two consecutive memory leak protection checks.
+_MEMORY_LEAK_CHECK_DELAY = 1.0
+
+# Number of bytes of memory usage allowed over the reference process size.
+_MAX_MEMORY_LEAK_SIZE = int(3e8)
+
+
+try:
+    from psutil import Process
+
+    _USE_PSUTIL = True
+
+    def _get_memory_usage(pid, force_gc=False):
+        if force_gc:
+            gc.collect()
+
+        mem_size = Process(pid).memory_info().rss
+        mp.util.debug(f"psutil return memory size: {mem_size}")
+        return mem_size
+
+except ImportError:
+    _USE_PSUTIL = False
+
+
+class _ThreadWakeup:
+    def __init__(self):
+        self._closed = False
+        self._reader, self._writer = mp.Pipe(duplex=False)
+
+    def close(self):
+        if not self._closed:
+            self._closed = True
+            self._writer.close()
+            self._reader.close()
+
+    def wakeup(self):
+        if not self._closed:
+            self._writer.send_bytes(b"")
+
+    def clear(self):
+        if not self._closed:
+            while self._reader.poll():
+                self._reader.recv_bytes()
+
+
+class _ExecutorFlags:
+    """necessary references to maintain executor states without preventing gc
+
+    It permits to keep the information needed by executor_manager_thread
+    and crash_detection_thread to maintain the pool without preventing the
+    garbage collection of unreferenced executors.
+    """
+
+    def __init__(self, shutdown_lock):
+
+        self.shutdown = False
+        self.broken = None
+        self.kill_workers = False
+        self.shutdown_lock = shutdown_lock
+
+    def flag_as_shutting_down(self, kill_workers=None):
+        with self.shutdown_lock:
+            self.shutdown = True
+            if kill_workers is not None:
+                self.kill_workers = kill_workers
+
+    def flag_as_broken(self, broken):
+        with self.shutdown_lock:
+            self.shutdown = True
+            self.broken = broken
+
+
+# Prior to 3.9, executor_manager_thread is created as daemon thread. This means
+# that it is not joined automatically when the interpreter is shutting down.
+# To work around this problem, an exit handler is installed to tell the
+# thread to exit when the interpreter is shutting down and then waits until
+# it finishes. The thread needs to be daemonized because the atexit hooks are
+# called after all non daemonized threads are joined.
+#
+# Starting 3.9, there exists a specific atexit hook to be called before joining
+# the threads so the executor_manager_thread does not need to be daemonized
+# anymore.
+#
+# The atexit hooks are registered when starting the first ProcessPoolExecutor
+# to avoid import having an effect on the interpreter.
+
+_global_shutdown = False
+_global_shutdown_lock = threading.Lock()
+_threads_wakeups = weakref.WeakKeyDictionary()
+
+
+def _python_exit():
+    global _global_shutdown
+    _global_shutdown = True
+
+    # Materialize the list of items to avoid error due to iterating over
+    # changing size dictionary.
+    items = list(_threads_wakeups.items())
+    if len(items) > 0:
+        mp.util.debug(
+            f"Interpreter shutting down. Waking up {len(items)}"
+            f"executor_manager_thread:\n{items}"
+        )
+
+    # Wake up the executor_manager_thread's so they can detect the interpreter
+    # is shutting down and exit.
+    for _, (shutdown_lock, thread_wakeup) in items:
+        with shutdown_lock:
+            thread_wakeup.wakeup()
+
+    # Collect the executor_manager_thread's to make sure we exit cleanly.
+    for thread, _ in items:
+        # This locks is to prevent situations where an executor is gc'ed in one
+        # thread while the atexit finalizer is running in another thread.
+        with _global_shutdown_lock:
+            thread.join()
+
+
+# With the fork context, _thread_wakeups is propagated to children.
+# Clear it after fork to avoid some situation that can cause some
+# freeze when joining the workers.
+mp.util.register_after_fork(_threads_wakeups, lambda obj: obj.clear())
+
+
+# Module variable to register the at_exit call
+process_pool_executor_at_exit = None
+
+# Controls how many more calls than processes will be queued in the call queue.
+# A smaller number will mean that processes spend more time idle waiting for
+# work while a larger number will make Future.cancel() succeed less frequently
+# (Futures in the call queue cannot be cancelled).
+EXTRA_QUEUED_CALLS = 1
+
+
+class _RemoteTraceback(Exception):
+    """Embed stringification of remote traceback in local traceback"""
+
+    def __init__(self, tb=None):
+        self.tb = f'\n"""\n{tb}"""'
+
+    def __str__(self):
+        return self.tb
+
+
+# Do not inherit from BaseException to mirror
+# concurrent.futures.process._ExceptionWithTraceback
+class _ExceptionWithTraceback:
+    def __init__(self, exc):
+        tb = getattr(exc, "__traceback__", None)
+        if tb is None:
+            _, _, tb = sys.exc_info()
+        tb = traceback.format_exception(type(exc), exc, tb)
+        tb = "".join(tb)
+        self.exc = exc
+        self.tb = tb
+
+    def __reduce__(self):
+        return _rebuild_exc, (self.exc, self.tb)
+
+
+def _rebuild_exc(exc, tb):
+    exc.__cause__ = _RemoteTraceback(tb)
+    return exc
+
+
+class _WorkItem:
+
+    __slots__ = ["future", "fn", "args", "kwargs"]
+
+    def __init__(self, future, fn, args, kwargs):
+        self.future = future
+        self.fn = fn
+        self.args = args
+        self.kwargs = kwargs
+
+
+class _ResultItem:
+    def __init__(self, work_id, exception=None, result=None):
+        self.work_id = work_id
+        self.exception = exception
+        self.result = result
+
+
+class _CallItem:
+    def __init__(self, work_id, fn, args, kwargs):
+        self.work_id = work_id
+        self.fn = fn
+        self.args = args
+        self.kwargs = kwargs
+
+        # Store the current loky_pickler so it is correctly set in the worker
+        self.loky_pickler = get_loky_pickler_name()
+
+    def __call__(self):
+        set_loky_pickler(self.loky_pickler)
+        return self.fn(*self.args, **self.kwargs)
+
+    def __repr__(self):
+        return (
+            f"CallItem({self.work_id}, {self.fn}, {self.args}, {self.kwargs})"
+        )
+
+
+class _SafeQueue(Queue):
+    """Safe Queue set exception to the future object linked to a job"""
+
+    def __init__(
+        self,
+        max_size=0,
+        ctx=None,
+        pending_work_items=None,
+        running_work_items=None,
+        thread_wakeup=None,
+        shutdown_lock=None,
+        reducers=None,
+    ):
+        self.thread_wakeup = thread_wakeup
+        self.shutdown_lock = shutdown_lock
+        self.pending_work_items = pending_work_items
+        self.running_work_items = running_work_items
+        super().__init__(max_size, reducers=reducers, ctx=ctx)
+
+    def _on_queue_feeder_error(self, e, obj):
+        if isinstance(obj, _CallItem):
+            # format traceback only works on python3
+            if isinstance(e, struct.error):
+                raised_error = RuntimeError(
+                    "The task could not be sent to the workers as it is too "
+                    "large for `send_bytes`."
+                )
+            else:
+                raised_error = PicklingError(
+                    "Could not pickle the task to send it to the workers."
+                )
+            tb = traceback.format_exception(
+                type(e), e, getattr(e, "__traceback__", None)
+            )
+            raised_error.__cause__ = _RemoteTraceback("".join(tb))
+            work_item = self.pending_work_items.pop(obj.work_id, None)
+            self.running_work_items.remove(obj.work_id)
+            # work_item can be None if another process terminated. In this
+            # case, the executor_manager_thread fails all work_items with
+            # BrokenProcessPool
+            if work_item is not None:
+                work_item.future.set_exception(raised_error)
+                del work_item
+            with self.shutdown_lock:
+                self.thread_wakeup.wakeup()
+        else:
+            super()._on_queue_feeder_error(e, obj)
+
+
+def _get_chunks(chunksize, *iterables):
+    """Iterates over zip()ed iterables in chunks."""
+    it = zip(*iterables)
+    while True:
+        chunk = tuple(itertools.islice(it, chunksize))
+        if not chunk:
+            return
+        yield chunk
+
+
+def _process_chunk(fn, chunk):
+    """Processes a chunk of an iterable passed to map.
+
+    Runs the function passed to map() on a chunk of the
+    iterable passed to map.
+
+    This function is run in a separate process.
+
+    """
+    return [fn(*args) for args in chunk]
+
+
+def _sendback_result(result_queue, work_id, result=None, exception=None):
+    """Safely send back the given result or exception"""
+    try:
+        result_queue.put(
+            _ResultItem(work_id, result=result, exception=exception)
+        )
+    except BaseException as e:
+        exc = _ExceptionWithTraceback(e)
+        result_queue.put(_ResultItem(work_id, exception=exc))
+
+
+def _enable_faulthandler_if_needed():
+    if "PYTHONFAULTHANDLER" in os.environ:
+        # Respect the environment variable to configure faulthandler. This
+        # makes it possible to never enable faulthandler in the loky workers by
+        # setting PYTHONFAULTHANDLER=0 explicitly in the environment.
+        mp.util.debug(
+            f"faulthandler explicitly configured by environment variable: "
+            f"PYTHONFAULTHANDLER={os.environ['PYTHONFAULTHANDLER']}."
+        )
+    else:
+        if faulthandler.is_enabled():
+            # Fault handler is already enabled, possibly via a custom
+            # initializer to customize the behavior.
+            mp.util.debug("faulthandler already enabled.")
+        else:
+            # Enable faulthandler by default with default paramaters otherwise.
+            mp.util.debug(
+                "Enabling faulthandler to report tracebacks on worker crashes."
+            )
+            faulthandler.enable()
+
+
+def _process_worker(
+    call_queue,
+    result_queue,
+    initializer,
+    initargs,
+    processes_management_lock,
+    timeout,
+    worker_exit_lock,
+    current_depth,
+):
+    """Evaluates calls from call_queue and places the results in result_queue.
+
+    This worker is run in a separate process.
+
+    Args:
+        call_queue: A ctx.Queue of _CallItems that will be read and
+            evaluated by the worker.
+        result_queue: A ctx.Queue of _ResultItems that will written
+            to by the worker.
+        initializer: A callable initializer, or None
+        initargs: A tuple of args for the initializer
+        processes_management_lock: A ctx.Lock avoiding worker timeout while
+            some workers are being spawned.
+        timeout: maximum time to wait for a new item in the call_queue. If that
+            time is expired, the worker will shutdown.
+        worker_exit_lock: Lock to avoid flagging the executor as broken on
+            workers timeout.
+        current_depth: Nested parallelism level, to avoid infinite spawning.
+    """
+    if initializer is not None:
+        try:
+            initializer(*initargs)
+        except BaseException:
+            LOGGER.critical("Exception in initializer:", exc_info=True)
+            # The parent will notice that the process stopped and
+            # mark the pool broken
+            return
+
+    # set the global _CURRENT_DEPTH mechanism to limit recursive call
+    global _CURRENT_DEPTH
+    _CURRENT_DEPTH = current_depth
+    _process_reference_size = None
+    _last_memory_leak_check = None
+    pid = os.getpid()
+
+    mp.util.debug(f"Worker started with timeout={timeout}")
+    _enable_faulthandler_if_needed()
+
+    while True:
+        try:
+            call_item = call_queue.get(block=True, timeout=timeout)
+            if call_item is None:
+                mp.util.info("Shutting down worker on sentinel")
+        except queue.Empty:
+            mp.util.info(f"Shutting down worker after timeout {timeout:0.3f}s")
+            if processes_management_lock.acquire(block=False):
+                processes_management_lock.release()
+                call_item = None
+            else:
+                mp.util.info("Could not acquire processes_management_lock")
+                continue
+        except BaseException:
+            previous_tb = traceback.format_exc()
+            try:
+                result_queue.put(_RemoteTraceback(previous_tb))
+            except BaseException:
+                # If we cannot format correctly the exception, at least print
+                # the traceback.
+                print(previous_tb)
+            mp.util.debug("Exiting with code 1")
+            sys.exit(1)
+        if call_item is None:
+            # Notify queue management thread about worker shutdown
+            result_queue.put(pid)
+            is_clean = worker_exit_lock.acquire(True, timeout=30)
+
+            # Early notify any loky executor running in this worker process
+            # (nested parallelism) that this process is about to shutdown to
+            # avoid a deadlock waiting undifinitely for the worker to finish.
+            _python_exit()
+
+            if is_clean:
+                mp.util.debug("Exited cleanly")
+            else:
+                mp.util.info("Main process did not release worker_exit")
+            return
+        try:
+            r = call_item()
+        except BaseException as e:
+            exc = _ExceptionWithTraceback(e)
+            result_queue.put(_ResultItem(call_item.work_id, exception=exc))
+        else:
+            _sendback_result(result_queue, call_item.work_id, result=r)
+            del r
+
+        # Free the resource as soon as possible, to avoid holding onto
+        # open files or shared memory that is not needed anymore
+        del call_item
+
+        if _USE_PSUTIL:
+            if _process_reference_size is None:
+                # Make reference measurement after the first call
+                _process_reference_size = _get_memory_usage(pid, force_gc=True)
+                _last_memory_leak_check = time()
+                continue
+            if time() - _last_memory_leak_check > _MEMORY_LEAK_CHECK_DELAY:
+                mem_usage = _get_memory_usage(pid)
+                _last_memory_leak_check = time()
+                if mem_usage - _process_reference_size < _MAX_MEMORY_LEAK_SIZE:
+                    # Memory usage stays within bounds: everything is fine.
+                    continue
+
+                # Check again memory usage; this time take the measurement
+                # after a forced garbage collection to break any reference
+                # cycles.
+                mem_usage = _get_memory_usage(pid, force_gc=True)
+                _last_memory_leak_check = time()
+                if mem_usage - _process_reference_size < _MAX_MEMORY_LEAK_SIZE:
+                    # The GC managed to free the memory: everything is fine.
+                    continue
+
+                # The process is leaking memory: let the master process
+                # know that we need to start a new worker.
+                mp.util.info("Memory leak detected: shutting down worker")
+                result_queue.put(pid)
+                with worker_exit_lock:
+                    mp.util.debug("Exit due to memory leak")
+                    return
+        else:
+            # if psutil is not installed, trigger gc.collect events
+            # regularly to limit potential memory leaks due to reference cycles
+            if _last_memory_leak_check is None or (
+                time() - _last_memory_leak_check > _MEMORY_LEAK_CHECK_DELAY
+            ):
+                gc.collect()
+                _last_memory_leak_check = time()
+
+
+class _ExecutorManagerThread(threading.Thread):
+    """Manages the communication between this process and the worker processes.
+
+    The manager is run in a local thread.
+
+    Args:
+        executor: A reference to the ProcessPoolExecutor that owns
+            this thread. A weakref will be own by the manager as well as
+            references to internal objects used to introspect the state of
+            the executor.
+    """
+
+    def __init__(self, executor):
+        # Store references to necessary internals of the executor.
+
+        # A _ThreadWakeup to allow waking up the executor_manager_thread from
+        # the main Thread and avoid deadlocks caused by permanently
+        # locked queues.
+        self.thread_wakeup = executor._executor_manager_thread_wakeup
+        self.shutdown_lock = executor._shutdown_lock
+
+        # A weakref.ref to the ProcessPoolExecutor that owns this thread. Used
+        # to determine if the ProcessPoolExecutor has been garbage collected
+        # and that the manager can exit.
+        # When the executor gets garbage collected, the weakref callback
+        # will wake up the queue management thread so that it can terminate
+        # if there is no pending work item.
+        def weakref_cb(
+            _,
+            thread_wakeup=self.thread_wakeup,
+            shutdown_lock=self.shutdown_lock,
+        ):
+            if mp is not None:
+                # At this point, the multiprocessing module can already be
+                # garbage collected. We only log debug info when still
+                # possible.
+                mp.util.debug(
+                    "Executor collected: triggering callback for"
+                    " QueueManager wakeup"
+                )
+            with shutdown_lock:
+                thread_wakeup.wakeup()
+
+        self.executor_reference = weakref.ref(executor, weakref_cb)
+
+        # The flags of the executor
+        self.executor_flags = executor._flags
+
+        # A list of the ctx.Process instances used as workers.
+        self.processes = executor._processes
+
+        # A ctx.Queue that will be filled with _CallItems derived from
+        # _WorkItems for processing by the process workers.
+        self.call_queue = executor._call_queue
+
+        # A ctx.SimpleQueue of _ResultItems generated by the process workers.
+        self.result_queue = executor._result_queue
+
+        # A queue.Queue of work ids e.g. Queue([5, 6, ...]).
+        self.work_ids_queue = executor._work_ids
+
+        # A dict mapping work ids to _WorkItems e.g.
+        #     {5: <_WorkItem...>, 6: <_WorkItem...>, ...}
+        self.pending_work_items = executor._pending_work_items
+
+        # A list of the work_ids that are currently running
+        self.running_work_items = executor._running_work_items
+
+        # A lock to avoid concurrent shutdown of workers on timeout and spawn
+        # of new processes or shut down
+        self.processes_management_lock = executor._processes_management_lock
+
+        super().__init__(name="ExecutorManagerThread")
+        if sys.version_info < (3, 9):
+            self.daemon = True
+
+    def run(self):
+        # Main loop for the executor manager thread.
+
+        while True:
+            self.add_call_item_to_queue()
+
+            result_item, is_broken, bpe = self.wait_result_broken_or_wakeup()
+
+            if is_broken:
+                self.terminate_broken(bpe)
+                return
+            if result_item is not None:
+                self.process_result_item(result_item)
+                # Delete reference to result_item to avoid keeping references
+                # while waiting on new results.
+                del result_item
+
+            if self.is_shutting_down():
+                self.flag_executor_shutting_down()
+
+                # Since no new work items can be added, it is safe to shutdown
+                # this thread if there are no pending work items.
+                if not self.pending_work_items:
+                    self.join_executor_internals()
+                    return
+
+    def add_call_item_to_queue(self):
+        # Fills call_queue with _WorkItems from pending_work_items.
+        # This function never blocks.
+        while True:
+            if self.call_queue.full():
+                return
+            try:
+                work_id = self.work_ids_queue.get(block=False)
+            except queue.Empty:
+                return
+            else:
+                work_item = self.pending_work_items[work_id]
+
+                if work_item.future.set_running_or_notify_cancel():
+                    self.running_work_items += [work_id]
+                    self.call_queue.put(
+                        _CallItem(
+                            work_id,
+                            work_item.fn,
+                            work_item.args,
+                            work_item.kwargs,
+                        ),
+                        block=True,
+                    )
+                else:
+                    del self.pending_work_items[work_id]
+                    continue
+
+    def wait_result_broken_or_wakeup(self):
+        # Wait for a result to be ready in the result_queue while checking
+        # that all worker processes are still running, or for a wake up
+        # signal send. The wake up signals come either from new tasks being
+        # submitted, from the executor being shutdown/gc-ed, or from the
+        # shutdown of the python interpreter.
+        result_reader = self.result_queue._reader
+        wakeup_reader = self.thread_wakeup._reader
+        readers = [result_reader, wakeup_reader]
+        worker_sentinels = [p.sentinel for p in list(self.processes.values())]
+        ready = wait(readers + worker_sentinels)
+
+        bpe = None
+        is_broken = True
+        result_item = None
+        if result_reader in ready:
+            try:
+                result_item = result_reader.recv()
+                if isinstance(result_item, _RemoteTraceback):
+                    bpe = BrokenProcessPool(
+                        "A task has failed to un-serialize. Please ensure that"
+                        " the arguments of the function are all picklable."
+                    )
+                    bpe.__cause__ = result_item
+                else:
+                    is_broken = False
+            except BaseException as e:
+                bpe = BrokenProcessPool(
+                    "A result has failed to un-serialize. Please ensure that "
+                    "the objects returned by the function are always "
+                    "picklable."
+                )
+                tb = traceback.format_exception(
+                    type(e), e, getattr(e, "__traceback__", None)
+                )
+                bpe.__cause__ = _RemoteTraceback("".join(tb))
+
+        elif wakeup_reader in ready:
+            # This is simply a wake-up event that might either trigger putting
+            # more tasks in the queue or trigger the clean up of resources.
+            is_broken = False
+        else:
+            # A worker has terminated and we don't know why, set the state of
+            # the executor as broken
+            exit_codes = ""
+            if sys.platform != "win32":
+                # In Windows, introspecting terminated workers exitcodes seems
+                # unstable, therefore they are not appended in the exception
+                # message.
+                exit_codes = (
+                    "\nThe exit codes of the workers are "
+                    f"{get_exitcodes_terminated_worker(self.processes)}"
+                )
+            mp.util.debug(
+                "A worker unexpectedly terminated. Workers that "
+                "might have caused the breakage: "
+                + str(
+                    {
+                        p.name: p.exitcode
+                        for p in list(self.processes.values())
+                        if p is not None and p.sentinel in ready
+                    }
+                )
+            )
+            bpe = TerminatedWorkerError(
+                "A worker process managed by the executor was unexpectedly "
+                "terminated. This could be caused by a segmentation fault "
+                "while calling the function or by an excessive memory usage "
+                "causing the Operating System to kill the worker.\n"
+                f"{exit_codes}\n"
+                "Detailed tracebacks of the workers should have been printed "
+                "to stderr in the executor process if faulthandler was not "
+                "disabled."
+            )
+
+        self.thread_wakeup.clear()
+
+        return result_item, is_broken, bpe
+
+    def process_result_item(self, result_item):
+        # Process the received a result_item. This can be either the PID of a
+        # worker that exited gracefully or a _ResultItem
+
+        if isinstance(result_item, int):
+            # Clean shutdown of a worker using its PID, either on request
+            # by the executor.shutdown method or by the timeout of the worker
+            # itself: we should not mark the executor as broken.
+            with self.processes_management_lock:
+                p = self.processes.pop(result_item, None)
+
+            # p can be None if the executor is concurrently shutting down.
+            if p is not None:
+                p._worker_exit_lock.release()
+                mp.util.debug(
+                    f"joining {p.name} when processing {p.pid} as result_item"
+                )
+                p.join()
+                del p
+
+            # Make sure the executor have the right number of worker, even if a
+            # worker timeout while some jobs were submitted. If some work is
+            # pending or there is less processes than running items, we need to
+            # start a new Process and raise a warning.
+            n_pending = len(self.pending_work_items)
+            n_running = len(self.running_work_items)
+            if n_pending - n_running > 0 or n_running > len(self.processes):
+                executor = self.executor_reference()
+                if (
+                    executor is not None
+                    and len(self.processes) < executor._max_workers
+                ):
+                    warnings.warn(
+                        "A worker stopped while some jobs were given to the "
+                        "executor. This can be caused by a too short worker "
+                        "timeout or by a memory leak.",
+                        UserWarning,
+                    )
+                    with executor._processes_management_lock:
+                        executor._adjust_process_count()
+                    executor = None
+        else:
+            # Received a _ResultItem so mark the future as completed.
+            work_item = self.pending_work_items.pop(result_item.work_id, None)
+            # work_item can be None if another process terminated (see above)
+            if work_item is not None:
+                if result_item.exception:
+                    work_item.future.set_exception(result_item.exception)
+                else:
+                    work_item.future.set_result(result_item.result)
+                self.running_work_items.remove(result_item.work_id)
+
+    def is_shutting_down(self):
+        # Check whether we should start shutting down the executor.
+        executor = self.executor_reference()
+        # No more work items can be added if:
+        #   - The interpreter is shutting down OR
+        #   - The executor that owns this thread is not broken AND
+        #        * The executor that owns this worker has been collected OR
+        #        * The executor that owns this worker has been shutdown.
+        # If the executor is broken, it should be detected in the next loop.
+        return _global_shutdown or (
+            (executor is None or self.executor_flags.shutdown)
+            and not self.executor_flags.broken
+        )
+
+    def terminate_broken(self, bpe):
+        # Terminate the executor because it is in a broken state. The bpe
+        # argument can be used to display more information on the error that
+        # lead the executor into becoming broken.
+
+        # Mark the process pool broken so that submits fail right now.
+        self.executor_flags.flag_as_broken(bpe)
+
+        # Mark pending tasks as failed.
+        for work_item in self.pending_work_items.values():
+            work_item.future.set_exception(bpe)
+            # Delete references to object. See issue16284
+            del work_item
+        self.pending_work_items.clear()
+
+        # Terminate remaining workers forcibly: the queues or their
+        # locks may be in a dirty state and block forever.
+        self.kill_workers(reason="broken executor")
+
+        # clean up resources
+        self.join_executor_internals()
+
+    def flag_executor_shutting_down(self):
+        # Flag the executor as shutting down and cancel remaining tasks if
+        # requested as early as possible if it is not gc-ed yet.
+        self.executor_flags.flag_as_shutting_down()
+
+        # Cancel pending work items if requested.
+        if self.executor_flags.kill_workers:
+            while self.pending_work_items:
+                _, work_item = self.pending_work_items.popitem()
+                work_item.future.set_exception(
+                    ShutdownExecutorError(
+                        "The Executor was shutdown with `kill_workers=True` "
+                        "before this job could complete."
+                    )
+                )
+                del work_item
+
+            # Kill the remaining worker forcibly to no waste time joining them
+            self.kill_workers(reason="executor shutting down")
+
+    def kill_workers(self, reason=""):
+        # Terminate the remaining workers using SIGKILL. This function also
+        # terminates descendant workers of the children in case there is some
+        # nested parallelism.
+        while self.processes:
+            _, p = self.processes.popitem()
+            mp.util.debug(f"terminate process {p.name}, reason: {reason}")
+            try:
+                kill_process_tree(p)
+            except ProcessLookupError:  # pragma: no cover
+                pass
+
+    def shutdown_workers(self):
+        # shutdown all workers in self.processes
+
+        # Create a list to avoid RuntimeError due to concurrent modification of
+        # processes. nb_children_alive is thus an upper bound. Also release the
+        # processes' _worker_exit_lock to accelerate the shutdown procedure, as
+        # there is no need for hand-shake here.
+        with self.processes_management_lock:
+            n_children_to_stop = 0
+            for p in list(self.processes.values()):
+                mp.util.debug(f"releasing worker exit lock on {p.name}")
+                p._worker_exit_lock.release()
+                n_children_to_stop += 1
+
+        mp.util.debug(f"found {n_children_to_stop} processes to stop")
+
+        # Send the right number of sentinels, to make sure all children are
+        # properly terminated. Do it with a mechanism that avoid hanging on
+        # Full queue when all workers have already been shutdown.
+        n_sentinels_sent = 0
+        cooldown_time = 0.001
+        while (
+            n_sentinels_sent < n_children_to_stop
+            and self.get_n_children_alive() > 0
+        ):
+            for _ in range(n_children_to_stop - n_sentinels_sent):
+                try:
+                    self.call_queue.put_nowait(None)
+                    n_sentinels_sent += 1
+                except queue.Full as e:
+                    if cooldown_time > 5.0:
+                        mp.util.info(
+                            "failed to send all sentinels and exit with error."
+                            f"\ncall_queue size={self.call_queue._maxsize}; "
+                            f" full is {self.call_queue.full()}; "
+                        )
+                        raise e
+                    mp.util.info(
+                        "full call_queue prevented to send all sentinels at "
+                        "once, waiting..."
+                    )
+                    sleep(cooldown_time)
+                    cooldown_time *= 1.2
+                    break
+
+        mp.util.debug(f"sent {n_sentinels_sent} sentinels to the call queue")
+
+    def join_executor_internals(self):
+        self.shutdown_workers()
+
+        # Release the queue's resources as soon as possible. Flag the feeder
+        # thread for clean exit to avoid having the crash detection thread flag
+        # the Executor as broken during the shutdown. This is safe as either:
+        #  * We don't need to communicate with the workers anymore
+        #  * There is nothing left in the Queue buffer except None sentinels
+        mp.util.debug("closing call_queue")
+        self.call_queue.close()
+        self.call_queue.join_thread()
+
+        # Closing result_queue
+        mp.util.debug("closing result_queue")
+        self.result_queue.close()
+
+        mp.util.debug("closing thread_wakeup")
+        with self.shutdown_lock:
+            self.thread_wakeup.close()
+
+        # If .join() is not called on the created processes then
+        # some ctx.Queue methods may deadlock on macOS.
+        with self.processes_management_lock:
+            mp.util.debug(f"joining {len(self.processes)} processes")
+            n_joined_processes = 0
+            while True:
+                try:
+                    pid, p = self.processes.popitem()
+                    mp.util.debug(f"joining process {p.name} with pid {pid}")
+                    p.join()
+                    n_joined_processes += 1
+                except KeyError:
+                    break
+
+            mp.util.debug(
+                "executor management thread clean shutdown of "
+                f"{n_joined_processes} workers"
+            )
+
+    def get_n_children_alive(self):
+        # This is an upper bound on the number of children alive.
+        with self.processes_management_lock:
+            return sum(p.is_alive() for p in list(self.processes.values()))
+
+
+_system_limits_checked = False
+_system_limited = None
+
+
+def _check_system_limits():
+    global _system_limits_checked, _system_limited
+    if _system_limits_checked and _system_limited:
+        raise NotImplementedError(_system_limited)
+    _system_limits_checked = True
+    try:
+        nsems_max = os.sysconf("SC_SEM_NSEMS_MAX")
+    except (AttributeError, ValueError):
+        # sysconf not available or setting not available
+        return
+    if nsems_max == -1:
+        # undetermined limit, assume that limit is determined
+        # by available memory only
+        return
+    if nsems_max >= 256:
+        # minimum number of semaphores available
+        # according to POSIX
+        return
+    _system_limited = (
+        f"system provides too few semaphores ({nsems_max} available, "
+        "256 necessary)"
+    )
+    raise NotImplementedError(_system_limited)
+
+
+def _chain_from_iterable_of_lists(iterable):
+    """
+    Specialized implementation of itertools.chain.from_iterable.
+    Each item in *iterable* should be a list.  This function is
+    careful not to keep references to yielded objects.
+    """
+    for element in iterable:
+        element.reverse()
+        while element:
+            yield element.pop()
+
+
+def _check_max_depth(context):
+    # Limit the maxmal recursion level
+    global _CURRENT_DEPTH
+    if context.get_start_method() == "fork" and _CURRENT_DEPTH > 0:
+        raise LokyRecursionError(
+            "Could not spawn extra nested processes at depth superior to "
+            "MAX_DEPTH=1. It is not possible to increase this limit when "
+            "using the 'fork' start method."
+        )
+
+    if 0 < MAX_DEPTH and _CURRENT_DEPTH + 1 > MAX_DEPTH:
+        raise LokyRecursionError(
+            "Could not spawn extra nested processes at depth superior to "
+            f"MAX_DEPTH={MAX_DEPTH}. If this is intendend, you can change "
+            "this limit with the LOKY_MAX_DEPTH environment variable."
+        )
+
+
+class LokyRecursionError(RuntimeError):
+    """A process tries to spawn too many levels of nested processes."""
+
+
+class BrokenProcessPool(_BPPException):
+    """
+    Raised when the executor is broken while a future was in the running state.
+    The cause can an error raised when unpickling the task in the worker
+    process or when unpickling the result value in the parent process. It can
+    also be caused by a worker process being terminated unexpectedly.
+    """
+
+
+class TerminatedWorkerError(BrokenProcessPool):
+    """
+    Raised when a process in a ProcessPoolExecutor terminated abruptly
+    while a future was in the running state.
+    """
+
+
+# Alias for backward compat (for code written for loky 1.1.4 and earlier). Do
+# not use in new code.
+BrokenExecutor = BrokenProcessPool
+
+
+class ShutdownExecutorError(RuntimeError):
+    """
+    Raised when a ProcessPoolExecutor is shutdown while a future was in the
+    running or pending state.
+    """
+
+
+class ProcessPoolExecutor(Executor):
+
+    _at_exit = None
+
+    def __init__(
+        self,
+        max_workers=None,
+        job_reducers=None,
+        result_reducers=None,
+        timeout=None,
+        context=None,
+        initializer=None,
+        initargs=(),
+        env=None,
+    ):
+        """Initializes a new ProcessPoolExecutor instance.
+
+        Args:
+            max_workers: int, optional (default: cpu_count())
+                The maximum number of processes that can be used to execute the
+                given calls. If None or not given then as many worker processes
+                will be created as the number of CPUs the current process
+                can use.
+            job_reducers, result_reducers: dict(type: reducer_func)
+                Custom reducer for pickling the jobs and the results from the
+                Executor. If only `job_reducers` is provided, `result_reducer`
+                will use the same reducers
+            timeout: int, optional (default: None)
+                Idle workers exit after timeout seconds. If a new job is
+                submitted after the timeout, the executor will start enough
+                new Python processes to make sure the pool of workers is full.
+            context: A multiprocessing context to launch the workers. This
+                object should provide SimpleQueue, Queue and Process.
+            initializer: An callable used to initialize worker processes.
+            initargs: A tuple of arguments to pass to the initializer.
+            env: A dict of environment variable to overwrite in the child
+                process. The environment variables are set before any module is
+                loaded. Note that this only works with the loky context.
+        """
+        _check_system_limits()
+
+        if max_workers is None:
+            self._max_workers = cpu_count()
+        else:
+            if max_workers <= 0:
+                raise ValueError("max_workers must be greater than 0")
+            self._max_workers = max_workers
+
+        if (
+            sys.platform == "win32"
+            and self._max_workers > _MAX_WINDOWS_WORKERS
+        ):
+            warnings.warn(
+                f"On Windows, max_workers cannot exceed {_MAX_WINDOWS_WORKERS} "
+                "due to limitations of the operating system."
+            )
+            self._max_workers = _MAX_WINDOWS_WORKERS
+
+        if context is None:
+            context = get_context()
+        self._context = context
+        self._env = env
+
+        self._initializer, self._initargs = _prepare_initializer(
+            initializer, initargs
+        )
+        _check_max_depth(self._context)
+
+        if result_reducers is None:
+            result_reducers = job_reducers
+
+        # Timeout
+        self._timeout = timeout
+
+        # Management thread
+        self._executor_manager_thread = None
+
+        # Map of pids to processes
+        self._processes = {}
+
+        # Internal variables of the ProcessPoolExecutor
+        self._processes = {}
+        self._queue_count = 0
+        self._pending_work_items = {}
+        self._running_work_items = []
+        self._work_ids = queue.Queue()
+        self._processes_management_lock = self._context.Lock()
+        self._executor_manager_thread = None
+        self._shutdown_lock = threading.Lock()
+
+        # _ThreadWakeup is a communication channel used to interrupt the wait
+        # of the main loop of executor_manager_thread from another thread (e.g.
+        # when calling executor.submit or executor.shutdown). We do not use the
+        # _result_queue to send wakeup signals to the executor_manager_thread
+        # as it could result in a deadlock if a worker process dies with the
+        # _result_queue write lock still acquired.
+        #
+        # _shutdown_lock must be locked to access _ThreadWakeup.wakeup.
+        self._executor_manager_thread_wakeup = _ThreadWakeup()
+
+        # Flag to hold the state of the Executor. This permits to introspect
+        # the Executor state even once it has been garbage collected.
+        self._flags = _ExecutorFlags(self._shutdown_lock)
+
+        # Finally setup the queues for interprocess communication
+        self._setup_queues(job_reducers, result_reducers)
+
+        mp.util.debug("ProcessPoolExecutor is setup")
+
+    def _setup_queues(self, job_reducers, result_reducers, queue_size=None):
+        # Make the call queue slightly larger than the number of processes to
+        # prevent the worker processes from idling. But don't make it too big
+        # because futures in the call queue cannot be cancelled.
+        if queue_size is None:
+            queue_size = 2 * self._max_workers + EXTRA_QUEUED_CALLS
+        self._call_queue = _SafeQueue(
+            max_size=queue_size,
+            pending_work_items=self._pending_work_items,
+            running_work_items=self._running_work_items,
+            thread_wakeup=self._executor_manager_thread_wakeup,
+            shutdown_lock=self._shutdown_lock,
+            reducers=job_reducers,
+            ctx=self._context,
+        )
+        # Killed worker processes can produce spurious "broken pipe"
+        # tracebacks in the queue's own worker thread. But we detect killed
+        # processes anyway, so silence the tracebacks.
+        self._call_queue._ignore_epipe = True
+
+        self._result_queue = SimpleQueue(
+            reducers=result_reducers, ctx=self._context
+        )
+
+    def _start_executor_manager_thread(self):
+        if self._executor_manager_thread is None:
+            mp.util.debug("_start_executor_manager_thread called")
+
+            # Start the processes so that their sentinels are known.
+            self._executor_manager_thread = _ExecutorManagerThread(self)
+            self._executor_manager_thread.start()
+
+            # register this executor in a mechanism that ensures it will wakeup
+            # when the interpreter is exiting.
+            _threads_wakeups[self._executor_manager_thread] = (
+                self._shutdown_lock,
+                self._executor_manager_thread_wakeup,
+            )
+
+            global process_pool_executor_at_exit
+            if process_pool_executor_at_exit is None:
+                # Ensure that the _python_exit function will be called before
+                # the multiprocessing.Queue._close finalizers which have an
+                # exitpriority of 10.
+
+                if sys.version_info < (3, 9):
+                    process_pool_executor_at_exit = mp.util.Finalize(
+                        None, _python_exit, exitpriority=20
+                    )
+                else:
+                    process_pool_executor_at_exit = threading._register_atexit(
+                        _python_exit
+                    )
+
+    def _adjust_process_count(self):
+        while len(self._processes) < self._max_workers:
+            worker_exit_lock = self._context.BoundedSemaphore(1)
+            args = (
+                self._call_queue,
+                self._result_queue,
+                self._initializer,
+                self._initargs,
+                self._processes_management_lock,
+                self._timeout,
+                worker_exit_lock,
+                _CURRENT_DEPTH + 1,
+            )
+            worker_exit_lock.acquire()
+            try:
+                # Try to spawn the process with some environment variable to
+                # overwrite but it only works with the loky context for now.
+                p = self._context.Process(
+                    target=_process_worker, args=args, env=self._env
+                )
+            except TypeError:
+                p = self._context.Process(target=_process_worker, args=args)
+            p._worker_exit_lock = worker_exit_lock
+            p.start()
+            self._processes[p.pid] = p
+        mp.util.debug(
+            f"Adjusted process count to {self._max_workers}: "
+            f"{[(p.name, pid) for pid, p in self._processes.items()]}"
+        )
+
+    def _ensure_executor_running(self):
+        """ensures all workers and management thread are running"""
+        with self._processes_management_lock:
+            if len(self._processes) != self._max_workers:
+                self._adjust_process_count()
+            self._start_executor_manager_thread()
+
+    def submit(self, fn, *args, **kwargs):
+        with self._flags.shutdown_lock:
+            if self._flags.broken is not None:
+                raise self._flags.broken
+            if self._flags.shutdown:
+                raise ShutdownExecutorError(
+                    "cannot schedule new futures after shutdown"
+                )
+
+            # Cannot submit a new calls once the interpreter is shutting down.
+            # This check avoids spawning new processes at exit.
+            if _global_shutdown:
+                raise RuntimeError(
+                    "cannot schedule new futures after interpreter shutdown"
+                )
+
+            f = Future()
+            w = _WorkItem(f, fn, args, kwargs)
+
+            self._pending_work_items[self._queue_count] = w
+            self._work_ids.put(self._queue_count)
+            self._queue_count += 1
+            # Wake up queue management thread
+            self._executor_manager_thread_wakeup.wakeup()
+
+            self._ensure_executor_running()
+            return f
+
+    submit.__doc__ = Executor.submit.__doc__
+
+    def map(self, fn, *iterables, **kwargs):
+        """Returns an iterator equivalent to map(fn, iter).
+
+        Args:
+            fn: A callable that will take as many arguments as there are
+                passed iterables.
+            timeout: The maximum number of seconds to wait. If None, then there
+                is no limit on the wait time.
+            chunksize: If greater than one, the iterables will be chopped into
+                chunks of size chunksize and submitted to the process pool.
+                If set to one, the items in the list will be sent one at a
+                time.
+
+        Returns:
+            An iterator equivalent to: map(func, *iterables) but the calls may
+            be evaluated out-of-order.
+
+        Raises:
+            TimeoutError: If the entire result iterator could not be generated
+                before the given timeout.
+            Exception: If fn(*args) raises for any values.
+        """
+        timeout = kwargs.get("timeout", None)
+        chunksize = kwargs.get("chunksize", 1)
+        if chunksize < 1:
+            raise ValueError("chunksize must be >= 1.")
+
+        results = super().map(
+            partial(_process_chunk, fn),
+            _get_chunks(chunksize, *iterables),
+            timeout=timeout,
+        )
+        return _chain_from_iterable_of_lists(results)
+
+    def shutdown(self, wait=True, kill_workers=False):
+        mp.util.debug(f"shutting down executor {self}")
+
+        self._flags.flag_as_shutting_down(kill_workers)
+        executor_manager_thread = self._executor_manager_thread
+        executor_manager_thread_wakeup = self._executor_manager_thread_wakeup
+
+        if executor_manager_thread_wakeup is not None:
+            # Wake up queue management thread
+            with self._shutdown_lock:
+                self._executor_manager_thread_wakeup.wakeup()
+
+        if executor_manager_thread is not None and wait:
+            # This locks avoids concurrent join if the interpreter
+            # is shutting down.
+            with _global_shutdown_lock:
+                executor_manager_thread.join()
+                _threads_wakeups.pop(executor_manager_thread, None)
+
+        # To reduce the risk of opening too many files, remove references to
+        # objects that use file descriptors.
+        self._executor_manager_thread = None
+        self._executor_manager_thread_wakeup = None
+        self._call_queue = None
+        self._result_queue = None
+        self._processes_management_lock = None
+
+    shutdown.__doc__ = Executor.shutdown.__doc__
diff --git a/shared/package-files/40/40f8c7ec17e12fc405c071e4ac36a4f2034e2cc95bd62b403b97e86e37682e9f/attr.h b/shared/package-files/40/40f8c7ec17e12fc405c071e4ac36a4f2034e2cc95bd62b403b97e86e37682e9f/attr.h
new file mode 100644
index 0000000000000000000000000000000000000000..1044db94d906ac5fcf6faab6ac7668187314598f
--- /dev/null
+++ b/shared/package-files/40/40f8c7ec17e12fc405c071e4ac36a4f2034e2cc95bd62b403b97e86e37682e9f/attr.h
@@ -0,0 +1,690 @@
+/*
+    pybind11/attr.h: Infrastructure for processing custom
+    type and function attributes
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "cast.h"
+
+#include <functional>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+/// \addtogroup annotations
+/// @{
+
+/// Annotation for methods
+struct is_method {
+    handle class_;
+    explicit is_method(const handle &c) : class_(c) {}
+};
+
+/// Annotation for setters
+struct is_setter {};
+
+/// Annotation for operators
+struct is_operator {};
+
+/// Annotation for classes that cannot be subclassed
+struct is_final {};
+
+/// Annotation for parent scope
+struct scope {
+    handle value;
+    explicit scope(const handle &s) : value(s) {}
+};
+
+/// Annotation for documentation
+struct doc {
+    const char *value;
+    explicit doc(const char *value) : value(value) {}
+};
+
+/// Annotation for function names
+struct name {
+    const char *value;
+    explicit name(const char *value) : value(value) {}
+};
+
+/// Annotation indicating that a function is an overload associated with a given "sibling"
+struct sibling {
+    handle value;
+    explicit sibling(const handle &value) : value(value.ptr()) {}
+};
+
+/// Annotation indicating that a class derives from another given type
+template <typename T>
+struct base {
+
+    PYBIND11_DEPRECATED(
+        "base<T>() was deprecated in favor of specifying 'T' as a template argument to class_")
+    base() = default;
+};
+
+/// Keep patient alive while nurse lives
+template <size_t Nurse, size_t Patient>
+struct keep_alive {};
+
+/// Annotation indicating that a class is involved in a multiple inheritance relationship
+struct multiple_inheritance {};
+
+/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class
+struct dynamic_attr {};
+
+/// Annotation which enables the buffer protocol for a type
+struct buffer_protocol {};
+
+/// Annotation which requests that a special metaclass is created for a type
+struct metaclass {
+    handle value;
+
+    PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.")
+    metaclass() = default;
+
+    /// Override pybind11's default metaclass
+    explicit metaclass(handle value) : value(value) {}
+};
+
+/// Specifies a custom callback with signature `void (PyHeapTypeObject*)` that
+/// may be used to customize the Python type.
+///
+/// The callback is invoked immediately before `PyType_Ready`.
+///
+/// Note: This is an advanced interface, and uses of it may require changes to
+/// work with later versions of pybind11.  You may wish to consult the
+/// implementation of `make_new_python_type` in `detail/classes.h` to understand
+/// the context in which the callback will be run.
+struct custom_type_setup {
+    using callback = std::function<void(PyHeapTypeObject *heap_type)>;
+
+    explicit custom_type_setup(callback value) : value(std::move(value)) {}
+
+    callback value;
+};
+
+/// Annotation that marks a class as local to the module:
+struct module_local {
+    const bool value;
+    constexpr explicit module_local(bool v = true) : value(v) {}
+};
+
+/// Annotation to mark enums as an arithmetic type
+struct arithmetic {};
+
+/// Mark a function for addition at the beginning of the existing overload chain instead of the end
+struct prepend {};
+
+/** \rst
+    A call policy which places one or more guard variables (``Ts...``) around the function call.
+
+    For example, this definition:
+
+    .. code-block:: cpp
+
+        m.def("foo", foo, py::call_guard<T>());
+
+    is equivalent to the following pseudocode:
+
+    .. code-block:: cpp
+
+        m.def("foo", [](args...) {
+            T scope_guard;
+            return foo(args...); // forwarded arguments
+        });
+ \endrst */
+template <typename... Ts>
+struct call_guard;
+
+template <>
+struct call_guard<> {
+    using type = detail::void_type;
+};
+
+template <typename T>
+struct call_guard<T> {
+    static_assert(std::is_default_constructible<T>::value,
+                  "The guard type must be default constructible");
+
+    using type = T;
+};
+
+template <typename T, typename... Ts>
+struct call_guard<T, Ts...> {
+    struct type {
+        T guard{}; // Compose multiple guard types with left-to-right default-constructor order
+        typename call_guard<Ts...>::type next{};
+    };
+};
+
+/// @} annotations
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+/* Forward declarations */
+enum op_id : int;
+enum op_type : int;
+struct undefined_t;
+template <op_id id, op_type ot, typename L = undefined_t, typename R = undefined_t>
+struct op_;
+void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret);
+
+/// Internal data structure which holds metadata about a keyword argument
+struct argument_record {
+    const char *name;  ///< Argument name
+    const char *descr; ///< Human-readable version of the argument value
+    handle value;      ///< Associated Python object
+    bool convert : 1;  ///< True if the argument is allowed to convert when loading
+    bool none : 1;     ///< True if None is allowed when loading
+
+    argument_record(const char *name, const char *descr, handle value, bool convert, bool none)
+        : name(name), descr(descr), value(value), convert(convert), none(none) {}
+};
+
+/// Internal data structure which holds metadata about a bound function (signature, overloads,
+/// etc.)
+struct function_record {
+    function_record()
+        : is_constructor(false), is_new_style_constructor(false), is_stateless(false),
+          is_operator(false), is_method(false), is_setter(false), has_args(false),
+          has_kwargs(false), prepend(false) {}
+
+    /// Function name
+    char *name = nullptr; /* why no C++ strings? They generate heavier code.. */
+
+    // User-specified documentation string
+    char *doc = nullptr;
+
+    /// Human-readable version of the function signature
+    char *signature = nullptr;
+
+    /// List of registered keyword arguments
+    std::vector<argument_record> args;
+
+    /// Pointer to lambda function which converts arguments and performs the actual call
+    handle (*impl)(function_call &) = nullptr;
+
+    /// Storage for the wrapped function pointer and captured data, if any
+    void *data[3] = {};
+
+    /// Pointer to custom destructor for 'data' (if needed)
+    void (*free_data)(function_record *ptr) = nullptr;
+
+    /// Return value policy associated with this function
+    return_value_policy policy = return_value_policy::automatic;
+
+    /// True if name == '__init__'
+    bool is_constructor : 1;
+
+    /// True if this is a new-style `__init__` defined in `detail/init.h`
+    bool is_new_style_constructor : 1;
+
+    /// True if this is a stateless function pointer
+    bool is_stateless : 1;
+
+    /// True if this is an operator (__add__), etc.
+    bool is_operator : 1;
+
+    /// True if this is a method
+    bool is_method : 1;
+
+    /// True if this is a setter
+    bool is_setter : 1;
+
+    /// True if the function has a '*args' argument
+    bool has_args : 1;
+
+    /// True if the function has a '**kwargs' argument
+    bool has_kwargs : 1;
+
+    /// True if this function is to be inserted at the beginning of the overload resolution chain
+    bool prepend : 1;
+
+    /// Number of arguments (including py::args and/or py::kwargs, if present)
+    std::uint16_t nargs;
+
+    /// Number of leading positional arguments, which are terminated by a py::args or py::kwargs
+    /// argument or by a py::kw_only annotation.
+    std::uint16_t nargs_pos = 0;
+
+    /// Number of leading arguments (counted in `nargs`) that are positional-only
+    std::uint16_t nargs_pos_only = 0;
+
+    /// Python method object
+    PyMethodDef *def = nullptr;
+
+    /// Python handle to the parent scope (a class or a module)
+    handle scope;
+
+    /// Python handle to the sibling function representing an overload chain
+    handle sibling;
+
+    /// Pointer to next overload
+    function_record *next = nullptr;
+};
+
+/// Special data structure which (temporarily) holds metadata about a bound class
+struct type_record {
+    PYBIND11_NOINLINE type_record()
+        : multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false),
+          default_holder(true), module_local(false), is_final(false) {}
+
+    /// Handle to the parent scope
+    handle scope;
+
+    /// Name of the class
+    const char *name = nullptr;
+
+    // Pointer to RTTI type_info data structure
+    const std::type_info *type = nullptr;
+
+    /// How large is the underlying C++ type?
+    size_t type_size = 0;
+
+    /// What is the alignment of the underlying C++ type?
+    size_t type_align = 0;
+
+    /// How large is the type's holder?
+    size_t holder_size = 0;
+
+    /// The global operator new can be overridden with a class-specific variant
+    void *(*operator_new)(size_t) = nullptr;
+
+    /// Function pointer to class_<..>::init_instance
+    void (*init_instance)(instance *, const void *) = nullptr;
+
+    /// Function pointer to class_<..>::dealloc
+    void (*dealloc)(detail::value_and_holder &) = nullptr;
+
+    /// List of base classes of the newly created type
+    list bases;
+
+    /// Optional docstring
+    const char *doc = nullptr;
+
+    /// Custom metaclass (optional)
+    handle metaclass;
+
+    /// Custom type setup.
+    custom_type_setup::callback custom_type_setup_callback;
+
+    /// Multiple inheritance marker
+    bool multiple_inheritance : 1;
+
+    /// Does the class manage a __dict__?
+    bool dynamic_attr : 1;
+
+    /// Does the class implement the buffer protocol?
+    bool buffer_protocol : 1;
+
+    /// Is the default (unique_ptr) holder type used?
+    bool default_holder : 1;
+
+    /// Is the class definition local to the module shared object?
+    bool module_local : 1;
+
+    /// Is the class inheritable from python classes?
+    bool is_final : 1;
+
+    PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *) ) {
+        auto *base_info = detail::get_type_info(base, false);
+        if (!base_info) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name)
+                          + "\" referenced unknown base type \"" + tname + "\"");
+        }
+
+        if (default_holder != base_info->default_holder) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name) + "\" "
+                          + (default_holder ? "does not have" : "has")
+                          + " a non-default holder type while its base \"" + tname + "\" "
+                          + (base_info->default_holder ? "does not" : "does"));
+        }
+
+        bases.append((PyObject *) base_info->type);
+
+#if PY_VERSION_HEX < 0x030B0000
+        dynamic_attr |= base_info->type->tp_dictoffset != 0;
+#else
+        dynamic_attr |= (base_info->type->tp_flags & Py_TPFLAGS_MANAGED_DICT) != 0;
+#endif
+
+        if (caster) {
+            base_info->implicit_casts.emplace_back(type, caster);
+        }
+    }
+};
+
+inline function_call::function_call(const function_record &f, handle p) : func(f), parent(p) {
+    args.reserve(f.nargs);
+    args_convert.reserve(f.nargs);
+}
+
+/// Tag for a new-style `__init__` defined in `detail/init.h`
+struct is_new_style_constructor {};
+
+/**
+ * Partial template specializations to process custom attributes provided to
+ * cpp_function_ and class_. These are either used to initialize the respective
+ * fields in the type_record and function_record data structures or executed at
+ * runtime to deal with custom call policies (e.g. keep_alive).
+ */
+template <typename T, typename SFINAE = void>
+struct process_attribute;
+
+template <typename T>
+struct process_attribute_default {
+    /// Default implementation: do nothing
+    static void init(const T &, function_record *) {}
+    static void init(const T &, type_record *) {}
+    static void precall(function_call &) {}
+    static void postcall(function_call &, handle) {}
+};
+
+/// Process an attribute specifying the function's name
+template <>
+struct process_attribute<name> : process_attribute_default<name> {
+    static void init(const name &n, function_record *r) { r->name = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring
+template <>
+struct process_attribute<doc> : process_attribute_default<doc> {
+    static void init(const doc &n, function_record *r) { r->doc = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring (provided as a C-style string)
+template <>
+struct process_attribute<const char *> : process_attribute_default<const char *> {
+    static void init(const char *d, function_record *r) { r->doc = const_cast<char *>(d); }
+    static void init(const char *d, type_record *r) { r->doc = d; }
+};
+template <>
+struct process_attribute<char *> : process_attribute<const char *> {};
+
+/// Process an attribute indicating the function's return value policy
+template <>
+struct process_attribute<return_value_policy> : process_attribute_default<return_value_policy> {
+    static void init(const return_value_policy &p, function_record *r) { r->policy = p; }
+};
+
+/// Process an attribute which indicates that this is an overloaded function associated with a
+/// given sibling
+template <>
+struct process_attribute<sibling> : process_attribute_default<sibling> {
+    static void init(const sibling &s, function_record *r) { r->sibling = s.value; }
+};
+
+/// Process an attribute which indicates that this function is a method
+template <>
+struct process_attribute<is_method> : process_attribute_default<is_method> {
+    static void init(const is_method &s, function_record *r) {
+        r->is_method = true;
+        r->scope = s.class_;
+    }
+};
+
+/// Process an attribute which indicates that this function is a setter
+template <>
+struct process_attribute<is_setter> : process_attribute_default<is_setter> {
+    static void init(const is_setter &, function_record *r) { r->is_setter = true; }
+};
+
+/// Process an attribute which indicates the parent scope of a method
+template <>
+struct process_attribute<scope> : process_attribute_default<scope> {
+    static void init(const scope &s, function_record *r) { r->scope = s.value; }
+};
+
+/// Process an attribute which indicates that this function is an operator
+template <>
+struct process_attribute<is_operator> : process_attribute_default<is_operator> {
+    static void init(const is_operator &, function_record *r) { r->is_operator = true; }
+};
+
+template <>
+struct process_attribute<is_new_style_constructor>
+    : process_attribute_default<is_new_style_constructor> {
+    static void init(const is_new_style_constructor &, function_record *r) {
+        r->is_new_style_constructor = true;
+    }
+};
+
+inline void check_kw_only_arg(const arg &a, function_record *r) {
+    if (r->args.size() > r->nargs_pos && (!a.name || a.name[0] == '\0')) {
+        pybind11_fail("arg(): cannot specify an unnamed argument after a kw_only() annotation or "
+                      "args() argument");
+    }
+}
+
+inline void append_self_arg_if_needed(function_record *r) {
+    if (r->is_method && r->args.empty()) {
+        r->args.emplace_back("self", nullptr, handle(), /*convert=*/true, /*none=*/false);
+    }
+}
+
+/// Process a keyword argument attribute (*without* a default value)
+template <>
+struct process_attribute<arg> : process_attribute_default<arg> {
+    static void init(const arg &a, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword argument attribute (*with* a default value)
+template <>
+struct process_attribute<arg_v> : process_attribute_default<arg_v> {
+    static void init(const arg_v &a, function_record *r) {
+        if (r->is_method && r->args.empty()) {
+            r->args.emplace_back(
+                "self", /*descr=*/nullptr, /*parent=*/handle(), /*convert=*/true, /*none=*/false);
+        }
+
+        if (!a.value) {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            std::string descr("'");
+            if (a.name) {
+                descr += std::string(a.name) + ": ";
+            }
+            descr += a.type + "'";
+            if (r->is_method) {
+                if (r->name) {
+                    descr += " in method '" + (std::string) str(r->scope) + "."
+                             + (std::string) r->name + "'";
+                } else {
+                    descr += " in method of '" + (std::string) str(r->scope) + "'";
+                }
+            } else if (r->name) {
+                descr += " in function '" + (std::string) r->name + "'";
+            }
+            pybind11_fail("arg(): could not convert default argument " + descr
+                          + " into a Python object (type not registered yet?)");
+#else
+            pybind11_fail("arg(): could not convert default argument "
+                          "into a Python object (type not registered yet?). "
+                          "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for "
+                          "more information.");
+#endif
+        }
+        r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword-only-arguments-follow pseudo argument
+template <>
+struct process_attribute<kw_only> : process_attribute_default<kw_only> {
+    static void init(const kw_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        if (r->has_args && r->nargs_pos != static_cast<std::uint16_t>(r->args.size())) {
+            pybind11_fail("Mismatched args() and kw_only(): they must occur at the same relative "
+                          "argument location (or omit kw_only() entirely)");
+        }
+        r->nargs_pos = static_cast<std::uint16_t>(r->args.size());
+    }
+};
+
+/// Process a positional-only-argument maker
+template <>
+struct process_attribute<pos_only> : process_attribute_default<pos_only> {
+    static void init(const pos_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->nargs_pos_only = static_cast<std::uint16_t>(r->args.size());
+        if (r->nargs_pos_only > r->nargs_pos) {
+            pybind11_fail("pos_only(): cannot follow a py::args() argument");
+        }
+        // It also can't follow a kw_only, but a static_assert in pybind11.h checks that
+    }
+};
+
+/// Process a parent class attribute.  Single inheritance only (class_ itself already guarantees
+/// that)
+template <typename T>
+struct process_attribute<T, enable_if_t<is_pyobject<T>::value>>
+    : process_attribute_default<handle> {
+    static void init(const handle &h, type_record *r) { r->bases.append(h); }
+};
+
+/// Process a parent class attribute (deprecated, does not support multiple inheritance)
+template <typename T>
+struct process_attribute<base<T>> : process_attribute_default<base<T>> {
+    static void init(const base<T> &, type_record *r) { r->add_base(typeid(T), nullptr); }
+};
+
+/// Process a multiple inheritance attribute
+template <>
+struct process_attribute<multiple_inheritance> : process_attribute_default<multiple_inheritance> {
+    static void init(const multiple_inheritance &, type_record *r) {
+        r->multiple_inheritance = true;
+    }
+};
+
+template <>
+struct process_attribute<dynamic_attr> : process_attribute_default<dynamic_attr> {
+    static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; }
+};
+
+template <>
+struct process_attribute<custom_type_setup> {
+    static void init(const custom_type_setup &value, type_record *r) {
+        r->custom_type_setup_callback = value.value;
+    }
+};
+
+template <>
+struct process_attribute<is_final> : process_attribute_default<is_final> {
+    static void init(const is_final &, type_record *r) { r->is_final = true; }
+};
+
+template <>
+struct process_attribute<buffer_protocol> : process_attribute_default<buffer_protocol> {
+    static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; }
+};
+
+template <>
+struct process_attribute<metaclass> : process_attribute_default<metaclass> {
+    static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; }
+};
+
+template <>
+struct process_attribute<module_local> : process_attribute_default<module_local> {
+    static void init(const module_local &l, type_record *r) { r->module_local = l.value; }
+};
+
+/// Process a 'prepend' attribute, putting this at the beginning of the overload chain
+template <>
+struct process_attribute<prepend> : process_attribute_default<prepend> {
+    static void init(const prepend &, function_record *r) { r->prepend = true; }
+};
+
+/// Process an 'arithmetic' attribute for enums (does nothing here)
+template <>
+struct process_attribute<arithmetic> : process_attribute_default<arithmetic> {};
+
+template <typename... Ts>
+struct process_attribute<call_guard<Ts...>> : process_attribute_default<call_guard<Ts...>> {};
+
+/**
+ * Process a keep_alive call policy -- invokes keep_alive_impl during the
+ * pre-call handler if both Nurse, Patient != 0 and use the post-call handler
+ * otherwise
+ */
+template <size_t Nurse, size_t Patient>
+struct process_attribute<keep_alive<Nurse, Patient>>
+    : public process_attribute_default<keep_alive<Nurse, Patient>> {
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void precall(function_call &call) {
+        keep_alive_impl(Nurse, Patient, call, handle());
+    }
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void postcall(function_call &, handle) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void precall(function_call &) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void postcall(function_call &call, handle ret) {
+        keep_alive_impl(Nurse, Patient, call, ret);
+    }
+};
+
+/// Recursively iterate over variadic template arguments
+template <typename... Args>
+struct process_attributes {
+    static void init(const Args &...args, function_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{
+            0, ((void) process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void init(const Args &...args, type_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void precall(function_call &call) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::precall(call), 0)...};
+    }
+    static void postcall(function_call &call, handle fn_ret) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call, fn_ret);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(fn_ret);
+        using expander = int[];
+        (void) expander{
+            0, (process_attribute<typename std::decay<Args>::type>::postcall(call, fn_ret), 0)...};
+    }
+};
+
+template <typename T>
+using is_call_guard = is_instantiation<call_guard, T>;
+
+/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found)
+template <typename... Extra>
+using extract_guard_t = typename exactly_one_t<is_call_guard, call_guard<>, Extra...>::type;
+
+/// Check the number of named arguments at compile time
+template <typename... Extra,
+          size_t named = constexpr_sum(std::is_base_of<arg, Extra>::value...),
+          size_t self = constexpr_sum(std::is_same<is_method, Extra>::value...)>
+constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(nargs, has_args, has_kwargs);
+    return named == 0 || (self + named + size_t(has_args) + size_t(has_kwargs)) == nargs;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/shared/package-files/40/40fa46dad1b5a598338ebb538e33aed860eeddc23a5294ac2ebb06fe8d635d89/linalg.py b/shared/package-files/40/40fa46dad1b5a598338ebb538e33aed860eeddc23a5294ac2ebb06fe8d635d89/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..c18360f6e6f92ff1389b4edf1c786731f3fbef22
--- /dev/null
+++ b/shared/package-files/40/40fa46dad1b5a598338ebb538e33aed860eeddc23a5294ac2ebb06fe8d635d89/linalg.py
@@ -0,0 +1,466 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _floating_dtypes,
+    _numeric_dtypes,
+    float32,
+    float64,
+    complex64,
+    complex128
+)
+from ._manipulation_functions import reshape
+from ._elementwise_functions import conj
+from ._array_object import Array
+
+from ..core.numeric import normalize_axis_tuple
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from ._typing import Literal, Optional, Sequence, Tuple, Union, Dtype
+
+from typing import NamedTuple
+
+import numpy.linalg
+import numpy as np
+
+class EighResult(NamedTuple):
+    eigenvalues: Array
+    eigenvectors: Array
+
+class QRResult(NamedTuple):
+    Q: Array
+    R: Array
+
+class SlogdetResult(NamedTuple):
+    sign: Array
+    logabsdet: Array
+
+class SVDResult(NamedTuple):
+    U: Array
+    S: Array
+    Vh: Array
+
+# Note: the inclusion of the upper keyword is different from
+# np.linalg.cholesky, which does not have it.
+def cholesky(x: Array, /, *, upper: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.cholesky <numpy.linalg.cholesky>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.cholesky.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in cholesky')
+    L = np.linalg.cholesky(x._array)
+    if upper:
+        U = Array._new(L).mT
+        if U.dtype in [complex64, complex128]:
+            U = conj(U)
+        return U
+    return Array._new(L)
+
+# Note: cross is the numpy top-level namespace, not np.linalg
+def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cross <numpy.cross>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in cross')
+    # Note: this is different from np.cross(), which broadcasts
+    if x1.shape != x2.shape:
+        raise ValueError('x1 and x2 must have the same shape')
+    if x1.ndim == 0:
+        raise ValueError('cross() requires arrays of dimension at least 1')
+    # Note: this is different from np.cross(), which allows dimension 2
+    if x1.shape[axis] != 3:
+        raise ValueError('cross() dimension must equal 3')
+    return Array._new(np.cross(x1._array, x2._array, axis=axis))
+
+def det(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.det <numpy.linalg.det>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.det.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in det')
+    return Array._new(np.linalg.det(x._array))
+
+# Note: diagonal is the numpy top-level namespace, not np.linalg
+def diagonal(x: Array, /, *, offset: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.diagonal <numpy.diagonal>`.
+
+    See its docstring for more information.
+    """
+    # Note: diagonal always operates on the last two axes, whereas np.diagonal
+    # operates on the first two axes by default
+    return Array._new(np.diagonal(x._array, offset=offset, axis1=-2, axis2=-1))
+
+
+def eigh(x: Array, /) -> EighResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigh <numpy.linalg.eigh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigh')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.eigh, which only returns a tuple.
+    return EighResult(*map(Array._new, np.linalg.eigh(x._array)))
+
+
+def eigvalsh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigvalsh <numpy.linalg.eigvalsh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigvalsh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigvalsh')
+
+    return Array._new(np.linalg.eigvalsh(x._array))
+
+def inv(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.inv <numpy.linalg.inv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.inv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in inv')
+
+    return Array._new(np.linalg.inv(x._array))
+
+
+# Note: matmul is the numpy top-level namespace but not in np.linalg
+def matmul(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.matmul.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in matmul')
+
+    return Array._new(np.matmul(x1._array, x2._array))
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf, 'fro', 'nuc']]], but Literal does not support floating-point
+# literals.
+def matrix_norm(x: Array, /, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in matrix_norm')
+
+    return Array._new(np.linalg.norm(x._array, axis=(-2, -1), keepdims=keepdims, ord=ord))
+
+
+def matrix_power(x: Array, n: int, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_power <numpy.matrix_power>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.matrix_power.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed for the first argument of matrix_power')
+
+    # np.matrix_power already checks if n is an integer
+    return Array._new(np.linalg.matrix_power(x._array, n))
+
+# Note: the keyword argument name rtol is different from np.linalg.matrix_rank
+def matrix_rank(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_rank <numpy.matrix_rank>`.
+
+    See its docstring for more information.
+    """
+    # Note: this is different from np.linalg.matrix_rank, which supports 1
+    # dimensional arrays.
+    if x.ndim < 2:
+        raise np.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
+    S = np.linalg.svd(x._array, compute_uv=False)
+    if rtol is None:
+        tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(S.dtype).eps
+    else:
+        if isinstance(rtol, Array):
+            rtol = rtol._array
+        # Note: this is different from np.linalg.matrix_rank, which does not multiply
+        # the tolerance by the largest singular value.
+        tol = S.max(axis=-1, keepdims=True)*np.asarray(rtol)[..., np.newaxis]
+    return Array._new(np.count_nonzero(S > tol, axis=-1))
+
+
+# Note: this function is new in the array API spec. Unlike transpose, it only
+# transposes the last two axes.
+def matrix_transpose(x: Array, /) -> Array:
+    if x.ndim < 2:
+        raise ValueError("x must be at least 2-dimensional for matrix_transpose")
+    return Array._new(np.swapaxes(x._array, -1, -2))
+
+# Note: outer is the numpy top-level namespace, not np.linalg
+def outer(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.outer <numpy.outer>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.outer.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in outer')
+
+    # Note: the restriction to only 1-dim arrays is different from np.outer
+    if x1.ndim != 1 or x2.ndim != 1:
+        raise ValueError('The input arrays to outer must be 1-dimensional')
+
+    return Array._new(np.outer(x1._array, x2._array))
+
+# Note: the keyword argument name rtol is different from np.linalg.pinv
+def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.pinv <numpy.linalg.pinv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.pinv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in pinv')
+
+    # Note: this is different from np.linalg.pinv, which does not multiply the
+    # default tolerance by max(M, N).
+    if rtol is None:
+        rtol = max(x.shape[-2:]) * np.finfo(x.dtype).eps
+    return Array._new(np.linalg.pinv(x._array, rcond=rtol))
+
+def qr(x: Array, /, *, mode: Literal['reduced', 'complete'] = 'reduced') -> QRResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.qr <numpy.linalg.qr>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.qr.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in qr')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.qr, which only returns a tuple.
+    return QRResult(*map(Array._new, np.linalg.qr(x._array, mode=mode)))
+
+def slogdet(x: Array, /) -> SlogdetResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.slogdet <numpy.linalg.slogdet>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.slogdet.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in slogdet')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.slogdet, which only returns a tuple.
+    return SlogdetResult(*map(Array._new, np.linalg.slogdet(x._array)))
+
+# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
+# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
+# of matrices. The np.linalg.solve behavior of allowing stacks of both
+# matrices and vectors is ambiguous c.f.
+# https://github.com/numpy/numpy/issues/15349 and
+# https://github.com/data-apis/array-api/issues/285.
+
+# To workaround this, the below is the code from np.linalg.solve except
+# only calling solve1 in the exactly 1D case.
+def _solve(a, b):
+    from ..linalg.linalg import (_makearray, _assert_stacked_2d,
+                                 _assert_stacked_square, _commonType,
+                                 isComplexType, get_linalg_error_extobj,
+                                 _raise_linalgerror_singular)
+    from ..linalg import _umath_linalg
+
+    a, _ = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    b, wrap = _makearray(b)
+    t, result_t = _commonType(a, b)
+
+    # This part is different from np.linalg.solve
+    if b.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    # This does nothing currently but is left in because it will be relevant
+    # when complex dtype support is added to the spec in 2022.
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    with np.errstate(call=_raise_linalgerror_singular, invalid='call',
+                     over='ignore', divide='ignore', under='ignore'):
+        r = gufunc(a, b, signature=signature)
+
+    return wrap(r.astype(result_t, copy=False))
+
+def solve(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.solve <numpy.linalg.solve>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.solve.
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in solve')
+
+    return Array._new(_solve(x1._array, x2._array))
+
+def svd(x: Array, /, *, full_matrices: bool = True) -> SVDResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.svd <numpy.linalg.svd>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.svd.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svd')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.svd, which only returns a tuple.
+    return SVDResult(*map(Array._new, np.linalg.svd(x._array, full_matrices=full_matrices)))
+
+# Note: svdvals is not in NumPy (but it is in SciPy). It is equivalent to
+# np.linalg.svd(compute_uv=False).
+def svdvals(x: Array, /) -> Union[Array, Tuple[Array, ...]]:
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svdvals')
+    return Array._new(np.linalg.svd(x._array, compute_uv=False))
+
+# Note: tensordot is the numpy top-level namespace but not in np.linalg
+
+# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
+def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
+    # Note: the restriction to numeric dtypes only is different from
+    # np.tensordot.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in tensordot')
+
+    return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
+
+# Note: trace is the numpy top-level namespace, not np.linalg
+def trace(x: Array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trace <numpy.trace>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in trace')
+
+    # Note: trace() works the same as sum() and prod() (see
+    # _statistical_functions.py)
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
+    # Note: trace always operates on the last two axes, whereas np.trace
+    # operates on the first two axes by default
+    return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1, dtype=dtype)))
+
+# Note: vecdot is not in NumPy
+def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in vecdot')
+    ndim = max(x1.ndim, x2.ndim)
+    x1_shape = (1,)*(ndim - x1.ndim) + tuple(x1.shape)
+    x2_shape = (1,)*(ndim - x2.ndim) + tuple(x2.shape)
+    if x1_shape[axis] != x2_shape[axis]:
+        raise ValueError("x1 and x2 must have the same size along the given axis")
+
+    x1_, x2_ = np.broadcast_arrays(x1._array, x2._array)
+    x1_ = np.moveaxis(x1_, axis, -1)
+    x2_ = np.moveaxis(x2_, axis, -1)
+
+    res = x1_[..., None, :] @ x2_[..., None]
+    return Array._new(res[..., 0, 0])
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf]]] but Literal does not support floating-point literals.
+def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in norm')
+
+    # np.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
+    # when axis=None and the input is 2-D, so to force a vector norm, we make
+    # it so the input is 1-D (for axis=None), or reshape so that norm is done
+    # on a single dimension.
+    a = x._array
+    if axis is None:
+        # Note: np.linalg.norm() doesn't handle 0-D arrays
+        a = a.ravel()
+        _axis = 0
+    elif isinstance(axis, tuple):
+        # Note: The axis argument supports any number of axes, whereas
+        # np.linalg.norm() only supports a single axis for vector norm.
+        normalized_axis = normalize_axis_tuple(axis, x.ndim)
+        rest = tuple(i for i in range(a.ndim) if i not in normalized_axis)
+        newshape = axis + rest
+        a = np.transpose(a, newshape).reshape(
+            (np.prod([a.shape[i] for i in axis], dtype=int), *[a.shape[i] for i in rest]))
+        _axis = 0
+    else:
+        _axis = axis
+
+    res = Array._new(np.linalg.norm(a, axis=_axis, ord=ord))
+
+    if keepdims:
+        # We can't reuse np.linalg.norm(keepdims) because of the reshape hacks
+        # above to avoid matrix norm logic.
+        shape = list(x.shape)
+        _axis = normalize_axis_tuple(range(x.ndim) if axis is None else axis, x.ndim)
+        for i in _axis:
+            shape[i] = 1
+        res = reshape(res, tuple(shape))
+
+    return res
+
+__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm']
diff --git a/shared/package-files/40/40fb8da47952aae4d7d78fabe186e135fc61df6c7f0f5ee3031b4ed9a427e460/test_lti.py b/shared/package-files/40/40fb8da47952aae4d7d78fabe186e135fc61df6c7f0f5ee3031b4ed9a427e460/test_lti.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d0f4b67e28c7a8422f4a2b5ef6bb803a4b9b6cf
--- /dev/null
+++ b/shared/package-files/40/40fb8da47952aae4d7d78fabe186e135fc61df6c7f0f5ee3031b4ed9a427e460/test_lti.py
@@ -0,0 +1,1245 @@
+from sympy.core.add import Add
+from sympy.core.function import Function
+from sympy.core.mul import Mul
+from sympy.core.numbers import (I, Rational, oo)
+from sympy.core.power import Pow
+from sympy.core.singleton import S
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.exponential import exp
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.matrices.dense import eye
+from sympy.polys.polytools import factor
+from sympy.polys.rootoftools import CRootOf
+from sympy.simplify.simplify import simplify
+from sympy.core.containers import Tuple
+from sympy.matrices import ImmutableMatrix, Matrix
+from sympy.physics.control import (TransferFunction, Series, Parallel,
+    Feedback, TransferFunctionMatrix, MIMOSeries, MIMOParallel, MIMOFeedback,
+    bilinear, backward_diff)
+from sympy.testing.pytest import raises
+
+a, x, b, s, g, d, p, k, a0, a1, a2, b0, b1, b2, tau, zeta, wn, T = symbols('a, x, b, s, g, d, p, k,\
+    a0:3, b0:3, tau, zeta, wn, T')
+TF1 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+TF2 = TransferFunction(k, 1, s)
+TF3 = TransferFunction(a2*p - s, a2*s + p, s)
+
+
+def test_TransferFunction_construction():
+    tf = TransferFunction(s + 1, s**2 + s + 1, s)
+    assert tf.num == (s + 1)
+    assert tf.den == (s**2 + s + 1)
+    assert tf.args == (s + 1, s**2 + s + 1, s)
+
+    tf1 = TransferFunction(s + 4, s - 5, s)
+    assert tf1.num == (s + 4)
+    assert tf1.den == (s - 5)
+    assert tf1.args == (s + 4, s - 5, s)
+
+    # using different polynomial variables.
+    tf2 = TransferFunction(p + 3, p**2 - 9, p)
+    assert tf2.num == (p + 3)
+    assert tf2.den == (p**2 - 9)
+    assert tf2.args == (p + 3, p**2 - 9, p)
+
+    tf3 = TransferFunction(p**3 + 5*p**2 + 4, p**4 + 3*p + 1, p)
+    assert tf3.args == (p**3 + 5*p**2 + 4, p**4 + 3*p + 1, p)
+
+    # no pole-zero cancellation on its own.
+    tf4 = TransferFunction((s + 3)*(s - 1), (s - 1)*(s + 5), s)
+    assert tf4.den == (s - 1)*(s + 5)
+    assert tf4.args == ((s + 3)*(s - 1), (s - 1)*(s + 5), s)
+
+    tf4_ = TransferFunction(p + 2, p + 2, p)
+    assert tf4_.args == (p + 2, p + 2, p)
+
+    tf5 = TransferFunction(s - 1, 4 - p, s)
+    assert tf5.args == (s - 1, 4 - p, s)
+
+    tf5_ = TransferFunction(s - 1, s - 1, s)
+    assert tf5_.args == (s - 1, s - 1, s)
+
+    tf6 = TransferFunction(5, 6, s)
+    assert tf6.num == 5
+    assert tf6.den == 6
+    assert tf6.args == (5, 6, s)
+
+    tf6_ = TransferFunction(1/2, 4, s)
+    assert tf6_.num == 0.5
+    assert tf6_.den == 4
+    assert tf6_.args == (0.500000000000000, 4, s)
+
+    tf7 = TransferFunction(3*s**2 + 2*p + 4*s, 8*p**2 + 7*s, s)
+    tf8 = TransferFunction(3*s**2 + 2*p + 4*s, 8*p**2 + 7*s, p)
+    assert not tf7 == tf8
+
+    tf7_ = TransferFunction(a0*s + a1*s**2 + a2*s**3, b0*p - b1*s, s)
+    tf8_ = TransferFunction(a0*s + a1*s**2 + a2*s**3, b0*p - b1*s, s)
+    assert tf7_ == tf8_
+    assert -(-tf7_) == tf7_ == -(-(-(-tf7_)))
+
+    tf9 = TransferFunction(a*s**3 + b*s**2 + g*s + d, d*p + g*p**2 + g*s, s)
+    assert tf9.args == (a*s**3 + b*s**2 + d + g*s, d*p + g*p**2 + g*s, s)
+
+    tf10 = TransferFunction(p**3 + d, g*s**2 + d*s + a, p)
+    tf10_ = TransferFunction(p**3 + d, g*s**2 + d*s + a, p)
+    assert tf10.args == (d + p**3, a + d*s + g*s**2, p)
+    assert tf10_ == tf10
+
+    tf11 = TransferFunction(a1*s + a0, b2*s**2 + b1*s + b0, s)
+    assert tf11.num == (a0 + a1*s)
+    assert tf11.den == (b0 + b1*s + b2*s**2)
+    assert tf11.args == (a0 + a1*s, b0 + b1*s + b2*s**2, s)
+
+    # when just the numerator is 0, leave the denominator alone.
+    tf12 = TransferFunction(0, p**2 - p + 1, p)
+    assert tf12.args == (0, p**2 - p + 1, p)
+
+    tf13 = TransferFunction(0, 1, s)
+    assert tf13.args == (0, 1, s)
+
+    # float exponents
+    tf14 = TransferFunction(a0*s**0.5 + a2*s**0.6 - a1, a1*p**(-8.7), s)
+    assert tf14.args == (a0*s**0.5 - a1 + a2*s**0.6, a1*p**(-8.7), s)
+
+    tf15 = TransferFunction(a2**2*p**(1/4) + a1*s**(-4/5), a0*s - p, p)
+    assert tf15.args == (a1*s**(-0.8) + a2**2*p**0.25, a0*s - p, p)
+
+    omega_o, k_p, k_o, k_i = symbols('omega_o, k_p, k_o, k_i')
+    tf18 = TransferFunction((k_p + k_o*s + k_i/s), s**2 + 2*omega_o*s + omega_o**2, s)
+    assert tf18.num == k_i/s + k_o*s + k_p
+    assert tf18.args == (k_i/s + k_o*s + k_p, omega_o**2 + 2*omega_o*s + s**2, s)
+
+    # ValueError when denominator is zero.
+    raises(ValueError, lambda: TransferFunction(4, 0, s))
+    raises(ValueError, lambda: TransferFunction(s, 0, s))
+    raises(ValueError, lambda: TransferFunction(0, 0, s))
+
+    raises(TypeError, lambda: TransferFunction(Matrix([1, 2, 3]), s, s))
+
+    raises(TypeError, lambda: TransferFunction(s**2 + 2*s - 1, s + 3, 3))
+    raises(TypeError, lambda: TransferFunction(p + 1, 5 - p, 4))
+    raises(TypeError, lambda: TransferFunction(3, 4, 8))
+
+
+def test_TransferFunction_functions():
+    # classmethod from_rational_expression
+    expr_1 = Mul(0, Pow(s, -1, evaluate=False), evaluate=False)
+    expr_2 = s/0
+    expr_3 = (p*s**2 + 5*s)/(s + 1)**3
+    expr_4 = 6
+    expr_5 = ((2 + 3*s)*(5 + 2*s))/((9 + 3*s)*(5 + 2*s**2))
+    expr_6 = (9*s**4 + 4*s**2 + 8)/((s + 1)*(s + 9))
+    tf = TransferFunction(s + 1, s**2 + 2, s)
+    delay = exp(-s/tau)
+    expr_7 = delay*tf.to_expr()
+    H1 = TransferFunction.from_rational_expression(expr_7, s)
+    H2 = TransferFunction(s + 1, (s**2 + 2)*exp(s/tau), s)
+    expr_8 = Add(2,  3*s/(s**2 + 1), evaluate=False)
+
+    assert TransferFunction.from_rational_expression(expr_1) == TransferFunction(0, s, s)
+    raises(ZeroDivisionError, lambda: TransferFunction.from_rational_expression(expr_2))
+    raises(ValueError, lambda: TransferFunction.from_rational_expression(expr_3))
+    assert TransferFunction.from_rational_expression(expr_3, s) == TransferFunction((p*s**2 + 5*s), (s + 1)**3, s)
+    assert TransferFunction.from_rational_expression(expr_3, p) == TransferFunction((p*s**2 + 5*s), (s + 1)**3, p)
+    raises(ValueError, lambda: TransferFunction.from_rational_expression(expr_4))
+    assert TransferFunction.from_rational_expression(expr_4, s) == TransferFunction(6, 1, s)
+    assert TransferFunction.from_rational_expression(expr_5, s) == \
+        TransferFunction((2 + 3*s)*(5 + 2*s), (9 + 3*s)*(5 + 2*s**2), s)
+    assert TransferFunction.from_rational_expression(expr_6, s) == \
+        TransferFunction((9*s**4 + 4*s**2 + 8), (s + 1)*(s + 9), s)
+    assert H1 == H2
+    assert TransferFunction.from_rational_expression(expr_8, s) == \
+        TransferFunction(2*s**2 + 3*s + 2, s**2 + 1, s)
+
+    # explicitly cancel poles and zeros.
+    tf0 = TransferFunction(s**5 + s**3 + s, s - s**2, s)
+    a = TransferFunction(-(s**4 + s**2 + 1), s - 1, s)
+    assert tf0.simplify() == simplify(tf0) == a
+
+    tf1 = TransferFunction((p + 3)*(p - 1), (p - 1)*(p + 5), p)
+    b = TransferFunction(p + 3, p + 5, p)
+    assert tf1.simplify() == simplify(tf1) == b
+
+    # expand the numerator and the denominator.
+    G1 = TransferFunction((1 - s)**2, (s**2 + 1)**2, s)
+    G2 = TransferFunction(1, -3, p)
+    c = (a2*s**p + a1*s**s + a0*p**p)*(p**s + s**p)
+    d = (b0*s**s + b1*p**s)*(b2*s*p + p**p)
+    e = a0*p**p*p**s + a0*p**p*s**p + a1*p**s*s**s + a1*s**p*s**s + a2*p**s*s**p + a2*s**(2*p)
+    f = b0*b2*p*s*s**s + b0*p**p*s**s + b1*b2*p*p**s*s + b1*p**p*p**s
+    g = a1*a2*s*s**p + a1*p*s + a2*b1*p*s*s**p + b1*p**2*s
+    G3 = TransferFunction(c, d, s)
+    G4 = TransferFunction(a0*s**s - b0*p**p, (a1*s + b1*s*p)*(a2*s**p + p), p)
+
+    assert G1.expand() == TransferFunction(s**2 - 2*s + 1, s**4 + 2*s**2 + 1, s)
+    assert tf1.expand() == TransferFunction(p**2 + 2*p - 3, p**2 + 4*p - 5, p)
+    assert G2.expand() == G2
+    assert G3.expand() == TransferFunction(e, f, s)
+    assert G4.expand() == TransferFunction(a0*s**s - b0*p**p, g, p)
+
+    # purely symbolic polynomials.
+    p1 = a1*s + a0
+    p2 = b2*s**2 + b1*s + b0
+    SP1 = TransferFunction(p1, p2, s)
+    expect1 = TransferFunction(2.0*s + 1.0, 5.0*s**2 + 4.0*s + 3.0, s)
+    expect1_ = TransferFunction(2*s + 1, 5*s**2 + 4*s + 3, s)
+    assert SP1.subs({a0: 1, a1: 2, b0: 3, b1: 4, b2: 5}) == expect1_
+    assert SP1.subs({a0: 1, a1: 2, b0: 3, b1: 4, b2: 5}).evalf() == expect1
+    assert expect1_.evalf() == expect1
+
+    c1, d0, d1, d2 = symbols('c1, d0:3')
+    p3, p4 = c1*p, d2*p**3 + d1*p**2 - d0
+    SP2 = TransferFunction(p3, p4, p)
+    expect2 = TransferFunction(2.0*p, 5.0*p**3 + 2.0*p**2 - 3.0, p)
+    expect2_ = TransferFunction(2*p, 5*p**3 + 2*p**2 - 3, p)
+    assert SP2.subs({c1: 2, d0: 3, d1: 2, d2: 5}) == expect2_
+    assert SP2.subs({c1: 2, d0: 3, d1: 2, d2: 5}).evalf() == expect2
+    assert expect2_.evalf() == expect2
+
+    SP3 = TransferFunction(a0*p**3 + a1*s**2 - b0*s + b1, a1*s + p, s)
+    expect3 = TransferFunction(2.0*p**3 + 4.0*s**2 - s + 5.0, p + 4.0*s, s)
+    expect3_ = TransferFunction(2*p**3 + 4*s**2 - s + 5, p + 4*s, s)
+    assert SP3.subs({a0: 2, a1: 4, b0: 1, b1: 5}) == expect3_
+    assert SP3.subs({a0: 2, a1: 4, b0: 1, b1: 5}).evalf() == expect3
+    assert expect3_.evalf() == expect3
+
+    SP4 = TransferFunction(s - a1*p**3, a0*s + p, p)
+    expect4 = TransferFunction(7.0*p**3 + s, p - s, p)
+    expect4_ = TransferFunction(7*p**3 + s, p - s, p)
+    assert SP4.subs({a0: -1, a1: -7}) == expect4_
+    assert SP4.subs({a0: -1, a1: -7}).evalf() == expect4
+    assert expect4_.evalf() == expect4
+
+    # Low-frequency (or DC) gain.
+    assert tf0.dc_gain() == 1
+    assert tf1.dc_gain() == Rational(3, 5)
+    assert SP2.dc_gain() == 0
+    assert expect4.dc_gain() == -1
+    assert expect2_.dc_gain() == 0
+    assert TransferFunction(1, s, s).dc_gain() == oo
+
+    # Poles of a transfer function.
+    tf_ = TransferFunction(x**3 - k, k, x)
+    _tf = TransferFunction(k, x**4 - k, x)
+    TF_ = TransferFunction(x**2, x**10 + x + x**2, x)
+    _TF = TransferFunction(x**10 + x + x**2, x**2, x)
+    assert G1.poles() == [I, I, -I, -I]
+    assert G2.poles() == []
+    assert tf1.poles() == [-5, 1]
+    assert expect4_.poles() == [s]
+    assert SP4.poles() == [-a0*s]
+    assert expect3.poles() == [-0.25*p]
+    assert str(expect2.poles()) == str([0.729001428685125, -0.564500714342563 - 0.710198984796332*I, -0.564500714342563 + 0.710198984796332*I])
+    assert str(expect1.poles()) == str([-0.4 - 0.66332495807108*I, -0.4 + 0.66332495807108*I])
+    assert _tf.poles() == [k**(Rational(1, 4)), -k**(Rational(1, 4)), I*k**(Rational(1, 4)), -I*k**(Rational(1, 4))]
+    assert TF_.poles() == [CRootOf(x**9 + x + 1, 0), 0, CRootOf(x**9 + x + 1, 1), CRootOf(x**9 + x + 1, 2),
+        CRootOf(x**9 + x + 1, 3), CRootOf(x**9 + x + 1, 4), CRootOf(x**9 + x + 1, 5), CRootOf(x**9 + x + 1, 6),
+        CRootOf(x**9 + x + 1, 7), CRootOf(x**9 + x + 1, 8)]
+    raises(NotImplementedError, lambda: TransferFunction(x**2, a0*x**10 + x + x**2, x).poles())
+
+    # Stability of a transfer function.
+    q, r = symbols('q, r', negative=True)
+    t = symbols('t', positive=True)
+    TF_ = TransferFunction(s**2 + a0 - a1*p, q*s - r, s)
+    stable_tf = TransferFunction(s**2 + a0 - a1*p, q*s - 1, s)
+    stable_tf_ = TransferFunction(s**2 + a0 - a1*p, q*s - t, s)
+
+    assert G1.is_stable() is False
+    assert G2.is_stable() is True
+    assert tf1.is_stable() is False   # as one pole is +ve, and the other is -ve.
+    assert expect2.is_stable() is False
+    assert expect1.is_stable() is True
+    assert stable_tf.is_stable() is True
+    assert stable_tf_.is_stable() is True
+    assert TF_.is_stable() is False
+    assert expect4_.is_stable() is None   # no assumption provided for the only pole 's'.
+    assert SP4.is_stable() is None
+
+    # Zeros of a transfer function.
+    assert G1.zeros() == [1, 1]
+    assert G2.zeros() == []
+    assert tf1.zeros() == [-3, 1]
+    assert expect4_.zeros() == [7**(Rational(2, 3))*(-s)**(Rational(1, 3))/7, -7**(Rational(2, 3))*(-s)**(Rational(1, 3))/14 -
+        sqrt(3)*7**(Rational(2, 3))*I*(-s)**(Rational(1, 3))/14, -7**(Rational(2, 3))*(-s)**(Rational(1, 3))/14 + sqrt(3)*7**(Rational(2, 3))*I*(-s)**(Rational(1, 3))/14]
+    assert SP4.zeros() == [(s/a1)**(Rational(1, 3)), -(s/a1)**(Rational(1, 3))/2 - sqrt(3)*I*(s/a1)**(Rational(1, 3))/2,
+        -(s/a1)**(Rational(1, 3))/2 + sqrt(3)*I*(s/a1)**(Rational(1, 3))/2]
+    assert str(expect3.zeros()) == str([0.125 - 1.11102430216445*sqrt(-0.405063291139241*p**3 - 1.0),
+        1.11102430216445*sqrt(-0.405063291139241*p**3 - 1.0) + 0.125])
+    assert tf_.zeros() == [k**(Rational(1, 3)), -k**(Rational(1, 3))/2 - sqrt(3)*I*k**(Rational(1, 3))/2,
+        -k**(Rational(1, 3))/2 + sqrt(3)*I*k**(Rational(1, 3))/2]
+    assert _TF.zeros() == [CRootOf(x**9 + x + 1, 0), 0, CRootOf(x**9 + x + 1, 1), CRootOf(x**9 + x + 1, 2),
+        CRootOf(x**9 + x + 1, 3), CRootOf(x**9 + x + 1, 4), CRootOf(x**9 + x + 1, 5), CRootOf(x**9 + x + 1, 6),
+        CRootOf(x**9 + x + 1, 7), CRootOf(x**9 + x + 1, 8)]
+    raises(NotImplementedError, lambda: TransferFunction(a0*x**10 + x + x**2, x**2, x).zeros())
+
+    # negation of TF.
+    tf2 = TransferFunction(s + 3, s**2 - s**3 + 9, s)
+    tf3 = TransferFunction(-3*p + 3, 1 - p, p)
+    assert -tf2 == TransferFunction(-s - 3, s**2 - s**3 + 9, s)
+    assert -tf3 == TransferFunction(3*p - 3, 1 - p, p)
+
+    # taking power of a TF.
+    tf4 = TransferFunction(p + 4, p - 3, p)
+    tf5 = TransferFunction(s**2 + 1, 1 - s, s)
+    expect2 = TransferFunction((s**2 + 1)**3, (1 - s)**3, s)
+    expect1 = TransferFunction((p + 4)**2, (p - 3)**2, p)
+    assert (tf4*tf4).doit() == tf4**2 == pow(tf4, 2) == expect1
+    assert (tf5*tf5*tf5).doit() == tf5**3 == pow(tf5, 3) == expect2
+    assert tf5**0 == pow(tf5, 0) == TransferFunction(1, 1, s)
+    assert Series(tf4).doit()**-1 == tf4**-1 == pow(tf4, -1) == TransferFunction(p - 3, p + 4, p)
+    assert (tf5*tf5).doit()**-1 == tf5**-2 == pow(tf5, -2) == TransferFunction((1 - s)**2, (s**2 + 1)**2, s)
+
+    raises(ValueError, lambda: tf4**(s**2 + s - 1))
+    raises(ValueError, lambda: tf5**s)
+    raises(ValueError, lambda: tf4**tf5)
+
+    # SymPy's own functions.
+    tf = TransferFunction(s - 1, s**2 - 2*s + 1, s)
+    tf6 = TransferFunction(s + p, p**2 - 5, s)
+    assert factor(tf) == TransferFunction(s - 1, (s - 1)**2, s)
+    assert tf.num.subs(s, 2) == tf.den.subs(s, 2) == 1
+    # subs & xreplace
+    assert tf.subs(s, 2) == TransferFunction(s - 1, s**2 - 2*s + 1, s)
+    assert tf6.subs(p, 3) == TransferFunction(s + 3, 4, s)
+    assert tf3.xreplace({p: s}) == TransferFunction(-3*s + 3, 1 - s, s)
+    raises(TypeError, lambda: tf3.xreplace({p: exp(2)}))
+    assert tf3.subs(p, exp(2)) == tf3
+
+    tf7 = TransferFunction(a0*s**p + a1*p**s, a2*p - s, s)
+    assert tf7.xreplace({s: k}) == TransferFunction(a0*k**p + a1*p**k, a2*p - k, k)
+    assert tf7.subs(s, k) == TransferFunction(a0*s**p + a1*p**s, a2*p - s, s)
+
+    # Conversion to Expr with to_expr()
+    tf8 = TransferFunction(a0*s**5 + 5*s**2 + 3, s**6 - 3, s)
+    tf9 = TransferFunction((5 + s), (5 + s)*(6 + s), s)
+    tf10 = TransferFunction(0, 1, s)
+    tf11 = TransferFunction(1, 1, s)
+    assert tf8.to_expr() == Mul((a0*s**5 + 5*s**2 + 3), Pow((s**6 - 3), -1, evaluate=False), evaluate=False)
+    assert tf9.to_expr() == Mul((s + 5), Pow((5 + s)*(6 + s), -1, evaluate=False), evaluate=False)
+    assert tf10.to_expr() == Mul(S(0), Pow(1, -1, evaluate=False), evaluate=False)
+    assert tf11.to_expr() == Pow(1, -1, evaluate=False)
+
+def test_TransferFunction_addition_and_subtraction():
+    tf1 = TransferFunction(s + 6, s - 5, s)
+    tf2 = TransferFunction(s + 3, s + 1, s)
+    tf3 = TransferFunction(s + 1, s**2 + s + 1, s)
+    tf4 = TransferFunction(p, 2 - p, p)
+
+    # addition
+    assert tf1 + tf2 == Parallel(tf1, tf2)
+    assert tf3 + tf1 == Parallel(tf3, tf1)
+    assert -tf1 + tf2 + tf3 == Parallel(-tf1, tf2, tf3)
+    assert tf1 + (tf2 + tf3) == Parallel(tf1, tf2, tf3)
+
+    c = symbols("c", commutative=False)
+    raises(ValueError, lambda: tf1 + Matrix([1, 2, 3]))
+    raises(ValueError, lambda: tf2 + c)
+    raises(ValueError, lambda: tf3 + tf4)
+    raises(ValueError, lambda: tf1 + (s - 1))
+    raises(ValueError, lambda: tf1 + 8)
+    raises(ValueError, lambda: (1 - p**3) + tf1)
+
+    # subtraction
+    assert tf1 - tf2 == Parallel(tf1, -tf2)
+    assert tf3 - tf2 == Parallel(tf3, -tf2)
+    assert -tf1 - tf3 == Parallel(-tf1, -tf3)
+    assert tf1 - tf2 + tf3 == Parallel(tf1, -tf2, tf3)
+
+    raises(ValueError, lambda: tf1 - Matrix([1, 2, 3]))
+    raises(ValueError, lambda: tf3 - tf4)
+    raises(ValueError, lambda: tf1 - (s - 1))
+    raises(ValueError, lambda: tf1 - 8)
+    raises(ValueError, lambda: (s + 5) - tf2)
+    raises(ValueError, lambda: (1 + p**4) - tf1)
+
+
+def test_TransferFunction_multiplication_and_division():
+    G1 = TransferFunction(s + 3, -s**3 + 9, s)
+    G2 = TransferFunction(s + 1, s - 5, s)
+    G3 = TransferFunction(p, p**4 - 6, p)
+    G4 = TransferFunction(p + 4, p - 5, p)
+    G5 = TransferFunction(s + 6, s - 5, s)
+    G6 = TransferFunction(s + 3, s + 1, s)
+    G7 = TransferFunction(1, 1, s)
+
+    # multiplication
+    assert G1*G2 == Series(G1, G2)
+    assert -G1*G5 == Series(-G1, G5)
+    assert -G2*G5*-G6 == Series(-G2, G5, -G6)
+    assert -G1*-G2*-G5*-G6 == Series(-G1, -G2, -G5, -G6)
+    assert G3*G4 == Series(G3, G4)
+    assert (G1*G2)*-(G5*G6) == \
+        Series(G1, G2, TransferFunction(-1, 1, s), Series(G5, G6))
+    assert G1*G2*(G5 + G6) == Series(G1, G2, Parallel(G5, G6))
+
+    c = symbols("c", commutative=False)
+    raises(ValueError, lambda: G3 * Matrix([1, 2, 3]))
+    raises(ValueError, lambda: G1 * c)
+    raises(ValueError, lambda: G3 * G5)
+    raises(ValueError, lambda: G5 * (s - 1))
+    raises(ValueError, lambda: 9 * G5)
+
+    raises(ValueError, lambda: G3 / Matrix([1, 2, 3]))
+    raises(ValueError, lambda: G6 / 0)
+    raises(ValueError, lambda: G3 / G5)
+    raises(ValueError, lambda: G5 / 2)
+    raises(ValueError, lambda: G5 / s**2)
+    raises(ValueError, lambda: (s - 4*s**2) / G2)
+    raises(ValueError, lambda: 0 / G4)
+    raises(ValueError, lambda: G5 / G6)
+    raises(ValueError, lambda: -G3 /G4)
+    raises(ValueError, lambda: G7 / (1 + G6))
+    raises(ValueError, lambda: G7 / (G5 * G6))
+    raises(ValueError, lambda: G7 / (G7 + (G5 + G6)))
+
+
+def test_TransferFunction_is_proper():
+    omega_o, zeta, tau = symbols('omega_o, zeta, tau')
+    G1 = TransferFunction(omega_o**2, s**2 + p*omega_o*zeta*s + omega_o**2, omega_o)
+    G2 = TransferFunction(tau - s**3, tau + p**4, tau)
+    G3 = TransferFunction(a*b*s**3 + s**2 - a*p + s, b - s*p**2, p)
+    G4 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
+    assert G1.is_proper
+    assert G2.is_proper
+    assert G3.is_proper
+    assert not G4.is_proper
+
+
+def test_TransferFunction_is_strictly_proper():
+    omega_o, zeta, tau = symbols('omega_o, zeta, tau')
+    tf1 = TransferFunction(omega_o**2, s**2 + p*omega_o*zeta*s + omega_o**2, omega_o)
+    tf2 = TransferFunction(tau - s**3, tau + p**4, tau)
+    tf3 = TransferFunction(a*b*s**3 + s**2 - a*p + s, b - s*p**2, p)
+    tf4 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
+    assert not tf1.is_strictly_proper
+    assert not tf2.is_strictly_proper
+    assert tf3.is_strictly_proper
+    assert not tf4.is_strictly_proper
+
+
+def test_TransferFunction_is_biproper():
+    tau, omega_o, zeta = symbols('tau, omega_o, zeta')
+    tf1 = TransferFunction(omega_o**2, s**2 + p*omega_o*zeta*s + omega_o**2, omega_o)
+    tf2 = TransferFunction(tau - s**3, tau + p**4, tau)
+    tf3 = TransferFunction(a*b*s**3 + s**2 - a*p + s, b - s*p**2, p)
+    tf4 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
+    assert tf1.is_biproper
+    assert tf2.is_biproper
+    assert not tf3.is_biproper
+    assert not tf4.is_biproper
+
+
+def test_Series_construction():
+    tf = TransferFunction(a0*s**3 + a1*s**2 - a2*s, b0*p**4 + b1*p**3 - b2*s*p, s)
+    tf2 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf3 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf4 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    inp = Function('X_d')(s)
+    out = Function('X')(s)
+
+    s0 = Series(tf, tf2)
+    assert s0.args == (tf, tf2)
+    assert s0.var == s
+
+    s1 = Series(Parallel(tf, -tf2), tf2)
+    assert s1.args == (Parallel(tf, -tf2), tf2)
+    assert s1.var == s
+
+    tf3_ = TransferFunction(inp, 1, s)
+    tf4_ = TransferFunction(-out, 1, s)
+    s2 = Series(tf, Parallel(tf3_, tf4_), tf2)
+    assert s2.args == (tf, Parallel(tf3_, tf4_), tf2)
+
+    s3 = Series(tf, tf2, tf4)
+    assert s3.args == (tf, tf2, tf4)
+
+    s4 = Series(tf3_, tf4_)
+    assert s4.args == (tf3_, tf4_)
+    assert s4.var == s
+
+    s6 = Series(tf2, tf4, Parallel(tf2, -tf), tf4)
+    assert s6.args == (tf2, tf4, Parallel(tf2, -tf), tf4)
+
+    s7 = Series(tf, tf2)
+    assert s0 == s7
+    assert not s0 == s2
+
+    raises(ValueError, lambda: Series(tf, tf3))
+    raises(ValueError, lambda: Series(tf, tf2, tf3, tf4))
+    raises(ValueError, lambda: Series(-tf3, tf2))
+    raises(TypeError, lambda: Series(2, tf, tf4))
+    raises(TypeError, lambda: Series(s**2 + p*s, tf3, tf2))
+    raises(TypeError, lambda: Series(tf3, Matrix([1, 2, 3, 4])))
+
+
+def test_MIMOSeries_construction():
+    tf_1 = TransferFunction(a0*s**3 + a1*s**2 - a2*s, b0*p**4 + b1*p**3 - b2*s*p, s)
+    tf_2 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf_3 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+
+    tfm_1 = TransferFunctionMatrix([[tf_1, tf_2, tf_3], [-tf_3, -tf_2, tf_1]])
+    tfm_2 = TransferFunctionMatrix([[-tf_2], [-tf_2], [-tf_3]])
+    tfm_3 = TransferFunctionMatrix([[-tf_3]])
+    tfm_4 = TransferFunctionMatrix([[TF3], [TF2], [-TF1]])
+    tfm_5 = TransferFunctionMatrix.from_Matrix(Matrix([1/p]), p)
+
+    s8 = MIMOSeries(tfm_2, tfm_1)
+    assert s8.args == (tfm_2, tfm_1)
+    assert s8.var == s
+    assert s8.shape == (s8.num_outputs, s8.num_inputs) == (2, 1)
+
+    s9 = MIMOSeries(tfm_3, tfm_2, tfm_1)
+    assert s9.args == (tfm_3, tfm_2, tfm_1)
+    assert s9.var == s
+    assert s9.shape == (s9.num_outputs, s9.num_inputs) == (2, 1)
+
+    s11 = MIMOSeries(tfm_3, MIMOParallel(-tfm_2, -tfm_4), tfm_1)
+    assert s11.args == (tfm_3, MIMOParallel(-tfm_2, -tfm_4), tfm_1)
+    assert s11.shape == (s11.num_outputs, s11.num_inputs) == (2, 1)
+
+    # arg cannot be empty tuple.
+    raises(ValueError, lambda: MIMOSeries())
+
+    # arg cannot contain SISO as well as MIMO systems.
+    raises(TypeError, lambda: MIMOSeries(tfm_1, tf_1))
+
+    # for all the adjacent transfer function matrices:
+    # no. of inputs of first TFM must be equal to the no. of outputs of the second TFM.
+    raises(ValueError, lambda: MIMOSeries(tfm_1, tfm_2, -tfm_1))
+
+    # all the TFMs must use the same complex variable.
+    raises(ValueError, lambda: MIMOSeries(tfm_3, tfm_5))
+
+    # Number or expression not allowed in the arguments.
+    raises(TypeError, lambda: MIMOSeries(2, tfm_2, tfm_3))
+    raises(TypeError, lambda: MIMOSeries(s**2 + p*s, -tfm_2, tfm_3))
+    raises(TypeError, lambda: MIMOSeries(Matrix([1/p]), tfm_3))
+
+
+def test_Series_functions():
+    tf1 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    tf2 = TransferFunction(k, 1, s)
+    tf3 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+
+    assert tf1*tf2*tf3 == Series(tf1, tf2, tf3) == Series(Series(tf1, tf2), tf3) \
+        == Series(tf1, Series(tf2, tf3))
+    assert tf1*(tf2 + tf3) == Series(tf1, Parallel(tf2, tf3))
+    assert tf1*tf2 + tf5 == Parallel(Series(tf1, tf2), tf5)
+    assert tf1*tf2 - tf5 == Parallel(Series(tf1, tf2), -tf5)
+    assert tf1*tf2 + tf3 + tf5 == Parallel(Series(tf1, tf2), tf3, tf5)
+    assert tf1*tf2 - tf3 - tf5 == Parallel(Series(tf1, tf2), -tf3, -tf5)
+    assert tf1*tf2 - tf3 + tf5 == Parallel(Series(tf1, tf2), -tf3, tf5)
+    assert tf1*tf2 + tf3*tf5 == Parallel(Series(tf1, tf2), Series(tf3, tf5))
+    assert tf1*tf2 - tf3*tf5 == Parallel(Series(tf1, tf2), Series(TransferFunction(-1, 1, s), Series(tf3, tf5)))
+    assert tf2*tf3*(tf2 - tf1)*tf3 == Series(tf2, tf3, Parallel(tf2, -tf1), tf3)
+    assert -tf1*tf2 == Series(-tf1, tf2)
+    assert -(tf1*tf2) == Series(TransferFunction(-1, 1, s), Series(tf1, tf2))
+    raises(ValueError, lambda: tf1*tf2*tf4)
+    raises(ValueError, lambda: tf1*(tf2 - tf4))
+    raises(ValueError, lambda: tf3*Matrix([1, 2, 3]))
+
+    # evaluate=True -> doit()
+    assert Series(tf1, tf2, evaluate=True) == Series(tf1, tf2).doit() == \
+        TransferFunction(k, s**2 + 2*s*wn*zeta + wn**2, s)
+    assert Series(tf1, tf2, Parallel(tf1, -tf3), evaluate=True) == Series(tf1, tf2, Parallel(tf1, -tf3)).doit() == \
+        TransferFunction(k*(a2*s + p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2)), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2)**2, s)
+    assert Series(tf2, tf1, -tf3, evaluate=True) == Series(tf2, tf1, -tf3).doit() == \
+        TransferFunction(k*(-a2*p + s), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert not Series(tf1, -tf2, evaluate=False) == Series(tf1, -tf2).doit()
+
+    assert Series(Parallel(tf1, tf2), Parallel(tf2, -tf3)).doit() == \
+        TransferFunction((k*(s**2 + 2*s*wn*zeta + wn**2) + 1)*(-a2*p + k*(a2*s + p) + s), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Series(-tf1, -tf2, -tf3).doit() == \
+        TransferFunction(k*(-a2*p + s), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert -Series(tf1, tf2, tf3).doit() == \
+        TransferFunction(-k*(a2*p - s), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Series(tf2, tf3, Parallel(tf2, -tf1), tf3).doit() == \
+        TransferFunction(k*(a2*p - s)**2*(k*(s**2 + 2*s*wn*zeta + wn**2) - 1), (a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2), s)
+
+    assert Series(tf1, tf2).rewrite(TransferFunction) == TransferFunction(k, s**2 + 2*s*wn*zeta + wn**2, s)
+    assert Series(tf2, tf1, -tf3).rewrite(TransferFunction) == \
+        TransferFunction(k*(-a2*p + s), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+
+    S1 = Series(Parallel(tf1, tf2), Parallel(tf2, -tf3))
+    assert S1.is_proper
+    assert not S1.is_strictly_proper
+    assert S1.is_biproper
+
+    S2 = Series(tf1, tf2, tf3)
+    assert S2.is_proper
+    assert S2.is_strictly_proper
+    assert not S2.is_biproper
+
+    S3 = Series(tf1, -tf2, Parallel(tf1, -tf3))
+    assert S3.is_proper
+    assert S3.is_strictly_proper
+    assert not S3.is_biproper
+
+
+def test_MIMOSeries_functions():
+    tfm1 = TransferFunctionMatrix([[TF1, TF2, TF3], [-TF3, -TF2, TF1]])
+    tfm2 = TransferFunctionMatrix([[-TF1], [-TF2], [-TF3]])
+    tfm3 = TransferFunctionMatrix([[-TF1]])
+    tfm4 = TransferFunctionMatrix([[-TF2, -TF3], [-TF1, TF2]])
+    tfm5 = TransferFunctionMatrix([[TF2, -TF2], [-TF3, -TF2]])
+    tfm6 = TransferFunctionMatrix([[-TF3], [TF1]])
+    tfm7 = TransferFunctionMatrix([[TF1], [-TF2]])
+
+    assert tfm1*tfm2 + tfm6 == MIMOParallel(MIMOSeries(tfm2, tfm1), tfm6)
+    assert tfm1*tfm2 + tfm7 + tfm6 == MIMOParallel(MIMOSeries(tfm2, tfm1), tfm7, tfm6)
+    assert tfm1*tfm2 - tfm6 - tfm7 == MIMOParallel(MIMOSeries(tfm2, tfm1), -tfm6, -tfm7)
+    assert tfm4*tfm5 + (tfm4 - tfm5) == MIMOParallel(MIMOSeries(tfm5, tfm4), tfm4, -tfm5)
+    assert tfm4*-tfm6 + (-tfm4*tfm6) == MIMOParallel(MIMOSeries(-tfm6, tfm4), MIMOSeries(tfm6, -tfm4))
+
+    raises(ValueError, lambda: tfm1*tfm2 + TF1)
+    raises(TypeError, lambda: tfm1*tfm2 + a0)
+    raises(TypeError, lambda: tfm4*tfm6 - (s - 1))
+    raises(TypeError, lambda: tfm4*-tfm6 - 8)
+    raises(TypeError, lambda: (-1 + p**5) + tfm1*tfm2)
+
+    # Shape criteria.
+
+    raises(TypeError, lambda: -tfm1*tfm2 + tfm4)
+    raises(TypeError, lambda: tfm1*tfm2 - tfm4 + tfm5)
+    raises(TypeError, lambda: tfm1*tfm2 - tfm4*tfm5)
+
+    assert tfm1*tfm2*-tfm3 == MIMOSeries(-tfm3, tfm2, tfm1)
+    assert (tfm1*-tfm2)*tfm3 == MIMOSeries(tfm3, -tfm2, tfm1)
+
+    # Multiplication of a Series object with a SISO TF not allowed.
+
+    raises(ValueError, lambda: tfm4*tfm5*TF1)
+    raises(TypeError, lambda: tfm4*tfm5*a1)
+    raises(TypeError, lambda: tfm4*-tfm5*(s - 2))
+    raises(TypeError, lambda: tfm5*tfm4*9)
+    raises(TypeError, lambda: (-p**3 + 1)*tfm5*tfm4)
+
+    # Transfer function matrix in the arguments.
+    assert (MIMOSeries(tfm2, tfm1, evaluate=True) == MIMOSeries(tfm2, tfm1).doit()
+        == TransferFunctionMatrix(((TransferFunction(-k**2*(a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2 + (-a2*p + s)*(a2*p - s)*(s**2 + 2*s*wn*zeta + wn**2)**2 - (a2*s + p)**2,
+        (a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2, s),),
+        (TransferFunction(k**2*(a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2 + (-a2*p + s)*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) + (a2*p - s)*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2),
+        (a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2, s),))))
+
+    # doit() should not cancel poles and zeros.
+    mat_1 = Matrix([[1/(1+s), (1+s)/(1+s**2+2*s)**3]])
+    mat_2 = Matrix([[(1+s)], [(1+s**2+2*s)**3/(1+s)]])
+    tm_1, tm_2 = TransferFunctionMatrix.from_Matrix(mat_1, s), TransferFunctionMatrix.from_Matrix(mat_2, s)
+    assert (MIMOSeries(tm_2, tm_1).doit()
+        == TransferFunctionMatrix(((TransferFunction(2*(s + 1)**2*(s**2 + 2*s + 1)**3, (s + 1)**2*(s**2 + 2*s + 1)**3, s),),)))
+    assert MIMOSeries(tm_2, tm_1).doit().simplify() == TransferFunctionMatrix(((TransferFunction(2, 1, s),),))
+
+    # calling doit() will expand the internal Series and Parallel objects.
+    assert (MIMOSeries(-tfm3, -tfm2, tfm1, evaluate=True)
+        == MIMOSeries(-tfm3, -tfm2, tfm1).doit()
+        == TransferFunctionMatrix(((TransferFunction(k**2*(a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2 + (a2*p - s)**2*(s**2 + 2*s*wn*zeta + wn**2)**2 + (a2*s + p)**2,
+        (a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**3, s),),
+        (TransferFunction(-k**2*(a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**2 + (-a2*p + s)*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) + (a2*p - s)*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2),
+        (a2*s + p)**2*(s**2 + 2*s*wn*zeta + wn**2)**3, s),))))
+    assert (MIMOSeries(MIMOParallel(tfm4, tfm5), tfm5, evaluate=True)
+        == MIMOSeries(MIMOParallel(tfm4, tfm5), tfm5).doit()
+        == TransferFunctionMatrix(((TransferFunction(-k*(-a2*s - p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2)), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s), TransferFunction(k*(-a2*p - \
+            k*(a2*s + p) + s), a2*s + p, s)), (TransferFunction(-k*(-a2*s - p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2)), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s), \
+            TransferFunction((-a2*p + s)*(-a2*p - k*(a2*s + p) + s), (a2*s + p)**2, s)))) == MIMOSeries(MIMOParallel(tfm4, tfm5), tfm5).rewrite(TransferFunctionMatrix))
+
+
+def test_Parallel_construction():
+    tf = TransferFunction(a0*s**3 + a1*s**2 - a2*s, b0*p**4 + b1*p**3 - b2*s*p, s)
+    tf2 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf3 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf4 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    inp = Function('X_d')(s)
+    out = Function('X')(s)
+
+    p0 = Parallel(tf, tf2)
+    assert p0.args == (tf, tf2)
+    assert p0.var == s
+
+    p1 = Parallel(Series(tf, -tf2), tf2)
+    assert p1.args == (Series(tf, -tf2), tf2)
+    assert p1.var == s
+
+    tf3_ = TransferFunction(inp, 1, s)
+    tf4_ = TransferFunction(-out, 1, s)
+    p2 = Parallel(tf, Series(tf3_, -tf4_), tf2)
+    assert p2.args == (tf, Series(tf3_, -tf4_), tf2)
+
+    p3 = Parallel(tf, tf2, tf4)
+    assert p3.args == (tf, tf2, tf4)
+
+    p4 = Parallel(tf3_, tf4_)
+    assert p4.args == (tf3_, tf4_)
+    assert p4.var == s
+
+    p5 = Parallel(tf, tf2)
+    assert p0 == p5
+    assert not p0 == p1
+
+    p6 = Parallel(tf2, tf4, Series(tf2, -tf4))
+    assert p6.args == (tf2, tf4, Series(tf2, -tf4))
+
+    p7 = Parallel(tf2, tf4, Series(tf2, -tf), tf4)
+    assert p7.args == (tf2, tf4, Series(tf2, -tf), tf4)
+
+    raises(ValueError, lambda: Parallel(tf, tf3))
+    raises(ValueError, lambda: Parallel(tf, tf2, tf3, tf4))
+    raises(ValueError, lambda: Parallel(-tf3, tf4))
+    raises(TypeError, lambda: Parallel(2, tf, tf4))
+    raises(TypeError, lambda: Parallel(s**2 + p*s, tf3, tf2))
+    raises(TypeError, lambda: Parallel(tf3, Matrix([1, 2, 3, 4])))
+
+
+def test_MIMOParallel_construction():
+    tfm1 = TransferFunctionMatrix([[TF1], [TF2], [TF3]])
+    tfm2 = TransferFunctionMatrix([[-TF3], [TF2], [TF1]])
+    tfm3 = TransferFunctionMatrix([[TF1]])
+    tfm4 = TransferFunctionMatrix([[TF2], [TF1], [TF3]])
+    tfm5 = TransferFunctionMatrix([[TF1, TF2], [TF2, TF1]])
+    tfm6 = TransferFunctionMatrix([[TF2, TF1], [TF1, TF2]])
+    tfm7 = TransferFunctionMatrix.from_Matrix(Matrix([[1/p]]), p)
+
+    p8 = MIMOParallel(tfm1, tfm2)
+    assert p8.args == (tfm1, tfm2)
+    assert p8.var == s
+    assert p8.shape == (p8.num_outputs, p8.num_inputs) == (3, 1)
+
+    p9 = MIMOParallel(MIMOSeries(tfm3, tfm1), tfm2)
+    assert p9.args == (MIMOSeries(tfm3, tfm1), tfm2)
+    assert p9.var == s
+    assert p9.shape == (p9.num_outputs, p9.num_inputs) == (3, 1)
+
+    p10 = MIMOParallel(tfm1, MIMOSeries(tfm3, tfm4), tfm2)
+    assert p10.args == (tfm1, MIMOSeries(tfm3, tfm4), tfm2)
+    assert p10.var == s
+    assert p10.shape == (p10.num_outputs, p10.num_inputs) == (3, 1)
+
+    p11 = MIMOParallel(tfm2, tfm1, tfm4)
+    assert p11.args == (tfm2, tfm1, tfm4)
+    assert p11.shape == (p11.num_outputs, p11.num_inputs) == (3, 1)
+
+    p12 = MIMOParallel(tfm6, tfm5)
+    assert p12.args == (tfm6, tfm5)
+    assert p12.shape == (p12.num_outputs, p12.num_inputs) == (2, 2)
+
+    p13 = MIMOParallel(tfm2, tfm4, MIMOSeries(-tfm3, tfm4), -tfm4)
+    assert p13.args == (tfm2, tfm4, MIMOSeries(-tfm3, tfm4), -tfm4)
+    assert p13.shape == (p13.num_outputs, p13.num_inputs) == (3, 1)
+
+    # arg cannot be empty tuple.
+    raises(TypeError, lambda: MIMOParallel(()))
+
+    # arg cannot contain SISO as well as MIMO systems.
+    raises(TypeError, lambda: MIMOParallel(tfm1, tfm2, TF1))
+
+    # all TFMs must have same shapes.
+    raises(TypeError, lambda: MIMOParallel(tfm1, tfm3, tfm4))
+
+    # all TFMs must be using the same complex variable.
+    raises(ValueError, lambda: MIMOParallel(tfm3, tfm7))
+
+    # Number or expression not allowed in the arguments.
+    raises(TypeError, lambda: MIMOParallel(2, tfm1, tfm4))
+    raises(TypeError, lambda: MIMOParallel(s**2 + p*s, -tfm4, tfm2))
+
+
+def test_Parallel_functions():
+    tf1 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    tf2 = TransferFunction(k, 1, s)
+    tf3 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+
+    assert tf1 + tf2 + tf3 == Parallel(tf1, tf2, tf3)
+    assert tf1 + tf2 + tf3 + tf5 == Parallel(tf1, tf2, tf3, tf5)
+    assert tf1 + tf2 - tf3 - tf5 == Parallel(tf1, tf2, -tf3, -tf5)
+    assert tf1 + tf2*tf3 == Parallel(tf1, Series(tf2, tf3))
+    assert tf1 - tf2*tf3 == Parallel(tf1, -Series(tf2,tf3))
+    assert -tf1 - tf2 == Parallel(-tf1, -tf2)
+    assert -(tf1 + tf2) == Series(TransferFunction(-1, 1, s), Parallel(tf1, tf2))
+    assert (tf2 + tf3)*tf1 == Series(Parallel(tf2, tf3), tf1)
+    assert (tf1 + tf2)*(tf3*tf5) == Series(Parallel(tf1, tf2), tf3, tf5)
+    assert -(tf2 + tf3)*-tf5 == Series(TransferFunction(-1, 1, s), Parallel(tf2, tf3), -tf5)
+    assert tf2 + tf3 + tf2*tf1 + tf5 == Parallel(tf2, tf3, Series(tf2, tf1), tf5)
+    assert tf2 + tf3 + tf2*tf1 - tf3 == Parallel(tf2, tf3, Series(tf2, tf1), -tf3)
+    assert (tf1 + tf2 + tf5)*(tf3 + tf5) == Series(Parallel(tf1, tf2, tf5), Parallel(tf3, tf5))
+    raises(ValueError, lambda: tf1 + tf2 + tf4)
+    raises(ValueError, lambda: tf1 - tf2*tf4)
+    raises(ValueError, lambda: tf3 + Matrix([1, 2, 3]))
+
+    # evaluate=True -> doit()
+    assert Parallel(tf1, tf2, evaluate=True) == Parallel(tf1, tf2).doit() == \
+        TransferFunction(k*(s**2 + 2*s*wn*zeta + wn**2) + 1, s**2 + 2*s*wn*zeta + wn**2, s)
+    assert Parallel(tf1, tf2, Series(-tf1, tf3), evaluate=True) == \
+        Parallel(tf1, tf2, Series(-tf1, tf3)).doit() == TransferFunction(k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2)**2 + \
+            (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2) + (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), (a2*s + p)*(s**2 + \
+                2*s*wn*zeta + wn**2)**2, s)
+    assert Parallel(tf2, tf1, -tf3, evaluate=True) == Parallel(tf2, tf1, -tf3).doit() == \
+        TransferFunction(a2*s + k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) + p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2) \
+            , (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert not Parallel(tf1, -tf2, evaluate=False) == Parallel(tf1, -tf2).doit()
+
+    assert Parallel(Series(tf1, tf2), Series(tf2, tf3)).doit() == \
+        TransferFunction(k*(a2*p - s)*(s**2 + 2*s*wn*zeta + wn**2) + k*(a2*s + p), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Parallel(-tf1, -tf2, -tf3).doit() == \
+        TransferFunction(-a2*s - k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) - p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + wn**2), \
+            (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert -Parallel(tf1, tf2, tf3).doit() == \
+        TransferFunction(-a2*s - k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) - p - (a2*p - s)*(s**2 + 2*s*wn*zeta + wn**2), \
+            (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Parallel(tf2, tf3, Series(tf2, -tf1), tf3).doit() == \
+        TransferFunction(k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) - k*(a2*s + p) + (2*a2*p - 2*s)*(s**2 + 2*s*wn*zeta \
+            + wn**2), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+
+    assert Parallel(tf1, tf2).rewrite(TransferFunction) == \
+        TransferFunction(k*(s**2 + 2*s*wn*zeta + wn**2) + 1, s**2 + 2*s*wn*zeta + wn**2, s)
+    assert Parallel(tf2, tf1, -tf3).rewrite(TransferFunction) == \
+        TransferFunction(a2*s + k*(a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2) + p + (-a2*p + s)*(s**2 + 2*s*wn*zeta + \
+             wn**2), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+
+    assert Parallel(tf1, Parallel(tf2, tf3)) == Parallel(tf1, tf2, tf3) == Parallel(Parallel(tf1, tf2), tf3)
+
+    P1 = Parallel(Series(tf1, tf2), Series(tf2, tf3))
+    assert P1.is_proper
+    assert not P1.is_strictly_proper
+    assert P1.is_biproper
+
+    P2 = Parallel(tf1, -tf2, -tf3)
+    assert P2.is_proper
+    assert not P2.is_strictly_proper
+    assert P2.is_biproper
+
+    P3 = Parallel(tf1, -tf2, Series(tf1, tf3))
+    assert P3.is_proper
+    assert not P3.is_strictly_proper
+    assert P3.is_biproper
+
+
+def test_MIMOParallel_functions():
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+
+    tfm1 = TransferFunctionMatrix([[TF1], [TF2], [TF3]])
+    tfm2 = TransferFunctionMatrix([[-TF2], [tf5], [-TF1]])
+    tfm3 = TransferFunctionMatrix([[tf5], [-tf5], [TF2]])
+    tfm4 = TransferFunctionMatrix([[TF2, -tf5], [TF1, tf5]])
+    tfm5 = TransferFunctionMatrix([[TF1, TF2], [TF3, -tf5]])
+    tfm6 = TransferFunctionMatrix([[-TF2]])
+    tfm7 = TransferFunctionMatrix([[tf4], [-tf4], [tf4]])
+
+    assert tfm1 + tfm2 + tfm3 == MIMOParallel(tfm1, tfm2, tfm3) == MIMOParallel(MIMOParallel(tfm1, tfm2), tfm3)
+    assert tfm2 - tfm1 - tfm3 == MIMOParallel(tfm2, -tfm1, -tfm3)
+    assert tfm2 - tfm3 + (-tfm1*tfm6*-tfm6) == MIMOParallel(tfm2, -tfm3, MIMOSeries(-tfm6, tfm6, -tfm1))
+    assert tfm1 + tfm1 - (-tfm1*tfm6) == MIMOParallel(tfm1, tfm1, -MIMOSeries(tfm6, -tfm1))
+    assert tfm2 - tfm3 - tfm1 + tfm2 == MIMOParallel(tfm2, -tfm3, -tfm1, tfm2)
+    assert tfm1 + tfm2 - tfm3 - tfm1 == MIMOParallel(tfm1, tfm2, -tfm3, -tfm1)
+    raises(ValueError, lambda: tfm1 + tfm2 + TF2)
+    raises(TypeError, lambda: tfm1 - tfm2 - a1)
+    raises(TypeError, lambda: tfm2 - tfm3 - (s - 1))
+    raises(TypeError, lambda: -tfm3 - tfm2 - 9)
+    raises(TypeError, lambda: (1 - p**3) - tfm3 - tfm2)
+    # All TFMs must use the same complex var. tfm7 uses 'p'.
+    raises(ValueError, lambda: tfm3 - tfm2 - tfm7)
+    raises(ValueError, lambda: tfm2 - tfm1 + tfm7)
+    # (tfm1 +/- tfm2) has (3, 1) shape while tfm4 has (2, 2) shape.
+    raises(TypeError, lambda: tfm1 + tfm2 + tfm4)
+    raises(TypeError, lambda: (tfm1 - tfm2) - tfm4)
+
+    assert (tfm1 + tfm2)*tfm6 == MIMOSeries(tfm6, MIMOParallel(tfm1, tfm2))
+    assert (tfm2 - tfm3)*tfm6*-tfm6 == MIMOSeries(-tfm6, tfm6, MIMOParallel(tfm2, -tfm3))
+    assert (tfm2 - tfm1 - tfm3)*(tfm6 + tfm6) == MIMOSeries(MIMOParallel(tfm6, tfm6), MIMOParallel(tfm2, -tfm1, -tfm3))
+    raises(ValueError, lambda: (tfm4 + tfm5)*TF1)
+    raises(TypeError, lambda: (tfm2 - tfm3)*a2)
+    raises(TypeError, lambda: (tfm3 + tfm2)*(s - 6))
+    raises(TypeError, lambda: (tfm1 + tfm2 + tfm3)*0)
+    raises(TypeError, lambda: (1 - p**3)*(tfm1 + tfm3))
+
+    # (tfm3 - tfm2) has (3, 1) shape while tfm4*tfm5 has (2, 2) shape.
+    raises(ValueError, lambda: (tfm3 - tfm2)*tfm4*tfm5)
+    # (tfm1 - tfm2) has (3, 1) shape while tfm5 has (2, 2) shape.
+    raises(ValueError, lambda: (tfm1 - tfm2)*tfm5)
+
+    # TFM in the arguments.
+    assert (MIMOParallel(tfm1, tfm2, evaluate=True) == MIMOParallel(tfm1, tfm2).doit()
+    == MIMOParallel(tfm1, tfm2).rewrite(TransferFunctionMatrix)
+    == TransferFunctionMatrix(((TransferFunction(-k*(s**2 + 2*s*wn*zeta + wn**2) + 1, s**2 + 2*s*wn*zeta + wn**2, s),), \
+        (TransferFunction(-a0 + a1*s**2 + a2*s + k*(a0 + s), a0 + s, s),), (TransferFunction(-a2*s - p + (a2*p - s)* \
+        (s**2 + 2*s*wn*zeta + wn**2), (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s),))))
+
+
+def test_Feedback_construction():
+    tf1 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    tf2 = TransferFunction(k, 1, s)
+    tf3 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+    tf6 = TransferFunction(s - p, p + s, p)
+
+    f1 = Feedback(TransferFunction(1, 1, s), tf1*tf2*tf3)
+    assert f1.args == (TransferFunction(1, 1, s), Series(tf1, tf2, tf3), -1)
+    assert f1.sys1 == TransferFunction(1, 1, s)
+    assert f1.sys2 == Series(tf1, tf2, tf3)
+    assert f1.var == s
+
+    f2 = Feedback(tf1, tf2*tf3)
+    assert f2.args == (tf1, Series(tf2, tf3), -1)
+    assert f2.sys1 == tf1
+    assert f2.sys2 == Series(tf2, tf3)
+    assert f2.var == s
+
+    f3 = Feedback(tf1*tf2, tf5)
+    assert f3.args == (Series(tf1, tf2), tf5, -1)
+    assert f3.sys1 == Series(tf1, tf2)
+
+    f4 = Feedback(tf4, tf6)
+    assert f4.args == (tf4, tf6, -1)
+    assert f4.sys1 == tf4
+    assert f4.var == p
+
+    f5 = Feedback(tf5, TransferFunction(1, 1, s))
+    assert f5.args == (tf5, TransferFunction(1, 1, s), -1)
+    assert f5.var == s
+    assert f5 == Feedback(tf5)  # When sys2 is not passed explicitly, it is assumed to be unit tf.
+
+    f6 = Feedback(TransferFunction(1, 1, p), tf4)
+    assert f6.args == (TransferFunction(1, 1, p), tf4, -1)
+    assert f6.var == p
+
+    f7 = -Feedback(tf4*tf6, TransferFunction(1, 1, p))
+    assert f7.args == (Series(TransferFunction(-1, 1, p), Series(tf4, tf6)), -TransferFunction(1, 1, p), -1)
+    assert f7.sys1 == Series(TransferFunction(-1, 1, p), Series(tf4, tf6))
+
+    # denominator can't be a Parallel instance
+    raises(TypeError, lambda: Feedback(tf1, tf2 + tf3))
+    raises(TypeError, lambda: Feedback(tf1, Matrix([1, 2, 3])))
+    raises(TypeError, lambda: Feedback(TransferFunction(1, 1, s), s - 1))
+    raises(TypeError, lambda: Feedback(1, 1))
+    # raises(ValueError, lambda: Feedback(TransferFunction(1, 1, s), TransferFunction(1, 1, s)))
+    raises(ValueError, lambda: Feedback(tf2, tf4*tf5))
+    raises(ValueError, lambda: Feedback(tf2, tf1, 1.5))  # `sign` can only be -1 or 1
+    raises(ValueError, lambda: Feedback(tf1, -tf1**-1))  # denominator can't be zero
+    raises(ValueError, lambda: Feedback(tf4, tf5))  # Both systems should use the same `var`
+
+
+def test_Feedback_functions():
+    tf = TransferFunction(1, 1, s)
+    tf1 = TransferFunction(1, s**2 + 2*zeta*wn*s + wn**2, s)
+    tf2 = TransferFunction(k, 1, s)
+    tf3 = TransferFunction(a2*p - s, a2*s + p, s)
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+    tf6 = TransferFunction(s - p, p + s, p)
+
+    assert tf / (tf + tf1) == Feedback(tf, tf1)
+    assert tf / (tf + tf1*tf2*tf3) == Feedback(tf, tf1*tf2*tf3)
+    assert tf1 / (tf + tf1*tf2*tf3) == Feedback(tf1, tf2*tf3)
+    assert (tf1*tf2) / (tf + tf1*tf2) == Feedback(tf1*tf2, tf)
+    assert (tf1*tf2) / (tf + tf1*tf2*tf5) == Feedback(tf1*tf2, tf5)
+    assert (tf1*tf2) / (tf + tf1*tf2*tf5*tf3) in (Feedback(tf1*tf2, tf5*tf3), Feedback(tf1*tf2, tf3*tf5))
+    assert tf4 / (TransferFunction(1, 1, p) + tf4*tf6) == Feedback(tf4, tf6)
+    assert tf5 / (tf + tf5) == Feedback(tf5, tf)
+
+    raises(TypeError, lambda: tf1*tf2*tf3 / (1 + tf1*tf2*tf3))
+    raises(ValueError, lambda: tf1*tf2*tf3 / tf3*tf5)
+    raises(ValueError, lambda: tf2*tf3 / (tf + tf2*tf3*tf4))
+
+    assert Feedback(tf, tf1*tf2*tf3).doit() == \
+        TransferFunction((a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), k*(a2*p - s) + \
+        (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Feedback(tf, tf1*tf2*tf3).sensitivity == \
+        1/(k*(a2*p - s)/((a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2)) + 1)
+    assert Feedback(tf1, tf2*tf3).doit() == \
+        TransferFunction((a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2), (k*(a2*p - s) + \
+        (a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2))*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Feedback(tf1, tf2*tf3).sensitivity == \
+        1/(k*(a2*p - s)/((a2*s + p)*(s**2 + 2*s*wn*zeta + wn**2)) + 1)
+    assert Feedback(tf1*tf2, tf5).doit() == \
+        TransferFunction(k*(a0 + s)*(s**2 + 2*s*wn*zeta + wn**2), (k*(-a0 + a1*s**2 + a2*s) + \
+        (a0 + s)*(s**2 + 2*s*wn*zeta + wn**2))*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Feedback(tf1*tf2, tf5, 1).sensitivity == \
+        1/(-k*(-a0 + a1*s**2 + a2*s)/((a0 + s)*(s**2 + 2*s*wn*zeta + wn**2)) + 1)
+    assert Feedback(tf4, tf6).doit() == \
+        TransferFunction(p*(p + s)*(a0*p + p**a1 - s), p*(p*(p + s) + (-p + s)*(a0*p + p**a1 - s)), p)
+    assert -Feedback(tf4*tf6, TransferFunction(1, 1, p)).doit() == \
+        TransferFunction(-p*(-p + s)*(p + s)*(a0*p + p**a1 - s), p*(p + s)*(p*(p + s) + (-p + s)*(a0*p + p**a1 - s)), p)
+    assert Feedback(tf, tf).doit() == TransferFunction(1, 2, s)
+
+    assert Feedback(tf1, tf2*tf5).rewrite(TransferFunction) == \
+        TransferFunction((a0 + s)*(s**2 + 2*s*wn*zeta + wn**2), (k*(-a0 + a1*s**2 + a2*s) + \
+        (a0 + s)*(s**2 + 2*s*wn*zeta + wn**2))*(s**2 + 2*s*wn*zeta + wn**2), s)
+    assert Feedback(TransferFunction(1, 1, p), tf4).rewrite(TransferFunction) == \
+        TransferFunction(p, a0*p + p + p**a1 - s, p)
+
+
+def test_MIMOFeedback_construction():
+    tf1 = TransferFunction(1, s, s)
+    tf2 = TransferFunction(s, s**3 - 1, s)
+    tf3 = TransferFunction(s, s + 1, s)
+    tf4 = TransferFunction(s, s**2 + 1, s)
+
+    tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf3, tf4]])
+    tfm_2 = TransferFunctionMatrix([[tf2, tf3], [tf4, tf1]])
+    tfm_3 = TransferFunctionMatrix([[tf3, tf4], [tf1, tf2]])
+
+    f1 = MIMOFeedback(tfm_1, tfm_2)
+    assert f1.args == (tfm_1, tfm_2, -1)
+    assert f1.sys1 == tfm_1
+    assert f1.sys2 == tfm_2
+    assert f1.var == s
+    assert f1.sign == -1
+    assert -(-f1) == f1
+
+    f2 = MIMOFeedback(tfm_2, tfm_1, 1)
+    assert f2.args == (tfm_2, tfm_1, 1)
+    assert f2.sys1 == tfm_2
+    assert f2.sys2 == tfm_1
+    assert f2.var == s
+    assert f2.sign == 1
+
+    f3 = MIMOFeedback(tfm_1, MIMOSeries(tfm_3, tfm_2))
+    assert f3.args == (tfm_1, MIMOSeries(tfm_3, tfm_2), -1)
+    assert f3.sys1 == tfm_1
+    assert f3.sys2 == MIMOSeries(tfm_3, tfm_2)
+    assert f3.var == s
+    assert f3.sign == -1
+
+    mat = Matrix([[1, 1/s], [0, 1]])
+    sys1 = controller = TransferFunctionMatrix.from_Matrix(mat, s)
+    f4 = MIMOFeedback(sys1, controller)
+    assert f4.args == (sys1, controller, -1)
+    assert f4.sys1 == f4.sys2 == sys1
+
+
+def test_MIMOFeedback_errors():
+    tf1 = TransferFunction(1, s, s)
+    tf2 = TransferFunction(s, s**3 - 1, s)
+    tf3 = TransferFunction(s, s - 1, s)
+    tf4 = TransferFunction(s, s**2 + 1, s)
+    tf5 = TransferFunction(1, 1, s)
+    tf6 = TransferFunction(-1, s - 1, s)
+
+    tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf3, tf4]])
+    tfm_2 = TransferFunctionMatrix([[tf2, tf3], [tf4, tf1]])
+    tfm_3 = TransferFunctionMatrix.from_Matrix(eye(2), var=s)
+    tfm_4 = TransferFunctionMatrix([[tf1, tf5], [tf5, tf5]])
+    tfm_5 = TransferFunctionMatrix([[-tf3, tf3], [tf3, tf6]])
+    # tfm_4 is inverse of tfm_5. Therefore tfm_5*tfm_4 = I
+    tfm_6 = TransferFunctionMatrix([[-tf3]])
+    tfm_7 = TransferFunctionMatrix([[tf3, tf4]])
+
+    # Unsupported Types
+    raises(TypeError, lambda: MIMOFeedback(tf1, tf2))
+    raises(TypeError, lambda: MIMOFeedback(MIMOParallel(tfm_1, tfm_2), tfm_3))
+    # Shape Errors
+    raises(ValueError, lambda: MIMOFeedback(tfm_1, tfm_6, 1))
+    raises(ValueError, lambda: MIMOFeedback(tfm_7, tfm_7))
+    # sign not 1/-1
+    raises(ValueError, lambda: MIMOFeedback(tfm_1, tfm_2, -2))
+    # Non-Invertible Systems
+    raises(ValueError, lambda: MIMOFeedback(tfm_5, tfm_4, 1))
+    raises(ValueError, lambda: MIMOFeedback(tfm_4, -tfm_5))
+    raises(ValueError, lambda: MIMOFeedback(tfm_3, tfm_3, 1))
+    # Variable not same in both the systems
+    tfm_8 = TransferFunctionMatrix.from_Matrix(eye(2), var=p)
+    raises(ValueError, lambda: MIMOFeedback(tfm_1, tfm_8, 1))
+
+
+def test_MIMOFeedback_functions():
+    tf1 = TransferFunction(1, s, s)
+    tf2 = TransferFunction(s, s - 1, s)
+    tf3 = TransferFunction(1, 1, s)
+    tf4 = TransferFunction(-1, s - 1, s)
+
+    tfm_1 = TransferFunctionMatrix.from_Matrix(eye(2), var=s)
+    tfm_2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf3]])
+    tfm_3 = TransferFunctionMatrix([[-tf2, tf2], [tf2, tf4]])
+    tfm_4 = TransferFunctionMatrix([[tf1, tf2], [-tf2, tf1]])
+
+    # sensitivity, doit(), rewrite()
+    F_1 = MIMOFeedback(tfm_2, tfm_3)
+    F_2 = MIMOFeedback(tfm_2, MIMOSeries(tfm_4, -tfm_1), 1)
+
+    assert F_1.sensitivity == Matrix([[S.Half, 0], [0, S.Half]])
+    assert F_2.sensitivity == Matrix([[(-2*s**4 + s**2)/(s**2 - s + 1),
+        (2*s**3 - s**2)/(s**2 - s + 1)], [-s**2, s]])
+
+    assert F_1.doit() == \
+        TransferFunctionMatrix(((TransferFunction(1, 2*s, s),
+        TransferFunction(1, 2, s)), (TransferFunction(1, 2, s),
+        TransferFunction(1, 2, s)))) == F_1.rewrite(TransferFunctionMatrix)
+    assert F_2.doit(cancel=False, expand=True) == \
+        TransferFunctionMatrix(((TransferFunction(-s**5 + 2*s**4 - 2*s**3 + s**2, s**5 - 2*s**4 + 3*s**3 - 2*s**2 + s, s),
+        TransferFunction(-2*s**4 + 2*s**3, s**2 - s + 1, s)), (TransferFunction(0, 1, s), TransferFunction(-s**2 + s, 1, s))))
+    assert F_2.doit(cancel=False) == \
+        TransferFunctionMatrix(((TransferFunction(s*(2*s**3 - s**2)*(s**2 - s + 1) + \
+        (-2*s**4 + s**2)*(s**2 - s + 1), s*(s**2 - s + 1)**2, s), TransferFunction(-2*s**4 + 2*s**3, s**2 - s + 1, s)),
+        (TransferFunction(0, 1, s), TransferFunction(-s**2 + s, 1, s))))
+    assert F_2.doit() == \
+        TransferFunctionMatrix(((TransferFunction(s*(-2*s**2 + s*(2*s - 1) + 1), s**2 - s + 1, s),
+        TransferFunction(-2*s**3*(s - 1), s**2 - s + 1, s)), (TransferFunction(0, 1, s), TransferFunction(s*(1 - s), 1, s))))
+    assert F_2.doit(expand=True) == \
+        TransferFunctionMatrix(((TransferFunction(-s**2 + s, s**2 - s + 1, s), TransferFunction(-2*s**4 + 2*s**3, s**2 - s + 1, s)),
+        (TransferFunction(0, 1, s), TransferFunction(-s**2 + s, 1, s))))
+
+    assert -(F_1.doit()) == (-F_1).doit()  # First negating then calculating vs calculating then negating.
+
+
+def test_TransferFunctionMatrix_construction():
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+    tf4 = TransferFunction(a0*p + p**a1 - s, p, p)
+
+    tfm3_ = TransferFunctionMatrix([[-TF3]])
+    assert tfm3_.shape == (tfm3_.num_outputs, tfm3_.num_inputs) == (1, 1)
+    assert tfm3_.args == Tuple(Tuple(Tuple(-TF3)))
+    assert tfm3_.var == s
+
+    tfm5 = TransferFunctionMatrix([[TF1, -TF2], [TF3, tf5]])
+    assert tfm5.shape == (tfm5.num_outputs, tfm5.num_inputs) == (2, 2)
+    assert tfm5.args == Tuple(Tuple(Tuple(TF1, -TF2), Tuple(TF3, tf5)))
+    assert tfm5.var == s
+
+    tfm7 = TransferFunctionMatrix([[TF1, TF2], [TF3, -tf5], [-tf5, TF2]])
+    assert tfm7.shape == (tfm7.num_outputs, tfm7.num_inputs) == (3, 2)
+    assert tfm7.args == Tuple(Tuple(Tuple(TF1, TF2), Tuple(TF3, -tf5), Tuple(-tf5, TF2)))
+    assert tfm7.var == s
+
+    # all transfer functions will use the same complex variable. tf4 uses 'p'.
+    raises(ValueError, lambda: TransferFunctionMatrix([[TF1], [TF2], [tf4]]))
+    raises(ValueError, lambda: TransferFunctionMatrix([[TF1, tf4], [TF3, tf5]]))
+
+    # length of all the lists in the TFM should be equal.
+    raises(ValueError, lambda: TransferFunctionMatrix([[TF1], [TF3, tf5]]))
+    raises(ValueError, lambda: TransferFunctionMatrix([[TF1, TF3], [tf5]]))
+
+    # lists should only support transfer functions in them.
+    raises(TypeError, lambda: TransferFunctionMatrix([[TF1, TF2], [TF3, Matrix([1, 2])]]))
+    raises(TypeError, lambda: TransferFunctionMatrix([[TF1, Matrix([1, 2])], [TF3, TF2]]))
+
+    # `arg` should strictly be nested list of TransferFunction
+    raises(ValueError, lambda: TransferFunctionMatrix([TF1, TF2, tf5]))
+    raises(ValueError, lambda: TransferFunctionMatrix([TF1]))
+
+def test_TransferFunctionMatrix_functions():
+    tf5 = TransferFunction(a1*s**2 + a2*s - a0, s + a0, s)
+
+    #  Classmethod (from_matrix)
+
+    mat_1 = ImmutableMatrix([
+        [s*(s + 1)*(s - 3)/(s**4 + 1), 2],
+        [p, p*(s + 1)/(s*(s**1 + 1))]
+        ])
+    mat_2 = ImmutableMatrix([[(2*s + 1)/(s**2 - 9)]])
+    mat_3 = ImmutableMatrix([[1, 2], [3, 4]])
+    assert TransferFunctionMatrix.from_Matrix(mat_1, s) == \
+        TransferFunctionMatrix([[TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(2, 1, s)],
+        [TransferFunction(p, 1, s), TransferFunction(p, s, s)]])
+    assert TransferFunctionMatrix.from_Matrix(mat_2, s) == \
+        TransferFunctionMatrix([[TransferFunction(2*s + 1, s**2 - 9, s)]])
+    assert TransferFunctionMatrix.from_Matrix(mat_3, p) == \
+        TransferFunctionMatrix([[TransferFunction(1, 1, p), TransferFunction(2, 1, p)],
+        [TransferFunction(3, 1, p), TransferFunction(4, 1, p)]])
+
+    #  Negating a TFM
+
+    tfm1 = TransferFunctionMatrix([[TF1], [TF2]])
+    assert -tfm1 == TransferFunctionMatrix([[-TF1], [-TF2]])
+
+    tfm2 = TransferFunctionMatrix([[TF1, TF2, TF3], [tf5, -TF1, -TF3]])
+    assert -tfm2 == TransferFunctionMatrix([[-TF1, -TF2, -TF3], [-tf5, TF1, TF3]])
+
+    # subs()
+
+    H_1 = TransferFunctionMatrix.from_Matrix(mat_1, s)
+    H_2 = TransferFunctionMatrix([[TransferFunction(a*p*s, k*s**2, s), TransferFunction(p*s, k*(s**2 - a), s)]])
+    assert H_1.subs(p, 1) == TransferFunctionMatrix([[TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(2, 1, s)], [TransferFunction(1, 1, s), TransferFunction(1, s, s)]])
+    assert H_1.subs({p: 1}) == TransferFunctionMatrix([[TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(2, 1, s)], [TransferFunction(1, 1, s), TransferFunction(1, s, s)]])
+    assert H_1.subs({p: 1, s: 1}) == TransferFunctionMatrix([[TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(2, 1, s)], [TransferFunction(1, 1, s), TransferFunction(1, s, s)]]) # This should ignore `s` as it is `var`
+    assert H_2.subs(p, 2) == TransferFunctionMatrix([[TransferFunction(2*a*s, k*s**2, s), TransferFunction(2*s, k*(-a + s**2), s)]])
+    assert H_2.subs(k, 1) == TransferFunctionMatrix([[TransferFunction(a*p*s, s**2, s), TransferFunction(p*s, -a + s**2, s)]])
+    assert H_2.subs(a, 0) == TransferFunctionMatrix([[TransferFunction(0, k*s**2, s), TransferFunction(p*s, k*s**2, s)]])
+    assert H_2.subs({p: 1, k: 1, a: a0}) == TransferFunctionMatrix([[TransferFunction(a0*s, s**2, s), TransferFunction(s, -a0 + s**2, s)]])
+
+    # transpose()
+
+    assert H_1.transpose() == TransferFunctionMatrix([[TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(p, 1, s)], [TransferFunction(2, 1, s), TransferFunction(p, s, s)]])
+    assert H_2.transpose() == TransferFunctionMatrix([[TransferFunction(a*p*s, k*s**2, s)], [TransferFunction(p*s, k*(-a + s**2), s)]])
+    assert H_1.transpose().transpose() == H_1
+    assert H_2.transpose().transpose() == H_2
+
+    # elem_poles()
+
+    assert H_1.elem_poles() == [[[-sqrt(2)/2 - sqrt(2)*I/2, -sqrt(2)/2 + sqrt(2)*I/2, sqrt(2)/2 - sqrt(2)*I/2, sqrt(2)/2 + sqrt(2)*I/2], []],
+        [[], [0]]]
+    assert H_2.elem_poles() == [[[0, 0], [sqrt(a), -sqrt(a)]]]
+    assert tfm2.elem_poles() == [[[wn*(-zeta + sqrt((zeta - 1)*(zeta + 1))), wn*(-zeta - sqrt((zeta - 1)*(zeta + 1)))], [], [-p/a2]],
+        [[-a0], [wn*(-zeta + sqrt((zeta - 1)*(zeta + 1))), wn*(-zeta - sqrt((zeta - 1)*(zeta + 1)))], [-p/a2]]]
+
+    # elem_zeros()
+
+    assert H_1.elem_zeros() == [[[-1, 0, 3], []], [[], []]]
+    assert H_2.elem_zeros() == [[[0], [0]]]
+    assert tfm2.elem_zeros() == [[[], [], [a2*p]],
+        [[-a2/(2*a1) - sqrt(4*a0*a1 + a2**2)/(2*a1), -a2/(2*a1) + sqrt(4*a0*a1 + a2**2)/(2*a1)], [], [a2*p]]]
+
+    # doit()
+
+    H_3 = TransferFunctionMatrix([[Series(TransferFunction(1, s**3 - 3, s), TransferFunction(s**2 - 2*s + 5, 1, s), TransferFunction(1, s, s))]])
+    H_4 = TransferFunctionMatrix([[Parallel(TransferFunction(s**3 - 3, 4*s**4 - s**2 - 2*s + 5, s), TransferFunction(4 - s**3, 4*s**4 - s**2 - 2*s + 5, s))]])
+
+    assert H_3.doit() == TransferFunctionMatrix([[TransferFunction(s**2 - 2*s + 5, s*(s**3 - 3), s)]])
+    assert H_4.doit() == TransferFunctionMatrix([[TransferFunction(1, 4*s**4 - s**2 - 2*s + 5, s)]])
+
+    # _flat()
+
+    assert H_1._flat() == [TransferFunction(s*(s - 3)*(s + 1), s**4 + 1, s), TransferFunction(2, 1, s), TransferFunction(p, 1, s), TransferFunction(p, s, s)]
+    assert H_2._flat() == [TransferFunction(a*p*s, k*s**2, s), TransferFunction(p*s, k*(-a + s**2), s)]
+    assert H_3._flat() == [Series(TransferFunction(1, s**3 - 3, s), TransferFunction(s**2 - 2*s + 5, 1, s), TransferFunction(1, s, s))]
+    assert H_4._flat() == [Parallel(TransferFunction(s**3 - 3, 4*s**4 - s**2 - 2*s + 5, s), TransferFunction(4 - s**3, 4*s**4 - s**2 - 2*s + 5, s))]
+
+    # evalf()
+
+    assert H_1.evalf() == \
+        TransferFunctionMatrix(((TransferFunction(s*(s - 3.0)*(s + 1.0), s**4 + 1.0, s), TransferFunction(2.0, 1, s)), (TransferFunction(1.0*p, 1, s), TransferFunction(p, s, s))))
+    assert H_2.subs({a:3.141, p:2.88, k:2}).evalf() == \
+        TransferFunctionMatrix(((TransferFunction(4.5230399999999999494093572138808667659759521484375, s, s),
+        TransferFunction(2.87999999999999989341858963598497211933135986328125*s, 2.0*s**2 - 6.282000000000000028421709430404007434844970703125, s)),))
+
+    # simplify()
+
+    H_5 = TransferFunctionMatrix([[TransferFunction(s**5 + s**3 + s, s - s**2, s),
+        TransferFunction((s + 3)*(s - 1), (s - 1)*(s + 5), s)]])
+
+    assert H_5.simplify() == simplify(H_5) == \
+        TransferFunctionMatrix(((TransferFunction(-s**4 - s**2 - 1, s - 1, s), TransferFunction(s + 3, s + 5, s)),))
+
+    # expand()
+
+    assert (H_1.expand()
+            == TransferFunctionMatrix(((TransferFunction(s**3 - 2*s**2 - 3*s, s**4 + 1, s), TransferFunction(2, 1, s)),
+            (TransferFunction(p, 1, s), TransferFunction(p, s, s)))))
+    assert H_5.expand() == \
+        TransferFunctionMatrix(((TransferFunction(s**5 + s**3 + s, -s**2 + s, s), TransferFunction(s**2 + 2*s - 3, s**2 + 4*s - 5, s)),))
+
+def test_TransferFunction_bilinear():
+    # simple transfer function, e.g. ohms law
+    tf = TransferFunction(1, a*s+b, s)
+    numZ, denZ = bilinear(tf, T)
+    # discretized transfer function with coefs from tf.bilinear()
+    tf_test_bilinear = TransferFunction(s*numZ[0]+numZ[1], s*denZ[0]+denZ[1], s)
+    # corresponding tf with manually calculated coefs
+    tf_test_manual = TransferFunction(s*T+T, s*(T*b+2*a)+T*b-2*a, s)
+
+    assert S.Zero == (tf_test_bilinear-tf_test_manual).simplify().num
+
+def test_TransferFunction_backward_diff():
+    # simple transfer function, e.g. ohms law
+    tf = TransferFunction(1, a*s+b, s)
+    numZ, denZ = backward_diff(tf, T)
+    # discretized transfer function with coefs from tf.bilinear()
+    tf_test_bilinear = TransferFunction(s*numZ[0]+numZ[1], s*denZ[0]+denZ[1], s)
+    # corresponding tf with manually calculated coefs
+    tf_test_manual = TransferFunction(s*T, s*(T*b+a)-a, s)
+
+    assert S.Zero == (tf_test_bilinear-tf_test_manual).simplify().num
diff --git a/shared/package-files/4e/4e00afd77c820f5b47d55abb4e12a4699754c4d26ee51c1ed9bcb3f168b0b908/adaptive_avg_pool2d_cpu_dispatch.h b/shared/package-files/4e/4e00afd77c820f5b47d55abb4e12a4699754c4d26ee51c1ed9bcb3f168b0b908/adaptive_avg_pool2d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c9a4fe2b177167fc3517555c5993275e4b4d6bb6
--- /dev/null
+++ b/shared/package-files/4e/4e00afd77c820f5b47d55abb4e12a4699754c4d26ee51c1ed9bcb3f168b0b908/adaptive_avg_pool2d_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size);
+TORCH_API at::Tensor & adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out);
+TORCH_API at::Tensor & adaptive_avg_pool2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size);
+TORCH_API at::Tensor & adaptive_avg_pool2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/4e/4e0542017f6be0c670dd03c7e9a13e779e6045970a37d1fd5fe313678bbe2e26/utils.py b/shared/package-files/4e/4e0542017f6be0c670dd03c7e9a13e779e6045970a37d1fd5fe313678bbe2e26/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..55bb58f3108c3d7004058494284ea6fb4b2fca7f
--- /dev/null
+++ b/shared/package-files/4e/4e0542017f6be0c670dd03c7e9a13e779e6045970a37d1fd5fe313678bbe2e26/utils.py
@@ -0,0 +1,553 @@
+"""
+Low-dependency indexing utilities.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AnyArrayLike
+
+    from pandas.core.frame import DataFrame
+    from pandas.core.indexes.base import Index
+
+# -----------------------------------------------------------
+# Indexer Identification
+
+
+def is_valid_positional_slice(slc: slice) -> bool:
+    """
+    Check if a slice object can be interpreted as a positional indexer.
+
+    Parameters
+    ----------
+    slc : slice
+
+    Returns
+    -------
+    bool
+
+    Notes
+    -----
+    A valid positional slice may also be interpreted as a label-based slice
+    depending on the index being sliced.
+    """
+    return (
+        lib.is_int_or_none(slc.start)
+        and lib.is_int_or_none(slc.stop)
+        and lib.is_int_or_none(slc.step)
+    )
+
+
+def is_list_like_indexer(key) -> bool:
+    """
+    Check if we have a list-like indexer that is *not* a NamedTuple.
+
+    Parameters
+    ----------
+    key : object
+
+    Returns
+    -------
+    bool
+    """
+    # allow a list_like, but exclude NamedTuples which can be indexers
+    return is_list_like(key) and not (isinstance(key, tuple) and type(key) is not tuple)
+
+
+def is_scalar_indexer(indexer, ndim: int) -> bool:
+    """
+    Return True if we are all scalar indexers.
+
+    Parameters
+    ----------
+    indexer : object
+    ndim : int
+        Number of dimensions in the object being indexed.
+
+    Returns
+    -------
+    bool
+    """
+    if ndim == 1 and is_integer(indexer):
+        # GH37748: allow indexer to be an integer for Series
+        return True
+    if isinstance(indexer, tuple) and len(indexer) == ndim:
+        return all(is_integer(x) for x in indexer)
+    return False
+
+
+def is_empty_indexer(indexer) -> bool:
+    """
+    Check if we have an empty indexer.
+
+    Parameters
+    ----------
+    indexer : object
+
+    Returns
+    -------
+    bool
+    """
+    if is_list_like(indexer) and not len(indexer):
+        return True
+    if not isinstance(indexer, tuple):
+        indexer = (indexer,)
+    return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)
+
+
+# -----------------------------------------------------------
+# Indexer Validation
+
+
+def check_setitem_lengths(indexer, value, values) -> bool:
+    """
+    Validate that value and indexer are the same length.
+
+    An special-case is allowed for when the indexer is a boolean array
+    and the number of true values equals the length of ``value``. In
+    this case, no exception is raised.
+
+    Parameters
+    ----------
+    indexer : sequence
+        Key for the setitem.
+    value : array-like
+        Value for the setitem.
+    values : array-like
+        Values being set into.
+
+    Returns
+    -------
+    bool
+        Whether this is an empty listlike setting which is a no-op.
+
+    Raises
+    ------
+    ValueError
+        When the indexer is an ndarray or list and the lengths don't match.
+    """
+    no_op = False
+
+    if isinstance(indexer, (np.ndarray, list)):
+        # We can ignore other listlikes because they are either
+        #  a) not necessarily 1-D indexers, e.g. tuple
+        #  b) boolean indexers e.g. BoolArray
+        if is_list_like(value):
+            if len(indexer) != len(value) and values.ndim == 1:
+                # boolean with truth values == len of the value is ok too
+                if isinstance(indexer, list):
+                    indexer = np.array(indexer)
+                if not (
+                    isinstance(indexer, np.ndarray)
+                    and indexer.dtype == np.bool_
+                    and indexer.sum() == len(value)
+                ):
+                    raise ValueError(
+                        "cannot set using a list-like indexer "
+                        "with a different length than the value"
+                    )
+            if not len(indexer):
+                no_op = True
+
+    elif isinstance(indexer, slice):
+        if is_list_like(value):
+            if len(value) != length_of_indexer(indexer, values) and values.ndim == 1:
+                # In case of two dimensional value is used row-wise and broadcasted
+                raise ValueError(
+                    "cannot set using a slice indexer with a "
+                    "different length than the value"
+                )
+            if not len(value):
+                no_op = True
+
+    return no_op
+
+
+def validate_indices(indices: np.ndarray, n: int) -> None:
+    """
+    Perform bounds-checking for an indexer.
+
+    -1 is allowed for indicating missing values.
+
+    Parameters
+    ----------
+    indices : ndarray
+    n : int
+        Length of the array being indexed.
+
+    Raises
+    ------
+    ValueError
+
+    Examples
+    --------
+    >>> validate_indices(np.array([1, 2]), 3) # OK
+
+    >>> validate_indices(np.array([1, -2]), 3)
+    Traceback (most recent call last):
+        ...
+    ValueError: negative dimensions are not allowed
+
+    >>> validate_indices(np.array([1, 2, 3]), 3)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+
+    >>> validate_indices(np.array([-1, -1]), 0) # OK
+
+    >>> validate_indices(np.array([0, 1]), 0)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+    """
+    if len(indices):
+        min_idx = indices.min()
+        if min_idx < -1:
+            msg = f"'indices' contains values less than allowed ({min_idx} < -1)"
+            raise ValueError(msg)
+
+        max_idx = indices.max()
+        if max_idx >= n:
+            raise IndexError("indices are out-of-bounds")
+
+
+# -----------------------------------------------------------
+# Indexer Conversion
+
+
+def maybe_convert_indices(indices, n: int, verify: bool = True) -> np.ndarray:
+    """
+    Attempt to convert indices into valid, positive indices.
+
+    If we have negative indices, translate to positive here.
+    If we have indices that are out-of-bounds, raise an IndexError.
+
+    Parameters
+    ----------
+    indices : array-like
+        Array of indices that we are to convert.
+    n : int
+        Number of elements in the array that we are indexing.
+    verify : bool, default True
+        Check that all entries are between 0 and n - 1, inclusive.
+
+    Returns
+    -------
+    array-like
+        An array-like of positive indices that correspond to the ones
+        that were passed in initially to this function.
+
+    Raises
+    ------
+    IndexError
+        One of the converted indices either exceeded the number of,
+        elements (specified by `n`), or was still negative.
+    """
+    if isinstance(indices, list):
+        indices = np.array(indices)
+        if len(indices) == 0:
+            # If `indices` is empty, np.array will return a float,
+            # and will cause indexing errors.
+            return np.empty(0, dtype=np.intp)
+
+    mask = indices < 0
+    if mask.any():
+        indices = indices.copy()
+        indices[mask] += n
+
+    if verify:
+        mask = (indices >= n) | (indices < 0)
+        if mask.any():
+            raise IndexError("indices are out-of-bounds")
+    return indices
+
+
+# -----------------------------------------------------------
+# Unsorted
+
+
+def length_of_indexer(indexer, target=None) -> int:
+    """
+    Return the expected length of target[indexer]
+
+    Returns
+    -------
+    int
+    """
+    if target is not None and isinstance(indexer, slice):
+        target_len = len(target)
+        start = indexer.start
+        stop = indexer.stop
+        step = indexer.step
+        if start is None:
+            start = 0
+        elif start < 0:
+            start += target_len
+        if stop is None or stop > target_len:
+            stop = target_len
+        elif stop < 0:
+            stop += target_len
+        if step is None:
+            step = 1
+        elif step < 0:
+            start, stop = stop + 1, start + 1
+            step = -step
+        return (stop - start + step - 1) // step
+    elif isinstance(indexer, (ABCSeries, ABCIndex, np.ndarray, list)):
+        if isinstance(indexer, list):
+            indexer = np.array(indexer)
+
+        if indexer.dtype == bool:
+            # GH#25774
+            return indexer.sum()
+        return len(indexer)
+    elif isinstance(indexer, range):
+        return (indexer.stop - indexer.start) // indexer.step
+    elif not is_list_like_indexer(indexer):
+        return 1
+    raise AssertionError("cannot find the length of the indexer")
+
+
+def disallow_ndim_indexing(result) -> None:
+    """
+    Helper function to disallow multi-dimensional indexing on 1D Series/Index.
+
+    GH#27125 indexer like idx[:, None] expands dim, but we cannot do that
+    and keep an index, so we used to return ndarray, which was deprecated
+    in GH#30588.
+    """
+    if np.ndim(result) > 1:
+        raise ValueError(
+            "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer "
+            "supported. Convert to a numpy array before indexing instead."
+        )
+
+
+def unpack_1tuple(tup):
+    """
+    If we have a length-1 tuple/list that contains a slice, unpack to just
+    the slice.
+
+    Notes
+    -----
+    The list case is deprecated.
+    """
+    if len(tup) == 1 and isinstance(tup[0], slice):
+        # if we don't have a MultiIndex, we may still be able to handle
+        #  a 1-tuple.  see test_1tuple_without_multiindex
+
+        if isinstance(tup, list):
+            # GH#31299
+            raise ValueError(
+                "Indexing with a single-item list containing a "
+                "slice is not allowed. Pass a tuple instead.",
+            )
+
+        return tup[0]
+    return tup
+
+
+def check_key_length(columns: Index, key, value: DataFrame) -> None:
+    """
+    Checks if a key used as indexer has the same length as the columns it is
+    associated with.
+
+    Parameters
+    ----------
+    columns : Index The columns of the DataFrame to index.
+    key : A list-like of keys to index with.
+    value : DataFrame The value to set for the keys.
+
+    Raises
+    ------
+    ValueError: If the length of key is not equal to the number of columns in value
+                or if the number of columns referenced by key is not equal to number
+                of columns.
+    """
+    if columns.is_unique:
+        if len(value.columns) != len(key):
+            raise ValueError("Columns must be same length as key")
+    else:
+        # Missing keys in columns are represented as -1
+        if len(columns.get_indexer_non_unique(key)[0]) != len(value.columns):
+            raise ValueError("Columns must be same length as key")
+
+
+def unpack_tuple_and_ellipses(item: tuple):
+    """
+    Possibly unpack arr[..., n] to arr[n]
+    """
+    if len(item) > 1:
+        # Note: we are assuming this indexing is being done on a 1D arraylike
+        if item[0] is Ellipsis:
+            item = item[1:]
+        elif item[-1] is Ellipsis:
+            item = item[:-1]
+
+    if len(item) > 1:
+        raise IndexError("too many indices for array.")
+
+    item = item[0]
+    return item
+
+
+# -----------------------------------------------------------
+# Public indexer validation
+
+
+def check_array_indexer(array: AnyArrayLike, indexer: Any) -> Any:
+    """
+    Check if `indexer` is a valid array indexer for `array`.
+
+    For a boolean mask, `array` and `indexer` are checked to have the same
+    length. The dtype is validated, and if it is an integer or boolean
+    ExtensionArray, it is checked if there are missing values present, and
+    it is converted to the appropriate numpy array. Other dtypes will raise
+    an error.
+
+    Non-array indexers (integer, slice, Ellipsis, tuples, ..) are passed
+    through as is.
+
+    Parameters
+    ----------
+    array : array-like
+        The array that is being indexed (only used for the length).
+    indexer : array-like or list-like
+        The array-like that's used to index. List-like input that is not yet
+        a numpy array or an ExtensionArray is converted to one. Other input
+        types are passed through as is.
+
+    Returns
+    -------
+    numpy.ndarray
+        The validated indexer as a numpy array that can be used to index.
+
+    Raises
+    ------
+    IndexError
+        When the lengths don't match.
+    ValueError
+        When `indexer` cannot be converted to a numpy ndarray to index
+        (e.g. presence of missing values).
+
+    See Also
+    --------
+    api.types.is_bool_dtype : Check if `key` is of boolean dtype.
+
+    Examples
+    --------
+    When checking a boolean mask, a boolean ndarray is returned when the
+    arguments are all valid.
+
+    >>> mask = pd.array([True, False])
+    >>> arr = pd.array([1, 2])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    An IndexError is raised when the lengths don't match.
+
+    >>> mask = pd.array([True, False, True])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    Traceback (most recent call last):
+    ...
+    IndexError: Boolean index has wrong length: 3 instead of 2.
+
+    NA values in a boolean array are treated as False.
+
+    >>> mask = pd.array([True, pd.NA])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    A numpy boolean mask will get passed through (if the length is correct):
+
+    >>> mask = np.array([True, False])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    Similarly for integer indexers, an integer ndarray is returned when it is
+    a valid indexer, otherwise an error is  (for integer indexers, a matching
+    length is not required):
+
+    >>> indexer = pd.array([0, 2], dtype="Int64")
+    >>> arr = pd.array([1, 2, 3])
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    array([0, 2])
+
+    >>> indexer = pd.array([0, pd.NA], dtype="Int64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    ValueError: Cannot index with an integer indexer containing NA values
+
+    For non-integer/boolean dtypes, an appropriate error is raised:
+
+    >>> indexer = np.array([0., 2.], dtype="float64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    IndexError: arrays used as indices must be of integer or boolean type
+    """
+    from pandas.core.construction import array as pd_array
+
+    # whatever is not an array-like is returned as-is (possible valid array
+    # indexers that are not array-like: integer, slice, Ellipsis, None)
+    # In this context, tuples are not considered as array-like, as they have
+    # a specific meaning in indexing (multi-dimensional indexing)
+    if is_list_like(indexer):
+        if isinstance(indexer, tuple):
+            return indexer
+    else:
+        return indexer
+
+    # convert list-likes to array
+    if not is_array_like(indexer):
+        indexer = pd_array(indexer)
+        if len(indexer) == 0:
+            # empty list is converted to float array by pd.array
+            indexer = np.array([], dtype=np.intp)
+
+    dtype = indexer.dtype
+    if is_bool_dtype(dtype):
+        if isinstance(dtype, ExtensionDtype):
+            indexer = indexer.to_numpy(dtype=bool, na_value=False)
+        else:
+            indexer = np.asarray(indexer, dtype=bool)
+
+        # GH26658
+        if len(indexer) != len(array):
+            raise IndexError(
+                f"Boolean index has wrong length: "
+                f"{len(indexer)} instead of {len(array)}"
+            )
+    elif is_integer_dtype(dtype):
+        try:
+            indexer = np.asarray(indexer, dtype=np.intp)
+        except ValueError as err:
+            raise ValueError(
+                "Cannot index with an integer indexer containing NA values"
+            ) from err
+    else:
+        raise IndexError("arrays used as indices must be of integer or boolean type")
+
+    return indexer
diff --git a/shared/package-files/4e/4e06ebb6e67cb75848438ab001027537d4011aa6d1b5b1d16106b2cb1bef9dea/conf.py b/shared/package-files/4e/4e06ebb6e67cb75848438ab001027537d4011aa6d1b5b1d16106b2cb1bef9dea/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..974115d6023763d7002959da7a376473140e7d03
--- /dev/null
+++ b/shared/package-files/4e/4e06ebb6e67cb75848438ab001027537d4011aa6d1b5b1d16106b2cb1bef9dea/conf.py
@@ -0,0 +1,46 @@
+from typing import Optional  # noqa: D100
+
+from duckdb.experimental.spark.exception import ContributionsAcceptedError
+
+
+class SparkConf:  # noqa: D101
+    def __init__(self) -> None:  # noqa: D107
+        raise NotImplementedError
+
+    def contains(self, key: str) -> bool:  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def get(self, key: str, defaultValue: Optional[str] = None) -> Optional[str]:  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def getAll(self) -> list[tuple[str, str]]:  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def set(self, key: str, value: str) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def setAll(self, pairs: list[tuple[str, str]]) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def setAppName(self, value: str) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def setExecutorEnv(  # noqa: D102
+        self, key: Optional[str] = None, value: Optional[str] = None, pairs: Optional[list[tuple[str, str]]] = None
+    ) -> "SparkConf":
+        raise ContributionsAcceptedError
+
+    def setIfMissing(self, key: str, value: str) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def setMaster(self, value: str) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def setSparkHome(self, value: str) -> "SparkConf":  # noqa: D102
+        raise ContributionsAcceptedError
+
+    def toDebugString(self) -> str:  # noqa: D102
+        raise ContributionsAcceptedError
+
+
+__all__ = ["SparkConf"]
diff --git a/shared/package-files/4e/4e080da9c447714e247648f9a628cb5052cc6a3e86f6fa40b03fa381d6c16956/testing.py b/shared/package-files/4e/4e080da9c447714e247648f9a628cb5052cc6a3e86f6fa40b03fa381d6c16956/testing.py
new file mode 100644
index 0000000000000000000000000000000000000000..fac20cf55508011f0d06511b30d846c1b26eeb4b
--- /dev/null
+++ b/shared/package-files/4e/4e080da9c447714e247648f9a628cb5052cc6a3e86f6fa40b03fa381d6c16956/testing.py
@@ -0,0 +1,378 @@
+import contextlib
+import dis
+import functools
+import logging
+import os.path
+import random
+import re
+import sys
+import types
+import unittest
+from typing import List, Optional, Sequence, Union
+from unittest.mock import patch
+
+np: Optional[types.ModuleType] = None
+try:
+    import numpy as np
+except ModuleNotFoundError:
+    np = None
+
+import torch
+from torch import fx
+from torch._dynamo.output_graph import OutputGraph
+
+from . import config, eval_frame, optimize_assert, reset
+from .bytecode_transformation import (
+    create_instruction,
+    debug_checks,
+    is_generator,
+    transform_code_object,
+)
+from .guards import CheckFunctionManager, GuardedCode
+from .utils import same
+
+unsupported = eval_frame.unsupported
+three = 3
+
+log = logging.getLogger(__name__)
+
+
+def clone_me(x):
+    if x is None:
+        return None
+    return x.detach().clone().requires_grad_(x.requires_grad)
+
+
+def named_parameters_for_optimized_module(mod):
+    assert isinstance(mod, eval_frame.OptimizedModule)
+    return mod._orig_mod.named_parameters
+
+
+def named_buffers_for_optimized_module(mod):
+    assert isinstance(mod, eval_frame.OptimizedModule)
+    return mod._orig_mod.named_buffers
+
+
+def remove_optimized_module_prefix(name) -> str:
+    return re.sub(r"^_orig_mod[.]", "", name)
+
+
+def collect_results(model, prediction, loss, example_inputs):
+    results = []
+    results.append(prediction)
+    results.append(loss)
+    # if isinstance(loss, torch.Tensor) and loss.item() > 1:
+    #     log.warning(
+    #         f"High loss value alert - {loss:.2f}. Can result in unstable gradients."
+    #     )
+
+    grads = dict()
+    params = dict()
+    for name, param in model.named_parameters():
+        if isinstance(model, eval_frame.OptimizedModule):
+            name = remove_optimized_module_prefix(name)
+        param_copy = param
+        grad = param.grad
+        # Treat None and zero grad as same
+        if param.grad is None:
+            grad = torch.zeros_like(param)
+        grads[name + ".grad"] = grad
+        params[name] = param_copy
+    results.append(grads)
+    results.append(params)
+    buffers = dict()
+    for name, buffer in model.named_buffers():
+        if isinstance(model, eval_frame.OptimizedModule):
+            name = remove_optimized_module_prefix(name)
+        buffers[name] = buffer
+    results.append(buffers)
+    for example in example_inputs:
+        if isinstance(example, (tuple, list)):
+            for inp in example:
+                if isinstance(inp, torch.Tensor):
+                    results.append(inp.grad)
+        else:
+            if isinstance(example, torch.Tensor):
+                results.append(example.grad)
+    return results
+
+
+def requires_bwd_pass(out):
+    if isinstance(out, torch.Tensor):
+        return out.requires_grad
+    elif isinstance(out, (list, tuple)):
+        return any(requires_bwd_pass(x) for x in out)
+    elif out is None:
+        return False
+    elif isinstance(out, int):
+        return False
+    raise NotImplementedError("Don't know how to reduce", type(out))
+
+
+def reduce_to_scalar_loss(out):
+    """Reduce the output of a model to get scalar loss"""
+    if isinstance(out, torch.Tensor):
+        # Mean does not work on integer tensors
+        return out.sum() / out.numel()
+    elif isinstance(out, (list, tuple)):
+        return sum([reduce_to_scalar_loss(x) for x in out]) / len(out)
+    elif type(out).__name__ in (
+        "MaskedLMOutput",
+        "Seq2SeqLMOutput",
+        "CausalLMOutputWithCrossAttentions",
+    ):
+        return reduce_to_scalar_loss(out.logits)
+    elif type(out).__name__ == "SquashedNormal":
+        return out.mean.sum()
+    elif isinstance(out, dict):
+        return sum([reduce_to_scalar_loss(value) for value in out.values()]) / len(
+            out.keys()
+        )
+    raise NotImplementedError("Don't know how to reduce", type(out))
+
+
+def debug_dir() -> str:
+    path = os.path.join(os.path.dirname(__file__), "../debug")
+    if not os.path.exists(path):
+        os.mkdir(path)
+    return path
+
+
+def debug_dump(name, code: types.CodeType, extra="") -> None:
+    with open(os.path.join(debug_dir(), name), "w") as fd:
+        fd.write(
+            f"{dis.Bytecode(code).info()}\n\n{dis.Bytecode(code).dis()}\n\n{extra}\n"
+        )
+
+
+def debug_insert_nops(
+    frame, cache_size, hooks, _, *, skip: int = 0
+) -> Optional[GuardedCode]:
+    """used to debug jump updates"""
+
+    def insert_nops(instructions, code_options):
+        instructions.insert(0, create_instruction("NOP"))
+        instructions.insert(0, create_instruction("NOP"))
+
+    if is_generator(frame.f_code):
+        return None
+
+    debug_checks(frame.f_code)
+    code = transform_code_object(frame.f_code, insert_nops)
+    graph = OutputGraph(
+        code_options={},
+        compiler_fn=None,
+        root_tx=None,
+        export=False,
+        export_constraints=None,
+        frame_state={"_id": 0},
+        # TODO: shouldn't this be f_locals/f_globals from frame?
+        local_scope=locals(),
+        global_scope=globals(),
+        f_code=frame.f_code,
+    )
+
+    return GuardedCode(code, CheckFunctionManager(graph).check_fn)
+
+
+class CompileCounter:
+    def __init__(self):
+        self.frame_count = 0
+        self.op_count = 0
+
+    def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
+        self.frame_count += 1
+        for node in gm.graph.nodes:
+            if "call" in node.op:
+                self.op_count += 1
+        return gm.forward
+
+    def clear(self):
+        self.frame_count = 0
+        self.op_count = 0
+
+
+class CompileCounterWithBackend:
+    def __init__(self, backend):
+        self.frame_count = 0
+        self.op_count = 0
+        self.backend = backend
+        self.graphs = []
+
+    def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
+        from .backends.registry import lookup_backend
+
+        self.frame_count += 1
+        for node in gm.graph.nodes:
+            if "call" in node.op:
+                self.op_count += 1
+        self.graphs.append(gm)
+        return lookup_backend(self.backend)(gm, example_inputs)
+
+
+# Equivalent to backend="eager", but also records graphs that
+# we can assert on
+class EagerAndRecordGraphs:
+    def __init__(self):
+        self.graphs = []
+
+    def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
+        self.graphs.append(gm)
+        return gm
+
+
+def strip_comment(code) -> str:
+    code = str(code)
+    return re.sub(r"(?m)^ *#.*\n?", "", code)
+
+
+def remove_trailing_space(code) -> str:
+    return "\n".join([line.rstrip() for line in code.split("\n")])
+
+
+def normalize_gm(gm_str) -> str:
+    # strip comments as comments have path to files which may differ from
+    # system to system.
+    return remove_trailing_space(strip_comment(gm_str))
+
+
+def standard_test(
+    self,
+    fn,
+    nargs,
+    expected_ops=None,
+    expected_ops_dynamic=None,
+    expected_frame_count=1,
+):
+    if not config.assume_static_by_default and expected_ops_dynamic is not None:
+        expected_ops = expected_ops_dynamic
+
+    actual = CompileCounter()
+
+    args1 = [torch.randn(10, 10) for _ in range(nargs)]
+    args2 = [torch.randn(10, 10) for _ in range(nargs)]
+    correct1 = fn(*args1)
+    correct2 = fn(*args2)
+    reset()
+    opt_fn = optimize_assert(actual)(fn)
+    val1a = opt_fn(*args1)
+    val2a = opt_fn(*args2)
+    val1b = opt_fn(*args1)
+    val2b = opt_fn(*args2)
+    reset()
+    self.assertTrue(same(val1a, correct1))
+    self.assertTrue(same(val1b, correct1))
+    self.assertTrue(same(val2a, correct2))
+    self.assertTrue(same(val2b, correct2))
+    self.assertEqual(actual.frame_count, expected_frame_count)
+    if expected_ops is not None:
+        self.assertEqual(actual.op_count, expected_ops)
+
+
+def dummy_fx_compile(gm: fx.GraphModule, example_inputs):
+    return gm.forward
+
+
+def format_speedup(speedup, pvalue, is_correct=True, pvalue_threshold=0.1):
+    if not is_correct:
+        return "ERROR"
+    if pvalue > pvalue_threshold:
+        return f"{speedup:.3f}x SAME"
+    return f"{speedup:.3f}x p={pvalue:.2f}"
+
+
+def rand_strided(
+    size: Sequence[int],
+    stride: Sequence[int],
+    dtype: torch.dtype = torch.float32,
+    device: Union[str, torch.device] = "cpu",
+    extra_size: int = 0,
+):
+    needed_size = (
+        sum((shape - 1) * stride for shape, stride in zip(size, stride))
+        + 1
+        + extra_size
+    )
+    if dtype.is_floating_point:
+        buffer = torch.randn(needed_size, dtype=dtype, device=device)
+    else:
+        buffer = torch.zeros(size=[needed_size], dtype=dtype, device=device)
+    return torch.as_strided(buffer, size, stride)
+
+
+def _make_fn_with_patches(fn, *patches):
+    @functools.wraps(fn)
+    def _fn(*args, **kwargs):
+        with contextlib.ExitStack() as stack:
+            for module, attr, val in patches:
+                stack.enter_context(patch.object(module, attr, val))
+
+            return fn(*args, **kwargs)
+
+    return _fn
+
+
+def make_test_cls_with_patches(cls, cls_prefix, fn_suffix, *patches, xfail_prop=None):
+    DummyTestClass = type(f"{cls_prefix}{cls.__name__}", cls.__bases__, {})
+    DummyTestClass.__qualname__ = DummyTestClass.__name__
+
+    for name in dir(cls):
+        if name.startswith("test_"):
+            fn = getattr(cls, name)
+            if not callable(fn):
+                setattr(DummyTestClass, name, getattr(cls, name))
+                continue
+            new_name = f"{name}{fn_suffix}"
+            new_fn = _make_fn_with_patches(fn, *patches)
+            new_fn.__name__ = new_name
+            if xfail_prop is not None and hasattr(fn, xfail_prop):
+                new_fn = unittest.expectedFailure(new_fn)
+            setattr(DummyTestClass, new_name, new_fn)
+        # NB: Doesn't handle slots correctly, but whatever
+        elif not hasattr(DummyTestClass, name):
+            setattr(DummyTestClass, name, getattr(cls, name))
+
+    return DummyTestClass
+
+
+# test Python 3.11+ specific features
+def skipIfNotPy311(fn):
+    if sys.version_info >= (3, 11):
+        return fn
+    return unittest.skip(fn)
+
+
+def xfailIfPy311(fn):
+    if sys.version_info >= (3, 11):
+        return unittest.expectedFailure(fn)
+    return fn
+
+
+# Controls tests generated in test/inductor/test_torchinductor_dynamic_shapes.py
+# and test/dynamo/test_dynamic_shapes.py
+def expectedFailureDynamic(fn):
+    fn._expected_failure_dynamic = True
+    return fn
+
+
+# Controls tests generated in test/inductor/test_torchinductor_codegen_dynamic_shapes.py
+def expectedFailureCodegenDynamic(fn):
+    fn._expected_failure_codegen_dynamic = True
+    return fn
+
+
+# Controls test generated in test/inductor/test_cpp_wrapper.py
+def expectedFailureDynamicWrapper(fn):
+    fn._expected_failure_dynamic_wrapper = True
+    return fn
+
+
+def reset_rng_state(use_xla=False):
+    torch.manual_seed(1337)
+    random.seed(1337)
+    if np:
+        np.random.seed(1337)
+    if use_xla:
+        import torch_xla.core.xla_model as xm
+
+        xm.set_rng_state(1337, str(xm.xla_device()))
diff --git a/shared/package-files/4e/4e0a199825735680203175113a4dc2d2efd5634e7364468ab6c7116c129d7ee5/maximum_meta_dispatch.h b/shared/package-files/4e/4e0a199825735680203175113a4dc2d2efd5634e7364468ab6c7116c129d7ee5/maximum_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5f5d690fbef2c8ffc4b27e0dd37ebddda29766e9
--- /dev/null
+++ b/shared/package-files/4e/4e0a199825735680203175113a4dc2d2efd5634e7364468ab6c7116c129d7ee5/maximum_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor maximum(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & maximum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & maximum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/4e/4e0c972b8d00e1da7c81e28878658cc0d9bfbf9f7b8d64262591fe1fe4d249d4/style_render.py b/shared/package-files/4e/4e0c972b8d00e1da7c81e28878658cc0d9bfbf9f7b8d64262591fe1fe4d249d4/style_render.py
new file mode 100644
index 0000000000000000000000000000000000000000..416b263ba84979d94306829cbdda57d51b013622
--- /dev/null
+++ b/shared/package-files/4e/4e0c972b8d00e1da7c81e28878658cc0d9bfbf9f7b8d64262591fe1fe4d249d4/style_render.py
@@ -0,0 +1,2497 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Sequence
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    DefaultDict,
+    Optional,
+    TypedDict,
+    Union,
+)
+from uuid import uuid4
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.common import (
+    is_complex,
+    is_float,
+    is_integer,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas import (
+    DataFrame,
+    Index,
+    IndexSlice,
+    MultiIndex,
+    Series,
+    isna,
+)
+from pandas.api.types import is_list_like
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        Level,
+    )
+jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.")
+from markupsafe import escape as escape_html  # markupsafe is jinja2 dependency
+
+BaseFormatter = Union[str, Callable]
+ExtFormatter = Union[BaseFormatter, dict[Any, Optional[BaseFormatter]]]
+CSSPair = tuple[str, Union[str, float]]
+CSSList = list[CSSPair]
+CSSProperties = Union[str, CSSList]
+
+
+class CSSDict(TypedDict):
+    selector: str
+    props: CSSProperties
+
+
+CSSStyles = list[CSSDict]
+Subset = Union[slice, Sequence, Index]
+
+
+class StylerRenderer:
+    """
+    Base class to process rendering a Styler with a specified jinja2 template.
+    """
+
+    loader = jinja2.PackageLoader("pandas", "io/formats/templates")
+    env = jinja2.Environment(loader=loader, trim_blocks=True)
+    template_html = env.get_template("html.tpl")
+    template_html_table = env.get_template("html_table.tpl")
+    template_html_style = env.get_template("html_style.tpl")
+    template_latex = env.get_template("latex.tpl")
+    template_string = env.get_template("string.tpl")
+
+    def __init__(
+        self,
+        data: DataFrame | Series,
+        uuid: str | None = None,
+        uuid_len: int = 5,
+        table_styles: CSSStyles | None = None,
+        table_attributes: str | None = None,
+        caption: str | tuple | list | None = None,
+        cell_ids: bool = True,
+        precision: int | None = None,
+    ) -> None:
+        # validate ordered args
+        if isinstance(data, Series):
+            data = data.to_frame()
+        if not isinstance(data, DataFrame):
+            raise TypeError("``data`` must be a Series or DataFrame")
+        self.data: DataFrame = data
+        self.index: Index = data.index
+        self.columns: Index = data.columns
+        if not isinstance(uuid_len, int) or uuid_len < 0:
+            raise TypeError("``uuid_len`` must be an integer in range [0, 32].")
+        self.uuid = uuid or uuid4().hex[: min(32, uuid_len)]
+        self.uuid_len = len(self.uuid)
+        self.table_styles = table_styles
+        self.table_attributes = table_attributes
+        self.caption = caption
+        self.cell_ids = cell_ids
+        self.css = {
+            "row_heading": "row_heading",
+            "col_heading": "col_heading",
+            "index_name": "index_name",
+            "col": "col",
+            "row": "row",
+            "col_trim": "col_trim",
+            "row_trim": "row_trim",
+            "level": "level",
+            "data": "data",
+            "blank": "blank",
+            "foot": "foot",
+        }
+        self.concatenated: list[StylerRenderer] = []
+        # add rendering variables
+        self.hide_index_names: bool = False
+        self.hide_column_names: bool = False
+        self.hide_index_: list = [False] * self.index.nlevels
+        self.hide_columns_: list = [False] * self.columns.nlevels
+        self.hidden_rows: Sequence[int] = []  # sequence for specific hidden rows/cols
+        self.hidden_columns: Sequence[int] = []
+        self.ctx: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.ctx_index: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.ctx_columns: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.cell_context: DefaultDict[tuple[int, int], str] = defaultdict(str)
+        self._todo: list[tuple[Callable, tuple, dict]] = []
+        self.tooltips: Tooltips | None = None
+        precision = (
+            get_option("styler.format.precision") if precision is None else precision
+        )
+        self._display_funcs: DefaultDict[  # maps (row, col) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+        self._display_funcs_index: DefaultDict[  # maps (row, level) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+        self._display_funcs_columns: DefaultDict[  # maps (level, col) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+
+    def _render(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        blank: str = "",
+    ):
+        """
+        Computes and applies styles and then generates the general render dicts.
+
+        Also extends the `ctx` and `ctx_index` attributes with those of concatenated
+        stylers for use within `_translate_latex`
+        """
+        self._compute()
+        dxs = []
+        ctx_len = len(self.index)
+        for i, concatenated in enumerate(self.concatenated):
+            concatenated.hide_index_ = self.hide_index_
+            concatenated.hidden_columns = self.hidden_columns
+            foot = f"{self.css['foot']}{i}"
+            concatenated.css = {
+                **self.css,
+                "data": f"{foot}_data",
+                "row_heading": f"{foot}_row_heading",
+                "row": f"{foot}_row",
+                "foot": f"{foot}_foot",
+            }
+            dx = concatenated._render(
+                sparse_index, sparse_columns, max_rows, max_cols, blank
+            )
+            dxs.append(dx)
+
+            for (r, c), v in concatenated.ctx.items():
+                self.ctx[(r + ctx_len, c)] = v
+            for (r, c), v in concatenated.ctx_index.items():
+                self.ctx_index[(r + ctx_len, c)] = v
+
+            ctx_len += len(concatenated.index)
+
+        d = self._translate(
+            sparse_index, sparse_columns, max_rows, max_cols, blank, dxs
+        )
+        return d
+
+    def _render_html(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        **kwargs,
+    ) -> str:
+        """
+        Renders the ``Styler`` including all applied styles to HTML.
+        Generates a dict with necessary kwargs passed to jinja2 template.
+        """
+        d = self._render(sparse_index, sparse_columns, max_rows, max_cols, "&nbsp;")
+        d.update(kwargs)
+        return self.template_html.render(
+            **d,
+            html_table_tpl=self.template_html_table,
+            html_style_tpl=self.template_html_style,
+        )
+
+    def _render_latex(
+        self, sparse_index: bool, sparse_columns: bool, clines: str | None, **kwargs
+    ) -> str:
+        """
+        Render a Styler in latex format
+        """
+        d = self._render(sparse_index, sparse_columns, None, None)
+        self._translate_latex(d, clines=clines)
+        self.template_latex.globals["parse_wrap"] = _parse_latex_table_wrapping
+        self.template_latex.globals["parse_table"] = _parse_latex_table_styles
+        self.template_latex.globals["parse_cell"] = _parse_latex_cell_styles
+        self.template_latex.globals["parse_header"] = _parse_latex_header_span
+        d.update(kwargs)
+        return self.template_latex.render(**d)
+
+    def _render_string(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        **kwargs,
+    ) -> str:
+        """
+        Render a Styler in string format
+        """
+        d = self._render(sparse_index, sparse_columns, max_rows, max_cols)
+        d.update(kwargs)
+        return self.template_string.render(**d)
+
+    def _compute(self):
+        """
+        Execute the style functions built up in `self._todo`.
+
+        Relies on the conventions that all style functions go through
+        .apply or .map. The append styles to apply as tuples of
+
+        (application method, *args, **kwargs)
+        """
+        self.ctx.clear()
+        self.ctx_index.clear()
+        self.ctx_columns.clear()
+        r = self
+        for func, args, kwargs in self._todo:
+            r = func(self)(*args, **kwargs)
+        return r
+
+    def _translate(
+        self,
+        sparse_index: bool,
+        sparse_cols: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        blank: str = "&nbsp;",
+        dxs: list[dict] | None = None,
+    ):
+        """
+        Process Styler data and settings into a dict for template rendering.
+
+        Convert data and settings from ``Styler`` attributes such as ``self.data``,
+        ``self.tooltips`` including applying any methods in ``self._todo``.
+
+        Parameters
+        ----------
+        sparse_index : bool
+            Whether to sparsify the index or print all hierarchical index elements.
+            Upstream defaults are typically to `pandas.options.styler.sparse.index`.
+        sparse_cols : bool
+            Whether to sparsify the columns or print all hierarchical column elements.
+            Upstream defaults are typically to `pandas.options.styler.sparse.columns`.
+        max_rows, max_cols : int, optional
+            Specific max rows and cols. max_elements always take precedence in render.
+        blank : str
+            Entry to top-left blank cells.
+        dxs : list[dict]
+            The render dicts of the concatenated Stylers.
+
+        Returns
+        -------
+        d : dict
+            The following structure: {uuid, table_styles, caption, head, body,
+            cellstyle, table_attributes}
+        """
+        if dxs is None:
+            dxs = []
+        self.css["blank_value"] = blank
+
+        # construct render dict
+        d = {
+            "uuid": self.uuid,
+            "table_styles": format_table_styles(self.table_styles or []),
+            "caption": self.caption,
+        }
+
+        max_elements = get_option("styler.render.max_elements")
+        max_rows = max_rows if max_rows else get_option("styler.render.max_rows")
+        max_cols = max_cols if max_cols else get_option("styler.render.max_columns")
+        max_rows, max_cols = _get_trimming_maximums(
+            len(self.data.index),
+            len(self.data.columns),
+            max_elements,
+            max_rows,
+            max_cols,
+        )
+
+        self.cellstyle_map_columns: DefaultDict[
+            tuple[CSSPair, ...], list[str]
+        ] = defaultdict(list)
+        head = self._translate_header(sparse_cols, max_cols)
+        d.update({"head": head})
+
+        # for sparsifying a MultiIndex and for use with latex clines
+        idx_lengths = _get_level_lengths(
+            self.index, sparse_index, max_rows, self.hidden_rows
+        )
+        d.update({"index_lengths": idx_lengths})
+
+        self.cellstyle_map: DefaultDict[tuple[CSSPair, ...], list[str]] = defaultdict(
+            list
+        )
+        self.cellstyle_map_index: DefaultDict[
+            tuple[CSSPair, ...], list[str]
+        ] = defaultdict(list)
+        body: list = self._translate_body(idx_lengths, max_rows, max_cols)
+        d.update({"body": body})
+
+        ctx_maps = {
+            "cellstyle": "cellstyle_map",
+            "cellstyle_index": "cellstyle_map_index",
+            "cellstyle_columns": "cellstyle_map_columns",
+        }  # add the cell_ids styles map to the render dictionary in right format
+        for k, attr in ctx_maps.items():
+            map = [
+                {"props": list(props), "selectors": selectors}
+                for props, selectors in getattr(self, attr).items()
+            ]
+            d.update({k: map})
+
+        for dx in dxs:  # self.concatenated is not empty
+            d["body"].extend(dx["body"])  # type: ignore[union-attr]
+            d["cellstyle"].extend(dx["cellstyle"])  # type: ignore[union-attr]
+            d["cellstyle_index"].extend(  # type: ignore[union-attr]
+                dx["cellstyle_index"]
+            )
+
+        table_attr = self.table_attributes
+        if not get_option("styler.html.mathjax"):
+            table_attr = table_attr or ""
+            if 'class="' in table_attr:
+                table_attr = table_attr.replace('class="', 'class="tex2jax_ignore ')
+            else:
+                table_attr += ' class="tex2jax_ignore"'
+        d.update({"table_attributes": table_attr})
+
+        if self.tooltips:
+            d = self.tooltips._translate(self, d)
+
+        return d
+
+    def _translate_header(self, sparsify_cols: bool, max_cols: int):
+        """
+        Build each <tr> within table <head> as a list
+
+        Using the structure:
+             +----------------------------+---------------+---------------------------+
+             |  index_blanks ...          | column_name_0 |  column_headers (level_0) |
+          1) |       ..                   |       ..      |             ..            |
+             |  index_blanks ...          | column_name_n |  column_headers (level_n) |
+             +----------------------------+---------------+---------------------------+
+          2) |  index_names (level_0 to level_n) ...      | column_blanks ...         |
+             +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        sparsify_cols : bool
+            Whether column_headers section will add colspan attributes (>1) to elements.
+        max_cols : int
+            Maximum number of columns to render. If exceeded will contain `...` filler.
+
+        Returns
+        -------
+        head : list
+            The associated HTML elements needed for template rendering.
+        """
+        # for sparsifying a MultiIndex
+        col_lengths = _get_level_lengths(
+            self.columns, sparsify_cols, max_cols, self.hidden_columns
+        )
+
+        clabels = self.data.columns.tolist()
+        if self.data.columns.nlevels == 1:
+            clabels = [[x] for x in clabels]
+        clabels = list(zip(*clabels))
+
+        head = []
+        # 1) column headers
+        for r, hide in enumerate(self.hide_columns_):
+            if hide or not clabels:
+                continue
+
+            header_row = self._generate_col_header_row(
+                (r, clabels), max_cols, col_lengths
+            )
+            head.append(header_row)
+
+        # 2) index names
+        if (
+            self.data.index.names
+            and com.any_not_none(*self.data.index.names)
+            and not all(self.hide_index_)
+            and not self.hide_index_names
+        ):
+            index_names_row = self._generate_index_names_row(
+                clabels, max_cols, col_lengths
+            )
+            head.append(index_names_row)
+
+        return head
+
+    def _generate_col_header_row(
+        self, iter: Sequence, max_cols: int, col_lengths: dict
+    ):
+        """
+        Generate the row containing column headers:
+
+         +----------------------------+---------------+---------------------------+
+         |  index_blanks ...          | column_name_i |  column_headers (level_i) |
+         +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Looping variables from outer scope
+        max_cols : int
+            Permissible number of columns
+        col_lengths :
+            c
+
+        Returns
+        -------
+        list of elements
+        """
+
+        r, clabels = iter
+
+        # number of index blanks is governed by number of hidden index levels
+        index_blanks = [
+            _element("th", self.css["blank"], self.css["blank_value"], True)
+        ] * (self.index.nlevels - sum(self.hide_index_) - 1)
+
+        name = self.data.columns.names[r]
+        column_name = [
+            _element(
+                "th",
+                (
+                    f"{self.css['blank']} {self.css['level']}{r}"
+                    if name is None
+                    else f"{self.css['index_name']} {self.css['level']}{r}"
+                ),
+                name
+                if (name is not None and not self.hide_column_names)
+                else self.css["blank_value"],
+                not all(self.hide_index_),
+            )
+        ]
+
+        column_headers: list = []
+        visible_col_count: int = 0
+        for c, value in enumerate(clabels[r]):
+            header_element_visible = _is_visible(c, r, col_lengths)
+            if header_element_visible:
+                visible_col_count += col_lengths.get((r, c), 0)
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                column_headers,
+                "th",
+                f"{self.css['col_heading']} {self.css['level']}{r} "
+                f"{self.css['col_trim']}",
+            ):
+                break
+
+            header_element = _element(
+                "th",
+                (
+                    f"{self.css['col_heading']} {self.css['level']}{r} "
+                    f"{self.css['col']}{c}"
+                ),
+                value,
+                header_element_visible,
+                display_value=self._display_funcs_columns[(r, c)](value),
+                attributes=(
+                    f'colspan="{col_lengths.get((r, c), 0)}"'
+                    if col_lengths.get((r, c), 0) > 1
+                    else ""
+                ),
+            )
+
+            if self.cell_ids:
+                header_element["id"] = f"{self.css['level']}{r}_{self.css['col']}{c}"
+            if (
+                header_element_visible
+                and (r, c) in self.ctx_columns
+                and self.ctx_columns[r, c]
+            ):
+                header_element["id"] = f"{self.css['level']}{r}_{self.css['col']}{c}"
+                self.cellstyle_map_columns[tuple(self.ctx_columns[r, c])].append(
+                    f"{self.css['level']}{r}_{self.css['col']}{c}"
+                )
+
+            column_headers.append(header_element)
+
+        return index_blanks + column_name + column_headers
+
+    def _generate_index_names_row(
+        self, iter: Sequence, max_cols: int, col_lengths: dict
+    ):
+        """
+        Generate the row containing index names
+
+         +----------------------------+---------------+---------------------------+
+         |  index_names (level_0 to level_n) ...      | column_blanks ...         |
+         +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Looping variables from outer scope
+        max_cols : int
+            Permissible number of columns
+
+        Returns
+        -------
+        list of elements
+        """
+
+        clabels = iter
+
+        index_names = [
+            _element(
+                "th",
+                f"{self.css['index_name']} {self.css['level']}{c}",
+                self.css["blank_value"] if name is None else name,
+                not self.hide_index_[c],
+            )
+            for c, name in enumerate(self.data.index.names)
+        ]
+
+        column_blanks: list = []
+        visible_col_count: int = 0
+        if clabels:
+            last_level = self.columns.nlevels - 1  # use last level since never sparsed
+            for c, value in enumerate(clabels[last_level]):
+                header_element_visible = _is_visible(c, last_level, col_lengths)
+                if header_element_visible:
+                    visible_col_count += 1
+                if self._check_trim(
+                    visible_col_count,
+                    max_cols,
+                    column_blanks,
+                    "th",
+                    f"{self.css['blank']} {self.css['col']}{c} {self.css['col_trim']}",
+                    self.css["blank_value"],
+                ):
+                    break
+
+                column_blanks.append(
+                    _element(
+                        "th",
+                        f"{self.css['blank']} {self.css['col']}{c}",
+                        self.css["blank_value"],
+                        c not in self.hidden_columns,
+                    )
+                )
+
+        return index_names + column_blanks
+
+    def _translate_body(self, idx_lengths: dict, max_rows: int, max_cols: int):
+        """
+        Build each <tr> within table <body> as a list
+
+        Use the following structure:
+          +--------------------------------------------+---------------------------+
+          |  index_header_0    ...    index_header_n   |  data_by_column   ...     |
+          +--------------------------------------------+---------------------------+
+
+        Also add elements to the cellstyle_map for more efficient grouped elements in
+        <style></style> block
+
+        Parameters
+        ----------
+        sparsify_index : bool
+            Whether index_headers section will add rowspan attributes (>1) to elements.
+
+        Returns
+        -------
+        body : list
+            The associated HTML elements needed for template rendering.
+        """
+        rlabels = self.data.index.tolist()
+        if not isinstance(self.data.index, MultiIndex):
+            rlabels = [[x] for x in rlabels]
+
+        body: list = []
+        visible_row_count: int = 0
+        for r, row_tup in [
+            z for z in enumerate(self.data.itertuples()) if z[0] not in self.hidden_rows
+        ]:
+            visible_row_count += 1
+            if self._check_trim(
+                visible_row_count,
+                max_rows,
+                body,
+                "row",
+            ):
+                break
+
+            body_row = self._generate_body_row(
+                (r, row_tup, rlabels), max_cols, idx_lengths
+            )
+            body.append(body_row)
+        return body
+
+    def _check_trim(
+        self,
+        count: int,
+        max: int,
+        obj: list,
+        element: str,
+        css: str | None = None,
+        value: str = "...",
+    ) -> bool:
+        """
+        Indicates whether to break render loops and append a trimming indicator
+
+        Parameters
+        ----------
+        count : int
+            The loop count of previous visible items.
+        max : int
+            The allowable rendered items in the loop.
+        obj : list
+            The current render collection of the rendered items.
+        element : str
+            The type of element to append in the case a trimming indicator is needed.
+        css : str, optional
+            The css to add to the trimming indicator element.
+        value : str, optional
+            The value of the elements display if necessary.
+
+        Returns
+        -------
+        result : bool
+            Whether a trimming element was required and appended.
+        """
+        if count > max:
+            if element == "row":
+                obj.append(self._generate_trimmed_row(max))
+            else:
+                obj.append(_element(element, css, value, True, attributes=""))
+            return True
+        return False
+
+    def _generate_trimmed_row(self, max_cols: int) -> list:
+        """
+        When a render has too many rows we generate a trimming row containing "..."
+
+        Parameters
+        ----------
+        max_cols : int
+            Number of permissible columns
+
+        Returns
+        -------
+        list of elements
+        """
+        index_headers = [
+            _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row_trim']}"
+                ),
+                "...",
+                not self.hide_index_[c],
+                attributes="",
+            )
+            for c in range(self.data.index.nlevels)
+        ]
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, _ in enumerate(self.columns):
+            data_element_visible = c not in self.hidden_columns
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row_trim']} {self.css['col_trim']}",
+            ):
+                break
+
+            data.append(
+                _element(
+                    "td",
+                    f"{self.css['data']} {self.css['col']}{c} {self.css['row_trim']}",
+                    "...",
+                    data_element_visible,
+                    attributes="",
+                )
+            )
+
+        return index_headers + data
+
+    def _generate_body_row(
+        self,
+        iter: tuple,
+        max_cols: int,
+        idx_lengths: dict,
+    ):
+        """
+        Generate a regular row for the body section of appropriate format.
+
+          +--------------------------------------------+---------------------------+
+          |  index_header_0    ...    index_header_n   |  data_by_column   ...     |
+          +--------------------------------------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Iterable from outer scope: row number, row data tuple, row index labels.
+        max_cols : int
+            Number of permissible columns.
+        idx_lengths : dict
+            A map of the sparsification structure of the index
+
+        Returns
+        -------
+            list of elements
+        """
+        r, row_tup, rlabels = iter
+
+        index_headers = []
+        for c, value in enumerate(rlabels[r]):
+            header_element_visible = (
+                _is_visible(r, c, idx_lengths) and not self.hide_index_[c]
+            )
+            header_element = _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row']}{r}"
+                ),
+                value,
+                header_element_visible,
+                display_value=self._display_funcs_index[(r, c)](value),
+                attributes=(
+                    f'rowspan="{idx_lengths.get((c, r), 0)}"'
+                    if idx_lengths.get((c, r), 0) > 1
+                    else ""
+                ),
+            )
+
+            if self.cell_ids:
+                header_element[
+                    "id"
+                ] = f"{self.css['level']}{c}_{self.css['row']}{r}"  # id is given
+            if (
+                header_element_visible
+                and (r, c) in self.ctx_index
+                and self.ctx_index[r, c]
+            ):
+                # always add id if a style is specified
+                header_element["id"] = f"{self.css['level']}{c}_{self.css['row']}{r}"
+                self.cellstyle_map_index[tuple(self.ctx_index[r, c])].append(
+                    f"{self.css['level']}{c}_{self.css['row']}{r}"
+                )
+
+            index_headers.append(header_element)
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, value in enumerate(row_tup[1:]):
+            data_element_visible = (
+                c not in self.hidden_columns and r not in self.hidden_rows
+            )
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row']}{r} {self.css['col_trim']}",
+            ):
+                break
+
+            # add custom classes from cell context
+            cls = ""
+            if (r, c) in self.cell_context:
+                cls = " " + self.cell_context[r, c]
+
+            data_element = _element(
+                "td",
+                (
+                    f"{self.css['data']} {self.css['row']}{r} "
+                    f"{self.css['col']}{c}{cls}"
+                ),
+                value,
+                data_element_visible,
+                attributes="",
+                display_value=self._display_funcs[(r, c)](value),
+            )
+
+            if self.cell_ids:
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+            if data_element_visible and (r, c) in self.ctx and self.ctx[r, c]:
+                # always add id if needed due to specified style
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+                self.cellstyle_map[tuple(self.ctx[r, c])].append(
+                    f"{self.css['row']}{r}_{self.css['col']}{c}"
+                )
+
+            data.append(data_element)
+
+        return index_headers + data
+
+    def _translate_latex(self, d: dict, clines: str | None) -> None:
+        r"""
+        Post-process the default render dict for the LaTeX template format.
+
+        Processing items included are:
+          - Remove hidden columns from the non-headers part of the body.
+          - Place cellstyles directly in td cells rather than use cellstyle_map.
+          - Remove hidden indexes or reinsert missing th elements if part of multiindex
+            or multirow sparsification (so that \multirow and \multicol work correctly).
+        """
+        index_levels = self.index.nlevels
+        visible_index_level_n = index_levels - sum(self.hide_index_)
+        d["head"] = [
+            [
+                {**col, "cellstyle": self.ctx_columns[r, c - visible_index_level_n]}
+                for c, col in enumerate(row)
+                if col["is_visible"]
+            ]
+            for r, row in enumerate(d["head"])
+        ]
+
+        def _concatenated_visible_rows(obj, n, row_indices):
+            """
+            Extract all visible row indices recursively from concatenated stylers.
+            """
+            row_indices.extend(
+                [r + n for r in range(len(obj.index)) if r not in obj.hidden_rows]
+            )
+            n += len(obj.index)
+            for concatenated in obj.concatenated:
+                n = _concatenated_visible_rows(concatenated, n, row_indices)
+            return n
+
+        def concatenated_visible_rows(obj):
+            row_indices: list[int] = []
+            _concatenated_visible_rows(obj, 0, row_indices)
+            # TODO try to consolidate the concat visible rows
+            # methods to a single function / recursion for simplicity
+            return row_indices
+
+        body = []
+        for r, row in zip(concatenated_visible_rows(self), d["body"]):
+            # note: cannot enumerate d["body"] because rows were dropped if hidden
+            # during _translate_body so must zip to acquire the true r-index associated
+            # with the ctx obj which contains the cell styles.
+            if all(self.hide_index_):
+                row_body_headers = []
+            else:
+                row_body_headers = [
+                    {
+                        **col,
+                        "display_value": col["display_value"]
+                        if col["is_visible"]
+                        else "",
+                        "cellstyle": self.ctx_index[r, c],
+                    }
+                    for c, col in enumerate(row[:index_levels])
+                    if (col["type"] == "th" and not self.hide_index_[c])
+                ]
+
+            row_body_cells = [
+                {**col, "cellstyle": self.ctx[r, c]}
+                for c, col in enumerate(row[index_levels:])
+                if (col["is_visible"] and col["type"] == "td")
+            ]
+
+            body.append(row_body_headers + row_body_cells)
+        d["body"] = body
+
+        # clines are determined from info on index_lengths and hidden_rows and input
+        # to a dict defining which row clines should be added in the template.
+        if clines not in [
+            None,
+            "all;data",
+            "all;index",
+            "skip-last;data",
+            "skip-last;index",
+        ]:
+            raise ValueError(
+                f"`clines` value of {clines} is invalid. Should either be None or one "
+                f"of 'all;data', 'all;index', 'skip-last;data', 'skip-last;index'."
+            )
+        if clines is not None:
+            data_len = len(row_body_cells) if "data" in clines and d["body"] else 0
+
+            d["clines"] = defaultdict(list)
+            visible_row_indexes: list[int] = [
+                r for r in range(len(self.data.index)) if r not in self.hidden_rows
+            ]
+            visible_index_levels: list[int] = [
+                i for i in range(index_levels) if not self.hide_index_[i]
+            ]
+            for rn, r in enumerate(visible_row_indexes):
+                for lvln, lvl in enumerate(visible_index_levels):
+                    if lvl == index_levels - 1 and "skip-last" in clines:
+                        continue
+                    idx_len = d["index_lengths"].get((lvl, r), None)
+                    if idx_len is not None:  # i.e. not a sparsified entry
+                        d["clines"][rn + idx_len].append(
+                            f"\\cline{{{lvln+1}-{len(visible_index_levels)+data_len}}}"
+                        )
+
+    def format(
+        self,
+        formatter: ExtFormatter | None = None,
+        subset: Subset | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of cells.
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        subset : label, array-like, IndexSlice, optional
+            A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
+            or single key, to `DataFrame.loc[:, <subset>]` where the columns are
+            prioritised, to limit ``data`` to *before* applying the function.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Use 'latex-math' to replace the characters the same way as in 'latex' mode,
+            except for math substrings, which either are surrounded
+            by two characters ``$`` or start with the character ``\(`` and
+            end with ``\)``. Escaping is done before ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML <a> tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+            .. versionadded:: 1.4.0
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index labels.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each cell in the
+        DataFrame. If ``formatter`` is ``None``, then the default formatter is used.
+        If a callable then that function should take a data value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to column names, and values should be string or
+        callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``subset`` argument defines which region to apply the formatting function
+        to. If the ``formatter`` argument is given in dict form but does not include
+        all columns within the subset then these columns will have the default formatter
+        applied. Any columns in the formatter dict excluded from the subset will
+        be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        When instantiating a Styler, default formatting can be applied be setting the
+        ``pandas.options``:
+
+          - ``styler.format.formatter``: default None.
+          - ``styler.format.na_rep``: default None.
+          - ``styler.format.precision``: default 6.
+          - ``styler.format.decimal``: default ".".
+          - ``styler.format.thousands``: default None.
+          - ``styler.format.escape``: default None.
+
+        .. warning::
+           `Styler.format` is ignored when using the output format `Styler.to_excel`,
+           since Excel and Python have inherrently different formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See examples.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[np.nan, 1.0, 'A'], [2.0, np.nan, 3.0]])
+        >>> df.style.format(na_rep='MISS', precision=3)  # doctest: +SKIP
+                0       1       2
+        0    MISS   1.000       A
+        1   2.000    MISS   3.000
+
+        Using a ``formatter`` specification on consistent column dtypes
+
+        >>> df.style.format('{:.2f}', na_rep='MISS', subset=[0,1])  # doctest: +SKIP
+                0      1          2
+        0    MISS   1.00          A
+        1    2.00   MISS   3.000000
+
+        Using the default ``formatter`` for unspecified columns
+
+        >>> df.style.format({0: '{:.2f}', 1: '£ {:.1f}'}, na_rep='MISS', precision=1)
+        ...  # doctest: +SKIP
+                 0      1     2
+        0    MISS   £ 1.0     A
+        1    2.00    MISS   3.0
+
+        Multiple ``na_rep`` or ``precision`` specifications under the default
+        ``formatter``.
+
+        >>> (df.style.format(na_rep='MISS', precision=1, subset=[0])
+        ...     .format(na_rep='PASS', precision=2, subset=[1, 2]))  # doctest: +SKIP
+                0      1      2
+        0    MISS   1.00      A
+        1     2.0   PASS   3.00
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format({0: '{:.1f}', 2: func}, precision=4, na_rep='MISS')
+        ...  # doctest: +SKIP
+                0        1        2
+        0    MISS   1.0000   STRING
+        1     2.0     MISS    FLOAT
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([['<div></div>', '"A&B"', None]])
+        >>> s = df.style.format(
+        ...     '<a href="a.com/{0}">{0}</a>', escape="html", na_rep="NA"
+        ...     )
+        >>> s.to_html()  # doctest: +SKIP
+        ...
+        <td .. ><a href="a.com/&lt;div&gt;&lt;/div&gt;">&lt;div&gt;&lt;/div&gt;</a></td>
+        <td .. ><a href="a.com/&#34;A&amp;B&#34;">&#34;A&amp;B&#34;</a></td>
+        <td .. >NA</td>
+        ...
+
+        Using a ``formatter`` with ``escape`` in 'latex' mode.
+
+        >>> df = pd.DataFrame([["123"], ["~ ^"], ["$%#"]])
+        >>> df.style.format("\\textbf{{{}}}", escape="latex").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbf{123} \\
+        1 & \textbf{\textasciitilde \space \textasciicircum } \\
+        2 & \textbf{\$\%\#} \\
+        \end{tabular}
+
+        Applying ``escape`` in 'latex-math' mode. In the example below
+        we enter math mode using the character ``$``.
+
+        >>> df = pd.DataFrame([[r"$\sum_{i=1}^{10} a_i$ a~b $\alpha \
+        ...     = \frac{\beta}{\zeta^2}$"], ["%#^ $ \$x^2 $"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & $\sum_{i=1}^{10} a_i$ a\textasciitilde b $\alpha = \frac{\beta}{\zeta^2}$ \\
+        1 & \%\#\textasciicircum \space $ \$x^2 $ \\
+        \end{tabular}
+
+        We can use the character ``\(`` to enter math mode and the character ``\)``
+        to close math mode.
+
+        >>> df = pd.DataFrame([[r"\(\sum_{i=1}^{10} a_i\) a~b \(\alpha \
+        ...     = \frac{\beta}{\zeta^2}\)"], ["%#^ \( \$x^2 \)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \(\sum_{i=1}^{10} a_i\) a\textasciitilde b \(\alpha
+        = \frac{\beta}{\zeta^2}\) \\
+        1 & \%\#\textasciicircum \space \( \$x^2 \) \\
+        \end{tabular}
+
+        If we have in one DataFrame cell a combination of both shorthands
+        for math formulas, the shorthand with the sign ``$`` will be applied.
+
+        >>> df = pd.DataFrame([[r"\( x^2 \)  $x^2$"], \
+        ...     [r"$\frac{\beta}{\zeta}$ \(\frac{\beta}{\zeta}\)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbackslash ( x\textasciicircum 2 \textbackslash )  $x^2$ \\
+        1 & $\frac{\beta}{\zeta}$ \textbackslash (\textbackslash
+        frac\{\textbackslash beta\}\{\textbackslash zeta\}\textbackslash ) \\
+        \end{tabular}
+
+        Pandas defines a `number-format` pseudo CSS attribute instead of the `.format`
+        method to create `to_excel` permissible formatting. Note that semi-colons are
+        CSS protected characters but used as separators in Excel's format string.
+        Replace semi-colons with the section separator character (ASCII-245) when
+        defining the formatting here.
+
+        >>> df = pd.DataFrame({"A": [1, 0, -1]})
+        >>> pseudo_css = "number-format: 0§[Red](0)§-§@;"
+        >>> filename = "formatted_file.xlsx"
+        >>> df.style.map(lambda v: pseudo_css).to_excel(filename) # doctest: +SKIP
+
+        .. figure:: ../../_static/style/format_excel_css.png
+        """
+        if all(
+            (
+                formatter is None,
+                subset is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            self._display_funcs.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        subset = slice(None) if subset is None else subset
+        subset = non_reducing_slice(subset)
+        data = self.data.loc[subset]
+
+        if not isinstance(formatter, dict):
+            formatter = {col: formatter for col in data.columns}
+
+        cis = self.columns.get_indexer_for(data.columns)
+        ris = self.index.get_indexer_for(data.index)
+        for ci in cis:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(self.columns[ci]),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+            for ri in ris:
+                self._display_funcs[(ri, ci)] = format_func
+
+        return self
+
+    def format_index(
+        self,
+        formatter: ExtFormatter | None = None,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of index labels or column headers.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        axis : {0, "index", 1, "columns"}
+            Whether to apply the formatter to the index or column headers.
+        level : int, str, list
+            The level(s) over which to apply the generic formatter.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Escaping is done before ``formatter``.
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML <a> tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format: Format the text display value of data cells.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each level label
+        in the DataFrame's index or column headers. If ``formatter`` is ``None``,
+        then the default formatter is used.
+        If a callable then that function should take a label value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to MultiIndex level numbers or names, and values should
+        be string or callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``level`` argument defines which levels of a MultiIndex to apply the
+        method to. If the ``formatter`` argument is given in dict form but does
+        not include all levels within the level argument then these unspecified levels
+        will have the default formatter applied. Any levels in the formatter dict
+        specifically excluded from the level argument will be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        .. warning::
+           `Styler.format_index` is ignored when using the output format
+           `Styler.to_excel`, since Excel and Python have inherrently different
+           formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See documentation for `Styler.format`.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=[2.0, np.nan, 4.0])
+        >>> df.style.format_index(axis=1, na_rep='MISS', precision=3)  # doctest: +SKIP
+            2.000    MISS   4.000
+        0       1       2       3
+
+        Using a ``formatter`` specification on consistent dtypes in a level
+
+        >>> df.style.format_index('{:.2f}', axis=1, na_rep='MISS')  # doctest: +SKIP
+             2.00   MISS    4.00
+        0       1      2       3
+
+        Using the default ``formatter`` for unspecified levels
+
+        >>> df = pd.DataFrame([[1, 2, 3]],
+        ...     columns=pd.MultiIndex.from_arrays([["a", "a", "b"],[2, np.nan, 4]]))
+        >>> df.style.format_index({0: lambda v: v.upper()}, axis=1, precision=1)
+        ...  # doctest: +SKIP
+                       A       B
+              2.0    nan     4.0
+        0       1      2       3
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format_index(func, axis=1, na_rep='MISS')
+        ...  # doctest: +SKIP
+                  STRING  STRING
+            FLOAT   MISS   FLOAT
+        0       1      2       3
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=['"A"', 'A&B', None])
+        >>> s = df.style.format_index('$ {0}', axis=1, escape="html", na_rep="NA")
+        ...  # doctest: +SKIP
+        <th .. >$ &#34;A&#34;</th>
+        <th .. >$ A&amp;B</th>
+        <th .. >NA</td>
+        ...
+
+        Using a ``formatter`` with LaTeX ``escape``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=["123", "~", "$%#"])
+        >>> df.style.format_index("\\textbf{{{}}}", escape="latex", axis=1).to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{lrrr}
+        {} & {\textbf{123}} & {\textbf{\textasciitilde }} & {\textbf{\$\%\#}} \\
+        0 & 1 & 2 & 3 \\
+        \end{tabular}
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+        levels_ = refactor_levels(level, obj)
+
+        if all(
+            (
+                formatter is None,
+                level is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            display_funcs_.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        if not isinstance(formatter, dict):
+            formatter = {level: formatter for level in levels_}
+        else:
+            formatter = {
+                obj._get_level_number(level): formatter_
+                for level, formatter_ in formatter.items()
+            }
+
+        for lvl in levels_:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(lvl),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+
+            for idx in [(i, lvl) if axis == 0 else (lvl, i) for i in range(len(obj))]:
+                display_funcs_[idx] = format_func
+
+        return self
+
+    def relabel_index(
+        self,
+        labels: Sequence | Index,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Relabel the index, or column header, keys to display a set of specified values.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        labels : list-like or Index
+            New labels to display. Must have same length as the underlying values not
+            hidden.
+        axis : {"index", 0, "columns", 1}
+            Apply to the index or columns.
+        level : int, str, list, optional
+            The level(s) over which to apply the new labels. If `None` will apply
+            to all levels of an Index or MultiIndex which are not hidden.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index or column headers.
+        Styler.hide: Hide the index, column headers, or specified data from display.
+
+        Notes
+        -----
+        As part of Styler, this method allows the display of an index to be
+        completely user-specified without affecting the underlying DataFrame data,
+        index, or column headers. This means that the flexibility of indexing is
+        maintained whilst the final display is customisable.
+
+        Since Styler is designed to be progressively constructed with method chaining,
+        this method is adapted to react to the **currently specified hidden elements**.
+        This is useful because it means one does not have to specify all the new
+        labels if the majority of an index, or column headers, have already been hidden.
+        The following produce equivalent display (note the length of ``labels`` in
+        each case).
+
+        .. code-block:: python
+
+            # relabel first, then hide
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.relabel_index(["A", "B", "C"]).hide([0,1])
+            # hide first, then relabel
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.hide([0,1]).relabel_index(["C"])
+
+        This method should be used, rather than :meth:`Styler.format_index`, in one of
+        the following cases (see examples):
+
+          - A specified set of labels are required which are not a function of the
+            underlying index keys.
+          - The function of the underlying index keys requires a counter variable,
+            such as those available upon enumeration.
+
+        Examples
+        --------
+        Basic use
+
+        >>> df = pd.DataFrame({"col": ["a", "b", "c"]})
+        >>> df.style.relabel_index(["A", "B", "C"])  # doctest: +SKIP
+             col
+        A      a
+        B      b
+        C      c
+
+        Chaining with pre-hidden elements
+
+        >>> df.style.hide([0,1]).relabel_index(["C"])  # doctest: +SKIP
+             col
+        C      c
+
+        Using a MultiIndex
+
+        >>> midx = pd.MultiIndex.from_product([[0, 1], [0, 1], [0, 1]])
+        >>> df = pd.DataFrame({"col": list(range(8))}, index=midx)
+        >>> styler = df.style  # doctest: +SKIP
+                  col
+        0  0  0     0
+              1     1
+           1  0     2
+              1     3
+        1  0  0     4
+              1     5
+           1  0     6
+              1     7
+        >>> styler.hide((midx.get_level_values(0)==0)|(midx.get_level_values(1)==0))
+        ...  # doctest: +SKIP
+        >>> styler.hide(level=[0,1])  # doctest: +SKIP
+        >>> styler.relabel_index(["binary6", "binary7"])  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        We can also achieve the above by indexing first and then re-labeling
+
+        >>> styler = df.loc[[(1,1,0), (1,1,1)]].style
+        >>> styler.hide(level=[0,1]).relabel_index(["binary6", "binary7"])
+        ...  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        Defining a formatting function which uses an enumeration counter. Also note
+        that the value of the index key is passed in the case of string labels so it
+        can also be inserted into the label, using curly brackets (or double curly
+        brackets if the string if pre-formatted),
+
+        >>> df = pd.DataFrame({"samples": np.random.rand(10)})
+        >>> styler = df.loc[np.random.randint(0,10,3)].style
+        >>> styler.relabel_index([f"sample{i+1} ({{}})" for i in range(3)])
+        ...  # doctest: +SKIP
+                         samples
+        sample1 (5)     0.315811
+        sample2 (0)     0.495941
+        sample3 (2)     0.067946
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+            hidden_labels, hidden_lvls = self.hidden_rows, self.hide_index_
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+            hidden_labels, hidden_lvls = self.hidden_columns, self.hide_columns_
+        visible_len = len(obj) - len(set(hidden_labels))
+        if len(labels) != visible_len:
+            raise ValueError(
+                "``labels`` must be of length equal to the number of "
+                f"visible labels along ``axis`` ({visible_len})."
+            )
+
+        if level is None:
+            level = [i for i in range(obj.nlevels) if not hidden_lvls[i]]
+        levels_ = refactor_levels(level, obj)
+
+        def alias_(x, value):
+            if isinstance(value, str):
+                return value.format(x)
+            return value
+
+        for ai, i in enumerate([i for i in range(len(obj)) if i not in hidden_labels]):
+            if len(levels_) == 1:
+                idx = (i, levels_[0]) if axis == 0 else (levels_[0], i)
+                display_funcs_[idx] = partial(alias_, value=labels[ai])
+            else:
+                for aj, lvl in enumerate(levels_):
+                    idx = (i, lvl) if axis == 0 else (lvl, i)
+                    display_funcs_[idx] = partial(alias_, value=labels[ai][aj])
+
+        return self
+
+
+def _element(
+    html_element: str,
+    html_class: str | None,
+    value: Any,
+    is_visible: bool,
+    **kwargs,
+) -> dict:
+    """
+    Template to return container with information for a <td></td> or <th></th> element.
+    """
+    if "display_value" not in kwargs:
+        kwargs["display_value"] = value
+    return {
+        "type": html_element,
+        "value": value,
+        "class": html_class,
+        "is_visible": is_visible,
+        **kwargs,
+    }
+
+
+def _get_trimming_maximums(
+    rn,
+    cn,
+    max_elements,
+    max_rows=None,
+    max_cols=None,
+    scaling_factor: float = 0.8,
+) -> tuple[int, int]:
+    """
+    Recursively reduce the number of rows and columns to satisfy max elements.
+
+    Parameters
+    ----------
+    rn, cn : int
+        The number of input rows / columns
+    max_elements : int
+        The number of allowable elements
+    max_rows, max_cols : int, optional
+        Directly specify an initial maximum rows or columns before compression.
+    scaling_factor : float
+        Factor at which to reduce the number of rows / columns to fit.
+
+    Returns
+    -------
+    rn, cn : tuple
+        New rn and cn values that satisfy the max_elements constraint
+    """
+
+    def scale_down(rn, cn):
+        if cn >= rn:
+            return rn, int(cn * scaling_factor)
+        else:
+            return int(rn * scaling_factor), cn
+
+    if max_rows:
+        rn = max_rows if rn > max_rows else rn
+    if max_cols:
+        cn = max_cols if cn > max_cols else cn
+
+    while rn * cn > max_elements:
+        rn, cn = scale_down(rn, cn)
+
+    return rn, cn
+
+
+def _get_level_lengths(
+    index: Index,
+    sparsify: bool,
+    max_index: int,
+    hidden_elements: Sequence[int] | None = None,
+):
+    """
+    Given an index, find the level length for each element.
+
+    Parameters
+    ----------
+    index : Index
+        Index or columns to determine lengths of each element
+    sparsify : bool
+        Whether to hide or show each distinct element in a MultiIndex
+    max_index : int
+        The maximum number of elements to analyse along the index due to trimming
+    hidden_elements : sequence of int
+        Index positions of elements hidden from display in the index affecting
+        length
+
+    Returns
+    -------
+    Dict :
+        Result is a dictionary of (level, initial_position): span
+    """
+    if isinstance(index, MultiIndex):
+        levels = index._format_multi(sparsify=lib.no_default, include_names=False)
+    else:
+        levels = index._format_flat(include_name=False)
+
+    if hidden_elements is None:
+        hidden_elements = []
+
+    lengths = {}
+    if not isinstance(index, MultiIndex):
+        for i, value in enumerate(levels):
+            if i not in hidden_elements:
+                lengths[(0, i)] = 1
+        return lengths
+
+    for i, lvl in enumerate(levels):
+        visible_row_count = 0  # used to break loop due to display trimming
+        for j, row in enumerate(lvl):
+            if visible_row_count > max_index:
+                break
+            if not sparsify:
+                # then lengths will always equal 1 since no aggregation.
+                if j not in hidden_elements:
+                    lengths[(i, j)] = 1
+                    visible_row_count += 1
+            elif (row is not lib.no_default) and (j not in hidden_elements):
+                # this element has not been sparsified so must be the start of section
+                last_label = j
+                lengths[(i, last_label)] = 1
+                visible_row_count += 1
+            elif row is not lib.no_default:
+                # even if the above is hidden, keep track of it in case length > 1 and
+                # later elements are visible
+                last_label = j
+                lengths[(i, last_label)] = 0
+            elif j not in hidden_elements:
+                # then element must be part of sparsified section and is visible
+                visible_row_count += 1
+                if visible_row_count > max_index:
+                    break  # do not add a length since the render trim limit reached
+                if lengths[(i, last_label)] == 0:
+                    # if previous iteration was first-of-section but hidden then offset
+                    last_label = j
+                    lengths[(i, last_label)] = 1
+                else:
+                    # else add to previous iteration
+                    lengths[(i, last_label)] += 1
+
+    non_zero_lengths = {
+        element: length for element, length in lengths.items() if length >= 1
+    }
+
+    return non_zero_lengths
+
+
+def _is_visible(idx_row, idx_col, lengths) -> bool:
+    """
+    Index -> {(idx_row, idx_col): bool}).
+    """
+    return (idx_col, idx_row) in lengths
+
+
+def format_table_styles(styles: CSSStyles) -> CSSStyles:
+    """
+    looks for multiple CSS selectors and separates them:
+    [{'selector': 'td, th', 'props': 'a:v;'}]
+        ---> [{'selector': 'td', 'props': 'a:v;'},
+              {'selector': 'th', 'props': 'a:v;'}]
+    """
+    return [
+        {"selector": selector, "props": css_dict["props"]}
+        for css_dict in styles
+        for selector in css_dict["selector"].split(",")
+    ]
+
+
+def _default_formatter(x: Any, precision: int, thousands: bool = False) -> Any:
+    """
+    Format the display of a value
+
+    Parameters
+    ----------
+    x : Any
+        Input variable to be formatted
+    precision : Int
+        Floating point precision used if ``x`` is float or complex.
+    thousands : bool, default False
+        Whether to group digits with thousands separated with ",".
+
+    Returns
+    -------
+    value : Any
+        Matches input type, or string if input is float or complex or int with sep.
+    """
+    if is_float(x) or is_complex(x):
+        return f"{x:,.{precision}f}" if thousands else f"{x:.{precision}f}"
+    elif is_integer(x):
+        return f"{x:,}" if thousands else str(x)
+    return x
+
+
+def _wrap_decimal_thousands(
+    formatter: Callable, decimal: str, thousands: str | None
+) -> Callable:
+    """
+    Takes a string formatting function and wraps logic to deal with thousands and
+    decimal parameters, in the case that they are non-standard and that the input
+    is a (float, complex, int).
+    """
+
+    def wrapper(x):
+        if is_float(x) or is_integer(x) or is_complex(x):
+            if decimal != "." and thousands is not None and thousands != ",":
+                return (
+                    formatter(x)
+                    .replace(",", "§_§-")  # rare string to avoid "," <-> "." clash.
+                    .replace(".", decimal)
+                    .replace("§_§-", thousands)
+                )
+            elif decimal != "." and (thousands is None or thousands == ","):
+                return formatter(x).replace(".", decimal)
+            elif decimal == "." and thousands is not None and thousands != ",":
+                return formatter(x).replace(",", thousands)
+        return formatter(x)
+
+    return wrapper
+
+
+def _str_escape(x, escape):
+    """if escaping: only use on str, else return input"""
+    if isinstance(x, str):
+        if escape == "html":
+            return escape_html(x)
+        elif escape == "latex":
+            return _escape_latex(x)
+        elif escape == "latex-math":
+            return _escape_latex_math(x)
+        else:
+            raise ValueError(
+                f"`escape` only permitted in {{'html', 'latex', 'latex-math'}}, \
+got {escape}"
+            )
+    return x
+
+
+def _render_href(x, format):
+    """uses regex to detect a common URL pattern and converts to href tag in format."""
+    if isinstance(x, str):
+        if format == "html":
+            href = '<a href="{0}" target="_blank">{0}</a>'
+        elif format == "latex":
+            href = r"\href{{{0}}}{{{0}}}"
+        else:
+            raise ValueError("``hyperlinks`` format can only be 'html' or 'latex'")
+        pat = r"((http|ftp)s?:\/\/|www.)[\w/\-?=%.:@]+\.[\w/\-&?=%.,':;~!@#$*()\[\]]+"
+        return re.sub(pat, lambda m: href.format(m.group(0)), x)
+    return x
+
+
+def _maybe_wrap_formatter(
+    formatter: BaseFormatter | None = None,
+    na_rep: str | None = None,
+    precision: int | None = None,
+    decimal: str = ".",
+    thousands: str | None = None,
+    escape: str | None = None,
+    hyperlinks: str | None = None,
+) -> Callable:
+    """
+    Allows formatters to be expressed as str, callable or None, where None returns
+    a default formatting function. wraps with na_rep, and precision where they are
+    available.
+    """
+    # Get initial func from input string, input callable, or from default factory
+    if isinstance(formatter, str):
+        func_0 = lambda x: formatter.format(x)
+    elif callable(formatter):
+        func_0 = formatter
+    elif formatter is None:
+        precision = (
+            get_option("styler.format.precision") if precision is None else precision
+        )
+        func_0 = partial(
+            _default_formatter, precision=precision, thousands=(thousands is not None)
+        )
+    else:
+        raise TypeError(f"'formatter' expected str or callable, got {type(formatter)}")
+
+    # Replace chars if escaping
+    if escape is not None:
+        func_1 = lambda x: func_0(_str_escape(x, escape=escape))
+    else:
+        func_1 = func_0
+
+    # Replace decimals and thousands if non-standard inputs detected
+    if decimal != "." or (thousands is not None and thousands != ","):
+        func_2 = _wrap_decimal_thousands(func_1, decimal=decimal, thousands=thousands)
+    else:
+        func_2 = func_1
+
+    # Render links
+    if hyperlinks is not None:
+        func_3 = lambda x: func_2(_render_href(x, format=hyperlinks))
+    else:
+        func_3 = func_2
+
+    # Replace missing values if na_rep
+    if na_rep is None:
+        return func_3
+    else:
+        return lambda x: na_rep if (isna(x) is True) else func_3(x)
+
+
+def non_reducing_slice(slice_: Subset):
+    """
+    Ensure that a slice doesn't reduce to a Series or Scalar.
+
+    Any user-passed `subset` should have this called on it
+    to make sure we're always working with DataFrames.
+    """
+    # default to column slice, like DataFrame
+    # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+    kinds = (ABCSeries, np.ndarray, Index, list, str)
+    if isinstance(slice_, kinds):
+        slice_ = IndexSlice[:, slice_]
+
+    def pred(part) -> bool:
+        """
+        Returns
+        -------
+        bool
+            True if slice does *not* reduce,
+            False if `part` is a tuple.
+        """
+        # true when slice does *not* reduce, False when part is a tuple,
+        # i.e. MultiIndex slice
+        if isinstance(part, tuple):
+            # GH#39421 check for sub-slice:
+            return any((isinstance(s, slice) or is_list_like(s)) for s in part)
+        else:
+            return isinstance(part, slice) or is_list_like(part)
+
+    if not is_list_like(slice_):
+        if not isinstance(slice_, slice):
+            # a 1-d slice, like df.loc[1]
+            slice_ = [[slice_]]
+        else:
+            # slice(a, b, c)
+            slice_ = [slice_]  # to tuplize later
+    else:
+        # error: Item "slice" of "Union[slice, Sequence[Any]]" has no attribute
+        # "__iter__" (not iterable) -> is specifically list_like in conditional
+        slice_ = [p if pred(p) else [p] for p in slice_]  # type: ignore[union-attr]
+    return tuple(slice_)
+
+
+def maybe_convert_css_to_tuples(style: CSSProperties) -> CSSList:
+    """
+    Convert css-string to sequence of tuples format if needed.
+    'color:red; border:1px solid black;' -> [('color', 'red'),
+                                             ('border','1px solid red')]
+    """
+    if isinstance(style, str):
+        s = style.split(";")
+        try:
+            return [
+                (x.split(":")[0].strip(), x.split(":")[1].strip())
+                for x in s
+                if x.strip() != ""
+            ]
+        except IndexError:
+            raise ValueError(
+                "Styles supplied as string must follow CSS rule formats, "
+                f"for example 'attr: val;'. '{style}' was given."
+            )
+    return style
+
+
+def refactor_levels(
+    level: Level | list[Level] | None,
+    obj: Index,
+) -> list[int]:
+    """
+    Returns a consistent levels arg for use in ``hide_index`` or ``hide_columns``.
+
+    Parameters
+    ----------
+    level : int, str, list
+        Original ``level`` arg supplied to above methods.
+    obj:
+        Either ``self.index`` or ``self.columns``
+
+    Returns
+    -------
+    list : refactored arg with a list of levels to hide
+    """
+    if level is None:
+        levels_: list[int] = list(range(obj.nlevels))
+    elif isinstance(level, int):
+        levels_ = [level]
+    elif isinstance(level, str):
+        levels_ = [obj._get_level_number(level)]
+    elif isinstance(level, list):
+        levels_ = [
+            obj._get_level_number(lev) if not isinstance(lev, int) else lev
+            for lev in level
+        ]
+    else:
+        raise ValueError("`level` must be of type `int`, `str` or list of such")
+    return levels_
+
+
+class Tooltips:
+    """
+    An extension to ``Styler`` that allows for and manipulates tooltips on hover
+    of ``<td>`` cells in the HTML result.
+
+    Parameters
+    ----------
+    css_name: str, default "pd-t"
+        Name of the CSS class that controls visualisation of tooltips.
+    css_props: list-like, default; see Notes
+        List of (attr, value) tuples defining properties of the CSS class.
+    tooltips: DataFrame, default empty
+        DataFrame of strings aligned with underlying Styler data for tooltip
+        display.
+
+    Notes
+    -----
+    The default properties for the tooltip CSS class are:
+
+        - visibility: hidden
+        - position: absolute
+        - z-index: 1
+        - background-color: black
+        - color: white
+        - transform: translate(-20px, -20px)
+
+    Hidden visibility is a key prerequisite to the hover functionality, and should
+    always be included in any manual properties specification.
+    """
+
+    def __init__(
+        self,
+        css_props: CSSProperties = [
+            ("visibility", "hidden"),
+            ("position", "absolute"),
+            ("z-index", 1),
+            ("background-color", "black"),
+            ("color", "white"),
+            ("transform", "translate(-20px, -20px)"),
+        ],
+        css_name: str = "pd-t",
+        tooltips: DataFrame = DataFrame(),
+    ) -> None:
+        self.class_name = css_name
+        self.class_properties = css_props
+        self.tt_data = tooltips
+        self.table_styles: CSSStyles = []
+
+    @property
+    def _class_styles(self):
+        """
+        Combine the ``_Tooltips`` CSS class name and CSS properties to the format
+        required to extend the underlying ``Styler`` `table_styles` to allow
+        tooltips to render in HTML.
+
+        Returns
+        -------
+        styles : List
+        """
+        return [
+            {
+                "selector": f".{self.class_name}",
+                "props": maybe_convert_css_to_tuples(self.class_properties),
+            }
+        ]
+
+    def _pseudo_css(self, uuid: str, name: str, row: int, col: int, text: str):
+        """
+        For every table data-cell that has a valid tooltip (not None, NaN or
+        empty string) must create two pseudo CSS entries for the specific
+        <td> element id which are added to overall table styles:
+        an on hover visibility change and a content change
+        dependent upon the user's chosen display string.
+
+        For example:
+            [{"selector": "T__row1_col1:hover .pd-t",
+             "props": [("visibility", "visible")]},
+            {"selector": "T__row1_col1 .pd-t::after",
+             "props": [("content", "Some Valid Text String")]}]
+
+        Parameters
+        ----------
+        uuid: str
+            The uuid of the Styler instance
+        name: str
+            The css-name of the class used for styling tooltips
+        row : int
+            The row index of the specified tooltip string data
+        col : int
+            The col index of the specified tooltip string data
+        text : str
+            The textual content of the tooltip to be displayed in HTML.
+
+        Returns
+        -------
+        pseudo_css : List
+        """
+        selector_id = "#T_" + uuid + "_row" + str(row) + "_col" + str(col)
+        return [
+            {
+                "selector": selector_id + f":hover .{name}",
+                "props": [("visibility", "visible")],
+            },
+            {
+                "selector": selector_id + f" .{name}::after",
+                "props": [("content", f'"{text}"')],
+            },
+        ]
+
+    def _translate(self, styler: StylerRenderer, d: dict):
+        """
+        Mutate the render dictionary to allow for tooltips:
+
+        - Add ``<span>`` HTML element to each data cells ``display_value``. Ignores
+          headers.
+        - Add table level CSS styles to control pseudo classes.
+
+        Parameters
+        ----------
+        styler_data : DataFrame
+            Underlying ``Styler`` DataFrame used for reindexing.
+        uuid : str
+            The underlying ``Styler`` uuid for CSS id.
+        d : dict
+            The dictionary prior to final render
+
+        Returns
+        -------
+        render_dict : Dict
+        """
+        self.tt_data = self.tt_data.reindex_like(styler.data)
+        if self.tt_data.empty:
+            return d
+
+        name = self.class_name
+        mask = (self.tt_data.isna()) | (self.tt_data.eq(""))  # empty string = no ttip
+        self.table_styles = [
+            style
+            for sublist in [
+                self._pseudo_css(styler.uuid, name, i, j, str(self.tt_data.iloc[i, j]))
+                for i in range(len(self.tt_data.index))
+                for j in range(len(self.tt_data.columns))
+                if not (
+                    mask.iloc[i, j]
+                    or i in styler.hidden_rows
+                    or j in styler.hidden_columns
+                )
+            ]
+            for style in sublist
+        ]
+
+        if self.table_styles:
+            # add span class to every cell only if at least 1 non-empty tooltip
+            for row in d["body"]:
+                for item in row:
+                    if item["type"] == "td":
+                        item["display_value"] = (
+                            str(item["display_value"])
+                            + f'<span class="{self.class_name}"></span>'
+                        )
+            d["table_styles"].extend(self._class_styles)
+            d["table_styles"].extend(self.table_styles)
+
+        return d
+
+
+def _parse_latex_table_wrapping(table_styles: CSSStyles, caption: str | None) -> bool:
+    """
+    Indicate whether LaTeX {tabular} should be wrapped with a {table} environment.
+
+    Parses the `table_styles` and detects any selectors which must be included outside
+    of {tabular}, i.e. indicating that wrapping must occur, and therefore return True,
+    or if a caption exists and requires similar.
+    """
+    IGNORED_WRAPPERS = ["toprule", "midrule", "bottomrule", "column_format"]
+    # ignored selectors are included with {tabular} so do not need wrapping
+    return (
+        table_styles is not None
+        and any(d["selector"] not in IGNORED_WRAPPERS for d in table_styles)
+    ) or caption is not None
+
+
+def _parse_latex_table_styles(table_styles: CSSStyles, selector: str) -> str | None:
+    """
+    Return the first 'props' 'value' from ``tables_styles`` identified by ``selector``.
+
+    Examples
+    --------
+    >>> table_styles = [{'selector': 'foo', 'props': [('attr','value')]},
+    ...                 {'selector': 'bar', 'props': [('attr', 'overwritten')]},
+    ...                 {'selector': 'bar', 'props': [('a1', 'baz'), ('a2', 'ignore')]}]
+    >>> _parse_latex_table_styles(table_styles, selector='bar')
+    'baz'
+
+    Notes
+    -----
+    The replacement of "§" with ":" is to avoid the CSS problem where ":" has structural
+    significance and cannot be used in LaTeX labels, but is often required by them.
+    """
+    for style in table_styles[::-1]:  # in reverse for most recently applied style
+        if style["selector"] == selector:
+            return str(style["props"][0][1]).replace("§", ":")
+    return None
+
+
+def _parse_latex_cell_styles(
+    latex_styles: CSSList, display_value: str, convert_css: bool = False
+) -> str:
+    r"""
+    Mutate the ``display_value`` string including LaTeX commands from ``latex_styles``.
+
+    This method builds a recursive latex chain of commands based on the
+    CSSList input, nested around ``display_value``.
+
+    If a CSS style is given as ('<command>', '<options>') this is translated to
+    '\<command><options>{display_value}', and this value is treated as the
+    display value for the next iteration.
+
+    The most recent style forms the inner component, for example for styles:
+    `[('c1', 'o1'), ('c2', 'o2')]` this returns: `\c1o1{\c2o2{display_value}}`
+
+    Sometimes latex commands have to be wrapped with curly braces in different ways:
+    We create some parsing flags to identify the different behaviours:
+
+     - `--rwrap`        : `\<command><options>{<display_value>}`
+     - `--wrap`         : `{\<command><options> <display_value>}`
+     - `--nowrap`       : `\<command><options> <display_value>`
+     - `--lwrap`        : `{\<command><options>} <display_value>`
+     - `--dwrap`        : `{\<command><options>}{<display_value>}`
+
+    For example for styles:
+    `[('c1', 'o1--wrap'), ('c2', 'o2')]` this returns: `{\c1o1 \c2o2{display_value}}
+    """
+    if convert_css:
+        latex_styles = _parse_latex_css_conversion(latex_styles)
+    for command, options in latex_styles[::-1]:  # in reverse for most recent style
+        formatter = {
+            "--wrap": f"{{\\{command}--to_parse {display_value}}}",
+            "--nowrap": f"\\{command}--to_parse {display_value}",
+            "--lwrap": f"{{\\{command}--to_parse}} {display_value}",
+            "--rwrap": f"\\{command}--to_parse{{{display_value}}}",
+            "--dwrap": f"{{\\{command}--to_parse}}{{{display_value}}}",
+        }
+        display_value = f"\\{command}{options} {display_value}"
+        for arg in ["--nowrap", "--wrap", "--lwrap", "--rwrap", "--dwrap"]:
+            if arg in str(options):
+                display_value = formatter[arg].replace(
+                    "--to_parse", _parse_latex_options_strip(value=options, arg=arg)
+                )
+                break  # only ever one purposeful entry
+    return display_value
+
+
+def _parse_latex_header_span(
+    cell: dict[str, Any],
+    multirow_align: str,
+    multicol_align: str,
+    wrap: bool = False,
+    convert_css: bool = False,
+) -> str:
+    r"""
+    Refactor the cell `display_value` if a 'colspan' or 'rowspan' attribute is present.
+
+    'rowspan' and 'colspan' do not occur simultaneouly. If they are detected then
+    the `display_value` is altered to a LaTeX `multirow` or `multicol` command
+    respectively, with the appropriate cell-span.
+
+    ``wrap`` is used to enclose the `display_value` in braces which is needed for
+    column headers using an siunitx package.
+
+    Requires the package {multirow}, whereas multicol support is usually built in
+    to the {tabular} environment.
+
+    Examples
+    --------
+    >>> cell = {'cellstyle': '', 'display_value':'text', 'attributes': 'colspan="3"'}
+    >>> _parse_latex_header_span(cell, 't', 'c')
+    '\\multicolumn{3}{c}{text}'
+    """
+    display_val = _parse_latex_cell_styles(
+        cell["cellstyle"], cell["display_value"], convert_css
+    )
+    if "attributes" in cell:
+        attrs = cell["attributes"]
+        if 'colspan="' in attrs:
+            colspan = attrs[attrs.find('colspan="') + 9 :]  # len('colspan="') = 9
+            colspan = int(colspan[: colspan.find('"')])
+            if "naive-l" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = " & {}" if wrap else " &"
+                return out + blanks * (colspan - 1)
+            elif "naive-r" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = "{} & " if wrap else "& "
+                return blanks * (colspan - 1) + out
+            return f"\\multicolumn{{{colspan}}}{{{multicol_align}}}{{{display_val}}}"
+        elif 'rowspan="' in attrs:
+            if multirow_align == "naive":
+                return display_val
+            rowspan = attrs[attrs.find('rowspan="') + 9 :]
+            rowspan = int(rowspan[: rowspan.find('"')])
+            return f"\\multirow[{multirow_align}]{{{rowspan}}}{{*}}{{{display_val}}}"
+    if wrap:
+        return f"{{{display_val}}}"
+    else:
+        return display_val
+
+
+def _parse_latex_options_strip(value: str | float, arg: str) -> str:
+    """
+    Strip a css_value which may have latex wrapping arguments, css comment identifiers,
+    and whitespaces, to a valid string for latex options parsing.
+
+    For example: 'red /* --wrap */  ' --> 'red'
+    """
+    return str(value).replace(arg, "").replace("/*", "").replace("*/", "").strip()
+
+
+def _parse_latex_css_conversion(styles: CSSList) -> CSSList:
+    """
+    Convert CSS (attribute,value) pairs to equivalent LaTeX (command,options) pairs.
+
+    Ignore conversion if tagged with `--latex` option, skipped if no conversion found.
+    """
+
+    def font_weight(value, arg):
+        if value in ("bold", "bolder"):
+            return "bfseries", f"{arg}"
+        return None
+
+    def font_style(value, arg):
+        if value == "italic":
+            return "itshape", f"{arg}"
+        if value == "oblique":
+            return "slshape", f"{arg}"
+        return None
+
+    def color(value, user_arg, command, comm_arg):
+        """
+        CSS colors have 5 formats to process:
+
+         - 6 digit hex code: "#ff23ee"     --> [HTML]{FF23EE}
+         - 3 digit hex code: "#f0e"        --> [HTML]{FF00EE}
+         - rgba: rgba(128, 255, 0, 0.5)    --> [rgb]{0.502, 1.000, 0.000}
+         - rgb: rgb(128, 255, 0,)          --> [rbg]{0.502, 1.000, 0.000}
+         - string: red                     --> {red}
+
+        Additionally rgb or rgba can be expressed in % which is also parsed.
+        """
+        arg = user_arg if user_arg != "" else comm_arg
+
+        if value[0] == "#" and len(value) == 7:  # color is hex code
+            return command, f"[HTML]{{{value[1:].upper()}}}{arg}"
+        if value[0] == "#" and len(value) == 4:  # color is short hex code
+            val = f"{value[1].upper()*2}{value[2].upper()*2}{value[3].upper()*2}"
+            return command, f"[HTML]{{{val}}}{arg}"
+        elif value[:3] == "rgb":  # color is rgb or rgba
+            r = re.findall("(?<=\\()[0-9\\s%]+(?=,)", value)[0].strip()
+            r = float(r[:-1]) / 100 if "%" in r else int(r) / 255
+            g = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[0].strip()
+            g = float(g[:-1]) / 100 if "%" in g else int(g) / 255
+            if value[3] == "a":  # color is rgba
+                b = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[1].strip()
+            else:  # color is rgb
+                b = re.findall("(?<=,)[0-9\\s%]+(?=\\))", value)[0].strip()
+            b = float(b[:-1]) / 100 if "%" in b else int(b) / 255
+            return command, f"[rgb]{{{r:.3f}, {g:.3f}, {b:.3f}}}{arg}"
+        else:
+            return command, f"{{{value}}}{arg}"  # color is likely string-named
+
+    CONVERTED_ATTRIBUTES: dict[str, Callable] = {
+        "font-weight": font_weight,
+        "background-color": partial(color, command="cellcolor", comm_arg="--lwrap"),
+        "color": partial(color, command="color", comm_arg=""),
+        "font-style": font_style,
+    }
+
+    latex_styles: CSSList = []
+    for attribute, value in styles:
+        if isinstance(value, str) and "--latex" in value:
+            # return the style without conversion but drop '--latex'
+            latex_styles.append((attribute, value.replace("--latex", "")))
+        if attribute in CONVERTED_ATTRIBUTES:
+            arg = ""
+            for x in ["--wrap", "--nowrap", "--lwrap", "--dwrap", "--rwrap"]:
+                if x in str(value):
+                    arg, value = x, _parse_latex_options_strip(value, x)
+                    break
+            latex_style = CONVERTED_ATTRIBUTES[attribute](value, arg)
+            if latex_style is not None:
+                latex_styles.extend([latex_style])
+    return latex_styles
+
+
+def _escape_latex(s: str) -> str:
+    r"""
+    Replace the characters ``&``, ``%``, ``$``, ``#``, ``_``, ``{``, ``}``,
+    ``~``, ``^``, and ``\`` in the string with LaTeX-safe sequences.
+
+    Use this if you need to display text that might contain such characters in LaTeX.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    return (
+        s.replace("\\", "ab2§=§8yz")  # rare string for final conversion: avoid \\ clash
+        .replace("ab2§=§8yz ", "ab2§=§8yz\\space ")  # since \backslash gobbles spaces
+        .replace("&", "\\&")
+        .replace("%", "\\%")
+        .replace("$", "\\$")
+        .replace("#", "\\#")
+        .replace("_", "\\_")
+        .replace("{", "\\{")
+        .replace("}", "\\}")
+        .replace("~ ", "~\\space ")  # since \textasciitilde gobbles spaces
+        .replace("~", "\\textasciitilde ")
+        .replace("^ ", "^\\space ")  # since \textasciicircum gobbles spaces
+        .replace("^", "\\textasciicircum ")
+        .replace("ab2§=§8yz", "\\textbackslash ")
+    )
+
+
+def _math_mode_with_dollar(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``$`` and end with ``$``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    pattern = re.compile(r"\$.*?\$")
+    pos = 0
+    ps = pattern.search(s, pos)
+    res = []
+    while ps:
+        res.append(_escape_latex(s[pos : ps.span()[0]]))
+        res.append(ps.group())
+        pos = ps.span()[1]
+        ps = pattern.search(s, pos)
+
+    res.append(_escape_latex(s[pos : len(s)]))
+    return "".join(res).replace(r"rt8§=§7wz", r"\$")
+
+
+def _math_mode_with_parentheses(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``\(`` and end with ``\)``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\(", r"LEFT§=§6yzLEFT").replace(r"\)", r"RIGHTab5§=§RIGHT")
+    res = []
+    for item in re.split(r"LEFT§=§6yz|ab5§=§RIGHT", s):
+        if item.startswith("LEFT") and item.endswith("RIGHT"):
+            res.append(item.replace("LEFT", r"\(").replace("RIGHT", r"\)"))
+        elif "LEFT" in item and "RIGHT" in item:
+            res.append(
+                _escape_latex(item).replace("LEFT", r"\(").replace("RIGHT", r"\)")
+            )
+        else:
+            res.append(
+                _escape_latex(item)
+                .replace("LEFT", r"\textbackslash (")
+                .replace("RIGHT", r"\textbackslash )")
+            )
+    return "".join(res)
+
+
+def _escape_latex_math(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which either are surrounded
+    by two characters ``$`` or start with the character ``\(`` and end with ``\)``,
+    are preserved without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    ps_d = re.compile(r"\$.*?\$").search(s, 0)
+    ps_p = re.compile(r"\(.*?\)").search(s, 0)
+    mode = []
+    if ps_d:
+        mode.append(ps_d.span()[0])
+    if ps_p:
+        mode.append(ps_p.span()[0])
+    if len(mode) == 0:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0]] == r"$":
+        return _math_mode_with_dollar(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0] - 1 : mode[0] + 1] == r"\(":
+        return _math_mode_with_parentheses(s.replace(r"rt8§=§7wz", r"\$"))
+    else:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))
diff --git a/shared/package-files/4e/4e0f2bf1f0bc706c45348ee5392399d9b576645e4d4a875f659665e822931d2d/copysign_ops.h b/shared/package-files/4e/4e0f2bf1f0bc706c45348ee5392399d9b576645e4d4a875f659665e822931d2d/copysign_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..0135d97f10621d20b3ecc4e233f51cc0219ab326
--- /dev/null
+++ b/shared/package-files/4e/4e0f2bf1f0bc706c45348ee5392399d9b576645e4d4a875f659665e822931d2d/copysign_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API copysign_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API copysign_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign.Tensor(Tensor self, Tensor other) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API copysign__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API copysign_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign.Scalar(Tensor self, Scalar other) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API copysign__Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API copysign_Scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::copysign")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/4e/4e0fa65f478c628c3b962f461811fc5037d8beb9e2b8d31b8a2143f8fe28b6d5/calibration.py b/shared/package-files/4e/4e0fa65f478c628c3b962f461811fc5037d8beb9e2b8d31b8a2143f8fe28b6d5/calibration.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b2bca2edfcc01bdec5ad60906061888d4fd7f8d
--- /dev/null
+++ b/shared/package-files/4e/4e0fa65f478c628c3b962f461811fc5037d8beb9e2b8d31b8a2143f8fe28b6d5/calibration.py
@@ -0,0 +1,1448 @@
+"""Methods for calibrating predicted probabilities."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from inspect import signature
+from math import log
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.optimize import minimize
+from scipy.special import expit
+
+from sklearn.utils import Bunch
+
+from ._loss import HalfBinomialLoss
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    RegressorMixin,
+    _fit_context,
+    clone,
+)
+from .frozen import FrozenEstimator
+from .isotonic import IsotonicRegression
+from .model_selection import LeaveOneOut, check_cv, cross_val_predict
+from .preprocessing import LabelEncoder, label_binarize
+from .svm import LinearSVC
+from .utils import _safe_indexing, column_or_1d, get_tags, indexable
+from .utils._param_validation import (
+    HasMethods,
+    Hidden,
+    Interval,
+    StrOptions,
+    validate_params,
+)
+from .utils._plotting import _BinaryClassifierCurveDisplayMixin, _validate_style_kwargs
+from .utils._response import _get_response_values, _process_predict_proba
+from .utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _routing_enabled,
+    process_routing,
+)
+from .utils.multiclass import check_classification_targets
+from .utils.parallel import Parallel, delayed
+from .utils.validation import (
+    _check_method_params,
+    _check_pos_label_consistency,
+    _check_response_method,
+    _check_sample_weight,
+    _num_samples,
+    check_consistent_length,
+    check_is_fitted,
+)
+
+
+class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
+    """Probability calibration with isotonic regression or logistic regression.
+
+    This class uses cross-validation to both estimate the parameters of a
+    classifier and subsequently calibrate a classifier. With
+    `ensemble=True`, for each cv split it
+    fits a copy of the base estimator to the training subset, and calibrates it
+    using the testing subset. For prediction, predicted probabilities are
+    averaged across these individual calibrated classifiers. When
+    `ensemble=False`, cross-validation is used to obtain unbiased predictions,
+    via :func:`~sklearn.model_selection.cross_val_predict`, which are then
+    used for calibration. For prediction, the base estimator, trained using all
+    the data, is used. This is the prediction method implemented when
+    `probabilities=True` for :class:`~sklearn.svm.SVC` and :class:`~sklearn.svm.NuSVC`
+    estimators (see :ref:`User Guide <scores_probabilities>` for details).
+
+    Already fitted classifiers can be calibrated by wrapping the model in a
+    :class:`~sklearn.frozen.FrozenEstimator`. In this case all provided
+    data is used for calibration. The user has to take care manually that data
+    for model fitting and calibration are disjoint.
+
+    The calibration is based on the :term:`decision_function` method of the
+    `estimator` if it exists, else on :term:`predict_proba`.
+
+    Read more in the :ref:`User Guide <calibration>`.
+    In order to learn more on the CalibratedClassifierCV class, see the
+    following calibration examples:
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`,
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`, and
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`.
+
+    Parameters
+    ----------
+    estimator : estimator instance, default=None
+        The classifier whose output need to be calibrated to provide more
+        accurate `predict_proba` outputs. The default classifier is
+        a :class:`~sklearn.svm.LinearSVC`.
+
+        .. versionadded:: 1.2
+
+    method : {'sigmoid', 'isotonic'}, default='sigmoid'
+        The method to use for calibration. Can be 'sigmoid' which
+        corresponds to Platt's method (i.e. a logistic regression model) or
+        'isotonic' which is a non-parametric approach. It is not advised to
+        use isotonic calibration with too few calibration samples
+        ``(<<1000)`` since it tends to overfit.
+
+    cv : int, cross-validation generator, or iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross-validation,
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used. If ``y`` is
+        neither binary nor multiclass, :class:`~sklearn.model_selection.KFold`
+        is used.
+
+        Refer to the :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+        .. versionchanged:: 0.22
+            ``cv`` default value if None changed from 3-fold to 5-fold.
+
+        .. versionchanged:: 1.6
+            `"prefit"` is deprecated. Use :class:`~sklearn.frozen.FrozenEstimator`
+            instead.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors.
+
+        Base estimator clones are fitted in parallel across cross-validation
+        iterations. Therefore parallelism happens only when `cv != "prefit"`.
+
+        See :term:`Glossary <n_jobs>` for more details.
+
+        .. versionadded:: 0.24
+
+    ensemble : bool, or "auto", default="auto"
+        Determines how the calibrator is fitted.
+
+        "auto" will use `False` if the `estimator` is a
+        :class:`~sklearn.frozen.FrozenEstimator`, and `True` otherwise.
+
+        If `True`, the `estimator` is fitted using training data, and
+        calibrated using testing data, for each `cv` fold. The final estimator
+        is an ensemble of `n_cv` fitted classifier and calibrator pairs, where
+        `n_cv` is the number of cross-validation folds. The output is the
+        average predicted probabilities of all pairs.
+
+        If `False`, `cv` is used to compute unbiased predictions, via
+        :func:`~sklearn.model_selection.cross_val_predict`, which are then
+        used for calibration. At prediction time, the classifier used is the
+        `estimator` trained on all the data.
+        Note that this method is also internally implemented  in
+        :mod:`sklearn.svm` estimators with the `probabilities=True` parameter.
+
+        .. versionadded:: 0.24
+
+        .. versionchanged:: 1.6
+            `"auto"` option is added and is the default.
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        The class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    calibrated_classifiers_ : list (len() equal to cv or 1 if `ensemble=False`)
+        The list of classifier and calibrator pairs.
+
+        - When `ensemble=True`, `n_cv` fitted `estimator` and calibrator pairs.
+          `n_cv` is the number of cross-validation folds.
+        - When `ensemble=False`, the `estimator`, fitted on all the data, and fitted
+          calibrator.
+
+        .. versionchanged:: 0.24
+            Single calibrated classifier case when `ensemble=False`.
+
+    See Also
+    --------
+    calibration_curve : Compute true and predicted probabilities
+        for a calibration curve.
+
+    References
+    ----------
+    .. [1] Obtaining calibrated probability estimates from decision trees
+           and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
+
+    .. [2] Transforming Classifier Scores into Accurate Multiclass
+           Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
+
+    .. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to
+           Regularized Likelihood Methods, J. Platt, (1999)
+
+    .. [4] Predicting Good Probabilities with Supervised Learning,
+           A. Niculescu-Mizil & R. Caruana, ICML 2005
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> from sklearn.calibration import CalibratedClassifierCV
+    >>> X, y = make_classification(n_samples=100, n_features=2,
+    ...                            n_redundant=0, random_state=42)
+    >>> base_clf = GaussianNB()
+    >>> calibrated_clf = CalibratedClassifierCV(base_clf, cv=3)
+    >>> calibrated_clf.fit(X, y)
+    CalibratedClassifierCV(...)
+    >>> len(calibrated_clf.calibrated_classifiers_)
+    3
+    >>> calibrated_clf.predict_proba(X)[:5, :]
+    array([[0.110, 0.889],
+           [0.072, 0.927],
+           [0.928, 0.072],
+           [0.928, 0.072],
+           [0.072, 0.928]])
+    >>> from sklearn.model_selection import train_test_split
+    >>> X, y = make_classification(n_samples=100, n_features=2,
+    ...                            n_redundant=0, random_state=42)
+    >>> X_train, X_calib, y_train, y_calib = train_test_split(
+    ...        X, y, random_state=42
+    ... )
+    >>> base_clf = GaussianNB()
+    >>> base_clf.fit(X_train, y_train)
+    GaussianNB()
+    >>> from sklearn.frozen import FrozenEstimator
+    >>> calibrated_clf = CalibratedClassifierCV(FrozenEstimator(base_clf))
+    >>> calibrated_clf.fit(X_calib, y_calib)
+    CalibratedClassifierCV(...)
+    >>> len(calibrated_clf.calibrated_classifiers_)
+    1
+    >>> calibrated_clf.predict_proba([[-0.5, 0.5]])
+    array([[0.936, 0.063]])
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [
+            HasMethods(["fit", "predict_proba"]),
+            HasMethods(["fit", "decision_function"]),
+            None,
+        ],
+        "method": [StrOptions({"isotonic", "sigmoid"})],
+        "cv": ["cv_object", Hidden(StrOptions({"prefit"}))],
+        "n_jobs": [Integral, None],
+        "ensemble": ["boolean", StrOptions({"auto"})],
+    }
+
+    def __init__(
+        self,
+        estimator=None,
+        *,
+        method="sigmoid",
+        cv=None,
+        n_jobs=None,
+        ensemble="auto",
+    ):
+        self.estimator = estimator
+        self.method = method
+        self.cv = cv
+        self.n_jobs = n_jobs
+        self.ensemble = ensemble
+
+    def _get_estimator(self):
+        """Resolve which estimator to return (default is LinearSVC)"""
+        if self.estimator is None:
+            # we want all classifiers that don't expose a random_state
+            # to be deterministic (and we don't want to expose this one).
+            estimator = LinearSVC(random_state=0)
+            if _routing_enabled():
+                estimator.set_fit_request(sample_weight=True)
+        else:
+            estimator = self.estimator
+
+        return estimator
+
+    @_fit_context(
+        # CalibratedClassifierCV.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, sample_weight=None, **fit_params):
+        """Fit the calibrated model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted.
+
+        **fit_params : dict
+            Parameters to pass to the `fit` method of the underlying
+            classifier.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        check_classification_targets(y)
+        X, y = indexable(X, y)
+        estimator = self._get_estimator()
+
+        _ensemble = self.ensemble
+        if _ensemble == "auto":
+            _ensemble = not isinstance(estimator, FrozenEstimator)
+
+        self.calibrated_classifiers_ = []
+        if self.cv == "prefit":
+            # TODO(1.8): Remove this code branch and cv='prefit'
+            warnings.warn(
+                "The `cv='prefit'` option is deprecated in 1.6 and will be removed in"
+                " 1.8. You can use CalibratedClassifierCV(FrozenEstimator(estimator))"
+                " instead.",
+                category=FutureWarning,
+            )
+            # `classes_` should be consistent with that of estimator
+            check_is_fitted(self.estimator, attributes=["classes_"])
+            self.classes_ = self.estimator.classes_
+
+            predictions, _ = _get_response_values(
+                estimator,
+                X,
+                response_method=["decision_function", "predict_proba"],
+            )
+            if predictions.ndim == 1:
+                # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+                predictions = predictions.reshape(-1, 1)
+
+            if sample_weight is not None:
+                # Check that the sample_weight dtype is consistent with the predictions
+                # to avoid unintentional upcasts.
+                sample_weight = _check_sample_weight(
+                    sample_weight, predictions, dtype=predictions.dtype
+                )
+
+            calibrated_classifier = _fit_calibrator(
+                estimator,
+                predictions,
+                y,
+                self.classes_,
+                self.method,
+                sample_weight,
+            )
+            self.calibrated_classifiers_.append(calibrated_classifier)
+        else:
+            # Set `classes_` using all `y`
+            label_encoder_ = LabelEncoder().fit(y)
+            self.classes_ = label_encoder_.classes_
+
+            if _routing_enabled():
+                routed_params = process_routing(
+                    self,
+                    "fit",
+                    sample_weight=sample_weight,
+                    **fit_params,
+                )
+            else:
+                # sample_weight checks
+                fit_parameters = signature(estimator.fit).parameters
+                supports_sw = "sample_weight" in fit_parameters
+                if sample_weight is not None and not supports_sw:
+                    estimator_name = type(estimator).__name__
+                    warnings.warn(
+                        f"Since {estimator_name} does not appear to accept"
+                        " sample_weight, sample weights will only be used for the"
+                        " calibration itself. This can be caused by a limitation of"
+                        " the current scikit-learn API. See the following issue for"
+                        " more details:"
+                        " https://github.com/scikit-learn/scikit-learn/issues/21134."
+                        " Be warned that the result of the calibration is likely to be"
+                        " incorrect."
+                    )
+                routed_params = Bunch()
+                routed_params.splitter = Bunch(split={})  # no routing for splitter
+                routed_params.estimator = Bunch(fit=fit_params)
+                if sample_weight is not None and supports_sw:
+                    routed_params.estimator.fit["sample_weight"] = sample_weight
+
+            # Check that each cross-validation fold can have at least one
+            # example per class
+            if isinstance(self.cv, int):
+                n_folds = self.cv
+            elif hasattr(self.cv, "n_splits"):
+                n_folds = self.cv.n_splits
+            else:
+                n_folds = None
+            if n_folds and np.any(np.unique(y, return_counts=True)[1] < n_folds):
+                raise ValueError(
+                    f"Requesting {n_folds}-fold "
+                    "cross-validation but provided less than "
+                    f"{n_folds} examples for at least one class."
+                )
+            if isinstance(self.cv, LeaveOneOut):
+                raise ValueError(
+                    "LeaveOneOut cross-validation does not allow"
+                    "all classes to be present in test splits. "
+                    "Please use a cross-validation generator that allows "
+                    "all classes to appear in every test and train split."
+                )
+            cv = check_cv(self.cv, y, classifier=True)
+
+            if _ensemble:
+                parallel = Parallel(n_jobs=self.n_jobs)
+                self.calibrated_classifiers_ = parallel(
+                    delayed(_fit_classifier_calibrator_pair)(
+                        clone(estimator),
+                        X,
+                        y,
+                        train=train,
+                        test=test,
+                        method=self.method,
+                        classes=self.classes_,
+                        sample_weight=sample_weight,
+                        fit_params=routed_params.estimator.fit,
+                    )
+                    for train, test in cv.split(X, y, **routed_params.splitter.split)
+                )
+            else:
+                this_estimator = clone(estimator)
+                method_name = _check_response_method(
+                    this_estimator,
+                    ["decision_function", "predict_proba"],
+                ).__name__
+                predictions = cross_val_predict(
+                    estimator=this_estimator,
+                    X=X,
+                    y=y,
+                    cv=cv,
+                    method=method_name,
+                    n_jobs=self.n_jobs,
+                    params=routed_params.estimator.fit,
+                )
+                if len(self.classes_) == 2:
+                    # Ensure shape (n_samples, 1) in the binary case
+                    if method_name == "predict_proba":
+                        # Select the probability column of the positive class
+                        predictions = _process_predict_proba(
+                            y_pred=predictions,
+                            target_type="binary",
+                            classes=self.classes_,
+                            pos_label=self.classes_[1],
+                        )
+                    predictions = predictions.reshape(-1, 1)
+
+                if sample_weight is not None:
+                    # Check that the sample_weight dtype is consistent with the
+                    # predictions to avoid unintentional upcasts.
+                    sample_weight = _check_sample_weight(
+                        sample_weight, predictions, dtype=predictions.dtype
+                    )
+
+                this_estimator.fit(X, y, **routed_params.estimator.fit)
+                # Note: Here we don't pass on fit_params because the supported
+                # calibrators don't support fit_params anyway
+                calibrated_classifier = _fit_calibrator(
+                    this_estimator,
+                    predictions,
+                    y,
+                    self.classes_,
+                    self.method,
+                    sample_weight,
+                )
+                self.calibrated_classifiers_.append(calibrated_classifier)
+
+        first_clf = self.calibrated_classifiers_[0].estimator
+        if hasattr(first_clf, "n_features_in_"):
+            self.n_features_in_ = first_clf.n_features_in_
+        if hasattr(first_clf, "feature_names_in_"):
+            self.feature_names_in_ = first_clf.feature_names_in_
+        return self
+
+    def predict_proba(self, X):
+        """Calibrated probabilities of classification.
+
+        This function returns calibrated probabilities of classification
+        according to each class on an array of test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict_proba`.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            The predicted probas.
+        """
+        check_is_fitted(self)
+        # Compute the arithmetic mean of the predictions of the calibrated
+        # classifiers
+        mean_proba = np.zeros((_num_samples(X), len(self.classes_)))
+        for calibrated_classifier in self.calibrated_classifiers_:
+            proba = calibrated_classifier.predict_proba(X)
+            mean_proba += proba
+
+        mean_proba /= len(self.calibrated_classifiers_)
+
+        return mean_proba
+
+    def predict(self, X):
+        """Predict the target of new samples.
+
+        The predicted class is the class that has the highest probability,
+        and can thus be different from the prediction of the uncalibrated classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict`.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,)
+            The predicted class.
+        """
+        check_is_fitted(self)
+        return self.classes_[np.argmax(self.predict_proba(X), axis=1)]
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add_self_request(self)
+            .add(
+                estimator=self._get_estimator(),
+                method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+            )
+            .add(
+                splitter=self.cv,
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
+            )
+        )
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = get_tags(self._get_estimator()).input_tags.sparse
+        return tags
+
+
+def _fit_classifier_calibrator_pair(
+    estimator,
+    X,
+    y,
+    train,
+    test,
+    method,
+    classes,
+    sample_weight=None,
+    fit_params=None,
+):
+    """Fit a classifier/calibration pair on a given train/test split.
+
+    Fit the classifier on the train set, compute its predictions on the test
+    set and use the predictions as input to fit the calibrator along with the
+    test labels.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Cloned base estimator.
+
+    X : array-like, shape (n_samples, n_features)
+        Sample data.
+
+    y : array-like, shape (n_samples,)
+        Targets.
+
+    train : ndarray, shape (n_train_indices,)
+        Indices of the training subset.
+
+    test : ndarray, shape (n_test_indices,)
+        Indices of the testing subset.
+
+    method : {'sigmoid', 'isotonic'}
+        Method to use for calibration.
+
+    classes : ndarray, shape (n_classes,)
+        The target classes.
+
+    sample_weight : array-like, default=None
+        Sample weights for `X`.
+
+    fit_params : dict, default=None
+        Parameters to pass to the `fit` method of the underlying
+        classifier.
+
+    Returns
+    -------
+    calibrated_classifier : _CalibratedClassifier instance
+    """
+    fit_params_train = _check_method_params(X, params=fit_params, indices=train)
+    X_train, y_train = _safe_indexing(X, train), _safe_indexing(y, train)
+    X_test, y_test = _safe_indexing(X, test), _safe_indexing(y, test)
+
+    estimator.fit(X_train, y_train, **fit_params_train)
+
+    predictions, _ = _get_response_values(
+        estimator,
+        X_test,
+        response_method=["decision_function", "predict_proba"],
+    )
+    if predictions.ndim == 1:
+        # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+        predictions = predictions.reshape(-1, 1)
+
+    if sample_weight is not None:
+        # Check that the sample_weight dtype is consistent with the predictions
+        # to avoid unintentional upcasts.
+        sample_weight = _check_sample_weight(sample_weight, X, dtype=predictions.dtype)
+        sw_test = _safe_indexing(sample_weight, test)
+    else:
+        sw_test = None
+    calibrated_classifier = _fit_calibrator(
+        estimator, predictions, y_test, classes, method, sample_weight=sw_test
+    )
+    return calibrated_classifier
+
+
+def _fit_calibrator(clf, predictions, y, classes, method, sample_weight=None):
+    """Fit calibrator(s) and return a `_CalibratedClassifier`
+    instance.
+
+    `n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted.
+    However, if `n_classes` equals 2, one calibrator is fitted.
+
+    Parameters
+    ----------
+    clf : estimator instance
+        Fitted classifier.
+
+    predictions : array-like, shape (n_samples, n_classes) or (n_samples, 1) \
+                    when binary.
+        Raw predictions returned by the un-calibrated base classifier.
+
+    y : array-like, shape (n_samples,)
+        The targets.
+
+    classes : ndarray, shape (n_classes,)
+        All the prediction classes.
+
+    method : {'sigmoid', 'isotonic'}
+        The method to use for calibration.
+
+    sample_weight : ndarray, shape (n_samples,), default=None
+        Sample weights. If None, then samples are equally weighted.
+
+    Returns
+    -------
+    pipeline : _CalibratedClassifier instance
+    """
+    Y = label_binarize(y, classes=classes)
+    label_encoder = LabelEncoder().fit(classes)
+    pos_class_indices = label_encoder.transform(clf.classes_)
+    calibrators = []
+    for class_idx, this_pred in zip(pos_class_indices, predictions.T):
+        if method == "isotonic":
+            calibrator = IsotonicRegression(out_of_bounds="clip")
+        else:  # "sigmoid"
+            calibrator = _SigmoidCalibration()
+        calibrator.fit(this_pred, Y[:, class_idx], sample_weight)
+        calibrators.append(calibrator)
+
+    pipeline = _CalibratedClassifier(clf, calibrators, method=method, classes=classes)
+    return pipeline
+
+
+class _CalibratedClassifier:
+    """Pipeline-like chaining a fitted classifier and its fitted calibrators.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Fitted classifier.
+
+    calibrators : list of fitted estimator instances
+        List of fitted calibrators (either 'IsotonicRegression' or
+        '_SigmoidCalibration'). The number of calibrators equals the number of
+        classes. However, if there are 2 classes, the list contains only one
+        fitted calibrator.
+
+    classes : array-like of shape (n_classes,)
+        All the prediction classes.
+
+    method : {'sigmoid', 'isotonic'}, default='sigmoid'
+        The method to use for calibration. Can be 'sigmoid' which
+        corresponds to Platt's method or 'isotonic' which is a
+        non-parametric approach based on isotonic regression.
+    """
+
+    def __init__(self, estimator, calibrators, *, classes, method="sigmoid"):
+        self.estimator = estimator
+        self.calibrators = calibrators
+        self.classes = classes
+        self.method = method
+
+    def predict_proba(self, X):
+        """Calculate calibrated probabilities.
+
+        Calculates classification calibrated probabilities
+        for each class, in a one-vs-all manner, for `X`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The sample data.
+
+        Returns
+        -------
+        proba : array, shape (n_samples, n_classes)
+            The predicted probabilities. Can be exact zeros.
+        """
+        predictions, _ = _get_response_values(
+            self.estimator,
+            X,
+            response_method=["decision_function", "predict_proba"],
+        )
+        if predictions.ndim == 1:
+            # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+            predictions = predictions.reshape(-1, 1)
+
+        n_classes = len(self.classes)
+
+        label_encoder = LabelEncoder().fit(self.classes)
+        pos_class_indices = label_encoder.transform(self.estimator.classes_)
+
+        proba = np.zeros((_num_samples(X), n_classes))
+        for class_idx, this_pred, calibrator in zip(
+            pos_class_indices, predictions.T, self.calibrators
+        ):
+            if n_classes == 2:
+                # When binary, `predictions` consists only of predictions for
+                # clf.classes_[1] but `pos_class_indices` = 0
+                class_idx += 1
+            proba[:, class_idx] = calibrator.predict(this_pred)
+
+        # Normalize the probabilities
+        if n_classes == 2:
+            proba[:, 0] = 1.0 - proba[:, 1]
+        else:
+            denominator = np.sum(proba, axis=1)[:, np.newaxis]
+            # In the edge case where for each class calibrator returns a null
+            # probability for a given sample, use the uniform distribution
+            # instead.
+            uniform_proba = np.full_like(proba, 1 / n_classes)
+            proba = np.divide(
+                proba, denominator, out=uniform_proba, where=denominator != 0
+            )
+
+        # Deal with cases where the predicted probability minimally exceeds 1.0
+        proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0
+
+        return proba
+
+
+# The max_abs_prediction_threshold was approximated using
+# logit(np.finfo(np.float64).eps) which is about -36
+def _sigmoid_calibration(
+    predictions, y, sample_weight=None, max_abs_prediction_threshold=30
+):
+    """Probability Calibration with sigmoid method (Platt 2000)
+
+    Parameters
+    ----------
+    predictions : ndarray of shape (n_samples,)
+        The decision function or predict proba for the samples.
+
+    y : ndarray of shape (n_samples,)
+        The targets.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights. If None, then samples are equally weighted.
+
+    Returns
+    -------
+    a : float
+        The slope.
+
+    b : float
+        The intercept.
+
+    References
+    ----------
+    Platt, "Probabilistic Outputs for Support Vector Machines"
+    """
+    predictions = column_or_1d(predictions)
+    y = column_or_1d(y)
+
+    F = predictions  # F follows Platt's notations
+
+    scale_constant = 1.0
+    max_prediction = np.max(np.abs(F))
+
+    # If the predictions have large values we scale them in order to bring
+    # them within a suitable range. This has no effect on the final
+    # (prediction) result because linear models like Logisitic Regression
+    # without a penalty are invariant to multiplying the features by a
+    # constant.
+    if max_prediction >= max_abs_prediction_threshold:
+        scale_constant = max_prediction
+        # We rescale the features in a copy: inplace rescaling could confuse
+        # the caller and make the code harder to reason about.
+        F = F / scale_constant
+
+    # Bayesian priors (see Platt end of section 2.2):
+    # It corresponds to the number of samples, taking into account the
+    # `sample_weight`.
+    mask_negative_samples = y <= 0
+    if sample_weight is not None:
+        prior0 = (sample_weight[mask_negative_samples]).sum()
+        prior1 = (sample_weight[~mask_negative_samples]).sum()
+    else:
+        prior0 = float(np.sum(mask_negative_samples))
+        prior1 = y.shape[0] - prior0
+    T = np.zeros_like(y, dtype=predictions.dtype)
+    T[y > 0] = (prior1 + 1.0) / (prior1 + 2.0)
+    T[y <= 0] = 1.0 / (prior0 + 2.0)
+
+    bin_loss = HalfBinomialLoss()
+
+    def loss_grad(AB):
+        # .astype below is needed to ensure y_true and raw_prediction have the
+        # same dtype. With result = np.float64(0) * np.array([1, 2], dtype=np.float32)
+        # - in Numpy 2, result.dtype is float64
+        # - in Numpy<2, result.dtype is float32
+        raw_prediction = -(AB[0] * F + AB[1]).astype(dtype=predictions.dtype)
+        l, g = bin_loss.loss_gradient(
+            y_true=T,
+            raw_prediction=raw_prediction,
+            sample_weight=sample_weight,
+        )
+        loss = l.sum()
+        # TODO: Remove casting to np.float64 when minimum supported SciPy is 1.11.2
+        # With SciPy >= 1.11.2, the LBFGS implementation will cast to float64
+        # https://github.com/scipy/scipy/pull/18825.
+        # Here we cast to float64 to support SciPy < 1.11.2
+        grad = np.asarray([-g @ F, -g.sum()], dtype=np.float64)
+        return loss, grad
+
+    AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))])
+
+    opt_result = minimize(
+        loss_grad,
+        AB0,
+        method="L-BFGS-B",
+        jac=True,
+        options={
+            "gtol": 1e-6,
+            "ftol": 64 * np.finfo(float).eps,
+        },
+    )
+    AB_ = opt_result.x
+
+    # The tuned multiplicative parameter is converted back to the original
+    # input feature scale. The offset parameter does not need rescaling since
+    # we did not rescale the outcome variable.
+    return AB_[0] / scale_constant, AB_[1]
+
+
+class _SigmoidCalibration(RegressorMixin, BaseEstimator):
+    """Sigmoid regression model.
+
+    Attributes
+    ----------
+    a_ : float
+        The slope.
+
+    b_ : float
+        The intercept.
+    """
+
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model using X, y as training data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples,)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Training target.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        X = column_or_1d(X)
+        y = column_or_1d(y)
+        X, y = indexable(X, y)
+
+        self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)
+        return self
+
+    def predict(self, T):
+        """Predict new data by linear interpolation.
+
+        Parameters
+        ----------
+        T : array-like of shape (n_samples,)
+            Data to predict from.
+
+        Returns
+        -------
+        T_ : ndarray of shape (n_samples,)
+            The predicted data.
+        """
+        T = column_or_1d(T)
+        return expit(-(self.a_ * T + self.b_))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_prob": ["array-like"],
+        "pos_label": [Real, str, "boolean", None],
+        "n_bins": [Interval(Integral, 1, None, closed="left")],
+        "strategy": [StrOptions({"uniform", "quantile"})],
+    },
+    prefer_skip_nested_validation=True,
+)
+def calibration_curve(
+    y_true,
+    y_prob,
+    *,
+    pos_label=None,
+    n_bins=5,
+    strategy="uniform",
+):
+    """Compute true and predicted probabilities for a calibration curve.
+
+    The method assumes the inputs come from a binary classifier, and
+    discretize the [0, 1] interval into bins.
+
+    Calibration curves may also be referred to as reliability diagrams.
+
+    Read more in the :ref:`User Guide <calibration>`.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True targets.
+
+    y_prob : array-like of shape (n_samples,)
+        Probabilities of the positive class.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+
+        .. versionadded:: 1.1
+
+    n_bins : int, default=5
+        Number of bins to discretize the [0, 1] interval. A bigger number
+        requires more data. Bins with no samples (i.e. without
+        corresponding values in `y_prob`) will not be returned, thus the
+        returned arrays may have less than `n_bins` values.
+
+    strategy : {'uniform', 'quantile'}, default='uniform'
+        Strategy used to define the widths of the bins.
+
+        uniform
+            The bins have identical widths.
+        quantile
+            The bins have the same number of samples and depend on `y_prob`.
+
+    Returns
+    -------
+    prob_true : ndarray of shape (n_bins,) or smaller
+        The proportion of samples whose class is the positive class, in each
+        bin (fraction of positives).
+
+    prob_pred : ndarray of shape (n_bins,) or smaller
+        The mean predicted probability in each bin.
+
+    See Also
+    --------
+    CalibrationDisplay.from_predictions : Plot calibration curve using true
+        and predicted labels.
+    CalibrationDisplay.from_estimator : Plot calibration curve using an
+        estimator and data.
+
+    References
+    ----------
+    Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
+    Probabilities With Supervised Learning, in Proceedings of the 22nd
+    International Conference on Machine Learning (ICML).
+    See section 4 (Qualitative Analysis of Predictions).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.calibration import calibration_curve
+    >>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
+    >>> y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9,  1.])
+    >>> prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3)
+    >>> prob_true
+    array([0. , 0.5, 1. ])
+    >>> prob_pred
+    array([0.2  , 0.525, 0.85 ])
+    """
+    y_true = column_or_1d(y_true)
+    y_prob = column_or_1d(y_prob)
+    check_consistent_length(y_true, y_prob)
+    pos_label = _check_pos_label_consistency(pos_label, y_true)
+
+    if y_prob.min() < 0 or y_prob.max() > 1:
+        raise ValueError("y_prob has values outside [0, 1].")
+
+    labels = np.unique(y_true)
+    if len(labels) > 2:
+        raise ValueError(
+            f"Only binary classification is supported. Provided labels {labels}."
+        )
+    y_true = y_true == pos_label
+
+    if strategy == "quantile":  # Determine bin edges by distribution of data
+        quantiles = np.linspace(0, 1, n_bins + 1)
+        bins = np.percentile(y_prob, quantiles * 100)
+    elif strategy == "uniform":
+        bins = np.linspace(0.0, 1.0, n_bins + 1)
+    else:
+        raise ValueError(
+            "Invalid entry to 'strategy' input. Strategy "
+            "must be either 'quantile' or 'uniform'."
+        )
+
+    binids = np.searchsorted(bins[1:-1], y_prob)
+
+    bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins))
+    bin_true = np.bincount(binids, weights=y_true, minlength=len(bins))
+    bin_total = np.bincount(binids, minlength=len(bins))
+
+    nonzero = bin_total != 0
+    prob_true = bin_true[nonzero] / bin_total[nonzero]
+    prob_pred = bin_sums[nonzero] / bin_total[nonzero]
+
+    return prob_true, prob_pred
+
+
+class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
+    """Calibration curve (also known as reliability diagram) visualization.
+
+    It is recommended to use
+    :func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or
+    :func:`~sklearn.calibration.CalibrationDisplay.from_predictions`
+    to create a `CalibrationDisplay`. All parameters are stored as attributes.
+
+    Read more about calibration in the :ref:`User Guide <calibration>` and
+    more about the scikit-learn visualization API in :ref:`visualizations`.
+
+    For an example on how to use the visualization, see
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`.
+
+    .. versionadded:: 1.0
+
+    Parameters
+    ----------
+    prob_true : ndarray of shape (n_bins,)
+        The proportion of samples whose class is the positive class (fraction
+        of positives), in each bin.
+
+    prob_pred : ndarray of shape (n_bins,)
+        The mean predicted probability in each bin.
+
+    y_prob : ndarray of shape (n_samples,)
+        Probability estimates for the positive class, for each sample.
+
+    estimator_name : str, default=None
+        Name of estimator. If None, the estimator name is not shown.
+
+    pos_label : int, float, bool or str, default=None
+        The positive class when computing the calibration curve.
+        By default, `pos_label` is set to `estimators.classes_[1]` when using
+        `from_estimator` and set to 1 when using `from_predictions`.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    line_ : matplotlib Artist
+        Calibration curve.
+
+    ax_ : matplotlib Axes
+        Axes with calibration curve.
+
+    figure_ : matplotlib Figure
+        Figure containing the curve.
+
+    See Also
+    --------
+    calibration_curve : Compute true and predicted probabilities for a
+        calibration curve.
+    CalibrationDisplay.from_predictions : Plot calibration curve using true
+        and predicted labels.
+    CalibrationDisplay.from_estimator : Plot calibration curve using an
+        estimator and data.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> from sklearn.calibration import calibration_curve, CalibrationDisplay
+    >>> X, y = make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, random_state=0)
+    >>> clf = LogisticRegression(random_state=0)
+    >>> clf.fit(X_train, y_train)
+    LogisticRegression(random_state=0)
+    >>> y_prob = clf.predict_proba(X_test)[:, 1]
+    >>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10)
+    >>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob)
+    >>> disp.plot()
+    <...>
+    """
+
+    def __init__(
+        self, prob_true, prob_pred, y_prob, *, estimator_name=None, pos_label=None
+    ):
+        self.prob_true = prob_true
+        self.prob_pred = prob_pred
+        self.y_prob = y_prob
+        self.estimator_name = estimator_name
+        self.pos_label = pos_label
+
+    def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
+        """Plot visualization.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Parameters
+        ----------
+        ax : Matplotlib Axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        name : str, default=None
+            Name for labeling curve. If `None`, use `estimator_name` if
+            not `None`, otherwise no labeling is shown.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`
+            Object that stores computed values.
+        """
+        self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
+
+        info_pos_label = (
+            f"(Positive class: {self.pos_label})" if self.pos_label is not None else ""
+        )
+
+        default_line_kwargs = {"marker": "s", "linestyle": "-"}
+        if name is not None:
+            default_line_kwargs["label"] = name
+        line_kwargs = _validate_style_kwargs(default_line_kwargs, kwargs)
+
+        ref_line_label = "Perfectly calibrated"
+        existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1]
+        if ref_line and not existing_ref_line:
+            self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label)
+        self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0]
+
+        # We always have to show the legend for at least the reference line
+        self.ax_.legend(loc="lower right")
+
+        xlabel = f"Mean predicted probability {info_pos_label}"
+        ylabel = f"Fraction of positives {info_pos_label}"
+        self.ax_.set(xlabel=xlabel, ylabel=ylabel)
+
+        return self
+
+    @classmethod
+    def from_estimator(
+        cls,
+        estimator,
+        X,
+        y,
+        *,
+        n_bins=5,
+        strategy="uniform",
+        pos_label=None,
+        name=None,
+        ax=None,
+        ref_line=True,
+        **kwargs,
+    ):
+        """Plot calibration curve using a binary classifier and data.
+
+        A calibration curve, also known as a reliability diagram, uses inputs
+        from a binary classifier and plots the average predicted probability
+        for each bin against the fraction of positive classes, on the
+        y-axis.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Read more about calibration in the :ref:`User Guide <calibration>` and
+        more about the scikit-learn visualization API in :ref:`visualizations`.
+
+        .. versionadded:: 1.0
+
+        Parameters
+        ----------
+        estimator : estimator instance
+            Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
+            in which the last estimator is a classifier. The classifier must
+            have a :term:`predict_proba` method.
+
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input values.
+
+        y : array-like of shape (n_samples,)
+            Binary target values.
+
+        n_bins : int, default=5
+            Number of bins to discretize the [0, 1] interval into when
+            calculating the calibration curve. A bigger number requires more
+            data.
+
+        strategy : {'uniform', 'quantile'}, default='uniform'
+            Strategy used to define the widths of the bins.
+
+            - `'uniform'`: The bins have identical widths.
+            - `'quantile'`: The bins have the same number of samples and depend
+              on predicted probabilities.
+
+        pos_label : int, float, bool or str, default=None
+            The positive class when computing the calibration curve.
+            By default, `estimators.classes_[1]` is considered as the
+            positive class.
+
+            .. versionadded:: 1.1
+
+        name : str, default=None
+            Name for labeling curve. If `None`, the name of the estimator is
+            used.
+
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`.
+            Object that stores computed values.
+
+        See Also
+        --------
+        CalibrationDisplay.from_predictions : Plot calibration curve using true
+            and predicted labels.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.model_selection import train_test_split
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.calibration import CalibrationDisplay
+        >>> X, y = make_classification(random_state=0)
+        >>> X_train, X_test, y_train, y_test = train_test_split(
+        ...     X, y, random_state=0)
+        >>> clf = LogisticRegression(random_state=0)
+        >>> clf.fit(X_train, y_train)
+        LogisticRegression(random_state=0)
+        >>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
+        >>> plt.show()
+        """
+        y_prob, pos_label, name = cls._validate_and_get_response_values(
+            estimator,
+            X,
+            y,
+            response_method="predict_proba",
+            pos_label=pos_label,
+            name=name,
+        )
+
+        return cls.from_predictions(
+            y,
+            y_prob,
+            n_bins=n_bins,
+            strategy=strategy,
+            pos_label=pos_label,
+            name=name,
+            ref_line=ref_line,
+            ax=ax,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_predictions(
+        cls,
+        y_true,
+        y_prob,
+        *,
+        n_bins=5,
+        strategy="uniform",
+        pos_label=None,
+        name=None,
+        ax=None,
+        ref_line=True,
+        **kwargs,
+    ):
+        """Plot calibration curve using true labels and predicted probabilities.
+
+        Calibration curve, also known as reliability diagram, uses inputs
+        from a binary classifier and plots the average predicted probability
+        for each bin against the fraction of positive classes, on the
+        y-axis.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Read more about calibration in the :ref:`User Guide <calibration>` and
+        more about the scikit-learn visualization API in :ref:`visualizations`.
+
+        .. versionadded:: 1.0
+
+        Parameters
+        ----------
+        y_true : array-like of shape (n_samples,)
+            True labels.
+
+        y_prob : array-like of shape (n_samples,)
+            The predicted probabilities of the positive class.
+
+        n_bins : int, default=5
+            Number of bins to discretize the [0, 1] interval into when
+            calculating the calibration curve. A bigger number requires more
+            data.
+
+        strategy : {'uniform', 'quantile'}, default='uniform'
+            Strategy used to define the widths of the bins.
+
+            - `'uniform'`: The bins have identical widths.
+            - `'quantile'`: The bins have the same number of samples and depend
+              on predicted probabilities.
+
+        pos_label : int, float, bool or str, default=None
+            The positive class when computing the calibration curve.
+            By default `pos_label` is set to 1.
+
+            .. versionadded:: 1.1
+
+        name : str, default=None
+            Name for labeling curve.
+
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`.
+            Object that stores computed values.
+
+        See Also
+        --------
+        CalibrationDisplay.from_estimator : Plot calibration curve using an
+            estimator and data.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.model_selection import train_test_split
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.calibration import CalibrationDisplay
+        >>> X, y = make_classification(random_state=0)
+        >>> X_train, X_test, y_train, y_test = train_test_split(
+        ...     X, y, random_state=0)
+        >>> clf = LogisticRegression(random_state=0)
+        >>> clf.fit(X_train, y_train)
+        LogisticRegression(random_state=0)
+        >>> y_prob = clf.predict_proba(X_test)[:, 1]
+        >>> disp = CalibrationDisplay.from_predictions(y_test, y_prob)
+        >>> plt.show()
+        """
+        pos_label_validated, name = cls._validate_from_predictions_params(
+            y_true, y_prob, sample_weight=None, pos_label=pos_label, name=name
+        )
+
+        prob_true, prob_pred = calibration_curve(
+            y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label
+        )
+
+        disp = cls(
+            prob_true=prob_true,
+            prob_pred=prob_pred,
+            y_prob=y_prob,
+            estimator_name=name,
+            pos_label=pos_label_validated,
+        )
+        return disp.plot(ax=ax, ref_line=ref_line, **kwargs)
diff --git a/shared/package-files/4e/4e0fedcb3b7c0f38b92385364e895d7b6e53ab30a8e86a6aa8e65722e24ad754/linalg_lstsq_native.h b/shared/package-files/4e/4e0fedcb3b7c0f38b92385364e895d7b6e53ab30a8e86a6aa8e65722e24ad754/linalg_lstsq_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4238e93a16d9ceec7b2c5ba9680dfee8342a0b06
--- /dev/null
+++ b/shared/package-files/4e/4e0fedcb3b7c0f38b92385364e895d7b6e53ab30a8e86a6aa8e65722e24ad754/linalg_lstsq_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> linalg_lstsq(const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond=c10::nullopt, c10::optional<c10::string_view> driver=c10::nullopt);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_out(const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond, c10::optional<c10::string_view> driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4e14d6fae0a12d7ffa8469bc1f1cbd69e875c9c21c140f83999c1a1d9c88ad20/hamming_window.h b/shared/package-files/4e/4e14d6fae0a12d7ffa8469bc1f1cbd69e875c9c21c140f83999c1a1d9c88ad20/hamming_window.h
new file mode 100644
index 0000000000000000000000000000000000000000..947b6012b1365854d2d55d28ba88882be9a213ae
--- /dev/null
+++ b/shared/package-files/4e/4e14d6fae0a12d7ffa8469bc1f1cbd69e875c9c21c140f83999c1a1d9c88ad20/hamming_window.h
@@ -0,0 +1,97 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/hamming_window_ops.h>
+
+namespace at {
+
+
+// aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, at::TensorOptions options={}) {
+    return at::_ops::hamming_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::hamming_window::call(window_length, dtype, layout, device, pin_memory);
+}
+
+// aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
+    return at::_ops::hamming_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::hamming_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
+}
+
+// aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, at::TensorOptions options={}) {
+    return at::_ops::hamming_window_periodic_alpha::call(window_length, periodic, alpha, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::hamming_window_periodic_alpha::call(window_length, periodic, alpha, dtype, layout, device, pin_memory);
+}
+
+// aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, at::TensorOptions options={}) {
+    return at::_ops::hamming_window_periodic_alpha_beta::call(window_length, periodic, alpha, beta, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::hamming_window_periodic_alpha_beta::call(window_length, periodic, alpha, beta, dtype, layout, device, pin_memory);
+}
+
+// aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length) {
+    return at::_ops::hamming_window_out::call(window_length, out);
+}
+// aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_outf(int64_t window_length, at::Tensor & out) {
+    return at::_ops::hamming_window_out::call(window_length, out);
+}
+
+// aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
+    return at::_ops::hamming_window_periodic_out::call(window_length, periodic, out);
+}
+// aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
+    return at::_ops::hamming_window_periodic_out::call(window_length, periodic, out);
+}
+
+// aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic, double alpha) {
+    return at::_ops::hamming_window_periodic_alpha_out::call(window_length, periodic, alpha, out);
+}
+// aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, double alpha, at::Tensor & out) {
+    return at::_ops::hamming_window_periodic_alpha_out::call(window_length, periodic, alpha, out);
+}
+
+// aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic, double alpha, double beta) {
+    return at::_ops::hamming_window_periodic_alpha_beta_out::call(window_length, periodic, alpha, beta, out);
+}
+// aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out) {
+    return at::_ops::hamming_window_periodic_alpha_beta_out::call(window_length, periodic, alpha, beta, out);
+}
+
+}
diff --git a/shared/package-files/4e/4e17da3da9549616f2672372bc133793eee89e5dde24aab9c02391ace1ad90c3/NS.py b/shared/package-files/4e/4e17da3da9549616f2672372bc133793eee89e5dde24aab9c02391ace1ad90c3/NS.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3f34ce90d53516f544b5e3c89b7759bf17771e4
--- /dev/null
+++ b/shared/package-files/4e/4e17da3da9549616f2672372bc133793eee89e5dde24aab9c02391ace1ad90c3/NS.py
@@ -0,0 +1,24 @@
+# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
+
+# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc.
+#
+# Permission to use, copy, modify, and distribute this software and its
+# documentation for any purpose with or without fee is hereby granted,
+# provided that the above copyright notice and this permission notice
+# appear in all copies.
+#
+# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
+# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
+# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+import dns.immutable
+import dns.rdtypes.nsbase
+
+
+@dns.immutable.immutable
+class NS(dns.rdtypes.nsbase.NSBase):
+    """NS record"""
diff --git a/shared/package-files/4e/4e188f481185604556de4c5f3b3cf60523848a344d6cc5f5b286d83416788393/test_qhull.py b/shared/package-files/4e/4e188f481185604556de4c5f3b3cf60523848a344d6cc5f5b286d83416788393/test_qhull.py
new file mode 100644
index 0000000000000000000000000000000000000000..adb7fcc2bdbaa2cffbe692737da0f868a86e2ad5
--- /dev/null
+++ b/shared/package-files/4e/4e188f481185604556de4c5f3b3cf60523848a344d6cc5f5b286d83416788393/test_qhull.py
@@ -0,0 +1,1308 @@
+import os
+import copy
+
+import numpy as np
+from numpy.testing import (assert_equal, assert_almost_equal,
+                           assert_, assert_allclose, assert_array_equal)
+import pytest
+from pytest import raises as assert_raises
+
+import scipy.spatial._qhull as qhull
+from scipy.spatial import cKDTree as KDTree  # type: ignore[attr-defined]
+from scipy.spatial import Voronoi
+
+import itertools
+
+def sorted_tuple(x):
+    return tuple(sorted(x))
+
+
+def assert_unordered_tuple_list_equal(a, b, tpl=tuple):
+    if isinstance(a, np.ndarray):
+        a = a.tolist()
+    if isinstance(b, np.ndarray):
+        b = b.tolist()
+    a = list(map(tpl, a))
+    a.sort()
+    b = list(map(tpl, b))
+    b.sort()
+    assert_equal(a, b)
+
+
+np.random.seed(1234)
+
+points = [(0,0), (0,1), (1,0), (1,1), (0.5, 0.5), (0.5, 1.5)]
+
+pathological_data_1 = np.array([
+    [-3.14,-3.14], [-3.14,-2.36], [-3.14,-1.57], [-3.14,-0.79],
+    [-3.14,0.0], [-3.14,0.79], [-3.14,1.57], [-3.14,2.36],
+    [-3.14,3.14], [-2.36,-3.14], [-2.36,-2.36], [-2.36,-1.57],
+    [-2.36,-0.79], [-2.36,0.0], [-2.36,0.79], [-2.36,1.57],
+    [-2.36,2.36], [-2.36,3.14], [-1.57,-0.79], [-1.57,0.79],
+    [-1.57,-1.57], [-1.57,0.0], [-1.57,1.57], [-1.57,-3.14],
+    [-1.57,-2.36], [-1.57,2.36], [-1.57,3.14], [-0.79,-1.57],
+    [-0.79,1.57], [-0.79,-3.14], [-0.79,-2.36], [-0.79,-0.79],
+    [-0.79,0.0], [-0.79,0.79], [-0.79,2.36], [-0.79,3.14],
+    [0.0,-3.14], [0.0,-2.36], [0.0,-1.57], [0.0,-0.79], [0.0,0.0],
+    [0.0,0.79], [0.0,1.57], [0.0,2.36], [0.0,3.14], [0.79,-3.14],
+    [0.79,-2.36], [0.79,-0.79], [0.79,0.0], [0.79,0.79],
+    [0.79,2.36], [0.79,3.14], [0.79,-1.57], [0.79,1.57],
+    [1.57,-3.14], [1.57,-2.36], [1.57,2.36], [1.57,3.14],
+    [1.57,-1.57], [1.57,0.0], [1.57,1.57], [1.57,-0.79],
+    [1.57,0.79], [2.36,-3.14], [2.36,-2.36], [2.36,-1.57],
+    [2.36,-0.79], [2.36,0.0], [2.36,0.79], [2.36,1.57],
+    [2.36,2.36], [2.36,3.14], [3.14,-3.14], [3.14,-2.36],
+    [3.14,-1.57], [3.14,-0.79], [3.14,0.0], [3.14,0.79],
+    [3.14,1.57], [3.14,2.36], [3.14,3.14],
+])
+
+pathological_data_2 = np.array([
+    [-1, -1], [-1, 0], [-1, 1],
+    [0, -1], [0, 0], [0, 1],
+    [1, -1 - np.finfo(np.float64).eps], [1, 0], [1, 1],
+])
+
+bug_2850_chunks = [np.random.rand(10, 2),
+                   np.array([[0,0], [0,1], [1,0], [1,1]])  # add corners
+                   ]
+
+# same with some additional chunks
+bug_2850_chunks_2 = (bug_2850_chunks +
+                     [np.random.rand(10, 2),
+                      0.25 + np.array([[0,0], [0,1], [1,0], [1,1]])])
+
+DATASETS = {
+    'some-points': np.asarray(points),
+    'random-2d': np.random.rand(30, 2),
+    'random-3d': np.random.rand(30, 3),
+    'random-4d': np.random.rand(30, 4),
+    'random-5d': np.random.rand(30, 5),
+    'random-6d': np.random.rand(10, 6),
+    'random-7d': np.random.rand(10, 7),
+    'random-8d': np.random.rand(10, 8),
+    'pathological-1': pathological_data_1,
+    'pathological-2': pathological_data_2
+}
+
+INCREMENTAL_DATASETS = {
+    'bug-2850': (bug_2850_chunks, None),
+    'bug-2850-2': (bug_2850_chunks_2, None),
+}
+
+
+def _add_inc_data(name, chunksize):
+    """
+    Generate incremental datasets from basic data sets
+    """
+    points = DATASETS[name]
+    ndim = points.shape[1]
+
+    opts = None
+    nmin = ndim + 2
+
+    if name == 'some-points':
+        # since Qz is not allowed, use QJ
+        opts = 'QJ Pp'
+    elif name == 'pathological-1':
+        # include enough points so that we get different x-coordinates
+        nmin = 12
+
+    chunks = [points[:nmin]]
+    for j in range(nmin, len(points), chunksize):
+        chunks.append(points[j:j+chunksize])
+
+    new_name = "%s-chunk-%d" % (name, chunksize)
+    assert new_name not in INCREMENTAL_DATASETS
+    INCREMENTAL_DATASETS[new_name] = (chunks, opts)
+
+
+for name in DATASETS:
+    for chunksize in 1, 4, 16:
+        _add_inc_data(name, chunksize)
+
+
+class Test_Qhull:
+    def test_swapping(self):
+        # Check that Qhull state swapping works
+
+        x = qhull._Qhull(b'v',
+                         np.array([[0,0],[0,1],[1,0],[1,1.],[0.5,0.5]]),
+                         b'Qz')
+        xd = copy.deepcopy(x.get_voronoi_diagram())
+
+        y = qhull._Qhull(b'v',
+                         np.array([[0,0],[0,1],[1,0],[1,2.]]),
+                         b'Qz')
+        yd = copy.deepcopy(y.get_voronoi_diagram())
+
+        xd2 = copy.deepcopy(x.get_voronoi_diagram())
+        x.close()
+        yd2 = copy.deepcopy(y.get_voronoi_diagram())
+        y.close()
+
+        assert_raises(RuntimeError, x.get_voronoi_diagram)
+        assert_raises(RuntimeError, y.get_voronoi_diagram)
+
+        assert_allclose(xd[0], xd2[0])
+        assert_unordered_tuple_list_equal(xd[1], xd2[1], tpl=sorted_tuple)
+        assert_unordered_tuple_list_equal(xd[2], xd2[2], tpl=sorted_tuple)
+        assert_unordered_tuple_list_equal(xd[3], xd2[3], tpl=sorted_tuple)
+        assert_array_equal(xd[4], xd2[4])
+
+        assert_allclose(yd[0], yd2[0])
+        assert_unordered_tuple_list_equal(yd[1], yd2[1], tpl=sorted_tuple)
+        assert_unordered_tuple_list_equal(yd[2], yd2[2], tpl=sorted_tuple)
+        assert_unordered_tuple_list_equal(yd[3], yd2[3], tpl=sorted_tuple)
+        assert_array_equal(yd[4], yd2[4])
+
+        x.close()
+        assert_raises(RuntimeError, x.get_voronoi_diagram)
+        y.close()
+        assert_raises(RuntimeError, y.get_voronoi_diagram)
+
+    def test_issue_8051(self):
+        points = np.array(
+            [[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],[2, 0], [2, 1], [2, 2]]
+        )
+        Voronoi(points)
+
+
+class TestUtilities:
+    """
+    Check that utility functions work.
+
+    """
+
+    def test_find_simplex(self):
+        # Simple check that simplex finding works
+        points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+
+        # +---+
+        # |\ 0|
+        # | \ |
+        # |1 \|
+        # +---+
+
+        assert_equal(tri.simplices, [[1, 3, 2], [3, 1, 0]])
+
+        for p in [(0.25, 0.25, 1),
+                  (0.75, 0.75, 0),
+                  (0.3, 0.2, 1)]:
+            i = tri.find_simplex(p[:2])
+            assert_equal(i, p[2], err_msg=f'{p!r}')
+            j = qhull.tsearch(tri, p[:2])
+            assert_equal(i, j)
+
+    def test_plane_distance(self):
+        # Compare plane distance from hyperplane equations obtained from Qhull
+        # to manually computed plane equations
+        x = np.array([(0,0), (1, 1), (1, 0), (0.99189033, 0.37674127),
+                      (0.99440079, 0.45182168)], dtype=np.float64)
+        p = np.array([0.99966555, 0.15685619], dtype=np.float64)
+
+        tri = qhull.Delaunay(x)
+
+        z = tri.lift_points(x)
+        pz = tri.lift_points(p)
+
+        dist = tri.plane_distance(p)
+
+        for j, v in enumerate(tri.simplices):
+            x1 = z[v[0]]
+            x2 = z[v[1]]
+            x3 = z[v[2]]
+
+            n = np.cross(x1 - x3, x2 - x3)
+            n /= np.sqrt(np.dot(n, n))
+            n *= -np.sign(n[2])
+
+            d = np.dot(n, pz - x3)
+
+            assert_almost_equal(dist[j], d)
+
+    def test_convex_hull(self):
+        # Simple check that the convex hull seems to works
+        points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+
+        # +---+
+        # |\ 0|
+        # | \ |
+        # |1 \|
+        # +---+
+
+        assert_equal(tri.convex_hull, [[3, 2], [1, 2], [1, 0], [3, 0]])
+
+    def test_volume_area(self):
+        #Basic check that we get back the correct volume and area for a cube
+        points = np.array([(0, 0, 0), (0, 1, 0), (1, 0, 0), (1, 1, 0),
+                           (0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
+        hull = qhull.ConvexHull(points)
+
+        assert_allclose(hull.volume, 1., rtol=1e-14,
+                        err_msg="Volume of cube is incorrect")
+        assert_allclose(hull.area, 6., rtol=1e-14,
+                        err_msg="Area of cube is incorrect")
+
+    def test_random_volume_area(self):
+        #Test that the results for a random 10-point convex are
+        #coherent with the output of qconvex Qt s FA
+        points = np.array([(0.362568364506, 0.472712355305, 0.347003084477),
+                           (0.733731893414, 0.634480295684, 0.950513180209),
+                           (0.511239955611, 0.876839441267, 0.418047827863),
+                           (0.0765906233393, 0.527373281342, 0.6509863541),
+                           (0.146694972056, 0.596725793348, 0.894860986685),
+                           (0.513808585741, 0.069576205858, 0.530890338876),
+                           (0.512343805118, 0.663537132612, 0.037689295973),
+                           (0.47282965018, 0.462176697655, 0.14061843691),
+                           (0.240584597123, 0.778660020591, 0.722913476339),
+                           (0.951271745935, 0.967000673944, 0.890661319684)])
+
+        hull = qhull.ConvexHull(points)
+        assert_allclose(hull.volume, 0.14562013, rtol=1e-07,
+                        err_msg="Volume of random polyhedron is incorrect")
+        assert_allclose(hull.area, 1.6670425, rtol=1e-07,
+                        err_msg="Area of random polyhedron is incorrect")
+
+    def test_incremental_volume_area_random_input(self):
+        """Test that incremental mode gives the same volume/area as
+        non-incremental mode and incremental mode with restart"""
+        nr_points = 20
+        dim = 3
+        points = np.random.random((nr_points, dim))
+        inc_hull = qhull.ConvexHull(points[:dim+1, :], incremental=True)
+        inc_restart_hull = qhull.ConvexHull(points[:dim+1, :], incremental=True)
+        for i in range(dim+1, nr_points):
+            hull = qhull.ConvexHull(points[:i+1, :])
+            inc_hull.add_points(points[i:i+1, :])
+            inc_restart_hull.add_points(points[i:i+1, :], restart=True)
+            assert_allclose(hull.volume, inc_hull.volume, rtol=1e-7)
+            assert_allclose(hull.volume, inc_restart_hull.volume, rtol=1e-7)
+            assert_allclose(hull.area, inc_hull.area, rtol=1e-7)
+            assert_allclose(hull.area, inc_restart_hull.area, rtol=1e-7)
+
+    def _check_barycentric_transforms(self, tri, err_msg="",
+                                      unit_cube=False,
+                                      unit_cube_tol=0):
+        """Check that a triangulation has reasonable barycentric transforms"""
+        vertices = tri.points[tri.simplices]
+        sc = 1/(tri.ndim + 1.0)
+        centroids = vertices.sum(axis=1) * sc
+
+        # Either: (i) the simplex has a `nan` barycentric transform,
+        # or, (ii) the centroid is in the simplex
+
+        def barycentric_transform(tr, x):
+            r = tr[:,-1,:]
+            Tinv = tr[:,:-1,:]
+            return np.einsum('ijk,ik->ij', Tinv, x - r)
+
+        eps = np.finfo(float).eps
+
+        c = barycentric_transform(tri.transform, centroids)
+        with np.errstate(invalid="ignore"):
+            ok = np.isnan(c).all(axis=1) | (abs(c - sc)/sc < 0.1).all(axis=1)
+
+        assert_(ok.all(), f"{err_msg} {np.nonzero(~ok)}")
+
+        # Invalid simplices must be (nearly) zero volume
+        q = vertices[:,:-1,:] - vertices[:,-1,None,:]
+        volume = np.array([np.linalg.det(q[k,:,:])
+                           for k in range(tri.nsimplex)])
+        ok = np.isfinite(tri.transform[:,0,0]) | (volume < np.sqrt(eps))
+        assert_(ok.all(), f"{err_msg} {np.nonzero(~ok)}")
+
+        # Also, find_simplex for the centroid should end up in some
+        # simplex for the non-degenerate cases
+        j = tri.find_simplex(centroids)
+        ok = (j != -1) | np.isnan(tri.transform[:,0,0])
+        assert_(ok.all(), f"{err_msg} {np.nonzero(~ok)}")
+
+        if unit_cube:
+            # If in unit cube, no interior point should be marked out of hull
+            at_boundary = (centroids <= unit_cube_tol).any(axis=1)
+            at_boundary |= (centroids >= 1 - unit_cube_tol).any(axis=1)
+
+            ok = (j != -1) | at_boundary
+            assert_(ok.all(), f"{err_msg} {np.nonzero(~ok)}")
+
+    @pytest.mark.fail_slow(10)
+    def test_degenerate_barycentric_transforms(self):
+        # The triangulation should not produce invalid barycentric
+        # transforms that stump the simplex finding
+        data = np.load(os.path.join(os.path.dirname(__file__), 'data',
+                                    'degenerate_pointset.npz'))
+        points = data['c']
+        data.close()
+
+        tri = qhull.Delaunay(points)
+
+        # Check that there are not too many invalid simplices
+        bad_count = np.isnan(tri.transform[:,0,0]).sum()
+        assert_(bad_count < 23, bad_count)
+
+        # Check the transforms
+        self._check_barycentric_transforms(tri)
+
+    @pytest.mark.slow
+    @pytest.mark.fail_slow(20)
+    # OK per https://github.com/scipy/scipy/pull/20487#discussion_r1572684869
+    def test_more_barycentric_transforms(self):
+        # Triangulate some "nasty" grids
+
+        eps = np.finfo(float).eps
+
+        npoints = {2: 70, 3: 11, 4: 5, 5: 3}
+
+        for ndim in range(2, 6):
+            # Generate an uniform grid in n-d unit cube
+            x = np.linspace(0, 1, npoints[ndim])
+            grid = np.c_[
+                list(map(np.ravel, np.broadcast_arrays(*np.ix_(*([x]*ndim)))))
+            ].T
+
+            err_msg = "ndim=%d" % ndim
+
+            # Check using regular grid
+            tri = qhull.Delaunay(grid)
+            self._check_barycentric_transforms(tri, err_msg=err_msg,
+                                               unit_cube=True)
+
+            # Check with eps-perturbations
+            np.random.seed(1234)
+            m = (np.random.rand(grid.shape[0]) < 0.2)
+            grid[m,:] += 2*eps*(np.random.rand(*grid[m,:].shape) - 0.5)
+
+            tri = qhull.Delaunay(grid)
+            self._check_barycentric_transforms(tri, err_msg=err_msg,
+                                               unit_cube=True,
+                                               unit_cube_tol=2*eps)
+
+            # Check with duplicated data
+            tri = qhull.Delaunay(np.r_[grid, grid])
+            self._check_barycentric_transforms(tri, err_msg=err_msg,
+                                               unit_cube=True,
+                                               unit_cube_tol=2*eps)
+
+
+class TestVertexNeighborVertices:
+    def _check(self, tri):
+        expected = [set() for j in range(tri.points.shape[0])]
+        for s in tri.simplices:
+            for a in s:
+                for b in s:
+                    if a != b:
+                        expected[a].add(b)
+
+        indptr, indices = tri.vertex_neighbor_vertices
+
+        got = [set(map(int, indices[indptr[j]:indptr[j+1]]))
+               for j in range(tri.points.shape[0])]
+
+        assert_equal(got, expected, err_msg=f"{got!r} != {expected!r}")
+
+    def test_triangle(self):
+        points = np.array([(0,0), (0,1), (1,0)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+        self._check(tri)
+
+    def test_rectangle(self):
+        points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+        self._check(tri)
+
+    def test_complicated(self):
+        points = np.array([(0,0), (0,1), (1,1), (1,0),
+                           (0.5, 0.5), (0.9, 0.5)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+        self._check(tri)
+
+
+class TestDelaunay:
+    """
+    Check that triangulation works.
+
+    """
+    def test_masked_array_fails(self):
+        masked_array = np.ma.masked_all(1)
+        assert_raises(ValueError, qhull.Delaunay, masked_array)
+
+    def test_array_with_nans_fails(self):
+        points_with_nan = np.array([(0,0), (0,1), (1,1), (1,np.nan)], dtype=np.float64)
+        assert_raises(ValueError, qhull.Delaunay, points_with_nan)
+
+    def test_nd_simplex(self):
+        # simple smoke test: triangulate a n-dimensional simplex
+        for nd in range(2, 8):
+            points = np.zeros((nd+1, nd))
+            for j in range(nd):
+                points[j,j] = 1.0
+            points[-1,:] = 1.0
+
+            tri = qhull.Delaunay(points)
+
+            tri.simplices.sort()
+
+            assert_equal(tri.simplices, np.arange(nd+1, dtype=int)[None, :])
+            assert_equal(tri.neighbors, -1 + np.zeros((nd+1), dtype=int)[None,:])
+
+    def test_2d_square(self):
+        # simple smoke test: 2d square
+        points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.float64)
+        tri = qhull.Delaunay(points)
+
+        assert_equal(tri.simplices, [[1, 3, 2], [3, 1, 0]])
+        assert_equal(tri.neighbors, [[-1, -1, 1], [-1, -1, 0]])
+
+    def test_duplicate_points(self):
+        x = np.array([0, 1, 0, 1], dtype=np.float64)
+        y = np.array([0, 0, 1, 1], dtype=np.float64)
+
+        xp = np.r_[x, x]
+        yp = np.r_[y, y]
+
+        # shouldn't fail on duplicate points
+        qhull.Delaunay(np.c_[x, y])
+        qhull.Delaunay(np.c_[xp, yp])
+
+    def test_pathological(self):
+        # both should succeed
+        points = DATASETS['pathological-1']
+        tri = qhull.Delaunay(points)
+        assert_equal(tri.points[tri.simplices].max(), points.max())
+        assert_equal(tri.points[tri.simplices].min(), points.min())
+
+        points = DATASETS['pathological-2']
+        tri = qhull.Delaunay(points)
+        assert_equal(tri.points[tri.simplices].max(), points.max())
+        assert_equal(tri.points[tri.simplices].min(), points.min())
+
+    def test_joggle(self):
+        # Check that the option QJ indeed guarantees that all input points
+        # occur as vertices of the triangulation
+
+        points = np.random.rand(10, 2)
+        points = np.r_[points, points]  # duplicate input data
+
+        tri = qhull.Delaunay(points, qhull_options="QJ Qbb Pp")
+        assert_array_equal(np.unique(tri.simplices.ravel()),
+                           np.arange(len(points)))
+
+    def test_coplanar(self):
+        # Check that the coplanar point output option indeed works
+        points = np.random.rand(10, 2)
+        points = np.r_[points, points]  # duplicate input data
+
+        tri = qhull.Delaunay(points)
+
+        assert_(len(np.unique(tri.simplices.ravel())) == len(points)//2)
+        assert_(len(tri.coplanar) == len(points)//2)
+
+        assert_(len(np.unique(tri.coplanar[:,2])) == len(points)//2)
+
+        assert_(np.all(tri.vertex_to_simplex >= 0))
+
+    def test_furthest_site(self):
+        points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
+        tri = qhull.Delaunay(points, furthest_site=True)
+
+        expected = np.array([(1, 4, 0), (4, 2, 0)])  # from Qhull
+        assert_array_equal(tri.simplices, expected)
+
+    @pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
+    def test_incremental(self, name):
+        # Test incremental construction of the triangulation
+
+        chunks, opts = INCREMENTAL_DATASETS[name]
+        points = np.concatenate(chunks, axis=0)
+
+        obj = qhull.Delaunay(chunks[0], incremental=True,
+                             qhull_options=opts)
+        for chunk in chunks[1:]:
+            obj.add_points(chunk)
+
+        obj2 = qhull.Delaunay(points)
+
+        obj3 = qhull.Delaunay(chunks[0], incremental=True,
+                              qhull_options=opts)
+        if len(chunks) > 1:
+            obj3.add_points(np.concatenate(chunks[1:], axis=0),
+                            restart=True)
+
+        # Check that the incremental mode agrees with upfront mode
+        if name.startswith('pathological'):
+            # XXX: These produce valid but different triangulations.
+            #      They look OK when plotted, but how to check them?
+
+            assert_array_equal(np.unique(obj.simplices.ravel()),
+                               np.arange(points.shape[0]))
+            assert_array_equal(np.unique(obj2.simplices.ravel()),
+                               np.arange(points.shape[0]))
+        else:
+            assert_unordered_tuple_list_equal(obj.simplices, obj2.simplices,
+                                              tpl=sorted_tuple)
+
+        assert_unordered_tuple_list_equal(obj2.simplices, obj3.simplices,
+                                          tpl=sorted_tuple)
+
+
+def assert_hulls_equal(points, facets_1, facets_2):
+    # Check that two convex hulls constructed from the same point set
+    # are equal
+
+    facets_1 = set(map(sorted_tuple, facets_1))
+    facets_2 = set(map(sorted_tuple, facets_2))
+
+    if facets_1 != facets_2 and points.shape[1] == 2:
+        # The direct check fails for the pathological cases
+        # --- then the convex hull from Delaunay differs (due
+        # to rounding error etc.) from the hull computed
+        # otherwise, by the question whether (tricoplanar)
+        # points that lie almost exactly on the hull are
+        # included as vertices of the hull or not.
+        #
+        # So we check the result, and accept it if the Delaunay
+        # hull line segments are a subset of the usual hull.
+
+        eps = 1000 * np.finfo(float).eps
+
+        for a, b in facets_1:
+            for ap, bp in facets_2:
+                t = points[bp] - points[ap]
+                t /= np.linalg.norm(t)       # tangent
+                n = np.array([-t[1], t[0]])  # normal
+
+                # check that the two line segments are parallel
+                # to the same line
+                c1 = np.dot(n, points[b] - points[ap])
+                c2 = np.dot(n, points[a] - points[ap])
+                if not np.allclose(np.dot(c1, n), 0):
+                    continue
+                if not np.allclose(np.dot(c2, n), 0):
+                    continue
+
+                # Check that the segment (a, b) is contained in (ap, bp)
+                c1 = np.dot(t, points[a] - points[ap])
+                c2 = np.dot(t, points[b] - points[ap])
+                c3 = np.dot(t, points[bp] - points[ap])
+                if c1 < -eps or c1 > c3 + eps:
+                    continue
+                if c2 < -eps or c2 > c3 + eps:
+                    continue
+
+                # OK:
+                break
+            else:
+                raise AssertionError("comparison fails")
+
+        # it was OK
+        return
+
+    assert_equal(facets_1, facets_2)
+
+
+class TestConvexHull:
+    def test_masked_array_fails(self):
+        masked_array = np.ma.masked_all(1)
+        assert_raises(ValueError, qhull.ConvexHull, masked_array)
+
+    def test_array_with_nans_fails(self):
+        points_with_nan = np.array([(0,0), (1,1), (2,np.nan)], dtype=np.float64)
+        assert_raises(ValueError, qhull.ConvexHull, points_with_nan)
+
+    @pytest.mark.parametrize("name", sorted(DATASETS))
+    def test_hull_consistency_tri(self, name):
+        # Check that a convex hull returned by qhull in ndim
+        # and the hull constructed from ndim delaunay agree
+        points = DATASETS[name]
+
+        tri = qhull.Delaunay(points)
+        hull = qhull.ConvexHull(points)
+
+        assert_hulls_equal(points, tri.convex_hull, hull.simplices)
+
+        # Check that the hull extremes are as expected
+        if points.shape[1] == 2:
+            assert_equal(np.unique(hull.simplices), np.sort(hull.vertices))
+        else:
+            assert_equal(np.unique(hull.simplices), hull.vertices)
+
+    @pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
+    def test_incremental(self, name):
+        # Test incremental construction of the convex hull
+        chunks, _ = INCREMENTAL_DATASETS[name]
+        points = np.concatenate(chunks, axis=0)
+
+        obj = qhull.ConvexHull(chunks[0], incremental=True)
+        for chunk in chunks[1:]:
+            obj.add_points(chunk)
+
+        obj2 = qhull.ConvexHull(points)
+
+        obj3 = qhull.ConvexHull(chunks[0], incremental=True)
+        if len(chunks) > 1:
+            obj3.add_points(np.concatenate(chunks[1:], axis=0),
+                            restart=True)
+
+        # Check that the incremental mode agrees with upfront mode
+        assert_hulls_equal(points, obj.simplices, obj2.simplices)
+        assert_hulls_equal(points, obj.simplices, obj3.simplices)
+
+    def test_vertices_2d(self):
+        # The vertices should be in counterclockwise order in 2-D
+        np.random.seed(1234)
+        points = np.random.rand(30, 2)
+
+        hull = qhull.ConvexHull(points)
+        assert_equal(np.unique(hull.simplices), np.sort(hull.vertices))
+
+        # Check counterclockwiseness
+        x, y = hull.points[hull.vertices].T
+        angle = np.arctan2(y - y.mean(), x - x.mean())
+        assert_(np.all(np.diff(np.unwrap(angle)) > 0))
+
+    def test_volume_area(self):
+        # Basic check that we get back the correct volume and area for a cube
+        points = np.array([(0, 0, 0), (0, 1, 0), (1, 0, 0), (1, 1, 0),
+                           (0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
+        tri = qhull.ConvexHull(points)
+
+        assert_allclose(tri.volume, 1., rtol=1e-14)
+        assert_allclose(tri.area, 6., rtol=1e-14)
+
+    @pytest.mark.parametrize("incremental", [False, True])
+    def test_good2d(self, incremental):
+        # Make sure the QGn option gives the correct value of "good".
+        points = np.array([[0.2, 0.2],
+                           [0.2, 0.4],
+                           [0.4, 0.4],
+                           [0.4, 0.2],
+                           [0.3, 0.6]])
+        hull = qhull.ConvexHull(points=points,
+                                incremental=incremental,
+                                qhull_options='QG4')
+        expected = np.array([False, True, False, False], dtype=bool)
+        actual = hull.good
+        assert_equal(actual, expected)
+
+    @pytest.mark.parametrize("visibility", [
+                              "QG4",  # visible=True
+                              "QG-4",  # visible=False
+                              ])
+    @pytest.mark.parametrize("new_gen, expected", [
+        # add generator that places QG4 inside hull
+        # so all facets are invisible
+        (np.array([[0.3, 0.7]]),
+         np.array([False, False, False, False, False], dtype=bool)),
+        # adding a generator on the opposite side of the square
+        # should preserve the single visible facet & add one invisible
+        # facet
+        (np.array([[0.3, -0.7]]),
+         np.array([False, True, False, False, False], dtype=bool)),
+        # split the visible facet on top of the square into two
+        # visible facets, with visibility at the end of the array
+        # because add_points concatenates
+        (np.array([[0.3, 0.41]]),
+         np.array([False, False, False, True, True], dtype=bool)),
+        # with our current Qhull options, coplanarity will not count
+        # for visibility; this case shifts one visible & one invisible
+        # facet & adds a coplanar facet
+        # simplex at index position 2 is the shifted visible facet
+        # the final simplex is the coplanar facet
+        (np.array([[0.5, 0.6], [0.6, 0.6]]),
+         np.array([False, False, True, False, False], dtype=bool)),
+        # place the new generator such that it envelops the query
+        # point within the convex hull, but only just barely within
+        # the double precision limit
+        # NOTE: testing exact degeneracy is less predictable than this
+        # scenario, perhaps because of the default Qt option we have
+        # enabled for Qhull to handle precision matters
+        (np.array([[0.3, 0.6 + 1e-16]]),
+         np.array([False, False, False, False, False], dtype=bool)),
+        ])
+    def test_good2d_incremental_changes(self, new_gen, expected,
+                                        visibility):
+        # use the usual square convex hull
+        # generators from test_good2d
+        points = np.array([[0.2, 0.2],
+                           [0.2, 0.4],
+                           [0.4, 0.4],
+                           [0.4, 0.2],
+                           [0.3, 0.6]])
+        hull = qhull.ConvexHull(points=points,
+                                incremental=True,
+                                qhull_options=visibility)
+        hull.add_points(new_gen)
+        actual = hull.good
+        if '-' in visibility:
+            expected = np.invert(expected)
+        assert_equal(actual, expected)
+
+    @pytest.mark.parametrize("incremental", [False, True])
+    def test_good2d_no_option(self, incremental):
+        # handle case where good attribute doesn't exist
+        # because Qgn or Qg-n wasn't specified
+        points = np.array([[0.2, 0.2],
+                           [0.2, 0.4],
+                           [0.4, 0.4],
+                           [0.4, 0.2],
+                           [0.3, 0.6]])
+        hull = qhull.ConvexHull(points=points,
+                                incremental=incremental)
+        actual = hull.good
+        assert actual is None
+        # preserve None after incremental addition
+        if incremental:
+            hull.add_points(np.zeros((1, 2)))
+            actual = hull.good
+            assert actual is None
+
+    @pytest.mark.parametrize("incremental", [False, True])
+    def test_good2d_inside(self, incremental):
+        # Make sure the QGn option gives the correct value of "good".
+        # When point n is inside the convex hull of the rest, good is
+        # all False.
+        points = np.array([[0.2, 0.2],
+                           [0.2, 0.4],
+                           [0.4, 0.4],
+                           [0.4, 0.2],
+                           [0.3, 0.3]])
+        hull = qhull.ConvexHull(points=points,
+                                incremental=incremental,
+                                qhull_options='QG4')
+        expected = np.array([False, False, False, False], dtype=bool)
+        actual = hull.good
+        assert_equal(actual, expected)
+
+    @pytest.mark.parametrize("incremental", [False, True])
+    def test_good3d(self, incremental):
+        # Make sure the QGn option gives the correct value of "good"
+        # for a 3d figure
+        points = np.array([[0.0, 0.0, 0.0],
+                           [0.90029516, -0.39187448, 0.18948093],
+                           [0.48676420, -0.72627633, 0.48536925],
+                           [0.57651530, -0.81179274, -0.09285832],
+                           [0.67846893, -0.71119562, 0.18406710]])
+        hull = qhull.ConvexHull(points=points,
+                                incremental=incremental,
+                                qhull_options='QG0')
+        expected = np.array([True, False, False, False], dtype=bool)
+        assert_equal(hull.good, expected)
+
+class TestVoronoi:
+
+    @pytest.mark.parametrize("qhull_opts, extra_pts", [
+        # option Qz (default for SciPy) will add
+        # an extra point at infinity
+        ("Qbb Qc Qz", 1),
+        ("Qbb Qc", 0),
+    ])
+    @pytest.mark.parametrize("n_pts", [50, 100])
+    @pytest.mark.parametrize("ndim", [2, 3])
+    def test_point_region_structure(self,
+                                    qhull_opts,
+                                    n_pts,
+                                    extra_pts,
+                                    ndim):
+        # see gh-16773
+        rng = np.random.default_rng(7790)
+        points = rng.random((n_pts, ndim))
+        vor = Voronoi(points, qhull_options=qhull_opts)
+        pt_region = vor.point_region
+        assert pt_region.max() == n_pts - 1 + extra_pts
+        assert pt_region.size == len(vor.regions) - extra_pts
+        assert len(vor.regions) == n_pts + extra_pts
+        assert vor.points.shape[0] == n_pts
+        # if there is an empty sublist in the Voronoi
+        # regions data structure, it should never be
+        # indexed because it corresponds to an internally
+        # added point at infinity and is not a member of the
+        # generators (input points)
+        if extra_pts:
+            sublens = [len(x) for x in vor.regions]
+            # only one point at infinity (empty region)
+            # is allowed
+            assert sublens.count(0) == 1
+            assert sublens.index(0) not in pt_region
+
+    def test_masked_array_fails(self):
+        masked_array = np.ma.masked_all(1)
+        assert_raises(ValueError, qhull.Voronoi, masked_array)
+
+    def test_simple(self):
+        # Simple case with known Voronoi diagram
+        points = [(0, 0), (0, 1), (0, 2),
+                  (1, 0), (1, 1), (1, 2),
+                  (2, 0), (2, 1), (2, 2)]
+
+        # qhull v o Fv Qbb Qc Qz < dat
+        output = """
+        2
+        5 10 1
+        -10.101 -10.101
+           0.5    0.5
+           0.5    1.5
+           1.5    0.5
+           1.5    1.5
+        2 0 1
+        3 2 0 1
+        2 0 2
+        3 3 0 1
+        4 1 2 4 3
+        3 4 0 2
+        2 0 3
+        3 4 0 3
+        2 0 4
+        0
+        12
+        4 0 3 0 1
+        4 0 1 0 1
+        4 1 4 1 2
+        4 1 2 0 2
+        4 2 5 0 2
+        4 3 4 1 3
+        4 3 6 0 3
+        4 4 5 2 4
+        4 4 7 3 4
+        4 5 8 0 4
+        4 6 7 0 3
+        4 7 8 0 4
+        """
+        self._compare_qvoronoi(points, output)
+
+    def _compare_qvoronoi(self, points, output, **kw):
+        """Compare to output from 'qvoronoi o Fv < data' to Voronoi()"""
+
+        # Parse output
+        output = [list(map(float, x.split())) for x in output.strip().splitlines()]
+        nvertex = int(output[1][0])
+        vertices = list(map(tuple, output[3:2+nvertex]))  # exclude inf
+        nregion = int(output[1][1])
+        regions = [[int(y)-1 for y in x[1:]]
+                   for x in output[2+nvertex:2+nvertex+nregion]]
+        ridge_points = [[int(y) for y in x[1:3]]
+                        for x in output[3+nvertex+nregion:]]
+        ridge_vertices = [[int(y)-1 for y in x[3:]]
+                          for x in output[3+nvertex+nregion:]]
+
+        # Compare results
+        vor = qhull.Voronoi(points, **kw)
+
+        def sorttuple(x):
+            return tuple(sorted(x))
+
+        assert_allclose(vor.vertices, vertices)
+        assert_equal(set(map(tuple, vor.regions)),
+                     set(map(tuple, regions)))
+
+        p1 = list(zip(list(map(sorttuple, ridge_points)),
+                      list(map(sorttuple, ridge_vertices))))
+        p2 = list(zip(list(map(sorttuple, vor.ridge_points.tolist())),
+                      list(map(sorttuple, vor.ridge_vertices))))
+        p1.sort()
+        p2.sort()
+
+        assert_equal(p1, p2)
+
+    @pytest.mark.parametrize("name", sorted(DATASETS))
+    def test_ridges(self, name):
+        # Check that the ridges computed by Voronoi indeed separate
+        # the regions of nearest neighborhood, by comparing the result
+        # to KDTree.
+
+        points = DATASETS[name]
+
+        tree = KDTree(points)
+        vor = qhull.Voronoi(points)
+
+        for p, v in vor.ridge_dict.items():
+            # consider only finite ridges
+            if not np.all(np.asarray(v) >= 0):
+                continue
+
+            ridge_midpoint = vor.vertices[v].mean(axis=0)
+            d = 1e-6 * (points[p[0]] - ridge_midpoint)
+
+            dist, k = tree.query(ridge_midpoint + d, k=1)
+            assert_equal(k, p[0])
+
+            dist, k = tree.query(ridge_midpoint - d, k=1)
+            assert_equal(k, p[1])
+
+    def test_furthest_site(self):
+        points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
+
+        # qhull v o Fv Qbb Qc Qu < dat
+        output = """
+        2
+        3 5 1
+        -10.101 -10.101
+        0.6000000000000001    0.5
+           0.5 0.6000000000000001
+        3 0 2 1
+        2 0 1
+        2 0 2
+        0
+        3 0 2 1
+        5
+        4 0 2 0 2
+        4 0 4 1 2
+        4 0 1 0 1
+        4 1 4 0 1
+        4 2 4 0 2
+        """
+        self._compare_qvoronoi(points, output, furthest_site=True)
+
+    def test_furthest_site_flag(self):
+        points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
+
+        vor = Voronoi(points)
+        assert_equal(vor.furthest_site,False)
+        vor = Voronoi(points,furthest_site=True)
+        assert_equal(vor.furthest_site,True)
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
+    def test_incremental(self, name):
+        # Test incremental construction of the triangulation
+
+        if INCREMENTAL_DATASETS[name][0][0].shape[1] > 3:
+            # too slow (testing of the result --- qhull is still fast)
+            return
+
+        chunks, opts = INCREMENTAL_DATASETS[name]
+        points = np.concatenate(chunks, axis=0)
+
+        obj = qhull.Voronoi(chunks[0], incremental=True,
+                             qhull_options=opts)
+        for chunk in chunks[1:]:
+            obj.add_points(chunk)
+
+        obj2 = qhull.Voronoi(points)
+
+        obj3 = qhull.Voronoi(chunks[0], incremental=True,
+                             qhull_options=opts)
+        if len(chunks) > 1:
+            obj3.add_points(np.concatenate(chunks[1:], axis=0),
+                            restart=True)
+
+        # -- Check that the incremental mode agrees with upfront mode
+        assert_equal(len(obj.point_region), len(obj2.point_region))
+        assert_equal(len(obj.point_region), len(obj3.point_region))
+
+        # The vertices may be in different order or duplicated in
+        # the incremental map
+        for objx in obj, obj3:
+            vertex_map = {-1: -1}
+            for i, v in enumerate(objx.vertices):
+                for j, v2 in enumerate(obj2.vertices):
+                    if np.allclose(v, v2):
+                        vertex_map[i] = j
+
+            def remap(x):
+                if hasattr(x, '__len__'):
+                    return tuple({remap(y) for y in x})
+                try:
+                    return vertex_map[x]
+                except KeyError as e:
+                    message = (f"incremental result has spurious vertex "
+                               f"at {objx.vertices[x]!r}")
+                    raise AssertionError(message) from e
+
+            def simplified(x):
+                items = set(map(sorted_tuple, x))
+                if () in items:
+                    items.remove(())
+                items = [x for x in items if len(x) > 1]
+                items.sort()
+                return items
+
+            assert_equal(
+                simplified(remap(objx.regions)),
+                simplified(obj2.regions)
+                )
+            assert_equal(
+                simplified(remap(objx.ridge_vertices)),
+                simplified(obj2.ridge_vertices)
+                )
+
+            # XXX: compare ridge_points --- not clear exactly how to do this
+
+
+class Test_HalfspaceIntersection:
+    def assert_unordered_allclose(self, arr1, arr2, rtol=1e-7):
+        """Check that every line in arr1 is only once in arr2"""
+        assert_equal(arr1.shape, arr2.shape)
+
+        truths = np.zeros((arr1.shape[0],), dtype=bool)
+        for l1 in arr1:
+            indexes = np.nonzero((abs(arr2 - l1) < rtol).all(axis=1))[0]
+            assert_equal(indexes.shape, (1,))
+            truths[indexes[0]] = True
+        assert_(truths.all())
+
+    @pytest.mark.parametrize("dt", [np.float64, int])
+    def test_cube_halfspace_intersection(self, dt):
+        halfspaces = np.array([[-1, 0, 0],
+                               [0, -1, 0],
+                               [1, 0, -2],
+                               [0, 1, -2]], dtype=dt)
+        feasible_point = np.array([1, 1], dtype=dt)
+
+        points = np.array([[0.0, 0.0], [2.0, 0.0], [0.0, 2.0], [2.0, 2.0]])
+
+        hull = qhull.HalfspaceIntersection(halfspaces, feasible_point)
+
+        assert_allclose(hull.intersections, points)
+
+    def test_self_dual_polytope_intersection(self):
+        fname = os.path.join(os.path.dirname(__file__), 'data',
+                             'selfdual-4d-polytope.txt')
+        ineqs = np.genfromtxt(fname)
+        halfspaces = -np.hstack((ineqs[:, 1:], ineqs[:, :1]))
+
+        feas_point = np.array([0., 0., 0., 0.])
+        hs = qhull.HalfspaceIntersection(halfspaces, feas_point)
+
+        assert_equal(hs.intersections.shape, (24, 4))
+
+        assert_almost_equal(hs.dual_volume, 32.0)
+        assert_equal(len(hs.dual_facets), 24)
+        for facet in hs.dual_facets:
+            assert_equal(len(facet), 6)
+
+        dists = halfspaces[:, -1] + halfspaces[:, :-1].dot(feas_point)
+        self.assert_unordered_allclose((halfspaces[:, :-1].T/dists).T, hs.dual_points)
+
+        points = itertools.permutations([0., 0., 0.5, -0.5])
+        for point in points:
+            assert_equal(np.sum((hs.intersections == point).all(axis=1)), 1)
+
+    def test_wrong_feasible_point(self):
+        halfspaces = np.array([[-1.0, 0.0, 0.0],
+                               [0.0, -1.0, 0.0],
+                               [1.0, 0.0, -1.0],
+                               [0.0, 1.0, -1.0]])
+        feasible_point = np.array([0.5, 0.5, 0.5])
+        #Feasible point is (ndim,) instead of (ndim-1,)
+        assert_raises(ValueError,
+                      qhull.HalfspaceIntersection, halfspaces, feasible_point)
+        feasible_point = np.array([[0.5], [0.5]])
+        #Feasible point is (ndim-1, 1) instead of (ndim-1,)
+        assert_raises(ValueError,
+                      qhull.HalfspaceIntersection, halfspaces, feasible_point)
+        feasible_point = np.array([[0.5, 0.5]])
+        #Feasible point is (1, ndim-1) instead of (ndim-1,)
+        assert_raises(ValueError,
+                      qhull.HalfspaceIntersection, halfspaces, feasible_point)
+
+        feasible_point = np.array([-0.5, -0.5])
+        #Feasible point is outside feasible region
+        assert_raises(qhull.QhullError,
+                      qhull.HalfspaceIntersection, halfspaces, feasible_point)
+
+    def test_incremental(self):
+        #Cube
+        halfspaces = np.array([[0., 0., -1., -0.5],
+                               [0., -1., 0., -0.5],
+                               [-1., 0., 0., -0.5],
+                               [1., 0., 0., -0.5],
+                               [0., 1., 0., -0.5],
+                               [0., 0., 1., -0.5]])
+        #Cut each summit
+        extra_normals = np.array([[1., 1., 1.],
+                                  [1., 1., -1.],
+                                  [1., -1., 1.],
+                                  [1, -1., -1.]])
+        offsets = np.array([[-1.]]*8)
+        extra_halfspaces = np.hstack((np.vstack((extra_normals, -extra_normals)),
+                                      offsets))
+
+        feas_point = np.array([0., 0., 0.])
+
+        inc_hs = qhull.HalfspaceIntersection(halfspaces, feas_point, incremental=True)
+
+        inc_res_hs = qhull.HalfspaceIntersection(halfspaces, feas_point,
+                                                 incremental=True)
+
+        for i, ehs in enumerate(extra_halfspaces):
+            inc_hs.add_halfspaces(ehs[np.newaxis, :])
+
+            inc_res_hs.add_halfspaces(ehs[np.newaxis, :], restart=True)
+
+            total = np.vstack((halfspaces, extra_halfspaces[:i+1, :]))
+
+            hs = qhull.HalfspaceIntersection(total, feas_point)
+
+            assert_allclose(inc_hs.halfspaces, inc_res_hs.halfspaces)
+            assert_allclose(inc_hs.halfspaces, hs.halfspaces)
+
+            #Direct computation and restart should have points in same order
+            assert_allclose(hs.intersections, inc_res_hs.intersections)
+            #Incremental will have points in different order than direct computation
+            self.assert_unordered_allclose(inc_hs.intersections, hs.intersections)
+
+        inc_hs.close()
+
+    def test_cube(self):
+        # Halfspaces of the cube:
+        halfspaces = np.array([[-1., 0., 0., 0.],  # x >= 0
+                               [1., 0., 0., -1.],  # x <= 1
+                               [0., -1., 0., 0.],  # y >= 0
+                               [0., 1., 0., -1.],  # y <= 1
+                               [0., 0., -1., 0.],  # z >= 0
+                               [0., 0., 1., -1.]])  # z <= 1
+        point = np.array([0.5, 0.5, 0.5])
+
+        hs = qhull.HalfspaceIntersection(halfspaces, point)
+
+        # qhalf H0.5,0.5,0.5 o < input.txt
+        qhalf_points = np.array([
+            [-2, 0, 0],
+            [2, 0, 0],
+            [0, -2, 0],
+            [0, 2, 0],
+            [0, 0, -2],
+            [0, 0, 2]])
+        qhalf_facets = [
+            [2, 4, 0],
+            [4, 2, 1],
+            [5, 2, 0],
+            [2, 5, 1],
+            [3, 4, 1],
+            [4, 3, 0],
+            [5, 3, 1],
+            [3, 5, 0]]
+
+        assert len(qhalf_facets) == len(hs.dual_facets)
+        for a, b in zip(qhalf_facets, hs.dual_facets):
+            assert set(a) == set(b)  # facet orientation can differ
+
+        assert_allclose(hs.dual_points, qhalf_points)
+
+    @pytest.mark.parametrize("k", range(1,4))
+    def test_halfspace_batch(self, k):
+        # Test that we can add halfspaces a few at a time
+        big_square = np.array([[ 1.,  0., -2.],
+                               [-1.,  0., -2.],
+                               [ 0.,  1., -2.],
+                               [ 0., -1., -2.]])
+
+        small_square = np.array([[ 1.,  0., -1.],
+                                 [-1.,  0., -1.],
+                                 [ 0.,  1., -1.],
+                                 [ 0., -1., -1.]])
+
+        hs = qhull.HalfspaceIntersection(big_square,
+                                         np.array([0.3141, 0.2718]),
+                                         incremental=True)
+
+        hs.add_halfspaces(small_square[0:k,:])
+        hs.add_halfspaces(small_square[k:4,:])
+        hs.close()
+
+        # Check the intersections are correct (they are the corners of the small square)
+        expected_intersections = np.array([[1., 1.],
+                                           [1., -1.],
+                                           [-1., 1.],
+                                           [-1., -1.]])
+        actual_intersections = hs.intersections
+        # They may be in any order, so just check that under some permutation 
+        # expected=actual.
+
+        ind1 = np.lexsort((actual_intersections[:, 1], actual_intersections[:, 0]))
+        ind2 = np.lexsort((expected_intersections[:, 1], expected_intersections[:, 0]))
+        assert_allclose(actual_intersections[ind1], expected_intersections[ind2])
+
+
+    @pytest.mark.parametrize("halfspaces", [
+    (np.array([-0.70613882, -0.45589431, 0.04178256])),
+    (np.array([[-0.70613882, -0.45589431,  0.04178256],
+               [0.70807342, -0.45464871, -0.45969769],
+               [0.,  0.76515026, -0.35614825]])),
+    ])
+    def test_gh_19865(self, halfspaces):
+        # starting off with a feasible interior point and
+        # adding halfspaces for which it is no longer feasible
+        # should result in an error rather than a problematic
+        # intersection polytope
+        initial_square =  np.array(
+                    [[1, 0, -1], [0, 1, -1], [-1, 0, -1], [0, -1, -1]]
+                )
+        incremental_intersector = qhull.HalfspaceIntersection(initial_square,
+                                                              np.zeros(2),
+                                                              incremental=True)
+        with pytest.raises(qhull.QhullError, match="feasible.*-0.706.*"):
+            incremental_intersector.add_halfspaces(halfspaces)
+
+
+    def test_gh_19865_3d(self):
+        # 3d case where closed half space is enforced for
+        # feasibility
+        halfspaces = np.array([[1, 1, 1, -1], # doesn't exclude origin
+                               [-1, -1, -1, -1], # doesn't exclude origin
+                               [1, 0, 0, 0]]) # the origin is on the line
+        initial_cube = np.array([[1, 0, 0, -1],
+                                 [-1, 0, 0, -1],
+                                 [0, 1, 0, -1],
+                                 [0, -1, 0, -1],
+                                 [0, 0, 1, -1],
+                                 [0, 0, -1, -1]])
+        incremental_intersector = qhull.HalfspaceIntersection(initial_cube,
+                                                              np.zeros(3),
+                                                              incremental=True)
+        with pytest.raises(qhull.QhullError, match="feasible.*[1 0 0 0]"):
+            incremental_intersector.add_halfspaces(halfspaces)
+
+
+    def test_2d_add_halfspace_input(self):
+        # incrementally added halfspaces should respect the 2D
+        # array shape requirement
+        initial_square =  np.array(
+                    [[1, 0, -1], [0, 1, -1], [-1, 0, -1], [0, -1, -1]]
+                )
+        incremental_intersector = qhull.HalfspaceIntersection(initial_square,
+                                                              np.zeros(2),
+                                                              incremental=True)
+        with pytest.raises(ValueError, match="2D array"):
+            incremental_intersector.add_halfspaces(np.ones((4, 4, 4)))
+
+    def test_1d_add_halfspace_input(self):
+        # we do allow 1D `halfspaces` input to add_halfspaces()
+        initial_square =  np.array(
+                    [[1, 0, -1], [0, 1, -1], [-1, 0, -1], [0, -1, -1]]
+                )
+        incremental_intersector = qhull.HalfspaceIntersection(initial_square,
+                                                              np.zeros(2),
+                                                              incremental=True)
+        assert_allclose(incremental_intersector.dual_vertices, np.arange(4))
+        incremental_intersector.add_halfspaces(np.array([2, 2, -1]))
+        assert_allclose(incremental_intersector.dual_vertices, np.arange(5))
+
+
+@pytest.mark.parametrize("diagram_type", [Voronoi, qhull.Delaunay])
+def test_gh_20623(diagram_type):
+    rng = np.random.default_rng(123)
+    invalid_data = rng.random((4, 10, 3))
+    with pytest.raises(ValueError, match="dimensions"):
+        diagram_type(invalid_data)
+
+
+def test_gh_21286():
+    generators = np.array([[0, 0], [0, 1.1], [1, 0], [1, 1]])
+    tri = qhull.Delaunay(generators)
+    # verify absence of segfault reported in ticket:
+    with pytest.raises(IndexError):
+        tri.find_simplex(1)
+    with pytest.raises(IndexError):
+        # strikingly, Delaunay object has shape
+        # () just like np.asanyarray(1) above
+        tri.find_simplex(tri)
+
+
+def test_find_simplex_ndim_err():
+    generators = np.array([[0, 0], [0, 1.1], [1, 0], [1, 1]])
+    tri = qhull.Delaunay(generators)
+    with pytest.raises(ValueError):
+        tri.find_simplex([2, 2, 2])
diff --git a/shared/package-files/4e/4e1996f288739dd8bee3e5adc955f14ab5b8d0c9cb233d57a2a45c9b6460487c/codata.py b/shared/package-files/4e/4e1996f288739dd8bee3e5adc955f14ab5b8d0c9cb233d57a2a45c9b6460487c/codata.py
new file mode 100644
index 0000000000000000000000000000000000000000..912e0bbf7c4f14d23ced4546b6704f7789996d97
--- /dev/null
+++ b/shared/package-files/4e/4e1996f288739dd8bee3e5adc955f14ab5b8d0c9cb233d57a2a45c9b6460487c/codata.py
@@ -0,0 +1,21 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.constants` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__ = [  # noqa: F822
+    'physical_constants', 'value', 'unit', 'precision', 'find',
+    'ConstantWarning', 'k', 'c',
+
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="constants", module="codata",
+                                   private_modules=["_codata"], all=__all__,
+                                   attribute=name)
diff --git a/shared/package-files/4e/4e1a694c718916b51f6a5e6d7b04bbd032f4d1d4b0935a2d6b3b95749ad38a81/testonechar_6.1_SOL2.mat b/shared/package-files/4e/4e1a694c718916b51f6a5e6d7b04bbd032f4d1d4b0935a2d6b3b95749ad38a81/testonechar_6.1_SOL2.mat
new file mode 100644
index 0000000000000000000000000000000000000000..3b5a428501a53ae7308c7b6edc42f4881820664d
Binary files /dev/null and b/shared/package-files/4e/4e1a694c718916b51f6a5e6d7b04bbd032f4d1d4b0935a2d6b3b95749ad38a81/testonechar_6.1_SOL2.mat differ
diff --git a/shared/package-files/4e/4e1aeb613108c109ddd5c39fc3efe9eb27516f57b6fbb2b9094edd2695d3c7e6/powsimp.py b/shared/package-files/4e/4e1aeb613108c109ddd5c39fc3efe9eb27516f57b6fbb2b9094edd2695d3c7e6/powsimp.py
new file mode 100644
index 0000000000000000000000000000000000000000..d179848a66438c9d7409a7f6ef9e1edbcec125e7
--- /dev/null
+++ b/shared/package-files/4e/4e1aeb613108c109ddd5c39fc3efe9eb27516f57b6fbb2b9094edd2695d3c7e6/powsimp.py
@@ -0,0 +1,714 @@
+from collections import defaultdict
+from functools import reduce
+from math import prod
+
+from sympy.core.function import expand_log, count_ops, _coeff_isneg
+from sympy.core import sympify, Basic, Dummy, S, Add, Mul, Pow, expand_mul, factor_terms
+from sympy.core.sorting import ordered, default_sort_key
+from sympy.core.numbers import Integer, Rational
+from sympy.core.mul import _keep_coeff
+from sympy.core.rules import Transform
+from sympy.functions import exp_polar, exp, log, root, polarify, unpolarify
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.polys import lcm, gcd
+from sympy.ntheory.factor_ import multiplicity
+
+
+
+def powsimp(expr, deep=False, combine='all', force=False, measure=count_ops):
+    """
+    Reduce expression by combining powers with similar bases and exponents.
+
+    Explanation
+    ===========
+
+    If ``deep`` is ``True`` then powsimp() will also simplify arguments of
+    functions. By default ``deep`` is set to ``False``.
+
+    If ``force`` is ``True`` then bases will be combined without checking for
+    assumptions, e.g. sqrt(x)*sqrt(y) -> sqrt(x*y) which is not true
+    if x and y are both negative.
+
+    You can make powsimp() only combine bases or only combine exponents by
+    changing combine='base' or combine='exp'.  By default, combine='all',
+    which does both.  combine='base' will only combine::
+
+         a   a          a                          2x      x
+        x * y  =>  (x*y)   as well as things like 2   =>  4
+
+    and combine='exp' will only combine
+    ::
+
+         a   b      (a + b)
+        x * x  =>  x
+
+    combine='exp' will strictly only combine exponents in the way that used
+    to be automatic.  Also use deep=True if you need the old behavior.
+
+    When combine='all', 'exp' is evaluated first.  Consider the first
+    example below for when there could be an ambiguity relating to this.
+    This is done so things like the second example can be completely
+    combined.  If you want 'base' combined first, do something like
+    powsimp(powsimp(expr, combine='base'), combine='exp').
+
+    Examples
+    ========
+
+    >>> from sympy import powsimp, exp, log, symbols
+    >>> from sympy.abc import x, y, z, n
+    >>> powsimp(x**y*x**z*y**z, combine='all')
+    x**(y + z)*y**z
+    >>> powsimp(x**y*x**z*y**z, combine='exp')
+    x**(y + z)*y**z
+    >>> powsimp(x**y*x**z*y**z, combine='base', force=True)
+    x**y*(x*y)**z
+
+    >>> powsimp(x**z*x**y*n**z*n**y, combine='all', force=True)
+    (n*x)**(y + z)
+    >>> powsimp(x**z*x**y*n**z*n**y, combine='exp')
+    n**(y + z)*x**(y + z)
+    >>> powsimp(x**z*x**y*n**z*n**y, combine='base', force=True)
+    (n*x)**y*(n*x)**z
+
+    >>> x, y = symbols('x y', positive=True)
+    >>> powsimp(log(exp(x)*exp(y)))
+    log(exp(x)*exp(y))
+    >>> powsimp(log(exp(x)*exp(y)), deep=True)
+    x + y
+
+    Radicals with Mul bases will be combined if combine='exp'
+
+    >>> from sympy import sqrt
+    >>> x, y = symbols('x y')
+
+    Two radicals are automatically joined through Mul:
+
+    >>> a=sqrt(x*sqrt(y))
+    >>> a*a**3 == a**4
+    True
+
+    But if an integer power of that radical has been
+    autoexpanded then Mul does not join the resulting factors:
+
+    >>> a**4 # auto expands to a Mul, no longer a Pow
+    x**2*y
+    >>> _*a # so Mul doesn't combine them
+    x**2*y*sqrt(x*sqrt(y))
+    >>> powsimp(_) # but powsimp will
+    (x*sqrt(y))**(5/2)
+    >>> powsimp(x*y*a) # but won't when doing so would violate assumptions
+    x*y*sqrt(x*sqrt(y))
+
+    """
+    def recurse(arg, **kwargs):
+        _deep = kwargs.get('deep', deep)
+        _combine = kwargs.get('combine', combine)
+        _force = kwargs.get('force', force)
+        _measure = kwargs.get('measure', measure)
+        return powsimp(arg, _deep, _combine, _force, _measure)
+
+    expr = sympify(expr)
+
+    if (not isinstance(expr, Basic) or isinstance(expr, MatrixSymbol) or (
+            expr.is_Atom or expr in (exp_polar(0), exp_polar(1)))):
+        return expr
+
+    if deep or expr.is_Add or expr.is_Mul and _y not in expr.args:
+        expr = expr.func(*[recurse(w) for w in expr.args])
+
+    if expr.is_Pow:
+        return recurse(expr*_y, deep=False)/_y
+
+    if not expr.is_Mul:
+        return expr
+
+    # handle the Mul
+    if combine in ('exp', 'all'):
+        # Collect base/exp data, while maintaining order in the
+        # non-commutative parts of the product
+        c_powers = defaultdict(list)
+        nc_part = []
+        newexpr = []
+        coeff = S.One
+        for term in expr.args:
+            if term.is_Rational:
+                coeff *= term
+                continue
+            if term.is_Pow:
+                term = _denest_pow(term)
+            if term.is_commutative:
+                b, e = term.as_base_exp()
+                if deep:
+                    b, e = [recurse(i) for i in [b, e]]
+                if b.is_Pow or isinstance(b, exp):
+                    # don't let smthg like sqrt(x**a) split into x**a, 1/2
+                    # or else it will be joined as x**(a/2) later
+                    b, e = b**e, S.One
+                c_powers[b].append(e)
+            else:
+                # This is the logic that combines exponents for equal,
+                # but non-commutative bases: A**x*A**y == A**(x+y).
+                if nc_part:
+                    b1, e1 = nc_part[-1].as_base_exp()
+                    b2, e2 = term.as_base_exp()
+                    if (b1 == b2 and
+                            e1.is_commutative and e2.is_commutative):
+                        nc_part[-1] = Pow(b1, Add(e1, e2))
+                        continue
+                nc_part.append(term)
+
+        # add up exponents of common bases
+        for b, e in ordered(iter(c_powers.items())):
+            # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are
+            # Numbers since autoevaluation will undo it, e.g.
+            # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4
+            if (b and b.is_Rational and not all(ei.is_Number for ei in e) and \
+                    coeff is not S.One and
+                    b not in (S.One, S.NegativeOne)):
+                m = multiplicity(abs(b), abs(coeff))
+                if m:
+                    e.append(m)
+                    coeff /= b**m
+            c_powers[b] = Add(*e)
+        if coeff is not S.One:
+            if coeff in c_powers:
+                c_powers[coeff] += S.One
+            else:
+                c_powers[coeff] = S.One
+
+        # convert to plain dictionary
+        c_powers = dict(c_powers)
+
+        # check for base and inverted base pairs
+        be = list(c_powers.items())
+        skip = set()  # skip if we already saw them
+        for b, e in be:
+            if b in skip:
+                continue
+            bpos = b.is_positive or b.is_polar
+            if bpos:
+                binv = 1/b
+                if b != binv and binv in c_powers:
+                    if b.as_numer_denom()[0] is S.One:
+                        c_powers.pop(b)
+                        c_powers[binv] -= e
+                    else:
+                        skip.add(binv)
+                        e = c_powers.pop(binv)
+                        c_powers[b] -= e
+
+        # check for base and negated base pairs
+        be = list(c_powers.items())
+        _n = S.NegativeOne
+        for b, e in be:
+            if (b.is_Symbol or b.is_Add) and -b in c_powers and b in c_powers:
+                if (b.is_positive is not None or e.is_integer):
+                    if e.is_integer or b.is_negative:
+                        c_powers[-b] += c_powers.pop(b)
+                    else:  # (-b).is_positive so use its e
+                        e = c_powers.pop(-b)
+                        c_powers[b] += e
+                    if _n in c_powers:
+                        c_powers[_n] += e
+                    else:
+                        c_powers[_n] = e
+
+        # filter c_powers and convert to a list
+        c_powers = [(b, e) for b, e in c_powers.items() if e]
+
+        # ==============================================================
+        # check for Mul bases of Rational powers that can be combined with
+        # separated bases, e.g. x*sqrt(x*y)*sqrt(x*sqrt(x*y)) ->
+        # (x*sqrt(x*y))**(3/2)
+        # ---------------- helper functions
+
+        def ratq(x):
+            '''Return Rational part of x's exponent as it appears in the bkey.
+            '''
+            return bkey(x)[0][1]
+
+        def bkey(b, e=None):
+            '''Return (b**s, c.q), c.p where e -> c*s. If e is not given then
+            it will be taken by using as_base_exp() on the input b.
+            e.g.
+                x**3/2 -> (x, 2), 3
+                x**y -> (x**y, 1), 1
+                x**(2*y/3) -> (x**y, 3), 2
+                exp(x/2) -> (exp(a), 2), 1
+
+            '''
+            if e is not None:  # coming from c_powers or from below
+                if e.is_Integer:
+                    return (b, S.One), e
+                elif e.is_Rational:
+                    return (b, Integer(e.q)), Integer(e.p)
+                else:
+                    c, m = e.as_coeff_Mul(rational=True)
+                    if c is not S.One:
+                        if m.is_integer:
+                            return (b, Integer(c.q)), m*Integer(c.p)
+                        return (b**m, Integer(c.q)), Integer(c.p)
+                    else:
+                        return (b**e, S.One), S.One
+            else:
+                return bkey(*b.as_base_exp())
+
+        def update(b):
+            '''Decide what to do with base, b. If its exponent is now an
+            integer multiple of the Rational denominator, then remove it
+            and put the factors of its base in the common_b dictionary or
+            update the existing bases if necessary. If it has been zeroed
+            out, simply remove the base.
+            '''
+            newe, r = divmod(common_b[b], b[1])
+            if not r:
+                common_b.pop(b)
+                if newe:
+                    for m in Mul.make_args(b[0]**newe):
+                        b, e = bkey(m)
+                        if b not in common_b:
+                            common_b[b] = 0
+                        common_b[b] += e
+                        if b[1] != 1:
+                            bases.append(b)
+        # ---------------- end of helper functions
+
+        # assemble a dictionary of the factors having a Rational power
+        common_b = {}
+        done = []
+        bases = []
+        for b, e in c_powers:
+            b, e = bkey(b, e)
+            if b in common_b:
+                common_b[b] = common_b[b] + e
+            else:
+                common_b[b] = e
+            if b[1] != 1 and b[0].is_Mul:
+                bases.append(b)
+        bases.sort(key=default_sort_key)  # this makes tie-breaking canonical
+        bases.sort(key=measure, reverse=True)  # handle longest first
+        for base in bases:
+            if base not in common_b:  # it may have been removed already
+                continue
+            b, exponent = base
+            last = False  # True when no factor of base is a radical
+            qlcm = 1  # the lcm of the radical denominators
+            while True:
+                bstart = b
+                qstart = qlcm
+
+                bb = []  # list of factors
+                ee = []  # (factor's expo. and it's current value in common_b)
+                for bi in Mul.make_args(b):
+                    bib, bie = bkey(bi)
+                    if bib not in common_b or common_b[bib] < bie:
+                        ee = bb = []  # failed
+                        break
+                    ee.append([bie, common_b[bib]])
+                    bb.append(bib)
+                if ee:
+                    # find the number of integral extractions possible
+                    # e.g. [(1, 2), (2, 2)] -> min(2/1, 2/2) -> 1
+                    min1 = ee[0][1]//ee[0][0]
+                    for i in range(1, len(ee)):
+                        rat = ee[i][1]//ee[i][0]
+                        if rat < 1:
+                            break
+                        min1 = min(min1, rat)
+                    else:
+                        # update base factor counts
+                        # e.g. if ee = [(2, 5), (3, 6)] then min1 = 2
+                        # and the new base counts will be 5-2*2 and 6-2*3
+                        for i in range(len(bb)):
+                            common_b[bb[i]] -= min1*ee[i][0]
+                            update(bb[i])
+                        # update the count of the base
+                        # e.g. x**2*y*sqrt(x*sqrt(y)) the count of x*sqrt(y)
+                        # will increase by 4 to give bkey (x*sqrt(y), 2, 5)
+                        common_b[base] += min1*qstart*exponent
+                if (last  # no more radicals in base
+                    or len(common_b) == 1  # nothing left to join with
+                    or all(k[1] == 1 for k in common_b)  # no rad's in common_b
+                        ):
+                    break
+                # see what we can exponentiate base by to remove any radicals
+                # so we know what to search for
+                # e.g. if base were x**(1/2)*y**(1/3) then we should
+                # exponentiate by 6 and look for powers of x and y in the ratio
+                # of 2 to 3
+                qlcm = lcm([ratq(bi) for bi in Mul.make_args(bstart)])
+                if qlcm == 1:
+                    break  # we are done
+                b = bstart**qlcm
+                qlcm *= qstart
+                if all(ratq(bi) == 1 for bi in Mul.make_args(b)):
+                    last = True  # we are going to be done after this next pass
+            # this base no longer can find anything to join with and
+            # since it was longer than any other we are done with it
+            b, q = base
+            done.append((b, common_b.pop(base)*Rational(1, q)))
+
+        # update c_powers and get ready to continue with powsimp
+        c_powers = done
+        # there may be terms still in common_b that were bases that were
+        # identified as needing processing, so remove those, too
+        for (b, q), e in common_b.items():
+            if (b.is_Pow or isinstance(b, exp)) and \
+                    q is not S.One and not b.exp.is_Rational:
+                b, be = b.as_base_exp()
+                b = b**(be/q)
+            else:
+                b = root(b, q)
+            c_powers.append((b, e))
+        check = len(c_powers)
+        c_powers = dict(c_powers)
+        assert len(c_powers) == check  # there should have been no duplicates
+        # ==============================================================
+
+        # rebuild the expression
+        newexpr = expr.func(*(newexpr + [Pow(b, e) for b, e in c_powers.items()]))
+        if combine == 'exp':
+            return expr.func(newexpr, expr.func(*nc_part))
+        else:
+            return recurse(expr.func(*nc_part), combine='base') * \
+                recurse(newexpr, combine='base')
+
+    elif combine == 'base':
+
+        # Build c_powers and nc_part.  These must both be lists not
+        # dicts because exp's are not combined.
+        c_powers = []
+        nc_part = []
+        for term in expr.args:
+            if term.is_commutative:
+                c_powers.append(list(term.as_base_exp()))
+            else:
+                nc_part.append(term)
+
+        # Pull out numerical coefficients from exponent if assumptions allow
+        # e.g., 2**(2*x) => 4**x
+        for i in range(len(c_powers)):
+            b, e = c_powers[i]
+            if not (all(x.is_nonnegative for x in b.as_numer_denom()) or e.is_integer or force or b.is_polar):
+                continue
+            exp_c, exp_t = e.as_coeff_Mul(rational=True)
+            if exp_c is not S.One and exp_t is not S.One:
+                c_powers[i] = [Pow(b, exp_c), exp_t]
+
+        # Combine bases whenever they have the same exponent and
+        # assumptions allow
+        # first gather the potential bases under the common exponent
+        c_exp = defaultdict(list)
+        for b, e in c_powers:
+            if deep:
+                e = recurse(e)
+            if e.is_Add and (b.is_positive or e.is_integer):
+                e = factor_terms(e)
+                if _coeff_isneg(e):
+                    e = -e
+                    b = 1/b
+            c_exp[e].append(b)
+        del c_powers
+
+        # Merge back in the results of the above to form a new product
+        c_powers = defaultdict(list)
+        for e in c_exp:
+            bases = c_exp[e]
+
+            # calculate the new base for e
+
+            if len(bases) == 1:
+                new_base = bases[0]
+            elif e.is_integer or force:
+                new_base = expr.func(*bases)
+            else:
+                # see which ones can be joined
+                unk = []
+                nonneg = []
+                neg = []
+                for bi in bases:
+                    if bi.is_negative:
+                        neg.append(bi)
+                    elif bi.is_nonnegative:
+                        nonneg.append(bi)
+                    elif bi.is_polar:
+                        nonneg.append(
+                            bi)  # polar can be treated like non-negative
+                    else:
+                        unk.append(bi)
+                if len(unk) == 1 and not neg or len(neg) == 1 and not unk:
+                    # a single neg or a single unk can join the rest
+                    nonneg.extend(unk + neg)
+                    unk = neg = []
+                elif neg:
+                    # their negative signs cancel in groups of 2*q if we know
+                    # that e = p/q else we have to treat them as unknown
+                    israt = False
+                    if e.is_Rational:
+                        israt = True
+                    else:
+                        p, d = e.as_numer_denom()
+                        if p.is_integer and d.is_integer:
+                            israt = True
+                    if israt:
+                        neg = [-w for w in neg]
+                        unk.extend([S.NegativeOne]*len(neg))
+                    else:
+                        unk.extend(neg)
+                        neg = []
+                    del israt
+
+                # these shouldn't be joined
+                for b in unk:
+                    c_powers[b].append(e)
+                # here is a new joined base
+                new_base = expr.func(*(nonneg + neg))
+                # if there are positive parts they will just get separated
+                # again unless some change is made
+
+                def _terms(e):
+                    # return the number of terms of this expression
+                    # when multiplied out -- assuming no joining of terms
+                    if e.is_Add:
+                        return sum([_terms(ai) for ai in e.args])
+                    if e.is_Mul:
+                        return prod([_terms(mi) for mi in e.args])
+                    return 1
+                xnew_base = expand_mul(new_base, deep=False)
+                if len(Add.make_args(xnew_base)) < _terms(new_base):
+                    new_base = factor_terms(xnew_base)
+
+            c_powers[new_base].append(e)
+
+        # break out the powers from c_powers now
+        c_part = [Pow(b, ei) for b, e in c_powers.items() for ei in e]
+
+        # we're done
+        return expr.func(*(c_part + nc_part))
+
+    else:
+        raise ValueError("combine must be one of ('all', 'exp', 'base').")
+
+
+def powdenest(eq, force=False, polar=False):
+    r"""
+    Collect exponents on powers as assumptions allow.
+
+    Explanation
+    ===========
+
+    Given ``(bb**be)**e``, this can be simplified as follows:
+        * if ``bb`` is positive, or
+        * ``e`` is an integer, or
+        * ``|be| < 1`` then this simplifies to ``bb**(be*e)``
+
+    Given a product of powers raised to a power, ``(bb1**be1 *
+    bb2**be2...)**e``, simplification can be done as follows:
+
+    - if e is positive, the gcd of all bei can be joined with e;
+    - all non-negative bb can be separated from those that are negative
+      and their gcd can be joined with e; autosimplification already
+      handles this separation.
+    - integer factors from powers that have integers in the denominator
+      of the exponent can be removed from any term and the gcd of such
+      integers can be joined with e
+
+    Setting ``force`` to ``True`` will make symbols that are not explicitly
+    negative behave as though they are positive, resulting in more
+    denesting.
+
+    Setting ``polar`` to ``True`` will do simplifications on the Riemann surface of
+    the logarithm, also resulting in more denestings.
+
+    When there are sums of logs in exp() then a product of powers may be
+    obtained e.g. ``exp(3*(log(a) + 2*log(b)))`` - > ``a**3*b**6``.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import a, b, x, y, z
+    >>> from sympy import Symbol, exp, log, sqrt, symbols, powdenest
+
+    >>> powdenest((x**(2*a/3))**(3*x))
+    (x**(2*a/3))**(3*x)
+    >>> powdenest(exp(3*x*log(2)))
+    2**(3*x)
+
+    Assumptions may prevent expansion:
+
+    >>> powdenest(sqrt(x**2))
+    sqrt(x**2)
+
+    >>> p = symbols('p', positive=True)
+    >>> powdenest(sqrt(p**2))
+    p
+
+    No other expansion is done.
+
+    >>> i, j = symbols('i,j', integer=True)
+    >>> powdenest((x**x)**(i + j)) # -X-> (x**x)**i*(x**x)**j
+    x**(x*(i + j))
+
+    But exp() will be denested by moving all non-log terms outside of
+    the function; this may result in the collapsing of the exp to a power
+    with a different base:
+
+    >>> powdenest(exp(3*y*log(x)))
+    x**(3*y)
+    >>> powdenest(exp(y*(log(a) + log(b))))
+    (a*b)**y
+    >>> powdenest(exp(3*(log(a) + log(b))))
+    a**3*b**3
+
+    If assumptions allow, symbols can also be moved to the outermost exponent:
+
+    >>> i = Symbol('i', integer=True)
+    >>> powdenest(((x**(2*i))**(3*y))**x)
+    ((x**(2*i))**(3*y))**x
+    >>> powdenest(((x**(2*i))**(3*y))**x, force=True)
+    x**(6*i*x*y)
+
+    >>> powdenest(((x**(2*a/3))**(3*y/i))**x)
+    ((x**(2*a/3))**(3*y/i))**x
+    >>> powdenest((x**(2*i)*y**(4*i))**z, force=True)
+    (x*y**2)**(2*i*z)
+
+    >>> n = Symbol('n', negative=True)
+
+    >>> powdenest((x**i)**y, force=True)
+    x**(i*y)
+    >>> powdenest((n**i)**x, force=True)
+    (n**i)**x
+
+    """
+    from sympy.simplify.simplify import posify
+
+    if force:
+        def _denest(b, e):
+            if not isinstance(b, (Pow, exp)):
+                return b.is_positive, Pow(b, e, evaluate=False)
+            return _denest(b.base, b.exp*e)
+        reps = []
+        for p in eq.atoms(Pow, exp):
+            if isinstance(p.base, (Pow, exp)):
+                ok, dp = _denest(*p.args)
+                if ok is not False:
+                    reps.append((p, dp))
+        if reps:
+            eq = eq.subs(reps)
+        eq, reps = posify(eq)
+        return powdenest(eq, force=False, polar=polar).xreplace(reps)
+
+    if polar:
+        eq, rep = polarify(eq)
+        return unpolarify(powdenest(unpolarify(eq, exponents_only=True)), rep)
+
+    new = powsimp(eq)
+    return new.xreplace(Transform(
+        _denest_pow, filter=lambda m: m.is_Pow or isinstance(m, exp)))
+
+_y = Dummy('y')
+
+
+def _denest_pow(eq):
+    """
+    Denest powers.
+
+    This is a helper function for powdenest that performs the actual
+    transformation.
+    """
+    from sympy.simplify.simplify import logcombine
+
+    b, e = eq.as_base_exp()
+    if b.is_Pow or isinstance(b, exp) and e != 1:
+        new = b._eval_power(e)
+        if new is not None:
+            eq = new
+            b, e = new.as_base_exp()
+
+    # denest exp with log terms in exponent
+    if b is S.Exp1 and e.is_Mul:
+        logs = []
+        other = []
+        for ei in e.args:
+            if any(isinstance(ai, log) for ai in Add.make_args(ei)):
+                logs.append(ei)
+            else:
+                other.append(ei)
+        logs = logcombine(Mul(*logs))
+        return Pow(exp(logs), Mul(*other))
+
+    _, be = b.as_base_exp()
+    if be is S.One and not (b.is_Mul or
+                            b.is_Rational and b.q != 1 or
+                            b.is_positive):
+        return eq
+
+    # denest eq which is either pos**e or Pow**e or Mul**e or
+    # Mul(b1**e1, b2**e2)
+
+    # handle polar numbers specially
+    polars, nonpolars = [], []
+    for bb in Mul.make_args(b):
+        if bb.is_polar:
+            polars.append(bb.as_base_exp())
+        else:
+            nonpolars.append(bb)
+    if len(polars) == 1 and not polars[0][0].is_Mul:
+        return Pow(polars[0][0], polars[0][1]*e)*powdenest(Mul(*nonpolars)**e)
+    elif polars:
+        return Mul(*[powdenest(bb**(ee*e)) for (bb, ee) in polars]) \
+            *powdenest(Mul(*nonpolars)**e)
+
+    if b.is_Integer:
+        # use log to see if there is a power here
+        logb = expand_log(log(b))
+        if logb.is_Mul:
+            c, logb = logb.args
+            e *= c
+            base = logb.args[0]
+            return Pow(base, e)
+
+    # if b is not a Mul or any factor is an atom then there is nothing to do
+    if not b.is_Mul or any(s.is_Atom for s in Mul.make_args(b)):
+        return eq
+
+    # let log handle the case of the base of the argument being a Mul, e.g.
+    # sqrt(x**(2*i)*y**(6*i)) -> x**i*y**(3**i) if x and y are positive; we
+    # will take the log, expand it, and then factor out the common powers that
+    # now appear as coefficient. We do this manually since terms_gcd pulls out
+    # fractions, terms_gcd(x+x*y/2) -> x*(y + 2)/2 and we don't want the 1/2;
+    # gcd won't pull out numerators from a fraction: gcd(3*x, 9*x/2) -> x but
+    # we want 3*x. Neither work with noncommutatives.
+
+    def nc_gcd(aa, bb):
+        a, b = [i.as_coeff_Mul() for i in [aa, bb]]
+        c = gcd(a[0], b[0]).as_numer_denom()[0]
+        g = Mul(*(a[1].args_cnc(cset=True)[0] & b[1].args_cnc(cset=True)[0]))
+        return _keep_coeff(c, g)
+
+    glogb = expand_log(log(b))
+    if glogb.is_Add:
+        args = glogb.args
+        g = reduce(nc_gcd, args)
+        if g != 1:
+            cg, rg = g.as_coeff_Mul()
+            glogb = _keep_coeff(cg, rg*Add(*[a/g for a in args]))
+
+    # now put the log back together again
+    if isinstance(glogb, log) or not glogb.is_Mul:
+        if glogb.args[0].is_Pow or isinstance(glogb.args[0], exp):
+            glogb = _denest_pow(glogb.args[0])
+            if (abs(glogb.exp) < 1) == True:
+                return Pow(glogb.base, glogb.exp*e)
+        return eq
+
+    # the log(b) was a Mul so join any adds with logcombine
+    add = []
+    other = []
+    for a in glogb.args:
+        if a.is_Add:
+            add.append(a)
+        else:
+            other.append(a)
+    return Pow(exp(logcombine(Mul(*add))), e*Mul(*other))
diff --git a/shared/package-files/4e/4e1b76f13b1c61638d432f40415196246f540ca46305f9a2d32b28f1f9ee28b1/batch_norm_gather_stats_compositeexplicitautograd_dispatch.h b/shared/package-files/4e/4e1b76f13b1c61638d432f40415196246f540ca46305f9a2d32b28f1f9ee28b1/batch_norm_gather_stats_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1df15dcfc2c1d4493f9c84f669927ba68950412
--- /dev/null
+++ b/shared/package-files/4e/4e1b76f13b1c61638d432f40415196246f540ca46305f9a2d32b28f1f9ee28b1/batch_norm_gather_stats_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_outf(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/4e/4e1c081ea566a2936541abd6afc15d4d4358c458f16c160494a7a73ec02ebc16/_copy_from_compositeexplicitautograd_dispatch.h b/shared/package-files/4e/4e1c081ea566a2936541abd6afc15d4d4358c458f16c160494a7a73ec02ebc16/_copy_from_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae70a40e850c9c062274d6c3865d59013219b5d2
--- /dev/null
+++ b/shared/package-files/4e/4e1c081ea566a2936541abd6afc15d4d4358c458f16c160494a7a73ec02ebc16/_copy_from_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _copy_from_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & dst, bool non_blocking=false);
+TORCH_API at::Tensor & _copy_from_outf(const at::Tensor & self, const at::Tensor & dst, bool non_blocking, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/4e/4e1dc94c079230f1dac221bc2e111b23a8c9b6de54f04c3d303b5a08f91f5d41/mathutils.py b/shared/package-files/4e/4e1dc94c079230f1dac221bc2e111b23a8c9b6de54f04c3d303b5a08f91f5d41/mathutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf7e95eff0af6d154c634fb5d2c743fa7a17ecc3
--- /dev/null
+++ b/shared/package-files/4e/4e1dc94c079230f1dac221bc2e111b23a8c9b6de54f04c3d303b5a08f91f5d41/mathutils.py
@@ -0,0 +1,261 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2013, Mahmoud Hashemi
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+#
+#    * Redistributions of source code must retain the above copyright
+#      notice, this list of conditions and the following disclaimer.
+#
+#    * Redistributions in binary form must reproduce the above
+#      copyright notice, this list of conditions and the following
+#      disclaimer in the documentation and/or other materials provided
+#      with the distribution.
+#
+#    * The names of the contributors may not be used to endorse or
+#      promote products derived from this software without specific
+#      prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+"""This module provides useful math functions on top of Python's
+built-in :mod:`math` module.
+"""
+from __future__ import division
+
+from math import ceil as _ceil, floor as _floor
+import bisect
+import binascii
+
+
+def clamp(x, lower=float('-inf'), upper=float('inf')):
+    """Limit a value to a given range.
+
+    Args:
+        x (int or float): Number to be clamped.
+        lower (int or float): Minimum value for x.
+        upper (int or float): Maximum value for x.
+
+    The returned value is guaranteed to be between *lower* and
+    *upper*. Integers, floats, and other comparable types can be
+    mixed.
+
+    >>> clamp(1.0, 0, 5)
+    1.0
+    >>> clamp(-1.0, 0, 5)
+    0
+    >>> clamp(101.0, 0, 5)
+    5
+    >>> clamp(123, upper=5)
+    5
+
+    Similar to `numpy's clip`_ function.
+
+    .. _numpy's clip: http://docs.scipy.org/doc/numpy/reference/generated/numpy.clip.html
+
+    """
+    if upper < lower:
+        raise ValueError('expected upper bound (%r) >= lower bound (%r)'
+                         % (upper, lower))
+    return min(max(x, lower), upper)
+
+
+def ceil(x, options=None):
+    """Return the ceiling of *x*. If *options* is set, return the smallest
+    integer or float from *options* that is greater than or equal to
+    *x*.
+
+    Args:
+        x (int or float): Number to be tested.
+        options (iterable): Optional iterable of arbitrary numbers
+          (ints or floats).
+
+    >>> VALID_CABLE_CSA = [1.5, 2.5, 4, 6, 10, 25, 35, 50]
+    >>> ceil(3.5, options=VALID_CABLE_CSA)
+    4
+    >>> ceil(4, options=VALID_CABLE_CSA)
+    4
+    """
+    if options is None:
+        return _ceil(x)
+    options = sorted(options)
+    i = bisect.bisect_left(options, x)
+    if i == len(options):
+        raise ValueError("no ceil options greater than or equal to: %r" % x)
+    return options[i]
+
+
+def floor(x, options=None):
+    """Return the floor of *x*. If *options* is set, return the largest
+    integer or float from *options* that is less than or equal to
+    *x*.
+
+    Args:
+        x (int or float): Number to be tested.
+        options (iterable): Optional iterable of arbitrary numbers
+          (ints or floats).
+
+    >>> VALID_CABLE_CSA = [1.5, 2.5, 4, 6, 10, 25, 35, 50]
+    >>> floor(3.5, options=VALID_CABLE_CSA)
+    2.5
+    >>> floor(2.5, options=VALID_CABLE_CSA)
+    2.5
+
+    """
+    if options is None:
+        return _floor(x)
+    options = sorted(options)
+
+    i = bisect.bisect_right(options, x)
+    if not i:
+        raise ValueError("no floor options less than or equal to: %r" % x)
+    return options[i - 1]
+
+
+try:
+    _int_types = (int, long)
+    bytes = str
+except NameError:
+    # py3 has no long
+    _int_types = (int,)
+    unicode = str
+
+
+class Bits(object):
+    '''
+    An immutable bit-string or bit-array object.
+    Provides list-like access to bits as bools,
+    as well as bitwise masking and shifting operators.
+    Bits also make it easy to convert between many
+    different useful representations:
+
+    * bytes -- good for serializing raw binary data
+    * int -- good for incrementing (e.g. to try all possible values)
+    * list of bools -- good for iterating over or treating as flags
+    * hex/bin string -- good for human readability
+
+    '''
+    __slots__ = ('val', 'len')
+
+    def __init__(self, val=0, len_=None):
+        if type(val) not in _int_types:
+            if type(val) is list:
+                val = ''.join(['1' if e else '0' for e in val])
+            if type(val) is bytes:
+                val = val.decode('ascii')
+            if type(val) is unicode:
+                if len_ is None:
+                    len_ = len(val)
+                    if val.startswith('0x'):
+                        len_ = (len_ - 2) * 4
+                if val.startswith('0x'):
+                    val = int(val, 16)
+                else:
+                    if val:
+                        val = int(val, 2)
+                    else:
+                        val = 0
+            if type(val) not in _int_types:
+                raise TypeError('initialized with bad type: {0}'.format(type(val).__name__))
+        if val < 0:
+            raise ValueError('Bits cannot represent negative values')
+        if len_ is None:
+            len_ = len('{0:b}'.format(val))
+        if val > 2 ** len_:
+            raise ValueError('value {0} cannot be represented with {1} bits'.format(val, len_))
+        self.val = val  # data is stored internally as integer
+        self.len = len_
+
+    def __getitem__(self, k):
+        if type(k) is slice:
+            return Bits(self.as_bin()[k])
+        if type(k) is int:
+            if k >= self.len:
+                raise IndexError(k)
+            return bool((1 << (self.len - k - 1)) & self.val)
+        raise TypeError(type(k))
+
+    def __len__(self):
+        return self.len
+
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return NotImplemented
+        return self.val == other.val and self.len == other.len
+
+    def __or__(self, other):
+        if type(self) is not type(other):
+            return NotImplemented
+        return Bits(self.val | other.val, max(self.len, other.len))
+
+    def __and__(self, other):
+        if type(self) is not type(other):
+            return NotImplemented
+        return Bits(self.val & other.val, max(self.len, other.len))
+
+    def __lshift__(self, other):
+        return Bits(self.val << other, self.len + other)
+
+    def __rshift__(self, other):
+        return Bits(self.val >> other, self.len - other)
+
+    def __hash__(self):
+        return hash(self.val)
+
+    def as_list(self):
+        return [c == '1' for c in self.as_bin()]
+
+    def as_bin(self):
+        return '{{0:0{0}b}}'.format(self.len).format(self.val)
+
+    def as_hex(self):
+        # make template to pad out to number of bytes necessary to represent bits
+        tmpl = '%0{0}X'.format(2 * (self.len // 8 + ((self.len % 8) != 0)))
+        ret = tmpl % self.val
+        return ret
+
+    def as_int(self):
+        return self.val
+
+    def as_bytes(self):
+        return binascii.unhexlify(self.as_hex())
+
+    @classmethod
+    def from_list(cls, list_):
+        return cls(list_)
+
+    @classmethod
+    def from_bin(cls, bin):
+        return cls(bin)
+
+    @classmethod
+    def from_hex(cls, hex):
+        if isinstance(hex, bytes):
+            hex = hex.decode('ascii')
+        if not hex.startswith('0x'):
+            hex = '0x' + hex
+        return cls(hex)
+
+    @classmethod
+    def from_int(cls, int_, len_=None):
+        return cls(int_, len_)
+
+    @classmethod
+    def from_bytes(cls, bytes_):
+        return cls.from_hex(binascii.hexlify(bytes_))
+
+    def __repr__(self):
+        cn = self.__class__.__name__
+        return "{0}('{1}')".format(cn, self.as_bin())
diff --git a/shared/package-files/4e/4e1fd01848d01379e21e6efbc47726b60454159c4577622c21486a346ca645d1/__init__.pyi b/shared/package-files/4e/4e1fd01848d01379e21e6efbc47726b60454159c4577622c21486a346ca645d1/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..82664dc4ab04d4e54c3752616384ef0014f6ad29
--- /dev/null
+++ b/shared/package-files/4e/4e1fd01848d01379e21e6efbc47726b60454159c4577622c21486a346ca645d1/__init__.pyi
@@ -0,0 +1,51 @@
+from typing import Any, Optional
+
+from werkzeug.wrappers import BaseRequest as Request, BaseResponse as Response
+
+PIN_TIME: Any
+
+def hash_pin(pin): ...
+def get_machine_id(): ...
+
+class _ConsoleFrame:
+    console: Any
+    id: Any
+    def __init__(self, namespace): ...
+
+def get_pin_and_cookie_name(app): ...
+
+class DebuggedApplication:
+    app: Any
+    evalex: Any
+    frames: Any
+    tracebacks: Any
+    request_key: Any
+    console_path: Any
+    console_init_func: Any
+    show_hidden_frames: Any
+    secret: Any
+    pin_logging: Any
+    pin: Any
+    def __init__(
+        self,
+        app,
+        evalex: bool = ...,
+        request_key: str = ...,
+        console_path: str = ...,
+        console_init_func: Optional[Any] = ...,
+        show_hidden_frames: bool = ...,
+        lodgeit_url: Optional[Any] = ...,
+        pin_security: bool = ...,
+        pin_logging: bool = ...,
+    ): ...
+    @property
+    def pin_cookie_name(self): ...
+    def debug_application(self, environ, start_response): ...
+    def execute_command(self, request, command, frame): ...
+    def display_console(self, request): ...
+    def paste_traceback(self, request, traceback): ...
+    def get_resource(self, request, filename): ...
+    def check_pin_trust(self, environ): ...
+    def pin_auth(self, request): ...
+    def log_pin_request(self): ...
+    def __call__(self, environ, start_response): ...
diff --git a/shared/package-files/4e/4e22c33db79517472480b54491a49e0da299f3072d7490ce97f1c4fd6779acab/Poland b/shared/package-files/4e/4e22c33db79517472480b54491a49e0da299f3072d7490ce97f1c4fd6779acab/Poland
new file mode 100644
index 0000000000000000000000000000000000000000..e33cf67171da78aa9e6eb02e50f9b9603da4c3f4
Binary files /dev/null and b/shared/package-files/4e/4e22c33db79517472480b54491a49e0da299f3072d7490ce97f1c4fd6779acab/Poland differ
diff --git a/shared/package-files/4e/4e22c33db79517472480b54491a49e0da299f3072d7490ce97f1c4fd6779acab/Warsaw b/shared/package-files/4e/4e22c33db79517472480b54491a49e0da299f3072d7490ce97f1c4fd6779acab/Warsaw
new file mode 100644
index 0000000000000000000000000000000000000000..e33cf67171da78aa9e6eb02e50f9b9603da4c3f4
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diff --git a/shared/package-files/4e/4e2838ff9b084c9003c3137979919e93b02d87ff9edec57c093339b7a5297945/impl.py b/shared/package-files/4e/4e2838ff9b084c9003c3137979919e93b02d87ff9edec57c093339b7a5297945/impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8542286242dcbb2036fff49c1d0e11fbbf9258b
--- /dev/null
+++ b/shared/package-files/4e/4e2838ff9b084c9003c3137979919e93b02d87ff9edec57c093339b7a5297945/impl.py
@@ -0,0 +1,385 @@
+import re
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torchaudio._internal import load_state_dict_from_url
+from torchaudio.functional import mu_law_decoding
+from torchaudio.models import Tacotron2, WaveRNN
+from torchaudio.transforms import GriffinLim, InverseMelScale
+
+from . import utils
+from .interface import Tacotron2TTSBundle
+
+__all__ = []
+
+_BASE_URL = "https://download.pytorch.org/torchaudio/models"
+
+
+################################################################################
+# Pipeline implementation - Text Processor
+################################################################################
+
+
+class _EnglishCharProcessor(Tacotron2TTSBundle.TextProcessor):
+    def __init__(self):
+        super().__init__()
+        self._tokens = utils._get_chars()
+        self._mapping = {s: i for i, s in enumerate(self._tokens)}
+
+    @property
+    def tokens(self):
+        return self._tokens
+
+    def __call__(self, texts: Union[str, List[str]]) -> Tuple[Tensor, Tensor]:
+        if isinstance(texts, str):
+            texts = [texts]
+        indices = [[self._mapping[c] for c in t.lower() if c in self._mapping] for t in texts]
+        return utils._to_tensor(indices)
+
+
+class _EnglishPhoneProcessor(Tacotron2TTSBundle.TextProcessor):
+    def __init__(self, *, dl_kwargs=None):
+        super().__init__()
+        self._tokens = utils._get_phones()
+        self._mapping = {p: i for i, p in enumerate(self._tokens)}
+        self._phonemizer = utils._load_phonemizer("en_us_cmudict_forward.pt", dl_kwargs=dl_kwargs)
+        self._pattern = r"(\[[A-Z]+?\]|[_!'(),.:;? -])"
+
+    @property
+    def tokens(self):
+        return self._tokens
+
+    def __call__(self, texts: Union[str, List[str]]) -> Tuple[Tensor, Tensor]:
+        if isinstance(texts, str):
+            texts = [texts]
+
+        indices = []
+        for phones in self._phonemizer(texts, lang="en_us"):
+            # '[F][UW][B][AA][R]!' -> ['F', 'UW', 'B', 'AA', 'R', '!']
+            ret = [re.sub(r"[\[\]]", "", r) for r in re.findall(self._pattern, phones)]
+            indices.append([self._mapping[p] for p in ret])
+        return utils._to_tensor(indices)
+
+
+################################################################################
+# Pipeline implementation - Vocoder
+################################################################################
+
+
+class _WaveRNNVocoder(torch.nn.Module, Tacotron2TTSBundle.Vocoder):
+    def __init__(self, model: WaveRNN, min_level_db: Optional[float] = -100):
+        super().__init__()
+        self._sample_rate = 22050
+        self._model = model
+        self._min_level_db = min_level_db
+
+    @property
+    def sample_rate(self):
+        return self._sample_rate
+
+    def forward(self, mel_spec, lengths=None):
+        mel_spec = torch.exp(mel_spec)
+        mel_spec = 20 * torch.log10(torch.clamp(mel_spec, min=1e-5))
+        if self._min_level_db is not None:
+            mel_spec = (self._min_level_db - mel_spec) / self._min_level_db
+            mel_spec = torch.clamp(mel_spec, min=0, max=1)
+        waveform, lengths = self._model.infer(mel_spec, lengths)
+        waveform = utils._unnormalize_waveform(waveform, self._model.n_bits)
+        waveform = mu_law_decoding(waveform, self._model.n_classes)
+        waveform = waveform.squeeze(1)
+        return waveform, lengths
+
+
+class _GriffinLimVocoder(torch.nn.Module, Tacotron2TTSBundle.Vocoder):
+    def __init__(self):
+        super().__init__()
+        self._sample_rate = 22050
+        self._inv_mel = InverseMelScale(
+            n_stft=(1024 // 2 + 1),
+            n_mels=80,
+            sample_rate=self.sample_rate,
+            f_min=0.0,
+            f_max=8000.0,
+            mel_scale="slaney",
+            norm="slaney",
+        )
+        self._griffin_lim = GriffinLim(
+            n_fft=1024,
+            power=1,
+            hop_length=256,
+            win_length=1024,
+        )
+
+    @property
+    def sample_rate(self):
+        return self._sample_rate
+
+    def forward(self, mel_spec, lengths=None):
+        mel_spec = torch.exp(mel_spec)
+        mel_spec = mel_spec.clone().detach().requires_grad_(True)
+        spec = self._inv_mel(mel_spec)
+        spec = spec.detach().requires_grad_(False)
+        waveforms = self._griffin_lim(spec)
+        return waveforms, lengths
+
+
+################################################################################
+# Bundle classes mixins
+################################################################################
+
+
+class _CharMixin:
+    def get_text_processor(self) -> Tacotron2TTSBundle.TextProcessor:
+        return _EnglishCharProcessor()
+
+
+class _PhoneMixin:
+    def get_text_processor(self, *, dl_kwargs=None) -> Tacotron2TTSBundle.TextProcessor:
+        return _EnglishPhoneProcessor(dl_kwargs=dl_kwargs)
+
+
+@dataclass
+class _Tacotron2Mixin:
+    _tacotron2_path: str
+    _tacotron2_params: Dict[str, Any]
+
+    def get_tacotron2(self, *, dl_kwargs=None) -> Tacotron2:
+        model = Tacotron2(**self._tacotron2_params)
+        url = f"{_BASE_URL}/{self._tacotron2_path}"
+        dl_kwargs = {} if dl_kwargs is None else dl_kwargs
+        state_dict = load_state_dict_from_url(url, **dl_kwargs)
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+
+
+@dataclass
+class _WaveRNNMixin:
+    _wavernn_path: Optional[str]
+    _wavernn_params: Optional[Dict[str, Any]]
+
+    def get_vocoder(self, *, dl_kwargs=None):
+        wavernn = self._get_wavernn(dl_kwargs=dl_kwargs)
+        return _WaveRNNVocoder(wavernn)
+
+    def _get_wavernn(self, *, dl_kwargs=None):
+        model = WaveRNN(**self._wavernn_params)
+        url = f"{_BASE_URL}/{self._wavernn_path}"
+        dl_kwargs = {} if dl_kwargs is None else dl_kwargs
+        state_dict = load_state_dict_from_url(url, **dl_kwargs)
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+
+
+class _GriffinLimMixin:
+    def get_vocoder(self, **_):
+        return _GriffinLimVocoder()
+
+
+################################################################################
+# Bundle classes
+################################################################################
+
+
+@dataclass
+class _Tacotron2WaveRNNCharBundle(_WaveRNNMixin, _Tacotron2Mixin, _CharMixin, Tacotron2TTSBundle):
+    pass
+
+
+@dataclass
+class _Tacotron2WaveRNNPhoneBundle(_WaveRNNMixin, _Tacotron2Mixin, _PhoneMixin, Tacotron2TTSBundle):
+    pass
+
+
+@dataclass
+class _Tacotron2GriffinLimCharBundle(_GriffinLimMixin, _Tacotron2Mixin, _CharMixin, Tacotron2TTSBundle):
+    pass
+
+
+@dataclass
+class _Tacotron2GriffinLimPhoneBundle(_GriffinLimMixin, _Tacotron2Mixin, _PhoneMixin, Tacotron2TTSBundle):
+    pass
+
+
+################################################################################
+# Instantiate bundle objects
+################################################################################
+
+
+TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH = _Tacotron2GriffinLimCharBundle(
+    _tacotron2_path="tacotron2_english_characters_1500_epochs_ljspeech.pth",
+    _tacotron2_params=utils._get_taco_params(n_symbols=38),
+)
+TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH.__doc__ = """Character-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs, and
+:py:class:`~torchaudio.transforms.GriffinLim` as vocoder.
+
+The text processor encodes the input texts character-by-character.
+
+You can find the training script `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_tacotron2>`__.
+The default parameters were used.
+
+Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage.
+
+Example - "Hello world! T T S stands for Text to Speech!"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+
+Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired,"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH_v2.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH_v2.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+"""  # noqa: E501
+
+TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH = _Tacotron2GriffinLimPhoneBundle(
+    _tacotron2_path="tacotron2_english_phonemes_1500_epochs_ljspeech.pth",
+    _tacotron2_params=utils._get_taco_params(n_symbols=96),
+)
+TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH.__doc__ = """Phoneme-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs and
+:py:class:`~torchaudio.transforms.GriffinLim` as vocoder.
+
+The text processor encodes the input texts based on phoneme.
+It uses `DeepPhonemizer <https://github.com/as-ideas/DeepPhonemizer>`__ to convert
+graphemes to phonemes.
+The model (*en_us_cmudict_forward*) was trained on
+`CMUDict <http://www.speech.cs.cmu.edu/cgi-bin/cmudict>`__.
+
+You can find the training script `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_tacotron2>`__.
+The text processor is set to the *"english_phonemes"*.
+
+Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage.
+
+Example - "Hello world! T T S stands for Text to Speech!"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+
+Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired,"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH_v2.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH_v2.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+
+"""  # noqa: E501
+
+TACOTRON2_WAVERNN_CHAR_LJSPEECH = _Tacotron2WaveRNNCharBundle(
+    _tacotron2_path="tacotron2_english_characters_1500_epochs_wavernn_ljspeech.pth",
+    _tacotron2_params=utils._get_taco_params(n_symbols=38),
+    _wavernn_path="wavernn_10k_epochs_8bits_ljspeech.pth",
+    _wavernn_params=utils._get_wrnn_params(),
+)
+TACOTRON2_WAVERNN_CHAR_LJSPEECH.__doc__ = """Character-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs and :py:class:`~torchaudio.models.WaveRNN` vocoder trained on 8 bits depth waveform of *LJSpeech* :cite:`ljspeech17` for 10,000 epochs.
+
+The text processor encodes the input texts character-by-character.
+
+You can find the training script `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_tacotron2>`__.
+The following parameters were used; ``win_length=1100``, ``hop_length=275``, ``n_fft=2048``,
+``mel_fmin=40``, and ``mel_fmax=11025``.
+
+You can find the training script `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_wavernn>`__.
+
+Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage.
+
+Example - "Hello world! T T S stands for Text to Speech!"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+
+Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired,"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH_v2.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH_v2.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+"""  # noqa: E501
+
+TACOTRON2_WAVERNN_PHONE_LJSPEECH = _Tacotron2WaveRNNPhoneBundle(
+    _tacotron2_path="tacotron2_english_phonemes_1500_epochs_wavernn_ljspeech.pth",
+    _tacotron2_params=utils._get_taco_params(n_symbols=96),
+    _wavernn_path="wavernn_10k_epochs_8bits_ljspeech.pth",
+    _wavernn_params=utils._get_wrnn_params(),
+)
+TACOTRON2_WAVERNN_PHONE_LJSPEECH.__doc__ = """Phoneme-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs, and
+:py:class:`~torchaudio.models.WaveRNN` vocoder trained on 8 bits depth waveform of *LJSpeech* :cite:`ljspeech17` for 10,000 epochs.
+
+The text processor encodes the input texts based on phoneme.
+It uses `DeepPhonemizer <https://github.com/as-ideas/DeepPhonemizer>`__ to convert
+graphemes to phonemes.
+The model (*en_us_cmudict_forward*) was trained on
+`CMUDict <http://www.speech.cs.cmu.edu/cgi-bin/cmudict>`__.
+
+You can find the training script for Tacotron2 `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_tacotron2>`__.
+The following parameters were used; ``win_length=1100``, ``hop_length=275``, ``n_fft=2048``,
+``mel_fmin=40``, and ``mel_fmax=11025``.
+
+You can find the training script for WaveRNN `here <https://github.com/pytorch/audio/tree/main/examples/pipeline_wavernn>`__.
+
+Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage.
+
+Example - "Hello world! T T S stands for Text to Speech!"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_PHONE_LJSPEECH.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_PHONE_LJSPEECH.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+
+
+Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired,"
+
+   .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_PHONE_LJSPEECH_v2.png
+      :alt: Spectrogram generated by Tacotron2
+
+   .. raw:: html
+
+      <audio controls="controls">
+         <source src="https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_PHONE_LJSPEECH_v2.wav" type="audio/wav">
+         Your browser does not support the <code>audio</code> element.
+      </audio>
+"""  # noqa: E501
diff --git a/shared/package-files/4e/4e361dedaf5c6ba8912622e3c34ea2b0b7994ee1a81803e68257836747187c9d/test_interval.py b/shared/package-files/4e/4e361dedaf5c6ba8912622e3c34ea2b0b7994ee1a81803e68257836747187c9d/test_interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..251fd8b7ddb00074e8ae27cce4a01d8f4f8fe151
--- /dev/null
+++ b/shared/package-files/4e/4e361dedaf5c6ba8912622e3c34ea2b0b7994ee1a81803e68257836747187c9d/test_interval.py
@@ -0,0 +1,453 @@
+from mpmath import *
+
+def test_interval_identity():
+    iv.dps = 15
+    assert mpi(2) == mpi(2, 2)
+    assert mpi(2) != mpi(-2, 2)
+    assert not (mpi(2) != mpi(2, 2))
+    assert mpi(-1, 1) == mpi(-1, 1)
+    assert str(mpi('0.1')) == "[0.099999999999999991673, 0.10000000000000000555]"
+    assert repr(mpi('0.1')) == "mpi('0.099999999999999992', '0.10000000000000001')"
+    u = mpi(-1, 3)
+    assert -1 in u
+    assert 2 in u
+    assert 3 in u
+    assert -1.1 not in u
+    assert 3.1 not in u
+    assert mpi(-1, 3) in u
+    assert mpi(0, 1) in u
+    assert mpi(-1.1, 2) not in u
+    assert mpi(2.5, 3.1) not in u
+    w = mpi(-inf, inf)
+    assert mpi(-5, 5) in w
+    assert mpi(2, inf) in w
+    assert mpi(0, 2) in mpi(0, 10)
+    assert not (3 in mpi(-inf, 0))
+
+def test_interval_hash():
+    assert hash(mpi(3)) == hash(3)
+    assert hash(mpi(3.25)) == hash(3.25)
+    assert hash(mpi(3,4)) == hash(mpi(3,4))
+    assert hash(iv.mpc(3)) == hash(3)
+    assert hash(iv.mpc(3,4)) == hash(3+4j)
+    assert hash(iv.mpc((1,3),(2,4))) == hash(iv.mpc((1,3),(2,4)))
+
+def test_interval_arithmetic():
+    iv.dps = 15
+    assert mpi(2) + mpi(3,4) == mpi(5,6)
+    assert mpi(1, 2)**2 == mpi(1, 4)
+    assert mpi(1) + mpi(0, 1e-50) == mpi(1, mpf('1.0000000000000002'))
+    x = 1 / (1 / mpi(3))
+    assert x.a < 3 < x.b
+    x = mpi(2) ** mpi(0.5)
+    iv.dps += 5
+    sq = iv.sqrt(2)
+    iv.dps -= 5
+    assert x.a < sq < x.b
+    assert mpi(1) / mpi(1, inf)
+    assert mpi(2, 3) / inf == mpi(0, 0)
+    assert mpi(0) / inf == 0
+    assert mpi(0) / 0 == mpi(-inf, inf)
+    assert mpi(inf) / 0 == mpi(-inf, inf)
+    assert mpi(0) * inf == mpi(-inf, inf)
+    assert 1 / mpi(2, inf) == mpi(0, 0.5)
+    assert str((mpi(50, 50) * mpi(-10, -10)) / 3) == \
+        '[-166.66666666666668561, -166.66666666666665719]'
+    assert mpi(0, 4) ** 3 == mpi(0, 64)
+    assert mpi(2,4).mid == 3
+    iv.dps = 30
+    a = mpi(iv.pi)
+    iv.dps = 15
+    b = +a
+    assert b.a < a.a
+    assert b.b > a.b
+    a = mpi(iv.pi)
+    assert a == +a
+    assert abs(mpi(-1,2)) == mpi(0,2)
+    assert abs(mpi(0.5,2)) == mpi(0.5,2)
+    assert abs(mpi(-3,2)) == mpi(0,3)
+    assert abs(mpi(-3,-0.5)) == mpi(0.5,3)
+    assert mpi(0) * mpi(2,3) == mpi(0)
+    assert mpi(2,3) * mpi(0) == mpi(0)
+    assert mpi(1,3).delta == 2
+    assert mpi(1,2) - mpi(3,4) == mpi(-3,-1)
+    assert mpi(-inf,0) - mpi(0,inf) == mpi(-inf,0)
+    assert mpi(-inf,0) - mpi(-inf,inf) == mpi(-inf,inf)
+    assert mpi(0,inf) - mpi(-inf,1) == mpi(-1,inf)
+
+def test_interval_mul():
+    assert mpi(-1, 0) * inf == mpi(-inf, 0)
+    assert mpi(-1, 0) * -inf == mpi(0, inf)
+    assert mpi(0, 1) * inf == mpi(0, inf)
+    assert mpi(0, 1) * mpi(0, inf) == mpi(0, inf)
+    assert mpi(-1, 1) * inf == mpi(-inf, inf)
+    assert mpi(-1, 1) * mpi(0, inf) == mpi(-inf, inf)
+    assert mpi(-1, 1) * mpi(-inf, inf) == mpi(-inf, inf)
+    assert mpi(-inf, 0) * mpi(0, 1) == mpi(-inf, 0)
+    assert mpi(-inf, 0) * mpi(0, 0) * mpi(-inf, 0)
+    assert mpi(-inf, 0) * mpi(-inf, inf) == mpi(-inf, inf)
+    assert mpi(-5,0)*mpi(-32,28) == mpi(-140,160)
+    assert mpi(2,3) * mpi(-1,2) == mpi(-3,6)
+    # Should be undefined?
+    assert mpi(inf, inf) * 0 == mpi(-inf, inf)
+    assert mpi(-inf, -inf) * 0 == mpi(-inf, inf)
+    assert mpi(0) * mpi(-inf,2) == mpi(-inf,inf)
+    assert mpi(0) * mpi(-2,inf) == mpi(-inf,inf)
+    assert mpi(-2,inf) * mpi(0) == mpi(-inf,inf)
+    assert mpi(-inf,2) * mpi(0) == mpi(-inf,inf)
+
+def test_interval_pow():
+    assert mpi(3)**2 == mpi(9, 9)
+    assert mpi(-3)**2 == mpi(9, 9)
+    assert mpi(-3, 1)**2 == mpi(0, 9)
+    assert mpi(-3, -1)**2 == mpi(1, 9)
+    assert mpi(-3, -1)**3 == mpi(-27, -1)
+    assert mpi(-3, 1)**3 == mpi(-27, 1)
+    assert mpi(-2, 3)**2 == mpi(0, 9)
+    assert mpi(-3, 2)**2 == mpi(0, 9)
+    assert mpi(4) ** -1 == mpi(0.25, 0.25)
+    assert mpi(-4) ** -1 == mpi(-0.25, -0.25)
+    assert mpi(4) ** -2 == mpi(0.0625, 0.0625)
+    assert mpi(-4) ** -2 == mpi(0.0625, 0.0625)
+    assert mpi(0, 1) ** inf == mpi(0, 1)
+    assert mpi(0, 1) ** -inf == mpi(1, inf)
+    assert mpi(0, inf) ** inf == mpi(0, inf)
+    assert mpi(0, inf) ** -inf == mpi(0, inf)
+    assert mpi(1, inf) ** inf == mpi(1, inf)
+    assert mpi(1, inf) ** -inf == mpi(0, 1)
+    assert mpi(2, 3) ** 1 == mpi(2, 3)
+    assert mpi(2, 3) ** 0 == 1
+    assert mpi(1,3) ** mpi(2) == mpi(1,9)
+
+def test_interval_sqrt():
+    assert mpi(4) ** 0.5 == mpi(2)
+
+def test_interval_div():
+    assert mpi(0.5, 1) / mpi(-1, 0) == mpi(-inf, -0.5)
+    assert mpi(0, 1) / mpi(0, 1) == mpi(0, inf)
+    assert mpi(inf, inf) / mpi(inf, inf) == mpi(0, inf)
+    assert mpi(inf, inf) / mpi(2, inf) == mpi(0, inf)
+    assert mpi(inf, inf) / mpi(2, 2) == mpi(inf, inf)
+    assert mpi(0, inf) / mpi(2, inf) == mpi(0, inf)
+    assert mpi(0, inf) / mpi(2, 2) == mpi(0, inf)
+    assert mpi(2, inf) / mpi(2, 2) == mpi(1, inf)
+    assert mpi(2, inf) / mpi(2, inf) == mpi(0, inf)
+    assert mpi(-4, 8) / mpi(1, inf) == mpi(-4, 8)
+    assert mpi(-4, 8) / mpi(0.5, inf) == mpi(-8, 16)
+    assert mpi(-inf, 8) / mpi(0.5, inf) == mpi(-inf, 16)
+    assert mpi(-inf, inf) / mpi(0.5, inf) == mpi(-inf, inf)
+    assert mpi(8, inf) / mpi(0.5, inf) == mpi(0, inf)
+    assert mpi(-8, inf) / mpi(0.5, inf) == mpi(-16, inf)
+    assert mpi(-4, 8) / mpi(inf, inf) == mpi(0, 0)
+    assert mpi(0, 8) / mpi(inf, inf) == mpi(0, 0)
+    assert mpi(0, 0) / mpi(inf, inf) == mpi(0, 0)
+    assert mpi(-inf, 0) / mpi(inf, inf) == mpi(-inf, 0)
+    assert mpi(-inf, 8) / mpi(inf, inf) == mpi(-inf, 0)
+    assert mpi(-inf, inf) / mpi(inf, inf) == mpi(-inf, inf)
+    assert mpi(-8, inf) / mpi(inf, inf) == mpi(0, inf)
+    assert mpi(0, inf) / mpi(inf, inf) == mpi(0, inf)
+    assert mpi(8, inf) / mpi(inf, inf) == mpi(0, inf)
+    assert mpi(inf, inf) / mpi(inf, inf) == mpi(0, inf)
+    assert mpi(-1, 2) / mpi(0, 1) == mpi(-inf, +inf)
+    assert mpi(0, 1) / mpi(0, 1) == mpi(0.0, +inf)
+    assert mpi(-1, 0) / mpi(0, 1) == mpi(-inf, 0.0)
+    assert mpi(-0.5, -0.25) / mpi(0, 1) == mpi(-inf, -0.25)
+    assert mpi(0.5, 1) / mpi(0, 1) == mpi(0.5, +inf)
+    assert mpi(0.5, 4) / mpi(0, 1) == mpi(0.5, +inf)
+    assert mpi(-1, -0.5) / mpi(0, 1) == mpi(-inf, -0.5)
+    assert mpi(-4, -0.5) / mpi(0, 1) == mpi(-inf, -0.5)
+    assert mpi(-1, 2) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(0, 1) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(-1, 0) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(-0.5, -0.25) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(0.5, 1) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(0.5, 4) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(-1, -0.5) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(-4, -0.5) / mpi(-2, 0.5) == mpi(-inf, +inf)
+    assert mpi(-1, 2) / mpi(-1, 0) == mpi(-inf, +inf)
+    assert mpi(0, 1) / mpi(-1, 0) == mpi(-inf, 0.0)
+    assert mpi(-1, 0) / mpi(-1, 0) == mpi(0.0, +inf)
+    assert mpi(-0.5, -0.25) / mpi(-1, 0) == mpi(0.25, +inf)
+    assert mpi(0.5, 1) / mpi(-1, 0) == mpi(-inf, -0.5)
+    assert mpi(0.5, 4) / mpi(-1, 0) == mpi(-inf, -0.5)
+    assert mpi(-1, -0.5) / mpi(-1, 0) == mpi(0.5, +inf)
+    assert mpi(-4, -0.5) / mpi(-1, 0) == mpi(0.5, +inf)
+    assert mpi(-1, 2) / mpi(0.5, 1) == mpi(-2.0, 4.0)
+    assert mpi(0, 1) / mpi(0.5, 1) == mpi(0.0, 2.0)
+    assert mpi(-1, 0) / mpi(0.5, 1) == mpi(-2.0, 0.0)
+    assert mpi(-0.5, -0.25) / mpi(0.5, 1) == mpi(-1.0, -0.25)
+    assert mpi(0.5, 1) / mpi(0.5, 1) == mpi(0.5, 2.0)
+    assert mpi(0.5, 4) / mpi(0.5, 1) == mpi(0.5, 8.0)
+    assert mpi(-1, -0.5) / mpi(0.5, 1) == mpi(-2.0, -0.5)
+    assert mpi(-4, -0.5) / mpi(0.5, 1) == mpi(-8.0, -0.5)
+    assert mpi(-1, 2) / mpi(-2, -0.5) == mpi(-4.0, 2.0)
+    assert mpi(0, 1) / mpi(-2, -0.5) == mpi(-2.0, 0.0)
+    assert mpi(-1, 0) / mpi(-2, -0.5) == mpi(0.0, 2.0)
+    assert mpi(-0.5, -0.25) / mpi(-2, -0.5) == mpi(0.125, 1.0)
+    assert mpi(0.5, 1) / mpi(-2, -0.5) == mpi(-2.0, -0.25)
+    assert mpi(0.5, 4) / mpi(-2, -0.5) == mpi(-8.0, -0.25)
+    assert mpi(-1, -0.5) / mpi(-2, -0.5) == mpi(0.25, 2.0)
+    assert mpi(-4, -0.5) / mpi(-2, -0.5) == mpi(0.25, 8.0)
+    # Should be undefined?
+    assert mpi(0, 0) / mpi(0, 0) == mpi(-inf, inf)
+    assert mpi(0, 0) / mpi(0, 1) == mpi(-inf, inf)
+
+def test_interval_cos_sin():
+    iv.dps = 15
+    cos = iv.cos
+    sin = iv.sin
+    tan = iv.tan
+    pi = iv.pi
+    # Around 0
+    assert cos(mpi(0)) == 1
+    assert sin(mpi(0)) == 0
+    assert cos(mpi(0,1)) == mpi(0.54030230586813965399, 1.0)
+    assert sin(mpi(0,1)) == mpi(0, 0.8414709848078966159)
+    assert cos(mpi(1,2)) == mpi(-0.4161468365471424069, 0.54030230586813976501)
+    assert sin(mpi(1,2)) == mpi(0.84147098480789650488, 1.0)
+    assert sin(mpi(1,2.5)) == mpi(0.59847214410395643824, 1.0)
+    assert cos(mpi(-1, 1)) == mpi(0.54030230586813965399, 1.0)
+    assert cos(mpi(-1, 0.5)) == mpi(0.54030230586813965399, 1.0)
+    assert cos(mpi(-1, 1.5)) == mpi(0.070737201667702906405, 1.0)
+    assert sin(mpi(-1,1)) == mpi(-0.8414709848078966159, 0.8414709848078966159)
+    assert sin(mpi(-1,0.5)) == mpi(-0.8414709848078966159, 0.47942553860420300538)
+    assert mpi(-0.8414709848078966159, 1.00000000000000002e-100) in sin(mpi(-1,1e-100))
+    assert mpi(-2.00000000000000004e-100, 1.00000000000000002e-100) in sin(mpi(-2e-100,1e-100))
+    # Same interval
+    assert cos(mpi(2, 2.5))
+    assert cos(mpi(3.5, 4)) == mpi(-0.93645668729079634129, -0.65364362086361182946)
+    assert cos(mpi(5, 5.5)) == mpi(0.28366218546322624627, 0.70866977429126010168)
+    assert mpi(0.59847214410395654927, 0.90929742682568170942) in sin(mpi(2, 2.5))
+    assert sin(mpi(3.5, 4)) == mpi(-0.75680249530792831347, -0.35078322768961983646)
+    assert sin(mpi(5, 5.5)) == mpi(-0.95892427466313856499, -0.70554032557039181306)
+    # Higher roots
+    iv.dps = 55
+    w = 4*10**50 + mpi(0.5)
+    for p in [15, 40, 80]:
+        iv.dps = p
+        assert 0 in sin(4*mpi(pi))
+        assert 0 in sin(4*10**50*mpi(pi))
+        assert 0 in cos((4+0.5)*mpi(pi))
+        assert 0 in cos(w*mpi(pi))
+        assert 1 in cos(4*mpi(pi))
+        assert 1 in cos(4*10**50*mpi(pi))
+    iv.dps = 15
+    assert cos(mpi(2,inf)) == mpi(-1,1)
+    assert sin(mpi(2,inf)) == mpi(-1,1)
+    assert cos(mpi(-inf,2)) == mpi(-1,1)
+    assert sin(mpi(-inf,2)) == mpi(-1,1)
+    u = tan(mpi(0.5,1))
+    assert mpf(u.a).ae(mp.tan(0.5))
+    assert mpf(u.b).ae(mp.tan(1))
+    v = iv.cot(mpi(0.5,1))
+    assert mpf(v.a).ae(mp.cot(1))
+    assert mpf(v.b).ae(mp.cot(0.5))
+    # Sanity check of evaluation at n*pi and (n+1/2)*pi
+    for n in range(-5,7,2):
+        x = iv.cos(n*iv.pi)
+        assert -1 in x
+        assert x >= -1
+        assert x != -1
+        x = iv.sin((n+0.5)*iv.pi)
+        assert -1 in x
+        assert x >= -1
+        assert x != -1
+    for n in range(-6,8,2):
+        x = iv.cos(n*iv.pi)
+        assert 1 in x
+        assert x <= 1
+        if n:
+            assert x != 1
+        x = iv.sin((n+0.5)*iv.pi)
+        assert 1 in x
+        assert x <= 1
+        assert x != 1
+    for n in range(-6,7):
+        x = iv.cos((n+0.5)*iv.pi)
+        assert x.a < 0 < x.b
+        x = iv.sin(n*iv.pi)
+        if n:
+            assert x.a < 0 < x.b
+
+def test_interval_complex():
+    # TODO: many more tests
+    iv.dps = 15
+    mp.dps = 15
+    assert iv.mpc(2,3) == 2+3j
+    assert iv.mpc(2,3) != 2+4j
+    assert iv.mpc(2,3) != 1+3j
+    assert 1+3j in iv.mpc([1,2],[3,4])
+    assert 2+5j not in iv.mpc([1,2],[3,4])
+    assert iv.mpc(1,2) + 1j == 1+3j
+    assert iv.mpc([1,2],[2,3]) + 2+3j == iv.mpc([3,4],[5,6])
+    assert iv.mpc([2,4],[4,8]) / 2 == iv.mpc([1,2],[2,4])
+    assert iv.mpc([1,2],[2,4]) * 2j == iv.mpc([-8,-4],[2,4])
+    assert iv.mpc([2,4],[4,8]) / 2j == iv.mpc([2,4],[-2,-1])
+    assert iv.exp(2+3j).ae(mp.exp(2+3j))
+    assert iv.log(2+3j).ae(mp.log(2+3j))
+    assert (iv.mpc(2,3) ** iv.mpc(0.5,2)).ae(mp.mpc(2,3) ** mp.mpc(0.5,2))
+    assert 1j in (iv.mpf(-1) ** 0.5)
+    assert 1j in (iv.mpc(-1) ** 0.5)
+    assert abs(iv.mpc(0)) == 0
+    assert abs(iv.mpc(inf)) == inf
+    assert abs(iv.mpc(3,4)) == 5
+    assert abs(iv.mpc(4)) == 4
+    assert abs(iv.mpc(0,4)) == 4
+    assert abs(iv.mpc(0,[2,3])) == iv.mpf([2,3])
+    assert abs(iv.mpc(0,[-3,2])) == iv.mpf([0,3])
+    assert abs(iv.mpc([3,5],[4,12])) == iv.mpf([5,13])
+    assert abs(iv.mpc([3,5],[-4,12])) == iv.mpf([3,13])
+    assert iv.mpc(2,3) ** 0 == 1
+    assert iv.mpc(2,3) ** 1 == (2+3j)
+    assert iv.mpc(2,3) ** 2 == (2+3j)**2
+    assert iv.mpc(2,3) ** 3 == (2+3j)**3
+    assert iv.mpc(2,3) ** 4 == (2+3j)**4
+    assert iv.mpc(2,3) ** 5 == (2+3j)**5
+    assert iv.mpc(2,2) ** (-1) == (2+2j) ** (-1)
+    assert iv.mpc(2,2) ** (-2) == (2+2j) ** (-2)
+    assert iv.cos(2).ae(mp.cos(2))
+    assert iv.sin(2).ae(mp.sin(2))
+    assert iv.cos(2+3j).ae(mp.cos(2+3j))
+    assert iv.sin(2+3j).ae(mp.sin(2+3j))
+
+def test_interval_complex_arg():
+    mp.dps = 15
+    iv.dps = 15
+    assert iv.arg(3) == 0
+    assert iv.arg(0) == 0
+    assert iv.arg([0,3]) == 0
+    assert iv.arg(-3).ae(pi)
+    assert iv.arg(2+3j).ae(iv.arg(2+3j))
+    z = iv.mpc([-2,-1],[3,4])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(-1+4j))
+    assert t.b.ae(mp.arg(-2+3j))
+    z = iv.mpc([-2,1],[3,4])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(1+3j))
+    assert t.b.ae(mp.arg(-2+3j))
+    z = iv.mpc([1,2],[3,4])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(2+3j))
+    assert t.b.ae(mp.arg(1+4j))
+    z = iv.mpc([1,2],[-2,3])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(1-2j))
+    assert t.b.ae(mp.arg(1+3j))
+    z = iv.mpc([1,2],[-4,-3])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(1-4j))
+    assert t.b.ae(mp.arg(2-3j))
+    z = iv.mpc([-1,2],[-4,-3])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(-1-3j))
+    assert t.b.ae(mp.arg(2-3j))
+    z = iv.mpc([-2,-1],[-4,-3])
+    t = iv.arg(z)
+    assert t.a.ae(mp.arg(-2-3j))
+    assert t.b.ae(mp.arg(-1-4j))
+    z = iv.mpc([-2,-1],[-3,3])
+    t = iv.arg(z)
+    assert t.a.ae(-mp.pi)
+    assert t.b.ae(mp.pi)
+    z = iv.mpc([-2,2],[-3,3])
+    t = iv.arg(z)
+    assert t.a.ae(-mp.pi)
+    assert t.b.ae(mp.pi)
+
+def test_interval_ae():
+    iv.dps = 15
+    x = iv.mpf([1,2])
+    assert x.ae(1) is None
+    assert x.ae(1.5) is None
+    assert x.ae(2) is None
+    assert x.ae(2.01) is False
+    assert x.ae(0.99) is False
+    x = iv.mpf(3.5)
+    assert x.ae(3.5) is True
+    assert x.ae(3.5+1e-15) is True
+    assert x.ae(3.5-1e-15) is True
+    assert x.ae(3.501) is False
+    assert x.ae(3.499) is False
+    assert x.ae(iv.mpf([3.5,3.501])) is None
+    assert x.ae(iv.mpf([3.5,4.5+1e-15])) is None
+
+def test_interval_nstr():
+    iv.dps = n = 30
+    x = mpi(1, 2)
+    # FIXME: error_dps should not be necessary
+    assert iv.nstr(x, n, mode='plusminus', error_dps=6) == '1.5 +- 0.5'
+    assert iv.nstr(x, n, mode='plusminus', use_spaces=False, error_dps=6) == '1.5+-0.5'
+    assert iv.nstr(x, n, mode='percent') == '1.5 (33.33%)'
+    assert iv.nstr(x, n, mode='brackets', use_spaces=False) == '[1.0,2.0]'
+    assert iv.nstr(x, n, mode='brackets' , brackets=('<', '>')) == '<1.0, 2.0>'
+    x = mpi('5.2582327113062393041', '5.2582327113062749951')
+    assert iv.nstr(x, n, mode='diff') == '5.2582327113062[393041, 749951]'
+    assert iv.nstr(iv.cos(mpi(1)), n, mode='diff', use_spaces=False) == '0.54030230586813971740093660744[2955,3053]'
+    assert iv.nstr(mpi('1e123', '1e129'), n, mode='diff') == '[1.0e+123, 1.0e+129]'
+    exp = iv.exp
+    assert iv.nstr(iv.exp(mpi('5000.1')), n, mode='diff') == '3.2797365856787867069110487[0926, 1191]e+2171'
+    iv.dps = 15
+
+def test_mpi_from_str():
+    iv.dps = 15
+    assert iv.convert('1.5 +- 0.5') == mpi(mpf('1.0'), mpf('2.0'))
+    assert mpi(1, 2) in iv.convert('1.5 (33.33333333333333333333333333333%)')
+    assert iv.convert('[1, 2]') == mpi(1, 2)
+    assert iv.convert('1[2, 3]') == mpi(12, 13)
+    assert iv.convert('1.[23,46]e-8') == mpi('1.23e-8', '1.46e-8')
+    assert iv.convert('12[3.4,5.9]e4') == mpi('123.4e+4', '125.9e4')
+
+def test_interval_gamma():
+    mp.dps = 15
+    iv.dps = 15
+    # TODO: need many more tests
+    assert iv.rgamma(0) == 0
+    assert iv.fac(0) == 1
+    assert iv.fac(1) == 1
+    assert iv.fac(2) == 2
+    assert iv.fac(3) == 6
+    assert iv.gamma(0) == [-inf,inf]
+    assert iv.gamma(1) == 1
+    assert iv.gamma(2) == 1
+    assert iv.gamma(3) == 2
+    assert -3.5449077018110320546 in iv.gamma(-0.5)
+    assert iv.loggamma(1) == 0
+    assert iv.loggamma(2) == 0
+    assert 0.69314718055994530942 in iv.loggamma(3)
+    # Test tight log-gamma endpoints based on monotonicity
+    xs = [iv.mpc([2,3],[1,4]),
+          iv.mpc([2,3],[-4,-1]),
+          iv.mpc([2,3],[-1,4]),
+          iv.mpc([2,3],[-4,1]),
+          iv.mpc([2,3],[-4,4]),
+          iv.mpc([-3,-2],[2,4]),
+          iv.mpc([-3,-2],[-4,-2])]
+    for x in xs:
+        ys = [mp.loggamma(mp.mpc(x.a,x.c)),
+              mp.loggamma(mp.mpc(x.b,x.c)),
+              mp.loggamma(mp.mpc(x.a,x.d)),
+              mp.loggamma(mp.mpc(x.b,x.d))]
+        if 0 in x.imag:
+            ys += [mp.loggamma(x.a), mp.loggamma(x.b)]
+        min_real = min([y.real for y in ys])
+        max_real = max([y.real for y in ys])
+        min_imag = min([y.imag for y in ys])
+        max_imag = max([y.imag for y in ys])
+        z = iv.loggamma(x)
+        assert z.a.ae(min_real)
+        assert z.b.ae(max_real)
+        assert z.c.ae(min_imag)
+        assert z.d.ae(max_imag)
+
+def test_interval_conversions():
+    mp.dps = 15
+    iv.dps = 15
+    for a, b in ((-0.0, 0), (0.0, 0.5), (1.0, 1), \
+                 ('-inf', 20.5), ('-inf', float(sqrt(2)))):
+        r = mpi(a, b)
+        assert int(r.b) == int(b)
+        assert float(r.a) == float(a)
+        assert float(r.b) == float(b)
+        assert complex(r.a) == complex(a)
+        assert complex(r.b) == complex(b)
diff --git a/shared/package-files/4e/4e377fa38bbd09b71ca590a5eccb0ef1c97c874a610f87bbdede2945fe7df3a0/_foreach_cosh_cuda_dispatch.h b/shared/package-files/4e/4e377fa38bbd09b71ca590a5eccb0ef1c97c874a610f87bbdede2945fe7df3a0/_foreach_cosh_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ebd2c1bce1bcbe9b400f5a33f260ad4cee13abe
--- /dev/null
+++ b/shared/package-files/4e/4e377fa38bbd09b71ca590a5eccb0ef1c97c874a610f87bbdede2945fe7df3a0/_foreach_cosh_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::vector<at::Tensor> _foreach_cosh(at::TensorList self);
+TORCH_API void _foreach_cosh_(at::TensorList self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/4e/4e3a84a7ff40b73c1611773aa98346e022a83aa814ef87e4514549615feb0665/slice_native.h b/shared/package-files/4e/4e3a84a7ff40b73c1611773aa98346e022a83aa814ef87e4514549615feb0665/slice_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..95125dce90a3cd9d06840c40653e2ea150eff7a8
--- /dev/null
+++ b/shared/package-files/4e/4e3a84a7ff40b73c1611773aa98346e022a83aa814ef87e4514549615feb0665/slice_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor slice(const at::Tensor & self, int64_t dim=0, c10::optional<int64_t> start=c10::nullopt, c10::optional<int64_t> end=c10::nullopt, int64_t step=1);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4e3c90998f5a3cf4ac6b3e1495f22b545fb8a7a0f7a0ed1d9ef6ad2e14e89537/__init__.py b/shared/package-files/4e/4e3c90998f5a3cf4ac6b3e1495f22b545fb8a7a0f7a0ed1d9ef6ad2e14e89537/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dee04d15cc7b4f501a0b263a4be598954f3016f2
--- /dev/null
+++ b/shared/package-files/4e/4e3c90998f5a3cf4ac6b3e1495f22b545fb8a7a0f7a0ed1d9ef6ad2e14e89537/__init__.py
@@ -0,0 +1,17 @@
+# Functional DataPipe
+from torch.utils.data.datapipes.map.callable import MapperMapDataPipe as Mapper
+from torch.utils.data.datapipes.map.combinatorics import ShufflerIterDataPipe as Shuffler
+from torch.utils.data.datapipes.map.combining import (
+    ConcaterMapDataPipe as Concater,
+    ZipperMapDataPipe as Zipper
+)
+from torch.utils.data.datapipes.map.grouping import (
+    BatcherMapDataPipe as Batcher
+)
+from torch.utils.data.datapipes.map.utils import SequenceWrapperMapDataPipe as SequenceWrapper
+
+
+__all__ = ['Batcher', 'Concater', 'Mapper', 'SequenceWrapper', 'Shuffler', 'Zipper']
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
diff --git a/shared/package-files/4e/4e3dc8fed9d0dff99f0112bfb1c0ec77d68ce5d2def53a3e19e68275ae4e30ac/_lebedev.py b/shared/package-files/4e/4e3dc8fed9d0dff99f0112bfb1c0ec77d68ce5d2def53a3e19e68275ae4e30ac/_lebedev.py
new file mode 100644
index 0000000000000000000000000000000000000000..da200972f9d475162f84294ed335149dc86fe94b
--- /dev/null
+++ b/shared/package-files/4e/4e3dc8fed9d0dff99f0112bfb1c0ec77d68ce5d2def53a3e19e68275ae4e30ac/_lebedev.py
@@ -0,0 +1,5450 @@
+# getLebedevSphere
+# Copyright (c) 2010, Robert Parrish
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+#
+#     * Redistributions of source code must retain the above copyright
+#       notice, this list of conditions and the following disclaimer.
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in
+#       the documentation and/or other materials provided with the distribution
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Brainlessly translated to Python
+
+import numpy as np
+from numpy import pi, zeros, sqrt
+
+
+__all__ = ['lebedev_rule']
+
+
+def get_lebedev_sphere(degree):
+    # getLebedevSphere
+    # @author Rob Parrish, The Sherrill Group, CCMST Georgia Tech
+    # @email robparrish@gmail.com
+    # @date 03/24/2010
+    #
+    # @description - function to compute normalized points and weights
+    # for Lebedev quadratures on the surface of the unit sphere at double precision.
+    # **********Relative error is generally expected to be ~2.0E-14 [1]********
+    # Lebedev quadratures are superbly accurate and efficient quadrature rules for
+    # approximating integrals of the form $v = \iint_{4\pi}  f(\Omega) \ \ud
+    # \Omega$, where $\Omega is the solid angle on the surface of the unit
+    # sphere. Lebedev quadratures integrate all spherical harmonics up to $l =
+    # order$, where $degree \approx order(order+1)/3$. These grids may be easily
+    # combined with radial quadratures to provide robust cubature formulae. For
+    # example, see 'A. Becke, 1988c, J. Chem. Phys., 88(4), pp. 2547' (The first
+    # paper on tractable molecular Density Functional Theory methods, of which
+    # Lebedev grids and numerical cubature are an intrinsic part).
+    #
+    # @param degree - positive integer specifying number of points in the
+    # requested quadrature. Allowed values are (degree -> order):
+    # degree: { 6, 14, 26, 38, 50, 74, 86, 110, 146, 170, 194, 230, 266, 302,
+    #   350, 434, 590, 770, 974, 1202, 1454, 1730, 2030, 2354, 2702, 3074,
+    #   3470, 3890, 4334, 4802, 5294, 5810 }
+    # order: {3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,35,41,47,53,59,65,71,77,
+    #   83,89,95,101,107,113,119,125,131}
+    #
+    #
+    # @return leb_tmp - struct containing fields:
+    #   x - x values of quadrature, constrained to unit sphere
+    #   y - y values of quadrature, constrained to unit sphere
+    #   z - z values of quadrature, constrained to unit sphere
+    #   w - quadrature weights, normalized to $4\pi$.
+    #
+    # @example: $\int_S x^2+y^2-z^2 \ud \Omega = 4.188790204786399$
+    #   f = @(x,y,z) x.^2+y.^2-z.^2
+    #   leb = getLebedevSphere(590)
+    #   v = f(leb.x,leb.y,leb.z)
+    #   int = sum(v.*leb.w)
+    #
+    # @citation - Translated from a Fortran code kindly provided by Christoph van
+    # Wuellen (Ruhr-Universitaet, Bochum, Germany), which in turn came from the
+    # original C routines coded by Dmitri Laikov (Moscow State University,
+    # Moscow, Russia). The MATLAB implementation of this code is designed for
+    # benchmarking of new DFT integration techniques to be implemented in the
+    # open source Psi4 ab initio quantum chemistry program.
+    #
+    # As per Professor Wuellen's request, any papers published using this code
+    # or its derivatives are requested to include the following citation:
+    #
+    # [1] V.I. Lebedev, and D.N. Laikov
+    #    "A quadrature formula for the sphere of the 131st
+    #     algebraic order of accuracy"
+    #    Doklady Mathematics, Vol. 59, No. 3, 1999, pp. 477-481.
+
+    class Leb:
+        x, y, z, w = None, None, None, None
+
+    leb_tmp = Leb()
+
+    leb_tmp.x = zeros(degree)
+    leb_tmp.y = zeros(degree)
+    leb_tmp.z = zeros(degree)
+    leb_tmp.w = zeros(degree)
+
+    start = 0
+    a = 0.0
+    b = 0.0
+
+    match degree:
+
+        case 6:
+
+            v = 0.1666666666666667E+0
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+
+        case 14:
+
+            v = 0.6666666666666667E-1
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.7500000000000000E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+
+        case 26:
+
+            v = 0.4761904761904762E-1
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3809523809523810E-1
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.3214285714285714E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+
+        case 38:
+
+            v = 0.9523809523809524E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3214285714285714E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.4597008433809831E+0
+            v = 0.2857142857142857E-1
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+
+        case 50:
+
+            v = 0.1269841269841270E-1
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2257495590828924E-1
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.2109375000000000E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.3015113445777636E+0
+            v = 0.2017333553791887E-1
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+
+        case 74:
+
+            v = 0.5130671797338464E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1660406956574204E-1
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = -0.2958603896103896E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.4803844614152614E+0
+            v = 0.2657620708215946E-1
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3207726489807764E+0
+            v = 0.1652217099371571E-1
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+
+        case 86:
+
+            v = 0.1154401154401154E-1
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1194390908585628E-1
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.3696028464541502E+0
+            v = 0.1111055571060340E-1
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6943540066026664E+0
+            v = 0.1187650129453714E-1
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3742430390903412E+0
+            v = 0.1181230374690448E-1
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+
+        case 110:
+
+            v = 0.3828270494937162E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.9793737512487512E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1851156353447362E+0
+            v = 0.8211737283191111E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6904210483822922E+0
+            v = 0.9942814891178103E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3956894730559419E+0
+            v = 0.9595471336070963E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4783690288121502E+0
+            v = 0.9694996361663028E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+
+        case 146:
+
+            v = 0.5996313688621381E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.7372999718620756E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.7210515360144488E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.6764410400114264E+0
+            v = 0.7116355493117555E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4174961227965453E+0
+            v = 0.6753829486314477E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1574676672039082E+0
+            v = 0.7574394159054034E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1403553811713183E+0
+            b = 0.4493328323269557E+0
+            v = 0.6991087353303262E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 170:
+
+            v = 0.5544842902037365E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.6071332770670752E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.6383674773515093E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2551252621114134E+0
+            v = 0.5183387587747790E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6743601460362766E+0
+            v = 0.6317929009813725E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4318910696719410E+0
+            v = 0.6201670006589077E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2613931360335988E+0
+            v = 0.5477143385137348E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4990453161796037E+0
+            b = 0.1446630744325115E+0
+            v = 0.5968383987681156E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 194:
+
+            v = 0.1782340447244611E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.5716905949977102E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.5573383178848738E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.6712973442695226E+0
+            v = 0.5608704082587997E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2892465627575439E+0
+            v = 0.5158237711805383E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4446933178717437E+0
+            v = 0.5518771467273614E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1299335447650067E+0
+            v = 0.4106777028169394E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3457702197611283E+0
+            v = 0.5051846064614808E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1590417105383530E+0
+            b = 0.8360360154824589E+0
+            v = 0.5530248916233094E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 230:
+
+            v = -0.5522639919727325E-1
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.4450274607445226E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.4492044687397611E+0
+            v = 0.4496841067921404E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2520419490210201E+0
+            v = 0.5049153450478750E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6981906658447242E+0
+            v = 0.3976408018051883E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6587405243460960E+0
+            v = 0.4401400650381014E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4038544050097660E-1
+            v = 0.1724544350544401E-1
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5823842309715585E+0
+            v = 0.4231083095357343E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3545877390518688E+0
+            v = 0.5198069864064399E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2272181808998187E+0
+            b = 0.4864661535886647E+0
+            v = 0.4695720972568883E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 266:
+
+            v = -0.1313769127326952E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = -0.2522728704859336E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.4186853881700583E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.7039373391585475E+0
+            v = 0.5315167977810885E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1012526248572414E+0
+            v = 0.4047142377086219E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4647448726420539E+0
+            v = 0.4112482394406990E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3277420654971629E+0
+            v = 0.3595584899758782E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6620338663699974E+0
+            v = 0.4256131351428158E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8506508083520399E+0
+            v = 0.4229582700647240E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3233484542692899E+0
+            b = 0.1153112011009701E+0
+            v = 0.4080914225780505E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2314790158712601E+0
+            b = 0.5244939240922365E+0
+            v = 0.4071467593830964E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 302:
+
+            v = 0.8545911725128148E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3599119285025571E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.3515640345570105E+0
+            v = 0.3449788424305883E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6566329410219612E+0
+            v = 0.3604822601419882E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4729054132581005E+0
+            v = 0.3576729661743367E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9618308522614784E-1
+            v = 0.2352101413689164E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2219645236294178E+0
+            v = 0.3108953122413675E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7011766416089545E+0
+            v = 0.3650045807677255E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2644152887060663E+0
+            v = 0.2982344963171804E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5718955891878961E+0
+            v = 0.3600820932216460E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2510034751770465E+0
+            b = 0.8000727494073952E+0
+            v = 0.3571540554273387E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1233548532583327E+0
+            b = 0.4127724083168531E+0
+            v = 0.3392312205006170E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 350:
+
+            v = 0.3006796749453936E-2
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3050627745650771E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.7068965463912316E+0
+            v = 0.1621104600288991E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4794682625712025E+0
+            v = 0.3005701484901752E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1927533154878019E+0
+            v = 0.2990992529653774E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6930357961327123E+0
+            v = 0.2982170644107595E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3608302115520091E+0
+            v = 0.2721564237310992E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6498486161496169E+0
+            v = 0.3033513795811141E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1932945013230339E+0
+            v = 0.3007949555218533E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3800494919899303E+0
+            v = 0.2881964603055307E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2899558825499574E+0
+            b = 0.7934537856582316E+0
+            v = 0.2958357626535696E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9684121455103957E-1
+            b = 0.8280801506686862E+0
+            v = 0.3036020026407088E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1833434647041659E+0
+            b = 0.9074658265305127E+0
+            v = 0.2832187403926303E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 434:
+
+            v = 0.5265897968224436E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2548219972002607E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.2512317418927307E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.6909346307509111E+0
+            v = 0.2530403801186355E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1774836054609158E+0
+            v = 0.2014279020918528E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4914342637784746E+0
+            v = 0.2501725168402936E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6456664707424256E+0
+            v = 0.2513267174597564E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2861289010307638E+0
+            v = 0.2302694782227416E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7568084367178018E-1
+            v = 0.1462495621594614E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3927259763368002E+0
+            v = 0.2445373437312980E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8818132877794288E+0
+            v = 0.2417442375638981E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9776428111182649E+0
+            v = 0.1910951282179532E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2054823696403044E+0
+            b = 0.8689460322872412E+0
+            v = 0.2416930044324775E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5905157048925271E+0
+            b = 0.7999278543857286E+0
+            v = 0.2512236854563495E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5550152361076807E+0
+            b = 0.7717462626915901E+0
+            v = 0.2496644054553086E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9371809858553722E+0
+            b = 0.3344363145343455E+0
+            v = 0.2236607760437849E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 590:
+
+            v = 0.3095121295306187E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1852379698597489E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.7040954938227469E+0
+            v = 0.1871790639277744E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6807744066455243E+0
+            v = 0.1858812585438317E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6372546939258752E+0
+            v = 0.1852028828296213E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5044419707800358E+0
+            v = 0.1846715956151242E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4215761784010967E+0
+            v = 0.1818471778162769E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3317920736472123E+0
+            v = 0.1749564657281154E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2384736701421887E+0
+            v = 0.1617210647254411E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1459036449157763E+0
+            v = 0.1384737234851692E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6095034115507196E-1
+            v = 0.9764331165051050E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6116843442009876E+0
+            v = 0.1857161196774078E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3964755348199858E+0
+            v = 0.1705153996395864E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1724782009907724E+0
+            v = 0.1300321685886048E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5610263808622060E+0
+            b = 0.3518280927733519E+0
+            v = 0.1842866472905286E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4742392842551980E+0
+            b = 0.2634716655937950E+0
+            v = 0.1802658934377451E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5984126497885380E+0
+            b = 0.1816640840360209E+0
+            v = 0.1849830560443660E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3791035407695563E+0
+            b = 0.1720795225656878E+0
+            v = 0.1713904507106709E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2778673190586244E+0
+            b = 0.8213021581932511E-1
+            v = 0.1555213603396808E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5033564271075117E+0
+            b = 0.8999205842074875E-1
+            v = 0.1802239128008525E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 770:
+
+            v = 0.2192942088181184E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1436433617319080E-2
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.1421940344335877E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.5087204410502360E-1
+            v = 0.6798123511050502E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1228198790178831E+0
+            v = 0.9913184235294912E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2026890814408786E+0
+            v = 0.1180207833238949E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2847745156464294E+0
+            v = 0.1296599602080921E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3656719078978026E+0
+            v = 0.1365871427428316E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4428264886713469E+0
+            v = 0.1402988604775325E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5140619627249735E+0
+            v = 0.1418645563595609E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6306401219166803E+0
+            v = 0.1421376741851662E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6716883332022612E+0
+            v = 0.1423996475490962E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6979792685336881E+0
+            v = 0.1431554042178567E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1446865674195309E+0
+            v = 0.9254401499865368E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3390263475411216E+0
+            v = 0.1250239995053509E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5335804651263506E+0
+            v = 0.1394365843329230E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6944024393349413E-1
+            b = 0.2355187894242326E+0
+            v = 0.1127089094671749E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2269004109529460E+0
+            b = 0.4102182474045730E+0
+            v = 0.1345753760910670E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8025574607775339E-1
+            b = 0.6214302417481605E+0
+            v = 0.1424957283316783E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1467999527896572E+0
+            b = 0.3245284345717394E+0
+            v = 0.1261523341237750E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1571507769824727E+0
+            b = 0.5224482189696630E+0
+            v = 0.1392547106052696E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2365702993157246E+0
+            b = 0.6017546634089558E+0
+            v = 0.1418761677877656E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7714815866765732E-1
+            b = 0.4346575516141163E+0
+            v = 0.1338366684479554E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3062936666210730E+0
+            b = 0.4908826589037616E+0
+            v = 0.1393700862676131E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3822477379524787E+0
+            b = 0.5648768149099500E+0
+            v = 0.1415914757466932E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 974:
+
+            v = 0.1438294190527431E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1125772288287004E-2
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.4292963545341347E-1
+            v = 0.4948029341949241E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1051426854086404E+0
+            v = 0.7357990109125470E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1750024867623087E+0
+            v = 0.8889132771304384E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2477653379650257E+0
+            v = 0.9888347838921435E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3206567123955957E+0
+            v = 0.1053299681709471E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3916520749849983E+0
+            v = 0.1092778807014578E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4590825874187624E+0
+            v = 0.1114389394063227E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5214563888415861E+0
+            v = 0.1123724788051555E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6253170244654199E+0
+            v = 0.1125239325243814E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6637926744523170E+0
+            v = 0.1126153271815905E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6910410398498301E+0
+            v = 0.1130286931123841E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7052907007457760E+0
+            v = 0.1134986534363955E-2
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1236686762657990E+0
+            v = 0.6823367927109931E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2940777114468387E+0
+            v = 0.9454158160447096E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4697753849207649E+0
+            v = 0.1074429975385679E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6334563241139567E+0
+            v = 0.1129300086569132E-2
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5974048614181342E-1
+            b = 0.2029128752777523E+0
+            v = 0.8436884500901954E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1375760408473636E+0
+            b = 0.4602621942484054E+0
+            v = 0.1075255720448885E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3391016526336286E+0
+            b = 0.5030673999662036E+0
+            v = 0.1108577236864462E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1271675191439820E+0
+            b = 0.2817606422442134E+0
+            v = 0.9566475323783357E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2693120740413512E+0
+            b = 0.4331561291720157E+0
+            v = 0.1080663250717391E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1419786452601918E+0
+            b = 0.6256167358580814E+0
+            v = 0.1126797131196295E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6709284600738255E-1
+            b = 0.3798395216859157E+0
+            v = 0.1022568715358061E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7057738183256172E-1
+            b = 0.5517505421423520E+0
+            v = 0.1108960267713108E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2783888477882155E+0
+            b = 0.6029619156159187E+0
+            v = 0.1122790653435766E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1979578938917407E+0
+            b = 0.3589606329589096E+0
+            v = 0.1032401847117460E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2087307061103274E+0
+            b = 0.5348666438135476E+0
+            v = 0.1107249382283854E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4055122137872836E+0
+            b = 0.5674997546074373E+0
+            v = 0.1121780048519972E-2
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 1202:
+
+            v = 0.1105189233267572E-3
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.9205232738090741E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.9133159786443561E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.3712636449657089E-1
+            v = 0.3690421898017899E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9140060412262223E-1
+            v = 0.5603990928680660E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1531077852469906E+0
+            v = 0.6865297629282609E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2180928891660612E+0
+            v = 0.7720338551145630E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2839874532200175E+0
+            v = 0.8301545958894795E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3491177600963764E+0
+            v = 0.8686692550179628E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4121431461444309E+0
+            v = 0.8927076285846890E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4718993627149127E+0
+            v = 0.9060820238568219E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5273145452842337E+0
+            v = 0.9119777254940867E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6209475332444019E+0
+            v = 0.9128720138604181E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6569722711857291E+0
+            v = 0.9130714935691735E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6841788309070143E+0
+            v = 0.9152873784554116E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7012604330123631E+0
+            v = 0.9187436274321654E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1072382215478166E+0
+            v = 0.5176977312965694E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2582068959496968E+0
+            v = 0.7331143682101417E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4172752955306717E+0
+            v = 0.8463232836379928E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5700366911792503E+0
+            v = 0.9031122694253992E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9827986018263947E+0
+            b = 0.1771774022615325E+0
+            v = 0.6485778453163257E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9624249230326228E+0
+            b = 0.2475716463426288E+0
+            v = 0.7435030910982369E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9402007994128811E+0
+            b = 0.3354616289066489E+0
+            v = 0.7998527891839054E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9320822040143202E+0
+            b = 0.3173615246611977E+0
+            v = 0.8101731497468018E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9043674199393299E+0
+            b = 0.4090268427085357E+0
+            v = 0.8483389574594331E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8912407560074747E+0
+            b = 0.3854291150669224E+0
+            v = 0.8556299257311812E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8676435628462708E+0
+            b = 0.4932221184851285E+0
+            v = 0.8803208679738260E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8581979986041619E+0
+            b = 0.4785320675922435E+0
+            v = 0.8811048182425720E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8396753624049856E+0
+            b = 0.4507422593157064E+0
+            v = 0.8850282341265444E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8165288564022188E+0
+            b = 0.5632123020762100E+0
+            v = 0.9021342299040653E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8015469370783529E+0
+            b = 0.5434303569693900E+0
+            v = 0.9010091677105086E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7773563069070351E+0
+            b = 0.5123518486419871E+0
+            v = 0.9022692938426915E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7661621213900394E+0
+            b = 0.6394279634749102E+0
+            v = 0.9158016174693465E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7553584143533510E+0
+            b = 0.6269805509024392E+0
+            v = 0.9131578003189435E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7344305757559503E+0
+            b = 0.6031161693096310E+0
+            v = 0.9107813579482705E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.7043837184021765E+0
+            b = 0.5693702498468441E+0
+            v = 0.9105760258970126E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 1454:
+
+            v = 0.7777160743261247E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.7557646413004701E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.3229290663413854E-1
+            v = 0.2841633806090617E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8036733271462222E-1
+            v = 0.4374419127053555E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1354289960531653E+0
+            v = 0.5417174740872172E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1938963861114426E+0
+            v = 0.6148000891358593E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2537343715011275E+0
+            v = 0.6664394485800705E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3135251434752570E+0
+            v = 0.7025039356923220E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3721558339375338E+0
+            v = 0.7268511789249627E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4286809575195696E+0
+            v = 0.7422637534208629E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4822510128282994E+0
+            v = 0.7509545035841214E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5320679333566263E+0
+            v = 0.7548535057718401E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6172998195394274E+0
+            v = 0.7554088969774001E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6510679849127481E+0
+            v = 0.7553147174442808E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6777315251687360E+0
+            v = 0.7564767653292297E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6963109410648741E+0
+            v = 0.7587991808518730E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7058935009831749E+0
+            v = 0.7608261832033027E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9955546194091857E+0
+            v = 0.4021680447874916E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9734115901794209E+0
+            v = 0.5804871793945964E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9275693732388626E+0
+            v = 0.6792151955945159E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.8568022422795103E+0
+            v = 0.7336741211286294E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.7623495553719372E+0
+            v = 0.7581866300989608E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5707522908892223E+0
+            b = 0.4387028039889501E+0
+            v = 0.7538257859800743E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5196463388403083E+0
+            b = 0.3858908414762617E+0
+            v = 0.7483517247053123E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4646337531215351E+0
+            b = 0.3301937372343854E+0
+            v = 0.7371763661112059E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4063901697557691E+0
+            b = 0.2725423573563777E+0
+            v = 0.7183448895756934E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3456329466643087E+0
+            b = 0.2139510237495250E+0
+            v = 0.6895815529822191E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2831395121050332E+0
+            b = 0.1555922309786647E+0
+            v = 0.6480105801792886E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2197682022925330E+0
+            b = 0.9892878979686097E-1
+            v = 0.5897558896594636E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1564696098650355E+0
+            b = 0.4598642910675510E-1
+            v = 0.5095708849247346E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6027356673721295E+0
+            b = 0.3376625140173426E+0
+            v = 0.7536906428909755E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5496032320255096E+0
+            b = 0.2822301309727988E+0
+            v = 0.7472505965575118E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4921707755234567E+0
+            b = 0.2248632342592540E+0
+            v = 0.7343017132279698E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4309422998598483E+0
+            b = 0.1666224723456479E+0
+            v = 0.7130871582177445E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3664108182313672E+0
+            b = 0.1086964901822169E+0
+            v = 0.6817022032112776E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2990189057758436E+0
+            b = 0.5251989784120085E-1
+            v = 0.6380941145604121E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6268724013144998E+0
+            b = 0.2297523657550023E+0
+            v = 0.7550381377920310E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5707324144834607E+0
+            b = 0.1723080607093800E+0
+            v = 0.7478646640144802E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5096360901960365E+0
+            b = 0.1140238465390513E+0
+            v = 0.7335918720601220E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4438729938312456E+0
+            b = 0.5611522095882537E-1
+            v = 0.7110120527658118E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6419978471082389E+0
+            b = 0.1164174423140873E+0
+            v = 0.7571363978689501E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5817218061802611E+0
+            b = 0.5797589531445219E-1
+            v = 0.7489908329079234E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 1730:
+
+            v = 0.6309049437420976E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.6398287705571748E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.6357185073530720E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2860923126194662E-1
+            v = 0.2221207162188168E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7142556767711522E-1
+            v = 0.3475784022286848E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1209199540995559E+0
+            v = 0.4350742443589804E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1738673106594379E+0
+            v = 0.4978569136522127E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2284645438467734E+0
+            v = 0.5435036221998053E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2834807671701512E+0
+            v = 0.5765913388219542E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3379680145467339E+0
+            v = 0.6001200359226003E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3911355454819537E+0
+            v = 0.6162178172717512E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4422860353001403E+0
+            v = 0.6265218152438485E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4907781568726057E+0
+            v = 0.6323987160974212E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5360006153211468E+0
+            v = 0.6350767851540569E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6142105973596603E+0
+            v = 0.6354362775297107E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6459300387977504E+0
+            v = 0.6352302462706235E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6718056125089225E+0
+            v = 0.6358117881417972E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6910888533186254E+0
+            v = 0.6373101590310117E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7030467416823252E+0
+            v = 0.6390428961368665E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8354951166354646E-1
+            v = 0.3186913449946576E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2050143009099486E+0
+            v = 0.4678028558591711E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3370208290706637E+0
+            v = 0.5538829697598626E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4689051484233963E+0
+            v = 0.6044475907190476E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5939400424557334E+0
+            v = 0.6313575103509012E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1394983311832261E+0
+            b = 0.4097581162050343E-1
+            v = 0.4078626431855630E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1967999180485014E+0
+            b = 0.8851987391293348E-1
+            v = 0.4759933057812725E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2546183732548967E+0
+            b = 0.1397680182969819E+0
+            v = 0.5268151186413440E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3121281074713875E+0
+            b = 0.1929452542226526E+0
+            v = 0.5643048560507316E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3685981078502492E+0
+            b = 0.2467898337061562E+0
+            v = 0.5914501076613073E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4233760321547856E+0
+            b = 0.3003104124785409E+0
+            v = 0.6104561257874195E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4758671236059246E+0
+            b = 0.3526684328175033E+0
+            v = 0.6230252860707806E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5255178579796463E+0
+            b = 0.4031134861145713E+0
+            v = 0.6305618761760796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5718025633734589E+0
+            b = 0.4509426448342351E+0
+            v = 0.6343092767597889E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2686927772723415E+0
+            b = 0.4711322502423248E-1
+            v = 0.5176268945737826E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3306006819904809E+0
+            b = 0.9784487303942695E-1
+            v = 0.5564840313313692E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3904906850594983E+0
+            b = 0.1505395810025273E+0
+            v = 0.5856426671038980E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4479957951904390E+0
+            b = 0.2039728156296050E+0
+            v = 0.6066386925777091E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5027076848919780E+0
+            b = 0.2571529941121107E+0
+            v = 0.6208824962234458E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5542087392260217E+0
+            b = 0.3092191375815670E+0
+            v = 0.6296314297822907E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6020850887375187E+0
+            b = 0.3593807506130276E+0
+            v = 0.6340423756791859E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4019851409179594E+0
+            b = 0.5063389934378671E-1
+            v = 0.5829627677107342E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4635614567449800E+0
+            b = 0.1032422269160612E+0
+            v = 0.6048693376081110E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5215860931591575E+0
+            b = 0.1566322094006254E+0
+            v = 0.6202362317732461E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5758202499099271E+0
+            b = 0.2098082827491099E+0
+            v = 0.6299005328403779E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6259893683876795E+0
+            b = 0.2618824114553391E+0
+            v = 0.6347722390609353E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5313795124811891E+0
+            b = 0.5263245019338556E-1
+            v = 0.6203778981238834E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5893317955931995E+0
+            b = 0.1061059730982005E+0
+            v = 0.6308414671239979E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6426246321215801E+0
+            b = 0.1594171564034221E+0
+            v = 0.6362706466959498E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6511904367376113E+0
+            b = 0.5354789536565540E-1
+            v = 0.6375414170333233E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 2030:
+
+            v = 0.4656031899197431E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.5421549195295507E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2540835336814348E-1
+            v = 0.1778522133346553E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6399322800504915E-1
+            v = 0.2811325405682796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1088269469804125E+0
+            v = 0.3548896312631459E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1570670798818287E+0
+            v = 0.4090310897173364E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2071163932282514E+0
+            v = 0.4493286134169965E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2578914044450844E+0
+            v = 0.4793728447962723E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3085687558169623E+0
+            v = 0.5015415319164265E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3584719706267024E+0
+            v = 0.5175127372677937E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4070135594428709E+0
+            v = 0.5285522262081019E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4536618626222638E+0
+            v = 0.5356832703713962E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4979195686463577E+0
+            v = 0.5397914736175170E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5393075111126999E+0
+            v = 0.5416899441599930E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6115617676843916E+0
+            v = 0.5419308476889938E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6414308435160159E+0
+            v = 0.5416936902030596E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6664099412721607E+0
+            v = 0.5419544338703164E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6859161771214913E+0
+            v = 0.5428983656630975E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6993625593503890E+0
+            v = 0.5442286500098193E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7062393387719380E+0
+            v = 0.5452250345057301E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7479028168349763E-1
+            v = 0.2568002497728530E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1848951153969366E+0
+            v = 0.3827211700292145E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3059529066581305E+0
+            v = 0.4579491561917824E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4285556101021362E+0
+            v = 0.5042003969083574E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5468758653496526E+0
+            v = 0.5312708889976025E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6565821978343439E+0
+            v = 0.5438401790747117E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1253901572367117E+0
+            b = 0.3681917226439641E-1
+            v = 0.3316041873197344E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1775721510383941E+0
+            b = 0.7982487607213301E-1
+            v = 0.3899113567153771E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2305693358216114E+0
+            b = 0.1264640966592335E+0
+            v = 0.4343343327201309E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2836502845992063E+0
+            b = 0.1751585683418957E+0
+            v = 0.4679415262318919E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3361794746232590E+0
+            b = 0.2247995907632670E+0
+            v = 0.4930847981631031E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3875979172264824E+0
+            b = 0.2745299257422246E+0
+            v = 0.5115031867540091E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4374019316999074E+0
+            b = 0.3236373482441118E+0
+            v = 0.5245217148457367E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4851275843340022E+0
+            b = 0.3714967859436741E+0
+            v = 0.5332041499895321E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5303391803806868E+0
+            b = 0.4175353646321745E+0
+            v = 0.5384583126021542E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5726197380596287E+0
+            b = 0.4612084406355461E+0
+            v = 0.5411067210798852E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2431520732564863E+0
+            b = 0.4258040133043952E-1
+            v = 0.4259797391468714E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3002096800895869E+0
+            b = 0.8869424306722721E-1
+            v = 0.4604931368460021E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3558554457457432E+0
+            b = 0.1368811706510655E+0
+            v = 0.4871814878255202E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4097782537048887E+0
+            b = 0.1860739985015033E+0
+            v = 0.5072242910074885E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4616337666067458E+0
+            b = 0.2354235077395853E+0
+            v = 0.5217069845235350E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5110707008417874E+0
+            b = 0.2842074921347011E+0
+            v = 0.5315785966280310E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5577415286163795E+0
+            b = 0.3317784414984102E+0
+            v = 0.5376833708758905E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6013060431366950E+0
+            b = 0.3775299002040700E+0
+            v = 0.5408032092069521E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3661596767261781E+0
+            b = 0.4599367887164592E-1
+            v = 0.4842744917904866E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4237633153506581E+0
+            b = 0.9404893773654421E-1
+            v = 0.5048926076188130E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4786328454658452E+0
+            b = 0.1431377109091971E+0
+            v = 0.5202607980478373E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5305702076789774E+0
+            b = 0.1924186388843570E+0
+            v = 0.5309932388325743E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5793436224231788E+0
+            b = 0.2411590944775190E+0
+            v = 0.5377419770895208E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6247069017094747E+0
+            b = 0.2886871491583605E+0
+            v = 0.5411696331677717E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4874315552535204E+0
+            b = 0.4804978774953206E-1
+            v = 0.5197996293282420E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5427337322059053E+0
+            b = 0.9716857199366665E-1
+            v = 0.5311120836622945E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5943493747246700E+0
+            b = 0.1465205839795055E+0
+            v = 0.5384309319956951E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6421314033564943E+0
+            b = 0.1953579449803574E+0
+            v = 0.5421859504051886E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6020628374713980E+0
+            b = 0.4916375015738108E-1
+            v = 0.5390948355046314E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6529222529856881E+0
+            b = 0.9861621540127005E-1
+            v = 0.5433312705027845E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 2354:
+
+            v = 0.3922616270665292E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.4703831750854424E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.4678202801282136E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2290024646530589E-1
+            v = 0.1437832228979900E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5779086652271284E-1
+            v = 0.2303572493577644E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9863103576375984E-1
+            v = 0.2933110752447454E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1428155792982185E+0
+            v = 0.3402905998359838E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1888978116601463E+0
+            v = 0.3759138466870372E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2359091682970210E+0
+            v = 0.4030638447899798E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2831228833706171E+0
+            v = 0.4236591432242211E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3299495857966693E+0
+            v = 0.4390522656946746E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3758840802660796E+0
+            v = 0.4502523466626247E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4204751831009480E+0
+            v = 0.4580577727783541E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4633068518751051E+0
+            v = 0.4631391616615899E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5039849474507313E+0
+            v = 0.4660928953698676E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5421265793440747E+0
+            v = 0.4674751807936953E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6092660230557310E+0
+            v = 0.4676414903932920E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6374654204984869E+0
+            v = 0.4674086492347870E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6615136472609892E+0
+            v = 0.4674928539483207E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6809487285958127E+0
+            v = 0.4680748979686447E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6952980021665196E+0
+            v = 0.4690449806389040E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7041245497695400E+0
+            v = 0.4699877075860818E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6744033088306065E-1
+            v = 0.2099942281069176E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1678684485334166E+0
+            v = 0.3172269150712804E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2793559049539613E+0
+            v = 0.3832051358546523E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3935264218057639E+0
+            v = 0.4252193818146985E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5052629268232558E+0
+            v = 0.4513807963755000E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6107905315437531E+0
+            v = 0.4657797469114178E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1135081039843524E+0
+            b = 0.3331954884662588E-1
+            v = 0.2733362800522836E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1612866626099378E+0
+            b = 0.7247167465436538E-1
+            v = 0.3235485368463559E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2100786550168205E+0
+            b = 0.1151539110849745E+0
+            v = 0.3624908726013453E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2592282009459942E+0
+            b = 0.1599491097143677E+0
+            v = 0.3925540070712828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3081740561320203E+0
+            b = 0.2058699956028027E+0
+            v = 0.4156129781116235E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3564289781578164E+0
+            b = 0.2521624953502911E+0
+            v = 0.4330644984623263E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4035587288240703E+0
+            b = 0.2982090785797674E+0
+            v = 0.4459677725921312E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4491671196373903E+0
+            b = 0.3434762087235733E+0
+            v = 0.4551593004456795E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4928854782917489E+0
+            b = 0.3874831357203437E+0
+            v = 0.4613341462749918E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5343646791958988E+0
+            b = 0.4297814821746926E+0
+            v = 0.4651019618269806E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5732683216530990E+0
+            b = 0.4699402260943537E+0
+            v = 0.4670249536100625E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2214131583218986E+0
+            b = 0.3873602040643895E-1
+            v = 0.3549555576441708E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2741796504750071E+0
+            b = 0.8089496256902013E-1
+            v = 0.3856108245249010E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3259797439149485E+0
+            b = 0.1251732177620872E+0
+            v = 0.4098622845756882E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3765441148826891E+0
+            b = 0.1706260286403185E+0
+            v = 0.4286328604268950E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4255773574530558E+0
+            b = 0.2165115147300408E+0
+            v = 0.4427802198993945E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4727795117058430E+0
+            b = 0.2622089812225259E+0
+            v = 0.4530473511488561E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5178546895819012E+0
+            b = 0.3071721431296201E+0
+            v = 0.4600805475703138E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5605141192097460E+0
+            b = 0.3508998998801138E+0
+            v = 0.4644599059958017E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6004763319352512E+0
+            b = 0.3929160876166931E+0
+            v = 0.4667274455712508E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3352842634946949E+0
+            b = 0.4202563457288019E-1
+            v = 0.4069360518020356E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3891971629814670E+0
+            b = 0.8614309758870850E-1
+            v = 0.4260442819919195E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4409875565542281E+0
+            b = 0.1314500879380001E+0
+            v = 0.4408678508029063E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4904893058592484E+0
+            b = 0.1772189657383859E+0
+            v = 0.4518748115548597E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5375056138769549E+0
+            b = 0.2228277110050294E+0
+            v = 0.4595564875375116E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5818255708669969E+0
+            b = 0.2677179935014386E+0
+            v = 0.4643988774315846E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6232334858144959E+0
+            b = 0.3113675035544165E+0
+            v = 0.4668827491646946E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4489485354492058E+0
+            b = 0.4409162378368174E-1
+            v = 0.4400541823741973E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5015136875933150E+0
+            b = 0.8939009917748489E-1
+            v = 0.4514512890193797E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5511300550512623E+0
+            b = 0.1351806029383365E+0
+            v = 0.4596198627347549E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5976720409858000E+0
+            b = 0.1808370355053196E+0
+            v = 0.4648659016801781E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6409956378989354E+0
+            b = 0.2257852192301602E+0
+            v = 0.4675502017157673E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5581222330827514E+0
+            b = 0.4532173421637160E-1
+            v = 0.4598494476455523E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6074705984161695E+0
+            b = 0.9117488031840314E-1
+            v = 0.4654916955152048E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6532272537379033E+0
+            b = 0.1369294213140155E+0
+            v = 0.4684709779505137E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6594761494500487E+0
+            b = 0.4589901487275583E-1
+            v = 0.4691445539106986E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 2702:
+
+            v = 0.2998675149888161E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.4077860529495355E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2065562538818703E-1
+            v = 0.1185349192520667E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5250918173022379E-1
+            v = 0.1913408643425751E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8993480082038376E-1
+            v = 0.2452886577209897E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1306023924436019E+0
+            v = 0.2862408183288702E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1732060388531418E+0
+            v = 0.3178032258257357E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2168727084820249E+0
+            v = 0.3422945667633690E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2609528309173586E+0
+            v = 0.3612790520235922E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3049252927938952E+0
+            v = 0.3758638229818521E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3483484138084404E+0
+            v = 0.3868711798859953E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3908321549106406E+0
+            v = 0.3949429933189938E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4320210071894814E+0
+            v = 0.4006068107541156E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4715824795890053E+0
+            v = 0.4043192149672723E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5091984794078453E+0
+            v = 0.4064947495808078E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5445580145650803E+0
+            v = 0.4075245619813152E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6072575796841768E+0
+            v = 0.4076423540893566E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6339484505755803E+0
+            v = 0.4074280862251555E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6570718257486958E+0
+            v = 0.4074163756012244E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6762557330090709E+0
+            v = 0.4077647795071246E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6911161696923790E+0
+            v = 0.4084517552782530E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7012841911659961E+0
+            v = 0.4092468459224052E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7064559272410020E+0
+            v = 0.4097872687240906E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6123554989894765E-1
+            v = 0.1738986811745028E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1533070348312393E+0
+            v = 0.2659616045280191E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2563902605244206E+0
+            v = 0.3240596008171533E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3629346991663361E+0
+            v = 0.3621195964432943E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4683949968987538E+0
+            v = 0.3868838330760539E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5694479240657952E+0
+            v = 0.4018911532693111E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6634465430993955E+0
+            v = 0.4089929432983252E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1033958573552305E+0
+            b = 0.3034544009063584E-1
+            v = 0.2279907527706409E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1473521412414395E+0
+            b = 0.6618803044247135E-1
+            v = 0.2715205490578897E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1924552158705967E+0
+            b = 0.1054431128987715E+0
+            v = 0.3057917896703976E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2381094362890328E+0
+            b = 0.1468263551238858E+0
+            v = 0.3326913052452555E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2838121707936760E+0
+            b = 0.1894486108187886E+0
+            v = 0.3537334711890037E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3291323133373415E+0
+            b = 0.2326374238761579E+0
+            v = 0.3700567500783129E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3736896978741460E+0
+            b = 0.2758485808485768E+0
+            v = 0.3825245372589122E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4171406040760013E+0
+            b = 0.3186179331996921E+0
+            v = 0.3918125171518296E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4591677985256915E+0
+            b = 0.3605329796303794E+0
+            v = 0.3984720419937579E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4994733831718418E+0
+            b = 0.4012147253586509E+0
+            v = 0.4029746003338211E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5377731830445096E+0
+            b = 0.4403050025570692E+0
+            v = 0.4057428632156627E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5737917830001331E+0
+            b = 0.4774565904277483E+0
+            v = 0.4071719274114857E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2027323586271389E+0
+            b = 0.3544122504976147E-1
+            v = 0.2990236950664119E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2516942375187273E+0
+            b = 0.7418304388646328E-1
+            v = 0.3262951734212878E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3000227995257181E+0
+            b = 0.1150502745727186E+0
+            v = 0.3482634608242413E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3474806691046342E+0
+            b = 0.1571963371209364E+0
+            v = 0.3656596681700892E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3938103180359209E+0
+            b = 0.1999631877247100E+0
+            v = 0.3791740467794218E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4387519590455703E+0
+            b = 0.2428073457846535E+0
+            v = 0.3894034450156905E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4820503960077787E+0
+            b = 0.2852575132906155E+0
+            v = 0.3968600245508371E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5234573778475101E+0
+            b = 0.3268884208674639E+0
+            v = 0.4019931351420050E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5627318647235282E+0
+            b = 0.3673033321675939E+0
+            v = 0.4052108801278599E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5996390607156954E+0
+            b = 0.4061211551830290E+0
+            v = 0.4068978613940934E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3084780753791947E+0
+            b = 0.3860125523100059E-1
+            v = 0.3454275351319704E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3589988275920223E+0
+            b = 0.7928938987104867E-1
+            v = 0.3629963537007920E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4078628415881973E+0
+            b = 0.1212614643030087E+0
+            v = 0.3770187233889873E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4549287258889735E+0
+            b = 0.1638770827382693E+0
+            v = 0.3878608613694378E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5000278512957279E+0
+            b = 0.2065965798260176E+0
+            v = 0.3959065270221274E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5429785044928199E+0
+            b = 0.2489436378852235E+0
+            v = 0.4015286975463570E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5835939850491711E+0
+            b = 0.2904811368946891E+0
+            v = 0.4050866785614717E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6216870353444856E+0
+            b = 0.3307941957666609E+0
+            v = 0.4069320185051913E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4151104662709091E+0
+            b = 0.4064829146052554E-1
+            v = 0.3760120964062763E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4649804275009218E+0
+            b = 0.8258424547294755E-1
+            v = 0.3870969564418064E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5124695757009662E+0
+            b = 0.1251841962027289E+0
+            v = 0.3955287790534055E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5574711100606224E+0
+            b = 0.1679107505976331E+0
+            v = 0.4015361911302668E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5998597333287227E+0
+            b = 0.2102805057358715E+0
+            v = 0.4053836986719548E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6395007148516600E+0
+            b = 0.2518418087774107E+0
+            v = 0.4073578673299117E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5188456224746252E+0
+            b = 0.4194321676077518E-1
+            v = 0.3954628379231406E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5664190707942778E+0
+            b = 0.8457661551921499E-1
+            v = 0.4017645508847530E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6110464353283153E+0
+            b = 0.1273652932519396E+0
+            v = 0.4059030348651293E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6526430302051563E+0
+            b = 0.1698173239076354E+0
+            v = 0.4080565809484880E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6167551880377548E+0
+            b = 0.4266398851548864E-1
+            v = 0.4063018753664651E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6607195418355383E+0
+            b = 0.8551925814238349E-1
+            v = 0.4087191292799671E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 3074:
+
+            v = 0.2599095953754734E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3603134089687541E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.3586067974412447E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1886108518723392E-1
+            v = 0.9831528474385880E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4800217244625303E-1
+            v = 0.1605023107954450E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8244922058397242E-1
+            v = 0.2072200131464099E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1200408362484023E+0
+            v = 0.2431297618814187E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1595773530809965E+0
+            v = 0.2711819064496707E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2002635973434064E+0
+            v = 0.2932762038321116E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2415127590139982E+0
+            v = 0.3107032514197368E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2828584158458477E+0
+            v = 0.3243808058921213E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3239091015338138E+0
+            v = 0.3349899091374030E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3643225097962194E+0
+            v = 0.3430580688505218E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4037897083691802E+0
+            v = 0.3490124109290343E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4420247515194127E+0
+            v = 0.3532148948561955E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4787572538464938E+0
+            v = 0.3559862669062833E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5137265251275234E+0
+            v = 0.3576224317551411E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5466764056654611E+0
+            v = 0.3584050533086076E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6054859420813535E+0
+            v = 0.3584903581373224E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6308106701764562E+0
+            v = 0.3582991879040586E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6530369230179584E+0
+            v = 0.3582371187963125E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6718609524611158E+0
+            v = 0.3584353631122350E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6869676499894013E+0
+            v = 0.3589120166517785E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6980467077240748E+0
+            v = 0.3595445704531601E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7048241721250522E+0
+            v = 0.3600943557111074E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5591105222058232E-1
+            v = 0.1456447096742039E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1407384078513916E+0
+            v = 0.2252370188283782E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2364035438976309E+0
+            v = 0.2766135443474897E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3360602737818170E+0
+            v = 0.3110729491500851E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4356292630054665E+0
+            v = 0.3342506712303391E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5321569415256174E+0
+            v = 0.3491981834026860E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6232956305040554E+0
+            v = 0.3576003604348932E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9469870086838469E-1
+            b = 0.2778748387309470E-1
+            v = 0.1921921305788564E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1353170300568141E+0
+            b = 0.6076569878628364E-1
+            v = 0.2301458216495632E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1771679481726077E+0
+            b = 0.9703072762711040E-1
+            v = 0.2604248549522893E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2197066664231751E+0
+            b = 0.1354112458524762E+0
+            v = 0.2845275425870697E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2624783557374927E+0
+            b = 0.1750996479744100E+0
+            v = 0.3036870897974840E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3050969521214442E+0
+            b = 0.2154896907449802E+0
+            v = 0.3188414832298066E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3472252637196021E+0
+            b = 0.2560954625740152E+0
+            v = 0.3307046414722089E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3885610219026360E+0
+            b = 0.2965070050624096E+0
+            v = 0.3398330969031360E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4288273776062765E+0
+            b = 0.3363641488734497E+0
+            v = 0.3466757899705373E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4677662471302948E+0
+            b = 0.3753400029836788E+0
+            v = 0.3516095923230054E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5051333589553359E+0
+            b = 0.4131297522144286E+0
+            v = 0.3549645184048486E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5406942145810492E+0
+            b = 0.4494423776081795E+0
+            v = 0.3570415969441392E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5742204122576457E+0
+            b = 0.4839938958841502E+0
+            v = 0.3581251798496118E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1865407027225188E+0
+            b = 0.3259144851070796E-1
+            v = 0.2543491329913348E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2321186453689432E+0
+            b = 0.6835679505297343E-1
+            v = 0.2786711051330776E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2773159142523882E+0
+            b = 0.1062284864451989E+0
+            v = 0.2985552361083679E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3219200192237254E+0
+            b = 0.1454404409323047E+0
+            v = 0.3145867929154039E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3657032593944029E+0
+            b = 0.1854018282582510E+0
+            v = 0.3273290662067609E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4084376778363622E+0
+            b = 0.2256297412014750E+0
+            v = 0.3372705511943501E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4499004945751427E+0
+            b = 0.2657104425000896E+0
+            v = 0.3448274437851510E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4898758141326335E+0
+            b = 0.3052755487631557E+0
+            v = 0.3503592783048583E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5281547442266309E+0
+            b = 0.3439863920645423E+0
+            v = 0.3541854792663162E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5645346989813992E+0
+            b = 0.3815229456121914E+0
+            v = 0.3565995517909428E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5988181252159848E+0
+            b = 0.4175752420966734E+0
+            v = 0.3578802078302898E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2850425424471603E+0
+            b = 0.3562149509862536E-1
+            v = 0.2958644592860982E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3324619433027876E+0
+            b = 0.7330318886871096E-1
+            v = 0.3119548129116835E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3785848333076282E+0
+            b = 0.1123226296008472E+0
+            v = 0.3250745225005984E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4232891028562115E+0
+            b = 0.1521084193337708E+0
+            v = 0.3355153415935208E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4664287050829722E+0
+            b = 0.1921844459223610E+0
+            v = 0.3435847568549328E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5078458493735726E+0
+            b = 0.2321360989678303E+0
+            v = 0.3495786831622488E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5473779816204180E+0
+            b = 0.2715886486360520E+0
+            v = 0.3537767805534621E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5848617133811376E+0
+            b = 0.3101924707571355E+0
+            v = 0.3564459815421428E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6201348281584888E+0
+            b = 0.3476121052890973E+0
+            v = 0.3578464061225468E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3852191185387871E+0
+            b = 0.3763224880035108E-1
+            v = 0.3239748762836212E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4325025061073423E+0
+            b = 0.7659581935637135E-1
+            v = 0.3345491784174287E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4778486229734490E+0
+            b = 0.1163381306083900E+0
+            v = 0.3429126177301782E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5211663693009000E+0
+            b = 0.1563890598752899E+0
+            v = 0.3492420343097421E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5623469504853703E+0
+            b = 0.1963320810149200E+0
+            v = 0.3537399050235257E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6012718188659246E+0
+            b = 0.2357847407258738E+0
+            v = 0.3566209152659172E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6378179206390117E+0
+            b = 0.2743846121244060E+0
+            v = 0.3581084321919782E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4836936460214534E+0
+            b = 0.3895902610739024E-1
+            v = 0.3426522117591512E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5293792562683797E+0
+            b = 0.7871246819312640E-1
+            v = 0.3491848770121379E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5726281253100033E+0
+            b = 0.1187963808202981E+0
+            v = 0.3539318235231476E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6133658776169068E+0
+            b = 0.1587914708061787E+0
+            v = 0.3570231438458694E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6515085491865307E+0
+            b = 0.1983058575227646E+0
+            v = 0.3586207335051714E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5778692716064976E+0
+            b = 0.3977209689791542E-1
+            v = 0.3541196205164025E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6207904288086192E+0
+            b = 0.7990157592981152E-1
+            v = 0.3574296911573953E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6608688171046802E+0
+            b = 0.1199671308754309E+0
+            v = 0.3591993279818963E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6656263089489130E+0
+            b = 0.4015955957805969E-1
+            v = 0.3595855034661997E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 3470:
+
+            v = 0.2040382730826330E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.3178149703889544E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1721420832906233E-1
+            v = 0.8288115128076110E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4408875374981770E-1
+            v = 0.1360883192522954E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7594680813878681E-1
+            v = 0.1766854454542662E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1108335359204799E+0
+            v = 0.2083153161230153E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1476517054388567E+0
+            v = 0.2333279544657158E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1856731870860615E+0
+            v = 0.2532809539930247E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2243634099428821E+0
+            v = 0.2692472184211158E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2633006881662727E+0
+            v = 0.2819949946811885E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3021340904916283E+0
+            v = 0.2920953593973030E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3405594048030089E+0
+            v = 0.2999889782948352E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3783044434007372E+0
+            v = 0.3060292120496902E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4151194767407910E+0
+            v = 0.3105109167522192E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4507705766443257E+0
+            v = 0.3136902387550312E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4850346056573187E+0
+            v = 0.3157984652454632E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5176950817792470E+0
+            v = 0.3170516518425422E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5485384240820989E+0
+            v = 0.3176568425633755E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6039117238943308E+0
+            v = 0.3177198411207062E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6279956655573113E+0
+            v = 0.3175519492394733E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6493636169568952E+0
+            v = 0.3174654952634756E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6677644117704504E+0
+            v = 0.3175676415467654E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6829368572115624E+0
+            v = 0.3178923417835410E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6946195818184121E+0
+            v = 0.3183788287531909E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7025711542057026E+0
+            v = 0.3188755151918807E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7066004767140119E+0
+            v = 0.3191916889313849E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5132537689946062E-1
+            v = 0.1231779611744508E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1297994661331225E+0
+            v = 0.1924661373839880E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2188852049401307E+0
+            v = 0.2380881867403424E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3123174824903457E+0
+            v = 0.2693100663037885E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4064037620738195E+0
+            v = 0.2908673382834366E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4984958396944782E+0
+            v = 0.3053914619381535E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5864975046021365E+0
+            v = 0.3143916684147777E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6686711634580175E+0
+            v = 0.3187042244055363E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.8715738780835950E-1
+            b = 0.2557175233367578E-1
+            v = 0.1635219535869790E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1248383123134007E+0
+            b = 0.5604823383376681E-1
+            v = 0.1968109917696070E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1638062693383378E+0
+            b = 0.8968568601900765E-1
+            v = 0.2236754342249974E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2035586203373176E+0
+            b = 0.1254086651976279E+0
+            v = 0.2453186687017181E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2436798975293774E+0
+            b = 0.1624780150162012E+0
+            v = 0.2627551791580541E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2838207507773806E+0
+            b = 0.2003422342683208E+0
+            v = 0.2767654860152220E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3236787502217692E+0
+            b = 0.2385628026255263E+0
+            v = 0.2879467027765895E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3629849554840691E+0
+            b = 0.2767731148783578E+0
+            v = 0.2967639918918702E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4014948081992087E+0
+            b = 0.3146542308245309E+0
+            v = 0.3035900684660351E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4389818379260225E+0
+            b = 0.3519196415895088E+0
+            v = 0.3087338237298308E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4752331143674377E+0
+            b = 0.3883050984023654E+0
+            v = 0.3124608838860167E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5100457318374018E+0
+            b = 0.4235613423908649E+0
+            v = 0.3150084294226743E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5432238388954868E+0
+            b = 0.4574484717196220E+0
+            v = 0.3165958398598402E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5745758685072442E+0
+            b = 0.4897311639255524E+0
+            v = 0.3174320440957372E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1723981437592809E+0
+            b = 0.3010630597881105E-1
+            v = 0.2182188909812599E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2149553257844597E+0
+            b = 0.6326031554204694E-1
+            v = 0.2399727933921445E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2573256081247422E+0
+            b = 0.9848566980258631E-1
+            v = 0.2579796133514652E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2993163751238106E+0
+            b = 0.1350835952384266E+0
+            v = 0.2727114052623535E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3407238005148000E+0
+            b = 0.1725184055442181E+0
+            v = 0.2846327656281355E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3813454978483264E+0
+            b = 0.2103559279730725E+0
+            v = 0.2941491102051334E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4209848104423343E+0
+            b = 0.2482278774554860E+0
+            v = 0.3016049492136107E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4594519699996300E+0
+            b = 0.2858099509982883E+0
+            v = 0.3072949726175648E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4965640166185930E+0
+            b = 0.3228075659915428E+0
+            v = 0.3114768142886460E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5321441655571562E+0
+            b = 0.3589459907204151E+0
+            v = 0.3143823673666223E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5660208438582166E+0
+            b = 0.3939630088864310E+0
+            v = 0.3162269764661535E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5980264315964364E+0
+            b = 0.4276029922949089E+0
+            v = 0.3172164663759821E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2644215852350733E+0
+            b = 0.3300939429072552E-1
+            v = 0.2554575398967435E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3090113743443063E+0
+            b = 0.6803887650078501E-1
+            v = 0.2701704069135677E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3525871079197808E+0
+            b = 0.1044326136206709E+0
+            v = 0.2823693413468940E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3950418005354029E+0
+            b = 0.1416751597517679E+0
+            v = 0.2922898463214289E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4362475663430163E+0
+            b = 0.1793408610504821E+0
+            v = 0.3001829062162428E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4760661812145854E+0
+            b = 0.2170630750175722E+0
+            v = 0.3062890864542953E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5143551042512103E+0
+            b = 0.2545145157815807E+0
+            v = 0.3108328279264746E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5509709026935597E+0
+            b = 0.2913940101706601E+0
+            v = 0.3140243146201245E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5857711030329428E+0
+            b = 0.3274169910910705E+0
+            v = 0.3160638030977130E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6186149917404392E+0
+            b = 0.3623081329317265E+0
+            v = 0.3171462882206275E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3586894569557064E+0
+            b = 0.3497354386450040E-1
+            v = 0.2812388416031796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4035266610019441E+0
+            b = 0.7129736739757095E-1
+            v = 0.2912137500288045E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4467775312332510E+0
+            b = 0.1084758620193165E+0
+            v = 0.2993241256502206E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4883638346608543E+0
+            b = 0.1460915689241772E+0
+            v = 0.3057101738983822E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5281908348434601E+0
+            b = 0.1837790832369980E+0
+            v = 0.3105319326251432E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5661542687149311E+0
+            b = 0.2212075390874021E+0
+            v = 0.3139565514428167E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6021450102031452E+0
+            b = 0.2580682841160985E+0
+            v = 0.3161543006806366E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6360520783610050E+0
+            b = 0.2940656362094121E+0
+            v = 0.3172985960613294E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4521611065087196E+0
+            b = 0.3631055365867002E-1
+            v = 0.2989400336901431E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4959365651560963E+0
+            b = 0.7348318468484350E-1
+            v = 0.3054555883947677E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5376815804038283E+0
+            b = 0.1111087643812648E+0
+            v = 0.3104764960807702E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5773314480243768E+0
+            b = 0.1488226085145408E+0
+            v = 0.3141015825977616E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6148113245575056E+0
+            b = 0.1862892274135151E+0
+            v = 0.3164520621159896E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6500407462842380E+0
+            b = 0.2231909701714456E+0
+            v = 0.3176652305912204E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5425151448707213E+0
+            b = 0.3718201306118944E-1
+            v = 0.3105097161023939E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5841860556907931E+0
+            b = 0.7483616335067346E-1
+            v = 0.3143014117890550E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6234632186851500E+0
+            b = 0.1125990834266120E+0
+            v = 0.3168172866287200E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6602934551848843E+0
+            b = 0.1501303813157619E+0
+            v = 0.3181401865570968E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6278573968375105E+0
+            b = 0.3767559930245720E-1
+            v = 0.3170663659156037E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6665611711264577E+0
+            b = 0.7548443301360158E-1
+            v = 0.3185447944625510E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 3890:
+
+            v = 0.1807395252196920E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2848008782238827E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.2836065837530581E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1587876419858352E-1
+            v = 0.7013149266673816E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4069193593751206E-1
+            v = 0.1162798021956766E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7025888115257997E-1
+            v = 0.1518728583972105E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1027495450028704E+0
+            v = 0.1798796108216934E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1371457730893426E+0
+            v = 0.2022593385972785E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1727758532671953E+0
+            v = 0.2203093105575464E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2091492038929037E+0
+            v = 0.2349294234299855E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2458813281751915E+0
+            v = 0.2467682058747003E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2826545859450066E+0
+            v = 0.2563092683572224E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3191957291799622E+0
+            v = 0.2639253896763318E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3552621469299578E+0
+            v = 0.2699137479265108E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3906329503406230E+0
+            v = 0.2745196420166739E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4251028614093031E+0
+            v = 0.2779529197397593E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4584777520111870E+0
+            v = 0.2803996086684265E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4905711358710193E+0
+            v = 0.2820302356715842E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5212011669847385E+0
+            v = 0.2830056747491068E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5501878488737995E+0
+            v = 0.2834808950776839E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6025037877479342E+0
+            v = 0.2835282339078929E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6254572689549016E+0
+            v = 0.2833819267065800E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6460107179528248E+0
+            v = 0.2832858336906784E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6639541138154251E+0
+            v = 0.2833268235451244E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6790688515667495E+0
+            v = 0.2835432677029253E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6911338580371512E+0
+            v = 0.2839091722743049E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6999385956126490E+0
+            v = 0.2843308178875841E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7053037748656896E+0
+            v = 0.2846703550533846E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4732224387180115E-1
+            v = 0.1051193406971900E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1202100529326803E+0
+            v = 0.1657871838796974E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2034304820664855E+0
+            v = 0.2064648113714232E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2912285643573002E+0
+            v = 0.2347942745819741E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3802361792726768E+0
+            v = 0.2547775326597726E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4680598511056146E+0
+            v = 0.2686876684847025E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5528151052155599E+0
+            v = 0.2778665755515867E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6329386307803041E+0
+            v = 0.2830996616782929E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.8056516651369069E-1
+            b = 0.2363454684003124E-1
+            v = 0.1403063340168372E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1156476077139389E+0
+            b = 0.5191291632545936E-1
+            v = 0.1696504125939477E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1520473382760421E+0
+            b = 0.8322715736994519E-1
+            v = 0.1935787242745390E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1892986699745931E+0
+            b = 0.1165855667993712E+0
+            v = 0.2130614510521968E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2270194446777792E+0
+            b = 0.1513077167409504E+0
+            v = 0.2289381265931048E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2648908185093273E+0
+            b = 0.1868882025807859E+0
+            v = 0.2418630292816186E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3026389259574136E+0
+            b = 0.2229277629776224E+0
+            v = 0.2523400495631193E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3400220296151384E+0
+            b = 0.2590951840746235E+0
+            v = 0.2607623973449605E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3768217953335510E+0
+            b = 0.2951047291750847E+0
+            v = 0.2674441032689209E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4128372900921884E+0
+            b = 0.3307019714169930E+0
+            v = 0.2726432360343356E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4478807131815630E+0
+            b = 0.3656544101087634E+0
+            v = 0.2765787685924545E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4817742034089257E+0
+            b = 0.3997448951939695E+0
+            v = 0.2794428690642224E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5143472814653344E+0
+            b = 0.4327667110812024E+0
+            v = 0.2814099002062895E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5454346213905650E+0
+            b = 0.4645196123532293E+0
+            v = 0.2826429531578994E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5748739313170252E+0
+            b = 0.4948063555703345E+0
+            v = 0.2832983542550884E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1599598738286342E+0
+            b = 0.2792357590048985E-1
+            v = 0.1886695565284976E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1998097412500951E+0
+            b = 0.5877141038139065E-1
+            v = 0.2081867882748234E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2396228952566202E+0
+            b = 0.9164573914691377E-1
+            v = 0.2245148680600796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2792228341097746E+0
+            b = 0.1259049641962687E+0
+            v = 0.2380370491511872E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3184251107546741E+0
+            b = 0.1610594823400863E+0
+            v = 0.2491398041852455E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3570481164426244E+0
+            b = 0.1967151653460898E+0
+            v = 0.2581632405881230E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3949164710492144E+0
+            b = 0.2325404606175168E+0
+            v = 0.2653965506227417E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4318617293970503E+0
+            b = 0.2682461141151439E+0
+            v = 0.2710857216747087E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4677221009931678E+0
+            b = 0.3035720116011973E+0
+            v = 0.2754434093903659E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5023417939270955E+0
+            b = 0.3382781859197439E+0
+            v = 0.2786579932519380E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5355701836636128E+0
+            b = 0.3721383065625942E+0
+            v = 0.2809011080679474E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5672608451328771E+0
+            b = 0.4049346360466055E+0
+            v = 0.2823336184560987E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5972704202540162E+0
+            b = 0.4364538098633802E+0
+            v = 0.2831101175806309E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2461687022333596E+0
+            b = 0.3070423166833368E-1
+            v = 0.2221679970354546E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2881774566286831E+0
+            b = 0.6338034669281885E-1
+            v = 0.2356185734270703E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3293963604116978E+0
+            b = 0.9742862487067941E-1
+            v = 0.2469228344805590E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3697303822241377E+0
+            b = 0.1323799532282290E+0
+            v = 0.2562726348642046E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4090663023135127E+0
+            b = 0.1678497018129336E+0
+            v = 0.2638756726753028E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4472819355411712E+0
+            b = 0.2035095105326114E+0
+            v = 0.2699311157390862E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4842513377231437E+0
+            b = 0.2390692566672091E+0
+            v = 0.2746233268403837E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5198477629962928E+0
+            b = 0.2742649818076149E+0
+            v = 0.2781225674454771E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5539453011883145E+0
+            b = 0.3088503806580094E+0
+            v = 0.2805881254045684E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5864196762401251E+0
+            b = 0.3425904245906614E+0
+            v = 0.2821719877004913E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6171484466668390E+0
+            b = 0.3752562294789468E+0
+            v = 0.2830222502333124E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3350337830565727E+0
+            b = 0.3261589934634747E-1
+            v = 0.2457995956744870E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3775773224758284E+0
+            b = 0.6658438928081572E-1
+            v = 0.2551474407503706E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4188155229848973E+0
+            b = 0.1014565797157954E+0
+            v = 0.2629065335195311E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4586805892009344E+0
+            b = 0.1368573320843822E+0
+            v = 0.2691900449925075E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4970895714224235E+0
+            b = 0.1724614851951608E+0
+            v = 0.2741275485754276E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5339505133960747E+0
+            b = 0.2079779381416412E+0
+            v = 0.2778530970122595E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5691665792531440E+0
+            b = 0.2431385788322288E+0
+            v = 0.2805010567646741E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6026387682680377E+0
+            b = 0.2776901883049853E+0
+            v = 0.2822055834031040E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6342676150163307E+0
+            b = 0.3113881356386632E+0
+            v = 0.2831016901243473E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4237951119537067E+0
+            b = 0.3394877848664351E-1
+            v = 0.2624474901131803E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4656918683234929E+0
+            b = 0.6880219556291447E-1
+            v = 0.2688034163039377E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5058857069185980E+0
+            b = 0.1041946859721635E+0
+            v = 0.2738932751287636E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5443204666713996E+0
+            b = 0.1398039738736393E+0
+            v = 0.2777944791242523E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5809298813759742E+0
+            b = 0.1753373381196155E+0
+            v = 0.2806011661660987E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6156416039447128E+0
+            b = 0.2105215793514010E+0
+            v = 0.2824181456597460E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6483801351066604E+0
+            b = 0.2450953312157051E+0
+            v = 0.2833585216577828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5103616577251688E+0
+            b = 0.3485560643800719E-1
+            v = 0.2738165236962878E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5506738792580681E+0
+            b = 0.7026308631512033E-1
+            v = 0.2778365208203180E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5889573040995292E+0
+            b = 0.1059035061296403E+0
+            v = 0.2807852940418966E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6251641589516930E+0
+            b = 0.1414823925236026E+0
+            v = 0.2827245949674705E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6592414921570178E+0
+            b = 0.1767207908214530E+0
+            v = 0.2837342344829828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5930314017533384E+0
+            b = 0.3542189339561672E-1
+            v = 0.2809233907610981E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6309812253390175E+0
+            b = 0.7109574040369549E-1
+            v = 0.2829930809742694E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6666296011353230E+0
+            b = 0.1067259792282730E+0
+            v = 0.2841097874111479E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6703715271049922E+0
+            b = 0.3569455268820809E-1
+            v = 0.2843455206008783E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 4334:
+
+            v = 0.1449063022537883E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2546377329828424E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1462896151831013E-1
+            v = 0.6018432961087496E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3769840812493139E-1
+            v = 0.1002286583263673E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6524701904096891E-1
+            v = 0.1315222931028093E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9560543416134648E-1
+            v = 0.1564213746876724E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1278335898929198E+0
+            v = 0.1765118841507736E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1613096104466031E+0
+            v = 0.1928737099311080E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1955806225745371E+0
+            v = 0.2062658534263270E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2302935218498028E+0
+            v = 0.2172395445953787E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2651584344113027E+0
+            v = 0.2262076188876047E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2999276825183209E+0
+            v = 0.2334885699462397E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3343828669718798E+0
+            v = 0.2393355273179203E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3683265013750518E+0
+            v = 0.2439559200468863E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4015763206518108E+0
+            v = 0.2475251866060002E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4339612026399770E+0
+            v = 0.2501965558158773E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4653180651114582E+0
+            v = 0.2521081407925925E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4954893331080803E+0
+            v = 0.2533881002388081E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5243207068924930E+0
+            v = 0.2541582900848261E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5516590479041704E+0
+            v = 0.2545365737525860E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6012371927804176E+0
+            v = 0.2545726993066799E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6231574466449819E+0
+            v = 0.2544456197465555E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6429416514181271E+0
+            v = 0.2543481596881064E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6604124272943595E+0
+            v = 0.2543506451429194E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6753851470408250E+0
+            v = 0.2544905675493763E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6876717970626160E+0
+            v = 0.2547611407344429E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6970895061319234E+0
+            v = 0.2551060375448869E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7034746912553310E+0
+            v = 0.2554291933816039E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7067017217542295E+0
+            v = 0.2556255710686343E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4382223501131123E-1
+            v = 0.9041339695118195E-4
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1117474077400006E+0
+            v = 0.1438426330079022E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1897153252911440E+0
+            v = 0.1802523089820518E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2724023009910331E+0
+            v = 0.2060052290565496E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3567163308709902E+0
+            v = 0.2245002248967466E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4404784483028087E+0
+            v = 0.2377059847731150E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5219833154161411E+0
+            v = 0.2468118955882525E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5998179868977553E+0
+            v = 0.2525410872966528E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6727803154548222E+0
+            v = 0.2553101409933397E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.7476563943166086E-1
+            b = 0.2193168509461185E-1
+            v = 0.1212879733668632E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1075341482001416E+0
+            b = 0.4826419281533887E-1
+            v = 0.1472872881270931E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1416344885203259E+0
+            b = 0.7751191883575742E-1
+            v = 0.1686846601010828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1766325315388586E+0
+            b = 0.1087558139247680E+0
+            v = 0.1862698414660208E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2121744174481514E+0
+            b = 0.1413661374253096E+0
+            v = 0.2007430956991861E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2479669443408145E+0
+            b = 0.1748768214258880E+0
+            v = 0.2126568125394796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2837600452294113E+0
+            b = 0.2089216406612073E+0
+            v = 0.2224394603372113E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3193344933193984E+0
+            b = 0.2431987685545972E+0
+            v = 0.2304264522673135E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3544935442438745E+0
+            b = 0.2774497054377770E+0
+            v = 0.2368854288424087E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3890571932288154E+0
+            b = 0.3114460356156915E+0
+            v = 0.2420352089461772E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4228581214259090E+0
+            b = 0.3449806851913012E+0
+            v = 0.2460597113081295E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4557387211304052E+0
+            b = 0.3778618641248256E+0
+            v = 0.2491181912257687E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4875487950541643E+0
+            b = 0.4099086391698978E+0
+            v = 0.2513528194205857E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5181436529962997E+0
+            b = 0.4409474925853973E+0
+            v = 0.2528943096693220E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5473824095600661E+0
+            b = 0.4708094517711291E+0
+            v = 0.2538660368488136E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5751263398976174E+0
+            b = 0.4993275140354637E+0
+            v = 0.2543868648299022E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1489515746840028E+0
+            b = 0.2599381993267017E-1
+            v = 0.1642595537825183E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1863656444351767E+0
+            b = 0.5479286532462190E-1
+            v = 0.1818246659849308E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2238602880356348E+0
+            b = 0.8556763251425254E-1
+            v = 0.1966565649492420E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2612723375728160E+0
+            b = 0.1177257802267011E+0
+            v = 0.2090677905657991E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2984332990206190E+0
+            b = 0.1508168456192700E+0
+            v = 0.2193820409510504E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3351786584663333E+0
+            b = 0.1844801892177727E+0
+            v = 0.2278870827661928E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3713505522209120E+0
+            b = 0.2184145236087598E+0
+            v = 0.2348283192282090E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4067981098954663E+0
+            b = 0.2523590641486229E+0
+            v = 0.2404139755581477E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4413769993687534E+0
+            b = 0.2860812976901373E+0
+            v = 0.2448227407760734E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4749487182516394E+0
+            b = 0.3193686757808996E+0
+            v = 0.2482110455592573E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5073798105075426E+0
+            b = 0.3520226949547602E+0
+            v = 0.2507192397774103E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5385410448878654E+0
+            b = 0.3838544395667890E+0
+            v = 0.2524765968534880E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5683065353670530E+0
+            b = 0.4146810037640963E+0
+            v = 0.2536052388539425E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5965527620663510E+0
+            b = 0.4443224094681121E+0
+            v = 0.2542230588033068E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2299227700856157E+0
+            b = 0.2865757664057584E-1
+            v = 0.1944817013047896E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2695752998553267E+0
+            b = 0.5923421684485993E-1
+            v = 0.2067862362746635E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3086178716611389E+0
+            b = 0.9117817776057715E-1
+            v = 0.2172440734649114E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3469649871659077E+0
+            b = 0.1240593814082605E+0
+            v = 0.2260125991723423E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3845153566319655E+0
+            b = 0.1575272058259175E+0
+            v = 0.2332655008689523E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4211600033403215E+0
+            b = 0.1912845163525413E+0
+            v = 0.2391699681532458E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4567867834329882E+0
+            b = 0.2250710177858171E+0
+            v = 0.2438801528273928E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4912829319232061E+0
+            b = 0.2586521303440910E+0
+            v = 0.2475370504260665E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5245364793303812E+0
+            b = 0.2918112242865407E+0
+            v = 0.2502707235640574E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5564369788915756E+0
+            b = 0.3243439239067890E+0
+            v = 0.2522031701054241E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5868757697775287E+0
+            b = 0.3560536787835351E+0
+            v = 0.2534511269978784E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6157458853519617E+0
+            b = 0.3867480821242581E+0
+            v = 0.2541284914955151E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3138461110672113E+0
+            b = 0.3051374637507278E-1
+            v = 0.2161509250688394E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3542495872050569E+0
+            b = 0.6237111233730755E-1
+            v = 0.2248778513437852E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3935751553120181E+0
+            b = 0.9516223952401907E-1
+            v = 0.2322388803404617E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4317634668111147E+0
+            b = 0.1285467341508517E+0
+            v = 0.2383265471001355E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4687413842250821E+0
+            b = 0.1622318931656033E+0
+            v = 0.2432476675019525E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5044274237060283E+0
+            b = 0.1959581153836453E+0
+            v = 0.2471122223750674E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5387354077925727E+0
+            b = 0.2294888081183837E+0
+            v = 0.2500291752486870E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5715768898356105E+0
+            b = 0.2626031152713945E+0
+            v = 0.2521055942764682E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6028627200136111E+0
+            b = 0.2950904075286713E+0
+            v = 0.2534472785575503E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6325039812653463E+0
+            b = 0.3267458451113286E+0
+            v = 0.2541599713080121E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3981986708423407E+0
+            b = 0.3183291458749821E-1
+            v = 0.2317380975862936E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4382791182133300E+0
+            b = 0.6459548193880908E-1
+            v = 0.2378550733719775E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4769233057218166E+0
+            b = 0.9795757037087952E-1
+            v = 0.2428884456739118E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5140823911194238E+0
+            b = 0.1316307235126655E+0
+            v = 0.2469002655757292E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5496977833862983E+0
+            b = 0.1653556486358704E+0
+            v = 0.2499657574265851E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5837047306512727E+0
+            b = 0.1988931724126510E+0
+            v = 0.2521676168486082E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6160349566926879E+0
+            b = 0.2320174581438950E+0
+            v = 0.2535935662645334E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6466185353209440E+0
+            b = 0.2645106562168662E+0
+            v = 0.2543356743363214E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4810835158795404E+0
+            b = 0.3275917807743992E-1
+            v = 0.2427353285201535E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5199925041324341E+0
+            b = 0.6612546183967181E-1
+            v = 0.2468258039744386E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5571717692207494E+0
+            b = 0.9981498331474143E-1
+            v = 0.2500060956440310E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5925789250836378E+0
+            b = 0.1335687001410374E+0
+            v = 0.2523238365420979E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6261658523859670E+0
+            b = 0.1671444402896463E+0
+            v = 0.2538399260252846E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6578811126669331E+0
+            b = 0.2003106382156076E+0
+            v = 0.2546255927268069E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5609624612998100E+0
+            b = 0.3337500940231335E-1
+            v = 0.2500583360048449E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5979959659984670E+0
+            b = 0.6708750335901803E-1
+            v = 0.2524777638260203E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6330523711054002E+0
+            b = 0.1008792126424850E+0
+            v = 0.2540951193860656E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6660960998103972E+0
+            b = 0.1345050343171794E+0
+            v = 0.2549524085027472E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6365384364585819E+0
+            b = 0.3372799460737052E-1
+            v = 0.2542569507009158E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6710994302899275E+0
+            b = 0.6755249309678028E-1
+            v = 0.2552114127580376E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 4802:
+
+            v = 0.9687521879420705E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2307897895367918E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.2297310852498558E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2335728608887064E-1
+            v = 0.7386265944001919E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4352987836550653E-1
+            v = 0.8257977698542210E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6439200521088801E-1
+            v = 0.9706044762057630E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.9003943631993181E-1
+            v = 0.1302393847117003E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1196706615548473E+0
+            v = 0.1541957004600968E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1511715412838134E+0
+            v = 0.1704459770092199E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1835982828503801E+0
+            v = 0.1827374890942906E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2165081259155405E+0
+            v = 0.1926360817436107E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2496208720417563E+0
+            v = 0.2008010239494833E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2827200673567900E+0
+            v = 0.2075635983209175E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3156190823994346E+0
+            v = 0.2131306638690909E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3481476793749115E+0
+            v = 0.2176562329937335E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3801466086947226E+0
+            v = 0.2212682262991018E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4114652119634011E+0
+            v = 0.2240799515668565E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4419598786519751E+0
+            v = 0.2261959816187525E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4714925949329543E+0
+            v = 0.2277156368808855E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4999293972879466E+0
+            v = 0.2287351772128336E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5271387221431248E+0
+            v = 0.2293490814084085E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5529896780837761E+0
+            v = 0.2296505312376273E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6000856099481712E+0
+            v = 0.2296793832318756E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6210562192785175E+0
+            v = 0.2295785443842974E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6401165879934240E+0
+            v = 0.2295017931529102E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6571144029244334E+0
+            v = 0.2295059638184868E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6718910821718863E+0
+            v = 0.2296232343237362E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6842845591099010E+0
+            v = 0.2298530178740771E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6941353476269816E+0
+            v = 0.2301579790280501E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7012965242212991E+0
+            v = 0.2304690404996513E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7056471428242644E+0
+            v = 0.2307027995907102E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4595557643585895E-1
+            v = 0.9312274696671092E-4
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1049316742435023E+0
+            v = 0.1199919385876926E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1773548879549274E+0
+            v = 0.1598039138877690E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2559071411236127E+0
+            v = 0.1822253763574900E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3358156837985898E+0
+            v = 0.1988579593655040E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4155835743763893E+0
+            v = 0.2112620102533307E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4937894296167472E+0
+            v = 0.2201594887699007E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5691569694793316E+0
+            v = 0.2261622590895036E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6405840854894251E+0
+            v = 0.2296458453435705E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.7345133894143348E-1
+            b = 0.2177844081486067E-1
+            v = 0.1006006990267000E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1009859834044931E+0
+            b = 0.4590362185775188E-1
+            v = 0.1227676689635876E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1324289619748758E+0
+            b = 0.7255063095690877E-1
+            v = 0.1467864280270117E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1654272109607127E+0
+            b = 0.1017825451960684E+0
+            v = 0.1644178912101232E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1990767186776461E+0
+            b = 0.1325652320980364E+0
+            v = 0.1777664890718961E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2330125945523278E+0
+            b = 0.1642765374496765E+0
+            v = 0.1884825664516690E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2670080611108287E+0
+            b = 0.1965360374337889E+0
+            v = 0.1973269246453848E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3008753376294316E+0
+            b = 0.2290726770542238E+0
+            v = 0.2046767775855328E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3344475596167860E+0
+            b = 0.2616645495370823E+0
+            v = 0.2107600125918040E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3675709724070786E+0
+            b = 0.2941150728843141E+0
+            v = 0.2157416362266829E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4001000887587812E+0
+            b = 0.3262440400919066E+0
+            v = 0.2197557816920721E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4318956350436028E+0
+            b = 0.3578835350611916E+0
+            v = 0.2229192611835437E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4628239056795531E+0
+            b = 0.3888751854043678E+0
+            v = 0.2253385110212775E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4927563229773636E+0
+            b = 0.4190678003222840E+0
+            v = 0.2271137107548774E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5215687136707969E+0
+            b = 0.4483151836883852E+0
+            v = 0.2283414092917525E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5491402346984905E+0
+            b = 0.4764740676087880E+0
+            v = 0.2291161673130077E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5753520160126075E+0
+            b = 0.5034021310998277E+0
+            v = 0.2295313908576598E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1388326356417754E+0
+            b = 0.2435436510372806E-1
+            v = 0.1438204721359031E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1743686900537244E+0
+            b = 0.5118897057342652E-1
+            v = 0.1607738025495257E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2099737037950268E+0
+            b = 0.8014695048539634E-1
+            v = 0.1741483853528379E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2454492590908548E+0
+            b = 0.1105117874155699E+0
+            v = 0.1851918467519151E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2807219257864278E+0
+            b = 0.1417950531570966E+0
+            v = 0.1944628638070613E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3156842271975842E+0
+            b = 0.1736604945719597E+0
+            v = 0.2022495446275152E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3502090945177752E+0
+            b = 0.2058466324693981E+0
+            v = 0.2087462382438514E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3841684849519686E+0
+            b = 0.2381284261195919E+0
+            v = 0.2141074754818308E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4174372367906016E+0
+            b = 0.2703031270422569E+0
+            v = 0.2184640913748162E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4498926465011892E+0
+            b = 0.3021845683091309E+0
+            v = 0.2219309165220329E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4814146229807701E+0
+            b = 0.3335993355165720E+0
+            v = 0.2246123118340624E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5118863625734701E+0
+            b = 0.3643833735518232E+0
+            v = 0.2266062766915125E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5411947455119144E+0
+            b = 0.3943789541958179E+0
+            v = 0.2280072952230796E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5692301500357246E+0
+            b = 0.4234320144403542E+0
+            v = 0.2289082025202583E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5958857204139576E+0
+            b = 0.4513897947419260E+0
+            v = 0.2294012695120025E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2156270284785766E+0
+            b = 0.2681225755444491E-1
+            v = 0.1722434488736947E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2532385054909710E+0
+            b = 0.5557495747805614E-1
+            v = 0.1830237421455091E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2902564617771537E+0
+            b = 0.8569368062950249E-1
+            v = 0.1923855349997633E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3266979823143256E+0
+            b = 0.1167367450324135E+0
+            v = 0.2004067861936271E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3625039627493614E+0
+            b = 0.1483861994003304E+0
+            v = 0.2071817297354263E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3975838937548699E+0
+            b = 0.1803821503011405E+0
+            v = 0.2128250834102103E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4318396099009774E+0
+            b = 0.2124962965666424E+0
+            v = 0.2174513719440102E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4651706555732742E+0
+            b = 0.2445221837805913E+0
+            v = 0.2211661839150214E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4974752649620969E+0
+            b = 0.2762701224322987E+0
+            v = 0.2240665257813102E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5286517579627517E+0
+            b = 0.3075627775211328E+0
+            v = 0.2262439516632620E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5586001195731895E+0
+            b = 0.3382311089826877E+0
+            v = 0.2277874557231869E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5872229902021319E+0
+            b = 0.3681108834741399E+0
+            v = 0.2287854314454994E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6144258616235123E+0
+            b = 0.3970397446872839E+0
+            v = 0.2293268499615575E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2951676508064861E+0
+            b = 0.2867499538750441E-1
+            v = 0.1912628201529828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3335085485472725E+0
+            b = 0.5867879341903510E-1
+            v = 0.1992499672238701E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3709561760636381E+0
+            b = 0.8961099205022284E-1
+            v = 0.2061275533454027E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4074722861667498E+0
+            b = 0.1211627927626297E+0
+            v = 0.2119318215968572E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4429923648839117E+0
+            b = 0.1530748903554898E+0
+            v = 0.2167416581882652E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4774428052721736E+0
+            b = 0.1851176436721877E+0
+            v = 0.2206430730516600E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5107446539535904E+0
+            b = 0.2170829107658179E+0
+            v = 0.2237186938699523E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5428151370542935E+0
+            b = 0.2487786689026271E+0
+            v = 0.2260480075032884E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5735699292556964E+0
+            b = 0.2800239952795016E+0
+            v = 0.2277098884558542E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6029253794562866E+0
+            b = 0.3106445702878119E+0
+            v = 0.2287845715109671E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6307998987073145E+0
+            b = 0.3404689500841194E+0
+            v = 0.2293547268236294E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3752652273692719E+0
+            b = 0.2997145098184479E-1
+            v = 0.2056073839852528E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4135383879344028E+0
+            b = 0.6086725898678011E-1
+            v = 0.2114235865831876E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4506113885153907E+0
+            b = 0.9238849548435643E-1
+            v = 0.2163175629770551E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4864401554606072E+0
+            b = 0.1242786603851851E+0
+            v = 0.2203392158111650E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5209708076611709E+0
+            b = 0.1563086731483386E+0
+            v = 0.2235473176847839E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5541422135830122E+0
+            b = 0.1882696509388506E+0
+            v = 0.2260024141501235E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5858880915113817E+0
+            b = 0.2199672979126059E+0
+            v = 0.2277675929329182E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6161399390603444E+0
+            b = 0.2512165482924867E+0
+            v = 0.2289102112284834E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6448296482255090E+0
+            b = 0.2818368701871888E+0
+            v = 0.2295027954625118E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4544796274917948E+0
+            b = 0.3088970405060312E-1
+            v = 0.2161281589879992E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4919389072146628E+0
+            b = 0.6240947677636835E-1
+            v = 0.2201980477395102E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5279313026985183E+0
+            b = 0.9430706144280313E-1
+            v = 0.2234952066593166E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5624169925571135E+0
+            b = 0.1263547818770374E+0
+            v = 0.2260540098520838E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5953484627093287E+0
+            b = 0.1583430788822594E+0
+            v = 0.2279157981899988E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6266730715339185E+0
+            b = 0.1900748462555988E+0
+            v = 0.2291296918565571E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6563363204278871E+0
+            b = 0.2213599519592567E+0
+            v = 0.2297533752536649E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5314574716585696E+0
+            b = 0.3152508811515374E-1
+            v = 0.2234927356465995E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5674614932298185E+0
+            b = 0.6343865291465561E-1
+            v = 0.2261288012985219E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6017706004970264E+0
+            b = 0.9551503504223951E-1
+            v = 0.2280818160923688E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6343471270264178E+0
+            b = 0.1275440099801196E+0
+            v = 0.2293773295180159E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6651494599127802E+0
+            b = 0.1593252037671960E+0
+            v = 0.2300528767338634E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6050184986005704E+0
+            b = 0.3192538338496105E-1
+            v = 0.2281893855065666E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6390163550880400E+0
+            b = 0.6402824353962306E-1
+            v = 0.2295720444840727E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6711199107088448E+0
+            b = 0.9609805077002909E-1
+            v = 0.2303227649026753E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6741354429572275E+0
+            b = 0.3211853196273233E-1
+            v = 0.2304831913227114E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 5294:
+
+            v = 0.9080510764308163E-4
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.2084824361987793E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.2303261686261450E-1
+            v = 0.5011105657239616E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3757208620162394E-1
+            v = 0.5942520409683854E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5821912033821852E-1
+            v = 0.9564394826109721E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.8403127529194872E-1
+            v = 0.1185530657126338E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1122927798060578E+0
+            v = 0.1364510114230331E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1420125319192987E+0
+            v = 0.1505828825605415E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1726396437341978E+0
+            v = 0.1619298749867023E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2038170058115696E+0
+            v = 0.1712450504267789E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2352849892876508E+0
+            v = 0.1789891098164999E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2668363354312461E+0
+            v = 0.1854474955629795E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2982941279900452E+0
+            v = 0.1908148636673661E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3295002922087076E+0
+            v = 0.1952377405281833E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3603094918363593E+0
+            v = 0.1988349254282232E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3905857895173920E+0
+            v = 0.2017079807160050E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4202005758160837E+0
+            v = 0.2039473082709094E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4490310061597227E+0
+            v = 0.2056360279288953E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4769586160311491E+0
+            v = 0.2068525823066865E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5038679887049750E+0
+            v = 0.2076724877534488E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5296454286519961E+0
+            v = 0.2081694278237885E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5541776207164850E+0
+            v = 0.2084157631219326E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5990467321921213E+0
+            v = 0.2084381531128593E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6191467096294587E+0
+            v = 0.2083476277129307E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6375251212901849E+0
+            v = 0.2082686194459732E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6540514381131168E+0
+            v = 0.2082475686112415E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6685899064391510E+0
+            v = 0.2083139860289915E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6810013009681648E+0
+            v = 0.2084745561831237E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6911469578730340E+0
+            v = 0.2087091313375890E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6988956915141736E+0
+            v = 0.2089718413297697E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7041335794868720E+0
+            v = 0.2092003303479793E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7067754398018567E+0
+            v = 0.2093336148263241E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3840368707853623E-1
+            v = 0.7591708117365267E-4
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9835485954117399E-1
+            v = 0.1083383968169186E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1665774947612998E+0
+            v = 0.1403019395292510E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2405702335362910E+0
+            v = 0.1615970179286436E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3165270770189046E+0
+            v = 0.1771144187504911E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3927386145645443E+0
+            v = 0.1887760022988168E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4678825918374656E+0
+            v = 0.1973474670768214E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5408022024266935E+0
+            v = 0.2033787661234659E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6104967445752438E+0
+            v = 0.2072343626517331E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6760910702685738E+0
+            v = 0.2091177834226918E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6655644120217392E-1
+            b = 0.1936508874588424E-1
+            v = 0.9316684484675566E-4
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.9446246161270182E-1
+            b = 0.4252442002115869E-1
+            v = 0.1116193688682976E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1242651925452509E+0
+            b = 0.6806529315354374E-1
+            v = 0.1298623551559414E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1553438064846751E+0
+            b = 0.9560957491205369E-1
+            v = 0.1450236832456426E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1871137110542670E+0
+            b = 0.1245931657452888E+0
+            v = 0.1572719958149914E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2192612628836257E+0
+            b = 0.1545385828778978E+0
+            v = 0.1673234785867195E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2515682807206955E+0
+            b = 0.1851004249723368E+0
+            v = 0.1756860118725188E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2838535866287290E+0
+            b = 0.2160182608272384E+0
+            v = 0.1826776290439367E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3159578817528521E+0
+            b = 0.2470799012277111E+0
+            v = 0.1885116347992865E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3477370882791392E+0
+            b = 0.2781014208986402E+0
+            v = 0.1933457860170574E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3790576960890540E+0
+            b = 0.3089172523515731E+0
+            v = 0.1973060671902064E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4097938317810200E+0
+            b = 0.3393750055472244E+0
+            v = 0.2004987099616311E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4398256572859637E+0
+            b = 0.3693322470987730E+0
+            v = 0.2030170909281499E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4690384114718480E+0
+            b = 0.3986541005609877E+0
+            v = 0.2049461460119080E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4973216048301053E+0
+            b = 0.4272112491408562E+0
+            v = 0.2063653565200186E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5245681526132446E+0
+            b = 0.4548781735309936E+0
+            v = 0.2073507927381027E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5506733911803888E+0
+            b = 0.4815315355023251E+0
+            v = 0.2079764593256122E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5755339829522475E+0
+            b = 0.5070486445801855E+0
+            v = 0.2083150534968778E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1305472386056362E+0
+            b = 0.2284970375722366E-1
+            v = 0.1262715121590664E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1637327908216477E+0
+            b = 0.4812254338288384E-1
+            v = 0.1414386128545972E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1972734634149637E+0
+            b = 0.7531734457511935E-1
+            v = 0.1538740401313898E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2308694653110130E+0
+            b = 0.1039043639882017E+0
+            v = 0.1642434942331432E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2643899218338160E+0
+            b = 0.1334526587117626E+0
+            v = 0.1729790609237496E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2977171599622171E+0
+            b = 0.1636414868936382E+0
+            v = 0.1803505190260828E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3307293903032310E+0
+            b = 0.1942195406166568E+0
+            v = 0.1865475350079657E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3633069198219073E+0
+            b = 0.2249752879943753E+0
+            v = 0.1917182669679069E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3953346955922727E+0
+            b = 0.2557218821820032E+0
+            v = 0.1959851709034382E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4267018394184914E+0
+            b = 0.2862897925213193E+0
+            v = 0.1994529548117882E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4573009622571704E+0
+            b = 0.3165224536636518E+0
+            v = 0.2022138911146548E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4870279559856109E+0
+            b = 0.3462730221636496E+0
+            v = 0.2043518024208592E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5157819581450322E+0
+            b = 0.3754016870282835E+0
+            v = 0.2059450313018110E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5434651666465393E+0
+            b = 0.4037733784993613E+0
+            v = 0.2070685715318472E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5699823887764627E+0
+            b = 0.4312557784139123E+0
+            v = 0.2077955310694373E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5952403350947741E+0
+            b = 0.4577175367122110E+0
+            v = 0.2081980387824712E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2025152599210369E+0
+            b = 0.2520253617719557E-1
+            v = 0.1521318610377956E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2381066653274425E+0
+            b = 0.5223254506119000E-1
+            v = 0.1622772720185755E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2732823383651612E+0
+            b = 0.8060669688588620E-1
+            v = 0.1710498139420709E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3080137692611118E+0
+            b = 0.1099335754081255E+0
+            v = 0.1785911149448736E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3422405614587601E+0
+            b = 0.1399120955959857E+0
+            v = 0.1850125313687736E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3758808773890420E+0
+            b = 0.1702977801651705E+0
+            v = 0.1904229703933298E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4088458383438932E+0
+            b = 0.2008799256601680E+0
+            v = 0.1949259956121987E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4410450550841152E+0
+            b = 0.2314703052180836E+0
+            v = 0.1986161545363960E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4723879420561312E+0
+            b = 0.2618972111375892E+0
+            v = 0.2015790585641370E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5027843561874343E+0
+            b = 0.2920013195600270E+0
+            v = 0.2038934198707418E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5321453674452458E+0
+            b = 0.3216322555190551E+0
+            v = 0.2056334060538251E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5603839113834030E+0
+            b = 0.3506456615934198E+0
+            v = 0.2068705959462289E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5874150706875146E+0
+            b = 0.3789007181306267E+0
+            v = 0.2076753906106002E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6131559381660038E+0
+            b = 0.4062580170572782E+0
+            v = 0.2081179391734803E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2778497016394506E+0
+            b = 0.2696271276876226E-1
+            v = 0.1700345216228943E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3143733562261912E+0
+            b = 0.5523469316960465E-1
+            v = 0.1774906779990410E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3501485810261827E+0
+            b = 0.8445193201626464E-1
+            v = 0.1839659377002642E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3851430322303653E+0
+            b = 0.1143263119336083E+0
+            v = 0.1894987462975169E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4193013979470415E+0
+            b = 0.1446177898344475E+0
+            v = 0.1941548809452595E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4525585960458567E+0
+            b = 0.1751165438438091E+0
+            v = 0.1980078427252384E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4848447779622947E+0
+            b = 0.2056338306745660E+0
+            v = 0.2011296284744488E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5160871208276894E+0
+            b = 0.2359965487229226E+0
+            v = 0.2035888456966776E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5462112185696926E+0
+            b = 0.2660430223139146E+0
+            v = 0.2054516325352142E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5751425068101757E+0
+            b = 0.2956193664498032E+0
+            v = 0.2067831033092635E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6028073872853596E+0
+            b = 0.3245763905312779E+0
+            v = 0.2076485320284876E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6291338275278409E+0
+            b = 0.3527670026206972E+0
+            v = 0.2081141439525255E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3541797528439391E+0
+            b = 0.2823853479435550E-1
+            v = 0.1834383015469222E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3908234972074657E+0
+            b = 0.5741296374713106E-1
+            v = 0.1889540591777677E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4264408450107590E+0
+            b = 0.8724646633650199E-1
+            v = 0.1936677023597375E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4609949666553286E+0
+            b = 0.1175034422915616E+0
+            v = 0.1976176495066504E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4944389496536006E+0
+            b = 0.1479755652628428E+0
+            v = 0.2008536004560983E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5267194884346086E+0
+            b = 0.1784740659484352E+0
+            v = 0.2034280351712291E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5577787810220990E+0
+            b = 0.2088245700431244E+0
+            v = 0.2053944466027758E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5875563763536670E+0
+            b = 0.2388628136570763E+0
+            v = 0.2068077642882360E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6159910016391269E+0
+            b = 0.2684308928769185E+0
+            v = 0.2077250949661599E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6430219602956268E+0
+            b = 0.2973740761960252E+0
+            v = 0.2082062440705320E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4300647036213646E+0
+            b = 0.2916399920493977E-1
+            v = 0.1934374486546626E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4661486308935531E+0
+            b = 0.5898803024755659E-1
+            v = 0.1974107010484300E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5009658555287261E+0
+            b = 0.8924162698525409E-1
+            v = 0.2007129290388658E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5344824270447704E+0
+            b = 0.1197185199637321E+0
+            v = 0.2033736947471293E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5666575997416371E+0
+            b = 0.1502300756161382E+0
+            v = 0.2054287125902493E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5974457471404752E+0
+            b = 0.1806004191913564E+0
+            v = 0.2069184936818894E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6267984444116886E+0
+            b = 0.2106621764786252E+0
+            v = 0.2078883689808782E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6546664713575417E+0
+            b = 0.2402526932671914E+0
+            v = 0.2083886366116359E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5042711004437253E+0
+            b = 0.2982529203607657E-1
+            v = 0.2006593275470817E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5392127456774380E+0
+            b = 0.6008728062339922E-1
+            v = 0.2033728426135397E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5726819437668618E+0
+            b = 0.9058227674571398E-1
+            v = 0.2055008781377608E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6046469254207278E+0
+            b = 0.1211219235803400E+0
+            v = 0.2070651783518502E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6350716157434952E+0
+            b = 0.1515286404791580E+0
+            v = 0.2080953335094320E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6639177679185454E+0
+            b = 0.1816314681255552E+0
+            v = 0.2086284998988521E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5757276040972253E+0
+            b = 0.3026991752575440E-1
+            v = 0.2055549387644668E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6090265823139755E+0
+            b = 0.6078402297870770E-1
+            v = 0.2071871850267654E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6406735344387661E+0
+            b = 0.9135459984176636E-1
+            v = 0.2082856600431965E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6706397927793709E+0
+            b = 0.1218024155966590E+0
+            v = 0.2088705858819358E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6435019674426665E+0
+            b = 0.3052608357660639E-1
+            v = 0.2083995867536322E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6747218676375681E+0
+            b = 0.6112185773983089E-1
+            v = 0.2090509712889637E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case 5810:
+
+            v = 0.9735347946175486E-5
+            leb_tmp, start = get_lebedev_recurrence_points(1, start, a, b, v, leb_tmp)
+            v = 0.1907581241803167E-3
+            leb_tmp, start = get_lebedev_recurrence_points(2, start, a, b, v, leb_tmp)
+            v = 0.1901059546737578E-3
+            leb_tmp, start = get_lebedev_recurrence_points(3, start, a, b, v, leb_tmp)
+            a = 0.1182361662400277E-1
+            v = 0.3926424538919212E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3062145009138958E-1
+            v = 0.6667905467294382E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5329794036834243E-1
+            v = 0.8868891315019135E-4
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7848165532862220E-1
+            v = 0.1066306000958872E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1054038157636201E+0
+            v = 0.1214506743336128E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1335577797766211E+0
+            v = 0.1338054681640871E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1625769955502252E+0
+            v = 0.1441677023628504E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.1921787193412792E+0
+            v = 0.1528880200826557E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2221340534690548E+0
+            v = 0.1602330623773609E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2522504912791132E+0
+            v = 0.1664102653445244E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.2823610860679697E+0
+            v = 0.1715845854011323E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3123173966267560E+0
+            v = 0.1758901000133069E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3419847036953789E+0
+            v = 0.1794382485256736E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3712386456999758E+0
+            v = 0.1823238106757407E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3999627649876828E+0
+            v = 0.1846293252959976E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4280466458648093E+0
+            v = 0.1864284079323098E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4553844360185711E+0
+            v = 0.1877882694626914E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.4818736094437834E+0
+            v = 0.1887716321852025E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5074138709260629E+0
+            v = 0.1894381638175673E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5319061304570707E+0
+            v = 0.1898454899533629E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5552514978677286E+0
+            v = 0.1900497929577815E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.5981009025246183E+0
+            v = 0.1900671501924092E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6173990192228116E+0
+            v = 0.1899837555533510E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6351365239411131E+0
+            v = 0.1899014113156229E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6512010228227200E+0
+            v = 0.1898581257705106E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6654758363948120E+0
+            v = 0.1898804756095753E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6778410414853370E+0
+            v = 0.1899793610426402E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6881760887484110E+0
+            v = 0.1901464554844117E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.6963645267094598E+0
+            v = 0.1903533246259542E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7023010617153579E+0
+            v = 0.1905556158463228E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.7059004636628753E+0
+            v = 0.1907037155663528E-3
+            leb_tmp, start = get_lebedev_recurrence_points(4, start, a, b, v, leb_tmp)
+            a = 0.3552470312472575E-1
+            v = 0.5992997844249967E-4
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.9151176620841283E-1
+            v = 0.9749059382456978E-4
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.1566197930068980E+0
+            v = 0.1241680804599158E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2265467599271907E+0
+            v = 0.1437626154299360E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.2988242318581361E+0
+            v = 0.1584200054793902E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.3717482419703886E+0
+            v = 0.1694436550982744E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.4440094491758889E+0
+            v = 0.1776617014018108E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5145337096756642E+0
+            v = 0.1836132434440077E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.5824053672860230E+0
+            v = 0.1876494727075983E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6468283961043370E+0
+            v = 0.1899906535336482E-3
+            leb_tmp, start = get_lebedev_recurrence_points(5, start, a, b, v, leb_tmp)
+            a = 0.6095964259104373E-1
+            b = 0.1787828275342931E-1
+            v = 0.8143252820767350E-4
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.8811962270959388E-1
+            b = 0.3953888740792096E-1
+            v = 0.9998859890887728E-4
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1165936722428831E+0
+            b = 0.6378121797722990E-1
+            v = 0.1156199403068359E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1460232857031785E+0
+            b = 0.8985890813745037E-1
+            v = 0.1287632092635513E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1761197110181755E+0
+            b = 0.1172606510576162E+0
+            v = 0.1398378643365139E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2066471190463718E+0
+            b = 0.1456102876970995E+0
+            v = 0.1491876468417391E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2374076026328152E+0
+            b = 0.1746153823011775E+0
+            v = 0.1570855679175456E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2682305474337051E+0
+            b = 0.2040383070295584E+0
+            v = 0.1637483948103775E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2989653312142369E+0
+            b = 0.2336788634003698E+0
+            v = 0.1693500566632843E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3294762752772209E+0
+            b = 0.2633632752654219E+0
+            v = 0.1740322769393633E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3596390887276086E+0
+            b = 0.2929369098051601E+0
+            v = 0.1779126637278296E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3893383046398812E+0
+            b = 0.3222592785275512E+0
+            v = 0.1810908108835412E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4184653789358347E+0
+            b = 0.3512004791195743E+0
+            v = 0.1836529132600190E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4469172319076166E+0
+            b = 0.3796385677684537E+0
+            v = 0.1856752841777379E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4745950813276976E+0
+            b = 0.4074575378263879E+0
+            v = 0.1872270566606832E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5014034601410262E+0
+            b = 0.4345456906027828E+0
+            v = 0.1883722645591307E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5272493404551239E+0
+            b = 0.4607942515205134E+0
+            v = 0.1891714324525297E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5520413051846366E+0
+            b = 0.4860961284181720E+0
+            v = 0.1896827480450146E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5756887237503077E+0
+            b = 0.5103447395342790E+0
+            v = 0.1899628417059528E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1225039430588352E+0
+            b = 0.2136455922655793E-1
+            v = 0.1123301829001669E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1539113217321372E+0
+            b = 0.4520926166137188E-1
+            v = 0.1253698826711277E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1856213098637712E+0
+            b = 0.7086468177864818E-1
+            v = 0.1366266117678531E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2174998728035131E+0
+            b = 0.9785239488772918E-1
+            v = 0.1462736856106918E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2494128336938330E+0
+            b = 0.1258106396267210E+0
+            v = 0.1545076466685412E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2812321562143480E+0
+            b = 0.1544529125047001E+0
+            v = 0.1615096280814007E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3128372276456111E+0
+            b = 0.1835433512202753E+0
+            v = 0.1674366639741759E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3441145160177973E+0
+            b = 0.2128813258619585E+0
+            v = 0.1724225002437900E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3749567714853510E+0
+            b = 0.2422913734880829E+0
+            v = 0.1765810822987288E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4052621732015610E+0
+            b = 0.2716163748391453E+0
+            v = 0.1800104126010751E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4349335453522385E+0
+            b = 0.3007127671240280E+0
+            v = 0.1827960437331284E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4638776641524965E+0
+            b = 0.3294470677216479E+0
+            v = 0.1850140300716308E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4920046410462687E+0
+            b = 0.3576932543699155E+0
+            v = 0.1867333507394938E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5192273554861704E+0
+            b = 0.3853307059757764E+0
+            v = 0.1880178688638289E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5454609081136522E+0
+            b = 0.4122425044452694E+0
+            v = 0.1889278925654758E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5706220661424140E+0
+            b = 0.4383139587781027E+0
+            v = 0.1895213832507346E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5946286755181518E+0
+            b = 0.4634312536300553E+0
+            v = 0.1898548277397420E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.1905370790924295E+0
+            b = 0.2371311537781979E-1
+            v = 0.1349105935937341E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2242518717748009E+0
+            b = 0.4917878059254806E-1
+            v = 0.1444060068369326E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2577190808025936E+0
+            b = 0.7595498960495142E-1
+            v = 0.1526797390930008E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2908724534927187E+0
+            b = 0.1036991083191100E+0
+            v = 0.1598208771406474E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3236354020056219E+0
+            b = 0.1321348584450234E+0
+            v = 0.1659354368615331E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3559267359304543E+0
+            b = 0.1610316571314789E+0
+            v = 0.1711279910946440E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3876637123676956E+0
+            b = 0.1901912080395707E+0
+            v = 0.1754952725601440E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4187636705218842E+0
+            b = 0.2194384950137950E+0
+            v = 0.1791247850802529E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4491449019883107E+0
+            b = 0.2486155334763858E+0
+            v = 0.1820954300877716E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4787270932425445E+0
+            b = 0.2775768931812335E+0
+            v = 0.1844788524548449E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5074315153055574E+0
+            b = 0.3061863786591120E+0
+            v = 0.1863409481706220E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5351810507738336E+0
+            b = 0.3343144718152556E+0
+            v = 0.1877433008795068E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5619001025975381E+0
+            b = 0.3618362729028427E+0
+            v = 0.1887444543705232E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5875144035268046E+0
+            b = 0.3886297583620408E+0
+            v = 0.1894009829375006E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6119507308734495E+0
+            b = 0.4145742277792031E+0
+            v = 0.1897683345035198E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2619733870119463E+0
+            b = 0.2540047186389353E-1
+            v = 0.1517327037467653E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.2968149743237949E+0
+            b = 0.5208107018543989E-1
+            v = 0.1587740557483543E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3310451504860488E+0
+            b = 0.7971828470885599E-1
+            v = 0.1649093382274097E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3646215567376676E+0
+            b = 0.1080465999177927E+0
+            v = 0.1701915216193265E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3974916785279360E+0
+            b = 0.1368413849366629E+0
+            v = 0.1746847753144065E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4295967403772029E+0
+            b = 0.1659073184763559E+0
+            v = 0.1784555512007570E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4608742854473447E+0
+            b = 0.1950703730454614E+0
+            v = 0.1815687562112174E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4912598858949903E+0
+            b = 0.2241721144376724E+0
+            v = 0.1840864370663302E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5206882758945558E+0
+            b = 0.2530655255406489E+0
+            v = 0.1860676785390006E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5490940914019819E+0
+            b = 0.2816118409731066E+0
+            v = 0.1875690583743703E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5764123302025542E+0
+            b = 0.3096780504593238E+0
+            v = 0.1886453236347225E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6025786004213506E+0
+            b = 0.3371348366394987E+0
+            v = 0.1893501123329645E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6275291964794956E+0
+            b = 0.3638547827694396E+0
+            v = 0.1897366184519868E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3348189479861771E+0
+            b = 0.2664841935537443E-1
+            v = 0.1643908815152736E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.3699515545855295E+0
+            b = 0.5424000066843495E-1
+            v = 0.1696300350907768E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4042003071474669E+0
+            b = 0.8251992715430854E-1
+            v = 0.1741553103844483E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4375320100182624E+0
+            b = 0.1112695182483710E+0
+            v = 0.1780015282386092E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4699054490335947E+0
+            b = 0.1402964116467816E+0
+            v = 0.1812116787077125E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5012739879431952E+0
+            b = 0.1694275117584291E+0
+            v = 0.1838323158085421E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5315874883754966E+0
+            b = 0.1985038235312689E+0
+            v = 0.1859113119837737E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5607937109622117E+0
+            b = 0.2273765660020893E+0
+            v = 0.1874969220221698E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5888393223495521E+0
+            b = 0.2559041492849764E+0
+            v = 0.1886375612681076E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6156705979160163E+0
+            b = 0.2839497251976899E+0
+            v = 0.1893819575809276E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6412338809078123E+0
+            b = 0.3113791060500690E+0
+            v = 0.1897794748256767E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4076051259257167E+0
+            b = 0.2757792290858463E-1
+            v = 0.1738963926584846E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4423788125791520E+0
+            b = 0.5584136834984293E-1
+            v = 0.1777442359873466E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4760480917328258E+0
+            b = 0.8457772087727143E-1
+            v = 0.1810010815068719E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5085838725946297E+0
+            b = 0.1135975846359248E+0
+            v = 0.1836920318248129E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5399513637391218E+0
+            b = 0.1427286904765053E+0
+            v = 0.1858489473214328E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5701118433636380E+0
+            b = 0.1718112740057635E+0
+            v = 0.1875079342496592E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5990240530606021E+0
+            b = 0.2006944855985351E+0
+            v = 0.1887080239102310E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6266452685139695E+0
+            b = 0.2292335090598907E+0
+            v = 0.1894905752176822E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6529320971415942E+0
+            b = 0.2572871512353714E+0
+            v = 0.1898991061200695E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.4791583834610126E+0
+            b = 0.2826094197735932E-1
+            v = 0.1809065016458791E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5130373952796940E+0
+            b = 0.5699871359683649E-1
+            v = 0.1836297121596799E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5456252429628476E+0
+            b = 0.8602712528554394E-1
+            v = 0.1858426916241869E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5768956329682385E+0
+            b = 0.1151748137221281E+0
+            v = 0.1875654101134641E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6068186944699046E+0
+            b = 0.1442811654136362E+0
+            v = 0.1888240751833503E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6353622248024907E+0
+            b = 0.1731930321657680E+0
+            v = 0.1896497383866979E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6624927035731797E+0
+            b = 0.2017619958756061E+0
+            v = 0.1900775530219121E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5484933508028488E+0
+            b = 0.2874219755907391E-1
+            v = 0.1858525041478814E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.5810207682142106E+0
+            b = 0.5778312123713695E-1
+            v = 0.1876248690077947E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6120955197181352E+0
+            b = 0.8695262371439526E-1
+            v = 0.1889404439064607E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6416944284294319E+0
+            b = 0.1160893767057166E+0
+            v = 0.1898168539265290E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6697926391731260E+0
+            b = 0.1450378826743251E+0
+            v = 0.1902779940661772E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6147594390585488E+0
+            b = 0.2904957622341456E-1
+            v = 0.1890125641731815E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6455390026356783E+0
+            b = 0.5823809152617197E-1
+            v = 0.1899434637795751E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6747258588365477E+0
+            b = 0.8740384899884715E-1
+            v = 0.1904520856831751E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+            a = 0.6772135750395347E+0
+            b = 0.2919946135808105E-1
+            v = 0.1905534498734563E-3
+            leb_tmp, start = get_lebedev_recurrence_points(6, start, a, b, v, leb_tmp)
+
+        case _:
+            raise Exception('Angular grid unrecognized, choices are 6, 14, 26, 38, 50, 74, 86, 110, 146, 170, 194, 230, 266, 302, 350, 434, 590, 770, 974, 1202, 1454, 1730, 2030, 2354, 2702, 3074, 3470, 3890, 4334, 4802, 5294, 5810')  # noqa: E501
+
+    leb_tmp.n = degree
+    return leb_tmp
+
+
+def get_lebedev_recurrence_points(type_, start, a, b, v, leb):
+    c = 0.0
+
+    match type_:
+
+        case 1:
+            a = 1.0
+
+            leb.x[start] = a
+            leb.y[start] = 0.0
+            leb.z[start] = 0.0
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = -a
+            leb.y[start + 1] = 0.0
+            leb.z[start + 1] = 0.0
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = 0.0
+            leb.y[start + 2] = a
+            leb.z[start + 2] = 0.0
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = 0.0
+            leb.y[start + 3] = -a
+            leb.z[start + 3] = 0.0
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = 0.0
+            leb.y[start + 4] = 0.0
+            leb.z[start + 4] = a
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = 0.0
+            leb.y[start + 5] = 0.0
+            leb.z[start + 5] = -a
+            leb.w[start + 5] = 4.0 * pi * v
+            start = start + 6
+
+        case 2:
+            a = sqrt(0.5)
+            leb.x[start] = 0.0
+            leb.y[start] = a
+            leb.z[start] = a
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = 0.0
+            leb.y[start + 1] = -a
+            leb.z[start + 1] = a
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = 0.0
+            leb.y[start + 2] = a
+            leb.z[start + 2] = -a
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = 0.0
+            leb.y[start + 3] = -a
+            leb.z[start + 3] = -a
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = a
+            leb.y[start + 4] = 0.0
+            leb.z[start + 4] = a
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = a
+            leb.y[start + 5] = 0.0
+            leb.z[start + 5] = -a
+            leb.w[start + 5] = 4.0 * pi * v
+
+            leb.x[start + 6] = -a
+            leb.y[start + 6] = 0.0
+            leb.z[start + 6] = a
+            leb.w[start + 6] = 4.0 * pi * v
+
+            leb.x[start + 7] = -a
+            leb.y[start + 7] = 0.0
+            leb.z[start + 7] = -a
+            leb.w[start + 7] = 4.0 * pi * v
+
+            leb.x[start + 8] = a
+            leb.y[start + 8] = a
+            leb.z[start + 8] = 0.0
+            leb.w[start + 8] = 4.0 * pi * v
+
+            leb.x[start + 9] = -a
+            leb.y[start + 9] = a
+            leb.z[start + 9] = 0.0
+            leb.w[start + 9] = 4.0 * pi * v
+
+            leb.x[start + 10] = a
+            leb.y[start + 10] = -a
+            leb.z[start + 10] = 0.0
+            leb.w[start + 10] = 4.0 * pi * v
+
+            leb.x[start + 11] = -a
+            leb.y[start + 11] = -a
+            leb.z[start + 11] = 0.0
+            leb.w[start + 11] = 4.0 * pi * v
+            start = start + 12
+
+        case 3:
+            a = sqrt(1.0 / 3.0)
+            leb.x[start] = a
+            leb.y[start] = a
+            leb.z[start] = a
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = -a
+            leb.y[start + 1] = a
+            leb.z[start + 1] = a
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = a
+            leb.y[start + 2] = -a
+            leb.z[start + 2] = a
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = a
+            leb.y[start + 3] = a
+            leb.z[start + 3] = -a
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = -a
+            leb.y[start + 4] = -a
+            leb.z[start + 4] = a
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = a
+            leb.y[start + 5] = -a
+            leb.z[start + 5] = -a
+            leb.w[start + 5] = 4.0 * pi * v
+
+            leb.x[start + 6] = -a
+            leb.y[start + 6] = a
+            leb.z[start + 6] = -a
+            leb.w[start + 6] = 4.0 * pi * v
+
+            leb.x[start + 7] = -a
+            leb.y[start + 7] = -a
+            leb.z[start + 7] = -a
+            leb.w[start + 7] = 4.0 * pi * v
+            start = start + 8
+
+        case 4:
+            # /* In this case A is inputed */
+            b = sqrt(1.0 - 2.0 * a * a)
+            leb.x[start] = a
+            leb.y[start] = a
+            leb.z[start] = b
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = -a
+            leb.y[start + 1] = a
+            leb.z[start + 1] = b
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = a
+            leb.y[start + 2] = -a
+            leb.z[start + 2] = b
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = a
+            leb.y[start + 3] = a
+            leb.z[start + 3] = -b
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = -a
+            leb.y[start + 4] = -a
+            leb.z[start + 4] = b
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = -a
+            leb.y[start + 5] = a
+            leb.z[start + 5] = -b
+            leb.w[start + 5] = 4.0 * pi * v
+
+            leb.x[start + 6] = a
+            leb.y[start + 6] = -a
+            leb.z[start + 6] = -b
+            leb.w[start + 6] = 4.0 * pi * v
+
+            leb.x[start + 7] = -a
+            leb.y[start + 7] = -a
+            leb.z[start + 7] = -b
+            leb.w[start + 7] = 4.0 * pi * v
+
+            leb.x[start + 8] = -a
+            leb.y[start + 8] = b
+            leb.z[start + 8] = a
+            leb.w[start + 8] = 4.0 * pi * v
+
+            leb.x[start + 9] = a
+            leb.y[start + 9] = -b
+            leb.z[start + 9] = a
+            leb.w[start + 9] = 4.0 * pi * v
+
+            leb.x[start + 10] = a
+            leb.y[start + 10] = b
+            leb.z[start + 10] = -a
+            leb.w[start + 10] = 4.0 * pi * v
+
+            leb.x[start + 11] = -a
+            leb.y[start + 11] = -b
+            leb.z[start + 11] = a
+            leb.w[start + 11] = 4.0 * pi * v
+
+            leb.x[start + 12] = -a
+            leb.y[start + 12] = b
+            leb.z[start + 12] = -a
+            leb.w[start + 12] = 4.0 * pi * v
+
+            leb.x[start + 13] = a
+            leb.y[start + 13] = -b
+            leb.z[start + 13] = -a
+            leb.w[start + 13] = 4.0 * pi * v
+
+            leb.x[start + 14] = -a
+            leb.y[start + 14] = -b
+            leb.z[start + 14] = -a
+            leb.w[start + 14] = 4.0 * pi * v
+
+            leb.x[start + 15] = a
+            leb.y[start + 15] = b
+            leb.z[start + 15] = a
+            leb.w[start + 15] = 4.0 * pi * v
+
+            leb.x[start + 16] = b
+            leb.y[start + 16] = a
+            leb.z[start + 16] = a
+            leb.w[start + 16] = 4.0 * pi * v
+
+            leb.x[start + 17] = -b
+            leb.y[start + 17] = a
+            leb.z[start + 17] = a
+            leb.w[start + 17] = 4.0 * pi * v
+
+            leb.x[start + 18] = b
+            leb.y[start + 18] = -a
+            leb.z[start + 18] = a
+            leb.w[start + 18] = 4.0 * pi * v
+
+            leb.x[start + 19] = b
+            leb.y[start + 19] = a
+            leb.z[start + 19] = -a
+            leb.w[start + 19] = 4.0 * pi * v
+
+            leb.x[start + 20] = -b
+            leb.y[start + 20] = -a
+            leb.z[start + 20] = a
+            leb.w[start + 20] = 4.0 * pi * v
+
+            leb.x[start + 21] = -b
+            leb.y[start + 21] = a
+            leb.z[start + 21] = -a
+            leb.w[start + 21] = 4.0 * pi * v
+
+            leb.x[start + 22] = b
+            leb.y[start + 22] = -a
+            leb.z[start + 22] = -a
+            leb.w[start + 22] = 4.0 * pi * v
+
+            leb.x[start + 23] = -b
+            leb.y[start + 23] = -a
+            leb.z[start + 23] = -a
+            leb.w[start + 23] = 4.0 * pi * v
+            start = start + 24
+
+        case 5:
+            # /* A is inputed in this case as well*/
+            b = sqrt(1 - a * a)
+            leb.x[start] = a
+            leb.y[start] = b
+            leb.z[start] = 0.0
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = -a
+            leb.y[start + 1] = b
+            leb.z[start + 1] = 0.0
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = a
+            leb.y[start + 2] = -b
+            leb.z[start + 2] = 0.0
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = -a
+            leb.y[start + 3] = -b
+            leb.z[start + 3] = 0.0
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = b
+            leb.y[start + 4] = a
+            leb.z[start + 4] = 0.0
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = -b
+            leb.y[start + 5] = a
+            leb.z[start + 5] = 0.0
+            leb.w[start + 5] = 4.0 * pi * v
+
+            leb.x[start + 6] = b
+            leb.y[start + 6] = -a
+            leb.z[start + 6] = 0.0
+            leb.w[start + 6] = 4.0 * pi * v
+
+            leb.x[start + 7] = -b
+            leb.y[start + 7] = -a
+            leb.z[start + 7] = 0.0
+            leb.w[start + 7] = 4.0 * pi * v
+
+            leb.x[start + 8] = a
+            leb.y[start + 8] = 0.0
+            leb.z[start + 8] = b
+            leb.w[start + 8] = 4.0 * pi * v
+
+            leb.x[start + 9] = -a
+            leb.y[start + 9] = 0.0
+            leb.z[start + 9] = b
+            leb.w[start + 9] = 4.0 * pi * v
+
+            leb.x[start + 10] = a
+            leb.y[start + 10] = 0.0
+            leb.z[start + 10] = -b
+            leb.w[start + 10] = 4.0 * pi * v
+
+            leb.x[start + 11] = -a
+            leb.y[start + 11] = 0.0
+            leb.z[start + 11] = -b
+            leb.w[start + 11] = 4.0 * pi * v
+
+            leb.x[start + 12] = b
+            leb.y[start + 12] = 0.0
+            leb.z[start + 12] = a
+            leb.w[start + 12] = 4.0 * pi * v
+
+            leb.x[start + 13] = -b
+            leb.y[start + 13] = 0.0
+            leb.z[start + 13] = a
+            leb.w[start + 13] = 4.0 * pi * v
+
+            leb.x[start + 14] = b
+            leb.y[start + 14] = 0.0
+            leb.z[start + 14] = -a
+            leb.w[start + 14] = 4.0 * pi * v
+
+            leb.x[start + 15] = -b
+            leb.y[start + 15] = 0.0
+            leb.z[start + 15] = -a
+            leb.w[start + 15] = 4.0 * pi * v
+
+            leb.x[start + 16] = 0.0
+            leb.y[start + 16] = a
+            leb.z[start + 16] = b
+            leb.w[start + 16] = 4.0 * pi * v
+
+            leb.x[start + 17] = 0.0
+            leb.y[start + 17] = -a
+            leb.z[start + 17] = b
+            leb.w[start + 17] = 4.0 * pi * v
+
+            leb.x[start + 18] = 0.0
+            leb.y[start + 18] = a
+            leb.z[start + 18] = -b
+            leb.w[start + 18] = 4.0 * pi * v
+
+            leb.x[start + 19] = 0.0
+            leb.y[start + 19] = -a
+            leb.z[start + 19] = -b
+            leb.w[start + 19] = 4.0 * pi * v
+
+            leb.x[start + 20] = 0.0
+            leb.y[start + 20] = b
+            leb.z[start + 20] = a
+            leb.w[start + 20] = 4.0 * pi * v
+
+            leb.x[start + 21] = 0.0
+            leb.y[start + 21] = -b
+            leb.z[start + 21] = a
+            leb.w[start + 21] = 4.0 * pi * v
+
+            leb.x[start + 22] = 0.0
+            leb.y[start + 22] = b
+            leb.z[start + 22] = -a
+            leb.w[start + 22] = 4.0 * pi * v
+
+            leb.x[start + 23] = 0.0
+            leb.y[start + 23] = -b
+            leb.z[start + 23] = -a
+            leb.w[start + 23] = 4.0 * pi * v
+            start = start + 24
+
+        case 6:
+            # /* both A and B are inputed in this case */
+            c = sqrt(1.0 - a * a - b * b)
+            leb.x[start] = a
+            leb.y[start] = b
+            leb.z[start] = c
+            leb.w[start] = 4.0 * pi * v
+
+            leb.x[start + 1] = -a
+            leb.y[start + 1] = b
+            leb.z[start + 1] = c
+            leb.w[start + 1] = 4.0 * pi * v
+
+            leb.x[start + 2] = a
+            leb.y[start + 2] = -b
+            leb.z[start + 2] = c
+            leb.w[start + 2] = 4.0 * pi * v
+
+            leb.x[start + 3] = a
+            leb.y[start + 3] = b
+            leb.z[start + 3] = -c
+            leb.w[start + 3] = 4.0 * pi * v
+
+            leb.x[start + 4] = -a
+            leb.y[start + 4] = -b
+            leb.z[start + 4] = c
+            leb.w[start + 4] = 4.0 * pi * v
+
+            leb.x[start + 5] = a
+            leb.y[start + 5] = -b
+            leb.z[start + 5] = -c
+            leb.w[start + 5] = 4.0 * pi * v
+
+            leb.x[start + 6] = -a
+            leb.y[start + 6] = b
+            leb.z[start + 6] = -c
+            leb.w[start + 6] = 4.0 * pi * v
+
+            leb.x[start + 7] = -a
+            leb.y[start + 7] = -b
+            leb.z[start + 7] = -c
+            leb.w[start + 7] = 4.0 * pi * v
+
+            leb.x[start + 8] = b
+            leb.y[start + 8] = a
+            leb.z[start + 8] = c
+            leb.w[start + 8] = 4.0 * pi * v
+
+            leb.x[start + 9] = -b
+            leb.y[start + 9] = a
+            leb.z[start + 9] = c
+            leb.w[start + 9] = 4.0 * pi * v
+
+            leb.x[start + 10] = b
+            leb.y[start + 10] = -a
+            leb.z[start + 10] = c
+            leb.w[start + 10] = 4.0 * pi * v
+
+            leb.x[start + 11] = b
+            leb.y[start + 11] = a
+            leb.z[start + 11] = -c
+            leb.w[start + 11] = 4.0 * pi * v
+
+            leb.x[start + 12] = -b
+            leb.y[start + 12] = -a
+            leb.z[start + 12] = c
+            leb.w[start + 12] = 4.0 * pi * v
+
+            leb.x[start + 13] = b
+            leb.y[start + 13] = -a
+            leb.z[start + 13] = -c
+            leb.w[start + 13] = 4.0 * pi * v
+
+            leb.x[start + 14] = -b
+            leb.y[start + 14] = a
+            leb.z[start + 14] = -c
+            leb.w[start + 14] = 4.0 * pi * v
+
+            leb.x[start + 15] = -b
+            leb.y[start + 15] = -a
+            leb.z[start + 15] = -c
+            leb.w[start + 15] = 4.0 * pi * v
+
+            leb.x[start + 16] = c
+            leb.y[start + 16] = a
+            leb.z[start + 16] = b
+            leb.w[start + 16] = 4.0 * pi * v
+
+            leb.x[start + 17] = -c
+            leb.y[start + 17] = a
+            leb.z[start + 17] = b
+            leb.w[start + 17] = 4.0 * pi * v
+
+            leb.x[start + 18] = c
+            leb.y[start + 18] = -a
+            leb.z[start + 18] = b
+            leb.w[start + 18] = 4.0 * pi * v
+
+            leb.x[start + 19] = c
+            leb.y[start + 19] = a
+            leb.z[start + 19] = -b
+            leb.w[start + 19] = 4.0 * pi * v
+
+            leb.x[start + 20] = -c
+            leb.y[start + 20] = -a
+            leb.z[start + 20] = b
+            leb.w[start + 20] = 4.0 * pi * v
+
+            leb.x[start + 21] = c
+            leb.y[start + 21] = -a
+            leb.z[start + 21] = -b
+            leb.w[start + 21] = 4.0 * pi * v
+
+            leb.x[start + 22] = -c
+            leb.y[start + 22] = a
+            leb.z[start + 22] = -b
+            leb.w[start + 22] = 4.0 * pi * v
+
+            leb.x[start + 23] = -c
+            leb.y[start + 23] = -a
+            leb.z[start + 23] = -b
+            leb.w[start + 23] = 4.0 * pi * v
+
+            leb.x[start + 24] = c
+            leb.y[start + 24] = b
+            leb.z[start + 24] = a
+            leb.w[start + 24] = 4.0 * pi * v
+
+            leb.x[start + 25] = -c
+            leb.y[start + 25] = b
+            leb.z[start + 25] = a
+            leb.w[start + 25] = 4.0 * pi * v
+
+            leb.x[start + 26] = c
+            leb.y[start + 26] = -b
+            leb.z[start + 26] = a
+            leb.w[start + 26] = 4.0 * pi * v
+
+            leb.x[start + 27] = c
+            leb.y[start + 27] = b
+            leb.z[start + 27] = -a
+            leb.w[start + 27] = 4.0 * pi * v
+
+            leb.x[start + 28] = -c
+            leb.y[start + 28] = -b
+            leb.z[start + 28] = a
+            leb.w[start + 28] = 4.0 * pi * v
+
+            leb.x[start + 29] = c
+            leb.y[start + 29] = -b
+            leb.z[start + 29] = -a
+            leb.w[start + 29] = 4.0 * pi * v
+
+            leb.x[start + 30] = -c
+            leb.y[start + 30] = b
+            leb.z[start + 30] = -a
+            leb.w[start + 30] = 4.0 * pi * v
+
+            leb.x[start + 31] = -c
+            leb.y[start + 31] = -b
+            leb.z[start + 31] = -a
+            leb.w[start + 31] = 4.0 * pi * v
+
+            leb.x[start + 32] = a
+            leb.y[start + 32] = c
+            leb.z[start + 32] = b
+            leb.w[start + 32] = 4.0 * pi * v
+
+            leb.x[start + 33] = -a
+            leb.y[start + 33] = c
+            leb.z[start + 33] = b
+            leb.w[start + 33] = 4.0 * pi * v
+
+            leb.x[start + 34] = a
+            leb.y[start + 34] = -c
+            leb.z[start + 34] = b
+            leb.w[start + 34] = 4.0 * pi * v
+
+            leb.x[start + 35] = a
+            leb.y[start + 35] = c
+            leb.z[start + 35] = -b
+            leb.w[start + 35] = 4.0 * pi * v
+
+            leb.x[start + 36] = -a
+            leb.y[start + 36] = -c
+            leb.z[start + 36] = b
+            leb.w[start + 36] = 4.0 * pi * v
+
+            leb.x[start + 37] = a
+            leb.y[start + 37] = -c
+            leb.z[start + 37] = -b
+            leb.w[start + 37] = 4.0 * pi * v
+
+            leb.x[start + 38] = -a
+            leb.y[start + 38] = c
+            leb.z[start + 38] = -b
+            leb.w[start + 38] = 4.0 * pi * v
+
+            leb.x[start + 39] = -a
+            leb.y[start + 39] = -c
+            leb.z[start + 39] = -b
+            leb.w[start + 39] = 4.0 * pi * v
+
+            leb.x[start + 40] = b
+            leb.y[start + 40] = c
+            leb.z[start + 40] = a
+            leb.w[start + 40] = 4.0 * pi * v
+
+            leb.x[start + 41] = -b
+            leb.y[start + 41] = c
+            leb.z[start + 41] = a
+            leb.w[start + 41] = 4.0 * pi * v
+
+            leb.x[start + 42] = b
+            leb.y[start + 42] = -c
+            leb.z[start + 42] = a
+            leb.w[start + 42] = 4.0 * pi * v
+
+            leb.x[start + 43] = b
+            leb.y[start + 43] = c
+            leb.z[start + 43] = -a
+            leb.w[start + 43] = 4.0 * pi * v
+
+            leb.x[start + 44] = -b
+            leb.y[start + 44] = -c
+            leb.z[start + 44] = a
+            leb.w[start + 44] = 4.0 * pi * v
+
+            leb.x[start + 45] = b
+            leb.y[start + 45] = -c
+            leb.z[start + 45] = -a
+            leb.w[start + 45] = 4.0 * pi * v
+
+            leb.x[start + 46] = -b
+            leb.y[start + 46] = c
+            leb.z[start + 46] = -a
+            leb.w[start + 46] = 4.0 * pi * v
+
+            leb.x[start + 47] = -b
+            leb.y[start + 47] = -c
+            leb.z[start + 47] = -a
+            leb.w[start + 47] = 4.0 * pi * v
+            start = start + 48
+
+        case _:
+            raise Exception('Bad grid order')
+
+    return leb, start
+
+
+def lebedev_rule(n):
+    r"""Lebedev quadrature.
+
+    Compute the sample points and weights for Lebedev quadrature [1]_
+    for integration of a function over the surface of a unit sphere.
+
+    Parameters
+    ----------
+    n : int
+        Quadrature order. See Notes for supported values.
+
+    Returns
+    -------
+    x : ndarray of shape ``(3, m)``
+        Sample points on the unit sphere in Cartesian coordinates.
+        ``m`` is the "degree" corresponding with the specified order; see Notes.
+    w : ndarray of shape ``(m,)``
+        Weights
+
+    Notes
+    -----
+    Implemented by translating the Matlab code of [2]_ to Python.
+
+    The available orders (argument `n`) are::
+
+        3, 5, 7, 9, 11, 13, 15, 17,
+        19, 21, 23, 25, 27, 29, 31, 35,
+        41, 47, 53, 59, 65, 71, 77, 83,
+        89, 95, 101, 107, 113, 119, 125, 131
+
+    The corresponding degrees ``m`` are::
+
+        6, 14, 26, 38, 50, 74, 86, 110,
+        146, 170, 194, 230, 266, 302, 350, 434,
+        590, 770, 974, 1202, 1454, 1730, 2030, 2354,
+        2702, 3074, 3470, 3890, 4334, 4802, 5294, 5810
+
+    References
+    ----------
+    .. [1] V.I. Lebedev, and D.N. Laikov. "A quadrature formula for the sphere of
+           the 131st algebraic order of accuracy". Doklady Mathematics, Vol. 59,
+           No. 3, 1999, pp. 477-481.
+    .. [2] R. Parrish. ``getLebedevSphere``. Matlab Central File Exchange.
+           https://www.mathworks.com/matlabcentral/fileexchange/27097-getlebedevsphere.
+    .. [3] Bellet, Jean-Baptiste, Matthieu Brachet, and Jean-Pierre Croisille.
+           "Quadrature and symmetry on the Cubed Sphere." Journal of Computational and
+           Applied Mathematics 409 (2022): 114142. :doi:`10.1016/j.cam.2022.114142`.
+
+    Examples
+    --------
+    An example given in [3]_ is integration of :math:`f(x, y, z) = \exp(x)` over a
+    sphere of radius :math:`1`; the reference there is ``14.7680137457653``.
+    Show the convergence to the expected result as the order increases:
+
+    >>> import matplotlib.pyplot as plt
+    >>> import numpy as np
+    >>> from scipy.integrate import lebedev_rule
+    >>>
+    >>> def f(x):
+    ...     return np.exp(x[0])
+    >>>
+    >>> res = []
+    >>> orders = np.arange(3, 20, 2)
+    >>> for n in orders:
+    ...     x, w = lebedev_rule(n)
+    ...     res.append(w @ f(x))
+    >>>
+    >>> ref = np.full_like(res, 14.7680137457653)
+    >>> err = abs(res - ref)/abs(ref)
+    >>> plt.semilogy(orders, err)
+    >>> plt.xlabel('order $n$')
+    >>> plt.ylabel('relative error')
+    >>> plt.title(r'Convergence for $f(x, y, z) = \exp(x)$')
+    >>> plt.show()
+
+    """
+    degree = [6, 14, 26, 38, 50, 74, 86, 110, 146, 170, 194, 230, 266, 302, 350,
+              434, 590, 770, 974, 1202, 1454, 1730, 2030, 2354, 2702, 3074, 3470,
+              3890, 4334, 4802, 5294, 5810]
+    order = [3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 35, 41, 47,
+             53, 59, 65, 71, 77, 83, 89, 95, 101, 107, 113, 119, 125, 131]
+    order_degree_map = dict(zip(order, degree))
+
+    if n not in order_degree_map:
+        message = f"Order {n=} not available. Available orders are {order}."
+        raise NotImplementedError(message)
+
+    degree = order_degree_map[n]
+    res = get_lebedev_sphere(degree)
+    x = np.stack((res.x, res.y, res.z))
+    w = res.w
+
+    return x, w
diff --git a/shared/package-files/4e/4e3e852443d6ef00c11603cbeeb00f976cd08f58aaf77b8b8e618da06b58678d/betareg.py b/shared/package-files/4e/4e3e852443d6ef00c11603cbeeb00f976cd08f58aaf77b8b8e618da06b58678d/betareg.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e834cf3e0d8d13085c4dee314f25a9213fd97e7
--- /dev/null
+++ b/shared/package-files/4e/4e3e852443d6ef00c11603cbeeb00f976cd08f58aaf77b8b8e618da06b58678d/betareg.py
@@ -0,0 +1,1025 @@
+"""
+Beta regression for modeling rates and proportions
+
+References
+----------
+Grün, Bettina, Ioannis Kosmidis, and Achim Zeileis. Extended beta regression
+in R: Shaken, stirred, mixed, and partitioned. No. 2011-22. Working Papers in
+Economics and Statistics, 2011.
+
+Smithson, Michael, and Jay Verkuilen. "A better lemon squeezer?
+Maximum-likelihood regression with beta-distributed dependent variables."
+Psychological methods 11.1 (2006): 54.
+"""
+
+import numpy as np
+from scipy.special import gammaln as lgamma
+
+from statsmodels.base.model import (
+    GenericLikelihoodModel,
+    GenericLikelihoodModelResults,
+    _LLRMixin,
+)
+import statsmodels.base.wrapper as wrap
+from statsmodels.formula._manager import FormulaManager
+from statsmodels.formula.formulatools import advance_eval_env
+from statsmodels.genmod import families
+import statsmodels.regression.linear_model as lm
+from statsmodels.tools._decorators import cache_readonly
+
+LOGIT_LINK = families.links.Logit()
+LOG_LINK = families.links.Log()
+
+_init_example = """
+
+    Beta regression with default of logit-link for exog and log-link
+    for precision.
+
+    >>> mod = BetaModel(endog, exog)
+    >>> rslt = mod.fit()
+    >>> print(rslt.summary())
+
+    We can also specify a formula and a specific structure and use the
+    identity-link for precision.
+
+    >>> from sm.families.links import identity
+    >>> Z = patsy.dmatrix('~ temp', dat, return_type='dataframe')
+    >>> mod = BetaModel.from_formula('iyield ~ C(batch, Treatment(10)) + temp',
+    ...                              dat, exog_precision=Z,
+    ...                              link_precision=identity())
+
+    In the case of proportion-data, we may think that the precision depends on
+    the number of measurements. E.g for sequence data, on the number of
+    sequence reads covering a site:
+
+    >>> Z = patsy.dmatrix('~ coverage', df)
+    >>> formula = 'methylation ~ disease + age + gender + coverage'
+    >>> mod = BetaModel.from_formula(formula, df, exog_precision=Z)
+    >>> rslt = mod.fit()
+
+"""
+
+
+class BetaModel(GenericLikelihoodModel):
+    __doc__ = f"""
+    Beta Regression
+
+    The Model is parameterized by mean and precision. Both can depend on
+    explanatory variables through link functions.
+
+    Parameters
+    ----------
+    endog : array_like
+        1d array of endogenous response variable.
+    exog : array_like
+        A nobs x k array where `nobs` is the number of observations and `k`
+        is the number of regressors. An intercept is not included by default
+        and should be added by the user (models specified using a formula
+        include an intercept by default). See `statsmodels.tools.add_constant`.
+    exog_precision : array_like, optional
+        2d array of variables for the precision.
+    link : a link instance, optional
+        Any link in sm.families.links for mean, should have range in
+        interval [0, 1]. Default is logit-link.
+    link_precision : a link instance, optional
+        Any link in sm.families.links for precision, should have
+        range in positive line. Default is log-link.
+    **kwds : extra keywords
+        Keyword options that will be handled by super classes.
+        Not all general keywords will be supported in this class.
+
+    Notes
+    -----
+    Status: experimental, new in 0.13.
+    Core results are verified, but api can change and some extra results
+    specific to Beta regression are missing.
+
+    Examples
+    --------
+    {_init_example}
+
+    See Also
+    --------
+    :ref:`links`
+
+    """
+
+    def __init__(
+        self,
+        endog,
+        exog,
+        exog_precision=None,
+        link=LOGIT_LINK,
+        link_precision=LOG_LINK,
+        **kwds,
+    ):
+
+        etmp = np.array(endog)
+        assert np.all((0 < etmp) & (etmp < 1))
+        if exog_precision is None:
+            extra_names = ["precision"]
+            exog_precision = np.ones((len(endog), 1), dtype="f")
+        else:
+            extra_names = [
+                f"precision-{zc}"
+                for zc in (
+                    exog_precision.columns
+                    if hasattr(exog_precision, "columns")
+                    else range(1, exog_precision.shape[1] + 1)
+                )
+            ]
+
+        kwds["extra_params_names"] = extra_names
+
+        super().__init__(endog, exog, exog_precision=exog_precision, **kwds)
+        self.link = link
+        self.link_precision = link_precision
+        # not needed, handled by super:
+        # self.exog_precision = exog_precision
+        # inherited df do not account for precision params
+        self.nobs = self.endog.shape[0]
+        self.k_extra = 1
+        self.df_model = self.nparams - 2
+        self.df_resid = self.nobs - self.nparams
+        assert len(self.exog_precision) == len(self.endog)
+        self.hess_type = "oim"
+        if "exog_precision" not in self._init_keys:
+            self._init_keys.extend(["exog_precision"])
+        self._init_keys.extend(["link", "link_precision"])
+        self._null_drop_keys = ["exog_precision"]
+        del kwds["extra_params_names"]
+        self._check_kwargs(kwds)
+        self.results_class = BetaResults
+        self.results_class_wrapper = BetaResultsWrapper
+
+    @classmethod
+    def from_formula(cls, formula, data, exog_precision_formula=None, *args, **kwargs):
+        """
+        Create a `BetaModel` from a formula and dataframe.
+
+        Parameters
+        ----------
+        formula : str or generic Formula object
+            The formula specifying the model for the mean.
+        data : array_like
+            The data for the model. See Notes.
+        exog_precision_formula : str, optional
+            The formula specifying the model for the precision. If None,
+            then the precision model only has a constant term (see the
+            `exog_precision` parameter of `BetaModel`).
+        *args
+            Additional positional arguments passed to the model.
+        **kwargs
+            Additional keyword arguments passed to the model.
+
+        Returns
+        -------
+        model : BetaModel instance
+        """
+        if exog_precision_formula is not None:
+
+            mgr = FormulaManager()
+            if "subset" in kwargs:
+                d = data.ix[kwargs["subset"]]
+                Z = mgr.get_matrices(exog_precision_formula, d, pandas=False)
+            else:
+                Z = mgr.get_matrices(exog_precision_formula, data, pandas=False)
+            kwargs["exog_precision"] = Z
+        advance_eval_env(kwargs)
+        return super().from_formula(formula, data, *args, **kwargs)
+
+    def _get_exogs(self):
+        return (self.exog, self.exog_precision)
+
+    def predict(self, params, exog=None, exog_precision=None, which="mean"):
+        """
+        Predict values for mean or precision
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision parameter.
+        which : str, optional
+            Statistic to predict. Default is "mean".
+
+            - "mean" : mean, conditional expectation E(endog | exog)
+            - "precision" : predicted precision
+            - "linear" : linear predictor for the mean function
+            - "linear-precision" : linear predictor for the precision parameter
+            - "var" : returns the estimated variance of endog implied by the
+              model.
+
+        Returns
+        -------
+        ndarray
+            The predicted values.
+        """
+        # compatibility with old names and misspelling
+        if which == "linpred":
+            which = "linear"
+        if which in ["linpred_precision", "linear_precision"]:
+            which = "linear-precision"
+
+        k_mean = self.exog.shape[1]
+        if which in ["mean", "linear"]:
+            if exog is None:
+                exog = self.exog
+            params_mean = params[:k_mean]
+            # Zparams = params[k_mean:]
+            linpred = np.dot(exog, params_mean)
+            if which == "mean":
+                mu = self.link.inverse(linpred)
+                res = mu
+            else:
+                res = linpred
+
+        elif which in ["precision", "linear-precision"]:
+            if exog_precision is None:
+                exog_precision = self.exog_precision
+            params_prec = params[k_mean:]
+            linpred_prec = np.dot(exog_precision, params_prec)
+
+            if which == "precision":
+                phi = self.link_precision.inverse(linpred_prec)
+                res = phi
+            else:
+                res = linpred_prec
+
+        elif which == "var":
+            res = self._predict_var(params, exog=exog, exog_precision=exog_precision)
+
+        else:
+            raise ValueError(f"which = {which} is not available")
+
+        return res
+
+    def _predict_precision(self, params, exog_precision=None):
+        """
+        Predict values for precision function for given exog_precision
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+
+        Returns
+        -------
+        ndarray
+            Predicted precision.
+        """
+        if exog_precision is None:
+            exog_precision = self.exog_precision
+
+        k_mean = self.exog.shape[1]
+        params_precision = params[k_mean:]
+        linpred_prec = np.dot(exog_precision, params_precision)
+        phi = self.link_precision.inverse(linpred_prec)
+
+        return phi
+
+    def _predict_var(self, params, exog=None, exog_precision=None):
+        """
+        Predict values for conditional variance V(endog | exog)
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+
+        Returns
+        -------
+        ndarray
+            Predicted conditional variance.
+        """
+        mean = self.predict(params, exog=exog)
+        precision = self._predict_precision(params, exog_precision=exog_precision)
+
+        var_endog = mean * (1 - mean) / (1 + precision)
+        return var_endog
+
+    def loglikeobs(self, params):
+        """
+        Loglikelihood for observations of the Beta regression model
+
+        Parameters
+        ----------
+        params : ndarray
+            The parameters of the model, coefficients for linear predictors
+            of the mean and of the precision function.
+
+        Returns
+        -------
+        loglike : ndarray
+            The log likelihood for each observation of the model evaluated
+            at `params`.
+        """
+        return self._llobs(self.endog, self.exog, self.exog_precision, params)
+
+    def _llobs(self, endog, exog, exog_precision, params):
+        """
+        Loglikelihood for observations with data arguments
+
+        Parameters
+        ----------
+        endog : ndarray
+            1d array of endogenous variable.
+        exog : ndarray
+            2d array of explanatory variables.
+        exog_precision : ndarray
+            2d array of explanatory variables for precision.
+        params : ndarray
+            The parameters of the model, coefficients for linear predictors
+            of the mean and of the precision function.
+
+        Returns
+        -------
+        loglike : ndarray
+            The log likelihood for each observation of the model evaluated
+            at `params`.
+        """
+        y, X, Z = endog, exog, exog_precision
+        nz = Z.shape[1]
+
+        params_mean = params[:-nz]
+        params_prec = params[-nz:]
+        linpred = np.dot(X, params_mean)
+        linpred_prec = np.dot(Z, params_prec)
+
+        mu = self.link.inverse(linpred)
+        phi = self.link_precision.inverse(linpred_prec)
+
+        eps_lb = 1e-200
+        alpha = np.clip(mu * phi, eps_lb, np.inf)
+        beta = np.clip((1 - mu) * phi, eps_lb, np.inf)
+
+        ll = (
+            lgamma(phi)
+            - lgamma(alpha)
+            - lgamma(beta)
+            + (mu * phi - 1) * np.log(y)
+            + (((1 - mu) * phi) - 1) * np.log(1 - y)
+        )
+
+        return ll
+
+    def score(self, params):
+        """
+        Returns the score vector of the log-likelihood
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+
+        Returns
+        -------
+        score : ndarray
+            First derivative of loglikelihood function.
+
+        References
+        ----------
+        .. [1] http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993
+        """
+        sf1, sf2 = self.score_factor(params)
+
+        d1 = np.dot(sf1, self.exog)
+        d2 = np.dot(sf2, self.exog_precision)
+        return np.concatenate((d1, d2))
+
+    def _score_check(self, params):
+        """
+        Inherited score with finite differences
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+
+        Returns
+        -------
+        ndarray
+            Score based on numerical derivatives.
+        """
+        return super().score(params)
+
+    def score_factor(self, params, endog=None):
+        """
+        Derivative of loglikelihood function w.r.t. linear predictors
+
+        This needs to be multiplied with the exog to obtain the score_obs.
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+        endog : ndarray, optional
+            Values to use instead of the model's endog. If None, then the
+            model's endog is used.
+
+        Returns
+        -------
+        sf1 : ndarray
+            Score factor for the mean parameters.
+        sf2 : ndarray
+            Score factor for the precision parameters.
+
+        Notes
+        -----
+        The score_obs can be obtained from the score factors ``sf1, sf2``
+        using
+
+            - d1 = sf1[:, None] * exog
+            - d2 = sf2[:, None] * exog_precision
+
+        """
+        from scipy import special
+
+        digamma = special.psi
+
+        y = self.endog if endog is None else endog
+        X, Z = self.exog, self.exog_precision
+        nz = Z.shape[1]
+        Xparams = params[:-nz]
+        Zparams = params[-nz:]
+
+        # NO LINKS
+        mu = self.link.inverse(np.dot(X, Xparams))
+        phi = self.link_precision.inverse(np.dot(Z, Zparams))
+
+        eps_lb = 1e-200  # lower bound for evaluating digamma, avoids -inf
+        alpha = np.clip(mu * phi, eps_lb, np.inf)
+        beta = np.clip((1 - mu) * phi, eps_lb, np.inf)
+
+        ystar = np.log(y / (1.0 - y))
+        dig_beta = digamma(beta)
+        mustar = digamma(alpha) - dig_beta
+        yt = np.log(1 - y)
+        mut = dig_beta - digamma(phi)
+
+        t = 1.0 / self.link.deriv(mu)
+        h = 1.0 / self.link_precision.deriv(phi)
+        sf1 = phi * t * (ystar - mustar)
+        sf2 = h * (mu * (ystar - mustar) + yt - mut)
+
+        return (sf1, sf2)
+
+    def score_hessian_factor(self, params, return_hessian=False, observed=True):
+        """
+        Derivatives of loglikelihood function w.r.t. linear predictors
+
+        This calculates score and hessian factors at the same time, because
+        there is a large overlap in calculations.
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+        return_hessian : bool, optional
+            If False, then only score_factors are returned
+            If True, the both score and hessian factors are returned
+        observed : bool, optional
+            If True, then the observed Hessian is returned (default).
+            If False, then the expected information matrix is returned.
+
+        Returns
+        -------
+        (sf1, sf2) : tuple
+            The score factors, as returned by ``score_factor``. Only
+            returned if `return_hessian` is False.
+        (sf1, sf2), (-jbb, -jbg, -jgg) : tuple of tuples
+            The score factors and a tuple with 3 hessian factors,
+            corresponding to the upper triangle of the Hessian matrix.
+            Only returned if `return_hessian` is True.
+            TODO: check why there are minus
+        """
+        from scipy import special
+
+        digamma = special.psi
+
+        y, X, Z = self.endog, self.exog, self.exog_precision
+        nz = Z.shape[1]
+        Xparams = params[:-nz]
+        Zparams = params[-nz:]
+
+        # NO LINKS
+        mu = self.link.inverse(np.dot(X, Xparams))
+        phi = self.link_precision.inverse(np.dot(Z, Zparams))
+
+        # We need to prevent mu = 0 and (1-mu) = 0 in digamma call
+        eps_lb = 1e-200  # lower bound for evaluating digamma, avoids -inf
+        alpha = np.clip(mu * phi, eps_lb, np.inf)
+        beta = np.clip((1 - mu) * phi, eps_lb, np.inf)
+
+        ystar = np.log(y / (1.0 - y))
+        dig_beta = digamma(beta)
+        mustar = digamma(alpha) - dig_beta
+        yt = np.log(1 - y)
+        mut = dig_beta - digamma(phi)
+
+        t = 1.0 / self.link.deriv(mu)
+        h = 1.0 / self.link_precision.deriv(phi)
+
+        ymu_star = ystar - mustar
+        sf1 = phi * t * ymu_star
+        sf2 = h * (mu * ymu_star + yt - mut)
+
+        if return_hessian:
+
+            def trigamma(x):
+                return special.polygamma(1, x)
+
+            trig_beta = trigamma(beta)
+            var_star = trigamma(alpha) + trig_beta
+            var_t = trig_beta - trigamma(phi)
+
+            c = -trig_beta
+            s = self.link.deriv2(mu)
+            q = self.link_precision.deriv2(phi)
+
+            jbb = (phi * t) * var_star
+            if observed:
+                jbb += s * t**2 * ymu_star
+
+            jbb *= t * phi
+
+            jbg = phi * t * h * (mu * var_star + c)
+            if observed:
+                jbg -= ymu_star * t * h
+
+            jgg = h**2 * (mu**2 * var_star + 2 * mu * c + var_t)
+            if observed:
+                jgg += (mu * ymu_star + yt - mut) * q * h**3  # **3 ?
+
+            return (sf1, sf2), (-jbb, -jbg, -jgg)
+        else:
+            return (sf1, sf2)
+
+    def score_obs(self, params):
+        """
+        Score, first derivative of the loglikelihood for each observation
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+
+        Returns
+        -------
+        score_obs : ndarray, 2d
+            The first derivative of the loglikelihood function evaluated at
+            params for each observation.
+        """
+        sf1, sf2 = self.score_factor(params)
+
+        # elementwise product for each row (observation)
+        d1 = sf1[:, None] * self.exog
+        d2 = sf2[:, None] * self.exog_precision
+        return np.column_stack((d1, d2))
+
+    def hessian(self, params, observed=None):
+        """
+        Hessian, second derivative of loglikelihood function
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which Hessian is evaluated.
+        observed : bool, optional
+            If True, then the observed Hessian is returned.
+            If False, then the expected information matrix is returned.
+            If None, the default, then the choice is made by the model's
+            ``hess_type`` attribute, which is "oim", i.e. observed, unless
+            ``fit`` was called with ``cov_type="eim"``.
+
+        Returns
+        -------
+        hessian : ndarray
+            Hessian, i.e., observed information, or expected information matrix.
+        """
+        if observed is None:
+            observed = self.hess_type != "eim"
+        _, hf = self.score_hessian_factor(
+            params, return_hessian=True, observed=observed
+        )
+
+        hf11, hf12, hf22 = hf
+
+        # elementwise product for each row (observation)
+        d11 = (self.exog.T * hf11).dot(self.exog)
+        d12 = (self.exog.T * hf12).dot(self.exog_precision)
+        d22 = (self.exog_precision.T * hf22).dot(self.exog_precision)
+        return np.block([[d11, d12], [d12.T, d22]])
+
+    def hessian_factor(self, params, observed=True):
+        """
+        Derivatives of loglikelihood function w.r.t. linear predictors
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which Hessian is evaluated.
+        observed : bool, optional
+            If True, then the observed Hessian is returned (default).
+            If False, then the expected information matrix is returned.
+
+        Returns
+        -------
+        tuple
+            The three hessian factors, corresponding to the upper triangle
+            of the Hessian matrix, as returned by ``score_hessian_factor``.
+        """
+        _, hf = self.score_hessian_factor(
+            params, return_hessian=True, observed=observed
+        )
+        return hf
+
+    def _start_params(self, niter=2, return_intermediate=False):
+        """
+        Find starting values
+
+        Parameters
+        ----------
+        niter : int, optional
+            Number of iterations of WLS approximation
+        return_intermediate : bool, optional
+            If False (default), then only the preliminary parameter estimate
+            will be returned.
+            If True, then also the two results instances of the WLS estimate
+            for mean parameters and for the precision parameters will be
+            returned.
+
+        Returns
+        -------
+        sp : ndarray
+            start parameters for the optimization
+        res_m2 : results instance, optional
+            Results instance for the WLS regression of the mean function.
+            Only returned if `return_intermediate` is True.
+        res_p2 : results instance, optional
+            Results instance for the WLS regression of the precision
+            function. Only returned if `return_intermediate` is True.
+
+        Notes
+        -----
+        This calculates a few iteration of weighted least squares. This is not
+        a full scoring algorithm.
+        """
+        # WLS of the mean equation uses the implied weights (inverse variance),
+        # WLS for the precision equations uses weights that only take
+        # account of the link transformation of the precision endog.
+        from statsmodels.regression.linear_model import OLS, WLS
+
+        res_m = OLS(self.link(self.endog), self.exog).fit()
+        fitted = self.link.inverse(res_m.fittedvalues)
+        resid = self.endog - fitted
+
+        prec_i = fitted * (1 - fitted) / np.maximum(np.abs(resid), 1e-2) ** 2 - 1
+        res_p = OLS(self.link_precision(prec_i), self.exog_precision).fit()
+        prec_fitted = self.link_precision.inverse(res_p.fittedvalues)
+        # sp = np.concatenate((res_m.params, res_p.params))
+
+        for _ in range(niter):
+            y_var_inv = (1 + prec_fitted) / (fitted * (1 - fitted))
+            # y_var = fitted * (1 - fitted) / (1 + prec_fitted)
+
+            ylink_var_inv = y_var_inv / self.link.deriv(fitted) ** 2
+            res_m2 = WLS(self.link(self.endog), self.exog, weights=ylink_var_inv).fit()
+            fitted = self.link.inverse(res_m2.fittedvalues)
+            resid2 = self.endog - fitted
+
+            prec_i2 = fitted * (1 - fitted) / np.maximum(np.abs(resid2), 1e-2) ** 2 - 1
+            w_p = 1.0 / self.link_precision.deriv(prec_fitted) ** 2
+            res_p2 = WLS(
+                self.link_precision(prec_i2), self.exog_precision, weights=w_p
+            ).fit()
+            prec_fitted = self.link_precision.inverse(res_p2.fittedvalues)
+            sp2 = np.concatenate((res_m2.params, res_p2.params))
+
+        if return_intermediate:
+            return sp2, res_m2, res_p2
+
+        return sp2
+
+    def fit(self, start_params=None, maxiter=1000, disp=False, method="bfgs", **kwds):
+        """
+        Fit the model by maximum likelihood
+
+        Parameters
+        ----------
+        start_params : array_like, optional
+            A vector of starting values for the regression
+            coefficients.  If None, a default is chosen.
+        maxiter : int, optional
+            The maximum number of iterations
+        disp : bool, optional
+            Show convergence stats.
+        method : str, optional
+            The optimization method to use.
+        kwds :
+            Keyword arguments for the optimizer.
+
+        Returns
+        -------
+        BetaResults
+            Results instance with fitted parameters.
+        """
+
+        if start_params is None:
+            start_params = self._start_params()
+        #           # http://www.ime.usp.br/~sferrari/beta.pdf suggests starting phi
+        #           # on page 8
+
+        if "cov_type" in kwds:
+            # this is a workaround because we cannot tell super to use eim
+            if kwds["cov_type"].lower() == "eim":
+                self.hess_type = "eim"
+                del kwds["cov_type"]
+        else:
+            self.hess_type = "oim"
+
+        res = super().fit(
+            start_params=start_params, maxiter=maxiter, method=method, disp=disp, **kwds
+        )
+        if not isinstance(res, BetaResultsWrapper):
+            # currently GenericLikelihoodModel doe not add wrapper
+            res = BetaResultsWrapper(res)
+        return res
+
+    def _deriv_mean_dparams(self, params):
+        """
+        Derivative of the expected endog with respect to the parameters
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+
+        Returns
+        -------
+        ndarray
+            The value of the derivative of the expected endog with respect
+            to the parameter vector.
+
+        Notes
+        -----
+        Not verified yet.
+        """
+        link = self.link
+        lin_pred = self.predict(params, which="linear")
+        idl = link.inverse_deriv(lin_pred)
+        dmat = self.exog * idl[:, None]
+        return np.column_stack((dmat, np.zeros(self.exog_precision.shape)))
+
+    def _deriv_score_obs_dendog(self, params):
+        """
+        Derivative of score_obs w.r.t. endog
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+
+        Returns
+        -------
+        derivative : ndarray, 2d
+            The derivative of the score_obs with respect to endog.
+        """
+        from statsmodels.tools.numdiff import _approx_fprime_cs_scalar
+
+        def f(y):
+            if y.ndim == 2 and y.shape[1] == 1:
+                y = y[:, 0]
+            sf = self.score_factor(params, endog=y)
+            return np.column_stack(sf)
+
+        dsf = _approx_fprime_cs_scalar(self.endog[:, None], f)
+        # deriv is 2d vector
+        d1 = dsf[:, :1] * self.exog
+        d2 = dsf[:, 1:2] * self.exog_precision
+
+        return np.column_stack((d1, d2))
+
+    def get_distribution_params(self, params, exog=None, exog_precision=None):
+        """
+        Return distribution parameters converted from model prediction
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+
+        Returns
+        -------
+        (alpha, beta) : tuple of ndarrays
+            Parameters for the scipy distribution to evaluate predictive
+            distribution.
+        """
+        mean = self.predict(params, exog=exog)
+        precision = self.predict(
+            params, exog_precision=exog_precision, which="precision"
+        )
+        return precision * mean, precision * (1 - mean)
+
+    def get_distribution(self, params, exog=None, exog_precision=None):
+        """
+        Return an instance of the predictive distribution
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+
+        Returns
+        -------
+        Instance of a scipy frozen distribution based on estimated
+        parameters.
+
+        See Also
+        --------
+        predict
+
+        Notes
+        -----
+        This function delegates to the predict method to handle exog and
+        exog_precision, which in turn makes any required transformations.
+
+        Due to the behavior of ``scipy.stats.distributions objects``, the
+        returned random number generator must be called with ``gen.rvs(n)``
+        where ``n`` is the number of observations in the data set used
+        to fit the model.  If any other value is used for ``n``, misleading
+        results will be produced.
+        """
+        from scipy import stats
+
+        args = self.get_distribution_params(
+            params, exog=exog, exog_precision=exog_precision
+        )
+        distr = stats.beta(*args)
+        return distr
+
+
+class BetaResults(GenericLikelihoodModelResults, _LLRMixin):
+    """
+    Results class for Beta regression
+
+    This class inherits from GenericLikelihoodModelResults and not all
+    inherited methods might be appropriate in this case.
+    """
+
+    # GenericLikelihoodModel doesn't define fittedvalues, residuals and similar
+    @cache_readonly
+    def fittedvalues(self):
+        """In-sample predicted mean, conditional expectation"""
+        return self.model.predict(self.params)
+
+    @cache_readonly
+    def fitted_precision(self):
+        """In-sample predicted precision"""
+        return self.model.predict(self.params, which="precision")
+
+    @cache_readonly
+    def resid(self):
+        """Response residual"""
+        return self.model.endog - self.fittedvalues
+
+    @cache_readonly
+    def resid_pearson(self):
+        """Pearson standardized residual"""
+        std = np.sqrt(self.model.predict(self.params, which="var"))
+        return self.resid / std
+
+    @cache_readonly
+    def prsquared(self):
+        """
+        Cox-Snell Likelihood-Ratio pseudo-R-squared
+
+        Computed as ``1 - exp((llnull - llf) * (2 / nobs))``.
+        """
+        return self.pseudo_rsquared(kind="lr")
+
+    def get_distribution_params(self, exog=None, exog_precision=None, transform=True):
+        """
+        Return distribution parameters converted from model prediction
+
+        Parameters
+        ----------
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+        transform : bool, optional
+            If transform is True and formulas have been used, then predictor
+            ``exog`` is passed through the formula processing. Default is True.
+
+        Returns
+        -------
+        (alpha, beta) : tuple of ndarrays
+            Parameters for the scipy distribution to evaluate predictive
+            distribution.
+        """
+        mean = self.predict(exog=exog, transform=transform)
+        precision = self.predict(
+            exog_precision=exog_precision, which="precision", transform=transform
+        )
+        return precision * mean, precision * (1 - mean)
+
+    def get_distribution(self, exog=None, exog_precision=None, transform=True):
+        """
+        Return an instance of the predictive distribution
+
+        Parameters
+        ----------
+        exog : array_like, optional
+            Array of predictor variables for mean.
+        exog_precision : array_like, optional
+            Array of predictor variables for precision.
+        transform : bool, optional
+            If transform is True and formulas have been used, then predictor
+            ``exog`` is passed through the formula processing. Default is True.
+
+        Returns
+        -------
+        Instance of a scipy frozen distribution based on estimated
+        parameters.
+
+        See Also
+        --------
+        predict
+
+        Notes
+        -----
+        This function delegates to the predict method to handle exog and
+        exog_precision, which in turn makes any required transformations.
+
+        Due to the behavior of ``scipy.stats.distributions objects``, the
+        returned random number generator must be called with ``gen.rvs(n)``
+        where ``n`` is the number of observations in the data set used
+        to fit the model.  If any other value is used for ``n``, misleading
+        results will be produced.
+        """
+        from scipy import stats
+
+        args = self.get_distribution_params(
+            exog=exog, exog_precision=exog_precision, transform=transform
+        )
+        args = (np.asarray(arg) for arg in args)
+        distr = stats.beta(*args)
+        return distr
+
+    def get_influence(self):
+        """
+        Get an instance of MLEInfluence with influence and outlier measures
+
+        Returns
+        -------
+        infl : MLEInfluence instance
+            The instance has methods to calculate the main influence and
+            outlier measures as attributes.
+
+        See Also
+        --------
+        statsmodels.stats.outliers_influence.MLEInfluence
+
+        Notes
+        -----
+        Support for multi-link and multi-exog models is still experimental
+        in MLEInfluence. Interface and some definitions might still change.
+
+        Difference to R betareg: Betareg has the same general leverage
+        as this model. However, they use a linear approximation hat matrix
+        to scale and studentize influence and residual statistics.
+        MLEInfluence uses the generalized leverage as hat_matrix_diag.
+        Additionally, MLEInfluence uses pearson residuals for residual
+        analysis.
+        """
+        from statsmodels.stats.outliers_influence import MLEInfluence
+
+        return MLEInfluence(self)
+
+    def bootstrap(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class BetaResultsWrapper(lm.RegressionResultsWrapper):
+    pass
+
+
+wrap.populate_wrapper(BetaResultsWrapper, BetaResults)
diff --git a/shared/package-files/4e/4e3f400abcd0a8d98348eeb17e9cf0d7612c4249d2cb043e646b9631b0dd1e74/runtime.py b/shared/package-files/4e/4e3f400abcd0a8d98348eeb17e9cf0d7612c4249d2cb043e646b9631b0dd1e74/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..20634d8f4b7de3e87c99ecd76a427f8879b3fd9e
--- /dev/null
+++ b/shared/package-files/4e/4e3f400abcd0a8d98348eeb17e9cf0d7612c4249d2cb043e646b9631b0dd1e74/runtime.py
@@ -0,0 +1,142 @@
+"""
+CUDA Runtime wrapper.
+
+This provides a very minimal set of bindings, since the Runtime API is not
+really used in Numba except for querying the Runtime version.
+"""
+
+import ctypes
+import functools
+import sys
+
+from numba.core import config
+from numba.cuda.cudadrv.driver import ERROR_MAP, make_logger
+from numba.cuda.cudadrv.error import CudaSupportError, CudaRuntimeError
+from numba.cuda.cudadrv.libs import open_cudalib
+from numba.cuda.cudadrv.rtapi import API_PROTOTYPES
+from numba.cuda.cudadrv import enums
+
+
+class CudaRuntimeAPIError(CudaRuntimeError):
+    """
+    Raised when there is an error accessing a C API from the CUDA Runtime.
+    """
+    def __init__(self, code, msg):
+        self.code = code
+        self.msg = msg
+        super().__init__(code, msg)
+
+    def __str__(self):
+        return "[%s] %s" % (self.code, self.msg)
+
+
+class Runtime:
+    """
+    Runtime object that lazily binds runtime API functions.
+    """
+
+    def __init__(self):
+        self.is_initialized = False
+
+    def _initialize(self):
+        # lazily initialize logger
+        global _logger
+        _logger = make_logger()
+
+        if config.DISABLE_CUDA:
+            msg = ("CUDA is disabled due to setting NUMBA_DISABLE_CUDA=1 "
+                   "in the environment, or because CUDA is unsupported on "
+                   "32-bit systems.")
+            raise CudaSupportError(msg)
+        self.lib = open_cudalib('cudart')
+
+        self.is_initialized = True
+
+    def __getattr__(self, fname):
+        # First request of a runtime API function
+        try:
+            proto = API_PROTOTYPES[fname]
+        except KeyError:
+            raise AttributeError(fname)
+        restype = proto[0]
+        argtypes = proto[1:]
+
+        if not self.is_initialized:
+            self._initialize()
+
+        # Find function in runtime library
+        libfn = self._find_api(fname)
+        libfn.restype = restype
+        libfn.argtypes = argtypes
+
+        safe_call = self._wrap_api_call(fname, libfn)
+        setattr(self, fname, safe_call)
+        return safe_call
+
+    def _wrap_api_call(self, fname, libfn):
+        @functools.wraps(libfn)
+        def safe_cuda_api_call(*args):
+            _logger.debug('call runtime api: %s', libfn.__name__)
+            retcode = libfn(*args)
+            self._check_error(fname, retcode)
+        return safe_cuda_api_call
+
+    def _check_error(self, fname, retcode):
+        if retcode != enums.CUDA_SUCCESS:
+            errname = ERROR_MAP.get(retcode, "cudaErrorUnknown")
+            msg = "Call to %s results in %s" % (fname, errname)
+            _logger.error(msg)
+            raise CudaRuntimeAPIError(retcode, msg)
+
+    def _find_api(self, fname):
+        try:
+            return getattr(self.lib, fname)
+        except AttributeError:
+            pass
+
+        # Not found.
+        # Delay missing function error to use
+        def absent_function(*args, **kws):
+            msg = "runtime missing function: %s."
+            raise CudaRuntimeError(msg % fname)
+
+        setattr(self, fname, absent_function)
+        return absent_function
+
+    def get_version(self):
+        """
+        Returns the CUDA Runtime version as a tuple (major, minor).
+        """
+        rtver = ctypes.c_int()
+        self.cudaRuntimeGetVersion(ctypes.byref(rtver))
+        # The version is encoded as (1000 * major) + (10 * minor)
+        major = rtver.value // 1000
+        minor = (rtver.value - (major * 1000)) // 10
+        return (major, minor)
+
+    def is_supported_version(self):
+        """
+        Returns True if the CUDA Runtime is a supported version.
+        """
+
+        return self.get_version() in self.supported_versions
+
+    @property
+    def supported_versions(self):
+        """A tuple of all supported CUDA toolkit versions. Versions are given in
+        the form ``(major_version, minor_version)``."""
+        if sys.platform not in ('linux', 'win32') or config.MACHINE_BITS != 64:
+            # Only 64-bit Linux and Windows are supported
+            return ()
+        return ((11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5), (11, 6),
+                (11, 7))
+
+
+runtime = Runtime()
+
+
+def get_version():
+    """
+    Return the runtime version as a tuple of (major, minor)
+    """
+    return runtime.get_version()
diff --git a/shared/package-files/4e/4e3f760adb4e8761f14b410a43ff7eb3130102c42b0ce50a34fd2780824574ff/regex.h b/shared/package-files/4e/4e3f760adb4e8761f14b410a43ff7eb3130102c42b0ce50a34fd2780824574ff/regex.h
new file mode 100644
index 0000000000000000000000000000000000000000..590fbac7153889129e7bca7652125980cb4457cd
--- /dev/null
+++ b/shared/package-files/4e/4e3f760adb4e8761f14b410a43ff7eb3130102c42b0ce50a34fd2780824574ff/regex.h
@@ -0,0 +1,51 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <cassert>
+#include <initializer_list>
+#include <regex>
+#include <string_view>
+#include <type_traits>
+
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace internal {
+
+/// Match regex against target and produce string_views out of matches.
+inline bool RegexMatch(const std::regex& regex, std::string_view target,
+                       std::initializer_list<std::string_view*> out_matches) {
+  assert(regex.mark_count() == out_matches.size());
+
+  std::match_results<decltype(target.begin())> match;
+  if (!std::regex_match(target.begin(), target.end(), match, regex)) {
+    return false;
+  }
+
+  // Match #0 is the whole matched sequence
+  assert(regex.mark_count() + 1 == match.size());
+  auto out_it = out_matches.begin();
+  for (size_t i = 1; i < match.size(); ++i) {
+    **out_it++ = target.substr(match.position(i), match.length(i));
+  }
+  return true;
+}
+
+}  // namespace internal
+}  // namespace arrow
diff --git a/shared/package-files/4e/4e44257bbb118cc23996a6e469d4e9f5299efc2c88f2231627241238f2b4983c/backend_exception.h b/shared/package-files/4e/4e44257bbb118cc23996a6e469d4e9f5299efc2c88f2231627241238f2b4983c/backend_exception.h
new file mode 100644
index 0000000000000000000000000000000000000000..0e100a60bdae150d0913819a8e343abe91f5d8b7
--- /dev/null
+++ b/shared/package-files/4e/4e44257bbb118cc23996a6e469d4e9f5299efc2c88f2231627241238f2b4983c/backend_exception.h
@@ -0,0 +1,54 @@
+#pragma once
+#include <c10/util/Exception.h>
+
+namespace c10 {
+class TORCH_API BackendRuntimeException : public c10::Error {
+ public:
+  // Use debug_handle to throw exception
+  BackendRuntimeException(
+      SourceLocation loc,
+      std::string msg,
+      int64_t debug_handle)
+      : c10::Error(loc, msg) {
+    debug_handles.push_back(debug_handle);
+  }
+  // If rethrowing, can push another debug_handle
+  // This is useful in couple of scenarios.
+  // 1. A submodule is lowered and lite interperter has CallMethod
+  //    to lowered module's method. In this case lowered module will throw with
+  //    a handle, plus there will be another debug handle corresponding
+  //    to the CallMethod node in lite interpreter. Both together give complete
+  //    trace. This function allows lite interpreter to rethrow with debug
+  //    handle it has for CallMethod.
+  // 2. Another scenarios is when lite interperter can make function calls or
+  //    the lowered backend also has function call ability. Thus we have
+  //    multiple function frames. Now we need a stack of handles to symbolicate
+  //    entire stack trace.
+  void pushDebugHandle(int64_t debug_handle) {
+    debug_handles.push_back(debug_handle);
+  }
+  const std::vector<int64_t>& getDebugHandles() {
+    return debug_handles;
+  }
+
+ private:
+  // Stores stack of debug handles.
+  std::vector<int64_t> debug_handles;
+};
+
+} // namespace c10
+#define TORCH_DELEGATED_BACKEND_THROW(cond, msg, debug_handle) \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                        \
+    throw ::c10::BackendRuntimeException(                      \
+        {__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, \
+        msg,                                                   \
+        debug_handle);                                         \
+  }
+
+#define TORCH_DELEGATED_BACKEND_RETHROW(e, debug_handle) \
+  do {                                                   \
+    e.pushDebugHandle(debug_handle);                     \
+    throw;                                               \
+  } while (false)
+
+#define DEBUG_HANDLE_UNKNOWN -1
diff --git a/shared/package-files/4e/4e4486fa7b48d9d75e544679cf10cb7fe1029a550e469c52daef26dc3c947c5c/gelu_backward_ops.h b/shared/package-files/4e/4e4486fa7b48d9d75e544679cf10cb7fe1029a550e469c52daef26dc3c947c5c/gelu_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2eaf088f78d2f5ffc8ab1476fb0744f2a420d36b
--- /dev/null
+++ b/shared/package-files/4e/4e4486fa7b48d9d75e544679cf10cb7fe1029a550e469c52daef26dc3c947c5c/gelu_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API gelu_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, c10::string_view, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gelu_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input);
+};
+
+struct TORCH_API gelu_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::string_view);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gelu_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/4e/4e45171106372b30d13e11934d5cd217aef45dfc0f2c065b4b0b1f7d9d2cbf9e/teraterm b/shared/package-files/4e/4e45171106372b30d13e11934d5cd217aef45dfc0f2c065b4b0b1f7d9d2cbf9e/teraterm
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diff --git a/shared/package-files/4e/4e4833d7b5e6605cd5c9bbd0f55a3d2c2855a3df11e4195b8ea08d30793ce833/util.cc b/shared/package-files/4e/4e4833d7b5e6605cd5c9bbd0f55a3d2c2855a3df11e4195b8ea08d30793ce833/util.cc
new file mode 100644
index 0000000000000000000000000000000000000000..cffe1eb956b3ed86cade1acfbba58635a71b20d5
--- /dev/null
+++ b/shared/package-files/4e/4e4833d7b5e6605cd5c9bbd0f55a3d2c2855a3df11e4195b8ea08d30793ce833/util.cc
@@ -0,0 +1,50 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#include "arrow/python/util.h"
+
+#include "arrow/array.h"
+#include "arrow/python/common.h"
+
+namespace arrow ::py {
+
+Result<std::shared_ptr<Array>> Arange(int64_t start, int64_t stop, int64_t step,
+                                      MemoryPool* pool) {
+  int64_t size;
+  if (step == 0) {
+    return Status::Invalid("Step must not be zero");
+  }
+  if (step > 0 && stop > start) {
+    // Ceiling division for positive step
+    size = (stop - start + step - 1) / step;
+  } else if (step < 0 && stop < start) {
+    // Ceiling division for negative step
+    size = (start - stop - step - 1) / (-step);
+  } else {
+    return MakeEmptyArray(int64());
+  }
+  std::shared_ptr<Buffer> data_buffer;
+  ARROW_ASSIGN_OR_RAISE(data_buffer, AllocateBuffer(size * sizeof(int64_t), pool));
+  auto values = reinterpret_cast<int64_t*>(data_buffer->mutable_data());
+  for (int64_t i = 0; i < size; ++i) {
+    values[i] = start + i * step;
+  }
+  auto data = ArrayData::Make(int64(), size, {nullptr, data_buffer}, 0);
+  return MakeArray(data);
+}
+
+}  // namespace arrow::py
diff --git a/shared/package-files/4e/4e4a551813ebbab8fc2f598837871a066cb29ad6044f302d139967856762baa4/slicer_internal.py b/shared/package-files/4e/4e4a551813ebbab8fc2f598837871a066cb29ad6044f302d139967856762baa4/slicer_internal.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2786349cbb86678b2da233ea3a95aecdc2f1a06
--- /dev/null
+++ b/shared/package-files/4e/4e4a551813ebbab8fc2f598837871a066cb29ad6044f302d139967856762baa4/slicer_internal.py
@@ -0,0 +1,631 @@
+""" Lower level layer for slicer.
+Mom's spaghetti.
+"""
+# TODO: Consider boolean array indexing.
+
+from typing import Any, AnyStr, Union, List, Tuple
+from abc import abstractmethod
+import numbers
+
+
+class AtomicSlicer:
+    """ Wrapping object that will unify slicing across data structures.
+
+    What we support:
+        Basic indexing (return references):
+        - (start:stop:step) slicing
+        - support ellipses
+        Advanced indexing (return references):
+        - integer array indexing
+
+    Numpy Reference:
+        Basic indexing (return views):
+        - (start:stop:step) slicing
+        - support ellipses and newaxis (alias for None)
+        Advanced indexing (return copy):
+        - integer array indexing, i.e. X[[1,2], [3,4]]
+        - boolean array indexing
+        - mixed array indexing (has integer array, ellipses, newaxis in same slice)
+    """
+
+    def __init__(self, o: Any, max_dim: Union[None, int, AnyStr] = "auto"):
+        """ Provides a consistent slicing API to the object provided.
+
+        Args:
+            o: Object to enable consistent slicing.
+                Currently supports numpy dense arrays, recursive lists ending with list or numpy.
+            max_dim: Max number of dimensions the wrapped object has.
+                If set to "auto", max dimensions will be inferred. This comes at compute cost.
+        """
+        self.o = o
+        self.max_dim = max_dim
+        if self.max_dim == "auto":
+            self.max_dim = UnifiedDataHandler.max_dim(o)
+
+    def __repr__(self) -> AnyStr:
+        """ Override default repr for human readability.
+
+        Returns:
+            String to display.
+        """
+        return f"{self.__class__.__name__}({self.o.__repr__()})"
+
+    def __getitem__(self, item: Any) -> Any:
+        """ Consistent slicing into wrapped object.
+
+        Args:
+            item: Slicing key of type integer or slice.
+
+        Returns:
+            Sliced object.
+
+        Raises:
+            ValueError: If slicing is not compatible with wrapped object.
+        """
+        # Turn item into tuple if not already.
+        index_tup = unify_slice(item, self.max_dim)
+
+        # Slice according to object type.
+        return UnifiedDataHandler.slice(self.o, index_tup, self.max_dim)
+
+
+def unify_slice(item: Any, max_dim: int, alias_lookup=None) -> Tuple:
+    """ Resolves aliases and ellipses in a slice item.
+
+    Args:
+        item: Slicing key that is passed to __getitem__.
+        max_dim: Max dimension of object to be sliced.
+        alias_lookup: AliasLookup structure.
+
+    Returns:
+        A tuple representation of the item.
+    """
+    item = _normalize_slice_key(item)
+    index_tup = _normalize_subkey_types(item)
+    index_tup = _handle_newaxis_ellipses(index_tup, max_dim)
+    if alias_lookup:
+        index_tup = _handle_aliases(index_tup, alias_lookup)
+    return index_tup
+
+
+def _normalize_subkey_types(index_tup: Tuple) -> Tuple:
+    """ Casts subkeys into basic types such as int.
+
+    Args:
+        key: Slicing key that is passed within __getitem__.
+
+    Returns:
+        Tuple with subkeys casted to basic types.
+    """
+    new_index_tup = []  # Gets casted to tuple at the end
+
+    np_int_types = {
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    }
+    for subkey in index_tup:
+        if _safe_isinstance(subkey, "numpy", np_int_types):
+            new_subkey = int(subkey)
+        elif _safe_isinstance(subkey, "numpy", "ndarray"):
+            if len(subkey.shape) == 1:
+                new_subkey = subkey.tolist()
+            else:
+                raise ValueError(f"Cannot use array of shape {subkey.shape} as subkey.")
+        else:
+            new_subkey = subkey
+
+        new_index_tup.append(new_subkey)
+    return tuple(new_index_tup)
+
+
+def _normalize_slice_key(key: Any) -> Tuple:
+    """ Normalizes slice key into always being a top-level tuple.
+
+    Args:
+        key: Slicing key that is passed within __getitem__.
+
+    Returns:
+        Expanded slice as a tuple.
+    """
+    if not isinstance(key, tuple):
+        return (key,)
+    else:
+        return key
+
+
+def _handle_newaxis_ellipses(index_tup: Tuple, max_dim: int) -> Tuple:
+    """ Expands newaxis and ellipses within a slice for simplification.
+    This code is mostly adapted from: https://github.com/clbarnes/h5py_like/blob/master/h5py_like/shape_utils.py#L111
+
+    Args:
+        index_tup: Slicing key as a tuple.
+        max_dim: Maximum number of dimensions in the respective sliceable object.
+
+    Returns:
+        Expanded slice as a tuple.
+    """
+    non_indexes = (None, Ellipsis)
+    concrete_indices = sum(idx not in non_indexes for idx in index_tup)
+    index_list = []
+    # newaxis_at = []
+    has_ellipsis = False
+    int_count = 0
+    for item in index_tup:
+        if isinstance(item, numbers.Number):
+            int_count += 1
+
+        # NOTE: If we need locations of new axis, re-enable this.
+        if item is None:  # pragma: no cover
+            pass
+            # newaxis_at.append(len(index_list) + len(newaxis_at) - int_count)
+        elif item == Ellipsis:
+            if has_ellipsis:  # pragma: no cover
+                raise IndexError("an index can only have a single ellipsis ('...')")
+            has_ellipsis = True
+            initial_len = len(index_list)
+            while len(index_list) + (concrete_indices - initial_len) < max_dim:
+                index_list.append(slice(None))
+        else:
+            index_list.append(item)
+
+    if len(index_list) > max_dim:  # pragma: no cover
+        raise IndexError("too many indices for array")
+    while len(index_list) < max_dim:
+        index_list.append(slice(None))
+
+    # return index_list, newaxis_at
+    return tuple(index_list)
+
+
+def _handle_aliases(index_tup: Tuple, alias_lookup) -> Tuple:
+    new_index_tup = []
+
+    def resolve(item, dim):
+        if isinstance(item, slice):
+            return item
+        # Replace element if in alias lookup, otherwise use original.
+        item = alias_lookup.get(dim, item, item)
+        return item
+
+    # Go through each element within the index and resolve if needed.
+    for dim, item in enumerate(index_tup):
+        if isinstance(item, list):
+            new_item = []
+            for sub_item in item:
+                new_item.append(resolve(sub_item, dim))
+        else:
+            new_item = resolve(item, dim)
+        new_index_tup.append(new_item)
+
+    return tuple(new_index_tup)
+
+
+class Tracked(AtomicSlicer):
+    """ Tracked defines an object that slicer wraps."""
+
+    def __init__(self, o: Any, dim: Union[int, List, tuple, None, str] = "auto"):
+        """ Defines an object that will be wrapped by slicer.
+
+        Args:
+            o: Object that will be tracked for slicer.
+            dim: Target dimension(s) slicer will index on for this object.
+        """
+        super().__init__(o)
+
+        # Protected attribute that can be overriden.
+        self._name = None
+
+        # Place dim into coordinate form.
+        if dim == "auto":
+            self.dim = list(range(self.max_dim))
+        elif dim is None:
+            self.dim = []
+        elif isinstance(dim, int):
+            self.dim = [dim]
+        elif isinstance(dim, list):
+            self.dim = dim
+        elif isinstance(dim, tuple):
+            self.dim = list(dim)
+        else:  # pragma: no cover
+            raise ValueError(f"Cannot handle dim of type: {type(dim)}")
+
+
+class Obj(Tracked):
+    """ An object that slicer wraps. """
+    def __init__(self, o, dim="auto"):
+        super().__init__(o, dim)
+
+
+class Alias(Tracked):
+    """ Defines a tracked object as well as additional __getitem__ keys. """
+    def __init__(self, o, dim):
+        if not (
+            isinstance(dim, int) or (isinstance(dim, (list, tuple)) and len(dim) <= 1)
+        ):  # pragma: no cover
+            raise ValueError("Aliases must track a single dimension")
+        super().__init__(o, dim)
+
+
+class AliasLookup:
+    def __init__(self, aliases):
+        self._lookup = {}
+
+        # Populate lookup and merge indexes.
+        for _, alias in aliases.items():
+            self.update(alias)
+
+    def update(self, alias):
+        if alias.dim is None or len(alias.dim) == 0:
+            return
+
+        dim = alias.dim[0]
+        if dim not in self._lookup:
+            self._lookup[dim] = {}
+
+        dim_lookup = self._lookup[dim]
+        # NOTE: Alias must be backed by either a list or dictionary.
+        itr = enumerate(alias.o) if isinstance(alias.o, list) else alias.o.items()
+        for i, x in itr:
+            if x not in dim_lookup:
+                dim_lookup[x] = set()
+            dim_lookup[x].add(i)
+
+    def delete(self, alias):
+        '''Delete an alias that exists from lookup'''
+        dim = alias.dim[0]
+        dim_lookup = self._lookup[dim]
+        # NOTE: Alias must be backed by either a list or dictionary.
+        itr = enumerate(alias.o) if isinstance(alias.o, list) else alias.o.items()
+        for i, x in itr:
+            del dim_lookup[x]
+
+    def get(self, dim, target, default=None):
+        if dim not in self._lookup:
+            return default
+
+        indexes = self._lookup[dim].get(target, None)
+        if indexes is None:
+            return default
+
+        if len(indexes) == 1:
+            return next(iter(indexes))
+        else:
+            return list(indexes)
+
+
+def resolve_dim(slicer_index: Tuple, slicer_dim: List) -> List:
+    """ Extracts new dim after applying slicing index and maps it back to the original index list. """
+
+    new_slicer_dim = []
+    reduced_mask = []
+
+    for _, curr_idx in enumerate(slicer_index):
+        if isinstance(curr_idx, (tuple, list, slice)):
+            reduced_mask.append(0)
+        else:
+            reduced_mask.append(1)
+
+    for curr_dim in slicer_dim:
+        if reduced_mask[curr_dim] == 0:
+            new_slicer_dim.append(curr_dim - sum(reduced_mask[:curr_dim]))
+
+    return new_slicer_dim
+
+
+def reduced_o(tracked: Tracked) -> Union[List, Any]:
+    os = [t.o for t in tracked]
+    os = os[0] if len(os) == 1 else os
+    return os
+
+
+class BaseHandler:
+    @classmethod
+    @abstractmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        raise NotImplementedError()  # pragma: no cover
+
+    @classmethod
+    @abstractmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        raise NotImplementedError()  # pragma: no cover
+
+    @classmethod
+    @abstractmethod
+    def max_dim(cls, o):
+        raise NotImplementedError()  # pragma: no cover
+
+    @classmethod
+    def default_alias(cls, o):
+        return []
+
+
+class SeriesHandler(BaseHandler):
+    @classmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        head_index = index_tup[0]
+        is_element = True if isinstance(head_index, int) else False
+        sliced_o = o.iloc[head_index]
+
+        return is_element, sliced_o, 1
+
+    @classmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        # NOTE: Series only has one dimension,
+        #       call slicer again to end the recursion.
+        return AtomicSlicer(o, max_dim=max_dim)[tail_index]
+
+    @classmethod
+    def max_dim(cls, o):
+        return len(o.shape)
+
+    @classmethod
+    def default_alias(cls, o):
+        index_alias = Alias(o.index.to_list(), 0)
+        index_alias._name = "index"
+        return [index_alias]
+
+
+class DataFrameHandler(BaseHandler):
+    @classmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        # NOTE: At head slice, we know there are two fixed dimensions.
+        cut_index = index_tup
+        is_element = True if isinstance(cut_index[-1], int) else False
+        sliced_o = o.iloc[cut_index]
+
+        return is_element, sliced_o, 2
+
+    @classmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        # NOTE: Dataframe has fixed dimensions,
+        #       call slicer again to end the recursion.
+        return AtomicSlicer(o, max_dim=max_dim)[tail_index]
+
+    @classmethod
+    def max_dim(cls, o):
+        return len(o.shape)
+
+    @classmethod
+    def default_alias(cls, o):
+        index_alias = Alias(o.index.to_list(), 0)
+        index_alias._name = "index"
+        column_alias = Alias(o.columns.to_list(), 1)
+        column_alias._name = "columns"
+        return [index_alias, column_alias]
+
+
+class ArrayHandler(BaseHandler):
+    @classmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        # Check if head is string
+        head_index, tail_index = index_tup[0], index_tup[1:]
+        cut = 1
+
+        for sub_index in tail_index:
+            if isinstance(sub_index, str) or cut == len(o.shape):
+                break
+            cut += 1
+
+        # Process native array dimensions
+        cut_index = index_tup[:cut]
+        is_element = any([True if isinstance(x, int) else False for x in cut_index])
+        sliced_o = o[cut_index]
+
+        return is_element, sliced_o, cut
+
+    @classmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        if flatten:
+            # NOTE: If we're dealing with a scipy matrix,
+            #       we have to manually flatten it ourselves
+            #       to keep consistent to the rest of slicer's API.
+            if _safe_isinstance(o, "scipy.sparse.csc", "csc_matrix"):
+                return AtomicSlicer(o.toarray().flatten(), max_dim=max_dim)[tail_index]
+            elif _safe_isinstance(o, "scipy.sparse.csr", "csr_matrix"):
+                return AtomicSlicer(o.toarray().flatten(), max_dim=max_dim)[tail_index]
+            elif _safe_isinstance(o, "scipy.sparse.dok", "dok_matrix"):
+                return AtomicSlicer(o.toarray().flatten(), max_dim=max_dim)[tail_index]
+            elif _safe_isinstance(o, "scipy.sparse.lil", "lil_matrix"):
+                return AtomicSlicer(o.toarray().flatten(), max_dim=max_dim)[tail_index]
+            else:
+                return AtomicSlicer(o, max_dim=max_dim)[tail_index]
+        else:
+            inner = [AtomicSlicer(e, max_dim=max_dim)[tail_index] for e in o]
+            if _safe_isinstance(o, "numpy", "ndarray"):
+                import numpy
+                if len(inner) > 0 and hasattr(inner[0], "__len__"):
+                    ragged = not all(len(x) == len(inner[0]) for x in inner)
+                else:
+                    ragged = False
+                if ragged:
+                    return numpy.array(inner, dtype=object)
+                else:
+                    return numpy.array(inner)
+            elif _safe_isinstance(o, "torch", "Tensor"):
+                import torch
+
+                if len(inner) > 0 and isinstance(inner[0], torch.Tensor):
+                    return torch.stack(inner)
+                else:
+                    return torch.tensor(inner)
+            elif _safe_isinstance(o, "scipy.sparse.csc", "csc_matrix"):
+                from scipy.sparse import vstack
+                out = vstack(inner, format='csc')
+                return out
+            elif _safe_isinstance(o, "scipy.sparse.csr", "csr_matrix"):
+                from scipy.sparse import vstack
+                out = vstack(inner, format='csr')
+                return out
+            elif _safe_isinstance(o, "scipy.sparse.dok", "dok_matrix"):
+                from scipy.sparse import vstack
+                out = vstack(inner, format='dok')
+                return out
+            elif _safe_isinstance(o, "scipy.sparse.lil", "lil_matrix"):
+                from scipy.sparse import vstack
+                out = vstack(inner, format='lil')
+                return out
+            else:
+                raise ValueError(f"Cannot handle type {type(o)}.")  # pragma: no cover
+
+    @classmethod
+    def max_dim(cls, o):
+        if _safe_isinstance(o, "numpy", "ndarray") and o.dtype == "object":
+            return max([UnifiedDataHandler.max_dim(x) for x in o], default=-1) + 1
+        else:
+            return len(o.shape)
+
+
+class DictHandler(BaseHandler):
+    @classmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        head_index = index_tup[0]
+        if isinstance(head_index, (tuple, list)) and len(index_tup) == 0:
+            return False, o, 1
+
+        if isinstance(head_index, (list, tuple)):
+            return (
+                False,
+                {
+                    sub_index: AtomicSlicer(o, max_dim=max_dim)[sub_index]
+                    for sub_index in head_index
+                },
+                1,
+            )
+        elif isinstance(head_index, slice):
+            if head_index == slice(None, None, None):
+                return False, o, 1
+            return False, o[head_index], 1
+        else:
+            return True, o[head_index], 1
+
+    @classmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        if flatten:
+            return AtomicSlicer(o, max_dim=max_dim)[tail_index]
+        else:
+            return {
+                k: AtomicSlicer(e, max_dim=max_dim)[tail_index] for k, e in o.items()
+            }
+
+    @classmethod
+    def max_dim(cls, o):
+        return max([UnifiedDataHandler.max_dim(x) for x in o.values()], default=-1) + 1
+
+
+class ListTupleHandler(BaseHandler):
+    @classmethod
+    def head_slice(cls, o, index_tup, max_dim):
+        head_index = index_tup[0]
+        if isinstance(head_index, (tuple, list)) and len(index_tup) == 0:
+            return False, o, 1
+
+        if isinstance(head_index, (list, tuple)):
+            if len(head_index) == 0:
+                return False, o, 1
+            else:
+                results = [
+                    AtomicSlicer(o, max_dim=max_dim)[sub_index]
+                    for sub_index in head_index
+                ]
+                results = tuple(results) if isinstance(o, tuple) else results
+                return False, results, 1
+        elif isinstance(head_index, slice):
+            return False, o[head_index], 1
+        elif isinstance(head_index, int):
+            return True, o[head_index], 1
+        else:  # pragma: no cover
+            raise ValueError(f"Invalid key {head_index} for {o}")
+
+    @classmethod
+    def tail_slice(cls, o, tail_index, max_dim, flatten=True):
+        if flatten:
+            return AtomicSlicer(o, max_dim=max_dim)[tail_index]
+        else:
+            results = [AtomicSlicer(e, max_dim=max_dim)[tail_index] for e in o]
+            return tuple(results) if isinstance(o, tuple) else results
+
+    @classmethod
+    def max_dim(cls, o):
+        return max([UnifiedDataHandler.max_dim(x) for x in o], default=-1) + 1
+
+
+class UnifiedDataHandler:
+    """ Registry that maps types to their unified slice calls."""
+
+    """ Class attribute that maps type to their unified slice calls."""
+    type_map = {
+        ("builtins", "list"): ListTupleHandler,
+        ("builtins", "tuple"): ListTupleHandler,
+        ("builtins", "dict"): DictHandler,
+        ("torch", "Tensor"): ArrayHandler,
+        ("numpy", "ndarray"): ArrayHandler,
+        ("scipy.sparse.csc", "csc_matrix"): ArrayHandler,
+        ("scipy.sparse.csr", "csr_matrix"): ArrayHandler,
+        ("scipy.sparse.dok", "dok_matrix"): ArrayHandler,
+        ("scipy.sparse.lil", "lil_matrix"): ArrayHandler,
+        ("pandas.core.frame", "DataFrame"): DataFrameHandler,
+        ("pandas.core.series", "Series"): SeriesHandler,
+    }
+
+    @classmethod
+    def slice(cls, o, index_tup, max_dim):
+        # NOTE: Unified handles base cases such as empty tuples, which
+        #       specialized handlers do not.
+        if isinstance(index_tup, (tuple, list)) and len(index_tup) == 0:
+            return o
+
+        # Slice as delegated by data handler.
+        o_type = _type_name(o)
+        head_slice = cls.type_map[o_type].head_slice
+        tail_slice = cls.type_map[o_type].tail_slice
+
+        is_element, sliced_o, cut = head_slice(o, index_tup, max_dim)
+        out = tail_slice(sliced_o, index_tup[cut:], max_dim - cut, is_element)
+        return out
+
+    @classmethod
+    def max_dim(cls, o):
+        o_type = _type_name(o)
+        if o_type not in cls.type_map:
+            return 0
+        return cls.type_map[o_type].max_dim(o)
+
+    @classmethod
+    def default_alias(cls, o):
+        o_type = _type_name(o)
+        if o_type not in cls.type_map:
+            return {}
+        return cls.type_map[o_type].default_alias(o)
+
+
+def _type_name(o: object) -> Tuple[str, str]:
+    return _handle_module_aliases(o.__class__.__module__), o.__class__.__name__
+
+
+def _safe_isinstance(
+    o: object, module_name: str, type_name: Union[str, set, tuple]
+) -> bool:
+    o_module, o_type = _type_name(o)
+    if isinstance(type_name, str):
+        return o_module == module_name and o_type == type_name
+    else:
+        return o_module == module_name and o_type in type_name
+
+
+def _handle_module_aliases(module_name):
+    # scipy modules such as "scipy.sparse.csc" were renamed to "scipy.sparse._csc" in v1.8
+    # Standardise by removing underscores for compatibility with either name
+    # Else just pass module name unchanged
+    module_map = {
+        "scipy.sparse._csc": "scipy.sparse.csc",
+        "scipy.sparse._csr": "scipy.sparse.csr",
+        "scipy.sparse._dok": "scipy.sparse.dok",
+        "scipy.sparse._lil": "scipy.sparse.lil",
+    }
+    return module_map.get(module_name, module_name)
diff --git a/shared/package-files/4e/4e4ac1b3190e3a239b8c11673792de91e0d58fb2790160d5f3d94449df46213c/portability.py b/shared/package-files/4e/4e4ac1b3190e3a239b8c11673792de91e0d58fb2790160d5f3d94449df46213c/portability.py
new file mode 100644
index 0000000000000000000000000000000000000000..69793adb60c2e4f1c5a86690445ffa0d10426cc2
--- /dev/null
+++ b/shared/package-files/4e/4e4ac1b3190e3a239b8c11673792de91e0d58fb2790160d5f3d94449df46213c/portability.py
@@ -0,0 +1,313 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+from __future__ import annotations
+
+import os
+import re
+import struct
+import subprocess
+import sys
+from logging import getLogger
+from os.path import basename, realpath
+
+from ..auxlib.ish import dals
+from ..base.constants import PREFIX_PLACEHOLDER
+from ..base.context import context
+from ..common.compat import on_linux, on_win
+from ..exceptions import BinaryPrefixReplacementError, CondaIOError
+from ..gateways.disk.update import CancelOperation, update_file_in_place_as_binary
+from ..models.enums import FileMode
+
+log = getLogger(__name__)
+
+
+# three capture groups: whole_shebang, executable, options
+SHEBANG_REGEX = (
+    rb"^(#!"  # pretty much the whole match string
+    rb"(?:[ ]*)"  # allow spaces between #! and beginning of the executable path
+    rb"(/(?:\\ |[^ \n\r\t])*)"  # the executable is the next text block without an escaped space or non-space whitespace character  # NOQA
+    rb"(.*)"  # the rest of the line can contain option flags
+    rb")$"
+)  # end whole_shebang group
+
+MAX_SHEBANG_LENGTH = 127 if on_linux else 512  # Not used on Windows
+
+# These are the most common file encodings that we run across when having to replace our
+# PREFIX_PLACEHOLDER string. They apply to binary and text formats.
+# More information/discussion: https://github.com/conda/conda/pull/9946
+POPULAR_ENCODINGS = (
+    "utf-8",
+    "utf-16-le",
+    "utf-16-be",
+    "utf-32-le",
+    "utf-32-be",
+)
+
+
+class _PaddingError(Exception):
+    pass
+
+
+def update_prefix(
+    path,
+    new_prefix,
+    placeholder=PREFIX_PLACEHOLDER,
+    mode=FileMode.text,
+    subdir=context.subdir,
+):
+    if on_win and mode == FileMode.text:
+        # force all prefix replacements to forward slashes to simplify need to escape backslashes
+        # replace with unix-style path separators
+        new_prefix = new_prefix.replace("\\", "/")
+
+    def _update_prefix(original_data):
+        # Step 1. do all prefix replacement
+        data = replace_prefix(mode, original_data, placeholder, new_prefix)
+
+        # Step 2. if the shebang is too long or the new prefix contains spaces, shorten it using
+        # /usr/bin/env trick -- NOTE: this trick assumes the environment WILL BE activated
+        if not on_win:
+            data = replace_long_shebang(mode, data)
+
+        # Step 3. if the before and after content is the same, skip writing
+        if data == original_data:
+            raise CancelOperation()
+
+        # Step 4. if we have a binary file, make sure the byte size is the same before
+        #         and after the update
+        if mode == FileMode.binary and len(data) != len(original_data):
+            raise BinaryPrefixReplacementError(
+                path, placeholder, new_prefix, len(original_data), len(data)
+            )
+
+        return data
+
+    updated = update_file_in_place_as_binary(realpath(path), _update_prefix)
+
+    if (
+        updated
+        and mode == FileMode.binary
+        and subdir == "osx-arm64"
+        and sys.platform == "darwin"
+    ):
+        # Apple arm64 needs signed executables
+        subprocess.run(
+            ["/usr/bin/codesign", "-s", "-", "-f", realpath(path)], capture_output=True
+        )
+
+
+def replace_prefix(
+    mode: FileMode, data: bytes, placeholder: str, new_prefix: str
+) -> bytes:
+    """
+    Replaces `placeholder` text with the `new_prefix` provided. The `mode` provided can
+    either be text or binary.
+
+    We use the `POPULAR_ENCODINGS` module level constant defined above to make several
+    passes at replacing the placeholder. We do this to account for as many encodings as
+    possible. If this causes any performance problems in the future, it could potentially
+    be removed (i.e. just using the most popular "utf-8" encoding").
+
+    More information/discussion available here: https://github.com/conda/conda/pull/9946
+    """
+    for encoding in POPULAR_ENCODINGS:
+        if mode == FileMode.text:
+            if not on_win:
+                # if new_prefix contains spaces, it might break the shebang!
+                # handle this by escaping the spaces early, which will trigger a
+                # /usr/bin/env replacement later on
+                newline_pos = data.find(b"\n")
+                if newline_pos > -1:
+                    shebang_line, rest_of_data = data[:newline_pos], data[newline_pos:]
+                    shebang_placeholder = f"#!{placeholder}".encode(encoding)
+                    if shebang_placeholder in shebang_line:
+                        escaped_shebang = f"#!{new_prefix}".replace(" ", "\\ ").encode(
+                            encoding
+                        )
+                        shebang_line = shebang_line.replace(
+                            shebang_placeholder, escaped_shebang
+                        )
+                        data = shebang_line + rest_of_data
+            # the rest of the file can be replaced normally
+            data = data.replace(
+                placeholder.encode(encoding), new_prefix.encode(encoding)
+            )
+        elif mode == FileMode.binary:
+            data = binary_replace(
+                data,
+                placeholder.encode(encoding),
+                new_prefix.encode(encoding),
+                encoding=encoding,
+            )
+        else:
+            raise CondaIOError("Invalid mode: %r" % mode)
+    return data
+
+
+def binary_replace(
+    data: bytes, search: bytes, replacement: bytes, encoding: str = "utf-8"
+) -> bytes:
+    """
+    Perform a binary replacement of `data`, where the placeholder `search` is
+    replaced with `replacement` and the remaining string is padded with null characters.
+    All input arguments are expected to be bytes objects.
+
+    Parameters
+    ----------
+    data:
+        The bytes object that will be searched and replaced
+    search:
+        The bytes object to find
+    replacement:
+        The bytes object that will replace `search`
+    encoding: str
+        The encoding of the expected string in the binary.
+    """
+    zeros = "\0".encode(encoding)
+    if on_win:
+        # on Windows for binary files, we currently only replace a pyzzer-type entry point
+        #   we skip all other prefix replacement
+        if has_pyzzer_entry_point(data):
+            return replace_pyzzer_entry_point_shebang(data, search, replacement)
+        else:
+            return data
+
+    def replace(match):
+        occurrences = match.group().count(search)
+        padding = (len(search) - len(replacement)) * occurrences
+        if padding < 0:
+            raise _PaddingError
+        return match.group().replace(search, replacement) + b"\0" * padding
+
+    original_data_len = len(data)
+    pat = re.compile(re.escape(search) + b"(?:(?!(?:" + zeros + b")).)*" + zeros)
+    data = pat.sub(replace, data)
+    assert len(data) == original_data_len
+
+    return data
+
+
+def has_pyzzer_entry_point(data):
+    pos = data.rfind(b"PK\x05\x06")
+    return pos >= 0
+
+
+def replace_pyzzer_entry_point_shebang(all_data, placeholder, new_prefix):
+    """Code adapted from pyzzer.  This is meant to deal with entry point exe's created by distlib,
+    which consist of a launcher, then a shebang, then a zip archive of the entry point code to run.
+    We need to change the shebang.
+    https://bitbucket.org/vinay.sajip/pyzzer/src/5d5740cb04308f067d5844a56fbe91e7a27efccc/pyzzer/__init__.py?at=default&fileviewer=file-view-default#__init__.py-112  # NOQA
+    """
+    # Copyright (c) 2013 Vinay Sajip.
+    #
+    # Permission is hereby granted, free of charge, to any person obtaining a copy
+    # of this software and associated documentation files (the "Software"), to deal
+    # in the Software without restriction, including without limitation the rights
+    # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    # copies of the Software, and to permit persons to whom the Software is
+    # furnished to do so, subject to the following conditions:
+    #
+    # The above copyright notice and this permission notice shall be included in
+    # all copies or substantial portions of the Software.
+    #
+    # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    # THE SOFTWARE.
+    launcher = shebang = None
+    pos = all_data.rfind(b"PK\x05\x06")
+    if pos >= 0:
+        end_cdr = all_data[pos + 12 : pos + 20]
+        cdr_size, cdr_offset = struct.unpack("<LL", end_cdr)
+        arc_pos = pos - cdr_size - cdr_offset
+        data = all_data[arc_pos:]
+        if arc_pos > 0:
+            pos = all_data.rfind(b"#!", 0, arc_pos)
+            if pos >= 0:
+                shebang = all_data[pos:arc_pos]
+                if pos > 0:
+                    launcher = all_data[:pos]
+
+        if data and shebang and launcher:
+            if hasattr(placeholder, "encode"):
+                placeholder = placeholder.encode("utf-8")
+            if hasattr(new_prefix, "encode"):
+                new_prefix = new_prefix.encode("utf-8")
+            shebang = shebang.replace(placeholder, new_prefix)
+            all_data = b"".join([launcher, shebang, data])
+    return all_data
+
+
+def replace_long_shebang(mode, data):
+    # this function only changes a shebang line if it exists and is greater than 127 characters
+    if mode == FileMode.text:
+        if not isinstance(data, bytes):
+            try:
+                data = bytes(data, encoding="utf-8")
+            except:
+                data = data.encode("utf-8")
+
+        shebang_match = re.match(SHEBANG_REGEX, data, re.MULTILINE)
+        if shebang_match:
+            whole_shebang, executable, options = shebang_match.groups()
+            prefix, executable_name = executable.decode("utf-8").rsplit("/", 1)
+            if len(whole_shebang) > MAX_SHEBANG_LENGTH or "\\ " in prefix:
+                new_shebang = (
+                    f"#!/usr/bin/env {executable_name}{options.decode('utf-8')}"
+                )
+                data = data.replace(whole_shebang, new_shebang.encode("utf-8"))
+
+    else:
+        # TODO: binary shebangs exist; figure this out in the future if text works well
+        pass
+    return data
+
+
+def generate_shebang_for_entry_point(executable, with_usr_bin_env=False):
+    """
+    This function can be used to generate a shebang line for Python entry points.
+
+    Use cases:
+    - At install/link time, to generate the `noarch: python` entry points.
+    - conda init uses it to create its own entry point during conda-build
+    """
+    shebang = f"#!{executable}\n"
+    if os.environ.get("CONDA_BUILD") == "1" and "/_h_env_placehold" in executable:
+        # This is being used during a conda-build process,
+        # which uses long prefixes on purpose. This will be replaced
+        # with the real environment prefix at install time. Do not
+        # do nothing for now.
+        return shebang
+
+    # In principle, the naive shebang will work as long as the path
+    # to the python executable does not contain spaces AND it's not
+    # longer than 127 characters. Otherwise, we must fix it
+    if len(shebang) > MAX_SHEBANG_LENGTH or " " in executable:
+        if with_usr_bin_env:
+            # This approach works well for all cases BUT it requires
+            # the executable to be in PATH. In other words, the environment
+            # needs to be activated!
+            shebang = f"#!/usr/bin/env {basename(executable)}\n"
+        else:
+            # This approach follows a method inspired by `pypa/distlib`
+            # https://github.com/pypa/distlib/blob/91aa92e64/distlib/scripts.py#L129
+            # Explanation: these lines are both valid Python and shell :)
+            # 1. Python will read it as a triple-quoted string; end of story
+            # 2. The shell will see:
+            #    * '' (empty string)
+            #    * 'exec' "path/with spaces/to/python" "this file" "arguments"
+            #    * # ''' (inline comment with three quotes, ignored by shell)
+            # This method works well BUT in some shells, $PS1 is dropped, which
+            # makes the prompt disappear. This is very problematic for the conda
+            # entry point! Details: https://github.com/conda/conda/issues/11885
+            shebang = dals(
+                f"""
+                #!/bin/sh
+                '''exec' "{executable}" "$0" "$@" #'''
+                """
+            )
+
+    return shebang
diff --git a/shared/package-files/4e/4e4af0c7ba8a2ea24826afee3f11b660345f1f7c10159ea4a1b4ad652d249cf2/test_query_eval.py b/shared/package-files/4e/4e4af0c7ba8a2ea24826afee3f11b660345f1f7c10159ea4a1b4ad652d249cf2/test_query_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffabf238a4884d3b7436c890da77e2700dc8bde4
--- /dev/null
+++ b/shared/package-files/4e/4e4af0c7ba8a2ea24826afee3f11b660345f1f7c10159ea4a1b4ad652d249cf2/test_query_eval.py
@@ -0,0 +1,1437 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    NumExprClobberingError,
+    UndefinedVariableError,
+)
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+
+@pytest.fixture(params=["python", "pandas"], ids=lambda x: x)
+def parser(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=["python", pytest.param("numexpr", marks=td.skip_if_no("numexpr"))],
+    ids=lambda x: x,
+)
+def engine(request):
+    return request.param
+
+
+def skip_if_no_pandas_parser(parser):
+    if parser != "pandas":
+        pytest.skip(f"cannot evaluate with parser={parser}")
+
+
+class TestCompat:
+    @pytest.fixture
+    def df(self):
+        return DataFrame({"A": [1, 2, 3]})
+
+    @pytest.fixture
+    def expected1(self, df):
+        return df[df.A > 0]
+
+    @pytest.fixture
+    def expected2(self, df):
+        return df.A + 1
+
+    def test_query_default(self, df, expected1, expected2):
+        # GH 12749
+        # this should always work, whether NUMEXPR_INSTALLED or not
+        result = df.query("A>0")
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1")
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_None(self, df, expected1, expected2):
+        result = df.query("A>0", engine=None)
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1", engine=None)
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_python(self, df, expected1, expected2):
+        result = df.query("A>0", engine="python")
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1", engine="python")
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_numexpr(self, df, expected1, expected2):
+        if NUMEXPR_INSTALLED:
+            result = df.query("A>0", engine="numexpr")
+            tm.assert_frame_equal(result, expected1)
+            result = df.eval("A+1", engine="numexpr")
+            tm.assert_series_equal(result, expected2, check_names=False)
+        else:
+            msg = (
+                r"'numexpr' is not installed or an unsupported version. "
+                r"Cannot use engine='numexpr' for query/eval if 'numexpr' is "
+                r"not installed"
+            )
+            with pytest.raises(ImportError, match=msg):
+                df.query("A>0", engine="numexpr")
+            with pytest.raises(ImportError, match=msg):
+                df.eval("A+1", engine="numexpr")
+
+
+class TestDataFrameEval:
+    # smaller hits python, larger hits numexpr
+    @pytest.mark.parametrize("n", [4, 4000])
+    @pytest.mark.parametrize(
+        "op_str,op,rop",
+        [
+            ("+", "__add__", "__radd__"),
+            ("-", "__sub__", "__rsub__"),
+            ("*", "__mul__", "__rmul__"),
+            ("/", "__truediv__", "__rtruediv__"),
+        ],
+    )
+    def test_ops(self, op_str, op, rop, n):
+        # tst ops and reversed ops in evaluation
+        # GH7198
+
+        df = DataFrame(1, index=range(n), columns=list("abcd"))
+        df.iloc[0] = 2
+        m = df.mean()
+
+        base = DataFrame(  # noqa: F841
+            np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
+        )
+
+        expected = eval(f"base {op_str} df")
+
+        # ops as strings
+        result = eval(f"m {op_str} df")
+        tm.assert_frame_equal(result, expected)
+
+        # these are commutative
+        if op in ["+", "*"]:
+            result = getattr(df, op)(m)
+            tm.assert_frame_equal(result, expected)
+
+        # these are not
+        elif op in ["-", "/"]:
+            result = getattr(df, rop)(m)
+            tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_sub_numexpr_path(self):
+        # GH7192: Note we need a large number of rows to ensure this
+        #  goes through the numexpr path
+        df = DataFrame({"A": np.random.default_rng(2).standard_normal(25000)})
+        df.iloc[0:5] = np.nan
+        expected = 1 - np.isnan(df.iloc[0:25])
+        result = (1 - np.isnan(df)).iloc[0:25]
+        tm.assert_frame_equal(result, expected)
+
+    def test_query_non_str(self):
+        # GH 11485
+        df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "b"]})
+
+        msg = "expr must be a string to be evaluated"
+        with pytest.raises(ValueError, match=msg):
+            df.query(lambda x: x.B == "b")
+
+        with pytest.raises(ValueError, match=msg):
+            df.query(111)
+
+    def test_query_empty_string(self):
+        # GH 13139
+        df = DataFrame({"A": [1, 2, 3]})
+
+        msg = "expr cannot be an empty string"
+        with pytest.raises(ValueError, match=msg):
+            df.query("")
+
+    def test_eval_resolvers_as_list(self):
+        # GH 14095
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab")
+        )
+        dict1 = {"a": 1}
+        dict2 = {"b": 2}
+        assert df.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
+        assert pd.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
+
+    def test_eval_resolvers_combined(self):
+        # GH 34966
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab")
+        )
+        dict1 = {"c": 2}
+
+        # Both input and default index/column resolvers should be usable
+        result = df.eval("a + b * c", resolvers=[dict1])
+
+        expected = df["a"] + df["b"] * dict1["c"]
+        tm.assert_series_equal(result, expected)
+
+    def test_eval_object_dtype_binop(self):
+        # GH#24883
+        df = DataFrame({"a1": ["Y", "N"]})
+        res = df.eval("c = ((a1 == 'Y') & True)")
+        expected = DataFrame({"a1": ["Y", "N"], "c": [True, False]})
+        tm.assert_frame_equal(res, expected)
+
+    def test_extension_array_eval(self, engine, parser, request):
+        # GH#58748
+        if engine == "numexpr":
+            mark = pytest.mark.xfail(
+                reason="numexpr does not support extension array dtypes"
+            )
+            request.applymarker(mark)
+        df = DataFrame({"a": pd.array([1, 2, 3]), "b": pd.array([4, 5, 6])})
+        result = df.eval("a / b", engine=engine, parser=parser)
+        expected = Series(pd.array([0.25, 0.40, 0.50]))
+        tm.assert_series_equal(result, expected)
+
+    def test_complex_eval(self, engine, parser):
+        # GH#21374
+        df = DataFrame({"a": [1 + 2j], "b": [1 + 1j]})
+        result = df.eval("a/b", engine=engine, parser=parser)
+        expected = Series([1.5 + 0.5j])
+        tm.assert_series_equal(result, expected)
+
+
+class TestDataFrameQueryWithMultiIndex:
+    def test_query_with_named_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.random.default_rng(2).choice(["eggs", "ham"], size=10)
+        index = MultiIndex.from_arrays([a, b], names=["color", "food"])
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        ind = Series(
+            df.index.get_level_values("color").values, index=index, name="color"
+        )
+
+        # equality
+        res1 = df.query('color == "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" == color', parser=parser, engine=engine)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('color != "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" != color', parser=parser, engine=engine)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('color == ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] == color', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('color != ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] != color', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["red"] in color', parser=parser, engine=engine)
+        res2 = df.query('"red" in color', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["red"] not in color', parser=parser, engine=engine)
+        res2 = df.query('"red" not in color', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+    def test_query_with_unnamed_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.random.default_rng(2).choice(["eggs", "ham"], size=10)
+        index = MultiIndex.from_arrays([a, b])
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+
+        res1 = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" == ilevel_0', parser=parser, engine=engine)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" != ilevel_0', parser=parser, engine=engine)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('ilevel_0 == ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] == ilevel_0', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('ilevel_0 != ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] != ilevel_0', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["red"] in ilevel_0', parser=parser, engine=engine)
+        res2 = df.query('"red" in ilevel_0', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["red"] not in ilevel_0', parser=parser, engine=engine)
+        res2 = df.query('"red" not in ilevel_0', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # ## LEVEL 1
+        ind = Series(df.index.get_level_values(1).values, index=index)
+        res1 = df.query('ilevel_1 == "eggs"', parser=parser, engine=engine)
+        res2 = df.query('"eggs" == ilevel_1', parser=parser, engine=engine)
+        exp = df[ind == "eggs"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('ilevel_1 != "eggs"', parser=parser, engine=engine)
+        res2 = df.query('"eggs" != ilevel_1', parser=parser, engine=engine)
+        exp = df[ind != "eggs"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('ilevel_1 == ["eggs"]', parser=parser, engine=engine)
+        res2 = df.query('["eggs"] == ilevel_1', parser=parser, engine=engine)
+        exp = df[ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('ilevel_1 != ["eggs"]', parser=parser, engine=engine)
+        res2 = df.query('["eggs"] != ilevel_1', parser=parser, engine=engine)
+        exp = df[~ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["eggs"] in ilevel_1', parser=parser, engine=engine)
+        res2 = df.query('"eggs" in ilevel_1', parser=parser, engine=engine)
+        exp = df[ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["eggs"] not in ilevel_1', parser=parser, engine=engine)
+        res2 = df.query('"eggs" not in ilevel_1', parser=parser, engine=engine)
+        exp = df[~ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+    def test_query_with_partially_named_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.arange(10)
+        index = MultiIndex.from_arrays([a, b])
+        index.names = [None, "rating"]
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        res = df.query("rating == 1", parser=parser, engine=engine)
+        ind = Series(
+            df.index.get_level_values("rating").values, index=index, name="rating"
+        )
+        exp = df[ind == 1]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query("rating != 1", parser=parser, engine=engine)
+        ind = Series(
+            df.index.get_level_values("rating").values, index=index, name="rating"
+        )
+        exp = df[ind != 1]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res, exp)
+
+    def test_query_multiindex_get_index_resolvers(self):
+        df = DataFrame(
+            np.ones((10, 3)),
+            index=MultiIndex.from_arrays(
+                [range(10) for _ in range(2)], names=["spam", "eggs"]
+            ),
+        )
+        resolvers = df._get_index_resolvers()
+
+        def to_series(mi, level):
+            level_values = mi.get_level_values(level)
+            s = level_values.to_series()
+            s.index = mi
+            return s
+
+        col_series = df.columns.to_series()
+        expected = {
+            "index": df.index,
+            "columns": col_series,
+            "spam": to_series(df.index, "spam"),
+            "eggs": to_series(df.index, "eggs"),
+            "clevel_0": col_series,
+        }
+        for k, v in resolvers.items():
+            if isinstance(v, Index):
+                assert v.is_(expected[k])
+            elif isinstance(v, Series):
+                tm.assert_series_equal(v, expected[k])
+            else:
+                raise AssertionError("object must be a Series or Index")
+
+
+@td.skip_if_no("numexpr")
+class TestDataFrameQueryNumExprPandas:
+    @pytest.fixture
+    def engine(self):
+        return "numexpr"
+
+    @pytest.fixture
+    def parser(self):
+        return "pandas"
+
+    def test_date_query_with_attribute_access(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query(
+            "@df.dates1 < 20130101 < @df.dates3", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_no_attribute_access(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_NaT(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates2"] = date_range("1/1/2013", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT(self, engine, parser):
+        n = 10
+        # Cast to object to avoid implicit cast when setting entry to pd.NaT below
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype(
+            {0: object}
+        )
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.iloc[0, 0] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT_duplicates(self, engine, parser):
+        n = 10
+        d = {}
+        d["dates1"] = date_range("1/1/2012", periods=n)
+        d["dates3"] = date_range("1/1/2014", periods=n)
+        df = DataFrame(d)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index.to_series() < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_non_date(self, engine, parser):
+        n = 10
+        df = DataFrame(
+            {"dates": date_range("1/1/2012", periods=n), "nondate": np.arange(n)}
+        )
+
+        result = df.query("dates == nondate", parser=parser, engine=engine)
+        assert len(result) == 0
+
+        result = df.query("dates != nondate", parser=parser, engine=engine)
+        tm.assert_frame_equal(result, df)
+
+        msg = r"Invalid comparison between dtype=datetime64\[ns\] and ndarray"
+        for op in ["<", ">", "<=", ">="]:
+            with pytest.raises(TypeError, match=msg):
+                df.query(f"dates {op} nondate", parser=parser, engine=engine)
+
+    def test_query_syntax_error(self, engine, parser):
+        df = DataFrame({"i": range(10), "+": range(3, 13), "r": range(4, 14)})
+        msg = "invalid syntax"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("i - +", engine=engine, parser=parser)
+
+    def test_query_scope(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 2)), columns=list("ab")
+        )
+
+        a, b = 1, 2  # noqa: F841
+        res = df.query("a > b", engine=engine, parser=parser)
+        expected = df[df.a > df.b]
+        tm.assert_frame_equal(res, expected)
+
+        res = df.query("@a > b", engine=engine, parser=parser)
+        expected = df[a > df.b]
+        tm.assert_frame_equal(res, expected)
+
+        # no local variable c
+        with pytest.raises(
+            UndefinedVariableError, match="local variable 'c' is not defined"
+        ):
+            df.query("@a > b > @c", engine=engine, parser=parser)
+
+        # no column named 'c'
+        with pytest.raises(UndefinedVariableError, match="name 'c' is not defined"):
+            df.query("@a > b > c", engine=engine, parser=parser)
+
+    def test_query_doesnt_pickup_local(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        # we don't pick up the local 'sin'
+        with pytest.raises(UndefinedVariableError, match="name 'sin' is not defined"):
+            df.query("sin > 5", engine=engine, parser=parser)
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        msg = "Variables in expression.+"
+        with pytest.raises(NumExprClobberingError, match=msg):
+            df.query("sin > 5", engine=engine, parser=parser)
+
+    def test_query(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=["a", "b", "c"]
+        )
+
+        tm.assert_frame_equal(
+            df.query("a < b", engine=engine, parser=parser), df[df.a < df.b]
+        )
+        tm.assert_frame_equal(
+            df.query("a + b > b * c", engine=engine, parser=parser),
+            df[df.a + df.b > df.b * df.c],
+        )
+
+    def test_query_index_with_name(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).integers(10, size=(10, 3)),
+            index=Index(range(10), name="blob"),
+            columns=["a", "b", "c"],
+        )
+        res = df.query("(blob < 5) & (a < b)", engine=engine, parser=parser)
+        expec = df[(df.index < 5) & (df.a < df.b)]
+        tm.assert_frame_equal(res, expec)
+
+        res = df.query("blob < b", engine=engine, parser=parser)
+        expec = df[df.index < df.b]
+
+        tm.assert_frame_equal(res, expec)
+
+    def test_query_index_without_name(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).integers(10, size=(10, 3)),
+            index=range(10),
+            columns=["a", "b", "c"],
+        )
+
+        # "index" should refer to the index
+        res = df.query("index < b", engine=engine, parser=parser)
+        expec = df[df.index < df.b]
+        tm.assert_frame_equal(res, expec)
+
+        # test against a scalar
+        res = df.query("index < 5", engine=engine, parser=parser)
+        expec = df[df.index < 5]
+        tm.assert_frame_equal(res, expec)
+
+    def test_nested_scope(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        expected = df[(df > 0) & (df2 > 0)]
+
+        result = df.query("(@df > 0) & (@df2 > 0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval("df[df > 0 and df2 > 0]", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval(
+            "df[df > 0 and df2 > 0 and df[df > 0] > 0]", engine=engine, parser=parser
+        )
+        expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval("df[(df>0) & (df2>0)]", engine=engine, parser=parser)
+        expected = df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_raises_on_local_self_reference(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+
+        # can't reference ourself b/c we're a local so @ is necessary
+        with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
+            df.query("df > 0", engine=engine, parser=parser)
+
+    def test_local_syntax(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((100, 10)),
+            columns=list("abcdefghij"),
+        )
+        b = 1
+        expect = df[df.a < b]
+        result = df.query("a < @b", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expect)
+
+        expect = df[df.a < df.b]
+        result = df.query("a < b", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expect)
+
+    def test_chained_cmp_and_in(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        cols = list("abc")
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((100, len(cols))), columns=cols
+        )
+        res = df.query(
+            "a < b < c and a not in b not in c", engine=engine, parser=parser
+        )
+        ind = (df.a < df.b) & (df.b < df.c) & ~df.b.isin(df.a) & ~df.c.isin(df.b)
+        expec = df[ind]
+        tm.assert_frame_equal(res, expec)
+
+    def test_local_variable_with_in(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        a = Series(np.random.default_rng(2).integers(3, size=15), name="a")
+        b = Series(np.random.default_rng(2).integers(10, size=15), name="b")
+        df = DataFrame({"a": a, "b": b})
+
+        expected = df.loc[(df.b - 1).isin(a)]
+        result = df.query("b - 1 in a", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+        b = Series(np.random.default_rng(2).integers(10, size=15), name="b")
+        expected = df.loc[(b - 1).isin(a)]
+        result = df.query("@b - 1 in a", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+    def test_at_inside_string(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        c = 1  # noqa: F841
+        df = DataFrame({"a": ["a", "a", "b", "b", "@c", "@c"]})
+        result = df.query('a == "@c"', engine=engine, parser=parser)
+        expected = df[df.a == "@c"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_query_undefined_local(self):
+        engine, parser = self.engine, self.parser
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(np.random.default_rng(2).random((10, 2)), columns=list("ab"))
+        with pytest.raises(
+            UndefinedVariableError, match="local variable 'c' is not defined"
+        ):
+            df.query("a == @c", engine=engine, parser=parser)
+
+    def test_index_resolvers_come_after_columns_with_the_same_name(
+        self, engine, parser
+    ):
+        n = 1  # noqa: F841
+        a = np.r_[20:101:20]
+
+        df = DataFrame(
+            {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)}
+        )
+        df.index.name = "index"
+        result = df.query("index > 5", engine=engine, parser=parser)
+        expected = df[df["index"] > 5]
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)}
+        )
+        result = df.query("ilevel_0 > 5", engine=engine, parser=parser)
+        expected = df.loc[df.index[df.index > 5]]
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame({"a": a, "b": np.random.default_rng(2).standard_normal(a.size)})
+        df.index.name = "a"
+        result = df.query("a > 5", engine=engine, parser=parser)
+        expected = df[df.a > 5]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.query("index > 5", engine=engine, parser=parser)
+        expected = df.loc[df.index[df.index > 5]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("op, f", [["==", operator.eq], ["!=", operator.ne]])
+    def test_inf(self, op, f, engine, parser):
+        n = 10
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).random(n),
+                "b": np.random.default_rng(2).random(n),
+            }
+        )
+        df.loc[::2, 0] = np.inf
+        q = f"a {op} inf"
+        expected = df[f(df.a, np.inf)]
+        result = df.query(q, engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+    def test_check_tz_aware_index_query(self, tz_aware_fixture):
+        # https://github.com/pandas-dev/pandas/issues/29463
+        tz = tz_aware_fixture
+        df_index = date_range(
+            start="2019-01-01", freq="1d", periods=10, tz=tz, name="time"
+        )
+        expected = DataFrame(index=df_index)
+        df = DataFrame(index=df_index)
+        result = df.query('"2018-01-03 00:00:00+00" < time')
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(df_index)
+        result = df.reset_index().query('"2018-01-03 00:00:00+00" < time')
+        tm.assert_frame_equal(result, expected)
+
+    def test_method_calls_in_query(self, engine, parser):
+        # https://github.com/pandas-dev/pandas/issues/22435
+        n = 10
+        df = DataFrame(
+            {
+                "a": 2 * np.random.default_rng(2).random(n),
+                "b": np.random.default_rng(2).random(n),
+            }
+        )
+        expected = df[df["a"].astype("int") == 0]
+        result = df.query("a.astype('int') == 0", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "a": np.where(
+                    np.random.default_rng(2).random(n) < 0.5,
+                    np.nan,
+                    np.random.default_rng(2).standard_normal(n),
+                ),
+                "b": np.random.default_rng(2).standard_normal(n),
+            }
+        )
+        expected = df[df["a"].notnull()]
+        result = df.query("a.notnull()", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numexpr")
+class TestDataFrameQueryNumExprPython(TestDataFrameQueryNumExprPandas):
+    @pytest.fixture
+    def engine(self):
+        return "numexpr"
+
+    @pytest.fixture
+    def parser(self):
+        return "python"
+
+    def test_date_query_no_attribute_access(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query(
+            "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_NaT(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates2"] = date_range("1/1/2013", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT
+        res = df.query(
+            "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query(
+            "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT(self, engine, parser):
+        n = 10
+        # Cast to object to avoid implicit cast when setting entry to pd.NaT below
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype(
+            {0: object}
+        )
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.iloc[0, 0] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query(
+            "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT_duplicates(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        msg = r"'BoolOp' nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+
+    def test_nested_scope(self, engine, parser):
+        # smoke test
+        x = 1  # noqa: F841
+        result = pd.eval("x + 1", engine=engine, parser=parser)
+        assert result == 2
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+
+        # don't have the pandas parser
+        msg = r"The '@' prefix is only supported by the pandas parser"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
+
+        with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
+            df.query("(df>0) & (df2>0)", engine=engine, parser=parser)
+
+        expected = df[(df > 0) & (df2 > 0)]
+        result = pd.eval("df[(df > 0) & (df2 > 0)]", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+        expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
+        result = pd.eval(
+            "df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]", engine=engine, parser=parser
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_query_numexpr_with_min_and_max_columns(self):
+        df = DataFrame({"min": [1, 2, 3], "max": [4, 5, 6]})
+        regex_to_match = (
+            r"Variables in expression \"\(min\) == \(1\)\" "
+            r"overlap with builtins: \('min'\)"
+        )
+        with pytest.raises(NumExprClobberingError, match=regex_to_match):
+            df.query("min == 1")
+
+        regex_to_match = (
+            r"Variables in expression \"\(max\) == \(1\)\" "
+            r"overlap with builtins: \('max'\)"
+        )
+        with pytest.raises(NumExprClobberingError, match=regex_to_match):
+            df.query("max == 1")
+
+
+class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas):
+    @pytest.fixture
+    def engine(self):
+        return "python"
+
+    @pytest.fixture
+    def parser(self):
+        return "pandas"
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        expected = df[df.index > 5]
+        result = df.query("sin > 5", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+
+class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython):
+    @pytest.fixture
+    def engine(self):
+        return "python"
+
+    @pytest.fixture
+    def parser(self):
+        return "python"
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        expected = df[df.index > 5]
+        result = df.query("sin > 5", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+
+class TestDataFrameQueryStrings:
+    def test_str_query_method(self, parser, engine):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"])
+        df["strings"] = Series(list("aabbccddee"))
+        expect = df[df.strings == "a"]
+
+        if parser != "pandas":
+            col = "strings"
+            lst = '"a"'
+
+            lhs = [col] * 2 + [lst] * 2
+            rhs = lhs[::-1]
+
+            eq, ne = "==", "!="
+            ops = 2 * ([eq] + [ne])
+            msg = r"'(Not)?In' nodes are not implemented"
+
+            for lhs, op, rhs in zip(lhs, ops, rhs):
+                ex = f"{lhs} {op} {rhs}"
+                with pytest.raises(NotImplementedError, match=msg):
+                    df.query(
+                        ex,
+                        engine=engine,
+                        parser=parser,
+                        local_dict={"strings": df.strings},
+                    )
+        else:
+            res = df.query('"a" == strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('strings == "a"', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+            tm.assert_frame_equal(res, df[df.strings.isin(["a"])])
+
+            expect = df[df.strings != "a"]
+            res = df.query('strings != "a"', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('"a" != strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+            tm.assert_frame_equal(res, df[~df.strings.isin(["a"])])
+
+    def test_str_list_query_method(self, parser, engine):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"])
+        df["strings"] = Series(list("aabbccddee"))
+        expect = df[df.strings.isin(["a", "b"])]
+
+        if parser != "pandas":
+            col = "strings"
+            lst = '["a", "b"]'
+
+            lhs = [col] * 2 + [lst] * 2
+            rhs = lhs[::-1]
+
+            eq, ne = "==", "!="
+            ops = 2 * ([eq] + [ne])
+            msg = r"'(Not)?In' nodes are not implemented"
+
+            for lhs, op, rhs in zip(lhs, ops, rhs):
+                ex = f"{lhs} {op} {rhs}"
+                with pytest.raises(NotImplementedError, match=msg):
+                    df.query(ex, engine=engine, parser=parser)
+        else:
+            res = df.query('strings == ["a", "b"]', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('["a", "b"] == strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            expect = df[~df.strings.isin(["a", "b"])]
+
+            res = df.query('strings != ["a", "b"]', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('["a", "b"] != strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+    def test_query_with_string_columns(self, parser, engine):
+        df = DataFrame(
+            {
+                "a": list("aaaabbbbcccc"),
+                "b": list("aabbccddeeff"),
+                "c": np.random.default_rng(2).integers(5, size=12),
+                "d": np.random.default_rng(2).integers(9, size=12),
+            }
+        )
+        if parser == "pandas":
+            res = df.query("a in b", parser=parser, engine=engine)
+            expec = df[df.a.isin(df.b)]
+            tm.assert_frame_equal(res, expec)
+
+            res = df.query("a in b and c < d", parser=parser, engine=engine)
+            expec = df[df.a.isin(df.b) & (df.c < df.d)]
+            tm.assert_frame_equal(res, expec)
+        else:
+            msg = r"'(Not)?In' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                df.query("a in b", parser=parser, engine=engine)
+
+            msg = r"'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                df.query("a in b and c < d", parser=parser, engine=engine)
+
+    def test_object_array_eq_ne(self, parser, engine):
+        df = DataFrame(
+            {
+                "a": list("aaaabbbbcccc"),
+                "b": list("aabbccddeeff"),
+                "c": np.random.default_rng(2).integers(5, size=12),
+                "d": np.random.default_rng(2).integers(9, size=12),
+            }
+        )
+        res = df.query("a == b", parser=parser, engine=engine)
+        exp = df[df.a == df.b]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query("a != b", parser=parser, engine=engine)
+        exp = df[df.a != df.b]
+        tm.assert_frame_equal(res, exp)
+
+    def test_query_with_nested_strings(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        events = [
+            f"page {n} {act}" for n in range(1, 4) for act in ["load", "exit"]
+        ] * 2
+        stamps1 = date_range("2014-01-01 0:00:01", freq="30s", periods=6)
+        stamps2 = date_range("2014-02-01 1:00:01", freq="30s", periods=6)
+        df = DataFrame(
+            {
+                "id": np.arange(1, 7).repeat(2),
+                "event": events,
+                "timestamp": stamps1.append(stamps2),
+            }
+        )
+
+        expected = df[df.event == '"page 1 load"']
+        res = df.query("""'"page 1 load"' in event""", parser=parser, engine=engine)
+        tm.assert_frame_equal(expected, res)
+
+    def test_query_with_nested_special_character(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame({"a": ["a", "b", "test & test"], "b": [1, 2, 3]})
+        res = df.query('a == "test & test"', parser=parser, engine=engine)
+        expec = df[df.a == "test & test"]
+        tm.assert_frame_equal(res, expec)
+
+    @pytest.mark.parametrize(
+        "op, func",
+        [
+            ["<", operator.lt],
+            [">", operator.gt],
+            ["<=", operator.le],
+            [">=", operator.ge],
+        ],
+    )
+    def test_query_lex_compare_strings(self, parser, engine, op, func):
+        a = Series(np.random.default_rng(2).choice(list("abcde"), 20))
+        b = Series(np.arange(a.size))
+        df = DataFrame({"X": a, "Y": b})
+
+        res = df.query(f'X {op} "d"', engine=engine, parser=parser)
+        expected = df[func(df.X, "d")]
+        tm.assert_frame_equal(res, expected)
+
+    def test_query_single_element_booleans(self, parser, engine):
+        columns = "bid", "bidsize", "ask", "asksize"
+        data = np.random.default_rng(2).integers(2, size=(1, len(columns))).astype(bool)
+        df = DataFrame(data, columns=columns)
+        res = df.query("bid & ask", engine=engine, parser=parser)
+        expected = df[df.bid & df.ask]
+        tm.assert_frame_equal(res, expected)
+
+    def test_query_string_scalar_variable(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame(
+            {
+                "Symbol": ["BUD US", "BUD US", "IBM US", "IBM US"],
+                "Price": [109.70, 109.72, 183.30, 183.35],
+            }
+        )
+        e = df[df.Symbol == "BUD US"]
+        symb = "BUD US"  # noqa: F841
+        r = df.query("Symbol == @symb", parser=parser, engine=engine)
+        tm.assert_frame_equal(e, r)
+
+    @pytest.mark.parametrize(
+        "in_list",
+        [
+            [None, "asdf", "ghjk"],
+            ["asdf", None, "ghjk"],
+            ["asdf", "ghjk", None],
+            [None, None, "asdf"],
+            ["asdf", None, None],
+            [None, None, None],
+        ],
+    )
+    def test_query_string_null_elements(self, in_list):
+        # GITHUB ISSUE #31516
+        parser = "pandas"
+        engine = "python"
+        expected = {i: value for i, value in enumerate(in_list) if value == "asdf"}
+
+        df_expected = DataFrame({"a": expected}, dtype="string")
+        df_expected.index = df_expected.index.astype("int64")
+        df = DataFrame({"a": in_list}, dtype="string")
+        res1 = df.query("a == 'asdf'", parser=parser, engine=engine)
+        res2 = df[df["a"] == "asdf"]
+        res3 = df.query("a <= 'asdf'", parser=parser, engine=engine)
+        tm.assert_frame_equal(res1, df_expected)
+        tm.assert_frame_equal(res1, res2)
+        tm.assert_frame_equal(res1, res3)
+        tm.assert_frame_equal(res2, res3)
+
+
+class TestDataFrameEvalWithFrame:
+    @pytest.fixture
+    def frame(self):
+        return DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=list("abc")
+        )
+
+    def test_simple_expr(self, frame, parser, engine):
+        res = frame.eval("a + b", engine=engine, parser=parser)
+        expect = frame.a + frame.b
+        tm.assert_series_equal(res, expect)
+
+    def test_bool_arith_expr(self, frame, parser, engine):
+        res = frame.eval("a[a < 1] + b", engine=engine, parser=parser)
+        expect = frame.a[frame.a < 1] + frame.b
+        tm.assert_series_equal(res, expect)
+
+    @pytest.mark.parametrize("op", ["+", "-", "*", "/"])
+    def test_invalid_type_for_operator_raises(self, parser, engine, op):
+        df = DataFrame({"a": [1, 2], "b": ["c", "d"]})
+        msg = r"unsupported operand type\(s\) for .+: '.+' and '.+'|Cannot"
+
+        with pytest.raises(TypeError, match=msg):
+            df.eval(f"a {op} b", engine=engine, parser=parser)
+
+
+class TestDataFrameQueryBacktickQuoting:
+    @pytest.fixture
+    def df(self):
+        """
+        Yields a dataframe with strings that may or may not need escaping
+        by backticks. The last two columns cannot be escaped by backticks
+        and should raise a ValueError.
+        """
+        yield DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B B": [3, 2, 1],
+                "C C": [4, 5, 6],
+                "C  C": [7, 4, 3],
+                "C_C": [8, 9, 10],
+                "D_D D": [11, 1, 101],
+                "E.E": [6, 3, 5],
+                "F-F": [8, 1, 10],
+                "1e1": [2, 4, 8],
+                "def": [10, 11, 2],
+                "A (x)": [4, 1, 3],
+                "B(x)": [1, 1, 5],
+                "B (x)": [2, 7, 4],
+                "  &^ :!€$?(} >    <++*''  ": [2, 5, 6],
+                "": [10, 11, 1],
+                " A": [4, 7, 9],
+                "  ": [1, 2, 1],
+                "it's": [6, 3, 1],
+                "that's": [9, 1, 8],
+                "☺": [8, 7, 6],
+                "foo#bar": [2, 4, 5],
+                1: [5, 7, 9],
+            }
+        )
+
+    def test_single_backtick_variable_query(self, df):
+        res = df.query("1 < `B B`")
+        expect = df[1 < df["B B"]]
+        tm.assert_frame_equal(res, expect)
+
+    def test_two_backtick_variables_query(self, df):
+        res = df.query("1 < `B B` and 4 < `C C`")
+        expect = df[(1 < df["B B"]) & (4 < df["C C"])]
+        tm.assert_frame_equal(res, expect)
+
+    def test_single_backtick_variable_expr(self, df):
+        res = df.eval("A + `B B`")
+        expect = df["A"] + df["B B"]
+        tm.assert_series_equal(res, expect)
+
+    def test_two_backtick_variables_expr(self, df):
+        res = df.eval("`B B` + `C C`")
+        expect = df["B B"] + df["C C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_already_underscore_variable(self, df):
+        res = df.eval("`C_C` + A")
+        expect = df["C_C"] + df["A"]
+        tm.assert_series_equal(res, expect)
+
+    def test_same_name_but_underscores(self, df):
+        res = df.eval("C_C + `C C`")
+        expect = df["C_C"] + df["C C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_mixed_underscores_and_spaces(self, df):
+        res = df.eval("A + `D_D D`")
+        expect = df["A"] + df["D_D D"]
+        tm.assert_series_equal(res, expect)
+
+    def test_backtick_quote_name_with_no_spaces(self, df):
+        res = df.eval("A + `C_C`")
+        expect = df["A"] + df["C_C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_special_characters(self, df):
+        res = df.eval("`E.E` + `F-F` - A")
+        expect = df["E.E"] + df["F-F"] - df["A"]
+        tm.assert_series_equal(res, expect)
+
+    def test_start_with_digit(self, df):
+        res = df.eval("A + `1e1`")
+        expect = df["A"] + df["1e1"]
+        tm.assert_series_equal(res, expect)
+
+    def test_keyword(self, df):
+        res = df.eval("A + `def`")
+        expect = df["A"] + df["def"]
+        tm.assert_series_equal(res, expect)
+
+    def test_unneeded_quoting(self, df):
+        res = df.query("`A` > 2")
+        expect = df[df["A"] > 2]
+        tm.assert_frame_equal(res, expect)
+
+    def test_parenthesis(self, df):
+        res = df.query("`A (x)` > 2")
+        expect = df[df["A (x)"] > 2]
+        tm.assert_frame_equal(res, expect)
+
+    def test_empty_string(self, df):
+        res = df.query("`` > 5")
+        expect = df[df[""] > 5]
+        tm.assert_frame_equal(res, expect)
+
+    def test_multiple_spaces(self, df):
+        res = df.query("`C  C` > 5")
+        expect = df[df["C  C"] > 5]
+        tm.assert_frame_equal(res, expect)
+
+    def test_start_with_spaces(self, df):
+        res = df.eval("` A` + `  `")
+        expect = df[" A"] + df["  "]
+        tm.assert_series_equal(res, expect)
+
+    def test_lots_of_operators_string(self, df):
+        res = df.query("`  &^ :!€$?(} >    <++*''  ` > 4")
+        expect = df[df["  &^ :!€$?(} >    <++*''  "] > 4]
+        tm.assert_frame_equal(res, expect)
+
+    def test_missing_attribute(self, df):
+        message = "module 'pandas' has no attribute 'thing'"
+        with pytest.raises(AttributeError, match=message):
+            df.eval("@pd.thing")
+
+    def test_failing_quote(self, df):
+        msg = r"(Could not convert ).*( to a valid Python identifier.)"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`it's` > `that's`")
+
+    def test_failing_character_outside_range(self, df):
+        msg = r"(Could not convert ).*( to a valid Python identifier.)"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`☺` > 4")
+
+    def test_failing_hashtag(self, df):
+        msg = "Failed to parse backticks"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`foo#bar` > 4")
+
+    def test_call_non_named_expression(self, df):
+        """
+        Only attributes and variables ('named functions') can be called.
+        .__call__() is not an allowed attribute because that would allow
+        calling anything.
+        https://github.com/pandas-dev/pandas/pull/32460
+        """
+
+        def func(*_):
+            return 1
+
+        funcs = [func]  # noqa: F841
+
+        df.eval("@func()")
+
+        with pytest.raises(TypeError, match="Only named functions are supported"):
+            df.eval("@funcs[0]()")
+
+        with pytest.raises(TypeError, match="Only named functions are supported"):
+            df.eval("@funcs[0].__call__()")
+
+    def test_ea_dtypes(self, any_numeric_ea_and_arrow_dtype):
+        # GH#29618
+        df = DataFrame(
+            [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype
+        )
+        warning = RuntimeWarning if NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.eval("c = b - a")
+        expected = DataFrame(
+            [[1, 2, 1], [3, 4, 1]],
+            columns=["a", "b", "c"],
+            dtype=any_numeric_ea_and_arrow_dtype,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_dtypes_and_scalar(self):
+        # GH#29618
+        df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"], dtype="Float64")
+        warning = RuntimeWarning if NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.eval("c = b - 1")
+        expected = DataFrame(
+            [[1, 2, 1], [3, 4, 3]], columns=["a", "b", "c"], dtype="Float64"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_dtypes_and_scalar_operation(self, any_numeric_ea_and_arrow_dtype):
+        # GH#29618
+        df = DataFrame(
+            [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype
+        )
+        result = df.eval("c = 2 - 1")
+        expected = DataFrame(
+            {
+                "a": Series([1, 3], dtype=any_numeric_ea_and_arrow_dtype),
+                "b": Series([2, 4], dtype=any_numeric_ea_and_arrow_dtype),
+                "c": Series([1, 1], dtype=result["c"].dtype),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"])
+    def test_query_ea_dtypes(self, dtype):
+        if dtype == "int64[pyarrow]":
+            pytest.importorskip("pyarrow")
+        # GH#50261
+        df = DataFrame({"a": Series([1, 2], dtype=dtype)})
+        ref = {2}  # noqa: F841
+        warning = RuntimeWarning if dtype == "Int64" and NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.query("a in @ref")
+        expected = DataFrame({"a": Series([2], dtype=dtype, index=[1])})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("engine", ["python", "numexpr"])
+    @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"])
+    def test_query_ea_equality_comparison(self, dtype, engine):
+        # GH#50261
+        warning = RuntimeWarning if engine == "numexpr" else None
+        if engine == "numexpr" and not NUMEXPR_INSTALLED:
+            pytest.skip("numexpr not installed")
+        if dtype == "int64[pyarrow]":
+            pytest.importorskip("pyarrow")
+        df = DataFrame(
+            {"A": Series([1, 1, 2], dtype="Int64"), "B": Series([1, 2, 2], dtype=dtype)}
+        )
+        with tm.assert_produces_warning(warning):
+            result = df.query("A == B", engine=engine)
+        expected = DataFrame(
+            {
+                "A": Series([1, 2], dtype="Int64", index=[0, 2]),
+                "B": Series([1, 2], dtype=dtype, index=[0, 2]),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_all_nat_in_object(self):
+        # GH#57068
+        now = pd.Timestamp.now("UTC")  # noqa: F841
+        df = DataFrame({"a": pd.to_datetime([None, None], utc=True)}, dtype=object)
+        result = df.query("a > @now")
+        expected = DataFrame({"a": []}, dtype=object)
+        tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/4e/4e4d98d50d9c386e97577310c66da73ffc28e9f40dafbd33ddfdc3f14fbc68a5/test_local_projection.py b/shared/package-files/4e/4e4d98d50d9c386e97577310c66da73ffc28e9f40dafbd33ddfdc3f14fbc68a5/test_local_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..b889fb3e2751daa731daec548fa76ec55666c604
--- /dev/null
+++ b/shared/package-files/4e/4e4d98d50d9c386e97577310c66da73ffc28e9f40dafbd33ddfdc3f14fbc68a5/test_local_projection.py
@@ -0,0 +1,591 @@
+"""
+Tests for LocalProjections (Jordà 2005) impulse response estimator.
+
+Reference
+---------
+Jordà, Ò. (2005). Estimation and Inference of Impulse Responses by Local
+Projections. American Economic Review, 95(1), 161-182.
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+
+import statsmodels.api as sm
+from statsmodels.tsa.vector_ar.local_proj import (
+    LocalProjections,
+    LocalProjectionsResults,
+    _hac_bandwidth,
+    _nw_cov,
+)
+
+# ---------------------------------------------------------------------------
+# DGP helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_var1(T=300, n=2, seed=42):
+    """Simulate a stable VAR(1) with known coefficient matrix A."""
+    rng = np.random.default_rng(seed)
+    if n == 2:
+        A = np.array([[0.5, 0.2], [0.0, 0.4]])  # lower-triangular, stable
+    else:
+        A = np.tril(rng.uniform(0, 1 / (n + 1), size=(n, n)))
+        max_eig = np.linalg.eigvalsh(A).max()
+        # scale to ensure stability
+        A *= 0.9 / max_eig
+    y = np.zeros((T, n))
+    e = rng.standard_normal((T, n))
+    for t in range(1, T):
+        y[t] = A @ y[t - 1] + e[t]
+    return y, A
+
+
+def _make_ar1(T=500, phi=0.6, seed=0):
+    """Simulate a univariate AR(1): y_t = phi * y_{t-1} + e_t."""
+    rng = np.random.default_rng(seed)
+    e = rng.standard_normal(T)
+    y = np.zeros(T)
+    for t in range(1, T):
+        y[t] = phi * y[t - 1] + e[t]
+    return y
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: helper functions
+# ---------------------------------------------------------------------------
+
+
+def test_hac_bandwidth_zero_horizon():
+    bw = _hac_bandwidth(0)
+    assert bw >= 1
+
+
+def test_hac_bandwidth_positive_horizon():
+    bw = _hac_bandwidth(5)
+    assert bw >= 5
+
+
+def test_hac_bandwidth_override():
+    assert _hac_bandwidth(10, nw_lags=3) == 3
+
+
+def test_nw_cov_shape():
+    rng = np.random.default_rng(0)
+    T, k = 100, 4
+    X = rng.standard_normal((T, k))
+    resid = rng.standard_normal(T)
+    V = _nw_cov(X, resid, nlags=3)
+    assert V.shape == (k, k)
+
+
+def test_nw_cov_symmetric():
+    rng = np.random.default_rng(1)
+    X = rng.standard_normal((80, 3))
+    resid = rng.standard_normal(80)
+    V = _nw_cov(X, resid, nlags=4)
+    assert_allclose(V, V.T, atol=1e-12)
+
+
+def test_nw_cov_psd():
+    rng = np.random.default_rng(2)
+    X = rng.standard_normal((100, 3))
+    resid = rng.standard_normal(100)
+    V = _nw_cov(X, resid, nlags=5)
+    eigvals = np.linalg.eigvalsh(V)
+    assert np.all(eigvals >= -1e-10)
+
+
+# ---------------------------------------------------------------------------
+# Constructor validation
+# ---------------------------------------------------------------------------
+
+
+def test_init_1d_endog():
+    rs = np.random.default_rng(238291)
+    y = rs.standard_normal(100)
+    lp = LocalProjections(y)
+    assert lp.endog.shape == (100, 1)
+
+
+def test_init_2d_endog():
+    rs = np.random.default_rng(238291)
+    y = rs.standard_normal((150, 3))
+    lp = LocalProjections(y, shock_idx=1)
+    assert lp.endog.shape == (150, 3)
+
+
+def test_init_default_shock_idx():
+    rs = np.random.default_rng(238291)
+    lp = LocalProjections(rs.standard_normal((100, 2)))
+    assert lp.shock_idx == [0]
+
+
+def test_init_multiple_shocks():
+    rs = np.random.default_rng(238291)
+    lp = LocalProjections(rs.standard_normal((100, 3)), shock_idx=[0, 2])
+    assert lp.shock_idx == [0, 2]
+
+
+def test_init_int_shock_idx():
+    rs = np.random.default_rng(238291)
+    lp = LocalProjections(rs.standard_normal((100, 3)), shock_idx=2)
+    assert lp.shock_idx == [2]
+
+
+def test_init_bad_shock_idx_raises():
+    rs = np.random.default_rng(238291)
+    with pytest.raises(ValueError, match="out of range"):
+        LocalProjections(rs.standard_normal((100, 2)), shock_idx=5)
+
+
+def test_init_negative_lags_raises():
+    rs = np.random.default_rng(238291)
+    with pytest.raises(ValueError, match="lags"):
+        LocalProjections(rs.standard_normal((100, 2)), lags=-1)
+
+
+def test_init_negative_horizons_raises():
+    rs = np.random.default_rng(238291)
+    with pytest.raises(ValueError, match="horizons"):
+        LocalProjections(rs.standard_normal((100, 2)), horizons=-1)
+
+
+def test_init_bad_trend_raises():
+    rs = np.random.default_rng(238291)
+    with pytest.raises(ValueError, match="trend"):
+        LocalProjections(rs.standard_normal((100, 2)), trend="x")
+
+
+def test_init_exog_wrong_length_raises():
+    rs = np.random.default_rng(238291)
+    with pytest.raises(ValueError, match="exog"):
+        LocalProjections(rs.standard_normal((100, 2)), exog=rs.standard_normal((50, 2)))
+
+
+def test_init_too_few_obs_raises():
+    rs = np.random.default_rng(238291)
+    y = rs.standard_normal((10, 2))
+    with pytest.raises(ValueError, match="Not enough"):
+        LocalProjections(y, lags=5, horizons=10).fit()
+
+
+def test_nobs_computed_correctly():
+    T, lags, horizons = 200, 4, 8
+    rs = np.random.default_rng(238291)
+    lp = LocalProjections(rs.standard_normal((T, 2)), lags=lags, horizons=horizons)
+    assert lp._nobs == T - lags - horizons
+
+
+# ---------------------------------------------------------------------------
+# IRF shape and basic properties
+# ---------------------------------------------------------------------------
+
+
+def test_fit_returns_results_object():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=5).fit()
+    assert isinstance(res, LocalProjectionsResults)
+
+
+def test_irfs_shape_univariate():
+    y = _make_ar1()
+    res = LocalProjections(y, lags=2, horizons=8).fit()
+    assert res.irfs.shape == (9, 1, 1)
+
+
+def test_irfs_shape_multivariate():
+    rng = np.random.default_rng(7)
+    y = rng.standard_normal((200, 3))
+    res = LocalProjections(y, lags=1, horizons=6).fit()
+    assert res.irfs.shape == (7, 3, 1)
+
+
+def test_irfs_shape_multiple_shocks():
+    y, _ = _make_var1(T=300, n=2)
+    res = LocalProjections(y, shock_idx=[0, 1], lags=1, horizons=5).fit()
+    assert res.irfs.shape == (6, 2, 2)
+
+
+def test_stderr_shape_matches_irfs():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=2, horizons=6).fit()
+    assert res.stderr.shape == res.irfs.shape
+
+
+def test_stderr_nonnegative():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=2, horizons=6).fit()
+    assert np.all(res.stderr >= 0)
+
+
+# ---------------------------------------------------------------------------
+# Accuracy: LP-IRF vs. true VAR(1) MA representation
+# ---------------------------------------------------------------------------
+
+
+def test_h0_response_equals_one_by_construction():
+    """At horizon 0, response of variable i to its own shock is 1
+    because the shock IS the contemporaneous value."""
+    y, _ = _make_var1(T=500)
+    res = LocalProjections(y, shock_idx=0, lags=2, horizons=0).fit()
+    # y0's response to shock in y0 at h=0 must be 1 (OLS on current value).
+    assert_allclose(res.irfs[0, 0, 0], 1.0, atol=1e-10)
+
+
+def test_ar1_irfs_match_phi_powers():
+    """LP-IRF for AR(1) at horizon h should be close to phi^h.
+
+    LP estimators are consistent but have higher variance than the indirect
+    VAR-based IRF, so we use a large sample and a generous tolerance.
+    """
+    phi = 0.6
+    y = _make_ar1(T=5000, phi=phi, seed=7)
+    res = LocalProjections(y, shock_idx=0, lags=1, horizons=6).fit()
+    true_irfs = phi ** np.arange(7)
+    assert_allclose(res.irfs[:, 0, 0], true_irfs, atol=0.08)
+
+
+def test_var1_irfs_match_ma_coefficients():
+    """LP-IRF at h should be close to A^h[:, shock_idx] for a VAR(1)."""
+    y, A = _make_var1(T=2000, seed=123)
+    res = LocalProjections(y, shock_idx=0, lags=1, horizons=6).fit()
+    Ah = np.eye(2)
+    for h in range(7):
+        assert_allclose(res.irfs[h, :, 0], Ah[:, 0], atol=0.08)
+        Ah = A @ Ah
+
+
+# ---------------------------------------------------------------------------
+# Confidence intervals
+# ---------------------------------------------------------------------------
+
+
+def test_conf_int_shape():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=5).fit()
+    ci = res.conf_int(alpha=0.05)
+    assert ci.shape == (6, 2, 1, 2)
+
+
+def test_conf_int_lower_le_irf_le_upper():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=5).fit()
+    ci = res.conf_int(alpha=0.05)
+    assert np.all(ci[..., 0] <= res.irfs)
+    assert np.all(res.irfs <= ci[..., 1])
+
+
+def test_conf_int_width_increases_with_alpha():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=5).fit()
+    width_90 = res.conf_int(alpha=0.10)[..., 1] - res.conf_int(alpha=0.10)[..., 0]
+    width_95 = res.conf_int(alpha=0.05)[..., 1] - res.conf_int(alpha=0.05)[..., 0]
+    assert np.all(width_95 >= width_90)
+
+
+# ---------------------------------------------------------------------------
+# Cumulative effects
+# ---------------------------------------------------------------------------
+
+
+def test_cumulative_effects_shape():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=6).fit()
+    ce = res.cumulative_effects()
+    assert ce.shape == res.irfs.shape
+
+
+def test_cumulative_effects_h0_equals_irf_h0():
+    y, _ = _make_var1()
+    res = LocalProjections(y, lags=1, horizons=6).fit()
+    assert_allclose(res.cumulative_effects()[0], res.irfs[0], atol=1e-12)
+
+
+def test_cumulative_effects_monotone_for_positive_irf():
+    """If all IRFs are positive, cumsum must be non-decreasing."""
+    # Use an AR(1) with positive phi so all IRFs are positive.
+    y = _make_ar1(T=1000, phi=0.7, seed=5)
+    res = LocalProjections(y, shock_idx=0, lags=1, horizons=6).fit()
+    if np.all(res.irfs >= 0):
+        ce = res.cumulative_effects()
+        assert np.all(np.diff(ce[:, 0, 0]) >= -1e-10)
+
+
+# ---------------------------------------------------------------------------
+# Trend specifications
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("trend", ["n", "c", "ct"])
+def test_fit_with_all_trend_types(trend):
+    y, _ = _make_var1(T=250)
+    res = LocalProjections(y, lags=1, horizons=5, trend=trend).fit()
+    assert res.irfs.shape == (6, 2, 1)
+    assert np.all(np.isfinite(res.irfs))
+
+
+# ---------------------------------------------------------------------------
+# Exogenous controls
+# ---------------------------------------------------------------------------
+
+
+def test_fit_with_exog():
+    rng = np.random.default_rng(10)
+    T = 200
+    y = rng.standard_normal((T, 2))
+    exog = rng.standard_normal((T, 2))
+    res = LocalProjections(y, lags=1, horizons=4, exog=exog).fit()
+    assert res.irfs.shape == (5, 2, 1)
+    assert np.all(np.isfinite(res.irfs))
+
+
+# ---------------------------------------------------------------------------
+# 1-D vs. 2-D-single-column input equivalence
+#
+# endog and exog can each be supplied as a 1-D array or as a 2-D array with
+# a single column; both must be accepted, normalized to shape (T, 1), and
+# -- for a given underlying dataset -- must produce numerically identical
+# fitted results regardless of which encoding was used.
+# ---------------------------------------------------------------------------
+
+
+def _shape_variants(rng, T):
+    """A single-variable dataset in both its 1-D and 2-D(1 col) encodings."""
+    x_1d = rng.standard_normal(T)
+    return {"1d": x_1d, "2d": x_1d[:, None]}
+
+
+@pytest.mark.parametrize("endog_kind", ["1d", "2d"])
+@pytest.mark.parametrize("exog_kind", ["none", "1d", "2d"])
+def test_single_column_shape_combinations(endog_kind, exog_kind):
+    rng = np.random.default_rng(20240917)
+    T = 150
+    endog_variants = _shape_variants(rng, T)
+    exog_variants = _shape_variants(rng, T)
+
+    endog = endog_variants[endog_kind]
+    exog = None if exog_kind == "none" else exog_variants[exog_kind]
+
+    lp = LocalProjections(endog, lags=1, horizons=4, exog=exog)
+    assert lp.endog.shape == (T, 1)
+    if exog is None:
+        assert lp.exog is None
+    else:
+        assert lp.exog.shape == (T, 1)
+
+    res = lp.fit()
+    assert res.irfs.shape == (5, 1, 1)
+    assert res.stderr.shape == (5, 1, 1)
+    assert np.all(np.isfinite(res.irfs))
+    assert np.all(np.isfinite(res.stderr))
+
+
+def test_single_column_shape_combinations_agree():
+    """The 1-D/2-D(1 col) distinction must be purely cosmetic: reshaping
+    either endog or exog from 1-D to 2-D(1 col) -- with the underlying data
+    held fixed -- must not change the fitted result. (Comparing across
+    different *exog* content, e.g. "none" vs. "1d", is intentionally not
+    done here: including exog is a genuinely different regression and is
+    not expected to agree with excluding it.)"""
+    rng = np.random.default_rng(20240917)
+    T = 150
+    endog_variants = _shape_variants(rng, T)
+    exog_variants = _shape_variants(rng, T)
+
+    results = {}
+    for endog_kind in ("1d", "2d"):
+        for exog_kind in ("none", "1d", "2d"):
+            exog = None if exog_kind == "none" else exog_variants[exog_kind]
+            lp = LocalProjections(
+                endog_variants[endog_kind], lags=1, horizons=4, exog=exog
+            )
+            results[(endog_kind, exog_kind)] = lp.fit()
+
+    # Reshaping endog (1d vs. 2d) must not matter, for each fixed exog.
+    for exog_kind in ("none", "1d", "2d"):
+        assert_allclose(
+            results[("1d", exog_kind)].irfs,
+            results[("2d", exog_kind)].irfs,
+            atol=1e-10,
+        )
+        assert_allclose(
+            results[("1d", exog_kind)].stderr,
+            results[("2d", exog_kind)].stderr,
+            atol=1e-10,
+        )
+
+    # Reshaping exog (1d vs. 2d) must not matter, for each fixed endog.
+    for endog_kind in ("1d", "2d"):
+        assert_allclose(
+            results[(endog_kind, "1d")].irfs,
+            results[(endog_kind, "2d")].irfs,
+            atol=1e-10,
+        )
+        assert_allclose(
+            results[(endog_kind, "1d")].stderr,
+            results[(endog_kind, "2d")].stderr,
+            atol=1e-10,
+        )
+
+
+# ---------------------------------------------------------------------------
+# nw_lags override
+# ---------------------------------------------------------------------------
+
+
+def test_nw_lags_override():
+    y, _ = _make_var1(T=300)
+    res1 = LocalProjections(y, lags=1, horizons=5, nw_lags=3).fit()
+    res2 = LocalProjections(y, lags=1, horizons=5, nw_lags=6).fit()
+    # IRF point estimates must be identical (same OLS).
+    assert_allclose(res1.irfs, res2.irfs, atol=1e-12)
+    # SEs will differ.
+    assert not np.allclose(res1.stderr, res2.stderr)
+
+
+# ---------------------------------------------------------------------------
+# Zero-lag model
+# ---------------------------------------------------------------------------
+
+
+def test_zero_lags():
+    y, _ = _make_var1(T=200)
+    res = LocalProjections(y, lags=0, horizons=4).fit()
+    assert res.irfs.shape == (5, 2, 1)
+    assert np.all(np.isfinite(res.irfs))
+
+
+# ---------------------------------------------------------------------------
+# Pandas input
+# ---------------------------------------------------------------------------
+
+
+def test_pandas_dataframe_input():
+    import pandas as pd
+
+    y, _ = _make_var1(T=200)
+    df = pd.DataFrame(y, columns=["gdp", "infl"])
+    lp = LocalProjections(df, shock_idx=0, lags=1, horizons=4)
+    assert lp.endog_names == ["gdp", "infl"]
+    assert lp.shock_names == ["gdp"]
+    res = lp.fit()
+    assert res.irfs.shape == (5, 2, 1)
+
+
+# ---------------------------------------------------------------------------
+# Repr
+# ---------------------------------------------------------------------------
+
+
+def test_repr():
+    y, _ = _make_var1(T=200)
+    res = LocalProjections(y, lags=1, horizons=6).fit()
+    r = repr(res)
+    assert "LocalProjectionsResults" in r
+    assert "horizons=6" in r
+
+
+@pytest.mark.matplotlib
+def test_plot_irfs(close_figures):
+    import matplotlib.figure
+
+    y, _ = _make_var1(T=200, n=4)
+    res = LocalProjections(y, lags=1, horizons=6).fit()
+    fig = res.plot_irfs()
+    assert isinstance(fig, matplotlib.figure.Figure)
+
+    res = LocalProjections(y, lags=1, shock_idx=[0, 1], horizons=6).fit()
+    fig = res.plot_irfs()
+    assert isinstance(fig, matplotlib.figure.Figure)
+
+
+# ---------------------------------------------------------------------------
+# Cross-check against an independent R implementation.
+#
+# statsmodels/tsa/vector_ar/tests/results/results_local_proj.R replicates
+# the LocalProjections regression construction (_build_regressors / fit)
+# using base R's lm() for OLS and sandwich::NeweyWest() for the HAC
+# covariance, on the real macrodata series already used in
+# docs/source/vector_ar.rst. The R script also cross-checks its own
+# hand-rolled Newey-West formula against sandwich::NeweyWest directly and
+# confirms they agree exactly, so this is a genuine independent check of
+# both the OLS point estimates and the HAC standard errors, not just two
+# copies of the same formula.
+# ---------------------------------------------------------------------------
+
+
+def _macrodata_log_diff(columns):
+    mdata = sm.datasets.macrodata.load_pandas().data
+    return np.log(mdata[columns]).diff().dropna()
+
+
+def test_against_r_base_case():
+    endog = _macrodata_log_diff(["realgdp", "realcons", "realinv"])
+    res = LocalProjections(
+        endog.values, shock_idx=0, lags=2, horizons=4, trend="c", nw_lags=4
+    ).fit()
+
+    # From results_local_proj.R, "Base case irfs"/"Base case stderr"
+    # (rows: horizon 0..4; columns: realgdp, realcons, realinv).
+    r_irfs = np.array(
+        [
+            [1.00000000000, 0.52364991703, 3.8482300128],
+            [0.17178466121, 0.09970743380, 1.1253122078],
+            [0.18322583380, 0.07627130443, 0.7568887880],
+            [-0.03403189708, 0.07068916493, -0.5903675451],
+            [0.08371270402, -0.05310649472, 0.6617451473],
+        ]
+    )
+    r_stderr = np.array(
+        [
+            [0.0, 0.06609432437, 0.3147000764],
+            [0.08948095300, 0.06672041817, 0.5282957309],
+            [0.07638810462, 0.05843115593, 0.4409788183],
+            [0.08192150214, 0.06149871824, 0.4797675369],
+            [0.07948064894, 0.06632669683, 0.3442574755],
+        ]
+    )
+
+    assert_allclose(res.irfs[:, :, 0], r_irfs, atol=1e-6)
+    # h=0 own-response stderr is ~0 by construction (perfect fit, see
+    # test_h0_response_equals_one_by_construction); both R and Python only
+    # agree on that entry up to floating-point noise, not to atol=1e-6.
+    assert_allclose(res.stderr[1:, :, 0], r_stderr[1:], atol=1e-6)
+    assert res.stderr[0, 0, 0] < 1e-8
+
+
+def test_against_r_with_exog():
+    endog = _macrodata_log_diff(["realgdp"])
+    exog = _macrodata_log_diff(["realgovt"])
+    res = LocalProjections(
+        endog.values,
+        shock_idx=0,
+        lags=1,
+        horizons=3,
+        trend="c",
+        nw_lags=3,
+        exog=exog.values,
+    ).fit()
+
+    # From results_local_proj.R, "Exog case irfs"/"Exog case stderr".
+    r_irfs = np.array(
+        [
+            [1.00000000000],
+            [0.28437561944],
+            [0.25751285158],
+            [0.07016128423],
+        ]
+    )
+    r_stderr = np.array(
+        [
+            [0.0],
+            [0.07910772625],
+            [0.08614744242],
+            [0.08139100144],
+        ]
+    )
+
+    assert_allclose(res.irfs[:, :, 0], r_irfs, atol=1e-6)
+    assert_allclose(res.stderr[1:, :, 0], r_stderr[1:], atol=1e-6)
+    assert res.stderr[0, 0, 0] < 1e-8
diff --git a/shared/package-files/4e/4e4e6ffb89096ee54aed592fb85f031a327b171e869dc476efa249a8035c51dc/bmm_cpu_dispatch.h b/shared/package-files/4e/4e4e6ffb89096ee54aed592fb85f031a327b171e869dc476efa249a8035c51dc/bmm_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..73dc06fa7266e150a023e6b3730f4e004eb0b551
--- /dev/null
+++ b/shared/package-files/4e/4e4e6ffb89096ee54aed592fb85f031a327b171e869dc476efa249a8035c51dc/bmm_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2);
+TORCH_API at::Tensor & bmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2);
+TORCH_API at::Tensor & bmm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/4e/4e4f0c554786bd7bc0d82f96f824b6601a8a9b8c70bbe52c9e811e795ab445b5/embedding_dense_backward_ops.h b/shared/package-files/4e/4e4f0c554786bd7bc0d82f96f824b6601a8a9b8c70bbe52c9e811e795ab445b5/embedding_dense_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4a5b482c1e32b02753b284b83855a5f84e4297e2
--- /dev/null
+++ b/shared/package-files/4e/4e4f0c554786bd7bc0d82f96f824b6601a8a9b8c70bbe52c9e811e795ab445b5/embedding_dense_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API embedding_dense_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::SymInt, c10::SymInt, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::embedding_dense_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq);
+};
+
+struct TORCH_API embedding_dense_backward_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, c10::SymInt, c10::SymInt, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::embedding_dense_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/4e/4e4ff3bf19cffaed46ee5551a02a8928bf99ed2aa5684dd6df8e8f3033d1bbe1/quantized_lowerings.py b/shared/package-files/4e/4e4ff3bf19cffaed46ee5551a02a8928bf99ed2aa5684dd6df8e8f3033d1bbe1/quantized_lowerings.py
new file mode 100644
index 0000000000000000000000000000000000000000..97818a6cb7e923e3aee5a0dcf929e6fd5ea17b85
--- /dev/null
+++ b/shared/package-files/4e/4e4ff3bf19cffaed46ee5551a02a8928bf99ed2aa5684dd6df8e8f3033d1bbe1/quantized_lowerings.py
@@ -0,0 +1,15 @@
+import torch
+
+
+def register_quantized_ops():
+    from . import lowering
+
+    quantized = torch.ops.quantized
+
+    lowering.add_needs_realized_inputs(
+        [
+            quantized.max_pool2d,
+        ]
+    )
+
+    lowering.make_fallback(quantized.max_pool2d)
diff --git a/shared/package-files/4e/4e57f9fec257652f834ad94ab09432fdee5bb83fd6ce5a05043338c5b39ddb33/arima211_css_results.py b/shared/package-files/4e/4e57f9fec257652f834ad94ab09432fdee5bb83fd6ce5a05043338c5b39ddb33/arima211_css_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f3552b0a2f9045df3188b36dc0af373d3c4ef59
--- /dev/null
+++ b/shared/package-files/4e/4e57f9fec257652f834ad94ab09432fdee5bb83fd6ce5a05043338c5b39ddb33/arima211_css_results.py
@@ -0,0 +1,1315 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+llf = np.array([-240.29558272688])
+
+nobs = np.array([202])
+
+k = np.array([5])
+
+k_exog = np.array([1])
+
+sigma = np.array([.79494581155191])
+
+chi2 = np.array([1213.6019521322])
+
+df_model = np.array([3])
+
+k_ar = np.array([2])
+
+k_ma = np.array([1])
+
+params = np.array([
+    .72428568600554,
+    1.1464248419014,
+    -.17024528879204,
+    -.87113675466923,
+    .63193884330392])
+
+cov_params = np.array([
+    .31218565961764,
+    -.01618380799341,
+    .00226345462929,
+    .01386291798401,
+    -.0036338799176,
+    -.01618380799341,
+    .00705713030623,
+    -.00395404914463,
+    -.00685704952799,
+    -.00018629958479,
+    .00226345462929,
+    -.00395404914463,
+    .00255884492061,
+    .00363586332269,
+    .00039879711931,
+    .01386291798401,
+    -.00685704952799,
+    .00363586332269,
+    .00751765532203,
+    .00008982556101,
+    -.0036338799176,
+    -.00018629958479,
+    .00039879711931,
+    .00008982556101,
+    .00077550533053]).reshape(5, 5)
+
+xb = np.array([
+    .72428566217422,
+    .72428566217422,
+    .56208884716034,
+    .53160965442657,
+    .45030161738396,
+    .45229381322861,
+    .38432359695435,
+    .40517011284828,
+    .36063131690025,
+    .30754271149635,
+    .32044330239296,
+    .29408219456673,
+    .27966624498367,
+    .29743707180023,
+    .25011941790581,
+    .27747189998627,
+    .24822402000427,
+    .23426930606365,
+    .27233305573463,
+    .23524768650532,
+    .26427435874939,
+    .21787133812904,
+    .22461311519146,
+    .22853142023087,
+    .24335558712482,
+    .22953669726849,
+    .25524401664734,
+    .22482520341873,
+    .26450532674789,
+    .31863233447075,
+    .27352628111839,
+    .33670437335968,
+    .25623551011086,
+    .28701293468475,
+    .315819054842,
+    .3238864839077,
+    .35844340920448,
+    .34399557113647,
+    .40348997712135,
+    .39373970031738,
+    .4022718667984,
+    .46476069092751,
+    .45762005448341,
+    .46842387318611,
+    .50536489486694,
+    .52051961421967,
+    .47866532206535,
+    .50378143787384,
+    .50863671302795,
+    .4302790760994,
+    .49568024277687,
+    .44652271270752,
+    .43774726986885,
+    .43010330200195,
+    .42344436049461,
+    .44517293572426,
+    .47460499405861,
+    .62086409330368,
+    .52550911903381,
+    .77532315254211,
+    .78466820716858,
+    .85438597202301,
+    .87056696414948,
+    1.0393311977386,
+    .99110960960388,
+    .85202795267105,
+    .91560190916061,
+    .89238166809082,
+    .88917690515518,
+    .72121334075928,
+    .84221452474594,
+    .8454754948616,
+    .82078683376312,
+    .95394861698151,
+    .84718400239944,
+    .839300096035,
+    .91501939296722,
+    .95743554830551,
+    1.0874761343002,
+    1.1326615810394,
+    1.1169674396515,
+    1.3300451040268,
+    1.4790810346603,
+    1.5027786493301,
+    1.7226468324661,
+    1.8395622968674,
+    1.5940405130386,
+    1.694568157196,
+    1.8241587877274,
+    1.7037791013718,
+    1.838702917099,
+    1.7334734201431,
+    1.4791669845581,
+    1.3007366657257,
+    1.7364456653595,
+    1.2694935798645,
+    .96595168113708,
+    1.1405370235443,
+    1.1328836679459,
+    1.1091921329498,
+    1.171138882637,
+    1.1465038061142,
+    1.0319484472275,
+    1.055313706398,
+    .93150246143341,
+    1.0844472646713,
+    .93333613872528,
+    .93137633800507,
+    1.0778160095215,
+    .38748729228973,
+    .77933365106583,
+    .75266307592392,
+    .88410103321075,
+    .94100385904312,
+    .91849637031555,
+    .96046274900436,
+    .92494148015976,
+    .98310285806656,
+    1.0272513628006,
+    1.0762135982513,
+    1.0743116140366,
+    1.254854798317,
+    1.1723403930664,
+    1.0479376316071,
+    1.3550333976746,
+    1.2255589962006,
+    1.2870025634766,
+    1.6643482446671,
+    1.3312928676605,
+    1.0657893419266,
+    1.1804157495499,
+    1.1335761547089,
+    1.137326002121,
+    1.1235628128052,
+    1.1115798950195,
+    1.1286649703979,
+    1.0989991426468,
+    1.0626485347748,
+    .96542054414749,
+    1.0419135093689,
+    .93033194541931,
+    .95628559589386,
+    1.027433514595,
+    .98328214883804,
+    1.0063992738724,
+    1.0645687580109,
+    .94354963302612,
+    .95077443122864,
+    1.0226324796677,
+    1.089217543602,
+    .97552293539047,
+    1.0441918373108,
+    1.052937746048,
+    .86785578727722,
+    .82579529285431,
+    .95432937145233,
+    .79897737503052,
+    .68320548534393,
+    .85365778207779,
+    .78336101770401,
+    .80072748661041,
+    .9089440703392,
+    .82500487565994,
+    .98515397310257,
+    .96745657920837,
+    1.0962044000626,
+    1.195325255394,
+    1.0824474096298,
+    1.2239117622375,
+    1.0142554044724,
+    1.0399018526077,
+    .80796521902084,
+    .7145761847496,
+    1.0631860494614,
+    .86374056339264,
+    .98086261749268,
+    1.0528303384781,
+    .86123734712601,
+    .80300676822662,
+    .96200370788574,
+    1.0364016294479,
+    .98456978797913,
+    1.1556725502014,
+    1.2025715112686,
+    1.0507286787033,
+    1.312912106514,
+    1.0682457685471,
+    2.0334177017212,
+    1.0775905847549,
+    1.2798084020615,
+    1.461397767067,
+    .72960823774338,
+    1.2498733997345,
+    1.466894865036,
+    1.286082983017,
+    1.3903408050537,
+    1.8483582735062,
+    1.4685434103012,
+    2.3107523918152,
+    .7711226940155,
+    -.31598940491676,
+    .68151205778122,
+    1.0212944746017])
+
+y = np.array([
+    np.nan,
+    29.704284667969,
+    29.712087631226,
+    29.881610870361,
+    29.820302963257,
+    29.992294311523,
+    29.934322357178,
+    30.155170440674,
+    30.200632095337,
+    30.117542266846,
+    30.24044418335,
+    30.274082183838,
+    30.319667816162,
+    30.507436752319,
+    30.470119476318,
+    30.657470703125,
+    30.68822479248,
+    30.714269638062,
+    30.962333679199,
+    30.985248565674,
+    31.204275131226,
+    31.16787147522,
+    31.244613647461,
+    31.348531723022,
+    31.523355484009,
+    31.609535217285,
+    31.835243225098,
+    31.874824523926,
+    32.144504547119,
+    32.5986328125,
+    32.723526000977,
+    33.186702728271,
+    33.156238555908,
+    33.387012481689,
+    33.7158203125,
+    34.023887634277,
+    34.458442687988,
+    34.743995666504,
+    35.303489685059,
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+    36.102272033691,
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+    37.257617950439,
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+    38.405364990234,
+    39.020519256592,
+    39.378665924072,
+    39.903781890869,
+    40.408638000488,
+    40.530277252197,
+    41.095680236816,
+    41.346523284912,
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+    41.930103302002,
+    42.223442077637,
+    42.645172119141,
+    43.174606323242,
+    44.320865631104,
+    44.725509643555,
+    46.37532043457,
+    47.584667205811,
+    48.954383850098,
+    50.170566558838,
+    52.039329528809,
+    53.291107177734,
+    53.852027893066,
+    54.915603637695,
+    55.792385101318,
+    56.6891746521,
+    56.821212768555,
+    57.842212677002,
+    58.745475769043,
+    59.5207862854,
+    60.953948974609,
+    61.6471824646,
+    62.439296722412,
+    63.615020751953,
+    64.857437133789,
+    66.587478637695,
+    68.23265838623,
+    69.616966247559,
+    71.930046081543,
+    74.479080200195,
+    76.702774047852,
+    79.722648620605,
+    82.739562988281,
+    84.194038391113,
+    86.394561767578,
+    89.024154663086,
+    90.803779602051,
+    93.33869934082,
+    95.133476257324,
+    95.879165649414,
+    96.300735473633,
+    99.236442565918,
+    99.369491577148,
+    98.865951538086,
+    99.940536499023,
+    100.93288421631,
+    101.90919494629,
+    103.27114105225,
+    104.44651031494,
+    105.13195037842,
+    106.15531158447,
+    106.63150024414,
+    108.08444976807,
+    108.63333129883,
+    109.43137359619,
+    110.9778137207,
+    109.08748626709,
+    110.27933502197,
+    110.95265960693,
+    112.28410339355,
+    113.64099884033,
+    114.71849822998,
+    115.96046447754,
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+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688,
+    .63193881511688])
+
+stdp = np.array([
+    .72428566217422,
+    .72428566217422,
+    .56208884716034,
+    .53160965442657,
+    .45030161738396,
+    .45229381322861,
+    .38432359695435,
+    .40517011284828,
+    .36063131690025,
+    .30754271149635,
+    .32044330239296,
+    .29408219456673,
+    .27966624498367,
+    .29743707180023,
+    .25011941790581,
+    .27747189998627,
+    .24822402000427,
+    .23426930606365,
+    .27233305573463,
+    .23524768650532,
+    .26427435874939,
+    .21787133812904,
+    .22461311519146,
+    .22853142023087,
+    .24335558712482,
+    .22953669726849,
+    .25524401664734,
+    .22482520341873,
+    .26450532674789,
+    .31863233447075,
+    .27352628111839,
+    .33670437335968,
+    .25623551011086,
+    .28701293468475,
+    .315819054842,
+    .3238864839077,
+    .35844340920448,
+    .34399557113647,
+    .40348997712135,
+    .39373970031738,
+    .4022718667984,
+    .46476069092751,
+    .45762005448341,
+    .46842387318611,
+    .50536489486694,
+    .52051961421967,
+    .47866532206535,
+    .50378143787384,
+    .50863671302795,
+    .4302790760994,
+    .49568024277687,
+    .44652271270752,
+    .43774726986885,
+    .43010330200195,
+    .42344436049461,
+    .44517293572426,
+    .47460499405861,
+    .62086409330368,
+    .52550911903381,
+    .77532315254211,
+    .78466820716858,
+    .85438597202301,
+    .87056696414948,
+    1.0393311977386,
+    .99110960960388,
+    .85202795267105,
+    .91560190916061,
+    .89238166809082,
+    .88917690515518,
+    .72121334075928,
+    .84221452474594,
+    .8454754948616,
+    .82078683376312,
+    .95394861698151,
+    .84718400239944,
+    .839300096035,
+    .91501939296722,
+    .95743554830551,
+    1.0874761343002,
+    1.1326615810394,
+    1.1169674396515,
+    1.3300451040268,
+    1.4790810346603,
+    1.5027786493301,
+    1.7226468324661,
+    1.8395622968674,
+    1.5940405130386,
+    1.694568157196,
+    1.8241587877274,
+    1.7037791013718,
+    1.838702917099,
+    1.7334734201431,
+    1.4791669845581,
+    1.3007366657257,
+    1.7364456653595,
+    1.2694935798645,
+    .96595168113708,
+    1.1405370235443,
+    1.1328836679459,
+    1.1091921329498,
+    1.171138882637,
+    1.1465038061142,
+    1.0319484472275,
+    1.055313706398,
+    .93150246143341,
+    1.0844472646713,
+    .93333613872528,
+    .93137633800507,
+    1.0778160095215,
+    .38748729228973,
+    .77933365106583,
+    .75266307592392,
+    .88410103321075,
+    .94100385904312,
+    .91849637031555,
+    .96046274900436,
+    .92494148015976,
+    .98310285806656,
+    1.0272513628006,
+    1.0762135982513,
+    1.0743116140366,
+    1.254854798317,
+    1.1723403930664,
+    1.0479376316071,
+    1.3550333976746,
+    1.2255589962006,
+    1.2870025634766,
+    1.6643482446671,
+    1.3312928676605,
+    1.0657893419266,
+    1.1804157495499,
+    1.1335761547089,
+    1.137326002121,
+    1.1235628128052,
+    1.1115798950195,
+    1.1286649703979,
+    1.0989991426468,
+    1.0626485347748,
+    .96542054414749,
+    1.0419135093689,
+    .93033194541931,
+    .95628559589386,
+    1.027433514595,
+    .98328214883804,
+    1.0063992738724,
+    1.0645687580109,
+    .94354963302612,
+    .95077443122864,
+    1.0226324796677,
+    1.089217543602,
+    .97552293539047,
+    1.0441918373108,
+    1.052937746048,
+    .86785578727722,
+    .82579529285431,
+    .95432937145233,
+    .79897737503052,
+    .68320548534393,
+    .85365778207779,
+    .78336101770401,
+    .80072748661041,
+    .9089440703392,
+    .82500487565994,
+    .98515397310257,
+    .96745657920837,
+    1.0962044000626,
+    1.195325255394,
+    1.0824474096298,
+    1.2239117622375,
+    1.0142554044724,
+    1.0399018526077,
+    .80796521902084,
+    .7145761847496,
+    1.0631860494614,
+    .86374056339264,
+    .98086261749268,
+    1.0528303384781,
+    .86123734712601,
+    .80300676822662,
+    .96200370788574,
+    1.0364016294479,
+    .98456978797913,
+    1.1556725502014,
+    1.2025715112686,
+    1.0507286787033,
+    1.312912106514,
+    1.0682457685471,
+    2.0334177017212,
+    1.0775905847549,
+    1.2798084020615,
+    1.461397767067,
+    .72960823774338,
+    1.2498733997345,
+    1.466894865036,
+    1.286082983017,
+    1.3903408050537,
+    1.8483582735062,
+    1.4685434103012,
+    2.3107523918152,
+    .7711226940155,
+    -.31598940491676,
+    .68151205778122,
+    1.0212944746017])
+
+icstats = np.array([
+    202,
+    np.nan,
+    -240.29558272688,
+    5,
+    490.59116545376,
+    507.13250394077])
+
+
+results = Bunch(
+    llf=llf,
+    nobs=nobs,
+    k=k,
+    k_exog=k_exog,
+    sigma=sigma,
+    chi2=chi2,
+    df_model=df_model,
+    k_ar=k_ar,
+    k_ma=k_ma,
+    params=params,
+    cov_params=cov_params,
+    xb=xb,
+    y=y,
+    resid=resid,
+    yr=yr,
+    mse=mse,
+    stdp=stdp,
+    icstats=icstats
+)
diff --git a/shared/package-files/4e/4e583b43fdf9bd4a731d70e074ee597aba03f3c8c36302bdc7e74650fb1fcc11/debugobj_r.py b/shared/package-files/4e/4e583b43fdf9bd4a731d70e074ee597aba03f3c8c36302bdc7e74650fb1fcc11/debugobj_r.py
new file mode 100644
index 0000000000000000000000000000000000000000..75e75ebe5acafcdffcff92fd385f6099c864b973
--- /dev/null
+++ b/shared/package-files/4e/4e583b43fdf9bd4a731d70e074ee597aba03f3c8c36302bdc7e74650fb1fcc11/debugobj_r.py
@@ -0,0 +1,41 @@
+from idlelib import rpc
+
+def remote_object_tree_item(item):
+    wrapper = WrappedObjectTreeItem(item)
+    oid = id(wrapper)
+    rpc.objecttable[oid] = wrapper
+    return oid
+
+class WrappedObjectTreeItem:
+    # Lives in PYTHON subprocess
+
+    def __init__(self, item):
+        self.__item = item
+
+    def __getattr__(self, name):
+        value = getattr(self.__item, name)
+        return value
+
+    def _GetSubList(self):
+        sub_list = self.__item._GetSubList()
+        return list(map(remote_object_tree_item, sub_list))
+
+class StubObjectTreeItem:
+    # Lives in IDLE process
+
+    def __init__(self, sockio, oid):
+        self.sockio = sockio
+        self.oid = oid
+
+    def __getattr__(self, name):
+        value = rpc.MethodProxy(self.sockio, self.oid, name)
+        return value
+
+    def _GetSubList(self):
+        sub_list = self.sockio.remotecall(self.oid, "_GetSubList", (), {})
+        return [StubObjectTreeItem(self.sockio, oid) for oid in sub_list]
+
+
+if __name__ == '__main__':
+    from unittest import main
+    main('idlelib.idle_test.test_debugobj_r', verbosity=2)
diff --git a/shared/package-files/4e/4e58f865450d271121bc0a28ed324aa96bf527bb4461a7f514431ecfe2bdc448/Ndjamena b/shared/package-files/4e/4e58f865450d271121bc0a28ed324aa96bf527bb4461a7f514431ecfe2bdc448/Ndjamena
new file mode 100644
index 0000000000000000000000000000000000000000..ecbc0966dc2dc01fd4f93139318eccc0dddce5a6
Binary files /dev/null and b/shared/package-files/4e/4e58f865450d271121bc0a28ed324aa96bf527bb4461a7f514431ecfe2bdc448/Ndjamena differ
diff --git a/shared/package-files/4e/4e5b08549722aa8dbd1d89c234db52eacfc85e3b3ec5c9ba2fe953bd0abee4ea/results_wpi1_ar3_stata.csv b/shared/package-files/4e/4e5b08549722aa8dbd1d89c234db52eacfc85e3b3ec5c9ba2fe953bd0abee4ea/results_wpi1_ar3_stata.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6126bd1eb8c0ee325aca0b917cc92f6646df7667
--- /dev/null
+++ b/shared/package-files/4e/4e5b08549722aa8dbd1d89c234db52eacfc85e3b3ec5c9ba2fe953bd0abee4ea/results_wpi1_ar3_stata.csv
@@ -0,0 +1 @@
+wpi,t,ln_wpi,dep1,sr1,sp1,sp2,sp3,sf1,sf2,sf3,sm1,sm2,sm3
30.7,1960q1,3.424263,,,,,,,,,,,
30.8,1960q2,3.427515,0,0.0794849,0,0,0,0.0999985,0.0785646,0.0712076,0.0999985,0.0431804,0.006482
30.7,1960q3,3.424263,0.0785646,-0.2294193,0.0785646,0.0999985,0.0785646,-0.0999985,0.0999985,0.0343236,-0.0999985,0.0999985,0.0431804
30.7,1960q4,3.424263,-0.0343236,0.0455185,-0.0343236,-0.0999985,0.0999985,-6.94E-18,-0.0999985,0.0999985,-6.94E-18,-0.0999985,0.0999985
30.8,1961q1,3.427515,0.0162772,0.1149192,0.0162772,0,-0.0999985,0.0999985,0,-0.0999985,0.0999985,0,-0.0999985
30.5,1961q2,3.417727,0.0269032,-0.4487194,0.0269032,0.0999985,0,-0.2999992,0.0999985,0,-0.2999992,0.0999985,0
30.5,1961q3,3.417727,-0.1485951,0.2039676,-0.1485951,-0.2999992,0.0999985,-2.78E-17,-0.2999992,0.0999985,-2.78E-17,-0.2999992,0.0999985
30.6,1961q4,3.421,-0.0027752,0.1410738,-0.0027752,0,-0.2999992,0.1000004,0,-0.2999992,0.1000004,0,-0.2999992
30.7,1962q1,3.424263,-0.0247031,0.171173,-0.0247031,0.1000004,0,0.1000004,0.1000004,0,0.1000004,0.1000004,0
30.6,1962q2,3.421,0.0622335,-0.2226887,0.0622335,0.1000004,0.1000004,-0.1000004,0.1000004,0.1000004,-0.1000004,0.1000004,0.1000004
30.7,1962q3,3.424263,-0.0173775,0.1611176,-0.0173775,-0.1000004,0.1000004,0.1000004,-0.1000004,0.1000004,0.1000004,-0.1000004,0.1000004
30.7,1962q4,3.424263,0.0689848,-0.0946914,0.0689848,0.1000004,-0.1000004,0,0.1000004,-0.1000004,0,0.1000004,-0.1000004
30.6,1963q1,3.421,-0.0162775,-0.1149214,-0.0162775,0,0.1000004,-0.1000004,0,0.1000004,-0.1000004,0,0.1000004
30.5,1963q2,3.417727,-0.0269037,-0.1003354,-0.0269037,-0.1000004,0,-0.1000004,-0.1000004,0,-0.1000004,-0.1000004,0
30.6,1963q3,3.421,-0.0622335,0.2226887,-0.0622335,-0.1000004,-0.1000004,0.1000004,-0.1000004,-0.1000004,0.1000004,-0.1000004,-0.1000004
30.7,1963q4,3.424263,0.0173775,0.1134114,0.0173775,0.1000004,-0.1000004,0.1000004,0.1000004,-0.1000004,0.1000004,0.1000004,-0.1000004
30.7,1964q1,3.424263,0.0364299,-0.0500051,0.0364299,0.1000004,0.1000004,0,0.1000004,0.1000004,0,0.1000004,0.1000004
30.6,1964q2,3.421,0.0353298,-0.1857596,0.0353298,0,0.1000004,-0.1000004,0,0.1000004,-0.1000004,0,0.1000004
30.7,1964q3,3.424263,-0.0269037,0.1741936,-0.0269037,-0.1000004,0,0.1000004,-0.1000004,0,0.1000004,-0.1000004,0
30.7,1964q4,3.424263,0.0431812,-0.0592722,0.0431812,0.1000004,-0.1000004,0,0.1000004,-0.1000004,0,0.1000004,-0.1000004
30.9,1965q1,3.430756,-0.0162775,0.2968695,-0.0162775,0,0.1000004,0.1999989,0,0.1000004,0.1999989,0,0.1000004
31.2,1965q2,3.440418,0.1312173,0.2316794,0.1312173,0.1999989,0,0.3000011,0.1999989,0,0.3000011,0.1999989,0
31.4,1965q3,3.446808,0.1771742,0.03133,0.1771742,0.3000011,0.1999989,0.1999989,0.3000011,0.1999989,0.1999989,0.3000011,0.1999989
31.6,1965q4,3.453157,0.185599,0.0197684,0.185599,0.1999989,0.3000011,0.2000008,0.1999989,0.3000011,0.2000008,0.1999989,0.3000011
32.1,1966q1,3.468856,0.2018778,0.4092119,0.2018778,0.2000008,0.1999989,0.4999981,0.2000008,0.1999989,0.4999981,0.2000008,0.1999989
32.2,1966q2,3.471967,0.3341939,-0.3214608,0.3341939,0.4999981,0.2000008,0.1000023,0.4999981,0.2000008,0.1000023,0.4999981,0.2000008
32.6,1966q3,3.484312,0.1519461,0.3404856,0.1519461,0.1000023,0.4999981,0.3999977,0.1000023,0.4999981,0.3999977,0.1000023,0.4999981
32.4,1966q4,3.478158,0.349371,-0.7540844,0.349371,0.3999977,0.1000023,-0.1999969,0.3999977,0.1000023,-0.1999969,0.3999977,0.1000023
32.3,1967q1,3.475067,-0.0415043,-0.0802967,-0.0415043,-0.1999969,0.3999977,-0.1000023,-0.1999969,0.3999977,-0.1000023,-0.1999969,0.3999977
32.3,1967q2,3.475067,0.0314533,-0.043174,0.0314533,-0.1000023,-0.1999969,0,-0.1000023,-0.1999969,0,-0.1000023,-0.1999969
32.4,1967q3,3.478158,-0.0611327,0.2211803,-0.0611327,0,-0.1000023,0.1000023,0,-0.1000023,0.1000023,0,-0.1000023
32.5,1967q4,3.48124,0.0269042,0.1003321,0.0269042,0.1000023,0,0.0999985,0.1000023,0,0.0999985,0.1000023,0
32.9,1968q1,3.493473,0.0622327,0.463635,0.0622327,0.0999985,0.1000023,0.4000015,0.0999985,0.1000023,0.4000015,0.0999985,0.1000023
33.1,1968q2,3.499533,0.2461595,-0.0633646,0.2461595,0.4000015,0.0999985,0.1999969,0.4000015,0.0999985,0.1999969,0.4000015,0.0999985
33.3,1968q3,3.505557,0.1693205,0.0421129,0.1693205,0.1999969,0.4000015,0.2000008,0.1999969,0.4000015,0.2000008,0.1999969,0.4000015
33.4,1968q4,3.508556,0.2276812,-0.1752573,0.2276812,0.2000008,0.1999969,0.1000023,0.2000008,0.1999969,0.1000023,0.2000008,0.1999969
33.9,1969q1,3.523415,0.123367,0.5169815,0.123367,0.1000023,0.2000008,0.5,0.1000023,0.2000008,0.5,0.1000023,0.2000008
34.4,1969q2,3.538057,0.3246694,0.2406658,0.3246694,0.5,0.1000023,0.5,0.5,0.1000023,0.5,0.5,0.1000023
34.7,1969q3,3.54674,0.3369705,-0.0507483,0.3369705,0.5,0.5,0.2999992,0.5,0.5,0.2999992,0.5,0.5
35,1969q4,3.555348,0.3347694,-0.047727,0.3347694,0.2999992,0.5,0.2999992,0.2999992,0.5,0.2999992,0.2999992,0.5
35.5,1970q1,3.569533,0.3157171,0.2529541,0.3157171,0.2999992,0.2999992,0.5,0.2999992,0.2999992,0.5,0.2999992,0.2999992
35.7,1970q2,3.575151,0.3695245,-0.2326951,0.3695245,0.5,0.2999992,0.2000008,0.5,0.2999992,0.2000008,0.5,0.2999992
35.9,1970q3,3.580737,0.2304558,-0.0418038,0.2304558,0.2000008,0.5,0.2000008,0.2000008,0.5,0.2000008,0.2000008,0.5
35.9,1970q4,3.580737,0.2534848,-0.3479433,0.2534848,0.2000008,0.2000008,0,0.2000008,0.2000008,0,0.2000008,0.2000008
36.5,1971q1,3.597312,0.0706596,0.7265917,0.0706596,0,0.2000008,0.5999985,0,0.2000008,0.5999985,0,0.2000008
36.9,1971q2,3.608212,0.3678494,0.0441333,0.3678494,0.5999985,0,0.4000015,0.5999985,0,0.4000015,0.5999985,0
37.2,1971q3,3.616309,0.267986,0.0439426,0.267986,0.4000015,0.5999985,0.2999992,0.4000015,0.5999985,0.2999992,0.4000015,0.5999985
37.2,1971q4,3.616309,0.3510466,-0.4818606,0.3510466,0.2999992,0.4000015,0,0.2999992,0.4000015,0,0.2999992,0.4000015
37.9,1972q1,3.634951,0.1317929,0.7799449,0.1317929,0,0.2999992,0.7000008,0,0.2999992,0.7000008,0,0.2999992
38.3,1972q2,3.64545,0.4463609,-0.06364,0.4463609,0.7000008,0,0.3999977,0.7000008,0,0.3999977,0.7000008,0
38.8,1972q3,3.65842,0.2775104,0.3053982,0.2775104,0.3999977,0.7000008,0.5,0.3999977,0.7000008,0.5,0.3999977,0.7000008
39.2,1972q4,3.668677,0.4822651,-0.1129183,0.4822651,0.5,0.3999977,0.4000015,0.5,0.3999977,0.4000015,0.5,0.3999977
41.1,1973q1,3.716008,0.3616737,2.111565,0.3616737,0.4000015,0.5,1.899998,0.4000015,0.5,1.899998,0.4000015,0.5
43.1,1973q2,3.763523,1.168557,1.141272,1.168557,1.899998,0.4000015,2,1.899998,0.4000015,2,1.899998,0.4000015
44.9,1973q3,3.804438,1.338354,0.6336781,1.338354,2,1.899998,1.800003,2,1.899998,1.800003,2,1.899998
45.3,1973q4,3.813307,1.62952,-1.687691,1.62952,1.800003,2,0.3999977,1.800003,2,0.3999977,1.800003,2
48.3,1974q1,3.877432,0.898369,2.884783,0.898369,0.3999977,1.800003,3,0.3999977,1.800003,3,0.3999977,1.800003
50,1974q2,3.912023,2.083784,-0.5267956,2.083784,3,0.3999977,1.700001,3,0.3999977,1.700001,3,0.3999977
53.6,1974q3,3.981549,1.285019,3.177633,1.285019,1.700001,3,3.599998,1.700001,3,3.599998,1.700001,3
55.4,1974q4,4.01458,2.833507,-1.418629,2.833507,3.599998,1.700001,1.800003,3.599998,1.700001,1.800003,3.599998,1.700001
55.4,1975q1,4.01458,1.730331,-2.375122,1.730331,1.800003,3.599998,2.22E-16,1.800003,3.599998,2.22E-16,1.800003,3.599998
56,1975q2,4.025352,1.100398,-0.6868683,1.100398,0,1.800003,0.5999985,0,1.800003,0.5999985,0,1.800003
57.2,1975q3,4.046554,0.7807068,0.5755398,0.7807068,0.5999985,0,1.200001,0.5999985,0,1.200001,0.5999985,0
57.8,1975q4,4.056989,0.6896428,-0.1230495,0.6896428,1.200001,0.5999985,0.5999985,1.200001,0.5999985,0.5999985,1.200001,0.5999985
58.1,1976q1,4.062166,0.5853766,-0.3917204,0.5853766,0.5999985,1.200001,0.2999992,0.5999985,1.200001,0.2999992,0.5999985,1.200001
59,1976q2,4.077538,0.5249205,0.5148513,0.5249205,0.2999992,0.5999985,0.9000015,0.2999992,0.5999985,0.9000015,0.2999992,0.5999985
59.7,1976q3,4.089332,0.6577644,0.0579753,0.6577644,0.9000015,0.2999992,0.7000008,0.9000015,0.2999992,0.7000008,0.9000015,0.2999992
60.2,1976q4,4.097672,0.5320962,-0.0440566,0.5320962,0.7000008,0.9000015,0.5,0.7000008,0.9000015,0.5,0.7000008,0.9000015
61.6,1977q1,4.120662,0.5624511,1.14965,0.5624511,0.5,0.7000008,1.399998,0.5,0.7000008,1.399998,0.5,0.7000008
63,1977q2,4.143135,0.9661546,0.5955157,0.9661546,1.399998,0.5,1.400002,1.399998,0.5,1.400002,1.399998,0.5
63.1,1977q3,4.144721,1.000284,-1.235768,1.000284,1.400002,1.399998,0.0999985,1.400002,1.399998,0.0999985,1.400002,1.399998
63.9,1977q4,4.15732,0.5473213,0.346841,0.5473213,0.0999985,1.400002,0.8000031,0.0999985,1.400002,0.8000031,0.0999985,1.400002
65.4,1978q1,4.180522,0.7924349,0.9712322,0.7924349,0.8000031,0.0999985,1.5,0.8000031,0.0999985,1.5,0.8000031,0.0999985
67.4,1978q2,4.210645,0.8926198,1.520034,0.8926198,1.5,0.8000031,2,1.5,0.8000031,2,1.5,0.8000031
68.4,1978q3,4.225373,1.403464,-0.5538111,1.403464,2,1.5,1,2,1.5,1,2,1.5
70,1978q4,4.248495,1.104647,0.6799389,1.104647,1,2,1.599998,1,2,1.599998,1,2
72.5,1979q1,4.283587,1.454646,1.434895,1.454646,1.599998,1,2.5,1.599998,1,2.5,1.599998,1
75.1,1979q2,4.31882,1.728132,1.196759,1.728132,2.5,1.599998,2.599998,2.5,1.599998,2.599998,2.5,1.599998
77.4,1979q3,4.348987,2.021395,0.3824289,2.021395,2.599998,2.5,2.300003,2.599998,2.5,2.300003,2.599998,2.5
80.2,1979q4,4.384523,2.105034,0.9539319,2.105034,2.300003,2.599998,2.799995,2.300003,2.599998,2.799995,2.300003,2.599998
83.9,1980q1,4.429626,2.365791,1.831395,2.365791,2.799995,2.300003,3.700005,2.799995,2.300003,3.700005,2.799995,2.300003
85.6,1980q2,4.449685,2.810381,-1.524157,2.810381,3.700005,2.799995,1.699997,3.700005,2.799995,1.699997,3.700005,2.799995
88.4,1980q3,4.481872,1.970985,1.137943,1.970985,1.699997,3.700005,2.800003,1.699997,3.700005,2.800003,1.699997,3.700005
90.4,1980q4,4.504244,2.592478,-0.8132593,2.592478,2.800003,1.699997,2,2.800003,1.699997,2,2.800003,1.699997
93.1,1981q1,4.533674,1.759534,1.290916,1.759534,2,2.800003,2.699997,2,2.800003,2.699997,2,2.800003
95.2,1981q2,4.55598,2.336114,-0.3241019,2.336114,2.699997,2,2.099998,2.699997,2,2.099998,2.699997,2
95.9,1981q3,4.563306,1.880126,-1.619881,1.880126,2.099998,2.699997,0.7000046,2.099998,2.699997,0.7000046,2.099998,2.699997
95.8,1981q4,4.562263,1.265696,-1.874607,1.265696,0.7000046,2.099998,-0.0999985,0.7000046,2.099998,-0.0999985,0.7000046,2.099998
96.6,1982q1,4.570579,0.555851,0.3351223,0.555851,-0.0999985,0.7000046,0.7999954,-0.0999985,0.7000046,0.7999954,-0.0999985,0.7000046
96.7,1982q2,4.571613,0.5927548,-0.6763771,0.5927548,0.7999954,-0.0999985,0.0999985,0.7999954,-0.0999985,0.0999985,0.7999954,-0.0999985
97.1,1982q3,4.575741,0.1031118,0.4075227,0.1031118,0.0999985,0.7999954,0.4000015,0.0999985,0.7999954,0.4000015,0.0999985,0.7999954
97.2,1982q4,4.576771,0.4267826,-0.448557,0.4267826,0.4000015,0.0999985,0.0999985,0.4000015,0.0999985,0.0999985,0.4000015,0.0999985
97.3,1983q1,4.577799,0.1166142,-0.0227969,0.1166142,0.0999985,0.4000015,0.1000061,0.0999985,0.4000015,0.1000061,0.0999985,0.4000015
97.6,1983q2,4.580877,0.1654509,0.1846811,0.1654509,0.1000061,0.0999985,0.2999954,0.1000061,0.0999985,0.2999954,0.1000061,0.0999985
98.6,1983q3,4.591071,0.1934489,1.107104,0.1934489,0.2999954,0.1000061,1,0.2999954,0.1000061,1,0.2999954,0.1000061
99.1,1983q4,4.596129,0.5814546,-0.1118079,0.5814546,1,0.2999954,0.5,1,0.2999954,0.5,1,0.2999954
100.2,1984q1,4.607168,0.4362065,0.9111474,0.4362065,0.5,1,1.099998,0.5,1,1.099998,0.5,1
100.8,1984q2,4.613138,0.885444,-0.3918035,0.885444,1.099998,0.5,0.6000061,1.099998,0.5,0.6000061,1.099998,0.5
100.6,1984q3,4.611152,0.5500512,-1.029557,0.5500512,0.6000061,1.099998,-0.2000046,0.6000061,1.099998,-0.2000046,0.6000061,1.099998
100.3,1984q4,4.608166,0.2355793,-0.7351512,0.2355793,-0.2000046,0.6000061,-0.2999954,-0.2000046,0.6000061,-0.2999954,-0.2000046,0.6000061
100.1,1985q1,4.60617,-0.0223489,-0.2438573,-0.0223489,-0.2999954,-0.2000046,-0.2000046,-0.2999954,-0.2000046,-0.2000046,-0.2999954,-0.2000046
100.2,1985q2,4.607168,-0.1856029,0.3920279,-0.1856029,-0.2000046,-0.2999954,0.0999985,-0.2000046,-0.2999954,0.0999985,-0.2000046,-0.2999954
99.5,1985q3,4.600158,-0.0437558,-0.9007829,-0.0437558,0.0999985,-0.2000046,-0.6999969,0.0999985,-0.2000046,-0.6999969,0.0999985,-0.2000046
100.1,1985q4,4.60617,-0.4110308,1.387779,-0.4110308,-0.6999969,0.0999985,0.5999985,-0.6999969,0.0999985,0.5999985,-0.6999969,0.0999985
98.6,1986q1,4.591071,0.2753627,-2.436934,0.2753627,0.5999985,-0.6999969,-1.5,0.5999985,-0.6999969,-1.5,0.5999985,-0.6999969
96.8,1986q2,4.572647,-0.9140747,-1.21605,-0.9140747,-1.5,0.5999985,-1.799995,-1.5,0.5999985,-1.799995,-1.5,0.5999985
96.3,1986q3,4.567468,-0.9367974,0.5995656,-0.9367974,-1.799995,-1.5,-0.5,-1.799995,-1.5,-0.5,-1.799995,-1.5
96.7,1986q4,4.571613,-0.8220589,1.677438,-0.8220589,-0.5,-1.799995,0.3999939,-0.5,-1.799995,0.3999939,-0.5,-1.799995
97.8,1987q1,4.582925,-0.3012679,1.923445,-0.3012679,0.3999939,-0.5,1.100006,0.3999939,-0.5,1.100006,0.3999939,-0.5
99.4,1987q2,4.599152,0.4888681,1.525182,0.4888681,1.100006,0.3999939,1.599998,1.100006,0.3999939,1.599998,1.100006,0.3999939
100.5,1987q3,4.610157,1.051314,0.0668259,1.051314,1.599998,1.100006,1.099998,1.599998,1.100006,1.099998,1.599998,1.100006
101,1987q4,4.61512,1.016036,-0.7083322,1.016036,1.099998,1.599998,0.5,1.099998,1.599998,0.5,1.099998,1.599998
101.6,1988q1,4.621044,0.7811794,-0.2486962,0.7811794,0.5,1.099998,0.5999985,0.5,1.099998,0.5999985,0.5,1.099998
103.2,1988q2,4.636669,0.6477114,1.307147,0.6477114,0.5999985,0.5,1.599998,0.5999985,0.5,1.599998,0.5999985,0.5
104.7,1988q3,4.651099,1.029488,0.6458436,1.029488,1.599998,0.5999985,1.5,1.599998,0.5999985,1.5,1.599998,0.5999985
105.2,1988q4,4.655863,1.097846,-0.8206275,1.097846,1.5,1.599998,0.5,1.5,1.599998,0.5,1.5,1.599998
107.5,1989q1,4.677491,0.819284,2.032494,0.819284,0.5,1.5,2.300003,0.5,1.5,2.300003,0.5,1.5
109.4,1989q2,4.695011,1.64695,0.3473487,1.64695,2.300003,0.5,1.900002,2.300003,0.5,1.900002,2.300003,0.5
109,1989q3,4.691348,1.349556,-2.401512,1.349556,1.900002,2.300003,-0.4000015,1.900002,2.300003,-0.4000015,1.900002,2.300003
109.4,1989q4,4.695011,0.5636495,-0.2246298,0.5636495,-0.4000015,1.900002,0.4000015,-0.4000015,1.900002,0.4000015,-0.4000015,1.900002
111,1990q1,4.70953,0.6629925,1.286172,0.6629925,0.4000015,-0.4000015,1.599998,0.4000015,-0.4000015,1.599998,0.4000015,-0.4000015
110.8,1990q2,4.707727,0.7782037,-1.342717,0.7782037,1.599998,0.4000015,-0.1999969,1.599998,0.4000015,-0.1999969,1.599998,0.4000015
112.8,1990q3,4.725616,0.1502198,2.539082,0.1502198,-0.1999969,1.599998,2,-0.1999969,1.599998,2,-0.1999969,1.599998
116.2,1990q4,4.755313,1.447948,2.679457,1.447948,2,-0.1999969,3.399994,2,-0.1999969,3.399994,2,-0.1999969
\ No newline at end of file
diff --git a/shared/package-files/4e/4e5c5ca38bf7cf4987bf82f107c5e947952b9c9fb071d4658fe38e67f97fce4e/quantization.h b/shared/package-files/4e/4e5c5ca38bf7cf4987bf82f107c5e947952b9c9fb071d4658fe38e67f97fce4e/quantization.h
new file mode 100644
index 0000000000000000000000000000000000000000..019b2349b184056b4bb3769340debbe2985650f6
--- /dev/null
+++ b/shared/package-files/4e/4e5c5ca38bf7cf4987bf82f107c5e947952b9c9fb071d4658fe38e67f97fce4e/quantization.h
@@ -0,0 +1,160 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/kernel.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+TORCH_API ExprHandle quantizePerTensorQParamFromArg(ArgValue arg);
+
+TORCH_API double immQScale(const BufHandle& qx);
+
+TORCH_API int64_t immQZero(const BufHandle& qx);
+
+TORCH_API ScalarType immQDType(const BufHandle& qx);
+
+TORCH_API bool isQuantized(const BufHandle& qx);
+
+TORCH_API Tensor computeQuantizePerTensor(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizePerTensorExternalCall(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedConv1d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedConv2dPrepack(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedConv1d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedConv2d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedConv2dRelu(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedLinear(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedLinearRelu(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedAdd(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+Tensor computeQuantizedAddExternalCall(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedMul(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedMulScalar(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedCat(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedRelu(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeDequantize(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeDequantizeExternalCall(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeUpsampleNearest2d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeUpsampleNearest2dExternalCall(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+TORCH_API Tensor computeQuantizedSigmoidExternalCall(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device);
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/4e/4e5e9eac4c3f66adddc156d5c8f5d15aa07cdeefe8e3090854539edf042b4aba/array_float32_4d.sav b/shared/package-files/4e/4e5e9eac4c3f66adddc156d5c8f5d15aa07cdeefe8e3090854539edf042b4aba/array_float32_4d.sav
new file mode 100644
index 0000000000000000000000000000000000000000..4bb951e274a399f091ff70b639d6e3b55ee1e122
Binary files /dev/null and b/shared/package-files/4e/4e5e9eac4c3f66adddc156d5c8f5d15aa07cdeefe8e3090854539edf042b4aba/array_float32_4d.sav differ
diff --git a/shared/package-files/4e/4e5f531cc0d9f8f9395a6f2c23580683f5390e1bac9b10fe159d1f51b714d16d/file_proxy.py b/shared/package-files/4e/4e5f531cc0d9f8f9395a6f2c23580683f5390e1bac9b10fe159d1f51b714d16d/file_proxy.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b0b0da6c2a62b2b1468c35ddd69f1bbb9b91aa8
--- /dev/null
+++ b/shared/package-files/4e/4e5f531cc0d9f8f9395a6f2c23580683f5390e1bac9b10fe159d1f51b714d16d/file_proxy.py
@@ -0,0 +1,57 @@
+import io
+from typing import IO, TYPE_CHECKING, Any, List
+
+from .ansi import AnsiDecoder
+from .text import Text
+
+if TYPE_CHECKING:
+    from .console import Console
+
+
+class FileProxy(io.TextIOBase):
+    """Wraps a file (e.g. sys.stdout) and redirects writes to a console."""
+
+    def __init__(self, console: "Console", file: IO[str]) -> None:
+        self.__console = console
+        self.__file = file
+        self.__buffer: List[str] = []
+        self.__ansi_decoder = AnsiDecoder()
+
+    @property
+    def rich_proxied_file(self) -> IO[str]:
+        """Get proxied file."""
+        return self.__file
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self.__file, name)
+
+    def write(self, text: str) -> int:
+        if not isinstance(text, str):
+            raise TypeError(f"write() argument must be str, not {type(text).__name__}")
+        buffer = self.__buffer
+        lines: List[str] = []
+        while text:
+            line, new_line, text = text.partition("\n")
+            if new_line:
+                lines.append("".join(buffer) + line)
+                buffer.clear()
+            else:
+                buffer.append(line)
+                break
+        if lines:
+            console = self.__console
+            with console:
+                output = Text("\n").join(
+                    self.__ansi_decoder.decode_line(line) for line in lines
+                )
+                console.print(output)
+        return len(text)
+
+    def flush(self) -> None:
+        output = "".join(self.__buffer)
+        if output:
+            self.__console.print(output)
+        del self.__buffer[:]
+
+    def fileno(self) -> int:
+        return self.__file.fileno()
diff --git a/shared/package-files/4e/4e5fc7d73a702dd48856eb5ce2f8be4300c27963f3046a4ddf537d22dcd391e4/test_getattr.py b/shared/package-files/4e/4e5fc7d73a702dd48856eb5ce2f8be4300c27963f3046a4ddf537d22dcd391e4/test_getattr.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0f5823600ca0378efe611c989ff293ea7740581
--- /dev/null
+++ b/shared/package-files/4e/4e5fc7d73a702dd48856eb5ce2f8be4300c27963f3046a4ddf537d22dcd391e4/test_getattr.py
@@ -0,0 +1,35 @@
+from importlib import import_module
+from pkgutil import walk_packages
+
+import matplotlib
+import pytest
+
+# Get the names of all matplotlib submodules,
+# except for the unit tests and private modules.
+module_names = [
+    m.name
+    for m in walk_packages(
+        path=matplotlib.__path__, prefix=f'{matplotlib.__name__}.'
+    )
+    if not m.name.startswith(__package__)
+    and not any(x.startswith('_') for x in m.name.split('.'))
+]
+
+
+@pytest.mark.parametrize('module_name', module_names)
+@pytest.mark.filterwarnings('ignore::DeprecationWarning')
+@pytest.mark.filterwarnings('ignore::ImportWarning')
+def test_getattr(module_name):
+    """
+    Test that __getattr__ methods raise AttributeError for unknown keys.
+    See #20822, #20855.
+    """
+    try:
+        module = import_module(module_name)
+    except (ImportError, RuntimeError, OSError) as e:
+        # Skip modules that cannot be imported due to missing dependencies
+        pytest.skip(f'Cannot import {module_name} due to {e}')
+
+    key = 'THIS_SYMBOL_SHOULD_NOT_EXIST'
+    if hasattr(module, key):
+        delattr(module, key)
diff --git a/shared/package-files/4e/4e61cdb02c71be9f8a0cb0f949a4e6784b96243cd44b9d44834e2e4967adf4f8/test_concat.py b/shared/package-files/4e/4e61cdb02c71be9f8a0cb0f949a4e6784b96243cd44b9d44834e2e4967adf4f8/test_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..feba574da548fd597c25103f67821145bccec9ed
--- /dev/null
+++ b/shared/package-files/4e/4e61cdb02c71be9f8a0cb0f949a4e6784b96243cd44b9d44834e2e4967adf4f8/test_concat.py
@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Int64", "Int64"], "Int64"),
+        (["UInt64", "UInt64"], "UInt64"),
+        (["Int8", "Int8"], "Int8"),
+        (["Int8", "Int16"], "Int16"),
+        (["UInt8", "Int8"], "Int16"),
+        (["Int32", "UInt32"], "Int64"),
+        (["Int64", "UInt64"], "Float64"),
+        (["Int64", "boolean"], "object"),
+        (["UInt8", "boolean"], "object"),
+    ],
+)
+def test_concat_series(to_concat_dtypes, result_dtype):
+    # we expect the same dtypes as we would get with non-masked inputs,
+    #  just masked where available.
+
+    result = pd.concat([pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes])
+    expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    # order doesn't matter for result
+    result = pd.concat(
+        [pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes[::-1]]
+    )
+    expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Int64", "int64"], "Int64"),
+        (["UInt64", "uint64"], "UInt64"),
+        (["Int8", "int8"], "Int8"),
+        (["Int8", "int16"], "Int16"),
+        (["UInt8", "int8"], "Int16"),
+        (["Int32", "uint32"], "Int64"),
+        (["Int64", "uint64"], "Float64"),
+        (["Int64", "bool"], "object"),
+        (["UInt8", "bool"], "object"),
+    ],
+)
+def test_concat_series_with_numpy(to_concat_dtypes, result_dtype):
+    # we expect the same dtypes as we would get with non-masked inputs,
+    #  just masked where available.
+
+    s1 = pd.Series([0, 1, pd.NA], dtype=to_concat_dtypes[0])
+    s2 = pd.Series(np.array([0, 1], dtype=to_concat_dtypes[1]))
+    result = pd.concat([s1, s2], ignore_index=True)
+    expected = pd.Series([0, 1, pd.NA, 0, 1], dtype=object).astype(result_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # order doesn't matter for result
+    result = pd.concat([s2, s1], ignore_index=True)
+    expected = pd.Series([0, 1, 0, 1, pd.NA], dtype=object).astype(result_dtype)
+    tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/4e/4e6230292bdf805ba4c772441f1ea050a5db7738aea5105b67fcab9c98975fbb/_dbscan_inner.cpython-310-x86_64-linux-gnu.so b/shared/package-files/4e/4e6230292bdf805ba4c772441f1ea050a5db7738aea5105b67fcab9c98975fbb/_dbscan_inner.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..ce45bf1d355ade266248a0f433e97bac698c22b8
Binary files /dev/null and b/shared/package-files/4e/4e6230292bdf805ba4c772441f1ea050a5db7738aea5105b67fcab9c98975fbb/_dbscan_inner.cpython-310-x86_64-linux-gnu.so differ
diff --git a/shared/package-files/4e/4e65937d087a2cb318d986fe99bd74a7f87317f1f297b79fe88610ecafbde772/TransformationHelper.h b/shared/package-files/4e/4e65937d087a2cb318d986fe99bd74a7f87317f1f297b79fe88610ecafbde772/TransformationHelper.h
new file mode 100644
index 0000000000000000000000000000000000000000..1061a732ddb7e74dbbce89850fe8191f81536c87
--- /dev/null
+++ b/shared/package-files/4e/4e65937d087a2cb318d986fe99bd74a7f87317f1f297b79fe88610ecafbde772/TransformationHelper.h
@@ -0,0 +1,173 @@
+#include <c10/macros/Macros.h>
+#include <c10/util/Half.h>
+#include <c10/util/BFloat16.h>
+#include <c10/util/MathConstants.h>
+#include <ATen/NumericUtils.h>
+#include <limits>
+#include <cstdint>
+#include <cassert>
+
+namespace at {
+
+// Using DistAccumType in accumulate types for distributions.
+// Note: Ideally we'd be using ATen/AccumulateType.h but looks
+// like the there is some inconsistency in how accumulate types
+// are mapped currently, e.g. for the cpu side, float is mapped
+// to double.
+template <typename T>
+struct DistAccumType {  };
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+template <> struct DistAccumType<half> { using type = float; };
+#endif
+template <> struct DistAccumType<BFloat16> { using type = float; };
+template <> struct DistAccumType<Half> { using type = float; };
+template <> struct DistAccumType<float> { using type = float; };
+template <> struct DistAccumType<double> { using type = double; };
+
+template <typename T>
+using dist_acctype = typename DistAccumType<T>::type;
+
+namespace transformation {
+
+/**
+ * A transformation function for `torch.Tensor.random_()`, when both `from` and `to` are specified.
+ * `range` is `to - from`
+ * `base` is `from`
+ */
+template <typename T, typename V>
+C10_HOST_DEVICE inline T uniform_int_from_to(V val, uint64_t range, int64_t base) {
+  return static_cast<T>(static_cast<int64_t>((val % range) + base));
+}
+
+/**
+ * A transformation function for `torch.Tensor.random_()`, when `from=min_value(int64_t)` and to=None
+ */
+template <typename T, typename V>
+C10_HOST_DEVICE inline T uniform_int_full_range(V val) {
+  return static_cast<T>(static_cast<int64_t>(val));
+}
+
+/**
+ * A transformation function for `torch.Tensor.random_()`, when used without specifying `from` and `to`.
+ * In order to prevent compiler warnings reported in GitHub issue 46391, T can't be float or double
+ * in this overloaded version
+ */
+template <typename T, typename V>
+C10_HOST_DEVICE inline typename std::enable_if<!(std::is_floating_point<T>::value), T>::type uniform_int(V val) {
+  if constexpr (std::is_same_v<T, bool>) {
+    return static_cast<bool>(val & 1);
+  } else if constexpr (std::is_same_v<T, int64_t>) {
+    return static_cast<T>(val % (static_cast<uint64_t>(std::numeric_limits<T>::max()) + 1));
+  } else if constexpr (std::is_same_v<T, at::Half> || std::is_same<T, at::BFloat16>::value) {
+    return static_cast<T>(val % static_cast<uint64_t>((1ULL << std::numeric_limits<T>::digits) + 1));
+  } else if constexpr (std::is_integral_v<T>) {
+    return static_cast<T>(val % (static_cast<uint64_t>(std::numeric_limits<T>::max()) + 1));
+  } else {
+    assert(false);
+    return 0;
+  }
+}
+
+/**
+ * An overloaded transformation function for `torch.Tensor.random_()`, when used without specifying `from` and `to`,
+ * added to fix compiler warnings reported in GitHub issue 46391. T is either float or double in this version.
+ */
+template<typename T, typename V>
+C10_HOST_DEVICE inline typename std::enable_if<std::is_floating_point<T>::value, T>::type uniform_int(V val) {
+  return static_cast<T>(val % static_cast<uint64_t>((1ULL << std::numeric_limits<T>::digits) + 1));
+}
+
+template <typename T, typename V>
+C10_HOST_DEVICE inline dist_acctype<T> uniform_real(V val, T from, T to) {
+  constexpr auto MASK = static_cast<V>((static_cast<uint64_t>(1) << std::numeric_limits<T>::digits) - 1);
+  constexpr auto DIVISOR = static_cast<dist_acctype<T>>(1) / (static_cast<uint64_t>(1) << std::numeric_limits<T>::digits);
+  dist_acctype<T> x = (val & MASK) * DIVISOR;
+  return (x * (to - from) + from);
+}
+
+/**
+ * Transforms normally distributed `val` with mean 0.0 and standard deviation 1.0 to
+ * normally distributed with `mean` and standard deviation `std`.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T normal(T val, T mean, T std) {
+  return val * std + mean;
+}
+
+/**
+ * Transforms uniformly distributed `val` between 0.0 and 1.0 to
+ * Cauchy distribution with location parameter `median` and scale parameter `sigma`.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T cauchy(T val, T median, T sigma) {
+  // https://en.wikipedia.org/wiki/Cauchy_distribution#Cumulative_distribution_function
+  // __tanf overflows and returns `inf/-inf` when (val > 1 - eps) or (val < 0 + eps),
+  // thus we clip those values.
+  constexpr T eps = std::numeric_limits<T>::epsilon();
+  constexpr T one_minus_eps = 1 - eps;
+  constexpr T zero_plus_eps = 0 + eps;
+  val = (val > one_minus_eps ? one_minus_eps : val);
+  val = (val < zero_plus_eps ? zero_plus_eps : val);
+  return median + sigma * at::tan(c10::pi<T> * (val - static_cast<T>(0.5)));
+}
+
+template <>
+C10_HOST_DEVICE inline double cauchy(double val, double median, double sigma) {
+  // https://en.wikipedia.org/wiki/Cauchy_distribution#Cumulative_distribution_function
+  return median + sigma * at::tan(c10::pi<double> * (val - static_cast<double>(0.5)));
+}
+
+/**
+ * Transforms uniformly distributed `val` between 0.0 and 1.0 to
+ * exponentially distributed with `lambda` parameter of the distribution.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T exponential(T val, T lambda) {
+  // https://en.wikipedia.org/wiki/Exponential_distribution#Generating_exponential_variates
+  // Different implementations for CUDA and CPU to preserve original logic
+  // TODO: must be investigated and unified!!!
+  // https://github.com/pytorch/pytorch/issues/38662
+#if defined(__CUDACC__) || defined(__HIPCC__)
+      // BEFORE TOUCHING THIS CODE READ: https://github.com/pytorch/pytorch/issues/16706
+      // curand_uniform has (0,1] bounds. log(1) is 0 and exponential excludes 0.
+      // we need log to be not 0, and not underflow when converted to half
+      // fast __logf approximation can underflow, so set log to -epsilon/2 for 1 or close to 1 args
+  auto log = val >= static_cast<T>(1.) - std::numeric_limits<T>::epsilon() / 2
+      ? -std::numeric_limits<T>::epsilon() / 2
+      : at::log(val);
+  return static_cast<T>(-1.0) / lambda * log;
+#else
+  return static_cast<T>(-1.0) / lambda * at::log1p(-val);
+#endif
+}
+
+/**
+ * Transforms uniformly distributed `val` between 0.0 and 1.0 to
+ * geometrically distributed with success probability `p`.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T geometric(T val, T p) {
+  // https://en.wikipedia.org/wiki/Geometric_distribution#Related_distributions
+  return static_cast<T>(::ceil(at::log(val) / at::log1p(-p)));
+}
+
+/**
+ * Transforms normally distributed `val` to log-normally distributed.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T log_normal(T val) {
+  // https://en.wikipedia.org/wiki/Log-normal_distribution#Mode,_median,_quantiles
+  return at::exp(val);
+}
+
+/**
+ * Transforms uniformly distributed `val` between 0.0 and 1.0 to
+ * bernoulli distributed with success probability `p`.
+ */
+template <typename T>
+C10_HOST_DEVICE inline T bernoulli(T val, T p) {
+  return val < p;
+}
+
+}} // namespace at::transformation
diff --git a/shared/package-files/4e/4e667fd1ffb2490fac6810254575747f8f48b709dee755415e7eab59cad6a874/Kwajalein b/shared/package-files/4e/4e667fd1ffb2490fac6810254575747f8f48b709dee755415e7eab59cad6a874/Kwajalein
new file mode 100644
index 0000000000000000000000000000000000000000..1887a607422edd499fdf24afe91a04294f1caf6f
Binary files /dev/null and b/shared/package-files/4e/4e667fd1ffb2490fac6810254575747f8f48b709dee755415e7eab59cad6a874/Kwajalein differ
diff --git a/shared/package-files/4e/4e709483c98702aecc21e3eb84d96504a27913992d66a750380419fe17ebc404/dropout.h b/shared/package-files/4e/4e709483c98702aecc21e3eb84d96504a27913992d66a750380419fe17ebc404/dropout.h
new file mode 100644
index 0000000000000000000000000000000000000000..7cc7dfb80fbd27541301e6fb0c81930c549d91ff
--- /dev/null
+++ b/shared/package-files/4e/4e709483c98702aecc21e3eb84d96504a27913992d66a750380419fe17ebc404/dropout.h
@@ -0,0 +1,190 @@
+#pragma once
+
+#include <torch/nn/cloneable.h>
+#include <torch/nn/options/dropout.h>
+#include <torch/nn/pimpl.h>
+#include <torch/types.h>
+
+#include <torch/csrc/Export.h>
+
+#include <cstddef>
+#include <vector>
+
+namespace torch {
+namespace nn {
+
+namespace detail {
+
+template <typename Derived>
+class _DropoutNd : public torch::nn::Cloneable<Derived> {
+ public:
+  _DropoutNd(double p) : _DropoutNd(DropoutOptions().p(p)){};
+
+  explicit _DropoutNd(const DropoutOptions& options_ = {}) : options(options_) {
+    // NOLINTNEXTLINE(clang-analyzer-optin.cplusplus.VirtualCall)
+    reset();
+  }
+
+  void reset() override {
+    TORCH_CHECK(
+        options.p() >= 0. && options.p() <= 1.,
+        "dropout probability has to be between 0 and 1, but got ",
+        options.p());
+  }
+
+  /// The options with which this `Module` was constructed.
+  DropoutOptions options;
+};
+
+} // namespace detail
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dropout ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Applies dropout over a 1-D input.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.Dropout to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::DropoutOptions` class to learn what
+/// constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// Dropout model(DropoutOptions().p(0.42).inplace(true));
+/// ```
+class TORCH_API DropoutImpl : public detail::_DropoutNd<DropoutImpl> {
+ public:
+  using detail::_DropoutNd<DropoutImpl>::_DropoutNd;
+
+  Tensor forward(Tensor input);
+
+  /// Pretty prints the `Dropout` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+/// A `ModuleHolder` subclass for `DropoutImpl`.
+/// See the documentation for `DropoutImpl` class to learn what methods it
+/// provides, and examples of how to use `Dropout` with
+/// `torch::nn::DropoutOptions`. See the documentation for `ModuleHolder` to
+/// learn about PyTorch's module storage semantics.
+TORCH_MODULE(Dropout);
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dropout2d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Applies dropout over a 2-D input.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.Dropout2d to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::Dropout2dOptions` class to learn what
+/// constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// Dropout2d model(Dropout2dOptions().p(0.42).inplace(true));
+/// ```
+class TORCH_API Dropout2dImpl : public detail::_DropoutNd<Dropout2dImpl> {
+ public:
+  using detail::_DropoutNd<Dropout2dImpl>::_DropoutNd;
+
+  Tensor forward(Tensor input);
+
+  /// Pretty prints the `Dropout2d` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+/// A `ModuleHolder` subclass for `Dropout2dImpl`.
+/// See the documentation for `Dropout2dImpl` class to learn what methods it
+/// provides, and examples of how to use `Dropout2d` with
+/// `torch::nn::Dropout2dOptions`. See the documentation for `ModuleHolder` to
+/// learn about PyTorch's module storage semantics.
+TORCH_MODULE(Dropout2d);
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dropout3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Applies dropout over a 3-D input.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.Dropout3d to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::Dropout3dOptions` class to learn what
+/// constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// Dropout3d model(Dropout3dOptions().p(0.42).inplace(true));
+/// ```
+class TORCH_API Dropout3dImpl : public detail::_DropoutNd<Dropout3dImpl> {
+ public:
+  using detail::_DropoutNd<Dropout3dImpl>::_DropoutNd;
+
+  Tensor forward(Tensor input);
+
+  /// Pretty prints the `Dropout3d` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+/// A `ModuleHolder` subclass for `Dropout3dImpl`.
+/// See the documentation for `Dropout3dImpl` class to learn what methods it
+/// provides, and examples of how to use `Dropout3d` with
+/// `torch::nn::Dropout3dOptions`. See the documentation for `ModuleHolder` to
+/// learn about PyTorch's module storage semantics.
+TORCH_MODULE(Dropout3d);
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AlphaDropout ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Applies Alpha Dropout over the input.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.AlphaDropout to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::AlphaDropoutOptions` class to learn
+/// what constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// AlphaDropout model(AlphaDropoutOptions(0.2).inplace(true));
+/// ```
+class TORCH_API AlphaDropoutImpl : public detail::_DropoutNd<AlphaDropoutImpl> {
+ public:
+  using detail::_DropoutNd<AlphaDropoutImpl>::_DropoutNd;
+
+  Tensor forward(const Tensor& input);
+
+  /// Pretty prints the `AlphaDropout` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+/// A `ModuleHolder` subclass for `AlphaDropoutImpl`.
+/// See the documentation for `AlphaDropoutImpl` class to learn what methods it
+/// provides, and examples of how to use `AlphaDropout` with
+/// `torch::nn::AlphaDropoutOptions`. See the documentation for `ModuleHolder`
+/// to learn about PyTorch's module storage semantics.
+TORCH_MODULE(AlphaDropout);
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FeatureAlphaDropout
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// See the documentation for `torch::nn::FeatureAlphaDropoutOptions` class to
+/// learn what constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// FeatureAlphaDropout model(FeatureAlphaDropoutOptions(0.2).inplace(true));
+/// ```
+class TORCH_API FeatureAlphaDropoutImpl
+    : public detail::_DropoutNd<FeatureAlphaDropoutImpl> {
+ public:
+  using detail::_DropoutNd<FeatureAlphaDropoutImpl>::_DropoutNd;
+
+  Tensor forward(const Tensor& input);
+
+  /// Pretty prints the `FeatureAlphaDropout` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+/// A `ModuleHolder` subclass for `FeatureAlphaDropoutImpl`.
+/// See the documentation for `FeatureAlphaDropoutImpl` class to learn what
+/// methods it provides, and examples of how to use `FeatureAlphaDropout` with
+/// `torch::nn::FeatureAlphaDropoutOptions`. See the documentation for
+/// `ModuleHolder` to learn about PyTorch's module storage semantics.
+TORCH_MODULE(FeatureAlphaDropout);
+
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/4e/4e7720668437bde0a122eaaa588ad69370ba7d02e4138bd7517d576e6bb4e0fa/autograd_metadata.h b/shared/package-files/4e/4e7720668437bde0a122eaaa588ad69370ba7d02e4138bd7517d576e6bb4e0fa/autograd_metadata.h
new file mode 100644
index 0000000000000000000000000000000000000000..1d5aefbd2010a76a05ab90de3c71d096377ecc82
--- /dev/null
+++ b/shared/package-files/4e/4e7720668437bde0a122eaaa588ad69370ba7d02e4138bd7517d576e6bb4e0fa/autograd_metadata.h
@@ -0,0 +1,25 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <cstdint>
+
+namespace torch {
+namespace distributed {
+namespace autograd {
+
+// This structure represents autograd metadata that we need to pass across
+// different nodes when we call an RPC which needs autograd computation.
+struct TORCH_API AutogradMetadata {
+  AutogradMetadata(int64_t autogradContextId, int64_t autogradMessageId);
+
+  // autogradContextId_ is a globally unique integer that identifies a
+  // particular distributed autograd pass.
+  int64_t autogradContextId;
+  // autogradMessageId_ is a globally unique integer that identifies a pair
+  // of send/recv autograd functions.
+  int64_t autogradMessageId;
+};
+
+} // namespace autograd
+} // namespace distributed
+} // namespace torch
diff --git a/shared/package-files/4e/4e79bf89ce24920da66942bb98095063e83346d1dd8183d9016687aa08545aa6/direct_url.json b/shared/package-files/4e/4e79bf89ce24920da66942bb98095063e83346d1dd8183d9016687aa08545aa6/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..44c254d71f587d9b76494b1c9b53811de059e7c3
--- /dev/null
+++ b/shared/package-files/4e/4e79bf89ce24920da66942bb98095063e83346d1dd8183d9016687aa08545aa6/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///croot/mkl_fft_1695058164594/work"}
\ No newline at end of file
diff --git a/shared/package-files/4e/4e79ddc91fd23db9960bf03efb4391074eab847ec84b6ffb923a617cf711a97a/osx.py b/shared/package-files/4e/4e79ddc91fd23db9960bf03efb4391074eab847ec84b6ffb923a617cf711a97a/osx.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b8a0cd98e21f48597780f285c9023f84c606bfa
--- /dev/null
+++ b/shared/package-files/4e/4e79ddc91fd23db9960bf03efb4391074eab847ec84b6ffb923a617cf711a97a/osx.py
@@ -0,0 +1,147 @@
+"""Inputhook for OS X
+
+Calls NSApp / CoreFoundation APIs via ctypes.
+"""
+
+# obj-c boilerplate from appnope, used under BSD 2-clause
+
+import ctypes
+import ctypes.util
+from threading import Event
+
+objc = ctypes.cdll.LoadLibrary(ctypes.util.find_library("objc"))  # type: ignore
+
+void_p = ctypes.c_void_p
+
+objc.objc_getClass.restype = void_p
+objc.sel_registerName.restype = void_p
+objc.objc_msgSend.restype = void_p
+objc.objc_msgSend.argtypes = [void_p, void_p]
+
+msg = objc.objc_msgSend
+
+def _utf8(s):
+    """ensure utf8 bytes"""
+    if not isinstance(s, bytes):
+        s = s.encode('utf8')
+    return s
+
+def n(name):
+    """create a selector name (for ObjC methods)"""
+    return objc.sel_registerName(_utf8(name))
+
+def C(classname):
+    """get an ObjC Class by name"""
+    return objc.objc_getClass(_utf8(classname))
+
+# end obj-c boilerplate from appnope
+
+# CoreFoundation C-API calls we will use:
+CoreFoundation = ctypes.cdll.LoadLibrary(ctypes.util.find_library("CoreFoundation"))  # type: ignore
+
+CFFileDescriptorCreate = CoreFoundation.CFFileDescriptorCreate
+CFFileDescriptorCreate.restype = void_p
+CFFileDescriptorCreate.argtypes = [void_p, ctypes.c_int, ctypes.c_bool, void_p, void_p]
+
+CFFileDescriptorGetNativeDescriptor = CoreFoundation.CFFileDescriptorGetNativeDescriptor
+CFFileDescriptorGetNativeDescriptor.restype = ctypes.c_int
+CFFileDescriptorGetNativeDescriptor.argtypes = [void_p]
+
+CFFileDescriptorEnableCallBacks = CoreFoundation.CFFileDescriptorEnableCallBacks
+CFFileDescriptorEnableCallBacks.restype = None
+CFFileDescriptorEnableCallBacks.argtypes = [void_p, ctypes.c_ulong]
+
+CFFileDescriptorCreateRunLoopSource = CoreFoundation.CFFileDescriptorCreateRunLoopSource
+CFFileDescriptorCreateRunLoopSource.restype = void_p
+CFFileDescriptorCreateRunLoopSource.argtypes = [void_p, void_p, void_p]
+
+CFRunLoopGetCurrent = CoreFoundation.CFRunLoopGetCurrent
+CFRunLoopGetCurrent.restype = void_p
+
+CFRunLoopAddSource = CoreFoundation.CFRunLoopAddSource
+CFRunLoopAddSource.restype = None
+CFRunLoopAddSource.argtypes = [void_p, void_p, void_p]
+
+CFRelease = CoreFoundation.CFRelease
+CFRelease.restype = None
+CFRelease.argtypes = [void_p]
+
+CFFileDescriptorInvalidate = CoreFoundation.CFFileDescriptorInvalidate
+CFFileDescriptorInvalidate.restype = None
+CFFileDescriptorInvalidate.argtypes = [void_p]
+
+# From CFFileDescriptor.h
+kCFFileDescriptorReadCallBack = 1
+kCFRunLoopCommonModes = void_p.in_dll(CoreFoundation, 'kCFRunLoopCommonModes')
+
+
+def _NSApp():
+    """Return the global NSApplication instance (NSApp)"""
+    objc.objc_msgSend.argtypes = [void_p, void_p]
+    return msg(C('NSApplication'), n('sharedApplication'))
+
+
+def _wake(NSApp):
+    """Wake the Application"""
+    objc.objc_msgSend.argtypes = [
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+        void_p,
+    ]
+    event = msg(
+        C("NSEvent"),
+        n(
+            "otherEventWithType:location:modifierFlags:"
+            "timestamp:windowNumber:context:subtype:data1:data2:"
+        ),
+        15,  # Type
+        0,  # location
+        0,  # flags
+        0,  # timestamp
+        0,  # window
+        None,  # context
+        0,  # subtype
+        0,  # data1
+        0,  # data2
+    )
+    objc.objc_msgSend.argtypes = [void_p, void_p, void_p, void_p]
+    msg(NSApp, n('postEvent:atStart:'), void_p(event), True)
+
+
+def _input_callback(fdref, flags, info):
+    """Callback to fire when there's input to be read"""
+    CFFileDescriptorInvalidate(fdref)
+    CFRelease(fdref)
+    NSApp = _NSApp()
+    objc.objc_msgSend.argtypes = [void_p, void_p, void_p]
+    msg(NSApp, n('stop:'), NSApp)
+    _wake(NSApp)
+
+_c_callback_func_type = ctypes.CFUNCTYPE(None, void_p, void_p, void_p)
+_c_input_callback = _c_callback_func_type(_input_callback)
+
+
+def _stop_on_read(fd):
+    """Register callback to stop eventloop when there's data on fd"""
+    fdref = CFFileDescriptorCreate(None, fd, False, _c_input_callback, None)
+    CFFileDescriptorEnableCallBacks(fdref, kCFFileDescriptorReadCallBack)
+    source = CFFileDescriptorCreateRunLoopSource(None, fdref, 0)
+    loop = CFRunLoopGetCurrent()
+    CFRunLoopAddSource(loop, source, kCFRunLoopCommonModes)
+    CFRelease(source)
+
+
+def inputhook(context):
+    """Inputhook for Cocoa (NSApp)"""
+    NSApp = _NSApp()
+    _stop_on_read(context.fileno())
+    objc.objc_msgSend.argtypes = [void_p, void_p]
+    msg(NSApp, n('run'))
diff --git a/shared/package-files/4e/4e79e6fe3baf2dc224d634006510eae9bbcc0ca82da0ff460c8c36b8c9910d21/rref_context.h b/shared/package-files/4e/4e79e6fe3baf2dc224d634006510eae9bbcc0ca82da0ff460c8c36b8c9910d21/rref_context.h
new file mode 100644
index 0000000000000000000000000000000000000000..87ffd4f868e3d76bcb5777650e2fa8a22391e7b2
--- /dev/null
+++ b/shared/package-files/4e/4e79e6fe3baf2dc224d634006510eae9bbcc0ca82da0ff460c8c36b8c9910d21/rref_context.h
@@ -0,0 +1,339 @@
+#pragma once
+
+#include <c10/util/Optional.h>
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/rpc_agent.h>
+#include <torch/csrc/distributed/rpc/rref_impl.h>
+#include <torch/csrc/distributed/rpc/types.h>
+#include <torch/csrc/distributed/rpc/utils.h>
+
+#include <atomic>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+namespace callback {
+// It's the callback for RemoteCall.
+void TORCH_API
+confirmPendingUser(const JitFuture& jitFuture, const ForkId& expectedForkId);
+
+// It's the callback for finishing creating owner rref, it returned deletedRRef,
+// so that the deletedRRef can be handled under GIL in python_functions.cpp if
+// deletedRRef contains python object.
+c10::intrusive_ptr<RRef> TORCH_API
+finishCreatingOwnerRRef(const JitFuture& jitFuture, const RRefId& rrefId);
+} // namespace callback
+
+// Manages RRef lifetime and keeps track of RRef forks.
+class TORCH_API RRefContext {
+ public:
+  static RRefContext& getInstance();
+  // NB: This method must be called before destructing RRefContext singleton.
+  // Similar to delForkOfOwner, this method returns a vector of OwnerRRefs that
+  // hold py::object. The call-site is also responsible for resetting those
+  // shared_ptr objects with a GIL. See comments at delForkOfOwner() for more
+  // details.
+  static std::vector<c10::intrusive_ptr<RRef>> destroyInstance(
+      bool ignoreRRefLeak = true);
+
+  static void handleException(const JitFuture& jitFuture);
+
+  // handle exception without throw ::c10::Error again
+  static void handleExceptionSilent(const JitFuture& jitFuture);
+
+  RRefContext(const RRefContext&) = delete;
+  RRefContext(RRefContext&& other) = delete;
+  void operator=(const RRefContext&) = delete;
+  RRefContext& operator=(RRefContext&& other) = delete;
+
+  ~RRefContext();
+
+  // get the worker id of the current worker
+  inline worker_id_t getWorkerId() const {
+    return agent_->getWorkerInfo().id_;
+  }
+
+  // get the worker name of the current worker
+  inline const std::string& getWorkerName() const {
+    return agent_->getWorkerInfo().name_;
+  }
+
+  //  generate a globally unique ID
+  inline GloballyUniqueId genGloballyUniqueId() {
+    return GloballyUniqueId(getWorkerId(), nextLocalId_++);
+  }
+
+  inline const std::shared_ptr<RpcAgent>& agent() const {
+    return agent_;
+  }
+
+  // create a ``UserRRef`` owned by the worker ``ownerId``
+  c10::intrusive_ptr<UserRRef> createUserRRef(
+      worker_id_t ownerId,
+      const TypePtr& type);
+
+  // Convert an RRefForkData into an RRef. This RRef could be user or owner.
+  // This RRef could have already existed before, or could be created in this
+  // method, we pass type here to validate or help the rref creation.
+  c10::intrusive_ptr<RRef> getOrCreateRRef(
+      const RRefForkData& rfd,
+      const TypePtr& type);
+
+  // Get the ``OwnerRRef`` of id ``rrefId``. If it does not exist, create a new
+  // one. This function is called in two places:
+  // 1. when processing ``rpc.remote()``, i.e., ``SCRIPT_REMOTE_CALL``
+  //    ``PYTHON_REMOTE_CALL``.
+  // 2. when unpickling ``OwnerRRef``.
+  // What's common in these two cases are, 1) the RRefId is already generated
+  // 2) the TypePtr is presented. So it can always create the ``OwnerRRef`` if
+  // it is not yet available.
+  c10::intrusive_ptr<OwnerRRef> getOrCreateOwnerRRef(
+      const RRefId& rrefId,
+      const TypePtr& type);
+
+  // Create an empty owner rref of type.
+  // This method is called to first time generate an ``OwnerRRef``, e.g.,
+  // 1) ``rpc.RRef(obj)``
+  // 2) create the ``OwnerRRef`` on `rpc.remote()` caller side.
+  // What's common in these two cases are, 1) the RRefId hasn't been generated
+  // 2) the TypePtr is presented.
+  c10::intrusive_ptr<OwnerRRef> createOwnerRRef(const TypePtr& type);
+
+  // Returns a Future of the OwnerRRef, which will be marked completed when
+  // ``OwnerRRef`` is created. This method is used when the TypePtr is not
+  // available, e.g., when processing to_here(). The forceCreated flag can be
+  // used to ensure that the rref is created on the owner, otherwise throw in
+  // cases where the user of this API expects this to return a completed future.
+  // Note that the return value is a intrusive_ptr to a c10::ivalue::Future that
+  // holds the RRef.
+  c10::intrusive_ptr<JitFuture> getOwnerRRef(
+      const RRefId& rrefId,
+      bool forceCreated = false);
+
+  // Adding the RRefId of an OwnerRRef into the forks_ map. This is useful when
+  // making a remote call to self, which as for now, still goes through serde
+  // and invokes request callback. In this case, the OwnerRRef has already been
+  // created on the send side, and we need to pass it to the receive side,
+  // instead of creating a new OwnerRRef. This is done by adding the OwnerRRef
+  // into owners_. However, that alone is not enough, as it could be deleted
+  // when all UserRRef die, which would then remove the OwnerRRef from owners_
+  // and this could happen before the self remote call finishes. To prevent
+  // that, this API adds the RRefId as a ForkId, which will then delete the
+  // ForkId when the self remote is done.
+  void addSelfAsFork(c10::intrusive_ptr<OwnerRRef>& rref);
+
+  // Register a fork of the ``OwnerRRef``, and inserts a intrusive_ptr of the
+  // ``OwnerRRef`` in a map to keep it alive.
+  void addForkOfOwner(const RRefId& rrefId, const ForkId& forkId);
+  // Performs the same function as addForkOfOwner but ignores duplicate
+  // requests. This idempotent function is used with RREF_FORK_REQUEST calls,
+  // whereas all other message types use the non-idempotent variant.
+  void addForkOfOwnerIfNotPresent(const RRefId& rrefId, const ForkId& forkId);
+  // Delete a fork of the ``OwnerRRef``. NB: this could trigger deletion on the
+  // IValue or py::object. For the later, this method will acquire GIL.
+  // NB: If this fork deletion triggered deleting OwnerRRef, this method will
+  // return a shared_ptr to the OwnerRRef, which is likely to be the last
+  // shared_ptr instance for it. Therefore, deleting this shared_ptr<OwnerRRef>
+  // will also trigger deleting the object it points to. If OwnerRRef holds a
+  // py::object, deleting it require GIL. The call site should guarded it with
+  // a GIL and reset the shared_ptr. The GIL-guarded deletion is intentionally
+  // left out of this function to avoid creating dependency on pybind.
+  c10::intrusive_ptr<RRef> delForkOfOwner(
+      const RRefId& rrefId,
+      const ForkId& forkId);
+
+  // Invoked when pickling an RRef to setup child/fork properly
+  RRefForkData prepareChildFork(const c10::intrusive_ptr<RRef>& rref);
+  // Invoked when unpickling an RRef to send RREF_FORK_REQUEST to owner and
+  // send RREF_CHILD_ACCEPT to the parent.
+  // NB: forkId is necessary here as the rref could be an OwnerRRef
+  void notifyOwnerAndParentOfFork(
+      const ForkId& forkId,
+      worker_id_t parent,
+      const c10::intrusive_ptr<RRef>& rref);
+
+  // When a UserRRef is forked to another worker (user or owner), it is added
+  // into pendingChildren_ to be held alive until it receives RREF_CHILD_ACCEPT
+  // from the child.
+  // NB: This is necessary for both user and owner child. As we do not have FIFO
+  // communication between workers, we need this strategy to make sure that all
+  // previously submitted rpc/remote calls are acked before sending out the
+  // RREF_USER_DELETE message. Otherwise, the OwnerRRef could be deleted too
+  // soon.
+  void addPendingChild(
+      const ForkId& forkId,
+      const c10::intrusive_ptr<RRef>& rref);
+  void delPendingChild(const ForkId& forkId);
+
+  // When a UserRRef is created, it is added into pendingUsers_ to be held alive
+  // until it receives RREF_USER_ACCEPT from the owner.
+  void addPendingUser(
+      const ForkId& forkId,
+      const c10::intrusive_ptr<RRef>& rref);
+  void delPendingUser(const ForkId& forkId);
+  void addConfirmedUser(
+      const ForkId& forkId,
+      const c10::intrusive_ptr<RRef>& rref);
+
+  // Retrieve a pending user given the fork ID. Throws if the user has already
+  // been confirmed (i.e. is no longer in the pendingUsers_ map).
+  c10::intrusive_ptr<RRef> getPendingUser(const ForkId& forkId);
+
+  // Start recording new pending UserRRefs. All pending UserRRefs introduced
+  // after this point will be put into the thread_local userTable_, which will
+  // then be consumed and cleared in waitForThreadLocalPendingRRefs().
+  void recordThreadLocalPendingRRefs();
+  // End recording new pending UserRRefs, and clear the thread_local userTable_.
+  // Returns a Future which will be marked as completed when all pending
+  // UserRRefs in the current userTable_ are confirmed by their owners. The bool
+  // value in the Future is unused.
+  // This method is useful to make sure RRefs in user function arguments are
+  // confirmed before launching user code.
+  // NB: Callers of this method does not need to keep the returned Future alive,
+  // because this Future is already captured in callbacks of the
+  // PendingUserState. If there is no pending UserRRefs, this method returns a
+  // completed future.
+  c10::intrusive_ptr<JitFuture> waitForThreadLocalPendingRRefs();
+  // Only call this function when there are errors during a recording session,
+  // and it is likely that waitForThreadLocalPendingRRefs() cannot be invoked
+  // properly.
+  // TODO: make this a context guard
+  void clearRecordedPendingRRefsOnError();
+
+  void delUser(
+      const worker_id_t owner,
+      const RRefId& rrefId,
+      const ForkId& forkId);
+  void delAllUsersAndUnforkedOwners(std::chrono::milliseconds timeoutMillis);
+
+  std::unordered_map<std::string, std::string> getDebugInfo();
+
+ private:
+  struct PendingUserState {
+    PendingUserState(c10::intrusive_ptr<RRef> rref)
+        : rref_(std::move(rref)),
+          confirmationFuture_(c10::make_intrusive<JitFuture>(BoolType::get())) {
+    }
+
+    inline void confirm() {
+      c10::static_intrusive_pointer_cast<UserRRef>(rref_)->confirm();
+      confirmationFuture_->markCompleted();
+    }
+
+    c10::intrusive_ptr<RRef> rref_;
+    // Use Future.wait() and Future.markCompleted() to block and unblock user
+    // functions. The bool value wrapped by the future_ is not used.
+    c10::intrusive_ptr<JitFuture> confirmationFuture_;
+  };
+
+  RRefContext(std::shared_ptr<RpcAgent>);
+
+  c10::intrusive_ptr<UserRRef> createUserRRef(
+      worker_id_t ownerId,
+      const RRefId& rrefId,
+      const ForkId& forkId,
+      const TypePtr& type);
+
+  void finishForkRequest(const ForkId& forkId, worker_id_t parent);
+
+  // If there is any leak on any RRef, this method will throw an error.
+  void checkRRefLeaks(bool ignoreRRefLeak);
+
+  static std::atomic<local_id_t> nextLocalId_;
+
+  const std::shared_ptr<RpcAgent> agent_;
+  mutable std::mutex mutex_;
+  // Keep OwnerRRefs alive until there is no living UserRRefs.
+  std::unordered_map<RRefId, c10::intrusive_ptr<RRef>, RRefId::Hash> owners_;
+  // A map to track OwnerRRefs that are requested but not yet created. This can
+  // happen if the to_here() message is processed on the owner before the
+  // corresponding creator rpc.remote() message. If this happens, instead of
+  // to_here() RPC thread to block waiting for the OwnerRRef creation, the
+  // RRefContext returns a Future, so that the RPC request processing logic can
+  // attach subsequent code as a callback to that Future.
+  // NB: the OwnerRRefs in this map must be cleared when the corresponding
+  // OwnerRRef is created. Note that the values in this map are intrusive_ptrs
+  // to c10::ivalue::Future that will be marked completed with the owner RRef.
+  std::unordered_map<RRefId, c10::intrusive_ptr<JitFuture>, RRefId::Hash>
+      pendingOwners_;
+  // Tracks known living UserRRefs of an OwnerRRef
+  std::unordered_map<
+      RRefId,
+      std::unordered_set<ForkId, ForkId::Hash>,
+      RRefId::Hash>
+      forks_;
+
+  // This cond var is used by deleteAllUsers(), a event notification is sent if
+  // number of pending UserRRef or UserRRef children is reduced, or
+  // number of owned OwnerRRef is reduced.
+  std::condition_variable deleteAllUsersCV_;
+  // The follow 3 maps keep UserRRefs alive by holding a intrusive_ptr to the
+  // RRef instances. A UserRRef must be added into this map if any of the
+  // following two conditions is true:
+  //
+  // (1) A UserRRef has not been accepted by owner yet.
+  //
+  //     It can be used or shared, but cannot be deleted, and hence kept alive
+  //     in this map. A message of type RREF_USER_ACCEPT will move the
+  //     corresponding RRef from pendingUsers_ map to confirmedUsers_ map.
+  std::unordered_map<ForkId, std::shared_ptr<PendingUserState>, ForkId::Hash>
+      pendingUsers_;
+  //     UserRRefs are added into this map when it is confirmed by the owner.
+  //     When destroying RRefContext this map helps to find local UserRRefs
+  //     and send delete messages if they are still not deleted by Python
+  //     garbage collection.
+  std::unordered_map<ForkId, c10::weak_intrusive_ptr<RRef>, ForkId::Hash>
+      confirmedUsers_;
+
+  // (2) A UserRRef has forked a child UserRRef which has not been accepted by
+  //     the owner yet.
+  //
+  //     In this case, this UserRRef cannot send out RREF_USER_DELETE message,
+  //     as it could potentially trigger the OwnerRRef been deleted before the
+  //     owner learns about the forked child.
+  std::unordered_map<ForkId, c10::intrusive_ptr<RRef>, ForkId::Hash>
+      pendingChildren_;
+
+  // The RRef context performs its operations through async RPC requests, in
+  // order to not block the user code. Therefore the RRef context's state may be
+  // lagging a bit behind what it is intended to be, while it waits for these
+  // requests to complete. To allow syncing when needed, we store the count of
+  // these pending requests, so that users can wait for it to reach zero.
+  std::atomic<int64_t> numPendingFutures_{0};
+
+  std::mutex destroyedMutex_;
+  bool destroyed_{false};
+
+  // Thread local states to keep UserRRefs deserialized from user function
+  // arguments.
+  static thread_local std::vector<std::shared_ptr<PendingUserState>> userTable_;
+  // A flag indicating whether subsequently created UserRRefs should be added to
+  // the thread_local userTable_. The flag is set to true before serializing
+  // RPC arguments and then set to false before running the corresponding
+  // user code. See addPendingUser and delPendingUser for more details.
+  // NB: The reason for having this flag is because addPendingUser are called in
+  // two cases, and we only want to track the 2nd case.
+  // (1) RRef as the return value: when calling rpc.remote, the UserRRef on the
+  //     caller side is added to the context using addPendingUser.
+  // (2) RRef as an argument: When running an RPC using RRefs as arguments, the
+  //     RRef is forwarded to the callee as new UserRRefs (if the callee is not
+  //     the owner). In this case, we block running the user function until all
+  //     UserRRefs are confirmed by the owner.
+  // This contract gurantees that no UserRRefs can be used remotely without
+  // confirmation. Note that, however, the UserRRef created by rpc.remote can
+  // still be passed to local functions as arguments and used there. This is by
+  // design, because this feature is especially useful when, say a master node
+  // creates multiple UserRRefs in a loop and then shares them with other nodes.
+  // Blocking every iteration in the loop until RRefs are confirmed will slow
+  // this down. This nuance on UserRRef can be interpreted as we only make
+  // exceptions for UserRRef creators. And using the UserRRef on its creator
+  // without confirmation is OK, because the creator would either call to_here
+  // or forward the UserRRef, and both would then require confirmations from the
+  // owner.
+  static thread_local bool recording_;
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch
diff --git a/shared/package-files/4e/4e7f1349939ad78b4693b472401ae1d3ef4e3b3e4e684d9611ee1297eb7c40d3/macrodata_jmulti_cs_lagorder.txt b/shared/package-files/4e/4e7f1349939ad78b4693b472401ae1d3ef4e3b3e4e684d9611ee1297eb7c40d3/macrodata_jmulti_cs_lagorder.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c86b94359b02e5f3e9155212223aea627df03f2b
--- /dev/null
+++ b/shared/package-files/4e/4e7f1349939ad78b4693b472401ae1d3ef4e3b3e4e684d9611ee1297eb7c40d3/macrodata_jmulti_cs_lagorder.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:26:11 ***
+OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA
+
+endogenous variables:     realcons realgdp realinv 
+deterministic variables:  CONST S1 S2 S3 
+sample range:             [1961 Q3, 2009 Q3], T = 193
+
+optimal number of lags (searched up to 10 lags of levels):
+Akaike Info Criterion:    4            
+Final Prediction Error:   4            
+Hannan-Quinn Criterion:   2            
+Schwarz Criterion:        2            
+
diff --git a/shared/package-files/4e/4e8285c4fb7b1d2984034d7870590c665efdaae33682917bb6670ff21d41b98b/FindPackageMessage.cmake b/shared/package-files/4e/4e8285c4fb7b1d2984034d7870590c665efdaae33682917bb6670ff21d41b98b/FindPackageMessage.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..6821cee4f77a9d84c74f2c140870a2163ae5a5f0
--- /dev/null
+++ b/shared/package-files/4e/4e8285c4fb7b1d2984034d7870590c665efdaae33682917bb6670ff21d41b98b/FindPackageMessage.cmake
@@ -0,0 +1,47 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#.rst:
+# FindPackageMessage
+# ------------------
+#
+#
+#
+# FIND_PACKAGE_MESSAGE(<name> "message for user" "find result details")
+#
+# This macro is intended to be used in FindXXX.cmake modules files.  It
+# will print a message once for each unique find result.  This is useful
+# for telling the user where a package was found.  The first argument
+# specifies the name (XXX) of the package.  The second argument
+# specifies the message to display.  The third argument lists details
+# about the find result so that if they change the message will be
+# displayed again.  The macro also obeys the QUIET argument to the
+# find_package command.
+#
+# Example:
+#
+# ::
+#
+#   if(X11_FOUND)
+#     FIND_PACKAGE_MESSAGE(X11 "Found X11: ${X11_X11_LIB}"
+#       "[${X11_X11_LIB}][${X11_INCLUDE_DIR}]")
+#   else()
+#    ...
+#   endif()
+
+function(FIND_PACKAGE_MESSAGE pkg msg details)
+  # Avoid printing a message repeatedly for the same find result.
+  if(NOT ${pkg}_FIND_QUIETLY)
+    string(REPLACE "\n" "" details "${details}")
+    set(DETAILS_VAR FIND_PACKAGE_MESSAGE_DETAILS_${pkg})
+    if(NOT "${details}" STREQUAL "${${DETAILS_VAR}}")
+      # The message has not yet been printed.
+      message(STATUS "${msg}")
+
+      # Save the find details in the cache to avoid printing the same
+      # message again.
+      set("${DETAILS_VAR}" "${details}"
+        CACHE INTERNAL "Details about finding ${pkg}")
+    endif()
+  endif()
+endfunction()
diff --git a/shared/package-files/4e/4e82d4eeb0703b0054fbadca116129c13f1c19027b46bf11c8e138cfd62cfffe/test_storemagic.py b/shared/package-files/4e/4e82d4eeb0703b0054fbadca116129c13f1c19027b46bf11c8e138cfd62cfffe/test_storemagic.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ac306bcddd86bcc132196e1702df49a937ba14f
--- /dev/null
+++ b/shared/package-files/4e/4e82d4eeb0703b0054fbadca116129c13f1c19027b46bf11c8e138cfd62cfffe/test_storemagic.py
@@ -0,0 +1,66 @@
+import tempfile, os
+from pathlib import Path
+
+from traitlets.config.loader import Config
+
+
+def setup_module():
+    ip.magic('load_ext storemagic')
+
+def test_store_restore():
+    assert 'bar' not in ip.user_ns, "Error: some other test leaked `bar` in user_ns"
+    assert 'foo' not in ip.user_ns, "Error: some other test leaked `foo` in user_ns"
+    assert 'foobar' not in ip.user_ns, "Error: some other test leaked `foobar` in user_ns"
+    assert 'foobaz' not in ip.user_ns, "Error: some other test leaked `foobaz` in user_ns"
+    ip.user_ns['foo'] = 78
+    ip.magic('alias bar echo "hello"')
+    ip.user_ns['foobar'] = 79
+    ip.user_ns['foobaz'] = '80'
+    tmpd = tempfile.mkdtemp()
+    ip.magic('cd ' + tmpd)
+    ip.magic('store foo')
+    ip.magic('store bar')
+    ip.magic('store foobar foobaz')
+
+    # Check storing
+    assert ip.db["autorestore/foo"] == 78
+    assert "bar" in ip.db["stored_aliases"]
+    assert ip.db["autorestore/foobar"] == 79
+    assert ip.db["autorestore/foobaz"] == "80"
+
+    # Remove those items
+    ip.user_ns.pop('foo', None)
+    ip.user_ns.pop('foobar', None)
+    ip.user_ns.pop('foobaz', None)
+    ip.alias_manager.undefine_alias('bar')
+    ip.magic('cd -')
+    ip.user_ns['_dh'][:] = []
+
+    # Check restoring
+    ip.magic("store -r foo bar foobar foobaz")
+    assert ip.user_ns["foo"] == 78
+    assert ip.alias_manager.is_alias("bar")
+    assert ip.user_ns["foobar"] == 79
+    assert ip.user_ns["foobaz"] == "80"
+
+    ip.magic("store -r")  # restores _dh too
+    assert any(Path(tmpd).samefile(p) for p in ip.user_ns["_dh"])
+
+    os.rmdir(tmpd)
+
+def test_autorestore():
+    ip.user_ns['foo'] = 95
+    ip.magic('store foo')
+    del ip.user_ns['foo']
+    c = Config()
+    c.StoreMagics.autorestore = False
+    orig_config = ip.config
+    try:
+        ip.config = c
+        ip.extension_manager.reload_extension("storemagic")
+        assert "foo" not in ip.user_ns
+        c.StoreMagics.autorestore = True
+        ip.extension_manager.reload_extension("storemagic")
+        assert ip.user_ns["foo"] == 95
+    finally:
+        ip.config = orig_config
diff --git a/shared/package-files/4e/4e869b0b78770c9199d71a138f01f2f4fef9da7add4a8bf1949b0633212a8d58/test_lib.py b/shared/package-files/4e/4e869b0b78770c9199d71a138f01f2f4fef9da7add4a8bf1949b0633212a8d58/test_lib.py
new file mode 100644
index 0000000000000000000000000000000000000000..17dae1879f3b818bac33f8825d0392d1c028975f
--- /dev/null
+++ b/shared/package-files/4e/4e869b0b78770c9199d71a138f01f2f4fef9da7add4a8bf1949b0633212a8d58/test_lib.py
@@ -0,0 +1,299 @@
+import pickle
+
+import numpy as np
+import pytest
+
+from pandas._libs import (
+    Timedelta,
+    lib,
+    writers as libwriters,
+)
+from pandas.compat import IS64
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestMisc:
+    def test_max_len_string_array(self):
+        arr = a = np.array(["foo", "b", np.nan], dtype="object")
+        assert libwriters.max_len_string_array(arr) == 3
+
+        # unicode
+        arr = a.astype("U").astype(object)
+        assert libwriters.max_len_string_array(arr) == 3
+
+        # bytes for python3
+        arr = a.astype("S").astype(object)
+        assert libwriters.max_len_string_array(arr) == 3
+
+        # raises
+        msg = "No matching signature found"
+        with pytest.raises(TypeError, match=msg):
+            libwriters.max_len_string_array(arr.astype("U"))
+
+    def test_fast_unique_multiple_list_gen_sort(self):
+        keys = [["p", "a"], ["n", "d"], ["a", "s"]]
+
+        gen = (key for key in keys)
+        expected = np.array(["a", "d", "n", "p", "s"])
+        out = lib.fast_unique_multiple_list_gen(gen, sort=True)
+        tm.assert_numpy_array_equal(np.array(out), expected)
+
+        gen = (key for key in keys)
+        expected = np.array(["p", "a", "n", "d", "s"])
+        out = lib.fast_unique_multiple_list_gen(gen, sort=False)
+        tm.assert_numpy_array_equal(np.array(out), expected)
+
+    def test_fast_multiget_timedelta_resos(self):
+        # This will become relevant for test_constructor_dict_timedelta64_index
+        #  once Timedelta constructor preserves reso when passed a
+        #  np.timedelta64 object
+        td = Timedelta(days=1)
+
+        mapping1 = {td: 1}
+        mapping2 = {td.as_unit("s"): 1}
+
+        oindex = Index([td * n for n in range(3)])._values.astype(object)
+
+        expected = lib.fast_multiget(mapping1, oindex)
+        result = lib.fast_multiget(mapping2, oindex)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # case that can't be cast to td64ns
+        td = Timedelta(np.timedelta64(146000, "D"))
+        assert hash(td) == hash(td.as_unit("ms"))
+        assert hash(td) == hash(td.as_unit("us"))
+        mapping1 = {td: 1}
+        mapping2 = {td.as_unit("ms"): 1}
+
+        oindex = Index([td * n for n in range(3)])._values.astype(object)
+
+        expected = lib.fast_multiget(mapping1, oindex)
+        result = lib.fast_multiget(mapping2, oindex)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestIndexing:
+    def test_maybe_indices_to_slice_left_edge(self):
+        target = np.arange(100)
+
+        # slice
+        indices = np.array([], dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize("end", [1, 2, 5, 20, 99])
+    @pytest.mark.parametrize("step", [1, 2, 4])
+    def test_maybe_indices_to_slice_left_edge_not_slice_end_steps(self, end, step):
+        target = np.arange(100)
+        indices = np.arange(0, end, step, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+        # reverse
+        indices = indices[::-1]
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize(
+        "case", [[2, 1, 2, 0], [2, 2, 1, 0], [0, 1, 2, 1], [-2, 0, 2], [2, 0, -2]]
+    )
+    def test_maybe_indices_to_slice_left_edge_not_slice(self, case):
+        # not slice
+        target = np.arange(100)
+        indices = np.array(case, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize("start", [0, 2, 5, 20, 97, 98])
+    @pytest.mark.parametrize("step", [1, 2, 4])
+    def test_maybe_indices_to_slice_right_edge(self, start, step):
+        target = np.arange(100)
+
+        # slice
+        indices = np.arange(start, 99, step, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+        # reverse
+        indices = indices[::-1]
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    def test_maybe_indices_to_slice_right_edge_not_slice(self):
+        # not slice
+        target = np.arange(100)
+        indices = np.array([97, 98, 99, 100], dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+
+        msg = "index 100 is out of bounds for axis (0|1) with size 100"
+
+        with pytest.raises(IndexError, match=msg):
+            target[indices]
+        with pytest.raises(IndexError, match=msg):
+            target[maybe_slice]
+
+        indices = np.array([100, 99, 98, 97], dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+
+        with pytest.raises(IndexError, match=msg):
+            target[indices]
+        with pytest.raises(IndexError, match=msg):
+            target[maybe_slice]
+
+    @pytest.mark.parametrize(
+        "case", [[99, 97, 99, 96], [99, 99, 98, 97], [98, 98, 97, 96]]
+    )
+    def test_maybe_indices_to_slice_right_edge_cases(self, case):
+        target = np.arange(100)
+        indices = np.array(case, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize("step", [1, 2, 4, 5, 8, 9])
+    def test_maybe_indices_to_slice_both_edges(self, step):
+        target = np.arange(10)
+
+        # slice
+        indices = np.arange(0, 9, step, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+        # reverse
+        indices = indices[::-1]
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize("case", [[4, 2, 0, -2], [2, 2, 1, 0], [0, 1, 2, 1]])
+    def test_maybe_indices_to_slice_both_edges_not_slice(self, case):
+        # not slice
+        target = np.arange(10)
+        indices = np.array(case, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize("start, end", [(2, 10), (5, 25), (65, 97)])
+    @pytest.mark.parametrize("step", [1, 2, 4, 20])
+    def test_maybe_indices_to_slice_middle(self, start, end, step):
+        target = np.arange(100)
+
+        # slice
+        indices = np.arange(start, end, step, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+        # reverse
+        indices = indices[::-1]
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    @pytest.mark.parametrize(
+        "case", [[14, 12, 10, 12], [12, 12, 11, 10], [10, 11, 12, 11]]
+    )
+    def test_maybe_indices_to_slice_middle_not_slice(self, case):
+        # not slice
+        target = np.arange(100)
+        indices = np.array(case, dtype=np.intp)
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
+
+        assert not isinstance(maybe_slice, slice)
+        tm.assert_numpy_array_equal(maybe_slice, indices)
+        tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
+
+    def test_maybe_booleans_to_slice(self):
+        arr = np.array([0, 0, 1, 1, 1, 0, 1], dtype=np.uint8)
+        result = lib.maybe_booleans_to_slice(arr)
+        assert result.dtype == np.bool_
+
+        result = lib.maybe_booleans_to_slice(arr[:0])
+        assert result == slice(0, 0)
+
+    def test_get_reverse_indexer(self):
+        indexer = np.array([-1, -1, 1, 2, 0, -1, 3, 4], dtype=np.intp)
+        result = lib.get_reverse_indexer(indexer, 5)
+        expected = np.array([4, 2, 3, 6, 7], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "int32"])
+    def test_is_range_indexer(self, dtype):
+        # GH#50592
+        left = np.arange(0, 100, dtype=dtype)
+        assert lib.is_range_indexer(left, 100)
+
+    @pytest.mark.skipif(
+        not IS64,
+        reason="2**31 is too big for Py_ssize_t on 32-bit. "
+        "It doesn't matter though since you cannot create an array that long on 32-bit",
+    )
+    @pytest.mark.parametrize("dtype", ["int64", "int32"])
+    def test_is_range_indexer_big_n(self, dtype):
+        # GH53616
+        left = np.arange(0, 100, dtype=dtype)
+
+        assert not lib.is_range_indexer(left, 2**31)
+
+    @pytest.mark.parametrize("dtype", ["int64", "int32"])
+    def test_is_range_indexer_not_equal(self, dtype):
+        # GH#50592
+        left = np.array([1, 2], dtype=dtype)
+        assert not lib.is_range_indexer(left, 2)
+
+    @pytest.mark.parametrize("dtype", ["int64", "int32"])
+    def test_is_range_indexer_not_equal_shape(self, dtype):
+        # GH#50592
+        left = np.array([0, 1, 2], dtype=dtype)
+        assert not lib.is_range_indexer(left, 2)
+
+
+def test_cache_readonly_preserve_docstrings():
+    # GH18197
+    assert Index.hasnans.__doc__ is not None
+
+
+def test_no_default_pickle():
+    # GH#40397
+    obj = tm.round_trip_pickle(lib.no_default)
+    assert obj is lib.no_default
+
+
+def test_ensure_string_array_copy():
+    # ensure the original array is not modified in case of copy=False with
+    # pickle-roundtripped object dtype array
+    # https://github.com/pandas-dev/pandas/issues/54654
+    arr = np.array(["a", None], dtype=object)
+    arr = pickle.loads(pickle.dumps(arr))
+    result = lib.ensure_string_array(arr, copy=False)
+    assert not np.shares_memory(arr, result)
+    assert arr[1] is None
+    assert result[1] is np.nan
diff --git a/shared/package-files/4e/4e8729a03a7f1bb1adb1c61726e8217e545227ecb510cb7c6cf71f9d749cb403/_rowwise_prune_compositeimplicitautograd_dispatch.h b/shared/package-files/4e/4e8729a03a7f1bb1adb1c61726e8217e545227ecb510cb7c6cf71f9d749cb403/_rowwise_prune_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..be3daf5ff8cdb9bb96a6753d24e0c688f7b54e2d
--- /dev/null
+++ b/shared/package-files/4e/4e8729a03a7f1bb1adb1c61726e8217e545227ecb510cb7c6cf71f9d749cb403/_rowwise_prune_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _rowwise_prune(const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/4e/4e8a7811e12e69074159db5e28c11c18e4de29e175f50f96a3febf0a3e643b34/ansi.py b/shared/package-files/4e/4e8a7811e12e69074159db5e28c11c18e4de29e175f50f96a3febf0a3e643b34/ansi.py
new file mode 100644
index 0000000000000000000000000000000000000000..11ec695ff79627463a0282d25079527562de9e42
--- /dev/null
+++ b/shared/package-files/4e/4e8a7811e12e69074159db5e28c11c18e4de29e175f50f96a3febf0a3e643b34/ansi.py
@@ -0,0 +1,102 @@
+# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
+'''
+This module generates ANSI character codes to printing colors to terminals.
+See: http://en.wikipedia.org/wiki/ANSI_escape_code
+'''
+
+CSI = '\033['
+OSC = '\033]'
+BEL = '\a'
+
+
+def code_to_chars(code):
+    return CSI + str(code) + 'm'
+
+def set_title(title):
+    return OSC + '2;' + title + BEL
+
+def clear_screen(mode=2):
+    return CSI + str(mode) + 'J'
+
+def clear_line(mode=2):
+    return CSI + str(mode) + 'K'
+
+
+class AnsiCodes(object):
+    def __init__(self):
+        # the subclasses declare class attributes which are numbers.
+        # Upon instantiation we define instance attributes, which are the same
+        # as the class attributes but wrapped with the ANSI escape sequence
+        for name in dir(self):
+            if not name.startswith('_'):
+                value = getattr(self, name)
+                setattr(self, name, code_to_chars(value))
+
+
+class AnsiCursor(object):
+    def UP(self, n=1):
+        return CSI + str(n) + 'A'
+    def DOWN(self, n=1):
+        return CSI + str(n) + 'B'
+    def FORWARD(self, n=1):
+        return CSI + str(n) + 'C'
+    def BACK(self, n=1):
+        return CSI + str(n) + 'D'
+    def POS(self, x=1, y=1):
+        return CSI + str(y) + ';' + str(x) + 'H'
+
+
+class AnsiFore(AnsiCodes):
+    BLACK           = 30
+    RED             = 31
+    GREEN           = 32
+    YELLOW          = 33
+    BLUE            = 34
+    MAGENTA         = 35
+    CYAN            = 36
+    WHITE           = 37
+    RESET           = 39
+
+    # These are fairly well supported, but not part of the standard.
+    LIGHTBLACK_EX   = 90
+    LIGHTRED_EX     = 91
+    LIGHTGREEN_EX   = 92
+    LIGHTYELLOW_EX  = 93
+    LIGHTBLUE_EX    = 94
+    LIGHTMAGENTA_EX = 95
+    LIGHTCYAN_EX    = 96
+    LIGHTWHITE_EX   = 97
+
+
+class AnsiBack(AnsiCodes):
+    BLACK           = 40
+    RED             = 41
+    GREEN           = 42
+    YELLOW          = 43
+    BLUE            = 44
+    MAGENTA         = 45
+    CYAN            = 46
+    WHITE           = 47
+    RESET           = 49
+
+    # These are fairly well supported, but not part of the standard.
+    LIGHTBLACK_EX   = 100
+    LIGHTRED_EX     = 101
+    LIGHTGREEN_EX   = 102
+    LIGHTYELLOW_EX  = 103
+    LIGHTBLUE_EX    = 104
+    LIGHTMAGENTA_EX = 105
+    LIGHTCYAN_EX    = 106
+    LIGHTWHITE_EX   = 107
+
+
+class AnsiStyle(AnsiCodes):
+    BRIGHT    = 1
+    DIM       = 2
+    NORMAL    = 22
+    RESET_ALL = 0
+
+Fore   = AnsiFore()
+Back   = AnsiBack()
+Style  = AnsiStyle()
+Cursor = AnsiCursor()
diff --git a/shared/package-files/4e/4e8c6320d3a64a7f266c479745254d32274426c3d46071181679cde0c6aabc7d/metadata.py b/shared/package-files/4e/4e8c6320d3a64a7f266c479745254d32274426c3d46071181679cde0c6aabc7d/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b0e6a9c3263cdafba53f6d2ecc713ca7955b15a
--- /dev/null
+++ b/shared/package-files/4e/4e8c6320d3a64a7f266c479745254d32274426c3d46071181679cde0c6aabc7d/metadata.py
@@ -0,0 +1,822 @@
+import email.feedparser
+import email.header
+import email.message
+import email.parser
+import email.policy
+import sys
+import typing
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
+
+from . import requirements, specifiers, utils, version as version_module
+
+T = typing.TypeVar("T")
+if sys.version_info[:2] >= (3, 8):  # pragma: no cover
+    from typing import Literal, TypedDict
+else:  # pragma: no cover
+    if typing.TYPE_CHECKING:
+        from typing_extensions import Literal, TypedDict
+    else:
+        try:
+            from typing_extensions import Literal, TypedDict
+        except ImportError:
+
+            class Literal:
+                def __init_subclass__(*_args, **_kwargs):
+                    pass
+
+            class TypedDict:
+                def __init_subclass__(*_args, **_kwargs):
+                    pass
+
+
+try:
+    ExceptionGroup = __builtins__.ExceptionGroup  # type: ignore[attr-defined]
+except AttributeError:
+
+    class ExceptionGroup(Exception):  # type: ignore[no-redef]  # noqa: N818
+        """A minimal implementation of :external:exc:`ExceptionGroup` from Python 3.11.
+
+        If :external:exc:`ExceptionGroup` is already defined by Python itself,
+        that version is used instead.
+        """
+
+        message: str
+        exceptions: List[Exception]
+
+        def __init__(self, message: str, exceptions: List[Exception]) -> None:
+            self.message = message
+            self.exceptions = exceptions
+
+        def __repr__(self) -> str:
+            return f"{self.__class__.__name__}({self.message!r}, {self.exceptions!r})"
+
+
+class InvalidMetadata(ValueError):
+    """A metadata field contains invalid data."""
+
+    field: str
+    """The name of the field that contains invalid data."""
+
+    def __init__(self, field: str, message: str) -> None:
+        self.field = field
+        super().__init__(message)
+
+
+# The RawMetadata class attempts to make as few assumptions about the underlying
+# serialization formats as possible. The idea is that as long as a serialization
+# formats offer some very basic primitives in *some* way then we can support
+# serializing to and from that format.
+class RawMetadata(TypedDict, total=False):
+    """A dictionary of raw core metadata.
+
+    Each field in core metadata maps to a key of this dictionary (when data is
+    provided). The key is lower-case and underscores are used instead of dashes
+    compared to the equivalent core metadata field. Any core metadata field that
+    can be specified multiple times or can hold multiple values in a single
+    field have a key with a plural name. See :class:`Metadata` whose attributes
+    match the keys of this dictionary.
+
+    Core metadata fields that can be specified multiple times are stored as a
+    list or dict depending on which is appropriate for the field. Any fields
+    which hold multiple values in a single field are stored as a list.
+
+    """
+
+    # Metadata 1.0 - PEP 241
+    metadata_version: str
+    name: str
+    version: str
+    platforms: List[str]
+    summary: str
+    description: str
+    keywords: List[str]
+    home_page: str
+    author: str
+    author_email: str
+    license: str
+
+    # Metadata 1.1 - PEP 314
+    supported_platforms: List[str]
+    download_url: str
+    classifiers: List[str]
+    requires: List[str]
+    provides: List[str]
+    obsoletes: List[str]
+
+    # Metadata 1.2 - PEP 345
+    maintainer: str
+    maintainer_email: str
+    requires_dist: List[str]
+    provides_dist: List[str]
+    obsoletes_dist: List[str]
+    requires_python: str
+    requires_external: List[str]
+    project_urls: Dict[str, str]
+
+    # Metadata 2.0
+    # PEP 426 attempted to completely revamp the metadata format
+    # but got stuck without ever being able to build consensus on
+    # it and ultimately ended up withdrawn.
+    #
+    # However, a number of tools had started emitting METADATA with
+    # `2.0` Metadata-Version, so for historical reasons, this version
+    # was skipped.
+
+    # Metadata 2.1 - PEP 566
+    description_content_type: str
+    provides_extra: List[str]
+
+    # Metadata 2.2 - PEP 643
+    dynamic: List[str]
+
+    # Metadata 2.3 - PEP 685
+    # No new fields were added in PEP 685, just some edge case were
+    # tightened up to provide better interoptability.
+
+
+_STRING_FIELDS = {
+    "author",
+    "author_email",
+    "description",
+    "description_content_type",
+    "download_url",
+    "home_page",
+    "license",
+    "maintainer",
+    "maintainer_email",
+    "metadata_version",
+    "name",
+    "requires_python",
+    "summary",
+    "version",
+}
+
+_LIST_FIELDS = {
+    "classifiers",
+    "dynamic",
+    "obsoletes",
+    "obsoletes_dist",
+    "platforms",
+    "provides",
+    "provides_dist",
+    "provides_extra",
+    "requires",
+    "requires_dist",
+    "requires_external",
+    "supported_platforms",
+}
+
+_DICT_FIELDS = {
+    "project_urls",
+}
+
+
+def _parse_keywords(data: str) -> List[str]:
+    """Split a string of comma-separate keyboards into a list of keywords."""
+    return [k.strip() for k in data.split(",")]
+
+
+def _parse_project_urls(data: List[str]) -> Dict[str, str]:
+    """Parse a list of label/URL string pairings separated by a comma."""
+    urls = {}
+    for pair in data:
+        # Our logic is slightly tricky here as we want to try and do
+        # *something* reasonable with malformed data.
+        #
+        # The main thing that we have to worry about, is data that does
+        # not have a ',' at all to split the label from the Value. There
+        # isn't a singular right answer here, and we will fail validation
+        # later on (if the caller is validating) so it doesn't *really*
+        # matter, but since the missing value has to be an empty str
+        # and our return value is dict[str, str], if we let the key
+        # be the missing value, then they'd have multiple '' values that
+        # overwrite each other in a accumulating dict.
+        #
+        # The other potentional issue is that it's possible to have the
+        # same label multiple times in the metadata, with no solid "right"
+        # answer with what to do in that case. As such, we'll do the only
+        # thing we can, which is treat the field as unparseable and add it
+        # to our list of unparsed fields.
+        parts = [p.strip() for p in pair.split(",", 1)]
+        parts.extend([""] * (max(0, 2 - len(parts))))  # Ensure 2 items
+
+        # TODO: The spec doesn't say anything about if the keys should be
+        #       considered case sensitive or not... logically they should
+        #       be case-preserving and case-insensitive, but doing that
+        #       would open up more cases where we might have duplicate
+        #       entries.
+        label, url = parts
+        if label in urls:
+            # The label already exists in our set of urls, so this field
+            # is unparseable, and we can just add the whole thing to our
+            # unparseable data and stop processing it.
+            raise KeyError("duplicate labels in project urls")
+        urls[label] = url
+
+    return urls
+
+
+def _get_payload(msg: email.message.Message, source: Union[bytes, str]) -> str:
+    """Get the body of the message."""
+    # If our source is a str, then our caller has managed encodings for us,
+    # and we don't need to deal with it.
+    if isinstance(source, str):
+        payload: str = msg.get_payload()
+        return payload
+    # If our source is a bytes, then we're managing the encoding and we need
+    # to deal with it.
+    else:
+        bpayload: bytes = msg.get_payload(decode=True)
+        try:
+            return bpayload.decode("utf8", "strict")
+        except UnicodeDecodeError:
+            raise ValueError("payload in an invalid encoding")
+
+
+# The various parse_FORMAT functions here are intended to be as lenient as
+# possible in their parsing, while still returning a correctly typed
+# RawMetadata.
+#
+# To aid in this, we also generally want to do as little touching of the
+# data as possible, except where there are possibly some historic holdovers
+# that make valid data awkward to work with.
+#
+# While this is a lower level, intermediate format than our ``Metadata``
+# class, some light touch ups can make a massive difference in usability.
+
+# Map METADATA fields to RawMetadata.
+_EMAIL_TO_RAW_MAPPING = {
+    "author": "author",
+    "author-email": "author_email",
+    "classifier": "classifiers",
+    "description": "description",
+    "description-content-type": "description_content_type",
+    "download-url": "download_url",
+    "dynamic": "dynamic",
+    "home-page": "home_page",
+    "keywords": "keywords",
+    "license": "license",
+    "maintainer": "maintainer",
+    "maintainer-email": "maintainer_email",
+    "metadata-version": "metadata_version",
+    "name": "name",
+    "obsoletes": "obsoletes",
+    "obsoletes-dist": "obsoletes_dist",
+    "platform": "platforms",
+    "project-url": "project_urls",
+    "provides": "provides",
+    "provides-dist": "provides_dist",
+    "provides-extra": "provides_extra",
+    "requires": "requires",
+    "requires-dist": "requires_dist",
+    "requires-external": "requires_external",
+    "requires-python": "requires_python",
+    "summary": "summary",
+    "supported-platform": "supported_platforms",
+    "version": "version",
+}
+_RAW_TO_EMAIL_MAPPING = {raw: email for email, raw in _EMAIL_TO_RAW_MAPPING.items()}
+
+
+def parse_email(data: Union[bytes, str]) -> Tuple[RawMetadata, Dict[str, List[str]]]:
+    """Parse a distribution's metadata stored as email headers (e.g. from ``METADATA``).
+
+    This function returns a two-item tuple of dicts. The first dict is of
+    recognized fields from the core metadata specification. Fields that can be
+    parsed and translated into Python's built-in types are converted
+    appropriately. All other fields are left as-is. Fields that are allowed to
+    appear multiple times are stored as lists.
+
+    The second dict contains all other fields from the metadata. This includes
+    any unrecognized fields. It also includes any fields which are expected to
+    be parsed into a built-in type but were not formatted appropriately. Finally,
+    any fields that are expected to appear only once but are repeated are
+    included in this dict.
+
+    """
+    raw: Dict[str, Union[str, List[str], Dict[str, str]]] = {}
+    unparsed: Dict[str, List[str]] = {}
+
+    if isinstance(data, str):
+        parsed = email.parser.Parser(policy=email.policy.compat32).parsestr(data)
+    else:
+        parsed = email.parser.BytesParser(policy=email.policy.compat32).parsebytes(data)
+
+    # We have to wrap parsed.keys() in a set, because in the case of multiple
+    # values for a key (a list), the key will appear multiple times in the
+    # list of keys, but we're avoiding that by using get_all().
+    for name in frozenset(parsed.keys()):
+        # Header names in RFC are case insensitive, so we'll normalize to all
+        # lower case to make comparisons easier.
+        name = name.lower()
+
+        # We use get_all() here, even for fields that aren't multiple use,
+        # because otherwise someone could have e.g. two Name fields, and we
+        # would just silently ignore it rather than doing something about it.
+        headers = parsed.get_all(name) or []
+
+        # The way the email module works when parsing bytes is that it
+        # unconditionally decodes the bytes as ascii using the surrogateescape
+        # handler. When you pull that data back out (such as with get_all() ),
+        # it looks to see if the str has any surrogate escapes, and if it does
+        # it wraps it in a Header object instead of returning the string.
+        #
+        # As such, we'll look for those Header objects, and fix up the encoding.
+        value = []
+        # Flag if we have run into any issues processing the headers, thus
+        # signalling that the data belongs in 'unparsed'.
+        valid_encoding = True
+        for h in headers:
+            # It's unclear if this can return more types than just a Header or
+            # a str, so we'll just assert here to make sure.
+            assert isinstance(h, (email.header.Header, str))
+
+            # If it's a header object, we need to do our little dance to get
+            # the real data out of it. In cases where there is invalid data
+            # we're going to end up with mojibake, but there's no obvious, good
+            # way around that without reimplementing parts of the Header object
+            # ourselves.
+            #
+            # That should be fine since, if mojibacked happens, this key is
+            # going into the unparsed dict anyways.
+            if isinstance(h, email.header.Header):
+                # The Header object stores it's data as chunks, and each chunk
+                # can be independently encoded, so we'll need to check each
+                # of them.
+                chunks: List[Tuple[bytes, Optional[str]]] = []
+                for bin, encoding in email.header.decode_header(h):
+                    try:
+                        bin.decode("utf8", "strict")
+                    except UnicodeDecodeError:
+                        # Enable mojibake.
+                        encoding = "latin1"
+                        valid_encoding = False
+                    else:
+                        encoding = "utf8"
+                    chunks.append((bin, encoding))
+
+                # Turn our chunks back into a Header object, then let that
+                # Header object do the right thing to turn them into a
+                # string for us.
+                value.append(str(email.header.make_header(chunks)))
+            # This is already a string, so just add it.
+            else:
+                value.append(h)
+
+        # We've processed all of our values to get them into a list of str,
+        # but we may have mojibake data, in which case this is an unparsed
+        # field.
+        if not valid_encoding:
+            unparsed[name] = value
+            continue
+
+        raw_name = _EMAIL_TO_RAW_MAPPING.get(name)
+        if raw_name is None:
+            # This is a bit of a weird situation, we've encountered a key that
+            # we don't know what it means, so we don't know whether it's meant
+            # to be a list or not.
+            #
+            # Since we can't really tell one way or another, we'll just leave it
+            # as a list, even though it may be a single item list, because that's
+            # what makes the most sense for email headers.
+            unparsed[name] = value
+            continue
+
+        # If this is one of our string fields, then we'll check to see if our
+        # value is a list of a single item. If it is then we'll assume that
+        # it was emitted as a single string, and unwrap the str from inside
+        # the list.
+        #
+        # If it's any other kind of data, then we haven't the faintest clue
+        # what we should parse it as, and we have to just add it to our list
+        # of unparsed stuff.
+        if raw_name in _STRING_FIELDS and len(value) == 1:
+            raw[raw_name] = value[0]
+        # If this is one of our list of string fields, then we can just assign
+        # the value, since email *only* has strings, and our get_all() call
+        # above ensures that this is a list.
+        elif raw_name in _LIST_FIELDS:
+            raw[raw_name] = value
+        # Special Case: Keywords
+        # The keywords field is implemented in the metadata spec as a str,
+        # but it conceptually is a list of strings, and is serialized using
+        # ", ".join(keywords), so we'll do some light data massaging to turn
+        # this into what it logically is.
+        elif raw_name == "keywords" and len(value) == 1:
+            raw[raw_name] = _parse_keywords(value[0])
+        # Special Case: Project-URL
+        # The project urls is implemented in the metadata spec as a list of
+        # specially-formatted strings that represent a key and a value, which
+        # is fundamentally a mapping, however the email format doesn't support
+        # mappings in a sane way, so it was crammed into a list of strings
+        # instead.
+        #
+        # We will do a little light data massaging to turn this into a map as
+        # it logically should be.
+        elif raw_name == "project_urls":
+            try:
+                raw[raw_name] = _parse_project_urls(value)
+            except KeyError:
+                unparsed[name] = value
+        # Nothing that we've done has managed to parse this, so it'll just
+        # throw it in our unparseable data and move on.
+        else:
+            unparsed[name] = value
+
+    # We need to support getting the Description from the message payload in
+    # addition to getting it from the the headers. This does mean, though, there
+    # is the possibility of it being set both ways, in which case we put both
+    # in 'unparsed' since we don't know which is right.
+    try:
+        payload = _get_payload(parsed, data)
+    except ValueError:
+        unparsed.setdefault("description", []).append(
+            parsed.get_payload(decode=isinstance(data, bytes))
+        )
+    else:
+        if payload:
+            # Check to see if we've already got a description, if so then both
+            # it, and this body move to unparseable.
+            if "description" in raw:
+                description_header = cast(str, raw.pop("description"))
+                unparsed.setdefault("description", []).extend(
+                    [description_header, payload]
+                )
+            elif "description" in unparsed:
+                unparsed["description"].append(payload)
+            else:
+                raw["description"] = payload
+
+    # We need to cast our `raw` to a metadata, because a TypedDict only support
+    # literal key names, but we're computing our key names on purpose, but the
+    # way this function is implemented, our `TypedDict` can only have valid key
+    # names.
+    return cast(RawMetadata, raw), unparsed
+
+
+_NOT_FOUND = object()
+
+
+# Keep the two values in sync.
+_VALID_METADATA_VERSIONS = ["1.0", "1.1", "1.2", "2.1", "2.2", "2.3"]
+_MetadataVersion = Literal["1.0", "1.1", "1.2", "2.1", "2.2", "2.3"]
+
+_REQUIRED_ATTRS = frozenset(["metadata_version", "name", "version"])
+
+
+class _Validator(Generic[T]):
+    """Validate a metadata field.
+
+    All _process_*() methods correspond to a core metadata field. The method is
+    called with the field's raw value. If the raw value is valid it is returned
+    in its "enriched" form (e.g. ``version.Version`` for the ``Version`` field).
+    If the raw value is invalid, :exc:`InvalidMetadata` is raised (with a cause
+    as appropriate).
+    """
+
+    name: str
+    raw_name: str
+    added: _MetadataVersion
+
+    def __init__(
+        self,
+        *,
+        added: _MetadataVersion = "1.0",
+    ) -> None:
+        self.added = added
+
+    def __set_name__(self, _owner: "Metadata", name: str) -> None:
+        self.name = name
+        self.raw_name = _RAW_TO_EMAIL_MAPPING[name]
+
+    def __get__(self, instance: "Metadata", _owner: Type["Metadata"]) -> T:
+        # With Python 3.8, the caching can be replaced with functools.cached_property().
+        # No need to check the cache as attribute lookup will resolve into the
+        # instance's __dict__ before __get__ is called.
+        cache = instance.__dict__
+        try:
+            value = instance._raw[self.name]  # type: ignore[literal-required]
+        except KeyError:
+            if self.name in _STRING_FIELDS:
+                value = ""
+            elif self.name in _LIST_FIELDS:
+                value = []
+            elif self.name in _DICT_FIELDS:
+                value = {}
+            else:  # pragma: no cover
+                assert False
+
+        try:
+            converter: Callable[[Any], T] = getattr(self, f"_process_{self.name}")
+        except AttributeError:
+            pass
+        else:
+            value = converter(value)
+
+        cache[self.name] = value
+        try:
+            del instance._raw[self.name]  # type: ignore[misc]
+        except KeyError:
+            pass
+
+        return cast(T, value)
+
+    def _invalid_metadata(
+        self, msg: str, cause: Optional[Exception] = None
+    ) -> InvalidMetadata:
+        exc = InvalidMetadata(
+            self.raw_name, msg.format_map({"field": repr(self.raw_name)})
+        )
+        exc.__cause__ = cause
+        return exc
+
+    def _process_metadata_version(self, value: str) -> _MetadataVersion:
+        # Implicitly makes Metadata-Version required.
+        if value not in _VALID_METADATA_VERSIONS:
+            raise self._invalid_metadata(f"{value!r} is not a valid metadata version")
+        return cast(_MetadataVersion, value)
+
+    def _process_name(self, value: str) -> str:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        # Validate the name as a side-effect.
+        try:
+            utils.canonicalize_name(value, validate=True)
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            )
+        else:
+            return value
+
+    def _process_version(self, value: str) -> version_module.Version:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        try:
+            return version_module.parse(value)
+        except version_module.InvalidVersion as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            )
+
+    def _process_summary(self, value: str) -> str:
+        """Check the field contains no newlines."""
+        if "\n" in value:
+            raise self._invalid_metadata("{field} must be a single line")
+        return value
+
+    def _process_description_content_type(self, value: str) -> str:
+        content_types = {"text/plain", "text/x-rst", "text/markdown"}
+        message = email.message.EmailMessage()
+        message["content-type"] = value
+
+        content_type, parameters = (
+            # Defaults to `text/plain` if parsing failed.
+            message.get_content_type().lower(),
+            message["content-type"].params,
+        )
+        # Check if content-type is valid or defaulted to `text/plain` and thus was
+        # not parseable.
+        if content_type not in content_types or content_type not in value.lower():
+            raise self._invalid_metadata(
+                f"{{field}} must be one of {list(content_types)}, not {value!r}"
+            )
+
+        charset = parameters.get("charset", "UTF-8")
+        if charset != "UTF-8":
+            raise self._invalid_metadata(
+                f"{{field}} can only specify the UTF-8 charset, not {list(charset)}"
+            )
+
+        markdown_variants = {"GFM", "CommonMark"}
+        variant = parameters.get("variant", "GFM")  # Use an acceptable default.
+        if content_type == "text/markdown" and variant not in markdown_variants:
+            raise self._invalid_metadata(
+                f"valid Markdown variants for {{field}} are {list(markdown_variants)}, "
+                f"not {variant!r}",
+            )
+        return value
+
+    def _process_dynamic(self, value: List[str]) -> List[str]:
+        for dynamic_field in map(str.lower, value):
+            if dynamic_field in {"name", "version", "metadata-version"}:
+                raise self._invalid_metadata(
+                    f"{value!r} is not allowed as a dynamic field"
+                )
+            elif dynamic_field not in _EMAIL_TO_RAW_MAPPING:
+                raise self._invalid_metadata(f"{value!r} is not a valid dynamic field")
+        return list(map(str.lower, value))
+
+    def _process_provides_extra(
+        self,
+        value: List[str],
+    ) -> List[utils.NormalizedName]:
+        normalized_names = []
+        try:
+            for name in value:
+                normalized_names.append(utils.canonicalize_name(name, validate=True))
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{name!r} is invalid for {{field}}", cause=exc
+            )
+        else:
+            return normalized_names
+
+    def _process_requires_python(self, value: str) -> specifiers.SpecifierSet:
+        try:
+            return specifiers.SpecifierSet(value)
+        except specifiers.InvalidSpecifier as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            )
+
+    def _process_requires_dist(
+        self,
+        value: List[str],
+    ) -> List[requirements.Requirement]:
+        reqs = []
+        try:
+            for req in value:
+                reqs.append(requirements.Requirement(req))
+        except requirements.InvalidRequirement as exc:
+            raise self._invalid_metadata(f"{req!r} is invalid for {{field}}", cause=exc)
+        else:
+            return reqs
+
+
+class Metadata:
+    """Representation of distribution metadata.
+
+    Compared to :class:`RawMetadata`, this class provides objects representing
+    metadata fields instead of only using built-in types. Any invalid metadata
+    will cause :exc:`InvalidMetadata` to be raised (with a
+    :py:attr:`~BaseException.__cause__` attribute as appropriate).
+    """
+
+    _raw: RawMetadata
+
+    @classmethod
+    def from_raw(cls, data: RawMetadata, *, validate: bool = True) -> "Metadata":
+        """Create an instance from :class:`RawMetadata`.
+
+        If *validate* is true, all metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        ins = cls()
+        ins._raw = data.copy()  # Mutations occur due to caching enriched values.
+
+        if validate:
+            exceptions: List[InvalidMetadata] = []
+            try:
+                metadata_version = ins.metadata_version
+                metadata_age = _VALID_METADATA_VERSIONS.index(metadata_version)
+            except InvalidMetadata as metadata_version_exc:
+                exceptions.append(metadata_version_exc)
+                metadata_version = None
+
+            # Make sure to check for the fields that are present, the required
+            # fields (so their absence can be reported).
+            fields_to_check = frozenset(ins._raw) | _REQUIRED_ATTRS
+            # Remove fields that have already been checked.
+            fields_to_check -= {"metadata_version"}
+
+            for key in fields_to_check:
+                try:
+                    if metadata_version:
+                        # Can't use getattr() as that triggers descriptor protocol which
+                        # will fail due to no value for the instance argument.
+                        try:
+                            field_metadata_version = cls.__dict__[key].added
+                        except KeyError:
+                            exc = InvalidMetadata(key, f"unrecognized field: {key!r}")
+                            exceptions.append(exc)
+                            continue
+                        field_age = _VALID_METADATA_VERSIONS.index(
+                            field_metadata_version
+                        )
+                        if field_age > metadata_age:
+                            field = _RAW_TO_EMAIL_MAPPING[key]
+                            exc = InvalidMetadata(
+                                field,
+                                "{field} introduced in metadata version "
+                                "{field_metadata_version}, not {metadata_version}",
+                            )
+                            exceptions.append(exc)
+                            continue
+                    getattr(ins, key)
+                except InvalidMetadata as exc:
+                    exceptions.append(exc)
+
+            if exceptions:
+                raise ExceptionGroup("invalid metadata", exceptions)
+
+        return ins
+
+    @classmethod
+    def from_email(
+        cls, data: Union[bytes, str], *, validate: bool = True
+    ) -> "Metadata":
+        """Parse metadata from email headers.
+
+        If *validate* is true, the metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        exceptions: list[InvalidMetadata] = []
+        raw, unparsed = parse_email(data)
+
+        if validate:
+            for unparsed_key in unparsed:
+                if unparsed_key in _EMAIL_TO_RAW_MAPPING:
+                    message = f"{unparsed_key!r} has invalid data"
+                else:
+                    message = f"unrecognized field: {unparsed_key!r}"
+                exceptions.append(InvalidMetadata(unparsed_key, message))
+
+            if exceptions:
+                raise ExceptionGroup("unparsed", exceptions)
+
+        try:
+            return cls.from_raw(raw, validate=validate)
+        except ExceptionGroup as exc_group:
+            exceptions.extend(exc_group.exceptions)
+            raise ExceptionGroup("invalid or unparsed metadata", exceptions) from None
+
+    metadata_version: _Validator[_MetadataVersion] = _Validator()
+    """:external:ref:`core-metadata-metadata-version`
+    (required; validated to be a valid metadata version)"""
+    name: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-name`
+    (required; validated using :func:`~packaging.utils.canonicalize_name` and its
+    *validate* parameter)"""
+    version: _Validator[version_module.Version] = _Validator()
+    """:external:ref:`core-metadata-version` (required)"""
+    dynamic: _Validator[List[str]] = _Validator(
+        added="2.2",
+    )
+    """:external:ref:`core-metadata-dynamic`
+    (validated against core metadata field names and lowercased)"""
+    platforms: _Validator[List[str]] = _Validator()
+    """:external:ref:`core-metadata-platform`"""
+    supported_platforms: _Validator[List[str]] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-supported-platform`"""
+    summary: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-summary` (validated to contain no newlines)"""
+    description: _Validator[str] = _Validator()  # TODO 2.1: can be in body
+    """:external:ref:`core-metadata-description`"""
+    description_content_type: _Validator[str] = _Validator(added="2.1")
+    """:external:ref:`core-metadata-description-content-type` (validated)"""
+    keywords: _Validator[List[str]] = _Validator()
+    """:external:ref:`core-metadata-keywords`"""
+    home_page: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-home-page`"""
+    download_url: _Validator[str] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-download-url`"""
+    author: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-author`"""
+    author_email: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-author-email`"""
+    maintainer: _Validator[str] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer`"""
+    maintainer_email: _Validator[str] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer-email`"""
+    license: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-license`"""
+    classifiers: _Validator[List[str]] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-classifier`"""
+    requires_dist: _Validator[List[requirements.Requirement]] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-requires-dist`"""
+    requires_python: _Validator[specifiers.SpecifierSet] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-requires-python`"""
+    # Because `Requires-External` allows for non-PEP 440 version specifiers, we
+    # don't do any processing on the values.
+    requires_external: _Validator[List[str]] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-requires-external`"""
+    project_urls: _Validator[Dict[str, str]] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-project-url`"""
+    # PEP 685 lets us raise an error if an extra doesn't pass `Name` validation
+    # regardless of metadata version.
+    provides_extra: _Validator[List[utils.NormalizedName]] = _Validator(
+        added="2.1",
+    )
+    """:external:ref:`core-metadata-provides-extra`"""
+    provides_dist: _Validator[List[str]] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-provides-dist`"""
+    obsoletes_dist: _Validator[List[str]] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-obsoletes-dist`"""
+    requires: _Validator[List[str]] = _Validator(added="1.1")
+    """``Requires`` (deprecated)"""
+    provides: _Validator[List[str]] = _Validator(added="1.1")
+    """``Provides`` (deprecated)"""
+    obsoletes: _Validator[List[str]] = _Validator(added="1.1")
+    """``Obsoletes`` (deprecated)"""
diff --git a/shared/package-files/4e/4e8c9e156778c41d463e84f826629a307500d03918eb362151f45a02cb4e4393/profiler.py b/shared/package-files/4e/4e8c9e156778c41d463e84f826629a307500d03918eb362151f45a02cb4e4393/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1b611b4ec29eea651b07fb33d2f61fa93e8eac6
--- /dev/null
+++ b/shared/package-files/4e/4e8c9e156778c41d463e84f826629a307500d03918eb362151f45a02cb4e4393/profiler.py
@@ -0,0 +1,839 @@
+import gzip
+import json
+import os
+import tempfile
+from abc import ABC, abstractmethod
+from enum import Enum
+from functools import partial
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+from warnings import warn
+
+from typing_extensions import Self
+
+import torch
+import torch.autograd.profiler as prof
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import (
+    _add_execution_trace_observer,
+    _disable_execution_trace_observer,
+    _enable_execution_trace_observer,
+    _ExperimentalConfig,
+    _remove_execution_trace_observer,
+)
+from torch.autograd import kineto_available, ProfilerActivity
+from torch.profiler._memory_profiler import MemoryProfile, MemoryProfileTimeline
+
+
+__all__ = [
+    "supported_activities",
+    "ProfilerAction",
+    "schedule",
+    "tensorboard_trace_handler",
+    "profile",
+    "ExecutionTraceObserver",
+]
+PROFILER_STEP_NAME = "ProfilerStep"
+
+
+def supported_activities():
+    """
+    Returns a set of supported profiler tracing activities.
+
+    Note: profiler uses CUPTI library to trace on-device CUDA kernels.
+    In case when CUDA is enabled but CUPTI is not available, passing
+    ``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA
+    profiling code (same as in the legacy ``torch.autograd.profiler``).
+    This, in turn, results in including CUDA time in the profiler table output,
+    but not in the JSON trace.
+    """
+    return torch.autograd._supported_activities()
+
+
+class _ITraceObserver(ABC):
+    """Abstract interface for a Trace observer.
+    This satisfies 3 methods: start, stop and cleanup"""
+
+    @abstractmethod
+    def start(self):
+        pass
+
+    @abstractmethod
+    def stop(self):
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        pass
+
+
+class _KinetoProfile:
+    """Low-level profiler wrap the autograd profile
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline``
+            for more details).
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPS of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces <https://arxiv.org/pdf/2305.14516.pdf>`__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler.
+
+    .. note::
+        This API is experimental and subject to change in the future.
+
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+    ):
+        self.activities = set(activities) if activities else supported_activities()
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.experimental_config = experimental_config
+        self.execution_trace_observer = execution_trace_observer
+        self.profiler: Optional[prof.profile] = None
+        self.mem_tl: Optional[MemoryProfileTimeline] = None
+        self.use_device = None
+        privateuse1_backend = _get_privateuse1_backend_name()
+        if privateuse1_backend != "privateuseone":
+            self.use_device = privateuse1_backend
+        # user-defined metadata to be amended to the trace
+        self.preset_metadata: Dict[str, str] = dict()
+
+    def start(self):
+        self.prepare_trace()
+        self.start_trace()
+
+    def stop(self):
+        self.stop_trace()
+
+    def prepare_trace(self):
+        self.profiler = prof.profile(
+            use_cuda=(ProfilerActivity.CUDA in self.activities),
+            use_cpu=(ProfilerActivity.CPU in self.activities),
+            use_mtia=(ProfilerActivity.MTIA in self.activities),
+            use_device=None,
+            record_shapes=self.record_shapes,
+            with_flops=self.with_flops,
+            profile_memory=self.profile_memory,
+            with_stack=self.with_stack,
+            with_modules=self.with_modules,
+            use_kineto=True,
+            experimental_config=self.experimental_config,
+        )
+        self.profiler._prepare_trace()
+
+    def start_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.start()
+        assert self.profiler is not None
+        self.profiler._start_trace()
+
+        if self.profile_memory:
+            self.add_metadata_json("profile_memory", "1")
+        if self.with_stack:
+            self.add_metadata_json("with_stack", "1")
+        if self.record_shapes:
+            self.add_metadata_json("record_shapes", "1")
+        if self.with_modules:
+            self.add_metadata_json("with_modules", "1")
+        if self.with_flops:
+            self.add_metadata_json("with_flops", "1")
+
+        if kineto_available():
+            dist_info = self._get_distributed_info()
+            if dist_info:
+                self.add_metadata_json("distributedInfo", json.dumps(dist_info))
+
+            if hasattr(torch, "_inductor"):
+                import torch._inductor.config as inductor_config
+
+                if inductor_config.triton.cudagraphs:
+                    os.environ["DISABLE_CUPTI_LAZY_REINIT"] = "1"
+                    self.add_metadata_json("DISABLE_CUPTI_LAZY_REINIT", "1")
+                    # FIXME: CUDA Graph does not work well with CUPTI teardown.
+                    #   1) crashes on 1st lazy CUPTI re-init after teardown (CUDA 11)
+                    #   2) crashes on 2nd non-lazy CUPTI re-init after teardown (CUDA 12)
+                    # Workaround: turn off CUPTI teardown when using CUDA Graphs.
+                    os.environ["TEARDOWN_CUPTI"] = "0"
+
+            # Insert the preset user metadata to the trace
+            for k, v in self.preset_metadata.items():
+                self.add_metadata_json(k, v)
+
+    def stop_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.stop()
+        assert self.profiler is not None
+        self.profiler.__exit__(None, None, None)
+
+    def export_chrome_trace(self, path: str):
+        """
+        Exports the collected trace in Chrome JSON format.
+        """
+        assert self.profiler
+        if path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+t", suffix=".json", delete=False)
+            fp.close()
+            retvalue = self.profiler.export_chrome_trace(fp.name)
+            with open(fp.name) as fin:
+                with gzip.open(path, "wt") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+            return retvalue
+        else:
+            return self.profiler.export_chrome_trace(path)
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        """Save stack traces in a file in a format suitable for visualization.
+
+        Args:
+            path (str): save stacks file to this location;
+            metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"
+
+        .. note::
+            Example of using FlameGraph tool:
+
+            - git clone https://github.com/brendangregg/FlameGraph
+            - cd FlameGraph
+            - ./flamegraph.pl --title "CPU time" --countname "us." profiler.stacks > perf_viz.svg
+        """
+        assert self.profiler
+        return self.profiler.export_stacks(path, metric)
+
+    def key_averages(
+        self, group_by_input_shape: bool = False, group_by_stack_n: int = 0
+    ):
+        """Averages events, grouping them by operator name and (optionally) input shapes and
+        stack.
+
+        .. note::
+            To use shape/stack functionality make sure to set record_shapes/with_stack
+            when creating profiler context manager.
+        """
+        assert self.profiler
+        return self.profiler.key_averages(group_by_input_shape, group_by_stack_n)
+
+    def events(self):
+        """
+        Returns the list of unaggregated profiler events,
+        to be used in the trace callback or after the profiling is finished
+        """
+        assert self.profiler
+        return self.profiler.function_events
+
+    def add_metadata(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a string value
+        into the trace file
+        """
+        wrapped_value = '"' + value.replace('"', '\\"') + '"'
+        torch.autograd._add_metadata_json(key, wrapped_value)
+
+    def add_metadata_json(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a valid json value
+        into the trace file
+        """
+        torch.autograd._add_metadata_json(key, value)
+
+    def preset_metadata_json(self, key: str, value: str):
+        """
+        Preset a user defined metadata when the profiler is not started
+        and added into the trace file later.
+        Metadata is in the format of a string key and a valid json value
+        """
+        self.preset_metadata[key] = value
+
+    def _get_distributed_info(self):
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            return None
+
+        backend = dist.get_backend()
+        dist_info = {
+            "backend": backend,
+            "rank": dist.get_rank(),
+            "world_size": dist.get_world_size(),
+            "pg_count": dist.get_pg_count(),
+            "pg_config": dist.distributed_c10d._get_all_pg_configs(),
+        }
+        if backend == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            dist_info["nccl_version"] = ".".join(str(v) for v in nccl_version)
+        return dist_info
+
+    def _memory_profile(self) -> MemoryProfile:
+        required = ("record_shapes", "profile_memory", "with_stack")
+        missing = [f"{i}=True" for i in required if not getattr(self, i)]
+        if missing:
+            raise ValueError(f"{', '.join(missing)} required for memory profiling.")
+
+        assert self.profiler is not None and self.profiler.kineto_results is not None
+        return MemoryProfile(self.profiler.kineto_results)
+
+    def export_memory_timeline(self, path: str, device: Optional[str] = None) -> None:
+        """Export memory event information from the profiler collected
+        tree for a given device, and export a timeline plot. There are 3
+        exportable files using ``export_memory_timeline``, each controlled by the
+        ``path``'s suffix.
+
+        - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline
+          plot will be embedded as a PNG file in the HTML file.
+
+        - For plot points consisting of ``[times, [sizes by category]]``, where
+          ``times`` are timestamps and ``sizes`` are memory usage for each category.
+          The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON
+          (``.json.gz``) depending on the suffix.
+
+        - For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory
+          event will consist of ``(timestamp, action, numbytes, category)``, where
+          ``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``,
+          and ``category`` is one of the enums from
+          ``torch.profiler._memory_profiler.Category``.
+
+        Output: Memory timeline written as gzipped JSON, JSON, or HTML.
+        """
+        # Default to device 0, if unset. Fallback on cpu.
+        if device is None and self.use_device and self.use_device != "cuda":
+            device = self.use_device + ":0"
+
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        # Construct the memory timeline plot data
+        self.mem_tl = MemoryProfileTimeline(self._memory_profile())
+
+        # Depending on the file suffix, save the data as json.gz or json.
+        # For html, we can embed the image into an HTML file.
+        if path.endswith(".html"):
+            self.mem_tl.export_memory_timeline_html(path, device)
+        elif path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+t", suffix=".json", delete=False)
+            fp.close()
+            if path.endswith("raw.json.gz"):
+                self.mem_tl.export_memory_timeline_raw(fp.name, device)
+            else:
+                self.mem_tl.export_memory_timeline(fp.name, device)
+            with open(fp.name) as fin:
+                with gzip.open(path, "wt") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+        else:
+            self.mem_tl.export_memory_timeline(path, device)
+
+
+class ProfilerAction(Enum):
+    """
+    Profiler actions that can be taken at the specified intervals
+    """
+
+    NONE = 0
+    WARMUP = 1
+    RECORD = 2
+    RECORD_AND_SAVE = 3
+
+
+def schedule(
+    *, wait: int, warmup: int, active: int, repeat: int = 0, skip_first: int = 0
+) -> Callable:
+    """
+    Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip
+    the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps,
+    then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps.
+    The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that
+    the cycles will continue until the profiling is finished.
+    """
+
+    def schedule_fn(step: int) -> ProfilerAction:
+        assert step >= 0
+        if step < skip_first:
+            return ProfilerAction.NONE
+        else:
+            step -= skip_first
+        num_steps = wait + warmup + active
+        if repeat > 0 and step / num_steps >= repeat:
+            return ProfilerAction.NONE
+        mod_step = step % num_steps
+        if mod_step < wait:
+            return ProfilerAction.NONE
+        elif mod_step < wait + warmup:
+            return ProfilerAction.WARMUP
+        else:
+            return (
+                ProfilerAction.RECORD
+                if mod_step < num_steps - 1
+                else ProfilerAction.RECORD_AND_SAVE
+            )
+
+    assert (
+        wait >= 0 and warmup >= 0 and active > 0 and repeat >= 0 and skip_first >= 0
+    ), "Invalid profiler schedule arguments"
+    if warmup == 0:
+        warn("Profiler won't be using warmup, this can skew profiler results")
+    return schedule_fn
+
+
+def _default_schedule_fn(_: int) -> ProfilerAction:
+    """
+    Default profiler behavior - immediately starts recording the events,
+    keeps doing it on every profiler step.
+    """
+    return ProfilerAction.RECORD
+
+
+def tensorboard_trace_handler(
+    dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False
+):
+    """
+    Outputs tracing files to directory of ``dir_name``, then that directory can be
+    directly delivered to tensorboard as logdir.
+    ``worker_name`` should be unique for each worker in distributed scenario,
+    it will be set to '[hostname]_[pid]' by default.
+    """
+    import os
+    import socket
+    import time
+
+    def handler_fn(prof) -> None:
+        nonlocal worker_name
+        if not os.path.isdir(dir_name):
+            try:
+                os.makedirs(dir_name, exist_ok=True)
+            except Exception as e:
+                raise RuntimeError("Can't create directory: " + dir_name) from e
+        if not worker_name:
+            worker_name = f"{socket.gethostname()}_{os.getpid()}"
+        # Use nanosecond here to avoid naming clash when exporting the trace
+        file_name = f"{worker_name}.{time.time_ns()}.pt.trace.json"
+        if use_gzip:
+            file_name = file_name + ".gz"
+        prof.export_chrome_trace(os.path.join(dir_name, file_name))
+
+    return handler_fn
+
+
+class profile(_KinetoProfile):
+    """Profiler context manager.
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
+        schedule (Callable): callable that takes step (int) as a single parameter and returns
+            ``ProfilerAction`` value that specifies the profiler action to perform at each step.
+        on_trace_ready (Callable): callable that is called at each step when ``schedule``
+            returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation.
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used for Kineto library features. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces <https://arxiv.org/pdf/2305.14516.pdf>`__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler. See the examples section below for a code sample.
+        use_cuda (bool):
+            .. deprecated:: 1.8.1
+                use ``activities`` instead.
+
+    .. note::
+        Use :func:`~torch.profiler.schedule` to generate the callable schedule.
+        Non-default schedules are useful when profiling long training jobs
+        and allow the user to obtain multiple traces at the different iterations
+        of the training process.
+        The default schedule simply records all the events continuously for the
+        duration of the context manager.
+
+    .. note::
+        Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
+
+        ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
+
+        After profiling, result files can be found in the specified directory. Use the command:
+
+        ``tensorboard --logdir dir_name``
+
+        to see the results in TensorBoard.
+        For more information, see
+        `PyTorch Profiler TensorBoard Plugin <https://github.com/pytorch/kineto/tree/master/tb_plugin>`__
+
+    .. note::
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+
+
+    Examples:
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ]
+        ) as p:
+            code_to_profile()
+        print(p.key_averages().table(
+            sort_by="self_cuda_time_total", row_limit=-1))
+
+    Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
+
+    .. code-block:: python
+
+        # Non-default profiler schedule allows user to turn profiler on and off
+        # on different iterations of the training loop;
+        # trace_handler is called every time a new trace becomes available
+        def trace_handler(prof):
+            print(prof.key_averages().table(
+                sort_by="self_cuda_time_total", row_limit=-1))
+            # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+
+            # In this example with wait=1, warmup=1, active=2, repeat=1,
+            # profiler will skip the first step/iteration,
+            # start warming up on the second, record
+            # the third and the forth iterations,
+            # after which the trace will become available
+            # and on_trace_ready (when set) is called;
+            # the cycle repeats starting with the next step
+
+            schedule=torch.profiler.schedule(
+                wait=1,
+                warmup=1,
+                active=2,
+                repeat=1),
+            on_trace_ready=trace_handler
+            # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
+            # used when outputting for tensorboard
+            ) as p:
+                for iter in range(N):
+                    code_iteration_to_profile(iter)
+                    # send a signal to the profiler that the next iteration has started
+                    p.step()
+
+    The following sample shows how to setup up an Execution Trace Observer (`execution_trace_observer`)
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            ...
+            execution_trace_observer=(
+                ExecutionTraceObserver().register_callback("./execution_trace.json")
+            ),
+        ) as p:
+            for iter in range(N):
+                code_iteration_to_profile(iter)
+                p.step()
+
+    You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py.
+    Note: One can also pass any object satisfying the _ITraceObserver interface.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        schedule: Optional[Callable[[int], ProfilerAction]] = None,
+        on_trace_ready: Optional[Callable[..., Any]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+        # deprecated:
+        use_cuda: Optional[bool] = None,
+    ):
+        activities_set = set(activities) if activities else supported_activities()
+        if use_cuda is not None:
+            warn("use_cuda is deprecated, use activities argument instead")
+            if use_cuda:
+                activities_set.add(ProfilerActivity.CUDA)
+            elif ProfilerActivity.CUDA in activities_set:
+                activities_set.remove(ProfilerActivity.CUDA)
+        assert len(activities_set) > 0, "No valid profiler activities found"
+
+        super().__init__(
+            activities=activities,
+            record_shapes=record_shapes,
+            profile_memory=profile_memory,
+            with_stack=with_stack,
+            with_flops=with_flops,
+            with_modules=with_modules,
+            experimental_config=experimental_config,
+            execution_trace_observer=execution_trace_observer,
+        )
+
+        if schedule:
+            self.schedule = schedule
+            # add step markers into the trace and table view
+            self.record_steps = True
+        else:
+            self.schedule = _default_schedule_fn
+            self.record_steps = False
+        self.on_trace_ready = on_trace_ready
+        self.step_num = 0
+        self.current_action = self.schedule(self.step_num)
+        self.step_rec_fn: Optional[prof.record_function] = None
+
+        self.action_map: Dict[
+            Tuple[ProfilerAction, Optional[ProfilerAction]], List[Any]
+        ] = {
+            # key is (prev_action, current_action), value is action list corresponding to the state pair.
+            (ProfilerAction.NONE, ProfilerAction.NONE): [],
+            (ProfilerAction.NONE, ProfilerAction.WARMUP): [self.prepare_trace],
+            (ProfilerAction.NONE, ProfilerAction.RECORD): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.NONE, ProfilerAction.RECORD_AND_SAVE): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: WARMUP followed by NONE"),
+                self.start_trace,
+                self.stop_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.WARMUP): [],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD): [self.start_trace],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD_AND_SAVE): [self.start_trace],
+            (ProfilerAction.RECORD, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: RECORD followed by NONE"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.WARMUP): [
+                partial(warn, "Incorrect schedule: RECORD followed by WARMUP"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD): [],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE): [],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.NONE): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.WARMUP): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD_AND_SAVE): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            # used for exit action
+            (ProfilerAction.WARMUP, None): [self.start_trace, self.stop_trace],
+            (ProfilerAction.RECORD, None): [self.stop_trace, self._trace_ready],
+            (ProfilerAction.RECORD_AND_SAVE, None): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+        }
+        # Start tracking increments to profiler step, this will be used
+        # by Kineto
+        prof.KinetoStepTracker.init_step_count(PROFILER_STEP_NAME)
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+        prof.KinetoStepTracker.erase_step_count(PROFILER_STEP_NAME)
+        if self.execution_trace_observer:
+            self.execution_trace_observer.cleanup()
+
+    def start(self):
+        self._transit_action(ProfilerAction.NONE, self.current_action)
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def stop(self):
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        self._transit_action(self.current_action, None)
+
+    def step(self):
+        """
+        Signals the profiler that the next profiling step has started.
+        """
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        prev_action = self.current_action
+        self.step_num += 1
+        self.current_action = self.schedule(self.step_num)
+
+        self._transit_action(prev_action, self.current_action)
+        prof.KinetoStepTracker.increment_step(PROFILER_STEP_NAME)
+
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def _trace_ready(self):
+        if self.on_trace_ready:
+            self.on_trace_ready(self)
+
+    def _transit_action(self, prev_action, current_action):
+        action_list = self.action_map.get((prev_action, current_action))
+        if action_list:
+            for action in action_list:
+                action()
+
+
+class ExecutionTraceObserver(_ITraceObserver):
+    """Execution Trace Observer
+
+    Each process can have a single ExecutionTraceObserver instance. The observer
+    can be added to record function callbacks via calling register_callback()
+    explicitly. Without calling unregister_callback(), repeated calls to
+    register_callback() will not add additional observers to record function
+    callbacks. Once an ExecutionTraceObserver is created, the start() and stop()
+    methods control when the event data is recorded.
+
+    Deleting or calling unregister_callback() will remove the observer from the
+    record function callbacks, finalize the output file, and will stop
+    incurring any overheads.
+    """
+
+    def __init__(self):
+        """
+        Initializes the default states.
+        """
+        self._registered = False
+        self._execution_trace_running = False
+
+    def __del__(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    def register_callback(self, output_file_path: str) -> Self:
+        """
+        Adds ET observer to record function callbacks. The data will be
+        written to output_file_path.
+        """
+        if not self._registered:
+            self._output_file_path = output_file_path
+            self._registered = _add_execution_trace_observer(output_file_path)
+        return self
+
+    def unregister_callback(self):
+        """
+        Removes ET observer from record function callbacks.
+        """
+        if self._registered:
+            self.stop()
+            _remove_execution_trace_observer()
+            self._registered = False
+
+    @property
+    def is_registered(self):
+        """
+        Returns True if the execution trace observer is registered, otherwise False.
+        """
+        return self._registered
+
+    def is_running(self):
+        """
+        Returns True if the observer is running, otherwise False.
+        """
+        return self._execution_trace_running
+
+    def start(self):
+        """
+        Starts to capture.
+        """
+        if self._registered and not self._execution_trace_running:
+            _enable_execution_trace_observer()
+            self._execution_trace_running = True
+
+    def stop(self):
+        """
+        Stops to capture.
+        """
+        if self._execution_trace_running:
+            _disable_execution_trace_observer()
+            self._execution_trace_running = False
+
+    def cleanup(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    def get_output_file_path(self) -> str:
+        """
+        Returns the output file name.
+        """
+        if self.is_registered:
+            return self._output_file_path
+        else:
+            raise RuntimeError(
+                "A callback to the ET profiler needs to be registered "
+                "first before getting the output file path"
+            )
diff --git a/shared/package-files/4e/4e8ca7ed9b9608e3c4267a425b4c77768bb6a9cbaef8b9da0c8c79c22e61e7c7/convert.py b/shared/package-files/4e/4e8ca7ed9b9608e3c4267a425b4c77768bb6a9cbaef8b9da0c8c79c22e61e7c7/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..c300093c2853a1384e99d1b76feee37cc18b6ebc
--- /dev/null
+++ b/shared/package-files/4e/4e8ca7ed9b9608e3c4267a425b4c77768bb6a9cbaef8b9da0c8c79c22e61e7c7/convert.py
@@ -0,0 +1,874 @@
+# Copyright (C) 2014 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+Tools for converting conda packages
+"""
+import glob
+import hashlib
+import json
+import os
+import re
+import shutil
+import sys
+import tarfile
+import tempfile
+from pathlib import Path
+
+from .utils import filter_info_files, walk
+
+
+def retrieve_c_extensions(file_path, show_imports=False):
+    """Check tarfile for compiled C files with '.pyd' or '.so' suffixes.
+
+    If a file ends in either .pyd or .so, it is a compiled C file.
+    Because compiled C code varies between platforms, it is not possible
+    to convert packages containing C extensions to other platforms.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package tar file
+
+    Keyword arguments:
+    show_imports (bool) -- output the C extensions included in the package
+    """
+    c_extension_pattern = re.compile(
+        r"(Lib\/|lib\/python\d\.\d\/|lib\/)(site-packages\/|lib-dynload)?(.*)"
+    )
+
+    imports = []
+    with tarfile.open(file_path) as tar:
+        for filename in tar.getnames():
+            if filename.endswith((".pyd", ".so")):
+                filename_match = c_extension_pattern.match(filename)
+                import_name = "import {}".format(
+                    filename_match.group(3).replace("/", ".")
+                )
+                imports.append(import_name)
+
+    return imports
+
+
+def retrieve_package_platform(file_path):
+    """Retrieve the platform and architecture of the source package.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package tar file
+    """
+    with tarfile.open(file_path) as tar:
+        index = json.loads(tar.extractfile("info/index.json").read().decode("utf-8"))
+
+    platform = index["platform"]
+
+    if index.get("arch") == "x86_64":
+        architecture = "64"
+    elif index.get("arch") == "x86":
+        architecture = "32"
+    else:
+        architecture = index.get("arch")
+
+    if platform.startswith("linux") or platform.startswith("osx"):
+        return ("unix", platform, architecture)
+    elif index["platform"].startswith("win"):
+        return ("win", platform, architecture)
+    else:
+        raise RuntimeError("Package platform not recognized.")
+
+
+def retrieve_python_version(file_path):
+    """Retrieve the python version from a path.
+
+    This function is overloaded to handle three separate cases:
+    when a path is a tar archive member path such as 'lib/python3.6/site-packages',
+    when a path is the file path to the source package tar file, and when a path
+    is the path to the temporary directory that contains the extracted contents
+    of the source package tar file. This allows one function to handle the three
+    most common cases of retrieving the python version from the source package.
+
+    Positional arguments:
+    file_path (str) -- the file path to a tar archive member, the file path
+        to the source tar file itself, or the file path to the
+        temporary directory containing the extracted source package contents
+    """
+    if "python" in file_path:
+        pattern = re.compile(r"python\d\.\d+")
+        matched = pattern.search(file_path)
+
+        if matched:
+            return matched.group(0)
+
+    else:
+        if file_path.endswith((".tar.bz2", ".tar")):
+            with tarfile.open(file_path) as tar:
+                index = json.loads(
+                    tar.extractfile("info/index.json").read().decode("utf-8")
+                )
+
+        else:
+            path_file = os.path.join(file_path, "info/index.json")
+
+            with open(path_file) as index_file:
+                index = json.load(index_file)
+
+        build_version_number = re.search(r"(.*)?(py)(\d\d)(.*)?", index["build"]).group(
+            3
+        )
+        build_version = re.sub(r"\A.*py\d\d.*\Z", "python", index["build"])
+
+        return f"{build_version}{build_version_number[0]}.{build_version_number[1]}"
+
+
+def extract_temporary_directory(file_path):
+    """Extract the source tar archive contents to a temporary directory.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package tar file
+    """
+    temporary_directory = tempfile.mkdtemp()
+
+    source = tarfile.open(file_path)
+    source.extractall(temporary_directory)
+    source.close()
+
+    return temporary_directory
+
+
+def update_dependencies(new_dependencies, existing_dependencies):
+    """Update the source package's existing dependencies.
+
+    When a user passes additional dependencies from the command line,
+    these dependencies will be added to the source package's existing dependencies.
+    If the dependencies passed from the command line are existing dependencies,
+    these existing dependencies are overwritten.
+
+    Positional arguments:
+    new_dependencies (List[str]) -- the dependencies passed from the command line
+    existing_dependencies (List[str]) -- the dependencies found in the source
+        package's index.json file
+    """
+    # split dependencies away from their version numbers since we need the names
+    # in order to evaluate duplication
+    dependency_names = {dependency.split()[0] for dependency in new_dependencies}
+    index_dependency_names = {index.split()[0] for index in existing_dependencies}
+
+    repeated_packages = index_dependency_names.intersection(dependency_names)
+
+    if len(repeated_packages) > 0:
+        for index_dependency in existing_dependencies:
+            for dependency in repeated_packages:
+                if index_dependency.startswith(dependency):
+                    existing_dependencies.remove(index_dependency)
+
+    existing_dependencies.extend(new_dependencies)
+
+    return existing_dependencies
+
+
+def update_index_file(temp_dir, target_platform, dependencies, verbose):
+    """Update the source package's index file with the target platform's information.
+
+    Positional arguments:
+    temp_dir (str) -- the file path to the temporary directory that contains
+        the source package's extracted contents
+    target_platform (str) -- the target platform and architecture in
+        the form of platform-architecture such as linux-64
+    dependencies (List[str]) -- the dependencies passed from the command line
+    verbose (bool) -- show output of items that are updated
+    """
+    index_file = os.path.join(temp_dir, "info/index.json")
+
+    with open(index_file) as file:
+        index = json.load(file)
+
+    platform, architecture = target_platform.split("-")
+    other_platforms = [
+        "linux-ppc64",
+        "linux-ppc64le",
+        "linux-s390x",
+        "linux-armv6l",
+        "linux-armv7l",
+        "linux-aarch64",
+    ]
+
+    if target_platform in other_platforms:
+        source_architecture = architecture
+    elif index.get("arch") == "x86_64":
+        source_architecture = "64"
+    else:
+        source_architecture = "32"
+
+    if verbose:
+        print("Updating platform from {} to {}".format(index["platform"], platform))
+        print("Updating subdir from {} to {}".format(index["subdir"], target_platform))
+        print(f"Updating architecture from {source_architecture} to {architecture}")
+
+    index["platform"] = platform
+    index["subdir"] = target_platform
+
+    if architecture == "64":
+        index["arch"] = "x86_64"
+    elif architecture == "32":
+        index["arch"] = "x86"
+    else:
+        index["arch"] = architecture
+
+    if dependencies:
+        index["depends"] = update_dependencies(dependencies, index["depends"])
+
+    with open(index_file, "w") as file:
+        json.dump(index, file, indent=2)
+
+    return index_file
+
+
+def update_lib_path(path, target_platform, temp_dir=None):
+    """Update the lib path found in the source package's paths.json file.
+
+    For conversions from unix to windows, the 'lib/pythonx.y/' paths are
+    renamed to 'Lib/' and vice versa for conversions from windows to unix.
+
+    Positional arguments:
+    path (str) -- path to rename in the paths.json file
+    target_platform (str) -- the platform to target: 'unix' or 'win'
+
+    Keyword arguments:
+    temp_dir (str) -- the file path to the temporary directory that
+        contains the source package's extracted contents
+    """
+    if target_platform == "win":
+        python_version = retrieve_python_version(path)
+        renamed_lib_path = re.sub(r"\Alib", "Lib", path).replace(python_version, "")
+
+    elif target_platform == "unix":
+        python_version = retrieve_python_version(temp_dir)
+        lib_python_version = os.path.join("lib", python_version).replace("\\", "\\\\")
+        renamed_lib_path = re.sub(
+            r"\ALib", lib_python_version, path.replace("\\", "\\\\")
+        )
+
+    return os.path.normpath(renamed_lib_path)
+
+
+def update_lib_contents(lib_directory, temp_dir, target_platform, file_path):
+    """Update the source package's 'lib' directory.
+
+    When converting from unix to windows, the 'lib' directory is renamed to
+    'Lib' and the contents inside the 'pythonx.y' directory are renamed to
+    exclude the 'pythonx.y' prefix. When converting from windows to unix,
+    the 'Lib' is renamed to 'lib' and the pythonx.y' prefix is added.
+
+    Positional arguments:
+    lib_directory (str) -- the file path to the 'lib' directory located in the
+        temporary directory that stores the package contents
+    temp_dir (str) -- the file path to the temporary directory that contains
+        the source package's extracted contents
+    target_platform (str) -- the platform to target: 'unix' or win'
+    file_path (str) -- the file path to the source package tar file
+    """
+    if target_platform == "win":
+        try:
+            for lib_file in glob.iglob(f"{lib_directory}/python*/**"):
+                if "site-packages" in lib_file:
+                    new_site_packages_path = os.path.join(
+                        temp_dir, os.path.join("lib", "site-packages")
+                    )
+                    os.renames(lib_file, new_site_packages_path)
+                else:
+                    if retrieve_python_version(lib_file) is not None:
+                        python_version = retrieve_python_version(lib_file)
+                        os.renames(lib_file, lib_file.replace(python_version, ""))
+        except OSError:
+            pass
+
+        try:
+            shutil.rmtree(glob.glob(f"{lib_directory}/python*")[0])
+        except IndexError:
+            pass
+
+        shutil.move(os.path.join(temp_dir, "lib"), os.path.join(temp_dir, "Lib"))
+
+    elif target_platform == "unix":
+        temp_dir = Path(temp_dir)
+        src_dir = temp_dir / "Lib"
+        dst_dir = temp_dir / "lib"
+
+        # rename Lib to lib (unix is case sensitive)
+        src_dir.rename(dst_dir)
+
+        # get contents (before adding the new directory below)
+        contents = tuple(dst_dir.glob("*"))
+
+        # create lib/pythonM.M directory
+        python_version = retrieve_python_version(file_path)
+        py_folder = dst_dir / python_version
+        py_folder.mkdir(parents=True, exist_ok=True)
+
+        # move contents of lib into lib/pythonM.M
+        for lib_file in contents:
+            lib_file.rename(py_folder / lib_file.name)
+
+
+def update_executable_path(temp_dir, file_path, target_platform):
+    """Update the name of the executable files found in the paths.json file.
+
+    When converting from unix to windows, executables are renamed with a '-script.py'
+    suffix. When converting from windows to unix, this suffix is removed. The
+    paths in paths.json need to be updated accordingly.
+
+    Positional arguments:
+    file_path (str) -- the file path to the executable to rename in paths.json
+    target_platform (str) -- the platform to target: 'unix' or 'win'
+    """
+    if target_platform == "win":
+        if os.path.basename(file_path).startswith(".") or is_binary_file(
+            temp_dir, file_path
+        ):
+            renamed_executable_path = re.sub(r"\Abin", "Scripts", file_path)
+        else:
+            renamed_path = os.path.splitext(re.sub(r"\Abin", "Scripts", file_path))[0]
+            renamed_executable_path = f"{renamed_path}-script.py"
+
+    elif target_platform == "unix":
+        renamed_path = re.sub(r"\AScripts", "bin", file_path)
+        renamed_executable_path = renamed_path.replace("-script.py", "")
+
+    return renamed_executable_path
+
+
+def update_executable_sha(package_directory, executable_path):
+    """Update the sha of executable scripts.
+
+    When moving from windows to linux, a shebang line is removed/added from
+    script files which requires to update the sha.
+
+    """
+    with open(os.path.join(package_directory, executable_path), "rb") as script_file:
+        script_file_contents = script_file.read()
+        return hashlib.sha256(script_file_contents).hexdigest()
+
+
+def update_executable_size(temp_dir, executable):
+    """Update the size of the converted executable files.
+
+    Positional arguments:
+    temp_dir (str) -- the file path to the temporary directory containing the source
+        package's extracted contents
+    executable (str) -- the executable whose size to update including its file extension
+
+    Returns:
+    byte size (int) of the executable file
+    """
+    return os.path.getsize(os.path.join(temp_dir, executable))
+
+
+def add_new_windows_path(executable_directory, executable):
+    """Add a new path to the paths.json file.
+
+    When an executable is renamed during a unix to windows conversion, a
+    an exe is also created. The paths.json file is updated with the
+    exe file's information.
+
+    Positional arguments:
+    executable_directory (str) -- the file path to temporary directory's 'Scripts' directory
+    executable (str) -- the filename of the script to add to paths.json
+    """
+    with open(os.path.join(executable_directory, executable), "rb") as script_file:
+        script_file_contents = script_file.read()
+        new_path = {
+            "_path": f"Scripts/{executable}",
+            "path_type": "hardlink",
+            "sha256": hashlib.sha256(script_file_contents).hexdigest(),
+            "size_in_bytes": os.path.getsize(
+                os.path.join(executable_directory, executable)
+            ),
+        }
+    return new_path
+
+
+def update_paths_file(temp_dir, target_platform):
+    """Update the paths.json file when converting between platforms.
+
+    Positional arguments:
+    temp_dir (str) -- the file path to the temporary directory containing the source
+        package's extracted contents
+    target_platform (str) -- the platform to target: 'unix' or 'win'
+    """
+    paths_file = os.path.join(temp_dir, "info/paths.json")
+
+    if os.path.isfile(paths_file):
+        with open(paths_file) as file:
+            paths = json.load(file)
+
+        if target_platform == "win":
+            for path in paths["paths"]:
+                if path["_path"].startswith("lib"):
+                    path["_path"] = update_lib_path(path["_path"], "win")
+
+                elif path["_path"].startswith("bin"):
+                    path["_path"] = update_executable_path(
+                        temp_dir, path["_path"], "win"
+                    )
+                    path["sha256"] = update_executable_sha(temp_dir, path["_path"])
+                    path["size_in_bytes"] = update_executable_size(
+                        temp_dir, path["_path"]
+                    )
+
+                path["_path"] = path["_path"].replace("\\", "/").replace("\\\\", "/")
+
+            script_directory = os.path.join(temp_dir, "Scripts")
+            if os.path.isdir(script_directory):
+                for script in os.listdir(script_directory):
+                    if script.endswith(".exe"):
+                        paths["paths"].append(
+                            add_new_windows_path(script_directory, script)
+                        )
+
+        elif target_platform == "unix":
+            for path in paths["paths"]:
+                if path["_path"].startswith("Lib"):
+                    path["_path"] = update_lib_path(path["_path"], "unix", temp_dir)
+
+                elif path["_path"].startswith("Scripts"):
+                    path["_path"] = update_executable_path(
+                        temp_dir, path["_path"], "unix"
+                    )
+                    path["sha256"] = update_executable_sha(temp_dir, path["_path"])
+                    path["size_in_bytes"] = update_executable_size(
+                        temp_dir, path["_path"]
+                    )
+
+                path["_path"] = path["_path"].replace("\\", "/").replace("\\\\", "/")
+
+                if path["_path"].endswith((".bat", ".exe")):
+                    paths["paths"].remove(path)
+
+        with open(paths_file, "w") as file:
+            json.dump(paths, file, indent=2)
+
+
+def retrieve_executable_name(executable):
+    """Retrieve the name of the executable to rename.
+
+    When converting between unix and windows, we need to be careful
+    that the executables are renamed without their file extensions.
+
+    Positional arguments:
+    executable (str) -- the executable to rename including its file extension
+    """
+    return os.path.splitext(os.path.basename(executable))[0]
+
+
+def is_binary_file(directory, executable):
+    """Read a file's contents to check whether it is a binary file.
+
+    When converting files, we need to check that binary files are not
+    converted.
+
+    Source: https://stackoverflow.com/questions/898669/
+
+    Positional arguments:
+    directory (str) -- the file path to the 'bin' or 'Scripts' directory
+    executable (str) -- the name of the executable to rename
+    """
+    file_path = os.path.join(directory, executable)
+
+    if os.path.isfile(file_path):
+        with open(file_path, "rb") as buffered_file:
+            file_contents = buffered_file.read(1024)
+
+        text_characters = bytearray(
+            {7, 8, 9, 10, 12, 13, 27}.union(set(range(0x20, 0x100)) - {0x7F})
+        )
+
+        return bool(file_contents.translate(None, text_characters))
+
+    return False
+
+
+def rename_executable(directory, executable, target_platform):
+    """Rename an executable file when converting between platforms.
+
+    When converting from unix to windows, each file inside the 'bin' directory
+    is renamed to include '-script.py' as a suffix. When converting from windows
+    to unix, each executable inside the 'Scripts' directory has its '-script.py'
+    suffix removed.
+
+    Positional arguments:
+    directory (str) -- the file path to the 'bin' or 'Scripts' directory
+    executable (str) -- the name of the executable to rename
+    target_platform (str) -- the platform to target: 'unix' or 'win'
+    """
+    old_executable_path = os.path.join(directory, executable)
+
+    if target_platform == "win":
+        new_executable_path = os.path.join(
+            directory, f"{retrieve_executable_name(executable)}-script.py"
+        )
+
+        with open(old_executable_path) as script_file_in:
+            lines = script_file_in.read().splitlines()
+
+        with open(old_executable_path, "w") as script_file_out:
+            for line in lines[1:]:
+                script_file_out.write(line + "\n")
+
+        os.renames(old_executable_path, new_executable_path)
+
+    else:
+        if old_executable_path.endswith(".py"):
+            new_executable_path = old_executable_path.replace("-script.py", "")
+
+            with open(old_executable_path) as script_file_in:
+                lines = script_file_in.read().splitlines()
+
+            with open(old_executable_path, "w") as script_file_out:
+                script_file_out.write(
+                    "#!/opt/conda/bin/python" + "\n"
+                )
+                for line in lines:
+                    script_file_out.write(line + "\n")
+
+            os.renames(old_executable_path, new_executable_path)
+
+
+def remove_executable(directory, executable):
+    """Remove an executable from the 'Scripts' directory.
+
+    When converting from windows to unix, the .exe or .bat files
+    need to be removed as they do not exist in unix packages.
+
+    Positional arguments:
+    directory (str) -- the file path to the 'Scripts' directory
+    executable (str) -- the filename of the executable to remove
+    """
+    if executable.endswith((".exe", ".bat")):
+        script = os.path.join(directory, executable)
+        os.remove(script)
+
+
+def create_exe_file(directory, executable, target_platform):
+    """Create an exe file for each executable during a unix to windows conversion.
+
+    Positional arguments:
+    directory (str) -- the file path to the 'Scripts' directory
+    executable (str) -- the filename of the executable to create an exe file for
+    target_platform -- the platform to target: 'win-64' or 'win-32'
+    """
+    exe_directory = os.path.dirname(__file__)
+
+    if target_platform.endswith("32"):
+        executable_file = os.path.join(exe_directory, "cli-32.exe")
+
+    else:
+        executable_file = os.path.join(exe_directory, "cli-64.exe")
+
+    renamed_executable_file = os.path.join(directory, f"{executable}.exe")
+
+    shutil.copyfile(executable_file, renamed_executable_file)
+
+
+def update_prefix_file(temp_dir, prefixes):
+    """Update the source package's 'has_prefix' file.
+
+    Each file in the 'bin' or 'Scripts' folder will be written
+    to the 'has_prefix' file located in the package's 'info' directory.
+
+    Positional arguments:
+    temp_dir (str) -- the file path to the temporary directory containing the source
+        package's extracted contents
+    prefixes (List[str])-- the prefixes to write to 'has_prefix'
+    """
+    has_prefix_file = os.path.join(temp_dir, "info/has_prefix")
+
+    with open(has_prefix_file, "w+") as prefix_file:
+        for prefix in prefixes:
+            prefix_file.write(prefix)
+
+
+def update_files_file(temp_dir, verbose):
+    """Update the source package's 'files' file.
+
+    The file path to each file that will be in the target archive is
+    written to the 'files' file.
+
+    Positional arguments:
+    temp_dir (str) -- the file path to the temporary directory containing the source
+        package's extracted contents
+    verbose (bool) -- show output of items that are updated
+    """
+    files_file = os.path.join(temp_dir, "info/files")
+
+    with open(files_file, "w") as files:
+        file_paths = []
+        for dirpath, dirnames, filenames in walk(temp_dir):
+            relative_dir = os.path.relpath(dirpath, temp_dir)
+            filenames = [os.path.join(relative_dir, f) for f in filenames]
+            for filename in filter_info_files(filenames, ""):
+                file_paths.append(filename.replace("\\", "/").replace("\\\\", "/"))
+                if verbose:
+                    print(f"Updating {filename}")
+
+        for file_path in sorted(file_paths):
+            files.write(file_path + "\n")
+
+
+def create_target_archive(file_path, temp_dir, platform, output_dir):
+    """Create the converted package's tar file.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    temp_dir (str) -- the file path to the temporary directory containing the source
+        package's extracted contents
+    platform (str) -- the platform to convert to: 'win-64', 'win-32', 'linux-64',
+        'linux-32', or 'osx-64'
+    """
+    output_directory = os.path.join(output_dir, platform)
+
+    if not os.path.isdir(output_directory):
+        os.makedirs(output_directory)
+
+    destination = os.path.join(output_directory, os.path.basename(file_path))
+
+    with tarfile.open(destination, "w:bz2") as target:
+        for dirpath, dirnames, filenames in walk(temp_dir):
+            relative_dir = os.path.relpath(dirpath, temp_dir)
+            filenames = [os.path.join(relative_dir, f) for f in filenames]
+            for filename in filenames:
+                target.add(os.path.join(temp_dir, filename), arcname=filename)
+
+
+def convert_between_unix_platforms(
+    file_path, output_dir, platform, dependencies, verbose
+):
+    """Convert package between unix platforms.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    output_dir (str) -- the file path to where to output the converted tar file
+    platform (str) -- the platform to convert to: 'linux-64', 'linux-32', or 'osx-64'
+    dependencies (List[str]) -- the dependencies passed from the command line
+    verbose (bool) -- show output of items that are updated
+    """
+    temp_dir = extract_temporary_directory(file_path)
+
+    update_index_file(temp_dir, platform, dependencies, verbose)
+
+    create_target_archive(file_path, temp_dir, platform, output_dir)
+
+    # we need to manually remove the temporary directory created by tempfile.mkdtemp
+    shutil.rmtree(temp_dir)
+
+
+def convert_between_windows_architechtures(
+    file_path, output_dir, platform, dependencies, verbose
+):
+    """Convert package between windows architectures.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    output_dir (str) -- the file path to where to output the converted tar file
+    platform (str) -- the platform to convert to: 'win-64' or 'win-32'
+    dependencies (List[str]) -- the dependencies passed from the command line
+    verbose (bool) -- show output of items that are updated
+    """
+    temp_dir = extract_temporary_directory(file_path)
+
+    update_index_file(temp_dir, platform, dependencies, verbose)
+
+    create_target_archive(file_path, temp_dir, platform, output_dir)
+
+    # we need to manually remove the temporary directory created by tempfile.mkdtemp
+    shutil.rmtree(temp_dir)
+
+
+def convert_from_unix_to_windows(
+    file_path, output_dir, platform, dependencies, verbose
+):
+    """Convert a package from a unix platform to windows.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    output_dir (str) -- the file path to where to output the converted tar file
+    platform (str) -- the platform to convert to: 'win-64' or 'win-32'
+    dependencies (List[str]) -- the dependencies passed from the command line
+    verbose (bool) -- show output of items that are updated
+    """
+    temp_dir = extract_temporary_directory(file_path)
+
+    prefixes = set()
+
+    for entry in os.listdir(temp_dir):
+        directory = os.path.join(temp_dir, entry)
+        if os.path.isdir(directory) and entry.strip(os.sep) == "lib":
+            update_lib_contents(directory, temp_dir, "win", file_path)
+
+        if os.path.isdir(directory) and entry.strip(os.sep) == "bin":
+            for script in os.listdir(directory):
+                if (
+                    os.path.isfile(os.path.join(directory, script))
+                    and not is_binary_file(directory, script)
+                    and not script.startswith(".")
+                ):
+                    rename_executable(directory, script, "win")
+                    create_exe_file(
+                        directory, retrieve_executable_name(script), platform
+                    )
+
+                    prefixes.add(
+                        f"/opt/conda text Scripts/{retrieve_executable_name(script)}-script.py\n"
+                    )
+
+            new_bin_path = os.path.join(temp_dir, "Scripts")
+            os.renames(directory, new_bin_path)
+
+    update_index_file(temp_dir, platform, dependencies, verbose)
+    update_prefix_file(temp_dir, prefixes)
+    update_paths_file(temp_dir, target_platform="win")
+    update_files_file(temp_dir, verbose)
+
+    create_target_archive(file_path, temp_dir, platform, output_dir)
+
+    shutil.rmtree(temp_dir)
+
+
+def convert_from_windows_to_unix(
+    file_path, output_dir, platform, dependencies, verbose
+):
+    """Convert a package from windows to a unix platform.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    output_dir (str) -- the file path to where to output the converted tar file
+    platform (str) -- the platform to convert to: 'linux-64', 'linux-32', or 'osx-64'
+    dependencies (List[str]) -- the dependencies passed from the command line
+    verbose (bool) -- show output of items that are updated
+    """
+    retrieve_python_version(file_path)
+    temp_dir = extract_temporary_directory(file_path)
+
+    prefixes = set()
+
+    for entry in os.listdir(temp_dir):
+        directory = os.path.join(temp_dir, entry)
+        if os.path.isdir(directory) and "Lib" in directory:
+            update_lib_contents(directory, temp_dir, "unix", file_path)
+
+        if os.path.isdir(directory) and "Scripts" in directory:
+            for script in os.listdir(directory):
+                if not is_binary_file(directory, script) and not script.startswith("."):
+                    rename_executable(directory, script, "unix")
+                    remove_executable(directory, script)
+
+                    prefixes.add(
+                        f"/opt/conda text bin/{retrieve_executable_name(script)}\n"
+                    )
+
+            new_bin_path = os.path.join(temp_dir, "bin")
+            os.renames(directory, new_bin_path)
+
+    update_index_file(temp_dir, platform, dependencies, verbose)
+    update_prefix_file(temp_dir, prefixes)
+    update_paths_file(temp_dir, target_platform="unix")
+    update_files_file(temp_dir, verbose)
+
+    create_target_archive(file_path, temp_dir, platform, output_dir)
+
+    shutil.rmtree(temp_dir)
+
+
+def conda_convert(
+    file_path,
+    output_dir=".",
+    show_imports=False,
+    platforms=None,
+    force=False,
+    dependencies=None,
+    verbose=False,
+    quiet=False,
+    dry_run=False,
+):
+    """Convert a conda package between different platforms and architectures.
+
+    Positional arguments:
+    file_path (str) -- the file path to the source package's tar file
+    output_dir (str) -- the file path to where to output the converted tar file
+    show_imports (bool) -- show all C extensions found in the source package
+    platforms (str) -- the platforms to convert to: 'win-64', 'win-32', 'linux-64',
+        'linux-32', 'osx-64', or 'all'
+    force (bool) -- force conversion of packages that contain C extensions
+    dependencies (List[str]) -- the new dependencies to add to the source package's
+        existing dependencies
+    verbose (bool) -- show output of items that are updated
+    quiet (bool) -- hide all output except warnings and errors
+    dry_run (bool) -- show which conversions will take place
+    """
+    if show_imports:
+        imports = retrieve_c_extensions(file_path)
+        if len(imports) == 0:
+            print("No imports found.")
+        else:
+            for c_extension in imports:
+                print(c_extension)
+        sys.exit()
+
+    if not show_imports and len(platforms) == 0:
+        sys.exit("Error: --platform option required for conda package conversion.")
+
+    if len(retrieve_c_extensions(file_path)) > 0 and not force:
+        sys.exit(
+            f"WARNING: Package {os.path.basename(file_path)} contains C extensions; skipping conversion. "
+            "Use -f to force conversion."
+        )
+
+    conversion_platform, source_platform, architecture = retrieve_package_platform(
+        file_path
+    )
+    source_platform_architecture = f"{source_platform}-{architecture}"
+
+    if "all" in platforms:
+        platforms = [
+            "osx-64",
+            "osx-arm64",
+            "linux-32",
+            "linux-64",
+            "linux-ppc64",
+            "linux-ppc64le",
+            "linux-s390x",
+            "linux-armv6l",
+            "linux-armv7l",
+            "linux-aarch64",
+            "win-32",
+            "win-64",
+            "win-arm64",
+        ]
+
+    for platform in platforms:
+        if platform == source_platform_architecture:
+            print(
+                f"Source platform '{source_platform_architecture}' and target platform '{platform}' are identical. "
+                "Skipping conversion."
+            )
+            continue
+
+        if not quiet:
+            print(
+                f"Converting {os.path.basename(file_path)} from {source_platform_architecture} to {platform}"
+            )
+
+        if platform.startswith(("osx", "linux")) and conversion_platform == "unix":
+            convert_between_unix_platforms(
+                file_path, output_dir, platform, dependencies, verbose
+            )
+
+        elif platform.startswith("win") and conversion_platform == "unix":
+            convert_from_unix_to_windows(
+                file_path, output_dir, platform, dependencies, verbose
+            )
+
+        elif platform.startswith(("osx", "linux")) and conversion_platform == "win":
+            convert_from_windows_to_unix(
+                file_path, output_dir, platform, dependencies, verbose
+            )
+
+        elif platform.startswith("win") and conversion_platform == "win":
+            convert_between_windows_architechtures(
+                file_path, output_dir, platform, dependencies, verbose
+            )
diff --git a/shared/package-files/4e/4e919ed7f7fc274d9ea400fe0f56ff0e2942635bf1d878034db9d12b342425f0/Setup b/shared/package-files/4e/4e919ed7f7fc274d9ea400fe0f56ff0e2942635bf1d878034db9d12b342425f0/Setup
new file mode 100644
index 0000000000000000000000000000000000000000..87c6a152f86eac094d3a8ff64d389986cae16e66
--- /dev/null
+++ b/shared/package-files/4e/4e919ed7f7fc274d9ea400fe0f56ff0e2942635bf1d878034db9d12b342425f0/Setup
@@ -0,0 +1,377 @@
+# -*- makefile -*-
+# The file Setup is used by the makesetup script to construct the files
+# Makefile and config.c, from Makefile.pre and config.c.in,
+# respectively.  Note that Makefile.pre is created from Makefile.pre.in
+# by the toplevel configure script.
+
+# (VPATH notes: Setup and Makefile.pre are in the build directory, as
+# are Makefile and config.c; the *.in files are in the source directory.)
+
+# Each line in this file describes one or more optional modules.
+# Modules configured here will not be compiled by the setup.py script,
+# so the file can be used to override setup.py's behavior.
+# Tag lines containing just the word "*static*", "*shared*" or "*disabled*"
+# (without the quotes but with the stars) are used to tag the following module
+# descriptions. Tag lines may alternate throughout this file.  Modules are
+# built statically when they are preceded by a "*static*" tag line or when
+# there is no tag line between the start of the file and the module
+# description.  Modules are built as a shared library when they are preceded by
+# a "*shared*" tag line.  Modules are not built at all, not by the Makefile,
+# nor by the setup.py script, when they are preceded by a "*disabled*" tag
+# line.
+
+# Lines have the following structure:
+#
+# <module> ... [<sourcefile> ...] [<cpparg> ...] [<library> ...]
+#
+# <sourcefile> is anything ending in .c (.C, .cc, .c++ are C++ files)
+# <cpparg> is anything starting with -I, -D, -U or -C
+# <library> is anything ending in .a or beginning with -l or -L
+# <module> is anything else but should be a valid Python
+# identifier (letters, digits, underscores, beginning with non-digit)
+#
+# (As the makesetup script changes, it may recognize some other
+# arguments as well, e.g. *.so and *.sl as libraries.  See the big
+# case statement in the makesetup script.)
+#
+# Lines can also have the form
+#
+# <name> = <value>
+#
+# which defines a Make variable definition inserted into Makefile.in
+#
+# The build process works like this:
+#
+# 1. Build all modules that are declared as static in Modules/Setup,
+#    combine them into libpythonxy.a, combine that into python.
+# 2. Build all modules that are listed as shared in Modules/Setup.
+# 3. Invoke setup.py. That builds all modules that
+#    a) are not builtin, and
+#    b) are not listed in Modules/Setup, and
+#    c) can be build on the target
+#
+# Therefore, modules declared to be shared will not be
+# included in the config.c file, nor in the list of objects to be
+# added to the library archive, and their linker options won't be
+# added to the linker options. Rules to create their .o files and
+# their shared libraries will still be added to the Makefile, and
+# their names will be collected in the Make variable SHAREDMODS.  This
+# is used to build modules as shared libraries.  (They can be
+# installed using "make sharedinstall", which is implied by the
+# toplevel "make install" target.)  (For compatibility,
+# *noconfig* has the same effect as *shared*.)
+#
+# NOTE: As a standard policy, as many modules as can be supported by a
+# platform should be present.  The distribution comes with all modules
+# enabled that are supported by most platforms and don't require you
+# to ftp sources from elsewhere.
+
+
+# Some special rules to define PYTHONPATH.
+# Edit the definitions below to indicate which options you are using.
+# Don't add any whitespace or comments!
+
+# Directories where library files get installed.
+# DESTLIB is for Python modules; MACHDESTLIB for shared libraries.
+DESTLIB=$(LIBDEST)
+MACHDESTLIB=$(BINLIBDEST)
+
+# NOTE: all the paths are now relative to the prefix that is computed
+# at run time!
+
+# Standard path -- don't edit.
+# No leading colon since this is the first entry.
+# Empty since this is now just the runtime prefix.
+DESTPATH=
+
+# Site specific path components -- should begin with : if non-empty
+SITEPATH=
+
+# Standard path components for test modules
+TESTPATH=
+
+COREPYTHONPATH=$(DESTPATH)$(SITEPATH)$(TESTPATH)
+PYTHONPATH=$(COREPYTHONPATH)
+
+
+# The modules listed here can't be built as shared libraries for
+# various reasons; therefore they are listed here instead of in the
+# normal order.
+
+# This only contains the minimal set of modules required to run the
+# setup.py script in the root of the Python source tree.
+
+posix -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal posixmodule.c # posix (UNIX) system calls
+errno errnomodule.c			# posix (UNIX) errno values
+pwd pwdmodule.c				# this is needed to find out the user's home dir
+					# if $HOME is not set
+_sre -DPy_BUILD_CORE_BUILTIN _sre.c	# Fredrik Lundh's new regular expressions
+_codecs _codecsmodule.c			# access to the builtin codecs and codec registry
+_weakref _weakref.c			# weak references
+_functools -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal _functoolsmodule.c   # Tools for working with functions and callable objects
+_operator -DPy_BUILD_CORE_BUILTIN _operator.c	        	# operator.add() and similar goodies
+_collections _collectionsmodule.c	# Container types
+_abc -DPy_BUILD_CORE_BUILTIN _abc.c	# Abstract base classes
+itertools itertoolsmodule.c		# Functions creating iterators for efficient looping
+atexit atexitmodule.c			# Register functions to be run at interpreter-shutdown
+_signal -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal signalmodule.c
+_stat _stat.c				# stat.h interface
+time -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal timemodule.c	# -lm # time operations and variables
+_thread -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal _threadmodule.c	# low-level threading interface
+
+# access to ISO C locale support
+_locale -DPy_BUILD_CORE_BUILTIN _localemodule.c  # -lintl
+
+# Standard I/O baseline
+_io -DPy_BUILD_CORE_BUILTIN -I$(srcdir)/Include/internal -I$(srcdir)/Modules/_io _io/_iomodule.c _io/iobase.c _io/fileio.c _io/bytesio.c _io/bufferedio.c _io/textio.c _io/stringio.c
+
+# faulthandler module
+faulthandler faulthandler.c
+
+# debug tool to trace memory blocks allocated by Python
+#
+# bpo-35053: The module must be builtin since _Py_NewReference()
+# can call _PyTraceMalloc_NewReference().
+_tracemalloc _tracemalloc.c
+
+# The rest of the modules listed in this file are all commented out by
+# default.  Usually they can be detected and built as dynamically
+# loaded modules by the new setup.py script added in Python 2.1.  If
+# you're on a platform that doesn't support dynamic loading, want to
+# compile modules statically into the Python binary, or need to
+# specify some odd set of compiler switches, you can uncomment the
+# appropriate lines below.
+
+# ======================================================================
+
+# The Python symtable module depends on .h files that setup.py doesn't track
+_symtable symtablemodule.c
+
+# Uncommenting the following line tells makesetup that all following
+# modules are to be built as shared libraries (see above for more
+# detail; also note that *static* or *disabled* cancels this effect):
+
+#*shared*
+
+# GNU readline.  Unlike previous Python incarnations, GNU readline is
+# now incorporated in an optional module, configured in the Setup file
+# instead of by a configure script switch.  You may have to insert a
+# -L option pointing to the directory where libreadline.* lives,
+# and you may have to change -ltermcap to -ltermlib or perhaps remove
+# it, depending on your system -- see the GNU readline instructions.
+# It's okay for this to be a shared library, too.
+
+#readline readline.c -lreadline -ltermcap
+
+
+# Modules that should always be present (non UNIX dependent):
+
+#array -DPy_BUILD_CORE_MODULE arraymodule.c	# array objects
+#cmath cmathmodule.c _math.c -DPy_BUILD_CORE_MODULE # -lm # complex math library functions
+#math mathmodule.c _math.c -DPy_BUILD_CORE_MODULE # -lm # math library functions, e.g. sin()
+#_contextvars _contextvarsmodule.c  # Context Variables
+#_struct -DPy_BUILD_CORE_MODULE _struct.c	# binary structure packing/unpacking
+#_weakref _weakref.c	# basic weak reference support
+#_testcapi _testcapimodule.c    # Python C API test module
+#_testinternalcapi _testinternalcapi.c -I$(srcdir)/Include/internal -DPy_BUILD_CORE_MODULE  # Python internal C API test module
+#_random _randommodule.c -DPy_BUILD_CORE_MODULE	# Random number generator
+#_elementtree -I$(srcdir)/Modules/expat -DHAVE_EXPAT_CONFIG_H -DUSE_PYEXPAT_CAPI _elementtree.c	# elementtree accelerator
+#_pickle -DPy_BUILD_CORE_MODULE _pickle.c	# pickle accelerator
+#_datetime _datetimemodule.c	# datetime accelerator
+#_zoneinfo _zoneinfo.c -DPy_BUILD_CORE_MODULE	# zoneinfo accelerator
+#_bisect _bisectmodule.c	# Bisection algorithms
+#_heapq _heapqmodule.c -DPy_BUILD_CORE_MODULE	# Heap queue algorithm
+#_asyncio _asynciomodule.c  # Fast asyncio Future
+#_json -I$(srcdir)/Include/internal -DPy_BUILD_CORE_BUILTIN _json.c	# _json speedups
+#_statistics _statisticsmodule.c # statistics accelerator
+
+#unicodedata unicodedata.c -DPy_BUILD_CORE_BUILTIN   # static Unicode character database
+
+
+# Modules with some UNIX dependencies -- on by default:
+# (If you have a really backward UNIX, select and socket may not be
+# supported...)
+
+#fcntl fcntlmodule.c	# fcntl(2) and ioctl(2)
+#spwd spwdmodule.c		# spwd(3)
+#grp grpmodule.c		# grp(3)
+#select selectmodule.c	# select(2); not on ancient System V
+
+# Memory-mapped files (also works on Win32).
+#mmap mmapmodule.c
+
+# CSV file helper
+#_csv _csv.c
+
+# Socket module helper for socket(2)
+#_socket socketmodule.c
+
+# Socket module helper for SSL support; you must comment out the other
+# socket line above, and edit the OPENSSL variable:
+# OPENSSL=/path/to/openssl/directory
+# _ssl _ssl.c \
+#     -I$(OPENSSL)/include -L$(OPENSSL)/lib \
+#     -lssl -lcrypto
+#_hashlib _hashopenssl.c \
+#     -I$(OPENSSL)/include -L$(OPENSSL)/lib \
+#     -lcrypto
+
+# To statically link OpenSSL:
+# _ssl _ssl.c \
+#     -I$(OPENSSL)/include -L$(OPENSSL)/lib \
+#     -l:libssl.a -Wl,--exclude-libs,libssl.a \
+#     -l:libcrypto.a -Wl,--exclude-libs,libcrypto.a
+#_hashlib _hashopenssl.c \
+#     -I$(OPENSSL)/include -L$(OPENSSL)/lib \
+#     -l:libcrypto.a -Wl,--exclude-libs,libcrypto.a
+
+# The crypt module is now disabled by default because it breaks builds
+# on many systems (where -lcrypt is needed), e.g. Linux (I believe).
+
+#_crypt _cryptmodule.c # -lcrypt	# crypt(3); needs -lcrypt on some systems
+
+
+# Some more UNIX dependent modules -- off by default, since these
+# are not supported by all UNIX systems:
+
+#nis nismodule.c -lnsl	# Sun yellow pages -- not everywhere
+#termios termios.c	# Steen Lumholt's termios module
+#resource resource.c	# Jeremy Hylton's rlimit interface
+
+#_posixsubprocess  -DPy_BUILD_CORE_BUILTIN _posixsubprocess.c  # POSIX subprocess module helper
+
+# Multimedia modules -- off by default.
+# These don't work for 64-bit platforms!!!
+# #993173 says audioop works on 64-bit platforms, though.
+# These represent audio samples or images as strings:
+
+#audioop audioop.c	# Operations on audio samples
+
+
+# Note that the _md5 and _sha modules are normally only built if the
+# system does not have the OpenSSL libs containing an optimized version.
+
+# The _md5 module implements the RSA Data Security, Inc. MD5
+# Message-Digest Algorithm, described in RFC 1321.
+
+#_md5 md5module.c
+
+
+# The _sha module implements the SHA checksum algorithms.
+# (NIST's Secure Hash Algorithms.)
+#_sha1 sha1module.c
+#_sha256 sha256module.c -DPy_BUILD_CORE_BUILTIN
+#_sha512 sha512module.c -DPy_BUILD_CORE_BUILTIN
+#_sha3 _sha3/sha3module.c
+
+# _blake module
+#_blake2 _blake2/blake2module.c _blake2/blake2b_impl.c _blake2/blake2s_impl.c
+
+# The _tkinter module.
+#
+# The command for _tkinter is long and site specific.  Please
+# uncomment and/or edit those parts as indicated.  If you don't have a
+# specific extension (e.g. Tix or BLT), leave the corresponding line
+# commented out.  (Leave the trailing backslashes in!  If you
+# experience strange errors, you may want to join all uncommented
+# lines and remove the backslashes -- the backslash interpretation is
+# done by the shell's "read" command and it may not be implemented on
+# every system.
+
+# *** Always uncomment this (leave the leading underscore in!):
+# _tkinter _tkinter.c tkappinit.c -DWITH_APPINIT \
+# *** Uncomment and edit to reflect where your Tcl/Tk libraries are:
+#	-L/usr/local/lib \
+# *** Uncomment and edit to reflect where your Tcl/Tk headers are:
+#	-I/usr/local/include \
+# *** Uncomment and edit to reflect where your X11 header files are:
+#	-I/usr/X11R6/include \
+# *** Or uncomment this for Solaris:
+#	-I/usr/openwin/include \
+# *** Uncomment and edit for Tix extension only:
+#	-DWITH_TIX -ltix8.1.8.2 \
+# *** Uncomment and edit for BLT extension only:
+#	-DWITH_BLT -I/usr/local/blt/blt8.0-unoff/include -lBLT8.0 \
+# *** Uncomment and edit for PIL (TkImaging) extension only:
+#     (See http://www.pythonware.com/products/pil/ for more info)
+#	-DWITH_PIL -I../Extensions/Imaging/libImaging  tkImaging.c \
+# *** Uncomment and edit for TOGL extension only:
+#	-DWITH_TOGL togl.c \
+# *** Uncomment and edit to reflect your Tcl/Tk versions:
+#	-ltk8.2 -ltcl8.2 \
+# *** Uncomment and edit to reflect where your X11 libraries are:
+#	-L/usr/X11R6/lib \
+# *** Or uncomment this for Solaris:
+#	-L/usr/openwin/lib \
+# *** Uncomment these for TOGL extension only:
+#	-lGL -lGLU -lXext -lXmu \
+# *** Uncomment for AIX:
+#	-lld \
+# *** Always uncomment this; X11 libraries to link with:
+#	-lX11
+
+# Lance Ellinghaus's syslog module
+#syslog syslogmodule.c		# syslog daemon interface
+
+
+# Curses support, requiring the System V version of curses, often
+# provided by the ncurses library.  e.g. on Linux, link with -lncurses
+# instead of -lcurses).
+
+#_curses _cursesmodule.c -lcurses -ltermcap -DPy_BUILD_CORE_MODULE
+# Wrapper for the panel library that's part of ncurses and SYSV curses.
+#_curses_panel _curses_panel.c -lpanel -lncurses
+
+
+# Modules that provide persistent dictionary-like semantics.  You will
+# probably want to arrange for at least one of them to be available on
+# your machine, though none are defined by default because of library
+# dependencies.  The Python module dbm/__init__.py provides an
+# implementation independent wrapper for these; dbm/dumb.py provides
+# similar functionality (but slower of course) implemented in Python.
+
+#_dbm _dbmmodule.c 	# dbm(3) may require -lndbm or similar
+
+# Anthony Baxter's gdbm module.  GNU dbm(3) will require -lgdbm:
+
+#_gdbm _gdbmmodule.c -I/usr/local/include -L/usr/local/lib -lgdbm
+
+
+# Helper module for various ascii-encoders
+#binascii binascii.c
+
+# Andrew Kuchling's zlib module.
+# This require zlib 1.1.3 (or later).
+# See http://www.gzip.org/zlib/
+#zlib zlibmodule.c -I$(prefix)/include -L$(exec_prefix)/lib -lz
+
+# Interface to the Expat XML parser
+# More information on Expat can be found at www.libexpat.org.
+#
+#pyexpat expat/xmlparse.c expat/xmlrole.c expat/xmltok.c pyexpat.c -I$(srcdir)/Modules/expat -DHAVE_EXPAT_CONFIG_H -DXML_POOR_ENTROPY -DUSE_PYEXPAT_CAPI
+
+# Hye-Shik Chang's CJKCodecs
+
+# multibytecodec is required for all the other CJK codec modules
+#_multibytecodec cjkcodecs/multibytecodec.c
+
+#_codecs_cn cjkcodecs/_codecs_cn.c
+#_codecs_hk cjkcodecs/_codecs_hk.c
+#_codecs_iso2022 cjkcodecs/_codecs_iso2022.c
+#_codecs_jp cjkcodecs/_codecs_jp.c
+#_codecs_kr cjkcodecs/_codecs_kr.c
+#_codecs_tw cjkcodecs/_codecs_tw.c
+
+# Example -- included for reference only:
+# xx xxmodule.c
+
+# Another example -- the 'xxsubtype' module shows C-level subtyping in action
+xxsubtype xxsubtype.c
+
+# Uncommenting the following line tells makesetup that all following modules
+# are not built (see above for more detail).
+#
+#*disabled*
+#
+#_sqlite3 _tkinter _curses pyexpat
+#_codecs_jp _codecs_kr _codecs_tw unicodedata
diff --git a/shared/package-files/4e/4e91c71cb824cf24fb6076f08feda2eb07916aaf88bf2dbe3149eb0e48dabbe5/markers.py b/shared/package-files/4e/4e91c71cb824cf24fb6076f08feda2eb07916aaf88bf2dbe3149eb0e48dabbe5/markers.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dc68410337dcf4619ef66a49d87cea8233bc057
--- /dev/null
+++ b/shared/package-files/4e/4e91c71cb824cf24fb6076f08feda2eb07916aaf88bf2dbe3149eb0e48dabbe5/markers.py
@@ -0,0 +1,152 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2012-2017 Vinay Sajip.
+# Licensed to the Python Software Foundation under a contributor agreement.
+# See LICENSE.txt and CONTRIBUTORS.txt.
+#
+"""
+Parser for the environment markers micro-language defined in PEP 508.
+"""
+
+# Note: In PEP 345, the micro-language was Python compatible, so the ast
+# module could be used to parse it. However, PEP 508 introduced operators such
+# as ~= and === which aren't in Python, necessitating a different approach.
+
+import os
+import re
+import sys
+import platform
+
+from .compat import string_types
+from .util import in_venv, parse_marker
+from .version import NormalizedVersion as NV
+
+__all__ = ['interpret']
+
+_VERSION_PATTERN = re.compile(r'((\d+(\.\d+)*\w*)|\'(\d+(\.\d+)*\w*)\'|\"(\d+(\.\d+)*\w*)\")')
+
+def _is_literal(o):
+    if not isinstance(o, string_types) or not o:
+        return False
+    return o[0] in '\'"'
+
+def _get_versions(s):
+    result = []
+    for m in _VERSION_PATTERN.finditer(s):
+        result.append(NV(m.groups()[0]))
+    return set(result)
+
+class Evaluator(object):
+    """
+    This class is used to evaluate marker expessions.
+    """
+
+    operations = {
+        '==': lambda x, y: x == y,
+        '===': lambda x, y: x == y,
+        '~=': lambda x, y: x == y or x > y,
+        '!=': lambda x, y: x != y,
+        '<':  lambda x, y: x < y,
+        '<=':  lambda x, y: x == y or x < y,
+        '>':  lambda x, y: x > y,
+        '>=':  lambda x, y: x == y or x > y,
+        'and': lambda x, y: x and y,
+        'or': lambda x, y: x or y,
+        'in': lambda x, y: x in y,
+        'not in': lambda x, y: x not in y,
+    }
+
+    def evaluate(self, expr, context):
+        """
+        Evaluate a marker expression returned by the :func:`parse_requirement`
+        function in the specified context.
+        """
+        if isinstance(expr, string_types):
+            if expr[0] in '\'"':
+                result = expr[1:-1]
+            else:
+                if expr not in context:
+                    raise SyntaxError('unknown variable: %s' % expr)
+                result = context[expr]
+        else:
+            assert isinstance(expr, dict)
+            op = expr['op']
+            if op not in self.operations:
+                raise NotImplementedError('op not implemented: %s' % op)
+            elhs = expr['lhs']
+            erhs = expr['rhs']
+            if _is_literal(expr['lhs']) and _is_literal(expr['rhs']):
+                raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs))
+
+            lhs = self.evaluate(elhs, context)
+            rhs = self.evaluate(erhs, context)
+            if ((elhs == 'python_version' or erhs == 'python_version') and
+                op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')):
+                lhs = NV(lhs)
+                rhs = NV(rhs)
+            elif elhs == 'python_version' and op in ('in', 'not in'):
+                lhs = NV(lhs)
+                rhs = _get_versions(rhs)
+            result = self.operations[op](lhs, rhs)
+        return result
+
+_DIGITS = re.compile(r'\d+\.\d+')
+
+def default_context():
+    def format_full_version(info):
+        version = '%s.%s.%s' % (info.major, info.minor, info.micro)
+        kind = info.releaselevel
+        if kind != 'final':
+            version += kind[0] + str(info.serial)
+        return version
+
+    if hasattr(sys, 'implementation'):
+        implementation_version = format_full_version(sys.implementation.version)
+        implementation_name = sys.implementation.name
+    else:
+        implementation_version = '0'
+        implementation_name = ''
+
+    ppv = platform.python_version()
+    m = _DIGITS.match(ppv)
+    pv = m.group(0)
+    result = {
+        'implementation_name': implementation_name,
+        'implementation_version': implementation_version,
+        'os_name': os.name,
+        'platform_machine': platform.machine(),
+        'platform_python_implementation': platform.python_implementation(),
+        'platform_release': platform.release(),
+        'platform_system': platform.system(),
+        'platform_version': platform.version(),
+        'platform_in_venv': str(in_venv()),
+        'python_full_version': ppv,
+        'python_version': pv,
+        'sys_platform': sys.platform,
+    }
+    return result
+
+DEFAULT_CONTEXT = default_context()
+del default_context
+
+evaluator = Evaluator()
+
+def interpret(marker, execution_context=None):
+    """
+    Interpret a marker and return a result depending on environment.
+
+    :param marker: The marker to interpret.
+    :type marker: str
+    :param execution_context: The context used for name lookup.
+    :type execution_context: mapping
+    """
+    try:
+        expr, rest = parse_marker(marker)
+    except Exception as e:
+        raise SyntaxError('Unable to interpret marker syntax: %s: %s' % (marker, e))
+    if rest and rest[0] != '#':
+        raise SyntaxError('unexpected trailing data in marker: %s: %s' % (marker, rest))
+    context = dict(DEFAULT_CONTEXT)
+    if execution_context:
+        context.update(execution_context)
+    return evaluator.evaluate(expr, context)
diff --git a/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/parent-validation-report.json b/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/parent-validation-report.json
new file mode 100644
index 0000000000000000000000000000000000000000..a473c7d2b4ca2e523122fafe69d4bf8ace479ef0
--- /dev/null
+++ b/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/parent-validation-report.json
@@ -0,0 +1,136384 @@
+{
+  "schema": "natural-disease-progression-report/v3",
+  "reward_metric": "panel_recall_at_5pct",
+  "panel": {
+    "panel_recall_at_5pct": 0.2919421213080196,
+    "panel_recall_at_1pct": 0.09295741601673842,
+    "panel_ppv_at_5pct": 0.0939891257826757,
+    "panel_auroc": 0.7996291654764027,
+    "panel_auprc": 0.10125228237396654,
+    "panel_calibration_slope_median": 0.6979622920082253,
+    "panel_prediction_to_observed_median": 0.8794374945956378,
+    "panel_ece_decile": 0.007624789959430965,
+    "endpoints_in_reward": [
+      "I50",
+      "I48",
+      "I21",
+      "I26",
+      "I82",
+      "E11",
+      "N18",
+      "N17",
+      "A41",
+      "J18",
+      "J96",
+      "K72",
+      "J44",
+      "C78"
+    ],
+    "min_positives": 20,
+    "endpoints": {
+      "I50": {
+        "name": "heart failure",
+        "eligible": 2408,
+        "positives": 52,
+        "family_history_excluded": 7,
+        "in_reward": true,
+        "recall_at_5pct": 0.25,
+        "ppv_at_5pct": 0.10743801652892562,
+        "recall_at_1pct": 0.09615384615384616,
+        "ppv_at_1pct": 0.2,
+        "auroc": 0.8074147838579078,
+        "auprc": 0.08793875549787784,
+        "prevalence": 0.02159468438538206,
+        "calibration": {
+          "observed_rate": 0.02159468438538206,
+          "mean_prediction": 0.020002026981464315,
+          "prediction_to_observed": 0.9262477109878091,
+          "slope": 0.6029579706820021,
+          "ece_decile": 0.012334665992229354
+        }
+      },
+      "I48": {
+        "name": "atrial fibrillation/flutter",
+        "eligible": 2385,
+        "positives": 32,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.15625,
+        "ppv_at_5pct": 0.041666666666666664,
+        "recall_at_1pct": 0.03125,
+        "ppv_at_1pct": 0.041666666666666664,
+        "auroc": 0.7546616022099447,
+        "auprc": 0.04294500730364608,
+        "prevalence": 0.013417190775681341,
+        "calibration": {
+          "observed_rate": 0.013417190775681341,
+          "mean_prediction": 0.014979020434708005,
+          "prediction_to_observed": 1.116405116774331,
+          "slope": 0.687940159699821,
+          "ece_decile": 0.0060299989951147735
+        }
+      },
+      "I21": {
+        "name": "acute myocardial infarction",
+        "eligible": 2671,
+        "positives": 23,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.2608695652173913,
+        "ppv_at_5pct": 0.04477611940298507,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.7980264022067516,
+        "auprc": 0.03249692370002018,
+        "prevalence": 0.008611007113440658,
+        "calibration": {
+          "observed_rate": 0.008611007113440658,
+          "mean_prediction": 0.012298636328861695,
+          "prediction_to_observed": 1.4282459841038952,
+          "slope": 0.6715979798029659,
+          "ece_decile": 0.006747536728333281
+        }
+      },
+      "I63": {
+        "name": "ischaemic stroke",
+        "eligible": 2662,
+        "positives": 14,
+        "family_history_excluded": 73,
+        "in_reward": false,
+        "recall_at_5pct": 0.07142857142857142,
+        "ppv_at_5pct": 0.007462686567164179,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.7497032801035822,
+        "auprc": 0.012747389791107186,
+        "prevalence": 0.005259203606311044,
+        "calibration": {
+          "observed_rate": 0.005259203606311044,
+          "mean_prediction": 0.004516370168816976,
+          "prediction_to_observed": 0.8587555278136277,
+          "slope": 0.5793251467178383,
+          "ece_decile": 0.0035474036486085473
+        }
+      },
+      "I61": {
+        "name": "intracerebral haemorrhage",
+        "eligible": 2802,
+        "positives": 9,
+        "family_history_excluded": 0,
+        "in_reward": false,
+        "recall_at_5pct": 0.2222222222222222,
+        "ppv_at_5pct": 0.014184397163120567,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.8557107053347655,
+        "auprc": 0.01677833107591628,
+        "prevalence": 0.0032119914346895075,
+        "calibration": {
+          "observed_rate": 0.0032119914346895075,
+          "mean_prediction": 0.0013339210787031272,
+          "prediction_to_observed": 0.4152940958362403,
+          "slope": 0.8374443595690628,
+          "ece_decile": 0.002312550028525053
+        }
+      },
+      "I26": {
+        "name": "pulmonary embolism",
+        "eligible": 2760,
+        "positives": 20,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.4,
+        "ppv_at_5pct": 0.057971014492753624,
+        "recall_at_1pct": 0.1,
+        "ppv_at_1pct": 0.07142857142857142,
+        "auroc": 0.7642883211678833,
+        "auprc": 0.05007780410894764,
+        "prevalence": 0.007246376811594203,
+        "calibration": {
+          "observed_rate": 0.007246376811594203,
+          "mean_prediction": 0.006046869523972577,
+          "prediction_to_observed": 0.8344679943082156,
+          "slope": 0.842880891744845,
+          "ece_decile": 0.0025784072902119396
+        }
+      },
+      "I82": {
+        "name": "venous thrombosis",
+        "eligible": 2739,
+        "positives": 35,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.22857142857142856,
+        "ppv_at_5pct": 0.058394160583941604,
+        "recall_at_1pct": 0.11428571428571428,
+        "ppv_at_1pct": 0.14285714285714285,
+        "auroc": 0.7599535080304312,
+        "auprc": 0.057948803939876656,
+        "prevalence": 0.012778386272362175,
+        "calibration": {
+          "observed_rate": 0.012778386272362175,
+          "mean_prediction": 0.011466290415688845,
+          "prediction_to_observed": 0.89731912710205,
+          "slope": 0.596559324407773,
+          "ece_decile": 0.0067192362099279
+        }
+      },
+      "I71": {
+        "name": "aortic aneurysm/dissection",
+        "eligible": 2769,
+        "positives": 5,
+        "family_history_excluded": 0,
+        "in_reward": false,
+        "recall_at_5pct": 0.2,
+        "ppv_at_5pct": 0.007194244604316547,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.7250361794500724,
+        "auprc": 0.006120057855075772,
+        "prevalence": 0.0018057060310581437,
+        "calibration": {
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+          "mean_prediction": 0.0032012284058225535,
+          "prediction_to_observed": 1.7728402911445302,
+          "slope": 0.48684463940243994,
+          "ece_decile": 0.002787396099907134
+        }
+      },
+      "E11": {
+        "name": "type 2 diabetes",
+        "eligible": 2161,
+        "positives": 21,
+        "family_history_excluded": 33,
+        "in_reward": true,
+        "recall_at_5pct": 0.14285714285714285,
+        "ppv_at_5pct": 0.027522935779816515,
+        "recall_at_1pct": 0.09523809523809523,
+        "ppv_at_1pct": 0.09090909090909091,
+        "auroc": 0.7168891855807744,
+        "auprc": 0.033930538865702804,
+        "prevalence": 0.00971772327626099,
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+          "mean_prediction": 0.008634882209574082,
+          "prediction_to_observed": 0.8885704978518852,
+          "slope": 0.5812263577857899,
+          "ece_decile": 0.005512725652656866
+        }
+      },
+      "N18": {
+        "name": "chronic kidney disease",
+        "eligible": 2439,
+        "positives": 49,
+        "family_history_excluded": 1,
+        "in_reward": true,
+        "recall_at_5pct": 0.3877551020408163,
+        "ppv_at_5pct": 0.1557377049180328,
+        "recall_at_1pct": 0.1836734693877551,
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+        "auprc": 0.25211184039326406,
+        "prevalence": 0.02009020090200902,
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+          "mean_prediction": 0.01155392999612531,
+          "prediction_to_observed": 0.5751027604193802,
+          "slope": 0.8111011564873142,
+          "ece_decile": 0.008838934681340647
+        }
+      },
+      "N17": {
+        "name": "acute kidney injury",
+        "eligible": 2292,
+        "positives": 104,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.25,
+        "ppv_at_5pct": 0.22608695652173913,
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+        "ppv_at_1pct": 0.34782608695652173,
+        "auroc": 0.797575938686542,
+        "auprc": 0.2023918789281792,
+        "prevalence": 0.04537521815008726,
+        "calibration": {
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+          "mean_prediction": 0.03972608160854717,
+          "prediction_to_observed": 0.8755017216037511,
+          "slope": 0.73968186332525,
+          "ece_decile": 0.013740939000447932
+        }
+      },
+      "A41": {
+        "name": "sepsis",
+        "eligible": 2661,
+        "positives": 76,
+        "family_history_excluded": 3,
+        "in_reward": true,
+        "recall_at_5pct": 0.15789473684210525,
+        "ppv_at_5pct": 0.08955223880597014,
+        "recall_at_1pct": 0.02631578947368421,
+        "ppv_at_1pct": 0.07407407407407407,
+        "auroc": 0.7436017509925684,
+        "auprc": 0.0682406334044092,
+        "prevalence": 0.028560691469372415,
+        "calibration": {
+          "observed_rate": 0.028560691469372415,
+          "mean_prediction": 0.028555506543272616,
+          "prediction_to_observed": 0.9998184593637952,
+          "slope": 0.5294065379500374,
+          "ece_decile": 0.015969131886175977
+        }
+      },
+      "J18": {
+        "name": "pneumonia",
+        "eligible": 2671,
+        "positives": 64,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.296875,
+        "ppv_at_5pct": 0.1417910447761194,
+        "recall_at_1pct": 0.078125,
+        "ppv_at_1pct": 0.18518518518518517,
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+        "prevalence": 0.023961063272182705,
+        "calibration": {
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+          "mean_prediction": 0.020740531419834646,
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+          "slope": 0.7079844243166296,
+          "ece_decile": 0.008551900471379867
+        }
+      },
+      "J96": {
+        "name": "respiratory failure",
+        "eligible": 2602,
+        "positives": 81,
+        "family_history_excluded": 3,
+        "in_reward": true,
+        "recall_at_5pct": 0.2345679012345679,
+        "ppv_at_5pct": 0.1450381679389313,
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+        "prevalence": 0.03112990007686395,
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+          "mean_prediction": 0.027499321550572435,
+          "prediction_to_observed": 0.8833732675875244,
+          "slope": 0.749395913794878,
+          "ece_decile": 0.008250310762226924
+        }
+      },
+      "K74": {
+        "name": "hepatic fibrosis/cirrhosis",
+        "eligible": 2705,
+        "positives": 5,
+        "family_history_excluded": 58,
+        "in_reward": false,
+        "recall_at_5pct": 1.0,
+        "ppv_at_5pct": 0.03676470588235294,
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+        "auroc": 0.9788888888888889,
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+        "prevalence": 0.0018484288354898336,
+        "calibration": {
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+          "slope": 1.1610842022428038,
+          "ece_decile": 0.0006027997337909938
+        }
+      },
+      "K72": {
+        "name": "hepatic failure",
+        "eligible": 2787,
+        "positives": 22,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.8181818181818182,
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+        "ppv_at_1pct": 0.25,
+        "auroc": 0.90785796481999,
+        "auprc": 0.2729943677168332,
+        "prevalence": 0.007893792608539648,
+        "calibration": {
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+          "slope": 0.8087629997458485,
+          "ece_decile": 0.0024545205180371596
+        }
+      },
+      "F03": {
+        "name": "dementia",
+        "eligible": 2665,
+        "positives": 13,
+        "family_history_excluded": 74,
+        "in_reward": false,
+        "recall_at_5pct": 0.15384615384615385,
+        "ppv_at_5pct": 0.014925373134328358,
+        "recall_at_1pct": 0.07692307692307693,
+        "ppv_at_1pct": 0.037037037037037035,
+        "auroc": 0.8556967165564451,
+        "auprc": 0.03242146786732503,
+        "prevalence": 0.004878048780487805,
+        "calibration": {
+          "observed_rate": 0.004878048780487805,
+          "mean_prediction": 0.007581928125184847,
+          "prediction_to_observed": 1.5542952656628937,
+          "slope": 0.7466749532582455,
+          "ece_decile": 0.00593869059052602
+        }
+      },
+      "J44": {
+        "name": "COPD",
+        "eligible": 2543,
+        "positives": 27,
+        "family_history_excluded": 27,
+        "in_reward": true,
+        "recall_at_5pct": 0.18518518518518517,
+        "ppv_at_5pct": 0.0390625,
+        "recall_at_1pct": 0.07407407407407407,
+        "ppv_at_1pct": 0.07692307692307693,
+        "auroc": 0.8250603544721191,
+        "auprc": 0.04397191372157165,
+        "prevalence": 0.01061738104600865,
+        "calibration": {
+          "observed_rate": 0.01061738104600865,
+          "mean_prediction": 0.009254721932879873,
+          "prediction_to_observed": 0.8716576990856859,
+          "slope": 0.8248071177877252,
+          "ece_decile": 0.004494019529389088
+        }
+      },
+      "J84": {
+        "name": "interstitial lung disease",
+        "eligible": 2817,
+        "positives": 7,
+        "family_history_excluded": 0,
+        "in_reward": false,
+        "recall_at_5pct": 0.5714285714285714,
+        "ppv_at_5pct": 0.028368794326241134,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.9408744280630401,
+        "auprc": 0.02503480111352393,
+        "prevalence": 0.0024849130280440185,
+        "calibration": {
+          "observed_rate": 0.0024849130280440185,
+          "mean_prediction": 0.0022958782960056444,
+          "prediction_to_observed": 0.9239270228354143,
+          "slope": 1.3218308024504446,
+          "ece_decile": 0.0016170877988415226
+        }
+      },
+      "C18": {
+        "name": "colon cancer",
+        "eligible": 2824,
+        "positives": 2,
+        "family_history_excluded": 0,
+        "in_reward": false,
+        "recall_at_5pct": 1.0,
+        "ppv_at_5pct": 0.014084507042253521,
+        "recall_at_1pct": 0.5,
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+        "auroc": 0.9904323175053154,
+        "auprc": 0.05115089514066496,
+        "prevalence": 0.000708215297450425,
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+          "prediction_to_observed": 1.2426806239423056,
+          "slope": 1.594277189115593,
+          "ece_decile": 0.000410310181815661
+        }
+      },
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diff --git a/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/validation-report.json b/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/validation-report.json
new file mode 100644
index 0000000000000000000000000000000000000000..a473c7d2b4ca2e523122fafe69d4bf8ace479ef0
--- /dev/null
+++ b/shared/package-files/4e/4e9735b5973aaa990f3bea89bcd9aa7c596c68b55d283dce698cee1f0fb2343b/validation-report.json
@@ -0,0 +1,136384 @@
+{
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+      "N28": {
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+      "N40": {
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+        "reason": null,
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+      },
+      "N73": {
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+          "nonempty_bins": 1
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9950756243404854
+      },
+      "N99": {
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+          "nonempty_bins": 1
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9845233907843827
+      },
+      "O69": {
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+        "positives": 6,
+        "negatives": 2737,
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+          "nonempty_bins": 3
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9648258881463243
+      },
+      "O99": {
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+        "positives": 3,
+        "negatives": 2649,
+        "auroc": 0.8881338870013842,
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+          "expected_calibration_error_10bin": 0.0008805672989701843,
+          "nonempty_bins": 3
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9328174463594794
+      },
+      "Q21": {
+        "valid": 2810,
+        "positives": 5,
+        "negatives": 2805,
+        "auroc": 0.6819251336898395,
+        "auprc": 0.00887253545046684,
+        "brier": 0.0017744603776350422,
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+          "mean_prediction": 0.0018642681379820339,
+          "calibration_in_the_large": 8.490870737705176e-05,
+          "expected_calibration_error_10bin": 8.490870737705176e-05,
+          "nonempty_bins": 1
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9883925430882871
+      },
+      "U07": {
+        "valid": 2828,
+        "positives": 3,
+        "negatives": 2825,
+        "auroc": 0.8181710914454278,
+        "auprc": 0.018480285146951814,
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+          "nonempty_bins": 2
+        },
+        "reason": null,
+        "coverage_in_primary_cohort": 0.9947238832219486
+      }
+    }
+  },
+  "strata": {
+    "age_18_64": {
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+      "age_sex_normalized_disease_time_brier": 1.2551212112826753,
+      "raw_disease_time_brier_macro": 0.004412908968928176,
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+      "macro_auprc_365d": 0.07155370549514653,
+      "macro_brier_365d": 0.006267574768522709,
+      "scorable_auroc_diseases": 244,
+      "death_time_brier": 0.019920437444744563
+    },
+    "age_65_plus": {
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+      "scorable_auroc_diseases": 239,
+      "death_time_brier": 0.07914451402758924
+    },
+    "sex_F": {
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+      "death_time_brier": 0.039864316730205115
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+    "sex_M": {
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+      "raw_disease_time_brier_macro": 0.006322373955699987,
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+      "macro_brier_365d": 0.009038749713549529,
+      "scorable_auroc_diseases": 250,
+      "death_time_brier": 0.056131969455323505
+    },
+    "no_future_disease_record": {
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+      "age_sex_normalized_disease_time_brier": 0.03253372827925402,
+      "raw_disease_time_brier_macro": 0.0001946859845714472,
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+      "macro_auprc_365d": null,
+      "macro_brier_365d": 0.00034002207507338256,
+      "scorable_auroc_diseases": 0,
+      "death_time_brier": 0.03266810476015538
+    },
+    "future_disease_record": {
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+      "age_sex_normalized_disease_time_brier": 4.798093944298928,
+      "raw_disease_time_brier_macro": 0.017718329781873113,
+      "macro_auroc_365d": 0.7139852124009918,
+      "macro_auprc_365d": 0.09217159809076704,
+      "macro_brier_365d": 0.024804864936011313,
+      "scorable_auroc_diseases": 258,
+      "death_time_brier": 0.07904435789100464
+    }
+  },
+  "confidence_intervals": null
+}
diff --git a/shared/package-files/4e/4e979e308224a0d3b82b2f1561dc5e8d4cf1995f11d52deddb9a1f383e4d8a57/options.pyi b/shared/package-files/4e/4e979e308224a0d3b82b2f1561dc5e8d4cf1995f11d52deddb9a1f383e4d8a57/options.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..63928972d8ecf5bad0b1f46c4437faa0eb0e5fbd
--- /dev/null
+++ b/shared/package-files/4e/4e979e308224a0d3b82b2f1561dc5e8d4cf1995f11d52deddb9a1f383e4d8a57/options.pyi
@@ -0,0 +1,275 @@
+from collections import OrderedDict
+from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Set, Tuple, Type, Union, Mapping, TypeVar
+
+from django.forms.forms import BaseForm
+from django.forms.formsets import BaseFormSet
+from typing_extensions import Literal, TypedDict
+
+from django.contrib.admin.filters import ListFilter
+from django.contrib.admin.models import LogEntry
+from django.contrib.admin.sites import AdminSite
+from django.contrib.admin.views.main import ChangeList
+from django.contrib.auth.forms import AdminPasswordChangeForm
+from django.contrib.contenttypes.models import ContentType
+from django.core.checks.messages import Error
+from django.core.paginator import Paginator
+from django.db.models.base import Model
+from django.db.models.fields.related import ForeignKey, ManyToManyField, RelatedField
+from django.db.models.options import Options
+from django.db.models.query import QuerySet
+from django.forms.fields import TypedChoiceField
+from django.forms.models import ModelChoiceField, ModelMultipleChoiceField
+from django.forms.widgets import Media
+from django.http.request import HttpRequest
+from django.http.response import HttpResponse, HttpResponseBase, HttpResponseRedirect, JsonResponse
+from django.template.response import TemplateResponse
+from django.urls.resolvers import URLPattern
+from django.utils.safestring import SafeText
+
+from django.db.models.fields import Field
+
+IS_POPUP_VAR: str
+TO_FIELD_VAR: str
+HORIZONTAL: Literal[1] = ...
+VERTICAL: Literal[2] = ...
+
+_Direction = Union[Literal[1], Literal[2]]
+
+def get_content_type_for_model(obj: Union[Type[Model], Model]) -> ContentType: ...
+def get_ul_class(radio_style: int) -> str: ...
+
+class IncorrectLookupParameters(Exception): ...
+
+FORMFIELD_FOR_DBFIELD_DEFAULTS: Any
+csrf_protect_m: Any
+
+class _OptionalFieldOpts(TypedDict, total=False):
+    classes: Sequence[str]
+    description: str
+
+class _FieldOpts(_OptionalFieldOpts, total=True):
+    fields: Sequence[Union[str, Sequence[str]]]
+
+# Workaround for mypy issue, a Sequence type should be preferred here.
+# https://github.com/python/mypy/issues/8921
+# _FieldsetSpec = Sequence[Tuple[Optional[str], _FieldOpts]]
+_T = TypeVar("_T")
+_ListOrTuple = Union[Tuple[_T, ...], List[_T]]
+_FieldsetSpec = _ListOrTuple[Tuple[Optional[str], _FieldOpts]]
+
+class BaseModelAdmin:
+    autocomplete_fields: Sequence[str] = ...
+    raw_id_fields: Sequence[str] = ...
+    fields: Sequence[Union[str, Sequence[str]]] = ...
+    exclude: Sequence[str] = ...
+    fieldsets: _FieldsetSpec = ...
+    form: Type[BaseForm] = ...
+    filter_vertical: Sequence[str] = ...
+    filter_horizontal: Sequence[str] = ...
+    radio_fields: Mapping[str, _Direction] = ...
+    prepopulated_fields: Mapping[str, Sequence[str]] = ...
+    formfield_overrides: Mapping[Type[Field], Mapping[str, Any]] = ...
+    readonly_fields: Sequence[Union[str, Callable[[Model], Any]]] = ...
+    ordering: Sequence[str] = ...
+    sortable_by: Sequence[str] = ...
+    view_on_site: bool = ...
+    show_full_result_count: bool = ...
+    checks_class: Any = ...
+    def check(self, **kwargs: Any) -> List[Union[str, Error]]: ...
+    def formfield_for_dbfield(
+        self, db_field: Field, request: Optional[HttpRequest], **kwargs: Any
+    ) -> Optional[Field]: ...
+    def formfield_for_choice_field(
+        self, db_field: Field, request: Optional[HttpRequest], **kwargs: Any
+    ) -> TypedChoiceField: ...
+    def get_field_queryset(
+        self, db: None, db_field: RelatedField, request: Optional[HttpRequest]
+    ) -> Optional[QuerySet]: ...
+    def formfield_for_foreignkey(
+        self, db_field: ForeignKey, request: Optional[HttpRequest], **kwargs: Any
+    ) -> Optional[ModelChoiceField]: ...
+    def formfield_for_manytomany(
+        self, db_field: ManyToManyField, request: Optional[HttpRequest], **kwargs: Any
+    ) -> ModelMultipleChoiceField: ...
+    def get_autocomplete_fields(self, request: HttpRequest) -> Tuple: ...
+    def get_view_on_site_url(self, obj: Optional[Model] = ...) -> Optional[str]: ...
+    def get_empty_value_display(self) -> SafeText: ...
+    def get_exclude(self, request: HttpRequest, obj: Optional[Model] = ...) -> Any: ...
+    def get_fields(self, request: HttpRequest, obj: Optional[Model] = ...) -> Sequence[Union[Callable, str]]: ...
+    def get_fieldsets(
+        self, request: HttpRequest, obj: Optional[Model] = ...
+    ) -> List[Tuple[Optional[str], Dict[str, Any]]]: ...
+    def get_ordering(self, request: HttpRequest) -> Union[List[str], Tuple]: ...
+    def get_readonly_fields(self, request: HttpRequest, obj: Optional[Model] = ...) -> Union[List[str], Tuple]: ...
+    def get_prepopulated_fields(self, request: HttpRequest, obj: Optional[Model] = ...) -> Dict[str, Tuple[str]]: ...
+    def get_queryset(self, request: HttpRequest) -> QuerySet: ...
+    def get_sortable_by(self, request: HttpRequest) -> Union[List[Callable], List[str], Tuple]: ...
+    def lookup_allowed(self, lookup: str, value: str) -> bool: ...
+    def to_field_allowed(self, request: HttpRequest, to_field: str) -> bool: ...
+    def has_add_permission(self, request: HttpRequest) -> bool: ...
+    def has_change_permission(self, request: HttpRequest, obj: Optional[Model] = ...) -> bool: ...
+    def has_delete_permission(self, request: HttpRequest, obj: Optional[Model] = ...) -> bool: ...
+    def has_view_permission(self, request: HttpRequest, obj: Optional[Model] = ...) -> bool: ...
+    def has_module_permission(self, request: HttpRequest) -> bool: ...
+
+class ModelAdmin(BaseModelAdmin):
+    list_display: Sequence[Union[str, Callable[[Model], Any]]] = ...
+    list_display_links: Optional[Sequence[Union[str, Callable]]] = ...
+    list_filter: Sequence[Union[str, Type[ListFilter], Tuple[str, Type[ListFilter]]]] = ...
+    list_select_related: Union[bool, Sequence[str]] = ...
+    list_per_page: int = ...
+    list_max_show_all: int = ...
+    list_editable: Sequence[str] = ...
+    search_fields: Sequence[str] = ...
+    date_hierarchy: Optional[str] = ...
+    save_as: bool = ...
+    save_as_continue: bool = ...
+    save_on_top: bool = ...
+    paginator: Type = ...
+    preserve_filters: bool = ...
+    inlines: Sequence[Type[InlineModelAdmin]] = ...
+    add_form_template: str = ...
+    change_form_template: str = ...
+    change_list_template: str = ...
+    delete_confirmation_template: str = ...
+    delete_selected_confirmation_template: str = ...
+    object_history_template: str = ...
+    popup_response_template: str = ...
+    actions: Sequence[Callable[[ModelAdmin, HttpRequest, QuerySet], None]] = ...
+    action_form: Any = ...
+    actions_on_top: bool = ...
+    actions_on_bottom: bool = ...
+    actions_selection_counter: bool = ...
+    model: Type[Model] = ...
+    opts: Options = ...
+    admin_site: AdminSite = ...
+    def __init__(self, model: Type[Model], admin_site: Optional[AdminSite]) -> None: ...
+    def get_inline_instances(self, request: HttpRequest, obj: Optional[Model] = ...) -> List[InlineModelAdmin]: ...
+    def get_urls(self) -> List[URLPattern]: ...
+    @property
+    def urls(self) -> List[URLPattern]: ...
+    @property
+    def media(self) -> Media: ...
+    def get_model_perms(self, request: HttpRequest) -> Dict[str, bool]: ...
+    def get_form(self, request: Any, obj: Optional[Any] = ..., change: bool = ..., **kwargs: Any): ...
+    def get_changelist(self, request: HttpRequest, **kwargs: Any) -> Type[ChangeList]: ...
+    def get_changelist_instance(self, request: HttpRequest) -> ChangeList: ...
+    def get_object(self, request: HttpRequest, object_id: str, from_field: None = ...) -> Optional[Model]: ...
+    def get_changelist_form(self, request: Any, **kwargs: Any): ...
+    def get_changelist_formset(self, request: Any, **kwargs: Any): ...
+    def get_formsets_with_inlines(self, request: HttpRequest, obj: Optional[Model] = ...) -> Iterator[Any]: ...
+    def get_paginator(
+        self,
+        request: HttpRequest,
+        queryset: QuerySet,
+        per_page: int,
+        orphans: int = ...,
+        allow_empty_first_page: bool = ...,
+    ) -> Paginator: ...
+    def log_addition(self, request: HttpRequest, object: Model, message: Any) -> LogEntry: ...
+    def log_change(self, request: HttpRequest, object: Model, message: Any) -> LogEntry: ...
+    def log_deletion(self, request: HttpRequest, object: Model, object_repr: str) -> LogEntry: ...
+    def action_checkbox(self, obj: Model) -> SafeText: ...
+    def get_actions(self, request: HttpRequest) -> OrderedDict: ...
+    def get_action_choices(
+        self, request: HttpRequest, default_choices: List[Tuple[str, str]] = ...
+    ) -> List[Tuple[str, str]]: ...
+    def get_action(self, action: Union[Callable, str]) -> Tuple[Callable, str, str]: ...
+    def get_list_display(self, request: HttpRequest) -> Sequence[str]: ...
+    def get_list_display_links(self, request: HttpRequest, list_display: Sequence[str]) -> Optional[Sequence[str]]: ...
+    def get_list_filter(self, request: HttpRequest) -> Sequence[str]: ...
+    def get_list_select_related(self, request: HttpRequest) -> Sequence[str]: ...
+    def get_search_fields(self, request: HttpRequest) -> List[str]: ...
+    def get_search_results(
+        self, request: HttpRequest, queryset: QuerySet, search_term: str
+    ) -> Tuple[QuerySet, bool]: ...
+    def get_preserved_filters(self, request: HttpRequest) -> str: ...
+    def _get_edited_object_pks(self, request: HttpRequest, prefix: str) -> List[str]: ...
+    def _get_list_editable_queryset(self, request: HttpRequest, prefix: str) -> QuerySet: ...
+    def construct_change_message(
+        self, request: HttpRequest, form: AdminPasswordChangeForm, formsets: None, add: bool = ...
+    ) -> List[Dict[str, Dict[str, List[str]]]]: ...
+    def message_user(
+        self,
+        request: HttpRequest,
+        message: str,
+        level: Union[int, str] = ...,
+        extra_tags: str = ...,
+        fail_silently: bool = ...,
+    ) -> None: ...
+    def save_form(self, request: Any, form: Any, change: Any): ...
+    def save_model(self, request: Any, obj: Any, form: Any, change: Any) -> None: ...
+    def delete_model(self, request: HttpRequest, obj: Model) -> None: ...
+    def delete_queryset(self, request: HttpRequest, queryset: QuerySet) -> None: ...
+    def save_formset(self, request: Any, form: Any, formset: Any, change: Any) -> None: ...
+    def save_related(self, request: Any, form: Any, formsets: Any, change: Any) -> None: ...
+    def render_change_form(
+        self,
+        request: Any,
+        context: Any,
+        add: bool = ...,
+        change: bool = ...,
+        form_url: str = ...,
+        obj: Optional[Any] = ...,
+    ): ...
+    def response_add(
+        self, request: HttpRequest, obj: Model, post_url_continue: Optional[str] = ...
+    ) -> HttpResponse: ...
+    def response_change(self, request: HttpRequest, obj: Model) -> HttpResponse: ...
+    def response_post_save_add(self, request: HttpRequest, obj: Model) -> HttpResponseRedirect: ...
+    def response_post_save_change(self, request: HttpRequest, obj: Model) -> HttpResponseRedirect: ...
+    def response_action(self, request: HttpRequest, queryset: QuerySet) -> Optional[HttpResponseBase]: ...
+    def response_delete(self, request: HttpRequest, obj_display: str, obj_id: int) -> HttpResponse: ...
+    def render_delete_form(self, request: Any, context: Any): ...
+    def get_inline_formsets(
+        self, request: HttpRequest, formsets: List[Any], inline_instances: List[Any], obj: Optional[Model] = ...
+    ) -> List[Any]: ...
+    def get_changeform_initial_data(self, request: HttpRequest) -> Dict[str, str]: ...
+    def changeform_view(
+        self,
+        request: HttpRequest,
+        object_id: Optional[str] = ...,
+        form_url: str = ...,
+        extra_context: Optional[Dict[str, bool]] = ...,
+    ) -> Any: ...
+    def autocomplete_view(self, request: HttpRequest) -> JsonResponse: ...
+    def add_view(self, request: HttpRequest, form_url: str = ..., extra_context: None = ...) -> HttpResponse: ...
+    def change_view(
+        self, request: HttpRequest, object_id: str, form_url: str = ..., extra_context: Optional[Dict[str, bool]] = ...
+    ) -> HttpResponse: ...
+    def changelist_view(
+        self, request: HttpRequest, extra_context: Optional[Dict[str, str]] = ...
+    ) -> TemplateResponse: ...
+    def get_deleted_objects(
+        self, objs: QuerySet, request: HttpRequest
+    ) -> Tuple[List[Any], Dict[Any, Any], Set[Any], List[Any]]: ...
+    def delete_view(self, request: HttpRequest, object_id: str, extra_context: None = ...) -> Any: ...
+    def history_view(self, request: HttpRequest, object_id: str, extra_context: None = ...) -> HttpResponse: ...
+
+class InlineModelAdmin(BaseModelAdmin):
+    model: Type[Model] = ...
+    fk_name: str = ...
+    formset: BaseFormSet = ...
+    extra: int = ...
+    min_num: Optional[int] = ...
+    max_num: Optional[int] = ...
+    template: str = ...
+    verbose_name: Optional[str] = ...
+    verbose_name_plural: Optional[str] = ...
+    can_delete: bool = ...
+    show_change_link: bool = ...
+    classes: Optional[Sequence[str]] = ...
+    admin_site: AdminSite = ...
+    parent_model: Any = ...
+    opts: Any = ...
+    has_registered_model: Any = ...
+    def __init__(self, parent_model: Union[Type[Model], Model], admin_site: AdminSite) -> None: ...
+    @property
+    def media(self) -> Media: ...
+    def get_extra(self, request: HttpRequest, obj: Optional[Model] = ..., **kwargs: Any) -> int: ...
+    def get_min_num(self, request: HttpRequest, obj: Optional[Model] = ..., **kwargs: Any) -> Optional[int]: ...
+    def get_max_num(self, request: HttpRequest, obj: Optional[Model] = ..., **kwargs: Any) -> Optional[int]: ...
+    def get_formset(self, request: Any, obj: Optional[Any] = ..., **kwargs: Any): ...
+
+class StackedInline(InlineModelAdmin): ...
+class TabularInline(InlineModelAdmin): ...
diff --git a/shared/package-files/4e/4e99366e9023dda609fa0a6d5ecd87fb802f6fc1aa58afdb6565506bdf8e7e45/ceil_cuda_dispatch.h b/shared/package-files/4e/4e99366e9023dda609fa0a6d5ecd87fb802f6fc1aa58afdb6565506bdf8e7e45/ceil_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f4613a6859dc0e3752f3cbece5267b16bbee823
--- /dev/null
+++ b/shared/package-files/4e/4e99366e9023dda609fa0a6d5ecd87fb802f6fc1aa58afdb6565506bdf8e7e45/ceil_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor ceil(const at::Tensor & self);
+TORCH_API at::Tensor & ceil_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & ceil_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & ceil_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/4e/4e9d68e216a224057731125e9e6bc65c56e6b7e444e2a33fa5de94dc686886ac/__init__.py b/shared/package-files/4e/4e9d68e216a224057731125e9e6bc65c56e6b7e444e2a33fa5de94dc686886ac/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fe77865a9740ab6c88465dbe4a5a41bc445a688
--- /dev/null
+++ b/shared/package-files/4e/4e9d68e216a224057731125e9e6bc65c56e6b7e444e2a33fa5de94dc686886ac/__init__.py
@@ -0,0 +1,75 @@
+from ._multi_channel import MVDR, PSD, RTFMVDR, SoudenMVDR
+from ._transforms import (
+    AddNoise,
+    AmplitudeToDB,
+    ComputeDeltas,
+    Convolve,
+    Deemphasis,
+    Fade,
+    FFTConvolve,
+    FrequencyMasking,
+    GriffinLim,
+    InverseMelScale,
+    InverseSpectrogram,
+    LFCC,
+    Loudness,
+    MelScale,
+    MelSpectrogram,
+    MFCC,
+    MuLawDecoding,
+    MuLawEncoding,
+    PitchShift,
+    Preemphasis,
+    Resample,
+    RNNTLoss,
+    SlidingWindowCmn,
+    SpecAugment,
+    SpectralCentroid,
+    Spectrogram,
+    Speed,
+    SpeedPerturbation,
+    TimeMasking,
+    TimeStretch,
+    Vad,
+    Vol,
+)
+
+
+__all__ = [
+    "AddNoise",
+    "AmplitudeToDB",
+    "ComputeDeltas",
+    "Convolve",
+    "Deemphasis",
+    "Fade",
+    "FFTConvolve",
+    "FrequencyMasking",
+    "GriffinLim",
+    "InverseMelScale",
+    "InverseSpectrogram",
+    "LFCC",
+    "Loudness",
+    "MFCC",
+    "MVDR",
+    "MelScale",
+    "MelSpectrogram",
+    "MuLawDecoding",
+    "MuLawEncoding",
+    "PSD",
+    "PitchShift",
+    "Preemphasis",
+    "RNNTLoss",
+    "RTFMVDR",
+    "Resample",
+    "SlidingWindowCmn",
+    "SoudenMVDR",
+    "SpecAugment",
+    "SpectralCentroid",
+    "Spectrogram",
+    "Speed",
+    "SpeedPerturbation",
+    "TimeMasking",
+    "TimeStretch",
+    "Vad",
+    "Vol",
+]
diff --git a/shared/package-files/4e/4e9e7495f8beba1d0454bfe288c98b0bcf9af2216b3392b0b0686f3195e6aa23/special_logit.h b/shared/package-files/4e/4e9e7495f8beba1d0454bfe288c98b0bcf9af2216b3392b0b0686f3195e6aa23/special_logit.h
new file mode 100644
index 0000000000000000000000000000000000000000..8a9fc94b5ecadb6ad1e8ce2ac40066653c52af2a
--- /dev/null
+++ b/shared/package-files/4e/4e9e7495f8beba1d0454bfe288c98b0bcf9af2216b3392b0b0686f3195e6aa23/special_logit.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/special_logit_ops.h>
+
+namespace at {
+
+
+// aten::special_logit(Tensor self, float? eps=None) -> Tensor
+inline at::Tensor special_logit(const at::Tensor & self, c10::optional<double> eps=c10::nullopt) {
+    return at::_ops::special_logit::call(self, eps);
+}
+
+// aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_logit_out(at::Tensor & out, const at::Tensor & self, c10::optional<double> eps=c10::nullopt) {
+    return at::_ops::special_logit_out::call(self, eps, out);
+}
+// aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_logit_outf(const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) {
+    return at::_ops::special_logit_out::call(self, eps, out);
+}
+
+}
diff --git a/shared/package-files/4e/4e9ed757e4b26420c21837042cf00c16342d140d468cacf88d9a8c6d2315b76a/svg.py b/shared/package-files/4e/4e9ed757e4b26420c21837042cf00c16342d140d468cacf88d9a8c6d2315b76a/svg.py
new file mode 100644
index 0000000000000000000000000000000000000000..10bd798e8ac89860397541b5ad6416e94147e675
--- /dev/null
+++ b/shared/package-files/4e/4e9ed757e4b26420c21837042cf00c16342d140d468cacf88d9a8c6d2315b76a/svg.py
@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+import re
+from functools import cache
+from itertools import chain, count
+from typing import Dict, Iterable, Iterator, List, Optional, Set, Tuple
+
+try:
+    from lxml import etree
+except ImportError:
+    # lxml is required for subsetting SVG, but we prefer to delay the import error
+    # until subset_glyphs() is called (i.e. if font to subset has an 'SVG ' table)
+    etree = None
+
+from fontTools import ttLib
+from fontTools.subset.util import _add_method
+from fontTools.ttLib.tables.S_V_G_ import SVGDocument
+
+__all__ = ["subset_glyphs"]
+
+
+GID_RE = re.compile(r"^glyph(\d+)$")
+
+NAMESPACES = {
+    "svg": "http://www.w3.org/2000/svg",
+    "xlink": "http://www.w3.org/1999/xlink",
+}
+XLINK_HREF = f'{{{NAMESPACES["xlink"]}}}href'
+
+
+@cache
+def xpath(path):
+    # compile XPath upfront, caching result to reuse on multiple elements
+    return etree.XPath(path, namespaces=NAMESPACES)
+
+
+def group_elements_by_id(tree: etree.Element) -> Dict[str, etree.Element]:
+    # select all svg elements with 'id' attribute no matter where they are
+    # including the root element itself:
+    # https://github.com/fonttools/fonttools/issues/2548
+    return {el.attrib["id"]: el for el in xpath("//svg:*[@id]")(tree)}
+
+
+def parse_css_declarations(style_attr: str) -> Dict[str, str]:
+    # https://developer.mozilla.org/en-US/docs/Web/SVG/Attribute/style
+    # https://developer.mozilla.org/en-US/docs/Web/CSS/Syntax#css_declarations
+    result = {}
+    for declaration in style_attr.split(";"):
+        if declaration.count(":") == 1:
+            property_name, value = declaration.split(":")
+            property_name = property_name.strip()
+            result[property_name] = value.strip()
+        elif declaration.strip():
+            raise ValueError(f"Invalid CSS declaration syntax: {declaration}")
+    return result
+
+
+def iter_referenced_ids(tree: etree.Element) -> Iterator[str]:
+    # Yield all the ids that can be reached via references from this element tree.
+    # We currently support xlink:href (as used by <use> and gradient templates),
+    # and local url(#...) links found in fill or clip-path attributes
+    # TODO(anthrotype): Check we aren't missing other supported kinds of reference
+    find_svg_elements_with_references = xpath(
+        ".//svg:*[ "
+        "starts-with(@xlink:href, '#') "
+        "or starts-with(@fill, 'url(#') "
+        "or starts-with(@clip-path, 'url(#') "
+        "or contains(@style, ':url(#') "
+        "]",
+    )
+    for el in chain([tree], find_svg_elements_with_references(tree)):
+        ref_id = href_local_target(el)
+        if ref_id is not None:
+            yield ref_id
+
+        attrs = el.attrib
+        if "style" in attrs:
+            attrs = {**dict(attrs), **parse_css_declarations(el.attrib["style"])}
+        for attr in ("fill", "clip-path"):
+            if attr in attrs:
+                value = attrs[attr]
+                if value.startswith("url(#") and value.endswith(")"):
+                    ref_id = value[5:-1]
+                    assert ref_id
+                    yield ref_id
+
+
+def closure_element_ids(
+    elements: Dict[str, etree.Element], element_ids: Set[str]
+) -> None:
+    # Expand the initial subset of element ids to include ids that can be reached
+    # via references from the initial set.
+    unvisited = element_ids
+    while unvisited:
+        referenced: Set[str] = set()
+        for el_id in unvisited:
+            if el_id not in elements:
+                # ignore dangling reference; not our job to validate svg
+                continue
+            referenced.update(iter_referenced_ids(elements[el_id]))
+        referenced -= element_ids
+        element_ids.update(referenced)
+        unvisited = referenced
+
+
+def subset_elements(el: etree.Element, retained_ids: Set[str]) -> bool:
+    # Keep elements if their id is in the subset, or any of their children's id is.
+    # Drop elements whose id is not in the subset, and either have no children,
+    # or all their children are being dropped.
+    if el.attrib.get("id") in retained_ids:
+        # if id is in the set, don't recurse; keep whole subtree
+        return True
+    # recursively subset all the children; we use a list comprehension instead
+    # of a parentheses-less generator expression because we don't want any() to
+    # short-circuit, as our function has a side effect of dropping empty elements.
+    if any([subset_elements(e, retained_ids) for e in el]):
+        return True
+    assert len(el) == 0
+    parent = el.getparent()
+    if parent is not None:
+        parent.remove(el)
+    return False
+
+
+def remap_glyph_ids(
+    svg: etree.Element, glyph_index_map: Dict[int, int]
+) -> Dict[str, str]:
+    # Given {old_gid: new_gid} map, rename all elements containing id="glyph{gid}"
+    # special attributes
+    elements = group_elements_by_id(svg)
+    id_map = {}
+    for el_id, el in elements.items():
+        m = GID_RE.match(el_id)
+        if not m:
+            continue
+        old_index = int(m.group(1))
+        new_index = glyph_index_map.get(old_index)
+        if new_index is not None:
+            if old_index == new_index:
+                continue
+            new_id = f"glyph{new_index}"
+        else:
+            # If the old index is missing, the element correspond to a glyph that was
+            # excluded from the font's subset.
+            # We rename it to avoid clashes with the new GIDs or other element ids.
+            new_id = f".{el_id}"
+            n = count(1)
+            while new_id in elements:
+                new_id = f"{new_id}.{next(n)}"
+
+        id_map[el_id] = new_id
+        el.attrib["id"] = new_id
+
+    return id_map
+
+
+def href_local_target(el: etree.Element) -> Optional[str]:
+    if XLINK_HREF in el.attrib:
+        href = el.attrib[XLINK_HREF]
+        if href.startswith("#") and len(href) > 1:
+            return href[1:]  # drop the leading #
+    return None
+
+
+def update_glyph_href_links(svg: etree.Element, id_map: Dict[str, str]) -> None:
+    # update all xlink:href="#glyph..." attributes to point to the new glyph ids
+    for el in xpath(".//svg:*[starts-with(@xlink:href, '#glyph')]")(svg):
+        old_id = href_local_target(el)
+        assert old_id is not None
+        if old_id in id_map:
+            new_id = id_map[old_id]
+            el.attrib[XLINK_HREF] = f"#{new_id}"
+
+
+def ranges(ints: Iterable[int]) -> Iterator[Tuple[int, int]]:
+    # Yield sorted, non-overlapping (min, max) ranges of consecutive integers
+    sorted_ints = iter(sorted(set(ints)))
+    try:
+        start = end = next(sorted_ints)
+    except StopIteration:
+        return
+    for v in sorted_ints:
+        if v - 1 == end:
+            end = v
+        else:
+            yield (start, end)
+            start = end = v
+    yield (start, end)
+
+
+@_add_method(ttLib.getTableClass("SVG "))
+def subset_glyphs(self, s) -> bool:
+    if etree is None:
+        raise ImportError("No module named 'lxml', required to subset SVG")
+
+    # glyph names (before subsetting)
+    glyph_order: List[str] = s.orig_glyph_order
+    # map from glyph names to original glyph indices
+    rev_orig_glyph_map: Dict[str, int] = s.reverseOrigGlyphMap
+    # map from original to new glyph indices (after subsetting)
+    glyph_index_map: Dict[int, int] = s.glyph_index_map
+
+    new_docs: List[SVGDocument] = []
+    for doc in self.docList:
+        glyphs = {
+            glyph_order[i] for i in range(doc.startGlyphID, doc.endGlyphID + 1)
+        }.intersection(s.glyphs)
+        if not glyphs:
+            # no intersection: we can drop the whole record
+            continue
+
+        svg = etree.fromstring(
+            # encode because fromstring dislikes xml encoding decl if input is str.
+            # SVG xml encoding must be utf-8 as per OT spec.
+            doc.data.encode("utf-8"),
+            parser=etree.XMLParser(
+                # Disable libxml2 security restrictions to support very deep trees.
+                # Without this we would get an error like this:
+                # `lxml.etree.XMLSyntaxError: internal error: Huge input lookup`
+                # when parsing big fonts e.g. noto-emoji-picosvg.ttf.
+                huge_tree=True,
+                # ignore blank text as it's not meaningful in OT-SVG; it also prevents
+                # dangling tail text after removing an element when pretty_print=True
+                remove_blank_text=True,
+                # don't replace entities; we don't expect any in OT-SVG and they may
+                # be abused for XXE attacks
+                resolve_entities=False,
+            ),
+        )
+
+        elements = group_elements_by_id(svg)
+        gids = {rev_orig_glyph_map[g] for g in glyphs}
+        element_ids = {f"glyph{i}" for i in gids}
+        closure_element_ids(elements, element_ids)
+
+        if not subset_elements(svg, element_ids):
+            continue
+
+        if not s.options.retain_gids:
+            id_map = remap_glyph_ids(svg, glyph_index_map)
+            update_glyph_href_links(svg, id_map)
+
+        new_doc = etree.tostring(svg, pretty_print=s.options.pretty_svg).decode("utf-8")
+
+        new_gids = (glyph_index_map[i] for i in gids)
+        for start, end in ranges(new_gids):
+            new_docs.append(SVGDocument(new_doc, start, end, doc.compressed))
+
+    self.docList = new_docs
+
+    return bool(self.docList)
diff --git a/shared/package-files/4e/4ea0e72451e229d8b05cfd066fac9a5cd01e891795e937417b8c0c4179974c8e/test_matfuncs.py b/shared/package-files/4e/4ea0e72451e229d8b05cfd066fac9a5cd01e891795e937417b8c0c4179974c8e/test_matfuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a468c39cad30f6f852cc6df64b98588fa70b5ff
--- /dev/null
+++ b/shared/package-files/4e/4ea0e72451e229d8b05cfd066fac9a5cd01e891795e937417b8c0c4179974c8e/test_matfuncs.py
@@ -0,0 +1,592 @@
+#
+# Created by: Pearu Peterson, March 2002
+#
+""" Test functions for scipy.linalg._matfuncs module
+
+"""
+import math
+
+import numpy as np
+from numpy import array, eye, exp, random
+from numpy.testing import (
+        assert_allclose, assert_, assert_array_almost_equal, assert_equal,
+        assert_array_almost_equal_nulp, suppress_warnings)
+
+from scipy.sparse import csc_array, SparseEfficiencyWarning
+from scipy.sparse._construct import eye_array
+from scipy.sparse.linalg._matfuncs import (expm, _expm,
+        ProductOperator, MatrixPowerOperator,
+        _onenorm_matrix_power_nnm, matrix_power)
+from scipy.sparse._sputils import matrix
+from scipy.linalg import logm
+from scipy.special import factorial, binom
+import scipy.sparse
+import scipy.sparse.linalg
+
+
+def _burkardt_13_power(n, p):
+    """
+    A helper function for testing matrix functions.
+
+    Parameters
+    ----------
+    n : integer greater than 1
+        Order of the square matrix to be returned.
+    p : non-negative integer
+        Power of the matrix.
+
+    Returns
+    -------
+    out : ndarray representing a square matrix
+        A Forsythe matrix of order n, raised to the power p.
+
+    """
+    # Input validation.
+    if n != int(n) or n < 2:
+        raise ValueError('n must be an integer greater than 1')
+    n = int(n)
+    if p != int(p) or p < 0:
+        raise ValueError('p must be a non-negative integer')
+    p = int(p)
+
+    # Construct the matrix explicitly.
+    a, b = divmod(p, n)
+    large = np.power(10.0, -n*a)
+    small = large * np.power(10.0, -n)
+    return np.diag([large]*(n-b), b) + np.diag([small]*b, b-n)
+
+
+def test_onenorm_matrix_power_nnm():
+    np.random.seed(1234)
+    for n in range(1, 5):
+        for p in range(5):
+            M = np.random.random((n, n))
+            Mp = np.linalg.matrix_power(M, p)
+            observed = _onenorm_matrix_power_nnm(M, p)
+            expected = np.linalg.norm(Mp, 1)
+            assert_allclose(observed, expected)
+
+def test_matrix_power():
+    np.random.seed(1234)
+    row, col = np.random.randint(0, 4, size=(2, 6))
+    data = np.random.random(size=(6,))
+    Amat = csc_array((data, (row, col)), shape=(4, 4))
+    A = csc_array((data, (row, col)), shape=(4, 4))
+    Adense = A.toarray()
+    for power in (2, 5, 6):
+        Apow = matrix_power(A, power).toarray()
+        Amat_pow = matrix_power(Amat, power).toarray()
+        Adense_pow = np.linalg.matrix_power(Adense, power)
+        assert_allclose(Apow, Adense_pow)
+        assert_allclose(Apow, Amat_pow)
+
+
+class TestExpM:
+    def test_zero_ndarray(self):
+        a = array([[0.,0],[0,0]])
+        assert_array_almost_equal(expm(a),[[1,0],[0,1]])
+
+    def test_zero_sparse(self):
+        a = csc_array([[0.,0],[0,0]])
+        assert_array_almost_equal(expm(a).toarray(),[[1,0],[0,1]])
+
+    def test_zero_matrix(self):
+        a = matrix([[0.,0],[0,0]])
+        assert_array_almost_equal(expm(a),[[1,0],[0,1]])
+
+    def test_misc_types(self):
+        A = expm(np.array([[1]]))
+        assert_allclose(expm(((1,),)), A)
+        assert_allclose(expm([[1]]), A)
+        assert_allclose(expm(matrix([[1]])), A)
+        assert_allclose(expm(np.array([[1]])), A)
+        assert_allclose(expm(csc_array([[1]])).toarray(), A)
+        B = expm(np.array([[1j]]))
+        assert_allclose(expm(((1j,),)), B)
+        assert_allclose(expm([[1j]]), B)
+        assert_allclose(expm(matrix([[1j]])), B)
+        assert_allclose(expm(csc_array([[1j]])).toarray(), B)
+
+    def test_bidiagonal_sparse(self):
+        A = csc_array([
+            [1, 3, 0],
+            [0, 1, 5],
+            [0, 0, 2]], dtype=float)
+        e1 = math.exp(1)
+        e2 = math.exp(2)
+        expected = np.array([
+            [e1, 3*e1, 15*(e2 - 2*e1)],
+            [0, e1, 5*(e2 - e1)],
+            [0, 0, e2]], dtype=float)
+        observed = expm(A).toarray()
+        assert_array_almost_equal(observed, expected)
+
+    def test_padecases_dtype_float(self):
+        for dtype in [np.float32, np.float64]:
+            for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
+                A = scale * eye(3, dtype=dtype)
+                observed = expm(A)
+                expected = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
+                assert_array_almost_equal_nulp(observed, expected, nulp=100)
+
+    def test_padecases_dtype_complex(self):
+        for dtype in [np.complex64, np.complex128]:
+            for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
+                A = scale * eye(3, dtype=dtype)
+                observed = expm(A)
+                expected = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
+                assert_array_almost_equal_nulp(observed, expected, nulp=100)
+
+    def test_padecases_dtype_sparse_float(self):
+        # float32 and complex64 lead to errors in spsolve/UMFpack
+        dtype = np.float64
+        for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
+            a = scale * eye_array(3, 3, dtype=dtype, format='csc')
+            e = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                exact_onenorm = _expm(a, use_exact_onenorm=True).toarray()
+                inexact_onenorm = _expm(a, use_exact_onenorm=False).toarray()
+            assert_array_almost_equal_nulp(exact_onenorm, e, nulp=100)
+            assert_array_almost_equal_nulp(inexact_onenorm, e, nulp=100)
+
+    def test_padecases_dtype_sparse_complex(self):
+        # float32 and complex64 lead to errors in spsolve/UMFpack
+        dtype = np.complex128
+        for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
+            a = scale * eye_array(3, 3, dtype=dtype, format='csc')
+            e = exp(scale) * eye(3, dtype=dtype)
+            with suppress_warnings() as sup:
+                sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure")
+                assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
+
+    def test_logm_consistency(self):
+        random.seed(1234)
+        for dtype in [np.float64, np.complex128]:
+            for n in range(1, 10):
+                for scale in [1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2]:
+                    # make logm(A) be of a given scale
+                    A = (eye(n) + random.rand(n, n) * scale).astype(dtype)
+                    if np.iscomplexobj(A):
+                        A = A + 1j * random.rand(n, n) * scale
+                    assert_array_almost_equal(expm(logm(A)), A)
+
+    def test_integer_matrix(self):
+        Q = np.array([
+            [-3, 1, 1, 1],
+            [1, -3, 1, 1],
+            [1, 1, -3, 1],
+            [1, 1, 1, -3]])
+        assert_allclose(expm(Q), expm(1.0 * Q))
+
+    def test_integer_matrix_2(self):
+        # Check for integer overflows
+        Q = np.array([[-500, 500, 0, 0],
+                      [0, -550, 360, 190],
+                      [0, 630, -630, 0],
+                      [0, 0, 0, 0]], dtype=np.int16)
+        assert_allclose(expm(Q), expm(1.0 * Q))
+
+        Q = csc_array(Q)
+        assert_allclose(expm(Q).toarray(), expm(1.0 * Q).toarray())
+
+    def test_triangularity_perturbation(self):
+        # Experiment (1) of
+        # Awad H. Al-Mohy and Nicholas J. Higham (2012)
+        # Improved Inverse Scaling and Squaring Algorithms
+        # for the Matrix Logarithm.
+        A = np.array([
+            [3.2346e-1, 3e4, 3e4, 3e4],
+            [0, 3.0089e-1, 3e4, 3e4],
+            [0, 0, 3.221e-1, 3e4],
+            [0, 0, 0, 3.0744e-1]],
+            dtype=float)
+        A_logm = np.array([
+            [-1.12867982029050462e+00, 9.61418377142025565e+04,
+             -4.52485573953179264e+09, 2.92496941103871812e+14],
+            [0.00000000000000000e+00, -1.20101052953082288e+00,
+             9.63469687211303099e+04, -4.68104828911105442e+09],
+            [0.00000000000000000e+00, 0.00000000000000000e+00,
+             -1.13289322264498393e+00, 9.53249183094775653e+04],
+            [0.00000000000000000e+00, 0.00000000000000000e+00,
+             0.00000000000000000e+00, -1.17947533272554850e+00]],
+            dtype=float)
+        assert_allclose(expm(A_logm), A, rtol=1e-4)
+
+        # Perturb the upper triangular matrix by tiny amounts,
+        # so that it becomes technically not upper triangular.
+        random.seed(1234)
+        tiny = 1e-17
+        A_logm_perturbed = A_logm.copy()
+        A_logm_perturbed[1, 0] = tiny
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "Ill-conditioned.*")
+            A_expm_logm_perturbed = expm(A_logm_perturbed)
+        rtol = 1e-4
+        atol = 100 * tiny
+        assert_(not np.allclose(A_expm_logm_perturbed, A, rtol=rtol, atol=atol))
+
+    def test_burkardt_1(self):
+        # This matrix is diagonal.
+        # The calculation of the matrix exponential is simple.
+        #
+        # This is the first of a series of matrix exponential tests
+        # collected by John Burkardt from the following sources.
+        #
+        # Alan Laub,
+        # Review of "Linear System Theory" by Joao Hespanha,
+        # SIAM Review,
+        # Volume 52, Number 4, December 2010, pages 779--781.
+        #
+        # Cleve Moler and Charles Van Loan,
+        # Nineteen Dubious Ways to Compute the Exponential of a Matrix,
+        # Twenty-Five Years Later,
+        # SIAM Review,
+        # Volume 45, Number 1, March 2003, pages 3--49.
+        #
+        # Cleve Moler,
+        # Cleve's Corner: A Balancing Act for the Matrix Exponential,
+        # 23 July 2012.
+        #
+        # Robert Ward,
+        # Numerical computation of the matrix exponential
+        # with accuracy estimate,
+        # SIAM Journal on Numerical Analysis,
+        # Volume 14, Number 4, September 1977, pages 600--610.
+        exp1 = np.exp(1)
+        exp2 = np.exp(2)
+        A = np.array([
+            [1, 0],
+            [0, 2],
+            ], dtype=float)
+        desired = np.array([
+            [exp1, 0],
+            [0, exp2],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_2(self):
+        # This matrix is symmetric.
+        # The calculation of the matrix exponential is straightforward.
+        A = np.array([
+            [1, 3],
+            [3, 2],
+            ], dtype=float)
+        desired = np.array([
+            [39.322809708033859, 46.166301438885753],
+            [46.166301438885768, 54.711576854329110],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_3(self):
+        # This example is due to Laub.
+        # This matrix is ill-suited for the Taylor series approach.
+        # As powers of A are computed, the entries blow up too quickly.
+        exp1 = np.exp(1)
+        exp39 = np.exp(39)
+        A = np.array([
+            [0, 1],
+            [-39, -40],
+            ], dtype=float)
+        desired = np.array([
+            [
+                39/(38*exp1) - 1/(38*exp39),
+                -np.expm1(-38) / (38*exp1)],
+            [
+                39*np.expm1(-38) / (38*exp1),
+                -1/(38*exp1) + 39/(38*exp39)],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_4(self):
+        # This example is due to Moler and Van Loan.
+        # The example will cause problems for the series summation approach,
+        # as well as for diagonal Pade approximations.
+        A = np.array([
+            [-49, 24],
+            [-64, 31],
+            ], dtype=float)
+        U = np.array([[3, 1], [4, 2]], dtype=float)
+        V = np.array([[1, -1/2], [-2, 3/2]], dtype=float)
+        w = np.array([-17, -1], dtype=float)
+        desired = np.dot(U * np.exp(w), V)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_5(self):
+        # This example is due to Moler and Van Loan.
+        # This matrix is strictly upper triangular
+        # All powers of A are zero beyond some (low) limit.
+        # This example will cause problems for Pade approximations.
+        A = np.array([
+            [0, 6, 0, 0],
+            [0, 0, 6, 0],
+            [0, 0, 0, 6],
+            [0, 0, 0, 0],
+            ], dtype=float)
+        desired = np.array([
+            [1, 6, 18, 36],
+            [0, 1, 6, 18],
+            [0, 0, 1, 6],
+            [0, 0, 0, 1],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_6(self):
+        # This example is due to Moler and Van Loan.
+        # This matrix does not have a complete set of eigenvectors.
+        # That means the eigenvector approach will fail.
+        exp1 = np.exp(1)
+        A = np.array([
+            [1, 1],
+            [0, 1],
+            ], dtype=float)
+        desired = np.array([
+            [exp1, exp1],
+            [0, exp1],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_7(self):
+        # This example is due to Moler and Van Loan.
+        # This matrix is very close to example 5.
+        # Mathematically, it has a complete set of eigenvectors.
+        # Numerically, however, the calculation will be suspect.
+        exp1 = np.exp(1)
+        eps = np.spacing(1)
+        A = np.array([
+            [1 + eps, 1],
+            [0, 1 - eps],
+            ], dtype=float)
+        desired = np.array([
+            [exp1, exp1],
+            [0, exp1],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_8(self):
+        # This matrix was an example in Wikipedia.
+        exp4 = np.exp(4)
+        exp16 = np.exp(16)
+        A = np.array([
+            [21, 17, 6],
+            [-5, -1, -6],
+            [4, 4, 16],
+            ], dtype=float)
+        desired = np.array([
+            [13*exp16 - exp4, 13*exp16 - 5*exp4, 2*exp16 - 2*exp4],
+            [-9*exp16 + exp4, -9*exp16 + 5*exp4, -2*exp16 + 2*exp4],
+            [16*exp16, 16*exp16, 4*exp16],
+            ], dtype=float) * 0.25
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_9(self):
+        # This matrix is due to the NAG Library.
+        # It is an example for function F01ECF.
+        A = np.array([
+            [1, 2, 2, 2],
+            [3, 1, 1, 2],
+            [3, 2, 1, 2],
+            [3, 3, 3, 1],
+            ], dtype=float)
+        desired = np.array([
+            [740.7038, 610.8500, 542.2743, 549.1753],
+            [731.2510, 603.5524, 535.0884, 542.2743],
+            [823.7630, 679.4257, 603.5524, 610.8500],
+            [998.4355, 823.7630, 731.2510, 740.7038],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_10(self):
+        # This is Ward's example #1.
+        # It is defective and nonderogatory.
+        A = np.array([
+            [4, 2, 0],
+            [1, 4, 1],
+            [1, 1, 4],
+            ], dtype=float)
+        assert_allclose(sorted(scipy.linalg.eigvals(A)), (3, 3, 6))
+        desired = np.array([
+            [147.8666224463699, 183.7651386463682, 71.79703239999647],
+            [127.7810855231823, 183.7651386463682, 91.88256932318415],
+            [127.7810855231824, 163.6796017231806, 111.9681062463718],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_11(self):
+        # This is Ward's example #2.
+        # It is a symmetric matrix.
+        A = np.array([
+            [29.87942128909879, 0.7815750847907159, -2.289519314033932],
+            [0.7815750847907159, 25.72656945571064, 8.680737820540137],
+            [-2.289519314033932, 8.680737820540137, 34.39400925519054],
+            ], dtype=float)
+        assert_allclose(scipy.linalg.eigvalsh(A), (20, 30, 40))
+        desired = np.array([
+             [
+                 5.496313853692378E+15,
+                 -1.823188097200898E+16,
+                 -3.047577080858001E+16],
+             [
+                -1.823188097200899E+16,
+                6.060522870222108E+16,
+                1.012918429302482E+17],
+             [
+                -3.047577080858001E+16,
+                1.012918429302482E+17,
+                1.692944112408493E+17],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_12(self):
+        # This is Ward's example #3.
+        # Ward's algorithm has difficulty estimating the accuracy
+        # of its results.
+        A = np.array([
+            [-131, 19, 18],
+            [-390, 56, 54],
+            [-387, 57, 52],
+            ], dtype=float)
+        assert_allclose(sorted(scipy.linalg.eigvals(A)), (-20, -2, -1))
+        desired = np.array([
+            [-1.509644158793135, 0.3678794391096522, 0.1353352811751005],
+            [-5.632570799891469, 1.471517758499875, 0.4060058435250609],
+            [-4.934938326088363, 1.103638317328798, 0.5413411267617766],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_burkardt_13(self):
+        # This is Ward's example #4.
+        # This is a version of the Forsythe matrix.
+        # The eigenvector problem is badly conditioned.
+        # Ward's algorithm has difficulty estimating the accuracy
+        # of its results for this problem.
+        #
+        # Check the construction of one instance of this family of matrices.
+        A4_actual = _burkardt_13_power(4, 1)
+        A4_desired = [[0, 1, 0, 0],
+                      [0, 0, 1, 0],
+                      [0, 0, 0, 1],
+                      [1e-4, 0, 0, 0]]
+        assert_allclose(A4_actual, A4_desired)
+        # Check the expm for a few instances.
+        for n in (2, 3, 4, 10):
+            # Approximate expm using Taylor series.
+            # This works well for this matrix family
+            # because each matrix in the summation,
+            # even before dividing by the factorial,
+            # is entrywise positive with max entry 10**(-floor(p/n)*n).
+            k = max(1, int(np.ceil(16/n)))
+            desired = np.zeros((n, n), dtype=float)
+            for p in range(n*k):
+                Ap = _burkardt_13_power(n, p)
+                assert_equal(np.min(Ap), 0)
+                assert_allclose(np.max(Ap), np.power(10, -np.floor(p/n)*n))
+                desired += Ap / factorial(p)
+            actual = expm(_burkardt_13_power(n, 1))
+            assert_allclose(actual, desired)
+
+    def test_burkardt_14(self):
+        # This is Moler's example.
+        # This badly scaled matrix caused problems for MATLAB's expm().
+        A = np.array([
+            [0, 1e-8, 0],
+            [-(2e10 + 4e8/6.), -3, 2e10],
+            [200./3., 0, -200./3.],
+            ], dtype=float)
+        desired = np.array([
+            [0.446849468283175, 1.54044157383952e-09, 0.462811453558774],
+            [-5743067.77947947, -0.0152830038686819, -4526542.71278401],
+            [0.447722977849494, 1.54270484519591e-09, 0.463480648837651],
+            ], dtype=float)
+        actual = expm(A)
+        assert_allclose(actual, desired)
+
+    def test_pascal(self):
+        # Test pascal triangle.
+        # Nilpotent exponential, used to trigger a failure (gh-8029)
+
+        for scale in [1.0, 1e-3, 1e-6]:
+            for n in range(0, 80, 3):
+                sc = scale ** np.arange(n, -1, -1)
+                if np.any(sc < 1e-300):
+                    break
+
+                A = np.diag(np.arange(1, n + 1), -1) * scale
+                B = expm(A)
+
+                got = B
+                expected = binom(np.arange(n + 1)[:,None],
+                                 np.arange(n + 1)[None,:]) * sc[None,:] / sc[:,None]
+                atol = 1e-13 * abs(expected).max()
+                assert_allclose(got, expected, atol=atol)
+
+    def test_matrix_input(self):
+        # Large np.matrix inputs should work, gh-5546
+        A = np.zeros((200, 200))
+        A[-1,0] = 1
+        B0 = expm(A)
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "the matrix subclass.*")
+            sup.filter(PendingDeprecationWarning, "the matrix subclass.*")
+            B = expm(np.matrix(A))
+        assert_allclose(B, B0)
+
+    def test_exp_sinch_overflow(self):
+        # Check overflow in intermediate steps is fixed (gh-11839)
+        L = np.array([[1.0, -0.5, -0.5, 0.0, 0.0, 0.0, 0.0],
+                      [0.0, 1.0, 0.0, -0.5, -0.5, 0.0, 0.0],
+                      [0.0, 0.0, 1.0, 0.0, 0.0, -0.5, -0.5],
+                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+        E0 = expm(-L)
+        E1 = expm(-2**11 * L)
+        E2 = E0
+        for j in range(11):
+            E2 = E2 @ E2
+
+        assert_allclose(E1, E2)
+
+
+class TestOperators:
+
+    def test_product_operator(self):
+        random.seed(1234)
+        n = 5
+        k = 2
+        nsamples = 10
+        for i in range(nsamples):
+            A = np.random.randn(n, n)
+            B = np.random.randn(n, n)
+            C = np.random.randn(n, n)
+            D = np.random.randn(n, k)
+            op = ProductOperator(A, B, C)
+            assert_allclose(op.matmat(D), A.dot(B).dot(C).dot(D))
+            assert_allclose(op.T.matmat(D), (A.dot(B).dot(C)).T.dot(D))
+
+    def test_matrix_power_operator(self):
+        random.seed(1234)
+        n = 5
+        k = 2
+        p = 3
+        nsamples = 10
+        for i in range(nsamples):
+            A = np.random.randn(n, n)
+            B = np.random.randn(n, k)
+            op = MatrixPowerOperator(A, p)
+            assert_allclose(op.matmat(B), np.linalg.matrix_power(A, p).dot(B))
+            assert_allclose(op.T.matmat(B), np.linalg.matrix_power(A, p).T.dot(B))
diff --git a/shared/package-files/4e/4ea19e65d841dc492dbe3e3c25f6d7d89affaac0a8beda168a6a5f5ff5622728/test_rename_axis.py b/shared/package-files/4e/4ea19e65d841dc492dbe3e3c25f6d7d89affaac0a8beda168a6a5f5ff5622728/test_rename_axis.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c095d697ede213be5c730e305bf1789810ac4a
--- /dev/null
+++ b/shared/package-files/4e/4ea19e65d841dc492dbe3e3c25f6d7d89affaac0a8beda168a6a5f5ff5622728/test_rename_axis.py
@@ -0,0 +1,47 @@
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesRenameAxis:
+    def test_rename_axis_mapper(self):
+        # GH 19978
+        mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])
+        ser = Series(list(range(len(mi))), index=mi)
+
+        result = ser.rename_axis(index={"ll": "foo"})
+        assert result.index.names == ["foo", "nn"]
+
+        result = ser.rename_axis(index=str.upper, axis=0)
+        assert result.index.names == ["LL", "NN"]
+
+        result = ser.rename_axis(index=["foo", "goo"])
+        assert result.index.names == ["foo", "goo"]
+
+        with pytest.raises(TypeError, match="unexpected"):
+            ser.rename_axis(columns="wrong")
+
+    def test_rename_axis_inplace(self, datetime_series):
+        # GH 15704
+        expected = datetime_series.rename_axis("foo")
+        result = datetime_series
+        no_return = result.rename_axis("foo", inplace=True)
+
+        assert no_return is None
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("kwargs", [{"mapper": None}, {"index": None}, {}])
+    def test_rename_axis_none(self, kwargs):
+        # GH 25034
+        index = Index(list("abc"), name="foo")
+        ser = Series([1, 2, 3], index=index)
+
+        result = ser.rename_axis(**kwargs)
+        expected_index = index.rename(None) if kwargs else index
+        expected = Series([1, 2, 3], index=expected_index)
+        tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/4e/4ea481aed165ef75db957f9694e3e1a87c68d3c6691371ba0ddacdcda6a67707/test_asymmetric.py b/shared/package-files/4e/4ea481aed165ef75db957f9694e3e1a87c68d3c6691371ba0ddacdcda6a67707/test_asymmetric.py
new file mode 100644
index 0000000000000000000000000000000000000000..27d6b4742d97e89508c0c4ce03deb5862ed272b7
--- /dev/null
+++ b/shared/package-files/4e/4ea481aed165ef75db957f9694e3e1a87c68d3c6691371ba0ddacdcda6a67707/test_asymmetric.py
@@ -0,0 +1,163 @@
+"""
+Created on Mon Mar  8 16:18:21 2021
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_less
+import pytest
+from scipy import stats
+
+import statsmodels.nonparametric.kernels_asymmetric as kern
+
+kernels_rplus = [
+    ("gamma", 0.1),
+    ("gamma2", 0.1),
+    ("invgamma", 0.02),
+    ("invgauss", 0.01),
+    ("recipinvgauss", 0.1),
+    ("bs", 0.1),
+    ("lognorm", 0.01),
+    ("weibull", 0.1),
+]
+
+kernels_unit = [
+    ("beta", 0.005),
+    ("beta2", 0.005),
+]
+
+
+class CheckKernels:
+
+    def test_kernels(self, case):
+        name, bw = case
+
+        rvs = self.rvs
+        x_plot = self.x_plot
+
+        kde = []
+        kce = []
+        for xi in x_plot:
+            kde.append(kern.pdf_kernel_asym(xi, rvs, bw, name))
+            kce.append(kern.cdf_kernel_asym(xi, rvs, bw, name))
+
+        kde = np.asarray(kde)
+        kce = np.asarray(kce)
+
+        # average mean squared error
+        amse = ((kde - self.pdf_dgp) ** 2).mean()
+        assert_array_less(amse, self.amse_pdf)
+        amse = ((kce - self.cdf_dgp) ** 2).mean()
+        assert_array_less(amse, self.amse_cdf)
+
+    def test_kernels_vectorized(self, case):
+        name, bw = case
+
+        rvs = self.rvs
+        x_plot = self.x_plot
+
+        kde = []
+        kce = []
+        for xi in x_plot:
+            kde.append(kern.pdf_kernel_asym(xi, rvs, bw, name))
+            kce.append(kern.cdf_kernel_asym(xi, rvs, bw, name))
+
+        kde = np.asarray(kde)
+        kce = np.asarray(kce)
+
+        kde1 = kern.pdf_kernel_asym(x_plot, rvs, bw, name)
+        kce1 = kern.cdf_kernel_asym(x_plot, rvs, bw, name)
+
+        assert_allclose(kde1, kde, rtol=1e-12)
+        assert_allclose(kce1, kce, rtol=1e-12)
+
+    def test_kernels_weights(self, case):
+        name, bw = case
+        rvs = self.rvs
+        x = self.x_plot
+
+        kde2 = kern.pdf_kernel_asym(x, rvs, bw, name)
+        kce2 = kern.cdf_kernel_asym(x, rvs, bw, name)
+
+        n = len(rvs)
+        w = np.ones(n) / n
+        kde1 = kern.pdf_kernel_asym(x, rvs, bw, name, weights=w)
+        kce1 = kern.cdf_kernel_asym(x, rvs, bw, name, weights=w)
+
+        assert_allclose(kde1, kde2, rtol=1e-12)
+        assert_allclose(kce1, kce2, rtol=1e-12)
+
+        # weights that do not add to 1 are valid, but do not produce pdf, cdf
+        n = len(rvs)
+        w = np.ones(n) / n * 2
+        kde1 = kern.pdf_kernel_asym(x, rvs, bw, name, weights=w)
+        kce1 = kern.cdf_kernel_asym(x, rvs, bw, name, weights=w)
+
+        assert_allclose(kde1, kde2 * 2, rtol=1e-12)
+        assert_allclose(kce1, kce2 * 2, rtol=1e-12)
+
+
+class TestKernelsRplus(CheckKernels):
+
+    @classmethod
+    def setup_class(cls):
+        b = 2
+        scale = 1.5
+        rs = np.random.RandomState(1)
+        nobs = 1000
+        distr0 = stats.gamma(b, scale=scale)
+        rvs = distr0.rvs(size=nobs, random_state=rs)
+        x_plot = np.linspace(0.5, 16, 51) + 1e-13
+
+        cls.rvs = rvs
+        cls.x_plot = x_plot
+        cls.pdf_dgp = distr0.pdf(x_plot)
+        cls.cdf_dgp = distr0.cdf(x_plot)
+        cls.amse_pdf = 1e-4  # tol for average mean squared error
+        cls.amse_cdf = 5e-4
+
+    @pytest.mark.parametrize("case", kernels_rplus)
+    def test_kernels(self, case):
+        super().test_kernels(case)
+
+    @pytest.mark.parametrize("case", kernels_rplus)
+    def test_kernels_vectorized(self, case):
+        super().test_kernels_vectorized(case)
+
+    @pytest.mark.parametrize("case", kernels_rplus)
+    def test_kernels_weights(self, case):
+        super().test_kernels_weights(case)
+
+
+class TestKernelsUnit(CheckKernels):
+
+    @classmethod
+    def setup_class(cls):
+        rs = np.random.RandomState(43784319)
+        nobs = 1000
+        distr0 = stats.beta(2, 3)
+        rvs = distr0.rvs(size=nobs, random_state=rs)
+        # Runtime warning if x_plot includes 0
+        x_plot = np.linspace(1e-10, 1, 51)
+
+        cls.rvs = rvs
+        cls.x_plot = x_plot
+        cls.pdf_dgp = distr0.pdf(x_plot)
+        cls.cdf_dgp = distr0.cdf(x_plot)
+        cls.amse_pdf = 0.02
+        cls.amse_cdf = 5e-3
+
+    @pytest.mark.parametrize("case", kernels_unit)
+    def test_kernels(self, case):
+        super().test_kernels(case)
+
+    @pytest.mark.parametrize("case", kernels_unit)
+    def test_kernels_vectorized(self, case):
+        super().test_kernels_vectorized(case)
+
+    @pytest.mark.parametrize("case", kernels_unit)
+    def test_kernels_weights(self, case):
+        super().test_kernels_weights(case)
diff --git a/shared/package-files/4e/4ea8d86f3774607a71d708ac160d3c275f704e983aced24b2e89e0658fe5a33b/GMT+7 b/shared/package-files/4e/4ea8d86f3774607a71d708ac160d3c275f704e983aced24b2e89e0658fe5a33b/GMT+7
new file mode 100644
index 0000000000000000000000000000000000000000..4e0b53a082f11f9b9debf5e110b97b1b0473c9a6
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diff --git a/shared/package-files/4e/4eaccb3f66f08fa154eaf7a5f27aa857ae35f7474182be60ab0ef7be01be2908/ImageMath.py b/shared/package-files/4e/4eaccb3f66f08fa154eaf7a5f27aa857ae35f7474182be60ab0ef7be01be2908/ImageMath.py
new file mode 100644
index 0000000000000000000000000000000000000000..b77f4bce5677ea7cad5284584f671c32ef936556
--- /dev/null
+++ b/shared/package-files/4e/4eaccb3f66f08fa154eaf7a5f27aa857ae35f7474182be60ab0ef7be01be2908/ImageMath.py
@@ -0,0 +1,265 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# a simple math add-on for the Python Imaging Library
+#
+# History:
+# 1999-02-15 fl   Original PIL Plus release
+# 2005-05-05 fl   Simplified and cleaned up for PIL 1.1.6
+# 2005-09-12 fl   Fixed int() and float() for Python 2.4.1
+#
+# Copyright (c) 1999-2005 by Secret Labs AB
+# Copyright (c) 2005 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import builtins
+
+from . import Image, _imagingmath
+
+
+class _Operand:
+    """Wraps an image operand, providing standard operators"""
+
+    def __init__(self, im):
+        self.im = im
+
+    def __fixup(self, im1):
+        # convert image to suitable mode
+        if isinstance(im1, _Operand):
+            # argument was an image.
+            if im1.im.mode in ("1", "L"):
+                return im1.im.convert("I")
+            elif im1.im.mode in ("I", "F"):
+                return im1.im
+            else:
+                msg = f"unsupported mode: {im1.im.mode}"
+                raise ValueError(msg)
+        else:
+            # argument was a constant
+            if isinstance(im1, (int, float)) and self.im.mode in ("1", "L", "I"):
+                return Image.new("I", self.im.size, im1)
+            else:
+                return Image.new("F", self.im.size, im1)
+
+    def apply(self, op, im1, im2=None, mode=None):
+        im1 = self.__fixup(im1)
+        if im2 is None:
+            # unary operation
+            out = Image.new(mode or im1.mode, im1.size, None)
+            im1.load()
+            try:
+                op = getattr(_imagingmath, op + "_" + im1.mode)
+            except AttributeError as e:
+                msg = f"bad operand type for '{op}'"
+                raise TypeError(msg) from e
+            _imagingmath.unop(op, out.im.id, im1.im.id)
+        else:
+            # binary operation
+            im2 = self.__fixup(im2)
+            if im1.mode != im2.mode:
+                # convert both arguments to floating point
+                if im1.mode != "F":
+                    im1 = im1.convert("F")
+                if im2.mode != "F":
+                    im2 = im2.convert("F")
+            if im1.size != im2.size:
+                # crop both arguments to a common size
+                size = (min(im1.size[0], im2.size[0]), min(im1.size[1], im2.size[1]))
+                if im1.size != size:
+                    im1 = im1.crop((0, 0) + size)
+                if im2.size != size:
+                    im2 = im2.crop((0, 0) + size)
+            out = Image.new(mode or im1.mode, im1.size, None)
+            im1.load()
+            im2.load()
+            try:
+                op = getattr(_imagingmath, op + "_" + im1.mode)
+            except AttributeError as e:
+                msg = f"bad operand type for '{op}'"
+                raise TypeError(msg) from e
+            _imagingmath.binop(op, out.im.id, im1.im.id, im2.im.id)
+        return _Operand(out)
+
+    # unary operators
+    def __bool__(self):
+        # an image is "true" if it contains at least one non-zero pixel
+        return self.im.getbbox() is not None
+
+    def __abs__(self):
+        return self.apply("abs", self)
+
+    def __pos__(self):
+        return self
+
+    def __neg__(self):
+        return self.apply("neg", self)
+
+    # binary operators
+    def __add__(self, other):
+        return self.apply("add", self, other)
+
+    def __radd__(self, other):
+        return self.apply("add", other, self)
+
+    def __sub__(self, other):
+        return self.apply("sub", self, other)
+
+    def __rsub__(self, other):
+        return self.apply("sub", other, self)
+
+    def __mul__(self, other):
+        return self.apply("mul", self, other)
+
+    def __rmul__(self, other):
+        return self.apply("mul", other, self)
+
+    def __truediv__(self, other):
+        return self.apply("div", self, other)
+
+    def __rtruediv__(self, other):
+        return self.apply("div", other, self)
+
+    def __mod__(self, other):
+        return self.apply("mod", self, other)
+
+    def __rmod__(self, other):
+        return self.apply("mod", other, self)
+
+    def __pow__(self, other):
+        return self.apply("pow", self, other)
+
+    def __rpow__(self, other):
+        return self.apply("pow", other, self)
+
+    # bitwise
+    def __invert__(self):
+        return self.apply("invert", self)
+
+    def __and__(self, other):
+        return self.apply("and", self, other)
+
+    def __rand__(self, other):
+        return self.apply("and", other, self)
+
+    def __or__(self, other):
+        return self.apply("or", self, other)
+
+    def __ror__(self, other):
+        return self.apply("or", other, self)
+
+    def __xor__(self, other):
+        return self.apply("xor", self, other)
+
+    def __rxor__(self, other):
+        return self.apply("xor", other, self)
+
+    def __lshift__(self, other):
+        return self.apply("lshift", self, other)
+
+    def __rshift__(self, other):
+        return self.apply("rshift", self, other)
+
+    # logical
+    def __eq__(self, other):
+        return self.apply("eq", self, other)
+
+    def __ne__(self, other):
+        return self.apply("ne", self, other)
+
+    def __lt__(self, other):
+        return self.apply("lt", self, other)
+
+    def __le__(self, other):
+        return self.apply("le", self, other)
+
+    def __gt__(self, other):
+        return self.apply("gt", self, other)
+
+    def __ge__(self, other):
+        return self.apply("ge", self, other)
+
+
+# conversions
+def imagemath_int(self):
+    return _Operand(self.im.convert("I"))
+
+
+def imagemath_float(self):
+    return _Operand(self.im.convert("F"))
+
+
+# logical
+def imagemath_equal(self, other):
+    return self.apply("eq", self, other, mode="I")
+
+
+def imagemath_notequal(self, other):
+    return self.apply("ne", self, other, mode="I")
+
+
+def imagemath_min(self, other):
+    return self.apply("min", self, other)
+
+
+def imagemath_max(self, other):
+    return self.apply("max", self, other)
+
+
+def imagemath_convert(self, mode):
+    return _Operand(self.im.convert(mode))
+
+
+ops = {}
+for k, v in list(globals().items()):
+    if k[:10] == "imagemath_":
+        ops[k[10:]] = v
+
+
+def eval(expression, _dict={}, **kw):
+    """
+    Evaluates an image expression.
+
+    :param expression: A string containing a Python-style expression.
+    :param options: Values to add to the evaluation context.  You
+                    can either use a dictionary, or one or more keyword
+                    arguments.
+    :return: The evaluated expression. This is usually an image object, but can
+             also be an integer, a floating point value, or a pixel tuple,
+             depending on the expression.
+    """
+
+    # build execution namespace
+    args = ops.copy()
+    for k in list(_dict.keys()) + list(kw.keys()):
+        if "__" in k or hasattr(builtins, k):
+            msg = f"'{k}' not allowed"
+            raise ValueError(msg)
+
+    args.update(_dict)
+    args.update(kw)
+    for k, v in args.items():
+        if hasattr(v, "im"):
+            args[k] = _Operand(v)
+
+    compiled_code = compile(expression, "<string>", "eval")
+
+    def scan(code):
+        for const in code.co_consts:
+            if type(const) is type(compiled_code):
+                scan(const)
+
+        for name in code.co_names:
+            if name not in args and name != "abs":
+                msg = f"'{name}' not allowed"
+                raise ValueError(msg)
+
+    scan(compiled_code)
+    out = builtins.eval(expression, {"__builtins": {"abs": abs}}, args)
+    try:
+        return out.im
+    except AttributeError:
+        return out
diff --git a/shared/package-files/4e/4ead6427e0eb7a378708e465d1184bc74185ed750894b296cdaa3bb1dc8a033d/_foreach_sqrt_cpu_dispatch.h b/shared/package-files/4e/4ead6427e0eb7a378708e465d1184bc74185ed750894b296cdaa3bb1dc8a033d/_foreach_sqrt_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..00fe4eaca57b954036ad132c1bbf076ce8724f5f
--- /dev/null
+++ b/shared/package-files/4e/4ead6427e0eb7a378708e465d1184bc74185ed750894b296cdaa3bb1dc8a033d/_foreach_sqrt_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::vector<at::Tensor> _foreach_sqrt(at::TensorList self);
+TORCH_API void _foreach_sqrt_(at::TensorList self);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/4e/4ead7b262649bb6fe72fd4364feee3aa5fccb838a729e00aaafb538aa071f3b5/test_savitzky_golay.py b/shared/package-files/4e/4ead7b262649bb6fe72fd4364feee3aa5fccb838a729e00aaafb538aa071f3b5/test_savitzky_golay.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d958e35d91b7d537e0bd3551b6cec3f45a4983
--- /dev/null
+++ b/shared/package-files/4e/4ead7b262649bb6fe72fd4364feee3aa5fccb838a729e00aaafb538aa071f3b5/test_savitzky_golay.py
@@ -0,0 +1,362 @@
+import pytest
+import numpy as np
+from numpy.testing import (assert_equal, 
+                           assert_array_equal,
+)
+
+from scipy._lib._array_api import (
+    assert_almost_equal, assert_array_almost_equal, xp_assert_close
+)
+
+from scipy.ndimage import convolve1d   # type: ignore[attr-defined]
+
+from scipy.signal import savgol_coeffs, savgol_filter
+from scipy.signal._savitzky_golay import _polyder
+
+
+def check_polyder(p, m, expected):
+    dp = _polyder(p, m)
+    assert_array_equal(dp, expected)
+
+
+def test_polyder():
+    cases = [
+        ([5], 0, [5]),
+        ([5], 1, [0]),
+        ([3, 2, 1], 0, [3, 2, 1]),
+        ([3, 2, 1], 1, [6, 2]),
+        ([3, 2, 1], 2, [6]),
+        ([3, 2, 1], 3, [0]),
+        ([[3, 2, 1], [5, 6, 7]], 0, [[3, 2, 1], [5, 6, 7]]),
+        ([[3, 2, 1], [5, 6, 7]], 1, [[6, 2], [10, 6]]),
+        ([[3, 2, 1], [5, 6, 7]], 2, [[6], [10]]),
+        ([[3, 2, 1], [5, 6, 7]], 3, [[0], [0]]),
+    ]
+    for p, m, expected in cases:
+        check_polyder(np.array(p).T, m, np.array(expected).T)
+
+
+#--------------------------------------------------------------------
+# savgol_coeffs tests
+#--------------------------------------------------------------------
+
+def alt_sg_coeffs(window_length, polyorder, pos):
+    """This is an alternative implementation of the SG coefficients.
+
+    It uses numpy.polyfit and numpy.polyval. The results should be
+    equivalent to those of savgol_coeffs(), but this implementation
+    is slower.
+
+    window_length should be odd.
+
+    """
+    if pos is None:
+        pos = window_length // 2
+    t = np.arange(window_length)
+    unit = (t == pos).astype(int)
+    h = np.polyval(np.polyfit(t, unit, polyorder), t)
+    return h
+
+
+def test_sg_coeffs_trivial():
+    # Test a trivial case of savgol_coeffs: polyorder = window_length - 1
+    h = savgol_coeffs(1, 0)
+    xp_assert_close(h, [1.0])
+
+    h = savgol_coeffs(3, 2)
+    xp_assert_close(h, [0.0, 1, 0], atol=1e-10)
+
+    h = savgol_coeffs(5, 4)
+    xp_assert_close(h, [0.0, 0, 1, 0, 0], atol=1e-10)
+
+    h = savgol_coeffs(5, 4, pos=1)
+    xp_assert_close(h, [0.0, 0, 0, 1, 0], atol=1e-10)
+
+    h = savgol_coeffs(5, 4, pos=1, use='dot')
+    xp_assert_close(h, [0.0, 1, 0, 0, 0], atol=1e-10)
+
+
+def compare_coeffs_to_alt(window_length, order):
+    # For the given window_length and order, compare the results
+    # of savgol_coeffs and alt_sg_coeffs for pos from 0 to window_length - 1.
+    # Also include pos=None.
+    for pos in [None] + list(range(window_length)):
+        h1 = savgol_coeffs(window_length, order, pos=pos, use='dot')
+        h2 = alt_sg_coeffs(window_length, order, pos=pos)
+        xp_assert_close(h1, h2, atol=1e-10,
+                        err_msg=("window_length = %d, order = %d, pos = %s" %
+                                 (window_length, order, pos)))
+
+
+def test_sg_coeffs_compare():
+    # Compare savgol_coeffs() to alt_sg_coeffs().
+    for window_length in range(1, 8, 2):
+        for order in range(window_length):
+            compare_coeffs_to_alt(window_length, order)
+
+
+def test_sg_coeffs_exact():
+    polyorder = 4
+    window_length = 9
+    halflen = window_length // 2
+
+    x = np.linspace(0, 21, 43)
+    delta = x[1] - x[0]
+
+    # The data is a cubic polynomial.  We'll use an order 4
+    # SG filter, so the filtered values should equal the input data
+    # (except within half window_length of the edges).
+    y = 0.5 * x ** 3 - x
+    h = savgol_coeffs(window_length, polyorder)
+    y0 = convolve1d(y, h)
+    xp_assert_close(y0[halflen:-halflen], y[halflen:-halflen])
+
+    # Check the same input, but use deriv=1.  dy is the exact result.
+    dy = 1.5 * x ** 2 - 1
+    h = savgol_coeffs(window_length, polyorder, deriv=1, delta=delta)
+    y1 = convolve1d(y, h)
+    xp_assert_close(y1[halflen:-halflen], dy[halflen:-halflen])
+
+    # Check the same input, but use deriv=2. d2y is the exact result.
+    d2y = 3.0 * x
+    h = savgol_coeffs(window_length, polyorder, deriv=2, delta=delta)
+    y2 = convolve1d(y, h)
+    xp_assert_close(y2[halflen:-halflen], d2y[halflen:-halflen])
+
+
+def test_sg_coeffs_deriv():
+    # The data in `x` is a sampled parabola, so using savgol_coeffs with an
+    # order 2 or higher polynomial should give exact results.
+    i = np.array([-2.0, 0.0, 2.0, 4.0, 6.0])
+    x = i ** 2 / 4
+    dx = i / 2
+    d2x = np.full_like(i, 0.5)
+    for pos in range(x.size):
+        coeffs0 = savgol_coeffs(5, 3, pos=pos, delta=2.0, use='dot')
+        xp_assert_close(coeffs0.dot(x), x[pos], atol=1e-10)
+        coeffs1 = savgol_coeffs(5, 3, pos=pos, delta=2.0, use='dot', deriv=1)
+        xp_assert_close(coeffs1.dot(x), dx[pos], atol=1e-10)
+        coeffs2 = savgol_coeffs(5, 3, pos=pos, delta=2.0, use='dot', deriv=2)
+        xp_assert_close(coeffs2.dot(x), d2x[pos], atol=1e-10)
+
+
+def test_sg_coeffs_deriv_gt_polyorder():
+    """
+    If deriv > polyorder, the coefficients should be all 0.
+    This is a regression test for a bug where, e.g.,
+        savgol_coeffs(5, polyorder=1, deriv=2)
+    raised an error.
+    """
+    coeffs = savgol_coeffs(5, polyorder=1, deriv=2)
+    assert_array_equal(coeffs, np.zeros(5))
+    coeffs = savgol_coeffs(7, polyorder=4, deriv=6)
+    assert_array_equal(coeffs, np.zeros(7))
+
+
+def test_sg_coeffs_large():
+    # Test that for large values of window_length and polyorder the array of
+    # coefficients returned is symmetric. The aim is to ensure that
+    # no potential numeric overflow occurs.
+    coeffs0 = savgol_coeffs(31, 9)
+    assert_array_almost_equal(coeffs0, coeffs0[::-1])
+    coeffs1 = savgol_coeffs(31, 9, deriv=1)
+    assert_array_almost_equal(coeffs1, -coeffs1[::-1])
+
+# --------------------------------------------------------------------
+# savgol_coeffs tests for even window length
+# --------------------------------------------------------------------
+
+
+def test_sg_coeffs_even_window_length():
+    # Simple case - deriv=0, polyorder=0, 1
+    window_lengths = [4, 6, 8, 10, 12, 14, 16]
+    for length in window_lengths:
+        h_p_d = savgol_coeffs(length, 0, 0)
+        xp_assert_close(h_p_d, np.ones_like(h_p_d) / length)
+
+    # Verify with closed forms
+    # deriv=1, polyorder=1, 2
+    def h_p_d_closed_form_1(k, m):
+        return 6*(k - 0.5)/((2*m + 1)*m*(2*m - 1))
+
+    # deriv=2, polyorder=2
+    def h_p_d_closed_form_2(k, m):
+        numer = 15*(-4*m**2 + 1 + 12*(k - 0.5)**2)
+        denom = 4*(2*m + 1)*(m + 1)*m*(m - 1)*(2*m - 1)
+        return numer/denom
+
+    for length in window_lengths:
+        m = length//2
+        expected_output = [h_p_d_closed_form_1(k, m)
+                           for k in range(-m + 1, m + 1)][::-1]
+        actual_output = savgol_coeffs(length, 1, 1)
+        xp_assert_close(expected_output, actual_output)
+        actual_output = savgol_coeffs(length, 2, 1)
+        xp_assert_close(expected_output, actual_output)
+
+        expected_output = [h_p_d_closed_form_2(k, m)
+                           for k in range(-m + 1, m + 1)][::-1]
+        actual_output = savgol_coeffs(length, 2, 2)
+        xp_assert_close(expected_output, actual_output)
+        actual_output = savgol_coeffs(length, 3, 2)
+        xp_assert_close(expected_output, actual_output)
+
+#--------------------------------------------------------------------
+# savgol_filter tests
+#--------------------------------------------------------------------
+
+
+def test_sg_filter_trivial():
+    """ Test some trivial edge cases for savgol_filter()."""
+    x = np.array([1.0])
+    y = savgol_filter(x, 1, 0)
+    assert_equal(y, [1.0])
+
+    # Input is a single value. With a window length of 3 and polyorder 1,
+    # the value in y is from the straight-line fit of (-1,0), (0,3) and
+    # (1, 0) at 0. This is just the average of the three values, hence 1.0.
+    x = np.array([3.0])
+    y = savgol_filter(x, 3, 1, mode='constant')
+    assert_almost_equal(y, [1.0], decimal=15)
+
+    x = np.array([3.0])
+    y = savgol_filter(x, 3, 1, mode='nearest')
+    assert_almost_equal(y, [3.0], decimal=15)
+
+    x = np.array([1.0] * 3)
+    y = savgol_filter(x, 3, 1, mode='wrap')
+    assert_almost_equal(y, [1.0, 1.0, 1.0], decimal=15)
+
+
+def test_sg_filter_basic():
+    # Some basic test cases for savgol_filter().
+    x = np.array([1.0, 2.0, 1.0])
+    y = savgol_filter(x, 3, 1, mode='constant')
+    xp_assert_close(y, [1.0, 4.0 / 3, 1.0])
+
+    y = savgol_filter(x, 3, 1, mode='mirror')
+    xp_assert_close(y, [5.0 / 3, 4.0 / 3, 5.0 / 3])
+
+    y = savgol_filter(x, 3, 1, mode='wrap')
+    xp_assert_close(y, [4.0 / 3, 4.0 / 3, 4.0 / 3])
+
+
+def test_sg_filter_2d():
+    x = np.array([[1.0, 2.0, 1.0],
+                  [2.0, 4.0, 2.0]])
+    expected = np.array([[1.0, 4.0 / 3, 1.0],
+                         [2.0, 8.0 / 3, 2.0]])
+    y = savgol_filter(x, 3, 1, mode='constant')
+    xp_assert_close(y, expected)
+
+    y = savgol_filter(x.T, 3, 1, mode='constant', axis=0)
+    xp_assert_close(y, expected.T)
+
+
+def test_sg_filter_interp_edges():
+    # Another test with low degree polynomial data, for which we can easily
+    # give the exact results. In this test, we use mode='interp', so
+    # savgol_filter should match the exact solution for the entire data set,
+    # including the edges.
+    t = np.linspace(-5, 5, 21)
+    delta = t[1] - t[0]
+    # Polynomial test data.
+    x = np.array([t,
+                  3 * t ** 2,
+                  t ** 3 - t])
+    dx = np.array([np.ones_like(t),
+                   6 * t,
+                   3 * t ** 2 - 1.0])
+    d2x = np.array([np.zeros_like(t),
+                    np.full_like(t, 6),
+                    6 * t])
+
+    window_length = 7
+
+    y = savgol_filter(x, window_length, 3, axis=-1, mode='interp')
+    xp_assert_close(y, x, atol=1e-12)
+
+    y1 = savgol_filter(x, window_length, 3, axis=-1, mode='interp',
+                       deriv=1, delta=delta)
+    xp_assert_close(y1, dx, atol=1e-12)
+
+    y2 = savgol_filter(x, window_length, 3, axis=-1, mode='interp',
+                       deriv=2, delta=delta)
+    xp_assert_close(y2, d2x, atol=1e-12)
+
+    # Transpose everything, and test again with axis=0.
+
+    x = x.T
+    dx = dx.T
+    d2x = d2x.T
+
+    y = savgol_filter(x, window_length, 3, axis=0, mode='interp')
+    xp_assert_close(y, x, atol=1e-12)
+
+    y1 = savgol_filter(x, window_length, 3, axis=0, mode='interp',
+                       deriv=1, delta=delta)
+    xp_assert_close(y1, dx, atol=1e-12)
+
+    y2 = savgol_filter(x, window_length, 3, axis=0, mode='interp',
+                       deriv=2, delta=delta)
+    xp_assert_close(y2, d2x, atol=1e-12)
+
+
+def test_sg_filter_interp_edges_3d():
+    # Test mode='interp' with a 3-D array.
+    t = np.linspace(-5, 5, 21)
+    delta = t[1] - t[0]
+    x1 = np.array([t, -t])
+    x2 = np.array([t ** 2, 3 * t ** 2 + 5])
+    x3 = np.array([t ** 3, 2 * t ** 3 + t ** 2 - 0.5 * t])
+    dx1 = np.array([np.ones_like(t), -np.ones_like(t)])
+    dx2 = np.array([2 * t, 6 * t])
+    dx3 = np.array([3 * t ** 2, 6 * t ** 2 + 2 * t - 0.5])
+
+    # z has shape (3, 2, 21)
+    z = np.array([x1, x2, x3])
+    dz = np.array([dx1, dx2, dx3])
+
+    y = savgol_filter(z, 7, 3, axis=-1, mode='interp', delta=delta)
+    xp_assert_close(y, z, atol=1e-10)
+
+    dy = savgol_filter(z, 7, 3, axis=-1, mode='interp', deriv=1, delta=delta)
+    xp_assert_close(dy, dz, atol=1e-10)
+
+    # z has shape (3, 21, 2)
+    z = np.array([x1.T, x2.T, x3.T])
+    dz = np.array([dx1.T, dx2.T, dx3.T])
+
+    y = savgol_filter(z, 7, 3, axis=1, mode='interp', delta=delta)
+    xp_assert_close(y, z, atol=1e-10)
+
+    dy = savgol_filter(z, 7, 3, axis=1, mode='interp', deriv=1, delta=delta)
+    xp_assert_close(dy, dz, atol=1e-10)
+
+    # z has shape (21, 3, 2)
+    z = z.swapaxes(0, 1).copy()
+    dz = dz.swapaxes(0, 1).copy()
+
+    y = savgol_filter(z, 7, 3, axis=0, mode='interp', delta=delta)
+    xp_assert_close(y, z, atol=1e-10)
+
+    dy = savgol_filter(z, 7, 3, axis=0, mode='interp', deriv=1, delta=delta)
+    xp_assert_close(dy, dz, atol=1e-10)
+
+
+def test_sg_filter_valid_window_length_3d():
+    """Tests that the window_length check is using the correct axis."""
+
+    x = np.ones((10, 20, 30))
+
+    savgol_filter(x, window_length=29, polyorder=3, mode='interp')
+
+    with pytest.raises(ValueError, match='window_length must be less than'):
+        # window_length is more than x.shape[-1].
+        savgol_filter(x, window_length=31, polyorder=3, mode='interp')
+
+    savgol_filter(x, window_length=9, polyorder=3, axis=0, mode='interp')
+
+    with pytest.raises(ValueError, match='window_length must be less than'):
+        # window_length is more than x.shape[0].
+        savgol_filter(x, window_length=11, polyorder=3, axis=0, mode='interp')
diff --git a/shared/package-files/4e/4eaff34e48e1f1eb9e049affaee932b6efd85a1c01bdd7509e505311861fdee0/network2.py b/shared/package-files/4e/4eaff34e48e1f1eb9e049affaee932b6efd85a1c01bdd7509e505311861fdee0/network2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdb583d0af92feaf238dd0bd61163d0c03cdaecc
--- /dev/null
+++ b/shared/package-files/4e/4eaff34e48e1f1eb9e049affaee932b6efd85a1c01bdd7509e505311861fdee0/network2.py
@@ -0,0 +1,11 @@
+# mypy: ignore-errors
+
+import torch.nn as nn
+
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 20)
+        self.relu = nn.ReLU()
diff --git a/shared/package-files/4e/4eb2ddd5b41d41c82f1f36ddf6ecf6956299935f278d546ebd4ac90bf109dfe9/simple_graph_executor_impl.h b/shared/package-files/4e/4eb2ddd5b41d41c82f1f36ddf6ecf6956299935f278d546ebd4ac90bf109dfe9/simple_graph_executor_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..34272000f0d1a3e2e808ce2bbe27ec4ab299380e
--- /dev/null
+++ b/shared/package-files/4e/4eb2ddd5b41d41c82f1f36ddf6ecf6956299935f278d546ebd4ac90bf109dfe9/simple_graph_executor_impl.h
@@ -0,0 +1,23 @@
+#pragma once
+#include <c10/util/Flags.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/runtime/graph_executor_impl.h>
+
+namespace torch::jit {
+
+struct TORCH_API SimpleGraphExecutorImpl : public GraphExecutorImplBase {
+  SimpleGraphExecutorImpl(
+      const std::shared_ptr<Graph>& graph,
+      std::string function_name);
+
+  const ExecutionPlan& getPlanFor(
+      Stack& stack,
+      c10::optional<size_t> remaining_bailout_depth) override;
+  GraphExecutorState getDebugState() override;
+  ~SimpleGraphExecutorImpl() override = default;
+
+ private:
+  c10::optional<ExecutionPlan> execution_plan_;
+};
+
+} // namespace torch::jit
diff --git a/shared/package-files/4e/4eb2fab7bf45d07d00407d32e428fb4ead3fa739387d5162fb8ab88c99ae8d84/einsumfunc.py b/shared/package-files/4e/4eb2fab7bf45d07d00407d32e428fb4ead3fa739387d5162fb8ab88c99ae8d84/einsumfunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..01966f0fe75b7e336a4237372e2d4cb0db0fbc84
--- /dev/null
+++ b/shared/package-files/4e/4eb2fab7bf45d07d00407d32e428fb4ead3fa739387d5162fb8ab88c99ae8d84/einsumfunc.py
@@ -0,0 +1,1443 @@
+"""
+Implementation of optimized einsum.
+
+"""
+import itertools
+import operator
+
+from numpy.core.multiarray import c_einsum
+from numpy.core.numeric import asanyarray, tensordot
+from numpy.core.overrides import array_function_dispatch
+
+__all__ = ['einsum', 'einsum_path']
+
+einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
+einsum_symbols_set = set(einsum_symbols)
+
+
+def _flop_count(idx_contraction, inner, num_terms, size_dictionary):
+    """
+    Computes the number of FLOPS in the contraction.
+
+    Parameters
+    ----------
+    idx_contraction : iterable
+        The indices involved in the contraction
+    inner : bool
+        Does this contraction require an inner product?
+    num_terms : int
+        The number of terms in a contraction
+    size_dictionary : dict
+        The size of each of the indices in idx_contraction
+
+    Returns
+    -------
+    flop_count : int
+        The total number of FLOPS required for the contraction.
+
+    Examples
+    --------
+
+    >>> _flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5})
+    30
+
+    >>> _flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5})
+    60
+
+    """
+
+    overall_size = _compute_size_by_dict(idx_contraction, size_dictionary)
+    op_factor = max(1, num_terms - 1)
+    if inner:
+        op_factor += 1
+
+    return overall_size * op_factor
+
+def _compute_size_by_dict(indices, idx_dict):
+    """
+    Computes the product of the elements in indices based on the dictionary
+    idx_dict.
+
+    Parameters
+    ----------
+    indices : iterable
+        Indices to base the product on.
+    idx_dict : dictionary
+        Dictionary of index sizes
+
+    Returns
+    -------
+    ret : int
+        The resulting product.
+
+    Examples
+    --------
+    >>> _compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5})
+    90
+
+    """
+    ret = 1
+    for i in indices:
+        ret *= idx_dict[i]
+    return ret
+
+
+def _find_contraction(positions, input_sets, output_set):
+    """
+    Finds the contraction for a given set of input and output sets.
+
+    Parameters
+    ----------
+    positions : iterable
+        Integer positions of terms used in the contraction.
+    input_sets : list
+        List of sets that represent the lhs side of the einsum subscript
+    output_set : set
+        Set that represents the rhs side of the overall einsum subscript
+
+    Returns
+    -------
+    new_result : set
+        The indices of the resulting contraction
+    remaining : list
+        List of sets that have not been contracted, the new set is appended to
+        the end of this list
+    idx_removed : set
+        Indices removed from the entire contraction
+    idx_contraction : set
+        The indices used in the current contraction
+
+    Examples
+    --------
+
+    # A simple dot product test case
+    >>> pos = (0, 1)
+    >>> isets = [set('ab'), set('bc')]
+    >>> oset = set('ac')
+    >>> _find_contraction(pos, isets, oset)
+    ({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'})
+
+    # A more complex case with additional terms in the contraction
+    >>> pos = (0, 2)
+    >>> isets = [set('abd'), set('ac'), set('bdc')]
+    >>> oset = set('ac')
+    >>> _find_contraction(pos, isets, oset)
+    ({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'})
+    """
+
+    idx_contract = set()
+    idx_remain = output_set.copy()
+    remaining = []
+    for ind, value in enumerate(input_sets):
+        if ind in positions:
+            idx_contract |= value
+        else:
+            remaining.append(value)
+            idx_remain |= value
+
+    new_result = idx_remain & idx_contract
+    idx_removed = (idx_contract - new_result)
+    remaining.append(new_result)
+
+    return (new_result, remaining, idx_removed, idx_contract)
+
+
+def _optimal_path(input_sets, output_set, idx_dict, memory_limit):
+    """
+    Computes all possible pair contractions, sieves the results based
+    on ``memory_limit`` and returns the lowest cost path. This algorithm
+    scales factorial with respect to the elements in the list ``input_sets``.
+
+    Parameters
+    ----------
+    input_sets : list
+        List of sets that represent the lhs side of the einsum subscript
+    output_set : set
+        Set that represents the rhs side of the overall einsum subscript
+    idx_dict : dictionary
+        Dictionary of index sizes
+    memory_limit : int
+        The maximum number of elements in a temporary array
+
+    Returns
+    -------
+    path : list
+        The optimal contraction order within the memory limit constraint.
+
+    Examples
+    --------
+    >>> isets = [set('abd'), set('ac'), set('bdc')]
+    >>> oset = set()
+    >>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
+    >>> _optimal_path(isets, oset, idx_sizes, 5000)
+    [(0, 2), (0, 1)]
+    """
+
+    full_results = [(0, [], input_sets)]
+    for iteration in range(len(input_sets) - 1):
+        iter_results = []
+
+        # Compute all unique pairs
+        for curr in full_results:
+            cost, positions, remaining = curr
+            for con in itertools.combinations(range(len(input_sets) - iteration), 2):
+
+                # Find the contraction
+                cont = _find_contraction(con, remaining, output_set)
+                new_result, new_input_sets, idx_removed, idx_contract = cont
+
+                # Sieve the results based on memory_limit
+                new_size = _compute_size_by_dict(new_result, idx_dict)
+                if new_size > memory_limit:
+                    continue
+
+                # Build (total_cost, positions, indices_remaining)
+                total_cost =  cost + _flop_count(idx_contract, idx_removed, len(con), idx_dict)
+                new_pos = positions + [con]
+                iter_results.append((total_cost, new_pos, new_input_sets))
+
+        # Update combinatorial list, if we did not find anything return best
+        # path + remaining contractions
+        if iter_results:
+            full_results = iter_results
+        else:
+            path = min(full_results, key=lambda x: x[0])[1]
+            path += [tuple(range(len(input_sets) - iteration))]
+            return path
+
+    # If we have not found anything return single einsum contraction
+    if len(full_results) == 0:
+        return [tuple(range(len(input_sets)))]
+
+    path = min(full_results, key=lambda x: x[0])[1]
+    return path
+
+def _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost):
+    """Compute the cost (removed size + flops) and resultant indices for
+    performing the contraction specified by ``positions``.
+
+    Parameters
+    ----------
+    positions : tuple of int
+        The locations of the proposed tensors to contract.
+    input_sets : list of sets
+        The indices found on each tensors.
+    output_set : set
+        The output indices of the expression.
+    idx_dict : dict
+        Mapping of each index to its size.
+    memory_limit : int
+        The total allowed size for an intermediary tensor.
+    path_cost : int
+        The contraction cost so far.
+    naive_cost : int
+        The cost of the unoptimized expression.
+
+    Returns
+    -------
+    cost : (int, int)
+        A tuple containing the size of any indices removed, and the flop cost.
+    positions : tuple of int
+        The locations of the proposed tensors to contract.
+    new_input_sets : list of sets
+        The resulting new list of indices if this proposed contraction is performed.
+
+    """
+
+    # Find the contraction
+    contract = _find_contraction(positions, input_sets, output_set)
+    idx_result, new_input_sets, idx_removed, idx_contract = contract
+
+    # Sieve the results based on memory_limit
+    new_size = _compute_size_by_dict(idx_result, idx_dict)
+    if new_size > memory_limit:
+        return None
+
+    # Build sort tuple
+    old_sizes = (_compute_size_by_dict(input_sets[p], idx_dict) for p in positions)
+    removed_size = sum(old_sizes) - new_size
+
+    # NB: removed_size used to be just the size of any removed indices i.e.:
+    #     helpers.compute_size_by_dict(idx_removed, idx_dict)
+    cost = _flop_count(idx_contract, idx_removed, len(positions), idx_dict)
+    sort = (-removed_size, cost)
+
+    # Sieve based on total cost as well
+    if (path_cost + cost) > naive_cost:
+        return None
+
+    # Add contraction to possible choices
+    return [sort, positions, new_input_sets]
+
+
+def _update_other_results(results, best):
+    """Update the positions and provisional input_sets of ``results`` based on
+    performing the contraction result ``best``. Remove any involving the tensors
+    contracted.
+
+    Parameters
+    ----------
+    results : list
+        List of contraction results produced by ``_parse_possible_contraction``.
+    best : list
+        The best contraction of ``results`` i.e. the one that will be performed.
+
+    Returns
+    -------
+    mod_results : list
+        The list of modified results, updated with outcome of ``best`` contraction.
+    """
+
+    best_con = best[1]
+    bx, by = best_con
+    mod_results = []
+
+    for cost, (x, y), con_sets in results:
+
+        # Ignore results involving tensors just contracted
+        if x in best_con or y in best_con:
+            continue
+
+        # Update the input_sets
+        del con_sets[by - int(by > x) - int(by > y)]
+        del con_sets[bx - int(bx > x) - int(bx > y)]
+        con_sets.insert(-1, best[2][-1])
+
+        # Update the position indices
+        mod_con = x - int(x > bx) - int(x > by), y - int(y > bx) - int(y > by)
+        mod_results.append((cost, mod_con, con_sets))
+
+    return mod_results
+
+def _greedy_path(input_sets, output_set, idx_dict, memory_limit):
+    """
+    Finds the path by contracting the best pair until the input list is
+    exhausted. The best pair is found by minimizing the tuple
+    ``(-prod(indices_removed), cost)``.  What this amounts to is prioritizing
+    matrix multiplication or inner product operations, then Hadamard like
+    operations, and finally outer operations. Outer products are limited by
+    ``memory_limit``. This algorithm scales cubically with respect to the
+    number of elements in the list ``input_sets``.
+
+    Parameters
+    ----------
+    input_sets : list
+        List of sets that represent the lhs side of the einsum subscript
+    output_set : set
+        Set that represents the rhs side of the overall einsum subscript
+    idx_dict : dictionary
+        Dictionary of index sizes
+    memory_limit : int
+        The maximum number of elements in a temporary array
+
+    Returns
+    -------
+    path : list
+        The greedy contraction order within the memory limit constraint.
+
+    Examples
+    --------
+    >>> isets = [set('abd'), set('ac'), set('bdc')]
+    >>> oset = set()
+    >>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
+    >>> _greedy_path(isets, oset, idx_sizes, 5000)
+    [(0, 2), (0, 1)]
+    """
+
+    # Handle trivial cases that leaked through
+    if len(input_sets) == 1:
+        return [(0,)]
+    elif len(input_sets) == 2:
+        return [(0, 1)]
+
+    # Build up a naive cost
+    contract = _find_contraction(range(len(input_sets)), input_sets, output_set)
+    idx_result, new_input_sets, idx_removed, idx_contract = contract
+    naive_cost = _flop_count(idx_contract, idx_removed, len(input_sets), idx_dict)
+
+    # Initially iterate over all pairs
+    comb_iter = itertools.combinations(range(len(input_sets)), 2)
+    known_contractions = []
+
+    path_cost = 0
+    path = []
+
+    for iteration in range(len(input_sets) - 1):
+
+        # Iterate over all pairs on first step, only previously found pairs on subsequent steps
+        for positions in comb_iter:
+
+            # Always initially ignore outer products
+            if input_sets[positions[0]].isdisjoint(input_sets[positions[1]]):
+                continue
+
+            result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost,
+                                                 naive_cost)
+            if result is not None:
+                known_contractions.append(result)
+
+        # If we do not have a inner contraction, rescan pairs including outer products
+        if len(known_contractions) == 0:
+
+            # Then check the outer products
+            for positions in itertools.combinations(range(len(input_sets)), 2):
+                result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit,
+                                                     path_cost, naive_cost)
+                if result is not None:
+                    known_contractions.append(result)
+
+            # If we still did not find any remaining contractions, default back to einsum like behavior
+            if len(known_contractions) == 0:
+                path.append(tuple(range(len(input_sets))))
+                break
+
+        # Sort based on first index
+        best = min(known_contractions, key=lambda x: x[0])
+
+        # Now propagate as many unused contractions as possible to next iteration
+        known_contractions = _update_other_results(known_contractions, best)
+
+        # Next iteration only compute contractions with the new tensor
+        # All other contractions have been accounted for
+        input_sets = best[2]
+        new_tensor_pos = len(input_sets) - 1
+        comb_iter = ((i, new_tensor_pos) for i in range(new_tensor_pos))
+
+        # Update path and total cost
+        path.append(best[1])
+        path_cost += best[0][1]
+
+    return path
+
+
+def _can_dot(inputs, result, idx_removed):
+    """
+    Checks if we can use BLAS (np.tensordot) call and its beneficial to do so.
+
+    Parameters
+    ----------
+    inputs : list of str
+        Specifies the subscripts for summation.
+    result : str
+        Resulting summation.
+    idx_removed : set
+        Indices that are removed in the summation
+
+
+    Returns
+    -------
+    type : bool
+        Returns true if BLAS should and can be used, else False
+
+    Notes
+    -----
+    If the operations is BLAS level 1 or 2 and is not already aligned
+    we default back to einsum as the memory movement to copy is more
+    costly than the operation itself.
+
+
+    Examples
+    --------
+
+    # Standard GEMM operation
+    >>> _can_dot(['ij', 'jk'], 'ik', set('j'))
+    True
+
+    # Can use the standard BLAS, but requires odd data movement
+    >>> _can_dot(['ijj', 'jk'], 'ik', set('j'))
+    False
+
+    # DDOT where the memory is not aligned
+    >>> _can_dot(['ijk', 'ikj'], '', set('ijk'))
+    False
+
+    """
+
+    # All `dot` calls remove indices
+    if len(idx_removed) == 0:
+        return False
+
+    # BLAS can only handle two operands
+    if len(inputs) != 2:
+        return False
+
+    input_left, input_right = inputs
+
+    for c in set(input_left + input_right):
+        # can't deal with repeated indices on same input or more than 2 total
+        nl, nr = input_left.count(c), input_right.count(c)
+        if (nl > 1) or (nr > 1) or (nl + nr > 2):
+            return False
+
+        # can't do implicit summation or dimension collapse e.g.
+        #     "ab,bc->c" (implicitly sum over 'a')
+        #     "ab,ca->ca" (take diagonal of 'a')
+        if nl + nr - 1 == int(c in result):
+            return False
+
+    # Build a few temporaries
+    set_left = set(input_left)
+    set_right = set(input_right)
+    keep_left = set_left - idx_removed
+    keep_right = set_right - idx_removed
+    rs = len(idx_removed)
+
+    # At this point we are a DOT, GEMV, or GEMM operation
+
+    # Handle inner products
+
+    # DDOT with aligned data
+    if input_left == input_right:
+        return True
+
+    # DDOT without aligned data (better to use einsum)
+    if set_left == set_right:
+        return False
+
+    # Handle the 4 possible (aligned) GEMV or GEMM cases
+
+    # GEMM or GEMV no transpose
+    if input_left[-rs:] == input_right[:rs]:
+        return True
+
+    # GEMM or GEMV transpose both
+    if input_left[:rs] == input_right[-rs:]:
+        return True
+
+    # GEMM or GEMV transpose right
+    if input_left[-rs:] == input_right[-rs:]:
+        return True
+
+    # GEMM or GEMV transpose left
+    if input_left[:rs] == input_right[:rs]:
+        return True
+
+    # Einsum is faster than GEMV if we have to copy data
+    if not keep_left or not keep_right:
+        return False
+
+    # We are a matrix-matrix product, but we need to copy data
+    return True
+
+
+def _parse_einsum_input(operands):
+    """
+    A reproduction of einsum c side einsum parsing in python.
+
+    Returns
+    -------
+    input_strings : str
+        Parsed input strings
+    output_string : str
+        Parsed output string
+    operands : list of array_like
+        The operands to use in the numpy contraction
+
+    Examples
+    --------
+    The operand list is simplified to reduce printing:
+
+    >>> np.random.seed(123)
+    >>> a = np.random.rand(4, 4)
+    >>> b = np.random.rand(4, 4, 4)
+    >>> _parse_einsum_input(('...a,...a->...', a, b))
+    ('za,xza', 'xz', [a, b]) # may vary
+
+    >>> _parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0]))
+    ('za,xza', 'xz', [a, b]) # may vary
+    """
+
+    if len(operands) == 0:
+        raise ValueError("No input operands")
+
+    if isinstance(operands[0], str):
+        subscripts = operands[0].replace(" ", "")
+        operands = [asanyarray(v) for v in operands[1:]]
+
+        # Ensure all characters are valid
+        for s in subscripts:
+            if s in '.,->':
+                continue
+            if s not in einsum_symbols:
+                raise ValueError("Character %s is not a valid symbol." % s)
+
+    else:
+        tmp_operands = list(operands)
+        operand_list = []
+        subscript_list = []
+        for p in range(len(operands) // 2):
+            operand_list.append(tmp_operands.pop(0))
+            subscript_list.append(tmp_operands.pop(0))
+
+        output_list = tmp_operands[-1] if len(tmp_operands) else None
+        operands = [asanyarray(v) for v in operand_list]
+        subscripts = ""
+        last = len(subscript_list) - 1
+        for num, sub in enumerate(subscript_list):
+            for s in sub:
+                if s is Ellipsis:
+                    subscripts += "..."
+                else:
+                    try:
+                        s = operator.index(s)
+                    except TypeError as e:
+                        raise TypeError("For this input type lists must contain "
+                                        "either int or Ellipsis") from e
+                    subscripts += einsum_symbols[s]
+            if num != last:
+                subscripts += ","
+
+        if output_list is not None:
+            subscripts += "->"
+            for s in output_list:
+                if s is Ellipsis:
+                    subscripts += "..."
+                else:
+                    try:
+                        s = operator.index(s)
+                    except TypeError as e:
+                        raise TypeError("For this input type lists must contain "
+                                        "either int or Ellipsis") from e
+                    subscripts += einsum_symbols[s]
+    # Check for proper "->"
+    if ("-" in subscripts) or (">" in subscripts):
+        invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1)
+        if invalid or (subscripts.count("->") != 1):
+            raise ValueError("Subscripts can only contain one '->'.")
+
+    # Parse ellipses
+    if "." in subscripts:
+        used = subscripts.replace(".", "").replace(",", "").replace("->", "")
+        unused = list(einsum_symbols_set - set(used))
+        ellipse_inds = "".join(unused)
+        longest = 0
+
+        if "->" in subscripts:
+            input_tmp, output_sub = subscripts.split("->")
+            split_subscripts = input_tmp.split(",")
+            out_sub = True
+        else:
+            split_subscripts = subscripts.split(',')
+            out_sub = False
+
+        for num, sub in enumerate(split_subscripts):
+            if "." in sub:
+                if (sub.count(".") != 3) or (sub.count("...") != 1):
+                    raise ValueError("Invalid Ellipses.")
+
+                # Take into account numerical values
+                if operands[num].shape == ():
+                    ellipse_count = 0
+                else:
+                    ellipse_count = max(operands[num].ndim, 1)
+                    ellipse_count -= (len(sub) - 3)
+
+                if ellipse_count > longest:
+                    longest = ellipse_count
+
+                if ellipse_count < 0:
+                    raise ValueError("Ellipses lengths do not match.")
+                elif ellipse_count == 0:
+                    split_subscripts[num] = sub.replace('...', '')
+                else:
+                    rep_inds = ellipse_inds[-ellipse_count:]
+                    split_subscripts[num] = sub.replace('...', rep_inds)
+
+        subscripts = ",".join(split_subscripts)
+        if longest == 0:
+            out_ellipse = ""
+        else:
+            out_ellipse = ellipse_inds[-longest:]
+
+        if out_sub:
+            subscripts += "->" + output_sub.replace("...", out_ellipse)
+        else:
+            # Special care for outputless ellipses
+            output_subscript = ""
+            tmp_subscripts = subscripts.replace(",", "")
+            for s in sorted(set(tmp_subscripts)):
+                if s not in (einsum_symbols):
+                    raise ValueError("Character %s is not a valid symbol." % s)
+                if tmp_subscripts.count(s) == 1:
+                    output_subscript += s
+            normal_inds = ''.join(sorted(set(output_subscript) -
+                                         set(out_ellipse)))
+
+            subscripts += "->" + out_ellipse + normal_inds
+
+    # Build output string if does not exist
+    if "->" in subscripts:
+        input_subscripts, output_subscript = subscripts.split("->")
+    else:
+        input_subscripts = subscripts
+        # Build output subscripts
+        tmp_subscripts = subscripts.replace(",", "")
+        output_subscript = ""
+        for s in sorted(set(tmp_subscripts)):
+            if s not in einsum_symbols:
+                raise ValueError("Character %s is not a valid symbol." % s)
+            if tmp_subscripts.count(s) == 1:
+                output_subscript += s
+
+    # Make sure output subscripts are in the input
+    for char in output_subscript:
+        if char not in input_subscripts:
+            raise ValueError("Output character %s did not appear in the input"
+                             % char)
+
+    # Make sure number operands is equivalent to the number of terms
+    if len(input_subscripts.split(',')) != len(operands):
+        raise ValueError("Number of einsum subscripts must be equal to the "
+                         "number of operands.")
+
+    return (input_subscripts, output_subscript, operands)
+
+
+def _einsum_path_dispatcher(*operands, optimize=None, einsum_call=None):
+    # NOTE: technically, we should only dispatch on array-like arguments, not
+    # subscripts (given as strings). But separating operands into
+    # arrays/subscripts is a little tricky/slow (given einsum's two supported
+    # signatures), so as a practical shortcut we dispatch on everything.
+    # Strings will be ignored for dispatching since they don't define
+    # __array_function__.
+    return operands
+
+
+@array_function_dispatch(_einsum_path_dispatcher, module='numpy')
+def einsum_path(*operands, optimize='greedy', einsum_call=False):
+    """
+    einsum_path(subscripts, *operands, optimize='greedy')
+
+    Evaluates the lowest cost contraction order for an einsum expression by
+    considering the creation of intermediate arrays.
+
+    Parameters
+    ----------
+    subscripts : str
+        Specifies the subscripts for summation.
+    *operands : list of array_like
+        These are the arrays for the operation.
+    optimize : {bool, list, tuple, 'greedy', 'optimal'}
+        Choose the type of path. If a tuple is provided, the second argument is
+        assumed to be the maximum intermediate size created. If only a single
+        argument is provided the largest input or output array size is used
+        as a maximum intermediate size.
+
+        * if a list is given that starts with ``einsum_path``, uses this as the
+          contraction path
+        * if False no optimization is taken
+        * if True defaults to the 'greedy' algorithm
+        * 'optimal' An algorithm that combinatorially explores all possible
+          ways of contracting the listed tensors and chooses the least costly
+          path. Scales exponentially with the number of terms in the
+          contraction.
+        * 'greedy' An algorithm that chooses the best pair contraction
+          at each step. Effectively, this algorithm searches the largest inner,
+          Hadamard, and then outer products at each step. Scales cubically with
+          the number of terms in the contraction. Equivalent to the 'optimal'
+          path for most contractions.
+
+        Default is 'greedy'.
+
+    Returns
+    -------
+    path : list of tuples
+        A list representation of the einsum path.
+    string_repr : str
+        A printable representation of the einsum path.
+
+    Notes
+    -----
+    The resulting path indicates which terms of the input contraction should be
+    contracted first, the result of this contraction is then appended to the
+    end of the contraction list. This list can then be iterated over until all
+    intermediate contractions are complete.
+
+    See Also
+    --------
+    einsum, linalg.multi_dot
+
+    Examples
+    --------
+
+    We can begin with a chain dot example. In this case, it is optimal to
+    contract the ``b`` and ``c`` tensors first as represented by the first
+    element of the path ``(1, 2)``. The resulting tensor is added to the end
+    of the contraction and the remaining contraction ``(0, 1)`` is then
+    completed.
+
+    >>> np.random.seed(123)
+    >>> a = np.random.rand(2, 2)
+    >>> b = np.random.rand(2, 5)
+    >>> c = np.random.rand(5, 2)
+    >>> path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy')
+    >>> print(path_info[0])
+    ['einsum_path', (1, 2), (0, 1)]
+    >>> print(path_info[1])
+      Complete contraction:  ij,jk,kl->il # may vary
+             Naive scaling:  4
+         Optimized scaling:  3
+          Naive FLOP count:  1.600e+02
+      Optimized FLOP count:  5.600e+01
+       Theoretical speedup:  2.857
+      Largest intermediate:  4.000e+00 elements
+    -------------------------------------------------------------------------
+    scaling                  current                                remaining
+    -------------------------------------------------------------------------
+       3                   kl,jk->jl                                ij,jl->il
+       3                   jl,ij->il                                   il->il
+
+
+    A more complex index transformation example.
+
+    >>> I = np.random.rand(10, 10, 10, 10)
+    >>> C = np.random.rand(10, 10)
+    >>> path_info = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C,
+    ...                            optimize='greedy')
+
+    >>> print(path_info[0])
+    ['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)]
+    >>> print(path_info[1]) 
+      Complete contraction:  ea,fb,abcd,gc,hd->efgh # may vary
+             Naive scaling:  8
+         Optimized scaling:  5
+          Naive FLOP count:  8.000e+08
+      Optimized FLOP count:  8.000e+05
+       Theoretical speedup:  1000.000
+      Largest intermediate:  1.000e+04 elements
+    --------------------------------------------------------------------------
+    scaling                  current                                remaining
+    --------------------------------------------------------------------------
+       5               abcd,ea->bcde                      fb,gc,hd,bcde->efgh
+       5               bcde,fb->cdef                         gc,hd,cdef->efgh
+       5               cdef,gc->defg                            hd,defg->efgh
+       5               defg,hd->efgh                               efgh->efgh
+    """
+
+    # Figure out what the path really is
+    path_type = optimize
+    if path_type is True:
+        path_type = 'greedy'
+    if path_type is None:
+        path_type = False
+
+    explicit_einsum_path = False
+    memory_limit = None
+
+    # No optimization or a named path algorithm
+    if (path_type is False) or isinstance(path_type, str):
+        pass
+
+    # Given an explicit path
+    elif len(path_type) and (path_type[0] == 'einsum_path'):
+        explicit_einsum_path = True
+
+    # Path tuple with memory limit
+    elif ((len(path_type) == 2) and isinstance(path_type[0], str) and
+            isinstance(path_type[1], (int, float))):
+        memory_limit = int(path_type[1])
+        path_type = path_type[0]
+
+    else:
+        raise TypeError("Did not understand the path: %s" % str(path_type))
+
+    # Hidden option, only einsum should call this
+    einsum_call_arg = einsum_call
+
+    # Python side parsing
+    input_subscripts, output_subscript, operands = _parse_einsum_input(operands)
+
+    # Build a few useful list and sets
+    input_list = input_subscripts.split(',')
+    input_sets = [set(x) for x in input_list]
+    output_set = set(output_subscript)
+    indices = set(input_subscripts.replace(',', ''))
+
+    # Get length of each unique dimension and ensure all dimensions are correct
+    dimension_dict = {}
+    broadcast_indices = [[] for x in range(len(input_list))]
+    for tnum, term in enumerate(input_list):
+        sh = operands[tnum].shape
+        if len(sh) != len(term):
+            raise ValueError("Einstein sum subscript %s does not contain the "
+                             "correct number of indices for operand %d."
+                             % (input_subscripts[tnum], tnum))
+        for cnum, char in enumerate(term):
+            dim = sh[cnum]
+
+            # Build out broadcast indices
+            if dim == 1:
+                broadcast_indices[tnum].append(char)
+
+            if char in dimension_dict.keys():
+                # For broadcasting cases we always want the largest dim size
+                if dimension_dict[char] == 1:
+                    dimension_dict[char] = dim
+                elif dim not in (1, dimension_dict[char]):
+                    raise ValueError("Size of label '%s' for operand %d (%d) "
+                                     "does not match previous terms (%d)."
+                                     % (char, tnum, dimension_dict[char], dim))
+            else:
+                dimension_dict[char] = dim
+
+    # Convert broadcast inds to sets
+    broadcast_indices = [set(x) for x in broadcast_indices]
+
+    # Compute size of each input array plus the output array
+    size_list = [_compute_size_by_dict(term, dimension_dict)
+                 for term in input_list + [output_subscript]]
+    max_size = max(size_list)
+
+    if memory_limit is None:
+        memory_arg = max_size
+    else:
+        memory_arg = memory_limit
+
+    # Compute naive cost
+    # This isn't quite right, need to look into exactly how einsum does this
+    inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0
+    naive_cost = _flop_count(indices, inner_product, len(input_list), dimension_dict)
+
+    # Compute the path
+    if explicit_einsum_path:
+        path = path_type[1:]
+    elif (
+        (path_type is False)
+        or (len(input_list) in [1, 2])
+        or (indices == output_set)
+    ):
+        # Nothing to be optimized, leave it to einsum
+        path = [tuple(range(len(input_list)))]
+    elif path_type == "greedy":
+        path = _greedy_path(input_sets, output_set, dimension_dict, memory_arg)
+    elif path_type == "optimal":
+        path = _optimal_path(input_sets, output_set, dimension_dict, memory_arg)
+    else:
+        raise KeyError("Path name %s not found", path_type)
+
+    cost_list, scale_list, size_list, contraction_list = [], [], [], []
+
+    # Build contraction tuple (positions, gemm, einsum_str, remaining)
+    for cnum, contract_inds in enumerate(path):
+        # Make sure we remove inds from right to left
+        contract_inds = tuple(sorted(list(contract_inds), reverse=True))
+
+        contract = _find_contraction(contract_inds, input_sets, output_set)
+        out_inds, input_sets, idx_removed, idx_contract = contract
+
+        cost = _flop_count(idx_contract, idx_removed, len(contract_inds), dimension_dict)
+        cost_list.append(cost)
+        scale_list.append(len(idx_contract))
+        size_list.append(_compute_size_by_dict(out_inds, dimension_dict))
+
+        bcast = set()
+        tmp_inputs = []
+        for x in contract_inds:
+            tmp_inputs.append(input_list.pop(x))
+            bcast |= broadcast_indices.pop(x)
+
+        new_bcast_inds = bcast - idx_removed
+
+        # If we're broadcasting, nix blas
+        if not len(idx_removed & bcast):
+            do_blas = _can_dot(tmp_inputs, out_inds, idx_removed)
+        else:
+            do_blas = False
+
+        # Last contraction
+        if (cnum - len(path)) == -1:
+            idx_result = output_subscript
+        else:
+            sort_result = [(dimension_dict[ind], ind) for ind in out_inds]
+            idx_result = "".join([x[1] for x in sorted(sort_result)])
+
+        input_list.append(idx_result)
+        broadcast_indices.append(new_bcast_inds)
+        einsum_str = ",".join(tmp_inputs) + "->" + idx_result
+
+        contraction = (contract_inds, idx_removed, einsum_str, input_list[:], do_blas)
+        contraction_list.append(contraction)
+
+    opt_cost = sum(cost_list) + 1
+
+    if len(input_list) != 1:
+        # Explicit "einsum_path" is usually trusted, but we detect this kind of
+        # mistake in order to prevent from returning an intermediate value.
+        raise RuntimeError(
+            "Invalid einsum_path is specified: {} more operands has to be "
+            "contracted.".format(len(input_list) - 1))
+
+    if einsum_call_arg:
+        return (operands, contraction_list)
+
+    # Return the path along with a nice string representation
+    overall_contraction = input_subscripts + "->" + output_subscript
+    header = ("scaling", "current", "remaining")
+
+    speedup = naive_cost / opt_cost
+    max_i = max(size_list)
+
+    path_print  = "  Complete contraction:  %s\n" % overall_contraction
+    path_print += "         Naive scaling:  %d\n" % len(indices)
+    path_print += "     Optimized scaling:  %d\n" % max(scale_list)
+    path_print += "      Naive FLOP count:  %.3e\n" % naive_cost
+    path_print += "  Optimized FLOP count:  %.3e\n" % opt_cost
+    path_print += "   Theoretical speedup:  %3.3f\n" % speedup
+    path_print += "  Largest intermediate:  %.3e elements\n" % max_i
+    path_print += "-" * 74 + "\n"
+    path_print += "%6s %24s %40s\n" % header
+    path_print += "-" * 74
+
+    for n, contraction in enumerate(contraction_list):
+        inds, idx_rm, einsum_str, remaining, blas = contraction
+        remaining_str = ",".join(remaining) + "->" + output_subscript
+        path_run = (scale_list[n], einsum_str, remaining_str)
+        path_print += "\n%4d    %24s %40s" % path_run
+
+    path = ['einsum_path'] + path
+    return (path, path_print)
+
+
+def _einsum_dispatcher(*operands, out=None, optimize=None, **kwargs):
+    # Arguably we dispatch on more arguments than we really should; see note in
+    # _einsum_path_dispatcher for why.
+    yield from operands
+    yield out
+
+
+# Rewrite einsum to handle different cases
+@array_function_dispatch(_einsum_dispatcher, module='numpy')
+def einsum(*operands, out=None, optimize=False, **kwargs):
+    """
+    einsum(subscripts, *operands, out=None, dtype=None, order='K',
+           casting='safe', optimize=False)
+
+    Evaluates the Einstein summation convention on the operands.
+
+    Using the Einstein summation convention, many common multi-dimensional,
+    linear algebraic array operations can be represented in a simple fashion.
+    In *implicit* mode `einsum` computes these values.
+
+    In *explicit* mode, `einsum` provides further flexibility to compute
+    other array operations that might not be considered classical Einstein
+    summation operations, by disabling, or forcing summation over specified
+    subscript labels.
+
+    See the notes and examples for clarification.
+
+    Parameters
+    ----------
+    subscripts : str
+        Specifies the subscripts for summation as comma separated list of
+        subscript labels. An implicit (classical Einstein summation)
+        calculation is performed unless the explicit indicator '->' is
+        included as well as subscript labels of the precise output form.
+    operands : list of array_like
+        These are the arrays for the operation.
+    out : ndarray, optional
+        If provided, the calculation is done into this array.
+    dtype : {data-type, None}, optional
+        If provided, forces the calculation to use the data type specified.
+        Note that you may have to also give a more liberal `casting`
+        parameter to allow the conversions. Default is None.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout of the output. 'C' means it should
+        be C contiguous. 'F' means it should be Fortran contiguous,
+        'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
+        'K' means it should be as close to the layout as the inputs as
+        is possible, including arbitrarily permuted axes.
+        Default is 'K'.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        Controls what kind of data casting may occur.  Setting this to
+        'unsafe' is not recommended, as it can adversely affect accumulations.
+
+          * 'no' means the data types should not be cast at all.
+          * 'equiv' means only byte-order changes are allowed.
+          * 'safe' means only casts which can preserve values are allowed.
+          * 'same_kind' means only safe casts or casts within a kind,
+            like float64 to float32, are allowed.
+          * 'unsafe' means any data conversions may be done.
+
+        Default is 'safe'.
+    optimize : {False, True, 'greedy', 'optimal'}, optional
+        Controls if intermediate optimization should occur. No optimization
+        will occur if False and True will default to the 'greedy' algorithm.
+        Also accepts an explicit contraction list from the ``np.einsum_path``
+        function. See ``np.einsum_path`` for more details. Defaults to False.
+
+    Returns
+    -------
+    output : ndarray
+        The calculation based on the Einstein summation convention.
+
+    See Also
+    --------
+    einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
+    einops :
+        similar verbose interface is provided by
+        `einops <https://github.com/arogozhnikov/einops>`_ package to cover
+        additional operations: transpose, reshape/flatten, repeat/tile,
+        squeeze/unsqueeze and reductions.
+    opt_einsum :
+        `opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_
+        optimizes contraction order for einsum-like expressions
+        in backend-agnostic manner.
+
+    Notes
+    -----
+    .. versionadded:: 1.6.0
+
+    The Einstein summation convention can be used to compute
+    many multi-dimensional, linear algebraic array operations. `einsum`
+    provides a succinct way of representing these.
+
+    A non-exhaustive list of these operations,
+    which can be computed by `einsum`, is shown below along with examples:
+
+    * Trace of an array, :py:func:`numpy.trace`.
+    * Return a diagonal, :py:func:`numpy.diag`.
+    * Array axis summations, :py:func:`numpy.sum`.
+    * Transpositions and permutations, :py:func:`numpy.transpose`.
+    * Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
+    * Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
+    * Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
+    * Tensor contractions, :py:func:`numpy.tensordot`.
+    * Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
+
+    The subscripts string is a comma-separated list of subscript labels,
+    where each label refers to a dimension of the corresponding operand.
+    Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
+    is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
+    appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
+    view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
+    describes traditional matrix multiplication and is equivalent to
+    :py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one
+    operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
+    to :py:func:`np.trace(a) <numpy.trace>`.
+
+    In *implicit mode*, the chosen subscripts are important
+    since the axes of the output are reordered alphabetically.  This
+    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
+    ``np.einsum('ji', a)`` takes its transpose. Additionally,
+    ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
+    ``np.einsum('ij,jh', a, b)`` returns the transpose of the
+    multiplication since subscript 'h' precedes subscript 'i'.
+
+    In *explicit mode* the output can be directly controlled by
+    specifying output subscript labels.  This requires the
+    identifier '->' as well as the list of output subscript labels.
+    This feature increases the flexibility of the function since
+    summing can be disabled or forced when required. The call
+    ``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`,
+    and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`.
+    The difference is that `einsum` does not allow broadcasting by default.
+    Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
+    order of the output subscript labels and therefore returns matrix
+    multiplication, unlike the example above in implicit mode.
+
+    To enable and control broadcasting, use an ellipsis.  Default
+    NumPy-style broadcasting is done by adding an ellipsis
+    to the left of each term, like ``np.einsum('...ii->...i', a)``.
+    To take the trace along the first and last axes,
+    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
+    product with the left-most indices instead of rightmost, one can do
+    ``np.einsum('ij...,jk...->ik...', a, b)``.
+
+    When there is only one operand, no axes are summed, and no output
+    parameter is provided, a view into the operand is returned instead
+    of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
+    produces a view (changed in version 1.10.0).
+
+    `einsum` also provides an alternative way to provide the subscripts
+    and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
+    If the output shape is not provided in this format `einsum` will be
+    calculated in implicit mode, otherwise it will be performed explicitly.
+    The examples below have corresponding `einsum` calls with the two
+    parameter methods.
+
+    .. versionadded:: 1.10.0
+
+    Views returned from einsum are now writeable whenever the input array
+    is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
+    have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>`
+    and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
+    of a 2D array.
+
+    .. versionadded:: 1.12.0
+
+    Added the ``optimize`` argument which will optimize the contraction order
+    of an einsum expression. For a contraction with three or more operands this
+    can greatly increase the computational efficiency at the cost of a larger
+    memory footprint during computation.
+
+    Typically a 'greedy' algorithm is applied which empirical tests have shown
+    returns the optimal path in the majority of cases. In some cases 'optimal'
+    will return the superlative path through a more expensive, exhaustive search.
+    For iterative calculations it may be advisable to calculate the optimal path
+    once and reuse that path by supplying it as an argument. An example is given
+    below.
+
+    See :py:func:`numpy.einsum_path` for more details.
+
+    Examples
+    --------
+    >>> a = np.arange(25).reshape(5,5)
+    >>> b = np.arange(5)
+    >>> c = np.arange(6).reshape(2,3)
+
+    Trace of a matrix:
+
+    >>> np.einsum('ii', a)
+    60
+    >>> np.einsum(a, [0,0])
+    60
+    >>> np.trace(a)
+    60
+
+    Extract the diagonal (requires explicit form):
+
+    >>> np.einsum('ii->i', a)
+    array([ 0,  6, 12, 18, 24])
+    >>> np.einsum(a, [0,0], [0])
+    array([ 0,  6, 12, 18, 24])
+    >>> np.diag(a)
+    array([ 0,  6, 12, 18, 24])
+
+    Sum over an axis (requires explicit form):
+
+    >>> np.einsum('ij->i', a)
+    array([ 10,  35,  60,  85, 110])
+    >>> np.einsum(a, [0,1], [0])
+    array([ 10,  35,  60,  85, 110])
+    >>> np.sum(a, axis=1)
+    array([ 10,  35,  60,  85, 110])
+
+    For higher dimensional arrays summing a single axis can be done with ellipsis:
+
+    >>> np.einsum('...j->...', a)
+    array([ 10,  35,  60,  85, 110])
+    >>> np.einsum(a, [Ellipsis,1], [Ellipsis])
+    array([ 10,  35,  60,  85, 110])
+
+    Compute a matrix transpose, or reorder any number of axes:
+
+    >>> np.einsum('ji', c)
+    array([[0, 3],
+           [1, 4],
+           [2, 5]])
+    >>> np.einsum('ij->ji', c)
+    array([[0, 3],
+           [1, 4],
+           [2, 5]])
+    >>> np.einsum(c, [1,0])
+    array([[0, 3],
+           [1, 4],
+           [2, 5]])
+    >>> np.transpose(c)
+    array([[0, 3],
+           [1, 4],
+           [2, 5]])
+
+    Vector inner products:
+
+    >>> np.einsum('i,i', b, b)
+    30
+    >>> np.einsum(b, [0], b, [0])
+    30
+    >>> np.inner(b,b)
+    30
+
+    Matrix vector multiplication:
+
+    >>> np.einsum('ij,j', a, b)
+    array([ 30,  80, 130, 180, 230])
+    >>> np.einsum(a, [0,1], b, [1])
+    array([ 30,  80, 130, 180, 230])
+    >>> np.dot(a, b)
+    array([ 30,  80, 130, 180, 230])
+    >>> np.einsum('...j,j', a, b)
+    array([ 30,  80, 130, 180, 230])
+
+    Broadcasting and scalar multiplication:
+
+    >>> np.einsum('..., ...', 3, c)
+    array([[ 0,  3,  6],
+           [ 9, 12, 15]])
+    >>> np.einsum(',ij', 3, c)
+    array([[ 0,  3,  6],
+           [ 9, 12, 15]])
+    >>> np.einsum(3, [Ellipsis], c, [Ellipsis])
+    array([[ 0,  3,  6],
+           [ 9, 12, 15]])
+    >>> np.multiply(3, c)
+    array([[ 0,  3,  6],
+           [ 9, 12, 15]])
+
+    Vector outer product:
+
+    >>> np.einsum('i,j', np.arange(2)+1, b)
+    array([[0, 1, 2, 3, 4],
+           [0, 2, 4, 6, 8]])
+    >>> np.einsum(np.arange(2)+1, [0], b, [1])
+    array([[0, 1, 2, 3, 4],
+           [0, 2, 4, 6, 8]])
+    >>> np.outer(np.arange(2)+1, b)
+    array([[0, 1, 2, 3, 4],
+           [0, 2, 4, 6, 8]])
+
+    Tensor contraction:
+
+    >>> a = np.arange(60.).reshape(3,4,5)
+    >>> b = np.arange(24.).reshape(4,3,2)
+    >>> np.einsum('ijk,jil->kl', a, b)
+    array([[4400., 4730.],
+           [4532., 4874.],
+           [4664., 5018.],
+           [4796., 5162.],
+           [4928., 5306.]])
+    >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
+    array([[4400., 4730.],
+           [4532., 4874.],
+           [4664., 5018.],
+           [4796., 5162.],
+           [4928., 5306.]])
+    >>> np.tensordot(a,b, axes=([1,0],[0,1]))
+    array([[4400., 4730.],
+           [4532., 4874.],
+           [4664., 5018.],
+           [4796., 5162.],
+           [4928., 5306.]])
+
+    Writeable returned arrays (since version 1.10.0):
+
+    >>> a = np.zeros((3, 3))
+    >>> np.einsum('ii->i', a)[:] = 1
+    >>> a
+    array([[1., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 1.]])
+
+    Example of ellipsis use:
+
+    >>> a = np.arange(6).reshape((3,2))
+    >>> b = np.arange(12).reshape((4,3))
+    >>> np.einsum('ki,jk->ij', a, b)
+    array([[10, 28, 46, 64],
+           [13, 40, 67, 94]])
+    >>> np.einsum('ki,...k->i...', a, b)
+    array([[10, 28, 46, 64],
+           [13, 40, 67, 94]])
+    >>> np.einsum('k...,jk', a, b)
+    array([[10, 28, 46, 64],
+           [13, 40, 67, 94]])
+
+    Chained array operations. For more complicated contractions, speed ups
+    might be achieved by repeatedly computing a 'greedy' path or pre-computing the
+    'optimal' path and repeatedly applying it, using an
+    `einsum_path` insertion (since version 1.12.0). Performance improvements can be
+    particularly significant with larger arrays:
+
+    >>> a = np.ones(64).reshape(2,4,8)
+
+    Basic `einsum`: ~1520ms  (benchmarked on 3.1GHz Intel i5.)
+
+    >>> for iteration in range(500):
+    ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
+
+    Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
+
+    >>> for iteration in range(500):
+    ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
+
+    Greedy `einsum` (faster optimal path approximation): ~160ms
+
+    >>> for iteration in range(500):
+    ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
+
+    Optimal `einsum` (best usage pattern in some use cases): ~110ms
+
+    >>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
+    >>> for iteration in range(500):
+    ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
+
+    """
+    # Special handling if out is specified
+    specified_out = out is not None
+
+    # If no optimization, run pure einsum
+    if optimize is False:
+        if specified_out:
+            kwargs['out'] = out
+        return c_einsum(*operands, **kwargs)
+
+    # Check the kwargs to avoid a more cryptic error later, without having to
+    # repeat default values here
+    valid_einsum_kwargs = ['dtype', 'order', 'casting']
+    unknown_kwargs = [k for (k, v) in kwargs.items() if
+                      k not in valid_einsum_kwargs]
+    if len(unknown_kwargs):
+        raise TypeError("Did not understand the following kwargs: %s"
+                        % unknown_kwargs)
+
+    # Build the contraction list and operand
+    operands, contraction_list = einsum_path(*operands, optimize=optimize,
+                                             einsum_call=True)
+
+    # Handle order kwarg for output array, c_einsum allows mixed case
+    output_order = kwargs.pop('order', 'K')
+    if output_order.upper() == 'A':
+        if all(arr.flags.f_contiguous for arr in operands):
+            output_order = 'F'
+        else:
+            output_order = 'C'
+
+    # Start contraction loop
+    for num, contraction in enumerate(contraction_list):
+        inds, idx_rm, einsum_str, remaining, blas = contraction
+        tmp_operands = [operands.pop(x) for x in inds]
+
+        # Do we need to deal with the output?
+        handle_out = specified_out and ((num + 1) == len(contraction_list))
+
+        # Call tensordot if still possible
+        if blas:
+            # Checks have already been handled
+            input_str, results_index = einsum_str.split('->')
+            input_left, input_right = input_str.split(',')
+
+            tensor_result = input_left + input_right
+            for s in idx_rm:
+                tensor_result = tensor_result.replace(s, "")
+
+            # Find indices to contract over
+            left_pos, right_pos = [], []
+            for s in sorted(idx_rm):
+                left_pos.append(input_left.find(s))
+                right_pos.append(input_right.find(s))
+
+            # Contract!
+            new_view = tensordot(*tmp_operands, axes=(tuple(left_pos), tuple(right_pos)))
+
+            # Build a new view if needed
+            if (tensor_result != results_index) or handle_out:
+                if handle_out:
+                    kwargs["out"] = out
+                new_view = c_einsum(tensor_result + '->' + results_index, new_view, **kwargs)
+
+        # Call einsum
+        else:
+            # If out was specified
+            if handle_out:
+                kwargs["out"] = out
+
+            # Do the contraction
+            new_view = c_einsum(einsum_str, *tmp_operands, **kwargs)
+
+        # Append new items and dereference what we can
+        operands.append(new_view)
+        del tmp_operands, new_view
+
+    if specified_out:
+        return out
+    else:
+        return asanyarray(operands[0], order=output_order)
diff --git a/shared/package-files/4e/4eb4173de103bfeb4339e9efa0eeb79535a8ef4afbb6ad76267213029ce10c7c/addcmul_cpu_dispatch.h b/shared/package-files/4e/4eb4173de103bfeb4339e9efa0eeb79535a8ef4afbb6ad76267213029ce10c7c/addcmul_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..42cf40440ed6389404c32ba0acf214e0a2fbe538
--- /dev/null
+++ b/shared/package-files/4e/4eb4173de103bfeb4339e9efa0eeb79535a8ef4afbb6ad76267213029ce10c7c/addcmul_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1);
+TORCH_API at::Tensor & addcmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1);
+TORCH_API at::Tensor & addcmul_outf(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out);
+TORCH_API at::Tensor & addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/4e/4eb47a3c29d81be9920a504ca21aa53fcaa76215cc52cc9d23e2feaae5c5c723/Whitehorse b/shared/package-files/4e/4eb47a3c29d81be9920a504ca21aa53fcaa76215cc52cc9d23e2feaae5c5c723/Whitehorse
new file mode 100644
index 0000000000000000000000000000000000000000..318c4a8e4003bcff00f9a21e294e7815f170235b
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diff --git a/shared/package-files/4e/4eb47a3c29d81be9920a504ca21aa53fcaa76215cc52cc9d23e2feaae5c5c723/Yukon b/shared/package-files/4e/4eb47a3c29d81be9920a504ca21aa53fcaa76215cc52cc9d23e2feaae5c5c723/Yukon
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diff --git a/shared/package-files/4e/4eb66a2a8dddacaf407ee8e0ee0ad94622cd726814afce2b9ef31ca23cd44672/caching.py b/shared/package-files/4e/4eb66a2a8dddacaf407ee8e0ee0ad94622cd726814afce2b9ef31ca23cd44672/caching.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1a51f1e2184f09d2ac7fdb8ce2e3596775ccce3
--- /dev/null
+++ b/shared/package-files/4e/4eb66a2a8dddacaf407ee8e0ee0ad94622cd726814afce2b9ef31ca23cd44672/caching.py
@@ -0,0 +1,881 @@
+from __future__ import annotations
+
+import collections
+import functools
+import logging
+import math
+import os
+import threading
+import warnings
+from concurrent.futures import Future, ThreadPoolExecutor
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Generic,
+    NamedTuple,
+    OrderedDict,
+    TypeVar,
+)
+
+if TYPE_CHECKING:
+    import mmap
+
+    from typing_extensions import ParamSpec
+
+    P = ParamSpec("P")
+else:
+    P = TypeVar("P")
+
+T = TypeVar("T")
+
+
+logger = logging.getLogger("fsspec")
+
+Fetcher = Callable[[int, int], bytes]  # Maps (start, end) to bytes
+
+
+class BaseCache:
+    """Pass-though cache: doesn't keep anything, calls every time
+
+    Acts as base class for other cachers
+
+    Parameters
+    ----------
+    blocksize: int
+        How far to read ahead in numbers of bytes
+    fetcher: func
+        Function of the form f(start, end) which gets bytes from remote as
+        specified
+    size: int
+        How big this file is
+    """
+
+    name: ClassVar[str] = "none"
+
+    def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
+        self.blocksize = blocksize
+        self.fetcher = fetcher
+        self.size = size
+
+    def _fetch(self, start: int | None, stop: int | None) -> bytes:
+        if start is None:
+            start = 0
+        if stop is None:
+            stop = self.size
+        if start >= self.size or start >= stop:
+            return b""
+        return self.fetcher(start, stop)
+
+
+class MMapCache(BaseCache):
+    """memory-mapped sparse file cache
+
+    Opens temporary file, which is filled blocks-wise when data is requested.
+    Ensure there is enough disc space in the temporary location.
+
+    This cache method might only work on posix
+    """
+
+    name = "mmap"
+
+    def __init__(
+        self,
+        blocksize: int,
+        fetcher: Fetcher,
+        size: int,
+        location: str | None = None,
+        blocks: set[int] | None = None,
+    ) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.blocks = set() if blocks is None else blocks
+        self.location = location
+        self.cache = self._makefile()
+
+    def _makefile(self) -> mmap.mmap | bytearray:
+        import mmap
+        import tempfile
+
+        if self.size == 0:
+            return bytearray()
+
+        # posix version
+        if self.location is None or not os.path.exists(self.location):
+            if self.location is None:
+                fd = tempfile.TemporaryFile()
+                self.blocks = set()
+            else:
+                fd = open(self.location, "wb+")
+            fd.seek(self.size - 1)
+            fd.write(b"1")
+            fd.flush()
+        else:
+            fd = open(self.location, "r+b")
+
+        return mmap.mmap(fd.fileno(), self.size)
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        logger.debug(f"MMap cache fetching {start}-{end}")
+        if start is None:
+            start = 0
+        if end is None:
+            end = self.size
+        if start >= self.size or start >= end:
+            return b""
+        start_block = start // self.blocksize
+        end_block = end // self.blocksize
+        need = [i for i in range(start_block, end_block + 1) if i not in self.blocks]
+        while need:
+            # TODO: not a for loop so we can consolidate blocks later to
+            # make fewer fetch calls; this could be parallel
+            i = need.pop(0)
+            sstart = i * self.blocksize
+            send = min(sstart + self.blocksize, self.size)
+            logger.debug(f"MMap get block #{i} ({sstart}-{send}")
+            self.cache[sstart:send] = self.fetcher(sstart, send)
+            self.blocks.add(i)
+
+        return self.cache[start:end]
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        # Remove the unpicklable entries.
+        del state["cache"]
+        return state
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        # Restore instance attributes
+        self.__dict__.update(state)
+        self.cache = self._makefile()
+
+
+class ReadAheadCache(BaseCache):
+    """Cache which reads only when we get beyond a block of data
+
+    This is a much simpler version of BytesCache, and does not attempt to
+    fill holes in the cache or keep fragments alive. It is best suited to
+    many small reads in a sequential order (e.g., reading lines from a file).
+    """
+
+    name = "readahead"
+
+    def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.cache = b""
+        self.start = 0
+        self.end = 0
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        if start is None:
+            start = 0
+        if end is None or end > self.size:
+            end = self.size
+        if start >= self.size or start >= end:
+            return b""
+        l = end - start
+        if start >= self.start and end <= self.end:
+            # cache hit
+            return self.cache[start - self.start : end - self.start]
+        elif self.start <= start < self.end:
+            # partial hit
+            part = self.cache[start - self.start :]
+            l -= len(part)
+            start = self.end
+        else:
+            # miss
+            part = b""
+        end = min(self.size, end + self.blocksize)
+        self.cache = self.fetcher(start, end)  # new block replaces old
+        self.start = start
+        self.end = self.start + len(self.cache)
+        return part + self.cache[:l]
+
+
+class FirstChunkCache(BaseCache):
+    """Caches the first block of a file only
+
+    This may be useful for file types where the metadata is stored in the header,
+    but is randomly accessed.
+    """
+
+    name = "first"
+
+    def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.cache: bytes | None = None
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        start = start or 0
+        end = end or self.size
+        if start < self.blocksize:
+            if self.cache is None:
+                if end > self.blocksize:
+                    data = self.fetcher(0, end)
+                    self.cache = data[: self.blocksize]
+                    return data[start:]
+                self.cache = self.fetcher(0, self.blocksize)
+            part = self.cache[start:end]
+            if end > self.blocksize:
+                part += self.fetcher(self.blocksize, end)
+            return part
+        else:
+            return self.fetcher(start, end)
+
+
+class BlockCache(BaseCache):
+    """
+    Cache holding memory as a set of blocks.
+
+    Requests are only ever made ``blocksize`` at a time, and are
+    stored in an LRU cache. The least recently accessed block is
+    discarded when more than ``maxblocks`` are stored.
+
+    Parameters
+    ----------
+    blocksize : int
+        The number of bytes to store in each block.
+        Requests are only ever made for ``blocksize``, so this
+        should balance the overhead of making a request against
+        the granularity of the blocks.
+    fetcher : Callable
+    size : int
+        The total size of the file being cached.
+    maxblocks : int
+        The maximum number of blocks to cache for. The maximum memory
+        use for this cache is then ``blocksize * maxblocks``.
+    """
+
+    name = "blockcache"
+
+    def __init__(
+        self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32
+    ) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.nblocks = math.ceil(size / blocksize)
+        self.maxblocks = maxblocks
+        self._fetch_block_cached = functools.lru_cache(maxblocks)(self._fetch_block)
+
+    def __repr__(self) -> str:
+        return (
+            f"<BlockCache blocksize={self.blocksize}, "
+            f"size={self.size}, nblocks={self.nblocks}>"
+        )
+
+    def cache_info(self):
+        """
+        The statistics on the block cache.
+
+        Returns
+        -------
+        NamedTuple
+            Returned directly from the LRU Cache used internally.
+        """
+        return self._fetch_block_cached.cache_info()
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__
+        del state["_fetch_block_cached"]
+        return state
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        self.__dict__.update(state)
+        self._fetch_block_cached = functools.lru_cache(state["maxblocks"])(
+            self._fetch_block
+        )
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        if start is None:
+            start = 0
+        if end is None:
+            end = self.size
+        if start >= self.size or start >= end:
+            return b""
+
+        # byte position -> block numbers
+        start_block_number = start // self.blocksize
+        end_block_number = end // self.blocksize
+
+        # these are cached, so safe to do multiple calls for the same start and end.
+        for block_number in range(start_block_number, end_block_number + 1):
+            self._fetch_block_cached(block_number)
+
+        return self._read_cache(
+            start,
+            end,
+            start_block_number=start_block_number,
+            end_block_number=end_block_number,
+        )
+
+    def _fetch_block(self, block_number: int) -> bytes:
+        """
+        Fetch the block of data for `block_number`.
+        """
+        if block_number > self.nblocks:
+            raise ValueError(
+                f"'block_number={block_number}' is greater than "
+                f"the number of blocks ({self.nblocks})"
+            )
+
+        start = block_number * self.blocksize
+        end = start + self.blocksize
+        logger.info("BlockCache fetching block %d", block_number)
+        block_contents = super()._fetch(start, end)
+        return block_contents
+
+    def _read_cache(
+        self, start: int, end: int, start_block_number: int, end_block_number: int
+    ) -> bytes:
+        """
+        Read from our block cache.
+
+        Parameters
+        ----------
+        start, end : int
+            The start and end byte positions.
+        start_block_number, end_block_number : int
+            The start and end block numbers.
+        """
+        start_pos = start % self.blocksize
+        end_pos = end % self.blocksize
+
+        if start_block_number == end_block_number:
+            block: bytes = self._fetch_block_cached(start_block_number)
+            return block[start_pos:end_pos]
+
+        else:
+            # read from the initial
+            out = [self._fetch_block_cached(start_block_number)[start_pos:]]
+
+            # intermediate blocks
+            # Note: it'd be nice to combine these into one big request. However
+            # that doesn't play nicely with our LRU cache.
+            out.extend(
+                map(
+                    self._fetch_block_cached,
+                    range(start_block_number + 1, end_block_number),
+                )
+            )
+
+            # final block
+            out.append(self._fetch_block_cached(end_block_number)[:end_pos])
+
+            return b"".join(out)
+
+
+class BytesCache(BaseCache):
+    """Cache which holds data in a in-memory bytes object
+
+    Implements read-ahead by the block size, for semi-random reads progressing
+    through the file.
+
+    Parameters
+    ----------
+    trim: bool
+        As we read more data, whether to discard the start of the buffer when
+        we are more than a blocksize ahead of it.
+    """
+
+    name: ClassVar[str] = "bytes"
+
+    def __init__(
+        self, blocksize: int, fetcher: Fetcher, size: int, trim: bool = True
+    ) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.cache = b""
+        self.start: int | None = None
+        self.end: int | None = None
+        self.trim = trim
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        # TODO: only set start/end after fetch, in case it fails?
+        # is this where retry logic might go?
+        if start is None:
+            start = 0
+        if end is None:
+            end = self.size
+        if start >= self.size or start >= end:
+            return b""
+        if (
+            self.start is not None
+            and start >= self.start
+            and self.end is not None
+            and end < self.end
+        ):
+            # cache hit: we have all the required data
+            offset = start - self.start
+            return self.cache[offset : offset + end - start]
+
+        if self.blocksize:
+            bend = min(self.size, end + self.blocksize)
+        else:
+            bend = end
+
+        if bend == start or start > self.size:
+            return b""
+
+        if (self.start is None or start < self.start) and (
+            self.end is None or end > self.end
+        ):
+            # First read, or extending both before and after
+            self.cache = self.fetcher(start, bend)
+            self.start = start
+        else:
+            assert self.start is not None
+            assert self.end is not None
+
+            if start < self.start:
+                if self.end is None or self.end - end > self.blocksize:
+                    self.cache = self.fetcher(start, bend)
+                    self.start = start
+                else:
+                    new = self.fetcher(start, self.start)
+                    self.start = start
+                    self.cache = new + self.cache
+            elif self.end is not None and bend > self.end:
+                if self.end > self.size:
+                    pass
+                elif end - self.end > self.blocksize:
+                    self.cache = self.fetcher(start, bend)
+                    self.start = start
+                else:
+                    new = self.fetcher(self.end, bend)
+                    self.cache = self.cache + new
+
+        self.end = self.start + len(self.cache)
+        offset = start - self.start
+        out = self.cache[offset : offset + end - start]
+        if self.trim:
+            num = (self.end - self.start) // (self.blocksize + 1)
+            if num > 1:
+                self.start += self.blocksize * num
+                self.cache = self.cache[self.blocksize * num :]
+        return out
+
+    def __len__(self) -> int:
+        return len(self.cache)
+
+
+class AllBytes(BaseCache):
+    """Cache entire contents of the file"""
+
+    name: ClassVar[str] = "all"
+
+    def __init__(
+        self,
+        blocksize: int | None = None,
+        fetcher: Fetcher | None = None,
+        size: int | None = None,
+        data: bytes | None = None,
+    ) -> None:
+        super().__init__(blocksize, fetcher, size)  # type: ignore[arg-type]
+        if data is None:
+            data = self.fetcher(0, self.size)
+        self.data = data
+
+    def _fetch(self, start: int | None, stop: int | None) -> bytes:
+        return self.data[start:stop]
+
+
+class KnownPartsOfAFile(BaseCache):
+    """
+    Cache holding known file parts.
+
+    Parameters
+    ----------
+    blocksize: int
+        How far to read ahead in numbers of bytes
+    fetcher: func
+        Function of the form f(start, end) which gets bytes from remote as
+        specified
+    size: int
+        How big this file is
+    data: dict
+        A dictionary mapping explicit `(start, stop)` file-offset tuples
+        with known bytes.
+    strict: bool, default True
+        Whether to fetch reads that go beyond a known byte-range boundary.
+        If `False`, any read that ends outside a known part will be zero
+        padded. Note that zero padding will not be used for reads that
+        begin outside a known byte-range.
+    """
+
+    name: ClassVar[str] = "parts"
+
+    def __init__(
+        self,
+        blocksize: int,
+        fetcher: Fetcher,
+        size: int,
+        data: dict[tuple[int, int], bytes] = {},
+        strict: bool = True,
+        **_: Any,
+    ):
+        super().__init__(blocksize, fetcher, size)
+        self.strict = strict
+
+        # simple consolidation of contiguous blocks
+        if data:
+            old_offsets = sorted(data.keys())
+            offsets = [old_offsets[0]]
+            blocks = [data.pop(old_offsets[0])]
+            for start, stop in old_offsets[1:]:
+                start0, stop0 = offsets[-1]
+                if start == stop0:
+                    offsets[-1] = (start0, stop)
+                    blocks[-1] += data.pop((start, stop))
+                else:
+                    offsets.append((start, stop))
+                    blocks.append(data.pop((start, stop)))
+
+            self.data = dict(zip(offsets, blocks))
+        else:
+            self.data = data
+
+    def _fetch(self, start: int | None, stop: int | None) -> bytes:
+        if start is None:
+            start = 0
+        if stop is None:
+            stop = self.size
+
+        out = b""
+        for (loc0, loc1), data in self.data.items():
+            # If self.strict=False, use zero-padded data
+            # for reads beyond the end of a "known" buffer
+            if loc0 <= start < loc1:
+                off = start - loc0
+                out = data[off : off + stop - start]
+                if not self.strict or loc0 <= stop <= loc1:
+                    # The request is within a known range, or
+                    # it begins within a known range, and we
+                    # are allowed to pad reads beyond the
+                    # buffer with zero
+                    out += b"\x00" * (stop - start - len(out))
+                    return out
+                else:
+                    # The request ends outside a known range,
+                    # and we are being "strict" about reads
+                    # beyond the buffer
+                    start = loc1
+                    break
+
+        # We only get here if there is a request outside the
+        # known parts of the file. In an ideal world, this
+        # should never happen
+        if self.fetcher is None:
+            # We cannot fetch the data, so raise an error
+            raise ValueError(f"Read is outside the known file parts: {(start, stop)}. ")
+        # We can fetch the data, but should warn the user
+        # that this may be slow
+        warnings.warn(
+            f"Read is outside the known file parts: {(start, stop)}. "
+            f"IO/caching performance may be poor!"
+        )
+        logger.debug(f"KnownPartsOfAFile cache fetching {start}-{stop}")
+        return out + super()._fetch(start, stop)
+
+
+class UpdatableLRU(Generic[P, T]):
+    """
+    Custom implementation of LRU cache that allows updating keys
+
+    Used by BackgroudBlockCache
+    """
+
+    class CacheInfo(NamedTuple):
+        hits: int
+        misses: int
+        maxsize: int
+        currsize: int
+
+    def __init__(self, func: Callable[P, T], max_size: int = 128) -> None:
+        self._cache: OrderedDict[Any, T] = collections.OrderedDict()
+        self._func = func
+        self._max_size = max_size
+        self._hits = 0
+        self._misses = 0
+        self._lock = threading.Lock()
+
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T:
+        if kwargs:
+            raise TypeError(f"Got unexpected keyword argument {kwargs.keys()}")
+        with self._lock:
+            if args in self._cache:
+                self._cache.move_to_end(args)
+                self._hits += 1
+                return self._cache[args]
+
+        result = self._func(*args, **kwargs)
+
+        with self._lock:
+            self._cache[args] = result
+            self._misses += 1
+            if len(self._cache) > self._max_size:
+                self._cache.popitem(last=False)
+
+        return result
+
+    def is_key_cached(self, *args: Any) -> bool:
+        with self._lock:
+            return args in self._cache
+
+    def add_key(self, result: T, *args: Any) -> None:
+        with self._lock:
+            self._cache[args] = result
+            if len(self._cache) > self._max_size:
+                self._cache.popitem(last=False)
+
+    def cache_info(self) -> UpdatableLRU.CacheInfo:
+        with self._lock:
+            return self.CacheInfo(
+                maxsize=self._max_size,
+                currsize=len(self._cache),
+                hits=self._hits,
+                misses=self._misses,
+            )
+
+
+class BackgroundBlockCache(BaseCache):
+    """
+    Cache holding memory as a set of blocks with pre-loading of
+    the next block in the background.
+
+    Requests are only ever made ``blocksize`` at a time, and are
+    stored in an LRU cache. The least recently accessed block is
+    discarded when more than ``maxblocks`` are stored. If the
+    next block is not in cache, it is loaded in a separate thread
+    in non-blocking way.
+
+    Parameters
+    ----------
+    blocksize : int
+        The number of bytes to store in each block.
+        Requests are only ever made for ``blocksize``, so this
+        should balance the overhead of making a request against
+        the granularity of the blocks.
+    fetcher : Callable
+    size : int
+        The total size of the file being cached.
+    maxblocks : int
+        The maximum number of blocks to cache for. The maximum memory
+        use for this cache is then ``blocksize * maxblocks``.
+    """
+
+    name: ClassVar[str] = "background"
+
+    def __init__(
+        self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32
+    ) -> None:
+        super().__init__(blocksize, fetcher, size)
+        self.nblocks = math.ceil(size / blocksize)
+        self.maxblocks = maxblocks
+        self._fetch_block_cached = UpdatableLRU(self._fetch_block, maxblocks)
+
+        self._thread_executor = ThreadPoolExecutor(max_workers=1)
+        self._fetch_future_block_number: int | None = None
+        self._fetch_future: Future[bytes] | None = None
+        self._fetch_future_lock = threading.Lock()
+
+    def __repr__(self) -> str:
+        return (
+            f"<BackgroundBlockCache blocksize={self.blocksize}, "
+            f"size={self.size}, nblocks={self.nblocks}>"
+        )
+
+    def cache_info(self) -> UpdatableLRU.CacheInfo:
+        """
+        The statistics on the block cache.
+
+        Returns
+        -------
+        NamedTuple
+            Returned directly from the LRU Cache used internally.
+        """
+        return self._fetch_block_cached.cache_info()
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__
+        del state["_fetch_block_cached"]
+        del state["_thread_executor"]
+        del state["_fetch_future_block_number"]
+        del state["_fetch_future"]
+        del state["_fetch_future_lock"]
+        return state
+
+    def __setstate__(self, state) -> None:
+        self.__dict__.update(state)
+        self._fetch_block_cached = UpdatableLRU(self._fetch_block, state["maxblocks"])
+        self._thread_executor = ThreadPoolExecutor(max_workers=1)
+        self._fetch_future_block_number = None
+        self._fetch_future = None
+        self._fetch_future_lock = threading.Lock()
+
+    def _fetch(self, start: int | None, end: int | None) -> bytes:
+        if start is None:
+            start = 0
+        if end is None:
+            end = self.size
+        if start >= self.size or start >= end:
+            return b""
+
+        # byte position -> block numbers
+        start_block_number = start // self.blocksize
+        end_block_number = end // self.blocksize
+
+        fetch_future_block_number = None
+        fetch_future = None
+        with self._fetch_future_lock:
+            # Background thread is running. Check we we can or must join it.
+            if self._fetch_future is not None:
+                assert self._fetch_future_block_number is not None
+                if self._fetch_future.done():
+                    logger.info("BlockCache joined background fetch without waiting.")
+                    self._fetch_block_cached.add_key(
+                        self._fetch_future.result(), self._fetch_future_block_number
+                    )
+                    # Cleanup the fetch variables. Done with fetching the block.
+                    self._fetch_future_block_number = None
+                    self._fetch_future = None
+                else:
+                    # Must join if we need the block for the current fetch
+                    must_join = bool(
+                        start_block_number
+                        <= self._fetch_future_block_number
+                        <= end_block_number
+                    )
+                    if must_join:
+                        # Copy to the local variables to release lock
+                        # before waiting for result
+                        fetch_future_block_number = self._fetch_future_block_number
+                        fetch_future = self._fetch_future
+
+                        # Cleanup the fetch variables. Have a local copy.
+                        self._fetch_future_block_number = None
+                        self._fetch_future = None
+
+        # Need to wait for the future for the current read
+        if fetch_future is not None:
+            logger.info("BlockCache waiting for background fetch.")
+            # Wait until result and put it in cache
+            self._fetch_block_cached.add_key(
+                fetch_future.result(), fetch_future_block_number
+            )
+
+        # these are cached, so safe to do multiple calls for the same start and end.
+        for block_number in range(start_block_number, end_block_number + 1):
+            self._fetch_block_cached(block_number)
+
+        # fetch next block in the background if nothing is running in the background,
+        # the block is within file and it is not already cached
+        end_block_plus_1 = end_block_number + 1
+        with self._fetch_future_lock:
+            if (
+                self._fetch_future is None
+                and end_block_plus_1 <= self.nblocks
+                and not self._fetch_block_cached.is_key_cached(end_block_plus_1)
+            ):
+                self._fetch_future_block_number = end_block_plus_1
+                self._fetch_future = self._thread_executor.submit(
+                    self._fetch_block, end_block_plus_1, "async"
+                )
+
+        return self._read_cache(
+            start,
+            end,
+            start_block_number=start_block_number,
+            end_block_number=end_block_number,
+        )
+
+    def _fetch_block(self, block_number: int, log_info: str = "sync") -> bytes:
+        """
+        Fetch the block of data for `block_number`.
+        """
+        if block_number > self.nblocks:
+            raise ValueError(
+                f"'block_number={block_number}' is greater than "
+                f"the number of blocks ({self.nblocks})"
+            )
+
+        start = block_number * self.blocksize
+        end = start + self.blocksize
+        logger.info("BlockCache fetching block (%s) %d", log_info, block_number)
+        block_contents = super()._fetch(start, end)
+        return block_contents
+
+    def _read_cache(
+        self, start: int, end: int, start_block_number: int, end_block_number: int
+    ) -> bytes:
+        """
+        Read from our block cache.
+
+        Parameters
+        ----------
+        start, end : int
+            The start and end byte positions.
+        start_block_number, end_block_number : int
+            The start and end block numbers.
+        """
+        start_pos = start % self.blocksize
+        end_pos = end % self.blocksize
+
+        if start_block_number == end_block_number:
+            block = self._fetch_block_cached(start_block_number)
+            return block[start_pos:end_pos]
+
+        else:
+            # read from the initial
+            out = [self._fetch_block_cached(start_block_number)[start_pos:]]
+
+            # intermediate blocks
+            # Note: it'd be nice to combine these into one big request. However
+            # that doesn't play nicely with our LRU cache.
+            out.extend(
+                map(
+                    self._fetch_block_cached,
+                    range(start_block_number + 1, end_block_number),
+                )
+            )
+
+            # final block
+            out.append(self._fetch_block_cached(end_block_number)[:end_pos])
+
+            return b"".join(out)
+
+
+caches: dict[str | None, type[BaseCache]] = {
+    # one custom case
+    None: BaseCache,
+}
+
+
+def register_cache(cls: type[BaseCache], clobber: bool = False) -> None:
+    """'Register' cache implementation.
+
+    Parameters
+    ----------
+    clobber: bool, optional
+        If set to True (default is False) - allow to overwrite existing
+        entry.
+
+    Raises
+    ------
+    ValueError
+    """
+    name = cls.name
+    if not clobber and name in caches:
+        raise ValueError(f"Cache with name {name!r} is already known: {caches[name]}")
+    caches[name] = cls
+
+
+for c in (
+    BaseCache,
+    MMapCache,
+    BytesCache,
+    ReadAheadCache,
+    BlockCache,
+    FirstChunkCache,
+    AllBytes,
+    KnownPartsOfAFile,
+    BackgroundBlockCache,
+):
+    register_cache(c)
diff --git a/shared/package-files/4e/4eb7d103def1fae790dc36a96cf91cd56b331f7311bc8f7ee011643d43141b22/ims-ansi b/shared/package-files/4e/4eb7d103def1fae790dc36a96cf91cd56b331f7311bc8f7ee011643d43141b22/ims-ansi
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diff --git a/shared/package-files/4e/4eb7d103def1fae790dc36a96cf91cd56b331f7311bc8f7ee011643d43141b22/ultimaII b/shared/package-files/4e/4eb7d103def1fae790dc36a96cf91cd56b331f7311bc8f7ee011643d43141b22/ultimaII
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diff --git a/shared/package-files/4e/4eb7e1746ea862160f99af026089081366f860bb37f6aed9584c16a847ae8281/resolve_neg_ops.h b/shared/package-files/4e/4eb7e1746ea862160f99af026089081366f860bb37f6aed9584c16a847ae8281/resolve_neg_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b2e46ded83721ecb444abfd2b2aade5a2ebb32d
--- /dev/null
+++ b/shared/package-files/4e/4eb7e1746ea862160f99af026089081366f860bb37f6aed9584c16a847ae8281/resolve_neg_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API resolve_neg {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::resolve_neg")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "resolve_neg(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/4e/4eb909e17c7f742f58acf75ec25b0ebd8b3fc5a83b813ddc3781e40ec2664fc2/testdouble_7.4_GLNX86.mat b/shared/package-files/4e/4eb909e17c7f742f58acf75ec25b0ebd8b3fc5a83b813ddc3781e40ec2664fc2/testdouble_7.4_GLNX86.mat
new file mode 100644
index 0000000000000000000000000000000000000000..9097bb08712d5bfccf172b0366573f503136228d
Binary files /dev/null and b/shared/package-files/4e/4eb909e17c7f742f58acf75ec25b0ebd8b3fc5a83b813ddc3781e40ec2664fc2/testdouble_7.4_GLNX86.mat differ
diff --git a/shared/package-files/4e/4eb9728db208bf6be069d29e014326a3e9a02380faa33a053981999beb05b137/index.py b/shared/package-files/4e/4eb9728db208bf6be069d29e014326a3e9a02380faa33a053981999beb05b137/index.py
new file mode 100644
index 0000000000000000000000000000000000000000..006f599270fa507aa5edf22bc16983f60889ef35
--- /dev/null
+++ b/shared/package-files/4e/4eb9728db208bf6be069d29e014326a3e9a02380faa33a053981999beb05b137/index.py
@@ -0,0 +1,15 @@
+from .distribution import Distribution
+
+class Index(dict):
+
+    def __setitem__(self, key, value):
+        if not isinstance(value, Distribution):
+            raise ValueError('Not a distribution: %r.' % value)
+        if key != '%s-%s' % (value.name, value.version):
+            raise ValueError('Key must match <name>-<version>.')
+        super(Index, self).__setitem__(key, value)
+
+    def add(self, distribution):
+        key = '%s-%s' % (distribution.name, distribution.version)
+        self[key] = distribution
+
diff --git a/shared/package-files/4e/4ebb6a6386e7196fe223c66922e9eb0747db42146ad758365e273e02513664fa/read_adapter_interface.h b/shared/package-files/4e/4ebb6a6386e7196fe223c66922e9eb0747db42146ad758365e273e02513664fa/read_adapter_interface.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a6b5b74a762e7c90b64a6f93717802f658dd164
--- /dev/null
+++ b/shared/package-files/4e/4ebb6a6386e7196fe223c66922e9eb0747db42146ad758365e273e02513664fa/read_adapter_interface.h
@@ -0,0 +1,23 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+
+#include "c10/macros/Macros.h"
+
+namespace caffe2 {
+namespace serialize {
+
+// this is the interface for the (file/stream/memory) reader in
+// PyTorchStreamReader. with this interface, we can extend the support
+// besides standard istream
+class TORCH_API ReadAdapterInterface {
+ public:
+  virtual size_t size() const = 0;
+  virtual size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
+      const = 0;
+  virtual ~ReadAdapterInterface();
+};
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/shared/package-files/4e/4ebde1edc24aa3e422d13f54809f4e29e946212c78475437c4717e41415cc3d3/pixel_unshuffle_ops.h b/shared/package-files/4e/4ebde1edc24aa3e422d13f54809f4e29e946212c78475437c4717e41415cc3d3/pixel_unshuffle_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..22851aec36be2917519c9fbe0621c3904b0755b9
--- /dev/null
+++ b/shared/package-files/4e/4ebde1edc24aa3e422d13f54809f4e29e946212c78475437c4717e41415cc3d3/pixel_unshuffle_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API pixel_unshuffle {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::pixel_unshuffle")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, int64_t downscale_factor);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t downscale_factor);
+};
+
+struct TORCH_API pixel_unshuffle_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::pixel_unshuffle")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, int64_t downscale_factor, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t downscale_factor, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/4e/4ebf58437226051ea8a862e579c31723b65f0a51dabaaeccd9f235578baddb3f/isinf.h b/shared/package-files/4e/4ebf58437226051ea8a862e579c31723b65f0a51dabaaeccd9f235578baddb3f/isinf.h
new file mode 100644
index 0000000000000000000000000000000000000000..9773e41211bbcd2dc44ea696e69c56463f90cb6f
--- /dev/null
+++ b/shared/package-files/4e/4ebf58437226051ea8a862e579c31723b65f0a51dabaaeccd9f235578baddb3f/isinf.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/isinf_ops.h>
+
+namespace at {
+
+
+// aten::isinf(Tensor self) -> Tensor
+inline at::Tensor isinf(const at::Tensor & self) {
+    return at::_ops::isinf::call(self);
+}
+
+// aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & isinf_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::isinf_out::call(self, out);
+}
+// aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & isinf_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::isinf_out::call(self, out);
+}
+
+}
diff --git a/shared/package-files/4e/4ec51a9a4c022b81315f389f61231eea2308312aac1f3f70d70609c571960c86/data_native.h b/shared/package-files/4e/4ec51a9a4c022b81315f389f61231eea2308312aac1f3f70d70609c571960c86/data_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..02c8430efe7bc377cf72c3ebff92d7dcb3456929
--- /dev/null
+++ b/shared/package-files/4e/4ec51a9a4c022b81315f389f61231eea2308312aac1f3f70d70609c571960c86/data_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor data(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4ec6e9a179376b5b5e74caf78bc04dacd0941c38354344e5ac737e2511bb2d8e/prelu.h b/shared/package-files/4e/4ec6e9a179376b5b5e74caf78bc04dacd0941c38354344e5ac737e2511bb2d8e/prelu.h
new file mode 100644
index 0000000000000000000000000000000000000000..80ffe1b58315ded3c6472196d849f045a9977fc2
--- /dev/null
+++ b/shared/package-files/4e/4ec6e9a179376b5b5e74caf78bc04dacd0941c38354344e5ac737e2511bb2d8e/prelu.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/prelu_ops.h>
+
+namespace at {
+
+
+// aten::prelu(Tensor self, Tensor weight) -> Tensor
+inline at::Tensor prelu(const at::Tensor & self, const at::Tensor & weight) {
+    return at::_ops::prelu::call(self, weight);
+}
+
+}
diff --git a/shared/package-files/4e/4ec97a3e48dea89f4ae0871ef162f9035a58984c350e6f516b01c60c2d36efc3/ApproximateClock.h b/shared/package-files/4e/4ec97a3e48dea89f4ae0871ef162f9035a58984c350e6f516b01c60c2d36efc3/ApproximateClock.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b3b5b6bb9aba103c8f92fd74fc2531e5eb0fc55
--- /dev/null
+++ b/shared/package-files/4e/4ec97a3e48dea89f4ae0871ef162f9035a58984c350e6f516b01c60c2d36efc3/ApproximateClock.h
@@ -0,0 +1,115 @@
+// Copyright 2023-present Facebook. All Rights Reserved.
+
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <array>
+#include <chrono>
+#include <cstddef>
+#include <cstdint>
+#include <ctime>
+#include <functional>
+#include <type_traits>
+
+#if defined(C10_IOS) && defined(C10_MOBILE)
+#include <sys/time.h> // for gettimeofday()
+#endif
+
+#if defined(__i386__) || defined(__x86_64__) || defined(__amd64__)
+#define C10_RDTSC
+#if defined(_MSC_VER)
+#include <intrin.h>
+#elif defined(__CUDACC__) || defined(__HIPCC__)
+#undef C10_RDTSC
+#elif defined(__clang__)
+// `__rdtsc` is available by default.
+// NB: This has to be first, because Clang will also define `__GNUC__`
+#elif defined(__GNUC__)
+#include <x86intrin.h>
+#else
+#undef C10_RDTSC
+#endif
+#endif
+
+namespace c10 {
+
+using time_t = int64_t;
+using steady_clock_t = std::conditional_t<
+    std::chrono::high_resolution_clock::is_steady,
+    std::chrono::high_resolution_clock,
+    std::chrono::steady_clock>;
+
+inline time_t getTimeSinceEpoch() {
+  auto now = std::chrono::system_clock::now().time_since_epoch();
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
+}
+
+inline time_t getTime(bool allow_monotonic = false) {
+#if defined(C10_IOS) && defined(C10_MOBILE)
+  // clock_gettime is only available on iOS 10.0 or newer. Unlike OS X, iOS
+  // can't rely on CLOCK_REALTIME, as it is defined no matter if clock_gettime
+  // is implemented or not
+  struct timeval now;
+  gettimeofday(&now, NULL);
+  return static_cast<time_t>(now.tv_sec) * 1000000000 +
+      static_cast<time_t>(now.tv_usec) * 1000;
+#elif defined(_WIN32) || defined(__MACH__)
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(
+             steady_clock_t::now().time_since_epoch())
+      .count();
+#else
+  // clock_gettime is *much* faster than std::chrono implementation on Linux
+  struct timespec t {};
+  auto mode = CLOCK_REALTIME;
+  if (allow_monotonic) {
+    mode = CLOCK_MONOTONIC;
+  }
+  clock_gettime(mode, &t);
+  return static_cast<time_t>(t.tv_sec) * 1000000000 +
+      static_cast<time_t>(t.tv_nsec);
+#endif
+}
+
+// We often do not need to capture true wall times. If a fast mechanism such
+// as TSC is available we can use that instead and convert back to epoch time
+// during post processing. This greatly reduce the clock's contribution to
+// profiling.
+//   http://btorpey.github.io/blog/2014/02/18/clock-sources-in-linux/
+//   https://quick-bench.com/q/r8opkkGZSJMu9wM_XTbDouq-0Io
+// TODO: We should use
+// `https://github.com/google/benchmark/blob/main/src/cycleclock.h`
+inline auto getApproximateTime() {
+#if defined(C10_RDTSC)
+  return static_cast<uint64_t>(__rdtsc());
+#else
+  return getTime();
+#endif
+}
+
+using approx_time_t = decltype(getApproximateTime());
+static_assert(
+    std::is_same_v<approx_time_t, int64_t> ||
+        std::is_same_v<approx_time_t, uint64_t>,
+    "Expected either int64_t (`getTime`) or uint64_t (some TSC reads).");
+
+// Convert `getCount` results to Nanoseconds since unix epoch.
+class C10_API ApproximateClockToUnixTimeConverter final {
+ public:
+  ApproximateClockToUnixTimeConverter();
+  std::function<time_t(approx_time_t)> makeConverter();
+
+  struct UnixAndApproximateTimePair {
+    time_t t_;
+    approx_time_t approx_t_;
+  };
+  static UnixAndApproximateTimePair measurePair();
+
+ private:
+  static constexpr size_t replicates = 1001;
+  using time_pairs = std::array<UnixAndApproximateTimePair, replicates>;
+  time_pairs measurePairs();
+
+  time_pairs start_times_;
+};
+
+} // namespace c10
diff --git a/shared/package-files/4e/4eca14856485eabba35aedcbeddb8ee0c45c2d9505804591acba4fce525b02ef/test_data.py b/shared/package-files/4e/4eca14856485eabba35aedcbeddb8ee0c45c2d9505804591acba4fce525b02ef/test_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..262ae0ae162628f6194fa71c598073e6d737b4c2
--- /dev/null
+++ b/shared/package-files/4e/4eca14856485eabba35aedcbeddb8ee0c45c2d9505804591acba4fce525b02ef/test_data.py
@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pd
+
+from statsmodels.tools import data
+
+
+def test_missing_data_pandas():
+    """
+    Fixes GH: #144
+    """
+    rs = np.random.RandomState(89099431)
+    X = rs.random((10, 5))
+    X[1, 2] = np.nan
+    df = pd.DataFrame(X)
+    vals, cnames, rnames = data.interpret_data(df)
+    np.testing.assert_equal(rnames.tolist(), [0, 2, 3, 4, 5, 6, 7, 8, 9])
+
+
+def test_dataframe():
+    rs = np.random.RandomState(89099431)
+    X = rs.random((10, 5))
+    df = pd.DataFrame(X)
+    vals, cnames, rnames = data.interpret_data(df)
+    np.testing.assert_equal(vals, df.values)
+    np.testing.assert_equal(rnames.tolist(), df.index.tolist())
+    np.testing.assert_equal(cnames, df.columns.tolist())
+
+
+def test_formula_engine_use_detection_577():
+    rs = np.random.RandomState(89099431)
+    x = rs.random((10, 2))
+    df = pd.DataFrame(x, columns=["var1", "var2"])
+    from statsmodels.formula._manager import FormulaManager
+
+    mgr = FormulaManager()
+    if mgr.engine == "patsy":
+        test_func = data._is_using_patsy
+    else:
+        test_func = data._is_using_formulaic
+    endog = mgr.get_matrices("var1 - 1", df, pandas=False)
+    assert test_func(endog, None)
+
+    exog = mgr.get_matrices("var2 - 1", df, pandas=False)
+    assert test_func(endog, exog)
+
+
+def test_as_array_with_name_series():
+    s = pd.Series([1], name="hello")
+    arr, name = data._as_array_with_name(s, "not_used")
+    np.testing.assert_array_equal(np.array([1]), arr)
+    assert name == "hello"
+
+
+def test_as_array_with_name_array():
+    arr, name = data._as_array_with_name(np.array([1]), "default")
+    np.testing.assert_array_equal(np.array([1]), arr)
+    assert name == "default"
diff --git a/shared/package-files/4e/4ecaac02e68f11ec1a406a6ce8a4b17e4f8af74f76157e0776360d0dd041f276/round_dance.py b/shared/package-files/4e/4ecaac02e68f11ec1a406a6ce8a4b17e4f8af74f76157e0776360d0dd041f276/round_dance.py
new file mode 100644
index 0000000000000000000000000000000000000000..10383614c6e974c7b0cbd3e9ad99ba8ee5371922
--- /dev/null
+++ b/shared/package-files/4e/4ecaac02e68f11ec1a406a6ce8a4b17e4f8af74f76157e0776360d0dd041f276/round_dance.py
@@ -0,0 +1,86 @@
+"""      turtle-example-suite:
+
+         tdemo_round_dance.py
+
+(Needs version 1.1 of the turtle module that
+comes with Python 3.1)
+
+Dancing turtles have a compound shape
+consisting of a series of triangles of
+decreasing size.
+
+Turtles march along a circle while rotating
+pairwise in opposite direction, with one
+exception. Does that breaking of symmetry
+enhance the attractiveness of the example?
+
+Press any key to stop the animation.
+
+Technically: demonstrates use of compound
+shapes, transformation of shapes as well as
+cloning turtles. The animation is
+controlled through update().
+"""
+
+from turtle import *
+
+def stop():
+    global running
+    running = False
+
+def main():
+    global running
+    clearscreen()
+    bgcolor("gray10")
+    tracer(False)
+    shape("triangle")
+    f =   0.793402
+    phi = 9.064678
+    s = 5
+    c = 1
+    # create compound shape
+    sh = Shape("compound")
+    for i in range(10):
+        shapesize(s)
+        p =get_shapepoly()
+        s *= f
+        c *= f
+        tilt(-phi)
+        sh.addcomponent(p, (c, 0.25, 1-c), "black")
+    register_shape("multitri", sh)
+    # create dancers
+    shapesize(1)
+    shape("multitri")
+    pu()
+    setpos(0, -200)
+    dancers = []
+    for i in range(180):
+        fd(7)
+        tilt(-4)
+        lt(2)
+        update()
+        if i % 12 == 0:
+            dancers.append(clone())
+    home()
+    # dance
+    running = True
+    onkeypress(stop)
+    listen()
+    cs = 1
+    while running:
+        ta = -4
+        for dancer in dancers:
+            dancer.fd(7)
+            dancer.lt(2)
+            dancer.tilt(ta)
+            ta = -4 if ta > 0 else 2
+        if cs < 180:
+            right(4)
+            shapesize(cs)
+            cs *= 1.005
+        update()
+    return "DONE!"
+
+if __name__=='__main__':
+    print(main())
+    mainloop()
diff --git a/shared/package-files/4e/4ecb6abb75942bc306af7a8f25bc1cb7c10f41fcd4ec0aea15f81b6737f67cac/main_search.py b/shared/package-files/4e/4ecb6abb75942bc306af7a8f25bc1cb7c10f41fcd4ec0aea15f81b6737f67cac/main_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6730bd584d6b28c48d3286689ba7a8cf32b1ae3
--- /dev/null
+++ b/shared/package-files/4e/4ecb6abb75942bc306af7a8f25bc1cb7c10f41fcd4ec0aea15f81b6737f67cac/main_search.py
@@ -0,0 +1,180 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+from collections import defaultdict
+from datetime import datetime, timezone
+
+from ..base.context import context
+from ..cli.common import stdout_json
+from ..common.io import Spinner, dashlist
+from ..core.envs_manager import query_all_prefixes
+from ..core.index import calculate_channel_urls
+from ..core.subdir_data import SubdirData
+from ..models.match_spec import MatchSpec
+from ..models.records import PackageRecord
+from ..models.version import VersionOrder
+from ..utils import human_bytes
+
+
+def execute(args, parser):
+    spec = MatchSpec(args.match_spec)
+    if spec.get_exact_value("subdir"):
+        subdirs = (spec.get_exact_value("subdir"),)
+    else:
+        subdirs = context.subdirs
+
+    if args.envs:
+        with Spinner(
+            "Searching environments for %s" % spec,
+            not context.verbosity and not context.quiet,
+            context.json,
+        ):
+            prefix_matches = query_all_prefixes(spec)
+            ordered_result = tuple(
+                {
+                    "location": prefix,
+                    "package_records": tuple(
+                        sorted(
+                            (
+                                PackageRecord.from_objects(prefix_rec)
+                                for prefix_rec in prefix_recs
+                            ),
+                            key=lambda prec: prec._pkey,
+                        )
+                    ),
+                }
+                for prefix, prefix_recs in prefix_matches
+            )
+        if context.json:
+            stdout_json(ordered_result)
+        elif args.info:
+            for pkg_group in ordered_result:
+                for prec in pkg_group["package_records"]:
+                    pretty_record(prec)
+        else:
+            builder = [
+                "# %-13s %15s %15s  %-20s %-20s"
+                % (
+                    "Name",
+                    "Version",
+                    "Build",
+                    "Channel",
+                    "Location",
+                )
+            ]
+            for pkg_group in ordered_result:
+                for prec in pkg_group["package_records"]:
+                    builder.append(
+                        "%-15s %15s %15s  %-20s %-20s"
+                        % (
+                            prec.name,
+                            prec.version,
+                            prec.build,
+                            prec.channel.name,
+                            pkg_group["location"],
+                        )
+                    )
+            print("\n".join(builder))
+        return 0
+
+    with Spinner(
+        "Loading channels", not context.verbosity and not context.quiet, context.json
+    ):
+        spec_channel = spec.get_exact_value("channel")
+        channel_urls = (spec_channel,) if spec_channel else context.channels
+
+        matches = sorted(
+            SubdirData.query_all(spec, channel_urls, subdirs),
+            key=lambda rec: (rec.name, VersionOrder(rec.version), rec.build),
+        )
+    if not matches and spec.get_exact_value("name"):
+        flex_spec = MatchSpec(spec, name="*%s*" % spec.name)
+        if not context.json:
+            print(f"No match found for: {spec}. Search: {flex_spec}")
+        matches = sorted(
+            SubdirData.query_all(flex_spec, channel_urls, subdirs),
+            key=lambda rec: (rec.name, VersionOrder(rec.version), rec.build),
+        )
+
+    if not matches:
+        channels_urls = tuple(
+            calculate_channel_urls(
+                channel_urls=context.channels,
+                prepend=not args.override_channels,
+                platform=subdirs[0],
+                use_local=args.use_local,
+            )
+        )
+        from ..exceptions import PackagesNotFoundError
+
+        raise PackagesNotFoundError((str(spec),), channels_urls)
+
+    if context.json:
+        json_obj = defaultdict(list)
+        for match in matches:
+            json_obj[match.name].append(match)
+        stdout_json(json_obj)
+
+    elif args.info:
+        for record in matches:
+            pretty_record(record)
+
+    else:
+        builder = [
+            "# %-18s %15s %15s  %-20s"
+            % (
+                "Name",
+                "Version",
+                "Build",
+                "Channel",
+            )
+        ]
+        for record in matches:
+            builder.append(
+                "%-20s %15s %15s  %-20s"
+                % (
+                    record.name,
+                    record.version,
+                    record.build,
+                    record.channel.name,
+                )
+            )
+        print("\n".join(builder))
+
+
+def pretty_record(record):
+    def push_line(display_name, attr_name):
+        value = getattr(record, attr_name, None)
+        if value is not None:
+            builder.append("%-12s: %s" % (display_name, value))
+
+    builder = []
+    builder.append(record.name + " " + record.version + " " + record.build)
+    builder.append("-" * len(builder[0]))
+
+    push_line("file name", "fn")
+    push_line("name", "name")
+    push_line("version", "version")
+    push_line("build", "build")
+    push_line("build number", "build_number")
+    builder.append("%-12s: %s" % ("size", human_bytes(record.size)))
+    push_line("license", "license")
+    push_line("subdir", "subdir")
+    push_line("url", "url")
+    push_line("md5", "md5")
+    if record.timestamp:
+        date_str = datetime.fromtimestamp(record.timestamp, timezone.utc).strftime(
+            "%Y-%m-%d %H:%M:%S %Z"
+        )
+        builder.append("%-12s: %s" % ("timestamp", date_str))
+    if record.track_features:
+        builder.append(
+            "%-12s: %s" % ("track_features", dashlist(record.track_features))
+        )
+    if record.constrains:
+        builder.append("%-12s: %s" % ("constraints", dashlist(record.constrains)))
+    builder.append(
+        "%-12s: %s"
+        % ("dependencies", dashlist(record.depends) if record.depends else "[]")
+    )
+    builder.append("\n")
+    print("\n".join(builder))
diff --git a/shared/package-files/4e/4ecc46eb891359cf4b7b0d8e1cbe3d3b3fded4d3fcf68f5028eebe95680dd0d6/replication_pad2d_meta.h b/shared/package-files/4e/4ecc46eb891359cf4b7b0d8e1cbe3d3b3fded4d3fcf68f5028eebe95680dd0d6/replication_pad2d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..987e4b236529170a60f68da27640d74e59a334bf
--- /dev/null
+++ b/shared/package-files/4e/4ecc46eb891359cf4b7b0d8e1cbe3d3b3fded4d3fcf68f5028eebe95680dd0d6/replication_pad2d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_replication_pad2d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> padding);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4ecd1d48064714595534aa1237b4236c7ae1ceeaa4dafc286a952eda08f92d71/kl_div_native.h b/shared/package-files/4e/4ecd1d48064714595534aa1237b4236c7ae1ceeaa4dafc286a952eda08f92d71/kl_div_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e7152e3146bdfa4350304d5acf36e1fc50b66da
--- /dev/null
+++ b/shared/package-files/4e/4ecd1d48064714595534aa1237b4236c7ae1ceeaa4dafc286a952eda08f92d71/kl_div_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor kl_div(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, bool log_target=false);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4ecd8f5e0da872b5ed5fd7a0baf707790e0907692da5de62b17b514e953d1796/ibm.py b/shared/package-files/4e/4ecd8f5e0da872b5ed5fd7a0baf707790e0907692da5de62b17b514e953d1796/ibm.py
new file mode 100644
index 0000000000000000000000000000000000000000..29927518c703581d7c4bf0aecd06fe2ea0904ed8
--- /dev/null
+++ b/shared/package-files/4e/4ecd8f5e0da872b5ed5fd7a0baf707790e0907692da5de62b17b514e953d1796/ibm.py
@@ -0,0 +1,97 @@
+import os
+import re
+import sys
+import subprocess
+
+from numpy.distutils.fcompiler import FCompiler
+from numpy.distutils.exec_command import find_executable
+from numpy.distutils.misc_util import make_temp_file
+from distutils import log
+
+compilers = ['IBMFCompiler']
+
+class IBMFCompiler(FCompiler):
+    compiler_type = 'ibm'
+    description = 'IBM XL Fortran Compiler'
+    version_pattern =  r'(xlf\(1\)\s*|)IBM XL Fortran ((Advanced Edition |)Version |Enterprise Edition V|for AIX, V)(?P<version>[^\s*]*)'
+    #IBM XL Fortran Enterprise Edition V10.1 for AIX \nVersion: 10.01.0000.0004
+
+    executables = {
+        'version_cmd'  : ["<F77>", "-qversion"],
+        'compiler_f77' : ["xlf"],
+        'compiler_fix' : ["xlf90", "-qfixed"],
+        'compiler_f90' : ["xlf90"],
+        'linker_so'    : ["xlf95"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    def get_version(self,*args,**kwds):
+        version = FCompiler.get_version(self,*args,**kwds)
+
+        if version is None and sys.platform.startswith('aix'):
+            # use lslpp to find out xlf version
+            lslpp = find_executable('lslpp')
+            xlf = find_executable('xlf')
+            if os.path.exists(xlf) and os.path.exists(lslpp):
+                try:
+                    o = subprocess.check_output([lslpp, '-Lc', 'xlfcmp'])
+                except (OSError, subprocess.CalledProcessError):
+                    pass
+                else:
+                    m = re.search(r'xlfcmp:(?P<version>\d+([.]\d+)+)', o)
+                    if m: version = m.group('version')
+
+        xlf_dir = '/etc/opt/ibmcmp/xlf'
+        if version is None and os.path.isdir(xlf_dir):
+            # linux:
+            # If the output of xlf does not contain version info
+            # (that's the case with xlf 8.1, for instance) then
+            # let's try another method:
+            l = sorted(os.listdir(xlf_dir))
+            l.reverse()
+            l = [d for d in l if os.path.isfile(os.path.join(xlf_dir, d, 'xlf.cfg'))]
+            if l:
+                from distutils.version import LooseVersion
+                self.version = version = LooseVersion(l[0])
+        return version
+
+    def get_flags(self):
+        return ['-qextname']
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_linker_so(self):
+        opt = []
+        if sys.platform=='darwin':
+            opt.append('-Wl,-bundle,-flat_namespace,-undefined,suppress')
+        else:
+            opt.append('-bshared')
+        version = self.get_version(ok_status=[0, 40])
+        if version is not None:
+            if sys.platform.startswith('aix'):
+                xlf_cfg = '/etc/xlf.cfg'
+            else:
+                xlf_cfg = '/etc/opt/ibmcmp/xlf/%s/xlf.cfg' % version
+            fo, new_cfg = make_temp_file(suffix='_xlf.cfg')
+            log.info('Creating '+new_cfg)
+            with open(xlf_cfg) as fi:
+                crt1_match = re.compile(r'\s*crt\s*=\s*(?P<path>.*)/crt1.o').match
+                for line in fi:
+                    m = crt1_match(line)
+                    if m:
+                        fo.write('crt = %s/bundle1.o\n' % (m.group('path')))
+                    else:
+                        fo.write(line)
+            fo.close()
+            opt.append('-F'+new_cfg)
+        return opt
+
+    def get_flags_opt(self):
+        return ['-O3']
+
+if __name__ == '__main__':
+    from numpy.distutils import customized_fcompiler
+    log.set_verbosity(2)
+    print(customized_fcompiler(compiler='ibm').get_version())
diff --git a/shared/package-files/4e/4ece565106067a569f4c99bdadfd1523c0f3dd95dcb5d5016ea09e87274d179e/binomial_native.h b/shared/package-files/4e/4ece565106067a569f4c99bdadfd1523c0f3dd95dcb5d5016ea09e87274d179e/binomial_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b2222602350f5272c7a3a90f0d1833bddf62e36
--- /dev/null
+++ b/shared/package-files/4e/4ece565106067a569f4c99bdadfd1523c0f3dd95dcb5d5016ea09e87274d179e/binomial_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & binomial_out(const at::Tensor & count, const at::Tensor & prob, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor _s_binomial_cpu(const at::Tensor & count, const at::Tensor & prob, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor _s_binomial_cuda(const at::Tensor & count, const at::Tensor & prob, c10::optional<at::Generator> generator=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/4e/4eceaaa2f8e42521f39a625931d0bb8b4e3c71845d1ac5d95c9309486224d619/TensorModeKernel.h b/shared/package-files/4e/4eceaaa2f8e42521f39a625931d0bb8b4e3c71845d1ac5d95c9309486224d619/TensorModeKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5660747997d4eb1ad56f79ec2d1f519921c05c2
--- /dev/null
+++ b/shared/package-files/4e/4eceaaa2f8e42521f39a625931d0bb8b4e3c71845d1ac5d95c9309486224d619/TensorModeKernel.h
@@ -0,0 +1,19 @@
+#pragma once
+#include <cstdint>
+
+namespace at {
+class TensorBase;
+}
+
+namespace at {
+namespace native {
+
+void launch_fused_mode_kernel(
+    const TensorBase &values, const TensorBase &indices,
+    const TensorBase &self, int64_t slice_size, int64_t slices);
+
+void launch_apply_mode_kernel(
+    const TensorBase &values, const TensorBase &indices,
+    const TensorBase &self, int64_t dim, int64_t ndim);
+
+}}  // namespace at::native
diff --git a/shared/package-files/4e/4ecec649862d8b6cfef32a0e25379d8fa6822e71a1ba02e50d177165ce2b3f1b/_censored_data.py b/shared/package-files/4e/4ecec649862d8b6cfef32a0e25379d8fa6822e71a1ba02e50d177165ce2b3f1b/_censored_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6fee500f1d97db0bae9ebff26824d4d894c7f39
--- /dev/null
+++ b/shared/package-files/4e/4ecec649862d8b6cfef32a0e25379d8fa6822e71a1ba02e50d177165ce2b3f1b/_censored_data.py
@@ -0,0 +1,459 @@
+import numpy as np
+
+
+def _validate_1d(a, name, allow_inf=False):
+    if np.ndim(a) != 1:
+        raise ValueError(f'`{name}` must be a one-dimensional sequence.')
+    if np.isnan(a).any():
+        raise ValueError(f'`{name}` must not contain nan.')
+    if not allow_inf and np.isinf(a).any():
+        raise ValueError(f'`{name}` must contain only finite values.')
+
+
+def _validate_interval(interval):
+    interval = np.asarray(interval)
+    if interval.shape == (0,):
+        # The input was a sequence with length 0.
+        interval = interval.reshape((0, 2))
+    if interval.ndim != 2 or interval.shape[-1] != 2:
+        raise ValueError('`interval` must be a two-dimensional array with '
+                         'shape (m, 2), where m is the number of '
+                         'interval-censored values, but got shape '
+                         f'{interval.shape}')
+
+    if np.isnan(interval).any():
+        raise ValueError('`interval` must not contain nan.')
+    if np.isinf(interval).all(axis=1).any():
+        raise ValueError('In each row in `interval`, both values must not'
+                         ' be infinite.')
+    if (interval[:, 0] > interval[:, 1]).any():
+        raise ValueError('In each row of `interval`, the left value must not'
+                         ' exceed the right value.')
+
+    uncensored_mask = interval[:, 0] == interval[:, 1]
+    left_mask = np.isinf(interval[:, 0])
+    right_mask = np.isinf(interval[:, 1])
+    interval_mask = np.isfinite(interval).all(axis=1) & ~uncensored_mask
+
+    uncensored2 = interval[uncensored_mask, 0]
+    left2 = interval[left_mask, 1]
+    right2 = interval[right_mask, 0]
+    interval2 = interval[interval_mask]
+
+    return uncensored2, left2, right2, interval2
+
+
+def _validate_x_censored(x, censored):
+    x = np.asarray(x)
+    if x.ndim != 1:
+        raise ValueError('`x` must be one-dimensional.')
+    censored = np.asarray(censored)
+    if censored.ndim != 1:
+        raise ValueError('`censored` must be one-dimensional.')
+    if (~np.isfinite(x)).any():
+        raise ValueError('`x` must not contain nan or inf.')
+    if censored.size != x.size:
+        raise ValueError('`x` and `censored` must have the same length.')
+    return x, censored.astype(bool)
+
+
+class CensoredData:
+    """
+    Instances of this class represent censored data.
+
+    Instances may be passed to the ``fit`` method of continuous
+    univariate SciPy distributions for maximum likelihood estimation.
+    The *only* method of the univariate continuous distributions that
+    understands `CensoredData` is the ``fit`` method.  An instance of
+    `CensoredData` can not be passed to methods such as ``pdf`` and
+    ``cdf``.
+
+    An observation is said to be *censored* when the precise value is unknown,
+    but it has a known upper and/or lower bound.  The conventional terminology
+    is:
+
+    * left-censored: an observation is below a certain value but it is
+      unknown by how much.
+    * right-censored: an observation is above a certain value but it is
+      unknown by how much.
+    * interval-censored: an observation lies somewhere on an interval between
+      two values.
+
+    Left-, right-, and interval-censored data can be represented by
+    `CensoredData`.
+
+    For convenience, the class methods ``left_censored`` and
+    ``right_censored`` are provided to create a `CensoredData`
+    instance from a single one-dimensional array of measurements
+    and a corresponding boolean array to indicate which measurements
+    are censored.  The class method ``interval_censored`` accepts two
+    one-dimensional arrays that hold the lower and upper bounds of the
+    intervals.
+
+    Parameters
+    ----------
+    uncensored : array_like, 1D
+        Uncensored observations.
+    left : array_like, 1D
+        Left-censored observations.
+    right : array_like, 1D
+        Right-censored observations.
+    interval : array_like, 2D, with shape (m, 2)
+        Interval-censored observations.  Each row ``interval[k, :]``
+        represents the interval for the kth interval-censored observation.
+
+    Notes
+    -----
+    In the input array `interval`, the lower bound of the interval may
+    be ``-inf``, and the upper bound may be ``inf``, but at least one must be
+    finite. When the lower bound is ``-inf``, the row represents a left-
+    censored observation, and when the upper bound is ``inf``, the row
+    represents a right-censored observation.  If the length of an interval
+    is 0 (i.e. ``interval[k, 0] == interval[k, 1]``, the observation is
+    treated as uncensored.  So one can represent all the types of censored
+    and uncensored data in ``interval``, but it is generally more convenient
+    to use `uncensored`, `left` and `right` for uncensored, left-censored and
+    right-censored observations, respectively.
+
+    Examples
+    --------
+    In the most general case, a censored data set may contain values that
+    are left-censored, right-censored, interval-censored, and uncensored.
+    For example, here we create a data set with five observations.  Two
+    are uncensored (values 1 and 1.5), one is a left-censored observation
+    of 0, one is a right-censored observation of 10 and one is
+    interval-censored in the interval [2, 3].
+
+    >>> import numpy as np
+    >>> from scipy.stats import CensoredData
+    >>> data = CensoredData(uncensored=[1, 1.5], left=[0], right=[10],
+    ...                     interval=[[2, 3]])
+    >>> print(data)
+    CensoredData(5 values: 2 not censored, 1 left-censored,
+    1 right-censored, 1 interval-censored)
+
+    Equivalently,
+
+    >>> data = CensoredData(interval=[[1, 1],
+    ...                               [1.5, 1.5],
+    ...                               [-np.inf, 0],
+    ...                               [10, np.inf],
+    ...                               [2, 3]])
+    >>> print(data)
+    CensoredData(5 values: 2 not censored, 1 left-censored,
+    1 right-censored, 1 interval-censored)
+
+    A common case is to have a mix of uncensored observations and censored
+    observations that are all right-censored (or all left-censored). For
+    example, consider an experiment in which six devices are started at
+    various times and left running until they fail.  Assume that time is
+    measured in hours, and the experiment is stopped after 30 hours, even
+    if all the devices have not failed by that time.  We might end up with
+    data such as this::
+
+        Device  Start-time  Fail-time  Time-to-failure
+           1         0         13           13
+           2         2         24           22
+           3         5         22           17
+           4         8         23           15
+           5        10        ***          >20
+           6        12        ***          >18
+
+    Two of the devices had not failed when the experiment was stopped;
+    the observations of the time-to-failure for these two devices are
+    right-censored.  We can represent this data with
+
+    >>> data = CensoredData(uncensored=[13, 22, 17, 15], right=[20, 18])
+    >>> print(data)
+    CensoredData(6 values: 4 not censored, 2 right-censored)
+
+    Alternatively, we can use the method `CensoredData.right_censored` to
+    create a representation of this data.  The time-to-failure observations
+    are put the list ``ttf``.  The ``censored`` list indicates which values
+    in ``ttf`` are censored.
+
+    >>> ttf = [13, 22, 17, 15, 20, 18]
+    >>> censored = [False, False, False, False, True, True]
+
+    Pass these lists to `CensoredData.right_censored` to create an
+    instance of `CensoredData`.
+
+    >>> data = CensoredData.right_censored(ttf, censored)
+    >>> print(data)
+    CensoredData(6 values: 4 not censored, 2 right-censored)
+
+    If the input data is interval censored and already stored in two
+    arrays, one holding the low end of the intervals and another
+    holding the high ends, the class method ``interval_censored`` can
+    be used to create the `CensoredData` instance.
+
+    This example creates an instance with four interval-censored values.
+    The intervals are [10, 11], [0.5, 1], [2, 3], and [12.5, 13.5].
+
+    >>> a = [10, 0.5, 2, 12.5]  # Low ends of the intervals
+    >>> b = [11, 1.0, 3, 13.5]  # High ends of the intervals
+    >>> data = CensoredData.interval_censored(low=a, high=b)
+    >>> print(data)
+    CensoredData(4 values: 0 not censored, 4 interval-censored)
+
+    Finally, we create and censor some data from the `weibull_min`
+    distribution, and then fit `weibull_min` to that data. We'll assume
+    that the location parameter is known to be 0.
+
+    >>> from scipy.stats import weibull_min
+    >>> rng = np.random.default_rng()
+
+    Create the random data set.
+
+    >>> x = weibull_min.rvs(2.5, loc=0, scale=30, size=250, random_state=rng)
+    >>> x[x > 40] = 40  # Right-censor values greater or equal to 40.
+
+    Create the `CensoredData` instance with the `right_censored` method.
+    The censored values are those where the value is 40.
+
+    >>> data = CensoredData.right_censored(x, x == 40)
+    >>> print(data)
+    CensoredData(250 values: 215 not censored, 35 right-censored)
+
+    35 values have been right-censored.
+
+    Fit `weibull_min` to the censored data.  We expect to shape and scale
+    to be approximately 2.5 and 30, respectively.
+
+    >>> weibull_min.fit(data, floc=0)
+    (2.3575922823897315, 0, 30.40650074451254)
+
+    """
+
+    def __init__(self, uncensored=None, *, left=None, right=None,
+                 interval=None):
+        if uncensored is None:
+            uncensored = []
+        if left is None:
+            left = []
+        if right is None:
+            right = []
+        if interval is None:
+            interval = np.empty((0, 2))
+
+        _validate_1d(uncensored, 'uncensored')
+        _validate_1d(left, 'left')
+        _validate_1d(right, 'right')
+        uncensored2, left2, right2, interval2 = _validate_interval(interval)
+
+        self._uncensored = np.concatenate((uncensored, uncensored2))
+        self._left = np.concatenate((left, left2))
+        self._right = np.concatenate((right, right2))
+        # Note that by construction, the private attribute _interval
+        # will be a 2D array that contains only finite values representing
+        # intervals with nonzero but finite length.
+        self._interval = interval2
+
+    def __repr__(self):
+        uncensored_str = " ".join(np.array_repr(self._uncensored).split())
+        left_str = " ".join(np.array_repr(self._left).split())
+        right_str = " ".join(np.array_repr(self._right).split())
+        interval_str = " ".join(np.array_repr(self._interval).split())
+        return (f"CensoredData(uncensored={uncensored_str}, left={left_str}, "
+                f"right={right_str}, interval={interval_str})")
+
+    def __str__(self):
+        num_nc = len(self._uncensored)
+        num_lc = len(self._left)
+        num_rc = len(self._right)
+        num_ic = len(self._interval)
+        n = num_nc + num_lc + num_rc + num_ic
+        parts = [f'{num_nc} not censored']
+        if num_lc > 0:
+            parts.append(f'{num_lc} left-censored')
+        if num_rc > 0:
+            parts.append(f'{num_rc} right-censored')
+        if num_ic > 0:
+            parts.append(f'{num_ic} interval-censored')
+        return f'CensoredData({n} values: ' + ', '.join(parts) + ')'
+
+    # This is not a complete implementation of the arithmetic operators.
+    # All we need is subtracting a scalar and dividing by a scalar.
+
+    def __sub__(self, other):
+        return CensoredData(uncensored=self._uncensored - other,
+                            left=self._left - other,
+                            right=self._right - other,
+                            interval=self._interval - other)
+
+    def __truediv__(self, other):
+        return CensoredData(uncensored=self._uncensored / other,
+                            left=self._left / other,
+                            right=self._right / other,
+                            interval=self._interval / other)
+
+    def __len__(self):
+        """
+        The number of values (censored and not censored).
+        """
+        return (len(self._uncensored) + len(self._left) + len(self._right)
+                + len(self._interval))
+
+    def num_censored(self):
+        """
+        Number of censored values.
+        """
+        return len(self._left) + len(self._right) + len(self._interval)
+
+    @classmethod
+    def right_censored(cls, x, censored):
+        """
+        Create a `CensoredData` instance of right-censored data.
+
+        Parameters
+        ----------
+        x : array_like
+            `x` is the array of observed data or measurements.
+            `x` must be a one-dimensional sequence of finite numbers.
+        censored : array_like of bool
+            `censored` must be a one-dimensional sequence of boolean
+            values.  If ``censored[k]`` is True, the corresponding value
+            in `x` is right-censored.  That is, the value ``x[k]``
+            is the lower bound of the true (but unknown) value.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of uncensored and right-censored values.
+
+        Examples
+        --------
+        >>> from scipy.stats import CensoredData
+
+        Two uncensored values (4 and 10) and two right-censored values
+        (24 and 25).
+
+        >>> data = CensoredData.right_censored([4, 10, 24, 25],
+        ...                                    [False, False, True, True])
+        >>> data
+        CensoredData(uncensored=array([ 4., 10.]),
+        left=array([], dtype=float64), right=array([24., 25.]),
+        interval=array([], shape=(0, 2), dtype=float64))
+        >>> print(data)
+        CensoredData(4 values: 2 not censored, 2 right-censored)
+        """
+        x, censored = _validate_x_censored(x, censored)
+        return cls(uncensored=x[~censored], right=x[censored])
+
+    @classmethod
+    def left_censored(cls, x, censored):
+        """
+        Create a `CensoredData` instance of left-censored data.
+
+        Parameters
+        ----------
+        x : array_like
+            `x` is the array of observed data or measurements.
+            `x` must be a one-dimensional sequence of finite numbers.
+        censored : array_like of bool
+            `censored` must be a one-dimensional sequence of boolean
+            values.  If ``censored[k]`` is True, the corresponding value
+            in `x` is left-censored.  That is, the value ``x[k]``
+            is the upper bound of the true (but unknown) value.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of uncensored and left-censored values.
+
+        Examples
+        --------
+        >>> from scipy.stats import CensoredData
+
+        Two uncensored values (0.12 and 0.033) and two left-censored values
+        (both 1e-3).
+
+        >>> data = CensoredData.left_censored([0.12, 0.033, 1e-3, 1e-3],
+        ...                                   [False, False, True, True])
+        >>> data
+        CensoredData(uncensored=array([0.12 , 0.033]),
+        left=array([0.001, 0.001]), right=array([], dtype=float64),
+        interval=array([], shape=(0, 2), dtype=float64))
+        >>> print(data)
+        CensoredData(4 values: 2 not censored, 2 left-censored)
+        """
+        x, censored = _validate_x_censored(x, censored)
+        return cls(uncensored=x[~censored], left=x[censored])
+
+    @classmethod
+    def interval_censored(cls, low, high):
+        """
+        Create a `CensoredData` instance of interval-censored data.
+
+        This method is useful when all the data is interval-censored, and
+        the low and high ends of the intervals are already stored in
+        separate one-dimensional arrays.
+
+        Parameters
+        ----------
+        low : array_like
+            The one-dimensional array containing the low ends of the
+            intervals.
+        high : array_like
+            The one-dimensional array containing the high ends of the
+            intervals.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of censored values.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy.stats import CensoredData
+
+        ``a`` and ``b`` are the low and high ends of a collection of
+        interval-censored values.
+
+        >>> a = [0.5, 2.0, 3.0, 5.5]
+        >>> b = [1.0, 2.5, 3.5, 7.0]
+        >>> data = CensoredData.interval_censored(low=a, high=b)
+        >>> print(data)
+        CensoredData(4 values: 0 not censored, 4 interval-censored)
+        """
+        _validate_1d(low, 'low', allow_inf=True)
+        _validate_1d(high, 'high', allow_inf=True)
+        if len(low) != len(high):
+            raise ValueError('`low` and `high` must have the same length.')
+        interval = np.column_stack((low, high))
+        uncensored, left, right, interval = _validate_interval(interval)
+        return cls(uncensored=uncensored, left=left, right=right,
+                   interval=interval)
+
+    def _uncensor(self):
+        """
+        This function is used when a non-censored version of the data
+        is needed to create a rough estimate of the parameters of a
+        distribution via the method of moments or some similar method.
+        The data is "uncensored" by taking the given endpoints as the
+        data for the left- or right-censored data, and the mean for the
+        interval-censored data.
+        """
+        data = np.concatenate((self._uncensored, self._left, self._right,
+                               self._interval.mean(axis=1)))
+        return data
+
+    def _supported(self, a, b):
+        """
+        Return a subset of self containing the values that are in
+        (or overlap with) the interval (a, b).
+        """
+        uncensored = self._uncensored
+        uncensored = uncensored[(a < uncensored) & (uncensored < b)]
+        left = self._left
+        left = left[a < left]
+        right = self._right
+        right = right[right < b]
+        interval = self._interval
+        interval = interval[(a < interval[:, 1]) & (interval[:, 0] < b)]
+        return CensoredData(uncensored, left=left, right=right,
+                            interval=interval)
diff --git a/shared/package-files/4e/4ed04578dec42932ada33be4c2e024b5b9ca38d1be6c21f58e51c3fb79f8116c/test_mangling.py b/shared/package-files/4e/4ed04578dec42932ada33be4c2e024b5b9ca38d1be6c21f58e51c3fb79f8116c/test_mangling.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad1ff95c8f4115cb53fa7cfc111d9dead2bb2bc3
--- /dev/null
+++ b/shared/package-files/4e/4ed04578dec42932ada33be4c2e024b5b9ca38d1be6c21f58e51c3fb79f8116c/test_mangling.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+"""
+Test function name mangling.
+The mangling affects the ABI of numba compiled binaries.
+"""
+from numba.core import types
+from numba.core.funcdesc import default_mangler
+from numba.tests.support import unittest, TestCase
+
+
+class TestMangling(TestCase):
+    def test_one_args(self):
+        fname = 'foo'
+        argtypes = types.int32,
+        name = default_mangler(fname, argtypes)
+        self.assertEqual(name, '_Z3fooi')
+
+    def test_two_args(self):
+        fname = 'foo'
+        argtypes = types.int32, types.float32
+        name = default_mangler(fname, argtypes)
+        self.assertEqual(name, '_Z3fooif')
+
+    def test_unicode_fname(self):
+        fname = u'foಠ'
+        argtypes = types.int32, types.float32
+        name = default_mangler(fname, argtypes)
+        self.assertIsInstance(name, str)
+        # manually encode it
+        unichar = fname[2]
+        enc = ''.join('_{:02x}'.format(c)
+                      for c in unichar.encode('utf8'))
+        text = 'fo' + enc
+        expect = '_Z{}{}if'.format(len(text), text)
+        self.assertEqual(name, expect)
+        # ensure result chars are in the right charset
+        self.assertRegex(name, r'^_Z[a-zA-Z0-9_\$]+$')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/4e/4ed05108437cf77088f374e3342fda8f7227c09c4194ff455aedbf2ab170e007/_foreach_lgamma_compositeexplicitautograd_dispatch.h b/shared/package-files/4e/4ed05108437cf77088f374e3342fda8f7227c09c4194ff455aedbf2ab170e007/_foreach_lgamma_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..025e9316e836822a6b73c145edb90ed52cab0c64
--- /dev/null
+++ b/shared/package-files/4e/4ed05108437cf77088f374e3342fda8f7227c09c4194ff455aedbf2ab170e007/_foreach_lgamma_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void _foreach_lgamma_out(at::TensorList out, at::TensorList self);
+TORCH_API void _foreach_lgamma_outf(at::TensorList self, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/4e/4ed16f0173e9d8a645b179d8a9d71f667216ced8a5ff7a9b5ebaafe27c30d689/__init__.py b/shared/package-files/4e/4ed16f0173e9d8a645b179d8a9d71f667216ced8a5ff7a9b5ebaafe27c30d689/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..02f34cc902057fc4a864dc967c6d8ac4faeb762d
--- /dev/null
+++ b/shared/package-files/4e/4ed16f0173e9d8a645b179d8a9d71f667216ced8a5ff7a9b5ebaafe27c30d689/__init__.py
@@ -0,0 +1 @@
+from .datetime import NPDatetime, NPTimedelta
diff --git a/shared/package-files/4e/4ed90a9eace5025a5fce794d0480da3541f10fcab5411111564963f95126e50e/ceil_div.h b/shared/package-files/4e/4ed90a9eace5025a5fce794d0480da3541f10fcab5411111564963f95126e50e/ceil_div.h
new file mode 100644
index 0000000000000000000000000000000000000000..37d67b232a22c11fa7dccf638b7897c0854ab8bd
--- /dev/null
+++ b/shared/package-files/4e/4ed90a9eace5025a5fce794d0480da3541f10fcab5411111564963f95126e50e/ceil_div.h
@@ -0,0 +1,24 @@
+#pragma once
+#include <c10/macros/Macros.h>
+#include <type_traits>
+
+namespace at {
+
+/**
+   Computes ceil(a / b)
+*/
+template <typename T, typename = std::enable_if_t<std::is_integral_v<T>>>
+C10_ALWAYS_INLINE C10_HOST_DEVICE T ceil_div(T a, T b) {
+  return (a + b - 1) / b;
+}
+
+/**
+   Computes ceil(a / b) * b; i.e., rounds up `a` to the next highest
+   multiple of b
+*/
+template <typename T>
+C10_ALWAYS_INLINE C10_HOST_DEVICE T round_up(T a, T b) {
+  return ceil_div(a, b) * b;
+}
+
+} // namespace at
diff --git a/shared/package-files/4e/4edf079d9d4e0b48f1d4c7caa79954bc793585657ae8cda85f9c7b2376672e11/replication_pad3d_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/4e/4edf079d9d4e0b48f1d4c7caa79954bc793585657ae8cda85f9c7b2376672e11/replication_pad3d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d14ed35d343f131dd660727d022b66f59de970d7
--- /dev/null
+++ b/shared/package-files/4e/4edf079d9d4e0b48f1d4c7caa79954bc793585657ae8cda85f9c7b2376672e11/replication_pad3d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor replication_pad3d(const at::Tensor & self, at::IntArrayRef padding);
+TORCH_API at::Tensor replication_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/4e/4edf57ce7d3edc4a6b3ea004b7de3af927acdc8f3e63060237e84c8be72d7e28/test_findlib.py b/shared/package-files/4e/4edf57ce7d3edc4a6b3ea004b7de3af927acdc8f3e63060237e84c8be72d7e28/test_findlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d42734ebeb04a1947ce48e78359e2660c727d4f
--- /dev/null
+++ b/shared/package-files/4e/4edf57ce7d3edc4a6b3ea004b7de3af927acdc8f3e63060237e84c8be72d7e28/test_findlib.py
@@ -0,0 +1,12 @@
+from numba.tests.support import TestCase, unittest
+from numba.misc import findlib
+
+
+class TestFindlib(TestCase):
+    def test_find_file_nonexistent_path(self):
+        candidates = findlib.find_file('libirrelevant.so', 'NONEXISTENT')
+        self.assertEqual(candidates, [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/4e/4ee0ed8c1451f1835c228342ffb00525cb39e41210897ccbc71c293f1a9f2b32/functional_utils.py b/shared/package-files/4e/4ee0ed8c1451f1835c228342ffb00525cb39e41210897ccbc71c293f1a9f2b32/functional_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddeaa0bae7c51b4e794e8f42ef31e557ed911953
--- /dev/null
+++ b/shared/package-files/4e/4ee0ed8c1451f1835c228342ffb00525cb39e41210897ccbc71c293f1a9f2b32/functional_utils.py
@@ -0,0 +1,370 @@
+"""
+This file contains utilities related to functionalization in AOTAutograd:
+1. converting to/from functional tensors
+2. detecting Tensor mutations - both metadata and Tensor value
+3. regenerating/replaying views from their base
+4. checking if a graph is functional i.e. whether it contains any mutation ops
+"""
+
+import torch
+from torch import Tensor
+from torch._subclasses.fake_tensor import FakeTensor
+from torch._subclasses.functional_tensor import FunctionalTensor
+from torch.fx.experimental.symbolic_shapes import definitely_true, sym_eq
+from torch.multiprocessing.reductions import StorageWeakRef
+from torch.utils._python_dispatch import (
+    is_traceable_wrapper_subclass,
+    transform_subclass,
+)
+
+
+def to_fun(t):
+    if isinstance(t, Tensor):
+        if is_traceable_wrapper_subclass(t):
+            # See Note [Functionalization always runs last]
+            # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper
+            # goes at the bottom.
+            # recurse here, so we can support nested wrapper subclasses
+            out = transform_subclass(t, lambda _, inner_t: to_fun(inner_t))
+            torch._mirror_autograd_meta_to(t, out)  # type: ignore[attr-defined]
+            return out
+        else:
+            return FunctionalTensor.to_functional(t)
+    else:
+        return t
+
+
+def sync_functional_tensor(t):
+    if is_traceable_wrapper_subclass(t):
+        attrs, ctx = t.__tensor_flatten__()  # type: ignore[attr-defined]
+        for attr in attrs:
+            sync_functional_tensor(getattr(t, attr))
+    else:
+        torch._sync(t)
+
+
+# When subclasses are involved, t here will usually look something like:
+# SubclassA(SubclassB(FunctionalTensor(_to_fun_tensor(FakeTensor))))
+def from_fun(t):
+    if isinstance(t, Tensor) and is_traceable_wrapper_subclass(t):
+        # See Note [Functionalization always runs last]
+        # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper
+        # goes at the bottom.
+        # recurse here, so we can support nested wrapper subclasses
+        out = transform_subclass(t, lambda _, inner_t: from_fun(inner_t))
+        torch._mirror_autograd_meta_to(t, out)  # type: ignore[attr-defined]
+        return out
+
+    if not isinstance(t, FunctionalTensor):
+        # quick sanity assert
+        if isinstance(t, torch.Tensor):
+            assert not torch._is_functional_tensor(t)  # type: ignore[attr-defined]
+        return t
+    sync_functional_tensor(t)
+    return torch._from_functional_tensor(t.elem)
+
+
+def is_fun(t):
+    if isinstance(t, Tensor) and is_traceable_wrapper_subclass(t):
+        # See Note [Functionalization always runs last]
+        # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper
+        # goes at the bottom.
+        # recurse here, so we can support nested wrapper subclasses
+        t_attrs, _ = t.__tensor_flatten__()  # type: ignore[attr-defined]
+        t_inners = [getattr(t, attr) for attr in t_attrs]
+        any_fun = any(is_fun(x) for x in t_inners)
+        all_fun = all(is_fun(x) for x in t_inners)
+        assert any_fun == all_fun
+        return any_fun
+
+    return isinstance(t, FunctionalTensor)
+
+
+# t here is either
+# (1) A FunctionalTensor(_to_functional_tensor(FakeTensor))
+# (2) A traceable tensor subclass that holds a FunctionalTensor
+# (3) Not a tensor
+def has_data_mutation(t):
+    if is_traceable_wrapper_subclass(t):
+        attrs, _ = t.__tensor_flatten__()
+        # A tensor subclass was updated if any of its inner elements were updated
+        return any(has_data_mutation(getattr(t, attr)) for attr in attrs)
+    else:
+        if isinstance(t, torch.Tensor):
+            assert isinstance(t, FunctionalTensor)
+            return torch._functionalize_has_data_mutation(t.elem)  # type: ignore[attr-defined]
+        return False
+
+
+def are_all_mutations_hidden_from_autograd(t):
+    if is_traceable_wrapper_subclass(t):
+        attrs, _ = t.__tensor_flatten__()
+        # If all inner elements are mutations hidden from autograd, then it is a mutation hidden from autograd.
+        return all(
+            are_all_mutations_hidden_from_autograd(getattr(t, attr)) for attr in attrs
+        )
+    elif isinstance(t, torch.Tensor):
+        assert isinstance(t, FunctionalTensor)
+        return torch._functionalize_are_all_mutations_hidden_from_autograd(t.elem)
+    else:
+        return False
+
+
+def are_all_mutations_under_no_grad_or_inference_mode(t):
+    if is_traceable_wrapper_subclass(t):
+        attrs, _ = t.__tensor_flatten__()
+        return all(
+            are_all_mutations_under_no_grad_or_inference_mode(getattr(t, attr))
+            for attr in attrs
+        )
+    else:
+        assert isinstance(t, FunctionalTensor)
+        return torch._functionalize_are_all_mutations_under_no_grad_or_inference_mode(
+            t.elem
+        )
+
+
+# f_arg here is either
+# (1) A FunctionalTensor(_to_functional_tensor(FakeTensor))
+# (2) A traceable tensor subclass that holds a FunctionalTensor
+# (3) Not a tensor
+# Assumption: arg promises to be the "original" tensor wrapped by f_arg
+# Note: "storage mutations" coming from set_() are a type of metadata mutation. So:
+# - check_only_storage_mutation=True: only return true if there was a storage mutation
+# - check_only_storage_mutation=Flse: return true if there was any metadata mutation (including a storage mutation)
+def has_metadata_mutation(f_arg, arg, *, check_only_storage_mutation: bool):
+    if is_traceable_wrapper_subclass(f_arg):
+        attrs, _ = f_arg.__tensor_flatten__()
+        # A tensor subclass was updated if any of its inner elements were updated
+        f_inner_ts = [getattr(f_arg, attr) for attr in attrs]
+        inner_ts = [getattr(arg, attr) for attr in attrs]
+        return any(
+            has_metadata_mutation(
+                f_inner_t,
+                inner_t,
+                check_only_storage_mutation=check_only_storage_mutation,
+            )
+            for f_inner_t, inner_t in zip(f_inner_ts, inner_ts)
+        )
+    else:
+        if not isinstance(f_arg, torch.Tensor):
+            assert not isinstance(arg, torch.Tensor)
+            return False
+        assert isinstance(f_arg, FunctionalTensor)
+        assert isinstance(arg, FakeTensor)
+
+        arg_after = torch._from_functional_tensor(f_arg.elem)
+        # This is true if the current tensor experienced at least one set_() call
+        maybe_storage_changed = torch._functionalize_was_storage_changed(f_arg.elem)  # type: ignore[attr-defined]
+        # However, multiple set_() calls can cancel out. So we also check whether the
+        # storage of the tensor has changed.
+        # Note: if an input experienced two set_() calls that cancel out, **and**
+        # it experiences an data mutation, we pessimistically think that the set_()
+        # call is necessary here. We could in theory fix this, but this will
+        # hopefully never happen in user code, and is not needed for fsdp.
+        same_storages = StorageWeakRef(arg.untyped_storage()) == StorageWeakRef(
+            arg_after.untyped_storage()
+        )
+        has_storage_metadata_mutation = maybe_storage_changed and not same_storages
+        if check_only_storage_mutation:
+            return has_storage_metadata_mutation
+
+        # storage metadata mutation is a type of metadata mutation, so return true if we saw one
+        if has_storage_metadata_mutation:
+            return True
+
+        maybe_metadata_mutated = torch._functionalize_has_metadata_mutation(f_arg.elem)  # type: ignore[attr-defined]
+        # This is true if the current tensor experienced at least one metadata mutation.
+        # So if false, we know there was no metadata mutation
+        if not maybe_metadata_mutated:
+            return False
+
+        # However, multi metadata mutations can cancel out.
+        # So we also check if the concrete sizes/strides on the tensor have changed.
+        same_sizes = arg.shape == arg_after.shape
+        same_strides = arg.stride() == arg_after.stride()
+        same_offsets = arg.storage_offset() == arg_after.storage_offset()
+        has_metadata_mutation_ = maybe_metadata_mutated and not (
+            same_sizes and same_strides and same_offsets
+        )
+        # We consider a tensor to have been metadata mutated if its storage was mutated through a set_() call.
+        return has_metadata_mutation_
+
+
+def gen_alias_from_base(aliased_base_tensor, target_meta_tensor, target_requires_grad):
+    # Try to do view-replay if possible.
+    # fall back to .as_strided() if we can't.
+    if target_meta_tensor._base is not None:
+        # The base that we want to replay our view off of might have a different shape than the view's original base.
+        b = target_meta_tensor._base
+        abt = aliased_base_tensor
+        # Don't unnecessarily call as_strided if nothing changed; as_strided's
+        # backward is poorly implemented and slow
+        if abt is not b and (
+            abt.size() != b.size()
+            or abt.stride() != b.stride()
+            or abt.storage_offset() != b.storage_offset()
+        ):
+            reshaped_base_tensor = aliased_base_tensor.as_strided(
+                b.size(), b.stride(), b.storage_offset()
+            )
+        else:
+            reshaped_base_tensor = aliased_base_tensor
+        out = target_meta_tensor._view_func(reshaped_base_tensor)
+        # This shape mismatch can happen due to a bug in inplace/view handling in autograd.
+        # Try putting a breakpoint here and running
+        # `test/functorch/test_aotdispatch TestAOTAutograd.test_output_all_alias_types`
+        # Also, https://github.com/pytorch/pytorch/issues/49825
+        #
+        # As a stopgap, we'll fall back to as_strided.
+        if out is not None and out.shape == target_meta_tensor.shape:
+            if aliased_base_tensor.requires_grad and not target_requires_grad:
+                out = out.detach()
+            elif not aliased_base_tensor.requires_grad and target_requires_grad:
+                out.requires_grad_(True)
+            return out
+    size = target_meta_tensor.size()
+    stride = target_meta_tensor.stride()
+    storage_offset = target_meta_tensor.storage_offset()
+    if aliased_base_tensor.is_complex() and not target_meta_tensor.is_complex():
+        aliased_out = torch.view_as_real(aliased_base_tensor).as_strided(
+            size, stride, storage_offset
+        )
+    elif not aliased_base_tensor.is_complex() and target_meta_tensor.is_complex():
+        aliased_out = torch.view_as_complex(aliased_base_tensor).as_strided(
+            size, stride, storage_offset
+        )
+    else:
+        aliased_out = aliased_base_tensor.as_strided(size, stride, storage_offset)
+    # For outputs aliasing inputs, we need to check if the requires-gradness has changed.
+    if aliased_base_tensor.requires_grad and not target_requires_grad:
+        aliased_out = aliased_out.detach()
+    elif not aliased_base_tensor.requires_grad and target_requires_grad:
+        aliased_out.requires_grad_(True)
+    # For outputs aliasing inputs, we need to check if the dtype has changed.
+    # as_strided() is the "most generic" view, but it does not cover cross-dtype views
+    if aliased_out.dtype != target_meta_tensor.dtype:
+        aliased_out = aliased_out.view(target_meta_tensor.dtype)
+    return aliased_out
+
+
+def has_same_metadata(t1, t2):
+    return (
+        definitely_true(sym_eq(t1.size(), t2.size()))
+        and definitely_true(sym_eq(t1.stride(), t2.stride()))
+        and definitely_true(t1.storage_offset() == t2.storage_offset())
+        and t1.is_conj() == t2.is_conj()
+        and t1.is_neg() == t2.is_neg()
+    )
+
+
+# new_arg and arg here are either:
+# (1) both a FakeTensor
+# (2) both a traceable tensor subclass that holds a FakeTensor
+# Pre-condition: the two args are the "old" and "new" inputs from running functionalization.
+# When we run functionalization and wrap our inputs into FunctionalTensors,
+# we can detect whether or not an input was mutated by checking to see if the inner tensor has changed
+#
+# Normally it would be enough just to check if arg is new_arg, which is normally enough for functionalization
+# to confirm that inputs were not mutated when running the user's model with functionalization on.
+# But when we have subclass inputs, we can't rely on that:
+# `from_fun(to_fun(x)) is x` will return False, because the call to `from_fun` constructs
+# a brand new subclass instance: we are calling __tensor_unflatten__, and going
+# from Subclass(FakeTensor) to Subclass(FunctionalTensor(FakeTensor))
+def was_tensor_updated(arg, new_arg):
+    if is_traceable_wrapper_subclass(arg):
+        assert is_traceable_wrapper_subclass(new_arg)
+        attrs, _ = arg.__tensor_flatten__()
+        new_attrs, _ = new_arg.__tensor_flatten__()
+        assert attrs == new_attrs
+        # A tensor subclass was updated if any of its inner elements were updated
+        return any(
+            was_tensor_updated(getattr(arg, attr), getattr(new_arg, attr))
+            for attr in attrs
+        )
+    else:
+        return arg is not new_arg
+
+
+# new_arg and arg here are either:
+# (1) both a FakeTensor
+# (2) both a traceable tensor subclass that holds a FakeTensor
+# Pre-condition: the two args are the "old" and "new" inputs from running functionalization.
+# When we run functionalization and wrap our inputs into FunctionalTensors,
+# we can detect whether or not an input was mutated by checking to see if the inner tensor has changed,
+# but shares storage with the old input
+def was_tensor_metadata_updated(arg, new_arg):
+    if is_traceable_wrapper_subclass(arg):
+        assert is_traceable_wrapper_subclass(new_arg)
+        attrs, _ = arg.__tensor_flatten__()
+        new_attrs, _ = new_arg.__tensor_flatten__()
+        assert attrs == new_attrs
+        # A tensor subclass was updated if any of its inner elements were updated
+        return any(
+            was_tensor_metadata_updated(getattr(arg, attr), getattr(new_arg, attr))
+            for attr in attrs
+        )
+    else:
+        return arg is not new_arg and StorageWeakRef(
+            arg.untyped_storage()
+        ) == StorageWeakRef(new_arg.untyped_storage())
+
+
+# Returns the number of detected copy_
+def assert_functional_graph(fx_g: torch.fx.Graph) -> int:
+    placeholders = set()
+    copy_count = 0
+    # NB: It would also be nice to verify that the mutations all happen at the
+    # end, but we also do some administrative views after mutations so this
+    # isn't actually true.  (TODO: Could this cause problems for Inductor?)
+    for n in fx_g.nodes:
+        if n.op == "placeholder":
+            placeholders.add(n)
+        if isinstance(n.target, torch._ops.OpOverload):
+            if n.target is torch.ops.aten.copy_.default:
+                suffix = True
+                # Can only copy_ into an input, and can only do so once
+                assert n.args[0] in placeholders
+                placeholders.remove(n.args[0])
+                copy_count += 1
+            else:
+                assert (
+                    not n.target._schema.is_mutable
+                ), f"aot_autograd expected to have an entirely functional graph, but found {n.format_node()}"
+    return copy_count
+
+
+def propagate_input_mutation_stacktraces(fx_g: torch.fx.Graph) -> None:
+    placeholders = set()
+    for n in fx_g.nodes:
+        if n.op == "placeholder":
+            placeholders.add(n)
+        if isinstance(n.target, torch._ops.OpOverload):
+            if n.target is torch.ops.aten.copy_.default:
+                # Can only copy_ into an input, and can only do so once
+                assert n.args[0] in placeholders
+                placeholders.remove(n.args[0])
+                copy_from_node = n.args[1]
+                # Pre-condition: every node has a "stack_trace" field in its meta,
+                # but copy_() nodes do not (since we manually added them during functionalization).
+                # Instead, we manually propagate here.
+                if "stack_trace" in copy_from_node.meta:
+                    assert "stack_trace" not in n.meta, str(n)
+                    n.meta["stack_trace"] = copy_from_node.meta["stack_trace"]
+
+
+def _check_if_mutation_can_be_in_graph(
+    keep_input_mutations: bool,
+    mutates_data,
+    mutates_metadata,
+    mutations_hidden_from_autograd,
+    mutations_under_no_grad_or_inference_mode,
+    requires_grad,
+):
+    if keep_input_mutations:
+        return mutates_data and (
+            (not mutates_metadata and not requires_grad)
+            or mutations_hidden_from_autograd
+            or mutations_under_no_grad_or_inference_mode
+        )
+    return False
diff --git a/shared/package-files/4e/4ee3335fe972af8fd5fea7a41e2dfd94978bc7e1a6d15e170c44fe431a35cfb9/Handle.h b/shared/package-files/4e/4ee3335fe972af8fd5fea7a41e2dfd94978bc7e1a6d15e170c44fe431a35cfb9/Handle.h
new file mode 100644
index 0000000000000000000000000000000000000000..f74638455817a894c774d9064cb981fc1c707dd5
--- /dev/null
+++ b/shared/package-files/4e/4ee3335fe972af8fd5fea7a41e2dfd94978bc7e1a6d15e170c44fe431a35cfb9/Handle.h
@@ -0,0 +1,9 @@
+#pragma once
+
+#include <ATen/cudnn/cudnn-wrapper.h>
+#include <ATen/cuda/ATenCUDAGeneral.h>
+
+namespace at { namespace native {
+
+TORCH_CUDA_CPP_API cudnnHandle_t getCudnnHandle();
+}} // namespace at::native
diff --git a/shared/package-files/4e/4ee774e2d0a3efe1cd1a1afdb3778f6a95523c79bc940ddfd40c19592ba7be21/expatreader.py b/shared/package-files/4e/4ee774e2d0a3efe1cd1a1afdb3778f6a95523c79bc940ddfd40c19592ba7be21/expatreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f7c87b126c9933a4071dc19ec797e2472f28e42
--- /dev/null
+++ b/shared/package-files/4e/4ee774e2d0a3efe1cd1a1afdb3778f6a95523c79bc940ddfd40c19592ba7be21/expatreader.py
@@ -0,0 +1,460 @@
+"""
+SAX driver for the pyexpat C module.  This driver works with
+pyexpat.__version__ == '2.22'.
+"""
+
+version = "0.20"
+
+from xml.sax._exceptions import *
+from xml.sax.handler import feature_validation, feature_namespaces
+from xml.sax.handler import feature_namespace_prefixes
+from xml.sax.handler import feature_external_ges, feature_external_pes
+from xml.sax.handler import feature_string_interning
+from xml.sax.handler import property_xml_string, property_interning_dict
+
+# xml.parsers.expat does not raise ImportError in Jython
+import sys
+if sys.platform[:4] == "java":
+    raise SAXReaderNotAvailable("expat not available in Java", None)
+del sys
+
+try:
+    from xml.parsers import expat
+except ImportError:
+    raise SAXReaderNotAvailable("expat not supported", None)
+else:
+    if not hasattr(expat, "ParserCreate"):
+        raise SAXReaderNotAvailable("expat not supported", None)
+from xml.sax import xmlreader, saxutils, handler
+
+AttributesImpl = xmlreader.AttributesImpl
+AttributesNSImpl = xmlreader.AttributesNSImpl
+
+# If we're using a sufficiently recent version of Python, we can use
+# weak references to avoid cycles between the parser and content
+# handler, otherwise we'll just have to pretend.
+try:
+    import _weakref
+except ImportError:
+    def _mkproxy(o):
+        return o
+else:
+    import weakref
+    _mkproxy = weakref.proxy
+    del weakref, _weakref
+
+class _ClosedParser:
+    pass
+
+# --- ExpatLocator
+
+class ExpatLocator(xmlreader.Locator):
+    """Locator for use with the ExpatParser class.
+
+    This uses a weak reference to the parser object to avoid creating
+    a circular reference between the parser and the content handler.
+    """
+    def __init__(self, parser):
+        self._ref = _mkproxy(parser)
+
+    def getColumnNumber(self):
+        parser = self._ref
+        if parser._parser is None:
+            return None
+        return parser._parser.ErrorColumnNumber
+
+    def getLineNumber(self):
+        parser = self._ref
+        if parser._parser is None:
+            return 1
+        return parser._parser.ErrorLineNumber
+
+    def getPublicId(self):
+        parser = self._ref
+        if parser is None:
+            return None
+        return parser._source.getPublicId()
+
+    def getSystemId(self):
+        parser = self._ref
+        if parser is None:
+            return None
+        return parser._source.getSystemId()
+
+
+# --- ExpatParser
+
+class ExpatParser(xmlreader.IncrementalParser, xmlreader.Locator):
+    """SAX driver for the pyexpat C module."""
+
+    def __init__(self, namespaceHandling=0, bufsize=2**16-20):
+        xmlreader.IncrementalParser.__init__(self, bufsize)
+        self._source = xmlreader.InputSource()
+        self._parser = None
+        self._namespaces = namespaceHandling
+        self._lex_handler_prop = None
+        self._parsing = False
+        self._entity_stack = []
+        self._external_ges = 0
+        self._interning = None
+
+    # XMLReader methods
+
+    def parse(self, source):
+        "Parse an XML document from a URL or an InputSource."
+        source = saxutils.prepare_input_source(source)
+
+        self._source = source
+        try:
+            self.reset()
+            self._cont_handler.setDocumentLocator(ExpatLocator(self))
+            xmlreader.IncrementalParser.parse(self, source)
+        except:
+            # bpo-30264: Close the source on error to not leak resources:
+            # xml.sax.parse() doesn't give access to the underlying parser
+            # to the caller
+            self._close_source()
+            raise
+
+    def prepareParser(self, source):
+        if source.getSystemId() is not None:
+            self._parser.SetBase(source.getSystemId())
+
+    # Redefined setContentHandler to allow changing handlers during parsing
+
+    def setContentHandler(self, handler):
+        xmlreader.IncrementalParser.setContentHandler(self, handler)
+        if self._parsing:
+            self._reset_cont_handler()
+
+    def getFeature(self, name):
+        if name == feature_namespaces:
+            return self._namespaces
+        elif name == feature_string_interning:
+            return self._interning is not None
+        elif name in (feature_validation, feature_external_pes,
+                      feature_namespace_prefixes):
+            return 0
+        elif name == feature_external_ges:
+            return self._external_ges
+        raise SAXNotRecognizedException("Feature '%s' not recognized" % name)
+
+    def setFeature(self, name, state):
+        if self._parsing:
+            raise SAXNotSupportedException("Cannot set features while parsing")
+
+        if name == feature_namespaces:
+            self._namespaces = state
+        elif name == feature_external_ges:
+            self._external_ges = state
+        elif name == feature_string_interning:
+            if state:
+                if self._interning is None:
+                    self._interning = {}
+            else:
+                self._interning = None
+        elif name == feature_validation:
+            if state:
+                raise SAXNotSupportedException(
+                    "expat does not support validation")
+        elif name == feature_external_pes:
+            if state:
+                raise SAXNotSupportedException(
+                    "expat does not read external parameter entities")
+        elif name == feature_namespace_prefixes:
+            if state:
+                raise SAXNotSupportedException(
+                    "expat does not report namespace prefixes")
+        else:
+            raise SAXNotRecognizedException(
+                "Feature '%s' not recognized" % name)
+
+    def getProperty(self, name):
+        if name == handler.property_lexical_handler:
+            return self._lex_handler_prop
+        elif name == property_interning_dict:
+            return self._interning
+        elif name == property_xml_string:
+            if self._parser:
+                if hasattr(self._parser, "GetInputContext"):
+                    return self._parser.GetInputContext()
+                else:
+                    raise SAXNotRecognizedException(
+                        "This version of expat does not support getting"
+                        " the XML string")
+            else:
+                raise SAXNotSupportedException(
+                    "XML string cannot be returned when not parsing")
+        raise SAXNotRecognizedException("Property '%s' not recognized" % name)
+
+    def setProperty(self, name, value):
+        if name == handler.property_lexical_handler:
+            self._lex_handler_prop = value
+            if self._parsing:
+                self._reset_lex_handler_prop()
+        elif name == property_interning_dict:
+            self._interning = value
+        elif name == property_xml_string:
+            raise SAXNotSupportedException("Property '%s' cannot be set" %
+                                           name)
+        else:
+            raise SAXNotRecognizedException("Property '%s' not recognized" %
+                                            name)
+
+    # IncrementalParser methods
+
+    def feed(self, data, isFinal=False):
+        if not self._parsing:
+            self.reset()
+            self._parsing = True
+            self._cont_handler.startDocument()
+
+        try:
+            # The isFinal parameter is internal to the expat reader.
+            # If it is set to true, expat will check validity of the entire
+            # document. When feeding chunks, they are not normally final -
+            # except when invoked from close.
+            self._parser.Parse(data, isFinal)
+        except expat.error as e:
+            exc = SAXParseException(expat.ErrorString(e.code), e, self)
+            # FIXME: when to invoke error()?
+            self._err_handler.fatalError(exc)
+
+    def flush(self):
+        if self._parser is None:
+            return
+
+        was_enabled = self._parser.GetReparseDeferralEnabled()
+        try:
+            self._parser.SetReparseDeferralEnabled(False)
+            self._parser.Parse(b"", False)
+        except expat.error as e:
+            exc = SAXParseException(expat.ErrorString(e.code), e, self)
+            self._err_handler.fatalError(exc)
+        finally:
+            self._parser.SetReparseDeferralEnabled(was_enabled)
+
+    def _close_source(self):
+        source = self._source
+        try:
+            file = source.getCharacterStream()
+            if file is not None:
+                file.close()
+        finally:
+            file = source.getByteStream()
+            if file is not None:
+                file.close()
+
+    def close(self):
+        if (self._entity_stack or self._parser is None or
+            isinstance(self._parser, _ClosedParser)):
+            # If we are completing an external entity, do nothing here
+            return
+        try:
+            self.feed(b"", isFinal=True)
+            self._cont_handler.endDocument()
+            self._parsing = False
+            # break cycle created by expat handlers pointing to our methods
+            self._parser = None
+        finally:
+            self._parsing = False
+            if self._parser is not None:
+                # Keep ErrorColumnNumber and ErrorLineNumber after closing.
+                parser = _ClosedParser()
+                parser.ErrorColumnNumber = self._parser.ErrorColumnNumber
+                parser.ErrorLineNumber = self._parser.ErrorLineNumber
+                self._parser = parser
+            self._close_source()
+
+    def _reset_cont_handler(self):
+        self._parser.ProcessingInstructionHandler = \
+                                    self._cont_handler.processingInstruction
+        self._parser.CharacterDataHandler = self._cont_handler.characters
+
+    def _reset_lex_handler_prop(self):
+        lex = self._lex_handler_prop
+        parser = self._parser
+        if lex is None:
+            parser.CommentHandler = None
+            parser.StartCdataSectionHandler = None
+            parser.EndCdataSectionHandler = None
+            parser.StartDoctypeDeclHandler = None
+            parser.EndDoctypeDeclHandler = None
+        else:
+            parser.CommentHandler = lex.comment
+            parser.StartCdataSectionHandler = lex.startCDATA
+            parser.EndCdataSectionHandler = lex.endCDATA
+            parser.StartDoctypeDeclHandler = self.start_doctype_decl
+            parser.EndDoctypeDeclHandler = lex.endDTD
+
+    def reset(self):
+        if self._namespaces:
+            self._parser = expat.ParserCreate(self._source.getEncoding(), " ",
+                                              intern=self._interning)
+            self._parser.namespace_prefixes = 1
+            self._parser.StartElementHandler = self.start_element_ns
+            self._parser.EndElementHandler = self.end_element_ns
+        else:
+            self._parser = expat.ParserCreate(self._source.getEncoding(),
+                                              intern = self._interning)
+            self._parser.StartElementHandler = self.start_element
+            self._parser.EndElementHandler = self.end_element
+
+        self._reset_cont_handler()
+        self._parser.UnparsedEntityDeclHandler = self.unparsed_entity_decl
+        self._parser.NotationDeclHandler = self.notation_decl
+        self._parser.StartNamespaceDeclHandler = self.start_namespace_decl
+        self._parser.EndNamespaceDeclHandler = self.end_namespace_decl
+
+        self._decl_handler_prop = None
+        if self._lex_handler_prop:
+            self._reset_lex_handler_prop()
+#         self._parser.DefaultHandler =
+#         self._parser.DefaultHandlerExpand =
+#         self._parser.NotStandaloneHandler =
+        self._parser.ExternalEntityRefHandler = self.external_entity_ref
+        try:
+            self._parser.SkippedEntityHandler = self.skipped_entity_handler
+        except AttributeError:
+            # This pyexpat does not support SkippedEntity
+            pass
+        self._parser.SetParamEntityParsing(
+            expat.XML_PARAM_ENTITY_PARSING_UNLESS_STANDALONE)
+
+        self._parsing = False
+        self._entity_stack = []
+
+    # Locator methods
+
+    def getColumnNumber(self):
+        if self._parser is None:
+            return None
+        return self._parser.ErrorColumnNumber
+
+    def getLineNumber(self):
+        if self._parser is None:
+            return 1
+        return self._parser.ErrorLineNumber
+
+    def getPublicId(self):
+        return self._source.getPublicId()
+
+    def getSystemId(self):
+        return self._source.getSystemId()
+
+    # event handlers
+    def start_element(self, name, attrs):
+        self._cont_handler.startElement(name, AttributesImpl(attrs))
+
+    def end_element(self, name):
+        self._cont_handler.endElement(name)
+
+    def start_element_ns(self, name, attrs):
+        pair = name.split()
+        if len(pair) == 1:
+            # no namespace
+            pair = (None, name)
+        elif len(pair) == 3:
+            pair = pair[0], pair[1]
+        else:
+            # default namespace
+            pair = tuple(pair)
+
+        newattrs = {}
+        qnames = {}
+        for (aname, value) in attrs.items():
+            parts = aname.split()
+            length = len(parts)
+            if length == 1:
+                # no namespace
+                qname = aname
+                apair = (None, aname)
+            elif length == 3:
+                qname = "%s:%s" % (parts[2], parts[1])
+                apair = parts[0], parts[1]
+            else:
+                # default namespace
+                qname = parts[1]
+                apair = tuple(parts)
+
+            newattrs[apair] = value
+            qnames[apair] = qname
+
+        self._cont_handler.startElementNS(pair, None,
+                                          AttributesNSImpl(newattrs, qnames))
+
+    def end_element_ns(self, name):
+        pair = name.split()
+        if len(pair) == 1:
+            pair = (None, name)
+        elif len(pair) == 3:
+            pair = pair[0], pair[1]
+        else:
+            pair = tuple(pair)
+
+        self._cont_handler.endElementNS(pair, None)
+
+    # this is not used (call directly to ContentHandler)
+    def processing_instruction(self, target, data):
+        self._cont_handler.processingInstruction(target, data)
+
+    # this is not used (call directly to ContentHandler)
+    def character_data(self, data):
+        self._cont_handler.characters(data)
+
+    def start_namespace_decl(self, prefix, uri):
+        self._cont_handler.startPrefixMapping(prefix, uri)
+
+    def end_namespace_decl(self, prefix):
+        self._cont_handler.endPrefixMapping(prefix)
+
+    def start_doctype_decl(self, name, sysid, pubid, has_internal_subset):
+        self._lex_handler_prop.startDTD(name, pubid, sysid)
+
+    def unparsed_entity_decl(self, name, base, sysid, pubid, notation_name):
+        self._dtd_handler.unparsedEntityDecl(name, pubid, sysid, notation_name)
+
+    def notation_decl(self, name, base, sysid, pubid):
+        self._dtd_handler.notationDecl(name, pubid, sysid)
+
+    def external_entity_ref(self, context, base, sysid, pubid):
+        if not self._external_ges:
+            return 1
+
+        source = self._ent_handler.resolveEntity(pubid, sysid)
+        source = saxutils.prepare_input_source(source,
+                                               self._source.getSystemId() or
+                                               "")
+
+        self._entity_stack.append((self._parser, self._source))
+        self._parser = self._parser.ExternalEntityParserCreate(context)
+        self._source = source
+
+        try:
+            xmlreader.IncrementalParser.parse(self, source)
+        except:
+            return 0  # FIXME: save error info here?
+
+        (self._parser, self._source) = self._entity_stack[-1]
+        del self._entity_stack[-1]
+        return 1
+
+    def skipped_entity_handler(self, name, is_pe):
+        if is_pe:
+            # The SAX spec requires to report skipped PEs with a '%'
+            name = '%'+name
+        self._cont_handler.skippedEntity(name)
+
+# ---
+
+def create_parser(*args, **kwargs):
+    return ExpatParser(*args, **kwargs)
+
+# ---
+
+if __name__ == "__main__":
+    import xml.sax.saxutils
+    p = create_parser()
+    p.setContentHandler(xml.sax.saxutils.XMLGenerator())
+    p.setErrorHandler(xml.sax.ErrorHandler())
+    p.parse("http://www.ibiblio.org/xml/examples/shakespeare/hamlet.xml")
diff --git a/shared/package-files/4e/4ee833ac741a7f0ec7dc6026d8b91de06f48d9ad9c60fce67fb4c8154c60dd5a/_bz2.cpython-310-x86_64-linux-gnu.so b/shared/package-files/4e/4ee833ac741a7f0ec7dc6026d8b91de06f48d9ad9c60fce67fb4c8154c60dd5a/_bz2.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a01cbf7a92fbf70a76b2b14ad1475944d2c44
Binary files /dev/null and b/shared/package-files/4e/4ee833ac741a7f0ec7dc6026d8b91de06f48d9ad9c60fce67fb4c8154c60dd5a/_bz2.cpython-310-x86_64-linux-gnu.so differ
diff --git a/shared/package-files/4e/4eeb81035942c6354e816b1633d8e181cf8fc86b97cda8cedd459629912ab990/test_array_with_attr.py b/shared/package-files/4e/4eeb81035942c6354e816b1633d8e181cf8fc86b97cda8cedd459629912ab990/test_array_with_attr.py
new file mode 100644
index 0000000000000000000000000000000000000000..3735fe40a0d67784b3603a177b6694e56e26d479
--- /dev/null
+++ b/shared/package-files/4e/4eeb81035942c6354e816b1633d8e181cf8fc86b97cda8cedd459629912ab990/test_array_with_attr.py
@@ -0,0 +1,33 @@
+import numpy as np
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.extension.array_with_attr import FloatAttrArray
+
+
+def test_concat_with_all_na():
+    # https://github.com/pandas-dev/pandas/pull/47762
+    # ensure that attribute of the column array is preserved (when it gets
+    # preserved in reindexing the array) during merge/concat
+    arr = FloatAttrArray(np.array([np.nan, np.nan], dtype="float64"), attr="test")
+
+    df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
+    df2 = pd.DataFrame({"key": [0, 1], "col2": [1, 2]})
+    result = pd.merge(df1, df2, on="key")
+    expected = pd.DataFrame({"col": arr, "key": [0, 1], "col2": [1, 2]})
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
+
+    df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
+    df2 = pd.DataFrame({"key": [0, 2], "col2": [1, 2]})
+    result = pd.merge(df1, df2, on="key")
+    expected = pd.DataFrame({"col": arr.take([0]), "key": [0], "col2": [1]})
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
+
+    result = pd.concat([df1.set_index("key"), df2.set_index("key")], axis=1)
+    expected = pd.DataFrame(
+        {"col": arr.take([0, 1, -1]), "col2": [1, np.nan, 2], "key": [0, 1, 2]}
+    ).set_index("key")
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
diff --git a/shared/package-files/4e/4eec71a45c9cf72e6ce5d407de16678c4a2f86864cd270d034f8df2b0e48e2e8/function.py b/shared/package-files/4e/4eec71a45c9cf72e6ce5d407de16678c4a2f86864cd270d034f8df2b0e48e2e8/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..db15050173e843b1d6d601f9bf556ec667e8188a
--- /dev/null
+++ b/shared/package-files/4e/4eec71a45c9cf72e6ce5d407de16678c4a2f86864cd270d034f8df2b0e48e2e8/function.py
@@ -0,0 +1,3385 @@
+"""
+There are three types of functions implemented in SymPy:
+
+    1) defined functions (in the sense that they can be evaluated) like
+       exp or sin; they have a name and a body:
+           f = exp
+    2) undefined function which have a name but no body. Undefined
+       functions can be defined using a Function class as follows:
+           f = Function('f')
+       (the result will be a Function instance)
+    3) anonymous function (or lambda function) which have a body (defined
+       with dummy variables) but have no name:
+           f = Lambda(x, exp(x)*x)
+           f = Lambda((x, y), exp(x)*y)
+    The fourth type of functions are composites, like (sin + cos)(x); these work in
+    SymPy core, but are not yet part of SymPy.
+
+    Examples
+    ========
+
+    >>> import sympy
+    >>> f = sympy.Function("f")
+    >>> from sympy.abc import x
+    >>> f(x)
+    f(x)
+    >>> print(sympy.srepr(f(x).func))
+    Function('f')
+    >>> f(x).args
+    (x,)
+
+"""
+
+from __future__ import annotations
+from typing import Any
+from collections.abc import Iterable
+
+from .add import Add
+from .basic import Basic, _atomic
+from .cache import cacheit
+from .containers import Tuple, Dict
+from .decorators import _sympifyit
+from .evalf import pure_complex
+from .expr import Expr, AtomicExpr
+from .logic import fuzzy_and, fuzzy_or, fuzzy_not, FuzzyBool
+from .mul import Mul
+from .numbers import Rational, Float, Integer
+from .operations import LatticeOp
+from .parameters import global_parameters
+from .rules import Transform
+from .singleton import S
+from .sympify import sympify, _sympify
+
+from .sorting import default_sort_key, ordered
+from sympy.utilities.exceptions import (sympy_deprecation_warning,
+                                        SymPyDeprecationWarning, ignore_warnings)
+from sympy.utilities.iterables import (has_dups, sift, iterable,
+    is_sequence, uniq, topological_sort)
+from sympy.utilities.lambdify import MPMATH_TRANSLATIONS
+from sympy.utilities.misc import as_int, filldedent, func_name
+
+import mpmath
+from mpmath.libmp.libmpf import prec_to_dps
+
+import inspect
+from collections import Counter
+
+def _coeff_isneg(a):
+    """Return True if the leading Number is negative.
+
+    Examples
+    ========
+
+    >>> from sympy.core.function import _coeff_isneg
+    >>> from sympy import S, Symbol, oo, pi
+    >>> _coeff_isneg(-3*pi)
+    True
+    >>> _coeff_isneg(S(3))
+    False
+    >>> _coeff_isneg(-oo)
+    True
+    >>> _coeff_isneg(Symbol('n', negative=True)) # coeff is 1
+    False
+
+    For matrix expressions:
+
+    >>> from sympy import MatrixSymbol, sqrt
+    >>> A = MatrixSymbol("A", 3, 3)
+    >>> _coeff_isneg(-sqrt(2)*A)
+    True
+    >>> _coeff_isneg(sqrt(2)*A)
+    False
+    """
+
+    if a.is_MatMul:
+        a = a.args[0]
+    if a.is_Mul:
+        a = a.args[0]
+    return a.is_Number and a.is_extended_negative
+
+
+class PoleError(Exception):
+    pass
+
+
+class ArgumentIndexError(ValueError):
+    def __str__(self):
+        return ("Invalid operation with argument number %s for Function %s" %
+               (self.args[1], self.args[0]))
+
+
+class BadSignatureError(TypeError):
+    '''Raised when a Lambda is created with an invalid signature'''
+    pass
+
+
+class BadArgumentsError(TypeError):
+    '''Raised when a Lambda is called with an incorrect number of arguments'''
+    pass
+
+
+# Python 3 version that does not raise a Deprecation warning
+def arity(cls):
+    """Return the arity of the function if it is known, else None.
+
+    Explanation
+    ===========
+
+    When default values are specified for some arguments, they are
+    optional and the arity is reported as a tuple of possible values.
+
+    Examples
+    ========
+
+    >>> from sympy import arity, log
+    >>> arity(lambda x: x)
+    1
+    >>> arity(log)
+    (1, 2)
+    >>> arity(lambda *x: sum(x)) is None
+    True
+    """
+    eval_ = getattr(cls, 'eval', cls)
+
+    parameters = inspect.signature(eval_).parameters.items()
+    if [p for _, p in parameters if p.kind == p.VAR_POSITIONAL]:
+        return
+    p_or_k = [p for _, p in parameters if p.kind == p.POSITIONAL_OR_KEYWORD]
+    # how many have no default and how many have a default value
+    no, yes = map(len, sift(p_or_k,
+        lambda p:p.default == p.empty, binary=True))
+    return no if not yes else tuple(range(no, no + yes + 1))
+
+class FunctionClass(type):
+    """
+    Base class for function classes. FunctionClass is a subclass of type.
+
+    Use Function('<function name>' [ , signature ]) to create
+    undefined function classes.
+    """
+    _new = type.__new__
+
+    def __init__(cls, *args, **kwargs):
+        # honor kwarg value or class-defined value before using
+        # the number of arguments in the eval function (if present)
+        nargs = kwargs.pop('nargs', cls.__dict__.get('nargs', arity(cls)))
+        if nargs is None and 'nargs' not in cls.__dict__:
+            for supcls in cls.__mro__:
+                if hasattr(supcls, '_nargs'):
+                    nargs = supcls._nargs
+                    break
+                else:
+                    continue
+
+        # Canonicalize nargs here; change to set in nargs.
+        if is_sequence(nargs):
+            if not nargs:
+                raise ValueError(filldedent('''
+                    Incorrectly specified nargs as %s:
+                    if there are no arguments, it should be
+                    `nargs = 0`;
+                    if there are any number of arguments,
+                    it should be
+                    `nargs = None`''' % str(nargs)))
+            nargs = tuple(ordered(set(nargs)))
+        elif nargs is not None:
+            nargs = (as_int(nargs),)
+        cls._nargs = nargs
+
+        # When __init__ is called from UndefinedFunction it is called with
+        # just one arg but when it is called from subclassing Function it is
+        # called with the usual (name, bases, namespace) type() signature.
+        if len(args) == 3:
+            namespace = args[2]
+            if 'eval' in namespace and not isinstance(namespace['eval'], classmethod):
+                raise TypeError("eval on Function subclasses should be a class method (defined with @classmethod)")
+
+    @property
+    def __signature__(self):
+        """
+        Allow Python 3's inspect.signature to give a useful signature for
+        Function subclasses.
+        """
+        # Python 3 only, but backports (like the one in IPython) still might
+        # call this.
+        try:
+            from inspect import signature
+        except ImportError:
+            return None
+
+        # TODO: Look at nargs
+        return signature(self.eval)
+
+    @property
+    def free_symbols(self):
+        return set()
+
+    @property
+    def xreplace(self):
+        # Function needs args so we define a property that returns
+        # a function that takes args...and then use that function
+        # to return the right value
+        return lambda rule, **_: rule.get(self, self)
+
+    @property
+    def nargs(self):
+        """Return a set of the allowed number of arguments for the function.
+
+        Examples
+        ========
+
+        >>> from sympy import Function
+        >>> f = Function('f')
+
+        If the function can take any number of arguments, the set of whole
+        numbers is returned:
+
+        >>> Function('f').nargs
+        Naturals0
+
+        If the function was initialized to accept one or more arguments, a
+        corresponding set will be returned:
+
+        >>> Function('f', nargs=1).nargs
+        {1}
+        >>> Function('f', nargs=(2, 1)).nargs
+        {1, 2}
+
+        The undefined function, after application, also has the nargs
+        attribute; the actual number of arguments is always available by
+        checking the ``args`` attribute:
+
+        >>> f = Function('f')
+        >>> f(1).nargs
+        Naturals0
+        >>> len(f(1).args)
+        1
+        """
+        from sympy.sets.sets import FiniteSet
+        # XXX it would be nice to handle this in __init__ but there are import
+        # problems with trying to import FiniteSet there
+        return FiniteSet(*self._nargs) if self._nargs else S.Naturals0
+
+    def _valid_nargs(self, n : int) -> bool:
+        """ Return True if the specified integer is a valid number of arguments
+
+        The number of arguments n is guaranteed to be an integer and positive
+
+        """
+        if self._nargs:
+            return n in self._nargs
+
+        nargs = self.nargs
+        return nargs is S.Naturals0 or n in nargs
+
+    def __repr__(cls):
+        return cls.__name__
+
+
+class Application(Basic, metaclass=FunctionClass):
+    """
+    Base class for applied functions.
+
+    Explanation
+    ===========
+
+    Instances of Application represent the result of applying an application of
+    any type to any object.
+    """
+
+    is_Function = True
+
+    @cacheit
+    def __new__(cls, *args, **options):
+        from sympy.sets.fancysets import Naturals0
+        from sympy.sets.sets import FiniteSet
+
+        args = list(map(sympify, args))
+        evaluate = options.pop('evaluate', global_parameters.evaluate)
+        # WildFunction (and anything else like it) may have nargs defined
+        # and we throw that value away here
+        options.pop('nargs', None)
+
+        if options:
+            raise ValueError("Unknown options: %s" % options)
+
+        if evaluate:
+            evaluated = cls.eval(*args)
+            if evaluated is not None:
+                return evaluated
+
+        obj = super().__new__(cls, *args, **options)
+
+        # make nargs uniform here
+        sentinel = object()
+        objnargs = getattr(obj, "nargs", sentinel)
+        if objnargs is not sentinel:
+            # things passing through here:
+            #  - functions subclassed from Function (e.g. myfunc(1).nargs)
+            #  - functions like cos(1).nargs
+            #  - AppliedUndef with given nargs like Function('f', nargs=1)(1).nargs
+            # Canonicalize nargs here
+            if is_sequence(objnargs):
+                nargs = tuple(ordered(set(objnargs)))
+            elif objnargs is not None:
+                nargs = (as_int(objnargs),)
+            else:
+                nargs = None
+        else:
+            # things passing through here:
+            #  - WildFunction('f').nargs
+            #  - AppliedUndef with no nargs like Function('f')(1).nargs
+            nargs = obj._nargs  # note the underscore here
+        # convert to FiniteSet
+        obj.nargs = FiniteSet(*nargs) if nargs else Naturals0()
+        return obj
+
+    @classmethod
+    def eval(cls, *args):
+        """
+        Returns a canonical form of cls applied to arguments args.
+
+        Explanation
+        ===========
+
+        The ``eval()`` method is called when the class ``cls`` is about to be
+        instantiated and it should return either some simplified instance
+        (possible of some other class), or if the class ``cls`` should be
+        unmodified, return None.
+
+        Examples of ``eval()`` for the function "sign"
+
+        .. code-block:: python
+
+            @classmethod
+            def eval(cls, arg):
+                if arg is S.NaN:
+                    return S.NaN
+                if arg.is_zero: return S.Zero
+                if arg.is_positive: return S.One
+                if arg.is_negative: return S.NegativeOne
+                if isinstance(arg, Mul):
+                    coeff, terms = arg.as_coeff_Mul(rational=True)
+                    if coeff is not S.One:
+                        return cls(coeff) * cls(terms)
+
+        """
+        return
+
+    @property
+    def func(self):
+        return self.__class__
+
+    def _eval_subs(self, old, new):
+        if (old.is_Function and new.is_Function and
+            callable(old) and callable(new) and
+            old == self.func and len(self.args) in new.nargs):
+            return new(*[i._subs(old, new) for i in self.args])
+
+
+class Function(Application, Expr):
+    r"""
+    Base class for applied mathematical functions.
+
+    It also serves as a constructor for undefined function classes.
+
+    See the :ref:`custom-functions` guide for details on how to subclass
+    ``Function`` and what methods can be defined.
+
+    Examples
+    ========
+
+    **Undefined Functions**
+
+    To create an undefined function, pass a string of the function name to
+    ``Function``.
+
+    >>> from sympy import Function, Symbol
+    >>> x = Symbol('x')
+    >>> f = Function('f')
+    >>> g = Function('g')(x)
+    >>> f
+    f
+    >>> f(x)
+    f(x)
+    >>> g
+    g(x)
+    >>> f(x).diff(x)
+    Derivative(f(x), x)
+    >>> g.diff(x)
+    Derivative(g(x), x)
+
+    Assumptions can be passed to ``Function`` the same as with a
+    :class:`~.Symbol`. Alternatively, you can use a ``Symbol`` with
+    assumptions for the function name and the function will inherit the name
+    and assumptions associated with the ``Symbol``:
+
+    >>> f_real = Function('f', real=True)
+    >>> f_real(x).is_real
+    True
+    >>> f_real_inherit = Function(Symbol('f', real=True))
+    >>> f_real_inherit(x).is_real
+    True
+
+    Note that assumptions on a function are unrelated to the assumptions on
+    the variables it is called on. If you want to add a relationship, subclass
+    ``Function`` and define custom assumptions handler methods. See the
+    :ref:`custom-functions-assumptions` section of the :ref:`custom-functions`
+    guide for more details.
+
+    **Custom Function Subclasses**
+
+    The :ref:`custom-functions` guide has several
+    :ref:`custom-functions-complete-examples` of how to subclass ``Function``
+    to create a custom function.
+
+    """
+
+    @property
+    def _diff_wrt(self):
+        return False
+
+    @cacheit
+    def __new__(cls, *args, **options):
+        # Handle calls like Function('f')
+        if cls is Function:
+            return UndefinedFunction(*args, **options)
+
+        n = len(args)
+
+        if not cls._valid_nargs(n):
+            # XXX: exception message must be in exactly this format to
+            # make it work with NumPy's functions like vectorize(). See,
+            # for example, https://github.com/numpy/numpy/issues/1697.
+            # The ideal solution would be just to attach metadata to
+            # the exception and change NumPy to take advantage of this.
+            temp = ('%(name)s takes %(qual)s %(args)s '
+                   'argument%(plural)s (%(given)s given)')
+            raise TypeError(temp % {
+                'name': cls,
+                'qual': 'exactly' if len(cls.nargs) == 1 else 'at least',
+                'args': min(cls.nargs),
+                'plural': 's'*(min(cls.nargs) != 1),
+                'given': n})
+
+        evaluate = options.get('evaluate', global_parameters.evaluate)
+        result = super().__new__(cls, *args, **options)
+        if evaluate and isinstance(result, cls) and result.args:
+            _should_evalf = [cls._should_evalf(a) for a in result.args]
+            pr2 = min(_should_evalf)
+            if pr2 > 0:
+                pr = max(_should_evalf)
+                result = result.evalf(prec_to_dps(pr))
+
+        return _sympify(result)
+
+    @classmethod
+    def _should_evalf(cls, arg):
+        """
+        Decide if the function should automatically evalf().
+
+        Explanation
+        ===========
+
+        By default (in this implementation), this happens if (and only if) the
+        ARG is a floating point number (including complex numbers).
+        This function is used by __new__.
+
+        Returns the precision to evalf to, or -1 if it should not evalf.
+        """
+        if arg.is_Float:
+            return arg._prec
+        if not arg.is_Add:
+            return -1
+        m = pure_complex(arg)
+        if m is None:
+            return -1
+        # the elements of m are of type Number, so have a _prec
+        return max(m[0]._prec, m[1]._prec)
+
+    @classmethod
+    def class_key(cls):
+        from sympy.sets.fancysets import Naturals0
+        funcs = {
+            'exp': 10,
+            'log': 11,
+            'sin': 20,
+            'cos': 21,
+            'tan': 22,
+            'cot': 23,
+            'sinh': 30,
+            'cosh': 31,
+            'tanh': 32,
+            'coth': 33,
+            'conjugate': 40,
+            're': 41,
+            'im': 42,
+            'arg': 43,
+        }
+        name = cls.__name__
+
+        try:
+            i = funcs[name]
+        except KeyError:
+            i = 0 if isinstance(cls.nargs, Naturals0) else 10000
+
+        return 4, i, name
+
+    def _eval_evalf(self, prec):
+
+        def _get_mpmath_func(fname):
+            """Lookup mpmath function based on name"""
+            if isinstance(self, AppliedUndef):
+                # Shouldn't lookup in mpmath but might have ._imp_
+                return None
+
+            if not hasattr(mpmath, fname):
+                fname = MPMATH_TRANSLATIONS.get(fname, None)
+                if fname is None:
+                    return None
+            return getattr(mpmath, fname)
+
+        _eval_mpmath = getattr(self, '_eval_mpmath', None)
+        if _eval_mpmath is None:
+            func = _get_mpmath_func(self.func.__name__)
+            args = self.args
+        else:
+            func, args = _eval_mpmath()
+
+        # Fall-back evaluation
+        if func is None:
+            imp = getattr(self, '_imp_', None)
+            if imp is None:
+                return None
+            try:
+                return Float(imp(*[i.evalf(prec) for i in self.args]), prec)
+            except (TypeError, ValueError):
+                return None
+
+        # Convert all args to mpf or mpc
+        # Convert the arguments to *higher* precision than requested for the
+        # final result.
+        # XXX + 5 is a guess, it is similar to what is used in evalf.py. Should
+        #     we be more intelligent about it?
+        try:
+            args = [arg._to_mpmath(prec + 5) for arg in args]
+            def bad(m):
+                from mpmath import mpf, mpc
+                # the precision of an mpf value is the last element
+                # if that is 1 (and m[1] is not 1 which would indicate a
+                # power of 2), then the eval failed; so check that none of
+                # the arguments failed to compute to a finite precision.
+                # Note: An mpc value has two parts, the re and imag tuple;
+                # check each of those parts, too. Anything else is allowed to
+                # pass
+                if isinstance(m, mpf):
+                    m = m._mpf_
+                    return m[1] !=1 and m[-1] == 1
+                elif isinstance(m, mpc):
+                    m, n = m._mpc_
+                    return m[1] !=1 and m[-1] == 1 and \
+                        n[1] !=1 and n[-1] == 1
+                else:
+                    return False
+            if any(bad(a) for a in args):
+                raise ValueError  # one or more args failed to compute with significance
+        except ValueError:
+            return
+
+        with mpmath.workprec(prec):
+            v = func(*args)
+
+        return Expr._from_mpmath(v, prec)
+
+    def _eval_derivative(self, s):
+        # f(x).diff(s) -> x.diff(s) * f.fdiff(1)(s)
+        i = 0
+        l = []
+        for a in self.args:
+            i += 1
+            da = a.diff(s)
+            if da.is_zero:
+                continue
+            try:
+                df = self.fdiff(i)
+            except ArgumentIndexError:
+                df = Function.fdiff(self, i)
+            l.append(df * da)
+        return Add(*l)
+
+    def _eval_is_commutative(self):
+        return fuzzy_and(a.is_commutative for a in self.args)
+
+    def _eval_is_meromorphic(self, x, a):
+        if not self.args:
+            return True
+        if any(arg.has(x) for arg in self.args[1:]):
+            return False
+
+        arg = self.args[0]
+        if not arg._eval_is_meromorphic(x, a):
+            return None
+
+        return fuzzy_not(type(self).is_singular(arg.subs(x, a)))
+
+    _singularities: FuzzyBool | tuple[Expr, ...] = None
+
+    @classmethod
+    def is_singular(cls, a):
+        """
+        Tests whether the argument is an essential singularity
+        or a branch point, or the functions is non-holomorphic.
+        """
+        ss = cls._singularities
+        if ss in (True, None, False):
+            return ss
+
+        return fuzzy_or(a.is_infinite if s is S.ComplexInfinity
+                        else (a - s).is_zero for s in ss)
+
+    def as_base_exp(self):
+        """
+        Returns the method as the 2-tuple (base, exponent).
+        """
+        return self, S.One
+
+    def _eval_aseries(self, n, args0, x, logx):
+        """
+        Compute an asymptotic expansion around args0, in terms of self.args.
+        This function is only used internally by _eval_nseries and should not
+        be called directly; derived classes can overwrite this to implement
+        asymptotic expansions.
+        """
+        raise PoleError(filldedent('''
+            Asymptotic expansion of %s around %s is
+            not implemented.''' % (type(self), args0)))
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        """
+        This function does compute series for multivariate functions,
+        but the expansion is always in terms of *one* variable.
+
+        Examples
+        ========
+
+        >>> from sympy import atan2
+        >>> from sympy.abc import x, y
+        >>> atan2(x, y).series(x, n=2)
+        atan2(0, y) + x/y + O(x**2)
+        >>> atan2(x, y).series(y, n=2)
+        -y/x + atan2(x, 0) + O(y**2)
+
+        This function also computes asymptotic expansions, if necessary
+        and possible:
+
+        >>> from sympy import loggamma
+        >>> loggamma(1/x)._eval_nseries(x,0,None)
+        -1/x - log(x)/x + log(x)/2 + O(1)
+
+        """
+        from .symbol import uniquely_named_symbol
+        from sympy.series.order import Order
+        from sympy.sets.sets import FiniteSet
+        args = self.args
+        args0 = [t.limit(x, 0) for t in args]
+        if any(t.is_finite is False for t in args0):
+            from .numbers import oo, zoo, nan
+            a = [t.as_leading_term(x, logx=logx) for t in args]
+            a0 = [t.limit(x, 0) for t in a]
+            if any(t.has(oo, -oo, zoo, nan) for t in a0):
+                return self._eval_aseries(n, args0, x, logx)
+            # Careful: the argument goes to oo, but only logarithmically so. We
+            # are supposed to do a power series expansion "around the
+            # logarithmic term". e.g.
+            #      f(1+x+log(x))
+            #     -> f(1+logx) + x*f'(1+logx) + O(x**2)
+            # where 'logx' is given in the argument
+            a = [t._eval_nseries(x, n, logx) for t in args]
+            z = [r - r0 for (r, r0) in zip(a, a0)]
+            p = [Dummy() for _ in z]
+            q = []
+            v = None
+            for ai, zi, pi in zip(a0, z, p):
+                if zi.has(x):
+                    if v is not None:
+                        raise NotImplementedError
+                    q.append(ai + pi)
+                    v = pi
+                else:
+                    q.append(ai)
+            e1 = self.func(*q)
+            if v is None:
+                return e1
+            s = e1._eval_nseries(v, n, logx)
+            o = s.getO()
+            s = s.removeO()
+            s = s.subs(v, zi).expand() + Order(o.expr.subs(v, zi), x)
+            return s
+        if (self.func.nargs is S.Naturals0
+                or (self.func.nargs == FiniteSet(1) and args0[0])
+                or any(c > 1 for c in self.func.nargs)):
+            e = self
+            e1 = e.expand()
+            if e == e1:
+                #for example when e = sin(x+1) or e = sin(cos(x))
+                #let's try the general algorithm
+                if len(e.args) == 1:
+                    # issue 14411
+                    e = e.func(e.args[0].cancel())
+                term = e.subs(x, S.Zero)
+                if term.is_finite is False or term is S.NaN:
+                    raise PoleError("Cannot expand %s around 0" % (self))
+                series = term
+                fact = S.One
+
+                _x = uniquely_named_symbol('xi', self)
+                e = e.subs(x, _x)
+                for i in range(1, n):
+                    fact *= Rational(i)
+                    e = e.diff(_x)
+                    subs = e.subs(_x, S.Zero)
+                    if subs is S.NaN:
+                        # try to evaluate a limit if we have to
+                        subs = e.limit(_x, S.Zero)
+                    if subs.is_finite is False:
+                        raise PoleError("Cannot expand %s around 0" % (self))
+                    term = subs*(x**i)/fact
+                    term = term.expand()
+                    series += term
+                return series + Order(x**n, x)
+            return e1.nseries(x, n=n, logx=logx)
+        arg = self.args[0]
+        l = []
+        g = None
+        # try to predict a number of terms needed
+        nterms = n + 2
+        cf = Order(arg.as_leading_term(x), x).getn()
+        if cf != 0:
+            nterms = (n/cf).ceiling()
+        for i in range(nterms):
+            g = self.taylor_term(i, arg, g)
+            g = g.nseries(x, n=n, logx=logx)
+            l.append(g)
+        return Add(*l) + Order(x**n, x)
+
+    def fdiff(self, argindex=1):
+        """
+        Returns the first derivative of the function.
+        """
+        if not (1 <= argindex <= len(self.args)):
+            raise ArgumentIndexError(self, argindex)
+        ix = argindex - 1
+        A = self.args[ix]
+        if A._diff_wrt:
+            if len(self.args) == 1 or not A.is_Symbol:
+                return _derivative_dispatch(self, A)
+            for i, v in enumerate(self.args):
+                if i != ix and A in v.free_symbols:
+                    # it can't be in any other argument's free symbols
+                    # issue 8510
+                    break
+            else:
+                return _derivative_dispatch(self, A)
+
+        # See issue 4624 and issue 4719, 5600 and 8510
+        D = Dummy('xi_%i' % argindex, dummy_index=hash(A))
+        args = self.args[:ix] + (D,) + self.args[ix + 1:]
+        return Subs(Derivative(self.func(*args), D), D, A)
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        """Stub that should be overridden by new Functions to return
+        the first non-zero term in a series if ever an x-dependent
+        argument whose leading term vanishes as x -> 0 might be encountered.
+        See, for example, cos._eval_as_leading_term.
+        """
+        from sympy.series.order import Order
+        args = [a.as_leading_term(x, logx=logx) for a in self.args]
+        o = Order(1, x)
+        if any(x in a.free_symbols and o.contains(a) for a in args):
+            # Whereas x and any finite number are contained in O(1, x),
+            # expressions like 1/x are not. If any arg simplified to a
+            # vanishing expression as x -> 0 (like x or x**2, but not
+            # 3, 1/x, etc...) then the _eval_as_leading_term is needed
+            # to supply the first non-zero term of the series,
+            #
+            # e.g. expression    leading term
+            #      ----------    ------------
+            #      cos(1/x)      cos(1/x)
+            #      cos(cos(x))   cos(1)
+            #      cos(x)        1        <- _eval_as_leading_term needed
+            #      sin(x)        x        <- _eval_as_leading_term needed
+            #
+            raise NotImplementedError(
+                '%s has no _eval_as_leading_term routine' % self.func)
+        else:
+            return self.func(*args)
+
+
+class AppliedUndef(Function):
+    """
+    Base class for expressions resulting from the application of an undefined
+    function.
+    """
+
+    is_number = False
+
+    def __new__(cls, *args, **options):
+        args = list(map(sympify, args))
+        u = [a.name for a in args if isinstance(a, UndefinedFunction)]
+        if u:
+            raise TypeError('Invalid argument: expecting an expression, not UndefinedFunction%s: %s' % (
+                's'*(len(u) > 1), ', '.join(u)))
+        obj = super().__new__(cls, *args, **options)
+        return obj
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        return self
+
+    @property
+    def _diff_wrt(self):
+        """
+        Allow derivatives wrt to undefined functions.
+
+        Examples
+        ========
+
+        >>> from sympy import Function, Symbol
+        >>> f = Function('f')
+        >>> x = Symbol('x')
+        >>> f(x)._diff_wrt
+        True
+        >>> f(x).diff(x)
+        Derivative(f(x), x)
+        """
+        return True
+
+
+class UndefSageHelper:
+    """
+    Helper to facilitate Sage conversion.
+    """
+    def __get__(self, ins, typ):
+        import sage.all as sage
+        if ins is None:
+            return lambda: sage.function(typ.__name__)
+        else:
+            args = [arg._sage_() for arg in ins.args]
+            return lambda : sage.function(ins.__class__.__name__)(*args)
+
+_undef_sage_helper = UndefSageHelper()
+
+class UndefinedFunction(FunctionClass):
+    """
+    The (meta)class of undefined functions.
+    """
+    def __new__(mcl, name, bases=(AppliedUndef,), __dict__=None, **kwargs):
+        from .symbol import _filter_assumptions
+        # Allow Function('f', real=True)
+        # and/or Function(Symbol('f', real=True))
+        assumptions, kwargs = _filter_assumptions(kwargs)
+        if isinstance(name, Symbol):
+            assumptions = name._merge(assumptions)
+            name = name.name
+        elif not isinstance(name, str):
+            raise TypeError('expecting string or Symbol for name')
+        else:
+            commutative = assumptions.get('commutative', None)
+            assumptions = Symbol(name, **assumptions).assumptions0
+            if commutative is None:
+                assumptions.pop('commutative')
+        __dict__ = __dict__ or {}
+        # put the `is_*` for into __dict__
+        __dict__.update({'is_%s' % k: v for k, v in assumptions.items()})
+        # You can add other attributes, although they do have to be hashable
+        # (but seriously, if you want to add anything other than assumptions,
+        # just subclass Function)
+        __dict__.update(kwargs)
+        # add back the sanitized assumptions without the is_ prefix
+        kwargs.update(assumptions)
+        # Save these for __eq__
+        __dict__.update({'_kwargs': kwargs})
+        # do this for pickling
+        __dict__['__module__'] = None
+        obj = super().__new__(mcl, name, bases, __dict__)
+        obj.name = name
+        obj._sage_ = _undef_sage_helper
+        return obj
+
+    def __instancecheck__(cls, instance):
+        return cls in type(instance).__mro__
+
+    _kwargs: dict[str, bool | None] = {}
+
+    def __hash__(self):
+        return hash((self.class_key(), frozenset(self._kwargs.items())))
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+            self.class_key() == other.class_key() and
+            self._kwargs == other._kwargs)
+
+    def __ne__(self, other):
+        return not self == other
+
+    @property
+    def _diff_wrt(self):
+        return False
+
+
+# XXX: The type: ignore on WildFunction is because mypy complains:
+#
+# sympy/core/function.py:939: error: Cannot determine type of 'sort_key' in
+# base class 'Expr'
+#
+# Somehow this is because of the @cacheit decorator but it is not clear how to
+# fix it.
+
+
+class WildFunction(Function, AtomicExpr):  # type: ignore
+    """
+    A WildFunction function matches any function (with its arguments).
+
+    Examples
+    ========
+
+    >>> from sympy import WildFunction, Function, cos
+    >>> from sympy.abc import x, y
+    >>> F = WildFunction('F')
+    >>> f = Function('f')
+    >>> F.nargs
+    Naturals0
+    >>> x.match(F)
+    >>> F.match(F)
+    {F_: F_}
+    >>> f(x).match(F)
+    {F_: f(x)}
+    >>> cos(x).match(F)
+    {F_: cos(x)}
+    >>> f(x, y).match(F)
+    {F_: f(x, y)}
+
+    To match functions with a given number of arguments, set ``nargs`` to the
+    desired value at instantiation:
+
+    >>> F = WildFunction('F', nargs=2)
+    >>> F.nargs
+    {2}
+    >>> f(x).match(F)
+    >>> f(x, y).match(F)
+    {F_: f(x, y)}
+
+    To match functions with a range of arguments, set ``nargs`` to a tuple
+    containing the desired number of arguments, e.g. if ``nargs = (1, 2)``
+    then functions with 1 or 2 arguments will be matched.
+
+    >>> F = WildFunction('F', nargs=(1, 2))
+    >>> F.nargs
+    {1, 2}
+    >>> f(x).match(F)
+    {F_: f(x)}
+    >>> f(x, y).match(F)
+    {F_: f(x, y)}
+    >>> f(x, y, 1).match(F)
+
+    """
+
+    # XXX: What is this class attribute used for?
+    include: set[Any] = set()
+
+    def __init__(cls, name, **assumptions):
+        from sympy.sets.sets import Set, FiniteSet
+        cls.name = name
+        nargs = assumptions.pop('nargs', S.Naturals0)
+        if not isinstance(nargs, Set):
+            # Canonicalize nargs here.  See also FunctionClass.
+            if is_sequence(nargs):
+                nargs = tuple(ordered(set(nargs)))
+            elif nargs is not None:
+                nargs = (as_int(nargs),)
+            nargs = FiniteSet(*nargs)
+        cls.nargs = nargs
+
+    def matches(self, expr, repl_dict=None, old=False):
+        if not isinstance(expr, (AppliedUndef, Function)):
+            return None
+        if len(expr.args) not in self.nargs:
+            return None
+
+        if repl_dict is None:
+            repl_dict = {}
+        else:
+            repl_dict = repl_dict.copy()
+
+        repl_dict[self] = expr
+        return repl_dict
+
+
+class Derivative(Expr):
+    """
+    Carries out differentiation of the given expression with respect to symbols.
+
+    Examples
+    ========
+
+    >>> from sympy import Derivative, Function, symbols, Subs
+    >>> from sympy.abc import x, y
+    >>> f, g = symbols('f g', cls=Function)
+
+    >>> Derivative(x**2, x, evaluate=True)
+    2*x
+
+    Denesting of derivatives retains the ordering of variables:
+
+        >>> Derivative(Derivative(f(x, y), y), x)
+        Derivative(f(x, y), y, x)
+
+    Contiguously identical symbols are merged into a tuple giving
+    the symbol and the count:
+
+        >>> Derivative(f(x), x, x, y, x)
+        Derivative(f(x), (x, 2), y, x)
+
+    If the derivative cannot be performed, and evaluate is True, the
+    order of the variables of differentiation will be made canonical:
+
+        >>> Derivative(f(x, y), y, x, evaluate=True)
+        Derivative(f(x, y), x, y)
+
+    Derivatives with respect to undefined functions can be calculated:
+
+        >>> Derivative(f(x)**2, f(x), evaluate=True)
+        2*f(x)
+
+    Such derivatives will show up when the chain rule is used to
+    evalulate a derivative:
+
+        >>> f(g(x)).diff(x)
+        Derivative(f(g(x)), g(x))*Derivative(g(x), x)
+
+    Substitution is used to represent derivatives of functions with
+    arguments that are not symbols or functions:
+
+        >>> f(2*x + 3).diff(x) == 2*Subs(f(y).diff(y), y, 2*x + 3)
+        True
+
+    Notes
+    =====
+
+    Simplification of high-order derivatives:
+
+    Because there can be a significant amount of simplification that can be
+    done when multiple differentiations are performed, results will be
+    automatically simplified in a fairly conservative fashion unless the
+    keyword ``simplify`` is set to False.
+
+        >>> from sympy import sqrt, diff, Function, symbols
+        >>> from sympy.abc import x, y, z
+        >>> f, g = symbols('f,g', cls=Function)
+
+        >>> e = sqrt((x + 1)**2 + x)
+        >>> diff(e, (x, 5), simplify=False).count_ops()
+        136
+        >>> diff(e, (x, 5)).count_ops()
+        30
+
+    Ordering of variables:
+
+    If evaluate is set to True and the expression cannot be evaluated, the
+    list of differentiation symbols will be sorted, that is, the expression is
+    assumed to have continuous derivatives up to the order asked.
+
+    Derivative wrt non-Symbols:
+
+    For the most part, one may not differentiate wrt non-symbols.
+    For example, we do not allow differentiation wrt `x*y` because
+    there are multiple ways of structurally defining where x*y appears
+    in an expression: a very strict definition would make
+    (x*y*z).diff(x*y) == 0. Derivatives wrt defined functions (like
+    cos(x)) are not allowed, either:
+
+        >>> (x*y*z).diff(x*y)
+        Traceback (most recent call last):
+        ...
+        ValueError: Can't calculate derivative wrt x*y.
+
+    To make it easier to work with variational calculus, however,
+    derivatives wrt AppliedUndef and Derivatives are allowed.
+    For example, in the Euler-Lagrange method one may write
+    F(t, u, v) where u = f(t) and v = f'(t). These variables can be
+    written explicitly as functions of time::
+
+        >>> from sympy.abc import t
+        >>> F = Function('F')
+        >>> U = f(t)
+        >>> V = U.diff(t)
+
+    The derivative wrt f(t) can be obtained directly:
+
+        >>> direct = F(t, U, V).diff(U)
+
+    When differentiation wrt a non-Symbol is attempted, the non-Symbol
+    is temporarily converted to a Symbol while the differentiation
+    is performed and the same answer is obtained:
+
+        >>> indirect = F(t, U, V).subs(U, x).diff(x).subs(x, U)
+        >>> assert direct == indirect
+
+    The implication of this non-symbol replacement is that all
+    functions are treated as independent of other functions and the
+    symbols are independent of the functions that contain them::
+
+        >>> x.diff(f(x))
+        0
+        >>> g(x).diff(f(x))
+        0
+
+    It also means that derivatives are assumed to depend only
+    on the variables of differentiation, not on anything contained
+    within the expression being differentiated::
+
+        >>> F = f(x)
+        >>> Fx = F.diff(x)
+        >>> Fx.diff(F)  # derivative depends on x, not F
+        0
+        >>> Fxx = Fx.diff(x)
+        >>> Fxx.diff(Fx)  # derivative depends on x, not Fx
+        0
+
+    The last example can be made explicit by showing the replacement
+    of Fx in Fxx with y:
+
+        >>> Fxx.subs(Fx, y)
+        Derivative(y, x)
+
+        Since that in itself will evaluate to zero, differentiating
+        wrt Fx will also be zero:
+
+        >>> _.doit()
+        0
+
+    Replacing undefined functions with concrete expressions
+
+    One must be careful to replace undefined functions with expressions
+    that contain variables consistent with the function definition and
+    the variables of differentiation or else insconsistent result will
+    be obtained. Consider the following example:
+
+    >>> eq = f(x)*g(y)
+    >>> eq.subs(f(x), x*y).diff(x, y).doit()
+    y*Derivative(g(y), y) + g(y)
+    >>> eq.diff(x, y).subs(f(x), x*y).doit()
+    y*Derivative(g(y), y)
+
+    The results differ because `f(x)` was replaced with an expression
+    that involved both variables of differentiation. In the abstract
+    case, differentiation of `f(x)` by `y` is 0; in the concrete case,
+    the presence of `y` made that derivative nonvanishing and produced
+    the extra `g(y)` term.
+
+    Defining differentiation for an object
+
+    An object must define ._eval_derivative(symbol) method that returns
+    the differentiation result. This function only needs to consider the
+    non-trivial case where expr contains symbol and it should call the diff()
+    method internally (not _eval_derivative); Derivative should be the only
+    one to call _eval_derivative.
+
+    Any class can allow derivatives to be taken with respect to
+    itself (while indicating its scalar nature). See the
+    docstring of Expr._diff_wrt.
+
+    See Also
+    ========
+    _sort_variable_count
+    """
+
+    is_Derivative = True
+
+    @property
+    def _diff_wrt(self):
+        """An expression may be differentiated wrt a Derivative if
+        it is in elementary form.
+
+        Examples
+        ========
+
+        >>> from sympy import Function, Derivative, cos
+        >>> from sympy.abc import x
+        >>> f = Function('f')
+
+        >>> Derivative(f(x), x)._diff_wrt
+        True
+        >>> Derivative(cos(x), x)._diff_wrt
+        False
+        >>> Derivative(x + 1, x)._diff_wrt
+        False
+
+        A Derivative might be an unevaluated form of what will not be
+        a valid variable of differentiation if evaluated. For example,
+
+        >>> Derivative(f(f(x)), x).doit()
+        Derivative(f(x), x)*Derivative(f(f(x)), f(x))
+
+        Such an expression will present the same ambiguities as arise
+        when dealing with any other product, like ``2*x``, so ``_diff_wrt``
+        is False:
+
+        >>> Derivative(f(f(x)), x)._diff_wrt
+        False
+        """
+        return self.expr._diff_wrt and isinstance(self.doit(), Derivative)
+
+    def __new__(cls, expr, *variables, **kwargs):
+        expr = sympify(expr)
+        symbols_or_none = getattr(expr, "free_symbols", None)
+        has_symbol_set = isinstance(symbols_or_none, set)
+
+        if not has_symbol_set:
+            raise ValueError(filldedent('''
+                Since there are no variables in the expression %s,
+                it cannot be differentiated.''' % expr))
+
+        # determine value for variables if it wasn't given
+        if not variables:
+            variables = expr.free_symbols
+            if len(variables) != 1:
+                if expr.is_number:
+                    return S.Zero
+                if len(variables) == 0:
+                    raise ValueError(filldedent('''
+                        Since there are no variables in the expression,
+                        the variable(s) of differentiation must be supplied
+                        to differentiate %s''' % expr))
+                else:
+                    raise ValueError(filldedent('''
+                        Since there is more than one variable in the
+                        expression, the variable(s) of differentiation
+                        must be supplied to differentiate %s''' % expr))
+
+        # Split the list of variables into a list of the variables we are diff
+        # wrt, where each element of the list has the form (s, count) where
+        # s is the entity to diff wrt and count is the order of the
+        # derivative.
+        variable_count = []
+        array_likes = (tuple, list, Tuple)
+
+        from sympy.tensor.array import Array, NDimArray
+
+        for i, v in enumerate(variables):
+            if isinstance(v, UndefinedFunction):
+                raise TypeError(
+                    "cannot differentiate wrt "
+                    "UndefinedFunction: %s" % v)
+
+            if isinstance(v, array_likes):
+                if len(v) == 0:
+                    # Ignore empty tuples: Derivative(expr, ... , (), ... )
+                    continue
+                if isinstance(v[0], array_likes):
+                    # Derive by array: Derivative(expr, ... , [[x, y, z]], ... )
+                    if len(v) == 1:
+                        v = Array(v[0])
+                        count = 1
+                    else:
+                        v, count = v
+                        v = Array(v)
+                else:
+                    v, count = v
+                if count == 0:
+                    continue
+                variable_count.append(Tuple(v, count))
+                continue
+
+            v = sympify(v)
+            if isinstance(v, Integer):
+                if i == 0:
+                    raise ValueError("First variable cannot be a number: %i" % v)
+                count = v
+                prev, prevcount = variable_count[-1]
+                if prevcount != 1:
+                    raise TypeError("tuple {} followed by number {}".format((prev, prevcount), v))
+                if count == 0:
+                    variable_count.pop()
+                else:
+                    variable_count[-1] = Tuple(prev, count)
+            else:
+                count = 1
+                variable_count.append(Tuple(v, count))
+
+        # light evaluation of contiguous, identical
+        # items: (x, 1), (x, 1) -> (x, 2)
+        merged = []
+        for t in variable_count:
+            v, c = t
+            if c.is_negative:
+                raise ValueError(
+                    'order of differentiation must be nonnegative')
+            if merged and merged[-1][0] == v:
+                c += merged[-1][1]
+                if not c:
+                    merged.pop()
+                else:
+                    merged[-1] = Tuple(v, c)
+            else:
+                merged.append(t)
+        variable_count = merged
+
+        # sanity check of variables of differentation; we waited
+        # until the counts were computed since some variables may
+        # have been removed because the count was 0
+        for v, c in variable_count:
+            # v must have _diff_wrt True
+            if not v._diff_wrt:
+                __ = ''  # filler to make error message neater
+                raise ValueError(filldedent('''
+                    Can't calculate derivative wrt %s.%s''' % (v,
+                    __)))
+
+        # We make a special case for 0th derivative, because there is no
+        # good way to unambiguously print this.
+        if len(variable_count) == 0:
+            return expr
+
+        evaluate = kwargs.get('evaluate', False)
+
+        if evaluate:
+            if isinstance(expr, Derivative):
+                expr = expr.canonical
+            variable_count = [
+                (v.canonical if isinstance(v, Derivative) else v, c)
+                for v, c in variable_count]
+
+            # Look for a quick exit if there are symbols that don't appear in
+            # expression at all. Note, this cannot check non-symbols like
+            # Derivatives as those can be created by intermediate
+            # derivatives.
+            zero = False
+            free = expr.free_symbols
+            from sympy.matrices.expressions.matexpr import MatrixExpr
+
+            for v, c in variable_count:
+                vfree = v.free_symbols
+                if c.is_positive and vfree:
+                    if isinstance(v, AppliedUndef):
+                        # these match exactly since
+                        # x.diff(f(x)) == g(x).diff(f(x)) == 0
+                        # and are not created by differentiation
+                        D = Dummy()
+                        if not expr.xreplace({v: D}).has(D):
+                            zero = True
+                            break
+                    elif isinstance(v, MatrixExpr):
+                        zero = False
+                        break
+                    elif isinstance(v, Symbol) and v not in free:
+                        zero = True
+                        break
+                    else:
+                        if not free & vfree:
+                            # e.g. v is IndexedBase or Matrix
+                            zero = True
+                            break
+            if zero:
+                return cls._get_zero_with_shape_like(expr)
+
+            # make the order of symbols canonical
+            #TODO: check if assumption of discontinuous derivatives exist
+            variable_count = cls._sort_variable_count(variable_count)
+
+        # denest
+        if isinstance(expr, Derivative):
+            variable_count = list(expr.variable_count) + variable_count
+            expr = expr.expr
+            return _derivative_dispatch(expr, *variable_count, **kwargs)
+
+        # we return here if evaluate is False or if there is no
+        # _eval_derivative method
+        if not evaluate or not hasattr(expr, '_eval_derivative'):
+            # return an unevaluated Derivative
+            if evaluate and variable_count == [(expr, 1)] and expr.is_scalar:
+                # special hack providing evaluation for classes
+                # that have defined is_scalar=True but have no
+                # _eval_derivative defined
+                return S.One
+            return Expr.__new__(cls, expr, *variable_count)
+
+        # evaluate the derivative by calling _eval_derivative method
+        # of expr for each variable
+        # -------------------------------------------------------------
+        nderivs = 0  # how many derivatives were performed
+        unhandled = []
+        from sympy.matrices.common import MatrixCommon
+        for i, (v, count) in enumerate(variable_count):
+
+            old_expr = expr
+            old_v = None
+
+            is_symbol = v.is_symbol or isinstance(v,
+                (Iterable, Tuple, MatrixCommon, NDimArray))
+
+            if not is_symbol:
+                old_v = v
+                v = Dummy('xi')
+                expr = expr.xreplace({old_v: v})
+                # Derivatives and UndefinedFunctions are independent
+                # of all others
+                clashing = not (isinstance(old_v, Derivative) or \
+                    isinstance(old_v, AppliedUndef))
+                if v not in expr.free_symbols and not clashing:
+                    return expr.diff(v)  # expr's version of 0
+                if not old_v.is_scalar and not hasattr(
+                        old_v, '_eval_derivative'):
+                    # special hack providing evaluation for classes
+                    # that have defined is_scalar=True but have no
+                    # _eval_derivative defined
+                    expr *= old_v.diff(old_v)
+
+            obj = cls._dispatch_eval_derivative_n_times(expr, v, count)
+            if obj is not None and obj.is_zero:
+                return obj
+
+            nderivs += count
+
+            if old_v is not None:
+                if obj is not None:
+                    # remove the dummy that was used
+                    obj = obj.subs(v, old_v)
+                # restore expr
+                expr = old_expr
+
+            if obj is None:
+                # we've already checked for quick-exit conditions
+                # that give 0 so the remaining variables
+                # are contained in the expression but the expression
+                # did not compute a derivative so we stop taking
+                # derivatives
+                unhandled = variable_count[i:]
+                break
+
+            expr = obj
+
+        # what we have so far can be made canonical
+        expr = expr.replace(
+            lambda x: isinstance(x, Derivative),
+            lambda x: x.canonical)
+
+        if unhandled:
+            if isinstance(expr, Derivative):
+                unhandled = list(expr.variable_count) + unhandled
+                expr = expr.expr
+            expr = Expr.__new__(cls, expr, *unhandled)
+
+        if (nderivs > 1) == True and kwargs.get('simplify', True):
+            from .exprtools import factor_terms
+            from sympy.simplify.simplify import signsimp
+            expr = factor_terms(signsimp(expr))
+        return expr
+
+    @property
+    def canonical(cls):
+        return cls.func(cls.expr,
+            *Derivative._sort_variable_count(cls.variable_count))
+
+    @classmethod
+    def _sort_variable_count(cls, vc):
+        """
+        Sort (variable, count) pairs into canonical order while
+        retaining order of variables that do not commute during
+        differentiation:
+
+        * symbols and functions commute with each other
+        * derivatives commute with each other
+        * a derivative does not commute with anything it contains
+        * any other object is not allowed to commute if it has
+          free symbols in common with another object
+
+        Examples
+        ========
+
+        >>> from sympy import Derivative, Function, symbols
+        >>> vsort = Derivative._sort_variable_count
+        >>> x, y, z = symbols('x y z')
+        >>> f, g, h = symbols('f g h', cls=Function)
+
+        Contiguous items are collapsed into one pair:
+
+        >>> vsort([(x, 1), (x, 1)])
+        [(x, 2)]
+        >>> vsort([(y, 1), (f(x), 1), (y, 1), (f(x), 1)])
+        [(y, 2), (f(x), 2)]
+
+        Ordering is canonical.
+
+        >>> def vsort0(*v):
+        ...     # docstring helper to
+        ...     # change vi -> (vi, 0), sort, and return vi vals
+        ...     return [i[0] for i in vsort([(i, 0) for i in v])]
+
+        >>> vsort0(y, x)
+        [x, y]
+        >>> vsort0(g(y), g(x), f(y))
+        [f(y), g(x), g(y)]
+
+        Symbols are sorted as far to the left as possible but never
+        move to the left of a derivative having the same symbol in
+        its variables; the same applies to AppliedUndef which are
+        always sorted after Symbols:
+
+        >>> dfx = f(x).diff(x)
+        >>> assert vsort0(dfx, y) == [y, dfx]
+        >>> assert vsort0(dfx, x) == [dfx, x]
+        """
+        if not vc:
+            return []
+        vc = list(vc)
+        if len(vc) == 1:
+            return [Tuple(*vc[0])]
+        V = list(range(len(vc)))
+        E = []
+        v = lambda i: vc[i][0]
+        D = Dummy()
+        def _block(d, v, wrt=False):
+            # return True if v should not come before d else False
+            if d == v:
+                return wrt
+            if d.is_Symbol:
+                return False
+            if isinstance(d, Derivative):
+                # a derivative blocks if any of it's variables contain
+                # v; the wrt flag will return True for an exact match
+                # and will cause an AppliedUndef to block if v is in
+                # the arguments
+                if any(_block(k, v, wrt=True)
+                        for k in d._wrt_variables):
+                    return True
+                return False
+            if not wrt and isinstance(d, AppliedUndef):
+                return False
+            if v.is_Symbol:
+                return v in d.free_symbols
+            if isinstance(v, AppliedUndef):
+                return _block(d.xreplace({v: D}), D)
+            return d.free_symbols & v.free_symbols
+        for i in range(len(vc)):
+            for j in range(i):
+                if _block(v(j), v(i)):
+                    E.append((j,i))
+        # this is the default ordering to use in case of ties
+        O = dict(zip(ordered(uniq([i for i, c in vc])), range(len(vc))))
+        ix = topological_sort((V, E), key=lambda i: O[v(i)])
+        # merge counts of contiguously identical items
+        merged = []
+        for v, c in [vc[i] for i in ix]:
+            if merged and merged[-1][0] == v:
+                merged[-1][1] += c
+            else:
+                merged.append([v, c])
+        return [Tuple(*i) for i in merged]
+
+    def _eval_is_commutative(self):
+        return self.expr.is_commutative
+
+    def _eval_derivative(self, v):
+        # If v (the variable of differentiation) is not in
+        # self.variables, we might be able to take the derivative.
+        if v not in self._wrt_variables:
+            dedv = self.expr.diff(v)
+            if isinstance(dedv, Derivative):
+                return dedv.func(dedv.expr, *(self.variable_count + dedv.variable_count))
+            # dedv (d(self.expr)/dv) could have simplified things such that the
+            # derivative wrt things in self.variables can now be done. Thus,
+            # we set evaluate=True to see if there are any other derivatives
+            # that can be done. The most common case is when dedv is a simple
+            # number so that the derivative wrt anything else will vanish.
+            return self.func(dedv, *self.variables, evaluate=True)
+        # In this case v was in self.variables so the derivative wrt v has
+        # already been attempted and was not computed, either because it
+        # couldn't be or evaluate=False originally.
+        variable_count = list(self.variable_count)
+        variable_count.append((v, 1))
+        return self.func(self.expr, *variable_count, evaluate=False)
+
+    def doit(self, **hints):
+        expr = self.expr
+        if hints.get('deep', True):
+            expr = expr.doit(**hints)
+        hints['evaluate'] = True
+        rv = self.func(expr, *self.variable_count, **hints)
+        if rv!= self and rv.has(Derivative):
+            rv =  rv.doit(**hints)
+        return rv
+
+    @_sympifyit('z0', NotImplementedError)
+    def doit_numerically(self, z0):
+        """
+        Evaluate the derivative at z numerically.
+
+        When we can represent derivatives at a point, this should be folded
+        into the normal evalf. For now, we need a special method.
+        """
+        if len(self.free_symbols) != 1 or len(self.variables) != 1:
+            raise NotImplementedError('partials and higher order derivatives')
+        z = list(self.free_symbols)[0]
+
+        def eval(x):
+            f0 = self.expr.subs(z, Expr._from_mpmath(x, prec=mpmath.mp.prec))
+            f0 = f0.evalf(prec_to_dps(mpmath.mp.prec))
+            return f0._to_mpmath(mpmath.mp.prec)
+        return Expr._from_mpmath(mpmath.diff(eval,
+                                             z0._to_mpmath(mpmath.mp.prec)),
+                                 mpmath.mp.prec)
+
+    @property
+    def expr(self):
+        return self._args[0]
+
+    @property
+    def _wrt_variables(self):
+        # return the variables of differentiation without
+        # respect to the type of count (int or symbolic)
+        return [i[0] for i in self.variable_count]
+
+    @property
+    def variables(self):
+        # TODO: deprecate?  YES, make this 'enumerated_variables' and
+        #       name _wrt_variables as variables
+        # TODO: support for `d^n`?
+        rv = []
+        for v, count in self.variable_count:
+            if not count.is_Integer:
+                raise TypeError(filldedent('''
+                Cannot give expansion for symbolic count. If you just
+                want a list of all variables of differentiation, use
+                _wrt_variables.'''))
+            rv.extend([v]*count)
+        return tuple(rv)
+
+    @property
+    def variable_count(self):
+        return self._args[1:]
+
+    @property
+    def derivative_count(self):
+        return sum([count for _, count in self.variable_count], 0)
+
+    @property
+    def free_symbols(self):
+        ret = self.expr.free_symbols
+        # Add symbolic counts to free_symbols
+        for _, count in self.variable_count:
+            ret.update(count.free_symbols)
+        return ret
+
+    @property
+    def kind(self):
+        return self.args[0].kind
+
+    def _eval_subs(self, old, new):
+        # The substitution (old, new) cannot be done inside
+        # Derivative(expr, vars) for a variety of reasons
+        # as handled below.
+        if old in self._wrt_variables:
+            # first handle the counts
+            expr = self.func(self.expr, *[(v, c.subs(old, new))
+                for v, c in self.variable_count])
+            if expr != self:
+                return expr._eval_subs(old, new)
+            # quick exit case
+            if not getattr(new, '_diff_wrt', False):
+                # case (0): new is not a valid variable of
+                # differentiation
+                if isinstance(old, Symbol):
+                    # don't introduce a new symbol if the old will do
+                    return Subs(self, old, new)
+                else:
+                    xi = Dummy('xi')
+                    return Subs(self.xreplace({old: xi}), xi, new)
+
+        # If both are Derivatives with the same expr, check if old is
+        # equivalent to self or if old is a subderivative of self.
+        if old.is_Derivative and old.expr == self.expr:
+            if self.canonical == old.canonical:
+                return new
+
+            # collections.Counter doesn't have __le__
+            def _subset(a, b):
+                return all((a[i] <= b[i]) == True for i in a)
+
+            old_vars = Counter(dict(reversed(old.variable_count)))
+            self_vars = Counter(dict(reversed(self.variable_count)))
+            if _subset(old_vars, self_vars):
+                return _derivative_dispatch(new, *(self_vars - old_vars).items()).canonical
+
+        args = list(self.args)
+        newargs = [x._subs(old, new) for x in args]
+        if args[0] == old:
+            # complete replacement of self.expr
+            # we already checked that the new is valid so we know
+            # it won't be a problem should it appear in variables
+            return _derivative_dispatch(*newargs)
+
+        if newargs[0] != args[0]:
+            # case (1) can't change expr by introducing something that is in
+            # the _wrt_variables if it was already in the expr
+            # e.g.
+            # for Derivative(f(x, g(y)), y), x cannot be replaced with
+            # anything that has y in it; for f(g(x), g(y)).diff(g(y))
+            # g(x) cannot be replaced with anything that has g(y)
+            syms = {vi: Dummy() for vi in self._wrt_variables
+                if not vi.is_Symbol}
+            wrt = {syms.get(vi, vi) for vi in self._wrt_variables}
+            forbidden = args[0].xreplace(syms).free_symbols & wrt
+            nfree = new.xreplace(syms).free_symbols
+            ofree = old.xreplace(syms).free_symbols
+            if (nfree - ofree) & forbidden:
+                return Subs(self, old, new)
+
+        viter = ((i, j) for ((i, _), (j, _)) in zip(newargs[1:], args[1:]))
+        if any(i != j for i, j in viter):  # a wrt-variable change
+            # case (2) can't change vars by introducing a variable
+            # that is contained in expr, e.g.
+            # for Derivative(f(z, g(h(x), y)), y), y cannot be changed to
+            # x, h(x), or g(h(x), y)
+            for a in _atomic(self.expr, recursive=True):
+                for i in range(1, len(newargs)):
+                    vi, _ = newargs[i]
+                    if a == vi and vi != args[i][0]:
+                        return Subs(self, old, new)
+            # more arg-wise checks
+            vc = newargs[1:]
+            oldv = self._wrt_variables
+            newe = self.expr
+            subs = []
+            for i, (vi, ci) in enumerate(vc):
+                if not vi._diff_wrt:
+                    # case (3) invalid differentiation expression so
+                    # create a replacement dummy
+                    xi = Dummy('xi_%i' % i)
+                    # replace the old valid variable with the dummy
+                    # in the expression
+                    newe = newe.xreplace({oldv[i]: xi})
+                    # and replace the bad variable with the dummy
+                    vc[i] = (xi, ci)
+                    # and record the dummy with the new (invalid)
+                    # differentiation expression
+                    subs.append((xi, vi))
+
+            if subs:
+                # handle any residual substitution in the expression
+                newe = newe._subs(old, new)
+                # return the Subs-wrapped derivative
+                return Subs(Derivative(newe, *vc), *zip(*subs))
+
+        # everything was ok
+        return _derivative_dispatch(*newargs)
+
+    def _eval_lseries(self, x, logx, cdir=0):
+        dx = self.variables
+        for term in self.expr.lseries(x, logx=logx, cdir=cdir):
+            yield self.func(term, *dx)
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        arg = self.expr.nseries(x, n=n, logx=logx)
+        o = arg.getO()
+        dx = self.variables
+        rv = [self.func(a, *dx) for a in Add.make_args(arg.removeO())]
+        if o:
+            rv.append(o/x)
+        return Add(*rv)
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        series_gen = self.expr.lseries(x)
+        d = S.Zero
+        for leading_term in series_gen:
+            d = diff(leading_term, *self.variables)
+            if d != 0:
+                break
+        return d
+
+    def as_finite_difference(self, points=1, x0=None, wrt=None):
+        """ Expresses a Derivative instance as a finite difference.
+
+        Parameters
+        ==========
+
+        points : sequence or coefficient, optional
+            If sequence: discrete values (length >= order+1) of the
+            independent variable used for generating the finite
+            difference weights.
+            If it is a coefficient, it will be used as the step-size
+            for generating an equidistant sequence of length order+1
+            centered around ``x0``. Default: 1 (step-size 1)
+
+        x0 : number or Symbol, optional
+            the value of the independent variable (``wrt``) at which the
+            derivative is to be approximated. Default: same as ``wrt``.
+
+        wrt : Symbol, optional
+            "with respect to" the variable for which the (partial)
+            derivative is to be approximated for. If not provided it
+            is required that the derivative is ordinary. Default: ``None``.
+
+
+        Examples
+        ========
+
+        >>> from sympy import symbols, Function, exp, sqrt, Symbol
+        >>> x, h = symbols('x h')
+        >>> f = Function('f')
+        >>> f(x).diff(x).as_finite_difference()
+        -f(x - 1/2) + f(x + 1/2)
+
+        The default step size and number of points are 1 and
+        ``order + 1`` respectively. We can change the step size by
+        passing a symbol as a parameter:
+
+        >>> f(x).diff(x).as_finite_difference(h)
+        -f(-h/2 + x)/h + f(h/2 + x)/h
+
+        We can also specify the discretized values to be used in a
+        sequence:
+
+        >>> f(x).diff(x).as_finite_difference([x, x+h, x+2*h])
+        -3*f(x)/(2*h) + 2*f(h + x)/h - f(2*h + x)/(2*h)
+
+        The algorithm is not restricted to use equidistant spacing, nor
+        do we need to make the approximation around ``x0``, but we can get
+        an expression estimating the derivative at an offset:
+
+        >>> e, sq2 = exp(1), sqrt(2)
+        >>> xl = [x-h, x+h, x+e*h]
+        >>> f(x).diff(x, 1).as_finite_difference(xl, x+h*sq2)  # doctest: +ELLIPSIS
+        2*h*((h + sqrt(2)*h)/(2*h) - (-sqrt(2)*h + h)/(2*h))*f(E*h + x)/...
+
+        To approximate ``Derivative`` around ``x0`` using a non-equidistant
+        spacing step, the algorithm supports assignment of undefined
+        functions to ``points``:
+
+        >>> dx = Function('dx')
+        >>> f(x).diff(x).as_finite_difference(points=dx(x), x0=x-h)
+        -f(-h + x - dx(-h + x)/2)/dx(-h + x) + f(-h + x + dx(-h + x)/2)/dx(-h + x)
+
+        Partial derivatives are also supported:
+
+        >>> y = Symbol('y')
+        >>> d2fdxdy=f(x,y).diff(x,y)
+        >>> d2fdxdy.as_finite_difference(wrt=x)
+        -Derivative(f(x - 1/2, y), y) + Derivative(f(x + 1/2, y), y)
+
+        We can apply ``as_finite_difference`` to ``Derivative`` instances in
+        compound expressions using ``replace``:
+
+        >>> (1 + 42**f(x).diff(x)).replace(lambda arg: arg.is_Derivative,
+        ...     lambda arg: arg.as_finite_difference())
+        42**(-f(x - 1/2) + f(x + 1/2)) + 1
+
+
+        See also
+        ========
+
+        sympy.calculus.finite_diff.apply_finite_diff
+        sympy.calculus.finite_diff.differentiate_finite
+        sympy.calculus.finite_diff.finite_diff_weights
+
+        """
+        from sympy.calculus.finite_diff import _as_finite_diff
+        return _as_finite_diff(self, points, x0, wrt)
+
+    @classmethod
+    def _get_zero_with_shape_like(cls, expr):
+        return S.Zero
+
+    @classmethod
+    def _dispatch_eval_derivative_n_times(cls, expr, v, count):
+        # Evaluate the derivative `n` times.  If
+        # `_eval_derivative_n_times` is not overridden by the current
+        # object, the default in `Basic` will call a loop over
+        # `_eval_derivative`:
+        return expr._eval_derivative_n_times(v, count)
+
+
+def _derivative_dispatch(expr, *variables, **kwargs):
+    from sympy.matrices.common import MatrixCommon
+    from sympy.matrices.expressions.matexpr import MatrixExpr
+    from sympy.tensor.array import NDimArray
+    array_types = (MatrixCommon, MatrixExpr, NDimArray, list, tuple, Tuple)
+    if isinstance(expr, array_types) or any(isinstance(i[0], array_types) if isinstance(i, (tuple, list, Tuple)) else isinstance(i, array_types) for i in variables):
+        from sympy.tensor.array.array_derivatives import ArrayDerivative
+        return ArrayDerivative(expr, *variables, **kwargs)
+    return Derivative(expr, *variables, **kwargs)
+
+
+class Lambda(Expr):
+    """
+    Lambda(x, expr) represents a lambda function similar to Python's
+    'lambda x: expr'. A function of several variables is written as
+    Lambda((x, y, ...), expr).
+
+    Examples
+    ========
+
+    A simple example:
+
+    >>> from sympy import Lambda
+    >>> from sympy.abc import x
+    >>> f = Lambda(x, x**2)
+    >>> f(4)
+    16
+
+    For multivariate functions, use:
+
+    >>> from sympy.abc import y, z, t
+    >>> f2 = Lambda((x, y, z, t), x + y**z + t**z)
+    >>> f2(1, 2, 3, 4)
+    73
+
+    It is also possible to unpack tuple arguments:
+
+    >>> f = Lambda(((x, y), z), x + y + z)
+    >>> f((1, 2), 3)
+    6
+
+    A handy shortcut for lots of arguments:
+
+    >>> p = x, y, z
+    >>> f = Lambda(p, x + y*z)
+    >>> f(*p)
+    x + y*z
+
+    """
+    is_Function = True
+
+    def __new__(cls, signature, expr):
+        if iterable(signature) and not isinstance(signature, (tuple, Tuple)):
+            sympy_deprecation_warning(
+                """
+                Using a non-tuple iterable as the first argument to Lambda
+                is deprecated. Use Lambda(tuple(args), expr) instead.
+                """,
+                deprecated_since_version="1.5",
+                active_deprecations_target="deprecated-non-tuple-lambda",
+            )
+            signature = tuple(signature)
+        sig = signature if iterable(signature) else (signature,)
+        sig = sympify(sig)
+        cls._check_signature(sig)
+
+        if len(sig) == 1 and sig[0] == expr:
+            return S.IdentityFunction
+
+        return Expr.__new__(cls, sig, sympify(expr))
+
+    @classmethod
+    def _check_signature(cls, sig):
+        syms = set()
+
+        def rcheck(args):
+            for a in args:
+                if a.is_symbol:
+                    if a in syms:
+                        raise BadSignatureError("Duplicate symbol %s" % a)
+                    syms.add(a)
+                elif isinstance(a, Tuple):
+                    rcheck(a)
+                else:
+                    raise BadSignatureError("Lambda signature should be only tuples"
+                        " and symbols, not %s" % a)
+
+        if not isinstance(sig, Tuple):
+            raise BadSignatureError("Lambda signature should be a tuple not %s" % sig)
+        # Recurse through the signature:
+        rcheck(sig)
+
+    @property
+    def signature(self):
+        """The expected form of the arguments to be unpacked into variables"""
+        return self._args[0]
+
+    @property
+    def expr(self):
+        """The return value of the function"""
+        return self._args[1]
+
+    @property
+    def variables(self):
+        """The variables used in the internal representation of the function"""
+        def _variables(args):
+            if isinstance(args, Tuple):
+                for arg in args:
+                    yield from _variables(arg)
+            else:
+                yield args
+        return tuple(_variables(self.signature))
+
+    @property
+    def nargs(self):
+        from sympy.sets.sets import FiniteSet
+        return FiniteSet(len(self.signature))
+
+    bound_symbols = variables
+
+    @property
+    def free_symbols(self):
+        return self.expr.free_symbols - set(self.variables)
+
+    def __call__(self, *args):
+        n = len(args)
+        if n not in self.nargs:  # Lambda only ever has 1 value in nargs
+            # XXX: exception message must be in exactly this format to
+            # make it work with NumPy's functions like vectorize(). See,
+            # for example, https://github.com/numpy/numpy/issues/1697.
+            # The ideal solution would be just to attach metadata to
+            # the exception and change NumPy to take advantage of this.
+            ## XXX does this apply to Lambda? If not, remove this comment.
+            temp = ('%(name)s takes exactly %(args)s '
+                   'argument%(plural)s (%(given)s given)')
+            raise BadArgumentsError(temp % {
+                'name': self,
+                'args': list(self.nargs)[0],
+                'plural': 's'*(list(self.nargs)[0] != 1),
+                'given': n})
+
+        d = self._match_signature(self.signature, args)
+
+        return self.expr.xreplace(d)
+
+    def _match_signature(self, sig, args):
+
+        symargmap = {}
+
+        def rmatch(pars, args):
+            for par, arg in zip(pars, args):
+                if par.is_symbol:
+                    symargmap[par] = arg
+                elif isinstance(par, Tuple):
+                    if not isinstance(arg, (tuple, Tuple)) or len(args) != len(pars):
+                        raise BadArgumentsError("Can't match %s and %s" % (args, pars))
+                    rmatch(par, arg)
+
+        rmatch(sig, args)
+
+        return symargmap
+
+    @property
+    def is_identity(self):
+        """Return ``True`` if this ``Lambda`` is an identity function. """
+        return self.signature == self.expr
+
+    def _eval_evalf(self, prec):
+        return self.func(self.args[0], self.args[1].evalf(n=prec_to_dps(prec)))
+
+
+class Subs(Expr):
+    """
+    Represents unevaluated substitutions of an expression.
+
+    ``Subs(expr, x, x0)`` represents the expression resulting
+    from substituting x with x0 in expr.
+
+    Parameters
+    ==========
+
+    expr : Expr
+        An expression.
+
+    x : tuple, variable
+        A variable or list of distinct variables.
+
+    x0 : tuple or list of tuples
+        A point or list of evaluation points
+        corresponding to those variables.
+
+    Examples
+    ========
+
+    >>> from sympy import Subs, Function, sin, cos
+    >>> from sympy.abc import x, y, z
+    >>> f = Function('f')
+
+    Subs are created when a particular substitution cannot be made. The
+    x in the derivative cannot be replaced with 0 because 0 is not a
+    valid variables of differentiation:
+
+    >>> f(x).diff(x).subs(x, 0)
+    Subs(Derivative(f(x), x), x, 0)
+
+    Once f is known, the derivative and evaluation at 0 can be done:
+
+    >>> _.subs(f, sin).doit() == sin(x).diff(x).subs(x, 0) == cos(0)
+    True
+
+    Subs can also be created directly with one or more variables:
+
+    >>> Subs(f(x)*sin(y) + z, (x, y), (0, 1))
+    Subs(z + f(x)*sin(y), (x, y), (0, 1))
+    >>> _.doit()
+    z + f(0)*sin(1)
+
+    Notes
+    =====
+
+    ``Subs`` objects are generally useful to represent unevaluated derivatives
+    calculated at a point.
+
+    The variables may be expressions, but they are subjected to the limitations
+    of subs(), so it is usually a good practice to use only symbols for
+    variables, since in that case there can be no ambiguity.
+
+    There's no automatic expansion - use the method .doit() to effect all
+    possible substitutions of the object and also of objects inside the
+    expression.
+
+    When evaluating derivatives at a point that is not a symbol, a Subs object
+    is returned. One is also able to calculate derivatives of Subs objects - in
+    this case the expression is always expanded (for the unevaluated form, use
+    Derivative()).
+
+    In order to allow expressions to combine before doit is done, a
+    representation of the Subs expression is used internally to make
+    expressions that are superficially different compare the same:
+
+    >>> a, b = Subs(x, x, 0), Subs(y, y, 0)
+    >>> a + b
+    2*Subs(x, x, 0)
+
+    This can lead to unexpected consequences when using methods
+    like `has` that are cached:
+
+    >>> s = Subs(x, x, 0)
+    >>> s.has(x), s.has(y)
+    (True, False)
+    >>> ss = s.subs(x, y)
+    >>> ss.has(x), ss.has(y)
+    (True, False)
+    >>> s, ss
+    (Subs(x, x, 0), Subs(y, y, 0))
+    """
+    def __new__(cls, expr, variables, point, **assumptions):
+        if not is_sequence(variables, Tuple):
+            variables = [variables]
+        variables = Tuple(*variables)
+
+        if has_dups(variables):
+            repeated = [str(v) for v, i in Counter(variables).items() if i > 1]
+            __ = ', '.join(repeated)
+            raise ValueError(filldedent('''
+                The following expressions appear more than once: %s
+                ''' % __))
+
+        point = Tuple(*(point if is_sequence(point, Tuple) else [point]))
+
+        if len(point) != len(variables):
+            raise ValueError('Number of point values must be the same as '
+                             'the number of variables.')
+
+        if not point:
+            return sympify(expr)
+
+        # denest
+        if isinstance(expr, Subs):
+            variables = expr.variables + variables
+            point = expr.point + point
+            expr = expr.expr
+        else:
+            expr = sympify(expr)
+
+        # use symbols with names equal to the point value (with prepended _)
+        # to give a variable-independent expression
+        pre = "_"
+        pts = sorted(set(point), key=default_sort_key)
+        from sympy.printing.str import StrPrinter
+        class CustomStrPrinter(StrPrinter):
+            def _print_Dummy(self, expr):
+                return str(expr) + str(expr.dummy_index)
+        def mystr(expr, **settings):
+            p = CustomStrPrinter(settings)
+            return p.doprint(expr)
+        while 1:
+            s_pts = {p: Symbol(pre + mystr(p)) for p in pts}
+            reps = [(v, s_pts[p])
+                for v, p in zip(variables, point)]
+            # if any underscore-prepended symbol is already a free symbol
+            # and is a variable with a different point value, then there
+            # is a clash, e.g. _0 clashes in Subs(_0 + _1, (_0, _1), (1, 0))
+            # because the new symbol that would be created is _1 but _1
+            # is already mapped to 0 so __0 and __1 are used for the new
+            # symbols
+            if any(r in expr.free_symbols and
+                   r in variables and
+                   Symbol(pre + mystr(point[variables.index(r)])) != r
+                   for _, r in reps):
+                pre += "_"
+                continue
+            break
+
+        obj = Expr.__new__(cls, expr, Tuple(*variables), point)
+        obj._expr = expr.xreplace(dict(reps))
+        return obj
+
+    def _eval_is_commutative(self):
+        return self.expr.is_commutative
+
+    def doit(self, **hints):
+        e, v, p = self.args
+
+        # remove self mappings
+        for i, (vi, pi) in enumerate(zip(v, p)):
+            if vi == pi:
+                v = v[:i] + v[i + 1:]
+                p = p[:i] + p[i + 1:]
+        if not v:
+            return self.expr
+
+        if isinstance(e, Derivative):
+            # apply functions first, e.g. f -> cos
+            undone = []
+            for i, vi in enumerate(v):
+                if isinstance(vi, FunctionClass):
+                    e = e.subs(vi, p[i])
+                else:
+                    undone.append((vi, p[i]))
+            if not isinstance(e, Derivative):
+                e = e.doit()
+            if isinstance(e, Derivative):
+                # do Subs that aren't related to differentiation
+                undone2 = []
+                D = Dummy()
+                arg = e.args[0]
+                for vi, pi in undone:
+                    if D not in e.xreplace({vi: D}).free_symbols:
+                        if arg.has(vi):
+                            e = e.subs(vi, pi)
+                    else:
+                        undone2.append((vi, pi))
+                undone = undone2
+                # differentiate wrt variables that are present
+                wrt = []
+                D = Dummy()
+                expr = e.expr
+                free = expr.free_symbols
+                for vi, ci in e.variable_count:
+                    if isinstance(vi, Symbol) and vi in free:
+                        expr = expr.diff((vi, ci))
+                    elif D in expr.subs(vi, D).free_symbols:
+                        expr = expr.diff((vi, ci))
+                    else:
+                        wrt.append((vi, ci))
+                # inject remaining subs
+                rv = expr.subs(undone)
+                # do remaining differentiation *in order given*
+                for vc in wrt:
+                    rv = rv.diff(vc)
+            else:
+                # inject remaining subs
+                rv = e.subs(undone)
+        else:
+            rv = e.doit(**hints).subs(list(zip(v, p)))
+
+        if hints.get('deep', True) and rv != self:
+            rv = rv.doit(**hints)
+        return rv
+
+    def evalf(self, prec=None, **options):
+        return self.doit().evalf(prec, **options)
+
+    n = evalf  # type:ignore
+
+    @property
+    def variables(self):
+        """The variables to be evaluated"""
+        return self._args[1]
+
+    bound_symbols = variables
+
+    @property
+    def expr(self):
+        """The expression on which the substitution operates"""
+        return self._args[0]
+
+    @property
+    def point(self):
+        """The values for which the variables are to be substituted"""
+        return self._args[2]
+
+    @property
+    def free_symbols(self):
+        return (self.expr.free_symbols - set(self.variables) |
+            set(self.point.free_symbols))
+
+    @property
+    def expr_free_symbols(self):
+        sympy_deprecation_warning("""
+        The expr_free_symbols property is deprecated. Use free_symbols to get
+        the free symbols of an expression.
+        """,
+            deprecated_since_version="1.9",
+            active_deprecations_target="deprecated-expr-free-symbols")
+        # Don't show the warning twice from the recursive call
+        with ignore_warnings(SymPyDeprecationWarning):
+            return (self.expr.expr_free_symbols - set(self.variables) |
+                    set(self.point.expr_free_symbols))
+
+    def __eq__(self, other):
+        if not isinstance(other, Subs):
+            return False
+        return self._hashable_content() == other._hashable_content()
+
+    def __ne__(self, other):
+        return not(self == other)
+
+    def __hash__(self):
+        return super().__hash__()
+
+    def _hashable_content(self):
+        return (self._expr.xreplace(self.canonical_variables),
+            ) + tuple(ordered([(v, p) for v, p in
+            zip(self.variables, self.point) if not self.expr.has(v)]))
+
+    def _eval_subs(self, old, new):
+        # Subs doit will do the variables in order; the semantics
+        # of subs for Subs is have the following invariant for
+        # Subs object foo:
+        #    foo.doit().subs(reps) == foo.subs(reps).doit()
+        pt = list(self.point)
+        if old in self.variables:
+            if _atomic(new) == {new} and not any(
+                    i.has(new) for i in self.args):
+                # the substitution is neutral
+                return self.xreplace({old: new})
+            # any occurrence of old before this point will get
+            # handled by replacements from here on
+            i = self.variables.index(old)
+            for j in range(i, len(self.variables)):
+                pt[j] = pt[j]._subs(old, new)
+            return self.func(self.expr, self.variables, pt)
+        v = [i._subs(old, new) for i in self.variables]
+        if v != list(self.variables):
+            return self.func(self.expr, self.variables + (old,), pt + [new])
+        expr = self.expr._subs(old, new)
+        pt = [i._subs(old, new) for i in self.point]
+        return self.func(expr, v, pt)
+
+    def _eval_derivative(self, s):
+        # Apply the chain rule of the derivative on the substitution variables:
+        f = self.expr
+        vp = V, P = self.variables, self.point
+        val = Add.fromiter(p.diff(s)*Subs(f.diff(v), *vp).doit()
+            for v, p in zip(V, P))
+
+        # these are all the free symbols in the expr
+        efree = f.free_symbols
+        # some symbols like IndexedBase include themselves and args
+        # as free symbols
+        compound = {i for i in efree if len(i.free_symbols) > 1}
+        # hide them and see what independent free symbols remain
+        dums = {Dummy() for i in compound}
+        masked = f.xreplace(dict(zip(compound, dums)))
+        ifree = masked.free_symbols - dums
+        # include the compound symbols
+        free = ifree | compound
+        # remove the variables already handled
+        free -= set(V)
+        # add back any free symbols of remaining compound symbols
+        free |= {i for j in free & compound for i in j.free_symbols}
+        # if symbols of s are in free then there is more to do
+        if free & s.free_symbols:
+            val += Subs(f.diff(s), self.variables, self.point).doit()
+        return val
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        if x in self.point:
+            # x is the variable being substituted into
+            apos = self.point.index(x)
+            other = self.variables[apos]
+        else:
+            other = x
+        arg = self.expr.nseries(other, n=n, logx=logx)
+        o = arg.getO()
+        terms = Add.make_args(arg.removeO())
+        rv = Add(*[self.func(a, *self.args[1:]) for a in terms])
+        if o:
+            rv += o.subs(other, x)
+        return rv
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        if x in self.point:
+            ipos = self.point.index(x)
+            xvar = self.variables[ipos]
+            return self.expr.as_leading_term(xvar)
+        if x in self.variables:
+            # if `x` is a dummy variable, it means it won't exist after the
+            # substitution has been performed:
+            return self
+        # The variable is independent of the substitution:
+        return self.expr.as_leading_term(x)
+
+
+def diff(f, *symbols, **kwargs):
+    """
+    Differentiate f with respect to symbols.
+
+    Explanation
+    ===========
+
+    This is just a wrapper to unify .diff() and the Derivative class; its
+    interface is similar to that of integrate().  You can use the same
+    shortcuts for multiple variables as with Derivative.  For example,
+    diff(f(x), x, x, x) and diff(f(x), x, 3) both return the third derivative
+    of f(x).
+
+    You can pass evaluate=False to get an unevaluated Derivative class.  Note
+    that if there are 0 symbols (such as diff(f(x), x, 0), then the result will
+    be the function (the zeroth derivative), even if evaluate=False.
+
+    Examples
+    ========
+
+    >>> from sympy import sin, cos, Function, diff
+    >>> from sympy.abc import x, y
+    >>> f = Function('f')
+
+    >>> diff(sin(x), x)
+    cos(x)
+    >>> diff(f(x), x, x, x)
+    Derivative(f(x), (x, 3))
+    >>> diff(f(x), x, 3)
+    Derivative(f(x), (x, 3))
+    >>> diff(sin(x)*cos(y), x, 2, y, 2)
+    sin(x)*cos(y)
+
+    >>> type(diff(sin(x), x))
+    cos
+    >>> type(diff(sin(x), x, evaluate=False))
+    <class 'sympy.core.function.Derivative'>
+    >>> type(diff(sin(x), x, 0))
+    sin
+    >>> type(diff(sin(x), x, 0, evaluate=False))
+    sin
+
+    >>> diff(sin(x))
+    cos(x)
+    >>> diff(sin(x*y))
+    Traceback (most recent call last):
+    ...
+    ValueError: specify differentiation variables to differentiate sin(x*y)
+
+    Note that ``diff(sin(x))`` syntax is meant only for convenience
+    in interactive sessions and should be avoided in library code.
+
+    References
+    ==========
+
+    .. [1] https://reference.wolfram.com/legacy/v5_2/Built-inFunctions/AlgebraicComputation/Calculus/D.html
+
+    See Also
+    ========
+
+    Derivative
+    idiff: computes the derivative implicitly
+
+    """
+    if hasattr(f, 'diff'):
+        return f.diff(*symbols, **kwargs)
+    kwargs.setdefault('evaluate', True)
+    return _derivative_dispatch(f, *symbols, **kwargs)
+
+
+def expand(e, deep=True, modulus=None, power_base=True, power_exp=True,
+        mul=True, log=True, multinomial=True, basic=True, **hints):
+    r"""
+    Expand an expression using methods given as hints.
+
+    Explanation
+    ===========
+
+    Hints evaluated unless explicitly set to False are:  ``basic``, ``log``,
+    ``multinomial``, ``mul``, ``power_base``, and ``power_exp`` The following
+    hints are supported but not applied unless set to True:  ``complex``,
+    ``func``, and ``trig``.  In addition, the following meta-hints are
+    supported by some or all of the other hints:  ``frac``, ``numer``,
+    ``denom``, ``modulus``, and ``force``.  ``deep`` is supported by all
+    hints.  Additionally, subclasses of Expr may define their own hints or
+    meta-hints.
+
+    The ``basic`` hint is used for any special rewriting of an object that
+    should be done automatically (along with the other hints like ``mul``)
+    when expand is called. This is a catch-all hint to handle any sort of
+    expansion that may not be described by the existing hint names. To use
+    this hint an object should override the ``_eval_expand_basic`` method.
+    Objects may also define their own expand methods, which are not run by
+    default.  See the API section below.
+
+    If ``deep`` is set to ``True`` (the default), things like arguments of
+    functions are recursively expanded.  Use ``deep=False`` to only expand on
+    the top level.
+
+    If the ``force`` hint is used, assumptions about variables will be ignored
+    in making the expansion.
+
+    Hints
+    =====
+
+    These hints are run by default
+
+    mul
+    ---
+
+    Distributes multiplication over addition:
+
+    >>> from sympy import cos, exp, sin
+    >>> from sympy.abc import x, y, z
+    >>> (y*(x + z)).expand(mul=True)
+    x*y + y*z
+
+    multinomial
+    -----------
+
+    Expand (x + y + ...)**n where n is a positive integer.
+
+    >>> ((x + y + z)**2).expand(multinomial=True)
+    x**2 + 2*x*y + 2*x*z + y**2 + 2*y*z + z**2
+
+    power_exp
+    ---------
+
+    Expand addition in exponents into multiplied bases.
+
+    >>> exp(x + y).expand(power_exp=True)
+    exp(x)*exp(y)
+    >>> (2**(x + y)).expand(power_exp=True)
+    2**x*2**y
+
+    power_base
+    ----------
+
+    Split powers of multiplied bases.
+
+    This only happens by default if assumptions allow, or if the
+    ``force`` meta-hint is used:
+
+    >>> ((x*y)**z).expand(power_base=True)
+    (x*y)**z
+    >>> ((x*y)**z).expand(power_base=True, force=True)
+    x**z*y**z
+    >>> ((2*y)**z).expand(power_base=True)
+    2**z*y**z
+
+    Note that in some cases where this expansion always holds, SymPy performs
+    it automatically:
+
+    >>> (x*y)**2
+    x**2*y**2
+
+    log
+    ---
+
+    Pull out power of an argument as a coefficient and split logs products
+    into sums of logs.
+
+    Note that these only work if the arguments of the log function have the
+    proper assumptions--the arguments must be positive and the exponents must
+    be real--or else the ``force`` hint must be True:
+
+    >>> from sympy import log, symbols
+    >>> log(x**2*y).expand(log=True)
+    log(x**2*y)
+    >>> log(x**2*y).expand(log=True, force=True)
+    2*log(x) + log(y)
+    >>> x, y = symbols('x,y', positive=True)
+    >>> log(x**2*y).expand(log=True)
+    2*log(x) + log(y)
+
+    basic
+    -----
+
+    This hint is intended primarily as a way for custom subclasses to enable
+    expansion by default.
+
+    These hints are not run by default:
+
+    complex
+    -------
+
+    Split an expression into real and imaginary parts.
+
+    >>> x, y = symbols('x,y')
+    >>> (x + y).expand(complex=True)
+    re(x) + re(y) + I*im(x) + I*im(y)
+    >>> cos(x).expand(complex=True)
+    -I*sin(re(x))*sinh(im(x)) + cos(re(x))*cosh(im(x))
+
+    Note that this is just a wrapper around ``as_real_imag()``.  Most objects
+    that wish to redefine ``_eval_expand_complex()`` should consider
+    redefining ``as_real_imag()`` instead.
+
+    func
+    ----
+
+    Expand other functions.
+
+    >>> from sympy import gamma
+    >>> gamma(x + 1).expand(func=True)
+    x*gamma(x)
+
+    trig
+    ----
+
+    Do trigonometric expansions.
+
+    >>> cos(x + y).expand(trig=True)
+    -sin(x)*sin(y) + cos(x)*cos(y)
+    >>> sin(2*x).expand(trig=True)
+    2*sin(x)*cos(x)
+
+    Note that the forms of ``sin(n*x)`` and ``cos(n*x)`` in terms of ``sin(x)``
+    and ``cos(x)`` are not unique, due to the identity `\sin^2(x) + \cos^2(x)
+    = 1`.  The current implementation uses the form obtained from Chebyshev
+    polynomials, but this may change.  See `this MathWorld article
+    <https://mathworld.wolfram.com/Multiple-AngleFormulas.html>`_ for more
+    information.
+
+    Notes
+    =====
+
+    - You can shut off unwanted methods::
+
+        >>> (exp(x + y)*(x + y)).expand()
+        x*exp(x)*exp(y) + y*exp(x)*exp(y)
+        >>> (exp(x + y)*(x + y)).expand(power_exp=False)
+        x*exp(x + y) + y*exp(x + y)
+        >>> (exp(x + y)*(x + y)).expand(mul=False)
+        (x + y)*exp(x)*exp(y)
+
+    - Use deep=False to only expand on the top level::
+
+        >>> exp(x + exp(x + y)).expand()
+        exp(x)*exp(exp(x)*exp(y))
+        >>> exp(x + exp(x + y)).expand(deep=False)
+        exp(x)*exp(exp(x + y))
+
+    - Hints are applied in an arbitrary, but consistent order (in the current
+      implementation, they are applied in alphabetical order, except
+      multinomial comes before mul, but this may change).  Because of this,
+      some hints may prevent expansion by other hints if they are applied
+      first. For example, ``mul`` may distribute multiplications and prevent
+      ``log`` and ``power_base`` from expanding them. Also, if ``mul`` is
+      applied before ``multinomial`, the expression might not be fully
+      distributed. The solution is to use the various ``expand_hint`` helper
+      functions or to use ``hint=False`` to this function to finely control
+      which hints are applied. Here are some examples::
+
+        >>> from sympy import expand, expand_mul, expand_power_base
+        >>> x, y, z = symbols('x,y,z', positive=True)
+
+        >>> expand(log(x*(y + z)))
+        log(x) + log(y + z)
+
+      Here, we see that ``log`` was applied before ``mul``.  To get the mul
+      expanded form, either of the following will work::
+
+        >>> expand_mul(log(x*(y + z)))
+        log(x*y + x*z)
+        >>> expand(log(x*(y + z)), log=False)
+        log(x*y + x*z)
+
+      A similar thing can happen with the ``power_base`` hint::
+
+        >>> expand((x*(y + z))**x)
+        (x*y + x*z)**x
+
+      To get the ``power_base`` expanded form, either of the following will
+      work::
+
+        >>> expand((x*(y + z))**x, mul=False)
+        x**x*(y + z)**x
+        >>> expand_power_base((x*(y + z))**x)
+        x**x*(y + z)**x
+
+        >>> expand((x + y)*y/x)
+        y + y**2/x
+
+      The parts of a rational expression can be targeted::
+
+        >>> expand((x + y)*y/x/(x + 1), frac=True)
+        (x*y + y**2)/(x**2 + x)
+        >>> expand((x + y)*y/x/(x + 1), numer=True)
+        (x*y + y**2)/(x*(x + 1))
+        >>> expand((x + y)*y/x/(x + 1), denom=True)
+        y*(x + y)/(x**2 + x)
+
+    - The ``modulus`` meta-hint can be used to reduce the coefficients of an
+      expression post-expansion::
+
+        >>> expand((3*x + 1)**2)
+        9*x**2 + 6*x + 1
+        >>> expand((3*x + 1)**2, modulus=5)
+        4*x**2 + x + 1
+
+    - Either ``expand()`` the function or ``.expand()`` the method can be
+      used.  Both are equivalent::
+
+        >>> expand((x + 1)**2)
+        x**2 + 2*x + 1
+        >>> ((x + 1)**2).expand()
+        x**2 + 2*x + 1
+
+    API
+    ===
+
+    Objects can define their own expand hints by defining
+    ``_eval_expand_hint()``.  The function should take the form::
+
+        def _eval_expand_hint(self, **hints):
+            # Only apply the method to the top-level expression
+            ...
+
+    See also the example below.  Objects should define ``_eval_expand_hint()``
+    methods only if ``hint`` applies to that specific object.  The generic
+    ``_eval_expand_hint()`` method defined in Expr will handle the no-op case.
+
+    Each hint should be responsible for expanding that hint only.
+    Furthermore, the expansion should be applied to the top-level expression
+    only.  ``expand()`` takes care of the recursion that happens when
+    ``deep=True``.
+
+    You should only call ``_eval_expand_hint()`` methods directly if you are
+    100% sure that the object has the method, as otherwise you are liable to
+    get unexpected ``AttributeError``s.  Note, again, that you do not need to
+    recursively apply the hint to args of your object: this is handled
+    automatically by ``expand()``.  ``_eval_expand_hint()`` should
+    generally not be used at all outside of an ``_eval_expand_hint()`` method.
+    If you want to apply a specific expansion from within another method, use
+    the public ``expand()`` function, method, or ``expand_hint()`` functions.
+
+    In order for expand to work, objects must be rebuildable by their args,
+    i.e., ``obj.func(*obj.args) == obj`` must hold.
+
+    Expand methods are passed ``**hints`` so that expand hints may use
+    'metahints'--hints that control how different expand methods are applied.
+    For example, the ``force=True`` hint described above that causes
+    ``expand(log=True)`` to ignore assumptions is such a metahint.  The
+    ``deep`` meta-hint is handled exclusively by ``expand()`` and is not
+    passed to ``_eval_expand_hint()`` methods.
+
+    Note that expansion hints should generally be methods that perform some
+    kind of 'expansion'.  For hints that simply rewrite an expression, use the
+    .rewrite() API.
+
+    Examples
+    ========
+
+    >>> from sympy import Expr, sympify
+    >>> class MyClass(Expr):
+    ...     def __new__(cls, *args):
+    ...         args = sympify(args)
+    ...         return Expr.__new__(cls, *args)
+    ...
+    ...     def _eval_expand_double(self, *, force=False, **hints):
+    ...         '''
+    ...         Doubles the args of MyClass.
+    ...
+    ...         If there more than four args, doubling is not performed,
+    ...         unless force=True is also used (False by default).
+    ...         '''
+    ...         if not force and len(self.args) > 4:
+    ...             return self
+    ...         return self.func(*(self.args + self.args))
+    ...
+    >>> a = MyClass(1, 2, MyClass(3, 4))
+    >>> a
+    MyClass(1, 2, MyClass(3, 4))
+    >>> a.expand(double=True)
+    MyClass(1, 2, MyClass(3, 4, 3, 4), 1, 2, MyClass(3, 4, 3, 4))
+    >>> a.expand(double=True, deep=False)
+    MyClass(1, 2, MyClass(3, 4), 1, 2, MyClass(3, 4))
+
+    >>> b = MyClass(1, 2, 3, 4, 5)
+    >>> b.expand(double=True)
+    MyClass(1, 2, 3, 4, 5)
+    >>> b.expand(double=True, force=True)
+    MyClass(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
+
+    See Also
+    ========
+
+    expand_log, expand_mul, expand_multinomial, expand_complex, expand_trig,
+    expand_power_base, expand_power_exp, expand_func, sympy.simplify.hyperexpand.hyperexpand
+
+    """
+    # don't modify this; modify the Expr.expand method
+    hints['power_base'] = power_base
+    hints['power_exp'] = power_exp
+    hints['mul'] = mul
+    hints['log'] = log
+    hints['multinomial'] = multinomial
+    hints['basic'] = basic
+    return sympify(e).expand(deep=deep, modulus=modulus, **hints)
+
+# This is a special application of two hints
+
+def _mexpand(expr, recursive=False):
+    # expand multinomials and then expand products; this may not always
+    # be sufficient to give a fully expanded expression (see
+    # test_issue_8247_8354 in test_arit)
+    if expr is None:
+        return
+    was = None
+    while was != expr:
+        was, expr = expr, expand_mul(expand_multinomial(expr))
+        if not recursive:
+            break
+    return expr
+
+
+# These are simple wrappers around single hints.
+
+
+def expand_mul(expr, deep=True):
+    """
+    Wrapper around expand that only uses the mul hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols, expand_mul, exp, log
+    >>> x, y = symbols('x,y', positive=True)
+    >>> expand_mul(exp(x+y)*(x+y)*log(x*y**2))
+    x*exp(x + y)*log(x*y**2) + y*exp(x + y)*log(x*y**2)
+
+    """
+    return sympify(expr).expand(deep=deep, mul=True, power_exp=False,
+    power_base=False, basic=False, multinomial=False, log=False)
+
+
+def expand_multinomial(expr, deep=True):
+    """
+    Wrapper around expand that only uses the multinomial hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols, expand_multinomial, exp
+    >>> x, y = symbols('x y', positive=True)
+    >>> expand_multinomial((x + exp(x + 1))**2)
+    x**2 + 2*x*exp(x + 1) + exp(2*x + 2)
+
+    """
+    return sympify(expr).expand(deep=deep, mul=False, power_exp=False,
+    power_base=False, basic=False, multinomial=True, log=False)
+
+
+def expand_log(expr, deep=True, force=False, factor=False):
+    """
+    Wrapper around expand that only uses the log hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols, expand_log, exp, log
+    >>> x, y = symbols('x,y', positive=True)
+    >>> expand_log(exp(x+y)*(x+y)*log(x*y**2))
+    (x + y)*(log(x) + 2*log(y))*exp(x + y)
+
+    """
+    from sympy.functions.elementary.exponential import log
+    if factor is False:
+        def _handle(x):
+            x1 = expand_mul(expand_log(x, deep=deep, force=force, factor=True))
+            if x1.count(log) <= x.count(log):
+                return x1
+            return x
+
+        expr = expr.replace(
+        lambda x: x.is_Mul and all(any(isinstance(i, log) and i.args[0].is_Rational
+        for i in Mul.make_args(j)) for j in x.as_numer_denom()),
+        _handle)
+
+    return sympify(expr).expand(deep=deep, log=True, mul=False,
+        power_exp=False, power_base=False, multinomial=False,
+        basic=False, force=force, factor=factor)
+
+
+def expand_func(expr, deep=True):
+    """
+    Wrapper around expand that only uses the func hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import expand_func, gamma
+    >>> from sympy.abc import x
+    >>> expand_func(gamma(x + 2))
+    x*(x + 1)*gamma(x)
+
+    """
+    return sympify(expr).expand(deep=deep, func=True, basic=False,
+    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
+
+
+def expand_trig(expr, deep=True):
+    """
+    Wrapper around expand that only uses the trig hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import expand_trig, sin
+    >>> from sympy.abc import x, y
+    >>> expand_trig(sin(x+y)*(x+y))
+    (x + y)*(sin(x)*cos(y) + sin(y)*cos(x))
+
+    """
+    return sympify(expr).expand(deep=deep, trig=True, basic=False,
+    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
+
+
+def expand_complex(expr, deep=True):
+    """
+    Wrapper around expand that only uses the complex hint.  See the expand
+    docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import expand_complex, exp, sqrt, I
+    >>> from sympy.abc import z
+    >>> expand_complex(exp(z))
+    I*exp(re(z))*sin(im(z)) + exp(re(z))*cos(im(z))
+    >>> expand_complex(sqrt(I))
+    sqrt(2)/2 + sqrt(2)*I/2
+
+    See Also
+    ========
+
+    sympy.core.expr.Expr.as_real_imag
+    """
+    return sympify(expr).expand(deep=deep, complex=True, basic=False,
+    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
+
+
+def expand_power_base(expr, deep=True, force=False):
+    """
+    Wrapper around expand that only uses the power_base hint.
+
+    A wrapper to expand(power_base=True) which separates a power with a base
+    that is a Mul into a product of powers, without performing any other
+    expansions, provided that assumptions about the power's base and exponent
+    allow.
+
+    deep=False (default is True) will only apply to the top-level expression.
+
+    force=True (default is False) will cause the expansion to ignore
+    assumptions about the base and exponent. When False, the expansion will
+    only happen if the base is non-negative or the exponent is an integer.
+
+    >>> from sympy.abc import x, y, z
+    >>> from sympy import expand_power_base, sin, cos, exp, Symbol
+
+    >>> (x*y)**2
+    x**2*y**2
+
+    >>> (2*x)**y
+    (2*x)**y
+    >>> expand_power_base(_)
+    2**y*x**y
+
+    >>> expand_power_base((x*y)**z)
+    (x*y)**z
+    >>> expand_power_base((x*y)**z, force=True)
+    x**z*y**z
+    >>> expand_power_base(sin((x*y)**z), deep=False)
+    sin((x*y)**z)
+    >>> expand_power_base(sin((x*y)**z), force=True)
+    sin(x**z*y**z)
+
+    >>> expand_power_base((2*sin(x))**y + (2*cos(x))**y)
+    2**y*sin(x)**y + 2**y*cos(x)**y
+
+    >>> expand_power_base((2*exp(y))**x)
+    2**x*exp(y)**x
+
+    >>> expand_power_base((2*cos(x))**y)
+    2**y*cos(x)**y
+
+    Notice that sums are left untouched. If this is not the desired behavior,
+    apply full ``expand()`` to the expression:
+
+    >>> expand_power_base(((x+y)*z)**2)
+    z**2*(x + y)**2
+    >>> (((x+y)*z)**2).expand()
+    x**2*z**2 + 2*x*y*z**2 + y**2*z**2
+
+    >>> expand_power_base((2*y)**(1+z))
+    2**(z + 1)*y**(z + 1)
+    >>> ((2*y)**(1+z)).expand()
+    2*2**z*y**(z + 1)
+
+    The power that is unexpanded can be expanded safely when
+    ``y != 0``, otherwise different values might be obtained for the expression:
+
+    >>> prev = _
+
+    If we indicate that ``y`` is positive but then replace it with
+    a value of 0 after expansion, the expression becomes 0:
+
+    >>> p = Symbol('p', positive=True)
+    >>> prev.subs(y, p).expand().subs(p, 0)
+    0
+
+    But if ``z = -1`` the expression would not be zero:
+
+    >>> prev.subs(y, 0).subs(z, -1)
+    1
+
+    See Also
+    ========
+
+    expand
+
+    """
+    return sympify(expr).expand(deep=deep, log=False, mul=False,
+        power_exp=False, power_base=True, multinomial=False,
+        basic=False, force=force)
+
+
+def expand_power_exp(expr, deep=True):
+    """
+    Wrapper around expand that only uses the power_exp hint.
+
+    See the expand docstring for more information.
+
+    Examples
+    ========
+
+    >>> from sympy import expand_power_exp, Symbol
+    >>> from sympy.abc import x, y
+    >>> expand_power_exp(3**(y + 2))
+    9*3**y
+    >>> expand_power_exp(x**(y + 2))
+    x**(y + 2)
+
+    If ``x = 0`` the value of the expression depends on the
+    value of ``y``; if the expression were expanded the result
+    would be 0. So expansion is only done if ``x != 0``:
+
+    >>> expand_power_exp(Symbol('x', zero=False)**(y + 2))
+    x**2*x**y
+    """
+    return sympify(expr).expand(deep=deep, complex=False, basic=False,
+    log=False, mul=False, power_exp=True, power_base=False, multinomial=False)
+
+
+def count_ops(expr, visual=False):
+    """
+    Return a representation (integer or expression) of the operations in expr.
+
+    Parameters
+    ==========
+
+    expr : Expr
+        If expr is an iterable, the sum of the op counts of the
+        items will be returned.
+
+    visual : bool, optional
+        If ``False`` (default) then the sum of the coefficients of the
+        visual expression will be returned.
+        If ``True`` then the number of each type of operation is shown
+        with the core class types (or their virtual equivalent) multiplied by the
+        number of times they occur.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import a, b, x, y
+    >>> from sympy import sin, count_ops
+
+    Although there is not a SUB object, minus signs are interpreted as
+    either negations or subtractions:
+
+    >>> (x - y).count_ops(visual=True)
+    SUB
+    >>> (-x).count_ops(visual=True)
+    NEG
+
+    Here, there are two Adds and a Pow:
+
+    >>> (1 + a + b**2).count_ops(visual=True)
+    2*ADD + POW
+
+    In the following, an Add, Mul, Pow and two functions:
+
+    >>> (sin(x)*x + sin(x)**2).count_ops(visual=True)
+    ADD + MUL + POW + 2*SIN
+
+    for a total of 5:
+
+    >>> (sin(x)*x + sin(x)**2).count_ops(visual=False)
+    5
+
+    Note that "what you type" is not always what you get. The expression
+    1/x/y is translated by sympy into 1/(x*y) so it gives a DIV and MUL rather
+    than two DIVs:
+
+    >>> (1/x/y).count_ops(visual=True)
+    DIV + MUL
+
+    The visual option can be used to demonstrate the difference in
+    operations for expressions in different forms. Here, the Horner
+    representation is compared with the expanded form of a polynomial:
+
+    >>> eq=x*(1 + x*(2 + x*(3 + x)))
+    >>> count_ops(eq.expand(), visual=True) - count_ops(eq, visual=True)
+    -MUL + 3*POW
+
+    The count_ops function also handles iterables:
+
+    >>> count_ops([x, sin(x), None, True, x + 2], visual=False)
+    2
+    >>> count_ops([x, sin(x), None, True, x + 2], visual=True)
+    ADD + SIN
+    >>> count_ops({x: sin(x), x + 2: y + 1}, visual=True)
+    2*ADD + SIN
+
+    """
+    from .relational import Relational
+    from sympy.concrete.summations import Sum
+    from sympy.integrals.integrals import Integral
+    from sympy.logic.boolalg import BooleanFunction
+    from sympy.simplify.radsimp import fraction
+
+    expr = sympify(expr)
+    if isinstance(expr, Expr) and not expr.is_Relational:
+
+        ops = []
+        args = [expr]
+        NEG = Symbol('NEG')
+        DIV = Symbol('DIV')
+        SUB = Symbol('SUB')
+        ADD = Symbol('ADD')
+        EXP = Symbol('EXP')
+        while args:
+            a = args.pop()
+
+            # if the following fails because the object is
+            # not Basic type, then the object should be fixed
+            # since it is the intention that all args of Basic
+            # should themselves be Basic
+            if a.is_Rational:
+                #-1/3 = NEG + DIV
+                if a is not S.One:
+                    if a.p < 0:
+                        ops.append(NEG)
+                    if a.q != 1:
+                        ops.append(DIV)
+                    continue
+            elif a.is_Mul or a.is_MatMul:
+                if _coeff_isneg(a):
+                    ops.append(NEG)
+                    if a.args[0] is S.NegativeOne:
+                        a = a.as_two_terms()[1]
+                    else:
+                        a = -a
+                n, d = fraction(a)
+                if n.is_Integer:
+                    ops.append(DIV)
+                    if n < 0:
+                        ops.append(NEG)
+                    args.append(d)
+                    continue  # won't be -Mul but could be Add
+                elif d is not S.One:
+                    if not d.is_Integer:
+                        args.append(d)
+                    ops.append(DIV)
+                    args.append(n)
+                    continue  # could be -Mul
+            elif a.is_Add or a.is_MatAdd:
+                aargs = list(a.args)
+                negs = 0
+                for i, ai in enumerate(aargs):
+                    if _coeff_isneg(ai):
+                        negs += 1
+                        args.append(-ai)
+                        if i > 0:
+                            ops.append(SUB)
+                    else:
+                        args.append(ai)
+                        if i > 0:
+                            ops.append(ADD)
+                if negs == len(aargs):  # -x - y = NEG + SUB
+                    ops.append(NEG)
+                elif _coeff_isneg(aargs[0]):  # -x + y = SUB, but already recorded ADD
+                    ops.append(SUB - ADD)
+                continue
+            if a.is_Pow and a.exp is S.NegativeOne:
+                ops.append(DIV)
+                args.append(a.base)  # won't be -Mul but could be Add
+                continue
+            if a == S.Exp1:
+                ops.append(EXP)
+                continue
+            if a.is_Pow and a.base == S.Exp1:
+                ops.append(EXP)
+                args.append(a.exp)
+                continue
+            if a.is_Mul or isinstance(a, LatticeOp):
+                o = Symbol(a.func.__name__.upper())
+                # count the args
+                ops.append(o*(len(a.args) - 1))
+            elif a.args and (
+                    a.is_Pow or
+                    a.is_Function or
+                    isinstance(a, Derivative) or
+                    isinstance(a, Integral) or
+                    isinstance(a, Sum)):
+                # if it's not in the list above we don't
+                # consider a.func something to count, e.g.
+                # Tuple, MatrixSymbol, etc...
+                if isinstance(a.func, UndefinedFunction):
+                    o = Symbol("FUNC_" + a.func.__name__.upper())
+                else:
+                    o = Symbol(a.func.__name__.upper())
+                ops.append(o)
+
+            if not a.is_Symbol:
+                args.extend(a.args)
+
+    elif isinstance(expr, Dict):
+        ops = [count_ops(k, visual=visual) +
+               count_ops(v, visual=visual) for k, v in expr.items()]
+    elif iterable(expr):
+        ops = [count_ops(i, visual=visual) for i in expr]
+    elif isinstance(expr, (Relational, BooleanFunction)):
+        ops = []
+        for arg in expr.args:
+            ops.append(count_ops(arg, visual=True))
+        o = Symbol(func_name(expr, short=True).upper())
+        ops.append(o)
+    elif not isinstance(expr, Basic):
+        ops = []
+    else:  # it's Basic not isinstance(expr, Expr):
+        if not isinstance(expr, Basic):
+            raise TypeError("Invalid type of expr")
+        else:
+            ops = []
+            args = [expr]
+            while args:
+                a = args.pop()
+
+                if a.args:
+                    o = Symbol(type(a).__name__.upper())
+                    if a.is_Boolean:
+                        ops.append(o*(len(a.args)-1))
+                    else:
+                        ops.append(o)
+                    args.extend(a.args)
+
+    if not ops:
+        if visual:
+            return S.Zero
+        return 0
+
+    ops = Add(*ops)
+
+    if visual:
+        return ops
+
+    if ops.is_Number:
+        return int(ops)
+
+    return sum(int((a.args or [1])[0]) for a in Add.make_args(ops))
+
+
+def nfloat(expr, n=15, exponent=False, dkeys=False):
+    """Make all Rationals in expr Floats except those in exponents
+    (unless the exponents flag is set to True) and those in undefined
+    functions. When processing dictionaries, do not modify the keys
+    unless ``dkeys=True``.
+
+    Examples
+    ========
+
+    >>> from sympy import nfloat, cos, pi, sqrt
+    >>> from sympy.abc import x, y
+    >>> nfloat(x**4 + x/2 + cos(pi/3) + 1 + sqrt(y))
+    x**4 + 0.5*x + sqrt(y) + 1.5
+    >>> nfloat(x**4 + sqrt(y), exponent=True)
+    x**4.0 + y**0.5
+
+    Container types are not modified:
+
+    >>> type(nfloat((1, 2))) is tuple
+    True
+    """
+    from sympy.matrices.matrices import MatrixBase
+
+    kw = {"n": n, "exponent": exponent, "dkeys": dkeys}
+
+    if isinstance(expr, MatrixBase):
+        return expr.applyfunc(lambda e: nfloat(e, **kw))
+
+    # handling of iterable containers
+    if iterable(expr, exclude=str):
+        if isinstance(expr, (dict, Dict)):
+            if dkeys:
+                args = [tuple((nfloat(i, **kw) for i in a))
+                    for a in expr.items()]
+            else:
+                args = [(k, nfloat(v, **kw)) for k, v in expr.items()]
+            if isinstance(expr, dict):
+                return type(expr)(args)
+            else:
+                return expr.func(*args)
+        elif isinstance(expr, Basic):
+            return expr.func(*[nfloat(a, **kw) for a in expr.args])
+        return type(expr)([nfloat(a, **kw) for a in expr])
+
+    rv = sympify(expr)
+
+    if rv.is_Number:
+        return Float(rv, n)
+    elif rv.is_number:
+        # evalf doesn't always set the precision
+        rv = rv.n(n)
+        if rv.is_Number:
+            rv = Float(rv.n(n), n)
+        else:
+            pass  # pure_complex(rv) is likely True
+        return rv
+    elif rv.is_Atom:
+        return rv
+    elif rv.is_Relational:
+        args_nfloat = (nfloat(arg, **kw) for arg in rv.args)
+        return rv.func(*args_nfloat)
+
+
+    # watch out for RootOf instances that don't like to have
+    # their exponents replaced with Dummies and also sometimes have
+    # problems with evaluating at low precision (issue 6393)
+    from sympy.polys.rootoftools import RootOf
+    rv = rv.xreplace({ro: ro.n(n) for ro in rv.atoms(RootOf)})
+
+    from .power import Pow
+    if not exponent:
+        reps = [(p, Pow(p.base, Dummy())) for p in rv.atoms(Pow)]
+        rv = rv.xreplace(dict(reps))
+    rv = rv.n(n)
+    if not exponent:
+        rv = rv.xreplace({d.exp: p.exp for p, d in reps})
+    else:
+        # Pow._eval_evalf special cases Integer exponents so if
+        # exponent is suppose to be handled we have to do so here
+        rv = rv.xreplace(Transform(
+            lambda x: Pow(x.base, Float(x.exp, n)),
+            lambda x: x.is_Pow and x.exp.is_Integer))
+
+    return rv.xreplace(Transform(
+        lambda x: x.func(*nfloat(x.args, n, exponent)),
+        lambda x: isinstance(x, Function) and not isinstance(x, AppliedUndef)))
+
+
+from .symbol import Dummy, Symbol
diff --git a/shared/package-files/4e/4eef2de24d0485600206fb07e6dd1394539575e278c08f4f5ee6fc64b77269d4/_plotting.py b/shared/package-files/4e/4eef2de24d0485600206fb07e6dd1394539575e278c08f4f5ee6fc64b77269d4/_plotting.py
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index 0000000000000000000000000000000000000000..1a3883b7db7f5d9f4f04dbecf0720230278e41dc
--- /dev/null
+++ b/shared/package-files/4e/4eef2de24d0485600206fb07e6dd1394539575e278c08f4f5ee6fc64b77269d4/_plotting.py
@@ -0,0 +1,419 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+import warnings
+from collections.abc import Mapping
+
+import numpy as np
+
+from . import check_consistent_length
+from ._optional_dependencies import check_matplotlib_support
+from ._response import _get_response_values_binary
+from .fixes import parse_version
+from .multiclass import type_of_target
+from .validation import _check_pos_label_consistency, _num_samples
+
+
+class _BinaryClassifierCurveDisplayMixin:
+    """Mixin class to be used in Displays requiring a binary classifier.
+
+    The aim of this class is to centralize some validations regarding the estimator and
+    the target and gather the response of the estimator.
+    """
+
+    def _validate_plot_params(self, *, ax=None, name=None):
+        check_matplotlib_support(f"{self.__class__.__name__}.plot")
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            _, ax = plt.subplots()
+
+        # Display classes are in process of changing from `estimator_name` to `name`.
+        # Try old attr name: `estimator_name` first.
+        if name is None:
+            name = getattr(self, "estimator_name", getattr(self, "name", None))
+        return ax, ax.figure, name
+
+    @classmethod
+    def _validate_and_get_response_values(
+        cls, estimator, X, y, *, response_method="auto", pos_label=None, name=None
+    ):
+        check_matplotlib_support(f"{cls.__name__}.from_estimator")
+
+        name = estimator.__class__.__name__ if name is None else name
+
+        y_pred, pos_label = _get_response_values_binary(
+            estimator,
+            X,
+            response_method=response_method,
+            pos_label=pos_label,
+        )
+
+        return y_pred, pos_label, name
+
+    @classmethod
+    def _validate_from_predictions_params(
+        cls, y_true, y_pred, *, sample_weight=None, pos_label=None, name=None
+    ):
+        check_matplotlib_support(f"{cls.__name__}.from_predictions")
+
+        if type_of_target(y_true) != "binary":
+            raise ValueError(
+                f"The target y is not binary. Got {type_of_target(y_true)} type of"
+                " target."
+            )
+
+        check_consistent_length(y_true, y_pred, sample_weight)
+        pos_label = _check_pos_label_consistency(pos_label, y_true)
+
+        name = name if name is not None else "Classifier"
+
+        return pos_label, name
+
+    @classmethod
+    def _validate_from_cv_results_params(
+        cls,
+        cv_results,
+        X,
+        y,
+        *,
+        sample_weight,
+        pos_label,
+    ):
+        check_matplotlib_support(f"{cls.__name__}.from_cv_results")
+
+        required_keys = {"estimator", "indices"}
+        if not all(key in cv_results for key in required_keys):
+            raise ValueError(
+                "`cv_results` does not contain one of the following required keys: "
+                f"{required_keys}. Set explicitly the parameters "
+                "`return_estimator=True` and `return_indices=True` to the function"
+                "`cross_validate`."
+            )
+
+        train_size, test_size = (
+            len(cv_results["indices"]["train"][0]),
+            len(cv_results["indices"]["test"][0]),
+        )
+
+        if _num_samples(X) != train_size + test_size:
+            raise ValueError(
+                "`X` does not contain the correct number of samples. "
+                f"Expected {train_size + test_size}, got {_num_samples(X)}."
+            )
+
+        if type_of_target(y) != "binary":
+            raise ValueError(
+                f"The target `y` is not binary. Got {type_of_target(y)} type of target."
+            )
+        check_consistent_length(X, y, sample_weight)
+
+        try:
+            pos_label = _check_pos_label_consistency(pos_label, y)
+        except ValueError as e:
+            # Adapt error message
+            raise ValueError(str(e).replace("y_true", "y"))
+
+        return pos_label
+
+    @staticmethod
+    def _get_legend_label(curve_legend_metric, curve_name, legend_metric_name):
+        """Helper to get legend label using `name` and `legend_metric`"""
+        if curve_legend_metric is not None and curve_name is not None:
+            label = f"{curve_name} ({legend_metric_name} = {curve_legend_metric:0.2f})"
+        elif curve_legend_metric is not None:
+            label = f"{legend_metric_name} = {curve_legend_metric:0.2f}"
+        elif curve_name is not None:
+            label = curve_name
+        else:
+            label = None
+        return label
+
+    @staticmethod
+    def _validate_curve_kwargs(
+        n_curves,
+        name,
+        legend_metric,
+        legend_metric_name,
+        curve_kwargs,
+        **kwargs,
+    ):
+        """Get validated line kwargs for each curve.
+
+        Parameters
+        ----------
+        n_curves : int
+            Number of curves.
+
+        name : list of str or None
+            Name for labeling legend entries.
+
+        legend_metric : dict
+            Dictionary with "mean" and "std" keys, or "metric" key of metric
+            values for each curve. If None, "label" will not contain metric values.
+
+        legend_metric_name : str
+            Name of the summary value provided in `legend_metrics`.
+
+        curve_kwargs : dict or list of dict or None
+            Dictionary with keywords passed to the matplotlib's `plot` function
+            to draw the individual curves. If a list is provided, the
+            parameters are applied to the curves sequentially. If a single
+            dictionary is provided, the same parameters are applied to all
+            curves.
+
+        **kwargs : dict
+            Deprecated. Keyword arguments to be passed to matplotlib's `plot`.
+        """
+        # TODO(1.9): Remove deprecated **kwargs
+        if curve_kwargs and kwargs:
+            raise ValueError(
+                "Cannot provide both `curve_kwargs` and `kwargs`. `**kwargs` is "
+                "deprecated in 1.7 and will be removed in 1.9. Pass all matplotlib "
+                "arguments to `curve_kwargs` as a dictionary."
+            )
+        if kwargs:
+            warnings.warn(
+                "`**kwargs` is deprecated and will be removed in 1.9. Pass all "
+                "matplotlib arguments to `curve_kwargs` as a dictionary instead.",
+                FutureWarning,
+            )
+            curve_kwargs = kwargs
+
+        if isinstance(curve_kwargs, list) and len(curve_kwargs) != n_curves:
+            raise ValueError(
+                f"`curve_kwargs` must be None, a dictionary or a list of length "
+                f"{n_curves}. Got: {curve_kwargs}."
+            )
+
+        # Ensure valid `name` and `curve_kwargs` combination.
+        if (
+            isinstance(name, list)
+            and len(name) != 1
+            and not isinstance(curve_kwargs, list)
+        ):
+            raise ValueError(
+                "To avoid labeling individual curves that have the same appearance, "
+                f"`curve_kwargs` should be a list of {n_curves} dictionaries. "
+                "Alternatively, set `name` to `None` or a single string to label "
+                "a single legend entry with mean ROC AUC score of all curves."
+            )
+
+        # Ensure `name` is of the correct length
+        if isinstance(name, str):
+            name = [name]
+        if isinstance(name, list) and len(name) == 1:
+            name = name * n_curves
+        name = [None] * n_curves if name is None else name
+
+        # Ensure `curve_kwargs` is of correct length
+        if isinstance(curve_kwargs, Mapping):
+            curve_kwargs = [curve_kwargs] * n_curves
+
+        default_multi_curve_kwargs = {"alpha": 0.5, "linestyle": "--", "color": "blue"}
+        if curve_kwargs is None:
+            if n_curves > 1:
+                curve_kwargs = [default_multi_curve_kwargs] * n_curves
+            else:
+                curve_kwargs = [{}]
+
+        labels = []
+        if "mean" in legend_metric:
+            label_aggregate = _BinaryClassifierCurveDisplayMixin._get_legend_label(
+                legend_metric["mean"], name[0], legend_metric_name
+            )
+            # Note: "std" always `None` when "mean" is `None` - no metric value added
+            # to label in this case
+            if legend_metric["std"] is not None:
+                # Add the "+/- std" to the end (in brackets if name provided)
+                if name[0] is not None:
+                    label_aggregate = (
+                        label_aggregate[:-1] + f" +/- {legend_metric['std']:0.2f})"
+                    )
+                else:
+                    label_aggregate = (
+                        label_aggregate + f" +/- {legend_metric['std']:0.2f}"
+                    )
+            # Add `label` for first curve only, set to `None` for remaining curves
+            labels.extend([label_aggregate] + [None] * (n_curves - 1))
+        else:
+            for curve_legend_metric, curve_name in zip(legend_metric["metric"], name):
+                labels.append(
+                    _BinaryClassifierCurveDisplayMixin._get_legend_label(
+                        curve_legend_metric, curve_name, legend_metric_name
+                    )
+                )
+
+        curve_kwargs_ = [
+            _validate_style_kwargs({"label": label}, curve_kwargs[fold_idx])
+            for fold_idx, label in enumerate(labels)
+        ]
+        return curve_kwargs_
+
+
+def _validate_score_name(score_name, scoring, negate_score):
+    """Validate the `score_name` parameter.
+
+    If `score_name` is provided, we just return it as-is.
+    If `score_name` is `None`, we use `Score` if `negate_score` is `False` and
+    `Negative score` otherwise.
+    If `score_name` is a string or a callable, we infer the name. We replace `_` by
+    spaces and capitalize the first letter. We remove `neg_` and replace it by
+    `"Negative"` if `negate_score` is `False` or just remove it otherwise.
+    """
+    if score_name is not None:
+        return score_name
+    elif scoring is None:
+        return "Negative score" if negate_score else "Score"
+    else:
+        score_name = scoring.__name__ if callable(scoring) else scoring
+        if negate_score:
+            if score_name.startswith("neg_"):
+                score_name = score_name[4:]
+            else:
+                score_name = f"Negative {score_name}"
+        elif score_name.startswith("neg_"):
+            score_name = f"Negative {score_name[4:]}"
+        score_name = score_name.replace("_", " ")
+        return score_name.capitalize()
+
+
+def _interval_max_min_ratio(data):
+    """Compute the ratio between the largest and smallest inter-point distances.
+
+    A value larger than 5 typically indicates that the parameter range would
+    better be displayed with a log scale while a linear scale would be more
+    suitable otherwise.
+    """
+    diff = np.diff(np.sort(data))
+    return diff.max() / diff.min()
+
+
+def _validate_style_kwargs(default_style_kwargs, user_style_kwargs):
+    """Create valid style kwargs by avoiding Matplotlib alias errors.
+
+    Matplotlib raises an error when, for example, 'color' and 'c', or 'linestyle' and
+    'ls', are specified together. To avoid this, we automatically keep only the one
+    specified by the user and raise an error if the user specifies both.
+
+    Parameters
+    ----------
+    default_style_kwargs : dict
+        The Matplotlib style kwargs used by default in the scikit-learn display.
+    user_style_kwargs : dict
+        The user-defined Matplotlib style kwargs.
+
+    Returns
+    -------
+    valid_style_kwargs : dict
+        The validated style kwargs taking into account both default and user-defined
+        Matplotlib style kwargs.
+    """
+
+    invalid_to_valid_kw = {
+        "ls": "linestyle",
+        "c": "color",
+        "ec": "edgecolor",
+        "fc": "facecolor",
+        "lw": "linewidth",
+        "mec": "markeredgecolor",
+        "mfcalt": "markerfacecoloralt",
+        "ms": "markersize",
+        "mew": "markeredgewidth",
+        "mfc": "markerfacecolor",
+        "aa": "antialiased",
+        "ds": "drawstyle",
+        "font": "fontproperties",
+        "family": "fontfamily",
+        "name": "fontname",
+        "size": "fontsize",
+        "stretch": "fontstretch",
+        "style": "fontstyle",
+        "variant": "fontvariant",
+        "weight": "fontweight",
+        "ha": "horizontalalignment",
+        "va": "verticalalignment",
+        "ma": "multialignment",
+    }
+    for invalid_key, valid_key in invalid_to_valid_kw.items():
+        if invalid_key in user_style_kwargs and valid_key in user_style_kwargs:
+            raise TypeError(
+                f"Got both {invalid_key} and {valid_key}, which are aliases of one "
+                "another"
+            )
+    valid_style_kwargs = default_style_kwargs.copy()
+
+    for key in user_style_kwargs.keys():
+        if key in invalid_to_valid_kw:
+            valid_style_kwargs[invalid_to_valid_kw[key]] = user_style_kwargs[key]
+        else:
+            valid_style_kwargs[key] = user_style_kwargs[key]
+
+    return valid_style_kwargs
+
+
+def _despine(ax):
+    """Remove the top and right spines of the plot.
+
+    Parameters
+    ----------
+    ax : matplotlib.axes.Axes
+        The axes of the plot to despine.
+    """
+    for s in ["top", "right"]:
+        ax.spines[s].set_visible(False)
+    for s in ["bottom", "left"]:
+        ax.spines[s].set_bounds(0, 1)
+
+
+def _deprecate_estimator_name(estimator_name, name, version):
+    """Deprecate `estimator_name` in favour of `name`."""
+    version = parse_version(version)
+    version_remove = f"{version.major}.{version.minor + 2}"
+    if estimator_name != "deprecated":
+        if name:
+            raise ValueError(
+                "Cannot provide both `estimator_name` and `name`. `estimator_name` "
+                f"is deprecated in {version} and will be removed in {version_remove}. "
+                "Use `name` only."
+            )
+        warnings.warn(
+            f"`estimator_name` is deprecated in {version} and will be removed in "
+            f"{version_remove}. Use `name` instead.",
+            FutureWarning,
+        )
+        return estimator_name
+    return name
+
+
+def _convert_to_list_leaving_none(param):
+    """Convert parameters to a list, leaving `None` as is."""
+    if param is None:
+        return None
+    if isinstance(param, list):
+        return param
+    return [param]
+
+
+def _check_param_lengths(required, optional, class_name):
+    """Check required and optional parameters are of the same length."""
+    optional_provided = {}
+    for name, param in optional.items():
+        if isinstance(param, list):
+            optional_provided[name] = param
+
+    all_params = {**required, **optional_provided}
+    if len({len(param) for param in all_params.values()}) > 1:
+        param_keys = [key for key in all_params.keys()]
+        # Note: below code requires `len(param_keys) >= 2`, which is the case for all
+        # display classes
+        params_formatted = " and ".join([", ".join(param_keys[:-1]), param_keys[-1]])
+        or_plot = ""
+        if "'name' (or self.name)" in param_keys:
+            or_plot = " (or `plot`)"
+        lengths_formatted = ", ".join(
+            f"{key}: {len(value)}" for key, value in all_params.items()
+        )
+        raise ValueError(
+            f"{params_formatted} from `{class_name}` initialization{or_plot}, "
+            f"should all be lists of the same length. Got: {lengths_formatted}"
+        )
diff --git a/shared/package-files/4e/4eef3bbd74960af465492fd2ab793c6a9b4e5f398c17d2508f81e4ea95b79b7b/enums.pyi b/shared/package-files/4e/4eef3bbd74960af465492fd2ab793c6a9b4e5f398c17d2508f81e4ea95b79b7b/enums.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ddfe548979a462a293b663dee67080e7b38a5a1d
--- /dev/null
+++ b/shared/package-files/4e/4eef3bbd74960af465492fd2ab793c6a9b4e5f398c17d2508f81e4ea95b79b7b/enums.pyi
@@ -0,0 +1,30 @@
+import enum
+from typing import Any, List, Tuple
+
+class ChoicesMeta(enum.EnumMeta):
+    names: List[str] = ...
+    choices: List[Tuple[Any, str]] = ...
+    labels: List[str] = ...
+    values: List[Any] = ...
+    def __contains__(self, item: Any) -> bool: ...
+
+class Choices(enum.Enum, metaclass=ChoicesMeta):
+    def __str__(self): ...
+
+# fake
+class _IntegerChoicesMeta(ChoicesMeta):
+    names: List[str] = ...
+    choices: List[Tuple[int, str]] = ...
+    labels: List[str] = ...
+    values: List[int] = ...
+
+class IntegerChoices(int, Choices, metaclass=_IntegerChoicesMeta): ...
+
+# fake
+class _TextChoicesMeta(ChoicesMeta):
+    names: List[str] = ...
+    choices: List[Tuple[str, str]] = ...
+    labels: List[str] = ...
+    values: List[str] = ...
+
+class TextChoices(str, Choices, metaclass=_TextChoicesMeta): ...
diff --git a/shared/package-files/4e/4ef13306f4b37f314274eb0c019d10811f79240e717f790064e361cb98045d11/Jakarta b/shared/package-files/4e/4ef13306f4b37f314274eb0c019d10811f79240e717f790064e361cb98045d11/Jakarta
new file mode 100644
index 0000000000000000000000000000000000000000..ec4bd5747a8c9c528dfd22c8e3171851784ade59
Binary files /dev/null and b/shared/package-files/4e/4ef13306f4b37f314274eb0c019d10811f79240e717f790064e361cb98045d11/Jakarta differ
diff --git a/shared/package-files/4e/4ef17d489a66c11eab09026f0af3b84f97a0c3702c890b020a8397b3fd144eb0/transforms.h b/shared/package-files/4e/4ef17d489a66c11eab09026f0af3b84f97a0c3702c890b020a8397b3fd144eb0/transforms.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5d92062e62d52dd2dac3ab39f76385f9bf1522f
--- /dev/null
+++ b/shared/package-files/4e/4ef17d489a66c11eab09026f0af3b84f97a0c3702c890b020a8397b3fd144eb0/transforms.h
@@ -0,0 +1,7 @@
+#pragma once
+
+#include <torch/data/transforms/base.h>
+#include <torch/data/transforms/collate.h>
+#include <torch/data/transforms/lambda.h>
+#include <torch/data/transforms/stack.h>
+#include <torch/data/transforms/tensor.h>
diff --git a/shared/package-files/4e/4ef430cb4098439b523007a1ca606a013e665bb96445f1ab934068e3a320a5c3/generic.py b/shared/package-files/4e/4ef430cb4098439b523007a1ca606a013e665bb96445f1ab934068e3a320a5c3/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff09fbd0c0da5b67102a917c587b4d96ce15f9a2
--- /dev/null
+++ b/shared/package-files/4e/4ef430cb4098439b523007a1ca606a013e665bb96445f1ab934068e3a320a5c3/generic.py
@@ -0,0 +1,580 @@
+"""
+Compute the shortest paths and path lengths between nodes in the graph.
+
+These algorithms work with undirected and directed graphs.
+
+"""
+import warnings
+
+import networkx as nx
+
+__all__ = [
+    "shortest_path",
+    "all_shortest_paths",
+    "shortest_path_length",
+    "average_shortest_path_length",
+    "has_path",
+]
+
+
+@nx._dispatch
+def has_path(G, source, target):
+    """Returns *True* if *G* has a path from *source* to *target*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+    """
+    try:
+        nx.shortest_path(G, source, target)
+    except nx.NetworkXNoPath:
+        return False
+    return True
+
+
+@nx._dispatch
+def shortest_path(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path. If not specified, compute shortest
+        paths for each possible starting node.
+
+    target : node, optional
+        Ending node for path. If not specified, compute shortest
+        paths to all possible nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    path: list or dictionary
+        All returned paths include both the source and target in the path.
+
+        If the source and target are both specified, return a single list
+        of nodes in a shortest path from the source to the target.
+
+        If only the source is specified, return a dictionary keyed by
+        targets with a list of nodes in a shortest path from the source
+        to one of the targets.
+
+        If only the target is specified, return a dictionary keyed by
+        sources with a list of nodes in a shortest path from one of the
+        sources to the target.
+
+        If neither the source nor target are specified return a dictionary
+        of dictionaries with path[source][target]=[list of nodes in path].
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> print(nx.shortest_path(G, source=0, target=4))
+    [0, 1, 2, 3, 4]
+    >>> p = nx.shortest_path(G, source=0)  # target not specified
+    >>> p[3] # shortest path from source=0 to target=3
+    [0, 1, 2, 3]
+    >>> p = nx.shortest_path(G, target=4)  # source not specified
+    >>> p[1] # shortest path from source=1 to target=4
+    [1, 2, 3, 4]
+    >>> p = nx.shortest_path(G)  # source, target not specified
+    >>> p[2][4] # shortest path from source=2 to target=4
+    [2, 3, 4]
+
+    Notes
+    -----
+    There may be more than one shortest path between a source and target.
+    This returns only one of them.
+
+    See Also
+    --------
+    all_pairs_shortest_path
+    all_pairs_dijkstra_path
+    all_pairs_bellman_ford_path
+    single_source_shortest_path
+    single_source_dijkstra_path
+    single_source_bellman_ford_path
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            msg = "shortest_path for all_pairs will return an iterator in v3.3"
+            warnings.warn(msg, DeprecationWarning)
+
+            # Find paths between all pairs.
+            if method == "unweighted":
+                paths = dict(nx.all_pairs_shortest_path(G))
+            elif method == "dijkstra":
+                paths = dict(nx.all_pairs_dijkstra_path(G, weight=weight))
+            else:  # method == 'bellman-ford':
+                paths = dict(nx.all_pairs_bellman_ford_path(G, weight=weight))
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, target)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, target, weight=weight)
+            # Now flip the paths so they go from a source to the target.
+            for target in paths:
+                paths[target] = list(reversed(paths[target]))
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, source)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                paths = nx.bidirectional_shortest_path(G, source, target)
+            elif method == "dijkstra":
+                _, paths = nx.bidirectional_dijkstra(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path(G, source, target, weight)
+    return paths
+
+
+@nx._dispatch
+def shortest_path_length(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest path lengths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        source nodes.
+
+    target : node, optional
+        Ending node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        target nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path length.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    length: int or iterator
+        If the source and target are both specified, return the length of
+        the shortest path from the source to the target.
+
+        If only the source is specified, return a dict keyed by target
+        to the shortest path length from the source to that target.
+
+        If only the target is specified, return a dict keyed by source
+        to the shortest path length from that source to the target.
+
+        If neither the source nor target are specified, return an iterator
+        over (source, dictionary) where dictionary is keyed by target to
+        shortest path length from source to that target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.shortest_path_length(G, source=0, target=4)
+    4
+    >>> p = nx.shortest_path_length(G, source=0)  # target not specified
+    >>> p[4]
+    4
+    >>> p = nx.shortest_path_length(G, target=4)  # source not specified
+    >>> p[0]
+    4
+    >>> p = dict(nx.shortest_path_length(G))  # source,target not specified
+    >>> p[0][4]
+    4
+
+    Notes
+    -----
+    The length of the path is always 1 less than the number of nodes involved
+    in the path since the length measures the number of edges followed.
+
+    For digraphs this returns the shortest directed path length. To find path
+    lengths in the reverse direction use G.reverse(copy=False) first to flip
+    the edge orientation.
+
+    See Also
+    --------
+    all_pairs_shortest_path_length
+    all_pairs_dijkstra_path_length
+    all_pairs_bellman_ford_path_length
+    single_source_shortest_path_length
+    single_source_dijkstra_path_length
+    single_source_bellman_ford_path_length
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            # Find paths between all pairs.
+            if method == "unweighted":
+                paths = nx.all_pairs_shortest_path_length(G)
+            elif method == "dijkstra":
+                paths = nx.all_pairs_dijkstra_path_length(G, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.all_pairs_bellman_ford_path_length(G, weight=weight)
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                path_length = nx.single_source_shortest_path_length
+                paths = path_length(G, target)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, target, weight=weight)
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path_length(G, source)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                p = nx.bidirectional_shortest_path(G, source, target)
+                paths = len(p) - 1
+            elif method == "dijkstra":
+                paths = nx.dijkstra_path_length(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path_length(G, source, target, weight)
+    return paths
+
+
+def average_shortest_path_length(G, weight=None, method=None):
+    r"""Returns the average shortest path length.
+
+    The average shortest path length is
+
+    .. math::
+
+       a =\sum_{\substack{s,t \in V \\ s\neq t}} \frac{d(s, t)}{n(n-1)}
+
+    where `V` is the set of nodes in `G`,
+    `d(s, t)` is the shortest path from `s` to `t`,
+    and `n` is the number of nodes in `G`.
+
+    .. versionchanged:: 3.0
+       An exception is raised for directed graphs that are not strongly
+       connected.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'unweighted' or 'djikstra')
+        The algorithm to use to compute the path lengths.
+        Supported options are 'unweighted', 'dijkstra', 'bellman-ford',
+        'floyd-warshall' and 'floyd-warshall-numpy'.
+        Other method values produce a ValueError.
+        The default method is 'unweighted' if `weight` is None,
+        otherwise the default method is 'dijkstra'.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is the null graph (that is, the graph on zero nodes).
+
+    NetworkXError
+        If `G` is not connected (or not strongly connected, in the case
+        of a directed graph).
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.average_shortest_path_length(G)
+    2.0
+
+    For disconnected graphs, you can compute the average shortest path
+    length for each component
+
+    >>> G = nx.Graph([(1, 2), (3, 4)])
+    >>> for C in (G.subgraph(c).copy() for c in nx.connected_components(G)):
+    ...     print(nx.average_shortest_path_length(C))
+    1.0
+    1.0
+
+    """
+    single_source_methods = ["unweighted", "dijkstra", "bellman-ford"]
+    all_pairs_methods = ["floyd-warshall", "floyd-warshall-numpy"]
+    supported_methods = single_source_methods + all_pairs_methods
+
+    if method is None:
+        method = "unweighted" if weight is None else "dijkstra"
+    if method not in supported_methods:
+        raise ValueError(f"method not supported: {method}")
+
+    n = len(G)
+    # For the special case of the null graph, raise an exception, since
+    # there are no paths in the null graph.
+    if n == 0:
+        msg = (
+            "the null graph has no paths, thus there is no average"
+            "shortest path length"
+        )
+        raise nx.NetworkXPointlessConcept(msg)
+    # For the special case of the trivial graph, return zero immediately.
+    if n == 1:
+        return 0
+    # Shortest path length is undefined if the graph is not strongly connected.
+    if G.is_directed() and not nx.is_strongly_connected(G):
+        raise nx.NetworkXError("Graph is not strongly connected.")
+    # Shortest path length is undefined if the graph is not connected.
+    if not G.is_directed() and not nx.is_connected(G):
+        raise nx.NetworkXError("Graph is not connected.")
+
+    # Compute all-pairs shortest paths.
+    def path_length(v):
+        if method == "unweighted":
+            return nx.single_source_shortest_path_length(G, v)
+        elif method == "dijkstra":
+            return nx.single_source_dijkstra_path_length(G, v, weight=weight)
+        elif method == "bellman-ford":
+            return nx.single_source_bellman_ford_path_length(G, v, weight=weight)
+
+    if method in single_source_methods:
+        # Sum the distances for each (ordered) pair of source and target node.
+        s = sum(l for u in G for l in path_length(u).values())
+    else:
+        if method == "floyd-warshall":
+            all_pairs = nx.floyd_warshall(G, weight=weight)
+            s = sum(sum(t.values()) for t in all_pairs.values())
+        elif method == "floyd-warshall-numpy":
+            s = nx.floyd_warshall_numpy(G, weight=weight).sum()
+    return s / (n * (n - 1))
+
+
+def all_shortest_paths(G, source, target, weight=None, method="dijkstra"):
+    """Compute all shortest simple paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path.
+
+    target : node
+       Ending node for path.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2])
+    >>> nx.add_path(G, [0, 10, 2])
+    >>> print([p for p in nx.all_shortest_paths(G, source=0, target=2)])
+    [[0, 1, 2], [0, 10, 2]]
+
+    Notes
+    -----
+    There may be many shortest paths between the source and target.  If G
+    contains zero-weight cycles, this function will not produce all shortest
+    paths because doing so would produce infinitely many paths of unbounded
+    length -- instead, we only produce the shortest simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    """
+    method = "unweighted" if weight is None else method
+    if method == "unweighted":
+        pred = nx.predecessor(G, source)
+    elif method == "dijkstra":
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, source, weight=weight)
+    elif method == "bellman-ford":
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, source, weight=weight)
+    else:
+        raise ValueError(f"method not supported: {method}")
+
+    return _build_paths_from_predecessors({source}, target, pred)
+
+
+def _build_paths_from_predecessors(sources, target, pred):
+    """Compute all simple paths to target, given the predecessors found in
+    pred, terminating when any source in sources is found.
+
+    Parameters
+    ----------
+    sources : set
+       Starting nodes for path.
+
+    target : node
+       Ending node for path.
+
+    pred : dict
+       A dictionary of predecessor lists, keyed by node
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Notes
+    -----
+    There may be many paths between the sources and target.  If there are
+    cycles among the predecessors, this function will not produce all
+    possible paths because doing so would produce infinitely many paths
+    of unbounded length -- instead, we only produce simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    all_shortest_paths
+    bellman_ford_path
+    """
+    if target not in pred:
+        raise nx.NetworkXNoPath(f"Target {target} cannot be reached from given sources")
+
+    seen = {target}
+    stack = [[target, 0]]
+    top = 0
+    while top >= 0:
+        node, i = stack[top]
+        if node in sources:
+            yield [p for p, n in reversed(stack[: top + 1])]
+        if len(pred[node]) > i:
+            stack[top][1] = i + 1
+            next = pred[node][i]
+            if next in seen:
+                continue
+            else:
+                seen.add(next)
+            top += 1
+            if top == len(stack):
+                stack.append([next, 0])
+            else:
+                stack[top][:] = [next, 0]
+        else:
+            seen.discard(node)
+            top -= 1
diff --git a/shared/package-files/4e/4ef7d3913e6d10050934451680b38decdc8cd74754674e9b17e7ea8b84eb621c/__init__.py b/shared/package-files/4e/4ef7d3913e6d10050934451680b38decdc8cd74754674e9b17e7ea8b84eb621c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..08e066a9a72f8633beb5982bffa2a02d472315e3
--- /dev/null
+++ b/shared/package-files/4e/4ef7d3913e6d10050934451680b38decdc8cd74754674e9b17e7ea8b84eb621c/__init__.py
@@ -0,0 +1,1985 @@
+from __future__ import annotations
+
+import operator
+import warnings
+import weakref
+
+from contextlib import nullcontext
+from enum import Enum
+from functools import cmp_to_key, reduce
+from typing import (
+    Any,
+    Callable,
+    cast,
+    List,
+    NamedTuple,
+    Optional,
+    overload,
+    Sequence,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    Union,
+)
+
+from typing_extensions import TypeAlias
+
+
+if TYPE_CHECKING:
+    # Import the following modules during type checking to enable code intelligence features,
+    # such as auto-completion in tools like pylance, even when these modules are not explicitly
+    # imported in user code.
+
+    import sympy
+
+import torch
+from torch import sym_float, sym_int, sym_max
+
+
+ShapeType: TypeAlias = Union[torch.Size, List[int], Tuple[int, ...]]
+StrideType: TypeAlias = Union[List[int], Tuple[int, ...]]
+DimsType: TypeAlias = Union[int, List[int], Tuple[int, ...]]
+DimsSequenceType: TypeAlias = Union[List[int], Tuple[int, ...]]
+# TODO: Type[torch.SymInt], Type[torch.SymFloat]
+NumberTypeType: TypeAlias = Union[Type[bool], Type[int], Type[float], Type[complex]]
+# TODO: This needs a lot more type annotations
+# NumberType = Union[bool, int, float, complex, torch.SymInt, torch.SymFloat]
+NumberType: TypeAlias = Union[bool, int, float, complex]
+RealNumberType: TypeAlias = Union[bool, int, float]
+
+Number = (bool, int, float, complex, torch.SymInt, torch.SymFloat)
+# I don't call it Integral because numbers.Integral includes bool, but IntLike
+# does not
+Dim = int
+IntLike = (int, torch.SymInt)
+FloatLike = (float, torch.SymFloat)
+IntWithoutSymInt = int
+FloatWithoutSymFloat = float
+DeviceLikeType: TypeAlias = Union[str, torch.device, int]
+Tensor = torch.Tensor
+
+
+torch_function_passthrough = {
+    torch.device,
+    torch.sym_not,
+    torch.sym_float,
+    torch.sym_int,
+    torch.sym_max,
+    torch.sym_min,
+    torch._sym_sqrt,  # type: ignore[attr-defined]
+    torch.sym_ite,
+    torch.Tensor.dim,
+    torch.Tensor.ndim.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.numel,
+    torch.Tensor.size,
+    torch.Tensor.storage_offset,
+    torch.Tensor.stride,
+    torch.Tensor.dtype.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.is_sparse.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.shape.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.device.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.requires_grad.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.layout.__get__,  # type: ignore[attr-defined]
+    torch.Tensor.is_contiguous,
+    # For TorchRefsMode only
+    torch.Tensor.__format__,
+    torch.Tensor.__repr__,
+    torch.Tensor.requires_grad.__get__,  # type: ignore[attr-defined]
+}
+
+
+TensorLikeType = torch.Tensor
+TensorLike = torch.Tensor
+TensorSequenceType: TypeAlias = Union[List[TensorLikeType], Tuple[TensorLikeType, ...]]
+TensorOrNumberLikeType: TypeAlias = Union[TensorLikeType, NumberType]
+
+CustomOutParamAnnotation = "__custom_out_param__"
+
+
+def same_shape(a: ShapeType, b: ShapeType, *, allow_rhs_unbacked=False) -> bool:
+    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+
+    if len(a) != len(b):
+        return False
+
+    for x, y in zip(a, b):
+        if allow_rhs_unbacked:
+            # TODO: We should check that the symbols are consistent
+            # with each other
+            if isinstance(y, torch.SymInt):
+                continue
+        # NB: Naively, you would not expect to have to do an oblivious guard
+        # here because there is seemingly no broadcasting here, but in fact we
+        # use this in some situations to determine if we need to do an expand
+        # on the tensor because they don't line up, so you can definitely end
+        # up trying to prove u0 != 1 in this situation.  See
+        # python test/test_proxy_tensor.py -k test_cumsum_unbacked
+        if guard_size_oblivious(x != y):
+            return False
+
+    return True
+
+
+def _maybe_get_pytype(t):
+    if t is torch.SymFloat:
+        return float
+    elif t is torch.SymInt:
+        return int
+    elif t is torch.SymBool:
+        return bool
+    else:
+        return t
+
+
+# TODO: look at using torch.testing.assert_close instead with an option
+#   to just compare metadata
+def compare_tensor_meta(
+    a: TensorLikeType,
+    b: TensorLikeType,
+    check_strides=False,
+    *,
+    allow_rhs_unbacked=False,
+    check_conj=True,
+):
+    """
+    Checks that two tensor likes have the same shape,
+    dtype and device.
+
+    In the future this will validate additional metadata, like
+    strides.
+    """
+    assert isinstance(a, TensorLike)
+    assert isinstance(b, TensorLike)
+
+    if not same_shape(a.shape, b.shape, allow_rhs_unbacked=allow_rhs_unbacked):
+        msg = f"Shapes {a.shape} and {b.shape} are not equal!"
+        raise AssertionError(msg)
+
+    if a.dtype != b.dtype:
+        msg = f"Dtypes {a.dtype} and {b.dtype} are not equal!"
+        raise AssertionError(msg)
+
+    if a.device != b.device:
+        # Handles special cuda:0 vs cuda case
+        # TODO: we should review why this happens and see about fixing it
+        if (str(a.device) == "cuda:0" or str(a.device) == "cuda") and (
+            str(b.device) == "cuda:0" or str(b.device) == "cuda"
+        ):
+            pass
+        else:
+            msg = f"Devices {a.device} and {b.device} are not equal!"
+            raise AssertionError(msg)
+
+    # Stride checking is currently disabled, see https://github.com/pytorch/pytorch/issues/78050
+    if check_strides:
+        same_strides, idx = check_significant_strides(a, b)
+        if not same_strides:
+            msg = f"Stride mismatch! Strides are {a.stride()} and {b.stride()} (mismatched at {idx})!"
+            raise RuntimeError(msg)
+
+        if a.storage_offset() != b.storage_offset():
+            msg = f"Storage offset mismatch! Storage offsets are {a.storage_offset()} and {b.storage_offset()}!"
+            raise RuntimeError(msg)
+
+    if check_conj:
+        if a.is_conj() != b.is_conj():
+            raise RuntimeError(
+                f"Conj mismatch! is_conj is set to {a.is_conj()} and {b.is_conj()}"
+            )
+
+    if a.is_neg() != b.is_neg():
+        raise RuntimeError(
+            f"Neg mismatch! is_neg is set to {a.is_neg()} and {b.is_neg()}"
+        )
+
+
+def _check_strides_helper(
+    a: TensorLikeType, b: TensorLikeType, *, only_cuda=True, significant_only=True
+) -> Tuple[bool, Optional[int]]:
+    # NOTE: only on CUDA because CPU elementwise strides are incorrect in PyTorch
+    # See https://github.com/pytorch/pytorch/issues/77553
+    # Only compares strides that are "meaningful" -- strides for dimensions with length > 1
+    # and for tensors with more than one element
+    if (
+        not only_cuda or a.device.type == "cuda" or b.device.type == "cuda"
+    ) and a.numel() > 0:
+        for idx in range(a.ndim):
+            check = not significant_only or a.shape[idx] > 1
+            if a.stride()[idx] != b.stride()[idx] and check:
+                return False, idx
+
+    return True, None
+
+
+def check_significant_strides(
+    a: TensorLikeType, b: TensorLikeType, *, only_cuda=True
+) -> Tuple[bool, Optional[int]]:
+    return _check_strides_helper(a, b, only_cuda=only_cuda, significant_only=True)
+
+
+def check_all_strides(
+    a: TensorLikeType, b: TensorLikeType, *, only_cuda=True
+) -> Tuple[bool, Optional[int]]:
+    return _check_strides_helper(a, b, only_cuda=only_cuda, significant_only=False)
+
+
+# This function is equivalent to compute_contiguous() from TensorImpl.cpp
+def is_contiguous(a: TensorLikeType) -> bool:
+    """
+    Tests whether a tensor is contiguous or not.
+
+    Tensors are contiguous when they have no elements,
+    one element, or when they have "nested" strides.
+    """
+    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+
+    if guard_size_oblivious(a.numel() < 2):
+        return True
+
+    expected_stride = 1
+    for x, y in reversed(tuple(zip(a.shape, a.stride()))):
+        # Skips checking strides when a dimension has length 1
+        if guard_size_oblivious(x == 1):
+            continue
+
+        if y != expected_stride:
+            return False
+        expected_stride = expected_stride * x
+
+    return True
+
+
+# This function is equivalent to compute_channels_last_contiguous_2d() in TensorImpl.cpp
+def is_channels_last_contiguous_2d(a: Tensor) -> bool:
+    # NHWC or not channels last 2D contiguous
+    if a.ndim != 4:
+        return False
+
+    expected_stride = 1
+    for idx in (1, 3, 2, 0):
+        length = a.shape[idx]
+        if length == 1:
+            continue
+
+        stride = a.stride()[idx]
+        if stride != expected_stride:
+            return False
+
+        expected_stride *= length
+
+    return True
+
+
+def is_channels_last_contiguous_3d(a: Tensor) -> bool:
+    # NDHWC or not channels last 3D contiguous
+    if a.ndim != 5:
+        return False
+
+    expected_stride = 1
+    for idx in (1, 4, 3, 2, 0):
+        length = a.shape[idx]
+        if length == 1:
+            continue
+
+        stride = a.stride()[idx]
+        if stride != expected_stride:
+            return False
+
+        expected_stride *= length
+
+    return True
+
+
+_memory_formats = {
+    torch.contiguous_format,
+    torch.preserve_format,
+    torch.channels_last,
+    torch.channels_last_3d,
+}
+
+
+def validate_memory_format(memory_format: torch.memory_format):
+    torch._check(
+        memory_format in _memory_formats,
+        lambda: f"Received unknown memory format {memory_format}!",
+    )
+
+
+def is_contiguous_for_memory_format(  # type: ignore[return]
+    a: Tensor, *, memory_format: torch.memory_format
+) -> bool:
+    validate_memory_format(memory_format)
+
+    if memory_format == torch.contiguous_format:
+        return is_contiguous(a)
+    if memory_format == torch.channels_last:
+        return is_channels_last_contiguous_2d(a)
+    if memory_format == torch.channels_last_3d:
+        return is_channels_last_contiguous_3d(a)
+
+    torch._check(
+        False,
+        lambda: f"is_contiguous received unsupported memory format {memory_format}",
+    )
+
+
+# NOTE: that tensors with no elements and channels last is ???
+def is_channels_last_contiguous(a: Tensor) -> bool:
+    """
+    True when a tensor is channels-last contiguous.
+
+    This requires that:
+
+      - the tensor is conceptually either 4 (NHWC) or 5 (NDHWC) dimensions
+      - if we name the tensor's dimensions NCHW or NCDHW, then the strides are such that the
+        stride of the 'C' dimension (Cs) is 1 and the strides corresponding to
+        each dimension (Xs) can be ordered Cs <= Ws <= Hs <= (Ds) <= Ns and are
+        "nested" -- so Ws = Cs * Cl, where Cl is the length of the 'C' dimension,
+        for example.
+    """
+    return is_channels_last_contiguous_2d(a) or is_channels_last_contiguous_3d(a)
+
+
+def is_non_overlapping_and_dense(a: Tensor) -> bool:
+    """
+    True when a tensor is non-overlapping and dense.
+
+    A tensor is non-overlapping and dense when there exists a permutation of
+    its dimensions that is contiguous.
+    """
+
+    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+
+    if a.is_sparse:
+        return False
+
+    # Short-circuits if the tensor is already contiguous or channels-last contiguous
+    if is_contiguous(a) or is_channels_last_contiguous(a):
+        return True
+
+    # The following is equivalent to compute_non_overlapping_and_dense in TensorImpl.cpp
+
+    # Short-circuits for tensors of rank one, which are
+    # non-overlapping and "dense" if their stride is one
+    if a.ndim == 1:
+        return a.stride()[0] == 1
+
+    # Checks that there exists a permutation of the strides s.t. the tensor would be contiguous
+    # Sorts (length, stride) pairs by stride
+    #
+    # This sort is done in a size-oblivious way, which helps if we do a
+    # comparison like 2048*u0 > u0; we just want this to return True
+    # (and not worry about what if u0 is zero).
+    class K(NamedTuple):
+        size: int
+        stride: int
+
+        def __lt__(self, other):
+            return guard_size_oblivious(self.stride < other.stride)
+
+        def __gt__(self, other):
+            return guard_size_oblivious(self.stride > other.stride)
+
+        def __le__(self, other):
+            return guard_size_oblivious(self.stride <= other.stride)
+
+        def __ge__(self, other):
+            return guard_size_oblivious(self.stride >= other.stride)
+
+        def __eq__(self, other):
+            return guard_size_oblivious(self.stride == other.stride)
+
+    lengths_and_strides = sorted(map(K, a.shape, a.stride()))
+
+    expected_stride = 1
+    for length, stride in lengths_and_strides:
+        if guard_size_oblivious(length == 1):
+            continue
+
+        if stride != expected_stride:
+            return False
+
+        expected_stride *= length
+
+    return True
+
+
+# NOTE: Based on the implementation in TensorIterator.cpp, but note that
+# the note [Computing output strides] is incorrect, because it
+# says that strides will be preserved even if they are not
+# "non overlapping and dense", but this is incorrect. The
+# output of elementwise operations are always given
+# non overlapping and dense strides.
+# This is also INCORRECT because it does not model TensorIterator's
+# short-circuit, which can cause different strides.
+def compute_elementwise_output_logical_to_physical_perm(
+    *tensors, _skip_checks=False
+) -> List[int]:
+    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+
+    if not _skip_checks and len(tensors) == 0:
+        msg = "Can't compute elementwise output strides for zero tensors!"
+        raise ValueError(msg)
+
+    if not _skip_checks:
+        check_same_shape(*tensors, allow_cpu_scalar_tensors=True)
+
+    # Filters the tensors to actual tensors
+    if not _skip_checks:
+        tensors = tuple(
+            a
+            for a in tensors
+            if isinstance(a, TensorLike) and not is_cpu_scalar_tensor(a)
+        )
+
+    # Short-circuits for CPU scalar case
+    if len(tensors) == 0:
+        return []
+
+    # Short-circuits for shapes with zero or one dimensions
+    # TODO: are these necessary?
+    ndim = tensors[0].ndim
+    if ndim == 0:
+        return []
+    if ndim == 1:
+        return [0]
+
+    # Short-circuits if contiguous, following the fake fast path.
+    # This reduces the number of guards we end up making
+    # TODO: do channels last too
+    is_contiguous = True
+    for t in tensors:
+        is_contiguous = is_contiguous and t.is_contiguous(
+            memory_format=torch.contiguous_format
+        )
+
+    if is_contiguous:
+        return list(range(ndim))
+
+    shape = tensors[0].shape
+
+    def should_swap(idx_a, idx_b):
+        for tensor in tensors:
+            stride_a = tensor.stride()[idx_a]
+            stride_b = tensor.stride()[idx_b]
+
+            if guard_size_oblivious(stride_a == 0) or guard_size_oblivious(
+                stride_b == 0
+            ):
+                continue
+
+            if guard_size_oblivious(stride_a < stride_b):
+                return -1
+
+            if guard_size_oblivious(stride_a > stride_b):
+                return 1
+
+            # stride_a == stride_b
+            if guard_size_oblivious(shape[idx_a] > shape[idx_b]):
+                return 1
+
+        # Note: this case is hit if all strides are zero,
+        # or all strides are equal and all dimensions have the same length
+        return 0
+
+    # The "sort" order for the permutation is back-to-front, but
+    # the natural order for permutations is front-to-back.  Do the
+    # sorting back-to-front and then reverse it on output.
+    #
+    # also, note this returns the logical to physical shape permutation
+    perm = list(reversed(range(ndim)))
+
+    # insertion sort with support for ambiguous comparisons
+    for i in range(1, ndim):
+        dim1 = i
+        for dim0 in reversed(range(i)):
+            comparison = should_swap(perm[dim0], perm[dim1])
+            if comparison > 0:
+                perm[dim0], perm[dim1] = perm[dim1], perm[dim0]
+                dim1 = dim0
+            elif comparison < 0:
+                break
+
+    return list(reversed(perm))
+
+
+def compute_elementwise_output_strides(*tensors) -> Tuple[int, ...]:
+    """
+    Computes the output strides for elementwise operations.
+    """
+    if len(tensors) == 0:
+        msg = "Can't compute elementwise output strides for zero tensors!"
+        raise ValueError(msg)
+
+    check_same_shape(*tensors, allow_cpu_scalar_tensors=True)
+
+    # Filters the tensors to actual tensors
+    tensors = tuple(
+        a for a in tensors if isinstance(a, TensorLike) and not is_cpu_scalar_tensor(a)
+    )
+
+    # Short-circuits for CPU scalar case
+    if len(tensors) == 0:
+        return ()
+
+    ndim = tensors[0].ndim
+    shape = tensors[0].shape
+
+    if ndim == 0:
+        return ()
+    if ndim == 1:
+        return (1,)
+
+    logical_to_physical_perm = compute_elementwise_output_logical_to_physical_perm(
+        *tensors, _skip_checks=True
+    )
+    permuted_shape = apply_perm(shape, logical_to_physical_perm)  # to physical
+
+    new_strides = make_contiguous_strides_for(permuted_shape)
+    permuted_strides = apply_perm(
+        new_strides, invert_perm(logical_to_physical_perm)
+    )  # to logical
+
+    return tuple(permuted_strides)
+
+
+# Identity permutation is [0, 1, 2]
+def apply_perm(inp, perm):
+    ndim = len(inp)
+    permuted_inp = [-1] * ndim
+    for idx, x in enumerate(perm):
+        permuted_inp[idx] = inp[x]
+    return permuted_inp
+
+
+def invert_perm(perm):
+    ndim = len(perm)
+    new_perm = [-1] * ndim
+    for idx, x in enumerate(perm):
+        new_perm[x] = idx
+    return new_perm
+
+
+#
+# Common helper functions
+#
+
+
+def validate_dim_length(length: int):
+    """
+    Validates that an object represents a valid
+    dimension length.
+    """
+
+    if isinstance(length, (int, torch.SymInt)):
+        torch._check_is_size(length)
+    else:
+        # sometimes called with sympy expression by inductor
+        assert length >= 0
+
+
+def validate_shape(shape: ShapeType):
+    """
+    Validates that a sequence represents a valid shape.
+    """
+
+    assert isinstance(shape, Sequence), type(shape)
+    for l in shape:
+        validate_dim_length(l)
+
+
+def validate_strides(strides: StrideType):
+    """
+    Verifies the object specifies valid strides.
+    """
+
+    assert isinstance(strides, Sequence)
+    for stride in strides:
+        assert stride >= 0
+
+
+def validate_idx(rank: int, idx: int):
+    """
+    Validates that idx is a valid index for the given shape.
+    Assumes the index is already canonicalized.
+    """
+
+    assert isinstance(idx, Dim)
+    assert isinstance(rank, Dim)
+
+    assert idx >= 0 and idx < rank or idx == 0
+
+
+def validate_dimension_indices(rank: int, indices: DimsSequenceType):
+    for idx in indices:
+        validate_idx(rank, idx)
+
+
+def validate_exclusive_idx(rank: int, ex_idx: int):
+    """
+    Validates that ex_idx is a valid exclusive index
+    for the given shape.
+    """
+
+    assert isinstance(ex_idx, Dim)
+    assert isinstance(rank, Dim)
+    assert ex_idx > 0 and ex_idx <= rank
+
+
+# "Wraps" a dim (up to one time) for the given rank, allowing dims to be
+# specified using negative indices. If `wrap_scalar` is true then scalar
+# tensors of rank 0 will allow dimensions in the range [-1, 0]. Otherwise,
+# idx should be in the range [-rank, rank-1].
+def canonicalize_dim(rank: int, idx: int, wrap_scalar: bool = True) -> int:
+    if rank < 0:
+        msg = f"Rank cannot be negative but got {rank}"
+        raise IndexError(msg)
+
+    if rank == 0:
+        if not wrap_scalar:
+            msg = f"Dimension specified as {idx} but tensor has no dimensions"
+            raise IndexError(msg)
+        rank = 1
+
+    if idx >= 0 and idx < rank:
+        return idx
+
+    if idx < 0:
+        _idx = idx + rank
+    else:
+        _idx = idx
+
+    if _idx < 0 or _idx >= rank:
+        # Same error message as in aten/src/ATen/WrapDimUtils.h:49
+        msg = f"Dimension out of range (expected to be in range of [{-rank}, {rank - 1}], but got {idx})"
+        raise IndexError(msg)
+
+    return _idx
+
+
+# Takes a dimension or sequence of dimensions and "wraps" them,
+# mapping negative offsets to positive ones
+@overload
+def canonicalize_dims(
+    rank: int, indices: Sequence[int], wrap_scalar: bool = True
+) -> Tuple[int, ...]:
+    pass
+
+
+@overload
+def canonicalize_dims(rank: int, indices: int, wrap_scalar: bool = True) -> int:
+    pass
+
+
+def canonicalize_dims(rank, indices, wrap_scalar=True):
+    if isinstance(indices, Dim):
+        return canonicalize_dim(rank, indices, wrap_scalar)
+
+    return tuple(canonicalize_dim(rank, x, wrap_scalar) for x in indices)
+
+
+def is_valid_permutation(rank: int, perm: DimsSequenceType) -> bool:
+    """
+    Validates that perm is a permutation of length rank.
+    """
+
+    if not isinstance(perm, Sequence):
+        return False
+
+    if not (tuple(sorted(perm)) == tuple(range(0, rank))):
+        return False
+
+    return True
+
+
+def is_same_shape(a: Sequence, b: Sequence) -> bool:
+    """
+    Compares two shapes a and b, returning True if they are the same
+    (their ranks and corresponding lengths match) and False otherwise.
+    """
+
+    return tuple(a) == tuple(b)
+
+
+def is_cpu_scalar_tensor(a: Any) -> bool:
+    return isinstance(a, TensorLike) and a.ndim == 0 and a.device.type == "cpu"
+
+
+def check_same_device(*args, allow_cpu_scalar_tensors):
+    """
+    Checks that all Tensors in args have the same device.
+
+    Raises a RuntimeError when:
+      - args contains an object whose type is not Tensor or Number
+      - two Tensor objects in args have different devices, unless one is a CPU scalar tensor and allow_cpu_scalar_tensors is True
+    """
+    # Short-circuits if all (one or fewer) arguments are trivially on the same device
+    if len(args) <= 1:
+        return
+
+    # Note: cannot initialize device to the first arg's device (it may not have one)
+    device = None
+    for arg in args:
+        if isinstance(arg, Number):
+            continue
+        elif isinstance(arg, TensorLike):
+            if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg):
+                continue
+
+            if device is None:
+                device = arg.device
+
+            if device != arg.device:
+                msg = (
+                    "Tensor on device "
+                    + str(arg.device)
+                    + " is not on the expected device "
+                    + str(device)
+                    + "!"
+                )
+                raise RuntimeError(msg)
+        else:
+            msg = (
+                "Unexpected type when checking for same device, " + str(type(arg)) + "!"
+            )
+            raise RuntimeError(msg)
+
+
+def canonicalize_device(device: DeviceLikeType) -> torch.device:
+    if isinstance(device, torch.device):
+        return device
+
+    assert isinstance(device, str)
+    return torch.device(device)
+
+
+# Asserts if any of the following are true:
+#   - a non-scalar or non-Tensor is given
+#   - the shape of any tensors is distinct
+def check_same_shape(*args, allow_cpu_scalar_tensors: bool):
+    """
+    Checks that all Tensors in args have the same shape.
+
+    Raises a RuntimeError when:
+      - args contains an object whose type is not Tensor or Number
+      - two Tensor objects in args have different devices
+    """
+    shape = None
+
+    for arg in args:
+        if isinstance(arg, Number):
+            continue
+        elif isinstance(arg, TensorLike):
+            if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg):
+                continue
+
+            if shape is None:
+                shape = arg.shape
+
+            if not is_same_shape(shape, arg.shape):
+                msg = f"Shape {arg.shape} is not the expected shape {shape}!"
+                raise RuntimeError(msg)
+        else:
+            msg = (
+                "Unexpected type when checking for same shape, " + str(type(arg)) + "!"
+            )
+            raise RuntimeError(msg)
+
+
+# Acquires a common shape, if it exists, from one or more tensor arguments,
+# filtering number arguments
+def extract_shape(*args, allow_cpu_scalar_tensors: bool) -> Optional[ShapeType]:
+    shape = None
+    scalar_shape = None
+
+    for arg in args:
+        if isinstance(arg, Number):
+            continue
+        elif isinstance(arg, TensorLike):
+            if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg):
+                scalar_shape = arg.shape
+                continue
+
+            if shape is None:
+                shape = arg.shape
+
+            if not is_same_shape(shape, arg.shape):
+                return None
+        else:
+            return None
+
+    return shape if shape is not None else scalar_shape
+
+
+# Extracts dimensions that might be passed either as a list/tuple or as varargs.
+# A typical case is Tensor.permute .
+def extract_dims_from_varargs(
+    dims: Union[DimsSequenceType, Tuple[DimsSequenceType, ...]]
+) -> DimsSequenceType:
+    if dims and isinstance(dims[0], Sequence):
+        assert len(dims) == 1
+        dims = cast(Tuple[DimsSequenceType], dims)
+        return dims[0]
+    else:
+        return cast(DimsSequenceType, dims)
+
+
+def extract_shape_from_varargs(
+    shape: Union[ShapeType, Tuple[ShapeType]],
+    validate=True,
+) -> Tuple[int, ...]:
+    """
+    Returns a shape from varargs.
+
+    In PyTorch, operations that accept shapes often accept them as varargs, like
+    foo(*shape). However a user can pass the shape as a sequence of integers,
+    like this:
+
+      foo(1, 2, 3)
+
+    or as a sequence of integers
+
+      foo((1, 2, 3))
+
+    In the first case shape will be a tuple of integers, and in the second case it's a tuple
+    containing a tuple of integers. This validates those inputs and canonicalizes them
+    to a tuple of integers.
+    """
+
+    # Handles tuple unwrapping
+    if len(shape) == 1 and isinstance(shape[0], Sequence):
+        shape = shape[0]
+
+    if validate:
+        validate_shape(shape)  # type: ignore[arg-type]
+    return shape  # type: ignore[return-value]
+
+
+def infer_size_shapes(a: ShapeType, b: ShapeType) -> Tuple[int, ...]:
+    ndim = max(len(a), len(b))
+    expandedSizes = [0] * ndim
+
+    for i in range(ndim - 1, -1, -1):
+        offset = ndim - 1 - i
+        dimA = len(a) - 1 - offset
+        dimB = len(b) - 1 - offset
+        sizeA = a[dimA] if dimA >= 0 else 1
+        sizeB = b[dimB] if dimB >= 0 else 1
+
+        torch._check(
+            (sizeA == sizeB) or (sizeA == 1) or (sizeB == 1),
+            lambda: (
+                f"The size of tensor a ({sizeA}) must match the size of "
+                f"tensor b ({sizeB}) at non-jagged dimension {i}"
+            ),
+        )
+
+        # 1s map to the other size (even 0)
+        expandedSizes[i] = sizeB if sizeA == 1 else sizeA
+
+    return tuple(expandedSizes)
+
+
+def infer_size(shape: ShapeType, numel: int) -> Tuple[int, ...]:
+    """
+    Infers the size of a dim with size -1, if it exists.
+    Also checks that new shape is compatible with the number of elements.
+    """
+    dim = None
+    newsize = 1
+    for i, d in enumerate(shape):
+        if d == -1:
+            torch._check(dim is None, lambda: "only one dimension can be inferred")
+            dim = i
+        elif d >= 0:
+            newsize *= d
+        else:
+            torch._check(False, lambda: f"invalid shape dimension {d}")
+    if dim is None:
+        torch._check(
+            numel == newsize,
+            lambda: f"shape '{list(shape)}' is invalid for input of size {numel}",
+        )
+    else:
+        from torch.fx.experimental.symbolic_shapes import definitely_true
+
+        torch._check(
+            newsize != 0,
+            lambda: (
+                f"cannot reshape tensor of 0 elements into shape {list(shape)} because the "
+                f"unspecified dimension size -1 can be any value and is ambiguous"
+                if definitely_true(numel == 0)
+                else f"shape '{list(shape)}' is invalid for input of size {numel}"
+            ),
+        )
+        torch._check(
+            numel % newsize == 0,
+            lambda: f"shape '{list(shape)}' is invalid for input of size {numel}",
+        )
+        # Convert to list to produce a compatible error message with core
+        # PyTorch, which prints sequences in square brackets.
+        shape = list(shape)
+        shape[dim] = numel // newsize
+        # NB: This is pretty important when you have unbacked SymInts.
+        # Suppose you have (i0, 12) resizing into (2, -1, 12).  The old
+        # range for i0 is typically [2, inf], which means if you divide
+        # by two the new range should be [1, inf].  But this is bad news
+        # if you have an unbacked SymInt: we need to reapply the unsound
+        # assumption that the size is >= 2.
+        torch._check_is_size(shape[dim])
+    return tuple(shape)
+
+
+_integer_dtypes = (
+    torch.uint8,
+    torch.uint16,
+    torch.uint32,
+    torch.uint64,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.int64,
+)
+_low_precision_dtypes = (torch.float16, torch.bfloat16, torch.complex32)
+_complex_dtypes = (torch.complex32, torch.complex64, torch.complex128)
+
+
+def is_boolean_dtype(dtype: torch.dtype) -> bool:
+    assert isinstance(dtype, torch.dtype)
+    return dtype is torch.bool
+
+
+def is_integer_dtype(dtype: torch.dtype) -> bool:
+    assert isinstance(dtype, torch.dtype)
+    return dtype in _integer_dtypes
+
+
+def is_low_precision_dtype(dtype: torch.dtype) -> bool:
+    assert isinstance(dtype, torch.dtype)
+    return dtype in _low_precision_dtypes
+
+
+def is_float_dtype(dtype: torch.dtype) -> bool:
+    assert isinstance(dtype, torch.dtype)
+    return dtype.is_floating_point
+
+
+def is_complex_dtype(dtype: torch.dtype) -> bool:
+    assert isinstance(dtype, torch.dtype)
+    return dtype in _complex_dtypes
+
+
+def is_grad_dtype(dtype: torch.dtype) -> bool:
+    """
+    Checks if the dtype can require a gradient.
+    """
+    return dtype.is_floating_point or is_complex_dtype(dtype)
+
+
+_complex_to_real_dtype_map = {
+    torch.complex128: torch.float64,
+    torch.complex64: torch.float32,
+    torch.complex32: torch.float16,
+}
+
+_real_to_complex_dtype_map = {
+    torch.float16: torch.complex32,
+    torch.bfloat16: torch.complex64,
+    torch.float32: torch.complex64,
+    torch.float64: torch.complex128,
+}
+
+
+def corresponding_real_dtype(dtype: torch.dtype) -> torch.dtype:
+    return _complex_to_real_dtype_map[dtype]
+
+
+def corresponding_complex_dtype(dtype: torch.dtype) -> torch.dtype:
+    return _real_to_complex_dtype_map[dtype]
+
+
+def dtype_to_type(dtype: torch.dtype) -> type:
+    """
+    Computes the corresponding Python type (AKA "type kind") for the
+    given dtype.
+    """
+    assert isinstance(dtype, torch.dtype)
+
+    if dtype is torch.bool:
+        return bool
+    if dtype in _integer_dtypes:
+        return int
+    if dtype.is_floating_point:
+        return float
+    if dtype in _complex_dtypes:
+        return complex
+
+    raise ValueError("Invalid dtype!")
+
+
+def dtype_to_type_ctor(dtype: torch.dtype) -> Callable[[NumberType], NumberType]:
+    """
+    Computes the corresponding Python type constructor for the
+    given dtype.
+    """
+    assert isinstance(dtype, torch.dtype)
+
+    if dtype is torch.bool:
+        return lambda x: bool(x)
+    if dtype in _integer_dtypes:
+        return sym_int
+    if dtype.is_floating_point:
+        return sym_float
+    if dtype in _complex_dtypes:
+        # TODO: type error here is real, replace with sym_complex
+        return lambda x: complex(x)  # type: ignore[arg-type]
+
+    raise ValueError("Invalid dtype!")
+
+
+def type_to_dtype(typ: type) -> torch.dtype:
+    """
+    Computes the corresponding dtype for a Number type.
+    """
+
+    assert isinstance(typ, type)
+
+    if typ is bool:
+        return torch.bool
+    if typ in [int, torch.SymInt]:
+        return torch.long
+    if typ in [float, torch.SymFloat]:
+        return torch.get_default_dtype()
+    # TODO: sym_complex_float?
+    if typ is complex:
+        return corresponding_complex_dtype(torch.get_default_dtype())
+
+    raise ValueError("Invalid type!")
+
+
+def get_dtype(x: Union[torch.Tensor, NumberType]):
+    if isinstance(x, torch.Tensor):
+        return x.dtype
+    else:
+        return type_to_dtype(type(x))
+
+
+_ordered_types = (bool, int, float, complex)
+
+
+def check_fp_or_complex(
+    dtype: torch.dtype, fn_name: str, allow_low_precision_dtypes: bool = True
+):
+    """
+    Checks whether the input is floating point or complex.
+    If allow_low_precision_dtypes is True, it allows having float16, bfloat16, and complex32
+    """
+    torch._check(
+        is_float_dtype(dtype) or is_complex_dtype(dtype),
+        lambda: f"{fn_name}: Expected a floating point or complex tensor as input. Got {dtype}",
+    )
+    torch._check(
+        allow_low_precision_dtypes or not is_low_precision_dtype(dtype),
+        lambda: f"{fn_name}: Half precision dtypes not supported. Got {dtype}",
+    )
+
+
+def check_is_matrix(A: TensorLikeType, f_name: str, arg_name: str = "A"):
+    torch._check(
+        len(A.shape) >= 2,
+        lambda: f"{f_name}: The input tensor {arg_name} must have at least 2 dimensions.",
+    )
+
+
+def get_higher_type(a: type, b: type) -> type:
+    """
+    Returns the higher of the two given Number types.
+
+    The types are ordered bool -> int -> float -> complex.
+    """
+    a, b = _maybe_get_pytype(a), _maybe_get_pytype(b)
+    # Type checking
+    if a not in _ordered_types or b not in _ordered_types:
+        raise RuntimeError(f"Expected builtin numeric types, found {a}, {b}")
+
+    if a is b:
+        return a
+
+    for typ in _ordered_types:
+        if a is typ:
+            return b
+        if b is typ:
+            return a
+
+    raise ValueError("Unknown Python scalar type!")
+
+
+# Returns the higher of two torch datatypes a and b or, if the two
+#   are not ordered relative to each other, the next
+#   higher datatype
+def get_higher_dtype(
+    a: Optional[Union[torch.dtype, TensorLikeType, NumberType]],
+    b: Optional[Union[torch.dtype, TensorLikeType, NumberType]],
+) -> Optional[torch.dtype]:
+    """
+    Computes the "lowest" datatype that is weakly
+    "higher" than both a and b.
+    """
+
+    # Type checking
+    assert a is None or isinstance(a, (torch.dtype, TensorLike, Number))
+    assert b is None or isinstance(b, (torch.dtype, TensorLike, Number))
+
+    def _extract_dtype(
+        x: Optional[Union[torch.dtype, TensorLikeType, NumberType]]
+    ) -> Optional[torch.dtype]:
+        if x is None:
+            return None
+        if isinstance(x, torch.dtype):
+            return x
+        if isinstance(x, TensorLike):
+            return x.dtype
+        if isinstance(x, Number):
+            return type_to_dtype(type(x))
+
+        raise RuntimeError("Unexpected type given to _extract_dtype!")
+
+    a, b = _extract_dtype(a), _extract_dtype(b)
+
+    if a is b:
+        return a
+
+    if a is None:
+        return b
+
+    if b is None:
+        return a
+
+    ordered_datatypes = (
+        (torch.bool,),
+        (torch.uint8, torch.int8),
+        (torch.int16,),
+        (torch.int32,),
+        (torch.int64,),
+        (torch.float16, torch.bfloat16),
+        (torch.float32,),
+        (torch.float64,),
+        (torch.complex32,),
+        (torch.complex64,),
+        (torch.complex128,),
+    )
+
+    for idx, dtypes in enumerate(ordered_datatypes):
+        if a in dtypes and b in dtypes:
+            return ordered_datatypes[idx + 1][0]
+        if a in dtypes:
+            return b
+        if b in dtypes:
+            return a
+
+    raise RuntimeError("Unexpected termination!")
+
+
+def check_pin_memory(pin_memory: bool):
+    torch._check_not_implemented(
+        not pin_memory, lambda: "PrimTorch does not support pinned memory"
+    )
+
+
+def check_layout(layout: torch.layout):
+    torch._check_not_implemented(
+        layout == torch.strided, lambda: f"PrimTorch doesn't support layout={layout}"
+    )
+
+
+# TODO: maybe unify with can_cast_to?
+def is_weakly_lesser_type(a: type, b: type) -> bool:
+    """
+    Compares two types, a and b, returning True if a is weakly "less" than b.
+
+    The comparison is determined by the following type ordering: bool, int, float, complex.
+    """
+
+    a, b = _maybe_get_pytype(a), _maybe_get_pytype(b)
+
+    if a not in _ordered_types or b not in _ordered_types:
+        raise RuntimeError(f"Expected builtin numeric types, found {a}, {b}")
+
+    for typ in _ordered_types:
+        if a == typ:
+            return True
+        if b == typ:
+            return False
+
+    raise RuntimeError("Unexpected termination!")
+
+
+def can_safe_cast_to(*, cast_to: torch.dtype, cast_from: torch.dtype) -> bool:
+    for fn in (is_complex_dtype, is_float_dtype, is_integer_dtype, is_boolean_dtype):
+        if fn(cast_to):
+            return True
+        if fn(cast_from):
+            return False
+
+    raise ValueError(f"Received unknown dtypes {cast_to}, {cast_from}!")
+
+
+def check_same_dtype(*args):
+    """
+    Checks that all Tensors in args have the same device and that all Numbers have the
+    same corresponding Python type.
+
+    Raises a RuntimeError when:
+      - args contains an object whose type is not Tensor or Number
+      - two Tensors objects in args have different dtypes
+      - two Number objects in args have different types
+      - there are Tensors and Numbers in args, and one of those Tensors corresponding
+          Python types is different from the type of one of those Numbers
+    """
+    full_dtype = None
+    scalar_type = None
+
+    for arg in args:
+        if isinstance(arg, Number):
+            # Scalar type checking is disabled (and may be removed in the future)
+            continue
+            # if scalar_type is None:
+            #     scalar_type = type(arg)
+
+            # if scalar_type is not type(arg):
+            #     msg = (
+            #         "Scalar of type "
+            #         + str(type(arg))
+            #         + " is not the expected type of "
+            #         + str(scalar_type)
+            #         + "!"
+            #     )
+            #     raise RuntimeError(msg)
+        elif isinstance(arg, TensorLike):
+            if full_dtype is None:
+                full_dtype = arg.dtype
+            if scalar_type is None:
+                scalar_type = dtype_to_type(arg.dtype)
+
+            if full_dtype is not arg.dtype:
+                msg = (
+                    "Tensor with dtype "
+                    + str(arg.dtype)
+                    + " is not the expected dtype of "
+                    + str(full_dtype)
+                    + "!"
+                )
+                raise RuntimeError(msg)
+
+            arg_type = dtype_to_type(arg.dtype)
+            if arg_type is not scalar_type:
+                msg = (
+                    "Tensor with corresponding Python type "
+                    + str(arg_type)
+                    + " is not the expected type of "
+                    + str(scalar_type)
+                    + "!"
+                )
+                raise RuntimeError(msg)
+        else:
+            msg = (
+                "Unexpected type when checking for same dtype, " + str(type(arg)) + "!"
+            )
+            raise RuntimeError(msg)
+
+
+# Maps datatypes to their computation types for elementwise operations
+_computation_dtype_map = {
+    torch.bfloat16: torch.float32,
+    torch.float16: torch.float32,
+    torch.complex32: torch.complex64,
+}
+
+
+def get_computation_dtype(dtype: torch.dtype) -> torch.dtype:
+    return _computation_dtype_map.get(dtype, dtype)
+
+
+_cpu_acc_type_map = {
+    torch.bfloat16: torch.float64,
+    torch.float16: torch.float64,
+    torch.float32: torch.float64,
+    torch.complex32: torch.complex128,
+    torch.complex64: torch.complex128,
+}
+
+
+def get_acc_type(dtype: torch.dtype, device: torch.device) -> torch.dtype:
+    # Equivalent to at::toAccumulateType, prefer computation_dtype where possible
+    if device.type == "cpu":
+        return _cpu_acc_type_map.get(dtype, dtype)
+    else:
+        return get_computation_dtype(dtype)
+
+
+class ELEMENTWISE_TYPE_PROMOTION_KIND(Enum):
+    DEFAULT = (0,)
+    NO_OPMATH = (1,)
+    INT_TO_FLOAT = (2,)
+    ALWAYS_BOOL = (3,)
+    COMPLEX_TO_FLOAT = (4,)
+    BOOL_TO_LONG = (5,)
+
+
+class REDUCTION_OUTPUT_TYPE_KIND(Enum):
+    SAME = (0,)
+    COMPLEX_TO_FLOAT = (1,)  # for complex types outputs corresponding real type
+    KEEP_PROMOTED_TYPE = (2,)  # keep output in opmath type, needed for mean
+    ALWAYS_BOOL = (3,)
+
+
+# Describes the return type of the primitive:
+#
+#   - NEW, a new tensor is created
+#   - VIEW, a view of an input tensor is returned
+#   - INPLACE, one or more input tensors is modified
+#
+# these descriptors are mututally exclusive and exhaustive.
+class RETURN_TYPE(Enum):
+    NEW = (0,)
+    VIEW = (1,)
+    INPLACE = (2,)
+
+
+# TODO: when NumberType contains the sym types, can simplify this
+def number_type(x: Union[NumberType, torch.SymInt, torch.SymFloat]) -> Type:
+    if isinstance(x, torch.SymInt):
+        return int
+    elif isinstance(x, torch.SymFloat):
+        return float
+    else:
+        return type(x)
+
+
+def expr_type(x: sympy.Expr) -> Type:
+    if x.is_integer:  # type: ignore[attr-defined]
+        return int
+    else:
+        # NB: Not strictly correct, but we don't support SymPy complex or bool.
+        return float
+
+
+# TODO: document type promotion kinds
+def elementwise_dtypes(
+    *_args,
+    type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND,
+) -> Tuple[torch.dtype, torch.dtype]:
+    """
+    Computes the computation and result dtypes for elementwise type promotion
+    on the given arguments and with the given elementwise type promotion kind.
+
+    Note that not all inputs to an elementwise operation necessarily participate in type promotion.
+    For example, the "alpha" parameter of torch.add does not participate in type promotion,
+    although it may be cast to the Python type corresponding to the computation dtype that
+    the type promotion algorithm determines.
+
+    Default elementwise type promotion, which all other type promotion kinds tweak (see below),
+    first decides which of four ordered types to use:
+
+    bool -> integer -> floating point -> complex
+
+    The selected type is the "lowest" type in the above list such that all number arguments
+    have a weakly "lower" type and all tensor arguments have a weakly lower corresponding
+    type for their dtype.
+
+    Once the type is determined, the particular result dtype is found. The dtypes are
+    partially ordered as follows:
+
+    bool -> uint8, int8 -> int16 -> int32 -> int64 ->
+      float16, bfloat16 -> float32 -> float64 -> complex32 -> complex64 -> complex128
+
+    The result dtype is selected by:
+      - if no tensor's dtype has the same corresponding type as the one selected,
+          then the result dtype is the (default) dtype corresponding to the selected type
+          (for example, 1.5 + an integer tensor has a result dtype of the default floating point dtype)
+      - if the result type is complex then the dtype is:
+        -  the default complex dtype if there are no floating point or complex tensors
+        -  if there are floating point or complex tensors with one or more dimensions, then
+            the complex dtype corresponding to the highest corresponding complex dtype among those tensors
+            (for example, double + cfloat -> cdouble)
+        -  if there are only floating point or complex tensors with zero dimensions, then
+            the complex dtype corresponding to the highest corresponding complex dtype among those tensors
+      - if the first two cases do not apply, the result dtype is the highest dtype among
+          all tensors with one or more dimensions of the output type, and if there are no such
+          tensors then it's the highest dtype among all tensors with zero dimensions of the output type
+          (for example, long + half -> half, even if the half tensor has zero dimensions)
+
+    The "corresponding complex dtypes" are:
+      float16    -> complex32
+      bfloat16   -> complex64
+      float32    -> complex64
+      float64    -> complex128
+      complex32  -> complex32
+      complex64  -> complex64
+      complex128 -> complex128
+
+    The DEFAULT type promotion kind computes per above, and then uses the result dtype to pick a computation
+    dtype by mapping low precision floating point and complex dtypes as follows:
+
+      float16   -> float32
+      bfloat16  -> float32
+      complex32 -> complex64
+
+    This is referred to as "op math", and the NO_OPMATH type promotion kind disables this mapping, making the
+    computation dtype the same as the result dtype when it's selected. NO_OPMATH is appropriate for kernels
+    which perform no mathematical operations on their tensors (see below for examples).
+
+    The INT_TO_FLOAT type promotion kind maps boolean and integer result dtypes to the default floating point dtype,
+    and computation dtypes to the appropriate op math dtype.
+
+    The COMPLEX_TO_FLOAT type promotion kind maps complex result dtypes to the corresponding float dtype, following this
+    mapping:
+
+        complex32  -> float16
+        complex64  -> float32
+        complex128 -> float64
+
+    Note that COMPLEX_TO_FLOAT derives the computation dtype as the DEFAULT setting does.
+
+    The BOOL_TO_LONG type promotion kind maps boolean computation and result dtypes to long.
+
+    The ALWAYS_BOOL type promotion kind always sets the result dtype to bool.
+
+    Example operators for each type promotion option:
+      DEFAULT                 : add
+      NO_OPMATH               : where, nextafter, cat
+      INT_TO_FLOAT            : sin
+      COMPLEX_TO_FLOAT        : abs
+      BOOL_TO_LONG            : pow
+      ALWAYS_BOOL             : eq
+
+    """
+
+    args = tuple(x for x in _args if x is not None)
+
+    highest_type: type = bool
+
+    # Import sympy locally, as importing it eagerly at a module level is too slow
+    # See https://dev-discuss.pytorch.org/t/delving-into-what-happens-when-you-import-torch/1589
+    import sympy
+
+    for x in args:
+        if not isinstance(x, (Number, TensorLike, sympy.Expr)):
+            msg = f"Unexpected type {str(type(x))} when computing elementwise type promotion!"
+            raise ValueError(msg)
+
+        if isinstance(x, Number):
+            highest_type = get_higher_type(highest_type, number_type(x))
+        elif isinstance(x, sympy.Expr):
+            highest_type = get_higher_type(highest_type, expr_type(x))
+        else:
+            # x is a TensorLike
+            highest_type = get_higher_type(highest_type, dtype_to_type(x.dtype))
+
+    result_dtype = None
+
+    def _find_highest_dtype_filtered(
+        args, filter, *, float_as_complex=False
+    ) -> Optional[torch.dtype]:
+        zero_dim_tensor_dtype = None
+        one_plus_dim_tensor_dtype = None
+        for x in args:
+            if isinstance(x, TensorLike) and filter(x.dtype):
+                _dtype = x.dtype
+                if float_as_complex and is_float_dtype(_dtype):
+                    _dtype = corresponding_complex_dtype(_dtype)
+                if x.ndim == 0:
+                    zero_dim_tensor_dtype = get_higher_dtype(
+                        zero_dim_tensor_dtype, _dtype
+                    )
+                else:
+                    # x.ndim > 0
+                    one_plus_dim_tensor_dtype = get_higher_dtype(
+                        one_plus_dim_tensor_dtype, _dtype
+                    )
+
+        # Prefers dtype of tensors with one or more dimensions
+        if one_plus_dim_tensor_dtype is not None:
+            return one_plus_dim_tensor_dtype
+
+        return zero_dim_tensor_dtype
+
+    if highest_type is float:
+        result_dtype = _find_highest_dtype_filtered(args, is_float_dtype)
+        result_dtype = (
+            torch.get_default_dtype() if result_dtype is None else result_dtype
+        )
+    elif highest_type is complex:
+        result_dtype = _find_highest_dtype_filtered(
+            args,
+            lambda x: is_float_dtype(x) or is_complex_dtype(x),
+            float_as_complex=True,
+        )
+        if result_dtype is None:
+            result_dtype = corresponding_complex_dtype(torch.get_default_dtype())
+    elif highest_type is int:
+        result_dtype = _find_highest_dtype_filtered(args, is_integer_dtype)
+        result_dtype = torch.long if result_dtype is None else result_dtype
+    else:
+        # highest_type is bool
+        result_dtype = torch.bool
+
+    if type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT:
+        return get_computation_dtype(result_dtype), result_dtype
+    elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH:
+        return result_dtype, result_dtype
+    elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT:
+        if is_integer_dtype(result_dtype) or is_boolean_dtype(result_dtype):
+            result_dtype = torch.get_default_dtype()
+        return get_computation_dtype(result_dtype), result_dtype
+    elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT:
+        # NOTE: computation can still occur in a complex dtype
+        computation_dtype = get_computation_dtype(result_dtype)
+        if is_complex_dtype(result_dtype):
+            result_dtype = corresponding_real_dtype(result_dtype)
+        return computation_dtype, result_dtype
+    elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG:
+        if is_boolean_dtype(result_dtype):
+            return torch.long, torch.long
+        return get_computation_dtype(result_dtype), result_dtype
+    elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL:
+        return get_computation_dtype(result_dtype), torch.bool
+    else:
+        raise ValueError(f"Unknown type promotion kind {str(type_promotion_kind)}")
+
+
+def reduction_dtypes(
+    arg,
+    output_dtype_kind: REDUCTION_OUTPUT_TYPE_KIND,
+    dtype: Optional[torch.dtype] = None,
+) -> Tuple[torch.dtype, Optional[torch.dtype]]:
+    # even though some reductions, like amin or amax, don't strictly require type promotion,
+    # all the math ops (including comparisons) are still defined only for a computation type,
+    # so promotion will still happen. We are doing it explicitly here
+    inp_dtype = dtype if dtype is not None else arg.dtype
+    computation_dtype = get_computation_dtype(inp_dtype)
+    if (
+        output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.SAME
+        or output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT
+    ):
+        result_dtype = dtype if dtype else arg.dtype
+        if (
+            output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT
+            and is_complex_dtype(result_dtype)
+        ):
+            result_dtype = corresponding_real_dtype(result_dtype)
+    elif output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.KEEP_PROMOTED_TYPE:
+        result_dtype = None
+    else:  # ALWAYS_BOOL
+        result_dtype = torch.bool
+    return computation_dtype, result_dtype
+
+
+# This function's logic is borrowed from the following functions defined in C++:
+# batched_matrix_contiguous_strides and contiguous_strides
+def make_contiguous_strides_for(
+    shape: ShapeType, row_major: bool = True
+) -> Tuple[int, ...]:
+    """
+    Returns the strides of a contiguous tensor if row_major
+    If row_major=True, it returns the strides of a contiguous batch of Fortran-contiguous matrices
+    This is often used when calling external libraries like BLAS/LAPACK/cuSolver...
+    """
+    # contiguous_strides from c10/util/strides.h
+    validate_shape(shape)
+    if not shape:
+        return ()
+
+    from torch.fx.experimental.symbolic_shapes import is_nested_int
+
+    multiplier = 1
+    strides = []
+    for l in reversed(shape):
+        strides.append(multiplier)
+        multiplier *= l if is_nested_int(l) else sym_max(l, 1)
+
+    result = tuple(reversed(strides))
+
+    # batched_matrix_contiguous_strides from aten/src/ATen/native/LinearAlgebraUtils.h
+    if row_major:
+        return result
+    else:
+        if len(shape) < 2:
+            return result
+        return result[:-2] + (1, max(shape[-2], 1))
+
+
+def make_channels_last_1d_strides_for(shape: ShapeType) -> Tuple[int, ...]:
+    torch._check(
+        len(shape) == 3,
+        lambda: "Only tensors of rank 3 can use the channels_last_1d memory format",
+    )
+
+    multiplier = 1
+    strides = [0] * 3
+    for idx in (1, -1, 0):
+        # NOTE: intentionally divergence from make_contiguous_strides_for
+        # This is consistent with eager
+        strides[idx] = multiplier
+        multiplier *= shape[idx]
+
+    return tuple(strides)
+
+
+def make_channels_last_2d_strides_for(shape: ShapeType) -> Tuple[int, ...]:
+    # TODO: maybe inform the user of channels_last_3d if rank of the tensor is 5?
+    torch._check(
+        len(shape) == 4,
+        lambda: "Only tensors of rank 4 can use the channels_last memory format",
+    )
+
+    multiplier = 1
+    strides = [0] * 4
+    for idx in (1, -1, -2, 0):
+        # NOTE: intentionally divergence from make_contiguous_strides_for
+        # This is consistent with eager
+        strides[idx] = multiplier
+        multiplier *= shape[idx]
+
+    return tuple(strides)
+
+
+def make_channels_last_3d_strides_for(shape: ShapeType) -> Tuple[int, ...]:
+    torch._check(
+        len(shape) == 5,
+        lambda: "Only tensors of rank 5 can use the channels_last_3d memory format",
+    )
+
+    multiplier = 1
+    strides = [0] * 5
+    for idx in (1, -1, -2, -3, 0):
+        # NOTE: intentionally divergence from make_contiguous_strides_for
+        # This is consistent with eager
+        strides[idx] = multiplier
+        multiplier *= shape[idx]
+
+    return tuple(strides)
+
+
+def make_channels_last_strides_for(shape: ShapeType) -> Tuple[int, ...]:
+    ndim = len(shape) if isinstance(shape, Sequence) else 1
+    if ndim == 3:
+        return make_channels_last_1d_strides_for(shape)
+    elif ndim == 4:
+        return make_channels_last_2d_strides_for(shape)
+    elif ndim == 5:
+        return make_channels_last_3d_strides_for(shape)
+    else:
+        raise RuntimeError(
+            f"no channels last format strides exist in {ndim} dimensions"
+        )
+
+
+def compute_reduction_output_shape(
+    shape: ShapeType, dimensions: Sequence
+) -> Tuple[int, ...]:
+    for idx in dimensions:
+        validate_idx(len(shape), idx)
+
+    new_shape = []
+    for idx in range(len(shape)):
+        if idx in dimensions:
+            continue
+
+        new_shape.append(shape[idx])
+
+    return tuple(new_shape)
+
+
+def validate_no_repeating_dims(dims: Sequence):
+    if len(dims) != len(set(dims)):
+        raise RuntimeError("duplicate value in the list of dims")
+
+
+def reduction_dims(shape: ShapeType, dims: Optional[Sequence]) -> Tuple[int, ...]:
+    if dims is None:
+        return tuple(range(len(shape)))
+    dims = tuple(canonicalize_dim(len(shape), idx) for idx in dims)
+    validate_no_repeating_dims(dims)
+    return dims
+
+
+def set_correction(
+    unbiased: Optional[bool] = None,
+    correction: Optional[NumberType] = None,
+) -> float:
+    if correction is not None and unbiased is not None:
+        raise RuntimeError("cannot specify both correction and unbiased arguments")
+    elif correction is None and unbiased is None:
+        correction = 1.0
+    elif correction is None and unbiased is not None:
+        correction = 0.0 if unbiased is False else 1.0
+    # NB: we don't actually support symint here, but it's harmless to accept
+    if not isinstance(correction, (IntLike, FloatLike)):
+        raise ValueError("correction argument should be integer or float")
+    if correction < 0:
+        raise ValueError("correction argument should be non-negative")
+    return sym_float(correction)
+
+
+def compute_required_storage_length(
+    shape: ShapeType, strides: StrideType, storage_offset: int
+) -> int:
+    """Computes the minimum storage size to hold the given tensor geometry.
+
+    Example
+    =======
+
+    This is the size of a newly allocated tensor's storage, in units of elements
+
+    >>> t = torch.empty((10, 20))
+    >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset())
+    200
+
+    >>> # xdoctest: +SKIP(failing)
+    >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11))
+    >>> size = compute_required_storage_length(t2.shape, t2.stride(), t2.storage_offset())
+    >>> size == t.storage().size()
+    True
+
+    A valid tensor may have a larger storage size, but never smaller
+
+    >>> slice = torch.empty(100)[20:40]
+    >>> slice.storage().size()
+    100
+
+    >>> compute_required_storage_length(slice.shape, slice.stride(), slice.storage_offset())
+    40
+
+    """
+    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+
+    # Short-circuits if the shape has no elements
+    if guard_size_oblivious(reduce(operator.mul, shape, 1) == 0):
+        return 0
+
+    max_offset = sum((x - 1) * y for x, y in zip(shape, strides))
+    # +1 to account for the first element which offsets are taken from
+    return 1 + storage_offset + max_offset
+
+
+def check_in_bounds_for_storage(
+    a: torch.TypedStorage, shape: ShapeType, strides: StrideType, storage_offset: int
+):
+    """
+    Determines if the given shape, strides, and offset are valid for the given storage.
+    """
+
+    required_length = compute_required_storage_length(shape, strides, storage_offset)
+    if a.size() < required_length:
+        msg = (
+            "Can't view a storage of size {} with an offset of {}, shape of {}, and strides of {}, "
+            "which requires a storage of size {}".format(
+                a.size(), storage_offset, str(shape), str(strides), required_length
+            )
+        )
+        raise ValueError(msg)
+
+
+# NOTE: This function should ideally be removed, but some Meta internal models
+# packaged with `torch.package` are using it, so it will have to be removed
+# at some point in the future when those models no longer use this function.
+def check(
+    b: bool, s: Callable[[], str], exc_type: Type[Exception] = RuntimeError
+) -> None:
+    """
+    Helper function for raising an error_type (default: RuntimeError) if a boolean condition fails.
+    Error message is a callable producing a string (to avoid wasting time
+    string formatting in non-error case, and also to make it easier for torchdynamo
+    to trace.)
+
+    .. note:: This function is planned for removal in the future. Please use
+        `torch._check*` functions instead.
+    """
+    warnings.warn(
+        DeprecationWarning(
+            "'torch._prims_common.check' will be removed in the future. Please use "
+            "'torch._check*' functions instead"
+        )
+    )
+    torch._check_with(exc_type, b, s)
+
+
+# This combines is_channels_last_strides_2d and is_channels_last_strides_3d in
+# c10/core/MemoryFormat.h into one function
+def are_strides_like_channels_last(
+    shape: Sequence[int], strides: Sequence[int]
+) -> bool:
+    ndim = len(shape)
+
+    if ndim == 4:
+        # Check for channels_last_2d
+        dim_order = [1, 3, 2, 0]
+    elif ndim == 5:
+        # Check for channels_last_3d
+        dim_order = [1, 4, 3, 2, 0]
+    else:
+        return False
+
+    if strides[1] == 0:
+        return False
+
+    min = 0
+    for d in dim_order:
+        if shape[d] == 0:
+            return False
+        if strides[d] < min:
+            return False
+        if d == 0 and min == strides[1]:
+            return False
+        min = strides[d]
+        if strides[d] > 1:
+            min *= shape[d]
+    return True
+
+
+def suggest_memory_format(x: TensorLikeType) -> torch.memory_format:
+    if x.layout != torch.strided:
+        return torch.contiguous_format
+
+    if are_strides_like_channels_last(x.shape, x.stride()):
+        return torch.channels_last if x.ndim == 4 else torch.channels_last_3d
+
+    return torch.contiguous_format
+
+
+def prod(xs: Sequence[NumberType]) -> NumberType:
+    """Product of elements in input sequence. Returns 1 for empty sequence"""
+    return reduce(operator.mul, xs, 1)
+
+
+def is_expandable_to(shape: ShapeType, desired: ShapeType) -> bool:
+    """Checks if a shape can be expanded to another shape.
+    This is equivalent to checking if the two shapes are broadcastable.
+    """
+    # This is a Python implementation of
+    # aten/src/ATen/ExpandUtils.h:is_expandable_to
+    if len(shape) > len(desired):
+        return False
+    for i in range(len(shape)):
+        if shape[-i - 1] != desired[-i - 1] and shape[-i - 1] != 1:
+            return False
+    return True
+
+
+def mask_tensor(mask: TensorLikeType, t: TensorLikeType):
+    """
+    Similar to torch.where(mask, t, 0) but if t is boolean,
+    result is also boolean and not promoted to int.
+    """
+    # torch.where(mask, t, False) is equivalent
+    # but feels hacky and might break in the future
+    if t.dtype is torch.bool:
+        return mask.logical_and(t)
+    else:
+        return torch.where(mask, t, 0)
+
+
+def get_aten_op(fn: Callable, name: str):
+    """
+    Given the __module__ of reference and its name, it returns
+    (our best guess of) the ATen name of the associated operation
+
+    Note: In ATen, the __name__ of a function within a module often
+    starts by the module name. E.g. linalg_eigh, or special_zeta
+    """
+    module = fn.__module__
+    prefix = "torch._refs"
+    assert module.startswith(prefix)
+    module = module[len(prefix) :]
+    # We want to go from .special / .nn.functional
+    # to special and special_ / nn_functional_
+    if module:
+        module = module[1:]
+        module = module.replace(".", "_")
+        module = module + "_"
+    return getattr(torch._ops.ops.aten, f"{module}{name}")
+
+
+def dtype_or_default(dtype: Optional[torch.dtype]) -> torch.dtype:
+    return dtype if dtype is not None else torch.get_default_dtype()
+
+
+def device_or_default(device: Optional[DeviceLikeType]) -> DeviceLikeType:
+    return device if device is not None else torch.device("cpu")
+
+
+def layout_or_default(layout: Optional[torch.layout]) -> torch.layout:
+    return layout if layout is not None else torch.strided
+
+
+def clone_preserve_strides(x):
+    needed_size = compute_required_storage_length(
+        x.size(), x.stride(), x.storage_offset()
+    )
+    # Our eager implementations for *_scatter ops are all primitives w.r.t autograd,
+    # so these as_strided() calls are not seen by autograd.
+    # We need to mimic this behavior in our ref/prim implementations.
+    # TODO: a better way to handle this would be with a new op, "_unsafe_as_strided"
+    # We should revisit this when we add a compositional as_strided op,
+    # and also as part of https://github.com/pytorch/pytorch/issues/90507
+    try:
+        old = torch._C._dispatch_tls_is_dispatch_key_excluded(
+            torch._C.DispatchKey.ADInplaceOrView
+        )
+        torch._C._dispatch_tls_set_dispatch_key_excluded(
+            torch._C.DispatchKey.ADInplaceOrView, True
+        )
+        buffer = torch.as_strided(x, (needed_size,), (1,), 0).clone()
+        return torch.as_strided(buffer, x.size(), x.stride(), x.storage_offset())
+    finally:
+        torch._C._dispatch_tls_set_dispatch_key_excluded(
+            torch._C.DispatchKey.ADInplaceOrView, old
+        )
+
+
+def alert_not_deterministic(caller: str):
+    if torch.are_deterministic_algorithms_enabled():
+        if torch.is_deterministic_algorithms_warn_only_enabled():
+            warnings.warn(
+                f"{caller} does not have a deterministic implementation, but you set "
+                f"'torch.use_deterministic_algorithms(True, warn_only=True)'. "
+                f"You can file an issue at https://github.com/pytorch/pytorch/issues "
+                f"to help us prioritize adding deterministic support for this operation."
+            )
+        else:
+            torch._check(
+                False,
+                lambda: (
+                    f"{caller} does not have a deterministic implementation, but you set "
+                    f"'torch.use_deterministic_algorithms(True)'. You can turn off "
+                    f"determinism just for this operation, or you can use the "
+                    f"'warn_only=True' option, if that's acceptable for your application. "
+                    f"You can also file an issue at https://github.com/pytorch/pytorch/issues "
+                    f"to help us prioritize adding deterministic support for this operation."
+                ),
+            )
+
+
+class CUDARngStateHelper:
+    @staticmethod
+    def get_torch_state_as_tuple(fake_mode=nullcontext()):
+        if not torch.cuda.is_available():
+            raise RuntimeError("CUDA not available")
+
+        with fake_mode:
+            seed = torch.tensor(torch.cuda.initial_seed())
+            offset = torch.tensor(torch.cuda._get_rng_state_offset())
+            return seed, offset
+
+    @staticmethod
+    def set_torch_state_tensor(seed, offset):
+        # Rng state is [64-bit seed, 64-bit offset]
+        seed_portion = seed.reshape([1]).view(torch.uint8)
+        offset_portion = offset.reshape([1]).view(torch.uint8)
+        new_state = torch.cat([seed_portion, offset_portion])
+        torch.cuda.set_rng_state(new_state)
+
+    @staticmethod
+    def set_new_offset(relative_offset):
+        torch.cuda._set_rng_state_offset(relative_offset.item())
diff --git a/shared/package-files/4e/4efa79279af9b83061f18f725bae4922d4e9f81e5f749e13f46d1fc5e94761ac/test_business_quarter.py b/shared/package-files/4e/4efa79279af9b83061f18f725bae4922d4e9f81e5f749e13f46d1fc5e94761ac/test_business_quarter.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d7a115054b7f20e3ab024eb31f266c18920f2c5
--- /dev/null
+++ b/shared/package-files/4e/4efa79279af9b83061f18f725bae4922d4e9f81e5f749e13f46d1fc5e94761ac/test_business_quarter.py
@@ -0,0 +1,315 @@
+"""
+Tests for the following offsets:
+- BQuarterBegin
+- BQuarterEnd
+"""
+from __future__ import annotations
+
+from datetime import datetime
+
+import pytest
+
+import pandas._testing as tm
+from pandas.tests.tseries.offsets.common import (
+    assert_is_on_offset,
+    assert_offset_equal,
+)
+
+from pandas.tseries.offsets import (
+    BQuarterBegin,
+    BQuarterEnd,
+)
+
+
+def test_quarterly_dont_normalize():
+    date = datetime(2012, 3, 31, 5, 30)
+
+    offsets = (BQuarterEnd, BQuarterBegin)
+
+    for klass in offsets:
+        result = date + klass()
+        assert result.time() == date.time()
+
+
+@pytest.mark.parametrize("offset", [BQuarterBegin(), BQuarterEnd()])
+def test_on_offset(offset):
+    dates = [
+        datetime(2016, m, d)
+        for m in [10, 11, 12]
+        for d in [1, 2, 3, 28, 29, 30, 31]
+        if not (m == 11 and d == 31)
+    ]
+    for date in dates:
+        res = offset.is_on_offset(date)
+        slow_version = date == (date + offset) - offset
+        assert res == slow_version
+
+
+class TestBQuarterBegin:
+    def test_repr(self):
+        expected = "<BusinessQuarterBegin: startingMonth=3>"
+        assert repr(BQuarterBegin()) == expected
+        expected = "<BusinessQuarterBegin: startingMonth=3>"
+        assert repr(BQuarterBegin(startingMonth=3)) == expected
+        expected = "<BusinessQuarterBegin: startingMonth=1>"
+        assert repr(BQuarterBegin(startingMonth=1)) == expected
+
+    def test_is_anchored(self):
+        msg = "BQuarterBegin.is_anchored is deprecated "
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert BQuarterBegin(startingMonth=1).is_anchored()
+            assert BQuarterBegin().is_anchored()
+            assert not BQuarterBegin(2, startingMonth=1).is_anchored()
+
+    def test_offset_corner_case(self):
+        # corner
+        offset = BQuarterBegin(n=-1, startingMonth=1)
+        assert datetime(2007, 4, 3) + offset == datetime(2007, 4, 2)
+
+    offset_cases = []
+    offset_cases.append(
+        (
+            BQuarterBegin(startingMonth=1),
+            {
+                datetime(2008, 1, 1): datetime(2008, 4, 1),
+                datetime(2008, 1, 31): datetime(2008, 4, 1),
+                datetime(2008, 2, 15): datetime(2008, 4, 1),
+                datetime(2008, 2, 29): datetime(2008, 4, 1),
+                datetime(2008, 3, 15): datetime(2008, 4, 1),
+                datetime(2008, 3, 31): datetime(2008, 4, 1),
+                datetime(2008, 4, 15): datetime(2008, 7, 1),
+                datetime(2007, 3, 15): datetime(2007, 4, 2),
+                datetime(2007, 2, 28): datetime(2007, 4, 2),
+                datetime(2007, 1, 1): datetime(2007, 4, 2),
+                datetime(2007, 4, 15): datetime(2007, 7, 2),
+                datetime(2007, 7, 1): datetime(2007, 7, 2),
+                datetime(2007, 4, 1): datetime(2007, 4, 2),
+                datetime(2007, 4, 2): datetime(2007, 7, 2),
+                datetime(2008, 4, 30): datetime(2008, 7, 1),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterBegin(startingMonth=2),
+            {
+                datetime(2008, 1, 1): datetime(2008, 2, 1),
+                datetime(2008, 1, 31): datetime(2008, 2, 1),
+                datetime(2008, 1, 15): datetime(2008, 2, 1),
+                datetime(2008, 2, 29): datetime(2008, 5, 1),
+                datetime(2008, 3, 15): datetime(2008, 5, 1),
+                datetime(2008, 3, 31): datetime(2008, 5, 1),
+                datetime(2008, 4, 15): datetime(2008, 5, 1),
+                datetime(2008, 8, 15): datetime(2008, 11, 3),
+                datetime(2008, 9, 15): datetime(2008, 11, 3),
+                datetime(2008, 11, 1): datetime(2008, 11, 3),
+                datetime(2008, 4, 30): datetime(2008, 5, 1),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterBegin(startingMonth=1, n=0),
+            {
+                datetime(2008, 1, 1): datetime(2008, 1, 1),
+                datetime(2007, 12, 31): datetime(2008, 1, 1),
+                datetime(2008, 2, 15): datetime(2008, 4, 1),
+                datetime(2008, 2, 29): datetime(2008, 4, 1),
+                datetime(2008, 1, 15): datetime(2008, 4, 1),
+                datetime(2008, 2, 27): datetime(2008, 4, 1),
+                datetime(2008, 3, 15): datetime(2008, 4, 1),
+                datetime(2007, 4, 1): datetime(2007, 4, 2),
+                datetime(2007, 4, 2): datetime(2007, 4, 2),
+                datetime(2007, 7, 1): datetime(2007, 7, 2),
+                datetime(2007, 4, 15): datetime(2007, 7, 2),
+                datetime(2007, 7, 2): datetime(2007, 7, 2),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterBegin(startingMonth=1, n=-1),
+            {
+                datetime(2008, 1, 1): datetime(2007, 10, 1),
+                datetime(2008, 1, 31): datetime(2008, 1, 1),
+                datetime(2008, 2, 15): datetime(2008, 1, 1),
+                datetime(2008, 2, 29): datetime(2008, 1, 1),
+                datetime(2008, 3, 15): datetime(2008, 1, 1),
+                datetime(2008, 3, 31): datetime(2008, 1, 1),
+                datetime(2008, 4, 15): datetime(2008, 4, 1),
+                datetime(2007, 7, 3): datetime(2007, 7, 2),
+                datetime(2007, 4, 3): datetime(2007, 4, 2),
+                datetime(2007, 7, 2): datetime(2007, 4, 2),
+                datetime(2008, 4, 1): datetime(2008, 1, 1),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterBegin(startingMonth=1, n=2),
+            {
+                datetime(2008, 1, 1): datetime(2008, 7, 1),
+                datetime(2008, 1, 15): datetime(2008, 7, 1),
+                datetime(2008, 2, 29): datetime(2008, 7, 1),
+                datetime(2008, 3, 15): datetime(2008, 7, 1),
+                datetime(2007, 3, 31): datetime(2007, 7, 2),
+                datetime(2007, 4, 15): datetime(2007, 10, 1),
+                datetime(2008, 4, 30): datetime(2008, 10, 1),
+            },
+        )
+    )
+
+    @pytest.mark.parametrize("case", offset_cases)
+    def test_offset(self, case):
+        offset, cases = case
+        for base, expected in cases.items():
+            assert_offset_equal(offset, base, expected)
+
+
+class TestBQuarterEnd:
+    def test_repr(self):
+        expected = "<BusinessQuarterEnd: startingMonth=3>"
+        assert repr(BQuarterEnd()) == expected
+        expected = "<BusinessQuarterEnd: startingMonth=3>"
+        assert repr(BQuarterEnd(startingMonth=3)) == expected
+        expected = "<BusinessQuarterEnd: startingMonth=1>"
+        assert repr(BQuarterEnd(startingMonth=1)) == expected
+
+    def test_is_anchored(self):
+        msg = "BQuarterEnd.is_anchored is deprecated "
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert BQuarterEnd(startingMonth=1).is_anchored()
+            assert BQuarterEnd().is_anchored()
+            assert not BQuarterEnd(2, startingMonth=1).is_anchored()
+
+    def test_offset_corner_case(self):
+        # corner
+        offset = BQuarterEnd(n=-1, startingMonth=1)
+        assert datetime(2010, 1, 31) + offset == datetime(2010, 1, 29)
+
+    offset_cases = []
+    offset_cases.append(
+        (
+            BQuarterEnd(startingMonth=1),
+            {
+                datetime(2008, 1, 1): datetime(2008, 1, 31),
+                datetime(2008, 1, 31): datetime(2008, 4, 30),
+                datetime(2008, 2, 15): datetime(2008, 4, 30),
+                datetime(2008, 2, 29): datetime(2008, 4, 30),
+                datetime(2008, 3, 15): datetime(2008, 4, 30),
+                datetime(2008, 3, 31): datetime(2008, 4, 30),
+                datetime(2008, 4, 15): datetime(2008, 4, 30),
+                datetime(2008, 4, 30): datetime(2008, 7, 31),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterEnd(startingMonth=2),
+            {
+                datetime(2008, 1, 1): datetime(2008, 2, 29),
+                datetime(2008, 1, 31): datetime(2008, 2, 29),
+                datetime(2008, 2, 15): datetime(2008, 2, 29),
+                datetime(2008, 2, 29): datetime(2008, 5, 30),
+                datetime(2008, 3, 15): datetime(2008, 5, 30),
+                datetime(2008, 3, 31): datetime(2008, 5, 30),
+                datetime(2008, 4, 15): datetime(2008, 5, 30),
+                datetime(2008, 4, 30): datetime(2008, 5, 30),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterEnd(startingMonth=1, n=0),
+            {
+                datetime(2008, 1, 1): datetime(2008, 1, 31),
+                datetime(2008, 1, 31): datetime(2008, 1, 31),
+                datetime(2008, 2, 15): datetime(2008, 4, 30),
+                datetime(2008, 2, 29): datetime(2008, 4, 30),
+                datetime(2008, 3, 15): datetime(2008, 4, 30),
+                datetime(2008, 3, 31): datetime(2008, 4, 30),
+                datetime(2008, 4, 15): datetime(2008, 4, 30),
+                datetime(2008, 4, 30): datetime(2008, 4, 30),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterEnd(startingMonth=1, n=-1),
+            {
+                datetime(2008, 1, 1): datetime(2007, 10, 31),
+                datetime(2008, 1, 31): datetime(2007, 10, 31),
+                datetime(2008, 2, 15): datetime(2008, 1, 31),
+                datetime(2008, 2, 29): datetime(2008, 1, 31),
+                datetime(2008, 3, 15): datetime(2008, 1, 31),
+                datetime(2008, 3, 31): datetime(2008, 1, 31),
+                datetime(2008, 4, 15): datetime(2008, 1, 31),
+                datetime(2008, 4, 30): datetime(2008, 1, 31),
+            },
+        )
+    )
+
+    offset_cases.append(
+        (
+            BQuarterEnd(startingMonth=1, n=2),
+            {
+                datetime(2008, 1, 31): datetime(2008, 7, 31),
+                datetime(2008, 2, 15): datetime(2008, 7, 31),
+                datetime(2008, 2, 29): datetime(2008, 7, 31),
+                datetime(2008, 3, 15): datetime(2008, 7, 31),
+                datetime(2008, 3, 31): datetime(2008, 7, 31),
+                datetime(2008, 4, 15): datetime(2008, 7, 31),
+                datetime(2008, 4, 30): datetime(2008, 10, 31),
+            },
+        )
+    )
+
+    @pytest.mark.parametrize("case", offset_cases)
+    def test_offset(self, case):
+        offset, cases = case
+        for base, expected in cases.items():
+            assert_offset_equal(offset, base, expected)
+
+    on_offset_cases = [
+        (BQuarterEnd(1, startingMonth=1), datetime(2008, 1, 31), True),
+        (BQuarterEnd(1, startingMonth=1), datetime(2007, 12, 31), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2008, 2, 29), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2007, 3, 30), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2007, 3, 31), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2008, 4, 30), True),
+        (BQuarterEnd(1, startingMonth=1), datetime(2008, 5, 30), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2007, 6, 29), False),
+        (BQuarterEnd(1, startingMonth=1), datetime(2007, 6, 30), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2008, 1, 31), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2007, 12, 31), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2008, 2, 29), True),
+        (BQuarterEnd(1, startingMonth=2), datetime(2007, 3, 30), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2007, 3, 31), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2008, 4, 30), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2008, 5, 30), True),
+        (BQuarterEnd(1, startingMonth=2), datetime(2007, 6, 29), False),
+        (BQuarterEnd(1, startingMonth=2), datetime(2007, 6, 30), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2008, 1, 31), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2007, 12, 31), True),
+        (BQuarterEnd(1, startingMonth=3), datetime(2008, 2, 29), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2007, 3, 30), True),
+        (BQuarterEnd(1, startingMonth=3), datetime(2007, 3, 31), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2008, 4, 30), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2008, 5, 30), False),
+        (BQuarterEnd(1, startingMonth=3), datetime(2007, 6, 29), True),
+        (BQuarterEnd(1, startingMonth=3), datetime(2007, 6, 30), False),
+    ]
+
+    @pytest.mark.parametrize("case", on_offset_cases)
+    def test_is_on_offset(self, case):
+        offset, dt, expected = case
+        assert_is_on_offset(offset, dt, expected)
diff --git a/shared/package-files/4e/4efb7488f4c36bccb70336adfa5d1f095820f3765289b799baa90195285b4c02/logging_utils.py b/shared/package-files/4e/4efb7488f4c36bccb70336adfa5d1f095820f3765289b799baa90195285b4c02/logging_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..28f82555ac974a46f94780473c9d19af81575423
--- /dev/null
+++ b/shared/package-files/4e/4efb7488f4c36bccb70336adfa5d1f095820f3765289b799baa90195285b4c02/logging_utils.py
@@ -0,0 +1,135 @@
+"""
+Contains utils for logging in AOTAutograd, including managing the names of the graphs under
+compilation, capturing user-friendly tracebacks, and debug messages.
+"""
+
+import collections
+from contextlib import contextmanager
+from typing import List, Tuple
+
+import torch
+import torch.fx.traceback as fx_traceback
+
+# This is a list since looking forward, we can have this arbitrarily nested.
+graph_being_compiled: List[str] = []
+# TODO: It would be nice to reset the numbering every time aot_id goes
+# up, but this is annoying to do right now (because we don't know if
+# an aot_id will come back from the dead), so right now this also happens
+# to be a globally unique number too (at the cost of wobbling if you change
+# how the graphs compile)
+nth_graph: int = 0
+model_name: str = "model"
+
+
+def set_model_name(name):
+    global model_name
+    model_name = name
+
+
+def get_aot_compilation_context() -> Tuple[List[str], str, int]:
+    return list(graph_being_compiled), model_name, nth_graph
+
+
+def get_aot_graph_name() -> str:
+    """
+    Returns the name of the graph being compiled.
+    """
+    global model_name, graph_being_compiled, nth_graph
+    return f"{model_name}__{'_'.join(graph_being_compiled)}_{nth_graph}"
+
+
+get_graph_being_compiled = get_aot_graph_name
+
+
+@contextmanager
+def track_graph_compiling(aot_config, graph_name):
+    global graph_being_compiled
+    # TODO: Don't shove the aot_id in here; set it in the context
+    graph_being_compiled = [f"{aot_config.aot_id}_{graph_name}"]
+    try:
+        yield
+    finally:
+        global nth_graph
+        nth_graph += 1
+        graph_being_compiled = []
+
+
+# Set up hooks so that during backward the fx's stack_trace is properly set
+callback_set = False
+
+
+def setup_stacktrace_preservation_hooks(roots: List):
+    def iter_graph(roots):
+        if not roots:
+            return
+        seen = set()
+        q = collections.deque()  # type: ignore[var-annotated]
+        for node in roots:
+            if node is not None and node not in seen:
+                seen.add(node)
+                q.append(node)
+
+        while q:
+            node = q.popleft()
+            for fn, _idx in node.next_functions:
+                if fn in seen or fn is None:
+                    continue
+                seen.add(fn)
+                q.append(fn)
+
+            yield node
+
+    def get_callback(saved_stack_):
+        def callback():
+            global callback_set
+            fx_traceback.set_stack_trace(saved_stack_)
+            callback_set = False
+
+        return callback
+
+    def get_prehook(stack_, seq_nr):
+        def prehook(grad_output):
+            global callback_set
+
+            if not callback_set:
+                torch.autograd.variable.Variable._execution_engine.queue_callback(  # type: ignore[attr-defined]
+                    get_callback(fx_traceback.format_stack())
+                )
+                callback_set = True
+
+            fx_traceback.set_stack_trace(stack_)
+            fx_traceback.set_grad_fn_seq_nr(seq_nr)
+
+        return prehook
+
+    def get_posthook(special_stack_, seq_nr):
+        def posthook(grad_input, grad_output):
+            fx_traceback.set_stack_trace(special_stack_)
+            fx_traceback.reset_grad_fn_seq_nr()
+
+        return posthook
+
+    for node in iter_graph(roots):
+        forward_node_stack = node.metadata.get("traceback_", [])
+        node.register_prehook(get_prehook(forward_node_stack, node._sequence_nr()))
+
+        special_stack = forward_node_stack.copy()
+        special_stack.append(
+            "Gradient addition node due to multiple use of tensor around:"
+        )
+        node.register_hook(get_posthook(special_stack, node._sequence_nr()))
+
+
+def describe_input(i, aot_config):
+    if i < aot_config.num_params_buffers:
+        return f"parameter/buffer {i}"
+    else:
+        return f"input {i - aot_config.num_params_buffers}"
+
+
+def format_guard_bug_msg(aot_config, expected):
+    return (
+        f"At compilation time, graph {aot_config.aot_id} was compiled under the "
+        f"assumption that {expected}, but at runtime this was not the case.  "
+        "This indicates a guard bug in AOTAutograd or Dynamo, please file a bug to PyTorch."
+    )
diff --git a/shared/package-files/4e/4eff988e2d6d9be8a479a78b3d709ca7561551af32dbde92355ed856ea49b4d8/parse.pyi b/shared/package-files/4e/4eff988e2d6d9be8a479a78b3d709ca7561551af32dbde92355ed856ea49b4d8/parse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..569b0732429d640bdf70d4aed225b5a935c276dc
--- /dev/null
+++ b/shared/package-files/4e/4eff988e2d6d9be8a479a78b3d709ca7561551af32dbde92355ed856ea49b4d8/parse.pyi
@@ -0,0 +1,29 @@
+from urllib import (
+    quote as quote,
+    quote_plus as quote_plus,
+    splitquery as splitquery,
+    splittag as splittag,
+    splituser as splituser,
+    unquote as unquote,
+    unquote_plus as unquote_plus,
+    urlencode as urlencode,
+)
+from urlparse import (
+    ParseResult as ParseResult,
+    SplitResult as SplitResult,
+    parse_qs as parse_qs,
+    parse_qsl as parse_qsl,
+    urldefrag as urldefrag,
+    urljoin as urljoin,
+    urlparse as urlparse,
+    urlsplit as urlsplit,
+    urlunparse as urlunparse,
+    urlunsplit as urlunsplit,
+    uses_fragment as uses_fragment,
+    uses_netloc as uses_netloc,
+    uses_params as uses_params,
+    uses_query as uses_query,
+    uses_relative as uses_relative,
+)
+
+unquote_to_bytes = unquote
diff --git a/shared/package-files/a0/a005429f3be921018a042516c3b5c2fcc3c53c26281733cdb5b45a2e2bbd95e1/import_legacy.h b/shared/package-files/a0/a005429f3be921018a042516c3b5c2fcc3c53c26281733cdb5b45a2e2bbd95e1/import_legacy.h
new file mode 100644
index 0000000000000000000000000000000000000000..a2618281095969011b9ee4483794c7e6ce1422b2
--- /dev/null
+++ b/shared/package-files/a0/a005429f3be921018a042516c3b5c2fcc3c53c26281733cdb5b45a2e2bbd95e1/import_legacy.h
@@ -0,0 +1,23 @@
+#pragma once
+
+#include <torch/csrc/jit/api/module.h>
+
+namespace caffe2 {
+namespace serialize {
+class PyTorchStreamReader;
+} // namespace serialize
+} // namespace caffe2
+
+namespace torch {
+namespace jit {
+
+struct CompilationUnit;
+
+// Deserializes a model in legacy format.
+Module LEGACY_deserialize(
+    std::shared_ptr<CompilationUnit> cu,
+    std::shared_ptr<caffe2::serialize::PyTorchStreamReader> reader,
+    const c10::optional<c10::Device>& device);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/a0/a01207f43ed2cdd09d8afee7d21640fa8e784c8e241450dd91cf8652d37fd5fd/_predictor.pyx b/shared/package-files/a0/a01207f43ed2cdd09d8afee7d21640fa8e784c8e241450dd91cf8652d37fd5fd/_predictor.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..8257fa974c4a00115180aaa7815389051f1d9db2
--- /dev/null
+++ b/shared/package-files/a0/a01207f43ed2cdd09d8afee7d21640fa8e784c8e241450dd91cf8652d37fd5fd/_predictor.pyx
@@ -0,0 +1,256 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from cython.parallel import prange
+from libc.math cimport isnan
+import numpy as np
+
+from ...utils._typedefs cimport intp_t, uint8_t
+from .common cimport X_DTYPE_C
+from .common cimport Y_DTYPE_C
+from .common import Y_DTYPE
+from .common cimport X_BINNED_DTYPE_C
+from .common cimport BITSET_INNER_DTYPE_C
+from .common cimport node_struct
+from ._bitset cimport in_bitset_2d_memoryview
+
+
+def _predict_from_raw_data(  # raw data = non-binned data
+        const node_struct [:] nodes,
+        const X_DTYPE_C [:, :] numeric_data,
+        const BITSET_INNER_DTYPE_C [:, ::1] raw_left_cat_bitsets,
+        const BITSET_INNER_DTYPE_C [:, ::1] known_cat_bitsets,
+        const unsigned int [::1] f_idx_map,
+        int n_threads,
+        Y_DTYPE_C [:] out):
+
+    cdef:
+        int i
+
+    for i in prange(numeric_data.shape[0], schedule='static', nogil=True,
+                    num_threads=n_threads):
+        out[i] = _predict_one_from_raw_data(
+            nodes, numeric_data, raw_left_cat_bitsets,
+            known_cat_bitsets,
+            f_idx_map, i)
+
+
+cdef inline Y_DTYPE_C _predict_one_from_raw_data(
+        const node_struct [:] nodes,
+        const X_DTYPE_C [:, :] numeric_data,
+        const BITSET_INNER_DTYPE_C [:, ::1] raw_left_cat_bitsets,
+        const BITSET_INNER_DTYPE_C [:, ::1] known_cat_bitsets,
+        const unsigned int [::1] f_idx_map,
+        const int row) noexcept nogil:
+    # Need to pass the whole array and the row index, else prange won't work.
+    # See issue Cython #2798
+
+    cdef:
+        node_struct node = nodes[0]
+        unsigned int node_idx = 0
+        X_DTYPE_C data_val
+
+    while True:
+        if node.is_leaf:
+            return node.value
+
+        data_val = numeric_data[row, node.feature_idx]
+
+        if isnan(data_val):
+            if node.missing_go_to_left:
+                node_idx = node.left
+            else:
+                node_idx = node.right
+        elif node.is_categorical:
+            if data_val < 0:
+                # data_val is not in the accepted range, so it is treated as missing value
+                node_idx = node.left if node.missing_go_to_left else node.right
+            elif in_bitset_2d_memoryview(
+                    raw_left_cat_bitsets,
+                    <X_BINNED_DTYPE_C>data_val,
+                    node.bitset_idx):
+                node_idx = node.left
+            elif in_bitset_2d_memoryview(
+                    known_cat_bitsets,
+                    <X_BINNED_DTYPE_C>data_val,
+                    f_idx_map[node.feature_idx]):
+                node_idx = node.right
+            else:
+                # Treat unknown categories as missing.
+                node_idx = node.left if node.missing_go_to_left else node.right
+        else:
+            if data_val <= node.num_threshold:
+                node_idx = node.left
+            else:
+                node_idx = node.right
+        node = nodes[node_idx]
+
+
+def _predict_from_binned_data(
+        node_struct [:] nodes,
+        const X_BINNED_DTYPE_C [:, :] binned_data,
+        BITSET_INNER_DTYPE_C [:, :] binned_left_cat_bitsets,
+        const uint8_t missing_values_bin_idx,
+        int n_threads,
+        Y_DTYPE_C [:] out):
+
+    cdef:
+        int i
+
+    for i in prange(binned_data.shape[0], schedule='static', nogil=True,
+                    num_threads=n_threads):
+        out[i] = _predict_one_from_binned_data(nodes,
+                                               binned_data,
+                                               binned_left_cat_bitsets, i,
+                                               missing_values_bin_idx)
+
+
+cdef inline Y_DTYPE_C _predict_one_from_binned_data(
+        node_struct [:] nodes,
+        const X_BINNED_DTYPE_C [:, :] binned_data,
+        const BITSET_INNER_DTYPE_C [:, :] binned_left_cat_bitsets,
+        const int row,
+        const uint8_t missing_values_bin_idx) noexcept nogil:
+    # Need to pass the whole array and the row index, else prange won't work.
+    # See issue Cython #2798
+
+    cdef:
+        node_struct node = nodes[0]
+        unsigned int node_idx = 0
+        X_BINNED_DTYPE_C data_val
+
+    while True:
+        if node.is_leaf:
+            return node.value
+
+        data_val = binned_data[row, node.feature_idx]
+
+        if data_val == missing_values_bin_idx:
+            if node.missing_go_to_left:
+                node_idx = node.left
+            else:
+                node_idx = node.right
+        elif node.is_categorical:
+            if in_bitset_2d_memoryview(
+                    binned_left_cat_bitsets,
+                    data_val,
+                    node.bitset_idx):
+                node_idx = node.left
+            else:
+                node_idx = node.right
+        else:
+            if data_val <= node.bin_threshold:
+                node_idx = node.left
+            else:
+                node_idx = node.right
+        node = nodes[node_idx]
+
+
+def _compute_partial_dependence(
+    node_struct [:] nodes,
+    const X_DTYPE_C [:, ::1] X,
+    const intp_t [:] target_features,
+    Y_DTYPE_C [:] out
+):
+    """Partial dependence of the response on the ``target_features`` set.
+
+    For each sample in ``X`` a tree traversal is performed.
+    Each traversal starts from the root with weight 1.0.
+
+    At each non-leaf node that splits on a target feature, either
+    the left child or the right child is visited based on the feature
+    value of the current sample, and the weight is not modified.
+    At each non-leaf node that splits on a complementary feature,
+    both children are visited and the weight is multiplied by the fraction
+    of training samples which went to each child.
+
+    At each leaf, the value of the node is multiplied by the current
+    weight (weights sum to 1 for all visited terminal nodes).
+
+    Parameters
+    ----------
+    nodes : view on array of PREDICTOR_RECORD_DTYPE, shape (n_nodes)
+        The array representing the predictor tree.
+    X : view on 2d ndarray, shape (n_samples, n_target_features)
+        The grid points on which the partial dependence should be
+        evaluated.
+    target_features : view on 1d ndarray of intp_t, shape (n_target_features)
+        The set of target features for which the partial dependence
+        should be evaluated.
+    out : view on 1d ndarray, shape (n_samples)
+        The value of the partial dependence function on each grid
+        point.
+    """
+
+    cdef:
+        unsigned int current_node_idx
+        unsigned int [:] node_idx_stack = np.zeros(shape=nodes.shape[0],
+                                                   dtype=np.uint32)
+        Y_DTYPE_C [::1] weight_stack = np.zeros(shape=nodes.shape[0],
+                                                dtype=Y_DTYPE)
+        node_struct * current_node  # pointer to avoid copying attributes
+
+        unsigned int sample_idx
+        intp_t feature_idx
+        unsigned stack_size
+        Y_DTYPE_C left_sample_frac
+        Y_DTYPE_C current_weight
+        Y_DTYPE_C total_weight  # used for sanity check only
+        bint is_target_feature
+
+    for sample_idx in range(X.shape[0]):
+        # init stacks for current sample
+        stack_size = 1
+        node_idx_stack[0] = 0  # root node
+        weight_stack[0] = 1  # all the samples are in the root node
+        total_weight = 0
+
+        while stack_size > 0:
+
+            # pop the stack
+            stack_size -= 1
+            current_node_idx = node_idx_stack[stack_size]
+            current_node = &nodes[current_node_idx]
+
+            if current_node.is_leaf:
+                out[sample_idx] += (weight_stack[stack_size] *
+                                    current_node.value)
+                total_weight += weight_stack[stack_size]
+            else:
+                # determine if the split feature is a target feature
+                is_target_feature = False
+                for feature_idx in range(target_features.shape[0]):
+                    if target_features[feature_idx] == current_node.feature_idx:
+                        is_target_feature = True
+                        break
+
+                if is_target_feature:
+                    # In this case, we push left or right child on stack
+                    if X[sample_idx, feature_idx] <= current_node.num_threshold:
+                        node_idx_stack[stack_size] = current_node.left
+                    else:
+                        node_idx_stack[stack_size] = current_node.right
+                    stack_size += 1
+                else:
+                    # In this case, we push both children onto the stack,
+                    # and give a weight proportional to the number of
+                    # samples going through each branch.
+
+                    # push left child
+                    node_idx_stack[stack_size] = current_node.left
+                    left_sample_frac = (
+                        <Y_DTYPE_C> nodes[current_node.left].count /
+                        current_node.count)
+                    current_weight = weight_stack[stack_size]
+                    weight_stack[stack_size] = current_weight * left_sample_frac
+                    stack_size += 1
+
+                    # push right child
+                    node_idx_stack[stack_size] = current_node.right
+                    weight_stack[stack_size] = (
+                        current_weight * (1 - left_sample_frac))
+                    stack_size += 1
+
+        # Sanity check. Should never happen.
+        if not (0.999 < total_weight < 1.001):
+            raise ValueError("Total weight should be 1.0 but was %.9f" %total_weight)
diff --git a/shared/package-files/a0/a01863a9310e5de6b09a2319f68b11e145ae2912357eecfaa2da266b5bca75c2/_foreach_abs_compositeexplicitautograd_dispatch.h b/shared/package-files/a0/a01863a9310e5de6b09a2319f68b11e145ae2912357eecfaa2da266b5bca75c2/_foreach_abs_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac1683c7b80cdfa64cb57aa44524de9b83c4bdc8
--- /dev/null
+++ b/shared/package-files/a0/a01863a9310e5de6b09a2319f68b11e145ae2912357eecfaa2da266b5bca75c2/_foreach_abs_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void _foreach_abs_out(at::TensorList out, at::TensorList self);
+TORCH_API void _foreach_abs_outf(at::TensorList self, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a02287eee69f37c475a3e8a35c6a13e54d49671c2fa0bcc9bbfd82ab28b68543/Aden b/shared/package-files/a0/a02287eee69f37c475a3e8a35c6a13e54d49671c2fa0bcc9bbfd82ab28b68543/Aden
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diff --git a/shared/package-files/a0/a02287eee69f37c475a3e8a35c6a13e54d49671c2fa0bcc9bbfd82ab28b68543/Kuwait b/shared/package-files/a0/a02287eee69f37c475a3e8a35c6a13e54d49671c2fa0bcc9bbfd82ab28b68543/Kuwait
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diff --git a/shared/package-files/a0/a027a494f2a6e8610ad306a9a85f3646dc94eadda6bb1282dae0c342b20e2d69/baddbmm_native.h b/shared/package-files/a0/a027a494f2a6e8610ad306a9a85f3646dc94eadda6bb1282dae0c342b20e2d69/baddbmm_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5062fd6e296bc170a33dcb91b8c2f47a4f87632b
--- /dev/null
+++ b/shared/package-files/a0/a027a494f2a6e8610ad306a9a85f3646dc94eadda6bb1282dae0c342b20e2d69/baddbmm_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/baddbmm_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_baddbmm_out_cpu : public at::meta::structured_baddbmm {
+void impl(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, const at::Tensor & out);
+};
+struct TORCH_API structured_baddbmm_out_cuda : public at::meta::structured_baddbmm {
+void impl(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, const at::Tensor & out);
+};
+TORCH_API at::Tensor & baddbmm_out_sparse_csr_cuda(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/a0/a02a48c16f94a37e96a79860bcbe6ed30b45e472a65c03631388fad5b39a7e82/test_summarization.py b/shared/package-files/a0/a02a48c16f94a37e96a79860bcbe6ed30b45e472a65c03631388fad5b39a7e82/test_summarization.py
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index 0000000000000000000000000000000000000000..859e0cf71e33c7ac2482392d518f05a69f4dcaf4
--- /dev/null
+++ b/shared/package-files/a0/a02a48c16f94a37e96a79860bcbe6ed30b45e472a65c03631388fad5b39a7e82/test_summarization.py
@@ -0,0 +1,641 @@
+"""
+Unit tests for dedensification and graph summarization
+"""
+import pytest
+
+import networkx as nx
+
+
+class TestDirectedDedensification:
+    def build_original_graph(self):
+        original_matrix = [
+            ("1", "BC"),
+            ("2", "ABC"),
+            ("3", ["A", "B", "6"]),
+            ("4", "ABC"),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+        ]
+        graph = nx.DiGraph()
+        for source, targets in original_matrix:
+            for target in targets:
+                graph.add_edge(source, target)
+        return graph
+
+    def build_compressed_graph(self):
+        compressed_matrix = [
+            ("1", "BC"),
+            ("2", ["ABC"]),
+            ("3", ["A", "B", "6"]),
+            ("4", ["ABC"]),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+            ("ABC", "ABC"),
+        ]
+        compressed_graph = nx.DiGraph()
+        for source, targets in compressed_matrix:
+            for target in targets:
+                compressed_graph.add_edge(source, target)
+        return compressed_graph
+
+    def test_empty(self):
+        """
+        Verify that an empty directed graph results in no compressor nodes
+        """
+        G = nx.DiGraph()
+        compressed_graph, c_nodes = nx.dedensify(G, threshold=2)
+        assert c_nodes == set()
+
+    @staticmethod
+    def densify(G, compressor_nodes, copy=True):
+        """
+        Reconstructs the original graph from a dedensified, directed graph
+
+        Parameters
+        ----------
+        G: dedensified graph
+           A networkx graph
+        compressor_nodes: iterable
+           Iterable of compressor nodes in the dedensified graph
+        inplace: bool, optional (default: False)
+           Indicates if densification should be done inplace
+
+        Returns
+        -------
+        G: graph
+           A densified networkx graph
+        """
+        if copy:
+            G = G.copy()
+        for compressor_node in compressor_nodes:
+            all_neighbors = set(nx.all_neighbors(G, compressor_node))
+            out_neighbors = set(G.neighbors(compressor_node))
+            for out_neighbor in out_neighbors:
+                G.remove_edge(compressor_node, out_neighbor)
+            in_neighbors = all_neighbors - out_neighbors
+            for in_neighbor in in_neighbors:
+                G.remove_edge(in_neighbor, compressor_node)
+                for out_neighbor in out_neighbors:
+                    G.add_edge(in_neighbor, out_neighbor)
+            G.remove_node(compressor_node)
+        return G
+
+    def setup_method(self):
+        self.c_nodes = ("ABC",)
+
+    def test_dedensify_edges(self):
+        """
+        Verifies that dedensify produced the correct edges to/from compressor
+        nodes in a directed graph
+        """
+        G = self.build_original_graph()
+        compressed_G = self.build_compressed_graph()
+        compressed_graph, c_nodes = nx.dedensify(G, threshold=2)
+        for s, t in compressed_graph.edges():
+            o_s = "".join(sorted(s))
+            o_t = "".join(sorted(t))
+            compressed_graph_exists = compressed_graph.has_edge(s, t)
+            verified_compressed_exists = compressed_G.has_edge(o_s, o_t)
+            assert compressed_graph_exists == verified_compressed_exists
+        assert len(c_nodes) == len(self.c_nodes)
+
+    def test_dedensify_edge_count(self):
+        """
+        Verifies that dedensify produced the correct number of comrpessor nodes
+        in a directed graph
+        """
+        G = self.build_original_graph()
+        original_edge_count = len(G.edges())
+        c_G, c_nodes = nx.dedensify(G, threshold=2)
+        compressed_edge_count = len(c_G.edges())
+        assert compressed_edge_count <= original_edge_count
+        compressed_G = self.build_compressed_graph()
+        assert compressed_edge_count == len(compressed_G.edges())
+
+    def test_densify_edges(self):
+        """
+        Verifies that densification produces the correct edges from the
+        original directed graph
+        """
+        compressed_G = self.build_compressed_graph()
+        original_graph = self.densify(compressed_G, self.c_nodes, copy=True)
+        G = self.build_original_graph()
+        for s, t in G.edges():
+            assert G.has_edge(s, t) == original_graph.has_edge(s, t)
+
+    def test_densify_edge_count(self):
+        """
+        Verifies that densification produces the correct number of edges in the
+        original directed graph
+        """
+        compressed_G = self.build_compressed_graph()
+        compressed_edge_count = len(compressed_G.edges())
+        original_graph = self.densify(compressed_G, self.c_nodes)
+        original_edge_count = len(original_graph.edges())
+        assert compressed_edge_count <= original_edge_count
+        G = self.build_original_graph()
+        assert original_edge_count == len(G.edges())
+
+
+class TestUnDirectedDedensification:
+    def build_original_graph(self):
+        """
+        Builds graph shown in the original research paper
+        """
+        original_matrix = [
+            ("1", "CB"),
+            ("2", "ABC"),
+            ("3", ["A", "B", "6"]),
+            ("4", "ABC"),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+        ]
+        graph = nx.Graph()
+        for source, targets in original_matrix:
+            for target in targets:
+                graph.add_edge(source, target)
+        return graph
+
+    def test_empty(self):
+        """
+        Verify that an empty undirected graph results in no compressor nodes
+        """
+        G = nx.Graph()
+        compressed_G, c_nodes = nx.dedensify(G, threshold=2)
+        assert c_nodes == set()
+
+    def setup_method(self):
+        self.c_nodes = ("6AB", "ABC")
+
+    def build_compressed_graph(self):
+        compressed_matrix = [
+            ("1", ["B", "C"]),
+            ("2", ["ABC"]),
+            ("3", ["6AB"]),
+            ("4", ["ABC"]),
+            ("5", ["6AB"]),
+            ("6", ["6AB", "A"]),
+            ("A", ["6AB", "ABC"]),
+            ("B", ["ABC", "6AB"]),
+            ("C", ["ABC"]),
+        ]
+        compressed_graph = nx.Graph()
+        for source, targets in compressed_matrix:
+            for target in targets:
+                compressed_graph.add_edge(source, target)
+        return compressed_graph
+
+    def test_dedensify_edges(self):
+        """
+        Verifies that dedensify produced correct compressor nodes and the
+        correct edges to/from the compressor nodes in an undirected graph
+        """
+        G = self.build_original_graph()
+        c_G, c_nodes = nx.dedensify(G, threshold=2)
+        v_compressed_G = self.build_compressed_graph()
+        for s, t in c_G.edges():
+            o_s = "".join(sorted(s))
+            o_t = "".join(sorted(t))
+            has_compressed_edge = c_G.has_edge(s, t)
+            verified_has_compressed_edge = v_compressed_G.has_edge(o_s, o_t)
+            assert has_compressed_edge == verified_has_compressed_edge
+        assert len(c_nodes) == len(self.c_nodes)
+
+    def test_dedensify_edge_count(self):
+        """
+        Verifies that dedensify produced the correct number of edges in an
+        undirected graph
+        """
+        G = self.build_original_graph()
+        c_G, c_nodes = nx.dedensify(G, threshold=2, copy=True)
+        compressed_edge_count = len(c_G.edges())
+        verified_original_edge_count = len(G.edges())
+        assert compressed_edge_count <= verified_original_edge_count
+        verified_compressed_G = self.build_compressed_graph()
+        verified_compressed_edge_count = len(verified_compressed_G.edges())
+        assert compressed_edge_count == verified_compressed_edge_count
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_summarization_empty(graph_type):
+    G = graph_type()
+    summary_graph = nx.snap_aggregation(G, node_attributes=("color",))
+    assert nx.is_isomorphic(summary_graph, G)
+
+
+class AbstractSNAP:
+    node_attributes = ("color",)
+
+    def build_original_graph(self):
+        pass
+
+    def build_summary_graph(self):
+        pass
+
+    def test_summary_graph(self):
+        original_graph = self.build_original_graph()
+        summary_graph = self.build_summary_graph()
+
+        relationship_attributes = ("type",)
+        generated_summary_graph = nx.snap_aggregation(
+            original_graph, self.node_attributes, relationship_attributes
+        )
+        relabeled_summary_graph = self.deterministic_labels(generated_summary_graph)
+        assert nx.is_isomorphic(summary_graph, relabeled_summary_graph)
+
+    def deterministic_labels(self, G):
+        node_labels = list(G.nodes)
+        node_labels = sorted(node_labels, key=lambda n: sorted(G.nodes[n]["group"])[0])
+        node_labels.sort()
+
+        label_mapping = {}
+        for index, node in enumerate(node_labels):
+            label = "Supernode-%s" % index
+            label_mapping[node] = label
+
+        return nx.relabel_nodes(G, label_mapping)
+
+
+class TestSNAPNoEdgeTypes(AbstractSNAP):
+    relationship_attributes = ()
+
+    def test_summary_graph(self):
+        original_graph = self.build_original_graph()
+        summary_graph = self.build_summary_graph()
+
+        relationship_attributes = ("type",)
+        generated_summary_graph = nx.snap_aggregation(
+            original_graph, self.node_attributes
+        )
+        relabeled_summary_graph = self.deterministic_labels(generated_summary_graph)
+        assert nx.is_isomorphic(summary_graph, relabeled_summary_graph)
+
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Red"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Blue"},
+            "H": {"color": "Blue"},
+            "I": {"color": "Yellow"},
+            "J": {"color": "Yellow"},
+            "K": {"color": "Yellow"},
+            "L": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "B"),
+            ("A", "C"),
+            ("A", "E"),
+            ("A", "I"),
+            ("B", "D"),
+            ("B", "J"),
+            ("B", "F"),
+            ("C", "G"),
+            ("D", "H"),
+            ("I", "J"),
+            ("J", "K"),
+            ("I", "L"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target in edges:
+            G.add_edge(source, target)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Red"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-0"),
+            ("Supernode-0", "Supernode-1"),
+            ("Supernode-0", "Supernode-2"),
+            ("Supernode-0", "Supernode-4"),
+            ("Supernode-1", "Supernode-3"),
+            ("Supernode-4", "Supernode-4"),
+            ("Supernode-4", "Supernode-5"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target in edges:
+            G.add_edge(source, target)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPUndirected(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Red"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Blue"},
+            "H": {"color": "Blue"},
+            "I": {"color": "Yellow"},
+            "J": {"color": "Yellow"},
+            "K": {"color": "Yellow"},
+            "L": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "B", "Strong"),
+            ("A", "C", "Weak"),
+            ("A", "E", "Strong"),
+            ("A", "I", "Weak"),
+            ("B", "D", "Weak"),
+            ("B", "J", "Weak"),
+            ("B", "F", "Strong"),
+            ("C", "G", "Weak"),
+            ("D", "H", "Weak"),
+            ("I", "J", "Strong"),
+            ("J", "K", "Strong"),
+            ("I", "L", "Strong"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Red"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-0", "Strong"),
+            ("Supernode-0", "Supernode-1", "Weak"),
+            ("Supernode-0", "Supernode-2", "Strong"),
+            ("Supernode-0", "Supernode-4", "Weak"),
+            ("Supernode-1", "Supernode-3", "Weak"),
+            ("Supernode-4", "Supernode-4", "Strong"),
+            ("Supernode-4", "Supernode-5", "Strong"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, types=[{"type": type}])
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPDirected(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Green"},
+            "D": {"color": "Green"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "C", "Strong"),
+            ("A", "E", "Strong"),
+            ("A", "F", "Weak"),
+            ("B", "D", "Strong"),
+            ("B", "E", "Weak"),
+            ("B", "F", "Strong"),
+            ("C", "G", "Strong"),
+            ("C", "F", "Strong"),
+            ("D", "E", "Strong"),
+            ("D", "H", "Strong"),
+            ("G", "E", "Strong"),
+            ("H", "F", "Strong"),
+        ]
+        G = nx.DiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Green"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-1", [{"type": "Strong"}]),
+            ("Supernode-0", "Supernode-2", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-1", "Supernode-2", [{"type": "Strong"}]),
+            ("Supernode-1", "Supernode-3", [{"type": "Strong"}]),
+            ("Supernode-3", "Supernode-2", [{"type": "Strong"}]),
+        ]
+        G = nx.DiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            G.add_edge(source, target, types=types)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPUndirectedMulti(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Blue"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+            "I": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "D", ["Weak", "Strong"]),
+            ("B", "E", ["Weak", "Strong"]),
+            ("D", "I", ["Strong"]),
+            ("E", "H", ["Strong"]),
+            ("F", "G", ["Weak"]),
+            ("I", "G", ["Weak", "Strong"]),
+            ("I", "H", ["Weak", "Strong"]),
+            ("G", "H", ["Weak", "Strong"]),
+        ]
+        G = nx.MultiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Blue"},
+            "Supernode-2": {"color": "Yellow"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Red"},
+        }
+        edges = [
+            ("Supernode-1", "Supernode-2", [{"type": "Weak"}]),
+            ("Supernode-2", "Supernode-4", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-3", "Supernode-4", [{"type": "Strong"}]),
+            ("Supernode-3", "Supernode-5", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-4", "Supernode-4", [{"type": "Weak"}, {"type": "Strong"}]),
+        ]
+        G = nx.MultiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPDirectedMulti(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Green"},
+            "D": {"color": "Green"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "C", ["Weak", "Strong"]),
+            ("A", "E", ["Strong"]),
+            ("A", "F", ["Weak"]),
+            ("B", "D", ["Weak", "Strong"]),
+            ("B", "E", ["Weak"]),
+            ("B", "F", ["Strong"]),
+            ("C", "G", ["Weak", "Strong"]),
+            ("C", "F", ["Strong"]),
+            ("D", "E", ["Strong"]),
+            ("D", "H", ["Weak", "Strong"]),
+            ("G", "E", ["Strong"]),
+            ("H", "F", ["Strong"]),
+        ]
+        G = nx.MultiDiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Blue"},
+            "Supernode-2": {"color": "Yellow"},
+            "Supernode-3": {"color": "Blue"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-1", ["Weak", "Strong"]),
+            ("Supernode-0", "Supernode-2", ["Weak", "Strong"]),
+            ("Supernode-1", "Supernode-2", ["Strong"]),
+            ("Supernode-1", "Supernode-3", ["Weak", "Strong"]),
+            ("Supernode-3", "Supernode-2", ["Strong"]),
+        ]
+        G = nx.MultiDiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
diff --git a/shared/package-files/a0/a02b5f06fed6f49a9840c53b9ee9129bc9152b5c32dfc53b75bde816c9d76942/utils.pyi b/shared/package-files/a0/a02b5f06fed6f49a9840c53b9ee9129bc9152b5c32dfc53b75bde816c9d76942/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3d1b1cccf2d10c41e32637dddbf57a6232d16572
--- /dev/null
+++ b/shared/package-files/a0/a02b5f06fed6f49a9840c53b9ee9129bc9152b5c32dfc53b75bde816c9d76942/utils.pyi
@@ -0,0 +1,87 @@
+from _typeshed import AnyPath
+from typing import IO, Any, Callable, Iterable, Optional, Protocol, Text, TypeVar, Union
+from typing_extensions import Literal
+
+from markupsafe import Markup as Markup, escape as escape, soft_unicode as soft_unicode
+
+missing: Any
+internal_code: Any
+concat: Any
+
+_CallableT = TypeVar("_CallableT", bound=Callable[..., Any])
+
+class _ContextFunction(Protocol[_CallableT]):
+    contextfunction: Literal[True]
+    __call__: _CallableT
+
+class _EvalContextFunction(Protocol[_CallableT]):
+    evalcontextfunction: Literal[True]
+    __call__: _CallableT
+
+class _EnvironmentFunction(Protocol[_CallableT]):
+    environmentfunction: Literal[True]
+    __call__: _CallableT
+
+def contextfunction(f: _CallableT) -> _ContextFunction[_CallableT]: ...
+def evalcontextfunction(f: _CallableT) -> _EvalContextFunction[_CallableT]: ...
+def environmentfunction(f: _CallableT) -> _EnvironmentFunction[_CallableT]: ...
+def internalcode(f: _CallableT) -> _CallableT: ...
+def is_undefined(obj: object) -> bool: ...
+def select_autoescape(
+    enabled_extensions: Iterable[str] = ...,
+    disabled_extensions: Iterable[str] = ...,
+    default_for_string: bool = ...,
+    default: bool = ...,
+) -> Callable[[str], bool]: ...
+def consume(iterable: Iterable[object]) -> None: ...
+def clear_caches() -> None: ...
+def import_string(import_name: str, silent: bool = ...) -> Any: ...
+def open_if_exists(filename: AnyPath, mode: str = ...) -> Optional[IO[Any]]: ...
+def object_type_repr(obj: object) -> str: ...
+def pformat(obj: object, verbose: bool = ...) -> str: ...
+def urlize(
+    text: Union[Markup, Text],
+    trim_url_limit: Optional[int] = ...,
+    rel: Optional[Union[Markup, Text]] = ...,
+    target: Optional[Union[Markup, Text]] = ...,
+) -> str: ...
+def generate_lorem_ipsum(n: int = ..., html: bool = ..., min: int = ..., max: int = ...) -> Union[Markup, str]: ...
+def unicode_urlencode(obj: object, charset: str = ..., for_qs: bool = ...) -> str: ...
+
+class LRUCache:
+    capacity: Any
+    def __init__(self, capacity) -> None: ...
+    def __getnewargs__(self): ...
+    def copy(self): ...
+    def get(self, key, default: Optional[Any] = ...): ...
+    def setdefault(self, key, default: Optional[Any] = ...): ...
+    def clear(self): ...
+    def __contains__(self, key): ...
+    def __len__(self): ...
+    def __getitem__(self, key): ...
+    def __setitem__(self, key, value): ...
+    def __delitem__(self, key): ...
+    def items(self): ...
+    def iteritems(self): ...
+    def values(self): ...
+    def itervalue(self): ...
+    def keys(self): ...
+    def iterkeys(self): ...
+    __iter__: Any
+    def __reversed__(self): ...
+    __copy__: Any
+
+class Cycler:
+    items: Any
+    def __init__(self, *items) -> None: ...
+    pos: int
+    def reset(self): ...
+    @property
+    def current(self): ...
+    def __next__(self): ...
+
+class Joiner:
+    sep: Any
+    used: bool
+    def __init__(self, sep: str = ...) -> None: ...
+    def __call__(self): ...
diff --git a/shared/package-files/a0/a02b9e66044dc5c35c5f76467627fdcba4aee1cc958606b85c777095cad82ceb/Japan b/shared/package-files/a0/a02b9e66044dc5c35c5f76467627fdcba4aee1cc958606b85c777095cad82ceb/Japan
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diff --git a/shared/package-files/a0/a02b9e66044dc5c35c5f76467627fdcba4aee1cc958606b85c777095cad82ceb/Tokyo b/shared/package-files/a0/a02b9e66044dc5c35c5f76467627fdcba4aee1cc958606b85c777095cad82ceb/Tokyo
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diff --git a/shared/package-files/a0/a02c06eaaa46d1b097b9ed95e7a7e30dc0bc700dab832e1bdcefe7bbf148f596/import_fairseq.py b/shared/package-files/a0/a02c06eaaa46d1b097b9ed95e7a7e30dc0bc700dab832e1bdcefe7bbf148f596/import_fairseq.py
new file mode 100644
index 0000000000000000000000000000000000000000..39791e9b7d75ac3c2eb1fcf4f9c3517e7483048c
--- /dev/null
+++ b/shared/package-files/a0/a02c06eaaa46d1b097b9ed95e7a7e30dc0bc700dab832e1bdcefe7bbf148f596/import_fairseq.py
@@ -0,0 +1,213 @@
+"""Import fariseq's wav2vec2.0 pretrained weights to torchaudios's format.
+
+For this module to work, you need `fairseq`.
+"""
+import re
+
+from torch.nn import Module
+
+from ..model import wav2vec2_model, Wav2Vec2Model
+
+
+def _parse_config(w2v_model):
+    encoder = w2v_model.encoder
+    conv_layers = w2v_model.feature_extractor.conv_layers
+
+    extractor_mode = "layer_norm"
+    if "GroupNorm" in conv_layers[0][2].__class__.__name__:
+        extractor_mode = "group_norm"
+    else:
+        extractor_mode = "layer_norm"
+
+    conv_layer_config = [(l[0].out_channels, l[0].kernel_size[0], l[0].stride[0]) for l in conv_layers]
+
+    if all(l[0].bias is None for l in conv_layers):
+        conv_bias = False
+    elif all(l[0].bias is not None for l in conv_layers):
+        conv_bias = True
+    else:
+        raise ValueError("Either all the convolutions layers have bias term or none of them should.")
+
+    config = {
+        "extractor_mode": extractor_mode,
+        "extractor_conv_layer_config": conv_layer_config,
+        "extractor_conv_bias": conv_bias,
+        "encoder_embed_dim": w2v_model.post_extract_proj.out_features,
+        "encoder_projection_dropout": w2v_model.dropout_input.p,
+        "encoder_pos_conv_kernel": encoder.pos_conv[0].kernel_size[0],
+        "encoder_pos_conv_groups": encoder.pos_conv[0].groups,
+        "encoder_num_layers": len(encoder.layers),
+        "encoder_num_heads": encoder.layers[0].self_attn.num_heads,
+        "encoder_attention_dropout": encoder.layers[0].self_attn.dropout_module.p,
+        "encoder_ff_interm_features": encoder.layers[0].fc1.out_features,
+        "encoder_ff_interm_dropout": encoder.layers[0].dropout2.p,
+        "encoder_dropout": encoder.layers[0].dropout3.p,
+        "encoder_layer_norm_first": encoder.layer_norm_first,
+        "encoder_layer_drop": encoder.layerdrop,
+    }
+    return config
+
+
+def _map_key(key):
+    key_ = key
+    if key.startswith("w2v_model."):
+        key = key.replace("w2v_model.", "")
+    if re.match(r"(mask_emb|quantizer|project_q|final_proj|mask_emb)", key):
+        return None
+    # Feature Extractor
+    # Group norm when "extractor_mode" is "default".
+    # (Only the first layer)
+    # "conv_layers.0.2.weight" -> "conv_layers.0.layer_norm.weight"
+    # "conv_layers.0.2.bias"   -> "conv_layers.0.layer_norm.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.0\.2\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.0.layer_norm.{match.group(1)}"
+    # Convolutions
+    # "conv_layers.X.0.weight" -> "conv_layers.X.conv.weight"
+    # "conv_layers.X.0.bias"   -> "conv_layers.X.conv.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.0\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.{match.group(1)}.conv.{match.group(2)}"
+    # Layer norm when "extractor_mode" is "layer_norm".
+    # "conv_layers.X.2.1.weight" -> "conv_layers.X.layer_norm.weight"
+    # "conv_layers.X.2.1.bias"   -> "conv_layers.X.layer_norm.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.2\.1\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.{match.group(1)}.layer_norm.{match.group(2)}"
+    match = re.match(r"post_extract_proj\.(weight|bias)", key)
+    # Encoder - Feature projection
+    if match:
+        return f"encoder.feature_projection.projection.{match.group(1)}"
+    match = re.match(r"layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.feature_projection.layer_norm.{match.group(1)}"
+    # Encoder - Transformer - Convolutional positional embedding
+    match = re.match(r"encoder\.pos_conv\.0\.(bias|weight_g|weight_v)", key)
+    if match:
+        return f"encoder.transformer.pos_conv_embed.conv.{match.group(1)}"
+    match = re.match(r"encoder\.layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layer_norm.{match.group(1)}"
+    # Encoder - Transformer - Self attention layers
+    match = re.match(r"encoder\.layers\.(\d+)\.self_attn\.((k_|v_|q_|out_)proj\.(weight|bias))", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.attention.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.self_attn_layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.layer_norm.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.fc1\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.intermediate_dense.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.fc2\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.output_dense.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.final_layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.final_layer_norm.{match.group(2)}"
+    match = re.match(r"proj\.(weight|bias)", key)
+    # Auxiliary Module
+    # Only relevant when loading fine-tuned models
+    if match:
+        return f"aux.{match.group(1)}"
+    # HuBERT Extension
+    if key in ["label_embs_concat"]:
+        return key
+    raise ValueError(f"Unexpected key: {key_}")
+
+
+def _convert_state_dict(state_dict):
+    converted = {}
+    for k, v in state_dict.items():
+        k = _map_key(k)
+        if k is not None:
+            converted[k] = v
+    return converted
+
+
+def import_fairseq_model(original: Module) -> Wav2Vec2Model:
+    """Builds :class:`Wav2Vec2Model` from the corresponding model object of
+    `fairseq <https://github.com/pytorch/fairseq>`_.
+
+    Args:
+        original (torch.nn.Module):
+            An instance of fairseq's Wav2Vec2.0 or HuBERT model.
+            One of ``fairseq.models.wav2vec.wav2vec2_asr.Wav2VecEncoder``,
+            ``fairseq.models.wav2vec.wav2vec2.Wav2Vec2Model`` or
+            ``fairseq.models.hubert.hubert_asr.HubertEncoder``.
+
+    Returns:
+        Wav2Vec2Model: Imported model.
+
+    Example - Loading pretrain-only model
+        >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model
+        >>>
+        >>> # Load model using fairseq
+        >>> model_file = 'wav2vec_small.pt'
+        >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file])
+        >>> original = model[0]
+        >>> imported = import_fairseq_model(original)
+        >>>
+        >>> # Perform feature extraction
+        >>> waveform, _ = torchaudio.load('audio.wav')
+        >>> features, _ = imported.extract_features(waveform)
+        >>>
+        >>> # Compare result with the original model from fairseq
+        >>> reference = original.feature_extractor(waveform).transpose(1, 2)
+        >>> torch.testing.assert_allclose(features, reference)
+
+    Example - Fine-tuned model
+        >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model
+        >>>
+        >>> # Load model using fairseq
+        >>> model_file = 'wav2vec_small_960h.pt'
+        >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file])
+        >>> original = model[0]
+        >>> imported = import_fairseq_model(original.w2v_encoder)
+        >>>
+        >>> # Perform encoding
+        >>> waveform, _ = torchaudio.load('audio.wav')
+        >>> emission, _ = imported(waveform)
+        >>>
+        >>> # Compare result with the original model from fairseq
+        >>> mask = torch.zeros_like(waveform)
+        >>> reference = original(waveform, mask)['encoder_out'].transpose(0, 1)
+        >>> torch.testing.assert_allclose(emission, reference)
+    """
+    class_ = original.__class__.__name__
+    if class_ == "Wav2Vec2Model":
+        return _import_wav2vec2_pretraining(original)
+    if class_ == "Wav2VecEncoder":
+        return _import_wav2vec2_finetuning(original)
+    if class_ == "HubertModel":
+        return _import_hubert_pretraining(original)
+    if class_ == "HubertEncoder":
+        return _import_hubert_finetuning(original)
+    raise ValueError(f"Expected an instance of `Wav2Vec2Model` or `Wav2VecEncoder`. Found: {class_}")
+
+
+def _import_wav2vec2_finetuning(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original.w2v_model)
+    model = wav2vec2_model(**config, aux_num_out=original.proj.out_features)
+    model.load_state_dict(_convert_state_dict(original.state_dict()))
+    return model
+
+
+def _import_wav2vec2_pretraining(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original)
+    model = wav2vec2_model(**config, aux_num_out=None)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
+
+
+def _import_hubert_finetuning(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original.w2v_model)
+    model = wav2vec2_model(**config, aux_num_out=original.proj.out_features)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
+
+
+def _import_hubert_pretraining(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original)
+    model = wav2vec2_model(**config, aux_num_out=None)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
diff --git a/shared/package-files/a0/a033a3eb91a39f96747d4300aa3394965e529c71896cd6503dd27e6b685eede5/patcomp.py b/shared/package-files/a0/a033a3eb91a39f96747d4300aa3394965e529c71896cd6503dd27e6b685eede5/patcomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..f57f4954b26ce7f59c44ce7cd67bf966fd594cd5
--- /dev/null
+++ b/shared/package-files/a0/a033a3eb91a39f96747d4300aa3394965e529c71896cd6503dd27e6b685eede5/patcomp.py
@@ -0,0 +1,204 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Pattern compiler.
+
+The grammar is taken from PatternGrammar.txt.
+
+The compiler compiles a pattern to a pytree.*Pattern instance.
+"""
+
+__author__ = "Guido van Rossum <guido@python.org>"
+
+# Python imports
+import io
+
+# Fairly local imports
+from .pgen2 import driver, literals, token, tokenize, parse, grammar
+
+# Really local imports
+from . import pytree
+from . import pygram
+
+
+class PatternSyntaxError(Exception):
+    pass
+
+
+def tokenize_wrapper(input):
+    """Tokenizes a string suppressing significant whitespace."""
+    skip = {token.NEWLINE, token.INDENT, token.DEDENT}
+    tokens = tokenize.generate_tokens(io.StringIO(input).readline)
+    for quintuple in tokens:
+        type, value, start, end, line_text = quintuple
+        if type not in skip:
+            yield quintuple
+
+
+class PatternCompiler(object):
+
+    def __init__(self, grammar_file=None):
+        """Initializer.
+
+        Takes an optional alternative filename for the pattern grammar.
+        """
+        if grammar_file is None:
+            self.grammar = pygram.pattern_grammar
+            self.syms = pygram.pattern_symbols
+        else:
+            self.grammar = driver.load_grammar(grammar_file)
+            self.syms = pygram.Symbols(self.grammar)
+        self.pygrammar = pygram.python_grammar
+        self.pysyms = pygram.python_symbols
+        self.driver = driver.Driver(self.grammar, convert=pattern_convert)
+
+    def compile_pattern(self, input, debug=False, with_tree=False):
+        """Compiles a pattern string to a nested pytree.*Pattern object."""
+        tokens = tokenize_wrapper(input)
+        try:
+            root = self.driver.parse_tokens(tokens, debug=debug)
+        except parse.ParseError as e:
+            raise PatternSyntaxError(str(e)) from None
+        if with_tree:
+            return self.compile_node(root), root
+        else:
+            return self.compile_node(root)
+
+    def compile_node(self, node):
+        """Compiles a node, recursively.
+
+        This is one big switch on the node type.
+        """
+        # XXX Optimize certain Wildcard-containing-Wildcard patterns
+        # that can be merged
+        if node.type == self.syms.Matcher:
+            node = node.children[0] # Avoid unneeded recursion
+
+        if node.type == self.syms.Alternatives:
+            # Skip the odd children since they are just '|' tokens
+            alts = [self.compile_node(ch) for ch in node.children[::2]]
+            if len(alts) == 1:
+                return alts[0]
+            p = pytree.WildcardPattern([[a] for a in alts], min=1, max=1)
+            return p.optimize()
+
+        if node.type == self.syms.Alternative:
+            units = [self.compile_node(ch) for ch in node.children]
+            if len(units) == 1:
+                return units[0]
+            p = pytree.WildcardPattern([units], min=1, max=1)
+            return p.optimize()
+
+        if node.type == self.syms.NegatedUnit:
+            pattern = self.compile_basic(node.children[1:])
+            p = pytree.NegatedPattern(pattern)
+            return p.optimize()
+
+        assert node.type == self.syms.Unit
+
+        name = None
+        nodes = node.children
+        if len(nodes) >= 3 and nodes[1].type == token.EQUAL:
+            name = nodes[0].value
+            nodes = nodes[2:]
+        repeat = None
+        if len(nodes) >= 2 and nodes[-1].type == self.syms.Repeater:
+            repeat = nodes[-1]
+            nodes = nodes[:-1]
+
+        # Now we've reduced it to: STRING | NAME [Details] | (...) | [...]
+        pattern = self.compile_basic(nodes, repeat)
+
+        if repeat is not None:
+            assert repeat.type == self.syms.Repeater
+            children = repeat.children
+            child = children[0]
+            if child.type == token.STAR:
+                min = 0
+                max = pytree.HUGE
+            elif child.type == token.PLUS:
+                min = 1
+                max = pytree.HUGE
+            elif child.type == token.LBRACE:
+                assert children[-1].type == token.RBRACE
+                assert  len(children) in (3, 5)
+                min = max = self.get_int(children[1])
+                if len(children) == 5:
+                    max = self.get_int(children[3])
+            else:
+                assert False
+            if min != 1 or max != 1:
+                pattern = pattern.optimize()
+                pattern = pytree.WildcardPattern([[pattern]], min=min, max=max)
+
+        if name is not None:
+            pattern.name = name
+        return pattern.optimize()
+
+    def compile_basic(self, nodes, repeat=None):
+        # Compile STRING | NAME [Details] | (...) | [...]
+        assert len(nodes) >= 1
+        node = nodes[0]
+        if node.type == token.STRING:
+            value = str(literals.evalString(node.value))
+            return pytree.LeafPattern(_type_of_literal(value), value)
+        elif node.type == token.NAME:
+            value = node.value
+            if value.isupper():
+                if value not in TOKEN_MAP:
+                    raise PatternSyntaxError("Invalid token: %r" % value)
+                if nodes[1:]:
+                    raise PatternSyntaxError("Can't have details for token")
+                return pytree.LeafPattern(TOKEN_MAP[value])
+            else:
+                if value == "any":
+                    type = None
+                elif not value.startswith("_"):
+                    type = getattr(self.pysyms, value, None)
+                    if type is None:
+                        raise PatternSyntaxError("Invalid symbol: %r" % value)
+                if nodes[1:]: # Details present
+                    content = [self.compile_node(nodes[1].children[1])]
+                else:
+                    content = None
+                return pytree.NodePattern(type, content)
+        elif node.value == "(":
+            return self.compile_node(nodes[1])
+        elif node.value == "[":
+            assert repeat is None
+            subpattern = self.compile_node(nodes[1])
+            return pytree.WildcardPattern([[subpattern]], min=0, max=1)
+        assert False, node
+
+    def get_int(self, node):
+        assert node.type == token.NUMBER
+        return int(node.value)
+
+
+# Map named tokens to the type value for a LeafPattern
+TOKEN_MAP = {"NAME": token.NAME,
+             "STRING": token.STRING,
+             "NUMBER": token.NUMBER,
+             "TOKEN": None}
+
+
+def _type_of_literal(value):
+    if value[0].isalpha():
+        return token.NAME
+    elif value in grammar.opmap:
+        return grammar.opmap[value]
+    else:
+        return None
+
+
+def pattern_convert(grammar, raw_node_info):
+    """Converts raw node information to a Node or Leaf instance."""
+    type, value, context, children = raw_node_info
+    if children or type in grammar.number2symbol:
+        return pytree.Node(type, children, context=context)
+    else:
+        return pytree.Leaf(type, value, context=context)
+
+
+def compile_pattern(pattern):
+    return PatternCompiler().compile_pattern(pattern)
diff --git a/shared/package-files/a0/a0341ab500dc02d5da6a848519bc63404c15be1ba366dcda82795e48d287ecbf/committee.dat b/shared/package-files/a0/a0341ab500dc02d5da6a848519bc63404c15be1ba366dcda82795e48d287ecbf/committee.dat
new file mode 100644
index 0000000000000000000000000000000000000000..369bfabe2ac5130803d92c8f7198a556b9af043e
--- /dev/null
+++ b/shared/package-files/a0/a0341ab500dc02d5da6a848519bc63404c15be1ba366dcda82795e48d287ecbf/committee.dat
@@ -0,0 +1,21 @@
+          	      SIZE SUBS  STAFF PRESTIGE POLICY CONSTIT SERVICE BILLS103 BILLS104
+Appropriations   	58   13    109 	 1	  0      0       0	 9	  6
+Budget   		42   0     39 	 1	  0      0       0	 101	  23
+Rules   		13   2     25 	 1	  0      0       0	 54	  44
+Ways_and_Means  	39   5     23 	 1	  0      0       0	 542	  355
+Banking   		51   5     61    0        1      0       0	 101	  125
+Economic_Educ_Oppor    	43   5     69    0        1      0       0	 158	  131
+Commerce   		49   4     79    0        1      0       0	 196	  271
+International_Relations 44   3     68    0        1      0       0	 40	  63
+Government_Reform   	51   7     99    0        1      0       0	 72	  149
+Judiciary   		35   5     56    0        1      0       0	 168	  253
+Agriculture   		49   5     46    0        0      1       0	 60	  81
+National_Security   	55   7     48    0        0      1       0	 75	  89
+Resources   		44   5     58    0        0      1       0	 98	  142
+TransInfrastructure   	61   6     74    0        0      1       0	 69 	  155
+Science   		50   4     58    0        0      1       0	 25	  27
+Small_Business   	43   4     29    0        0      1       0	 9	  8
+Veterans_Affairs   	33   3     36    0        0      1       0	 41	  28
+House_Oversight   	12   0     24    0        0      0       1	 233	  68
+Stds_of_Conduct   	10   0     9     0        0      0       1	 0	  1
+Intelligence   		16   2     24    0        0      0       1	 2	  4
diff --git a/shared/package-files/a0/a049a1f526b36e2f379a542b8a82ce9178ee686cb0b92c5f01aaf630b7d35248/argwhere_compositeimplicitautograd_dispatch.h b/shared/package-files/a0/a049a1f526b36e2f379a542b8a82ce9178ee686cb0b92c5f01aaf630b7d35248/argwhere_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..30dfe3e6224d1e596e2b7c6a5111f183df0d028b
--- /dev/null
+++ b/shared/package-files/a0/a049a1f526b36e2f379a542b8a82ce9178ee686cb0b92c5f01aaf630b7d35248/argwhere_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor argwhere(const at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a04f0f9ffef4df396b10e8b59db4e9d76986719b0d8504095afc74cffec35908/entry_points.py b/shared/package-files/a0/a04f0f9ffef4df396b10e8b59db4e9d76986719b0d8504095afc74cffec35908/entry_points.py
new file mode 100644
index 0000000000000000000000000000000000000000..82a010da423eaab3a3a2ee49e815daa6a2db57ff
--- /dev/null
+++ b/shared/package-files/a0/a04f0f9ffef4df396b10e8b59db4e9d76986719b0d8504095afc74cffec35908/entry_points.py
@@ -0,0 +1,37 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Copyright the Hypothesis Authors.
+# Individual contributors are listed in AUTHORS.rst and the git log.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+
+"""Run all functions registered for the "hypothesis" entry point.
+
+This can be used with `st.register_type_strategy` to register strategies for your
+custom types, running the relevant code when *hypothesis* is imported instead of
+your package.
+"""
+
+import importlib.metadata
+import os
+
+
+def get_entry_points():
+    try:
+        eps = importlib.metadata.entry_points(group="hypothesis")
+    except TypeError:  # pragma: no cover
+        # Load-time selection requires Python >= 3.10.  See also
+        # https://importlib-metadata.readthedocs.io/en/latest/using.html
+        eps = importlib.metadata.entry_points().get("hypothesis", [])
+    yield from eps
+
+
+def run():
+    if not os.environ.get("HYPOTHESIS_NO_PLUGINS"):
+        for entry in get_entry_points():  # pragma: no cover
+            hook = entry.load()
+            if callable(hook):
+                hook()
diff --git a/shared/package-files/a0/a05774d4db7d6ded41374f68b1b678c5f672d1e8454cddea9ebe7841aa4b4e63/_lof.py b/shared/package-files/a0/a05774d4db7d6ded41374f68b1b678c5f672d1e8454cddea9ebe7841aa4b4e63/_lof.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9f00be42570e2841e5445b5fd68e1dec5413c6a
--- /dev/null
+++ b/shared/package-files/a0/a05774d4db7d6ded41374f68b1b678c5f672d1e8454cddea9ebe7841aa4b4e63/_lof.py
@@ -0,0 +1,518 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Real
+
+import numpy as np
+
+from ..base import OutlierMixin, _fit_context
+from ..utils import check_array
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.metaestimators import available_if
+from ..utils.validation import check_is_fitted
+from ._base import KNeighborsMixin, NeighborsBase
+
+__all__ = ["LocalOutlierFactor"]
+
+
+class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
+    """Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
+
+    The anomaly score of each sample is called the Local Outlier Factor.
+    It measures the local deviation of the density of a given sample with respect
+    to its neighbors.
+    It is local in that the anomaly score depends on how isolated the object
+    is with respect to the surrounding neighborhood.
+    More precisely, locality is given by k-nearest neighbors, whose distance
+    is used to estimate the local density.
+    By comparing the local density of a sample to the local densities of its
+    neighbors, one can identify samples that have a substantially lower density
+    than their neighbors. These are considered outliers.
+
+    .. versionadded:: 0.19
+
+    Parameters
+    ----------
+    n_neighbors : int, default=20
+        Number of neighbors to use by default for :meth:`kneighbors` queries.
+        If n_neighbors is larger than the number of samples provided,
+        all samples will be used.
+
+    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
+        Algorithm used to compute the nearest neighbors:
+
+        - 'ball_tree' will use :class:`BallTree`
+        - 'kd_tree' will use :class:`KDTree`
+        - 'brute' will use a brute-force search.
+        - 'auto' will attempt to decide the most appropriate algorithm
+          based on the values passed to :meth:`fit` method.
+
+        Note: fitting on sparse input will override the setting of
+        this parameter, using brute force.
+
+    leaf_size : int, default=30
+        Leaf is size passed to :class:`BallTree` or :class:`KDTree`. This can
+        affect the speed of the construction and query, as well as the memory
+        required to store the tree. The optimal value depends on the
+        nature of the problem.
+
+    metric : str or callable, default='minkowski'
+        Metric to use for distance computation. Default is "minkowski", which
+        results in the standard Euclidean distance when p = 2. See the
+        documentation of `scipy.spatial.distance
+        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+        the metrics listed in
+        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
+        values.
+
+        If metric is "precomputed", X is assumed to be a distance matrix and
+        must be square during fit. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors.
+
+        If metric is a callable function, it takes two arrays representing 1D
+        vectors as inputs and must return one value indicating the distance
+        between those vectors. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
+
+    p : float, default=2
+        Parameter for the Minkowski metric from
+        :func:`sklearn.metrics.pairwise_distances`. When p = 1, this
+        is equivalent to using manhattan_distance (l1), and euclidean_distance
+        (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
+
+    metric_params : dict, default=None
+        Additional keyword arguments for the metric function.
+
+    contamination : 'auto' or float, default='auto'
+        The amount of contamination of the data set, i.e. the proportion
+        of outliers in the data set. When fitting this is used to define the
+        threshold on the scores of the samples.
+
+        - if 'auto', the threshold is determined as in the
+          original paper,
+        - if a float, the contamination should be in the range (0, 0.5].
+
+        .. versionchanged:: 0.22
+           The default value of ``contamination`` changed from 0.1
+           to ``'auto'``.
+
+    novelty : bool, default=False
+        By default, LocalOutlierFactor is only meant to be used for outlier
+        detection (novelty=False). Set novelty to True if you want to use
+        LocalOutlierFactor for novelty detection. In this case be aware that
+        you should only use predict, decision_function and score_samples
+        on new unseen data and not on the training set; and note that the
+        results obtained this way may differ from the standard LOF results.
+
+        .. versionadded:: 0.20
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run for neighbors search.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    Attributes
+    ----------
+    negative_outlier_factor_ : ndarray of shape (n_samples,)
+        The opposite LOF of the training samples. The higher, the more normal.
+        Inliers tend to have a LOF score close to 1
+        (``negative_outlier_factor_`` close to -1), while outliers tend to have
+        a larger LOF score.
+
+        The local outlier factor (LOF) of a sample captures its
+        supposed 'degree of abnormality'.
+        It is the average of the ratio of the local reachability density of
+        a sample and those of its k-nearest neighbors.
+
+    n_neighbors_ : int
+        The actual number of neighbors used for :meth:`kneighbors` queries.
+
+    offset_ : float
+        Offset used to obtain binary labels from the raw scores.
+        Observations having a negative_outlier_factor smaller than `offset_`
+        are detected as abnormal.
+        The offset is set to -1.5 (inliers score around -1), except when a
+        contamination parameter different than "auto" is provided. In that
+        case, the offset is defined in such a way we obtain the expected
+        number of outliers in training.
+
+        .. versionadded:: 0.20
+
+    effective_metric_ : str
+        The effective metric used for the distance computation.
+
+    effective_metric_params_ : dict
+        The effective additional keyword arguments for the metric function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_samples_fit_ : int
+        It is the number of samples in the fitted data.
+
+    See Also
+    --------
+    sklearn.svm.OneClassSVM: Unsupervised Outlier Detection using
+        Support Vector Machine.
+
+    References
+    ----------
+    .. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May).
+           LOF: identifying density-based local outliers. In ACM sigmod record.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.neighbors import LocalOutlierFactor
+    >>> X = [[-1.1], [0.2], [101.1], [0.3]]
+    >>> clf = LocalOutlierFactor(n_neighbors=2)
+    >>> clf.fit_predict(X)
+    array([ 1,  1, -1,  1])
+    >>> clf.negative_outlier_factor_
+    array([ -0.9821,  -1.0370, -73.3697,  -0.9821])
+    """
+
+    _parameter_constraints: dict = {
+        **NeighborsBase._parameter_constraints,
+        "contamination": [
+            StrOptions({"auto"}),
+            Interval(Real, 0, 0.5, closed="right"),
+        ],
+        "novelty": ["boolean"],
+    }
+    _parameter_constraints.pop("radius")
+
+    def __init__(
+        self,
+        n_neighbors=20,
+        *,
+        algorithm="auto",
+        leaf_size=30,
+        metric="minkowski",
+        p=2,
+        metric_params=None,
+        contamination="auto",
+        novelty=False,
+        n_jobs=None,
+    ):
+        super().__init__(
+            n_neighbors=n_neighbors,
+            algorithm=algorithm,
+            leaf_size=leaf_size,
+            metric=metric,
+            p=p,
+            metric_params=metric_params,
+            n_jobs=n_jobs,
+        )
+        self.contamination = contamination
+        self.novelty = novelty
+
+    def _check_novelty_fit_predict(self):
+        if self.novelty:
+            msg = (
+                "fit_predict is not available when novelty=True. Use "
+                "novelty=False if you want to predict on the training set."
+            )
+            raise AttributeError(msg)
+        return True
+
+    @available_if(_check_novelty_fit_predict)
+    def fit_predict(self, X, y=None):
+        """Fit the model to the training set X and return the labels.
+
+        **Not available for novelty detection (when novelty is set to True).**
+        Label is 1 for an inlier and -1 for an outlier according to the LOF
+        score and the contamination parameter.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), default=None
+            The query sample or samples to compute the Local Outlier Factor
+            w.r.t. the training samples.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        is_inlier : ndarray of shape (n_samples,)
+            Returns -1 for anomalies/outliers and 1 for inliers.
+        """
+
+        # As fit_predict would be different from fit.predict, fit_predict is
+        # only available for outlier detection (novelty=False)
+
+        return self.fit(X)._predict()
+
+    @_fit_context(
+        # LocalOutlierFactor.metric is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y=None):
+        """Fit the local outlier factor detector from the training dataset.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
+                (n_samples, n_samples) if metric='precomputed'
+            Training data.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : LocalOutlierFactor
+            The fitted local outlier factor detector.
+        """
+        self._fit(X)
+
+        n_samples = self.n_samples_fit_
+        if self.n_neighbors > n_samples:
+            warnings.warn(
+                "n_neighbors (%s) is greater than the "
+                "total number of samples (%s). n_neighbors "
+                "will be set to (n_samples - 1) for estimation."
+                % (self.n_neighbors, n_samples)
+            )
+        self.n_neighbors_ = max(1, min(self.n_neighbors, n_samples - 1))
+
+        self._distances_fit_X_, _neighbors_indices_fit_X_ = self.kneighbors(
+            n_neighbors=self.n_neighbors_
+        )
+
+        if self._fit_X.dtype == np.float32:
+            self._distances_fit_X_ = self._distances_fit_X_.astype(
+                self._fit_X.dtype,
+                copy=False,
+            )
+
+        self._lrd = self._local_reachability_density(
+            self._distances_fit_X_, _neighbors_indices_fit_X_
+        )
+
+        # Compute lof score over training samples to define offset_:
+        lrd_ratios_array = (
+            self._lrd[_neighbors_indices_fit_X_] / self._lrd[:, np.newaxis]
+        )
+
+        self.negative_outlier_factor_ = -np.mean(lrd_ratios_array, axis=1)
+
+        if self.contamination == "auto":
+            # inliers score around -1 (the higher, the less abnormal).
+            self.offset_ = -1.5
+        else:
+            self.offset_ = np.percentile(
+                self.negative_outlier_factor_, 100.0 * self.contamination
+            )
+
+        # Verify if negative_outlier_factor_ values are within acceptable range.
+        # Novelty must also be false to detect outliers
+        if np.min(self.negative_outlier_factor_) < -1e7 and not self.novelty:
+            warnings.warn(
+                "Duplicate values are leading to incorrect results. "
+                "Increase the number of neighbors for more accurate results."
+            )
+
+        return self
+
+    def _check_novelty_predict(self):
+        if not self.novelty:
+            msg = (
+                "predict is not available when novelty=False, use "
+                "fit_predict if you want to predict on training data. Use "
+                "novelty=True if you want to use LOF for novelty detection "
+                "and predict on new unseen data."
+            )
+            raise AttributeError(msg)
+        return True
+
+    @available_if(_check_novelty_predict)
+    def predict(self, X=None):
+        """Predict the labels (1 inlier, -1 outlier) of X according to LOF.
+
+        **Only available for novelty detection (when novelty is set to True).**
+        This method allows to generalize prediction to *new observations* (not
+        in the training set). Note that the result of ``clf.fit(X)`` then
+        ``clf.predict(X)`` with ``novelty=True`` may differ from the result
+        obtained by ``clf.fit_predict(X)`` with ``novelty=False``.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The query sample or samples to compute the Local Outlier Factor
+            w.r.t. the training samples.
+
+        Returns
+        -------
+        is_inlier : ndarray of shape (n_samples,)
+            Returns -1 for anomalies/outliers and +1 for inliers.
+        """
+        return self._predict(X)
+
+    def _predict(self, X=None):
+        """Predict the labels (1 inlier, -1 outlier) of X according to LOF.
+
+        If X is None, returns the same as fit_predict(X_train).
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), default=None
+            The query sample or samples to compute the Local Outlier Factor
+            w.r.t. the training samples. If None, makes prediction on the
+            training data without considering them as their own neighbors.
+
+        Returns
+        -------
+        is_inlier : ndarray of shape (n_samples,)
+            Returns -1 for anomalies/outliers and +1 for inliers.
+        """
+        check_is_fitted(self)
+
+        if X is not None:
+            shifted_opposite_lof_scores = self.decision_function(X)
+            is_inlier = np.ones(shifted_opposite_lof_scores.shape[0], dtype=int)
+            is_inlier[shifted_opposite_lof_scores < 0] = -1
+        else:
+            is_inlier = np.ones(self.n_samples_fit_, dtype=int)
+            is_inlier[self.negative_outlier_factor_ < self.offset_] = -1
+
+        return is_inlier
+
+    def _check_novelty_decision_function(self):
+        if not self.novelty:
+            msg = (
+                "decision_function is not available when novelty=False. "
+                "Use novelty=True if you want to use LOF for novelty "
+                "detection and compute decision_function for new unseen "
+                "data. Note that the opposite LOF of the training samples "
+                "is always available by considering the "
+                "negative_outlier_factor_ attribute."
+            )
+            raise AttributeError(msg)
+        return True
+
+    @available_if(_check_novelty_decision_function)
+    def decision_function(self, X):
+        """Shifted opposite of the Local Outlier Factor of X.
+
+        Bigger is better, i.e. large values correspond to inliers.
+
+        **Only available for novelty detection (when novelty is set to True).**
+        The shift offset allows a zero threshold for being an outlier.
+        The argument X is supposed to contain *new data*: if X contains a
+        point from training, it considers the later in its own neighborhood.
+        Also, the samples in X are not considered in the neighborhood of any
+        point.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The query sample or samples to compute the Local Outlier Factor
+            w.r.t. the training samples.
+
+        Returns
+        -------
+        shifted_opposite_lof_scores : ndarray of shape (n_samples,)
+            The shifted opposite of the Local Outlier Factor of each input
+            samples. The lower, the more abnormal. Negative scores represent
+            outliers, positive scores represent inliers.
+        """
+        return self.score_samples(X) - self.offset_
+
+    def _check_novelty_score_samples(self):
+        if not self.novelty:
+            msg = (
+                "score_samples is not available when novelty=False. The "
+                "scores of the training samples are always available "
+                "through the negative_outlier_factor_ attribute. Use "
+                "novelty=True if you want to use LOF for novelty detection "
+                "and compute score_samples for new unseen data."
+            )
+            raise AttributeError(msg)
+        return True
+
+    @available_if(_check_novelty_score_samples)
+    def score_samples(self, X):
+        """Opposite of the Local Outlier Factor of X.
+
+        It is the opposite as bigger is better, i.e. large values correspond
+        to inliers.
+
+        **Only available for novelty detection (when novelty is set to True).**
+        The argument X is supposed to contain *new data*: if X contains a
+        point from training, it considers the later in its own neighborhood.
+        Also, the samples in X are not considered in the neighborhood of any
+        point. Because of this, the scores obtained via ``score_samples`` may
+        differ from the standard LOF scores.
+        The standard LOF scores for the training data is available via the
+        ``negative_outlier_factor_`` attribute.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The query sample or samples to compute the Local Outlier Factor
+            w.r.t. the training samples.
+
+        Returns
+        -------
+        opposite_lof_scores : ndarray of shape (n_samples,)
+            The opposite of the Local Outlier Factor of each input samples.
+            The lower, the more abnormal.
+        """
+        check_is_fitted(self)
+        X = check_array(X, accept_sparse="csr")
+
+        distances_X, neighbors_indices_X = self.kneighbors(
+            X, n_neighbors=self.n_neighbors_
+        )
+
+        if X.dtype == np.float32:
+            distances_X = distances_X.astype(X.dtype, copy=False)
+
+        X_lrd = self._local_reachability_density(
+            distances_X,
+            neighbors_indices_X,
+        )
+
+        lrd_ratios_array = self._lrd[neighbors_indices_X] / X_lrd[:, np.newaxis]
+
+        # as bigger is better:
+        return -np.mean(lrd_ratios_array, axis=1)
+
+    def _local_reachability_density(self, distances_X, neighbors_indices):
+        """The local reachability density (LRD)
+
+        The LRD of a sample is the inverse of the average reachability
+        distance of its k-nearest neighbors.
+
+        Parameters
+        ----------
+        distances_X : ndarray of shape (n_queries, self.n_neighbors)
+            Distances to the neighbors (in the training samples `self._fit_X`)
+            of each query point to compute the LRD.
+
+        neighbors_indices : ndarray of shape (n_queries, self.n_neighbors)
+            Neighbors indices (of each query point) among training samples
+            self._fit_X.
+
+        Returns
+        -------
+        local_reachability_density : ndarray of shape (n_queries,)
+            The local reachability density of each sample.
+        """
+        dist_k = self._distances_fit_X_[neighbors_indices, self.n_neighbors_ - 1]
+        reach_dist_array = np.maximum(distances_X, dist_k)
+
+        # 1e-10 to avoid `nan' when nb of duplicates > n_neighbors_:
+        return 1.0 / (np.mean(reach_dist_array, axis=1) + 1e-10)
diff --git a/shared/package-files/a0/a05f749af5adba58bd48867877521542eee40388761785072cf53292bbce2129/test_melt.py b/shared/package-files/a0/a05f749af5adba58bd48867877521542eee40388761785072cf53292bbce2129/test_melt.py
new file mode 100644
index 0000000000000000000000000000000000000000..72fd72df60761406c7b9fad702d149baba1004df
--- /dev/null
+++ b/shared/package-files/a0/a05f749af5adba58bd48867877521542eee40388761785072cf53292bbce2129/test_melt.py
@@ -0,0 +1,1258 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    date_range,
+    lreshape,
+    melt,
+    wide_to_long,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df():
+    res = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        columns=Index(list("ABCD")),
+        index=date_range("2000-01-01", periods=10, freq="B"),
+    )
+    res["id1"] = (res["A"] > 0).astype(np.int64)
+    res["id2"] = (res["B"] > 0).astype(np.int64)
+    return res
+
+
+@pytest.fixture
+def df1():
+    res = DataFrame(
+        [
+            [1.067683, -1.110463, 0.20867],
+            [-1.321405, 0.368915, -1.055342],
+            [-0.807333, 0.08298, -0.873361],
+        ]
+    )
+    res.columns = [list("ABC"), list("abc")]
+    res.columns.names = ["CAP", "low"]
+    return res
+
+
+@pytest.fixture
+def var_name():
+    return "var"
+
+
+@pytest.fixture
+def value_name():
+    return "val"
+
+
+class TestMelt:
+    def test_top_level_method(self, df):
+        result = melt(df)
+        assert result.columns.tolist() == ["variable", "value"]
+
+    def test_method_signatures(self, df, df1, var_name, value_name):
+        tm.assert_frame_equal(df.melt(), melt(df))
+
+        tm.assert_frame_equal(
+            df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"]),
+            melt(df, id_vars=["id1", "id2"], value_vars=["A", "B"]),
+        )
+
+        tm.assert_frame_equal(
+            df.melt(var_name=var_name, value_name=value_name),
+            melt(df, var_name=var_name, value_name=value_name),
+        )
+
+        tm.assert_frame_equal(df1.melt(col_level=0), melt(df1, col_level=0))
+
+    def test_default_col_names(self, df):
+        result = df.melt()
+        assert result.columns.tolist() == ["variable", "value"]
+
+        result1 = df.melt(id_vars=["id1"])
+        assert result1.columns.tolist() == ["id1", "variable", "value"]
+
+        result2 = df.melt(id_vars=["id1", "id2"])
+        assert result2.columns.tolist() == ["id1", "id2", "variable", "value"]
+
+    def test_value_vars(self, df):
+        result3 = df.melt(id_vars=["id1", "id2"], value_vars="A")
+        assert len(result3) == 10
+
+        result4 = df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"])
+        expected4 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", "value"],
+        )
+        tm.assert_frame_equal(result4, expected4)
+
+    @pytest.mark.parametrize("type_", (tuple, list, np.array))
+    def test_value_vars_types(self, type_, df):
+        # GH 15348
+        expected = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", "value"],
+        )
+        result = df.melt(id_vars=["id1", "id2"], value_vars=type_(("A", "B")))
+        tm.assert_frame_equal(result, expected)
+
+    def test_vars_work_with_multiindex(self, df1):
+        expected = DataFrame(
+            {
+                ("A", "a"): df1[("A", "a")],
+                "CAP": ["B"] * len(df1),
+                "low": ["b"] * len(df1),
+                "value": df1[("B", "b")],
+            },
+            columns=[("A", "a"), "CAP", "low", "value"],
+        )
+
+        result = df1.melt(id_vars=[("A", "a")], value_vars=[("B", "b")])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "id_vars, value_vars, col_level, expected",
+        [
+            (
+                ["A"],
+                ["B"],
+                0,
+                DataFrame(
+                    {
+                        "A": {0: 1.067683, 1: -1.321405, 2: -0.807333},
+                        "CAP": {0: "B", 1: "B", 2: "B"},
+                        "value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
+                    }
+                ),
+            ),
+            (
+                ["a"],
+                ["b"],
+                1,
+                DataFrame(
+                    {
+                        "a": {0: 1.067683, 1: -1.321405, 2: -0.807333},
+                        "low": {0: "b", 1: "b", 2: "b"},
+                        "value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
+                    }
+                ),
+            ),
+        ],
+    )
+    def test_single_vars_work_with_multiindex(
+        self, id_vars, value_vars, col_level, expected, df1
+    ):
+        result = df1.melt(id_vars, value_vars, col_level=col_level)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "id_vars, value_vars",
+        [
+            [("A", "a"), [("B", "b")]],
+            [[("A", "a")], ("B", "b")],
+            [("A", "a"), ("B", "b")],
+        ],
+    )
+    def test_tuple_vars_fail_with_multiindex(self, id_vars, value_vars, df1):
+        # melt should fail with an informative error message if
+        # the columns have a MultiIndex and a tuple is passed
+        # for id_vars or value_vars.
+        msg = r"(id|value)_vars must be a list of tuples when columns are a MultiIndex"
+        with pytest.raises(ValueError, match=msg):
+            df1.melt(id_vars=id_vars, value_vars=value_vars)
+
+    def test_custom_var_name(self, df, var_name):
+        result5 = df.melt(var_name=var_name)
+        assert result5.columns.tolist() == ["var", "value"]
+
+        result6 = df.melt(id_vars=["id1"], var_name=var_name)
+        assert result6.columns.tolist() == ["id1", "var", "value"]
+
+        result7 = df.melt(id_vars=["id1", "id2"], var_name=var_name)
+        assert result7.columns.tolist() == ["id1", "id2", "var", "value"]
+
+        result8 = df.melt(id_vars=["id1", "id2"], value_vars="A", var_name=var_name)
+        assert result8.columns.tolist() == ["id1", "id2", "var", "value"]
+
+        result9 = df.melt(
+            id_vars=["id1", "id2"], value_vars=["A", "B"], var_name=var_name
+        )
+        expected9 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                var_name: ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", var_name, "value"],
+        )
+        tm.assert_frame_equal(result9, expected9)
+
+    def test_custom_value_name(self, df, value_name):
+        result10 = df.melt(value_name=value_name)
+        assert result10.columns.tolist() == ["variable", "val"]
+
+        result11 = df.melt(id_vars=["id1"], value_name=value_name)
+        assert result11.columns.tolist() == ["id1", "variable", "val"]
+
+        result12 = df.melt(id_vars=["id1", "id2"], value_name=value_name)
+        assert result12.columns.tolist() == ["id1", "id2", "variable", "val"]
+
+        result13 = df.melt(
+            id_vars=["id1", "id2"], value_vars="A", value_name=value_name
+        )
+        assert result13.columns.tolist() == ["id1", "id2", "variable", "val"]
+
+        result14 = df.melt(
+            id_vars=["id1", "id2"], value_vars=["A", "B"], value_name=value_name
+        )
+        expected14 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                value_name: (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", value_name],
+        )
+        tm.assert_frame_equal(result14, expected14)
+
+    def test_custom_var_and_value_name(self, df, value_name, var_name):
+        result15 = df.melt(var_name=var_name, value_name=value_name)
+        assert result15.columns.tolist() == ["var", "val"]
+
+        result16 = df.melt(id_vars=["id1"], var_name=var_name, value_name=value_name)
+        assert result16.columns.tolist() == ["id1", "var", "val"]
+
+        result17 = df.melt(
+            id_vars=["id1", "id2"], var_name=var_name, value_name=value_name
+        )
+        assert result17.columns.tolist() == ["id1", "id2", "var", "val"]
+
+        result18 = df.melt(
+            id_vars=["id1", "id2"],
+            value_vars="A",
+            var_name=var_name,
+            value_name=value_name,
+        )
+        assert result18.columns.tolist() == ["id1", "id2", "var", "val"]
+
+        result19 = df.melt(
+            id_vars=["id1", "id2"],
+            value_vars=["A", "B"],
+            var_name=var_name,
+            value_name=value_name,
+        )
+        expected19 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                var_name: ["A"] * 10 + ["B"] * 10,
+                value_name: (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", var_name, value_name],
+        )
+        tm.assert_frame_equal(result19, expected19)
+
+        df20 = df.copy()
+        df20.columns.name = "foo"
+        result20 = df20.melt()
+        assert result20.columns.tolist() == ["foo", "value"]
+
+    @pytest.mark.parametrize("col_level", [0, "CAP"])
+    def test_col_level(self, col_level, df1):
+        res = df1.melt(col_level=col_level)
+        assert res.columns.tolist() == ["CAP", "value"]
+
+    def test_multiindex(self, df1):
+        res = df1.melt()
+        assert res.columns.tolist() == ["CAP", "low", "value"]
+
+    @pytest.mark.parametrize(
+        "col",
+        [
+            pd.Series(date_range("2010", periods=5, tz="US/Pacific")),
+            pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
+            pd.Series([0, 1, 0, 0, 0]),
+        ],
+    )
+    def test_pandas_dtypes(self, col):
+        # GH 15785
+        df = DataFrame(
+            {"klass": range(5), "col": col, "attr1": [1, 0, 0, 0, 0], "attr2": col}
+        )
+        expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col], ignore_index=True)
+        result = melt(
+            df, id_vars=["klass", "col"], var_name="attribute", value_name="value"
+        )
+        expected = DataFrame(
+            {
+                0: list(range(5)) * 2,
+                1: pd.concat([col] * 2, ignore_index=True),
+                2: ["attr1"] * 5 + ["attr2"] * 5,
+                3: expected_value,
+            }
+        )
+        expected.columns = ["klass", "col", "attribute", "value"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_preserve_category(self):
+        # GH 15853
+        data = DataFrame({"A": [1, 2], "B": pd.Categorical(["X", "Y"])})
+        result = melt(data, ["B"], ["A"])
+        expected = DataFrame(
+            {"B": pd.Categorical(["X", "Y"]), "variable": ["A", "A"], "value": [1, 2]}
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_missing_columns_raises(self):
+        # GH-23575
+        # This test is to ensure that pandas raises an error if melting is
+        # attempted with column names absent from the dataframe
+
+        # Generate data
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)), columns=list("abcd")
+        )
+
+        # Try to melt with missing `value_vars` column name
+        msg = "The following id_vars or value_vars are not present in the DataFrame:"
+        with pytest.raises(KeyError, match=msg):
+            df.melt(["a", "b"], ["C", "d"])
+
+        # Try to melt with missing `id_vars` column name
+        with pytest.raises(KeyError, match=msg):
+            df.melt(["A", "b"], ["c", "d"])
+
+        # Multiple missing
+        with pytest.raises(
+            KeyError,
+            match=msg,
+        ):
+            df.melt(["a", "b", "not_here", "or_there"], ["c", "d"])
+
+        # Multiindex melt fails if column is missing from multilevel melt
+        multi = df.copy()
+        multi.columns = [list("ABCD"), list("abcd")]
+        with pytest.raises(KeyError, match=msg):
+            multi.melt([("E", "a")], [("B", "b")])
+        # Multiindex fails if column is missing from single level melt
+        with pytest.raises(KeyError, match=msg):
+            multi.melt(["A"], ["F"], col_level=0)
+
+    def test_melt_mixed_int_str_id_vars(self):
+        # GH 29718
+        df = DataFrame({0: ["foo"], "a": ["bar"], "b": [1], "d": [2]})
+        result = melt(df, id_vars=[0, "a"], value_vars=["b", "d"])
+        expected = DataFrame(
+            {0: ["foo"] * 2, "a": ["bar"] * 2, "variable": list("bd"), "value": [1, 2]}
+        )
+        # the df's columns are mixed type and thus object -> preserves object dtype
+        expected["variable"] = expected["variable"].astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_mixed_int_str_value_vars(self):
+        # GH 29718
+        df = DataFrame({0: ["foo"], "a": ["bar"]})
+        result = melt(df, value_vars=[0, "a"])
+        expected = DataFrame({"variable": [0, "a"], "value": ["foo", "bar"]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_index(self):
+        # GH 17440
+        df = DataFrame({"foo": [0], "bar": [1]}, index=["first"])
+        result = melt(df, ignore_index=False)
+        expected = DataFrame(
+            {"variable": ["foo", "bar"], "value": [0, 1]}, index=["first", "first"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_multiindex(self):
+        # GH 17440
+        index = pd.MultiIndex.from_tuples(
+            [("first", "second"), ("first", "third")], names=["baz", "foobar"]
+        )
+        df = DataFrame({"foo": [0, 1], "bar": [2, 3]}, index=index)
+        result = melt(df, ignore_index=False)
+
+        expected_index = pd.MultiIndex.from_tuples(
+            [("first", "second"), ("first", "third")] * 2, names=["baz", "foobar"]
+        )
+        expected = DataFrame(
+            {"variable": ["foo"] * 2 + ["bar"] * 2, "value": [0, 1, 2, 3]},
+            index=expected_index,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_index_name_and_type(self):
+        # GH 17440
+        index = Index(["foo", "bar"], dtype="category", name="baz")
+        df = DataFrame({"x": [0, 1], "y": [2, 3]}, index=index)
+        result = melt(df, ignore_index=False)
+
+        expected_index = Index(["foo", "bar"] * 2, dtype="category", name="baz")
+        expected = DataFrame(
+            {"variable": ["x", "x", "y", "y"], "value": [0, 1, 2, 3]},
+            index=expected_index,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_with_duplicate_columns(self):
+        # GH#41951
+        df = DataFrame([["id", 2, 3]], columns=["a", "b", "b"])
+        result = df.melt(id_vars=["a"], value_vars=["b"])
+        expected = DataFrame(
+            [["id", "b", 2], ["id", "b", 3]], columns=["a", "variable", "value"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["Int8", "Int64"])
+    def test_melt_ea_dtype(self, dtype):
+        # GH#41570
+        df = DataFrame(
+            {
+                "a": pd.Series([1, 2], dtype="Int8"),
+                "b": pd.Series([3, 4], dtype=dtype),
+            }
+        )
+        result = df.melt()
+        expected = DataFrame(
+            {
+                "variable": ["a", "a", "b", "b"],
+                "value": pd.Series([1, 2, 3, 4], dtype=dtype),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_ea_columns(self):
+        # GH 54297
+        df = DataFrame(
+            {
+                "A": {0: "a", 1: "b", 2: "c"},
+                "B": {0: 1, 1: 3, 2: 5},
+                "C": {0: 2, 1: 4, 2: 6},
+            }
+        )
+        df.columns = df.columns.astype("string[python]")
+        result = df.melt(id_vars=["A"], value_vars=["B"])
+        expected = DataFrame(
+            {
+                "A": list("abc"),
+                "variable": pd.Series(["B"] * 3, dtype="string[python]"),
+                "value": [1, 3, 5],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_preserves_datetime(self):
+        df = DataFrame(
+            data=[
+                {
+                    "type": "A0",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/10", tz="Asia/Tokyo"),
+                },
+                {
+                    "type": "A1",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/11", tz="Asia/Tokyo"),
+                },
+            ],
+            index=["aaaa", "bbbb"],
+        )
+        result = df.melt(
+            id_vars=["type"],
+            value_vars=["start_date", "end_date"],
+            var_name="start/end",
+            value_name="date",
+        )
+        expected = DataFrame(
+            {
+                "type": {0: "A0", 1: "A1", 2: "A0", 3: "A1"},
+                "start/end": {
+                    0: "start_date",
+                    1: "start_date",
+                    2: "end_date",
+                    3: "end_date",
+                },
+                "date": {
+                    0: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    1: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    2: pd.Timestamp("2023-03-10 00:00:00+0900", tz="Asia/Tokyo"),
+                    3: pd.Timestamp("2023-03-11 00:00:00+0900", tz="Asia/Tokyo"),
+                },
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_allows_non_scalar_id_vars(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        result = df.melt(
+            id_vars="a",
+            var_name=0,
+            value_name=1,
+        )
+        expected = DataFrame({"a": [1, 2, 3], 0: ["b"] * 3, 1: [4, 5, 6]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_allows_non_string_var_name(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        result = df.melt(
+            id_vars=["a"],
+            var_name=0,
+            value_name=1,
+        )
+        expected = DataFrame({"a": [1, 2, 3], 0: ["b"] * 3, 1: [4, 5, 6]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_non_scalar_var_name_raises(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        with pytest.raises(ValueError, match=r".* must be a scalar."):
+            df.melt(id_vars=["a"], var_name=[1, 2])
+
+
+class TestLreshape:
+    def test_pairs(self):
+        data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [1766, 3301, 1454, 3139, 4133],
+            "id": [101, 102, 103, 104, 105],
+            "sex": ["Male", "Female", "Female", "Female", "Female"],
+            "visitdt1": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+            ],
+            "visitdt2": ["21jan2009", np.nan, "22jan2009", "31dec2008", "03feb2009"],
+            "visitdt3": ["05feb2009", np.nan, np.nan, "02jan2009", "15feb2009"],
+            "wt1": [1823, 3338, 1549, 3298, 4306],
+            "wt2": [2011.0, np.nan, 1892.0, 3338.0, 4575.0],
+            "wt3": [2293.0, np.nan, np.nan, 3377.0, 4805.0],
+        }
+
+        df = DataFrame(data)
+
+        spec = {
+            "visitdt": [f"visitdt{i:d}" for i in range(1, 4)],
+            "wt": [f"wt{i:d}" for i in range(1, 4)],
+        }
+        result = lreshape(df, spec)
+
+        exp_data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                1454,
+                3139,
+                4133,
+                1766,
+                3139,
+                4133,
+            ],
+            "id": [101, 102, 103, 104, 105, 101, 103, 104, 105, 101, 104, 105],
+            "sex": [
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+            ],
+            "visitdt": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+                "21jan2009",
+                "22jan2009",
+                "31dec2008",
+                "03feb2009",
+                "05feb2009",
+                "02jan2009",
+                "15feb2009",
+            ],
+            "wt": [
+                1823.0,
+                3338.0,
+                1549.0,
+                3298.0,
+                4306.0,
+                2011.0,
+                1892.0,
+                3338.0,
+                4575.0,
+                2293.0,
+                3377.0,
+                4805.0,
+            ],
+        }
+        exp = DataFrame(exp_data, columns=result.columns)
+        tm.assert_frame_equal(result, exp)
+
+        result = lreshape(df, spec, dropna=False)
+        exp_data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+            ],
+            "id": [
+                101,
+                102,
+                103,
+                104,
+                105,
+                101,
+                102,
+                103,
+                104,
+                105,
+                101,
+                102,
+                103,
+                104,
+                105,
+            ],
+            "sex": [
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+            ],
+            "visitdt": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+                "21jan2009",
+                np.nan,
+                "22jan2009",
+                "31dec2008",
+                "03feb2009",
+                "05feb2009",
+                np.nan,
+                np.nan,
+                "02jan2009",
+                "15feb2009",
+            ],
+            "wt": [
+                1823.0,
+                3338.0,
+                1549.0,
+                3298.0,
+                4306.0,
+                2011.0,
+                np.nan,
+                1892.0,
+                3338.0,
+                4575.0,
+                2293.0,
+                np.nan,
+                np.nan,
+                3377.0,
+                4805.0,
+            ],
+        }
+        exp = DataFrame(exp_data, columns=result.columns)
+        tm.assert_frame_equal(result, exp)
+
+        spec = {
+            "visitdt": [f"visitdt{i:d}" for i in range(1, 3)],
+            "wt": [f"wt{i:d}" for i in range(1, 4)],
+        }
+        msg = "All column lists must be same length"
+        with pytest.raises(ValueError, match=msg):
+            lreshape(df, spec)
+
+
+class TestWideToLong:
+    def test_simple(self):
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A1970": {0: "a", 1: "b", 2: "c"},
+                "A1980": {0: "d", 1: "e", 2: "f"},
+                "B1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A": ["a", "b", "c", "d", "e", "f"],
+            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_stubs(self):
+        # GH9204 wide_to_long call should not modify 'stubs' list
+        df = DataFrame([[0, 1, 2, 3, 8], [4, 5, 6, 7, 9]])
+        df.columns = ["id", "inc1", "inc2", "edu1", "edu2"]
+        stubs = ["inc", "edu"]
+
+        wide_to_long(df, stubs, i="id", j="age")
+
+        assert stubs == ["inc", "edu"]
+
+    def test_separating_character(self):
+        # GH14779
+
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A.1970": {0: "a", 1: "b", 2: "c"},
+                "A.1980": {0: "d", 1: "e", 2: "f"},
+                "B.1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B.1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A": ["a", "b", "c", "d", "e", "f"],
+            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=".")
+        tm.assert_frame_equal(result, expected)
+
+    def test_escapable_characters(self):
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A(quarterly)1970": {0: "a", 1: "b", 2: "c"},
+                "A(quarterly)1980": {0: "d", 1: "e", 2: "f"},
+                "B(quarterly)1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B(quarterly)1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A(quarterly)": ["a", "b", "c", "d", "e", "f"],
+            "B(quarterly)": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[
+            ["X", "A(quarterly)", "B(quarterly)"]
+        ]
+        result = wide_to_long(df, ["A(quarterly)", "B(quarterly)"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unbalanced(self):
+        # test that we can have a varying amount of time variables
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": ["X1", "X2", "X1", "X2"],
+            "A": [1.0, 2.0, 3.0, 4.0],
+            "B": [5.0, 6.0, np.nan, np.nan],
+            "id": [0, 1, 0, 1],
+            "year": [2010, 2010, 2011, 2011],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_character_overlap(self):
+        # Test we handle overlapping characters in both id_vars and value_vars
+        df = DataFrame(
+            {
+                "A11": ["a11", "a22", "a33"],
+                "A12": ["a21", "a22", "a23"],
+                "B11": ["b11", "b12", "b13"],
+                "B12": ["b21", "b22", "b23"],
+                "BB11": [1, 2, 3],
+                "BB12": [4, 5, 6],
+                "BBBX": [91, 92, 93],
+                "BBBZ": [91, 92, 93],
+            }
+        )
+        df["id"] = df.index
+        expected = DataFrame(
+            {
+                "BBBX": [91, 92, 93, 91, 92, 93],
+                "BBBZ": [91, 92, 93, 91, 92, 93],
+                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
+                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
+                "BB": [1, 2, 3, 4, 5, 6],
+                "id": [0, 1, 2, 0, 1, 2],
+                "year": [11, 11, 11, 12, 12, 12],
+            }
+        )
+        expected = expected.set_index(["id", "year"])[["BBBX", "BBBZ", "A", "B", "BB"]]
+        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_invalid_separator(self):
+        # if an invalid separator is supplied a empty data frame is returned
+        sep = "nope!"
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": "",
+            "A2010": [],
+            "A2011": [],
+            "B2010": [],
+            "id": [],
+            "year": [],
+            "A": [],
+            "B": [],
+        }
+        expected = DataFrame(exp_data).astype({"year": np.int64})
+        expected = expected.set_index(["id", "year"])[
+            ["X", "A2010", "A2011", "B2010", "A", "B"]
+        ]
+        expected.index = expected.index.set_levels([0, 1], level=0)
+        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=sep)
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_num_string_disambiguation(self):
+        # Test that we can disambiguate number value_vars from
+        # string value_vars
+        df = DataFrame(
+            {
+                "A11": ["a11", "a22", "a33"],
+                "A12": ["a21", "a22", "a23"],
+                "B11": ["b11", "b12", "b13"],
+                "B12": ["b21", "b22", "b23"],
+                "BB11": [1, 2, 3],
+                "BB12": [4, 5, 6],
+                "Arating": [91, 92, 93],
+                "Arating_old": [91, 92, 93],
+            }
+        )
+        df["id"] = df.index
+        expected = DataFrame(
+            {
+                "Arating": [91, 92, 93, 91, 92, 93],
+                "Arating_old": [91, 92, 93, 91, 92, 93],
+                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
+                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
+                "BB": [1, 2, 3, 4, 5, 6],
+                "id": [0, 1, 2, 0, 1, 2],
+                "year": [11, 11, 11, 12, 12, 12],
+            }
+        )
+        expected = expected.set_index(["id", "year"])[
+            ["Arating", "Arating_old", "A", "B", "BB"]
+        ]
+        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_invalid_suffixtype(self):
+        # If all stubs names end with a string, but a numeric suffix is
+        # assumed,  an empty data frame is returned
+        df = DataFrame(
+            {
+                "Aone": [1.0, 2.0],
+                "Atwo": [3.0, 4.0],
+                "Bone": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": "",
+            "Aone": [],
+            "Atwo": [],
+            "Bone": [],
+            "id": [],
+            "year": [],
+            "A": [],
+            "B": [],
+        }
+        expected = DataFrame(exp_data).astype({"year": np.int64})
+
+        expected = expected.set_index(["id", "year"])
+        expected.index = expected.index.set_levels([0, 1], level=0)
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_multiple_id_columns(self):
+        # Taken from http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm
+        df = DataFrame(
+            {
+                "famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+                "birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
+                "ht1": [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+                "ht2": [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9],
+            }
+        )
+        expected = DataFrame(
+            {
+                "ht": [
+                    2.8,
+                    3.4,
+                    2.9,
+                    3.8,
+                    2.2,
+                    2.9,
+                    2.0,
+                    3.2,
+                    1.8,
+                    2.8,
+                    1.9,
+                    2.4,
+                    2.2,
+                    3.3,
+                    2.3,
+                    3.4,
+                    2.1,
+                    2.9,
+                ],
+                "famid": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
+                "birth": [1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3],
+                "age": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
+            }
+        )
+        expected = expected.set_index(["famid", "birth", "age"])[["ht"]]
+        result = wide_to_long(df, "ht", i=["famid", "birth"], j="age")
+        tm.assert_frame_equal(result, expected)
+
+    def test_non_unique_idvars(self):
+        # GH16382
+        # Raise an error message if non unique id vars (i) are passed
+        df = DataFrame(
+            {"A_A1": [1, 2, 3, 4, 5], "B_B1": [1, 2, 3, 4, 5], "x": [1, 1, 1, 1, 1]}
+        )
+        msg = "the id variables need to uniquely identify each row"
+        with pytest.raises(ValueError, match=msg):
+            wide_to_long(df, ["A_A", "B_B"], i="x", j="colname")
+
+    def test_cast_j_int(self):
+        df = DataFrame(
+            {
+                "actor_1": ["CCH Pounder", "Johnny Depp", "Christoph Waltz"],
+                "actor_2": ["Joel David Moore", "Orlando Bloom", "Rory Kinnear"],
+                "actor_fb_likes_1": [1000.0, 40000.0, 11000.0],
+                "actor_fb_likes_2": [936.0, 5000.0, 393.0],
+                "title": ["Avatar", "Pirates of the Caribbean", "Spectre"],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "actor": [
+                    "CCH Pounder",
+                    "Johnny Depp",
+                    "Christoph Waltz",
+                    "Joel David Moore",
+                    "Orlando Bloom",
+                    "Rory Kinnear",
+                ],
+                "actor_fb_likes": [1000.0, 40000.0, 11000.0, 936.0, 5000.0, 393.0],
+                "num": [1, 1, 1, 2, 2, 2],
+                "title": [
+                    "Avatar",
+                    "Pirates of the Caribbean",
+                    "Spectre",
+                    "Avatar",
+                    "Pirates of the Caribbean",
+                    "Spectre",
+                ],
+            }
+        ).set_index(["title", "num"])
+        result = wide_to_long(
+            df, ["actor", "actor_fb_likes"], i="title", j="num", sep="_"
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_identical_stubnames(self):
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "A": ["X1", "X2"],
+            }
+        )
+        msg = "stubname can't be identical to a column name"
+        with pytest.raises(ValueError, match=msg):
+            wide_to_long(df, ["A", "B"], i="A", j="colname")
+
+    def test_nonnumeric_suffix(self):
+        df = DataFrame(
+            {
+                "treatment_placebo": [1.0, 2.0],
+                "treatment_test": [3.0, 4.0],
+                "result_placebo": [5.0, 6.0],
+                "A": ["X1", "X2"],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2"],
+                "colname": ["placebo", "placebo", "test", "test"],
+                "result": [5.0, 6.0, np.nan, np.nan],
+                "treatment": [1.0, 2.0, 3.0, 4.0],
+            }
+        )
+        expected = expected.set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix="[a-z]+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mixed_type_suffix(self):
+        df = DataFrame(
+            {
+                "A": ["X1", "X2"],
+                "result_1": [0, 9],
+                "result_foo": [5.0, 6.0],
+                "treatment_1": [1.0, 2.0],
+                "treatment_foo": [3.0, 4.0],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2"],
+                "colname": ["1", "1", "foo", "foo"],
+                "result": [0.0, 9.0, 5.0, 6.0],
+                "treatment": [1.0, 2.0, 3.0, 4.0],
+            }
+        ).set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix=".+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_float_suffix(self):
+        df = DataFrame(
+            {
+                "treatment_1.1": [1.0, 2.0],
+                "treatment_2.1": [3.0, 4.0],
+                "result_1.2": [5.0, 6.0],
+                "result_1": [0, 9],
+                "A": ["X1", "X2"],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2", "X1", "X2", "X1", "X2"],
+                "colname": [1.2, 1.2, 1.0, 1.0, 1.1, 1.1, 2.1, 2.1],
+                "result": [5.0, 6.0, 0.0, 9.0, np.nan, np.nan, np.nan, np.nan],
+                "treatment": [np.nan, np.nan, np.nan, np.nan, 1.0, 2.0, 3.0, 4.0],
+            }
+        )
+        expected = expected.set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix="[0-9.]+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_col_substring_of_stubname(self):
+        # GH22468
+        # Don't raise ValueError when a column name is a substring
+        # of a stubname that's been passed as a string
+        wide_data = {
+            "node_id": {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
+            "A": {0: 0.80, 1: 0.0, 2: 0.25, 3: 1.0, 4: 0.81},
+            "PA0": {0: 0.74, 1: 0.56, 2: 0.56, 3: 0.98, 4: 0.6},
+            "PA1": {0: 0.77, 1: 0.64, 2: 0.52, 3: 0.98, 4: 0.67},
+            "PA3": {0: 0.34, 1: 0.70, 2: 0.52, 3: 0.98, 4: 0.67},
+        }
+        wide_df = DataFrame.from_dict(wide_data)
+        expected = wide_to_long(wide_df, stubnames=["PA"], i=["node_id", "A"], j="time")
+        result = wide_to_long(wide_df, stubnames="PA", i=["node_id", "A"], j="time")
+        tm.assert_frame_equal(result, expected)
+
+    def test_raise_of_column_name_value(self):
+        # GH34731, enforced in 2.0
+        # raise a ValueError if the resultant value column name matches
+        # a name in the dataframe already (default name is "value")
+        df = DataFrame({"col": list("ABC"), "value": range(10, 16, 2)})
+
+        with pytest.raises(
+            ValueError, match=re.escape("value_name (value) cannot match")
+        ):
+            df.melt(id_vars="value", value_name="value")
+
+    def test_missing_stubname(self, request, any_string_dtype, using_infer_string):
+        if using_infer_string and any_string_dtype == "object":
+            # triggers object dtype inference warning of dtype=object
+            request.applymarker(pytest.mark.xfail(reason="TODO(infer_string)"))
+        # GH46044
+        df = DataFrame({"id": ["1", "2"], "a-1": [100, 200], "a-2": [300, 400]})
+        df = df.astype({"id": any_string_dtype})
+        result = wide_to_long(
+            df,
+            stubnames=["a", "b"],
+            i="id",
+            j="num",
+            sep="-",
+        )
+        index = Index(
+            [("1", 1), ("2", 1), ("1", 2), ("2", 2)],
+            name=("id", "num"),
+        )
+        expected = DataFrame(
+            {"a": [100, 200, 300, 400], "b": [np.nan] * 4},
+            index=index,
+        )
+        new_level = expected.index.levels[0].astype(any_string_dtype)
+        if any_string_dtype == "object":
+            new_level = expected.index.levels[0].astype("str")
+        expected.index = expected.index.set_levels(new_level, level=0)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_wide_to_long_string_columns(string_storage):
+    # GH 57066
+    string_dtype = pd.StringDtype(string_storage, na_value=np.nan)
+    df = DataFrame(
+        {
+            "ID": {0: 1},
+            "R_test1": {0: 1},
+            "R_test2": {0: 1},
+            "R_test3": {0: 2},
+            "D": {0: 1},
+        }
+    )
+    df.columns = df.columns.astype(string_dtype)
+    result = wide_to_long(
+        df, stubnames="R", i="ID", j="UNPIVOTED", sep="_", suffix=".*"
+    )
+    expected = DataFrame(
+        [[1, 1], [1, 1], [1, 2]],
+        columns=Index(["D", "R"]),
+        index=pd.MultiIndex.from_arrays(
+            [
+                [1, 1, 1],
+                Index(["test1", "test2", "test3"], dtype=string_dtype),
+            ],
+            names=["ID", "UNPIVOTED"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/a0/a064b9370b79737add3a87f3aefdb48843d5cb8f010be749b8ac2ea2b4e55b72/cit500 b/shared/package-files/a0/a064b9370b79737add3a87f3aefdb48843d5cb8f010be749b8ac2ea2b4e55b72/cit500
new file mode 100644
index 0000000000000000000000000000000000000000..69047cd2c001c4eaca47503c3bb8fded61d24ad9
Binary files /dev/null and b/shared/package-files/a0/a064b9370b79737add3a87f3aefdb48843d5cb8f010be749b8ac2ea2b4e55b72/cit500 differ
diff --git a/shared/package-files/a0/a065ee36b4220bb4818416a183a49e38a8f0b5c199e7ee36f27a526e157c6fe4/stride.h b/shared/package-files/a0/a065ee36b4220bb4818416a183a49e38a8f0b5c199e7ee36f27a526e157c6fe4/stride.h
new file mode 100644
index 0000000000000000000000000000000000000000..1923284ec2d446f13f98b81f554a231767d692b6
--- /dev/null
+++ b/shared/package-files/a0/a065ee36b4220bb4818416a183a49e38a8f0b5c199e7ee36f27a526e157c6fe4/stride.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/stride_ops.h>
+
+namespace at {
+
+
+// aten::stride.int(Tensor self, int dim) -> int
+inline int64_t __dispatch_stride(const at::Tensor & self, int64_t dim) {
+    return at::_ops::stride_int::call(self, dim);
+}
+
+// aten::stride.Dimname(Tensor self, Dimname dim) -> int
+inline int64_t stride(const at::Tensor & self, at::Dimname dim) {
+    return at::_ops::stride_Dimname::call(self, dim);
+}
+
+}
diff --git a/shared/package-files/a0/a068f52236610a87f90470c9f67b62a741cc70cb7e37b745c6f140561d447e51/screen+fkeys b/shared/package-files/a0/a068f52236610a87f90470c9f67b62a741cc70cb7e37b745c6f140561d447e51/screen+fkeys
new file mode 100644
index 0000000000000000000000000000000000000000..a3564ab154d525b9d3cea7eb70485cf0b10400f9
Binary files /dev/null and b/shared/package-files/a0/a068f52236610a87f90470c9f67b62a741cc70cb7e37b745c6f140561d447e51/screen+fkeys differ
diff --git a/shared/package-files/a0/a0691fc9559dec148db5d27aadbacb5acb0e54ff527dd3e790b3be9018da8dce/poisson_compositeexplicitautograd_dispatch.h b/shared/package-files/a0/a0691fc9559dec148db5d27aadbacb5acb0e54ff527dd3e790b3be9018da8dce/poisson_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cd3c3836c364a6d29bed6f8d96fa38fbb57f200
--- /dev/null
+++ b/shared/package-files/a0/a0691fc9559dec148db5d27aadbacb5acb0e54ff527dd3e790b3be9018da8dce/poisson_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & poisson_out(at::Tensor & out, const at::Tensor & self, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & poisson_outf(const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a07505ddff594deaec7d3350a18cec84a89a519abc6829ccc6bf4cef898eef9e/TensorWrapper.h b/shared/package-files/a0/a07505ddff594deaec7d3350a18cec84a89a519abc6829ccc6bf4cef898eef9e/TensorWrapper.h
new file mode 100644
index 0000000000000000000000000000000000000000..b90cd124c27e6a4e2e2860d6ccfe71ca92d10eea
--- /dev/null
+++ b/shared/package-files/a0/a07505ddff594deaec7d3350a18cec84a89a519abc6829ccc6bf4cef898eef9e/TensorWrapper.h
@@ -0,0 +1,103 @@
+// Copyright (c) Facebook, Inc. and its affiliates.
+// All rights reserved.
+//
+// This source code is licensed under the BSD-style license found in the
+// LICENSE file in the root directory of this source tree.
+
+#pragma once
+
+#include <ATen/functorch/Macros.h>
+#include <ATen/Tensor.h>
+#include <ATen/functorch/Interpreter.h>
+
+namespace at::functorch {
+
+// NOTE: [functorch's TensorWrapper]
+//
+// Taking better suggestions for a name. TensorWrapper is the wrapper Tensor
+// Subclass for functorch's grad-based transforms (grad, vjp, jvp). It is
+// analogous to how vmap uses BatchedTensor as the wrapper Tensor subclass.
+//
+// If you're familiar with the Tensor-Variable merge, TensorWrapper is effectively
+// another Variable.
+//
+// Consider grad(grad(torch.sin))(x). This wraps `x` as TensorWrapper(TensorWrapper(x)).
+// The reason why is so that each TensorWrapper can hold its own AutogradMeta and
+// participate in a **separate** autograd graph.
+//
+// There are alternative designs we could have chosen (e.g. each grad transform
+// stores a weak map of Tensor -> AutogradMeta); the benefit of the TensorWrapper
+// design is that we can re-use existing VariableType kernels (i.e. Autograd kernels)
+// without much modification. Since a TensorWrapper looks like a regular Tensor,
+// the VariableType kernel can pull out the AutogradMeta struct from where it
+// expects and extend the autograd graph
+
+struct TORCH_API TensorWrapper : public c10::TensorImpl {
+  explicit TensorWrapper(
+      c10::DispatchKeySet key_set,
+      Tensor value,
+      int64_t level,
+      std::shared_ptr<bool> is_alive,
+      bool is_immutable = false,  // if true, this came from an operation that aliases an immutable tensor
+      bool use_value_sizes_strides = true);
+
+  void refreshMetadata();
+
+  const Tensor& value() const {
+    return value_;
+  }
+  optional<int64_t> level() const {
+    if (is_alive()) {
+      return level_;
+    }
+    return {};
+  }
+  bool is_immutable() const {
+    return is_immutable_;
+  }
+  bool is_alive() const;
+
+  // Overrides necessary for autograd
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+    const c10::VariableVersion& version_counter,
+    bool allow_tensor_metadata_change) const override;
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      c10::VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change) const override;
+  void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override;
+
+ private:
+  const char* tensorimpl_type_name() const override;
+  Tensor value_;
+  int64_t level_;
+  bool is_immutable_;
+
+  // TensorWrapper receives a boolean flag on whether or not the Grad Interpreter
+  // that created it is still alive or not.
+  // If the Grad Interpreter is no longer alive then it attempts to behave like
+  // a regular Tensor.
+  //
+  // When we exit the level, this wrapper may be marked as "not alive".
+  // Wrappers that are not alive:
+  // 1) May still have autograd metadata on them
+  // 2) Forward dispatches to the underlying value()
+  std::shared_ptr<bool> is_alive_;
+};
+
+// There are two variants of makeTensorWrapper: one that accepts a level
+// and one that accepts an Interpreter.
+//
+// The one that accepts a level tries to automatically get the life handle from the
+// interpreter on the DynamicLayerStack.
+// It needs to be used with caution: if the interpreter is not on the
+// DynamicLayerStack, then we won't be able to find the life handle.
+//
+// In practice this isn't a problem: when we're constructing TensorWrapper in
+// Python, the corresponding interpreter is on the stack.
+TORCH_API Tensor makeTensorWrapper(const Tensor& tensor, int64_t level, bool is_immutable=false);
+TORCH_API Tensor makeTensorWrapper(const Tensor& tensor, const Interpreter& interpreter, bool is_immutable=false);
+TORCH_API TensorWrapper* maybeGetTensorWrapper(const Tensor& tensor);
+TORCH_API void dumpTensor(std::ostream & ss, const Tensor& tensor);
+TORCH_API void dumpTensorCout(const Tensor& tensor);
+
+} // namespace at::functorch
diff --git a/shared/package-files/a0/a07a96e2be7c1ecd946edbb80573557556597fe1dd180b56f825c75c0bfa8d87/fitdb_h.index b/shared/package-files/a0/a07a96e2be7c1ecd946edbb80573557556597fe1dd180b56f825c75c0bfa8d87/fitdb_h.index
new file mode 100644
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diff --git a/shared/package-files/a0/a08573b8aff74c3e57482dbf003344f107cb846345cdf36fb3e54f2c29aa2f66/test_image.py b/shared/package-files/a0/a08573b8aff74c3e57482dbf003344f107cb846345cdf36fb3e54f2c29aa2f66/test_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d1d302913281a34138e4b598a02ebce303438df
--- /dev/null
+++ b/shared/package-files/a0/a08573b8aff74c3e57482dbf003344f107cb846345cdf36fb3e54f2c29aa2f66/test_image.py
@@ -0,0 +1,1781 @@
+from contextlib import ExitStack
+from copy import copy
+import functools
+import io
+import os
+from pathlib import Path
+import platform
+import sys
+import urllib.request
+
+import numpy as np
+from numpy.testing import assert_array_equal
+from PIL import Image
+
+import matplotlib as mpl
+from matplotlib import (
+    colors, image as mimage, patches, pyplot as plt, style, rcParams)
+from matplotlib.image import (AxesImage, BboxImage, FigureImage,
+                              NonUniformImage, PcolorImage)
+from matplotlib.testing.decorators import check_figures_equal, image_comparison
+from matplotlib.transforms import Bbox, Affine2D, TransformedBbox
+import matplotlib.ticker as mticker
+
+import pytest
+
+
+@image_comparison(['interp_alpha.png'], remove_text=True)
+def test_alpha_interp():
+    """Test the interpolation of the alpha channel on RGBA images"""
+    fig, (axl, axr) = plt.subplots(1, 2)
+    # full green image
+    img = np.zeros((5, 5, 4))
+    img[..., 1] = np.ones((5, 5))
+    # transparent under main diagonal
+    img[..., 3] = np.tril(np.ones((5, 5), dtype=np.uint8))
+    axl.imshow(img, interpolation="none")
+    axr.imshow(img, interpolation="bilinear")
+
+
+@image_comparison(['interp_nearest_vs_none'], tol=3.7,  # For Ghostscript 10.06+.
+                  extensions=['pdf', 'svg'], remove_text=True)
+def test_interp_nearest_vs_none():
+    """Test the effect of "nearest" and "none" interpolation"""
+    # Setting dpi to something really small makes the difference very
+    # visible. This works fine with pdf, since the dpi setting doesn't
+    # affect anything but images, but the agg output becomes unusably
+    # small.
+    rcParams['savefig.dpi'] = 3
+    X = np.array([[[218, 165, 32], [122, 103, 238]],
+                  [[127, 255, 0], [255, 99, 71]]], dtype=np.uint8)
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    ax1.imshow(X, interpolation='none')
+    ax1.set_title('interpolation none')
+    ax2.imshow(X, interpolation='nearest')
+    ax2.set_title('interpolation nearest')
+
+
+@pytest.mark.parametrize('suppressComposite', [False, True])
+@image_comparison(['figimage'], extensions=['png', 'pdf'])
+def test_figimage(suppressComposite):
+    fig = plt.figure(figsize=(2, 2), dpi=100)
+    fig.suppressComposite = suppressComposite
+    x, y = np.ix_(np.arange(100) / 100.0, np.arange(100) / 100)
+    z = np.sin(x**2 + y**2 - x*y)
+    c = np.sin(20*x**2 + 50*y**2)
+    img = z + c/5
+
+    fig.figimage(img, xo=0, yo=0, origin='lower')
+    fig.figimage(img[::-1, :], xo=0, yo=100, origin='lower')
+    fig.figimage(img[:, ::-1], xo=100, yo=0, origin='lower')
+    fig.figimage(img[::-1, ::-1], xo=100, yo=100, origin='lower')
+
+
+def test_image_python_io():
+    fig, ax = plt.subplots()
+    ax.plot([1, 2, 3])
+    buffer = io.BytesIO()
+    fig.savefig(buffer)
+    buffer.seek(0)
+    plt.imread(buffer)
+
+
+@pytest.mark.parametrize(
+    "img_size, fig_size, interpolation",
+    [(5, 2, "hanning"),  # data larger than figure.
+     (5, 5, "nearest"),  # exact resample.
+     (5, 10, "nearest"),  # double sample.
+     (3, 2.9, "hanning"),  # <3 upsample.
+     (3, 9.1, "nearest"),  # >3 upsample.
+     ])
+@check_figures_equal(extensions=['png'])
+def test_imshow_antialiased(fig_test, fig_ref,
+                            img_size, fig_size, interpolation):
+    np.random.seed(19680801)
+    dpi = plt.rcParams["savefig.dpi"]
+    A = np.random.rand(int(dpi * img_size), int(dpi * img_size))
+    for fig in [fig_test, fig_ref]:
+        fig.set_size_inches(fig_size, fig_size)
+    ax = fig_test.subplots()
+    ax.set_position([0, 0, 1, 1])
+    ax.imshow(A, interpolation='auto')
+    ax = fig_ref.subplots()
+    ax.set_position([0, 0, 1, 1])
+    ax.imshow(A, interpolation=interpolation)
+
+
+@check_figures_equal(extensions=['png'])
+def test_imshow_zoom(fig_test, fig_ref):
+    # should be less than 3 upsample, so should be nearest...
+    np.random.seed(19680801)
+    dpi = plt.rcParams["savefig.dpi"]
+    A = np.random.rand(int(dpi * 3), int(dpi * 3))
+    for fig in [fig_test, fig_ref]:
+        fig.set_size_inches(2.9, 2.9)
+    ax = fig_test.subplots()
+    ax.imshow(A, interpolation='auto')
+    ax.set_xlim([10, 20])
+    ax.set_ylim([10, 20])
+    ax = fig_ref.subplots()
+    ax.imshow(A, interpolation='nearest')
+    ax.set_xlim([10, 20])
+    ax.set_ylim([10, 20])
+
+
+@check_figures_equal(extensions=['png'])
+def test_imshow_pil(fig_test, fig_ref):
+    style.use("default")
+    png_path = Path(__file__).parent / "baseline_images/pngsuite/basn3p04.png"
+    tiff_path = Path(__file__).parent / "baseline_images/test_image/uint16.tif"
+    axs = fig_test.subplots(2)
+    axs[0].imshow(Image.open(png_path))
+    axs[1].imshow(Image.open(tiff_path))
+    axs = fig_ref.subplots(2)
+    axs[0].imshow(plt.imread(png_path))
+    axs[1].imshow(plt.imread(tiff_path))
+
+
+def test_imread_pil_uint16():
+    img = plt.imread(os.path.join(os.path.dirname(__file__),
+                     'baseline_images', 'test_image', 'uint16.tif'))
+    assert img.dtype == np.uint16
+    assert np.sum(img) == 134184960
+
+
+def test_imread_fspath():
+    img = plt.imread(
+        Path(__file__).parent / 'baseline_images/test_image/uint16.tif')
+    assert img.dtype == np.uint16
+    assert np.sum(img) == 134184960
+
+
+@pytest.mark.parametrize("fmt", ["png", "jpg", "jpeg", "tiff"])
+def test_imsave(fmt):
+    has_alpha = fmt not in ["jpg", "jpeg"]
+
+    # The goal here is that the user can specify an output logical DPI
+    # for the image, but this will not actually add any extra pixels
+    # to the image, it will merely be used for metadata purposes.
+
+    # So we do the traditional case (dpi == 1), and the new case (dpi
+    # == 100) and read the resulting PNG files back in and make sure
+    # the data is 100% identical.
+    np.random.seed(1)
+    # The height of 1856 pixels was selected because going through creating an
+    # actual dpi=100 figure to save the image to a Pillow-provided format would
+    # cause a rounding error resulting in a final image of shape 1855.
+    data = np.random.rand(1856, 2)
+
+    buff_dpi1 = io.BytesIO()
+    plt.imsave(buff_dpi1, data, format=fmt, dpi=1)
+
+    buff_dpi100 = io.BytesIO()
+    plt.imsave(buff_dpi100, data, format=fmt, dpi=100)
+
+    buff_dpi1.seek(0)
+    arr_dpi1 = plt.imread(buff_dpi1, format=fmt)
+
+    buff_dpi100.seek(0)
+    arr_dpi100 = plt.imread(buff_dpi100, format=fmt)
+
+    assert arr_dpi1.shape == (1856, 2, 3 + has_alpha)
+    assert arr_dpi100.shape == (1856, 2, 3 + has_alpha)
+
+    assert_array_equal(arr_dpi1, arr_dpi100)
+
+
+def test_imsave_python_sequences():
+    # Tests saving an image with data passed using Python sequence types
+    # such as lists or tuples.
+
+    # RGB image: 3 rows × 2 columns, with float values in [0.0, 1.0]
+    img_data = [
+        [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0)],
+        [(0.0, 0.0, 1.0), (1.0, 1.0, 0.0)],
+        [(0.0, 1.0, 1.0), (1.0, 0.0, 1.0)],
+    ]
+
+    buff = io.BytesIO()
+    plt.imsave(buff, img_data, format="png")
+    buff.seek(0)
+    read_img = plt.imread(buff)
+
+    assert_array_equal(
+        np.array(img_data),
+        read_img[:, :, :3]  # Drop alpha if present
+    )
+
+
+@pytest.mark.parametrize("origin", ["upper", "lower"])
+def test_imsave_rgba_origin(origin):
+    # test that imsave always passes c-contiguous arrays down to pillow
+    buf = io.BytesIO()
+    result = np.zeros((10, 10, 4), dtype='uint8')
+    mimage.imsave(buf, arr=result, format="png", origin=origin)
+
+
+@pytest.mark.parametrize("fmt", ["png", "pdf", "ps", "eps", "svg"])
+def test_imsave_fspath(fmt):
+    plt.imsave(Path(os.devnull), np.array([[0, 1]]), format=fmt)
+
+
+def test_imsave_color_alpha():
+    # Test that imsave accept arrays with ndim=3 where the third dimension is
+    # color and alpha without raising any exceptions, and that the data is
+    # acceptably preserved through a save/read roundtrip.
+    np.random.seed(1)
+
+    for origin in ['lower', 'upper']:
+        data = np.random.rand(16, 16, 4)
+        buff = io.BytesIO()
+        plt.imsave(buff, data, origin=origin, format="png")
+
+        buff.seek(0)
+        arr_buf = plt.imread(buff)
+
+        # Recreate the float -> uint8 conversion of the data
+        # We can only expect to be the same with 8 bits of precision,
+        # since that's what the PNG file used.
+        data = (255*data).astype('uint8')
+        if origin == 'lower':
+            data = data[::-1]
+        arr_buf = (255*arr_buf).astype('uint8')
+
+        assert_array_equal(data, arr_buf)
+
+
+def test_imsave_pil_kwargs_png():
+    from PIL.PngImagePlugin import PngInfo
+    buf = io.BytesIO()
+    pnginfo = PngInfo()
+    pnginfo.add_text("Software", "test")
+    plt.imsave(buf, [[0, 1], [2, 3]],
+               format="png", pil_kwargs={"pnginfo": pnginfo})
+    im = Image.open(buf)
+    assert im.info["Software"] == "test"
+
+
+def test_imsave_pil_kwargs_tiff():
+    from PIL.TiffTags import TAGS_V2 as TAGS
+    buf = io.BytesIO()
+    pil_kwargs = {"description": "test image"}
+    plt.imsave(buf, [[0, 1], [2, 3]], format="tiff", pil_kwargs=pil_kwargs)
+    assert len(pil_kwargs) == 1
+    im = Image.open(buf)
+    tags = {TAGS[k].name: v for k, v in im.tag_v2.items()}
+    assert tags["ImageDescription"] == "test image"
+
+
+@image_comparison(['image_alpha'], remove_text=True)
+def test_image_alpha():
+    np.random.seed(0)
+    Z = np.random.rand(6, 6)
+
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
+    ax1.imshow(Z, alpha=1.0, interpolation='none')
+    ax2.imshow(Z, alpha=0.5, interpolation='none')
+    ax3.imshow(Z, alpha=0.5, interpolation='nearest')
+
+
+@mpl.style.context('mpl20')
+@check_figures_equal(extensions=['png'])
+def test_imshow_alpha(fig_test, fig_ref):
+    np.random.seed(19680801)
+
+    rgbf = np.random.rand(6, 6, 3)
+    rgbu = np.uint8(rgbf * 255)
+    ((ax0, ax1), (ax2, ax3)) = fig_test.subplots(2, 2)
+    ax0.imshow(rgbf, alpha=0.5)
+    ax1.imshow(rgbf, alpha=0.75)
+    ax2.imshow(rgbu, alpha=0.5)
+    ax3.imshow(rgbu, alpha=0.75)
+
+    rgbaf = np.concatenate((rgbf, np.ones((6, 6, 1))), axis=2)
+    rgbau = np.concatenate((rgbu, np.full((6, 6, 1), 255, np.uint8)), axis=2)
+    ((ax0, ax1), (ax2, ax3)) = fig_ref.subplots(2, 2)
+    rgbaf[:, :, 3] = 0.5
+    ax0.imshow(rgbaf)
+    rgbaf[:, :, 3] = 0.75
+    ax1.imshow(rgbaf)
+    rgbau[:, :, 3] = 127
+    ax2.imshow(rgbau)
+    rgbau[:, :, 3] = 191
+    ax3.imshow(rgbau)
+
+
+def test_cursor_data():
+    from matplotlib.backend_bases import MouseEvent
+
+    fig, ax = plt.subplots()
+    im = ax.imshow(np.arange(100).reshape(10, 10), origin='upper')
+
+    x, y = 4, 4
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) == 44
+
+    # Now try for a point outside the image
+    # Tests issue #4957
+    x, y = 10.1, 4
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) is None
+
+    # Hmm, something is wrong here... I get 0, not None...
+    # But, this works further down in the tests with extents flipped
+    # x, y = 0.1, -0.1
+    # xdisp, ydisp = ax.transData.transform([x, y])
+    # event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    # z = im.get_cursor_data(event)
+    # assert z is None, "Did not get None, got %d" % z
+
+    ax.clear()
+    # Now try with the extents flipped.
+    im = ax.imshow(np.arange(100).reshape(10, 10), origin='lower')
+
+    x, y = 4, 4
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) == 44
+
+    fig, ax = plt.subplots()
+    im = ax.imshow(np.arange(100).reshape(10, 10), extent=[0, 0.5, 0, 0.5])
+
+    x, y = 0.25, 0.25
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) == 55
+
+    # Now try for a point outside the image
+    # Tests issue #4957
+    x, y = 0.75, 0.25
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) is None
+
+    x, y = 0.01, -0.01
+    xdisp, ydisp = ax.transData.transform([x, y])
+
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) is None
+
+    # Now try with additional transform applied to the image artist
+    trans = Affine2D().scale(2).rotate(0.5)
+    im = ax.imshow(np.arange(100).reshape(10, 10),
+                   transform=trans + ax.transData)
+    x, y = 3, 10
+    xdisp, ydisp = ax.transData.transform([x, y])
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) == 44
+
+
+@pytest.mark.parametrize("xy, data", [
+    # x/y coords chosen to be 0.5 above boundaries so they lie within image pixels
+    [[0.5, 0.5], 0 + 0],
+    [[0.5, 1.5], 0 + 1],
+    [[4.5, 0.5], 16 + 0],
+    [[8.5, 0.5], 16 + 0],
+    [[9.5, 2.5], 81 + 4],
+    [[-1, 0.5], None],
+    [[0.5, -1], None],
+    ]
+)
+def test_cursor_data_nonuniform(xy, data):
+    from matplotlib.backend_bases import MouseEvent
+
+    # Non-linear set of x-values
+    x = np.array([0, 1, 4, 9, 16])
+    y = np.array([0, 1, 2, 3, 4])
+    z = x[np.newaxis, :]**2 + y[:, np.newaxis]**2
+
+    fig, ax = plt.subplots()
+    im = NonUniformImage(ax, extent=(x.min(), x.max(), y.min(), y.max()))
+    im.set_data(x, y, z)
+    ax.add_image(im)
+    # Set lower min lim so we can test cursor outside image
+    ax.set_xlim(x.min() - 2, x.max())
+    ax.set_ylim(y.min() - 2, y.max())
+
+    xdisp, ydisp = ax.transData.transform(xy)
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.get_cursor_data(event) == data, (im.get_cursor_data(event), data)
+
+
+@pytest.mark.parametrize(
+    "data, text", [
+        ([[10001, 10000]], "[10001.000]"),
+        ([[.123, .987]], "[0.123]"),
+        ([[np.nan, 1, 2]], "[]"),
+        ([[1, 1+1e-15]], "[1.0000000000000000]"),
+        ([[-1, -1]], "[-1.0]"),
+        ([[0, 0]], "[0.00]"),
+    ])
+def test_format_cursor_data(data, text):
+    from matplotlib.backend_bases import MouseEvent
+
+    fig, ax = plt.subplots()
+    im = ax.imshow(data)
+
+    xdisp, ydisp = ax.transData.transform([0, 0])
+    event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
+    assert im.format_cursor_data(im.get_cursor_data(event)) == text
+
+
+@image_comparison(['image_clip'], style='mpl20')
+def test_image_clip():
+    d = [[1, 2], [3, 4]]
+
+    fig, ax = plt.subplots()
+    im = ax.imshow(d)
+    patch = patches.Circle((0, 0), radius=1, transform=ax.transData)
+    im.set_clip_path(patch)
+
+
+@image_comparison(['image_cliprect'], style='mpl20')
+def test_image_cliprect():
+    fig, ax = plt.subplots()
+    d = [[1, 2], [3, 4]]
+
+    im = ax.imshow(d, extent=(0, 5, 0, 5))
+
+    rect = patches.Rectangle(
+        xy=(1, 1), width=2, height=2, transform=im.axes.transData)
+    im.set_clip_path(rect)
+
+
+@check_figures_equal(extensions=['png'])
+def test_imshow_10_10_1(fig_test, fig_ref):
+    # 10x10x1 should be the same as 10x10
+    arr = np.arange(100).reshape((10, 10, 1))
+    ax = fig_ref.subplots()
+    ax.imshow(arr[:, :, 0], interpolation="bilinear", extent=(1, 2, 1, 2))
+    ax.set_xlim(0, 3)
+    ax.set_ylim(0, 3)
+
+    ax = fig_test.subplots()
+    ax.imshow(arr, interpolation="bilinear", extent=(1, 2, 1, 2))
+    ax.set_xlim(0, 3)
+    ax.set_ylim(0, 3)
+
+
+def test_imshow_10_10_2():
+    fig, ax = plt.subplots()
+    arr = np.arange(200).reshape((10, 10, 2))
+    with pytest.raises(TypeError):
+        ax.imshow(arr)
+
+
+def test_imshow_10_10_5():
+    fig, ax = plt.subplots()
+    arr = np.arange(500).reshape((10, 10, 5))
+    with pytest.raises(TypeError):
+        ax.imshow(arr)
+
+
+@image_comparison(['no_interpolation_origin'], remove_text=True)
+def test_no_interpolation_origin():
+    fig, axs = plt.subplots(2)
+    axs[0].imshow(np.arange(100).reshape((2, 50)), origin="lower",
+                  interpolation='none')
+    axs[1].imshow(np.arange(100).reshape((2, 50)), interpolation='none')
+
+
+@image_comparison(['image_shift'], remove_text=True, extensions=['pdf', 'svg'])
+def test_image_shift():
+    imgData = [[1 / x + 1 / y for x in range(1, 100)] for y in range(1, 100)]
+    tMin = 734717.945208
+    tMax = 734717.946366
+
+    fig, ax = plt.subplots()
+    ax.imshow(imgData, norm=colors.LogNorm(), interpolation='none',
+              extent=(tMin, tMax, 1, 100))
+    ax.set_aspect('auto')
+
+
+def test_image_edges():
+    fig = plt.figure(figsize=[1, 1])
+    ax = fig.add_axes([0, 0, 1, 1], frameon=False)
+
+    data = np.tile(np.arange(12), 15).reshape(20, 9)
+
+    im = ax.imshow(data, origin='upper', extent=[-10, 10, -10, 10],
+                   interpolation='none', cmap='gray')
+
+    x = y = 2
+    ax.set_xlim([-x, x])
+    ax.set_ylim([-y, y])
+
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    buf = io.BytesIO()
+    fig.savefig(buf, facecolor=(0, 1, 0))
+
+    buf.seek(0)
+
+    im = plt.imread(buf)
+    r, g, b, a = sum(im[:, 0])
+    r, g, b, a = sum(im[:, -1])
+
+    assert g != 100, 'Expected a non-green edge - but sadly, it was.'
+
+
+@image_comparison(['image_composite_background'],
+                  remove_text=True, style='mpl20')
+def test_image_composite_background():
+    fig, ax = plt.subplots()
+    arr = np.arange(12).reshape(4, 3)
+    ax.imshow(arr, extent=[0, 2, 15, 0])
+    ax.imshow(arr, extent=[4, 6, 15, 0])
+    ax.set_facecolor((1, 0, 0, 0.5))
+    ax.set_xlim([0, 12])
+
+
+@image_comparison(['image_composite_alpha'], remove_text=True)
+def test_image_composite_alpha():
+    """
+    Tests that the alpha value is recognized and correctly applied in the
+    process of compositing images together.
+    """
+    fig, ax = plt.subplots()
+    arr = np.zeros((11, 21, 4))
+    arr[:, :, 0] = 1
+    arr[:, :, 3] = np.concatenate(
+        (np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))
+    arr2 = np.zeros((21, 11, 4))
+    arr2[:, :, 0] = 1
+    arr2[:, :, 1] = 1
+    arr2[:, :, 3] = np.concatenate(
+        (np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))[:, np.newaxis]
+    ax.imshow(arr, extent=[1, 2, 5, 0], alpha=0.3)
+    ax.imshow(arr, extent=[2, 3, 5, 0], alpha=0.6)
+    ax.imshow(arr, extent=[3, 4, 5, 0])
+    ax.imshow(arr2, extent=[0, 5, 1, 2])
+    ax.imshow(arr2, extent=[0, 5, 2, 3], alpha=0.6)
+    ax.imshow(arr2, extent=[0, 5, 3, 4], alpha=0.3)
+    ax.set_facecolor((0, 0.5, 0, 1))
+    ax.set_xlim([0, 5])
+    ax.set_ylim([5, 0])
+
+
+@check_figures_equal(extensions=["pdf"])
+def test_clip_path_disables_compositing(fig_test, fig_ref):
+    t = np.arange(9).reshape((3, 3))
+    for fig in [fig_test, fig_ref]:
+        ax = fig.add_subplot()
+        ax.imshow(t, clip_path=(mpl.path.Path([(0, 0), (0, 1), (1, 0)]),
+                                ax.transData))
+        ax.imshow(t, clip_path=(mpl.path.Path([(1, 1), (1, 2), (2, 1)]),
+                                ax.transData))
+    fig_ref.suppressComposite = True
+
+
+@image_comparison(['rasterize_10dpi'],
+                  extensions=['pdf', 'svg'], remove_text=True, style='mpl20')
+def test_rasterize_dpi():
+    # This test should check rasterized rendering with high output resolution.
+    # It plots a rasterized line and a normal image with imshow.  So it will
+    # catch when images end up in the wrong place in case of non-standard dpi
+    # setting.  Instead of high-res rasterization I use low-res.  Therefore
+    # the fact that the resolution is non-standard is easily checked by
+    # image_comparison.
+    img = np.asarray([[1, 2], [3, 4]])
+
+    fig, axs = plt.subplots(1, 3, figsize=(3, 1))
+
+    axs[0].imshow(img)
+
+    axs[1].plot([0, 1], [0, 1], linewidth=20., rasterized=True)
+    axs[1].set(xlim=(0, 1), ylim=(-1, 2))
+
+    axs[2].plot([0, 1], [0, 1], linewidth=20.)
+    axs[2].set(xlim=(0, 1), ylim=(-1, 2))
+
+    # Low-dpi PDF rasterization errors prevent proper image comparison tests.
+    # Hide detailed structures like the axes spines.
+    for ax in axs:
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.spines[:].set_visible(False)
+
+    rcParams['savefig.dpi'] = 10
+
+
+@image_comparison(['bbox_image_inverted'], remove_text=True, style='mpl20')
+def test_bbox_image_inverted():
+    # This is just used to produce an image to feed to BboxImage
+    image = np.arange(100).reshape((10, 10))
+
+    fig, ax = plt.subplots()
+    bbox_im = BboxImage(
+        TransformedBbox(Bbox([[100, 100], [0, 0]]), ax.transData),
+        interpolation='nearest')
+    bbox_im.set_data(image)
+    bbox_im.set_clip_on(False)
+    ax.set_xlim(0, 100)
+    ax.set_ylim(0, 100)
+    ax.add_artist(bbox_im)
+
+    image = np.identity(10)
+
+    bbox_im = BboxImage(TransformedBbox(Bbox([[0.1, 0.2], [0.3, 0.25]]),
+                                        ax.get_figure().transFigure),
+                        interpolation='nearest')
+    bbox_im.set_data(image)
+    bbox_im.set_clip_on(False)
+    ax.add_artist(bbox_im)
+
+
+def test_get_window_extent_for_AxisImage():
+    # Create a figure of known size (1000x1000 pixels), place an image
+    # object at a given location and check that get_window_extent()
+    # returns the correct bounding box values (in pixels).
+
+    im = np.array([[0.25, 0.75, 1.0, 0.75], [0.1, 0.65, 0.5, 0.4],
+                   [0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
+    fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
+    ax.set_position([0, 0, 1, 1])
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    im_obj = ax.imshow(
+        im, extent=[0.4, 0.7, 0.2, 0.9], interpolation='nearest')
+
+    fig.canvas.draw()
+    renderer = fig.canvas.renderer
+    im_bbox = im_obj.get_window_extent(renderer)
+
+    assert_array_equal(im_bbox.get_points(), [[400, 200], [700, 900]])
+
+    fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
+    ax.set_position([0, 0, 1, 1])
+    ax.set_xlim(1, 2)
+    ax.set_ylim(0, 1)
+    im_obj = ax.imshow(
+        im, extent=[0.4, 0.7, 0.2, 0.9], interpolation='nearest',
+        transform=ax.transAxes)
+
+    fig.canvas.draw()
+    renderer = fig.canvas.renderer
+    im_bbox = im_obj.get_window_extent(renderer)
+
+    assert_array_equal(im_bbox.get_points(), [[400, 200], [700, 900]])
+
+
+@image_comparison(['zoom_and_clip_upper_origin.png'],
+                  remove_text=True, style='mpl20')
+def test_zoom_and_clip_upper_origin():
+    image = np.arange(100)
+    image = image.reshape((10, 10))
+
+    fig, ax = plt.subplots()
+    ax.imshow(image)
+    ax.set_ylim(2.0, -0.5)
+    ax.set_xlim(-0.5, 2.0)
+
+
+def test_nonuniformimage_setcmap():
+    ax = plt.gca()
+    im = NonUniformImage(ax)
+    im.set_cmap('Blues')
+
+
+def test_nonuniformimage_setnorm():
+    ax = plt.gca()
+    im = NonUniformImage(ax)
+    im.set_norm(plt.Normalize())
+
+
+def test_jpeg_2d():
+    # smoke test that mode-L pillow images work.
+    imd = np.ones((10, 10), dtype='uint8')
+    for i in range(10):
+        imd[i, :] = np.linspace(0.0, 1.0, 10) * 255
+    im = Image.new('L', (10, 10))
+    im.putdata(imd.flatten())
+    fig, ax = plt.subplots()
+    ax.imshow(im)
+
+
+def test_jpeg_alpha():
+    plt.figure(figsize=(1, 1), dpi=300)
+    # Create an image that is all black, with a gradient from 0-1 in
+    # the alpha channel from left to right.
+    im = np.zeros((300, 300, 4), dtype=float)
+    im[..., 3] = np.linspace(0.0, 1.0, 300)
+
+    plt.figimage(im)
+
+    buff = io.BytesIO()
+    plt.savefig(buff, facecolor="red", format='jpg', dpi=300)
+
+    buff.seek(0)
+    image = Image.open(buff)
+
+    # If this fails, there will be only one color (all black). If this
+    # is working, we should have all 256 shades of grey represented.
+    num_colors = len(image.getcolors(256))
+    assert 175 <= num_colors <= 230
+    # The fully transparent part should be red.
+    corner_pixel = image.getpixel((0, 0))
+    assert corner_pixel == (254, 0, 0)
+
+
+def test_axesimage_setdata():
+    ax = plt.gca()
+    im = AxesImage(ax)
+    z = np.arange(12, dtype=float).reshape((4, 3))
+    im.set_data(z)
+    z[0, 0] = 9.9
+    assert im._A[0, 0] == 0, 'value changed'
+
+
+def test_figureimage_setdata():
+    fig = plt.gcf()
+    im = FigureImage(fig)
+    z = np.arange(12, dtype=float).reshape((4, 3))
+    im.set_data(z)
+    z[0, 0] = 9.9
+    assert im._A[0, 0] == 0, 'value changed'
+
+
+@pytest.mark.parametrize(
+    "image_cls,x,y,a", [
+        (NonUniformImage,
+         np.arange(3.), np.arange(4.), np.arange(12.).reshape((4, 3))),
+        (PcolorImage,
+         np.arange(3.), np.arange(4.), np.arange(6.).reshape((3, 2))),
+    ])
+def test_setdata_xya(image_cls, x, y, a):
+    ax = plt.gca()
+    im = image_cls(ax)
+    im.set_data(x, y, a)
+    x[0] = y[0] = a[0, 0] = 9.9
+    assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
+    im.set_data(x, y, a.reshape((*a.shape, -1)))  # Just a smoketest.
+
+
+def test_minimized_rasterized():
+    # This ensures that the rasterized content in the colorbars is
+    # only as thick as the colorbar, and doesn't extend to other parts
+    # of the image.  See #5814.  While the original bug exists only
+    # in Postscript, the best way to detect it is to generate SVG
+    # and then parse the output to make sure the two colorbar images
+    # are the same size.
+    from xml.etree import ElementTree
+
+    np.random.seed(0)
+    data = np.random.rand(10, 10)
+
+    fig, ax = plt.subplots(1, 2)
+    p1 = ax[0].pcolormesh(data)
+    p2 = ax[1].pcolormesh(data)
+
+    plt.colorbar(p1, ax=ax[0])
+    plt.colorbar(p2, ax=ax[1])
+
+    buff = io.BytesIO()
+    plt.savefig(buff, format='svg')
+
+    buff = io.BytesIO(buff.getvalue())
+    tree = ElementTree.parse(buff)
+    width = None
+    for image in tree.iter('image'):
+        if width is None:
+            width = image['width']
+        else:
+            if image['width'] != width:
+                assert False
+
+
+def test_load_from_url():
+    path = Path(__file__).parent / "baseline_images/pngsuite/basn3p04.png"
+    url = ('file:'
+           + ('///' if sys.platform == 'win32' else '')
+           + path.resolve().as_posix())
+    with pytest.raises(ValueError, match="Please open the URL"):
+        plt.imread(url)
+    with urllib.request.urlopen(url) as file:
+        plt.imread(file)
+
+
+@image_comparison(['log_scale_image'], remove_text=True)
+def test_log_scale_image():
+    Z = np.zeros((10, 10))
+    Z[::2] = 1
+
+    fig, ax = plt.subplots()
+    ax.imshow(Z, extent=[1, 100, 1, 100], cmap='viridis', vmax=1, vmin=-1,
+              aspect='auto')
+    ax.set(yscale='log')
+
+
+@image_comparison(['rotate_image'], remove_text=True)
+def test_rotate_image():
+    delta = 0.25
+    x = y = np.arange(-3.0, 3.0, delta)
+    X, Y = np.meshgrid(x, y)
+    Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
+    Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
+          (2 * np.pi * 0.5 * 1.5))
+    Z = Z2 - Z1  # difference of Gaussians
+
+    fig, ax1 = plt.subplots(1, 1)
+    im1 = ax1.imshow(Z, interpolation='none', cmap='viridis',
+                     origin='lower',
+                     extent=[-2, 4, -3, 2], clip_on=True)
+
+    trans_data2 = Affine2D().rotate_deg(30) + ax1.transData
+    im1.set_transform(trans_data2)
+
+    # display intended extent of the image
+    x1, x2, y1, y2 = im1.get_extent()
+
+    ax1.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "r--", lw=3,
+             transform=trans_data2)
+
+    ax1.set_xlim(2, 5)
+    ax1.set_ylim(0, 4)
+
+
+def test_image_preserve_size():
+    buff = io.BytesIO()
+
+    im = np.zeros((481, 321))
+    plt.imsave(buff, im, format="png")
+
+    buff.seek(0)
+    img = plt.imread(buff)
+
+    assert img.shape[:2] == im.shape
+
+
+def test_image_preserve_size2():
+    n = 7
+    data = np.identity(n, float)
+
+    fig = plt.figure(figsize=(n, n), frameon=False)
+    ax = fig.add_axes((0.0, 0.0, 1.0, 1.0))
+    ax.set_axis_off()
+    ax.imshow(data, interpolation='nearest', origin='lower', aspect='auto')
+    buff = io.BytesIO()
+    fig.savefig(buff, dpi=1)
+
+    buff.seek(0)
+    img = plt.imread(buff)
+
+    assert img.shape == (7, 7, 4)
+
+    assert_array_equal(np.asarray(img[:, :, 0], bool),
+                       np.identity(n, bool)[::-1])
+
+
+@image_comparison(['mask_image_over_under.png'], remove_text=True, tol=1.0)
+def test_mask_image_over_under():
+
+    delta = 0.025
+    x = y = np.arange(-3.0, 3.0, delta)
+    X, Y = np.meshgrid(x, y)
+    Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
+    Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
+          (2 * np.pi * 0.5 * 1.5))
+    Z = 10*(Z2 - Z1)  # difference of Gaussians
+
+    palette = plt.cm.gray.with_extremes(over='r', under='g', bad='b')
+    Zm = np.ma.masked_where(Z > 1.2, Z)
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    im = ax1.imshow(Zm, interpolation='bilinear',
+                    cmap=palette,
+                    norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
+                    origin='lower', extent=[-3, 3, -3, 3])
+    ax1.set_title('Green=low, Red=high, Blue=bad')
+    fig.colorbar(im, extend='both', orientation='horizontal',
+                 ax=ax1, aspect=10)
+
+    im = ax2.imshow(Zm, interpolation='nearest',
+                    cmap=palette,
+                    norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
+                                             ncolors=256, clip=False),
+                    origin='lower', extent=[-3, 3, -3, 3])
+    ax2.set_title('With BoundaryNorm')
+    fig.colorbar(im, extend='both', spacing='proportional',
+                 orientation='horizontal', ax=ax2, aspect=10)
+
+
+@image_comparison(['mask_image'], remove_text=True)
+def test_mask_image():
+    # Test mask image two ways: Using nans and using a masked array.
+
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+
+    A = np.ones((5, 5))
+    A[1:2, 1:2] = np.nan
+
+    ax1.imshow(A, interpolation='nearest')
+
+    A = np.zeros((5, 5), dtype=bool)
+    A[1:2, 1:2] = True
+    A = np.ma.masked_array(np.ones((5, 5), dtype=np.uint16), A)
+
+    ax2.imshow(A, interpolation='nearest')
+
+
+def test_mask_image_all():
+    # Test behavior with an image that is entirely masked does not warn
+    data = np.full((2, 2), np.nan)
+    fig, ax = plt.subplots()
+    ax.imshow(data)
+    fig.canvas.draw_idle()  # would emit a warning
+
+
+@image_comparison(['imshow_endianess.png'], remove_text=True)
+def test_imshow_endianess():
+    x = np.arange(10)
+    X, Y = np.meshgrid(x, x)
+    Z = np.hypot(X - 5, Y - 5)
+
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+
+    kwargs = dict(origin="lower", interpolation='nearest', cmap='viridis')
+
+    ax1.imshow(Z.astype('<f8'), **kwargs)
+    ax2.imshow(Z.astype('>f8'), **kwargs)
+
+
+@image_comparison(['imshow_masked_interpolation'],
+                  tol=0 if platform.machine() == 'x86_64' else 0.01,
+                  remove_text=True, style='mpl20')
+def test_imshow_masked_interpolation():
+
+    cmap = mpl.colormaps['viridis'].with_extremes(over='r', under='b', bad='k')
+
+    N = 20
+    n = colors.Normalize(vmin=0, vmax=N*N-1)
+
+    data = np.arange(N*N, dtype=float).reshape(N, N)
+
+    data[5, 5] = -1
+    # This will cause crazy ringing for the higher-order
+    # interpolations
+    data[15, 5] = 1e5
+
+    # data[3, 3] = np.nan
+
+    data[15, 15] = np.inf
+
+    mask = np.zeros_like(data).astype('bool')
+    mask[5, 15] = True
+
+    data = np.ma.masked_array(data, mask)
+
+    fig, ax_grid = plt.subplots(3, 6)
+    interps = sorted(mimage._interpd_)
+    interps.remove('auto')
+    interps.remove('antialiased')
+
+    for interp, ax in zip(interps, ax_grid.ravel()):
+        ax.set_title(interp)
+        ax.imshow(data, norm=n, cmap=cmap, interpolation=interp)
+        ax.axis('off')
+
+
+def test_imshow_no_warn_invalid():
+    plt.imshow([[1, 2], [3, np.nan]])  # Check that no warning is emitted.
+
+
+@pytest.mark.parametrize(
+    'dtype', [np.dtype(s) for s in 'u2 u4 i2 i4 i8 f4 f8'.split()])
+def test_imshow_clips_rgb_to_valid_range(dtype):
+    arr = np.arange(300, dtype=dtype).reshape((10, 10, 3))
+    if dtype.kind != 'u':
+        arr -= 10
+    too_low = arr < 0
+    too_high = arr > 255
+    if dtype.kind == 'f':
+        arr = arr / 255
+    _, ax = plt.subplots()
+    out = ax.imshow(arr).get_array()
+    assert (out[too_low] == 0).all()
+    if dtype.kind == 'f':
+        assert (out[too_high] == 1).all()
+        assert out.dtype.kind == 'f'
+    else:
+        assert (out[too_high] == 255).all()
+        assert out.dtype == np.uint8
+
+
+@image_comparison(['imshow_flatfield.png'], remove_text=True, style='mpl20')
+def test_imshow_flatfield():
+    fig, ax = plt.subplots()
+    im = ax.imshow(np.ones((5, 5)), interpolation='nearest')
+    im.set_clim(.5, 1.5)
+
+
+@image_comparison(['imshow_bignumbers.png'], remove_text=True, style='mpl20')
+def test_imshow_bignumbers():
+    rcParams['image.interpolation'] = 'nearest'
+    # putting a big number in an array of integers shouldn't
+    # ruin the dynamic range of the resolved bits.
+    fig, ax = plt.subplots()
+    img = np.array([[1, 2, 1e12], [3, 1, 4]], dtype=np.uint64)
+    pc = ax.imshow(img)
+    pc.set_clim(0, 5)
+
+
+@image_comparison(['imshow_bignumbers_real.png'],
+                  remove_text=True, style='mpl20')
+def test_imshow_bignumbers_real():
+    rcParams['image.interpolation'] = 'nearest'
+    # putting a big number in an array of integers shouldn't
+    # ruin the dynamic range of the resolved bits.
+    fig, ax = plt.subplots()
+    img = np.array([[2., 1., 1.e22], [4., 1., 3.]])
+    pc = ax.imshow(img)
+    pc.set_clim(0, 5)
+
+
+@pytest.mark.parametrize(
+    "make_norm",
+    [colors.Normalize,
+     colors.LogNorm,
+     lambda: colors.SymLogNorm(1),
+     lambda: colors.PowerNorm(1)])
+def test_empty_imshow(make_norm):
+    fig, ax = plt.subplots()
+    with pytest.warns(UserWarning,
+                      match="Attempting to set identical low and high xlims"):
+        im = ax.imshow([[]], norm=make_norm())
+    im.set_extent([-5, 5, -5, 5])
+    fig.canvas.draw()
+
+    with pytest.raises(RuntimeError):
+        im.make_image(fig.canvas.get_renderer())
+
+
+def test_imshow_float16():
+    fig, ax = plt.subplots()
+    ax.imshow(np.zeros((3, 3), dtype=np.float16))
+    # Ensure that drawing doesn't cause crash.
+    fig.canvas.draw()
+
+
+def test_imshow_float128():
+    fig, ax = plt.subplots()
+    ax.imshow(np.zeros((3, 3), dtype=np.longdouble))
+    with (ExitStack() if np.can_cast(np.longdouble, np.float64, "equiv")
+          else pytest.warns(UserWarning)):
+        # Ensure that drawing doesn't cause crash.
+        fig.canvas.draw()
+
+
+def test_imshow_bool():
+    fig, ax = plt.subplots()
+    ax.imshow(np.array([[True, False], [False, True]], dtype=bool))
+
+
+def test_full_invalid():
+    fig, ax = plt.subplots()
+    ax.imshow(np.full((10, 10), np.nan))
+
+    fig.canvas.draw()
+
+
+@pytest.mark.parametrize("fmt,counted",
+                         [("ps", b" colorimage"), ("svg", b"<image")])
+@pytest.mark.parametrize("composite_image,count", [(True, 1), (False, 2)])
+def test_composite(fmt, counted, composite_image, count):
+    # Test that figures can be saved with and without combining multiple images
+    # (on a single set of axes) into a single composite image.
+    X, Y = np.meshgrid(np.arange(-5, 5, 1), np.arange(-5, 5, 1))
+    Z = np.sin(Y ** 2)
+
+    fig, ax = plt.subplots()
+    ax.set_xlim(0, 3)
+    ax.imshow(Z, extent=[0, 1, 0, 1])
+    ax.imshow(Z[::-1], extent=[2, 3, 0, 1])
+    plt.rcParams['image.composite_image'] = composite_image
+    buf = io.BytesIO()
+    fig.savefig(buf, format=fmt)
+    assert buf.getvalue().count(counted) == count
+
+
+def test_relim():
+    fig, ax = plt.subplots()
+    ax.imshow([[0]], extent=(0, 1, 0, 1))
+    ax.relim()
+    ax.autoscale()
+    assert ax.get_xlim() == ax.get_ylim() == (0, 1)
+
+
+def test_unclipped():
+    fig, ax = plt.subplots()
+    ax.set_axis_off()
+    im = ax.imshow([[0, 0], [0, 0]], aspect="auto", extent=(-10, 10, -10, 10),
+                   cmap='gray', clip_on=False)
+    ax.set(xlim=(0, 1), ylim=(0, 1))
+    fig.canvas.draw()
+    # The unclipped image should fill the *entire* figure and be black.
+    # Ignore alpha for this comparison.
+    assert (np.array(fig.canvas.buffer_rgba())[..., :3] == 0).all()
+
+
+def test_respects_bbox():
+    fig, axs = plt.subplots(2)
+    for ax in axs:
+        ax.set_axis_off()
+    im = axs[1].imshow([[0, 1], [2, 3]], aspect="auto", extent=(0, 1, 0, 1))
+    im.set_clip_path(None)
+    # Make the image invisible in axs[1], but visible in axs[0] if we pan
+    # axs[1] up.
+    im.set_clip_box(axs[0].bbox)
+    buf_before = io.BytesIO()
+    fig.savefig(buf_before, format="rgba")
+    assert {*buf_before.getvalue()} == {0xff}  # All white.
+    axs[1].set(ylim=(-1, 0))
+    buf_after = io.BytesIO()
+    fig.savefig(buf_after, format="rgba")
+    assert buf_before.getvalue() != buf_after.getvalue()  # Not all white.
+
+
+def test_image_cursor_formatting():
+    fig, ax = plt.subplots()
+    # Create a dummy image to be able to call format_cursor_data
+    im = ax.imshow(np.zeros((4, 4)))
+
+    data = np.ma.masked_array([0], mask=[True])
+    assert im.format_cursor_data(data) == '[]'
+
+    data = np.ma.masked_array([0], mask=[False])
+    assert im.format_cursor_data(data) == '[0]'
+
+    data = np.nan
+    assert im.format_cursor_data(data) == '[nan]'
+
+
+@check_figures_equal()
+def test_image_array_alpha(fig_test, fig_ref):
+    """Per-pixel alpha channel test."""
+    x = np.linspace(0, 1)
+    xx, yy = np.meshgrid(x, x)
+
+    zz = np.exp(- 3 * ((xx - 0.5) ** 2) + (yy - 0.7 ** 2))
+    alpha = zz / zz.max()
+
+    cmap = mpl.colormaps['viridis']
+    ax = fig_test.add_subplot()
+    ax.imshow(zz, alpha=alpha, cmap=cmap, interpolation='nearest')
+
+    ax = fig_ref.add_subplot()
+    rgba = cmap(colors.Normalize()(zz))
+    rgba[..., -1] = alpha
+    ax.imshow(rgba, interpolation='nearest')
+
+
+def test_image_array_alpha_validation():
+    with pytest.raises(TypeError, match="alpha must be a float, two-d"):
+        plt.imshow(np.zeros((2, 2)), alpha=[1, 1])
+
+
+@mpl.style.context('mpl20')
+def test_exact_vmin():
+    cmap = copy(mpl.colormaps["autumn_r"])
+    cmap.set_under(color="lightgrey")
+
+    # make the image exactly 190 pixels wide
+    fig = plt.figure(figsize=(1.9, 0.1), dpi=100)
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    data = np.array(
+        [[-1, -1, -1, 0, 0, 0, 0, 43, 79, 95, 66, 1, -1, -1, -1, 0, 0, 0, 34]],
+        dtype=float,
+    )
+
+    im = ax.imshow(data, aspect="auto", cmap=cmap, vmin=0, vmax=100)
+    ax.axis("off")
+    fig.canvas.draw()
+
+    # get the RGBA slice from the image
+    from_image = im.make_image(fig.canvas.renderer)[0][0]
+    # expand the input to be 190 long and run through norm / cmap
+    direct_computation = (
+        im.cmap(im.norm((data * ([[1]] * 10)).T.ravel())) * 255
+    ).astype(int)
+
+    # check than the RBGA values are the same
+    assert np.all(from_image == direct_computation)
+
+
+@image_comparison(['image_placement'], extensions=['svg', 'pdf'],
+                  remove_text=True, style='mpl20')
+def test_image_placement():
+    """
+    The red box should line up exactly with the outside of the image.
+    """
+    fig, ax = plt.subplots()
+    ax.plot([0, 0, 1, 1, 0], [0, 1, 1, 0, 0], color='r', lw=0.1)
+    np.random.seed(19680801)
+    ax.imshow(np.random.randn(16, 16), cmap='Blues', extent=(0, 1, 0, 1),
+              interpolation='none', vmin=-1, vmax=1)
+    ax.set_xlim(-0.1, 1+0.1)
+    ax.set_ylim(-0.1, 1+0.1)
+
+
+# A basic ndarray subclass that implements a quantity
+# It does not implement an entire unit system or all quantity math.
+# There is just enough implemented to test handling of ndarray
+# subclasses.
+class QuantityND(np.ndarray):
+    def __new__(cls, input_array, units):
+        obj = np.asarray(input_array).view(cls)
+        obj.units = units
+        return obj
+
+    def __array_finalize__(self, obj):
+        self.units = getattr(obj, "units", None)
+
+    def __getitem__(self, item):
+        units = getattr(self, "units", None)
+        ret = super().__getitem__(item)
+        if isinstance(ret, QuantityND) or units is not None:
+            ret = QuantityND(ret, units)
+        return ret
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        func = getattr(ufunc, method)
+        if "out" in kwargs:
+            return NotImplemented
+        if len(inputs) == 1:
+            i0 = inputs[0]
+            unit = getattr(i0, "units", "dimensionless")
+            out_arr = func(np.asarray(i0), **kwargs)
+        elif len(inputs) == 2:
+            i0 = inputs[0]
+            i1 = inputs[1]
+            u0 = getattr(i0, "units", "dimensionless")
+            u1 = getattr(i1, "units", "dimensionless")
+            u0 = u1 if u0 is None else u0
+            u1 = u0 if u1 is None else u1
+            if ufunc in [np.add, np.subtract]:
+                if u0 != u1:
+                    raise ValueError
+                unit = u0
+            elif ufunc == np.multiply:
+                unit = f"{u0}*{u1}"
+            elif ufunc == np.divide:
+                unit = f"{u0}/({u1})"
+            elif ufunc in (np.greater, np.greater_equal,
+                           np.equal, np.not_equal,
+                           np.less, np.less_equal):
+                # Comparisons produce unitless booleans for output
+                unit = None
+            else:
+                return NotImplemented
+            out_arr = func(i0.view(np.ndarray), i1.view(np.ndarray), **kwargs)
+        else:
+            return NotImplemented
+        if unit is None:
+            out_arr = np.array(out_arr)
+        else:
+            out_arr = QuantityND(out_arr, unit)
+        return out_arr
+
+    @property
+    def v(self):
+        return self.view(np.ndarray)
+
+
+def test_quantitynd():
+    q = QuantityND([1, 2], "m")
+    q0, q1 = q[:]
+    assert np.all(q.v == np.asarray([1, 2]))
+    assert q.units == "m"
+    assert np.all((q0 + q1).v == np.asarray([3]))
+    assert (q0 * q1).units == "m*m"
+    assert (q1 / q0).units == "m/(m)"
+    with pytest.raises(ValueError):
+        q0 + QuantityND(1, "s")
+
+
+def test_imshow_quantitynd():
+    # generate a dummy ndarray subclass
+    arr = QuantityND(np.ones((2, 2)), "m")
+    fig, ax = plt.subplots()
+    ax.imshow(arr)
+    # executing the draw should not raise an exception
+    fig.canvas.draw()
+
+
+@check_figures_equal(extensions=['png'])
+def test_norm_change(fig_test, fig_ref):
+    # LogNorm should not mask anything invalid permanently.
+    data = np.full((5, 5), 1, dtype=np.float64)
+    data[0:2, :] = -1
+
+    masked_data = np.ma.array(data, mask=False)
+    masked_data.mask[0:2, 0:2] = True
+
+    cmap = mpl.colormaps['viridis'].with_extremes(under='w')
+
+    ax = fig_test.subplots()
+    im = ax.imshow(data, norm=colors.LogNorm(vmin=0.5, vmax=1),
+                   extent=(0, 5, 0, 5), interpolation='nearest', cmap=cmap)
+    im.set_norm(colors.Normalize(vmin=-2, vmax=2))
+    im = ax.imshow(masked_data, norm=colors.LogNorm(vmin=0.5, vmax=1),
+                   extent=(5, 10, 5, 10), interpolation='nearest', cmap=cmap)
+    im.set_norm(colors.Normalize(vmin=-2, vmax=2))
+    ax.set(xlim=(0, 10), ylim=(0, 10))
+
+    ax = fig_ref.subplots()
+    ax.imshow(data, norm=colors.Normalize(vmin=-2, vmax=2),
+              extent=(0, 5, 0, 5), interpolation='nearest', cmap=cmap)
+    ax.imshow(masked_data, norm=colors.Normalize(vmin=-2, vmax=2),
+              extent=(5, 10, 5, 10), interpolation='nearest', cmap=cmap)
+    ax.set(xlim=(0, 10), ylim=(0, 10))
+
+
+@pytest.mark.parametrize('x', [-1, 1])
+@check_figures_equal(extensions=['png'])
+def test_huge_range_log(fig_test, fig_ref, x):
+    # parametrize over bad lognorm -1 values and large range 1 -> 1e20
+    data = np.full((5, 5), x, dtype=np.float64)
+    data[0:2, :] = 1E20
+
+    ax = fig_test.subplots()
+    ax.imshow(data, norm=colors.LogNorm(vmin=1, vmax=data.max()),
+              interpolation='nearest', cmap='viridis')
+
+    data = np.full((5, 5), x, dtype=np.float64)
+    data[0:2, :] = 1000
+
+    ax = fig_ref.subplots()
+    cmap = mpl.colormaps['viridis'].with_extremes(under='w')
+    ax.imshow(data, norm=colors.Normalize(vmin=1, vmax=data.max()),
+              interpolation='nearest', cmap=cmap)
+
+
+@check_figures_equal(extensions=['png'])
+def test_spy_box(fig_test, fig_ref):
+    # setting up reference and test
+    ax_test = fig_test.subplots(1, 3)
+    ax_ref = fig_ref.subplots(1, 3)
+
+    plot_data = (
+        [[1, 1], [1, 1]],
+        [[0, 0], [0, 0]],
+        [[0, 1], [1, 0]],
+    )
+    plot_titles = ["ones", "zeros", "mixed"]
+
+    for i, (z, title) in enumerate(zip(plot_data, plot_titles)):
+        ax_test[i].set_title(title)
+        ax_test[i].spy(z)
+        ax_ref[i].set_title(title)
+        ax_ref[i].imshow(z, interpolation='nearest',
+                            aspect='equal', origin='upper', cmap='Greys',
+                            vmin=0, vmax=1)
+        ax_ref[i].set_xlim(-0.5, 1.5)
+        ax_ref[i].set_ylim(1.5, -0.5)
+        ax_ref[i].xaxis.tick_top()
+        ax_ref[i].title.set_y(1.05)
+        ax_ref[i].xaxis.set_ticks_position('both')
+        ax_ref[i].xaxis.set_major_locator(
+            mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)
+        )
+        ax_ref[i].yaxis.set_major_locator(
+            mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)
+        )
+
+
+@image_comparison(["nonuniform_and_pcolor.png"], style="mpl20")
+def test_nonuniform_and_pcolor():
+    axs = plt.figure(figsize=(3, 3)).subplots(3, sharex=True, sharey=True)
+    for ax, interpolation in zip(axs, ["nearest", "bilinear"]):
+        im = NonUniformImage(ax, interpolation=interpolation)
+        im.set_data(np.arange(3) ** 2, np.arange(3) ** 2,
+                    np.arange(9).reshape((3, 3)))
+        ax.add_image(im)
+    axs[2].pcolorfast(  # PcolorImage
+        np.arange(4) ** 2, np.arange(4) ** 2, np.arange(9).reshape((3, 3)))
+    for ax in axs:
+        ax.set_axis_off()
+        # NonUniformImage "leaks" out of extents, not PColorImage.
+        ax.set(xlim=(0, 10))
+
+
+@image_comparison(["nonuniform_logscale.png"], style="mpl20")
+def test_nonuniform_logscale():
+    _, axs = plt.subplots(ncols=3, nrows=1)
+
+    for i in range(3):
+        ax = axs[i]
+        im = NonUniformImage(ax)
+        im.set_data(np.arange(1, 4) ** 2, np.arange(1, 4) ** 2,
+                    np.arange(9).reshape((3, 3)))
+        ax.set_xlim(1, 16)
+        ax.set_ylim(1, 16)
+        ax.set_box_aspect(1)
+        if i == 1:
+            ax.set_xscale("log", base=2)
+            ax.set_yscale("log", base=2)
+        if i == 2:
+            ax.set_xscale("log", base=4)
+            ax.set_yscale("log", base=4)
+        ax.add_image(im)
+
+
+@image_comparison(['rgba_antialias.png'], style='mpl20', remove_text=True, tol=0.02)
+def test_rgba_antialias():
+    fig, axs = plt.subplots(2, 2, figsize=(3.5, 3.5), sharex=False,
+                            sharey=False, constrained_layout=True)
+    N = 250
+    aa = np.ones((N, N))
+    aa[::2, :] = -1
+
+    x = np.arange(N) / N - 0.5
+    y = np.arange(N) / N - 0.5
+
+    X, Y = np.meshgrid(x, y)
+    R = np.sqrt(X**2 + Y**2)
+    f0 = 10
+    k = 75
+    # aliased concentric circles
+    a = np.sin(np.pi * 2 * (f0 * R + k * R**2 / 2))
+
+    # stripes on lhs
+    a[:int(N/2), :][R[:int(N/2), :] < 0.4] = -1
+    a[:int(N/2), :][R[:int(N/2), :] < 0.3] = 1
+    aa[:, int(N/2):] = a[:, int(N/2):]
+
+    # set some over/unders and NaNs
+    aa[20:50, 20:50] = np.nan
+    aa[70:90, 70:90] = 1e6
+    aa[70:90, 20:30] = -1e6
+    aa[70:90, 195:215] = 1e6
+    aa[20:30, 195:215] = -1e6
+
+    cmap = copy(plt.cm.RdBu_r)
+    cmap.set_over('yellow')
+    cmap.set_under('cyan')
+
+    axs = axs.flatten()
+    # zoom in
+    axs[0].imshow(aa, interpolation='nearest', cmap=cmap, vmin=-1.2, vmax=1.2)
+    axs[0].set_xlim([N/2-25, N/2+25])
+    axs[0].set_ylim([N/2+50, N/2-10])
+
+    # no anti-alias
+    axs[1].imshow(aa, interpolation='nearest', cmap=cmap, vmin=-1.2, vmax=1.2)
+
+    # data antialias: Note no purples, and white in circle.  Note
+    # that alternating red and blue stripes become white.
+    axs[2].imshow(aa, interpolation='auto', interpolation_stage='data',
+                  cmap=cmap, vmin=-1.2, vmax=1.2)
+
+    # rgba antialias: Note purples at boundary with circle.  Note that
+    # alternating red and blue stripes become purple
+    axs[3].imshow(aa, interpolation='auto', interpolation_stage='rgba',
+                  cmap=cmap, vmin=-1.2, vmax=1.2)
+
+
+@check_figures_equal(extensions=('png', ))
+def test_upsample_interpolation_stage(fig_test, fig_ref):
+    """
+    Show that interpolation_stage='auto' gives the same as 'data'
+    for upsampling.
+    """
+    # Fixing random state for reproducibility.  This non-standard seed
+    # gives red splotches for 'rgba'.
+    np.random.seed(19680801+9)
+
+    grid = np.random.rand(4, 4)
+    ax = fig_ref.subplots()
+    ax.imshow(grid, interpolation='bilinear', cmap='viridis',
+              interpolation_stage='data')
+
+    ax = fig_test.subplots()
+    ax.imshow(grid, interpolation='bilinear', cmap='viridis',
+              interpolation_stage='auto')
+
+
+@check_figures_equal(extensions=('png', ))
+def test_downsample_interpolation_stage(fig_test, fig_ref):
+    """
+    Show that interpolation_stage='auto' gives the same as 'rgba'
+    for downsampling.
+    """
+    # Fixing random state for reproducibility
+    np.random.seed(19680801)
+
+    grid = np.random.rand(4000, 4000)
+    ax = fig_ref.subplots()
+    ax.imshow(grid, interpolation='auto', cmap='viridis',
+              interpolation_stage='rgba')
+
+    ax = fig_test.subplots()
+    ax.imshow(grid, interpolation='auto', cmap='viridis',
+              interpolation_stage='auto')
+
+
+def test_rc_interpolation_stage():
+    for val in ["data", "rgba"]:
+        with mpl.rc_context({"image.interpolation_stage": val}):
+            assert plt.imshow([[1, 2]]).get_interpolation_stage() == val
+    for val in ["DATA", "foo", None]:
+        with pytest.raises(ValueError):
+            mpl.rcParams["image.interpolation_stage"] = val
+
+
+# We check for the warning with a draw() in the test, but we also need to
+# filter the warning as it is emitted by the figure test decorator
+@pytest.mark.filterwarnings(r'ignore:Data with more than .* '
+                            'cannot be accurately displayed')
+@pytest.mark.parametrize('origin', ['upper', 'lower'])
+@pytest.mark.parametrize(
+    'dim, size, msg', [['row', 2**23, r'2\*\*23 columns'],
+                       ['col', 2**24, r'2\*\*24 rows']])
+@check_figures_equal(extensions=('png', ))
+def test_large_image(fig_test, fig_ref, dim, size, msg, origin):
+    # Check that Matplotlib downsamples images that are too big for AGG
+    # See issue #19276. Currently the fix only works for png output but not
+    # pdf or svg output.
+    ax_test = fig_test.subplots()
+    ax_ref = fig_ref.subplots()
+
+    array = np.zeros((1, size + 2))
+    array[:, array.size // 2:] = 1
+    if dim == 'col':
+        array = array.T
+    im = ax_test.imshow(array, vmin=0, vmax=1,
+                        aspect='auto', extent=(0, 1, 0, 1),
+                        interpolation='none',
+                        origin=origin)
+
+    with pytest.warns(UserWarning,
+                      match=f'Data with more than {msg} cannot be '
+                      'accurately displayed.'):
+        fig_test.canvas.draw()
+
+    array = np.zeros((1, 2))
+    array[:, 1] = 1
+    if dim == 'col':
+        array = array.T
+    im = ax_ref.imshow(array, vmin=0, vmax=1, aspect='auto',
+                       extent=(0, 1, 0, 1),
+                       interpolation='none',
+                       origin=origin)
+
+
+@check_figures_equal(extensions=["png"])
+def test_str_norms(fig_test, fig_ref):
+    t = np.random.rand(10, 10) * .8 + .1  # between 0 and 1
+    axts = fig_test.subplots(1, 5)
+    axts[0].imshow(t, norm="log")
+    axts[1].imshow(t, norm="log", vmin=.2)
+    axts[2].imshow(t, norm="symlog")
+    axts[3].imshow(t, norm="symlog", vmin=.3, vmax=.7)
+    axts[4].imshow(t, norm="logit", vmin=.3, vmax=.7)
+    axrs = fig_ref.subplots(1, 5)
+    axrs[0].imshow(t, norm=colors.LogNorm())
+    axrs[1].imshow(t, norm=colors.LogNorm(vmin=.2))
+    # same linthresh as SymmetricalLogScale's default.
+    axrs[2].imshow(t, norm=colors.SymLogNorm(linthresh=2))
+    axrs[3].imshow(t, norm=colors.SymLogNorm(linthresh=2, vmin=.3, vmax=.7))
+    axrs[4].imshow(t, norm="logit", clim=(.3, .7))
+
+    assert type(axts[0].images[0].norm) is colors.LogNorm  # Exactly that class
+    with pytest.raises(ValueError):
+        axts[0].imshow(t, norm="foobar")
+
+
+def test__resample_valid_output():
+    resample = functools.partial(mpl._image.resample, transform=Affine2D())
+    with pytest.raises(TypeError, match="incompatible function arguments"):
+        resample(np.zeros((9, 9)), None)
+    with pytest.raises(ValueError, match="different dimensionalities"):
+        resample(np.zeros((9, 9)), np.zeros((9, 9, 4)))
+    with pytest.raises(ValueError, match="different dimensionalities"):
+        resample(np.zeros((9, 9, 4)), np.zeros((9, 9)))
+    with pytest.raises(ValueError, match="3D input array must be RGBA"):
+        resample(np.zeros((9, 9, 3)), np.zeros((9, 9, 4)))
+    with pytest.raises(ValueError, match="3D output array must be RGBA"):
+        resample(np.zeros((9, 9, 4)), np.zeros((9, 9, 3)))
+    with pytest.raises(ValueError, match="mismatched types"):
+        resample(np.zeros((9, 9), np.uint8), np.zeros((9, 9)))
+    with pytest.raises(ValueError, match="must be C-contiguous"):
+        resample(np.zeros((9, 9)), np.zeros((9, 9)).T)
+
+    out = np.zeros((9, 9))
+    out.flags.writeable = False
+    with pytest.raises(ValueError, match="Output array must be writeable"):
+        resample(np.zeros((9, 9)), out)
+
+
+def test_axesimage_get_shape():
+    # generate dummy image to test get_shape method
+    ax = plt.gca()
+    im = AxesImage(ax)
+    with pytest.raises(RuntimeError, match="You must first set the image array"):
+        im.get_shape()
+    z = np.arange(12, dtype=float).reshape((4, 3))
+    im.set_data(z)
+    assert im.get_shape() == (4, 3)
+    assert im.get_size() == im.get_shape()
+
+
+def test_non_transdata_image_does_not_touch_aspect():
+    ax = plt.figure().add_subplot()
+    im = np.arange(4).reshape((2, 2))
+    ax.imshow(im, transform=ax.transAxes)
+    assert ax.get_aspect() == "auto"
+    ax.imshow(im, transform=Affine2D().scale(2) + ax.transData)
+    assert ax.get_aspect() == 1
+    ax.imshow(im, transform=ax.transAxes, aspect=2)
+    assert ax.get_aspect() == 2
+
+
+@image_comparison(
+    ['downsampling.png'], style='mpl20', remove_text=True, tol=0.09)
+def test_downsampling():
+    N = 450
+    x = np.arange(N) / N - 0.5
+    y = np.arange(N) / N - 0.5
+    aa = np.ones((N, N))
+    aa[::2, :] = -1
+
+    X, Y = np.meshgrid(x, y)
+    R = np.sqrt(X**2 + Y**2)
+    f0 = 5
+    k = 100
+    a = np.sin(np.pi * 2 * (f0 * R + k * R**2 / 2))
+    # make the left hand side of this
+    a[:int(N / 2), :][R[:int(N / 2), :] < 0.4] = -1
+    a[:int(N / 2), :][R[:int(N / 2), :] < 0.3] = 1
+    aa[:, int(N / 3):] = a[:, int(N / 3):]
+    a = aa
+
+    fig, axs = plt.subplots(2, 3, figsize=(7, 6), layout='compressed')
+    axs[0, 0].imshow(a, interpolation='nearest', interpolation_stage='rgba',
+                     cmap='RdBu_r')
+    axs[0, 0].set_xlim(125, 175)
+    axs[0, 0].set_ylim(250, 200)
+    axs[0, 0].set_title('Zoom')
+
+    for ax, interp, space in zip(axs.flat[1:], ['nearest', 'nearest', 'hanning',
+                                                'hanning', 'auto'],
+                                 ['data', 'rgba', 'data', 'rgba', 'auto']):
+        ax.imshow(a, interpolation=interp, interpolation_stage=space,
+                  cmap='RdBu_r')
+        ax.set_title(f"interpolation='{interp}'\nspace='{space}'")
+
+
+@image_comparison(
+    ['downsampling_speckle.png'], style='mpl20', remove_text=True, tol=0.09)
+def test_downsampling_speckle():
+    fig, axs = plt.subplots(1, 2, figsize=(5, 2.7), sharex=True, sharey=True,
+                            layout="compressed")
+    axs = axs.flatten()
+    img = ((np.arange(1024).reshape(-1, 1) * np.ones(720)) // 50).T
+
+    cm = plt.get_cmap("viridis")
+    cm.set_over("m")
+    norm = colors.LogNorm(vmin=3, vmax=11)
+
+    # old default cannot be tested because it creates over/under speckles
+    # in the following that are machine dependent.
+
+    axs[0].set_title("interpolation='auto', stage='rgba'")
+    axs[0].imshow(np.triu(img), cmap=cm, norm=norm, interpolation_stage='rgba')
+
+    # Should be same as previous
+    axs[1].set_title("interpolation='auto', stage='auto'")
+    axs[1].imshow(np.triu(img), cmap=cm, norm=norm)
+
+
+@image_comparison(
+    ['upsampling.png'], style='mpl20', remove_text=True)
+def test_upsampling():
+
+    np.random.seed(19680801+9)  # need this seed to get yellow next to blue
+    a = np.random.rand(4, 4)
+
+    fig, axs = plt.subplots(1, 3, figsize=(6.5, 3), layout='compressed')
+    im = axs[0].imshow(a, cmap='viridis')
+    axs[0].set_title(
+        "interpolation='auto'\nstage='antialaised'\n(default for upsampling)")
+
+    # probably what people want:
+    axs[1].imshow(a, cmap='viridis', interpolation='sinc')
+    axs[1].set_title(
+        "interpolation='sinc'\nstage='auto'\n(default for upsampling)")
+
+    # probably not what people want:
+    axs[2].imshow(a, cmap='viridis', interpolation='sinc', interpolation_stage='rgba')
+    axs[2].set_title("interpolation='sinc'\nstage='rgba'")
+    fig.colorbar(im, ax=axs, shrink=0.7, extend='both')
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    ('float64', 'float32', 'int16', 'uint16', 'int8', 'uint8'),
+)
+@pytest.mark.parametrize('ndim', (2, 3))
+def test_resample_dtypes(dtype, ndim):
+    # Issue 28448, incorrect dtype comparisons in C++ image_resample can raise
+    # ValueError: arrays must be of dtype byte, short, float32 or float64
+    rng = np.random.default_rng(4181)
+    shape = (2, 2) if ndim == 2 else (2, 2, 3)
+    data = rng.uniform(size=shape).astype(np.dtype(dtype, copy=True))
+    fig, ax = plt.subplots()
+    axes_image = ax.imshow(data)
+    # Before fix the following raises ValueError for some dtypes.
+    axes_image.make_image(None)[0]
+
+
+@pytest.mark.parametrize('intp_stage', ('data', 'rgba'))
+@check_figures_equal()
+def test_interpolation_stage_rgba_respects_alpha_param(fig_test, fig_ref, intp_stage):
+    axs_tst = fig_test.subplots(2, 3)
+    axs_ref = fig_ref.subplots(2, 3)
+    ny, nx = 3, 3
+    scalar_alpha = 0.5
+    array_alpha = np.random.rand(ny, nx)
+
+    # When the image does not have an alpha channel, alpha should be specified
+    # by the user or default to 1.0
+    im_rgb = np.random.rand(ny, nx, 3)
+    im_concat_default_a = np.ones((ny, nx, 1))  # alpha defaults to 1.0
+    im_rgba = np.concatenate(  # combine rgb channels with array alpha
+        (im_rgb, array_alpha.reshape((ny, nx, 1))), axis=-1
+    )
+    axs_tst[0][0].imshow(im_rgb)
+    axs_ref[0][0].imshow(np.concatenate((im_rgb, im_concat_default_a), axis=-1))
+    axs_tst[0][1].imshow(im_rgb, interpolation_stage=intp_stage, alpha=scalar_alpha)
+    axs_ref[0][1].imshow(
+        np.concatenate(  # combine rgb channels with broadcasted scalar alpha
+            (im_rgb, scalar_alpha * im_concat_default_a), axis=-1
+        ), interpolation_stage=intp_stage
+    )
+    axs_tst[0][2].imshow(im_rgb, interpolation_stage=intp_stage, alpha=array_alpha)
+    axs_ref[0][2].imshow(im_rgba, interpolation_stage=intp_stage)
+
+    # When the image already has an alpha channel, multiply it by the
+    # scalar alpha param, or replace it by the array alpha param
+    axs_tst[1][0].imshow(im_rgba)
+    axs_ref[1][0].imshow(im_rgb, alpha=array_alpha)
+    axs_tst[1][1].imshow(im_rgba, interpolation_stage=intp_stage, alpha=scalar_alpha)
+    axs_ref[1][1].imshow(
+        np.concatenate(  # combine rgb channels with scaled array alpha
+            (im_rgb, scalar_alpha * array_alpha.reshape((ny, nx, 1))), axis=-1
+        ), interpolation_stage=intp_stage
+    )
+    new_array_alpha = np.random.rand(ny, nx)
+    axs_tst[1][2].imshow(im_rgba, interpolation_stage=intp_stage, alpha=new_array_alpha)
+    axs_ref[1][2].imshow(
+        np.concatenate(  # combine rgb channels with new array alpha
+            (im_rgb, new_array_alpha.reshape((ny, nx, 1))), axis=-1
+        ), interpolation_stage=intp_stage
+    )
diff --git a/shared/package-files/a0/a0869d2c9451a944b87f059edc5d93c1d415888b98b9247b8aeb5489d9dcba7d/urls.pyi b/shared/package-files/a0/a0869d2c9451a944b87f059edc5d93c1d415888b98b9247b8aeb5489d9dcba7d/urls.pyi
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index 0000000000000000000000000000000000000000..99cbb7c43327aa77c9f5e200fa108cb6c90188c5
--- /dev/null
+++ b/shared/package-files/a0/a0869d2c9451a944b87f059edc5d93c1d415888b98b9247b8aeb5489d9dcba7d/urls.pyi
@@ -0,0 +1,3 @@
+from typing import Any, List
+
+urlpatterns: List[Any] = ...
diff --git a/shared/package-files/a0/a0893267a67636250bd3db124ef0052bb22571f403895830b2c15ee8db6cc881/test_nlargest.py b/shared/package-files/a0/a0893267a67636250bd3db124ef0052bb22571f403895830b2c15ee8db6cc881/test_nlargest.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8de1cd89e3974f2dff0d775f35655b8caa02a08
--- /dev/null
+++ b/shared/package-files/a0/a0893267a67636250bd3db124ef0052bb22571f403895830b2c15ee8db6cc881/test_nlargest.py
@@ -0,0 +1,248 @@
+"""
+Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
+but are implicitly also testing nsmallest_foo.
+"""
+from itertools import product
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+main_dtypes = [
+    "datetime",
+    "datetimetz",
+    "timedelta",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "float32",
+    "float64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+]
+
+
+@pytest.fixture
+def s_main_dtypes():
+    """
+    A DataFrame with many dtypes
+
+    * datetime
+    * datetimetz
+    * timedelta
+    * [u]int{8,16,32,64}
+    * float{32,64}
+
+    The columns are the name of the dtype.
+    """
+    df = pd.DataFrame(
+        {
+            "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
+            "datetimetz": pd.to_datetime(
+                ["2003", "2002", "2001", "2002", "2005"]
+            ).tz_localize("US/Eastern"),
+            "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
+        }
+    )
+
+    for dtype in [
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "float32",
+        "float64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    ]:
+        df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
+
+    return df
+
+
+@pytest.fixture(params=main_dtypes)
+def s_main_dtypes_split(request, s_main_dtypes):
+    """Each series in s_main_dtypes."""
+    return s_main_dtypes[request.param]
+
+
+def assert_check_nselect_boundary(vals, dtype, method):
+    # helper function for 'test_boundary_{dtype}' tests
+    ser = Series(vals, dtype=dtype)
+    result = getattr(ser, method)(3)
+    expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
+    expected = ser.loc[expected_idxr]
+    tm.assert_series_equal(result, expected)
+
+
+class TestSeriesNLargestNSmallest:
+    @pytest.mark.parametrize(
+        "r",
+        [
+            Series([3.0, 2, 1, 2, "5"], dtype="object"),
+            Series([3.0, 2, 1, 2, 5], dtype="object"),
+            # not supported on some archs
+            # Series([3., 2, 1, 2, 5], dtype='complex256'),
+            Series([3.0, 2, 1, 2, 5], dtype="complex128"),
+            Series(list("abcde")),
+            Series(list("abcde"), dtype="category"),
+        ],
+    )
+    def test_nlargest_error(self, r):
+        dt = r.dtype
+        msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}"
+        args = 2, len(r), 0, -1
+        methods = r.nlargest, r.nsmallest
+        for method, arg in product(methods, args):
+            with pytest.raises(TypeError, match=msg):
+                method(arg)
+
+    def test_nsmallest_nlargest(self, s_main_dtypes_split):
+        # float, int, datetime64 (use i8), timedelts64 (same),
+        # object that are numbers, object that are strings
+        ser = s_main_dtypes_split
+
+        tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]])
+        tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]])
+
+        empty = ser.iloc[0:0]
+        tm.assert_series_equal(ser.nsmallest(0), empty)
+        tm.assert_series_equal(ser.nsmallest(-1), empty)
+        tm.assert_series_equal(ser.nlargest(0), empty)
+        tm.assert_series_equal(ser.nlargest(-1), empty)
+
+        tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values())
+        tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values())
+        tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]])
+        tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]])
+
+    def test_nlargest_misc(self):
+        ser = Series([3.0, np.nan, 1, 2, 5])
+        result = ser.nlargest()
+        expected = ser.iloc[[4, 0, 3, 2, 1]]
+        tm.assert_series_equal(result, expected)
+        result = ser.nsmallest()
+        expected = ser.iloc[[2, 3, 0, 4, 1]]
+        tm.assert_series_equal(result, expected)
+
+        msg = 'keep must be either "first", "last"'
+        with pytest.raises(ValueError, match=msg):
+            ser.nsmallest(keep="invalid")
+        with pytest.raises(ValueError, match=msg):
+            ser.nlargest(keep="invalid")
+
+        # GH#15297
+        ser = Series([1] * 5, index=[1, 2, 3, 4, 5])
+        expected_first = Series([1] * 3, index=[1, 2, 3])
+        expected_last = Series([1] * 3, index=[5, 4, 3])
+
+        result = ser.nsmallest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nsmallest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+        result = ser.nlargest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nlargest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+    @pytest.mark.parametrize("n", range(1, 5))
+    def test_nlargest_n(self, n):
+        # GH 13412
+        ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
+        result = ser.nlargest(n)
+        expected = ser.sort_values(ascending=False).head(n)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(n)
+        expected = ser.sort_values().head(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype):
+        # GH#21426
+        dtype_info = np.iinfo(any_int_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val, min_val + 1, max_val - 1, max_val]
+        assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method)
+
+    def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype):
+        # GH#21426
+        dtype_info = np.finfo(float_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype)
+        vals = [min_val, min_2nd, max_2nd, max_val]
+        assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method)
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_nlargest_boundary_datetimelike(self, nselect_method, dtype):
+        # GH#21426
+        # use int64 bounds and +1 to min_val since true minimum is NaT
+        # (include min_val/NaT at end to maintain same expected_idxr)
+        dtype_info = np.iinfo("int64")
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
+        assert_check_nselect_boundary(vals, dtype, nselect_method)
+
+    def test_nlargest_duplicate_keep_all_ties(self):
+        # see GH#16818
+        ser = Series([10, 9, 8, 7, 7, 7, 7, 6])
+        result = ser.nlargest(4, keep="all")
+        expected = Series([10, 9, 8, 7, 7, 7, 7])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(2, keep="all")
+        expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
+    )
+    def test_nlargest_boolean(self, data, expected):
+        # GH#26154 : ensure True > False
+        ser = Series(data)
+        result = ser.nlargest(1)
+        expected = Series(expected)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_nullable(self, any_numeric_ea_dtype):
+        # GH#42816
+        dtype = any_numeric_ea_dtype
+        if dtype.startswith("UInt"):
+            # Can't cast from negative float to uint on some platforms
+            arr = np.random.default_rng(2).integers(1, 10, 10)
+        else:
+            arr = np.random.default_rng(2).standard_normal(10)
+        arr = arr.astype(dtype.lower(), copy=False)
+
+        ser = Series(arr.copy(), dtype=dtype)
+        ser[1] = pd.NA
+        result = ser.nlargest(5)
+
+        expected = (
+            Series(np.delete(arr, 1), index=ser.index.delete(1))
+            .nlargest(5)
+            .astype(dtype)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_nsmallest_nan_when_keep_is_all(self):
+        # GH#46589
+        s = Series([1, 2, 3, 3, 3, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1.0, 2.0, 3.0, 3.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+        s = Series([1, 2, None, None, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1, 2, None, None, None])
+        tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/a0/a090a97cd7e817ca0ab5ee48926bb85c1758bffe2926a80518e7fce88ab8eeef/proxy_fix.pyi b/shared/package-files/a0/a090a97cd7e817ca0ab5ee48926bb85c1758bffe2926a80518e7fce88ab8eeef/proxy_fix.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0522e2beb121e13abbd20b3da81e87a955bb1cdd
--- /dev/null
+++ b/shared/package-files/a0/a090a97cd7e817ca0ab5ee48926bb85c1758bffe2926a80518e7fce88ab8eeef/proxy_fix.pyi
@@ -0,0 +1,23 @@
+from _typeshed.wsgi import StartResponse, WSGIApplication, WSGIEnvironment
+from typing import Iterable, Optional
+
+class ProxyFix(object):
+    app: WSGIApplication
+    x_for: int
+    x_proto: int
+    x_host: int
+    x_port: int
+    x_prefix: int
+    num_proxies: int
+    def __init__(
+        self,
+        app: WSGIApplication,
+        num_proxies: Optional[int] = ...,
+        x_for: int = ...,
+        x_proto: int = ...,
+        x_host: int = ...,
+        x_port: int = ...,
+        x_prefix: int = ...,
+    ) -> None: ...
+    def get_remote_addr(self, forwarded_for: Iterable[str]) -> Optional[str]: ...
+    def __call__(self, environ: WSGIEnvironment, start_response: StartResponse) -> Iterable[bytes]: ...
diff --git a/shared/package-files/a0/a091fdd8780b5ff9de7950eead12b354e8a1f7c44dd0aabf278e51a5edef5967/xterm-x10mouse b/shared/package-files/a0/a091fdd8780b5ff9de7950eead12b354e8a1f7c44dd0aabf278e51a5edef5967/xterm-x10mouse
new file mode 100644
index 0000000000000000000000000000000000000000..c1ceab02fa98588e289c7083745bd9599e4cc546
Binary files /dev/null and b/shared/package-files/a0/a091fdd8780b5ff9de7950eead12b354e8a1f7c44dd0aabf278e51a5edef5967/xterm-x10mouse differ
diff --git a/shared/package-files/a0/a0a133cfc78e82e1f71ce628408e7d10a38552ba3e3228ebd113838c1ce44484/fix_types.py b/shared/package-files/a0/a0a133cfc78e82e1f71ce628408e7d10a38552ba3e3228ebd113838c1ce44484/fix_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..67bf51f2f5b85a6a2f6a4c6427a5d9893f0c4350
--- /dev/null
+++ b/shared/package-files/a0/a0a133cfc78e82e1f71ce628408e7d10a38552ba3e3228ebd113838c1ce44484/fix_types.py
@@ -0,0 +1,61 @@
+# Copyright 2007 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Fixer for removing uses of the types module.
+
+These work for only the known names in the types module.  The forms above
+can include types. or not.  ie, It is assumed the module is imported either as:
+
+    import types
+    from types import ... # either * or specific types
+
+The import statements are not modified.
+
+There should be another fixer that handles at least the following constants:
+
+   type([]) -> list
+   type(()) -> tuple
+   type('') -> str
+
+"""
+
+# Local imports
+from .. import fixer_base
+from ..fixer_util import Name
+
+_TYPE_MAPPING = {
+        'BooleanType' : 'bool',
+        'BufferType' : 'memoryview',
+        'ClassType' : 'type',
+        'ComplexType' : 'complex',
+        'DictType': 'dict',
+        'DictionaryType' : 'dict',
+        'EllipsisType' : 'type(Ellipsis)',
+        #'FileType' : 'io.IOBase',
+        'FloatType': 'float',
+        'IntType': 'int',
+        'ListType': 'list',
+        'LongType': 'int',
+        'ObjectType' : 'object',
+        'NoneType': 'type(None)',
+        'NotImplementedType' : 'type(NotImplemented)',
+        'SliceType' : 'slice',
+        'StringType': 'bytes', # XXX ?
+        'StringTypes' : '(str,)', # XXX ?
+        'TupleType': 'tuple',
+        'TypeType' : 'type',
+        'UnicodeType': 'str',
+        'XRangeType' : 'range',
+    }
+
+_pats = ["power< 'types' trailer< '.' name='%s' > >" % t for t in _TYPE_MAPPING]
+
+class FixTypes(fixer_base.BaseFix):
+    BM_compatible = True
+    PATTERN = '|'.join(_pats)
+
+    def transform(self, node, results):
+        new_value = _TYPE_MAPPING.get(results["name"].value)
+        if new_value:
+            return Name(new_value, prefix=node.prefix)
+        return None
diff --git a/shared/package-files/a0/a0a8c5e26611c1bc91a97ae85d6add59b98a2fa5c92ff7c25467ea88bd6e4ce3/_ridge.py b/shared/package-files/a0/a0a8c5e26611c1bc91a97ae85d6add59b98a2fa5c92ff7c25467ea88bd6e4ce3/_ridge.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a55291a70ace22716d07fecffc931c8dadb093e
--- /dev/null
+++ b/shared/package-files/a0/a0a8c5e26611c1bc91a97ae85d6add59b98a2fa5c92ff7c25467ea88bd6e4ce3/_ridge.py
@@ -0,0 +1,2899 @@
+"""
+Ridge regression
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numbers
+import warnings
+from abc import ABCMeta, abstractmethod
+from functools import partial
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import linalg, optimize, sparse
+from scipy.sparse import linalg as sp_linalg
+
+from sklearn.base import BaseEstimator
+
+from ..base import MultiOutputMixin, RegressorMixin, _fit_context, is_classifier
+from ..exceptions import ConvergenceWarning
+from ..metrics import check_scoring, get_scorer_names
+from ..model_selection import GridSearchCV
+from ..preprocessing import LabelBinarizer
+from ..utils import (
+    Bunch,
+    check_array,
+    check_consistent_length,
+    check_scalar,
+    column_or_1d,
+    compute_sample_weight,
+)
+from ..utils._array_api import (
+    _is_numpy_namespace,
+    _ravel,
+    device,
+    get_namespace,
+    get_namespace_and_device,
+)
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.extmath import row_norms, safe_sparse_dot
+from ..utils.fixes import _sparse_linalg_cg
+from ..utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    _routing_enabled,
+    process_routing,
+)
+from ..utils.sparsefuncs import mean_variance_axis
+from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
+from ._base import LinearClassifierMixin, LinearModel, _preprocess_data, _rescale_data
+from ._sag import sag_solver
+
+
+def _get_rescaled_operator(X, X_offset, sample_weight_sqrt):
+    """Create LinearOperator for matrix products with implicit centering.
+
+    Matrix product `LinearOperator @ coef` returns `(X - X_offset) @ coef`.
+    """
+
+    def matvec(b):
+        return X.dot(b) - sample_weight_sqrt * b.dot(X_offset)
+
+    def rmatvec(b):
+        return X.T.dot(b) - X_offset * b.dot(sample_weight_sqrt)
+
+    X1 = sparse.linalg.LinearOperator(shape=X.shape, matvec=matvec, rmatvec=rmatvec)
+    return X1
+
+
+def _solve_sparse_cg(
+    X,
+    y,
+    alpha,
+    max_iter=None,
+    tol=1e-4,
+    verbose=0,
+    X_offset=None,
+    X_scale=None,
+    sample_weight_sqrt=None,
+):
+    if sample_weight_sqrt is None:
+        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)
+
+    n_samples, n_features = X.shape
+
+    if X_offset is None or X_scale is None:
+        X1 = sp_linalg.aslinearoperator(X)
+    else:
+        X_offset_scale = X_offset / X_scale
+        X1 = _get_rescaled_operator(X, X_offset_scale, sample_weight_sqrt)
+
+    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)
+
+    if n_features > n_samples:
+
+        def create_mv(curr_alpha):
+            def _mv(x):
+                return X1.matvec(X1.rmatvec(x)) + curr_alpha * x
+
+            return _mv
+
+    else:
+
+        def create_mv(curr_alpha):
+            def _mv(x):
+                return X1.rmatvec(X1.matvec(x)) + curr_alpha * x
+
+            return _mv
+
+    for i in range(y.shape[1]):
+        y_column = y[:, i]
+
+        mv = create_mv(alpha[i])
+        if n_features > n_samples:
+            # kernel ridge
+            # w = X.T * inv(X X^t + alpha*Id) y
+            C = sp_linalg.LinearOperator(
+                (n_samples, n_samples), matvec=mv, dtype=X.dtype
+            )
+            coef, info = _sparse_linalg_cg(C, y_column, rtol=tol)
+            coefs[i] = X1.rmatvec(coef)
+        else:
+            # linear ridge
+            # w = inv(X^t X + alpha*Id) * X.T y
+            y_column = X1.rmatvec(y_column)
+            C = sp_linalg.LinearOperator(
+                (n_features, n_features), matvec=mv, dtype=X.dtype
+            )
+            coefs[i], info = _sparse_linalg_cg(C, y_column, maxiter=max_iter, rtol=tol)
+
+        if info < 0:
+            raise ValueError("Failed with error code %d" % info)
+
+        if max_iter is None and info > 0 and verbose:
+            warnings.warn(
+                "sparse_cg did not converge after %d iterations." % info,
+                ConvergenceWarning,
+            )
+
+    return coefs
+
+
+def _solve_lsqr(
+    X,
+    y,
+    *,
+    alpha,
+    fit_intercept=True,
+    max_iter=None,
+    tol=1e-4,
+    X_offset=None,
+    X_scale=None,
+    sample_weight_sqrt=None,
+):
+    """Solve Ridge regression via LSQR.
+
+    We expect that y is always mean centered.
+    If X is dense, we expect it to be mean centered such that we can solve
+        ||y - Xw||_2^2 + alpha * ||w||_2^2
+
+    If X is sparse, we expect X_offset to be given such that we can solve
+        ||y - (X - X_offset)w||_2^2 + alpha * ||w||_2^2
+
+    With sample weights S=diag(sample_weight), this becomes
+        ||sqrt(S) (y - (X - X_offset) w)||_2^2 + alpha * ||w||_2^2
+    and we expect y and X to already be rescaled, i.e. sqrt(S) @ y, sqrt(S) @ X. In
+    this case, X_offset is the sample_weight weighted mean of X before scaling by
+    sqrt(S). The objective then reads
+       ||y - (X - sqrt(S) X_offset) w)||_2^2 + alpha * ||w||_2^2
+    """
+    if sample_weight_sqrt is None:
+        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)
+
+    if sparse.issparse(X) and fit_intercept:
+        X_offset_scale = X_offset / X_scale
+        X1 = _get_rescaled_operator(X, X_offset_scale, sample_weight_sqrt)
+    else:
+        # No need to touch anything
+        X1 = X
+
+    n_samples, n_features = X.shape
+    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)
+    n_iter = np.empty(y.shape[1], dtype=np.int32)
+
+    # According to the lsqr documentation, alpha = damp^2.
+    sqrt_alpha = np.sqrt(alpha)
+
+    for i in range(y.shape[1]):
+        y_column = y[:, i]
+        info = sp_linalg.lsqr(
+            X1, y_column, damp=sqrt_alpha[i], atol=tol, btol=tol, iter_lim=max_iter
+        )
+        coefs[i] = info[0]
+        n_iter[i] = info[2]
+
+    return coefs, n_iter
+
+
+def _solve_cholesky(X, y, alpha):
+    # w = inv(X^t X + alpha*Id) * X.T y
+    n_features = X.shape[1]
+    n_targets = y.shape[1]
+
+    A = safe_sparse_dot(X.T, X, dense_output=True)
+    Xy = safe_sparse_dot(X.T, y, dense_output=True)
+
+    one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]])
+
+    if one_alpha:
+        A.flat[:: n_features + 1] += alpha[0]
+        return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
+    else:
+        coefs = np.empty([n_targets, n_features], dtype=X.dtype)
+        for coef, target, current_alpha in zip(coefs, Xy.T, alpha):
+            A.flat[:: n_features + 1] += current_alpha
+            coef[:] = linalg.solve(A, target, assume_a="pos", overwrite_a=False).ravel()
+            A.flat[:: n_features + 1] -= current_alpha
+        return coefs
+
+
+def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False):
+    # dual_coef = inv(X X^t + alpha*Id) y
+    n_samples = K.shape[0]
+    n_targets = y.shape[1]
+
+    if copy:
+        K = K.copy()
+
+    alpha = np.atleast_1d(alpha)
+    one_alpha = (alpha == alpha[0]).all()
+    has_sw = isinstance(sample_weight, np.ndarray) or sample_weight not in [1.0, None]
+
+    if has_sw:
+        # Unlike other solvers, we need to support sample_weight directly
+        # because K might be a pre-computed kernel.
+        sw = np.sqrt(np.atleast_1d(sample_weight))
+        y = y * sw[:, np.newaxis]
+        K *= np.outer(sw, sw)
+
+    if one_alpha:
+        # Only one penalty, we can solve multi-target problems in one time.
+        K.flat[:: n_samples + 1] += alpha[0]
+
+        try:
+            # Note: we must use overwrite_a=False in order to be able to
+            #       use the fall-back solution below in case a LinAlgError
+            #       is raised
+            dual_coef = linalg.solve(K, y, assume_a="pos", overwrite_a=False)
+        except np.linalg.LinAlgError:
+            warnings.warn(
+                "Singular matrix in solving dual problem. Using "
+                "least-squares solution instead."
+            )
+            dual_coef = linalg.lstsq(K, y)[0]
+
+        # K is expensive to compute and store in memory so change it back in
+        # case it was user-given.
+        K.flat[:: n_samples + 1] -= alpha[0]
+
+        if has_sw:
+            dual_coef *= sw[:, np.newaxis]
+
+        return dual_coef
+    else:
+        # One penalty per target. We need to solve each target separately.
+        dual_coefs = np.empty([n_targets, n_samples], K.dtype)
+
+        for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha):
+            K.flat[:: n_samples + 1] += current_alpha
+
+            dual_coef[:] = linalg.solve(
+                K, target, assume_a="pos", overwrite_a=False
+            ).ravel()
+
+            K.flat[:: n_samples + 1] -= current_alpha
+
+        if has_sw:
+            dual_coefs *= sw[np.newaxis, :]
+
+        return dual_coefs.T
+
+
+def _solve_svd(X, y, alpha, xp=None):
+    xp, _ = get_namespace(X, xp=xp)
+    U, s, Vt = xp.linalg.svd(X, full_matrices=False)
+    idx = s > 1e-15  # same default value as scipy.linalg.pinv
+    s_nnz = s[idx][:, None]
+    UTy = U.T @ y
+    d = xp.zeros((s.shape[0], alpha.shape[0]), dtype=X.dtype, device=device(X))
+    d[idx] = s_nnz / (s_nnz**2 + alpha)
+    d_UT_y = d * UTy
+    return (Vt.T @ d_UT_y).T
+
+
+def _solve_lbfgs(
+    X,
+    y,
+    alpha,
+    positive=True,
+    max_iter=None,
+    tol=1e-4,
+    X_offset=None,
+    X_scale=None,
+    sample_weight_sqrt=None,
+):
+    """Solve ridge regression with LBFGS.
+
+    The main purpose is fitting with forcing coefficients to be positive.
+    For unconstrained ridge regression, there are faster dedicated solver methods.
+    Note that with positive bounds on the coefficients, LBFGS seems faster
+    than scipy.optimize.lsq_linear.
+    """
+    n_samples, n_features = X.shape
+
+    options = {}
+    if max_iter is not None:
+        options["maxiter"] = max_iter
+    config = {
+        "method": "L-BFGS-B",
+        "tol": tol,
+        "jac": True,
+        "options": options,
+    }
+    if positive:
+        config["bounds"] = [(0, np.inf)] * n_features
+
+    if X_offset is not None and X_scale is not None:
+        X_offset_scale = X_offset / X_scale
+    else:
+        X_offset_scale = None
+
+    if sample_weight_sqrt is None:
+        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)
+
+    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)
+
+    for i in range(y.shape[1]):
+        x0 = np.zeros((n_features,))
+        y_column = y[:, i]
+
+        def func(w):
+            residual = X.dot(w) - y_column
+            if X_offset_scale is not None:
+                residual -= sample_weight_sqrt * w.dot(X_offset_scale)
+            f = 0.5 * residual.dot(residual) + 0.5 * alpha[i] * w.dot(w)
+            grad = X.T @ residual + alpha[i] * w
+            if X_offset_scale is not None:
+                grad -= X_offset_scale * residual.dot(sample_weight_sqrt)
+
+            return f, grad
+
+        result = optimize.minimize(func, x0, **config)
+        if not result["success"]:
+            warnings.warn(
+                (
+                    "The lbfgs solver did not converge. Try increasing max_iter "
+                    f"or tol. Currently: max_iter={max_iter} and tol={tol}"
+                ),
+                ConvergenceWarning,
+            )
+        coefs[i] = result["x"]
+
+    return coefs
+
+
+def _get_valid_accept_sparse(is_X_sparse, solver):
+    if is_X_sparse and solver in ["auto", "sag", "saga"]:
+        return "csr"
+    else:
+        return ["csr", "csc", "coo"]
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix", sp_linalg.LinearOperator],
+        "y": ["array-like"],
+        "alpha": [Interval(Real, 0, None, closed="left"), "array-like"],
+        "sample_weight": [
+            Interval(Real, None, None, closed="neither"),
+            "array-like",
+            None,
+        ],
+        "solver": [
+            StrOptions(
+                {"auto", "svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"}
+            )
+        ],
+        "max_iter": [Interval(Integral, 0, None, closed="left"), None],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "verbose": ["verbose"],
+        "positive": ["boolean"],
+        "random_state": ["random_state"],
+        "return_n_iter": ["boolean"],
+        "return_intercept": ["boolean"],
+        "check_input": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def ridge_regression(
+    X,
+    y,
+    alpha,
+    *,
+    sample_weight=None,
+    solver="auto",
+    max_iter=None,
+    tol=1e-4,
+    verbose=0,
+    positive=False,
+    random_state=None,
+    return_n_iter=False,
+    return_intercept=False,
+    check_input=True,
+):
+    """Solve the ridge equation by the method of normal equations.
+
+    Read more in the :ref:`User Guide <ridge_regression>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix, LinearOperator} of shape \
+        (n_samples, n_features)
+        Training data.
+
+    y : array-like of shape (n_samples,) or (n_samples, n_targets)
+        Target values.
+
+    alpha : float or array-like of shape (n_targets,)
+        Constant that multiplies the L2 term, controlling regularization
+        strength. `alpha` must be a non-negative float i.e. in `[0, inf)`.
+
+        When `alpha = 0`, the objective is equivalent to ordinary least
+        squares, solved by the :class:`LinearRegression` object. For numerical
+        reasons, using `alpha = 0` with the `Ridge` object is not advised.
+        Instead, you should use the :class:`LinearRegression` object.
+
+        If an array is passed, penalties are assumed to be specific to the
+        targets. Hence they must correspond in number.
+
+    sample_weight : float or array-like of shape (n_samples,), default=None
+        Individual weights for each sample. If given a float, every sample
+        will have the same weight. If sample_weight is not None and
+        solver='auto', the solver will be set to 'cholesky'.
+
+        .. versionadded:: 0.17
+
+    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
+            'sag', 'saga', 'lbfgs'}, default='auto'
+        Solver to use in the computational routines:
+
+        - 'auto' chooses the solver automatically based on the type of data.
+
+        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
+          coefficients. It is the most stable solver, in particular more stable
+          for singular matrices than 'cholesky' at the cost of being slower.
+
+        - 'cholesky' uses the standard scipy.linalg.solve function to
+          obtain a closed-form solution via a Cholesky decomposition of
+          dot(X.T, X)
+
+        - 'sparse_cg' uses the conjugate gradient solver as found in
+          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
+          more appropriate than 'cholesky' for large-scale data
+          (possibility to set `tol` and `max_iter`).
+
+        - 'lsqr' uses the dedicated regularized least-squares routine
+          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
+          procedure.
+
+        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
+          its improved, unbiased version named SAGA. Both methods also use an
+          iterative procedure, and are often faster than other solvers when
+          both n_samples and n_features are large. Note that 'sag' and
+          'saga' fast convergence is only guaranteed on features with
+          approximately the same scale. You can preprocess the data with a
+          scaler from sklearn.preprocessing.
+
+        - 'lbfgs' uses L-BFGS-B algorithm implemented in
+          `scipy.optimize.minimize`. It can be used only when `positive`
+          is True.
+
+        All solvers except 'svd' support both dense and sparse data. However, only
+        'lsqr', 'sag', 'sparse_cg', and 'lbfgs' support sparse input when
+        `fit_intercept` is True.
+
+        .. versionadded:: 0.17
+           Stochastic Average Gradient descent solver.
+        .. versionadded:: 0.19
+           SAGA solver.
+
+    max_iter : int, default=None
+        Maximum number of iterations for conjugate gradient solver.
+        For the 'sparse_cg' and 'lsqr' solvers, the default value is determined
+        by scipy.sparse.linalg. For 'sag' and saga solver, the default value is
+        1000. For 'lbfgs' solver, the default value is 15000.
+
+    tol : float, default=1e-4
+        Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
+        'cholesky'.
+
+        .. versionchanged:: 1.2
+           Default value changed from 1e-3 to 1e-4 for consistency with other linear
+           models.
+
+    verbose : int, default=0
+        Verbosity level. Setting verbose > 0 will display additional
+        information depending on the solver used.
+
+    positive : bool, default=False
+        When set to ``True``, forces the coefficients to be positive.
+        Only 'lbfgs' solver is supported in this case.
+
+    random_state : int, RandomState instance, default=None
+        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
+        See :term:`Glossary <random_state>` for details.
+
+    return_n_iter : bool, default=False
+        If True, the method also returns `n_iter`, the actual number of
+        iteration performed by the solver.
+
+        .. versionadded:: 0.17
+
+    return_intercept : bool, default=False
+        If True and if X is sparse, the method also returns the intercept,
+        and the solver is automatically changed to 'sag'. This is only a
+        temporary fix for fitting the intercept with sparse data. For dense
+        data, use sklearn.linear_model._preprocess_data before your regression.
+
+        .. versionadded:: 0.17
+
+    check_input : bool, default=True
+        If False, the input arrays X and y will not be checked.
+
+        .. versionadded:: 0.21
+
+    Returns
+    -------
+    coef : ndarray of shape (n_features,) or (n_targets, n_features)
+        Weight vector(s).
+
+    n_iter : int, optional
+        The actual number of iteration performed by the solver.
+        Only returned if `return_n_iter` is True.
+
+    intercept : float or ndarray of shape (n_targets,)
+        The intercept of the model. Only returned if `return_intercept`
+        is True and if X is a scipy sparse array.
+
+    Notes
+    -----
+    This function won't compute the intercept.
+
+    Regularization improves the conditioning of the problem and
+    reduces the variance of the estimates. Larger values specify stronger
+    regularization. Alpha corresponds to ``1 / (2C)`` in other linear
+    models such as :class:`~sklearn.linear_model.LogisticRegression` or
+    :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are
+    assumed to be specific to the targets. Hence they must correspond in
+    number.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_regression
+    >>> from sklearn.linear_model import ridge_regression
+    >>> rng = np.random.RandomState(0)
+    >>> X = rng.randn(100, 4)
+    >>> y = 2.0 * X[:, 0] - 1.0 * X[:, 1] + 0.1 * rng.standard_normal(100)
+    >>> coef, intercept = ridge_regression(X, y, alpha=1.0, return_intercept=True,
+    ...                                    random_state=0)
+    >>> coef
+    array([ 1.97, -1., -2.69e-3, -9.27e-4 ])
+    >>> intercept
+    np.float64(-.0012)
+    """
+    return _ridge_regression(
+        X,
+        y,
+        alpha,
+        sample_weight=sample_weight,
+        solver=solver,
+        max_iter=max_iter,
+        tol=tol,
+        verbose=verbose,
+        positive=positive,
+        random_state=random_state,
+        return_n_iter=return_n_iter,
+        return_intercept=return_intercept,
+        X_scale=None,
+        X_offset=None,
+        check_input=check_input,
+    )
+
+
+def _ridge_regression(
+    X,
+    y,
+    alpha,
+    sample_weight=None,
+    solver="auto",
+    max_iter=None,
+    tol=1e-4,
+    verbose=0,
+    positive=False,
+    random_state=None,
+    return_n_iter=False,
+    return_intercept=False,
+    return_solver=False,
+    X_scale=None,
+    X_offset=None,
+    check_input=True,
+    fit_intercept=False,
+):
+    xp, is_array_api_compliant, device_ = get_namespace_and_device(
+        X, y, sample_weight, X_scale, X_offset
+    )
+    is_numpy_namespace = _is_numpy_namespace(xp)
+    X_is_sparse = sparse.issparse(X)
+
+    has_sw = sample_weight is not None
+
+    solver = resolve_solver(solver, positive, return_intercept, X_is_sparse, xp)
+
+    if is_numpy_namespace and not X_is_sparse:
+        X = np.asarray(X)
+
+    if not is_numpy_namespace and solver != "svd":
+        raise ValueError(
+            f"Array API dispatch to namespace {xp.__name__} only supports "
+            f"solver 'svd'. Got '{solver}'."
+        )
+
+    if positive and solver != "lbfgs":
+        raise ValueError(
+            "When positive=True, only 'lbfgs' solver can be used. "
+            f"Please change solver {solver} to 'lbfgs' "
+            "or set positive=False."
+        )
+
+    if solver == "lbfgs" and not positive:
+        raise ValueError(
+            "'lbfgs' solver can be used only when positive=True. "
+            "Please use another solver."
+        )
+
+    if return_intercept and solver != "sag":
+        raise ValueError(
+            "In Ridge, only 'sag' solver can directly fit the "
+            "intercept. Please change solver to 'sag' or set "
+            "return_intercept=False."
+        )
+
+    if check_input:
+        _dtype = [xp.float64, xp.float32]
+        _accept_sparse = _get_valid_accept_sparse(X_is_sparse, solver)
+        X = check_array(X, accept_sparse=_accept_sparse, dtype=_dtype, order="C")
+        y = check_array(y, dtype=X.dtype, ensure_2d=False, order=None)
+    check_consistent_length(X, y)
+
+    n_samples, n_features = X.shape
+
+    if y.ndim > 2:
+        raise ValueError("Target y has the wrong shape %s" % str(y.shape))
+
+    if y.ndim == 1:
+        y = xp.reshape(y, (-1, 1))
+
+    n_samples_, n_targets = y.shape
+
+    if n_samples != n_samples_:
+        raise ValueError(
+            "Number of samples in X and y does not correspond: %d != %d"
+            % (n_samples, n_samples_)
+        )
+
+    if has_sw:
+        sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+
+        if solver not in ["sag", "saga"]:
+            # SAG supports sample_weight directly. For other solvers,
+            # we implement sample_weight via a simple rescaling.
+            X, y, sample_weight_sqrt = _rescale_data(X, y, sample_weight)
+
+    # Some callers of this method might pass alpha as single
+    # element array which already has been validated.
+    if alpha is not None and not isinstance(alpha, type(xp.asarray([0.0]))):
+        alpha = check_scalar(
+            alpha,
+            "alpha",
+            target_type=numbers.Real,
+            min_val=0.0,
+            include_boundaries="left",
+        )
+
+    # There should be either 1 or n_targets penalties
+    alpha = _ravel(xp.asarray(alpha, device=device_, dtype=X.dtype), xp=xp)
+    if alpha.shape[0] not in [1, n_targets]:
+        raise ValueError(
+            "Number of targets and number of penalties do not correspond: %d != %d"
+            % (alpha.shape[0], n_targets)
+        )
+
+    if alpha.shape[0] == 1 and n_targets > 1:
+        alpha = xp.full(
+            shape=(n_targets,), fill_value=alpha[0], dtype=alpha.dtype, device=device_
+        )
+
+    n_iter = None
+    if solver == "sparse_cg":
+        coef = _solve_sparse_cg(
+            X,
+            y,
+            alpha,
+            max_iter=max_iter,
+            tol=tol,
+            verbose=verbose,
+            X_offset=X_offset,
+            X_scale=X_scale,
+            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
+        )
+
+    elif solver == "lsqr":
+        coef, n_iter = _solve_lsqr(
+            X,
+            y,
+            alpha=alpha,
+            fit_intercept=fit_intercept,
+            max_iter=max_iter,
+            tol=tol,
+            X_offset=X_offset,
+            X_scale=X_scale,
+            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
+        )
+
+    elif solver == "cholesky":
+        if n_features > n_samples:
+            K = safe_sparse_dot(X, X.T, dense_output=True)
+            try:
+                dual_coef = _solve_cholesky_kernel(K, y, alpha)
+
+                coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T
+            except linalg.LinAlgError:
+                # use SVD solver if matrix is singular
+                solver = "svd"
+        else:
+            try:
+                coef = _solve_cholesky(X, y, alpha)
+            except linalg.LinAlgError:
+                # use SVD solver if matrix is singular
+                solver = "svd"
+
+    elif solver in ["sag", "saga"]:
+        # precompute max_squared_sum for all targets
+        max_squared_sum = row_norms(X, squared=True).max()
+
+        coef = np.empty((y.shape[1], n_features), dtype=X.dtype)
+        n_iter = np.empty(y.shape[1], dtype=np.int32)
+        intercept = np.zeros((y.shape[1],), dtype=X.dtype)
+        for i, (alpha_i, target) in enumerate(zip(alpha, y.T)):
+            init = {
+                "coef": np.zeros((n_features + int(return_intercept), 1), dtype=X.dtype)
+            }
+            coef_, n_iter_, _ = sag_solver(
+                X,
+                target.ravel(),
+                sample_weight,
+                "squared",
+                alpha_i,
+                0,
+                max_iter,
+                tol,
+                verbose,
+                random_state,
+                False,
+                max_squared_sum,
+                init,
+                is_saga=solver == "saga",
+            )
+            if return_intercept:
+                coef[i] = coef_[:-1]
+                intercept[i] = coef_[-1]
+            else:
+                coef[i] = coef_
+            n_iter[i] = n_iter_
+
+        if intercept.shape[0] == 1:
+            intercept = intercept[0]
+
+    elif solver == "lbfgs":
+        coef = _solve_lbfgs(
+            X,
+            y,
+            alpha,
+            positive=positive,
+            tol=tol,
+            max_iter=max_iter,
+            X_offset=X_offset,
+            X_scale=X_scale,
+            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
+        )
+
+    if solver == "svd":
+        if X_is_sparse:
+            raise TypeError("SVD solver does not support sparse inputs currently")
+        coef = _solve_svd(X, y, alpha, xp)
+
+    if n_targets == 1:
+        coef = _ravel(coef)
+
+    coef = xp.asarray(coef)
+
+    if return_n_iter and return_intercept:
+        res = coef, n_iter, intercept
+    elif return_intercept:
+        res = coef, intercept
+    elif return_n_iter:
+        res = coef, n_iter
+    else:
+        res = coef
+
+    return (*res, solver) if return_solver else res
+
+
+def resolve_solver(solver, positive, return_intercept, is_sparse, xp):
+    if solver != "auto":
+        return solver
+
+    is_numpy_namespace = _is_numpy_namespace(xp)
+
+    auto_solver_np = resolve_solver_for_numpy(positive, return_intercept, is_sparse)
+    if is_numpy_namespace:
+        return auto_solver_np
+
+    if positive:
+        raise ValueError(
+            "The solvers that support positive fitting do not support "
+            f"Array API dispatch to namespace {xp.__name__}. Please "
+            "either disable Array API dispatch, or use a numpy-like "
+            "namespace, or set `positive=False`."
+        )
+
+    # At the moment, Array API dispatch only supports the "svd" solver.
+    solver = "svd"
+    if solver != auto_solver_np:
+        warnings.warn(
+            f"Using Array API dispatch to namespace {xp.__name__} with "
+            f"`solver='auto'` will result in using the solver '{solver}'. "
+            "The results may differ from those when using a Numpy array, "
+            f"because in that case the preferred solver would be {auto_solver_np}. "
+            f"Set `solver='{solver}'` to suppress this warning."
+        )
+
+    return solver
+
+
+def resolve_solver_for_numpy(positive, return_intercept, is_sparse):
+    if positive:
+        return "lbfgs"
+
+    if return_intercept:
+        # sag supports fitting intercept directly
+        return "sag"
+
+    if not is_sparse:
+        return "cholesky"
+
+    return "sparse_cg"
+
+
+class _BaseRidge(LinearModel, metaclass=ABCMeta):
+    _parameter_constraints: dict = {
+        "alpha": [Interval(Real, 0, None, closed="left"), np.ndarray],
+        "fit_intercept": ["boolean"],
+        "copy_X": ["boolean"],
+        "max_iter": [Interval(Integral, 1, None, closed="left"), None],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "solver": [
+            StrOptions(
+                {"auto", "svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"}
+            )
+        ],
+        "positive": ["boolean"],
+        "random_state": ["random_state"],
+    }
+
+    @abstractmethod
+    def __init__(
+        self,
+        alpha=1.0,
+        *,
+        fit_intercept=True,
+        copy_X=True,
+        max_iter=None,
+        tol=1e-4,
+        solver="auto",
+        positive=False,
+        random_state=None,
+    ):
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.copy_X = copy_X
+        self.max_iter = max_iter
+        self.tol = tol
+        self.solver = solver
+        self.positive = positive
+        self.random_state = random_state
+
+    def fit(self, X, y, sample_weight=None):
+        xp, is_array_api_compliant = get_namespace(X, y, sample_weight)
+
+        if self.solver == "lbfgs" and not self.positive:
+            raise ValueError(
+                "'lbfgs' solver can be used only when positive=True. "
+                "Please use another solver."
+            )
+
+        if self.positive:
+            if self.solver not in ["auto", "lbfgs"]:
+                raise ValueError(
+                    f"solver='{self.solver}' does not support positive fitting. Please"
+                    " set the solver to 'auto' or 'lbfgs', or set `positive=False`"
+                )
+            else:
+                solver = self.solver
+        elif sparse.issparse(X) and self.fit_intercept:
+            if self.solver not in ["auto", "lbfgs", "lsqr", "sag", "sparse_cg"]:
+                raise ValueError(
+                    "solver='{}' does not support fitting the intercept "
+                    "on sparse data. Please set the solver to 'auto' or "
+                    "'lsqr', 'sparse_cg', 'sag', 'lbfgs' "
+                    "or set `fit_intercept=False`".format(self.solver)
+                )
+            if self.solver in ["lsqr", "lbfgs"]:
+                solver = self.solver
+            elif self.solver == "sag" and self.max_iter is None and self.tol > 1e-4:
+                warnings.warn(
+                    '"sag" solver requires many iterations to fit '
+                    "an intercept with sparse inputs. Either set the "
+                    'solver to "auto" or "sparse_cg", or set a low '
+                    '"tol" and a high "max_iter" (especially if inputs are '
+                    "not standardized)."
+                )
+                solver = "sag"
+            else:
+                solver = "sparse_cg"
+        else:
+            solver = self.solver
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+
+        # when X is sparse we only remove offset from y
+        X, y, X_offset, y_offset, X_scale = _preprocess_data(
+            X,
+            y,
+            fit_intercept=self.fit_intercept,
+            copy=self.copy_X,
+            sample_weight=sample_weight,
+        )
+
+        if solver == "sag" and sparse.issparse(X) and self.fit_intercept:
+            self.coef_, self.n_iter_, self.intercept_, self.solver_ = _ridge_regression(
+                X,
+                y,
+                alpha=self.alpha,
+                sample_weight=sample_weight,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver="sag",
+                positive=self.positive,
+                random_state=self.random_state,
+                return_n_iter=True,
+                return_intercept=True,
+                return_solver=True,
+                check_input=False,
+            )
+            # add the offset which was subtracted by _preprocess_data
+            self.intercept_ += y_offset
+
+        else:
+            if sparse.issparse(X) and self.fit_intercept:
+                # required to fit intercept with sparse_cg and lbfgs solver
+                params = {"X_offset": X_offset, "X_scale": X_scale}
+            else:
+                # for dense matrices or when intercept is set to 0
+                params = {}
+
+            self.coef_, self.n_iter_, self.solver_ = _ridge_regression(
+                X,
+                y,
+                alpha=self.alpha,
+                sample_weight=sample_weight,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver=solver,
+                positive=self.positive,
+                random_state=self.random_state,
+                return_n_iter=True,
+                return_intercept=False,
+                return_solver=True,
+                check_input=False,
+                fit_intercept=self.fit_intercept,
+                **params,
+            )
+            self._set_intercept(X_offset, y_offset, X_scale)
+
+        return self
+
+
+class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
+    """Linear least squares with l2 regularization.
+
+    Minimizes the objective function::
+
+    ||y - Xw||^2_2 + alpha * ||w||^2_2
+
+    This model solves a regression model where the loss function is
+    the linear least squares function and regularization is given by
+    the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
+    This estimator has built-in support for multi-variate regression
+    (i.e., when y is a 2d-array of shape (n_samples, n_targets)).
+
+    Read more in the :ref:`User Guide <ridge_regression>`.
+
+    Parameters
+    ----------
+    alpha : {float, ndarray of shape (n_targets,)}, default=1.0
+        Constant that multiplies the L2 term, controlling regularization
+        strength. `alpha` must be a non-negative float i.e. in `[0, inf)`.
+
+        When `alpha = 0`, the objective is equivalent to ordinary least
+        squares, solved by the :class:`LinearRegression` object. For numerical
+        reasons, using `alpha = 0` with the `Ridge` object is not advised.
+        Instead, you should use the :class:`LinearRegression` object.
+
+        If an array is passed, penalties are assumed to be specific to the
+        targets. Hence they must correspond in number.
+
+    fit_intercept : bool, default=True
+        Whether to fit the intercept for this model. If set
+        to false, no intercept will be used in calculations
+        (i.e. ``X`` and ``y`` are expected to be centered).
+
+    copy_X : bool, default=True
+        If True, X will be copied; else, it may be overwritten.
+
+    max_iter : int, default=None
+        Maximum number of iterations for conjugate gradient solver.
+        For 'sparse_cg' and 'lsqr' solvers, the default value is determined
+        by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.
+        For 'lbfgs' solver, the default value is 15000.
+
+    tol : float, default=1e-4
+        The precision of the solution (`coef_`) is determined by `tol` which
+        specifies a different convergence criterion for each solver:
+
+        - 'svd': `tol` has no impact.
+
+        - 'cholesky': `tol` has no impact.
+
+        - 'sparse_cg': norm of residuals smaller than `tol`.
+
+        - 'lsqr': `tol` is set as atol and btol of scipy.sparse.linalg.lsqr,
+          which control the norm of the residual vector in terms of the norms of
+          matrix and coefficients.
+
+        - 'sag' and 'saga': relative change of coef smaller than `tol`.
+
+        - 'lbfgs': maximum of the absolute (projected) gradient=max|residuals|
+          smaller than `tol`.
+
+        .. versionchanged:: 1.2
+           Default value changed from 1e-3 to 1e-4 for consistency with other linear
+           models.
+
+    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
+            'sag', 'saga', 'lbfgs'}, default='auto'
+        Solver to use in the computational routines:
+
+        - 'auto' chooses the solver automatically based on the type of data.
+
+        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
+          coefficients. It is the most stable solver, in particular more stable
+          for singular matrices than 'cholesky' at the cost of being slower.
+
+        - 'cholesky' uses the standard scipy.linalg.solve function to
+          obtain a closed-form solution.
+
+        - 'sparse_cg' uses the conjugate gradient solver as found in
+          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
+          more appropriate than 'cholesky' for large-scale data
+          (possibility to set `tol` and `max_iter`).
+
+        - 'lsqr' uses the dedicated regularized least-squares routine
+          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
+          procedure.
+
+        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
+          its improved, unbiased version named SAGA. Both methods also use an
+          iterative procedure, and are often faster than other solvers when
+          both n_samples and n_features are large. Note that 'sag' and
+          'saga' fast convergence is only guaranteed on features with
+          approximately the same scale. You can preprocess the data with a
+          scaler from sklearn.preprocessing.
+
+        - 'lbfgs' uses L-BFGS-B algorithm implemented in
+          `scipy.optimize.minimize`. It can be used only when `positive`
+          is True.
+
+        All solvers except 'svd' support both dense and sparse data. However, only
+        'lsqr', 'sag', 'sparse_cg', and 'lbfgs' support sparse input when
+        `fit_intercept` is True.
+
+        .. versionadded:: 0.17
+           Stochastic Average Gradient descent solver.
+        .. versionadded:: 0.19
+           SAGA solver.
+
+    positive : bool, default=False
+        When set to ``True``, forces the coefficients to be positive.
+        Only 'lbfgs' solver is supported in this case.
+
+    random_state : int, RandomState instance, default=None
+        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
+        See :term:`Glossary <random_state>` for details.
+
+        .. versionadded:: 0.17
+           `random_state` to support Stochastic Average Gradient.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
+        Weight vector(s).
+
+    intercept_ : float or ndarray of shape (n_targets,)
+        Independent term in decision function. Set to 0.0 if
+        ``fit_intercept = False``.
+
+    n_iter_ : None or ndarray of shape (n_targets,)
+        Actual number of iterations for each target. Available only for
+        sag and lsqr solvers. Other solvers will return None.
+
+        .. versionadded:: 0.17
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    solver_ : str
+        The solver that was used at fit time by the computational
+        routines.
+
+        .. versionadded:: 1.5
+
+    See Also
+    --------
+    RidgeClassifier : Ridge classifier.
+    RidgeCV : Ridge regression with built-in cross validation.
+    :class:`~sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression
+        combines ridge regression with the kernel trick.
+
+    Notes
+    -----
+    Regularization improves the conditioning of the problem and
+    reduces the variance of the estimates. Larger values specify stronger
+    regularization. Alpha corresponds to ``1 / (2C)`` in other linear
+    models such as :class:`~sklearn.linear_model.LogisticRegression` or
+    :class:`~sklearn.svm.LinearSVC`.
+
+    Examples
+    --------
+    >>> from sklearn.linear_model import Ridge
+    >>> import numpy as np
+    >>> n_samples, n_features = 10, 5
+    >>> rng = np.random.RandomState(0)
+    >>> y = rng.randn(n_samples)
+    >>> X = rng.randn(n_samples, n_features)
+    >>> clf = Ridge(alpha=1.0)
+    >>> clf.fit(X, y)
+    Ridge()
+    """
+
+    def __init__(
+        self,
+        alpha=1.0,
+        *,
+        fit_intercept=True,
+        copy_X=True,
+        max_iter=None,
+        tol=1e-4,
+        solver="auto",
+        positive=False,
+        random_state=None,
+    ):
+        super().__init__(
+            alpha=alpha,
+            fit_intercept=fit_intercept,
+            copy_X=copy_X,
+            max_iter=max_iter,
+            tol=tol,
+            solver=solver,
+            positive=positive,
+            random_state=random_state,
+        )
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit Ridge regression model.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
+            Target values.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), self.solver)
+        xp, _ = get_namespace(X, y, sample_weight)
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse=_accept_sparse,
+            dtype=[xp.float64, xp.float32],
+            force_writeable=True,
+            multi_output=True,
+            y_numeric=True,
+        )
+        return super().fit(X, y, sample_weight=sample_weight)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.array_api_support = True
+        tags.input_tags.sparse = (self.solver != "svd") and (
+            self.solver != "cholesky" or not self.fit_intercept
+        )
+        return tags
+
+
+class _RidgeClassifierMixin(LinearClassifierMixin):
+    def _prepare_data(self, X, y, sample_weight, solver):
+        """Validate `X` and `y` and binarize `y`.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target values.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+        solver : str
+            The solver used in `Ridge` to know which sparse format to support.
+
+        Returns
+        -------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Validated training data.
+
+        y : ndarray of shape (n_samples,)
+            Validated target values.
+
+        sample_weight : ndarray of shape (n_samples,)
+            Validated sample weights.
+
+        Y : ndarray of shape (n_samples, n_classes)
+            The binarized version of `y`.
+        """
+        accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), solver)
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse=accept_sparse,
+            multi_output=True,
+            y_numeric=False,
+            force_writeable=True,
+        )
+
+        self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
+        Y = self._label_binarizer.fit_transform(y)
+        if not self._label_binarizer.y_type_.startswith("multilabel"):
+            y = column_or_1d(y, warn=True)
+
+        sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+        if self.class_weight:
+            sample_weight = sample_weight * compute_sample_weight(self.class_weight, y)
+        return X, y, sample_weight, Y
+
+    def predict(self, X):
+        """Predict class labels for samples in `X`.
+
+        Parameters
+        ----------
+        X : {array-like, spare matrix} of shape (n_samples, n_features)
+            The data matrix for which we want to predict the targets.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,) or (n_samples, n_outputs)
+            Vector or matrix containing the predictions. In binary and
+            multiclass problems, this is a vector containing `n_samples`. In
+            a multilabel problem, it returns a matrix of shape
+            `(n_samples, n_outputs)`.
+        """
+        check_is_fitted(self, attributes=["_label_binarizer"])
+        if self._label_binarizer.y_type_.startswith("multilabel"):
+            # Threshold such that the negative label is -1 and positive label
+            # is 1 to use the inverse transform of the label binarizer fitted
+            # during fit.
+            scores = 2 * (self.decision_function(X) > 0) - 1
+            return self._label_binarizer.inverse_transform(scores)
+        return super().predict(X)
+
+    @property
+    def classes_(self):
+        """Classes labels."""
+        return self._label_binarizer.classes_
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.classifier_tags.multi_label = True
+        return tags
+
+
+class RidgeClassifier(_RidgeClassifierMixin, _BaseRidge):
+    """Classifier using Ridge regression.
+
+    This classifier first converts the target values into ``{-1, 1}`` and
+    then treats the problem as a regression task (multi-output regression in
+    the multiclass case).
+
+    Read more in the :ref:`User Guide <ridge_regression>`.
+
+    Parameters
+    ----------
+    alpha : float, default=1.0
+        Regularization strength; must be a positive float. Regularization
+        improves the conditioning of the problem and reduces the variance of
+        the estimates. Larger values specify stronger regularization.
+        Alpha corresponds to ``1 / (2C)`` in other linear models such as
+        :class:`~sklearn.linear_model.LogisticRegression` or
+        :class:`~sklearn.svm.LinearSVC`.
+
+    fit_intercept : bool, default=True
+        Whether to calculate the intercept for this model. If set to false, no
+        intercept will be used in calculations (e.g. data is expected to be
+        already centered).
+
+    copy_X : bool, default=True
+        If True, X will be copied; else, it may be overwritten.
+
+    max_iter : int, default=None
+        Maximum number of iterations for conjugate gradient solver.
+        The default value is determined by scipy.sparse.linalg.
+
+    tol : float, default=1e-4
+        The precision of the solution (`coef_`) is determined by `tol` which
+        specifies a different convergence criterion for each solver:
+
+        - 'svd': `tol` has no impact.
+
+        - 'cholesky': `tol` has no impact.
+
+        - 'sparse_cg': norm of residuals smaller than `tol`.
+
+        - 'lsqr': `tol` is set as atol and btol of scipy.sparse.linalg.lsqr,
+          which control the norm of the residual vector in terms of the norms of
+          matrix and coefficients.
+
+        - 'sag' and 'saga': relative change of coef smaller than `tol`.
+
+        - 'lbfgs': maximum of the absolute (projected) gradient=max|residuals|
+          smaller than `tol`.
+
+        .. versionchanged:: 1.2
+           Default value changed from 1e-3 to 1e-4 for consistency with other linear
+           models.
+
+    class_weight : dict or 'balanced', default=None
+        Weights associated with classes in the form ``{class_label: weight}``.
+        If not given, all classes are supposed to have weight one.
+
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``.
+
+    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
+            'sag', 'saga', 'lbfgs'}, default='auto'
+        Solver to use in the computational routines:
+
+        - 'auto' chooses the solver automatically based on the type of data.
+
+        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
+          coefficients. It is the most stable solver, in particular more stable
+          for singular matrices than 'cholesky' at the cost of being slower.
+
+        - 'cholesky' uses the standard scipy.linalg.solve function to
+          obtain a closed-form solution.
+
+        - 'sparse_cg' uses the conjugate gradient solver as found in
+          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
+          more appropriate than 'cholesky' for large-scale data
+          (possibility to set `tol` and `max_iter`).
+
+        - 'lsqr' uses the dedicated regularized least-squares routine
+          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
+          procedure.
+
+        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
+          its unbiased and more flexible version named SAGA. Both methods
+          use an iterative procedure, and are often faster than other solvers
+          when both n_samples and n_features are large. Note that 'sag' and
+          'saga' fast convergence is only guaranteed on features with
+          approximately the same scale. You can preprocess the data with a
+          scaler from sklearn.preprocessing.
+
+          .. versionadded:: 0.17
+             Stochastic Average Gradient descent solver.
+          .. versionadded:: 0.19
+             SAGA solver.
+
+        - 'lbfgs' uses L-BFGS-B algorithm implemented in
+          `scipy.optimize.minimize`. It can be used only when `positive`
+          is True.
+
+    positive : bool, default=False
+        When set to ``True``, forces the coefficients to be positive.
+        Only 'lbfgs' solver is supported in this case.
+
+    random_state : int, RandomState instance, default=None
+        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
+        See :term:`Glossary <random_state>` for details.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)
+        Coefficient of the features in the decision function.
+
+        ``coef_`` is of shape (1, n_features) when the given problem is binary.
+
+    intercept_ : float or ndarray of shape (n_targets,)
+        Independent term in decision function. Set to 0.0 if
+        ``fit_intercept = False``.
+
+    n_iter_ : None or ndarray of shape (n_targets,)
+        Actual number of iterations for each target. Available only for
+        sag and lsqr solvers. Other solvers will return None.
+
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    solver_ : str
+        The solver that was used at fit time by the computational
+        routines.
+
+        .. versionadded:: 1.5
+
+    See Also
+    --------
+    Ridge : Ridge regression.
+    RidgeClassifierCV :  Ridge classifier with built-in cross validation.
+
+    Notes
+    -----
+    For multi-class classification, n_class classifiers are trained in
+    a one-versus-all approach. Concretely, this is implemented by taking
+    advantage of the multi-variate response support in Ridge.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.linear_model import RidgeClassifier
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> clf = RidgeClassifier().fit(X, y)
+    >>> clf.score(X, y)
+    0.9595...
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseRidge._parameter_constraints,
+        "class_weight": [dict, StrOptions({"balanced"}), None],
+    }
+
+    def __init__(
+        self,
+        alpha=1.0,
+        *,
+        fit_intercept=True,
+        copy_X=True,
+        max_iter=None,
+        tol=1e-4,
+        class_weight=None,
+        solver="auto",
+        positive=False,
+        random_state=None,
+    ):
+        super().__init__(
+            alpha=alpha,
+            fit_intercept=fit_intercept,
+            copy_X=copy_X,
+            max_iter=max_iter,
+            tol=tol,
+            solver=solver,
+            positive=positive,
+            random_state=random_state,
+        )
+        self.class_weight = class_weight
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit Ridge classifier model.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target values.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+            .. versionadded:: 0.17
+               *sample_weight* support to RidgeClassifier.
+
+        Returns
+        -------
+        self : object
+            Instance of the estimator.
+        """
+        X, y, sample_weight, Y = self._prepare_data(X, y, sample_weight, self.solver)
+
+        super().fit(X, Y, sample_weight=sample_weight)
+        return self
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = (self.solver != "svd") and (
+            self.solver != "cholesky" or not self.fit_intercept
+        )
+        return tags
+
+
+def _check_gcv_mode(X, gcv_mode):
+    if gcv_mode in ["eigen", "svd"]:
+        return gcv_mode
+    # if X has more rows than columns, use decomposition of X^T.X,
+    # otherwise X.X^T
+    if X.shape[0] > X.shape[1]:
+        return "svd"
+    return "eigen"
+
+
+def _find_smallest_angle(query, vectors):
+    """Find the column of vectors that is most aligned with the query.
+
+    Both query and the columns of vectors must have their l2 norm equal to 1.
+
+    Parameters
+    ----------
+    query : ndarray of shape (n_samples,)
+        Normalized query vector.
+
+    vectors : ndarray of shape (n_samples, n_features)
+        Vectors to which we compare query, as columns. Must be normalized.
+    """
+    abs_cosine = np.abs(query.dot(vectors))
+    index = np.argmax(abs_cosine)
+    return index
+
+
+class _X_CenterStackOp(sparse.linalg.LinearOperator):
+    """Behaves as centered and scaled X with an added intercept column.
+
+    This operator behaves as
+    np.hstack([X - sqrt_sw[:, None] * X_mean, sqrt_sw[:, None]])
+    """
+
+    def __init__(self, X, X_mean, sqrt_sw):
+        n_samples, n_features = X.shape
+        super().__init__(X.dtype, (n_samples, n_features + 1))
+        self.X = X
+        self.X_mean = X_mean
+        self.sqrt_sw = sqrt_sw
+
+    def _matvec(self, v):
+        v = v.ravel()
+        return (
+            safe_sparse_dot(self.X, v[:-1], dense_output=True)
+            - self.sqrt_sw * self.X_mean.dot(v[:-1])
+            + v[-1] * self.sqrt_sw
+        )
+
+    def _matmat(self, v):
+        return (
+            safe_sparse_dot(self.X, v[:-1], dense_output=True)
+            - self.sqrt_sw[:, None] * self.X_mean.dot(v[:-1])
+            + v[-1] * self.sqrt_sw[:, None]
+        )
+
+    def _transpose(self):
+        return _XT_CenterStackOp(self.X, self.X_mean, self.sqrt_sw)
+
+
+class _XT_CenterStackOp(sparse.linalg.LinearOperator):
+    """Behaves as transposed centered and scaled X with an intercept column.
+
+    This operator behaves as
+    np.hstack([X - sqrt_sw[:, None] * X_mean, sqrt_sw[:, None]]).T
+    """
+
+    def __init__(self, X, X_mean, sqrt_sw):
+        n_samples, n_features = X.shape
+        super().__init__(X.dtype, (n_features + 1, n_samples))
+        self.X = X
+        self.X_mean = X_mean
+        self.sqrt_sw = sqrt_sw
+
+    def _matvec(self, v):
+        v = v.ravel()
+        n_features = self.shape[0]
+        res = np.empty(n_features, dtype=self.X.dtype)
+        res[:-1] = safe_sparse_dot(self.X.T, v, dense_output=True) - (
+            self.X_mean * self.sqrt_sw.dot(v)
+        )
+        res[-1] = np.dot(v, self.sqrt_sw)
+        return res
+
+    def _matmat(self, v):
+        n_features = self.shape[0]
+        res = np.empty((n_features, v.shape[1]), dtype=self.X.dtype)
+        res[:-1] = safe_sparse_dot(self.X.T, v, dense_output=True) - self.X_mean[
+            :, None
+        ] * self.sqrt_sw.dot(v)
+        res[-1] = np.dot(self.sqrt_sw, v)
+        return res
+
+
+class _IdentityRegressor(RegressorMixin, BaseEstimator):
+    """Fake regressor which will directly output the prediction."""
+
+    def decision_function(self, y_predict):
+        return y_predict
+
+    def predict(self, y_predict):
+        return y_predict
+
+
+class _IdentityClassifier(LinearClassifierMixin, BaseEstimator):
+    """Fake classifier which will directly output the prediction.
+
+    We inherit from LinearClassifierMixin to get the proper shape for the
+    output `y`.
+    """
+
+    def __init__(self, classes):
+        self.classes_ = classes
+
+    def decision_function(self, y_predict):
+        return y_predict
+
+
+class _RidgeGCV(LinearModel):
+    """Ridge regression with built-in Leave-one-out Cross-Validation.
+
+    This class is not intended to be used directly. Use RidgeCV instead.
+
+    `_RidgeGCV` uses a Generalized Cross-Validation for model selection. It's an
+    efficient approximation of leave-one-out cross-validation (LOO-CV), where instead of
+    computing multiple models by excluding one data point at a time, it uses an
+    algebraic shortcut to approximate the LOO-CV error, making it faster and
+    computationally more efficient.
+
+    Using a naive grid-search approach with a leave-one-out cross-validation in contrast
+    requires to fit `n_samples` models to compute the prediction error for each sample
+    and then to repeat this process for each alpha in the grid.
+
+    Here, the prediction error for each sample is computed by solving a **single**
+    linear system (in other words a single model) via a matrix factorization (i.e.
+    eigendecomposition or SVD) solving the problem stated in the Notes section. Finally,
+    we need to repeat this process for each alpha in the grid. The detailed complexity
+    is further discussed in Sect. 4 in [1].
+
+    This algebraic approach is only applicable for regularized least squares
+    problems. It could potentially be extended to kernel ridge regression.
+
+    See the Notes section and references for more details regarding the formulation
+    and the linear system that is solved.
+
+    Notes
+    -----
+
+    We want to solve (K + alpha*Id)c = y,
+    where K = X X^T is the kernel matrix.
+
+    Let G = (K + alpha*Id).
+
+    Dual solution: c = G^-1y
+    Primal solution: w = X^T c
+
+    Compute eigendecomposition K = Q V Q^T.
+    Then G^-1 = Q (V + alpha*Id)^-1 Q^T,
+    where (V + alpha*Id) is diagonal.
+    It is thus inexpensive to inverse for many alphas.
+
+    Let loov be the vector of prediction values for each example
+    when the model was fitted with all examples but this example.
+
+    loov = (KG^-1Y - diag(KG^-1)Y) / diag(I-KG^-1)
+
+    Let looe be the vector of prediction errors for each example
+    when the model was fitted with all examples but this example.
+
+    looe = y - loov = c / diag(G^-1)
+
+    The best score (negative mean squared error or user-provided scoring) is
+    stored in the `best_score_` attribute, and the selected hyperparameter in
+    `alpha_`.
+
+    References
+    ----------
+    [1] http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2007-025.pdf
+    [2] https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
+    """
+
+    def __init__(
+        self,
+        alphas=(0.1, 1.0, 10.0),
+        *,
+        fit_intercept=True,
+        scoring=None,
+        copy_X=True,
+        gcv_mode=None,
+        store_cv_results=False,
+        is_clf=False,
+        alpha_per_target=False,
+    ):
+        self.alphas = alphas
+        self.fit_intercept = fit_intercept
+        self.scoring = scoring
+        self.copy_X = copy_X
+        self.gcv_mode = gcv_mode
+        self.store_cv_results = store_cv_results
+        self.is_clf = is_clf
+        self.alpha_per_target = alpha_per_target
+
+    @staticmethod
+    def _decomp_diag(v_prime, Q):
+        # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))
+        return (v_prime * Q**2).sum(axis=-1)
+
+    @staticmethod
+    def _diag_dot(D, B):
+        # compute dot(diag(D), B)
+        if len(B.shape) > 1:
+            # handle case where B is > 1-d
+            D = D[(slice(None),) + (np.newaxis,) * (len(B.shape) - 1)]
+        return D * B
+
+    def _compute_gram(self, X, sqrt_sw):
+        """Computes the Gram matrix XX^T with possible centering.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            The preprocessed design matrix.
+
+        sqrt_sw : ndarray of shape (n_samples,)
+            square roots of sample weights
+
+        Returns
+        -------
+        gram : ndarray of shape (n_samples, n_samples)
+            The Gram matrix.
+        X_mean : ndarray of shape (n_feature,)
+            The weighted mean of ``X`` for each feature.
+
+        Notes
+        -----
+        When X is dense the centering has been done in preprocessing
+        so the mean is 0 and we just compute XX^T.
+
+        When X is sparse it has not been centered in preprocessing, but it has
+        been scaled by sqrt(sample weights).
+
+        When self.fit_intercept is False no centering is done.
+
+        The centered X is never actually computed because centering would break
+        the sparsity of X.
+        """
+        center = self.fit_intercept and sparse.issparse(X)
+        if not center:
+            # in this case centering has been done in preprocessing
+            # or we are not fitting an intercept.
+            X_mean = np.zeros(X.shape[1], dtype=X.dtype)
+            return safe_sparse_dot(X, X.T, dense_output=True), X_mean
+        # X is sparse
+        n_samples = X.shape[0]
+        sample_weight_matrix = sparse.dia_matrix(
+            (sqrt_sw, 0), shape=(n_samples, n_samples)
+        )
+        X_weighted = sample_weight_matrix.dot(X)
+        X_mean, _ = mean_variance_axis(X_weighted, axis=0)
+        X_mean *= n_samples / sqrt_sw.dot(sqrt_sw)
+        X_mX = sqrt_sw[:, None] * safe_sparse_dot(X_mean, X.T, dense_output=True)
+        X_mX_m = np.outer(sqrt_sw, sqrt_sw) * np.dot(X_mean, X_mean)
+        return (
+            safe_sparse_dot(X, X.T, dense_output=True) + X_mX_m - X_mX - X_mX.T,
+            X_mean,
+        )
+
+    def _compute_covariance(self, X, sqrt_sw):
+        """Computes covariance matrix X^TX with possible centering.
+
+        Parameters
+        ----------
+        X : sparse matrix of shape (n_samples, n_features)
+            The preprocessed design matrix.
+
+        sqrt_sw : ndarray of shape (n_samples,)
+            square roots of sample weights
+
+        Returns
+        -------
+        covariance : ndarray of shape (n_features, n_features)
+            The covariance matrix.
+        X_mean : ndarray of shape (n_feature,)
+            The weighted mean of ``X`` for each feature.
+
+        Notes
+        -----
+        Since X is sparse it has not been centered in preprocessing, but it has
+        been scaled by sqrt(sample weights).
+
+        When self.fit_intercept is False no centering is done.
+
+        The centered X is never actually computed because centering would break
+        the sparsity of X.
+        """
+        if not self.fit_intercept:
+            # in this case centering has been done in preprocessing
+            # or we are not fitting an intercept.
+            X_mean = np.zeros(X.shape[1], dtype=X.dtype)
+            return safe_sparse_dot(X.T, X, dense_output=True), X_mean
+        # this function only gets called for sparse X
+        n_samples = X.shape[0]
+        sample_weight_matrix = sparse.dia_matrix(
+            (sqrt_sw, 0), shape=(n_samples, n_samples)
+        )
+        X_weighted = sample_weight_matrix.dot(X)
+        X_mean, _ = mean_variance_axis(X_weighted, axis=0)
+        X_mean = X_mean * n_samples / sqrt_sw.dot(sqrt_sw)
+        weight_sum = sqrt_sw.dot(sqrt_sw)
+        return (
+            safe_sparse_dot(X.T, X, dense_output=True)
+            - weight_sum * np.outer(X_mean, X_mean),
+            X_mean,
+        )
+
+    def _sparse_multidot_diag(self, X, A, X_mean, sqrt_sw):
+        """Compute the diagonal of (X - X_mean).dot(A).dot((X - X_mean).T)
+        without explicitly centering X nor computing X.dot(A)
+        when X is sparse.
+
+        Parameters
+        ----------
+        X : sparse matrix of shape (n_samples, n_features)
+
+        A : ndarray of shape (n_features, n_features)
+
+        X_mean : ndarray of shape (n_features,)
+
+        sqrt_sw : ndarray of shape (n_features,)
+            square roots of sample weights
+
+        Returns
+        -------
+        diag : np.ndarray, shape (n_samples,)
+            The computed diagonal.
+        """
+        intercept_col = scale = sqrt_sw
+        batch_size = X.shape[1]
+        diag = np.empty(X.shape[0], dtype=X.dtype)
+        for start in range(0, X.shape[0], batch_size):
+            batch = slice(start, min(X.shape[0], start + batch_size), 1)
+            X_batch = np.empty(
+                (X[batch].shape[0], X.shape[1] + self.fit_intercept), dtype=X.dtype
+            )
+            if self.fit_intercept:
+                X_batch[:, :-1] = X[batch].toarray() - X_mean * scale[batch][:, None]
+                X_batch[:, -1] = intercept_col[batch]
+            else:
+                X_batch = X[batch].toarray()
+            diag[batch] = (X_batch.dot(A) * X_batch).sum(axis=1)
+        return diag
+
+    def _eigen_decompose_gram(self, X, y, sqrt_sw):
+        """Eigendecomposition of X.X^T, used when n_samples <= n_features."""
+        # if X is dense it has already been centered in preprocessing
+        K, X_mean = self._compute_gram(X, sqrt_sw)
+        if self.fit_intercept:
+            # to emulate centering X with sample weights,
+            # ie removing the weighted average, we add a column
+            # containing the square roots of the sample weights.
+            # by centering, it is orthogonal to the other columns
+            K += np.outer(sqrt_sw, sqrt_sw)
+        eigvals, Q = linalg.eigh(K)
+        QT_y = np.dot(Q.T, y)
+        return X_mean, eigvals, Q, QT_y
+
+    def _solve_eigen_gram(self, alpha, y, sqrt_sw, X_mean, eigvals, Q, QT_y):
+        """Compute dual coefficients and diagonal of G^-1.
+
+        Used when we have a decomposition of X.X^T (n_samples <= n_features).
+        """
+        w = 1.0 / (eigvals + alpha)
+        if self.fit_intercept:
+            # the vector containing the square roots of the sample weights (1
+            # when no sample weights) is the eigenvector of XX^T which
+            # corresponds to the intercept; we cancel the regularization on
+            # this dimension. the corresponding eigenvalue is
+            # sum(sample_weight).
+            normalized_sw = sqrt_sw / np.linalg.norm(sqrt_sw)
+            intercept_dim = _find_smallest_angle(normalized_sw, Q)
+            w[intercept_dim] = 0  # cancel regularization for the intercept
+
+        c = np.dot(Q, self._diag_dot(w, QT_y))
+        G_inverse_diag = self._decomp_diag(w, Q)
+        # handle case where y is 2-d
+        if len(y.shape) != 1:
+            G_inverse_diag = G_inverse_diag[:, np.newaxis]
+        return G_inverse_diag, c
+
+    def _eigen_decompose_covariance(self, X, y, sqrt_sw):
+        """Eigendecomposition of X^T.X, used when n_samples > n_features
+        and X is sparse.
+        """
+        n_samples, n_features = X.shape
+        cov = np.empty((n_features + 1, n_features + 1), dtype=X.dtype)
+        cov[:-1, :-1], X_mean = self._compute_covariance(X, sqrt_sw)
+        if not self.fit_intercept:
+            cov = cov[:-1, :-1]
+        # to emulate centering X with sample weights,
+        # ie removing the weighted average, we add a column
+        # containing the square roots of the sample weights.
+        # by centering, it is orthogonal to the other columns
+        # when all samples have the same weight we add a column of 1
+        else:
+            cov[-1] = 0
+            cov[:, -1] = 0
+            cov[-1, -1] = sqrt_sw.dot(sqrt_sw)
+        nullspace_dim = max(0, n_features - n_samples)
+        eigvals, V = linalg.eigh(cov)
+        # remove eigenvalues and vectors in the null space of X^T.X
+        eigvals = eigvals[nullspace_dim:]
+        V = V[:, nullspace_dim:]
+        return X_mean, eigvals, V, X
+
+    def _solve_eigen_covariance_no_intercept(
+        self, alpha, y, sqrt_sw, X_mean, eigvals, V, X
+    ):
+        """Compute dual coefficients and diagonal of G^-1.
+
+        Used when we have a decomposition of X^T.X
+        (n_samples > n_features and X is sparse), and not fitting an intercept.
+        """
+        w = 1 / (eigvals + alpha)
+        A = (V * w).dot(V.T)
+        AXy = A.dot(safe_sparse_dot(X.T, y, dense_output=True))
+        y_hat = safe_sparse_dot(X, AXy, dense_output=True)
+        hat_diag = self._sparse_multidot_diag(X, A, X_mean, sqrt_sw)
+        if len(y.shape) != 1:
+            # handle case where y is 2-d
+            hat_diag = hat_diag[:, np.newaxis]
+        return (1 - hat_diag) / alpha, (y - y_hat) / alpha
+
+    def _solve_eigen_covariance_intercept(
+        self, alpha, y, sqrt_sw, X_mean, eigvals, V, X
+    ):
+        """Compute dual coefficients and diagonal of G^-1.
+
+        Used when we have a decomposition of X^T.X
+        (n_samples > n_features and X is sparse),
+        and we are fitting an intercept.
+        """
+        # the vector [0, 0, ..., 0, 1]
+        # is the eigenvector of X^TX which
+        # corresponds to the intercept; we cancel the regularization on
+        # this dimension. the corresponding eigenvalue is
+        # sum(sample_weight), e.g. n when uniform sample weights.
+        intercept_sv = np.zeros(V.shape[0])
+        intercept_sv[-1] = 1
+        intercept_dim = _find_smallest_angle(intercept_sv, V)
+        w = 1 / (eigvals + alpha)
+        w[intercept_dim] = 1 / eigvals[intercept_dim]
+        A = (V * w).dot(V.T)
+        # add a column to X containing the square roots of sample weights
+        X_op = _X_CenterStackOp(X, X_mean, sqrt_sw)
+        AXy = A.dot(X_op.T.dot(y))
+        y_hat = X_op.dot(AXy)
+        hat_diag = self._sparse_multidot_diag(X, A, X_mean, sqrt_sw)
+        # return (1 - hat_diag), (y - y_hat)
+        if len(y.shape) != 1:
+            # handle case where y is 2-d
+            hat_diag = hat_diag[:, np.newaxis]
+        return (1 - hat_diag) / alpha, (y - y_hat) / alpha
+
+    def _solve_eigen_covariance(self, alpha, y, sqrt_sw, X_mean, eigvals, V, X):
+        """Compute dual coefficients and diagonal of G^-1.
+
+        Used when we have a decomposition of X^T.X
+        (n_samples > n_features and X is sparse).
+        """
+        if self.fit_intercept:
+            return self._solve_eigen_covariance_intercept(
+                alpha, y, sqrt_sw, X_mean, eigvals, V, X
+            )
+        return self._solve_eigen_covariance_no_intercept(
+            alpha, y, sqrt_sw, X_mean, eigvals, V, X
+        )
+
+    def _svd_decompose_design_matrix(self, X, y, sqrt_sw):
+        # X already centered
+        X_mean = np.zeros(X.shape[1], dtype=X.dtype)
+        if self.fit_intercept:
+            # to emulate fit_intercept=True situation, add a column
+            # containing the square roots of the sample weights
+            # by centering, the other columns are orthogonal to that one
+            intercept_column = sqrt_sw[:, None]
+            X = np.hstack((X, intercept_column))
+        U, singvals, _ = linalg.svd(X, full_matrices=0)
+        singvals_sq = singvals**2
+        UT_y = np.dot(U.T, y)
+        return X_mean, singvals_sq, U, UT_y
+
+    def _solve_svd_design_matrix(self, alpha, y, sqrt_sw, X_mean, singvals_sq, U, UT_y):
+        """Compute dual coefficients and diagonal of G^-1.
+
+        Used when we have an SVD decomposition of X
+        (n_samples > n_features and X is dense).
+        """
+        w = ((singvals_sq + alpha) ** -1) - (alpha**-1)
+        if self.fit_intercept:
+            # detect intercept column
+            normalized_sw = sqrt_sw / np.linalg.norm(sqrt_sw)
+            intercept_dim = _find_smallest_angle(normalized_sw, U)
+            # cancel the regularization for the intercept
+            w[intercept_dim] = -(alpha**-1)
+        c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha**-1) * y
+        G_inverse_diag = self._decomp_diag(w, U) + (alpha**-1)
+        if len(y.shape) != 1:
+            # handle case where y is 2-d
+            G_inverse_diag = G_inverse_diag[:, np.newaxis]
+        return G_inverse_diag, c
+
+    def fit(self, X, y, sample_weight=None, score_params=None):
+        """Fit Ridge regression model with gcv.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Training data. Will be cast to float64 if necessary.
+
+        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
+            Target values. Will be cast to float64 if necessary.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight. Note that the scale of `sample_weight`
+            has an impact on the loss; i.e. multiplying all weights by `k`
+            is equivalent to setting `alpha / k`.
+
+        score_params : dict, default=None
+            Parameters to be passed to the underlying scorer.
+
+            .. versionadded:: 1.5
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
+        Returns
+        -------
+        self : object
+        """
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse=["csr", "csc", "coo"],
+            dtype=[np.float64],
+            multi_output=True,
+            y_numeric=True,
+        )
+
+        # alpha_per_target cannot be used in classifier mode. All subclasses
+        # of _RidgeGCV that are classifiers keep alpha_per_target at its
+        # default value: False, so the condition below should never happen.
+        assert not (self.is_clf and self.alpha_per_target)
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+
+        self.alphas = np.asarray(self.alphas)
+
+        unscaled_y = y
+        X, y, X_offset, y_offset, X_scale = _preprocess_data(
+            X,
+            y,
+            fit_intercept=self.fit_intercept,
+            copy=self.copy_X,
+            sample_weight=sample_weight,
+        )
+
+        gcv_mode = _check_gcv_mode(X, self.gcv_mode)
+
+        if gcv_mode == "eigen":
+            decompose = self._eigen_decompose_gram
+            solve = self._solve_eigen_gram
+        elif gcv_mode == "svd":
+            if sparse.issparse(X):
+                decompose = self._eigen_decompose_covariance
+                solve = self._solve_eigen_covariance
+            else:
+                decompose = self._svd_decompose_design_matrix
+                solve = self._solve_svd_design_matrix
+
+        n_samples = X.shape[0]
+
+        if sample_weight is not None:
+            X, y, sqrt_sw = _rescale_data(X, y, sample_weight)
+        else:
+            sqrt_sw = np.ones(n_samples, dtype=X.dtype)
+
+        X_mean, *decomposition = decompose(X, y, sqrt_sw)
+
+        n_y = 1 if len(y.shape) == 1 else y.shape[1]
+        n_alphas = 1 if np.ndim(self.alphas) == 0 else len(self.alphas)
+
+        if self.store_cv_results:
+            self.cv_results_ = np.empty((n_samples * n_y, n_alphas), dtype=X.dtype)
+
+        best_coef, best_score, best_alpha = None, None, None
+
+        for i, alpha in enumerate(np.atleast_1d(self.alphas)):
+            G_inverse_diag, c = solve(float(alpha), y, sqrt_sw, X_mean, *decomposition)
+            if self.scoring is None:
+                squared_errors = (c / G_inverse_diag) ** 2
+                alpha_score = self._score_without_scorer(squared_errors=squared_errors)
+                if self.store_cv_results:
+                    self.cv_results_[:, i] = squared_errors.ravel()
+            else:
+                predictions = y - (c / G_inverse_diag)
+                # Rescale predictions back to original scale
+                if sample_weight is not None:  # avoid the unnecessary division by ones
+                    if predictions.ndim > 1:
+                        predictions /= sqrt_sw[:, None]
+                    else:
+                        predictions /= sqrt_sw
+                predictions += y_offset
+
+                if self.store_cv_results:
+                    self.cv_results_[:, i] = predictions.ravel()
+
+                score_params = score_params or {}
+                alpha_score = self._score(
+                    predictions=predictions,
+                    y=unscaled_y,
+                    n_y=n_y,
+                    scorer=self.scoring,
+                    score_params=score_params,
+                )
+
+            # Keep track of the best model
+            if best_score is None:
+                # initialize
+                if self.alpha_per_target and n_y > 1:
+                    best_coef = c
+                    best_score = np.atleast_1d(alpha_score)
+                    best_alpha = np.full(n_y, alpha)
+                else:
+                    best_coef = c
+                    best_score = alpha_score
+                    best_alpha = alpha
+            else:
+                # update
+                if self.alpha_per_target and n_y > 1:
+                    to_update = alpha_score > best_score
+                    best_coef[:, to_update] = c[:, to_update]
+                    best_score[to_update] = alpha_score[to_update]
+                    best_alpha[to_update] = alpha
+                elif alpha_score > best_score:
+                    best_coef, best_score, best_alpha = c, alpha_score, alpha
+
+        self.alpha_ = best_alpha
+        self.best_score_ = best_score
+        self.dual_coef_ = best_coef
+        self.coef_ = safe_sparse_dot(self.dual_coef_.T, X)
+        if y.ndim == 1 or y.shape[1] == 1:
+            self.coef_ = self.coef_.ravel()
+
+        if sparse.issparse(X):
+            X_offset = X_mean * X_scale
+        else:
+            X_offset += X_mean * X_scale
+        self._set_intercept(X_offset, y_offset, X_scale)
+
+        if self.store_cv_results:
+            if len(y.shape) == 1:
+                cv_results_shape = n_samples, n_alphas
+            else:
+                cv_results_shape = n_samples, n_y, n_alphas
+            self.cv_results_ = self.cv_results_.reshape(cv_results_shape)
+
+        return self
+
+    def _score_without_scorer(self, squared_errors):
+        """Performs scoring using squared errors when the scorer is None."""
+        if self.alpha_per_target:
+            _score = -squared_errors.mean(axis=0)
+        else:
+            _score = -squared_errors.mean()
+
+        return _score
+
+    def _score(self, *, predictions, y, n_y, scorer, score_params):
+        """Performs scoring with the specified scorer using the
+        predictions and the true y values.
+        """
+        if self.is_clf:
+            identity_estimator = _IdentityClassifier(classes=np.arange(n_y))
+            _score = scorer(
+                identity_estimator,
+                predictions,
+                y.argmax(axis=1),
+                **score_params,
+            )
+        else:
+            identity_estimator = _IdentityRegressor()
+            if self.alpha_per_target:
+                _score = np.array(
+                    [
+                        scorer(
+                            identity_estimator,
+                            predictions[:, j],
+                            y[:, j],
+                            **score_params,
+                        )
+                        for j in range(n_y)
+                    ]
+                )
+            else:
+                _score = scorer(identity_estimator, predictions, y, **score_params)
+
+        return _score
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # Required since this is neither a RegressorMixin nor a ClassifierMixin
+        tags.target_tags.required = True
+        return tags
+
+
+class _BaseRidgeCV(LinearModel):
+    _parameter_constraints: dict = {
+        "alphas": ["array-like", Interval(Real, 0, None, closed="neither")],
+        "fit_intercept": ["boolean"],
+        "scoring": [StrOptions(set(get_scorer_names())), callable, None],
+        "cv": ["cv_object"],
+        "gcv_mode": [StrOptions({"auto", "svd", "eigen"}), None],
+        "store_cv_results": ["boolean"],
+        "alpha_per_target": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        alphas=(0.1, 1.0, 10.0),
+        *,
+        fit_intercept=True,
+        scoring=None,
+        cv=None,
+        gcv_mode=None,
+        store_cv_results=False,
+        alpha_per_target=False,
+    ):
+        self.alphas = alphas
+        self.fit_intercept = fit_intercept
+        self.scoring = scoring
+        self.cv = cv
+        self.gcv_mode = gcv_mode
+        self.store_cv_results = store_cv_results
+        self.alpha_per_target = alpha_per_target
+
+    def fit(self, X, y, sample_weight=None, **params):
+        """Fit Ridge regression model with cv.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data. If using GCV, will be cast to float64
+            if necessary.
+
+        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
+            Target values. Will be cast to X's dtype if necessary.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+        **params : dict, default=None
+            Extra parameters for the underlying scorer.
+
+            .. versionadded:: 1.5
+                Only available if `enable_metadata_routing=True`,
+                which can be set by using
+                ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+
+        Notes
+        -----
+        When sample_weight is provided, the selected hyperparameter may depend
+        on whether we use leave-one-out cross-validation (cv=None)
+        or another form of cross-validation, because only leave-one-out
+        cross-validation takes the sample weights into account when computing
+        the validation score.
+        """
+        _raise_for_params(params, self, "fit")
+        cv = self.cv
+        scorer = self._get_scorer()
+
+        # `_RidgeGCV` does not work for alpha = 0
+        if cv is None:
+            check_scalar_alpha = partial(
+                check_scalar,
+                target_type=numbers.Real,
+                min_val=0.0,
+                include_boundaries="neither",
+            )
+        else:
+            check_scalar_alpha = partial(
+                check_scalar,
+                target_type=numbers.Real,
+                min_val=0.0,
+                include_boundaries="left",
+            )
+
+        if isinstance(self.alphas, (np.ndarray, list, tuple)):
+            n_alphas = 1 if np.ndim(self.alphas) == 0 else len(self.alphas)
+            if n_alphas != 1:
+                for index, alpha in enumerate(self.alphas):
+                    alpha = check_scalar_alpha(alpha, f"alphas[{index}]")
+            else:
+                self.alphas[0] = check_scalar_alpha(self.alphas[0], "alphas")
+        alphas = np.asarray(self.alphas)
+
+        if sample_weight is not None:
+            params["sample_weight"] = sample_weight
+
+        if cv is None:
+            if _routing_enabled():
+                routed_params = process_routing(
+                    self,
+                    "fit",
+                    **params,
+                )
+            else:
+                routed_params = Bunch(scorer=Bunch(score={}))
+                if sample_weight is not None:
+                    routed_params.scorer.score["sample_weight"] = sample_weight
+
+            # reset `scorer` variable to original user-intend if no scoring is passed
+            if self.scoring is None:
+                scorer = None
+
+            estimator = _RidgeGCV(
+                alphas,
+                fit_intercept=self.fit_intercept,
+                scoring=scorer,
+                gcv_mode=self.gcv_mode,
+                store_cv_results=self.store_cv_results,
+                is_clf=is_classifier(self),
+                alpha_per_target=self.alpha_per_target,
+            )
+            estimator.fit(
+                X,
+                y,
+                sample_weight=sample_weight,
+                score_params=routed_params.scorer.score,
+            )
+            self.alpha_ = estimator.alpha_
+            self.best_score_ = estimator.best_score_
+            if self.store_cv_results:
+                self.cv_results_ = estimator.cv_results_
+        else:
+            if self.store_cv_results:
+                raise ValueError("cv!=None and store_cv_results=True are incompatible")
+            if self.alpha_per_target:
+                raise ValueError("cv!=None and alpha_per_target=True are incompatible")
+
+            parameters = {"alpha": alphas}
+            solver = "sparse_cg" if sparse.issparse(X) else "auto"
+            model = RidgeClassifier if is_classifier(self) else Ridge
+            estimator = model(
+                fit_intercept=self.fit_intercept,
+                solver=solver,
+            )
+            if _routing_enabled():
+                estimator.set_fit_request(sample_weight=True)
+
+            grid_search = GridSearchCV(
+                estimator,
+                parameters,
+                cv=cv,
+                scoring=scorer,
+            )
+
+            grid_search.fit(X, y, **params)
+            estimator = grid_search.best_estimator_
+            self.alpha_ = grid_search.best_estimator_.alpha
+            self.best_score_ = grid_search.best_score_
+
+        self.coef_ = estimator.coef_
+        self.intercept_ = estimator.intercept_
+        self.n_features_in_ = estimator.n_features_in_
+        if hasattr(estimator, "feature_names_in_"):
+            self.feature_names_in_ = estimator.feature_names_in_
+
+        return self
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.5
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add_self_request(self)
+            .add(
+                scorer=self.scoring,
+                method_mapping=MethodMapping().add(caller="fit", callee="score"),
+            )
+            .add(
+                splitter=self.cv,
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
+            )
+        )
+        return router
+
+    def _get_scorer(self):
+        scorer = check_scoring(estimator=self, scoring=self.scoring, allow_none=True)
+        if _routing_enabled() and self.scoring is None:
+            # This estimator passes an array of 1s as sample_weight even if
+            # sample_weight is not provided by the user. Therefore we need to
+            # always request it. But we don't set it if it's passed explicitly
+            # by the user.
+            scorer.set_score_request(sample_weight=True)
+        return scorer
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
+
+
+class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
+    """Ridge regression with built-in cross-validation.
+
+    See glossary entry for :term:`cross-validation estimator`.
+
+    By default, it performs efficient Leave-One-Out Cross-Validation.
+
+    Read more in the :ref:`User Guide <ridge_regression>`.
+
+    Parameters
+    ----------
+    alphas : array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)
+        Array of alpha values to try.
+        Regularization strength; must be a positive float. Regularization
+        improves the conditioning of the problem and reduces the variance of
+        the estimates. Larger values specify stronger regularization.
+        Alpha corresponds to ``1 / (2C)`` in other linear models such as
+        :class:`~sklearn.linear_model.LogisticRegression` or
+        :class:`~sklearn.svm.LinearSVC`.
+        If using Leave-One-Out cross-validation, alphas must be strictly positive.
+
+    fit_intercept : bool, default=True
+        Whether to calculate the intercept for this model. If set
+        to false, no intercept will be used in calculations
+        (i.e. data is expected to be centered).
+
+    scoring : str, callable, default=None
+        The scoring method to use for cross-validation. Options:
+
+        - str: see :ref:`scoring_string_names` for options.
+        - callable: a scorer callable object (e.g., function) with signature
+          ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details.
+        - `None`: negative :ref:`mean squared error <mean_squared_error>` if cv is
+          None (i.e. when using leave-one-out cross-validation), or
+          :ref:`coefficient of determination <r2_score>` (:math:`R^2`) otherwise.
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the efficient Leave-One-Out cross-validation
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used, else,
+        :class:`~sklearn.model_selection.KFold` is used.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+    gcv_mode : {'auto', 'svd', 'eigen'}, default='auto'
+        Flag indicating which strategy to use when performing
+        Leave-One-Out Cross-Validation. Options are::
+
+            'auto' : use 'svd' if n_samples > n_features, otherwise use 'eigen'
+            'svd' : force use of singular value decomposition of X when X is
+                dense, eigenvalue decomposition of X^T.X when X is sparse.
+            'eigen' : force computation via eigendecomposition of X.X^T
+
+        The 'auto' mode is the default and is intended to pick the cheaper
+        option of the two depending on the shape of the training data.
+
+    store_cv_results : bool, default=False
+        Flag indicating if the cross-validation values corresponding to
+        each alpha should be stored in the ``cv_results_`` attribute (see
+        below). This flag is only compatible with ``cv=None`` (i.e. using
+        Leave-One-Out Cross-Validation).
+
+        .. versionchanged:: 1.5
+            Parameter name changed from `store_cv_values` to `store_cv_results`.
+
+    alpha_per_target : bool, default=False
+        Flag indicating whether to optimize the alpha value (picked from the
+        `alphas` parameter list) for each target separately (for multi-output
+        settings: multiple prediction targets). When set to `True`, after
+        fitting, the `alpha_` attribute will contain a value for each target.
+        When set to `False`, a single alpha is used for all targets.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    cv_results_ : ndarray of shape (n_samples, n_alphas) or \
+            shape (n_samples, n_targets, n_alphas), optional
+        Cross-validation values for each alpha (only available if
+        ``store_cv_results=True`` and ``cv=None``). After ``fit()`` has been
+        called, this attribute will contain the mean squared errors if
+        `scoring is None` otherwise it will contain standardized per point
+        prediction values.
+
+        .. versionchanged:: 1.5
+            `cv_values_` changed to `cv_results_`.
+
+    coef_ : ndarray of shape (n_features) or (n_targets, n_features)
+        Weight vector(s).
+
+    intercept_ : float or ndarray of shape (n_targets,)
+        Independent term in decision function. Set to 0.0 if
+        ``fit_intercept = False``.
+
+    alpha_ : float or ndarray of shape (n_targets,)
+        Estimated regularization parameter, or, if ``alpha_per_target=True``,
+        the estimated regularization parameter for each target.
+
+    best_score_ : float or ndarray of shape (n_targets,)
+        Score of base estimator with best alpha, or, if
+        ``alpha_per_target=True``, a score for each target.
+
+        .. versionadded:: 0.23
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    Ridge : Ridge regression.
+    RidgeClassifier : Classifier based on ridge regression on {-1, 1} labels.
+    RidgeClassifierCV : Ridge classifier with built-in cross validation.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_diabetes
+    >>> from sklearn.linear_model import RidgeCV
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
+    >>> clf.score(X, y)
+    0.5166...
+    """
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None, **params):
+        """Fit Ridge regression model with cv.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data. If using GCV, will be cast to float64
+            if necessary.
+
+        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
+            Target values. Will be cast to X's dtype if necessary.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+        **params : dict, default=None
+            Parameters to be passed to the underlying scorer.
+
+            .. versionadded:: 1.5
+                Only available if `enable_metadata_routing=True`,
+                which can be set by using
+                ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+
+        Notes
+        -----
+        When sample_weight is provided, the selected hyperparameter may depend
+        on whether we use leave-one-out cross-validation (cv=None)
+        or another form of cross-validation, because only leave-one-out
+        cross-validation takes the sample weights into account when computing
+        the validation score.
+        """
+        super().fit(X, y, sample_weight=sample_weight, **params)
+        return self
+
+
+class RidgeClassifierCV(_RidgeClassifierMixin, _BaseRidgeCV):
+    """Ridge classifier with built-in cross-validation.
+
+    See glossary entry for :term:`cross-validation estimator`.
+
+    By default, it performs Leave-One-Out Cross-Validation. Currently,
+    only the n_features > n_samples case is handled efficiently.
+
+    Read more in the :ref:`User Guide <ridge_regression>`.
+
+    Parameters
+    ----------
+    alphas : array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)
+        Array of alpha values to try.
+        Regularization strength; must be a positive float. Regularization
+        improves the conditioning of the problem and reduces the variance of
+        the estimates. Larger values specify stronger regularization.
+        Alpha corresponds to ``1 / (2C)`` in other linear models such as
+        :class:`~sklearn.linear_model.LogisticRegression` or
+        :class:`~sklearn.svm.LinearSVC`.
+        If using Leave-One-Out cross-validation, alphas must be strictly positive.
+
+    fit_intercept : bool, default=True
+        Whether to calculate the intercept for this model. If set
+        to false, no intercept will be used in calculations
+        (i.e. data is expected to be centered).
+
+    scoring : str, callable, default=None
+        The scoring method to use for cross-validation. Options:
+
+        - str: see :ref:`scoring_string_names` for options.
+        - callable: a scorer callable object (e.g., function) with signature
+          ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details.
+        - `None`: negative :ref:`mean squared error <mean_squared_error>` if cv is
+          None (i.e. when using leave-one-out cross-validation), or
+          :ref:`accuracy <accuracy_score>` otherwise.
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the efficient Leave-One-Out cross-validation
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+    class_weight : dict or 'balanced', default=None
+        Weights associated with classes in the form ``{class_label: weight}``.
+        If not given, all classes are supposed to have weight one.
+
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``.
+
+    store_cv_results : bool, default=False
+        Flag indicating if the cross-validation results corresponding to
+        each alpha should be stored in the ``cv_results_`` attribute (see
+        below). This flag is only compatible with ``cv=None`` (i.e. using
+        Leave-One-Out Cross-Validation).
+
+        .. versionchanged:: 1.5
+            Parameter name changed from `store_cv_values` to `store_cv_results`.
+
+    Attributes
+    ----------
+    cv_results_ : ndarray of shape (n_samples, n_targets, n_alphas), optional
+        Cross-validation results for each alpha (only if ``store_cv_results=True`` and
+        ``cv=None``). After ``fit()`` has been called, this attribute will
+        contain the mean squared errors if `scoring is None` otherwise it
+        will contain standardized per point prediction values.
+
+        .. versionchanged:: 1.5
+            `cv_values_` changed to `cv_results_`.
+
+    coef_ : ndarray of shape (1, n_features) or (n_targets, n_features)
+        Coefficient of the features in the decision function.
+
+        ``coef_`` is of shape (1, n_features) when the given problem is binary.
+
+    intercept_ : float or ndarray of shape (n_targets,)
+        Independent term in decision function. Set to 0.0 if
+        ``fit_intercept = False``.
+
+    alpha_ : float
+        Estimated regularization parameter.
+
+    best_score_ : float
+        Score of base estimator with best alpha.
+
+        .. versionadded:: 0.23
+
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    Ridge : Ridge regression.
+    RidgeClassifier : Ridge classifier.
+    RidgeCV : Ridge regression with built-in cross validation.
+
+    Notes
+    -----
+    For multi-class classification, n_class classifiers are trained in
+    a one-versus-all approach. Concretely, this is implemented by taking
+    advantage of the multi-variate response support in Ridge.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.linear_model import RidgeClassifierCV
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
+    >>> clf.score(X, y)
+    0.9630...
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseRidgeCV._parameter_constraints,
+        "class_weight": [dict, StrOptions({"balanced"}), None],
+    }
+    for param in ("gcv_mode", "alpha_per_target"):
+        _parameter_constraints.pop(param)
+
+    def __init__(
+        self,
+        alphas=(0.1, 1.0, 10.0),
+        *,
+        fit_intercept=True,
+        scoring=None,
+        cv=None,
+        class_weight=None,
+        store_cv_results=False,
+    ):
+        super().__init__(
+            alphas=alphas,
+            fit_intercept=fit_intercept,
+            scoring=scoring,
+            cv=cv,
+            store_cv_results=store_cv_results,
+        )
+        self.class_weight = class_weight
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None, **params):
+        """Fit Ridge classifier with cv.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples
+            and `n_features` is the number of features. When using GCV,
+            will be cast to float64 if necessary.
+
+        y : ndarray of shape (n_samples,)
+            Target values. Will be cast to X's dtype if necessary.
+
+        sample_weight : float or ndarray of shape (n_samples,), default=None
+            Individual weights for each sample. If given a float, every sample
+            will have the same weight.
+
+        **params : dict, default=None
+            Parameters to be passed to the underlying scorer.
+
+            .. versionadded:: 1.5
+                Only available if `enable_metadata_routing=True`,
+                which can be set by using
+                ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        # `RidgeClassifier` does not accept "sag" or "saga" solver and thus support
+        # csr, csc, and coo sparse matrices. By using solver="eigen" we force to accept
+        # all sparse format.
+        X, y, sample_weight, Y = self._prepare_data(X, y, sample_weight, solver="eigen")
+
+        # If cv is None, gcv mode will be used and we used the binarized Y
+        # since y will not be binarized in _RidgeGCV estimator.
+        # If cv is not None, a GridSearchCV with some RidgeClassifier
+        # estimators are used where y will be binarized. Thus, we pass y
+        # instead of the binarized Y.
+        target = Y if self.cv is None else y
+        super().fit(X, target, sample_weight=sample_weight, **params)
+        return self
diff --git a/shared/package-files/a0/a0af2d7b10fafcc9b4b8724b63cd043c3d9ee01d9ac07d3ef84516567ce42b0f/sin_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/a0/a0af2d7b10fafcc9b4b8724b63cd043c3d9ee01d9ac07d3ef84516567ce42b0f/sin_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..996e28f921ece8d52101b984686e1b6745694e3b
--- /dev/null
+++ b/shared/package-files/a0/a0af2d7b10fafcc9b4b8724b63cd043c3d9ee01d9ac07d3ef84516567ce42b0f/sin_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor sin(const at::Tensor & self);
+TORCH_API at::Tensor & sin_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/a0/a0b91235a1b93c202cd17685ea02b8f98db201f71f627a8560a8985a31ce80df/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_cuda_dispatch.h b/shared/package-files/a0/a0b91235a1b93c202cd17685ea02b8f98db201f71f627a8560a8985a31ce80df/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2bfb7862cd1a3c3f826a782f7ff89debaa18920f
--- /dev/null
+++ b/shared/package-files/a0/a0b91235a1b93c202cd17685ea02b8f98db201f71f627a8560a8985a31ce80df/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/a0/a0be0d3e5699aaf5d7d7776d8ce53997755d749e568e7024b9afebf98623a1d8/contiguous_compositeimplicitautograd_dispatch.h b/shared/package-files/a0/a0be0d3e5699aaf5d7d7776d8ce53997755d749e568e7024b9afebf98623a1d8/contiguous_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb3e7fb2aa9e185f819848aae4de4cf7a85f32a3
--- /dev/null
+++ b/shared/package-files/a0/a0be0d3e5699aaf5d7d7776d8ce53997755d749e568e7024b9afebf98623a1d8/contiguous_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor contiguous(const at::Tensor & self, at::MemoryFormat memory_format=MemoryFormat::Contiguous);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a0bf280fe27aac19b56cc0a5bce156457b6e5173103dbb1f867dcd5d212bc2fe/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h b/shared/package-files/a0/a0bf280fe27aac19b56cc0a5bce156457b6e5173103dbb1f867dcd5d212bc2fe/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2493a1c1ba3b9743552f059e43855e6d56a10fbd
--- /dev/null
+++ b/shared/package-files/a0/a0bf280fe27aac19b56cc0a5bce156457b6e5173103dbb1f867dcd5d212bc2fe/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a0cb66641d8ea7ac9e66f157b24303654013c0b9a0442838c718773ab715a926/tensorrt.py b/shared/package-files/a0/a0cb66641d8ea7ac9e66f157b24303654013c0b9a0442838c718773ab715a926/tensorrt.py
new file mode 100644
index 0000000000000000000000000000000000000000..1868919ea7621be35555c41c985e900614d50e63
--- /dev/null
+++ b/shared/package-files/a0/a0cb66641d8ea7ac9e66f157b24303654013c0b9a0442838c718773ab715a926/tensorrt.py
@@ -0,0 +1,14 @@
+# mypy: ignore-errors
+
+# import torch  # type: ignore[import]
+# from .common import device_from_inputs, fake_tensor_unsupported  # type: ignore[import]
+# from .registry import register_backend  # type: ignore[import]
+
+"""
+Placeholder for TensorRT backend for dynamo via torch-tensorrt
+"""
+
+# @register_backend
+# def tensorrt(gm, example_inputs):
+#    import torch_tensorrt # type: ignore[import]
+#    pass
diff --git a/shared/package-files/a0/a0d1e78cb17e9fe3de261a586a57095d43ee022846996a196368103c94a23e3d/_backward_compositeimplicitautograd_dispatch.h b/shared/package-files/a0/a0d1e78cb17e9fe3de261a586a57095d43ee022846996a196368103c94a23e3d/_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8a192cb8630f67a4120da9443dccc94c583b924b
--- /dev/null
+++ b/shared/package-files/a0/a0d1e78cb17e9fe3de261a586a57095d43ee022846996a196368103c94a23e3d/_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API void _backward(const at::Tensor & self, at::TensorList inputs, const c10::optional<at::Tensor> & gradient={}, c10::optional<bool> retain_graph=c10::nullopt, bool create_graph=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a0d6172012fb80470c65b1fac3bbf50140051fd01a38d2d39e1f71ac9d3746ff/_fused_adam_native.h b/shared/package-files/a0/a0d6172012fb80470c65b1fac3bbf50140051fd01a38d2d39e1f71ac9d3746ff/_fused_adam_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e141ae580d7f0c744058315d8b5e259aff2f1772
--- /dev/null
+++ b/shared/package-files/a0/a0d6172012fb80470c65b1fac3bbf50140051fd01a38d2d39e1f71ac9d3746ff/_fused_adam_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adam(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={});
+TORCH_API void _fused_adam_out(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out);
+TORCH_API void _fused_adam_kernel_cuda_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={});
+TORCH_API ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adam(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={});
+TORCH_API void _fused_adam_tensor_lr_out(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out);
+TORCH_API void _fused_adam_kernel_cuda_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={});
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/a0/a0debdca48044433c5014ab129c333e09d788258269559e3a50586bf13c687fe/_sobol_engine_ff_compositeimplicitautograd_dispatch.h b/shared/package-files/a0/a0debdca48044433c5014ab129c333e09d788258269559e3a50586bf13c687fe/_sobol_engine_ff_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc1ad5ce5b53668e64275280a19d5224039bd432
--- /dev/null
+++ b/shared/package-files/a0/a0debdca48044433c5014ab129c333e09d788258269559e3a50586bf13c687fe/_sobol_engine_ff_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor & _sobol_engine_ff_(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/a0/a0eae3588ffb91cb9086913e756d17ce5ce547aa7186e22b3a5b205b6cb37093/test_lowess_simple.csv b/shared/package-files/a0/a0eae3588ffb91cb9086913e756d17ce5ce547aa7186e22b3a5b205b6cb37093/test_lowess_simple.csv
new file mode 100644
index 0000000000000000000000000000000000000000..33cff13bf22fb7e0b2cd445315fd28d8b5a84bc3
--- /dev/null
+++ b/shared/package-files/a0/a0eae3588ffb91cb9086913e756d17ce5ce547aa7186e22b3a5b205b6cb37093/test_lowess_simple.csv
@@ -0,0 +1,21 @@
+"x","y","out"
+0,-0.76741118,-0.626034455349546
+1,0.69245631,0.56507171201094
+2,2.39950921,1.75962718897954
+3,2.53647578,2.95796332584499
+4,2.32918222,4.15606361537761
+5,5.6595567,5.34733969366442
+6,6.66367639,6.52229821799894
+7,4.95611415,7.70815938803622
+8,8.8123281,8.87590555190343
+9,10.45977518,9.940975860297
+10,11.21428038,10.8981138457948
+11,12.29296866,11.7851424727769
+12,12.78028477,12.6188717296918
+13,12.7597147,13.409849737403
+14,13.78278698,14.1516996584552
+15,15.24549405,14.9180658146586
+16,16.25987014,15.695660019874
+17,16.09290966,16.4783034134255
+18,16.54311784,17.2617441530539
+19,18.68219495,18.0459201716397
diff --git a/shared/package-files/a0/a0ee9b39b5c05df1114c4fdda34fe0bcd3bb28a41d93c1121822da971e739404/_triton_scaled_dot_attention_ops.h b/shared/package-files/a0/a0ee9b39b5c05df1114c4fdda34fe0bcd3bb28a41d93c1121822da971e739404/_triton_scaled_dot_attention_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..fec042e1effaa81f6c65e0b212f372e3a42a45e8
--- /dev/null
+++ b/shared/package-files/a0/a0ee9b39b5c05df1114c4fdda34fe0bcd3bb28a41d93c1121822da971e739404/_triton_scaled_dot_attention_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _triton_scaled_dot_attention {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, double);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_triton_scaled_dot_attention")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor")
+  static at::Tensor call(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p);
+};
+
+struct TORCH_API _triton_scaled_dot_attention_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, double, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_triton_scaled_dot_attention")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/a0/a0f80d842e220d286b2154f1790b05dc8b5edb17eaed7f2e679fd5124e7a8675/atan2_ops.h b/shared/package-files/a0/a0f80d842e220d286b2154f1790b05dc8b5edb17eaed7f2e679fd5124e7a8675/atan2_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..909f8b432177a9b0a6fb36deb34d715e72276701
--- /dev/null
+++ b/shared/package-files/a0/a0f80d842e220d286b2154f1790b05dc8b5edb17eaed7f2e679fd5124e7a8675/atan2_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API atan2_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::atan2")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API atan2_ {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::atan2_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API atan2 {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::atan2")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "atan2(Tensor self, Tensor other) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/a0/a0fc1785a8f2a200a3124d703d49550601b7741ccffc861713c2ddaa07a537dd/LICENCE b/shared/package-files/a0/a0fc1785a8f2a200a3124d703d49550601b7741ccffc861713c2ddaa07a537dd/LICENCE
new file mode 100644
index 0000000000000000000000000000000000000000..5b3cab7fb59874bed3d4b6a018ebd05f3b40de0d
--- /dev/null
+++ b/shared/package-files/a0/a0fc1785a8f2a200a3124d703d49550601b7741ccffc861713c2ddaa07a537dd/LICENCE
@@ -0,0 +1,49 @@
+`tqdm` is a product of collaborative work.
+Unless otherwise stated, all authors (see commit logs) retain copyright
+for their respective work, and release the work under the MIT licence
+(text below).
+
+Exceptions or notable authors are listed below
+in reverse chronological order:
+
+* files: *
+  MPLv2.0 2015-2021 (c) Casper da Costa-Luis
+  [casperdcl](https://github.com/casperdcl).
+* files: tqdm/_tqdm.py
+  MIT 2016 (c) [PR #96] on behalf of Google Inc.
+* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
+  MIT 2013 (c) Noam Yorav-Raphael, original author.
+
+[PR #96]: https://github.com/tqdm/tqdm/pull/96
+
+
+Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
+-----------------------------------------------
+
+This Source Code Form is subject to the terms of the
+Mozilla Public License, v. 2.0.
+If a copy of the MPL was not distributed with this project,
+You can obtain one at https://mozilla.org/MPL/2.0/.
+
+
+MIT License (MIT)
+-----------------
+
+Copyright (c) 2013 noamraph
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/shared/package-files/b5/b5014033740d28f77307856a5067445058cd3d85d564b029c069d2bacd86431f/type_fwd.h b/shared/package-files/b5/b5014033740d28f77307856a5067445058cd3d85d564b029c069d2bacd86431f/type_fwd.h
new file mode 100644
index 0000000000000000000000000000000000000000..2f22bbea36dbbf1e3da7ce10975a9584accb989e
--- /dev/null
+++ b/shared/package-files/b5/b5014033740d28f77307856a5067445058cd3d85d564b029c069d2bacd86431f/type_fwd.h
@@ -0,0 +1,65 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+namespace arrow {
+namespace internal {
+class Uri;
+}
+namespace flight {
+struct Action;
+struct ActionType;
+template <typename T>
+class AsyncListener;
+class AsyncListenerBase;
+class AsyncRpc;
+struct BasicAuth;
+class ClientAuthHandler;
+class ClientMiddleware;
+class ClientMiddlewareFactory;
+struct Criteria;
+class FlightCallOptions;
+struct FlightClientOptions;
+struct FlightDescriptor;
+struct FlightEndpoint;
+class FlightInfo;
+class PollInfo;
+class FlightListing;
+class FlightMetadataReader;
+class FlightMetadataWriter;
+struct FlightPayload;
+class FlightServerBase;
+class FlightServerOptions;
+class FlightStreamReader;
+class FlightStreamWriter;
+struct Location;
+struct Result;
+class ResultStream;
+struct SchemaResult;
+class ServerCallContext;
+class ServerMiddleware;
+class ServerMiddlewareFactory;
+struct Ticket;
+namespace internal {
+class AsyncRpc;
+class ClientTransport;
+struct FlightData;
+class ServerTransport;
+}  // namespace internal
+}  // namespace flight
+}  // namespace arrow
diff --git a/shared/package-files/b5/b5049885088ef7866153fdef6d20960b4d22e8792617111f1aa8749606c3e73d/upsample_bilinear2d_native.h b/shared/package-files/b5/b5049885088ef7866153fdef6d20960b4d22e8792617111f1aa8749606c3e73d/upsample_bilinear2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5b486ca6a64aee9ce4fc725e99f77571160f7011
--- /dev/null
+++ b/shared/package-files/b5/b5049885088ef7866153fdef6d20960b4d22e8792617111f1aa8749606c3e73d/upsample_bilinear2d_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_bilinear2d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_bilinear2d_out_cpu : public at::meta::structured_upsample_bilinear2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_bilinear2d_out_cuda : public at::meta::structured_upsample_bilinear2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, const at::Tensor & out);
+};
+TORCH_API at::Tensor upsample_bilinear2d_quantized_cpu(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h=c10::nullopt, c10::optional<double> scales_w=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/b5/b508898aea46c22f3603184725f9e84d3beb544bb1d4359e5ddc730c366246cd/indenter.py b/shared/package-files/b5/b508898aea46c22f3603184725f9e84d3beb544bb1d4359e5ddc730c366246cd/indenter.py
new file mode 100644
index 0000000000000000000000000000000000000000..782fe9ce99ce310524a6136ab42701357e7aa55e
--- /dev/null
+++ b/shared/package-files/b5/b508898aea46c22f3603184725f9e84d3beb544bb1d4359e5ddc730c366246cd/indenter.py
@@ -0,0 +1,112 @@
+"Provides a post-lexer for implementing Python-style indentation."
+
+from abc import ABC, abstractmethod
+from typing import List, Iterator
+
+from .exceptions import LarkError
+from .lark import PostLex
+from .lexer import Token
+
+###{standalone
+
+class DedentError(LarkError):
+    pass
+
+class Indenter(PostLex, ABC):
+    paren_level: int
+    indent_level: List[int]
+
+    def __init__(self) -> None:
+        self.paren_level = 0
+        self.indent_level = [0]
+        assert self.tab_len > 0
+
+    def handle_NL(self, token: Token) -> Iterator[Token]:
+        if self.paren_level > 0:
+            return
+
+        yield token
+
+        indent_str = token.rsplit('\n', 1)[1] # Tabs and spaces
+        indent = indent_str.count(' ') + indent_str.count('\t') * self.tab_len
+
+        if indent > self.indent_level[-1]:
+            self.indent_level.append(indent)
+            yield Token.new_borrow_pos(self.INDENT_type, indent_str, token)
+        else:
+            while indent < self.indent_level[-1]:
+                self.indent_level.pop()
+                yield Token.new_borrow_pos(self.DEDENT_type, indent_str, token)
+
+            if indent != self.indent_level[-1]:
+                raise DedentError('Unexpected dedent to column %s. Expected dedent to %s' % (indent, self.indent_level[-1]))
+
+    def _process(self, stream):
+        for token in stream:
+            if token.type == self.NL_type:
+                yield from self.handle_NL(token)
+            else:
+                yield token
+
+            if token.type in self.OPEN_PAREN_types:
+                self.paren_level += 1
+            elif token.type in self.CLOSE_PAREN_types:
+                self.paren_level -= 1
+                assert self.paren_level >= 0
+
+        while len(self.indent_level) > 1:
+            self.indent_level.pop()
+            yield Token(self.DEDENT_type, '')
+
+        assert self.indent_level == [0], self.indent_level
+
+    def process(self, stream):
+        self.paren_level = 0
+        self.indent_level = [0]
+        return self._process(stream)
+
+    # XXX Hack for ContextualLexer. Maybe there's a more elegant solution?
+    @property
+    def always_accept(self):
+        return (self.NL_type,)
+
+    @property
+    @abstractmethod
+    def NL_type(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def OPEN_PAREN_types(self) -> List[str]:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def CLOSE_PAREN_types(self) -> List[str]:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def INDENT_type(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def DEDENT_type(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def tab_len(self) -> int:
+        raise NotImplementedError()
+
+
+class PythonIndenter(Indenter):
+    NL_type = '_NEWLINE'
+    OPEN_PAREN_types = ['LPAR', 'LSQB', 'LBRACE']
+    CLOSE_PAREN_types = ['RPAR', 'RSQB', 'RBRACE']
+    INDENT_type = '_INDENT'
+    DEDENT_type = '_DEDENT'
+    tab_len = 8
+
+###}
diff --git a/shared/package-files/b5/b509291df48f709d11afdd7d5a2dbc2bcf3b6648a6fe8e2b26ad9ac01ea06b85/linux-16color b/shared/package-files/b5/b509291df48f709d11afdd7d5a2dbc2bcf3b6648a6fe8e2b26ad9ac01ea06b85/linux-16color
new file mode 100644
index 0000000000000000000000000000000000000000..3e6eb5a815b5faaa2e2a67463689a35beed160dc
Binary files /dev/null and b/shared/package-files/b5/b509291df48f709d11afdd7d5a2dbc2bcf3b6648a6fe8e2b26ad9ac01ea06b85/linux-16color differ
diff --git a/shared/package-files/b5/b50b9415af18a9576c1cf39c52103094c281f14934f077a30e10c275af255ecd/test_autolev.py b/shared/package-files/b5/b50b9415af18a9576c1cf39c52103094c281f14934f077a30e10c275af255ecd/test_autolev.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfcaef13565c5e2187dc6e90113b407a7967c331
--- /dev/null
+++ b/shared/package-files/b5/b50b9415af18a9576c1cf39c52103094c281f14934f077a30e10c275af255ecd/test_autolev.py
@@ -0,0 +1,178 @@
+import os
+
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.external import import_module
+from sympy.testing.pytest import skip
+from sympy.parsing.autolev import parse_autolev
+
+antlr4 = import_module("antlr4")
+
+if not antlr4:
+    disabled = True
+
+FILE_DIR = os.path.dirname(
+    os.path.dirname(os.path.abspath(os.path.realpath(__file__))))
+
+
+def _test_examples(in_filename, out_filename, test_name=""):
+
+    in_file_path = os.path.join(FILE_DIR, 'autolev', 'test-examples',
+                                in_filename)
+    correct_file_path = os.path.join(FILE_DIR, 'autolev', 'test-examples',
+                                     out_filename)
+    with open(in_file_path) as f:
+        generated_code = parse_autolev(f, include_numeric=True)
+
+    with open(correct_file_path) as f:
+        for idx, line1 in enumerate(f):
+            if line1.startswith("#"):
+                break
+            try:
+                line2 = generated_code.split('\n')[idx]
+                assert line1.rstrip() == line2.rstrip()
+            except Exception:
+                msg = 'mismatch in ' + test_name + ' in line no: {0}'
+                raise AssertionError(msg.format(idx+1))
+
+
+def test_rule_tests():
+
+    l = ["ruletest1", "ruletest2", "ruletest3", "ruletest4", "ruletest5",
+         "ruletest6", "ruletest7", "ruletest8", "ruletest9", "ruletest10",
+         "ruletest11", "ruletest12"]
+
+    for i in l:
+        in_filepath = i + ".al"
+        out_filepath = i + ".py"
+        _test_examples(in_filepath, out_filepath, i)
+
+
+def test_pydy_examples():
+
+    l = ["mass_spring_damper", "chaos_pendulum", "double_pendulum",
+         "non_min_pendulum"]
+
+    for i in l:
+        in_filepath = os.path.join("pydy-example-repo", i + ".al")
+        out_filepath = os.path.join("pydy-example-repo", i + ".py")
+        _test_examples(in_filepath, out_filepath, i)
+
+
+def test_autolev_tutorial():
+
+    dir_path = os.path.join(FILE_DIR, 'autolev', 'test-examples',
+                            'autolev-tutorial')
+
+    if os.path.isdir(dir_path):
+        l = ["tutor1", "tutor2", "tutor3", "tutor4", "tutor5", "tutor6",
+             "tutor7"]
+        for i in l:
+            in_filepath = os.path.join("autolev-tutorial", i + ".al")
+            out_filepath = os.path.join("autolev-tutorial", i + ".py")
+            _test_examples(in_filepath, out_filepath, i)
+
+
+def test_dynamics_online():
+
+    dir_path = os.path.join(FILE_DIR, 'autolev', 'test-examples',
+                            'dynamics-online')
+
+    if os.path.isdir(dir_path):
+        ch1 = ["1-4", "1-5", "1-6", "1-7", "1-8", "1-9_1", "1-9_2", "1-9_3"]
+        ch2 = ["2-1", "2-2", "2-3", "2-4", "2-5", "2-6", "2-7", "2-8", "2-9",
+               "circular"]
+        ch3 = ["3-1_1", "3-1_2", "3-2_1", "3-2_2", "3-2_3", "3-2_4", "3-2_5",
+               "3-3"]
+        ch4 = ["4-1_1", "4-2_1", "4-4_1", "4-4_2", "4-5_1", "4-5_2"]
+        chapters = [(ch1, "ch1"), (ch2, "ch2"), (ch3, "ch3"), (ch4, "ch4")]
+        for ch, name in chapters:
+            for i in ch:
+                in_filepath = os.path.join("dynamics-online", name, i + ".al")
+                out_filepath = os.path.join("dynamics-online", name, i + ".py")
+                _test_examples(in_filepath, out_filepath, i)
+
+
+def test_output_01():
+    """Autolev example calculates the position, velocity, and acceleration of a
+    point and expresses in a single reference frame::
+
+          (1) FRAMES C,D,F
+          (2) VARIABLES FD'',DC''
+          (3) CONSTANTS R,L
+          (4) POINTS O,E
+          (5) SIMPROT(F,D,1,FD)
+       -> (6) F_D = [1, 0, 0; 0, COS(FD), -SIN(FD); 0, SIN(FD), COS(FD)]
+          (7) SIMPROT(D,C,2,DC)
+       -> (8) D_C = [COS(DC), 0, SIN(DC); 0, 1, 0; -SIN(DC), 0, COS(DC)]
+          (9) W_C_F> = EXPRESS(W_C_F>, F)
+       -> (10) W_C_F> = FD'*F1> + COS(FD)*DC'*F2> + SIN(FD)*DC'*F3>
+          (11) P_O_E>=R*D2>-L*C1>
+          (12) P_O_E>=EXPRESS(P_O_E>, D)
+       -> (13) P_O_E> = -L*COS(DC)*D1> + R*D2> + L*SIN(DC)*D3>
+          (14) V_E_F>=EXPRESS(DT(P_O_E>,F),D)
+       -> (15) V_E_F> = L*SIN(DC)*DC'*D1> - L*SIN(DC)*FD'*D2> + (R*FD'+L*COS(DC)*DC')*D3>
+          (16) A_E_F>=EXPRESS(DT(V_E_F>,F),D)
+       -> (17) A_E_F> = L*(COS(DC)*DC'^2+SIN(DC)*DC'')*D1> + (-R*FD'^2-2*L*COS(DC)*DC'*FD'-L*SIN(DC)*FD'')*D2> + (R*FD''+L*COS(DC)*DC''-L*SIN(DC)*DC'^2-L*SIN(DC)*FD'^2)*D3>
+
+    """
+
+    if not antlr4:
+        skip('Test skipped: antlr4 is not installed.')
+
+    autolev_input = """\
+FRAMES C,D,F
+VARIABLES FD'',DC''
+CONSTANTS R,L
+POINTS O,E
+SIMPROT(F,D,1,FD)
+SIMPROT(D,C,2,DC)
+W_C_F>=EXPRESS(W_C_F>,F)
+P_O_E>=R*D2>-L*C1>
+P_O_E>=EXPRESS(P_O_E>,D)
+V_E_F>=EXPRESS(DT(P_O_E>,F),D)
+A_E_F>=EXPRESS(DT(V_E_F>,F),D)\
+"""
+
+    sympy_input = parse_autolev(autolev_input)
+
+    g = {}
+    l = {}
+    exec(sympy_input, g, l)
+
+    w_c_f = l['frame_c'].ang_vel_in(l['frame_f'])
+    # P_O_E> means "the position of point E wrt to point O"
+    p_o_e = l['point_e'].pos_from(l['point_o'])
+    v_e_f = l['point_e'].vel(l['frame_f'])
+    a_e_f = l['point_e'].acc(l['frame_f'])
+
+    # NOTE : The Autolev outputs above were manually transformed into
+    # equivalent SymPy physics vector expressions. Would be nice to automate
+    # this transformation.
+    expected_w_c_f = (l['fd'].diff()*l['frame_f'].x +
+                      cos(l['fd'])*l['dc'].diff()*l['frame_f'].y +
+                      sin(l['fd'])*l['dc'].diff()*l['frame_f'].z)
+
+    assert (w_c_f - expected_w_c_f).simplify() == 0
+
+    expected_p_o_e = (-l['l']*cos(l['dc'])*l['frame_d'].x +
+                      l['r']*l['frame_d'].y +
+                      l['l']*sin(l['dc'])*l['frame_d'].z)
+
+    assert (p_o_e - expected_p_o_e).simplify() == 0
+
+    expected_v_e_f = (l['l']*sin(l['dc'])*l['dc'].diff()*l['frame_d'].x -
+                      l['l']*sin(l['dc'])*l['fd'].diff()*l['frame_d'].y +
+                      (l['r']*l['fd'].diff() +
+                       l['l']*cos(l['dc'])*l['dc'].diff())*l['frame_d'].z)
+    assert (v_e_f - expected_v_e_f).simplify() == 0
+
+    expected_a_e_f = (l['l']*(cos(l['dc'])*l['dc'].diff()**2 +
+                              sin(l['dc'])*l['dc'].diff().diff())*l['frame_d'].x +
+                      (-l['r']*l['fd'].diff()**2 -
+                       2*l['l']*cos(l['dc'])*l['dc'].diff()*l['fd'].diff() -
+                       l['l']*sin(l['dc'])*l['fd'].diff().diff())*l['frame_d'].y +
+                      (l['r']*l['fd'].diff().diff() +
+                       l['l']*cos(l['dc'])*l['dc'].diff().diff() -
+                       l['l']*sin(l['dc'])*l['dc'].diff()**2 -
+                       l['l']*sin(l['dc'])*l['fd'].diff()**2)*l['frame_d'].z)
+    assert (a_e_f - expected_a_e_f).simplify() == 0
diff --git a/shared/package-files/b5/b5166019760429c4d6150180f1c2dd81136488e21c36b564e605c9dc7366f1db/bterm b/shared/package-files/b5/b5166019760429c4d6150180f1c2dd81136488e21c36b564e605c9dc7366f1db/bterm
new file mode 100644
index 0000000000000000000000000000000000000000..134f63e816c93c71f6198ca0df976c1ee7979982
Binary files /dev/null and b/shared/package-files/b5/b5166019760429c4d6150180f1c2dd81136488e21c36b564e605c9dc7366f1db/bterm differ
diff --git a/shared/package-files/b5/b5171e32758a78450854f40867775d4aca58665bc920ebece04fcfcc153af02a/fix_apply.py b/shared/package-files/b5/b5171e32758a78450854f40867775d4aca58665bc920ebece04fcfcc153af02a/fix_apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..6408582c42647741416968f92c351a5581bc5e3e
--- /dev/null
+++ b/shared/package-files/b5/b5171e32758a78450854f40867775d4aca58665bc920ebece04fcfcc153af02a/fix_apply.py
@@ -0,0 +1,68 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Fixer for apply().
+
+This converts apply(func, v, k) into (func)(*v, **k)."""
+
+# Local imports
+from .. import pytree
+from ..pgen2 import token
+from .. import fixer_base
+from ..fixer_util import Call, Comma, parenthesize
+
+class FixApply(fixer_base.BaseFix):
+    BM_compatible = True
+
+    PATTERN = """
+    power< 'apply'
+        trailer<
+            '('
+            arglist<
+                (not argument<NAME '=' any>) func=any ','
+                (not argument<NAME '=' any>) args=any [','
+                (not argument<NAME '=' any>) kwds=any] [',']
+            >
+            ')'
+        >
+    >
+    """
+
+    def transform(self, node, results):
+        syms = self.syms
+        assert results
+        func = results["func"]
+        args = results["args"]
+        kwds = results.get("kwds")
+        # I feel like we should be able to express this logic in the
+        # PATTERN above but I don't know how to do it so...
+        if args:
+            if (args.type == self.syms.argument and
+                args.children[0].value in {'**', '*'}):
+                return  # Make no change.
+        if kwds and (kwds.type == self.syms.argument and
+                     kwds.children[0].value == '**'):
+            return  # Make no change.
+        prefix = node.prefix
+        func = func.clone()
+        if (func.type not in (token.NAME, syms.atom) and
+            (func.type != syms.power or
+             func.children[-2].type == token.DOUBLESTAR)):
+            # Need to parenthesize
+            func = parenthesize(func)
+        func.prefix = ""
+        args = args.clone()
+        args.prefix = ""
+        if kwds is not None:
+            kwds = kwds.clone()
+            kwds.prefix = ""
+        l_newargs = [pytree.Leaf(token.STAR, "*"), args]
+        if kwds is not None:
+            l_newargs.extend([Comma(),
+                              pytree.Leaf(token.DOUBLESTAR, "**"),
+                              kwds])
+            l_newargs[-2].prefix = " " # that's the ** token
+        # XXX Sometimes we could be cleverer, e.g. apply(f, (x, y) + t)
+        # can be translated into f(x, y, *t) instead of f(*(x, y) + t)
+        #new = pytree.Node(syms.power, (func, ArgList(l_newargs)))
+        return Call(func, l_newargs, prefix=prefix)
diff --git a/shared/package-files/b5/b5177b70f87ae8de6d5380dd3bbd05f31d45a8e0dc199dc7b6b623178052b3dc/udf.cc b/shared/package-files/b5/b5177b70f87ae8de6d5380dd3bbd05f31d45a8e0dc199dc7b6b623178052b3dc/udf.cc
new file mode 100644
index 0000000000000000000000000000000000000000..940e403ef693bad99aeab11022cb5c4e868a4854
--- /dev/null
+++ b/shared/package-files/b5/b5177b70f87ae8de6d5380dd3bbd05f31d45a8e0dc199dc7b6b623178052b3dc/udf.cc
@@ -0,0 +1,707 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#include "arrow/python/udf.h"
+
+#include "arrow/array/array_nested.h"
+#include "arrow/array/builder_base.h"
+#include "arrow/buffer_builder.h"
+#include "arrow/compute/api_aggregate.h"
+#include "arrow/compute/api_vector.h"
+#include "arrow/compute/function.h"
+#include "arrow/compute/kernel.h"
+#include "arrow/compute/row/grouper.h"
+#include "arrow/python/common.h"
+#include "arrow/python/vendored/pythoncapi_compat.h"
+#include "arrow/table.h"
+#include "arrow/util/checked_cast.h"
+#include "arrow/util/logging.h"
+
+namespace arrow {
+using compute::ExecSpan;
+using compute::Grouper;
+using compute::KernelContext;
+using compute::KernelState;
+using internal::checked_cast;
+
+namespace py {
+namespace {
+
+struct PythonUdfKernelState : public compute::KernelState {
+  // NOTE: this KernelState constructor doesn't require the GIL.
+  // If it did, the corresponding KernelInit::operator() should be wrapped
+  // within SafeCallIntoPython (GH-43487).
+  explicit PythonUdfKernelState(std::shared_ptr<OwnedRefNoGIL> function)
+      : function(std::move(function)) {}
+
+  std::shared_ptr<OwnedRefNoGIL> function;
+};
+
+struct PythonUdfKernelInit {
+  explicit PythonUdfKernelInit(std::shared_ptr<OwnedRefNoGIL> function)
+      : function(std::move(function)) {}
+
+  Result<std::unique_ptr<compute::KernelState>> operator()(
+      compute::KernelContext*, const compute::KernelInitArgs&) {
+    return std::make_unique<PythonUdfKernelState>(function);
+  }
+
+  std::shared_ptr<OwnedRefNoGIL> function;
+};
+
+struct ScalarUdfAggregator : public compute::KernelState {
+  virtual Status Consume(compute::KernelContext* ctx, const compute::ExecSpan& batch) = 0;
+  virtual Status MergeFrom(compute::KernelContext* ctx, compute::KernelState&& src) = 0;
+  virtual Status Finalize(compute::KernelContext* ctx, Datum* out) = 0;
+};
+
+struct HashUdfAggregator : public compute::KernelState {
+  virtual Status Resize(KernelContext* ctx, int64_t size) = 0;
+  virtual Status Consume(KernelContext* ctx, const ExecSpan& batch) = 0;
+  virtual Status Merge(KernelContext* ct, KernelState&& other, const ArrayData&) = 0;
+  virtual Status Finalize(KernelContext* ctx, Datum* out) = 0;
+};
+
+Status AggregateUdfConsume(compute::KernelContext* ctx, const compute::ExecSpan& batch) {
+  return checked_cast<ScalarUdfAggregator*>(ctx->state())->Consume(ctx, batch);
+}
+
+Status AggregateUdfMerge(compute::KernelContext* ctx, compute::KernelState&& src,
+                         compute::KernelState* dst) {
+  return checked_cast<ScalarUdfAggregator*>(dst)->MergeFrom(ctx, std::move(src));
+}
+
+Status AggregateUdfFinalize(compute::KernelContext* ctx, arrow::Datum* out) {
+  return checked_cast<ScalarUdfAggregator*>(ctx->state())->Finalize(ctx, out);
+}
+
+Status HashAggregateUdfResize(KernelContext* ctx, int64_t size) {
+  return checked_cast<HashUdfAggregator*>(ctx->state())->Resize(ctx, size);
+}
+
+Status HashAggregateUdfConsume(KernelContext* ctx, const ExecSpan& batch) {
+  return checked_cast<HashUdfAggregator*>(ctx->state())->Consume(ctx, batch);
+}
+
+Status HashAggregateUdfMerge(KernelContext* ctx, KernelState&& src,
+                             const ArrayData& group_id_mapping) {
+  return checked_cast<HashUdfAggregator*>(ctx->state())
+      ->Merge(ctx, std::move(src), group_id_mapping);
+}
+
+Status HashAggregateUdfFinalize(KernelContext* ctx, Datum* out) {
+  return checked_cast<HashUdfAggregator*>(ctx->state())->Finalize(ctx, out);
+}
+
+struct PythonTableUdfKernelInit {
+  PythonTableUdfKernelInit(std::shared_ptr<OwnedRefNoGIL> function_maker,
+                           UdfWrapperCallback cb)
+      : function_maker(std::move(function_maker)), cb(std::move(cb)) {}
+
+  Result<std::unique_ptr<compute::KernelState>> operator()(
+      compute::KernelContext* ctx, const compute::KernelInitArgs&) {
+    return SafeCallIntoPython(
+        [this, ctx]() -> Result<std::unique_ptr<compute::KernelState>> {
+          UdfContext udf_context{ctx->memory_pool(), /*batch_length=*/0};
+          OwnedRef empty_tuple(PyTuple_New(0));
+          auto function = std::make_shared<OwnedRefNoGIL>(
+              cb(function_maker->obj(), udf_context, empty_tuple.obj()));
+          RETURN_NOT_OK(CheckPyError());
+          if (!PyCallable_Check(function->obj())) {
+            return Status::TypeError("Expected a callable Python object.");
+          }
+          return std::make_unique<PythonUdfKernelState>(std::move(function));
+        });
+  }
+
+  std::shared_ptr<OwnedRefNoGIL> function_maker;
+  UdfWrapperCallback cb;
+};
+
+struct PythonUdfScalarAggregatorImpl : public ScalarUdfAggregator {
+  PythonUdfScalarAggregatorImpl(std::shared_ptr<OwnedRefNoGIL> function,
+                                UdfWrapperCallback cb,
+                                std::vector<std::shared_ptr<DataType>> input_types,
+                                std::shared_ptr<DataType> output_type)
+      : function(std::move(function)),
+        cb(std::move(cb)),
+        output_type(std::move(output_type)) {
+    std::vector<std::shared_ptr<Field>> fields;
+    for (size_t i = 0; i < input_types.size(); i++) {
+      fields.push_back(field("", input_types[i]));
+    }
+    input_schema = schema(std::move(fields));
+  };
+
+  Status Consume(compute::KernelContext* ctx, const compute::ExecSpan& batch) override {
+    ARROW_ASSIGN_OR_RAISE(
+        auto rb, batch.ToExecBatch().ToRecordBatch(input_schema, ctx->memory_pool()));
+    values.push_back(std::move(rb));
+    return Status::OK();
+  }
+
+  Status MergeFrom(compute::KernelContext* ctx, compute::KernelState&& src) override {
+    auto& other_values = checked_cast<PythonUdfScalarAggregatorImpl&>(src).values;
+    values.insert(values.end(), std::make_move_iterator(other_values.begin()),
+                  std::make_move_iterator(other_values.end()));
+
+    other_values.erase(other_values.begin(), other_values.end());
+    return Status::OK();
+  }
+
+  Status Finalize(compute::KernelContext* ctx, Datum* out) override {
+    auto state =
+        arrow::internal::checked_cast<PythonUdfScalarAggregatorImpl*>(ctx->state());
+    const int num_args = input_schema->num_fields();
+
+    // Note: The way that batches are concatenated together
+    // would result in using double amount of the memory.
+    // This is OK for now because non decomposable aggregate
+    // UDF is supposed to be used with segmented aggregation
+    // where the size of the segment is more or less constant
+    // so doubling that is not a big deal. This can be also
+    // improved in the future to use more efficient way to
+    // concatenate.
+    ARROW_ASSIGN_OR_RAISE(auto table,
+                          arrow::Table::FromRecordBatches(input_schema, values));
+    ARROW_ASSIGN_OR_RAISE(table, table->CombineChunks(ctx->memory_pool()));
+    UdfContext udf_context{ctx->memory_pool(), table->num_rows()};
+
+    if (table->num_rows() == 0) {
+      return Status::Invalid("Finalized is called with empty inputs");
+    }
+
+    RETURN_NOT_OK(SafeCallIntoPython([&] {
+      std::unique_ptr<OwnedRef> result;
+      OwnedRef arg_tuple(PyTuple_New(num_args));
+      RETURN_NOT_OK(CheckPyError());
+
+      for (int arg_id = 0; arg_id < num_args; arg_id++) {
+        // Since we combined chunks there is only one chunk
+        std::shared_ptr<Array> c_data = table->column(arg_id)->chunk(0);
+        PyObject* data = wrap_array(c_data);
+        PyTuple_SetItem(arg_tuple.obj(), arg_id, data);
+      }
+      result =
+          std::make_unique<OwnedRef>(cb(function->obj(), udf_context, arg_tuple.obj()));
+      RETURN_NOT_OK(CheckPyError());
+      // unwrapping the output for expected output type
+      if (is_scalar(result->obj())) {
+        ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Scalar> val, unwrap_scalar(result->obj()));
+        if (*output_type != *val->type) {
+          return Status::TypeError("Expected output datatype ", output_type->ToString(),
+                                   ", but function returned datatype ",
+                                   val->type->ToString());
+        }
+        out->value = std::move(val);
+        return Status::OK();
+      }
+      return Status::TypeError("Unexpected output type: ",
+                               Py_TYPE(result->obj())->tp_name, " (expected Scalar)");
+    }));
+    return Status::OK();
+  }
+
+  std::shared_ptr<OwnedRefNoGIL> function;
+  UdfWrapperCallback cb;
+  std::vector<std::shared_ptr<RecordBatch>> values;
+  std::shared_ptr<Schema> input_schema;
+  std::shared_ptr<DataType> output_type;
+};
+
+struct PythonUdfHashAggregatorImpl : public HashUdfAggregator {
+  PythonUdfHashAggregatorImpl(std::shared_ptr<OwnedRefNoGIL> function,
+                              UdfWrapperCallback cb,
+                              std::vector<std::shared_ptr<DataType>> input_types,
+                              std::shared_ptr<DataType> output_type)
+      : function(std::move(function)),
+        cb(std::move(cb)),
+        output_type(std::move(output_type)) {
+    std::vector<std::shared_ptr<Field>> fields;
+    fields.reserve(input_types.size());
+    for (size_t i = 0; i < input_types.size(); i++) {
+      fields.push_back(field("", input_types[i]));
+    }
+    input_schema = schema(std::move(fields));
+  };
+
+  // same as ApplyGrouping in partition.cc
+  // replicated the code here to avoid complicating the dependencies
+  static Result<RecordBatchVector> ApplyGroupings(
+      const ListArray& groupings, const std::shared_ptr<RecordBatch>& batch) {
+    ARROW_ASSIGN_OR_RAISE(Datum sorted,
+                          compute::Take(batch, groupings.data()->child_data[0]));
+
+    const auto& sorted_batch = *sorted.record_batch();
+
+    RecordBatchVector out(static_cast<size_t>(groupings.length()));
+    for (size_t i = 0; i < out.size(); ++i) {
+      out[i] = sorted_batch.Slice(groupings.value_offset(i), groupings.value_length(i));
+    }
+
+    return out;
+  }
+
+  Status Resize(KernelContext* ctx, int64_t new_num_groups) override {
+    // We only need to change num_groups in resize
+    // similar to other hash aggregate kernels
+    num_groups = new_num_groups;
+    return Status::OK();
+  }
+
+  Status Consume(KernelContext* ctx, const ExecSpan& batch) override {
+    ARROW_ASSIGN_OR_RAISE(
+        std::shared_ptr<RecordBatch> rb,
+        batch.ToExecBatch().ToRecordBatch(input_schema, ctx->memory_pool()));
+
+    // This is similar to GroupedListImpl
+    // last array is the group id
+    const ArraySpan& groups_array_data = batch[batch.num_values() - 1].array;
+    ARROW_DCHECK_EQ(groups_array_data.offset, 0);
+    int64_t batch_num_values = groups_array_data.length;
+    const auto* batch_groups = groups_array_data.GetValues<uint32_t>(1);
+    RETURN_NOT_OK(groups.Append(batch_groups, batch_num_values));
+    values.push_back(std::move(rb));
+    num_values += batch_num_values;
+    return Status::OK();
+  }
+  Status Merge(KernelContext* ctx, KernelState&& other_state,
+               const ArrayData& group_id_mapping) override {
+    // This is similar to GroupedListImpl
+    auto& other = checked_cast<PythonUdfHashAggregatorImpl&>(other_state);
+    auto& other_values = other.values;
+    const uint32_t* other_raw_groups = other.groups.data();
+    values.insert(values.end(), std::make_move_iterator(other_values.begin()),
+                  std::make_move_iterator(other_values.end()));
+
+    auto g = group_id_mapping.GetValues<uint32_t>(1);
+    for (uint32_t other_g = 0; static_cast<int64_t>(other_g) < other.num_values;
+         ++other_g) {
+      // Different state can have different group_id mappings, so we
+      // need to translate the ids
+      RETURN_NOT_OK(groups.Append(g[other_raw_groups[other_g]]));
+    }
+
+    num_values += other.num_values;
+    return Status::OK();
+  }
+
+  Status Finalize(KernelContext* ctx, Datum* out) override {
+    // Exclude the last column which is the group id
+    const int num_args = input_schema->num_fields() - 1;
+
+    ARROW_ASSIGN_OR_RAISE(auto groups_buffer, groups.Finish());
+    ARROW_ASSIGN_OR_RAISE(auto groupings,
+                          Grouper::MakeGroupings(UInt32Array(num_values, groups_buffer),
+                                                 static_cast<uint32_t>(num_groups)));
+
+    ARROW_ASSIGN_OR_RAISE(auto table,
+                          arrow::Table::FromRecordBatches(input_schema, values));
+    ARROW_ASSIGN_OR_RAISE(auto rb, table->CombineChunksToBatch(ctx->memory_pool()));
+    UdfContext udf_context{ctx->memory_pool(), table->num_rows()};
+
+    if (rb->num_rows() == 0) {
+      *out = Datum();
+      return Status::OK();
+    }
+
+    ARROW_ASSIGN_OR_RAISE(RecordBatchVector rbs, ApplyGroupings(*groupings, rb));
+
+    return SafeCallIntoPython([&] {
+      ARROW_ASSIGN_OR_RAISE(std::unique_ptr<ArrayBuilder> builder,
+                            MakeBuilder(output_type, ctx->memory_pool()));
+      for (auto& group_rb : rbs) {
+        std::unique_ptr<OwnedRef> result;
+        OwnedRef arg_tuple(PyTuple_New(num_args));
+        RETURN_NOT_OK(CheckPyError());
+
+        for (int arg_id = 0; arg_id < num_args; arg_id++) {
+          // Since we combined chunks there is only one chunk
+          std::shared_ptr<Array> c_data = group_rb->column(arg_id);
+          PyObject* data = wrap_array(c_data);
+          PyTuple_SetItem(arg_tuple.obj(), arg_id, data);
+        }
+
+        result =
+            std::make_unique<OwnedRef>(cb(function->obj(), udf_context, arg_tuple.obj()));
+        RETURN_NOT_OK(CheckPyError());
+
+        // unwrapping the output for expected output type
+        if (is_scalar(result->obj())) {
+          ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Scalar> val,
+                                unwrap_scalar(result->obj()));
+          if (*output_type != *val->type) {
+            return Status::TypeError("Expected output datatype ", output_type->ToString(),
+                                     ", but function returned datatype ",
+                                     val->type->ToString());
+          }
+          ARROW_RETURN_NOT_OK(builder->AppendScalar(std::move(*val)));
+        } else {
+          return Status::TypeError("Unexpected output type: ",
+                                   Py_TYPE(result->obj())->tp_name, " (expected Scalar)");
+        }
+      }
+      ARROW_ASSIGN_OR_RAISE(auto result, builder->Finish());
+      out->value = std::move(result->data());
+      return Status::OK();
+    });
+  }
+
+  std::shared_ptr<OwnedRefNoGIL> function;
+  UdfWrapperCallback cb;
+  // Accumulated input batches
+  std::vector<std::shared_ptr<RecordBatch>> values;
+  // Group ids - extracted from the last column from the batch
+  TypedBufferBuilder<uint32_t> groups;
+  int64_t num_groups = 0;
+  int64_t num_values = 0;
+  std::shared_ptr<Schema> input_schema;
+  std::shared_ptr<DataType> output_type;
+};
+
+struct PythonUdf : public PythonUdfKernelState {
+  PythonUdf(std::shared_ptr<OwnedRefNoGIL> function, UdfWrapperCallback cb,
+            std::vector<TypeHolder> input_types, compute::OutputType output_type)
+      : PythonUdfKernelState(std::move(function)),
+        cb(std::move(cb)),
+        input_types(std::move(input_types)),
+        output_type(std::move(output_type)) {}
+
+  UdfWrapperCallback cb;
+  std::vector<TypeHolder> input_types;
+  compute::OutputType output_type;
+  TypeHolder resolved_type;
+
+  Result<TypeHolder> ResolveType(compute::KernelContext* ctx,
+                                 const std::vector<TypeHolder>& types) {
+    if (input_types == types) {
+      if (!resolved_type) {
+        ARROW_ASSIGN_OR_RAISE(resolved_type, output_type.Resolve(ctx, input_types));
+      }
+      return resolved_type;
+    }
+    return output_type.Resolve(ctx, types);
+  }
+
+  Status Exec(compute::KernelContext* ctx, const compute::ExecSpan& batch,
+              compute::ExecResult* out) {
+    auto state = arrow::internal::checked_cast<PythonUdfKernelState*>(ctx->state());
+    PyObject* function = state->function->obj();
+    const int num_args = batch.num_values();
+    UdfContext udf_context{ctx->memory_pool(), batch.length};
+
+    OwnedRef arg_tuple(PyTuple_New(num_args));
+    RETURN_NOT_OK(CheckPyError());
+    for (int arg_id = 0; arg_id < num_args; arg_id++) {
+      if (batch[arg_id].is_scalar()) {
+        std::shared_ptr<Scalar> c_data = batch[arg_id].scalar->GetSharedPtr();
+        PyObject* data = wrap_scalar(c_data);
+        PyTuple_SetItem(arg_tuple.obj(), arg_id, data);
+      } else {
+        std::shared_ptr<Array> c_data = batch[arg_id].array.ToArray();
+        PyObject* data = wrap_array(c_data);
+        PyTuple_SetItem(arg_tuple.obj(), arg_id, data);
+      }
+    }
+
+    OwnedRef result(cb(function, udf_context, arg_tuple.obj()));
+    RETURN_NOT_OK(CheckPyError());
+    // unwrapping the output for expected output type
+    if (is_array(result.obj())) {
+      ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Array> val, unwrap_array(result.obj()));
+      ARROW_ASSIGN_OR_RAISE(TypeHolder type, ResolveType(ctx, batch.GetTypes()));
+      if (type.type == NULLPTR) {
+        return Status::TypeError("expected output datatype is null");
+      }
+      if (*type.type != *val->type()) {
+        return Status::TypeError("Expected output datatype ", type.type->ToString(),
+                                 ", but function returned datatype ",
+                                 val->type()->ToString());
+      }
+      out->value = std::move(val->data());
+      return Status::OK();
+    } else {
+      return Status::TypeError("Unexpected output type: ", Py_TYPE(result.obj())->tp_name,
+                               " (expected Array)");
+    }
+    return Status::OK();
+  }
+};
+
+Status PythonUdfExec(compute::KernelContext* ctx, const compute::ExecSpan& batch,
+                     compute::ExecResult* out) {
+  auto udf = static_cast<PythonUdf*>(ctx->kernel()->data.get());
+  return SafeCallIntoPython([&]() -> Status { return udf->Exec(ctx, batch, out); });
+}
+
+template <class Function, class Kernel>
+Status RegisterUdf(PyObject* function, compute::KernelInit kernel_init,
+                   UdfWrapperCallback cb, const UdfOptions& options,
+                   compute::FunctionRegistry* registry) {
+  if (!PyCallable_Check(function)) {
+    return Status::TypeError("Expected a callable Python object.");
+  }
+  auto scalar_func =
+      std::make_shared<Function>(options.func_name, options.arity, options.func_doc);
+  std::vector<compute::InputType> input_types;
+  for (const auto& in_dtype : options.input_types) {
+    input_types.emplace_back(in_dtype);
+  }
+  compute::OutputType output_type(options.output_type);
+  // Take reference before wrapping with OwnedRefNoGIL
+  Py_INCREF(function);
+  auto udf_data = std::make_shared<PythonUdf>(
+      std::make_shared<OwnedRefNoGIL>(function), cb,
+      TypeHolder::FromTypes(options.input_types), options.output_type);
+  Kernel kernel(
+      compute::KernelSignature::Make(std::move(input_types), std::move(output_type),
+                                     options.arity.is_varargs),
+      PythonUdfExec, kernel_init);
+  kernel.data = std::move(udf_data);
+
+  kernel.mem_allocation = compute::MemAllocation::NO_PREALLOCATE;
+  kernel.null_handling = compute::NullHandling::COMPUTED_NO_PREALLOCATE;
+  RETURN_NOT_OK(scalar_func->AddKernel(std::move(kernel)));
+  if (registry == NULLPTR) {
+    registry = compute::GetFunctionRegistry();
+  }
+  RETURN_NOT_OK(registry->AddFunction(std::move(scalar_func)));
+  return Status::OK();
+}
+
+}  // namespace
+
+Status RegisterScalarFunction(PyObject* function, UdfWrapperCallback cb,
+                              const UdfOptions& options,
+                              compute::FunctionRegistry* registry) {
+  return RegisterUdf<compute::ScalarFunction, compute::ScalarKernel>(
+      function, PythonUdfKernelInit{std::make_shared<OwnedRefNoGIL>(function)}, cb,
+      options, registry);
+}
+
+Status RegisterVectorFunction(PyObject* function, UdfWrapperCallback cb,
+                              const UdfOptions& options,
+                              compute::FunctionRegistry* registry) {
+  return RegisterUdf<compute::VectorFunction, compute::VectorKernel>(
+      function, PythonUdfKernelInit{std::make_shared<OwnedRefNoGIL>(function)}, cb,
+      options, registry);
+}
+
+Status RegisterTabularFunction(PyObject* function, UdfWrapperCallback cb,
+                               const UdfOptions& options,
+                               compute::FunctionRegistry* registry) {
+  if (options.arity.num_args != 0 || options.arity.is_varargs) {
+    return Status::NotImplemented("tabular function of non-null arity");
+  }
+  if (options.output_type->id() != Type::type::STRUCT) {
+    return Status::Invalid("tabular function with non-struct output");
+  }
+  return RegisterUdf<compute::ScalarFunction, compute::ScalarKernel>(
+      function, PythonTableUdfKernelInit{std::make_shared<OwnedRefNoGIL>(function), cb},
+      cb, options, registry);
+}
+
+Status RegisterScalarAggregateFunction(PyObject* function, UdfWrapperCallback cb,
+                                       const UdfOptions& options,
+                                       compute::FunctionRegistry* registry) {
+  if (!PyCallable_Check(function)) {
+    return Status::TypeError("Expected a callable Python object.");
+  }
+
+  if (registry == NULLPTR) {
+    registry = compute::GetFunctionRegistry();
+  }
+
+  static auto default_scalar_aggregate_options =
+      compute::ScalarAggregateOptions::Defaults();
+  auto aggregate_func = std::make_shared<compute::ScalarAggregateFunction>(
+      options.func_name, options.arity, options.func_doc,
+      &default_scalar_aggregate_options);
+
+  std::vector<compute::InputType> input_types;
+  for (const auto& in_dtype : options.input_types) {
+    input_types.emplace_back(in_dtype);
+  }
+  compute::OutputType output_type(options.output_type);
+
+  // Take reference before wrapping with OwnedRefNoGIL
+  Py_INCREF(function);
+  auto function_ref = std::make_shared<OwnedRefNoGIL>(function);
+
+  compute::KernelInit init = [cb, function_ref, options](
+                                 compute::KernelContext* ctx,
+                                 const compute::KernelInitArgs& args)
+      -> Result<std::unique_ptr<compute::KernelState>> {
+    return std::make_unique<PythonUdfScalarAggregatorImpl>(
+        function_ref, cb, options.input_types, options.output_type);
+  };
+
+  auto sig = compute::KernelSignature::Make(
+      std::move(input_types), std::move(output_type), options.arity.is_varargs);
+  compute::ScalarAggregateKernel kernel(std::move(sig), std::move(init),
+                                        AggregateUdfConsume, AggregateUdfMerge,
+                                        AggregateUdfFinalize, /*ordered=*/false);
+  RETURN_NOT_OK(aggregate_func->AddKernel(std::move(kernel)));
+  RETURN_NOT_OK(registry->AddFunction(std::move(aggregate_func)));
+  return Status::OK();
+}
+
+/// \brief Create a new UdfOptions with adjustment for hash kernel
+/// \param options User provided udf options
+UdfOptions AdjustForHashAggregate(const UdfOptions& options) {
+  UdfOptions hash_options;
+  // Append hash_ before the function name to separate from the scalar
+  // version
+  hash_options.func_name = "hash_" + options.func_name;
+  // Extend input types with group id. Group id is appended by the group
+  // aggregation node. Here we change both arity and input types
+  if (options.arity.is_varargs) {
+    hash_options.arity = options.arity;
+  } else {
+    hash_options.arity = compute::Arity(options.arity.num_args + 1, false);
+  }
+  // Changing the function doc shouldn't be necessarily because group id
+  // is not user visible, however, this is currently needed to pass the
+  // function validation. The name group_id_array is consistent with
+  // hash kernels in hash_aggregate.cc
+  hash_options.func_doc = options.func_doc;
+  hash_options.func_doc.arg_names.emplace_back("group_id_array");
+  std::vector<std::shared_ptr<DataType>> input_dtypes = options.input_types;
+  input_dtypes.emplace_back(uint32());
+  hash_options.input_types = std::move(input_dtypes);
+  hash_options.output_type = options.output_type;
+  return hash_options;
+}
+
+Status RegisterHashAggregateFunction(PyObject* function, UdfWrapperCallback cb,
+                                     const UdfOptions& options,
+                                     compute::FunctionRegistry* registry) {
+  if (!PyCallable_Check(function)) {
+    return Status::TypeError("Expected a callable Python object.");
+  }
+
+  if (registry == NULLPTR) {
+    registry = compute::GetFunctionRegistry();
+  }
+
+  UdfOptions hash_options = AdjustForHashAggregate(options);
+
+  std::vector<compute::InputType> input_types;
+  for (const auto& in_dtype : hash_options.input_types) {
+    input_types.emplace_back(in_dtype);
+  }
+  compute::OutputType output_type(hash_options.output_type);
+
+  static auto default_hash_aggregate_options =
+      compute::ScalarAggregateOptions::Defaults();
+  auto hash_aggregate_func = std::make_shared<compute::HashAggregateFunction>(
+      hash_options.func_name, hash_options.arity, hash_options.func_doc,
+      &default_hash_aggregate_options);
+
+  // Take reference before wrapping with OwnedRefNoGIL
+  Py_INCREF(function);
+  auto function_ref = std::make_shared<OwnedRefNoGIL>(function);
+  compute::KernelInit init = [function_ref, cb, hash_options](
+                                 compute::KernelContext* ctx,
+                                 const compute::KernelInitArgs& args)
+      -> Result<std::unique_ptr<compute::KernelState>> {
+    return std::make_unique<PythonUdfHashAggregatorImpl>(
+        function_ref, cb, hash_options.input_types, hash_options.output_type);
+  };
+
+  auto sig = compute::KernelSignature::Make(
+      std::move(input_types), std::move(output_type), hash_options.arity.is_varargs);
+
+  compute::HashAggregateKernel kernel(
+      std::move(sig), std::move(init), HashAggregateUdfResize, HashAggregateUdfConsume,
+      HashAggregateUdfMerge, HashAggregateUdfFinalize, /*ordered=*/false);
+  RETURN_NOT_OK(hash_aggregate_func->AddKernel(std::move(kernel)));
+  RETURN_NOT_OK(registry->AddFunction(std::move(hash_aggregate_func)));
+  return Status::OK();
+}
+
+Status RegisterAggregateFunction(PyObject* function, UdfWrapperCallback cb,
+                                 const UdfOptions& options,
+                                 compute::FunctionRegistry* registry) {
+  RETURN_NOT_OK(RegisterScalarAggregateFunction(function, cb, options, registry));
+  RETURN_NOT_OK(RegisterHashAggregateFunction(function, cb, options, registry));
+
+  return Status::OK();
+}
+
+Result<std::shared_ptr<RecordBatchReader>> CallTabularFunction(
+    const std::string& func_name, const std::vector<Datum>& args,
+    compute::FunctionRegistry* registry) {
+  if (args.size() != 0) {
+    return Status::NotImplemented("non-empty arguments to tabular function");
+  }
+  if (registry == NULLPTR) {
+    registry = compute::GetFunctionRegistry();
+  }
+  ARROW_ASSIGN_OR_RAISE(auto func, registry->GetFunction(func_name));
+  if (func->kind() != compute::Function::SCALAR) {
+    return Status::Invalid("tabular function of non-scalar kind");
+  }
+  auto arity = func->arity();
+  if (arity.num_args != 0 || arity.is_varargs) {
+    return Status::NotImplemented("tabular function of non-null arity");
+  }
+  auto kernels =
+      arrow::internal::checked_pointer_cast<compute::ScalarFunction>(func)->kernels();
+  if (kernels.size() != 1) {
+    return Status::NotImplemented("tabular function with non-single kernel");
+  }
+  const compute::ScalarKernel* kernel = kernels[0];
+  auto out_type = kernel->signature->out_type();
+  if (out_type.kind() != compute::OutputType::FIXED) {
+    return Status::Invalid("tabular kernel of non-fixed kind");
+  }
+  auto datatype = out_type.type();
+  if (datatype->id() != Type::type::STRUCT) {
+    return Status::Invalid("tabular kernel with non-struct output");
+  }
+  auto struct_type = arrow::internal::checked_cast<StructType*>(datatype.get());
+  auto schema = ::arrow::schema(struct_type->fields());
+  std::vector<TypeHolder> in_types;
+  ARROW_ASSIGN_OR_RAISE(auto func_exec,
+                        GetFunctionExecutor(func_name, in_types, NULLPTR, registry));
+  auto next_func = [schema, func_exec = std::move(
+                                func_exec)]() -> Result<std::shared_ptr<RecordBatch>> {
+    std::vector<Datum> args;
+    // passed_length of -1 or 0 with args.size() of 0 leads to an empty ExecSpanIterator
+    // in exec.cc and to never invoking the source function, so 1 is passed instead
+    // TODO: GH-33612: Support batch size in user-defined tabular functions
+    ARROW_ASSIGN_OR_RAISE(auto datum, func_exec->Execute(args, /*passed_length=*/1));
+    if (!datum.is_array()) {
+      return Status::Invalid("UDF result of non-array kind");
+    }
+    std::shared_ptr<Array> array = datum.make_array();
+    if (array->length() == 0) {
+      return IterationTraits<std::shared_ptr<RecordBatch>>::End();
+    }
+    ARROW_ASSIGN_OR_RAISE(auto batch, RecordBatch::FromStructArray(std::move(array)));
+    if (!schema->Equals(batch->schema())) {
+      return Status::Invalid("UDF result with shape not conforming to schema");
+    }
+    return std::move(batch);
+  };
+  return RecordBatchReader::MakeFromIterator(MakeFunctionIterator(std::move(next_func)),
+                                             schema);
+}
+
+}  // namespace py
+}  // namespace arrow
diff --git a/shared/package-files/b5/b517b4cb6fbcfd40a8e8e7091e98641753504f68b57332c58abffa8777aafdca/RECORD b/shared/package-files/b5/b517b4cb6fbcfd40a8e8e7091e98641753504f68b57332c58abffa8777aafdca/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..aa1e9489e182b8412d4ad6f220115325ee81c1f6
--- /dev/null
+++ b/shared/package-files/b5/b517b4cb6fbcfd40a8e8e7091e98641753504f68b57332c58abffa8777aafdca/RECORD
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diff --git a/shared/package-files/b5/b51c9e3f35952f0bc6c597b79cb9786f2678096602b170734f075459902f7174/_cast_Half_compositeimplicitautograd_dispatch.h b/shared/package-files/b5/b51c9e3f35952f0bc6c597b79cb9786f2678096602b170734f075459902f7174/_cast_Half_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..18f52404c537743e032003f1bcc4cb6b2b9f84ee
--- /dev/null
+++ b/shared/package-files/b5/b51c9e3f35952f0bc6c597b79cb9786f2678096602b170734f075459902f7174/_cast_Half_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor _cast_Half(const at::Tensor & self, bool non_blocking=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/b5/b51da92457e10f1cd47d19f3e3c4089c4749681f75fe7f6af6c02276ca3b740c/ansi80x30 b/shared/package-files/b5/b51da92457e10f1cd47d19f3e3c4089c4749681f75fe7f6af6c02276ca3b740c/ansi80x30
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diff --git a/shared/package-files/b5/b51da92457e10f1cd47d19f3e3c4089c4749681f75fe7f6af6c02276ca3b740c/cons30 b/shared/package-files/b5/b51da92457e10f1cd47d19f3e3c4089c4749681f75fe7f6af6c02276ca3b740c/cons30
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diff --git a/shared/package-files/b5/b51e6e21fd77e7501616aaea36d9979e14169ca444989699a8a0736964553134/Punta_Arenas b/shared/package-files/b5/b51e6e21fd77e7501616aaea36d9979e14169ca444989699a8a0736964553134/Punta_Arenas
new file mode 100644
index 0000000000000000000000000000000000000000..411a839b84d3c629bf7eb623024077efe614ed07
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diff --git a/shared/package-files/b5/b5223b146331c3ad0655047a9ea53c247c8d2490f35d2e6e46ccf526dfac7b1c/fixtures.py b/shared/package-files/b5/b5223b146331c3ad0655047a9ea53c247c8d2490f35d2e6e46ccf526dfac7b1c/fixtures.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f107b3248497f45bde6ec4f48bc8fae661a37d8
--- /dev/null
+++ b/shared/package-files/b5/b5223b146331c3ad0655047a9ea53c247c8d2490f35d2e6e46ccf526dfac7b1c/fixtures.py
@@ -0,0 +1,123 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+import json
+import os
+import socket
+from pathlib import Path
+from shutil import which
+
+import boto3
+import pytest
+from botocore.client import Config
+from xprocess import ProcessStarter
+
+MINIO_EXE = which("minio")
+
+
+def minio_s3_server(xprocess, tmp_path):
+    """
+    Mock a local S3 server using `minio`
+
+    This requires:
+    - pytest-xprocess: runs the background process
+    - minio: the executable must be in PATH
+
+    Note, the given S3 server will be EMPTY! The test function needs
+    to populate it. You can use
+    `conda.testing.helpers.populate_s3_server` for that.
+    """
+
+    class Minio:
+        # The 'name' below will be the name of the S3 bucket containing
+        # keys like `noarch/repodata.json`
+        name = "minio_s3_server"
+        port = 9000
+
+        def __init__(self):
+            (Path(tmp_path) / self.name).mkdir()
+
+        @property
+        def server_url(self):
+            return f"http://localhost:{self.port}/{self.name}"
+
+        def populate_bucket(self, endpoint, bucket_name, channel_dir):
+            """Prepare the s3 connection for our minio instance"""
+            # Make the minio bucket public first
+            # https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-bucket-policies.html#set-a-bucket-policy
+            session = boto3.session.Session()
+            client = session.client(
+                "s3",
+                endpoint_url=endpoint,
+                aws_access_key_id="minioadmin",
+                aws_secret_access_key="minioadmin",
+                config=Config(signature_version="s3v4"),
+                region_name="us-east-1",
+            )
+            bucket_policy = json.dumps(
+                {
+                    "Version": "2012-10-17",
+                    "Statement": [
+                        {
+                            "Sid": "AddPerm",
+                            "Effect": "Allow",
+                            "Principal": "*",
+                            "Action": ["s3:GetObject"],
+                            "Resource": f"arn:aws:s3:::{bucket_name}/*",
+                        }
+                    ],
+                }
+            )
+            client.put_bucket_policy(Bucket=bucket_name, Policy=bucket_policy)
+
+            # Minio has to start with an empty directory; once available,
+            # we can import all channel files by "uploading" them
+            for current, _, files in os.walk(channel_dir):
+                for f in files:
+                    path = Path(current, f)
+                    key = path.relative_to(channel_dir)
+                    client.upload_file(
+                        str(path),
+                        bucket_name,
+                        str(key),
+                        ExtraArgs={"ACL": "public-read"},
+                    )
+
+    print("Starting mock_s3_server")
+    minio = Minio()
+
+    class Starter(ProcessStarter):
+        pattern = "MinIO Object Storage Server"
+        terminate_on_interrupt = True
+        timeout = 10
+        args = [
+            MINIO_EXE,
+            "server",
+            f"--address=:{minio.port}",
+            tmp_path,
+        ]
+
+        def startup_check(self, port=minio.port):
+            s = socket.socket()
+            address = "localhost"
+            error = False
+            try:
+                s.connect((address, port))
+            except Exception as e:
+                print(
+                    "something's wrong with %s:%d. Exception is %s" % (address, port, e)
+                )
+                error = True
+            finally:
+                s.close()
+
+            return not error
+
+    # ensure process is running and return its logfile
+    pid, logfile = xprocess.ensure(minio.name, Starter)
+    print(f"Server (PID: {pid}) log file can be found here: {logfile}")
+    yield minio
+    xprocess.getinfo(minio.name).terminate()
+
+
+if MINIO_EXE is not None:
+    minio_s3_server = pytest.fixture()(minio_s3_server)
diff --git a/shared/package-files/b5/b52299d1b28ba4839bc5a499f3a201a64ecfb11ea27491970f66d9ce0c9ae6cb/parser.pyi b/shared/package-files/b5/b52299d1b28ba4839bc5a499f3a201a64ecfb11ea27491970f66d9ce0c9ae6cb/parser.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fdcc2eaf7ccf04eeef12211563f583754f2f954c
--- /dev/null
+++ b/shared/package-files/b5/b52299d1b28ba4839bc5a499f3a201a64ecfb11ea27491970f66d9ce0c9ae6cb/parser.pyi
@@ -0,0 +1,65 @@
+from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
+
+from click.core import Context
+
+def _unpack_args(args: Iterable[str], nargs_spec: Iterable[int]) -> Tuple[Tuple[Optional[Tuple[str, ...]], ...], List[str]]: ...
+def split_opt(opt: str) -> Tuple[str, str]: ...
+def normalize_opt(opt: str, ctx: Context) -> str: ...
+def split_arg_string(string: str) -> List[str]: ...
+
+class Option:
+    dest: str
+    action: str
+    nargs: int
+    const: Any
+    obj: Any
+    prefixes: Set[str]
+    _short_opts: List[str]
+    _long_opts: List[str]
+    def __init__(
+        self,
+        opts: Iterable[str],
+        dest: str,
+        action: Optional[str] = ...,
+        nargs: int = ...,
+        const: Optional[Any] = ...,
+        obj: Optional[Any] = ...,
+    ) -> None: ...
+    @property
+    def takes_value(self) -> bool: ...
+    def process(self, value: Any, state: ParsingState) -> None: ...
+
+class Argument:
+    dest: str
+    nargs: int
+    obj: Any
+    def __init__(self, dest: str, nargs: int = ..., obj: Optional[Any] = ...) -> None: ...
+    def process(self, value: Any, state: ParsingState) -> None: ...
+
+class ParsingState:
+    opts: Dict[str, Any]
+    largs: List[str]
+    rargs: List[str]
+    order: List[Any]
+    def __init__(self, rargs: List[str]) -> None: ...
+
+class OptionParser:
+    ctx: Optional[Context]
+    allow_interspersed_args: bool
+    ignore_unknown_options: bool
+    _short_opt: Dict[str, Option]
+    _long_opt: Dict[str, Option]
+    _opt_prefixes: Set[str]
+    _args: List[Argument]
+    def __init__(self, ctx: Optional[Context] = ...) -> None: ...
+    def add_option(
+        self,
+        opts: Iterable[str],
+        dest: str,
+        action: Optional[str] = ...,
+        nargs: int = ...,
+        const: Optional[Any] = ...,
+        obj: Optional[Any] = ...,
+    ) -> None: ...
+    def add_argument(self, dest: str, nargs: int = ..., obj: Optional[Any] = ...) -> None: ...
+    def parse_args(self, args: List[str]) -> Tuple[Dict[str, Any], List[str], List[Any]]: ...
diff --git a/shared/package-files/b5/b522d333a325e5297cf7116c15fc5d12aafad95f1021cf397c5a848fb0052031/hardshrink_cuda_dispatch.h b/shared/package-files/b5/b522d333a325e5297cf7116c15fc5d12aafad95f1021cf397c5a848fb0052031/hardshrink_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..125d4fe0aae09a885acec9c3dcae7b1a246a7ad3
--- /dev/null
+++ b/shared/package-files/b5/b522d333a325e5297cf7116c15fc5d12aafad95f1021cf397c5a848fb0052031/hardshrink_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd=0.5);
+TORCH_API at::Tensor & hardshrink_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd=0.5);
+TORCH_API at::Tensor & hardshrink_outf(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/b5/b5244db5512526b91c7e7d41c8373fa1c850e99024ec3ac8fb766b125a1a9604/cos_meta_dispatch.h b/shared/package-files/b5/b5244db5512526b91c7e7d41c8373fa1c850e99024ec3ac8fb766b125a1a9604/cos_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..16bf9233c1daeef3db4864f43d60175023d0953d
--- /dev/null
+++ b/shared/package-files/b5/b5244db5512526b91c7e7d41c8373fa1c850e99024ec3ac8fb766b125a1a9604/cos_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor cos(const at::Tensor & self);
+TORCH_API at::Tensor & cos_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & cos_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & cos_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/b5/b525ee6b21f0feceed0acfe49256fd5024d58edeeece49b555be8910a74f4440/xterm+nopcfkeys b/shared/package-files/b5/b525ee6b21f0feceed0acfe49256fd5024d58edeeece49b555be8910a74f4440/xterm+nopcfkeys
new file mode 100644
index 0000000000000000000000000000000000000000..6101caecb199ebb75316beedae2b764a57f117ce
Binary files /dev/null and b/shared/package-files/b5/b525ee6b21f0feceed0acfe49256fd5024d58edeeece49b555be8910a74f4440/xterm+nopcfkeys differ
diff --git a/shared/package-files/b5/b52b4115622832d20991e5c6a83c7cb1251b3983c47ee9d3d8d7a5754faafa1f/test_eval.py b/shared/package-files/b5/b52b4115622832d20991e5c6a83c7cb1251b3983c47ee9d3d8d7a5754faafa1f/test_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..859cbd30cfeadb29add9cabd770be0d6bd67d2d1
--- /dev/null
+++ b/shared/package-files/b5/b52b4115622832d20991e5c6a83c7cb1251b3983c47ee9d3d8d7a5754faafa1f/test_eval.py
@@ -0,0 +1,2006 @@
+from __future__ import annotations
+
+from functools import reduce
+from itertools import product
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY312
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    NumExprClobberingError,
+    PerformanceWarning,
+    UndefinedVariableError,
+)
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_list_like,
+    is_scalar,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.computation import (
+    expr,
+    pytables,
+)
+from pandas.core.computation.engines import ENGINES
+from pandas.core.computation.expr import (
+    BaseExprVisitor,
+    PandasExprVisitor,
+    PythonExprVisitor,
+)
+from pandas.core.computation.expressions import (
+    NUMEXPR_INSTALLED,
+    USE_NUMEXPR,
+)
+from pandas.core.computation.ops import (
+    ARITH_OPS_SYMS,
+    SPECIAL_CASE_ARITH_OPS_SYMS,
+    _binary_math_ops,
+    _binary_ops_dict,
+    _unary_math_ops,
+)
+from pandas.core.computation.scope import DEFAULT_GLOBALS
+from pandas.util.version import Version
+
+numexpr = import_optional_dependency("numexpr", errors="ignore")
+
+
+@pytest.fixture(
+    params=(
+        pytest.param(
+            engine,
+            marks=[
+                pytest.mark.skipif(
+                    engine == "numexpr" and not USE_NUMEXPR,
+                    reason=f"numexpr enabled->{USE_NUMEXPR}, "
+                    f"installed->{NUMEXPR_INSTALLED}",
+                ),
+                td.skip_if_no("numexpr"),
+            ],
+        )
+        for engine in ENGINES
+    )
+)
+def engine(request):
+    return request.param
+
+
+@pytest.fixture(params=expr.PARSERS)
+def parser(request):
+    return request.param
+
+
+def _eval_single_bin(lhs, cmp1, rhs, engine):
+    c = _binary_ops_dict[cmp1]
+    if ENGINES[engine].has_neg_frac:
+        try:
+            return c(lhs, rhs)
+        except ValueError as e:
+            if str(e).startswith(
+                "negative number cannot be raised to a fractional power"
+            ):
+                return np.nan
+            raise
+    return c(lhs, rhs)
+
+
+# TODO: using range(5) here is a kludge
+@pytest.fixture(
+    params=list(range(5)),
+    ids=["DataFrame", "Series", "SeriesNaN", "DataFrameNaN", "float"],
+)
+def lhs(request):
+    nan_df1 = DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
+    nan_df1[nan_df1 > 0.5] = np.nan
+
+    opts = (
+        DataFrame(np.random.default_rng(2).standard_normal((10, 5))),
+        Series(np.random.default_rng(2).standard_normal(5)),
+        Series([1, 2, np.nan, np.nan, 5]),
+        nan_df1,
+        np.random.default_rng(2).standard_normal(),
+    )
+    return opts[request.param]
+
+
+rhs = lhs
+midhs = lhs
+
+
+@pytest.fixture
+def idx_func_dict():
+    return {
+        "i": lambda n: Index(np.arange(n), dtype=np.int64),
+        "f": lambda n: Index(np.arange(n), dtype=np.float64),
+        "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
+        "dt": lambda n: date_range("2020-01-01", periods=n),
+        "td": lambda n: timedelta_range("1 day", periods=n),
+        "p": lambda n: period_range("2020-01-01", periods=n, freq="D"),
+    }
+
+
+class TestEval:
+    @pytest.mark.parametrize(
+        "cmp1",
+        ["!=", "==", "<=", ">=", "<", ">"],
+        ids=["ne", "eq", "le", "ge", "lt", "gt"],
+    )
+    @pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"])
+    @pytest.mark.parametrize("binop", expr.BOOL_OPS_SYMS)
+    def test_complex_cmp_ops(self, cmp1, cmp2, binop, lhs, rhs, engine, parser):
+        if parser == "python" and binop in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, rhs, engine)
+        rhs_new = _eval_single_bin(lhs, cmp2, rhs, engine)
+        expected = _eval_single_bin(lhs_new, binop, rhs_new, engine)
+
+        ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("cmp_op", expr.CMP_OPS_SYMS)
+    def test_simple_cmp_ops(self, cmp_op, lhs, rhs, engine, parser):
+        lhs = lhs < 0
+        rhs = rhs < 0
+
+        if parser == "python" and cmp_op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"lhs {cmp_op} rhs"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        ex = f"lhs {cmp_op} rhs"
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')bool(`|')",
+                "argument of type 'bool' is not iterable",
+            ]
+        )
+        if cmp_op in ("in", "not in") and not is_list_like(rhs):
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            expected = _eval_single_bin(lhs, cmp_op, rhs, engine)
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("op", expr.CMP_OPS_SYMS)
+    def test_compound_invert_op(self, op, lhs, rhs, request, engine, parser):
+        if parser == "python" and op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"~(lhs {op} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        if (
+            is_float(lhs)
+            and not is_float(rhs)
+            and op in ["in", "not in"]
+            and engine == "python"
+            and parser == "pandas"
+        ):
+            mark = pytest.mark.xfail(
+                reason="Looks like expected is negative, unclear whether "
+                "expected is incorrect or result is incorrect"
+            )
+            request.applymarker(mark)
+        skip_these = ["in", "not in"]
+        ex = f"~(lhs {op} rhs)"
+
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')float(`|')",
+                "argument of type 'float' is not iterable",
+            ]
+        )
+        if is_scalar(rhs) and op in skip_these:
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            # compound
+            if is_scalar(lhs) and is_scalar(rhs):
+                lhs, rhs = (np.array([x]) for x in (lhs, rhs))
+            expected = _eval_single_bin(lhs, op, rhs, engine)
+            if is_scalar(expected):
+                expected = not expected
+            else:
+                expected = ~expected
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_almost_equal(expected, result)
+
+    @pytest.mark.parametrize("cmp1", ["<", ">"])
+    @pytest.mark.parametrize("cmp2", ["<", ">"])
+    def test_chained_cmp_op(self, cmp1, cmp2, lhs, midhs, rhs, engine, parser):
+        mid = midhs
+        if parser == "python":
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex1, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, mid, engine)
+        rhs_new = _eval_single_bin(mid, cmp2, rhs, engine)
+
+        if lhs_new is not None and rhs_new is not None:
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            ex2 = f"lhs {cmp1} mid and mid {cmp2} rhs"
+            ex3 = f"(lhs {cmp1} mid) & (mid {cmp2} rhs)"
+            expected = _eval_single_bin(lhs_new, "&", rhs_new, engine)
+
+            for ex in (ex1, ex2, ex3):
+                result = pd.eval(ex, engine=engine, parser=parser)
+
+                tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arith1", sorted(set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS))
+    )
+    def test_binary_arith_ops(self, arith1, lhs, rhs, engine, parser):
+        ex = f"lhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = _eval_single_bin(lhs, arith1, rhs, engine)
+
+        tm.assert_almost_equal(result, expected)
+        ex = f"lhs {arith1} rhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        nlhs = _eval_single_bin(lhs, arith1, rhs, engine)
+        try:
+            nlhs, ghs = nlhs.align(rhs)
+        except (ValueError, TypeError, AttributeError):
+            # ValueError: series frame or frame series align
+            # TypeError, AttributeError: series or frame with scalar align
+            return
+        else:
+            if engine == "numexpr":
+                import numexpr as ne
+
+                # direct numpy comparison
+                expected = ne.evaluate(f"nlhs {arith1} ghs")
+                # Update assert statement due to unreliable numerical
+                # precision component (GH37328)
+                # TODO: update testing code so that assert_almost_equal statement
+                #  can be replaced again by the assert_numpy_array_equal statement
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                expected = eval(f"nlhs {arith1} ghs")
+                tm.assert_almost_equal(result, expected)
+
+    # modulus, pow, and floor division require special casing
+
+    def test_modulus(self, lhs, rhs, engine, parser):
+        ex = r"lhs % rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = lhs % rhs
+        tm.assert_almost_equal(result, expected)
+
+        if engine == "numexpr":
+            import numexpr as ne
+
+            expected = ne.evaluate(r"expected % rhs")
+            if isinstance(result, (DataFrame, Series)):
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                tm.assert_almost_equal(result, expected.item())
+        else:
+            expected = _eval_single_bin(expected, "%", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_floor_division(self, lhs, rhs, engine, parser):
+        ex = "lhs // rhs"
+
+        if engine == "python" or (
+            engine == "numexpr" and Version(numexpr.__version__) >= Version("2.13.0")
+        ):
+            res = pd.eval(ex, engine=engine, parser=parser)
+            expected = lhs // rhs
+            tm.assert_equal(res, expected)
+        else:
+            msg = (
+                r"unsupported operand type\(s\) for //: 'VariableNode' and "
+                "'VariableNode'"
+            )
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                    engine=engine,
+                    parser=parser,
+                )
+
+    @td.skip_if_windows
+    def test_pow(self, lhs, rhs, engine, parser):
+        # odd failure on win32 platform, so skip
+        ex = "lhs ** rhs"
+        expected = _eval_single_bin(lhs, "**", rhs, engine)
+        result = pd.eval(ex, engine=engine, parser=parser)
+
+        if (
+            is_scalar(lhs)
+            and is_scalar(rhs)
+            and isinstance(expected, (complex, np.complexfloating))
+            and np.isnan(result)
+        ):
+            msg = "(DataFrame.columns|numpy array) are different"
+            with pytest.raises(AssertionError, match=msg):
+                tm.assert_numpy_array_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected)
+
+            ex = "(lhs ** rhs) ** rhs"
+            result = pd.eval(ex, engine=engine, parser=parser)
+
+            middle = _eval_single_bin(lhs, "**", rhs, engine)
+            expected = _eval_single_bin(middle, "**", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_check_single_invert_op(self, lhs, engine, parser):
+        # simple
+        try:
+            elb = lhs.astype(bool)
+        except AttributeError:
+            elb = np.array([bool(lhs)])
+        expected = ~elb
+        result = pd.eval("~elb", engine=engine, parser=parser)
+        tm.assert_almost_equal(expected, result)
+
+    def test_frame_invert(self, engine, parser):
+        expr = "~lhs"
+
+        # ~ ##
+        # frame
+        # float always raises
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        if engine == "numexpr" and Version(numexpr.__version__) < Version("2.13.0"):
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+        # bool always works
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # object raises
+        lhs = DataFrame(
+            {"b": ["a", 1, 2.0], "c": np.random.default_rng(2).standard_normal(3) > 0.5}
+        )
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_series_invert(self, engine, parser):
+        # ~ ####
+        expr = "~lhs"
+
+        # series
+        # float raises
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                result = pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        if engine == "numexpr" and Version(numexpr.__version__) < Version("2.13.0"):
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+        # bool
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # float
+        # int
+        # bool
+
+        # object
+        lhs = Series(["a", 1, 2.0])
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_frame_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # int
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+    def test_series_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # int
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2))),
+            # Int
+            DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))),
+            # bool doesn't work with numexpr but works elsewhere
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5),
+        ],
+    )
+    def test_frame_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            Series(np.random.default_rng(2).standard_normal(5)),
+            # Int
+            Series(np.random.default_rng(2).integers(5, size=5)),
+            # bool doesn't work with numexpr but works elsewhere
+            Series(np.random.default_rng(2).standard_normal(5) > 0.5),
+        ],
+    )
+    def test_series_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+    def test_scalar_unary(self, engine, parser):
+        msg = "bad operand type for unary ~: 'float'"
+        warn = None
+        if PY312 and not (engine == "numexpr" and parser == "pandas"):
+            warn = DeprecationWarning
+        with pytest.raises(TypeError, match=msg):
+            pd.eval("~1.0", engine=engine, parser=parser)
+
+        assert pd.eval("-1.0", parser=parser, engine=engine) == -1.0
+        assert pd.eval("+1.0", parser=parser, engine=engine) == +1.0
+        assert pd.eval("~1", parser=parser, engine=engine) == ~1
+        assert pd.eval("-1", parser=parser, engine=engine) == -1
+        assert pd.eval("+1", parser=parser, engine=engine) == +1
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~True", parser=parser, engine=engine) == ~True
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~False", parser=parser, engine=engine) == ~False
+        assert pd.eval("-True", parser=parser, engine=engine) == -True
+        assert pd.eval("-False", parser=parser, engine=engine) == -False
+        assert pd.eval("+True", parser=parser, engine=engine) == +True
+        assert pd.eval("+False", parser=parser, engine=engine) == +False
+
+    def test_unary_in_array(self):
+        # GH 11235
+        # TODO: 2022-01-29: result return list with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        result = np.array(
+            pd.eval("[-True, True, +True, -False, False, +False, -37, 37, ~37, +37]"),
+            dtype=np.object_,
+        )
+        expected = np.array(
+            [
+                -True,
+                True,
+                +True,
+                -False,
+                False,
+                +False,
+                -37,
+                37,
+                ~37,
+                +37,
+            ],
+            dtype=np.object_,
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"])
+    def test_float_comparison_bin_op(self, float_numpy_dtype, expr):
+        # GH 16363
+        df = DataFrame({"x": np.array([0], dtype=float_numpy_dtype)})
+        res = df.eval(expr)
+        assert res.values == np.array([False])
+
+    def test_unary_in_function(self):
+        # GH 46471
+        df = DataFrame({"x": [0, 1, np.nan]})
+
+        result = df.eval("x.fillna(-1)")
+        expected = df.x.fillna(-1)
+        # column name becomes None if using numexpr
+        # only check names when the engine is not numexpr
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+        result = df.eval("x.shift(1, fill_value=-1)")
+        expected = df.x.shift(1, fill_value=-1)
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+    @pytest.mark.parametrize(
+        "ex",
+        (
+            "1 or 2",
+            "1 and 2",
+            "a and b",
+            "a or b",
+            "1 or 2 and (3 + 2) > 3",
+            "2 * x > 2 or 1 and 2",
+            "2 * df > 3 and 1 or a",
+        ),
+    )
+    def test_disallow_scalar_bool_ops(self, ex, engine, parser):
+        x, a, b = np.random.default_rng(2).standard_normal(3), 1, 2  # noqa: F841
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 2)))  # noqa: F841
+
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+    def test_identical(self, engine, parser):
+        # see gh-10546
+        x = 1
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1
+        assert is_scalar(result)
+
+        x = 1.5
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1.5
+        assert is_scalar(result)
+
+        x = False
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert not result
+        assert is_bool(result)
+        assert is_scalar(result)
+
+        x = np.array([1])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1]))
+        assert result.shape == (1,)
+
+        x = np.array([1.5])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1.5]))
+        assert result.shape == (1,)
+
+        x = np.array([False])  # noqa: F841
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([False]))
+        assert result.shape == (1,)
+
+    def test_line_continuation(self, engine, parser):
+        # GH 11149
+        exp = """1 + 2 * \
+        5 - 1 + 2 """
+        result = pd.eval(exp, engine=engine, parser=parser)
+        assert result == 12
+
+    def test_float_truncation(self, engine, parser):
+        # GH 14241
+        exp = "1000000000.006"
+        result = pd.eval(exp, engine=engine, parser=parser)
+        expected = np.float64(exp)
+        assert result == expected
+
+        df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]})
+        cutoff = 1000000000.0006
+        result = df.query(f"A < {cutoff:.4f}")
+        assert result.empty
+
+        cutoff = 1000000000.0010
+        result = df.query(f"A > {cutoff:.4f}")
+        expected = df.loc[[1, 2], :]
+        tm.assert_frame_equal(expected, result)
+
+        exact = 1000000000.0011
+        result = df.query(f"A == {exact:.4f}")
+        expected = df.loc[[1], :]
+        tm.assert_frame_equal(expected, result)
+
+    def test_disallow_python_keywords(self):
+        # GH 18221
+        df = DataFrame([[0, 0, 0]], columns=["foo", "bar", "class"])
+        msg = "Python keyword not valid identifier in numexpr query"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("class == 0")
+
+        df = DataFrame()
+        df.index.name = "lambda"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("lambda == 0")
+
+    def test_true_false_logic(self):
+        # GH 25823
+        # This behavior is deprecated in Python 3.12
+        with tm.maybe_produces_warning(
+            DeprecationWarning, PY312, check_stacklevel=False
+        ):
+            assert pd.eval("not True") == -2
+            assert pd.eval("not False") == -1
+            assert pd.eval("True and not True") == 0
+
+    def test_and_logic_string_match(self):
+        # GH 25823
+        event = Series({"a": "hello"})
+        assert pd.eval(f"{event.str.match('hello').a}")
+        assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}")
+
+
+# -------------------------------------
+# gh-12388: Typecasting rules consistency with python
+
+
+class TestTypeCasting:
+    @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
+    # maybe someday... numexpr has too many upcasting rules now
+    # chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float']))
+    @pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")])
+    def test_binop_typecasting(
+        self, engine, parser, op, complex_or_float_dtype, left_right, request
+    ):
+        # GH#21374
+        dtype = complex_or_float_dtype
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dtype)
+        left, right = left_right
+        s = f"{left} {op} {right}"
+        res = pd.eval(s, engine=engine, parser=parser)
+        if dtype == "complex64" and engine == "numexpr":
+            mark = pytest.mark.xfail(
+                reason="numexpr issue with complex that are upcast "
+                "to complex 128 "
+                "https://github.com/pydata/numexpr/issues/492"
+            )
+            request.applymarker(mark)
+        assert df.values.dtype == dtype
+        assert res.values.dtype == dtype
+        tm.assert_frame_equal(res, eval(s), check_exact=False)
+
+
+# -------------------------------------
+# Basic and complex alignment
+
+
+def should_warn(*args):
+    not_mono = not any(map(operator.attrgetter("is_monotonic_increasing"), args))
+    only_one_dt = reduce(
+        operator.xor, (issubclass(x.dtype.type, np.datetime64) for x in args)
+    )
+    return not_mono and only_one_dt
+
+
+class TestAlignment:
+    index_types = ["i", "s", "dt"]
+    lhs_index_types = index_types + ["s"]  # 'p'
+
+    def test_align_nested_unary_op(self, engine, parser):
+        s = "df * ~2"
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        res = pd.eval(s, engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df * ~2)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("lr_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("rr_idx_type", index_types)
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    def test_basic_frame_alignment(
+        self, engine, parser, lr_idx_type, rr_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[lr_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 10)),
+            index=idx_func_dict[rr_idx_type](20),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        # only warns if not monotonic and not sortable
+        if should_warn(df.index, df2.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2)
+
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("c_idx_type", lhs_index_types)
+    def test_frame_comparison(
+        self, engine, parser, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        res = pd.eval("df < 2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < 2)
+
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal(df.shape),
+            index=df.index,
+            columns=df.columns,
+        )
+        res = pd.eval("df < df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_medium_complex_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 2)),
+            index=idx_func_dict[r1](3),
+            columns=idx_func_dict[c1](2),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)),
+            index=idx_func_dict[r2](4),
+            columns=idx_func_dict[c2](2),
+        )
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)),
+            index=idx_func_dict[r2](5),
+            columns=idx_func_dict[c2](2),
+        )
+        if should_warn(df.index, df2.index, df3.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2 + df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    def test_basic_frame_series_alignment(
+        self, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + s", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + s", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else df + s
+        else:
+            expected = df + s
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize(
+        "r_idx_type, c_idx_type",
+        list(product(["i", "s"], ["i", "s"])) + [("dt", "dt")],
+    )
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_basic_series_frame_alignment(
+        self, request, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        if (
+            engine == "numexpr"
+            and parser in ("pandas", "python")
+            and index_name == "index"
+            and r_idx_type == "i"
+            and c_idx_type == "s"
+        ):
+            reason = (
+                f"Flaky column ordering when engine={engine}, "
+                f"parser={parser}, index_name={index_name}, "
+                f"r_idx_type={r_idx_type}, c_idx_type={c_idx_type}"
+            )
+            request.applymarker(pytest.mark.xfail(reason=reason, strict=False))
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 7)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](7),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+        if should_warn(s.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("s + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("s + df", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else s + df
+        else:
+            expected = s + df
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("op", ["+", "*"])
+    def test_series_frame_commutativity(
+        self, engine, parser, index_name, op, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        lhs = f"s {op} df"
+        rhs = f"df {op} s"
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                a = pd.eval(lhs, engine=engine, parser=parser)
+            with tm.assert_produces_warning(RuntimeWarning):
+                b = pd.eval(rhs, engine=engine, parser=parser)
+        else:
+            a = pd.eval(lhs, engine=engine, parser=parser)
+            b = pd.eval(rhs, engine=engine, parser=parser)
+
+        if r_idx_type != "dt" and c_idx_type != "dt":
+            if engine == "numexpr":
+                tm.assert_frame_equal(a, b)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_complex_series_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        n = 3
+        m1 = 5
+        m2 = 2 * m1
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((m1, n)),
+            index=idx_func_dict[r1](m1),
+            columns=idx_func_dict[c1](n),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((m2, n)),
+            index=idx_func_dict[r2](m2),
+            columns=idx_func_dict[c2](n),
+        )
+        index = df2.columns
+        ser = Series(np.random.default_rng(2).standard_normal(n), index[:n])
+
+        if r2 == "dt" or c2 == "dt":
+            if engine == "numexpr":
+                expected2 = df2.add(ser)
+            else:
+                expected2 = df2 + ser
+        else:
+            expected2 = df2 + ser
+
+        if r1 == "dt" or c1 == "dt":
+            if engine == "numexpr":
+                expected = expected2.add(df)
+            else:
+                expected = expected2 + df
+        else:
+            expected = expected2 + df
+
+        if should_warn(df2.index, ser.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        assert res.shape == expected.shape
+        tm.assert_frame_equal(res, expected)
+
+    def test_performance_warning_for_poor_alignment(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((1000, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        if engine == "numexpr":
+            seen = PerformanceWarning
+        else:
+            seen = False
+
+        with tm.assert_produces_warning(seen):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        s = Series(np.random.default_rng(2).standard_normal(1000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10000)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+
+        is_python_engine = engine == "python"
+
+        if not is_python_engine:
+            wrn = PerformanceWarning
+        else:
+            wrn = False
+
+        with tm.assert_produces_warning(wrn) as w:
+            pd.eval("df + s", engine=engine, parser=parser)
+
+            if not is_python_engine:
+                assert len(w) == 1
+                msg = str(w[0].message)
+                logged = np.log10(s.size - df.shape[1])
+                expected = (
+                    f"Alignment difference on axis 1 is larger "
+                    f"than an order of magnitude on term 'df', "
+                    f"by more than {logged:.4g}; performance may suffer."
+                )
+                assert msg == expected
+
+
+# ------------------------------------
+# Slightly more complex ops
+
+
+class TestOperations:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    def test_simple_arith_ops(self, engine, parser):
+        exclude_arith = []
+        if parser == "python":
+            exclude_arith = ["in", "not in"]
+
+        arith_ops = [
+            op
+            for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
+            if op not in exclude_arith
+        ]
+
+        ops = (op for op in arith_ops if op != "//")
+
+        for op in ops:
+            ex = f"1 {op} 1"
+            ex2 = f"x {op} 1"
+            ex3 = f"1 {op} (x + 1)"
+
+            if op in ("in", "not in"):
+                msg = "argument of type 'int' is not iterable"
+                with pytest.raises(TypeError, match=msg):
+                    pd.eval(ex, engine=engine, parser=parser)
+            else:
+                expec = _eval_single_bin(1, op, 1, engine)
+                x = self.eval(ex, engine=engine, parser=parser)
+                assert x == expec
+
+                expec = _eval_single_bin(x, op, 1, engine)
+                y = self.eval(ex2, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+                expec = _eval_single_bin(1, op, x + 1, engine)
+                y = self.eval(ex3, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_simple_bool_ops(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_bool_ops_with_constants(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    def test_4d_ndarray_fails(self):
+        x = np.random.default_rng(2).standard_normal((3, 4, 5, 6))
+        y = Series(np.random.default_rng(2).standard_normal(10))
+        msg = "N-dimensional objects, where N > 2, are not supported with eval"
+        with pytest.raises(NotImplementedError, match=msg):
+            self.eval("x + y", local_dict={"x": x, "y": y})
+
+    def test_constant(self):
+        x = self.eval("1")
+        assert x == 1
+
+    def test_single_variable(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+        df2 = self.eval("df", local_dict={"df": df})
+        tm.assert_frame_equal(df, df2)
+
+    def test_failing_subscript_with_name_error(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        with pytest.raises(NameError, match="name 'x' is not defined"):
+            self.eval("df[x > 2] > 2")
+
+    def test_lhs_expression_subscript(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        result = self.eval("(df + 1)[df > 2]", local_dict={"df": df})
+        expected = (df + 1)[df > 2]
+        tm.assert_frame_equal(result, expected)
+
+    def test_attr_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        expr1 = "df.a < df.b"
+        expec1 = df.a < df.b
+        expr2 = "df.a + df.b + df.c"
+        expec2 = df.a + df.b + df.c
+        expr3 = "df.a + df.b + df.c[df.b < 0]"
+        expec3 = df.a + df.b + df.c[df.b < 0]
+        exprs = expr1, expr2, expr3
+        expecs = expec1, expec2, expec3
+        for e, expec in zip(exprs, expecs):
+            tm.assert_series_equal(expec, self.eval(e, local_dict={"df": df}))
+
+    def test_assignment_fails(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        expr1 = "df = df2"
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expr1, local_dict={"df": df, "df2": df2})
+
+    def test_assignment_column_multiple_raise(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignees
+        with pytest.raises(SyntaxError, match="invalid syntax"):
+            df.eval("d c = a + b")
+
+    def test_assignment_column_invalid_assign(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # invalid assignees
+        msg = "left hand side of an assignment must be a single name"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("d,c = a + b")
+
+    def test_assignment_column_invalid_assign_function_call(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        msg = "cannot assign to function call"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval('Timestamp("20131001") = a + b')
+
+    def test_assignment_single_assign_existing(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - existing variable
+        expected = df.copy()
+        expected["a"] = expected["a"] + expected["b"]
+        df.eval("a = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_new(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - new variable
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        df.eval("c = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_local_overlap(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        df = df.copy()
+        a = 1  # noqa: F841
+        df.eval("a = 1 + b", inplace=True)
+
+        expected = df.copy()
+        expected["a"] = 1 + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_name(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        a = 1  # noqa: F841
+        old_a = df.a.copy()
+        df.eval("a = a + b", inplace=True)
+        result = old_a + df.b
+        tm.assert_series_equal(result, df.a, check_names=False)
+        assert result.name is None
+
+    def test_assignment_multiple_raises(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignment
+        df.eval("c = a + b", inplace=True)
+        msg = "can only assign a single expression"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("c = a = b")
+
+    def test_assignment_explicit(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # explicit targets
+        self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True)
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_column_in(self):
+        # GH 11235
+        df = DataFrame({"a": [11], "b": [-32]})
+        result = df.eval("a in [11, -32]")
+        expected = Series([True])
+        # TODO: 2022-01-29: Name check failed with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        tm.assert_series_equal(result, expected, check_names=False)
+
+    @pytest.mark.xfail(reason="Unknown: Omitted test_ in name prior.")
+    def test_assignment_not_inplace(self):
+        # see gh-9297
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        actual = df.eval("c = a + b", inplace=False)
+        assert actual is not None
+
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_multi_line_expression(self, warn_copy_on_write):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        answer = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        answer = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        # multi-line not valid if not all assignments
+        msg = "Multi-line expressions are only valid if all expressions contain"
+        with pytest.raises(ValueError, match=msg):
+            df.eval(
+                """
+            a = b + 2
+            b - 2""",
+                inplace=False,
+            )
+
+    def test_multi_line_expression_not_inplace(self):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        df = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        df = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+    def test_multi_line_expression_local_variable(self):
+        # GH 15342
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        local_var = 7
+        expected["c"] = expected["a"] * local_var
+        expected["d"] = expected["c"] + local_var
+        answer = df.eval(
+            """
+        c = a * @local_var
+        d = c + @local_var
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(1, 7)
+        expected["d"] = expected["c"] + local_func(1, 7)
+        answer = df.eval(
+            """
+        c = a * @local_func(1, 7)
+        d = c + @local_func(1, 7)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable_with_kwargs(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(b=7, a=1)
+        expected["d"] = expected["c"] + local_func(b=7, a=1)
+        answer = df.eval(
+            """
+        c = a * @local_func(b=7, a=1)
+        d = c + @local_func(b=7, a=1)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_assignment_in_query(self):
+        # GH 8664
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        df_orig = df.copy()
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            df.query("a = 1")
+        tm.assert_frame_equal(df, df_orig)
+
+    def test_query_inplace(self):
+        # see gh-11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+        expected = expected[expected["a"] == 2]
+        df.query("a == 2", inplace=True)
+        tm.assert_frame_equal(expected, df)
+
+        df = {}
+        expected = {"a": 3}
+
+        self.eval("a = 1 + 2", target=df, inplace=True)
+        tm.assert_dict_equal(df, expected)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2], np.array([]), (1, 3)])
+    def test_cannot_item_assign(self, invalid_target):
+        msg = "Cannot assign expression output to target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=True)
+
+        if hasattr(invalid_target, "copy"):
+            with pytest.raises(ValueError, match=msg):
+                self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", (1, 3)])
+    def test_cannot_copy_item(self, invalid_target):
+        msg = "Cannot return a copy of the target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("target", [1, "cat", [1, 2], np.array([]), (1, 3), {1: 2}])
+    def test_inplace_no_assignment(self, target):
+        expression = "1 + 2"
+
+        assert self.eval(expression, target=target, inplace=False) == 3
+
+        msg = "Cannot operate inplace if there is no assignment"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=target, inplace=True)
+
+    def test_basic_period_index_boolean_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        e = df < 2
+        r = self.eval("df < 2", local_dict={"df": df})
+        x = df < 2
+
+        tm.assert_frame_equal(r, e)
+        tm.assert_frame_equal(x, e)
+
+    def test_basic_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df < 2 + 3]", local_dict={"df": df})
+        e = df[df < 2 + 3]
+        tm.assert_frame_equal(r, e)
+
+    def test_nested_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df[df < 2] < 2] + df * 2", local_dict={"df": df})
+        e = df[df[df < 2] < 2] + df * 2
+        tm.assert_frame_equal(r, e)
+
+    def test_date_boolean(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        res = self.eval(
+            "df.dates1 < 20130101",
+            local_dict={"df": df},
+            engine=engine,
+            parser=parser,
+        )
+        expec = df.dates1 < "20130101"
+        tm.assert_series_equal(res, expec, check_names=False)
+
+    def test_simple_in_ops(self, engine, parser):
+        if parser != "python":
+            res = pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] in ([3], 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[[3]] in [[[3]], 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) in [(3,), 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) not in [(3,), 2]", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("[(3,)] in [[(3,)], 2]", engine=engine, parser=parser)
+            assert res
+        else:
+            msg = "'In' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[(3,)] in (1, 2, [(3,)])", engine=engine, parser=parser)
+            msg = "'NotIn' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[3] not in (1, 2, [[3]])", engine=engine, parser=parser)
+
+    def test_check_many_exprs(self, engine, parser):
+        a = 1  # noqa: F841
+        expr = " * ".join("a" * 33)
+        expected = 1
+        res = pd.eval(expr, engine=engine, parser=parser)
+        assert res == expected
+
+    @pytest.mark.parametrize(
+        "expr",
+        [
+            "df > 2 and df > 3",
+            "df > 2 or df > 3",
+            "not df > 2",
+        ],
+    )
+    def test_fails_and_or_not(self, expr, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        if parser == "python":
+            msg = "'BoolOp' nodes are not implemented"
+            if "not" in expr:
+                msg = "'Not' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(
+                    expr,
+                    local_dict={"df": df},
+                    parser=parser,
+                    engine=engine,
+                )
+        else:
+            # smoke-test, should not raise
+            pd.eval(
+                expr,
+                local_dict={"df": df},
+                parser=parser,
+                engine=engine,
+            )
+
+    @pytest.mark.parametrize("char", ["|", "&"])
+    def test_fails_ampersand_pipe(self, char, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        ex = f"(df + 2)[df > 1] > 0 {char} (df > 0)"
+        if parser == "python":
+            msg = "cannot evaluate scalar only bool ops"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex, parser=parser, engine=engine)
+        else:
+            # smoke-test, should not raise
+            pd.eval(ex, parser=parser, engine=engine)
+
+
+class TestMath:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    @pytest.mark.skipif(
+        not NUMEXPR_INSTALLED, reason="Unary ops only implemented for numexpr"
+    )
+    @pytest.mark.parametrize("fn", _unary_math_ops)
+    def test_unary_functions(self, fn):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        a = df.a
+
+        expr = f"{fn}(a)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize("fn", _binary_math_ops)
+    def test_binary_functions(self, fn):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        a = df.a
+        b = df.b
+
+        expr = f"{fn}(a, b)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a, b)
+        tm.assert_almost_equal(got, expect, check_names=False)
+
+    def test_df_use_case(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval(
+            "e = arctan2(sin(a), b)",
+            engine=engine,
+            parser=parser,
+            inplace=True,
+        )
+        got = df.e
+        expect = np.arctan2(np.sin(df.a), df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_df_arithmetic_subexpression(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval("e = sin(a + b)", engine=engine, parser=parser, inplace=True)
+        got = df.e
+        expect = np.sin(df.a + df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize(
+        "dtype, expect_dtype",
+        [
+            (np.int32, np.float64),
+            (np.int64, np.float64),
+            (np.float32, np.float32),
+            (np.float64, np.float64),
+            pytest.param(np.complex128, np.complex128, marks=td.skip_if_windows),
+        ],
+    )
+    def test_result_types(self, dtype, expect_dtype, engine, parser):
+        # xref https://github.com/pandas-dev/pandas/issues/12293
+        #  this fails on Windows, apparently a floating point precision issue
+
+        # Did not test complex64 because DataFrame is converting it to
+        # complex128. Due to https://github.com/pandas-dev/pandas/issues/10952
+        df = DataFrame(
+            {"a": np.random.default_rng(2).standard_normal(10).astype(dtype)}
+        )
+        assert df.a.dtype == dtype
+        df.eval("b = sin(a)", engine=engine, parser=parser, inplace=True)
+        got = df.b
+        expect = np.sin(df.a)
+        assert expect.dtype == got.dtype
+        assert expect_dtype == got.dtype
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_undefined_func(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = '"mysin" is not a supported function'
+
+        with pytest.raises(ValueError, match=msg):
+            df.eval("mysin(a)", engine=engine, parser=parser)
+
+    def test_keyword_arg(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = 'Function "sin" does not support keyword arguments'
+
+        with pytest.raises(TypeError, match=msg):
+            df.eval("sin(x=a)", engine=engine, parser=parser)
+
+
+_var_s = np.random.default_rng(2).standard_normal(10)
+
+
+class TestScope:
+    def test_global_scope(self, engine, parser):
+        e = "_var_s * 2"
+        tm.assert_numpy_array_equal(
+            _var_s * 2, pd.eval(e, engine=engine, parser=parser)
+        )
+
+    def test_no_new_locals(self, engine, parser):
+        x = 1
+        lcls = locals().copy()
+        pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser)
+        lcls2 = locals().copy()
+        lcls2.pop("lcls")
+        assert lcls == lcls2
+
+    def test_no_new_globals(self, engine, parser):
+        x = 1  # noqa: F841
+        gbls = globals().copy()
+        pd.eval("x + 1", engine=engine, parser=parser)
+        gbls2 = globals().copy()
+        assert gbls == gbls2
+
+    def test_empty_locals(self, engine, parser):
+        # GH 47084
+        x = 1  # noqa: F841
+        msg = "name 'x' is not defined"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval("x + 1", engine=engine, parser=parser, local_dict={})
+
+    def test_empty_globals(self, engine, parser):
+        # GH 47084
+        msg = "name '_var_s' is not defined"
+        e = "_var_s * 2"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval(e, engine=engine, parser=parser, global_dict={})
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_engine():
+    msg = "Invalid engine 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, engine="asdf")
+
+
+@td.skip_if_no("numexpr")
+@pytest.mark.parametrize(
+    ("use_numexpr", "expected"),
+    (
+        (True, "numexpr"),
+        (False, "python"),
+    ),
+)
+def test_numexpr_option_respected(use_numexpr, expected):
+    # GH 32556
+    from pandas.core.computation.eval import _check_engine
+
+    with pd.option_context("compute.use_numexpr", use_numexpr):
+        result = _check_engine(None)
+        assert result == expected
+
+
+@td.skip_if_no("numexpr")
+def test_numexpr_option_incompatible_op():
+    # GH 32556
+    with pd.option_context("compute.use_numexpr", False):
+        df = DataFrame(
+            {"A": [True, False, True, False, None, None], "B": [1, 2, 3, 4, 5, 6]}
+        )
+        result = df.query("A.isnull()")
+        expected = DataFrame({"A": [None, None], "B": [5, 6]}, index=[4, 5])
+        tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_parser():
+    msg = "Invalid parser 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, parser="asdf")
+
+
+_parsers: dict[str, type[BaseExprVisitor]] = {
+    "python": PythonExprVisitor,
+    "pytables": pytables.PyTablesExprVisitor,
+    "pandas": PandasExprVisitor,
+}
+
+
+@pytest.mark.parametrize("engine", ENGINES)
+@pytest.mark.parametrize("parser", _parsers)
+def test_disallowed_nodes(engine, parser):
+    VisitorClass = _parsers[parser]
+    inst = VisitorClass("x + 1", engine, parser)
+
+    for ops in VisitorClass.unsupported_nodes:
+        msg = "nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            getattr(inst, ops)()
+
+
+def test_syntax_error_exprs(engine, parser):
+    e = "s +"
+    with pytest.raises(SyntaxError, match="invalid syntax"):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+def test_name_error_exprs(engine, parser):
+    e = "s + t"
+    msg = "name 's' is not defined"
+    with pytest.raises(NameError, match=msg):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("express", ["a + @b", "@a + b", "@a + @b"])
+def test_invalid_local_variable_reference(engine, parser, express):
+    a, b = 1, 2  # noqa: F841
+
+    if parser != "pandas":
+        with pytest.raises(SyntaxError, match="The '@' prefix is only"):
+            pd.eval(express, engine=engine, parser=parser)
+    else:
+        with pytest.raises(SyntaxError, match="The '@' prefix is not"):
+            pd.eval(express, engine=engine, parser=parser)
+
+
+def test_numexpr_builtin_raises(engine, parser):
+    sin, dotted_line = 1, 2
+    if engine == "numexpr":
+        msg = "Variables in expression .+"
+        with pytest.raises(NumExprClobberingError, match=msg):
+            pd.eval("sin + dotted_line", engine=engine, parser=parser)
+    else:
+        res = pd.eval("sin + dotted_line", engine=engine, parser=parser)
+        assert res == sin + dotted_line
+
+
+def test_bad_resolver_raises(engine, parser):
+    cannot_resolve = 42, 3.0
+    with pytest.raises(TypeError, match="Resolver of type .+"):
+        pd.eval("1 + 2", resolvers=cannot_resolve, engine=engine, parser=parser)
+
+
+def test_empty_string_raises(engine, parser):
+    # GH 13139
+    with pytest.raises(ValueError, match="expr cannot be an empty string"):
+        pd.eval("", engine=engine, parser=parser)
+
+
+def test_more_than_one_expression_raises(engine, parser):
+    with pytest.raises(SyntaxError, match="only a single expression is allowed"):
+        pd.eval("1 + 1; 2 + 2", engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("cmp", ("and", "or"))
+@pytest.mark.parametrize("lhs", (int, float))
+@pytest.mark.parametrize("rhs", (int, float))
+def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser):
+    gen = {
+        int: lambda: np.random.default_rng(2).integers(10),
+        float: np.random.default_rng(2).standard_normal,
+    }
+
+    mid = gen[lhs]()  # noqa: F841
+    lhs = gen[lhs]()
+    rhs = gen[rhs]()
+
+    ex1 = f"lhs {cmp} mid {cmp} rhs"
+    ex2 = f"lhs {cmp} mid and mid {cmp} rhs"
+    ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)"
+    for ex in (ex1, ex2, ex3):
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        "'x'",
+        "...",
+    ],
+)
+def test_equals_various(other):
+    df = DataFrame({"A": ["a", "b", "c"]}, dtype=object)
+    result = df.eval(f"A == {other}")
+    expected = Series([False, False, False], name="A")
+    if USE_NUMEXPR:
+        # https://github.com/pandas-dev/pandas/issues/10239
+        # lose name with numexpr engine. Remove when that's fixed.
+        expected.name = None
+    tm.assert_series_equal(result, expected)
+
+
+def test_inf(engine, parser):
+    s = "inf + 1"
+    expected = np.inf
+    result = pd.eval(s, engine=engine, parser=parser)
+    assert result == expected
+
+
+@pytest.mark.parametrize("column", ["Temp(°C)", "Capacitance(μF)"])
+def test_query_token(engine, column):
+    # See: https://github.com/pandas-dev/pandas/pull/42826
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((5, 2)), columns=[column, "b"]
+    )
+    expected = df[df[column] > 5]
+    query_string = f"`{column}` > 5"
+    result = df.query(query_string, engine=engine)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_negate_lt_eq_le(engine, parser):
+    df = DataFrame([[0, 10], [1, 20]], columns=["cat", "count"])
+    expected = df[~(df.cat > 0)]
+
+    result = df.query("~(cat > 0)", engine=engine, parser=parser)
+    tm.assert_frame_equal(result, expected)
+
+    if parser == "python":
+        msg = "'Not' nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            df.query("not (cat > 0)", engine=engine, parser=parser)
+    else:
+        result = df.query("not (cat > 0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "column",
+    DEFAULT_GLOBALS.keys(),
+)
+def test_eval_no_support_column_name(request, column):
+    # GH 44603
+    if column in ["True", "False", "inf", "Inf"]:
+        request.applymarker(
+            pytest.mark.xfail(
+                raises=KeyError,
+                reason=f"GH 47859 DataFrame eval not supported with {column}",
+            )
+        )
+
+    df = DataFrame(
+        np.random.default_rng(2).integers(0, 100, size=(10, 2)),
+        columns=[column, "col1"],
+    )
+    expected = df[df[column] > 6]
+    result = df.query(f"{column}>6")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_set_inplace(using_copy_on_write, warn_copy_on_write):
+    # https://github.com/pandas-dev/pandas/issues/47449
+    # Ensure we don't only update the DataFrame inplace, but also the actual
+    # column values, such that references to this column also get updated
+    df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+    result_view = df[:]
+    ser = df["A"]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.eval("A = B + C", inplace=True)
+    expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]})
+    tm.assert_frame_equal(df, expected)
+    if not using_copy_on_write:
+        tm.assert_series_equal(ser, expected["A"])
+        tm.assert_series_equal(result_view["A"], expected["A"])
+    else:
+        expected = Series([1, 2, 3], name="A")
+        tm.assert_series_equal(ser, expected)
+        tm.assert_series_equal(result_view["A"], expected)
+
+
+class TestValidate:
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, value):
+        msg = 'For argument "inplace" expected type bool, received type'
+        with pytest.raises(ValueError, match=msg):
+            pd.eval("2+2", inplace=value)
diff --git a/shared/package-files/b5/b533763a21518c69484fe3e4ac22e2fa605f52670067c344a6b1528f047f2b95/bdf.py b/shared/package-files/b5/b533763a21518c69484fe3e4ac22e2fa605f52670067c344a6b1528f047f2b95/bdf.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b47a642b976e623edc9047f6465e328095dcd2
--- /dev/null
+++ b/shared/package-files/b5/b533763a21518c69484fe3e4ac22e2fa605f52670067c344a6b1528f047f2b95/bdf.py
@@ -0,0 +1,478 @@
+import numpy as np
+from scipy.linalg import lu_factor, lu_solve
+from scipy.sparse import issparse, csc_matrix, eye
+from scipy.sparse.linalg import splu
+from scipy.optimize._numdiff import group_columns
+from .common import (validate_max_step, validate_tol, select_initial_step,
+                     norm, EPS, num_jac, validate_first_step,
+                     warn_extraneous)
+from .base import OdeSolver, DenseOutput
+
+
+MAX_ORDER = 5
+NEWTON_MAXITER = 4
+MIN_FACTOR = 0.2
+MAX_FACTOR = 10
+
+
+def compute_R(order, factor):
+    """Compute the matrix for changing the differences array."""
+    I = np.arange(1, order + 1)[:, None]
+    J = np.arange(1, order + 1)
+    M = np.zeros((order + 1, order + 1))
+    M[1:, 1:] = (I - 1 - factor * J) / I
+    M[0] = 1
+    return np.cumprod(M, axis=0)
+
+
+def change_D(D, order, factor):
+    """Change differences array in-place when step size is changed."""
+    R = compute_R(order, factor)
+    U = compute_R(order, 1)
+    RU = R.dot(U)
+    D[:order + 1] = np.dot(RU.T, D[:order + 1])
+
+
+def solve_bdf_system(fun, t_new, y_predict, c, psi, LU, solve_lu, scale, tol):
+    """Solve the algebraic system resulting from BDF method."""
+    d = 0
+    y = y_predict.copy()
+    dy_norm_old = None
+    converged = False
+    for k in range(NEWTON_MAXITER):
+        f = fun(t_new, y)
+        if not np.all(np.isfinite(f)):
+            break
+
+        dy = solve_lu(LU, c * f - psi - d)
+        dy_norm = norm(dy / scale)
+
+        if dy_norm_old is None:
+            rate = None
+        else:
+            rate = dy_norm / dy_norm_old
+
+        if (rate is not None and (rate >= 1 or
+                rate ** (NEWTON_MAXITER - k) / (1 - rate) * dy_norm > tol)):
+            break
+
+        y += dy
+        d += dy
+
+        if (dy_norm == 0 or
+                rate is not None and rate / (1 - rate) * dy_norm < tol):
+            converged = True
+            break
+
+        dy_norm_old = dy_norm
+
+    return converged, k + 1, y, d
+
+
+class BDF(OdeSolver):
+    """Implicit method based on backward-differentiation formulas.
+
+    This is a variable order method with the order varying automatically from
+    1 to 5. The general framework of the BDF algorithm is described in [1]_.
+    This class implements a quasi-constant step size as explained in [2]_.
+    The error estimation strategy for the constant-step BDF is derived in [3]_.
+    An accuracy enhancement using modified formulas (NDF) [2]_ is also implemented.
+
+    Can be applied in the complex domain.
+
+    Parameters
+    ----------
+    fun : callable
+        Right-hand side of the system: the time derivative of the state ``y``
+        at time ``t``. The calling signature is ``fun(t, y)``, where ``t`` is a
+        scalar and ``y`` is an ndarray with ``len(y) = len(y0)``. ``fun`` must
+        return an array of the same shape as ``y``. See `vectorized` for more
+        information.
+    t0 : float
+        Initial time.
+    y0 : array_like, shape (n,)
+        Initial state.
+    t_bound : float
+        Boundary time - the integration won't continue beyond it. It also
+        determines the direction of the integration.
+    first_step : float or None, optional
+        Initial step size. Default is ``None`` which means that the algorithm
+        should choose.
+    max_step : float, optional
+        Maximum allowed step size. Default is np.inf, i.e., the step size is not
+        bounded and determined solely by the solver.
+    rtol, atol : float and array_like, optional
+        Relative and absolute tolerances. The solver keeps the local error
+        estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
+        relative accuracy (number of correct digits), while `atol` controls
+        absolute accuracy (number of correct decimal places). To achieve the
+        desired `rtol`, set `atol` to be smaller than the smallest value that
+        can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
+        allowable error. If `atol` is larger than ``rtol * abs(y)`` the
+        number of correct digits is not guaranteed. Conversely, to achieve the
+        desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
+        than `atol`. If components of y have different scales, it might be
+        beneficial to set different `atol` values for different components by
+        passing array_like with shape (n,) for `atol`. Default values are
+        1e-3 for `rtol` and 1e-6 for `atol`.
+    jac : {None, array_like, sparse_matrix, callable}, optional
+        Jacobian matrix of the right-hand side of the system with respect to y,
+        required by this method. The Jacobian matrix has shape (n, n) and its
+        element (i, j) is equal to ``d f_i / d y_j``.
+        There are three ways to define the Jacobian:
+
+            * If array_like or sparse_matrix, the Jacobian is assumed to
+              be constant.
+            * If callable, the Jacobian is assumed to depend on both
+              t and y; it will be called as ``jac(t, y)`` as necessary.
+              For the 'Radau' and 'BDF' methods, the return value might be a
+              sparse matrix.
+            * If None (default), the Jacobian will be approximated by
+              finite differences.
+
+        It is generally recommended to provide the Jacobian rather than
+        relying on a finite-difference approximation.
+    jac_sparsity : {None, array_like, sparse matrix}, optional
+        Defines a sparsity structure of the Jacobian matrix for a
+        finite-difference approximation. Its shape must be (n, n). This argument
+        is ignored if `jac` is not `None`. If the Jacobian has only few non-zero
+        elements in *each* row, providing the sparsity structure will greatly
+        speed up the computations [4]_. A zero entry means that a corresponding
+        element in the Jacobian is always zero. If None (default), the Jacobian
+        is assumed to be dense.
+    vectorized : bool, optional
+        Whether `fun` can be called in a vectorized fashion. Default is False.
+
+        If ``vectorized`` is False, `fun` will always be called with ``y`` of
+        shape ``(n,)``, where ``n = len(y0)``.
+
+        If ``vectorized`` is True, `fun` may be called with ``y`` of shape
+        ``(n, k)``, where ``k`` is an integer. In this case, `fun` must behave
+        such that ``fun(t, y)[:, i] == fun(t, y[:, i])`` (i.e. each column of
+        the returned array is the time derivative of the state corresponding
+        with a column of ``y``).
+
+        Setting ``vectorized=True`` allows for faster finite difference
+        approximation of the Jacobian by this method, but may result in slower
+        execution overall in some circumstances (e.g. small ``len(y0)``).
+
+    Attributes
+    ----------
+    n : int
+        Number of equations.
+    status : string
+        Current status of the solver: 'running', 'finished' or 'failed'.
+    t_bound : float
+        Boundary time.
+    direction : float
+        Integration direction: +1 or -1.
+    t : float
+        Current time.
+    y : ndarray
+        Current state.
+    t_old : float
+        Previous time. None if no steps were made yet.
+    step_size : float
+        Size of the last successful step. None if no steps were made yet.
+    nfev : int
+        Number of evaluations of the right-hand side.
+    njev : int
+        Number of evaluations of the Jacobian.
+    nlu : int
+        Number of LU decompositions.
+
+    References
+    ----------
+    .. [1] G. D. Byrne, A. C. Hindmarsh, "A Polyalgorithm for the Numerical
+           Solution of Ordinary Differential Equations", ACM Transactions on
+           Mathematical Software, Vol. 1, No. 1, pp. 71-96, March 1975.
+    .. [2] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
+           COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
+    .. [3] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations I:
+           Nonstiff Problems", Sec. III.2.
+    .. [4] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
+           sparse Jacobian matrices", Journal of the Institute of Mathematics
+           and its Applications, 13, pp. 117-120, 1974.
+    """
+    def __init__(self, fun, t0, y0, t_bound, max_step=np.inf,
+                 rtol=1e-3, atol=1e-6, jac=None, jac_sparsity=None,
+                 vectorized=False, first_step=None, **extraneous):
+        warn_extraneous(extraneous)
+        super().__init__(fun, t0, y0, t_bound, vectorized,
+                         support_complex=True)
+        self.max_step = validate_max_step(max_step)
+        self.rtol, self.atol = validate_tol(rtol, atol, self.n)
+        f = self.fun(self.t, self.y)
+        if first_step is None:
+            self.h_abs = select_initial_step(self.fun, self.t, self.y, 
+                                             t_bound, max_step, f,
+                                             self.direction, 1,
+                                             self.rtol, self.atol)
+        else:
+            self.h_abs = validate_first_step(first_step, t0, t_bound)
+        self.h_abs_old = None
+        self.error_norm_old = None
+
+        self.newton_tol = max(10 * EPS / rtol, min(0.03, rtol ** 0.5))
+
+        self.jac_factor = None
+        self.jac, self.J = self._validate_jac(jac, jac_sparsity)
+        if issparse(self.J):
+            def lu(A):
+                self.nlu += 1
+                return splu(A)
+
+            def solve_lu(LU, b):
+                return LU.solve(b)
+
+            I = eye(self.n, format='csc', dtype=self.y.dtype)
+        else:
+            def lu(A):
+                self.nlu += 1
+                return lu_factor(A, overwrite_a=True)
+
+            def solve_lu(LU, b):
+                return lu_solve(LU, b, overwrite_b=True)
+
+            I = np.identity(self.n, dtype=self.y.dtype)
+
+        self.lu = lu
+        self.solve_lu = solve_lu
+        self.I = I
+
+        kappa = np.array([0, -0.1850, -1/9, -0.0823, -0.0415, 0])
+        self.gamma = np.hstack((0, np.cumsum(1 / np.arange(1, MAX_ORDER + 1))))
+        self.alpha = (1 - kappa) * self.gamma
+        self.error_const = kappa * self.gamma + 1 / np.arange(1, MAX_ORDER + 2)
+
+        D = np.empty((MAX_ORDER + 3, self.n), dtype=self.y.dtype)
+        D[0] = self.y
+        D[1] = f * self.h_abs * self.direction
+        self.D = D
+
+        self.order = 1
+        self.n_equal_steps = 0
+        self.LU = None
+
+    def _validate_jac(self, jac, sparsity):
+        t0 = self.t
+        y0 = self.y
+
+        if jac is None:
+            if sparsity is not None:
+                if issparse(sparsity):
+                    sparsity = csc_matrix(sparsity)
+                groups = group_columns(sparsity)
+                sparsity = (sparsity, groups)
+
+            def jac_wrapped(t, y):
+                self.njev += 1
+                f = self.fun_single(t, y)
+                J, self.jac_factor = num_jac(self.fun_vectorized, t, y, f,
+                                             self.atol, self.jac_factor,
+                                             sparsity)
+                return J
+            J = jac_wrapped(t0, y0)
+        elif callable(jac):
+            J = jac(t0, y0)
+            self.njev += 1
+            if issparse(J):
+                J = csc_matrix(J, dtype=y0.dtype)
+
+                def jac_wrapped(t, y):
+                    self.njev += 1
+                    return csc_matrix(jac(t, y), dtype=y0.dtype)
+            else:
+                J = np.asarray(J, dtype=y0.dtype)
+
+                def jac_wrapped(t, y):
+                    self.njev += 1
+                    return np.asarray(jac(t, y), dtype=y0.dtype)
+
+            if J.shape != (self.n, self.n):
+                raise ValueError(f"`jac` is expected to have shape {(self.n, self.n)},"
+                                 f" but actually has {J.shape}.")
+        else:
+            if issparse(jac):
+                J = csc_matrix(jac, dtype=y0.dtype)
+            else:
+                J = np.asarray(jac, dtype=y0.dtype)
+
+            if J.shape != (self.n, self.n):
+                raise ValueError(f"`jac` is expected to have shape {(self.n, self.n)},"
+                                 f" but actually has {J.shape}.")
+            jac_wrapped = None
+
+        return jac_wrapped, J
+
+    def _step_impl(self):
+        t = self.t
+        D = self.D
+
+        max_step = self.max_step
+        min_step = 10 * np.abs(np.nextafter(t, self.direction * np.inf) - t)
+        if self.h_abs > max_step:
+            h_abs = max_step
+            change_D(D, self.order, max_step / self.h_abs)
+            self.n_equal_steps = 0
+        elif self.h_abs < min_step:
+            h_abs = min_step
+            change_D(D, self.order, min_step / self.h_abs)
+            self.n_equal_steps = 0
+        else:
+            h_abs = self.h_abs
+
+        atol = self.atol
+        rtol = self.rtol
+        order = self.order
+
+        alpha = self.alpha
+        gamma = self.gamma
+        error_const = self.error_const
+
+        J = self.J
+        LU = self.LU
+        current_jac = self.jac is None
+
+        step_accepted = False
+        while not step_accepted:
+            if h_abs < min_step:
+                return False, self.TOO_SMALL_STEP
+
+            h = h_abs * self.direction
+            t_new = t + h
+
+            if self.direction * (t_new - self.t_bound) > 0:
+                t_new = self.t_bound
+                change_D(D, order, np.abs(t_new - t) / h_abs)
+                self.n_equal_steps = 0
+                LU = None
+
+            h = t_new - t
+            h_abs = np.abs(h)
+
+            y_predict = np.sum(D[:order + 1], axis=0)
+
+            scale = atol + rtol * np.abs(y_predict)
+            psi = np.dot(D[1: order + 1].T, gamma[1: order + 1]) / alpha[order]
+
+            converged = False
+            c = h / alpha[order]
+            while not converged:
+                if LU is None:
+                    LU = self.lu(self.I - c * J)
+
+                converged, n_iter, y_new, d = solve_bdf_system(
+                    self.fun, t_new, y_predict, c, psi, LU, self.solve_lu,
+                    scale, self.newton_tol)
+
+                if not converged:
+                    if current_jac:
+                        break
+                    J = self.jac(t_new, y_predict)
+                    LU = None
+                    current_jac = True
+
+            if not converged:
+                factor = 0.5
+                h_abs *= factor
+                change_D(D, order, factor)
+                self.n_equal_steps = 0
+                LU = None
+                continue
+
+            safety = 0.9 * (2 * NEWTON_MAXITER + 1) / (2 * NEWTON_MAXITER
+                                                       + n_iter)
+
+            scale = atol + rtol * np.abs(y_new)
+            error = error_const[order] * d
+            error_norm = norm(error / scale)
+
+            if error_norm > 1:
+                factor = max(MIN_FACTOR,
+                             safety * error_norm ** (-1 / (order + 1)))
+                h_abs *= factor
+                change_D(D, order, factor)
+                self.n_equal_steps = 0
+                # As we didn't have problems with convergence, we don't
+                # reset LU here.
+            else:
+                step_accepted = True
+
+        self.n_equal_steps += 1
+
+        self.t = t_new
+        self.y = y_new
+
+        self.h_abs = h_abs
+        self.J = J
+        self.LU = LU
+
+        # Update differences. The principal relation here is
+        # D^{j + 1} y_n = D^{j} y_n - D^{j} y_{n - 1}. Keep in mind that D
+        # contained difference for previous interpolating polynomial and
+        # d = D^{k + 1} y_n. Thus this elegant code follows.
+        D[order + 2] = d - D[order + 1]
+        D[order + 1] = d
+        for i in reversed(range(order + 1)):
+            D[i] += D[i + 1]
+
+        if self.n_equal_steps < order + 1:
+            return True, None
+
+        if order > 1:
+            error_m = error_const[order - 1] * D[order]
+            error_m_norm = norm(error_m / scale)
+        else:
+            error_m_norm = np.inf
+
+        if order < MAX_ORDER:
+            error_p = error_const[order + 1] * D[order + 2]
+            error_p_norm = norm(error_p / scale)
+        else:
+            error_p_norm = np.inf
+
+        error_norms = np.array([error_m_norm, error_norm, error_p_norm])
+        with np.errstate(divide='ignore'):
+            factors = error_norms ** (-1 / np.arange(order, order + 3))
+
+        delta_order = np.argmax(factors) - 1
+        order += delta_order
+        self.order = order
+
+        factor = min(MAX_FACTOR, safety * np.max(factors))
+        self.h_abs *= factor
+        change_D(D, order, factor)
+        self.n_equal_steps = 0
+        self.LU = None
+
+        return True, None
+
+    def _dense_output_impl(self):
+        return BdfDenseOutput(self.t_old, self.t, self.h_abs * self.direction,
+                              self.order, self.D[:self.order + 1].copy())
+
+
+class BdfDenseOutput(DenseOutput):
+    def __init__(self, t_old, t, h, order, D):
+        super().__init__(t_old, t)
+        self.order = order
+        self.t_shift = self.t - h * np.arange(self.order)
+        self.denom = h * (1 + np.arange(self.order))
+        self.D = D
+
+    def _call_impl(self, t):
+        if t.ndim == 0:
+            x = (t - self.t_shift) / self.denom
+            p = np.cumprod(x)
+        else:
+            x = (t - self.t_shift[:, None]) / self.denom[:, None]
+            p = np.cumprod(x, axis=0)
+
+        y = np.dot(self.D[1:].T, p)
+        if y.ndim == 1:
+            y += self.D[0]
+        else:
+            y += self.D[0, :, None]
+
+        return y
diff --git a/shared/package-files/b5/b541795565f2f20963102f6c316f6c49577a3f870c867b45c5dfac1e3ea57886/results_simulation_smoothing3_variates.csv b/shared/package-files/b5/b541795565f2f20963102f6c316f6c49577a3f870c867b45c5dfac1e3ea57886/results_simulation_smoothing3_variates.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5458ea8c5e7114c38431484296691cfd24feac8f
--- /dev/null
+++ b/shared/package-files/b5/b541795565f2f20963102f6c316f6c49577a3f870c867b45c5dfac1e3ea57886/results_simulation_smoothing3_variates.csv
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diff --git a/shared/package-files/b5/b547282c4cf5fdebcfd02167b16720b614aaa0480f061713e7abbf3b5fe36508/_validate_sparse_coo_tensor_args_ops.h b/shared/package-files/b5/b547282c4cf5fdebcfd02167b16720b614aaa0480f061713e7abbf3b5fe36508/_validate_sparse_coo_tensor_args_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..64fa7379f354b36b3665ca4a2822fe4a0908a805
--- /dev/null
+++ b/shared/package-files/b5/b547282c4cf5fdebcfd02167b16720b614aaa0480f061713e7abbf3b5fe36508/_validate_sparse_coo_tensor_args_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _validate_sparse_coo_tensor_args {
+  using schema = void (const at::Tensor &, const at::Tensor &, at::IntArrayRef, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_validate_sparse_coo_tensor_args")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None) -> ()")
+  static void call(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<bool> is_coalesced);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<bool> is_coalesced);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/b5/b54a0223d767811d80b898c000a5c4949222cf6426e002467572a7f5cf2559a3/test_memory_async.py b/shared/package-files/b5/b54a0223d767811d80b898c000a5c4949222cf6426e002467572a7f5cf2559a3/test_memory_async.py
new file mode 100644
index 0000000000000000000000000000000000000000..a22f3066985cbd12b87b9ffa9b7f2bb85e04a3bc
--- /dev/null
+++ b/shared/package-files/b5/b54a0223d767811d80b898c000a5c4949222cf6426e002467572a7f5cf2559a3/test_memory_async.py
@@ -0,0 +1,180 @@
+import asyncio
+import gc
+import shutil
+
+import pytest
+
+from joblib.memory import (
+    AsyncMemorizedFunc,
+    AsyncNotMemorizedFunc,
+    MemorizedResult,
+    Memory,
+    NotMemorizedResult,
+)
+from joblib.test.common import np, with_numpy
+from joblib.testing import raises
+
+from .test_memory import corrupt_single_cache_item, monkeypatch_cached_func_warn
+
+
+async def check_identity_lazy_async(func, accumulator, location):
+    """Similar to check_identity_lazy_async for coroutine functions"""
+    memory = Memory(location=location, verbose=0)
+    func = memory.cache(func)
+    for i in range(3):
+        for _ in range(2):
+            value = await func(i)
+            assert value == i
+            assert len(accumulator) == i + 1
+
+
+@pytest.mark.asyncio
+async def test_memory_integration_async(tmpdir):
+    accumulator = list()
+
+    async def f(n):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return n
+
+    await check_identity_lazy_async(f, accumulator, tmpdir.strpath)
+
+    # Now test clearing
+    for compress in (False, True):
+        for mmap_mode in ("r", None):
+            memory = Memory(
+                location=tmpdir.strpath,
+                verbose=10,
+                mmap_mode=mmap_mode,
+                compress=compress,
+            )
+            # First clear the cache directory, to check that our code can
+            # handle that
+            # NOTE: this line would raise an exception, as the database
+            # file is still open; we ignore the error since we want to
+            # test what happens if the directory disappears
+            shutil.rmtree(tmpdir.strpath, ignore_errors=True)
+            g = memory.cache(f)
+            await g(1)
+            g.clear(warn=False)
+            current_accumulator = len(accumulator)
+            out = await g(1)
+
+        assert len(accumulator) == current_accumulator + 1
+        # Also, check that Memory.eval works similarly
+        evaled = await memory.eval(f, 1)
+        assert evaled == out
+        assert len(accumulator) == current_accumulator + 1
+
+    # Now do a smoke test with a function defined in __main__, as the name
+    # mangling rules are more complex
+    f.__module__ = "__main__"
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    await memory.cache(f)(1)
+
+
+@pytest.mark.asyncio
+async def test_no_memory_async():
+    accumulator = list()
+
+    async def ff(x):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return x
+
+    memory = Memory(location=None, verbose=0)
+    gg = memory.cache(ff)
+    for _ in range(4):
+        current_accumulator = len(accumulator)
+        await gg(1)
+        assert len(accumulator) == current_accumulator + 1
+
+
+@with_numpy
+@pytest.mark.asyncio
+async def test_memory_numpy_check_mmap_mode_async(tmpdir, monkeypatch):
+    """Check that mmap_mode is respected even at the first call"""
+
+    memory = Memory(location=tmpdir.strpath, mmap_mode="r", verbose=0)
+
+    @memory.cache()
+    async def twice(a):
+        return a * 2
+
+    a = np.ones(3)
+    b = await twice(a)
+    c = await twice(a)
+
+    assert isinstance(c, np.memmap)
+    assert c.mode == "r"
+
+    assert isinstance(b, np.memmap)
+    assert b.mode == "r"
+
+    # Corrupts the file,  Deleting b and c mmaps
+    # is necessary to be able edit the file
+    del b
+    del c
+    gc.collect()
+    corrupt_single_cache_item(memory)
+
+    # Make sure that corrupting the file causes recomputation and that
+    # a warning is issued.
+    recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
+    d = await twice(a)
+    assert len(recorded_warnings) == 1
+    exception_msg = "Exception while loading results"
+    assert exception_msg in recorded_warnings[0]
+    # Asserts that the recomputation returns a mmap
+    assert isinstance(d, np.memmap)
+    assert d.mode == "r"
+
+
+@pytest.mark.asyncio
+async def test_call_and_shelve_async(tmpdir):
+    async def f(x, y=1):
+        await asyncio.sleep(0.1)
+        return x**2 + y
+
+    # Test MemorizedFunc outputting a reference to cache.
+    for func, Result in zip(
+        (
+            AsyncMemorizedFunc(f, tmpdir.strpath),
+            AsyncNotMemorizedFunc(f),
+            Memory(location=tmpdir.strpath, verbose=0).cache(f),
+            Memory(location=None).cache(f),
+        ),
+        (
+            MemorizedResult,
+            NotMemorizedResult,
+            MemorizedResult,
+            NotMemorizedResult,
+        ),
+    ):
+        for _ in range(2):
+            result = await func.call_and_shelve(2)
+            assert isinstance(result, Result)
+            assert result.get() == 5
+
+        result.clear()
+        with raises(KeyError):
+            result.get()
+        result.clear()  # Do nothing if there is no cache.
+
+
+@pytest.mark.asyncio
+async def test_memorized_func_call_async(memory):
+    async def ff(x, counter):
+        await asyncio.sleep(0.1)
+        counter[x] = counter.get(x, 0) + 1
+        return counter[x]
+
+    gg = memory.cache(ff, ignore=["counter"])
+
+    counter = {}
+    assert await gg(2, counter) == 1
+    assert await gg(2, counter) == 1
+
+    x, meta = await gg.call(2, counter)
+    assert x == 2, "f has not been called properly"
+    assert isinstance(meta, dict), "Metadata are not returned by MemorizedFunc.call."
diff --git a/shared/package-files/b5/b5528129b6e7dbdd3f26c15b274c7b960aeb4d5c5e497b314c854bde713d4bec/linalg_eigvalsh.h b/shared/package-files/b5/b5528129b6e7dbdd3f26c15b274c7b960aeb4d5c5e497b314c854bde713d4bec/linalg_eigvalsh.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5ba455f87acbdcb4c3b44a90f057759b7ffa5af
--- /dev/null
+++ b/shared/package-files/b5/b5528129b6e7dbdd3f26c15b274c7b960aeb4d5c5e497b314c854bde713d4bec/linalg_eigvalsh.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/linalg_eigvalsh_ops.h>
+
+namespace at {
+
+
+// aten::linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor
+inline at::Tensor linalg_eigvalsh(const at::Tensor & self, c10::string_view UPLO="L") {
+    return at::_ops::linalg_eigvalsh::call(self, UPLO);
+}
+
+// aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_eigvalsh_out(at::Tensor & out, const at::Tensor & self, c10::string_view UPLO="L") {
+    return at::_ops::linalg_eigvalsh_out::call(self, UPLO, out);
+}
+// aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_eigvalsh_outf(const at::Tensor & self, c10::string_view UPLO, at::Tensor & out) {
+    return at::_ops::linalg_eigvalsh_out::call(self, UPLO, out);
+}
+
+}
diff --git a/shared/package-files/b5/b55550d445ad093e0e12be861e8980f5e8ded234970b1d1aed89c05fe9f54e7a/unsqueeze_native.h b/shared/package-files/b5/b55550d445ad093e0e12be861e8980f5e8ded234970b1d1aed89c05fe9f54e7a/unsqueeze_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c6ce1d6d9f7ca01aa4b908fda0590e63c28d4ce
--- /dev/null
+++ b/shared/package-files/b5/b55550d445ad093e0e12be861e8980f5e8ded234970b1d1aed89c05fe9f54e7a/unsqueeze_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor unsqueeze(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_nested(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_sparse(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_quantized(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & unsqueeze_(at::Tensor & self, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/b5/b557af4f1af00bd6320a00ca0c236bb3167263a1e3f32336178e24a9ac0c0a0b/_bracket.py b/shared/package-files/b5/b557af4f1af00bd6320a00ca0c236bb3167263a1e3f32336178e24a9ac0c0a0b/_bracket.py
new file mode 100644
index 0000000000000000000000000000000000000000..263243c612d08ebdc9939cc892771b49ac766d0c
--- /dev/null
+++ b/shared/package-files/b5/b557af4f1af00bd6320a00ca0c236bb3167263a1e3f32336178e24a9ac0c0a0b/_bracket.py
@@ -0,0 +1,713 @@
+import numpy as np
+import scipy._lib._elementwise_iterative_method as eim
+from scipy._lib._util import _RichResult
+from scipy._lib._array_api import array_namespace, xp_ravel, xp_default_dtype
+
+_ELIMITS = -1  # used in _bracket_root
+_ESTOPONESIDE = 2  # used in _bracket_root
+
+def _bracket_root_iv(func, xl0, xr0, xmin, xmax, factor, args, maxiter):
+
+    if not callable(func):
+        raise ValueError('`func` must be callable.')
+
+    if not np.iterable(args):
+        args = (args,)
+
+    xp = array_namespace(xl0)
+    xl0 = xp.asarray(xl0)[()]
+    if (not xp.isdtype(xl0.dtype, "numeric")
+        or xp.isdtype(xl0.dtype, "complex floating")):
+        raise ValueError('`xl0` must be numeric and real.')
+    if not xp.isdtype(xl0.dtype, "real floating"):
+        xl0 = xp.asarray(xl0, dtype=xp_default_dtype(xp))
+
+    # If xr0 is not supplied, fill with a dummy value for the sake of
+    # broadcasting. We need to wait until xmax has been validated to
+    # compute the default value.
+    xr0_not_supplied = False
+    if xr0 is None:
+        xr0 = xp.nan
+        xr0_not_supplied = True
+
+    xmin = -xp.inf if xmin is None else xmin
+    xmax = xp.inf if xmax is None else xmax
+    factor = 2. if factor is None else factor
+    xl0, xr0, xmin, xmax, factor = xp.broadcast_arrays(
+        xl0, xp.asarray(xr0), xp.asarray(xmin), xp.asarray(xmax), xp.asarray(factor))
+
+    if (not xp.isdtype(xr0.dtype, "numeric")
+        or xp.isdtype(xr0.dtype, "complex floating")):
+        raise ValueError('`xr0` must be numeric and real.')
+
+    if (not xp.isdtype(xmin.dtype, "numeric")
+        or xp.isdtype(xmin.dtype, "complex floating")):
+        raise ValueError('`xmin` must be numeric and real.')
+
+    if (not xp.isdtype(xmax.dtype, "numeric")
+        or xp.isdtype(xmax.dtype, "complex floating")):
+        raise ValueError('`xmax` must be numeric and real.')
+
+    if (not xp.isdtype(factor.dtype, "numeric")
+        or xp.isdtype(factor.dtype, "complex floating")):
+        raise ValueError('`factor` must be numeric and real.')
+    if not xp.all(factor > 1):
+        raise ValueError('All elements of `factor` must be greater than 1.')
+
+    # Calculate the default value of xr0 if a value has not been supplied.
+    # Be careful to ensure xr0 is not larger than xmax.
+    if xr0_not_supplied:
+        xr0 = xl0 + xp.minimum((xmax - xl0)/ 8, xp.asarray(1.0))
+        xr0 = xp.astype(xr0, xl0.dtype, copy=False)
+
+    maxiter = xp.asarray(maxiter)
+    message = '`maxiter` must be a non-negative integer.'
+    if (not xp.isdtype(maxiter.dtype, "numeric") or maxiter.shape != tuple()
+            or xp.isdtype(maxiter.dtype, "complex floating")):
+        raise ValueError(message)
+    maxiter_int = int(maxiter[()])
+    if not maxiter == maxiter_int or maxiter < 0:
+        raise ValueError(message)
+
+    return func, xl0, xr0, xmin, xmax, factor, args, maxiter, xp
+
+
+def _bracket_root(func, xl0, xr0=None, *, xmin=None, xmax=None, factor=None,
+                  args=(), maxiter=1000):
+    """Bracket the root of a monotonic scalar function of one variable
+
+    This function works elementwise when `xl0`, `xr0`, `xmin`, `xmax`, `factor`, and
+    the elements of `args` are broadcastable arrays.
+
+    Parameters
+    ----------
+    func : callable
+        The function for which the root is to be bracketed.
+        The signature must be::
+
+            func(x: ndarray, *args) -> ndarray
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with `x`. ``func`` must be an elementwise function: each element
+        ``func(x)[i]`` must equal ``func(x[i])`` for all indices ``i``.
+    xl0, xr0: float array_like
+        Starting guess of bracket, which need not contain a root. If `xr0` is
+        not provided, ``xr0 = xl0 + 1``. Must be broadcastable with one another.
+    xmin, xmax : float array_like, optional
+        Minimum and maximum allowable endpoints of the bracket, inclusive. Must
+        be broadcastable with `xl0` and `xr0`.
+    factor : float array_like, default: 2
+        The factor used to grow the bracket. See notes for details.
+    args : tuple, optional
+        Additional positional arguments to be passed to `func`.  Must be arrays
+        broadcastable with `xl0`, `xr0`, `xmin`, and `xmax`. If the callable to be
+        bracketed requires arguments that are not broadcastable with these
+        arrays, wrap that callable with `func` such that `func` accepts
+        only `x` and broadcastable arrays.
+    maxiter : int, optional
+        The maximum number of iterations of the algorithm to perform.
+
+    Returns
+    -------
+    res : _RichResult
+        An instance of `scipy._lib._util._RichResult` with the following
+        attributes. The descriptions are written as though the values will be
+        scalars; however, if `func` returns an array, the outputs will be
+        arrays of the same shape.
+
+        xl, xr : float
+            The lower and upper ends of the bracket, if the algorithm
+            terminated successfully.
+        fl, fr : float
+            The function value at the lower and upper ends of the bracket.
+        nfev : int
+            The number of function evaluations required to find the bracket.
+            This is distinct from the number of times `func` is *called*
+            because the function may evaluated at multiple points in a single
+            call.
+        nit : int
+            The number of iterations of the algorithm that were performed.
+        status : int
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm produced a valid bracket.
+            - ``-1`` : The bracket expanded to the allowable limits without finding a bracket.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : Iteration was terminated by `callback`.
+            - ``-5``: The initial bracket does not satisfy `xmin <= xl0 < xr0 < xmax`.
+            - ``1`` : The algorithm is proceeding normally (in `callback` only).
+            - ``2`` : A bracket was found in the opposite search direction (in `callback` only).
+
+        success : bool
+            ``True`` when the algorithm terminated successfully (status ``0``).
+
+    Notes
+    -----
+    This function generalizes an algorithm found in pieces throughout
+    `scipy.stats`. The strategy is to iteratively grow the bracket ``(l, r)``
+     until ``func(l) < 0 < func(r)``. The bracket grows to the left as follows.
+
+    - If `xmin` is not provided, the distance between `xl0` and `l` is iteratively
+      increased by `factor`.
+    - If `xmin` is provided, the distance between `xmin` and `l` is iteratively
+      decreased by `factor`. Note that this also *increases* the bracket size.
+
+    Growth of the bracket to the right is analogous.
+
+    Growth of the bracket in one direction stops when the endpoint is no longer
+    finite, the function value at the endpoint is no longer finite, or the
+    endpoint reaches its limiting value (`xmin` or `xmax`). Iteration terminates
+    when the bracket stops growing in both directions, the bracket surrounds
+    the root, or a root is found (accidentally).
+
+    If two brackets are found - that is, a bracket is found on both sides in
+    the same iteration, the smaller of the two is returned.
+    If roots of the function are found, both `l` and `r` are set to the
+    leftmost root.
+
+    """  # noqa: E501
+    # Todo:
+    # - find bracket with sign change in specified direction
+    # - Add tolerance
+    # - allow factor < 1?
+
+    callback = None  # works; I just don't want to test it
+    temp = _bracket_root_iv(func, xl0, xr0, xmin, xmax, factor, args, maxiter)
+    func, xl0, xr0, xmin, xmax, factor, args, maxiter, xp = temp
+
+    xs = (xl0, xr0)
+    temp = eim._initialize(func, xs, args)
+    func, xs, fs, args, shape, dtype, xp = temp  # line split for PEP8
+    xl0, xr0 = xs
+    xmin = xp_ravel(xp.astype(xp.broadcast_to(xmin, shape), dtype, copy=False), xp=xp)
+    xmax = xp_ravel(xp.astype(xp.broadcast_to(xmax, shape), dtype, copy=False), xp=xp)
+    invalid_bracket = ~((xmin <= xl0) & (xl0 < xr0) & (xr0 <= xmax))
+
+    # The approach is to treat the left and right searches as though they were
+    # (almost) totally independent one-sided bracket searches. (The interaction
+    # is considered when checking for termination and preparing the result
+    # object.)
+    # `x` is the "moving" end of the bracket
+    x = xp.concat(xs)
+    f = xp.concat(fs)
+    invalid_bracket = xp.concat((invalid_bracket, invalid_bracket))
+    n = x.shape[0] // 2
+
+    # `x_last` is the previous location of the moving end of the bracket. If
+    # the signs of `f` and `f_last` are different, `x` and `x_last` form a
+    # bracket.
+    x_last = xp.concat((x[n:], x[:n]))
+    f_last = xp.concat((f[n:], f[:n]))
+    # `x0` is the "fixed" end of the bracket.
+    x0 = x_last
+    # We don't need to retain the corresponding function value, since the
+    # fixed end of the bracket is only needed to compute the new value of the
+    # moving end; it is never returned.
+    limit = xp.concat((xmin, xmax))
+
+    factor = xp_ravel(xp.broadcast_to(factor, shape), xp=xp)
+    factor = xp.astype(factor, dtype, copy=False)
+    factor = xp.concat((factor, factor))
+
+    active = xp.arange(2*n)
+    args = [xp.concat((arg, arg)) for arg in args]
+
+    # This is needed due to inner workings of `eim._loop`.
+    # We're abusing it a tiny bit.
+    shape = shape + (2,)
+
+    # `d` is for "distance".
+    # For searches without a limit, the distance between the fixed end of the
+    # bracket `x0` and the moving end `x` will grow by `factor` each iteration.
+    # For searches with a limit, the distance between the `limit` and moving
+    # end of the bracket `x` will shrink by `factor` each iteration.
+    i = xp.isinf(limit)
+    ni = ~i
+    d = xp.zeros_like(x)
+    d[i] = x[i] - x0[i]
+    d[ni] = limit[ni] - x[ni]
+
+    status = xp.full_like(x, eim._EINPROGRESS, dtype=xp.int32)  # in progress
+    status[invalid_bracket] = eim._EINPUTERR
+    nit, nfev = 0, 1  # one function evaluation per side performed above
+
+    work = _RichResult(x=x, x0=x0, f=f, limit=limit, factor=factor,
+                       active=active, d=d, x_last=x_last, f_last=f_last,
+                       nit=nit, nfev=nfev, status=status, args=args,
+                       xl=xp.nan, xr=xp.nan, fl=xp.nan, fr=xp.nan, n=n)
+    res_work_pairs = [('status', 'status'), ('xl', 'xl'), ('xr', 'xr'),
+                      ('nit', 'nit'), ('nfev', 'nfev'), ('fl', 'fl'),
+                      ('fr', 'fr'), ('x', 'x'), ('f', 'f'),
+                      ('x_last', 'x_last'), ('f_last', 'f_last')]
+
+    def pre_func_eval(work):
+        # Initialize moving end of bracket
+        x = xp.zeros_like(work.x)
+
+        # Unlimited brackets grow by `factor` by increasing distance from fixed
+        # end to moving end.
+        i = xp.isinf(work.limit)  # indices of unlimited brackets
+        work.d[i] *= work.factor[i]
+        x[i] = work.x0[i] + work.d[i]
+
+        # Limited brackets grow by decreasing the distance from the limit to
+        # the moving end.
+        ni = ~i  # indices of limited brackets
+        work.d[ni] /= work.factor[ni]
+        x[ni] = work.limit[ni] - work.d[ni]
+
+        return x
+
+    def post_func_eval(x, f, work):
+        # Keep track of the previous location of the moving end so that we can
+        # return a narrower bracket. (The alternative is to remember the
+        # original fixed end, but then the bracket would be wider than needed.)
+        work.x_last = work.x
+        work.f_last = work.f
+        work.x = x
+        work.f = f
+
+    def check_termination(work):
+        # Condition 0: initial bracket is invalid
+        stop = (work.status == eim._EINPUTERR)
+
+        # Condition 1: a valid bracket (or the root itself) has been found
+        sf = xp.sign(work.f)
+        sf_last = xp.sign(work.f_last)
+        i = ((sf_last == -sf) | (sf_last == 0) | (sf == 0)) & ~stop
+        work.status[i] = eim._ECONVERGED
+        stop[i] = True
+
+        # Condition 2: the other side's search found a valid bracket.
+        # (If we just found a bracket with the rightward search, we can stop
+        #  the leftward search, and vice-versa.)
+        # To do this, we need to set the status of the other side's search;
+        # this is tricky because `work.status` contains only the *active*
+        # elements, so we don't immediately know the index of the element we
+        # need to set - or even if it's still there. (That search may have
+        # terminated already, e.g. by reaching its `limit`.)
+        # To facilitate this, `work.active` contains a unit integer index of
+        # each search. Index `k` (`k < n)` and `k + n` correspond with a
+        # leftward and rightward search, respectively. Elements are removed
+        # from `work.active` just as they are removed from `work.status`, so
+        # we use `work.active` to help find the right location in
+        # `work.status`.
+        # Get the integer indices of the elements that can also stop
+        also_stop = (work.active[i] + work.n) % (2*work.n)
+        # Check whether they are still active. We want to find the indices
+        # in work.active where the associated values in work.active are
+        # contained in also_stop. xp.searchsorted let's us take advantage
+        # of work.active being sorted, but requires some hackery because
+        # searchsorted solves the separate but related problem of finding
+        # the indices where the values in also_stop should be added to
+        # maintain sorted order.
+        j = xp.searchsorted(work.active, also_stop)
+        # If the location exceeds the length of the `work.active`, they are
+        # not there. This happens when a value in also_stop is larger than
+        # the greatest value in work.active. This case needs special handling
+        # because we cannot simply check that also_stop == work.active[j].
+        mask = j < work.active.shape[0]
+        # Note that we also have to use the mask to filter also_stop to ensure
+        # that also_stop and j will still have the same shape.
+        j, also_stop = j[mask], also_stop[mask]
+        j = j[also_stop == work.active[j]]
+        # Now convert these to boolean indices to use with `work.status`.
+        i = xp.zeros_like(stop)
+        i[j] = True  # boolean indices of elements that can also stop
+        i = i & ~stop
+        work.status[i] = _ESTOPONESIDE
+        stop[i] = True
+
+        # Condition 3: moving end of bracket reaches limit
+        i = (work.x == work.limit) & ~stop
+        work.status[i] = _ELIMITS
+        stop[i] = True
+
+        # Condition 4: non-finite value encountered
+        i = ~(xp.isfinite(work.x) & xp.isfinite(work.f)) & ~stop
+        work.status[i] = eim._EVALUEERR
+        stop[i] = True
+
+        return stop
+
+    def post_termination_check(work):
+        pass
+
+    def customize_result(res, shape):
+        n = res['x'].shape[0] // 2
+
+        # To avoid ambiguity, below we refer to `xl0`, the initial left endpoint
+        # as `a` and `xr0`, the initial right endpoint, as `b`.
+        # Because we treat the two one-sided searches as though they were
+        # independent, what we keep track of in `work` and what we want to
+        # return in `res` look quite different. Combine the results from the
+        # two one-sided searches before reporting the results to the user.
+        # - "a" refers to the leftward search (the moving end started at `a`)
+        # - "b" refers to the rightward search (the moving end started at `b`)
+        # - "l" refers to the left end of the bracket (closer to -oo)
+        # - "r" refers to the right end of the bracket (closer to +oo)
+        xal = res['x'][:n]
+        xar = res['x_last'][:n]
+        xbl = res['x_last'][n:]
+        xbr = res['x'][n:]
+
+        fal = res['f'][:n]
+        far = res['f_last'][:n]
+        fbl = res['f_last'][n:]
+        fbr = res['f'][n:]
+
+        # Initialize the brackets and corresponding function values to return
+        # to the user. Brackets may not be valid (e.g. there is no root,
+        # there weren't enough iterations, NaN encountered), but we still need
+        # to return something. One option would be all NaNs, but what I've
+        # chosen here is the left- and right-most points at which the function
+        # has been evaluated. This gives the user some information about what
+        # interval of the real line has been searched and shows that there is
+        # no sign change between the two ends.
+        xl = xp.asarray(xal, copy=True)
+        fl = xp.asarray(fal, copy=True)
+        xr = xp.asarray(xbr, copy=True)
+        fr = xp.asarray(fbr, copy=True)
+
+        # `status` indicates whether the bracket is valid or not. If so,
+        # we want to adjust the bracket we return to be the narrowest possible
+        # given the points at which we evaluated the function.
+        # For example if bracket "a" is valid and smaller than bracket "b" OR
+        # if bracket "a" is valid and bracket "b" is not valid, we want to
+        # return bracket "a" (and vice versa).
+        sa = res['status'][:n]
+        sb = res['status'][n:]
+
+        da = xar - xal
+        db = xbr - xbl
+
+        i1 = ((da <= db) & (sa == 0)) | ((sa == 0) & (sb != 0))
+        i2 = ((db <= da) & (sb == 0)) | ((sb == 0) & (sa != 0))
+
+        xr[i1] = xar[i1]
+        fr[i1] = far[i1]
+        xl[i2] = xbl[i2]
+        fl[i2] = fbl[i2]
+
+        # Finish assembling the result object
+        res['xl'] = xl
+        res['xr'] = xr
+        res['fl'] = fl
+        res['fr'] = fr
+
+        res['nit'] = xp.maximum(res['nit'][:n], res['nit'][n:])
+        res['nfev'] = res['nfev'][:n] + res['nfev'][n:]
+        # If the status on one side is zero, the status is zero. In any case,
+        # report the status from one side only.
+        res['status'] = xp.where(sa == 0, sa, sb)
+        res['success'] = (res['status'] == 0)
+
+        del res['x']
+        del res['f']
+        del res['x_last']
+        del res['f_last']
+
+        return shape[:-1]
+
+    return eim._loop(work, callback, shape, maxiter, func, args, dtype,
+                     pre_func_eval, post_func_eval, check_termination,
+                     post_termination_check, customize_result, res_work_pairs,
+                     xp)
+
+
+def _bracket_minimum_iv(func, xm0, xl0, xr0, xmin, xmax, factor, args, maxiter):
+
+    if not callable(func):
+        raise ValueError('`func` must be callable.')
+
+    if not np.iterable(args):
+        args = (args,)
+
+    xp = array_namespace(xm0)
+    xm0 = xp.asarray(xm0)[()]
+    if (not xp.isdtype(xm0.dtype, "numeric")
+        or xp.isdtype(xm0.dtype, "complex floating")):
+        raise ValueError('`xm0` must be numeric and real.')
+    if not xp.isdtype(xm0.dtype, "real floating"):
+        xm0 = xp.asarray(xm0, dtype=xp_default_dtype(xp))
+
+    xmin = -xp.inf if xmin is None else xmin
+    xmax = xp.inf if xmax is None else xmax
+
+    # If xl0 (xr0) is not supplied, fill with a dummy value for the sake
+    # of broadcasting. We need to wait until xmin (xmax) has been validated
+    # to compute the default values.
+    xl0_not_supplied = False
+    if xl0 is None:
+        xl0 = xp.nan
+        xl0_not_supplied = True
+
+    xr0_not_supplied = False
+    if xr0 is None:
+        xr0 = xp.nan
+        xr0_not_supplied = True
+
+    factor = 2.0 if factor is None else factor
+    xl0, xm0, xr0, xmin, xmax, factor = xp.broadcast_arrays(
+        xp.asarray(xl0), xm0, xp.asarray(xr0), xp.asarray(xmin),
+        xp.asarray(xmax), xp.asarray(factor)
+    )
+
+    if (not xp.isdtype(xl0.dtype, "numeric")
+        or xp.isdtype(xl0.dtype, "complex floating")):
+        raise ValueError('`xl0` must be numeric and real.')
+
+    if (not xp.isdtype(xr0.dtype, "numeric")
+        or xp.isdtype(xr0.dtype, "complex floating")):
+        raise ValueError('`xr0` must be numeric and real.')
+
+    if (not xp.isdtype(xmin.dtype, "numeric")
+        or xp.isdtype(xmin.dtype, "complex floating")):
+        raise ValueError('`xmin` must be numeric and real.')
+
+    if (not xp.isdtype(xmax.dtype, "numeric")
+        or xp.isdtype(xmax.dtype, "complex floating")):
+        raise ValueError('`xmax` must be numeric and real.')
+
+    if (not xp.isdtype(factor.dtype, "numeric")
+        or xp.isdtype(factor.dtype, "complex floating")):
+        raise ValueError('`factor` must be numeric and real.')
+    if not xp.all(factor > 1):
+        raise ValueError('All elements of `factor` must be greater than 1.')
+
+    # Calculate default values of xl0 and/or xr0 if they have not been supplied
+    # by the user. We need to be careful to ensure xl0 and xr0 are not outside
+    # of (xmin, xmax).
+    if xl0_not_supplied:
+        xl0 = xm0 - xp.minimum((xm0 - xmin)/16, xp.asarray(0.5))
+        xl0 = xp.astype(xl0, xm0.dtype, copy=False)
+    if xr0_not_supplied:
+        xr0 = xm0 + xp.minimum((xmax - xm0)/16, xp.asarray(0.5))
+        xr0 = xp.astype(xr0, xm0.dtype, copy=False)
+
+    maxiter = xp.asarray(maxiter)
+    message = '`maxiter` must be a non-negative integer.'
+    if (not xp.isdtype(maxiter.dtype, "numeric") or maxiter.shape != tuple()
+            or xp.isdtype(maxiter.dtype, "complex floating")):
+        raise ValueError(message)
+    maxiter_int = int(maxiter[()])
+    if not maxiter == maxiter_int or maxiter < 0:
+        raise ValueError(message)
+
+    return func, xm0, xl0, xr0, xmin, xmax, factor, args, maxiter, xp
+
+
+def _bracket_minimum(func, xm0, *, xl0=None, xr0=None, xmin=None, xmax=None,
+                     factor=None, args=(), maxiter=1000):
+    """Bracket the minimum of a unimodal scalar function of one variable
+
+    This function works elementwise when `xm0`, `xl0`, `xr0`, `xmin`, `xmax`,
+    and the elements of `args` are broadcastable arrays.
+
+    Parameters
+    ----------
+    func : callable
+        The function for which the minimum is to be bracketed.
+        The signature must be::
+
+            func(x: ndarray, *args) -> ndarray
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``. `func` must be an elementwise function: each element
+        ``func(x)[i]`` must equal ``func(x[i])`` for all indices `i`.
+    xm0: float array_like
+        Starting guess for middle point of bracket.
+    xl0, xr0: float array_like, optional
+        Starting guesses for left and right endpoints of the bracket. Must be
+        broadcastable with one another and with `xm0`.
+    xmin, xmax : float array_like, optional
+        Minimum and maximum allowable endpoints of the bracket, inclusive. Must
+        be broadcastable with `xl0`, `xm0`, and `xr0`.
+    factor : float array_like, optional
+        Controls expansion of bracket endpoint in downhill direction. Works
+        differently in the cases where a limit is set in the downhill direction
+        with `xmax` or `xmin`. See Notes.
+    args : tuple, optional
+        Additional positional arguments to be passed to `func`.  Must be arrays
+        broadcastable with `xl0`, `xm0`, `xr0`, `xmin`, and `xmax`. If the
+        callable to be bracketed requires arguments that are not broadcastable
+        with these arrays, wrap that callable with `func` such that `func`
+        accepts only ``x`` and broadcastable arrays.
+    maxiter : int, optional
+        The maximum number of iterations of the algorithm to perform. The number
+        of function evaluations is three greater than the number of iterations.
+
+    Returns
+    -------
+    res : _RichResult
+        An instance of `scipy._lib._util._RichResult` with the following
+        attributes. The descriptions are written as though the values will be
+        scalars; however, if `func` returns an array, the outputs will be
+        arrays of the same shape.
+
+        xl, xm, xr : float
+            The left, middle, and right points of the bracket, if the algorithm
+            terminated successfully.
+        fl, fm, fr : float
+            The function value at the left, middle, and right points of the bracket.
+        nfev : int
+            The number of function evaluations required to find the bracket.
+        nit : int
+            The number of iterations of the algorithm that were performed.
+        status : int
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm produced a valid bracket.
+            - ``-1`` : The bracket expanded to the allowable limits. Assuming
+                       unimodality, this implies the endpoint at the limit is a
+                       minimizer.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : ``None`` shall pass.
+            - ``-5`` : The initial bracket does not satisfy
+                       `xmin <= xl0 < xm0 < xr0 <= xmax`.
+
+        success : bool
+            ``True`` when the algorithm terminated successfully (status ``0``).
+
+    Notes
+    -----
+    Similar to `scipy.optimize.bracket`, this function seeks to find real
+    points ``xl < xm < xr`` such that ``f(xl) >= f(xm)`` and ``f(xr) >= f(xm)``,
+    where at least one of the inequalities is strict. Unlike `scipy.optimize.bracket`,
+    this function can operate in a vectorized manner on array input, so long as
+    the input arrays are broadcastable with each other. Also unlike
+    `scipy.optimize.bracket`, users may specify minimum and maximum endpoints
+    for the desired bracket.
+
+    Given an initial trio of points ``xl = xl0``, ``xm = xm0``, ``xr = xr0``,
+    the algorithm checks if these points already give a valid bracket. If not,
+    a new endpoint, ``w`` is chosen in the "downhill" direction, ``xm`` becomes the new
+    opposite endpoint, and either `xl` or `xr` becomes the new middle point,
+    depending on which direction is downhill. The algorithm repeats from here.
+
+    The new endpoint `w` is chosen differently depending on whether or not a
+    boundary `xmin` or `xmax` has been set in the downhill direction. Without
+    loss of generality, suppose the downhill direction is to the right, so that
+    ``f(xl) > f(xm) > f(xr)``. If there is no boundary to the right, then `w`
+    is chosen to be ``xr + factor * (xr - xm)`` where `factor` is controlled by
+    the user (defaults to 2.0) so that step sizes increase in geometric proportion.
+    If there is a boundary, `xmax` in this case, then `w` is chosen to be
+    ``xmax - (xmax - xr)/factor``, with steps slowing to a stop at
+    `xmax`. This cautious approach ensures that a minimum near but distinct from
+    the boundary isn't missed while also detecting whether or not the `xmax` is
+    a minimizer when `xmax` is reached after a finite number of steps.
+    """  # noqa: E501
+    callback = None  # works; I just don't want to test it
+
+    temp = _bracket_minimum_iv(func, xm0, xl0, xr0, xmin, xmax, factor, args, maxiter)
+    func, xm0, xl0, xr0, xmin, xmax, factor, args, maxiter, xp = temp
+
+    xs = (xl0, xm0, xr0)
+    temp = eim._initialize(func, xs, args)
+    func, xs, fs, args, shape, dtype, xp = temp
+
+    xl0, xm0, xr0 = xs
+    fl0, fm0, fr0 = fs
+    xmin = xp.astype(xp.broadcast_to(xmin, shape), dtype, copy=False)
+    xmin = xp_ravel(xmin, xp=xp)
+    xmax = xp.astype(xp.broadcast_to(xmax, shape), dtype, copy=False)
+    xmax = xp_ravel(xmax, xp=xp)
+    invalid_bracket = ~((xmin <= xl0) & (xl0 < xm0) & (xm0 < xr0) & (xr0 <= xmax))
+    # We will modify factor later on so make a copy. np.broadcast_to returns
+    # a read-only view.
+    factor = xp.astype(xp.broadcast_to(factor, shape), dtype, copy=True)
+    factor = xp_ravel(factor)
+
+    # To simplify the logic, swap xl and xr if f(xl) < f(xr). We should always be
+    # marching downhill in the direction from xl to xr.
+    comp = fl0 < fr0
+    xl0[comp], xr0[comp] = xr0[comp], xl0[comp]
+    fl0[comp], fr0[comp] = fr0[comp], fl0[comp]
+    # We only need the boundary in the direction we're traveling.
+    limit = xp.where(comp, xmin, xmax)
+
+    unlimited = xp.isinf(limit)
+    limited = ~unlimited
+    step = xp.empty_like(xl0)
+
+    step[unlimited] = (xr0[unlimited] - xm0[unlimited])
+    step[limited] = (limit[limited] - xr0[limited])
+
+    # Step size is divided by factor for case where there is a limit.
+    factor[limited] = 1 / factor[limited]
+
+    status = xp.full_like(xl0, eim._EINPROGRESS, dtype=xp.int32)
+    status[invalid_bracket] = eim._EINPUTERR
+    nit, nfev = 0, 3
+
+    work = _RichResult(xl=xl0, xm=xm0, xr=xr0, xr0=xr0, fl=fl0, fm=fm0, fr=fr0,
+                       step=step, limit=limit, limited=limited, factor=factor, nit=nit,
+                       nfev=nfev, status=status, args=args)
+
+    res_work_pairs = [('status', 'status'), ('xl', 'xl'), ('xm', 'xm'), ('xr', 'xr'),
+                      ('nit', 'nit'), ('nfev', 'nfev'), ('fl', 'fl'), ('fm', 'fm'),
+                      ('fr', 'fr')]
+
+    def pre_func_eval(work):
+        work.step *= work.factor
+        x = xp.empty_like(work.xr)
+        x[~work.limited] = work.xr0[~work.limited] + work.step[~work.limited]
+        x[work.limited] = work.limit[work.limited] - work.step[work.limited]
+        # Since the new bracket endpoint is calculated from an offset with the
+        # limit, it may be the case that the new endpoint equals the old endpoint,
+        # when the old endpoint is sufficiently close to the limit. We use the
+        # limit itself as the new endpoint in these cases.
+        x[work.limited] = xp.where(
+            x[work.limited] == work.xr[work.limited],
+            work.limit[work.limited],
+            x[work.limited],
+        )
+        return x
+
+    def post_func_eval(x, f, work):
+        work.xl, work.xm, work.xr = work.xm, work.xr, x
+        work.fl, work.fm, work.fr = work.fm, work.fr, f
+
+    def check_termination(work):
+        # Condition 0: Initial bracket is invalid.
+        stop = (work.status == eim._EINPUTERR)
+
+        # Condition 1: A valid bracket has been found.
+        i = (
+            (work.fl >= work.fm) & (work.fr > work.fm)
+            | (work.fl > work.fm) & (work.fr >= work.fm)
+        ) & ~stop
+        work.status[i] = eim._ECONVERGED
+        stop[i] = True
+
+        # Condition 2: Moving end of bracket reaches limit.
+        i = (work.xr == work.limit) & ~stop
+        work.status[i] = _ELIMITS
+        stop[i] = True
+
+        # Condition 3: non-finite value encountered
+        i = ~(xp.isfinite(work.xr) & xp.isfinite(work.fr)) & ~stop
+        work.status[i] = eim._EVALUEERR
+        stop[i] = True
+
+        return stop
+
+    def post_termination_check(work):
+        pass
+
+    def customize_result(res, shape):
+        # Reorder entries of xl and xr if they were swapped due to f(xl0) < f(xr0).
+        comp = res['xl'] > res['xr']
+        res['xl'][comp], res['xr'][comp] = res['xr'][comp], res['xl'][comp]
+        res['fl'][comp], res['fr'][comp] = res['fr'][comp], res['fl'][comp]
+        return shape
+
+    return eim._loop(work, callback, shape,
+                     maxiter, func, args, dtype,
+                     pre_func_eval, post_func_eval,
+                     check_termination, post_termination_check,
+                     customize_result, res_work_pairs, xp)
diff --git a/shared/package-files/b5/b56af8c32d7913272c7141b5cdcebb7f17ed4b574cab0e823645cea88bdd6e9c/trace_backward_compositeimplicitautograd_dispatch.h b/shared/package-files/b5/b56af8c32d7913272c7141b5cdcebb7f17ed4b574cab0e823645cea88bdd6e9c/trace_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5e95242aa2af6716eb71736e1bc4211e34276502
--- /dev/null
+++ b/shared/package-files/b5/b56af8c32d7913272c7141b5cdcebb7f17ed4b574cab0e823645cea88bdd6e9c/trace_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes);
+TORCH_API at::Tensor trace_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef sizes);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/b5/b56bdcbd830509a13ad27255bc3aeba2feecb49becd4a4183b2ae1977773714b/GMT+11 b/shared/package-files/b5/b56bdcbd830509a13ad27255bc3aeba2feecb49becd4a4183b2ae1977773714b/GMT+11
new file mode 100644
index 0000000000000000000000000000000000000000..d969982309e5ca7d32979a7dad814ca307d2cd8d
Binary files /dev/null and b/shared/package-files/b5/b56bdcbd830509a13ad27255bc3aeba2feecb49becd4a4183b2ae1977773714b/GMT+11 differ
diff --git a/shared/package-files/b5/b56e7a906fb6903de34d3c705e30c6c1f27dc31a4f84bedf5adb319cc073b9e0/_dataset.pyx b/shared/package-files/b5/b56e7a906fb6903de34d3c705e30c6c1f27dc31a4f84bedf5adb319cc073b9e0/_dataset.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..666fd2c1cc5018378f832f8552ff21bf88e616a9
--- /dev/null
+++ b/shared/package-files/b5/b56e7a906fb6903de34d3c705e30c6c1f27dc31a4f84bedf5adb319cc073b9e0/_dataset.pyx
@@ -0,0 +1,4228 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# cython: language_level = 3
+
+"""Dataset is currently unstable. APIs subject to change without notice."""
+
+from cython.operator cimport dereference as deref
+
+import codecs
+import collections
+from libcpp cimport bool
+
+import pyarrow as pa
+from pyarrow.lib cimport *
+from pyarrow.lib import ArrowTypeError, frombytes, tobytes, _pac
+from pyarrow.includes.libarrow_dataset cimport *
+from pyarrow._acero cimport ExecNodeOptions
+from pyarrow._compute cimport Expression, _bind
+from pyarrow._compute import _forbid_instantiation
+from pyarrow._fs cimport FileSystem, FileSelector, FileInfo
+from pyarrow._csv cimport (
+    ConvertOptions, ParseOptions, ReadOptions, WriteOptions)
+from pyarrow.util import _is_iterable, _is_path_like, _stringify_path
+from pyarrow._json cimport ParseOptions as JsonParseOptions
+from pyarrow._json cimport ReadOptions as JsonReadOptions
+
+try:
+    import pyarrow.substrait as pa_substrait
+except ImportError:
+    pa_substrait = None
+
+
+_DEFAULT_BATCH_SIZE = 2**17
+_DEFAULT_BATCH_READAHEAD = 16
+_DEFAULT_FRAGMENT_READAHEAD = 4
+
+
+# Initialise support for Datasets in ExecPlan
+Initialize()
+
+
+_orc_fileformat = None
+_orc_imported = False
+
+
+def _get_orc_fileformat():
+    """
+    Import OrcFileFormat on first usage (to avoid circular import issue
+    when `pyarrow._dataset_orc` would be imported first)
+    """
+    global _orc_fileformat
+    global _orc_imported
+    if not _orc_imported:
+        try:
+            from pyarrow._dataset_orc import OrcFileFormat
+            _orc_fileformat = OrcFileFormat
+        except ImportError as e:
+            _orc_fileformat = None
+        finally:
+            _orc_imported = True
+    return _orc_fileformat
+
+
+_dataset_pq = False
+
+
+def _get_parquet_classes():
+    """
+    Import Parquet class files on first usage (to avoid circular import issue
+    when `pyarrow._dataset_parquet` would be imported first)
+    """
+    global _dataset_pq
+    if _dataset_pq is False:
+        try:
+            import pyarrow._dataset_parquet as _dataset_pq
+        except ImportError:
+            _dataset_pq = None
+
+
+def _get_parquet_symbol(name):
+    """
+    Get a symbol from pyarrow.parquet if the latter is importable, otherwise
+    return None.
+    """
+    _get_parquet_classes()
+    return _dataset_pq and getattr(_dataset_pq, name)
+
+
+cdef CFileSource _make_file_source(object file, FileSystem filesystem=None, object file_size=None):
+
+    cdef:
+        CFileSource c_source
+        shared_ptr[CFileSystem] c_filesystem
+        CFileInfo c_info
+        c_string c_path
+        shared_ptr[CRandomAccessFile] c_file
+        shared_ptr[CBuffer] c_buffer
+        int64_t c_size
+
+    if isinstance(file, Buffer):
+        c_buffer = pyarrow_unwrap_buffer(file)
+        c_source = CFileSource(move(c_buffer))
+    elif _is_path_like(file):
+        if filesystem is None:
+            raise ValueError("cannot construct a FileSource from "
+                             "a path without a FileSystem")
+        c_filesystem = filesystem.unwrap()
+        c_path = tobytes(_stringify_path(file))
+
+        if file_size is not None:
+            c_size = file_size
+            c_info = FileInfo(c_path, size=c_size).unwrap()
+            c_source = CFileSource(move(c_info), move(c_filesystem))
+        else:
+            c_source = CFileSource(move(c_path), move(c_filesystem))
+    elif hasattr(file, 'read'):
+        # Optimistically hope this is file-like
+        c_file = get_native_file(file, False).get_random_access_file()
+        c_source = CFileSource(move(c_file))
+
+    else:
+        raise TypeError("cannot construct a FileSource "
+                        "from " + str(file))
+
+    return c_source
+
+
+cdef CSegmentEncoding _get_segment_encoding(str segment_encoding):
+    if segment_encoding == "none":
+        return CSegmentEncoding_None
+    elif segment_encoding == "uri":
+        return CSegmentEncoding_Uri
+    raise ValueError(f"Unknown segment encoding: {segment_encoding}")
+
+
+cdef str _wrap_segment_encoding(CSegmentEncoding segment_encoding):
+    if segment_encoding == CSegmentEncoding_None:
+        return "none"
+    elif segment_encoding == CSegmentEncoding_Uri:
+        return "uri"
+    raise ValueError("Unknown segment encoding")
+
+
+cdef Expression _true = Expression._scalar(True)
+
+
+cdef class Dataset(_Weakrefable):
+    """
+    Collection of data fragments and potentially child datasets.
+
+    Arrow Datasets allow you to query against data that has been split across
+    multiple files. This sharding of data may indicate partitioning, which
+    can accelerate queries that only touch some partitions (files).
+    """
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CDataset]& sp):
+        self.wrapped = sp
+        self.dataset = sp.get()
+        self._scan_options = dict()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CDataset]& sp):
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            'union': UnionDataset,
+            'filesystem': FileSystemDataset,
+            'in-memory': InMemoryDataset,
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            raise TypeError(type_name)
+
+        cdef Dataset self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    cdef shared_ptr[CDataset] unwrap(self) nogil:
+        return self.wrapped
+
+    @property
+    def partition_expression(self):
+        """
+        An Expression which evaluates to true for all data viewed by this
+        Dataset.
+        """
+        return Expression.wrap(self.dataset.partition_expression())
+
+    def replace_schema(self, Schema schema not None):
+        """
+        Return a copy of this Dataset with a different schema.
+
+        The copy will view the same Fragments. If the new schema is not
+        compatible with the original dataset's schema then an error will
+        be raised.
+
+        Parameters
+        ----------
+        schema : Schema
+            The new dataset schema.
+        """
+        cdef shared_ptr[CDataset] copy = GetResultValue(
+            self.dataset.ReplaceSchema(pyarrow_unwrap_schema(schema))
+        )
+
+        d = Dataset.wrap(move(copy))
+        if self._scan_options:
+            # Preserve scan options if set.
+            d._scan_options = self._scan_options.copy()
+        return d
+
+    def get_fragments(self, Expression filter=None):
+        """Returns an iterator over the fragments in this dataset.
+
+        Parameters
+        ----------
+        filter : Expression, default None
+            Return fragments matching the optional filter, either using the
+            partition_expression or internal information like Parquet's
+            statistics.
+
+        Returns
+        -------
+        fragments : iterator of Fragment
+        """
+        if self._scan_options.get("filter") is not None:
+            # Accessing fragments of a filtered dataset is not supported.
+            # It would be unclear if you wanted to filter the fragments
+            # or the rows in those fragments.
+            raise ValueError(
+                "Retrieving fragments of a filtered or projected "
+                "dataset is not allowed. Remove the filtering."
+            )
+
+        return self._get_fragments(filter)
+
+    def _get_fragments(self, Expression filter):
+        cdef:
+            CExpression c_filter
+
+        if filter is None:
+            c_fragments = move(GetResultValue(self.dataset.GetFragments()))
+        else:
+            c_filter = _bind(filter, self.schema)
+            c_fragments = move(GetResultValue(
+                self.dataset.GetFragments(c_filter)))
+
+        for maybe_fragment in c_fragments:
+            yield Fragment.wrap(GetResultValue(move(maybe_fragment)))
+
+    def _scanner_options(self, options):
+        """Returns the default options to create a new Scanner.
+
+        This is automatically invoked by :meth:`Dataset.scanner`
+        and there is no need to use it.
+        """
+        new_options = options.copy()
+
+        # at the moment only support filter
+        requested_filter = options.get("filter")
+        if pa_substrait and isinstance(requested_filter, pa_substrait.BoundExpressions):
+            expressions = list(requested_filter.expressions.values())
+            if len(expressions) != 1:
+                raise ValueError(
+                    "Only one BoundExpressions with a single expression are supported")
+            new_options["filter"] = requested_filter = expressions[0]
+
+        current_filter = self._scan_options.get("filter")
+        if requested_filter is not None and current_filter is not None:
+            new_options["filter"] = current_filter & requested_filter
+        elif current_filter is not None:
+            new_options["filter"] = current_filter
+
+        return new_options
+
+    def scanner(self,
+                object columns=None,
+                object filter=None,
+                int batch_size=_DEFAULT_BATCH_SIZE,
+                int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                FragmentScanOptions fragment_scan_options=None,
+                bint use_threads=True,
+                bint cache_metadata=True,
+                MemoryPool memory_pool=None):
+        """
+        Build a scan operation against the dataset.
+
+        Data is not loaded immediately. Instead, this produces a Scanner,
+        which exposes further operations (e.g. loading all data as a
+        table, counting rows).
+
+        See the :meth:`Scanner.from_dataset` method for further information.
+
+        Parameters
+        ----------
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        scanner : Scanner
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
+        ...                   'n_legs': [2, 2, 4, 4, 5, 100],
+        ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
+        ...                              "Brittle stars", "Centipede"]})
+        >>>
+        >>> import pyarrow.parquet as pq
+        >>> pq.write_table(table, "dataset_scanner.parquet")
+
+        >>> import pyarrow.dataset as ds
+        >>> dataset = ds.dataset("dataset_scanner.parquet")
+
+        Selecting a subset of the columns:
+
+        >>> dataset.scanner(columns=["year", "n_legs"]).to_table()
+        pyarrow.Table
+        year: int64
+        n_legs: int64
+        ----
+        year: [[2020,2022,2021,2022,2019,2021]]
+        n_legs: [[2,2,4,4,5,100]]
+
+        Projecting selected columns using an expression:
+
+        >>> dataset.scanner(columns={
+        ...     "n_legs_uint": ds.field("n_legs").cast("uint8"),
+        ... }).to_table()
+        pyarrow.Table
+        n_legs_uint: uint8
+        ----
+        n_legs_uint: [[2,2,4,4,5,100]]
+
+        Filtering rows while scanning:
+
+        >>> dataset.scanner(filter=ds.field("year") > 2020).to_table()
+        pyarrow.Table
+        year: int64
+        n_legs: int64
+        animal: string
+        ----
+        year: [[2022,2021,2022,2021]]
+        n_legs: [[2,4,4,100]]
+        animal: [["Parrot","Dog","Horse","Centipede"]]
+        """
+        return Scanner.from_dataset(
+            self,
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        )
+
+    def to_batches(self,
+                   object columns=None,
+                   Expression filter=None,
+                   int batch_size=_DEFAULT_BATCH_SIZE,
+                   int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                   int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                   FragmentScanOptions fragment_scan_options=None,
+                   bint use_threads=True,
+                   bint cache_metadata=True,
+                   MemoryPool memory_pool=None):
+        """
+        Read the dataset as materialized record batches.
+
+        Parameters
+        ----------
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        record_batches : iterator of RecordBatch
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).to_batches()
+
+    def to_table(self,
+                 object columns=None,
+                 Expression filter=None,
+                 int batch_size=_DEFAULT_BATCH_SIZE,
+                 int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                 int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                 FragmentScanOptions fragment_scan_options=None,
+                 bint use_threads=True,
+                 bint cache_metadata=True,
+                 MemoryPool memory_pool=None):
+        """
+        Read the dataset to an Arrow table.
+
+        Note that this method reads all the selected data from the dataset
+        into memory.
+
+        Parameters
+        ----------
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        table : Table
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).to_table()
+
+    def take(self,
+             object indices,
+             object columns=None,
+             Expression filter=None,
+             int batch_size=_DEFAULT_BATCH_SIZE,
+             int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+             int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+             FragmentScanOptions fragment_scan_options=None,
+             bint use_threads=True,
+             bint cache_metadata=True,
+             MemoryPool memory_pool=None):
+        """
+        Select rows of data by index.
+
+        Parameters
+        ----------
+        indices : Array or array-like
+            indices of rows to select in the dataset.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        table : Table
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).take(indices)
+
+    def head(self,
+             int num_rows,
+             object columns=None,
+             Expression filter=None,
+             int batch_size=_DEFAULT_BATCH_SIZE,
+             int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+             int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+             FragmentScanOptions fragment_scan_options=None,
+             bint use_threads=True,
+             bint cache_metadata=True,
+             MemoryPool memory_pool=None):
+        """
+        Load the first N rows of the dataset.
+
+        Parameters
+        ----------
+        num_rows : int
+            The number of rows to load.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        table : Table
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).head(num_rows)
+
+    def count_rows(self,
+                   Expression filter=None,
+                   int batch_size=_DEFAULT_BATCH_SIZE,
+                   int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                   int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                   FragmentScanOptions fragment_scan_options=None,
+                   bint use_threads=True,
+                   bint cache_metadata=True,
+                   MemoryPool memory_pool=None):
+        """
+        Count rows matching the scanner filter.
+
+        Parameters
+        ----------
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        count : int
+        """
+        return self.scanner(
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).count_rows()
+
+    @property
+    def schema(self):
+        """The common schema of the full Dataset"""
+        return pyarrow_wrap_schema(self.dataset.schema())
+
+    def filter(self, expression not None):
+        """
+        Apply a row filter to the dataset.
+
+        Parameters
+        ----------
+        expression : Expression
+            The filter that should be applied to the dataset.
+
+        Returns
+        -------
+        Dataset
+        """
+        cdef:
+            Dataset filtered_dataset
+
+        new_filter = expression
+        current_filter = self._scan_options.get("filter")
+        if current_filter is not None and new_filter is not None:
+            new_filter = current_filter & new_filter
+
+        filtered_dataset = self.__class__.__new__(self.__class__)
+        filtered_dataset.init(self.wrapped)
+        filtered_dataset._scan_options = dict(filter=new_filter)
+        return filtered_dataset
+
+    def sort_by(self, sorting, **kwargs):
+        """
+        Sort the Dataset by one or multiple columns.
+
+        Parameters
+        ----------
+        sorting : str or list[tuple(name, order)]
+            Name of the column to use to sort (ascending), or
+            a list of multiple sorting conditions where
+            each entry is a tuple with column name
+            and sorting order ("ascending" or "descending")
+        **kwargs : dict, optional
+            Additional sorting options.
+            As allowed by :class:`SortOptions`
+
+        Returns
+        -------
+        InMemoryDataset
+            A new dataset sorted according to the sort keys.
+        """
+        if isinstance(sorting, str):
+            sorting = [(sorting, "ascending")]
+
+        res = _pac()._sort_source(
+            self, output_type=InMemoryDataset, sort_keys=sorting, **kwargs
+        )
+        return res
+
+    def join(self, right_dataset, keys, right_keys=None, join_type="left outer",
+             left_suffix=None, right_suffix=None, coalesce_keys=True,
+             use_threads=True):
+        """
+        Perform a join between this dataset and another one.
+
+        Result of the join will be a new dataset, where further
+        operations can be applied.
+
+        Parameters
+        ----------
+        right_dataset : dataset
+            The dataset to join to the current one, acting as the right dataset
+            in the join operation.
+        keys : str or list[str]
+            The columns from current dataset that should be used as keys
+            of the join operation left side.
+        right_keys : str or list[str], default None
+            The columns from the right_dataset that should be used as keys
+            on the join operation right side.
+            When ``None`` use the same key names as the left dataset.
+        join_type : str, default "left outer"
+            The kind of join that should be performed, one of
+            ("left semi", "right semi", "left anti", "right anti",
+            "inner", "left outer", "right outer", "full outer")
+        left_suffix : str, default None
+            Which suffix to add to right column names. This prevents confusion
+            when the columns in left and right datasets have colliding names.
+        right_suffix : str, default None
+            Which suffix to add to the left column names. This prevents confusion
+            when the columns in left and right datasets have colliding names.
+        coalesce_keys : bool, default True
+            If the duplicated keys should be omitted from one of the sides
+            in the join result.
+        use_threads : bool, default True
+            Whenever to use multithreading or not.
+
+        Returns
+        -------
+        InMemoryDataset
+        """
+        if right_keys is None:
+            right_keys = keys
+        return _pac()._perform_join(
+            join_type, self, keys, right_dataset, right_keys,
+            left_suffix=left_suffix, right_suffix=right_suffix,
+            use_threads=use_threads, coalesce_keys=coalesce_keys,
+            output_type=InMemoryDataset
+        )
+
+    def join_asof(self, right_dataset, on, by, tolerance, right_on=None, right_by=None):
+        """
+        Perform an asof join between this dataset and another one.
+
+        This is similar to a left-join except that we match on nearest key rather
+        than equal keys. Both datasets must be sorted by the key. This type of join
+        is most useful for time series data that are not perfectly aligned.
+
+        Optionally match on equivalent keys with "by" before searching with "on".
+
+        Result of the join will be a new Dataset, where further
+        operations can be applied.
+
+        Parameters
+        ----------
+        right_dataset : dataset
+            The dataset to join to the current one, acting as the right dataset
+            in the join operation.
+        on : str
+            The column from current dataset that should be used as the "on" key
+            of the join operation left side.
+
+            An inexact match is used on the "on" key, i.e. a row is considered a
+            match if and only if ``right.on - left.on`` is in the range
+            ``[min(0, tolerance), max(0, tolerance)]``.
+
+            The input table must be sorted by the "on" key. Must be a single
+            field of a common type.
+
+            Currently, the "on" key must be an integer, date, or timestamp type.
+        by : str or list[str]
+            The columns from current dataset that should be used as the keys
+            of the join operation left side. The join operation is then done
+            only for the matches in these columns.
+        tolerance : int
+            The tolerance for inexact "on" key matching. A right row is considered
+            a match with a left row if ``right.on - left.on`` is in the range
+            ``[min(0, tolerance), max(0, tolerance)]``. ``tolerance`` may be:
+
+            - negative, in which case a past-as-of-join occurs
+              (match iff ``tolerance <= right.on - left.on <= 0``);
+            - or positive, in which case a future-as-of-join occurs
+              (match iff ``0 <= right.on - left.on <= tolerance``);
+            - or zero, in which case an exact-as-of-join occurs
+              (match iff ``right.on == left.on``).
+
+            The tolerance is interpreted in the same units as the "on" key.
+        right_on : str or list[str], default None
+            The columns from the right_dataset that should be used as the on key
+            on the join operation right side.
+            When ``None`` use the same key name as the left dataset.
+        right_by : str or list[str], default None
+            The columns from the right_dataset that should be used as by keys
+            on the join operation right side.
+            When ``None`` use the same key names as the left dataset.
+
+        Returns
+        -------
+        InMemoryDataset
+        """
+        if right_on is None:
+            right_on = on
+        if right_by is None:
+            right_by = by
+        return _pac()._perform_join_asof(self, on, by,
+                                         right_dataset, right_on, right_by,
+                                         tolerance, output_type=InMemoryDataset)
+
+
+cdef class InMemoryDataset(Dataset):
+    """
+    A Dataset wrapping in-memory data.
+
+    Parameters
+    ----------
+    source : RecordBatch, Table, list, tuple
+        The data for this dataset. Can be a RecordBatch, Table, list of
+        RecordBatch/Table, iterable of RecordBatch, or a RecordBatchReader
+        If an iterable is provided, the schema must also be provided.
+    schema : Schema, optional
+        Only required if passing an iterable as the source
+    """
+
+    cdef:
+        CInMemoryDataset* in_memory_dataset
+
+    def __init__(self, source, Schema schema=None):
+        cdef:
+            shared_ptr[CInMemoryDataset] in_memory_dataset
+
+        if isinstance(source, (pa.RecordBatch, pa.Table)):
+            source = [source]
+
+        if isinstance(source, (list, tuple, pa.RecordBatchReader)):
+            batches = []
+            for item in source:
+                if isinstance(item, pa.RecordBatch):
+                    batches.append(item)
+                elif isinstance(item, pa.Table):
+                    batches.extend(item.to_batches())
+                else:
+                    raise TypeError(
+                        'Expected a list of tables or batches. The given list '
+                        'contains a ' + type(item).__name__)
+                if schema is None:
+                    schema = item.schema
+                elif not schema.equals(item.schema):
+                    raise ArrowTypeError(
+                        f'Item has schema\n{item.schema}\nwhich does not '
+                        f'match expected schema\n{schema}')
+            if not batches and schema is None:
+                raise ValueError('Must provide schema to construct in-memory '
+                                 'dataset from an empty list')
+            table = pa.Table.from_batches(batches, schema=schema)
+            in_memory_dataset = make_shared[CInMemoryDataset](
+                pyarrow_unwrap_table(table))
+        else:
+            raise TypeError(
+                'Expected a Table, RecordBatch, list of Table/RecordBatch, '
+                'or RecordBatchReader instead of the given type: ' +
+                type(source).__name__
+            )
+
+        self.init(<shared_ptr[CDataset]> in_memory_dataset)
+
+    cdef void init(self, const shared_ptr[CDataset]& sp):
+        Dataset.init(self, sp)
+        self.in_memory_dataset = <CInMemoryDataset*> sp.get()
+
+
+cdef class UnionDataset(Dataset):
+    """
+    A Dataset wrapping child datasets.
+
+    Children's schemas must agree with the provided schema.
+
+    Parameters
+    ----------
+    schema : Schema
+        A known schema to conform to.
+    children : list of Dataset
+        One or more input children
+    """
+
+    cdef:
+        CUnionDataset* union_dataset
+
+    def __init__(self, Schema schema not None, children):
+        cdef:
+            Dataset child
+            CDatasetVector c_children
+            shared_ptr[CUnionDataset] union_dataset
+
+        for child in children:
+            c_children.push_back(child.wrapped)
+
+        union_dataset = GetResultValue(CUnionDataset.Make(
+            pyarrow_unwrap_schema(schema), move(c_children)))
+        self.init(<shared_ptr[CDataset]> union_dataset)
+
+    cdef void init(self, const shared_ptr[CDataset]& sp):
+        Dataset.init(self, sp)
+        self.union_dataset = <CUnionDataset*> sp.get()
+
+    def __reduce__(self):
+        return UnionDataset, (self.schema, self.children)
+
+    @property
+    def children(self):
+        cdef CDatasetVector children = self.union_dataset.children()
+        return [Dataset.wrap(children[i]) for i in range(children.size())]
+
+
+cdef class FileSystemDataset(Dataset):
+    """
+    A Dataset of file fragments.
+
+    A FileSystemDataset is composed of one or more FileFragment.
+
+    Parameters
+    ----------
+    fragments : list[Fragments]
+        List of fragments to consume.
+    schema : Schema
+        The top-level schema of the Dataset.
+    format : FileFormat
+        File format of the fragments, currently only ParquetFileFormat,
+        IpcFileFormat, CsvFileFormat, and JsonFileFormat are supported.
+    filesystem : FileSystem
+        FileSystem of the fragments.
+    root_partition : Expression, optional
+        The top-level partition of the DataDataset.
+    """
+
+    cdef:
+        CFileSystemDataset* filesystem_dataset
+
+    def __init__(self, fragments, Schema schema, FileFormat format,
+                 FileSystem filesystem=None, root_partition=None):
+        cdef:
+            FileFragment fragment=None
+            vector[shared_ptr[CFileFragment]] c_fragments
+            CResult[shared_ptr[CDataset]] result
+            shared_ptr[CFileSystem] c_filesystem
+
+        if root_partition is None:
+            root_partition = _true
+        elif not isinstance(root_partition, Expression):
+            raise TypeError(
+                "Argument 'root_partition' has incorrect type (expected "
+                f"Expression, got {type(root_partition)})"
+            )
+
+        for fragment in fragments:
+            c_fragments.push_back(
+                static_pointer_cast[CFileFragment, CFragment](
+                    fragment.unwrap()))
+
+            if filesystem is None:
+                filesystem = fragment.filesystem
+
+        if filesystem is not None:
+            c_filesystem = filesystem.unwrap()
+
+        result = CFileSystemDataset.Make(
+            pyarrow_unwrap_schema(schema),
+            (<Expression> root_partition).unwrap(),
+            format.unwrap(),
+            c_filesystem,
+            c_fragments
+        )
+        self.init(GetResultValue(result))
+
+    @property
+    def filesystem(self):
+        return FileSystem.wrap(self.filesystem_dataset.filesystem())
+
+    @property
+    def partitioning(self):
+        """
+        The partitioning of the Dataset source, if discovered.
+
+        If the FileSystemDataset is created using the ``dataset()`` factory
+        function with a partitioning specified, this will return the
+        finalized Partitioning object from the dataset discovery. In all
+        other cases, this returns None.
+        """
+        c_partitioning = self.filesystem_dataset.partitioning()
+        if c_partitioning.get() == nullptr:
+            return None
+        try:
+            return Partitioning.wrap(c_partitioning)
+        except TypeError:
+            # e.g. type_name "default"
+            return None
+
+    cdef void init(self, const shared_ptr[CDataset]& sp):
+        Dataset.init(self, sp)
+        self.filesystem_dataset = <CFileSystemDataset*> sp.get()
+
+    def __reduce__(self):
+        return FileSystemDataset, (
+            list(self.get_fragments()),
+            self.schema,
+            self.format,
+            self.filesystem,
+            self.partition_expression
+        )
+
+    @classmethod
+    def from_paths(cls, paths, schema=None, format=None,
+                   filesystem=None, partitions=None, root_partition=None):
+        """
+        A Dataset created from a list of paths on a particular filesystem.
+
+        Parameters
+        ----------
+        paths : list of str
+            List of file paths to create the fragments from.
+        schema : Schema
+            The top-level schema of the DataDataset.
+        format : FileFormat
+            File format to create fragments from, currently only
+            ParquetFileFormat, IpcFileFormat, CsvFileFormat, and JsonFileFormat are supported.
+        filesystem : FileSystem
+            The filesystem which files are from.
+        partitions : list[Expression], optional
+            Attach additional partition information for the file paths.
+        root_partition : Expression, optional
+            The top-level partition of the DataDataset.
+        """
+        if root_partition is None:
+            root_partition = _true
+
+        for arg, class_, name in [
+            (schema, Schema, 'schema'),
+            (format, FileFormat, 'format'),
+            (filesystem, FileSystem, 'filesystem'),
+            (root_partition, Expression, 'root_partition')
+        ]:
+            if not isinstance(arg, class_):
+                raise TypeError(
+                    f"Argument '{name}' has incorrect type (expected {class_.__name__}, "
+                    f"got {type(arg)})"
+                )
+
+        partitions = partitions or [_true] * len(paths)
+
+        if len(paths) != len(partitions):
+            raise ValueError(
+                'The number of files resulting from paths_or_selector '
+                'must be equal to the number of partitions.'
+            )
+
+        fragments = [
+            format.make_fragment(path, filesystem, partitions[i])
+            for i, path in enumerate(paths)
+        ]
+        return FileSystemDataset(fragments, schema, format,
+                                 filesystem, root_partition)
+
+    @property
+    def files(self):
+        """List of the files"""
+        cdef vector[c_string] files = self.filesystem_dataset.files()
+        return [frombytes(f) for f in files]
+
+    @property
+    def format(self):
+        """The FileFormat of this source."""
+        return FileFormat.wrap(self.filesystem_dataset.format())
+
+
+cdef class FileWriteOptions(_Weakrefable):
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CFileWriteOptions]& sp):
+        self.wrapped = sp
+        self.c_options = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CFileWriteOptions]& sp):
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            'csv': CsvFileWriteOptions,
+            'ipc': IpcFileWriteOptions,
+            'parquet': _get_parquet_symbol('ParquetFileWriteOptions'),
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            raise TypeError(type_name)
+
+        cdef FileWriteOptions self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    @property
+    def format(self):
+        return FileFormat.wrap(self.c_options.format())
+
+    cdef inline shared_ptr[CFileWriteOptions] unwrap(self):
+        return self.wrapped
+
+
+cdef class FileFormat(_Weakrefable):
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CFileFormat]& sp):
+        self.wrapped = sp
+        self.format = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CFileFormat]& sp):
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            'ipc': IpcFileFormat,
+            'csv': CsvFileFormat,
+            'json': JsonFileFormat,
+            'parquet': _get_parquet_symbol('ParquetFileFormat'),
+            'orc': _get_orc_fileformat(),
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            raise TypeError(type_name)
+
+        cdef FileFormat self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    cdef WrittenFile _finish_write(self, path, base_dir,
+                                   CFileWriter* file_writer):
+        parquet_metadata = None
+        size = GetResultValue(file_writer.GetBytesWritten())
+        return WrittenFile(path, parquet_metadata, size)
+
+    cdef inline shared_ptr[CFileFormat] unwrap(self):
+        return self.wrapped
+
+    def inspect(self, file, filesystem=None):
+        """
+        Infer the schema of a file.
+
+        Parameters
+        ----------
+        file : file-like object, path-like or str
+            The file or file path to infer a schema from.
+        filesystem : Filesystem, optional
+            If `filesystem` is given, `file` must be a string and specifies
+            the path of the file to read from the filesystem.
+
+        Returns
+        -------
+        schema : Schema
+            The schema inferred from the file
+        """
+        cdef:
+            CFileSource c_source = _make_file_source(file, filesystem, file_size=None)
+            CResult[shared_ptr[CSchema]] c_result
+        with nogil:
+            c_result = self.format.Inspect(c_source)
+        c_schema = GetResultValue(c_result)
+        return pyarrow_wrap_schema(move(c_schema))
+
+    def make_fragment(self, file, filesystem=None,
+                      Expression partition_expression=None,
+                      *, file_size=None):
+        """
+        Make a FileFragment from a given file.
+
+        Parameters
+        ----------
+        file : file-like object, path-like or str
+            The file or file path to make a fragment from.
+        filesystem : Filesystem, optional
+            If `filesystem` is given, `file` must be a string and specifies
+            the path of the file to read from the filesystem.
+        partition_expression : Expression, optional
+            An expression that is guaranteed true for all rows in the fragment.  Allows
+            fragment to be potentially skipped while scanning with a filter.
+        file_size : int, optional
+            The size of the file in bytes. Can improve performance with high-latency filesystems
+            when file size needs to be known before reading.
+
+        Returns
+        -------
+        fragment : Fragment
+            The file fragment
+        """
+        if partition_expression is None:
+            partition_expression = _true
+        c_source = _make_file_source(file, filesystem, file_size)
+        c_fragment = <shared_ptr[CFragment]> GetResultValue(
+            self.format.MakeFragment(move(c_source),
+                                     partition_expression.unwrap(),
+                                     <shared_ptr[CSchema]>nullptr))
+        return Fragment.wrap(move(c_fragment))
+
+    def make_write_options(self):
+        sp_write_options = self.format.DefaultWriteOptions()
+        if sp_write_options.get() == nullptr:
+            # DefaultWriteOptions() may return `nullptr` which means that
+            # the format does not yet support writing datasets.
+            raise NotImplementedError(
+                "Writing datasets not yet implemented for this file format."
+            )
+        return FileWriteOptions.wrap(sp_write_options)
+
+    @property
+    def default_extname(self):
+        return frombytes(self.format.type_name())
+
+    @property
+    def default_fragment_scan_options(self):
+        dfso = FragmentScanOptions.wrap(
+            self.wrapped.get().default_fragment_scan_options)
+        # CsvFileFormat stores a Python-specific encoding field that needs
+        # to be restored because it does not exist in the C++ struct
+        if isinstance(self, CsvFileFormat):
+            if self._read_options_py is not None:
+                dfso.read_options = self._read_options_py
+        return dfso
+
+    @default_fragment_scan_options.setter
+    def default_fragment_scan_options(self, FragmentScanOptions options):
+        if options is None:
+            self.wrapped.get().default_fragment_scan_options =\
+                <shared_ptr[CFragmentScanOptions]>nullptr
+        else:
+            self._set_default_fragment_scan_options(options)
+
+    cdef _set_default_fragment_scan_options(self, FragmentScanOptions options):
+        raise ValueError(f"Cannot set fragment scan options for "
+                         f"'{options.type_name}' on {self.__class__.__name__}")
+
+    def __eq__(self, other):
+        try:
+            return self.equals(other)
+        except TypeError:
+            return False
+
+
+cdef class Fragment(_Weakrefable):
+    """Fragment of data from a Dataset."""
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CFragment]& sp):
+        self.wrapped = sp
+        self.fragment = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CFragment]& sp):
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            # IpcFileFormat, CsvFileFormat, JsonFileFormat and OrcFileFormat do not have
+            # corresponding subclasses of FileFragment
+            'ipc': FileFragment,
+            'csv': FileFragment,
+            'json': FileFragment,
+            'orc': FileFragment,
+            'parquet': _get_parquet_symbol('ParquetFileFragment'),
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            class_ = Fragment
+
+        cdef Fragment self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    cdef inline shared_ptr[CFragment] unwrap(self):
+        return self.wrapped
+
+    @property
+    def physical_schema(self):
+        """Return the physical schema of this Fragment. This schema can be
+        different from the dataset read schema."""
+        cdef:
+            CResult[shared_ptr[CSchema]] maybe_schema
+        with nogil:
+            maybe_schema = self.fragment.ReadPhysicalSchema()
+        return pyarrow_wrap_schema(GetResultValue(maybe_schema))
+
+    @property
+    def partition_expression(self):
+        """An Expression which evaluates to true for all data viewed by this
+        Fragment.
+        """
+        return Expression.wrap(self.fragment.partition_expression())
+
+    def scanner(self,
+                Schema schema=None,
+                object columns=None,
+                Expression filter=None,
+                int batch_size=_DEFAULT_BATCH_SIZE,
+                int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                FragmentScanOptions fragment_scan_options=None,
+                bint use_threads=True,
+                bint cache_metadata=True,
+                MemoryPool memory_pool=None):
+        """
+        Build a scan operation against the fragment.
+
+        Data is not loaded immediately. Instead, this produces a Scanner,
+        which exposes further operations (e.g. loading all data as a
+        table, counting rows).
+
+        Parameters
+        ----------
+        schema : Schema
+            Schema to use for scanning. This is used to unify a Fragment to
+            its Dataset's schema. If not specified this will use the
+            Fragment's physical schema which might differ for each Fragment.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        scanner : Scanner
+        """
+        return Scanner.from_fragment(
+            self,
+            schema=schema,
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        )
+
+    def to_batches(self,
+                   Schema schema=None,
+                   object columns=None,
+                   Expression filter=None,
+                   int batch_size=_DEFAULT_BATCH_SIZE,
+                   int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                   int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                   FragmentScanOptions fragment_scan_options=None,
+                   bint use_threads=True,
+                   bint cache_metadata=True,
+                   MemoryPool memory_pool=None):
+        """
+        Read the fragment as materialized record batches.
+
+        Parameters
+        ----------
+        schema : Schema, optional
+            Concrete schema to use for scanning.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        record_batches : iterator of RecordBatch
+        """
+        return Scanner.from_fragment(
+            self,
+            schema=schema,
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).to_batches()
+
+    def to_table(self,
+                 Schema schema=None,
+                 object columns=None,
+                 Expression filter=None,
+                 int batch_size=_DEFAULT_BATCH_SIZE,
+                 int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                 int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                 FragmentScanOptions fragment_scan_options=None,
+                 bint use_threads=True,
+                 bint cache_metadata=True,
+                 MemoryPool memory_pool=None):
+        """
+        Convert this Fragment into a Table.
+
+        Use this convenience utility with care. This will serially materialize
+        the Scan result in memory before creating the Table.
+
+        Parameters
+        ----------
+        schema : Schema, optional
+            Concrete schema to use for scanning.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        table : Table
+        """
+        return self.scanner(
+            schema=schema,
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).to_table()
+
+    def take(self,
+             object indices,
+             object columns=None,
+             Expression filter=None,
+             int batch_size=_DEFAULT_BATCH_SIZE,
+             int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+             int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+             FragmentScanOptions fragment_scan_options=None,
+             bint use_threads=True,
+             bint cache_metadata=True,
+             MemoryPool memory_pool=None):
+        """
+        Select rows of data by index.
+
+        Parameters
+        ----------
+        indices : Array or array-like
+            The indices of row to select in the dataset.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        Table
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).take(indices)
+
+    def head(self,
+             int num_rows,
+             object columns=None,
+             Expression filter=None,
+             int batch_size=_DEFAULT_BATCH_SIZE,
+             int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+             int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+             FragmentScanOptions fragment_scan_options=None,
+             bint use_threads=True,
+             bint cache_metadata=True,
+             MemoryPool memory_pool=None):
+        """
+        Load the first N rows of the fragment.
+
+        Parameters
+        ----------
+        num_rows : int
+            The number of rows to load.
+        columns : list of str, default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        Table
+        """
+        return self.scanner(
+            columns=columns,
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).head(num_rows)
+
+    def count_rows(self,
+                   Expression filter=None,
+                   int batch_size=_DEFAULT_BATCH_SIZE,
+                   int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                   int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                   FragmentScanOptions fragment_scan_options=None,
+                   bint use_threads=True,
+                   bint cache_metadata=True,
+                   MemoryPool memory_pool=None):
+        """
+        Count rows matching the scanner filter.
+
+        Parameters
+        ----------
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+
+        Returns
+        -------
+        count : int
+        """
+        return self.scanner(
+            filter=filter,
+            batch_size=batch_size,
+            batch_readahead=batch_readahead,
+            fragment_readahead=fragment_readahead,
+            fragment_scan_options=fragment_scan_options,
+            use_threads=use_threads,
+            cache_metadata=cache_metadata,
+            memory_pool=memory_pool
+        ).count_rows()
+
+
+cdef class FileFragment(Fragment):
+    """A Fragment representing a data file."""
+
+    cdef void init(self, const shared_ptr[CFragment]& sp):
+        Fragment.init(self, sp)
+        self.file_fragment = <CFileFragment*> sp.get()
+
+    def __repr__(self):
+        type_name = frombytes(self.fragment.type_name())
+        if type_name != "parquet":
+            typ = f" type={type_name}"
+        else:
+            # parquet has a subclass -> type embedded in class name
+            typ = ""
+        partition_dict = get_partition_keys(self.partition_expression)
+        partition = ", ".join(
+            [f"{key}={val}" for key, val in partition_dict.items()]
+        )
+        if partition:
+            partition = f" partition=[{partition}]"
+        return f"<pyarrow.dataset.{self.__class__.__name__}{typ} path={self.path}{partition}>"
+
+    def __reduce__(self):
+        buffer = self.buffer
+        return self.format.make_fragment, (
+            self.path if buffer is None else buffer,
+            self.filesystem,
+            self.partition_expression
+        )
+
+    def open(self):
+        """
+        Open a NativeFile of the buffer or file viewed by this fragment.
+        """
+        cdef:
+            shared_ptr[CFileSystem] c_filesystem
+            shared_ptr[CRandomAccessFile] opened
+            c_string c_path
+            NativeFile out = NativeFile()
+
+        # Handle each of the cases in _make_file_source
+        if self.buffer is not None:
+            return pa.BufferReader(self.buffer)
+
+        if self.file_fragment.source().filesystem() != nullptr:
+            c_path = tobytes(self.file_fragment.source().path())
+            with nogil:
+                c_filesystem = self.file_fragment.source().filesystem()
+                opened = GetResultValue(c_filesystem.get().OpenInputFile(c_path))
+        else:
+            with nogil:
+                opened = GetResultValue(self.file_fragment.source().Open())
+
+        out.set_random_access_file(opened)
+        out.is_readable = True
+        return out
+
+    @property
+    def path(self):
+        """
+        The path of the data file viewed by this fragment, if it views a
+        file. If instead it views a buffer, this will be "<Buffer>".
+        """
+        return frombytes(self.file_fragment.source().path())
+
+    @property
+    def filesystem(self):
+        """
+        The FileSystem containing the data file viewed by this fragment, if
+        it views a file. If instead it views a buffer, this will be None.
+        """
+        cdef:
+            shared_ptr[CFileSystem] c_fs
+        c_fs = self.file_fragment.source().filesystem()
+
+        if c_fs.get() == nullptr:
+            return None
+
+        return FileSystem.wrap(c_fs)
+
+    @property
+    def buffer(self):
+        """
+        The buffer viewed by this fragment, if it views a buffer. If
+        instead it views a file, this will be None.
+        """
+        cdef:
+            shared_ptr[CBuffer] c_buffer
+        c_buffer = self.file_fragment.source().buffer()
+
+        if c_buffer.get() == nullptr:
+            return None
+
+        return pyarrow_wrap_buffer(c_buffer)
+
+    @property
+    def format(self):
+        """
+        The format of the data file viewed by this fragment.
+        """
+        return FileFormat.wrap(self.file_fragment.format())
+
+
+cdef class FragmentScanOptions(_Weakrefable):
+    """Scan options specific to a particular fragment and scan operation."""
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp):
+        self.wrapped = sp
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CFragmentScanOptions]& sp):
+        if not sp:
+            return None
+
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            'csv': CsvFragmentScanOptions,
+            'json': JsonFragmentScanOptions,
+            'parquet': _get_parquet_symbol('ParquetFragmentScanOptions'),
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            raise TypeError(type_name)
+
+        cdef FragmentScanOptions self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    @property
+    def type_name(self):
+        return frombytes(self.wrapped.get().type_name())
+
+    def __eq__(self, other):
+        try:
+            return self.equals(other)
+        except TypeError:
+            return False
+
+
+cdef class IpcFileWriteOptions(FileWriteOptions):
+    cdef:
+        CIpcFileWriteOptions* ipc_options
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    @property
+    def write_options(self):
+        out = IpcWriteOptions()
+        out.c_options = CIpcWriteOptions(deref(self.ipc_options.options))
+        return out
+
+    @write_options.setter
+    def write_options(self, IpcWriteOptions write_options not None):
+        self.ipc_options.options.reset(
+            new CIpcWriteOptions(write_options.c_options))
+
+    cdef void init(self, const shared_ptr[CFileWriteOptions]& sp):
+        FileWriteOptions.init(self, sp)
+        self.ipc_options = <CIpcFileWriteOptions*> sp.get()
+
+
+cdef class IpcFileFormat(FileFormat):
+
+    def __init__(self):
+        self.init(shared_ptr[CFileFormat](new CIpcFileFormat()))
+
+    def equals(self, IpcFileFormat other):
+        """
+        Parameters
+        ----------
+        other : pyarrow.dataset.IpcFileFormat
+
+        Returns
+        -------
+        True
+        """
+        return True
+
+    def make_write_options(self, **kwargs):
+        """
+        Parameters
+        ----------
+        **kwargs : dict
+
+        Returns
+        -------
+        pyarrow.ipc.IpcWriteOptions
+        """
+        cdef IpcFileWriteOptions opts = \
+            <IpcFileWriteOptions> FileFormat.make_write_options(self)
+        opts.write_options = IpcWriteOptions(**kwargs)
+        return opts
+
+    @property
+    def default_extname(self):
+        return "arrow"
+
+    def __reduce__(self):
+        return IpcFileFormat, tuple()
+
+
+cdef class FeatherFileFormat(IpcFileFormat):
+
+    @property
+    def default_extname(self):
+        return "feather"
+
+
+cdef class CsvFileFormat(FileFormat):
+    """
+    FileFormat for CSV files.
+
+    Parameters
+    ----------
+    parse_options : pyarrow.csv.ParseOptions
+        Options regarding CSV parsing.
+    default_fragment_scan_options : CsvFragmentScanOptions
+        Default options for fragments scan.
+    convert_options : pyarrow.csv.ConvertOptions
+        Options regarding value conversion.
+    read_options : pyarrow.csv.ReadOptions
+        General read options.
+    """
+    cdef:
+        CCsvFileFormat* csv_format
+        # The encoding field in ReadOptions does not exist in the C++ struct.
+        # We need to store it here and override it when reading
+        # default_fragment_scan_options.read_options
+        public ReadOptions _read_options_py
+
+    # Avoid mistakingly creating attributes
+    __slots__ = ()
+
+    def __init__(self, ParseOptions parse_options=None,
+                 default_fragment_scan_options=None,
+                 ConvertOptions convert_options=None,
+                 ReadOptions read_options=None):
+        self.init(shared_ptr[CFileFormat](new CCsvFileFormat()))
+        if parse_options is not None:
+            self.parse_options = parse_options
+        if convert_options is not None or read_options is not None:
+            if default_fragment_scan_options:
+                raise ValueError('If `default_fragment_scan_options` is '
+                                 'given, cannot specify convert_options '
+                                 'or read_options')
+            self.default_fragment_scan_options = CsvFragmentScanOptions(
+                convert_options=convert_options, read_options=read_options)
+        elif isinstance(default_fragment_scan_options, dict):
+            self.default_fragment_scan_options = CsvFragmentScanOptions(
+                **default_fragment_scan_options)
+        elif isinstance(default_fragment_scan_options, CsvFragmentScanOptions):
+            self.default_fragment_scan_options = default_fragment_scan_options
+        elif default_fragment_scan_options is not None:
+            raise TypeError('`default_fragment_scan_options` must be either '
+                            'a dictionary or an instance of '
+                            'CsvFragmentScanOptions')
+        if read_options is not None:
+            self._read_options_py = read_options
+
+    cdef void init(self, const shared_ptr[CFileFormat]& sp):
+        FileFormat.init(self, sp)
+        self.csv_format = <CCsvFileFormat*> sp.get()
+
+    def make_write_options(self, **kwargs):
+        """
+        Parameters
+        ----------
+        **kwargs : dict
+
+        Returns
+        -------
+        pyarrow.csv.WriteOptions
+        """
+        cdef CsvFileWriteOptions opts = \
+            <CsvFileWriteOptions> FileFormat.make_write_options(self)
+        opts.write_options = WriteOptions(**kwargs)
+        return opts
+
+    @property
+    def parse_options(self):
+        return ParseOptions.wrap(self.csv_format.parse_options)
+
+    @parse_options.setter
+    def parse_options(self, ParseOptions parse_options not None):
+        self.csv_format.parse_options = deref(parse_options.options)
+
+    cdef _set_default_fragment_scan_options(self, FragmentScanOptions options):
+        if options.type_name == 'csv':
+            self.csv_format.default_fragment_scan_options = options.wrapped
+            self.default_fragment_scan_options.read_options = options.read_options
+            self._read_options_py = options.read_options
+        else:
+            super()._set_default_fragment_scan_options(options)
+
+    def equals(self, CsvFileFormat other):
+        """
+        Parameters
+        ----------
+        other : pyarrow.dataset.CsvFileFormat
+
+        Returns
+        -------
+        bool
+        """
+        return (
+            self.parse_options.equals(other.parse_options) and
+            self.default_fragment_scan_options ==
+            other.default_fragment_scan_options)
+
+    def __reduce__(self):
+        return CsvFileFormat, (self.parse_options,
+                               self.default_fragment_scan_options)
+
+    def __repr__(self):
+        return f"<CsvFileFormat parse_options={self.parse_options}>"
+
+
+cdef class CsvFragmentScanOptions(FragmentScanOptions):
+    """
+    Scan-specific options for CSV fragments.
+
+    Parameters
+    ----------
+    convert_options : pyarrow.csv.ConvertOptions
+        Options regarding value conversion.
+    read_options : pyarrow.csv.ReadOptions
+        General read options.
+    """
+
+    cdef:
+        CCsvFragmentScanOptions* csv_options
+        # The encoding field in ReadOptions does not exist in the C++ struct.
+        # We need to store it here and override it when reading read_options
+        ReadOptions _read_options_py
+
+    # Avoid mistakingly creating attributes
+    __slots__ = ()
+
+    def __init__(self, ConvertOptions convert_options=None,
+                 ReadOptions read_options=None):
+        self.init(shared_ptr[CFragmentScanOptions](
+            new CCsvFragmentScanOptions()))
+        if convert_options is not None:
+            self.convert_options = convert_options
+        if read_options is not None:
+            self.read_options = read_options
+            self._read_options_py = read_options
+
+    cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp):
+        FragmentScanOptions.init(self, sp)
+        self.csv_options = <CCsvFragmentScanOptions*> sp.get()
+
+    @property
+    def convert_options(self):
+        return ConvertOptions.wrap(self.csv_options.convert_options)
+
+    @convert_options.setter
+    def convert_options(self, ConvertOptions convert_options not None):
+        self.csv_options.convert_options = deref(convert_options.options)
+
+    @property
+    def read_options(self):
+        read_options = ReadOptions.wrap(self.csv_options.read_options)
+        if self._read_options_py is not None:
+            read_options.encoding = self._read_options_py.encoding
+        return read_options
+
+    @read_options.setter
+    def read_options(self, ReadOptions read_options not None):
+        self.csv_options.read_options = deref(read_options.options)
+        self._read_options_py = read_options
+        if codecs.lookup(read_options.encoding).name != 'utf-8':
+            self.csv_options.stream_transform_func = deref(
+                make_streamwrap_func(read_options.encoding, 'utf-8'))
+
+    def equals(self, CsvFragmentScanOptions other):
+        """
+        Parameters
+        ----------
+        other : pyarrow.dataset.CsvFragmentScanOptions
+
+        Returns
+        -------
+        bool
+        """
+        return (
+            other and
+            self.convert_options.equals(other.convert_options) and
+            self.read_options.equals(other.read_options))
+
+    def __reduce__(self):
+        return CsvFragmentScanOptions, (self.convert_options,
+                                        self.read_options)
+
+
+cdef class CsvFileWriteOptions(FileWriteOptions):
+    cdef:
+        CCsvFileWriteOptions* csv_options
+        object _properties
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    @property
+    def write_options(self):
+        return WriteOptions.wrap(deref(self.csv_options.write_options))
+
+    @write_options.setter
+    def write_options(self, WriteOptions write_options not None):
+        self.csv_options.write_options.reset(
+            new CCSVWriteOptions(deref(write_options.options)))
+
+    cdef void init(self, const shared_ptr[CFileWriteOptions]& sp):
+        FileWriteOptions.init(self, sp)
+        self.csv_options = <CCsvFileWriteOptions*> sp.get()
+
+
+cdef class JsonFileFormat(FileFormat):
+    """
+    FileFormat for JSON files.
+
+    Parameters
+    ----------
+    default_fragment_scan_options : JsonFragmentScanOptions
+        Default options for fragments scan.
+    parse_options : pyarrow.json.ParseOptions
+        Options regarding json parsing.
+    read_options : pyarrow.json.ReadOptions
+        General read options.
+    """
+    cdef:
+        CJsonFileFormat* json_format
+
+    # Avoid mistakingly creating attributes
+    __slots__ = ()
+
+    def __init__(self, default_fragment_scan_options=None,
+                 JsonParseOptions parse_options=None,
+                 JsonReadOptions read_options=None):
+        self.init(shared_ptr[CFileFormat](new CJsonFileFormat()))
+        if parse_options is not None or read_options is not None:
+            if default_fragment_scan_options is not None:
+                raise ValueError('If `default_fragment_scan_options` is '
+                                 'given, cannot specify read_options')
+            self.default_fragment_scan_options = JsonFragmentScanOptions(
+                parse_options=parse_options,
+                read_options=read_options)
+        elif isinstance(default_fragment_scan_options, dict):
+            self.default_fragment_scan_options = JsonFragmentScanOptions(
+                **default_fragment_scan_options)
+        elif isinstance(default_fragment_scan_options, JsonFragmentScanOptions):
+            self.default_fragment_scan_options = default_fragment_scan_options
+        elif default_fragment_scan_options is not None:
+            raise TypeError('`default_fragment_scan_options` must be either '
+                            'a dictionary or an instance of '
+                            'JsonFragmentScanOptions')
+
+    cdef void init(self, const shared_ptr[CFileFormat]& sp):
+        FileFormat.init(self, sp)
+        self.json_format = <CJsonFileFormat*> sp.get()
+
+    cdef _set_default_fragment_scan_options(self, FragmentScanOptions options):
+        if options.type_name == 'json':
+            self.json_format.default_fragment_scan_options = options.wrapped
+            self.default_fragment_scan_options.read_options = options.read_options
+            self.default_fragment_scan_options.parse_options = options.parse_options
+        else:
+            super()._set_default_fragment_scan_options(options)
+
+    def equals(self, JsonFileFormat other):
+        """
+        Parameters
+        ----------
+        other : pyarrow.dataset.JsonFileFormat
+
+        Returns
+        -------
+        bool
+        """
+        return (other and
+                self.default_fragment_scan_options ==
+                other.default_fragment_scan_options)
+
+    def __reduce__(self):
+        return JsonFileFormat, (self.default_fragment_scan_options,)
+
+    def __repr__(self):
+        return "<JsonFileFormat>"
+
+
+cdef class JsonFragmentScanOptions(FragmentScanOptions):
+    """
+    Scan-specific options for JSON fragments.
+
+    Parameters
+    ----------
+    parse_options : pyarrow.json.ParseOptions
+        Options regarding JSON parsing.
+    read_options : pyarrow.json.ReadOptions
+        General read options.
+    """
+    cdef:
+        CJsonFragmentScanOptions* json_options
+
+     # Avoid mistakingly creating attributes
+    __slots__ = ()
+
+    def __init__(self, JsonParseOptions parse_options=None,
+                 JsonReadOptions read_options=None):
+        self.init(shared_ptr[CFragmentScanOptions](
+            new CJsonFragmentScanOptions()))
+        if parse_options is not None:
+            self.parse_options = parse_options
+        if read_options is not None:
+            self.read_options = read_options
+
+    cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp):
+        FragmentScanOptions.init(self, sp)
+        self.json_options = <CJsonFragmentScanOptions*> sp.get()
+
+    @property
+    def parse_options(self):
+        return JsonParseOptions.wrap(self.json_options.parse_options)
+
+    @parse_options.setter
+    def parse_options(self, JsonParseOptions parse_options not None):
+        self.json_options.parse_options = parse_options.options
+
+    @property
+    def read_options(self):
+        return JsonReadOptions.wrap(self.json_options.read_options)
+
+    @read_options.setter
+    def read_options(self, JsonReadOptions read_options not None):
+        self.json_options.read_options = read_options.options
+
+    def equals(self, JsonFragmentScanOptions other):
+        """
+        Parameters
+        ----------
+        other : pyarrow.dataset.JsonFragmentScanOptions
+
+        Returns
+        -------
+        bool
+        """
+        return (
+            other and
+            self.read_options.equals(other.read_options) and
+            self.parse_options.equals(other.parse_options))
+
+    def __reduce__(self):
+        return JsonFragmentScanOptions, (self.parse_options, self.read_options)
+
+
+cdef class Partitioning(_Weakrefable):
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef init(self, const shared_ptr[CPartitioning]& sp):
+        self.wrapped = sp
+        self.partitioning = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CPartitioning]& sp):
+        type_name = frombytes(sp.get().type_name())
+
+        classes = {
+            'directory': DirectoryPartitioning,
+            'hive': HivePartitioning,
+            'filename': FilenamePartitioning,
+        }
+
+        class_ = classes.get(type_name, None)
+        if class_ is None:
+            raise TypeError(type_name)
+
+        cdef Partitioning self = class_.__new__(class_)
+        self.init(sp)
+        return self
+
+    cdef inline shared_ptr[CPartitioning] unwrap(self):
+        return self.wrapped
+
+    def __eq__(self, other):
+        if isinstance(other, Partitioning):
+            return self.partitioning.Equals(deref((<Partitioning>other).unwrap()))
+        return False
+
+    def parse(self, path):
+        """
+        Parse a path into a partition expression.
+
+        Parameters
+        ----------
+        path : str
+
+        Returns
+        -------
+        pyarrow.dataset.Expression
+        """
+        cdef CResult[CExpression] result
+        result = self.partitioning.Parse(tobytes(path))
+        return Expression.wrap(GetResultValue(result))
+
+    def format(self, expr):
+        """
+        Convert a filter expression into a tuple of (directory, filename) using
+        the current partitioning scheme
+
+        Parameters
+        ----------
+        expr : pyarrow.dataset.Expression
+
+        Returns
+        -------
+        tuple[str, str]
+
+        Examples
+        --------
+
+        Specify the Schema for paths like "/2009/June":
+
+        >>> import pyarrow as pa
+        >>> import pyarrow.dataset as ds
+        >>> import pyarrow.compute as pc
+        >>> part = ds.partitioning(pa.schema([("year", pa.int16()),
+        ...                                   ("month", pa.string())]))
+        >>> part.format(
+        ...     (pc.field("year") == 1862) & (pc.field("month") == "Jan")
+        ... )
+        ('1862/Jan', '')
+        """
+        cdef:
+            CPartitionPathFormat result
+
+        result = GetResultValue(self.partitioning.Format(
+            Expression.unwrap(expr)
+        ))
+
+        return frombytes(result.directory), frombytes(result.filename)
+
+    @property
+    def schema(self):
+        """The arrow Schema attached to the partitioning."""
+        return pyarrow_wrap_schema(self.partitioning.schema())
+
+
+cdef class PartitioningFactory(_Weakrefable):
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef init(self, const shared_ptr[CPartitioningFactory]& sp):
+        self.wrapped = sp
+        self.factory = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CPartitioningFactory]& sp,
+              object constructor, object options):
+        cdef PartitioningFactory self = PartitioningFactory.__new__(
+            PartitioningFactory
+        )
+        self.init(sp)
+        self.constructor = constructor
+        self.options = options
+        return self
+
+    cdef inline shared_ptr[CPartitioningFactory] unwrap(self):
+        return self.wrapped
+
+    def __reduce__(self):
+        return self.constructor, self.options
+
+    @property
+    def type_name(self):
+        return frombytes(self.factory.type_name())
+
+
+cdef vector[shared_ptr[CArray]] _partitioning_dictionaries(
+        Schema schema, dictionaries) except *:
+    cdef:
+        vector[shared_ptr[CArray]] c_dictionaries
+
+    dictionaries = dictionaries or {}
+
+    for field in schema:
+        dictionary = dictionaries.get(field.name)
+
+        if (isinstance(field.type, pa.DictionaryType) and
+                dictionary is not None):
+            c_dictionaries.push_back(pyarrow_unwrap_array(dictionary))
+        else:
+            c_dictionaries.push_back(<shared_ptr[CArray]> nullptr)
+
+    return c_dictionaries
+
+
+cdef class KeyValuePartitioning(Partitioning):
+
+    cdef:
+        CKeyValuePartitioning* keyvalue_partitioning
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef init(self, const shared_ptr[CPartitioning]& sp):
+        Partitioning.init(self, sp)
+        self.keyvalue_partitioning = <CKeyValuePartitioning*> sp.get()
+        self.wrapped = sp
+        self.partitioning = sp.get()
+
+    def __reduce__(self):
+        dictionaries = self.dictionaries
+        if dictionaries:
+            dictionaries = dict(zip(self.schema.names, dictionaries))
+        segment_encoding = _wrap_segment_encoding(
+            deref(self.keyvalue_partitioning).segment_encoding()
+        )
+        return self.__class__, (self.schema, dictionaries, segment_encoding)
+
+    @property
+    def dictionaries(self):
+        """
+        The unique values for each partition field, if available.
+
+        Those values are only available if the Partitioning object was
+        created through dataset discovery from a PartitioningFactory, or
+        if the dictionaries were manually specified in the constructor.
+        If no dictionary field is available, this returns an empty list.
+        """
+        cdef vector[shared_ptr[CArray]] c_arrays
+        c_arrays = self.keyvalue_partitioning.dictionaries()
+        res = []
+        for arr in c_arrays:
+            if arr.get() == nullptr:
+                # Partitioning object has not been created through
+                # inspected Factory
+                res.append(None)
+            else:
+                res.append(pyarrow_wrap_array(arr))
+        return res
+
+
+def _constructor_directory_partitioning_factory(*args):
+    return DirectoryPartitioning.discover(*args)
+
+
+cdef class DirectoryPartitioning(KeyValuePartitioning):
+    """
+    A Partitioning based on a specified Schema.
+
+    The DirectoryPartitioning expects one segment in the file path for each
+    field in the schema (all fields are required to be present).
+    For example given schema<year:int16, month:int8> the path "/2009/11" would
+    be parsed to ("year"_ == 2009 and "month"_ == 11).
+
+    Parameters
+    ----------
+    schema : Schema
+        The schema that describes the partitions present in the file path.
+    dictionaries : dict[str, Array]
+        If the type of any field of `schema` is a dictionary type, the
+        corresponding entry of `dictionaries` must be an array containing
+        every value which may be taken by the corresponding column or an
+        error will be raised in parsing.
+    segment_encoding : str, default "uri"
+        After splitting paths into segments, decode the segments. Valid
+        values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+    Returns
+    -------
+    DirectoryPartitioning
+
+    Examples
+    --------
+    >>> from pyarrow.dataset import DirectoryPartitioning
+    >>> partitioning = DirectoryPartitioning(
+    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
+    >>> print(partitioning.parse("/2009/11/"))
+    ((year == 2009) and (month == 11))
+    """
+
+    cdef:
+        CDirectoryPartitioning* directory_partitioning
+
+    def __init__(self, Schema schema not None, dictionaries=None,
+                 segment_encoding="uri"):
+        cdef:
+            shared_ptr[CDirectoryPartitioning] c_partitioning
+            CKeyValuePartitioningOptions c_options
+
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+        c_partitioning = make_shared[CDirectoryPartitioning](
+            pyarrow_unwrap_schema(schema),
+            _partitioning_dictionaries(schema, dictionaries),
+            c_options,
+        )
+        self.init(<shared_ptr[CPartitioning]> c_partitioning)
+
+    cdef init(self, const shared_ptr[CPartitioning]& sp):
+        KeyValuePartitioning.init(self, sp)
+        self.directory_partitioning = <CDirectoryPartitioning*> sp.get()
+
+    @staticmethod
+    def discover(field_names=None, infer_dictionary=False,
+                 max_partition_dictionary_size=0,
+                 schema=None, segment_encoding="uri"):
+        """
+        Discover a DirectoryPartitioning.
+
+        Parameters
+        ----------
+        field_names : list of str
+            The names to associate with the values from the subdirectory names.
+            If schema is given, will be populated from the schema.
+        infer_dictionary : bool, default False
+            When inferring a schema for partition fields, yield dictionary
+            encoded types instead of plain types. This can be more efficient
+            when materializing virtual columns, and Expressions parsed by the
+            finished Partitioning will include dictionaries of all unique
+            inspected values for each field.
+        max_partition_dictionary_size : int, default 0
+            Synonymous with infer_dictionary for backwards compatibility with
+            1.0: setting this to -1 or None is equivalent to passing
+            infer_dictionary=True.
+        schema : Schema, default None
+            Use this schema instead of inferring a schema from partition
+            values. Partition values will be validated against this schema
+            before accumulation into the Partitioning's dictionary.
+        segment_encoding : str, default "uri"
+            After splitting paths into segments, decode the segments. Valid
+            values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+        Returns
+        -------
+        PartitioningFactory
+            To be used in the FileSystemFactoryOptions.
+        """
+        cdef:
+            CPartitioningFactoryOptions c_options
+            vector[c_string] c_field_names
+
+        if max_partition_dictionary_size in {-1, None}:
+            infer_dictionary = True
+        elif max_partition_dictionary_size != 0:
+            raise NotImplementedError("max_partition_dictionary_size must be "
+                                      "0, -1, or None")
+
+        if infer_dictionary:
+            c_options.infer_dictionary = True
+
+        if schema:
+            c_options.schema = pyarrow_unwrap_schema(schema)
+            c_field_names = [tobytes(f.name) for f in schema]
+        elif not field_names:
+            raise ValueError(
+                "Neither field_names nor schema was passed; "
+                "cannot infer field_names")
+        else:
+            c_field_names = [tobytes(s) for s in field_names]
+
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+
+        return PartitioningFactory.wrap(
+            CDirectoryPartitioning.MakeFactory(c_field_names, c_options),
+            _constructor_directory_partitioning_factory,
+            (field_names, infer_dictionary, max_partition_dictionary_size,
+             schema, segment_encoding)
+        )
+
+
+def _constructor_hive_partitioning_factory(*args):
+    return HivePartitioning.discover(*args)
+
+
+cdef class HivePartitioning(KeyValuePartitioning):
+    """
+    A Partitioning for "/$key=$value/" nested directories as found in
+    Apache Hive.
+
+    Multi-level, directory based partitioning scheme originating from
+    Apache Hive with all data files stored in the leaf directories. Data is
+    partitioned by static values of a particular column in the schema.
+    Partition keys are represented in the form $key=$value in directory names.
+    Field order is ignored, as are missing or unrecognized field names.
+
+    For example, given schema<year:int16, month:int8, day:int8>, a possible
+    path would be "/year=2009/month=11/day=15".
+
+    Parameters
+    ----------
+    schema : Schema
+        The schema that describes the partitions present in the file path.
+    dictionaries : dict[str, Array]
+        If the type of any field of `schema` is a dictionary type, the
+        corresponding entry of `dictionaries` must be an array containing
+        every value which may be taken by the corresponding column or an
+        error will be raised in parsing.
+    null_fallback : str, default "__HIVE_DEFAULT_PARTITION__"
+        If any field is None then this fallback will be used as a label
+    segment_encoding : str, default "uri"
+        After splitting paths into segments, decode the segments. Valid
+        values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+    Returns
+    -------
+    HivePartitioning
+
+    Examples
+    --------
+    >>> from pyarrow.dataset import HivePartitioning
+    >>> partitioning = HivePartitioning(
+    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
+    >>> print(partitioning.parse("/year=2009/month=11/"))
+    ((year == 2009) and (month == 11))
+
+    """
+
+    cdef:
+        CHivePartitioning* hive_partitioning
+
+    def __init__(self,
+                 Schema schema not None,
+                 dictionaries=None,
+                 null_fallback="__HIVE_DEFAULT_PARTITION__",
+                 segment_encoding="uri"):
+
+        cdef:
+            shared_ptr[CHivePartitioning] c_partitioning
+            CHivePartitioningOptions c_options
+
+        c_options.null_fallback = tobytes(null_fallback)
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+
+        c_partitioning = make_shared[CHivePartitioning](
+            pyarrow_unwrap_schema(schema),
+            _partitioning_dictionaries(schema, dictionaries),
+            c_options,
+        )
+        self.init(<shared_ptr[CPartitioning]> c_partitioning)
+
+    cdef init(self, const shared_ptr[CPartitioning]& sp):
+        KeyValuePartitioning.init(self, sp)
+        self.hive_partitioning = <CHivePartitioning*> sp.get()
+
+    def __reduce__(self):
+        dictionaries = self.dictionaries
+        if dictionaries:
+            dictionaries = dict(zip(self.schema.names, dictionaries))
+        segment_encoding = _wrap_segment_encoding(
+            deref(self.keyvalue_partitioning).segment_encoding()
+        )
+        null_fallback = frombytes(deref(self.hive_partitioning).null_fallback())
+        return HivePartitioning, (
+            self.schema, dictionaries, null_fallback, segment_encoding
+        )
+
+    @staticmethod
+    def discover(infer_dictionary=False,
+                 max_partition_dictionary_size=0,
+                 null_fallback="__HIVE_DEFAULT_PARTITION__",
+                 schema=None,
+                 segment_encoding="uri"):
+        """
+        Discover a HivePartitioning.
+
+        Parameters
+        ----------
+        infer_dictionary : bool, default False
+            When inferring a schema for partition fields, yield dictionary
+            encoded types instead of plain. This can be more efficient when
+            materializing virtual columns, and Expressions parsed by the
+            finished Partitioning will include dictionaries of all unique
+            inspected values for each field.
+        max_partition_dictionary_size : int, default 0
+            Synonymous with infer_dictionary for backwards compatibility with
+            1.0: setting this to -1 or None is equivalent to passing
+            infer_dictionary=True.
+        null_fallback : str, default "__HIVE_DEFAULT_PARTITION__"
+            When inferring a schema for partition fields this value will be
+            replaced by null.  The default is set to __HIVE_DEFAULT_PARTITION__
+            for compatibility with Spark
+        schema : Schema, default None
+            Use this schema instead of inferring a schema from partition
+            values. Partition values will be validated against this schema
+            before accumulation into the Partitioning's dictionary.
+        segment_encoding : str, default "uri"
+            After splitting paths into segments, decode the segments. Valid
+            values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+        Returns
+        -------
+        PartitioningFactory
+            To be used in the FileSystemFactoryOptions.
+        """
+        cdef:
+            CHivePartitioningFactoryOptions c_options
+
+        if max_partition_dictionary_size in {-1, None}:
+            infer_dictionary = True
+        elif max_partition_dictionary_size != 0:
+            raise NotImplementedError("max_partition_dictionary_size must be "
+                                      "0, -1, or None")
+
+        if infer_dictionary:
+            c_options.infer_dictionary = True
+
+        c_options.null_fallback = tobytes(null_fallback)
+
+        if schema:
+            c_options.schema = pyarrow_unwrap_schema(schema)
+
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+
+        return PartitioningFactory.wrap(
+            CHivePartitioning.MakeFactory(c_options),
+            _constructor_hive_partitioning_factory,
+            (infer_dictionary, max_partition_dictionary_size, null_fallback,
+             schema, segment_encoding),
+        )
+
+
+def _constructor_filename_partitioning_factory(*args):
+    return FilenamePartitioning.discover(*args)
+
+
+cdef class FilenamePartitioning(KeyValuePartitioning):
+    """
+    A Partitioning based on a specified Schema.
+
+    The FilenamePartitioning expects one segment in the file name for each
+    field in the schema (all fields are required to be present) separated
+    by '_'. For example given schema<year:int16, month:int8> the name
+    ``"2009_11_"`` would be parsed to ("year" == 2009 and "month" == 11).
+
+    Parameters
+    ----------
+    schema : Schema
+        The schema that describes the partitions present in the file path.
+    dictionaries : dict[str, Array]
+        If the type of any field of `schema` is a dictionary type, the
+        corresponding entry of `dictionaries` must be an array containing
+        every value which may be taken by the corresponding column or an
+        error will be raised in parsing.
+    segment_encoding : str, default "uri"
+        After splitting paths into segments, decode the segments. Valid
+        values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+    Returns
+    -------
+    FilenamePartitioning
+
+    Examples
+    --------
+    >>> from pyarrow.dataset import FilenamePartitioning
+    >>> partitioning = FilenamePartitioning(
+    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
+    >>> print(partitioning.parse("2009_11_data.parquet"))
+    ((year == 2009) and (month == 11))
+    """
+
+    cdef:
+        CFilenamePartitioning* filename_partitioning
+
+    def __init__(self, Schema schema not None, dictionaries=None,
+                 segment_encoding="uri"):
+        cdef:
+            shared_ptr[CFilenamePartitioning] c_partitioning
+            CKeyValuePartitioningOptions c_options
+
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+        c_partitioning = make_shared[CFilenamePartitioning](
+            pyarrow_unwrap_schema(schema),
+            _partitioning_dictionaries(schema, dictionaries),
+            c_options,
+        )
+        self.init(<shared_ptr[CPartitioning]> c_partitioning)
+
+    cdef init(self, const shared_ptr[CPartitioning]& sp):
+        KeyValuePartitioning.init(self, sp)
+        self.filename_partitioning = <CFilenamePartitioning*> sp.get()
+
+    @staticmethod
+    def discover(field_names=None, infer_dictionary=False,
+                 schema=None, segment_encoding="uri"):
+        """
+        Discover a FilenamePartitioning.
+
+        Parameters
+        ----------
+        field_names : list of str
+            The names to associate with the values from the subdirectory names.
+            If schema is given, will be populated from the schema.
+        infer_dictionary : bool, default False
+            When inferring a schema for partition fields, yield dictionary
+            encoded types instead of plain types. This can be more efficient
+            when materializing virtual columns, and Expressions parsed by the
+            finished Partitioning will include dictionaries of all unique
+            inspected values for each field.
+        schema : Schema, default None
+            Use this schema instead of inferring a schema from partition
+            values. Partition values will be validated against this schema
+            before accumulation into the Partitioning's dictionary.
+        segment_encoding : str, default "uri"
+            After splitting paths into segments, decode the segments. Valid
+            values are "uri" (URI-decode segments) and "none" (leave as-is).
+
+        Returns
+        -------
+        PartitioningFactory
+            To be used in the FileSystemFactoryOptions.
+        """
+        cdef:
+            CPartitioningFactoryOptions c_options
+            vector[c_string] c_field_names
+
+        if infer_dictionary:
+            c_options.infer_dictionary = True
+
+        if schema:
+            c_options.schema = pyarrow_unwrap_schema(schema)
+            c_field_names = [tobytes(f.name) for f in schema]
+        elif not field_names:
+            raise TypeError(
+                "Neither field_names nor schema was passed; "
+                "cannot infer field_names")
+        else:
+            c_field_names = [tobytes(s) for s in field_names]
+
+        c_options.segment_encoding = _get_segment_encoding(segment_encoding)
+
+        return PartitioningFactory.wrap(
+            CFilenamePartitioning.MakeFactory(c_field_names, c_options),
+            _constructor_filename_partitioning_factory,
+            (field_names, infer_dictionary, schema, segment_encoding)
+        )
+
+
+cdef class DatasetFactory(_Weakrefable):
+    """
+    DatasetFactory is used to create a Dataset, inspect the Schema
+    of the fragments contained in it, and declare a partitioning.
+    """
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef init(self, const shared_ptr[CDatasetFactory]& sp):
+        self.wrapped = sp
+        self.factory = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CDatasetFactory]& sp):
+        cdef DatasetFactory self = \
+            DatasetFactory.__new__(DatasetFactory)
+        self.init(sp)
+        return self
+
+    cdef inline shared_ptr[CDatasetFactory] unwrap(self) nogil:
+        return self.wrapped
+
+    @property
+    def root_partition(self):
+        return Expression.wrap(self.factory.root_partition())
+
+    @root_partition.setter
+    def root_partition(self, Expression expr):
+        check_status(self.factory.SetRootPartition(expr.unwrap()))
+
+    def inspect_schemas(self):
+        cdef CResult[vector[shared_ptr[CSchema]]] result
+        cdef CInspectOptions options
+        with nogil:
+            result = self.factory.InspectSchemas(options)
+
+        schemas = []
+        for s in GetResultValue(result):
+            schemas.append(pyarrow_wrap_schema(s))
+        return schemas
+
+    def inspect(self, *, promote_options="default", fragments=None):
+        """
+        Inspect data fragments and return a common Schema.
+
+        Parameters
+        ----------
+        promote_options : str, default "default"
+            Control how to unify types. Accepts strings "default" and "permissive".
+            Default: types must match exactly, except nulls can be merged with other types.
+            Permissive: types are promoted when possible.
+        fragments : int, default None
+            How many fragments should be inspected to infer the unified schema.
+            Use ``None`` to inspect all fragments.
+
+        Returns
+        -------
+        Schema
+        """
+        cdef:
+            CInspectOptions options
+            CResult[shared_ptr[CSchema]] result
+
+        options.field_merge_options = _parse_field_merge_options(promote_options)
+
+        if fragments is None:
+            options.fragments = -1  # InspectOptions::kInspectAllFragments
+        elif isinstance(fragments, int) and fragments >= 0:
+            options.fragments = fragments
+        else:
+            raise ValueError(
+                f"Fragment count must be a non-negative int or None; got {fragments!r}")
+
+        with nogil:
+            result = self.factory.Inspect(options)
+        return pyarrow_wrap_schema(GetResultValue(result))
+
+    def finish(self, Schema schema=None):
+        """
+        Create a Dataset using the inspected schema or an explicit schema
+        (if given).
+
+        Parameters
+        ----------
+        schema : Schema, default None
+            The schema to conform the source to.  If None, the inspected
+            schema is used.
+
+        Returns
+        -------
+        Dataset
+        """
+        cdef:
+            shared_ptr[CSchema] sp_schema
+            CResult[shared_ptr[CDataset]] result
+
+        if schema is not None:
+            sp_schema = pyarrow_unwrap_schema(schema)
+            with nogil:
+                result = self.factory.FinishWithSchema(sp_schema)
+        else:
+            with nogil:
+                result = self.factory.Finish()
+
+        return Dataset.wrap(GetResultValue(result))
+
+
+cdef class FileSystemFactoryOptions(_Weakrefable):
+    """
+    Influences the discovery of filesystem paths.
+
+    Parameters
+    ----------
+    partition_base_dir : str, optional
+        For the purposes of applying the partitioning, paths will be
+        stripped of the partition_base_dir. Files not matching the
+        partition_base_dir prefix will be skipped for partitioning discovery.
+        The ignored files will still be part of the Dataset, but will not
+        have partition information.
+    partitioning : Partitioning/PartitioningFactory, optional
+       Apply the Partitioning to every discovered Fragment. See Partitioning or
+       PartitioningFactory documentation.
+    exclude_invalid_files : bool, optional (default False)
+        If True, invalid files will be excluded (file format specific check).
+        This will incur IO for each files in a serial and single threaded
+        fashion. Disabling this feature will skip the IO, but unsupported
+        files may be present in the Dataset (resulting in an error at scan
+        time).
+    selector_ignore_prefixes : list, optional
+        When discovering from a Selector (and not from an explicit file list),
+        ignore files and directories matching any of these prefixes.
+        By default this is ['.', '_'].
+    """
+
+    cdef:
+        CFileSystemFactoryOptions options
+
+    __slots__ = ()  # avoid mistakingly creating attributes
+
+    def __init__(self, partition_base_dir=None, partitioning=None,
+                 exclude_invalid_files=None,
+                 list selector_ignore_prefixes=None):
+        if isinstance(partitioning, PartitioningFactory):
+            self.partitioning_factory = partitioning
+        elif isinstance(partitioning, Partitioning):
+            self.partitioning = partitioning
+
+        if partition_base_dir is not None:
+            self.partition_base_dir = partition_base_dir
+        if exclude_invalid_files is not None:
+            self.exclude_invalid_files = exclude_invalid_files
+        if selector_ignore_prefixes is not None:
+            self.selector_ignore_prefixes = selector_ignore_prefixes
+
+    cdef inline CFileSystemFactoryOptions unwrap(self):
+        return self.options
+
+    @property
+    def partitioning(self):
+        """Partitioning to apply to discovered files.
+
+        NOTE: setting this property will overwrite partitioning_factory.
+        """
+        c_partitioning = self.options.partitioning.partitioning()
+        if c_partitioning.get() == nullptr:
+            return None
+        return Partitioning.wrap(c_partitioning)
+
+    @partitioning.setter
+    def partitioning(self, Partitioning value):
+        self.options.partitioning = (<Partitioning> value).unwrap()
+
+    @property
+    def partitioning_factory(self):
+        """PartitioningFactory to apply to discovered files and
+        discover a Partitioning.
+
+        NOTE: setting this property will overwrite partitioning.
+        """
+        c_factory = self.options.partitioning.factory()
+        if c_factory.get() == nullptr:
+            return None
+        return PartitioningFactory.wrap(c_factory, None, None)
+
+    @partitioning_factory.setter
+    def partitioning_factory(self, PartitioningFactory value):
+        self.options.partitioning = (<PartitioningFactory> value).unwrap()
+
+    @property
+    def partition_base_dir(self):
+        """
+        Base directory to strip paths before applying the partitioning.
+        """
+        return frombytes(self.options.partition_base_dir)
+
+    @partition_base_dir.setter
+    def partition_base_dir(self, value):
+        self.options.partition_base_dir = tobytes(value)
+
+    @property
+    def exclude_invalid_files(self):
+        """Whether to exclude invalid files."""
+        return self.options.exclude_invalid_files
+
+    @exclude_invalid_files.setter
+    def exclude_invalid_files(self, bint value):
+        self.options.exclude_invalid_files = value
+
+    @property
+    def selector_ignore_prefixes(self):
+        """
+        List of prefixes. Files matching one of those prefixes will be
+        ignored by the discovery process.
+        """
+        return [frombytes(p) for p in self.options.selector_ignore_prefixes]
+
+    @selector_ignore_prefixes.setter
+    def selector_ignore_prefixes(self, values):
+        self.options.selector_ignore_prefixes = [tobytes(v) for v in values]
+
+
+cdef vector[CFileInfo] unwrap_finfos(finfos):
+    cdef vector[CFileInfo] o_vect
+    for fi in finfos:
+        o_vect.push_back((<FileInfo> fi).unwrap())
+    return o_vect
+
+
+cdef class FileSystemDatasetFactory(DatasetFactory):
+    """
+    Create a DatasetFactory from a list of paths with schema inspection.
+
+    Parameters
+    ----------
+    filesystem : pyarrow.fs.FileSystem
+        Filesystem to discover.
+    paths_or_selector : pyarrow.fs.FileSelector or list of path-likes
+        Either a Selector object or a list of path-like objects.
+    format : FileFormat
+        Currently only ParquetFileFormat and IpcFileFormat are supported.
+    options : FileSystemFactoryOptions, optional
+        Various flags influencing the discovery of filesystem paths.
+    """
+
+    cdef:
+        CFileSystemDatasetFactory* filesystem_factory
+
+    def __init__(self, FileSystem filesystem not None, paths_or_selector,
+                 FileFormat format not None,
+                 FileSystemFactoryOptions options=None):
+        cdef:
+            vector[c_string] paths
+            vector[CFileInfo] finfos
+            CFileSelector c_selector
+            CResult[shared_ptr[CDatasetFactory]] result
+            shared_ptr[CFileSystem] c_filesystem
+            shared_ptr[CFileFormat] c_format
+            CFileSystemFactoryOptions c_options
+
+        options = options or FileSystemFactoryOptions()
+        c_options = options.unwrap()
+        c_filesystem = filesystem.unwrap()
+        c_format = format.unwrap()
+
+        if isinstance(paths_or_selector, FileSelector):
+            with nogil:
+                c_selector = (<FileSelector> paths_or_selector).selector
+                result = CFileSystemDatasetFactory.MakeFromSelector(
+                    c_filesystem,
+                    c_selector,
+                    c_format,
+                    c_options
+                )
+        elif isinstance(paths_or_selector, (list, tuple)):
+            if len(paths_or_selector) > 0 and isinstance(paths_or_selector[0], FileInfo):
+                finfos = unwrap_finfos(paths_or_selector)
+                with nogil:
+                    result = CFileSystemDatasetFactory.MakeFromFileInfos(
+                        c_filesystem,
+                        finfos,
+                        c_format,
+                        c_options
+                    )
+            else:
+                paths = [tobytes(s) for s in paths_or_selector]
+                with nogil:
+                    result = CFileSystemDatasetFactory.MakeFromPaths(
+                        c_filesystem,
+                        paths,
+                        c_format,
+                        c_options
+                    )
+        else:
+            raise TypeError('Must pass either paths or a FileSelector, but '
+                            f'passed {type(paths_or_selector)}')
+
+        self.init(GetResultValue(result))
+
+    cdef init(self, shared_ptr[CDatasetFactory]& sp):
+        DatasetFactory.init(self, sp)
+        self.filesystem_factory = <CFileSystemDatasetFactory*> sp.get()
+
+
+cdef class UnionDatasetFactory(DatasetFactory):
+    """
+    Provides a way to inspect/discover a Dataset's expected schema before
+    materialization.
+
+    Parameters
+    ----------
+    factories : list of DatasetFactory
+    """
+
+    cdef:
+        CUnionDatasetFactory* union_factory
+
+    def __init__(self, list factories):
+        cdef:
+            DatasetFactory factory
+            vector[shared_ptr[CDatasetFactory]] c_factories
+        for factory in factories:
+            c_factories.push_back(factory.unwrap())
+        self.init(GetResultValue(CUnionDatasetFactory.Make(c_factories)))
+
+    cdef init(self, const shared_ptr[CDatasetFactory]& sp):
+        DatasetFactory.init(self, sp)
+        self.union_factory = <CUnionDatasetFactory*> sp.get()
+
+
+cdef class RecordBatchIterator(_Weakrefable):
+    """An iterator over a sequence of record batches."""
+    cdef:
+        # An object that must be kept alive with the iterator.
+        object iterator_owner
+        # Iterator is a non-POD type and Cython uses offsetof, leading
+        # to a compiler warning unless wrapped like so
+        SharedPtrNoGIL[CRecordBatchIterator] iterator
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__, subclasses_instead=False)
+
+    @staticmethod
+    cdef wrap(object owner, CRecordBatchIterator iterator):
+        cdef RecordBatchIterator self = \
+            RecordBatchIterator.__new__(RecordBatchIterator)
+        self.iterator_owner = owner
+        self.iterator = make_shared[CRecordBatchIterator](move(iterator))
+        return self
+
+    cdef inline shared_ptr[CRecordBatchIterator] unwrap(self) nogil:
+        return self.iterator
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        cdef shared_ptr[CRecordBatch] record_batch
+        with nogil:
+            record_batch = GetResultValue(move(self.iterator.get().Next()))
+        if record_batch == NULL:
+            raise StopIteration
+        return pyarrow_wrap_batch(record_batch)
+
+
+class TaggedRecordBatch(collections.namedtuple(
+        "TaggedRecordBatch", ["record_batch", "fragment"])):
+    """
+    A combination of a record batch and the fragment it came from.
+
+    Parameters
+    ----------
+    record_batch : RecordBatch
+        The record batch.
+    fragment : Fragment
+        Fragment of the record batch.
+    """
+
+
+cdef class TaggedRecordBatchIterator(_Weakrefable):
+    """An iterator over a sequence of record batches with fragments."""
+    cdef:
+        object iterator_owner
+        SharedPtrNoGIL[CTaggedRecordBatchIterator] iterator
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__, subclasses_instead=False)
+
+    @staticmethod
+    cdef wrap(object owner, CTaggedRecordBatchIterator iterator):
+        cdef TaggedRecordBatchIterator self = \
+            TaggedRecordBatchIterator.__new__(TaggedRecordBatchIterator)
+        self.iterator_owner = owner
+        self.iterator = make_shared[CTaggedRecordBatchIterator](
+            move(iterator))
+        return self
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        cdef CTaggedRecordBatch batch
+        with nogil:
+            batch = GetResultValue(move(self.iterator.get().Next()))
+        if batch.record_batch == NULL:
+            raise StopIteration
+        return TaggedRecordBatch(
+            record_batch=pyarrow_wrap_batch(batch.record_batch),
+            fragment=Fragment.wrap(batch.fragment))
+
+
+cdef void _populate_builder(const shared_ptr[CScannerBuilder]& ptr,
+                            object columns=None, Expression filter=None,
+                            int batch_size=_DEFAULT_BATCH_SIZE,
+                            int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                            int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                            bint use_threads=True, bint cache_metadata=True,
+                            MemoryPool memory_pool=None,
+                            FragmentScanOptions fragment_scan_options=None)\
+        except *:
+    cdef:
+        CScannerBuilder *builder
+        vector[CExpression] c_exprs
+
+    builder = ptr.get()
+
+    check_status(builder.Filter(_bind(
+        filter, pyarrow_wrap_schema(builder.schema()))))
+
+    if columns is not None:
+        if pa_substrait and isinstance(columns, pa_substrait.BoundExpressions):
+            columns = columns.expressions
+
+        if isinstance(columns, dict):
+            for expr in columns.values():
+                if not isinstance(expr, Expression):
+                    raise TypeError(
+                        "Expected an Expression for a 'column' dictionary "
+                        f"value, got {type(expr)} instead"
+                    )
+                c_exprs.push_back((<Expression> expr).unwrap())
+
+            check_status(
+                builder.Project(c_exprs, [tobytes(c) for c in columns.keys()])
+            )
+        elif isinstance(columns, list):
+            check_status(builder.ProjectColumns([tobytes(c) for c in columns]))
+        else:
+            raise ValueError(
+                "Expected a list or a dict for 'columns', "
+                f"got {type(columns)} instead."
+            )
+
+    check_status(builder.BatchSize(batch_size))
+    check_status(builder.BatchReadahead(batch_readahead))
+    check_status(builder.FragmentReadahead(fragment_readahead))
+    check_status(builder.UseThreads(use_threads))
+    check_status(builder.CacheMetadata(cache_metadata))
+    check_status(builder.Pool(maybe_unbox_memory_pool(memory_pool)))
+    if fragment_scan_options:
+        check_status(
+            builder.FragmentScanOptions(fragment_scan_options.wrapped))
+
+
+cdef class Scanner(_Weakrefable):
+    """A materialized scan operation with context and options bound.
+
+    A scanner is the class that glues the scan tasks, data fragments and data
+    sources together.
+    """
+
+    def __init__(self):
+        _forbid_instantiation(self.__class__)
+
+    cdef void init(self, const shared_ptr[CScanner]& sp):
+        self.wrapped = sp
+        self.scanner = sp.get()
+
+    @staticmethod
+    cdef wrap(const shared_ptr[CScanner]& sp):
+        cdef Scanner self = Scanner.__new__(Scanner)
+        self.init(sp)
+        return self
+
+    cdef inline shared_ptr[CScanner] unwrap(self):
+        return self.wrapped
+
+    @staticmethod
+    cdef shared_ptr[CScanOptions] _make_scan_options(Dataset dataset, dict py_scanoptions) except *:
+        cdef:
+            shared_ptr[CScannerBuilder] builder = make_shared[CScannerBuilder](dataset.unwrap())
+
+        py_scanoptions = dataset._scanner_options(py_scanoptions)
+
+        # Need to explicitly expand the arguments as Cython doesn't support
+        # keyword expansion in cdef functions.
+        _populate_builder(
+            builder,
+            columns=py_scanoptions.get("columns"),
+            filter=py_scanoptions.get("filter"),
+            batch_size=py_scanoptions.get("batch_size", _DEFAULT_BATCH_SIZE),
+            batch_readahead=py_scanoptions.get(
+                "batch_readahead", _DEFAULT_BATCH_READAHEAD),
+            fragment_readahead=py_scanoptions.get(
+                "fragment_readahead", _DEFAULT_FRAGMENT_READAHEAD),
+            use_threads=py_scanoptions.get("use_threads", True),
+            cache_metadata=py_scanoptions.get("cache_metadata", True),
+            memory_pool=py_scanoptions.get("memory_pool"),
+            fragment_scan_options=py_scanoptions.get("fragment_scan_options"))
+
+        return GetResultValue(deref(builder).GetScanOptions())
+
+    @staticmethod
+    def from_dataset(Dataset dataset not None, *,
+                     object columns=None,
+                     object filter=None,
+                     int batch_size=_DEFAULT_BATCH_SIZE,
+                     int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                     int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                     FragmentScanOptions fragment_scan_options=None,
+                     bint use_threads=True, bint cache_metadata=True,
+                     MemoryPool memory_pool=None):
+        """
+        Create Scanner from Dataset,
+
+        Parameters
+        ----------
+        dataset : Dataset
+            Dataset to scan.
+        columns : list[str] or dict[str, Expression], default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+        """
+        cdef:
+            shared_ptr[CScanOptions] options
+            shared_ptr[CScannerBuilder] builder
+            shared_ptr[CScanner] scanner
+
+        options = Scanner._make_scan_options(
+            dataset,
+            dict(columns=columns, filter=filter, batch_size=batch_size,
+                 batch_readahead=batch_readahead,
+                 fragment_readahead=fragment_readahead, use_threads=use_threads,
+                 cache_metadata=cache_metadata, memory_pool=memory_pool,
+                 fragment_scan_options=fragment_scan_options)
+        )
+        builder = make_shared[CScannerBuilder](dataset.unwrap(), options)
+        scanner = GetResultValue(builder.get().Finish())
+        return Scanner.wrap(scanner)
+
+    @staticmethod
+    def from_fragment(Fragment fragment not None, *, Schema schema=None,
+                      object columns=None, Expression filter=None,
+                      int batch_size=_DEFAULT_BATCH_SIZE,
+                      int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                      int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                      FragmentScanOptions fragment_scan_options=None,
+                      bint use_threads=True, bint cache_metadata=True,
+                      MemoryPool memory_pool=None):
+        """
+        Create Scanner from Fragment,
+
+        Parameters
+        ----------
+        fragment : Fragment
+            fragment to scan.
+        schema : Schema, optional
+            The schema of the fragment.
+        columns : list[str] or dict[str, Expression], default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+        """
+        cdef:
+            shared_ptr[CScanOptions] options = make_shared[CScanOptions]()
+            shared_ptr[CScannerBuilder] builder
+            shared_ptr[CScanner] scanner
+
+        schema = schema or fragment.physical_schema
+
+        builder = make_shared[CScannerBuilder](pyarrow_unwrap_schema(schema),
+                                               fragment.unwrap(), options)
+        _populate_builder(builder, columns=columns, filter=filter,
+                          batch_size=batch_size, batch_readahead=batch_readahead,
+                          fragment_readahead=fragment_readahead,
+                          use_threads=use_threads, cache_metadata=cache_metadata,
+                          memory_pool=memory_pool,
+                          fragment_scan_options=fragment_scan_options)
+
+        scanner = GetResultValue(builder.get().Finish())
+        return Scanner.wrap(scanner)
+
+    @staticmethod
+    def from_batches(source, *, Schema schema=None, object columns=None,
+                     Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE,
+                     int batch_readahead=_DEFAULT_BATCH_READAHEAD,
+                     int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD,
+                     FragmentScanOptions fragment_scan_options=None,
+                     bint use_threads=True, bint cache_metadata=True,
+                     MemoryPool memory_pool=None):
+        """
+        Create a Scanner from an iterator of batches.
+
+        This creates a scanner which can be used only once. It is
+        intended to support writing a dataset (which takes a scanner)
+        from a source which can be read only once (e.g. a
+        RecordBatchReader or generator).
+
+        Parameters
+        ----------
+        source : Iterator or Arrow-compatible stream object
+            The iterator of Batches. This can be a pyarrow RecordBatchReader,
+            any object that implements the Arrow PyCapsule Protocol for
+            streams, or an actual Python iterator of RecordBatches.
+        schema : Schema
+            The schema of the batches (required when passing a Python
+            iterator).
+        columns : list[str] or dict[str, Expression], default None
+            The columns to project. This can be a list of column names to
+            include (order and duplicates will be preserved), or a dictionary
+            with {new_column_name: expression} values for more advanced
+            projections.
+
+            The list of columns or expressions may use the special fields
+            `__batch_index` (the index of the batch within the fragment),
+            `__fragment_index` (the index of the fragment within the dataset),
+            `__last_in_fragment` (whether the batch is last in fragment), and
+            `__filename` (the name of the source file or a description of the
+            source fragment).
+
+            The columns will be passed down to Datasets and corresponding data
+            fragments to avoid loading, copying, and deserializing columns
+            that will not be required further down the compute chain.
+            By default all of the available columns are projected. Raises
+            an exception if any of the referenced column names does not exist
+            in the dataset's Schema.
+        filter : Expression, default None
+            Scan will return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the
+            partition information or internal metadata found in the data
+            source, e.g. Parquet statistics. Otherwise filters the loaded
+            RecordBatches before yielding them.
+        batch_size : int, default 131_072
+            The maximum row count for scanned record batches. If scanned
+            record batches are overflowing memory then this method can be
+            called to reduce their size.
+        batch_readahead : int, default 16
+            The number of batches to read ahead in a file. This might not work
+            for all file formats. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_readahead : int, default 4
+            The number of files to read ahead. Increasing this number will increase
+            RAM usage but could also improve IO utilization.
+        fragment_scan_options : FragmentScanOptions, default None
+            Options specific to a particular scan and fragment type, which
+            can change between different scans of the same dataset.
+        use_threads : bool, default True
+            If enabled, then maximum parallelism will be used determined by
+            the number of available CPU cores.
+        cache_metadata : bool, default True
+            If enabled, metadata may be cached when scanning to speed up
+            repeated scans.
+        memory_pool : MemoryPool, default None
+            For memory allocations, if required. If not specified, uses the
+            default pool.
+        """
+        cdef:
+            shared_ptr[CScannerBuilder] builder
+            shared_ptr[CScanner] scanner
+            RecordBatchReader reader
+        if isinstance(source, pa.ipc.RecordBatchReader):
+            if schema:
+                raise ValueError('Cannot specify a schema when providing '
+                                 'a RecordBatchReader')
+            reader = source
+        elif hasattr(source, "__arrow_c_stream__"):
+            if schema:
+                raise ValueError(
+                    'Cannot specify a schema when providing an object '
+                    'implementing the Arrow PyCapsule Protocol')
+            reader = pa.ipc.RecordBatchReader.from_stream(source)
+        elif _is_iterable(source):
+            if schema is None:
+                raise ValueError('Must provide schema to construct scanner '
+                                 'from an iterable')
+            reader = pa.ipc.RecordBatchReader.from_batches(schema, source)
+        else:
+            raise TypeError('Expected a RecordBatchReader or an iterable of '
+                            'batches instead of the given type: ' +
+                            type(source).__name__)
+        builder = CScannerBuilder.FromRecordBatchReader(reader.reader)
+        _populate_builder(builder, columns=columns, filter=filter,
+                          batch_size=batch_size, batch_readahead=batch_readahead,
+                          fragment_readahead=fragment_readahead, use_threads=use_threads,
+                          cache_metadata=cache_metadata, memory_pool=memory_pool,
+                          fragment_scan_options=fragment_scan_options)
+        scanner = GetResultValue(builder.get().Finish())
+        return Scanner.wrap(scanner)
+
+    @property
+    def dataset_schema(self):
+        """The schema with which batches will be read from fragments."""
+        return pyarrow_wrap_schema(
+            self.scanner.options().get().dataset_schema)
+
+    @property
+    def projected_schema(self):
+        """
+        The materialized schema of the data, accounting for projections.
+
+        This is the schema of any data returned from the scanner.
+        """
+        return pyarrow_wrap_schema(
+            self.scanner.options().get().projected_schema)
+
+    def to_batches(self):
+        """
+        Consume a Scanner in record batches.
+
+        Returns
+        -------
+        record_batches : iterator of RecordBatch
+        """
+        def _iterator(batch_iter):
+            for batch in batch_iter:
+                yield batch.record_batch
+        # Don't make ourselves a generator so errors are raised immediately
+        return _iterator(self.scan_batches())
+
+    def scan_batches(self):
+        """
+        Consume a Scanner in record batches with corresponding fragments.
+
+        Returns
+        -------
+        record_batches : iterator of TaggedRecordBatch
+        """
+        cdef CTaggedRecordBatchIterator iterator
+        with nogil:
+            iterator = move(GetResultValue(self.scanner.ScanBatches()))
+        # Don't make ourselves a generator so errors are raised immediately
+        return TaggedRecordBatchIterator.wrap(self, move(iterator))
+
+    def to_table(self):
+        """
+        Convert a Scanner into a Table.
+
+        Use this convenience utility with care. This will serially materialize
+        the Scan result in memory before creating the Table.
+
+        Returns
+        -------
+        Table
+        """
+        cdef CResult[shared_ptr[CTable]] result
+
+        with nogil:
+            result = self.scanner.ToTable()
+
+        return pyarrow_wrap_table(GetResultValue(result))
+
+    def take(self, object indices):
+        """
+        Select rows of data by index.
+
+        Will only consume as many batches of the underlying dataset as
+        needed. Otherwise, this is equivalent to
+        ``to_table().take(indices)``.
+
+        Parameters
+        ----------
+        indices : Array or array-like
+            indices of rows to select in the dataset.
+
+        Returns
+        -------
+        Table
+        """
+        cdef CResult[shared_ptr[CTable]] result
+        cdef shared_ptr[CArray] c_indices
+
+        if not isinstance(indices, pa.Array):
+            indices = pa.array(indices)
+        c_indices = pyarrow_unwrap_array(indices)
+
+        with nogil:
+            result = self.scanner.TakeRows(deref(c_indices))
+        return pyarrow_wrap_table(GetResultValue(result))
+
+    def head(self, int num_rows):
+        """
+        Load the first N rows of the dataset.
+
+        Parameters
+        ----------
+        num_rows : int
+            The number of rows to load.
+
+        Returns
+        -------
+        Table
+        """
+        cdef CResult[shared_ptr[CTable]] result
+        with nogil:
+            result = self.scanner.Head(num_rows)
+        return pyarrow_wrap_table(GetResultValue(result))
+
+    def count_rows(self):
+        """
+        Count rows matching the scanner filter.
+
+        Returns
+        -------
+        count : int
+        """
+        cdef CResult[int64_t] result
+        with nogil:
+            result = self.scanner.CountRows()
+        return GetResultValue(result)
+
+    def to_reader(self):
+        """Consume this scanner as a RecordBatchReader.
+
+        Returns
+        -------
+        RecordBatchReader
+        """
+        cdef RecordBatchReader reader
+        reader = RecordBatchReader.__new__(RecordBatchReader)
+        reader.reader = GetResultValue(self.scanner.ToRecordBatchReader())
+        return reader
+
+
+def get_partition_keys(Expression partition_expression):
+    """
+    Extract partition keys (equality constraints between a field and a scalar)
+    from an expression as a dict mapping the field's name to its value.
+
+    NB: All expressions yielded by a HivePartitioning or DirectoryPartitioning
+    will be conjunctions of equality conditions and are accessible through this
+    function. Other subexpressions will be ignored.
+
+    Parameters
+    ----------
+    partition_expression : pyarrow.dataset.Expression
+
+    Returns
+    -------
+    dict
+
+    Examples
+    --------
+
+    For example, an expression of
+    <pyarrow.dataset.Expression ((part == A:string) and (year == 2016:int32))>
+    is converted to {'part': 'A', 'year': 2016}
+    """
+    cdef:
+        CExpression expr = partition_expression.unwrap()
+        pair[CFieldRef, CDatum] ref_val
+
+    out = {}
+    for ref_val in GetResultValue(CExtractKnownFieldValues(expr)).map:
+        assert ref_val.first.name() != nullptr
+        assert ref_val.second.kind() == DatumType_SCALAR
+        val = pyarrow_wrap_scalar(ref_val.second.scalar())
+        out[frombytes(deref(ref_val.first.name()))] = val.as_py()
+    return out
+
+
+cdef class WrittenFile(_Weakrefable):
+    """
+    Metadata information about files written as
+    part of a dataset write operation
+
+    Parameters
+    ----------
+    path : str
+        Path to the file.
+    metadata : pyarrow.parquet.FileMetaData, optional
+        For Parquet files, the Parquet file metadata.
+    size : int
+        The size of the file in bytes.
+    """
+
+    def __init__(self, path, metadata, size):
+        self.path = path
+        self.metadata = metadata
+        self.size = size
+
+
+cdef void _filesystemdataset_write_visitor(
+        dict visit_args,
+        CFileWriter* file_writer):
+    cdef:
+        str path
+        str base_dir
+        WrittenFile written_file
+        FileFormat file_format
+
+    path = frombytes(deref(file_writer).destination().path)
+    base_dir = frombytes(visit_args['base_dir'])
+    file_format = FileFormat.wrap(file_writer.format())
+    written_file = file_format._finish_write(path, base_dir, file_writer)
+    visit_args['file_visitor'](written_file)
+
+
+def _filesystemdataset_write(
+    Scanner data not None,
+    object base_dir not None,
+    str basename_template not None,
+    FileSystem filesystem not None,
+    Partitioning partitioning not None,
+    bool preserve_order,
+    FileWriteOptions file_options not None,
+    int max_partitions,
+    object file_visitor,
+    str existing_data_behavior not None,
+    int max_open_files,
+    int max_rows_per_file,
+    int min_rows_per_group,
+    int max_rows_per_group,
+    bool create_dir
+):
+    """
+    CFileSystemDataset.Write wrapper
+    """
+    cdef:
+        CFileSystemDatasetWriteOptions c_options
+        shared_ptr[CScanner] c_scanner
+        dict visit_args
+
+    c_options.file_write_options = file_options.unwrap()
+    c_options.filesystem = filesystem.unwrap()
+    c_options.base_dir = tobytes(_stringify_path(base_dir))
+    c_options.partitioning = partitioning.unwrap()
+    c_options.preserve_order = preserve_order
+    c_options.max_partitions = max_partitions
+    c_options.max_open_files = max_open_files
+    c_options.max_rows_per_file = max_rows_per_file
+    c_options.max_rows_per_group = max_rows_per_group
+    c_options.min_rows_per_group = min_rows_per_group
+    c_options.basename_template = tobytes(basename_template)
+    if existing_data_behavior == 'error':
+        c_options.existing_data_behavior = ExistingDataBehavior_ERROR
+    elif existing_data_behavior == 'overwrite_or_ignore':
+        c_options.existing_data_behavior =\
+            ExistingDataBehavior_OVERWRITE_OR_IGNORE
+    elif existing_data_behavior == 'delete_matching':
+        c_options.existing_data_behavior = ExistingDataBehavior_DELETE_MATCHING
+    else:
+        raise ValueError(
+            ("existing_data_behavior must be one of 'error', ",
+             "'overwrite_or_ignore' or 'delete_matching'")
+        )
+    c_options.create_dir = create_dir
+
+    if file_visitor is not None:
+        visit_args = {'base_dir': c_options.base_dir,
+                      'file_visitor': file_visitor}
+        # Need to use post_finish because parquet metadata is not available
+        # until after Finish has been called
+        c_options.writer_post_finish = BindFunction[cb_writer_finish_internal](
+            &_filesystemdataset_write_visitor, visit_args)
+
+    c_scanner = data.unwrap()
+    with nogil:
+        check_status(CFileSystemDataset.Write(c_options, c_scanner))
+
+
+cdef class _ScanNodeOptions(ExecNodeOptions):
+
+    def _set_options(self, Dataset dataset, dict scan_options):
+        cdef:
+            shared_ptr[CScanOptions] c_scan_options
+            bint require_sequenced_output=False
+            bint implicit_ordering=False
+
+        c_scan_options = Scanner._make_scan_options(dataset, scan_options)
+
+        require_sequenced_output=scan_options.get("require_sequenced_output", False)
+        implicit_ordering=scan_options.get("implicit_ordering", False)
+
+        self.wrapped.reset(
+            new CScanNodeOptions(dataset.unwrap(), c_scan_options, require_sequenced_output, implicit_ordering)
+        )
+
+
+class ScanNodeOptions(_ScanNodeOptions):
+    """
+    A Source node which yields batches from a Dataset scan.
+
+    This is the option class for the "scan" node factory.
+
+    This node is capable of applying pushdown projections or filters
+    to the file readers which reduce the amount of data that needs to
+    be read (if supported by the file format). But note that this does not
+    construct associated filter or project nodes to perform the final
+    filtering or projection. Rather, you may supply the same filter
+    expression or projection to the scan node that you also supply
+    to the filter or project node.
+
+    Yielded batches will be augmented with fragment/batch indices when
+    implicit_ordering=True to enable stable ordering for simple ExecPlans.
+
+    Parameters
+    ----------
+    dataset : pyarrow.dataset.Dataset
+        The table which acts as the data source.
+    **kwargs : dict, optional
+        Scan options. See `Scanner.from_dataset` for possible arguments.
+    require_sequenced_output : bool, default False
+        Batches are yielded sequentially, like single-threaded
+    implicit_ordering : bool, default False
+        Preserve implicit ordering of data.
+    """
+
+    def __init__(self, Dataset dataset, **kwargs):
+        self._set_options(dataset, kwargs)
diff --git a/shared/package-files/b5/b5735508c29c82415fe06113929531fd462f379ebee78b58082334fb4e0e227e/__init__.py b/shared/package-files/b5/b5735508c29c82415fe06113929531fd462f379ebee78b58082334fb4e0e227e/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..82b3aa56c653cd1241872c67e9d9016df04a6c5a
--- /dev/null
+++ b/shared/package-files/b5/b5735508c29c82415fe06113929531fd462f379ebee78b58082334fb4e0e227e/__init__.py
@@ -0,0 +1,29 @@
+def __getattr__(key: str):
+    # These imports need to be lazy to avoid circular import errors
+    if key == "hash_array":
+        from pandas.core.util.hashing import hash_array
+
+        return hash_array
+    if key == "hash_pandas_object":
+        from pandas.core.util.hashing import hash_pandas_object
+
+        return hash_pandas_object
+    if key == "Appender":
+        from pandas.util._decorators import Appender
+
+        return Appender
+    if key == "Substitution":
+        from pandas.util._decorators import Substitution
+
+        return Substitution
+
+    if key == "cache_readonly":
+        from pandas.util._decorators import cache_readonly
+
+        return cache_readonly
+
+    raise AttributeError(f"module 'pandas.util' has no attribute '{key}'")
+
+
+def capitalize_first_letter(s):
+    return s[:1].upper() + s[1:]
diff --git a/shared/package-files/b5/b57426b591cc4ae171daca21a9cf25f01735882f414c4354fb8a8438c881e8a0/training_provenance.json b/shared/package-files/b5/b57426b591cc4ae171daca21a9cf25f01735882f414c4354fb8a8438c881e8a0/training_provenance.json
new file mode 100644
index 0000000000000000000000000000000000000000..1e8d193f135c79e133954af02ae2d34d6f3c551a
--- /dev/null
+++ b/shared/package-files/b5/b57426b591cc4ae171daca21a9cf25f01735882f414c4354fb8a8438c881e8a0/training_provenance.json
@@ -0,0 +1,104477 @@
+{
+  "initialization": {
+    "distribution": "Normal(0,0.01)",
+    "seed": 20261021,
+    "pretrained_weights": false
+  },
+  "training_data": "all 19880 supplied training shots per condition; all 130 conditions",
+  "train_manifest_sha256": "ce46c21e3595cfc5885bbe2b6481105317d2e6267498ec4572601469825914b8",
+  "validation_manifest_sha256": "482bb7268c6c827ebbf1f756ee6994750acfcd011adab08eaa2ada703e5faea1",
+  "calibration_exception": "Supplied even-fitted DEMs include validation syndromes but no logical labels or test data.",
+  "optimizer": "L-BFGS-B binary cross entropy plus 1e-5 L2, maximum 300 iterations",
+  "selection": "First trained candidate; validation reported without parameter fitting or hyperparameter selection",
+  "test_data_used": false,
+  "released_per_shot_predictions_used": false,
+  "history": [
+    {
+      "group": "d3_bX_center_3_5",
+      "train_loss": 0.49244210477855016,
+      "iterations": 88,
+      "failure": 0.26941105769230766,
+      "plain_failure": 0.2847205528846154,
+      "correlated_failure": 0.2704326923076923,
+      "coset_failure": 0.2847205528846154,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_5_3",
+      "train_loss": 0.4947914557869911,
+      "iterations": 85,
+      "failure": 0.2706430288461538,
+      "plain_failure": 0.29103064903846154,
+      "correlated_failure": 0.27076322115384616,
+      "coset_failure": 0.29103064903846154,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_5_7",
+      "train_loss": 0.4786369068642458,
+      "iterations": 79,
+      "failure": 0.25964543269230766,
+      "plain_failure": 0.2741887019230769,
+      "correlated_failure": 0.2598106971153846,
+      "coset_failure": 0.2741887019230769,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bX_center_7_5",
+      "train_loss": 0.48299120621578956,
+      "iterations": 81,
+      "failure": 0.26062199519230766,
+      "plain_failure": 0.2762920673076923,
+      "correlated_failure": 0.2615234375,
+      "coset_failure": 0.2762920673076923,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_3_5",
+      "train_loss": 0.4903180822446507,
+      "iterations": 62,
+      "failure": 0.2718299278846154,
+      "plain_failure": 0.2878605769230769,
+      "correlated_failure": 0.2736778846153846,
+      "coset_failure": 0.2878605769230769,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_5_3",
+      "train_loss": 0.4886621962442568,
+      "iterations": 90,
+      "failure": 0.25896935096153845,
+      "plain_failure": 0.27548076923076925,
+      "correlated_failure": 0.2588942307692308,
+      "coset_failure": 0.27548076923076925,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_5_7",
+      "train_loss": 0.4822654448634678,
+      "iterations": 71,
+      "failure": 0.2594200721153846,
+      "plain_failure": 0.2823016826923077,
+      "correlated_failure": 0.25961538461538464,
+      "coset_failure": 0.2823016826923077,
+      "optimizer_success": true
+    },
+    {
+      "group": "d3_bZ_center_7_5",
+      "train_loss": 0.47379411844328495,
+      "iterations": 69,
+      "failure": 0.2542067307692308,
+      "plain_failure": 0.27393329326923077,
+      "correlated_failure": 0.2547776442307692,
+      "coset_failure": 0.27393329326923077,
+      "optimizer_success": true
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+      "runtime": "Two exact neural passes share one physical-prior computation; preserve all retained incumbent dispatches;fullreplay/scoring/readonlyrelocation/projection before any publication",
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+      "predictions": "/run-state/workspace/research/context_upgrades/turbo1/predictions",
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+      "verified_config": "turbo settings equal; physical DEM/plans and calibration/ensemble weights have identical SHA256",
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+        "dems/surface_code_bZ_d5_r01_center_5_5.dem",
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+        "dems/surface_code_bZ_d5_r23_center_5_5.dem",
+        "dems/surface_code_bZ_d5_r25_center_5_5.dem",
+        "plans/surface_code_bX_d5_r01_center_5_5.npz",
+        "plans/surface_code_bZ_d5_r01_center_5_5.npz",
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+            "elapsed_seconds": 49.39001536369324
+          },
+          {
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+            "elapsed_seconds": 51.99720120429993
+          },
+          {
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+            },
+            "condition_mean_gate_diagnostic": {
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+            "std_expert0_coefficient": 0.05232278257608414,
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+              "decision_differences": 0
+            },
+            "elapsed_seconds": 54.61855983734131
+          },
+          {
+            "condition": "surface_code_bZ_d5_r19_center_5_5",
+            "basis": "Z",
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+            "equal_trained_experts": {
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+            "dynamic_gate": {
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+            },
+            "condition_mean_gate_diagnostic": {
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+            "gate_corrected_equal_errors": 9,
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+            "std_expert0_coefficient": 0.050149668008089066,
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+            "elapsed_seconds": 57.23480463027954
+          },
+          {
+            "condition": "surface_code_bZ_d5_r21_center_5_5",
+            "basis": "Z",
+            "rounds": 21,
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+            "equal_trained_experts": {
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+            "dynamic_gate": {
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+            },
+            "condition_mean_gate_diagnostic": {
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+            },
+            "gate_corrected_equal_errors": 19,
+            "gate_introduced_equal_errors": 13,
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+            "std_expert0_coefficient": 0.047953102737665176,
+            "original_joint_inference_comparison": {
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+            },
+            "elapsed_seconds": 59.855043172836304
+          },
+          {
+            "condition": "surface_code_bZ_d5_r23_center_5_5",
+            "basis": "Z",
+            "rounds": 23,
+            "shots": 5120,
+            "expert0": {
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+            },
+            "expert1": {
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+            },
+            "equal_trained_experts": {
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+              "nll": 0.598072503622755
+            },
+            "dynamic_gate": {
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+              "nll": 0.5981155884918229
+            },
+            "condition_mean_gate_diagnostic": {
+              "failure": 0.354296875,
+              "nll": 0.5980860524780912
+            },
+            "gate_corrected_equal_errors": 14,
+            "gate_introduced_equal_errors": 17,
+            "mean_expert0_coefficient": 0.4296036660671234,
+            "std_expert0_coefficient": 0.046422749757766724,
+            "original_joint_inference_comparison": {
+              "max_probability_difference": 0.0,
+              "decision_differences": 0
+            },
+            "elapsed_seconds": 63.31757092475891
+          },
+          {
+            "condition": "surface_code_bZ_d5_r25_center_5_5",
+            "basis": "Z",
+            "rounds": 25,
+            "shots": 5120,
+            "expert0": {
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+              "nll": 0.6270165313839197
+            },
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+            },
+            "equal_trained_experts": {
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+            },
+            "dynamic_gate": {
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+            },
+            "condition_mean_gate_diagnostic": {
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+            },
+            "gate_corrected_equal_errors": 14,
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+            "mean_expert0_coefficient": 0.4255400598049164,
+            "std_expert0_coefficient": 0.04466654360294342,
+            "original_joint_inference_comparison": {
+              "max_probability_difference": 0.0,
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+            },
+            "elapsed_seconds": 66.79905652999878
+          }
+        ],
+        "seconds": 66.8079903125763,
+        "test_data_used": false,
+        "source_sha256": {
+          "joint_gate_ablation.py": "ea765d87c0c7495f3871bec5abe05200c866c6dcd52493f6068c99ac9ebfa818",
+          "joint_mixture.py": "07b111c55b7a380d387ef0955cb4ce894f6c76f6f84a5488c6ac80ae09415137",
+          "model.py": "3b2d4b34a8945ddfe82d76e28b4aa917f11933ed885c0cdcd2bc389448a871fb"
+        }
+      }
+    },
+    "new_optimizer_updates": 0,
+    "new_trainable_parameters": 0,
+    "test_data_used": false,
+    "initialization": "All extracted parameters descend from the joint study session-trained random-initialization parents; exact tensors are serialized without further fitting",
+    "selection_scope": "Adaptive follow-up proposed after observing validation branch diagnostics. All13 durations perbasis are included. Transductive condition-mean gates are excluded. This is not an independent confirmation dataset."
+  },
+  "package_validation": {
+    "performance": 0.7674034705528846,
+    "failure_by_distance": {
+      "d3": 0.23975736177884616,
+      "d5": 0.22543569711538464
+    },
+    "scorer": "Supplied qecbench.scoring on all 130 validation conditions and 665600 shots",
+    "contract_checks": [
+      {
+        "condition": "surface_code_bX_d3_r01_center_3_5",
+        "shots": 1,
+        "maximum_probability_difference": 0.0
+      },
+      {
+        "condition": "surface_code_bX_d3_r25_center_5_3",
+        "shots": 37,
+        "maximum_probability_difference": 0.0
+      },
+      {
+        "condition": "surface_code_bZ_d3_r13_center_5_7",
+        "shots": 1025,
+        "maximum_probability_difference": 0.0
+      },
+      {
+        "condition": "surface_code_bX_d5_r01_center_5_5",
+        "shots": 7,
+        "maximum_probability_difference": 0.0
+      },
+      {
+        "condition": "surface_code_bZ_d5_r09_center_5_5",
+        "shots": 257,
+        "maximum_probability_difference": 0.0
+      },
+      {
+        "condition": "surface_code_bX_d5_r25_center_5_5",
+        "shots": 17,
+        "maximum_probability_difference": 0.0
+      }
+    ],
+    "prediction_generation": "All26 d5 conditions replayed from the assembled package; unchanged d3 predictions retained. Full trusted scoring and relocated arbitrary-ID/subset checks.",
+    "runtime_evidence": "Full133120-shot d5 replay took 356.06s on one H100; d3 paths unchanged from the recorded incumbent.",
+    "d5_validation_runtime_seconds": 356.0588037967682,
+    "runtime_vs_cached": {
+      "scope": "Assembled runtime versus cached candidate predictions, including physical-prior recomputation and compilation/batching differences; descriptive, not a causal precision attribution",
+      "maximum_probability_difference": 4.988908767700195e-05,
+      "decision_differences": 1,
+      "source_sha256": "2e3e81d122f9d626d486d050c38d86bfd3e55bc6cd8e0e270b9e8eabbf69539a"
+    },
+    "contract_execution": {
+      "requested_verifier_uid": 10001,
+      "effective_uid": 1000,
+      "effective_gid": 1000,
+      "supplementary_groups": [],
+      "identity_status": "Development process is already unprivileged and cannot change UID; verifier UID10001 was not executed",
+      "model_and_input_copy": "Owned by development UID 1000,directories0555,files0444,relocated under a fresh accessible temporary parent",
+      "output_directory": "Owned by execution UID 1000,mode0700",
+      "PYTHONPATH": "",
+      "backend": "Runtime default; full selected subset cases",
+      "scope": "Local contract check; not a Docker/network/mount-isolation test"
+    }
+  }
+}
diff --git a/shared/package-files/b5/b5793ec751875a5679a9b3b62792be9ebec845e96821a9a9e93a9c591b770d4e/chain_matmul_compositeimplicitautograd_dispatch.h b/shared/package-files/b5/b5793ec751875a5679a9b3b62792be9ebec845e96821a9a9e93a9c591b770d4e/chain_matmul_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..379d1b7a5afe03d1bff0118f71418db7c0d00e89
--- /dev/null
+++ b/shared/package-files/b5/b5793ec751875a5679a9b3b62792be9ebec845e96821a9a9e93a9c591b770d4e/chain_matmul_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor chain_matmul(at::TensorList matrices);
+TORCH_API at::Tensor & chain_matmul_out(at::Tensor & out, at::TensorList matrices);
+TORCH_API at::Tensor & chain_matmul_outf(at::TensorList matrices, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/b5/b57e7c299c8f3f4decb689d39899e7be047c27e40bf0c4fa8ba20b1ed159ffc5/admonition.pyi b/shared/package-files/b5/b57e7c299c8f3f4decb689d39899e7be047c27e40bf0c4fa8ba20b1ed159ffc5/admonition.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..24005e29fd97c52d9912e87c156266aa867657e6
--- /dev/null
+++ b/shared/package-files/b5/b57e7c299c8f3f4decb689d39899e7be047c27e40bf0c4fa8ba20b1ed159ffc5/admonition.pyi
@@ -0,0 +1,15 @@
+from typing import Any, Pattern
+
+from markdown.blockprocessors import BlockProcessor
+from markdown.extensions import Extension
+
+class AdmonitionExtension(Extension): ...
+
+class AdmonitionProcessor(BlockProcessor):
+    CLASSNAME: str = ...
+    CLASSNAME_TITLE: str = ...
+    RE: Pattern
+    RE_SPACES: Any
+    def get_class_and_title(self, match): ...
+
+def makeExtension(**kwargs): ...
diff --git a/shared/package-files/b5/b582ad953b97318607bdfa66ae1742aa5ce4d01232a219766fee1bd1203a9c18/test_lsqr.py b/shared/package-files/b5/b582ad953b97318607bdfa66ae1742aa5ce4d01232a219766fee1bd1203a9c18/test_lsqr.py
new file mode 100644
index 0000000000000000000000000000000000000000..d77048af48a6b4495d23c9bc9a3b2d71466bade6
--- /dev/null
+++ b/shared/package-files/b5/b582ad953b97318607bdfa66ae1742aa5ce4d01232a219766fee1bd1203a9c18/test_lsqr.py
@@ -0,0 +1,120 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_equal, assert_equal
+import pytest
+import scipy.sparse
+import scipy.sparse.linalg
+from scipy.sparse.linalg import lsqr
+
+# Set up a test problem
+n = 35
+G = np.eye(n)
+normal = np.random.normal
+norm = np.linalg.norm
+
+for jj in range(5):
+    gg = normal(size=n)
+    hh = gg * gg.T
+    G += (hh + hh.T) * 0.5
+    G += normal(size=n) * normal(size=n)
+
+b = normal(size=n)
+
+# tolerance for atol/btol keywords of lsqr()
+tol = 2e-10
+# tolerances for testing the results of the lsqr() call with assert_allclose
+# These tolerances are a bit fragile - see discussion in gh-15301.
+atol_test = 4e-10
+rtol_test = 2e-8
+show = False
+maxit = None
+
+
+def test_lsqr_basic():
+    b_copy = b.copy()
+    xo, *_ = lsqr(G, b, show=show, atol=tol, btol=tol, iter_lim=maxit)
+    assert_array_equal(b_copy, b)
+
+    svx = np.linalg.solve(G, b)
+    assert_allclose(xo, svx, atol=atol_test, rtol=rtol_test)
+
+    # Now the same but with damp > 0.
+    # This is equivalent to solving the extended system:
+    # ( G      ) @ x = ( b )
+    # ( damp*I )       ( 0 )
+    damp = 1.5
+    xo, *_ = lsqr(
+        G, b, damp=damp, show=show, atol=tol, btol=tol, iter_lim=maxit)
+
+    Gext = np.r_[G, damp * np.eye(G.shape[1])]
+    bext = np.r_[b, np.zeros(G.shape[1])]
+    svx, *_ = np.linalg.lstsq(Gext, bext, rcond=None)
+    assert_allclose(xo, svx, atol=atol_test, rtol=rtol_test)
+
+
+def test_gh_2466():
+    row = np.array([0, 0])
+    col = np.array([0, 1])
+    val = np.array([1, -1])
+    A = scipy.sparse.coo_array((val, (row, col)), shape=(1, 2))
+    b = np.asarray([4])
+    lsqr(A, b)
+
+
+def test_well_conditioned_problems():
+    # Test that sparse the lsqr solver returns the right solution
+    # on various problems with different random seeds.
+    # This is a non-regression test for a potential ZeroDivisionError
+    # raised when computing the `test2` & `test3` convergence conditions.
+    n = 10
+    A_sparse = scipy.sparse.eye_array(n, n)
+    A_dense = A_sparse.toarray()
+
+    with np.errstate(invalid='raise'):
+        for seed in range(30):
+            rng = np.random.RandomState(seed + 10)
+            beta = rng.rand(n)
+            beta[beta == 0] = 0.00001  # ensure that all the betas are not null
+            b = A_sparse @ beta[:, np.newaxis]
+            output = lsqr(A_sparse, b, show=show)
+
+            # Check that the termination condition corresponds to an approximate
+            # solution to Ax = b
+            assert_equal(output[1], 1)
+            solution = output[0]
+
+            # Check that we recover the ground truth solution
+            assert_allclose(solution, beta)
+
+            # Sanity check: compare to the dense array solver
+            reference_solution = np.linalg.solve(A_dense, b).ravel()
+            assert_allclose(solution, reference_solution)
+
+
+def test_b_shapes():
+    # Test b being a scalar.
+    A = np.array([[1.0, 2.0]])
+    b = 3.0
+    x = lsqr(A, b)[0]
+    assert norm(A.dot(x) - b) == pytest.approx(0)
+
+    # Test b being a column vector.
+    A = np.eye(10)
+    b = np.ones((10, 1))
+    x = lsqr(A, b)[0]
+    assert norm(A.dot(x) - b.ravel()) == pytest.approx(0)
+
+
+def test_initialization():
+    # Test the default setting is the same as zeros
+    b_copy = b.copy()
+    x_ref = lsqr(G, b, show=show, atol=tol, btol=tol, iter_lim=maxit)
+    x0 = np.zeros(x_ref[0].shape)
+    x = lsqr(G, b, show=show, atol=tol, btol=tol, iter_lim=maxit, x0=x0)
+    assert_array_equal(b_copy, b)
+    assert_allclose(x_ref[0], x[0])
+
+    # Test warm-start with single iteration
+    x0 = lsqr(G, b, show=show, atol=tol, btol=tol, iter_lim=1)[0]
+    x = lsqr(G, b, show=show, atol=tol, btol=tol, iter_lim=maxit, x0=x0)
+    assert_allclose(x_ref[0], x[0])
+    assert_array_equal(b_copy, b)
diff --git a/shared/package-files/b5/b58d529c2ecd0533d3f9c3dc4c01f5017ed296563ff2990ac89adda223bb5ac1/log_sigmoid_backward.h b/shared/package-files/b5/b58d529c2ecd0533d3f9c3dc4c01f5017ed296563ff2990ac89adda223bb5ac1/log_sigmoid_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e341fb599a50be0412c4517f59663ac524538f5
--- /dev/null
+++ b/shared/package-files/b5/b58d529c2ecd0533d3f9c3dc4c01f5017ed296563ff2990ac89adda223bb5ac1/log_sigmoid_backward.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/log_sigmoid_backward_ops.h>
+
+namespace at {
+
+
+// aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) {
+    return at::_ops::log_sigmoid_backward_grad_input::call(grad_output, self, buffer, grad_input);
+}
+// aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & log_sigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) {
+    return at::_ops::log_sigmoid_backward_grad_input::call(grad_output, self, buffer, grad_input);
+}
+
+// aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor
+inline at::Tensor log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) {
+    return at::_ops::log_sigmoid_backward::call(grad_output, self, buffer);
+}
+
+}
diff --git a/shared/package-files/b5/b58daa7281ae8608eb6cdde16aa16932cc15f2fd8a3fa56efb158a398d17ca43/_linalg_eigh_cpu_dispatch.h b/shared/package-files/b5/b58daa7281ae8608eb6cdde16aa16932cc15f2fd8a3fa56efb158a398d17ca43/_linalg_eigh_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..34ce1d188551928520447d8844b3fe99fe206c60
--- /dev/null
+++ b/shared/package-files/b5/b58daa7281ae8608eb6cdde16aa16932cc15f2fd8a3fa56efb158a398d17ca43/_linalg_eigh_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _linalg_eigh(const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_outf(const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/b5/b5962daae02ca2b93d570c37bf45aef79a6604f808efed229924eba84a9f92ce/take_native.h b/shared/package-files/b5/b5962daae02ca2b93d570c37bf45aef79a6604f808efed229924eba84a9f92ce/take_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..1ea5b9b93335c3f1b92ac3701f83ac5a2c79942e
--- /dev/null
+++ b/shared/package-files/b5/b5962daae02ca2b93d570c37bf45aef79a6604f808efed229924eba84a9f92ce/take_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor take(const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_out(const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/b5/b597271fb13f4677b65b03bec19bee5e877583060d9d8f4ecda2466ec2b438e4/unbind_copy_compositeexplicitautograd_dispatch.h b/shared/package-files/b5/b597271fb13f4677b65b03bec19bee5e877583060d9d8f4ecda2466ec2b438e4/unbind_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..66be9d3fabf684b97acf382b385ba54d205dec8e
--- /dev/null
+++ b/shared/package-files/b5/b597271fb13f4677b65b03bec19bee5e877583060d9d8f4ecda2466ec2b438e4/unbind_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void unbind_copy_out(at::TensorList out, const at::Tensor & self, int64_t dim=0);
+TORCH_API void unbind_copy_outf(const at::Tensor & self, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/b5/b59af94124586de66632e1c65bb6741d9e2d8ffece83639858f58a164f34df26/PngImagePlugin.py b/shared/package-files/b5/b59af94124586de66632e1c65bb6741d9e2d8ffece83639858f58a164f34df26/PngImagePlugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4ed93880115571ec013f219688b34d558167020
--- /dev/null
+++ b/shared/package-files/b5/b59af94124586de66632e1c65bb6741d9e2d8ffece83639858f58a164f34df26/PngImagePlugin.py
@@ -0,0 +1,1460 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# PNG support code
+#
+# See "PNG (Portable Network Graphics) Specification, version 1.0;
+# W3C Recommendation", 1996-10-01, Thomas Boutell (ed.).
+#
+# history:
+# 1996-05-06 fl   Created (couldn't resist it)
+# 1996-12-14 fl   Upgraded, added read and verify support (0.2)
+# 1996-12-15 fl   Separate PNG stream parser
+# 1996-12-29 fl   Added write support, added getchunks
+# 1996-12-30 fl   Eliminated circular references in decoder (0.3)
+# 1998-07-12 fl   Read/write 16-bit images as mode I (0.4)
+# 2001-02-08 fl   Added transparency support (from Zircon) (0.5)
+# 2001-04-16 fl   Don't close data source in "open" method (0.6)
+# 2004-02-24 fl   Don't even pretend to support interlaced files (0.7)
+# 2004-08-31 fl   Do basic sanity check on chunk identifiers (0.8)
+# 2004-09-20 fl   Added PngInfo chunk container
+# 2004-12-18 fl   Added DPI read support (based on code by Niki Spahiev)
+# 2008-08-13 fl   Added tRNS support for RGB images
+# 2009-03-06 fl   Support for preserving ICC profiles (by Florian Hoech)
+# 2009-03-08 fl   Added zTXT support (from Lowell Alleman)
+# 2009-03-29 fl   Read interlaced PNG files (from Conrado Porto Lopes Gouvua)
+#
+# Copyright (c) 1997-2009 by Secret Labs AB
+# Copyright (c) 1996 by Fredrik Lundh
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import itertools
+import logging
+import re
+import struct
+import warnings
+import zlib
+from enum import IntEnum
+
+from . import Image, ImageChops, ImageFile, ImagePalette, ImageSequence
+from ._binary import i16be as i16
+from ._binary import i32be as i32
+from ._binary import o8
+from ._binary import o16be as o16
+from ._binary import o32be as o32
+
+logger = logging.getLogger(__name__)
+
+is_cid = re.compile(rb"\w\w\w\w").match
+
+
+_MAGIC = b"\211PNG\r\n\032\n"
+
+
+_MODES = {
+    # supported bits/color combinations, and corresponding modes/rawmodes
+    # Grayscale
+    (1, 0): ("1", "1"),
+    (2, 0): ("L", "L;2"),
+    (4, 0): ("L", "L;4"),
+    (8, 0): ("L", "L"),
+    (16, 0): ("I", "I;16B"),
+    # Truecolour
+    (8, 2): ("RGB", "RGB"),
+    (16, 2): ("RGB", "RGB;16B"),
+    # Indexed-colour
+    (1, 3): ("P", "P;1"),
+    (2, 3): ("P", "P;2"),
+    (4, 3): ("P", "P;4"),
+    (8, 3): ("P", "P"),
+    # Grayscale with alpha
+    (8, 4): ("LA", "LA"),
+    (16, 4): ("RGBA", "LA;16B"),  # LA;16B->LA not yet available
+    # Truecolour with alpha
+    (8, 6): ("RGBA", "RGBA"),
+    (16, 6): ("RGBA", "RGBA;16B"),
+}
+
+
+_simple_palette = re.compile(b"^\xff*\x00\xff*$")
+
+MAX_TEXT_CHUNK = ImageFile.SAFEBLOCK
+"""
+Maximum decompressed size for a iTXt or zTXt chunk.
+Eliminates decompression bombs where compressed chunks can expand 1000x.
+See :ref:`Text in PNG File Format<png-text>`.
+"""
+MAX_TEXT_MEMORY = 64 * MAX_TEXT_CHUNK
+"""
+Set the maximum total text chunk size.
+See :ref:`Text in PNG File Format<png-text>`.
+"""
+
+
+# APNG frame disposal modes
+class Disposal(IntEnum):
+    OP_NONE = 0
+    """
+    No disposal is done on this frame before rendering the next frame.
+    See :ref:`Saving APNG sequences<apng-saving>`.
+    """
+    OP_BACKGROUND = 1
+    """
+    This frame’s modified region is cleared to fully transparent black before rendering
+    the next frame.
+    See :ref:`Saving APNG sequences<apng-saving>`.
+    """
+    OP_PREVIOUS = 2
+    """
+    This frame’s modified region is reverted to the previous frame’s contents before
+    rendering the next frame.
+    See :ref:`Saving APNG sequences<apng-saving>`.
+    """
+
+
+# APNG frame blend modes
+class Blend(IntEnum):
+    OP_SOURCE = 0
+    """
+    All color components of this frame, including alpha, overwrite the previous output
+    image contents.
+    See :ref:`Saving APNG sequences<apng-saving>`.
+    """
+    OP_OVER = 1
+    """
+    This frame should be alpha composited with the previous output image contents.
+    See :ref:`Saving APNG sequences<apng-saving>`.
+    """
+
+
+def _safe_zlib_decompress(s):
+    dobj = zlib.decompressobj()
+    plaintext = dobj.decompress(s, MAX_TEXT_CHUNK)
+    if dobj.unconsumed_tail:
+        msg = "Decompressed Data Too Large"
+        raise ValueError(msg)
+    return plaintext
+
+
+def _crc32(data, seed=0):
+    return zlib.crc32(data, seed) & 0xFFFFFFFF
+
+
+# --------------------------------------------------------------------
+# Support classes.  Suitable for PNG and related formats like MNG etc.
+
+
+class ChunkStream:
+    def __init__(self, fp):
+        self.fp = fp
+        self.queue = []
+
+    def read(self):
+        """Fetch a new chunk. Returns header information."""
+        cid = None
+
+        if self.queue:
+            cid, pos, length = self.queue.pop()
+            self.fp.seek(pos)
+        else:
+            s = self.fp.read(8)
+            cid = s[4:]
+            pos = self.fp.tell()
+            length = i32(s)
+
+        if not is_cid(cid):
+            if not ImageFile.LOAD_TRUNCATED_IMAGES:
+                msg = f"broken PNG file (chunk {repr(cid)})"
+                raise SyntaxError(msg)
+
+        return cid, pos, length
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+    def close(self):
+        self.queue = self.fp = None
+
+    def push(self, cid, pos, length):
+        self.queue.append((cid, pos, length))
+
+    def call(self, cid, pos, length):
+        """Call the appropriate chunk handler"""
+
+        logger.debug("STREAM %r %s %s", cid, pos, length)
+        return getattr(self, "chunk_" + cid.decode("ascii"))(pos, length)
+
+    def crc(self, cid, data):
+        """Read and verify checksum"""
+
+        # Skip CRC checks for ancillary chunks if allowed to load truncated
+        # images
+        # 5th byte of first char is 1 [specs, section 5.4]
+        if ImageFile.LOAD_TRUNCATED_IMAGES and (cid[0] >> 5 & 1):
+            self.crc_skip(cid, data)
+            return
+
+        try:
+            crc1 = _crc32(data, _crc32(cid))
+            crc2 = i32(self.fp.read(4))
+            if crc1 != crc2:
+                msg = f"broken PNG file (bad header checksum in {repr(cid)})"
+                raise SyntaxError(msg)
+        except struct.error as e:
+            msg = f"broken PNG file (incomplete checksum in {repr(cid)})"
+            raise SyntaxError(msg) from e
+
+    def crc_skip(self, cid, data):
+        """Read checksum"""
+
+        self.fp.read(4)
+
+    def verify(self, endchunk=b"IEND"):
+        # Simple approach; just calculate checksum for all remaining
+        # blocks.  Must be called directly after open.
+
+        cids = []
+
+        while True:
+            try:
+                cid, pos, length = self.read()
+            except struct.error as e:
+                msg = "truncated PNG file"
+                raise OSError(msg) from e
+
+            if cid == endchunk:
+                break
+            self.crc(cid, ImageFile._safe_read(self.fp, length))
+            cids.append(cid)
+
+        return cids
+
+
+class iTXt(str):
+    """
+    Subclass of string to allow iTXt chunks to look like strings while
+    keeping their extra information
+
+    """
+
+    @staticmethod
+    def __new__(cls, text, lang=None, tkey=None):
+        """
+        :param cls: the class to use when creating the instance
+        :param text: value for this key
+        :param lang: language code
+        :param tkey: UTF-8 version of the key name
+        """
+
+        self = str.__new__(cls, text)
+        self.lang = lang
+        self.tkey = tkey
+        return self
+
+
+class PngInfo:
+    """
+    PNG chunk container (for use with save(pnginfo=))
+
+    """
+
+    def __init__(self):
+        self.chunks = []
+
+    def add(self, cid, data, after_idat=False):
+        """Appends an arbitrary chunk. Use with caution.
+
+        :param cid: a byte string, 4 bytes long.
+        :param data: a byte string of the encoded data
+        :param after_idat: for use with private chunks. Whether the chunk
+                           should be written after IDAT
+
+        """
+
+        chunk = [cid, data]
+        if after_idat:
+            chunk.append(True)
+        self.chunks.append(tuple(chunk))
+
+    def add_itxt(self, key, value, lang="", tkey="", zip=False):
+        """Appends an iTXt chunk.
+
+        :param key: latin-1 encodable text key name
+        :param value: value for this key
+        :param lang: language code
+        :param tkey: UTF-8 version of the key name
+        :param zip: compression flag
+
+        """
+
+        if not isinstance(key, bytes):
+            key = key.encode("latin-1", "strict")
+        if not isinstance(value, bytes):
+            value = value.encode("utf-8", "strict")
+        if not isinstance(lang, bytes):
+            lang = lang.encode("utf-8", "strict")
+        if not isinstance(tkey, bytes):
+            tkey = tkey.encode("utf-8", "strict")
+
+        if zip:
+            self.add(
+                b"iTXt",
+                key + b"\0\x01\0" + lang + b"\0" + tkey + b"\0" + zlib.compress(value),
+            )
+        else:
+            self.add(b"iTXt", key + b"\0\0\0" + lang + b"\0" + tkey + b"\0" + value)
+
+    def add_text(self, key, value, zip=False):
+        """Appends a text chunk.
+
+        :param key: latin-1 encodable text key name
+        :param value: value for this key, text or an
+           :py:class:`PIL.PngImagePlugin.iTXt` instance
+        :param zip: compression flag
+
+        """
+        if isinstance(value, iTXt):
+            return self.add_itxt(key, value, value.lang, value.tkey, zip=zip)
+
+        # The tEXt chunk stores latin-1 text
+        if not isinstance(value, bytes):
+            try:
+                value = value.encode("latin-1", "strict")
+            except UnicodeError:
+                return self.add_itxt(key, value, zip=zip)
+
+        if not isinstance(key, bytes):
+            key = key.encode("latin-1", "strict")
+
+        if zip:
+            self.add(b"zTXt", key + b"\0\0" + zlib.compress(value))
+        else:
+            self.add(b"tEXt", key + b"\0" + value)
+
+
+# --------------------------------------------------------------------
+# PNG image stream (IHDR/IEND)
+
+
+class PngStream(ChunkStream):
+    def __init__(self, fp):
+        super().__init__(fp)
+
+        # local copies of Image attributes
+        self.im_info = {}
+        self.im_text = {}
+        self.im_size = (0, 0)
+        self.im_mode = None
+        self.im_tile = None
+        self.im_palette = None
+        self.im_custom_mimetype = None
+        self.im_n_frames = None
+        self._seq_num = None
+        self.rewind_state = None
+
+        self.text_memory = 0
+
+    def check_text_memory(self, chunklen):
+        self.text_memory += chunklen
+        if self.text_memory > MAX_TEXT_MEMORY:
+            msg = (
+                "Too much memory used in text chunks: "
+                f"{self.text_memory}>MAX_TEXT_MEMORY"
+            )
+            raise ValueError(msg)
+
+    def save_rewind(self):
+        self.rewind_state = {
+            "info": self.im_info.copy(),
+            "tile": self.im_tile,
+            "seq_num": self._seq_num,
+        }
+
+    def rewind(self):
+        self.im_info = self.rewind_state["info"]
+        self.im_tile = self.rewind_state["tile"]
+        self._seq_num = self.rewind_state["seq_num"]
+
+    def chunk_iCCP(self, pos, length):
+        # ICC profile
+        s = ImageFile._safe_read(self.fp, length)
+        # according to PNG spec, the iCCP chunk contains:
+        # Profile name  1-79 bytes (character string)
+        # Null separator        1 byte (null character)
+        # Compression method    1 byte (0)
+        # Compressed profile    n bytes (zlib with deflate compression)
+        i = s.find(b"\0")
+        logger.debug("iCCP profile name %r", s[:i])
+        logger.debug("Compression method %s", s[i])
+        comp_method = s[i]
+        if comp_method != 0:
+            msg = f"Unknown compression method {comp_method} in iCCP chunk"
+            raise SyntaxError(msg)
+        try:
+            icc_profile = _safe_zlib_decompress(s[i + 2 :])
+        except ValueError:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                icc_profile = None
+            else:
+                raise
+        except zlib.error:
+            icc_profile = None  # FIXME
+        self.im_info["icc_profile"] = icc_profile
+        return s
+
+    def chunk_IHDR(self, pos, length):
+        # image header
+        s = ImageFile._safe_read(self.fp, length)
+        if length < 13:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                return s
+            msg = "Truncated IHDR chunk"
+            raise ValueError(msg)
+        self.im_size = i32(s, 0), i32(s, 4)
+        try:
+            self.im_mode, self.im_rawmode = _MODES[(s[8], s[9])]
+        except Exception:
+            pass
+        if s[12]:
+            self.im_info["interlace"] = 1
+        if s[11]:
+            msg = "unknown filter category"
+            raise SyntaxError(msg)
+        return s
+
+    def chunk_IDAT(self, pos, length):
+        # image data
+        if "bbox" in self.im_info:
+            tile = [("zip", self.im_info["bbox"], pos, self.im_rawmode)]
+        else:
+            if self.im_n_frames is not None:
+                self.im_info["default_image"] = True
+            tile = [("zip", (0, 0) + self.im_size, pos, self.im_rawmode)]
+        self.im_tile = tile
+        self.im_idat = length
+        msg = "image data found"
+        raise EOFError(msg)
+
+    def chunk_IEND(self, pos, length):
+        msg = "end of PNG image"
+        raise EOFError(msg)
+
+    def chunk_PLTE(self, pos, length):
+        # palette
+        s = ImageFile._safe_read(self.fp, length)
+        if self.im_mode == "P":
+            self.im_palette = "RGB", s
+        return s
+
+    def chunk_tRNS(self, pos, length):
+        # transparency
+        s = ImageFile._safe_read(self.fp, length)
+        if self.im_mode == "P":
+            if _simple_palette.match(s):
+                # tRNS contains only one full-transparent entry,
+                # other entries are full opaque
+                i = s.find(b"\0")
+                if i >= 0:
+                    self.im_info["transparency"] = i
+            else:
+                # otherwise, we have a byte string with one alpha value
+                # for each palette entry
+                self.im_info["transparency"] = s
+        elif self.im_mode in ("1", "L", "I"):
+            self.im_info["transparency"] = i16(s)
+        elif self.im_mode == "RGB":
+            self.im_info["transparency"] = i16(s), i16(s, 2), i16(s, 4)
+        return s
+
+    def chunk_gAMA(self, pos, length):
+        # gamma setting
+        s = ImageFile._safe_read(self.fp, length)
+        self.im_info["gamma"] = i32(s) / 100000.0
+        return s
+
+    def chunk_cHRM(self, pos, length):
+        # chromaticity, 8 unsigned ints, actual value is scaled by 100,000
+        # WP x,y, Red x,y, Green x,y Blue x,y
+
+        s = ImageFile._safe_read(self.fp, length)
+        raw_vals = struct.unpack(">%dI" % (len(s) // 4), s)
+        self.im_info["chromaticity"] = tuple(elt / 100000.0 for elt in raw_vals)
+        return s
+
+    def chunk_sRGB(self, pos, length):
+        # srgb rendering intent, 1 byte
+        # 0 perceptual
+        # 1 relative colorimetric
+        # 2 saturation
+        # 3 absolute colorimetric
+
+        s = ImageFile._safe_read(self.fp, length)
+        if length < 1:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                return s
+            msg = "Truncated sRGB chunk"
+            raise ValueError(msg)
+        self.im_info["srgb"] = s[0]
+        return s
+
+    def chunk_pHYs(self, pos, length):
+        # pixels per unit
+        s = ImageFile._safe_read(self.fp, length)
+        if length < 9:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                return s
+            msg = "Truncated pHYs chunk"
+            raise ValueError(msg)
+        px, py = i32(s, 0), i32(s, 4)
+        unit = s[8]
+        if unit == 1:  # meter
+            dpi = px * 0.0254, py * 0.0254
+            self.im_info["dpi"] = dpi
+        elif unit == 0:
+            self.im_info["aspect"] = px, py
+        return s
+
+    def chunk_tEXt(self, pos, length):
+        # text
+        s = ImageFile._safe_read(self.fp, length)
+        try:
+            k, v = s.split(b"\0", 1)
+        except ValueError:
+            # fallback for broken tEXt tags
+            k = s
+            v = b""
+        if k:
+            k = k.decode("latin-1", "strict")
+            v_str = v.decode("latin-1", "replace")
+
+            self.im_info[k] = v if k == "exif" else v_str
+            self.im_text[k] = v_str
+            self.check_text_memory(len(v_str))
+
+        return s
+
+    def chunk_zTXt(self, pos, length):
+        # compressed text
+        s = ImageFile._safe_read(self.fp, length)
+        try:
+            k, v = s.split(b"\0", 1)
+        except ValueError:
+            k = s
+            v = b""
+        if v:
+            comp_method = v[0]
+        else:
+            comp_method = 0
+        if comp_method != 0:
+            msg = f"Unknown compression method {comp_method} in zTXt chunk"
+            raise SyntaxError(msg)
+        try:
+            v = _safe_zlib_decompress(v[1:])
+        except ValueError:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                v = b""
+            else:
+                raise
+        except zlib.error:
+            v = b""
+
+        if k:
+            k = k.decode("latin-1", "strict")
+            v = v.decode("latin-1", "replace")
+
+            self.im_info[k] = self.im_text[k] = v
+            self.check_text_memory(len(v))
+
+        return s
+
+    def chunk_iTXt(self, pos, length):
+        # international text
+        r = s = ImageFile._safe_read(self.fp, length)
+        try:
+            k, r = r.split(b"\0", 1)
+        except ValueError:
+            return s
+        if len(r) < 2:
+            return s
+        cf, cm, r = r[0], r[1], r[2:]
+        try:
+            lang, tk, v = r.split(b"\0", 2)
+        except ValueError:
+            return s
+        if cf != 0:
+            if cm == 0:
+                try:
+                    v = _safe_zlib_decompress(v)
+                except ValueError:
+                    if ImageFile.LOAD_TRUNCATED_IMAGES:
+                        return s
+                    else:
+                        raise
+                except zlib.error:
+                    return s
+            else:
+                return s
+        try:
+            k = k.decode("latin-1", "strict")
+            lang = lang.decode("utf-8", "strict")
+            tk = tk.decode("utf-8", "strict")
+            v = v.decode("utf-8", "strict")
+        except UnicodeError:
+            return s
+
+        self.im_info[k] = self.im_text[k] = iTXt(v, lang, tk)
+        self.check_text_memory(len(v))
+
+        return s
+
+    def chunk_eXIf(self, pos, length):
+        s = ImageFile._safe_read(self.fp, length)
+        self.im_info["exif"] = b"Exif\x00\x00" + s
+        return s
+
+    # APNG chunks
+    def chunk_acTL(self, pos, length):
+        s = ImageFile._safe_read(self.fp, length)
+        if length < 8:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                return s
+            msg = "APNG contains truncated acTL chunk"
+            raise ValueError(msg)
+        if self.im_n_frames is not None:
+            self.im_n_frames = None
+            warnings.warn("Invalid APNG, will use default PNG image if possible")
+            return s
+        n_frames = i32(s)
+        if n_frames == 0 or n_frames > 0x80000000:
+            warnings.warn("Invalid APNG, will use default PNG image if possible")
+            return s
+        self.im_n_frames = n_frames
+        self.im_info["loop"] = i32(s, 4)
+        self.im_custom_mimetype = "image/apng"
+        return s
+
+    def chunk_fcTL(self, pos, length):
+        s = ImageFile._safe_read(self.fp, length)
+        if length < 26:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                return s
+            msg = "APNG contains truncated fcTL chunk"
+            raise ValueError(msg)
+        seq = i32(s)
+        if (self._seq_num is None and seq != 0) or (
+            self._seq_num is not None and self._seq_num != seq - 1
+        ):
+            msg = "APNG contains frame sequence errors"
+            raise SyntaxError(msg)
+        self._seq_num = seq
+        width, height = i32(s, 4), i32(s, 8)
+        px, py = i32(s, 12), i32(s, 16)
+        im_w, im_h = self.im_size
+        if px + width > im_w or py + height > im_h:
+            msg = "APNG contains invalid frames"
+            raise SyntaxError(msg)
+        self.im_info["bbox"] = (px, py, px + width, py + height)
+        delay_num, delay_den = i16(s, 20), i16(s, 22)
+        if delay_den == 0:
+            delay_den = 100
+        self.im_info["duration"] = float(delay_num) / float(delay_den) * 1000
+        self.im_info["disposal"] = s[24]
+        self.im_info["blend"] = s[25]
+        return s
+
+    def chunk_fdAT(self, pos, length):
+        if length < 4:
+            if ImageFile.LOAD_TRUNCATED_IMAGES:
+                s = ImageFile._safe_read(self.fp, length)
+                return s
+            msg = "APNG contains truncated fDAT chunk"
+            raise ValueError(msg)
+        s = ImageFile._safe_read(self.fp, 4)
+        seq = i32(s)
+        if self._seq_num != seq - 1:
+            msg = "APNG contains frame sequence errors"
+            raise SyntaxError(msg)
+        self._seq_num = seq
+        return self.chunk_IDAT(pos + 4, length - 4)
+
+
+# --------------------------------------------------------------------
+# PNG reader
+
+
+def _accept(prefix):
+    return prefix[:8] == _MAGIC
+
+
+##
+# Image plugin for PNG images.
+
+
+class PngImageFile(ImageFile.ImageFile):
+    format = "PNG"
+    format_description = "Portable network graphics"
+
+    def _open(self):
+        if not _accept(self.fp.read(8)):
+            msg = "not a PNG file"
+            raise SyntaxError(msg)
+        self._fp = self.fp
+        self.__frame = 0
+
+        #
+        # Parse headers up to the first IDAT or fDAT chunk
+
+        self.private_chunks = []
+        self.png = PngStream(self.fp)
+
+        while True:
+            #
+            # get next chunk
+
+            cid, pos, length = self.png.read()
+
+            try:
+                s = self.png.call(cid, pos, length)
+            except EOFError:
+                break
+            except AttributeError:
+                logger.debug("%r %s %s (unknown)", cid, pos, length)
+                s = ImageFile._safe_read(self.fp, length)
+                if cid[1:2].islower():
+                    self.private_chunks.append((cid, s))
+
+            self.png.crc(cid, s)
+
+        #
+        # Copy relevant attributes from the PngStream.  An alternative
+        # would be to let the PngStream class modify these attributes
+        # directly, but that introduces circular references which are
+        # difficult to break if things go wrong in the decoder...
+        # (believe me, I've tried ;-)
+
+        self._mode = self.png.im_mode
+        self._size = self.png.im_size
+        self.info = self.png.im_info
+        self._text = None
+        self.tile = self.png.im_tile
+        self.custom_mimetype = self.png.im_custom_mimetype
+        self.n_frames = self.png.im_n_frames or 1
+        self.default_image = self.info.get("default_image", False)
+
+        if self.png.im_palette:
+            rawmode, data = self.png.im_palette
+            self.palette = ImagePalette.raw(rawmode, data)
+
+        if cid == b"fdAT":
+            self.__prepare_idat = length - 4
+        else:
+            self.__prepare_idat = length  # used by load_prepare()
+
+        if self.png.im_n_frames is not None:
+            self._close_exclusive_fp_after_loading = False
+            self.png.save_rewind()
+            self.__rewind_idat = self.__prepare_idat
+            self.__rewind = self._fp.tell()
+            if self.default_image:
+                # IDAT chunk contains default image and not first animation frame
+                self.n_frames += 1
+            self._seek(0)
+        self.is_animated = self.n_frames > 1
+
+    @property
+    def text(self):
+        # experimental
+        if self._text is None:
+            # iTxt, tEXt and zTXt chunks may appear at the end of the file
+            # So load the file to ensure that they are read
+            if self.is_animated:
+                frame = self.__frame
+                # for APNG, seek to the final frame before loading
+                self.seek(self.n_frames - 1)
+            self.load()
+            if self.is_animated:
+                self.seek(frame)
+        return self._text
+
+    def verify(self):
+        """Verify PNG file"""
+
+        if self.fp is None:
+            msg = "verify must be called directly after open"
+            raise RuntimeError(msg)
+
+        # back up to beginning of IDAT block
+        self.fp.seek(self.tile[0][2] - 8)
+
+        self.png.verify()
+        self.png.close()
+
+        if self._exclusive_fp:
+            self.fp.close()
+        self.fp = None
+
+    def seek(self, frame):
+        if not self._seek_check(frame):
+            return
+        if frame < self.__frame:
+            self._seek(0, True)
+
+        last_frame = self.__frame
+        for f in range(self.__frame + 1, frame + 1):
+            try:
+                self._seek(f)
+            except EOFError as e:
+                self.seek(last_frame)
+                msg = "no more images in APNG file"
+                raise EOFError(msg) from e
+
+    def _seek(self, frame, rewind=False):
+        if frame == 0:
+            if rewind:
+                self._fp.seek(self.__rewind)
+                self.png.rewind()
+                self.__prepare_idat = self.__rewind_idat
+                self.im = None
+                if self.pyaccess:
+                    self.pyaccess = None
+                self.info = self.png.im_info
+                self.tile = self.png.im_tile
+                self.fp = self._fp
+            self._prev_im = None
+            self.dispose = None
+            self.default_image = self.info.get("default_image", False)
+            self.dispose_op = self.info.get("disposal")
+            self.blend_op = self.info.get("blend")
+            self.dispose_extent = self.info.get("bbox")
+            self.__frame = 0
+        else:
+            if frame != self.__frame + 1:
+                msg = f"cannot seek to frame {frame}"
+                raise ValueError(msg)
+
+            # ensure previous frame was loaded
+            self.load()
+
+            if self.dispose:
+                self.im.paste(self.dispose, self.dispose_extent)
+            self._prev_im = self.im.copy()
+
+            self.fp = self._fp
+
+            # advance to the next frame
+            if self.__prepare_idat:
+                ImageFile._safe_read(self.fp, self.__prepare_idat)
+                self.__prepare_idat = 0
+            frame_start = False
+            while True:
+                self.fp.read(4)  # CRC
+
+                try:
+                    cid, pos, length = self.png.read()
+                except (struct.error, SyntaxError):
+                    break
+
+                if cid == b"IEND":
+                    msg = "No more images in APNG file"
+                    raise EOFError(msg)
+                if cid == b"fcTL":
+                    if frame_start:
+                        # there must be at least one fdAT chunk between fcTL chunks
+                        msg = "APNG missing frame data"
+                        raise SyntaxError(msg)
+                    frame_start = True
+
+                try:
+                    self.png.call(cid, pos, length)
+                except UnicodeDecodeError:
+                    break
+                except EOFError:
+                    if cid == b"fdAT":
+                        length -= 4
+                        if frame_start:
+                            self.__prepare_idat = length
+                            break
+                    ImageFile._safe_read(self.fp, length)
+                except AttributeError:
+                    logger.debug("%r %s %s (unknown)", cid, pos, length)
+                    ImageFile._safe_read(self.fp, length)
+
+            self.__frame = frame
+            self.tile = self.png.im_tile
+            self.dispose_op = self.info.get("disposal")
+            self.blend_op = self.info.get("blend")
+            self.dispose_extent = self.info.get("bbox")
+
+            if not self.tile:
+                msg = "image not found in APNG frame"
+                raise EOFError(msg)
+
+        # setup frame disposal (actual disposal done when needed in the next _seek())
+        if self._prev_im is None and self.dispose_op == Disposal.OP_PREVIOUS:
+            self.dispose_op = Disposal.OP_BACKGROUND
+
+        if self.dispose_op == Disposal.OP_PREVIOUS:
+            self.dispose = self._prev_im.copy()
+            self.dispose = self._crop(self.dispose, self.dispose_extent)
+        elif self.dispose_op == Disposal.OP_BACKGROUND:
+            self.dispose = Image.core.fill(self.mode, self.size)
+            self.dispose = self._crop(self.dispose, self.dispose_extent)
+        else:
+            self.dispose = None
+
+    def tell(self):
+        return self.__frame
+
+    def load_prepare(self):
+        """internal: prepare to read PNG file"""
+
+        if self.info.get("interlace"):
+            self.decoderconfig = self.decoderconfig + (1,)
+
+        self.__idat = self.__prepare_idat  # used by load_read()
+        ImageFile.ImageFile.load_prepare(self)
+
+    def load_read(self, read_bytes):
+        """internal: read more image data"""
+
+        while self.__idat == 0:
+            # end of chunk, skip forward to next one
+
+            self.fp.read(4)  # CRC
+
+            cid, pos, length = self.png.read()
+
+            if cid not in [b"IDAT", b"DDAT", b"fdAT"]:
+                self.png.push(cid, pos, length)
+                return b""
+
+            if cid == b"fdAT":
+                try:
+                    self.png.call(cid, pos, length)
+                except EOFError:
+                    pass
+                self.__idat = length - 4  # sequence_num has already been read
+            else:
+                self.__idat = length  # empty chunks are allowed
+
+        # read more data from this chunk
+        if read_bytes <= 0:
+            read_bytes = self.__idat
+        else:
+            read_bytes = min(read_bytes, self.__idat)
+
+        self.__idat = self.__idat - read_bytes
+
+        return self.fp.read(read_bytes)
+
+    def load_end(self):
+        """internal: finished reading image data"""
+        if self.__idat != 0:
+            self.fp.read(self.__idat)
+        while True:
+            self.fp.read(4)  # CRC
+
+            try:
+                cid, pos, length = self.png.read()
+            except (struct.error, SyntaxError):
+                break
+
+            if cid == b"IEND":
+                break
+            elif cid == b"fcTL" and self.is_animated:
+                # start of the next frame, stop reading
+                self.__prepare_idat = 0
+                self.png.push(cid, pos, length)
+                break
+
+            try:
+                self.png.call(cid, pos, length)
+            except UnicodeDecodeError:
+                break
+            except EOFError:
+                if cid == b"fdAT":
+                    length -= 4
+                ImageFile._safe_read(self.fp, length)
+            except AttributeError:
+                logger.debug("%r %s %s (unknown)", cid, pos, length)
+                s = ImageFile._safe_read(self.fp, length)
+                if cid[1:2].islower():
+                    self.private_chunks.append((cid, s, True))
+        self._text = self.png.im_text
+        if not self.is_animated:
+            self.png.close()
+            self.png = None
+        else:
+            if self._prev_im and self.blend_op == Blend.OP_OVER:
+                updated = self._crop(self.im, self.dispose_extent)
+                if self.im.mode == "RGB" and "transparency" in self.info:
+                    mask = updated.convert_transparent(
+                        "RGBA", self.info["transparency"]
+                    )
+                else:
+                    mask = updated.convert("RGBA")
+                self._prev_im.paste(updated, self.dispose_extent, mask)
+                self.im = self._prev_im
+                if self.pyaccess:
+                    self.pyaccess = None
+
+    def _getexif(self):
+        if "exif" not in self.info:
+            self.load()
+        if "exif" not in self.info and "Raw profile type exif" not in self.info:
+            return None
+        return self.getexif()._get_merged_dict()
+
+    def getexif(self):
+        if "exif" not in self.info:
+            self.load()
+
+        return super().getexif()
+
+    def getxmp(self):
+        """
+        Returns a dictionary containing the XMP tags.
+        Requires defusedxml to be installed.
+
+        :returns: XMP tags in a dictionary.
+        """
+        return (
+            self._getxmp(self.info["XML:com.adobe.xmp"])
+            if "XML:com.adobe.xmp" in self.info
+            else {}
+        )
+
+
+# --------------------------------------------------------------------
+# PNG writer
+
+_OUTMODES = {
+    # supported PIL modes, and corresponding rawmodes/bits/color combinations
+    "1": ("1", b"\x01\x00"),
+    "L;1": ("L;1", b"\x01\x00"),
+    "L;2": ("L;2", b"\x02\x00"),
+    "L;4": ("L;4", b"\x04\x00"),
+    "L": ("L", b"\x08\x00"),
+    "LA": ("LA", b"\x08\x04"),
+    "I": ("I;16B", b"\x10\x00"),
+    "I;16": ("I;16B", b"\x10\x00"),
+    "I;16B": ("I;16B", b"\x10\x00"),
+    "P;1": ("P;1", b"\x01\x03"),
+    "P;2": ("P;2", b"\x02\x03"),
+    "P;4": ("P;4", b"\x04\x03"),
+    "P": ("P", b"\x08\x03"),
+    "RGB": ("RGB", b"\x08\x02"),
+    "RGBA": ("RGBA", b"\x08\x06"),
+}
+
+
+def putchunk(fp, cid, *data):
+    """Write a PNG chunk (including CRC field)"""
+
+    data = b"".join(data)
+
+    fp.write(o32(len(data)) + cid)
+    fp.write(data)
+    crc = _crc32(data, _crc32(cid))
+    fp.write(o32(crc))
+
+
+class _idat:
+    # wrap output from the encoder in IDAT chunks
+
+    def __init__(self, fp, chunk):
+        self.fp = fp
+        self.chunk = chunk
+
+    def write(self, data):
+        self.chunk(self.fp, b"IDAT", data)
+
+
+class _fdat:
+    # wrap encoder output in fdAT chunks
+
+    def __init__(self, fp, chunk, seq_num):
+        self.fp = fp
+        self.chunk = chunk
+        self.seq_num = seq_num
+
+    def write(self, data):
+        self.chunk(self.fp, b"fdAT", o32(self.seq_num), data)
+        self.seq_num += 1
+
+
+def _write_multiple_frames(im, fp, chunk, rawmode, default_image, append_images):
+    duration = im.encoderinfo.get("duration", im.info.get("duration", 0))
+    loop = im.encoderinfo.get("loop", im.info.get("loop", 0))
+    disposal = im.encoderinfo.get("disposal", im.info.get("disposal", Disposal.OP_NONE))
+    blend = im.encoderinfo.get("blend", im.info.get("blend", Blend.OP_SOURCE))
+
+    if default_image:
+        chain = itertools.chain(append_images)
+    else:
+        chain = itertools.chain([im], append_images)
+
+    im_frames = []
+    frame_count = 0
+    for im_seq in chain:
+        for im_frame in ImageSequence.Iterator(im_seq):
+            if im_frame.mode == rawmode:
+                im_frame = im_frame.copy()
+            else:
+                im_frame = im_frame.convert(rawmode)
+            encoderinfo = im.encoderinfo.copy()
+            if isinstance(duration, (list, tuple)):
+                encoderinfo["duration"] = duration[frame_count]
+            if isinstance(disposal, (list, tuple)):
+                encoderinfo["disposal"] = disposal[frame_count]
+            if isinstance(blend, (list, tuple)):
+                encoderinfo["blend"] = blend[frame_count]
+            frame_count += 1
+
+            if im_frames:
+                previous = im_frames[-1]
+                prev_disposal = previous["encoderinfo"].get("disposal")
+                prev_blend = previous["encoderinfo"].get("blend")
+                if prev_disposal == Disposal.OP_PREVIOUS and len(im_frames) < 2:
+                    prev_disposal = Disposal.OP_BACKGROUND
+
+                if prev_disposal == Disposal.OP_BACKGROUND:
+                    base_im = previous["im"].copy()
+                    dispose = Image.core.fill("RGBA", im.size, (0, 0, 0, 0))
+                    bbox = previous["bbox"]
+                    if bbox:
+                        dispose = dispose.crop(bbox)
+                    else:
+                        bbox = (0, 0) + im.size
+                    base_im.paste(dispose, bbox)
+                elif prev_disposal == Disposal.OP_PREVIOUS:
+                    base_im = im_frames[-2]["im"]
+                else:
+                    base_im = previous["im"]
+                delta = ImageChops.subtract_modulo(
+                    im_frame.convert("RGBA"), base_im.convert("RGBA")
+                )
+                bbox = delta.getbbox(alpha_only=False)
+                if (
+                    not bbox
+                    and prev_disposal == encoderinfo.get("disposal")
+                    and prev_blend == encoderinfo.get("blend")
+                ):
+                    previous["encoderinfo"]["duration"] += encoderinfo.get(
+                        "duration", duration
+                    )
+                    continue
+            else:
+                bbox = None
+            if "duration" not in encoderinfo:
+                encoderinfo["duration"] = duration
+            im_frames.append({"im": im_frame, "bbox": bbox, "encoderinfo": encoderinfo})
+
+    if len(im_frames) == 1 and not default_image:
+        return im_frames[0]["im"]
+
+    # animation control
+    chunk(
+        fp,
+        b"acTL",
+        o32(len(im_frames)),  # 0: num_frames
+        o32(loop),  # 4: num_plays
+    )
+
+    # default image IDAT (if it exists)
+    if default_image:
+        if im.mode != rawmode:
+            im = im.convert(rawmode)
+        ImageFile._save(im, _idat(fp, chunk), [("zip", (0, 0) + im.size, 0, rawmode)])
+
+    seq_num = 0
+    for frame, frame_data in enumerate(im_frames):
+        im_frame = frame_data["im"]
+        if not frame_data["bbox"]:
+            bbox = (0, 0) + im_frame.size
+        else:
+            bbox = frame_data["bbox"]
+            im_frame = im_frame.crop(bbox)
+        size = im_frame.size
+        encoderinfo = frame_data["encoderinfo"]
+        frame_duration = int(round(encoderinfo["duration"]))
+        frame_disposal = encoderinfo.get("disposal", disposal)
+        frame_blend = encoderinfo.get("blend", blend)
+        # frame control
+        chunk(
+            fp,
+            b"fcTL",
+            o32(seq_num),  # sequence_number
+            o32(size[0]),  # width
+            o32(size[1]),  # height
+            o32(bbox[0]),  # x_offset
+            o32(bbox[1]),  # y_offset
+            o16(frame_duration),  # delay_numerator
+            o16(1000),  # delay_denominator
+            o8(frame_disposal),  # dispose_op
+            o8(frame_blend),  # blend_op
+        )
+        seq_num += 1
+        # frame data
+        if frame == 0 and not default_image:
+            # first frame must be in IDAT chunks for backwards compatibility
+            ImageFile._save(
+                im_frame,
+                _idat(fp, chunk),
+                [("zip", (0, 0) + im_frame.size, 0, rawmode)],
+            )
+        else:
+            fdat_chunks = _fdat(fp, chunk, seq_num)
+            ImageFile._save(
+                im_frame,
+                fdat_chunks,
+                [("zip", (0, 0) + im_frame.size, 0, rawmode)],
+            )
+            seq_num = fdat_chunks.seq_num
+
+
+def _save_all(im, fp, filename):
+    _save(im, fp, filename, save_all=True)
+
+
+def _save(im, fp, filename, chunk=putchunk, save_all=False):
+    # save an image to disk (called by the save method)
+
+    if save_all:
+        default_image = im.encoderinfo.get(
+            "default_image", im.info.get("default_image")
+        )
+        modes = set()
+        append_images = im.encoderinfo.get("append_images", [])
+        for im_seq in itertools.chain([im], append_images):
+            for im_frame in ImageSequence.Iterator(im_seq):
+                modes.add(im_frame.mode)
+        for mode in ("RGBA", "RGB", "P"):
+            if mode in modes:
+                break
+        else:
+            mode = modes.pop()
+    else:
+        mode = im.mode
+
+    if mode == "P":
+        #
+        # attempt to minimize storage requirements for palette images
+        if "bits" in im.encoderinfo:
+            # number of bits specified by user
+            colors = min(1 << im.encoderinfo["bits"], 256)
+        else:
+            # check palette contents
+            if im.palette:
+                colors = max(min(len(im.palette.getdata()[1]) // 3, 256), 1)
+            else:
+                colors = 256
+
+        if colors <= 16:
+            if colors <= 2:
+                bits = 1
+            elif colors <= 4:
+                bits = 2
+            else:
+                bits = 4
+            mode = f"{mode};{bits}"
+
+    # encoder options
+    im.encoderconfig = (
+        im.encoderinfo.get("optimize", False),
+        im.encoderinfo.get("compress_level", -1),
+        im.encoderinfo.get("compress_type", -1),
+        im.encoderinfo.get("dictionary", b""),
+    )
+
+    # get the corresponding PNG mode
+    try:
+        rawmode, mode = _OUTMODES[mode]
+    except KeyError as e:
+        msg = f"cannot write mode {mode} as PNG"
+        raise OSError(msg) from e
+
+    #
+    # write minimal PNG file
+
+    fp.write(_MAGIC)
+
+    chunk(
+        fp,
+        b"IHDR",
+        o32(im.size[0]),  # 0: size
+        o32(im.size[1]),
+        mode,  # 8: depth/type
+        b"\0",  # 10: compression
+        b"\0",  # 11: filter category
+        b"\0",  # 12: interlace flag
+    )
+
+    chunks = [b"cHRM", b"gAMA", b"sBIT", b"sRGB", b"tIME"]
+
+    icc = im.encoderinfo.get("icc_profile", im.info.get("icc_profile"))
+    if icc:
+        # ICC profile
+        # according to PNG spec, the iCCP chunk contains:
+        # Profile name  1-79 bytes (character string)
+        # Null separator        1 byte (null character)
+        # Compression method    1 byte (0)
+        # Compressed profile    n bytes (zlib with deflate compression)
+        name = b"ICC Profile"
+        data = name + b"\0\0" + zlib.compress(icc)
+        chunk(fp, b"iCCP", data)
+
+        # You must either have sRGB or iCCP.
+        # Disallow sRGB chunks when an iCCP-chunk has been emitted.
+        chunks.remove(b"sRGB")
+
+    info = im.encoderinfo.get("pnginfo")
+    if info:
+        chunks_multiple_allowed = [b"sPLT", b"iTXt", b"tEXt", b"zTXt"]
+        for info_chunk in info.chunks:
+            cid, data = info_chunk[:2]
+            if cid in chunks:
+                chunks.remove(cid)
+                chunk(fp, cid, data)
+            elif cid in chunks_multiple_allowed:
+                chunk(fp, cid, data)
+            elif cid[1:2].islower():
+                # Private chunk
+                after_idat = info_chunk[2:3]
+                if not after_idat:
+                    chunk(fp, cid, data)
+
+    if im.mode == "P":
+        palette_byte_number = colors * 3
+        palette_bytes = im.im.getpalette("RGB")[:palette_byte_number]
+        while len(palette_bytes) < palette_byte_number:
+            palette_bytes += b"\0"
+        chunk(fp, b"PLTE", palette_bytes)
+
+    transparency = im.encoderinfo.get("transparency", im.info.get("transparency", None))
+
+    if transparency or transparency == 0:
+        if im.mode == "P":
+            # limit to actual palette size
+            alpha_bytes = colors
+            if isinstance(transparency, bytes):
+                chunk(fp, b"tRNS", transparency[:alpha_bytes])
+            else:
+                transparency = max(0, min(255, transparency))
+                alpha = b"\xFF" * transparency + b"\0"
+                chunk(fp, b"tRNS", alpha[:alpha_bytes])
+        elif im.mode in ("1", "L", "I"):
+            transparency = max(0, min(65535, transparency))
+            chunk(fp, b"tRNS", o16(transparency))
+        elif im.mode == "RGB":
+            red, green, blue = transparency
+            chunk(fp, b"tRNS", o16(red) + o16(green) + o16(blue))
+        else:
+            if "transparency" in im.encoderinfo:
+                # don't bother with transparency if it's an RGBA
+                # and it's in the info dict. It's probably just stale.
+                msg = "cannot use transparency for this mode"
+                raise OSError(msg)
+    else:
+        if im.mode == "P" and im.im.getpalettemode() == "RGBA":
+            alpha = im.im.getpalette("RGBA", "A")
+            alpha_bytes = colors
+            chunk(fp, b"tRNS", alpha[:alpha_bytes])
+
+    dpi = im.encoderinfo.get("dpi")
+    if dpi:
+        chunk(
+            fp,
+            b"pHYs",
+            o32(int(dpi[0] / 0.0254 + 0.5)),
+            o32(int(dpi[1] / 0.0254 + 0.5)),
+            b"\x01",
+        )
+
+    if info:
+        chunks = [b"bKGD", b"hIST"]
+        for info_chunk in info.chunks:
+            cid, data = info_chunk[:2]
+            if cid in chunks:
+                chunks.remove(cid)
+                chunk(fp, cid, data)
+
+    exif = im.encoderinfo.get("exif")
+    if exif:
+        if isinstance(exif, Image.Exif):
+            exif = exif.tobytes(8)
+        if exif.startswith(b"Exif\x00\x00"):
+            exif = exif[6:]
+        chunk(fp, b"eXIf", exif)
+
+    if save_all:
+        im = _write_multiple_frames(
+            im, fp, chunk, rawmode, default_image, append_images
+        )
+    if im:
+        ImageFile._save(im, _idat(fp, chunk), [("zip", (0, 0) + im.size, 0, rawmode)])
+
+    if info:
+        for info_chunk in info.chunks:
+            cid, data = info_chunk[:2]
+            if cid[1:2].islower():
+                # Private chunk
+                after_idat = info_chunk[2:3]
+                if after_idat:
+                    chunk(fp, cid, data)
+
+    chunk(fp, b"IEND", b"")
+
+    if hasattr(fp, "flush"):
+        fp.flush()
+
+
+# --------------------------------------------------------------------
+# PNG chunk converter
+
+
+def getchunks(im, **params):
+    """Return a list of PNG chunks representing this image."""
+
+    class collector:
+        data = []
+
+        def write(self, data):
+            pass
+
+        def append(self, chunk):
+            self.data.append(chunk)
+
+    def append(fp, cid, *data):
+        data = b"".join(data)
+        crc = o32(_crc32(data, _crc32(cid)))
+        fp.append((cid, data, crc))
+
+    fp = collector()
+
+    try:
+        im.encoderinfo = params
+        _save(im, fp, None, append)
+    finally:
+        del im.encoderinfo
+
+    return fp.data
+
+
+# --------------------------------------------------------------------
+# Registry
+
+Image.register_open(PngImageFile.format, PngImageFile, _accept)
+Image.register_save(PngImageFile.format, _save)
+Image.register_save_all(PngImageFile.format, _save_all)
+
+Image.register_extensions(PngImageFile.format, [".png", ".apng"])
+
+Image.register_mime(PngImageFile.format, "image/png")
diff --git a/shared/package-files/b5/b5a46bdddb836842b53dfce3f4dd5d16e6695dfcbc7b8b8522d7098912c04304/__init__.py b/shared/package-files/b5/b5a46bdddb836842b53dfce3f4dd5d16e6695dfcbc7b8b8522d7098912c04304/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..609cb14fdcc38c48270acd5803f4bfe603c39e71
--- /dev/null
+++ b/shared/package-files/b5/b5a46bdddb836842b53dfce3f4dd5d16e6695dfcbc7b8b8522d7098912c04304/__init__.py
@@ -0,0 +1,47 @@
+from .cmuarctic import CMUARCTIC
+from .cmudict import CMUDict
+from .commonvoice import COMMONVOICE
+from .dr_vctk import DR_VCTK
+from .fluentcommands import FluentSpeechCommands
+from .gtzan import GTZAN
+from .iemocap import IEMOCAP
+from .librilight_limited import LibriLightLimited
+from .librimix import LibriMix
+from .librispeech import LIBRISPEECH
+from .librispeech_biasing import LibriSpeechBiasing
+from .libritts import LIBRITTS
+from .ljspeech import LJSPEECH
+from .musdb_hq import MUSDB_HQ
+from .quesst14 import QUESST14
+from .snips import Snips
+from .speechcommands import SPEECHCOMMANDS
+from .tedlium import TEDLIUM
+from .vctk import VCTK_092
+from .voxceleb1 import VoxCeleb1Identification, VoxCeleb1Verification
+from .yesno import YESNO
+
+
+__all__ = [
+    "COMMONVOICE",
+    "LIBRISPEECH",
+    "LibriSpeechBiasing",
+    "LibriLightLimited",
+    "SPEECHCOMMANDS",
+    "VCTK_092",
+    "DR_VCTK",
+    "YESNO",
+    "LJSPEECH",
+    "GTZAN",
+    "CMUARCTIC",
+    "CMUDict",
+    "LibriMix",
+    "LIBRITTS",
+    "TEDLIUM",
+    "QUESST14",
+    "MUSDB_HQ",
+    "FluentSpeechCommands",
+    "VoxCeleb1Identification",
+    "VoxCeleb1Verification",
+    "IEMOCAP",
+    "Snips",
+]
diff --git a/shared/package-files/b5/b5a7dd9c150ace971ba0df76fe2fdbc30cb8ace1402fa22168a7e0db9658f4a6/test_penalized.py b/shared/package-files/b5/b5a7dd9c150ace971ba0df76fe2fdbc30cb8ace1402fa22168a7e0db9658f4a6/test_penalized.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fbb9257aad1f7d151379461cc6e6068c4606873
--- /dev/null
+++ b/shared/package-files/b5/b5a7dd9c150ace971ba0df76fe2fdbc30cb8ace1402fa22168a7e0db9658f4a6/test_penalized.py
@@ -0,0 +1,859 @@
+"""
+Created on Sun May 10 12:39:33 2015
+
+Author: Josef Perktold
+License: BSD-3
+"""
+
+import warnings
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+
+from statsmodels.base._penalized import PenalizedMixin
+import statsmodels.base._penalties as smpen
+from statsmodels.discrete.discrete_model import Logit, Poisson, Probit
+from statsmodels.genmod.families import family
+from statsmodels.genmod.generalized_linear_model import GLM
+from statsmodels.sandbox.regression.penalized import TheilGLS
+
+
+class PoissonPenalized(PenalizedMixin, Poisson):
+    pass
+
+
+class LogitPenalized(PenalizedMixin, Logit):
+    pass
+
+
+class ProbitPenalized(PenalizedMixin, Probit):
+    pass
+
+
+class GLMPenalized(PenalizedMixin, GLM):
+    pass
+
+
+class CheckPenalizedPoisson:
+
+    @classmethod
+    def setup_class(cls):
+        # simulate data
+        rs = np.random.RandomState(987865)
+
+        nobs, k_vars = 500, 10
+        k_nonzero = 4
+        x = (rs.rand(nobs, k_vars) + 0.5 * (rs.rand(nobs, 1) - 0.5)) * 2 - 1
+        x *= 1.2
+        x[:, 0] = 1
+        beta = np.zeros(k_vars)
+        beta[:k_nonzero] = 1.0 / np.arange(1, k_nonzero + 1)
+        linpred = x.dot(beta)
+        y = cls._generate_endog(linpred)
+
+        cls.k_nonzero = k_nonzero
+        cls.x = x
+        cls.y = y
+
+        # defaults to be overwritten by subclasses
+        cls.rtol = 1e-4
+        cls.atol = 1e-6
+        cls.exog_index = slice(None, None, None)
+        cls.k_params = k_vars
+        cls.skip_hessian = False  # can be overwritten in _initialize
+        cls.penalty = smpen.SCADSmoothed(0.1, c0=0.0001)  # default for tests
+        cls._initialize()
+
+    @classmethod
+    def _generate_endog(cls, linpred):
+        mu = np.exp(linpred)
+        rs = np.random.RandomState(999)
+        y = rs.poisson(mu)
+        return y
+
+    def test_params_table(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_equal((res1.params != 0).sum(), self.k_params)
+        assert_allclose(
+            res1.params[self.exog_index], res2.params, rtol=self.rtol, atol=self.atol
+        )
+        assert_allclose(
+            res1.bse[self.exog_index], res2.bse, rtol=self.rtol, atol=self.atol
+        )
+        with warnings.catch_warnings():
+            # silence scipy distribution warnigns becaus of zero bse
+            warnings.simplefilter("ignore", RuntimeWarning)
+            assert_allclose(
+                res1.pvalues[self.exog_index],
+                res2.pvalues,
+                rtol=self.rtol,
+                atol=self.atol,
+            )
+        assert_allclose(res1.predict(), res2.predict(), rtol=0.05)
+
+    @pytest.mark.smoke
+    def test_summary(self):
+        self.res1.summary()
+
+    @pytest.mark.smoke
+    def test_summary2(self):
+        summ = self.res1.summary2()
+        assert isinstance(summ.as_latex(), str)
+        assert isinstance(summ.as_html(), str)
+        assert isinstance(summ.as_text(), str)
+
+    def test_numdiff(self):
+        res1 = self.res1
+
+        # avoid checking score at MLE, score close to zero
+        p = res1.params * 0.98
+        # GLM concentrates out scale which affects derivatives, see #4616
+        kwds = {"scale": 1} if isinstance(res1.model, GLM) else {}
+
+        assert_allclose(
+            res1.model.score(p, **kwds)[self.exog_index],
+            res1.model.score_numdiff(p, **kwds)[self.exog_index],
+            rtol=0.025,
+        )
+
+        if not self.skip_hessian:
+            if isinstance(self.exog_index, slice):
+                idx1 = idx2 = self.exog_index
+            else:
+                idx1 = self.exog_index[:, None]
+                idx2 = self.exog_index
+
+            h1 = res1.model.hessian(res1.params, **kwds)[idx1, idx2]
+            h2 = res1.model.hessian_numdiff(res1.params, **kwds)[idx1, idx2]
+            assert_allclose(h1, h2, rtol=0.02)
+
+
+class TestPenalizedPoissonNonePenal(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+
+        modp = Poisson(y, x)
+        cls.res2 = modp.fit(disp=0)
+
+        mod = PoissonPenalized(y, x)  # default no penalty
+        mod.pen_weight = 0
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.atol = 5e-6
+
+
+class TestPenalizedPoissonNoPenal(CheckPenalizedPoisson):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+
+        modp = Poisson(y, x)
+        cls.res2 = modp.fit(disp=0)
+
+        mod = PoissonPenalized(y, x)
+        mod.pen_weight = 0
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.atol = 5e-6
+
+
+class TestPenalizedGLMPoissonNoPenal(CheckPenalizedPoisson):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+
+        modp = GLM(y, x, family=family.Poisson())
+        cls.res2 = modp.fit()
+
+        mod = GLMPenalized(y, x, family=family.Poisson(), penal=cls.penalty)
+        mod.pen_weight = 0
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.atol = 5e-6
+
+
+class TestPenalizedPoissonOracle(CheckPenalizedPoisson):
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = Poisson(y, x[:, : cls.k_nonzero])
+        cls.res2 = modp.fit(disp=0)
+
+        mod = PoissonPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 1.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+
+class TestPenalizedGLMPoissonOracle(CheckPenalizedPoisson):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Poisson())
+        cls.res2 = modp.fit()
+
+        mod = GLMPenalized(y, x, family=family.Poisson(), penal=cls.penalty)
+        mod.pen_weight *= 1.5  # same as discrete Poisson
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+
+class TestPenalizedPoissonOracleHC(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        cov_type = "HC0"
+        modp = Poisson(y, x[:, : cls.k_nonzero])
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        mod = PoissonPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 1.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+    def test_cov_type(self):
+        res1 = self.res1
+        res2 = self.res2
+
+        assert_equal(self.res1.cov_type, "HC0")
+        cov_kwds = {
+            "description": "Standard Errors are heteroscedasticity robust (HC0)",
+            "adjust_df": False,
+            "use_t": False,
+            "scaling_factor": None,
+        }
+        assert_equal(self.res1.cov_kwds, cov_kwds)
+        # numbers are regression test using bfgs
+        params = np.array(
+            [
+                0.96817787574701109,
+                0.43674374940137434,
+                0.33096260487556745,
+                0.27415680046693747,
+            ]
+        )
+        bse = np.array(
+            [
+                0.028126650444581985,
+                0.033099984564283147,
+                0.033184585514904545,
+                0.034282504130503301,
+            ]
+        )
+        assert_allclose(res2.params[: self.k_nonzero], params, atol=1e-5)
+        assert_allclose(res2.bse[: self.k_nonzero], bse, rtol=1e-6)
+        assert_allclose(res1.params[: self.k_nonzero], params, atol=self.atol)
+        assert_allclose(res1.bse[: self.k_nonzero], bse, rtol=0.02)
+
+
+class TestPenalizedGLMPoissonOracleHC(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Poisson())
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        mod = GLMPenalized(y, x, family=family.Poisson(), penal=cls.penalty)
+        mod.pen_weight *= 1.5  # same as ddiscrete Poisson
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+
+class TestPenalizedPoissonGLMOracleHC(CheckPenalizedPoisson):
+    # compare discrete Poisson and GLM-Poisson
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        cov_type = "HC0"
+        modp = PoissonPenalized(y, x, penal=cls.penalty)
+        modp.pen_weight *= 1.5  # same as discrete Poisson 1.5
+        modp.penal.tau = 0.05
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        mod = GLMPenalized(y, x, family=family.Poisson(), penal=cls.penalty)
+        mod.pen_weight *= 1.5  # same as discrete Poisson 1.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, None, None)
+
+        cls.atol = 1e-4
+
+
+class TestPenalizedPoissonOraclePenalized(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = PoissonPenalized(y, x[:, : cls.k_nonzero], penal=cls.penalty)
+        cls.res2 = modp.fit(method="bfgs", maxiter=100, disp=0)
+
+        mod = PoissonPenalized(y, x, penal=cls.penalty)
+        # mod.pen_weight *= 1.5
+        # mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, trim=False, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-3
+
+
+class TestPenalizedPoissonOraclePenalized2(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = PoissonPenalized(y, x[:, : cls.k_nonzero], penal=cls.penalty)
+        modp.pen_weight *= 10  # need to penalize more to get oracle selection
+        modp.penal.tau = 0.05
+        sp2 = np.array([0.96817921, 0.43673551, 0.33096011, 0.27416614])
+        cls.res2 = modp.fit(start_params=sp2 * 0.5, method="bfgs", maxiter=100, disp=0)
+
+        params_notrim = np.array(
+            [
+                9.68178874e-01,
+                4.36744981e-01,
+                3.30965041e-01,
+                2.74161883e-01,
+                -2.58988461e-06,
+                -1.24352640e-06,
+                4.48584458e-08,
+                -2.46876149e-06,
+                -1.02471074e-05,
+                -4.39248098e-06,
+            ]
+        )
+
+        mod = PoissonPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 10  # need to penalize more to get oracle selection
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(
+            start_params=params_notrim * 0.5,
+            method="bfgs",
+            maxiter=100,
+            trim=True,
+            disp=0,
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-8
+        cls.k_params = cls.k_nonzero
+
+    def test_zeros(self):
+
+        # first test for trimmed result
+        assert_equal(self.res1.params[self.k_nonzero :], 0)
+        # we also set bse to zero, TODO: check fit_regularized
+        assert_equal(self.res1.bse[self.k_nonzero :], 0)
+
+
+class TestPenalizedPoissonOraclePenalized2HC(CheckPenalizedPoisson):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        cov_type = "HC0"
+        modp = PoissonPenalized(y, x[:, : cls.k_nonzero], penal=cls.penalty)
+        modp.pen_weight *= 10  # need to penalize more to get oracle selection
+        modp.penal.tau = 0.05
+        sp2 = np.array([0.96817921, 0.43673551, 0.33096011, 0.27416614])
+        cls.res2 = modp.fit(
+            start_params=sp2 * 0.5,
+            cov_type=cov_type,
+            method="bfgs",
+            maxiter=100,
+            disp=0,
+        )
+
+        params_notrim = np.array(
+            [
+                9.68178874e-01,
+                4.36744981e-01,
+                3.30965041e-01,
+                2.74161883e-01,
+                -2.58988461e-06,
+                -1.24352640e-06,
+                4.48584458e-08,
+                -2.46876149e-06,
+                -1.02471074e-05,
+                -4.39248098e-06,
+            ]
+        )
+
+        mod = PoissonPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 10  # need to penalize more to get oracle selection
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(
+            start_params=params_notrim * 0.5,
+            cov_type=cov_type,
+            method="bfgs",
+            maxiter=100,
+            trim=True,
+            disp=0,
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+        cls.atol = 1e-12
+        cls.k_params = cls.k_nonzero
+
+    def test_cov_type(self):
+        res1 = self.res1
+        res2 = self.res2
+
+        assert_equal(self.res1.cov_type, "HC0")
+        assert_equal(self.res1.results_constrained.cov_type, "HC0")
+        cov_kwds = {
+            "description": "Standard Errors are heteroscedasticity robust (HC0)",
+            "adjust_df": False,
+            "use_t": False,
+            "scaling_factor": None,
+        }
+        assert_equal(self.res1.cov_kwds, cov_kwds)
+        assert_equal(self.res1.cov_kwds, self.res1.results_constrained.cov_kwds)
+
+        # numbers are regression test using bfgs
+        params = np.array(
+            [
+                0.96817787574701109,
+                0.43674374940137434,
+                0.33096260487556745,
+                0.27415680046693747,
+            ]
+        )
+        bse = np.array(
+            [
+                0.028126650444581985,
+                0.033099984564283147,
+                0.033184585514904545,
+                0.034282504130503301,
+            ]
+        )
+        assert_allclose(res2.params[: self.k_nonzero], params, atol=1e-5)
+        assert_allclose(res2.bse[: self.k_nonzero], bse, rtol=5e-6)
+        assert_allclose(res1.params[: self.k_nonzero], params, atol=1e-5)
+        assert_allclose(res1.bse[: self.k_nonzero], bse, rtol=5e-6)
+
+
+# the following classes are copies of Poisson with model adjustments
+
+
+class CheckPenalizedLogit(CheckPenalizedPoisson):
+
+    @classmethod
+    def _generate_endog(cls, linpred):
+        mu = 1 / (1 + np.exp(-linpred + linpred.mean() - 0.5))
+        rs = np.random.RandomState(999)
+        y = rs.rand(len(mu)) < mu
+        return y
+
+
+class TestPenalizedLogitNoPenal(CheckPenalizedLogit):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+
+        modp = Logit(y, x)
+        cls.res2 = modp.fit(disp=0)
+
+        mod = LogitPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight = 0
+        cls.res1 = mod.fit(disp=0)
+
+        cls.atol = 1e-4  # why not closer ?
+
+
+class TestPenalizedLogitOracle(CheckPenalizedLogit):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = Logit(y, x[:, : cls.k_nonzero])
+        cls.res2 = modp.fit(disp=0)
+
+        mod = LogitPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 0.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+
+class TestPenalizedGLMLogitOracle(CheckPenalizedLogit):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Binomial())
+        cls.res2 = modp.fit(disp=0)
+
+        mod = GLMPenalized(y, x, family=family.Binomial(), penal=cls.penalty)
+        mod.pen_weight *= 0.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-3
+
+
+class TestPenalizedLogitOraclePenalized(CheckPenalizedLogit):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = LogitPenalized(y, x[:, : cls.k_nonzero], penal=cls.penalty)
+        cls.res2 = modp.fit(method="bfgs", maxiter=100, disp=0)
+
+        mod = LogitPenalized(y, x, penal=cls.penalty)
+        # mod.pen_weight *= 1.5
+        # mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, trim=False, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-3
+
+
+class TestPenalizedLogitOraclePenalized2(CheckPenalizedLogit):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        modp = LogitPenalized(y, x[:, : cls.k_nonzero], penal=cls.penalty)
+        modp.pen_weight *= 0.5
+        modp.penal.tau = 0.05
+        cls.res2 = modp.fit(method="bfgs", maxiter=100, disp=0)
+
+        mod = LogitPenalized(y, x, penal=cls.penalty)
+        mod.pen_weight *= 0.5
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(method="bfgs", maxiter=100, trim=True, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-8
+        cls.k_params = cls.k_nonzero
+
+    def test_zeros(self):
+
+        # test for trimmed result
+        assert_equal(self.res1.params[self.k_nonzero :], 0)
+        # we also set bse to zero
+        assert_equal(self.res1.bse[self.k_nonzero :], 0)
+
+
+# the following classes are copies of Poisson with model adjustments
+class CheckPenalizedBinomCount(CheckPenalizedPoisson):
+
+    @classmethod
+    def _generate_endog(cls, linpred):
+        mu = 1 / (1 + np.exp(-linpred + linpred.mean() - 0.5))
+        rs = np.random.RandomState(999)
+        n_trials = 5 * np.ones(len(mu), int)
+        n_trials[: len(mu) // 2] += 5
+        y = rs.binomial(n_trials, mu)
+        return np.column_stack((y, n_trials - y))
+
+
+class TestPenalizedGLMBinomCountNoPenal(CheckPenalizedBinomCount):
+    # TODO: check, adjust cov_type
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        x = x[:, :4]
+        offset = -0.25 * np.ones(len(y))  # also check offset
+        modp = GLM(y, x, family=family.Binomial(), offset=offset)
+        cls.res2 = modp.fit(method="bfgs", max_start_irls=100)
+
+        mod = GLMPenalized(
+            y, x, family=family.Binomial(), offset=offset, penal=cls.penalty
+        )
+        mod.pen_weight = 0
+        cls.res1 = mod.fit(
+            method="bfgs",
+            max_start_irls=3,
+            maxiter=100,
+            disp=0,
+            start_params=cls.res2.params * 0.9,
+        )
+
+        cls.atol = 1e-10
+        cls.k_params = 4
+
+    def test_deriv(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_allclose(
+            res1.model.score(res2.params * 0.98),
+            res2.model.score(res2.params * 0.98),
+            rtol=1e-10,
+        )
+        assert_allclose(
+            res1.model.score_obs(res2.params * 0.98),
+            res2.model.score_obs(res2.params * 0.98),
+            rtol=1e-10,
+        )
+
+
+class TestPenalizedGLMBinomCountOracleHC(CheckPenalizedBinomCount):
+    # TODO: There are still problems with this case
+    # using the standard optimization, I get convergence failures and
+    # different estimates depending on details, e.g., small changes in pen_weight
+    # most likely convexity fails with SCAD in this case
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        offset = -0.25 * np.ones(len(y))  # also check offset
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Binomial(), offset=offset)
+        cls.res2 = modp.fit(cov_type=cov_type, method="newton", maxiter=1000, disp=0)
+
+        mod = GLMPenalized(
+            y, x, family=family.Binomial(), offset=offset, penal=cls.penalty
+        )
+        mod.pen_weight *= 1  # lower than in other cases
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(
+            cov_type=cov_type, method="bfgs", max_start_irls=0, maxiter=100, disp=0
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-3
+
+
+class TestPenalizedGLMBinomCountOracleHC2(CheckPenalizedBinomCount):
+    # TODO: There are still problems with this case, see other class
+    # with trimming of small parameters, needs larger trim threshold
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        offset = -0.25 * np.ones(len(y))  # also check offset
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Binomial(), offset=offset)
+        cls.res2 = modp.fit(cov_type=cov_type, method="newton", maxiter=1000, disp=0)
+
+        mod = GLMPenalized(
+            y, x, family=family.Binomial(), offset=offset, penal=cls.penalty
+        )
+        mod.pen_weight *= 1  # lower than in other cases
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(
+            cov_type=cov_type,
+            method="bfgs",
+            max_start_irls=0,
+            maxiter=100,
+            disp=0,
+            trim=0.001,
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 1e-3
+        cls.k_params = cls.k_nonzero
+
+
+# the following classes are copies of Poisson with model adjustments
+class CheckPenalizedGaussian(CheckPenalizedPoisson):
+
+    @classmethod
+    def _generate_endog(cls, linpred):
+        sig_e = np.sqrt(0.1)
+        rs = np.random.RandomState(999)
+        y = linpred + sig_e * rs.rand(len(linpred))
+        return y
+
+
+class TestPenalizedGLMGaussianOracleHC(CheckPenalizedGaussian):
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        # adding 10 to avoid strict rtol at predicted values close to zero
+        y = y + 10
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Gaussian())
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        mod = GLMPenalized(y, x, family=family.Gaussian(), penal=cls.penalty)
+        mod.pen_weight *= 1.5  # same as discrete Poisson
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+
+        cls.atol = 5e-6
+        cls.rtol = 1e-6
+
+
+class TestPenalizedGLMGaussianOracleHC2(CheckPenalizedGaussian):
+    # with trimming
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        # adding 10 to avoid strict rtol at predicted values close to zero
+        y = y + 10
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Gaussian())
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        mod = GLMPenalized(y, x, family=family.Gaussian(), penal=cls.penalty)
+        mod.pen_weight *= 1.5  # same as discrete Poisson
+        mod.penal.tau = 0.05
+        cls.res1 = mod.fit(
+            cov_type=cov_type, method="bfgs", maxiter=100, disp=0, trim=True
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+        cls.k_params = cls.k_nonzero
+        cls.atol = 1e-5
+        cls.rtol = 1e-5
+
+
+class TestPenalizedGLMGaussianL2(CheckPenalizedGaussian):
+    # L2 penalty on redundant exog
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        # adding 10 to avoid strict rtol at predicted values close to zero
+        y = y + 10
+        cov_type = "HC0"
+        modp = GLM(y, x[:, : cls.k_nonzero], family=family.Gaussian())
+        cls.res2 = modp.fit(cov_type=cov_type, method="bfgs", maxiter=100, disp=0)
+
+        weights = (np.arange(x.shape[1]) >= 4).astype(float)
+        mod = GLMPenalized(
+            y,
+            x,
+            family=family.Gaussian(),
+            penal=smpen.L2ConstraintsPenalty(weights=weights),
+        )
+        # make pen_weight large to force redundant to close to zero
+        mod.pen_weight *= 500
+        cls.res1 = mod.fit(
+            cov_type=cov_type, method="bfgs", maxiter=100, disp=0, trim=False
+        )
+
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+        cls.k_params = x.shape[1]
+        cls.atol = 1e-5
+        cls.rtol = 1e-5
+
+
+class TestPenalizedGLMGaussianL2Theil(CheckPenalizedGaussian):
+    # L2 penalty on redundant exog
+
+    @classmethod
+    def _initialize(cls):
+        y, x = cls.y, cls.x
+        # adding 10 to avoid strict rtol at predicted values close to zero
+        y = y + 10
+        k = x.shape[1]
+        cov_type = "HC0"
+        restriction = np.eye(k)[2:]
+        modp = TheilGLS(y, x, r_matrix=restriction)
+        # the corresponding Theil penweight seems to be 2 * nobs / sigma2_e
+        cls.res2 = modp.fit(pen_weight=120.74564413221599 * 1000, use_t=False)
+
+        pen = smpen.L2ConstraintsPenalty(restriction=restriction)
+        mod = GLMPenalized(y, x, family=family.Gaussian(), penal=pen)
+        # use default weight for GLMPenalized
+        mod.pen_weight *= 1
+        cls.res1 = mod.fit(
+            cov_type=cov_type, method="bfgs", maxiter=100, disp=0, trim=False
+        )
+
+        cls.k_nonzero = k
+        cls.exog_index = slice(None, cls.k_nonzero, None)
+        cls.k_params = x.shape[1]
+        cls.atol = 1e-5
+        cls.rtol = 1e-5
+
+    def test_params_table(self):
+        # override inherited because match is not good except for params and predict
+        # The cov_type in GLMPenalized and in TheilGLS are not the same
+        # both use sandwiches but TheilGLS sandwich is not HC
+        # relative difference in bse up to 7%
+        res1 = self.res1
+        res2 = self.res2
+        assert_equal((res1.params != 0).sum(), self.k_params)
+        assert_allclose(res1.params, res2.params, rtol=self.rtol, atol=self.atol)
+
+        exog_index = slice(None, None, None)
+        assert_allclose(
+            res1.bse[exog_index], res2.bse[exog_index], rtol=0.1, atol=self.atol
+        )
+        assert_allclose(
+            res1.tvalues[exog_index], res2.tvalues[exog_index], rtol=0.08, atol=5e-3
+        )
+        assert_allclose(
+            res1.pvalues[exog_index], res2.pvalues[exog_index], rtol=0.1, atol=5e-3
+        )
+        assert_allclose(res1.predict(), res2.predict(), rtol=1e-5)
+
+
+def test_loglikeobs_sums_to_loglike():
+    # PenalizedMixin.loglikeobs had no test coverage. Per-observation
+    # penalized log-likelihoods should sum to the (already tested)
+    # aggregate penalized loglike at the same params -- the penalty term is
+    # divided by nobs_llf inside loglikeobs specifically so that summing it
+    # back up reproduces the un-divided penalty used by loglike.
+    rng = np.random.default_rng(87654)
+    nobs, k_vars = 60, 3
+    x = rng.standard_normal((nobs, k_vars))
+    x[:, 0] = 1
+    beta = np.array([0.5, 0.3, -0.2])
+    y = rng.poisson(np.exp(x @ beta))
+
+    mod = PoissonPenalized(y, x, penal=smpen.SCADSmoothed(0.1, c0=0.0001))
+    params = beta + 0.1
+
+    for pen_weight in [None, 0, 5.0]:
+        llobs = mod.loglikeobs(params, pen_weight=pen_weight)
+        assert llobs.shape == (nobs,)
+        assert_allclose(
+            llobs.sum(), mod.loglike(params, pen_weight=pen_weight), rtol=1e-10
+        )
diff --git a/shared/package-files/b5/b5b14699aa8bac5f25129d079fcb0eb8fcc3e6bcc8a4e54e79e068064dff5ba8/test_all_random_functions.py b/shared/package-files/b5/b5b14699aa8bac5f25129d079fcb0eb8fcc3e6bcc8a4e54e79e068064dff5ba8/test_all_random_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..828c3137aa1c9183d45279769809eff8c0642b70
--- /dev/null
+++ b/shared/package-files/b5/b5b14699aa8bac5f25129d079fcb0eb8fcc3e6bcc8a4e54e79e068064dff5ba8/test_all_random_functions.py
@@ -0,0 +1,247 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+import random
+
+import networkx as nx
+from networkx.algorithms import approximation as approx
+from networkx.algorithms import threshold
+
+progress = 0
+
+# store the random numbers after setting a global seed
+np.random.seed(42)
+np_rv = np.random.rand()
+random.seed(42)
+py_rv = random.random()
+
+
+def t(f, *args, **kwds):
+    """call one function and check if global RNG changed"""
+    global progress
+    progress += 1
+    print(progress, ",", end="")
+
+    f(*args, **kwds)
+
+    after_np_rv = np.random.rand()
+    # if np_rv != after_np_rv:
+    #    print(np_rv, after_np_rv, "don't match np!")
+    assert np_rv == after_np_rv
+    np.random.seed(42)
+
+    after_py_rv = random.random()
+    # if py_rv != after_py_rv:
+    #    print(py_rv, after_py_rv, "don't match py!")
+    assert py_rv == after_py_rv
+    random.seed(42)
+
+
+def run_all_random_functions(seed):
+    n = 20
+    m = 10
+    k = l = 2
+    s = v = 10
+    p = q = p1 = p2 = p_in = p_out = 0.4
+    alpha = radius = theta = 0.75
+    sizes = (20, 20, 10)
+    colors = [1, 2, 3]
+    G = nx.barbell_graph(12, 20)
+    H = nx.cycle_graph(3)
+    H.add_weighted_edges_from((u, v, 0.2) for u, v in H.edges)
+    deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1]
+    in_degree_sequence = w = sequence = aseq = bseq = deg_sequence
+
+    # print("starting...")
+    t(nx.maximal_independent_set, G, seed=seed)
+    t(nx.rich_club_coefficient, G, seed=seed, normalized=False)
+    t(nx.random_reference, G, seed=seed)
+    t(nx.lattice_reference, G, seed=seed)
+    t(nx.sigma, G, 1, 2, seed=seed)
+    t(nx.omega, G, 1, 2, seed=seed)
+    # print("out of smallworld.py")
+    t(nx.double_edge_swap, G, seed=seed)
+    # print("starting connected_double_edge_swap")
+    t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed)
+    # print("ending connected_double_edge_swap")
+    t(nx.random_layout, G, seed=seed)
+    t(nx.fruchterman_reingold_layout, G, seed=seed)
+    t(nx.algebraic_connectivity, G, seed=seed)
+    t(nx.fiedler_vector, G, seed=seed)
+    t(nx.spectral_ordering, G, seed=seed)
+    # print('starting average_clustering')
+    t(approx.average_clustering, G, seed=seed)
+    t(approx.simulated_annealing_tsp, H, "greedy", source=1, seed=seed)
+    t(approx.threshold_accepting_tsp, H, "greedy", source=1, seed=seed)
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, wt: approx.simulated_annealing_tsp(G, "greedy", wt, seed=seed),
+    )
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, wt: approx.threshold_accepting_tsp(G, "greedy", wt, seed=seed),
+    )
+    t(nx.betweenness_centrality, G, seed=seed)
+    t(nx.edge_betweenness_centrality, G, seed=seed)
+    t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed)
+    # print("kernighan")
+    t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed)
+    # nx.algorithms.community.asyn_lpa_communities(G, seed=seed)
+    t(nx.algorithms.tree.greedy_branching, G, seed=seed)
+    t(nx.algorithms.tree.Edmonds, G, seed=seed)
+    # print('done with graph argument functions')
+
+    t(nx.spectral_graph_forge, G, alpha, seed=seed)
+    t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed)
+    t(
+        nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation,
+        G,
+        k,
+        seed=seed,
+    )
+    t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed)
+
+    cs = ["d", "i", "i", "d", "d", "i"]
+    t(threshold.swap_d, cs, seed=seed)
+    t(nx.configuration_model, deg_sequence, seed=seed)
+    t(
+        nx.directed_configuration_model,
+        in_degree_sequence,
+        in_degree_sequence,
+        seed=seed,
+    )
+    t(nx.expected_degree_graph, w, seed=seed)
+    t(nx.random_degree_sequence_graph, sequence, seed=seed)
+    joint_degrees = {
+        1: {4: 1},
+        2: {2: 2, 3: 2, 4: 2},
+        3: {2: 2, 4: 1},
+        4: {1: 1, 2: 2, 3: 1},
+    }
+    t(nx.joint_degree_graph, joint_degrees, seed=seed)
+    joint_degree_sequence = [
+        (1, 0),
+        (1, 0),
+        (1, 0),
+        (2, 0),
+        (1, 0),
+        (2, 1),
+        (0, 1),
+        (0, 1),
+    ]
+    t(nx.random_clustered_graph, joint_degree_sequence, seed=seed)
+    constructor = [(3, 3, 0.5), (10, 10, 0.7)]
+    t(nx.random_shell_graph, constructor, seed=seed)
+    t(nx.random_triad, G.to_directed(), seed=seed)
+    mapping = {1: 0.4, 2: 0.3, 3: 0.3}
+    t(nx.utils.random_weighted_sample, mapping, k, seed=seed)
+    t(nx.utils.weighted_choice, mapping, seed=seed)
+    t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed)
+    t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed)
+
+    def kernel_integral(u, w, z):
+        return z - w
+
+    t(nx.random_kernel_graph, n, kernel_integral, seed=seed)
+
+    sizes = [75, 75, 300]
+    probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    t(nx.stochastic_block_model, sizes, probs, seed=seed)
+    t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed)
+
+    # print("starting generator functions")
+    t(threshold.random_threshold_sequence, n, p, seed=seed)
+    t(nx.tournament.random_tournament, n, seed=seed)
+    t(nx.relaxed_caveman_graph, l, k, p, seed=seed)
+    t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed)
+    t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed)
+    t(nx.gn_graph, n, seed=seed)
+    t(nx.gnr_graph, n, p, seed=seed)
+    t(nx.gnc_graph, n, seed=seed)
+    t(nx.scale_free_graph, n, seed=seed)
+    t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed)
+    t(nx.random_k_out_graph, n, k, alpha, seed=seed)
+    N = 1000
+    t(nx.partial_duplication_graph, N, n, p, q, seed=seed)
+    t(nx.duplication_divergence_graph, n, p, seed=seed)
+    t(nx.random_geometric_graph, n, radius, seed=seed)
+    t(nx.soft_random_geometric_graph, n, radius, seed=seed)
+    t(nx.geographical_threshold_graph, n, theta, seed=seed)
+    t(nx.waxman_graph, n, seed=seed)
+    t(nx.navigable_small_world_graph, n, seed=seed)
+    t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed)
+    t(nx.uniform_random_intersection_graph, n, m, p, seed=seed)
+    t(nx.k_random_intersection_graph, n, m, k, seed=seed)
+
+    t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed)
+    t(nx.fast_gnp_random_graph, n, p, seed=seed)
+    t(nx.gnp_random_graph, n, p, seed=seed)
+    t(nx.dense_gnm_random_graph, n, m, seed=seed)
+    t(nx.gnm_random_graph, n, m, seed=seed)
+    t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.random_regular_graph, 3, n, seed=seed)
+    t(nx.barabasi_albert_graph, n, m, seed=seed)
+    t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed)
+    t(nx.powerlaw_cluster_graph, n, m, p, seed=seed)
+    t(nx.random_lobster, n, p1, p2, seed=seed)
+    t(nx.random_powerlaw_tree, n, seed=seed, tries=5000)
+    t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000)
+    t(nx.random_tree, n, seed=seed)
+    t(nx.utils.powerlaw_sequence, n, seed=seed)
+    t(nx.utils.zipf_rv, 2.3, seed=seed)
+    cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0]
+    t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed)
+    t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed)
+    t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed)
+    LFR = nx.generators.LFR_benchmark_graph
+    t(
+        LFR,
+        25,
+        3,
+        1.5,
+        0.1,
+        average_degree=3,
+        min_community=10,
+        seed=seed,
+        max_community=20,
+    )
+    t(nx.random_internet_as_graph, n, seed=seed)
+    # print("done")
+
+
+# choose to test an integer seed, or whether a single RNG can be everywhere
+# np_rng = np.random.RandomState(14)
+# seed = np_rng
+# seed = 14
+
+
+@pytest.mark.slow
+# print("NetworkX Version:", nx.__version__)
+def test_rng_interface():
+    global progress
+
+    # try different kinds of seeds
+    for seed in [14, np.random.RandomState(14)]:
+        np.random.seed(42)
+        random.seed(42)
+        run_all_random_functions(seed)
+        progress = 0
+
+        # check that both global RNGs are unaffected
+        after_np_rv = np.random.rand()
+        #        if np_rv != after_np_rv:
+        #            print(np_rv, after_np_rv, "don't match np!")
+        assert np_rv == after_np_rv
+        after_py_rv = random.random()
+        #        if py_rv != after_py_rv:
+        #            print(py_rv, after_py_rv, "don't match py!")
+        assert py_rv == after_py_rv
+
+
+#        print("\nDone testing seed:", seed)
+
+# test_rng_interface()
diff --git a/shared/package-files/b5/b5b50c5921973c6c5e494a8dd1846aa2a539c7e892c5268c5126e5b849e72ebe/_prelu_kernel_backward_native.h b/shared/package-files/b5/b5b50c5921973c6c5e494a8dd1846aa2a539c7e892c5268c5126e5b849e72ebe/_prelu_kernel_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..15d52270ab5230bf98bdcb40fd6b455c2123d0b7
--- /dev/null
+++ b/shared/package-files/b5/b5b50c5921973c6c5e494a8dd1846aa2a539c7e892c5268c5126e5b849e72ebe/_prelu_kernel_backward_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _prelu_kernel_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> mkldnn_prelu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/b5/b5bb2055b443dfa5ab8e4644078c62d5fbce5d3eb6af2d29f06fd277e8f90ab6/ssd.py b/shared/package-files/b5/b5bb2055b443dfa5ab8e4644078c62d5fbce5d3eb6af2d29f06fd277e8f90ab6/ssd.py
new file mode 100644
index 0000000000000000000000000000000000000000..87062d2bc88a5bf17625e0530116aba22941c538
--- /dev/null
+++ b/shared/package-files/b5/b5bb2055b443dfa5ab8e4644078c62d5fbce5d3eb6af2d29f06fd277e8f90ab6/ssd.py
@@ -0,0 +1,682 @@
+import warnings
+from collections import OrderedDict
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+from ...ops import boxes as box_ops
+from ...transforms._presets import ObjectDetection
+from ...utils import _log_api_usage_once
+from .._api import register_model, Weights, WeightsEnum
+from .._meta import _COCO_CATEGORIES
+from .._utils import _ovewrite_value_param, handle_legacy_interface
+from ..vgg import VGG, vgg16, VGG16_Weights
+from . import _utils as det_utils
+from .anchor_utils import DefaultBoxGenerator
+from .backbone_utils import _validate_trainable_layers
+from .transform import GeneralizedRCNNTransform
+
+
+__all__ = [
+    "SSD300_VGG16_Weights",
+    "ssd300_vgg16",
+]
+
+
+class SSD300_VGG16_Weights(WeightsEnum):
+    COCO_V1 = Weights(
+        url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
+        transforms=ObjectDetection,
+        meta={
+            "num_params": 35641826,
+            "categories": _COCO_CATEGORIES,
+            "min_size": (1, 1),
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
+            "_metrics": {
+                "COCO-val2017": {
+                    "box_map": 25.1,
+                }
+            },
+            "_ops": 34.858,
+            "_file_size": 135.988,
+            "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
+        },
+    )
+    DEFAULT = COCO_V1
+
+
+def _xavier_init(conv: nn.Module):
+    for layer in conv.modules():
+        if isinstance(layer, nn.Conv2d):
+            torch.nn.init.xavier_uniform_(layer.weight)
+            if layer.bias is not None:
+                torch.nn.init.constant_(layer.bias, 0.0)
+
+
+class SSDHead(nn.Module):
+    def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int):
+        super().__init__()
+        self.classification_head = SSDClassificationHead(in_channels, num_anchors, num_classes)
+        self.regression_head = SSDRegressionHead(in_channels, num_anchors)
+
+    def forward(self, x: List[Tensor]) -> Dict[str, Tensor]:
+        return {
+            "bbox_regression": self.regression_head(x),
+            "cls_logits": self.classification_head(x),
+        }
+
+
+class SSDScoringHead(nn.Module):
+    def __init__(self, module_list: nn.ModuleList, num_columns: int):
+        super().__init__()
+        self.module_list = module_list
+        self.num_columns = num_columns
+
+    def _get_result_from_module_list(self, x: Tensor, idx: int) -> Tensor:
+        """
+        This is equivalent to self.module_list[idx](x),
+        but torchscript doesn't support this yet
+        """
+        num_blocks = len(self.module_list)
+        if idx < 0:
+            idx += num_blocks
+        out = x
+        for i, module in enumerate(self.module_list):
+            if i == idx:
+                out = module(x)
+        return out
+
+    def forward(self, x: List[Tensor]) -> Tensor:
+        all_results = []
+
+        for i, features in enumerate(x):
+            results = self._get_result_from_module_list(features, i)
+
+            # Permute output from (N, A * K, H, W) to (N, HWA, K).
+            N, _, H, W = results.shape
+            results = results.view(N, -1, self.num_columns, H, W)
+            results = results.permute(0, 3, 4, 1, 2)
+            results = results.reshape(N, -1, self.num_columns)  # Size=(N, HWA, K)
+
+            all_results.append(results)
+
+        return torch.cat(all_results, dim=1)
+
+
+class SSDClassificationHead(SSDScoringHead):
+    def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int):
+        cls_logits = nn.ModuleList()
+        for channels, anchors in zip(in_channels, num_anchors):
+            cls_logits.append(nn.Conv2d(channels, num_classes * anchors, kernel_size=3, padding=1))
+        _xavier_init(cls_logits)
+        super().__init__(cls_logits, num_classes)
+
+
+class SSDRegressionHead(SSDScoringHead):
+    def __init__(self, in_channels: List[int], num_anchors: List[int]):
+        bbox_reg = nn.ModuleList()
+        for channels, anchors in zip(in_channels, num_anchors):
+            bbox_reg.append(nn.Conv2d(channels, 4 * anchors, kernel_size=3, padding=1))
+        _xavier_init(bbox_reg)
+        super().__init__(bbox_reg, 4)
+
+
+class SSD(nn.Module):
+    """
+    Implements SSD architecture from `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
+
+    The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
+    image, and should be in 0-1 range. Different images can have different sizes, but they will be resized
+    to a fixed size before passing it to the backbone.
+
+    The behavior of the model changes depending on if it is in training or evaluation mode.
+
+    During training, the model expects both the input tensors and targets (list of dictionary),
+    containing:
+        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
+          ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
+        - labels (Int64Tensor[N]): the class label for each ground-truth box
+
+    The model returns a Dict[Tensor] during training, containing the classification and regression
+    losses.
+
+    During inference, the model requires only the input tensors, and returns the post-processed
+    predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
+    follows, where ``N`` is the number of detections:
+
+        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
+          ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
+        - labels (Int64Tensor[N]): the predicted labels for each detection
+        - scores (Tensor[N]): the scores for each detection
+
+    Args:
+        backbone (nn.Module): the network used to compute the features for the model.
+            It should contain an out_channels attribute with the list of the output channels of
+            each feature map. The backbone should return a single Tensor or an OrderedDict[Tensor].
+        anchor_generator (DefaultBoxGenerator): module that generates the default boxes for a
+            set of feature maps.
+        size (Tuple[int, int]): the width and height to which images will be rescaled before feeding them
+            to the backbone.
+        num_classes (int): number of output classes of the model (including the background).
+        image_mean (Tuple[float, float, float]): mean values used for input normalization.
+            They are generally the mean values of the dataset on which the backbone has been trained
+            on
+        image_std (Tuple[float, float, float]): std values used for input normalization.
+            They are generally the std values of the dataset on which the backbone has been trained on
+        head (nn.Module, optional): Module run on top of the backbone features. Defaults to a module containing
+            a classification and regression module.
+        score_thresh (float): Score threshold used for postprocessing the detections.
+        nms_thresh (float): NMS threshold used for postprocessing the detections.
+        detections_per_img (int): Number of best detections to keep after NMS.
+        iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
+            considered as positive during training.
+        topk_candidates (int): Number of best detections to keep before NMS.
+        positive_fraction (float): a number between 0 and 1 which indicates the proportion of positive
+            proposals used during the training of the classification head. It is used to estimate the negative to
+            positive ratio.
+    """
+
+    __annotations__ = {
+        "box_coder": det_utils.BoxCoder,
+        "proposal_matcher": det_utils.Matcher,
+    }
+
+    def __init__(
+        self,
+        backbone: nn.Module,
+        anchor_generator: DefaultBoxGenerator,
+        size: Tuple[int, int],
+        num_classes: int,
+        image_mean: Optional[List[float]] = None,
+        image_std: Optional[List[float]] = None,
+        head: Optional[nn.Module] = None,
+        score_thresh: float = 0.01,
+        nms_thresh: float = 0.45,
+        detections_per_img: int = 200,
+        iou_thresh: float = 0.5,
+        topk_candidates: int = 400,
+        positive_fraction: float = 0.25,
+        **kwargs: Any,
+    ):
+        super().__init__()
+        _log_api_usage_once(self)
+
+        self.backbone = backbone
+
+        self.anchor_generator = anchor_generator
+
+        self.box_coder = det_utils.BoxCoder(weights=(10.0, 10.0, 5.0, 5.0))
+
+        if head is None:
+            if hasattr(backbone, "out_channels"):
+                out_channels = backbone.out_channels
+            else:
+                out_channels = det_utils.retrieve_out_channels(backbone, size)
+
+            if len(out_channels) != len(anchor_generator.aspect_ratios):
+                raise ValueError(
+                    f"The length of the output channels from the backbone ({len(out_channels)}) do not match the length of the anchor generator aspect ratios ({len(anchor_generator.aspect_ratios)})"
+                )
+
+            num_anchors = self.anchor_generator.num_anchors_per_location()
+            head = SSDHead(out_channels, num_anchors, num_classes)
+        self.head = head
+
+        self.proposal_matcher = det_utils.SSDMatcher(iou_thresh)
+
+        if image_mean is None:
+            image_mean = [0.485, 0.456, 0.406]
+        if image_std is None:
+            image_std = [0.229, 0.224, 0.225]
+        self.transform = GeneralizedRCNNTransform(
+            min(size), max(size), image_mean, image_std, size_divisible=1, fixed_size=size, **kwargs
+        )
+
+        self.score_thresh = score_thresh
+        self.nms_thresh = nms_thresh
+        self.detections_per_img = detections_per_img
+        self.topk_candidates = topk_candidates
+        self.neg_to_pos_ratio = (1.0 - positive_fraction) / positive_fraction
+
+        # used only on torchscript mode
+        self._has_warned = False
+
+    @torch.jit.unused
+    def eager_outputs(
+        self, losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]]
+    ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
+        if self.training:
+            return losses
+
+        return detections
+
+    def compute_loss(
+        self,
+        targets: List[Dict[str, Tensor]],
+        head_outputs: Dict[str, Tensor],
+        anchors: List[Tensor],
+        matched_idxs: List[Tensor],
+    ) -> Dict[str, Tensor]:
+        bbox_regression = head_outputs["bbox_regression"]
+        cls_logits = head_outputs["cls_logits"]
+
+        # Match original targets with default boxes
+        num_foreground = 0
+        bbox_loss = []
+        cls_targets = []
+        for (
+            targets_per_image,
+            bbox_regression_per_image,
+            cls_logits_per_image,
+            anchors_per_image,
+            matched_idxs_per_image,
+        ) in zip(targets, bbox_regression, cls_logits, anchors, matched_idxs):
+            # produce the matching between boxes and targets
+            foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
+            foreground_matched_idxs_per_image = matched_idxs_per_image[foreground_idxs_per_image]
+            num_foreground += foreground_matched_idxs_per_image.numel()
+
+            # Calculate regression loss
+            matched_gt_boxes_per_image = targets_per_image["boxes"][foreground_matched_idxs_per_image]
+            bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :]
+            anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]
+            target_regression = self.box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image)
+            bbox_loss.append(
+                torch.nn.functional.smooth_l1_loss(bbox_regression_per_image, target_regression, reduction="sum")
+            )
+
+            # Estimate ground truth for class targets
+            gt_classes_target = torch.zeros(
+                (cls_logits_per_image.size(0),),
+                dtype=targets_per_image["labels"].dtype,
+                device=targets_per_image["labels"].device,
+            )
+            gt_classes_target[foreground_idxs_per_image] = targets_per_image["labels"][
+                foreground_matched_idxs_per_image
+            ]
+            cls_targets.append(gt_classes_target)
+
+        bbox_loss = torch.stack(bbox_loss)
+        cls_targets = torch.stack(cls_targets)
+
+        # Calculate classification loss
+        num_classes = cls_logits.size(-1)
+        cls_loss = F.cross_entropy(cls_logits.view(-1, num_classes), cls_targets.view(-1), reduction="none").view(
+            cls_targets.size()
+        )
+
+        # Hard Negative Sampling
+        foreground_idxs = cls_targets > 0
+        num_negative = self.neg_to_pos_ratio * foreground_idxs.sum(1, keepdim=True)
+        # num_negative[num_negative < self.neg_to_pos_ratio] = self.neg_to_pos_ratio
+        negative_loss = cls_loss.clone()
+        negative_loss[foreground_idxs] = -float("inf")  # use -inf to detect positive values that creeped in the sample
+        values, idx = negative_loss.sort(1, descending=True)
+        # background_idxs = torch.logical_and(idx.sort(1)[1] < num_negative, torch.isfinite(values))
+        background_idxs = idx.sort(1)[1] < num_negative
+
+        N = max(1, num_foreground)
+        return {
+            "bbox_regression": bbox_loss.sum() / N,
+            "classification": (cls_loss[foreground_idxs].sum() + cls_loss[background_idxs].sum()) / N,
+        }
+
+    def forward(
+        self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None
+    ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
+        if self.training:
+            if targets is None:
+                torch._assert(False, "targets should not be none when in training mode")
+            else:
+                for target in targets:
+                    boxes = target["boxes"]
+                    if isinstance(boxes, torch.Tensor):
+                        torch._assert(
+                            len(boxes.shape) == 2 and boxes.shape[-1] == 4,
+                            f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.",
+                        )
+                    else:
+                        torch._assert(False, f"Expected target boxes to be of type Tensor, got {type(boxes)}.")
+
+        # get the original image sizes
+        original_image_sizes: List[Tuple[int, int]] = []
+        for img in images:
+            val = img.shape[-2:]
+            torch._assert(
+                len(val) == 2,
+                f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}",
+            )
+            original_image_sizes.append((val[0], val[1]))
+
+        # transform the input
+        images, targets = self.transform(images, targets)
+
+        # Check for degenerate boxes
+        if targets is not None:
+            for target_idx, target in enumerate(targets):
+                boxes = target["boxes"]
+                degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
+                if degenerate_boxes.any():
+                    bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0]
+                    degen_bb: List[float] = boxes[bb_idx].tolist()
+                    torch._assert(
+                        False,
+                        "All bounding boxes should have positive height and width."
+                        f" Found invalid box {degen_bb} for target at index {target_idx}.",
+                    )
+
+        # get the features from the backbone
+        features = self.backbone(images.tensors)
+        if isinstance(features, torch.Tensor):
+            features = OrderedDict([("0", features)])
+
+        features = list(features.values())
+
+        # compute the ssd heads outputs using the features
+        head_outputs = self.head(features)
+
+        # create the set of anchors
+        anchors = self.anchor_generator(images, features)
+
+        losses = {}
+        detections: List[Dict[str, Tensor]] = []
+        if self.training:
+            matched_idxs = []
+            if targets is None:
+                torch._assert(False, "targets should not be none when in training mode")
+            else:
+                for anchors_per_image, targets_per_image in zip(anchors, targets):
+                    if targets_per_image["boxes"].numel() == 0:
+                        matched_idxs.append(
+                            torch.full(
+                                (anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device
+                            )
+                        )
+                        continue
+
+                    match_quality_matrix = box_ops.box_iou(targets_per_image["boxes"], anchors_per_image)
+                    matched_idxs.append(self.proposal_matcher(match_quality_matrix))
+
+                losses = self.compute_loss(targets, head_outputs, anchors, matched_idxs)
+        else:
+            detections = self.postprocess_detections(head_outputs, anchors, images.image_sizes)
+            detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
+
+        if torch.jit.is_scripting():
+            if not self._has_warned:
+                warnings.warn("SSD always returns a (Losses, Detections) tuple in scripting")
+                self._has_warned = True
+            return losses, detections
+        return self.eager_outputs(losses, detections)
+
+    def postprocess_detections(
+        self, head_outputs: Dict[str, Tensor], image_anchors: List[Tensor], image_shapes: List[Tuple[int, int]]
+    ) -> List[Dict[str, Tensor]]:
+        bbox_regression = head_outputs["bbox_regression"]
+        pred_scores = F.softmax(head_outputs["cls_logits"], dim=-1)
+
+        num_classes = pred_scores.size(-1)
+        device = pred_scores.device
+
+        detections: List[Dict[str, Tensor]] = []
+
+        for boxes, scores, anchors, image_shape in zip(bbox_regression, pred_scores, image_anchors, image_shapes):
+            boxes = self.box_coder.decode_single(boxes, anchors)
+            boxes = box_ops.clip_boxes_to_image(boxes, image_shape)
+
+            image_boxes = []
+            image_scores = []
+            image_labels = []
+            for label in range(1, num_classes):
+                score = scores[:, label]
+
+                keep_idxs = score > self.score_thresh
+                score = score[keep_idxs]
+                box = boxes[keep_idxs]
+
+                # keep only topk scoring predictions
+                num_topk = det_utils._topk_min(score, self.topk_candidates, 0)
+                score, idxs = score.topk(num_topk)
+                box = box[idxs]
+
+                image_boxes.append(box)
+                image_scores.append(score)
+                image_labels.append(torch.full_like(score, fill_value=label, dtype=torch.int64, device=device))
+
+            image_boxes = torch.cat(image_boxes, dim=0)
+            image_scores = torch.cat(image_scores, dim=0)
+            image_labels = torch.cat(image_labels, dim=0)
+
+            # non-maximum suppression
+            keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh)
+            keep = keep[: self.detections_per_img]
+
+            detections.append(
+                {
+                    "boxes": image_boxes[keep],
+                    "scores": image_scores[keep],
+                    "labels": image_labels[keep],
+                }
+            )
+        return detections
+
+
+class SSDFeatureExtractorVGG(nn.Module):
+    def __init__(self, backbone: nn.Module, highres: bool):
+        super().__init__()
+
+        _, _, maxpool3_pos, maxpool4_pos, _ = (i for i, layer in enumerate(backbone) if isinstance(layer, nn.MaxPool2d))
+
+        # Patch ceil_mode for maxpool3 to get the same WxH output sizes as the paper
+        backbone[maxpool3_pos].ceil_mode = True
+
+        # parameters used for L2 regularization + rescaling
+        self.scale_weight = nn.Parameter(torch.ones(512) * 20)
+
+        # Multiple Feature maps - page 4, Fig 2 of SSD paper
+        self.features = nn.Sequential(*backbone[:maxpool4_pos])  # until conv4_3
+
+        # SSD300 case - page 4, Fig 2 of SSD paper
+        extra = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Conv2d(1024, 256, kernel_size=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=2),  # conv8_2
+                    nn.ReLU(inplace=True),
+                ),
+                nn.Sequential(
+                    nn.Conv2d(512, 128, kernel_size=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),  # conv9_2
+                    nn.ReLU(inplace=True),
+                ),
+                nn.Sequential(
+                    nn.Conv2d(256, 128, kernel_size=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(128, 256, kernel_size=3),  # conv10_2
+                    nn.ReLU(inplace=True),
+                ),
+                nn.Sequential(
+                    nn.Conv2d(256, 128, kernel_size=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(128, 256, kernel_size=3),  # conv11_2
+                    nn.ReLU(inplace=True),
+                ),
+            ]
+        )
+        if highres:
+            # Additional layers for the SSD512 case. See page 11, footernote 5.
+            extra.append(
+                nn.Sequential(
+                    nn.Conv2d(256, 128, kernel_size=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(128, 256, kernel_size=4),  # conv12_2
+                    nn.ReLU(inplace=True),
+                )
+            )
+        _xavier_init(extra)
+
+        fc = nn.Sequential(
+            nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=False),  # add modified maxpool5
+            nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6),  # FC6 with atrous
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1),  # FC7
+            nn.ReLU(inplace=True),
+        )
+        _xavier_init(fc)
+        extra.insert(
+            0,
+            nn.Sequential(
+                *backbone[maxpool4_pos:-1],  # until conv5_3, skip maxpool5
+                fc,
+            ),
+        )
+        self.extra = extra
+
+    def forward(self, x: Tensor) -> Dict[str, Tensor]:
+        # L2 regularization + Rescaling of 1st block's feature map
+        x = self.features(x)
+        rescaled = self.scale_weight.view(1, -1, 1, 1) * F.normalize(x)
+        output = [rescaled]
+
+        # Calculating Feature maps for the rest blocks
+        for block in self.extra:
+            x = block(x)
+            output.append(x)
+
+        return OrderedDict([(str(i), v) for i, v in enumerate(output)])
+
+
+def _vgg_extractor(backbone: VGG, highres: bool, trainable_layers: int):
+    backbone = backbone.features
+    # Gather the indices of maxpools. These are the locations of output blocks.
+    stage_indices = [0] + [i for i, b in enumerate(backbone) if isinstance(b, nn.MaxPool2d)][:-1]
+    num_stages = len(stage_indices)
+
+    # find the index of the layer from which we won't freeze
+    torch._assert(
+        0 <= trainable_layers <= num_stages,
+        f"trainable_layers should be in the range [0, {num_stages}]. Instead got {trainable_layers}",
+    )
+    freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers]
+
+    for b in backbone[:freeze_before]:
+        for parameter in b.parameters():
+            parameter.requires_grad_(False)
+
+    return SSDFeatureExtractorVGG(backbone, highres)
+
+
+@register_model()
+@handle_legacy_interface(
+    weights=("pretrained", SSD300_VGG16_Weights.COCO_V1),
+    weights_backbone=("pretrained_backbone", VGG16_Weights.IMAGENET1K_FEATURES),
+)
+def ssd300_vgg16(
+    *,
+    weights: Optional[SSD300_VGG16_Weights] = None,
+    progress: bool = True,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[VGG16_Weights] = VGG16_Weights.IMAGENET1K_FEATURES,
+    trainable_backbone_layers: Optional[int] = None,
+    **kwargs: Any,
+) -> SSD:
+    """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector
+    <https://arxiv.org/abs/1512.02325>`_ paper.
+
+    .. betastatus:: detection module
+
+    The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
+    image, and should be in 0-1 range. Different images can have different sizes, but they will be resized
+    to a fixed size before passing it to the backbone.
+
+    The behavior of the model changes depending on if it is in training or evaluation mode.
+
+    During training, the model expects both the input tensors and targets (list of dictionary),
+    containing:
+
+        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
+          ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
+        - labels (Int64Tensor[N]): the class label for each ground-truth box
+
+    The model returns a Dict[Tensor] during training, containing the classification and regression
+    losses.
+
+    During inference, the model requires only the input tensors, and returns the post-processed
+    predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
+    follows, where ``N`` is the number of detections:
+
+        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
+          ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
+        - labels (Int64Tensor[N]): the predicted labels for each detection
+        - scores (Tensor[N]): the scores for each detection
+
+    Example:
+
+        >>> model = torchvision.models.detection.ssd300_vgg16(weights=SSD300_VGG16_Weights.DEFAULT)
+        >>> model.eval()
+        >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
+        >>> predictions = model(x)
+
+    Args:
+        weights (:class:`~torchvision.models.detection.SSD300_VGG16_Weights`, optional): The pretrained
+                weights to use. See
+                :class:`~torchvision.models.detection.SSD300_VGG16_Weights`
+                below for more details, and possible values. By default, no
+                pre-trained weights are used.
+        progress (bool, optional): If True, displays a progress bar of the download to stderr
+            Default is True.
+        num_classes (int, optional): number of output classes of the model (including the background)
+        weights_backbone (:class:`~torchvision.models.VGG16_Weights`, optional): The pretrained weights for the
+            backbone
+        trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block.
+            Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
+            passed (the default) this value is set to 4.
+        **kwargs: parameters passed to the ``torchvision.models.detection.SSD``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.detection.SSD300_VGG16_Weights
+        :members:
+    """
+    weights = SSD300_VGG16_Weights.verify(weights)
+    weights_backbone = VGG16_Weights.verify(weights_backbone)
+
+    if "size" in kwargs:
+        warnings.warn("The size of the model is already fixed; ignoring the parameter.")
+
+    if weights is not None:
+        weights_backbone = None
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
+
+    trainable_backbone_layers = _validate_trainable_layers(
+        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4
+    )
+
+    # Use custom backbones more appropriate for SSD
+    backbone = vgg16(weights=weights_backbone, progress=progress)
+    backbone = _vgg_extractor(backbone, False, trainable_backbone_layers)
+    anchor_generator = DefaultBoxGenerator(
+        [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
+        scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
+        steps=[8, 16, 32, 64, 100, 300],
+    )
+
+    defaults = {
+        # Rescale the input in a way compatible to the backbone
+        "image_mean": [0.48235, 0.45882, 0.40784],
+        "image_std": [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0],  # undo the 0-1 scaling of toTensor
+    }
+    kwargs: Any = {**defaults, **kwargs}
+    model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
+
+    return model
diff --git a/shared/package-files/b5/b5c478d21a0258244b94bfbc3ed14c6bb3f9b2af2a6a30d2305c5ddb943dd33e/table.pyi b/shared/package-files/b5/b5c478d21a0258244b94bfbc3ed14c6bb3f9b2af2a6a30d2305c5ddb943dd33e/table.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..07d2427f66dc184efaf46af83e6733d157bc089b
--- /dev/null
+++ b/shared/package-files/b5/b5c478d21a0258244b94bfbc3ed14c6bb3f9b2af2a6a30d2305c5ddb943dd33e/table.pyi
@@ -0,0 +1,87 @@
+from .artist import Artist
+from .axes import Axes
+from .backend_bases import RendererBase
+from .patches import Rectangle
+from .path import Path
+from .text import Text
+from .transforms import Bbox
+from .typing import ColorType
+
+from collections.abc import Sequence
+from typing import Any, Literal, TYPE_CHECKING
+
+from pandas import DataFrame
+
+class Cell(Rectangle):
+    PAD: float
+    def __init__(
+        self,
+        xy: tuple[float, float],
+        width: float,
+        height: float,
+        *,
+        edgecolor: ColorType = ...,
+        facecolor: ColorType = ...,
+        fill: bool = ...,
+        text: str = ...,
+        loc: Literal["left", "center", "right"] = ...,
+        fontproperties: dict[str, Any] | None = ...,
+        visible_edges: str | None = ...
+    ) -> None: ...
+    def get_text(self) -> Text: ...
+    def set_fontsize(self, size: float) -> None: ...
+    def get_fontsize(self) -> float: ...
+    def auto_set_font_size(self, renderer: RendererBase) -> float: ...
+    def get_text_bounds(
+        self, renderer: RendererBase
+    ) -> tuple[float, float, float, float]: ...
+    def get_required_width(self, renderer: RendererBase) -> float: ...
+    def set_text_props(self, **kwargs) -> None: ...
+    @property
+    def visible_edges(self) -> str: ...
+    @visible_edges.setter
+    def visible_edges(self, value: str | None) -> None: ...
+    def get_path(self) -> Path: ...
+
+CustomCell = Cell
+
+class Table(Artist):
+    codes: dict[str, int]
+    FONTSIZE: float
+    AXESPAD: float
+    def __init__(
+        self, ax: Axes, loc: str | None = ..., bbox: Bbox | None = ..., **kwargs
+    ) -> None: ...
+    def add_cell(self, row: int, col: int, *args, **kwargs) -> Cell: ...
+    def __setitem__(self, position: tuple[int, int], cell: Cell) -> None: ...
+    def __getitem__(self, position: tuple[int, int]) -> Cell: ...
+    @property
+    def edges(self) -> str | None: ...
+    @edges.setter
+    def edges(self, value: str | None) -> None: ...
+    def draw(self, renderer) -> None: ...
+    def get_children(self) -> list[Artist]: ...
+    def get_window_extent(self, renderer: RendererBase | None = ...) -> Bbox: ...
+    def auto_set_column_width(self, col: int | Sequence[int]) -> None: ...
+    def auto_set_font_size(self, value: bool = ...) -> None: ...
+    def scale(self, xscale: float, yscale: float) -> None: ...
+    def set_fontsize(self, size: float) -> None: ...
+    def get_celld(self) -> dict[tuple[int, int], Cell]: ...
+
+def table(
+    ax: Axes,
+    cellText: Sequence[Sequence[str]] | DataFrame | None = ...,
+    cellColours: Sequence[Sequence[ColorType]] | None = ...,
+    cellLoc: Literal["left", "center", "right"] = ...,
+    colWidths: Sequence[float] | None = ...,
+    rowLabels: Sequence[str] | None = ...,
+    rowColours: Sequence[ColorType] | None = ...,
+    rowLoc: Literal["left", "center", "right"] = ...,
+    colLabels: Sequence[str] | None = ...,
+    colColours: Sequence[ColorType] | None = ...,
+    colLoc: Literal["left", "center", "right"] = ...,
+    loc: str = ...,
+    bbox: Bbox | None = ...,
+    edges: str = ...,
+    **kwargs
+) -> Table: ...
diff --git a/shared/package-files/b5/b5c79ac6e7d4ffc2e8cb1fcc69a4925432fff3f31c7cca074ac6c282942bfdeb/batch_norm_backward_elemt_ops.h b/shared/package-files/b5/b5c79ac6e7d4ffc2e8cb1fcc69a4925432fff3f31c7cca074ac6c282942bfdeb/batch_norm_backward_elemt_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..296de3ba59fcda0f234375d5d77fe9999fb17340
--- /dev/null
+++ b/shared/package-files/b5/b5c79ac6e7d4ffc2e8cb1fcc69a4925432fff3f31c7cca074ac6c282942bfdeb/batch_norm_backward_elemt_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API batch_norm_backward_elemt {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, const at::Tensor &, const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::batch_norm_backward_elemt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count);
+};
+
+struct TORCH_API batch_norm_backward_elemt_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, const at::Tensor &, const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::batch_norm_backward_elemt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/b5/b5c7c22a436f3675157ac7590092a7a17649ce03c6d36c9c3824f8d6582357fc/_cummin_helper_cpu_dispatch.h b/shared/package-files/b5/b5c7c22a436f3675157ac7590092a7a17649ce03c6d36c9c3824f8d6582357fc/_cummin_helper_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ebe8141ba94fef859465f64ccad97d64f176da25
--- /dev/null
+++ b/shared/package-files/b5/b5c7c22a436f3675157ac7590092a7a17649ce03c6d36c9c3824f8d6582357fc/_cummin_helper_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API void _cummin_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/b5/b5cb7ff7859e7d282d98fc43ab081b0dda5dc659dc3385cf65740c759a7e6a6c/LICENSE b/shared/package-files/b5/b5cb7ff7859e7d282d98fc43ab081b0dda5dc659dc3385cf65740c759a7e6a6c/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..5ae193c94d0ca44b222ff24d76eaf41709ce2b4f
--- /dev/null
+++ b/shared/package-files/b5/b5cb7ff7859e7d282d98fc43ab081b0dda5dc659dc3385cf65740c759a7e6a6c/LICENSE
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) Microsoft Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/shared/package-files/b5/b5cbc7ab7f983c65df484eb558ca2713025a69c0696044ecd187c023ef24dc67/ncr7900iv b/shared/package-files/b5/b5cbc7ab7f983c65df484eb558ca2713025a69c0696044ecd187c023ef24dc67/ncr7900iv
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index 0000000000000000000000000000000000000000..d2950fb84c26f2db4ff2e97e4cfaa4c395ddbe87
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diff --git a/shared/package-files/b5/b5d7681adeee826c934ebc8c7aa4af53a144f6f81a8ec3187eb48b432b4c115d/_xlrd.py b/shared/package-files/b5/b5d7681adeee826c934ebc8c7aa4af53a144f6f81a8ec3187eb48b432b4c115d/_xlrd.py
new file mode 100644
index 0000000000000000000000000000000000000000..a444970792e6e65faf3d8947b721fff59487d994
--- /dev/null
+++ b/shared/package-files/b5/b5d7681adeee826c934ebc8c7aa4af53a144f6f81a8ec3187eb48b432b4c115d/_xlrd.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+from datetime import time
+import math
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from xlrd import Book
+
+    from pandas._typing import (
+        Scalar,
+        StorageOptions,
+    )
+
+
+class XlrdReader(BaseExcelReader["Book"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using xlrd engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        err_msg = "Install xlrd >= 2.0.1 for xls Excel support"
+        import_optional_dependency("xlrd", extra=err_msg)
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Book]:
+        from xlrd import Book
+
+        return Book
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> Book:
+        from xlrd import open_workbook
+
+        if hasattr(filepath_or_buffer, "read"):
+            data = filepath_or_buffer.read()
+            return open_workbook(file_contents=data, **engine_kwargs)
+        else:
+            return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self):
+        return self.book.sheet_names()
+
+    def get_sheet_by_name(self, name):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.sheet_by_name(name)
+
+    def get_sheet_by_index(self, index):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        from xlrd import (
+            XL_CELL_BOOLEAN,
+            XL_CELL_DATE,
+            XL_CELL_ERROR,
+            XL_CELL_NUMBER,
+            xldate,
+        )
+
+        epoch1904 = self.book.datemode
+
+        def _parse_cell(cell_contents, cell_typ):
+            """
+            converts the contents of the cell into a pandas appropriate object
+            """
+            if cell_typ == XL_CELL_DATE:
+                # Use the newer xlrd datetime handling.
+                try:
+                    cell_contents = xldate.xldate_as_datetime(cell_contents, epoch1904)
+                except OverflowError:
+                    return cell_contents
+
+                # Excel doesn't distinguish between dates and time,
+                # so we treat dates on the epoch as times only.
+                # Also, Excel supports 1900 and 1904 epochs.
+                year = (cell_contents.timetuple())[0:3]
+                if (not epoch1904 and year == (1899, 12, 31)) or (
+                    epoch1904 and year == (1904, 1, 1)
+                ):
+                    cell_contents = time(
+                        cell_contents.hour,
+                        cell_contents.minute,
+                        cell_contents.second,
+                        cell_contents.microsecond,
+                    )
+
+            elif cell_typ == XL_CELL_ERROR:
+                cell_contents = np.nan
+            elif cell_typ == XL_CELL_BOOLEAN:
+                cell_contents = bool(cell_contents)
+            elif cell_typ == XL_CELL_NUMBER:
+                # GH5394 - Excel 'numbers' are always floats
+                # it's a minimal perf hit and less surprising
+                if math.isfinite(cell_contents):
+                    # GH54564 - don't attempt to convert NaN/Inf
+                    val = int(cell_contents)
+                    if val == cell_contents:
+                        cell_contents = val
+            return cell_contents
+
+        data = []
+
+        nrows = sheet.nrows
+        if file_rows_needed is not None:
+            nrows = min(nrows, file_rows_needed)
+        for i in range(nrows):
+            row = [
+                _parse_cell(value, typ)
+                for value, typ in zip(sheet.row_values(i), sheet.row_types(i))
+            ]
+            data.append(row)
+
+        return data
diff --git a/shared/package-files/b5/b5de92ed006fa8483ee1f59101782d8a716fbba284b55f054d713f393456ebfa/wy160-42-w b/shared/package-files/b5/b5de92ed006fa8483ee1f59101782d8a716fbba284b55f054d713f393456ebfa/wy160-42-w
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diff --git a/shared/package-files/b5/b5de92ed006fa8483ee1f59101782d8a716fbba284b55f054d713f393456ebfa/wyse160-42-w b/shared/package-files/b5/b5de92ed006fa8483ee1f59101782d8a716fbba284b55f054d713f393456ebfa/wyse160-42-w
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diff --git a/shared/package-files/b5/b5e1ec5d37cfc3fab8d7deed5dccf63affac715c431cff93fcba5c10b98e3272/test_query.py b/shared/package-files/b5/b5e1ec5d37cfc3fab8d7deed5dccf63affac715c431cff93fcba5c10b98e3272/test_query.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a7d58d335b2a01f13c54a505f643f24dd58801
--- /dev/null
+++ b/shared/package-files/b5/b5e1ec5d37cfc3fab8d7deed5dccf63affac715c431cff93fcba5c10b98e3272/test_query.py
@@ -0,0 +1,2408 @@
+from sympy.abc import t, w, x, y, z, n, k, m, p, i
+from sympy.assumptions import (ask, AssumptionsContext, Q, register_handler,
+        remove_handler)
+from sympy.assumptions.assume import assuming, global_assumptions, Predicate
+from sympy.assumptions.cnf import CNF, Literal
+from sympy.assumptions.facts import (single_fact_lookup,
+    get_known_facts, generate_known_facts_dict, get_known_facts_keys)
+from sympy.assumptions.handlers import AskHandler
+from sympy.assumptions.ask_generated import (get_all_known_facts,
+    get_known_facts_dict)
+from sympy.core.add import Add
+from sympy.core.numbers import (I, Integer, Rational, oo, zoo, pi)
+from sympy.core.singleton import S
+from sympy.core.power import Pow
+from sympy.core.symbol import Str, symbols, Symbol
+from sympy.functions.combinatorial.factorials import factorial
+from sympy.functions.elementary.complexes import (Abs, im, re, sign)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (
+    acos, acot, asin, atan, cos, cot, sin, tan)
+from sympy.logic.boolalg import Equivalent, Implies, Xor, And, to_cnf
+from sympy.matrices import Matrix, SparseMatrix
+from sympy.testing.pytest import (XFAIL, slow, raises, warns_deprecated_sympy,
+    _both_exp_pow)
+import math
+
+
+def test_int_1():
+    z = 1
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is True
+    assert ask(Q.rational(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is True
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_int_11():
+    z = 11
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is True
+    assert ask(Q.rational(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is True
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is True
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_int_12():
+    z = 12
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is True
+    assert ask(Q.rational(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is True
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is True
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_float_1():
+    z = 1.0
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is None
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is None
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    z = 7.2123
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is None
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is None
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    # test for issue #12168
+    assert ask(Q.rational(math.pi)) is None
+
+
+def test_zero_0():
+    z = Integer(0)
+    assert ask(Q.nonzero(z)) is False
+    assert ask(Q.zero(z)) is True
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is True
+    assert ask(Q.rational(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is False
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is True
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is True
+
+
+def test_negativeone():
+    z = Integer(-1)
+    assert ask(Q.nonzero(z)) is True
+    assert ask(Q.zero(z)) is False
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is True
+    assert ask(Q.rational(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is False
+    assert ask(Q.negative(z)) is True
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is True
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_infinity():
+    assert ask(Q.commutative(oo)) is True
+    assert ask(Q.integer(oo)) is False
+    assert ask(Q.rational(oo)) is False
+    assert ask(Q.algebraic(oo)) is False
+    assert ask(Q.real(oo)) is False
+    assert ask(Q.extended_real(oo)) is True
+    assert ask(Q.complex(oo)) is False
+    assert ask(Q.irrational(oo)) is False
+    assert ask(Q.imaginary(oo)) is False
+    assert ask(Q.positive(oo)) is False
+    assert ask(Q.extended_positive(oo)) is True
+    assert ask(Q.negative(oo)) is False
+    assert ask(Q.even(oo)) is False
+    assert ask(Q.odd(oo)) is False
+    assert ask(Q.finite(oo)) is False
+    assert ask(Q.infinite(oo)) is True
+    assert ask(Q.prime(oo)) is False
+    assert ask(Q.composite(oo)) is False
+    assert ask(Q.hermitian(oo)) is False
+    assert ask(Q.antihermitian(oo)) is False
+    assert ask(Q.positive_infinite(oo)) is True
+    assert ask(Q.negative_infinite(oo)) is False
+
+
+def test_neg_infinity():
+    mm = S.NegativeInfinity
+    assert ask(Q.commutative(mm)) is True
+    assert ask(Q.integer(mm)) is False
+    assert ask(Q.rational(mm)) is False
+    assert ask(Q.algebraic(mm)) is False
+    assert ask(Q.real(mm)) is False
+    assert ask(Q.extended_real(mm)) is True
+    assert ask(Q.complex(mm)) is False
+    assert ask(Q.irrational(mm)) is False
+    assert ask(Q.imaginary(mm)) is False
+    assert ask(Q.positive(mm)) is False
+    assert ask(Q.negative(mm)) is False
+    assert ask(Q.extended_negative(mm)) is True
+    assert ask(Q.even(mm)) is False
+    assert ask(Q.odd(mm)) is False
+    assert ask(Q.finite(mm)) is False
+    assert ask(Q.infinite(oo)) is True
+    assert ask(Q.prime(mm)) is False
+    assert ask(Q.composite(mm)) is False
+    assert ask(Q.hermitian(mm)) is False
+    assert ask(Q.antihermitian(mm)) is False
+    assert ask(Q.positive_infinite(-oo)) is False
+    assert ask(Q.negative_infinite(-oo)) is True
+
+
+def test_complex_infinity():
+    assert ask(Q.commutative(zoo)) is True
+    assert ask(Q.integer(zoo)) is False
+    assert ask(Q.rational(zoo)) is False
+    assert ask(Q.algebraic(zoo)) is False
+    assert ask(Q.real(zoo)) is False
+    assert ask(Q.extended_real(zoo)) is False
+    assert ask(Q.complex(zoo)) is False
+    assert ask(Q.irrational(zoo)) is False
+    assert ask(Q.imaginary(zoo)) is False
+    assert ask(Q.positive(zoo)) is False
+    assert ask(Q.negative(zoo)) is False
+    assert ask(Q.zero(zoo)) is False
+    assert ask(Q.nonzero(zoo)) is False
+    assert ask(Q.even(zoo)) is False
+    assert ask(Q.odd(zoo)) is False
+    assert ask(Q.finite(zoo)) is False
+    assert ask(Q.infinite(zoo)) is True
+    assert ask(Q.prime(zoo)) is False
+    assert ask(Q.composite(zoo)) is False
+    assert ask(Q.hermitian(zoo)) is False
+    assert ask(Q.antihermitian(zoo)) is False
+    assert ask(Q.positive_infinite(zoo)) is False
+    assert ask(Q.negative_infinite(zoo)) is False
+
+
+def test_nan():
+    nan = S.NaN
+    assert ask(Q.commutative(nan)) is True
+    assert ask(Q.integer(nan)) is None
+    assert ask(Q.rational(nan)) is None
+    assert ask(Q.algebraic(nan)) is None
+    assert ask(Q.real(nan)) is None
+    assert ask(Q.extended_real(nan)) is None
+    assert ask(Q.complex(nan)) is None
+    assert ask(Q.irrational(nan)) is None
+    assert ask(Q.imaginary(nan)) is None
+    assert ask(Q.positive(nan)) is None
+    assert ask(Q.nonzero(nan)) is None
+    assert ask(Q.zero(nan)) is None
+    assert ask(Q.even(nan)) is None
+    assert ask(Q.odd(nan)) is None
+    assert ask(Q.finite(nan)) is None
+    assert ask(Q.infinite(nan)) is None
+    assert ask(Q.prime(nan)) is None
+    assert ask(Q.composite(nan)) is None
+    assert ask(Q.hermitian(nan)) is None
+    assert ask(Q.antihermitian(nan)) is None
+
+
+def test_Rational_number():
+    r = Rational(3, 4)
+    assert ask(Q.commutative(r)) is True
+    assert ask(Q.integer(r)) is False
+    assert ask(Q.rational(r)) is True
+    assert ask(Q.real(r)) is True
+    assert ask(Q.complex(r)) is True
+    assert ask(Q.irrational(r)) is False
+    assert ask(Q.imaginary(r)) is False
+    assert ask(Q.positive(r)) is True
+    assert ask(Q.negative(r)) is False
+    assert ask(Q.even(r)) is False
+    assert ask(Q.odd(r)) is False
+    assert ask(Q.finite(r)) is True
+    assert ask(Q.prime(r)) is False
+    assert ask(Q.composite(r)) is False
+    assert ask(Q.hermitian(r)) is True
+    assert ask(Q.antihermitian(r)) is False
+
+    r = Rational(1, 4)
+    assert ask(Q.positive(r)) is True
+    assert ask(Q.negative(r)) is False
+
+    r = Rational(5, 4)
+    assert ask(Q.negative(r)) is False
+    assert ask(Q.positive(r)) is True
+
+    r = Rational(5, 3)
+    assert ask(Q.positive(r)) is True
+    assert ask(Q.negative(r)) is False
+
+    r = Rational(-3, 4)
+    assert ask(Q.positive(r)) is False
+    assert ask(Q.negative(r)) is True
+
+    r = Rational(-1, 4)
+    assert ask(Q.positive(r)) is False
+    assert ask(Q.negative(r)) is True
+
+    r = Rational(-5, 4)
+    assert ask(Q.negative(r)) is True
+    assert ask(Q.positive(r)) is False
+
+    r = Rational(-5, 3)
+    assert ask(Q.positive(r)) is False
+    assert ask(Q.negative(r)) is True
+
+
+def test_sqrt_2():
+    z = sqrt(2)
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_pi():
+    z = S.Pi
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    z = S.Pi + 1
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    z = 2*S.Pi
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    z = S.Pi ** 2
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+    z = (1 + S.Pi) ** 2
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_E():
+    z = S.Exp1
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is False
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_GoldenRatio():
+    z = S.GoldenRatio
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_TribonacciConstant():
+    z = S.TribonacciConstant
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is True
+    assert ask(Q.real(z)) is True
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is True
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is True
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is True
+    assert ask(Q.antihermitian(z)) is False
+
+
+def test_I():
+    z = I
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is True
+    assert ask(Q.real(z)) is False
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is True
+    assert ask(Q.positive(z)) is False
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is False
+    assert ask(Q.antihermitian(z)) is True
+
+    z = 1 + I
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is True
+    assert ask(Q.real(z)) is False
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is False
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is False
+    assert ask(Q.antihermitian(z)) is False
+
+    z = I*(1 + I)
+    assert ask(Q.commutative(z)) is True
+    assert ask(Q.integer(z)) is False
+    assert ask(Q.rational(z)) is False
+    assert ask(Q.algebraic(z)) is True
+    assert ask(Q.real(z)) is False
+    assert ask(Q.complex(z)) is True
+    assert ask(Q.irrational(z)) is False
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.positive(z)) is False
+    assert ask(Q.negative(z)) is False
+    assert ask(Q.even(z)) is False
+    assert ask(Q.odd(z)) is False
+    assert ask(Q.finite(z)) is True
+    assert ask(Q.prime(z)) is False
+    assert ask(Q.composite(z)) is False
+    assert ask(Q.hermitian(z)) is False
+    assert ask(Q.antihermitian(z)) is False
+
+    z = I**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (-I)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (3*I)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is False
+
+    z = (1)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (-1)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (1+I)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is False
+
+    z = (I)**(I+3)
+    assert ask(Q.imaginary(z)) is True
+    assert ask(Q.real(z)) is False
+
+    z = (I)**(I+2)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (I)**(2)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+    z = (I)**(3)
+    assert ask(Q.imaginary(z)) is True
+    assert ask(Q.real(z)) is False
+
+    z = (3)**(I)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is False
+
+    z = (I)**(0)
+    assert ask(Q.imaginary(z)) is False
+    assert ask(Q.real(z)) is True
+
+def test_bounded():
+    x, y, z = symbols('x,y,z')
+    assert ask(Q.finite(x)) is None
+    assert ask(Q.finite(x), Q.finite(x)) is True
+    assert ask(Q.finite(x), Q.finite(y)) is None
+    assert ask(Q.finite(x), Q.complex(x)) is True
+    assert ask(Q.finite(x), Q.extended_real(x)) is None
+
+    assert ask(Q.finite(x + 1)) is None
+    assert ask(Q.finite(x + 1), Q.finite(x)) is True
+    a = x + y
+    x, y = a.args
+    # B + B
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)) is True
+    assert ask(Q.finite(a), Q.positive(x) & Q.finite(y)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)) is True
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)) is True
+    assert ask(Q.finite(a), Q.positive(x) & Q.finite(y)
+        & ~Q.positive(y)) is True
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.positive(x)
+        & Q.positive(y)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y) & ~Q.positive(x)
+        & ~Q.positive(y)) is True
+    # B + U
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)) is False
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)) is False
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.positive_infinite(y)) is False
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.positive_infinite(y)) is False
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)
+        & ~Q.positive(y)) is False
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.positive(x)
+        & Q.positive_infinite(y)) is False
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.positive(x) & ~Q.finite(y)
+        & ~Q.positive(y)) is False
+    # B + ?
+    assert ask(Q.finite(a), Q.finite(x)) is None
+    assert ask(Q.finite(a), Q.positive(x)) is None
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.positive(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.positive(x)
+        & Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.positive(x)
+        & ~Q.positive(y)) is None
+    # U + U
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.positive_infinite(y)) is False
+    assert ask(Q.finite(a), Q.positive_infinite(x) & ~Q.finite(y)
+        & ~Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.extended_positive(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & ~Q.extended_positive(x) & ~Q.extended_positive(y)) is False
+    # U + ?
+    assert ask(Q.finite(a), ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & Q.positive_infinite(y)) is False
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & ~Q.finite(y) & ~Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), ~Q.extended_positive(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), ~Q.extended_positive(x) & ~Q.finite(y)
+        & ~Q.extended_positive(y)) is False
+    # ? + ?
+    assert ask(Q.finite(a)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)) is None
+    assert ask(Q.finite(a), Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & ~Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), ~Q.extended_positive(x)
+        & Q.extended_positive(y)) is None
+    assert ask(Q.finite(a), ~Q.extended_positive(x)
+        & ~Q.extended_positive(y)) is None
+
+    x, y, z = symbols('x,y,z')
+    a = x + y + z
+    x, y, z = a.args
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.negative(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.finite(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.finite(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.extended_positive(y)
+        & Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative_infinite(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative_infinite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative_infinite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative_infinite(y)
+        & Q.extended_negative(z)) is False
+    assert ask(Q.finite(a), Q.negative(x)
+        & Q.negative_infinite(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.negative_infinite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & ~Q.finite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & ~Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & ~Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive_infinite(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive_infinite(y)
+        & Q.negative_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) &
+         Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.positive_infinite(y)
+        & Q.extended_positive(z)) is False
+    assert ask(Q.finite(a), Q.negative(x) & Q.extended_negative(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.extended_negative(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x)) is None
+    assert ask(Q.finite(a), Q.negative(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative(x) & Q.extended_positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.finite(z)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.negative_infinite(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.negative_infinite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.negative_infinite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.negative_infinite(y)
+        & Q.extended_negative(z)) is False
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.negative_infinite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.negative_infinite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive_infinite(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive_infinite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.positive_infinite(y)
+        & Q.extended_positive(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.extended_negative(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.extended_negative(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x)) is None
+    assert ask(Q.finite(a), Q.finite(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.extended_positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & Q.positive(z)) is True
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.negative_infinite(y)
+        & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.negative_infinite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.negative_infinite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.negative_infinite(y)
+        & Q.extended_negative(z)) is False
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.negative_infinite(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.negative_infinite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & ~Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive_infinite(y)
+        & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive_infinite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.positive_infinite(y)
+        & Q.extended_positive(z)) is False
+    assert ask(Q.finite(a), Q.positive(x) & Q.extended_negative(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.extended_negative(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x)) is None
+    assert ask(Q.finite(a), Q.positive(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive(x) & Q.extended_positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y) & Q.negative_infinite(z)) is False
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y)& Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y) & Q.extended_negative(z)) is False
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.negative_infinite(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & ~Q.finite(y) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & ~Q.finite(y) & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & ~Q.finite(y) & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & ~Q.finite(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.positive_infinite(y) & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.positive_infinite(y) & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.positive_infinite(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.extended_negative(y) & Q.extended_negative(z)) is False
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.extended_negative(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.negative_infinite(x)
+        & Q.extended_positive(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.positive_infinite(z)
+        & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.positive_infinite(y)
+        & Q.positive_infinite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.positive_infinite(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.positive_infinite(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.extended_negative(y)
+        & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.extended_negative(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.extended_positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.positive_infinite(y) & Q.positive_infinite(z)) is False
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.positive_infinite(y) & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.positive_infinite(y)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.positive_infinite(y) & Q.extended_positive(z)) is False
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.extended_negative(y) & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.extended_negative(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.positive_infinite(x)
+        & Q.extended_positive(y) & Q.extended_positive(z)) is False
+    assert ask(Q.finite(a), Q.extended_negative(x)
+        & Q.extended_negative(y) & Q.extended_negative(z)) is None
+    assert ask(Q.finite(a), Q.extended_negative(x)
+        & Q.extended_negative(y)) is None
+    assert ask(Q.finite(a), Q.extended_negative(x)
+        & Q.extended_negative(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.extended_negative(x)) is None
+    assert ask(Q.finite(a), Q.extended_negative(x)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.extended_negative(x)
+        & Q.extended_positive(y) & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a)) is None
+    assert ask(Q.finite(a), Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.extended_positive(y)
+        & Q.extended_positive(z)) is None
+    assert ask(Q.finite(a), Q.extended_positive(x)
+        & Q.extended_positive(y) & Q.extended_positive(z)) is None
+
+    assert ask(Q.finite(2*x)) is None
+    assert ask(Q.finite(2*x), Q.finite(x)) is True
+
+    x, y, z = symbols('x,y,z')
+    a = x*y
+    x, y = a.args
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)) is True
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)) is False
+    assert ask(Q.finite(a), Q.finite(x)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.finite(y)) is False
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)) is False
+    assert ask(Q.finite(a), ~Q.finite(x)) is None
+    assert ask(Q.finite(a), Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(y)) is None
+    assert ask(Q.finite(a)) is None
+    a = x*y*z
+    x, y, z = a.args
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & Q.finite(z)) is True
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.finite(x) & Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(x)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.finite(y)
+        & Q.finite(z)) is False
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & Q.finite(z)) is False
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)
+        & ~Q.finite(z)) is False
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(x)) is None
+    assert ask(Q.finite(a), Q.finite(y) & Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(y) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), Q.finite(y)) is None
+    assert ask(Q.finite(a), ~Q.finite(y) & Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(y) & ~Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(y)) is None
+    assert ask(Q.finite(a), Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(z)) is None
+    assert ask(Q.finite(a), ~Q.finite(z) & Q.extended_nonzero(x)
+        & Q.extended_nonzero(y) & Q.extended_nonzero(z)) is None
+    assert ask(Q.finite(a), Q.extended_nonzero(x) & ~Q.finite(y)
+        & Q.extended_nonzero(y) & ~Q.finite(z)
+        & Q.extended_nonzero(z)) is False
+
+    x, y, z = symbols('x,y,z')
+    assert ask(Q.finite(x**2)) is None
+    assert ask(Q.finite(2**x)) is None
+    assert ask(Q.finite(2**x), Q.finite(x)) is True
+    assert ask(Q.finite(x**x)) is None
+    assert ask(Q.finite(S.Half ** x)) is None
+    assert ask(Q.finite(S.Half ** x), Q.extended_positive(x)) is True
+    assert ask(Q.finite(S.Half ** x), Q.extended_negative(x)) is None
+    assert ask(Q.finite(2**x), Q.extended_negative(x)) is True
+    assert ask(Q.finite(sqrt(x))) is None
+    assert ask(Q.finite(2**x), ~Q.finite(x)) is False
+    assert ask(Q.finite(x**2), ~Q.finite(x)) is False
+
+    # sign function
+    assert ask(Q.finite(sign(x))) is True
+    assert ask(Q.finite(sign(x)), ~Q.finite(x)) is True
+
+    # exponential functions
+    assert ask(Q.finite(log(x))) is None
+    assert ask(Q.finite(log(x)), Q.finite(x)) is None
+    assert ask(Q.finite(log(x)), ~Q.zero(x)) is True
+    assert ask(Q.finite(log(x)), Q.infinite(x)) is False
+    assert ask(Q.finite(log(x)), Q.zero(x)) is False
+    assert ask(Q.finite(exp(x))) is None
+    assert ask(Q.finite(exp(x)), Q.finite(x)) is True
+    assert ask(Q.finite(exp(2))) is True
+
+    # trigonometric functions
+    assert ask(Q.finite(sin(x))) is True
+    assert ask(Q.finite(sin(x)), ~Q.finite(x)) is True
+    assert ask(Q.finite(cos(x))) is True
+    assert ask(Q.finite(cos(x)), ~Q.finite(x)) is True
+    assert ask(Q.finite(2*sin(x))) is True
+    assert ask(Q.finite(sin(x)**2)) is True
+    assert ask(Q.finite(cos(x)**2)) is True
+    assert ask(Q.finite(cos(x) + sin(x))) is True
+
+
+@XFAIL
+def test_bounded_xfail():
+    """We need to support relations in ask for this to work"""
+    assert ask(Q.finite(sin(x)**x)) is True
+    assert ask(Q.finite(cos(x)**x)) is True
+
+
+def test_commutative():
+    """By default objects are Q.commutative that is why it returns True
+    for both key=True and key=False"""
+    assert ask(Q.commutative(x)) is True
+    assert ask(Q.commutative(x), ~Q.commutative(x)) is False
+    assert ask(Q.commutative(x), Q.complex(x)) is True
+    assert ask(Q.commutative(x), Q.imaginary(x)) is True
+    assert ask(Q.commutative(x), Q.real(x)) is True
+    assert ask(Q.commutative(x), Q.positive(x)) is True
+    assert ask(Q.commutative(x), ~Q.commutative(y)) is True
+
+    assert ask(Q.commutative(2*x)) is True
+    assert ask(Q.commutative(2*x), ~Q.commutative(x)) is False
+
+    assert ask(Q.commutative(x + 1)) is True
+    assert ask(Q.commutative(x + 1), ~Q.commutative(x)) is False
+
+    assert ask(Q.commutative(x**2)) is True
+    assert ask(Q.commutative(x**2), ~Q.commutative(x)) is False
+
+    assert ask(Q.commutative(log(x))) is True
+
+
+@_both_exp_pow
+def test_complex():
+    assert ask(Q.complex(x)) is None
+    assert ask(Q.complex(x), Q.complex(x)) is True
+    assert ask(Q.complex(x), Q.complex(y)) is None
+    assert ask(Q.complex(x), ~Q.complex(x)) is False
+    assert ask(Q.complex(x), Q.real(x)) is True
+    assert ask(Q.complex(x), ~Q.real(x)) is None
+    assert ask(Q.complex(x), Q.rational(x)) is True
+    assert ask(Q.complex(x), Q.irrational(x)) is True
+    assert ask(Q.complex(x), Q.positive(x)) is True
+    assert ask(Q.complex(x), Q.imaginary(x)) is True
+    assert ask(Q.complex(x), Q.algebraic(x)) is True
+
+    # a+b
+    assert ask(Q.complex(x + 1), Q.complex(x)) is True
+    assert ask(Q.complex(x + 1), Q.real(x)) is True
+    assert ask(Q.complex(x + 1), Q.rational(x)) is True
+    assert ask(Q.complex(x + 1), Q.irrational(x)) is True
+    assert ask(Q.complex(x + 1), Q.imaginary(x)) is True
+    assert ask(Q.complex(x + 1), Q.integer(x)) is True
+    assert ask(Q.complex(x + 1), Q.even(x)) is True
+    assert ask(Q.complex(x + 1), Q.odd(x)) is True
+    assert ask(Q.complex(x + y), Q.complex(x) & Q.complex(y)) is True
+    assert ask(Q.complex(x + y), Q.real(x) & Q.imaginary(y)) is True
+
+    # a*x +b
+    assert ask(Q.complex(2*x + 1), Q.complex(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.real(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.positive(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.rational(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.irrational(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.imaginary(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.integer(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.even(x)) is True
+    assert ask(Q.complex(2*x + 1), Q.odd(x)) is True
+
+    # x**2
+    assert ask(Q.complex(x**2), Q.complex(x)) is True
+    assert ask(Q.complex(x**2), Q.real(x)) is True
+    assert ask(Q.complex(x**2), Q.positive(x)) is True
+    assert ask(Q.complex(x**2), Q.rational(x)) is True
+    assert ask(Q.complex(x**2), Q.irrational(x)) is True
+    assert ask(Q.complex(x**2), Q.imaginary(x)) is True
+    assert ask(Q.complex(x**2), Q.integer(x)) is True
+    assert ask(Q.complex(x**2), Q.even(x)) is True
+    assert ask(Q.complex(x**2), Q.odd(x)) is True
+
+    # 2**x
+    assert ask(Q.complex(2**x), Q.complex(x)) is True
+    assert ask(Q.complex(2**x), Q.real(x)) is True
+    assert ask(Q.complex(2**x), Q.positive(x)) is True
+    assert ask(Q.complex(2**x), Q.rational(x)) is True
+    assert ask(Q.complex(2**x), Q.irrational(x)) is True
+    assert ask(Q.complex(2**x), Q.imaginary(x)) is True
+    assert ask(Q.complex(2**x), Q.integer(x)) is True
+    assert ask(Q.complex(2**x), Q.even(x)) is True
+    assert ask(Q.complex(2**x), Q.odd(x)) is True
+    assert ask(Q.complex(x**y), Q.complex(x) & Q.complex(y)) is True
+
+    # trigonometric expressions
+    assert ask(Q.complex(sin(x))) is True
+    assert ask(Q.complex(sin(2*x + 1))) is True
+    assert ask(Q.complex(cos(x))) is True
+    assert ask(Q.complex(cos(2*x + 1))) is True
+
+    # exponential
+    assert ask(Q.complex(exp(x))) is True
+    assert ask(Q.complex(exp(x))) is True
+
+    # Q.complexes
+    assert ask(Q.complex(Abs(x))) is True
+    assert ask(Q.complex(re(x))) is True
+    assert ask(Q.complex(im(x))) is True
+
+
+def test_even_query():
+    assert ask(Q.even(x)) is None
+    assert ask(Q.even(x), Q.integer(x)) is None
+    assert ask(Q.even(x), ~Q.integer(x)) is False
+    assert ask(Q.even(x), Q.rational(x)) is None
+    assert ask(Q.even(x), Q.positive(x)) is None
+
+    assert ask(Q.even(2*x)) is None
+    assert ask(Q.even(2*x), Q.integer(x)) is True
+    assert ask(Q.even(2*x), Q.even(x)) is True
+    assert ask(Q.even(2*x), Q.irrational(x)) is False
+    assert ask(Q.even(2*x), Q.odd(x)) is True
+    assert ask(Q.even(2*x), ~Q.integer(x)) is None
+    assert ask(Q.even(3*x), Q.integer(x)) is None
+    assert ask(Q.even(3*x), Q.even(x)) is True
+    assert ask(Q.even(3*x), Q.odd(x)) is False
+
+    assert ask(Q.even(x + 1), Q.odd(x)) is True
+    assert ask(Q.even(x + 1), Q.even(x)) is False
+    assert ask(Q.even(x + 2), Q.odd(x)) is False
+    assert ask(Q.even(x + 2), Q.even(x)) is True
+    assert ask(Q.even(7 - x), Q.odd(x)) is True
+    assert ask(Q.even(7 + x), Q.odd(x)) is True
+    assert ask(Q.even(x + y), Q.odd(x) & Q.odd(y)) is True
+    assert ask(Q.even(x + y), Q.odd(x) & Q.even(y)) is False
+    assert ask(Q.even(x + y), Q.even(x) & Q.even(y)) is True
+
+    assert ask(Q.even(2*x + 1), Q.integer(x)) is False
+    assert ask(Q.even(2*x*y), Q.rational(x) & Q.rational(x)) is None
+    assert ask(Q.even(2*x*y), Q.irrational(x) & Q.irrational(x)) is None
+
+    assert ask(Q.even(x + y + z), Q.odd(x) & Q.odd(y) & Q.even(z)) is True
+    assert ask(Q.even(x + y + z + t),
+        Q.odd(x) & Q.odd(y) & Q.even(z) & Q.integer(t)) is None
+
+    assert ask(Q.even(Abs(x)), Q.even(x)) is True
+    assert ask(Q.even(Abs(x)), ~Q.even(x)) is None
+    assert ask(Q.even(re(x)), Q.even(x)) is True
+    assert ask(Q.even(re(x)), ~Q.even(x)) is None
+    assert ask(Q.even(im(x)), Q.even(x)) is True
+    assert ask(Q.even(im(x)), Q.real(x)) is True
+
+    assert ask(Q.even((-1)**n), Q.integer(n)) is False
+
+    assert ask(Q.even(k**2), Q.even(k)) is True
+    assert ask(Q.even(n**2), Q.odd(n)) is False
+    assert ask(Q.even(2**k), Q.even(k)) is None
+    assert ask(Q.even(x**2)) is None
+
+    assert ask(Q.even(k**m), Q.even(k) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.even(n**m), Q.odd(n) & Q.integer(m) & ~Q.negative(m)) is False
+
+    assert ask(Q.even(k**p), Q.even(k) & Q.integer(p) & Q.positive(p)) is True
+    assert ask(Q.even(n**p), Q.odd(n) & Q.integer(p) & Q.positive(p)) is False
+
+    assert ask(Q.even(m**k), Q.even(k) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.even(p**k), Q.even(k) & Q.integer(p) & Q.positive(p)) is None
+
+    assert ask(Q.even(m**n), Q.odd(n) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.even(p**n), Q.odd(n) & Q.integer(p) & Q.positive(p)) is None
+
+    assert ask(Q.even(k**x), Q.even(k)) is None
+    assert ask(Q.even(n**x), Q.odd(n)) is None
+
+    assert ask(Q.even(x*y), Q.integer(x) & Q.integer(y)) is None
+    assert ask(Q.even(x*x), Q.integer(x)) is None
+    assert ask(Q.even(x*(x + y)), Q.integer(x) & Q.odd(y)) is True
+    assert ask(Q.even(x*(x + y)), Q.integer(x) & Q.even(y)) is None
+
+
+@XFAIL
+def test_evenness_in_ternary_integer_product_with_odd():
+    # Tests that oddness inference is independent of term ordering.
+    # Term ordering at the point of testing depends on SymPy's symbol order, so
+    # we try to force a different order by modifying symbol names.
+    assert ask(Q.even(x*y*(y + z)), Q.integer(x) & Q.integer(y) & Q.odd(z)) is True
+    assert ask(Q.even(y*x*(x + z)), Q.integer(x) & Q.integer(y) & Q.odd(z)) is True
+
+
+def test_evenness_in_ternary_integer_product_with_even():
+    assert ask(Q.even(x*y*(y + z)), Q.integer(x) & Q.integer(y) & Q.even(z)) is None
+
+
+def test_extended_real():
+    assert ask(Q.extended_real(x), Q.positive_infinite(x)) is True
+    assert ask(Q.extended_real(x), Q.positive(x)) is True
+    assert ask(Q.extended_real(x), Q.zero(x)) is True
+    assert ask(Q.extended_real(x), Q.negative(x)) is True
+    assert ask(Q.extended_real(x), Q.negative_infinite(x)) is True
+
+    assert ask(Q.extended_real(-x), Q.positive(x)) is True
+    assert ask(Q.extended_real(-x), Q.negative(x)) is True
+
+    assert ask(Q.extended_real(x + S.Infinity), Q.real(x)) is True
+
+    assert ask(Q.extended_real(x), Q.infinite(x)) is None
+
+
+@_both_exp_pow
+def test_rational():
+    assert ask(Q.rational(x), Q.integer(x)) is True
+    assert ask(Q.rational(x), Q.irrational(x)) is False
+    assert ask(Q.rational(x), Q.real(x)) is None
+    assert ask(Q.rational(x), Q.positive(x)) is None
+    assert ask(Q.rational(x), Q.negative(x)) is None
+    assert ask(Q.rational(x), Q.nonzero(x)) is None
+    assert ask(Q.rational(x), ~Q.algebraic(x)) is False
+
+    assert ask(Q.rational(2*x), Q.rational(x)) is True
+    assert ask(Q.rational(2*x), Q.integer(x)) is True
+    assert ask(Q.rational(2*x), Q.even(x)) is True
+    assert ask(Q.rational(2*x), Q.odd(x)) is True
+    assert ask(Q.rational(2*x), Q.irrational(x)) is False
+
+    assert ask(Q.rational(x/2), Q.rational(x)) is True
+    assert ask(Q.rational(x/2), Q.integer(x)) is True
+    assert ask(Q.rational(x/2), Q.even(x)) is True
+    assert ask(Q.rational(x/2), Q.odd(x)) is True
+    assert ask(Q.rational(x/2), Q.irrational(x)) is False
+
+    assert ask(Q.rational(1/x), Q.rational(x)) is True
+    assert ask(Q.rational(1/x), Q.integer(x)) is True
+    assert ask(Q.rational(1/x), Q.even(x)) is True
+    assert ask(Q.rational(1/x), Q.odd(x)) is True
+    assert ask(Q.rational(1/x), Q.irrational(x)) is False
+
+    assert ask(Q.rational(2/x), Q.rational(x)) is True
+    assert ask(Q.rational(2/x), Q.integer(x)) is True
+    assert ask(Q.rational(2/x), Q.even(x)) is True
+    assert ask(Q.rational(2/x), Q.odd(x)) is True
+    assert ask(Q.rational(2/x), Q.irrational(x)) is False
+
+    assert ask(Q.rational(x), ~Q.algebraic(x)) is False
+
+    # with multiple symbols
+    assert ask(Q.rational(x*y), Q.irrational(x) & Q.irrational(y)) is None
+    assert ask(Q.rational(y/x), Q.rational(x) & Q.rational(y)) is True
+    assert ask(Q.rational(y/x), Q.integer(x) & Q.rational(y)) is True
+    assert ask(Q.rational(y/x), Q.even(x) & Q.rational(y)) is True
+    assert ask(Q.rational(y/x), Q.odd(x) & Q.rational(y)) is True
+    assert ask(Q.rational(y/x), Q.irrational(x) & Q.rational(y)) is False
+
+    for f in [exp, sin, tan, asin, atan, cos]:
+        assert ask(Q.rational(f(7))) is False
+        assert ask(Q.rational(f(7, evaluate=False))) is False
+        assert ask(Q.rational(f(0, evaluate=False))) is True
+        assert ask(Q.rational(f(x)), Q.rational(x)) is None
+        assert ask(Q.rational(f(x)), Q.rational(x) & Q.nonzero(x)) is False
+
+    for g in [log, acos]:
+        assert ask(Q.rational(g(7))) is False
+        assert ask(Q.rational(g(7, evaluate=False))) is False
+        assert ask(Q.rational(g(1, evaluate=False))) is True
+        assert ask(Q.rational(g(x)), Q.rational(x)) is None
+        assert ask(Q.rational(g(x)), Q.rational(x) & Q.nonzero(x - 1)) is False
+
+    for h in [cot, acot]:
+        assert ask(Q.rational(h(7))) is False
+        assert ask(Q.rational(h(7, evaluate=False))) is False
+        assert ask(Q.rational(h(x)), Q.rational(x)) is False
+
+
+def test_hermitian():
+    assert ask(Q.hermitian(x)) is None
+    assert ask(Q.hermitian(x), Q.antihermitian(x)) is None
+    assert ask(Q.hermitian(x), Q.imaginary(x)) is False
+    assert ask(Q.hermitian(x), Q.prime(x)) is True
+    assert ask(Q.hermitian(x), Q.real(x)) is True
+    assert ask(Q.hermitian(x), Q.zero(x)) is True
+
+    assert ask(Q.hermitian(x + 1), Q.antihermitian(x)) is None
+    assert ask(Q.hermitian(x + 1), Q.complex(x)) is None
+    assert ask(Q.hermitian(x + 1), Q.hermitian(x)) is True
+    assert ask(Q.hermitian(x + 1), Q.imaginary(x)) is False
+    assert ask(Q.hermitian(x + 1), Q.real(x)) is True
+    assert ask(Q.hermitian(x + I), Q.antihermitian(x)) is None
+    assert ask(Q.hermitian(x + I), Q.complex(x)) is None
+    assert ask(Q.hermitian(x + I), Q.hermitian(x)) is False
+    assert ask(Q.hermitian(x + I), Q.imaginary(x)) is None
+    assert ask(Q.hermitian(x + I), Q.real(x)) is False
+    assert ask(
+        Q.hermitian(x + y), Q.antihermitian(x) & Q.antihermitian(y)) is None
+    assert ask(Q.hermitian(x + y), Q.antihermitian(x) & Q.complex(y)) is None
+    assert ask(
+        Q.hermitian(x + y), Q.antihermitian(x) & Q.hermitian(y)) is None
+    assert ask(Q.hermitian(x + y), Q.antihermitian(x) & Q.imaginary(y)) is None
+    assert ask(Q.hermitian(x + y), Q.antihermitian(x) & Q.real(y)) is None
+    assert ask(Q.hermitian(x + y), Q.hermitian(x) & Q.complex(y)) is None
+    assert ask(Q.hermitian(x + y), Q.hermitian(x) & Q.hermitian(y)) is True
+    assert ask(Q.hermitian(x + y), Q.hermitian(x) & Q.imaginary(y)) is False
+    assert ask(Q.hermitian(x + y), Q.hermitian(x) & Q.real(y)) is True
+    assert ask(Q.hermitian(x + y), Q.imaginary(x) & Q.complex(y)) is None
+    assert ask(Q.hermitian(x + y), Q.imaginary(x) & Q.imaginary(y)) is None
+    assert ask(Q.hermitian(x + y), Q.imaginary(x) & Q.real(y)) is False
+    assert ask(Q.hermitian(x + y), Q.real(x) & Q.complex(y)) is None
+    assert ask(Q.hermitian(x + y), Q.real(x) & Q.real(y)) is True
+
+    assert ask(Q.hermitian(I*x), Q.antihermitian(x)) is True
+    assert ask(Q.hermitian(I*x), Q.complex(x)) is None
+    assert ask(Q.hermitian(I*x), Q.hermitian(x)) is False
+    assert ask(Q.hermitian(I*x), Q.imaginary(x)) is True
+    assert ask(Q.hermitian(I*x), Q.real(x)) is False
+    assert ask(Q.hermitian(x*y), Q.hermitian(x) & Q.real(y)) is True
+
+    assert ask(
+        Q.hermitian(x + y + z), Q.real(x) & Q.real(y) & Q.real(z)) is True
+    assert ask(Q.hermitian(x + y + z),
+        Q.real(x) & Q.real(y) & Q.imaginary(z)) is False
+    assert ask(Q.hermitian(x + y + z),
+        Q.real(x) & Q.imaginary(y) & Q.imaginary(z)) is None
+    assert ask(Q.hermitian(x + y + z),
+        Q.imaginary(x) & Q.imaginary(y) & Q.imaginary(z)) is None
+
+    assert ask(Q.antihermitian(x)) is None
+    assert ask(Q.antihermitian(x), Q.real(x)) is False
+    assert ask(Q.antihermitian(x), Q.prime(x)) is False
+
+    assert ask(Q.antihermitian(x + 1), Q.antihermitian(x)) is False
+    assert ask(Q.antihermitian(x + 1), Q.complex(x)) is None
+    assert ask(Q.antihermitian(x + 1), Q.hermitian(x)) is None
+    assert ask(Q.antihermitian(x + 1), Q.imaginary(x)) is False
+    assert ask(Q.antihermitian(x + 1), Q.real(x)) is None
+    assert ask(Q.antihermitian(x + I), Q.antihermitian(x)) is True
+    assert ask(Q.antihermitian(x + I), Q.complex(x)) is None
+    assert ask(Q.antihermitian(x + I), Q.hermitian(x)) is None
+    assert ask(Q.antihermitian(x + I), Q.imaginary(x)) is True
+    assert ask(Q.antihermitian(x + I), Q.real(x)) is False
+    assert ask(Q.antihermitian(x), Q.zero(x)) is True
+
+    assert ask(
+        Q.antihermitian(x + y), Q.antihermitian(x) & Q.antihermitian(y)
+    ) is True
+    assert ask(
+        Q.antihermitian(x + y), Q.antihermitian(x) & Q.complex(y)) is None
+    assert ask(
+        Q.antihermitian(x + y), Q.antihermitian(x) & Q.hermitian(y)) is None
+    assert ask(
+        Q.antihermitian(x + y), Q.antihermitian(x) & Q.imaginary(y)) is True
+    assert ask(Q.antihermitian(x + y), Q.antihermitian(x) & Q.real(y)
+        ) is False
+    assert ask(Q.antihermitian(x + y), Q.hermitian(x) & Q.complex(y)) is None
+    assert ask(Q.antihermitian(x + y), Q.hermitian(x) & Q.hermitian(y)
+        ) is None
+    assert ask(
+        Q.antihermitian(x + y), Q.hermitian(x) & Q.imaginary(y)) is None
+    assert ask(Q.antihermitian(x + y), Q.hermitian(x) & Q.real(y)) is None
+    assert ask(Q.antihermitian(x + y), Q.imaginary(x) & Q.complex(y)) is None
+    assert ask(Q.antihermitian(x + y), Q.imaginary(x) & Q.imaginary(y)) is True
+    assert ask(Q.antihermitian(x + y), Q.imaginary(x) & Q.real(y)) is False
+    assert ask(Q.antihermitian(x + y), Q.real(x) & Q.complex(y)) is None
+    assert ask(Q.antihermitian(x + y), Q.real(x) & Q.real(y)) is None
+
+    assert ask(Q.antihermitian(I*x), Q.real(x)) is True
+    assert ask(Q.antihermitian(I*x), Q.antihermitian(x)) is False
+    assert ask(Q.antihermitian(I*x), Q.complex(x)) is None
+    assert ask(Q.antihermitian(x*y), Q.antihermitian(x) & Q.real(y)) is True
+
+    assert ask(Q.antihermitian(x + y + z),
+        Q.real(x) & Q.real(y) & Q.real(z)) is None
+    assert ask(Q.antihermitian(x + y + z),
+        Q.real(x) & Q.real(y) & Q.imaginary(z)) is None
+    assert ask(Q.antihermitian(x + y + z),
+        Q.real(x) & Q.imaginary(y) & Q.imaginary(z)) is False
+    assert ask(Q.antihermitian(x + y + z),
+        Q.imaginary(x) & Q.imaginary(y) & Q.imaginary(z)) is True
+
+
+@_both_exp_pow
+def test_imaginary():
+    assert ask(Q.imaginary(x)) is None
+    assert ask(Q.imaginary(x), Q.real(x)) is False
+    assert ask(Q.imaginary(x), Q.prime(x)) is False
+
+    assert ask(Q.imaginary(x + 1), Q.real(x)) is False
+    assert ask(Q.imaginary(x + 1), Q.imaginary(x)) is False
+    assert ask(Q.imaginary(x + I), Q.real(x)) is False
+    assert ask(Q.imaginary(x + I), Q.imaginary(x)) is True
+    assert ask(Q.imaginary(x + y), Q.imaginary(x) & Q.imaginary(y)) is True
+    assert ask(Q.imaginary(x + y), Q.real(x) & Q.real(y)) is False
+    assert ask(Q.imaginary(x + y), Q.imaginary(x) & Q.real(y)) is False
+    assert ask(Q.imaginary(x + y), Q.complex(x) & Q.real(y)) is None
+    assert ask(
+        Q.imaginary(x + y + z), Q.real(x) & Q.real(y) & Q.real(z)) is False
+    assert ask(Q.imaginary(x + y + z),
+        Q.real(x) & Q.real(y) & Q.imaginary(z)) is None
+    assert ask(Q.imaginary(x + y + z),
+        Q.real(x) & Q.imaginary(y) & Q.imaginary(z)) is False
+
+    assert ask(Q.imaginary(I*x), Q.real(x)) is True
+    assert ask(Q.imaginary(I*x), Q.imaginary(x)) is False
+    assert ask(Q.imaginary(I*x), Q.complex(x)) is None
+    assert ask(Q.imaginary(x*y), Q.imaginary(x) & Q.real(y)) is True
+    assert ask(Q.imaginary(x*y), Q.real(x) & Q.real(y)) is False
+
+    assert ask(Q.imaginary(I**x), Q.negative(x)) is None
+    assert ask(Q.imaginary(I**x), Q.positive(x)) is None
+    assert ask(Q.imaginary(I**x), Q.even(x)) is False
+    assert ask(Q.imaginary(I**x), Q.odd(x)) is True
+    assert ask(Q.imaginary(I**x), Q.imaginary(x)) is False
+    assert ask(Q.imaginary((2*I)**x), Q.imaginary(x)) is False
+    assert ask(Q.imaginary(x**0), Q.imaginary(x)) is False
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.imaginary(y)) is None
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.real(y)) is None
+    assert ask(Q.imaginary(x**y), Q.real(x) & Q.imaginary(y)) is None
+    assert ask(Q.imaginary(x**y), Q.real(x) & Q.real(y)) is None
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.integer(y)) is None
+    assert ask(Q.imaginary(x**y), Q.imaginary(y) & Q.integer(x)) is None
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.odd(y)) is True
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.rational(y)) is None
+    assert ask(Q.imaginary(x**y), Q.imaginary(x) & Q.even(y)) is False
+
+    assert ask(Q.imaginary(x**y), Q.real(x) & Q.integer(y)) is False
+    assert ask(Q.imaginary(x**y), Q.positive(x) & Q.real(y)) is False
+    assert ask(Q.imaginary(x**y), Q.negative(x) & Q.real(y)) is None
+    assert ask(Q.imaginary(x**y), Q.negative(x) & Q.real(y) & ~Q.rational(y)) is False
+    assert ask(Q.imaginary(x**y), Q.integer(x) & Q.imaginary(y)) is None
+    assert ask(Q.imaginary(x**y), Q.negative(x) & Q.rational(y) & Q.integer(2*y)) is True
+    assert ask(Q.imaginary(x**y), Q.negative(x) & Q.rational(y) & ~Q.integer(2*y)) is False
+    assert ask(Q.imaginary(x**y), Q.negative(x) & Q.rational(y)) is None
+    assert ask(Q.imaginary(x**y), Q.real(x) & Q.rational(y) & ~Q.integer(2*y)) is False
+    assert ask(Q.imaginary(x**y), Q.real(x) & Q.rational(y) & Q.integer(2*y)) is None
+
+    # logarithm
+    assert ask(Q.imaginary(log(I))) is True
+    assert ask(Q.imaginary(log(2*I))) is False
+    assert ask(Q.imaginary(log(I + 1))) is False
+    assert ask(Q.imaginary(log(x)), Q.complex(x)) is None
+    assert ask(Q.imaginary(log(x)), Q.imaginary(x)) is None
+    assert ask(Q.imaginary(log(x)), Q.positive(x)) is False
+    assert ask(Q.imaginary(log(exp(x))), Q.complex(x)) is None
+    assert ask(Q.imaginary(log(exp(x))), Q.imaginary(x)) is None  # zoo/I/a+I*b
+    assert ask(Q.imaginary(log(exp(I)))) is True
+
+    # exponential
+    assert ask(Q.imaginary(exp(x)**x), Q.imaginary(x)) is False
+    eq = Pow(exp(pi*I*x, evaluate=False), x, evaluate=False)
+    assert ask(Q.imaginary(eq), Q.even(x)) is False
+    eq = Pow(exp(pi*I*x/2, evaluate=False), x, evaluate=False)
+    assert ask(Q.imaginary(eq), Q.odd(x)) is True
+    assert ask(Q.imaginary(exp(3*I*pi*x)**x), Q.integer(x)) is False
+    assert ask(Q.imaginary(exp(2*pi*I, evaluate=False))) is False
+    assert ask(Q.imaginary(exp(pi*I/2, evaluate=False))) is True
+
+    # issue 7886
+    assert ask(Q.imaginary(Pow(x, Rational(1, 4))), Q.real(x) & Q.negative(x)) is False
+
+
+def test_integer():
+    assert ask(Q.integer(x)) is None
+    assert ask(Q.integer(x), Q.integer(x)) is True
+    assert ask(Q.integer(x), ~Q.integer(x)) is False
+    assert ask(Q.integer(x), ~Q.real(x)) is False
+    assert ask(Q.integer(x), ~Q.positive(x)) is None
+    assert ask(Q.integer(x), Q.even(x) | Q.odd(x)) is True
+
+    assert ask(Q.integer(2*x), Q.integer(x)) is True
+    assert ask(Q.integer(2*x), Q.even(x)) is True
+    assert ask(Q.integer(2*x), Q.prime(x)) is True
+    assert ask(Q.integer(2*x), Q.rational(x)) is None
+    assert ask(Q.integer(2*x), Q.real(x)) is None
+    assert ask(Q.integer(sqrt(2)*x), Q.integer(x)) is False
+    assert ask(Q.integer(sqrt(2)*x), Q.irrational(x)) is None
+
+    assert ask(Q.integer(x/2), Q.odd(x)) is False
+    assert ask(Q.integer(x/2), Q.even(x)) is True
+    assert ask(Q.integer(x/3), Q.odd(x)) is None
+    assert ask(Q.integer(x/3), Q.even(x)) is None
+
+
+def test_negative():
+    assert ask(Q.negative(x), Q.negative(x)) is True
+    assert ask(Q.negative(x), Q.positive(x)) is False
+    assert ask(Q.negative(x), ~Q.real(x)) is False
+    assert ask(Q.negative(x), Q.prime(x)) is False
+    assert ask(Q.negative(x), ~Q.prime(x)) is None
+
+    assert ask(Q.negative(-x), Q.positive(x)) is True
+    assert ask(Q.negative(-x), ~Q.positive(x)) is None
+    assert ask(Q.negative(-x), Q.negative(x)) is False
+    assert ask(Q.negative(-x), Q.positive(x)) is True
+
+    assert ask(Q.negative(x - 1), Q.negative(x)) is True
+    assert ask(Q.negative(x + y)) is None
+    assert ask(Q.negative(x + y), Q.negative(x)) is None
+    assert ask(Q.negative(x + y), Q.negative(x) & Q.negative(y)) is True
+    assert ask(Q.negative(x + y), Q.negative(x) & Q.nonpositive(y)) is True
+    assert ask(Q.negative(2 + I)) is False
+    # although this could be False, it is representative of expressions
+    # that don't evaluate to a zero with precision
+    assert ask(Q.negative(cos(I)**2 + sin(I)**2 - 1)) is None
+    assert ask(Q.negative(-I + I*(cos(2)**2 + sin(2)**2))) is None
+
+    assert ask(Q.negative(x**2)) is None
+    assert ask(Q.negative(x**2), Q.real(x)) is False
+    assert ask(Q.negative(x**1.4), Q.real(x)) is None
+
+    assert ask(Q.negative(x**I), Q.positive(x)) is None
+
+    assert ask(Q.negative(x*y)) is None
+    assert ask(Q.negative(x*y), Q.positive(x) & Q.positive(y)) is False
+    assert ask(Q.negative(x*y), Q.positive(x) & Q.negative(y)) is True
+    assert ask(Q.negative(x*y), Q.complex(x) & Q.complex(y)) is None
+
+    assert ask(Q.negative(x**y)) is None
+    assert ask(Q.negative(x**y), Q.negative(x) & Q.even(y)) is False
+    assert ask(Q.negative(x**y), Q.negative(x) & Q.odd(y)) is True
+    assert ask(Q.negative(x**y), Q.positive(x) & Q.integer(y)) is False
+
+    assert ask(Q.negative(Abs(x))) is False
+
+
+def test_nonzero():
+    assert ask(Q.nonzero(x)) is None
+    assert ask(Q.nonzero(x), Q.real(x)) is None
+    assert ask(Q.nonzero(x), Q.positive(x)) is True
+    assert ask(Q.nonzero(x), Q.negative(x)) is True
+    assert ask(Q.nonzero(x), Q.negative(x) | Q.positive(x)) is True
+
+    assert ask(Q.nonzero(x + y)) is None
+    assert ask(Q.nonzero(x + y), Q.positive(x) & Q.positive(y)) is True
+    assert ask(Q.nonzero(x + y), Q.positive(x) & Q.negative(y)) is None
+    assert ask(Q.nonzero(x + y), Q.negative(x) & Q.negative(y)) is True
+
+    assert ask(Q.nonzero(2*x)) is None
+    assert ask(Q.nonzero(2*x), Q.positive(x)) is True
+    assert ask(Q.nonzero(2*x), Q.negative(x)) is True
+    assert ask(Q.nonzero(x*y), Q.nonzero(x)) is None
+    assert ask(Q.nonzero(x*y), Q.nonzero(x) & Q.nonzero(y)) is True
+
+    assert ask(Q.nonzero(x**y), Q.nonzero(x)) is True
+
+    assert ask(Q.nonzero(Abs(x))) is None
+    assert ask(Q.nonzero(Abs(x)), Q.nonzero(x)) is True
+
+    assert ask(Q.nonzero(log(exp(2*I)))) is False
+    # although this could be False, it is representative of expressions
+    # that don't evaluate to a zero with precision
+    assert ask(Q.nonzero(cos(1)**2 + sin(1)**2 - 1)) is None
+
+
+def test_zero():
+    assert ask(Q.zero(x)) is None
+    assert ask(Q.zero(x), Q.real(x)) is None
+    assert ask(Q.zero(x), Q.positive(x)) is False
+    assert ask(Q.zero(x), Q.negative(x)) is False
+    assert ask(Q.zero(x), Q.negative(x) | Q.positive(x)) is False
+
+    assert ask(Q.zero(x), Q.nonnegative(x) & Q.nonpositive(x)) is True
+
+    assert ask(Q.zero(x + y)) is None
+    assert ask(Q.zero(x + y), Q.positive(x) & Q.positive(y)) is False
+    assert ask(Q.zero(x + y), Q.positive(x) & Q.negative(y)) is None
+    assert ask(Q.zero(x + y), Q.negative(x) & Q.negative(y)) is False
+
+    assert ask(Q.zero(2*x)) is None
+    assert ask(Q.zero(2*x), Q.positive(x)) is False
+    assert ask(Q.zero(2*x), Q.negative(x)) is False
+    assert ask(Q.zero(x*y), Q.nonzero(x)) is None
+
+    assert ask(Q.zero(Abs(x))) is None
+    assert ask(Q.zero(Abs(x)), Q.zero(x)) is True
+
+    assert ask(Q.integer(x), Q.zero(x)) is True
+    assert ask(Q.even(x), Q.zero(x)) is True
+    assert ask(Q.odd(x), Q.zero(x)) is False
+    assert ask(Q.zero(x), Q.even(x)) is None
+    assert ask(Q.zero(x), Q.odd(x)) is False
+    assert ask(Q.zero(x) | Q.zero(y), Q.zero(x*y)) is True
+
+
+def test_odd_query():
+    assert ask(Q.odd(x)) is None
+    assert ask(Q.odd(x), Q.odd(x)) is True
+    assert ask(Q.odd(x), Q.integer(x)) is None
+    assert ask(Q.odd(x), ~Q.integer(x)) is False
+    assert ask(Q.odd(x), Q.rational(x)) is None
+    assert ask(Q.odd(x), Q.positive(x)) is None
+
+    assert ask(Q.odd(-x), Q.odd(x)) is True
+
+    assert ask(Q.odd(2*x)) is None
+    assert ask(Q.odd(2*x), Q.integer(x)) is False
+    assert ask(Q.odd(2*x), Q.odd(x)) is False
+    assert ask(Q.odd(2*x), Q.irrational(x)) is False
+    assert ask(Q.odd(2*x), ~Q.integer(x)) is None
+    assert ask(Q.odd(3*x), Q.integer(x)) is None
+
+    assert ask(Q.odd(x/3), Q.odd(x)) is None
+    assert ask(Q.odd(x/3), Q.even(x)) is None
+
+    assert ask(Q.odd(x + 1), Q.even(x)) is True
+    assert ask(Q.odd(x + 2), Q.even(x)) is False
+    assert ask(Q.odd(x + 2), Q.odd(x)) is True
+    assert ask(Q.odd(3 - x), Q.odd(x)) is False
+    assert ask(Q.odd(3 - x), Q.even(x)) is True
+    assert ask(Q.odd(3 + x), Q.odd(x)) is False
+    assert ask(Q.odd(3 + x), Q.even(x)) is True
+    assert ask(Q.odd(x + y), Q.odd(x) & Q.odd(y)) is False
+    assert ask(Q.odd(x + y), Q.odd(x) & Q.even(y)) is True
+    assert ask(Q.odd(x - y), Q.even(x) & Q.odd(y)) is True
+    assert ask(Q.odd(x - y), Q.odd(x) & Q.odd(y)) is False
+
+    assert ask(Q.odd(x + y + z), Q.odd(x) & Q.odd(y) & Q.even(z)) is False
+    assert ask(Q.odd(x + y + z + t),
+        Q.odd(x) & Q.odd(y) & Q.even(z) & Q.integer(t)) is None
+
+    assert ask(Q.odd(2*x + 1), Q.integer(x)) is True
+    assert ask(Q.odd(2*x + y), Q.integer(x) & Q.odd(y)) is True
+    assert ask(Q.odd(2*x + y), Q.integer(x) & Q.even(y)) is False
+    assert ask(Q.odd(2*x + y), Q.integer(x) & Q.integer(y)) is None
+    assert ask(Q.odd(x*y), Q.odd(x) & Q.even(y)) is False
+    assert ask(Q.odd(x*y), Q.odd(x) & Q.odd(y)) is True
+    assert ask(Q.odd(2*x*y), Q.rational(x) & Q.rational(x)) is None
+    assert ask(Q.odd(2*x*y), Q.irrational(x) & Q.irrational(x)) is None
+
+    assert ask(Q.odd(Abs(x)), Q.odd(x)) is True
+
+    assert ask(Q.odd((-1)**n), Q.integer(n)) is True
+
+    assert ask(Q.odd(k**2), Q.even(k)) is False
+    assert ask(Q.odd(n**2), Q.odd(n)) is True
+    assert ask(Q.odd(3**k), Q.even(k)) is None
+
+    assert ask(Q.odd(k**m), Q.even(k) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.odd(n**m), Q.odd(n) & Q.integer(m) & ~Q.negative(m)) is True
+
+    assert ask(Q.odd(k**p), Q.even(k) & Q.integer(p) & Q.positive(p)) is False
+    assert ask(Q.odd(n**p), Q.odd(n) & Q.integer(p) & Q.positive(p)) is True
+
+    assert ask(Q.odd(m**k), Q.even(k) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.odd(p**k), Q.even(k) & Q.integer(p) & Q.positive(p)) is None
+
+    assert ask(Q.odd(m**n), Q.odd(n) & Q.integer(m) & ~Q.negative(m)) is None
+    assert ask(Q.odd(p**n), Q.odd(n) & Q.integer(p) & Q.positive(p)) is None
+
+    assert ask(Q.odd(k**x), Q.even(k)) is None
+    assert ask(Q.odd(n**x), Q.odd(n)) is None
+
+    assert ask(Q.odd(x*y), Q.integer(x) & Q.integer(y)) is None
+    assert ask(Q.odd(x*x), Q.integer(x)) is None
+    assert ask(Q.odd(x*(x + y)), Q.integer(x) & Q.odd(y)) is False
+    assert ask(Q.odd(x*(x + y)), Q.integer(x) & Q.even(y)) is None
+
+
+@XFAIL
+def test_oddness_in_ternary_integer_product_with_odd():
+    # Tests that oddness inference is independent of term ordering.
+    # Term ordering at the point of testing depends on SymPy's symbol order, so
+    # we try to force a different order by modifying symbol names.
+    assert ask(Q.odd(x*y*(y + z)), Q.integer(x) & Q.integer(y) & Q.odd(z)) is False
+    assert ask(Q.odd(y*x*(x + z)), Q.integer(x) & Q.integer(y) & Q.odd(z)) is False
+
+
+def test_oddness_in_ternary_integer_product_with_even():
+    assert ask(Q.odd(x*y*(y + z)), Q.integer(x) & Q.integer(y) & Q.even(z)) is None
+
+
+def test_prime():
+    assert ask(Q.prime(x), Q.prime(x)) is True
+    assert ask(Q.prime(x), ~Q.prime(x)) is False
+    assert ask(Q.prime(x), Q.integer(x)) is None
+    assert ask(Q.prime(x), ~Q.integer(x)) is False
+
+    assert ask(Q.prime(2*x), Q.integer(x)) is None
+    assert ask(Q.prime(x*y)) is None
+    assert ask(Q.prime(x*y), Q.prime(x)) is None
+    assert ask(Q.prime(x*y), Q.integer(x) & Q.integer(y)) is None
+    assert ask(Q.prime(4*x), Q.integer(x)) is False
+    assert ask(Q.prime(4*x)) is None
+
+    assert ask(Q.prime(x**2), Q.integer(x)) is False
+    assert ask(Q.prime(x**2), Q.prime(x)) is False
+    assert ask(Q.prime(x**y), Q.integer(x) & Q.integer(y)) is False
+
+
+@_both_exp_pow
+def test_positive():
+    assert ask(Q.positive(x), Q.positive(x)) is True
+    assert ask(Q.positive(x), Q.negative(x)) is False
+    assert ask(Q.positive(x), Q.nonzero(x)) is None
+
+    assert ask(Q.positive(-x), Q.positive(x)) is False
+    assert ask(Q.positive(-x), Q.negative(x)) is True
+
+    assert ask(Q.positive(x + y), Q.positive(x) & Q.positive(y)) is True
+    assert ask(Q.positive(x + y), Q.positive(x) & Q.nonnegative(y)) is True
+    assert ask(Q.positive(x + y), Q.positive(x) & Q.negative(y)) is None
+    assert ask(Q.positive(x + y), Q.positive(x) & Q.imaginary(y)) is False
+
+    assert ask(Q.positive(2*x), Q.positive(x)) is True
+    assumptions = Q.positive(x) & Q.negative(y) & Q.negative(z) & Q.positive(w)
+    assert ask(Q.positive(x*y*z)) is None
+    assert ask(Q.positive(x*y*z), assumptions) is True
+    assert ask(Q.positive(-x*y*z), assumptions) is False
+
+    assert ask(Q.positive(x**I), Q.positive(x)) is None
+
+    assert ask(Q.positive(x**2), Q.positive(x)) is True
+    assert ask(Q.positive(x**2), Q.negative(x)) is True
+    assert ask(Q.positive(x**3), Q.negative(x)) is False
+    assert ask(Q.positive(1/(1 + x**2)), Q.real(x)) is True
+    assert ask(Q.positive(2**I)) is False
+    assert ask(Q.positive(2 + I)) is False
+    # although this could be False, it is representative of expressions
+    # that don't evaluate to a zero with precision
+    assert ask(Q.positive(cos(I)**2 + sin(I)**2 - 1)) is None
+    assert ask(Q.positive(-I + I*(cos(2)**2 + sin(2)**2))) is None
+
+    #exponential
+    assert ask(Q.positive(exp(x)), Q.real(x)) is True
+    assert ask(~Q.negative(exp(x)), Q.real(x)) is True
+    assert ask(Q.positive(x + exp(x)), Q.real(x)) is None
+    assert ask(Q.positive(exp(x)), Q.imaginary(x)) is None
+    assert ask(Q.positive(exp(2*pi*I, evaluate=False)), Q.imaginary(x)) is True
+    assert ask(Q.negative(exp(pi*I, evaluate=False)), Q.imaginary(x)) is True
+    assert ask(Q.positive(exp(x*pi*I)), Q.even(x)) is True
+    assert ask(Q.positive(exp(x*pi*I)), Q.odd(x)) is False
+    assert ask(Q.positive(exp(x*pi*I)), Q.real(x)) is None
+
+    # logarithm
+    assert ask(Q.positive(log(x)), Q.imaginary(x)) is False
+    assert ask(Q.positive(log(x)), Q.negative(x)) is False
+    assert ask(Q.positive(log(x)), Q.positive(x)) is None
+    assert ask(Q.positive(log(x + 2)), Q.positive(x)) is True
+
+    # factorial
+    assert ask(Q.positive(factorial(x)), Q.integer(x) & Q.positive(x))
+    assert ask(Q.positive(factorial(x)), Q.integer(x)) is None
+
+    #absolute value
+    assert ask(Q.positive(Abs(x))) is None  # Abs(0) = 0
+    assert ask(Q.positive(Abs(x)), Q.positive(x)) is True
+
+
+def test_nonpositive():
+    assert ask(Q.nonpositive(-1))
+    assert ask(Q.nonpositive(0))
+    assert ask(Q.nonpositive(1)) is False
+    assert ask(~Q.positive(x), Q.nonpositive(x))
+    assert ask(Q.nonpositive(x), Q.positive(x)) is False
+    assert ask(Q.nonpositive(sqrt(-1))) is False
+    assert ask(Q.nonpositive(x), Q.imaginary(x)) is False
+
+
+def test_nonnegative():
+    assert ask(Q.nonnegative(-1)) is False
+    assert ask(Q.nonnegative(0))
+    assert ask(Q.nonnegative(1))
+    assert ask(~Q.negative(x), Q.nonnegative(x))
+    assert ask(Q.nonnegative(x), Q.negative(x)) is False
+    assert ask(Q.nonnegative(sqrt(-1))) is False
+    assert ask(Q.nonnegative(x), Q.imaginary(x)) is False
+
+def test_real_basic():
+    assert ask(Q.real(x)) is None
+    assert ask(Q.real(x), Q.real(x)) is True
+    assert ask(Q.real(x), Q.nonzero(x)) is True
+    assert ask(Q.real(x), Q.positive(x)) is True
+    assert ask(Q.real(x), Q.negative(x)) is True
+    assert ask(Q.real(x), Q.integer(x)) is True
+    assert ask(Q.real(x), Q.even(x)) is True
+    assert ask(Q.real(x), Q.prime(x)) is True
+
+    assert ask(Q.real(x/sqrt(2)), Q.real(x)) is True
+    assert ask(Q.real(x/sqrt(-2)), Q.real(x)) is False
+
+    assert ask(Q.real(x + 1), Q.real(x)) is True
+    assert ask(Q.real(x + I), Q.real(x)) is False
+    assert ask(Q.real(x + I), Q.complex(x)) is None
+
+    assert ask(Q.real(2*x), Q.real(x)) is True
+    assert ask(Q.real(I*x), Q.real(x)) is False
+    assert ask(Q.real(I*x), Q.imaginary(x)) is True
+    assert ask(Q.real(I*x), Q.complex(x)) is None
+
+
+def test_real_pow():
+    assert ask(Q.real(x**2), Q.real(x)) is True
+    assert ask(Q.real(sqrt(x)), Q.negative(x)) is False
+    assert ask(Q.real(x**y), Q.real(x) & Q.integer(y)) is True
+    assert ask(Q.real(x**y), Q.real(x) & Q.real(y)) is None
+    assert ask(Q.real(x**y), Q.positive(x) & Q.real(y)) is True
+    assert ask(Q.real(x**y), Q.imaginary(x) & Q.imaginary(y)) is None  # I**I or (2*I)**I
+    assert ask(Q.real(x**y), Q.imaginary(x) & Q.real(y)) is None  # I**1 or I**0
+    assert ask(Q.real(x**y), Q.real(x) & Q.imaginary(y)) is None  # could be exp(2*pi*I) or 2**I
+    assert ask(Q.real(x**0), Q.imaginary(x)) is True
+    assert ask(Q.real(x**y), Q.real(x) & Q.integer(y)) is True
+    assert ask(Q.real(x**y), Q.positive(x) & Q.real(y)) is True
+    assert ask(Q.real(x**y), Q.real(x) & Q.rational(y)) is None
+    assert ask(Q.real(x**y), Q.imaginary(x) & Q.integer(y)) is None
+    assert ask(Q.real(x**y), Q.imaginary(x) & Q.odd(y)) is False
+    assert ask(Q.real(x**y), Q.imaginary(x) & Q.even(y)) is True
+    assert ask(Q.real(x**(y/z)), Q.real(x) & Q.real(y/z) & Q.rational(y/z) & Q.even(z) & Q.positive(x)) is True
+    assert ask(Q.real(x**(y/z)), Q.real(x) & Q.rational(y/z) & Q.even(z) & Q.negative(x)) is False
+    assert ask(Q.real(x**(y/z)), Q.real(x) & Q.integer(y/z)) is True
+    assert ask(Q.real(x**(y/z)), Q.real(x) & Q.real(y/z) & Q.positive(x)) is True
+    assert ask(Q.real(x**(y/z)), Q.real(x) & Q.real(y/z) & Q.negative(x)) is False
+    assert ask(Q.real((-I)**i), Q.imaginary(i)) is True
+    assert ask(Q.real(I**i), Q.imaginary(i)) is True
+    assert ask(Q.real(i**i), Q.imaginary(i)) is None  # i might be 2*I
+    assert ask(Q.real(x**i), Q.imaginary(i)) is None  # x could be 0
+    assert ask(Q.real(x**(I*pi/log(x))), Q.real(x)) is True
+
+
+@_both_exp_pow
+def test_real_functions():
+    # trigonometric functions
+    assert ask(Q.real(sin(x))) is None
+    assert ask(Q.real(cos(x))) is None
+    assert ask(Q.real(sin(x)), Q.real(x)) is True
+    assert ask(Q.real(cos(x)), Q.real(x)) is True
+
+    # exponential function
+    assert ask(Q.real(exp(x))) is None
+    assert ask(Q.real(exp(x)), Q.real(x)) is True
+    assert ask(Q.real(x + exp(x)), Q.real(x)) is True
+    assert ask(Q.real(exp(2*pi*I, evaluate=False))) is True
+    assert ask(Q.real(exp(pi*I, evaluate=False))) is True
+    assert ask(Q.real(exp(pi*I/2, evaluate=False))) is False
+
+    # logarithm
+    assert ask(Q.real(log(I))) is False
+    assert ask(Q.real(log(2*I))) is False
+    assert ask(Q.real(log(I + 1))) is False
+    assert ask(Q.real(log(x)), Q.complex(x)) is None
+    assert ask(Q.real(log(x)), Q.imaginary(x)) is False
+    assert ask(Q.real(log(exp(x))), Q.imaginary(x)) is None  # exp(2*pi*I) is 1, log(exp(pi*I)) is pi*I (disregarding periodicity)
+    assert ask(Q.real(log(exp(x))), Q.complex(x)) is None
+    eq = Pow(exp(2*pi*I*x, evaluate=False), x, evaluate=False)
+    assert ask(Q.real(eq), Q.integer(x)) is True
+    assert ask(Q.real(exp(x)**x), Q.imaginary(x)) is True
+    assert ask(Q.real(exp(x)**x), Q.complex(x)) is None
+
+    # Q.complexes
+    assert ask(Q.real(re(x))) is True
+    assert ask(Q.real(im(x))) is True
+
+
+def test_matrix():
+
+    # hermitian
+    assert ask(Q.hermitian(Matrix([[2, 2 + I, 4], [2 - I, 3, I], [4, -I, 1]]))) == True
+    assert ask(Q.hermitian(Matrix([[2, 2 + I, 4], [2 + I, 3, I], [4, -I, 1]]))) == False
+    z = symbols('z', complex=True)
+    assert ask(Q.hermitian(Matrix([[2, 2 + I, z], [2 - I, 3, I], [4, -I, 1]]))) == None
+    assert ask(Q.hermitian(SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11))))) == True
+    assert ask(Q.hermitian(SparseMatrix(((25, 15, -5), (15, I, 0), (-5, 0, 11))))) == False
+    assert ask(Q.hermitian(SparseMatrix(((25, 15, -5), (15, z, 0), (-5, 0, 11))))) == None
+
+    # antihermitian
+    A = Matrix([[0, -2 - I, 0], [2 - I, 0, -I], [0, -I, 0]])
+    B = Matrix([[-I, 2 + I, 0], [-2 + I, 0, 2 + I], [0, -2 + I, -I]])
+    assert ask(Q.antihermitian(A)) is True
+    assert ask(Q.antihermitian(B)) is True
+    assert ask(Q.antihermitian(A**2)) is False
+    C = (B**3)
+    C.simplify()
+    assert ask(Q.antihermitian(C)) is True
+    _A = Matrix([[0, -2 - I, 0], [z, 0, -I], [0, -I, 0]])
+    assert ask(Q.antihermitian(_A)) is None
+
+
+@_both_exp_pow
+def test_algebraic():
+    assert ask(Q.algebraic(x)) is None
+
+    assert ask(Q.algebraic(I)) is True
+    assert ask(Q.algebraic(2*I)) is True
+    assert ask(Q.algebraic(I/3)) is True
+
+    assert ask(Q.algebraic(sqrt(7))) is True
+    assert ask(Q.algebraic(2*sqrt(7))) is True
+    assert ask(Q.algebraic(sqrt(7)/3)) is True
+
+    assert ask(Q.algebraic(I*sqrt(3))) is True
+    assert ask(Q.algebraic(sqrt(1 + I*sqrt(3)))) is True
+
+    assert ask(Q.algebraic(1 + I*sqrt(3)**Rational(17, 31))) is True
+    assert ask(Q.algebraic(1 + I*sqrt(3)**(17/pi))) is False
+
+    for f in [exp, sin, tan, asin, atan, cos]:
+        assert ask(Q.algebraic(f(7))) is False
+        assert ask(Q.algebraic(f(7, evaluate=False))) is False
+        assert ask(Q.algebraic(f(0, evaluate=False))) is True
+        assert ask(Q.algebraic(f(x)), Q.algebraic(x)) is None
+        assert ask(Q.algebraic(f(x)), Q.algebraic(x) & Q.nonzero(x)) is False
+
+    for g in [log, acos]:
+        assert ask(Q.algebraic(g(7))) is False
+        assert ask(Q.algebraic(g(7, evaluate=False))) is False
+        assert ask(Q.algebraic(g(1, evaluate=False))) is True
+        assert ask(Q.algebraic(g(x)), Q.algebraic(x)) is None
+        assert ask(Q.algebraic(g(x)), Q.algebraic(x) & Q.nonzero(x - 1)) is False
+
+    for h in [cot, acot]:
+        assert ask(Q.algebraic(h(7))) is False
+        assert ask(Q.algebraic(h(7, evaluate=False))) is False
+        assert ask(Q.algebraic(h(x)), Q.algebraic(x)) is False
+
+    assert ask(Q.algebraic(sqrt(sin(7)))) is False
+    assert ask(Q.algebraic(sqrt(y + I*sqrt(7)))) is None
+
+    assert ask(Q.algebraic(2.47)) is True
+
+    assert ask(Q.algebraic(x), Q.transcendental(x)) is False
+    assert ask(Q.transcendental(x), Q.algebraic(x)) is False
+
+
+def test_global():
+    """Test ask with global assumptions"""
+    assert ask(Q.integer(x)) is None
+    global_assumptions.add(Q.integer(x))
+    assert ask(Q.integer(x)) is True
+    global_assumptions.clear()
+    assert ask(Q.integer(x)) is None
+
+
+def test_custom_context():
+    """Test ask with custom assumptions context"""
+    assert ask(Q.integer(x)) is None
+    local_context = AssumptionsContext()
+    local_context.add(Q.integer(x))
+    assert ask(Q.integer(x), context=local_context) is True
+    assert ask(Q.integer(x)) is None
+
+
+def test_functions_in_assumptions():
+    assert ask(Q.negative(x), Q.real(x) >> Q.positive(x)) is False
+    assert ask(Q.negative(x), Equivalent(Q.real(x), Q.positive(x))) is False
+    assert ask(Q.negative(x), Xor(Q.real(x), Q.negative(x))) is False
+
+
+def test_composite_ask():
+    assert ask(Q.negative(x) & Q.integer(x),
+        assumptions=Q.real(x) >> Q.positive(x)) is False
+
+
+def test_composite_proposition():
+    assert ask(True) is True
+    assert ask(False) is False
+    assert ask(~Q.negative(x), Q.positive(x)) is True
+    assert ask(~Q.real(x), Q.commutative(x)) is None
+    assert ask(Q.negative(x) & Q.integer(x), Q.positive(x)) is False
+    assert ask(Q.negative(x) & Q.integer(x)) is None
+    assert ask(Q.real(x) | Q.integer(x), Q.positive(x)) is True
+    assert ask(Q.real(x) | Q.integer(x)) is None
+    assert ask(Q.real(x) >> Q.positive(x), Q.negative(x)) is False
+    assert ask(Implies(
+        Q.real(x), Q.positive(x), evaluate=False), Q.negative(x)) is False
+    assert ask(Implies(Q.real(x), Q.positive(x), evaluate=False)) is None
+    assert ask(Equivalent(Q.integer(x), Q.even(x)), Q.even(x)) is True
+    assert ask(Equivalent(Q.integer(x), Q.even(x))) is None
+    assert ask(Equivalent(Q.positive(x), Q.integer(x)), Q.integer(x)) is None
+    assert ask(Q.real(x) | Q.integer(x), Q.real(x) | Q.integer(x)) is True
+
+def test_tautology():
+    assert ask(Q.real(x) | ~Q.real(x)) is True
+    assert ask(Q.real(x) & ~Q.real(x)) is False
+
+def test_composite_assumptions():
+    assert ask(Q.real(x), Q.real(x) & Q.real(y)) is True
+    assert ask(Q.positive(x), Q.positive(x) | Q.positive(y)) is None
+    assert ask(Q.positive(x), Q.real(x) >> Q.positive(y)) is None
+    assert ask(Q.real(x), ~(Q.real(x) >> Q.real(y))) is True
+
+def test_key_extensibility():
+    """test that you can add keys to the ask system at runtime"""
+    # make sure the key is not defined
+    raises(AttributeError, lambda: ask(Q.my_key(x)))
+
+    # Old handler system
+    class MyAskHandler(AskHandler):
+        @staticmethod
+        def Symbol(expr, assumptions):
+            return True
+    try:
+        with warns_deprecated_sympy():
+            register_handler('my_key', MyAskHandler)
+        with warns_deprecated_sympy():
+            assert ask(Q.my_key(x)) is True
+        with warns_deprecated_sympy():
+            assert ask(Q.my_key(x + 1)) is None
+    finally:
+        # We have to disable the stacklevel testing here because this raises
+        # the warning twice from two different places
+        with warns_deprecated_sympy():
+            remove_handler('my_key', MyAskHandler)
+        del Q.my_key
+    raises(AttributeError, lambda: ask(Q.my_key(x)))
+
+    # New handler system
+    class MyPredicate(Predicate):
+        pass
+    try:
+        Q.my_key = MyPredicate()
+        @Q.my_key.register(Symbol)
+        def _(expr, assumptions):
+            return True
+        assert ask(Q.my_key(x)) is True
+        assert ask(Q.my_key(x+1)) is None
+    finally:
+        del Q.my_key
+    raises(AttributeError, lambda: ask(Q.my_key(x)))
+
+
+def test_type_extensibility():
+    """test that new types can be added to the ask system at runtime
+    """
+    from sympy.core import Basic
+
+    class MyType(Basic):
+        pass
+
+    @Q.prime.register(MyType)
+    def _(expr, assumptions):
+        return True
+
+    assert ask(Q.prime(MyType())) is True
+
+
+def test_single_fact_lookup():
+    known_facts = And(Implies(Q.integer, Q.rational),
+                      Implies(Q.rational, Q.real),
+                      Implies(Q.real, Q.complex))
+    known_facts_keys = {Q.integer, Q.rational, Q.real, Q.complex}
+
+    known_facts_cnf = to_cnf(known_facts)
+    mapping = single_fact_lookup(known_facts_keys, known_facts_cnf)
+
+    assert mapping[Q.rational] == {Q.real, Q.rational, Q.complex}
+
+
+def test_generate_known_facts_dict():
+    known_facts = And(Implies(Q.integer(x), Q.rational(x)),
+                      Implies(Q.rational(x), Q.real(x)),
+                      Implies(Q.real(x), Q.complex(x)))
+    known_facts_keys = {Q.integer(x), Q.rational(x), Q.real(x), Q.complex(x)}
+
+    assert generate_known_facts_dict(known_facts_keys, known_facts) == \
+        {Q.complex: ({Q.complex}, set()),
+         Q.integer: ({Q.complex, Q.integer, Q.rational, Q.real}, set()),
+         Q.rational: ({Q.complex, Q.rational, Q.real}, set()),
+         Q.real: ({Q.complex, Q.real}, set())}
+
+
+@slow
+def test_known_facts_consistent():
+    """"Test that ask_generated.py is up-to-date"""
+    x = Symbol('x')
+    fact = get_known_facts(x)
+    # test cnf clauses of fact between unary predicates
+    cnf = CNF.to_CNF(fact)
+    clauses = set()
+    for cl in cnf.clauses:
+        clauses.add(frozenset(Literal(lit.arg.function, lit.is_Not) for lit in sorted(cl, key=str)))
+    assert get_all_known_facts() == clauses
+    # test dictionary of fact between unary predicates
+    keys = [pred(x) for pred in get_known_facts_keys()]
+    mapping = generate_known_facts_dict(keys, fact)
+    assert get_known_facts_dict() == mapping
+
+
+def test_Add_queries():
+    assert ask(Q.prime(12345678901234567890 + (cos(1)**2 + sin(1)**2))) is True
+    assert ask(Q.even(Add(S(2), S(2), evaluate=0))) is True
+    assert ask(Q.prime(Add(S(2), S(2), evaluate=0))) is False
+    assert ask(Q.integer(Add(S(2), S(2), evaluate=0))) is True
+
+
+def test_positive_assuming():
+    with assuming(Q.positive(x + 1)):
+        assert not ask(Q.positive(x))
+
+
+def test_issue_5421():
+    raises(TypeError, lambda: ask(pi/log(x), Q.real))
+
+
+def test_issue_3906():
+    raises(TypeError, lambda: ask(Q.positive))
+
+
+def test_issue_5833():
+    assert ask(Q.positive(log(x)**2), Q.positive(x)) is None
+    assert ask(~Q.negative(log(x)**2), Q.positive(x)) is True
+
+
+def test_issue_6732():
+    raises(ValueError, lambda: ask(Q.positive(x), Q.positive(x) & Q.negative(x)))
+    raises(ValueError, lambda: ask(Q.negative(x), Q.positive(x) & Q.negative(x)))
+
+
+def test_issue_7246():
+    assert ask(Q.positive(atan(p)), Q.positive(p)) is True
+    assert ask(Q.positive(atan(p)), Q.negative(p)) is False
+    assert ask(Q.positive(atan(p)), Q.zero(p)) is False
+    assert ask(Q.positive(atan(x))) is None
+
+    assert ask(Q.positive(asin(p)), Q.positive(p)) is None
+    assert ask(Q.positive(asin(p)), Q.zero(p)) is None
+    assert ask(Q.positive(asin(Rational(1, 7)))) is True
+    assert ask(Q.positive(asin(x)), Q.positive(x) & Q.nonpositive(x - 1)) is True
+    assert ask(Q.positive(asin(x)), Q.negative(x) & Q.nonnegative(x + 1)) is False
+
+    assert ask(Q.positive(acos(p)), Q.positive(p)) is None
+    assert ask(Q.positive(acos(Rational(1, 7)))) is True
+    assert ask(Q.positive(acos(x)), Q.nonnegative(x + 1) & Q.nonpositive(x - 1)) is True
+    assert ask(Q.positive(acos(x)), Q.nonnegative(x - 1)) is None
+
+    assert ask(Q.positive(acot(x)), Q.positive(x)) is True
+    assert ask(Q.positive(acot(x)), Q.real(x)) is True
+    assert ask(Q.positive(acot(x)), Q.imaginary(x)) is False
+    assert ask(Q.positive(acot(x))) is None
+
+
+@XFAIL
+def test_issue_7246_failing():
+    #Move this test to test_issue_7246 once
+    #the new assumptions module is improved.
+    assert ask(Q.positive(acos(x)), Q.zero(x)) is True
+
+
+def test_check_old_assumption():
+    x = symbols('x', real=True)
+    assert ask(Q.real(x)) is True
+    assert ask(Q.imaginary(x)) is False
+    assert ask(Q.complex(x)) is True
+
+    x = symbols('x', imaginary=True)
+    assert ask(Q.real(x)) is False
+    assert ask(Q.imaginary(x)) is True
+    assert ask(Q.complex(x)) is True
+
+    x = symbols('x', complex=True)
+    assert ask(Q.real(x)) is None
+    assert ask(Q.complex(x)) is True
+
+    x = symbols('x', positive=True)
+    assert ask(Q.positive(x)) is True
+    assert ask(Q.negative(x)) is False
+    assert ask(Q.real(x)) is True
+
+    x = symbols('x', commutative=False)
+    assert ask(Q.commutative(x)) is False
+
+    x = symbols('x', negative=True)
+    assert ask(Q.positive(x)) is False
+    assert ask(Q.negative(x)) is True
+
+    x = symbols('x', nonnegative=True)
+    assert ask(Q.negative(x)) is False
+    assert ask(Q.positive(x)) is None
+    assert ask(Q.zero(x)) is None
+
+    x = symbols('x', finite=True)
+    assert ask(Q.finite(x)) is True
+
+    x = symbols('x', prime=True)
+    assert ask(Q.prime(x)) is True
+    assert ask(Q.composite(x)) is False
+
+    x = symbols('x', composite=True)
+    assert ask(Q.prime(x)) is False
+    assert ask(Q.composite(x)) is True
+
+    x = symbols('x', even=True)
+    assert ask(Q.even(x)) is True
+    assert ask(Q.odd(x)) is False
+
+    x = symbols('x', odd=True)
+    assert ask(Q.even(x)) is False
+    assert ask(Q.odd(x)) is True
+
+    x = symbols('x', nonzero=True)
+    assert ask(Q.nonzero(x)) is True
+    assert ask(Q.zero(x)) is False
+
+    x = symbols('x', zero=True)
+    assert ask(Q.zero(x)) is True
+
+    x = symbols('x', integer=True)
+    assert ask(Q.integer(x)) is True
+
+    x = symbols('x', rational=True)
+    assert ask(Q.rational(x)) is True
+    assert ask(Q.irrational(x)) is False
+
+    x = symbols('x', irrational=True)
+    assert ask(Q.irrational(x)) is True
+    assert ask(Q.rational(x)) is False
+
+
+def test_issue_9636():
+    assert ask(Q.integer(1.0)) is False
+    assert ask(Q.prime(3.0)) is False
+    assert ask(Q.composite(4.0)) is False
+    assert ask(Q.even(2.0)) is False
+    assert ask(Q.odd(3.0)) is False
+
+
+def test_autosimp_used_to_fail():
+    # See issue #9807
+    assert ask(Q.imaginary(0**I)) is None
+    assert ask(Q.imaginary(0**(-I))) is None
+    assert ask(Q.real(0**I)) is None
+    assert ask(Q.real(0**(-I))) is None
+
+
+def test_custom_AskHandler():
+    from sympy.logic.boolalg import conjuncts
+
+    # Old handler system
+    class MersenneHandler(AskHandler):
+        @staticmethod
+        def Integer(expr, assumptions):
+            if ask(Q.integer(log(expr + 1, 2))):
+                return True
+        @staticmethod
+        def Symbol(expr, assumptions):
+            if expr in conjuncts(assumptions):
+                return True
+    try:
+        with warns_deprecated_sympy():
+            register_handler('mersenne', MersenneHandler)
+        n = Symbol('n', integer=True)
+        with warns_deprecated_sympy():
+            assert ask(Q.mersenne(7))
+        with warns_deprecated_sympy():
+            assert ask(Q.mersenne(n), Q.mersenne(n))
+    finally:
+        del Q.mersenne
+
+    # New handler system
+    class MersennePredicate(Predicate):
+        pass
+    try:
+        Q.mersenne = MersennePredicate()
+        @Q.mersenne.register(Integer)
+        def _(expr, assumptions):
+            if ask(Q.integer(log(expr + 1, 2))):
+                return True
+        @Q.mersenne.register(Symbol)
+        def _(expr, assumptions):
+            if expr in conjuncts(assumptions):
+                return True
+        assert ask(Q.mersenne(7))
+        assert ask(Q.mersenne(n), Q.mersenne(n))
+    finally:
+        del Q.mersenne
+
+
+def test_polyadic_predicate():
+
+    class SexyPredicate(Predicate):
+        pass
+    try:
+        Q.sexyprime = SexyPredicate()
+
+        @Q.sexyprime.register(Integer, Integer)
+        def _(int1, int2, assumptions):
+            args = sorted([int1, int2])
+            if not all(ask(Q.prime(a), assumptions) for a in args):
+                return False
+            return args[1] - args[0] == 6
+
+        @Q.sexyprime.register(Integer, Integer, Integer)
+        def _(int1, int2, int3, assumptions):
+            args = sorted([int1, int2, int3])
+            if not all(ask(Q.prime(a), assumptions) for a in args):
+                return False
+            return args[2] - args[1] == 6 and args[1] - args[0] == 6
+
+        assert ask(Q.sexyprime(5, 11))
+        assert ask(Q.sexyprime(7, 13, 19))
+    finally:
+        del Q.sexyprime
+
+
+def test_Predicate_handler_is_unique():
+
+    # Undefined predicate does not have a handler
+    assert Predicate('mypredicate').handler is None
+
+    # Handler of defined predicate is unique to the class
+    class MyPredicate(Predicate):
+        pass
+    mp1 = MyPredicate(Str('mp1'))
+    mp2 = MyPredicate(Str('mp2'))
+    assert mp1.handler is mp2.handler
+
+
+def test_relational():
+    assert ask(Q.eq(x, 0), Q.zero(x))
+    assert not ask(Q.eq(x, 0), Q.nonzero(x))
+    assert not ask(Q.ne(x, 0), Q.zero(x))
+    assert ask(Q.ne(x, 0), Q.nonzero(x))
diff --git a/shared/package-files/b5/b5e8cd13eed82f5a5d48fe2d84440a23984e69699743fede4117e6441eba0168/fft_ifft_native.h b/shared/package-files/b5/b5e8cd13eed82f5a5d48fe2d84440a23984e69699743fede4117e6441eba0168/fft_ifft_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ebad60dec6402dc014e118e83432eb34582205be
--- /dev/null
+++ b/shared/package-files/b5/b5e8cd13eed82f5a5d48fe2d84440a23984e69699743fede4117e6441eba0168/fft_ifft_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor fft_ifft_symint(const at::Tensor & self, c10::optional<c10::SymInt> n=c10::nullopt, int64_t dim=-1, c10::optional<c10::string_view> norm=c10::nullopt);
+TORCH_API at::Tensor & fft_ifft_symint_out(const at::Tensor & self, c10::optional<c10::SymInt> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/b5/b5eda5fb25967e49451e76b0f85da293cab676a112d9cc6b9c4d6fa09c4c18bd/test_maple.py b/shared/package-files/b5/b5eda5fb25967e49451e76b0f85da293cab676a112d9cc6b9c4d6fa09c4c18bd/test_maple.py
new file mode 100644
index 0000000000000000000000000000000000000000..95cc262ea669d7f6ae7e358d9fd99ac1857c882d
--- /dev/null
+++ b/shared/package-files/b5/b5eda5fb25967e49451e76b0f85da293cab676a112d9cc6b9c4d6fa09c4c18bd/test_maple.py
@@ -0,0 +1,384 @@
+from sympy.core import (S, pi, oo, symbols, Function, Rational, Integer,
+                        Tuple, Symbol, Eq, Ne, Le, Lt, Gt, Ge)
+from sympy.core import EulerGamma, GoldenRatio, Catalan, Lambda, Mul, Pow
+from sympy.functions import Piecewise, sqrt, ceiling, exp, sin, cos, sinc, lucas
+from sympy.testing.pytest import raises
+from sympy.utilities.lambdify import implemented_function
+from sympy.matrices import (eye, Matrix, MatrixSymbol, Identity,
+                            HadamardProduct, SparseMatrix)
+from sympy.functions.special.bessel import besseli
+
+from sympy.printing.maple import maple_code
+
+x, y, z = symbols('x,y,z')
+
+
+def test_Integer():
+    assert maple_code(Integer(67)) == "67"
+    assert maple_code(Integer(-1)) == "-1"
+
+
+def test_Rational():
+    assert maple_code(Rational(3, 7)) == "3/7"
+    assert maple_code(Rational(18, 9)) == "2"
+    assert maple_code(Rational(3, -7)) == "-3/7"
+    assert maple_code(Rational(-3, -7)) == "3/7"
+    assert maple_code(x + Rational(3, 7)) == "x + 3/7"
+    assert maple_code(Rational(3, 7) * x) == '(3/7)*x'
+
+
+def test_Relational():
+    assert maple_code(Eq(x, y)) == "x = y"
+    assert maple_code(Ne(x, y)) == "x <> y"
+    assert maple_code(Le(x, y)) == "x <= y"
+    assert maple_code(Lt(x, y)) == "x < y"
+    assert maple_code(Gt(x, y)) == "x > y"
+    assert maple_code(Ge(x, y)) == "x >= y"
+
+
+def test_Function():
+    assert maple_code(sin(x) ** cos(x)) == "sin(x)^cos(x)"
+    assert maple_code(abs(x)) == "abs(x)"
+    assert maple_code(ceiling(x)) == "ceil(x)"
+
+
+def test_Pow():
+    assert maple_code(x ** 3) == "x^3"
+    assert maple_code(x ** (y ** 3)) == "x^(y^3)"
+
+    assert maple_code((x ** 3) ** y) == "(x^3)^y"
+    assert maple_code(x ** Rational(2, 3)) == 'x^(2/3)'
+
+    g = implemented_function('g', Lambda(x, 2 * x))
+    assert maple_code(1 / (g(x) * 3.5) ** (x - y ** x) / (x ** 2 + y)) == \
+           "(3.5*2*x)^(-x + y^x)/(x^2 + y)"
+    # For issue 14160
+    assert maple_code(Mul(-2, x, Pow(Mul(y, y, evaluate=False), -1, evaluate=False),
+                          evaluate=False)) == '-2*x/(y*y)'
+
+
+def test_basic_ops():
+    assert maple_code(x * y) == "x*y"
+    assert maple_code(x + y) == "x + y"
+    assert maple_code(x - y) == "x - y"
+    assert maple_code(-x) == "-x"
+
+
+def test_1_over_x_and_sqrt():
+    # 1.0 and 0.5 would do something different in regular StrPrinter,
+    # but these are exact in IEEE floating point so no different here.
+    assert maple_code(1 / x) == '1/x'
+    assert maple_code(x ** -1) == maple_code(x ** -1.0) == '1/x'
+    assert maple_code(1 / sqrt(x)) == '1/sqrt(x)'
+    assert maple_code(x ** -S.Half) == maple_code(x ** -0.5) == '1/sqrt(x)'
+    assert maple_code(sqrt(x)) == 'sqrt(x)'
+    assert maple_code(x ** S.Half) == maple_code(x ** 0.5) == 'sqrt(x)'
+    assert maple_code(1 / pi) == '1/Pi'
+    assert maple_code(pi ** -1) == maple_code(pi ** -1.0) == '1/Pi'
+    assert maple_code(pi ** -0.5) == '1/sqrt(Pi)'
+
+
+def test_mix_number_mult_symbols():
+    assert maple_code(3 * x) == "3*x"
+    assert maple_code(pi * x) == "Pi*x"
+    assert maple_code(3 / x) == "3/x"
+    assert maple_code(pi / x) == "Pi/x"
+    assert maple_code(x / 3) == '(1/3)*x'
+    assert maple_code(x / pi) == "x/Pi"
+    assert maple_code(x * y) == "x*y"
+    assert maple_code(3 * x * y) == "3*x*y"
+    assert maple_code(3 * pi * x * y) == "3*Pi*x*y"
+    assert maple_code(x / y) == "x/y"
+    assert maple_code(3 * x / y) == "3*x/y"
+    assert maple_code(x * y / z) == "x*y/z"
+    assert maple_code(x / y * z) == "x*z/y"
+    assert maple_code(1 / x / y) == "1/(x*y)"
+    assert maple_code(2 * pi * x / y / z) == "2*Pi*x/(y*z)"
+    assert maple_code(3 * pi / x) == "3*Pi/x"
+    assert maple_code(S(3) / 5) == "3/5"
+    assert maple_code(S(3) / 5 * x) == '(3/5)*x'
+    assert maple_code(x / y / z) == "x/(y*z)"
+    assert maple_code((x + y) / z) == "(x + y)/z"
+    assert maple_code((x + y) / (z + x)) == "(x + y)/(x + z)"
+    assert maple_code((x + y) / EulerGamma) == '(x + y)/gamma'
+    assert maple_code(x / 3 / pi) == '(1/3)*x/Pi'
+    assert maple_code(S(3) / 5 * x * y / pi) == '(3/5)*x*y/Pi'
+
+
+def test_mix_number_pow_symbols():
+    assert maple_code(pi ** 3) == 'Pi^3'
+    assert maple_code(x ** 2) == 'x^2'
+
+    assert maple_code(x ** (pi ** 3)) == 'x^(Pi^3)'
+    assert maple_code(x ** y) == 'x^y'
+
+    assert maple_code(x ** (y ** z)) == 'x^(y^z)'
+    assert maple_code((x ** y) ** z) == '(x^y)^z'
+
+
+def test_imag():
+    I = S('I')
+    assert maple_code(I) == "I"
+    assert maple_code(5 * I) == "5*I"
+
+    assert maple_code((S(3) / 2) * I) == "(3/2)*I"
+    assert maple_code(3 + 4 * I) == "3 + 4*I"
+
+
+def test_constants():
+    assert maple_code(pi) == "Pi"
+    assert maple_code(oo) == "infinity"
+    assert maple_code(-oo) == "-infinity"
+    assert maple_code(S.NegativeInfinity) == "-infinity"
+    assert maple_code(S.NaN) == "undefined"
+    assert maple_code(S.Exp1) == "exp(1)"
+    assert maple_code(exp(1)) == "exp(1)"
+
+
+def test_constants_other():
+    assert maple_code(2 * GoldenRatio) == '2*(1/2 + (1/2)*sqrt(5))'
+    assert maple_code(2 * Catalan) == '2*Catalan'
+    assert maple_code(2 * EulerGamma) == "2*gamma"
+
+
+def test_boolean():
+    assert maple_code(x & y) == "x && y"
+    assert maple_code(x | y) == "x || y"
+    assert maple_code(~x) == "!x"
+    assert maple_code(x & y & z) == "x && y && z"
+    assert maple_code(x | y | z) == "x || y || z"
+    assert maple_code((x & y) | z) == "z || x && y"
+    assert maple_code((x | y) & z) == "z && (x || y)"
+
+
+def test_Matrices():
+    assert maple_code(Matrix(1, 1, [10])) == \
+           'Matrix([[10]], storage = rectangular)'
+
+    A = Matrix([[1, sin(x / 2), abs(x)],
+                [0, 1, pi],
+                [0, exp(1), ceiling(x)]])
+    expected = \
+        'Matrix(' \
+        '[[1, sin((1/2)*x), abs(x)],' \
+        ' [0, 1, Pi],' \
+        ' [0, exp(1), ceil(x)]], ' \
+        'storage = rectangular)'
+    assert maple_code(A) == expected
+
+    # row and columns
+    assert maple_code(A[:, 0]) == \
+           'Matrix([[1], [0], [0]], storage = rectangular)'
+    assert maple_code(A[0, :]) == \
+           'Matrix([[1, sin((1/2)*x), abs(x)]], storage = rectangular)'
+    assert maple_code(Matrix([[x, x - y, -y]])) == \
+           'Matrix([[x, x - y, -y]], storage = rectangular)'
+
+    # empty matrices
+    assert maple_code(Matrix(0, 0, [])) == \
+           'Matrix([], storage = rectangular)'
+    assert maple_code(Matrix(0, 3, [])) == \
+           'Matrix([], storage = rectangular)'
+
+def test_SparseMatrices():
+    assert maple_code(SparseMatrix(Identity(2))) == 'Matrix([[1, 0], [0, 1]], storage = sparse)'
+
+
+def test_vector_entries_hadamard():
+    # For a row or column, user might to use the other dimension
+    A = Matrix([[1, sin(2 / x), 3 * pi / x / 5]])
+    assert maple_code(A) == \
+           'Matrix([[1, sin(2/x), (3/5)*Pi/x]], storage = rectangular)'
+    assert maple_code(A.T) == \
+           'Matrix([[1], [sin(2/x)], [(3/5)*Pi/x]], storage = rectangular)'
+
+
+def test_Matrices_entries_not_hadamard():
+    A = Matrix([[1, sin(2 / x), 3 * pi / x / 5], [1, 2, x * y]])
+    expected = \
+        'Matrix([[1, sin(2/x), (3/5)*Pi/x], [1, 2, x*y]], ' \
+        'storage = rectangular)'
+    assert maple_code(A) == expected
+
+
+def test_MatrixSymbol():
+    n = Symbol('n', integer=True)
+    A = MatrixSymbol('A', n, n)
+    B = MatrixSymbol('B', n, n)
+    assert maple_code(A * B) == "A.B"
+    assert maple_code(B * A) == "B.A"
+    assert maple_code(2 * A * B) == "2*A.B"
+    assert maple_code(B * 2 * A) == "2*B.A"
+
+    assert maple_code(
+        A * (B + 3 * Identity(n))) == "A.(3*Matrix(n, shape = identity) + B)"
+
+    assert maple_code(A ** (x ** 2)) == "MatrixPower(A, x^2)"
+    assert maple_code(A ** 3) == "MatrixPower(A, 3)"
+    assert maple_code(A ** (S.Half)) == "MatrixPower(A, 1/2)"
+
+
+def test_special_matrices():
+    assert maple_code(6 * Identity(3)) == "6*Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]], storage = sparse)"
+    assert maple_code(Identity(x)) == 'Matrix(x, shape = identity)'
+
+
+def test_containers():
+    assert maple_code([1, 2, 3, [4, 5, [6, 7]], 8, [9, 10], 11]) == \
+           "[1, 2, 3, [4, 5, [6, 7]], 8, [9, 10], 11]"
+
+    assert maple_code((1, 2, (3, 4))) == "[1, 2, [3, 4]]"
+    assert maple_code([1]) == "[1]"
+    assert maple_code((1,)) == "[1]"
+    assert maple_code(Tuple(*[1, 2, 3])) == "[1, 2, 3]"
+    assert maple_code((1, x * y, (3, x ** 2))) == "[1, x*y, [3, x^2]]"
+    # scalar, matrix, empty matrix and empty list
+
+    assert maple_code((1, eye(3), Matrix(0, 0, []), [])) == \
+           "[1, Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]], storage = rectangular), Matrix([], storage = rectangular), []]"
+
+
+def test_maple_noninline():
+    source = maple_code((x + y)/Catalan, assign_to='me', inline=False)
+    expected = "me := (x + y)/Catalan"
+
+    assert source == expected
+
+
+def test_maple_matrix_assign_to():
+    A = Matrix([[1, 2, 3]])
+    assert maple_code(A, assign_to='a') == "a := Matrix([[1, 2, 3]], storage = rectangular)"
+    A = Matrix([[1, 2], [3, 4]])
+    assert maple_code(A, assign_to='A') == "A := Matrix([[1, 2], [3, 4]], storage = rectangular)"
+
+
+def test_maple_matrix_assign_to_more():
+    # assigning to Symbol or MatrixSymbol requires lhs/rhs match
+    A = Matrix([[1, 2, 3]])
+    B = MatrixSymbol('B', 1, 3)
+    C = MatrixSymbol('C', 2, 3)
+    assert maple_code(A, assign_to=B) == "B := Matrix([[1, 2, 3]], storage = rectangular)"
+    raises(ValueError, lambda: maple_code(A, assign_to=x))
+    raises(ValueError, lambda: maple_code(A, assign_to=C))
+
+
+def test_maple_matrix_1x1():
+    A = Matrix([[3]])
+    assert maple_code(A, assign_to='B') == "B := Matrix([[3]], storage = rectangular)"
+
+
+def test_maple_matrix_elements():
+    A = Matrix([[x, 2, x * y]])
+
+    assert maple_code(A[0, 0] ** 2 + A[0, 1] + A[0, 2]) == "x^2 + x*y + 2"
+    AA = MatrixSymbol('AA', 1, 3)
+    assert maple_code(AA) == "AA"
+
+    assert maple_code(AA[0, 0] ** 2 + sin(AA[0, 1]) + AA[0, 2]) == \
+           "sin(AA[1, 2]) + AA[1, 1]^2 + AA[1, 3]"
+    assert maple_code(sum(AA)) == "AA[1, 1] + AA[1, 2] + AA[1, 3]"
+
+
+def test_maple_boolean():
+    assert maple_code(True) == "true"
+    assert maple_code(S.true) == "true"
+    assert maple_code(False) == "false"
+    assert maple_code(S.false) == "false"
+
+
+def test_sparse():
+    M = SparseMatrix(5, 6, {})
+    M[2, 2] = 10
+    M[1, 2] = 20
+    M[1, 3] = 22
+    M[0, 3] = 30
+    M[3, 0] = x * y
+    assert maple_code(M) == \
+           'Matrix([[0, 0, 0, 30, 0, 0],' \
+           ' [0, 0, 20, 22, 0, 0],' \
+           ' [0, 0, 10, 0, 0, 0],' \
+           ' [x*y, 0, 0, 0, 0, 0],' \
+           ' [0, 0, 0, 0, 0, 0]], ' \
+           'storage = sparse)'
+
+# Not an important point.
+def test_maple_not_supported():
+    assert maple_code(S.ComplexInfinity) == (
+        "# Not supported in maple:\n"
+        "# ComplexInfinity\n"
+        "zoo"
+    )  # PROBLEM
+
+
+def test_MatrixElement_printing():
+    # test cases for issue #11821
+    A = MatrixSymbol("A", 1, 3)
+    B = MatrixSymbol("B", 1, 3)
+
+    assert (maple_code(A[0, 0]) == "A[1, 1]")
+    assert (maple_code(3 * A[0, 0]) == "3*A[1, 1]")
+
+    F = A-B
+
+    assert (maple_code(F[0,0]) == "A[1, 1] - B[1, 1]")
+
+
+def test_hadamard():
+    A = MatrixSymbol('A', 3, 3)
+    B = MatrixSymbol('B', 3, 3)
+    v = MatrixSymbol('v', 3, 1)
+    h = MatrixSymbol('h', 1, 3)
+    C = HadamardProduct(A, B)
+    assert maple_code(C) == "A*B"
+
+    assert maple_code(C * v) == "(A*B).v"
+    # HadamardProduct is higher than dot product.
+
+    assert maple_code(h * C * v) == "h.(A*B).v"
+
+    assert maple_code(C * A) == "(A*B).A"
+    # mixing Hadamard and scalar strange b/c we vectorize scalars
+
+    assert maple_code(C * x * y) == "x*y*(A*B)"
+
+
+def test_maple_piecewise():
+    expr = Piecewise((x, x < 1), (x ** 2, True))
+
+    assert maple_code(expr) == "piecewise(x < 1, x, x^2)"
+    assert maple_code(expr, assign_to="r") == (
+        "r := piecewise(x < 1, x, x^2)")
+
+    expr = Piecewise((x ** 2, x < 1), (x ** 3, x < 2), (x ** 4, x < 3), (x ** 5, True))
+    expected = "piecewise(x < 1, x^2, x < 2, x^3, x < 3, x^4, x^5)"
+    assert maple_code(expr) == expected
+    assert maple_code(expr, assign_to="r") == "r := " + expected
+
+    # Check that Piecewise without a True (default) condition error
+    expr = Piecewise((x, x < 1), (x ** 2, x > 1), (sin(x), x > 0))
+    raises(ValueError, lambda: maple_code(expr))
+
+
+def test_maple_piecewise_times_const():
+    pw = Piecewise((x, x < 1), (x ** 2, True))
+
+    assert maple_code(2 * pw) == "2*piecewise(x < 1, x, x^2)"
+    assert maple_code(pw / x) == "piecewise(x < 1, x, x^2)/x"
+    assert maple_code(pw / (x * y)) == "piecewise(x < 1, x, x^2)/(x*y)"
+    assert maple_code(pw / 3) == "(1/3)*piecewise(x < 1, x, x^2)"
+
+
+def test_maple_derivatives():
+    f = Function('f')
+    assert maple_code(f(x).diff(x)) == 'diff(f(x), x)'
+    assert maple_code(f(x).diff(x, 2)) == 'diff(f(x), x$2)'
+
+
+def test_automatic_rewrites():
+    assert maple_code(lucas(x)) == '2^(-x)*((1 - sqrt(5))^x + (1 + sqrt(5))^x)'
+    assert maple_code(sinc(x)) == 'piecewise(x <> 0, sin(x)/x, 1)'
+
+
+def test_specfun():
+    assert maple_code('asin(x)') == 'arcsin(x)'
+    assert maple_code(besseli(x, y)) == 'BesselI(x, y)'
diff --git a/shared/package-files/b5/b5ede3c9fca20dfb27154d0c118c3ec01e630605a0c385406c8a50cc602cf55b/filesystem.pyi b/shared/package-files/b5/b5ede3c9fca20dfb27154d0c118c3ec01e630605a0c385406c8a50cc602cf55b/filesystem.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..58695fa28f1b3c304c4535f7c802a46ea6c8a522
--- /dev/null
+++ b/shared/package-files/b5/b5ede3c9fca20dfb27154d0c118c3ec01e630605a0c385406c8a50cc602cf55b/filesystem.pyi
@@ -0,0 +1,7 @@
+from typing import Any
+
+has_likely_buggy_unicode_filesystem: Any
+
+class BrokenFilesystemWarning(RuntimeWarning, UnicodeWarning): ...
+
+def get_filesystem_encoding(): ...
diff --git a/shared/package-files/b5/b5f10cd03431c8b91413cee51ddb8c365139e4628836d019a3863f62c026489a/hatch.py b/shared/package-files/b5/b5f10cd03431c8b91413cee51ddb8c365139e4628836d019a3863f62c026489a/hatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cbd042e1628bdfa2172ca1f64eb146d8f8e54fb
--- /dev/null
+++ b/shared/package-files/b5/b5f10cd03431c8b91413cee51ddb8c365139e4628836d019a3863f62c026489a/hatch.py
@@ -0,0 +1,225 @@
+"""Contains classes for generating hatch patterns."""
+
+import numpy as np
+
+from matplotlib import _api
+from matplotlib.path import Path
+
+
+class HatchPatternBase:
+    """The base class for a hatch pattern."""
+    pass
+
+
+class HorizontalHatch(HatchPatternBase):
+    def __init__(self, hatch, density):
+        self.num_lines = int((hatch.count('-') + hatch.count('+')) * density)
+        self.num_vertices = self.num_lines * 2
+
+    def set_vertices_and_codes(self, vertices, codes):
+        steps, stepsize = np.linspace(0.0, 1.0, self.num_lines, False,
+                                      retstep=True)
+        steps += stepsize / 2.
+        vertices[0::2, 0] = 0.0
+        vertices[0::2, 1] = steps
+        vertices[1::2, 0] = 1.0
+        vertices[1::2, 1] = steps
+        codes[0::2] = Path.MOVETO
+        codes[1::2] = Path.LINETO
+
+
+class VerticalHatch(HatchPatternBase):
+    def __init__(self, hatch, density):
+        self.num_lines = int((hatch.count('|') + hatch.count('+')) * density)
+        self.num_vertices = self.num_lines * 2
+
+    def set_vertices_and_codes(self, vertices, codes):
+        steps, stepsize = np.linspace(0.0, 1.0, self.num_lines, False,
+                                      retstep=True)
+        steps += stepsize / 2.
+        vertices[0::2, 0] = steps
+        vertices[0::2, 1] = 0.0
+        vertices[1::2, 0] = steps
+        vertices[1::2, 1] = 1.0
+        codes[0::2] = Path.MOVETO
+        codes[1::2] = Path.LINETO
+
+
+class NorthEastHatch(HatchPatternBase):
+    def __init__(self, hatch, density):
+        self.num_lines = int(
+            (hatch.count('/') + hatch.count('x') + hatch.count('X')) * density)
+        if self.num_lines:
+            self.num_vertices = (self.num_lines + 1) * 2
+        else:
+            self.num_vertices = 0
+
+    def set_vertices_and_codes(self, vertices, codes):
+        steps = np.linspace(-0.5, 0.5, self.num_lines + 1)
+        vertices[0::2, 0] = 0.0 + steps
+        vertices[0::2, 1] = 0.0 - steps
+        vertices[1::2, 0] = 1.0 + steps
+        vertices[1::2, 1] = 1.0 - steps
+        codes[0::2] = Path.MOVETO
+        codes[1::2] = Path.LINETO
+
+
+class SouthEastHatch(HatchPatternBase):
+    def __init__(self, hatch, density):
+        self.num_lines = int(
+            (hatch.count('\\') + hatch.count('x') + hatch.count('X'))
+            * density)
+        if self.num_lines:
+            self.num_vertices = (self.num_lines + 1) * 2
+        else:
+            self.num_vertices = 0
+
+    def set_vertices_and_codes(self, vertices, codes):
+        steps = np.linspace(-0.5, 0.5, self.num_lines + 1)
+        vertices[0::2, 0] = 0.0 + steps
+        vertices[0::2, 1] = 1.0 + steps
+        vertices[1::2, 0] = 1.0 + steps
+        vertices[1::2, 1] = 0.0 + steps
+        codes[0::2] = Path.MOVETO
+        codes[1::2] = Path.LINETO
+
+
+class Shapes(HatchPatternBase):
+    filled = False
+
+    def __init__(self, hatch, density):
+        if self.num_rows == 0:
+            self.num_shapes = 0
+            self.num_vertices = 0
+        else:
+            self.num_shapes = ((self.num_rows // 2 + 1) * (self.num_rows + 1) +
+                               (self.num_rows // 2) * self.num_rows)
+            self.num_vertices = (self.num_shapes *
+                                 len(self.shape_vertices) *
+                                 (1 if self.filled else 2))
+
+    def set_vertices_and_codes(self, vertices, codes):
+        offset = 1.0 / self.num_rows
+        shape_vertices = self.shape_vertices * offset * self.size
+        shape_codes = self.shape_codes
+        if not self.filled:
+            shape_vertices = np.concatenate(  # Forward, then backward.
+                [shape_vertices, shape_vertices[::-1] * 0.9])
+            shape_codes = np.concatenate([shape_codes, shape_codes])
+        vertices_parts = []
+        codes_parts = []
+        for row in range(self.num_rows + 1):
+            if row % 2 == 0:
+                cols = np.linspace(0, 1, self.num_rows + 1)
+            else:
+                cols = np.linspace(offset / 2, 1 - offset / 2, self.num_rows)
+            row_pos = row * offset
+            for col_pos in cols:
+                vertices_parts.append(shape_vertices + [col_pos, row_pos])
+                codes_parts.append(shape_codes)
+        np.concatenate(vertices_parts, out=vertices)
+        np.concatenate(codes_parts, out=codes)
+
+
+class Circles(Shapes):
+    def __init__(self, hatch, density):
+        path = Path.unit_circle()
+        self.shape_vertices = path.vertices
+        self.shape_codes = path.codes
+        super().__init__(hatch, density)
+
+
+class SmallCircles(Circles):
+    size = 0.2
+
+    def __init__(self, hatch, density):
+        self.num_rows = (hatch.count('o')) * density
+        super().__init__(hatch, density)
+
+
+class LargeCircles(Circles):
+    size = 0.35
+
+    def __init__(self, hatch, density):
+        self.num_rows = (hatch.count('O')) * density
+        super().__init__(hatch, density)
+
+
+class SmallFilledCircles(Circles):
+    size = 0.1
+    filled = True
+
+    def __init__(self, hatch, density):
+        self.num_rows = (hatch.count('.')) * density
+        super().__init__(hatch, density)
+
+
+class Stars(Shapes):
+    size = 1.0 / 3.0
+    filled = True
+
+    def __init__(self, hatch, density):
+        self.num_rows = (hatch.count('*')) * density
+        path = Path.unit_regular_star(5)
+        self.shape_vertices = path.vertices
+        self.shape_codes = np.full(len(self.shape_vertices), Path.LINETO,
+                                   dtype=Path.code_type)
+        self.shape_codes[0] = Path.MOVETO
+        super().__init__(hatch, density)
+
+_hatch_types = [
+    HorizontalHatch,
+    VerticalHatch,
+    NorthEastHatch,
+    SouthEastHatch,
+    SmallCircles,
+    LargeCircles,
+    SmallFilledCircles,
+    Stars
+    ]
+
+
+def _validate_hatch_pattern(hatch):
+    valid_hatch_patterns = set(r'-+|/\xXoO.*')
+    if hatch is not None:
+        invalids = set(hatch).difference(valid_hatch_patterns)
+        if invalids:
+            valid = ''.join(sorted(valid_hatch_patterns))
+            invalids = ''.join(sorted(invalids))
+            _api.warn_deprecated(
+                '3.4',
+                removal='3.11',  # one release after custom hatches (#20690)
+                message=f'hatch must consist of a string of "{valid}" or '
+                        'None, but found the following invalid values '
+                        f'"{invalids}". Passing invalid values is deprecated '
+                        'since %(since)s and will become an error in %(removal)s.'
+            )
+
+
+def get_path(hatchpattern, density=6):
+    """
+    Given a hatch specifier, *hatchpattern*, generates Path to render
+    the hatch in a unit square.  *density* is the number of lines per
+    unit square.
+    """
+    density = int(density)
+
+    patterns = [hatch_type(hatchpattern, density)
+                for hatch_type in _hatch_types]
+    num_vertices = sum([pattern.num_vertices for pattern in patterns])
+
+    if num_vertices == 0:
+        return Path(np.empty((0, 2)))
+
+    vertices = np.empty((num_vertices, 2))
+    codes = np.empty(num_vertices, Path.code_type)
+
+    cursor = 0
+    for pattern in patterns:
+        if pattern.num_vertices != 0:
+            vertices_chunk = vertices[cursor:cursor + pattern.num_vertices]
+            codes_chunk = codes[cursor:cursor + pattern.num_vertices]
+            pattern.set_vertices_and_codes(vertices_chunk, codes_chunk)
+            cursor += pattern.num_vertices
+
+    return Path(vertices, codes)
diff --git a/shared/package-files/b5/b5f1abe06156e1b058d1faf2b10c0342d9731e617de416fc709c8dc9e00ca238/__init__.py b/shared/package-files/b5/b5f1abe06156e1b058d1faf2b10c0342d9731e617de416fc709c8dc9e00ca238/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8395c6fe9806f48876f950c4340e39386d8f016e
--- /dev/null
+++ b/shared/package-files/b5/b5f1abe06156e1b058d1faf2b10c0342d9731e617de416fc709c8dc9e00ca238/__init__.py
@@ -0,0 +1,7 @@
+from statsmodels.tools._test_runner import PytestTester
+
+from .qsturng_ import p_keys, psturng, qsturng, v_keys
+
+__all__ = ["p_keys", "psturng", "qsturng", "test", "v_keys"]
+
+test = PytestTester()
diff --git a/shared/package-files/b5/b5f768fdba1384727e618233426731e924c6f1c3894be35808c4dccaca933e9e/conv.py b/shared/package-files/b5/b5f768fdba1384727e618233426731e924c6f1c3894be35808c4dccaca933e9e/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbfeb2959f4b4c4030c5496fd3a4f666c9330569
--- /dev/null
+++ b/shared/package-files/b5/b5f768fdba1384727e618233426731e924c6f1c3894be35808c4dccaca933e9e/conv.py
@@ -0,0 +1,19 @@
+# flake8: noqa: F401
+r"""Quantized Reference Modules.
+
+This module is in the process of migration to
+`torch/ao/nn/quantized/reference`, and is kept here for
+compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate file under the `torch/ao/nn/quantized/reference`,
+while adding an import statement here.
+"""
+
+from torch.ao.nn.quantized.reference.modules.conv import _ConvNd
+from torch.ao.nn.quantized.reference.modules.conv import Conv1d
+from torch.ao.nn.quantized.reference.modules.conv import Conv2d
+from torch.ao.nn.quantized.reference.modules.conv import Conv3d
+from torch.ao.nn.quantized.reference.modules.conv import _ConvTransposeNd
+from torch.ao.nn.quantized.reference.modules.conv import ConvTranspose1d
+from torch.ao.nn.quantized.reference.modules.conv import ConvTranspose2d
+from torch.ao.nn.quantized.reference.modules.conv import ConvTranspose3d
diff --git a/shared/package-files/b5/b5fbdd4bccf06d8abf751e4cd86a6545927411ee0185e7373e1a126db8824bfd/web.py b/shared/package-files/b5/b5fbdd4bccf06d8abf751e4cd86a6545927411ee0185e7373e1a126db8824bfd/web.py
new file mode 100644
index 0000000000000000000000000000000000000000..6859074ceca12e90f272106a85885c7b88614100
--- /dev/null
+++ b/shared/package-files/b5/b5fbdd4bccf06d8abf751e4cd86a6545927411ee0185e7373e1a126db8824bfd/web.py
@@ -0,0 +1,100 @@
+"""Open the system browser to search or view online documentation"""
+from urllib.parse import urlencode
+import webbrowser
+
+from statsmodels import __version__
+
+BASE_URL = "https://www.statsmodels.org/"
+
+
+def _generate_url(func, stable):
+    """
+    Return a documentation URL for func or raise ValueError
+
+    Parameters
+    ----------
+    func : None, str, or callable
+        Either None to get the base documentation URL, a string to search
+        the documentation, or a statsmodels function to link to its
+        generated API documentation.
+    stable : bool
+        Flag indicating whether to use the stable documentation (True) or
+        the development documentation (False).
+
+    Returns
+    -------
+    str
+        The documentation URL.
+
+    Raises
+    ------
+    ValueError
+        If func is a callable that is not part of statsmodels, or if func
+        is not a str, callable, or None.
+    """
+    url = BASE_URL
+    if stable:
+        url += "stable/"
+    else:
+        url += "devel/"
+
+    if func is None:
+        return url
+    elif isinstance(func, str):
+        url += "search.html?"
+        url += urlencode({"q": func})
+        url += "&check_keywords=yes&area=default"
+    else:
+        try:
+            func_name = func.__name__
+            func_module = func.__module__
+            if not func_module.startswith("statsmodels."):
+                raise ValueError("Function must be from statsmodels")
+            url += "generated/"
+            url += func_module + "." + func_name + ".html"
+        except AttributeError as exc:
+            raise ValueError("Input not understood") from exc
+    return url
+
+
+def webdoc(func=None, stable=None):
+    """
+    Open a browser and display online documentation
+
+    Parameters
+    ----------
+    func : None, str, or callable, optional
+        Either a string to search the documentation or a function
+    stable : bool, optional
+        Flag indicating whether to use the stable documentation (True) or
+        the development documentation (False).  If not provided, opens
+        the stable documentation if the current version of statsmodels is a
+        release
+
+    Examples
+    --------
+    >>> import statsmodels.api as sm
+
+    Documentation site
+
+    >>> sm.webdoc()
+
+    Search for glm in docs
+
+    >>> sm.webdoc('glm')
+
+    Go to current generated help for OLS
+
+    >>> sm.webdoc(sm.OLS, stable=False)
+
+    Notes
+    -----
+    By default, open stable documentation if the current version of
+    statsmodels is a release.  Otherwise opens the development documentation.
+
+    Uses the default system browser.
+
+    """
+    stable = __version__ if "dev" not in __version__ else stable
+    url_or_error = _generate_url(func, stable)
+    webbrowser.open(url_or_error)
diff --git a/shared/package-files/b5/b5ffe38aff15d130b11a3d94941dddddb7af79afa1ebf286ef9ac088b797b633/mach b/shared/package-files/b5/b5ffe38aff15d130b11a3d94941dddddb7af79afa1ebf286ef9ac088b797b633/mach
new file mode 100644
index 0000000000000000000000000000000000000000..32c3b53175f46ebfd845f432091fdf024a842c04
Binary files /dev/null and b/shared/package-files/b5/b5ffe38aff15d130b11a3d94941dddddb7af79afa1ebf286ef9ac088b797b633/mach differ
diff --git a/shared/package-files/b5/b5ffe8aaa5729cc313579455d4d78dc9d71831a0d41b39fb92655d63e64a8a5f/count_nonzero_compositeexplicitautograd_dispatch.h b/shared/package-files/b5/b5ffe8aaa5729cc313579455d4d78dc9d71831a0d41b39fb92655d63e64a8a5f/count_nonzero_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d2e0bbf7e028d8082cc0025a4152559a50e06b61
--- /dev/null
+++ b/shared/package-files/b5/b5ffe8aaa5729cc313579455d4d78dc9d71831a0d41b39fb92655d63e64a8a5f/count_nonzero_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,27 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & count_nonzero_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor & count_nonzero_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+TORCH_API at::Tensor count_nonzero(const at::Tensor & self, c10::optional<int64_t> dim=c10::nullopt);
+TORCH_API at::Tensor & count_nonzero_out(at::Tensor & out, const at::Tensor & self, c10::optional<int64_t> dim=c10::nullopt);
+TORCH_API at::Tensor & count_nonzero_outf(const at::Tensor & self, c10::optional<int64_t> dim, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/c0/c003d351dd06c92bf9a505d337b0a7ef9fad113598338eb2e3bdd40b15e7fa74/test_array_api.py b/shared/package-files/c0/c003d351dd06c92bf9a505d337b0a7ef9fad113598338eb2e3bdd40b15e7fa74/test_array_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..f425eb4327fe042a3cf8eb79cf3402f23b526ae7
--- /dev/null
+++ b/shared/package-files/c0/c003d351dd06c92bf9a505d337b0a7ef9fad113598338eb2e3bdd40b15e7fa74/test_array_api.py
@@ -0,0 +1,191 @@
+import numpy as np
+import pytest
+
+from scipy.conftest import array_api_compatible
+from scipy._lib._array_api import (
+    _GLOBAL_CONFIG, array_namespace, _asarray, xp_copy, xp_assert_equal, is_numpy,
+    np_compat, xp_default_dtype
+)
+from scipy._lib._array_api_no_0d import xp_assert_equal as xp_assert_equal_no_0d
+
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+@pytest.mark.skipif(not _GLOBAL_CONFIG["SCIPY_ARRAY_API"],
+        reason="Array API test; set environment variable SCIPY_ARRAY_API=1 to run it")
+class TestArrayAPI:
+
+    def test_array_namespace(self):
+        x, y = np.array([0, 1, 2]), np.array([0, 1, 2])
+        xp = array_namespace(x, y)
+        assert 'array_api_compat.numpy' in xp.__name__
+
+        _GLOBAL_CONFIG["SCIPY_ARRAY_API"] = False
+        xp = array_namespace(x, y)
+        assert 'array_api_compat.numpy' in xp.__name__
+        _GLOBAL_CONFIG["SCIPY_ARRAY_API"] = True
+
+    @array_api_compatible
+    def test_asarray(self, xp):
+        x, y = _asarray([0, 1, 2], xp=xp), _asarray(np.arange(3), xp=xp)
+        ref = xp.asarray([0, 1, 2])
+        xp_assert_equal(x, ref)
+        xp_assert_equal(y, ref)
+
+    @pytest.mark.filterwarnings("ignore: the matrix subclass")
+    def test_raises(self):
+        msg = "of type `numpy.ma.MaskedArray` are not supported"
+        with pytest.raises(TypeError, match=msg):
+            array_namespace(np.ma.array(1), np.array(1))
+
+        msg = "of type `numpy.matrix` are not supported"
+        with pytest.raises(TypeError, match=msg):
+            array_namespace(np.array(1), np.matrix(1))
+
+        msg = "only boolean and numerical dtypes are supported"
+        with pytest.raises(TypeError, match=msg):
+            array_namespace([object()])
+        with pytest.raises(TypeError, match=msg):
+            array_namespace('abc')
+
+    def test_array_likes(self):
+        # should be no exceptions
+        array_namespace([0, 1, 2])
+        array_namespace(1, 2, 3)
+        array_namespace(1)
+
+    @skip_xp_backends('jax.numpy',
+                      reason="JAX arrays do not support item assignment")
+    @pytest.mark.usefixtures("skip_xp_backends")
+    @array_api_compatible
+    def test_copy(self, xp):
+        for _xp in [xp, None]:
+            x = xp.asarray([1, 2, 3])
+            y = xp_copy(x, xp=_xp)
+            # with numpy we'd want to use np.shared_memory, but that's not specified
+            # in the array-api
+            x[0] = 10
+            x[1] = 11
+            x[2] = 12
+
+            assert x[0] != y[0]
+            assert x[1] != y[1]
+            assert x[2] != y[2]
+            assert id(x) != id(y)
+
+    @array_api_compatible
+    @pytest.mark.parametrize('dtype', ['int32', 'int64', 'float32', 'float64'])
+    @pytest.mark.parametrize('shape', [(), (3,)])
+    def test_strict_checks(self, xp, dtype, shape):
+        # Check that `_strict_check` behaves as expected
+        dtype = getattr(xp, dtype)
+        x = xp.broadcast_to(xp.asarray(1, dtype=dtype), shape)
+        x = x if shape else x[()]
+        y = np_compat.asarray(1)[()]
+
+        kwarg_names = ["check_namespace", "check_dtype", "check_shape", "check_0d"]
+        options = dict(zip(kwarg_names, [True, False, False, False]))
+        if xp == np:
+            xp_assert_equal(x, y, **options)
+        else:
+            with pytest.raises(AssertionError, match="Namespaces do not match."):
+                xp_assert_equal(x, y, **options)
+
+        options = dict(zip(kwarg_names, [False, True, False, False]))
+        if y.dtype.name in str(x.dtype):
+            xp_assert_equal(x, y, **options)
+        else:
+            with pytest.raises(AssertionError, match="dtypes do not match."):
+                xp_assert_equal(x, y, **options)
+
+        options = dict(zip(kwarg_names, [False, False, True, False]))
+        if x.shape == y.shape:
+            xp_assert_equal(x, y, **options)
+        else:
+            with pytest.raises(AssertionError, match="Shapes do not match."):
+                xp_assert_equal(x, xp.asarray(y), **options)
+
+        options = dict(zip(kwarg_names, [False, False, False, True]))
+        if is_numpy(xp) and x.shape == y.shape:
+            xp_assert_equal(x, y, **options)
+        elif is_numpy(xp):
+            with pytest.raises(AssertionError, match="Array-ness does not match."):
+                xp_assert_equal(x, y, **options)
+
+
+    @array_api_compatible
+    def test_check_scalar(self, xp):
+        if not is_numpy(xp):
+            pytest.skip("Scalars only exist in NumPy")
+
+        # identity always passes
+        xp_assert_equal(xp.float64(0), xp.float64(0))
+        xp_assert_equal(xp.asarray(0.), xp.asarray(0.))
+        xp_assert_equal(xp.float64(0), xp.float64(0), check_0d=False)
+        xp_assert_equal(xp.asarray(0.), xp.asarray(0.), check_0d=False)
+
+        # Check default convention: 0d-arrays are distinguished from scalars
+        message = "Array-ness does not match:.*"
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal(xp.asarray(0.), xp.float64(0))
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal(xp.float64(0), xp.asarray(0.))
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal(xp.asarray(42), xp.int64(42))
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal(xp.int64(42), xp.asarray(42))
+
+        # with `check_0d=False`, scalars-vs-0d passes (if values match)
+        xp_assert_equal(xp.asarray(0.), xp.float64(0), check_0d=False)
+        xp_assert_equal(xp.float64(0), xp.asarray(0.), check_0d=False)
+        # also with regular python objects
+        xp_assert_equal(xp.asarray(0.), 0., check_0d=False)
+        xp_assert_equal(0., xp.asarray(0.), check_0d=False)
+        xp_assert_equal(xp.asarray(42), 42, check_0d=False)
+        xp_assert_equal(42, xp.asarray(42), check_0d=False)
+
+        # as an alternative to `check_0d=False`, explicitly expect scalar
+        xp_assert_equal(xp.float64(0), xp.asarray(0.)[()])
+
+
+    @array_api_compatible
+    def test_check_scalar_no_0d(self, xp):
+        if not is_numpy(xp):
+            pytest.skip("Scalars only exist in NumPy")
+
+        # identity passes, if first argument is not 0d (or check_0d=True)
+        xp_assert_equal_no_0d(xp.float64(0), xp.float64(0))
+        xp_assert_equal_no_0d(xp.float64(0), xp.float64(0), check_0d=True)
+        xp_assert_equal_no_0d(xp.asarray(0.), xp.asarray(0.), check_0d=True)
+
+        # by default, 0d values are forbidden as the first argument
+        message = "Result is a NumPy 0d-array.*"
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.asarray(0.), xp.asarray(0.))
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.asarray(0.), xp.float64(0))
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.asarray(42), xp.int64(42))
+
+        # Check default convention: 0d-arrays are NOT distinguished from scalars
+        xp_assert_equal_no_0d(xp.float64(0), xp.asarray(0.))
+        xp_assert_equal_no_0d(xp.int64(42), xp.asarray(42))
+
+        # opt in to 0d-check remains possible
+        message = "Array-ness does not match:.*"
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.asarray(0.), xp.float64(0), check_0d=True)
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.float64(0), xp.asarray(0.), check_0d=True)
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.asarray(42), xp.int64(0), check_0d=True)
+        with pytest.raises(AssertionError, match=message):
+            xp_assert_equal_no_0d(xp.int64(0), xp.asarray(42), check_0d=True)
+
+        # scalars-vs-0d passes (if values match) also with regular python objects
+        xp_assert_equal_no_0d(0., xp.asarray(0.))
+        xp_assert_equal_no_0d(42, xp.asarray(42))
+
+    @array_api_compatible
+    def test_default_dtype(self, xp):
+        assert xp_default_dtype(xp) == xp.asarray(1.).dtype
diff --git a/shared/package-files/c0/c003dfa402dc27f518bc600e5ace4362a71ab0c63f9fe7840ce98393288e571b/connectivity.py b/shared/package-files/c0/c003dfa402dc27f518bc600e5ace4362a71ab0c63f9fe7840ce98393288e571b/connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..82b98f828808f35c170614d403fb62614617f41b
--- /dev/null
+++ b/shared/package-files/c0/c003dfa402dc27f518bc600e5ace4362a71ab0c63f9fe7840ce98393288e571b/connectivity.py
@@ -0,0 +1,812 @@
+"""
+Flow based connectivity algorithms
+"""
+
+import itertools
+from operator import itemgetter
+
+import networkx as nx
+
+# Define the default maximum flow function to use in all flow based
+# connectivity algorithms.
+from networkx.algorithms.flow import (
+    boykov_kolmogorov,
+    build_residual_network,
+    dinitz,
+    edmonds_karp,
+    shortest_augmenting_path,
+)
+
+default_flow_func = edmonds_karp
+
+from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
+
+__all__ = [
+    "average_node_connectivity",
+    "local_node_connectivity",
+    "node_connectivity",
+    "local_edge_connectivity",
+    "edge_connectivity",
+    "all_pairs_node_connectivity",
+]
+
+
+def local_node_connectivity(
+    G, s, t, flow_func=None, auxiliary=None, residual=None, cutoff=None
+):
+    r"""Computes local node connectivity for nodes s and t.
+
+    Local node connectivity for two non adjacent nodes s and t is the
+    minimum number of nodes that must be removed (along with their incident
+    edges) to disconnect them.
+
+    This is a flow based implementation of node connectivity. We compute the
+    maximum flow on an auxiliary digraph build from the original input
+    graph (see below for details).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    s : node
+        Source node
+
+    t : node
+        Target node
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The choice
+        of the default function may change from version to version and
+        should not be relied on. Default value: None.
+
+    auxiliary : NetworkX DiGraph
+        Auxiliary digraph to compute flow based node connectivity. It has
+        to have a graph attribute called mapping with a dictionary mapping
+        node names in G and in the auxiliary digraph. If provided
+        it will be reused instead of recreated. Default value: None.
+
+    residual : NetworkX DiGraph
+        Residual network to compute maximum flow. If provided it will be
+        reused instead of recreated. Default value: None.
+
+    cutoff : integer, float, or None (default: None)
+        If specified, the maximum flow algorithm will terminate when the
+        flow value reaches or exceeds the cutoff. This only works for flows
+        that support the cutoff parameter (most do) and is ignored otherwise.
+
+    Returns
+    -------
+    K : integer
+        local node connectivity for nodes s and t
+
+    Examples
+    --------
+    This function is not imported in the base NetworkX namespace, so you
+    have to explicitly import it from the connectivity package:
+
+    >>> from networkx.algorithms.connectivity import local_node_connectivity
+
+    We use in this example the platonic icosahedral graph, which has node
+    connectivity 5.
+
+    >>> G = nx.icosahedral_graph()
+    >>> local_node_connectivity(G, 0, 6)
+    5
+
+    If you need to compute local connectivity on several pairs of
+    nodes in the same graph, it is recommended that you reuse the
+    data structures that NetworkX uses in the computation: the
+    auxiliary digraph for node connectivity, and the residual
+    network for the underlying maximum flow computation.
+
+    Example of how to compute local node connectivity among
+    all pairs of nodes of the platonic icosahedral graph reusing
+    the data structures.
+
+    >>> import itertools
+    >>> # You also have to explicitly import the function for
+    >>> # building the auxiliary digraph from the connectivity package
+    >>> from networkx.algorithms.connectivity import build_auxiliary_node_connectivity
+    ...
+    >>> H = build_auxiliary_node_connectivity(G)
+    >>> # And the function for building the residual network from the
+    >>> # flow package
+    >>> from networkx.algorithms.flow import build_residual_network
+    >>> # Note that the auxiliary digraph has an edge attribute named capacity
+    >>> R = build_residual_network(H, "capacity")
+    >>> result = dict.fromkeys(G, dict())
+    >>> # Reuse the auxiliary digraph and the residual network by passing them
+    >>> # as parameters
+    >>> for u, v in itertools.combinations(G, 2):
+    ...     k = local_node_connectivity(G, u, v, auxiliary=H, residual=R)
+    ...     result[u][v] = k
+    ...
+    >>> all(result[u][v] == 5 for u, v in itertools.combinations(G, 2))
+    True
+
+    You can also use alternative flow algorithms for computing node
+    connectivity. For instance, in dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better than
+    the default :meth:`edmonds_karp` which is faster for sparse
+    networks with highly skewed degree distributions. Alternative flow
+    functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> local_node_connectivity(G, 0, 6, flow_func=shortest_augmenting_path)
+    5
+
+    Notes
+    -----
+    This is a flow based implementation of node connectivity. We compute the
+    maximum flow using, by default, the :meth:`edmonds_karp` algorithm (see:
+    :meth:`maximum_flow`) on an auxiliary digraph build from the original
+    input graph:
+
+    For an undirected graph G having `n` nodes and `m` edges we derive a
+    directed graph H with `2n` nodes and `2m+n` arcs by replacing each
+    original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
+    arc in H. Then for each edge (`u`, `v`) in G we add two arcs
+    (`u_B`, `v_A`) and (`v_B`, `u_A`) in H. Finally we set the attribute
+    capacity = 1 for each arc in H [1]_ .
+
+    For a directed graph G having `n` nodes and `m` arcs we derive a
+    directed graph H with `2n` nodes and `m+n` arcs by replacing each
+    original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
+    arc (`v_A`, `v_B`) in H. Then for each arc (`u`, `v`) in G we add one arc
+    (`u_B`, `v_A`) in H. Finally we set the attribute capacity = 1 for
+    each arc in H.
+
+    This is equal to the local node connectivity because the value of
+    a maximum s-t-flow is equal to the capacity of a minimum s-t-cut.
+
+    See also
+    --------
+    :meth:`local_edge_connectivity`
+    :meth:`node_connectivity`
+    :meth:`minimum_node_cut`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and
+        Erlebach, 'Network Analysis: Methodological Foundations', Lecture
+        Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.
+        http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
+
+    """
+    if flow_func is None:
+        flow_func = default_flow_func
+
+    if auxiliary is None:
+        H = build_auxiliary_node_connectivity(G)
+    else:
+        H = auxiliary
+
+    mapping = H.graph.get("mapping", None)
+    if mapping is None:
+        raise nx.NetworkXError("Invalid auxiliary digraph.")
+
+    kwargs = {"flow_func": flow_func, "residual": residual}
+    if flow_func is shortest_augmenting_path:
+        kwargs["cutoff"] = cutoff
+        kwargs["two_phase"] = True
+    elif flow_func is edmonds_karp:
+        kwargs["cutoff"] = cutoff
+    elif flow_func is dinitz:
+        kwargs["cutoff"] = cutoff
+    elif flow_func is boykov_kolmogorov:
+        kwargs["cutoff"] = cutoff
+
+    return nx.maximum_flow_value(H, f"{mapping[s]}B", f"{mapping[t]}A", **kwargs)
+
+
+def node_connectivity(G, s=None, t=None, flow_func=None):
+    r"""Returns node connectivity for a graph or digraph G.
+
+    Node connectivity is equal to the minimum number of nodes that
+    must be removed to disconnect G or render it trivial. If source
+    and target nodes are provided, this function returns the local node
+    connectivity: the minimum number of nodes that must be removed to break
+    all paths from source to target in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    Returns
+    -------
+    K : integer
+        Node connectivity of G, or local node connectivity if source
+        and target are provided.
+
+    Examples
+    --------
+    >>> # Platonic icosahedral graph is 5-node-connected
+    >>> G = nx.icosahedral_graph()
+    >>> nx.node_connectivity(G)
+    5
+
+    You can use alternative flow algorithms for the underlying maximum
+    flow computation. In dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better
+    than the default :meth:`edmonds_karp`, which is faster for
+    sparse networks with highly skewed degree distributions. Alternative
+    flow functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> nx.node_connectivity(G, flow_func=shortest_augmenting_path)
+    5
+
+    If you specify a pair of nodes (source and target) as parameters,
+    this function returns the value of local node connectivity.
+
+    >>> nx.node_connectivity(G, 3, 7)
+    5
+
+    If you need to perform several local computations among different
+    pairs of nodes on the same graph, it is recommended that you reuse
+    the data structures used in the maximum flow computations. See
+    :meth:`local_node_connectivity` for details.
+
+    Notes
+    -----
+    This is a flow based implementation of node connectivity. The
+    algorithm works by solving $O((n-\delta-1+\delta(\delta-1)/2))$
+    maximum flow problems on an auxiliary digraph. Where $\delta$
+    is the minimum degree of G. For details about the auxiliary
+    digraph and the computation of local node connectivity see
+    :meth:`local_node_connectivity`. This implementation is based
+    on algorithm 11 in [1]_.
+
+    See also
+    --------
+    :meth:`local_node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # Local node connectivity
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return local_node_connectivity(G, s, t, flow_func=flow_func)
+
+    # Global node connectivity
+    if G.is_directed():
+        if not nx.is_weakly_connected(G):
+            return 0
+        iter_func = itertools.permutations
+        # It is necessary to consider both predecessors
+        # and successors for directed graphs
+
+        def neighbors(v):
+            return itertools.chain.from_iterable([G.predecessors(v), G.successors(v)])
+
+    else:
+        if not nx.is_connected(G):
+            return 0
+        iter_func = itertools.combinations
+        neighbors = G.neighbors
+
+    # Reuse the auxiliary digraph and the residual network
+    H = build_auxiliary_node_connectivity(G)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
+
+    # Pick a node with minimum degree
+    # Node connectivity is bounded by degree.
+    v, K = min(G.degree(), key=itemgetter(1))
+    # compute local node connectivity with all its non-neighbors nodes
+    for w in set(G) - set(neighbors(v)) - {v}:
+        kwargs["cutoff"] = K
+        K = min(K, local_node_connectivity(G, v, w, **kwargs))
+    # Also for non adjacent pairs of neighbors of v
+    for x, y in iter_func(neighbors(v), 2):
+        if y in G[x]:
+            continue
+        kwargs["cutoff"] = K
+        K = min(K, local_node_connectivity(G, x, y, **kwargs))
+
+    return K
+
+
+def average_node_connectivity(G, flow_func=None):
+    r"""Returns the average connectivity of a graph G.
+
+    The average connectivity `\bar{\kappa}` of a graph G is the average
+    of local node connectivity over all pairs of nodes of G [1]_ .
+
+    .. math::
+
+        \bar{\kappa}(G) = \frac{\sum_{u,v} \kappa_{G}(u,v)}{{n \choose 2}}
+
+    Parameters
+    ----------
+
+    G : NetworkX graph
+        Undirected graph
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See :meth:`local_node_connectivity`
+        for details. The choice of the default function may change from
+        version to version and should not be relied on. Default value: None.
+
+    Returns
+    -------
+    K : float
+        Average node connectivity
+
+    See also
+    --------
+    :meth:`local_node_connectivity`
+    :meth:`node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1]  Beineke, L., O. Oellermann, and R. Pippert (2002). The average
+            connectivity of a graph. Discrete mathematics 252(1-3), 31-45.
+            http://www.sciencedirect.com/science/article/pii/S0012365X01001807
+
+    """
+    if G.is_directed():
+        iter_func = itertools.permutations
+    else:
+        iter_func = itertools.combinations
+
+    # Reuse the auxiliary digraph and the residual network
+    H = build_auxiliary_node_connectivity(G)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
+
+    num, den = 0, 0
+    for u, v in iter_func(G, 2):
+        num += local_node_connectivity(G, u, v, **kwargs)
+        den += 1
+
+    if den == 0:  # Null Graph
+        return 0
+    return num / den
+
+
+def all_pairs_node_connectivity(G, nbunch=None, flow_func=None):
+    """Compute node connectivity between all pairs of nodes of G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    nbunch: container
+        Container of nodes. If provided node connectivity will be computed
+        only over pairs of nodes in nbunch.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    Returns
+    -------
+    all_pairs : dict
+        A dictionary with node connectivity between all pairs of nodes
+        in G, or in nbunch if provided.
+
+    See also
+    --------
+    :meth:`local_node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`local_edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    """
+    if nbunch is None:
+        nbunch = G
+    else:
+        nbunch = set(nbunch)
+
+    directed = G.is_directed()
+    if directed:
+        iter_func = itertools.permutations
+    else:
+        iter_func = itertools.combinations
+
+    all_pairs = {n: {} for n in nbunch}
+
+    # Reuse auxiliary digraph and residual network
+    H = build_auxiliary_node_connectivity(G)
+    mapping = H.graph["mapping"]
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
+
+    for u, v in iter_func(nbunch, 2):
+        K = local_node_connectivity(G, u, v, **kwargs)
+        all_pairs[u][v] = K
+        if not directed:
+            all_pairs[v][u] = K
+
+    return all_pairs
+
+
+def local_edge_connectivity(
+    G, s, t, flow_func=None, auxiliary=None, residual=None, cutoff=None
+):
+    r"""Returns local edge connectivity for nodes s and t in G.
+
+    Local edge connectivity for two nodes s and t is the minimum number
+    of edges that must be removed to disconnect them.
+
+    This is a flow based implementation of edge connectivity. We compute the
+    maximum flow on an auxiliary digraph build from the original
+    network (see below for details). This is equal to the local edge
+    connectivity because the value of a maximum s-t-flow is equal to the
+    capacity of a minimum s-t-cut (Ford and Fulkerson theorem) [1]_ .
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected or directed graph
+
+    s : node
+        Source node
+
+    t : node
+        Target node
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    auxiliary : NetworkX DiGraph
+        Auxiliary digraph for computing flow based edge connectivity. If
+        provided it will be reused instead of recreated. Default value: None.
+
+    residual : NetworkX DiGraph
+        Residual network to compute maximum flow. If provided it will be
+        reused instead of recreated. Default value: None.
+
+    cutoff : integer, float, or None (default: None)
+        If specified, the maximum flow algorithm will terminate when the
+        flow value reaches or exceeds the cutoff. This only works for flows
+        that support the cutoff parameter (most do) and is ignored otherwise.
+
+    Returns
+    -------
+    K : integer
+        local edge connectivity for nodes s and t.
+
+    Examples
+    --------
+    This function is not imported in the base NetworkX namespace, so you
+    have to explicitly import it from the connectivity package:
+
+    >>> from networkx.algorithms.connectivity import local_edge_connectivity
+
+    We use in this example the platonic icosahedral graph, which has edge
+    connectivity 5.
+
+    >>> G = nx.icosahedral_graph()
+    >>> local_edge_connectivity(G, 0, 6)
+    5
+
+    If you need to compute local connectivity on several pairs of
+    nodes in the same graph, it is recommended that you reuse the
+    data structures that NetworkX uses in the computation: the
+    auxiliary digraph for edge connectivity, and the residual
+    network for the underlying maximum flow computation.
+
+    Example of how to compute local edge connectivity among
+    all pairs of nodes of the platonic icosahedral graph reusing
+    the data structures.
+
+    >>> import itertools
+    >>> # You also have to explicitly import the function for
+    >>> # building the auxiliary digraph from the connectivity package
+    >>> from networkx.algorithms.connectivity import build_auxiliary_edge_connectivity
+    >>> H = build_auxiliary_edge_connectivity(G)
+    >>> # And the function for building the residual network from the
+    >>> # flow package
+    >>> from networkx.algorithms.flow import build_residual_network
+    >>> # Note that the auxiliary digraph has an edge attribute named capacity
+    >>> R = build_residual_network(H, "capacity")
+    >>> result = dict.fromkeys(G, dict())
+    >>> # Reuse the auxiliary digraph and the residual network by passing them
+    >>> # as parameters
+    >>> for u, v in itertools.combinations(G, 2):
+    ...     k = local_edge_connectivity(G, u, v, auxiliary=H, residual=R)
+    ...     result[u][v] = k
+    >>> all(result[u][v] == 5 for u, v in itertools.combinations(G, 2))
+    True
+
+    You can also use alternative flow algorithms for computing edge
+    connectivity. For instance, in dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better than
+    the default :meth:`edmonds_karp` which is faster for sparse
+    networks with highly skewed degree distributions. Alternative flow
+    functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> local_edge_connectivity(G, 0, 6, flow_func=shortest_augmenting_path)
+    5
+
+    Notes
+    -----
+    This is a flow based implementation of edge connectivity. We compute the
+    maximum flow using, by default, the :meth:`edmonds_karp` algorithm on an
+    auxiliary digraph build from the original input graph:
+
+    If the input graph is undirected, we replace each edge (`u`,`v`) with
+    two reciprocal arcs (`u`, `v`) and (`v`, `u`) and then we set the attribute
+    'capacity' for each arc to 1. If the input graph is directed we simply
+    add the 'capacity' attribute. This is an implementation of algorithm 1
+    in [1]_.
+
+    The maximum flow in the auxiliary network is equal to the local edge
+    connectivity because the value of a maximum s-t-flow is equal to the
+    capacity of a minimum s-t-cut (Ford and Fulkerson theorem).
+
+    See also
+    --------
+    :meth:`edge_connectivity`
+    :meth:`local_node_connectivity`
+    :meth:`node_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if flow_func is None:
+        flow_func = default_flow_func
+
+    if auxiliary is None:
+        H = build_auxiliary_edge_connectivity(G)
+    else:
+        H = auxiliary
+
+    kwargs = {"flow_func": flow_func, "residual": residual}
+    if flow_func is shortest_augmenting_path:
+        kwargs["cutoff"] = cutoff
+        kwargs["two_phase"] = True
+    elif flow_func is edmonds_karp:
+        kwargs["cutoff"] = cutoff
+    elif flow_func is dinitz:
+        kwargs["cutoff"] = cutoff
+    elif flow_func is boykov_kolmogorov:
+        kwargs["cutoff"] = cutoff
+
+    return nx.maximum_flow_value(H, s, t, **kwargs)
+
+
+def edge_connectivity(G, s=None, t=None, flow_func=None, cutoff=None):
+    r"""Returns the edge connectivity of the graph or digraph G.
+
+    The edge connectivity is equal to the minimum number of edges that
+    must be removed to disconnect G or render it trivial. If source
+    and target nodes are provided, this function returns the local edge
+    connectivity: the minimum number of edges that must be removed to
+    break all paths from source to target in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected or directed graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    cutoff : integer, float, or None (default: None)
+        If specified, the maximum flow algorithm will terminate when the
+        flow value reaches or exceeds the cutoff. This only works for flows
+        that support the cutoff parameter (most do) and is ignored otherwise.
+
+    Returns
+    -------
+    K : integer
+        Edge connectivity for G, or local edge connectivity if source
+        and target were provided
+
+    Examples
+    --------
+    >>> # Platonic icosahedral graph is 5-edge-connected
+    >>> G = nx.icosahedral_graph()
+    >>> nx.edge_connectivity(G)
+    5
+
+    You can use alternative flow algorithms for the underlying
+    maximum flow computation. In dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better
+    than the default :meth:`edmonds_karp`, which is faster for
+    sparse networks with highly skewed degree distributions.
+    Alternative flow functions have to be explicitly imported
+    from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> nx.edge_connectivity(G, flow_func=shortest_augmenting_path)
+    5
+
+    If you specify a pair of nodes (source and target) as parameters,
+    this function returns the value of local edge connectivity.
+
+    >>> nx.edge_connectivity(G, 3, 7)
+    5
+
+    If you need to perform several local computations among different
+    pairs of nodes on the same graph, it is recommended that you reuse
+    the data structures used in the maximum flow computations. See
+    :meth:`local_edge_connectivity` for details.
+
+    Notes
+    -----
+    This is a flow based implementation of global edge connectivity.
+    For undirected graphs the algorithm works by finding a 'small'
+    dominating set of nodes of G (see algorithm 7 in [1]_ ) and
+    computing local maximum flow (see :meth:`local_edge_connectivity`)
+    between an arbitrary node in the dominating set and the rest of
+    nodes in it. This is an implementation of algorithm 6 in [1]_ .
+    For directed graphs, the algorithm does n calls to the maximum
+    flow function. This is an implementation of algorithm 8 in [1]_ .
+
+    See also
+    --------
+    :meth:`local_edge_connectivity`
+    :meth:`local_node_connectivity`
+    :meth:`node_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+    :meth:`k_edge_components`
+    :meth:`k_edge_subgraphs`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # Local edge connectivity
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return local_edge_connectivity(G, s, t, flow_func=flow_func, cutoff=cutoff)
+
+    # Global edge connectivity
+    # reuse auxiliary digraph and residual network
+    H = build_auxiliary_edge_connectivity(G)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
+
+    if G.is_directed():
+        # Algorithm 8 in [1]
+        if not nx.is_weakly_connected(G):
+            return 0
+
+        # initial value for \lambda is minimum degree
+        L = min(d for n, d in G.degree())
+        nodes = list(G)
+        n = len(nodes)
+
+        if cutoff is not None:
+            L = min(cutoff, L)
+
+        for i in range(n):
+            kwargs["cutoff"] = L
+            try:
+                L = min(L, local_edge_connectivity(G, nodes[i], nodes[i + 1], **kwargs))
+            except IndexError:  # last node!
+                L = min(L, local_edge_connectivity(G, nodes[i], nodes[0], **kwargs))
+        return L
+    else:  # undirected
+        # Algorithm 6 in [1]
+        if not nx.is_connected(G):
+            return 0
+
+        # initial value for \lambda is minimum degree
+        L = min(d for n, d in G.degree())
+
+        if cutoff is not None:
+            L = min(cutoff, L)
+
+        # A dominating set is \lambda-covering
+        # We need a dominating set with at least two nodes
+        for node in G:
+            D = nx.dominating_set(G, start_with=node)
+            v = D.pop()
+            if D:
+                break
+        else:
+            # in complete graphs the dominating sets will always be of one node
+            # thus we return min degree
+            return L
+
+        for w in D:
+            kwargs["cutoff"] = L
+            L = min(L, local_edge_connectivity(G, v, w, **kwargs))
+
+        return L
diff --git a/shared/package-files/c0/c00559ffdffedd8d78c73ceea519390534d17b01497a4476bdff68231e0cef9d/__init__.py b/shared/package-files/c0/c00559ffdffedd8d78c73ceea519390534d17b01497a4476bdff68231e0cef9d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cf3b507d7f908a3eff1b3e01db7164ee9209807
--- /dev/null
+++ b/shared/package-files/c0/c00559ffdffedd8d78c73ceea519390534d17b01497a4476bdff68231e0cef9d/__init__.py
@@ -0,0 +1,51 @@
+"""
+A platform independent file lock that supports the with-statement.
+
+.. autodata:: filelock.__version__
+   :no-value:
+
+"""
+from __future__ import annotations
+
+import sys
+import warnings
+from typing import TYPE_CHECKING
+
+from ._api import AcquireReturnProxy, BaseFileLock
+from ._error import Timeout
+from ._soft import SoftFileLock
+from ._unix import UnixFileLock, has_fcntl
+from ._windows import WindowsFileLock
+from .version import version
+
+#: version of the project as a string
+__version__: str = version
+
+
+if sys.platform == "win32":  # pragma: win32 cover
+    _FileLock: type[BaseFileLock] = WindowsFileLock
+else:  # pragma: win32 no cover # noqa: PLR5501
+    if has_fcntl:
+        _FileLock: type[BaseFileLock] = UnixFileLock
+    else:
+        _FileLock = SoftFileLock
+        if warnings is not None:
+            warnings.warn("only soft file lock is available", stacklevel=2)
+
+if TYPE_CHECKING:
+    FileLock = SoftFileLock
+else:
+    #: Alias for the lock, which should be used for the current platform.
+    FileLock = _FileLock
+
+
+__all__ = [
+    "__version__",
+    "FileLock",
+    "SoftFileLock",
+    "Timeout",
+    "UnixFileLock",
+    "WindowsFileLock",
+    "BaseFileLock",
+    "AcquireReturnProxy",
+]
diff --git a/shared/package-files/c0/c0077fe5e60254985b066f1a1600ab9a2f5ee628affa3fd88455f6452cb50934/miopen_convolution_transpose_ops.h b/shared/package-files/c0/c0077fe5e60254985b066f1a1600ab9a2f5ee628affa3fd88455f6452cb50934/miopen_convolution_transpose_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b02c35e95665e41b285bbcbe8ee8fcef45e0ee6
--- /dev/null
+++ b/shared/package-files/c0/c0077fe5e60254985b066f1a1600ab9a2f5ee628affa3fd88455f6452cb50934/miopen_convolution_transpose_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API miopen_convolution_transpose {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::miopen_convolution_transpose")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic);
+};
+
+struct TORCH_API miopen_convolution_transpose_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt, bool, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::miopen_convolution_transpose")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c0/c0079301aa2091db1e1659780ee97b027b5951529f25b3e5c34f6fb16ab9d40f/is_same_size_compositeexplicitautograd_dispatch.h b/shared/package-files/c0/c0079301aa2091db1e1659780ee97b027b5951529f25b3e5c34f6fb16ab9d40f/is_same_size_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c7b8206515af1375770ce99df6ac17821cc3dd7
--- /dev/null
+++ b/shared/package-files/c0/c0079301aa2091db1e1659780ee97b027b5951529f25b3e5c34f6fb16ab9d40f/is_same_size_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API bool is_same_size(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/c0/c00b9f30658bfecd40c369779b201f045b6b44e54f7acddd32836e0c4a0dc13f/Magadan b/shared/package-files/c0/c00b9f30658bfecd40c369779b201f045b6b44e54f7acddd32836e0c4a0dc13f/Magadan
new file mode 100644
index 0000000000000000000000000000000000000000..16bac8444656c393288dcc0209a96c7c3f487a19
Binary files /dev/null and b/shared/package-files/c0/c00b9f30658bfecd40c369779b201f045b6b44e54f7acddd32836e0c4a0dc13f/Magadan differ
diff --git a/shared/package-files/c0/c010fa48495f337c3ebba25c83236cca87d73efa96d5ee1896d81e1c4e1768e7/Scripts.py b/shared/package-files/c0/c010fa48495f337c3ebba25c83236cca87d73efa96d5ee1896d81e1c4e1768e7/Scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..dac10667b423a6a6681e0a67234de37db00611b9
--- /dev/null
+++ b/shared/package-files/c0/c010fa48495f337c3ebba25c83236cca87d73efa96d5ee1896d81e1c4e1768e7/Scripts.py
@@ -0,0 +1,3639 @@
+# -*- coding: utf-8 -*-
+#
+# NOTE: This file was auto-generated with MetaTools/buildUCD.py.
+# Source: https://unicode.org/Public/UNIDATA/Scripts.txt
+# License: http://unicode.org/copyright.html#License
+#
+# Scripts-17.0.0.txt
+# Date: 2025-07-24, 13:28:55 GMT
+# © 2025 Unicode®, Inc.
+# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries.
+# For terms of use and license, see https://www.unicode.org/terms_of_use.html
+#
+# Unicode Character Database
+#   For documentation, see https://www.unicode.org/reports/tr44/
+# For more information, see:
+#   UAX #24, Unicode Script Property: https://www.unicode.org/reports/tr24/
+#     Especially the sections:
+#       https://www.unicode.org/reports/tr24/#Assignment_Script_Values
+#       https://www.unicode.org/reports/tr24/#Assignment_ScriptX_Values
+#
+
+RANGES = [
+    0x0000,  # .. 0x0040 ; Common
+    0x0041,  # .. 0x005A ; Latin
+    0x005B,  # .. 0x0060 ; Common
+    0x0061,  # .. 0x007A ; Latin
+    0x007B,  # .. 0x00A9 ; Common
+    0x00AA,  # .. 0x00AA ; Latin
+    0x00AB,  # .. 0x00B9 ; Common
+    0x00BA,  # .. 0x00BA ; Latin
+    0x00BB,  # .. 0x00BF ; Common
+    0x00C0,  # .. 0x00D6 ; Latin
+    0x00D7,  # .. 0x00D7 ; Common
+    0x00D8,  # .. 0x00F6 ; Latin
+    0x00F7,  # .. 0x00F7 ; Common
+    0x00F8,  # .. 0x02B8 ; Latin
+    0x02B9,  # .. 0x02DF ; Common
+    0x02E0,  # .. 0x02E4 ; Latin
+    0x02E5,  # .. 0x02E9 ; Common
+    0x02EA,  # .. 0x02EB ; Bopomofo
+    0x02EC,  # .. 0x02FF ; Common
+    0x0300,  # .. 0x036F ; Inherited
+    0x0370,  # .. 0x0373 ; Greek
+    0x0374,  # .. 0x0374 ; Common
+    0x0375,  # .. 0x0377 ; Greek
+    0x0378,  # .. 0x0379 ; Unknown
+    0x037A,  # .. 0x037D ; Greek
+    0x037E,  # .. 0x037E ; Common
+    0x037F,  # .. 0x037F ; Greek
+    0x0380,  # .. 0x0383 ; Unknown
+    0x0384,  # .. 0x0384 ; Greek
+    0x0385,  # .. 0x0385 ; Common
+    0x0386,  # .. 0x0386 ; Greek
+    0x0387,  # .. 0x0387 ; Common
+    0x0388,  # .. 0x038A ; Greek
+    0x038B,  # .. 0x038B ; Unknown
+    0x038C,  # .. 0x038C ; Greek
+    0x038D,  # .. 0x038D ; Unknown
+    0x038E,  # .. 0x03A1 ; Greek
+    0x03A2,  # .. 0x03A2 ; Unknown
+    0x03A3,  # .. 0x03E1 ; Greek
+    0x03E2,  # .. 0x03EF ; Coptic
+    0x03F0,  # .. 0x03FF ; Greek
+    0x0400,  # .. 0x0484 ; Cyrillic
+    0x0485,  # .. 0x0486 ; Inherited
+    0x0487,  # .. 0x052F ; Cyrillic
+    0x0530,  # .. 0x0530 ; Unknown
+    0x0531,  # .. 0x0556 ; Armenian
+    0x0557,  # .. 0x0558 ; Unknown
+    0x0559,  # .. 0x058A ; Armenian
+    0x058B,  # .. 0x058C ; Unknown
+    0x058D,  # .. 0x058F ; Armenian
+    0x0590,  # .. 0x0590 ; Unknown
+    0x0591,  # .. 0x05C7 ; Hebrew
+    0x05C8,  # .. 0x05CF ; Unknown
+    0x05D0,  # .. 0x05EA ; Hebrew
+    0x05EB,  # .. 0x05EE ; Unknown
+    0x05EF,  # .. 0x05F4 ; Hebrew
+    0x05F5,  # .. 0x05FF ; Unknown
+    0x0600,  # .. 0x0604 ; Arabic
+    0x0605,  # .. 0x0605 ; Common
+    0x0606,  # .. 0x060B ; Arabic
+    0x060C,  # .. 0x060C ; Common
+    0x060D,  # .. 0x061A ; Arabic
+    0x061B,  # .. 0x061B ; Common
+    0x061C,  # .. 0x061E ; Arabic
+    0x061F,  # .. 0x061F ; Common
+    0x0620,  # .. 0x063F ; Arabic
+    0x0640,  # .. 0x0640 ; Common
+    0x0641,  # .. 0x064A ; Arabic
+    0x064B,  # .. 0x0655 ; Inherited
+    0x0656,  # .. 0x066F ; Arabic
+    0x0670,  # .. 0x0670 ; Inherited
+    0x0671,  # .. 0x06DC ; Arabic
+    0x06DD,  # .. 0x06DD ; Common
+    0x06DE,  # .. 0x06FF ; Arabic
+    0x0700,  # .. 0x070D ; Syriac
+    0x070E,  # .. 0x070E ; Unknown
+    0x070F,  # .. 0x074A ; Syriac
+    0x074B,  # .. 0x074C ; Unknown
+    0x074D,  # .. 0x074F ; Syriac
+    0x0750,  # .. 0x077F ; Arabic
+    0x0780,  # .. 0x07B1 ; Thaana
+    0x07B2,  # .. 0x07BF ; Unknown
+    0x07C0,  # .. 0x07FA ; Nko
+    0x07FB,  # .. 0x07FC ; Unknown
+    0x07FD,  # .. 0x07FF ; Nko
+    0x0800,  # .. 0x082D ; Samaritan
+    0x082E,  # .. 0x082F ; Unknown
+    0x0830,  # .. 0x083E ; Samaritan
+    0x083F,  # .. 0x083F ; Unknown
+    0x0840,  # .. 0x085B ; Mandaic
+    0x085C,  # .. 0x085D ; Unknown
+    0x085E,  # .. 0x085E ; Mandaic
+    0x085F,  # .. 0x085F ; Unknown
+    0x0860,  # .. 0x086A ; Syriac
+    0x086B,  # .. 0x086F ; Unknown
+    0x0870,  # .. 0x0891 ; Arabic
+    0x0892,  # .. 0x0896 ; Unknown
+    0x0897,  # .. 0x08E1 ; Arabic
+    0x08E2,  # .. 0x08E2 ; Common
+    0x08E3,  # .. 0x08FF ; Arabic
+    0x0900,  # .. 0x0950 ; Devanagari
+    0x0951,  # .. 0x0954 ; Inherited
+    0x0955,  # .. 0x0963 ; Devanagari
+    0x0964,  # .. 0x0965 ; Common
+    0x0966,  # .. 0x097F ; Devanagari
+    0x0980,  # .. 0x0983 ; Bengali
+    0x0984,  # .. 0x0984 ; Unknown
+    0x0985,  # .. 0x098C ; Bengali
+    0x098D,  # .. 0x098E ; Unknown
+    0x098F,  # .. 0x0990 ; Bengali
+    0x0991,  # .. 0x0992 ; Unknown
+    0x0993,  # .. 0x09A8 ; Bengali
+    0x09A9,  # .. 0x09A9 ; Unknown
+    0x09AA,  # .. 0x09B0 ; Bengali
+    0x09B1,  # .. 0x09B1 ; Unknown
+    0x09B2,  # .. 0x09B2 ; Bengali
+    0x09B3,  # .. 0x09B5 ; Unknown
+    0x09B6,  # .. 0x09B9 ; Bengali
+    0x09BA,  # .. 0x09BB ; Unknown
+    0x09BC,  # .. 0x09C4 ; Bengali
+    0x09C5,  # .. 0x09C6 ; Unknown
+    0x09C7,  # .. 0x09C8 ; Bengali
+    0x09C9,  # .. 0x09CA ; Unknown
+    0x09CB,  # .. 0x09CE ; Bengali
+    0x09CF,  # .. 0x09D6 ; Unknown
+    0x09D7,  # .. 0x09D7 ; Bengali
+    0x09D8,  # .. 0x09DB ; Unknown
+    0x09DC,  # .. 0x09DD ; Bengali
+    0x09DE,  # .. 0x09DE ; Unknown
+    0x09DF,  # .. 0x09E3 ; Bengali
+    0x09E4,  # .. 0x09E5 ; Unknown
+    0x09E6,  # .. 0x09FE ; Bengali
+    0x09FF,  # .. 0x0A00 ; Unknown
+    0x0A01,  # .. 0x0A03 ; Gurmukhi
+    0x0A04,  # .. 0x0A04 ; Unknown
+    0x0A05,  # .. 0x0A0A ; Gurmukhi
+    0x0A0B,  # .. 0x0A0E ; Unknown
+    0x0A0F,  # .. 0x0A10 ; Gurmukhi
+    0x0A11,  # .. 0x0A12 ; Unknown
+    0x0A13,  # .. 0x0A28 ; Gurmukhi
+    0x0A29,  # .. 0x0A29 ; Unknown
+    0x0A2A,  # .. 0x0A30 ; Gurmukhi
+    0x0A31,  # .. 0x0A31 ; Unknown
+    0x0A32,  # .. 0x0A33 ; Gurmukhi
+    0x0A34,  # .. 0x0A34 ; Unknown
+    0x0A35,  # .. 0x0A36 ; Gurmukhi
+    0x0A37,  # .. 0x0A37 ; Unknown
+    0x0A38,  # .. 0x0A39 ; Gurmukhi
+    0x0A3A,  # .. 0x0A3B ; Unknown
+    0x0A3C,  # .. 0x0A3C ; Gurmukhi
+    0x0A3D,  # .. 0x0A3D ; Unknown
+    0x0A3E,  # .. 0x0A42 ; Gurmukhi
+    0x0A43,  # .. 0x0A46 ; Unknown
+    0x0A47,  # .. 0x0A48 ; Gurmukhi
+    0x0A49,  # .. 0x0A4A ; Unknown
+    0x0A4B,  # .. 0x0A4D ; Gurmukhi
+    0x0A4E,  # .. 0x0A50 ; Unknown
+    0x0A51,  # .. 0x0A51 ; Gurmukhi
+    0x0A52,  # .. 0x0A58 ; Unknown
+    0x0A59,  # .. 0x0A5C ; Gurmukhi
+    0x0A5D,  # .. 0x0A5D ; Unknown
+    0x0A5E,  # .. 0x0A5E ; Gurmukhi
+    0x0A5F,  # .. 0x0A65 ; Unknown
+    0x0A66,  # .. 0x0A76 ; Gurmukhi
+    0x0A77,  # .. 0x0A80 ; Unknown
+    0x0A81,  # .. 0x0A83 ; Gujarati
+    0x0A84,  # .. 0x0A84 ; Unknown
+    0x0A85,  # .. 0x0A8D ; Gujarati
+    0x0A8E,  # .. 0x0A8E ; Unknown
+    0x0A8F,  # .. 0x0A91 ; Gujarati
+    0x0A92,  # .. 0x0A92 ; Unknown
+    0x0A93,  # .. 0x0AA8 ; Gujarati
+    0x0AA9,  # .. 0x0AA9 ; Unknown
+    0x0AAA,  # .. 0x0AB0 ; Gujarati
+    0x0AB1,  # .. 0x0AB1 ; Unknown
+    0x0AB2,  # .. 0x0AB3 ; Gujarati
+    0x0AB4,  # .. 0x0AB4 ; Unknown
+    0x0AB5,  # .. 0x0AB9 ; Gujarati
+    0x0ABA,  # .. 0x0ABB ; Unknown
+    0x0ABC,  # .. 0x0AC5 ; Gujarati
+    0x0AC6,  # .. 0x0AC6 ; Unknown
+    0x0AC7,  # .. 0x0AC9 ; Gujarati
+    0x0ACA,  # .. 0x0ACA ; Unknown
+    0x0ACB,  # .. 0x0ACD ; Gujarati
+    0x0ACE,  # .. 0x0ACF ; Unknown
+    0x0AD0,  # .. 0x0AD0 ; Gujarati
+    0x0AD1,  # .. 0x0ADF ; Unknown
+    0x0AE0,  # .. 0x0AE3 ; Gujarati
+    0x0AE4,  # .. 0x0AE5 ; Unknown
+    0x0AE6,  # .. 0x0AF1 ; Gujarati
+    0x0AF2,  # .. 0x0AF8 ; Unknown
+    0x0AF9,  # .. 0x0AFF ; Gujarati
+    0x0B00,  # .. 0x0B00 ; Unknown
+    0x0B01,  # .. 0x0B03 ; Oriya
+    0x0B04,  # .. 0x0B04 ; Unknown
+    0x0B05,  # .. 0x0B0C ; Oriya
+    0x0B0D,  # .. 0x0B0E ; Unknown
+    0x0B0F,  # .. 0x0B10 ; Oriya
+    0x0B11,  # .. 0x0B12 ; Unknown
+    0x0B13,  # .. 0x0B28 ; Oriya
+    0x0B29,  # .. 0x0B29 ; Unknown
+    0x0B2A,  # .. 0x0B30 ; Oriya
+    0x0B31,  # .. 0x0B31 ; Unknown
+    0x0B32,  # .. 0x0B33 ; Oriya
+    0x0B34,  # .. 0x0B34 ; Unknown
+    0x0B35,  # .. 0x0B39 ; Oriya
+    0x0B3A,  # .. 0x0B3B ; Unknown
+    0x0B3C,  # .. 0x0B44 ; Oriya
+    0x0B45,  # .. 0x0B46 ; Unknown
+    0x0B47,  # .. 0x0B48 ; Oriya
+    0x0B49,  # .. 0x0B4A ; Unknown
+    0x0B4B,  # .. 0x0B4D ; Oriya
+    0x0B4E,  # .. 0x0B54 ; Unknown
+    0x0B55,  # .. 0x0B57 ; Oriya
+    0x0B58,  # .. 0x0B5B ; Unknown
+    0x0B5C,  # .. 0x0B5D ; Oriya
+    0x0B5E,  # .. 0x0B5E ; Unknown
+    0x0B5F,  # .. 0x0B63 ; Oriya
+    0x0B64,  # .. 0x0B65 ; Unknown
+    0x0B66,  # .. 0x0B77 ; Oriya
+    0x0B78,  # .. 0x0B81 ; Unknown
+    0x0B82,  # .. 0x0B83 ; Tamil
+    0x0B84,  # .. 0x0B84 ; Unknown
+    0x0B85,  # .. 0x0B8A ; Tamil
+    0x0B8B,  # .. 0x0B8D ; Unknown
+    0x0B8E,  # .. 0x0B90 ; Tamil
+    0x0B91,  # .. 0x0B91 ; Unknown
+    0x0B92,  # .. 0x0B95 ; Tamil
+    0x0B96,  # .. 0x0B98 ; Unknown
+    0x0B99,  # .. 0x0B9A ; Tamil
+    0x0B9B,  # .. 0x0B9B ; Unknown
+    0x0B9C,  # .. 0x0B9C ; Tamil
+    0x0B9D,  # .. 0x0B9D ; Unknown
+    0x0B9E,  # .. 0x0B9F ; Tamil
+    0x0BA0,  # .. 0x0BA2 ; Unknown
+    0x0BA3,  # .. 0x0BA4 ; Tamil
+    0x0BA5,  # .. 0x0BA7 ; Unknown
+    0x0BA8,  # .. 0x0BAA ; Tamil
+    0x0BAB,  # .. 0x0BAD ; Unknown
+    0x0BAE,  # .. 0x0BB9 ; Tamil
+    0x0BBA,  # .. 0x0BBD ; Unknown
+    0x0BBE,  # .. 0x0BC2 ; Tamil
+    0x0BC3,  # .. 0x0BC5 ; Unknown
+    0x0BC6,  # .. 0x0BC8 ; Tamil
+    0x0BC9,  # .. 0x0BC9 ; Unknown
+    0x0BCA,  # .. 0x0BCD ; Tamil
+    0x0BCE,  # .. 0x0BCF ; Unknown
+    0x0BD0,  # .. 0x0BD0 ; Tamil
+    0x0BD1,  # .. 0x0BD6 ; Unknown
+    0x0BD7,  # .. 0x0BD7 ; Tamil
+    0x0BD8,  # .. 0x0BE5 ; Unknown
+    0x0BE6,  # .. 0x0BFA ; Tamil
+    0x0BFB,  # .. 0x0BFF ; Unknown
+    0x0C00,  # .. 0x0C0C ; Telugu
+    0x0C0D,  # .. 0x0C0D ; Unknown
+    0x0C0E,  # .. 0x0C10 ; Telugu
+    0x0C11,  # .. 0x0C11 ; Unknown
+    0x0C12,  # .. 0x0C28 ; Telugu
+    0x0C29,  # .. 0x0C29 ; Unknown
+    0x0C2A,  # .. 0x0C39 ; Telugu
+    0x0C3A,  # .. 0x0C3B ; Unknown
+    0x0C3C,  # .. 0x0C44 ; Telugu
+    0x0C45,  # .. 0x0C45 ; Unknown
+    0x0C46,  # .. 0x0C48 ; Telugu
+    0x0C49,  # .. 0x0C49 ; Unknown
+    0x0C4A,  # .. 0x0C4D ; Telugu
+    0x0C4E,  # .. 0x0C54 ; Unknown
+    0x0C55,  # .. 0x0C56 ; Telugu
+    0x0C57,  # .. 0x0C57 ; Unknown
+    0x0C58,  # .. 0x0C5A ; Telugu
+    0x0C5B,  # .. 0x0C5B ; Unknown
+    0x0C5C,  # .. 0x0C5D ; Telugu
+    0x0C5E,  # .. 0x0C5F ; Unknown
+    0x0C60,  # .. 0x0C63 ; Telugu
+    0x0C64,  # .. 0x0C65 ; Unknown
+    0x0C66,  # .. 0x0C6F ; Telugu
+    0x0C70,  # .. 0x0C76 ; Unknown
+    0x0C77,  # .. 0x0C7F ; Telugu
+    0x0C80,  # .. 0x0C8C ; Kannada
+    0x0C8D,  # .. 0x0C8D ; Unknown
+    0x0C8E,  # .. 0x0C90 ; Kannada
+    0x0C91,  # .. 0x0C91 ; Unknown
+    0x0C92,  # .. 0x0CA8 ; Kannada
+    0x0CA9,  # .. 0x0CA9 ; Unknown
+    0x0CAA,  # .. 0x0CB3 ; Kannada
+    0x0CB4,  # .. 0x0CB4 ; Unknown
+    0x0CB5,  # .. 0x0CB9 ; Kannada
+    0x0CBA,  # .. 0x0CBB ; Unknown
+    0x0CBC,  # .. 0x0CC4 ; Kannada
+    0x0CC5,  # .. 0x0CC5 ; Unknown
+    0x0CC6,  # .. 0x0CC8 ; Kannada
+    0x0CC9,  # .. 0x0CC9 ; Unknown
+    0x0CCA,  # .. 0x0CCD ; Kannada
+    0x0CCE,  # .. 0x0CD4 ; Unknown
+    0x0CD5,  # .. 0x0CD6 ; Kannada
+    0x0CD7,  # .. 0x0CDB ; Unknown
+    0x0CDC,  # .. 0x0CDE ; Kannada
+    0x0CDF,  # .. 0x0CDF ; Unknown
+    0x0CE0,  # .. 0x0CE3 ; Kannada
+    0x0CE4,  # .. 0x0CE5 ; Unknown
+    0x0CE6,  # .. 0x0CEF ; Kannada
+    0x0CF0,  # .. 0x0CF0 ; Unknown
+    0x0CF1,  # .. 0x0CF3 ; Kannada
+    0x0CF4,  # .. 0x0CFF ; Unknown
+    0x0D00,  # .. 0x0D0C ; Malayalam
+    0x0D0D,  # .. 0x0D0D ; Unknown
+    0x0D0E,  # .. 0x0D10 ; Malayalam
+    0x0D11,  # .. 0x0D11 ; Unknown
+    0x0D12,  # .. 0x0D44 ; Malayalam
+    0x0D45,  # .. 0x0D45 ; Unknown
+    0x0D46,  # .. 0x0D48 ; Malayalam
+    0x0D49,  # .. 0x0D49 ; Unknown
+    0x0D4A,  # .. 0x0D4F ; Malayalam
+    0x0D50,  # .. 0x0D53 ; Unknown
+    0x0D54,  # .. 0x0D63 ; Malayalam
+    0x0D64,  # .. 0x0D65 ; Unknown
+    0x0D66,  # .. 0x0D7F ; Malayalam
+    0x0D80,  # .. 0x0D80 ; Unknown
+    0x0D81,  # .. 0x0D83 ; Sinhala
+    0x0D84,  # .. 0x0D84 ; Unknown
+    0x0D85,  # .. 0x0D96 ; Sinhala
+    0x0D97,  # .. 0x0D99 ; Unknown
+    0x0D9A,  # .. 0x0DB1 ; Sinhala
+    0x0DB2,  # .. 0x0DB2 ; Unknown
+    0x0DB3,  # .. 0x0DBB ; Sinhala
+    0x0DBC,  # .. 0x0DBC ; Unknown
+    0x0DBD,  # .. 0x0DBD ; Sinhala
+    0x0DBE,  # .. 0x0DBF ; Unknown
+    0x0DC0,  # .. 0x0DC6 ; Sinhala
+    0x0DC7,  # .. 0x0DC9 ; Unknown
+    0x0DCA,  # .. 0x0DCA ; Sinhala
+    0x0DCB,  # .. 0x0DCE ; Unknown
+    0x0DCF,  # .. 0x0DD4 ; Sinhala
+    0x0DD5,  # .. 0x0DD5 ; Unknown
+    0x0DD6,  # .. 0x0DD6 ; Sinhala
+    0x0DD7,  # .. 0x0DD7 ; Unknown
+    0x0DD8,  # .. 0x0DDF ; Sinhala
+    0x0DE0,  # .. 0x0DE5 ; Unknown
+    0x0DE6,  # .. 0x0DEF ; Sinhala
+    0x0DF0,  # .. 0x0DF1 ; Unknown
+    0x0DF2,  # .. 0x0DF4 ; Sinhala
+    0x0DF5,  # .. 0x0E00 ; Unknown
+    0x0E01,  # .. 0x0E3A ; Thai
+    0x0E3B,  # .. 0x0E3E ; Unknown
+    0x0E3F,  # .. 0x0E3F ; Common
+    0x0E40,  # .. 0x0E5B ; Thai
+    0x0E5C,  # .. 0x0E80 ; Unknown
+    0x0E81,  # .. 0x0E82 ; Lao
+    0x0E83,  # .. 0x0E83 ; Unknown
+    0x0E84,  # .. 0x0E84 ; Lao
+    0x0E85,  # .. 0x0E85 ; Unknown
+    0x0E86,  # .. 0x0E8A ; Lao
+    0x0E8B,  # .. 0x0E8B ; Unknown
+    0x0E8C,  # .. 0x0EA3 ; Lao
+    0x0EA4,  # .. 0x0EA4 ; Unknown
+    0x0EA5,  # .. 0x0EA5 ; Lao
+    0x0EA6,  # .. 0x0EA6 ; Unknown
+    0x0EA7,  # .. 0x0EBD ; Lao
+    0x0EBE,  # .. 0x0EBF ; Unknown
+    0x0EC0,  # .. 0x0EC4 ; Lao
+    0x0EC5,  # .. 0x0EC5 ; Unknown
+    0x0EC6,  # .. 0x0EC6 ; Lao
+    0x0EC7,  # .. 0x0EC7 ; Unknown
+    0x0EC8,  # .. 0x0ECE ; Lao
+    0x0ECF,  # .. 0x0ECF ; Unknown
+    0x0ED0,  # .. 0x0ED9 ; Lao
+    0x0EDA,  # .. 0x0EDB ; Unknown
+    0x0EDC,  # .. 0x0EDF ; Lao
+    0x0EE0,  # .. 0x0EFF ; Unknown
+    0x0F00,  # .. 0x0F47 ; Tibetan
+    0x0F48,  # .. 0x0F48 ; Unknown
+    0x0F49,  # .. 0x0F6C ; Tibetan
+    0x0F6D,  # .. 0x0F70 ; Unknown
+    0x0F71,  # .. 0x0F97 ; Tibetan
+    0x0F98,  # .. 0x0F98 ; Unknown
+    0x0F99,  # .. 0x0FBC ; Tibetan
+    0x0FBD,  # .. 0x0FBD ; Unknown
+    0x0FBE,  # .. 0x0FCC ; Tibetan
+    0x0FCD,  # .. 0x0FCD ; Unknown
+    0x0FCE,  # .. 0x0FD4 ; Tibetan
+    0x0FD5,  # .. 0x0FD8 ; Common
+    0x0FD9,  # .. 0x0FDA ; Tibetan
+    0x0FDB,  # .. 0x0FFF ; Unknown
+    0x1000,  # .. 0x109F ; Myanmar
+    0x10A0,  # .. 0x10C5 ; Georgian
+    0x10C6,  # .. 0x10C6 ; Unknown
+    0x10C7,  # .. 0x10C7 ; Georgian
+    0x10C8,  # .. 0x10CC ; Unknown
+    0x10CD,  # .. 0x10CD ; Georgian
+    0x10CE,  # .. 0x10CF ; Unknown
+    0x10D0,  # .. 0x10FA ; Georgian
+    0x10FB,  # .. 0x10FB ; Common
+    0x10FC,  # .. 0x10FF ; Georgian
+    0x1100,  # .. 0x11FF ; Hangul
+    0x1200,  # .. 0x1248 ; Ethiopic
+    0x1249,  # .. 0x1249 ; Unknown
+    0x124A,  # .. 0x124D ; Ethiopic
+    0x124E,  # .. 0x124F ; Unknown
+    0x1250,  # .. 0x1256 ; Ethiopic
+    0x1257,  # .. 0x1257 ; Unknown
+    0x1258,  # .. 0x1258 ; Ethiopic
+    0x1259,  # .. 0x1259 ; Unknown
+    0x125A,  # .. 0x125D ; Ethiopic
+    0x125E,  # .. 0x125F ; Unknown
+    0x1260,  # .. 0x1288 ; Ethiopic
+    0x1289,  # .. 0x1289 ; Unknown
+    0x128A,  # .. 0x128D ; Ethiopic
+    0x128E,  # .. 0x128F ; Unknown
+    0x1290,  # .. 0x12B0 ; Ethiopic
+    0x12B1,  # .. 0x12B1 ; Unknown
+    0x12B2,  # .. 0x12B5 ; Ethiopic
+    0x12B6,  # .. 0x12B7 ; Unknown
+    0x12B8,  # .. 0x12BE ; Ethiopic
+    0x12BF,  # .. 0x12BF ; Unknown
+    0x12C0,  # .. 0x12C0 ; Ethiopic
+    0x12C1,  # .. 0x12C1 ; Unknown
+    0x12C2,  # .. 0x12C5 ; Ethiopic
+    0x12C6,  # .. 0x12C7 ; Unknown
+    0x12C8,  # .. 0x12D6 ; Ethiopic
+    0x12D7,  # .. 0x12D7 ; Unknown
+    0x12D8,  # .. 0x1310 ; Ethiopic
+    0x1311,  # .. 0x1311 ; Unknown
+    0x1312,  # .. 0x1315 ; Ethiopic
+    0x1316,  # .. 0x1317 ; Unknown
+    0x1318,  # .. 0x135A ; Ethiopic
+    0x135B,  # .. 0x135C ; Unknown
+    0x135D,  # .. 0x137C ; Ethiopic
+    0x137D,  # .. 0x137F ; Unknown
+    0x1380,  # .. 0x1399 ; Ethiopic
+    0x139A,  # .. 0x139F ; Unknown
+    0x13A0,  # .. 0x13F5 ; Cherokee
+    0x13F6,  # .. 0x13F7 ; Unknown
+    0x13F8,  # .. 0x13FD ; Cherokee
+    0x13FE,  # .. 0x13FF ; Unknown
+    0x1400,  # .. 0x167F ; Canadian_Aboriginal
+    0x1680,  # .. 0x169C ; Ogham
+    0x169D,  # .. 0x169F ; Unknown
+    0x16A0,  # .. 0x16EA ; Runic
+    0x16EB,  # .. 0x16ED ; Common
+    0x16EE,  # .. 0x16F8 ; Runic
+    0x16F9,  # .. 0x16FF ; Unknown
+    0x1700,  # .. 0x1715 ; Tagalog
+    0x1716,  # .. 0x171E ; Unknown
+    0x171F,  # .. 0x171F ; Tagalog
+    0x1720,  # .. 0x1734 ; Hanunoo
+    0x1735,  # .. 0x1736 ; Common
+    0x1737,  # .. 0x173F ; Unknown
+    0x1740,  # .. 0x1753 ; Buhid
+    0x1754,  # .. 0x175F ; Unknown
+    0x1760,  # .. 0x176C ; Tagbanwa
+    0x176D,  # .. 0x176D ; Unknown
+    0x176E,  # .. 0x1770 ; Tagbanwa
+    0x1771,  # .. 0x1771 ; Unknown
+    0x1772,  # .. 0x1773 ; Tagbanwa
+    0x1774,  # .. 0x177F ; Unknown
+    0x1780,  # .. 0x17DD ; Khmer
+    0x17DE,  # .. 0x17DF ; Unknown
+    0x17E0,  # .. 0x17E9 ; Khmer
+    0x17EA,  # .. 0x17EF ; Unknown
+    0x17F0,  # .. 0x17F9 ; Khmer
+    0x17FA,  # .. 0x17FF ; Unknown
+    0x1800,  # .. 0x1801 ; Mongolian
+    0x1802,  # .. 0x1803 ; Common
+    0x1804,  # .. 0x1804 ; Mongolian
+    0x1805,  # .. 0x1805 ; Common
+    0x1806,  # .. 0x1819 ; Mongolian
+    0x181A,  # .. 0x181F ; Unknown
+    0x1820,  # .. 0x1878 ; Mongolian
+    0x1879,  # .. 0x187F ; Unknown
+    0x1880,  # .. 0x18AA ; Mongolian
+    0x18AB,  # .. 0x18AF ; Unknown
+    0x18B0,  # .. 0x18F5 ; Canadian_Aboriginal
+    0x18F6,  # .. 0x18FF ; Unknown
+    0x1900,  # .. 0x191E ; Limbu
+    0x191F,  # .. 0x191F ; Unknown
+    0x1920,  # .. 0x192B ; Limbu
+    0x192C,  # .. 0x192F ; Unknown
+    0x1930,  # .. 0x193B ; Limbu
+    0x193C,  # .. 0x193F ; Unknown
+    0x1940,  # .. 0x1940 ; Limbu
+    0x1941,  # .. 0x1943 ; Unknown
+    0x1944,  # .. 0x194F ; Limbu
+    0x1950,  # .. 0x196D ; Tai_Le
+    0x196E,  # .. 0x196F ; Unknown
+    0x1970,  # .. 0x1974 ; Tai_Le
+    0x1975,  # .. 0x197F ; Unknown
+    0x1980,  # .. 0x19AB ; New_Tai_Lue
+    0x19AC,  # .. 0x19AF ; Unknown
+    0x19B0,  # .. 0x19C9 ; New_Tai_Lue
+    0x19CA,  # .. 0x19CF ; Unknown
+    0x19D0,  # .. 0x19DA ; New_Tai_Lue
+    0x19DB,  # .. 0x19DD ; Unknown
+    0x19DE,  # .. 0x19DF ; New_Tai_Lue
+    0x19E0,  # .. 0x19FF ; Khmer
+    0x1A00,  # .. 0x1A1B ; Buginese
+    0x1A1C,  # .. 0x1A1D ; Unknown
+    0x1A1E,  # .. 0x1A1F ; Buginese
+    0x1A20,  # .. 0x1A5E ; Tai_Tham
+    0x1A5F,  # .. 0x1A5F ; Unknown
+    0x1A60,  # .. 0x1A7C ; Tai_Tham
+    0x1A7D,  # .. 0x1A7E ; Unknown
+    0x1A7F,  # .. 0x1A89 ; Tai_Tham
+    0x1A8A,  # .. 0x1A8F ; Unknown
+    0x1A90,  # .. 0x1A99 ; Tai_Tham
+    0x1A9A,  # .. 0x1A9F ; Unknown
+    0x1AA0,  # .. 0x1AAD ; Tai_Tham
+    0x1AAE,  # .. 0x1AAF ; Unknown
+    0x1AB0,  # .. 0x1ADD ; Inherited
+    0x1ADE,  # .. 0x1ADF ; Unknown
+    0x1AE0,  # .. 0x1AEB ; Inherited
+    0x1AEC,  # .. 0x1AFF ; Unknown
+    0x1B00,  # .. 0x1B4C ; Balinese
+    0x1B4D,  # .. 0x1B4D ; Unknown
+    0x1B4E,  # .. 0x1B7F ; Balinese
+    0x1B80,  # .. 0x1BBF ; Sundanese
+    0x1BC0,  # .. 0x1BF3 ; Batak
+    0x1BF4,  # .. 0x1BFB ; Unknown
+    0x1BFC,  # .. 0x1BFF ; Batak
+    0x1C00,  # .. 0x1C37 ; Lepcha
+    0x1C38,  # .. 0x1C3A ; Unknown
+    0x1C3B,  # .. 0x1C49 ; Lepcha
+    0x1C4A,  # .. 0x1C4C ; Unknown
+    0x1C4D,  # .. 0x1C4F ; Lepcha
+    0x1C50,  # .. 0x1C7F ; Ol_Chiki
+    0x1C80,  # .. 0x1C8A ; Cyrillic
+    0x1C8B,  # .. 0x1C8F ; Unknown
+    0x1C90,  # .. 0x1CBA ; Georgian
+    0x1CBB,  # .. 0x1CBC ; Unknown
+    0x1CBD,  # .. 0x1CBF ; Georgian
+    0x1CC0,  # .. 0x1CC7 ; Sundanese
+    0x1CC8,  # .. 0x1CCF ; Unknown
+    0x1CD0,  # .. 0x1CD2 ; Inherited
+    0x1CD3,  # .. 0x1CD3 ; Common
+    0x1CD4,  # .. 0x1CE0 ; Inherited
+    0x1CE1,  # .. 0x1CE1 ; Common
+    0x1CE2,  # .. 0x1CE8 ; Inherited
+    0x1CE9,  # .. 0x1CEC ; Common
+    0x1CED,  # .. 0x1CED ; Inherited
+    0x1CEE,  # .. 0x1CF3 ; Common
+    0x1CF4,  # .. 0x1CF4 ; Inherited
+    0x1CF5,  # .. 0x1CF7 ; Common
+    0x1CF8,  # .. 0x1CF9 ; Inherited
+    0x1CFA,  # .. 0x1CFA ; Common
+    0x1CFB,  # .. 0x1CFF ; Unknown
+    0x1D00,  # .. 0x1D25 ; Latin
+    0x1D26,  # .. 0x1D2A ; Greek
+    0x1D2B,  # .. 0x1D2B ; Cyrillic
+    0x1D2C,  # .. 0x1D5C ; Latin
+    0x1D5D,  # .. 0x1D61 ; Greek
+    0x1D62,  # .. 0x1D65 ; Latin
+    0x1D66,  # .. 0x1D6A ; Greek
+    0x1D6B,  # .. 0x1D77 ; Latin
+    0x1D78,  # .. 0x1D78 ; Cyrillic
+    0x1D79,  # .. 0x1DBE ; Latin
+    0x1DBF,  # .. 0x1DBF ; Greek
+    0x1DC0,  # .. 0x1DFF ; Inherited
+    0x1E00,  # .. 0x1EFF ; Latin
+    0x1F00,  # .. 0x1F15 ; Greek
+    0x1F16,  # .. 0x1F17 ; Unknown
+    0x1F18,  # .. 0x1F1D ; Greek
+    0x1F1E,  # .. 0x1F1F ; Unknown
+    0x1F20,  # .. 0x1F45 ; Greek
+    0x1F46,  # .. 0x1F47 ; Unknown
+    0x1F48,  # .. 0x1F4D ; Greek
+    0x1F4E,  # .. 0x1F4F ; Unknown
+    0x1F50,  # .. 0x1F57 ; Greek
+    0x1F58,  # .. 0x1F58 ; Unknown
+    0x1F59,  # .. 0x1F59 ; Greek
+    0x1F5A,  # .. 0x1F5A ; Unknown
+    0x1F5B,  # .. 0x1F5B ; Greek
+    0x1F5C,  # .. 0x1F5C ; Unknown
+    0x1F5D,  # .. 0x1F5D ; Greek
+    0x1F5E,  # .. 0x1F5E ; Unknown
+    0x1F5F,  # .. 0x1F7D ; Greek
+    0x1F7E,  # .. 0x1F7F ; Unknown
+    0x1F80,  # .. 0x1FB4 ; Greek
+    0x1FB5,  # .. 0x1FB5 ; Unknown
+    0x1FB6,  # .. 0x1FC4 ; Greek
+    0x1FC5,  # .. 0x1FC5 ; Unknown
+    0x1FC6,  # .. 0x1FD3 ; Greek
+    0x1FD4,  # .. 0x1FD5 ; Unknown
+    0x1FD6,  # .. 0x1FDB ; Greek
+    0x1FDC,  # .. 0x1FDC ; Unknown
+    0x1FDD,  # .. 0x1FEF ; Greek
+    0x1FF0,  # .. 0x1FF1 ; Unknown
+    0x1FF2,  # .. 0x1FF4 ; Greek
+    0x1FF5,  # .. 0x1FF5 ; Unknown
+    0x1FF6,  # .. 0x1FFE ; Greek
+    0x1FFF,  # .. 0x1FFF ; Unknown
+    0x2000,  # .. 0x200B ; Common
+    0x200C,  # .. 0x200D ; Inherited
+    0x200E,  # .. 0x2064 ; Common
+    0x2065,  # .. 0x2065 ; Unknown
+    0x2066,  # .. 0x2070 ; Common
+    0x2071,  # .. 0x2071 ; Latin
+    0x2072,  # .. 0x2073 ; Unknown
+    0x2074,  # .. 0x207E ; Common
+    0x207F,  # .. 0x207F ; Latin
+    0x2080,  # .. 0x208E ; Common
+    0x208F,  # .. 0x208F ; Unknown
+    0x2090,  # .. 0x209C ; Latin
+    0x209D,  # .. 0x209F ; Unknown
+    0x20A0,  # .. 0x20C1 ; Common
+    0x20C2,  # .. 0x20CF ; Unknown
+    0x20D0,  # .. 0x20F0 ; Inherited
+    0x20F1,  # .. 0x20FF ; Unknown
+    0x2100,  # .. 0x2125 ; Common
+    0x2126,  # .. 0x2126 ; Greek
+    0x2127,  # .. 0x2129 ; Common
+    0x212A,  # .. 0x212B ; Latin
+    0x212C,  # .. 0x2131 ; Common
+    0x2132,  # .. 0x2132 ; Latin
+    0x2133,  # .. 0x214D ; Common
+    0x214E,  # .. 0x214E ; Latin
+    0x214F,  # .. 0x215F ; Common
+    0x2160,  # .. 0x2188 ; Latin
+    0x2189,  # .. 0x218B ; Common
+    0x218C,  # .. 0x218F ; Unknown
+    0x2190,  # .. 0x2429 ; Common
+    0x242A,  # .. 0x243F ; Unknown
+    0x2440,  # .. 0x244A ; Common
+    0x244B,  # .. 0x245F ; Unknown
+    0x2460,  # .. 0x27FF ; Common
+    0x2800,  # .. 0x28FF ; Braille
+    0x2900,  # .. 0x2B73 ; Common
+    0x2B74,  # .. 0x2B75 ; Unknown
+    0x2B76,  # .. 0x2BFF ; Common
+    0x2C00,  # .. 0x2C5F ; Glagolitic
+    0x2C60,  # .. 0x2C7F ; Latin
+    0x2C80,  # .. 0x2CF3 ; Coptic
+    0x2CF4,  # .. 0x2CF8 ; Unknown
+    0x2CF9,  # .. 0x2CFF ; Coptic
+    0x2D00,  # .. 0x2D25 ; Georgian
+    0x2D26,  # .. 0x2D26 ; Unknown
+    0x2D27,  # .. 0x2D27 ; Georgian
+    0x2D28,  # .. 0x2D2C ; Unknown
+    0x2D2D,  # .. 0x2D2D ; Georgian
+    0x2D2E,  # .. 0x2D2F ; Unknown
+    0x2D30,  # .. 0x2D67 ; Tifinagh
+    0x2D68,  # .. 0x2D6E ; Unknown
+    0x2D6F,  # .. 0x2D70 ; Tifinagh
+    0x2D71,  # .. 0x2D7E ; Unknown
+    0x2D7F,  # .. 0x2D7F ; Tifinagh
+    0x2D80,  # .. 0x2D96 ; Ethiopic
+    0x2D97,  # .. 0x2D9F ; Unknown
+    0x2DA0,  # .. 0x2DA6 ; Ethiopic
+    0x2DA7,  # .. 0x2DA7 ; Unknown
+    0x2DA8,  # .. 0x2DAE ; Ethiopic
+    0x2DAF,  # .. 0x2DAF ; Unknown
+    0x2DB0,  # .. 0x2DB6 ; Ethiopic
+    0x2DB7,  # .. 0x2DB7 ; Unknown
+    0x2DB8,  # .. 0x2DBE ; Ethiopic
+    0x2DBF,  # .. 0x2DBF ; Unknown
+    0x2DC0,  # .. 0x2DC6 ; Ethiopic
+    0x2DC7,  # .. 0x2DC7 ; Unknown
+    0x2DC8,  # .. 0x2DCE ; Ethiopic
+    0x2DCF,  # .. 0x2DCF ; Unknown
+    0x2DD0,  # .. 0x2DD6 ; Ethiopic
+    0x2DD7,  # .. 0x2DD7 ; Unknown
+    0x2DD8,  # .. 0x2DDE ; Ethiopic
+    0x2DDF,  # .. 0x2DDF ; Unknown
+    0x2DE0,  # .. 0x2DFF ; Cyrillic
+    0x2E00,  # .. 0x2E5D ; Common
+    0x2E5E,  # .. 0x2E7F ; Unknown
+    0x2E80,  # .. 0x2E99 ; Han
+    0x2E9A,  # .. 0x2E9A ; Unknown
+    0x2E9B,  # .. 0x2EF3 ; Han
+    0x2EF4,  # .. 0x2EFF ; Unknown
+    0x2F00,  # .. 0x2FD5 ; Han
+    0x2FD6,  # .. 0x2FEF ; Unknown
+    0x2FF0,  # .. 0x3004 ; Common
+    0x3005,  # .. 0x3005 ; Han
+    0x3006,  # .. 0x3006 ; Common
+    0x3007,  # .. 0x3007 ; Han
+    0x3008,  # .. 0x3020 ; Common
+    0x3021,  # .. 0x3029 ; Han
+    0x302A,  # .. 0x302D ; Inherited
+    0x302E,  # .. 0x302F ; Hangul
+    0x3030,  # .. 0x3037 ; Common
+    0x3038,  # .. 0x303B ; Han
+    0x303C,  # .. 0x303F ; Common
+    0x3040,  # .. 0x3040 ; Unknown
+    0x3041,  # .. 0x3096 ; Hiragana
+    0x3097,  # .. 0x3098 ; Unknown
+    0x3099,  # .. 0x309A ; Inherited
+    0x309B,  # .. 0x309C ; Common
+    0x309D,  # .. 0x309F ; Hiragana
+    0x30A0,  # .. 0x30A0 ; Common
+    0x30A1,  # .. 0x30FA ; Katakana
+    0x30FB,  # .. 0x30FC ; Common
+    0x30FD,  # .. 0x30FF ; Katakana
+    0x3100,  # .. 0x3104 ; Unknown
+    0x3105,  # .. 0x312F ; Bopomofo
+    0x3130,  # .. 0x3130 ; Unknown
+    0x3131,  # .. 0x318E ; Hangul
+    0x318F,  # .. 0x318F ; Unknown
+    0x3190,  # .. 0x319F ; Common
+    0x31A0,  # .. 0x31BF ; Bopomofo
+    0x31C0,  # .. 0x31E5 ; Common
+    0x31E6,  # .. 0x31EE ; Unknown
+    0x31EF,  # .. 0x31EF ; Common
+    0x31F0,  # .. 0x31FF ; Katakana
+    0x3200,  # .. 0x321E ; Hangul
+    0x321F,  # .. 0x321F ; Unknown
+    0x3220,  # .. 0x325F ; Common
+    0x3260,  # .. 0x327E ; Hangul
+    0x327F,  # .. 0x32CF ; Common
+    0x32D0,  # .. 0x32FE ; Katakana
+    0x32FF,  # .. 0x32FF ; Common
+    0x3300,  # .. 0x3357 ; Katakana
+    0x3358,  # .. 0x33FF ; Common
+    0x3400,  # .. 0x4DBF ; Han
+    0x4DC0,  # .. 0x4DFF ; Common
+    0x4E00,  # .. 0x9FFF ; Han
+    0xA000,  # .. 0xA48C ; Yi
+    0xA48D,  # .. 0xA48F ; Unknown
+    0xA490,  # .. 0xA4C6 ; Yi
+    0xA4C7,  # .. 0xA4CF ; Unknown
+    0xA4D0,  # .. 0xA4FF ; Lisu
+    0xA500,  # .. 0xA62B ; Vai
+    0xA62C,  # .. 0xA63F ; Unknown
+    0xA640,  # .. 0xA69F ; Cyrillic
+    0xA6A0,  # .. 0xA6F7 ; Bamum
+    0xA6F8,  # .. 0xA6FF ; Unknown
+    0xA700,  # .. 0xA721 ; Common
+    0xA722,  # .. 0xA787 ; Latin
+    0xA788,  # .. 0xA78A ; Common
+    0xA78B,  # .. 0xA7DC ; Latin
+    0xA7DD,  # .. 0xA7F0 ; Unknown
+    0xA7F1,  # .. 0xA7FF ; Latin
+    0xA800,  # .. 0xA82C ; Syloti_Nagri
+    0xA82D,  # .. 0xA82F ; Unknown
+    0xA830,  # .. 0xA839 ; Common
+    0xA83A,  # .. 0xA83F ; Unknown
+    0xA840,  # .. 0xA877 ; Phags_Pa
+    0xA878,  # .. 0xA87F ; Unknown
+    0xA880,  # .. 0xA8C5 ; Saurashtra
+    0xA8C6,  # .. 0xA8CD ; Unknown
+    0xA8CE,  # .. 0xA8D9 ; Saurashtra
+    0xA8DA,  # .. 0xA8DF ; Unknown
+    0xA8E0,  # .. 0xA8FF ; Devanagari
+    0xA900,  # .. 0xA92D ; Kayah_Li
+    0xA92E,  # .. 0xA92E ; Common
+    0xA92F,  # .. 0xA92F ; Kayah_Li
+    0xA930,  # .. 0xA953 ; Rejang
+    0xA954,  # .. 0xA95E ; Unknown
+    0xA95F,  # .. 0xA95F ; Rejang
+    0xA960,  # .. 0xA97C ; Hangul
+    0xA97D,  # .. 0xA97F ; Unknown
+    0xA980,  # .. 0xA9CD ; Javanese
+    0xA9CE,  # .. 0xA9CE ; Unknown
+    0xA9CF,  # .. 0xA9CF ; Common
+    0xA9D0,  # .. 0xA9D9 ; Javanese
+    0xA9DA,  # .. 0xA9DD ; Unknown
+    0xA9DE,  # .. 0xA9DF ; Javanese
+    0xA9E0,  # .. 0xA9FE ; Myanmar
+    0xA9FF,  # .. 0xA9FF ; Unknown
+    0xAA00,  # .. 0xAA36 ; Cham
+    0xAA37,  # .. 0xAA3F ; Unknown
+    0xAA40,  # .. 0xAA4D ; Cham
+    0xAA4E,  # .. 0xAA4F ; Unknown
+    0xAA50,  # .. 0xAA59 ; Cham
+    0xAA5A,  # .. 0xAA5B ; Unknown
+    0xAA5C,  # .. 0xAA5F ; Cham
+    0xAA60,  # .. 0xAA7F ; Myanmar
+    0xAA80,  # .. 0xAAC2 ; Tai_Viet
+    0xAAC3,  # .. 0xAADA ; Unknown
+    0xAADB,  # .. 0xAADF ; Tai_Viet
+    0xAAE0,  # .. 0xAAF6 ; Meetei_Mayek
+    0xAAF7,  # .. 0xAB00 ; Unknown
+    0xAB01,  # .. 0xAB06 ; Ethiopic
+    0xAB07,  # .. 0xAB08 ; Unknown
+    0xAB09,  # .. 0xAB0E ; Ethiopic
+    0xAB0F,  # .. 0xAB10 ; Unknown
+    0xAB11,  # .. 0xAB16 ; Ethiopic
+    0xAB17,  # .. 0xAB1F ; Unknown
+    0xAB20,  # .. 0xAB26 ; Ethiopic
+    0xAB27,  # .. 0xAB27 ; Unknown
+    0xAB28,  # .. 0xAB2E ; Ethiopic
+    0xAB2F,  # .. 0xAB2F ; Unknown
+    0xAB30,  # .. 0xAB5A ; Latin
+    0xAB5B,  # .. 0xAB5B ; Common
+    0xAB5C,  # .. 0xAB64 ; Latin
+    0xAB65,  # .. 0xAB65 ; Greek
+    0xAB66,  # .. 0xAB69 ; Latin
+    0xAB6A,  # .. 0xAB6B ; Common
+    0xAB6C,  # .. 0xAB6F ; Unknown
+    0xAB70,  # .. 0xABBF ; Cherokee
+    0xABC0,  # .. 0xABED ; Meetei_Mayek
+    0xABEE,  # .. 0xABEF ; Unknown
+    0xABF0,  # .. 0xABF9 ; Meetei_Mayek
+    0xABFA,  # .. 0xABFF ; Unknown
+    0xAC00,  # .. 0xD7A3 ; Hangul
+    0xD7A4,  # .. 0xD7AF ; Unknown
+    0xD7B0,  # .. 0xD7C6 ; Hangul
+    0xD7C7,  # .. 0xD7CA ; Unknown
+    0xD7CB,  # .. 0xD7FB ; Hangul
+    0xD7FC,  # .. 0xF8FF ; Unknown
+    0xF900,  # .. 0xFA6D ; Han
+    0xFA6E,  # .. 0xFA6F ; Unknown
+    0xFA70,  # .. 0xFAD9 ; Han
+    0xFADA,  # .. 0xFAFF ; Unknown
+    0xFB00,  # .. 0xFB06 ; Latin
+    0xFB07,  # .. 0xFB12 ; Unknown
+    0xFB13,  # .. 0xFB17 ; Armenian
+    0xFB18,  # .. 0xFB1C ; Unknown
+    0xFB1D,  # .. 0xFB36 ; Hebrew
+    0xFB37,  # .. 0xFB37 ; Unknown
+    0xFB38,  # .. 0xFB3C ; Hebrew
+    0xFB3D,  # .. 0xFB3D ; Unknown
+    0xFB3E,  # .. 0xFB3E ; Hebrew
+    0xFB3F,  # .. 0xFB3F ; Unknown
+    0xFB40,  # .. 0xFB41 ; Hebrew
+    0xFB42,  # .. 0xFB42 ; Unknown
+    0xFB43,  # .. 0xFB44 ; Hebrew
+    0xFB45,  # .. 0xFB45 ; Unknown
+    0xFB46,  # .. 0xFB4F ; Hebrew
+    0xFB50,  # .. 0xFD3D ; Arabic
+    0xFD3E,  # .. 0xFD3F ; Common
+    0xFD40,  # .. 0xFDCF ; Arabic
+    0xFDD0,  # .. 0xFDEF ; Unknown
+    0xFDF0,  # .. 0xFDFF ; Arabic
+    0xFE00,  # .. 0xFE0F ; Inherited
+    0xFE10,  # .. 0xFE19 ; Common
+    0xFE1A,  # .. 0xFE1F ; Unknown
+    0xFE20,  # .. 0xFE2D ; Inherited
+    0xFE2E,  # .. 0xFE2F ; Cyrillic
+    0xFE30,  # .. 0xFE52 ; Common
+    0xFE53,  # .. 0xFE53 ; Unknown
+    0xFE54,  # .. 0xFE66 ; Common
+    0xFE67,  # .. 0xFE67 ; Unknown
+    0xFE68,  # .. 0xFE6B ; Common
+    0xFE6C,  # .. 0xFE6F ; Unknown
+    0xFE70,  # .. 0xFE74 ; Arabic
+    0xFE75,  # .. 0xFE75 ; Unknown
+    0xFE76,  # .. 0xFEFC ; Arabic
+    0xFEFD,  # .. 0xFEFE ; Unknown
+    0xFEFF,  # .. 0xFEFF ; Common
+    0xFF00,  # .. 0xFF00 ; Unknown
+    0xFF01,  # .. 0xFF20 ; Common
+    0xFF21,  # .. 0xFF3A ; Latin
+    0xFF3B,  # .. 0xFF40 ; Common
+    0xFF41,  # .. 0xFF5A ; Latin
+    0xFF5B,  # .. 0xFF65 ; Common
+    0xFF66,  # .. 0xFF6F ; Katakana
+    0xFF70,  # .. 0xFF70 ; Common
+    0xFF71,  # .. 0xFF9D ; Katakana
+    0xFF9E,  # .. 0xFF9F ; Common
+    0xFFA0,  # .. 0xFFBE ; Hangul
+    0xFFBF,  # .. 0xFFC1 ; Unknown
+    0xFFC2,  # .. 0xFFC7 ; Hangul
+    0xFFC8,  # .. 0xFFC9 ; Unknown
+    0xFFCA,  # .. 0xFFCF ; Hangul
+    0xFFD0,  # .. 0xFFD1 ; Unknown
+    0xFFD2,  # .. 0xFFD7 ; Hangul
+    0xFFD8,  # .. 0xFFD9 ; Unknown
+    0xFFDA,  # .. 0xFFDC ; Hangul
+    0xFFDD,  # .. 0xFFDF ; Unknown
+    0xFFE0,  # .. 0xFFE6 ; Common
+    0xFFE7,  # .. 0xFFE7 ; Unknown
+    0xFFE8,  # .. 0xFFEE ; Common
+    0xFFEF,  # .. 0xFFF8 ; Unknown
+    0xFFF9,  # .. 0xFFFD ; Common
+    0xFFFE,  # .. 0xFFFF ; Unknown
+    0x10000,  # .. 0x1000B ; Linear_B
+    0x1000C,  # .. 0x1000C ; Unknown
+    0x1000D,  # .. 0x10026 ; Linear_B
+    0x10027,  # .. 0x10027 ; Unknown
+    0x10028,  # .. 0x1003A ; Linear_B
+    0x1003B,  # .. 0x1003B ; Unknown
+    0x1003C,  # .. 0x1003D ; Linear_B
+    0x1003E,  # .. 0x1003E ; Unknown
+    0x1003F,  # .. 0x1004D ; Linear_B
+    0x1004E,  # .. 0x1004F ; Unknown
+    0x10050,  # .. 0x1005D ; Linear_B
+    0x1005E,  # .. 0x1007F ; Unknown
+    0x10080,  # .. 0x100FA ; Linear_B
+    0x100FB,  # .. 0x100FF ; Unknown
+    0x10100,  # .. 0x10102 ; Common
+    0x10103,  # .. 0x10106 ; Unknown
+    0x10107,  # .. 0x10133 ; Common
+    0x10134,  # .. 0x10136 ; Unknown
+    0x10137,  # .. 0x1013F ; Common
+    0x10140,  # .. 0x1018E ; Greek
+    0x1018F,  # .. 0x1018F ; Unknown
+    0x10190,  # .. 0x1019C ; Common
+    0x1019D,  # .. 0x1019F ; Unknown
+    0x101A0,  # .. 0x101A0 ; Greek
+    0x101A1,  # .. 0x101CF ; Unknown
+    0x101D0,  # .. 0x101FC ; Common
+    0x101FD,  # .. 0x101FD ; Inherited
+    0x101FE,  # .. 0x1027F ; Unknown
+    0x10280,  # .. 0x1029C ; Lycian
+    0x1029D,  # .. 0x1029F ; Unknown
+    0x102A0,  # .. 0x102D0 ; Carian
+    0x102D1,  # .. 0x102DF ; Unknown
+    0x102E0,  # .. 0x102E0 ; Inherited
+    0x102E1,  # .. 0x102FB ; Common
+    0x102FC,  # .. 0x102FF ; Unknown
+    0x10300,  # .. 0x10323 ; Old_Italic
+    0x10324,  # .. 0x1032C ; Unknown
+    0x1032D,  # .. 0x1032F ; Old_Italic
+    0x10330,  # .. 0x1034A ; Gothic
+    0x1034B,  # .. 0x1034F ; Unknown
+    0x10350,  # .. 0x1037A ; Old_Permic
+    0x1037B,  # .. 0x1037F ; Unknown
+    0x10380,  # .. 0x1039D ; Ugaritic
+    0x1039E,  # .. 0x1039E ; Unknown
+    0x1039F,  # .. 0x1039F ; Ugaritic
+    0x103A0,  # .. 0x103C3 ; Old_Persian
+    0x103C4,  # .. 0x103C7 ; Unknown
+    0x103C8,  # .. 0x103D5 ; Old_Persian
+    0x103D6,  # .. 0x103FF ; Unknown
+    0x10400,  # .. 0x1044F ; Deseret
+    0x10450,  # .. 0x1047F ; Shavian
+    0x10480,  # .. 0x1049D ; Osmanya
+    0x1049E,  # .. 0x1049F ; Unknown
+    0x104A0,  # .. 0x104A9 ; Osmanya
+    0x104AA,  # .. 0x104AF ; Unknown
+    0x104B0,  # .. 0x104D3 ; Osage
+    0x104D4,  # .. 0x104D7 ; Unknown
+    0x104D8,  # .. 0x104FB ; Osage
+    0x104FC,  # .. 0x104FF ; Unknown
+    0x10500,  # .. 0x10527 ; Elbasan
+    0x10528,  # .. 0x1052F ; Unknown
+    0x10530,  # .. 0x10563 ; Caucasian_Albanian
+    0x10564,  # .. 0x1056E ; Unknown
+    0x1056F,  # .. 0x1056F ; Caucasian_Albanian
+    0x10570,  # .. 0x1057A ; Vithkuqi
+    0x1057B,  # .. 0x1057B ; Unknown
+    0x1057C,  # .. 0x1058A ; Vithkuqi
+    0x1058B,  # .. 0x1058B ; Unknown
+    0x1058C,  # .. 0x10592 ; Vithkuqi
+    0x10593,  # .. 0x10593 ; Unknown
+    0x10594,  # .. 0x10595 ; Vithkuqi
+    0x10596,  # .. 0x10596 ; Unknown
+    0x10597,  # .. 0x105A1 ; Vithkuqi
+    0x105A2,  # .. 0x105A2 ; Unknown
+    0x105A3,  # .. 0x105B1 ; Vithkuqi
+    0x105B2,  # .. 0x105B2 ; Unknown
+    0x105B3,  # .. 0x105B9 ; Vithkuqi
+    0x105BA,  # .. 0x105BA ; Unknown
+    0x105BB,  # .. 0x105BC ; Vithkuqi
+    0x105BD,  # .. 0x105BF ; Unknown
+    0x105C0,  # .. 0x105F3 ; Todhri
+    0x105F4,  # .. 0x105FF ; Unknown
+    0x10600,  # .. 0x10736 ; Linear_A
+    0x10737,  # .. 0x1073F ; Unknown
+    0x10740,  # .. 0x10755 ; Linear_A
+    0x10756,  # .. 0x1075F ; Unknown
+    0x10760,  # .. 0x10767 ; Linear_A
+    0x10768,  # .. 0x1077F ; Unknown
+    0x10780,  # .. 0x10785 ; Latin
+    0x10786,  # .. 0x10786 ; Unknown
+    0x10787,  # .. 0x107B0 ; Latin
+    0x107B1,  # .. 0x107B1 ; Unknown
+    0x107B2,  # .. 0x107BA ; Latin
+    0x107BB,  # .. 0x107FF ; Unknown
+    0x10800,  # .. 0x10805 ; Cypriot
+    0x10806,  # .. 0x10807 ; Unknown
+    0x10808,  # .. 0x10808 ; Cypriot
+    0x10809,  # .. 0x10809 ; Unknown
+    0x1080A,  # .. 0x10835 ; Cypriot
+    0x10836,  # .. 0x10836 ; Unknown
+    0x10837,  # .. 0x10838 ; Cypriot
+    0x10839,  # .. 0x1083B ; Unknown
+    0x1083C,  # .. 0x1083C ; Cypriot
+    0x1083D,  # .. 0x1083E ; Unknown
+    0x1083F,  # .. 0x1083F ; Cypriot
+    0x10840,  # .. 0x10855 ; Imperial_Aramaic
+    0x10856,  # .. 0x10856 ; Unknown
+    0x10857,  # .. 0x1085F ; Imperial_Aramaic
+    0x10860,  # .. 0x1087F ; Palmyrene
+    0x10880,  # .. 0x1089E ; Nabataean
+    0x1089F,  # .. 0x108A6 ; Unknown
+    0x108A7,  # .. 0x108AF ; Nabataean
+    0x108B0,  # .. 0x108DF ; Unknown
+    0x108E0,  # .. 0x108F2 ; Hatran
+    0x108F3,  # .. 0x108F3 ; Unknown
+    0x108F4,  # .. 0x108F5 ; Hatran
+    0x108F6,  # .. 0x108FA ; Unknown
+    0x108FB,  # .. 0x108FF ; Hatran
+    0x10900,  # .. 0x1091B ; Phoenician
+    0x1091C,  # .. 0x1091E ; Unknown
+    0x1091F,  # .. 0x1091F ; Phoenician
+    0x10920,  # .. 0x10939 ; Lydian
+    0x1093A,  # .. 0x1093E ; Unknown
+    0x1093F,  # .. 0x1093F ; Lydian
+    0x10940,  # .. 0x10959 ; Sidetic
+    0x1095A,  # .. 0x1097F ; Unknown
+    0x10980,  # .. 0x1099F ; Meroitic_Hieroglyphs
+    0x109A0,  # .. 0x109B7 ; Meroitic_Cursive
+    0x109B8,  # .. 0x109BB ; Unknown
+    0x109BC,  # .. 0x109CF ; Meroitic_Cursive
+    0x109D0,  # .. 0x109D1 ; Unknown
+    0x109D2,  # .. 0x109FF ; Meroitic_Cursive
+    0x10A00,  # .. 0x10A03 ; Kharoshthi
+    0x10A04,  # .. 0x10A04 ; Unknown
+    0x10A05,  # .. 0x10A06 ; Kharoshthi
+    0x10A07,  # .. 0x10A0B ; Unknown
+    0x10A0C,  # .. 0x10A13 ; Kharoshthi
+    0x10A14,  # .. 0x10A14 ; Unknown
+    0x10A15,  # .. 0x10A17 ; Kharoshthi
+    0x10A18,  # .. 0x10A18 ; Unknown
+    0x10A19,  # .. 0x10A35 ; Kharoshthi
+    0x10A36,  # .. 0x10A37 ; Unknown
+    0x10A38,  # .. 0x10A3A ; Kharoshthi
+    0x10A3B,  # .. 0x10A3E ; Unknown
+    0x10A3F,  # .. 0x10A48 ; Kharoshthi
+    0x10A49,  # .. 0x10A4F ; Unknown
+    0x10A50,  # .. 0x10A58 ; Kharoshthi
+    0x10A59,  # .. 0x10A5F ; Unknown
+    0x10A60,  # .. 0x10A7F ; Old_South_Arabian
+    0x10A80,  # .. 0x10A9F ; Old_North_Arabian
+    0x10AA0,  # .. 0x10ABF ; Unknown
+    0x10AC0,  # .. 0x10AE6 ; Manichaean
+    0x10AE7,  # .. 0x10AEA ; Unknown
+    0x10AEB,  # .. 0x10AF6 ; Manichaean
+    0x10AF7,  # .. 0x10AFF ; Unknown
+    0x10B00,  # .. 0x10B35 ; Avestan
+    0x10B36,  # .. 0x10B38 ; Unknown
+    0x10B39,  # .. 0x10B3F ; Avestan
+    0x10B40,  # .. 0x10B55 ; Inscriptional_Parthian
+    0x10B56,  # .. 0x10B57 ; Unknown
+    0x10B58,  # .. 0x10B5F ; Inscriptional_Parthian
+    0x10B60,  # .. 0x10B72 ; Inscriptional_Pahlavi
+    0x10B73,  # .. 0x10B77 ; Unknown
+    0x10B78,  # .. 0x10B7F ; Inscriptional_Pahlavi
+    0x10B80,  # .. 0x10B91 ; Psalter_Pahlavi
+    0x10B92,  # .. 0x10B98 ; Unknown
+    0x10B99,  # .. 0x10B9C ; Psalter_Pahlavi
+    0x10B9D,  # .. 0x10BA8 ; Unknown
+    0x10BA9,  # .. 0x10BAF ; Psalter_Pahlavi
+    0x10BB0,  # .. 0x10BFF ; Unknown
+    0x10C00,  # .. 0x10C48 ; Old_Turkic
+    0x10C49,  # .. 0x10C7F ; Unknown
+    0x10C80,  # .. 0x10CB2 ; Old_Hungarian
+    0x10CB3,  # .. 0x10CBF ; Unknown
+    0x10CC0,  # .. 0x10CF2 ; Old_Hungarian
+    0x10CF3,  # .. 0x10CF9 ; Unknown
+    0x10CFA,  # .. 0x10CFF ; Old_Hungarian
+    0x10D00,  # .. 0x10D27 ; Hanifi_Rohingya
+    0x10D28,  # .. 0x10D2F ; Unknown
+    0x10D30,  # .. 0x10D39 ; Hanifi_Rohingya
+    0x10D3A,  # .. 0x10D3F ; Unknown
+    0x10D40,  # .. 0x10D65 ; Garay
+    0x10D66,  # .. 0x10D68 ; Unknown
+    0x10D69,  # .. 0x10D85 ; Garay
+    0x10D86,  # .. 0x10D8D ; Unknown
+    0x10D8E,  # .. 0x10D8F ; Garay
+    0x10D90,  # .. 0x10E5F ; Unknown
+    0x10E60,  # .. 0x10E7E ; Arabic
+    0x10E7F,  # .. 0x10E7F ; Unknown
+    0x10E80,  # .. 0x10EA9 ; Yezidi
+    0x10EAA,  # .. 0x10EAA ; Unknown
+    0x10EAB,  # .. 0x10EAD ; Yezidi
+    0x10EAE,  # .. 0x10EAF ; Unknown
+    0x10EB0,  # .. 0x10EB1 ; Yezidi
+    0x10EB2,  # .. 0x10EC1 ; Unknown
+    0x10EC2,  # .. 0x10EC7 ; Arabic
+    0x10EC8,  # .. 0x10ECF ; Unknown
+    0x10ED0,  # .. 0x10ED8 ; Arabic
+    0x10ED9,  # .. 0x10EF9 ; Unknown
+    0x10EFA,  # .. 0x10EFF ; Arabic
+    0x10F00,  # .. 0x10F27 ; Old_Sogdian
+    0x10F28,  # .. 0x10F2F ; Unknown
+    0x10F30,  # .. 0x10F59 ; Sogdian
+    0x10F5A,  # .. 0x10F6F ; Unknown
+    0x10F70,  # .. 0x10F89 ; Old_Uyghur
+    0x10F8A,  # .. 0x10FAF ; Unknown
+    0x10FB0,  # .. 0x10FCB ; Chorasmian
+    0x10FCC,  # .. 0x10FDF ; Unknown
+    0x10FE0,  # .. 0x10FF6 ; Elymaic
+    0x10FF7,  # .. 0x10FFF ; Unknown
+    0x11000,  # .. 0x1104D ; Brahmi
+    0x1104E,  # .. 0x11051 ; Unknown
+    0x11052,  # .. 0x11075 ; Brahmi
+    0x11076,  # .. 0x1107E ; Unknown
+    0x1107F,  # .. 0x1107F ; Brahmi
+    0x11080,  # .. 0x110C2 ; Kaithi
+    0x110C3,  # .. 0x110CC ; Unknown
+    0x110CD,  # .. 0x110CD ; Kaithi
+    0x110CE,  # .. 0x110CF ; Unknown
+    0x110D0,  # .. 0x110E8 ; Sora_Sompeng
+    0x110E9,  # .. 0x110EF ; Unknown
+    0x110F0,  # .. 0x110F9 ; Sora_Sompeng
+    0x110FA,  # .. 0x110FF ; Unknown
+    0x11100,  # .. 0x11134 ; Chakma
+    0x11135,  # .. 0x11135 ; Unknown
+    0x11136,  # .. 0x11147 ; Chakma
+    0x11148,  # .. 0x1114F ; Unknown
+    0x11150,  # .. 0x11176 ; Mahajani
+    0x11177,  # .. 0x1117F ; Unknown
+    0x11180,  # .. 0x111DF ; Sharada
+    0x111E0,  # .. 0x111E0 ; Unknown
+    0x111E1,  # .. 0x111F4 ; Sinhala
+    0x111F5,  # .. 0x111FF ; Unknown
+    0x11200,  # .. 0x11211 ; Khojki
+    0x11212,  # .. 0x11212 ; Unknown
+    0x11213,  # .. 0x11241 ; Khojki
+    0x11242,  # .. 0x1127F ; Unknown
+    0x11280,  # .. 0x11286 ; Multani
+    0x11287,  # .. 0x11287 ; Unknown
+    0x11288,  # .. 0x11288 ; Multani
+    0x11289,  # .. 0x11289 ; Unknown
+    0x1128A,  # .. 0x1128D ; Multani
+    0x1128E,  # .. 0x1128E ; Unknown
+    0x1128F,  # .. 0x1129D ; Multani
+    0x1129E,  # .. 0x1129E ; Unknown
+    0x1129F,  # .. 0x112A9 ; Multani
+    0x112AA,  # .. 0x112AF ; Unknown
+    0x112B0,  # .. 0x112EA ; Khudawadi
+    0x112EB,  # .. 0x112EF ; Unknown
+    0x112F0,  # .. 0x112F9 ; Khudawadi
+    0x112FA,  # .. 0x112FF ; Unknown
+    0x11300,  # .. 0x11303 ; Grantha
+    0x11304,  # .. 0x11304 ; Unknown
+    0x11305,  # .. 0x1130C ; Grantha
+    0x1130D,  # .. 0x1130E ; Unknown
+    0x1130F,  # .. 0x11310 ; Grantha
+    0x11311,  # .. 0x11312 ; Unknown
+    0x11313,  # .. 0x11328 ; Grantha
+    0x11329,  # .. 0x11329 ; Unknown
+    0x1132A,  # .. 0x11330 ; Grantha
+    0x11331,  # .. 0x11331 ; Unknown
+    0x11332,  # .. 0x11333 ; Grantha
+    0x11334,  # .. 0x11334 ; Unknown
+    0x11335,  # .. 0x11339 ; Grantha
+    0x1133A,  # .. 0x1133A ; Unknown
+    0x1133B,  # .. 0x1133B ; Inherited
+    0x1133C,  # .. 0x11344 ; Grantha
+    0x11345,  # .. 0x11346 ; Unknown
+    0x11347,  # .. 0x11348 ; Grantha
+    0x11349,  # .. 0x1134A ; Unknown
+    0x1134B,  # .. 0x1134D ; Grantha
+    0x1134E,  # .. 0x1134F ; Unknown
+    0x11350,  # .. 0x11350 ; Grantha
+    0x11351,  # .. 0x11356 ; Unknown
+    0x11357,  # .. 0x11357 ; Grantha
+    0x11358,  # .. 0x1135C ; Unknown
+    0x1135D,  # .. 0x11363 ; Grantha
+    0x11364,  # .. 0x11365 ; Unknown
+    0x11366,  # .. 0x1136C ; Grantha
+    0x1136D,  # .. 0x1136F ; Unknown
+    0x11370,  # .. 0x11374 ; Grantha
+    0x11375,  # .. 0x1137F ; Unknown
+    0x11380,  # .. 0x11389 ; Tulu_Tigalari
+    0x1138A,  # .. 0x1138A ; Unknown
+    0x1138B,  # .. 0x1138B ; Tulu_Tigalari
+    0x1138C,  # .. 0x1138D ; Unknown
+    0x1138E,  # .. 0x1138E ; Tulu_Tigalari
+    0x1138F,  # .. 0x1138F ; Unknown
+    0x11390,  # .. 0x113B5 ; Tulu_Tigalari
+    0x113B6,  # .. 0x113B6 ; Unknown
+    0x113B7,  # .. 0x113C0 ; Tulu_Tigalari
+    0x113C1,  # .. 0x113C1 ; Unknown
+    0x113C2,  # .. 0x113C2 ; Tulu_Tigalari
+    0x113C3,  # .. 0x113C4 ; Unknown
+    0x113C5,  # .. 0x113C5 ; Tulu_Tigalari
+    0x113C6,  # .. 0x113C6 ; Unknown
+    0x113C7,  # .. 0x113CA ; Tulu_Tigalari
+    0x113CB,  # .. 0x113CB ; Unknown
+    0x113CC,  # .. 0x113D5 ; Tulu_Tigalari
+    0x113D6,  # .. 0x113D6 ; Unknown
+    0x113D7,  # .. 0x113D8 ; Tulu_Tigalari
+    0x113D9,  # .. 0x113E0 ; Unknown
+    0x113E1,  # .. 0x113E2 ; Tulu_Tigalari
+    0x113E3,  # .. 0x113FF ; Unknown
+    0x11400,  # .. 0x1145B ; Newa
+    0x1145C,  # .. 0x1145C ; Unknown
+    0x1145D,  # .. 0x11461 ; Newa
+    0x11462,  # .. 0x1147F ; Unknown
+    0x11480,  # .. 0x114C7 ; Tirhuta
+    0x114C8,  # .. 0x114CF ; Unknown
+    0x114D0,  # .. 0x114D9 ; Tirhuta
+    0x114DA,  # .. 0x1157F ; Unknown
+    0x11580,  # .. 0x115B5 ; Siddham
+    0x115B6,  # .. 0x115B7 ; Unknown
+    0x115B8,  # .. 0x115DD ; Siddham
+    0x115DE,  # .. 0x115FF ; Unknown
+    0x11600,  # .. 0x11644 ; Modi
+    0x11645,  # .. 0x1164F ; Unknown
+    0x11650,  # .. 0x11659 ; Modi
+    0x1165A,  # .. 0x1165F ; Unknown
+    0x11660,  # .. 0x1166C ; Mongolian
+    0x1166D,  # .. 0x1167F ; Unknown
+    0x11680,  # .. 0x116B9 ; Takri
+    0x116BA,  # .. 0x116BF ; Unknown
+    0x116C0,  # .. 0x116C9 ; Takri
+    0x116CA,  # .. 0x116CF ; Unknown
+    0x116D0,  # .. 0x116E3 ; Myanmar
+    0x116E4,  # .. 0x116FF ; Unknown
+    0x11700,  # .. 0x1171A ; Ahom
+    0x1171B,  # .. 0x1171C ; Unknown
+    0x1171D,  # .. 0x1172B ; Ahom
+    0x1172C,  # .. 0x1172F ; Unknown
+    0x11730,  # .. 0x11746 ; Ahom
+    0x11747,  # .. 0x117FF ; Unknown
+    0x11800,  # .. 0x1183B ; Dogra
+    0x1183C,  # .. 0x1189F ; Unknown
+    0x118A0,  # .. 0x118F2 ; Warang_Citi
+    0x118F3,  # .. 0x118FE ; Unknown
+    0x118FF,  # .. 0x118FF ; Warang_Citi
+    0x11900,  # .. 0x11906 ; Dives_Akuru
+    0x11907,  # .. 0x11908 ; Unknown
+    0x11909,  # .. 0x11909 ; Dives_Akuru
+    0x1190A,  # .. 0x1190B ; Unknown
+    0x1190C,  # .. 0x11913 ; Dives_Akuru
+    0x11914,  # .. 0x11914 ; Unknown
+    0x11915,  # .. 0x11916 ; Dives_Akuru
+    0x11917,  # .. 0x11917 ; Unknown
+    0x11918,  # .. 0x11935 ; Dives_Akuru
+    0x11936,  # .. 0x11936 ; Unknown
+    0x11937,  # .. 0x11938 ; Dives_Akuru
+    0x11939,  # .. 0x1193A ; Unknown
+    0x1193B,  # .. 0x11946 ; Dives_Akuru
+    0x11947,  # .. 0x1194F ; Unknown
+    0x11950,  # .. 0x11959 ; Dives_Akuru
+    0x1195A,  # .. 0x1199F ; Unknown
+    0x119A0,  # .. 0x119A7 ; Nandinagari
+    0x119A8,  # .. 0x119A9 ; Unknown
+    0x119AA,  # .. 0x119D7 ; Nandinagari
+    0x119D8,  # .. 0x119D9 ; Unknown
+    0x119DA,  # .. 0x119E4 ; Nandinagari
+    0x119E5,  # .. 0x119FF ; Unknown
+    0x11A00,  # .. 0x11A47 ; Zanabazar_Square
+    0x11A48,  # .. 0x11A4F ; Unknown
+    0x11A50,  # .. 0x11AA2 ; Soyombo
+    0x11AA3,  # .. 0x11AAF ; Unknown
+    0x11AB0,  # .. 0x11ABF ; Canadian_Aboriginal
+    0x11AC0,  # .. 0x11AF8 ; Pau_Cin_Hau
+    0x11AF9,  # .. 0x11AFF ; Unknown
+    0x11B00,  # .. 0x11B09 ; Devanagari
+    0x11B0A,  # .. 0x11B5F ; Unknown
+    0x11B60,  # .. 0x11B67 ; Sharada
+    0x11B68,  # .. 0x11BBF ; Unknown
+    0x11BC0,  # .. 0x11BE1 ; Sunuwar
+    0x11BE2,  # .. 0x11BEF ; Unknown
+    0x11BF0,  # .. 0x11BF9 ; Sunuwar
+    0x11BFA,  # .. 0x11BFF ; Unknown
+    0x11C00,  # .. 0x11C08 ; Bhaiksuki
+    0x11C09,  # .. 0x11C09 ; Unknown
+    0x11C0A,  # .. 0x11C36 ; Bhaiksuki
+    0x11C37,  # .. 0x11C37 ; Unknown
+    0x11C38,  # .. 0x11C45 ; Bhaiksuki
+    0x11C46,  # .. 0x11C4F ; Unknown
+    0x11C50,  # .. 0x11C6C ; Bhaiksuki
+    0x11C6D,  # .. 0x11C6F ; Unknown
+    0x11C70,  # .. 0x11C8F ; Marchen
+    0x11C90,  # .. 0x11C91 ; Unknown
+    0x11C92,  # .. 0x11CA7 ; Marchen
+    0x11CA8,  # .. 0x11CA8 ; Unknown
+    0x11CA9,  # .. 0x11CB6 ; Marchen
+    0x11CB7,  # .. 0x11CFF ; Unknown
+    0x11D00,  # .. 0x11D06 ; Masaram_Gondi
+    0x11D07,  # .. 0x11D07 ; Unknown
+    0x11D08,  # .. 0x11D09 ; Masaram_Gondi
+    0x11D0A,  # .. 0x11D0A ; Unknown
+    0x11D0B,  # .. 0x11D36 ; Masaram_Gondi
+    0x11D37,  # .. 0x11D39 ; Unknown
+    0x11D3A,  # .. 0x11D3A ; Masaram_Gondi
+    0x11D3B,  # .. 0x11D3B ; Unknown
+    0x11D3C,  # .. 0x11D3D ; Masaram_Gondi
+    0x11D3E,  # .. 0x11D3E ; Unknown
+    0x11D3F,  # .. 0x11D47 ; Masaram_Gondi
+    0x11D48,  # .. 0x11D4F ; Unknown
+    0x11D50,  # .. 0x11D59 ; Masaram_Gondi
+    0x11D5A,  # .. 0x11D5F ; Unknown
+    0x11D60,  # .. 0x11D65 ; Gunjala_Gondi
+    0x11D66,  # .. 0x11D66 ; Unknown
+    0x11D67,  # .. 0x11D68 ; Gunjala_Gondi
+    0x11D69,  # .. 0x11D69 ; Unknown
+    0x11D6A,  # .. 0x11D8E ; Gunjala_Gondi
+    0x11D8F,  # .. 0x11D8F ; Unknown
+    0x11D90,  # .. 0x11D91 ; Gunjala_Gondi
+    0x11D92,  # .. 0x11D92 ; Unknown
+    0x11D93,  # .. 0x11D98 ; Gunjala_Gondi
+    0x11D99,  # .. 0x11D9F ; Unknown
+    0x11DA0,  # .. 0x11DA9 ; Gunjala_Gondi
+    0x11DAA,  # .. 0x11DAF ; Unknown
+    0x11DB0,  # .. 0x11DDB ; Tolong_Siki
+    0x11DDC,  # .. 0x11DDF ; Unknown
+    0x11DE0,  # .. 0x11DE9 ; Tolong_Siki
+    0x11DEA,  # .. 0x11EDF ; Unknown
+    0x11EE0,  # .. 0x11EF8 ; Makasar
+    0x11EF9,  # .. 0x11EFF ; Unknown
+    0x11F00,  # .. 0x11F10 ; Kawi
+    0x11F11,  # .. 0x11F11 ; Unknown
+    0x11F12,  # .. 0x11F3A ; Kawi
+    0x11F3B,  # .. 0x11F3D ; Unknown
+    0x11F3E,  # .. 0x11F5A ; Kawi
+    0x11F5B,  # .. 0x11FAF ; Unknown
+    0x11FB0,  # .. 0x11FB0 ; Lisu
+    0x11FB1,  # .. 0x11FBF ; Unknown
+    0x11FC0,  # .. 0x11FF1 ; Tamil
+    0x11FF2,  # .. 0x11FFE ; Unknown
+    0x11FFF,  # .. 0x11FFF ; Tamil
+    0x12000,  # .. 0x12399 ; Cuneiform
+    0x1239A,  # .. 0x123FF ; Unknown
+    0x12400,  # .. 0x1246E ; Cuneiform
+    0x1246F,  # .. 0x1246F ; Unknown
+    0x12470,  # .. 0x12474 ; Cuneiform
+    0x12475,  # .. 0x1247F ; Unknown
+    0x12480,  # .. 0x12543 ; Cuneiform
+    0x12544,  # .. 0x12F8F ; Unknown
+    0x12F90,  # .. 0x12FF2 ; Cypro_Minoan
+    0x12FF3,  # .. 0x12FFF ; Unknown
+    0x13000,  # .. 0x13455 ; Egyptian_Hieroglyphs
+    0x13456,  # .. 0x1345F ; Unknown
+    0x13460,  # .. 0x143FA ; Egyptian_Hieroglyphs
+    0x143FB,  # .. 0x143FF ; Unknown
+    0x14400,  # .. 0x14646 ; Anatolian_Hieroglyphs
+    0x14647,  # .. 0x160FF ; Unknown
+    0x16100,  # .. 0x16139 ; Gurung_Khema
+    0x1613A,  # .. 0x167FF ; Unknown
+    0x16800,  # .. 0x16A38 ; Bamum
+    0x16A39,  # .. 0x16A3F ; Unknown
+    0x16A40,  # .. 0x16A5E ; Mro
+    0x16A5F,  # .. 0x16A5F ; Unknown
+    0x16A60,  # .. 0x16A69 ; Mro
+    0x16A6A,  # .. 0x16A6D ; Unknown
+    0x16A6E,  # .. 0x16A6F ; Mro
+    0x16A70,  # .. 0x16ABE ; Tangsa
+    0x16ABF,  # .. 0x16ABF ; Unknown
+    0x16AC0,  # .. 0x16AC9 ; Tangsa
+    0x16ACA,  # .. 0x16ACF ; Unknown
+    0x16AD0,  # .. 0x16AED ; Bassa_Vah
+    0x16AEE,  # .. 0x16AEF ; Unknown
+    0x16AF0,  # .. 0x16AF5 ; Bassa_Vah
+    0x16AF6,  # .. 0x16AFF ; Unknown
+    0x16B00,  # .. 0x16B45 ; Pahawh_Hmong
+    0x16B46,  # .. 0x16B4F ; Unknown
+    0x16B50,  # .. 0x16B59 ; Pahawh_Hmong
+    0x16B5A,  # .. 0x16B5A ; Unknown
+    0x16B5B,  # .. 0x16B61 ; Pahawh_Hmong
+    0x16B62,  # .. 0x16B62 ; Unknown
+    0x16B63,  # .. 0x16B77 ; Pahawh_Hmong
+    0x16B78,  # .. 0x16B7C ; Unknown
+    0x16B7D,  # .. 0x16B8F ; Pahawh_Hmong
+    0x16B90,  # .. 0x16D3F ; Unknown
+    0x16D40,  # .. 0x16D79 ; Kirat_Rai
+    0x16D7A,  # .. 0x16E3F ; Unknown
+    0x16E40,  # .. 0x16E9A ; Medefaidrin
+    0x16E9B,  # .. 0x16E9F ; Unknown
+    0x16EA0,  # .. 0x16EB8 ; Beria_Erfe
+    0x16EB9,  # .. 0x16EBA ; Unknown
+    0x16EBB,  # .. 0x16ED3 ; Beria_Erfe
+    0x16ED4,  # .. 0x16EFF ; Unknown
+    0x16F00,  # .. 0x16F4A ; Miao
+    0x16F4B,  # .. 0x16F4E ; Unknown
+    0x16F4F,  # .. 0x16F87 ; Miao
+    0x16F88,  # .. 0x16F8E ; Unknown
+    0x16F8F,  # .. 0x16F9F ; Miao
+    0x16FA0,  # .. 0x16FDF ; Unknown
+    0x16FE0,  # .. 0x16FE0 ; Tangut
+    0x16FE1,  # .. 0x16FE1 ; Nushu
+    0x16FE2,  # .. 0x16FE3 ; Han
+    0x16FE4,  # .. 0x16FE4 ; Khitan_Small_Script
+    0x16FE5,  # .. 0x16FEF ; Unknown
+    0x16FF0,  # .. 0x16FF6 ; Han
+    0x16FF7,  # .. 0x16FFF ; Unknown
+    0x17000,  # .. 0x18AFF ; Tangut
+    0x18B00,  # .. 0x18CD5 ; Khitan_Small_Script
+    0x18CD6,  # .. 0x18CFE ; Unknown
+    0x18CFF,  # .. 0x18CFF ; Khitan_Small_Script
+    0x18D00,  # .. 0x18D1E ; Tangut
+    0x18D1F,  # .. 0x18D7F ; Unknown
+    0x18D80,  # .. 0x18DF2 ; Tangut
+    0x18DF3,  # .. 0x1AFEF ; Unknown
+    0x1AFF0,  # .. 0x1AFF3 ; Katakana
+    0x1AFF4,  # .. 0x1AFF4 ; Unknown
+    0x1AFF5,  # .. 0x1AFFB ; Katakana
+    0x1AFFC,  # .. 0x1AFFC ; Unknown
+    0x1AFFD,  # .. 0x1AFFE ; Katakana
+    0x1AFFF,  # .. 0x1AFFF ; Unknown
+    0x1B000,  # .. 0x1B000 ; Katakana
+    0x1B001,  # .. 0x1B11F ; Hiragana
+    0x1B120,  # .. 0x1B122 ; Katakana
+    0x1B123,  # .. 0x1B131 ; Unknown
+    0x1B132,  # .. 0x1B132 ; Hiragana
+    0x1B133,  # .. 0x1B14F ; Unknown
+    0x1B150,  # .. 0x1B152 ; Hiragana
+    0x1B153,  # .. 0x1B154 ; Unknown
+    0x1B155,  # .. 0x1B155 ; Katakana
+    0x1B156,  # .. 0x1B163 ; Unknown
+    0x1B164,  # .. 0x1B167 ; Katakana
+    0x1B168,  # .. 0x1B16F ; Unknown
+    0x1B170,  # .. 0x1B2FB ; Nushu
+    0x1B2FC,  # .. 0x1BBFF ; Unknown
+    0x1BC00,  # .. 0x1BC6A ; Duployan
+    0x1BC6B,  # .. 0x1BC6F ; Unknown
+    0x1BC70,  # .. 0x1BC7C ; Duployan
+    0x1BC7D,  # .. 0x1BC7F ; Unknown
+    0x1BC80,  # .. 0x1BC88 ; Duployan
+    0x1BC89,  # .. 0x1BC8F ; Unknown
+    0x1BC90,  # .. 0x1BC99 ; Duployan
+    0x1BC9A,  # .. 0x1BC9B ; Unknown
+    0x1BC9C,  # .. 0x1BC9F ; Duployan
+    0x1BCA0,  # .. 0x1BCA3 ; Common
+    0x1BCA4,  # .. 0x1CBFF ; Unknown
+    0x1CC00,  # .. 0x1CCFC ; Common
+    0x1CCFD,  # .. 0x1CCFF ; Unknown
+    0x1CD00,  # .. 0x1CEB3 ; Common
+    0x1CEB4,  # .. 0x1CEB9 ; Unknown
+    0x1CEBA,  # .. 0x1CED0 ; Common
+    0x1CED1,  # .. 0x1CEDF ; Unknown
+    0x1CEE0,  # .. 0x1CEF0 ; Common
+    0x1CEF1,  # .. 0x1CEFF ; Unknown
+    0x1CF00,  # .. 0x1CF2D ; Inherited
+    0x1CF2E,  # .. 0x1CF2F ; Unknown
+    0x1CF30,  # .. 0x1CF46 ; Inherited
+    0x1CF47,  # .. 0x1CF4F ; Unknown
+    0x1CF50,  # .. 0x1CFC3 ; Common
+    0x1CFC4,  # .. 0x1CFFF ; Unknown
+    0x1D000,  # .. 0x1D0F5 ; Common
+    0x1D0F6,  # .. 0x1D0FF ; Unknown
+    0x1D100,  # .. 0x1D126 ; Common
+    0x1D127,  # .. 0x1D128 ; Unknown
+    0x1D129,  # .. 0x1D166 ; Common
+    0x1D167,  # .. 0x1D169 ; Inherited
+    0x1D16A,  # .. 0x1D17A ; Common
+    0x1D17B,  # .. 0x1D182 ; Inherited
+    0x1D183,  # .. 0x1D184 ; Common
+    0x1D185,  # .. 0x1D18B ; Inherited
+    0x1D18C,  # .. 0x1D1A9 ; Common
+    0x1D1AA,  # .. 0x1D1AD ; Inherited
+    0x1D1AE,  # .. 0x1D1EA ; Common
+    0x1D1EB,  # .. 0x1D1FF ; Unknown
+    0x1D200,  # .. 0x1D245 ; Greek
+    0x1D246,  # .. 0x1D2BF ; Unknown
+    0x1D2C0,  # .. 0x1D2D3 ; Common
+    0x1D2D4,  # .. 0x1D2DF ; Unknown
+    0x1D2E0,  # .. 0x1D2F3 ; Common
+    0x1D2F4,  # .. 0x1D2FF ; Unknown
+    0x1D300,  # .. 0x1D356 ; Common
+    0x1D357,  # .. 0x1D35F ; Unknown
+    0x1D360,  # .. 0x1D378 ; Common
+    0x1D379,  # .. 0x1D3FF ; Unknown
+    0x1D400,  # .. 0x1D454 ; Common
+    0x1D455,  # .. 0x1D455 ; Unknown
+    0x1D456,  # .. 0x1D49C ; Common
+    0x1D49D,  # .. 0x1D49D ; Unknown
+    0x1D49E,  # .. 0x1D49F ; Common
+    0x1D4A0,  # .. 0x1D4A1 ; Unknown
+    0x1D4A2,  # .. 0x1D4A2 ; Common
+    0x1D4A3,  # .. 0x1D4A4 ; Unknown
+    0x1D4A5,  # .. 0x1D4A6 ; Common
+    0x1D4A7,  # .. 0x1D4A8 ; Unknown
+    0x1D4A9,  # .. 0x1D4AC ; Common
+    0x1D4AD,  # .. 0x1D4AD ; Unknown
+    0x1D4AE,  # .. 0x1D4B9 ; Common
+    0x1D4BA,  # .. 0x1D4BA ; Unknown
+    0x1D4BB,  # .. 0x1D4BB ; Common
+    0x1D4BC,  # .. 0x1D4BC ; Unknown
+    0x1D4BD,  # .. 0x1D4C3 ; Common
+    0x1D4C4,  # .. 0x1D4C4 ; Unknown
+    0x1D4C5,  # .. 0x1D505 ; Common
+    0x1D506,  # .. 0x1D506 ; Unknown
+    0x1D507,  # .. 0x1D50A ; Common
+    0x1D50B,  # .. 0x1D50C ; Unknown
+    0x1D50D,  # .. 0x1D514 ; Common
+    0x1D515,  # .. 0x1D515 ; Unknown
+    0x1D516,  # .. 0x1D51C ; Common
+    0x1D51D,  # .. 0x1D51D ; Unknown
+    0x1D51E,  # .. 0x1D539 ; Common
+    0x1D53A,  # .. 0x1D53A ; Unknown
+    0x1D53B,  # .. 0x1D53E ; Common
+    0x1D53F,  # .. 0x1D53F ; Unknown
+    0x1D540,  # .. 0x1D544 ; Common
+    0x1D545,  # .. 0x1D545 ; Unknown
+    0x1D546,  # .. 0x1D546 ; Common
+    0x1D547,  # .. 0x1D549 ; Unknown
+    0x1D54A,  # .. 0x1D550 ; Common
+    0x1D551,  # .. 0x1D551 ; Unknown
+    0x1D552,  # .. 0x1D6A5 ; Common
+    0x1D6A6,  # .. 0x1D6A7 ; Unknown
+    0x1D6A8,  # .. 0x1D7CB ; Common
+    0x1D7CC,  # .. 0x1D7CD ; Unknown
+    0x1D7CE,  # .. 0x1D7FF ; Common
+    0x1D800,  # .. 0x1DA8B ; SignWriting
+    0x1DA8C,  # .. 0x1DA9A ; Unknown
+    0x1DA9B,  # .. 0x1DA9F ; SignWriting
+    0x1DAA0,  # .. 0x1DAA0 ; Unknown
+    0x1DAA1,  # .. 0x1DAAF ; SignWriting
+    0x1DAB0,  # .. 0x1DEFF ; Unknown
+    0x1DF00,  # .. 0x1DF1E ; Latin
+    0x1DF1F,  # .. 0x1DF24 ; Unknown
+    0x1DF25,  # .. 0x1DF2A ; Latin
+    0x1DF2B,  # .. 0x1DFFF ; Unknown
+    0x1E000,  # .. 0x1E006 ; Glagolitic
+    0x1E007,  # .. 0x1E007 ; Unknown
+    0x1E008,  # .. 0x1E018 ; Glagolitic
+    0x1E019,  # .. 0x1E01A ; Unknown
+    0x1E01B,  # .. 0x1E021 ; Glagolitic
+    0x1E022,  # .. 0x1E022 ; Unknown
+    0x1E023,  # .. 0x1E024 ; Glagolitic
+    0x1E025,  # .. 0x1E025 ; Unknown
+    0x1E026,  # .. 0x1E02A ; Glagolitic
+    0x1E02B,  # .. 0x1E02F ; Unknown
+    0x1E030,  # .. 0x1E06D ; Cyrillic
+    0x1E06E,  # .. 0x1E08E ; Unknown
+    0x1E08F,  # .. 0x1E08F ; Cyrillic
+    0x1E090,  # .. 0x1E0FF ; Unknown
+    0x1E100,  # .. 0x1E12C ; Nyiakeng_Puachue_Hmong
+    0x1E12D,  # .. 0x1E12F ; Unknown
+    0x1E130,  # .. 0x1E13D ; Nyiakeng_Puachue_Hmong
+    0x1E13E,  # .. 0x1E13F ; Unknown
+    0x1E140,  # .. 0x1E149 ; Nyiakeng_Puachue_Hmong
+    0x1E14A,  # .. 0x1E14D ; Unknown
+    0x1E14E,  # .. 0x1E14F ; Nyiakeng_Puachue_Hmong
+    0x1E150,  # .. 0x1E28F ; Unknown
+    0x1E290,  # .. 0x1E2AE ; Toto
+    0x1E2AF,  # .. 0x1E2BF ; Unknown
+    0x1E2C0,  # .. 0x1E2F9 ; Wancho
+    0x1E2FA,  # .. 0x1E2FE ; Unknown
+    0x1E2FF,  # .. 0x1E2FF ; Wancho
+    0x1E300,  # .. 0x1E4CF ; Unknown
+    0x1E4D0,  # .. 0x1E4F9 ; Nag_Mundari
+    0x1E4FA,  # .. 0x1E5CF ; Unknown
+    0x1E5D0,  # .. 0x1E5FA ; Ol_Onal
+    0x1E5FB,  # .. 0x1E5FE ; Unknown
+    0x1E5FF,  # .. 0x1E5FF ; Ol_Onal
+    0x1E600,  # .. 0x1E6BF ; Unknown
+    0x1E6C0,  # .. 0x1E6DE ; Tai_Yo
+    0x1E6DF,  # .. 0x1E6DF ; Unknown
+    0x1E6E0,  # .. 0x1E6F5 ; Tai_Yo
+    0x1E6F6,  # .. 0x1E6FD ; Unknown
+    0x1E6FE,  # .. 0x1E6FF ; Tai_Yo
+    0x1E700,  # .. 0x1E7DF ; Unknown
+    0x1E7E0,  # .. 0x1E7E6 ; Ethiopic
+    0x1E7E7,  # .. 0x1E7E7 ; Unknown
+    0x1E7E8,  # .. 0x1E7EB ; Ethiopic
+    0x1E7EC,  # .. 0x1E7EC ; Unknown
+    0x1E7ED,  # .. 0x1E7EE ; Ethiopic
+    0x1E7EF,  # .. 0x1E7EF ; Unknown
+    0x1E7F0,  # .. 0x1E7FE ; Ethiopic
+    0x1E7FF,  # .. 0x1E7FF ; Unknown
+    0x1E800,  # .. 0x1E8C4 ; Mende_Kikakui
+    0x1E8C5,  # .. 0x1E8C6 ; Unknown
+    0x1E8C7,  # .. 0x1E8D6 ; Mende_Kikakui
+    0x1E8D7,  # .. 0x1E8FF ; Unknown
+    0x1E900,  # .. 0x1E94B ; Adlam
+    0x1E94C,  # .. 0x1E94F ; Unknown
+    0x1E950,  # .. 0x1E959 ; Adlam
+    0x1E95A,  # .. 0x1E95D ; Unknown
+    0x1E95E,  # .. 0x1E95F ; Adlam
+    0x1E960,  # .. 0x1EC70 ; Unknown
+    0x1EC71,  # .. 0x1ECB4 ; Common
+    0x1ECB5,  # .. 0x1ED00 ; Unknown
+    0x1ED01,  # .. 0x1ED3D ; Common
+    0x1ED3E,  # .. 0x1EDFF ; Unknown
+    0x1EE00,  # .. 0x1EE03 ; Arabic
+    0x1EE04,  # .. 0x1EE04 ; Unknown
+    0x1EE05,  # .. 0x1EE1F ; Arabic
+    0x1EE20,  # .. 0x1EE20 ; Unknown
+    0x1EE21,  # .. 0x1EE22 ; Arabic
+    0x1EE23,  # .. 0x1EE23 ; Unknown
+    0x1EE24,  # .. 0x1EE24 ; Arabic
+    0x1EE25,  # .. 0x1EE26 ; Unknown
+    0x1EE27,  # .. 0x1EE27 ; Arabic
+    0x1EE28,  # .. 0x1EE28 ; Unknown
+    0x1EE29,  # .. 0x1EE32 ; Arabic
+    0x1EE33,  # .. 0x1EE33 ; Unknown
+    0x1EE34,  # .. 0x1EE37 ; Arabic
+    0x1EE38,  # .. 0x1EE38 ; Unknown
+    0x1EE39,  # .. 0x1EE39 ; Arabic
+    0x1EE3A,  # .. 0x1EE3A ; Unknown
+    0x1EE3B,  # .. 0x1EE3B ; Arabic
+    0x1EE3C,  # .. 0x1EE41 ; Unknown
+    0x1EE42,  # .. 0x1EE42 ; Arabic
+    0x1EE43,  # .. 0x1EE46 ; Unknown
+    0x1EE47,  # .. 0x1EE47 ; Arabic
+    0x1EE48,  # .. 0x1EE48 ; Unknown
+    0x1EE49,  # .. 0x1EE49 ; Arabic
+    0x1EE4A,  # .. 0x1EE4A ; Unknown
+    0x1EE4B,  # .. 0x1EE4B ; Arabic
+    0x1EE4C,  # .. 0x1EE4C ; Unknown
+    0x1EE4D,  # .. 0x1EE4F ; Arabic
+    0x1EE50,  # .. 0x1EE50 ; Unknown
+    0x1EE51,  # .. 0x1EE52 ; Arabic
+    0x1EE53,  # .. 0x1EE53 ; Unknown
+    0x1EE54,  # .. 0x1EE54 ; Arabic
+    0x1EE55,  # .. 0x1EE56 ; Unknown
+    0x1EE57,  # .. 0x1EE57 ; Arabic
+    0x1EE58,  # .. 0x1EE58 ; Unknown
+    0x1EE59,  # .. 0x1EE59 ; Arabic
+    0x1EE5A,  # .. 0x1EE5A ; Unknown
+    0x1EE5B,  # .. 0x1EE5B ; Arabic
+    0x1EE5C,  # .. 0x1EE5C ; Unknown
+    0x1EE5D,  # .. 0x1EE5D ; Arabic
+    0x1EE5E,  # .. 0x1EE5E ; Unknown
+    0x1EE5F,  # .. 0x1EE5F ; Arabic
+    0x1EE60,  # .. 0x1EE60 ; Unknown
+    0x1EE61,  # .. 0x1EE62 ; Arabic
+    0x1EE63,  # .. 0x1EE63 ; Unknown
+    0x1EE64,  # .. 0x1EE64 ; Arabic
+    0x1EE65,  # .. 0x1EE66 ; Unknown
+    0x1EE67,  # .. 0x1EE6A ; Arabic
+    0x1EE6B,  # .. 0x1EE6B ; Unknown
+    0x1EE6C,  # .. 0x1EE72 ; Arabic
+    0x1EE73,  # .. 0x1EE73 ; Unknown
+    0x1EE74,  # .. 0x1EE77 ; Arabic
+    0x1EE78,  # .. 0x1EE78 ; Unknown
+    0x1EE79,  # .. 0x1EE7C ; Arabic
+    0x1EE7D,  # .. 0x1EE7D ; Unknown
+    0x1EE7E,  # .. 0x1EE7E ; Arabic
+    0x1EE7F,  # .. 0x1EE7F ; Unknown
+    0x1EE80,  # .. 0x1EE89 ; Arabic
+    0x1EE8A,  # .. 0x1EE8A ; Unknown
+    0x1EE8B,  # .. 0x1EE9B ; Arabic
+    0x1EE9C,  # .. 0x1EEA0 ; Unknown
+    0x1EEA1,  # .. 0x1EEA3 ; Arabic
+    0x1EEA4,  # .. 0x1EEA4 ; Unknown
+    0x1EEA5,  # .. 0x1EEA9 ; Arabic
+    0x1EEAA,  # .. 0x1EEAA ; Unknown
+    0x1EEAB,  # .. 0x1EEBB ; Arabic
+    0x1EEBC,  # .. 0x1EEEF ; Unknown
+    0x1EEF0,  # .. 0x1EEF1 ; Arabic
+    0x1EEF2,  # .. 0x1EFFF ; Unknown
+    0x1F000,  # .. 0x1F02B ; Common
+    0x1F02C,  # .. 0x1F02F ; Unknown
+    0x1F030,  # .. 0x1F093 ; Common
+    0x1F094,  # .. 0x1F09F ; Unknown
+    0x1F0A0,  # .. 0x1F0AE ; Common
+    0x1F0AF,  # .. 0x1F0B0 ; Unknown
+    0x1F0B1,  # .. 0x1F0BF ; Common
+    0x1F0C0,  # .. 0x1F0C0 ; Unknown
+    0x1F0C1,  # .. 0x1F0CF ; Common
+    0x1F0D0,  # .. 0x1F0D0 ; Unknown
+    0x1F0D1,  # .. 0x1F0F5 ; Common
+    0x1F0F6,  # .. 0x1F0FF ; Unknown
+    0x1F100,  # .. 0x1F1AD ; Common
+    0x1F1AE,  # .. 0x1F1E5 ; Unknown
+    0x1F1E6,  # .. 0x1F1FF ; Common
+    0x1F200,  # .. 0x1F200 ; Hiragana
+    0x1F201,  # .. 0x1F202 ; Common
+    0x1F203,  # .. 0x1F20F ; Unknown
+    0x1F210,  # .. 0x1F23B ; Common
+    0x1F23C,  # .. 0x1F23F ; Unknown
+    0x1F240,  # .. 0x1F248 ; Common
+    0x1F249,  # .. 0x1F24F ; Unknown
+    0x1F250,  # .. 0x1F251 ; Common
+    0x1F252,  # .. 0x1F25F ; Unknown
+    0x1F260,  # .. 0x1F265 ; Common
+    0x1F266,  # .. 0x1F2FF ; Unknown
+    0x1F300,  # .. 0x1F6D8 ; Common
+    0x1F6D9,  # .. 0x1F6DB ; Unknown
+    0x1F6DC,  # .. 0x1F6EC ; Common
+    0x1F6ED,  # .. 0x1F6EF ; Unknown
+    0x1F6F0,  # .. 0x1F6FC ; Common
+    0x1F6FD,  # .. 0x1F6FF ; Unknown
+    0x1F700,  # .. 0x1F7D9 ; Common
+    0x1F7DA,  # .. 0x1F7DF ; Unknown
+    0x1F7E0,  # .. 0x1F7EB ; Common
+    0x1F7EC,  # .. 0x1F7EF ; Unknown
+    0x1F7F0,  # .. 0x1F7F0 ; Common
+    0x1F7F1,  # .. 0x1F7FF ; Unknown
+    0x1F800,  # .. 0x1F80B ; Common
+    0x1F80C,  # .. 0x1F80F ; Unknown
+    0x1F810,  # .. 0x1F847 ; Common
+    0x1F848,  # .. 0x1F84F ; Unknown
+    0x1F850,  # .. 0x1F859 ; Common
+    0x1F85A,  # .. 0x1F85F ; Unknown
+    0x1F860,  # .. 0x1F887 ; Common
+    0x1F888,  # .. 0x1F88F ; Unknown
+    0x1F890,  # .. 0x1F8AD ; Common
+    0x1F8AE,  # .. 0x1F8AF ; Unknown
+    0x1F8B0,  # .. 0x1F8BB ; Common
+    0x1F8BC,  # .. 0x1F8BF ; Unknown
+    0x1F8C0,  # .. 0x1F8C1 ; Common
+    0x1F8C2,  # .. 0x1F8CF ; Unknown
+    0x1F8D0,  # .. 0x1F8D8 ; Common
+    0x1F8D9,  # .. 0x1F8FF ; Unknown
+    0x1F900,  # .. 0x1FA57 ; Common
+    0x1FA58,  # .. 0x1FA5F ; Unknown
+    0x1FA60,  # .. 0x1FA6D ; Common
+    0x1FA6E,  # .. 0x1FA6F ; Unknown
+    0x1FA70,  # .. 0x1FA7C ; Common
+    0x1FA7D,  # .. 0x1FA7F ; Unknown
+    0x1FA80,  # .. 0x1FA8A ; Common
+    0x1FA8B,  # .. 0x1FA8D ; Unknown
+    0x1FA8E,  # .. 0x1FAC6 ; Common
+    0x1FAC7,  # .. 0x1FAC7 ; Unknown
+    0x1FAC8,  # .. 0x1FAC8 ; Common
+    0x1FAC9,  # .. 0x1FACC ; Unknown
+    0x1FACD,  # .. 0x1FADC ; Common
+    0x1FADD,  # .. 0x1FADE ; Unknown
+    0x1FADF,  # .. 0x1FAEA ; Common
+    0x1FAEB,  # .. 0x1FAEE ; Unknown
+    0x1FAEF,  # .. 0x1FAF8 ; Common
+    0x1FAF9,  # .. 0x1FAFF ; Unknown
+    0x1FB00,  # .. 0x1FB92 ; Common
+    0x1FB93,  # .. 0x1FB93 ; Unknown
+    0x1FB94,  # .. 0x1FBFA ; Common
+    0x1FBFB,  # .. 0x1FFFF ; Unknown
+    0x20000,  # .. 0x2A6DF ; Han
+    0x2A6E0,  # .. 0x2A6FF ; Unknown
+    0x2A700,  # .. 0x2B81D ; Han
+    0x2B81E,  # .. 0x2B81F ; Unknown
+    0x2B820,  # .. 0x2CEAD ; Han
+    0x2CEAE,  # .. 0x2CEAF ; Unknown
+    0x2CEB0,  # .. 0x2EBE0 ; Han
+    0x2EBE1,  # .. 0x2EBEF ; Unknown
+    0x2EBF0,  # .. 0x2EE5D ; Han
+    0x2EE5E,  # .. 0x2F7FF ; Unknown
+    0x2F800,  # .. 0x2FA1D ; Han
+    0x2FA1E,  # .. 0x2FFFF ; Unknown
+    0x30000,  # .. 0x3134A ; Han
+    0x3134B,  # .. 0x3134F ; Unknown
+    0x31350,  # .. 0x33479 ; Han
+    0x3347A,  # .. 0xE0000 ; Unknown
+    0xE0001,  # .. 0xE0001 ; Common
+    0xE0002,  # .. 0xE001F ; Unknown
+    0xE0020,  # .. 0xE007F ; Common
+    0xE0080,  # .. 0xE00FF ; Unknown
+    0xE0100,  # .. 0xE01EF ; Inherited
+    0xE01F0,  # .. 0x10FFFF ; Unknown
+]
+
+VALUES = [
+    "Zyyy",  # 0000..0040 ; Common
+    "Latn",  # 0041..005A ; Latin
+    "Zyyy",  # 005B..0060 ; Common
+    "Latn",  # 0061..007A ; Latin
+    "Zyyy",  # 007B..00A9 ; Common
+    "Latn",  # 00AA..00AA ; Latin
+    "Zyyy",  # 00AB..00B9 ; Common
+    "Latn",  # 00BA..00BA ; Latin
+    "Zyyy",  # 00BB..00BF ; Common
+    "Latn",  # 00C0..00D6 ; Latin
+    "Zyyy",  # 00D7..00D7 ; Common
+    "Latn",  # 00D8..00F6 ; Latin
+    "Zyyy",  # 00F7..00F7 ; Common
+    "Latn",  # 00F8..02B8 ; Latin
+    "Zyyy",  # 02B9..02DF ; Common
+    "Latn",  # 02E0..02E4 ; Latin
+    "Zyyy",  # 02E5..02E9 ; Common
+    "Bopo",  # 02EA..02EB ; Bopomofo
+    "Zyyy",  # 02EC..02FF ; Common
+    "Zinh",  # 0300..036F ; Inherited
+    "Grek",  # 0370..0373 ; Greek
+    "Zyyy",  # 0374..0374 ; Common
+    "Grek",  # 0375..0377 ; Greek
+    "Zzzz",  # 0378..0379 ; Unknown
+    "Grek",  # 037A..037D ; Greek
+    "Zyyy",  # 037E..037E ; Common
+    "Grek",  # 037F..037F ; Greek
+    "Zzzz",  # 0380..0383 ; Unknown
+    "Grek",  # 0384..0384 ; Greek
+    "Zyyy",  # 0385..0385 ; Common
+    "Grek",  # 0386..0386 ; Greek
+    "Zyyy",  # 0387..0387 ; Common
+    "Grek",  # 0388..038A ; Greek
+    "Zzzz",  # 038B..038B ; Unknown
+    "Grek",  # 038C..038C ; Greek
+    "Zzzz",  # 038D..038D ; Unknown
+    "Grek",  # 038E..03A1 ; Greek
+    "Zzzz",  # 03A2..03A2 ; Unknown
+    "Grek",  # 03A3..03E1 ; Greek
+    "Copt",  # 03E2..03EF ; Coptic
+    "Grek",  # 03F0..03FF ; Greek
+    "Cyrl",  # 0400..0484 ; Cyrillic
+    "Zinh",  # 0485..0486 ; Inherited
+    "Cyrl",  # 0487..052F ; Cyrillic
+    "Zzzz",  # 0530..0530 ; Unknown
+    "Armn",  # 0531..0556 ; Armenian
+    "Zzzz",  # 0557..0558 ; Unknown
+    "Armn",  # 0559..058A ; Armenian
+    "Zzzz",  # 058B..058C ; Unknown
+    "Armn",  # 058D..058F ; Armenian
+    "Zzzz",  # 0590..0590 ; Unknown
+    "Hebr",  # 0591..05C7 ; Hebrew
+    "Zzzz",  # 05C8..05CF ; Unknown
+    "Hebr",  # 05D0..05EA ; Hebrew
+    "Zzzz",  # 05EB..05EE ; Unknown
+    "Hebr",  # 05EF..05F4 ; Hebrew
+    "Zzzz",  # 05F5..05FF ; Unknown
+    "Arab",  # 0600..0604 ; Arabic
+    "Zyyy",  # 0605..0605 ; Common
+    "Arab",  # 0606..060B ; Arabic
+    "Zyyy",  # 060C..060C ; Common
+    "Arab",  # 060D..061A ; Arabic
+    "Zyyy",  # 061B..061B ; Common
+    "Arab",  # 061C..061E ; Arabic
+    "Zyyy",  # 061F..061F ; Common
+    "Arab",  # 0620..063F ; Arabic
+    "Zyyy",  # 0640..0640 ; Common
+    "Arab",  # 0641..064A ; Arabic
+    "Zinh",  # 064B..0655 ; Inherited
+    "Arab",  # 0656..066F ; Arabic
+    "Zinh",  # 0670..0670 ; Inherited
+    "Arab",  # 0671..06DC ; Arabic
+    "Zyyy",  # 06DD..06DD ; Common
+    "Arab",  # 06DE..06FF ; Arabic
+    "Syrc",  # 0700..070D ; Syriac
+    "Zzzz",  # 070E..070E ; Unknown
+    "Syrc",  # 070F..074A ; Syriac
+    "Zzzz",  # 074B..074C ; Unknown
+    "Syrc",  # 074D..074F ; Syriac
+    "Arab",  # 0750..077F ; Arabic
+    "Thaa",  # 0780..07B1 ; Thaana
+    "Zzzz",  # 07B2..07BF ; Unknown
+    "Nkoo",  # 07C0..07FA ; Nko
+    "Zzzz",  # 07FB..07FC ; Unknown
+    "Nkoo",  # 07FD..07FF ; Nko
+    "Samr",  # 0800..082D ; Samaritan
+    "Zzzz",  # 082E..082F ; Unknown
+    "Samr",  # 0830..083E ; Samaritan
+    "Zzzz",  # 083F..083F ; Unknown
+    "Mand",  # 0840..085B ; Mandaic
+    "Zzzz",  # 085C..085D ; Unknown
+    "Mand",  # 085E..085E ; Mandaic
+    "Zzzz",  # 085F..085F ; Unknown
+    "Syrc",  # 0860..086A ; Syriac
+    "Zzzz",  # 086B..086F ; Unknown
+    "Arab",  # 0870..0891 ; Arabic
+    "Zzzz",  # 0892..0896 ; Unknown
+    "Arab",  # 0897..08E1 ; Arabic
+    "Zyyy",  # 08E2..08E2 ; Common
+    "Arab",  # 08E3..08FF ; Arabic
+    "Deva",  # 0900..0950 ; Devanagari
+    "Zinh",  # 0951..0954 ; Inherited
+    "Deva",  # 0955..0963 ; Devanagari
+    "Zyyy",  # 0964..0965 ; Common
+    "Deva",  # 0966..097F ; Devanagari
+    "Beng",  # 0980..0983 ; Bengali
+    "Zzzz",  # 0984..0984 ; Unknown
+    "Beng",  # 0985..098C ; Bengali
+    "Zzzz",  # 098D..098E ; Unknown
+    "Beng",  # 098F..0990 ; Bengali
+    "Zzzz",  # 0991..0992 ; Unknown
+    "Beng",  # 0993..09A8 ; Bengali
+    "Zzzz",  # 09A9..09A9 ; Unknown
+    "Beng",  # 09AA..09B0 ; Bengali
+    "Zzzz",  # 09B1..09B1 ; Unknown
+    "Beng",  # 09B2..09B2 ; Bengali
+    "Zzzz",  # 09B3..09B5 ; Unknown
+    "Beng",  # 09B6..09B9 ; Bengali
+    "Zzzz",  # 09BA..09BB ; Unknown
+    "Beng",  # 09BC..09C4 ; Bengali
+    "Zzzz",  # 09C5..09C6 ; Unknown
+    "Beng",  # 09C7..09C8 ; Bengali
+    "Zzzz",  # 09C9..09CA ; Unknown
+    "Beng",  # 09CB..09CE ; Bengali
+    "Zzzz",  # 09CF..09D6 ; Unknown
+    "Beng",  # 09D7..09D7 ; Bengali
+    "Zzzz",  # 09D8..09DB ; Unknown
+    "Beng",  # 09DC..09DD ; Bengali
+    "Zzzz",  # 09DE..09DE ; Unknown
+    "Beng",  # 09DF..09E3 ; Bengali
+    "Zzzz",  # 09E4..09E5 ; Unknown
+    "Beng",  # 09E6..09FE ; Bengali
+    "Zzzz",  # 09FF..0A00 ; Unknown
+    "Guru",  # 0A01..0A03 ; Gurmukhi
+    "Zzzz",  # 0A04..0A04 ; Unknown
+    "Guru",  # 0A05..0A0A ; Gurmukhi
+    "Zzzz",  # 0A0B..0A0E ; Unknown
+    "Guru",  # 0A0F..0A10 ; Gurmukhi
+    "Zzzz",  # 0A11..0A12 ; Unknown
+    "Guru",  # 0A13..0A28 ; Gurmukhi
+    "Zzzz",  # 0A29..0A29 ; Unknown
+    "Guru",  # 0A2A..0A30 ; Gurmukhi
+    "Zzzz",  # 0A31..0A31 ; Unknown
+    "Guru",  # 0A32..0A33 ; Gurmukhi
+    "Zzzz",  # 0A34..0A34 ; Unknown
+    "Guru",  # 0A35..0A36 ; Gurmukhi
+    "Zzzz",  # 0A37..0A37 ; Unknown
+    "Guru",  # 0A38..0A39 ; Gurmukhi
+    "Zzzz",  # 0A3A..0A3B ; Unknown
+    "Guru",  # 0A3C..0A3C ; Gurmukhi
+    "Zzzz",  # 0A3D..0A3D ; Unknown
+    "Guru",  # 0A3E..0A42 ; Gurmukhi
+    "Zzzz",  # 0A43..0A46 ; Unknown
+    "Guru",  # 0A47..0A48 ; Gurmukhi
+    "Zzzz",  # 0A49..0A4A ; Unknown
+    "Guru",  # 0A4B..0A4D ; Gurmukhi
+    "Zzzz",  # 0A4E..0A50 ; Unknown
+    "Guru",  # 0A51..0A51 ; Gurmukhi
+    "Zzzz",  # 0A52..0A58 ; Unknown
+    "Guru",  # 0A59..0A5C ; Gurmukhi
+    "Zzzz",  # 0A5D..0A5D ; Unknown
+    "Guru",  # 0A5E..0A5E ; Gurmukhi
+    "Zzzz",  # 0A5F..0A65 ; Unknown
+    "Guru",  # 0A66..0A76 ; Gurmukhi
+    "Zzzz",  # 0A77..0A80 ; Unknown
+    "Gujr",  # 0A81..0A83 ; Gujarati
+    "Zzzz",  # 0A84..0A84 ; Unknown
+    "Gujr",  # 0A85..0A8D ; Gujarati
+    "Zzzz",  # 0A8E..0A8E ; Unknown
+    "Gujr",  # 0A8F..0A91 ; Gujarati
+    "Zzzz",  # 0A92..0A92 ; Unknown
+    "Gujr",  # 0A93..0AA8 ; Gujarati
+    "Zzzz",  # 0AA9..0AA9 ; Unknown
+    "Gujr",  # 0AAA..0AB0 ; Gujarati
+    "Zzzz",  # 0AB1..0AB1 ; Unknown
+    "Gujr",  # 0AB2..0AB3 ; Gujarati
+    "Zzzz",  # 0AB4..0AB4 ; Unknown
+    "Gujr",  # 0AB5..0AB9 ; Gujarati
+    "Zzzz",  # 0ABA..0ABB ; Unknown
+    "Gujr",  # 0ABC..0AC5 ; Gujarati
+    "Zzzz",  # 0AC6..0AC6 ; Unknown
+    "Gujr",  # 0AC7..0AC9 ; Gujarati
+    "Zzzz",  # 0ACA..0ACA ; Unknown
+    "Gujr",  # 0ACB..0ACD ; Gujarati
+    "Zzzz",  # 0ACE..0ACF ; Unknown
+    "Gujr",  # 0AD0..0AD0 ; Gujarati
+    "Zzzz",  # 0AD1..0ADF ; Unknown
+    "Gujr",  # 0AE0..0AE3 ; Gujarati
+    "Zzzz",  # 0AE4..0AE5 ; Unknown
+    "Gujr",  # 0AE6..0AF1 ; Gujarati
+    "Zzzz",  # 0AF2..0AF8 ; Unknown
+    "Gujr",  # 0AF9..0AFF ; Gujarati
+    "Zzzz",  # 0B00..0B00 ; Unknown
+    "Orya",  # 0B01..0B03 ; Oriya
+    "Zzzz",  # 0B04..0B04 ; Unknown
+    "Orya",  # 0B05..0B0C ; Oriya
+    "Zzzz",  # 0B0D..0B0E ; Unknown
+    "Orya",  # 0B0F..0B10 ; Oriya
+    "Zzzz",  # 0B11..0B12 ; Unknown
+    "Orya",  # 0B13..0B28 ; Oriya
+    "Zzzz",  # 0B29..0B29 ; Unknown
+    "Orya",  # 0B2A..0B30 ; Oriya
+    "Zzzz",  # 0B31..0B31 ; Unknown
+    "Orya",  # 0B32..0B33 ; Oriya
+    "Zzzz",  # 0B34..0B34 ; Unknown
+    "Orya",  # 0B35..0B39 ; Oriya
+    "Zzzz",  # 0B3A..0B3B ; Unknown
+    "Orya",  # 0B3C..0B44 ; Oriya
+    "Zzzz",  # 0B45..0B46 ; Unknown
+    "Orya",  # 0B47..0B48 ; Oriya
+    "Zzzz",  # 0B49..0B4A ; Unknown
+    "Orya",  # 0B4B..0B4D ; Oriya
+    "Zzzz",  # 0B4E..0B54 ; Unknown
+    "Orya",  # 0B55..0B57 ; Oriya
+    "Zzzz",  # 0B58..0B5B ; Unknown
+    "Orya",  # 0B5C..0B5D ; Oriya
+    "Zzzz",  # 0B5E..0B5E ; Unknown
+    "Orya",  # 0B5F..0B63 ; Oriya
+    "Zzzz",  # 0B64..0B65 ; Unknown
+    "Orya",  # 0B66..0B77 ; Oriya
+    "Zzzz",  # 0B78..0B81 ; Unknown
+    "Taml",  # 0B82..0B83 ; Tamil
+    "Zzzz",  # 0B84..0B84 ; Unknown
+    "Taml",  # 0B85..0B8A ; Tamil
+    "Zzzz",  # 0B8B..0B8D ; Unknown
+    "Taml",  # 0B8E..0B90 ; Tamil
+    "Zzzz",  # 0B91..0B91 ; Unknown
+    "Taml",  # 0B92..0B95 ; Tamil
+    "Zzzz",  # 0B96..0B98 ; Unknown
+    "Taml",  # 0B99..0B9A ; Tamil
+    "Zzzz",  # 0B9B..0B9B ; Unknown
+    "Taml",  # 0B9C..0B9C ; Tamil
+    "Zzzz",  # 0B9D..0B9D ; Unknown
+    "Taml",  # 0B9E..0B9F ; Tamil
+    "Zzzz",  # 0BA0..0BA2 ; Unknown
+    "Taml",  # 0BA3..0BA4 ; Tamil
+    "Zzzz",  # 0BA5..0BA7 ; Unknown
+    "Taml",  # 0BA8..0BAA ; Tamil
+    "Zzzz",  # 0BAB..0BAD ; Unknown
+    "Taml",  # 0BAE..0BB9 ; Tamil
+    "Zzzz",  # 0BBA..0BBD ; Unknown
+    "Taml",  # 0BBE..0BC2 ; Tamil
+    "Zzzz",  # 0BC3..0BC5 ; Unknown
+    "Taml",  # 0BC6..0BC8 ; Tamil
+    "Zzzz",  # 0BC9..0BC9 ; Unknown
+    "Taml",  # 0BCA..0BCD ; Tamil
+    "Zzzz",  # 0BCE..0BCF ; Unknown
+    "Taml",  # 0BD0..0BD0 ; Tamil
+    "Zzzz",  # 0BD1..0BD6 ; Unknown
+    "Taml",  # 0BD7..0BD7 ; Tamil
+    "Zzzz",  # 0BD8..0BE5 ; Unknown
+    "Taml",  # 0BE6..0BFA ; Tamil
+    "Zzzz",  # 0BFB..0BFF ; Unknown
+    "Telu",  # 0C00..0C0C ; Telugu
+    "Zzzz",  # 0C0D..0C0D ; Unknown
+    "Telu",  # 0C0E..0C10 ; Telugu
+    "Zzzz",  # 0C11..0C11 ; Unknown
+    "Telu",  # 0C12..0C28 ; Telugu
+    "Zzzz",  # 0C29..0C29 ; Unknown
+    "Telu",  # 0C2A..0C39 ; Telugu
+    "Zzzz",  # 0C3A..0C3B ; Unknown
+    "Telu",  # 0C3C..0C44 ; Telugu
+    "Zzzz",  # 0C45..0C45 ; Unknown
+    "Telu",  # 0C46..0C48 ; Telugu
+    "Zzzz",  # 0C49..0C49 ; Unknown
+    "Telu",  # 0C4A..0C4D ; Telugu
+    "Zzzz",  # 0C4E..0C54 ; Unknown
+    "Telu",  # 0C55..0C56 ; Telugu
+    "Zzzz",  # 0C57..0C57 ; Unknown
+    "Telu",  # 0C58..0C5A ; Telugu
+    "Zzzz",  # 0C5B..0C5B ; Unknown
+    "Telu",  # 0C5C..0C5D ; Telugu
+    "Zzzz",  # 0C5E..0C5F ; Unknown
+    "Telu",  # 0C60..0C63 ; Telugu
+    "Zzzz",  # 0C64..0C65 ; Unknown
+    "Telu",  # 0C66..0C6F ; Telugu
+    "Zzzz",  # 0C70..0C76 ; Unknown
+    "Telu",  # 0C77..0C7F ; Telugu
+    "Knda",  # 0C80..0C8C ; Kannada
+    "Zzzz",  # 0C8D..0C8D ; Unknown
+    "Knda",  # 0C8E..0C90 ; Kannada
+    "Zzzz",  # 0C91..0C91 ; Unknown
+    "Knda",  # 0C92..0CA8 ; Kannada
+    "Zzzz",  # 0CA9..0CA9 ; Unknown
+    "Knda",  # 0CAA..0CB3 ; Kannada
+    "Zzzz",  # 0CB4..0CB4 ; Unknown
+    "Knda",  # 0CB5..0CB9 ; Kannada
+    "Zzzz",  # 0CBA..0CBB ; Unknown
+    "Knda",  # 0CBC..0CC4 ; Kannada
+    "Zzzz",  # 0CC5..0CC5 ; Unknown
+    "Knda",  # 0CC6..0CC8 ; Kannada
+    "Zzzz",  # 0CC9..0CC9 ; Unknown
+    "Knda",  # 0CCA..0CCD ; Kannada
+    "Zzzz",  # 0CCE..0CD4 ; Unknown
+    "Knda",  # 0CD5..0CD6 ; Kannada
+    "Zzzz",  # 0CD7..0CDB ; Unknown
+    "Knda",  # 0CDC..0CDE ; Kannada
+    "Zzzz",  # 0CDF..0CDF ; Unknown
+    "Knda",  # 0CE0..0CE3 ; Kannada
+    "Zzzz",  # 0CE4..0CE5 ; Unknown
+    "Knda",  # 0CE6..0CEF ; Kannada
+    "Zzzz",  # 0CF0..0CF0 ; Unknown
+    "Knda",  # 0CF1..0CF3 ; Kannada
+    "Zzzz",  # 0CF4..0CFF ; Unknown
+    "Mlym",  # 0D00..0D0C ; Malayalam
+    "Zzzz",  # 0D0D..0D0D ; Unknown
+    "Mlym",  # 0D0E..0D10 ; Malayalam
+    "Zzzz",  # 0D11..0D11 ; Unknown
+    "Mlym",  # 0D12..0D44 ; Malayalam
+    "Zzzz",  # 0D45..0D45 ; Unknown
+    "Mlym",  # 0D46..0D48 ; Malayalam
+    "Zzzz",  # 0D49..0D49 ; Unknown
+    "Mlym",  # 0D4A..0D4F ; Malayalam
+    "Zzzz",  # 0D50..0D53 ; Unknown
+    "Mlym",  # 0D54..0D63 ; Malayalam
+    "Zzzz",  # 0D64..0D65 ; Unknown
+    "Mlym",  # 0D66..0D7F ; Malayalam
+    "Zzzz",  # 0D80..0D80 ; Unknown
+    "Sinh",  # 0D81..0D83 ; Sinhala
+    "Zzzz",  # 0D84..0D84 ; Unknown
+    "Sinh",  # 0D85..0D96 ; Sinhala
+    "Zzzz",  # 0D97..0D99 ; Unknown
+    "Sinh",  # 0D9A..0DB1 ; Sinhala
+    "Zzzz",  # 0DB2..0DB2 ; Unknown
+    "Sinh",  # 0DB3..0DBB ; Sinhala
+    "Zzzz",  # 0DBC..0DBC ; Unknown
+    "Sinh",  # 0DBD..0DBD ; Sinhala
+    "Zzzz",  # 0DBE..0DBF ; Unknown
+    "Sinh",  # 0DC0..0DC6 ; Sinhala
+    "Zzzz",  # 0DC7..0DC9 ; Unknown
+    "Sinh",  # 0DCA..0DCA ; Sinhala
+    "Zzzz",  # 0DCB..0DCE ; Unknown
+    "Sinh",  # 0DCF..0DD4 ; Sinhala
+    "Zzzz",  # 0DD5..0DD5 ; Unknown
+    "Sinh",  # 0DD6..0DD6 ; Sinhala
+    "Zzzz",  # 0DD7..0DD7 ; Unknown
+    "Sinh",  # 0DD8..0DDF ; Sinhala
+    "Zzzz",  # 0DE0..0DE5 ; Unknown
+    "Sinh",  # 0DE6..0DEF ; Sinhala
+    "Zzzz",  # 0DF0..0DF1 ; Unknown
+    "Sinh",  # 0DF2..0DF4 ; Sinhala
+    "Zzzz",  # 0DF5..0E00 ; Unknown
+    "Thai",  # 0E01..0E3A ; Thai
+    "Zzzz",  # 0E3B..0E3E ; Unknown
+    "Zyyy",  # 0E3F..0E3F ; Common
+    "Thai",  # 0E40..0E5B ; Thai
+    "Zzzz",  # 0E5C..0E80 ; Unknown
+    "Laoo",  # 0E81..0E82 ; Lao
+    "Zzzz",  # 0E83..0E83 ; Unknown
+    "Laoo",  # 0E84..0E84 ; Lao
+    "Zzzz",  # 0E85..0E85 ; Unknown
+    "Laoo",  # 0E86..0E8A ; Lao
+    "Zzzz",  # 0E8B..0E8B ; Unknown
+    "Laoo",  # 0E8C..0EA3 ; Lao
+    "Zzzz",  # 0EA4..0EA4 ; Unknown
+    "Laoo",  # 0EA5..0EA5 ; Lao
+    "Zzzz",  # 0EA6..0EA6 ; Unknown
+    "Laoo",  # 0EA7..0EBD ; Lao
+    "Zzzz",  # 0EBE..0EBF ; Unknown
+    "Laoo",  # 0EC0..0EC4 ; Lao
+    "Zzzz",  # 0EC5..0EC5 ; Unknown
+    "Laoo",  # 0EC6..0EC6 ; Lao
+    "Zzzz",  # 0EC7..0EC7 ; Unknown
+    "Laoo",  # 0EC8..0ECE ; Lao
+    "Zzzz",  # 0ECF..0ECF ; Unknown
+    "Laoo",  # 0ED0..0ED9 ; Lao
+    "Zzzz",  # 0EDA..0EDB ; Unknown
+    "Laoo",  # 0EDC..0EDF ; Lao
+    "Zzzz",  # 0EE0..0EFF ; Unknown
+    "Tibt",  # 0F00..0F47 ; Tibetan
+    "Zzzz",  # 0F48..0F48 ; Unknown
+    "Tibt",  # 0F49..0F6C ; Tibetan
+    "Zzzz",  # 0F6D..0F70 ; Unknown
+    "Tibt",  # 0F71..0F97 ; Tibetan
+    "Zzzz",  # 0F98..0F98 ; Unknown
+    "Tibt",  # 0F99..0FBC ; Tibetan
+    "Zzzz",  # 0FBD..0FBD ; Unknown
+    "Tibt",  # 0FBE..0FCC ; Tibetan
+    "Zzzz",  # 0FCD..0FCD ; Unknown
+    "Tibt",  # 0FCE..0FD4 ; Tibetan
+    "Zyyy",  # 0FD5..0FD8 ; Common
+    "Tibt",  # 0FD9..0FDA ; Tibetan
+    "Zzzz",  # 0FDB..0FFF ; Unknown
+    "Mymr",  # 1000..109F ; Myanmar
+    "Geor",  # 10A0..10C5 ; Georgian
+    "Zzzz",  # 10C6..10C6 ; Unknown
+    "Geor",  # 10C7..10C7 ; Georgian
+    "Zzzz",  # 10C8..10CC ; Unknown
+    "Geor",  # 10CD..10CD ; Georgian
+    "Zzzz",  # 10CE..10CF ; Unknown
+    "Geor",  # 10D0..10FA ; Georgian
+    "Zyyy",  # 10FB..10FB ; Common
+    "Geor",  # 10FC..10FF ; Georgian
+    "Hang",  # 1100..11FF ; Hangul
+    "Ethi",  # 1200..1248 ; Ethiopic
+    "Zzzz",  # 1249..1249 ; Unknown
+    "Ethi",  # 124A..124D ; Ethiopic
+    "Zzzz",  # 124E..124F ; Unknown
+    "Ethi",  # 1250..1256 ; Ethiopic
+    "Zzzz",  # 1257..1257 ; Unknown
+    "Ethi",  # 1258..1258 ; Ethiopic
+    "Zzzz",  # 1259..1259 ; Unknown
+    "Ethi",  # 125A..125D ; Ethiopic
+    "Zzzz",  # 125E..125F ; Unknown
+    "Ethi",  # 1260..1288 ; Ethiopic
+    "Zzzz",  # 1289..1289 ; Unknown
+    "Ethi",  # 128A..128D ; Ethiopic
+    "Zzzz",  # 128E..128F ; Unknown
+    "Ethi",  # 1290..12B0 ; Ethiopic
+    "Zzzz",  # 12B1..12B1 ; Unknown
+    "Ethi",  # 12B2..12B5 ; Ethiopic
+    "Zzzz",  # 12B6..12B7 ; Unknown
+    "Ethi",  # 12B8..12BE ; Ethiopic
+    "Zzzz",  # 12BF..12BF ; Unknown
+    "Ethi",  # 12C0..12C0 ; Ethiopic
+    "Zzzz",  # 12C1..12C1 ; Unknown
+    "Ethi",  # 12C2..12C5 ; Ethiopic
+    "Zzzz",  # 12C6..12C7 ; Unknown
+    "Ethi",  # 12C8..12D6 ; Ethiopic
+    "Zzzz",  # 12D7..12D7 ; Unknown
+    "Ethi",  # 12D8..1310 ; Ethiopic
+    "Zzzz",  # 1311..1311 ; Unknown
+    "Ethi",  # 1312..1315 ; Ethiopic
+    "Zzzz",  # 1316..1317 ; Unknown
+    "Ethi",  # 1318..135A ; Ethiopic
+    "Zzzz",  # 135B..135C ; Unknown
+    "Ethi",  # 135D..137C ; Ethiopic
+    "Zzzz",  # 137D..137F ; Unknown
+    "Ethi",  # 1380..1399 ; Ethiopic
+    "Zzzz",  # 139A..139F ; Unknown
+    "Cher",  # 13A0..13F5 ; Cherokee
+    "Zzzz",  # 13F6..13F7 ; Unknown
+    "Cher",  # 13F8..13FD ; Cherokee
+    "Zzzz",  # 13FE..13FF ; Unknown
+    "Cans",  # 1400..167F ; Canadian_Aboriginal
+    "Ogam",  # 1680..169C ; Ogham
+    "Zzzz",  # 169D..169F ; Unknown
+    "Runr",  # 16A0..16EA ; Runic
+    "Zyyy",  # 16EB..16ED ; Common
+    "Runr",  # 16EE..16F8 ; Runic
+    "Zzzz",  # 16F9..16FF ; Unknown
+    "Tglg",  # 1700..1715 ; Tagalog
+    "Zzzz",  # 1716..171E ; Unknown
+    "Tglg",  # 171F..171F ; Tagalog
+    "Hano",  # 1720..1734 ; Hanunoo
+    "Zyyy",  # 1735..1736 ; Common
+    "Zzzz",  # 1737..173F ; Unknown
+    "Buhd",  # 1740..1753 ; Buhid
+    "Zzzz",  # 1754..175F ; Unknown
+    "Tagb",  # 1760..176C ; Tagbanwa
+    "Zzzz",  # 176D..176D ; Unknown
+    "Tagb",  # 176E..1770 ; Tagbanwa
+    "Zzzz",  # 1771..1771 ; Unknown
+    "Tagb",  # 1772..1773 ; Tagbanwa
+    "Zzzz",  # 1774..177F ; Unknown
+    "Khmr",  # 1780..17DD ; Khmer
+    "Zzzz",  # 17DE..17DF ; Unknown
+    "Khmr",  # 17E0..17E9 ; Khmer
+    "Zzzz",  # 17EA..17EF ; Unknown
+    "Khmr",  # 17F0..17F9 ; Khmer
+    "Zzzz",  # 17FA..17FF ; Unknown
+    "Mong",  # 1800..1801 ; Mongolian
+    "Zyyy",  # 1802..1803 ; Common
+    "Mong",  # 1804..1804 ; Mongolian
+    "Zyyy",  # 1805..1805 ; Common
+    "Mong",  # 1806..1819 ; Mongolian
+    "Zzzz",  # 181A..181F ; Unknown
+    "Mong",  # 1820..1878 ; Mongolian
+    "Zzzz",  # 1879..187F ; Unknown
+    "Mong",  # 1880..18AA ; Mongolian
+    "Zzzz",  # 18AB..18AF ; Unknown
+    "Cans",  # 18B0..18F5 ; Canadian_Aboriginal
+    "Zzzz",  # 18F6..18FF ; Unknown
+    "Limb",  # 1900..191E ; Limbu
+    "Zzzz",  # 191F..191F ; Unknown
+    "Limb",  # 1920..192B ; Limbu
+    "Zzzz",  # 192C..192F ; Unknown
+    "Limb",  # 1930..193B ; Limbu
+    "Zzzz",  # 193C..193F ; Unknown
+    "Limb",  # 1940..1940 ; Limbu
+    "Zzzz",  # 1941..1943 ; Unknown
+    "Limb",  # 1944..194F ; Limbu
+    "Tale",  # 1950..196D ; Tai_Le
+    "Zzzz",  # 196E..196F ; Unknown
+    "Tale",  # 1970..1974 ; Tai_Le
+    "Zzzz",  # 1975..197F ; Unknown
+    "Talu",  # 1980..19AB ; New_Tai_Lue
+    "Zzzz",  # 19AC..19AF ; Unknown
+    "Talu",  # 19B0..19C9 ; New_Tai_Lue
+    "Zzzz",  # 19CA..19CF ; Unknown
+    "Talu",  # 19D0..19DA ; New_Tai_Lue
+    "Zzzz",  # 19DB..19DD ; Unknown
+    "Talu",  # 19DE..19DF ; New_Tai_Lue
+    "Khmr",  # 19E0..19FF ; Khmer
+    "Bugi",  # 1A00..1A1B ; Buginese
+    "Zzzz",  # 1A1C..1A1D ; Unknown
+    "Bugi",  # 1A1E..1A1F ; Buginese
+    "Lana",  # 1A20..1A5E ; Tai_Tham
+    "Zzzz",  # 1A5F..1A5F ; Unknown
+    "Lana",  # 1A60..1A7C ; Tai_Tham
+    "Zzzz",  # 1A7D..1A7E ; Unknown
+    "Lana",  # 1A7F..1A89 ; Tai_Tham
+    "Zzzz",  # 1A8A..1A8F ; Unknown
+    "Lana",  # 1A90..1A99 ; Tai_Tham
+    "Zzzz",  # 1A9A..1A9F ; Unknown
+    "Lana",  # 1AA0..1AAD ; Tai_Tham
+    "Zzzz",  # 1AAE..1AAF ; Unknown
+    "Zinh",  # 1AB0..1ADD ; Inherited
+    "Zzzz",  # 1ADE..1ADF ; Unknown
+    "Zinh",  # 1AE0..1AEB ; Inherited
+    "Zzzz",  # 1AEC..1AFF ; Unknown
+    "Bali",  # 1B00..1B4C ; Balinese
+    "Zzzz",  # 1B4D..1B4D ; Unknown
+    "Bali",  # 1B4E..1B7F ; Balinese
+    "Sund",  # 1B80..1BBF ; Sundanese
+    "Batk",  # 1BC0..1BF3 ; Batak
+    "Zzzz",  # 1BF4..1BFB ; Unknown
+    "Batk",  # 1BFC..1BFF ; Batak
+    "Lepc",  # 1C00..1C37 ; Lepcha
+    "Zzzz",  # 1C38..1C3A ; Unknown
+    "Lepc",  # 1C3B..1C49 ; Lepcha
+    "Zzzz",  # 1C4A..1C4C ; Unknown
+    "Lepc",  # 1C4D..1C4F ; Lepcha
+    "Olck",  # 1C50..1C7F ; Ol_Chiki
+    "Cyrl",  # 1C80..1C8A ; Cyrillic
+    "Zzzz",  # 1C8B..1C8F ; Unknown
+    "Geor",  # 1C90..1CBA ; Georgian
+    "Zzzz",  # 1CBB..1CBC ; Unknown
+    "Geor",  # 1CBD..1CBF ; Georgian
+    "Sund",  # 1CC0..1CC7 ; Sundanese
+    "Zzzz",  # 1CC8..1CCF ; Unknown
+    "Zinh",  # 1CD0..1CD2 ; Inherited
+    "Zyyy",  # 1CD3..1CD3 ; Common
+    "Zinh",  # 1CD4..1CE0 ; Inherited
+    "Zyyy",  # 1CE1..1CE1 ; Common
+    "Zinh",  # 1CE2..1CE8 ; Inherited
+    "Zyyy",  # 1CE9..1CEC ; Common
+    "Zinh",  # 1CED..1CED ; Inherited
+    "Zyyy",  # 1CEE..1CF3 ; Common
+    "Zinh",  # 1CF4..1CF4 ; Inherited
+    "Zyyy",  # 1CF5..1CF7 ; Common
+    "Zinh",  # 1CF8..1CF9 ; Inherited
+    "Zyyy",  # 1CFA..1CFA ; Common
+    "Zzzz",  # 1CFB..1CFF ; Unknown
+    "Latn",  # 1D00..1D25 ; Latin
+    "Grek",  # 1D26..1D2A ; Greek
+    "Cyrl",  # 1D2B..1D2B ; Cyrillic
+    "Latn",  # 1D2C..1D5C ; Latin
+    "Grek",  # 1D5D..1D61 ; Greek
+    "Latn",  # 1D62..1D65 ; Latin
+    "Grek",  # 1D66..1D6A ; Greek
+    "Latn",  # 1D6B..1D77 ; Latin
+    "Cyrl",  # 1D78..1D78 ; Cyrillic
+    "Latn",  # 1D79..1DBE ; Latin
+    "Grek",  # 1DBF..1DBF ; Greek
+    "Zinh",  # 1DC0..1DFF ; Inherited
+    "Latn",  # 1E00..1EFF ; Latin
+    "Grek",  # 1F00..1F15 ; Greek
+    "Zzzz",  # 1F16..1F17 ; Unknown
+    "Grek",  # 1F18..1F1D ; Greek
+    "Zzzz",  # 1F1E..1F1F ; Unknown
+    "Grek",  # 1F20..1F45 ; Greek
+    "Zzzz",  # 1F46..1F47 ; Unknown
+    "Grek",  # 1F48..1F4D ; Greek
+    "Zzzz",  # 1F4E..1F4F ; Unknown
+    "Grek",  # 1F50..1F57 ; Greek
+    "Zzzz",  # 1F58..1F58 ; Unknown
+    "Grek",  # 1F59..1F59 ; Greek
+    "Zzzz",  # 1F5A..1F5A ; Unknown
+    "Grek",  # 1F5B..1F5B ; Greek
+    "Zzzz",  # 1F5C..1F5C ; Unknown
+    "Grek",  # 1F5D..1F5D ; Greek
+    "Zzzz",  # 1F5E..1F5E ; Unknown
+    "Grek",  # 1F5F..1F7D ; Greek
+    "Zzzz",  # 1F7E..1F7F ; Unknown
+    "Grek",  # 1F80..1FB4 ; Greek
+    "Zzzz",  # 1FB5..1FB5 ; Unknown
+    "Grek",  # 1FB6..1FC4 ; Greek
+    "Zzzz",  # 1FC5..1FC5 ; Unknown
+    "Grek",  # 1FC6..1FD3 ; Greek
+    "Zzzz",  # 1FD4..1FD5 ; Unknown
+    "Grek",  # 1FD6..1FDB ; Greek
+    "Zzzz",  # 1FDC..1FDC ; Unknown
+    "Grek",  # 1FDD..1FEF ; Greek
+    "Zzzz",  # 1FF0..1FF1 ; Unknown
+    "Grek",  # 1FF2..1FF4 ; Greek
+    "Zzzz",  # 1FF5..1FF5 ; Unknown
+    "Grek",  # 1FF6..1FFE ; Greek
+    "Zzzz",  # 1FFF..1FFF ; Unknown
+    "Zyyy",  # 2000..200B ; Common
+    "Zinh",  # 200C..200D ; Inherited
+    "Zyyy",  # 200E..2064 ; Common
+    "Zzzz",  # 2065..2065 ; Unknown
+    "Zyyy",  # 2066..2070 ; Common
+    "Latn",  # 2071..2071 ; Latin
+    "Zzzz",  # 2072..2073 ; Unknown
+    "Zyyy",  # 2074..207E ; Common
+    "Latn",  # 207F..207F ; Latin
+    "Zyyy",  # 2080..208E ; Common
+    "Zzzz",  # 208F..208F ; Unknown
+    "Latn",  # 2090..209C ; Latin
+    "Zzzz",  # 209D..209F ; Unknown
+    "Zyyy",  # 20A0..20C1 ; Common
+    "Zzzz",  # 20C2..20CF ; Unknown
+    "Zinh",  # 20D0..20F0 ; Inherited
+    "Zzzz",  # 20F1..20FF ; Unknown
+    "Zyyy",  # 2100..2125 ; Common
+    "Grek",  # 2126..2126 ; Greek
+    "Zyyy",  # 2127..2129 ; Common
+    "Latn",  # 212A..212B ; Latin
+    "Zyyy",  # 212C..2131 ; Common
+    "Latn",  # 2132..2132 ; Latin
+    "Zyyy",  # 2133..214D ; Common
+    "Latn",  # 214E..214E ; Latin
+    "Zyyy",  # 214F..215F ; Common
+    "Latn",  # 2160..2188 ; Latin
+    "Zyyy",  # 2189..218B ; Common
+    "Zzzz",  # 218C..218F ; Unknown
+    "Zyyy",  # 2190..2429 ; Common
+    "Zzzz",  # 242A..243F ; Unknown
+    "Zyyy",  # 2440..244A ; Common
+    "Zzzz",  # 244B..245F ; Unknown
+    "Zyyy",  # 2460..27FF ; Common
+    "Brai",  # 2800..28FF ; Braille
+    "Zyyy",  # 2900..2B73 ; Common
+    "Zzzz",  # 2B74..2B75 ; Unknown
+    "Zyyy",  # 2B76..2BFF ; Common
+    "Glag",  # 2C00..2C5F ; Glagolitic
+    "Latn",  # 2C60..2C7F ; Latin
+    "Copt",  # 2C80..2CF3 ; Coptic
+    "Zzzz",  # 2CF4..2CF8 ; Unknown
+    "Copt",  # 2CF9..2CFF ; Coptic
+    "Geor",  # 2D00..2D25 ; Georgian
+    "Zzzz",  # 2D26..2D26 ; Unknown
+    "Geor",  # 2D27..2D27 ; Georgian
+    "Zzzz",  # 2D28..2D2C ; Unknown
+    "Geor",  # 2D2D..2D2D ; Georgian
+    "Zzzz",  # 2D2E..2D2F ; Unknown
+    "Tfng",  # 2D30..2D67 ; Tifinagh
+    "Zzzz",  # 2D68..2D6E ; Unknown
+    "Tfng",  # 2D6F..2D70 ; Tifinagh
+    "Zzzz",  # 2D71..2D7E ; Unknown
+    "Tfng",  # 2D7F..2D7F ; Tifinagh
+    "Ethi",  # 2D80..2D96 ; Ethiopic
+    "Zzzz",  # 2D97..2D9F ; Unknown
+    "Ethi",  # 2DA0..2DA6 ; Ethiopic
+    "Zzzz",  # 2DA7..2DA7 ; Unknown
+    "Ethi",  # 2DA8..2DAE ; Ethiopic
+    "Zzzz",  # 2DAF..2DAF ; Unknown
+    "Ethi",  # 2DB0..2DB6 ; Ethiopic
+    "Zzzz",  # 2DB7..2DB7 ; Unknown
+    "Ethi",  # 2DB8..2DBE ; Ethiopic
+    "Zzzz",  # 2DBF..2DBF ; Unknown
+    "Ethi",  # 2DC0..2DC6 ; Ethiopic
+    "Zzzz",  # 2DC7..2DC7 ; Unknown
+    "Ethi",  # 2DC8..2DCE ; Ethiopic
+    "Zzzz",  # 2DCF..2DCF ; Unknown
+    "Ethi",  # 2DD0..2DD6 ; Ethiopic
+    "Zzzz",  # 2DD7..2DD7 ; Unknown
+    "Ethi",  # 2DD8..2DDE ; Ethiopic
+    "Zzzz",  # 2DDF..2DDF ; Unknown
+    "Cyrl",  # 2DE0..2DFF ; Cyrillic
+    "Zyyy",  # 2E00..2E5D ; Common
+    "Zzzz",  # 2E5E..2E7F ; Unknown
+    "Hani",  # 2E80..2E99 ; Han
+    "Zzzz",  # 2E9A..2E9A ; Unknown
+    "Hani",  # 2E9B..2EF3 ; Han
+    "Zzzz",  # 2EF4..2EFF ; Unknown
+    "Hani",  # 2F00..2FD5 ; Han
+    "Zzzz",  # 2FD6..2FEF ; Unknown
+    "Zyyy",  # 2FF0..3004 ; Common
+    "Hani",  # 3005..3005 ; Han
+    "Zyyy",  # 3006..3006 ; Common
+    "Hani",  # 3007..3007 ; Han
+    "Zyyy",  # 3008..3020 ; Common
+    "Hani",  # 3021..3029 ; Han
+    "Zinh",  # 302A..302D ; Inherited
+    "Hang",  # 302E..302F ; Hangul
+    "Zyyy",  # 3030..3037 ; Common
+    "Hani",  # 3038..303B ; Han
+    "Zyyy",  # 303C..303F ; Common
+    "Zzzz",  # 3040..3040 ; Unknown
+    "Hira",  # 3041..3096 ; Hiragana
+    "Zzzz",  # 3097..3098 ; Unknown
+    "Zinh",  # 3099..309A ; Inherited
+    "Zyyy",  # 309B..309C ; Common
+    "Hira",  # 309D..309F ; Hiragana
+    "Zyyy",  # 30A0..30A0 ; Common
+    "Kana",  # 30A1..30FA ; Katakana
+    "Zyyy",  # 30FB..30FC ; Common
+    "Kana",  # 30FD..30FF ; Katakana
+    "Zzzz",  # 3100..3104 ; Unknown
+    "Bopo",  # 3105..312F ; Bopomofo
+    "Zzzz",  # 3130..3130 ; Unknown
+    "Hang",  # 3131..318E ; Hangul
+    "Zzzz",  # 318F..318F ; Unknown
+    "Zyyy",  # 3190..319F ; Common
+    "Bopo",  # 31A0..31BF ; Bopomofo
+    "Zyyy",  # 31C0..31E5 ; Common
+    "Zzzz",  # 31E6..31EE ; Unknown
+    "Zyyy",  # 31EF..31EF ; Common
+    "Kana",  # 31F0..31FF ; Katakana
+    "Hang",  # 3200..321E ; Hangul
+    "Zzzz",  # 321F..321F ; Unknown
+    "Zyyy",  # 3220..325F ; Common
+    "Hang",  # 3260..327E ; Hangul
+    "Zyyy",  # 327F..32CF ; Common
+    "Kana",  # 32D0..32FE ; Katakana
+    "Zyyy",  # 32FF..32FF ; Common
+    "Kana",  # 3300..3357 ; Katakana
+    "Zyyy",  # 3358..33FF ; Common
+    "Hani",  # 3400..4DBF ; Han
+    "Zyyy",  # 4DC0..4DFF ; Common
+    "Hani",  # 4E00..9FFF ; Han
+    "Yiii",  # A000..A48C ; Yi
+    "Zzzz",  # A48D..A48F ; Unknown
+    "Yiii",  # A490..A4C6 ; Yi
+    "Zzzz",  # A4C7..A4CF ; Unknown
+    "Lisu",  # A4D0..A4FF ; Lisu
+    "Vaii",  # A500..A62B ; Vai
+    "Zzzz",  # A62C..A63F ; Unknown
+    "Cyrl",  # A640..A69F ; Cyrillic
+    "Bamu",  # A6A0..A6F7 ; Bamum
+    "Zzzz",  # A6F8..A6FF ; Unknown
+    "Zyyy",  # A700..A721 ; Common
+    "Latn",  # A722..A787 ; Latin
+    "Zyyy",  # A788..A78A ; Common
+    "Latn",  # A78B..A7DC ; Latin
+    "Zzzz",  # A7DD..A7F0 ; Unknown
+    "Latn",  # A7F1..A7FF ; Latin
+    "Sylo",  # A800..A82C ; Syloti_Nagri
+    "Zzzz",  # A82D..A82F ; Unknown
+    "Zyyy",  # A830..A839 ; Common
+    "Zzzz",  # A83A..A83F ; Unknown
+    "Phag",  # A840..A877 ; Phags_Pa
+    "Zzzz",  # A878..A87F ; Unknown
+    "Saur",  # A880..A8C5 ; Saurashtra
+    "Zzzz",  # A8C6..A8CD ; Unknown
+    "Saur",  # A8CE..A8D9 ; Saurashtra
+    "Zzzz",  # A8DA..A8DF ; Unknown
+    "Deva",  # A8E0..A8FF ; Devanagari
+    "Kali",  # A900..A92D ; Kayah_Li
+    "Zyyy",  # A92E..A92E ; Common
+    "Kali",  # A92F..A92F ; Kayah_Li
+    "Rjng",  # A930..A953 ; Rejang
+    "Zzzz",  # A954..A95E ; Unknown
+    "Rjng",  # A95F..A95F ; Rejang
+    "Hang",  # A960..A97C ; Hangul
+    "Zzzz",  # A97D..A97F ; Unknown
+    "Java",  # A980..A9CD ; Javanese
+    "Zzzz",  # A9CE..A9CE ; Unknown
+    "Zyyy",  # A9CF..A9CF ; Common
+    "Java",  # A9D0..A9D9 ; Javanese
+    "Zzzz",  # A9DA..A9DD ; Unknown
+    "Java",  # A9DE..A9DF ; Javanese
+    "Mymr",  # A9E0..A9FE ; Myanmar
+    "Zzzz",  # A9FF..A9FF ; Unknown
+    "Cham",  # AA00..AA36 ; Cham
+    "Zzzz",  # AA37..AA3F ; Unknown
+    "Cham",  # AA40..AA4D ; Cham
+    "Zzzz",  # AA4E..AA4F ; Unknown
+    "Cham",  # AA50..AA59 ; Cham
+    "Zzzz",  # AA5A..AA5B ; Unknown
+    "Cham",  # AA5C..AA5F ; Cham
+    "Mymr",  # AA60..AA7F ; Myanmar
+    "Tavt",  # AA80..AAC2 ; Tai_Viet
+    "Zzzz",  # AAC3..AADA ; Unknown
+    "Tavt",  # AADB..AADF ; Tai_Viet
+    "Mtei",  # AAE0..AAF6 ; Meetei_Mayek
+    "Zzzz",  # AAF7..AB00 ; Unknown
+    "Ethi",  # AB01..AB06 ; Ethiopic
+    "Zzzz",  # AB07..AB08 ; Unknown
+    "Ethi",  # AB09..AB0E ; Ethiopic
+    "Zzzz",  # AB0F..AB10 ; Unknown
+    "Ethi",  # AB11..AB16 ; Ethiopic
+    "Zzzz",  # AB17..AB1F ; Unknown
+    "Ethi",  # AB20..AB26 ; Ethiopic
+    "Zzzz",  # AB27..AB27 ; Unknown
+    "Ethi",  # AB28..AB2E ; Ethiopic
+    "Zzzz",  # AB2F..AB2F ; Unknown
+    "Latn",  # AB30..AB5A ; Latin
+    "Zyyy",  # AB5B..AB5B ; Common
+    "Latn",  # AB5C..AB64 ; Latin
+    "Grek",  # AB65..AB65 ; Greek
+    "Latn",  # AB66..AB69 ; Latin
+    "Zyyy",  # AB6A..AB6B ; Common
+    "Zzzz",  # AB6C..AB6F ; Unknown
+    "Cher",  # AB70..ABBF ; Cherokee
+    "Mtei",  # ABC0..ABED ; Meetei_Mayek
+    "Zzzz",  # ABEE..ABEF ; Unknown
+    "Mtei",  # ABF0..ABF9 ; Meetei_Mayek
+    "Zzzz",  # ABFA..ABFF ; Unknown
+    "Hang",  # AC00..D7A3 ; Hangul
+    "Zzzz",  # D7A4..D7AF ; Unknown
+    "Hang",  # D7B0..D7C6 ; Hangul
+    "Zzzz",  # D7C7..D7CA ; Unknown
+    "Hang",  # D7CB..D7FB ; Hangul
+    "Zzzz",  # D7FC..F8FF ; Unknown
+    "Hani",  # F900..FA6D ; Han
+    "Zzzz",  # FA6E..FA6F ; Unknown
+    "Hani",  # FA70..FAD9 ; Han
+    "Zzzz",  # FADA..FAFF ; Unknown
+    "Latn",  # FB00..FB06 ; Latin
+    "Zzzz",  # FB07..FB12 ; Unknown
+    "Armn",  # FB13..FB17 ; Armenian
+    "Zzzz",  # FB18..FB1C ; Unknown
+    "Hebr",  # FB1D..FB36 ; Hebrew
+    "Zzzz",  # FB37..FB37 ; Unknown
+    "Hebr",  # FB38..FB3C ; Hebrew
+    "Zzzz",  # FB3D..FB3D ; Unknown
+    "Hebr",  # FB3E..FB3E ; Hebrew
+    "Zzzz",  # FB3F..FB3F ; Unknown
+    "Hebr",  # FB40..FB41 ; Hebrew
+    "Zzzz",  # FB42..FB42 ; Unknown
+    "Hebr",  # FB43..FB44 ; Hebrew
+    "Zzzz",  # FB45..FB45 ; Unknown
+    "Hebr",  # FB46..FB4F ; Hebrew
+    "Arab",  # FB50..FD3D ; Arabic
+    "Zyyy",  # FD3E..FD3F ; Common
+    "Arab",  # FD40..FDCF ; Arabic
+    "Zzzz",  # FDD0..FDEF ; Unknown
+    "Arab",  # FDF0..FDFF ; Arabic
+    "Zinh",  # FE00..FE0F ; Inherited
+    "Zyyy",  # FE10..FE19 ; Common
+    "Zzzz",  # FE1A..FE1F ; Unknown
+    "Zinh",  # FE20..FE2D ; Inherited
+    "Cyrl",  # FE2E..FE2F ; Cyrillic
+    "Zyyy",  # FE30..FE52 ; Common
+    "Zzzz",  # FE53..FE53 ; Unknown
+    "Zyyy",  # FE54..FE66 ; Common
+    "Zzzz",  # FE67..FE67 ; Unknown
+    "Zyyy",  # FE68..FE6B ; Common
+    "Zzzz",  # FE6C..FE6F ; Unknown
+    "Arab",  # FE70..FE74 ; Arabic
+    "Zzzz",  # FE75..FE75 ; Unknown
+    "Arab",  # FE76..FEFC ; Arabic
+    "Zzzz",  # FEFD..FEFE ; Unknown
+    "Zyyy",  # FEFF..FEFF ; Common
+    "Zzzz",  # FF00..FF00 ; Unknown
+    "Zyyy",  # FF01..FF20 ; Common
+    "Latn",  # FF21..FF3A ; Latin
+    "Zyyy",  # FF3B..FF40 ; Common
+    "Latn",  # FF41..FF5A ; Latin
+    "Zyyy",  # FF5B..FF65 ; Common
+    "Kana",  # FF66..FF6F ; Katakana
+    "Zyyy",  # FF70..FF70 ; Common
+    "Kana",  # FF71..FF9D ; Katakana
+    "Zyyy",  # FF9E..FF9F ; Common
+    "Hang",  # FFA0..FFBE ; Hangul
+    "Zzzz",  # FFBF..FFC1 ; Unknown
+    "Hang",  # FFC2..FFC7 ; Hangul
+    "Zzzz",  # FFC8..FFC9 ; Unknown
+    "Hang",  # FFCA..FFCF ; Hangul
+    "Zzzz",  # FFD0..FFD1 ; Unknown
+    "Hang",  # FFD2..FFD7 ; Hangul
+    "Zzzz",  # FFD8..FFD9 ; Unknown
+    "Hang",  # FFDA..FFDC ; Hangul
+    "Zzzz",  # FFDD..FFDF ; Unknown
+    "Zyyy",  # FFE0..FFE6 ; Common
+    "Zzzz",  # FFE7..FFE7 ; Unknown
+    "Zyyy",  # FFE8..FFEE ; Common
+    "Zzzz",  # FFEF..FFF8 ; Unknown
+    "Zyyy",  # FFF9..FFFD ; Common
+    "Zzzz",  # FFFE..FFFF ; Unknown
+    "Linb",  # 10000..1000B ; Linear_B
+    "Zzzz",  # 1000C..1000C ; Unknown
+    "Linb",  # 1000D..10026 ; Linear_B
+    "Zzzz",  # 10027..10027 ; Unknown
+    "Linb",  # 10028..1003A ; Linear_B
+    "Zzzz",  # 1003B..1003B ; Unknown
+    "Linb",  # 1003C..1003D ; Linear_B
+    "Zzzz",  # 1003E..1003E ; Unknown
+    "Linb",  # 1003F..1004D ; Linear_B
+    "Zzzz",  # 1004E..1004F ; Unknown
+    "Linb",  # 10050..1005D ; Linear_B
+    "Zzzz",  # 1005E..1007F ; Unknown
+    "Linb",  # 10080..100FA ; Linear_B
+    "Zzzz",  # 100FB..100FF ; Unknown
+    "Zyyy",  # 10100..10102 ; Common
+    "Zzzz",  # 10103..10106 ; Unknown
+    "Zyyy",  # 10107..10133 ; Common
+    "Zzzz",  # 10134..10136 ; Unknown
+    "Zyyy",  # 10137..1013F ; Common
+    "Grek",  # 10140..1018E ; Greek
+    "Zzzz",  # 1018F..1018F ; Unknown
+    "Zyyy",  # 10190..1019C ; Common
+    "Zzzz",  # 1019D..1019F ; Unknown
+    "Grek",  # 101A0..101A0 ; Greek
+    "Zzzz",  # 101A1..101CF ; Unknown
+    "Zyyy",  # 101D0..101FC ; Common
+    "Zinh",  # 101FD..101FD ; Inherited
+    "Zzzz",  # 101FE..1027F ; Unknown
+    "Lyci",  # 10280..1029C ; Lycian
+    "Zzzz",  # 1029D..1029F ; Unknown
+    "Cari",  # 102A0..102D0 ; Carian
+    "Zzzz",  # 102D1..102DF ; Unknown
+    "Zinh",  # 102E0..102E0 ; Inherited
+    "Zyyy",  # 102E1..102FB ; Common
+    "Zzzz",  # 102FC..102FF ; Unknown
+    "Ital",  # 10300..10323 ; Old_Italic
+    "Zzzz",  # 10324..1032C ; Unknown
+    "Ital",  # 1032D..1032F ; Old_Italic
+    "Goth",  # 10330..1034A ; Gothic
+    "Zzzz",  # 1034B..1034F ; Unknown
+    "Perm",  # 10350..1037A ; Old_Permic
+    "Zzzz",  # 1037B..1037F ; Unknown
+    "Ugar",  # 10380..1039D ; Ugaritic
+    "Zzzz",  # 1039E..1039E ; Unknown
+    "Ugar",  # 1039F..1039F ; Ugaritic
+    "Xpeo",  # 103A0..103C3 ; Old_Persian
+    "Zzzz",  # 103C4..103C7 ; Unknown
+    "Xpeo",  # 103C8..103D5 ; Old_Persian
+    "Zzzz",  # 103D6..103FF ; Unknown
+    "Dsrt",  # 10400..1044F ; Deseret
+    "Shaw",  # 10450..1047F ; Shavian
+    "Osma",  # 10480..1049D ; Osmanya
+    "Zzzz",  # 1049E..1049F ; Unknown
+    "Osma",  # 104A0..104A9 ; Osmanya
+    "Zzzz",  # 104AA..104AF ; Unknown
+    "Osge",  # 104B0..104D3 ; Osage
+    "Zzzz",  # 104D4..104D7 ; Unknown
+    "Osge",  # 104D8..104FB ; Osage
+    "Zzzz",  # 104FC..104FF ; Unknown
+    "Elba",  # 10500..10527 ; Elbasan
+    "Zzzz",  # 10528..1052F ; Unknown
+    "Aghb",  # 10530..10563 ; Caucasian_Albanian
+    "Zzzz",  # 10564..1056E ; Unknown
+    "Aghb",  # 1056F..1056F ; Caucasian_Albanian
+    "Vith",  # 10570..1057A ; Vithkuqi
+    "Zzzz",  # 1057B..1057B ; Unknown
+    "Vith",  # 1057C..1058A ; Vithkuqi
+    "Zzzz",  # 1058B..1058B ; Unknown
+    "Vith",  # 1058C..10592 ; Vithkuqi
+    "Zzzz",  # 10593..10593 ; Unknown
+    "Vith",  # 10594..10595 ; Vithkuqi
+    "Zzzz",  # 10596..10596 ; Unknown
+    "Vith",  # 10597..105A1 ; Vithkuqi
+    "Zzzz",  # 105A2..105A2 ; Unknown
+    "Vith",  # 105A3..105B1 ; Vithkuqi
+    "Zzzz",  # 105B2..105B2 ; Unknown
+    "Vith",  # 105B3..105B9 ; Vithkuqi
+    "Zzzz",  # 105BA..105BA ; Unknown
+    "Vith",  # 105BB..105BC ; Vithkuqi
+    "Zzzz",  # 105BD..105BF ; Unknown
+    "Todr",  # 105C0..105F3 ; Todhri
+    "Zzzz",  # 105F4..105FF ; Unknown
+    "Lina",  # 10600..10736 ; Linear_A
+    "Zzzz",  # 10737..1073F ; Unknown
+    "Lina",  # 10740..10755 ; Linear_A
+    "Zzzz",  # 10756..1075F ; Unknown
+    "Lina",  # 10760..10767 ; Linear_A
+    "Zzzz",  # 10768..1077F ; Unknown
+    "Latn",  # 10780..10785 ; Latin
+    "Zzzz",  # 10786..10786 ; Unknown
+    "Latn",  # 10787..107B0 ; Latin
+    "Zzzz",  # 107B1..107B1 ; Unknown
+    "Latn",  # 107B2..107BA ; Latin
+    "Zzzz",  # 107BB..107FF ; Unknown
+    "Cprt",  # 10800..10805 ; Cypriot
+    "Zzzz",  # 10806..10807 ; Unknown
+    "Cprt",  # 10808..10808 ; Cypriot
+    "Zzzz",  # 10809..10809 ; Unknown
+    "Cprt",  # 1080A..10835 ; Cypriot
+    "Zzzz",  # 10836..10836 ; Unknown
+    "Cprt",  # 10837..10838 ; Cypriot
+    "Zzzz",  # 10839..1083B ; Unknown
+    "Cprt",  # 1083C..1083C ; Cypriot
+    "Zzzz",  # 1083D..1083E ; Unknown
+    "Cprt",  # 1083F..1083F ; Cypriot
+    "Armi",  # 10840..10855 ; Imperial_Aramaic
+    "Zzzz",  # 10856..10856 ; Unknown
+    "Armi",  # 10857..1085F ; Imperial_Aramaic
+    "Palm",  # 10860..1087F ; Palmyrene
+    "Nbat",  # 10880..1089E ; Nabataean
+    "Zzzz",  # 1089F..108A6 ; Unknown
+    "Nbat",  # 108A7..108AF ; Nabataean
+    "Zzzz",  # 108B0..108DF ; Unknown
+    "Hatr",  # 108E0..108F2 ; Hatran
+    "Zzzz",  # 108F3..108F3 ; Unknown
+    "Hatr",  # 108F4..108F5 ; Hatran
+    "Zzzz",  # 108F6..108FA ; Unknown
+    "Hatr",  # 108FB..108FF ; Hatran
+    "Phnx",  # 10900..1091B ; Phoenician
+    "Zzzz",  # 1091C..1091E ; Unknown
+    "Phnx",  # 1091F..1091F ; Phoenician
+    "Lydi",  # 10920..10939 ; Lydian
+    "Zzzz",  # 1093A..1093E ; Unknown
+    "Lydi",  # 1093F..1093F ; Lydian
+    "Sidt",  # 10940..10959 ; Sidetic
+    "Zzzz",  # 1095A..1097F ; Unknown
+    "Mero",  # 10980..1099F ; Meroitic_Hieroglyphs
+    "Merc",  # 109A0..109B7 ; Meroitic_Cursive
+    "Zzzz",  # 109B8..109BB ; Unknown
+    "Merc",  # 109BC..109CF ; Meroitic_Cursive
+    "Zzzz",  # 109D0..109D1 ; Unknown
+    "Merc",  # 109D2..109FF ; Meroitic_Cursive
+    "Khar",  # 10A00..10A03 ; Kharoshthi
+    "Zzzz",  # 10A04..10A04 ; Unknown
+    "Khar",  # 10A05..10A06 ; Kharoshthi
+    "Zzzz",  # 10A07..10A0B ; Unknown
+    "Khar",  # 10A0C..10A13 ; Kharoshthi
+    "Zzzz",  # 10A14..10A14 ; Unknown
+    "Khar",  # 10A15..10A17 ; Kharoshthi
+    "Zzzz",  # 10A18..10A18 ; Unknown
+    "Khar",  # 10A19..10A35 ; Kharoshthi
+    "Zzzz",  # 10A36..10A37 ; Unknown
+    "Khar",  # 10A38..10A3A ; Kharoshthi
+    "Zzzz",  # 10A3B..10A3E ; Unknown
+    "Khar",  # 10A3F..10A48 ; Kharoshthi
+    "Zzzz",  # 10A49..10A4F ; Unknown
+    "Khar",  # 10A50..10A58 ; Kharoshthi
+    "Zzzz",  # 10A59..10A5F ; Unknown
+    "Sarb",  # 10A60..10A7F ; Old_South_Arabian
+    "Narb",  # 10A80..10A9F ; Old_North_Arabian
+    "Zzzz",  # 10AA0..10ABF ; Unknown
+    "Mani",  # 10AC0..10AE6 ; Manichaean
+    "Zzzz",  # 10AE7..10AEA ; Unknown
+    "Mani",  # 10AEB..10AF6 ; Manichaean
+    "Zzzz",  # 10AF7..10AFF ; Unknown
+    "Avst",  # 10B00..10B35 ; Avestan
+    "Zzzz",  # 10B36..10B38 ; Unknown
+    "Avst",  # 10B39..10B3F ; Avestan
+    "Prti",  # 10B40..10B55 ; Inscriptional_Parthian
+    "Zzzz",  # 10B56..10B57 ; Unknown
+    "Prti",  # 10B58..10B5F ; Inscriptional_Parthian
+    "Phli",  # 10B60..10B72 ; Inscriptional_Pahlavi
+    "Zzzz",  # 10B73..10B77 ; Unknown
+    "Phli",  # 10B78..10B7F ; Inscriptional_Pahlavi
+    "Phlp",  # 10B80..10B91 ; Psalter_Pahlavi
+    "Zzzz",  # 10B92..10B98 ; Unknown
+    "Phlp",  # 10B99..10B9C ; Psalter_Pahlavi
+    "Zzzz",  # 10B9D..10BA8 ; Unknown
+    "Phlp",  # 10BA9..10BAF ; Psalter_Pahlavi
+    "Zzzz",  # 10BB0..10BFF ; Unknown
+    "Orkh",  # 10C00..10C48 ; Old_Turkic
+    "Zzzz",  # 10C49..10C7F ; Unknown
+    "Hung",  # 10C80..10CB2 ; Old_Hungarian
+    "Zzzz",  # 10CB3..10CBF ; Unknown
+    "Hung",  # 10CC0..10CF2 ; Old_Hungarian
+    "Zzzz",  # 10CF3..10CF9 ; Unknown
+    "Hung",  # 10CFA..10CFF ; Old_Hungarian
+    "Rohg",  # 10D00..10D27 ; Hanifi_Rohingya
+    "Zzzz",  # 10D28..10D2F ; Unknown
+    "Rohg",  # 10D30..10D39 ; Hanifi_Rohingya
+    "Zzzz",  # 10D3A..10D3F ; Unknown
+    "Gara",  # 10D40..10D65 ; Garay
+    "Zzzz",  # 10D66..10D68 ; Unknown
+    "Gara",  # 10D69..10D85 ; Garay
+    "Zzzz",  # 10D86..10D8D ; Unknown
+    "Gara",  # 10D8E..10D8F ; Garay
+    "Zzzz",  # 10D90..10E5F ; Unknown
+    "Arab",  # 10E60..10E7E ; Arabic
+    "Zzzz",  # 10E7F..10E7F ; Unknown
+    "Yezi",  # 10E80..10EA9 ; Yezidi
+    "Zzzz",  # 10EAA..10EAA ; Unknown
+    "Yezi",  # 10EAB..10EAD ; Yezidi
+    "Zzzz",  # 10EAE..10EAF ; Unknown
+    "Yezi",  # 10EB0..10EB1 ; Yezidi
+    "Zzzz",  # 10EB2..10EC1 ; Unknown
+    "Arab",  # 10EC2..10EC7 ; Arabic
+    "Zzzz",  # 10EC8..10ECF ; Unknown
+    "Arab",  # 10ED0..10ED8 ; Arabic
+    "Zzzz",  # 10ED9..10EF9 ; Unknown
+    "Arab",  # 10EFA..10EFF ; Arabic
+    "Sogo",  # 10F00..10F27 ; Old_Sogdian
+    "Zzzz",  # 10F28..10F2F ; Unknown
+    "Sogd",  # 10F30..10F59 ; Sogdian
+    "Zzzz",  # 10F5A..10F6F ; Unknown
+    "Ougr",  # 10F70..10F89 ; Old_Uyghur
+    "Zzzz",  # 10F8A..10FAF ; Unknown
+    "Chrs",  # 10FB0..10FCB ; Chorasmian
+    "Zzzz",  # 10FCC..10FDF ; Unknown
+    "Elym",  # 10FE0..10FF6 ; Elymaic
+    "Zzzz",  # 10FF7..10FFF ; Unknown
+    "Brah",  # 11000..1104D ; Brahmi
+    "Zzzz",  # 1104E..11051 ; Unknown
+    "Brah",  # 11052..11075 ; Brahmi
+    "Zzzz",  # 11076..1107E ; Unknown
+    "Brah",  # 1107F..1107F ; Brahmi
+    "Kthi",  # 11080..110C2 ; Kaithi
+    "Zzzz",  # 110C3..110CC ; Unknown
+    "Kthi",  # 110CD..110CD ; Kaithi
+    "Zzzz",  # 110CE..110CF ; Unknown
+    "Sora",  # 110D0..110E8 ; Sora_Sompeng
+    "Zzzz",  # 110E9..110EF ; Unknown
+    "Sora",  # 110F0..110F9 ; Sora_Sompeng
+    "Zzzz",  # 110FA..110FF ; Unknown
+    "Cakm",  # 11100..11134 ; Chakma
+    "Zzzz",  # 11135..11135 ; Unknown
+    "Cakm",  # 11136..11147 ; Chakma
+    "Zzzz",  # 11148..1114F ; Unknown
+    "Mahj",  # 11150..11176 ; Mahajani
+    "Zzzz",  # 11177..1117F ; Unknown
+    "Shrd",  # 11180..111DF ; Sharada
+    "Zzzz",  # 111E0..111E0 ; Unknown
+    "Sinh",  # 111E1..111F4 ; Sinhala
+    "Zzzz",  # 111F5..111FF ; Unknown
+    "Khoj",  # 11200..11211 ; Khojki
+    "Zzzz",  # 11212..11212 ; Unknown
+    "Khoj",  # 11213..11241 ; Khojki
+    "Zzzz",  # 11242..1127F ; Unknown
+    "Mult",  # 11280..11286 ; Multani
+    "Zzzz",  # 11287..11287 ; Unknown
+    "Mult",  # 11288..11288 ; Multani
+    "Zzzz",  # 11289..11289 ; Unknown
+    "Mult",  # 1128A..1128D ; Multani
+    "Zzzz",  # 1128E..1128E ; Unknown
+    "Mult",  # 1128F..1129D ; Multani
+    "Zzzz",  # 1129E..1129E ; Unknown
+    "Mult",  # 1129F..112A9 ; Multani
+    "Zzzz",  # 112AA..112AF ; Unknown
+    "Sind",  # 112B0..112EA ; Khudawadi
+    "Zzzz",  # 112EB..112EF ; Unknown
+    "Sind",  # 112F0..112F9 ; Khudawadi
+    "Zzzz",  # 112FA..112FF ; Unknown
+    "Gran",  # 11300..11303 ; Grantha
+    "Zzzz",  # 11304..11304 ; Unknown
+    "Gran",  # 11305..1130C ; Grantha
+    "Zzzz",  # 1130D..1130E ; Unknown
+    "Gran",  # 1130F..11310 ; Grantha
+    "Zzzz",  # 11311..11312 ; Unknown
+    "Gran",  # 11313..11328 ; Grantha
+    "Zzzz",  # 11329..11329 ; Unknown
+    "Gran",  # 1132A..11330 ; Grantha
+    "Zzzz",  # 11331..11331 ; Unknown
+    "Gran",  # 11332..11333 ; Grantha
+    "Zzzz",  # 11334..11334 ; Unknown
+    "Gran",  # 11335..11339 ; Grantha
+    "Zzzz",  # 1133A..1133A ; Unknown
+    "Zinh",  # 1133B..1133B ; Inherited
+    "Gran",  # 1133C..11344 ; Grantha
+    "Zzzz",  # 11345..11346 ; Unknown
+    "Gran",  # 11347..11348 ; Grantha
+    "Zzzz",  # 11349..1134A ; Unknown
+    "Gran",  # 1134B..1134D ; Grantha
+    "Zzzz",  # 1134E..1134F ; Unknown
+    "Gran",  # 11350..11350 ; Grantha
+    "Zzzz",  # 11351..11356 ; Unknown
+    "Gran",  # 11357..11357 ; Grantha
+    "Zzzz",  # 11358..1135C ; Unknown
+    "Gran",  # 1135D..11363 ; Grantha
+    "Zzzz",  # 11364..11365 ; Unknown
+    "Gran",  # 11366..1136C ; Grantha
+    "Zzzz",  # 1136D..1136F ; Unknown
+    "Gran",  # 11370..11374 ; Grantha
+    "Zzzz",  # 11375..1137F ; Unknown
+    "Tutg",  # 11380..11389 ; Tulu_Tigalari
+    "Zzzz",  # 1138A..1138A ; Unknown
+    "Tutg",  # 1138B..1138B ; Tulu_Tigalari
+    "Zzzz",  # 1138C..1138D ; Unknown
+    "Tutg",  # 1138E..1138E ; Tulu_Tigalari
+    "Zzzz",  # 1138F..1138F ; Unknown
+    "Tutg",  # 11390..113B5 ; Tulu_Tigalari
+    "Zzzz",  # 113B6..113B6 ; Unknown
+    "Tutg",  # 113B7..113C0 ; Tulu_Tigalari
+    "Zzzz",  # 113C1..113C1 ; Unknown
+    "Tutg",  # 113C2..113C2 ; Tulu_Tigalari
+    "Zzzz",  # 113C3..113C4 ; Unknown
+    "Tutg",  # 113C5..113C5 ; Tulu_Tigalari
+    "Zzzz",  # 113C6..113C6 ; Unknown
+    "Tutg",  # 113C7..113CA ; Tulu_Tigalari
+    "Zzzz",  # 113CB..113CB ; Unknown
+    "Tutg",  # 113CC..113D5 ; Tulu_Tigalari
+    "Zzzz",  # 113D6..113D6 ; Unknown
+    "Tutg",  # 113D7..113D8 ; Tulu_Tigalari
+    "Zzzz",  # 113D9..113E0 ; Unknown
+    "Tutg",  # 113E1..113E2 ; Tulu_Tigalari
+    "Zzzz",  # 113E3..113FF ; Unknown
+    "Newa",  # 11400..1145B ; Newa
+    "Zzzz",  # 1145C..1145C ; Unknown
+    "Newa",  # 1145D..11461 ; Newa
+    "Zzzz",  # 11462..1147F ; Unknown
+    "Tirh",  # 11480..114C7 ; Tirhuta
+    "Zzzz",  # 114C8..114CF ; Unknown
+    "Tirh",  # 114D0..114D9 ; Tirhuta
+    "Zzzz",  # 114DA..1157F ; Unknown
+    "Sidd",  # 11580..115B5 ; Siddham
+    "Zzzz",  # 115B6..115B7 ; Unknown
+    "Sidd",  # 115B8..115DD ; Siddham
+    "Zzzz",  # 115DE..115FF ; Unknown
+    "Modi",  # 11600..11644 ; Modi
+    "Zzzz",  # 11645..1164F ; Unknown
+    "Modi",  # 11650..11659 ; Modi
+    "Zzzz",  # 1165A..1165F ; Unknown
+    "Mong",  # 11660..1166C ; Mongolian
+    "Zzzz",  # 1166D..1167F ; Unknown
+    "Takr",  # 11680..116B9 ; Takri
+    "Zzzz",  # 116BA..116BF ; Unknown
+    "Takr",  # 116C0..116C9 ; Takri
+    "Zzzz",  # 116CA..116CF ; Unknown
+    "Mymr",  # 116D0..116E3 ; Myanmar
+    "Zzzz",  # 116E4..116FF ; Unknown
+    "Ahom",  # 11700..1171A ; Ahom
+    "Zzzz",  # 1171B..1171C ; Unknown
+    "Ahom",  # 1171D..1172B ; Ahom
+    "Zzzz",  # 1172C..1172F ; Unknown
+    "Ahom",  # 11730..11746 ; Ahom
+    "Zzzz",  # 11747..117FF ; Unknown
+    "Dogr",  # 11800..1183B ; Dogra
+    "Zzzz",  # 1183C..1189F ; Unknown
+    "Wara",  # 118A0..118F2 ; Warang_Citi
+    "Zzzz",  # 118F3..118FE ; Unknown
+    "Wara",  # 118FF..118FF ; Warang_Citi
+    "Diak",  # 11900..11906 ; Dives_Akuru
+    "Zzzz",  # 11907..11908 ; Unknown
+    "Diak",  # 11909..11909 ; Dives_Akuru
+    "Zzzz",  # 1190A..1190B ; Unknown
+    "Diak",  # 1190C..11913 ; Dives_Akuru
+    "Zzzz",  # 11914..11914 ; Unknown
+    "Diak",  # 11915..11916 ; Dives_Akuru
+    "Zzzz",  # 11917..11917 ; Unknown
+    "Diak",  # 11918..11935 ; Dives_Akuru
+    "Zzzz",  # 11936..11936 ; Unknown
+    "Diak",  # 11937..11938 ; Dives_Akuru
+    "Zzzz",  # 11939..1193A ; Unknown
+    "Diak",  # 1193B..11946 ; Dives_Akuru
+    "Zzzz",  # 11947..1194F ; Unknown
+    "Diak",  # 11950..11959 ; Dives_Akuru
+    "Zzzz",  # 1195A..1199F ; Unknown
+    "Nand",  # 119A0..119A7 ; Nandinagari
+    "Zzzz",  # 119A8..119A9 ; Unknown
+    "Nand",  # 119AA..119D7 ; Nandinagari
+    "Zzzz",  # 119D8..119D9 ; Unknown
+    "Nand",  # 119DA..119E4 ; Nandinagari
+    "Zzzz",  # 119E5..119FF ; Unknown
+    "Zanb",  # 11A00..11A47 ; Zanabazar_Square
+    "Zzzz",  # 11A48..11A4F ; Unknown
+    "Soyo",  # 11A50..11AA2 ; Soyombo
+    "Zzzz",  # 11AA3..11AAF ; Unknown
+    "Cans",  # 11AB0..11ABF ; Canadian_Aboriginal
+    "Pauc",  # 11AC0..11AF8 ; Pau_Cin_Hau
+    "Zzzz",  # 11AF9..11AFF ; Unknown
+    "Deva",  # 11B00..11B09 ; Devanagari
+    "Zzzz",  # 11B0A..11B5F ; Unknown
+    "Shrd",  # 11B60..11B67 ; Sharada
+    "Zzzz",  # 11B68..11BBF ; Unknown
+    "Sunu",  # 11BC0..11BE1 ; Sunuwar
+    "Zzzz",  # 11BE2..11BEF ; Unknown
+    "Sunu",  # 11BF0..11BF9 ; Sunuwar
+    "Zzzz",  # 11BFA..11BFF ; Unknown
+    "Bhks",  # 11C00..11C08 ; Bhaiksuki
+    "Zzzz",  # 11C09..11C09 ; Unknown
+    "Bhks",  # 11C0A..11C36 ; Bhaiksuki
+    "Zzzz",  # 11C37..11C37 ; Unknown
+    "Bhks",  # 11C38..11C45 ; Bhaiksuki
+    "Zzzz",  # 11C46..11C4F ; Unknown
+    "Bhks",  # 11C50..11C6C ; Bhaiksuki
+    "Zzzz",  # 11C6D..11C6F ; Unknown
+    "Marc",  # 11C70..11C8F ; Marchen
+    "Zzzz",  # 11C90..11C91 ; Unknown
+    "Marc",  # 11C92..11CA7 ; Marchen
+    "Zzzz",  # 11CA8..11CA8 ; Unknown
+    "Marc",  # 11CA9..11CB6 ; Marchen
+    "Zzzz",  # 11CB7..11CFF ; Unknown
+    "Gonm",  # 11D00..11D06 ; Masaram_Gondi
+    "Zzzz",  # 11D07..11D07 ; Unknown
+    "Gonm",  # 11D08..11D09 ; Masaram_Gondi
+    "Zzzz",  # 11D0A..11D0A ; Unknown
+    "Gonm",  # 11D0B..11D36 ; Masaram_Gondi
+    "Zzzz",  # 11D37..11D39 ; Unknown
+    "Gonm",  # 11D3A..11D3A ; Masaram_Gondi
+    "Zzzz",  # 11D3B..11D3B ; Unknown
+    "Gonm",  # 11D3C..11D3D ; Masaram_Gondi
+    "Zzzz",  # 11D3E..11D3E ; Unknown
+    "Gonm",  # 11D3F..11D47 ; Masaram_Gondi
+    "Zzzz",  # 11D48..11D4F ; Unknown
+    "Gonm",  # 11D50..11D59 ; Masaram_Gondi
+    "Zzzz",  # 11D5A..11D5F ; Unknown
+    "Gong",  # 11D60..11D65 ; Gunjala_Gondi
+    "Zzzz",  # 11D66..11D66 ; Unknown
+    "Gong",  # 11D67..11D68 ; Gunjala_Gondi
+    "Zzzz",  # 11D69..11D69 ; Unknown
+    "Gong",  # 11D6A..11D8E ; Gunjala_Gondi
+    "Zzzz",  # 11D8F..11D8F ; Unknown
+    "Gong",  # 11D90..11D91 ; Gunjala_Gondi
+    "Zzzz",  # 11D92..11D92 ; Unknown
+    "Gong",  # 11D93..11D98 ; Gunjala_Gondi
+    "Zzzz",  # 11D99..11D9F ; Unknown
+    "Gong",  # 11DA0..11DA9 ; Gunjala_Gondi
+    "Zzzz",  # 11DAA..11DAF ; Unknown
+    "Tols",  # 11DB0..11DDB ; Tolong_Siki
+    "Zzzz",  # 11DDC..11DDF ; Unknown
+    "Tols",  # 11DE0..11DE9 ; Tolong_Siki
+    "Zzzz",  # 11DEA..11EDF ; Unknown
+    "Maka",  # 11EE0..11EF8 ; Makasar
+    "Zzzz",  # 11EF9..11EFF ; Unknown
+    "Kawi",  # 11F00..11F10 ; Kawi
+    "Zzzz",  # 11F11..11F11 ; Unknown
+    "Kawi",  # 11F12..11F3A ; Kawi
+    "Zzzz",  # 11F3B..11F3D ; Unknown
+    "Kawi",  # 11F3E..11F5A ; Kawi
+    "Zzzz",  # 11F5B..11FAF ; Unknown
+    "Lisu",  # 11FB0..11FB0 ; Lisu
+    "Zzzz",  # 11FB1..11FBF ; Unknown
+    "Taml",  # 11FC0..11FF1 ; Tamil
+    "Zzzz",  # 11FF2..11FFE ; Unknown
+    "Taml",  # 11FFF..11FFF ; Tamil
+    "Xsux",  # 12000..12399 ; Cuneiform
+    "Zzzz",  # 1239A..123FF ; Unknown
+    "Xsux",  # 12400..1246E ; Cuneiform
+    "Zzzz",  # 1246F..1246F ; Unknown
+    "Xsux",  # 12470..12474 ; Cuneiform
+    "Zzzz",  # 12475..1247F ; Unknown
+    "Xsux",  # 12480..12543 ; Cuneiform
+    "Zzzz",  # 12544..12F8F ; Unknown
+    "Cpmn",  # 12F90..12FF2 ; Cypro_Minoan
+    "Zzzz",  # 12FF3..12FFF ; Unknown
+    "Egyp",  # 13000..13455 ; Egyptian_Hieroglyphs
+    "Zzzz",  # 13456..1345F ; Unknown
+    "Egyp",  # 13460..143FA ; Egyptian_Hieroglyphs
+    "Zzzz",  # 143FB..143FF ; Unknown
+    "Hluw",  # 14400..14646 ; Anatolian_Hieroglyphs
+    "Zzzz",  # 14647..160FF ; Unknown
+    "Gukh",  # 16100..16139 ; Gurung_Khema
+    "Zzzz",  # 1613A..167FF ; Unknown
+    "Bamu",  # 16800..16A38 ; Bamum
+    "Zzzz",  # 16A39..16A3F ; Unknown
+    "Mroo",  # 16A40..16A5E ; Mro
+    "Zzzz",  # 16A5F..16A5F ; Unknown
+    "Mroo",  # 16A60..16A69 ; Mro
+    "Zzzz",  # 16A6A..16A6D ; Unknown
+    "Mroo",  # 16A6E..16A6F ; Mro
+    "Tnsa",  # 16A70..16ABE ; Tangsa
+    "Zzzz",  # 16ABF..16ABF ; Unknown
+    "Tnsa",  # 16AC0..16AC9 ; Tangsa
+    "Zzzz",  # 16ACA..16ACF ; Unknown
+    "Bass",  # 16AD0..16AED ; Bassa_Vah
+    "Zzzz",  # 16AEE..16AEF ; Unknown
+    "Bass",  # 16AF0..16AF5 ; Bassa_Vah
+    "Zzzz",  # 16AF6..16AFF ; Unknown
+    "Hmng",  # 16B00..16B45 ; Pahawh_Hmong
+    "Zzzz",  # 16B46..16B4F ; Unknown
+    "Hmng",  # 16B50..16B59 ; Pahawh_Hmong
+    "Zzzz",  # 16B5A..16B5A ; Unknown
+    "Hmng",  # 16B5B..16B61 ; Pahawh_Hmong
+    "Zzzz",  # 16B62..16B62 ; Unknown
+    "Hmng",  # 16B63..16B77 ; Pahawh_Hmong
+    "Zzzz",  # 16B78..16B7C ; Unknown
+    "Hmng",  # 16B7D..16B8F ; Pahawh_Hmong
+    "Zzzz",  # 16B90..16D3F ; Unknown
+    "Krai",  # 16D40..16D79 ; Kirat_Rai
+    "Zzzz",  # 16D7A..16E3F ; Unknown
+    "Medf",  # 16E40..16E9A ; Medefaidrin
+    "Zzzz",  # 16E9B..16E9F ; Unknown
+    "Berf",  # 16EA0..16EB8 ; Beria_Erfe
+    "Zzzz",  # 16EB9..16EBA ; Unknown
+    "Berf",  # 16EBB..16ED3 ; Beria_Erfe
+    "Zzzz",  # 16ED4..16EFF ; Unknown
+    "Plrd",  # 16F00..16F4A ; Miao
+    "Zzzz",  # 16F4B..16F4E ; Unknown
+    "Plrd",  # 16F4F..16F87 ; Miao
+    "Zzzz",  # 16F88..16F8E ; Unknown
+    "Plrd",  # 16F8F..16F9F ; Miao
+    "Zzzz",  # 16FA0..16FDF ; Unknown
+    "Tang",  # 16FE0..16FE0 ; Tangut
+    "Nshu",  # 16FE1..16FE1 ; Nushu
+    "Hani",  # 16FE2..16FE3 ; Han
+    "Kits",  # 16FE4..16FE4 ; Khitan_Small_Script
+    "Zzzz",  # 16FE5..16FEF ; Unknown
+    "Hani",  # 16FF0..16FF6 ; Han
+    "Zzzz",  # 16FF7..16FFF ; Unknown
+    "Tang",  # 17000..18AFF ; Tangut
+    "Kits",  # 18B00..18CD5 ; Khitan_Small_Script
+    "Zzzz",  # 18CD6..18CFE ; Unknown
+    "Kits",  # 18CFF..18CFF ; Khitan_Small_Script
+    "Tang",  # 18D00..18D1E ; Tangut
+    "Zzzz",  # 18D1F..18D7F ; Unknown
+    "Tang",  # 18D80..18DF2 ; Tangut
+    "Zzzz",  # 18DF3..1AFEF ; Unknown
+    "Kana",  # 1AFF0..1AFF3 ; Katakana
+    "Zzzz",  # 1AFF4..1AFF4 ; Unknown
+    "Kana",  # 1AFF5..1AFFB ; Katakana
+    "Zzzz",  # 1AFFC..1AFFC ; Unknown
+    "Kana",  # 1AFFD..1AFFE ; Katakana
+    "Zzzz",  # 1AFFF..1AFFF ; Unknown
+    "Kana",  # 1B000..1B000 ; Katakana
+    "Hira",  # 1B001..1B11F ; Hiragana
+    "Kana",  # 1B120..1B122 ; Katakana
+    "Zzzz",  # 1B123..1B131 ; Unknown
+    "Hira",  # 1B132..1B132 ; Hiragana
+    "Zzzz",  # 1B133..1B14F ; Unknown
+    "Hira",  # 1B150..1B152 ; Hiragana
+    "Zzzz",  # 1B153..1B154 ; Unknown
+    "Kana",  # 1B155..1B155 ; Katakana
+    "Zzzz",  # 1B156..1B163 ; Unknown
+    "Kana",  # 1B164..1B167 ; Katakana
+    "Zzzz",  # 1B168..1B16F ; Unknown
+    "Nshu",  # 1B170..1B2FB ; Nushu
+    "Zzzz",  # 1B2FC..1BBFF ; Unknown
+    "Dupl",  # 1BC00..1BC6A ; Duployan
+    "Zzzz",  # 1BC6B..1BC6F ; Unknown
+    "Dupl",  # 1BC70..1BC7C ; Duployan
+    "Zzzz",  # 1BC7D..1BC7F ; Unknown
+    "Dupl",  # 1BC80..1BC88 ; Duployan
+    "Zzzz",  # 1BC89..1BC8F ; Unknown
+    "Dupl",  # 1BC90..1BC99 ; Duployan
+    "Zzzz",  # 1BC9A..1BC9B ; Unknown
+    "Dupl",  # 1BC9C..1BC9F ; Duployan
+    "Zyyy",  # 1BCA0..1BCA3 ; Common
+    "Zzzz",  # 1BCA4..1CBFF ; Unknown
+    "Zyyy",  # 1CC00..1CCFC ; Common
+    "Zzzz",  # 1CCFD..1CCFF ; Unknown
+    "Zyyy",  # 1CD00..1CEB3 ; Common
+    "Zzzz",  # 1CEB4..1CEB9 ; Unknown
+    "Zyyy",  # 1CEBA..1CED0 ; Common
+    "Zzzz",  # 1CED1..1CEDF ; Unknown
+    "Zyyy",  # 1CEE0..1CEF0 ; Common
+    "Zzzz",  # 1CEF1..1CEFF ; Unknown
+    "Zinh",  # 1CF00..1CF2D ; Inherited
+    "Zzzz",  # 1CF2E..1CF2F ; Unknown
+    "Zinh",  # 1CF30..1CF46 ; Inherited
+    "Zzzz",  # 1CF47..1CF4F ; Unknown
+    "Zyyy",  # 1CF50..1CFC3 ; Common
+    "Zzzz",  # 1CFC4..1CFFF ; Unknown
+    "Zyyy",  # 1D000..1D0F5 ; Common
+    "Zzzz",  # 1D0F6..1D0FF ; Unknown
+    "Zyyy",  # 1D100..1D126 ; Common
+    "Zzzz",  # 1D127..1D128 ; Unknown
+    "Zyyy",  # 1D129..1D166 ; Common
+    "Zinh",  # 1D167..1D169 ; Inherited
+    "Zyyy",  # 1D16A..1D17A ; Common
+    "Zinh",  # 1D17B..1D182 ; Inherited
+    "Zyyy",  # 1D183..1D184 ; Common
+    "Zinh",  # 1D185..1D18B ; Inherited
+    "Zyyy",  # 1D18C..1D1A9 ; Common
+    "Zinh",  # 1D1AA..1D1AD ; Inherited
+    "Zyyy",  # 1D1AE..1D1EA ; Common
+    "Zzzz",  # 1D1EB..1D1FF ; Unknown
+    "Grek",  # 1D200..1D245 ; Greek
+    "Zzzz",  # 1D246..1D2BF ; Unknown
+    "Zyyy",  # 1D2C0..1D2D3 ; Common
+    "Zzzz",  # 1D2D4..1D2DF ; Unknown
+    "Zyyy",  # 1D2E0..1D2F3 ; Common
+    "Zzzz",  # 1D2F4..1D2FF ; Unknown
+    "Zyyy",  # 1D300..1D356 ; Common
+    "Zzzz",  # 1D357..1D35F ; Unknown
+    "Zyyy",  # 1D360..1D378 ; Common
+    "Zzzz",  # 1D379..1D3FF ; Unknown
+    "Zyyy",  # 1D400..1D454 ; Common
+    "Zzzz",  # 1D455..1D455 ; Unknown
+    "Zyyy",  # 1D456..1D49C ; Common
+    "Zzzz",  # 1D49D..1D49D ; Unknown
+    "Zyyy",  # 1D49E..1D49F ; Common
+    "Zzzz",  # 1D4A0..1D4A1 ; Unknown
+    "Zyyy",  # 1D4A2..1D4A2 ; Common
+    "Zzzz",  # 1D4A3..1D4A4 ; Unknown
+    "Zyyy",  # 1D4A5..1D4A6 ; Common
+    "Zzzz",  # 1D4A7..1D4A8 ; Unknown
+    "Zyyy",  # 1D4A9..1D4AC ; Common
+    "Zzzz",  # 1D4AD..1D4AD ; Unknown
+    "Zyyy",  # 1D4AE..1D4B9 ; Common
+    "Zzzz",  # 1D4BA..1D4BA ; Unknown
+    "Zyyy",  # 1D4BB..1D4BB ; Common
+    "Zzzz",  # 1D4BC..1D4BC ; Unknown
+    "Zyyy",  # 1D4BD..1D4C3 ; Common
+    "Zzzz",  # 1D4C4..1D4C4 ; Unknown
+    "Zyyy",  # 1D4C5..1D505 ; Common
+    "Zzzz",  # 1D506..1D506 ; Unknown
+    "Zyyy",  # 1D507..1D50A ; Common
+    "Zzzz",  # 1D50B..1D50C ; Unknown
+    "Zyyy",  # 1D50D..1D514 ; Common
+    "Zzzz",  # 1D515..1D515 ; Unknown
+    "Zyyy",  # 1D516..1D51C ; Common
+    "Zzzz",  # 1D51D..1D51D ; Unknown
+    "Zyyy",  # 1D51E..1D539 ; Common
+    "Zzzz",  # 1D53A..1D53A ; Unknown
+    "Zyyy",  # 1D53B..1D53E ; Common
+    "Zzzz",  # 1D53F..1D53F ; Unknown
+    "Zyyy",  # 1D540..1D544 ; Common
+    "Zzzz",  # 1D545..1D545 ; Unknown
+    "Zyyy",  # 1D546..1D546 ; Common
+    "Zzzz",  # 1D547..1D549 ; Unknown
+    "Zyyy",  # 1D54A..1D550 ; Common
+    "Zzzz",  # 1D551..1D551 ; Unknown
+    "Zyyy",  # 1D552..1D6A5 ; Common
+    "Zzzz",  # 1D6A6..1D6A7 ; Unknown
+    "Zyyy",  # 1D6A8..1D7CB ; Common
+    "Zzzz",  # 1D7CC..1D7CD ; Unknown
+    "Zyyy",  # 1D7CE..1D7FF ; Common
+    "Sgnw",  # 1D800..1DA8B ; SignWriting
+    "Zzzz",  # 1DA8C..1DA9A ; Unknown
+    "Sgnw",  # 1DA9B..1DA9F ; SignWriting
+    "Zzzz",  # 1DAA0..1DAA0 ; Unknown
+    "Sgnw",  # 1DAA1..1DAAF ; SignWriting
+    "Zzzz",  # 1DAB0..1DEFF ; Unknown
+    "Latn",  # 1DF00..1DF1E ; Latin
+    "Zzzz",  # 1DF1F..1DF24 ; Unknown
+    "Latn",  # 1DF25..1DF2A ; Latin
+    "Zzzz",  # 1DF2B..1DFFF ; Unknown
+    "Glag",  # 1E000..1E006 ; Glagolitic
+    "Zzzz",  # 1E007..1E007 ; Unknown
+    "Glag",  # 1E008..1E018 ; Glagolitic
+    "Zzzz",  # 1E019..1E01A ; Unknown
+    "Glag",  # 1E01B..1E021 ; Glagolitic
+    "Zzzz",  # 1E022..1E022 ; Unknown
+    "Glag",  # 1E023..1E024 ; Glagolitic
+    "Zzzz",  # 1E025..1E025 ; Unknown
+    "Glag",  # 1E026..1E02A ; Glagolitic
+    "Zzzz",  # 1E02B..1E02F ; Unknown
+    "Cyrl",  # 1E030..1E06D ; Cyrillic
+    "Zzzz",  # 1E06E..1E08E ; Unknown
+    "Cyrl",  # 1E08F..1E08F ; Cyrillic
+    "Zzzz",  # 1E090..1E0FF ; Unknown
+    "Hmnp",  # 1E100..1E12C ; Nyiakeng_Puachue_Hmong
+    "Zzzz",  # 1E12D..1E12F ; Unknown
+    "Hmnp",  # 1E130..1E13D ; Nyiakeng_Puachue_Hmong
+    "Zzzz",  # 1E13E..1E13F ; Unknown
+    "Hmnp",  # 1E140..1E149 ; Nyiakeng_Puachue_Hmong
+    "Zzzz",  # 1E14A..1E14D ; Unknown
+    "Hmnp",  # 1E14E..1E14F ; Nyiakeng_Puachue_Hmong
+    "Zzzz",  # 1E150..1E28F ; Unknown
+    "Toto",  # 1E290..1E2AE ; Toto
+    "Zzzz",  # 1E2AF..1E2BF ; Unknown
+    "Wcho",  # 1E2C0..1E2F9 ; Wancho
+    "Zzzz",  # 1E2FA..1E2FE ; Unknown
+    "Wcho",  # 1E2FF..1E2FF ; Wancho
+    "Zzzz",  # 1E300..1E4CF ; Unknown
+    "Nagm",  # 1E4D0..1E4F9 ; Nag_Mundari
+    "Zzzz",  # 1E4FA..1E5CF ; Unknown
+    "Onao",  # 1E5D0..1E5FA ; Ol_Onal
+    "Zzzz",  # 1E5FB..1E5FE ; Unknown
+    "Onao",  # 1E5FF..1E5FF ; Ol_Onal
+    "Zzzz",  # 1E600..1E6BF ; Unknown
+    "Tayo",  # 1E6C0..1E6DE ; Tai_Yo
+    "Zzzz",  # 1E6DF..1E6DF ; Unknown
+    "Tayo",  # 1E6E0..1E6F5 ; Tai_Yo
+    "Zzzz",  # 1E6F6..1E6FD ; Unknown
+    "Tayo",  # 1E6FE..1E6FF ; Tai_Yo
+    "Zzzz",  # 1E700..1E7DF ; Unknown
+    "Ethi",  # 1E7E0..1E7E6 ; Ethiopic
+    "Zzzz",  # 1E7E7..1E7E7 ; Unknown
+    "Ethi",  # 1E7E8..1E7EB ; Ethiopic
+    "Zzzz",  # 1E7EC..1E7EC ; Unknown
+    "Ethi",  # 1E7ED..1E7EE ; Ethiopic
+    "Zzzz",  # 1E7EF..1E7EF ; Unknown
+    "Ethi",  # 1E7F0..1E7FE ; Ethiopic
+    "Zzzz",  # 1E7FF..1E7FF ; Unknown
+    "Mend",  # 1E800..1E8C4 ; Mende_Kikakui
+    "Zzzz",  # 1E8C5..1E8C6 ; Unknown
+    "Mend",  # 1E8C7..1E8D6 ; Mende_Kikakui
+    "Zzzz",  # 1E8D7..1E8FF ; Unknown
+    "Adlm",  # 1E900..1E94B ; Adlam
+    "Zzzz",  # 1E94C..1E94F ; Unknown
+    "Adlm",  # 1E950..1E959 ; Adlam
+    "Zzzz",  # 1E95A..1E95D ; Unknown
+    "Adlm",  # 1E95E..1E95F ; Adlam
+    "Zzzz",  # 1E960..1EC70 ; Unknown
+    "Zyyy",  # 1EC71..1ECB4 ; Common
+    "Zzzz",  # 1ECB5..1ED00 ; Unknown
+    "Zyyy",  # 1ED01..1ED3D ; Common
+    "Zzzz",  # 1ED3E..1EDFF ; Unknown
+    "Arab",  # 1EE00..1EE03 ; Arabic
+    "Zzzz",  # 1EE04..1EE04 ; Unknown
+    "Arab",  # 1EE05..1EE1F ; Arabic
+    "Zzzz",  # 1EE20..1EE20 ; Unknown
+    "Arab",  # 1EE21..1EE22 ; Arabic
+    "Zzzz",  # 1EE23..1EE23 ; Unknown
+    "Arab",  # 1EE24..1EE24 ; Arabic
+    "Zzzz",  # 1EE25..1EE26 ; Unknown
+    "Arab",  # 1EE27..1EE27 ; Arabic
+    "Zzzz",  # 1EE28..1EE28 ; Unknown
+    "Arab",  # 1EE29..1EE32 ; Arabic
+    "Zzzz",  # 1EE33..1EE33 ; Unknown
+    "Arab",  # 1EE34..1EE37 ; Arabic
+    "Zzzz",  # 1EE38..1EE38 ; Unknown
+    "Arab",  # 1EE39..1EE39 ; Arabic
+    "Zzzz",  # 1EE3A..1EE3A ; Unknown
+    "Arab",  # 1EE3B..1EE3B ; Arabic
+    "Zzzz",  # 1EE3C..1EE41 ; Unknown
+    "Arab",  # 1EE42..1EE42 ; Arabic
+    "Zzzz",  # 1EE43..1EE46 ; Unknown
+    "Arab",  # 1EE47..1EE47 ; Arabic
+    "Zzzz",  # 1EE48..1EE48 ; Unknown
+    "Arab",  # 1EE49..1EE49 ; Arabic
+    "Zzzz",  # 1EE4A..1EE4A ; Unknown
+    "Arab",  # 1EE4B..1EE4B ; Arabic
+    "Zzzz",  # 1EE4C..1EE4C ; Unknown
+    "Arab",  # 1EE4D..1EE4F ; Arabic
+    "Zzzz",  # 1EE50..1EE50 ; Unknown
+    "Arab",  # 1EE51..1EE52 ; Arabic
+    "Zzzz",  # 1EE53..1EE53 ; Unknown
+    "Arab",  # 1EE54..1EE54 ; Arabic
+    "Zzzz",  # 1EE55..1EE56 ; Unknown
+    "Arab",  # 1EE57..1EE57 ; Arabic
+    "Zzzz",  # 1EE58..1EE58 ; Unknown
+    "Arab",  # 1EE59..1EE59 ; Arabic
+    "Zzzz",  # 1EE5A..1EE5A ; Unknown
+    "Arab",  # 1EE5B..1EE5B ; Arabic
+    "Zzzz",  # 1EE5C..1EE5C ; Unknown
+    "Arab",  # 1EE5D..1EE5D ; Arabic
+    "Zzzz",  # 1EE5E..1EE5E ; Unknown
+    "Arab",  # 1EE5F..1EE5F ; Arabic
+    "Zzzz",  # 1EE60..1EE60 ; Unknown
+    "Arab",  # 1EE61..1EE62 ; Arabic
+    "Zzzz",  # 1EE63..1EE63 ; Unknown
+    "Arab",  # 1EE64..1EE64 ; Arabic
+    "Zzzz",  # 1EE65..1EE66 ; Unknown
+    "Arab",  # 1EE67..1EE6A ; Arabic
+    "Zzzz",  # 1EE6B..1EE6B ; Unknown
+    "Arab",  # 1EE6C..1EE72 ; Arabic
+    "Zzzz",  # 1EE73..1EE73 ; Unknown
+    "Arab",  # 1EE74..1EE77 ; Arabic
+    "Zzzz",  # 1EE78..1EE78 ; Unknown
+    "Arab",  # 1EE79..1EE7C ; Arabic
+    "Zzzz",  # 1EE7D..1EE7D ; Unknown
+    "Arab",  # 1EE7E..1EE7E ; Arabic
+    "Zzzz",  # 1EE7F..1EE7F ; Unknown
+    "Arab",  # 1EE80..1EE89 ; Arabic
+    "Zzzz",  # 1EE8A..1EE8A ; Unknown
+    "Arab",  # 1EE8B..1EE9B ; Arabic
+    "Zzzz",  # 1EE9C..1EEA0 ; Unknown
+    "Arab",  # 1EEA1..1EEA3 ; Arabic
+    "Zzzz",  # 1EEA4..1EEA4 ; Unknown
+    "Arab",  # 1EEA5..1EEA9 ; Arabic
+    "Zzzz",  # 1EEAA..1EEAA ; Unknown
+    "Arab",  # 1EEAB..1EEBB ; Arabic
+    "Zzzz",  # 1EEBC..1EEEF ; Unknown
+    "Arab",  # 1EEF0..1EEF1 ; Arabic
+    "Zzzz",  # 1EEF2..1EFFF ; Unknown
+    "Zyyy",  # 1F000..1F02B ; Common
+    "Zzzz",  # 1F02C..1F02F ; Unknown
+    "Zyyy",  # 1F030..1F093 ; Common
+    "Zzzz",  # 1F094..1F09F ; Unknown
+    "Zyyy",  # 1F0A0..1F0AE ; Common
+    "Zzzz",  # 1F0AF..1F0B0 ; Unknown
+    "Zyyy",  # 1F0B1..1F0BF ; Common
+    "Zzzz",  # 1F0C0..1F0C0 ; Unknown
+    "Zyyy",  # 1F0C1..1F0CF ; Common
+    "Zzzz",  # 1F0D0..1F0D0 ; Unknown
+    "Zyyy",  # 1F0D1..1F0F5 ; Common
+    "Zzzz",  # 1F0F6..1F0FF ; Unknown
+    "Zyyy",  # 1F100..1F1AD ; Common
+    "Zzzz",  # 1F1AE..1F1E5 ; Unknown
+    "Zyyy",  # 1F1E6..1F1FF ; Common
+    "Hira",  # 1F200..1F200 ; Hiragana
+    "Zyyy",  # 1F201..1F202 ; Common
+    "Zzzz",  # 1F203..1F20F ; Unknown
+    "Zyyy",  # 1F210..1F23B ; Common
+    "Zzzz",  # 1F23C..1F23F ; Unknown
+    "Zyyy",  # 1F240..1F248 ; Common
+    "Zzzz",  # 1F249..1F24F ; Unknown
+    "Zyyy",  # 1F250..1F251 ; Common
+    "Zzzz",  # 1F252..1F25F ; Unknown
+    "Zyyy",  # 1F260..1F265 ; Common
+    "Zzzz",  # 1F266..1F2FF ; Unknown
+    "Zyyy",  # 1F300..1F6D8 ; Common
+    "Zzzz",  # 1F6D9..1F6DB ; Unknown
+    "Zyyy",  # 1F6DC..1F6EC ; Common
+    "Zzzz",  # 1F6ED..1F6EF ; Unknown
+    "Zyyy",  # 1F6F0..1F6FC ; Common
+    "Zzzz",  # 1F6FD..1F6FF ; Unknown
+    "Zyyy",  # 1F700..1F7D9 ; Common
+    "Zzzz",  # 1F7DA..1F7DF ; Unknown
+    "Zyyy",  # 1F7E0..1F7EB ; Common
+    "Zzzz",  # 1F7EC..1F7EF ; Unknown
+    "Zyyy",  # 1F7F0..1F7F0 ; Common
+    "Zzzz",  # 1F7F1..1F7FF ; Unknown
+    "Zyyy",  # 1F800..1F80B ; Common
+    "Zzzz",  # 1F80C..1F80F ; Unknown
+    "Zyyy",  # 1F810..1F847 ; Common
+    "Zzzz",  # 1F848..1F84F ; Unknown
+    "Zyyy",  # 1F850..1F859 ; Common
+    "Zzzz",  # 1F85A..1F85F ; Unknown
+    "Zyyy",  # 1F860..1F887 ; Common
+    "Zzzz",  # 1F888..1F88F ; Unknown
+    "Zyyy",  # 1F890..1F8AD ; Common
+    "Zzzz",  # 1F8AE..1F8AF ; Unknown
+    "Zyyy",  # 1F8B0..1F8BB ; Common
+    "Zzzz",  # 1F8BC..1F8BF ; Unknown
+    "Zyyy",  # 1F8C0..1F8C1 ; Common
+    "Zzzz",  # 1F8C2..1F8CF ; Unknown
+    "Zyyy",  # 1F8D0..1F8D8 ; Common
+    "Zzzz",  # 1F8D9..1F8FF ; Unknown
+    "Zyyy",  # 1F900..1FA57 ; Common
+    "Zzzz",  # 1FA58..1FA5F ; Unknown
+    "Zyyy",  # 1FA60..1FA6D ; Common
+    "Zzzz",  # 1FA6E..1FA6F ; Unknown
+    "Zyyy",  # 1FA70..1FA7C ; Common
+    "Zzzz",  # 1FA7D..1FA7F ; Unknown
+    "Zyyy",  # 1FA80..1FA8A ; Common
+    "Zzzz",  # 1FA8B..1FA8D ; Unknown
+    "Zyyy",  # 1FA8E..1FAC6 ; Common
+    "Zzzz",  # 1FAC7..1FAC7 ; Unknown
+    "Zyyy",  # 1FAC8..1FAC8 ; Common
+    "Zzzz",  # 1FAC9..1FACC ; Unknown
+    "Zyyy",  # 1FACD..1FADC ; Common
+    "Zzzz",  # 1FADD..1FADE ; Unknown
+    "Zyyy",  # 1FADF..1FAEA ; Common
+    "Zzzz",  # 1FAEB..1FAEE ; Unknown
+    "Zyyy",  # 1FAEF..1FAF8 ; Common
+    "Zzzz",  # 1FAF9..1FAFF ; Unknown
+    "Zyyy",  # 1FB00..1FB92 ; Common
+    "Zzzz",  # 1FB93..1FB93 ; Unknown
+    "Zyyy",  # 1FB94..1FBFA ; Common
+    "Zzzz",  # 1FBFB..1FFFF ; Unknown
+    "Hani",  # 20000..2A6DF ; Han
+    "Zzzz",  # 2A6E0..2A6FF ; Unknown
+    "Hani",  # 2A700..2B81D ; Han
+    "Zzzz",  # 2B81E..2B81F ; Unknown
+    "Hani",  # 2B820..2CEAD ; Han
+    "Zzzz",  # 2CEAE..2CEAF ; Unknown
+    "Hani",  # 2CEB0..2EBE0 ; Han
+    "Zzzz",  # 2EBE1..2EBEF ; Unknown
+    "Hani",  # 2EBF0..2EE5D ; Han
+    "Zzzz",  # 2EE5E..2F7FF ; Unknown
+    "Hani",  # 2F800..2FA1D ; Han
+    "Zzzz",  # 2FA1E..2FFFF ; Unknown
+    "Hani",  # 30000..3134A ; Han
+    "Zzzz",  # 3134B..3134F ; Unknown
+    "Hani",  # 31350..33479 ; Han
+    "Zzzz",  # 3347A..E0000 ; Unknown
+    "Zyyy",  # E0001..E0001 ; Common
+    "Zzzz",  # E0002..E001F ; Unknown
+    "Zyyy",  # E0020..E007F ; Common
+    "Zzzz",  # E0080..E00FF ; Unknown
+    "Zinh",  # E0100..E01EF ; Inherited
+    "Zzzz",  # E01F0..10FFFF ; Unknown
+]
+
+NAMES = {
+    "Adlm": "Adlam",
+    "Aghb": "Caucasian_Albanian",
+    "Ahom": "Ahom",
+    "Arab": "Arabic",
+    "Armi": "Imperial_Aramaic",
+    "Armn": "Armenian",
+    "Avst": "Avestan",
+    "Bali": "Balinese",
+    "Bamu": "Bamum",
+    "Bass": "Bassa_Vah",
+    "Batk": "Batak",
+    "Beng": "Bengali",
+    "Berf": "Beria_Erfe",
+    "Bhks": "Bhaiksuki",
+    "Bopo": "Bopomofo",
+    "Brah": "Brahmi",
+    "Brai": "Braille",
+    "Bugi": "Buginese",
+    "Buhd": "Buhid",
+    "Cakm": "Chakma",
+    "Cans": "Canadian_Aboriginal",
+    "Cari": "Carian",
+    "Cham": "Cham",
+    "Cher": "Cherokee",
+    "Chrs": "Chorasmian",
+    "Copt": "Coptic",
+    "Cpmn": "Cypro_Minoan",
+    "Cprt": "Cypriot",
+    "Cyrl": "Cyrillic",
+    "Deva": "Devanagari",
+    "Diak": "Dives_Akuru",
+    "Dogr": "Dogra",
+    "Dsrt": "Deseret",
+    "Dupl": "Duployan",
+    "Egyp": "Egyptian_Hieroglyphs",
+    "Elba": "Elbasan",
+    "Elym": "Elymaic",
+    "Ethi": "Ethiopic",
+    "Gara": "Garay",
+    "Geor": "Georgian",
+    "Glag": "Glagolitic",
+    "Gong": "Gunjala_Gondi",
+    "Gonm": "Masaram_Gondi",
+    "Goth": "Gothic",
+    "Gran": "Grantha",
+    "Grek": "Greek",
+    "Gujr": "Gujarati",
+    "Gukh": "Gurung_Khema",
+    "Guru": "Gurmukhi",
+    "Hang": "Hangul",
+    "Hani": "Han",
+    "Hano": "Hanunoo",
+    "Hatr": "Hatran",
+    "Hebr": "Hebrew",
+    "Hira": "Hiragana",
+    "Hluw": "Anatolian_Hieroglyphs",
+    "Hmng": "Pahawh_Hmong",
+    "Hmnp": "Nyiakeng_Puachue_Hmong",
+    "Hrkt": "Katakana_Or_Hiragana",
+    "Hung": "Old_Hungarian",
+    "Ital": "Old_Italic",
+    "Java": "Javanese",
+    "Kali": "Kayah_Li",
+    "Kana": "Katakana",
+    "Kawi": "Kawi",
+    "Khar": "Kharoshthi",
+    "Khmr": "Khmer",
+    "Khoj": "Khojki",
+    "Kits": "Khitan_Small_Script",
+    "Knda": "Kannada",
+    "Krai": "Kirat_Rai",
+    "Kthi": "Kaithi",
+    "Lana": "Tai_Tham",
+    "Laoo": "Lao",
+    "Latn": "Latin",
+    "Lepc": "Lepcha",
+    "Limb": "Limbu",
+    "Lina": "Linear_A",
+    "Linb": "Linear_B",
+    "Lisu": "Lisu",
+    "Lyci": "Lycian",
+    "Lydi": "Lydian",
+    "Mahj": "Mahajani",
+    "Maka": "Makasar",
+    "Mand": "Mandaic",
+    "Mani": "Manichaean",
+    "Marc": "Marchen",
+    "Medf": "Medefaidrin",
+    "Mend": "Mende_Kikakui",
+    "Merc": "Meroitic_Cursive",
+    "Mero": "Meroitic_Hieroglyphs",
+    "Mlym": "Malayalam",
+    "Modi": "Modi",
+    "Mong": "Mongolian",
+    "Mroo": "Mro",
+    "Mtei": "Meetei_Mayek",
+    "Mult": "Multani",
+    "Mymr": "Myanmar",
+    "Nagm": "Nag_Mundari",
+    "Nand": "Nandinagari",
+    "Narb": "Old_North_Arabian",
+    "Nbat": "Nabataean",
+    "Newa": "Newa",
+    "Nkoo": "Nko",
+    "Nshu": "Nushu",
+    "Ogam": "Ogham",
+    "Olck": "Ol_Chiki",
+    "Onao": "Ol_Onal",
+    "Orkh": "Old_Turkic",
+    "Orya": "Oriya",
+    "Osge": "Osage",
+    "Osma": "Osmanya",
+    "Ougr": "Old_Uyghur",
+    "Palm": "Palmyrene",
+    "Pauc": "Pau_Cin_Hau",
+    "Perm": "Old_Permic",
+    "Phag": "Phags_Pa",
+    "Phli": "Inscriptional_Pahlavi",
+    "Phlp": "Psalter_Pahlavi",
+    "Phnx": "Phoenician",
+    "Plrd": "Miao",
+    "Prti": "Inscriptional_Parthian",
+    "Rjng": "Rejang",
+    "Rohg": "Hanifi_Rohingya",
+    "Runr": "Runic",
+    "Samr": "Samaritan",
+    "Sarb": "Old_South_Arabian",
+    "Saur": "Saurashtra",
+    "Sgnw": "SignWriting",
+    "Shaw": "Shavian",
+    "Shrd": "Sharada",
+    "Sidd": "Siddham",
+    "Sidt": "Sidetic",
+    "Sind": "Khudawadi",
+    "Sinh": "Sinhala",
+    "Sogd": "Sogdian",
+    "Sogo": "Old_Sogdian",
+    "Sora": "Sora_Sompeng",
+    "Soyo": "Soyombo",
+    "Sund": "Sundanese",
+    "Sunu": "Sunuwar",
+    "Sylo": "Syloti_Nagri",
+    "Syrc": "Syriac",
+    "Tagb": "Tagbanwa",
+    "Takr": "Takri",
+    "Tale": "Tai_Le",
+    "Talu": "New_Tai_Lue",
+    "Taml": "Tamil",
+    "Tang": "Tangut",
+    "Tavt": "Tai_Viet",
+    "Tayo": "Tai_Yo",
+    "Telu": "Telugu",
+    "Tfng": "Tifinagh",
+    "Tglg": "Tagalog",
+    "Thaa": "Thaana",
+    "Thai": "Thai",
+    "Tibt": "Tibetan",
+    "Tirh": "Tirhuta",
+    "Tnsa": "Tangsa",
+    "Todr": "Todhri",
+    "Tols": "Tolong_Siki",
+    "Toto": "Toto",
+    "Tutg": "Tulu_Tigalari",
+    "Ugar": "Ugaritic",
+    "Vaii": "Vai",
+    "Vith": "Vithkuqi",
+    "Wara": "Warang_Citi",
+    "Wcho": "Wancho",
+    "Xpeo": "Old_Persian",
+    "Xsux": "Cuneiform",
+    "Yezi": "Yezidi",
+    "Yiii": "Yi",
+    "Zanb": "Zanabazar_Square",
+    "Zinh": "Inherited",
+    "Zyyy": "Common",
+    "Zzzz": "Unknown",
+}
diff --git a/shared/package-files/c0/c011ed4eb79790335336eb434f6f5817cdf503a71afd156b10373eeec24c3040/python_arg_parsing.h b/shared/package-files/c0/c011ed4eb79790335336eb434f6f5817cdf503a71afd156b10373eeec24c3040/python_arg_parsing.h
new file mode 100644
index 0000000000000000000000000000000000000000..7701e97fe9189cb49a2c98cfa5cc1f5e41190941
--- /dev/null
+++ b/shared/package-files/c0/c011ed4eb79790335336eb434f6f5817cdf503a71afd156b10373eeec24c3040/python_arg_parsing.h
@@ -0,0 +1,53 @@
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <torch/csrc/python_headers.h>
+
+#include <torch/csrc/utils/python_arg_parser.h>
+
+namespace torch {
+namespace autograd {
+namespace utils {
+
+// The parameter allow_copy is to accept copy for Tensor.to (and by proxy
+// PackedSequences.to) but not nn.Module.to.
+inline std::tuple<
+    c10::optional<at::Device>,
+    c10::optional<at::ScalarType>,
+    bool,
+    bool,
+    c10::optional<at::MemoryFormat>>
+parse_to_conversion(PythonArgs& r, bool allow_copy) {
+  if (r.idx == 0) {
+    if (!allow_copy && !r.isNone(3))
+      throw std::runtime_error(".to() does not accept copy argument");
+    return std::make_tuple(
+        r.deviceOptional(0),
+        r.scalartypeOptional(1),
+        r.toBool(2),
+        r.toBool(3),
+        r.memoryformatOptional(4));
+  } else if (r.idx == 1) {
+    if (!allow_copy && !r.isNone(2))
+      throw std::runtime_error(".to() does not accept copy argument");
+    return std::make_tuple(
+        c10::nullopt,
+        r.scalartype(0),
+        r.toBool(1),
+        r.toBool(2),
+        r.memoryformatOptional(3));
+  } else {
+    auto tensor = r.tensor(0);
+    if (!allow_copy && !r.isNone(2))
+      throw std::runtime_error(".to() does not accept copy argument");
+    return std::make_tuple(
+        tensor.device(),
+        tensor.scalar_type(),
+        r.toBool(1),
+        r.toBool(2),
+        r.memoryformatOptional(3));
+  }
+}
+} // namespace utils
+} // namespace autograd
+} // namespace torch
diff --git a/shared/package-files/c0/c0175a8a7b007de0d42b0b0b2b2aad5cb942be5c08086c28f721d3a851b4e8ef/_foreach_add.h b/shared/package-files/c0/c0175a8a7b007de0d42b0b0b2b2aad5cb942be5c08086c28f721d3a851b4e8ef/_foreach_add.h
new file mode 100644
index 0000000000000000000000000000000000000000..c721860797c2dbc9b7b2170fab0d1b896bf5666d
--- /dev/null
+++ b/shared/package-files/c0/c0175a8a7b007de0d42b0b0b2b2aad5cb942be5c08086c28f721d3a851b4e8ef/_foreach_add.h
@@ -0,0 +1,101 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_foreach_add_ops.h>
+
+namespace at {
+
+
+// aten::_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_add_Scalar::call(self, scalar);
+}
+
+// aten::_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
+inline void _foreach_add_(at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_add__Scalar::call(self, scalar);
+}
+
+// aten::_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add_List::call(self, other, alpha);
+}
+
+// aten::_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()
+inline void _foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add__List::call(self, other, alpha);
+}
+
+// aten::_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_add_ScalarList::call(self, scalars);
+}
+
+// aten::_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
+inline void _foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_add__ScalarList::call(self, scalars);
+}
+
+// aten::_foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add_Tensor::call(self, other, alpha);
+}
+
+// aten::_foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> ()
+inline void _foreach_add_(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add__Tensor::call(self, other, alpha);
+}
+
+// aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
+inline void _foreach_add_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_add_Scalar_out::call(self, scalar, out);
+}
+// aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
+inline void _foreach_add_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
+    return at::_ops::_foreach_add_Scalar_out::call(self, scalar, out);
+}
+
+// aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
+inline void _foreach_add_out(at::TensorList out, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add_List_out::call(self, other, alpha, out);
+}
+// aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
+inline void _foreach_add_outf(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) {
+    return at::_ops::_foreach_add_List_out::call(self, other, alpha, out);
+}
+
+// aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
+inline void _foreach_add_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_add_ScalarList_out::call(self, scalars, out);
+}
+// aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
+inline void _foreach_add_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
+    return at::_ops::_foreach_add_ScalarList_out::call(self, scalars, out);
+}
+
+// aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
+inline void _foreach_add_out(at::TensorList out, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::_foreach_add_Tensor_out::call(self, other, alpha, out);
+}
+// aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
+inline void _foreach_add_outf(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha, at::TensorList out) {
+    return at::_ops::_foreach_add_Tensor_out::call(self, other, alpha, out);
+}
+
+}
diff --git a/shared/package-files/c0/c01df2402aff277894ede7d493bc2c2335c815030dfa0fa7315b0d6db3870caf/mit-krb5-gssapi.pc b/shared/package-files/c0/c01df2402aff277894ede7d493bc2c2335c815030dfa0fa7315b0d6db3870caf/mit-krb5-gssapi.pc
new file mode 100644
index 0000000000000000000000000000000000000000..cf8b48288ea38ca6add29331c5556f46e1a88d01
--- /dev/null
+++ b/shared/package-files/c0/c01df2402aff277894ede7d493bc2c2335c815030dfa0fa7315b0d6db3870caf/mit-krb5-gssapi.pc
@@ -0,0 +1,11 @@
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: mit-krb5-gssapi
+Description: Kerberos implementation of the GSSAPI
+Version: 1.20.1
+Requires.private: mit-krb5
+Cflags: -I${includedir}
+Libs: -L${libdir} -lgssapi_krb5
diff --git a/shared/package-files/c0/c01f0f08f0aa62aa58477608740aeba32609dd6b047391a11b5c4a029d03499a/pythoncapi_compat.h b/shared/package-files/c0/c01f0f08f0aa62aa58477608740aeba32609dd6b047391a11b5c4a029d03499a/pythoncapi_compat.h
new file mode 100644
index 0000000000000000000000000000000000000000..077678c4780beb92f997499d7551fde83eaccead
--- /dev/null
+++ b/shared/package-files/c0/c01f0f08f0aa62aa58477608740aeba32609dd6b047391a11b5c4a029d03499a/pythoncapi_compat.h
@@ -0,0 +1,1696 @@
+// Header file providing new C API functions to old Python versions.
+//
+// File distributed under the Zero Clause BSD (0BSD) license.
+// Copyright Contributors to the pythoncapi_compat project.
+//
+// Homepage:
+// https://github.com/python/pythoncapi_compat
+//
+// Latest version:
+// https://raw.githubusercontent.com/python/pythoncapi-compat/0041177c4f348c8952b4c8980b2c90856e61c7c7/pythoncapi_compat.h
+//
+// SPDX-License-Identifier: 0BSD
+
+#ifndef PYTHONCAPI_COMPAT
+#define PYTHONCAPI_COMPAT
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <Python.h>
+
+// Python 3.11.0b4 added PyFrame_Back() to Python.h
+#if PY_VERSION_HEX < 0x030b00B4 && !defined(PYPY_VERSION)
+#  include "frameobject.h"        // PyFrameObject, PyFrame_GetBack()
+#endif
+
+
+#ifndef _Py_CAST
+#  define _Py_CAST(type, expr) ((type)(expr))
+#endif
+
+// Static inline functions should use _Py_NULL rather than using directly NULL
+// to prevent C++ compiler warnings. On C23 and newer and on C++11 and newer,
+// _Py_NULL is defined as nullptr.
+#if (defined (__STDC_VERSION__) && __STDC_VERSION__ > 201710L) \
+        || (defined(__cplusplus) && __cplusplus >= 201103)
+#  define _Py_NULL nullptr
+#else
+#  define _Py_NULL NULL
+#endif
+
+// Cast argument to PyObject* type.
+#ifndef _PyObject_CAST
+#  define _PyObject_CAST(op) _Py_CAST(PyObject*, op)
+#endif
+
+#ifndef Py_BUILD_ASSERT
+#  define Py_BUILD_ASSERT(cond) \
+        do { \
+            (void)sizeof(char [1 - 2 * !(cond)]); \
+        } while(0)
+#endif
+
+
+// bpo-42262 added Py_NewRef() to Python 3.10.0a3
+#if PY_VERSION_HEX < 0x030A00A3 && !defined(Py_NewRef)
+static inline PyObject* _Py_NewRef(PyObject *obj)
+{
+    Py_INCREF(obj);
+    return obj;
+}
+#define Py_NewRef(obj) _Py_NewRef(_PyObject_CAST(obj))
+#endif
+
+
+// bpo-42262 added Py_XNewRef() to Python 3.10.0a3
+#if PY_VERSION_HEX < 0x030A00A3 && !defined(Py_XNewRef)
+static inline PyObject* _Py_XNewRef(PyObject *obj)
+{
+    Py_XINCREF(obj);
+    return obj;
+}
+#define Py_XNewRef(obj) _Py_XNewRef(_PyObject_CAST(obj))
+#endif
+
+
+// bpo-39573 added Py_SET_REFCNT() to Python 3.9.0a4
+#if PY_VERSION_HEX < 0x030900A4 && !defined(Py_SET_REFCNT)
+static inline void _Py_SET_REFCNT(PyObject *ob, Py_ssize_t refcnt)
+{
+    ob->ob_refcnt = refcnt;
+}
+#define Py_SET_REFCNT(ob, refcnt) _Py_SET_REFCNT(_PyObject_CAST(ob), refcnt)
+#endif
+
+
+// Py_SETREF() and Py_XSETREF() were added to Python 3.5.2.
+// It is excluded from the limited C API.
+#if (PY_VERSION_HEX < 0x03050200 && !defined(Py_SETREF)) && !defined(Py_LIMITED_API)
+#define Py_SETREF(dst, src)                                     \
+    do {                                                        \
+        PyObject **_tmp_dst_ptr = _Py_CAST(PyObject**, &(dst)); \
+        PyObject *_tmp_dst = (*_tmp_dst_ptr);                   \
+        *_tmp_dst_ptr = _PyObject_CAST(src);                    \
+        Py_DECREF(_tmp_dst);                                    \
+    } while (0)
+
+#define Py_XSETREF(dst, src)                                    \
+    do {                                                        \
+        PyObject **_tmp_dst_ptr = _Py_CAST(PyObject**, &(dst)); \
+        PyObject *_tmp_dst = (*_tmp_dst_ptr);                   \
+        *_tmp_dst_ptr = _PyObject_CAST(src);                    \
+        Py_XDECREF(_tmp_dst);                                   \
+    } while (0)
+#endif
+
+
+// bpo-43753 added Py_Is(), Py_IsNone(), Py_IsTrue() and Py_IsFalse()
+// to Python 3.10.0b1.
+#if PY_VERSION_HEX < 0x030A00B1 && !defined(Py_Is)
+#  define Py_Is(x, y) ((x) == (y))
+#endif
+#if PY_VERSION_HEX < 0x030A00B1 && !defined(Py_IsNone)
+#  define Py_IsNone(x) Py_Is(x, Py_None)
+#endif
+#if (PY_VERSION_HEX < 0x030A00B1 || defined(PYPY_VERSION)) && !defined(Py_IsTrue)
+#  define Py_IsTrue(x) Py_Is(x, Py_True)
+#endif
+#if (PY_VERSION_HEX < 0x030A00B1 || defined(PYPY_VERSION)) && !defined(Py_IsFalse)
+#  define Py_IsFalse(x) Py_Is(x, Py_False)
+#endif
+
+
+// bpo-39573 added Py_SET_TYPE() to Python 3.9.0a4
+#if PY_VERSION_HEX < 0x030900A4 && !defined(Py_SET_TYPE)
+static inline void _Py_SET_TYPE(PyObject *ob, PyTypeObject *type)
+{
+    ob->ob_type = type;
+}
+#define Py_SET_TYPE(ob, type) _Py_SET_TYPE(_PyObject_CAST(ob), type)
+#endif
+
+
+// bpo-39573 added Py_SET_SIZE() to Python 3.9.0a4
+#if PY_VERSION_HEX < 0x030900A4 && !defined(Py_SET_SIZE)
+static inline void _Py_SET_SIZE(PyVarObject *ob, Py_ssize_t size)
+{
+    ob->ob_size = size;
+}
+#define Py_SET_SIZE(ob, size) _Py_SET_SIZE((PyVarObject*)(ob), size)
+#endif
+
+
+// bpo-40421 added PyFrame_GetCode() to Python 3.9.0b1
+#if PY_VERSION_HEX < 0x030900B1 || defined(PYPY_VERSION)
+static inline PyCodeObject* PyFrame_GetCode(PyFrameObject *frame)
+{
+    assert(frame != _Py_NULL);
+    assert(frame->f_code != _Py_NULL);
+    return _Py_CAST(PyCodeObject*, Py_NewRef(frame->f_code));
+}
+#endif
+
+static inline PyCodeObject* _PyFrame_GetCodeBorrow(PyFrameObject *frame)
+{
+    PyCodeObject *code = PyFrame_GetCode(frame);
+    Py_DECREF(code);
+    return code;
+}
+
+
+// bpo-40421 added PyFrame_GetBack() to Python 3.9.0b1
+#if PY_VERSION_HEX < 0x030900B1 && !defined(PYPY_VERSION)
+static inline PyFrameObject* PyFrame_GetBack(PyFrameObject *frame)
+{
+    assert(frame != _Py_NULL);
+    return _Py_CAST(PyFrameObject*, Py_XNewRef(frame->f_back));
+}
+#endif
+
+#if !defined(PYPY_VERSION)
+static inline PyFrameObject* _PyFrame_GetBackBorrow(PyFrameObject *frame)
+{
+    PyFrameObject *back = PyFrame_GetBack(frame);
+    Py_XDECREF(back);
+    return back;
+}
+#endif
+
+
+// bpo-40421 added PyFrame_GetLocals() to Python 3.11.0a7
+#if PY_VERSION_HEX < 0x030B00A7 && !defined(PYPY_VERSION)
+static inline PyObject* PyFrame_GetLocals(PyFrameObject *frame)
+{
+#if PY_VERSION_HEX >= 0x030400B1
+    if (PyFrame_FastToLocalsWithError(frame) < 0) {
+        return NULL;
+    }
+#else
+    PyFrame_FastToLocals(frame);
+#endif
+    return Py_NewRef(frame->f_locals);
+}
+#endif
+
+
+// bpo-40421 added PyFrame_GetGlobals() to Python 3.11.0a7
+#if PY_VERSION_HEX < 0x030B00A7 && !defined(PYPY_VERSION)
+static inline PyObject* PyFrame_GetGlobals(PyFrameObject *frame)
+{
+    return Py_NewRef(frame->f_globals);
+}
+#endif
+
+
+// bpo-40421 added PyFrame_GetBuiltins() to Python 3.11.0a7
+#if PY_VERSION_HEX < 0x030B00A7 && !defined(PYPY_VERSION)
+static inline PyObject* PyFrame_GetBuiltins(PyFrameObject *frame)
+{
+    return Py_NewRef(frame->f_builtins);
+}
+#endif
+
+
+// bpo-40421 added PyFrame_GetLasti() to Python 3.11.0b1
+#if PY_VERSION_HEX < 0x030B00B1 && !defined(PYPY_VERSION)
+static inline int PyFrame_GetLasti(PyFrameObject *frame)
+{
+#if PY_VERSION_HEX >= 0x030A00A7
+    // bpo-27129: Since Python 3.10.0a7, f_lasti is an instruction offset,
+    // not a bytes offset anymore. Python uses 16-bit "wordcode" (2 bytes)
+    // instructions.
+    if (frame->f_lasti < 0) {
+        return -1;
+    }
+    return frame->f_lasti * 2;
+#else
+    return frame->f_lasti;
+#endif
+}
+#endif
+
+
+// gh-91248 added PyFrame_GetVar() to Python 3.12.0a2
+#if PY_VERSION_HEX < 0x030C00A2 && !defined(PYPY_VERSION)
+static inline PyObject* PyFrame_GetVar(PyFrameObject *frame, PyObject *name)
+{
+    PyObject *locals, *value;
+
+    locals = PyFrame_GetLocals(frame);
+    if (locals == NULL) {
+        return NULL;
+    }
+#if PY_VERSION_HEX >= 0x03000000
+    value = PyDict_GetItemWithError(locals, name);
+#else
+    value = _PyDict_GetItemWithError(locals, name);
+#endif
+    Py_DECREF(locals);
+
+    if (value == NULL) {
+        if (PyErr_Occurred()) {
+            return NULL;
+        }
+#if PY_VERSION_HEX >= 0x03000000
+        PyErr_Format(PyExc_NameError, "variable %R does not exist", name);
+#else
+        PyErr_SetString(PyExc_NameError, "variable does not exist");
+#endif
+        return NULL;
+    }
+    return Py_NewRef(value);
+}
+#endif
+
+
+// gh-91248 added PyFrame_GetVarString() to Python 3.12.0a2
+#if PY_VERSION_HEX < 0x030C00A2 && !defined(PYPY_VERSION)
+static inline PyObject*
+PyFrame_GetVarString(PyFrameObject *frame, const char *name)
+{
+    PyObject *name_obj, *value;
+#if PY_VERSION_HEX >= 0x03000000
+    name_obj = PyUnicode_FromString(name);
+#else
+    name_obj = PyString_FromString(name);
+#endif
+    if (name_obj == NULL) {
+        return NULL;
+    }
+    value = PyFrame_GetVar(frame, name_obj);
+    Py_DECREF(name_obj);
+    return value;
+}
+#endif
+
+
+// bpo-39947 added PyThreadState_GetInterpreter() to Python 3.9.0a5
+#if PY_VERSION_HEX < 0x030900A5 || defined(PYPY_VERSION)
+static inline PyInterpreterState *
+PyThreadState_GetInterpreter(PyThreadState *tstate)
+{
+    assert(tstate != _Py_NULL);
+    return tstate->interp;
+}
+#endif
+
+
+// bpo-40429 added PyThreadState_GetFrame() to Python 3.9.0b1
+#if PY_VERSION_HEX < 0x030900B1 && !defined(PYPY_VERSION)
+static inline PyFrameObject* PyThreadState_GetFrame(PyThreadState *tstate)
+{
+    assert(tstate != _Py_NULL);
+    return _Py_CAST(PyFrameObject *, Py_XNewRef(tstate->frame));
+}
+#endif
+
+#if !defined(PYPY_VERSION)
+static inline PyFrameObject*
+_PyThreadState_GetFrameBorrow(PyThreadState *tstate)
+{
+    PyFrameObject *frame = PyThreadState_GetFrame(tstate);
+    Py_XDECREF(frame);
+    return frame;
+}
+#endif
+
+
+// bpo-39947 added PyInterpreterState_Get() to Python 3.9.0a5
+#if PY_VERSION_HEX < 0x030900A5 || defined(PYPY_VERSION)
+static inline PyInterpreterState* PyInterpreterState_Get(void)
+{
+    PyThreadState *tstate;
+    PyInterpreterState *interp;
+
+    tstate = PyThreadState_GET();
+    if (tstate == _Py_NULL) {
+        Py_FatalError("GIL released (tstate is NULL)");
+    }
+    interp = tstate->interp;
+    if (interp == _Py_NULL) {
+        Py_FatalError("no current interpreter");
+    }
+    return interp;
+}
+#endif
+
+
+// bpo-39947 added PyInterpreterState_Get() to Python 3.9.0a6
+#if 0x030700A1 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x030900A6 && !defined(PYPY_VERSION)
+static inline uint64_t PyThreadState_GetID(PyThreadState *tstate)
+{
+    assert(tstate != _Py_NULL);
+    return tstate->id;
+}
+#endif
+
+// bpo-43760 added PyThreadState_EnterTracing() to Python 3.11.0a2
+#if PY_VERSION_HEX < 0x030B00A2 && !defined(PYPY_VERSION)
+static inline void PyThreadState_EnterTracing(PyThreadState *tstate)
+{
+    tstate->tracing++;
+#if PY_VERSION_HEX >= 0x030A00A1
+    tstate->cframe->use_tracing = 0;
+#else
+    tstate->use_tracing = 0;
+#endif
+}
+#endif
+
+// bpo-43760 added PyThreadState_LeaveTracing() to Python 3.11.0a2
+#if PY_VERSION_HEX < 0x030B00A2 && !defined(PYPY_VERSION)
+static inline void PyThreadState_LeaveTracing(PyThreadState *tstate)
+{
+    int use_tracing = (tstate->c_tracefunc != _Py_NULL
+                       || tstate->c_profilefunc != _Py_NULL);
+    tstate->tracing--;
+#if PY_VERSION_HEX >= 0x030A00A1
+    tstate->cframe->use_tracing = use_tracing;
+#else
+    tstate->use_tracing = use_tracing;
+#endif
+}
+#endif
+
+
+// bpo-37194 added PyObject_CallNoArgs() to Python 3.9.0a1
+// PyObject_CallNoArgs() added to PyPy 3.9.16-v7.3.11
+#if !defined(PyObject_CallNoArgs) && PY_VERSION_HEX < 0x030900A1
+static inline PyObject* PyObject_CallNoArgs(PyObject *func)
+{
+    return PyObject_CallFunctionObjArgs(func, NULL);
+}
+#endif
+
+
+// bpo-39245 made PyObject_CallOneArg() public (previously called
+// _PyObject_CallOneArg) in Python 3.9.0a4
+// PyObject_CallOneArg() added to PyPy 3.9.16-v7.3.11
+#if !defined(PyObject_CallOneArg) && PY_VERSION_HEX < 0x030900A4
+static inline PyObject* PyObject_CallOneArg(PyObject *func, PyObject *arg)
+{
+    return PyObject_CallFunctionObjArgs(func, arg, NULL);
+}
+#endif
+
+
+// bpo-1635741 added PyModule_AddObjectRef() to Python 3.10.0a3
+#if PY_VERSION_HEX < 0x030A00A3
+static inline int
+PyModule_AddObjectRef(PyObject *module, const char *name, PyObject *value)
+{
+    int res;
+
+    if (!value && !PyErr_Occurred()) {
+        // PyModule_AddObject() raises TypeError in this case
+        PyErr_SetString(PyExc_SystemError,
+                        "PyModule_AddObjectRef() must be called "
+                        "with an exception raised if value is NULL");
+        return -1;
+    }
+
+    Py_XINCREF(value);
+    res = PyModule_AddObject(module, name, value);
+    if (res < 0) {
+        Py_XDECREF(value);
+    }
+    return res;
+}
+#endif
+
+
+// bpo-40024 added PyModule_AddType() to Python 3.9.0a5
+#if PY_VERSION_HEX < 0x030900A5
+static inline int PyModule_AddType(PyObject *module, PyTypeObject *type)
+{
+    const char *name, *dot;
+
+    if (PyType_Ready(type) < 0) {
+        return -1;
+    }
+
+    // inline _PyType_Name()
+    name = type->tp_name;
+    assert(name != _Py_NULL);
+    dot = strrchr(name, '.');
+    if (dot != _Py_NULL) {
+        name = dot + 1;
+    }
+
+    return PyModule_AddObjectRef(module, name, _PyObject_CAST(type));
+}
+#endif
+
+
+// bpo-40241 added PyObject_GC_IsTracked() to Python 3.9.0a6.
+// bpo-4688 added _PyObject_GC_IS_TRACKED() to Python 2.7.0a2.
+#if PY_VERSION_HEX < 0x030900A6 && !defined(PYPY_VERSION)
+static inline int PyObject_GC_IsTracked(PyObject* obj)
+{
+    return (PyObject_IS_GC(obj) && _PyObject_GC_IS_TRACKED(obj));
+}
+#endif
+
+// bpo-40241 added PyObject_GC_IsFinalized() to Python 3.9.0a6.
+// bpo-18112 added _PyGCHead_FINALIZED() to Python 3.4.0 final.
+#if PY_VERSION_HEX < 0x030900A6 && PY_VERSION_HEX >= 0x030400F0 && !defined(PYPY_VERSION)
+static inline int PyObject_GC_IsFinalized(PyObject *obj)
+{
+    PyGC_Head *gc = _Py_CAST(PyGC_Head*, obj) - 1;
+    return (PyObject_IS_GC(obj) && _PyGCHead_FINALIZED(gc));
+}
+#endif
+
+
+// bpo-39573 added Py_IS_TYPE() to Python 3.9.0a4
+#if PY_VERSION_HEX < 0x030900A4 && !defined(Py_IS_TYPE)
+static inline int _Py_IS_TYPE(PyObject *ob, PyTypeObject *type) {
+    return Py_TYPE(ob) == type;
+}
+#define Py_IS_TYPE(ob, type) _Py_IS_TYPE(_PyObject_CAST(ob), type)
+#endif
+
+
+// bpo-46906 added PyFloat_Pack2() and PyFloat_Unpack2() to Python 3.11a7.
+// bpo-11734 added _PyFloat_Pack2() and _PyFloat_Unpack2() to Python 3.6.0b1.
+// Python 3.11a2 moved _PyFloat_Pack2() and _PyFloat_Unpack2() to the internal
+// C API: Python 3.11a2-3.11a6 versions are not supported.
+#if 0x030600B1 <= PY_VERSION_HEX && PY_VERSION_HEX <= 0x030B00A1 && !defined(PYPY_VERSION)
+static inline int PyFloat_Pack2(double x, char *p, int le)
+{ return _PyFloat_Pack2(x, (unsigned char*)p, le); }
+
+static inline double PyFloat_Unpack2(const char *p, int le)
+{ return _PyFloat_Unpack2((const unsigned char *)p, le); }
+#endif
+
+
+// bpo-46906 added PyFloat_Pack4(), PyFloat_Pack8(), PyFloat_Unpack4() and
+// PyFloat_Unpack8() to Python 3.11a7.
+// Python 3.11a2 moved _PyFloat_Pack4(), _PyFloat_Pack8(), _PyFloat_Unpack4()
+// and _PyFloat_Unpack8() to the internal C API: Python 3.11a2-3.11a6 versions
+// are not supported.
+#if PY_VERSION_HEX <= 0x030B00A1 && !defined(PYPY_VERSION)
+static inline int PyFloat_Pack4(double x, char *p, int le)
+{ return _PyFloat_Pack4(x, (unsigned char*)p, le); }
+
+static inline int PyFloat_Pack8(double x, char *p, int le)
+{ return _PyFloat_Pack8(x, (unsigned char*)p, le); }
+
+static inline double PyFloat_Unpack4(const char *p, int le)
+{ return _PyFloat_Unpack4((const unsigned char *)p, le); }
+
+static inline double PyFloat_Unpack8(const char *p, int le)
+{ return _PyFloat_Unpack8((const unsigned char *)p, le); }
+#endif
+
+
+// gh-92154 added PyCode_GetCode() to Python 3.11.0b1
+#if PY_VERSION_HEX < 0x030B00B1 && !defined(PYPY_VERSION)
+static inline PyObject* PyCode_GetCode(PyCodeObject *code)
+{
+    return Py_NewRef(code->co_code);
+}
+#endif
+
+
+// gh-95008 added PyCode_GetVarnames() to Python 3.11.0rc1
+#if PY_VERSION_HEX < 0x030B00C1 && !defined(PYPY_VERSION)
+static inline PyObject* PyCode_GetVarnames(PyCodeObject *code)
+{
+    return Py_NewRef(code->co_varnames);
+}
+#endif
+
+// gh-95008 added PyCode_GetFreevars() to Python 3.11.0rc1
+#if PY_VERSION_HEX < 0x030B00C1 && !defined(PYPY_VERSION)
+static inline PyObject* PyCode_GetFreevars(PyCodeObject *code)
+{
+    return Py_NewRef(code->co_freevars);
+}
+#endif
+
+// gh-95008 added PyCode_GetCellvars() to Python 3.11.0rc1
+#if PY_VERSION_HEX < 0x030B00C1 && !defined(PYPY_VERSION)
+static inline PyObject* PyCode_GetCellvars(PyCodeObject *code)
+{
+    return Py_NewRef(code->co_cellvars);
+}
+#endif
+
+
+// Py_UNUSED() was added to Python 3.4.0b2.
+#if PY_VERSION_HEX < 0x030400B2 && !defined(Py_UNUSED)
+#  if defined(__GNUC__) || defined(__clang__)
+#    define Py_UNUSED(name) _unused_ ## name __attribute__((unused))
+#  else
+#    define Py_UNUSED(name) _unused_ ## name
+#  endif
+#endif
+
+
+// gh-105922 added PyImport_AddModuleRef() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A0
+static inline PyObject* PyImport_AddModuleRef(const char *name)
+{
+    return Py_XNewRef(PyImport_AddModule(name));
+}
+#endif
+
+
+// gh-105927 added PyWeakref_GetRef() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D0000
+static inline int PyWeakref_GetRef(PyObject *ref, PyObject **pobj)
+{
+    PyObject *obj;
+    if (ref != NULL && !PyWeakref_Check(ref)) {
+        *pobj = NULL;
+        PyErr_SetString(PyExc_TypeError, "expected a weakref");
+        return -1;
+    }
+    obj = PyWeakref_GetObject(ref);
+    if (obj == NULL) {
+        // SystemError if ref is NULL
+        *pobj = NULL;
+        return -1;
+    }
+    if (obj == Py_None) {
+        *pobj = NULL;
+        return 0;
+    }
+    *pobj = Py_NewRef(obj);
+    return (*pobj != NULL);
+}
+#endif
+
+
+// bpo-36974 added PY_VECTORCALL_ARGUMENTS_OFFSET to Python 3.8b1
+#ifndef PY_VECTORCALL_ARGUMENTS_OFFSET
+#  define PY_VECTORCALL_ARGUMENTS_OFFSET (_Py_CAST(size_t, 1) << (8 * sizeof(size_t) - 1))
+#endif
+
+// bpo-36974 added PyVectorcall_NARGS() to Python 3.8b1
+#if PY_VERSION_HEX < 0x030800B1
+static inline Py_ssize_t PyVectorcall_NARGS(size_t n)
+{
+    return n & ~PY_VECTORCALL_ARGUMENTS_OFFSET;
+}
+#endif
+
+
+// gh-105922 added PyObject_Vectorcall() to Python 3.9.0a4
+#if PY_VERSION_HEX < 0x030900A4
+static inline PyObject*
+PyObject_Vectorcall(PyObject *callable, PyObject *const *args,
+                     size_t nargsf, PyObject *kwnames)
+{
+#if PY_VERSION_HEX >= 0x030800B1 && !defined(PYPY_VERSION)
+    // bpo-36974 added _PyObject_Vectorcall() to Python 3.8.0b1
+    return _PyObject_Vectorcall(callable, args, nargsf, kwnames);
+#else
+    PyObject *posargs = NULL, *kwargs = NULL;
+    PyObject *res;
+    Py_ssize_t nposargs, nkwargs, i;
+
+    if (nargsf != 0 && args == NULL) {
+        PyErr_BadInternalCall();
+        goto error;
+    }
+    if (kwnames != NULL && !PyTuple_Check(kwnames)) {
+        PyErr_BadInternalCall();
+        goto error;
+    }
+
+    nposargs = (Py_ssize_t)PyVectorcall_NARGS(nargsf);
+    if (kwnames) {
+        nkwargs = PyTuple_GET_SIZE(kwnames);
+    }
+    else {
+        nkwargs = 0;
+    }
+
+    posargs = PyTuple_New(nposargs);
+    if (posargs == NULL) {
+        goto error;
+    }
+    if (nposargs) {
+        for (i=0; i < nposargs; i++) {
+            PyTuple_SET_ITEM(posargs, i, Py_NewRef(*args));
+            args++;
+        }
+    }
+
+    if (nkwargs) {
+        kwargs = PyDict_New();
+        if (kwargs == NULL) {
+            goto error;
+        }
+
+        for (i = 0; i < nkwargs; i++) {
+            PyObject *key = PyTuple_GET_ITEM(kwnames, i);
+            PyObject *value = *args;
+            args++;
+            if (PyDict_SetItem(kwargs, key, value) < 0) {
+                goto error;
+            }
+        }
+    }
+    else {
+        kwargs = NULL;
+    }
+
+    res = PyObject_Call(callable, posargs, kwargs);
+    Py_DECREF(posargs);
+    Py_XDECREF(kwargs);
+    return res;
+
+error:
+    Py_DECREF(posargs);
+    Py_XDECREF(kwargs);
+    return NULL;
+#endif
+}
+#endif
+
+
+// gh-106521 added PyObject_GetOptionalAttr() and
+// PyObject_GetOptionalAttrString() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyObject_GetOptionalAttr(PyObject *obj, PyObject *attr_name, PyObject **result)
+{
+    // bpo-32571 added _PyObject_LookupAttr() to Python 3.7.0b1
+#if PY_VERSION_HEX >= 0x030700B1 && !defined(PYPY_VERSION)
+    return _PyObject_LookupAttr(obj, attr_name, result);
+#else
+    *result = PyObject_GetAttr(obj, attr_name);
+    if (*result != NULL) {
+        return 1;
+    }
+    if (!PyErr_Occurred()) {
+        return 0;
+    }
+    if (PyErr_ExceptionMatches(PyExc_AttributeError)) {
+        PyErr_Clear();
+        return 0;
+    }
+    return -1;
+#endif
+}
+
+static inline int
+PyObject_GetOptionalAttrString(PyObject *obj, const char *attr_name, PyObject **result)
+{
+    PyObject *name_obj;
+    int rc;
+#if PY_VERSION_HEX >= 0x03000000
+    name_obj = PyUnicode_FromString(attr_name);
+#else
+    name_obj = PyString_FromString(attr_name);
+#endif
+    if (name_obj == NULL) {
+        *result = NULL;
+        return -1;
+    }
+    rc = PyObject_GetOptionalAttr(obj, name_obj, result);
+    Py_DECREF(name_obj);
+    return rc;
+}
+#endif
+
+
+// gh-106307 added PyObject_GetOptionalAttr() and
+// PyMapping_GetOptionalItemString() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyMapping_GetOptionalItem(PyObject *obj, PyObject *key, PyObject **result)
+{
+    *result = PyObject_GetItem(obj, key);
+    if (*result) {
+        return 1;
+    }
+    if (!PyErr_ExceptionMatches(PyExc_KeyError)) {
+        return -1;
+    }
+    PyErr_Clear();
+    return 0;
+}
+
+static inline int
+PyMapping_GetOptionalItemString(PyObject *obj, const char *key, PyObject **result)
+{
+    PyObject *key_obj;
+    int rc;
+#if PY_VERSION_HEX >= 0x03000000
+    key_obj = PyUnicode_FromString(key);
+#else
+    key_obj = PyString_FromString(key);
+#endif
+    if (key_obj == NULL) {
+        *result = NULL;
+        return -1;
+    }
+    rc = PyMapping_GetOptionalItem(obj, key_obj, result);
+    Py_DECREF(key_obj);
+    return rc;
+}
+#endif
+
+// gh-108511 added PyMapping_HasKeyWithError() and
+// PyMapping_HasKeyStringWithError() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyMapping_HasKeyWithError(PyObject *obj, PyObject *key)
+{
+    PyObject *res;
+    int rc = PyMapping_GetOptionalItem(obj, key, &res);
+    Py_XDECREF(res);
+    return rc;
+}
+
+static inline int
+PyMapping_HasKeyStringWithError(PyObject *obj, const char *key)
+{
+    PyObject *res;
+    int rc = PyMapping_GetOptionalItemString(obj, key, &res);
+    Py_XDECREF(res);
+    return rc;
+}
+#endif
+
+
+// gh-108511 added PyObject_HasAttrWithError() and
+// PyObject_HasAttrStringWithError() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyObject_HasAttrWithError(PyObject *obj, PyObject *attr)
+{
+    PyObject *res;
+    int rc = PyObject_GetOptionalAttr(obj, attr, &res);
+    Py_XDECREF(res);
+    return rc;
+}
+
+static inline int
+PyObject_HasAttrStringWithError(PyObject *obj, const char *attr)
+{
+    PyObject *res;
+    int rc = PyObject_GetOptionalAttrString(obj, attr, &res);
+    Py_XDECREF(res);
+    return rc;
+}
+#endif
+
+
+// gh-106004 added PyDict_GetItemRef() and PyDict_GetItemStringRef()
+// to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyDict_GetItemRef(PyObject *mp, PyObject *key, PyObject **result)
+{
+#if PY_VERSION_HEX >= 0x03000000
+    PyObject *item = PyDict_GetItemWithError(mp, key);
+#else
+    PyObject *item = _PyDict_GetItemWithError(mp, key);
+#endif
+    if (item != NULL) {
+        *result = Py_NewRef(item);
+        return 1;  // found
+    }
+    if (!PyErr_Occurred()) {
+        *result = NULL;
+        return 0;  // not found
+    }
+    *result = NULL;
+    return -1;
+}
+
+static inline int
+PyDict_GetItemStringRef(PyObject *mp, const char *key, PyObject **result)
+{
+    int res;
+#if PY_VERSION_HEX >= 0x03000000
+    PyObject *key_obj = PyUnicode_FromString(key);
+#else
+    PyObject *key_obj = PyString_FromString(key);
+#endif
+    if (key_obj == NULL) {
+        *result = NULL;
+        return -1;
+    }
+    res = PyDict_GetItemRef(mp, key_obj, result);
+    Py_DECREF(key_obj);
+    return res;
+}
+#endif
+
+
+// gh-106307 added PyModule_Add() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyModule_Add(PyObject *mod, const char *name, PyObject *value)
+{
+    int res = PyModule_AddObjectRef(mod, name, value);
+    Py_XDECREF(value);
+    return res;
+}
+#endif
+
+
+// gh-108014 added Py_IsFinalizing() to Python 3.13.0a1
+// bpo-1856 added _Py_Finalizing to Python 3.2.1b1.
+// _Py_IsFinalizing() was added to PyPy 7.3.0.
+#if (0x030201B1 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x030D00A1) \
+        && (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x7030000)
+static inline int Py_IsFinalizing(void)
+{
+#if PY_VERSION_HEX >= 0x030700A1
+    // _Py_IsFinalizing() was added to Python 3.7.0a1.
+    return _Py_IsFinalizing();
+#else
+    return (_Py_Finalizing != NULL);
+#endif
+}
+#endif
+
+
+// gh-108323 added PyDict_ContainsString() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int PyDict_ContainsString(PyObject *op, const char *key)
+{
+    PyObject *key_obj = PyUnicode_FromString(key);
+    if (key_obj == NULL) {
+        return -1;
+    }
+    int res = PyDict_Contains(op, key_obj);
+    Py_DECREF(key_obj);
+    return res;
+}
+#endif
+
+
+// gh-108445 added PyLong_AsInt() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int PyLong_AsInt(PyObject *obj)
+{
+#ifdef PYPY_VERSION
+    long value = PyLong_AsLong(obj);
+    if (value == -1 && PyErr_Occurred()) {
+        return -1;
+    }
+    if (value < (long)INT_MIN || (long)INT_MAX < value) {
+        PyErr_SetString(PyExc_OverflowError,
+                        "Python int too large to convert to C int");
+        return -1;
+    }
+    return (int)value;
+#else
+    return _PyLong_AsInt(obj);
+#endif
+}
+#endif
+
+
+// gh-107073 added PyObject_VisitManagedDict() to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyObject_VisitManagedDict(PyObject *obj, visitproc visit, void *arg)
+{
+    PyObject **dict = _PyObject_GetDictPtr(obj);
+    if (*dict == NULL) {
+        return -1;
+    }
+    Py_VISIT(*dict);
+    return 0;
+}
+
+static inline void
+PyObject_ClearManagedDict(PyObject *obj)
+{
+    PyObject **dict = _PyObject_GetDictPtr(obj);
+    if (*dict == NULL) {
+        return;
+    }
+    Py_CLEAR(*dict);
+}
+#endif
+
+// gh-108867 added PyThreadState_GetUnchecked() to Python 3.13.0a1
+// Python 3.5.2 added _PyThreadState_UncheckedGet().
+#if PY_VERSION_HEX >= 0x03050200 && PY_VERSION_HEX < 0x030D00A1
+static inline PyThreadState*
+PyThreadState_GetUnchecked(void)
+{
+    return _PyThreadState_UncheckedGet();
+}
+#endif
+
+// gh-110289 added PyUnicode_EqualToUTF8() and PyUnicode_EqualToUTF8AndSize()
+// to Python 3.13.0a1
+#if PY_VERSION_HEX < 0x030D00A1
+static inline int
+PyUnicode_EqualToUTF8AndSize(PyObject *unicode, const char *str, Py_ssize_t str_len)
+{
+    Py_ssize_t len;
+    const void *utf8;
+    PyObject *exc_type, *exc_value, *exc_tb;
+    int res;
+
+    // API cannot report errors so save/restore the exception
+    PyErr_Fetch(&exc_type, &exc_value, &exc_tb);
+
+    // Python 3.3.0a1 added PyUnicode_AsUTF8AndSize()
+#if PY_VERSION_HEX >= 0x030300A1
+    if (PyUnicode_IS_ASCII(unicode)) {
+        utf8 = PyUnicode_DATA(unicode);
+        len = PyUnicode_GET_LENGTH(unicode);
+    }
+    else {
+        utf8 = PyUnicode_AsUTF8AndSize(unicode, &len);
+        if (utf8 == NULL) {
+            // Memory allocation failure. The API cannot report error,
+            // so ignore the exception and return 0.
+            res = 0;
+            goto done;
+        }
+    }
+
+    if (len != str_len) {
+        res = 0;
+        goto done;
+    }
+    res = (memcmp(utf8, str, (size_t)len) == 0);
+#else
+    PyObject *bytes = PyUnicode_AsUTF8String(unicode);
+    if (bytes == NULL) {
+        // Memory allocation failure. The API cannot report error,
+        // so ignore the exception and return 0.
+        res = 0;
+        goto done;
+    }
+
+#if PY_VERSION_HEX >= 0x03000000
+    len = PyBytes_GET_SIZE(bytes);
+    utf8 = PyBytes_AS_STRING(bytes);
+#else
+    len = PyString_GET_SIZE(bytes);
+    utf8 = PyString_AS_STRING(bytes);
+#endif
+    if (len != str_len) {
+        Py_DECREF(bytes);
+        res = 0;
+        goto done;
+    }
+
+    res = (memcmp(utf8, str, (size_t)len) == 0);
+    Py_DECREF(bytes);
+#endif
+
+done:
+    PyErr_Restore(exc_type, exc_value, exc_tb);
+    return res;
+}
+
+static inline int
+PyUnicode_EqualToUTF8(PyObject *unicode, const char *str)
+{
+    return PyUnicode_EqualToUTF8AndSize(unicode, str, (Py_ssize_t)strlen(str));
+}
+#endif
+
+
+// gh-111138 added PyList_Extend() and PyList_Clear() to Python 3.13.0a2
+#if PY_VERSION_HEX < 0x030D00A2
+static inline int
+PyList_Extend(PyObject *list, PyObject *iterable)
+{
+    return PyList_SetSlice(list, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, iterable);
+}
+
+static inline int
+PyList_Clear(PyObject *list)
+{
+    return PyList_SetSlice(list, 0, PY_SSIZE_T_MAX, NULL);
+}
+#endif
+
+// gh-111262 added PyDict_Pop() and PyDict_PopString() to Python 3.13.0a2
+#if PY_VERSION_HEX < 0x030D00A2
+static inline int
+PyDict_Pop(PyObject *dict, PyObject *key, PyObject **result)
+{
+    PyObject *value;
+
+    if (!PyDict_Check(dict)) {
+        PyErr_BadInternalCall();
+        if (result) {
+            *result = NULL;
+        }
+        return -1;
+    }
+
+    // bpo-16991 added _PyDict_Pop() to Python 3.5.0b2.
+    // Python 3.6.0b3 changed _PyDict_Pop() first argument type to PyObject*.
+    // Python 3.13.0a1 removed _PyDict_Pop().
+#if defined(PYPY_VERSION) || PY_VERSION_HEX < 0x030500b2 || PY_VERSION_HEX >= 0x030D0000
+    value = PyObject_CallMethod(dict, "pop", "O", key);
+#elif PY_VERSION_HEX < 0x030600b3
+    value = _PyDict_Pop(_Py_CAST(PyDictObject*, dict), key, NULL);
+#else
+    value = _PyDict_Pop(dict, key, NULL);
+#endif
+    if (value == NULL) {
+        if (result) {
+            *result = NULL;
+        }
+        if (PyErr_Occurred() && !PyErr_ExceptionMatches(PyExc_KeyError)) {
+            return -1;
+        }
+        PyErr_Clear();
+        return 0;
+    }
+    if (result) {
+        *result = value;
+    }
+    else {
+        Py_DECREF(value);
+    }
+    return 1;
+}
+
+static inline int
+PyDict_PopString(PyObject *dict, const char *key, PyObject **result)
+{
+    PyObject *key_obj = PyUnicode_FromString(key);
+    if (key_obj == NULL) {
+        if (result != NULL) {
+            *result = NULL;
+        }
+        return -1;
+    }
+
+    int res = PyDict_Pop(dict, key_obj, result);
+    Py_DECREF(key_obj);
+    return res;
+}
+#endif
+
+
+#if PY_VERSION_HEX < 0x030200A4
+// Python 3.2.0a4 added Py_hash_t type
+typedef Py_ssize_t Py_hash_t;
+#endif
+
+
+// gh-111545 added Py_HashPointer() to Python 3.13.0a3
+#if PY_VERSION_HEX < 0x030D00A3
+static inline Py_hash_t Py_HashPointer(const void *ptr)
+{
+#if PY_VERSION_HEX >= 0x030900A4 && !defined(PYPY_VERSION)
+    return _Py_HashPointer(ptr);
+#else
+    return _Py_HashPointer(_Py_CAST(void*, ptr));
+#endif
+}
+#endif
+
+
+// Python 3.13a4 added a PyTime API.
+// Use the private API added to Python 3.5.
+#if PY_VERSION_HEX < 0x030D00A4 && PY_VERSION_HEX  >= 0x03050000
+typedef _PyTime_t PyTime_t;
+#define PyTime_MIN _PyTime_MIN
+#define PyTime_MAX _PyTime_MAX
+
+static inline double PyTime_AsSecondsDouble(PyTime_t t)
+{ return _PyTime_AsSecondsDouble(t); }
+
+static inline int PyTime_Monotonic(PyTime_t *result)
+{ return _PyTime_GetMonotonicClockWithInfo(result, NULL); }
+
+static inline int PyTime_Time(PyTime_t *result)
+{ return _PyTime_GetSystemClockWithInfo(result, NULL); }
+
+static inline int PyTime_PerfCounter(PyTime_t *result)
+{
+#if PY_VERSION_HEX >= 0x03070000 && !defined(PYPY_VERSION)
+    return _PyTime_GetPerfCounterWithInfo(result, NULL);
+#elif PY_VERSION_HEX >= 0x03070000
+    // Call time.perf_counter_ns() and convert Python int object to PyTime_t.
+    // Cache time.perf_counter_ns() function for best performance.
+    static PyObject *func = NULL;
+    if (func == NULL) {
+        PyObject *mod = PyImport_ImportModule("time");
+        if (mod == NULL) {
+            return -1;
+        }
+
+        func = PyObject_GetAttrString(mod, "perf_counter_ns");
+        Py_DECREF(mod);
+        if (func == NULL) {
+            return -1;
+        }
+    }
+
+    PyObject *res = PyObject_CallNoArgs(func);
+    if (res == NULL) {
+        return -1;
+    }
+    long long value = PyLong_AsLongLong(res);
+    Py_DECREF(res);
+
+    if (value == -1 && PyErr_Occurred()) {
+        return -1;
+    }
+
+    Py_BUILD_ASSERT(sizeof(value) >= sizeof(PyTime_t));
+    *result = (PyTime_t)value;
+    return 0;
+#else
+    // Call time.perf_counter() and convert C double to PyTime_t.
+    // Cache time.perf_counter() function for best performance.
+    static PyObject *func = NULL;
+    if (func == NULL) {
+        PyObject *mod = PyImport_ImportModule("time");
+        if (mod == NULL) {
+            return -1;
+        }
+
+        func = PyObject_GetAttrString(mod, "perf_counter");
+        Py_DECREF(mod);
+        if (func == NULL) {
+            return -1;
+        }
+    }
+
+    PyObject *res = PyObject_CallNoArgs(func);
+    if (res == NULL) {
+        return -1;
+    }
+    double d = PyFloat_AsDouble(res);
+    Py_DECREF(res);
+
+    if (d == -1.0 && PyErr_Occurred()) {
+        return -1;
+    }
+
+    // Avoid floor() to avoid having to link to libm
+    *result = (PyTime_t)(d * 1e9);
+    return 0;
+#endif
+}
+
+#endif
+
+// gh-111389 added hash constants to Python 3.13.0a5. These constants were
+// added first as private macros to Python 3.4.0b1 and PyPy 7.3.9.
+#if (!defined(PyHASH_BITS) \
+     && ((!defined(PYPY_VERSION) && PY_VERSION_HEX >= 0x030400B1) \
+         || (defined(PYPY_VERSION) && PY_VERSION_HEX >= 0x03070000 \
+             && PYPY_VERSION_NUM >= 0x07090000)))
+#  define PyHASH_BITS _PyHASH_BITS
+#  define PyHASH_MODULUS _PyHASH_MODULUS
+#  define PyHASH_INF _PyHASH_INF
+#  define PyHASH_IMAG _PyHASH_IMAG
+#endif
+
+
+// gh-111545 added Py_GetConstant() and Py_GetConstantBorrowed()
+// to Python 3.13.0a6
+#if PY_VERSION_HEX < 0x030D00A6 && !defined(Py_CONSTANT_NONE)
+
+#define Py_CONSTANT_NONE 0
+#define Py_CONSTANT_FALSE 1
+#define Py_CONSTANT_TRUE 2
+#define Py_CONSTANT_ELLIPSIS 3
+#define Py_CONSTANT_NOT_IMPLEMENTED 4
+#define Py_CONSTANT_ZERO 5
+#define Py_CONSTANT_ONE 6
+#define Py_CONSTANT_EMPTY_STR 7
+#define Py_CONSTANT_EMPTY_BYTES 8
+#define Py_CONSTANT_EMPTY_TUPLE 9
+
+static inline PyObject* Py_GetConstant(unsigned int constant_id)
+{
+    static PyObject* constants[Py_CONSTANT_EMPTY_TUPLE + 1] = {NULL};
+
+    if (constants[Py_CONSTANT_NONE] == NULL) {
+        constants[Py_CONSTANT_NONE] = Py_None;
+        constants[Py_CONSTANT_FALSE] = Py_False;
+        constants[Py_CONSTANT_TRUE] = Py_True;
+        constants[Py_CONSTANT_ELLIPSIS] = Py_Ellipsis;
+        constants[Py_CONSTANT_NOT_IMPLEMENTED] = Py_NotImplemented;
+
+        constants[Py_CONSTANT_ZERO] = PyLong_FromLong(0);
+        if (constants[Py_CONSTANT_ZERO] == NULL) {
+            goto fatal_error;
+        }
+
+        constants[Py_CONSTANT_ONE] = PyLong_FromLong(1);
+        if (constants[Py_CONSTANT_ONE] == NULL) {
+            goto fatal_error;
+        }
+
+        constants[Py_CONSTANT_EMPTY_STR] = PyUnicode_FromStringAndSize("", 0);
+        if (constants[Py_CONSTANT_EMPTY_STR] == NULL) {
+            goto fatal_error;
+        }
+
+        constants[Py_CONSTANT_EMPTY_BYTES] = PyBytes_FromStringAndSize("", 0);
+        if (constants[Py_CONSTANT_EMPTY_BYTES] == NULL) {
+            goto fatal_error;
+        }
+
+        constants[Py_CONSTANT_EMPTY_TUPLE] = PyTuple_New(0);
+        if (constants[Py_CONSTANT_EMPTY_TUPLE] == NULL) {
+            goto fatal_error;
+        }
+        // goto dance to avoid compiler warnings about Py_FatalError()
+        goto init_done;
+
+fatal_error:
+        // This case should never happen
+        Py_FatalError("Py_GetConstant() failed to get constants");
+    }
+
+init_done:
+    if (constant_id <= Py_CONSTANT_EMPTY_TUPLE) {
+        return Py_NewRef(constants[constant_id]);
+    }
+    else {
+        PyErr_BadInternalCall();
+        return NULL;
+    }
+}
+
+static inline PyObject* Py_GetConstantBorrowed(unsigned int constant_id)
+{
+    PyObject *obj = Py_GetConstant(constant_id);
+    Py_XDECREF(obj);
+    return obj;
+}
+#endif
+
+
+// gh-114329 added PyList_GetItemRef() to Python 3.13.0a4
+#if PY_VERSION_HEX < 0x030D00A4
+static inline PyObject *
+PyList_GetItemRef(PyObject *op, Py_ssize_t index)
+{
+    PyObject *item = PyList_GetItem(op, index);
+    Py_XINCREF(item);
+    return item;
+}
+#endif
+
+
+// gh-114329 added PyList_GetItemRef() to Python 3.13.0a4
+#if PY_VERSION_HEX < 0x030D00A4
+static inline int
+PyDict_SetDefaultRef(PyObject *d, PyObject *key, PyObject *default_value,
+                     PyObject **result)
+{
+    PyObject *value;
+    if (PyDict_GetItemRef(d, key, &value) < 0) {
+        // get error
+        if (result) {
+            *result = NULL;
+        }
+        return -1;
+    }
+    if (value != NULL) {
+        // present
+        if (result) {
+            *result = value;
+        }
+        else {
+            Py_DECREF(value);
+        }
+        return 1;
+    }
+
+    // missing: set the item
+    if (PyDict_SetItem(d, key, default_value) < 0) {
+        // set error
+        if (result) {
+            *result = NULL;
+        }
+        return -1;
+    }
+    if (result) {
+        *result = Py_NewRef(default_value);
+    }
+    return 0;
+}
+#endif
+
+#if PY_VERSION_HEX < 0x030D00B3
+#  define Py_BEGIN_CRITICAL_SECTION(op) {
+#  define Py_END_CRITICAL_SECTION() }
+#  define Py_BEGIN_CRITICAL_SECTION2(a, b) {
+#  define Py_END_CRITICAL_SECTION2() }
+#endif
+
+#if PY_VERSION_HEX < 0x030E0000 && PY_VERSION_HEX >= 0x03060000 && !defined(PYPY_VERSION)
+typedef struct PyUnicodeWriter PyUnicodeWriter;
+
+static inline void PyUnicodeWriter_Discard(PyUnicodeWriter *writer)
+{
+    _PyUnicodeWriter_Dealloc((_PyUnicodeWriter*)writer);
+    PyMem_Free(writer);
+}
+
+static inline PyUnicodeWriter* PyUnicodeWriter_Create(Py_ssize_t length)
+{
+    if (length < 0) {
+        PyErr_SetString(PyExc_ValueError,
+                        "length must be positive");
+        return NULL;
+    }
+
+    const size_t size = sizeof(_PyUnicodeWriter);
+    PyUnicodeWriter *pub_writer = (PyUnicodeWriter *)PyMem_Malloc(size);
+    if (pub_writer == _Py_NULL) {
+        PyErr_NoMemory();
+        return _Py_NULL;
+    }
+    _PyUnicodeWriter *writer = (_PyUnicodeWriter *)pub_writer;
+
+    _PyUnicodeWriter_Init(writer);
+    if (_PyUnicodeWriter_Prepare(writer, length, 127) < 0) {
+        PyUnicodeWriter_Discard(pub_writer);
+        return NULL;
+    }
+    writer->overallocate = 1;
+    return pub_writer;
+}
+
+static inline PyObject* PyUnicodeWriter_Finish(PyUnicodeWriter *writer)
+{
+    PyObject *str = _PyUnicodeWriter_Finish((_PyUnicodeWriter*)writer);
+    assert(((_PyUnicodeWriter*)writer)->buffer == NULL);
+    PyMem_Free(writer);
+    return str;
+}
+
+static inline int
+PyUnicodeWriter_WriteChar(PyUnicodeWriter *writer, Py_UCS4 ch)
+{
+    if (ch > 0x10ffff) {
+        PyErr_SetString(PyExc_ValueError,
+                        "character must be in range(0x110000)");
+        return -1;
+    }
+
+    return _PyUnicodeWriter_WriteChar((_PyUnicodeWriter*)writer, ch);
+}
+
+static inline int
+PyUnicodeWriter_WriteStr(PyUnicodeWriter *writer, PyObject *obj)
+{
+    PyObject *str = PyObject_Str(obj);
+    if (str == NULL) {
+        return -1;
+    }
+
+    int res = _PyUnicodeWriter_WriteStr((_PyUnicodeWriter*)writer, str);
+    Py_DECREF(str);
+    return res;
+}
+
+static inline int
+PyUnicodeWriter_WriteRepr(PyUnicodeWriter *writer, PyObject *obj)
+{
+    PyObject *str = PyObject_Repr(obj);
+    if (str == NULL) {
+        return -1;
+    }
+
+    int res = _PyUnicodeWriter_WriteStr((_PyUnicodeWriter*)writer, str);
+    Py_DECREF(str);
+    return res;
+}
+
+static inline int
+PyUnicodeWriter_WriteUTF8(PyUnicodeWriter *writer,
+                          const char *str, Py_ssize_t size)
+{
+    if (size < 0) {
+        size = (Py_ssize_t)strlen(str);
+    }
+
+    PyObject *str_obj = PyUnicode_FromStringAndSize(str, size);
+    if (str_obj == _Py_NULL) {
+        return -1;
+    }
+
+    int res = _PyUnicodeWriter_WriteStr((_PyUnicodeWriter*)writer, str_obj);
+    Py_DECREF(str_obj);
+    return res;
+}
+
+static inline int
+PyUnicodeWriter_WriteWideChar(PyUnicodeWriter *writer,
+                              const wchar_t *str, Py_ssize_t size)
+{
+    if (size < 0) {
+        size = (Py_ssize_t)wcslen(str);
+    }
+
+    PyObject *str_obj = PyUnicode_FromWideChar(str, size);
+    if (str_obj == _Py_NULL) {
+        return -1;
+    }
+
+    int res = _PyUnicodeWriter_WriteStr((_PyUnicodeWriter*)writer, str_obj);
+    Py_DECREF(str_obj);
+    return res;
+}
+
+static inline int
+PyUnicodeWriter_WriteSubstring(PyUnicodeWriter *writer, PyObject *str,
+                               Py_ssize_t start, Py_ssize_t end)
+{
+    if (!PyUnicode_Check(str)) {
+        PyErr_Format(PyExc_TypeError, "expect str, not %T", str);
+        return -1;
+    }
+    if (start < 0 || start > end) {
+        PyErr_Format(PyExc_ValueError, "invalid start argument");
+        return -1;
+    }
+    if (end > PyUnicode_GET_LENGTH(str)) {
+        PyErr_Format(PyExc_ValueError, "invalid end argument");
+        return -1;
+    }
+
+    return _PyUnicodeWriter_WriteSubstring((_PyUnicodeWriter*)writer, str,
+                                           start, end);
+}
+
+static inline int
+PyUnicodeWriter_Format(PyUnicodeWriter *writer, const char *format, ...)
+{
+    va_list vargs;
+    va_start(vargs, format);
+    PyObject *str = PyUnicode_FromFormatV(format, vargs);
+    va_end(vargs);
+    if (str == _Py_NULL) {
+        return -1;
+    }
+
+    int res = _PyUnicodeWriter_WriteStr((_PyUnicodeWriter*)writer, str);
+    Py_DECREF(str);
+    return res;
+}
+#endif  // PY_VERSION_HEX < 0x030E0000
+
+// gh-116560 added PyLong_GetSign() to Python 3.14.0a0
+#if PY_VERSION_HEX < 0x030E00A0
+static inline int PyLong_GetSign(PyObject *obj, int *sign)
+{
+    if (!PyLong_Check(obj)) {
+        PyErr_Format(PyExc_TypeError, "expect int, got %s", Py_TYPE(obj)->tp_name);
+        return -1;
+    }
+
+    *sign = _PyLong_Sign(obj);
+    return 0;
+}
+#endif
+
+
+// gh-124502 added PyUnicode_Equal() to Python 3.14.0a0
+#if PY_VERSION_HEX < 0x030E00A0
+static inline int PyUnicode_Equal(PyObject *str1, PyObject *str2)
+{
+    if (!PyUnicode_Check(str1)) {
+        PyErr_Format(PyExc_TypeError, "first argument must be str, not %s",
+                     Py_TYPE(str1)->tp_name);
+        return -1;
+    }
+    if (!PyUnicode_Check(str2)) {
+        PyErr_Format(PyExc_TypeError, "second argument must be str, not %s",
+                     Py_TYPE(str2)->tp_name);
+        return -1;
+    }
+
+#if PY_VERSION_HEX >= 0x030d0000 && !defined(PYPY_VERSION)
+    PyAPI_FUNC(int) _PyUnicode_Equal(PyObject *str1, PyObject *str2);
+
+    return _PyUnicode_Equal(str1, str2);
+#elif PY_VERSION_HEX >= 0x03060000 && !defined(PYPY_VERSION)
+    return _PyUnicode_EQ(str1, str2);
+#elif PY_VERSION_HEX >= 0x03090000 && defined(PYPY_VERSION)
+    return _PyUnicode_EQ(str1, str2);
+#else
+    return (PyUnicode_Compare(str1, str2) == 0);
+#endif
+}
+#endif
+
+
+// gh-121645 added PyBytes_Join() to Python 3.14.0a0
+#if PY_VERSION_HEX < 0x030E00A0
+static inline PyObject* PyBytes_Join(PyObject *sep, PyObject *iterable)
+{
+    return _PyBytes_Join(sep, iterable);
+}
+#endif
+
+
+#if PY_VERSION_HEX < 0x030E00A0
+static inline Py_hash_t Py_HashBuffer(const void *ptr, Py_ssize_t len)
+{
+#if PY_VERSION_HEX >= 0x03000000 && !defined(PYPY_VERSION)
+    PyAPI_FUNC(Py_hash_t) _Py_HashBytes(const void *src, Py_ssize_t len);
+
+    return _Py_HashBytes(ptr, len);
+#else
+    Py_hash_t hash;
+    PyObject *bytes = PyBytes_FromStringAndSize((const char*)ptr, len);
+    if (bytes == NULL) {
+        return -1;
+    }
+    hash = PyObject_Hash(bytes);
+    Py_DECREF(bytes);
+    return hash;
+#endif
+}
+#endif
+
+
+#if PY_VERSION_HEX < 0x030E00A0
+static inline int PyIter_NextItem(PyObject *iter, PyObject **item)
+{
+    iternextfunc tp_iternext;
+
+    assert(iter != NULL);
+    assert(item != NULL);
+
+    tp_iternext = Py_TYPE(iter)->tp_iternext;
+    if (tp_iternext == NULL) {
+        *item = NULL;
+        PyErr_Format(PyExc_TypeError, "expected an iterator, got '%s'",
+                     Py_TYPE(iter)->tp_name);
+        return -1;
+    }
+
+    if ((*item = tp_iternext(iter))) {
+        return 1;
+    }
+    if (!PyErr_Occurred()) {
+        return 0;
+    }
+    if (PyErr_ExceptionMatches(PyExc_StopIteration)) {
+        PyErr_Clear();
+        return 0;
+    }
+    return -1;
+}
+#endif
+
+
+#if PY_VERSION_HEX < 0x030E00A0
+static inline PyObject* PyLong_FromInt32(int32_t value)
+{
+    Py_BUILD_ASSERT(sizeof(long) >= 4);
+    return PyLong_FromLong(value);
+}
+
+static inline PyObject* PyLong_FromInt64(int64_t value)
+{
+    Py_BUILD_ASSERT(sizeof(long long) >= 8);
+    return PyLong_FromLongLong(value);
+}
+
+static inline PyObject* PyLong_FromUInt32(uint32_t value)
+{
+    Py_BUILD_ASSERT(sizeof(unsigned long) >= 4);
+    return PyLong_FromUnsignedLong(value);
+}
+
+static inline PyObject* PyLong_FromUInt64(uint64_t value)
+{
+    Py_BUILD_ASSERT(sizeof(unsigned long long) >= 8);
+    return PyLong_FromUnsignedLongLong(value);
+}
+
+static inline int PyLong_AsInt32(PyObject *obj, int32_t *pvalue)
+{
+    Py_BUILD_ASSERT(sizeof(int) == 4);
+    int value = PyLong_AsInt(obj);
+    if (value == -1 && PyErr_Occurred()) {
+        return -1;
+    }
+    *pvalue = (int32_t)value;
+    return 0;
+}
+
+static inline int PyLong_AsInt64(PyObject *obj, int64_t *pvalue)
+{
+    Py_BUILD_ASSERT(sizeof(long long) == 8);
+    long long value = PyLong_AsLongLong(obj);
+    if (value == -1 && PyErr_Occurred()) {
+        return -1;
+    }
+    *pvalue = (int64_t)value;
+    return 0;
+}
+
+static inline int PyLong_AsUInt32(PyObject *obj, uint32_t *pvalue)
+{
+    Py_BUILD_ASSERT(sizeof(long) >= 4);
+    unsigned long value = PyLong_AsUnsignedLong(obj);
+    if (value == (unsigned long)-1 && PyErr_Occurred()) {
+        return -1;
+    }
+#if SIZEOF_LONG > 4
+    if ((unsigned long)UINT32_MAX < value) {
+        PyErr_SetString(PyExc_OverflowError,
+                        "Python int too large to convert to C uint32_t");
+        return -1;
+    }
+#endif
+    *pvalue = (uint32_t)value;
+    return 0;
+}
+
+static inline int PyLong_AsUInt64(PyObject *obj, uint64_t *pvalue)
+{
+    Py_BUILD_ASSERT(sizeof(long long) == 8);
+    unsigned long long value = PyLong_AsUnsignedLongLong(obj);
+    if (value == (unsigned long long)-1 && PyErr_Occurred()) {
+        return -1;
+    }
+    *pvalue = (uint64_t)value;
+    return 0;
+}
+#endif
+
+
+#ifdef __cplusplus
+}
+#endif
+#endif  // PYTHONCAPI_COMPAT
diff --git a/shared/package-files/c0/c01fb469a2ec7b260dd58384402c8ac71b31da9249d546cfa839b625c6f8cf4f/movedim_compositeimplicitautograd_dispatch.h b/shared/package-files/c0/c01fb469a2ec7b260dd58384402c8ac71b31da9249d546cfa839b625c6f8cf4f/movedim_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0543008089c86b9cc6fd7da8cf8fa41e22b0ceab
--- /dev/null
+++ b/shared/package-files/c0/c01fb469a2ec7b260dd58384402c8ac71b31da9249d546cfa839b625c6f8cf4f/movedim_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor movedim(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination);
+TORCH_API at::Tensor movedim(const at::Tensor & self, int64_t source, int64_t destination);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/c0/c0276bf58e6619e8ac21c85110afdeb4633eb7cc1e26a4bd5a680c13cd4d3e45/_qn.pyx b/shared/package-files/c0/c0276bf58e6619e8ac21c85110afdeb4633eb7cc1e26a4bd5a680c13cd4d3e45/_qn.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..dcef2e15ebc5d7c61a98670e37e40f8c8077d081
--- /dev/null
+++ b/shared/package-files/c0/c0276bf58e6619e8ac21c85110afdeb4633eb7cc1e26a4bd5a680c13cd4d3e45/_qn.pyx
@@ -0,0 +1,135 @@
+#!python
+#cython: wraparound=False, boundscheck=False, cdivision=True
+
+import numpy as np
+
+
+def _high_weighted_median(double[::1] a, int[::1] weights):
+    """
+    Computes a weighted high median of a. This is defined as the
+    smallest a[j] such that the sum over all a[i]<=a[j] is strictly
+    greater than half the total sum of the weights
+    """
+    cdef:
+        double[::1] a_cp = np.copy(a)
+        int[::1] weights_cp = np.copy(weights)
+        int n = a_cp.shape[0]
+        double[::1] sorted_a = np.zeros((n,), dtype=np.double)
+        double[::1] a_cand = np.zeros((n,), dtype=np.double)
+        int[::1] weights_cand = np.zeros((n,), dtype=np.intc)
+        int kcand = 0
+        int wleft, wright, wmid, wtot, wrest = 0
+        double trial = 0
+    wtot = np.sum(weights_cp)
+    while True:
+        wleft = 0
+        wmid = 0
+        wright = 0
+        for i in range(n):
+            sorted_a[i] = a_cp[i]
+        sorted_a = np.partition(sorted_a, kth=n//2)
+        trial = sorted_a[n//2]
+        for i in range(n):
+            if a_cp[i] < trial:
+                wleft = wleft + weights_cp[i]
+            elif a_cp[i] > trial:
+                wright = wright + weights_cp[i]
+            else:
+                wmid = wmid + weights_cp[i]
+        kcand = 0
+        if 2 * (wrest + wleft) > wtot:
+            for i in range(n):
+                if a_cp[i] < trial:
+                    a_cand[kcand] = a_cp[i]
+                    weights_cand[kcand] = weights_cp[i]
+                    kcand = kcand + 1
+        elif 2 * (wrest + wleft + wmid) <= wtot:
+            for i in range(n):
+                if a_cp[i] > trial:
+                    a_cand[kcand] = a_cp[i]
+                    weights_cand[kcand] = weights_cp[i]
+                    kcand = kcand + 1
+            wrest = wrest + wleft + wmid
+        else:
+            break
+        n = kcand
+        for i in range(n):
+            a_cp[i] = a_cand[i]
+            weights_cp[i] = weights_cand[i]
+    return trial
+
+
+def _qn(double[:] a, double c):
+    """
+    Computes the Qn robust estimator of scale, a more efficient alternative
+    to the MAD. The implementation follows the algorithm described in Croux
+    and Rousseeuw (1992).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    c : float, optional
+        The normalization constant, used to get consistent estimates of the
+        standard deviation at the normal distribution.  Defined as
+        1/(np.sqrt(2) * scipy.stats.norm.ppf(5/8)), which is 2.219144.
+
+    Returns
+    -------
+    The Qn robust estimator of scale
+    """
+    cdef:
+        int n = a.shape[0]
+        int h = n/2 + 1
+        int k = h * (h - 1) / 2
+        int n_left = n * (n + 1) / 2
+        int n_right = n * n
+        int k_new = k + n_left
+        Py_ssize_t i, j, jh, l = 0
+        int sump, sumq = 0
+        double trial, output = 0
+        double[::1] a_sorted = np.sort(a)
+        int[::1] left = np.array([n - i + 1 for i in range(0, n)], dtype=np.intc)
+        int[::1] right = np.array([n if i <= h else n - (i - h) for i in range(0, n)], dtype=np.intc)
+        int[::1] weights = np.zeros((n,), dtype=np.intc)
+        double[::1] work = np.zeros((n,), dtype=np.double)
+        int[::1] p = np.zeros((n,), dtype=np.intc)
+        int[::1] q = np.zeros((n,), dtype=np.intc)
+    while n_right - n_left > n:
+        j = 0
+        for i in range(1, n):
+            if left[i] <= right[i]:
+                weights[j] = right[i] - left[i] + 1
+                jh = left[i] + weights[j] // 2
+                work[j] = a_sorted[i] - a_sorted[n - jh]
+                j = j + 1
+        trial = _high_weighted_median(work[:j], weights[:j])
+        j = 0
+        for i in range(n - 1, -1, -1):
+            while j < n and (a_sorted[i] - a_sorted[n - j - 1]) < trial:
+                j = j + 1
+            p[i] = j
+        j = n + 1
+        for i in range(n):
+            while (a_sorted[i] - a_sorted[n - j + 1]) > trial:
+                j = j - 1
+            q[i] = j
+        sump = np.sum(p)
+        sumq = np.sum(q) - n
+        if k_new <= sump:
+            right = np.copy(p)
+            n_right = sump
+        elif k_new > sumq:
+            left = np.copy(q)
+            n_left = sumq
+        else:
+            output = c * trial
+            return output
+    j = 0
+    for i in range(1, n):
+        for l in range(left[i], right[i] + 1):
+            work[j] = a_sorted[i] - a_sorted[n - l]
+            j = j + 1
+    k_new = k_new - (n_left + 1)
+    output = c * np.sort(work[:j])[k_new]
+    return output
diff --git a/shared/package-files/c0/c02c9d6e3fa052aab2d1097f44598705ace4ed66fbdb6a9aef548841146f7b57/environment.py b/shared/package-files/c0/c02c9d6e3fa052aab2d1097f44598705ace4ed66fbdb6a9aef548841146f7b57/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffc5feb59defcbabc02fd96d728a94d307e84c4a
--- /dev/null
+++ b/shared/package-files/c0/c02c9d6e3fa052aab2d1097f44598705ace4ed66fbdb6a9aef548841146f7b57/environment.py
@@ -0,0 +1,64 @@
+import weakref
+import importlib
+
+from numba import _dynfunc
+
+
+class Environment(_dynfunc.Environment):
+    """Stores globals and constant pyobjects for runtime.
+
+    It is often needed to convert b/w nopython objects and pyobjects.
+    """
+    __slots__ = ('env_name', '__weakref__')
+    # A weak-value dictionary to store live environment with env_name as the
+    # key.
+    _memo = weakref.WeakValueDictionary()
+
+    @classmethod
+    def from_fndesc(cls, fndesc):
+        try:
+            # Avoid creating new Env
+            return cls._memo[fndesc.env_name]
+        except KeyError:
+            inst = cls(fndesc.lookup_globals())
+            inst.env_name = fndesc.env_name
+            cls._memo[fndesc.env_name] = inst
+            return inst
+
+    def can_cache(self):
+        is_dyn = '__name__' not in self.globals
+        return not is_dyn
+
+    def __reduce__(self):
+        return _rebuild_env, (
+            self.globals.get('__name__'),
+            self.consts,
+            self.env_name,
+        )
+
+    def __del__(self):
+        return
+
+    def __repr__(self):
+        return f"<Environment {self.env_name!r} >"
+
+
+def _rebuild_env(modname, consts, env_name):
+    env = lookup_environment(env_name)
+    if env is not None:
+        return env
+
+    mod = importlib.import_module(modname)
+    env = Environment(mod.__dict__)
+    env.consts[:] = consts
+    env.env_name = env_name
+    # Cache loaded object
+    Environment._memo[env_name] = env
+    return env
+
+
+def lookup_environment(env_name):
+    """Returns the Environment object for the given name;
+    or None if not found
+    """
+    return Environment._memo.get(env_name)
diff --git a/shared/package-files/c0/c02f4a544e94aa45951e9c96f4553885076a455455989961681175117b1a0fc5/_tritools.py b/shared/package-files/c0/c02f4a544e94aa45951e9c96f4553885076a455455989961681175117b1a0fc5/_tritools.py
new file mode 100644
index 0000000000000000000000000000000000000000..9837309f7e810ba1c9d41aa52a9856cb6e1aa062
--- /dev/null
+++ b/shared/package-files/c0/c02f4a544e94aa45951e9c96f4553885076a455455989961681175117b1a0fc5/_tritools.py
@@ -0,0 +1,263 @@
+"""
+Tools for triangular grids.
+"""
+
+import numpy as np
+
+from matplotlib import _api
+from matplotlib.tri import Triangulation
+
+
+class TriAnalyzer:
+    """
+    Define basic tools for triangular mesh analysis and improvement.
+
+    A TriAnalyzer encapsulates a `.Triangulation` object and provides basic
+    tools for mesh analysis and mesh improvement.
+
+    Attributes
+    ----------
+    scale_factors
+
+    Parameters
+    ----------
+    triangulation : `~matplotlib.tri.Triangulation`
+        The encapsulated triangulation to analyze.
+    """
+
+    def __init__(self, triangulation):
+        _api.check_isinstance(Triangulation, triangulation=triangulation)
+        self._triangulation = triangulation
+
+    @property
+    def scale_factors(self):
+        """
+        Factors to rescale the triangulation into a unit square.
+
+        Returns
+        -------
+        (float, float)
+            Scaling factors (kx, ky) so that the triangulation
+            ``[triangulation.x * kx, triangulation.y * ky]``
+            fits exactly inside a unit square.
+        """
+        compressed_triangles = self._triangulation.get_masked_triangles()
+        node_used = (np.bincount(np.ravel(compressed_triangles),
+                                 minlength=self._triangulation.x.size) != 0)
+        return (1 / np.ptp(self._triangulation.x[node_used]),
+                1 / np.ptp(self._triangulation.y[node_used]))
+
+    def circle_ratios(self, rescale=True):
+        """
+        Return a measure of the triangulation triangles flatness.
+
+        The ratio of the incircle radius over the circumcircle radius is a
+        widely used indicator of a triangle flatness.
+        It is always ``<= 0.5`` and ``== 0.5`` only for equilateral
+        triangles. Circle ratios below 0.01 denote very flat triangles.
+
+        To avoid unduly low values due to a difference of scale between the 2
+        axis, the triangular mesh can first be rescaled to fit inside a unit
+        square with `scale_factors` (Only if *rescale* is True, which is
+        its default value).
+
+        Parameters
+        ----------
+        rescale : bool, default: True
+            If True, internally rescale (based on `scale_factors`), so that the
+            (unmasked) triangles fit exactly inside a unit square mesh.
+
+        Returns
+        -------
+        masked array
+            Ratio of the incircle radius over the circumcircle radius, for
+            each 'rescaled' triangle of the encapsulated triangulation.
+            Values corresponding to masked triangles are masked out.
+
+        """
+        # Coords rescaling
+        if rescale:
+            (kx, ky) = self.scale_factors
+        else:
+            (kx, ky) = (1.0, 1.0)
+        pts = np.vstack([self._triangulation.x*kx,
+                         self._triangulation.y*ky]).T
+        tri_pts = pts[self._triangulation.triangles]
+        # Computes the 3 side lengths
+        a = tri_pts[:, 1, :] - tri_pts[:, 0, :]
+        b = tri_pts[:, 2, :] - tri_pts[:, 1, :]
+        c = tri_pts[:, 0, :] - tri_pts[:, 2, :]
+        a = np.hypot(a[:, 0], a[:, 1])
+        b = np.hypot(b[:, 0], b[:, 1])
+        c = np.hypot(c[:, 0], c[:, 1])
+        # circumcircle and incircle radii
+        s = (a+b+c)*0.5
+        prod = s*(a+b-s)*(a+c-s)*(b+c-s)
+        # We have to deal with flat triangles with infinite circum_radius
+        bool_flat = (prod == 0.)
+        if np.any(bool_flat):
+            # Pathologic flow
+            ntri = tri_pts.shape[0]
+            circum_radius = np.empty(ntri, dtype=np.float64)
+            circum_radius[bool_flat] = np.inf
+            abc = a*b*c
+            circum_radius[~bool_flat] = abc[~bool_flat] / (
+                4.0*np.sqrt(prod[~bool_flat]))
+        else:
+            # Normal optimized flow
+            circum_radius = (a*b*c) / (4.0*np.sqrt(prod))
+        in_radius = (a*b*c) / (4.0*circum_radius*s)
+        circle_ratio = in_radius/circum_radius
+        mask = self._triangulation.mask
+        if mask is None:
+            return circle_ratio
+        else:
+            return np.ma.array(circle_ratio, mask=mask)
+
+    def get_flat_tri_mask(self, min_circle_ratio=0.01, rescale=True):
+        """
+        Eliminate excessively flat border triangles from the triangulation.
+
+        Returns a mask *new_mask* which allows to clean the encapsulated
+        triangulation from its border-located flat triangles
+        (according to their :meth:`circle_ratios`).
+        This mask is meant to be subsequently applied to the triangulation
+        using `.Triangulation.set_mask`.
+        *new_mask* is an extension of the initial triangulation mask
+        in the sense that an initially masked triangle will remain masked.
+
+        The *new_mask* array is computed recursively; at each step flat
+        triangles are removed only if they share a side with the current mesh
+        border. Thus, no new holes in the triangulated domain will be created.
+
+        Parameters
+        ----------
+        min_circle_ratio : float, default: 0.01
+            Border triangles with incircle/circumcircle radii ratio r/R will
+            be removed if r/R < *min_circle_ratio*.
+        rescale : bool, default: True
+            If True, first, internally rescale (based on `scale_factors`) so
+            that the (unmasked) triangles fit exactly inside a unit square
+            mesh.  This rescaling accounts for the difference of scale which
+            might exist between the 2 axis.
+
+        Returns
+        -------
+        array of bool
+            Mask to apply to encapsulated triangulation.
+            All the initially masked triangles remain masked in the
+            *new_mask*.
+
+        Notes
+        -----
+        The rationale behind this function is that a Delaunay
+        triangulation - of an unstructured set of points - sometimes contains
+        almost flat triangles at its border, leading to artifacts in plots
+        (especially for high-resolution contouring).
+        Masked with computed *new_mask*, the encapsulated
+        triangulation would contain no more unmasked border triangles
+        with a circle ratio below *min_circle_ratio*, thus improving the
+        mesh quality for subsequent plots or interpolation.
+        """
+        # Recursively computes the mask_current_borders, true if a triangle is
+        # at the border of the mesh OR touching the border through a chain of
+        # invalid aspect ratio masked_triangles.
+        ntri = self._triangulation.triangles.shape[0]
+        mask_bad_ratio = self.circle_ratios(rescale) < min_circle_ratio
+
+        current_mask = self._triangulation.mask
+        if current_mask is None:
+            current_mask = np.zeros(ntri, dtype=bool)
+        valid_neighbors = np.copy(self._triangulation.neighbors)
+        renum_neighbors = np.arange(ntri, dtype=np.int32)
+        nadd = -1
+        while nadd != 0:
+            # The active wavefront is the triangles from the border (unmasked
+            # but with a least 1 neighbor equal to -1
+            wavefront = (np.min(valid_neighbors, axis=1) == -1) & ~current_mask
+            # The element from the active wavefront will be masked if their
+            # circle ratio is bad.
+            added_mask = wavefront & mask_bad_ratio
+            current_mask = added_mask | current_mask
+            nadd = np.sum(added_mask)
+
+            # now we have to update the tables valid_neighbors
+            valid_neighbors[added_mask, :] = -1
+            renum_neighbors[added_mask] = -1
+            valid_neighbors = np.where(valid_neighbors == -1, -1,
+                                       renum_neighbors[valid_neighbors])
+
+        return np.ma.filled(current_mask, True)
+
+    def _get_compressed_triangulation(self):
+        """
+        Compress (if masked) the encapsulated triangulation.
+
+        Returns minimal-length triangles array (*compressed_triangles*) and
+        coordinates arrays (*compressed_x*, *compressed_y*) that can still
+        describe the unmasked triangles of the encapsulated triangulation.
+
+        Returns
+        -------
+        compressed_triangles : array-like
+            the returned compressed triangulation triangles
+        compressed_x : array-like
+            the returned compressed triangulation 1st coordinate
+        compressed_y : array-like
+            the returned compressed triangulation 2nd coordinate
+        tri_renum : int array
+            renumbering table to translate the triangle numbers from the
+            encapsulated triangulation into the new (compressed) renumbering.
+            -1 for masked triangles (deleted from *compressed_triangles*).
+        node_renum : int array
+            renumbering table to translate the point numbers from the
+            encapsulated triangulation into the new (compressed) renumbering.
+            -1 for unused points (i.e. those deleted from *compressed_x* and
+            *compressed_y*).
+
+        """
+        # Valid triangles and renumbering
+        tri_mask = self._triangulation.mask
+        compressed_triangles = self._triangulation.get_masked_triangles()
+        ntri = self._triangulation.triangles.shape[0]
+        if tri_mask is not None:
+            tri_renum = self._total_to_compress_renum(~tri_mask)
+        else:
+            tri_renum = np.arange(ntri, dtype=np.int32)
+
+        # Valid nodes and renumbering
+        valid_node = (np.bincount(np.ravel(compressed_triangles),
+                                  minlength=self._triangulation.x.size) != 0)
+        compressed_x = self._triangulation.x[valid_node]
+        compressed_y = self._triangulation.y[valid_node]
+        node_renum = self._total_to_compress_renum(valid_node)
+
+        # Now renumbering the valid triangles nodes
+        compressed_triangles = node_renum[compressed_triangles]
+
+        return (compressed_triangles, compressed_x, compressed_y, tri_renum,
+                node_renum)
+
+    @staticmethod
+    def _total_to_compress_renum(valid):
+        """
+        Parameters
+        ----------
+        valid : 1D bool array
+            Validity mask.
+
+        Returns
+        -------
+        int array
+            Array so that (`valid_array` being a compressed array
+            based on a `masked_array` with mask ~*valid*):
+
+            - For all i with valid[i] = True:
+              valid_array[renum[i]] = masked_array[i]
+            - For all i with valid[i] = False:
+              renum[i] = -1 (invalid value)
+        """
+        renum = np.full(np.size(valid), -1, dtype=np.int32)
+        n_valid = np.sum(valid)
+        renum[valid] = np.arange(n_valid, dtype=np.int32)
+        return renum
diff --git a/shared/package-files/c0/c0323ab87c3932845c821f29678c27b6d9be77bb676193dee5434131db607214/test_iplib.py b/shared/package-files/c0/c0323ab87c3932845c821f29678c27b6d9be77bb676193dee5434131db607214/test_iplib.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5e51fbc246891981c91da5ec8193cd685d22694
--- /dev/null
+++ b/shared/package-files/c0/c0323ab87c3932845c821f29678c27b6d9be77bb676193dee5434131db607214/test_iplib.py
@@ -0,0 +1,246 @@
+"""Tests for the key interactiveshell module, where the main ipython class is defined.
+"""
+
+import stack_data
+import sys
+
+SV_VERSION = tuple([int(x) for x in stack_data.__version__.split(".")[0:2]])
+
+
+def test_reset():
+    """reset must clear most namespaces."""
+
+    # Check that reset runs without error
+    ip.reset()
+
+    # Once we've reset it (to clear of any junk that might have been there from
+    # other tests, we can count how many variables are in the user's namespace
+    nvars_user_ns = len(ip.user_ns)
+    nvars_hidden = len(ip.user_ns_hidden)
+
+    # Now add a few variables to user_ns, and check that reset clears them
+    ip.user_ns['x'] = 1
+    ip.user_ns['y'] = 1
+    ip.reset()
+    
+    # Finally, check that all namespaces have only as many variables as we
+    # expect to find in them:
+    assert len(ip.user_ns) == nvars_user_ns
+    assert len(ip.user_ns_hidden) == nvars_hidden
+
+
+# Tests for reporting of exceptions in various modes, handling of SystemExit,
+# and %tb functionality.  This is really a mix of testing ultraTB and interactiveshell.
+
+def doctest_tb_plain():
+    """
+    In [18]: xmode plain
+    Exception reporting mode: Plain
+
+    In [19]: run simpleerr.py
+    Traceback (most recent call last):
+      File ...:...
+        bar(mode)
+      File ...:... in bar
+        div0()
+      File ...:... in div0
+        x/y
+    ZeroDivisionError: ...
+    """
+
+
+def doctest_tb_context():
+    """
+    In [3]: xmode context
+    Exception reporting mode: Context
+
+    In [4]: run simpleerr.py
+    ---------------------------------------------------------------------------
+    ZeroDivisionError                         Traceback (most recent call last)
+    <BLANKLINE>
+    ...
+         30     except IndexError:
+         31         mode = 'div'
+    ---> 33     bar(mode)
+    <BLANKLINE>
+    ... in bar(mode)
+         15     "bar"
+         16     if mode=='div':
+    ---> 17         div0()
+         18     elif mode=='exit':
+         19         try:
+    <BLANKLINE>
+    ... in div0()
+          6     x = 1
+          7     y = 0
+    ----> 8     x/y
+    <BLANKLINE>
+    ZeroDivisionError: ..."""
+
+
+def doctest_tb_verbose():
+    """
+    In [5]: xmode verbose
+    Exception reporting mode: Verbose
+
+    In [6]: run simpleerr.py
+    ---------------------------------------------------------------------------
+    ZeroDivisionError                         Traceback (most recent call last)
+    <BLANKLINE>
+    ...
+         30     except IndexError:
+         31         mode = 'div'
+    ---> 33     bar(mode)
+            mode = 'div'
+    <BLANKLINE>
+    ... in bar(mode='div')
+         15     "bar"
+         16     if mode=='div':
+    ---> 17         div0()
+         18     elif mode=='exit':
+         19         try:
+    <BLANKLINE>
+    ... in div0()
+          6     x = 1
+          7     y = 0
+    ----> 8     x/y
+            x = 1
+            y = 0
+    <BLANKLINE>
+    ZeroDivisionError: ...
+    """
+
+
+def doctest_tb_sysexit():
+    """
+    In [17]: %xmode plain
+    Exception reporting mode: Plain
+
+    In [18]: %run simpleerr.py exit
+    An exception has occurred, use %tb to see the full traceback.
+    SystemExit: (1, 'Mode = exit')
+
+    In [19]: %run simpleerr.py exit 2
+    An exception has occurred, use %tb to see the full traceback.
+    SystemExit: (2, 'Mode = exit')
+
+    In [20]: %tb
+    Traceback (most recent call last):
+      File ...:... in execfile
+        exec(compiler(f.read(), fname, "exec"), glob, loc)
+      File ...:...
+        bar(mode)
+      File ...:... in bar
+        sysexit(stat, mode)
+      File ...:... in sysexit
+        raise SystemExit(stat, f"Mode = {mode}")
+    SystemExit: (2, 'Mode = exit')
+
+    In [21]: %xmode context
+    Exception reporting mode: Context
+
+    In [22]: %tb
+    ---------------------------------------------------------------------------
+    SystemExit                                Traceback (most recent call last)
+    File ..., in execfile(fname, glob, loc, compiler)
+         ... with open(fname, "rb") as f:
+         ...     compiler = compiler or compile
+    ---> ...     exec(compiler(f.read(), fname, "exec"), glob, loc)
+    ...
+         30     except IndexError:
+         31         mode = 'div'
+    ---> 33     bar(mode)
+    <BLANKLINE>
+    ...bar(mode)
+         21         except:
+         22             stat = 1
+    ---> 23         sysexit(stat, mode)
+         24     else:
+         25         raise ValueError('Unknown mode')
+    <BLANKLINE>
+    ...sysexit(stat, mode)
+         10 def sysexit(stat, mode):
+    ---> 11     raise SystemExit(stat, f"Mode = {mode}")
+    <BLANKLINE>
+    SystemExit: (2, 'Mode = exit')
+    """
+
+
+if SV_VERSION < (0, 6):
+
+    def doctest_tb_sysexit_verbose_stack_data_05():
+        """
+        In [18]: %run simpleerr.py exit
+        An exception has occurred, use %tb to see the full traceback.
+        SystemExit: (1, 'Mode = exit')
+
+        In [19]: %run simpleerr.py exit 2
+        An exception has occurred, use %tb to see the full traceback.
+        SystemExit: (2, 'Mode = exit')
+
+        In [23]: %xmode verbose
+        Exception reporting mode: Verbose
+
+        In [24]: %tb
+        ---------------------------------------------------------------------------
+        SystemExit                                Traceback (most recent call last)
+        <BLANKLINE>
+        ...
+            30     except IndexError:
+            31         mode = 'div'
+        ---> 33     bar(mode)
+                mode = 'exit'
+        <BLANKLINE>
+        ... in bar(mode='exit')
+            ...     except:
+            ...         stat = 1
+        ---> ...     sysexit(stat, mode)
+                mode = 'exit'
+                stat = 2
+            ...     else:
+            ...         raise ValueError('Unknown mode')
+        <BLANKLINE>
+        ... in sysexit(stat=2, mode='exit')
+            10 def sysexit(stat, mode):
+        ---> 11     raise SystemExit(stat, f"Mode = {mode}")
+                stat = 2
+        <BLANKLINE>
+        SystemExit: (2, 'Mode = exit')
+        """
+
+
+def test_run_cell():
+    import textwrap
+
+    ip.run_cell("a = 10\na+=1")
+    ip.run_cell("assert a == 11\nassert 1")
+
+    assert ip.user_ns["a"] == 11
+    complex = textwrap.dedent(
+        """
+    if 1:
+        print "hello"
+        if 1:
+            print "world"
+        
+    if 2:
+        print "foo"
+
+    if 3:
+        print "bar"
+
+    if 4:
+        print "bar"
+    
+    """
+    )
+    # Simply verifies that this kind of input is run
+    ip.run_cell(complex)
+    
+
+def test_db():
+    """Test the internal database used for variable persistence."""
+    ip.db["__unittest_"] = 12
+    assert ip.db["__unittest_"] == 12
+    del ip.db["__unittest_"]
+    assert "__unittest_" not in ip.db
diff --git a/shared/package-files/c0/c03318f95cfcebacda5a58c0b03703b93dd938050fe08d95a63a240188c733ab/nl.msg b/shared/package-files/c0/c03318f95cfcebacda5a58c0b03703b93dd938050fe08d95a63a240188c733ab/nl.msg
new file mode 100644
index 0000000000000000000000000000000000000000..cd52630617625fbfccd3e8d34a0abba38b9b2acb
--- /dev/null
+++ b/shared/package-files/c0/c03318f95cfcebacda5a58c0b03703b93dd938050fe08d95a63a240188c733ab/nl.msg
@@ -0,0 +1,125 @@
+::msgcat::mcset nl "Widget Demonstration" "Demonstratie van widgets"
+::msgcat::mcset nl "tkWidgetDemo" "tkWidgetDemo"
+::msgcat::mcset nl "&File"     "&Bestand"
+::msgcat::mcset nl "About..."  "Info..."
+::msgcat::mcset nl "&About..." "&Info..."
+::msgcat::mcset nl "<F1>"      "<F1>"
+::msgcat::mcset nl "&Quit"     "&Einde"
+::msgcat::mcset nl "Meta+Q"    "Meta+E"		;# Displayed hotkey
+::msgcat::mcset nl "Meta-q"    "Meta-e"		;# Actual binding sequence
+::msgcat::mcset nl "Ctrl+Q"    "Ctrl+E"		;# Displayed hotkey
+::msgcat::mcset nl "Control-q" "Control-e"	;# Actual binding sequence
+::msgcat::mcset nl "Dismiss"   "Sluiten"
+::msgcat::mcset nl "See Variables"    "Bekijk Variabelen"
+::msgcat::mcset nl "Variable Values"  "Waarden Variabelen"
+::msgcat::mcset nl "OK"        "OK"
+::msgcat::mcset nl "Run the \"%s\" sample program" "Start voorbeeld \"%s\""
+::msgcat::mcset nl "Print Code" "Code Afdrukken"
+::msgcat::mcset nl "Demo code: %s" "Code van Demo %s"
+::msgcat::mcset nl "About Widget Demo" "Over deze demonstratie"
+::msgcat::mcset nl "Tk widget demonstration" "Demonstratie van Tk widgets"
+::msgcat::mcset nl "Copyright © %s"
+
+::msgcat::mcset nl "Tk Widget Demonstrations" "Demonstratie van Tk widgets"
+::msgcat::mcset nl "This application provides a front end for several short scripts" \
+    "Dit programma is een schil rond enkele korte scripts waarmee"
+::msgcat::mcset nl "that demonstrate what you can do with Tk widgets.  Each of the" \
+    "gedemonstreerd wordt wat je kunt doen met Tk widgets.  Elk van de"
+::msgcat::mcset nl "numbered lines below describes a demonstration;  you can click on" \
+    "genummerde regels hieronder omschrijft een demonstratie;  je kunt de"
+::msgcat::mcset nl "it to invoke the demonstration.  Once the demonstration window" \
+    "demonstratie starten door op de regel te klikken."
+::msgcat::mcset nl "appears, you can click the" \
+    "Zodra het nieuwe venster verschijnt, kun je op de knop"
+::msgcat::mcset nl "See Code"  "Bekijk Code"	;# This is also button text!
+::msgcat::mcset nl "button to see the Tcl/Tk code that created the demonstration.  If" \
+    "drukken om de achterliggende Tcl/Tk code te zien.  Als je dat wilt,"
+::msgcat::mcset nl "you wish, you can edit the code and click the" \
+    "kun je de code wijzigen en op de knop"
+::msgcat::mcset nl "Rerun Demo" "Herstart Demo"	;# This is also button text!
+::msgcat::mcset nl "button in the code window to reinvoke the demonstration with the" \
+    "drukken in het codevenster om de demonstratie uit te voeren met de"
+::msgcat::mcset nl "modified code." \
+    "nieuwe code."
+
+::msgcat::mcset nl "Labels, buttons, checkbuttons, and radiobuttons" \
+    "Labels, knoppen, vinkjes/aankruishokjes en radioknoppen"
+
+::msgcat::mcset nl "Labels (text and bitmaps)" "Labels (tekst en plaatjes)"
+::msgcat::mcset nl "Labels and UNICODE text"   "Labels en tekst in UNICODE"
+::msgcat::mcset nl "Buttons" "Buttons (drukknoppen)"
+::msgcat::mcset nl "Check-buttons (select any of a group)" \
+    "Check-buttons (een of meer uit een groep)"
+::msgcat::mcset nl "Radio-buttons (select one of a group)" \
+    "Radio-buttons (een van een groep)"
+::msgcat::mcset nl "A 15-puzzle game made out of buttons" \
+    "Een schuifpuzzel van buttons"
+::msgcat::mcset nl "Iconic buttons that use bitmaps" \
+    "Buttons met pictogrammen"
+::msgcat::mcset nl "Two labels displaying images" \
+    "Twee labels met plaatjes in plaats van tekst"
+::msgcat::mcset nl "A simple user interface for viewing images" \
+    "Een eenvoudige user-interface voor het bekijken van plaatjes"
+::msgcat::mcset nl "Labelled frames" \
+    "Kaders met bijschrift"
+
+::msgcat::mcset nl "Listboxes" "Keuzelijsten"
+::msgcat::mcset nl "The 50 states" "De 50 staten van de VS"
+::msgcat::mcset nl "Colors: change the color scheme for the application" \
+         "Kleuren: verander het kleurenschema voor het programma"
+::msgcat::mcset nl "A collection of famous and infamous sayings" \
+         "Beroemde en beruchte citaten en gezegden"
+
+::msgcat::mcset nl "Entries and Spin-boxes"     "Invulvelden en Spinboxen"
+::msgcat::mcset nl "Entries without scrollbars" "Invulvelden zonder schuifbalk"
+::msgcat::mcset nl "Entries with scrollbars"    "Invulvelden met schuifbalk"
+::msgcat::mcset nl "Validated entries and password fields" \
+         "Invulvelden met controle of wachtwoorden"
+::msgcat::mcset nl "Spin-boxes" "Spinboxen"
+::msgcat::mcset nl "Simple Rolodex-like form" "Simpel kaartsysteem"
+
+::msgcat::mcset nl "Text" "Tekst"
+::msgcat::mcset nl "Basic editable text" "Voorbeeld met te wijzigen tekst"
+::msgcat::mcset nl "Text display styles" "Tekst met verschillende stijlen"
+::msgcat::mcset nl "Hypertext (tag bindings)" \
+    "Hypertext (verwijzingen via \"tags\")"
+::msgcat::mcset nl "A text widget with embedded windows" \
+    "Tekstwidget met windows erin"
+::msgcat::mcset nl "A search tool built with a text widget" \
+    "Zoeken in tekst met behulp van een tekstwidget"
+
+::msgcat::mcset nl "Canvases" "Canvaswidgets"
+::msgcat::mcset nl "The canvas item types"     "Objecten in een canvas"
+::msgcat::mcset nl "A simple 2-D plot"         "Eenvoudige 2D-grafiek"
+::msgcat::mcset nl "Text items in canvases"    "Tekstobjecten in een canvas"
+::msgcat::mcset nl "An editor for arrowheads on canvas lines" \
+    "Editor voor de vorm van de pijl (begin/eind van een lijn)"
+::msgcat::mcset nl "A ruler with adjustable tab stops" \
+    "Een meetlat met aanpasbare ruiters"
+::msgcat::mcset nl "A building floor plan"     "Plattegrond van een gebouw"
+::msgcat::mcset nl "A simple scrollable canvas" "Een schuifbaar canvas"
+
+::msgcat::mcset nl "Scales" "Schaalverdelingen"
+::msgcat::mcset nl "Horizontal scale"          "Horizontale schaal"
+::msgcat::mcset nl "Vertical scale"          "Verticale schaal"
+
+::msgcat::mcset nl "Paned Windows" "Vensters opgedeeld in stukken"
+::msgcat::mcset nl "Horizontal paned window"   "Horizontaal gedeeld venster"
+::msgcat::mcset nl "Vertical paned window"     "Verticaal gedeeld venster"
+
+::msgcat::mcset nl "Menus" "Menu's"
+::msgcat::mcset nl "Menus and cascades (sub-menus)" \
+    "Menu's en cascades (submenu's)"
+::msgcat::mcset nl "Menu-buttons" "Menu-buttons"
+
+::msgcat::mcset nl "Common Dialogs" "Veel voorkomende dialoogvensters"
+::msgcat::mcset nl "Message boxes"  "Mededeling (message box)"
+::msgcat::mcset nl "File selection dialog"     "Selectie van bestanden"
+::msgcat::mcset nl "Color picker"              "Kleurenpalet"
+
+::msgcat::mcset nl "Miscellaneous"             "Diversen"
+::msgcat::mcset nl "The built-in bitmaps"      "Ingebouwde plaatjes"
+::msgcat::mcset nl "A dialog box with a local grab" \
+    "Een dialoogvenster met een locale \"grab\""
+::msgcat::mcset nl "A dialog box with a global grab" \
+    "Een dialoogvenster met een globale \"grab\""
diff --git a/shared/package-files/c0/c036f48435b733035c313e99cd825cf7390941bde5a349785aa9251e72b2d61e/__version__.py b/shared/package-files/c0/c036f48435b733035c313e99cd825cf7390941bde5a349785aa9251e72b2d61e/__version__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1b2ad28dd298c7e6eb77a9672edbfb4de16ccfb
--- /dev/null
+++ b/shared/package-files/c0/c036f48435b733035c313e99cd825cf7390941bde5a349785aa9251e72b2d61e/__version__.py
@@ -0,0 +1,4 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+__version__ = version = '23.5.2'
+__version_tuple__ = version_tuple = (23, 5, 2)
diff --git a/shared/package-files/c0/c0378f6ebbea8e6fcb79d978a7db6140a9e7844f1c0b55b45a20acec68e173b8/det.h b/shared/package-files/c0/c0378f6ebbea8e6fcb79d978a7db6140a9e7844f1c0b55b45a20acec68e173b8/det.h
new file mode 100644
index 0000000000000000000000000000000000000000..14701e817162df7f89d8034e319fc288b7d6d11d
--- /dev/null
+++ b/shared/package-files/c0/c0378f6ebbea8e6fcb79d978a7db6140a9e7844f1c0b55b45a20acec68e173b8/det.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/det_ops.h>
+
+namespace at {
+
+
+// aten::det(Tensor self) -> Tensor
+inline at::Tensor det(const at::Tensor & self) {
+    return at::_ops::det::call(self);
+}
+
+}
diff --git a/shared/package-files/c0/c0407017ad3624b1c4ac15045e24ba8d7aa6c58008cc7a5adcc305ae77afeabb/single_figure.html b/shared/package-files/c0/c0407017ad3624b1c4ac15045e24ba8d7aa6c58008cc7a5adcc305ae77afeabb/single_figure.html
new file mode 100644
index 0000000000000000000000000000000000000000..ceaaab00669ff62e0724eff610dcb74adcff1674
--- /dev/null
+++ b/shared/package-files/c0/c0407017ad3624b1c4ac15045e24ba8d7aa6c58008cc7a5adcc305ae77afeabb/single_figure.html
@@ -0,0 +1,39 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <link rel="stylesheet" href="{{ prefix }}/_static/css/page.css" type="text/css">
+    <link rel="stylesheet" href="{{ prefix }}/_static/css/boilerplate.css" type="text/css">
+    <link rel="stylesheet" href="{{ prefix }}/_static/css/fbm.css" type="text/css">
+    <link rel="stylesheet" href="{{ prefix }}/_static/css/mpl.css" type="text/css">
+    <script src="{{ prefix }}/_static/js/mpl_tornado.js"></script>
+    <script src="{{ prefix }}/js/mpl.js"></script>
+    <script>
+      function ready(fn) {
+        if (document.readyState != "loading") {
+          fn();
+        } else {
+          document.addEventListener("DOMContentLoaded", fn);
+        }
+      }
+
+      ready(
+        function () {
+          var websocket_type = mpl.get_websocket_type();
+          var uri = "{{ ws_uri }}" + {{ str(fig_id) }} + "/ws";
+          if (window.location.protocol === 'https:') uri = uri.replace('ws:', 'wss:')
+          var websocket = new websocket_type(uri);
+          var fig = new mpl.figure(
+              {{ str(fig_id) }}, websocket, mpl_ondownload,
+              document.getElementById("figure"));
+        }
+      );
+    </script>
+
+    <title>matplotlib</title>
+  </head>
+
+  <body>
+    <div id="mpl-warnings" class="mpl-warnings"></div>
+    <div id="figure" style="margin: 10px 10px;"></div>
+  </body>
+</html>
diff --git a/shared/package-files/c0/c040b5d90b92f035e0a4cf59301f92675661d86038f78a7e71105942f9d5e9d2/mimetypes.pyi b/shared/package-files/c0/c040b5d90b92f035e0a4cf59301f92675661d86038f78a7e71105942f9d5e9d2/mimetypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..43f27dda4d7bca3b0e6aef878da831971a77e478
--- /dev/null
+++ b/shared/package-files/c0/c040b5d90b92f035e0a4cf59301f92675661d86038f78a7e71105942f9d5e9d2/mimetypes.pyi
@@ -0,0 +1,36 @@
+import sys
+from typing import IO, Dict, List, Optional, Sequence, Text, Tuple, Union
+
+if sys.version_info >= (3, 8):
+    from os import PathLike
+    def guess_type(url: Union[Text, PathLike[str]], strict: bool = ...) -> Tuple[Optional[str], Optional[str]]: ...
+
+else:
+    def guess_type(url: Text, strict: bool = ...) -> Tuple[Optional[str], Optional[str]]: ...
+
+def guess_all_extensions(type: str, strict: bool = ...) -> List[str]: ...
+def guess_extension(type: str, strict: bool = ...) -> Optional[str]: ...
+def init(files: Optional[Sequence[str]] = ...) -> None: ...
+def read_mime_types(filename: str) -> Optional[Dict[str, str]]: ...
+def add_type(type: str, ext: str, strict: bool = ...) -> None: ...
+
+inited: bool
+knownfiles: List[str]
+suffix_map: Dict[str, str]
+encodings_map: Dict[str, str]
+types_map: Dict[str, str]
+common_types: Dict[str, str]
+
+class MimeTypes:
+    suffix_map: Dict[str, str]
+    encodings_map: Dict[str, str]
+    types_map: Tuple[Dict[str, str], Dict[str, str]]
+    types_map_inv: Tuple[Dict[str, str], Dict[str, str]]
+    def __init__(self, filenames: Tuple[str, ...] = ..., strict: bool = ...) -> None: ...
+    def guess_extension(self, type: str, strict: bool = ...) -> Optional[str]: ...
+    def guess_type(self, url: str, strict: bool = ...) -> Tuple[Optional[str], Optional[str]]: ...
+    def guess_all_extensions(self, type: str, strict: bool = ...) -> List[str]: ...
+    def read(self, filename: str, strict: bool = ...) -> None: ...
+    def readfp(self, fp: IO[str], strict: bool = ...) -> None: ...
+    if sys.platform == "win32":
+        def read_windows_registry(self, strict: bool = ...) -> None: ...
diff --git a/shared/package-files/c0/c0410090cd455a952f6b3fb6bad76fcce0279b73e7c417620f2fb0da89b899c3/ne_cpu_dispatch.h b/shared/package-files/c0/c0410090cd455a952f6b3fb6bad76fcce0279b73e7c417620f2fb0da89b899c3/ne_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..81257f73d74f27bbe76f7367de7fc05fdc848742
--- /dev/null
+++ b/shared/package-files/c0/c0410090cd455a952f6b3fb6bad76fcce0279b73e7c417620f2fb0da89b899c3/ne_cpu_dispatch.h
@@ -0,0 +1,30 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor ne(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & ne_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+TORCH_API at::Tensor & ne_(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor ne(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & ne_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & ne_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/c0/c0545be5c1800623685120a7bca99dbff28831c06d5f53d7f3db4f15fbe06d02/utils.h b/shared/package-files/c0/c0545be5c1800623685120a7bca99dbff28831c06d5f53d7f3db4f15fbe06d02/utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..332619a31270d709fda1c4f85248ae71546debde
--- /dev/null
+++ b/shared/package-files/c0/c0545be5c1800623685120a7bca99dbff28831c06d5f53d7f3db4f15fbe06d02/utils.h
@@ -0,0 +1,420 @@
+// Copyright 2010 the V8 project authors. All rights reserved.
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are
+// met:
+//
+//     * Redistributions of source code must retain the above copyright
+//       notice, this list of conditions and the following disclaimer.
+//     * Redistributions in binary form must reproduce the above
+//       copyright notice, this list of conditions and the following
+//       disclaimer in the documentation and/or other materials provided
+//       with the distribution.
+//     * Neither the name of Google Inc. nor the names of its
+//       contributors may be used to endorse or promote products derived
+//       from this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef DOUBLE_CONVERSION_UTILS_H_
+#define DOUBLE_CONVERSION_UTILS_H_
+
+// Use DOUBLE_CONVERSION_NON_PREFIXED_MACROS to get unprefixed macros as was
+// the case in double-conversion releases prior to 3.1.6
+
+#include <cstdlib>
+#include <cstring>
+
+// For pre-C++11 compatibility
+#if __cplusplus >= 201103L
+#define DOUBLE_CONVERSION_NULLPTR nullptr
+#else
+#define DOUBLE_CONVERSION_NULLPTR NULL
+#endif
+
+#include <cassert>
+#ifndef DOUBLE_CONVERSION_ASSERT
+#define DOUBLE_CONVERSION_ASSERT(condition)         \
+    assert(condition)
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(ASSERT)
+#define ASSERT DOUBLE_CONVERSION_ASSERT
+#endif
+
+#ifndef DOUBLE_CONVERSION_UNIMPLEMENTED
+#define DOUBLE_CONVERSION_UNIMPLEMENTED() (abort())
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(UNIMPLEMENTED)
+#define UNIMPLEMENTED DOUBLE_CONVERSION_UNIMPLEMENTED
+#endif
+
+#ifndef DOUBLE_CONVERSION_NO_RETURN
+#ifdef _MSC_VER
+#define DOUBLE_CONVERSION_NO_RETURN __declspec(noreturn)
+#else
+#define DOUBLE_CONVERSION_NO_RETURN __attribute__((noreturn))
+#endif
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(NO_RETURN)
+#define NO_RETURN DOUBLE_CONVERSION_NO_RETURN
+#endif
+
+#ifndef DOUBLE_CONVERSION_UNREACHABLE
+#ifdef _MSC_VER
+void DOUBLE_CONVERSION_NO_RETURN abort_noreturn();
+inline void abort_noreturn() { abort(); }
+#define DOUBLE_CONVERSION_UNREACHABLE()   (abort_noreturn())
+#else
+#define DOUBLE_CONVERSION_UNREACHABLE()   (abort())
+#endif
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(UNREACHABLE)
+#define UNREACHABLE DOUBLE_CONVERSION_UNREACHABLE
+#endif
+
+// Not all compilers support __has_attribute and combining a check for both
+// ifdef and __has_attribute on the same preprocessor line isn't portable.
+#ifdef __has_attribute
+#   define DOUBLE_CONVERSION_HAS_ATTRIBUTE(x) __has_attribute(x)
+#else
+#   define DOUBLE_CONVERSION_HAS_ATTRIBUTE(x) 0
+#endif
+
+#ifndef DOUBLE_CONVERSION_UNUSED
+#if DOUBLE_CONVERSION_HAS_ATTRIBUTE(unused)
+#define DOUBLE_CONVERSION_UNUSED __attribute__((unused))
+#else
+#define DOUBLE_CONVERSION_UNUSED
+#endif
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(UNUSED)
+#define UNUSED DOUBLE_CONVERSION_UNUSED
+#endif
+
+#if DOUBLE_CONVERSION_HAS_ATTRIBUTE(uninitialized)
+#define DOUBLE_CONVERSION_STACK_UNINITIALIZED __attribute__((uninitialized))
+#else
+#define DOUBLE_CONVERSION_STACK_UNINITIALIZED
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(STACK_UNINITIALIZED)
+#define STACK_UNINITIALIZED DOUBLE_CONVERSION_STACK_UNINITIALIZED
+#endif
+
+// Double operations detection based on target architecture.
+// Linux uses a 80bit wide floating point stack on x86. This induces double
+// rounding, which in turn leads to wrong results.
+// An easy way to test if the floating-point operations are correct is to
+// evaluate: 89255.0/1e22. If the floating-point stack is 64 bits wide then
+// the result is equal to 89255e-22.
+// The best way to test this, is to create a division-function and to compare
+// the output of the division with the expected result. (Inlining must be
+// disabled.)
+// On Linux,x86 89255e-22 != Div_double(89255.0/1e22)
+//
+// For example:
+/*
+// -- in div.c
+double Div_double(double x, double y) { return x / y; }
+
+// -- in main.c
+double Div_double(double x, double y);  // Forward declaration.
+
+int main(int argc, char** argv) {
+  return Div_double(89255.0, 1e22) == 89255e-22;
+}
+*/
+// Run as follows ./main || echo "correct"
+//
+// If it prints "correct" then the architecture should be here, in the "correct" section.
+#if defined(_M_X64) || defined(__x86_64__) || \
+    defined(__ARMEL__) || defined(__avr32__) || defined(_M_ARM) || defined(_M_ARM64) || \
+    defined(__hppa__) || defined(__ia64__) || \
+    defined(__mips__) || \
+    defined(__loongarch__) || \
+    defined(__nios2__) || defined(__ghs) || \
+    defined(__powerpc__) || defined(__ppc__) || defined(__ppc64__) || \
+    defined(_POWER) || defined(_ARCH_PPC) || defined(_ARCH_PPC64) || \
+    defined(__sparc__) || defined(__sparc) || defined(__s390__) || \
+    defined(__SH4__) || defined(__alpha__) || \
+    defined(_MIPS_ARCH_MIPS32R2) || defined(__ARMEB__) ||\
+    defined(__AARCH64EL__) || defined(__aarch64__) || defined(__AARCH64EB__) || \
+    defined(__riscv) || defined(__e2k__) || \
+    defined(__or1k__) || defined(__arc__) || defined(__ARC64__) || \
+    defined(__microblaze__) || defined(__XTENSA__) || \
+    defined(__EMSCRIPTEN__) || defined(__wasm32__)
+#define DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS 1
+#elif defined(__mc68000__) || \
+    defined(__pnacl__) || defined(__native_client__)
+#undef DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS
+#elif defined(_M_IX86) || defined(__i386__) || defined(__i386)
+#if defined(_WIN32)
+// Windows uses a 64bit wide floating point stack.
+#define DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS 1
+#else
+#undef DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS
+#endif  // _WIN32
+#else
+#error Target architecture was not detected as supported by Double-Conversion.
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(CORRECT_DOUBLE_OPERATIONS)
+#define CORRECT_DOUBLE_OPERATIONS DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS
+#endif
+
+#if defined(_WIN32) && !defined(__MINGW32__)
+
+typedef signed char int8_t;
+typedef unsigned char uint8_t;
+typedef short int16_t;  // NOLINT
+typedef unsigned short uint16_t;  // NOLINT
+typedef int int32_t;
+typedef unsigned int uint32_t;
+typedef __int64 int64_t;
+typedef unsigned __int64 uint64_t;
+// intptr_t and friends are defined in crtdefs.h through stdio.h.
+
+#else
+
+#include <stdint.h>
+
+#endif
+
+typedef uint16_t uc16;
+
+// The following macro works on both 32 and 64-bit platforms.
+// Usage: instead of writing 0x1234567890123456
+//      write DOUBLE_CONVERSION_UINT64_2PART_C(0x12345678,90123456);
+#define DOUBLE_CONVERSION_UINT64_2PART_C(a, b) (((static_cast<uint64_t>(a) << 32) + 0x##b##u))
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(UINT64_2PART_C)
+#define UINT64_2PART_C DOUBLE_CONVERSION_UINT64_2PART_C
+#endif
+
+// The expression DOUBLE_CONVERSION_ARRAY_SIZE(a) is a compile-time constant of type
+// size_t which represents the number of elements of the given
+// array. You should only use DOUBLE_CONVERSION_ARRAY_SIZE on statically allocated
+// arrays.
+#ifndef DOUBLE_CONVERSION_ARRAY_SIZE
+#define DOUBLE_CONVERSION_ARRAY_SIZE(a)                                   \
+  ((sizeof(a) / sizeof(*(a))) /                         \
+  static_cast<size_t>(!(sizeof(a) % sizeof(*(a)))))
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(ARRAY_SIZE)
+#define ARRAY_SIZE DOUBLE_CONVERSION_ARRAY_SIZE
+#endif
+
+// A macro to disallow the evil copy constructor and operator= functions
+// This should be used in the private: declarations for a class
+#ifndef DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN
+#define DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN(TypeName)      \
+  TypeName(const TypeName&);                    \
+  void operator=(const TypeName&)
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(DC_DISALLOW_COPY_AND_ASSIGN)
+#define DC_DISALLOW_COPY_AND_ASSIGN DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN
+#endif
+
+// A macro to disallow all the implicit constructors, namely the
+// default constructor, copy constructor and operator= functions.
+//
+// This should be used in the private: declarations for a class
+// that wants to prevent anyone from instantiating it. This is
+// especially useful for classes containing only static methods.
+#ifndef DOUBLE_CONVERSION_DISALLOW_IMPLICIT_CONSTRUCTORS
+#define DOUBLE_CONVERSION_DISALLOW_IMPLICIT_CONSTRUCTORS(TypeName) \
+  TypeName();                                    \
+  DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN(TypeName)
+#endif
+#if defined(DOUBLE_CONVERSION_NON_PREFIXED_MACROS) && !defined(DC_DISALLOW_IMPLICIT_CONSTRUCTORS)
+#define DC_DISALLOW_IMPLICIT_CONSTRUCTORS DOUBLE_CONVERSION_DISALLOW_IMPLICIT_CONSTRUCTORS
+#endif
+
+namespace arrow_vendored {
+namespace double_conversion {
+
+inline int StrLength(const char* string) {
+  size_t length = strlen(string);
+  DOUBLE_CONVERSION_ASSERT(length == static_cast<size_t>(static_cast<int>(length)));
+  return static_cast<int>(length);
+}
+
+// This is a simplified version of V8's Vector class.
+template <typename T>
+class Vector {
+ public:
+  Vector() : start_(DOUBLE_CONVERSION_NULLPTR), length_(0) {}
+  Vector(T* data, int len) : start_(data), length_(len) {
+    DOUBLE_CONVERSION_ASSERT(len == 0 || (len > 0 && data != DOUBLE_CONVERSION_NULLPTR));
+  }
+
+  // Returns a vector using the same backing storage as this one,
+  // spanning from and including 'from', to but not including 'to'.
+  Vector<T> SubVector(int from, int to) {
+    DOUBLE_CONVERSION_ASSERT(to <= length_);
+    DOUBLE_CONVERSION_ASSERT(from < to);
+    DOUBLE_CONVERSION_ASSERT(0 <= from);
+    return Vector<T>(start() + from, to - from);
+  }
+
+  // Returns the length of the vector.
+  int length() const { return length_; }
+
+  // Returns whether or not the vector is empty.
+  bool is_empty() const { return length_ == 0; }
+
+  // Returns the pointer to the start of the data in the vector.
+  T* start() const { return start_; }
+
+  // Access individual vector elements - checks bounds in debug mode.
+  T& operator[](int index) const {
+    DOUBLE_CONVERSION_ASSERT(0 <= index && index < length_);
+    return start_[index];
+  }
+
+  T& first() { return start_[0]; }
+
+  T& last() { return start_[length_ - 1]; }
+
+  void pop_back() {
+    DOUBLE_CONVERSION_ASSERT(!is_empty());
+    --length_;
+  }
+
+ private:
+  T* start_;
+  int length_;
+};
+
+
+// Helper class for building result strings in a character buffer. The
+// purpose of the class is to use safe operations that checks the
+// buffer bounds on all operations in debug mode.
+class StringBuilder {
+ public:
+  StringBuilder(char* buffer, int buffer_size)
+      : buffer_(buffer, buffer_size), position_(0) { }
+
+  ~StringBuilder() { if (!is_finalized()) Finalize(); }
+
+  int size() const { return buffer_.length(); }
+
+  // Get the current position in the builder.
+  int position() const {
+    DOUBLE_CONVERSION_ASSERT(!is_finalized());
+    return position_;
+  }
+
+  // Reset the position.
+  void Reset() { position_ = 0; }
+
+  // Add a single character to the builder. It is not allowed to add
+  // 0-characters; use the Finalize() method to terminate the string
+  // instead.
+  void AddCharacter(char c) {
+    DOUBLE_CONVERSION_ASSERT(c != '\0');
+    DOUBLE_CONVERSION_ASSERT(!is_finalized() && position_ < buffer_.length());
+    buffer_[position_++] = c;
+  }
+
+  // Add an entire string to the builder. Uses strlen() internally to
+  // compute the length of the input string.
+  void AddString(const char* s) {
+    AddSubstring(s, StrLength(s));
+  }
+
+  // Add the first 'n' characters of the given string 's' to the
+  // builder. The input string must have enough characters.
+  void AddSubstring(const char* s, int n) {
+    DOUBLE_CONVERSION_ASSERT(!is_finalized() && position_ + n < buffer_.length());
+    DOUBLE_CONVERSION_ASSERT(static_cast<size_t>(n) <= strlen(s));
+    memmove(&buffer_[position_], s, static_cast<size_t>(n));
+    position_ += n;
+  }
+
+
+  // Add character padding to the builder. If count is non-positive,
+  // nothing is added to the builder.
+  void AddPadding(char c, int count) {
+    for (int i = 0; i < count; i++) {
+      AddCharacter(c);
+    }
+  }
+
+  // Finalize the string by 0-terminating it and returning the buffer.
+  char* Finalize() {
+    DOUBLE_CONVERSION_ASSERT(!is_finalized() && position_ < buffer_.length());
+    buffer_[position_] = '\0';
+    // Make sure nobody managed to add a 0-character to the
+    // buffer while building the string.
+    DOUBLE_CONVERSION_ASSERT(strlen(buffer_.start()) == static_cast<size_t>(position_));
+    position_ = -1;
+    DOUBLE_CONVERSION_ASSERT(is_finalized());
+    return buffer_.start();
+  }
+
+ private:
+  Vector<char> buffer_;
+  int position_;
+
+  bool is_finalized() const { return position_ < 0; }
+
+  DOUBLE_CONVERSION_DISALLOW_IMPLICIT_CONSTRUCTORS(StringBuilder);
+};
+
+// The type-based aliasing rule allows the compiler to assume that pointers of
+// different types (for some definition of different) never alias each other.
+// Thus the following code does not work:
+//
+// float f = foo();
+// int fbits = *(int*)(&f);
+//
+// The compiler 'knows' that the int pointer can't refer to f since the types
+// don't match, so the compiler may cache f in a register, leaving random data
+// in fbits.  Using C++ style casts makes no difference, however a pointer to
+// char data is assumed to alias any other pointer.  This is the 'memcpy
+// exception'.
+//
+// Bit_cast uses the memcpy exception to move the bits from a variable of one
+// type of a variable of another type.  Of course the end result is likely to
+// be implementation dependent.  Most compilers (gcc-4.2 and MSVC 2005)
+// will completely optimize BitCast away.
+//
+// There is an additional use for BitCast.
+// Recent gccs will warn when they see casts that may result in breakage due to
+// the type-based aliasing rule.  If you have checked that there is no breakage
+// you can use BitCast to cast one pointer type to another.  This confuses gcc
+// enough that it can no longer see that you have cast one pointer type to
+// another thus avoiding the warning.
+template <class Dest, class Source>
+Dest BitCast(const Source& source) {
+  // Compile time assertion: sizeof(Dest) == sizeof(Source)
+  // A compile error here means your Dest and Source have different sizes.
+#if __cplusplus >= 201103L
+  static_assert(sizeof(Dest) == sizeof(Source),
+                "source and destination size mismatch");
+#else
+  DOUBLE_CONVERSION_UNUSED
+  typedef char VerifySizesAreEqual[sizeof(Dest) == sizeof(Source) ? 1 : -1];
+#endif
+
+  Dest dest;
+  memmove(&dest, &source, sizeof(dest));
+  return dest;
+}
+
+template <class Dest, class Source>
+Dest BitCast(Source* source) {
+  return BitCast<Dest>(reinterpret_cast<uintptr_t>(source));
+}
+
+}  // namespace double_conversion
+}  // namespace arrow_vendored
+
+#endif  // DOUBLE_CONVERSION_UTILS_H_
diff --git a/shared/package-files/c0/c05ef4cf16ab4689c0baf9f79e87f178a63535720b34ef6ca8f84b9793303d41/config.h b/shared/package-files/c0/c05ef4cf16ab4689c0baf9f79e87f178a63535720b34ef6ca8f84b9793303d41/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..16d2b80701536166c817ad609db14f342e6c94d0
--- /dev/null
+++ b/shared/package-files/c0/c05ef4cf16ab4689c0baf9f79e87f178a63535720b34ef6ca8f84b9793303d41/config.h
@@ -0,0 +1,28 @@
+#pragma once
+#include <c10/macros/Export.h>
+#include <c10/util/Flags.h>
+
+C10_DECLARE_bool(torch_lazy_ir_debug);
+C10_DECLARE_bool(torch_lazy_handle_special_scalars);
+C10_DECLARE_bool(torch_lazy_all_numbers_special_scalars);
+C10_DECLARE_bool(torch_lazy_param_aliasing);
+C10_DECLARE_bool(torch_lazy_reuse_ir);
+C10_DECLARE_bool(torch_lazy_use_thread_pool);
+C10_DECLARE_bool(torch_lazy_enable_device_data_cache);
+
+C10_DECLARE_int(torch_lazy_compilation_cache_size);
+C10_DECLARE_int(torch_lazy_device_data_cache_size);
+C10_DECLARE_int(torch_lazy_io_thread_pool_size);
+C10_DECLARE_int(torch_lazy_metrics_samples);
+C10_DECLARE_int(torch_lazy_trim_graph_check_frequency);
+C10_DECLARE_int(torch_lazy_trim_graph_size);
+
+C10_DECLARE_string(torch_lazy_metrics_percentiles);
+
+C10_DECLARE_int(torch_lazy_shape_cache_size);
+
+namespace torch {
+namespace lazy {
+TORCH_API std::string& getLTCForceFallback();
+}
+} // namespace torch
diff --git a/shared/package-files/c0/c0639292cbc55fbc1de9d97b628cfcdfa684c99f83ea3c62e11f6405a69e12d6/xmlutils.pyi b/shared/package-files/c0/c0639292cbc55fbc1de9d97b628cfcdfa684c99f83ea3c62e11f6405a69e12d6/xmlutils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..45d8c4c3a2f294e4c65ed4a463655ff659f055bc
--- /dev/null
+++ b/shared/package-files/c0/c0639292cbc55fbc1de9d97b628cfcdfa684c99f83ea3c62e11f6405a69e12d6/xmlutils.pyi
@@ -0,0 +1,11 @@
+from typing import Dict, Optional
+from xml.sax.saxutils import XMLGenerator
+
+class UnserializableContentError(ValueError): ...
+
+class SimplerXMLGenerator(XMLGenerator):
+    def addQuickElement(
+        self, name: str, contents: Optional[str] = ..., attrs: Optional[Dict[str, str]] = ...
+    ) -> None: ...
+    def characters(self, content: str) -> None: ...
+    def startElement(self, name: str, attrs: Dict[str, str]) -> None: ...
diff --git a/shared/package-files/c0/c065cb588f9d0613a20e6374beb1696569905d69e10aeebcb48f9bb01e4f5995/rsqrt_ops.h b/shared/package-files/c0/c065cb588f9d0613a20e6374beb1696569905d69e10aeebcb48f9bb01e4f5995/rsqrt_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b77261394e5560d7c14cb9d33efef98d2986f7c9
--- /dev/null
+++ b/shared/package-files/c0/c065cb588f9d0613a20e6374beb1696569905d69e10aeebcb48f9bb01e4f5995/rsqrt_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API rsqrt {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::rsqrt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "rsqrt(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API rsqrt_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::rsqrt_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "rsqrt_(Tensor(a!) self) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API rsqrt_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::rsqrt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c0/c0671b9256e3072f3751f915718b252f31d7721b1e2fcc822867fb307853398f/test_correlation.py b/shared/package-files/c0/c0671b9256e3072f3751f915718b252f31d7721b1e2fcc822867fb307853398f/test_correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..596cdd2c4228010d2c8ffed82a0fd97946bc9b91
--- /dev/null
+++ b/shared/package-files/c0/c0671b9256e3072f3751f915718b252f31d7721b1e2fcc822867fb307853398f/test_correlation.py
@@ -0,0 +1,63 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+from statsmodels.stats.correlation_tools import (
+    GaussianMultivariateKernel,
+    kernel_covariance,
+)
+
+
+def test_kernel_covariance():
+
+    rs = np.random.RandomState(342)
+
+    # Number of independent observations
+    ng = 1000
+
+    # Dimension of the process
+    p = 3
+
+    # Each component of the process in an AR(r) with 10 values
+    # observed on a grid
+    r = 0.5
+    ii = np.arange(10)
+    qm = r ** np.abs(np.subtract.outer(ii, ii))
+    qm = np.linalg.cholesky(qm)
+
+    exog, groups, pos = [], [], []
+    for j in range(ng):
+        pos1 = np.arange(10)[:, None]
+        groups1 = j * np.ones(10)
+
+        # The components are independent AR processes
+        ex1 = rs.normal(size=(10, 3))
+
+        ex1 = np.dot(qm, ex1)
+        pos.append(pos1)
+        groups.append(groups1)
+        exog.append(ex1)
+
+    groups = np.concatenate(groups)
+    pos = np.concatenate(pos, axis=0)
+    exog = np.concatenate(exog, axis=0)
+
+    for j in range(4):
+
+        if j == 0:
+            kernel = None
+            bw = None
+        elif j == 1:
+            kernel = GaussianMultivariateKernel()
+            bw = None
+        elif j == 2:
+            kernel = GaussianMultivariateKernel()
+            bw = 1
+        elif j == 3:
+            kernel = GaussianMultivariateKernel()
+            bw = kernel.set_default_bw(pos)
+
+        cv = kernel_covariance(exog, pos, groups, kernel=kernel, bw=bw)
+        assert_allclose(cv(0, 0), np.eye(p), atol=0.1, rtol=0.01)
+        assert_allclose(cv(0, 1), 0.5 * np.eye(p), atol=0.1, rtol=0.01)
+        assert_allclose(cv(0, 2), 0.25 * np.eye(p), atol=0.1, rtol=0.01)
+        assert_allclose(cv(1, 2), 0.5 * np.eye(p), atol=0.1, rtol=0.01)
diff --git a/shared/package-files/c0/c06734f1c7544c10c9be1522c15a93ac14bba35f448bec104675a5927cbb8793/test_multivariate_colormaps.py b/shared/package-files/c0/c06734f1c7544c10c9be1522c15a93ac14bba35f448bec104675a5927cbb8793/test_multivariate_colormaps.py
new file mode 100644
index 0000000000000000000000000000000000000000..81a2e6adeb356969ed3bf5b9acf77980fc40ac19
--- /dev/null
+++ b/shared/package-files/c0/c06734f1c7544c10c9be1522c15a93ac14bba35f448bec104675a5927cbb8793/test_multivariate_colormaps.py
@@ -0,0 +1,564 @@
+import numpy as np
+from numpy.testing import assert_array_equal, assert_allclose
+import matplotlib.pyplot as plt
+from matplotlib.testing.decorators import (image_comparison,
+                                           remove_ticks_and_titles)
+import matplotlib as mpl
+import pytest
+from pathlib import Path
+from io import BytesIO
+from PIL import Image
+import base64
+
+
+@image_comparison(["bivariate_cmap_shapes.png"])
+def test_bivariate_cmap_shapes():
+    x_0 = np.repeat(np.linspace(-0.1, 1.1, 10, dtype='float32')[None, :], 10, axis=0)
+    x_1 = x_0.T
+
+    fig, axes = plt.subplots(1, 4, figsize=(10, 2))
+
+    # shape = 'square'
+    cmap = mpl.bivar_colormaps['BiPeak']
+    axes[0].imshow(cmap((x_0, x_1)), interpolation='nearest')
+
+    # shape = 'circle'
+    cmap = mpl.bivar_colormaps['BiCone']
+    axes[1].imshow(cmap((x_0, x_1)), interpolation='nearest')
+
+    # shape = 'ignore'
+    cmap = mpl.bivar_colormaps['BiPeak']
+    cmap = cmap.with_extremes(shape='ignore')
+    axes[2].imshow(cmap((x_0, x_1)), interpolation='nearest')
+
+    # shape = circleignore
+    cmap = mpl.bivar_colormaps['BiCone']
+    cmap = cmap.with_extremes(shape='circleignore')
+    axes[3].imshow(cmap((x_0, x_1)), interpolation='nearest')
+    remove_ticks_and_titles(fig)
+
+
+def test_multivar_creation():
+    # test creation of a custom multivariate colorbar
+    blues = mpl.colormaps['Blues']
+    cmap = mpl.colors.MultivarColormap((blues, 'Oranges'), 'sRGB_sub')
+    y, x = np.mgrid[0:3, 0:3]/2
+    im = cmap((y, x))
+    res = np.array([[[0.96862745, 0.94509804, 0.92156863, 1],
+                     [0.96004614, 0.53504037, 0.23277201, 1],
+                     [0.46666667, 0.1372549, 0.01568627, 1]],
+                    [[0.41708574, 0.64141484, 0.75980008, 1],
+                     [0.40850442, 0.23135717, 0.07100346, 1],
+                     [0, 0, 0, 1]],
+                    [[0.03137255, 0.14901961, 0.34117647, 1],
+                     [0.02279123, 0, 0, 1],
+                     [0, 0, 0, 1]]])
+    assert_allclose(im,  res, atol=0.01)
+
+    with pytest.raises(ValueError, match="colormaps must be a list of"):
+        cmap = mpl.colors.MultivarColormap((blues, [blues]), 'sRGB_sub')
+    with pytest.raises(ValueError, match="A MultivarColormap must"):
+        cmap = mpl.colors.MultivarColormap('blues', 'sRGB_sub')
+    with pytest.raises(ValueError, match="A MultivarColormap must"):
+        cmap = mpl.colors.MultivarColormap((blues), 'sRGB_sub')
+
+
+@image_comparison(["multivar_alpha_mixing.png"])
+def test_multivar_alpha_mixing():
+    # test creation of a custom colormap using 'rainbow'
+    # and a colormap that goes from alpha = 1 to alpha = 0
+    rainbow = mpl.colormaps['rainbow']
+    alpha = np.zeros((256, 4))
+    alpha[:, 3] = np.linspace(1, 0, 256)
+    alpha_cmap = mpl.colors.LinearSegmentedColormap.from_list('from_list', alpha)
+
+    cmap = mpl.colors.MultivarColormap((rainbow, alpha_cmap), 'sRGB_add')
+    y, x = np.mgrid[0:10, 0:10]/9
+    im = cmap((y, x))
+
+    fig, ax = plt.subplots()
+    ax.imshow(im, interpolation='nearest')
+    remove_ticks_and_titles(fig)
+
+
+def test_multivar_cmap_call():
+    cmap = mpl.multivar_colormaps['2VarAddA']
+    assert_array_equal(cmap((0.0, 0.0)), (0, 0, 0, 1))
+    assert_array_equal(cmap((1.0, 1.0)), (1, 1, 1, 1))
+    assert_allclose(cmap((0.0, 0.0), alpha=0.1), (0, 0, 0, 0.1), atol=0.1)
+
+    cmap = mpl.multivar_colormaps['2VarSubA']
+    assert_array_equal(cmap((0.0, 0.0)), (1, 1, 1, 1))
+    assert_allclose(cmap((1.0, 1.0)), (0, 0, 0, 1), atol=0.1)
+
+    # check outside and bad
+    cs = cmap([(0., 0., 0., 1.2, np.nan), (0., 1.2, np.nan, 0., 0., )])
+    assert_allclose(cs, [[1., 1., 1., 1.],
+                         [0.801, 0.426, 0.119, 1.],
+                         [0., 0., 0., 0.],
+                         [0.199, 0.574, 0.881, 1.],
+                         [0., 0., 0., 0.]])
+
+    assert_array_equal(cmap((0.0, 0.0), bytes=True), (255, 255, 255, 255))
+
+    with pytest.raises(ValueError, match="alpha is array-like but its shape"):
+        cs = cmap([(0, 5, 9), (0, 0, 0)], alpha=(0.5, 0.3))
+
+    with pytest.raises(ValueError, match="For the selected colormap the data"):
+        cs = cmap([(0, 5, 9), (0, 0, 0), (0, 0, 0)])
+
+    with pytest.raises(ValueError, match="clip cannot be false"):
+        cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, clip=False)
+    # Tests calling a multivariate colormap with integer values
+    cmap = mpl.multivar_colormaps['2VarSubA']
+
+    # call only integers
+    cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)])
+    res = np.array([[1, 1, 1, 1],
+                     [0.85176471, 0.91029412, 0.96023529, 1],
+                     [0.70452941, 0.82764706, 0.93358824, 1],
+                     [0.94358824, 0.88505882, 0.83511765, 1],
+                     [0.89729412, 0.77417647, 0.66823529, 1],
+                     [0, 0, 0, 1]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # call only integers, wrong byte order
+    swapped_dt = np.dtype(int).newbyteorder()
+    cs = cmap([np.array([0, 50, 100, 0, 0, 300], dtype=swapped_dt),
+               np.array([0, 0, 0, 50, 100, 300], dtype=swapped_dt)])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # call mix floats integers
+    # check calling with bytes = True
+    cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)], bytes=True)
+    res = np.array([[255, 255, 255, 255],
+                     [217, 232, 244, 255],
+                     [179, 211, 238, 255],
+                     [240, 225, 212, 255],
+                     [228, 197, 170, 255],
+                     [0,   0,   0, 255]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    cs = cmap([(0, 50, 100, 0, 0, 300), (0, 0, 0, 50, 100, 300)], alpha=0.5)
+    res = np.array([[1, 1, 1, 0.5],
+                     [0.85176471, 0.91029412, 0.96023529, 0.5],
+                     [0.70452941, 0.82764706, 0.93358824, 0.5],
+                     [0.94358824, 0.88505882, 0.83511765, 0.5],
+                     [0.89729412, 0.77417647, 0.66823529, 0.5],
+                     [0, 0, 0, 0.5]])
+    assert_allclose(cs,  res, atol=0.01)
+    # call with tuple
+    assert_allclose(cmap((100, 120), bytes=True, alpha=0.5),
+                    [149, 142, 136, 127], atol=0.01)
+
+    # alpha and bytes
+    cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True, alpha=0.5)
+    res = np.array([[0, 0, 255, 127],
+                    [141, 0, 255, 127],
+                    [255, 0, 255, 127],
+                    [0, 115, 255, 127],
+                    [0, 255, 255, 127],
+                    [255, 255, 255, 127]])
+
+    # bad alpha shape
+    with pytest.raises(ValueError, match="alpha is array-like but its shape"):
+        cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, alpha=(0.5, 0.3))
+
+    cmap = cmap.with_extremes(bad=(1, 1, 1, 1))
+    cs = cmap([(0., 1.1, np.nan), (0., 1.2, 1.)])
+    res = np.array([[1., 1., 1., 1.],
+                   [0., 0., 0., 1.],
+                   [1., 1., 1., 1.]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # call outside with tuple
+    assert_allclose(cmap((300, 300), bytes=True, alpha=0.5),
+                    [0, 0, 0, 127], atol=0.01)
+    with pytest.raises(ValueError,
+                       match="For the selected colormap the data must have"):
+        cs = cmap((0, 5, 9))
+
+    # test over/under
+    cmap = mpl.multivar_colormaps['2VarAddA']
+    with pytest.raises(ValueError, match='i.e. be of length 2'):
+        cmap.with_extremes(over=0)
+    with pytest.raises(ValueError, match='i.e. be of length 2'):
+        cmap.with_extremes(under=0)
+
+    cmap = cmap.with_extremes(under=[(0, 0, 0, 0)]*2)
+    assert_allclose((0, 0, 0, 0), cmap((-1., 0)), atol=1e-2)
+    cmap = cmap.with_extremes(over=[(0, 0, 0, 0)]*2)
+    assert_allclose((0, 0, 0, 0), cmap((2., 0)), atol=1e-2)
+
+
+def test_multivar_bad_mode():
+    cmap = mpl.multivar_colormaps['2VarSubA']
+    with pytest.raises(ValueError, match="is not a valid value for"):
+        cmap = mpl.colors.MultivarColormap(cmap[:], 'bad')
+
+
+def test_multivar_resample():
+    cmap = mpl.multivar_colormaps['3VarAddA']
+    cmap_resampled = cmap.resampled((None, 10, 3))
+
+    assert_allclose(cmap_resampled[1](0.25), (0.093, 0.116, 0.059, 1.0))
+    assert_allclose(cmap_resampled((0, 0.25, 0)), (0.093, 0.116, 0.059, 1.0))
+    assert_allclose(cmap_resampled((1, 0.25, 1)), (0.417271, 0.264624, 0.274976, 1.),
+                                   atol=0.01)
+
+    with pytest.raises(ValueError, match="lutshape must be of length"):
+        cmap = cmap.resampled(4)
+
+
+def test_bivar_cmap_call_tuple():
+    cmap = mpl.bivar_colormaps['BiOrangeBlue']
+    assert_allclose(cmap((1.0, 1.0)), (1, 1, 1, 1), atol=0.01)
+    assert_allclose(cmap((0.0, 0.0)), (0, 0, 0, 1), atol=0.1)
+    assert_allclose(cmap((0.0, 0.0), alpha=0.1), (0, 0, 0, 0.1), atol=0.1)
+
+
+def test_bivar_cmap_call():
+    """
+    Tests calling a bivariate colormap with integer values
+    """
+    im = np.ones((10, 12, 4))
+    im[:, :, 0] = np.linspace(0, 1, 10)[:, np.newaxis]
+    im[:, :, 1] = np.linspace(0, 1, 12)[np.newaxis, :]
+    cmap = mpl.colors.BivarColormapFromImage(im)
+
+    # call only integers
+    cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)])
+    res = np.array([[0, 0, 1, 1],
+                   [0.556, 0, 1, 1],
+                   [1, 0, 1, 1],
+                   [0, 0.454, 1, 1],
+                   [0, 1, 1, 1],
+                   [1, 1, 1, 1]])
+    assert_allclose(cs,  res, atol=0.01)
+    # call only integers, wrong byte order
+    swapped_dt = np.dtype(int).newbyteorder()
+    cs = cmap([np.array([0, 5, 9, 0, 0, 10], dtype=swapped_dt),
+               np.array([0, 0, 0, 5, 11, 12], dtype=swapped_dt)])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # call mix floats integers
+    cmap = cmap.with_extremes(outside=(1, 0, 0, 0))
+    cs = cmap([(0.5, 0), (0, 3)])
+    res = np.array([[0.555, 0, 1, 1],
+                    [0, 0.2727, 1, 1]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # check calling with bytes = True
+    cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True)
+    res = np.array([[0, 0, 255, 255],
+                    [141, 0, 255, 255],
+                    [255, 0, 255, 255],
+                    [0, 115, 255, 255],
+                    [0, 255, 255, 255],
+                    [255, 255, 255, 255]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    # test alpha
+    cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], alpha=0.5)
+    res = np.array([[0, 0, 1, 0.5],
+                    [0.556, 0, 1, 0.5],
+                    [1, 0, 1, 0.5],
+                    [0, 0.454, 1, 0.5],
+                    [0, 1, 1, 0.5],
+                    [1, 1, 1, 0.5]])
+    assert_allclose(cs,  res, atol=0.01)
+    # call with tuple
+    assert_allclose(cmap((10, 12), bytes=True, alpha=0.5),
+                    [255, 255, 255, 127], atol=0.01)
+
+    # alpha and bytes
+    cs = cmap([(0, 5, 9, 0, 0, 10), (0, 0, 0, 5, 11, 12)], bytes=True, alpha=0.5)
+    res = np.array([[0, 0, 255, 127],
+                    [141, 0, 255, 127],
+                    [255, 0, 255, 127],
+                    [0, 115, 255, 127],
+                    [0, 255, 255, 127],
+                    [255, 255, 255, 127]])
+
+    # bad alpha shape
+    with pytest.raises(ValueError, match="alpha is array-like but its shape"):
+        cs = cmap([(0, 5, 9), (0, 0, 0)], bytes=True, alpha=(0.5, 0.3))
+
+    # set shape to 'ignore'.
+    # final point is outside colormap and should then receive
+    # the 'outside' (in this case [1,0,0,0])
+    # also test 'bad' (in this case [1,1,1,0])
+    cmap = cmap.with_extremes(outside=(1, 0, 0, 0), bad=(1, 1, 1, 0), shape='ignore')
+    cs = cmap([(0., 1.1, np.nan), (0., 1.2, 1.)])
+    res = np.array([[0, 0, 1, 1],
+                    [1, 0, 0, 0],
+                    [1, 1, 1, 0]])
+    assert_allclose(cs,  res, atol=0.01)
+    # call outside with tuple
+    assert_allclose(cmap((10, 12), bytes=True, alpha=0.5),
+                    [255, 0, 0, 127], atol=0.01)
+    # with integers
+    cs = cmap([(0, 10), (0, 12)])
+    res = np.array([[0, 0, 1, 1],
+                    [1, 0, 0, 0]])
+    assert_allclose(cs,  res, atol=0.01)
+
+    with pytest.raises(ValueError,
+                       match="For a `BivarColormap` the data must have"):
+        cs = cmap((0, 5, 9))
+
+    cmap = cmap.with_extremes(shape='circle')
+    with pytest.raises(NotImplementedError,
+                       match="only implemented for use with with floats"):
+        cs = cmap([(0, 5, 9, 0, 0, 9), (0, 0, 0, 5, 11, 11)])
+
+    # test origin
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].with_extremes(origin=(0.5, 0.5))
+    assert_allclose(cmap[0](0.5),
+                    (0.50244140625, 0.5024222412109375, 0.50244140625, 1))
+    assert_allclose(cmap[1](0.5),
+                    (0.50244140625, 0.5024222412109375, 0.50244140625, 1))
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].with_extremes(origin=(1, 1))
+    assert_allclose(cmap[0](1.),
+                    (0.99853515625, 0.9985467529296875, 0.99853515625, 1.0))
+    assert_allclose(cmap[1](1.),
+                    (0.99853515625, 0.9985467529296875, 0.99853515625, 1.0))
+    with pytest.raises(KeyError,
+                       match="only 0 or 1 are valid keys"):
+        cs = cmap[2]
+
+
+def test_bivar_getitem():
+    """Test __getitem__  on BivarColormap"""
+    xA = ([.0, .25, .5, .75, 1., -1, 2], [.5]*7)
+    xB = ([.5]*7, [.0, .25, .5, .75, 1., -1, 2])
+
+    cmaps = mpl.bivar_colormaps['BiPeak']
+    assert_array_equal(cmaps(xA), cmaps[0](xA[0]))
+    assert_array_equal(cmaps(xB), cmaps[1](xB[1]))
+
+    cmaps = cmaps.with_extremes(shape='ignore')
+    assert_array_equal(cmaps(xA), cmaps[0](xA[0]))
+    assert_array_equal(cmaps(xB), cmaps[1](xB[1]))
+
+    xA = ([.0, .25, .5, .75, 1., -1, 2], [.0]*7)
+    xB = ([.0]*7, [.0, .25, .5, .75, 1., -1, 2])
+    cmaps = mpl.bivar_colormaps['BiOrangeBlue']
+    assert_array_equal(cmaps(xA), cmaps[0](xA[0]))
+    assert_array_equal(cmaps(xB), cmaps[1](xB[1]))
+
+    cmaps = cmaps.with_extremes(shape='ignore')
+    assert_array_equal(cmaps(xA), cmaps[0](xA[0]))
+    assert_array_equal(cmaps(xB), cmaps[1](xB[1]))
+
+
+def test_bivar_cmap_bad_shape():
+    """
+    Tests calling a bivariate colormap with integer values
+    """
+    cmap = mpl.bivar_colormaps['BiCone']
+    _ = cmap.lut
+    with pytest.raises(ValueError,
+                       match="is not a valid value for shape"):
+        cmap.with_extremes(shape='bad_shape')
+
+    with pytest.raises(ValueError,
+                       match="is not a valid value for shape"):
+        mpl.colors.BivarColormapFromImage(np.ones((3, 3, 4)),
+                                          shape='bad_shape')
+
+
+def test_bivar_cmap_bad_lut():
+    """
+    Tests calling a bivariate colormap with integer values
+    """
+    with pytest.raises(ValueError,
+                       match="The lut must be an array of shape"):
+        cmap = mpl.colors.BivarColormapFromImage(np.ones((3, 3, 5)))
+
+
+def test_bivar_cmap_from_image():
+    """
+    This tests the creation and use of a bivariate colormap
+    generated from an image
+    """
+
+    data_0 = np.arange(6).reshape((2, 3))/5
+    data_1 = np.arange(6).reshape((3, 2)).T/5
+
+    # bivariate colormap from array
+    cim = np.ones((10, 12, 3))
+    cim[:, :, 0] = np.arange(10)[:, np.newaxis]/10
+    cim[:, :, 1] = np.arange(12)[np.newaxis, :]/12
+
+    cmap = mpl.colors.BivarColormapFromImage(cim)
+    im = cmap((data_0, data_1))
+    res = np.array([[[0, 0, 1, 1],
+                    [0.2, 0.33333333, 1, 1],
+                    [0.4, 0.75, 1, 1]],
+                   [[0.6, 0.16666667, 1, 1],
+                    [0.8, 0.58333333, 1, 1],
+                    [0.9, 0.91666667, 1, 1]]])
+    assert_allclose(im,  res, atol=0.01)
+
+    # input as unit8
+    cim = np.ones((10, 12, 3))*255
+    cim[:, :, 0] = np.arange(10)[:, np.newaxis]/10*255
+    cim[:, :, 1] = np.arange(12)[np.newaxis, :]/12*255
+
+    cmap = mpl.colors.BivarColormapFromImage(cim.astype(np.uint8))
+    im = cmap((data_0, data_1))
+    res = np.array([[[0, 0, 1, 1],
+                    [0.2, 0.33333333, 1, 1],
+                    [0.4, 0.75, 1, 1]],
+                   [[0.6, 0.16666667, 1, 1],
+                    [0.8, 0.58333333, 1, 1],
+                    [0.9, 0.91666667, 1, 1]]])
+    assert_allclose(im,  res, atol=0.01)
+
+    # bivariate colormap from array
+    png_path = Path(__file__).parent / "baseline_images/pngsuite/basn2c16.png"
+    cim = Image.open(png_path)
+    cim = np.asarray(cim.convert('RGBA'))
+
+    cmap = mpl.colors.BivarColormapFromImage(cim)
+    im = cmap((data_0, data_1), bytes=True)
+    res = np.array([[[255, 255,   0, 255],
+                     [156, 206,   0, 255],
+                     [49, 156,  49, 255]],
+                    [[206,  99,   0, 255],
+                     [99,  49, 107, 255],
+                     [0,   0, 255, 255]]])
+    assert_allclose(im,  res, atol=0.01)
+
+
+def test_bivar_resample():
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((2, 2))
+    assert_allclose(cmap((0.25, 0.25)), (0, 0, 0, 1), atol=1e-2)
+
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((-2, 2))
+    assert_allclose(cmap((0.25, 0.25)), (1., 0.5, 0., 1.), atol=1e-2)
+
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((2, -2))
+    assert_allclose(cmap((0.25, 0.25)), (0., 0.5, 1., 1.), atol=1e-2)
+
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].resampled((-2, -2))
+    assert_allclose(cmap((0.25, 0.25)), (1, 1, 1, 1), atol=1e-2)
+
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].reversed()
+    assert_allclose(cmap((0.25, 0.25)), (0.748535, 0.748547, 0.748535, 1.), atol=1e-2)
+    cmap = mpl.bivar_colormaps['BiOrangeBlue'].transposed()
+    assert_allclose(cmap((0.25, 0.25)), (0.252441, 0.252422, 0.252441, 1.), atol=1e-2)
+
+    with pytest.raises(ValueError, match="lutshape must be of length"):
+        cmap = cmap.resampled(4)
+
+
+def test_bivariate_repr_png():
+    cmap = mpl.bivar_colormaps['BiCone']
+    png = cmap._repr_png_()
+    assert len(png) > 0
+    img = Image.open(BytesIO(png))
+    assert img.width > 0
+    assert img.height > 0
+    assert 'Title' in img.text
+    assert 'Description' in img.text
+    assert 'Author' in img.text
+    assert 'Software' in img.text
+
+
+def test_bivariate_repr_html():
+    cmap = mpl.bivar_colormaps['BiCone']
+    html = cmap._repr_html_()
+    assert len(html) > 0
+    png = cmap._repr_png_()
+    assert base64.b64encode(png).decode('ascii') in html
+    assert cmap.name in html
+    assert html.startswith('<div')
+    assert html.endswith('</div>')
+
+
+def test_multivariate_repr_png():
+    cmap = mpl.multivar_colormaps['3VarAddA']
+    png = cmap._repr_png_()
+    assert len(png) > 0
+    img = Image.open(BytesIO(png))
+    assert img.width > 0
+    assert img.height > 0
+    assert 'Title' in img.text
+    assert 'Description' in img.text
+    assert 'Author' in img.text
+    assert 'Software' in img.text
+
+
+def test_multivariate_repr_html():
+    cmap = mpl.multivar_colormaps['3VarAddA']
+    html = cmap._repr_html_()
+    assert len(html) > 0
+    for c in cmap:
+        png = c._repr_png_()
+        assert base64.b64encode(png).decode('ascii') in html
+    assert cmap.name in html
+    assert html.startswith('<div')
+    assert html.endswith('</div>')
+
+
+def test_bivar_eq():
+    """
+    Tests equality between multivariate colormaps
+    """
+    cmap_0 = mpl.bivar_colormaps['BiPeak']
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    assert (cmap_0 == cmap_1) is True
+
+    cmap_1 = mpl.multivar_colormaps['2VarAddA']
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.bivar_colormaps['BiCone']
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    cmap_1 = cmap_1.with_extremes(bad='k')
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    cmap_1 = cmap_1.with_extremes(outside='k')
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    cmap_1._init()
+    cmap_1._lut *= 0.5
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    cmap_1 = cmap_1.with_extremes(shape='ignore')
+    assert (cmap_0 == cmap_1) is False
+
+
+def test_multivar_eq():
+    """
+    Tests equality between multivariate colormaps
+    """
+    cmap_0 = mpl.multivar_colormaps['2VarAddA']
+
+    cmap_1 = mpl.multivar_colormaps['2VarAddA']
+    assert (cmap_0 == cmap_1) is True
+
+    cmap_1 = mpl.bivar_colormaps['BiPeak']
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.colors.MultivarColormap([cmap_0[0]]*2,
+                                         'sRGB_add')
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.multivar_colormaps['3VarAddA']
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.multivar_colormaps['2VarAddA']
+    cmap_1 = cmap_1.with_extremes(bad='k')
+    assert (cmap_0 == cmap_1) is False
+
+    cmap_1 = mpl.multivar_colormaps['2VarAddA']
+    cmap_1 = mpl.colors.MultivarColormap(cmap_1[:], 'sRGB_sub')
+    assert (cmap_0 == cmap_1) is False
diff --git a/shared/package-files/c0/c06754b2847ea30faed8b4488da6a48dcc0f0722906fcf94f8a3978536d6f708/db.pyi b/shared/package-files/c0/c06754b2847ea30faed8b4488da6a48dcc0f0722906fcf94f8a3978536d6f708/db.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..82fb34a79d337f55c5588f74912714da513a8520
--- /dev/null
+++ b/shared/package-files/c0/c06754b2847ea30faed8b4488da6a48dcc0f0722906fcf94f8a3978536d6f708/db.pyi
@@ -0,0 +1,22 @@
+from typing import Any, Dict
+
+from django.core.cache.backends.base import BaseCache
+
+class Options:
+    db_table: str = ...
+    app_label: str = ...
+    model_name: str = ...
+    verbose_name: str = ...
+    verbose_name_plural: str = ...
+    object_name: str = ...
+    abstract: bool = ...
+    managed: bool = ...
+    proxy: bool = ...
+    swapped: bool = ...
+    def __init__(self, table: str) -> None: ...
+
+class BaseDatabaseCache(BaseCache):
+    cache_model_class: Any = ...
+    def __init__(self, table: str, params: Dict[str, Any]) -> None: ...
+
+class DatabaseCache(BaseDatabaseCache): ...
diff --git a/shared/package-files/c0/c06bd5aada7ab707b4a5d9c3625c6c607ddbf1e0089e9bf2e5e7f2327babbab4/a210 b/shared/package-files/c0/c06bd5aada7ab707b4a5d9c3625c6c607ddbf1e0089e9bf2e5e7f2327babbab4/a210
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diff --git a/shared/package-files/c0/c06bd5aada7ab707b4a5d9c3625c6c607ddbf1e0089e9bf2e5e7f2327babbab4/ampex210 b/shared/package-files/c0/c06bd5aada7ab707b4a5d9c3625c6c607ddbf1e0089e9bf2e5e7f2327babbab4/ampex210
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diff --git a/shared/package-files/c0/c06ed8eaeba4eaf806bee279df38777bba778742c7c6f85e893a44f2e539f144/api.py b/shared/package-files/c0/c06ed8eaeba4eaf806bee279df38777bba778742c7c6f85e893a44f2e539f144/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..131579955fb30a2db8636dffa58cc170b54f5535
--- /dev/null
+++ b/shared/package-files/c0/c06ed8eaeba4eaf806bee279df38777bba778742c7c6f85e893a44f2e539f144/api.py
@@ -0,0 +1,243 @@
+"""Base API."""
+from __future__ import annotations
+
+import os
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import sys
+
+    if sys.version_info >= (3, 8):  # pragma: no cover (py38+)
+        from typing import Literal
+    else:  # pragma: no cover (py38+)
+        from typing_extensions import Literal
+
+
+class PlatformDirsABC(ABC):
+    """Abstract base class for platform directories."""
+
+    def __init__(  # noqa: PLR0913
+        self,
+        appname: str | None = None,
+        appauthor: str | None | Literal[False] = None,
+        version: str | None = None,
+        roaming: bool = False,  # noqa: FBT001, FBT002
+        multipath: bool = False,  # noqa: FBT001, FBT002
+        opinion: bool = True,  # noqa: FBT001, FBT002
+        ensure_exists: bool = False,  # noqa: FBT001, FBT002
+    ) -> None:
+        """
+        Create a new platform directory.
+
+        :param appname: See `appname`.
+        :param appauthor: See `appauthor`.
+        :param version: See `version`.
+        :param roaming: See `roaming`.
+        :param multipath: See `multipath`.
+        :param opinion: See `opinion`.
+        :param ensure_exists: See `ensure_exists`.
+        """
+        self.appname = appname  #: The name of application.
+        self.appauthor = appauthor
+        """
+        The name of the app author or distributing body for this application. Typically, it is the owning company name.
+        Defaults to `appname`. You may pass ``False`` to disable it.
+        """
+        self.version = version
+        """
+        An optional version path element to append to the path. You might want to use this if you want multiple versions
+        of your app to be able to run independently. If used, this would typically be ``<major>.<minor>``.
+        """
+        self.roaming = roaming
+        """
+        Whether to use the roaming appdata directory on Windows. That means that for users on a Windows network setup
+        for roaming profiles, this user data will be synced on login (see
+        `here <http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>`_).
+        """
+        self.multipath = multipath
+        """
+        An optional parameter only applicable to Unix/Linux which indicates that the entire list of data dirs should be
+        returned. By default, the first item would only be returned.
+        """
+        self.opinion = opinion  #: A flag to indicating to use opinionated values.
+        self.ensure_exists = ensure_exists
+        """
+        Optionally create the directory (and any missing parents) upon access if it does not exist.
+        By default, no directories are created.
+        """
+
+    def _append_app_name_and_version(self, *base: str) -> str:
+        params = list(base[1:])
+        if self.appname:
+            params.append(self.appname)
+            if self.version:
+                params.append(self.version)
+        path = os.path.join(base[0], *params)  # noqa: PTH118
+        self._optionally_create_directory(path)
+        return path
+
+    def _optionally_create_directory(self, path: str) -> None:
+        if self.ensure_exists:
+            Path(path).mkdir(parents=True, exist_ok=True)
+
+    @property
+    @abstractmethod
+    def user_data_dir(self) -> str:
+        """:return: data directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users"""
+
+    @property
+    @abstractmethod
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users"""
+
+    @property
+    @abstractmethod
+    def user_cache_dir(self) -> str:
+        """:return: cache directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users"""
+
+    @property
+    @abstractmethod
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_runtime_dir(self) -> str:
+        """:return: runtime directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users"""
+
+    @property
+    def user_data_path(self) -> Path:
+        """:return: data path tied to the user"""
+        return Path(self.user_data_dir)
+
+    @property
+    def site_data_path(self) -> Path:
+        """:return: data path shared by users"""
+        return Path(self.site_data_dir)
+
+    @property
+    def user_config_path(self) -> Path:
+        """:return: config path tied to the user"""
+        return Path(self.user_config_dir)
+
+    @property
+    def site_config_path(self) -> Path:
+        """:return: config path shared by the users"""
+        return Path(self.site_config_dir)
+
+    @property
+    def user_cache_path(self) -> Path:
+        """:return: cache path tied to the user"""
+        return Path(self.user_cache_dir)
+
+    @property
+    def site_cache_path(self) -> Path:
+        """:return: cache path shared by users"""
+        return Path(self.site_cache_dir)
+
+    @property
+    def user_state_path(self) -> Path:
+        """:return: state path tied to the user"""
+        return Path(self.user_state_dir)
+
+    @property
+    def user_log_path(self) -> Path:
+        """:return: log path tied to the user"""
+        return Path(self.user_log_dir)
+
+    @property
+    def user_documents_path(self) -> Path:
+        """:return: documents path tied to the user"""
+        return Path(self.user_documents_dir)
+
+    @property
+    def user_downloads_path(self) -> Path:
+        """:return: downloads path tied to the user"""
+        return Path(self.user_downloads_dir)
+
+    @property
+    def user_pictures_path(self) -> Path:
+        """:return: pictures path tied to the user"""
+        return Path(self.user_pictures_dir)
+
+    @property
+    def user_videos_path(self) -> Path:
+        """:return: videos path tied to the user"""
+        return Path(self.user_videos_dir)
+
+    @property
+    def user_music_path(self) -> Path:
+        """:return: music path tied to the user"""
+        return Path(self.user_music_dir)
+
+    @property
+    def user_desktop_path(self) -> Path:
+        """:return: desktop path tied to the user"""
+        return Path(self.user_desktop_dir)
+
+    @property
+    def user_runtime_path(self) -> Path:
+        """:return: runtime path tied to the user"""
+        return Path(self.user_runtime_dir)
+
+    @property
+    def site_runtime_path(self) -> Path:
+        """:return: runtime path shared by users"""
+        return Path(self.site_runtime_dir)
diff --git a/shared/package-files/c0/c0732a06e0c2e101be8e7b5da89cc9690d561b2735801a4ca466cae681ce921e/tensor_factory_functions.py b/shared/package-files/c0/c0732a06e0c2e101be8e7b5da89cc9690d561b2735801a4ca466cae681ce921e/tensor_factory_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..47aa9c500466daadf282633d43f0335e0a8c0b70
--- /dev/null
+++ b/shared/package-files/c0/c0732a06e0c2e101be8e7b5da89cc9690d561b2735801a4ca466cae681ce921e/tensor_factory_functions.py
@@ -0,0 +1,48 @@
+import torch
+
+"""
+tensor_factory_functions defines the list of torch functions that create tensors.
+The list is grabbed by searching thru native_functions.yaml by the following
+regular expression:
+
+  cat native_functions.yaml | grep 'func:' | grep -v "Tensor.*->" | grep "[-]>.*Tensor"
+
+It's possible that new tensor factory functions are added making this list stale.
+Use at your own risk or regenerate the list.
+"""
+tensor_factory_functions = (
+    torch._cudnn_init_dropout_state,
+    torch.arange,
+    torch.bartlett_window,
+    torch.blackman_window,
+    torch._empty_affine_quantized,
+    torch.empty_strided,
+    torch.eye,
+    torch.full,
+    torch.from_file,
+    torch.hann_window,
+    torch.hamming_window,
+    torch.kaiser_window,
+    torch.linspace,
+    torch.logspace,
+    torch.ones,
+    torch.scalar_tensor,
+    torch.rand,
+    torch.randint,
+    torch.randn,
+    torch.randperm,
+    torch.range,
+    torch._efficientzerotensor,
+    torch.zeros,
+    torch.tril_indices,
+    torch.triu_indices,
+    # Note: the following functions match the regular expression search above but
+    # they are not available in the torch module. Comment out.
+    # torch._sparse_coo_tensor_with_dims,
+    # torch.fft_fftfreq,
+    # torch.fft_rfftfreq,
+) + (
+    # torch.tensor is special since it's not in native_functions.yaml
+    # add it separately
+    torch.tensor,
+)
diff --git a/shared/package-files/c0/c076e9195f2da1d69dcfa308cc91da9437d18e2288d7ae2ab3a88c70782c56ed/doccer.py b/shared/package-files/c0/c076e9195f2da1d69dcfa308cc91da9437d18e2288d7ae2ab3a88c70782c56ed/doccer.py
new file mode 100644
index 0000000000000000000000000000000000000000..74cabc8c2fd14fe6424b8aad828c329ecdaee4b2
--- /dev/null
+++ b/shared/package-files/c0/c076e9195f2da1d69dcfa308cc91da9437d18e2288d7ae2ab3a88c70782c56ed/doccer.py
@@ -0,0 +1,6 @@
+import warnings
+warnings.warn(
+    "scipy.misc.doccer is deprecated and will be removed in 2.0.0",
+    DeprecationWarning,
+    stacklevel=2
+)
diff --git a/shared/package-files/c0/c07c57afb47f35388aa86fd21f7c087505c92e5757db8bb06d85ddd05b608b0e/concat_opt.h b/shared/package-files/c0/c07c57afb47f35388aa86fd21f7c087505c92e5757db8bb06d85ddd05b608b0e/concat_opt.h
new file mode 100644
index 0000000000000000000000000000000000000000..4fdd86f36a46dd0c1be0c53f1731c8e686ba1796
--- /dev/null
+++ b/shared/package-files/c0/c07c57afb47f35388aa86fd21f7c087505c92e5757db8bb06d85ddd05b608b0e/concat_opt.h
@@ -0,0 +1,19 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+// Eliminates common inputs among `aten::cat` ops.
+TORCH_API bool EliminateConcatCommonInputs(const std::shared_ptr<Graph>& graph);
+
+// Expands `aten::cat` ops into `aten::copy` ops and eliminates redudancies
+// in the buffers used for concatenation if possible.
+TORCH_API void ExpandConcatAndEliminateRedundancy(
+    const std::shared_ptr<Graph>& graph);
+
+TORCH_API bool CombineConcats(const std::shared_ptr<Graph>& graph);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/c0/c07e88d6846f3a923a4529f1908624c87a09ff22855d7eb1d0a9054bec639b6c/mse_loss_meta_dispatch.h b/shared/package-files/c0/c07e88d6846f3a923a4529f1908624c87a09ff22855d7eb1d0a9054bec639b6c/mse_loss_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3352a9f373be6eae8a3726a156a8d6b99f4872a8
--- /dev/null
+++ b/shared/package-files/c0/c07e88d6846f3a923a4529f1908624c87a09ff22855d7eb1d0a9054bec639b6c/mse_loss_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean);
+TORCH_API at::Tensor & mse_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean);
+TORCH_API at::Tensor & mse_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/c0/c081db214332c2d8de0676471b02f683adac6da829eb0ace363db1f72dfaa738/cpuid_schema.json b/shared/package-files/c0/c081db214332c2d8de0676471b02f683adac6da829eb0ace363db1f72dfaa738/cpuid_schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..8dc5b982eac66a8230a107962882a11d4982240a
--- /dev/null
+++ b/shared/package-files/c0/c081db214332c2d8de0676471b02f683adac6da829eb0ace363db1f72dfaa738/cpuid_schema.json
@@ -0,0 +1,134 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Schema for microarchitecture definitions and feature aliases",
+  "type": "object",
+  "additionalProperties": false,
+  "properties": {
+    "vendor": {
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "description": {
+          "type": "string"
+        },
+        "input": {
+          "type": "object",
+          "additionalProperties": false,
+          "properties": {
+            "eax": {
+              "type": "integer"
+            },
+            "ecx": {
+              "type": "integer"
+            }
+          }
+        }
+      }
+    },
+    "highest_extension_support": {
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "description": {
+          "type": "string"
+        },
+        "input": {
+          "type": "object",
+          "additionalProperties": false,
+          "properties": {
+            "eax": {
+              "type": "integer"
+            },
+            "ecx": {
+              "type": "integer"
+            }
+          }
+        }
+      }
+    },
+    "flags": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "additionalProperties": false,
+        "properties": {
+          "description": {
+            "type": "string"
+          },
+          "input": {
+            "type": "object",
+            "additionalProperties": false,
+            "properties": {
+              "eax": {
+                "type": "integer"
+              },
+              "ecx": {
+                "type": "integer"
+              }
+            }
+          },
+          "bits": {
+            "type": "array",
+            "items": {
+              "type": "object",
+              "additionalProperties": false,
+              "properties": {
+                "name": {
+                  "type": "string"
+                },
+                "register": {
+                  "type": "string"
+                },
+                "bit": {
+                  "type": "integer"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "extension-flags": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "additionalProperties": false,
+        "properties": {
+          "description": {
+            "type": "string"
+          },
+          "input": {
+            "type": "object",
+            "additionalProperties": false,
+            "properties": {
+              "eax": {
+                "type": "integer"
+              },
+              "ecx": {
+                "type": "integer"
+              }
+            }
+          },
+          "bits": {
+            "type": "array",
+            "items": {
+              "type": "object",
+              "additionalProperties": false,
+              "properties": {
+                "name": {
+                  "type": "string"
+                },
+                "register": {
+                  "type": "string"
+                },
+                "bit": {
+                  "type": "integer"
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/shared/package-files/c0/c08462a7d8133366e12145e67812434be010686328cd323cdbb8b986c402e4a7/vip b/shared/package-files/c0/c08462a7d8133366e12145e67812434be010686328cd323cdbb8b986c402e4a7/vip
new file mode 100644
index 0000000000000000000000000000000000000000..010a8d112077bf9ac312b30db13723ebd316a30f
Binary files /dev/null and b/shared/package-files/c0/c08462a7d8133366e12145e67812434be010686328cd323cdbb8b986c402e4a7/vip differ
diff --git a/shared/package-files/c0/c08e844893e93dfff2dd9f81771f55b5e532212289c46ddd994dbc99354a735d/trapz_compositeimplicitautograd_dispatch.h b/shared/package-files/c0/c08e844893e93dfff2dd9f81771f55b5e532212289c46ddd994dbc99354a735d/trapz_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dfe5056e4d41771e59fa82ad47cdd68456127f1
--- /dev/null
+++ b/shared/package-files/c0/c08e844893e93dfff2dd9f81771f55b5e532212289c46ddd994dbc99354a735d/trapz_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1);
+TORCH_API at::Tensor trapz(const at::Tensor & y, double dx=1, int64_t dim=-1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/expatbuilder.pyi b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/expatbuilder.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..964e6fa3f42695effe5d4f31e2102a6e785360d4
--- /dev/null
+++ b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/expatbuilder.pyi
@@ -0,0 +1,3 @@
+from typing import Any
+
+def __getattr__(name: str) -> Any: ...  # incomplete
diff --git a/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/minicompat.pyi b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/minicompat.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..964e6fa3f42695effe5d4f31e2102a6e785360d4
--- /dev/null
+++ b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/minicompat.pyi
@@ -0,0 +1,3 @@
+from typing import Any
+
+def __getattr__(name: str) -> Any: ...  # incomplete
diff --git a/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/pulldom.pyi b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/pulldom.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..964e6fa3f42695effe5d4f31e2102a6e785360d4
--- /dev/null
+++ b/shared/package-files/c0/c08fdebb51bcc9a6aab911e667d9984608f2e3334d8490b7f394e348ca1a9918/pulldom.pyi
@@ -0,0 +1,3 @@
+from typing import Any
+
+def __getattr__(name: str) -> Any: ...  # incomplete
diff --git a/shared/package-files/c0/c090be072963864c92c4fd4307800c6883cc9656056644625b4aa8b82aafae1c/edge_kcomponents.py b/shared/package-files/c0/c090be072963864c92c4fd4307800c6883cc9656056644625b4aa8b82aafae1c/edge_kcomponents.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc85e26441b04a130c84b3133b50db50ddc18eea
--- /dev/null
+++ b/shared/package-files/c0/c090be072963864c92c4fd4307800c6883cc9656056644625b4aa8b82aafae1c/edge_kcomponents.py
@@ -0,0 +1,581 @@
+"""
+Algorithms for finding k-edge-connected components and subgraphs.
+
+A k-edge-connected component (k-edge-cc) is a maximal set of nodes in G, such
+that all pairs of node have an edge-connectivity of at least k.
+
+A k-edge-connected subgraph (k-edge-subgraph) is a maximal set of nodes in G,
+such that the subgraph of G defined by the nodes has an edge-connectivity at
+least k.
+"""
+import itertools as it
+from functools import partial
+
+import networkx as nx
+from networkx.algorithms import bridges
+from networkx.utils import arbitrary_element, not_implemented_for
+
+__all__ = [
+    "k_edge_components",
+    "k_edge_subgraphs",
+    "bridge_components",
+    "EdgeComponentAuxGraph",
+]
+
+
+@not_implemented_for("multigraph")
+def k_edge_components(G, k):
+    """Generates nodes in each maximal k-edge-connected component in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    k : Integer
+        Desired edge connectivity
+
+    Returns
+    -------
+    k_edge_components : a generator of k-edge-ccs. Each set of returned nodes
+       will have k-edge-connectivity in the graph G.
+
+    See Also
+    --------
+    :func:`local_edge_connectivity`
+    :func:`k_edge_subgraphs` : similar to this function, but the subgraph
+        defined by the nodes must also have k-edge-connectivity.
+    :func:`k_components` : similar to this function, but uses node-connectivity
+        instead of edge-connectivity
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is a multigraph.
+
+    ValueError:
+        If k is less than 1
+
+    Notes
+    -----
+    Attempts to use the most efficient implementation available based on k.
+    If k=1, this is simply connected components for directed graphs and
+    connected components for undirected graphs.
+    If k=2 on an efficient bridge connected component algorithm from _[1] is
+    run based on the chain decomposition.
+    Otherwise, the algorithm from _[2] is used.
+
+    Examples
+    --------
+    >>> import itertools as it
+    >>> from networkx.utils import pairwise
+    >>> paths = [
+    ...     (1, 2, 4, 3, 1, 4),
+    ...     (5, 6, 7, 8, 5, 7, 8, 6),
+    ... ]
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(it.chain(*paths))
+    >>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
+    >>> # note this returns {1, 4} unlike k_edge_subgraphs
+    >>> sorted(map(sorted, nx.k_edge_components(G, k=3)))
+    [[1, 4], [2], [3], [5, 6, 7, 8]]
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bridge_%28graph_theory%29
+    .. [2] Wang, Tianhao, et al. (2015) A simple algorithm for finding all
+        k-edge-connected components.
+        http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
+    """
+    # Compute k-edge-ccs using the most efficient algorithms available.
+    if k < 1:
+        raise ValueError("k cannot be less than 1")
+    if G.is_directed():
+        if k == 1:
+            return nx.strongly_connected_components(G)
+        else:
+            # TODO: investigate https://arxiv.org/abs/1412.6466 for k=2
+            aux_graph = EdgeComponentAuxGraph.construct(G)
+            return aux_graph.k_edge_components(k)
+    else:
+        if k == 1:
+            return nx.connected_components(G)
+        elif k == 2:
+            return bridge_components(G)
+        else:
+            aux_graph = EdgeComponentAuxGraph.construct(G)
+            return aux_graph.k_edge_components(k)
+
+
+@not_implemented_for("multigraph")
+def k_edge_subgraphs(G, k):
+    """Generates nodes in each maximal k-edge-connected subgraph in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    k : Integer
+        Desired edge connectivity
+
+    Returns
+    -------
+    k_edge_subgraphs : a generator of k-edge-subgraphs
+        Each k-edge-subgraph is a maximal set of nodes that defines a subgraph
+        of G that is k-edge-connected.
+
+    See Also
+    --------
+    :func:`edge_connectivity`
+    :func:`k_edge_components` : similar to this function, but nodes only
+        need to have k-edge-connctivity within the graph G and the subgraphs
+        might not be k-edge-connected.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is a multigraph.
+
+    ValueError:
+        If k is less than 1
+
+    Notes
+    -----
+    Attempts to use the most efficient implementation available based on k.
+    If k=1, or k=2 and the graph is undirected, then this simply calls
+    `k_edge_components`.  Otherwise the algorithm from _[1] is used.
+
+    Examples
+    --------
+    >>> import itertools as it
+    >>> from networkx.utils import pairwise
+    >>> paths = [
+    ...     (1, 2, 4, 3, 1, 4),
+    ...     (5, 6, 7, 8, 5, 7, 8, 6),
+    ... ]
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(it.chain(*paths))
+    >>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
+    >>> # note this does not return {1, 4} unlike k_edge_components
+    >>> sorted(map(sorted, nx.k_edge_subgraphs(G, k=3)))
+    [[1], [2], [3], [4], [5, 6, 7, 8]]
+
+    References
+    ----------
+    .. [1] Zhou, Liu, et al. (2012) Finding maximal k-edge-connected subgraphs
+        from a large graph.  ACM International Conference on Extending Database
+        Technology 2012 480-–491.
+        https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf
+    """
+    if k < 1:
+        raise ValueError("k cannot be less than 1")
+    if G.is_directed():
+        if k <= 1:
+            # For directed graphs ,
+            # When k == 1, k-edge-ccs and k-edge-subgraphs are the same
+            return k_edge_components(G, k)
+        else:
+            return _k_edge_subgraphs_nodes(G, k)
+    else:
+        if k <= 2:
+            # For undirected graphs,
+            # when k <= 2, k-edge-ccs and k-edge-subgraphs are the same
+            return k_edge_components(G, k)
+        else:
+            return _k_edge_subgraphs_nodes(G, k)
+
+
+def _k_edge_subgraphs_nodes(G, k):
+    """Helper to get the nodes from the subgraphs.
+
+    This allows k_edge_subgraphs to return a generator.
+    """
+    for C in general_k_edge_subgraphs(G, k):
+        yield set(C.nodes())
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def bridge_components(G):
+    """Finds all bridge-connected components G.
+
+    Parameters
+    ----------
+    G : NetworkX undirected graph
+
+    Returns
+    -------
+    bridge_components : a generator of 2-edge-connected components
+
+
+    See Also
+    --------
+    :func:`k_edge_subgraphs` : this function is a special case for an
+        undirected graph where k=2.
+    :func:`biconnected_components` : similar to this function, but is defined
+        using 2-node-connectivity instead of 2-edge-connectivity.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is directed or a multigraph.
+
+    Notes
+    -----
+    Bridge-connected components are also known as 2-edge-connected components.
+
+    Examples
+    --------
+    >>> # The barbell graph with parameter zero has a single bridge
+    >>> G = nx.barbell_graph(5, 0)
+    >>> from networkx.algorithms.connectivity.edge_kcomponents import bridge_components
+    >>> sorted(map(sorted, bridge_components(G)))
+    [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
+    """
+    H = G.copy()
+    H.remove_edges_from(bridges(G))
+    yield from nx.connected_components(H)
+
+
+class EdgeComponentAuxGraph:
+    r"""A simple algorithm to find all k-edge-connected components in a graph.
+
+    Constructing the auxiliary graph (which may take some time) allows for the
+    k-edge-ccs to be found in linear time for arbitrary k.
+
+    Notes
+    -----
+    This implementation is based on [1]_. The idea is to construct an auxiliary
+    graph from which the k-edge-ccs can be extracted in linear time. The
+    auxiliary graph is constructed in $O(|V|\cdot F)$ operations, where F is the
+    complexity of max flow. Querying the components takes an additional $O(|V|)$
+    operations. This algorithm can be slow for large graphs, but it handles an
+    arbitrary k and works for both directed and undirected inputs.
+
+    The undirected case for k=1 is exactly connected components.
+    The undirected case for k=2 is exactly bridge connected components.
+    The directed case for k=1 is exactly strongly connected components.
+
+    References
+    ----------
+    .. [1] Wang, Tianhao, et al. (2015) A simple algorithm for finding all
+        k-edge-connected components.
+        http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
+
+    Examples
+    --------
+    >>> import itertools as it
+    >>> from networkx.utils import pairwise
+    >>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph
+    >>> # Build an interesting graph with multiple levels of k-edge-ccs
+    >>> paths = [
+    ...     (1, 2, 3, 4, 1, 3, 4, 2),  # a 3-edge-cc (a 4 clique)
+    ...     (5, 6, 7, 5),  # a 2-edge-cc (a 3 clique)
+    ...     (1, 5),  # combine first two ccs into a 1-edge-cc
+    ...     (0,),  # add an additional disconnected 1-edge-cc
+    ... ]
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(it.chain(*paths))
+    >>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
+    >>> # Constructing the AuxGraph takes about O(n ** 4)
+    >>> aux_graph = EdgeComponentAuxGraph.construct(G)
+    >>> # Once constructed, querying takes O(n)
+    >>> sorted(map(sorted, aux_graph.k_edge_components(k=1)))
+    [[0], [1, 2, 3, 4, 5, 6, 7]]
+    >>> sorted(map(sorted, aux_graph.k_edge_components(k=2)))
+    [[0], [1, 2, 3, 4], [5, 6, 7]]
+    >>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
+    [[0], [1, 2, 3, 4], [5], [6], [7]]
+    >>> sorted(map(sorted, aux_graph.k_edge_components(k=4)))
+    [[0], [1], [2], [3], [4], [5], [6], [7]]
+
+    The auxiliary graph is primarily used for k-edge-ccs but it
+    can also speed up the queries of k-edge-subgraphs by refining the
+    search space.
+
+    >>> import itertools as it
+    >>> from networkx.utils import pairwise
+    >>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph
+    >>> paths = [
+    ...     (1, 2, 4, 3, 1, 4),
+    ... ]
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(it.chain(*paths))
+    >>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
+    >>> aux_graph = EdgeComponentAuxGraph.construct(G)
+    >>> sorted(map(sorted, aux_graph.k_edge_subgraphs(k=3)))
+    [[1], [2], [3], [4]]
+    >>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
+    [[1, 4], [2], [3]]
+    """
+
+    # @not_implemented_for('multigraph')  # TODO: fix decor for classmethods
+    @classmethod
+    def construct(EdgeComponentAuxGraph, G):
+        """Builds an auxiliary graph encoding edge-connectivity between nodes.
+
+        Notes
+        -----
+        Given G=(V, E), initialize an empty auxiliary graph A.
+        Choose an arbitrary source node s.  Initialize a set N of available
+        nodes (that can be used as the sink). The algorithm picks an
+        arbitrary node t from N - {s}, and then computes the minimum st-cut
+        (S, T) with value w. If G is directed the minimum of the st-cut or
+        the ts-cut is used instead. Then, the edge (s, t) is added to the
+        auxiliary graph with weight w. The algorithm is called recursively
+        first using S as the available nodes and s as the source, and then
+        using T and t. Recursion stops when the source is the only available
+        node.
+
+        Parameters
+        ----------
+        G : NetworkX graph
+        """
+        # workaround for classmethod decorator
+        not_implemented_for("multigraph")(lambda G: G)(G)
+
+        def _recursive_build(H, A, source, avail):
+            # Terminate once the flow has been compute to every node.
+            if {source} == avail:
+                return
+            # pick an arbitrary node as the sink
+            sink = arbitrary_element(avail - {source})
+            # find the minimum cut and its weight
+            value, (S, T) = nx.minimum_cut(H, source, sink)
+            if H.is_directed():
+                # check if the reverse direction has a smaller cut
+                value_, (T_, S_) = nx.minimum_cut(H, sink, source)
+                if value_ < value:
+                    value, S, T = value_, S_, T_
+            # add edge with weight of cut to the aux graph
+            A.add_edge(source, sink, weight=value)
+            # recursively call until all but one node is used
+            _recursive_build(H, A, source, avail.intersection(S))
+            _recursive_build(H, A, sink, avail.intersection(T))
+
+        # Copy input to ensure all edges have unit capacity
+        H = G.__class__()
+        H.add_nodes_from(G.nodes())
+        H.add_edges_from(G.edges(), capacity=1)
+
+        # A is the auxiliary graph to be constructed
+        # It is a weighted undirected tree
+        A = nx.Graph()
+
+        # Pick an arbitrary node as the source
+        if H.number_of_nodes() > 0:
+            source = arbitrary_element(H.nodes())
+            # Initialize a set of elements that can be chosen as the sink
+            avail = set(H.nodes())
+
+            # This constructs A
+            _recursive_build(H, A, source, avail)
+
+        # This class is a container the holds the auxiliary graph A and
+        # provides access the k_edge_components function.
+        self = EdgeComponentAuxGraph()
+        self.A = A
+        self.H = H
+        return self
+
+    def k_edge_components(self, k):
+        """Queries the auxiliary graph for k-edge-connected components.
+
+        Parameters
+        ----------
+        k : Integer
+            Desired edge connectivity
+
+        Returns
+        -------
+        k_edge_components : a generator of k-edge-ccs
+
+        Notes
+        -----
+        Given the auxiliary graph, the k-edge-connected components can be
+        determined in linear time by removing all edges with weights less than
+        k from the auxiliary graph.  The resulting connected components are the
+        k-edge-ccs in the original graph.
+        """
+        if k < 1:
+            raise ValueError("k cannot be less than 1")
+        A = self.A
+        # "traverse the auxiliary graph A and delete all edges with weights less
+        # than k"
+        aux_weights = nx.get_edge_attributes(A, "weight")
+        # Create a relevant graph with the auxiliary edges with weights >= k
+        R = nx.Graph()
+        R.add_nodes_from(A.nodes())
+        R.add_edges_from(e for e, w in aux_weights.items() if w >= k)
+
+        # Return the nodes that are k-edge-connected in the original graph
+        yield from nx.connected_components(R)
+
+    def k_edge_subgraphs(self, k):
+        """Queries the auxiliary graph for k-edge-connected subgraphs.
+
+        Parameters
+        ----------
+        k : Integer
+            Desired edge connectivity
+
+        Returns
+        -------
+        k_edge_subgraphs : a generator of k-edge-subgraphs
+
+        Notes
+        -----
+        Refines the k-edge-ccs into k-edge-subgraphs. The running time is more
+        than $O(|V|)$.
+
+        For single values of k it is faster to use `nx.k_edge_subgraphs`.
+        But for multiple values of k, it can be faster to build AuxGraph and
+        then use this method.
+        """
+        if k < 1:
+            raise ValueError("k cannot be less than 1")
+        H = self.H
+        A = self.A
+        # "traverse the auxiliary graph A and delete all edges with weights less
+        # than k"
+        aux_weights = nx.get_edge_attributes(A, "weight")
+        # Create a relevant graph with the auxiliary edges with weights >= k
+        R = nx.Graph()
+        R.add_nodes_from(A.nodes())
+        R.add_edges_from(e for e, w in aux_weights.items() if w >= k)
+
+        # Return the components whose subgraphs are k-edge-connected
+        for cc in nx.connected_components(R):
+            if len(cc) < k:
+                # Early return optimization
+                for node in cc:
+                    yield {node}
+            else:
+                # Call subgraph solution to refine the results
+                C = H.subgraph(cc)
+                yield from k_edge_subgraphs(C, k)
+
+
+def _low_degree_nodes(G, k, nbunch=None):
+    """Helper for finding nodes with degree less than k."""
+    # Nodes with degree less than k cannot be k-edge-connected.
+    if G.is_directed():
+        # Consider both in and out degree in the directed case
+        seen = set()
+        for node, degree in G.out_degree(nbunch):
+            if degree < k:
+                seen.add(node)
+                yield node
+        for node, degree in G.in_degree(nbunch):
+            if node not in seen and degree < k:
+                seen.add(node)
+                yield node
+    else:
+        # Only the degree matters in the undirected case
+        for node, degree in G.degree(nbunch):
+            if degree < k:
+                yield node
+
+
+def _high_degree_components(G, k):
+    """Helper for filtering components that can't be k-edge-connected.
+
+    Removes and generates each node with degree less than k.  Then generates
+    remaining components where all nodes have degree at least k.
+    """
+    # Iteravely remove parts of the graph that are not k-edge-connected
+    H = G.copy()
+    singletons = set(_low_degree_nodes(H, k))
+    while singletons:
+        # Only search neighbors of removed nodes
+        nbunch = set(it.chain.from_iterable(map(H.neighbors, singletons)))
+        nbunch.difference_update(singletons)
+        H.remove_nodes_from(singletons)
+        for node in singletons:
+            yield {node}
+        singletons = set(_low_degree_nodes(H, k, nbunch))
+
+    # Note: remaining connected components may not be k-edge-connected
+    if G.is_directed():
+        yield from nx.strongly_connected_components(H)
+    else:
+        yield from nx.connected_components(H)
+
+
+def general_k_edge_subgraphs(G, k):
+    """General algorithm to find all maximal k-edge-connected subgraphs in G.
+
+    Returns
+    -------
+    k_edge_subgraphs : a generator of nx.Graphs that are k-edge-subgraphs
+        Each k-edge-subgraph is a maximal set of nodes that defines a subgraph
+        of G that is k-edge-connected.
+
+    Notes
+    -----
+    Implementation of the basic algorithm from _[1].  The basic idea is to find
+    a global minimum cut of the graph. If the cut value is at least k, then the
+    graph is a k-edge-connected subgraph and can be added to the results.
+    Otherwise, the cut is used to split the graph in two and the procedure is
+    applied recursively. If the graph is just a single node, then it is also
+    added to the results. At the end, each result is either guaranteed to be
+    a single node or a subgraph of G that is k-edge-connected.
+
+    This implementation contains optimizations for reducing the number of calls
+    to max-flow, but there are other optimizations in _[1] that could be
+    implemented.
+
+    References
+    ----------
+    .. [1] Zhou, Liu, et al. (2012) Finding maximal k-edge-connected subgraphs
+        from a large graph.  ACM International Conference on Extending Database
+        Technology 2012 480-–491.
+        https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf
+
+    Examples
+    --------
+    >>> from networkx.utils import pairwise
+    >>> paths = [
+    ...     (11, 12, 13, 14, 11, 13, 14, 12),  # a 4-clique
+    ...     (21, 22, 23, 24, 21, 23, 24, 22),  # another 4-clique
+    ...     # connect the cliques with high degree but low connectivity
+    ...     (50, 13),
+    ...     (12, 50, 22),
+    ...     (13, 102, 23),
+    ...     (14, 101, 24),
+    ... ]
+    >>> G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
+    >>> sorted(map(len, k_edge_subgraphs(G, k=3)))
+    [1, 1, 1, 4, 4]
+    """
+    if k < 1:
+        raise ValueError("k cannot be less than 1")
+
+    # Node pruning optimization (incorporates early return)
+    # find_ccs is either connected_components/strongly_connected_components
+    find_ccs = partial(_high_degree_components, k=k)
+
+    # Quick return optimization
+    if G.number_of_nodes() < k:
+        for node in G.nodes():
+            yield G.subgraph([node]).copy()
+        return
+
+    # Intermediate results
+    R0 = {G.subgraph(cc).copy() for cc in find_ccs(G)}
+    # Subdivide CCs in the intermediate results until they are k-conn
+    while R0:
+        G1 = R0.pop()
+        if G1.number_of_nodes() == 1:
+            yield G1
+        else:
+            # Find a global minimum cut
+            cut_edges = nx.minimum_edge_cut(G1)
+            cut_value = len(cut_edges)
+            if cut_value < k:
+                # G1 is not k-edge-connected, so subdivide it
+                G1.remove_edges_from(cut_edges)
+                for cc in find_ccs(G1):
+                    R0.add(G1.subgraph(cc).copy())
+            else:
+                # Otherwise we found a k-edge-connected subgraph
+                yield G1
diff --git a/shared/package-files/c0/c0945dee73c25d49016384e4917911397ca4b2251386d7932370cf32dfa60bf1/atleast_1d_native.h b/shared/package-files/c0/c0945dee73c25d49016384e4917911397ca4b2251386d7932370cf32dfa60bf1/atleast_1d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c0af1e3ea343adb06a8ba57de2147c64d0a01389
--- /dev/null
+++ b/shared/package-files/c0/c0945dee73c25d49016384e4917911397ca4b2251386d7932370cf32dfa60bf1/atleast_1d_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor atleast_1d(const at::Tensor & self);
+TORCH_API ::std::vector<at::Tensor> atleast_1d(at::TensorList tensors);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c0/c0987a6260dfabc85a1a32439e20da554b0320a123deb4ff381e2beae2c8efb6/test_codegen_rust.py b/shared/package-files/c0/c0987a6260dfabc85a1a32439e20da554b0320a123deb4ff381e2beae2c8efb6/test_codegen_rust.py
new file mode 100644
index 0000000000000000000000000000000000000000..235cc6350e051ab1a2915284aa4669274030943b
--- /dev/null
+++ b/shared/package-files/c0/c0987a6260dfabc85a1a32439e20da554b0320a123deb4ff381e2beae2c8efb6/test_codegen_rust.py
@@ -0,0 +1,401 @@
+from io import StringIO
+
+from sympy.core import S, symbols, pi, Catalan, EulerGamma, Function
+from sympy.core.relational import Equality
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.utilities.codegen import RustCodeGen, codegen, make_routine
+from sympy.testing.pytest import XFAIL
+import sympy
+
+
+x, y, z = symbols('x,y,z')
+
+
+def test_empty_rust_code():
+    code_gen = RustCodeGen()
+    output = StringIO()
+    code_gen.dump_rs([], output, "file", header=False, empty=False)
+    source = output.getvalue()
+    assert source == ""
+
+
+def test_simple_rust_code():
+    name_expr = ("test", (x + y)*z)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    assert result[0] == "test.rs"
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64, z: f64) -> f64 {\n"
+        "    let out1 = z*(x + y);\n"
+        "    out1\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_simple_code_with_header():
+    name_expr = ("test", (x + y)*z)
+    result, = codegen(name_expr, "Rust", header=True, empty=False)
+    assert result[0] == "test.rs"
+    source = result[1]
+    version_str = "Code generated with SymPy %s" % sympy.__version__
+    version_line = version_str.center(76).rstrip()
+    expected = (
+        "/*\n"
+        " *%(version_line)s\n"
+        " *\n"
+        " *              See http://www.sympy.org/ for more information.\n"
+        " *\n"
+        " *                       This file is part of 'project'\n"
+        " */\n"
+        "fn test(x: f64, y: f64, z: f64) -> f64 {\n"
+        "    let out1 = z*(x + y);\n"
+        "    out1\n"
+        "}\n"
+    ) % {'version_line': version_line}
+    assert source == expected
+
+
+def test_simple_code_nameout():
+    expr = Equality(z, (x + y))
+    name_expr = ("test", expr)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64) -> f64 {\n"
+        "    let z = x + y;\n"
+        "    z\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_numbersymbol():
+    name_expr = ("test", pi**Catalan)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test() -> f64 {\n"
+        "    const Catalan: f64 = %s;\n"
+        "    let out1 = PI.powf(Catalan);\n"
+        "    out1\n"
+        "}\n"
+    ) % Catalan.evalf(17)
+    assert source == expected
+
+
+@XFAIL
+def test_numbersymbol_inline():
+    # FIXME: how to pass inline to the RustCodePrinter?
+    name_expr = ("test", [pi**Catalan, EulerGamma])
+    result, = codegen(name_expr, "Rust", header=False,
+                      empty=False, inline=True)
+    source = result[1]
+    expected = (
+        "fn test() -> (f64, f64) {\n"
+        "    const Catalan: f64 = %s;\n"
+        "    const EulerGamma: f64 = %s;\n"
+        "    let out1 = PI.powf(Catalan);\n"
+        "    let out2 = EulerGamma);\n"
+        "    (out1, out2)\n"
+        "}\n"
+    ) % (Catalan.evalf(17), EulerGamma.evalf(17))
+    assert source == expected
+
+
+def test_argument_order():
+    expr = x + y
+    routine = make_routine("test", expr, argument_sequence=[z, x, y], language="rust")
+    code_gen = RustCodeGen()
+    output = StringIO()
+    code_gen.dump_rs([routine], output, "test", header=False, empty=False)
+    source = output.getvalue()
+    expected = (
+        "fn test(z: f64, x: f64, y: f64) -> f64 {\n"
+        "    let out1 = x + y;\n"
+        "    out1\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_multiple_results_rust():
+    # Here the output order is the input order
+    expr1 = (x + y)*z
+    expr2 = (x - y)*z
+    name_expr = ("test", [expr1, expr2])
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64, z: f64) -> (f64, f64) {\n"
+        "    let out1 = z*(x + y);\n"
+        "    let out2 = z*(x - y);\n"
+        "    (out1, out2)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_results_named_unordered():
+    # Here output order is based on name_expr
+    A, B, C = symbols('A,B,C')
+    expr1 = Equality(C, (x + y)*z)
+    expr2 = Equality(A, (x - y)*z)
+    expr3 = Equality(B, 2*x)
+    name_expr = ("test", [expr1, expr2, expr3])
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64, z: f64) -> (f64, f64, f64) {\n"
+        "    let C = z*(x + y);\n"
+        "    let A = z*(x - y);\n"
+        "    let B = 2*x;\n"
+        "    (C, A, B)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_results_named_ordered():
+    A, B, C = symbols('A,B,C')
+    expr1 = Equality(C, (x + y)*z)
+    expr2 = Equality(A, (x - y)*z)
+    expr3 = Equality(B, 2*x)
+    name_expr = ("test", [expr1, expr2, expr3])
+    result = codegen(name_expr, "Rust", header=False, empty=False,
+                     argument_sequence=(x, z, y))
+    assert result[0][0] == "test.rs"
+    source = result[0][1]
+    expected = (
+        "fn test(x: f64, z: f64, y: f64) -> (f64, f64, f64) {\n"
+        "    let C = z*(x + y);\n"
+        "    let A = z*(x - y);\n"
+        "    let B = 2*x;\n"
+        "    (C, A, B)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_complicated_rs_codegen():
+    from sympy.functions.elementary.trigonometric import (cos, sin, tan)
+    name_expr = ("testlong",
+            [ ((sin(x) + cos(y) + tan(z))**3).expand(),
+            cos(cos(cos(cos(cos(cos(cos(cos(x + y + z))))))))
+    ])
+    result = codegen(name_expr, "Rust", header=False, empty=False)
+    assert result[0][0] == "testlong.rs"
+    source = result[0][1]
+    expected = (
+        "fn testlong(x: f64, y: f64, z: f64) -> (f64, f64) {\n"
+        "    let out1 = x.sin().powi(3) + 3*x.sin().powi(2)*y.cos()"
+        " + 3*x.sin().powi(2)*z.tan() + 3*x.sin()*y.cos().powi(2)"
+        " + 6*x.sin()*y.cos()*z.tan() + 3*x.sin()*z.tan().powi(2)"
+        " + y.cos().powi(3) + 3*y.cos().powi(2)*z.tan()"
+        " + 3*y.cos()*z.tan().powi(2) + z.tan().powi(3);\n"
+        "    let out2 = (x + y + z).cos().cos().cos().cos()"
+        ".cos().cos().cos().cos();\n"
+        "    (out1, out2)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_output_arg_mixed_unordered():
+    # named outputs are alphabetical, unnamed output appear in the given order
+    from sympy.functions.elementary.trigonometric import (cos, sin)
+    a = symbols("a")
+    name_expr = ("foo", [cos(2*x), Equality(y, sin(x)), cos(x), Equality(a, sin(2*x))])
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    assert result[0] == "foo.rs"
+    source = result[1];
+    expected = (
+        "fn foo(x: f64) -> (f64, f64, f64, f64) {\n"
+        "    let out1 = (2*x).cos();\n"
+        "    let y = x.sin();\n"
+        "    let out3 = x.cos();\n"
+        "    let a = (2*x).sin();\n"
+        "    (out1, y, out3, a)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_piecewise_():
+    pw = Piecewise((0, x < -1), (x**2, x <= 1), (-x+2, x > 1), (1, True), evaluate=False)
+    name_expr = ("pwtest", pw)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn pwtest(x: f64) -> f64 {\n"
+        "    let out1 = if (x < -1) {\n"
+        "        0\n"
+        "    } else if (x <= 1) {\n"
+        "        x.powi(2)\n"
+        "    } else if (x > 1) {\n"
+        "        2 - x\n"
+        "    } else {\n"
+        "        1\n"
+        "    };\n"
+        "    out1\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+@XFAIL
+def test_piecewise_inline():
+    # FIXME: how to pass inline to the RustCodePrinter?
+    pw = Piecewise((0, x < -1), (x**2, x <= 1), (-x+2, x > 1), (1, True))
+    name_expr = ("pwtest", pw)
+    result, = codegen(name_expr, "Rust", header=False, empty=False,
+                      inline=True)
+    source = result[1]
+    expected = (
+        "fn pwtest(x: f64) -> f64 {\n"
+        "    let out1 = if (x < -1) { 0 } else if (x <= 1) { x.powi(2) }"
+        " else if (x > 1) { -x + 2 } else { 1 };\n"
+        "    out1\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_multifcns_per_file():
+    name_expr = [ ("foo", [2*x, 3*y]), ("bar", [y**2, 4*y]) ]
+    result = codegen(name_expr, "Rust", header=False, empty=False)
+    assert result[0][0] == "foo.rs"
+    source = result[0][1];
+    expected = (
+        "fn foo(x: f64, y: f64) -> (f64, f64) {\n"
+        "    let out1 = 2*x;\n"
+        "    let out2 = 3*y;\n"
+        "    (out1, out2)\n"
+        "}\n"
+        "fn bar(y: f64) -> (f64, f64) {\n"
+        "    let out1 = y.powi(2);\n"
+        "    let out2 = 4*y;\n"
+        "    (out1, out2)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_multifcns_per_file_w_header():
+    name_expr = [ ("foo", [2*x, 3*y]), ("bar", [y**2, 4*y]) ]
+    result = codegen(name_expr, "Rust", header=True, empty=False)
+    assert result[0][0] == "foo.rs"
+    source = result[0][1];
+    version_str = "Code generated with SymPy %s" % sympy.__version__
+    version_line = version_str.center(76).rstrip()
+    expected = (
+        "/*\n"
+        " *%(version_line)s\n"
+        " *\n"
+        " *              See http://www.sympy.org/ for more information.\n"
+        " *\n"
+        " *                       This file is part of 'project'\n"
+        " */\n"
+        "fn foo(x: f64, y: f64) -> (f64, f64) {\n"
+        "    let out1 = 2*x;\n"
+        "    let out2 = 3*y;\n"
+        "    (out1, out2)\n"
+        "}\n"
+        "fn bar(y: f64) -> (f64, f64) {\n"
+        "    let out1 = y.powi(2);\n"
+        "    let out2 = 4*y;\n"
+        "    (out1, out2)\n"
+        "}\n"
+    ) % {'version_line': version_line}
+    assert source == expected
+
+
+def test_filename_match_prefix():
+    name_expr = [ ("foo", [2*x, 3*y]), ("bar", [y**2, 4*y]) ]
+    result, = codegen(name_expr, "Rust", prefix="baz", header=False,
+                     empty=False)
+    assert result[0] == "baz.rs"
+
+
+def test_InOutArgument():
+    expr = Equality(x, x**2)
+    name_expr = ("mysqr", expr)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn mysqr(x: f64) -> f64 {\n"
+        "    let x = x.powi(2);\n"
+        "    x\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_InOutArgument_order():
+    # can specify the order as (x, y)
+    expr = Equality(x, x**2 + y)
+    name_expr = ("test", expr)
+    result, = codegen(name_expr, "Rust", header=False,
+                      empty=False, argument_sequence=(x,y))
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64) -> f64 {\n"
+        "    let x = x.powi(2) + y;\n"
+        "    x\n"
+        "}\n"
+    )
+    assert source == expected
+    # make sure it gives (x, y) not (y, x)
+    expr = Equality(x, x**2 + y)
+    name_expr = ("test", expr)
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test(x: f64, y: f64) -> f64 {\n"
+        "    let x = x.powi(2) + y;\n"
+        "    x\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_not_supported():
+    f = Function('f')
+    name_expr = ("test", [f(x).diff(x), S.ComplexInfinity])
+    result, = codegen(name_expr, "Rust", header=False, empty=False)
+    source = result[1]
+    expected = (
+        "fn test(x: f64) -> (f64, f64) {\n"
+        "    // unsupported: Derivative(f(x), x)\n"
+        "    // unsupported: zoo\n"
+        "    let out1 = Derivative(f(x), x);\n"
+        "    let out2 = zoo;\n"
+        "    (out1, out2)\n"
+        "}\n"
+    )
+    assert source == expected
+
+
+def test_global_vars_rust():
+    x, y, z, t = symbols("x y z t")
+    result = codegen(('f', x*y), "Rust", header=False, empty=False,
+                     global_vars=(y,))
+    source = result[0][1]
+    expected = (
+        "fn f(x: f64) -> f64 {\n"
+        "    let out1 = x*y;\n"
+        "    out1\n"
+        "}\n"
+        )
+    assert source == expected
+
+    result = codegen(('f', x*y+z), "Rust", header=False, empty=False,
+                     argument_sequence=(x, y), global_vars=(z, t))
+    source = result[0][1]
+    expected = (
+        "fn f(x: f64, y: f64) -> f64 {\n"
+        "    let out1 = x*y + z;\n"
+        "    out1\n"
+        "}\n"
+    )
+    assert source == expected
diff --git a/shared/package-files/c0/c0a16cfbe6b674abb6534c984f06af296a01668b208f0967f25f8a4348e625a3/no_fixer_cls.py b/shared/package-files/c0/c0a16cfbe6b674abb6534c984f06af296a01668b208f0967f25f8a4348e625a3/no_fixer_cls.py
new file mode 100644
index 0000000000000000000000000000000000000000..506f794951705843bef4dd7540490959b0224940
--- /dev/null
+++ b/shared/package-files/c0/c0a16cfbe6b674abb6534c984f06af296a01668b208f0967f25f8a4348e625a3/no_fixer_cls.py
@@ -0,0 +1 @@
+# This is empty so trying to fetch the fixer class gives an AttributeError
diff --git a/shared/package-files/c0/c0a9943582966edcb8ae75f1969920ba97952340183e3c8210817af36b9c08bb/unbind_compositeimplicitautograd_dispatch.h b/shared/package-files/c0/c0a9943582966edcb8ae75f1969920ba97952340183e3c8210817af36b9c08bb/unbind_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a43b0dc24081fedc1f04edac2e4b7b521fd40f34
--- /dev/null
+++ b/shared/package-files/c0/c0a9943582966edcb8ae75f1969920ba97952340183e3c8210817af36b9c08bb/unbind_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unbind(const at::Tensor & self, at::Dimname dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/c0/c0ac158b870c50a423be62659d2607393ad95f4f3561f0b3c4e8ca3ae5ea2f53/roi_align.py b/shared/package-files/c0/c0ac158b870c50a423be62659d2607393ad95f4f3561f0b3c4e8ca3ae5ea2f53/roi_align.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d505c140ee6b3af5bd2183ad32aef244c771497
--- /dev/null
+++ b/shared/package-files/c0/c0ac158b870c50a423be62659d2607393ad95f4f3561f0b3c4e8ca3ae5ea2f53/roi_align.py
@@ -0,0 +1,274 @@
+from typing import List, Union
+
+import torch
+import torch._dynamo
+import torch.fx
+from torch import nn, Tensor
+from torch.jit.annotations import BroadcastingList2
+from torch.nn.modules.utils import _pair
+from torchvision.extension import _assert_has_ops, _has_ops
+
+from ..utils import _log_api_usage_once
+from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
+
+
+# NB: all inputs are tensors
+def _bilinear_interpolate(
+    input,  # [N, C, H, W]
+    roi_batch_ind,  # [K]
+    y,  # [K, PH, IY]
+    x,  # [K, PW, IX]
+    ymask,  # [K, IY]
+    xmask,  # [K, IX]
+):
+    _, channels, height, width = input.size()
+
+    # deal with inverse element out of feature map boundary
+    y = y.clamp(min=0)
+    x = x.clamp(min=0)
+    y_low = y.int()
+    x_low = x.int()
+    y_high = torch.where(y_low >= height - 1, height - 1, y_low + 1)
+    y_low = torch.where(y_low >= height - 1, height - 1, y_low)
+    y = torch.where(y_low >= height - 1, y.to(input.dtype), y)
+
+    x_high = torch.where(x_low >= width - 1, width - 1, x_low + 1)
+    x_low = torch.where(x_low >= width - 1, width - 1, x_low)
+    x = torch.where(x_low >= width - 1, x.to(input.dtype), x)
+
+    ly = y - y_low
+    lx = x - x_low
+    hy = 1.0 - ly
+    hx = 1.0 - lx
+
+    # do bilinear interpolation, but respect the masking!
+    # TODO: It's possible the masking here is unnecessary if y and
+    # x were clamped appropriately; hard to tell
+    def masked_index(
+        y,  # [K, PH, IY]
+        x,  # [K, PW, IX]
+    ):
+        if ymask is not None:
+            assert xmask is not None
+            y = torch.where(ymask[:, None, :], y, 0)
+            x = torch.where(xmask[:, None, :], x, 0)
+        return input[
+            roi_batch_ind[:, None, None, None, None, None],
+            torch.arange(channels, device=input.device)[None, :, None, None, None, None],
+            y[:, None, :, None, :, None],  # prev [K, PH, IY]
+            x[:, None, None, :, None, :],  # prev [K, PW, IX]
+        ]  # [K, C, PH, PW, IY, IX]
+
+    v1 = masked_index(y_low, x_low)
+    v2 = masked_index(y_low, x_high)
+    v3 = masked_index(y_high, x_low)
+    v4 = masked_index(y_high, x_high)
+
+    # all ws preemptively [K, C, PH, PW, IY, IX]
+    def outer_prod(y, x):
+        return y[:, None, :, None, :, None] * x[:, None, None, :, None, :]
+
+    w1 = outer_prod(hy, hx)
+    w2 = outer_prod(hy, lx)
+    w3 = outer_prod(ly, hx)
+    w4 = outer_prod(ly, lx)
+
+    val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+    return val
+
+
+# TODO: this doesn't actually cache
+# TODO: main library should make this easier to do
+def maybe_cast(tensor):
+    if torch.is_autocast_enabled() and tensor.is_cuda and tensor.dtype != torch.double:
+        return tensor.float()
+    else:
+        return tensor
+
+
+# This is a slow but pure Python and differentiable implementation of
+# roi_align.  It potentially is a good basis for Inductor compilation
+# (but I have not benchmarked it) but today it is solely used for the
+# fact that its backwards can be implemented deterministically,
+# which is needed for the PT2 benchmark suite.
+#
+# It is transcribed directly off of the roi_align CUDA kernel, see
+# https://dev-discuss.pytorch.org/t/a-pure-python-implementation-of-roi-align-that-looks-just-like-its-cuda-kernel/1266
+@torch._dynamo.allow_in_graph
+def _roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
+    orig_dtype = input.dtype
+
+    input = maybe_cast(input)
+    rois = maybe_cast(rois)
+
+    _, _, height, width = input.size()
+
+    ph = torch.arange(pooled_height, device=input.device)  # [PH]
+    pw = torch.arange(pooled_width, device=input.device)  # [PW]
+
+    # input: [N, C, H, W]
+    # rois: [K, 5]
+
+    roi_batch_ind = rois[:, 0].int()  # [K]
+    offset = 0.5 if aligned else 0.0
+    roi_start_w = rois[:, 1] * spatial_scale - offset  # [K]
+    roi_start_h = rois[:, 2] * spatial_scale - offset  # [K]
+    roi_end_w = rois[:, 3] * spatial_scale - offset  # [K]
+    roi_end_h = rois[:, 4] * spatial_scale - offset  # [K]
+
+    roi_width = roi_end_w - roi_start_w  # [K]
+    roi_height = roi_end_h - roi_start_h  # [K]
+    if not aligned:
+        roi_width = torch.clamp(roi_width, min=1.0)  # [K]
+        roi_height = torch.clamp(roi_height, min=1.0)  # [K]
+
+    bin_size_h = roi_height / pooled_height  # [K]
+    bin_size_w = roi_width / pooled_width  # [K]
+
+    exact_sampling = sampling_ratio > 0
+
+    roi_bin_grid_h = sampling_ratio if exact_sampling else torch.ceil(roi_height / pooled_height)  # scalar or [K]
+    roi_bin_grid_w = sampling_ratio if exact_sampling else torch.ceil(roi_width / pooled_width)  # scalar or [K]
+
+    """
+    iy, ix = dims(2)
+    """
+
+    if exact_sampling:
+        count = max(roi_bin_grid_h * roi_bin_grid_w, 1)  # scalar
+        iy = torch.arange(roi_bin_grid_h, device=input.device)  # [IY]
+        ix = torch.arange(roi_bin_grid_w, device=input.device)  # [IX]
+        ymask = None
+        xmask = None
+    else:
+        count = torch.clamp(roi_bin_grid_h * roi_bin_grid_w, min=1)  # [K]
+        # When doing adaptive sampling, the number of samples we need to do
+        # is data-dependent based on how big the ROIs are.  This is a bit
+        # awkward because first-class dims can't actually handle this.
+        # So instead, we inefficiently suppose that we needed to sample ALL
+        # the points and mask out things that turned out to be unnecessary
+        iy = torch.arange(height, device=input.device)  # [IY]
+        ix = torch.arange(width, device=input.device)  # [IX]
+        ymask = iy[None, :] < roi_bin_grid_h[:, None]  # [K, IY]
+        xmask = ix[None, :] < roi_bin_grid_w[:, None]  # [K, IX]
+
+    def from_K(t):
+        return t[:, None, None]
+
+    y = (
+        from_K(roi_start_h)
+        + ph[None, :, None] * from_K(bin_size_h)
+        + (iy[None, None, :] + 0.5).to(input.dtype) * from_K(bin_size_h / roi_bin_grid_h)
+    )  # [K, PH, IY]
+    x = (
+        from_K(roi_start_w)
+        + pw[None, :, None] * from_K(bin_size_w)
+        + (ix[None, None, :] + 0.5).to(input.dtype) * from_K(bin_size_w / roi_bin_grid_w)
+    )  # [K, PW, IX]
+    val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
+
+    # Mask out samples that weren't actually adaptively needed
+    if not exact_sampling:
+        val = torch.where(ymask[:, None, None, None, :, None], val, 0)
+        val = torch.where(xmask[:, None, None, None, None, :], val, 0)
+
+    output = val.sum((-1, -2))  # remove IY, IX ~> [K, C, PH, PW]
+    if isinstance(count, torch.Tensor):
+        output /= count[:, None, None, None]
+    else:
+        output /= count
+
+    output = output.to(orig_dtype)
+
+    return output
+
+
+@torch.fx.wrap
+def roi_align(
+    input: Tensor,
+    boxes: Union[Tensor, List[Tensor]],
+    output_size: BroadcastingList2[int],
+    spatial_scale: float = 1.0,
+    sampling_ratio: int = -1,
+    aligned: bool = False,
+) -> Tensor:
+    """
+    Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN.
+
+    Args:
+        input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element
+            contains ``C`` feature maps of dimensions ``H x W``.
+            If the tensor is quantized, we expect a batch size of ``N == 1``.
+        boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
+            format where the regions will be taken from.
+            The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
+            If a single Tensor is passed, then the first column should
+            contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``.
+            If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i
+            in the batch.
+        output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling
+            is performed, as (height, width).
+        spatial_scale (float): a scaling factor that maps the box coordinates to
+            the input coordinates. For example, if your boxes are defined on the scale
+            of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of
+            the original image), you'll want to set this to 0.5. Default: 1.0
+        sampling_ratio (int): number of sampling points in the interpolation grid
+            used to compute the output value of each pooled output bin. If > 0,
+            then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If
+            <= 0, then an adaptive number of grid points are used (computed as
+            ``ceil(roi_width / output_width)``, and likewise for height). Default: -1
+        aligned (bool): If False, use the legacy implementation.
+            If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two
+            neighboring pixel indices. This version is used in Detectron2
+
+    Returns:
+        Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs.
+    """
+    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
+        _log_api_usage_once(roi_align)
+    check_roi_boxes_shape(boxes)
+    rois = boxes
+    output_size = _pair(output_size)
+    if not isinstance(rois, torch.Tensor):
+        rois = convert_boxes_to_roi_format(rois)
+    if not torch.jit.is_scripting():
+        if not _has_ops() or (torch.are_deterministic_algorithms_enabled() and (input.is_cuda or input.is_mps)):
+            return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
+    _assert_has_ops()
+    return torch.ops.torchvision.roi_align(
+        input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned
+    )
+
+
+class RoIAlign(nn.Module):
+    """
+    See :func:`roi_align`.
+    """
+
+    def __init__(
+        self,
+        output_size: BroadcastingList2[int],
+        spatial_scale: float,
+        sampling_ratio: int,
+        aligned: bool = False,
+    ):
+        super().__init__()
+        _log_api_usage_once(self)
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+        self.sampling_ratio = sampling_ratio
+        self.aligned = aligned
+
+    def forward(self, input: Tensor, rois: Union[Tensor, List[Tensor]]) -> Tensor:
+        return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned)
+
+    def __repr__(self) -> str:
+        s = (
+            f"{self.__class__.__name__}("
+            f"output_size={self.output_size}"
+            f", spatial_scale={self.spatial_scale}"
+            f", sampling_ratio={self.sampling_ratio}"
+            f", aligned={self.aligned}"
+            f")"
+        )
+        return s
diff --git a/shared/package-files/c0/c0ad4d55d448985ff85952e542b5e4b9fba7bc4d2a2ebe5dc39d2ff3a28f228d/callgrind.h b/shared/package-files/c0/c0ad4d55d448985ff85952e542b5e4b9fba7bc4d2a2ebe5dc39d2ff3a28f228d/callgrind.h
new file mode 100644
index 0000000000000000000000000000000000000000..f078cc82b95daf94d2bea51f1e1b1a8c12daea23
--- /dev/null
+++ b/shared/package-files/c0/c0ad4d55d448985ff85952e542b5e4b9fba7bc4d2a2ebe5dc39d2ff3a28f228d/callgrind.h
@@ -0,0 +1,129 @@
+
+/*
+   ----------------------------------------------------------------
+
+   Notice that the following BSD-style license applies to this one
+   file (callgrind.h) only.  The rest of Valgrind is licensed under the
+   terms of the GNU General Public License, version 2, unless
+   otherwise indicated.  See the COPYING file in the source
+   distribution for details.
+
+   ----------------------------------------------------------------
+
+   This file is part of callgrind, a valgrind tool for cache simulation
+   and call tree tracing.
+
+   Copyright (C) 2003-2017 Josef Weidendorfer.  All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   1. Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+   2. The origin of this software must not be misrepresented; you must
+      not claim that you wrote the original software.  If you use this
+      software in a product, an acknowledgment in the product
+      documentation would be appreciated but is not required.
+
+   3. Altered source versions must be plainly marked as such, and must
+      not be misrepresented as being the original software.
+
+   4. The name of the author may not be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+   OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+   ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+   DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+   DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+   GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+   INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+   WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+   ----------------------------------------------------------------
+
+   Notice that the above BSD-style license applies to this one file
+   (callgrind.h) only.  The entire rest of Valgrind is licensed under
+   the terms of the GNU General Public License, version 2.  See the
+   COPYING file in the source distribution for details.
+
+   ----------------------------------------------------------------
+*/
+
+#ifndef __CALLGRIND_H
+#define __CALLGRIND_H
+
+#include "valgrind.h"
+
+/* !! ABIWARNING !! ABIWARNING !! ABIWARNING !! ABIWARNING !!
+   This enum comprises an ABI exported by Valgrind to programs
+   which use client requests.  DO NOT CHANGE THE ORDER OF THESE
+   ENTRIES, NOR DELETE ANY -- add new ones at the end.
+
+   The identification ('C','T') for Callgrind has historical
+   reasons: it was called "Calltree" before. Besides, ('C','G') would
+   clash with cachegrind.
+ */
+
+typedef
+   enum {
+      VG_USERREQ__DUMP_STATS = VG_USERREQ_TOOL_BASE('C','T'),
+      VG_USERREQ__ZERO_STATS,
+      VG_USERREQ__TOGGLE_COLLECT,
+      VG_USERREQ__DUMP_STATS_AT,
+      VG_USERREQ__START_INSTRUMENTATION,
+      VG_USERREQ__STOP_INSTRUMENTATION
+   } Vg_CallgrindClientRequest;
+
+/* Dump current state of cost centers, and zero them afterwards */
+#define CALLGRIND_DUMP_STATS                                    \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DUMP_STATS,       \
+                                  0, 0, 0, 0, 0)
+
+/* Dump current state of cost centers, and zero them afterwards.
+   The argument is appended to a string stating the reason which triggered
+   the dump. This string is written as a description field into the
+   profile data dump. */
+#define CALLGRIND_DUMP_STATS_AT(pos_str)                        \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DUMP_STATS_AT,    \
+                                  pos_str, 0, 0, 0, 0)
+
+/* Zero cost centers */
+#define CALLGRIND_ZERO_STATS                                    \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__ZERO_STATS,       \
+                                  0, 0, 0, 0, 0)
+
+/* Toggles collection state.
+   The collection state specifies whether the happening of events
+   should be noted or if they are to be ignored. Events are noted
+   by increment of counters in a cost center */
+#define CALLGRIND_TOGGLE_COLLECT                                \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__TOGGLE_COLLECT,   \
+                                  0, 0, 0, 0, 0)
+
+/* Start full callgrind instrumentation if not already switched on.
+   When cache simulation is done, it will flush the simulated cache;
+   this will lead to an artificial cache warmup phase afterwards with
+   cache misses which would not have happened in reality. */
+#define CALLGRIND_START_INSTRUMENTATION                              \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__START_INSTRUMENTATION, \
+                                  0, 0, 0, 0, 0)
+
+/* Stop full callgrind instrumentation if not already switched off.
+   This flushes Valgrinds translation cache, and does no additional
+   instrumentation afterwards, which effectivly will run at the same
+   speed as the "none" tool (ie. at minimal slowdown).
+   Use this to bypass Callgrind aggregation for uninteresting code parts.
+   To start Callgrind in this mode to ignore the setup phase, use
+   the option "--instr-atstart=no". */
+#define CALLGRIND_STOP_INSTRUMENTATION                               \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__STOP_INSTRUMENTATION,  \
+                                  0, 0, 0, 0, 0)
+
+#endif /* __CALLGRIND_H */
diff --git a/shared/package-files/c0/c0af2da3a7dfc8cbb2cab94d26ef7eee7ce138049946bfb1fcc59d4a077f1bfe/views.pyi b/shared/package-files/c0/c0af2da3a7dfc8cbb2cab94d26ef7eee7ce138049946bfb1fcc59d4a077f1bfe/views.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..05f704120c7120361f40f29208906ad54149a35c
--- /dev/null
+++ b/shared/package-files/c0/c0af2da3a7dfc8cbb2cab94d26ef7eee7ce138049946bfb1fcc59d4a077f1bfe/views.pyi
@@ -0,0 +1,8 @@
+from typing import Union
+
+from django.http.request import HttpRequest
+from django.http.response import HttpResponseRedirect
+
+def shortcut(
+    request: HttpRequest, content_type_id: Union[int, str], object_id: Union[int, str]
+) -> HttpResponseRedirect: ...
diff --git a/shared/package-files/c0/c0af7c1f9acba8cf60b40607d32eb1b6aa4ac5d8911628613a24b97cab78628a/wrappers.py b/shared/package-files/c0/c0af7c1f9acba8cf60b40607d32eb1b6aa4ac5d8911628613a24b97cab78628a/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa575dc78c065326ab70673cac6baf4237cb0c0
--- /dev/null
+++ b/shared/package-files/c0/c0af7c1f9acba8cf60b40607d32eb1b6aa4ac5d8911628613a24b97cab78628a/wrappers.py
@@ -0,0 +1,401 @@
+import inspect
+import warnings
+from functools import wraps
+from itertools import chain
+
+from typing import Callable, NamedTuple, Optional, overload, Sequence, Tuple
+
+import torch
+import torch._prims_common as utils
+from torch._prims_common import (
+    CustomOutParamAnnotation,
+    ELEMENTWISE_TYPE_PROMOTION_KIND,
+    Number,
+    NumberType,
+    ShapeType,
+    TensorLike,
+    TensorLikeType,
+)
+from torch.utils import _pytree as pytree
+from torch.utils._pytree import tree_flatten, tree_unflatten
+
+
+@overload
+def _maybe_convert_to_dtype(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType:
+    pass
+
+
+@overload
+def _maybe_convert_to_dtype(a: NumberType, dtype: torch.dtype) -> NumberType:
+    pass
+
+
+@overload
+def _maybe_convert_to_dtype(a: Sequence, dtype: torch.dtype) -> Sequence:
+    pass
+
+
+@overload
+def _maybe_convert_to_dtype(a: None, dtype: torch.dtype) -> None:
+    pass
+
+
+# TODO: implement ref.cast with an option to enforce safe casting
+def _maybe_convert_to_dtype(a, dtype):
+    if isinstance(a, TensorLike):
+        if a.dtype != dtype:
+            return a.to(dtype)
+        return a
+    if isinstance(a, Number):
+        return utils.dtype_to_type_ctor(dtype)(a)  # type: ignore[arg-type]
+    if isinstance(a, Sequence):
+        return tuple(_maybe_convert_to_dtype(x, dtype) for x in a)
+    # Passthrough None because some functions wrapped with type promotion
+    # wrapper might have optional args
+    if a is None:
+        return None
+
+    raise ValueError(f"Received type {type(a)} that is neither a tensor or a number!")
+
+
+def _maybe_convert_to_type(a: NumberType, typ: type) -> NumberType:
+    if not isinstance(a, Number):
+        msg = f"Found unknown type {type(a)} when trying to convert scalars!"
+        raise ValueError(msg)
+    if not utils.is_weakly_lesser_type(type(a), typ):
+        msg = f"Scalar {a} of type {type(a)} cannot be safely cast to type {typ}!"
+        raise ValueError(msg)
+
+    return typ(a)
+
+
+def _annotation_has_type(*, typ, annotation):
+    if hasattr(annotation, "__args__"):
+        for a in annotation.__args__:
+            if _annotation_has_type(typ=typ, annotation=a):
+                return True
+        return False
+
+    return typ is annotation
+
+
+class elementwise_type_promotion_wrapper:
+    """
+    Adds elementwise type promotion to a Python reference implementation.
+
+    Takes two kwargs, type_promoting_args and type_promotion_kind.
+
+    type_promoting_args must be a string Sequence specifiying the argument names of all
+    arguments that participate in type promotion (and should be type promoted). If the
+    arg specifies a Sequence-type then every element of the Sequence will participate in
+    type promotion.
+
+    type_promotion_kind must be one of the kinds specified by ELEMENTWISE_TYPE_PROMOTION_KIND.
+    See its documentation for details.
+
+    The return_dtype will be coerced to the wrapped function's dtype arg if it is available and
+    not None.
+
+    Other type promotion behavior, like validating the Python type of scalar arguments, must
+    be handled separately.
+    """
+
+    def __init__(
+        self,
+        *,
+        type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND,
+        type_promoting_args: Optional[Sequence[str]] = None,
+    ):
+        self.type_promoting_arg_names = type_promoting_args
+        self.type_promotion_kind = type_promotion_kind
+
+    def __call__(self, fn: Callable) -> Callable:
+        sig = inspect.signature(fn)
+
+        @wraps(fn)
+        def _fn(*args, **kwargs):
+            bound = sig.bind(*args, **kwargs)
+            type_promoting_args = tuple(
+                bound.arguments[x]
+                for x in self.type_promoting_arg_names  # type: ignore[union-attr]
+                if x in bound.arguments.keys()
+            )
+
+            flattened_type_promoting_args = pytree.arg_tree_leaves(*type_promoting_args)
+            compute_dtype, result_dtype = utils.elementwise_dtypes(
+                *flattened_type_promoting_args,
+                type_promotion_kind=self.type_promotion_kind,
+            )
+
+            promoted_args = {
+                x: _maybe_convert_to_dtype(bound.arguments[x], compute_dtype)
+                for x in self.type_promoting_arg_names  # type: ignore[union-attr]
+                if x in bound.arguments.keys()
+            }
+            bound.arguments.update(promoted_args)
+
+            result = fn(**bound.arguments)
+
+            # Override the return_dtype if a dtype arg is present and not None
+            if "dtype" in bound.arguments:
+                maybe_dtype = bound.arguments["dtype"]
+                if maybe_dtype:  # dtype cannot be None
+                    result_dtype = maybe_dtype
+
+            if isinstance(result, TensorLike):
+                return _maybe_convert_to_dtype(result, result_dtype)
+            if isinstance(result, Sequence):
+                return tuple(_maybe_convert_to_dtype(x, result_dtype) for x in result)
+            raise AssertionError(f"Unhandled result type: {type(result)}")
+
+        _fn.__signature__ = sig  # type: ignore[attr-defined]
+        return _fn
+
+
+# Returns True if resize is necessary
+def _resize_output_check(out: TensorLikeType, shape: ShapeType):
+    # If the shapes are correct there's nothing to do
+    if utils.same_shape(out.shape, shape):
+        return False
+    if out.numel() != 0:
+        msg = (
+            f"An output with one or more elements was resized since it had shape {str(out.shape)} "
+            "which does not match the required output shape {str(shape)}. "
+            "This behavior is deprecated, and in a future PyTorch release outputs will not "
+            "be resized unless they have zero elements. "
+            "You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0)."
+        )
+        warnings.warn(msg)
+    return True
+
+
+# TODO: handle tuples of tensors
+def _maybe_resize_out(out: TensorLikeType, shape: ShapeType):
+    if _resize_output_check(out, shape):
+        return out.resize_(shape)
+    else:
+        return out
+
+
+def _safe_copy_out(
+    *, copy_from: TensorLikeType, copy_to: TensorLikeType, exact_dtype: bool = False
+):
+    # Checks same device
+    if copy_from.device != copy_to.device:
+        msg = "Attempting to copy from device {} to device {}, but cross-device copies are not allowed!".format(
+            copy_from.device, copy_to.device
+        )
+        raise RuntimeError(msg)
+
+    # Checks safe cast
+    if exact_dtype:
+        torch._check(
+            copy_from.dtype == copy_to.dtype,
+            lambda: f"Expected out tensor to have dtype {copy_from.dtype} "
+            f"but got {copy_to.dtype} instead",
+        )
+    else:
+        torch._check(
+            utils.can_safe_cast_to(cast_from=copy_from.dtype, cast_to=copy_to.dtype),
+            lambda: f"Attempting to cast from {copy_from.dtype} to out tensor with dtype {copy_to.dtype}, "
+            "but this can't be cast because it is not safe!",
+        )
+
+    return copy_to.copy_(copy_from)
+
+
+def out_wrapper(*out_names: str, exact_dtype: bool = False, pass_is_out: bool = False):
+    # The wrapped function needs to convert the output parameters to ensure
+    # compatibility between the Python API (which always uses "out" as the
+    # parameter name and may be a tuple) and the Aten API (which may have
+    # multiple output parameters and use different parameter names such as
+    # "grad_input", "indices" or "values".)
+
+    default_out_names = ("out",)
+    if len(out_names) == 0:
+        # Use default in out name
+        out_names = default_out_names
+
+    is_tensor = len(out_names) == 1
+
+    def _out_wrapper(fn: Callable) -> Callable:
+        """
+        Adds the out parameter to a Python reference.
+        """
+        out_type = (
+            TensorLikeType
+            if is_tensor
+            else Tuple[tuple(TensorLikeType for _ in range(len(out_names)))]
+        )
+        return_type = (
+            TensorLikeType
+            if is_tensor
+            else NamedTuple(
+                f"return_types_{fn.__name__}", [(o, TensorLikeType) for o in out_names]
+            )
+        )
+
+        sig = inspect.signature(fn)
+        factory_kwargs = ("device", "dtype")
+        is_factory_fn = all(p in sig.parameters for p in factory_kwargs)
+
+        @wraps(fn)
+        def _fn(*args, out=None, **kwargs):
+            if is_factory_fn and out is not None:
+                for k in factory_kwargs:
+                    out_attr = getattr(out, k)
+                    if k not in kwargs:
+                        kwargs[k] = out_attr
+            if pass_is_out:
+                result = fn(*args, is_out=(out is not None), **kwargs)
+            else:
+                result = fn(*args, **kwargs)
+            assert (
+                isinstance(result, TensorLike)
+                and is_tensor
+                or isinstance(result, Tuple)  # type: ignore[arg-type]
+                and len(result) == len(out_names)
+            )
+            if out is not None:
+                # Naively you might expect this assert to be true, but
+                # it's not:
+                #
+                #   assert type(out) == type(result)
+                #
+                # The reason is that functions under this wrapper can
+                # get registered to the Meta dispatch key, and that
+                # means they can be executed in a context where tensor
+                # subclasses are disabled (with no_dispatch), which is a
+                # handy way for an is-a tensor subclass (e.g.,
+                # FakeTensor) to have the normal meta backend create a
+                # meta tensor, to be wrapped once it gets returned.
+                # In this situation, you will get a FakeTensor as
+                # the output tensor, but not the result--which will
+                # be a normal meta tensor, but this is perfectly
+                # harmless.
+                if is_tensor:
+                    assert isinstance(out, TensorLike)
+                    # These two operations are done in-place
+                    _maybe_resize_out(out, result.shape)
+                    _safe_copy_out(copy_from=result, copy_to=out, exact_dtype=exact_dtype)  # type: ignore[arg-type]
+                else:
+                    assert isinstance(out, Tuple)  # type: ignore[arg-type]
+                    torch._check_type(
+                        len(out) == len(result),
+                        lambda: f"expected tuple of {len(result)} elements but got {len(out)}",
+                    )
+                    for r, o in zip(result, out):
+                        # These two operations are done in-place
+                        _maybe_resize_out(o, r.shape)
+                        _safe_copy_out(copy_from=r, copy_to=o, exact_dtype=exact_dtype)  # type: ignore[arg-type]
+            else:
+                out = result
+            # mypy does not see through  the definition of out_type given that it's in a different scope
+            return out if is_tensor else return_type(*out)  # type: ignore[operator]
+
+        out_param = inspect.Parameter(
+            "out",
+            kind=inspect.Parameter.KEYWORD_ONLY,
+            default=None,
+            annotation=out_type,
+        )
+        # Mark that the function now returns a tuple
+        assert isinstance(sig.return_annotation, str) or sig.return_annotation in (
+            sig.empty,
+            out_type,
+        )
+        params = chain(sig.parameters.values(), (out_param,))
+        _fn.__signature__ = inspect.Signature(  # type: ignore[attr-defined]
+            parameters=params, return_annotation=return_type  # type: ignore[arg-type]
+        )
+
+        _fn.__annotations__ = fn.__annotations__
+        _fn.__annotations__["out"] = out_type
+        _fn.__annotations__["return"] = return_type
+
+        # In the special case of having a single tensor out parameter with a
+        # name other than out, add a special annotation to name the parameter
+        if is_tensor and out_names != default_out_names:
+            _fn.__annotations__[CustomOutParamAnnotation] = out_names[0]
+
+        # Add an indicator attribute that can be used in special cases
+        # where having a function wrapped by `out_wrapper` is not desirable e.g.
+        # jit
+        _fn._torch_decompositions_out_wrapper = f"This function is wrapped by {out_wrapper.__module__}.out_wrapper"  # type: ignore[attr-defined]
+
+        return _fn
+
+    return _out_wrapper
+
+
+def _maybe_remove_out_wrapper(fn: Callable):
+    return inspect.unwrap(
+        fn,
+        stop=lambda f: not hasattr(f, "_torch_decompositions_out_wrapper"),
+    )
+
+
+def backwards_not_supported(prim):
+    def redispatch_prim(args, kwargs):
+        with torch._C._AutoDispatchBelowAutograd():
+            old = torch._C._dispatch_tls_is_dispatch_key_excluded(
+                torch._C.DispatchKey.ADInplaceOrView
+            )
+            return prim(*args, **kwargs)
+
+    class BackwardsNotSupported(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, args_spec, *flat_args):
+            args, kwargs = tree_unflatten(flat_args, args_spec)  # type: ignore[arg-type]
+            return redispatch_prim(args, kwargs)
+
+        @staticmethod
+        def backward(ctx, *args):
+            raise RuntimeError("backwards not supported on prim")
+
+    @wraps(prim)
+    def _autograd_impl(*args, **kwargs):
+        flat_args, args_spec = tree_flatten((args, kwargs))
+        if torch.is_grad_enabled() and any(
+            a.requires_grad for a in flat_args if isinstance(a, torch.Tensor)
+        ):
+            # TODO: There is a subtle bug here: prims like copy_to
+            # return their input argument after mutating it; and custom
+            # autograd function will incorrectly turn the result into
+            # a view which will fail test_python_ref_executor tests.
+            # At the moment, we sidestep this by observing that the
+            # unit tests don't ever try to run the executor with
+            # autograd, so we don't exercise the buggy case, but if
+            # you ever want to feed autograd through this, be aware
+            # of it!  We need a way of properly implementing autograd
+            # for mutating operations in Python to do this.
+            return BackwardsNotSupported.apply(args_spec, *flat_args)
+        else:
+            return redispatch_prim(args, kwargs)
+
+    return _autograd_impl
+
+
+# TODO: when tracing this will add torch tensors and not TensorMeta objects
+# to the trace -- we should fix this by adding a tracing context and NumberMeta classes
+# TODO: this wrapper is currently untested
+def elementwise_unary_scalar_wrapper(fn: Callable) -> Callable:
+    """
+    Allows unary operators that accept tensors to work with Python numbers.
+    """
+    sig = inspect.signature(fn)
+
+    @wraps(fn)
+    def _fn(*args, **kwargs):
+        if len(args) > 0 and isinstance(args[0], Number):
+            dtype = utils.type_to_dtype(type(args[0]))
+            args_ = list(args)
+            args_[0] = torch.tensor(args[0], dtype=dtype)
+            result = fn(*args_, **kwargs)
+            assert isinstance(result, torch.Tensor)
+            return result.item()
+
+        return fn(*args, **kwargs)
+
+    _fn.__signature__ = sig  # type: ignore[attr-defined]
+    return _fn
diff --git a/shared/package-files/c0/c0c1621e0ce6b2459cab45c7c2d05b928431fd4799f75ee122b2804d2bf523a1/upsample_nearest1d_ops.h b/shared/package-files/c0/c0c1621e0ce6b2459cab45c7c2d05b928431fd4799f75ee122b2804d2bf523a1/upsample_nearest1d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..741fae22935fc5e8405a637582b2faa3d69466d7
--- /dev/null
+++ b/shared/package-files/c0/c0c1621e0ce6b2459cab45c7c2d05b928431fd4799f75ee122b2804d2bf523a1/upsample_nearest1d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest1d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, c10::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::upsample_nearest1d")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "vec")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor")
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_nearest1d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::upsample_nearest1d")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out);
+};
+
+struct TORCH_API upsample_nearest1d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::upsample_nearest1d")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c0/c0c57b6c7e6807496e2c3476b6e9c7acb694c6c0fffac2212e73698f56e540fb/test_system_info.py b/shared/package-files/c0/c0c57b6c7e6807496e2c3476b6e9c7acb694c6c0fffac2212e73698f56e540fb/test_system_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bcc09050503e7f1bb3e94eecc902f512a9e42a1
--- /dev/null
+++ b/shared/package-files/c0/c0c57b6c7e6807496e2c3476b6e9c7acb694c6c0fffac2212e73698f56e540fb/test_system_info.py
@@ -0,0 +1,334 @@
+import os
+import shutil
+import pytest
+from tempfile import mkstemp, mkdtemp
+from subprocess import Popen, PIPE
+import importlib.metadata
+from distutils.errors import DistutilsError
+
+from numpy.testing import assert_, assert_equal, assert_raises
+from numpy.distutils import ccompiler, customized_ccompiler
+from numpy.distutils.system_info import system_info, ConfigParser, mkl_info
+from numpy.distutils.system_info import AliasedOptionError
+from numpy.distutils.system_info import default_lib_dirs, default_include_dirs
+from numpy.distutils import _shell_utils
+
+
+try:
+    if importlib.metadata.version('setuptools') >= '60':
+        # pkg-resources gives deprecation warnings, and there may be more
+        # issues. We only support setuptools <60
+        pytest.skip("setuptools is too new", allow_module_level=True)
+except importlib.metadata.PackageNotFoundError:
+    # we don't require `setuptools`; if it is not found, continue
+    pass
+
+
+def get_class(name, notfound_action=1):
+    """
+    notfound_action:
+      0 - do nothing
+      1 - display warning message
+      2 - raise error
+    """
+    cl = {'temp1': Temp1Info,
+          'temp2': Temp2Info,
+          'duplicate_options': DuplicateOptionInfo,
+          }.get(name.lower(), _system_info)
+    return cl()
+
+simple_site = """
+[ALL]
+library_dirs = {dir1:s}{pathsep:s}{dir2:s}
+libraries = {lib1:s},{lib2:s}
+extra_compile_args = -I/fake/directory -I"/path with/spaces" -Os
+runtime_library_dirs = {dir1:s}
+
+[temp1]
+library_dirs = {dir1:s}
+libraries = {lib1:s}
+runtime_library_dirs = {dir1:s}
+
+[temp2]
+library_dirs = {dir2:s}
+libraries = {lib2:s}
+extra_link_args = -Wl,-rpath={lib2_escaped:s}
+rpath = {dir2:s}
+
+[duplicate_options]
+mylib_libs = {lib1:s}
+libraries = {lib2:s}
+"""
+site_cfg = simple_site
+
+fakelib_c_text = """
+/* This file is generated from numpy/distutils/testing/test_system_info.py */
+#include<stdio.h>
+void foo(void) {
+   printf("Hello foo");
+}
+void bar(void) {
+   printf("Hello bar");
+}
+"""
+
+def have_compiler():
+    """ Return True if there appears to be an executable compiler
+    """
+    compiler = customized_ccompiler()
+    try:
+        cmd = compiler.compiler  # Unix compilers
+    except AttributeError:
+        try:
+            if not compiler.initialized:
+                compiler.initialize()  # MSVC is different
+        except (DistutilsError, ValueError):
+            return False
+        cmd = [compiler.cc]
+    try:
+        p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+        p.stdout.close()
+        p.stderr.close()
+        p.wait()
+    except OSError:
+        return False
+    return True
+
+
+HAVE_COMPILER = have_compiler()
+
+
+class _system_info(system_info):
+
+    def __init__(self,
+                 default_lib_dirs=default_lib_dirs,
+                 default_include_dirs=default_include_dirs,
+                 verbosity=1,
+                 ):
+        self.__class__.info = {}
+        self.local_prefixes = []
+        defaults = {'library_dirs': '',
+                    'include_dirs': '',
+                    'runtime_library_dirs': '',
+                    'rpath': '',
+                    'src_dirs': '',
+                    'search_static_first': "0",
+                    'extra_compile_args': '',
+                    'extra_link_args': ''}
+        self.cp = ConfigParser(defaults)
+        # We have to parse the config files afterwards
+        # to have a consistent temporary filepath
+
+    def _check_libs(self, lib_dirs, libs, opt_libs, exts):
+        """Override _check_libs to return with all dirs """
+        info = {'libraries': libs, 'library_dirs': lib_dirs}
+        return info
+
+
+class Temp1Info(_system_info):
+    """For testing purposes"""
+    section = 'temp1'
+
+
+class Temp2Info(_system_info):
+    """For testing purposes"""
+    section = 'temp2'
+
+class DuplicateOptionInfo(_system_info):
+    """For testing purposes"""
+    section = 'duplicate_options'
+
+
+class TestSystemInfoReading:
+
+    def setup_method(self):
+        """ Create the libraries """
+        # Create 2 sources and 2 libraries
+        self._dir1 = mkdtemp()
+        self._src1 = os.path.join(self._dir1, 'foo.c')
+        self._lib1 = os.path.join(self._dir1, 'libfoo.so')
+        self._dir2 = mkdtemp()
+        self._src2 = os.path.join(self._dir2, 'bar.c')
+        self._lib2 = os.path.join(self._dir2, 'libbar.so')
+        # Update local site.cfg
+        global simple_site, site_cfg
+        site_cfg = simple_site.format(**{
+            'dir1': self._dir1,
+            'lib1': self._lib1,
+            'dir2': self._dir2,
+            'lib2': self._lib2,
+            'pathsep': os.pathsep,
+            'lib2_escaped': _shell_utils.NativeParser.join([self._lib2])
+        })
+        # Write site.cfg
+        fd, self._sitecfg = mkstemp()
+        os.close(fd)
+        with open(self._sitecfg, 'w') as fd:
+            fd.write(site_cfg)
+        # Write the sources
+        with open(self._src1, 'w') as fd:
+            fd.write(fakelib_c_text)
+        with open(self._src2, 'w') as fd:
+            fd.write(fakelib_c_text)
+        # We create all class-instances
+
+        def site_and_parse(c, site_cfg):
+            c.files = [site_cfg]
+            c.parse_config_files()
+            return c
+        self.c_default = site_and_parse(get_class('default'), self._sitecfg)
+        self.c_temp1 = site_and_parse(get_class('temp1'), self._sitecfg)
+        self.c_temp2 = site_and_parse(get_class('temp2'), self._sitecfg)
+        self.c_dup_options = site_and_parse(get_class('duplicate_options'),
+                                            self._sitecfg)
+
+    def teardown_method(self):
+        # Do each removal separately
+        try:
+            shutil.rmtree(self._dir1)
+        except Exception:
+            pass
+        try:
+            shutil.rmtree(self._dir2)
+        except Exception:
+            pass
+        try:
+            os.remove(self._sitecfg)
+        except Exception:
+            pass
+
+    def test_all(self):
+        # Read in all information in the ALL block
+        tsi = self.c_default
+        assert_equal(tsi.get_lib_dirs(), [self._dir1, self._dir2])
+        assert_equal(tsi.get_libraries(), [self._lib1, self._lib2])
+        assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
+        extra = tsi.calc_extra_info()
+        assert_equal(extra['extra_compile_args'], ['-I/fake/directory', '-I/path with/spaces', '-Os'])
+
+    def test_temp1(self):
+        # Read in all information in the temp1 block
+        tsi = self.c_temp1
+        assert_equal(tsi.get_lib_dirs(), [self._dir1])
+        assert_equal(tsi.get_libraries(), [self._lib1])
+        assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
+
+    def test_temp2(self):
+        # Read in all information in the temp2 block
+        tsi = self.c_temp2
+        assert_equal(tsi.get_lib_dirs(), [self._dir2])
+        assert_equal(tsi.get_libraries(), [self._lib2])
+        # Now from rpath and not runtime_library_dirs
+        assert_equal(tsi.get_runtime_lib_dirs(key='rpath'), [self._dir2])
+        extra = tsi.calc_extra_info()
+        assert_equal(extra['extra_link_args'], ['-Wl,-rpath=' + self._lib2])
+
+    def test_duplicate_options(self):
+        # Ensure that duplicates are raising an AliasedOptionError
+        tsi = self.c_dup_options
+        assert_raises(AliasedOptionError, tsi.get_option_single, "mylib_libs", "libraries")
+        assert_equal(tsi.get_libs("mylib_libs", [self._lib1]), [self._lib1])
+        assert_equal(tsi.get_libs("libraries", [self._lib2]), [self._lib2])
+
+    @pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
+    def test_compile1(self):
+        # Compile source and link the first source
+        c = customized_ccompiler()
+        previousDir = os.getcwd()
+        try:
+            # Change directory to not screw up directories
+            os.chdir(self._dir1)
+            c.compile([os.path.basename(self._src1)], output_dir=self._dir1)
+            # Ensure that the object exists
+            assert_(os.path.isfile(self._src1.replace('.c', '.o')) or
+                    os.path.isfile(self._src1.replace('.c', '.obj')))
+        finally:
+            os.chdir(previousDir)
+
+    @pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
+    @pytest.mark.skipif('msvc' in repr(ccompiler.new_compiler()),
+                         reason="Fails with MSVC compiler ")
+    def test_compile2(self):
+        # Compile source and link the second source
+        tsi = self.c_temp2
+        c = customized_ccompiler()
+        extra_link_args = tsi.calc_extra_info()['extra_link_args']
+        previousDir = os.getcwd()
+        try:
+            # Change directory to not screw up directories
+            os.chdir(self._dir2)
+            c.compile([os.path.basename(self._src2)], output_dir=self._dir2,
+                      extra_postargs=extra_link_args)
+            # Ensure that the object exists
+            assert_(os.path.isfile(self._src2.replace('.c', '.o')))
+        finally:
+            os.chdir(previousDir)
+
+    HAS_MKL = "mkl_rt" in mkl_info().calc_libraries_info().get("libraries", [])
+
+    @pytest.mark.xfail(HAS_MKL, reason=("`[DEFAULT]` override doesn't work if "
+                                        "numpy is built with MKL support"))
+    def test_overrides(self):
+        previousDir = os.getcwd()
+        cfg = os.path.join(self._dir1, 'site.cfg')
+        shutil.copy(self._sitecfg, cfg)
+        try:
+            os.chdir(self._dir1)
+            # Check that the '[ALL]' section does not override
+            # missing values from other sections
+            info = mkl_info()
+            lib_dirs = info.cp['ALL']['library_dirs'].split(os.pathsep)
+            assert info.get_lib_dirs() != lib_dirs
+
+            # But if we copy the values to a '[mkl]' section the value
+            # is correct
+            with open(cfg) as fid:
+                mkl = fid.read().replace('[ALL]', '[mkl]', 1)
+            with open(cfg, 'w') as fid:
+                fid.write(mkl)
+            info = mkl_info()
+            assert info.get_lib_dirs() == lib_dirs
+
+            # Also, the values will be taken from a section named '[DEFAULT]'
+            with open(cfg) as fid:
+                dflt = fid.read().replace('[mkl]', '[DEFAULT]', 1)
+            with open(cfg, 'w') as fid:
+                fid.write(dflt)
+            info = mkl_info()
+            assert info.get_lib_dirs() == lib_dirs
+        finally:
+            os.chdir(previousDir)
+
+
+def test_distutils_parse_env_order(monkeypatch):
+    from numpy.distutils.system_info import _parse_env_order
+    env = 'NPY_TESTS_DISTUTILS_PARSE_ENV_ORDER'
+
+    base_order = list('abcdef')
+
+    monkeypatch.setenv(env, 'b,i,e,f')
+    order, unknown = _parse_env_order(base_order, env)
+    assert len(order) == 3
+    assert order == list('bef')
+    assert len(unknown) == 1
+
+    # For when LAPACK/BLAS optimization is disabled
+    monkeypatch.setenv(env, '')
+    order, unknown = _parse_env_order(base_order, env)
+    assert len(order) == 0
+    assert len(unknown) == 0
+
+    for prefix in '^!':
+        monkeypatch.setenv(env, f'{prefix}b,i,e')
+        order, unknown = _parse_env_order(base_order, env)
+        assert len(order) == 4
+        assert order == list('acdf')
+        assert len(unknown) == 1
+
+    with pytest.raises(ValueError):
+        monkeypatch.setenv(env, 'b,^e,i')
+        _parse_env_order(base_order, env)
+
+    with pytest.raises(ValueError):
+        monkeypatch.setenv(env, '!b,^e,i')
+        _parse_env_order(base_order, env)
diff --git a/shared/package-files/c0/c0cef459e54f909eaefe2cd263e6074de624351b1937b821989fd0830e24ae4c/special_modified_bessel_i1_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/c0/c0cef459e54f909eaefe2cd263e6074de624351b1937b821989fd0830e24ae4c/special_modified_bessel_i1_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc65d02221d7a96f3fe33ed28d5184543b88943f
--- /dev/null
+++ b/shared/package-files/c0/c0cef459e54f909eaefe2cd263e6074de624351b1937b821989fd0830e24ae4c/special_modified_bessel_i1_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_modified_bessel_i1(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/c0/c0d690aa892a1ec266ad9f4d4086a8e7c653d214a49131f5e08eff3aaf2d003b/test_combine.py b/shared/package-files/c0/c0d690aa892a1ec266ad9f4d4086a8e7c653d214a49131f5e08eff3aaf2d003b/test_combine.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc6a67e4e1f320dbb71220d33ba03eaf788bcb4b
--- /dev/null
+++ b/shared/package-files/c0/c0d690aa892a1ec266ad9f4d4086a8e7c653d214a49131f5e08eff3aaf2d003b/test_combine.py
@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestCombine:
+    @pytest.mark.parametrize(
+        "data",
+        [
+            pd.date_range("2000", periods=4),
+            pd.date_range("2000", periods=4, tz="US/Central"),
+            pd.period_range("2000", periods=4),
+            pd.timedelta_range(0, periods=4),
+        ],
+    )
+    def test_combine_datetlike_udf(self, data):
+        # GH#23079
+        df = pd.DataFrame({"A": data})
+        other = df.copy()
+        df.iloc[1, 0] = None
+
+        def combiner(a, b):
+            return b
+
+        result = df.combine(other, combiner)
+        tm.assert_frame_equal(result, other)
+
+    def test_combine_generic(self, float_frame):
+        df1 = float_frame
+        df2 = float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]]
+
+        combined = df1.combine(df2, np.add)
+        combined2 = df2.combine(df1, np.add)
+        assert combined["D"].isna().all()
+        assert combined2["D"].isna().all()
+
+        chunk = combined.loc[combined.index[:-5], ["A", "B", "C"]]
+        chunk2 = combined2.loc[combined2.index[:-5], ["A", "B", "C"]]
+
+        exp = (
+            float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]].reindex_like(chunk)
+            * 2
+        )
+        tm.assert_frame_equal(chunk, exp)
+        tm.assert_frame_equal(chunk2, exp)
diff --git a/shared/package-files/c0/c0d7ed9df5cb7e2ca376e1187aaf0c49f13c2b538a6991b45a998a581a9693aa/test_getitem.py b/shared/package-files/c0/c0d7ed9df5cb7e2ca376e1187aaf0c49f13c2b538a6991b45a998a581a9693aa/test_getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..b86e233110e882d3c9a71720bfc0b725bfd46923
--- /dev/null
+++ b/shared/package-files/c0/c0d7ed9df5cb7e2ca376e1187aaf0c49f13c2b538a6991b45a998a581a9693aa/test_getitem.py
@@ -0,0 +1,410 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.indexing import IndexingError
+
+# ----------------------------------------------------------------------------
+# test indexing of Series with multi-level Index
+# ----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "access_method",
+    [lambda s, x: s[:, x], lambda s, x: s.loc[:, x], lambda s, x: s.xs(x, level=1)],
+)
+@pytest.mark.parametrize(
+    "level1_value, expected",
+    [(0, Series([1], index=[0])), (1, Series([2, 3], index=[1, 2]))],
+)
+def test_series_getitem_multiindex(access_method, level1_value, expected):
+    # GH 6018
+    # series regression getitem with a multi-index
+
+    mi = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 1)], names=["A", "B"])
+    ser = Series([1, 2, 3], index=mi)
+    expected.index.name = "A"
+
+    result = access_method(ser, level1_value)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("level0_value", ["D", "A"])
+def test_series_getitem_duplicates_multiindex(level0_value):
+    # GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise
+    # the appropriate error, only in PY3 of course!
+
+    index = MultiIndex(
+        levels=[[level0_value, "B", "C"], [0, 26, 27, 37, 57, 67, 75, 82]],
+        codes=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
+        names=["tag", "day"],
+    )
+    arr = np.random.default_rng(2).standard_normal((len(index), 1))
+    df = DataFrame(arr, index=index, columns=["val"])
+
+    # confirm indexing on missing value raises KeyError
+    if level0_value != "A":
+        with pytest.raises(KeyError, match=r"^'A'$"):
+            df.val["A"]
+
+    with pytest.raises(KeyError, match=r"^'X'$"):
+        df.val["X"]
+
+    result = df.val[level0_value]
+    expected = Series(
+        arr.ravel()[0:3], name="val", index=Index([26, 37, 57], name="day")
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_getitem(multiindex_year_month_day_dataframe_random_data, indexer_sl):
+    s = multiindex_year_month_day_dataframe_random_data["A"]
+    expected = s.reindex(s.index[42:65])
+    expected.index = expected.index.droplevel(0).droplevel(0)
+
+    result = indexer_sl(s)[2000, 3]
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_getitem_returns_scalar(
+    multiindex_year_month_day_dataframe_random_data, indexer_sl
+):
+    s = multiindex_year_month_day_dataframe_random_data["A"]
+    expected = s.iloc[49]
+
+    result = indexer_sl(s)[2000, 3, 10]
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "indexer,expected_error,expected_error_msg",
+    [
+        (lambda s: s.__getitem__((2000, 3, 4)), KeyError, r"^\(2000, 3, 4\)$"),
+        (lambda s: s[(2000, 3, 4)], KeyError, r"^\(2000, 3, 4\)$"),
+        (lambda s: s.loc[(2000, 3, 4)], KeyError, r"^\(2000, 3, 4\)$"),
+        (lambda s: s.loc[(2000, 3, 4, 5)], IndexingError, "Too many indexers"),
+        (lambda s: s.__getitem__(len(s)), KeyError, ""),  # match should include len(s)
+        (lambda s: s[len(s)], KeyError, ""),  # match should include len(s)
+        (
+            lambda s: s.iloc[len(s)],
+            IndexError,
+            "single positional indexer is out-of-bounds",
+        ),
+    ],
+)
+def test_series_getitem_indexing_errors(
+    multiindex_year_month_day_dataframe_random_data,
+    indexer,
+    expected_error,
+    expected_error_msg,
+):
+    s = multiindex_year_month_day_dataframe_random_data["A"]
+    with pytest.raises(expected_error, match=expected_error_msg):
+        indexer(s)
+
+
+def test_series_getitem_corner_generator(
+    multiindex_year_month_day_dataframe_random_data,
+):
+    s = multiindex_year_month_day_dataframe_random_data["A"]
+    result = s[(x > 0 for x in s)]
+    expected = s[s > 0]
+    tm.assert_series_equal(result, expected)
+
+
+# ----------------------------------------------------------------------------
+# test indexing of DataFrame with multi-level Index
+# ----------------------------------------------------------------------------
+
+
+def test_getitem_simple(multiindex_dataframe_random_data):
+    df = multiindex_dataframe_random_data.T
+    expected = df.values[:, 0]
+    result = df["foo", "one"].values
+    tm.assert_almost_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "indexer,expected_error_msg",
+    [
+        (lambda df: df[("foo", "four")], r"^\('foo', 'four'\)$"),
+        (lambda df: df["foobar"], r"^'foobar'$"),
+    ],
+)
+def test_frame_getitem_simple_key_error(
+    multiindex_dataframe_random_data, indexer, expected_error_msg
+):
+    df = multiindex_dataframe_random_data.T
+    with pytest.raises(KeyError, match=expected_error_msg):
+        indexer(df)
+
+
+def test_tuple_string_column_names():
+    # GH#50372
+    mi = MultiIndex.from_tuples([("a", "aa"), ("a", "ab"), ("b", "ba"), ("b", "bb")])
+    df = DataFrame([range(4), range(1, 5), range(2, 6)], columns=mi)
+    df["single_index"] = 0
+
+    df_flat = df.copy()
+    df_flat.columns = df_flat.columns.to_flat_index()
+    df_flat["new_single_index"] = 0
+
+    result = df_flat[[("a", "aa"), "new_single_index"]]
+    expected = DataFrame(
+        [[0, 0], [1, 0], [2, 0]], columns=Index([("a", "aa"), "new_single_index"])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_getitem_multicolumn_empty_level():
+    df = DataFrame({"a": ["1", "2", "3"], "b": ["2", "3", "4"]})
+    df.columns = [
+        ["level1 item1", "level1 item2"],
+        ["", "level2 item2"],
+        ["level3 item1", "level3 item2"],
+    ]
+
+    result = df["level1 item1"]
+    expected = DataFrame(
+        [["1"], ["2"], ["3"]], index=df.index, columns=["level3 item1"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "indexer,expected_slice",
+    [
+        (lambda df: df["foo"], slice(3)),
+        (lambda df: df["bar"], slice(3, 5)),
+        (lambda df: df.loc[:, "bar"], slice(3, 5)),
+    ],
+)
+def test_frame_getitem_toplevel(
+    multiindex_dataframe_random_data, indexer, expected_slice
+):
+    df = multiindex_dataframe_random_data.T
+    expected = df.reindex(columns=df.columns[expected_slice])
+    expected.columns = expected.columns.droplevel(0)
+    result = indexer(df)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_mixed_depth_get():
+    arrays = [
+        ["a", "top", "top", "routine1", "routine1", "routine2"],
+        ["", "OD", "OD", "result1", "result2", "result1"],
+        ["", "wx", "wy", "", "", ""],
+    ]
+
+    tuples = sorted(zip(*arrays))
+    index = MultiIndex.from_tuples(tuples)
+    df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index)
+
+    result = df["a"]
+    expected = df["a", "", ""].rename("a")
+    tm.assert_series_equal(result, expected)
+
+    result = df["routine1", "result1"]
+    expected = df["routine1", "result1", ""]
+    expected = expected.rename(("routine1", "result1"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_frame_getitem_nan_multiindex(nulls_fixture):
+    # GH#29751
+    # loc on a multiindex containing nan values
+    n = nulls_fixture  # for code readability
+    cols = ["a", "b", "c"]
+    df = DataFrame(
+        [[11, n, 13], [21, n, 23], [31, n, 33], [41, n, 43]],
+        columns=cols,
+    ).set_index(["a", "b"])
+    df["c"] = df["c"].astype("int64")
+
+    idx = (21, n)
+    result = df.loc[:idx]
+    expected = DataFrame([[11, n, 13], [21, n, 23]], columns=cols).set_index(["a", "b"])
+    expected["c"] = expected["c"].astype("int64")
+    tm.assert_frame_equal(result, expected)
+
+    result = df.loc[idx:]
+    expected = DataFrame(
+        [[21, n, 23], [31, n, 33], [41, n, 43]], columns=cols
+    ).set_index(["a", "b"])
+    expected["c"] = expected["c"].astype("int64")
+    tm.assert_frame_equal(result, expected)
+
+    idx1, idx2 = (21, n), (31, n)
+    result = df.loc[idx1:idx2]
+    expected = DataFrame([[21, n, 23], [31, n, 33]], columns=cols).set_index(["a", "b"])
+    expected["c"] = expected["c"].astype("int64")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "indexer,expected",
+    [
+        (
+            (["b"], ["bar", np.nan]),
+            (
+                DataFrame(
+                    [[2, 3], [5, 6]],
+                    columns=MultiIndex.from_tuples([("b", "bar"), ("b", np.nan)]),
+                    dtype="int64",
+                )
+            ),
+        ),
+        (
+            (["a", "b"]),
+            (
+                DataFrame(
+                    [[1, 2, 3], [4, 5, 6]],
+                    columns=MultiIndex.from_tuples(
+                        [("a", "foo"), ("b", "bar"), ("b", np.nan)]
+                    ),
+                    dtype="int64",
+                )
+            ),
+        ),
+        (
+            (["b"]),
+            (
+                DataFrame(
+                    [[2, 3], [5, 6]],
+                    columns=MultiIndex.from_tuples([("b", "bar"), ("b", np.nan)]),
+                    dtype="int64",
+                )
+            ),
+        ),
+        (
+            (["b"], ["bar"]),
+            (
+                DataFrame(
+                    [[2], [5]],
+                    columns=MultiIndex.from_tuples([("b", "bar")]),
+                    dtype="int64",
+                )
+            ),
+        ),
+        (
+            (["b"], [np.nan]),
+            (
+                DataFrame(
+                    [[3], [6]],
+                    columns=MultiIndex(
+                        codes=[[1], [-1]], levels=[["a", "b"], ["bar", "foo"]]
+                    ),
+                    dtype="int64",
+                )
+            ),
+        ),
+        (("b", np.nan), Series([3, 6], dtype="int64", name=("b", np.nan))),
+    ],
+)
+def test_frame_getitem_nan_cols_multiindex(
+    indexer,
+    expected,
+    nulls_fixture,
+):
+    # Slicing MultiIndex including levels with nan values, for more information
+    # see GH#25154
+    df = DataFrame(
+        [[1, 2, 3], [4, 5, 6]],
+        columns=MultiIndex.from_tuples(
+            [("a", "foo"), ("b", "bar"), ("b", nulls_fixture)]
+        ),
+        dtype="int64",
+    )
+
+    result = df.loc[:, indexer]
+    tm.assert_equal(result, expected)
+
+
+# ----------------------------------------------------------------------------
+# test indexing of DataFrame with multi-level Index with duplicates
+# ----------------------------------------------------------------------------
+
+
+@pytest.fixture
+def dataframe_with_duplicate_index():
+    """Fixture for DataFrame used in tests for gh-4145 and gh-4146"""
+    data = [["a", "d", "e", "c", "f", "b"], [1, 4, 5, 3, 6, 2], [1, 4, 5, 3, 6, 2]]
+    index = ["h1", "h3", "h5"]
+    columns = MultiIndex(
+        levels=[["A", "B"], ["A1", "A2", "B1", "B2"]],
+        codes=[[0, 0, 0, 1, 1, 1], [0, 3, 3, 0, 1, 2]],
+        names=["main", "sub"],
+    )
+    return DataFrame(data, index=index, columns=columns)
+
+
+@pytest.mark.parametrize(
+    "indexer", [lambda df: df[("A", "A1")], lambda df: df.loc[:, ("A", "A1")]]
+)
+def test_frame_mi_access(dataframe_with_duplicate_index, indexer):
+    # GH 4145
+    df = dataframe_with_duplicate_index
+    index = Index(["h1", "h3", "h5"])
+    columns = MultiIndex.from_tuples([("A", "A1")], names=["main", "sub"])
+    expected = DataFrame([["a", 1, 1]], index=columns, columns=index).T
+
+    result = indexer(df)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_mi_access_returns_series(dataframe_with_duplicate_index):
+    # GH 4146, not returning a block manager when selecting a unique index
+    # from a duplicate index
+    # as of 4879, this returns a Series (which is similar to what happens
+    # with a non-unique)
+    df = dataframe_with_duplicate_index
+    expected = Series(["a", 1, 1], index=["h1", "h3", "h5"], name="A1")
+    result = df["A"]["A1"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_frame_mi_access_returns_frame(dataframe_with_duplicate_index):
+    # selecting a non_unique from the 2nd level
+    df = dataframe_with_duplicate_index
+    expected = DataFrame(
+        [["d", 4, 4], ["e", 5, 5]],
+        index=Index(["B2", "B2"], name="sub"),
+        columns=["h1", "h3", "h5"],
+    ).T
+    result = df["A"]["B2"]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_mi_empty_slice():
+    # GH 15454
+    df = DataFrame(0, index=range(2), columns=MultiIndex.from_product([[1], [2]]))
+    result = df[[]]
+    expected = DataFrame(
+        index=[0, 1], columns=MultiIndex(levels=[[1], [2]], codes=[[], []])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_loc_empty_multiindex():
+    # GH#36936
+    arrays = [["a", "a", "b", "a"], ["a", "a", "b", "b"]]
+    index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
+    df = DataFrame([1, 2, 3, 4], index=index, columns=["value"])
+
+    # loc on empty multiindex == loc with False mask
+    empty_multiindex = df.loc[df.loc[:, "value"] == 0, :].index
+    result = df.loc[empty_multiindex, :]
+    expected = df.loc[[False] * len(df.index), :]
+    tm.assert_frame_equal(result, expected)
+
+    # replacing value with loc on empty multiindex
+    df.loc[df.loc[df.loc[:, "value"] == 0].index, "value"] = 5
+    result = df
+    expected = DataFrame([1, 2, 3, 4], index=index, columns=["value"])
+    tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/c0/c0dcc6fb3111cd2fd71a5b3e9c13d55722d11dadac9149649f4fb99f4b6b3160/locators.py b/shared/package-files/c0/c0dcc6fb3111cd2fd71a5b3e9c13d55722d11dadac9149649f4fb99f4b6b3160/locators.py
new file mode 100644
index 0000000000000000000000000000000000000000..966ebc0e37d6104a8e0e1fefe9dc526f39409ce2
--- /dev/null
+++ b/shared/package-files/c0/c0dcc6fb3111cd2fd71a5b3e9c13d55722d11dadac9149649f4fb99f4b6b3160/locators.py
@@ -0,0 +1,1300 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2012-2015 Vinay Sajip.
+# Licensed to the Python Software Foundation under a contributor agreement.
+# See LICENSE.txt and CONTRIBUTORS.txt.
+#
+
+import gzip
+from io import BytesIO
+import json
+import logging
+import os
+import posixpath
+import re
+try:
+    import threading
+except ImportError:  # pragma: no cover
+    import dummy_threading as threading
+import zlib
+
+from . import DistlibException
+from .compat import (urljoin, urlparse, urlunparse, url2pathname, pathname2url,
+                     queue, quote, unescape, build_opener,
+                     HTTPRedirectHandler as BaseRedirectHandler, text_type,
+                     Request, HTTPError, URLError)
+from .database import Distribution, DistributionPath, make_dist
+from .metadata import Metadata, MetadataInvalidError
+from .util import (cached_property, ensure_slash, split_filename, get_project_data,
+                   parse_requirement, parse_name_and_version, ServerProxy,
+                   normalize_name)
+from .version import get_scheme, UnsupportedVersionError
+from .wheel import Wheel, is_compatible
+
+logger = logging.getLogger(__name__)
+
+HASHER_HASH = re.compile(r'^(\w+)=([a-f0-9]+)')
+CHARSET = re.compile(r';\s*charset\s*=\s*(.*)\s*$', re.I)
+HTML_CONTENT_TYPE = re.compile('text/html|application/x(ht)?ml')
+DEFAULT_INDEX = 'https://pypi.org/pypi'
+
+def get_all_distribution_names(url=None):
+    """
+    Return all distribution names known by an index.
+    :param url: The URL of the index.
+    :return: A list of all known distribution names.
+    """
+    if url is None:
+        url = DEFAULT_INDEX
+    client = ServerProxy(url, timeout=3.0)
+    try:
+        return client.list_packages()
+    finally:
+        client('close')()
+
+class RedirectHandler(BaseRedirectHandler):
+    """
+    A class to work around a bug in some Python 3.2.x releases.
+    """
+    # There's a bug in the base version for some 3.2.x
+    # (e.g. 3.2.2 on Ubuntu Oneiric). If a Location header
+    # returns e.g. /abc, it bails because it says the scheme ''
+    # is bogus, when actually it should use the request's
+    # URL for the scheme. See Python issue #13696.
+    def http_error_302(self, req, fp, code, msg, headers):
+        # Some servers (incorrectly) return multiple Location headers
+        # (so probably same goes for URI).  Use first header.
+        newurl = None
+        for key in ('location', 'uri'):
+            if key in headers:
+                newurl = headers[key]
+                break
+        if newurl is None:  # pragma: no cover
+            return
+        urlparts = urlparse(newurl)
+        if urlparts.scheme == '':
+            newurl = urljoin(req.get_full_url(), newurl)
+            if hasattr(headers, 'replace_header'):
+                headers.replace_header(key, newurl)
+            else:
+                headers[key] = newurl
+        return BaseRedirectHandler.http_error_302(self, req, fp, code, msg,
+                                                  headers)
+
+    http_error_301 = http_error_303 = http_error_307 = http_error_302
+
+class Locator(object):
+    """
+    A base class for locators - things that locate distributions.
+    """
+    source_extensions = ('.tar.gz', '.tar.bz2', '.tar', '.zip', '.tgz', '.tbz')
+    binary_extensions = ('.egg', '.exe', '.whl')
+    excluded_extensions = ('.pdf',)
+
+    # A list of tags indicating which wheels you want to match. The default
+    # value of None matches against the tags compatible with the running
+    # Python. If you want to match other values, set wheel_tags on a locator
+    # instance to a list of tuples (pyver, abi, arch) which you want to match.
+    wheel_tags = None
+
+    downloadable_extensions = source_extensions + ('.whl',)
+
+    def __init__(self, scheme='default'):
+        """
+        Initialise an instance.
+        :param scheme: Because locators look for most recent versions, they
+                       need to know the version scheme to use. This specifies
+                       the current PEP-recommended scheme - use ``'legacy'``
+                       if you need to support existing distributions on PyPI.
+        """
+        self._cache = {}
+        self.scheme = scheme
+        # Because of bugs in some of the handlers on some of the platforms,
+        # we use our own opener rather than just using urlopen.
+        self.opener = build_opener(RedirectHandler())
+        # If get_project() is called from locate(), the matcher instance
+        # is set from the requirement passed to locate(). See issue #18 for
+        # why this can be useful to know.
+        self.matcher = None
+        self.errors = queue.Queue()
+
+    def get_errors(self):
+        """
+        Return any errors which have occurred.
+        """
+        result = []
+        while not self.errors.empty():  # pragma: no cover
+            try:
+                e = self.errors.get(False)
+                result.append(e)
+            except self.errors.Empty:
+                continue
+            self.errors.task_done()
+        return result
+
+    def clear_errors(self):
+        """
+        Clear any errors which may have been logged.
+        """
+        # Just get the errors and throw them away
+        self.get_errors()
+
+    def clear_cache(self):
+        self._cache.clear()
+
+    def _get_scheme(self):
+        return self._scheme
+
+    def _set_scheme(self, value):
+        self._scheme = value
+
+    scheme = property(_get_scheme, _set_scheme)
+
+    def _get_project(self, name):
+        """
+        For a given project, get a dictionary mapping available versions to Distribution
+        instances.
+
+        This should be implemented in subclasses.
+
+        If called from a locate() request, self.matcher will be set to a
+        matcher for the requirement to satisfy, otherwise it will be None.
+        """
+        raise NotImplementedError('Please implement in the subclass')
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        raise NotImplementedError('Please implement in the subclass')
+
+    def get_project(self, name):
+        """
+        For a given project, get a dictionary mapping available versions to Distribution
+        instances.
+
+        This calls _get_project to do all the work, and just implements a caching layer on top.
+        """
+        if self._cache is None:  # pragma: no cover
+            result = self._get_project(name)
+        elif name in self._cache:
+            result = self._cache[name]
+        else:
+            self.clear_errors()
+            result = self._get_project(name)
+            self._cache[name] = result
+        return result
+
+    def score_url(self, url):
+        """
+        Give an url a score which can be used to choose preferred URLs
+        for a given project release.
+        """
+        t = urlparse(url)
+        basename = posixpath.basename(t.path)
+        compatible = True
+        is_wheel = basename.endswith('.whl')
+        is_downloadable = basename.endswith(self.downloadable_extensions)
+        if is_wheel:
+            compatible = is_compatible(Wheel(basename), self.wheel_tags)
+        return (t.scheme == 'https', 'pypi.org' in t.netloc,
+                is_downloadable, is_wheel, compatible, basename)
+
+    def prefer_url(self, url1, url2):
+        """
+        Choose one of two URLs where both are candidates for distribution
+        archives for the same version of a distribution (for example,
+        .tar.gz vs. zip).
+
+        The current implementation favours https:// URLs over http://, archives
+        from PyPI over those from other locations, wheel compatibility (if a
+        wheel) and then the archive name.
+        """
+        result = url2
+        if url1:
+            s1 = self.score_url(url1)
+            s2 = self.score_url(url2)
+            if s1 > s2:
+                result = url1
+            if result != url2:
+                logger.debug('Not replacing %r with %r', url1, url2)
+            else:
+                logger.debug('Replacing %r with %r', url1, url2)
+        return result
+
+    def split_filename(self, filename, project_name):
+        """
+        Attempt to split a filename in project name, version and Python version.
+        """
+        return split_filename(filename, project_name)
+
+    def convert_url_to_download_info(self, url, project_name):
+        """
+        See if a URL is a candidate for a download URL for a project (the URL
+        has typically been scraped from an HTML page).
+
+        If it is, a dictionary is returned with keys "name", "version",
+        "filename" and "url"; otherwise, None is returned.
+        """
+        def same_project(name1, name2):
+            return normalize_name(name1) == normalize_name(name2)
+
+        result = None
+        scheme, netloc, path, params, query, frag = urlparse(url)
+        if frag.lower().startswith('egg='):  # pragma: no cover
+            logger.debug('%s: version hint in fragment: %r',
+                         project_name, frag)
+        m = HASHER_HASH.match(frag)
+        if m:
+            algo, digest = m.groups()
+        else:
+            algo, digest = None, None
+        origpath = path
+        if path and path[-1] == '/':  # pragma: no cover
+            path = path[:-1]
+        if path.endswith('.whl'):
+            try:
+                wheel = Wheel(path)
+                if not is_compatible(wheel, self.wheel_tags):
+                    logger.debug('Wheel not compatible: %s', path)
+                else:
+                    if project_name is None:
+                        include = True
+                    else:
+                        include = same_project(wheel.name, project_name)
+                    if include:
+                        result = {
+                            'name': wheel.name,
+                            'version': wheel.version,
+                            'filename': wheel.filename,
+                            'url': urlunparse((scheme, netloc, origpath,
+                                               params, query, '')),
+                            'python-version': ', '.join(
+                                ['.'.join(list(v[2:])) for v in wheel.pyver]),
+                        }
+            except Exception as e:  # pragma: no cover
+                logger.warning('invalid path for wheel: %s', path)
+        elif not path.endswith(self.downloadable_extensions):  # pragma: no cover
+            logger.debug('Not downloadable: %s', path)
+        else:  # downloadable extension
+            path = filename = posixpath.basename(path)
+            for ext in self.downloadable_extensions:
+                if path.endswith(ext):
+                    path = path[:-len(ext)]
+                    t = self.split_filename(path, project_name)
+                    if not t:  # pragma: no cover
+                        logger.debug('No match for project/version: %s', path)
+                    else:
+                        name, version, pyver = t
+                        if not project_name or same_project(project_name, name):
+                            result = {
+                                'name': name,
+                                'version': version,
+                                'filename': filename,
+                                'url': urlunparse((scheme, netloc, origpath,
+                                                   params, query, '')),
+                                #'packagetype': 'sdist',
+                            }
+                            if pyver:  # pragma: no cover
+                                result['python-version'] = pyver
+                    break
+        if result and algo:
+            result['%s_digest' % algo] = digest
+        return result
+
+    def _get_digest(self, info):
+        """
+        Get a digest from a dictionary by looking at a "digests" dictionary
+        or keys of the form 'algo_digest'.
+
+        Returns a 2-tuple (algo, digest) if found, else None. Currently
+        looks only for SHA256, then MD5.
+        """
+        result = None
+        if 'digests' in info:
+            digests = info['digests']
+            for algo in ('sha256', 'md5'):
+                if algo in digests:
+                    result = (algo, digests[algo])
+                    break
+        if not result:
+            for algo in ('sha256', 'md5'):
+                key = '%s_digest' % algo
+                if key in info:
+                    result = (algo, info[key])
+                    break
+        return result
+
+    def _update_version_data(self, result, info):
+        """
+        Update a result dictionary (the final result from _get_project) with a
+        dictionary for a specific version, which typically holds information
+        gleaned from a filename or URL for an archive for the distribution.
+        """
+        name = info.pop('name')
+        version = info.pop('version')
+        if version in result:
+            dist = result[version]
+            md = dist.metadata
+        else:
+            dist = make_dist(name, version, scheme=self.scheme)
+            md = dist.metadata
+        dist.digest = digest = self._get_digest(info)
+        url = info['url']
+        result['digests'][url] = digest
+        if md.source_url != info['url']:
+            md.source_url = self.prefer_url(md.source_url, url)
+            result['urls'].setdefault(version, set()).add(url)
+        dist.locator = self
+        result[version] = dist
+
+    def locate(self, requirement, prereleases=False):
+        """
+        Find the most recent distribution which matches the given
+        requirement.
+
+        :param requirement: A requirement of the form 'foo (1.0)' or perhaps
+                            'foo (>= 1.0, < 2.0, != 1.3)'
+        :param prereleases: If ``True``, allow pre-release versions
+                            to be located. Otherwise, pre-release versions
+                            are not returned.
+        :return: A :class:`Distribution` instance, or ``None`` if no such
+                 distribution could be located.
+        """
+        result = None
+        r = parse_requirement(requirement)
+        if r is None:  # pragma: no cover
+            raise DistlibException('Not a valid requirement: %r' % requirement)
+        scheme = get_scheme(self.scheme)
+        self.matcher = matcher = scheme.matcher(r.requirement)
+        logger.debug('matcher: %s (%s)', matcher, type(matcher).__name__)
+        versions = self.get_project(r.name)
+        if len(versions) > 2:   # urls and digests keys are present
+            # sometimes, versions are invalid
+            slist = []
+            vcls = matcher.version_class
+            for k in versions:
+                if k in ('urls', 'digests'):
+                    continue
+                try:
+                    if not matcher.match(k):
+                        pass  # logger.debug('%s did not match %r', matcher, k)
+                    else:
+                        if prereleases or not vcls(k).is_prerelease:
+                            slist.append(k)
+                        # else:
+                            # logger.debug('skipping pre-release '
+                                         # 'version %s of %s', k, matcher.name)
+                except Exception:  # pragma: no cover
+                    logger.warning('error matching %s with %r', matcher, k)
+                    pass # slist.append(k)
+            if len(slist) > 1:
+                slist = sorted(slist, key=scheme.key)
+            if slist:
+                logger.debug('sorted list: %s', slist)
+                version = slist[-1]
+                result = versions[version]
+        if result:
+            if r.extras:
+                result.extras = r.extras
+            result.download_urls = versions.get('urls', {}).get(version, set())
+            d = {}
+            sd = versions.get('digests', {})
+            for url in result.download_urls:
+                if url in sd:  # pragma: no cover
+                    d[url] = sd[url]
+            result.digests = d
+        self.matcher = None
+        return result
+
+
+class PyPIRPCLocator(Locator):
+    """
+    This locator uses XML-RPC to locate distributions. It therefore
+    cannot be used with simple mirrors (that only mirror file content).
+    """
+    def __init__(self, url, **kwargs):
+        """
+        Initialise an instance.
+
+        :param url: The URL to use for XML-RPC.
+        :param kwargs: Passed to the superclass constructor.
+        """
+        super(PyPIRPCLocator, self).__init__(**kwargs)
+        self.base_url = url
+        self.client = ServerProxy(url, timeout=3.0)
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        return set(self.client.list_packages())
+
+    def _get_project(self, name):
+        result = {'urls': {}, 'digests': {}}
+        versions = self.client.package_releases(name, True)
+        for v in versions:
+            urls = self.client.release_urls(name, v)
+            data = self.client.release_data(name, v)
+            metadata = Metadata(scheme=self.scheme)
+            metadata.name = data['name']
+            metadata.version = data['version']
+            metadata.license = data.get('license')
+            metadata.keywords = data.get('keywords', [])
+            metadata.summary = data.get('summary')
+            dist = Distribution(metadata)
+            if urls:
+                info = urls[0]
+                metadata.source_url = info['url']
+                dist.digest = self._get_digest(info)
+                dist.locator = self
+                result[v] = dist
+                for info in urls:
+                    url = info['url']
+                    digest = self._get_digest(info)
+                    result['urls'].setdefault(v, set()).add(url)
+                    result['digests'][url] = digest
+        return result
+
+class PyPIJSONLocator(Locator):
+    """
+    This locator uses PyPI's JSON interface. It's very limited in functionality
+    and probably not worth using.
+    """
+    def __init__(self, url, **kwargs):
+        super(PyPIJSONLocator, self).__init__(**kwargs)
+        self.base_url = ensure_slash(url)
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        raise NotImplementedError('Not available from this locator')
+
+    def _get_project(self, name):
+        result = {'urls': {}, 'digests': {}}
+        url = urljoin(self.base_url, '%s/json' % quote(name))
+        try:
+            resp = self.opener.open(url)
+            data = resp.read().decode() # for now
+            d = json.loads(data)
+            md = Metadata(scheme=self.scheme)
+            data = d['info']
+            md.name = data['name']
+            md.version = data['version']
+            md.license = data.get('license')
+            md.keywords = data.get('keywords', [])
+            md.summary = data.get('summary')
+            dist = Distribution(md)
+            dist.locator = self
+            urls = d['urls']
+            result[md.version] = dist
+            for info in d['urls']:
+                url = info['url']
+                dist.download_urls.add(url)
+                dist.digests[url] = self._get_digest(info)
+                result['urls'].setdefault(md.version, set()).add(url)
+                result['digests'][url] = self._get_digest(info)
+            # Now get other releases
+            for version, infos in d['releases'].items():
+                if version == md.version:
+                    continue    # already done
+                omd = Metadata(scheme=self.scheme)
+                omd.name = md.name
+                omd.version = version
+                odist = Distribution(omd)
+                odist.locator = self
+                result[version] = odist
+                for info in infos:
+                    url = info['url']
+                    odist.download_urls.add(url)
+                    odist.digests[url] = self._get_digest(info)
+                    result['urls'].setdefault(version, set()).add(url)
+                    result['digests'][url] = self._get_digest(info)
+#            for info in urls:
+#                md.source_url = info['url']
+#                dist.digest = self._get_digest(info)
+#                dist.locator = self
+#                for info in urls:
+#                    url = info['url']
+#                    result['urls'].setdefault(md.version, set()).add(url)
+#                    result['digests'][url] = self._get_digest(info)
+        except Exception as e:
+            self.errors.put(text_type(e))
+            logger.exception('JSON fetch failed: %s', e)
+        return result
+
+
+class Page(object):
+    """
+    This class represents a scraped HTML page.
+    """
+    # The following slightly hairy-looking regex just looks for the contents of
+    # an anchor link, which has an attribute "href" either immediately preceded
+    # or immediately followed by a "rel" attribute. The attribute values can be
+    # declared with double quotes, single quotes or no quotes - which leads to
+    # the length of the expression.
+    _href = re.compile("""
+(rel\\s*=\\s*(?:"(?P<rel1>[^"]*)"|'(?P<rel2>[^']*)'|(?P<rel3>[^>\\s\n]*))\\s+)?
+href\\s*=\\s*(?:"(?P<url1>[^"]*)"|'(?P<url2>[^']*)'|(?P<url3>[^>\\s\n]*))
+(\\s+rel\\s*=\\s*(?:"(?P<rel4>[^"]*)"|'(?P<rel5>[^']*)'|(?P<rel6>[^>\\s\n]*)))?
+""", re.I | re.S | re.X)
+    _base = re.compile(r"""<base\s+href\s*=\s*['"]?([^'">]+)""", re.I | re.S)
+
+    def __init__(self, data, url):
+        """
+        Initialise an instance with the Unicode page contents and the URL they
+        came from.
+        """
+        self.data = data
+        self.base_url = self.url = url
+        m = self._base.search(self.data)
+        if m:
+            self.base_url = m.group(1)
+
+    _clean_re = re.compile(r'[^a-z0-9$&+,/:;=?@.#%_\\|-]', re.I)
+
+    @cached_property
+    def links(self):
+        """
+        Return the URLs of all the links on a page together with information
+        about their "rel" attribute, for determining which ones to treat as
+        downloads and which ones to queue for further scraping.
+        """
+        def clean(url):
+            "Tidy up an URL."
+            scheme, netloc, path, params, query, frag = urlparse(url)
+            return urlunparse((scheme, netloc, quote(path),
+                               params, query, frag))
+
+        result = set()
+        for match in self._href.finditer(self.data):
+            d = match.groupdict('')
+            rel = (d['rel1'] or d['rel2'] or d['rel3'] or
+                   d['rel4'] or d['rel5'] or d['rel6'])
+            url = d['url1'] or d['url2'] or d['url3']
+            url = urljoin(self.base_url, url)
+            url = unescape(url)
+            url = self._clean_re.sub(lambda m: '%%%2x' % ord(m.group(0)), url)
+            result.add((url, rel))
+        # We sort the result, hoping to bring the most recent versions
+        # to the front
+        result = sorted(result, key=lambda t: t[0], reverse=True)
+        return result
+
+
+class SimpleScrapingLocator(Locator):
+    """
+    A locator which scrapes HTML pages to locate downloads for a distribution.
+    This runs multiple threads to do the I/O; performance is at least as good
+    as pip's PackageFinder, which works in an analogous fashion.
+    """
+
+    # These are used to deal with various Content-Encoding schemes.
+    decoders = {
+        'deflate': zlib.decompress,
+        'gzip': lambda b: gzip.GzipFile(fileobj=BytesIO(b)).read(),
+        'none': lambda b: b,
+    }
+
+    def __init__(self, url, timeout=None, num_workers=10, **kwargs):
+        """
+        Initialise an instance.
+        :param url: The root URL to use for scraping.
+        :param timeout: The timeout, in seconds, to be applied to requests.
+                        This defaults to ``None`` (no timeout specified).
+        :param num_workers: The number of worker threads you want to do I/O,
+                            This defaults to 10.
+        :param kwargs: Passed to the superclass.
+        """
+        super(SimpleScrapingLocator, self).__init__(**kwargs)
+        self.base_url = ensure_slash(url)
+        self.timeout = timeout
+        self._page_cache = {}
+        self._seen = set()
+        self._to_fetch = queue.Queue()
+        self._bad_hosts = set()
+        self.skip_externals = False
+        self.num_workers = num_workers
+        self._lock = threading.RLock()
+        # See issue #45: we need to be resilient when the locator is used
+        # in a thread, e.g. with concurrent.futures. We can't use self._lock
+        # as it is for coordinating our internal threads - the ones created
+        # in _prepare_threads.
+        self._gplock = threading.RLock()
+        self.platform_check = False  # See issue #112
+
+    def _prepare_threads(self):
+        """
+        Threads are created only when get_project is called, and terminate
+        before it returns. They are there primarily to parallelise I/O (i.e.
+        fetching web pages).
+        """
+        self._threads = []
+        for i in range(self.num_workers):
+            t = threading.Thread(target=self._fetch)
+            t.daemon = True
+            t.start()
+            self._threads.append(t)
+
+    def _wait_threads(self):
+        """
+        Tell all the threads to terminate (by sending a sentinel value) and
+        wait for them to do so.
+        """
+        # Note that you need two loops, since you can't say which
+        # thread will get each sentinel
+        for t in self._threads:
+            self._to_fetch.put(None)    # sentinel
+        for t in self._threads:
+            t.join()
+        self._threads = []
+
+    def _get_project(self, name):
+        result = {'urls': {}, 'digests': {}}
+        with self._gplock:
+            self.result = result
+            self.project_name = name
+            url = urljoin(self.base_url, '%s/' % quote(name))
+            self._seen.clear()
+            self._page_cache.clear()
+            self._prepare_threads()
+            try:
+                logger.debug('Queueing %s', url)
+                self._to_fetch.put(url)
+                self._to_fetch.join()
+            finally:
+                self._wait_threads()
+            del self.result
+        return result
+
+    platform_dependent = re.compile(r'\b(linux_(i\d86|x86_64|arm\w+)|'
+                                    r'win(32|_amd64)|macosx_?\d+)\b', re.I)
+
+    def _is_platform_dependent(self, url):
+        """
+        Does an URL refer to a platform-specific download?
+        """
+        return self.platform_dependent.search(url)
+
+    def _process_download(self, url):
+        """
+        See if an URL is a suitable download for a project.
+
+        If it is, register information in the result dictionary (for
+        _get_project) about the specific version it's for.
+
+        Note that the return value isn't actually used other than as a boolean
+        value.
+        """
+        if self.platform_check and self._is_platform_dependent(url):
+            info = None
+        else:
+            info = self.convert_url_to_download_info(url, self.project_name)
+        logger.debug('process_download: %s -> %s', url, info)
+        if info:
+            with self._lock:    # needed because self.result is shared
+                self._update_version_data(self.result, info)
+        return info
+
+    def _should_queue(self, link, referrer, rel):
+        """
+        Determine whether a link URL from a referring page and with a
+        particular "rel" attribute should be queued for scraping.
+        """
+        scheme, netloc, path, _, _, _ = urlparse(link)
+        if path.endswith(self.source_extensions + self.binary_extensions +
+                         self.excluded_extensions):
+            result = False
+        elif self.skip_externals and not link.startswith(self.base_url):
+            result = False
+        elif not referrer.startswith(self.base_url):
+            result = False
+        elif rel not in ('homepage', 'download'):
+            result = False
+        elif scheme not in ('http', 'https', 'ftp'):
+            result = False
+        elif self._is_platform_dependent(link):
+            result = False
+        else:
+            host = netloc.split(':', 1)[0]
+            if host.lower() == 'localhost':
+                result = False
+            else:
+                result = True
+        logger.debug('should_queue: %s (%s) from %s -> %s', link, rel,
+                     referrer, result)
+        return result
+
+    def _fetch(self):
+        """
+        Get a URL to fetch from the work queue, get the HTML page, examine its
+        links for download candidates and candidates for further scraping.
+
+        This is a handy method to run in a thread.
+        """
+        while True:
+            url = self._to_fetch.get()
+            try:
+                if url:
+                    page = self.get_page(url)
+                    if page is None:    # e.g. after an error
+                        continue
+                    for link, rel in page.links:
+                        if link not in self._seen:
+                            try:
+                                self._seen.add(link)
+                                if (not self._process_download(link) and
+                                    self._should_queue(link, url, rel)):
+                                    logger.debug('Queueing %s from %s', link, url)
+                                    self._to_fetch.put(link)
+                            except MetadataInvalidError:  # e.g. invalid versions
+                                pass
+            except Exception as e:  # pragma: no cover
+                self.errors.put(text_type(e))
+            finally:
+                # always do this, to avoid hangs :-)
+                self._to_fetch.task_done()
+            if not url:
+                #logger.debug('Sentinel seen, quitting.')
+                break
+
+    def get_page(self, url):
+        """
+        Get the HTML for an URL, possibly from an in-memory cache.
+
+        XXX TODO Note: this cache is never actually cleared. It's assumed that
+        the data won't get stale over the lifetime of a locator instance (not
+        necessarily true for the default_locator).
+        """
+        # http://peak.telecommunity.com/DevCenter/EasyInstall#package-index-api
+        scheme, netloc, path, _, _, _ = urlparse(url)
+        if scheme == 'file' and os.path.isdir(url2pathname(path)):
+            url = urljoin(ensure_slash(url), 'index.html')
+
+        if url in self._page_cache:
+            result = self._page_cache[url]
+            logger.debug('Returning %s from cache: %s', url, result)
+        else:
+            host = netloc.split(':', 1)[0]
+            result = None
+            if host in self._bad_hosts:
+                logger.debug('Skipping %s due to bad host %s', url, host)
+            else:
+                req = Request(url, headers={'Accept-encoding': 'identity'})
+                try:
+                    logger.debug('Fetching %s', url)
+                    resp = self.opener.open(req, timeout=self.timeout)
+                    logger.debug('Fetched %s', url)
+                    headers = resp.info()
+                    content_type = headers.get('Content-Type', '')
+                    if HTML_CONTENT_TYPE.match(content_type):
+                        final_url = resp.geturl()
+                        data = resp.read()
+                        encoding = headers.get('Content-Encoding')
+                        if encoding:
+                            decoder = self.decoders[encoding]   # fail if not found
+                            data = decoder(data)
+                        encoding = 'utf-8'
+                        m = CHARSET.search(content_type)
+                        if m:
+                            encoding = m.group(1)
+                        try:
+                            data = data.decode(encoding)
+                        except UnicodeError:  # pragma: no cover
+                            data = data.decode('latin-1')    # fallback
+                        result = Page(data, final_url)
+                        self._page_cache[final_url] = result
+                except HTTPError as e:
+                    if e.code != 404:
+                        logger.exception('Fetch failed: %s: %s', url, e)
+                except URLError as e:  # pragma: no cover
+                    logger.exception('Fetch failed: %s: %s', url, e)
+                    with self._lock:
+                        self._bad_hosts.add(host)
+                except Exception as e:  # pragma: no cover
+                    logger.exception('Fetch failed: %s: %s', url, e)
+                finally:
+                    self._page_cache[url] = result   # even if None (failure)
+        return result
+
+    _distname_re = re.compile('<a href=[^>]*>([^<]+)<')
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        result = set()
+        page = self.get_page(self.base_url)
+        if not page:
+            raise DistlibException('Unable to get %s' % self.base_url)
+        for match in self._distname_re.finditer(page.data):
+            result.add(match.group(1))
+        return result
+
+class DirectoryLocator(Locator):
+    """
+    This class locates distributions in a directory tree.
+    """
+
+    def __init__(self, path, **kwargs):
+        """
+        Initialise an instance.
+        :param path: The root of the directory tree to search.
+        :param kwargs: Passed to the superclass constructor,
+                       except for:
+                       * recursive - if True (the default), subdirectories are
+                         recursed into. If False, only the top-level directory
+                         is searched,
+        """
+        self.recursive = kwargs.pop('recursive', True)
+        super(DirectoryLocator, self).__init__(**kwargs)
+        path = os.path.abspath(path)
+        if not os.path.isdir(path):  # pragma: no cover
+            raise DistlibException('Not a directory: %r' % path)
+        self.base_dir = path
+
+    def should_include(self, filename, parent):
+        """
+        Should a filename be considered as a candidate for a distribution
+        archive? As well as the filename, the directory which contains it
+        is provided, though not used by the current implementation.
+        """
+        return filename.endswith(self.downloadable_extensions)
+
+    def _get_project(self, name):
+        result = {'urls': {}, 'digests': {}}
+        for root, dirs, files in os.walk(self.base_dir):
+            for fn in files:
+                if self.should_include(fn, root):
+                    fn = os.path.join(root, fn)
+                    url = urlunparse(('file', '',
+                                      pathname2url(os.path.abspath(fn)),
+                                      '', '', ''))
+                    info = self.convert_url_to_download_info(url, name)
+                    if info:
+                        self._update_version_data(result, info)
+            if not self.recursive:
+                break
+        return result
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        result = set()
+        for root, dirs, files in os.walk(self.base_dir):
+            for fn in files:
+                if self.should_include(fn, root):
+                    fn = os.path.join(root, fn)
+                    url = urlunparse(('file', '',
+                                      pathname2url(os.path.abspath(fn)),
+                                      '', '', ''))
+                    info = self.convert_url_to_download_info(url, None)
+                    if info:
+                        result.add(info['name'])
+            if not self.recursive:
+                break
+        return result
+
+class JSONLocator(Locator):
+    """
+    This locator uses special extended metadata (not available on PyPI) and is
+    the basis of performant dependency resolution in distlib. Other locators
+    require archive downloads before dependencies can be determined! As you
+    might imagine, that can be slow.
+    """
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        raise NotImplementedError('Not available from this locator')
+
+    def _get_project(self, name):
+        result = {'urls': {}, 'digests': {}}
+        data = get_project_data(name)
+        if data:
+            for info in data.get('files', []):
+                if info['ptype'] != 'sdist' or info['pyversion'] != 'source':
+                    continue
+                # We don't store summary in project metadata as it makes
+                # the data bigger for no benefit during dependency
+                # resolution
+                dist = make_dist(data['name'], info['version'],
+                                 summary=data.get('summary',
+                                                  'Placeholder for summary'),
+                                 scheme=self.scheme)
+                md = dist.metadata
+                md.source_url = info['url']
+                # TODO SHA256 digest
+                if 'digest' in info and info['digest']:
+                    dist.digest = ('md5', info['digest'])
+                md.dependencies = info.get('requirements', {})
+                dist.exports = info.get('exports', {})
+                result[dist.version] = dist
+                result['urls'].setdefault(dist.version, set()).add(info['url'])
+        return result
+
+class DistPathLocator(Locator):
+    """
+    This locator finds installed distributions in a path. It can be useful for
+    adding to an :class:`AggregatingLocator`.
+    """
+    def __init__(self, distpath, **kwargs):
+        """
+        Initialise an instance.
+
+        :param distpath: A :class:`DistributionPath` instance to search.
+        """
+        super(DistPathLocator, self).__init__(**kwargs)
+        assert isinstance(distpath, DistributionPath)
+        self.distpath = distpath
+
+    def _get_project(self, name):
+        dist = self.distpath.get_distribution(name)
+        if dist is None:
+            result = {'urls': {}, 'digests': {}}
+        else:
+            result = {
+                dist.version: dist,
+                'urls': {dist.version: set([dist.source_url])},
+                'digests': {dist.version: set([None])}
+            }
+        return result
+
+
+class AggregatingLocator(Locator):
+    """
+    This class allows you to chain and/or merge a list of locators.
+    """
+    def __init__(self, *locators, **kwargs):
+        """
+        Initialise an instance.
+
+        :param locators: The list of locators to search.
+        :param kwargs: Passed to the superclass constructor,
+                       except for:
+                       * merge - if False (the default), the first successful
+                         search from any of the locators is returned. If True,
+                         the results from all locators are merged (this can be
+                         slow).
+        """
+        self.merge = kwargs.pop('merge', False)
+        self.locators = locators
+        super(AggregatingLocator, self).__init__(**kwargs)
+
+    def clear_cache(self):
+        super(AggregatingLocator, self).clear_cache()
+        for locator in self.locators:
+            locator.clear_cache()
+
+    def _set_scheme(self, value):
+        self._scheme = value
+        for locator in self.locators:
+            locator.scheme = value
+
+    scheme = property(Locator.scheme.fget, _set_scheme)
+
+    def _get_project(self, name):
+        result = {}
+        for locator in self.locators:
+            d = locator.get_project(name)
+            if d:
+                if self.merge:
+                    files = result.get('urls', {})
+                    digests = result.get('digests', {})
+                    # next line could overwrite result['urls'], result['digests']
+                    result.update(d)
+                    df = result.get('urls')
+                    if files and df:
+                        for k, v in files.items():
+                            if k in df:
+                                df[k] |= v
+                            else:
+                                df[k] = v
+                    dd = result.get('digests')
+                    if digests and dd:
+                        dd.update(digests)
+                else:
+                    # See issue #18. If any dists are found and we're looking
+                    # for specific constraints, we only return something if
+                    # a match is found. For example, if a DirectoryLocator
+                    # returns just foo (1.0) while we're looking for
+                    # foo (>= 2.0), we'll pretend there was nothing there so
+                    # that subsequent locators can be queried. Otherwise we
+                    # would just return foo (1.0) which would then lead to a
+                    # failure to find foo (>= 2.0), because other locators
+                    # weren't searched. Note that this only matters when
+                    # merge=False.
+                    if self.matcher is None:
+                        found = True
+                    else:
+                        found = False
+                        for k in d:
+                            if self.matcher.match(k):
+                                found = True
+                                break
+                    if found:
+                        result = d
+                        break
+        return result
+
+    def get_distribution_names(self):
+        """
+        Return all the distribution names known to this locator.
+        """
+        result = set()
+        for locator in self.locators:
+            try:
+                result |= locator.get_distribution_names()
+            except NotImplementedError:
+                pass
+        return result
+
+
+# We use a legacy scheme simply because most of the dists on PyPI use legacy
+# versions which don't conform to PEP 440.
+default_locator = AggregatingLocator(
+                    # JSONLocator(), # don't use as PEP 426 is withdrawn
+                    SimpleScrapingLocator('https://pypi.org/simple/',
+                                          timeout=3.0),
+                    scheme='legacy')
+
+locate = default_locator.locate
+
+
+class DependencyFinder(object):
+    """
+    Locate dependencies for distributions.
+    """
+
+    def __init__(self, locator=None):
+        """
+        Initialise an instance, using the specified locator
+        to locate distributions.
+        """
+        self.locator = locator or default_locator
+        self.scheme = get_scheme(self.locator.scheme)
+
+    def add_distribution(self, dist):
+        """
+        Add a distribution to the finder. This will update internal information
+        about who provides what.
+        :param dist: The distribution to add.
+        """
+        logger.debug('adding distribution %s', dist)
+        name = dist.key
+        self.dists_by_name[name] = dist
+        self.dists[(name, dist.version)] = dist
+        for p in dist.provides:
+            name, version = parse_name_and_version(p)
+            logger.debug('Add to provided: %s, %s, %s', name, version, dist)
+            self.provided.setdefault(name, set()).add((version, dist))
+
+    def remove_distribution(self, dist):
+        """
+        Remove a distribution from the finder. This will update internal
+        information about who provides what.
+        :param dist: The distribution to remove.
+        """
+        logger.debug('removing distribution %s', dist)
+        name = dist.key
+        del self.dists_by_name[name]
+        del self.dists[(name, dist.version)]
+        for p in dist.provides:
+            name, version = parse_name_and_version(p)
+            logger.debug('Remove from provided: %s, %s, %s', name, version, dist)
+            s = self.provided[name]
+            s.remove((version, dist))
+            if not s:
+                del self.provided[name]
+
+    def get_matcher(self, reqt):
+        """
+        Get a version matcher for a requirement.
+        :param reqt: The requirement
+        :type reqt: str
+        :return: A version matcher (an instance of
+                 :class:`distlib.version.Matcher`).
+        """
+        try:
+            matcher = self.scheme.matcher(reqt)
+        except UnsupportedVersionError:  # pragma: no cover
+            # XXX compat-mode if cannot read the version
+            name = reqt.split()[0]
+            matcher = self.scheme.matcher(name)
+        return matcher
+
+    def find_providers(self, reqt):
+        """
+        Find the distributions which can fulfill a requirement.
+
+        :param reqt: The requirement.
+         :type reqt: str
+        :return: A set of distribution which can fulfill the requirement.
+        """
+        matcher = self.get_matcher(reqt)
+        name = matcher.key   # case-insensitive
+        result = set()
+        provided = self.provided
+        if name in provided:
+            for version, provider in provided[name]:
+                try:
+                    match = matcher.match(version)
+                except UnsupportedVersionError:
+                    match = False
+
+                if match:
+                    result.add(provider)
+                    break
+        return result
+
+    def try_to_replace(self, provider, other, problems):
+        """
+        Attempt to replace one provider with another. This is typically used
+        when resolving dependencies from multiple sources, e.g. A requires
+        (B >= 1.0) while C requires (B >= 1.1).
+
+        For successful replacement, ``provider`` must meet all the requirements
+        which ``other`` fulfills.
+
+        :param provider: The provider we are trying to replace with.
+        :param other: The provider we're trying to replace.
+        :param problems: If False is returned, this will contain what
+                         problems prevented replacement. This is currently
+                         a tuple of the literal string 'cantreplace',
+                         ``provider``, ``other``  and the set of requirements
+                         that ``provider`` couldn't fulfill.
+        :return: True if we can replace ``other`` with ``provider``, else
+                 False.
+        """
+        rlist = self.reqts[other]
+        unmatched = set()
+        for s in rlist:
+            matcher = self.get_matcher(s)
+            if not matcher.match(provider.version):
+                unmatched.add(s)
+        if unmatched:
+            # can't replace other with provider
+            problems.add(('cantreplace', provider, other,
+                          frozenset(unmatched)))
+            result = False
+        else:
+            # can replace other with provider
+            self.remove_distribution(other)
+            del self.reqts[other]
+            for s in rlist:
+                self.reqts.setdefault(provider, set()).add(s)
+            self.add_distribution(provider)
+            result = True
+        return result
+
+    def find(self, requirement, meta_extras=None, prereleases=False):
+        """
+        Find a distribution and all distributions it depends on.
+
+        :param requirement: The requirement specifying the distribution to
+                            find, or a Distribution instance.
+        :param meta_extras: A list of meta extras such as :test:, :build: and
+                            so on.
+        :param prereleases: If ``True``, allow pre-release versions to be
+                            returned - otherwise, don't return prereleases
+                            unless they're all that's available.
+
+        Return a set of :class:`Distribution` instances and a set of
+        problems.
+
+        The distributions returned should be such that they have the
+        :attr:`required` attribute set to ``True`` if they were
+        from the ``requirement`` passed to ``find()``, and they have the
+        :attr:`build_time_dependency` attribute set to ``True`` unless they
+        are post-installation dependencies of the ``requirement``.
+
+        The problems should be a tuple consisting of the string
+        ``'unsatisfied'`` and the requirement which couldn't be satisfied
+        by any distribution known to the locator.
+        """
+
+        self.provided = {}
+        self.dists = {}
+        self.dists_by_name = {}
+        self.reqts = {}
+
+        meta_extras = set(meta_extras or [])
+        if ':*:' in meta_extras:
+            meta_extras.remove(':*:')
+            # :meta: and :run: are implicitly included
+            meta_extras |= set([':test:', ':build:', ':dev:'])
+
+        if isinstance(requirement, Distribution):
+            dist = odist = requirement
+            logger.debug('passed %s as requirement', odist)
+        else:
+            dist = odist = self.locator.locate(requirement,
+                                               prereleases=prereleases)
+            if dist is None:
+                raise DistlibException('Unable to locate %r' % requirement)
+            logger.debug('located %s', odist)
+        dist.requested = True
+        problems = set()
+        todo = set([dist])
+        install_dists = set([odist])
+        while todo:
+            dist = todo.pop()
+            name = dist.key     # case-insensitive
+            if name not in self.dists_by_name:
+                self.add_distribution(dist)
+            else:
+                #import pdb; pdb.set_trace()
+                other = self.dists_by_name[name]
+                if other != dist:
+                    self.try_to_replace(dist, other, problems)
+
+            ireqts = dist.run_requires | dist.meta_requires
+            sreqts = dist.build_requires
+            ereqts = set()
+            if meta_extras and dist in install_dists:
+                for key in ('test', 'build', 'dev'):
+                    e = ':%s:' % key
+                    if e in meta_extras:
+                        ereqts |= getattr(dist, '%s_requires' % key)
+            all_reqts = ireqts | sreqts | ereqts
+            for r in all_reqts:
+                providers = self.find_providers(r)
+                if not providers:
+                    logger.debug('No providers found for %r', r)
+                    provider = self.locator.locate(r, prereleases=prereleases)
+                    # If no provider is found and we didn't consider
+                    # prereleases, consider them now.
+                    if provider is None and not prereleases:
+                        provider = self.locator.locate(r, prereleases=True)
+                    if provider is None:
+                        logger.debug('Cannot satisfy %r', r)
+                        problems.add(('unsatisfied', r))
+                    else:
+                        n, v = provider.key, provider.version
+                        if (n, v) not in self.dists:
+                            todo.add(provider)
+                        providers.add(provider)
+                        if r in ireqts and dist in install_dists:
+                            install_dists.add(provider)
+                            logger.debug('Adding %s to install_dists',
+                                         provider.name_and_version)
+                for p in providers:
+                    name = p.key
+                    if name not in self.dists_by_name:
+                        self.reqts.setdefault(p, set()).add(r)
+                    else:
+                        other = self.dists_by_name[name]
+                        if other != p:
+                            # see if other can be replaced by p
+                            self.try_to_replace(p, other, problems)
+
+        dists = set(self.dists.values())
+        for dist in dists:
+            dist.build_time_dependency = dist not in install_dists
+            if dist.build_time_dependency:
+                logger.debug('%s is a build-time dependency only.',
+                             dist.name_and_version)
+        logger.debug('find done for %s', odist)
+        return dists, problems
diff --git a/shared/package-files/c0/c0ddad8d67429c843126b303c06bbdaaf3fcaf743edf7781d87902bf2741514a/Module.h b/shared/package-files/c0/c0ddad8d67429c843126b303c06bbdaaf3fcaf743edf7781d87902bf2741514a/Module.h
new file mode 100644
index 0000000000000000000000000000000000000000..71ff8c4fcb85e2c9e55fb4c0660ef506b6fda6e6
--- /dev/null
+++ b/shared/package-files/c0/c0ddad8d67429c843126b303c06bbdaaf3fcaf743edf7781d87902bf2741514a/Module.h
@@ -0,0 +1,6 @@
+#ifndef THP_MODULE_INC
+#define THP_MODULE_INC
+
+#define THP_STATELESS_ATTRIBUTE_NAME "_torch"
+
+#endif
diff --git a/shared/package-files/c0/c0de4e1a63afed930855afd4b6a1886df7bb9b27b7733d74d11dfa2295534339/_discretization.py b/shared/package-files/c0/c0de4e1a63afed930855afd4b6a1886df7bb9b27b7733d74d11dfa2295534339/_discretization.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef5081080bda1813d4f16b9931dc58cf608c9818
--- /dev/null
+++ b/shared/package-files/c0/c0de4e1a63afed930855afd4b6a1886df7bb9b27b7733d74d11dfa2295534339/_discretization.py
@@ -0,0 +1,548 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+import warnings
+from numbers import Integral
+
+import numpy as np
+
+from ..base import BaseEstimator, TransformerMixin, _fit_context
+from ..utils import resample
+from ..utils._param_validation import Interval, Options, StrOptions
+from ..utils.stats import _averaged_weighted_percentile, _weighted_percentile
+from ..utils.validation import (
+    _check_feature_names_in,
+    _check_sample_weight,
+    check_array,
+    check_is_fitted,
+    validate_data,
+)
+from ._encoders import OneHotEncoder
+
+
+class KBinsDiscretizer(TransformerMixin, BaseEstimator):
+    """
+    Bin continuous data into intervals.
+
+    Read more in the :ref:`User Guide <preprocessing_discretization>`.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    n_bins : int or array-like of shape (n_features,), default=5
+        The number of bins to produce. Raises ValueError if ``n_bins < 2``.
+
+    encode : {'onehot', 'onehot-dense', 'ordinal'}, default='onehot'
+        Method used to encode the transformed result.
+
+        - 'onehot': Encode the transformed result with one-hot encoding
+          and return a sparse matrix. Ignored features are always
+          stacked to the right.
+        - 'onehot-dense': Encode the transformed result with one-hot encoding
+          and return a dense array. Ignored features are always
+          stacked to the right.
+        - 'ordinal': Return the bin identifier encoded as an integer value.
+
+    strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
+        Strategy used to define the widths of the bins.
+
+        - 'uniform': All bins in each feature have identical widths.
+        - 'quantile': All bins in each feature have the same number of points.
+        - 'kmeans': Values in each bin have the same nearest center of a 1D
+          k-means cluster.
+
+        For an example of the different strategies see:
+        :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`.
+
+    quantile_method : {"inverted_cdf", "averaged_inverted_cdf",
+            "closest_observation", "interpolated_inverted_cdf", "hazen",
+            "weibull", "linear", "median_unbiased", "normal_unbiased"},
+            default="linear"
+            Method to pass on to np.percentile calculation when using
+            strategy="quantile". Only `averaged_inverted_cdf` and `inverted_cdf`
+            support the use of `sample_weight != None` when subsampling is not
+            active.
+
+            .. versionadded:: 1.7
+
+    dtype : {np.float32, np.float64}, default=None
+        The desired data-type for the output. If None, output dtype is
+        consistent with input dtype. Only np.float32 and np.float64 are
+        supported.
+
+        .. versionadded:: 0.24
+
+    subsample : int or None, default=200_000
+        Maximum number of samples, used to fit the model, for computational
+        efficiency.
+        `subsample=None` means that all the training samples are used when
+        computing the quantiles that determine the binning thresholds.
+        Since quantile computation relies on sorting each column of `X` and
+        that sorting has an `n log(n)` time complexity,
+        it is recommended to use subsampling on datasets with a
+        very large number of samples.
+
+        .. versionchanged:: 1.3
+            The default value of `subsample` changed from `None` to `200_000` when
+            `strategy="quantile"`.
+
+        .. versionchanged:: 1.5
+            The default value of `subsample` changed from `None` to `200_000` when
+            `strategy="uniform"` or `strategy="kmeans"`.
+
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for subsampling.
+        Pass an int for reproducible results across multiple function calls.
+        See the `subsample` parameter for more details.
+        See :term:`Glossary <random_state>`.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    bin_edges_ : ndarray of ndarray of shape (n_features,)
+        The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )``
+        Ignored features will have empty arrays.
+
+    n_bins_ : ndarray of shape (n_features,), dtype=np.int64
+        Number of bins per feature. Bins whose width are too small
+        (i.e., <= 1e-8) are removed with a warning.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    Binarizer : Class used to bin values as ``0`` or
+        ``1`` based on a parameter ``threshold``.
+
+    Notes
+    -----
+
+    For a visualization of discretization on different datasets refer to
+    :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`.
+    On the effect of discretization on linear models see:
+    :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`.
+
+    In bin edges for feature ``i``, the first and last values are used only for
+    ``inverse_transform``. During transform, bin edges are extended to::
+
+      np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])
+
+    You can combine ``KBinsDiscretizer`` with
+    :class:`~sklearn.compose.ColumnTransformer` if you only want to preprocess
+    part of the features.
+
+    ``KBinsDiscretizer`` might produce constant features (e.g., when
+    ``encode = 'onehot'`` and certain bins do not contain any data).
+    These features can be removed with feature selection algorithms
+    (e.g., :class:`~sklearn.feature_selection.VarianceThreshold`).
+
+    Examples
+    --------
+    >>> from sklearn.preprocessing import KBinsDiscretizer
+    >>> X = [[-2, 1, -4,   -1],
+    ...      [-1, 2, -3, -0.5],
+    ...      [ 0, 3, -2,  0.5],
+    ...      [ 1, 4, -1,    2]]
+    >>> est = KBinsDiscretizer(
+    ...     n_bins=3, encode='ordinal', strategy='uniform'
+    ... )
+    >>> est.fit(X)
+    KBinsDiscretizer(...)
+    >>> Xt = est.transform(X)
+    >>> Xt  # doctest: +SKIP
+    array([[ 0., 0., 0., 0.],
+           [ 1., 1., 1., 0.],
+           [ 2., 2., 2., 1.],
+           [ 2., 2., 2., 2.]])
+
+    Sometimes it may be useful to convert the data back into the original
+    feature space. The ``inverse_transform`` function converts the binned
+    data into the original feature space. Each value will be equal to the mean
+    of the two bin edges.
+
+    >>> est.bin_edges_[0]
+    array([-2., -1.,  0.,  1.])
+    >>> est.inverse_transform(Xt)
+    array([[-1.5,  1.5, -3.5, -0.5],
+           [-0.5,  2.5, -2.5, -0.5],
+           [ 0.5,  3.5, -1.5,  0.5],
+           [ 0.5,  3.5, -1.5,  1.5]])
+    """
+
+    _parameter_constraints: dict = {
+        "n_bins": [Interval(Integral, 2, None, closed="left"), "array-like"],
+        "encode": [StrOptions({"onehot", "onehot-dense", "ordinal"})],
+        "strategy": [StrOptions({"uniform", "quantile", "kmeans"})],
+        "quantile_method": [
+            StrOptions(
+                {
+                    "warn",
+                    "inverted_cdf",
+                    "averaged_inverted_cdf",
+                    "closest_observation",
+                    "interpolated_inverted_cdf",
+                    "hazen",
+                    "weibull",
+                    "linear",
+                    "median_unbiased",
+                    "normal_unbiased",
+                }
+            )
+        ],
+        "dtype": [Options(type, {np.float64, np.float32}), None],
+        "subsample": [Interval(Integral, 1, None, closed="left"), None],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_bins=5,
+        *,
+        encode="onehot",
+        strategy="quantile",
+        quantile_method="warn",
+        dtype=None,
+        subsample=200_000,
+        random_state=None,
+    ):
+        self.n_bins = n_bins
+        self.encode = encode
+        self.strategy = strategy
+        self.quantile_method = quantile_method
+        self.dtype = dtype
+        self.subsample = subsample
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None, sample_weight=None):
+        """
+        Fit the estimator.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Data to be discretized.
+
+        y : None
+            Ignored. This parameter exists only for compatibility with
+            :class:`~sklearn.pipeline.Pipeline`.
+
+        sample_weight : ndarray of shape (n_samples,)
+            Contains weight values to be associated with each sample.
+
+            .. versionadded:: 1.3
+
+            .. versionchanged:: 1.7
+               Added support for strategy="uniform".
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = validate_data(self, X, dtype="numeric")
+
+        if self.dtype in (np.float64, np.float32):
+            output_dtype = self.dtype
+        else:  # self.dtype is None
+            output_dtype = X.dtype
+
+        n_samples, n_features = X.shape
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+
+        if self.subsample is not None and n_samples > self.subsample:
+            # Take a subsample of `X`
+            # When resampling, it is important to subsample **with replacement** to
+            # preserve the distribution, in particular in the presence of a few data
+            # points with large weights. You can check this by setting `replace=False`
+            # in sklearn.utils.test.test_indexing.test_resample_weighted and check that
+            # it fails as a justification for this claim.
+            X = resample(
+                X,
+                replace=True,
+                n_samples=self.subsample,
+                random_state=self.random_state,
+                sample_weight=sample_weight,
+            )
+            # Since we already used the weights when resampling when provided,
+            # we set them back to `None` to avoid accounting for the weights twice
+            # in subsequent operations to compute weight-aware bin edges with
+            # quantiles or k-means.
+            sample_weight = None
+
+        n_features = X.shape[1]
+        n_bins = self._validate_n_bins(n_features)
+
+        bin_edges = np.zeros(n_features, dtype=object)
+
+        # TODO(1.9): remove and switch to quantile_method="averaged_inverted_cdf"
+        # by default.
+        quantile_method = self.quantile_method
+        if self.strategy == "quantile" and quantile_method == "warn":
+            warnings.warn(
+                "The current default behavior, quantile_method='linear', will be "
+                "changed to quantile_method='averaged_inverted_cdf' in "
+                "scikit-learn version 1.9 to naturally support sample weight "
+                "equivalence properties by default. Pass "
+                "quantile_method='averaged_inverted_cdf' explicitly to silence this "
+                "warning.",
+                FutureWarning,
+            )
+            quantile_method = "linear"
+
+        if (
+            self.strategy == "quantile"
+            and quantile_method not in ["inverted_cdf", "averaged_inverted_cdf"]
+            and sample_weight is not None
+        ):
+            raise ValueError(
+                "When fitting with strategy='quantile' and sample weights, "
+                "quantile_method should either be set to 'averaged_inverted_cdf' or "
+                f"'inverted_cdf', got quantile_method='{quantile_method}' instead."
+            )
+
+        if self.strategy != "quantile" and sample_weight is not None:
+            # Prepare a mask to filter out zero-weight samples when extracting
+            # the min and max values of each columns which are needed for the
+            # "uniform" and "kmeans" strategies.
+            nnz_weight_mask = sample_weight != 0
+        else:
+            # Otherwise, all samples are used. Use a slice to avoid creating a
+            # new array.
+            nnz_weight_mask = slice(None)
+
+        for jj in range(n_features):
+            column = X[:, jj]
+            col_min = column[nnz_weight_mask].min()
+            col_max = column[nnz_weight_mask].max()
+
+            if col_min == col_max:
+                warnings.warn(
+                    "Feature %d is constant and will be replaced with 0." % jj
+                )
+                n_bins[jj] = 1
+                bin_edges[jj] = np.array([-np.inf, np.inf])
+                continue
+
+            if self.strategy == "uniform":
+                bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1)
+
+            elif self.strategy == "quantile":
+                percentile_levels = np.linspace(0, 100, n_bins[jj] + 1)
+
+                # method="linear" is the implicit default for any numpy
+                # version. So we keep it version independent in that case by
+                # using an empty param dict.
+                percentile_kwargs = {}
+                if quantile_method != "linear" and sample_weight is None:
+                    percentile_kwargs["method"] = quantile_method
+
+                if sample_weight is None:
+                    bin_edges[jj] = np.asarray(
+                        np.percentile(column, percentile_levels, **percentile_kwargs),
+                        dtype=np.float64,
+                    )
+                else:
+                    # TODO: make _weighted_percentile and
+                    # _averaged_weighted_percentile accept an array of
+                    # quantiles instead of calling it multiple times and
+                    # sorting the column multiple times as a result.
+                    percentile_func = {
+                        "inverted_cdf": _weighted_percentile,
+                        "averaged_inverted_cdf": _averaged_weighted_percentile,
+                    }[quantile_method]
+                    bin_edges[jj] = np.asarray(
+                        [
+                            percentile_func(column, sample_weight, percentile_rank=p)
+                            for p in percentile_levels
+                        ],
+                        dtype=np.float64,
+                    )
+            elif self.strategy == "kmeans":
+                from ..cluster import KMeans  # fixes import loops
+
+                # Deterministic initialization with uniform spacing
+                uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1)
+                init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5
+
+                # 1D k-means procedure
+                km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)
+                centers = km.fit(
+                    column[:, None], sample_weight=sample_weight
+                ).cluster_centers_[:, 0]
+                # Must sort, centers may be unsorted even with sorted init
+                centers.sort()
+                bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5
+                bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max]
+
+            # Remove bins whose width are too small (i.e., <= 1e-8)
+            if self.strategy in ("quantile", "kmeans"):
+                mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8
+                bin_edges[jj] = bin_edges[jj][mask]
+                if len(bin_edges[jj]) - 1 != n_bins[jj]:
+                    warnings.warn(
+                        "Bins whose width are too small (i.e., <= "
+                        "1e-8) in feature %d are removed. Consider "
+                        "decreasing the number of bins." % jj
+                    )
+                    n_bins[jj] = len(bin_edges[jj]) - 1
+
+        self.bin_edges_ = bin_edges
+        self.n_bins_ = n_bins
+
+        if "onehot" in self.encode:
+            self._encoder = OneHotEncoder(
+                categories=[np.arange(i) for i in self.n_bins_],
+                sparse_output=self.encode == "onehot",
+                dtype=output_dtype,
+            )
+            # Fit the OneHotEncoder with toy datasets
+            # so that it's ready for use after the KBinsDiscretizer is fitted
+            self._encoder.fit(np.zeros((1, len(self.n_bins_))))
+
+        return self
+
+    def _validate_n_bins(self, n_features):
+        """Returns n_bins_, the number of bins per feature."""
+        orig_bins = self.n_bins
+        if isinstance(orig_bins, Integral):
+            return np.full(n_features, orig_bins, dtype=int)
+
+        n_bins = check_array(orig_bins, dtype=int, copy=True, ensure_2d=False)
+
+        if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
+            raise ValueError("n_bins must be a scalar or array of shape (n_features,).")
+
+        bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
+
+        violating_indices = np.where(bad_nbins_value)[0]
+        if violating_indices.shape[0] > 0:
+            indices = ", ".join(str(i) for i in violating_indices)
+            raise ValueError(
+                "{} received an invalid number "
+                "of bins at indices {}. Number of bins "
+                "must be at least 2, and must be an int.".format(
+                    KBinsDiscretizer.__name__, indices
+                )
+            )
+        return n_bins
+
+    def transform(self, X):
+        """
+        Discretize the data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Data to be discretized.
+
+        Returns
+        -------
+        Xt : {ndarray, sparse matrix}, dtype={np.float32, np.float64}
+            Data in the binned space. Will be a sparse matrix if
+            `self.encode='onehot'` and ndarray otherwise.
+        """
+        check_is_fitted(self)
+
+        # check input and attribute dtypes
+        dtype = (np.float64, np.float32) if self.dtype is None else self.dtype
+        Xt = validate_data(self, X, copy=True, dtype=dtype, reset=False)
+
+        bin_edges = self.bin_edges_
+        for jj in range(Xt.shape[1]):
+            Xt[:, jj] = np.searchsorted(bin_edges[jj][1:-1], Xt[:, jj], side="right")
+
+        if self.encode == "ordinal":
+            return Xt
+
+        dtype_init = None
+        if "onehot" in self.encode:
+            dtype_init = self._encoder.dtype
+            self._encoder.dtype = Xt.dtype
+        try:
+            Xt_enc = self._encoder.transform(Xt)
+        finally:
+            # revert the initial dtype to avoid modifying self.
+            self._encoder.dtype = dtype_init
+        return Xt_enc
+
+    def inverse_transform(self, X):
+        """
+        Transform discretized data back to original feature space.
+
+        Note that this function does not regenerate the original data
+        due to discretization rounding.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Transformed data in the binned space.
+
+        Returns
+        -------
+        X_original : ndarray, dtype={np.float32, np.float64}
+            Data in the original feature space.
+        """
+
+        check_is_fitted(self)
+
+        if "onehot" in self.encode:
+            X = self._encoder.inverse_transform(X)
+
+        Xinv = check_array(X, copy=True, dtype=(np.float64, np.float32))
+        n_features = self.n_bins_.shape[0]
+        if Xinv.shape[1] != n_features:
+            raise ValueError(
+                "Incorrect number of features. Expecting {}, received {}.".format(
+                    n_features, Xinv.shape[1]
+                )
+            )
+
+        for jj in range(n_features):
+            bin_edges = self.bin_edges_[jj]
+            bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5
+            Xinv[:, jj] = bin_centers[(Xinv[:, jj]).astype(np.int64)]
+
+        return Xinv
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "n_features_in_")
+        input_features = _check_feature_names_in(self, input_features)
+        if hasattr(self, "_encoder"):
+            return self._encoder.get_feature_names_out(input_features)
+
+        # ordinal encoding
+        return input_features
diff --git a/shared/package-files/c0/c0e0bcd89efa67b90e90db88c9a5f2c35e1bed65cb3b1b3c5685c54924d7b1b7/metadata.py b/shared/package-files/c0/c0e0bcd89efa67b90e90db88c9a5f2c35e1bed65cb3b1b3c5685c54924d7b1b7/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..633b6de8fc13928f59ca00b645666745c173dc25
--- /dev/null
+++ b/shared/package-files/c0/c0e0bcd89efa67b90e90db88c9a5f2c35e1bed65cb3b1b3c5685c54924d7b1b7/metadata.py
@@ -0,0 +1,2976 @@
+# Copyright (C) 2014 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+from __future__ import annotations
+
+import copy
+import hashlib
+import json
+import os
+import re
+import sys
+import time
+import warnings
+from collections import OrderedDict
+from functools import lru_cache
+from os.path import isfile, join
+from typing import TYPE_CHECKING, overload
+
+from bs4 import UnicodeDammit
+from conda.gateways.disk.read import compute_sum
+
+from . import exceptions, utils, variants
+from .conda_interface import MatchSpec, envs_dirs
+from .config import Config, get_or_merge_config
+from .features import feature_list
+from .license_family import ensure_valid_license_family
+from .utils import (
+    DEFAULT_SUBDIRS,
+    HashableDict,
+    ensure_list,
+    expand_globs,
+    find_recipe,
+    get_installed_packages,
+    insert_variant_versions,
+    on_win,
+)
+
+if TYPE_CHECKING:
+    from typing import Literal
+
+try:
+    import yaml
+except ImportError:
+    sys.exit(
+        "Error: could not import yaml (required to read meta.yaml "
+        "files of conda recipes)"
+    )
+
+try:
+    Loader = yaml.CLoader
+except AttributeError:
+    Loader = yaml.Loader
+
+
+class StringifyNumbersLoader(Loader):
+    @classmethod
+    def remove_implicit_resolver(cls, tag):
+        if "yaml_implicit_resolvers" not in cls.__dict__:
+            cls.yaml_implicit_resolvers = {
+                k: v[:] for k, v in cls.yaml_implicit_resolvers.items()
+            }
+        for ch in tuple(cls.yaml_implicit_resolvers):
+            resolvers = [(t, r) for t, r in cls.yaml_implicit_resolvers[ch] if t != tag]
+            if resolvers:
+                cls.yaml_implicit_resolvers[ch] = resolvers
+            else:
+                del cls.yaml_implicit_resolvers[ch]
+
+    @classmethod
+    def remove_constructor(cls, tag):
+        if "yaml_constructors" not in cls.__dict__:
+            cls.yaml_constructors = cls.yaml_constructors.copy()
+        if tag in cls.yaml_constructors:
+            del cls.yaml_constructors[tag]
+
+
+StringifyNumbersLoader.remove_implicit_resolver("tag:yaml.org,2002:float")
+StringifyNumbersLoader.remove_implicit_resolver("tag:yaml.org,2002:int")
+StringifyNumbersLoader.remove_constructor("tag:yaml.org,2002:float")
+StringifyNumbersLoader.remove_constructor("tag:yaml.org,2002:int")
+
+# arches that don't follow exact names in the subdir need to be mapped here
+ARCH_MAP = {"32": "x86", "64": "x86_64"}
+
+NOARCH_TYPES = ("python", "generic", True)
+
+# we originally matched outputs based on output name. Unfortunately, that
+#    doesn't work when outputs are templated - we want to match un-rendered
+#    text, but we have rendered names.
+# We overcome that divide by finding the output index in a rendered set of
+#    outputs, so our names match, then we use that numeric index with this
+#    regex, which extract all outputs in order.
+# Stop condition is one of 3 things:
+#    \w at the start of a line (next top-level section)
+#    \Z (end of file)
+#    next output, as delineated by "- name" or "- type"
+output_re = re.compile(
+    r"^\ +-\ +(?:name|type):.+?(?=^\w|\Z|^\ +-\ +(?:name|type))", flags=re.M | re.S
+)
+numpy_xx_re = re.compile(
+    r"(numpy\s*x\.x)|pin_compatible\([\'\"]numpy.*max_pin=[\'\"]x\.x[\'\"]"
+)
+# TODO: there's probably a way to combine these, but I can't figure out how to many the x
+#     capturing group optional.
+numpy_compatible_x_re = re.compile(
+    r"pin_\w+\([\'\"]numpy[\'\"].*((?<=x_pin=[\'\"])[x\.]*(?=[\'\"]))"
+)
+numpy_compatible_re = re.compile(r"pin_\w+\([\'\"]numpy[\'\"]")
+
+# used to avoid recomputing/rescanning recipe contents for used variables
+used_vars_cache = {}
+
+
+def get_selectors(config: Config) -> dict[str, bool]:
+    """Aggregates selectors for use in recipe templating.
+
+    Derives selectors from the config and variants to be injected
+    into the Jinja environment prior to templating.
+
+    Args:
+        config (Config): The config object
+
+    Returns:
+        dict[str, bool]: Dictionary of on/off selectors for Jinja
+    """
+    # Remember to update the docs of any of this changes
+    plat = config.host_subdir
+    d = dict(
+        linux32=bool(plat == "linux-32"),
+        linux64=bool(plat == "linux-64"),
+        arm=plat.startswith("linux-arm"),
+        unix=plat.startswith(("linux-", "osx-", "emscripten-")),
+        win32=bool(plat == "win-32"),
+        win64=bool(plat == "win-64"),
+        os=os,
+        environ=os.environ,
+        nomkl=bool(int(os.environ.get("FEATURE_NOMKL", False))),
+    )
+
+    # Add the current platform to the list of subdirs to enable conda-build
+    # to bootstrap new platforms without a new conda release.
+    subdirs = list(DEFAULT_SUBDIRS) + [plat]
+
+    # filter out noarch and other weird subdirs
+    subdirs = [subdir for subdir in subdirs if "-" in subdir]
+
+    subdir_oses = {subdir.split("-")[0] for subdir in subdirs}
+    subdir_archs = {subdir.split("-")[1] for subdir in subdirs}
+
+    for subdir_os in subdir_oses:
+        d[subdir_os] = plat.startswith(f"{subdir_os}-")
+
+    for arch in subdir_archs:
+        arch_full = ARCH_MAP.get(arch, arch)
+        d[arch_full] = plat.endswith(f"-{arch}")
+        if arch == "32":
+            d["x86"] = plat.endswith(("-32", "-64"))
+
+    defaults = variants.get_default_variant(config)
+    py = config.variant.get("python", defaults["python"])
+    # there are times when python comes in as a tuple
+    if not hasattr(py, "split"):
+        py = py[0]
+    # go from "3.6 *_cython" -> "36"
+    # or from "3.6.9" -> "36"
+    py_major, py_minor, *_ = py.split(" ")[0].split(".")
+    py = int(f"{py_major}{py_minor}")
+
+    d["build_platform"] = config.build_subdir
+
+    d.update(
+        dict(
+            py=py,
+            py3k=bool(py_major == "3"),
+            py2k=bool(py_major == "2"),
+            py26=bool(py == 26),
+            py27=bool(py == 27),
+            py33=bool(py == 33),
+            py34=bool(py == 34),
+            py35=bool(py == 35),
+            py36=bool(py == 36),
+        )
+    )
+
+    np = config.variant.get("numpy")
+    if not np:
+        np = defaults["numpy"]
+        if config.verbose:
+            utils.get_logger(__name__).warn(
+                "No numpy version specified in conda_build_config.yaml.  "
+                "Falling back to default numpy value of {}".format(defaults["numpy"])
+            )
+    d["np"] = int("".join(np.split(".")[:2]))
+
+    pl = config.variant.get("perl", defaults["perl"])
+    d["pl"] = pl
+
+    lua = config.variant.get("lua", defaults["lua"])
+    d["lua"] = lua
+    d["luajit"] = bool(lua[0] == "2")
+
+    for feature, value in feature_list:
+        d[feature] = value
+    d.update(os.environ)
+
+    # here we try to do some type conversion for more intuitive usage.  Otherwise,
+    #    values like 35 are strings by default, making relational operations confusing.
+    # We also convert "True" and things like that to booleans.
+    for k, v in config.variant.items():
+        if k not in d:
+            try:
+                d[k] = int(v)
+            except (TypeError, ValueError):
+                if isinstance(v, str) and v.lower() in ("false", "true"):
+                    v = v.lower() == "true"
+                d[k] = v
+    return d
+
+
+def ns_cfg(config: Config) -> dict[str, bool]:
+    warnings.warn(
+        "`conda_build.metadata.ns_cfg` is pending deprecation and will be removed in a "
+        "future release. Please use `conda_build.metadata.get_selectors` instead.",
+        PendingDeprecationWarning,
+    )
+    return get_selectors(config)
+
+
+# Selectors must be either:
+# - at end of the line
+# - embedded (anywhere) within a comment
+#
+# Notes:
+# - [([^\[\]]+)\] means "find a pair of brackets containing any
+#                 NON-bracket chars, and capture the contents"
+# - (?(2)[^\(\)]*)$ means "allow trailing characters iff group 2 (#.*) was found."
+#                 Skip markdown link syntax.
+sel_pat = re.compile(r"(.+?)\s*(#.*)?\[([^\[\]]+)\](?(2)[^\(\)]*)$")
+
+
+# this function extracts the variable name from a NameError exception, it has the form of:
+# "NameError: name 'var' is not defined", where var is the variable that is not defined. This gets
+#    returned
+def parseNameNotFound(error):
+    m = re.search("'(.+?)'", str(error))
+    if len(m.groups()) == 1:
+        return m.group(1)
+    else:
+        return ""
+
+
+# We evaluate the selector and return True (keep this line) or False (drop this line)
+# If we encounter a NameError (unknown variable in selector), then we replace it by False and
+#     re-run the evaluation
+def eval_selector(selector_string, namespace, variants_in_place):
+    try:
+        # TODO: is there a way to do this without eval?  Eval allows arbitrary
+        #    code execution.
+        return eval(selector_string, namespace, {})
+    except NameError as e:
+        missing_var = parseNameNotFound(e)
+        if variants_in_place:
+            log = utils.get_logger(__name__)
+            log.debug(
+                "Treating unknown selector '" + missing_var + "' as if it was False."
+            )
+        next_string = selector_string.replace(missing_var, "False")
+        return eval_selector(next_string, namespace, variants_in_place)
+
+
+def select_lines(data, namespace, variants_in_place):
+    lines = []
+
+    for i, line in enumerate(data.splitlines()):
+        line = line.rstrip()
+
+        trailing_quote = ""
+        if line and line[-1] in ("'", '"'):
+            trailing_quote = line[-1]
+
+        if line.lstrip().startswith("#"):
+            # Don't bother with comment only lines
+            continue
+        m = sel_pat.match(line)
+        if m:
+            cond = m.group(3)
+            try:
+                if eval_selector(cond, namespace, variants_in_place):
+                    lines.append(m.group(1) + trailing_quote)
+            except Exception as e:
+                sys.exit(
+                    """\
+Error: Invalid selector in meta.yaml line %d:
+offending line:
+%s
+exception:
+%s
+"""
+                    % (i + 1, line, str(e))
+                )
+        else:
+            lines.append(line)
+    return "\n".join(lines) + "\n"
+
+
+def yamlize(data):
+    try:
+        return yaml.load(data, Loader=StringifyNumbersLoader)
+    except yaml.error.YAMLError as e:
+        if "{{" in data:
+            try:
+                import jinja2
+
+                jinja2  # Avoid pyflakes failure: 'jinja2' imported but unused
+            except ImportError:
+                raise exceptions.UnableToParseMissingJinja2(original=e)
+        print("Problematic recipe:", file=sys.stderr)
+        print(data, file=sys.stderr)
+        raise exceptions.UnableToParse(original=e)
+
+
+def ensure_valid_fields(meta):
+    pin_depends = meta.get("build", {}).get("pin_depends", "")
+    if pin_depends and pin_depends not in ("", "record", "strict"):
+        raise RuntimeError(
+            f"build/pin_depends must be 'record' or 'strict' - not '{pin_depends}'"
+        )
+
+
+def _trim_None_strings(meta_dict):
+    log = utils.get_logger(__name__)
+    for key, value in meta_dict.items():
+        if hasattr(value, "keys"):
+            meta_dict[key] = _trim_None_strings(value)
+        elif value and hasattr(value, "__iter__") or isinstance(value, str):
+            if isinstance(value, str):
+                meta_dict[key] = None if "None" in value else value
+            else:
+                # support lists of dicts (homogeneous)
+                keep = []
+                if hasattr(next(iter(value)), "keys"):
+                    for d in value:
+                        trimmed_dict = _trim_None_strings(d)
+                        if trimmed_dict:
+                            keep.append(trimmed_dict)
+                # support lists of strings (homogeneous)
+                else:
+                    keep = [i for i in value if i not in ("None", "NoneType")]
+                meta_dict[key] = keep
+        else:
+            log.debug(
+                f"found unrecognized data type in dictionary: {value}, type: {type(value)}"
+            )
+    return meta_dict
+
+
+def ensure_valid_noarch_value(meta):
+    build_noarch = meta.get("build", {}).get("noarch")
+    if build_noarch and build_noarch not in NOARCH_TYPES:
+        raise exceptions.CondaBuildException(
+            "Invalid value for noarch: %s" % build_noarch
+        )
+
+
+def _get_all_dependencies(metadata, envs=("host", "build", "run")):
+    reqs = []
+    for _env in envs:
+        reqs.extend(metadata.meta.get("requirements", {}).get(_env, []))
+    return reqs
+
+
+def check_circular_dependencies(render_order, config=None):
+    if config and config.host_subdir != config.build_subdir:
+        # When cross compiling build dependencies are already built
+        # and cannot come from the recipe as subpackages
+        envs = ("host", "run")
+    else:
+        envs = ("build", "host", "run")
+    pairs = []
+    for idx, m in enumerate(render_order.values()):
+        for other_m in list(render_order.values())[idx + 1 :]:
+            if any(
+                m.name() == dep or dep.startswith(m.name() + " ")
+                for dep in _get_all_dependencies(other_m, envs=envs)
+            ) and any(
+                other_m.name() == dep or dep.startswith(other_m.name() + " ")
+                for dep in _get_all_dependencies(m, envs=envs)
+            ):
+                pairs.append((m.name(), other_m.name()))
+    if pairs:
+        error = "Circular dependencies in recipe: \n"
+        for pair in pairs:
+            error += "    {} <-> {}\n".format(*pair)
+        raise exceptions.RecipeError(error)
+
+
+def _variants_equal(metadata, output_metadata):
+    match = True
+    for key, val in metadata.config.variant.items():
+        if (
+            key in output_metadata.config.variant
+            and val != output_metadata.config.variant[key]
+        ):
+            match = False
+    return match
+
+
+def ensure_matching_hashes(output_metadata):
+    envs = "build", "host", "run"
+    problemos = []
+    for _, m in output_metadata.values():
+        for _, om in output_metadata.values():
+            if m != om:
+                run_exports = om.get_value("build/run_exports", [])
+                if hasattr(run_exports, "keys"):
+                    run_exports_list = []
+                    for export_type in utils.RUN_EXPORTS_TYPES:
+                        run_exports_list = run_exports_list + run_exports.get(
+                            export_type, []
+                        )
+                    run_exports = run_exports_list
+                deps = _get_all_dependencies(om, envs) + run_exports
+                for dep in deps:
+                    if (
+                        dep.startswith(m.name() + " ")
+                        and len(dep.split(" ")) == 3
+                        and dep.split(" ")[-1] != m.build_id()
+                        and _variants_equal(m, om)
+                    ):
+                        problemos.append((m.name(), m.build_id(), dep, om.name()))
+
+    if problemos:
+        error = ""
+        for prob in problemos:
+            error += "Mismatching package: {} (id {}); dep: {}; consumer package: {}\n".format(
+                *prob
+            )
+        raise exceptions.RecipeError(
+            "Mismatching hashes in recipe. Exact pins in dependencies "
+            "that contribute to the hash often cause this. Can you "
+            "change one or more exact pins to version bound constraints?\n"
+            "Involved packages were:\n" + error
+        )
+
+
+def parse(data, config, path=None):
+    data = select_lines(
+        data,
+        get_selectors(config),
+        variants_in_place=bool(config.variant),
+    )
+    res = yamlize(data)
+    # ensure the result is a dict
+    if res is None:
+        res = {}
+    for field in FIELDS:
+        if field not in res:
+            continue
+        # ensure that empty fields are dicts (otherwise selectors can cause invalid fields)
+        if not res[field]:
+            res[field] = {}
+        # source field may be either a dictionary, or a list of dictionaries
+        if field in OPTIONALLY_ITERABLE_FIELDS:
+            if not (
+                isinstance(res[field], dict)
+                or (hasattr(res[field], "__iter__") and not isinstance(res[field], str))
+            ):
+                raise RuntimeError(
+                    f"The {field} field should be a dict or list of dicts, not "
+                    f"{res[field].__class__.__name__} in file {path}."
+                )
+        else:
+            if not isinstance(res[field], dict):
+                raise RuntimeError(
+                    f"The {field} field should be a dict, not "
+                    f"{res[field].__class__.__name__} in file {path}."
+                )
+
+    ensure_valid_fields(res)
+    ensure_valid_license_family(res)
+    ensure_valid_noarch_value(res)
+    return sanitize(res)
+
+
+TRUES = {"y", "on", "true", "yes"}
+FALSES = {"n", "no", "false", "off"}
+
+# If you update this please update the example in
+# conda-docs/docs/source/build.rst
+FIELDS = {
+    "package": {
+        "name": None,
+        "version": str,
+    },
+    "source": {
+        "fn": None,
+        "url": None,
+        "md5": str,
+        "sha1": None,
+        "sha256": None,
+        "path": str,
+        "path_via_symlink": None,
+        "git_url": str,
+        "git_tag": str,
+        "git_branch": str,
+        "git_rev": str,
+        "git_depth": None,
+        "hg_url": None,
+        "hg_tag": None,
+        "svn_url": str,
+        "svn_rev": None,
+        "svn_ignore_externals": None,
+        "svn_username": None,
+        "svn_password": None,
+        "folder": None,
+        "no_hoist": None,
+        "patches": list,
+    },
+    "build": {
+        "number": None,
+        "string": str,
+        "entry_points": list,
+        "osx_is_app": bool,
+        "disable_pip": None,
+        "features": list,
+        "track_features": list,
+        "preserve_egg_dir": bool,
+        "no_link": None,
+        "binary_relocation": bool,
+        "script": list,
+        "noarch": str,
+        "noarch_python": bool,
+        "has_prefix_files": None,
+        "binary_has_prefix_files": None,
+        "ignore_prefix_files": None,
+        "detect_binary_files_with_prefix": bool,
+        "skip_compile_pyc": list,
+        "rpaths": None,
+        "rpaths_patcher": None,
+        "script_env": list,
+        "always_include_files": None,
+        "skip": bool,
+        "msvc_compiler": str,
+        "pin_depends": str,  # still experimental
+        "include_recipe": None,
+        "preferred_env": str,
+        "preferred_env_executable_paths": list,
+        "run_exports": list,
+        "ignore_run_exports": list,
+        "ignore_run_exports_from": list,
+        "requires_features": dict,
+        "provides_features": dict,
+        "force_use_keys": list,
+        "force_ignore_keys": list,
+        "merge_build_host": None,
+        "pre-link": str,
+        "post-link": str,
+        "pre-unlink": str,
+        "missing_dso_whitelist": None,
+        "error_overdepending": None,
+        "error_overlinking": None,
+        "overlinking_ignore_patterns": [],
+    },
+    "outputs": {
+        "name": None,
+        "version": None,
+        "number": None,
+        "entry_points": None,
+        "script": None,
+        "script_interpreter": None,
+        "build": None,
+        "requirements": None,
+        "test": None,
+        "about": None,
+        "extra": None,
+        "files": None,
+        "type": None,
+        "run_exports": None,
+        "target": None,
+    },
+    "requirements": {
+        "build": list,
+        "host": list,
+        "run": list,
+        "conflicts": list,
+        "run_constrained": list,
+    },
+    "app": {
+        "entry": None,
+        "icon": None,
+        "summary": None,
+        "type": None,
+        "cli_opts": None,
+        "own_environment": bool,
+    },
+    "test": {
+        "requires": list,
+        "commands": list,
+        "files": list,
+        "imports": list,
+        "source_files": list,
+        "downstreams": list,
+    },
+    "about": {
+        "home": None,
+        # these are URLs
+        "dev_url": None,
+        "doc_url": None,
+        "doc_source_url": None,
+        "license_url": None,
+        # text
+        "license": None,
+        "summary": None,
+        "description": None,
+        "license_family": None,
+        # lists
+        "identifiers": list,
+        "tags": list,
+        "keywords": list,
+        # paths in source tree
+        "license_file": None,
+        "prelink_message": None,
+        "readme": None,
+    },
+    "extra": {},
+}
+
+# Fields that may either be a dictionary or a list of dictionaries.
+OPTIONALLY_ITERABLE_FIELDS = ("source", "outputs")
+
+
+def sanitize(meta):
+    """
+    Sanitize the meta-data to remove aliases/handle deprecation
+    """
+    sanitize_funs = {
+        "source": [_git_clean],
+        "package": [_str_version],
+        "build": [_str_version],
+    }
+    for section, funs in sanitize_funs.items():
+        if section in meta:
+            for func in funs:
+                section_data = meta[section]
+                # section is a dictionary
+                if hasattr(section_data, "keys"):
+                    section_data = func(section_data)
+                # section is a list of dictionaries
+                else:
+                    section_data = [func(_d) for _d in section_data]
+                meta[section] = section_data
+    return meta
+
+
+def _git_clean(source_meta):
+    """
+    Reduce the redundancy in git specification by removing git_tag and
+    git_branch.
+
+    If one is specified, copy to git_rev.
+
+    If more than one field is used to specified, exit
+    and complain.
+    """
+
+    git_rev_tags_old = ("git_branch", "git_tag")
+    git_rev = "git_rev"
+
+    git_rev_tags = (git_rev,) + git_rev_tags_old
+
+    has_rev_tags = tuple(bool(source_meta.get(tag, "")) for tag in git_rev_tags)
+    if sum(has_rev_tags) > 1:
+        msg = "Error: multiple git_revs:"
+        msg += ", ".join(
+            f"{key}" for key, has in zip(git_rev_tags, has_rev_tags) if has
+        )
+        sys.exit(msg)
+
+    # make a copy of the input so we have no side-effects
+    ret_meta = source_meta.copy()
+    # loop over the old versions
+    for key, has in zip(git_rev_tags[1:], has_rev_tags[1:]):
+        # update if needed
+        if has:
+            ret_meta[git_rev_tags[0]] = ret_meta[key]
+        # and remove
+        ret_meta.pop(key, None)
+
+    return ret_meta
+
+
+def _str_version(package_meta):
+    if "version" in package_meta:
+        package_meta["version"] = str(package_meta.get("version", ""))
+    if "msvc_compiler" in package_meta:
+        package_meta["msvc_compiler"] = str(package_meta.get("msvc_compiler", ""))
+    return package_meta
+
+
+def check_bad_chrs(s, field):
+    bad_chrs = "=@#$%^&*:;\"'\\|<>?/ "
+    if field in ("package/version", "build/string"):
+        bad_chrs += "-"
+    if field != "package/version":
+        bad_chrs += "!"
+    for c in bad_chrs:
+        if c in s:
+            sys.exit(f"Error: bad character '{c}' in {field}: {s}")
+
+
+def get_package_version_pin(build_reqs, name):
+    version = ""
+    for spec in build_reqs:
+        if spec.split()[0] == name and len(spec.split()) > 1:
+            version = spec.split()[1]
+    return version
+
+
+def build_string_from_metadata(metadata):
+    if metadata.meta.get("build", {}).get("string"):
+        build_str = metadata.get_value("build/string")
+    else:
+        res = []
+        build_or_host = "host" if metadata.is_cross else "build"
+        build_pkg_names = [ms.name for ms in metadata.ms_depends(build_or_host)]
+        build_deps = metadata.meta.get("requirements", {}).get(build_or_host, [])
+        # TODO: this is the bit that puts in strings like py27np111 in the filename.  It would be
+        #    nice to get rid of this, since the hash supercedes that functionally, but not clear
+        #    whether anyone's tools depend on this file naming right now.
+        for s, names, places in (
+            ("np", "numpy", 2),
+            ("py", "python", 2),
+            ("pl", "perl", 3),
+            ("lua", "lua", 2),
+            ("r", ("r", "r-base"), 2),
+            ("mro", "mro-base", 3),
+            ("mro", "mro-base_impl", 3),
+        ):
+            for ms in metadata.ms_depends("run"):
+                for name in ensure_list(names):
+                    if ms.name == name and name in build_pkg_names:
+                        # only append numpy when it is actually pinned
+                        if name == "numpy" and not metadata.numpy_xx:
+                            continue
+                        if metadata.noarch == name or (
+                            metadata.get_value("build/noarch_python")
+                            and name == "python"
+                        ):
+                            res.append(s)
+                        else:
+                            pkg_names = list(ensure_list(names))
+                            pkg_names.extend(
+                                [
+                                    _n.replace("-", "_")
+                                    for _n in ensure_list(names)
+                                    if "-" in _n
+                                ]
+                            )
+                            for _n in pkg_names:
+                                variant_version = get_package_version_pin(
+                                    build_deps, _n
+                                ) or metadata.config.variant.get(
+                                    _n.replace("-", "_"), ""
+                                )
+                                if variant_version:
+                                    break
+                            entry = "".join([s] + variant_version.split(".")[:places])
+                            if entry not in res:
+                                res.append(entry)
+
+        features = ensure_list(metadata.get_value("build/features", []))
+        if res:
+            res.append("_")
+        if features:
+            res.extend(("_".join(features), "_"))
+        res.append(str(metadata.build_number()))
+        build_str = "".join(res)
+    return build_str
+
+
+# This really belongs in conda, and it is int conda.cli.common,
+#   but we don't presently have an API there.
+def _get_env_path(env_name_or_path):
+    if not os.path.isdir(env_name_or_path):
+        for envs_dir in list(envs_dirs) + [os.getcwd()]:
+            path = os.path.join(envs_dir, env_name_or_path)
+            if os.path.isdir(path):
+                env_name_or_path = path
+                break
+    bootstrap_metadir = os.path.join(env_name_or_path, "conda-meta")
+    if not os.path.isdir(bootstrap_metadir):
+        print("Bootstrap environment '%s' not found" % env_name_or_path)
+        sys.exit(1)
+    return env_name_or_path
+
+
+def _get_dependencies_from_environment(env_name_or_path):
+    path = _get_env_path(env_name_or_path)
+    # construct build requirements that replicate the given bootstrap environment
+    # and concatenate them to the build requirements from the recipe
+    bootstrap_metadata = get_installed_packages(path)
+    bootstrap_requirements = []
+    for package, data in bootstrap_metadata.items():
+        bootstrap_requirements.append(
+            "{} {} {}".format(package, data["version"], data["build"])
+        )
+    return {"requirements": {"build": bootstrap_requirements}}
+
+
+def toposort(output_metadata_map):
+    """This function is used to work out the order to run the install scripts
+    for split packages based on any interdependencies. The result is just
+    a re-ordering of outputs such that we can run them in that order and
+    reset the initial set of files in the install prefix after each. This
+    will naturally lead to non-overlapping files in each package and also
+    the correct files being present during the install and test procedures,
+    provided they are run in this order."""
+    from .conda_interface import _toposort
+
+    # We only care about the conda packages built by this recipe. Non-conda
+    # packages get sorted to the end.
+    these_packages = [
+        output_d["name"]
+        for output_d in output_metadata_map
+        if output_d.get("type", "conda").startswith("conda")
+    ]
+    topodict = dict()
+    order = dict()
+    endorder = set()
+
+    for idx, (output_d, output_m) in enumerate(output_metadata_map.items()):
+        if output_d.get("type", "conda").startswith("conda"):
+            deps = output_m.get_value("requirements/run", []) + output_m.get_value(
+                "requirements/host", []
+            )
+            if not output_m.is_cross:
+                deps.extend(output_m.get_value("requirements/build", []))
+            name = output_d["name"]
+            order[name] = idx
+            topodict[name] = set()
+            for dep in deps:
+                dep = dep.split(" ")[0]
+                if dep in these_packages:
+                    topodict[name].update((dep,))
+        else:
+            endorder.add(idx)
+
+    topo_order = list(_toposort(topodict))
+    keys = [
+        k
+        for pkgname in topo_order
+        for k in output_metadata_map.keys()
+        if "name" in k and k["name"] == pkgname
+    ]
+    # not sure that this is working...  not everything has 'name', and not sure how this pans out
+    #    may end up excluding packages without the 'name' field
+    keys.extend(
+        [
+            k
+            for pkgname in endorder
+            for k in output_metadata_map.keys()
+            if ("name" in k and k["name"] == pkgname) or "name" not in k
+        ]
+    )
+    result = OrderedDict()
+    for key in keys:
+        result[key] = output_metadata_map[key]
+    return result
+
+
+def get_output_dicts_from_metadata(metadata, outputs=None):
+    outputs = outputs or metadata.get_section("outputs")
+
+    if not outputs:
+        outputs = [{"name": metadata.name()}]
+    else:
+        assert not hasattr(outputs, "keys"), (
+            "outputs specified as dictionary, but must be a "
+            "list of dictionaries.  YAML syntax is: \n\n"
+            "outputs:\n    - name: subpkg\n\n"
+            "(note the - before the inner dictionary)"
+        )
+        # make a metapackage for the top-level package if the top-level requirements
+        #     mention a subpackage,
+        # but only if a matching output name is not explicitly provided
+        if metadata.uses_subpackage and not any(
+            metadata.name() == out.get("name", "") for out in outputs
+        ):
+            outputs.append(OrderedDict(name=metadata.name()))
+    for out in outputs:
+        if (
+            "package:" in metadata.get_recipe_text()
+            and out.get("name") == metadata.name()
+        ):
+            combine_top_level_metadata_with_output(metadata, out)
+    return outputs
+
+
+def finalize_outputs_pass(
+    base_metadata,
+    render_order,
+    pass_no,
+    outputs=None,
+    permit_unsatisfiable_variants=False,
+    bypass_env_check=False,
+):
+    from .render import finalize_metadata
+
+    outputs = OrderedDict()
+    # each of these outputs can have a different set of dependency versions from each other,
+    #    but also from base_metadata
+    for output_d, metadata in render_order.values():
+        if metadata.skip():
+            continue
+        try:
+            log = utils.get_logger(__name__)
+            # We should reparse the top-level recipe to get all of our dependencies fixed up.
+            # we base things on base_metadata because it has the record of the full origin recipe
+            if base_metadata.config.verbose:
+                log.info(f"Attempting to finalize metadata for {metadata.name()}")
+            # Using base_metadata is important for keeping the reference to the parent recipe
+            om = base_metadata.copy()
+            # other_outputs is the context of what's available for
+            # pin_subpackage. It's stored on the metadata object here, but not
+            # on base_metadata, which om is a copy of. Before we do
+            # re-rendering of om's metadata, we need to have other_outputs in
+            # place, so it can refer to it for any pin_subpackage stuff it has.
+            om.other_outputs = metadata.other_outputs
+            om.config.variant = metadata.config.variant
+            parent_metadata = om.copy()
+
+            om.other_outputs.update(outputs)
+            om.final = False
+            # get the new output_d from the reparsed top-level metadata, so that we have any
+            #    exact subpackage version/build string info
+            output_d = om.get_rendered_output(metadata.name()) or {
+                "name": metadata.name()
+            }
+
+            om = om.get_output_metadata(output_d)
+            replacements = None
+            if "replacements" in parent_metadata.config.variant:
+                replacements = parent_metadata.config.variant["replacements"]
+                del parent_metadata.config.variant["replacements"]
+            parent_metadata.parse_until_resolved()
+            if replacements:
+                parent_metadata.config.variant["replacements"] = replacements
+
+            if not bypass_env_check:
+                fm = finalize_metadata(
+                    om,
+                    parent_metadata=parent_metadata,
+                    permit_unsatisfiable_variants=permit_unsatisfiable_variants,
+                )
+            else:
+                fm = om
+            if not output_d.get("type") or output_d.get("type").startswith("conda"):
+                outputs[
+                    (
+                        fm.name(),
+                        HashableDict(
+                            {
+                                k: copy.deepcopy(fm.config.variant[k])
+                                for k in fm.get_used_vars()
+                            }
+                        ),
+                    )
+                ] = (output_d, fm)
+        except exceptions.DependencyNeedsBuildingError as e:
+            if not permit_unsatisfiable_variants:
+                raise
+            else:
+                log = utils.get_logger(__name__)
+                log.warn(
+                    "Could not finalize metadata due to missing dependencies: "
+                    f"{e.packages}"
+                )
+                outputs[
+                    (
+                        metadata.name(),
+                        HashableDict(
+                            {
+                                k: copy.deepcopy(metadata.config.variant[k])
+                                for k in metadata.get_used_vars()
+                            }
+                        ),
+                    )
+                ] = (output_d, metadata)
+    # in-place modification
+    base_metadata.other_outputs = outputs
+    base_metadata.final = False
+    final_outputs = OrderedDict()
+    for k, (out_d, m) in outputs.items():
+        final_outputs[
+            (
+                m.name(),
+                HashableDict(
+                    {k: copy.deepcopy(m.config.variant[k]) for k in m.get_used_vars()}
+                ),
+            )
+        ] = (out_d, m)
+    return final_outputs
+
+
+def get_updated_output_dict_from_reparsed_metadata(original_dict, new_outputs):
+    output_d = original_dict
+    if "name" in original_dict:
+        output_ds = [
+            out
+            for out in new_outputs
+            if "name" in out and out["name"] == original_dict["name"]
+        ]
+        assert len(output_ds) == 1
+        output_d = output_ds[0]
+    return output_d
+
+
+def _filter_recipe_text(text, extract_pattern=None):
+    if extract_pattern:
+        match = re.search(extract_pattern, text, flags=re.MULTILINE | re.DOTALL)
+        text = (
+            "\n".join({string for string in match.groups() if string}) if match else ""
+        )
+    return text
+
+
+@lru_cache(maxsize=None)
+def read_meta_file(meta_path):
+    with open(meta_path, "rb") as f:
+        recipe_text = UnicodeDammit(f.read()).unicode_markup
+    if hasattr(recipe_text, "decode"):
+        recipe_text = recipe_text.decode()
+    return recipe_text
+
+
+def combine_top_level_metadata_with_output(metadata, output):
+    """Merge top-level metadata into output when output is same name as top-level"""
+    sections = ("requirements", "build", "about")
+    for section in sections:
+        metadata_section = metadata.meta.get(section, {}) or {}
+        output_section = output.get(section, {}) or {}
+        if section == "requirements":
+            output_section = utils.expand_reqs(output.get(section, {}))
+        for k, v in metadata_section.items():
+            if k not in output_section:
+                output_section[k] = v
+        output[section] = output_section
+        # synchronize them
+        metadata.meta[section] = output_section
+
+
+def trim_build_only_deps(metadata, requirements_used):
+    """things can be used as dependencies or elsewhere in the recipe.  If it's only used
+    elsewhere, keep it. If it's a dep-related thing, only keep it if
+    it's in the build deps."""
+
+    # filter out things that occur only in run requirements.  These don't actually affect the
+    #     outcome of the package.
+    output_reqs = utils.expand_reqs(metadata.meta.get("requirements", {}))
+    build_reqs = utils.ensure_list(output_reqs.get("build", []))
+    host_reqs = utils.ensure_list(output_reqs.get("host", []))
+    run_reqs = output_reqs.get("run", [])
+    build_reqs = {req.split()[0].replace("-", "_") for req in build_reqs if req}
+    host_reqs = {req.split()[0].replace("-", "_") for req in host_reqs if req}
+
+    to_remove = set()
+    ignore_build_only_deps = sorted(
+        utils.ensure_list(metadata.config.variant.get("ignore_build_only_deps", []))
+    )
+
+    for dep in requirements_used:
+        # filter out stuff that's only in run deps
+        if dep in run_reqs:
+            if (
+                dep not in build_reqs
+                and dep not in host_reqs
+                and dep in requirements_used
+            ):
+                to_remove.add(dep)
+        else:
+            if (
+                dep in build_reqs
+                and dep not in host_reqs
+                and dep in requirements_used
+                and dep in ignore_build_only_deps
+            ):
+                to_remove.add(dep)
+
+    return requirements_used - to_remove
+
+
+def _hash_dependencies(hashing_dependencies, hash_length):
+    hash_ = hashlib.sha1(json.dumps(hashing_dependencies, sort_keys=True).encode())
+    # save only the first HASH_LENGTH characters - should be more than
+    #    enough, since these only need to be unique within one version
+    # plus one is for the h - zero pad on the front, trim to match HASH_LENGTH
+    return f"h{hash_.hexdigest()}"[: hash_length + 1]
+
+
+class MetaData:
+    def __init__(self, path, config=None, variant=None):
+        self.undefined_jinja_vars = []
+        self.config = get_or_merge_config(config, variant=variant)
+
+        if isfile(path):
+            self._meta_path = path
+            self._meta_name = os.path.basename(path)
+            self.path = os.path.dirname(path)
+        else:
+            self._meta_path = find_recipe(path)
+            self._meta_name = "meta.yaml"
+            self.path = os.path.dirname(self.meta_path)
+        self.requirements_path = join(self.path, "requirements.txt")
+
+        # Start with bare-minimum contents so we can call environ.get_dict() with impunity
+        # We'll immediately replace these contents in parse_again()
+        self.meta = dict()
+
+        # This is the 'first pass' parse of meta.yaml, so not all variables are defined yet
+        # (e.g. GIT_FULL_HASH, etc. are undefined)
+        # Therefore, undefined jinja variables are permitted here
+        # In the second pass, we'll be more strict. See build.build()
+        # Primarily for debugging.  Ensure that metadata is not altered after "finalizing"
+        self.parse_again(permit_undefined_jinja=True, allow_no_other_outputs=True)
+        self.config.disable_pip = self.disable_pip
+        # establish whether this recipe should squish build and host together
+
+    @property
+    def is_cross(self) -> bool:
+        return bool(
+            self.get_depends_top_and_out("host")
+            or "host" in self.get_section("requirements")
+        )
+
+    @property
+    def final(self) -> bool:
+        return bool(self.get_value("extra/final"))
+
+    @final.setter
+    def final(self, value: bool) -> None:
+        self.meta.setdefault("extra", {})["final"] = bool(value)
+
+    @property
+    def disable_pip(self) -> bool:
+        return bool(self.config.disable_pip or self.get_value("build/disable_pip"))
+
+    @disable_pip.setter
+    def disable_pip(self, value: bool) -> None:
+        self.config.disable_pip = bool(value)
+        self.meta.setdefault("build", {})["disable_pip"] = bool(value)
+
+    def append_metadata_sections(
+        self, sections_file_or_dict, merge, raise_on_clobber=False
+    ):
+        """Append to or replace subsections to meta.yaml
+
+        This is used to alter input recipes, so that a given requirement or
+        setting is applied without manually altering the input recipe. It is
+        intended for vendors who want to extend existing recipes without
+        necessarily removing information. pass merge=False to replace sections.
+        """
+        if hasattr(sections_file_or_dict, "keys"):
+            build_config = sections_file_or_dict
+        else:
+            with open(sections_file_or_dict) as configfile:
+                build_config = parse(configfile.read(), config=self.config)
+        utils.merge_or_update_dict(
+            self.meta,
+            build_config,
+            self.path,
+            merge=merge,
+            raise_on_clobber=raise_on_clobber,
+        )
+
+    @property
+    def is_output(self) -> str:
+        parent_name = self.get_value("extra/parent_recipe", {}).get("name")
+        return parent_name and parent_name != self.name()
+
+    def parse_again(
+        self,
+        permit_undefined_jinja=False,
+        allow_no_other_outputs=False,
+        bypass_env_check=False,
+        **kw,
+    ):
+        """Redo parsing for key-value pairs that are not initialized in the
+        first pass.
+
+        config: a conda-build Config object.  If None, the config object passed at creation
+                time is used.
+
+        permit_undefined_jinja: If True, *any* use of undefined jinja variables will
+                                evaluate to an emtpy string, without emitting an error.
+        """
+        assert not self.final, "modifying metadata after finalization"
+
+        log = utils.get_logger(__name__)
+        if kw:
+            log.warn(
+                "using unsupported internal conda-build function `parse_again`.  Please use "
+                "conda_build.api.render instead."
+            )
+
+        append_sections_file = None
+        clobber_sections_file = None
+        # we sometimes create metadata from dictionaries, in which case we'll have no path
+        if self.meta_path:
+            self.meta = parse(
+                self._get_contents(
+                    permit_undefined_jinja,
+                    allow_no_other_outputs=allow_no_other_outputs,
+                    bypass_env_check=bypass_env_check,
+                ),
+                config=self.config,
+                path=self.meta_path,
+            )
+
+            append_sections_file = os.path.join(self.path, "recipe_append.yaml")
+            clobber_sections_file = os.path.join(self.path, "recipe_clobber.yaml")
+
+        append_sections_file = self.config.append_sections_file or append_sections_file
+        if append_sections_file and not os.path.isfile(append_sections_file):
+            log.debug(
+                "input append sections file did not exist: %s", append_sections_file
+            )
+            append_sections_file = None
+        clobber_sections_file = (
+            self.config.clobber_sections_file or clobber_sections_file
+        )
+        if clobber_sections_file and not os.path.isfile(clobber_sections_file):
+            log.debug(
+                "input clobber sections file did not exist: %s", clobber_sections_file
+            )
+            clobber_sections_file = None
+
+        if append_sections_file:
+            self.append_metadata_sections(append_sections_file, merge=True)
+        if clobber_sections_file:
+            self.append_metadata_sections(clobber_sections_file, merge=False)
+        if self.config.bootstrap:
+            dependencies = _get_dependencies_from_environment(self.config.bootstrap)
+            self.append_metadata_sections(dependencies, merge=True)
+
+        if "error_overlinking" in self.get_section("build"):
+            self.config.error_overlinking = self.meta["build"]["error_overlinking"]
+        if "error_overdepending" in self.get_section("build"):
+            self.config.error_overdepending = self.meta["build"]["error_overdepending"]
+
+        self.validate_features()
+        self.ensure_no_pip_requirements()
+
+    def ensure_no_pip_requirements(self):
+        for key in ("requirements/build", "requirements/run", "test/requires"):
+            if any(hasattr(item, "keys") for item in (self.get_value(key) or [])):
+                raise ValueError(
+                    "Dictionaries are not supported as values in requirements sections"
+                    ".  Note that pip requirements as used in conda-env "
+                    "environment.yml files are not supported by conda-build."
+                )
+
+    def append_requirements(self):
+        """For dynamic determination of build or run reqs, based on configuration"""
+        run_reqs = self.meta.setdefault("requirements", {}).setdefault("run", [])
+        if (
+            self.get_value("build/osx_is_app", False)
+            and self.config.platform == "osx"
+            and "python.app" not in run_reqs
+        ):
+            run_reqs.append("python.app")
+
+    def parse_until_resolved(
+        self, allow_no_other_outputs=False, bypass_env_check=False
+    ):
+        """variant contains key-value mapping for additional functions and values
+        for jinja2 variables"""
+        # undefined_jinja_vars is refreshed by self.parse again
+        undefined_jinja_vars = ()
+        # store the "final" state that we think we're in.  reloading the meta.yaml file
+        #   can reset it (to True)
+        final = self.final
+        # always parse again at least once.
+        self.parse_again(
+            permit_undefined_jinja=True,
+            allow_no_other_outputs=allow_no_other_outputs,
+            bypass_env_check=bypass_env_check,
+        )
+        self.final = final
+
+        while set(undefined_jinja_vars) != set(self.undefined_jinja_vars):
+            undefined_jinja_vars = self.undefined_jinja_vars
+            self.parse_again(
+                permit_undefined_jinja=True,
+                allow_no_other_outputs=allow_no_other_outputs,
+                bypass_env_check=bypass_env_check,
+            )
+            self.final = final
+        if undefined_jinja_vars:
+            self.parse_again(
+                permit_undefined_jinja=False,
+                allow_no_other_outputs=allow_no_other_outputs,
+                bypass_env_check=bypass_env_check,
+            )
+            sys.exit(
+                f"Undefined Jinja2 variables remain ({self.undefined_jinja_vars}).  Please enable "
+                "source downloading and try again."
+            )
+
+        # always parse again at the end, too.
+        self.parse_again(
+            permit_undefined_jinja=False,
+            allow_no_other_outputs=allow_no_other_outputs,
+            bypass_env_check=bypass_env_check,
+        )
+        self.final = final
+
+    @classmethod
+    def fromstring(cls, metadata, config=None, variant=None):
+        m = super().__new__(cls)
+        m.path = ""
+        m._meta_path = ""
+        m.requirements_path = ""
+        config = config or Config(variant=variant)
+        m.meta = parse(metadata, config=config, path="")
+        m.config = config
+        m.parse_again(permit_undefined_jinja=True)
+        return m
+
+    @classmethod
+    def fromdict(cls, metadata, config=None, variant=None):
+        """
+        Create a MetaData object from metadata dict directly.
+        """
+        m = super().__new__(cls)
+        m.path = ""
+        m._meta_path = ""
+        m.requirements_path = ""
+        m.meta = sanitize(metadata)
+        m.config = config or Config(variant=variant)
+        m.undefined_jinja_vars = []
+        m.final = False
+        return m
+
+    @overload
+    def get_section(self, section: Literal["source", "outputs"]) -> list[dict]:
+        ...
+
+    @overload
+    def get_section(
+        self,
+        section: Literal[
+            "package",
+            "build",
+            "requirements",
+            "app",
+            "test",
+            "about",
+            "extra",
+        ],
+    ) -> dict:
+        ...
+
+    def get_section(self, name):
+        section = self.meta.get(name)
+        if name in OPTIONALLY_ITERABLE_FIELDS:
+            if not section:
+                return []
+            elif isinstance(section, dict):
+                return [section]
+            elif not isinstance(section, list):
+                raise ValueError(f"Expected {name} to be a list")
+        else:
+            if not section:
+                return {}
+            elif not isinstance(section, dict):
+                raise ValueError(f"Expected {name} to be a dict")
+        return section
+
+    def get_value(self, name, default=None, autotype=True):
+        """
+        Get a value from a meta.yaml.
+        :param field: Field to return, e.g. 'package/name'.
+                      If the section might be a list, specify an index,
+                      e.g. 'source/0/git_url'.
+        :param default: Default object to return if field doesn't exist
+        :param autotype: If True, return the default type of field if one exists.
+                         False will return the default object.
+        :return: The named value from meta.yaml
+        """
+        names = name.split("/")
+        assert len(names) in (2, 3), "Bad field name: " + name
+        if len(names) == 2:
+            section, key = names
+            index = None
+        elif len(names) == 3:
+            section, index, key = names
+            assert section in OPTIONALLY_ITERABLE_FIELDS, (
+                "Section is not a list: " + section
+            )
+            index = int(index)
+
+        # get correct default
+        if autotype and default is None and FIELDS.get(section, {}).get(key):
+            default = FIELDS[section][key]()
+
+        section_data = self.get_section(section)
+        if isinstance(section_data, dict):
+            assert (
+                not index
+            ), f"Got non-zero index ({index}), but section {section} is not a list."
+        elif isinstance(section_data, list):
+            # The 'source' section can be written a list, in which case the name
+            # is passed in with an index, e.g. get_value('source/0/git_url')
+            if index is None:
+                log = utils.get_logger(__name__)
+                log.warn(
+                    f"No index specified in get_value('{name}'). Assuming index 0."
+                )
+                index = 0
+
+            if not section_data:
+                section_data = {}
+            else:
+                section_data = section_data[index]
+                assert isinstance(
+                    section_data, dict
+                ), f"Expected {section}/{index} to be a dict"
+
+        value = section_data.get(key, default)
+
+        # handle yaml 1.1 boolean values
+        if isinstance(value, str):
+            if value.lower() in TRUES:
+                value = True
+            elif value.lower() in FALSES:
+                value = False
+
+        if value is None:
+            value = default
+
+        return value
+
+    def check_fields(self):
+        def check_field(key, section):
+            if key not in FIELDS[section]:
+                raise ValueError(f"in section {section!r}: unknown key {key!r}")
+
+        for section, submeta in self.meta.items():
+            # anything goes in the extra section
+            if section == "extra":
+                continue
+            if section not in FIELDS:
+                raise ValueError("unknown section: %s" % section)
+            for key_or_dict in submeta:
+                if section in OPTIONALLY_ITERABLE_FIELDS and isinstance(
+                    key_or_dict, dict
+                ):
+                    for key in key_or_dict.keys():
+                        check_field(key, section)
+                else:
+                    check_field(key_or_dict, section)
+        return True
+
+    def name(self) -> str:
+        name = self.get_value("package/name", "")
+        if not name and self.final:
+            sys.exit("Error: package/name missing in: %r" % self.meta_path)
+        name = str(name)
+        if name != name.lower():
+            sys.exit("Error: package/name must be lowercase, got: %r" % name)
+        check_bad_chrs(name, "package/name")
+        return name
+
+    def version(self) -> str:
+        version = self.get_value("package/version", "")
+        if not version and not self.get_section("outputs") and self.final:
+            sys.exit("Error: package/version missing in: %r" % self.meta_path)
+        version = str(version)
+        check_bad_chrs(version, "package/version")
+        if self.final and version.startswith("."):
+            raise ValueError(
+                "Fully-rendered version can't start with period -  got %s", version
+            )
+        return version
+
+    def build_number(self):
+        number = self.get_value("build/number")
+
+        # build number can come back as None if no setting (or jinja intermediate)
+        if not number:
+            return 0
+
+        try:
+            return int(number)
+        except (ValueError, TypeError):
+            raise ValueError(
+                f"Build number was invalid value '{number}'. Must be an integer."
+            )
+
+    def get_depends_top_and_out(self, typ):
+        meta_requirements = ensure_list(self.get_value("requirements/" + typ, []))[:]
+        req_names = {req.split()[0] for req in meta_requirements if req}
+        extra_reqs = []
+        # this is for the edge case of requirements for top-level being
+        # partially defined in a similarly named output
+        if not self.is_output:
+            matching_output = [
+                out
+                for out in self.get_section("outputs")
+                if out.get("name") == self.name()
+            ]
+            if matching_output:
+                extra_reqs = utils.expand_reqs(
+                    matching_output[0].get("requirements", [])
+                ).get(typ, [])
+                extra_reqs = [
+                    dep for dep in extra_reqs if dep.split()[0] not in req_names
+                ]
+        meta_requirements = [
+            req for req in (set(meta_requirements) | set(extra_reqs)) if req
+        ]
+        return meta_requirements
+
+    def ms_depends(self, typ="run"):
+        names = ("python", "numpy", "perl", "lua")
+        name_ver_list = [
+            (name, self.config.variant[name])
+            for name in names
+            if self.config.variant.get(name)
+        ]
+        if self.config.variant.get("r_base"):
+            # r is kept for legacy installations, r-base deprecates it.
+            name_ver_list.extend(
+                [
+                    ("r", self.config.variant["r_base"]),
+                    ("r-base", self.config.variant["r_base"]),
+                ]
+            )
+        specs = OrderedDict()
+        for spec in ensure_list(self.get_value("requirements/" + typ, [])):
+            if not spec:
+                continue
+            try:
+                ms = MatchSpec(spec)
+            except AssertionError:
+                raise RuntimeError("Invalid package specification: %r" % spec)
+            except (AttributeError, ValueError) as e:
+                raise RuntimeError(
+                    "Received dictionary as spec.  Note that pip requirements are "
+                    "not supported in conda-build meta.yaml.  Error message: " + str(e)
+                )
+            if ms.name == self.name() and not (
+                typ == "build" and self.config.host_subdir != self.config.build_subdir
+            ):
+                raise RuntimeError("%s cannot depend on itself" % self.name())
+            for name, ver in name_ver_list:
+                if ms.name == name:
+                    if self.noarch:
+                        continue
+
+            for c in "=!@#$%^&*:;\"'\\|<>?/":
+                if c in ms.name:
+                    sys.exit(
+                        f"Error: bad character '{c}' in package name "
+                        f"dependency '{ms.name}'"
+                    )
+            parts = spec.split()
+            if len(parts) >= 2:
+                if parts[1] in {">", ">=", "=", "==", "!=", "<", "<="}:
+                    msg = (
+                        f"Error: bad character '{parts[1]}' in package version "
+                        f"dependency '{ms.name}'"
+                    )
+                    if len(parts) >= 3:
+                        msg += f"\nPerhaps you meant '{ms.name} {parts[1]}{parts[2]}'"
+                    sys.exit(msg)
+            specs[spec] = ms
+        return list(specs.values())
+
+    def get_hash_contents(self):
+        """
+        # A hash will be added if all of these are true for any dependency:
+        #
+        # 1. package is an explicit dependency in build, host, or run deps
+        # 2. package has a matching entry in conda_build_config.yaml which is a pin to a specific
+        #    version, not a lower bound
+        # 3. that package is not ignored by ignore_version
+        #
+        # The hash is computed based on the pinning value, NOT the build
+        #    dependency build string. This means hashes won't change as often,
+        #    but it also means that if run_exports is overly permissive,
+        #    software may break more often.
+        #
+        # A hash will also ALWAYS be added when a compiler package is a build
+        #    or host dependency. Reasoning for that is that the compiler
+        #    package represents compiler flags and other things that can and do
+        #    dramatically change compatibility. It is much more risky to drop
+        #    this info (by dropping the hash) than it is for other software.
+
+        # used variables - anything with a value in conda_build_config.yaml that applies to this
+        #    recipe.  Includes compiler if compiler jinja2 function is used.
+        """
+        dependencies = set(self.get_used_vars())
+
+        trim_build_only_deps(self, dependencies)
+
+        # filter out ignored versions
+        build_string_excludes = ["python", "r_base", "perl", "lua"]
+        build_string_excludes.extend(
+            ensure_list(self.config.variant.get("ignore_version", []))
+        )
+        if "numpy" in dependencies:
+            pin_compatible, not_xx = self.uses_numpy_pin_compatible_without_xx
+            # numpy_xx means it is accounted for in the build string, with npXYY
+            # if not pin_compatible, then we don't care about the usage, and omit it from the hash.
+            if self.numpy_xx or (pin_compatible and not not_xx):
+                build_string_excludes.append("numpy")
+        # always exclude older stuff that's always in the build string (py, np, pl, r, lua)
+        if build_string_excludes:
+            exclude_pattern = re.compile(
+                "|".join(rf"{exc}[\s$]?.*" for exc in build_string_excludes)
+            )
+            dependencies = [
+                req
+                for req in dependencies
+                if not exclude_pattern.match(req) or " " in self.config.variant[req]
+            ]
+
+        # retrieve values - this dictionary is what makes up the hash.
+
+        # if dependencies are only 'target_platform' then ignore that.
+        if dependencies == ["target_platform"]:
+            hash_contents = {}
+        else:
+            hash_contents = {key: self.config.variant[key] for key in dependencies}
+
+        # include virtual packages in run
+        run_reqs = self.meta.get("requirements", {}).get("run", [])
+        virtual_pkgs = [req for req in run_reqs if req.startswith("__")]
+
+        # add name -> match spec mapping for virtual packages
+        hash_contents.update({pkg.split(" ")[0]: pkg for pkg in virtual_pkgs})
+        return hash_contents
+
+    def hash_dependencies(self):
+        """With arbitrary pinning, we can't depend on the build string as done in
+        build_string_from_metadata - there's just too much info.  Instead, we keep that as-is, to
+        not be disruptive, but we add this extra hash, which is just a way of distinguishing files
+        on disk.  The actual determination of dependencies is done in the repository metadata.
+
+        This was revised in conda-build 3.1.0: hashing caused too many package
+            rebuilds. We reduce the scope to include only the pins added by conda_build_config.yaml,
+            and no longer hash files that contribute to the recipe.
+        """
+        hash_ = ""
+        hashing_dependencies = self.get_hash_contents()
+        if hashing_dependencies:
+            return _hash_dependencies(hashing_dependencies, self.config.hash_length)
+        return hash_
+
+    def build_id(self):
+        manual_build_string = self.get_value("build/string")
+        # we need the raw recipe for this metadata (possibly an output), so that we can say whether
+        #    PKG_HASH is used for anything.
+        raw_recipe_text = self.extract_package_and_build_text()
+        if not manual_build_string and not raw_recipe_text:
+            raise RuntimeError(
+                f"Couldn't extract raw recipe text for {self.name()} output"
+            )
+        raw_recipe_text = self.extract_package_and_build_text()
+        raw_manual_build_string = re.search(r"\s*string:", raw_recipe_text)
+        # user setting their own build string.  Don't modify it.
+        if manual_build_string and not (
+            raw_manual_build_string
+            and re.findall(r"h\{\{\s*PKG_HASH\s*\}\}", raw_manual_build_string.string)
+        ):
+            check_bad_chrs(manual_build_string, "build/string")
+            out = manual_build_string
+        else:
+            # default; build/string not set or uses PKG_HASH variable, so we should fill in the hash
+            out = build_string_from_metadata(self)
+            if self.config.filename_hashing and self.final:
+                hash_ = self.hash_dependencies()
+                if not re.findall("h[0-9a-f]{%s}" % self.config.hash_length, out):
+                    ret = out.rsplit("_", 1)
+                    try:
+                        int(ret[0])
+                        out = "_".join((hash_, str(ret[0]))) if hash_ else str(ret[0])
+                    except ValueError:
+                        out = ret[0] + hash_
+                    if len(ret) > 1:
+                        out = "_".join([out] + ret[1:])
+                else:
+                    out = re.sub("h[0-9a-f]{%s}" % self.config.hash_length, hash_, out)
+        return out
+
+    def dist(self):
+        return f"{self.name()}-{self.version()}-{self.build_id()}"
+
+    def pkg_fn(self):
+        return "%s.tar.bz2" % self.dist()
+
+    def is_app(self):
+        return bool(self.get_value("app/entry"))
+
+    def app_meta(self):
+        d = {"type": "app"}
+        if self.get_value("app/icon"):
+            d["icon"] = "%s.png" % compute_sum(
+                join(self.path, self.get_value("app/icon")), "md5"
+            )
+
+        for field, key in [
+            ("app/entry", "app_entry"),
+            ("app/type", "app_type"),
+            ("app/cli_opts", "app_cli_opts"),
+            ("app/summary", "summary"),
+            ("app/own_environment", "app_own_environment"),
+        ]:
+            value = self.get_value(field)
+            if value:
+                d[key] = value
+        return d
+
+    def info_index(self):
+        arch = (
+            "noarch" if self.config.target_subdir == "noarch" else self.config.host_arch
+        )
+        d = dict(
+            name=self.name(),
+            version=self.version(),
+            build=self.build_id(),
+            build_number=self.build_number(),
+            platform=self.config.host_platform
+            if (self.config.host_platform != "noarch" and arch != "noarch")
+            else None,
+            arch=ARCH_MAP.get(arch, arch),
+            subdir=self.config.target_subdir,
+            depends=sorted(" ".join(ms.spec.split()) for ms in self.ms_depends()),
+            timestamp=int(time.time() * 1000),
+        )
+        for key in ("license", "license_family"):
+            value = self.get_value("about/" + key)
+            if value:
+                d[key] = value
+
+        preferred_env = self.get_value("build/preferred_env")
+        if preferred_env:
+            d["preferred_env"] = preferred_env
+
+        # conda 4.4+ optional dependencies
+        constrains = ensure_list(self.get_value("requirements/run_constrained"))
+        # filter None values
+        constrains = [v for v in constrains if v]
+        if constrains:
+            d["constrains"] = constrains
+
+        if self.get_value("build/features"):
+            d["features"] = " ".join(self.get_value("build/features"))
+        if self.get_value("build/track_features"):
+            d["track_features"] = " ".join(self.get_value("build/track_features"))
+        if self.get_value("build/provides_features"):
+            d["provides_features"] = self.get_value("build/provides_features")
+        if self.get_value("build/requires_features"):
+            d["requires_features"] = self.get_value("build/requires_features")
+        if self.noarch:
+            d["platform"] = d["arch"] = None
+            d["subdir"] = "noarch"
+            # These are new-style noarch settings.  the self.noarch setting can be True in 2 ways:
+            #    if noarch: True or if noarch_python: True.  This is disambiguation.
+            build_noarch = self.get_value("build/noarch")
+            if build_noarch:
+                d["noarch"] = build_noarch
+        if self.is_app():
+            d.update(self.app_meta())
+        return d
+
+    def has_prefix_files(self):
+        ret = ensure_list(self.get_value("build/has_prefix_files", []))
+        if not isinstance(ret, list):
+            raise RuntimeError("build/has_prefix_files should be a list of paths")
+        if on_win:
+            if any("\\" in i for i in ret):
+                raise RuntimeError(
+                    "build/has_prefix_files paths must use / "
+                    "as the path delimiter on Windows"
+                )
+        return expand_globs(ret, self.config.host_prefix)
+
+    def ignore_prefix_files(self):
+        ret = self.get_value("build/ignore_prefix_files", False)
+        if not isinstance(ret, (list, bool)):
+            raise RuntimeError(
+                "build/ignore_prefix_files should be boolean or a list of paths "
+                "(optionally globs)"
+            )
+        if on_win:
+            if isinstance(ret, list) and any("\\" in i for i in ret):
+                raise RuntimeError(
+                    "build/ignore_prefix_files paths must use / "
+                    "as the path delimiter on Windows"
+                )
+        return (
+            expand_globs(ret, self.config.host_prefix) if isinstance(ret, list) else ret
+        )
+
+    def always_include_files(self):
+        files = ensure_list(self.get_value("build/always_include_files", []))
+        if any("\\" in i for i in files):
+            raise RuntimeError(
+                "build/always_include_files paths must use / "
+                "as the path delimiter on Windows"
+            )
+        if on_win:
+            files = [f.replace("/", "\\") for f in files]
+
+        return expand_globs(files, self.config.host_prefix)
+
+    def ignore_verify_codes(self):
+        return ensure_list(self.get_value("build/ignore_verify_codes", []))
+
+    def binary_relocation(self):
+        ret = self.get_value("build/binary_relocation", True)
+        if not isinstance(ret, (list, bool)):
+            raise RuntimeError(
+                "build/binary_relocation should be boolean or a list of paths "
+                "(optionally globs)"
+            )
+        if on_win:
+            if isinstance(ret, list) and any("\\" in i for i in ret):
+                raise RuntimeError(
+                    "build/binary_relocation paths must use / "
+                    "as the path delimiter on Windows"
+                )
+        return (
+            expand_globs(ret, self.config.host_prefix) if isinstance(ret, list) else ret
+        )
+
+    def include_recipe(self) -> bool:
+        return bool(self.get_value("build/include_recipe", True))
+
+    def binary_has_prefix_files(self):
+        ret = ensure_list(self.get_value("build/binary_has_prefix_files", []))
+        if not isinstance(ret, list):
+            raise RuntimeError(
+                "build/binary_has_prefix_files should be a list of paths"
+            )
+        if on_win:
+            if any("\\" in i for i in ret):
+                raise RuntimeError(
+                    "build/binary_has_prefix_files paths must use / "
+                    "as the path delimiter on Windows"
+                )
+        return expand_globs(ret, self.config.host_prefix)
+
+    def skip(self) -> bool:
+        return bool(self.get_value("build/skip", False))
+
+    def _get_contents(
+        self,
+        permit_undefined_jinja,
+        allow_no_other_outputs=False,
+        bypass_env_check=False,
+        template_string=None,
+        skip_build_id=False,
+        alt_name=None,
+        variant=None,
+    ):
+        """
+        Get the contents of our [meta.yaml|conda.yaml] file.
+        If jinja is installed, then the template.render function is called
+        before standard conda macro processors.
+
+        permit_undefined_jinja: If True, *any* use of undefined jinja variables will
+                                evaluate to an emtpy string, without emitting an error.
+        """
+        try:
+            import jinja2
+        except ImportError:
+            print("There was an error importing jinja2.", file=sys.stderr)
+            print(
+                "Please run `conda install jinja2` to enable jinja template support",
+                file=sys.stderr,
+            )  # noqa
+            with open(self.meta_path) as fd:
+                return fd.read()
+
+        from .jinja_context import (
+            FilteredLoader,
+            UndefinedNeverFail,
+            context_processor,
+        )
+
+        path, filename = os.path.split(self.meta_path)
+        loaders = [  # search relative to '<conda_root>/Lib/site-packages/conda_build/templates'
+            jinja2.PackageLoader("conda_build"),
+            # search relative to RECIPE_DIR
+            jinja2.FileSystemLoader(path),
+        ]
+
+        # search relative to current conda environment directory
+        conda_env_path = os.environ.get(
+            "CONDA_DEFAULT_ENV"
+        )  # path to current conda environment
+        if conda_env_path and os.path.isdir(conda_env_path):
+            conda_env_path = os.path.abspath(conda_env_path)
+            conda_env_path = conda_env_path.replace("\\", "/")  # need unix-style path
+            env_loader = jinja2.FileSystemLoader(conda_env_path)
+            loaders.append(jinja2.PrefixLoader({"$CONDA_DEFAULT_ENV": env_loader}))
+
+        undefined_type = jinja2.StrictUndefined
+        if permit_undefined_jinja:
+            # The UndefinedNeverFail class keeps a global list of all undefined names
+            # Clear any leftover names from the last parse.
+            UndefinedNeverFail.all_undefined_names = []
+            undefined_type = UndefinedNeverFail
+
+        loader = FilteredLoader(jinja2.ChoiceLoader(loaders), config=self.config)
+        env = jinja2.Environment(loader=loader, undefined=undefined_type)
+
+        from .environ import get_dict
+
+        env.globals.update(get_selectors(self.config))
+        env.globals.update(get_dict(m=self, skip_build_id=skip_build_id))
+        env.globals.update({"CONDA_BUILD_STATE": "RENDER"})
+        env.globals.update(
+            context_processor(
+                self,
+                path,
+                config=self.config,
+                permit_undefined_jinja=permit_undefined_jinja,
+                allow_no_other_outputs=allow_no_other_outputs,
+                bypass_env_check=bypass_env_check,
+                skip_build_id=skip_build_id,
+                variant=variant,
+            )
+        )
+        # override PKG_NAME with custom value.  This gets used when an output needs to pretend
+        #   that it is top-level when getting the top-level recipe data.
+        if alt_name:
+            env.globals.update({"PKG_NAME": alt_name})
+
+        # Future goal here.  Not supporting jinja2 on replaced sections right now.
+
+        # we write a temporary file, so that we can dynamically replace sections in the meta.yaml
+        #     file on disk.  These replaced sections also need to have jinja2 filling in templates.
+        # The really hard part here is that we need to operate on plain text, because we need to
+        #     keep selectors and all that.
+
+        try:
+            if template_string:
+                template = env.from_string(template_string)
+            elif filename:
+                template = env.get_or_select_template(filename)
+            else:
+                template = env.from_string("")
+
+            os.environ["CONDA_BUILD_STATE"] = "RENDER"
+            rendered = template.render(environment=env)
+
+            if permit_undefined_jinja:
+                self.undefined_jinja_vars = UndefinedNeverFail.all_undefined_names
+            else:
+                self.undefined_jinja_vars = []
+
+        except jinja2.TemplateError as ex:
+            if "'None' has not attribute" in str(ex):
+                ex = "Failed to run jinja context function"
+            sys.exit(
+                f"Error: Failed to render jinja template in {self.meta_path}:\n{str(ex)}"
+            )
+        finally:
+            if "CONDA_BUILD_STATE" in os.environ:
+                del os.environ["CONDA_BUILD_STATE"]
+        return rendered
+
+    def __unicode__(self):
+        """
+        String representation of the MetaData.
+        """
+        return str(self.__dict__)
+
+    def __str__(self):
+        return self.__unicode__()
+
+    def __repr__(self):
+        """
+        String representation of the MetaData.
+        """
+        return self.__str__()
+
+    @property
+    def meta_path(self):
+        meta_path = (
+            self._meta_path
+            # get the parent recipe path if this is a subpackage
+            or self.get_value("extra/parent_recipe", {}).get("path", "")
+        )
+        if meta_path and os.path.basename(meta_path) != self._meta_name:
+            meta_path = os.path.join(meta_path, self._meta_name)
+        return meta_path
+
+    @property
+    def uses_setup_py_in_meta(self):
+        meta_text = ""
+        if self.meta_path:
+            with open(self.meta_path, "rb") as f:
+                meta_text = UnicodeDammit(f.read()).unicode_markup
+        return "load_setup_py_data" in meta_text or "load_setuptools" in meta_text
+
+    @property
+    def uses_regex_in_meta(self):
+        meta_text = ""
+        if self.meta_path:
+            with open(self.meta_path, "rb") as f:
+                meta_text = UnicodeDammit(f.read()).unicode_markup
+        return "load_file_regex" in meta_text
+
+    @property
+    def uses_load_file_data_in_meta(self):
+        meta_text = ""
+        if self.meta_path:
+            with open(self.meta_path, "rb") as f:
+                meta_text = UnicodeDammit(f.read()).unicode_markup
+        return "load_file_data" in meta_text
+
+    @property
+    def needs_source_for_render(self):
+        return (
+            self.uses_vcs_in_meta
+            or self.uses_setup_py_in_meta
+            or self.uses_regex_in_meta
+            or self.uses_load_file_data_in_meta
+        )
+
+    @property
+    def uses_jinja(self):
+        if not self.meta_path:
+            return False
+        with open(self.meta_path, "rb") as f:
+            meta_text = UnicodeDammit(f.read()).unicode_markup
+            matches = re.findall(r"{{.*}}", meta_text)
+        return len(matches) > 0
+
+    @property
+    def uses_vcs_in_meta(self) -> Literal["git", "svn", "mercurial"] | None:
+        """returns name of vcs used if recipe contains metadata associated with version control systems.
+        If this metadata is present, a download/copy will be forced in parse_or_try_download.
+        """
+        vcs = None
+        vcs_types = ["git", "svn", "hg"]
+        # We would get here if we use Jinja2 templating, but specify source with path.
+        if self.meta_path:
+            with open(self.meta_path, "rb") as f:
+                meta_text = UnicodeDammit(f.read()).unicode_markup
+                for _vcs in vcs_types:
+                    matches = re.findall(rf"{_vcs.upper()}_[^\.\s\'\"]+", meta_text)
+                    if len(matches) > 0 and _vcs != self.get_value("package/name"):
+                        if _vcs == "hg":
+                            _vcs = "mercurial"
+                        vcs = _vcs
+                        break
+        return vcs
+
+    @property
+    def uses_vcs_in_build(self) -> Literal["git", "svn", "mercurial"] | None:
+        # TODO :: Re-work this. Is it even useful? We can declare any vcs in our build deps.
+        build_script = "bld.bat" if on_win else "build.sh"
+        build_script = os.path.join(self.path, build_script)
+        for recipe_file in (build_script, self.meta_path):
+            if os.path.isfile(recipe_file):
+                vcs_types = ["git", "svn", "hg"]
+                with open(self.meta_path, "rb") as f:
+                    build_script = UnicodeDammit(f.read()).unicode_markup
+                    for vcs in vcs_types:
+                        # commands are assumed to have 3 parts:
+                        #   1. the vcs command, optionally with an exe extension
+                        #   2. a subcommand - for example, "clone"
+                        #   3. a target url or other argument
+                        matches = re.findall(
+                            rf"{vcs}(?:\.exe)?(?:\s+\w+\s+[\w\/\.:@]+)",
+                            build_script,
+                            flags=re.IGNORECASE,
+                        )
+                        if len(matches) > 0 and vcs != self.get_value("package/name"):
+                            if vcs == "hg":
+                                vcs = "mercurial"
+                            return vcs
+        return None
+
+    def get_recipe_text(
+        self, extract_pattern=None, force_top_level=False, apply_selectors=True
+    ):
+        meta_path = self.meta_path
+        if meta_path:
+            recipe_text = read_meta_file(meta_path)
+            if self.is_output and not force_top_level:
+                recipe_text = self.extract_single_output_text(
+                    self.name(), getattr(self, "type", None)
+                )
+        else:
+            from .render import output_yaml
+
+            recipe_text = output_yaml(self)
+        recipe_text = _filter_recipe_text(recipe_text, extract_pattern)
+        if apply_selectors:
+            recipe_text = select_lines(
+                recipe_text,
+                get_selectors(self.config),
+                variants_in_place=bool(self.config.variant),
+            )
+        return recipe_text.rstrip()
+
+    def extract_requirements_text(self, force_top_level=False):
+        # outputs are already filtered into each output for us
+        f = r"(^\s*requirements:.*?)(?=^\s*test:|^\s*extra:|^\s*about:|^\s*-\s+name:|^outputs:|\Z)"
+        if "package:" in self.get_recipe_text(force_top_level=force_top_level):
+            # match top-level requirements - start of line means top-level requirements
+            #    ^requirements:.*?
+            # match output with similar name
+            #    (?:-\s+name:\s+%s.*?)requirements:.*?
+            # terminate match of other sections
+            #    (?=^\s*-\sname|^\s*test:|^\s*extra:|^\s*about:|^outputs:|\Z)
+            f = r"(^requirements:.*?)(?=^test:|^extra:|^about:|^outputs:|\Z)"
+        return self.get_recipe_text(f, force_top_level=force_top_level)
+
+    def extract_outputs_text(self, apply_selectors=True):
+        return self.get_recipe_text(
+            r"(^outputs:.*?)(?=^test:|^extra:|^about:|\Z)",
+            force_top_level=True,
+            apply_selectors=apply_selectors,
+        )
+
+    def extract_source_text(self):
+        return self.get_recipe_text(
+            r"(\s*source:.*?)(?=^build:|^requirements:|^test:|^extra:|^about:|^outputs:|\Z)"
+        )
+
+    def extract_package_and_build_text(self):
+        return self.get_recipe_text(
+            r"(^.*?)(?=^requirements:|^test:|^extra:|^about:|^outputs:|\Z)"
+        )
+
+    def extract_single_output_text(
+        self, output_name, output_type, apply_selectors=True
+    ):
+        # first, need to figure out which index in our list of outputs the name matches.
+        #    We have to do this on rendered data, because templates can be used in output names
+        recipe_text = self.extract_outputs_text(apply_selectors=apply_selectors)
+        output_matches = output_re.findall(recipe_text)
+        outputs = self.meta.get("outputs") or (
+            self.parent_outputs if hasattr(self, "parent_outputs") else None
+        )
+        if not outputs:
+            outputs = [{"name": self.name()}]
+
+        try:
+            if output_type:
+                output_tuples = [
+                    (
+                        out.get("name", self.name()),
+                        out.get(
+                            "type",
+                            "conda_v2"
+                            if self.config.conda_pkg_format == "2"
+                            else "conda",
+                        ),
+                    )
+                    for out in outputs
+                ]
+                output_index = output_tuples.index((output_name, output_type))
+            else:
+                output_tuples = [out.get("name", self.name()) for out in outputs]
+                output_index = output_tuples.index(output_name)
+            output = output_matches[output_index] if output_matches else ""
+        except ValueError:
+            if not self.path and self.meta.get("extra", {}).get("parent_recipe"):
+                utils.get_logger(__name__).warn(
+                    f"Didn't match any output in raw metadata.  Target value was: {output_name}"
+                )
+                output = ""
+            else:
+                output = self.name()
+        return output
+
+    @property
+    def numpy_xx(self) -> bool:
+        """This is legacy syntax that we need to support for a while.  numpy x.x means
+        "pin run as build" for numpy.  It was special-cased to only numpy."""
+        text = self.extract_requirements_text()
+        return bool(numpy_xx_re.search(text))
+
+    @property
+    def uses_numpy_pin_compatible_without_xx(self) -> tuple[bool, bool]:
+        text = self.extract_requirements_text()
+        compatible_search = numpy_compatible_re.search(text)
+        max_pin_search = None
+        if compatible_search:
+            max_pin_search = numpy_compatible_x_re.search(text)
+        # compatible_search matches simply use of pin_compatible('numpy')
+        # max_pin_search quantifies the actual number of x's in the max_pin field.  The max_pin
+        #     field can be absent, which is equivalent to a single 'x'
+        return (
+            bool(compatible_search),
+            max_pin_search.group(1).count("x") != 2 if max_pin_search else True,
+        )
+
+    @property
+    def uses_subpackage(self):
+        outputs = self.get_section("outputs")
+        in_reqs = False
+        for out in outputs:
+            if "name" in out:
+                name_re = re.compile(r"^{}(\s|\Z|$)".format(out["name"]))
+                in_reqs = any(
+                    name_re.match(req) for req in self.get_depends_top_and_out("run")
+                )
+                if in_reqs:
+                    break
+        subpackage_pin = False
+        if not in_reqs and self.meta_path:
+            data = self.extract_requirements_text(force_top_level=True)
+            if data:
+                subpackage_pin = re.search(r"{{\s*pin_subpackage\(.*\)\s*}}", data)
+        return in_reqs or bool(subpackage_pin)
+
+    @property
+    def uses_new_style_compiler_activation(self):
+        text = self.extract_requirements_text()
+        return bool(re.search(r"\{\{\s*compiler\(.*\)\s*\}\}", text))
+
+    def validate_features(self):
+        if any(
+            "-" in feature for feature in ensure_list(self.get_value("build/features"))
+        ):
+            raise ValueError(
+                "- is a disallowed character in features.  Please change this "
+                "character in your recipe."
+            )
+
+    def copy(self):
+        new = copy.copy(self)
+        new.config = self.config.copy()
+        new.config.variant = copy.deepcopy(self.config.variant)
+        new.meta = copy.deepcopy(self.meta)
+        new.type = getattr(
+            self, "type", "conda_v2" if self.config.conda_pkg_format == "2" else "conda"
+        )
+        return new
+
+    @property
+    def noarch(self):
+        return self.get_value("build/noarch")
+
+    @noarch.setter
+    def noarch(self, value: str | None) -> None:
+        self.meta.setdefault("build", {})["noarch"] = value
+        if not self.noarch_python and not value:
+            self.config.reset_platform()
+        elif value:
+            self.config.host_platform = "noarch"
+
+    @property
+    def noarch_python(self) -> bool:
+        return bool(self.get_value("build/noarch_python"))
+
+    @noarch_python.setter
+    def noarch_python(self, value: bool) -> None:
+        self.meta.setdefault("build", {})["noarch_python"] = value
+        if not self.noarch and not value:
+            self.config.reset_platform()
+        elif value:
+            self.config.host_platform = "noarch"
+
+    @property
+    def variant_in_source(self):
+        variant = self.config.variant
+        self.config.variant = {}
+        self.parse_again(
+            permit_undefined_jinja=True,
+            allow_no_other_outputs=True,
+            bypass_env_check=True,
+        )
+        vars_in_recipe = set(self.undefined_jinja_vars)
+        self.config.variant = variant
+
+        for key in vars_in_recipe & set(variant.keys()):
+            # We use this variant in the top-level recipe.
+            # constrain the stored variants to only this version in the output
+            #     variant mapping
+            if re.search(
+                r"\s*\{\{\s*%s\s*(?:.*?)?\}\}" % key, self.extract_source_text()
+            ):
+                return True
+        return False
+
+    @property
+    def pin_depends(self):
+        return self.get_value("build/pin_depends", "").lower()
+
+    @property
+    def source_provided(self):
+        return not self.get_section("source") or (
+            os.path.isdir(self.config.work_dir) and os.listdir(self.config.work_dir)
+        )
+
+    def reconcile_metadata_with_output_dict(self, output_metadata, output_dict):
+        output_metadata.meta["package"]["name"] = output_dict.get("name", self.name())
+
+        # make sure that subpackages do not duplicate tests from top-level recipe
+        test = output_metadata.meta.get("test", {})
+        if output_dict.get("name") != self.name() or not (
+            output_dict.get("script") or output_dict.get("files")
+        ):
+            if "commands" in test:
+                del test["commands"]
+            if "imports" in test:
+                del test["imports"]
+
+        # make sure that subpackages do not duplicate top-level entry-points or run_exports
+        build = output_metadata.meta.get("build", {})
+        transfer_keys = "entry_points", "run_exports", "script"
+        for key in transfer_keys:
+            if key in output_dict:
+                build[key] = output_dict[key]
+            elif key in build:
+                del build[key]
+        output_metadata.meta["build"] = build
+
+        # reset this so that reparsing does not reset the metadata name
+        output_metadata._meta_path = ""
+
+    def get_output_metadata(self, output):
+        if output.get("name") == self.name():
+            output_metadata = self.copy()
+            output_metadata.type = output.get(
+                "type", "conda_v2" if self.config.conda_pkg_format == "2" else "conda"
+            )
+
+        else:
+            output_metadata = self.copy()
+            output_reqs = utils.expand_reqs(output.get("requirements", {}))
+            build_reqs = output_reqs.get("build", [])
+            host_reqs = output_reqs.get("host", [])
+            run_reqs = output_reqs.get("run", [])
+            constrain_reqs = output_reqs.get("run_constrained", [])
+            # pass through any other unrecognized req types
+            other_reqs = {
+                k: v
+                for k, v in output_reqs.items()
+                if k not in ("build", "host", "run", "run_constrained")
+            }
+
+            if output.get("target"):
+                output_metadata.config.target_subdir = output["target"]
+
+            if self.name() != output.get("name") or (
+                output.get("script") or output.get("files")
+            ):
+                self.reconcile_metadata_with_output_dict(output_metadata, output)
+
+            output_metadata.type = output.get(
+                "type", "conda_v2" if self.config.conda_pkg_format == "2" else "conda"
+            )
+
+            if "name" in output:
+                # since we are copying reqs from the top-level package, which
+                #   can depend on subpackages, make sure that we filter out
+                #   subpackages so that they don't depend on themselves
+                subpackage_pattern = re.compile(
+                    r"(?:^{}(?:\s|$|\Z))".format(output["name"])
+                )
+                if build_reqs:
+                    build_reqs = [
+                        req for req in build_reqs if not subpackage_pattern.match(req)
+                    ]
+                if host_reqs:
+                    host_reqs = [
+                        req for req in host_reqs if not subpackage_pattern.match(req)
+                    ]
+                if run_reqs:
+                    run_reqs = [
+                        req for req in run_reqs if not subpackage_pattern.match(req)
+                    ]
+
+            requirements = {}
+            requirements.update({"build": build_reqs}) if build_reqs else None
+            requirements.update({"host": host_reqs}) if host_reqs else None
+            requirements.update({"run": run_reqs}) if run_reqs else None
+            requirements.update(
+                {"run_constrained": constrain_reqs}
+            ) if constrain_reqs else None
+            requirements.update(other_reqs)
+            output_metadata.meta["requirements"] = requirements
+
+            output_metadata.meta["package"]["version"] = (
+                output.get("version") or self.version()
+            )
+            output_metadata.final = False
+            output_metadata.noarch = output.get("noarch", False)
+            output_metadata.noarch_python = output.get("noarch_python", False)
+            # primarily for tests - make sure that we keep the platform consistent (setting noarch
+            #      would reset it)
+            if (
+                not (output_metadata.noarch or output_metadata.noarch_python)
+                and self.config.platform != output_metadata.config.platform
+            ):
+                output_metadata.config.platform = self.config.platform
+
+            build = output_metadata.meta.get("build", {})
+            # legacy (conda build 2.1.x - 3.0.25). Newer stuff should just emulate
+            #   the top-level recipe, with full sections for build, test, about
+            if "number" in output:
+                build["number"] = output["number"]
+            if "string" in output:
+                build["string"] = output["string"]
+            if "run_exports" in output and output["run_exports"]:
+                build["run_exports"] = output["run_exports"]
+            if "track_features" in output and output["track_features"]:
+                build["track_features"] = output["track_features"]
+            if "features" in output and output["features"]:
+                build["features"] = output["features"]
+
+            # 3.0.26+ - just pass through the whole build section from the output.
+            #    It clobbers everything else, aside from build number
+            if "build" in output:
+                build = output["build"]
+                if build is None:
+                    build = {}
+                if "number" not in build:
+                    build["number"] = output.get(
+                        "number", output_metadata.build_number()
+                    )
+            output_metadata.meta["build"] = build
+            if "test" in output:
+                output_metadata.meta["test"] = output["test"]
+            if "about" in output:
+                output_metadata.meta["about"] = output["about"]
+            self.append_parent_metadata(output_metadata)
+        return output_metadata
+
+    def append_parent_metadata(self, out_metadata):
+        extra = self.meta.get("extra", {})
+        extra["parent_recipe"] = {
+            "path": self.path,
+            "name": self.name(),
+            "version": self.version(),
+        }
+        out_metadata.meta["extra"] = extra
+
+    def get_reduced_variant_set(self, used_variables):
+        # reduce variable space to limit work we need to do
+        full_collapsed_variants = variants.list_of_dicts_to_dict_of_lists(
+            self.config.variants
+        )
+        reduced_collapsed_variants = full_collapsed_variants.copy()
+        reduce_keys = set(self.config.variants[0].keys()) - set(used_variables)
+
+        zip_key_groups = self.config.variant.get("zip_keys", [])
+        zip_key_groups = (
+            [zip_key_groups]
+            if zip_key_groups and isinstance(zip_key_groups[0], str)
+            else zip_key_groups
+        )
+        used_zip_key_groups = [
+            group for group in zip_key_groups if any(set(group) & set(used_variables))
+        ]
+
+        extend_keys = full_collapsed_variants.get("extend_keys", [])
+        reduce_keys = [
+            key
+            for key in reduce_keys
+            if not any(key in group for group in used_zip_key_groups)
+            and key not in extend_keys
+        ]
+        for key in reduce_keys:
+            values = full_collapsed_variants.get(key)
+            if (
+                values is not None
+                and len(values)
+                and not hasattr(values, "keys")
+                and key != "zip_keys"
+            ):
+                # save only one element from this key
+                reduced_collapsed_variants[key] = utils.ensure_list(next(iter(values)))
+
+        out = variants.dict_of_lists_to_list_of_dicts(reduced_collapsed_variants)
+        return out
+
+    def get_output_metadata_set(
+        self,
+        permit_undefined_jinja=False,
+        permit_unsatisfiable_variants=False,
+        bypass_env_check=False,
+    ):
+        from .source import provide
+
+        out_metadata_map = {}
+        if self.final:
+            outputs = get_output_dicts_from_metadata(self)[0]
+            output_tuples = [(outputs, self)]
+        else:
+            all_output_metadata = OrderedDict()
+
+            used_variables = self.get_used_loop_vars(force_global=True)
+            top_loop = (
+                self.get_reduced_variant_set(used_variables) or self.config.variants[:1]
+            )
+
+            for variant in (
+                top_loop
+                if (hasattr(self.config, "variants") and self.config.variants)
+                else [self.config.variant]
+            ):
+                ref_metadata = self.copy()
+                ref_metadata.config.variant = variant
+                if ref_metadata.needs_source_for_render and self.variant_in_source:
+                    ref_metadata.parse_again()
+                    utils.rm_rf(ref_metadata.config.work_dir)
+                    provide(ref_metadata)
+                    ref_metadata.parse_again()
+                try:
+                    ref_metadata.parse_until_resolved(
+                        allow_no_other_outputs=True, bypass_env_check=True
+                    )
+                except SystemExit:
+                    pass
+                outputs = get_output_dicts_from_metadata(ref_metadata)
+
+                try:
+                    for out in outputs:
+                        requirements = out.get("requirements")
+                        if requirements:
+                            requirements = utils.expand_reqs(requirements)
+                            for env in ("build", "host", "run"):
+                                insert_variant_versions(requirements, variant, env)
+                            out["requirements"] = requirements
+                        out_metadata = ref_metadata.get_output_metadata(out)
+
+                        # keeping track of other outputs is necessary for correct functioning of the
+                        #    pin_subpackage jinja2 function.  It's important that we store all of
+                        #    our outputs so that they can be referred to in later rendering.  We
+                        #    also refine this collection as each output metadata object is
+                        #    finalized - see the finalize_outputs_pass function
+                        all_output_metadata[
+                            (
+                                out_metadata.name(),
+                                HashableDict(
+                                    {
+                                        k: copy.deepcopy(out_metadata.config.variant[k])
+                                        for k in out_metadata.get_used_vars()
+                                    }
+                                ),
+                            )
+                        ] = (out, out_metadata)
+                        out_metadata_map[HashableDict(out)] = out_metadata
+                        ref_metadata.other_outputs = (
+                            out_metadata.other_outputs
+                        ) = all_output_metadata
+                except SystemExit:
+                    if not permit_undefined_jinja:
+                        raise
+                    out_metadata_map = {}
+
+            assert out_metadata_map, (
+                "Error: output metadata set is empty.  Please file an issue"
+                " on the conda-build tracker at https://github.com/conda/conda-build/issues"
+            )
+
+            # format here is {output_dict: metadata_object}
+            render_order = toposort(out_metadata_map)
+            check_circular_dependencies(render_order, config=self.config)
+            conda_packages = OrderedDict()
+            non_conda_packages = []
+
+            for output_d, m in render_order.items():
+                if not output_d.get("type") or output_d["type"] in (
+                    "conda",
+                    "conda_v2",
+                ):
+                    conda_packages[
+                        m.name(),
+                        HashableDict(
+                            {
+                                k: copy.deepcopy(m.config.variant[k])
+                                for k in m.get_used_vars()
+                            }
+                        ),
+                    ] = (output_d, m)
+                elif output_d.get("type") == "wheel":
+                    if not output_d.get("requirements", {}).get("build") or not any(
+                        "pip" in req for req in output_d["requirements"]["build"]
+                    ):
+                        build_reqs = output_d.get("requirements", {}).get("build", [])
+                        build_reqs.extend(
+                            ["pip", "python {}".format(m.config.variant["python"])]
+                        )
+                        output_d["requirements"] = output_d.get("requirements", {})
+                        output_d["requirements"]["build"] = build_reqs
+                        m.meta["requirements"] = m.get_section("requirements")
+                        m.meta["requirements"]["build"] = build_reqs
+                    non_conda_packages.append((output_d, m))
+                else:
+                    # for wheels and other non-conda packages, just append them at the end.
+                    #    no deduplication with hashes currently.
+                    # hard part about including any part of output_d
+                    #    outside of this func is that it is harder to
+                    #    obtain an exact match elsewhere
+                    non_conda_packages.append((output_d, m))
+
+            # early stages don't need to do the finalization.  Skip it until the later stages
+            #     when we need it.
+            if not permit_undefined_jinja and not ref_metadata.skip():
+                conda_packages = finalize_outputs_pass(
+                    ref_metadata,
+                    conda_packages,
+                    pass_no=0,
+                    permit_unsatisfiable_variants=permit_unsatisfiable_variants,
+                    bypass_env_check=bypass_env_check,
+                )
+
+                # Sanity check: if any exact pins of any subpackages, make sure that they match
+                ensure_matching_hashes(conda_packages)
+            final_conda_packages = []
+            for out_d, m in conda_packages.values():
+                # We arbitrarily mark all output metadata as final, regardless
+                #    of if it truly is or not. This is done to add sane hashes
+                #    to unfinalizable packages, so that they are differentiable
+                #    from one another. This is mostly a test concern than an
+                #    actual one, as any "final" recipe returned here will still
+                #    barf if anyone tries to actually build it.
+                m.final = True
+                final_conda_packages.append((out_d, m))
+            output_tuples = final_conda_packages + non_conda_packages
+        return output_tuples
+
+    def get_loop_vars(self):
+        _variants = (
+            self.config.input_variants
+            if hasattr(self.config, "input_variants")
+            else self.config.variants
+        )
+        return variants.get_vars(_variants, loop_only=True)
+
+    def get_used_loop_vars(self, force_top_level=False, force_global=False):
+        return {
+            var
+            for var in self.get_used_vars(
+                force_top_level=force_top_level, force_global=force_global
+            )
+            if var in self.get_loop_vars()
+        }
+
+    def get_rendered_recipe_text(
+        self, permit_undefined_jinja=False, extract_pattern=None
+    ):
+        template_string = self.get_recipe_text(
+            extract_pattern=extract_pattern, force_top_level=True
+        ).rstrip()
+
+        return (
+            yaml.safe_load(
+                self._get_contents(
+                    permit_undefined_jinja=permit_undefined_jinja,
+                    template_string=template_string,
+                    skip_build_id=False,
+                )
+            )
+            or {}
+        )
+
+    def get_rendered_outputs_section(self, permit_undefined_jinja=False, variant=None):
+        extract_pattern = r"(.*)package:"
+        template_string = "\n".join(
+            (
+                self.get_recipe_text(
+                    extract_pattern=extract_pattern, force_top_level=True
+                ),
+                # second item: the output text for this metadata
+                #    object (might be output)
+                self.extract_outputs_text(),
+            )
+        ).rstrip()
+
+        outputs = (
+            yaml.safe_load(
+                self._get_contents(
+                    permit_undefined_jinja=permit_undefined_jinja,
+                    template_string=template_string,
+                    skip_build_id=False,
+                    allow_no_other_outputs=permit_undefined_jinja,
+                    variant=variant,
+                )
+            )
+            or {}
+        ).get("outputs", [])
+        return get_output_dicts_from_metadata(self, outputs=outputs)
+
+    def get_rendered_output(self, name, permit_undefined_jinja=False, variant=None):
+        """This is for obtaining the rendered, parsed, dictionary-object representation of an
+        output. It's not useful for saying what variables are used. You need earlier, more raw
+        versions of the metadata for that. It is useful, however, for getting updated, re-rendered
+        contents of outputs."""
+        output = None
+        for output_ in self.get_rendered_outputs_section(
+            permit_undefined_jinja=permit_undefined_jinja, variant=variant
+        ):
+            if output_.get("name") == name:
+                output = output_
+                break
+        return output
+
+    @property
+    def force_ignore_keys(self):
+        return ensure_list(self.get_value("build/force_ignore_keys"))
+
+    @property
+    def force_use_keys(self):
+        return ensure_list(self.get_value("build/force_use_keys"))
+
+    def get_used_vars(self, force_top_level=False, force_global=False):
+        global used_vars_cache
+        recipe_dir = self.path
+
+        # `HashableDict` does not handle lists of other dictionaries correctly. Also it
+        # is constructed inplace, taking references to sub-elements of the input dict
+        # and thus corrupting it. Also, this was being called in 3 places in this function
+        # so caching it is probably a good thing.
+        hashed_variants = HashableDict(copy.deepcopy(self.config.variant))
+        if hasattr(self.config, "used_vars"):
+            used_vars = self.config.used_vars
+        elif (
+            self.name(),
+            recipe_dir,
+            force_top_level,
+            force_global,
+            self.config.subdir,
+            hashed_variants,
+        ) in used_vars_cache:
+            used_vars = used_vars_cache[
+                (
+                    self.name(),
+                    recipe_dir,
+                    force_top_level,
+                    force_global,
+                    self.config.subdir,
+                    hashed_variants,
+                )
+            ]
+        else:
+            meta_yaml_reqs = self._get_used_vars_meta_yaml(
+                force_top_level=force_top_level, force_global=force_global
+            )
+            is_output = "package:" not in self.get_recipe_text()
+
+            if is_output:
+                script_reqs = self._get_used_vars_output_script()
+            else:
+                script_reqs = self._get_used_vars_build_scripts()
+
+            used_vars = meta_yaml_reqs | script_reqs
+            # force target_platform to always be included, because it determines behavior
+            if "target_platform" in self.config.variant and not self.noarch:
+                used_vars.add("target_platform")
+            # and channel_targets too.
+            if "channel_targets" in self.config.variant:
+                used_vars.add("channel_targets")
+
+            if self.force_use_keys or self.force_ignore_keys:
+                used_vars = (used_vars - set(self.force_ignore_keys)) | set(
+                    self.force_use_keys
+                )
+
+            used_vars_cache[
+                (
+                    self.name(),
+                    recipe_dir,
+                    force_top_level,
+                    force_global,
+                    self.config.subdir,
+                    hashed_variants,
+                )
+            ] = used_vars
+        return used_vars
+
+    def _get_used_vars_meta_yaml_helper(
+        self, force_top_level=False, force_global=False, apply_selectors=False
+    ):
+        if force_global:
+            recipe_text = self.get_recipe_text(
+                force_top_level=force_top_level, apply_selectors=apply_selectors
+            )
+            # a bit hacky.  When we force global, we don't distinguish
+            #     between requirements and the rest
+            reqs_text = recipe_text
+        else:
+            if self.is_output and not force_top_level:
+                recipe_text = self.extract_single_output_text(
+                    self.name(),
+                    getattr(self, "type", None),
+                    apply_selectors=apply_selectors,
+                )
+            else:
+                recipe_text = self.get_recipe_text(
+                    force_top_level=force_top_level, apply_selectors=apply_selectors
+                ).replace(
+                    self.extract_outputs_text(apply_selectors=apply_selectors).strip(),
+                    "",
+                ) + self.extract_single_output_text(
+                    self.name(),
+                    getattr(self, "type", None),
+                    apply_selectors=apply_selectors,
+                )
+            reqs_re = re.compile(
+                r"requirements:.+?(?=^\w|\Z|^\s+-\s(?=name|type))", flags=re.M | re.S
+            )
+            reqs_text = reqs_re.search(recipe_text)
+            reqs_text = reqs_text.group() if reqs_text else ""
+
+        return reqs_text, recipe_text
+
+    def _get_used_vars_meta_yaml(self, force_top_level=False, force_global=False):
+        # make variant dict hashable so that memoization works
+        variant_keys = tuple(sorted(self.config.variant.keys()))
+
+        reqs_text, recipe_text = self._get_used_vars_meta_yaml_helper(
+            force_top_level=force_top_level,
+            force_global=force_global,
+            apply_selectors=False,
+        )
+
+        all_used_selectors = variants.find_used_variables_in_text(
+            variant_keys, recipe_text, selectors_only=True
+        )
+
+        reqs_text, recipe_text = self._get_used_vars_meta_yaml_helper(
+            force_top_level=force_top_level,
+            force_global=force_global,
+            apply_selectors=True,
+        )
+        all_used_reqs = variants.find_used_variables_in_text(
+            variant_keys, recipe_text, selectors_only=False
+        )
+
+        all_used = all_used_reqs.union(all_used_selectors)
+
+        # things that are only used in requirements need further consideration,
+        #   for omitting things that are only used in run
+        if force_global:
+            used = all_used
+        else:
+            requirements_used = variants.find_used_variables_in_text(
+                variant_keys, reqs_text
+            )
+            outside_reqs_used = all_used - requirements_used
+
+            requirements_used = trim_build_only_deps(self, requirements_used)
+            used = outside_reqs_used | requirements_used
+
+        return used
+
+    def _get_used_vars_build_scripts(self):
+        used_vars = set()
+        buildsh = os.path.join(self.path, "build.sh")
+        if os.path.isfile(buildsh):
+            used_vars.update(
+                variants.find_used_variables_in_shell_script(
+                    self.config.variant, buildsh
+                )
+            )
+        bldbat = os.path.join(self.path, "bld.bat")
+        if self.config.platform == "win" and os.path.isfile(bldbat):
+            used_vars.update(
+                variants.find_used_variables_in_batch_script(
+                    self.config.variant, bldbat
+                )
+            )
+        return used_vars
+
+    def _get_used_vars_output_script(self):
+        this_output = (
+            self.get_rendered_output(self.name(), permit_undefined_jinja=True) or {}
+        )
+        used_vars = set()
+        if "script" in this_output:
+            script = os.path.join(self.path, this_output["script"])
+            if os.path.splitext(script)[1] == ".sh":
+                used_vars.update(
+                    variants.find_used_variables_in_shell_script(
+                        self.config.variant, script
+                    )
+                )
+            elif os.path.splitext(script)[1] == ".bat":
+                used_vars.update(
+                    variants.find_used_variables_in_batch_script(
+                        self.config.variant, script
+                    )
+                )
+            else:
+                log = utils.get_logger(__name__)
+                log.warn(
+                    f"Not detecting used variables in output script {script}; conda-build only knows "
+                    "how to search .sh and .bat files right now."
+                )
+        return used_vars
+
+    def get_variants_as_dict_of_lists(self):
+        return variants.list_of_dicts_to_dict_of_lists(self.config.variants)
+
+    def clean(self):
+        """This ensures that clean is called with the correct build id"""
+        self.config.clean()
+
+    @property
+    def activate_build_script(self) -> bool:
+        return bool(
+            self.config.activate
+            and self.get_value("build/activate_in_script") is not False
+        )
+
+    @property
+    def build_is_host(self) -> bool:
+        manual_overrides = (
+            self.get_value("build/merge_build_host") is True
+            or self.config.build_is_host
+        )
+        manually_disabled = self.get_value("build/merge_build_host") is False
+        return manual_overrides or (
+            self.config.subdirs_same
+            and not manually_disabled
+            and "host" not in self.meta.get("requirements", {})
+            and not self.uses_new_style_compiler_activation
+        )
+
+    def get_top_level_recipe_without_outputs(self):
+        recipe_no_outputs = self.get_recipe_text(force_top_level=True).replace(
+            self.extract_outputs_text(), ""
+        )
+        top_no_outputs = {}
+        # because we're an output, calls to PKG_NAME used in the top-level
+        #    content will reflect our current name, not the top-level name. We
+        #    fix that here by replacing any PKG_NAME instances with the known
+        #    parent name
+        parent_recipe = self.meta.get("extra", {}).get("parent_recipe", {})
+        alt_name = parent_recipe["name"] if self.is_output else None
+        if recipe_no_outputs:
+            top_no_outputs = yaml.safe_load(
+                self._get_contents(
+                    False, template_string=recipe_no_outputs, alt_name=alt_name
+                )
+            )
+        return top_no_outputs or {}
+
+    def get_test_deps(self, py_files, pl_files, lua_files, r_files):
+        specs = [f"{self.name()} {self.version()} {self.build_id()}"]
+
+        # add packages listed in the run environment and test/requires
+        specs.extend(ms.spec for ms in self.ms_depends("run"))
+        specs += utils.ensure_list(self.get_value("test/requires", []))
+
+        if py_files:
+            # as the tests are run by python, ensure that python is installed.
+            # (If they already provided python as a run or test requirement,
+            #  this won't hurt anything.)
+            specs += ["python"]
+        if pl_files:
+            # as the tests are run by perl, we need to specify it
+            specs += ["perl"]
+        if lua_files:
+            # not sure how this shakes out
+            specs += ["lua"]
+        if r_files and not any(s.split()[0] in ("r-base", "mro-base") for s in specs):
+            # not sure how this shakes out
+            specs += ["r-base"]
+
+        specs.extend(utils.ensure_list(self.config.extra_deps))
+        return specs
diff --git a/shared/package-files/c0/c0e24d22bed93dafc36aba95875b3b78cd271bfc28eacb6220f1d9631aaf8ac7/_python_dispatch.py b/shared/package-files/c0/c0e24d22bed93dafc36aba95875b3b78cd271bfc28eacb6220f1d9631aaf8ac7/_python_dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c774f2d0e16dfb1de6813a6abcdd30d4e251d33
--- /dev/null
+++ b/shared/package-files/c0/c0e24d22bed93dafc36aba95875b3b78cd271bfc28eacb6220f1d9631aaf8ac7/_python_dispatch.py
@@ -0,0 +1,495 @@
+import contextlib
+from typing import Optional, Union, List, Set, Dict, Any
+
+import warnings
+from dataclasses import dataclass
+import torch
+import torchgen
+from torch._C import _len_torch_dispatch_stack, _get_dispatch_stack_at, \
+    _pop_torch_dispatch_stack, _push_on_torch_dispatch_stack, DispatchKey
+
+
+# TODO: Limitations and things about enable_torch_dispatch_mode we should fix before exposing it:
+# - We need a better user-facing api for _DisableTorchDispatch that
+#   is able to selectively disable __torch_dispatch__ of a particular class.
+# - It doesn't work with the tensor constructors (torch.tensor, torch.Tensor)
+# - Better name (see https://github.com/pytorch/pytorch/pull/63496#discussion_r694091694)
+
+class TorchDispatchMode:
+    """
+    A ``TorchDispatchMode`` allows you to override the meaning of all
+    ``__torch_dispatch__`` overrideable functions within a dynamic scope,
+    without having to actually create a tensor subclass or manually
+    monkey-patch functions in the PyTorch API.  Some common situations
+    where you should use a mode:
+
+        * You want to override the meaning of factory functions, or other
+          functions that do not otherwise take a tensor as an argument
+          (these cannot be overridden with tensor subclasses).
+
+        * You want to override the behavior of all functions without needing
+          to wrap your inputs in tensor subclasses; e.g., if you are just
+          interested in logging intermediate computations.
+
+        * You want to control the order of execution of various tensor
+          subclasses explicitly, rather than implicitly via the return of
+          ``NotImplemented``.
+
+    Independent subclasses of :class:`TorchDispatchMode` are compositional:
+    modes can be pushed onto a stack using ``with MyMode():``.
+    When you call functions in the PyTorch API inside your
+    ``__torch_dispatch__`` implementation, by default, they will forward on to
+    the next mode on the mode stack.  If you want recursively call back into
+    your current ``__torch_dispatch__`` implementation, either explicitly
+    invoke ``self.__torch_dispatch__(...)``, or use the context manager
+    ``__torch_dispatch__(self)`` to make PyTorch
+    API self-referential (beware of infinite loops, in this case!)
+    """
+
+    def __init__(self, _dispatch_key=None):
+        if _dispatch_key is not None:
+            assert isinstance(_dispatch_key, torch._C.DispatchKey)
+            self.__dict__['_dispatch_key'] = _dispatch_key
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        raise NotImplementedError()
+
+    def __enter__(self):
+        _push_mode(self)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        mb_dk_or_mode_key = self.__dict__.get("_dispatch_key", None)
+        if mb_dk_or_mode_key is None:
+            # Today, mode keys are not used at all in the per-dispatch-key-mode logic (for pre-dispatch)
+            # We should probably revisit this.
+            mb_dk_or_mode_key = self.__dict__.get("_mode_key", None)
+        _pop_mode(mb_dk_or_mode_key)
+
+    @classmethod
+    def push(cls, *args, **kwargs):
+        warnings.warn("`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`")
+        instance = cls(*args, **kwargs)
+        return instance
+
+def _get_current_dispatch_mode():
+    stack_len = _len_torch_dispatch_stack()
+    # Return a user mode on the stack if there are any
+    if stack_len > 0:
+        return _get_dispatch_stack_at(stack_len - 1)
+    return None
+
+
+def _detect_functional_mode():
+    from torch._ops import _get_dispatch_mode_pre_dispatch
+    pre_dispatch_functional_mode = _get_dispatch_mode_pre_dispatch(torch._C._TorchDispatchModeKey.FUNCTIONAL)
+    post_dispatch_functional_mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL)
+
+    assert (pre_dispatch_functional_mode is None) or (post_dispatch_functional_mode is None)
+
+    if pre_dispatch_functional_mode is None:
+        return post_dispatch_functional_mode
+
+    return pre_dispatch_functional_mode
+
+def _unset_infra_mode(key):
+    from torch._ops import unset_mode_pre_dispatch, _get_dispatch_mode_pre_dispatch
+    pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(key)
+    post_dispatch_mode = torch._C._get_dispatch_mode(key)
+    if pre_dispatch_mode and post_dispatch_mode:
+        raise AssertionError("Can't have active infra mode on both pre and post dispatch mode stack")
+
+    if pre_dispatch_mode:
+        mode = unset_mode_pre_dispatch(key)
+        return mode
+    if post_dispatch_mode:
+        return torch._C._unset_dispatch_mode(key)
+
+
+def _disable_infra_mode(key):
+    assert key in (torch._C._TorchDispatchModeKey.FUNCTIONAL, torch._C._TorchDispatchModeKey.PROXY)
+    mode_unset = _unset_infra_mode(key)
+    try:
+        yield mode_unset
+    finally:
+        if mode_unset is not None:
+            _push_mode(mode_unset)
+
+
+def _get_current_dispatch_mode_stack():
+    stack_len = _len_torch_dispatch_stack()
+    return [_get_dispatch_stack_at(i) for i in range(stack_len)]
+
+
+def _push_mode(mode):
+    k = mode._dispatch_key if hasattr(mode, "_dispatch_key") else None
+    assert k is None or k == torch._C.DispatchKey.PreDispatch
+    if k is None:
+        _push_on_torch_dispatch_stack(mode)
+        return
+
+    from torch._ops import get_cached_ops, _set_mode_pre_dispatch
+    # See Note [Not Caching Per-Dispatch-Key Mode Handlers]
+    # Clear the cache of every op that has been used so far, for this particular key.
+    ks = torch._C._functionality_to_backend_keys(k)
+    for op in get_cached_ops():
+        for key in ks:
+            op._uncache_dispatch(key)
+    _set_mode_pre_dispatch(mode)
+
+
+def _pop_mode(k: Optional[Union[DispatchKey, torch._C._TorchDispatchModeKey]] = None):
+    if k == torch._C.DispatchKey.PreDispatch:  # type: ignore[attr-defined]
+        from torch._ops import _pop_mode_from_pre_dispatch
+        return _pop_mode_from_pre_dispatch()
+
+    if k is None or isinstance(k, torch._C._TorchDispatchModeKey):
+        return _pop_torch_dispatch_stack(k)
+
+@contextlib.contextmanager
+def _pop_mode_temporarily(k: Optional[DispatchKey] = None):
+    old = _pop_mode(k)
+    try:
+        yield old
+    finally:
+        _push_mode(old)
+
+@contextlib.contextmanager
+def _disable_current_modes():
+    from torch._ops import _len_torch_dispatch_stack_pre_dispatch, _pop_mode_from_pre_dispatch
+    from torch._subclasses.functional_tensor import FunctionalTensorMode
+    from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode
+    mode_len_pre_dispatch = _len_torch_dispatch_stack_pre_dispatch()
+    old_pre_dispatch_modes = [_pop_mode_from_pre_dispatch() for _ in range(mode_len_pre_dispatch)]
+
+    has_proxy_mode_in_pre_dispatch = False
+    has_functional_mode_in_pre_dispatch = False
+
+    for i in old_pre_dispatch_modes:
+        if isinstance(i, ProxyTorchDispatchMode):
+            has_proxy_mode_in_pre_dispatch = True
+        if isinstance(i, FunctionalTensorMode):
+            has_functional_mode_in_pre_dispatch = True
+
+    mode_len = _len_torch_dispatch_stack()
+    old_modes = [_pop_mode() for _ in range(mode_len)]
+
+    for old in old_modes:
+        if isinstance(old, FunctionalTensorMode) and has_functional_mode_in_pre_dispatch:
+            raise AssertionError("Can't have FunctionalMode available both in PreDispatch and Python Key")
+        if isinstance(old, ProxyTorchDispatchMode) and has_proxy_mode_in_pre_dispatch:
+            raise AssertionError("Can't have ProxyTorchDispatchMode available both in PreDispatch and Python Key")
+
+    # Manually disable proxy and fake modes, if any are active
+    try:
+        yield old_pre_dispatch_modes + old_modes
+    finally:
+        for mode in reversed(old_modes):
+            _push_mode(mode)
+        for mode in reversed(old_pre_dispatch_modes):
+            _push_mode(mode)
+
+
+class BaseTorchDispatchMode(TorchDispatchMode):
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        return func(*args, **kwargs)
+
+def is_traceable_wrapper_subclass(t):
+    """
+    Returns whether or not a tensor subclass that implements __torch_dispatch__
+    is 'traceable' with torch.compile.
+    In order for a tensor subclass to support TorchDispatchMode-style tracing in PT2,
+    It must implement two magic methods: __tensor_flatten__ and __tensor_unflatten__.
+    It is also expected to obey some restrictions around traceability and aliasing:
+        * The subclass's __torch_dispatch__() implementation should desugar into pytorch
+            dispatcher operations that can be traced into a graph.
+        * The subclass should use return_and_correct_aliasing(). This is needed today to make
+            sure that torch.compile does the right thing in a few cases around input mutation
+            and output aliasing.
+
+    Expected magic method signatures:
+        attrs, ctx = t.__tensor_flatten__()
+            attrs: list of attribute name strings for inner tensors
+            ctx: dict containing any other subclass-specific metadata needed for unflattening
+
+        t = MySubClass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride)
+            inner_tensors: dict mapping attribute name -> tensor for each inner tensor
+            ctx: dict with subclass metadata in the form that __tensor_flatten__() produces
+            outer_size: expected (possibly symbolic) size that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the shape info to be constructed. In most cases, this arg can be
+                safely ignored.
+            outer_stride: expected (possibly symbolic) stride that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the stride info to be constructed. In most cases, this arg can be
+                safely ignored.
+    """
+    is_subclass = isinstance(t, torch.Tensor) and type(t) != torch.Tensor
+    return is_subclass and hasattr(t, "__tensor_flatten__") and hasattr(t, "__tensor_unflatten__")
+
+def transform_subclass(t, callback, outer_size=None, outer_stride=None):
+    """
+    Given a traceable, wrapper tensor subclass ``t`` that implements
+    ``__torch_dispatch__`` and holds some inner tensors,
+    and a callback of type ``Callable[[str, torch.Tensor], torch.Tensor]``,
+    `transform_subclass` will construct a fresh instance of the wrapper tensor subclass.
+    It will do so by grabbing each inner tensor attribute from the wrapper,
+    passing them into ``callback`` to get a transformed tensor,
+    and putting each transformed tensor into the fresh tensor subclass instance.
+
+    Note: this function will not handle ensuring that the fresh subclass
+    gets the same (autograd, and aliasing) metadata as the original tensor.
+    This is generally handled in other subsystems like AOTAutograd.
+    """
+    outer_size = outer_size if outer_size is not None else t.size()
+    outer_stride = outer_stride if outer_stride is not None else t.stride()
+
+    attrs, ctx = t.__tensor_flatten__()
+    transformed_tensors_dict = {}
+    for attr in attrs:
+        transformed_tensors_dict[attr] = callback(attr, getattr(t, attr))
+    sub = type(t).__tensor_unflatten__(
+        transformed_tensors_dict, ctx, outer_size, outer_stride
+    )
+
+    # NB: Purposefully guard here to simplify the inner / outer symbols.
+    # Using sym_eq() for symbolic comparison can result in an expression that's too
+    # difficult to guard on, so we use == here.
+    assert sub.shape == outer_size, \
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have " \
+        f"shape equal to {outer_size}, but got: {sub.shape}"
+    assert sub.stride() == outer_stride, \
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have " \
+        f"stride equal to {outer_stride}, but got: {sub.stride()}"
+
+    return sub
+
+def _correct_storage_aliasing(func, schema_info, args, outs):
+    """
+    Given: an OpOverload, a SchemaInfo (cached information from torchgen about schema),
+    and the inputs/outputs to the OpOverload,
+    this function checks to see if func is a view operator
+    (by checking if any of the outputs in the op's schema
+     are immutable aliases of inputs).
+    If so, this function manually aliases the storage of the output tensor
+    with its corresponding input tensor alias.
+    It does this by unsafely overwriting the storage field of the output tensor
+    to be the same storage as the input.
+    """
+    assert isinstance(func, torch._ops.OpOverload)
+    assert isinstance(args, tuple)
+    assert isinstance(outs, (list, tuple))
+    flat_outs = torch.utils._pytree.tree_leaves(outs)
+
+    def alias_non_inplace_storage(arg, ret):
+        # This is hopefully a reasonable assert:
+        # subclasses that rely on this API for output aliasing
+        # should always return wrapper tensor subclasses for us to manually alias.
+        # in theory if a subclass that needs this API wants to sometimes return
+        # plain tensors, we could remove the assert and just not perform the aliasing,
+        # but it seems safer to learn more about this case first.
+        if is_traceable_wrapper_subclass(arg) or is_traceable_wrapper_subclass(ret):
+            ret_list = ret if isinstance(ret, list) else [ret]
+            for r in ret_list:
+                assert type(arg) == type(r), f"""Called {str(func)} with input of type {type(arg)}
+and output of type {type(ret)}. But expected types to match."""
+        # Need to run under no_dispatch, because we explicitly do **not**
+        # want our subclass to intercept the set_() call.
+        # instead, our subclass should directly have its storage swapped out.
+        with torch.utils._mode_utils.no_dispatch():
+            # See Note: [Fake Tensor Dispatch Keys]
+            # we're borrowing the way it modifies dispatch key TLS.
+            meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+            torch._C._set_meta_in_tls_dispatch_include(True)
+            try:
+                # directly calling this overload, and passing ret.shape, because we **explicitly**
+                # don't want to reset the sizes on ret, if the storage implies a size change.
+                # Why?
+                # The purpose of this API is *not* to change the size/strides of our output- we assume it's already correct.
+                # We just want to "fix up" the storage aliasing, without modifying or output's metadata.
+                # Example: out = inp.expand(inp.shape[0], inp.shape[0])
+                #     This requires swapping the storage of out to be the same as inp,
+                #     but we do *not* want it to change the sizes/strides that were compute for out.
+
+                if isinstance(ret, list):
+                    for r in ret:
+                        torch.ops.aten.set_.source_Storage_storage_offset(
+                            r, arg.untyped_storage(), r.storage_offset(), r.shape, r.stride())
+                else:
+                    assert isinstance(ret, torch.Tensor), f"type: {type(ret)}"
+                    torch.ops.aten.set_.source_Storage_storage_offset(
+                        ret, arg.untyped_storage(), ret.storage_offset(), ret.shape, ret.stride()
+                    )
+            finally:
+                torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+
+    def is_read_only_alias_match(arg, ret):
+        shared_aliases = arg.alias_set & ret.alias_set
+        return len(shared_aliases) > 0 and not arg.is_write
+
+    num_args = len(func._schema.arguments)
+    num_returns = len(func._schema.returns)
+    for arg_idx in range(num_args):
+        for return_idx in range(num_returns):
+            if is_read_only_alias_match(schema_info.args[arg_idx], schema_info.outs[return_idx]):
+                alias_non_inplace_storage(args[arg_idx], outs[return_idx])
+
+# This abstracts over the fact that in return_and_correct_aliasing,
+# we sometimes use torchgen schema parsing (for aten ops, since torchscript's schema parsing is sometimes buggy),
+# and sometimes use torchscript schema parsing (for custom ops, for which torchgen parsing is untested).
+@dataclass
+class AliasInfo:
+    alias_set: Set[str]
+    is_write: bool
+    name: Optional[str]
+
+@dataclass
+class SchemaInfo:
+    args: List[AliasInfo]
+    outs: List[AliasInfo]
+
+# Can't import torch._ops.OpOverload due to circular reference
+parsed_schema_map: Dict[Any, SchemaInfo] = {}
+
+# Given an OpOverload, returns schema information on it.
+# This is cached for efficiency, since it can involve running torchgen
+def get_alias_info(func) -> SchemaInfo:
+    if func in parsed_schema_map:
+        return parsed_schema_map[func]
+    # For ATen ops: use torchgen (since torchscript parser doesn't handle alias annotations
+    # properly for some ops that output tensorlists)
+    if func.namespace == "aten":
+        torchgen_schema_str = str(func._schema)
+        assert torchgen_schema_str.startswith("aten::")
+        # remove the aten:: namespace, which is added by the torchscript parser,
+        # and torchgen doesn't know how to handle
+        torchgen_schema_str = torchgen_schema_str[6:]
+        import re
+        # the torchscript parser ends up converting int[2]=1 into int[2]=[1, 1],
+        # which torchgen chokes on.
+        torchgen_schema_str = re.sub(r'=\[[0, ]+\]', '=0', torchgen_schema_str)
+        torchgen_schema_str = re.sub(r'=\[[1, ]+\]', '=1', torchgen_schema_str)
+        # for aten::rot90
+        torchgen_schema_str = torchgen_schema_str.replace("=[0, 1]", "=[0,1]")
+        torchgen_schema = torchgen.model.FunctionSchema.parse(torchgen_schema_str)
+        arg_schemas = [AliasInfo(
+            alias_set=set() if a.annotation is None else set(a.annotation.alias_set),
+            is_write=a.annotation is not None and a.annotation.is_write,
+            name=a.name,
+        ) for a in torchgen_schema.arguments.flat_all]
+        out_schemas = [AliasInfo(
+            alias_set=set() if a.annotation is None else set(a.annotation.alias_set),
+            is_write=a.annotation is not None and a.annotation.is_write,
+            name=a.name,
+        ) for a in torchgen_schema.returns]
+    else:
+        # For non-aten ops, torchgen is untested so we rely on torchscript schema parsing
+        arg_schemas = [AliasInfo(
+            alias_set=set() if a.alias_info is None else set(a.alias_info.before_set),
+            is_write=a.alias_info is not None and a.alias_info.is_write,
+            name=a.name,
+        ) for a in func._schema.arguments]
+        out_schemas = [AliasInfo(
+            alias_set=set() if a.alias_info is None else set(a.alias_info.before_set),
+            is_write=a.alias_info is not None and a.alias_info.is_write,
+            name=a.name,
+        ) for a in func._schema.returns]
+    schema_info = SchemaInfo(args=arg_schemas, outs=out_schemas)
+    parsed_schema_map[func] = schema_info
+    return schema_info
+
+def return_and_correct_aliasing(func, args, kwargs, out):
+    """
+    This function should be used by wrapper tensor ``__torch_dispatch__`` subclasses
+    that would like to work with torch.compile. It ensures that the subclass
+    properly implements the aliasing behavior of every op,
+    which is needed for correctness in AOTAutograd.
+    This function will handle:
+
+        * When we see a view op, we will alias the storages of any
+          input and output tensor subclasses
+
+        * When we see an inplace or out= op, we will directly
+          return the corresponding input tensor, instead of returning
+          a (potentially) fresh output tensor.
+    """
+
+    # Caching here because torchgen parsing is definitely not fast, and this function is called
+    # once for every op in the graph during functionalization.
+    schema_info = get_alias_info(func)
+
+    def get_write_alias(x):
+        if len(x.alias_set) == 0:
+            return None
+        alias_set = list(x.alias_set)
+        # torchscript allows for complicated alias sets, but our dispatcher ops only really involve simple aliasing
+        assert len(alias_set) == 1
+        if x.is_write:
+            return alias_set[0]
+        return None
+
+    def get_arg_from_alias(output_alias, schema_info, args, kwargs):
+        new_args, new_kwargs = torch.fx.operator_schemas.normalize_function(func, args=args, kwargs=kwargs)
+
+        arg_indices = [
+            i for i, a in enumerate(schema_info.args)
+            if output_alias in a.alias_set
+        ]
+        # For any dispatcher op with an output alias, we expect it to map to exactly one alias in the schema's input arguments.
+        assert len(arg_indices) == 1
+        idx = arg_indices[0]
+        arg_info = schema_info.args[idx]
+        if arg_info.name is not None and arg_info.name in new_kwargs:
+            return new_kwargs[arg_info.name]
+        return new_args[idx]
+
+    # Fix up the storages of any outs so that they point to the same storage as the input,
+    # if func is a view op.
+    _correct_storage_aliasing(func, schema_info, args, (out,) if not isinstance(out, tuple) else out)
+
+    # For inplace_view ops in particular, we'll try hard to make sure that the wrapper subclass's
+    # metadata is set correctly.
+    if torch.Tag.inplace_view in func.tags:
+        # no_dispatch() to make sure that we secretly change the metadata on the wrapper,
+        # but don't end up dispatching the op anywhere else.
+        mutated_args = [x for i, x in enumerate(args) if get_write_alias(schema_info.args[i]) is not None]
+        # Assumption: we have a very small number of inplace_view ops that follow a strict schema:
+        # there is only a single argument that gets its metadata mutated.
+        assert len(mutated_args) == 1
+        # This check exists because we generally *do* want to update the metadata of any wrapper subclasses,
+        # but FunctionalTensor is special: it overrides all size/stride calls to plumb to the inner tensor.
+        # so we don't actually need to update the metadata (and attempting to do so causes errors)
+        from torch._subclasses.functional_tensor import FunctionalTensor
+        if not isinstance(mutated_args[0], FunctionalTensor):
+            with torch.utils._mode_utils.no_dispatch():
+                # See Note: [Fake Tensor Dispatch Keys]
+                # we're borrowing the way it modifies dispatch key TLS.
+                meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+                torch._C._set_meta_in_tls_dispatch_include(True)
+                try:
+                    func(*args, **kwargs)
+                finally:
+                    torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+
+    # Next: we need to make sure to return inputs directly, if the output is a mutable alias (e.g. add_()).
+
+    # simple case: none of our outputs have mutable aliases, so we can return the output as-is
+    if not any(get_write_alias(r) is not None for r in schema_info.outs):
+        return out
+
+    # simplifying assumption: we don't have **any** ops with return types like "-> (Tensor(a!), Tensor)"
+    if not all(get_write_alias(r) is not None for r in schema_info.outs):
+        raise RuntimeError("Unsupported schema: " + str(func._schema))
+
+    if len(func._schema.returns) == 1:
+        return get_arg_from_alias(get_write_alias(schema_info.outs[0]), schema_info, args, kwargs)
+
+    # In the multi-return case, all aten ops return a tuple / list, so cast accordingly.
+    outs_to_return = type(out)([
+        get_arg_from_alias(get_write_alias(schema_info.outs[i]), schema_info, args, kwargs)
+        if get_write_alias(r) is not None else o
+        for ((i, r), o) in zip(enumerate(schema_info.outs), out)
+    ])
+    return outs_to_return
diff --git a/shared/package-files/c0/c0e4e5ab5d9e167fb59d2e4d68ab381c35ee0114f9b529d42a7cbe5e3f22f06b/test_merge.py b/shared/package-files/c0/c0e4e5ab5d9e167fb59d2e4d68ab381c35ee0114f9b529d42a7cbe5e3f22f06b/test_merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8292afe3c0fe7b74db647037de4b1601f6a7581
--- /dev/null
+++ b/shared/package-files/c0/c0e4e5ab5d9e167fb59d2e4d68ab381c35ee0114f9b529d42a7cbe5e3f22f06b/test_merge.py
@@ -0,0 +1,3030 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY314
+
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.merge import (
+    MergeError,
+    merge,
+)
+
+
+def get_test_data(ngroups=8, n=50):
+    unique_groups = list(range(ngroups))
+    arr = np.asarray(np.tile(unique_groups, n // ngroups))
+
+    if len(arr) < n:
+        arr = np.asarray(list(arr) + unique_groups[: n - len(arr)])
+
+    np.random.default_rng(2).shuffle(arr)
+    return arr
+
+
+def get_series():
+    return [
+        Series([1], dtype="int64"),
+        Series([1], dtype="Int64"),
+        Series([1.23]),
+        Series(["foo"]),
+        Series([True]),
+        Series([pd.Timestamp("2018-01-01")]),
+        Series([pd.Timestamp("2018-01-01", tz="US/Eastern")]),
+    ]
+
+
+def get_series_na():
+    return [
+        Series([np.nan], dtype="Int64"),
+        Series([np.nan], dtype="float"),
+        Series([np.nan], dtype="object"),
+        Series([pd.NaT]),
+    ]
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype(request):
+    """
+    A parametrized fixture returning a variety of Series of different
+    dtypes
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype2(request):
+    """
+    A duplicate of the series_of_dtype fixture, so that it can be used
+    twice by a single function
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series_na(), ids=lambda x: x.dtype.name)
+def series_of_dtype_all_na(request):
+    """
+    A parametrized fixture returning a variety of Series with all NA
+    values
+    """
+    return request.param
+
+
+@pytest.fixture
+def dfs_for_indicator():
+    df1 = DataFrame({"col1": [0, 1], "col_conflict": [1, 2], "col_left": ["a", "b"]})
+    df2 = DataFrame(
+        {
+            "col1": [1, 2, 3, 4, 5],
+            "col_conflict": [1, 2, 3, 4, 5],
+            "col_right": [2, 2, 2, 2, 2],
+        }
+    )
+    return df1, df2
+
+
+class TestMerge:
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            {
+                "key1": get_test_data(),
+                "key2": get_test_data(),
+                "data1": np.random.default_rng(2).standard_normal(50),
+                "data2": np.random.default_rng(2).standard_normal(50),
+            }
+        )
+
+        # exclude a couple keys for fun
+        df = df[df["key2"] > 1]
+        return df
+
+    @pytest.fixture
+    def df2(self):
+        return DataFrame(
+            {
+                "key1": get_test_data(n=10),
+                "key2": get_test_data(ngroups=4, n=10),
+                "value": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+
+    @pytest.fixture
+    def left(self):
+        return DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+
+    @pytest.fixture
+    def right(self):
+        return DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+    def test_merge_inner_join_empty(self):
+        # GH 15328
+        df_empty = DataFrame()
+        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
+        result = merge(df_empty, df_a, left_index=True, right_index=True)
+        expected = DataFrame({"a": []}, dtype="int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_common(self, df, df2):
+        joined = merge(df, df2)
+        exp = merge(df, df2, on=["key1", "key2"])
+        tm.assert_frame_equal(joined, exp)
+
+    def test_merge_non_string_columns(self):
+        # https://github.com/pandas-dev/pandas/issues/17962
+        # Checks that method runs for non string column names
+        left = DataFrame(
+            {0: [1, 0, 1, 0], 1: [0, 1, 0, 0], 2: [0, 0, 2, 0], 3: [1, 0, 0, 3]}
+        )
+
+        right = left.astype(float)
+        expected = left
+        result = merge(left, right)
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_index_as_on_arg(self, df, df2):
+        # GH14355
+
+        left = df.set_index("key1")
+        right = df2.set_index("key1")
+        result = merge(left, right, on="key1")
+        expected = merge(df, df2, on="key1").set_index("key1")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_index_singlekey_right_vs_left(self):
+        left = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+        right = DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=False
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=False
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=True
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=True
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+    def test_merge_index_singlekey_inner(self):
+        left = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+        right = DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+        # inner join
+        result = merge(left, right, left_on="key", right_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected)
+
+        result = merge(right, left, right_on="key", left_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+    def test_merge_misspecified(self, df, df2, left, right):
+        msg = "Must pass right_on or right_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, left_index=True)
+        msg = "Must pass left_on or left_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, right_index=True)
+
+        msg = (
+            'Can only pass argument "on" OR "left_on" and "right_on", not '
+            "a combination of both"
+        )
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, left, left_on="key", on="key")
+
+        msg = r"len\(right_on\) must equal len\(left_on\)"
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, left_on=["key1"], right_on=["key1", "key2"])
+
+    def test_index_and_on_parameters_confusion(self, df, df2):
+        msg = "right_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=False,
+                right_index=["key1", "key2"],
+            )
+        msg = "left_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=False,
+            )
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=["key1", "key2"],
+            )
+
+    def test_merge_overlap(self, left):
+        merged = merge(left, left, on="key")
+        exp_len = (left["key"].value_counts() ** 2).sum()
+        assert len(merged) == exp_len
+        assert "v1_x" in merged
+        assert "v1_y" in merged
+
+    def test_merge_different_column_key_names(self):
+        left = DataFrame({"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 4]})
+        right = DataFrame({"rkey": ["foo", "bar", "qux", "foo"], "value": [5, 6, 7, 8]})
+
+        merged = left.merge(
+            right, left_on="lkey", right_on="rkey", how="outer", sort=True
+        )
+
+        exp = Series(["bar", "baz", "foo", "foo", "foo", "foo", np.nan], name="lkey")
+        tm.assert_series_equal(merged["lkey"], exp)
+
+        exp = Series(["bar", np.nan, "foo", "foo", "foo", "foo", "qux"], name="rkey")
+        tm.assert_series_equal(merged["rkey"], exp)
+
+        exp = Series([2, 3, 1, 1, 4, 4, np.nan], name="value_x")
+        tm.assert_series_equal(merged["value_x"], exp)
+
+        exp = Series([6, np.nan, 5, 8, 5, 8, 7], name="value_y")
+        tm.assert_series_equal(merged["value_y"], exp)
+
+    def test_merge_copy(self):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=True)
+
+        merged["a"] = 6
+        assert (left["a"] == 0).all()
+
+        merged["d"] = "peekaboo"
+        assert (right["d"] == "bar").all()
+
+    def test_merge_nocopy(self, using_array_manager, using_infer_string):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=False)
+
+        assert np.shares_memory(merged["a"]._values, left["a"]._values)
+        if not using_infer_string:
+            assert np.shares_memory(merged["d"]._values, right["d"]._values)
+
+    def test_intelligently_handle_join_key(self):
+        # #733, be a bit more 1337 about not returning unconsolidated DataFrame
+
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": list(range(5))}, columns=["value", "key"]
+        )
+        right = DataFrame({"key": [1, 1, 2, 3, 4, 5], "rvalue": list(range(6))})
+
+        joined = merge(left, right, on="key", how="outer")
+        expected = DataFrame(
+            {
+                "key": [1, 1, 1, 1, 2, 2, 3, 4, 5],
+                "value": np.array([0, 0, 1, 1, 2, 3, 4, np.nan, np.nan]),
+                "rvalue": [0, 1, 0, 1, 2, 2, 3, 4, 5],
+            },
+            columns=["value", "key", "rvalue"],
+        )
+        tm.assert_frame_equal(joined, expected)
+
+    def test_merge_join_key_dtype_cast(self):
+        # #8596
+
+        df1 = DataFrame({"key": [1], "v1": [10]})
+        df2 = DataFrame({"key": [2], "v1": [20]})
+        df = merge(df1, df2, how="outer")
+        assert df["key"].dtype == "int64"
+
+        df1 = DataFrame({"key": [True], "v1": [1]})
+        df2 = DataFrame({"key": [False], "v1": [0]})
+        df = merge(df1, df2, how="outer")
+
+        # GH13169
+        # GH#40073
+        assert df["key"].dtype == "bool"
+
+        df1 = DataFrame({"val": [1]})
+        df2 = DataFrame({"val": [2]})
+        lkey = np.array([1])
+        rkey = np.array([2])
+        df = merge(df1, df2, left_on=lkey, right_on=rkey, how="outer")
+        assert df["key_0"].dtype == np.dtype(int)
+
+    def test_handle_join_key_pass_array(self):
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": np.arange(5)},
+            columns=["value", "key"],
+            dtype="int64",
+        )
+        right = DataFrame({"rvalue": np.arange(6)}, dtype="int64")
+        key = np.array([1, 1, 2, 3, 4, 5], dtype="int64")
+
+        merged = merge(left, right, left_on="key", right_on=key, how="outer")
+        merged2 = merge(right, left, left_on=key, right_on="key", how="outer")
+
+        tm.assert_series_equal(merged["key"], merged2["key"])
+        assert merged["key"].notna().all()
+        assert merged2["key"].notna().all()
+
+        left = DataFrame({"value": np.arange(5)}, columns=["value"])
+        right = DataFrame({"rvalue": np.arange(6)})
+        lkey = np.array([1, 1, 2, 2, 3])
+        rkey = np.array([1, 1, 2, 3, 4, 5])
+
+        merged = merge(left, right, left_on=lkey, right_on=rkey, how="outer")
+        expected = Series([1, 1, 1, 1, 2, 2, 3, 4, 5], dtype=int, name="key_0")
+        tm.assert_series_equal(merged["key_0"], expected)
+
+        left = DataFrame({"value": np.arange(3)})
+        right = DataFrame({"rvalue": np.arange(6)})
+
+        key = np.array([0, 1, 1, 2, 2, 3], dtype=np.int64)
+        merged = merge(left, right, left_index=True, right_on=key, how="outer")
+        tm.assert_series_equal(merged["key_0"], Series(key, name="key_0"))
+
+    def test_no_overlap_more_informative_error(self):
+        dt = datetime.now()
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+
+        msg = (
+            "No common columns to perform merge on. "
+            f"Merge options: left_on={None}, right_on={None}, "
+            f"left_index={False}, right_index={False}"
+        )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(df1, df2)
+
+    def test_merge_non_unique_indexes(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        dt4 = datetime(2012, 5, 4)
+
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+        # Not monotonic
+        df1 = DataFrame({"x": ["a", "b", "q"]}, index=[dt2, dt, dt4])
+        df2 = DataFrame(
+            {"y": ["c", "d", "e", "f", "g", "h"]}, index=[dt3, dt3, dt2, dt2, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+        df1 = DataFrame({"x": ["a", "b"]}, index=[dt, dt])
+        df2 = DataFrame({"y": ["c", "d"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+    def test_merge_non_unique_index_many_to_many(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        df1 = DataFrame({"x": ["a", "b", "c", "d"]}, index=[dt2, dt2, dt, dt])
+        df2 = DataFrame(
+            {"y": ["e", "f", "g", " h", "i"]}, index=[dt2, dt2, dt3, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+    def test_left_merge_empty_dataframe(self):
+        left = DataFrame({"key": [1], "value": [2]})
+        right = DataFrame({"key": []})
+
+        result = merge(left, right, on="key", how="left")
+        tm.assert_frame_equal(result, left)
+
+        result = merge(right, left, on="key", how="right")
+        tm.assert_frame_equal(result, left)
+
+    @pytest.mark.parametrize("how", ["inner", "left", "right", "outer"])
+    def test_merge_empty_dataframe(self, index, how):
+        # GH52777
+        left = DataFrame([], index=index[:0])
+        right = left.copy()
+
+        result = left.join(right, how=how)
+        tm.assert_frame_equal(result, left)
+
+    @pytest.mark.parametrize(
+        "kwarg",
+        [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+            {"left_on": "a", "right_index": True},
+            {"left_on": "a", "right_on": "x"},
+        ],
+    )
+    def test_merge_left_empty_right_empty(self, join_type, kwarg):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_in = DataFrame(columns=["a", "b", "c", "x", "y", "z"], dtype=object)
+
+        result = merge(left, right, how=join_type, **kwarg)
+        tm.assert_frame_equal(result, exp_in)
+
+    def test_merge_left_empty_right_notempty(self):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": np.array([np.nan] * 3, dtype=object),
+                "b": np.array([np.nan] * 3, dtype=object),
+                "c": np.array([np.nan] * 3, dtype=object),
+                "x": [1, 4, 7],
+                "y": [2, 5, 8],
+                "z": [3, 6, 9],
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        for kwarg in [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+        ]:
+            check1(exp_in, kwarg)
+            check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_index": True}
+        check1(exp_in, kwarg)
+        exp_out["a"] = [0, 1, 2]
+        check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_on": "x"}
+        check1(exp_in, kwarg)
+        exp_out["a"] = np.array([np.nan] * 3, dtype=object)
+        check2(exp_out, kwarg)
+
+    def test_merge_left_notempty_right_empty(self):
+        # GH 10824
+        left = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": [1, 4, 7],
+                "b": [2, 5, 8],
+                "c": [3, 6, 9],
+                "x": np.array([np.nan] * 3, dtype=object),
+                "y": np.array([np.nan] * 3, dtype=object),
+                "z": np.array([np.nan] * 3, dtype=object),
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+        # result will have object dtype
+        exp_in.index = exp_in.index.astype(object)
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+            # TODO: should the next loop be un-indented? doing so breaks this test
+            for kwarg in [
+                {"left_index": True, "right_index": True},
+                {"left_index": True, "right_on": "x"},
+                {"left_on": "a", "right_index": True},
+                {"left_on": "a", "right_on": "x"},
+            ]:
+                check1(exp_in, kwarg)
+                check2(exp_out, kwarg)
+
+    def test_merge_empty_frame(self, series_of_dtype, series_of_dtype2):
+        # GH 25183
+        df = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype2},
+            columns=["key", "value"],
+        )
+        df_empty = df[:0]
+        expected = DataFrame(
+            {
+                "key": Series(dtype=df.dtypes["key"]),
+                "value_x": Series(dtype=df.dtypes["value"]),
+                "value_y": Series(dtype=df.dtypes["value"]),
+            },
+            columns=["key", "value_x", "value_y"],
+        )
+        actual = df_empty.merge(df, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_all_na_column(self, series_of_dtype, series_of_dtype_all_na):
+        # GH 25183
+        df_left = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        df_right = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        expected = DataFrame(
+            {
+                "key": series_of_dtype,
+                "value_x": series_of_dtype_all_na,
+                "value_y": series_of_dtype_all_na,
+            },
+            columns=["key", "value_x", "value_y"],
+        )
+        actual = df_left.merge(df_right, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_nosort(self):
+        # GH#2098
+
+        d = {
+            "var1": np.random.default_rng(2).integers(0, 10, size=10),
+            "var2": np.random.default_rng(2).integers(0, 10, size=10),
+            "var3": [
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2010, 2, 3),
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2012, 4, 3),
+                datetime(2012, 3, 4),
+                datetime(2008, 5, 1),
+                datetime(2010, 2, 3),
+                datetime(2012, 2, 3),
+            ],
+        }
+        df = DataFrame.from_dict(d)
+        var3 = df.var3.unique()
+        var3 = np.sort(var3)
+        new = DataFrame.from_dict(
+            {"var3": var3, "var8": np.random.default_rng(2).random(7)}
+        )
+
+        result = df.merge(new, on="var3", sort=False)
+        exp = merge(df, new, on="var3", sort=False)
+        tm.assert_frame_equal(result, exp)
+
+        assert (df.var3.unique() == result.var3.unique()).all()
+
+    @pytest.mark.parametrize(
+        ("sort", "values"), [(False, [1, 1, 0, 1, 1]), (True, [0, 1, 1, 1, 1])]
+    )
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_same_order_left_right(self, sort, values, how):
+        # GH#35382
+        df = DataFrame({"a": [1, 0, 1]})
+
+        result = df.merge(df, on="a", how=how, sort=sort)
+        expected = DataFrame(values, columns=["a"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_nan_right(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0, 1]})
+        df2 = DataFrame({"i1": [0], "i3": [0]})
+        result = df1.join(df2, on="i1", rsuffix="_")
+        expected = (
+            DataFrame(
+                {
+                    "i1": {0: 0.0, 1: 1},
+                    "i2": {0: 0, 1: 1},
+                    "i1_": {0: 0, 1: np.nan},
+                    "i3": {0: 0.0, 1: np.nan},
+                    None: {0: 0, 1: 0},
+                },
+                columns=Index(["i1", "i2", "i1_", "i3", None], dtype=object),
+            )
+            .set_index(None)
+            .reset_index()[["i1", "i2", "i1_", "i3"]]
+        )
+        result.columns = result.columns.astype("object")
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_merge_nan_right2(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0.5, 1.5]})
+        df2 = DataFrame({"i1": [0], "i3": [0.7]})
+        result = df1.join(df2, rsuffix="_", on="i1")
+        expected = DataFrame(
+            {
+                "i1": {0: 0, 1: 1},
+                "i1_": {0: 0.0, 1: np.nan},
+                "i2": {0: 0.5, 1: 1.5},
+                "i3": {0: 0.69999999999999996, 1: np.nan},
+            }
+        )[["i1", "i2", "i1_", "i3"]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+    )
+    def test_merge_type(self, df, df2):
+        class NotADataFrame(DataFrame):
+            @property
+            def _constructor(self):
+                return NotADataFrame
+
+        nad = NotADataFrame(df)
+        result = nad.merge(df2, on="key1")
+
+        assert isinstance(result, NotADataFrame)
+
+    def test_join_append_timedeltas(self, using_array_manager):
+        # timedelta64 issues with join/merge
+        # GH 5695
+
+        d = DataFrame.from_dict(
+            {"d": [datetime(2013, 11, 5, 5, 56)], "t": [timedelta(0, 22500)]}
+        )
+        df = DataFrame(columns=list("dt"))
+        msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+        warn = FutureWarning
+        if using_array_manager:
+            warn = None
+        with tm.assert_produces_warning(warn, match=msg):
+            df = concat([df, d], ignore_index=True)
+            result = concat([df, d], ignore_index=True)
+        expected = DataFrame(
+            {
+                "d": [datetime(2013, 11, 5, 5, 56), datetime(2013, 11, 5, 5, 56)],
+                "t": [timedelta(0, 22500), timedelta(0, 22500)],
+            }
+        )
+        if using_array_manager:
+            # TODO(ArrayManager) decide on exact casting rules in concat
+            expected = expected.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_append_timedeltas2(self):
+        # timedelta64 issues with join/merge
+        # GH 5695
+        td = np.timedelta64(300000000)
+        lhs = DataFrame(Series([td, td], index=["A", "B"]))
+        rhs = DataFrame(Series([td], index=["A"]))
+
+        result = lhs.join(rhs, rsuffix="r", how="left")
+        expected = DataFrame(
+            {
+                "0": Series([td, td], index=list("AB")),
+                "0r": Series([td, pd.NaT], index=list("AB")),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_datetime_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"datetime64[{unit}]"
+
+        if unit in ["D", "h", "m"]:
+            # not supported so we cast to the nearest supported unit, seconds
+            exp_dtype = "datetime64[s]"
+        else:
+            exp_dtype = dtype
+        df2 = ser.astype(exp_dtype).to_frame("days")
+        assert df2["days"].dtype == exp_dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        days = np.array(["nat", "nat"], dtype=exp_dtype)
+        days = pd.core.arrays.DatetimeArray._simple_new(days, dtype=days.dtype)
+        exp = DataFrame(
+            {
+                "entity_id": [101, 102],
+                "days": days,
+            },
+            columns=["entity_id", "days"],
+        )
+        assert exp["days"].dtype == exp_dtype
+        tm.assert_frame_equal(result, exp)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_timedelta_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"m8[{unit}]"
+        if unit in ["D", "h", "m"]:
+            # We cannot astype, instead do nearest supported unit, i.e. "s"
+            msg = "Supported resolutions are 's', 'ms', 'us', 'ns'"
+            with pytest.raises(ValueError, match=msg):
+                ser.astype(dtype)
+
+            df2 = ser.astype("m8[s]").to_frame("days")
+        else:
+            df2 = ser.astype(dtype).to_frame("days")
+            assert df2["days"].dtype == dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        exp = DataFrame(
+            {"entity_id": [101, 102], "days": np.array(["nat", "nat"], dtype=dtype)},
+            columns=["entity_id", "days"],
+        )
+        tm.assert_frame_equal(result, exp)
+
+    def test_overlapping_columns_error_message(self):
+        df = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+        df2 = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+
+        df.columns = ["key", "foo", "foo"]
+        df2.columns = ["key", "bar", "bar"]
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "v1": [4, 5, 6],
+                "v2": [7, 8, 9],
+                "v3": [4, 5, 6],
+                "v4": [7, 8, 9],
+            }
+        )
+        expected.columns = ["key", "foo", "foo", "bar", "bar"]
+        tm.assert_frame_equal(merge(df, df2), expected)
+
+        # #2649, #10639
+        df2.columns = ["key1", "foo", "foo"]
+        msg = r"Data columns not unique: Index\(\['foo'\], dtype='object|str'\)"
+        with pytest.raises(MergeError, match=msg):
+            merge(df, df2)
+
+    def test_merge_on_datetime64tz(self):
+        # GH11405
+        left = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+                "value": [1, 2],
+            }
+        )
+        right = DataFrame(
+            {
+                "key": pd.date_range("20151011", periods=3, tz="US/Eastern"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=4, tz="US/Eastern"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_values(self):
+        left = DataFrame(
+            {
+                "key": [1, 2],
+                "value": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+            }
+        )
+        right = DataFrame(
+            {
+                "key": [2, 3],
+                "value": pd.date_range("20151011", periods=2, tz="US/Eastern"),
+            }
+        )
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(pd.date_range("20151010", periods=2, tz="US/Eastern"))
+                + [pd.NaT],
+                "value_y": [pd.NaT]
+                + list(pd.date_range("20151011", periods=2, tz="US/Eastern")),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "datetime64[ns, US/Eastern]"
+        assert result["value_y"].dtype == "datetime64[ns, US/Eastern]"
+
+    def test_merge_on_datetime64tz_empty(self):
+        # https://github.com/pandas-dev/pandas/issues/25014
+        dtz = pd.DatetimeTZDtype(tz="UTC")
+        right = DataFrame(
+            {
+                "date": DatetimeIndex(["2018"], dtype=dtz),
+                "value": [4.0],
+                "date2": DatetimeIndex(["2019"], dtype=dtz),
+            },
+            columns=["date", "value", "date2"],
+        )
+        left = right[:0]
+        result = left.merge(right, on="date")
+        expected = DataFrame(
+            {
+                "date": Series(dtype=dtz),
+                "value_x": Series(dtype=float),
+                "date2_x": Series(dtype=dtz),
+                "value_y": Series(dtype=float),
+                "date2_y": Series(dtype=dtz),
+            },
+            columns=["date", "value_x", "date2_x", "value_y", "date2_y"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_with_dst_transition(self):
+        # GH 18885
+        df1 = DataFrame(
+            pd.date_range("2017-10-29 01:00", periods=4, freq="h", tz="Europe/Madrid"),
+            columns=["date"],
+        )
+        df1["value"] = 1
+        df2 = DataFrame(
+            {
+                "date": pd.to_datetime(
+                    [
+                        "2017-10-29 03:00:00",
+                        "2017-10-29 04:00:00",
+                        "2017-10-29 05:00:00",
+                    ]
+                ),
+                "value": 2,
+            }
+        )
+        df2["date"] = df2["date"].dt.tz_localize("UTC").dt.tz_convert("Europe/Madrid")
+        result = merge(df1, df2, how="outer", on="date")
+        expected = DataFrame(
+            {
+                "date": pd.date_range(
+                    "2017-10-29 01:00", periods=7, freq="h", tz="Europe/Madrid"
+                ),
+                "value_x": [1] * 4 + [np.nan] * 3,
+                "value_y": [np.nan] * 4 + [2] * 3,
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_non_unique_period_index(self):
+        # GH #16871
+        index = pd.period_range("2016-01-01", periods=16, freq="M")
+        df = DataFrame(list(range(len(index))), index=index, columns=["pnum"])
+        df2 = concat([df, df])
+        result = df.merge(df2, left_index=True, right_index=True, how="inner")
+        expected = DataFrame(
+            np.tile(np.arange(16, dtype=np.int64).repeat(2).reshape(-1, 1), 2),
+            columns=["pnum_x", "pnum_y"],
+            index=df2.sort_index().index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_periods(self):
+        left = DataFrame(
+            {"key": pd.period_range("20151010", periods=2, freq="D"), "value": [1, 2]}
+        )
+        right = DataFrame(
+            {
+                "key": pd.period_range("20151011", periods=3, freq="D"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.period_range("20151010", periods=4, freq="D"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_period_values(self):
+        left = DataFrame(
+            {"key": [1, 2], "value": pd.period_range("20151010", periods=2, freq="D")}
+        )
+        right = DataFrame(
+            {"key": [2, 3], "value": pd.period_range("20151011", periods=2, freq="D")}
+        )
+
+        exp_x = pd.period_range("20151010", periods=2, freq="D")
+        exp_y = pd.period_range("20151011", periods=2, freq="D")
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(exp_x) + [pd.NaT],
+                "value_y": [pd.NaT] + list(exp_y),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "Period[D]"
+        assert result["value_y"].dtype == "Period[D]"
+
+    def test_indicator(self, dfs_for_indicator):
+        # PR #10054. xref #7412 and closes #8790.
+        df1, df2 = dfs_for_indicator
+        df1_copy = df1.copy()
+
+        df2_copy = df2.copy()
+
+        df_result = DataFrame(
+            {
+                "col1": [0, 1, 2, 3, 4, 5],
+                "col_conflict_x": [1, 2, np.nan, np.nan, np.nan, np.nan],
+                "col_left": ["a", "b", np.nan, np.nan, np.nan, np.nan],
+                "col_conflict_y": [np.nan, 1, 2, 3, 4, 5],
+                "col_right": [np.nan, 2, 2, 2, 2, 2],
+            }
+        )
+        df_result["_merge"] = Categorical(
+            [
+                "left_only",
+                "both",
+                "right_only",
+                "right_only",
+                "right_only",
+                "right_only",
+            ],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        df_result = df_result[
+            [
+                "col1",
+                "col_conflict_x",
+                "col_left",
+                "col_conflict_y",
+                "col_right",
+                "_merge",
+            ]
+        ]
+
+        test = merge(df1, df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+        test = df1.merge(df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+
+        # No side effects
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check with custom name
+        df_result_custom_name = df_result
+        df_result_custom_name = df_result_custom_name.rename(
+            columns={"_merge": "custom_name"}
+        )
+
+        test_custom_name = merge(
+            df1, df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+        test_custom_name = df1.merge(
+            df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+
+    def test_merge_indicator_arg_validation(self, dfs_for_indicator):
+        # Check only accepts strings and booleans
+        df1, df2 = dfs_for_indicator
+
+        msg = "indicator option can only accept boolean or string arguments"
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on="col1", how="outer", indicator=5)
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(df2, on="col1", how="outer", indicator=5)
+
+    def test_merge_indicator_result_integrity(self, dfs_for_indicator):
+        # Check result integrity
+        df1, df2 = dfs_for_indicator
+
+        test2 = merge(df1, df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+        test2 = df1.merge(df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+
+        test3 = merge(df1, df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+        test3 = df1.merge(df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+
+        test4 = merge(df1, df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+        test4 = df1.merge(df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+
+    def test_merge_indicator_invalid(self, dfs_for_indicator):
+        # Check if working name in df
+        df1, _ = dfs_for_indicator
+
+        for i in ["_right_indicator", "_left_indicator", "_merge"]:
+            df_badcolumn = DataFrame({"col1": [1, 2], i: [2, 2]})
+
+            msg = (
+                "Cannot use `indicator=True` option when data contains a "
+                f"column named {i}|"
+                "Cannot use name of an existing column for indicator column"
+            )
+            with pytest.raises(ValueError, match=msg):
+                merge(df1, df_badcolumn, on="col1", how="outer", indicator=True)
+            with pytest.raises(ValueError, match=msg):
+                df1.merge(df_badcolumn, on="col1", how="outer", indicator=True)
+
+        # Check for name conflict with custom name
+        df_badcolumn = DataFrame({"col1": [1, 2], "custom_column_name": [2, 2]})
+
+        msg = "Cannot use name of an existing column for indicator column"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df1,
+                df_badcolumn,
+                on="col1",
+                how="outer",
+                indicator="custom_column_name",
+            )
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(
+                df_badcolumn, on="col1", how="outer", indicator="custom_column_name"
+            )
+
+    def test_merge_indicator_multiple_columns(self):
+        # Merge on multiple columns
+        df3 = DataFrame({"col1": [0, 1], "col2": ["a", "b"]})
+
+        df4 = DataFrame({"col1": [1, 1, 3], "col2": ["b", "x", "y"]})
+
+        hand_coded_result = DataFrame(
+            {"col1": [0, 1, 1, 3], "col2": ["a", "b", "x", "y"]}
+        )
+        hand_coded_result["_merge"] = Categorical(
+            ["left_only", "both", "right_only", "right_only"],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        test5 = merge(df3, df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+        test5 = df3.merge(df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+
+    def test_validation(self):
+        left = DataFrame(
+            {"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
+            index=range(4),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
+            },
+            index=range(5),
+        )
+
+        # Make sure no side effects.
+        left_copy = left.copy()
+        right_copy = right.copy()
+
+        result = merge(left, right, left_index=True, right_index=True, validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+
+        # make sure merge still correct
+        expected = DataFrame(
+            {
+                "a_x": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a_y": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+            columns=["a_x", "b", "a_y", "c"],
+        )
+
+        result = merge(
+            left, right, left_index=True, right_index=True, validate="one_to_one"
+        )
+        tm.assert_frame_equal(result, expected)
+
+        expected_2 = DataFrame(
+            {
+                "a": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+        )
+
+        result = merge(left, right, on="a", validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+        tm.assert_frame_equal(result, expected_2)
+
+        result = merge(left, right, on="a", validate="one_to_one")
+        tm.assert_frame_equal(result, expected_2)
+
+        # One index, one column
+        expected_3 = DataFrame(
+            {
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            columns=["b", "a", "c"],
+            index=range(4),
+        )
+
+        left_index_reset = left.set_index("a")
+        result = merge(
+            left_index_reset,
+            right,
+            left_index=True,
+            right_on="a",
+            validate="one_to_one",
+        )
+        tm.assert_frame_equal(result, expected_3)
+
+        # Dups on right
+        right_w_dups = concat([right, DataFrame({"a": ["e"], "c": ["moo"]}, index=[4])])
+        merge(
+            left,
+            right_w_dups,
+            left_index=True,
+            right_index=True,
+            validate="one_to_many",
+        )
+
+        msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right_w_dups, on="a", validate="one_to_one")
+
+        # Dups on left
+        left_w_dups = concat(
+            [left, DataFrame({"a": ["a"], "c": ["cow"]}, index=[3])], sort=True
+        )
+        merge(
+            left_w_dups,
+            right,
+            left_index=True,
+            right_index=True,
+            validate="many_to_one",
+        )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right, on="a", validate="one_to_one")
+
+        # Dups on both
+        merge(left_w_dups, right_w_dups, on="a", validate="many_to_many")
+
+        msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="many_to_one",
+            )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right_w_dups, on="a", validate="one_to_many")
+
+        # Check invalid arguments
+        msg = (
+            '"jibberish" is not a valid argument. '
+            "Valid arguments are:\n"
+            '- "1:1"\n'
+            '- "1:m"\n'
+            '- "m:1"\n'
+            '- "m:m"\n'
+            '- "one_to_one"\n'
+            '- "one_to_many"\n'
+            '- "many_to_one"\n'
+            '- "many_to_many"'
+        )
+        with pytest.raises(ValueError, match=msg):
+            merge(left, right, on="a", validate="jibberish")
+
+        # Two column merge, dups in both, but jointly no dups.
+        left = DataFrame(
+            {
+                "a": ["a", "a", "b", "b"],
+                "b": [0, 1, 0, 1],
+                "c": ["cat", "dog", "weasel", "horse"],
+            },
+            index=range(4),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        expected_multi = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "c": ["cat", "dog", "weasel"],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        msg = (
+            "Merge keys are not unique in either left or right dataset; "
+            "not a one-to-one merge"
+        )
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right, on="a", validate="1:1")
+
+        result = merge(left, right, on=["a", "b"], validate="1:1")
+        tm.assert_frame_equal(result, expected_multi)
+
+    def test_merge_two_empty_df_no_division_error(self):
+        # GH17776, PR #17846
+        a = DataFrame({"a": [], "b": [], "c": []})
+        with np.errstate(divide="raise"):
+            merge(a, a, on=("a", "b"))
+
+    @pytest.mark.parametrize("how", ["right", "outer"])
+    @pytest.mark.parametrize(
+        "index,expected_index",
+        [
+            (
+                CategoricalIndex([1, 2, 4]),
+                CategoricalIndex([1, 2, 4, None, None, None]),
+            ),
+            (
+                DatetimeIndex(
+                    ["2001-01-01", "2002-02-02", "2003-03-03"], dtype="M8[ns]"
+                ),
+                DatetimeIndex(
+                    ["2001-01-01", "2002-02-02", "2003-03-03", pd.NaT, pd.NaT, pd.NaT],
+                    dtype="M8[ns]",
+                ),
+            ),
+            *[
+                (
+                    Index([1, 2, 3], dtype=dtyp),
+                    Index([1, 2, 3, None, None, None], dtype=np.float64),
+                )
+                for dtyp in tm.ALL_REAL_NUMPY_DTYPES
+            ],
+            (
+                IntervalIndex.from_tuples([(1, 2), (2, 3), (3, 4)]),
+                IntervalIndex.from_tuples(
+                    [(1, 2), (2, 3), (3, 4), np.nan, np.nan, np.nan]
+                ),
+            ),
+            (
+                PeriodIndex(["2001-01-01", "2001-01-02", "2001-01-03"], freq="D"),
+                PeriodIndex(
+                    ["2001-01-01", "2001-01-02", "2001-01-03", pd.NaT, pd.NaT, pd.NaT],
+                    freq="D",
+                ),
+            ),
+            (
+                TimedeltaIndex(["1d", "2d", "3d"]),
+                TimedeltaIndex(["1d", "2d", "3d", pd.NaT, pd.NaT, pd.NaT]),
+            ),
+        ],
+    )
+    def test_merge_on_index_with_more_values(self, how, index, expected_index):
+        # GH 24212
+        # pd.merge gets [0, 1, 2, -1, -1, -1] as left_indexer, ensure that
+        # -1 is interpreted as a missing value instead of the last element
+        df1 = DataFrame({"a": [0, 1, 2], "key": [0, 1, 2]}, index=index)
+        df2 = DataFrame({"b": [0, 1, 2, 3, 4, 5]})
+        result = df1.merge(df2, left_on="key", right_index=True, how=how)
+        expected = DataFrame(
+            [
+                [0, 0, 0],
+                [1, 1, 1],
+                [2, 2, 2],
+                [np.nan, 3, 3],
+                [np.nan, 4, 4],
+                [np.nan, 5, 5],
+            ],
+            columns=["a", "key", "b"],
+        )
+        expected.set_index(expected_index, inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_right_index_right(self):
+        # Note: the expected output here is probably incorrect.
+        # See https://github.com/pandas-dev/pandas/issues/17257 for more.
+        # We include this as a regression test for GH-24897.
+        left = DataFrame({"a": [1, 2, 3], "key": [0, 1, 1]})
+        right = DataFrame({"b": [1, 2, 3]})
+
+        expected = DataFrame(
+            {"a": [1, 2, 3, None], "key": [0, 1, 1, 2], "b": [1, 2, 2, 3]},
+            columns=["a", "key", "b"],
+            index=[0, 1, 2, np.nan],
+        )
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_preserves_row_order(self, how):
+        # GH 27453
+        left_df = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        right_df = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        result = left_df.merge(right_df, on=["animal", "max_speed"], how=how)
+        if how == "right":
+            expected = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        else:
+            expected = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_take_missing_values_from_index_of_other_dtype(self):
+        # GH 24212
+        left = DataFrame(
+            {
+                "a": [1, 2, 3],
+                "key": Categorical(["a", "a", "b"], categories=list("abc")),
+            }
+        )
+        right = DataFrame({"b": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        expected = DataFrame(
+            {
+                "a": [1, 2, 3, None],
+                "key": Categorical(["a", "a", "b", "c"]),
+                "b": [1, 1, 2, 3],
+            },
+            index=[0, 1, 2, np.nan],
+        )
+        expected = expected.reindex(columns=["a", "key", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_readonly(self):
+        # https://github.com/pandas-dev/pandas/issues/27943
+        data1 = DataFrame(
+            np.arange(20).reshape((4, 5)) + 1, columns=["a", "b", "c", "d", "e"]
+        )
+        data2 = DataFrame(
+            np.arange(20).reshape((5, 4)) + 1, columns=["a", "b", "x", "y"]
+        )
+
+        # make each underlying block array / column array read-only
+        for arr in data1._mgr.arrays:
+            arr.flags.writeable = False
+
+        data1.merge(data2)  # no error
+
+
+def _check_merge(x, y):
+    for how in ["inner", "left", "outer"]:
+        for sort in [True, False]:
+            result = x.join(y, how=how, sort=sort)
+
+            expected = merge(x.reset_index(), y.reset_index(), how=how, sort=sort)
+            expected = expected.set_index("index")
+
+            # TODO check_names on merge?
+            tm.assert_frame_equal(result, expected, check_names=False)
+
+
+class TestMergeDtypes:
+    @pytest.mark.parametrize(
+        "right_vals", [["foo", "bar"], Series(["foo", "bar"]).astype("category")]
+    )
+    def test_different(self, right_vals):
+        left = DataFrame(
+            {
+                "A": ["foo", "bar"],
+                "B": Series(["foo", "bar"]).astype("category"),
+                "C": [1, 2],
+                "D": [1.0, 2.0],
+                "E": Series([1, 2], dtype="uint64"),
+                "F": Series([1, 2], dtype="int32"),
+            }
+        )
+        right = DataFrame({"A": right_vals})
+
+        # GH 9780
+        # We allow merging on object and categorical cols and cast
+        # categorical cols to object
+        result = merge(left, right, on="A")
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "d1", [np.int64, np.int32, np.intc, np.int16, np.int8, np.uint8]
+    )
+    @pytest.mark.parametrize("d2", [np.int64, np.float64, np.float32, np.float16])
+    def test_join_multi_dtypes(self, d1, d2):
+        dtype1 = np.dtype(d1)
+        dtype2 = np.dtype(d2)
+
+        left = DataFrame(
+            {
+                "k1": np.array([0, 1, 2] * 8, dtype=dtype1),
+                "k2": ["foo", "bar"] * 12,
+                "v": np.array(np.arange(24), dtype=np.int64),
+            }
+        )
+
+        index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
+        right = DataFrame({"v2": np.array([5, 7], dtype=dtype2)}, index=index)
+
+        result = left.join(right, on=["k1", "k2"])
+
+        expected = left.copy()
+
+        if dtype2.kind == "i":
+            dtype2 = np.dtype("float64")
+        expected["v2"] = np.array(np.nan, dtype=dtype2)
+        expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
+        expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on=["k1", "k2"], sort=True)
+        expected.sort_values(["k1", "k2"], kind="mergesort", inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "int_vals, float_vals, exp_vals",
+        [
+            ([1, 2, 3], [1.0, 2.0, 3.0], {"X": [1, 2, 3], "Y": [1.0, 2.0, 3.0]}),
+            ([1, 2, 3], [1.0, 3.0], {"X": [1, 3], "Y": [1.0, 3.0]}),
+            ([1, 2], [1.0, 2.0, 3.0], {"X": [1, 2], "Y": [1.0, 2.0]}),
+        ],
+    )
+    def test_merge_on_ints_floats(self, int_vals, float_vals, exp_vals):
+        # GH 16572
+        # Check that float column is not cast to object if
+        # merging on float and int columns
+        A = DataFrame({"X": int_vals})
+        B = DataFrame({"Y": float_vals})
+        expected = DataFrame(exp_vals)
+
+        result = A.merge(B, left_on="X", right_on="Y")
+        tm.assert_frame_equal(result, expected)
+
+        result = B.merge(A, left_on="Y", right_on="X")
+        tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_key_dtype_cast(self):
+        # GH 17044
+        df1 = DataFrame({"key": [1.0, 2.0], "v1": [10, 20]}, columns=["key", "v1"])
+        df2 = DataFrame({"key": [2], "v2": [200]}, columns=["key", "v2"])
+        result = df1.merge(df2, on="key", how="left")
+        expected = DataFrame(
+            {"key": [1.0, 2.0], "v1": [10, 20], "v2": [np.nan, 200.0]},
+            columns=["key", "v1", "v2"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_ints_floats_warning(self):
+        # GH 16572
+        # merge will produce a warning when merging on int and
+        # float columns where the float values are not exactly
+        # equal to their int representation
+        A = DataFrame({"X": [1, 2, 3]})
+        B = DataFrame({"Y": [1.1, 2.5, 3.0]})
+        expected = DataFrame({"X": [3], "Y": [3.0]})
+
+        with tm.assert_produces_warning(UserWarning):
+            result = A.merge(B, left_on="X", right_on="Y")
+            tm.assert_frame_equal(result, expected)
+
+        with tm.assert_produces_warning(UserWarning):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+        # test no warning if float has NaNs
+        B = DataFrame({"Y": [np.nan, np.nan, 3.0]})
+
+        with tm.assert_produces_warning(None):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_incompat_infer_boolean_object(self):
+        # GH21119: bool + object bool merge OK
+        df1 = DataFrame({"key": Series([True, False], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_incompat_infer_boolean_object_with_missing(self):
+        # GH21119: bool + object bool merge OK
+        # with missing value
+        df1 = DataFrame({"key": Series([True, False, np.nan], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # merge on category coerces to object
+            ([0, 1, 2], Series(["a", "b", "a"]).astype("category")),
+            ([0.0, 1.0, 2.0], Series(["a", "b", "a"]).astype("category")),
+            # no not infer
+            ([0, 1], Series([False, True], dtype=object)),
+            ([0, 1], Series([False, True], dtype=bool)),
+        ],
+    )
+    def test_merge_incompat_dtypes_are_ok(self, df1_vals, df2_vals):
+        # these are explicitly allowed incompat merges, that pass thru
+        # the result type is dependent on if the values on the rhs are
+        # inferred, otherwise these will be coerced to object
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        result = merge(df1, df2, on=["A"])
+        assert is_object_dtype(result.A.dtype)
+        result = merge(df2, df1, on=["A"])
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # do not infer to numeric
+            (Series([1, 2], dtype="uint64"), ["a", "b", "c"]),
+            (Series([1, 2], dtype="int32"), ["a", "b", "c"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            ([0.0, 1.0, 2.0], ["0", "1", "2"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            (
+                pd.date_range("1/1/2011", periods=2, freq="D"),
+                ["2011-01-01", "2011-01-02"],
+            ),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0, 1]),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0.0, 1.0]),
+            (
+                pd.date_range("20130101", periods=3),
+                pd.date_range("20130101", periods=3, tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_merge_incompat_dtypes_error(self, df1_vals, df2_vals):
+        # GH 9780, GH 15800
+        # Raise a ValueError when a user tries to merge on
+        # dtypes that are incompatible (e.g., obj and int/float)
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        msg = (
+            f"You are trying to merge on {df1['A'].dtype} and {df2['A'].dtype} "
+            "columns for key 'A'. If you wish to proceed you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on=["A"])
+
+        # Check that error still raised when swapping order of dataframes
+        msg = (
+            f"You are trying to merge on {df2['A'].dtype} and {df1['A'].dtype} "
+            "columns for key 'A'. If you wish to proceed you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df2, df1, on=["A"])
+
+        # Check that error still raised when merging on multiple columns
+        # The error message should mention the first incompatible column
+        if len(df1_vals) == len(df2_vals):
+            # Column A in df1 and df2 is of compatible (the same) dtype
+            # Columns B and C in df1 and df2 are of incompatible dtypes
+            df3 = DataFrame({"A": df2_vals, "B": df1_vals, "C": df1_vals})
+            df4 = DataFrame({"A": df2_vals, "B": df2_vals, "C": df2_vals})
+
+            # Check that error raised correctly when merging all columns A, B, and C
+            # The error message should mention key 'B'
+            msg = (
+                f"You are trying to merge on {df3['B'].dtype} and {df4['B'].dtype} "
+                "columns for key 'B'. If you wish to proceed you should use pd.concat"
+            )
+            msg = re.escape(msg)
+            with pytest.raises(ValueError, match=msg):
+                merge(df3, df4)
+
+            # Check that error raised correctly when merging columns A and C
+            # The error message should mention key 'C'
+            msg = (
+                f"You are trying to merge on {df3['C'].dtype} and {df4['C'].dtype} "
+                "columns for key 'C'. If you wish to proceed you should use pd.concat"
+            )
+            msg = re.escape(msg)
+            with pytest.raises(ValueError, match=msg):
+                merge(df3, df4, on=["A", "C"])
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            ([1, 2], "outer"),
+            ([], "inner"),
+            ([2], "right"),
+            ([1], "left"),
+        ],
+    )
+    def test_merge_EA_dtype(self, any_numeric_ea_dtype, how, expected_data):
+        # GH#40073
+        d1 = DataFrame([(1,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        d2 = DataFrame([(2,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        result = merge(d1, d2, how=how)
+        exp_index = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_index, columns=["id"], dtype=any_numeric_ea_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            (["a", "b"], "outer"),
+            ([], "inner"),
+            (["b"], "right"),
+            (["a"], "left"),
+        ],
+    )
+    def test_merge_string_dtype(self, how, expected_data, any_string_dtype):
+        # GH#40073
+        d1 = DataFrame([("a",)], columns=["id"], dtype=any_string_dtype)
+        d2 = DataFrame([("b",)], columns=["id"], dtype=any_string_dtype)
+        result = merge(d1, d2, how=how)
+        exp_idx = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_idx, columns=["id"], dtype=any_string_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "how, expected_data",
+        [
+            ("inner", [[True, 1, 4], [False, 5, 3]]),
+            ("outer", [[False, 5, 3], [True, 1, 4]]),
+            ("left", [[True, 1, 4], [False, 5, 3]]),
+            ("right", [[False, 5, 3], [True, 1, 4]]),
+        ],
+    )
+    def test_merge_bool_dtype(self, how, expected_data):
+        # GH#40073
+        df1 = DataFrame({"A": [True, False], "B": [1, 5]})
+        df2 = DataFrame({"A": [False, True], "C": [3, 4]})
+        result = merge(df1, df2, how=how)
+        expected = DataFrame(expected_data, columns=["A", "B", "C"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_ea_with_string(self, join_type, string_dtype):
+        # GH 43734 Avoid the use of `assign` with multi-index
+        df1 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", "4", None],
+                ("lvl0", "lvl1-b"): ["4", "5", "6", "7", "8"],
+            },
+            dtype=pd.StringDtype(),
+        )
+        df1_copy = df1.copy()
+        df2 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", pd.NA, "5"],
+                ("lvl0", "lvl1-c"): ["7", "8", "9", pd.NA, "11"],
+            },
+            dtype=string_dtype,
+        )
+        df2_copy = df2.copy()
+        merged = merge(left=df1, right=df2, on=[("lvl0", "lvl1-a")], how=join_type)
+
+        # No change in df1 and df2
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check the expected types for the merged data frame
+        expected = Series(
+            [np.dtype("O"), pd.StringDtype(), np.dtype("O")],
+            index=MultiIndex.from_tuples(
+                [("lvl0", "lvl1-a"), ("lvl0", "lvl1-b"), ("lvl0", "lvl1-c")]
+            ),
+        )
+        tm.assert_series_equal(merged.dtypes, expected)
+
+    @pytest.mark.parametrize(
+        "left_empty, how, exp",
+        [
+            (False, "left", "left"),
+            (False, "right", "empty"),
+            (False, "inner", "empty"),
+            (False, "outer", "left"),
+            (False, "cross", "empty_cross"),
+            (True, "left", "empty"),
+            (True, "right", "right"),
+            (True, "inner", "empty"),
+            (True, "outer", "right"),
+            (True, "cross", "empty_cross"),
+        ],
+    )
+    def test_merge_empty(self, left_empty, how, exp):
+        left = DataFrame({"A": [2, 1], "B": [3, 4]})
+        right = DataFrame({"A": [1], "C": [5]}, dtype="int64")
+
+        if left_empty:
+            left = left.head(0)
+        else:
+            right = right.head(0)
+
+        result = left.merge(right, how=how)
+
+        if exp == "left":
+            expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
+        elif exp == "right":
+            expected = DataFrame({"A": [1], "B": [np.nan], "C": [5]})
+        elif exp == "empty":
+            expected = DataFrame(columns=["A", "B", "C"], dtype="int64")
+        elif exp == "empty_cross":
+            expected = DataFrame(columns=["A_x", "B", "A_y", "C"], dtype="int64")
+
+        if how == "outer":
+            expected = expected.sort_values("A", ignore_index=True)
+
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left():
+    return DataFrame(
+        {
+            "X": Series(
+                np.random.default_rng(2).choice(["foo", "bar"], size=(10,))
+            ).astype(CategoricalDtype(["foo", "bar"])),
+            "Y": np.random.default_rng(2).choice(["one", "two", "three"], size=(10,)),
+        }
+    )
+
+
+@pytest.fixture
+def right():
+    return DataFrame(
+        {
+            "X": Series(["foo", "bar"]).astype(CategoricalDtype(["foo", "bar"])),
+            "Z": [1, 2],
+        }
+    )
+
+
+class TestMergeCategorical:
+    def test_identical(self, left, using_infer_string):
+        # merging on the same, should preserve dtypes
+        merged = merge(left, left, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "str"
+        expected = Series(
+            [CategoricalDtype(categories=["foo", "bar"]), dtype, dtype],
+            index=["X", "Y_x", "Y_y"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_basic(self, left, right, using_infer_string):
+        # we have matching Categorical dtypes in X
+        # so should preserve the merged column
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "str"
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                dtype,
+                np.dtype("int64"),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_merge_categorical(self):
+        # GH 9426
+
+        right = DataFrame(
+            {
+                "c": {0: "a", 1: "b", 2: "c", 3: "d", 4: "e"},
+                "d": {0: "null", 1: "null", 2: "null", 3: "null", 4: "null"},
+            }
+        )
+        left = DataFrame(
+            {
+                "a": {0: "f", 1: "f", 2: "f", 3: "f", 4: "f"},
+                "b": {0: "g", 1: "g", 2: "g", 3: "g", 4: "g"},
+            }
+        )
+        df = merge(left, right, how="left", left_on="b", right_on="c")
+
+        # object-object
+        expected = df.copy()
+
+        # object-cat
+        # note that we propagate the category
+        # because we don't have any matching rows
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        result = merge(left, cright, how="left", left_on="b", right_on="c")
+        expected["d"] = expected["d"].astype(CategoricalDtype(["null"]))
+        tm.assert_frame_equal(result, expected)
+
+        # cat-object
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+        # cat-cat
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+    def tests_merge_categorical_unordered_equal(self):
+        # GH-19551
+        df1 = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"], categories=["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "Foo": Categorical(["C", "B", "A"], categories=["C", "B", "A"]),
+                "Right": ["C1", "B1", "A1"],
+            }
+        )
+        result = merge(df1, df2, on=["Foo"])
+        expected = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+                "Right": ["A1", "B1", "C1"],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_multiindex_merge_with_unordered_categoricalindex(self, ordered):
+        # GH 36973
+        pcat = CategoricalDtype(categories=["P2", "P1"], ordered=ordered)
+        df1 = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+            }
+        ).set_index(["id", "p"])
+        df2 = DataFrame(
+            {
+                "id": ["A", "C", "C"],
+                "p": Categorical(["P2", "P2", "P1"], dtype=pcat),
+                "d1": [10, 11, 12],
+            }
+        ).set_index(["id", "p"])
+        result = merge(df1, df2, how="left", left_index=True, right_index=True)
+        expected = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+                "d1": [11.0, 12.0, np.nan],
+            }
+        ).set_index(["id", "p"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_other_columns(self, left, right, using_infer_string):
+        # non-merge columns should preserve if possible
+        right = right.assign(Z=right.Z.astype("category"))
+
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "str"
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                dtype,
+                CategoricalDtype(categories=[1, 2]),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+        # categories are preserved
+        assert left.X.values._categories_match_up_to_permutation(merged.X.values)
+        assert right.Z.values._categories_match_up_to_permutation(merged.Z.values)
+
+    @pytest.mark.parametrize(
+        "change",
+        [
+            lambda x: x,
+            lambda x: x.astype(CategoricalDtype(["foo", "bar", "bah"])),
+            lambda x: x.astype(CategoricalDtype(ordered=True)),
+        ],
+    )
+    def test_dtype_on_merged_different(
+        self, change, join_type, left, right, using_infer_string
+    ):
+        # our merging columns, X now has 2 different dtypes
+        # so we must be object as a result
+
+        X = change(right.X.astype("object"))
+        right = right.assign(X=X)
+        assert isinstance(left.X.values.dtype, CategoricalDtype)
+        # assert not left.X.values._categories_match_up_to_permutation(right.X.values)
+
+        merged = merge(left, right, on="X", how=join_type)
+
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "str"
+        expected = Series([dtype, dtype, np.dtype("int64")], index=["X", "Y", "Z"])
+        tm.assert_series_equal(result, expected)
+
+    def test_self_join_multiple_categories(self):
+        # GH 16767
+        # non-duplicates should work with multiple categories
+        m = 5
+        df = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] * m,
+                "b": ["t", "w", "x", "y", "z"] * 2 * m,
+                "c": [
+                    letter
+                    for each in ["m", "n", "u", "p", "o"]
+                    for letter in [each] * 2 * m
+                ],
+                "d": [
+                    letter
+                    for each in [
+                        "aa",
+                        "bb",
+                        "cc",
+                        "dd",
+                        "ee",
+                        "ff",
+                        "gg",
+                        "hh",
+                        "ii",
+                        "jj",
+                    ]
+                    for letter in [each] * m
+                ],
+            }
+        )
+
+        # change them all to categorical variables
+        df = df.apply(lambda x: x.astype("category"))
+
+        # self-join should equal ourselves
+        result = merge(df, df, on=list(df.columns))
+
+        tm.assert_frame_equal(result, df)
+
+    def test_dtype_on_categorical_dates(self):
+        # GH 16900
+        # dates should not be coerced to ints
+
+        df = DataFrame(
+            [[date(2001, 1, 1), 1.1], [date(2001, 1, 2), 1.3]], columns=["date", "num2"]
+        )
+        df["date"] = df["date"].astype("category")
+
+        df2 = DataFrame(
+            [[date(2001, 1, 1), 1.3], [date(2001, 1, 3), 1.4]], columns=["date", "num4"]
+        )
+        df2["date"] = df2["date"].astype("category")
+
+        expected_outer = DataFrame(
+            [
+                [pd.Timestamp("2001-01-01").date(), 1.1, 1.3],
+                [pd.Timestamp("2001-01-02").date(), 1.3, np.nan],
+                [pd.Timestamp("2001-01-03").date(), np.nan, 1.4],
+            ],
+            columns=["date", "num2", "num4"],
+        )
+        result_outer = merge(df, df2, how="outer", on=["date"])
+        tm.assert_frame_equal(result_outer, expected_outer)
+
+        expected_inner = DataFrame(
+            [[pd.Timestamp("2001-01-01").date(), 1.1, 1.3]],
+            columns=["date", "num2", "num4"],
+        )
+        result_inner = merge(df, df2, how="inner", on=["date"])
+        tm.assert_frame_equal(result_inner, expected_inner)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    @pytest.mark.parametrize(
+        "category_column,categories,expected_categories",
+        [
+            ([False, True, True, False], [True, False], [True, False]),
+            ([2, 1, 1, 2], [1, 2], [1, 2]),
+            (["False", "True", "True", "False"], ["True", "False"], ["True", "False"]),
+        ],
+    )
+    def test_merging_with_bool_or_int_cateorical_column(
+        self, category_column, categories, expected_categories, ordered
+    ):
+        # GH 17187
+        # merging with a boolean/int categorical column
+        df1 = DataFrame({"id": [1, 2, 3, 4], "cat": category_column})
+        df1["cat"] = df1["cat"].astype(CategoricalDtype(categories, ordered=ordered))
+        df2 = DataFrame({"id": [2, 4], "num": [1, 9]})
+        result = df1.merge(df2)
+        expected = DataFrame({"id": [2, 4], "cat": expected_categories, "num": [1, 9]})
+        expected["cat"] = expected["cat"].astype(
+            CategoricalDtype(categories, ordered=ordered)
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_on_int_array(self):
+        # GH 23020
+        df = DataFrame({"A": Series([1, 2, np.nan], dtype="Int64"), "B": 1})
+        result = merge(df, df, on="A")
+        expected = DataFrame(
+            {"A": Series([1, 2, np.nan], dtype="Int64"), "B_x": 1, "B_y": 1}
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left_df():
+    return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
+
+
+@pytest.fixture
+def right_df():
+    return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
+
+
+class TestMergeOnIndexes:
+    @pytest.mark.parametrize(
+        "how, sort, expected",
+        [
+            ("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])),
+            ("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])),
+            (
+                "left",
+                False,
+                DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]),
+            ),
+            (
+                "left",
+                True,
+                DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]),
+            ),
+            (
+                "right",
+                False,
+                DataFrame(
+                    {"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]
+                ),
+            ),
+            (
+                "right",
+                True,
+                DataFrame(
+                    {"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]
+                ),
+            ),
+            (
+                "outer",
+                False,
+                DataFrame(
+                    {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                    index=[0, 1, 2, 3],
+                ),
+            ),
+            (
+                "outer",
+                True,
+                DataFrame(
+                    {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                    index=[0, 1, 2, 3],
+                ),
+            ),
+        ],
+    )
+    def test_merge_on_indexes(self, left_df, right_df, how, sort, expected):
+        result = merge(
+            left_df, right_df, left_index=True, right_index=True, how=how, sort=sort
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [Index([1, 2], dtype=dtyp, name="index_col") for dtyp in tm.ALL_REAL_NUMPY_DTYPES]
+    + [
+        CategoricalIndex(["A", "B"], categories=["A", "B"], name="index_col"),
+        RangeIndex(start=0, stop=2, name="index_col"),
+        DatetimeIndex(["2018-01-01", "2018-01-02"], name="index_col"),
+    ],
+    ids=lambda x: f"{type(x).__name__}[{x.dtype}]",
+)
+def test_merge_index_types(index):
+    # gh-20777
+    # assert key access is consistent across index types
+    left = DataFrame({"left_data": [1, 2]}, index=index)
+    right = DataFrame({"right_data": [1.0, 2.0]}, index=index)
+
+    result = left.merge(right, on=["index_col"])
+
+    expected = DataFrame({"left_data": [1, 2], "right_data": [1.0, 2.0]}, index=index)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "on,left_on,right_on,left_index,right_index,nm",
+    [
+        (["outer", "inner"], None, None, False, False, "B"),
+        (None, None, None, True, True, "B"),
+        (None, ["outer", "inner"], None, False, True, "B"),
+        (None, None, ["outer", "inner"], True, False, "B"),
+        (["outer", "inner"], None, None, False, False, None),
+        (None, None, None, True, True, None),
+        (None, ["outer", "inner"], None, False, True, None),
+        (None, None, ["outer", "inner"], True, False, None),
+    ],
+)
+def test_merge_series(on, left_on, right_on, left_index, right_index, nm):
+    # GH 21220
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=nm,
+    )
+    expected = DataFrame(
+        {"A": [2, 4], "B": [1, 3]},
+        index=MultiIndex.from_product([["a", "b"], [1]], names=["outer", "inner"]),
+    )
+    if nm is not None:
+        result = merge(
+            a,
+            b,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        msg = "Cannot merge a Series without a name"
+        with pytest.raises(ValueError, match=msg):
+            result = merge(
+                a,
+                b,
+                on=on,
+                left_on=left_on,
+                right_on=right_on,
+                left_index=left_index,
+                right_index=right_index,
+            )
+
+
+def test_merge_series_multilevel():
+    # GH#47946
+    # GH 40993: For raising, enforced in 2.0
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=("B", "C"),
+    )
+    with pytest.raises(
+        MergeError, match="Not allowed to merge between different levels"
+    ):
+        merge(a, b, on=["outer", "inner"])
+
+
+@pytest.mark.parametrize(
+    "col1, col2, kwargs, expected_cols",
+    [
+        (0, 0, {"suffixes": ("", "_dup")}, ["0", "0_dup"]),
+        (0, 0, {"suffixes": (None, "_dup")}, [0, "0_dup"]),
+        (0, 0, {"suffixes": ("_x", "_y")}, ["0_x", "0_y"]),
+        (0, 0, {"suffixes": ["_x", "_y"]}, ["0_x", "0_y"]),
+        ("a", 0, {"suffixes": (None, "_y")}, ["a", 0]),
+        (0.0, 0.0, {"suffixes": ("_x", None)}, ["0.0_x", 0.0]),
+        ("b", "b", {"suffixes": (None, "_y")}, ["b", "b_y"]),
+        ("a", "a", {"suffixes": ("_x", None)}, ["a_x", "a"]),
+        ("a", "b", {"suffixes": ("_x", None)}, ["a", "b"]),
+        ("a", "a", {"suffixes": (None, "_x")}, ["a", "a_x"]),
+        (0, 0, {"suffixes": ("_a", None)}, ["0_a", 0]),
+        ("a", "a", {}, ["a_x", "a_y"]),
+        (0, 0, {}, ["0_x", "0_y"]),
+    ],
+)
+def test_merge_suffix(col1, col2, kwargs, expected_cols):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [4, 5, 6]})
+
+    expected = DataFrame([[1, 4], [2, 5], [3, 6]], columns=expected_cols)
+
+    result = a.merge(b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(a, b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how,expected",
+    [
+        (
+            "right",
+            DataFrame(
+                {"A": [100, 200, 300], "B1": [60, 70, np.nan], "B2": [600, 700, 800]}
+            ),
+        ),
+        (
+            "outer",
+            DataFrame(
+                {
+                    "A": [1, 100, 200, 300],
+                    "B1": [80, 60, 70, np.nan],
+                    "B2": [np.nan, 600, 700, 800],
+                }
+            ),
+        ),
+    ],
+)
+def test_merge_duplicate_suffix(how, expected):
+    left_df = DataFrame({"A": [100, 200, 1], "B": [60, 70, 80]})
+    right_df = DataFrame({"A": [100, 200, 300], "B": [600, 700, 800]})
+    result = merge(left_df, right_df, on="A", how=how, suffixes=("_x", "_x"))
+    expected.columns = ["A", "B_x", "B_x"]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes",
+    [("a", "a", (None, None)), ("a", "a", ("", None)), (0, 0, (None, ""))],
+)
+def test_merge_suffix_error(col1, col2, suffixes):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    # TODO: might reconsider current raise behaviour, see issue 24782
+    msg = "columns overlap but no suffix specified"
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("suffixes", [{"left", "right"}, {"left": 0, "right": 0}])
+def test_merge_suffix_raises(suffixes):
+    a = DataFrame({"a": [1, 2, 3]})
+    b = DataFrame({"b": [3, 4, 5]})
+
+    with pytest.raises(TypeError, match="Passing 'suffixes' as a"):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+TWO_GOT_THREE = "2, got 3" if PY314 else "2"
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes, msg",
+    [
+        (
+            "a",
+            "a",
+            ("a", "b", "c"),
+            (rf"too many values to unpack \(expected {TWO_GOT_THREE}\)"),
+        ),
+        ("a", "a", tuple("a"), r"not enough values to unpack \(expected 2, got 1\)"),
+    ],
+)
+def test_merge_suffix_length_error(col1, col2, suffixes, msg):
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("cat_dtype", ["one", "two"])
+@pytest.mark.parametrize("reverse", [True, False])
+def test_merge_equal_cat_dtypes(cat_dtype, reverse):
+    # see gh-22501
+    cat_dtypes = {
+        "one": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+        "two": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+    }
+
+    df1 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]), "left": [1, 2, 3]}
+    ).set_index("foo")
+
+    data_foo = ["a", "b", "c"]
+    data_right = [1, 2, 3]
+
+    if reverse:
+        data_foo.reverse()
+        data_right.reverse()
+
+    df2 = DataFrame(
+        {"foo": Series(data_foo).astype(cat_dtypes[cat_dtype]), "right": data_right}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {
+            "left": [1, 2, 3],
+            "right": [1, 2, 3],
+            "foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]),
+        }
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_equal_cat_dtypes2():
+    # see gh-22501
+    cat_dtype = CategoricalDtype(categories=["a", "b", "c"], ordered=False)
+
+    # Test Data
+    df1 = DataFrame(
+        {"foo": Series(["a", "b"]).astype(cat_dtype), "left": [1, 2]}
+    ).set_index("foo")
+
+    df2 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtype), "right": [3, 2, 1]}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {"left": [1, 2], "right": [3, 2], "foo": Series(["a", "b"]).astype(cat_dtype)}
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_on_cat_and_ext_array():
+    # GH 28668
+    right = DataFrame(
+        {"a": Series([pd.Interval(0, 1), pd.Interval(1, 2)], dtype="interval")}
+    )
+    left = right.copy()
+    left["a"] = left["a"].astype("category")
+
+    result = merge(left, right, how="inner", on="a")
+    expected = right.copy()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_multiindex_columns():
+    # Issue #28518
+    # Verify that merging two dataframes give the expected labels
+    # The original cause of this issue come from a bug lexsort_depth and is tested in
+    # test_lexsort_depth
+
+    letters = ["a", "b", "c", "d"]
+    numbers = ["1", "2", "3"]
+    index = MultiIndex.from_product((letters, numbers), names=["outer", "inner"])
+
+    frame_x = DataFrame(columns=index)
+    frame_x["id"] = ""
+    frame_y = DataFrame(columns=index)
+    frame_y["id"] = ""
+
+    l_suf = "_x"
+    r_suf = "_y"
+    result = frame_x.merge(frame_y, on="id", suffixes=((l_suf, r_suf)))
+
+    # Constructing the expected results
+    tuples = [(letter + l_suf, num) for letter in letters for num in numbers]
+    tuples += [("id", "")]
+    tuples += [(letter + r_suf, num) for letter in letters for num in numbers]
+
+    expected_index = MultiIndex.from_tuples(tuples, names=["outer", "inner"])
+    expected = DataFrame(columns=expected_index)
+
+    tm.assert_frame_equal(result, expected, check_dtype=False)
+
+
+def test_merge_datetime_upcast_dtype():
+    # https://github.com/pandas-dev/pandas/issues/31208
+    df1 = DataFrame({"x": ["a", "b", "c"], "y": ["1", "2", "4"]})
+    df2 = DataFrame(
+        {"y": ["1", "2", "3"], "z": pd.to_datetime(["2000", "2001", "2002"])}
+    )
+    result = merge(df1, df2, how="left", on="y")
+    expected = DataFrame(
+        {
+            "x": ["a", "b", "c"],
+            "y": ["1", "2", "4"],
+            "z": pd.to_datetime(["2000", "2001", "NaT"]),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("n_categories", [5, 128])
+def test_categorical_non_unique_monotonic(n_categories):
+    # GH 28189
+    # With n_categories as 5, we test the int8 case is hit in libjoin,
+    # with n_categories as 128 we test the int16 case.
+    left_index = CategoricalIndex([0] + list(range(n_categories)))
+    df1 = DataFrame(range(n_categories + 1), columns=["value"], index=left_index)
+    df2 = DataFrame(
+        [[6]],
+        columns=["value"],
+        index=CategoricalIndex([0], categories=list(range(n_categories))),
+    )
+
+    result = merge(df1, df2, how="left", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[i, 6.0] if i < 2 else [i, np.nan] for i in range(n_categories + 1)],
+        columns=["value_x", "value_y"],
+        index=left_index,
+    )
+    tm.assert_frame_equal(expected, result)
+
+
+def test_merge_join_categorical_multiindex():
+    # From issue 16627
+    a = {
+        "Cat1": Categorical(["a", "b", "a", "c", "a", "b"], ["a", "b", "c"]),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(["a", "b", "c", "a", "b", "c"], ["a", "b", "c"]),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+    # Same test, but with ordered categorical
+    a = {
+        "Cat1": Categorical(
+            ["a", "b", "a", "c", "a", "b"], ["b", "a", "c"], ordered=True
+        ),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(
+            ["a", "b", "c", "a", "b", "c"], ["b", "a", "c"], ordered=True
+        ),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a", "left_index": True}, ["left_on", "left_index"]),
+        ({"right_on": "a", "right_index": True}, ["right_on", "right_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_duplicates(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Can only pass argument "{err_msg[0]}" OR "{err_msg[1]}" not both\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a"}, ["right_on", "right_index"]),
+        ({"right_on": "a"}, ["left_on", "left_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_missing(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Must pass "{err_msg[0]}" OR "{err_msg[1]}"\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"right_index": True},
+        {"left_index": True},
+    ],
+)
+def test_merge_join_cols_error_reporting_on_and_index(func, kwargs):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = (
+        r'Can only pass argument "on" OR "left_index" '
+        r'and "right_index", not a combination of both\.'
+    )
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, on="a", **kwargs)
+
+
+def test_merge_right_left_index():
+    # GH#38616
+    left = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    right = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    result = merge(left, right, how="right", left_index=True, right_on="x")
+    expected = DataFrame(
+        {
+            "x": [1, 1],
+            "x_x": [1, 1],
+            "z_x": ["foo", "foo"],
+            "x_y": [1, 1],
+            "z_y": ["foo", "foo"],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_result_empty_index_and_on():
+    # GH#33814
+    df1 = DataFrame({"a": [1], "b": [2]}).set_index(["a", "b"])
+    df2 = DataFrame({"b": [1]}).set_index(["b"])
+    expected = DataFrame({"a": [], "b": []}, dtype=np.int64).set_index(["a", "b"])
+    result = merge(df1, df2, left_on=["b"], right_index=True)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(df2, df1, left_index=True, right_on=["b"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_suffixes_produce_dup_columns_raises():
+    # GH#22818; Enforced in 2.0
+    left = DataFrame({"a": [1, 2, 3], "b": 1, "b_x": 2})
+    right = DataFrame({"a": [1, 2, 3], "b": 2})
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(right, left, on="a", suffixes=("_y", "_x"))
+
+
+def test_merge_duplicate_columns_with_suffix_no_warning():
+    # GH#22818
+    # Do not raise warning when duplicates are caused by duplicates in origin
+    left = DataFrame([[1, 1, 1], [2, 2, 2]], columns=["a", "b", "b"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    result = merge(left, right, on="a")
+    expected = DataFrame([[1, 1, 1, 2]], columns=["a", "b_x", "b_x", "b_y"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_duplicate_columns_with_suffix_causing_another_duplicate_raises():
+    # GH#22818, Enforced in 2.0
+    # This should raise warning because suffixes cause another collision
+    left = DataFrame([[1, 1, 1, 1], [2, 2, 2, 2]], columns=["a", "b", "b", "b_x"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+
+def test_merge_string_float_column_result():
+    # GH 13353
+    df1 = DataFrame([[1, 2], [3, 4]], columns=Index(["a", 114.0]))
+    df2 = DataFrame([[9, 10], [11, 12]], columns=["x", "y"])
+    result = merge(df2, df1, how="inner", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[9, 10, 1, 2], [11, 12, 3, 4]], columns=Index(["x", "y", "a", 114.0])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_mergeerror_on_left_index_mismatched_dtypes():
+    # GH 22449
+    df_1 = DataFrame(data=["X"], columns=["C"], index=[22])
+    df_2 = DataFrame(data=["X"], columns=["C"], index=[999])
+    with pytest.raises(MergeError, match="Can only pass argument"):
+        merge(df_1, df_2, on=["C"], left_index=True)
+
+
+def test_merge_on_left_categoricalindex():
+    # GH#48464 don't raise when left_on is a CategoricalIndex
+    ci = CategoricalIndex(range(3))
+
+    right = DataFrame({"A": ci, "B": range(3)})
+    left = DataFrame({"C": range(3, 6)})
+
+    res = merge(left, right, left_on=ci, right_on="A")
+    expected = merge(left, right, left_on=ci._data, right_on="A")
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize("dtype", [None, "Int64"])
+def test_merge_outer_with_NaN(dtype):
+    # GH#43550
+    left = DataFrame({"key": [1, 2], "col1": [1, 2]}, dtype=dtype)
+    right = DataFrame({"key": [np.nan, np.nan], "col2": [3, 4]}, dtype=dtype)
+    result = merge(left, right, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [1, 2, np.nan, np.nan],
+            "col1": [1, 2, np.nan, np.nan],
+            "col2": [np.nan, np.nan, 3, 4],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # switch left and right
+    result = merge(right, left, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [1, 2, np.nan, np.nan],
+            "col2": [np.nan, np.nan, 3, 4],
+            "col1": [1, 2, np.nan, np.nan],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_different_index_names():
+    # GH#45094
+    left = DataFrame({"a": [1]}, index=Index([1], name="c"))
+    right = DataFrame({"a": [1]}, index=Index([1], name="d"))
+    result = merge(left, right, left_on="c", right_on="d")
+    expected = DataFrame({"a_x": [1], "a_y": 1})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype)
+    result = left.merge(right, how=join_type)
+    expected = DataFrame({"a": [1, 2, 3], "b": 1, "c": 2}, dtype=any_numeric_ea_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea_and_non_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype.lower())
+    result = left.merge(right, how=join_type)
+    expected = DataFrame(
+        {
+            "a": Series([1, 2, 3], dtype=any_numeric_ea_dtype),
+            "b": Series([1, 1, 1], dtype=any_numeric_ea_dtype),
+            "c": Series([2, 2, 2], dtype=any_numeric_ea_dtype.lower()),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "int64[pyarrow]"])
+def test_merge_arrow_and_numpy_dtypes(dtype):
+    # GH#52406
+    pytest.importorskip("pyarrow")
+    df = DataFrame({"a": [1, 2]}, dtype=dtype)
+    df2 = DataFrame({"a": [1, 2]}, dtype="int64[pyarrow]")
+    result = df.merge(df2)
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+    result = df2.merge(df)
+    expected = df2.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["inner", "left", "outer", "right"])
+@pytest.mark.parametrize("tz", [None, "America/Chicago"])
+def test_merge_datetime_different_resolution(tz, how):
+    # https://github.com/pandas-dev/pandas/issues/53200
+    vals = [
+        pd.Timestamp(2023, 5, 12, tz=tz),
+        pd.Timestamp(2023, 5, 13, tz=tz),
+        pd.Timestamp(2023, 5, 14, tz=tz),
+    ]
+    df1 = DataFrame({"t": vals[:2], "a": [1.0, 2.0]})
+    df1["t"] = df1["t"].dt.as_unit("ns")
+    df2 = DataFrame({"t": vals[1:], "b": [1.0, 2.0]})
+    df2["t"] = df2["t"].dt.as_unit("s")
+
+    expected = DataFrame({"t": vals, "a": [1.0, 2.0, np.nan], "b": [np.nan, 1.0, 2.0]})
+    expected["t"] = expected["t"].dt.as_unit("ns")
+    if how == "inner":
+        expected = expected.iloc[[1]].reset_index(drop=True)
+    elif how == "left":
+        expected = expected.iloc[[0, 1]]
+    elif how == "right":
+        expected = expected.iloc[[1, 2]].reset_index(drop=True)
+
+    result = df1.merge(df2, on="t", how=how)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_multiindex_single_level():
+    # GH52331
+    df = DataFrame({"col": ["A", "B"]})
+    df2 = DataFrame(
+        data={"b": [100]},
+        index=MultiIndex.from_tuples([("A",), ("C",)], names=["col"]),
+    )
+    expected = DataFrame({"col": ["A", "B"], "b": [100, np.nan]})
+
+    result = df.merge(df2, left_on=["col"], right_index=True, how="left")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("on_index", [True, False])
+@pytest.mark.parametrize("left_unique", [True, False])
+@pytest.mark.parametrize("left_monotonic", [True, False])
+@pytest.mark.parametrize("right_unique", [True, False])
+@pytest.mark.parametrize("right_monotonic", [True, False])
+def test_merge_combinations(
+    how, sort, on_index, left_unique, left_monotonic, right_unique, right_monotonic
+):
+    # GH 54611
+    left = [2, 3]
+    if left_unique:
+        left.append(4 if left_monotonic else 1)
+    else:
+        left.append(3 if left_monotonic else 2)
+
+    right = [2, 3]
+    if right_unique:
+        right.append(4 if right_monotonic else 1)
+    else:
+        right.append(3 if right_monotonic else 2)
+
+    left = DataFrame({"key": left})
+    right = DataFrame({"key": right})
+
+    if on_index:
+        left = left.set_index("key")
+        right = right.set_index("key")
+        on_kwargs = {"left_index": True, "right_index": True}
+    else:
+        on_kwargs = {"on": "key"}
+
+    result = merge(left, right, how=how, sort=sort, **on_kwargs)
+
+    if on_index:
+        left = left.reset_index()
+        right = right.reset_index()
+
+    if how in ["left", "right", "inner"]:
+        if how in ["left", "inner"]:
+            expected, other, other_unique = left, right, right_unique
+        else:
+            expected, other, other_unique = right, left, left_unique
+        if how == "inner":
+            keep_values = set(left["key"].values).intersection(right["key"].values)
+            keep_mask = expected["key"].isin(keep_values)
+            expected = expected[keep_mask]
+        if sort:
+            expected = expected.sort_values("key")
+        if not other_unique:
+            other_value_counts = other["key"].value_counts()
+            repeats = other_value_counts.reindex(expected["key"].values, fill_value=1)
+            repeats = repeats.astype(np.intp)
+            expected = expected["key"].repeat(repeats.values)
+            expected = expected.to_frame()
+    elif how == "outer":
+        left_counts = left["key"].value_counts()
+        right_counts = right["key"].value_counts()
+        expected_counts = left_counts.mul(right_counts, fill_value=1)
+        expected_counts = expected_counts.astype(np.intp)
+        expected = expected_counts.index.values.repeat(expected_counts.values)
+        expected = DataFrame({"key": expected})
+        expected = expected.sort_values("key")
+
+    if on_index:
+        expected = expected.set_index("key")
+    else:
+        expected = expected.reset_index(drop=True)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea_int_and_float_numpy():
+    # GH#46178
+    df1 = DataFrame([1.0, np.nan], dtype=pd.Int64Dtype())
+    df2 = DataFrame([1.5])
+    expected = DataFrame(columns=[0], dtype="Int64")
+
+    with tm.assert_produces_warning(UserWarning, match="You are merging"):
+        result = df1.merge(df2)
+    tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(UserWarning, match="You are merging"):
+        result = df2.merge(df1)
+    tm.assert_frame_equal(result, expected.astype("float64"))
+
+    df2 = DataFrame([1.0])
+    expected = DataFrame([1], columns=[0], dtype="Int64")
+    result = df1.merge(df2)
+    tm.assert_frame_equal(result, expected)
+
+    result = df2.merge(df1)
+    tm.assert_frame_equal(result, expected.astype("float64"))
+
+
+def test_merge_arrow_string_index(any_string_dtype):
+    # GH#54894
+    pytest.importorskip("pyarrow")
+    left = DataFrame({"a": ["a", "b"]}, dtype=any_string_dtype)
+    right = DataFrame({"b": 1}, index=Index(["a", "c"], dtype=any_string_dtype))
+    result = left.merge(right, left_on="a", right_index=True, how="left")
+    expected = DataFrame(
+        {"a": Series(["a", "b"], dtype=any_string_dtype), "b": [1, np.nan]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("left_empty", [True, False])
+@pytest.mark.parametrize("right_empty", [True, False])
+def test_merge_empty_frames_column_order(left_empty, right_empty):
+    # GH 51929
+    df1 = DataFrame(1, index=[0], columns=["A", "B"])
+    df2 = DataFrame(1, index=[0], columns=["A", "C", "D"])
+
+    if left_empty:
+        df1 = df1.iloc[:0]
+    if right_empty:
+        df2 = df2.iloc[:0]
+
+    result = merge(df1, df2, on=["A"], how="outer")
+    expected = DataFrame(1, index=[0], columns=["A", "B", "C", "D"])
+    if left_empty and right_empty:
+        expected = expected.iloc[:0]
+    elif left_empty:
+        expected["B"] = np.nan
+    elif right_empty:
+        expected[["C", "D"]] = np.nan
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+def test_merge_datetime_and_timedelta(how):
+    left = DataFrame({"key": Series([1, None], dtype="datetime64[ns]")})
+    right = DataFrame({"key": Series([1], dtype="timedelta64[ns]")})
+
+    msg = (
+        f"You are trying to merge on {left['key'].dtype} and {right['key'].dtype} "
+        "columns for key 'key'. If you wish to proceed you should use pd.concat"
+    )
+    with pytest.raises(ValueError, match=re.escape(msg)):
+        left.merge(right, on="key", how=how)
+
+    msg = (
+        f"You are trying to merge on {right['key'].dtype} and {left['key'].dtype} "
+        "columns for key 'key'. If you wish to proceed you should use pd.concat"
+    )
+    with pytest.raises(ValueError, match=re.escape(msg)):
+        right.merge(left, on="key", how=how)
diff --git a/shared/package-files/c0/c0e66acd7ee28a9907dfd84e1ea065926adeb4945b632f8aecfb2764fc9a2455/sf_error.py b/shared/package-files/c0/c0e66acd7ee28a9907dfd84e1ea065926adeb4945b632f8aecfb2764fc9a2455/sf_error.py
new file mode 100644
index 0000000000000000000000000000000000000000..00ff73756acd4219a4ba94eb089bce7d4c32266d
--- /dev/null
+++ b/shared/package-files/c0/c0e66acd7ee28a9907dfd84e1ea065926adeb4945b632f8aecfb2764fc9a2455/sf_error.py
@@ -0,0 +1,20 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.special` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__ = [  # noqa: F822
+    'SpecialFunctionWarning',
+    'SpecialFunctionError'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="special", module="sf_error",
+                                   private_modules=["_sf_error"], all=__all__,
+                                   attribute=name)
diff --git a/shared/package-files/c0/c0ea166ef878f8cfd0fc6cb4f28c82c1a1dab7bab99b69040b75acbda12d73d4/msort_ops.h b/shared/package-files/c0/c0ea166ef878f8cfd0fc6cb4f28c82c1a1dab7bab99b69040b75acbda12d73d4/msort_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..9267b868350507a795e67689b743d512e897f1b6
--- /dev/null
+++ b/shared/package-files/c0/c0ea166ef878f8cfd0fc6cb4f28c82c1a1dab7bab99b69040b75acbda12d73d4/msort_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API msort_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::msort")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+struct TORCH_API msort {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::msort")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "msort(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c0/c0f024efac9fac6f43d7e233234903925a624d5aa0a78c6c1068ce35ef65098e/test_cython_aggregations.py b/shared/package-files/c0/c0f024efac9fac6f43d7e233234903925a624d5aa0a78c6c1068ce35ef65098e/test_cython_aggregations.py
new file mode 100644
index 0000000000000000000000000000000000000000..c60cb6ea74ec0aa90cf089841c853c657e1b4c00
--- /dev/null
+++ b/shared/package-files/c0/c0f024efac9fac6f43d7e233234903925a624d5aa0a78c6c1068ce35ef65098e/test_cython_aggregations.py
@@ -0,0 +1,111 @@
+from functools import partial
+import sys
+
+import numpy as np
+import pytest
+
+import pandas._libs.window.aggregations as window_aggregations
+
+from pandas import Series
+import pandas._testing as tm
+
+
+def _get_rolling_aggregations():
+    # list pairs of name and function
+    # each function has this signature:
+    # (const float64_t[:] values, ndarray[int64_t] start,
+    #  ndarray[int64_t] end, int64_t minp) -> np.ndarray
+    named_roll_aggs = (
+        [
+            ("roll_sum", window_aggregations.roll_sum),
+            ("roll_mean", window_aggregations.roll_mean),
+        ]
+        + [
+            (f"roll_var({ddof})", partial(window_aggregations.roll_var, ddof=ddof))
+            for ddof in [0, 1]
+        ]
+        + [
+            ("roll_skew", window_aggregations.roll_skew),
+            ("roll_kurt", window_aggregations.roll_kurt),
+            ("roll_median_c", window_aggregations.roll_median_c),
+            ("roll_max", window_aggregations.roll_max),
+            ("roll_min", window_aggregations.roll_min),
+        ]
+        + [
+            (
+                f"roll_quantile({quantile},{interpolation})",
+                partial(
+                    window_aggregations.roll_quantile,
+                    quantile=quantile,
+                    interpolation=interpolation,
+                ),
+            )
+            for quantile in [0.0001, 0.5, 0.9999]
+            for interpolation in window_aggregations.interpolation_types
+        ]
+        + [
+            (
+                f"roll_rank({percentile},{method},{ascending})",
+                partial(
+                    window_aggregations.roll_rank,
+                    percentile=percentile,
+                    method=method,
+                    ascending=ascending,
+                ),
+            )
+            for percentile in [True, False]
+            for method in window_aggregations.rolling_rank_tiebreakers.keys()
+            for ascending in [True, False]
+        ]
+    )
+    # unzip to a list of 2 tuples, names and functions
+    unzipped = list(zip(*named_roll_aggs))
+    return {"ids": unzipped[0], "params": unzipped[1]}
+
+
+_rolling_aggregations = _get_rolling_aggregations()
+
+
+@pytest.fixture(
+    params=_rolling_aggregations["params"], ids=_rolling_aggregations["ids"]
+)
+def rolling_aggregation(request):
+    """Make a rolling aggregation function as fixture."""
+    return request.param
+
+
+def test_rolling_aggregation_boundary_consistency(rolling_aggregation):
+    # GH-45647
+    minp, step, width, size, selection = 0, 1, 3, 11, [2, 7]
+    values = np.arange(1, 1 + size, dtype=np.float64)
+    end = np.arange(width, size, step, dtype=np.int64)
+    start = end - width
+    selarr = np.array(selection, dtype=np.int32)
+    result = Series(rolling_aggregation(values, start[selarr], end[selarr], minp))
+    expected = Series(rolling_aggregation(values, start, end, minp)[selarr])
+    tm.assert_equal(expected, result)
+
+
+def test_rolling_aggregation_with_unused_elements(rolling_aggregation):
+    # GH-45647
+    minp, width = 0, 5  # width at least 4 for kurt
+    size = 2 * width + 5
+    values = np.arange(1, size + 1, dtype=np.float64)
+    values[width : width + 2] = sys.float_info.min
+    values[width + 2] = np.nan
+    values[width + 3 : width + 5] = sys.float_info.max
+    start = np.array([0, size - width], dtype=np.int64)
+    end = np.array([width, size], dtype=np.int64)
+    loc = np.array(
+        [j for i in range(len(start)) for j in range(start[i], end[i])],
+        dtype=np.int32,
+    )
+    result = Series(rolling_aggregation(values, start, end, minp))
+    compact_values = np.array(values[loc], dtype=np.float64)
+    compact_start = np.arange(0, len(start) * width, width, dtype=np.int64)
+    compact_end = compact_start + width
+    expected = Series(
+        rolling_aggregation(compact_values, compact_start, compact_end, minp)
+    )
+    assert np.isfinite(expected.values).all(), "Not all expected values are finite"
+    tm.assert_equal(expected, result)
diff --git a/shared/package-files/c0/c0f5de91a579724de35cd4126659ee079eaddbeab3602668fcb8c9ad9284e7ff/graph_rewrite_helper.h b/shared/package-files/c0/c0f5de91a579724de35cd4126659ee079eaddbeab3602668fcb8c9ad9284e7ff/graph_rewrite_helper.h
new file mode 100644
index 0000000000000000000000000000000000000000..0920830babb8b326e994339e8c479593091d36cb
--- /dev/null
+++ b/shared/package-files/c0/c0f5de91a579724de35cd4126659ee079eaddbeab3602668fcb8c9ad9284e7ff/graph_rewrite_helper.h
@@ -0,0 +1,54 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/ir/irparser.h>
+#include <torch/csrc/jit/ir/subgraph_matcher.h>
+#include <torch/csrc/jit/passes/subgraph_rewrite.h>
+
+namespace torch {
+namespace jit {
+namespace graph_rewrite_helper {
+
+std::string getFuncName(Value* func_value);
+Value* getValue(
+    const std::string& name,
+    const std::unordered_map<const Value*, Value*>& match_vmap,
+    const std::unordered_map<std::string, Value*>& vmap);
+c10::optional<IValue> getIValue(
+    const std::string& name,
+    const std::unordered_map<const Value*, Value*>& match_vmap,
+    const std::unordered_map<std::string, Value*>& vmap);
+TORCH_API void replaceConvolutionWithAtenConv(std::shared_ptr<Graph>& graph);
+
+bool isClampFusable(
+    const Match& match,
+    const std::unordered_map<std::string, Value*>& vmap);
+
+// This struct contains a compiled IR patterns slated for use in the
+// findPatternMatches function. The struct encapsulates the common
+// information from parseIR that is used in conjunction with the
+// pattern matching facility. A const instance of this struct can
+// also be stored away to cache the compiled IR pattern and reduce
+// runtime cost
+struct PatternInfo {
+  std::string pattern_string;
+  std::unique_ptr<Graph> pattern_graph;
+  std::unordered_map<std::string, Value*> vmap;
+  std::vector<MatchFilter> filters;
+
+  static PatternInfo parse_from_str(
+      std::string pattern_string,
+      const std::vector<MatchFilter>& filters = {}) {
+    PatternInfo rv{
+        std::move(pattern_string),
+        std::make_unique<Graph>(),
+        decltype(vmap){},
+        filters};
+    parseIR(rv.pattern_string, rv.pattern_graph.get(), rv.vmap);
+    return rv;
+  }
+};
+
+} // namespace graph_rewrite_helper
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/c0/c0fe4344f1b75763dc5dca31fa1a0c63adf6e70da57598ca2e441adaeceb6344/_trace_wrapped_higher_order_op.py b/shared/package-files/c0/c0fe4344f1b75763dc5dca31fa1a0c63adf6e70da57598ca2e441adaeceb6344/_trace_wrapped_higher_order_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e22cafcc6dd5efeac0d05a81c8a9612270bd1d3
--- /dev/null
+++ b/shared/package-files/c0/c0fe4344f1b75763dc5dca31fa1a0c63adf6e70da57598ca2e441adaeceb6344/_trace_wrapped_higher_order_op.py
@@ -0,0 +1,120 @@
+import torch
+from torch._C import DispatchKey
+from torch._higher_order_ops.utils import autograd_not_implemented
+
+from torch._ops import HigherOrderOperator
+from torch._subclasses import FakeTensorMode
+from torch.fx.experimental._backward_state import BackwardState
+
+from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree
+from torch.utils._python_dispatch import _get_current_dispatch_mode
+from torch.utils._pytree import tree_map_only
+
+
+__all__ = ["trace_wrapped"]
+
+
+# trace_wrapped(*args, fn) is equivalent to fn(*args), but with a twist:
+# if you make_fx trace through this call, we will not actually trace into fn; instead,
+# we will directly insert it as a call_function to fn in the graph.
+# (Unlike make_fx, Dynamo WILL inline into fn.)
+# You can think of this as a one off allow_in_graph equivalent for proxy tensor tracing.
+#
+# Because proxy tensor tracing does not actually run the function, there are
+# requirements on the behavior of fn. We are still figuring it out, but here is the current state:
+#
+# 1) fn SHOULD only take a single argument, which must be a tensor
+# 2) fn MUST return a new tensor with the same metadata as the original tensor
+#    (e.g., zeros_like(input) is a permissible implementation of fn).
+#    This is verified via an extra assert that is inserted into the traced graph.
+# 3) fn MAY have side effects, but it MAY NOT perform metadata mutation on other tensors
+#    participating in proxy tensor tracing (it MAY mutate other tensors, it MAY mutate Python state)
+# These requirements stem from the requirement that we need to continue performing proxy tensor tracing,
+# which assumes accurate fake tensor metadata, without actually running fn.
+# In the future, we may allow for a "meta" function associated with fn to allow for more interesting input-output patterns.
+#
+# Note that tensors / Python state are allowed to be mutated.
+# This is relaxed constraint is not always sound, but it is sound for backward tracing with fake
+# tensors as it takes place in AOTAutograd, as the backward pass is guaranteed not to depend on concrete
+# tensor values (via fake tensor) or Python state (because the autograd engine doesn't depend on Python).
+#
+# The intended use case for this function is to allow AOTAutograd to defer complex
+# backward hooks to compiled autograd. AOTAutograd performs a make_fx trace which preserves
+# the function call as is in the graph, and only when we Dynamo through the backward graph in
+# compiled autograd do we inline into the function.
+
+
+def trace_wrapped(*args, **kwargs):
+    with torch.no_grad():
+        return _trace_wrapped_op(*args, **kwargs)
+
+
+# TODO(jansel): need to ensure this does not get DCEed
+_trace_wrapped_op = HigherOrderOperator("trace_wrapped")
+
+
+def _assert_meta(grad, size, stride, dtype):
+    assert grad.size() == size, "size mismatch"
+    assert grad.stride() == stride, "stride mismatch"
+    assert grad.dtype == dtype, "dtype mismatch"
+    return grad
+
+
+@_trace_wrapped_op.py_impl(ProxyTorchDispatchMode)
+def inner_trace(mode, *args, bw_state=None, **kwargs):
+    def self_invoke(*args, **dyn_kwargs):
+        with torch.no_grad():
+            return _trace_wrapped_op(*args, **dyn_kwargs, **kwargs)
+
+    def unwrap_proxies(x):
+        if isinstance(x, torch.Tensor):
+            return mode.tracer.unwrap_proxy(x)
+        if isinstance(x, (list, tuple)):
+            return type(x)(map(unwrap_proxies, x))
+        if x is None:
+            return None
+        raise AssertionError(f"unhandled type: {type(x)}")
+
+    proxy_kwargs = {}
+    if bw_state is not None:
+        assert isinstance(bw_state, BackwardState) and bw_state.proxy is not None
+        proxy_kwargs["bw_state"] = bw_state.proxy
+    out_proxy = mode.tracer.create_proxy(
+        "call_function",
+        self_invoke,
+        unwrap_proxies(args),
+        proxy_kwargs,
+        name="trace_wrapped",
+    )
+
+    if args[0] is None:
+        grad = args[1]  # module backward hooks
+    else:
+        grad = args[0]  # other backward hooks
+    grad = tree_map_only(torch.Tensor, torch.empty_like, grad)
+    track_tensor_tree(grad, out_proxy, constant=None, tracer=mode.tracer)
+    return grad
+
+
+@_trace_wrapped_op.py_impl(FakeTensorMode)
+def inner_fake(*args, **kwargs):
+    raise RuntimeError("This op should never be invoked here")
+
+
+@_trace_wrapped_op.py_impl(DispatchKey.CompositeExplicitAutograd)
+def _trace_wrapped_op_dense(*args, fn, **kwargs):
+    mode = _get_current_dispatch_mode()
+    assert mode is None, "Mode should never be enabled for CPU/CUDA key"
+    return fn(*args, **kwargs)
+
+
+_trace_wrapped_op.py_impl(DispatchKey.Autograd)(
+    autograd_not_implemented(_trace_wrapped_op, deferred_error=True)
+)
+
+
+@_trace_wrapped_op.py_functionalize_impl
+def _trace_wrapped_functionalized(ctx, *args, **kwargs):
+    unwrapped_args = ctx.unwrap_tensors(args)
+    with ctx.redispatch_to_next():
+        return ctx.wrap_tensors(_trace_wrapped_op(*unwrapped_args, **kwargs))
diff --git a/shared/package-files/d8/d80045127d48b0816f577b41a9d943bbc0f47295d8e0895bca8d50803cebde99/test_getitem.py b/shared/package-files/d8/d80045127d48b0816f577b41a9d943bbc0f47295d8e0895bca8d50803cebde99/test_getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..9891684e9597c00ade0c9f2b5c69bb0cf4d6df46
--- /dev/null
+++ b/shared/package-files/d8/d80045127d48b0816f577b41a9d943bbc0f47295d8e0895bca8d50803cebde99/test_getitem.py
@@ -0,0 +1,733 @@
+"""
+Series.__getitem__ test classes are organized by the type of key passed.
+"""
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import (
+    conversion,
+    timezones,
+)
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.indexing import IndexingError
+
+from pandas.tseries.offsets import BDay
+
+
+class TestSeriesGetitemScalars:
+    def test_getitem_object_index_float_string(self):
+        # GH#17286
+        ser = Series([1] * 4, index=Index(["a", "b", "c", 1.0]))
+        assert ser["a"] == 1
+        assert ser[1.0] == 1
+
+    def test_getitem_float_keys_tuple_values(self):
+        # see GH#13509
+
+        # unique Index
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name="foo")
+        result = ser[0.0]
+        assert result == (1, 1)
+
+        # non-unique Index
+        expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name="foo")
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name="foo")
+
+        result = ser[0.0]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_unrecognized_scalar(self):
+        # GH#32684 a scalar key that is not recognized by lib.is_scalar
+
+        # a series that might be produced via `frame.dtypes`
+        ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")])
+
+        key = ser.index[1]
+
+        result = ser[key]
+        assert result == 2
+
+    def test_getitem_negative_out_of_bounds(self):
+        ser = Series(["a"] * 10, index=["a"] * 10)
+
+        msg = "index -11 is out of bounds for axis 0 with size 10|index out of bounds"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                ser[-11]
+
+    def test_getitem_out_of_bounds_indexerror(self, datetime_series):
+        # don't segfault, GH#495
+        msg = r"index \d+ is out of bounds for axis 0 with size \d+"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                datetime_series[len(datetime_series)]
+
+    def test_getitem_out_of_bounds_empty_rangeindex_keyerror(self):
+        # GH#917
+        # With a RangeIndex, an int key gives a KeyError
+        ser = Series([], dtype=object)
+        with pytest.raises(KeyError, match="-1"):
+            ser[-1]
+
+    def test_getitem_keyerror_with_integer_index(self, any_int_numpy_dtype):
+        dtype = any_int_numpy_dtype
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6),
+            index=Index([0, 0, 1, 1, 2, 2], dtype=dtype),
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+        # not monotonic
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6), index=[2, 2, 0, 0, 1, 1]
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+    def test_getitem_int64(self, datetime_series):
+        idx = np.int64(5)
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = datetime_series[idx]
+        assert res == datetime_series.iloc[5]
+
+    def test_getitem_full_range(self):
+        # github.com/pandas-dev/pandas/commit/4f433773141d2eb384325714a2776bcc5b2e20f7
+        ser = Series(range(5), index=list(range(5)))
+        result = ser[list(range(5))]
+        tm.assert_series_equal(result, ser)
+
+    # ------------------------------------------------------------------
+    # Series with DatetimeIndex
+
+    @pytest.mark.parametrize("tzstr", ["Europe/Berlin", "dateutil/Europe/Berlin"])
+    def test_getitem_pydatetime_tz(self, tzstr):
+        tz = timezones.maybe_get_tz(tzstr)
+
+        index = date_range(
+            start="2012-12-24 16:00", end="2012-12-24 18:00", freq="h", tz=tzstr
+        )
+        ts = Series(index=index, data=index.hour)
+        time_pandas = Timestamp("2012-12-24 17:00", tz=tzstr)
+
+        dt = datetime(2012, 12, 24, 17, 0)
+        time_datetime = conversion.localize_pydatetime(dt, tz)
+        assert ts[time_pandas] == ts[time_datetime]
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_string_index_alias_tz_aware(self, tz):
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        ser = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        result = ser["1/3/2000"]
+        tm.assert_almost_equal(result, ser.iloc[2])
+
+    def test_getitem_time_object(self):
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        mask = (rng.hour == 9) & (rng.minute == 30)
+        result = ts[time(9, 30)]
+        expected = ts[mask]
+        result.index = result.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    # ------------------------------------------------------------------
+    # Series with CategoricalIndex
+
+    def test_getitem_scalar_categorical_index(self):
+        cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")])
+
+        ser = Series([1, 2], index=cats)
+
+        expected = ser.iloc[0]
+        result = ser[cats[0]]
+        assert result == expected
+
+    def test_getitem_numeric_categorical_listlike_matches_scalar(self):
+        # GH#15470
+        ser = Series(["a", "b", "c"], index=pd.CategoricalIndex([2, 1, 0]))
+
+        # 0 is treated as a label
+        assert ser[0] == "c"
+
+        # the listlike analogue should also be treated as labels
+        res = ser[[0]]
+        expected = ser.iloc[-1:]
+        tm.assert_series_equal(res, expected)
+
+        res2 = ser[[0, 1, 2]]
+        tm.assert_series_equal(res2, ser.iloc[::-1])
+
+    def test_getitem_integer_categorical_not_positional(self):
+        # GH#14865
+        ser = Series(["a", "b", "c"], index=Index([1, 2, 3], dtype="category"))
+        assert ser.get(3) == "c"
+        assert ser[3] == "c"
+
+    def test_getitem_str_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        key = "6 days, 23:11:12"
+        indexer = rng.get_loc(key)
+        assert indexer == 133
+
+        result = ser[key]
+        assert result == ser.iloc[133]
+
+        msg = r"^Timedelta\('50 days 00:00:00'\)$"
+        with pytest.raises(KeyError, match=msg):
+            rng.get_loc("50 days")
+        with pytest.raises(KeyError, match=msg):
+            ser["50 days"]
+
+    def test_getitem_bool_index_positional(self):
+        # GH#48653
+        ser = Series({True: 1, False: 0})
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[0]
+        assert result == 1
+
+
+class TestSeriesGetitemSlices:
+    def test_getitem_partial_str_slice_with_datetimeindex(self):
+        # GH#34860
+        arr = date_range("1/1/2008", "1/1/2009")
+        ser = arr.to_series()
+        result = ser["2008"]
+
+        rng = date_range(start="2008-01-01", end="2008-12-31")
+        expected = Series(rng, index=rng)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_slice_strings_with_datetimeindex(self):
+        idx = DatetimeIndex(
+            ["1/1/2000", "1/2/2000", "1/2/2000", "1/3/2000", "1/4/2000"]
+        )
+
+        ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+        result = ts["1/2/2000":]
+        expected = ts[1:]
+        tm.assert_series_equal(result, expected)
+
+        result = ts["1/2/2000":"1/3/2000"]
+        expected = ts[1:4]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["5 day":"6 day"]
+        expected = ser.iloc[86:134]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["5 day":]
+        expected = ser.iloc[86:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[:"6 day"]
+        expected = ser.iloc[:134]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_high_reso_with_timedeltaindex(self):
+        # higher reso
+        rng = timedelta_range("1 day 10:11:12", freq="us", periods=2000)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["1 day 10:11:12":]
+        expected = ser.iloc[0:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 day 10:11:12.001":]
+        expected = ser.iloc[1000:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 days, 10:11:12.001001"]
+        assert result == ser.iloc[1001]
+
+    def test_getitem_slice_2d(self, datetime_series):
+        # GH#30588 multi-dimensional indexing deprecated
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            datetime_series[:, np.newaxis]
+
+    def test_getitem_median_slice_bug(self):
+        index = date_range("20090415", "20090519", freq="2B")
+        ser = Series(np.random.default_rng(2).standard_normal(13), index=index)
+
+        indexer = [slice(6, 7, None)]
+        msg = "Indexing with a single-item list"
+        with pytest.raises(ValueError, match=msg):
+            # GH#31299
+            ser[indexer]
+        # but we're OK with a single-element tuple
+        result = ser[(indexer[0],)]
+        expected = ser[indexer[0]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "slc, positions",
+        [
+            [slice(date(2018, 1, 1), None), [0, 1, 2]],
+            [slice(date(2019, 1, 2), None), [2]],
+            [slice(date(2020, 1, 1), None), []],
+            [slice(None, date(2020, 1, 1)), [0, 1, 2]],
+            [slice(None, date(2019, 1, 1)), [0]],
+        ],
+    )
+    def test_getitem_slice_date(self, slc, positions):
+        # https://github.com/pandas-dev/pandas/issues/31501
+        ser = Series(
+            [0, 1, 2],
+            DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
+        )
+        result = ser[slc]
+        expected = ser.take(positions)
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_slice_float_raises(self, datetime_series):
+        msg = (
+            "cannot do slice indexing on DatetimeIndex with these indexers "
+            r"\[{key}\] of type float"
+        )
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.0")):
+            datetime_series[4.0:10.0]
+
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.5")):
+            datetime_series[4.5:10.0]
+
+    def test_getitem_slice_bug(self):
+        ser = Series(range(10), index=list(range(10)))
+        result = ser[-12:]
+        tm.assert_series_equal(result, ser)
+
+        result = ser[-7:]
+        tm.assert_series_equal(result, ser[3:])
+
+        result = ser[:-12]
+        tm.assert_series_equal(result, ser[:0])
+
+    def test_getitem_slice_integers(self):
+        ser = Series(
+            np.random.default_rng(2).standard_normal(8),
+            index=[2, 4, 6, 8, 10, 12, 14, 16],
+        )
+
+        result = ser[:4]
+        expected = Series(ser.values[:4], index=[2, 4, 6, 8])
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesGetitemListLike:
+    @pytest.mark.parametrize("box", [list, np.array, Index, Series])
+    def test_getitem_no_matches(self, box):
+        # GH#33462 we expect the same behavior for list/ndarray/Index/Series
+        ser = Series(["A", "B"])
+
+        key = Series(["C"])
+        key = box(key)
+
+        msg = r"None of \[Index\(\['C'\], dtype='object|str'\)\] are in the \[index\]"
+        with pytest.raises(KeyError, match=msg):
+            ser[key]
+
+    def test_getitem_intlist_intindex_periodvalues(self):
+        ser = Series(period_range("2000-01-01", periods=10, freq="D"))
+
+        result = ser[[2, 4]]
+        exp = Series(
+            [pd.Period("2000-01-03", freq="D"), pd.Period("2000-01-05", freq="D")],
+            index=[2, 4],
+            dtype="Period[D]",
+        )
+        tm.assert_series_equal(result, exp)
+        assert result.dtype == "Period[D]"
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    def test_getitem_intlist_intervalindex_non_int(self, box):
+        # GH#33404 fall back to positional since ints are unambiguous
+        dti = date_range("2000-01-03", periods=3)._with_freq(None)
+        ii = pd.IntervalIndex.from_breaks(dti)
+        ser = Series(range(len(ii)), index=ii)
+
+        expected = ser.iloc[:1]
+        key = box([0])
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[key]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.uint64])
+    def test_getitem_intlist_multiindex_numeric_level(self, dtype, box):
+        # GH#33404 do _not_ fall back to positional since ints are ambiguous
+        idx = Index(range(4)).astype(dtype)
+        dti = date_range("2000-01-03", periods=3)
+        mi = pd.MultiIndex.from_product([idx, dti])
+        ser = Series(range(len(mi))[::-1], index=mi)
+
+        key = box([5])
+        with pytest.raises(KeyError, match="5"):
+            ser[key]
+
+    def test_getitem_uint_array_key(self, any_unsigned_int_numpy_dtype):
+        # GH #37218
+        ser = Series([1, 2, 3])
+        key = np.array([4], dtype=any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(KeyError, match="4"):
+            ser[key]
+        with pytest.raises(KeyError, match="4"):
+            ser.loc[key]
+
+
+class TestGetitemBooleanMask:
+    def test_getitem_boolean(self, string_series):
+        ser = string_series
+        mask = ser > ser.median()
+
+        # passing list is OK
+        result = ser[list(mask)]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+        tm.assert_index_equal(result.index, ser.index[mask])
+
+    def test_getitem_boolean_empty(self):
+        ser = Series([], dtype=np.int64)
+        ser.index.name = "index_name"
+        ser = ser[ser.isna()]
+        assert ser.index.name == "index_name"
+        assert ser.dtype == np.int64
+
+        # GH#5877
+        # indexing with empty series
+        ser = Series(["A", "B"], dtype=object)
+        expected = Series(dtype=object, index=Index([], dtype="int64"))
+        result = ser[Series([], dtype=object)]
+        tm.assert_series_equal(result, expected)
+
+        # invalid because of the boolean indexer
+        # that's empty or not-aligned
+        msg = (
+            r"Unalignable boolean Series provided as indexer \(index of "
+            r"the boolean Series and of the indexed object do not match"
+        )
+        with pytest.raises(IndexingError, match=msg):
+            ser[Series([], dtype=bool)]
+
+        with pytest.raises(IndexingError, match=msg):
+            ser[Series([True], dtype=bool)]
+
+    def test_getitem_boolean_object(self, string_series):
+        # using column from DataFrame
+
+        ser = string_series
+        mask = ser > ser.median()
+        omask = mask.astype(object)
+
+        # getitem
+        result = ser[omask]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+
+        # setitem
+        s2 = ser.copy()
+        cop = ser.copy()
+        cop[omask] = 5
+        s2[mask] = 5
+        tm.assert_series_equal(cop, s2)
+
+        # nans raise exception
+        omask[5:10] = np.nan
+        msg = "Cannot mask with non-boolean array containing NA / NaN values"
+        with pytest.raises(ValueError, match=msg):
+            ser[omask]
+        with pytest.raises(ValueError, match=msg):
+            ser[omask] = 5
+
+    def test_getitem_boolean_dt64_copies(self):
+        # GH#36210
+        dti = date_range("2016-01-01", periods=4, tz="US/Pacific")
+        key = np.array([True, True, False, False])
+
+        ser = Series(dti._data)
+
+        res = ser[key]
+        assert res._values._ndarray.base is None
+
+        # compare with numeric case for reference
+        ser2 = Series(range(4))
+        res2 = ser2[key]
+        assert res2._values.base is None
+
+    def test_getitem_boolean_corner(self, datetime_series):
+        ts = datetime_series
+        mask_shifted = ts.shift(1, freq=BDay()) > ts.median()
+
+        msg = (
+            r"Unalignable boolean Series provided as indexer \(index of "
+            r"the boolean Series and of the indexed object do not match"
+        )
+        with pytest.raises(IndexingError, match=msg):
+            ts[mask_shifted]
+
+        with pytest.raises(IndexingError, match=msg):
+            ts.loc[mask_shifted]
+
+    def test_getitem_boolean_different_order(self, string_series):
+        ordered = string_series.sort_values()
+
+        sel = string_series[ordered > 0]
+        exp = string_series[string_series > 0]
+        tm.assert_series_equal(sel, exp)
+
+    def test_getitem_boolean_contiguous_preserve_freq(self):
+        rng = date_range("1/1/2000", "3/1/2000", freq="B")
+
+        mask = np.zeros(len(rng), dtype=bool)
+        mask[10:20] = True
+
+        masked = rng[mask]
+        expected = rng[10:20]
+        assert expected.freq == rng.freq
+        tm.assert_index_equal(masked, expected)
+
+        mask[22] = True
+        masked = rng[mask]
+        assert masked.freq is None
+
+
+class TestGetitemCallable:
+    def test_getitem_callable(self):
+        # GH#12533
+        ser = Series(4, index=list("ABCD"))
+        result = ser[lambda x: "A"]
+        assert result == ser.loc["A"]
+
+        result = ser[lambda x: ["A", "B"]]
+        expected = ser.loc[["A", "B"]]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[lambda x: [True, False, True, True]]
+        expected = ser.iloc[[0, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+
+def test_getitem_generator(string_series):
+    gen = (x > 0 for x in string_series)
+    result = string_series[gen]
+    result2 = string_series[iter(string_series > 0)]
+    expected = string_series[string_series > 0]
+    tm.assert_series_equal(result, expected)
+    tm.assert_series_equal(result2, expected)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        Series([0, 1]),
+        Series(date_range("2012-01-01", periods=2)),
+        Series(date_range("2012-01-01", periods=2, tz="CET")),
+    ],
+)
+def test_getitem_ndim_deprecated(series):
+    with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+        series[:, None]
+
+
+def test_getitem_multilevel_scalar_slice_not_implemented(
+    multiindex_year_month_day_dataframe_random_data,
+):
+    # not implementing this for now
+    df = multiindex_year_month_day_dataframe_random_data
+    ser = df["A"]
+
+    msg = r"\(2000, slice\(3, 4, None\)\)"
+    with pytest.raises(TypeError, match=msg):
+        ser[2000, 3:4]
+
+
+def test_getitem_dataframe_raises():
+    rng = list(range(10))
+    ser = Series(10, index=rng)
+    df = DataFrame(rng, index=rng)
+    msg = (
+        "Indexing a Series with DataFrame is not supported, "
+        "use the appropriate DataFrame column"
+    )
+    with pytest.raises(TypeError, match=msg):
+        ser[df > 5]
+
+
+def test_getitem_assignment_series_alignment():
+    # https://github.com/pandas-dev/pandas/issues/37427
+    # with getitem, when assigning with a Series, it is not first aligned
+    ser = Series(range(10))
+    idx = np.array([2, 4, 9])
+    ser[idx] = Series([10, 11, 12])
+    expected = Series([0, 1, 10, 3, 11, 5, 6, 7, 8, 12])
+    tm.assert_series_equal(ser, expected)
+
+
+def test_getitem_duplicate_index_mistyped_key_raises_keyerror():
+    # GH#29189 float_index.get_loc(None) should raise KeyError, not TypeError
+    ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
+    with pytest.raises(KeyError, match="None"):
+        ser[None]
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index.get_loc(None)
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index._engine.get_loc(None)
+
+
+def test_getitem_1tuple_slice_without_multiindex():
+    ser = Series(range(5))
+    key = (slice(3),)
+
+    result = ser[key]
+    expected = ser[key[0]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_preserve_name(datetime_series):
+    result = datetime_series[datetime_series > 0]
+    assert result.name == datetime_series.name
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = datetime_series[[0, 2, 4]]
+    assert result.name == datetime_series.name
+
+    result = datetime_series[5:10]
+    assert result.name == datetime_series.name
+
+
+def test_getitem_with_integer_labels():
+    # integer indexes, be careful
+    ser = Series(
+        np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
+    )
+    inds = [0, 2, 5, 7, 8]
+    arr_inds = np.array([0, 2, 5, 7, 8])
+    with pytest.raises(KeyError, match="not in index"):
+        ser[inds]
+
+    with pytest.raises(KeyError, match="not in index"):
+        ser[arr_inds]
+
+
+def test_getitem_missing(datetime_series):
+    # missing
+    d = datetime_series.index[0] - BDay()
+    msg = r"Timestamp\('1999-12-31 00:00:00'\)"
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[d]
+
+
+def test_getitem_fancy(string_series, object_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        slice1 = string_series[[1, 2, 3]]
+        slice2 = object_series[[1, 2, 3]]
+    assert string_series.index[2] == slice1.index[1]
+    assert object_series.index[2] == slice2.index[1]
+    assert string_series.iloc[2] == slice1.iloc[1]
+    assert object_series.iloc[2] == slice2.iloc[1]
+
+
+def test_getitem_box_float64(datetime_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        value = datetime_series[5]
+    assert isinstance(value, np.float64)
+
+
+def test_getitem_unordered_dup():
+    obj = Series(range(5), index=["c", "a", "a", "b", "b"])
+    assert is_scalar(obj["c"])
+    assert obj["c"] == 0
+
+
+def test_getitem_dups():
+    ser = Series(range(5), index=["A", "A", "B", "C", "C"], dtype=np.int64)
+    expected = Series([3, 4], index=["C", "C"], dtype=np.int64)
+    result = ser["C"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_categorical_str():
+    # GH#31765
+    ser = Series(range(5), index=Categorical(["a", "b", "c", "a", "b"]))
+    result = ser["a"]
+    expected = ser.iloc[[0, 3]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_slice_can_reorder_not_uniquely_indexed():
+    ser = Series(1, index=["a", "a", "b", "b", "c"])
+    ser[::-1]  # it works!
+
+
+@pytest.mark.parametrize("index_vals", ["aabcd", "aadcb"])
+def test_duplicated_index_getitem_positional_indexer(index_vals):
+    # GH 11747
+    s = Series(range(5), index=list(index_vals))
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s[3]
+    assert result == 3
+
+
+class TestGetitemDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key] = 1
diff --git a/shared/package-files/d8/d80cb3b2d45a2601d7b9f17b452a60060b550f7066b1d5c581de92491c09a8a5/process.pyi b/shared/package-files/d8/d80cb3b2d45a2601d7b9f17b452a60060b550f7066b1d5c581de92491c09a8a5/process.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9ab9628e0e32d896a7b8b38d821bdf86a8ae1881
--- /dev/null
+++ b/shared/package-files/d8/d80cb3b2d45a2601d7b9f17b452a60060b550f7066b1d5c581de92491c09a8a5/process.pyi
@@ -0,0 +1,37 @@
+from typing import Any, Optional
+
+def current_process(): ...
+def active_children(): ...
+
+class Process:
+    def __init__(
+        self, group: Optional[Any] = ..., target: Optional[Any] = ..., name: Optional[Any] = ..., args=..., kwargs=...
+    ): ...
+    def run(self): ...
+    def start(self): ...
+    def terminate(self): ...
+    def join(self, timeout: Optional[Any] = ...): ...
+    def is_alive(self): ...
+    @property
+    def name(self): ...
+    @name.setter
+    def name(self, name): ...
+    @property
+    def daemon(self): ...
+    @daemon.setter
+    def daemon(self, daemonic): ...
+    @property
+    def authkey(self): ...
+    @authkey.setter
+    def authkey(self, authkey): ...
+    @property
+    def exitcode(self): ...
+    @property
+    def ident(self): ...
+    pid: Any
+
+class AuthenticationString(bytes):
+    def __reduce__(self): ...
+
+class _MainProcess(Process):
+    def __init__(self): ...
diff --git a/shared/package-files/d8/d810cd8b0dabf5bb91f4b948eb9896b9a25e4d2850faf3d746cbdf45306a3b22/common_dtensor.py b/shared/package-files/d8/d810cd8b0dabf5bb91f4b948eb9896b9a25e4d2850faf3d746cbdf45306a3b22/common_dtensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..41e72c6e54c790a01882ec40ffb193af6d6f999a
--- /dev/null
+++ b/shared/package-files/d8/d810cd8b0dabf5bb91f4b948eb9896b9a25e4d2850faf3d746cbdf45306a3b22/common_dtensor.py
@@ -0,0 +1,486 @@
+# mypy: ignore-errors
+
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import itertools
+from dataclasses import dataclass
+import sys
+from functools import wraps
+from typing import (
+    Any,
+    Callable,
+    Iterator,
+    Tuple,
+    Dict,
+    List,
+    Sequence,
+    TypeVar,
+    cast,
+)
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
+from torch.testing._internal.common_distributed import (
+    MultiProcessTestCase,
+    MultiThreadedTestCase,
+    TEST_SKIPS,
+    skip_if_lt_x_gpu,
+)
+
+
+from torch.distributed._tensor import (
+    DeviceMesh,
+    Shard,
+    Replicate,
+    distribute_tensor,
+)
+from torch.distributed._tensor.placement_types import Placement
+
+DEVICE_TYPE = "cuda" if torch.cuda.is_available() and torch.cuda.device_count() > 1 else "cpu"
+PG_BACKEND = "nccl" if DEVICE_TYPE == "cuda" else "gloo"
+
+NUM_DEVICES = 4
+
+# We use this as a proxy for "multiple GPUs exist"
+if torch.cuda.is_available() and torch.cuda.device_count() > 1:
+    # when we actually have multiple GPUs, relax the requirement to smaller counts.
+    NUM_DEVICES = min(NUM_DEVICES, torch.cuda.device_count())
+
+T = TypeVar("T")
+
+
+# simple RMSNorm layer for testing
+class RMSNormPython(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = torch.nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x)
+        return output * self.weight
+
+class MLPModule(nn.Module):
+    def __init__(self, device):
+        super().__init__()
+        torch.manual_seed(5)
+        self.net1 = nn.Linear(10, 16, device=device)
+        self.relu = nn.ReLU()
+        self.net2 = nn.Linear(16, 10, device=device)
+
+    def forward(self, x):
+        return self.net2(self.relu(self.net1(x)))
+
+    def reset_parameters(self):
+        self.net1.reset_parameters()
+        self.net2.reset_parameters()
+
+
+@dataclass
+class ModelArgs:
+    n_layers: int = 2
+    vocab_size: int = 16
+    max_seq_len: int = 16
+    dim: int = 8
+    n_heads: int = 4
+    dropout_p: float = 0.1
+    use_attn_mask: bool = True
+    weight_tying: bool = True
+    checkpoint_activations: bool = False
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        assert args.dim % args.n_heads == 0
+        self.head_dim = args.dim // args.n_heads
+        self.n_heads = args.n_heads
+        self.dropout_p = args.dropout_p
+        self.resid_dropout = nn.Dropout(args.dropout_p)
+        self.use_attn_mask = args.use_attn_mask
+
+        self.wq = nn.Linear(args.dim, args.dim, bias=False)
+        self.wk = nn.Linear(args.dim, args.dim, bias=False)
+        self.wv = nn.Linear(args.dim, args.dim, bias=False)
+        self.wo = nn.Linear(args.dim, args.dim, bias=False)
+
+    def forward(self, x):
+        bsz, seq_len, _ = x.size()
+        queries, keys, values = self.wq(x), self.wk(x), self.wv(x)
+        queries = queries.view(bsz, seq_len, self.n_heads, self.head_dim)
+        keys = keys.view(bsz, seq_len, self.n_heads, self.head_dim)
+        values = values.view(bsz, seq_len, self.n_heads, self.head_dim)
+
+        queries = queries.transpose(1, 2)  # (bsz, n_heads, seq_len, head_dim)
+        keys = keys.transpose(1, 2)  # (bsz, n_heads, seq_len, head_dim)
+        values = values.transpose(1, 2)  # (bsz, n_heads, seq_len, head_dim)
+
+        output = F.scaled_dot_product_attention(
+            queries, keys, values, None,
+            self.dropout_p if self.training else 0,
+            self.use_attn_mask,
+        )
+        output = output.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
+        return self.resid_dropout(self.wo(output))
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout_p):
+        super().__init__()
+        self.w1 = nn.Linear(dim, hidden_dim)
+        self.gelu = nn.GELU()
+        self.w2 = nn.Linear(hidden_dim, dim)
+        self.resid_dropout = nn.Dropout(dropout_p)
+
+    def forward(self, x):
+        return self.resid_dropout(self.w2(self.gelu(self.w1(x))))
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.attention_norm = nn.LayerNorm(args.dim)
+        self.attention = Attention(args)
+        self.ffn_norm = nn.LayerNorm(args.dim)
+        self.feed_forward = FeedForward(args.dim, hidden_dim=4 * args.dim, dropout_p=args.dropout_p)
+
+    def forward(self, x):
+        h = x + self.attention(self.attention_norm(x))
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+# A toy transformer model, partly inspired by the nanoGPT model:
+# https://github.com/karpathy/nanoGPT.
+class Transformer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        assert args.vocab_size is not None
+        assert args.max_seq_len is not None
+        self.max_seq_len = args.max_seq_len
+        self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
+        self.pos_embeddings = nn.Embedding(args.max_seq_len, args.dim)
+        self.dropout = nn.Dropout(args.dropout_p)
+        self.layers = nn.ModuleList()
+        for _ in range(args.n_layers):
+            self.layers.append(TransformerBlock(args))
+        self.norm = nn.LayerNorm(args.dim)
+        self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
+        if args.weight_tying:
+            self.output.weight = self.tok_embeddings.weight
+        self.checkpoint_activations = args.checkpoint_activations
+
+    def forward(self, tokens):
+        _bsz, seq_len = tokens.size()
+        assert seq_len <= self.max_seq_len
+        h = self.tok_embeddings(tokens)
+        pos = torch.arange(0, seq_len, device=tokens.device)
+        p = self.pos_embeddings(pos)  # positional embeddings of shape (seq_len, dim)
+        h = h + p
+        h = self.dropout(h)
+        for layer in self.layers:
+            if self.checkpoint_activations:
+                h = torch.utils.checkpoint.checkpoint(layer, h, use_reentrant=False)
+            else:
+                h = layer(h)
+        h = self.norm(h)
+        output = self.output(h).float()
+        return output
+
+
+def skip_unless_torch_gpu(method: T) -> T:
+    """
+    Test decorator which skips the test unless there's a GPU available to torch.
+
+    >>> # xdoctest: +SKIP
+    >>> @skip_unless_torch_gpu
+    >>> def test_some_method(self) -> None:
+    >>>   ...
+    """
+    # The builtin @skip_if_no_gpu relies on os.environ['WORLD_SIZE'] being set.
+    return cast(T, skip_if_lt_x_gpu(NUM_DEVICES)(method))
+
+
+class DTensorTestBase(MultiProcessTestCase):
+    @property
+    def world_size(self) -> int:
+        return NUM_DEVICES
+
+    @property
+    def backend(self) -> str:
+        return PG_BACKEND
+
+    def build_device_mesh(self) -> DeviceMesh:
+        return DeviceMesh(DEVICE_TYPE, list(range(self.world_size)))
+
+    def init_pg(self) -> None:
+        if "nccl" in self.backend and torch.cuda.device_count() < self.world_size:
+            sys.exit(TEST_SKIPS[f"multi-gpu-{self.world_size}"].exit_code)
+
+        if self.backend not in ["nccl", "gloo", "mpi", "cpu:gloo,cuda:nccl"]:
+            raise RuntimeError(f"Backend {self.backend} not supported!")
+
+        dist.init_process_group(
+            backend=self.backend,
+            world_size=self.world_size,
+            rank=self.rank,  # pyre-ignore[16]
+            init_method=f"file://{self.file_name}",  # pyre-ignore[16]
+        )
+
+        # set device for nccl pg for collectives
+        if "nccl" in self.backend:
+            torch.cuda.set_device(self.rank)
+
+    def destroy_pg(self) -> None:
+        # Wait for all ranks to reach here before starting shutdown.
+        # FIXME dist.barrier deadlocks with multiple threads and NCCL: https://github.com/pytorch/pytorch/issues/95895
+        # dist.all_reduce(torch.zeros((1,), device="cuda" if torch.cuda.is_available() else "cpu"))
+        # FIXME can't use the above all_reduce as it causes hangs on bionic and focal. It hangs:
+        #  test_dtensor.py  -- DTensorMeshTest.test_dtensor_device_mesh_device_conversion
+        dist.barrier()
+        dist.destroy_process_group()
+
+    def setUp(self) -> None:
+        super().setUp()
+        self._spawn_processes()
+
+    # pyre-ignore[2]:
+    def _test_op(self, mesh: DeviceMesh, op_call, *args, **kwargs) -> None:
+        out = op_call(*args, **kwargs)
+        dtc = DTensorConverter(mesh, args, kwargs)
+        for d_args, d_kwargs in dtc:
+            # pyre can't find assertTrue anymore?
+            self.assertEqual(dtc.successful(), True)
+            d_out = op_call(*d_args, **d_kwargs)
+            self.assertEqual(d_out.full_tensor(), out)
+
+    def run_subtests(self, *args, **kwargs):
+        return run_subtests(self, *args, **kwargs)
+
+
+TestFunc = Callable[[object], object]
+
+# wrapper to initialize comms (processgroup)
+def with_comms(func: TestFunc) -> TestFunc:
+    assert func is not None
+
+    @wraps(func)  # pyre-ignore[6]
+    def wrapper(
+        self, *args: Tuple[object], **kwargs: Dict[str, Any]  # type: ignore[misc]
+    ) -> None:
+        # if backend not specified, and cuda available, then use nccl, else gloo
+        if torch.cuda.is_available() and torch.cuda.device_count() >= self.world_size:
+            self.device_type = "cuda"
+        else:
+            self.device_type = "cpu"
+
+        self.init_pg()
+        func(self, *args, **kwargs)  # type: ignore[misc]
+        self.destroy_pg()
+
+    return wrapper
+
+
+def run_subtests(
+    cls_inst,
+    subtest_config: Dict[str, List[Any]],
+    test_fn: Callable,
+    *test_args,
+    **test_kwargs: Any,
+):
+    """
+    Runs a test function given by ``test_fn`` as a subtest according to the
+    configurations specified by ``subtest_config``. This amortizes the
+    costly setup overhead (including process spawn and initializing the
+    process group) over the subtests.
+
+    Args:
+        subtest_config (Dict[str, List[Any]]): A mapping from subtest
+            keyword argument name to a list of its possible values.
+        test_fn (Callable): A callable that runs the actual test.
+        test_args: Positional arguments to pass to ``test_fn``.
+        test_kwargs: Keyword arguments to pass to ``test_fn``.
+    """
+    # Convert the config mapping to a list to have a fixed order
+    subtest_config_items: List[Tuple[str, List[Any]]] = list(subtest_config.items())
+    subtest_config_keys: List[str] = [item[0] for item in subtest_config_items]
+    subtest_config_values: List[List[Any]] = [item[1] for item in subtest_config_items]
+    for values in itertools.product(*subtest_config_values):
+        # Map keyword to chosen value
+        subtest_kwargs = dict(zip(subtest_config_keys, values))
+        with cls_inst.subTest(**subtest_kwargs):
+            test_fn(*test_args, **test_kwargs, **subtest_kwargs)
+        dist.barrier()
+
+
+class DTensorOpTestBase(MultiThreadedTestCase):
+    @property
+    def world_size(self) -> int:
+        return NUM_DEVICES
+
+    @property
+    def device_type(self) -> str:
+        return DEVICE_TYPE
+
+    def build_device_mesh(self):
+        return DeviceMesh(self.device_type, list(range(self.world_size)))
+
+    def setUp(self) -> None:
+        super().setUp()
+        self._spawn_threads()
+
+
+# This is a class for converting args/kwargs of an op into distributed args/kwargs
+class DTensorConverter:
+    def __init__(
+        self,
+        mesh: DeviceMesh,
+        args: Tuple[object, ...],
+        kwargs: Dict[str, object],
+    ) -> None:
+        self.hit = 0
+        self.miss = 0
+        self.mesh = mesh
+        self.args = args
+        self.kwargs = kwargs
+        flatten_args, flatten_args_spec = tree_flatten(args)
+        flatten_kwargs, flatten_kwargs_spec = tree_flatten(kwargs)
+
+        self.flatten_args: List[object] = flatten_args
+        self.flatten_args_spec: TreeSpec = flatten_args_spec
+        self.flatten_kwargs: List[object] = flatten_kwargs
+        self.flatten_kwargs_spec: TreeSpec = flatten_kwargs_spec
+
+        choices_for_args = []
+        for arg in self.flatten_args:
+            if isinstance(arg, torch.Tensor):
+                choices_for_args.append(self.gen_sharding_choices_for_arg(arg))
+
+        for arg in self.flatten_kwargs:
+            if isinstance(arg, torch.Tensor):
+                choices_for_args.append(self.gen_sharding_choices_for_arg(arg))
+
+        self.sharding_combs: Iterator[Sequence[Placement]] = iter(
+            itertools.product(*choices_for_args)
+        )
+
+    def successful(self) -> bool:
+        return self.hit > 0 and self.miss == 0
+
+    def is_supported_tensor(self, t: torch.Tensor) -> bool:
+        # TODO: dist tensor need to support quantized and sparse
+        # tensors, quantized tensor might be relatively easy, but
+        # sparse tensor have special layouts that we need to possibly
+        # deal with, until we are clear about them, we don't officially
+        # support them.
+        return not any(
+            [
+                t.is_sparse_csr,
+                t.is_sparse,
+                t.is_mkldnn,
+                t.is_quantized,
+                t.is_nested,
+                torch._is_functional_tensor(t),
+                t.is_neg(),
+                t.is_conj(),
+                t.device.type in ("lazy", "meta"),
+                # We need a way to test if a tensor is batched but there
+                # is no official APi to do it
+                # torch._C._is_batched(t),
+            ]
+        )
+
+    def gen_sharding_choices_for_arg(
+        self, arg: torch.Tensor
+    ) -> Sequence[Placement]:
+        mesh_size = self.mesh.size()
+        sharding_choices: List[Placement] = [Replicate()]
+        # c10d collective does not support bool tensor
+        # for bool tensor we treat it as replicated
+        if arg.dtype != torch.bool:
+            # only generating choices with: replicate, or sharding
+            # evenly on a dimension that could be sharded
+            sharding_choices = sharding_choices + [
+                Shard(i)
+                for i, s in enumerate(arg.shape)
+                if s > 1 and s % mesh_size == 0
+            ]
+        # TODO: add multi mesh choices
+        # all_choices = itertools.product(
+        #     *(self.mesh.ndim * [sharding_choices])
+        # )
+        return sharding_choices
+
+    def __iter__(self) -> "DTensorConverter":
+        return self
+
+    def __next__(self) -> Tuple[Tuple[object, ...], Dict[str, object]]:
+        try:
+            next_sharding_choices = next(self.sharding_combs)
+            idx = 0
+
+            new_args: List[object] = []
+            for arg in self.flatten_args:
+                if isinstance(arg, torch.Tensor):
+                    new_args.append(
+                        self.to_dist_tensor(
+                            arg, self.mesh, [next_sharding_choices[idx]]
+                        )
+                    )
+                    idx += 1
+                else:
+                    new_args.append(arg)
+
+            new_kwargs: List[object] = []
+            for arg in self.flatten_kwargs:
+                if isinstance(arg, torch.Tensor):
+                    new_kwargs.append(
+                        self.to_dist_tensor(
+                            arg, self.mesh, [next_sharding_choices[idx]]
+                        )
+                    )
+                    idx += 1
+                else:
+                    new_kwargs.append(arg)
+
+            return (
+                tree_unflatten(new_args, self.flatten_args_spec),
+                tree_unflatten(new_kwargs, self.flatten_kwargs_spec),
+            )
+        except StopIteration as e:
+            raise StopIteration from e
+
+    def to_dist_tensor(
+        self, t: torch.Tensor, mesh: DeviceMesh, placements: List[Placement]
+    ) -> torch.Tensor:
+        if type(t) is torch.Tensor or type(t) is nn.Parameter:
+            if self.is_supported_tensor(t):
+                self.hit += 1
+                if t.ndim == 0:
+                    # scalar tensor by default will be replicated
+                    r = distribute_tensor(t, mesh, [Replicate()] * mesh.ndim)
+                else:
+                    # distribute non-scalar tensors
+                    r = distribute_tensor(t, mesh, placements)
+                if type(t) is nn.Parameter:
+                    r = nn.Parameter(  # type: ignore[assignment]
+                        r, requires_grad=r.requires_grad
+                    )
+                return r
+            else:
+                self.miss += 1
+                return t
+        elif torch.overrides.is_tensor_like(t):
+            # Blindly converting tensor subclasses to dist tensor can cause
+            # unpredictable problems, we explicitly disable this conversion
+            # for now (i.e. we don't support DTensor holding tensor subclass
+            # until there's a strong reason later).
+            self.miss += 1
+            return t
+        else:
+            raise RuntimeError(
+                f"Trying to convert to DTensor, but got {type(t)}"
+            )
diff --git a/shared/package-files/d8/d814f17c3ccdaad4a88d81c7c3c07b71c302539f7657826147fbe3b7891d45e4/_cufft_get_plan_cache_size_compositeimplicitautograd_dispatch.h b/shared/package-files/d8/d814f17c3ccdaad4a88d81c7c3c07b71c302539f7657826147fbe3b7891d45e4/_cufft_get_plan_cache_size_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c498537f98a1d734b7c6989526f42bae8b7daa15
--- /dev/null
+++ b/shared/package-files/d8/d814f17c3ccdaad4a88d81c7c3c07b71c302539f7657826147fbe3b7891d45e4/_cufft_get_plan_cache_size_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API int64_t _cufft_get_plan_cache_size(at::DeviceIndex device_index);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/d8/d8158d607bb090e5c4d2847b157260dfa81dc376e473c57714e4d5ba5d1b53da/_use_cudnn_rnn_flatten_weight_native.h b/shared/package-files/d8/d8158d607bb090e5c4d2847b157260dfa81dc376e473c57714e4d5ba5d1b53da/_use_cudnn_rnn_flatten_weight_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a5cc35b4a7e0e919d0a42fba127aab216f37b3d
--- /dev/null
+++ b/shared/package-files/d8/d8158d607bb090e5c4d2847b157260dfa81dc376e473c57714e4d5ba5d1b53da/_use_cudnn_rnn_flatten_weight_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API bool _use_cudnn_rnn_flatten_weight();
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/d8/d81a6a2e54f6131bce3a2eef9e32b99c1e05a7e9b9da57623da5cca31e6ec2e8/lexer.py b/shared/package-files/d8/d81a6a2e54f6131bce3a2eef9e32b99c1e05a7e9b9da57623da5cca31e6ec2e8/lexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb2c1b46b6928363a1db20306c379b12668c5a47
--- /dev/null
+++ b/shared/package-files/d8/d81a6a2e54f6131bce3a2eef9e32b99c1e05a7e9b9da57623da5cca31e6ec2e8/lexer.py
@@ -0,0 +1,943 @@
+"""
+    pygments.lexer
+    ~~~~~~~~~~~~~~
+
+    Base lexer classes.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+import sys
+import time
+
+from pip._vendor.pygments.filter import apply_filters, Filter
+from pip._vendor.pygments.filters import get_filter_by_name
+from pip._vendor.pygments.token import Error, Text, Other, Whitespace, _TokenType
+from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
+    make_analysator, Future, guess_decode
+from pip._vendor.pygments.regexopt import regex_opt
+
+__all__ = ['Lexer', 'RegexLexer', 'ExtendedRegexLexer', 'DelegatingLexer',
+           'LexerContext', 'include', 'inherit', 'bygroups', 'using', 'this',
+           'default', 'words', 'line_re']
+
+line_re = re.compile('.*?\n')
+
+_encoding_map = [(b'\xef\xbb\xbf', 'utf-8'),
+                 (b'\xff\xfe\0\0', 'utf-32'),
+                 (b'\0\0\xfe\xff', 'utf-32be'),
+                 (b'\xff\xfe', 'utf-16'),
+                 (b'\xfe\xff', 'utf-16be')]
+
+_default_analyse = staticmethod(lambda x: 0.0)
+
+
+class LexerMeta(type):
+    """
+    This metaclass automagically converts ``analyse_text`` methods into
+    static methods which always return float values.
+    """
+
+    def __new__(mcs, name, bases, d):
+        if 'analyse_text' in d:
+            d['analyse_text'] = make_analysator(d['analyse_text'])
+        return type.__new__(mcs, name, bases, d)
+
+
+class Lexer(metaclass=LexerMeta):
+    """
+    Lexer for a specific language.
+
+    See also :doc:`lexerdevelopment`, a high-level guide to writing
+    lexers.
+
+    Lexer classes have attributes used for choosing the most appropriate
+    lexer based on various criteria.
+
+    .. autoattribute:: name
+       :no-value:
+    .. autoattribute:: aliases
+       :no-value:
+    .. autoattribute:: filenames
+       :no-value:
+    .. autoattribute:: alias_filenames
+    .. autoattribute:: mimetypes
+       :no-value:
+    .. autoattribute:: priority
+
+    Lexers included in Pygments should have an additional attribute:
+
+    .. autoattribute:: url
+       :no-value:
+
+    You can pass options to the constructor. The basic options recognized
+    by all lexers and processed by the base `Lexer` class are:
+
+    ``stripnl``
+        Strip leading and trailing newlines from the input (default: True).
+    ``stripall``
+        Strip all leading and trailing whitespace from the input
+        (default: False).
+    ``ensurenl``
+        Make sure that the input ends with a newline (default: True).  This
+        is required for some lexers that consume input linewise.
+
+        .. versionadded:: 1.3
+
+    ``tabsize``
+        If given and greater than 0, expand tabs in the input (default: 0).
+    ``encoding``
+        If given, must be an encoding name. This encoding will be used to
+        convert the input string to Unicode, if it is not already a Unicode
+        string (default: ``'guess'``, which uses a simple UTF-8 / Locale /
+        Latin1 detection.  Can also be ``'chardet'`` to use the chardet
+        library, if it is installed.
+    ``inencoding``
+        Overrides the ``encoding`` if given.
+    """
+
+    #: Full name of the lexer, in human-readable form
+    name = None
+
+    #: A list of short, unique identifiers that can be used to look
+    #: up the lexer from a list, e.g., using `get_lexer_by_name()`.
+    aliases = []
+
+    #: A list of `fnmatch` patterns that match filenames which contain
+    #: content for this lexer. The patterns in this list should be unique among
+    #: all lexers.
+    filenames = []
+
+    #: A list of `fnmatch` patterns that match filenames which may or may not
+    #: contain content for this lexer. This list is used by the
+    #: :func:`.guess_lexer_for_filename()` function, to determine which lexers
+    #: are then included in guessing the correct one. That means that
+    #: e.g. every lexer for HTML and a template language should include
+    #: ``\*.html`` in this list.
+    alias_filenames = []
+
+    #: A list of MIME types for content that can be lexed with this lexer.
+    mimetypes = []
+
+    #: Priority, should multiple lexers match and no content is provided
+    priority = 0
+
+    #: URL of the language specification/definition. Used in the Pygments
+    #: documentation.
+    url = None
+
+    def __init__(self, **options):
+        """
+        This constructor takes arbitrary options as keyword arguments.
+        Every subclass must first process its own options and then call
+        the `Lexer` constructor, since it processes the basic
+        options like `stripnl`.
+
+        An example looks like this:
+
+        .. sourcecode:: python
+
+           def __init__(self, **options):
+               self.compress = options.get('compress', '')
+               Lexer.__init__(self, **options)
+
+        As these options must all be specifiable as strings (due to the
+        command line usage), there are various utility functions
+        available to help with that, see `Utilities`_.
+        """
+        self.options = options
+        self.stripnl = get_bool_opt(options, 'stripnl', True)
+        self.stripall = get_bool_opt(options, 'stripall', False)
+        self.ensurenl = get_bool_opt(options, 'ensurenl', True)
+        self.tabsize = get_int_opt(options, 'tabsize', 0)
+        self.encoding = options.get('encoding', 'guess')
+        self.encoding = options.get('inencoding') or self.encoding
+        self.filters = []
+        for filter_ in get_list_opt(options, 'filters', ()):
+            self.add_filter(filter_)
+
+    def __repr__(self):
+        if self.options:
+            return '<pygments.lexers.%s with %r>' % (self.__class__.__name__,
+                                                     self.options)
+        else:
+            return '<pygments.lexers.%s>' % self.__class__.__name__
+
+    def add_filter(self, filter_, **options):
+        """
+        Add a new stream filter to this lexer.
+        """
+        if not isinstance(filter_, Filter):
+            filter_ = get_filter_by_name(filter_, **options)
+        self.filters.append(filter_)
+
+    def analyse_text(text):
+        """
+        A static method which is called for lexer guessing.
+
+        It should analyse the text and return a float in the range
+        from ``0.0`` to ``1.0``.  If it returns ``0.0``, the lexer
+        will not be selected as the most probable one, if it returns
+        ``1.0``, it will be selected immediately.  This is used by
+        `guess_lexer`.
+
+        The `LexerMeta` metaclass automatically wraps this function so
+        that it works like a static method (no ``self`` or ``cls``
+        parameter) and the return value is automatically converted to
+        `float`. If the return value is an object that is boolean `False`
+        it's the same as if the return values was ``0.0``.
+        """
+
+    def get_tokens(self, text, unfiltered=False):
+        """
+        This method is the basic interface of a lexer. It is called by
+        the `highlight()` function. It must process the text and return an
+        iterable of ``(tokentype, value)`` pairs from `text`.
+
+        Normally, you don't need to override this method. The default
+        implementation processes the options recognized by all lexers
+        (`stripnl`, `stripall` and so on), and then yields all tokens
+        from `get_tokens_unprocessed()`, with the ``index`` dropped.
+
+        If `unfiltered` is set to `True`, the filtering mechanism is
+        bypassed even if filters are defined.
+        """
+        if not isinstance(text, str):
+            if self.encoding == 'guess':
+                text, _ = guess_decode(text)
+            elif self.encoding == 'chardet':
+                try:
+                    from pip._vendor import chardet
+                except ImportError as e:
+                    raise ImportError('To enable chardet encoding guessing, '
+                                      'please install the chardet library '
+                                      'from http://chardet.feedparser.org/') from e
+                # check for BOM first
+                decoded = None
+                for bom, encoding in _encoding_map:
+                    if text.startswith(bom):
+                        decoded = text[len(bom):].decode(encoding, 'replace')
+                        break
+                # no BOM found, so use chardet
+                if decoded is None:
+                    enc = chardet.detect(text[:1024])  # Guess using first 1KB
+                    decoded = text.decode(enc.get('encoding') or 'utf-8',
+                                          'replace')
+                text = decoded
+            else:
+                text = text.decode(self.encoding)
+                if text.startswith('\ufeff'):
+                    text = text[len('\ufeff'):]
+        else:
+            if text.startswith('\ufeff'):
+                text = text[len('\ufeff'):]
+
+        # text now *is* a unicode string
+        text = text.replace('\r\n', '\n')
+        text = text.replace('\r', '\n')
+        if self.stripall:
+            text = text.strip()
+        elif self.stripnl:
+            text = text.strip('\n')
+        if self.tabsize > 0:
+            text = text.expandtabs(self.tabsize)
+        if self.ensurenl and not text.endswith('\n'):
+            text += '\n'
+
+        def streamer():
+            for _, t, v in self.get_tokens_unprocessed(text):
+                yield t, v
+        stream = streamer()
+        if not unfiltered:
+            stream = apply_filters(stream, self.filters, self)
+        return stream
+
+    def get_tokens_unprocessed(self, text):
+        """
+        This method should process the text and return an iterable of
+        ``(index, tokentype, value)`` tuples where ``index`` is the starting
+        position of the token within the input text.
+
+        It must be overridden by subclasses. It is recommended to
+        implement it as a generator to maximize effectiveness.
+        """
+        raise NotImplementedError
+
+
+class DelegatingLexer(Lexer):
+    """
+    This lexer takes two lexer as arguments. A root lexer and
+    a language lexer. First everything is scanned using the language
+    lexer, afterwards all ``Other`` tokens are lexed using the root
+    lexer.
+
+    The lexers from the ``template`` lexer package use this base lexer.
+    """
+
+    def __init__(self, _root_lexer, _language_lexer, _needle=Other, **options):
+        self.root_lexer = _root_lexer(**options)
+        self.language_lexer = _language_lexer(**options)
+        self.needle = _needle
+        Lexer.__init__(self, **options)
+
+    def get_tokens_unprocessed(self, text):
+        buffered = ''
+        insertions = []
+        lng_buffer = []
+        for i, t, v in self.language_lexer.get_tokens_unprocessed(text):
+            if t is self.needle:
+                if lng_buffer:
+                    insertions.append((len(buffered), lng_buffer))
+                    lng_buffer = []
+                buffered += v
+            else:
+                lng_buffer.append((i, t, v))
+        if lng_buffer:
+            insertions.append((len(buffered), lng_buffer))
+        return do_insertions(insertions,
+                             self.root_lexer.get_tokens_unprocessed(buffered))
+
+
+# ------------------------------------------------------------------------------
+# RegexLexer and ExtendedRegexLexer
+#
+
+
+class include(str):  # pylint: disable=invalid-name
+    """
+    Indicates that a state should include rules from another state.
+    """
+    pass
+
+
+class _inherit:
+    """
+    Indicates the a state should inherit from its superclass.
+    """
+    def __repr__(self):
+        return 'inherit'
+
+inherit = _inherit()  # pylint: disable=invalid-name
+
+
+class combined(tuple):  # pylint: disable=invalid-name
+    """
+    Indicates a state combined from multiple states.
+    """
+
+    def __new__(cls, *args):
+        return tuple.__new__(cls, args)
+
+    def __init__(self, *args):
+        # tuple.__init__ doesn't do anything
+        pass
+
+
+class _PseudoMatch:
+    """
+    A pseudo match object constructed from a string.
+    """
+
+    def __init__(self, start, text):
+        self._text = text
+        self._start = start
+
+    def start(self, arg=None):
+        return self._start
+
+    def end(self, arg=None):
+        return self._start + len(self._text)
+
+    def group(self, arg=None):
+        if arg:
+            raise IndexError('No such group')
+        return self._text
+
+    def groups(self):
+        return (self._text,)
+
+    def groupdict(self):
+        return {}
+
+
+def bygroups(*args):
+    """
+    Callback that yields multiple actions for each group in the match.
+    """
+    def callback(lexer, match, ctx=None):
+        for i, action in enumerate(args):
+            if action is None:
+                continue
+            elif type(action) is _TokenType:
+                data = match.group(i + 1)
+                if data:
+                    yield match.start(i + 1), action, data
+            else:
+                data = match.group(i + 1)
+                if data is not None:
+                    if ctx:
+                        ctx.pos = match.start(i + 1)
+                    for item in action(lexer,
+                                       _PseudoMatch(match.start(i + 1), data), ctx):
+                        if item:
+                            yield item
+        if ctx:
+            ctx.pos = match.end()
+    return callback
+
+
+class _This:
+    """
+    Special singleton used for indicating the caller class.
+    Used by ``using``.
+    """
+
+this = _This()
+
+
+def using(_other, **kwargs):
+    """
+    Callback that processes the match with a different lexer.
+
+    The keyword arguments are forwarded to the lexer, except `state` which
+    is handled separately.
+
+    `state` specifies the state that the new lexer will start in, and can
+    be an enumerable such as ('root', 'inline', 'string') or a simple
+    string which is assumed to be on top of the root state.
+
+    Note: For that to work, `_other` must not be an `ExtendedRegexLexer`.
+    """
+    gt_kwargs = {}
+    if 'state' in kwargs:
+        s = kwargs.pop('state')
+        if isinstance(s, (list, tuple)):
+            gt_kwargs['stack'] = s
+        else:
+            gt_kwargs['stack'] = ('root', s)
+
+    if _other is this:
+        def callback(lexer, match, ctx=None):
+            # if keyword arguments are given the callback
+            # function has to create a new lexer instance
+            if kwargs:
+                # XXX: cache that somehow
+                kwargs.update(lexer.options)
+                lx = lexer.__class__(**kwargs)
+            else:
+                lx = lexer
+            s = match.start()
+            for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
+                yield i + s, t, v
+            if ctx:
+                ctx.pos = match.end()
+    else:
+        def callback(lexer, match, ctx=None):
+            # XXX: cache that somehow
+            kwargs.update(lexer.options)
+            lx = _other(**kwargs)
+
+            s = match.start()
+            for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
+                yield i + s, t, v
+            if ctx:
+                ctx.pos = match.end()
+    return callback
+
+
+class default:
+    """
+    Indicates a state or state action (e.g. #pop) to apply.
+    For example default('#pop') is equivalent to ('', Token, '#pop')
+    Note that state tuples may be used as well.
+
+    .. versionadded:: 2.0
+    """
+    def __init__(self, state):
+        self.state = state
+
+
+class words(Future):
+    """
+    Indicates a list of literal words that is transformed into an optimized
+    regex that matches any of the words.
+
+    .. versionadded:: 2.0
+    """
+    def __init__(self, words, prefix='', suffix=''):
+        self.words = words
+        self.prefix = prefix
+        self.suffix = suffix
+
+    def get(self):
+        return regex_opt(self.words, prefix=self.prefix, suffix=self.suffix)
+
+
+class RegexLexerMeta(LexerMeta):
+    """
+    Metaclass for RegexLexer, creates the self._tokens attribute from
+    self.tokens on the first instantiation.
+    """
+
+    def _process_regex(cls, regex, rflags, state):
+        """Preprocess the regular expression component of a token definition."""
+        if isinstance(regex, Future):
+            regex = regex.get()
+        return re.compile(regex, rflags).match
+
+    def _process_token(cls, token):
+        """Preprocess the token component of a token definition."""
+        assert type(token) is _TokenType or callable(token), \
+            'token type must be simple type or callable, not %r' % (token,)
+        return token
+
+    def _process_new_state(cls, new_state, unprocessed, processed):
+        """Preprocess the state transition action of a token definition."""
+        if isinstance(new_state, str):
+            # an existing state
+            if new_state == '#pop':
+                return -1
+            elif new_state in unprocessed:
+                return (new_state,)
+            elif new_state == '#push':
+                return new_state
+            elif new_state[:5] == '#pop:':
+                return -int(new_state[5:])
+            else:
+                assert False, 'unknown new state %r' % new_state
+        elif isinstance(new_state, combined):
+            # combine a new state from existing ones
+            tmp_state = '_tmp_%d' % cls._tmpname
+            cls._tmpname += 1
+            itokens = []
+            for istate in new_state:
+                assert istate != new_state, 'circular state ref %r' % istate
+                itokens.extend(cls._process_state(unprocessed,
+                                                  processed, istate))
+            processed[tmp_state] = itokens
+            return (tmp_state,)
+        elif isinstance(new_state, tuple):
+            # push more than one state
+            for istate in new_state:
+                assert (istate in unprocessed or
+                        istate in ('#pop', '#push')), \
+                    'unknown new state ' + istate
+            return new_state
+        else:
+            assert False, 'unknown new state def %r' % new_state
+
+    def _process_state(cls, unprocessed, processed, state):
+        """Preprocess a single state definition."""
+        assert type(state) is str, "wrong state name %r" % state
+        assert state[0] != '#', "invalid state name %r" % state
+        if state in processed:
+            return processed[state]
+        tokens = processed[state] = []
+        rflags = cls.flags
+        for tdef in unprocessed[state]:
+            if isinstance(tdef, include):
+                # it's a state reference
+                assert tdef != state, "circular state reference %r" % state
+                tokens.extend(cls._process_state(unprocessed, processed,
+                                                 str(tdef)))
+                continue
+            if isinstance(tdef, _inherit):
+                # should be processed already, but may not in the case of:
+                # 1. the state has no counterpart in any parent
+                # 2. the state includes more than one 'inherit'
+                continue
+            if isinstance(tdef, default):
+                new_state = cls._process_new_state(tdef.state, unprocessed, processed)
+                tokens.append((re.compile('').match, None, new_state))
+                continue
+
+            assert type(tdef) is tuple, "wrong rule def %r" % tdef
+
+            try:
+                rex = cls._process_regex(tdef[0], rflags, state)
+            except Exception as err:
+                raise ValueError("uncompilable regex %r in state %r of %r: %s" %
+                                 (tdef[0], state, cls, err)) from err
+
+            token = cls._process_token(tdef[1])
+
+            if len(tdef) == 2:
+                new_state = None
+            else:
+                new_state = cls._process_new_state(tdef[2],
+                                                   unprocessed, processed)
+
+            tokens.append((rex, token, new_state))
+        return tokens
+
+    def process_tokendef(cls, name, tokendefs=None):
+        """Preprocess a dictionary of token definitions."""
+        processed = cls._all_tokens[name] = {}
+        tokendefs = tokendefs or cls.tokens[name]
+        for state in list(tokendefs):
+            cls._process_state(tokendefs, processed, state)
+        return processed
+
+    def get_tokendefs(cls):
+        """
+        Merge tokens from superclasses in MRO order, returning a single tokendef
+        dictionary.
+
+        Any state that is not defined by a subclass will be inherited
+        automatically.  States that *are* defined by subclasses will, by
+        default, override that state in the superclass.  If a subclass wishes to
+        inherit definitions from a superclass, it can use the special value
+        "inherit", which will cause the superclass' state definition to be
+        included at that point in the state.
+        """
+        tokens = {}
+        inheritable = {}
+        for c in cls.__mro__:
+            toks = c.__dict__.get('tokens', {})
+
+            for state, items in toks.items():
+                curitems = tokens.get(state)
+                if curitems is None:
+                    # N.b. because this is assigned by reference, sufficiently
+                    # deep hierarchies are processed incrementally (e.g. for
+                    # A(B), B(C), C(RegexLexer), B will be premodified so X(B)
+                    # will not see any inherits in B).
+                    tokens[state] = items
+                    try:
+                        inherit_ndx = items.index(inherit)
+                    except ValueError:
+                        continue
+                    inheritable[state] = inherit_ndx
+                    continue
+
+                inherit_ndx = inheritable.pop(state, None)
+                if inherit_ndx is None:
+                    continue
+
+                # Replace the "inherit" value with the items
+                curitems[inherit_ndx:inherit_ndx+1] = items
+                try:
+                    # N.b. this is the index in items (that is, the superclass
+                    # copy), so offset required when storing below.
+                    new_inh_ndx = items.index(inherit)
+                except ValueError:
+                    pass
+                else:
+                    inheritable[state] = inherit_ndx + new_inh_ndx
+
+        return tokens
+
+    def __call__(cls, *args, **kwds):
+        """Instantiate cls after preprocessing its token definitions."""
+        if '_tokens' not in cls.__dict__:
+            cls._all_tokens = {}
+            cls._tmpname = 0
+            if hasattr(cls, 'token_variants') and cls.token_variants:
+                # don't process yet
+                pass
+            else:
+                cls._tokens = cls.process_tokendef('', cls.get_tokendefs())
+
+        return type.__call__(cls, *args, **kwds)
+
+
+class RegexLexer(Lexer, metaclass=RegexLexerMeta):
+    """
+    Base for simple stateful regular expression-based lexers.
+    Simplifies the lexing process so that you need only
+    provide a list of states and regular expressions.
+    """
+
+    #: Flags for compiling the regular expressions.
+    #: Defaults to MULTILINE.
+    flags = re.MULTILINE
+
+    #: At all time there is a stack of states. Initially, the stack contains
+    #: a single state 'root'. The top of the stack is called "the current state".
+    #:
+    #: Dict of ``{'state': [(regex, tokentype, new_state), ...], ...}``
+    #:
+    #: ``new_state`` can be omitted to signify no state transition.
+    #: If ``new_state`` is a string, it is pushed on the stack. This ensure
+    #: the new current state is ``new_state``.
+    #: If ``new_state`` is a tuple of strings, all of those strings are pushed
+    #: on the stack and the current state will be the last element of the list.
+    #: ``new_state`` can also be ``combined('state1', 'state2', ...)``
+    #: to signify a new, anonymous state combined from the rules of two
+    #: or more existing ones.
+    #: Furthermore, it can be '#pop' to signify going back one step in
+    #: the state stack, or '#push' to push the current state on the stack
+    #: again. Note that if you push while in a combined state, the combined
+    #: state itself is pushed, and not only the state in which the rule is
+    #: defined.
+    #:
+    #: The tuple can also be replaced with ``include('state')``, in which
+    #: case the rules from the state named by the string are included in the
+    #: current one.
+    tokens = {}
+
+    def get_tokens_unprocessed(self, text, stack=('root',)):
+        """
+        Split ``text`` into (tokentype, text) pairs.
+
+        ``stack`` is the initial stack (default: ``['root']``)
+        """
+        pos = 0
+        tokendefs = self._tokens
+        statestack = list(stack)
+        statetokens = tokendefs[statestack[-1]]
+        while 1:
+            for rexmatch, action, new_state in statetokens:
+                m = rexmatch(text, pos)
+                if m:
+                    if action is not None:
+                        if type(action) is _TokenType:
+                            yield pos, action, m.group()
+                        else:
+                            yield from action(self, m)
+                    pos = m.end()
+                    if new_state is not None:
+                        # state transition
+                        if isinstance(new_state, tuple):
+                            for state in new_state:
+                                if state == '#pop':
+                                    if len(statestack) > 1:
+                                        statestack.pop()
+                                elif state == '#push':
+                                    statestack.append(statestack[-1])
+                                else:
+                                    statestack.append(state)
+                        elif isinstance(new_state, int):
+                            # pop, but keep at least one state on the stack
+                            # (random code leading to unexpected pops should
+                            # not allow exceptions)
+                            if abs(new_state) >= len(statestack):
+                                del statestack[1:]
+                            else:
+                                del statestack[new_state:]
+                        elif new_state == '#push':
+                            statestack.append(statestack[-1])
+                        else:
+                            assert False, "wrong state def: %r" % new_state
+                        statetokens = tokendefs[statestack[-1]]
+                    break
+            else:
+                # We are here only if all state tokens have been considered
+                # and there was not a match on any of them.
+                try:
+                    if text[pos] == '\n':
+                        # at EOL, reset state to "root"
+                        statestack = ['root']
+                        statetokens = tokendefs['root']
+                        yield pos, Whitespace, '\n'
+                        pos += 1
+                        continue
+                    yield pos, Error, text[pos]
+                    pos += 1
+                except IndexError:
+                    break
+
+
+class LexerContext:
+    """
+    A helper object that holds lexer position data.
+    """
+
+    def __init__(self, text, pos, stack=None, end=None):
+        self.text = text
+        self.pos = pos
+        self.end = end or len(text)  # end=0 not supported ;-)
+        self.stack = stack or ['root']
+
+    def __repr__(self):
+        return 'LexerContext(%r, %r, %r)' % (
+            self.text, self.pos, self.stack)
+
+
+class ExtendedRegexLexer(RegexLexer):
+    """
+    A RegexLexer that uses a context object to store its state.
+    """
+
+    def get_tokens_unprocessed(self, text=None, context=None):
+        """
+        Split ``text`` into (tokentype, text) pairs.
+        If ``context`` is given, use this lexer context instead.
+        """
+        tokendefs = self._tokens
+        if not context:
+            ctx = LexerContext(text, 0)
+            statetokens = tokendefs['root']
+        else:
+            ctx = context
+            statetokens = tokendefs[ctx.stack[-1]]
+            text = ctx.text
+        while 1:
+            for rexmatch, action, new_state in statetokens:
+                m = rexmatch(text, ctx.pos, ctx.end)
+                if m:
+                    if action is not None:
+                        if type(action) is _TokenType:
+                            yield ctx.pos, action, m.group()
+                            ctx.pos = m.end()
+                        else:
+                            yield from action(self, m, ctx)
+                            if not new_state:
+                                # altered the state stack?
+                                statetokens = tokendefs[ctx.stack[-1]]
+                    # CAUTION: callback must set ctx.pos!
+                    if new_state is not None:
+                        # state transition
+                        if isinstance(new_state, tuple):
+                            for state in new_state:
+                                if state == '#pop':
+                                    if len(ctx.stack) > 1:
+                                        ctx.stack.pop()
+                                elif state == '#push':
+                                    ctx.stack.append(ctx.stack[-1])
+                                else:
+                                    ctx.stack.append(state)
+                        elif isinstance(new_state, int):
+                            # see RegexLexer for why this check is made
+                            if abs(new_state) >= len(ctx.stack):
+                                del ctx.stack[1:]
+                            else:
+                                del ctx.stack[new_state:]
+                        elif new_state == '#push':
+                            ctx.stack.append(ctx.stack[-1])
+                        else:
+                            assert False, "wrong state def: %r" % new_state
+                        statetokens = tokendefs[ctx.stack[-1]]
+                    break
+            else:
+                try:
+                    if ctx.pos >= ctx.end:
+                        break
+                    if text[ctx.pos] == '\n':
+                        # at EOL, reset state to "root"
+                        ctx.stack = ['root']
+                        statetokens = tokendefs['root']
+                        yield ctx.pos, Text, '\n'
+                        ctx.pos += 1
+                        continue
+                    yield ctx.pos, Error, text[ctx.pos]
+                    ctx.pos += 1
+                except IndexError:
+                    break
+
+
+def do_insertions(insertions, tokens):
+    """
+    Helper for lexers which must combine the results of several
+    sublexers.
+
+    ``insertions`` is a list of ``(index, itokens)`` pairs.
+    Each ``itokens`` iterable should be inserted at position
+    ``index`` into the token stream given by the ``tokens``
+    argument.
+
+    The result is a combined token stream.
+
+    TODO: clean up the code here.
+    """
+    insertions = iter(insertions)
+    try:
+        index, itokens = next(insertions)
+    except StopIteration:
+        # no insertions
+        yield from tokens
+        return
+
+    realpos = None
+    insleft = True
+
+    # iterate over the token stream where we want to insert
+    # the tokens from the insertion list.
+    for i, t, v in tokens:
+        # first iteration. store the position of first item
+        if realpos is None:
+            realpos = i
+        oldi = 0
+        while insleft and i + len(v) >= index:
+            tmpval = v[oldi:index - i]
+            if tmpval:
+                yield realpos, t, tmpval
+                realpos += len(tmpval)
+            for it_index, it_token, it_value in itokens:
+                yield realpos, it_token, it_value
+                realpos += len(it_value)
+            oldi = index - i
+            try:
+                index, itokens = next(insertions)
+            except StopIteration:
+                insleft = False
+                break  # not strictly necessary
+        if oldi < len(v):
+            yield realpos, t, v[oldi:]
+            realpos += len(v) - oldi
+
+    # leftover tokens
+    while insleft:
+        # no normal tokens, set realpos to zero
+        realpos = realpos or 0
+        for p, t, v in itokens:
+            yield realpos, t, v
+            realpos += len(v)
+        try:
+            index, itokens = next(insertions)
+        except StopIteration:
+            insleft = False
+            break  # not strictly necessary
+
+
+class ProfilingRegexLexerMeta(RegexLexerMeta):
+    """Metaclass for ProfilingRegexLexer, collects regex timing info."""
+
+    def _process_regex(cls, regex, rflags, state):
+        if isinstance(regex, words):
+            rex = regex_opt(regex.words, prefix=regex.prefix,
+                            suffix=regex.suffix)
+        else:
+            rex = regex
+        compiled = re.compile(rex, rflags)
+
+        def match_func(text, pos, endpos=sys.maxsize):
+            info = cls._prof_data[-1].setdefault((state, rex), [0, 0.0])
+            t0 = time.time()
+            res = compiled.match(text, pos, endpos)
+            t1 = time.time()
+            info[0] += 1
+            info[1] += t1 - t0
+            return res
+        return match_func
+
+
+class ProfilingRegexLexer(RegexLexer, metaclass=ProfilingRegexLexerMeta):
+    """Drop-in replacement for RegexLexer that does profiling of its regexes."""
+
+    _prof_data = []
+    _prof_sort_index = 4  # defaults to time per call
+
+    def get_tokens_unprocessed(self, text, stack=('root',)):
+        # this needs to be a stack, since using(this) will produce nested calls
+        self.__class__._prof_data.append({})
+        yield from RegexLexer.get_tokens_unprocessed(self, text, stack)
+        rawdata = self.__class__._prof_data.pop()
+        data = sorted(((s, repr(r).strip('u\'').replace('\\\\', '\\')[:65],
+                        n, 1000 * t, 1000 * t / n)
+                       for ((s, r), (n, t)) in rawdata.items()),
+                      key=lambda x: x[self._prof_sort_index],
+                      reverse=True)
+        sum_total = sum(x[3] for x in data)
+
+        print()
+        print('Profiling result for %s lexing %d chars in %.3f ms' %
+              (self.__class__.__name__, len(text), sum_total))
+        print('=' * 110)
+        print('%-20s %-64s ncalls  tottime  percall' % ('state', 'regex'))
+        print('-' * 110)
+        for d in data:
+            print('%-20s %-65s %5d %8.4f %8.4f' % d)
+        print('=' * 110)
diff --git a/shared/package-files/d8/d8260580aeee630d12b7e4f933e180861f33801df25349879a34b543a618acd0/_elementwise.py b/shared/package-files/d8/d8260580aeee630d12b7e4f933e180861f33801df25349879a34b543a618acd0/_elementwise.py
new file mode 100644
index 0000000000000000000000000000000000000000..883c644dbcbbb954ea3e3c184bd610b28f05cca8
--- /dev/null
+++ b/shared/package-files/d8/d8260580aeee630d12b7e4f933e180861f33801df25349879a34b543a618acd0/_elementwise.py
@@ -0,0 +1,801 @@
+from scipy.optimize._bracket import _bracket_root, _bracket_minimum
+from scipy.optimize._chandrupatla import _chandrupatla, _chandrupatla_minimize
+from scipy._lib._util import _RichResult
+
+
+def find_root(f, init, /, *, args=(), tolerances=None, maxiter=None, callback=None):
+    """Find the root of a monotonic, real-valued function of a real variable.
+
+    For each element of the output of `f`, `find_root` seeks the scalar
+    root that makes the element 0. This function currently uses Chandrupatla's
+    bracketing algorithm [1]_ and therefore requires argument `init` to
+    provide a bracket around the root: the function values at the two endpoints
+    must have opposite signs.
+
+    Provided a valid bracket, `find_root` is guaranteed to converge to a solution
+    that satisfies the provided `tolerances` if the function is continuous within
+    the bracket.
+
+    This function works elementwise when `init` and `args` contain (broadcastable)
+    arrays.
+
+    Parameters
+    ----------
+    f : callable
+        The function whose root is desired. The signature must be::
+
+            f(x: array, *args) -> array
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+
+        `f` must be an elementwise function: each element ``f(x)[i]``
+        must equal ``f(x[i])`` for all indices ``i``. It must not mutate the
+        array ``x`` or the arrays in ``args``.
+
+        `find_root` seeks an array ``x`` such that ``f(x)`` is an array of zeros.
+    init : 2-tuple of float array_like
+        The lower and upper endpoints of a bracket surrounding the desired root.
+        A bracket is valid if arrays ``xl, xr = init`` satisfy ``xl < xr`` and
+        ``sign(f(xl)) == -sign(f(xr))`` elementwise. Arrays be broadcastable with
+        one another and `args`.
+    args : tuple of array_like, optional
+        Additional positional array arguments to be passed to `f`. Arrays
+        must be broadcastable with one another and the arrays of `init`.
+        If the callable for which the root is desired requires arguments that are
+        not broadcastable with `x`, wrap that callable with `f` such that `f`
+        accepts only `x` and broadcastable ``*args``.
+    tolerances : dictionary of floats, optional
+        Absolute and relative tolerances on the root and function value.
+        Valid keys of the dictionary are:
+
+        - ``xatol`` - absolute tolerance on the root
+        - ``xrtol`` - relative tolerance on the root
+        - ``fatol`` - absolute tolerance on the function value
+        - ``frtol`` - relative tolerance on the function value
+
+        See Notes for default values and explicit termination conditions.
+    maxiter : int, optional
+        The maximum number of iterations of the algorithm to perform.
+        The default is the maximum possible number of bisections within
+        the (normal) floating point numbers of the relevant dtype.
+    callback : callable, optional
+        An optional user-supplied function to be called before the first
+        iteration and after each iteration.
+        Called as ``callback(res)``, where ``res`` is a ``_RichResult``
+        similar to that returned by `find_root` (but containing the current
+        iterate's values of all variables). If `callback` raises a
+        ``StopIteration``, the algorithm will terminate immediately and
+        `find_root` will return a result. `callback` must not mutate
+        `res` or its attributes.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.
+
+        success : bool array
+            ``True`` where the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm converged to the specified tolerances.
+            - ``-1`` : The initial bracket was invalid.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : Iteration was terminated by `callback`.
+            - ``1`` : The algorithm is proceeding normally (in `callback` only).
+
+        x : float array
+            The root of the function, if the algorithm terminated successfully.
+        f_x : float array
+            The value of `f` evaluated at `x`.
+        nfev : int array
+            The number of abscissae at which `f` was evaluated to find the root.
+            This is distinct from the number of times `f` is *called* because the
+            the function may evaluated at multiple points in a single call.
+        nit : int array
+            The number of iterations of the algorithm that were performed.
+        bracket : tuple of float arrays
+            The lower and upper endpoints of the final bracket.
+        f_bracket : tuple of float arrays
+            The value of `f` evaluated at the lower and upper endpoints of the
+            bracket.
+
+    Notes
+    -----
+    Implemented based on Chandrupatla's original paper [1]_.
+
+    Let:
+
+    -  ``a, b = init`` be the left and right endpoints of the initial bracket,
+    - ``xl`` and ``xr`` be the left and right endpoints of the final bracket,
+    - ``xmin = xl if abs(f(xl)) <= abs(f(xr)) else xr`` be the final bracket
+      endpoint with the smaller function value, and
+    - ``fmin0 = min(f(a), f(b))`` be the minimum of the two values of the
+      function evaluated at the initial bracket endpoints.
+
+    Then the algorithm is considered to have converged when
+
+    - ``abs(xr - xl) < xatol + abs(xmin) * xrtol`` or
+    - ``fun(xmin) <= fatol + abs(fmin0) * frtol``.
+
+    This is equivalent to the termination condition described in [1]_ with
+    ``xrtol = 4e-10``, ``xatol = 1e-5``, and ``fatol = frtol = 0``.
+    However, the default values of the `tolerances` dictionary are
+    ``xatol = 4*tiny``, ``xrtol = 4*eps``, ``frtol = 0``, and ``fatol = tiny``,
+    where ``eps`` and ``tiny`` are the precision and smallest normal number
+    of the result ``dtype`` of function inputs and outputs.
+
+    References
+    ----------
+
+    .. [1] Chandrupatla, Tirupathi R.
+        "A new hybrid quadratic/bisection algorithm for finding the zero of a
+        nonlinear function without using derivatives".
+        Advances in Engineering Software, 28(3), 145-149.
+        https://doi.org/10.1016/s0965-9978(96)00051-8
+
+    See Also
+    --------
+    bracket_root
+
+    Examples
+    --------
+    Suppose we wish to find the root of the following function.
+
+    >>> def f(x, c=5):
+    ...     return x**3 - 2*x - c
+
+    First, we must find a valid bracket. The function is not monotonic,
+    but `bracket_root` may be able to provide a bracket.
+
+    >>> from scipy.optimize import elementwise
+    >>> res_bracket = elementwise.bracket_root(f, 0)
+    >>> res_bracket.success
+    True
+    >>> res_bracket.bracket
+    (2.0, 4.0)
+
+    Indeed, the values of the function at the bracket endpoints have
+    opposite signs.
+
+    >>> res_bracket.f_bracket
+    (-1.0, 51.0)
+
+    Once we have a valid bracket, `find_root` can be used to provide
+    a precise root.
+
+    >>> res_root = elementwise.find_root(f, res_bracket.bracket)
+    >>> res_root.x
+    2.0945514815423265
+
+    The final bracket is only a few ULPs wide, so the error between
+    this value and the true root cannot be much smaller within values
+    that are representable in double precision arithmetic.
+
+    >>> import numpy as np
+    >>> xl, xr = res_root.bracket
+    >>> (xr - xl) / np.spacing(xl)
+    2.0
+    >>> res_root.f_bracket
+    (-8.881784197001252e-16, 9.769962616701378e-15)
+
+    `bracket_root` and `find_root` accept arrays for most arguments.
+    For instance, to find the root for a few values of the parameter ``c``
+    at once:
+
+    >>> c = np.asarray([3, 4, 5])
+    >>> res_bracket = elementwise.bracket_root(f, 0, args=(c,))
+    >>> res_bracket.bracket
+    (array([1., 1., 2.]), array([2., 2., 4.]))
+    >>> res_root = elementwise.find_root(f, res_bracket.bracket, args=(c,))
+    >>> res_root.x
+    array([1.8932892 , 2.        , 2.09455148])
+
+    """
+
+    def reformat_result(res_in):
+        res_out = _RichResult()
+        res_out.status = res_in.status
+        res_out.success = res_in.success
+        res_out.x = res_in.x
+        res_out.f_x = res_in.fun
+        res_out.nfev = res_in.nfev
+        res_out.nit = res_in.nit
+        res_out.bracket = (res_in.xl, res_in.xr)
+        res_out.f_bracket = (res_in.fl, res_in.fr)
+        res_out._order_keys = ['success', 'status', 'x', 'f_x',
+                               'nfev', 'nit', 'bracket', 'f_bracket']
+        return res_out
+
+    xl, xr = init
+    default_tolerances = dict(xatol=None, xrtol=None, fatol=None, frtol=0)
+    tolerances = {} if tolerances is None else tolerances
+    default_tolerances.update(tolerances)
+    tolerances = default_tolerances
+
+    if callable(callback):
+        def _callback(res):
+            return callback(reformat_result(res))
+    else:
+        _callback = callback
+
+    res = _chandrupatla(f, xl, xr, args=args, **tolerances,
+                        maxiter=maxiter, callback=_callback)
+    return reformat_result(res)
+
+
+def find_minimum(f, init, /, *, args=(), tolerances=None, maxiter=100, callback=None):
+    """Find the minimum of an unimodal, real-valued function of a real variable.
+
+    For each element of the output of `f`, `find_minimum` seeks the scalar minimizer
+    that minimizes the element. This function currently uses Chandrupatla's
+    bracketing minimization algorithm [1]_ and therefore requires argument `init`
+    to provide a three-point minimization bracket: ``x1 < x2 < x3`` such that
+    ``func(x1) >= func(x2) <= func(x3)``, where one of the inequalities is strict.
+
+    Provided a valid bracket, `find_minimum` is guaranteed to converge to a local
+    minimum that satisfies the provided `tolerances` if the function is continuous
+    within the bracket.
+
+    This function works elementwise when `init` and `args` contain (broadcastable)
+    arrays.
+
+    Parameters
+    ----------
+    f : callable
+        The function whose minimizer is desired. The signature must be::
+
+            f(x: array, *args) -> array
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+
+        `f` must be an elementwise function: each element ``f(x)[i]``
+        must equal ``f(x[i])`` for all indices ``i``. It must not mutate the
+        array ``x`` or the arrays in ``args``.
+
+        `find_minimum` seeks an array ``x`` such that ``f(x)`` is an array of
+        local minima.
+    init : 3-tuple of float array_like
+        The abscissae of a standard scalar minimization bracket. A bracket is
+        valid if arrays ``x1, x2, x3 = init`` satisfy ``x1 < x2 < x3`` and
+        ``func(x1) >= func(x2) <= func(x3)``, where one of the inequalities
+        is strict. Arrays must be broadcastable with one another and the arrays
+        of `args`.
+    args : tuple of array_like, optional
+        Additional positional array arguments to be passed to `f`. Arrays
+        must be broadcastable with one another and the arrays of `init`.
+        If the callable for which the root is desired requires arguments that are
+        not broadcastable with `x`, wrap that callable with `f` such that `f`
+        accepts only `x` and broadcastable ``*args``.
+    tolerances : dictionary of floats, optional
+        Absolute and relative tolerances on the root and function value.
+        Valid keys of the dictionary are:
+
+        - ``xatol`` - absolute tolerance on the root
+        - ``xrtol`` - relative tolerance on the root
+        - ``fatol`` - absolute tolerance on the function value
+        - ``frtol`` - relative tolerance on the function value
+
+        See Notes for default values and explicit termination conditions.
+    maxiter : int, default: 100
+        The maximum number of iterations of the algorithm to perform.
+    callback : callable, optional
+        An optional user-supplied function to be called before the first
+        iteration and after each iteration.
+        Called as ``callback(res)``, where ``res`` is a ``_RichResult``
+        similar to that returned by `find_minimum` (but containing the current
+        iterate's values of all variables). If `callback` raises a
+        ``StopIteration``, the algorithm will terminate immediately and
+        `find_root` will return a result. `callback` must not mutate
+        `res` or its attributes.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.
+
+        success : bool array
+            ``True`` where the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm converged to the specified tolerances.
+            - ``-1`` : The algorithm encountered an invalid bracket.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : Iteration was terminated by `callback`.
+            - ``1`` : The algorithm is proceeding normally (in `callback` only).
+
+        x : float array
+            The minimizer of the function, if the algorithm terminated successfully.
+        f_x : float array
+            The value of `f` evaluated at `x`.
+        nfev : int array
+            The number of abscissae at which `f` was evaluated to find the root.
+            This is distinct from the number of times `f` is *called* because the
+            the function may evaluated at multiple points in a single call.
+        nit : int array
+            The number of iterations of the algorithm that were performed.
+        bracket : tuple of float arrays
+            The final three-point bracket.
+        f_bracket : tuple of float arrays
+            The value of `f` evaluated at the bracket points.
+
+    Notes
+    -----
+    Implemented based on Chandrupatla's original paper [1]_.
+
+    If ``xl < xm < xr`` are the points of the bracket and ``fl >= fm <= fr``
+    (where one of the inequalities is strict) are the values of `f` evaluated
+    at those points, then the algorithm is considered to have converged when:
+
+    - ``xr - xl <= abs(xm)*xrtol + xatol`` or
+    - ``(fl - 2*fm + fr)/2 <= abs(fm)*frtol + fatol``.
+
+    Note that first of these differs from the termination conditions described
+    in [1]_.
+
+    The default value of `xrtol` is the square root of the precision of the
+    appropriate dtype, and ``xatol = fatol = frtol`` is the smallest normal
+    number of the appropriate dtype.
+
+    References
+    ----------
+
+    .. [1] Chandrupatla, Tirupathi R. (1998).
+        "An efficient quadratic fit-sectioning algorithm for minimization
+        without derivatives".
+        Computer Methods in Applied Mechanics and Engineering, 152 (1-2),
+        211-217. https://doi.org/10.1016/S0045-7825(97)00190-4
+
+    See Also
+    --------
+    bracket_minimum
+
+    Examples
+    --------
+    Suppose we wish to minimize the following function.
+
+    >>> def f(x, c=1):
+    ...     return (x - c)**2 + 2
+
+    First, we must find a valid bracket. The function is unimodal,
+    so `bracket_minium` will easily find a bracket.
+
+    >>> from scipy.optimize import elementwise
+    >>> res_bracket = elementwise.bracket_minimum(f, 0)
+    >>> res_bracket.success
+    True
+    >>> res_bracket.bracket
+    (0.0, 0.5, 1.5)
+
+    Indeed, the bracket points are ordered and the function value
+    at the middle bracket point is less than at the surrounding
+    points.
+
+    >>> xl, xm, xr = res_bracket.bracket
+    >>> fl, fm, fr = res_bracket.f_bracket
+    >>> (xl < xm < xr) and (fl > fm <= fr)
+    True
+
+    Once we have a valid bracket, `find_minimum` can be used to provide
+    an estimate of the minimizer.
+
+    >>> res_minimum = elementwise.find_minimum(f, res_bracket.bracket)
+    >>> res_minimum.x
+    1.0000000149011612
+
+    The function value changes by only a few ULPs within the bracket, so
+    the minimizer cannot be determined much more precisely by evaluating
+    the function alone (i.e. we would need its derivative to do better).
+
+    >>> import numpy as np
+    >>> fl, fm, fr = res_minimum.f_bracket
+    >>> (fl - fm) / np.spacing(fm), (fr - fm) / np.spacing(fm)
+    (0.0, 2.0)
+
+    Therefore, a precise minimum of the function is given by:
+
+    >>> res_minimum.f_x
+    2.0
+
+    `bracket_minimum` and `find_minimum` accept arrays for most arguments.
+    For instance, to find the minimizers and minima for a few values of the
+    parameter ``c`` at once:
+
+    >>> c = np.asarray([1, 1.5, 2])
+    >>> res_bracket = elementwise.bracket_minimum(f, 0, args=(c,))
+    >>> res_bracket.bracket
+    (array([0. , 0.5, 0.5]), array([0.5, 1.5, 1.5]), array([1.5, 2.5, 2.5]))
+    >>> res_minimum = elementwise.find_minimum(f, res_bracket.bracket, args=(c,))
+    >>> res_minimum.x
+    array([1.00000001, 1.5       , 2.        ])
+    >>> res_minimum.f_x
+    array([2., 2., 2.])
+
+    """
+
+    def reformat_result(res_in):
+        res_out = _RichResult()
+        res_out.status = res_in.status
+        res_out.success = res_in.success
+        res_out.x = res_in.x
+        res_out.f_x = res_in.fun
+        res_out.nfev = res_in.nfev
+        res_out.nit = res_in.nit
+        res_out.bracket = (res_in.xl, res_in.xm, res_in.xr)
+        res_out.f_bracket = (res_in.fl, res_in.fm, res_in.fr)
+        res_out._order_keys = ['success', 'status', 'x', 'f_x',
+                               'nfev', 'nit', 'bracket', 'f_bracket']
+        return res_out
+
+    xl, xm, xr = init
+    default_tolerances = dict(xatol=None, xrtol=None, fatol=None, frtol=None)
+    tolerances = {} if tolerances is None else tolerances
+    default_tolerances.update(tolerances)
+    tolerances = default_tolerances
+
+    if callable(callback):
+        def _callback(res):
+            return callback(reformat_result(res))
+    else:
+        _callback = callback
+
+    res = _chandrupatla_minimize(f, xl, xm, xr, args=args, **tolerances,
+                                 maxiter=maxiter, callback=_callback)
+    return reformat_result(res)
+
+
+def bracket_root(f, xl0, xr0=None, *, xmin=None, xmax=None, factor=None, args=(),
+                 maxiter=1000):
+    """Bracket the root of a monotonic, real-valued function of a real variable.
+
+    For each element of the output of `f`, `bracket_root` seeks the scalar
+    bracket endpoints ``xl`` and ``xr`` such that ``sign(f(xl)) == -sign(f(xr))``
+    elementwise.
+
+    The function is guaranteed to find a valid bracket if the function is monotonic,
+    but it may find a bracket under other conditions.
+
+    This function works elementwise when `xl0`, `xr0`, `xmin`, `xmax`, `factor`, and
+    the elements of `args` are (mutually broadcastable) arrays.
+
+    Parameters
+    ----------
+    f : callable
+        The function for which the root is to be bracketed. The signature must be::
+
+            f(x: array, *args) -> array
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+
+        `f` must be an elementwise function: each element ``f(x)[i]``
+        must equal ``f(x[i])`` for all indices ``i``. It must not mutate the
+        array ``x`` or the arrays in ``args``.
+    xl0, xr0: float array_like
+        Starting guess of bracket, which need not contain a root. If `xr0` is
+        not provided, ``xr0 = xl0 + 1``. Must be broadcastable with all other
+        array inputs.
+    xmin, xmax : float array_like, optional
+        Minimum and maximum allowable endpoints of the bracket, inclusive. Must
+        be broadcastable with all other array inputs.
+    factor : float array_like, default: 2
+        The factor used to grow the bracket. See Notes.
+    args : tuple of array_like, optional
+        Additional positional array arguments to be passed to `f`.
+        If the callable for which the root is desired requires arguments that are
+        not broadcastable with `x`, wrap that callable with `f` such that `f`
+        accepts only `x` and broadcastable ``*args``.
+    maxiter : int, default: 1000
+        The maximum number of iterations of the algorithm to perform.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.
+
+        success : bool array
+            ``True`` where the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm produced a valid bracket.
+            - ``-1`` : The bracket expanded to the allowable limits without success.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : Iteration was terminated by `callback`.
+            - ``-5``: The initial bracket does not satisfy`xmin <= xl0 < xr0 < xmax`.
+            
+        bracket : 2-tuple of float arrays
+            The lower and upper endpoints of the bracket, if the algorithm
+            terminated successfully.
+        f_bracket : 2-tuple of float arrays
+            The values of `f` evaluated at the endpoints of ``res.bracket``,
+            respectively.
+        nfev : int array
+            The number of abscissae at which `f` was evaluated to find the root.
+            This is distinct from the number of times `f` is *called* because the
+            the function may evaluated at multiple points in a single call.
+        nit : int array
+            The number of iterations of the algorithm that were performed.
+
+    Notes
+    -----
+    This function generalizes an algorithm found in pieces throughout the
+    `scipy.stats` codebase. The strategy is to iteratively grow the bracket `(l, r)`
+    until ``f(l) < 0 < f(r)`` or ``f(r) < 0 < f(l)``. The bracket grows to the left
+    as follows.
+
+    - If `xmin` is not provided, the distance between `xl0` and `l` is iteratively
+      increased by `factor`.
+    - If `xmin` is provided, the distance between `xmin` and `l` is iteratively
+      decreased by `factor`. Note that this also *increases* the bracket size.
+
+    Growth of the bracket to the right is analogous.
+
+    Growth of the bracket in one direction stops when the endpoint is no longer
+    finite, the function value at the endpoint is no longer finite, or the
+    endpoint reaches its limiting value (`xmin` or `xmax`). Iteration terminates
+    when the bracket stops growing in both directions, the bracket surrounds
+    the root, or a root is found (by chance).
+
+    If two brackets are found - that is, a bracket is found on both sides in
+    the same iteration, the smaller of the two is returned.
+    
+    If roots of the function are found, both `xl` and `xr` are set to the
+    leftmost root.
+    
+    See Also
+    --------
+    find_root
+
+    Examples
+    --------
+    Suppose we wish to find the root of the following function.
+
+    >>> def f(x, c=5):
+    ...     return x**3 - 2*x - c
+
+    First, we must find a valid bracket. The function is not monotonic,
+    but `bracket_root` may be able to provide a bracket.
+
+    >>> from scipy.optimize import elementwise
+    >>> res_bracket = elementwise.bracket_root(f, 0)
+    >>> res_bracket.success
+    True
+    >>> res_bracket.bracket
+    (2.0, 4.0)
+
+    Indeed, the values of the function at the bracket endpoints have
+    opposite signs.
+
+    >>> res_bracket.f_bracket
+    (-1.0, 51.0)
+
+    Once we have a valid bracket, `find_root` can be used to provide
+    a precise root.
+
+    >>> res_root = elementwise.find_root(f, res_bracket.bracket)
+    >>> res_root.x
+    2.0945514815423265
+
+    `bracket_root` and `find_root` accept arrays for most arguments.
+    For instance, to find the root for a few values of the parameter ``c``
+    at once:
+
+    >>> import numpy as np
+    >>> c = np.asarray([3, 4, 5])
+    >>> res_bracket = elementwise.bracket_root(f, 0, args=(c,))
+    >>> res_bracket.bracket
+    (array([1., 1., 2.]), array([2., 2., 4.]))
+    >>> res_root = elementwise.find_root(f, res_bracket.bracket, args=(c,))
+    >>> res_root.x
+    array([1.8932892 , 2.        , 2.09455148])
+
+    """  # noqa: E501
+
+    res = _bracket_root(f, xl0, xr0=xr0, xmin=xmin, xmax=xmax, factor=factor,
+                        args=args, maxiter=maxiter)
+    res.bracket = res.xl, res.xr
+    res.f_bracket = res.fl, res.fr
+    del res.xl
+    del res.xr
+    del res.fl
+    del res.fr
+    return res
+
+
+def bracket_minimum(f, xm0, *, xl0=None, xr0=None, xmin=None, xmax=None,
+                     factor=None, args=(), maxiter=1000):
+    """Bracket the minimum of a unimodal, real-valued function of a real variable.
+
+    For each element of the output of `f`, `bracket_minimum` seeks the scalar
+    bracket points ``xl < xm < xr`` such that ``fl >= fm <= fr`` where one of the
+    inequalities is strict.
+
+    The function is guaranteed to find a valid bracket if the function is
+    strongly unimodal, but it may find a bracket under other conditions.
+
+    This function works elementwise when `xm0`, `xl0`, `xr0`, `xmin`, `xmax`, `factor`,
+    and the elements of `args` are (mutually broadcastable) arrays.
+
+    Parameters
+    ----------
+    f : callable
+        The function for which the root is to be bracketed. The signature must be::
+
+            f(x: array, *args) -> array
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+
+        `f` must be an elementwise function: each element ``f(x)[i]``
+        must equal ``f(x[i])`` for all indices ``i``. It must not mutate the
+        array ``x`` or the arrays in ``args``.
+    xm0: float array_like
+        Starting guess for middle point of bracket.
+    xl0, xr0: float array_like, optional
+        Starting guesses for left and right endpoints of the bracket. Must
+        be broadcastable with all other array inputs.
+    xmin, xmax : float array_like, optional
+        Minimum and maximum allowable endpoints of the bracket, inclusive. Must
+        be broadcastable with all other array inputs.
+    factor : float array_like, default: 2
+        The factor used to grow the bracket. See Notes.
+    args : tuple of array_like, optional
+        Additional positional array arguments to be passed to `f`.
+        If the callable for which the root is desired requires arguments that are
+        not broadcastable with `x`, wrap that callable with `f` such that `f`
+        accepts only `x` and broadcastable ``*args``.
+    maxiter : int, default: 1000
+        The maximum number of iterations of the algorithm to perform.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.
+
+        success : bool array
+            ``True`` where the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm produced a valid bracket.
+            - ``-1`` : The bracket expanded to the allowable limits. Assuming
+              unimodality, this implies the endpoint at the limit is a minimizer.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+            - ``-4`` : ``None`` shall pass.
+            - ``-5`` : The initial bracket does not satisfy
+              `xmin <= xl0 < xm0 < xr0 <= xmax`.
+
+        bracket : 3-tuple of float arrays
+            The left, middle, and right points of the bracket, if the algorithm
+            terminated successfully.
+        f_bracket : 3-tuple of float arrays
+            The function value at the left, middle, and right points of the bracket.
+        nfev : int array
+            The number of abscissae at which `f` was evaluated to find the root.
+            This is distinct from the number of times `f` is *called* because the
+            the function may evaluated at multiple points in a single call.
+        nit : int array
+            The number of iterations of the algorithm that were performed.
+
+    Notes
+    -----
+    Similar to `scipy.optimize.bracket`, this function seeks to find real
+    points ``xl < xm < xr`` such that ``f(xl) >= f(xm)`` and ``f(xr) >= f(xm)``,
+    where at least one of the inequalities is strict. Unlike `scipy.optimize.bracket`,
+    this function can operate in a vectorized manner on array input, so long as
+    the input arrays are broadcastable with each other. Also unlike
+    `scipy.optimize.bracket`, users may specify minimum and maximum endpoints
+    for the desired bracket.
+
+    Given an initial trio of points ``xl = xl0``, ``xm = xm0``, ``xr = xr0``,
+    the algorithm checks if these points already give a valid bracket. If not,
+    a new endpoint, ``w`` is chosen in the "downhill" direction, ``xm`` becomes the new
+    opposite endpoint, and either `xl` or `xr` becomes the new middle point,
+    depending on which direction is downhill. The algorithm repeats from here.
+
+    The new endpoint `w` is chosen differently depending on whether or not a
+    boundary `xmin` or `xmax` has been set in the downhill direction. Without
+    loss of generality, suppose the downhill direction is to the right, so that
+    ``f(xl) > f(xm) > f(xr)``. If there is no boundary to the right, then `w`
+    is chosen to be ``xr + factor * (xr - xm)`` where `factor` is controlled by
+    the user (defaults to 2.0) so that step sizes increase in geometric proportion.
+    If there is a boundary, `xmax` in this case, then `w` is chosen to be
+    ``xmax - (xmax - xr)/factor``, with steps slowing to a stop at
+    `xmax`. This cautious approach ensures that a minimum near but distinct from
+    the boundary isn't missed while also detecting whether or not the `xmax` is
+    a minimizer when `xmax` is reached after a finite number of steps.
+
+    See Also
+    --------
+    scipy.optimize.bracket
+    scipy.optimize.elementwise.find_minimum
+
+    Examples
+    --------
+    Suppose we wish to minimize the following function.
+
+    >>> def f(x, c=1):
+    ...     return (x - c)**2 + 2
+
+    First, we must find a valid bracket. The function is unimodal,
+    so `bracket_minium` will easily find a bracket.
+
+    >>> from scipy.optimize import elementwise
+    >>> res_bracket = elementwise.bracket_minimum(f, 0)
+    >>> res_bracket.success
+    True
+    >>> res_bracket.bracket
+    (0.0, 0.5, 1.5)
+
+    Indeed, the bracket points are ordered and the function value
+    at the middle bracket point is less than at the surrounding
+    points.
+
+    >>> xl, xm, xr = res_bracket.bracket
+    >>> fl, fm, fr = res_bracket.f_bracket
+    >>> (xl < xm < xr) and (fl > fm <= fr)
+    True
+
+    Once we have a valid bracket, `find_minimum` can be used to provide
+    an estimate of the minimizer.
+
+    >>> res_minimum = elementwise.find_minimum(f, res_bracket.bracket)
+    >>> res_minimum.x
+    1.0000000149011612
+
+    `bracket_minimum` and `find_minimum` accept arrays for most arguments.
+    For instance, to find the minimizers and minima for a few values of the
+    parameter ``c`` at once:
+
+    >>> import numpy as np
+    >>> c = np.asarray([1, 1.5, 2])
+    >>> res_bracket = elementwise.bracket_minimum(f, 0, args=(c,))
+    >>> res_bracket.bracket
+    (array([0. , 0.5, 0.5]), array([0.5, 1.5, 1.5]), array([1.5, 2.5, 2.5]))
+    >>> res_minimum = elementwise.find_minimum(f, res_bracket.bracket, args=(c,))
+    >>> res_minimum.x
+    array([1.00000001, 1.5       , 2.        ])
+    >>> res_minimum.f_x
+    array([2., 2., 2.])
+
+    """  # noqa: E501
+
+    res = _bracket_minimum(f, xm0, xl0=xl0, xr0=xr0, xmin=xmin, xmax=xmax,
+                           factor=factor, args=args, maxiter=maxiter)
+    res.bracket = res.xl, res.xm, res.xr
+    res.f_bracket = res.fl, res.fm, res.fr
+    del res.xl
+    del res.xm
+    del res.xr
+    del res.fl
+    del res.fm
+    del res.fr
+    return res
diff --git a/shared/package-files/d8/d82642d7ed3469da9995c309543344462f68b56840af55885afe372a68f3a223/_foreach_erf_ops.h b/shared/package-files/d8/d82642d7ed3469da9995c309543344462f68b56840af55885afe372a68f3a223/_foreach_erf_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa73463899450a81ab8a42ee4160c42bba470445
--- /dev/null
+++ b/shared/package-files/d8/d82642d7ed3469da9995c309543344462f68b56840af55885afe372a68f3a223/_foreach_erf_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_erf {
+  using schema = ::std::vector<at::Tensor> (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_erf")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_erf(Tensor[] self) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_erf_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_erf_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_erf_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_erf_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_erf")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/d8/d82a28e0786ae131773b2e02160fe7a41b2e6c90dd143a3cdb1fe9b6e5857138/test_common.py b/shared/package-files/d8/d82a28e0786ae131773b2e02160fe7a41b2e6c90dd143a3cdb1fe9b6e5857138/test_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..39522876e8f24589174fa6ce2f6890ad552e5899
--- /dev/null
+++ b/shared/package-files/d8/d82a28e0786ae131773b2e02160fe7a41b2e6c90dd143a3cdb1fe9b6e5857138/test_common.py
@@ -0,0 +1,2348 @@
+import math
+from functools import partial
+from inspect import signature
+from itertools import chain, permutations, product
+
+import numpy as np
+import pytest
+
+from sklearn._config import config_context
+from sklearn.datasets import make_multilabel_classification
+from sklearn.exceptions import UndefinedMetricWarning
+from sklearn.metrics import (
+    accuracy_score,
+    average_precision_score,
+    balanced_accuracy_score,
+    brier_score_loss,
+    cohen_kappa_score,
+    confusion_matrix,
+    coverage_error,
+    d2_absolute_error_score,
+    d2_pinball_score,
+    d2_tweedie_score,
+    dcg_score,
+    det_curve,
+    explained_variance_score,
+    f1_score,
+    fbeta_score,
+    hamming_loss,
+    hinge_loss,
+    jaccard_score,
+    label_ranking_average_precision_score,
+    label_ranking_loss,
+    log_loss,
+    matthews_corrcoef,
+    max_error,
+    mean_absolute_error,
+    mean_absolute_percentage_error,
+    mean_gamma_deviance,
+    mean_pinball_loss,
+    mean_poisson_deviance,
+    mean_squared_error,
+    mean_squared_log_error,
+    mean_tweedie_deviance,
+    median_absolute_error,
+    multilabel_confusion_matrix,
+    ndcg_score,
+    precision_recall_curve,
+    precision_score,
+    r2_score,
+    recall_score,
+    roc_auc_score,
+    roc_curve,
+    root_mean_squared_error,
+    root_mean_squared_log_error,
+    top_k_accuracy_score,
+    zero_one_loss,
+)
+from sklearn.metrics._base import _average_binary_score
+from sklearn.metrics.pairwise import (
+    additive_chi2_kernel,
+    chi2_kernel,
+    cosine_distances,
+    cosine_similarity,
+    euclidean_distances,
+    linear_kernel,
+    paired_cosine_distances,
+    paired_euclidean_distances,
+    polynomial_kernel,
+    rbf_kernel,
+    sigmoid_kernel,
+)
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.utils import shuffle
+from sklearn.utils._array_api import (
+    _atol_for_type,
+    _convert_to_numpy,
+    _get_namespace_device_dtype_ids,
+    yield_namespace_device_dtype_combinations,
+)
+from sklearn.utils._testing import (
+    _array_api_for_tests,
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_equal,
+    assert_array_less,
+    ignore_warnings,
+)
+from sklearn.utils.fixes import COO_CONTAINERS, parse_version, sp_version
+from sklearn.utils.multiclass import type_of_target
+from sklearn.utils.validation import _num_samples, check_random_state
+
+# Note toward developers about metric testing
+# -------------------------------------------
+# It is often possible to write one general test for several metrics:
+#
+#   - invariance properties, e.g. invariance to sample order
+#   - common behavior for an argument, e.g. the "normalize" with value True
+#     will return the mean of the metrics and with value False will return
+#     the sum of the metrics.
+#
+# In order to improve the overall metric testing, it is a good idea to write
+# first a specific test for the given metric and then add a general test for
+# all metrics that have the same behavior.
+#
+# Two types of datastructures are used in order to implement this system:
+# dictionaries of metrics and lists of metrics with common properties.
+#
+# Dictionaries of metrics
+# ------------------------
+# The goal of having those dictionaries is to have an easy way to call a
+# particular metric and associate a name to each function:
+#
+#   - REGRESSION_METRICS: all regression metrics.
+#   - CLASSIFICATION_METRICS: all classification metrics
+#     which compare a ground truth and the estimated targets as returned by a
+#     classifier.
+#   - THRESHOLDED_METRICS: all classification metrics which
+#     compare a ground truth and a score, e.g. estimated probabilities or
+#     decision function (format might vary)
+#
+# Those dictionaries will be used to test systematically some invariance
+# properties, e.g. invariance toward several input layout.
+#
+
+REGRESSION_METRICS = {
+    "max_error": max_error,
+    "mean_absolute_error": mean_absolute_error,
+    "mean_squared_error": mean_squared_error,
+    "mean_squared_log_error": mean_squared_log_error,
+    "mean_pinball_loss": mean_pinball_loss,
+    "median_absolute_error": median_absolute_error,
+    "mean_absolute_percentage_error": mean_absolute_percentage_error,
+    "explained_variance_score": explained_variance_score,
+    "r2_score": partial(r2_score, multioutput="variance_weighted"),
+    "root_mean_squared_error": root_mean_squared_error,
+    "root_mean_squared_log_error": root_mean_squared_log_error,
+    "mean_normal_deviance": partial(mean_tweedie_deviance, power=0),
+    "mean_poisson_deviance": mean_poisson_deviance,
+    "mean_gamma_deviance": mean_gamma_deviance,
+    "mean_compound_poisson_deviance": partial(mean_tweedie_deviance, power=1.4),
+    "d2_tweedie_score": partial(d2_tweedie_score, power=1.4),
+    "d2_pinball_score": d2_pinball_score,
+    "d2_absolute_error_score": d2_absolute_error_score,
+}
+
+CLASSIFICATION_METRICS = {
+    "accuracy_score": accuracy_score,
+    "balanced_accuracy_score": balanced_accuracy_score,
+    "adjusted_balanced_accuracy_score": partial(balanced_accuracy_score, adjusted=True),
+    "unnormalized_accuracy_score": partial(accuracy_score, normalize=False),
+    # `confusion_matrix` returns absolute values and hence behaves unnormalized
+    # . Naming it with an unnormalized_ prefix is necessary for this module to
+    # skip sample_weight scaling checks which will fail for unnormalized
+    # metrics.
+    "unnormalized_confusion_matrix": confusion_matrix,
+    "normalized_confusion_matrix": lambda *args, **kwargs: (
+        confusion_matrix(*args, **kwargs).astype("float")
+        / confusion_matrix(*args, **kwargs).sum(axis=1)[:, np.newaxis]
+    ),
+    "unnormalized_multilabel_confusion_matrix": multilabel_confusion_matrix,
+    "unnormalized_multilabel_confusion_matrix_sample": partial(
+        multilabel_confusion_matrix, samplewise=True
+    ),
+    "hamming_loss": hamming_loss,
+    "zero_one_loss": zero_one_loss,
+    "unnormalized_zero_one_loss": partial(zero_one_loss, normalize=False),
+    # These are needed to test averaging
+    "jaccard_score": jaccard_score,
+    "precision_score": precision_score,
+    "recall_score": recall_score,
+    "f1_score": f1_score,
+    "f2_score": partial(fbeta_score, beta=2),
+    "f0.5_score": partial(fbeta_score, beta=0.5),
+    "matthews_corrcoef_score": matthews_corrcoef,
+    "weighted_f0.5_score": partial(fbeta_score, average="weighted", beta=0.5),
+    "weighted_f1_score": partial(f1_score, average="weighted"),
+    "weighted_f2_score": partial(fbeta_score, average="weighted", beta=2),
+    "weighted_precision_score": partial(precision_score, average="weighted"),
+    "weighted_recall_score": partial(recall_score, average="weighted"),
+    "weighted_jaccard_score": partial(jaccard_score, average="weighted"),
+    "micro_f0.5_score": partial(fbeta_score, average="micro", beta=0.5),
+    "micro_f1_score": partial(f1_score, average="micro"),
+    "micro_f2_score": partial(fbeta_score, average="micro", beta=2),
+    "micro_precision_score": partial(precision_score, average="micro"),
+    "micro_recall_score": partial(recall_score, average="micro"),
+    "micro_jaccard_score": partial(jaccard_score, average="micro"),
+    "macro_f0.5_score": partial(fbeta_score, average="macro", beta=0.5),
+    "macro_f1_score": partial(f1_score, average="macro"),
+    "macro_f2_score": partial(fbeta_score, average="macro", beta=2),
+    "macro_precision_score": partial(precision_score, average="macro"),
+    "macro_recall_score": partial(recall_score, average="macro"),
+    "macro_jaccard_score": partial(jaccard_score, average="macro"),
+    "samples_f0.5_score": partial(fbeta_score, average="samples", beta=0.5),
+    "samples_f1_score": partial(f1_score, average="samples"),
+    "samples_f2_score": partial(fbeta_score, average="samples", beta=2),
+    "samples_precision_score": partial(precision_score, average="samples"),
+    "samples_recall_score": partial(recall_score, average="samples"),
+    "samples_jaccard_score": partial(jaccard_score, average="samples"),
+    "cohen_kappa_score": cohen_kappa_score,
+}
+
+
+def precision_recall_curve_padded_thresholds(*args, **kwargs):
+    """
+    The dimensions of precision-recall pairs and the threshold array as
+    returned by the precision_recall_curve do not match. See
+    func:`sklearn.metrics.precision_recall_curve`
+
+    This prevents implicit conversion of return value triple to an higher
+    dimensional np.array of dtype('float64') (it will be of dtype('object)
+    instead). This again is needed for assert_array_equal to work correctly.
+
+    As a workaround we pad the threshold array with NaN values to match
+    the dimension of precision and recall arrays respectively.
+    """
+    precision, recall, thresholds = precision_recall_curve(*args, **kwargs)
+
+    pad_threshholds = len(precision) - len(thresholds)
+
+    return np.array(
+        [
+            precision,
+            recall,
+            np.pad(
+                thresholds.astype(np.float64),
+                pad_width=(0, pad_threshholds),
+                mode="constant",
+                constant_values=[np.nan],
+            ),
+        ]
+    )
+
+
+CURVE_METRICS = {
+    "roc_curve": roc_curve,
+    "precision_recall_curve": precision_recall_curve_padded_thresholds,
+    "det_curve": det_curve,
+}
+
+THRESHOLDED_METRICS = {
+    "coverage_error": coverage_error,
+    "label_ranking_loss": label_ranking_loss,
+    "log_loss": log_loss,
+    "unnormalized_log_loss": partial(log_loss, normalize=False),
+    "hinge_loss": hinge_loss,
+    "brier_score_loss": brier_score_loss,
+    "roc_auc_score": roc_auc_score,  # default: average="macro"
+    "weighted_roc_auc": partial(roc_auc_score, average="weighted"),
+    "samples_roc_auc": partial(roc_auc_score, average="samples"),
+    "micro_roc_auc": partial(roc_auc_score, average="micro"),
+    "ovr_roc_auc": partial(roc_auc_score, average="macro", multi_class="ovr"),
+    "weighted_ovr_roc_auc": partial(
+        roc_auc_score, average="weighted", multi_class="ovr"
+    ),
+    "ovo_roc_auc": partial(roc_auc_score, average="macro", multi_class="ovo"),
+    "weighted_ovo_roc_auc": partial(
+        roc_auc_score, average="weighted", multi_class="ovo"
+    ),
+    "partial_roc_auc": partial(roc_auc_score, max_fpr=0.5),
+    "average_precision_score": average_precision_score,  # default: average="macro"
+    "weighted_average_precision_score": partial(
+        average_precision_score, average="weighted"
+    ),
+    "samples_average_precision_score": partial(
+        average_precision_score, average="samples"
+    ),
+    "micro_average_precision_score": partial(average_precision_score, average="micro"),
+    "label_ranking_average_precision_score": label_ranking_average_precision_score,
+    "ndcg_score": ndcg_score,
+    "dcg_score": dcg_score,
+    "top_k_accuracy_score": top_k_accuracy_score,
+}
+
+ALL_METRICS = dict()
+ALL_METRICS.update(THRESHOLDED_METRICS)
+ALL_METRICS.update(CLASSIFICATION_METRICS)
+ALL_METRICS.update(REGRESSION_METRICS)
+ALL_METRICS.update(CURVE_METRICS)
+
+# Lists of metrics with common properties
+# ---------------------------------------
+# Lists of metrics with common properties are used to test systematically some
+# functionalities and invariance, e.g. SYMMETRIC_METRICS lists all metrics that
+# are symmetric with respect to their input argument y_true and y_pred.
+#
+# When you add a new metric or functionality, check if a general test
+# is already written.
+
+# Those metrics don't support binary inputs
+METRIC_UNDEFINED_BINARY = {
+    "samples_f0.5_score",
+    "samples_f1_score",
+    "samples_f2_score",
+    "samples_precision_score",
+    "samples_recall_score",
+    "samples_jaccard_score",
+    "coverage_error",
+    "unnormalized_multilabel_confusion_matrix_sample",
+    "label_ranking_loss",
+    "label_ranking_average_precision_score",
+    "dcg_score",
+    "ndcg_score",
+}
+
+# Those metrics don't support multiclass inputs
+METRIC_UNDEFINED_MULTICLASS = {
+    "micro_roc_auc",
+    "samples_roc_auc",
+    "partial_roc_auc",
+    "roc_auc_score",
+    "weighted_roc_auc",
+    "jaccard_score",
+    # with default average='binary', multiclass is prohibited
+    "precision_score",
+    "recall_score",
+    "f1_score",
+    "f2_score",
+    "f0.5_score",
+    # curves
+    "roc_curve",
+    "precision_recall_curve",
+    "det_curve",
+}
+
+# Metric undefined with "binary" or "multiclass" input
+METRIC_UNDEFINED_BINARY_MULTICLASS = METRIC_UNDEFINED_BINARY.union(
+    METRIC_UNDEFINED_MULTICLASS
+)
+
+# Metrics with an "average" argument
+METRICS_WITH_AVERAGING = {
+    "precision_score",
+    "recall_score",
+    "f1_score",
+    "f2_score",
+    "f0.5_score",
+    "jaccard_score",
+}
+
+# Threshold-based metrics with an "average" argument
+THRESHOLDED_METRICS_WITH_AVERAGING = {
+    "roc_auc_score",
+    "average_precision_score",
+    "partial_roc_auc",
+}
+
+# Metrics with a "pos_label" argument
+METRICS_WITH_POS_LABEL = {
+    "roc_curve",
+    "precision_recall_curve",
+    "det_curve",
+    "brier_score_loss",
+    "precision_score",
+    "recall_score",
+    "f1_score",
+    "f2_score",
+    "f0.5_score",
+    "jaccard_score",
+    "average_precision_score",
+    "weighted_average_precision_score",
+    "micro_average_precision_score",
+    "samples_average_precision_score",
+}
+
+# Metrics with a "labels" argument
+# TODO: Handle multi_class metrics that has a labels argument as well as a
+# decision function argument. e.g hinge_loss
+METRICS_WITH_LABELS = {
+    "unnormalized_confusion_matrix",
+    "normalized_confusion_matrix",
+    "roc_curve",
+    "precision_recall_curve",
+    "det_curve",
+    "precision_score",
+    "recall_score",
+    "f1_score",
+    "f2_score",
+    "f0.5_score",
+    "jaccard_score",
+    "weighted_f0.5_score",
+    "weighted_f1_score",
+    "weighted_f2_score",
+    "weighted_precision_score",
+    "weighted_recall_score",
+    "weighted_jaccard_score",
+    "micro_f0.5_score",
+    "micro_f1_score",
+    "micro_f2_score",
+    "micro_precision_score",
+    "micro_recall_score",
+    "micro_jaccard_score",
+    "macro_f0.5_score",
+    "macro_f1_score",
+    "macro_f2_score",
+    "macro_precision_score",
+    "macro_recall_score",
+    "macro_jaccard_score",
+    "unnormalized_multilabel_confusion_matrix",
+    "unnormalized_multilabel_confusion_matrix_sample",
+    "cohen_kappa_score",
+    "log_loss",
+    "brier_score_loss",
+}
+
+# Metrics with a "normalize" option
+METRICS_WITH_NORMALIZE_OPTION = {
+    "accuracy_score",
+    "top_k_accuracy_score",
+    "zero_one_loss",
+}
+
+# Threshold-based metrics with "multilabel-indicator" format support
+THRESHOLDED_MULTILABEL_METRICS = {
+    "log_loss",
+    "unnormalized_log_loss",
+    "brier_score_loss",
+    "roc_auc_score",
+    "weighted_roc_auc",
+    "samples_roc_auc",
+    "micro_roc_auc",
+    "partial_roc_auc",
+    "average_precision_score",
+    "weighted_average_precision_score",
+    "samples_average_precision_score",
+    "micro_average_precision_score",
+    "coverage_error",
+    "label_ranking_loss",
+    "ndcg_score",
+    "dcg_score",
+    "label_ranking_average_precision_score",
+}
+
+# Classification metrics with  "multilabel-indicator" format
+MULTILABELS_METRICS = {
+    "accuracy_score",
+    "unnormalized_accuracy_score",
+    "hamming_loss",
+    "zero_one_loss",
+    "unnormalized_zero_one_loss",
+    "weighted_f0.5_score",
+    "weighted_f1_score",
+    "weighted_f2_score",
+    "weighted_precision_score",
+    "weighted_recall_score",
+    "weighted_jaccard_score",
+    "macro_f0.5_score",
+    "macro_f1_score",
+    "macro_f2_score",
+    "macro_precision_score",
+    "macro_recall_score",
+    "macro_jaccard_score",
+    "micro_f0.5_score",
+    "micro_f1_score",
+    "micro_f2_score",
+    "micro_precision_score",
+    "micro_recall_score",
+    "micro_jaccard_score",
+    "unnormalized_multilabel_confusion_matrix",
+    "samples_f0.5_score",
+    "samples_f1_score",
+    "samples_f2_score",
+    "samples_precision_score",
+    "samples_recall_score",
+    "samples_jaccard_score",
+}
+
+# Regression metrics with "multioutput-continuous" format support
+MULTIOUTPUT_METRICS = {
+    "mean_absolute_error",
+    "median_absolute_error",
+    "mean_squared_error",
+    "mean_squared_log_error",
+    "r2_score",
+    "root_mean_squared_error",
+    "root_mean_squared_log_error",
+    "explained_variance_score",
+    "mean_absolute_percentage_error",
+    "mean_pinball_loss",
+    "d2_pinball_score",
+    "d2_absolute_error_score",
+}
+
+# Symmetric with respect to their input arguments y_true and y_pred
+# metric(y_true, y_pred) == metric(y_pred, y_true).
+SYMMETRIC_METRICS = {
+    "accuracy_score",
+    "unnormalized_accuracy_score",
+    "hamming_loss",
+    "zero_one_loss",
+    "unnormalized_zero_one_loss",
+    "micro_jaccard_score",
+    "macro_jaccard_score",
+    "jaccard_score",
+    "samples_jaccard_score",
+    "f1_score",
+    "micro_f1_score",
+    "macro_f1_score",
+    "weighted_recall_score",
+    "mean_squared_log_error",
+    "root_mean_squared_error",
+    "root_mean_squared_log_error",
+    # P = R = F = accuracy in multiclass case
+    "micro_f0.5_score",
+    "micro_f1_score",
+    "micro_f2_score",
+    "micro_precision_score",
+    "micro_recall_score",
+    "matthews_corrcoef_score",
+    "mean_absolute_error",
+    "mean_squared_error",
+    "median_absolute_error",
+    "max_error",
+    # Pinball loss is only symmetric for alpha=0.5 which is the default.
+    "mean_pinball_loss",
+    "cohen_kappa_score",
+    "mean_normal_deviance",
+}
+
+# Asymmetric with respect to their input arguments y_true and y_pred
+# metric(y_true, y_pred) != metric(y_pred, y_true).
+NOT_SYMMETRIC_METRICS = {
+    "balanced_accuracy_score",
+    "adjusted_balanced_accuracy_score",
+    "explained_variance_score",
+    "r2_score",
+    "unnormalized_confusion_matrix",
+    "normalized_confusion_matrix",
+    "roc_curve",
+    "precision_recall_curve",
+    "det_curve",
+    "precision_score",
+    "recall_score",
+    "f2_score",
+    "f0.5_score",
+    "weighted_f0.5_score",
+    "weighted_f1_score",
+    "weighted_f2_score",
+    "weighted_precision_score",
+    "weighted_jaccard_score",
+    "unnormalized_multilabel_confusion_matrix",
+    "macro_f0.5_score",
+    "macro_f2_score",
+    "macro_precision_score",
+    "macro_recall_score",
+    "hinge_loss",
+    "mean_gamma_deviance",
+    "mean_poisson_deviance",
+    "mean_compound_poisson_deviance",
+    "d2_tweedie_score",
+    "d2_pinball_score",
+    "d2_absolute_error_score",
+    "mean_absolute_percentage_error",
+}
+
+
+# No Sample weight support
+METRICS_WITHOUT_SAMPLE_WEIGHT = {
+    "median_absolute_error",
+    "max_error",
+    "ovo_roc_auc",
+    "weighted_ovo_roc_auc",
+}
+
+METRICS_REQUIRE_POSITIVE_Y = {
+    "mean_poisson_deviance",
+    "mean_gamma_deviance",
+    "mean_compound_poisson_deviance",
+    "d2_tweedie_score",
+}
+
+# Metrics involving y = log(1+x)
+METRICS_WITH_LOG1P_Y = {
+    "mean_squared_log_error",
+    "root_mean_squared_log_error",
+}
+
+
+def _require_positive_targets(y1, y2):
+    """Make targets strictly positive"""
+    offset = abs(min(y1.min(), y2.min())) + 1
+    y1 += offset
+    y2 += offset
+    return y1, y2
+
+
+def _require_log1p_targets(y1, y2):
+    """Make targets strictly larger than -1"""
+    offset = abs(min(y1.min(), y2.min())) - 0.99
+    y1 = y1.astype(np.float64)
+    y2 = y2.astype(np.float64)
+    y1 += offset
+    y2 += offset
+    return y1, y2
+
+
+def test_symmetry_consistency():
+    # We shouldn't forget any metrics
+    assert (
+        SYMMETRIC_METRICS
+        | NOT_SYMMETRIC_METRICS
+        | set(THRESHOLDED_METRICS)
+        | METRIC_UNDEFINED_BINARY_MULTICLASS
+    ) == set(ALL_METRICS)
+
+    assert (SYMMETRIC_METRICS & NOT_SYMMETRIC_METRICS) == set()
+
+
+@pytest.mark.parametrize("name", sorted(SYMMETRIC_METRICS))
+def test_symmetric_metric(name):
+    # Test the symmetry of score and loss functions
+    random_state = check_random_state(0)
+    y_true = random_state.randint(0, 2, size=(20,))
+    y_pred = random_state.randint(0, 2, size=(20,))
+
+    if name in METRICS_REQUIRE_POSITIVE_Y:
+        y_true, y_pred = _require_positive_targets(y_true, y_pred)
+
+    elif name in METRICS_WITH_LOG1P_Y:
+        y_true, y_pred = _require_log1p_targets(y_true, y_pred)
+
+    y_true_bin = random_state.randint(0, 2, size=(20, 25))
+    y_pred_bin = random_state.randint(0, 2, size=(20, 25))
+
+    metric = ALL_METRICS[name]
+    if name in METRIC_UNDEFINED_BINARY:
+        if name in MULTILABELS_METRICS:
+            assert_allclose(
+                metric(y_true_bin, y_pred_bin),
+                metric(y_pred_bin, y_true_bin),
+                err_msg="%s is not symmetric" % name,
+            )
+        else:
+            assert False, "This case is currently unhandled"
+    else:
+        assert_allclose(
+            metric(y_true, y_pred),
+            metric(y_pred, y_true),
+            err_msg="%s is not symmetric" % name,
+        )
+
+
+@pytest.mark.parametrize("name", sorted(NOT_SYMMETRIC_METRICS))
+def test_not_symmetric_metric(name):
+    # Test the symmetry of score and loss functions
+    random_state = check_random_state(0)
+    metric = ALL_METRICS[name]
+
+    # The metric can be accidentally symmetric on a random draw.
+    # We run several random draws to check that at least of them
+    # gives an asymmetric result.
+    always_symmetric = True
+    for _ in range(5):
+        y_true = random_state.randint(0, 2, size=(20,))
+        y_pred = random_state.randint(0, 2, size=(20,))
+
+        if name in METRICS_REQUIRE_POSITIVE_Y:
+            y_true, y_pred = _require_positive_targets(y_true, y_pred)
+
+        nominal = metric(y_true, y_pred)
+        swapped = metric(y_pred, y_true)
+        if not np.allclose(nominal, swapped):
+            always_symmetric = False
+            break
+
+    if always_symmetric:
+        raise ValueError(f"{name} seems to be symmetric")
+
+
+def test_symmetry_tests():
+    # check test_symmetric_metric and test_not_symmetric_metric
+    sym = "accuracy_score"
+    not_sym = "recall_score"
+    # test_symmetric_metric passes on a symmetric metric
+    # but fails on a not symmetric metric
+    test_symmetric_metric(sym)
+    with pytest.raises(AssertionError, match=f"{not_sym} is not symmetric"):
+        test_symmetric_metric(not_sym)
+    # test_not_symmetric_metric passes on a not symmetric metric
+    # but fails on a symmetric metric
+    test_not_symmetric_metric(not_sym)
+    with pytest.raises(ValueError, match=f"{sym} seems to be symmetric"):
+        test_not_symmetric_metric(sym)
+
+
+@pytest.mark.parametrize(
+    "name", sorted(set(ALL_METRICS) - METRIC_UNDEFINED_BINARY_MULTICLASS)
+)
+def test_sample_order_invariance(name):
+    random_state = check_random_state(0)
+    y_true = random_state.randint(0, 2, size=(20,))
+    y_pred = random_state.randint(0, 2, size=(20,))
+
+    if name in METRICS_REQUIRE_POSITIVE_Y:
+        y_true, y_pred = _require_positive_targets(y_true, y_pred)
+    elif name in METRICS_WITH_LOG1P_Y:
+        y_true, y_pred = _require_log1p_targets(y_true, y_pred)
+
+    y_true_shuffle, y_pred_shuffle = shuffle(y_true, y_pred, random_state=0)
+
+    with ignore_warnings():
+        metric = ALL_METRICS[name]
+        assert_allclose(
+            metric(y_true, y_pred),
+            metric(y_true_shuffle, y_pred_shuffle),
+            err_msg="%s is not sample order invariant" % name,
+        )
+
+
+def test_sample_order_invariance_multilabel_and_multioutput():
+    random_state = check_random_state(0)
+
+    # Generate some data
+    y_true = random_state.randint(0, 2, size=(20, 25))
+    y_pred = random_state.randint(0, 2, size=(20, 25))
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
+
+    y_true_shuffle, y_pred_shuffle, y_score_shuffle = shuffle(
+        y_true, y_pred, y_score, random_state=0
+    )
+
+    for name in MULTILABELS_METRICS:
+        metric = ALL_METRICS[name]
+        assert_allclose(
+            metric(y_true, y_pred),
+            metric(y_true_shuffle, y_pred_shuffle),
+            err_msg="%s is not sample order invariant" % name,
+        )
+
+    for name in THRESHOLDED_MULTILABEL_METRICS:
+        metric = ALL_METRICS[name]
+        assert_allclose(
+            metric(y_true, y_score),
+            metric(y_true_shuffle, y_score_shuffle),
+            err_msg="%s is not sample order invariant" % name,
+        )
+
+    for name in MULTIOUTPUT_METRICS:
+        metric = ALL_METRICS[name]
+        assert_allclose(
+            metric(y_true, y_score),
+            metric(y_true_shuffle, y_score_shuffle),
+            err_msg="%s is not sample order invariant" % name,
+        )
+        assert_allclose(
+            metric(y_true, y_pred),
+            metric(y_true_shuffle, y_pred_shuffle),
+            err_msg="%s is not sample order invariant" % name,
+        )
+
+
+@pytest.mark.parametrize(
+    "name", sorted(set(ALL_METRICS) - METRIC_UNDEFINED_BINARY_MULTICLASS)
+)
+def test_format_invariance_with_1d_vectors(name):
+    random_state = check_random_state(0)
+    y1 = random_state.randint(0, 2, size=(20,))
+    y2 = random_state.randint(0, 2, size=(20,))
+
+    if name in METRICS_REQUIRE_POSITIVE_Y:
+        y1, y2 = _require_positive_targets(y1, y2)
+    elif name in METRICS_WITH_LOG1P_Y:
+        y1, y2 = _require_log1p_targets(y1, y2)
+
+    y1_list = list(y1)
+    y2_list = list(y2)
+
+    y1_1d, y2_1d = np.array(y1), np.array(y2)
+    assert_array_equal(y1_1d.ndim, 1)
+    assert_array_equal(y2_1d.ndim, 1)
+    y1_column = np.reshape(y1_1d, (-1, 1))
+    y2_column = np.reshape(y2_1d, (-1, 1))
+    y1_row = np.reshape(y1_1d, (1, -1))
+    y2_row = np.reshape(y2_1d, (1, -1))
+
+    with ignore_warnings():
+        metric = ALL_METRICS[name]
+
+        measure = metric(y1, y2)
+
+        assert_allclose(
+            metric(y1_list, y2_list),
+            measure,
+            err_msg="%s is not representation invariant with list" % name,
+        )
+
+        assert_allclose(
+            metric(y1_1d, y2_1d),
+            measure,
+            err_msg="%s is not representation invariant with np-array-1d" % name,
+        )
+
+        assert_allclose(
+            metric(y1_column, y2_column),
+            measure,
+            err_msg="%s is not representation invariant with np-array-column" % name,
+        )
+
+        # Mix format support
+        assert_allclose(
+            metric(y1_1d, y2_list),
+            measure,
+            err_msg="%s is not representation invariant with mix np-array-1d and list"
+            % name,
+        )
+
+        assert_allclose(
+            metric(y1_list, y2_1d),
+            measure,
+            err_msg="%s is not representation invariant with mix np-array-1d and list"
+            % name,
+        )
+
+        assert_allclose(
+            metric(y1_1d, y2_column),
+            measure,
+            err_msg=(
+                "%s is not representation invariant with mix "
+                "np-array-1d and np-array-column"
+            )
+            % name,
+        )
+
+        assert_allclose(
+            metric(y1_column, y2_1d),
+            measure,
+            err_msg=(
+                "%s is not representation invariant with mix "
+                "np-array-1d and np-array-column"
+            )
+            % name,
+        )
+
+        assert_allclose(
+            metric(y1_list, y2_column),
+            measure,
+            err_msg=(
+                "%s is not representation invariant with mix list and np-array-column"
+            )
+            % name,
+        )
+
+        assert_allclose(
+            metric(y1_column, y2_list),
+            measure,
+            err_msg=(
+                "%s is not representation invariant with mix list and np-array-column"
+            )
+            % name,
+        )
+
+        # These mix representations aren't allowed
+        with pytest.raises(ValueError):
+            metric(y1_1d, y2_row)
+        with pytest.raises(ValueError):
+            metric(y1_row, y2_1d)
+        with pytest.raises(ValueError):
+            metric(y1_list, y2_row)
+        with pytest.raises(ValueError):
+            metric(y1_row, y2_list)
+        with pytest.raises(ValueError):
+            metric(y1_column, y2_row)
+        with pytest.raises(ValueError):
+            metric(y1_row, y2_column)
+
+        # NB: We do not test for y1_row, y2_row as these may be
+        # interpreted as multilabel or multioutput data.
+        if name not in (
+            MULTIOUTPUT_METRICS | THRESHOLDED_MULTILABEL_METRICS | MULTILABELS_METRICS
+        ):
+            if "roc_auc" in name:
+                # for consistency between the `roc_cuve` and `roc_auc_score`
+                # np.nan is returned and an `UndefinedMetricWarning` is raised
+                with pytest.warns(UndefinedMetricWarning):
+                    assert math.isnan(metric(y1_row, y2_row))
+            else:
+                with pytest.raises(ValueError):
+                    metric(y1_row, y2_row)
+
+
+@pytest.mark.parametrize(
+    "name", sorted(set(CLASSIFICATION_METRICS) - METRIC_UNDEFINED_BINARY_MULTICLASS)
+)
+def test_classification_invariance_string_vs_numbers_labels(name):
+    # Ensure that classification metrics with string labels are invariant
+    random_state = check_random_state(0)
+    y1 = random_state.randint(0, 2, size=(20,))
+    y2 = random_state.randint(0, 2, size=(20,))
+
+    y1_str = np.array(["eggs", "spam"])[y1]
+    y2_str = np.array(["eggs", "spam"])[y2]
+
+    pos_label_str = "spam"
+    labels_str = ["eggs", "spam"]
+
+    with ignore_warnings():
+        metric = CLASSIFICATION_METRICS[name]
+        measure_with_number = metric(y1, y2)
+
+        # Ugly, but handle case with a pos_label and label
+        metric_str = metric
+        if name in METRICS_WITH_POS_LABEL:
+            metric_str = partial(metric_str, pos_label=pos_label_str)
+
+        measure_with_str = metric_str(y1_str, y2_str)
+
+        assert_array_equal(
+            measure_with_number,
+            measure_with_str,
+            err_msg="{0} failed string vs number invariance test".format(name),
+        )
+
+        measure_with_strobj = metric_str(y1_str.astype("O"), y2_str.astype("O"))
+        assert_array_equal(
+            measure_with_number,
+            measure_with_strobj,
+            err_msg="{0} failed string object vs number invariance test".format(name),
+        )
+
+        if name in METRICS_WITH_LABELS:
+            metric_str = partial(metric_str, labels=labels_str)
+            measure_with_str = metric_str(y1_str, y2_str)
+            assert_array_equal(
+                measure_with_number,
+                measure_with_str,
+                err_msg="{0} failed string vs number  invariance test".format(name),
+            )
+
+            measure_with_strobj = metric_str(y1_str.astype("O"), y2_str.astype("O"))
+            assert_array_equal(
+                measure_with_number,
+                measure_with_strobj,
+                err_msg="{0} failed string vs number  invariance test".format(name),
+            )
+
+
+@pytest.mark.parametrize("name", THRESHOLDED_METRICS)
+def test_thresholded_invariance_string_vs_numbers_labels(name):
+    # Ensure that thresholded metrics with string labels are invariant
+    random_state = check_random_state(0)
+    y1 = random_state.randint(0, 2, size=(20,))
+    y2 = random_state.randint(0, 2, size=(20,))
+
+    y1_str = np.array(["eggs", "spam"])[y1]
+
+    pos_label_str = "spam"
+
+    with ignore_warnings():
+        metric = THRESHOLDED_METRICS[name]
+        if name not in METRIC_UNDEFINED_BINARY:
+            # Ugly, but handle case with a pos_label and label
+            metric_str = metric
+            if name in METRICS_WITH_POS_LABEL:
+                metric_str = partial(metric_str, pos_label=pos_label_str)
+
+            measure_with_number = metric(y1, y2)
+            measure_with_str = metric_str(y1_str, y2)
+            assert_array_equal(
+                measure_with_number,
+                measure_with_str,
+                err_msg="{0} failed string vs number invariance test".format(name),
+            )
+
+            measure_with_strobj = metric_str(y1_str.astype("O"), y2)
+            assert_array_equal(
+                measure_with_number,
+                measure_with_strobj,
+                err_msg="{0} failed string object vs number invariance test".format(
+                    name
+                ),
+            )
+        else:
+            # TODO those metrics doesn't support string label yet
+            with pytest.raises(ValueError):
+                metric(y1_str, y2)
+            with pytest.raises(ValueError):
+                metric(y1_str.astype("O"), y2)
+
+
+invalids_nan_inf = [
+    ([0, 1], [np.inf, np.inf]),
+    ([0, 1], [np.nan, np.nan]),
+    ([0, 1], [np.nan, np.inf]),
+    ([0, 1], [np.inf, 1]),
+    ([0, 1], [np.nan, 1]),
+]
+
+
+@pytest.mark.parametrize(
+    "metric", chain(THRESHOLDED_METRICS.values(), REGRESSION_METRICS.values())
+)
+@pytest.mark.parametrize("y_true, y_score", invalids_nan_inf)
+def test_regression_thresholded_inf_nan_input(metric, y_true, y_score):
+    # Reshape since coverage_error only accepts 2D arrays.
+    if metric == coverage_error:
+        y_true = [y_true]
+        y_score = [y_score]
+    with pytest.raises(ValueError, match=r"contains (NaN|infinity)"):
+        metric(y_true, y_score)
+
+
+@pytest.mark.parametrize("metric", CLASSIFICATION_METRICS.values())
+@pytest.mark.parametrize(
+    "y_true, y_score",
+    invalids_nan_inf
+    +
+    # Add an additional case for classification only
+    # non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/6809
+    [
+        ([np.nan, 1, 2], [1, 2, 3]),
+        ([np.inf, 1, 2], [1, 2, 3]),
+    ],
+)
+def test_classification_inf_nan_input(metric, y_true, y_score):
+    """check that classification metrics raise a message mentioning the
+    occurrence of non-finite values in the target vectors."""
+    if not np.isfinite(y_true).all():
+        input_name = "y_true"
+        if np.isnan(y_true).any():
+            unexpected_value = "NaN"
+        else:
+            unexpected_value = "infinity or a value too large"
+    else:
+        input_name = "y_pred"
+        if np.isnan(y_score).any():
+            unexpected_value = "NaN"
+        else:
+            unexpected_value = "infinity or a value too large"
+
+    err_msg = f"Input {input_name} contains {unexpected_value}"
+
+    with pytest.raises(ValueError, match=err_msg):
+        metric(y_true, y_score)
+
+
+@pytest.mark.parametrize("metric", CLASSIFICATION_METRICS.values())
+def test_classification_binary_continuous_input(metric):
+    """check that classification metrics raise a message of mixed type data
+    with continuous/binary target vectors."""
+    y_true, y_score = ["a", "b", "a"], [0.1, 0.2, 0.3]
+    err_msg = (
+        "Classification metrics can't handle a mix of binary and continuous targets"
+    )
+    with pytest.raises(ValueError, match=err_msg):
+        metric(y_true, y_score)
+
+
+def check_single_sample(name):
+    # Non-regression test: scores should work with a single sample.
+    # This is important for leave-one-out cross validation.
+    # Score functions tested are those that formerly called np.squeeze,
+    # which turns an array of size 1 into a 0-d array (!).
+    metric = ALL_METRICS[name]
+
+    # assert that no exception is thrown
+    if name in METRICS_REQUIRE_POSITIVE_Y:
+        values = [1, 2]
+    elif name in METRICS_WITH_LOG1P_Y:
+        values = [-0.7, 1]
+    else:
+        values = [0, 1]
+    for i, j in product(values, repeat=2):
+        metric([i], [j])
+
+
+def check_single_sample_multioutput(name):
+    metric = ALL_METRICS[name]
+    for i, j, k, l in product([0, 1], repeat=4):
+        metric(np.array([[i, j]]), np.array([[k, l]]))
+
+
+# filter many metric specific warnings
+@pytest.mark.filterwarnings("ignore")
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS)
+        # Those metrics are not always defined with one sample
+        # or in multiclass classification
+        - METRIC_UNDEFINED_BINARY_MULTICLASS
+        - set(THRESHOLDED_METRICS)
+    ),
+)
+def test_single_sample(name):
+    check_single_sample(name)
+
+
+# filter many metric specific warnings
+@pytest.mark.filterwarnings("ignore")
+@pytest.mark.parametrize("name", sorted(MULTIOUTPUT_METRICS | MULTILABELS_METRICS))
+def test_single_sample_multioutput(name):
+    check_single_sample_multioutput(name)
+
+
+@pytest.mark.parametrize("name", sorted(MULTIOUTPUT_METRICS))
+def test_multioutput_number_of_output_differ(name):
+    y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
+    y_pred = np.array([[0, 0], [1, 0], [0, 0]])
+
+    metric = ALL_METRICS[name]
+    with pytest.raises(ValueError):
+        metric(y_true, y_pred)
+
+
+@pytest.mark.parametrize("name", sorted(MULTIOUTPUT_METRICS))
+def test_multioutput_regression_invariance_to_dimension_shuffling(name):
+    # test invariance to dimension shuffling
+    random_state = check_random_state(0)
+    y_true = random_state.uniform(0, 2, size=(20, 5))
+    y_pred = random_state.uniform(0, 2, size=(20, 5))
+
+    metric = ALL_METRICS[name]
+    error = metric(y_true, y_pred)
+
+    for _ in range(3):
+        perm = random_state.permutation(y_true.shape[1])
+        assert_allclose(
+            metric(y_true[:, perm], y_pred[:, perm]),
+            error,
+            err_msg="%s is not dimension shuffling invariant" % (name),
+        )
+
+
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.UndefinedMetricWarning")
+@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
+def test_multilabel_representation_invariance(coo_container):
+    # Generate some data
+    n_classes = 4
+    n_samples = 50
+
+    _, y1 = make_multilabel_classification(
+        n_features=1,
+        n_classes=n_classes,
+        random_state=0,
+        n_samples=n_samples,
+        allow_unlabeled=True,
+    )
+    _, y2 = make_multilabel_classification(
+        n_features=1,
+        n_classes=n_classes,
+        random_state=1,
+        n_samples=n_samples,
+        allow_unlabeled=True,
+    )
+
+    # To make sure at least one empty label is present
+    y1 = np.vstack([y1, [[0] * n_classes]])
+    y2 = np.vstack([y2, [[0] * n_classes]])
+
+    y1_sparse_indicator = coo_container(y1)
+    y2_sparse_indicator = coo_container(y2)
+
+    y1_list_array_indicator = list(y1)
+    y2_list_array_indicator = list(y2)
+
+    y1_list_list_indicator = [list(a) for a in y1_list_array_indicator]
+    y2_list_list_indicator = [list(a) for a in y2_list_array_indicator]
+
+    for name in MULTILABELS_METRICS:
+        metric = ALL_METRICS[name]
+
+        # XXX cruel hack to work with partial functions
+        if isinstance(metric, partial):
+            metric.__module__ = "tmp"
+            metric.__name__ = name
+
+        measure = metric(y1, y2)
+
+        # Check representation invariance
+        assert_allclose(
+            metric(y1_sparse_indicator, y2_sparse_indicator),
+            measure,
+            err_msg=(
+                "%s failed representation invariance between "
+                "dense and sparse indicator formats."
+            )
+            % name,
+        )
+        assert_almost_equal(
+            metric(y1_list_list_indicator, y2_list_list_indicator),
+            measure,
+            err_msg=(
+                "%s failed representation invariance  "
+                "between dense array and list of list "
+                "indicator formats."
+            )
+            % name,
+        )
+        assert_almost_equal(
+            metric(y1_list_array_indicator, y2_list_array_indicator),
+            measure,
+            err_msg=(
+                "%s failed representation invariance  "
+                "between dense and list of array "
+                "indicator formats."
+            )
+            % name,
+        )
+
+
+@pytest.mark.parametrize("name", sorted(MULTILABELS_METRICS))
+def test_raise_value_error_multilabel_sequences(name):
+    # make sure the multilabel-sequence format raises ValueError
+    multilabel_sequences = [
+        [[1], [2], [0, 1]],
+        [(), (2), (0, 1)],
+        [[]],
+        [()],
+        np.array([[], [1, 2]], dtype="object"),
+    ]
+
+    metric = ALL_METRICS[name]
+    for seq in multilabel_sequences:
+        with pytest.raises(ValueError):
+            metric(seq, seq)
+
+
+@pytest.mark.parametrize("name", sorted(METRICS_WITH_NORMALIZE_OPTION))
+def test_normalize_option_binary_classification(name):
+    # Test in the binary case
+    n_classes = 2
+    n_samples = 20
+    random_state = check_random_state(0)
+
+    y_true = random_state.randint(0, n_classes, size=(n_samples,))
+    y_pred = random_state.randint(0, n_classes, size=(n_samples,))
+    y_score = random_state.normal(size=y_true.shape)
+
+    metrics = ALL_METRICS[name]
+    pred = y_score if name in THRESHOLDED_METRICS else y_pred
+    measure_normalized = metrics(y_true, pred, normalize=True)
+    measure_not_normalized = metrics(y_true, pred, normalize=False)
+
+    assert_array_less(
+        -1.0 * measure_normalized,
+        0,
+        err_msg="We failed to test correctly the normalize option",
+    )
+
+    assert_allclose(
+        measure_normalized,
+        measure_not_normalized / n_samples,
+        err_msg=f"Failed with {name}",
+    )
+
+
+@pytest.mark.parametrize("name", sorted(METRICS_WITH_NORMALIZE_OPTION))
+def test_normalize_option_multiclass_classification(name):
+    # Test in the multiclass case
+    n_classes = 4
+    n_samples = 20
+    random_state = check_random_state(0)
+
+    y_true = random_state.randint(0, n_classes, size=(n_samples,))
+    y_pred = random_state.randint(0, n_classes, size=(n_samples,))
+    y_score = random_state.uniform(size=(n_samples, n_classes))
+
+    metrics = ALL_METRICS[name]
+    pred = y_score if name in THRESHOLDED_METRICS else y_pred
+    measure_normalized = metrics(y_true, pred, normalize=True)
+    measure_not_normalized = metrics(y_true, pred, normalize=False)
+
+    assert_array_less(
+        -1.0 * measure_normalized,
+        0,
+        err_msg="We failed to test correctly the normalize option",
+    )
+
+    assert_allclose(
+        measure_normalized,
+        measure_not_normalized / n_samples,
+        err_msg=f"Failed with {name}",
+    )
+
+
+@pytest.mark.parametrize(
+    "name", sorted(METRICS_WITH_NORMALIZE_OPTION.intersection(MULTILABELS_METRICS))
+)
+def test_normalize_option_multilabel_classification(name):
+    # Test in the multilabel case
+    n_classes = 4
+    n_samples = 100
+    random_state = check_random_state(0)
+
+    # for both random_state 0 and 1, y_true and y_pred has at least one
+    # unlabelled entry
+    _, y_true = make_multilabel_classification(
+        n_features=1,
+        n_classes=n_classes,
+        random_state=0,
+        allow_unlabeled=True,
+        n_samples=n_samples,
+    )
+    _, y_pred = make_multilabel_classification(
+        n_features=1,
+        n_classes=n_classes,
+        random_state=1,
+        allow_unlabeled=True,
+        n_samples=n_samples,
+    )
+
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # To make sure at least one empty label is present
+    y_true += [0] * n_classes
+    y_pred += [0] * n_classes
+
+    metrics = ALL_METRICS[name]
+    pred = y_score if name in THRESHOLDED_METRICS else y_pred
+    measure_normalized = metrics(y_true, pred, normalize=True)
+    measure_not_normalized = metrics(y_true, pred, normalize=False)
+
+    assert_array_less(
+        -1.0 * measure_normalized,
+        0,
+        err_msg="We failed to test correctly the normalize option",
+    )
+
+    assert_allclose(
+        measure_normalized,
+        measure_not_normalized / n_samples,
+        err_msg=f"Failed with {name}",
+    )
+
+
+def _check_averaging(
+    metric, y_true, y_pred, y_true_binarize, y_pred_binarize, is_multilabel
+):
+    n_samples, n_classes = y_true_binarize.shape
+
+    # No averaging
+    label_measure = metric(y_true, y_pred, average=None)
+    assert_allclose(
+        label_measure,
+        [
+            metric(y_true_binarize[:, i], y_pred_binarize[:, i])
+            for i in range(n_classes)
+        ],
+    )
+
+    # Micro measure
+    micro_measure = metric(y_true, y_pred, average="micro")
+    assert_allclose(
+        micro_measure, metric(y_true_binarize.ravel(), y_pred_binarize.ravel())
+    )
+
+    # Macro measure
+    macro_measure = metric(y_true, y_pred, average="macro")
+    assert_allclose(macro_measure, np.mean(label_measure))
+
+    # Weighted measure
+    weights = np.sum(y_true_binarize, axis=0, dtype=int)
+
+    if np.sum(weights) != 0:
+        weighted_measure = metric(y_true, y_pred, average="weighted")
+        assert_allclose(weighted_measure, np.average(label_measure, weights=weights))
+    else:
+        weighted_measure = metric(y_true, y_pred, average="weighted")
+        assert_allclose(weighted_measure, 0)
+
+    # Sample measure
+    if is_multilabel:
+        sample_measure = metric(y_true, y_pred, average="samples")
+        assert_allclose(
+            sample_measure,
+            np.mean(
+                [
+                    metric(y_true_binarize[i], y_pred_binarize[i])
+                    for i in range(n_samples)
+                ]
+            ),
+        )
+
+    with pytest.raises(ValueError):
+        metric(y_true, y_pred, average="unknown")
+    with pytest.raises(ValueError):
+        metric(y_true, y_pred, average="garbage")
+
+
+def check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score):
+    is_multilabel = type_of_target(y_true).startswith("multilabel")
+
+    metric = ALL_METRICS[name]
+
+    if name in METRICS_WITH_AVERAGING:
+        _check_averaging(
+            metric, y_true, y_pred, y_true_binarize, y_pred_binarize, is_multilabel
+        )
+    elif name in THRESHOLDED_METRICS_WITH_AVERAGING:
+        _check_averaging(
+            metric, y_true, y_score, y_true_binarize, y_score, is_multilabel
+        )
+    else:
+        raise ValueError("Metric is not recorded as having an average option")
+
+
+@pytest.mark.parametrize("name", sorted(METRICS_WITH_AVERAGING))
+def test_averaging_multiclass(name):
+    n_samples, n_classes = 50, 3
+    random_state = check_random_state(0)
+    y_true = random_state.randint(0, n_classes, size=(n_samples,))
+    y_pred = random_state.randint(0, n_classes, size=(n_samples,))
+    y_score = random_state.uniform(size=(n_samples, n_classes))
+
+    lb = LabelBinarizer().fit(y_true)
+    y_true_binarize = lb.transform(y_true)
+    y_pred_binarize = lb.transform(y_pred)
+
+    check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)
+
+
+@pytest.mark.parametrize(
+    "name", sorted(METRICS_WITH_AVERAGING | THRESHOLDED_METRICS_WITH_AVERAGING)
+)
+def test_averaging_multilabel(name):
+    n_samples, n_classes = 40, 5
+    _, y = make_multilabel_classification(
+        n_features=1,
+        n_classes=n_classes,
+        random_state=5,
+        n_samples=n_samples,
+        allow_unlabeled=False,
+    )
+    y_true = y[:20]
+    y_pred = y[20:]
+    y_score = check_random_state(0).normal(size=(20, n_classes))
+    y_true_binarize = y_true
+    y_pred_binarize = y_pred
+
+    check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)
+
+
+@pytest.mark.parametrize("name", sorted(METRICS_WITH_AVERAGING))
+def test_averaging_multilabel_all_zeroes(name):
+    y_true = np.zeros((20, 3))
+    y_pred = np.zeros((20, 3))
+    y_score = np.zeros((20, 3))
+    y_true_binarize = y_true
+    y_pred_binarize = y_pred
+
+    check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)
+
+
+def test_averaging_binary_multilabel_all_zeroes():
+    y_true = np.zeros((20, 3))
+    y_pred = np.zeros((20, 3))
+    y_true_binarize = y_true
+    y_pred_binarize = y_pred
+    # Test _average_binary_score for weight.sum() == 0
+    binary_metric = lambda y_true, y_score, average="macro": _average_binary_score(
+        precision_score, y_true, y_score, average
+    )
+    _check_averaging(
+        binary_metric,
+        y_true,
+        y_pred,
+        y_true_binarize,
+        y_pred_binarize,
+        is_multilabel=True,
+    )
+
+
+@pytest.mark.parametrize("name", sorted(METRICS_WITH_AVERAGING))
+def test_averaging_multilabel_all_ones(name):
+    y_true = np.ones((20, 3))
+    y_pred = np.ones((20, 3))
+    y_score = np.ones((20, 3))
+    y_true_binarize = y_true
+    y_pred_binarize = y_pred
+
+    check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)
+
+
+def check_sample_weight_invariance(name, metric, y1, y2):
+    rng = np.random.RandomState(0)
+    sample_weight = rng.randint(1, 10, size=len(y1))
+
+    # top_k_accuracy_score always lead to a perfect score for k > 1 in the
+    # binary case
+    metric = partial(metric, k=1) if name == "top_k_accuracy_score" else metric
+
+    # check that unit weights gives the same score as no weight
+    unweighted_score = metric(y1, y2, sample_weight=None)
+
+    assert_allclose(
+        unweighted_score,
+        metric(y1, y2, sample_weight=np.ones(shape=len(y1))),
+        err_msg="For %s sample_weight=None is not equivalent to sample_weight=ones"
+        % name,
+    )
+
+    # check that the weighted and unweighted scores are unequal
+    weighted_score = metric(y1, y2, sample_weight=sample_weight)
+
+    # use context manager to supply custom error message
+    with pytest.raises(AssertionError):
+        assert_allclose(unweighted_score, weighted_score)
+        raise ValueError(
+            "Unweighted and weighted scores are unexpectedly "
+            "almost equal (%s) and (%s) "
+            "for %s" % (unweighted_score, weighted_score, name)
+        )
+
+    # check that sample_weight can be a list
+    weighted_score_list = metric(y1, y2, sample_weight=sample_weight.tolist())
+    assert_allclose(
+        weighted_score,
+        weighted_score_list,
+        err_msg=(
+            "Weighted scores for array and list "
+            "sample_weight input are not equal (%s != %s) for %s"
+        )
+        % (weighted_score, weighted_score_list, name),
+    )
+
+    # check that integer weights is the same as repeated samples
+    repeat_weighted_score = metric(
+        np.repeat(y1, sample_weight, axis=0),
+        np.repeat(y2, sample_weight, axis=0),
+        sample_weight=None,
+    )
+    assert_allclose(
+        weighted_score,
+        repeat_weighted_score,
+        err_msg="Weighting %s is not equal to repeating samples" % name,
+    )
+
+    # check that ignoring a fraction of the samples is equivalent to setting
+    # the corresponding weights to zero
+    sample_weight_subset = sample_weight[1::2]
+    sample_weight_zeroed = np.copy(sample_weight)
+    sample_weight_zeroed[::2] = 0
+    y1_subset = y1[1::2]
+    y2_subset = y2[1::2]
+    weighted_score_subset = metric(
+        y1_subset, y2_subset, sample_weight=sample_weight_subset
+    )
+    weighted_score_zeroed = metric(y1, y2, sample_weight=sample_weight_zeroed)
+    assert_allclose(
+        weighted_score_subset,
+        weighted_score_zeroed,
+        err_msg=(
+            "Zeroing weights does not give the same result as "
+            "removing the corresponding samples (%s != %s) for %s"
+        )
+        % (weighted_score_zeroed, weighted_score_subset, name),
+    )
+
+    if not name.startswith("unnormalized"):
+        # check that the score is invariant under scaling of the weights by a
+        # common factor
+        for scaling in [2, 0.3]:
+            assert_allclose(
+                weighted_score,
+                metric(y1, y2, sample_weight=sample_weight * scaling),
+                err_msg="%s sample_weight is not invariant under scaling" % name,
+            )
+
+    # Check that if number of samples in y_true and sample_weight are not
+    # equal, meaningful error is raised.
+    error_message = (
+        r"Found input variables with inconsistent numbers of "
+        r"samples: \[{}, {}, {}\]".format(
+            _num_samples(y1), _num_samples(y2), _num_samples(sample_weight) * 2
+        )
+    )
+    with pytest.raises(ValueError, match=error_message):
+        metric(y1, y2, sample_weight=np.hstack([sample_weight, sample_weight]))
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS).intersection(set(REGRESSION_METRICS))
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+    ),
+)
+def test_regression_sample_weight_invariance(name):
+    n_samples = 50
+    random_state = check_random_state(0)
+    # regression
+    y_true = random_state.random_sample(size=(n_samples,))
+    y_pred = random_state.random_sample(size=(n_samples,))
+    metric = ALL_METRICS[name]
+    check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS).intersection(set(REGRESSION_METRICS))
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+    ),
+)
+def test_regression_with_invalid_sample_weight(name):
+    # Check that `sample_weight` with incorrect length raises error
+    n_samples = 50
+    random_state = check_random_state(0)
+    y_true = random_state.random_sample(size=(n_samples,))
+    y_pred = random_state.random_sample(size=(n_samples,))
+    metric = ALL_METRICS[name]
+
+    sample_weight = random_state.random_sample(size=(n_samples - 1,))
+    with pytest.raises(ValueError, match="Found input variables with inconsistent"):
+        metric(y_true, y_pred, sample_weight=sample_weight)
+
+    sample_weight = random_state.random_sample(size=(n_samples * 2,)).reshape(
+        (n_samples, 2)
+    )
+    with pytest.raises(ValueError, match="Sample weights must be 1D array or scalar"):
+        metric(y_true, y_pred, sample_weight=sample_weight)
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS)
+        - set(REGRESSION_METRICS)
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+        - METRIC_UNDEFINED_BINARY
+    ),
+)
+def test_binary_sample_weight_invariance(name):
+    # binary
+    n_samples = 50
+    random_state = check_random_state(0)
+    y_true = random_state.randint(0, 2, size=(n_samples,))
+    y_pred = random_state.randint(0, 2, size=(n_samples,))
+    y_score = random_state.random_sample(size=(n_samples,))
+    metric = ALL_METRICS[name]
+    if name in THRESHOLDED_METRICS:
+        check_sample_weight_invariance(name, metric, y_true, y_score)
+    else:
+        check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS)
+        - set(REGRESSION_METRICS)
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+        - METRIC_UNDEFINED_BINARY_MULTICLASS
+    ),
+)
+def test_multiclass_sample_weight_invariance(name):
+    # multiclass
+    n_samples = 50
+    random_state = check_random_state(0)
+    y_true = random_state.randint(0, 5, size=(n_samples,))
+    y_pred = random_state.randint(0, 5, size=(n_samples,))
+    y_score = random_state.random_sample(size=(n_samples, 5))
+    metric = ALL_METRICS[name]
+    if name in THRESHOLDED_METRICS:
+        # softmax
+        temp = np.exp(-y_score)
+        y_score_norm = temp / temp.sum(axis=-1).reshape(-1, 1)
+        check_sample_weight_invariance(name, metric, y_true, y_score_norm)
+    else:
+        check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        (MULTILABELS_METRICS | THRESHOLDED_MULTILABEL_METRICS)
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+    ),
+)
+def test_multilabel_sample_weight_invariance(name):
+    # multilabel indicator
+    random_state = check_random_state(0)
+    _, ya = make_multilabel_classification(
+        n_features=1, n_classes=10, random_state=0, n_samples=50, allow_unlabeled=False
+    )
+    _, yb = make_multilabel_classification(
+        n_features=1, n_classes=10, random_state=1, n_samples=50, allow_unlabeled=False
+    )
+    y_true = np.vstack([ya, yb])
+    y_pred = np.vstack([ya, ya])
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
+
+    metric = ALL_METRICS[name]
+    if name in THRESHOLDED_METRICS:
+        check_sample_weight_invariance(name, metric, y_true, y_score)
+    else:
+        check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+
+@pytest.mark.parametrize(
+    "name",
+    sorted(MULTIOUTPUT_METRICS - METRICS_WITHOUT_SAMPLE_WEIGHT),
+)
+def test_multioutput_sample_weight_invariance(name):
+    random_state = check_random_state(0)
+    y_true = random_state.uniform(0, 2, size=(20, 5))
+    y_pred = random_state.uniform(0, 2, size=(20, 5))
+
+    metric = ALL_METRICS[name]
+    check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+
+def test_no_averaging_labels():
+    # test labels argument when not using averaging
+    # in multi-class and multi-label cases
+    y_true_multilabel = np.array([[1, 1, 0, 0], [1, 1, 0, 0]])
+    y_pred_multilabel = np.array([[0, 0, 1, 1], [0, 1, 1, 0]])
+    y_true_multiclass = np.array([0, 1, 2])
+    y_pred_multiclass = np.array([0, 2, 3])
+    labels = np.array([3, 0, 1, 2])
+    _, inverse_labels = np.unique(labels, return_inverse=True)
+
+    for name in METRICS_WITH_AVERAGING:
+        for y_true, y_pred in [
+            [y_true_multiclass, y_pred_multiclass],
+            [y_true_multilabel, y_pred_multilabel],
+        ]:
+            if name not in MULTILABELS_METRICS and y_pred.ndim > 1:
+                continue
+
+            metric = ALL_METRICS[name]
+
+            score_labels = metric(y_true, y_pred, labels=labels, average=None)
+            score = metric(y_true, y_pred, average=None)
+            assert_array_equal(score_labels, score[inverse_labels])
+
+
+@pytest.mark.parametrize(
+    "name", sorted(MULTILABELS_METRICS - {"unnormalized_multilabel_confusion_matrix"})
+)
+def test_multilabel_label_permutations_invariance(name):
+    random_state = check_random_state(0)
+    n_samples, n_classes = 20, 4
+
+    y_true = random_state.randint(0, 2, size=(n_samples, n_classes))
+    y_score = random_state.randint(0, 2, size=(n_samples, n_classes))
+
+    metric = ALL_METRICS[name]
+    score = metric(y_true, y_score)
+
+    for perm in permutations(range(n_classes), n_classes):
+        y_score_perm = y_score[:, perm]
+        y_true_perm = y_true[:, perm]
+
+        current_score = metric(y_true_perm, y_score_perm)
+        assert_almost_equal(score, current_score)
+
+
+@pytest.mark.parametrize(
+    "name", sorted(THRESHOLDED_MULTILABEL_METRICS | MULTIOUTPUT_METRICS)
+)
+def test_thresholded_multilabel_multioutput_permutations_invariance(name):
+    random_state = check_random_state(0)
+    n_samples, n_classes = 20, 4
+    y_true = random_state.randint(0, 2, size=(n_samples, n_classes))
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
+
+    # Makes sure all samples have at least one label. This works around errors
+    # when running metrics where average="sample"
+    y_true[y_true.sum(1) == 4, 0] = 0
+    y_true[y_true.sum(1) == 0, 0] = 1
+
+    metric = ALL_METRICS[name]
+    score = metric(y_true, y_score)
+
+    for perm in permutations(range(n_classes), n_classes):
+        y_score_perm = y_score[:, perm]
+        y_true_perm = y_true[:, perm]
+
+        current_score = metric(y_true_perm, y_score_perm)
+        if metric == mean_absolute_percentage_error:
+            assert np.isfinite(current_score)
+            assert current_score > 1e6
+            # Here we are not comparing the values in case of MAPE because
+            # whenever y_true value is exactly zero, the MAPE value doesn't
+            # signify anything. Thus, in this case we are just expecting
+            # very large finite value.
+        else:
+            assert_almost_equal(score, current_score)
+
+
+@pytest.mark.parametrize(
+    "name", sorted(set(THRESHOLDED_METRICS) - METRIC_UNDEFINED_BINARY_MULTICLASS)
+)
+def test_thresholded_metric_permutation_invariance(name):
+    n_samples, n_classes = 100, 3
+    random_state = check_random_state(0)
+
+    y_score = random_state.rand(n_samples, n_classes)
+    temp = np.exp(-y_score)
+    y_score = temp / temp.sum(axis=-1).reshape(-1, 1)
+    y_true = random_state.randint(0, n_classes, size=n_samples)
+
+    metric = ALL_METRICS[name]
+    score = metric(y_true, y_score)
+    for perm in permutations(range(n_classes), n_classes):
+        inverse_perm = np.zeros(n_classes, dtype=int)
+        inverse_perm[list(perm)] = np.arange(n_classes)
+        y_score_perm = y_score[:, inverse_perm]
+        y_true_perm = np.take(perm, y_true)
+
+        current_score = metric(y_true_perm, y_score_perm)
+        assert_almost_equal(score, current_score)
+
+
+@pytest.mark.parametrize("metric_name", CLASSIFICATION_METRICS)
+def test_metrics_consistent_type_error(metric_name):
+    # check that an understable message is raised when the type between y_true
+    # and y_pred mismatch
+    rng = np.random.RandomState(42)
+    y1 = np.array(["spam"] * 3 + ["eggs"] * 2, dtype=object)
+    y2 = rng.randint(0, 2, size=y1.size)
+
+    err_msg = "Labels in y_true and y_pred should be of the same type."
+    with pytest.raises(TypeError, match=err_msg):
+        CLASSIFICATION_METRICS[metric_name](y1, y2)
+
+
+@pytest.mark.parametrize(
+    "metric, y_pred_threshold",
+    [
+        (average_precision_score, True),
+        (brier_score_loss, True),
+        (f1_score, False),
+        (partial(fbeta_score, beta=1), False),
+        (jaccard_score, False),
+        (precision_recall_curve, True),
+        (precision_score, False),
+        (recall_score, False),
+        (roc_curve, True),
+    ],
+)
+@pytest.mark.parametrize("dtype_y_str", [str, object])
+def test_metrics_pos_label_error_str(metric, y_pred_threshold, dtype_y_str):
+    # check that the error message if `pos_label` is not specified and the
+    # targets is made of strings.
+    rng = np.random.RandomState(42)
+    y1 = np.array(["spam"] * 3 + ["eggs"] * 2, dtype=dtype_y_str)
+    y2 = rng.randint(0, 2, size=y1.size)
+
+    if not y_pred_threshold:
+        y2 = np.array(["spam", "eggs"], dtype=dtype_y_str)[y2]
+
+    err_msg_pos_label_None = (
+        "y_true takes value in {'eggs', 'spam'} and pos_label is not "
+        "specified: either make y_true take value in {0, 1} or {-1, 1} or "
+        "pass pos_label explicit"
+    )
+    err_msg_pos_label_1 = (
+        r"pos_label=1 is not a valid label. It should be one of \['eggs', 'spam'\]"
+    )
+
+    pos_label_default = signature(metric).parameters["pos_label"].default
+
+    err_msg = err_msg_pos_label_1 if pos_label_default == 1 else err_msg_pos_label_None
+    with pytest.raises(ValueError, match=err_msg):
+        metric(y1, y2)
+
+
+def check_array_api_metric(
+    metric, array_namespace, device, dtype_name, a_np, b_np, **metric_kwargs
+):
+    xp = _array_api_for_tests(array_namespace, device)
+
+    a_xp = xp.asarray(a_np, device=device)
+    b_xp = xp.asarray(b_np, device=device)
+
+    metric_np = metric(a_np, b_np, **metric_kwargs)
+
+    if metric_kwargs.get("sample_weight") is not None:
+        metric_kwargs["sample_weight"] = xp.asarray(
+            metric_kwargs["sample_weight"], device=device
+        )
+
+    multioutput = metric_kwargs.get("multioutput")
+    if isinstance(multioutput, np.ndarray):
+        metric_kwargs["multioutput"] = xp.asarray(multioutput, device=device)
+
+    # When array API dispatch is disabled, and np.asarray works (for example PyTorch
+    # with CPU device), calling the metric function with such numpy compatible inputs
+    # should work (albeit by implicitly converting to numpy arrays instead of
+    # dispatching to the array library).
+    try:
+        np.asarray(a_xp)
+        np.asarray(b_xp)
+        numpy_as_array_works = True
+    except (TypeError, RuntimeError, ValueError):
+        # PyTorch with CUDA device and CuPy raise TypeError consistently.
+        # array-api-strict chose to raise RuntimeError instead. NumPy raises
+        # a ValueError if the `__array__` dunder does not return an array.
+        # Exception type may need to be updated in the future for other libraries.
+        numpy_as_array_works = False
+
+    if numpy_as_array_works:
+        metric_xp = metric(a_xp, b_xp, **metric_kwargs)
+        assert_allclose(
+            metric_xp,
+            metric_np,
+            atol=_atol_for_type(dtype_name),
+        )
+        metric_xp_mixed_1 = metric(a_np, b_xp, **metric_kwargs)
+        assert_allclose(
+            metric_xp_mixed_1,
+            metric_np,
+            atol=_atol_for_type(dtype_name),
+        )
+        metric_xp_mixed_2 = metric(a_xp, b_np, **metric_kwargs)
+        assert_allclose(
+            metric_xp_mixed_2,
+            metric_np,
+            atol=_atol_for_type(dtype_name),
+        )
+
+    with config_context(array_api_dispatch=True):
+        metric_xp = metric(a_xp, b_xp, **metric_kwargs)
+
+        assert_allclose(
+            _convert_to_numpy(xp.asarray(metric_xp), xp),
+            metric_np,
+            atol=_atol_for_type(dtype_name),
+        )
+
+
+def check_array_api_binary_classification_metric(
+    metric, array_namespace, device, dtype_name
+):
+    y_true_np = np.array([0, 0, 1, 1])
+    y_pred_np = np.array([0, 1, 0, 1])
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=None,
+    )
+
+    sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=sample_weight,
+    )
+
+
+def check_array_api_multiclass_classification_metric(
+    metric, array_namespace, device, dtype_name
+):
+    y_true_np = np.array([0, 1, 2, 3])
+    y_pred_np = np.array([0, 1, 0, 2])
+
+    additional_params = {
+        "average": ("micro", "macro", "weighted"),
+        "beta": (0.2, 0.5, 0.8),
+    }
+    metric_kwargs_combinations = _get_metric_kwargs_for_array_api_testing(
+        metric=metric,
+        params=additional_params,
+    )
+    for metric_kwargs in metric_kwargs_combinations:
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=y_true_np,
+            b_np=y_pred_np,
+            sample_weight=None,
+            **metric_kwargs,
+        )
+
+        sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
+
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=y_true_np,
+            b_np=y_pred_np,
+            sample_weight=sample_weight,
+            **metric_kwargs,
+        )
+
+
+def check_array_api_multilabel_classification_metric(
+    metric, array_namespace, device, dtype_name
+):
+    y_true_np = np.array([[1, 1], [0, 1], [0, 0]], dtype=dtype_name)
+    y_pred_np = np.array([[1, 1], [1, 1], [1, 1]], dtype=dtype_name)
+
+    additional_params = {
+        "average": ("micro", "macro", "weighted"),
+        "beta": (0.2, 0.5, 0.8),
+    }
+    metric_kwargs_combinations = _get_metric_kwargs_for_array_api_testing(
+        metric=metric,
+        params=additional_params,
+    )
+    for metric_kwargs in metric_kwargs_combinations:
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=y_true_np,
+            b_np=y_pred_np,
+            sample_weight=None,
+            **metric_kwargs,
+        )
+
+        sample_weight = np.array([0.0, 0.1, 2.0], dtype=dtype_name)
+
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=y_true_np,
+            b_np=y_pred_np,
+            sample_weight=sample_weight,
+            **metric_kwargs,
+        )
+
+
+def check_array_api_regression_metric(metric, array_namespace, device, dtype_name):
+    func_name = metric.func.__name__ if isinstance(metric, partial) else metric.__name__
+    if func_name == "mean_poisson_deviance" and sp_version < parse_version("1.14.0"):
+        pytest.skip(
+            "mean_poisson_deviance's dependency `xlogy` is available as of scipy 1.14.0"
+        )
+
+    y_true_np = np.array([2.0, 0.1, 1.0, 4.0], dtype=dtype_name)
+    y_pred_np = np.array([0.5, 0.5, 2, 2], dtype=dtype_name)
+
+    metric_kwargs = {}
+    metric_params = signature(metric).parameters
+
+    if "sample_weight" in metric_params:
+        metric_kwargs["sample_weight"] = None
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        **metric_kwargs,
+    )
+
+    if "sample_weight" in metric_params:
+        metric_kwargs["sample_weight"] = np.array(
+            [0.1, 2.0, 1.5, 0.5], dtype=dtype_name
+        )
+
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=y_true_np,
+            b_np=y_pred_np,
+            **metric_kwargs,
+        )
+
+
+def check_array_api_regression_metric_multioutput(
+    metric, array_namespace, device, dtype_name
+):
+    y_true_np = np.array([[1, 3, 2], [1, 2, 2]], dtype=dtype_name)
+    y_pred_np = np.array([[1, 4, 4], [1, 1, 1]], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=None,
+    )
+
+    sample_weight = np.array([0.1, 2.0], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=sample_weight,
+    )
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        multioutput=np.array([0.1, 0.3, 0.7], dtype=dtype_name),
+    )
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        multioutput="raw_values",
+    )
+
+
+def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name):
+    X_np = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=dtype_name)
+    Y_np = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]], dtype=dtype_name)
+
+    metric_kwargs = {}
+    if "dense_output" in signature(metric).parameters:
+        metric_kwargs["dense_output"] = False
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=X_np,
+            b_np=Y_np,
+            **metric_kwargs,
+        )
+        metric_kwargs["dense_output"] = True
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=X_np,
+        b_np=Y_np,
+        **metric_kwargs,
+    )
+
+
+array_api_metric_checkers = {
+    accuracy_score: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    f1_score: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    fbeta_score: [
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    jaccard_score: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    multilabel_confusion_matrix: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    precision_score: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    recall_score: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    zero_one_loss: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    hamming_loss: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
+    mean_tweedie_deviance: [check_array_api_regression_metric],
+    partial(mean_tweedie_deviance, power=-0.5): [check_array_api_regression_metric],
+    partial(mean_tweedie_deviance, power=1.5): [check_array_api_regression_metric],
+    r2_score: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    cosine_similarity: [check_array_api_metric_pairwise],
+    explained_variance_score: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    mean_absolute_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    mean_pinball_loss: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    mean_squared_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    mean_squared_log_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    d2_tweedie_score: [
+        check_array_api_regression_metric,
+    ],
+    paired_cosine_distances: [check_array_api_metric_pairwise],
+    mean_poisson_deviance: [check_array_api_regression_metric],
+    additive_chi2_kernel: [check_array_api_metric_pairwise],
+    mean_gamma_deviance: [check_array_api_regression_metric],
+    max_error: [check_array_api_regression_metric],
+    mean_absolute_percentage_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    chi2_kernel: [check_array_api_metric_pairwise],
+    paired_euclidean_distances: [check_array_api_metric_pairwise],
+    cosine_distances: [check_array_api_metric_pairwise],
+    euclidean_distances: [check_array_api_metric_pairwise],
+    linear_kernel: [check_array_api_metric_pairwise],
+    polynomial_kernel: [check_array_api_metric_pairwise],
+    rbf_kernel: [check_array_api_metric_pairwise],
+    root_mean_squared_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    root_mean_squared_log_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    sigmoid_kernel: [check_array_api_metric_pairwise],
+}
+
+
+def yield_metric_checker_combinations(metric_checkers=array_api_metric_checkers):
+    for metric, checkers in metric_checkers.items():
+        for checker in checkers:
+            yield metric, checker
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype_name",
+    yield_namespace_device_dtype_combinations(),
+    ids=_get_namespace_device_dtype_ids,
+)
+@pytest.mark.parametrize("metric, check_func", yield_metric_checker_combinations())
+def test_array_api_compliance(metric, array_namespace, device, dtype_name, check_func):
+    check_func(metric, array_namespace, device, dtype_name)
+
+
+@pytest.mark.parametrize("df_lib_name", ["pandas", "polars"])
+@pytest.mark.parametrize("metric_name", sorted(ALL_METRICS))
+def test_metrics_dataframe_series(metric_name, df_lib_name):
+    df_lib = pytest.importorskip(df_lib_name)
+
+    y_pred = df_lib.Series([0.0, 1.0, 0, 1.0])
+    y_true = df_lib.Series([1.0, 0.0, 0.0, 0.0])
+
+    metric = ALL_METRICS[metric_name]
+    try:
+        expected_metric = metric(y_pred.to_numpy(), y_true.to_numpy())
+    except ValueError:
+        pytest.skip(f"{metric_name} can not deal with 1d inputs")
+
+    assert_allclose(metric(y_pred, y_true), expected_metric)
+
+
+def _get_metric_kwargs_for_array_api_testing(metric, params):
+    """Helper function to enable specifying a variety of additional params and
+    their corresponding values, so that they can be passed to a metric function
+    when testing for array api compliance."""
+    metric_kwargs_combinations = [{}]
+    for param, values in params.items():
+        if param not in signature(metric).parameters:
+            continue
+
+        new_combinations = []
+        for kwargs in metric_kwargs_combinations:
+            for value in values:
+                new_kwargs = kwargs.copy()
+                new_kwargs[param] = value
+                new_combinations.append(new_kwargs)
+
+        metric_kwargs_combinations = new_combinations
+
+    return metric_kwargs_combinations
+
+
+@pytest.mark.parametrize("name", sorted(ALL_METRICS))
+def test_returned_value_consistency(name):
+    """Ensure that the returned values of all metrics are consistent.
+
+    It can either be a float, a numpy array, or a tuple of floats or numpy arrays.
+    It should not be a numpy float64 or float32.
+    """
+
+    rng = np.random.RandomState(0)
+    y_true = rng.randint(0, 2, size=(20,))
+    y_pred = rng.randint(0, 2, size=(20,))
+
+    if name in METRICS_REQUIRE_POSITIVE_Y:
+        y_true, y_pred = _require_positive_targets(y_true, y_pred)
+
+    if name in METRIC_UNDEFINED_BINARY:
+        y_true = rng.randint(0, 2, size=(20, 3))
+        y_pred = rng.randint(0, 2, size=(20, 3))
+
+    metric = ALL_METRICS[name]
+    score = metric(y_true, y_pred)
+
+    assert isinstance(score, (float, np.ndarray, tuple))
+    assert not isinstance(score, (np.float64, np.float32))
+
+    if isinstance(score, tuple):
+        assert all(isinstance(v, float) for v in score) or all(
+            isinstance(v, np.ndarray) for v in score
+        )
diff --git a/shared/package-files/d8/d82bb62d033af8ae1d6abaa11efd4433f6e59a96299eb048217d469cbfebc4a1/CERT.py b/shared/package-files/d8/d82bb62d033af8ae1d6abaa11efd4433f6e59a96299eb048217d469cbfebc4a1/CERT.py
new file mode 100644
index 0000000000000000000000000000000000000000..f369cc858a7347706f06955827b95890052aa6f5
--- /dev/null
+++ b/shared/package-files/d8/d82bb62d033af8ae1d6abaa11efd4433f6e59a96299eb048217d469cbfebc4a1/CERT.py
@@ -0,0 +1,116 @@
+# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
+
+# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc.
+#
+# Permission to use, copy, modify, and distribute this software and its
+# documentation for any purpose with or without fee is hereby granted,
+# provided that the above copyright notice and this permission notice
+# appear in all copies.
+#
+# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
+# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
+# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+import base64
+import struct
+
+import dns.dnssectypes
+import dns.exception
+import dns.immutable
+import dns.rdata
+import dns.tokenizer
+
+_ctype_by_value = {
+    1: "PKIX",
+    2: "SPKI",
+    3: "PGP",
+    4: "IPKIX",
+    5: "ISPKI",
+    6: "IPGP",
+    7: "ACPKIX",
+    8: "IACPKIX",
+    253: "URI",
+    254: "OID",
+}
+
+_ctype_by_name = {
+    "PKIX": 1,
+    "SPKI": 2,
+    "PGP": 3,
+    "IPKIX": 4,
+    "ISPKI": 5,
+    "IPGP": 6,
+    "ACPKIX": 7,
+    "IACPKIX": 8,
+    "URI": 253,
+    "OID": 254,
+}
+
+
+def _ctype_from_text(what):
+    v = _ctype_by_name.get(what)
+    if v is not None:
+        return v
+    return int(what)
+
+
+def _ctype_to_text(what):
+    v = _ctype_by_value.get(what)
+    if v is not None:
+        return v
+    return str(what)
+
+
+@dns.immutable.immutable
+class CERT(dns.rdata.Rdata):
+    """CERT record"""
+
+    # see RFC 4398
+
+    __slots__ = ["certificate_type", "key_tag", "algorithm", "certificate"]
+
+    def __init__(
+        self, rdclass, rdtype, certificate_type, key_tag, algorithm, certificate
+    ):
+        super().__init__(rdclass, rdtype)
+        self.certificate_type = self._as_uint16(certificate_type)
+        self.key_tag = self._as_uint16(key_tag)
+        self.algorithm = self._as_uint8(algorithm)
+        self.certificate = self._as_bytes(certificate)
+
+    def to_text(self, origin=None, relativize=True, **kw):
+        certificate_type = _ctype_to_text(self.certificate_type)
+        return "%s %d %s %s" % (
+            certificate_type,
+            self.key_tag,
+            dns.dnssectypes.Algorithm.to_text(self.algorithm),
+            dns.rdata._base64ify(self.certificate, **kw),
+        )
+
+    @classmethod
+    def from_text(
+        cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None
+    ):
+        certificate_type = _ctype_from_text(tok.get_string())
+        key_tag = tok.get_uint16()
+        algorithm = dns.dnssectypes.Algorithm.from_text(tok.get_string())
+        b64 = tok.concatenate_remaining_identifiers().encode()
+        certificate = base64.b64decode(b64)
+        return cls(rdclass, rdtype, certificate_type, key_tag, algorithm, certificate)
+
+    def _to_wire(self, file, compress=None, origin=None, canonicalize=False):
+        prefix = struct.pack(
+            "!HHB", self.certificate_type, self.key_tag, self.algorithm
+        )
+        file.write(prefix)
+        file.write(self.certificate)
+
+    @classmethod
+    def from_wire_parser(cls, rdclass, rdtype, parser, origin=None):
+        (certificate_type, key_tag, algorithm) = parser.get_struct("!HHB")
+        certificate = parser.get_remaining()
+        return cls(rdclass, rdtype, certificate_type, key_tag, algorithm, certificate)
diff --git a/shared/package-files/d8/d83593cf83ef70f4104e3ef2fcbdf48a3d6c208dce2add25d0afdacec1462c53/postproc.py b/shared/package-files/d8/d83593cf83ef70f4104e3ef2fcbdf48a3d6c208dce2add25d0afdacec1462c53/postproc.py
new file mode 100644
index 0000000000000000000000000000000000000000..eab5310ca3e4ca89507e6cfb700f8855891e30e7
--- /dev/null
+++ b/shared/package-files/d8/d83593cf83ef70f4104e3ef2fcbdf48a3d6c208dce2add25d0afdacec1462c53/postproc.py
@@ -0,0 +1,239 @@
+from functools import cached_property
+from numba.core import ir, analysis, transforms, ir_utils
+
+
+class YieldPoint(object):
+
+    def __init__(self, block, inst):
+        assert isinstance(block, ir.Block)
+        assert isinstance(inst, ir.Yield)
+        self.block = block
+        self.inst = inst
+        self.live_vars = None
+        self.weak_live_vars = None
+
+
+class GeneratorInfo(object):
+
+    def __init__(self):
+        # { index: YieldPoint }
+        self.yield_points = {}
+        # Ordered list of variable names
+        self.state_vars = []
+
+    def get_yield_points(self):
+        """
+        Return an iterable of YieldPoint instances.
+        """
+        return self.yield_points.values()
+
+
+class VariableLifetime(object):
+    """
+    For lazily building information of variable lifetime
+    """
+    def __init__(self, blocks):
+        self._blocks = blocks
+
+    @cached_property
+    def cfg(self):
+        return analysis.compute_cfg_from_blocks(self._blocks)
+
+    @cached_property
+    def usedefs(self):
+        return analysis.compute_use_defs(self._blocks)
+
+    @cached_property
+    def livemap(self):
+        return analysis.compute_live_map(self.cfg, self._blocks,
+                                         self.usedefs.usemap,
+                                         self.usedefs.defmap)
+
+    @cached_property
+    def deadmaps(self):
+        return analysis.compute_dead_maps(self.cfg, self._blocks, self.livemap,
+                                          self.usedefs.defmap)
+
+
+# other packages that define new nodes add calls for inserting dels
+# format: {type:function}
+ir_extension_insert_dels = {}
+
+
+class PostProcessor(object):
+    """
+    A post-processor for Numba IR.
+    """
+
+    def __init__(self, func_ir):
+        self.func_ir = func_ir
+
+    def run(self, emit_dels: bool = False, extend_lifetimes: bool = False):
+        """
+        Run the following passes over Numba IR:
+        - canonicalize the CFG
+        - emit explicit `del` instructions for variables
+        - compute lifetime of variables
+        - compute generator info (if function is a generator function)
+        """
+        self.func_ir.blocks = transforms.canonicalize_cfg(self.func_ir.blocks)
+        vlt = VariableLifetime(self.func_ir.blocks)
+        self.func_ir.variable_lifetime = vlt
+
+        bev = analysis.compute_live_variables(vlt.cfg, self.func_ir.blocks,
+                                              vlt.usedefs.defmap,
+                                              vlt.deadmaps.combined)
+        for offset, ir_block in self.func_ir.blocks.items():
+            self.func_ir.block_entry_vars[ir_block] = bev[offset]
+
+        if self.func_ir.is_generator:
+            self.func_ir.generator_info = GeneratorInfo()
+            self._compute_generator_info()
+        else:
+            self.func_ir.generator_info = None
+
+        # Emit del nodes, do this last as the generator info parsing generates
+        # and then strips dels as part of its analysis.
+        if emit_dels:
+            self._insert_var_dels(extend_lifetimes=extend_lifetimes)
+
+    def _populate_generator_info(self):
+        """
+        Fill `index` for the Yield instruction and create YieldPoints.
+        """
+        dct = self.func_ir.generator_info.yield_points
+        assert not dct, 'rerunning _populate_generator_info'
+        for block in self.func_ir.blocks.values():
+            for inst in block.body:
+                if isinstance(inst, ir.Assign):
+                    yieldinst = inst.value
+                    if isinstance(yieldinst, ir.Yield):
+                        index = len(dct) + 1
+                        yieldinst.index = index
+                        yp = YieldPoint(block, yieldinst)
+                        dct[yieldinst.index] = yp
+
+    def _compute_generator_info(self):
+        """
+        Compute the generator's state variables as the union of live variables
+        at all yield points.
+        """
+        # generate del info, it's used in analysis here, strip it out at the end
+        self._insert_var_dels()
+        self._populate_generator_info()
+        gi = self.func_ir.generator_info
+        for yp in gi.get_yield_points():
+            live_vars = set(self.func_ir.get_block_entry_vars(yp.block))
+            weak_live_vars = set()
+            stmts = iter(yp.block.body)
+            for stmt in stmts:
+                if isinstance(stmt, ir.Assign):
+                    if stmt.value is yp.inst:
+                        break
+                    live_vars.add(stmt.target.name)
+                elif isinstance(stmt, ir.Del):
+                    live_vars.remove(stmt.value)
+            else:
+                assert 0, "couldn't find yield point"
+            # Try to optimize out any live vars that are deleted immediately
+            # after the yield point.
+            for stmt in stmts:
+                if isinstance(stmt, ir.Del):
+                    name = stmt.value
+                    if name in live_vars:
+                        live_vars.remove(name)
+                        weak_live_vars.add(name)
+                else:
+                    break
+            yp.live_vars = live_vars
+            yp.weak_live_vars = weak_live_vars
+
+        st = set()
+        for yp in gi.get_yield_points():
+            st |= yp.live_vars
+            st |= yp.weak_live_vars
+        gi.state_vars = sorted(st)
+        self.remove_dels()
+
+    def _insert_var_dels(self, extend_lifetimes=False):
+        """
+        Insert del statements for each variable.
+        Returns a 2-tuple of (variable definition map, variable deletion map)
+        which indicates variables defined and deleted in each block.
+
+        The algorithm avoids relying on explicit knowledge on loops and
+        distinguish between variables that are defined locally vs variables that
+        come from incoming blocks.
+        We start with simple usage (variable reference) and definition (variable
+        creation) maps on each block. Propagate the liveness info to predecessor
+        blocks until it stabilize, at which point we know which variables must
+        exist before entering each block. Then, we compute the end of variable
+        lives and insert del statements accordingly. Variables are deleted after
+        the last use. Variable referenced by terminators (e.g. conditional
+        branch and return) are deleted by the successors or the caller.
+        """
+        vlt = self.func_ir.variable_lifetime
+        self._patch_var_dels(vlt.deadmaps.internal, vlt.deadmaps.escaping,
+                             extend_lifetimes=extend_lifetimes)
+
+    def _patch_var_dels(self, internal_dead_map, escaping_dead_map,
+                        extend_lifetimes=False):
+        """
+        Insert delete in each block
+        """
+        for offset, ir_block in self.func_ir.blocks.items():
+            # for each internal var, insert delete after the last use
+            internal_dead_set = internal_dead_map[offset].copy()
+            delete_pts = []
+            # for each statement in reverse order
+            for stmt in reversed(ir_block.body[:-1]):
+                # internal vars that are used here
+                live_set = set(v.name for v in stmt.list_vars())
+                dead_set = live_set & internal_dead_set
+                for T, def_func in ir_extension_insert_dels.items():
+                    if isinstance(stmt, T):
+                        done_dels = def_func(stmt, dead_set)
+                        dead_set -= done_dels
+                        internal_dead_set -= done_dels
+                # used here but not afterwards
+                delete_pts.append((stmt, dead_set))
+                internal_dead_set -= dead_set
+
+            # rewrite body and insert dels
+            body = []
+            lastloc = ir_block.loc
+            del_store = []
+            for stmt, delete_set in reversed(delete_pts):
+                # If using extended lifetimes then the Dels are all put at the
+                # block end just ahead of the terminator, so associate their
+                # location with the terminator.
+                if extend_lifetimes:
+                    lastloc = ir_block.body[-1].loc
+                else:
+                    lastloc = stmt.loc
+                # Ignore dels (assuming no user inserted deletes)
+                if not isinstance(stmt, ir.Del):
+                    body.append(stmt)
+                # note: the reverse sort is not necessary for correctness
+                #       it is just to minimize changes to test for now
+                for var_name in sorted(delete_set, reverse=True):
+                    delnode = ir.Del(var_name, loc=lastloc)
+                    if extend_lifetimes:
+                        del_store.append(delnode)
+                    else:
+                        body.append(delnode)
+            if extend_lifetimes:
+                body.extend(del_store)
+            body.append(ir_block.body[-1])  # terminator
+            ir_block.body = body
+
+            # vars to delete at the start
+            escape_dead_set = escaping_dead_map[offset]
+            for var_name in sorted(escape_dead_set):
+                ir_block.prepend(ir.Del(var_name, loc=ir_block.body[0].loc))
+
+    def remove_dels(self):
+        """
+        Strips the IR of Del nodes
+        """
+        ir_utils.remove_dels(self.func_ir.blocks)
diff --git a/shared/package-files/d8/d837ca22ba2c9d9ebc0731bb04b24d00047af7604dd12bd986506ce8cf3a9795/_variation.py b/shared/package-files/d8/d837ca22ba2c9d9ebc0731bb04b24d00047af7604dd12bd986506ce8cf3a9795/_variation.py
new file mode 100644
index 0000000000000000000000000000000000000000..9febde9ec8f1ae967634ed799f5f7ba2d817318e
--- /dev/null
+++ b/shared/package-files/d8/d837ca22ba2c9d9ebc0731bb04b24d00047af7604dd12bd986506ce8cf3a9795/_variation.py
@@ -0,0 +1,128 @@
+import numpy as np
+
+from scipy._lib._util import _get_nan
+from scipy._lib._array_api import array_namespace, xp_copysign
+
+from ._axis_nan_policy import _axis_nan_policy_factory
+
+
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x: (x,)
+)
+def variation(a, axis=0, nan_policy='propagate', ddof=0, *, keepdims=False):
+    """
+    Compute the coefficient of variation.
+
+    The coefficient of variation is the standard deviation divided by the
+    mean.  This function is equivalent to::
+
+        np.std(x, axis=axis, ddof=ddof) / np.mean(x)
+
+    The default for ``ddof`` is 0, but many definitions of the coefficient
+    of variation use the square root of the unbiased sample variance
+    for the sample standard deviation, which corresponds to ``ddof=1``.
+
+    The function does not take the absolute value of the mean of the data,
+    so the return value is negative if the mean is negative.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int or None, optional
+        Axis along which to calculate the coefficient of variation.
+        Default is 0. If None, compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains ``nan``.
+        The following options are available:
+
+          * 'propagate': return ``nan``
+          * 'raise': raise an exception
+          * 'omit': perform the calculation with ``nan`` values omitted
+
+        The default is 'propagate'.
+    ddof : int, optional
+        Gives the "Delta Degrees Of Freedom" used when computing the
+        standard deviation.  The divisor used in the calculation of the
+        standard deviation is ``N - ddof``, where ``N`` is the number of
+        elements.  `ddof` must be less than ``N``; if it isn't, the result
+        will be ``nan`` or ``inf``, depending on ``N`` and the values in
+        the array.  By default `ddof` is zero for backwards compatibility,
+        but it is recommended to use ``ddof=1`` to ensure that the sample
+        standard deviation is computed as the square root of the unbiased
+        sample variance.
+
+    Returns
+    -------
+    variation : ndarray
+        The calculated variation along the requested axis.
+
+    Notes
+    -----
+    There are several edge cases that are handled without generating a
+    warning:
+
+    * If both the mean and the standard deviation are zero, ``nan``
+      is returned.
+    * If the mean is zero and the standard deviation is nonzero, ``inf``
+      is returned.
+    * If the input has length zero (either because the array has zero
+      length, or all the input values are ``nan`` and ``nan_policy`` is
+      ``'omit'``), ``nan`` is returned.
+    * If the input contains ``inf``, ``nan`` is returned.
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import variation
+    >>> variation([1, 2, 3, 4, 5], ddof=1)
+    0.5270462766947299
+
+    Compute the variation along a given dimension of an array that contains
+    a few ``nan`` values:
+
+    >>> x = np.array([[  10.0, np.nan, 11.0, 19.0, 23.0, 29.0, 98.0],
+    ...               [  29.0,   30.0, 32.0, 33.0, 35.0, 56.0, 57.0],
+    ...               [np.nan, np.nan, 12.0, 13.0, 16.0, 16.0, 17.0]])
+    >>> variation(x, axis=1, ddof=1, nan_policy='omit')
+    array([1.05109361, 0.31428986, 0.146483  ])
+
+    """
+    xp = array_namespace(a)
+    a = xp.asarray(a)
+    # `nan_policy` and `keepdims` are handled by `_axis_nan_policy`
+    # `axis=None` is only handled for NumPy backend
+    if axis is None:
+        a = xp.reshape(a, (-1,))
+        axis = 0
+
+    n = a.shape[axis]
+    NaN = _get_nan(a)
+
+    if a.size == 0 or ddof > n:
+        # Handle as a special case to avoid spurious warnings.
+        # The return values, if any, are all nan.
+        shp = list(a.shape)
+        shp.pop(axis)
+        result = xp.full(shp, fill_value=NaN)
+        return result[()] if result.ndim == 0 else result
+
+    mean_a = xp.mean(a, axis=axis)
+
+    if ddof == n:
+        # Another special case.  Result is either inf or nan.
+        std_a = xp.std(a, axis=axis, correction=0)
+        result = xp.where(std_a > 0, xp_copysign(xp.asarray(xp.inf), mean_a), NaN)
+        return result[()] if result.ndim == 0 else result
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        std_a = xp.std(a, axis=axis, correction=ddof)
+        result = std_a / mean_a
+
+    return result[()] if result.ndim == 0 else result
diff --git a/shared/package-files/d8/d83c526360b062b2b1fee4f440a1e8af399e91c7fa6dc296c50e65669e8f45ca/constants.pyi b/shared/package-files/d8/d83c526360b062b2b1fee4f440a1e8af399e91c7fa6dc296c50e65669e8f45ca/constants.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7c26cd619bb9e289b1c6512d41c44cb4d63bb032
--- /dev/null
+++ b/shared/package-files/d8/d83c526360b062b2b1fee4f440a1e8af399e91c7fa6dc296c50e65669e8f45ca/constants.pyi
@@ -0,0 +1,166 @@
+from typing import Any
+
+BATCH_WRITE_ITEM: str
+DESCRIBE_TABLE: str
+BATCH_GET_ITEM: str
+CREATE_TABLE: str
+UPDATE_TABLE: str
+DELETE_TABLE: str
+LIST_TABLES: str
+UPDATE_ITEM: str
+DELETE_ITEM: str
+GET_ITEM: str
+PUT_ITEM: str
+QUERY: str
+SCAN: str
+GLOBAL_SECONDARY_INDEX_UPDATES: str
+RETURN_ITEM_COLL_METRICS: str
+EXCLUSIVE_START_TABLE_NAME: str
+RETURN_CONSUMED_CAPACITY: str
+COMPARISON_OPERATOR: str
+SCAN_INDEX_FORWARD: str
+ATTR_DEFINITIONS: str
+ATTR_VALUE_LIST: str
+TABLE_DESCRIPTION: str
+UNPROCESSED_KEYS: str
+UNPROCESSED_ITEMS: str
+CONSISTENT_READ: str
+DELETE_REQUEST: str
+RETURN_VALUES: str
+REQUEST_ITEMS: str
+ATTRS_TO_GET: str
+ATTR_UPDATES: str
+TABLE_STATUS: str
+SCAN_FILTER: str
+TABLE_NAME: str
+KEY_SCHEMA: str
+ATTR_NAME: str
+ATTR_TYPE: str
+ITEM_COUNT: str
+CAMEL_COUNT: str
+PUT_REQUEST: str
+INDEX_NAME: str
+ATTRIBUTES: str
+TABLE_KEY: str
+RESPONSES: str
+RANGE_KEY: str
+KEY_TYPE: str
+ACTION: str
+UPDATE: str
+EXISTS: str
+SELECT: str
+ACTIVE: str
+LIMIT: str
+ITEMS: str
+ITEM: str
+KEYS: str
+UTC: str
+KEY: str
+DEFAULT_ENCODING: str
+DEFAULT_REGION: str
+DATETIME_FORMAT: str
+SERVICE_NAME: str
+HTTP_OK: int
+HTTP_BAD_REQUEST: int
+PROVISIONED_THROUGHPUT: str
+READ_CAPACITY_UNITS: str
+WRITE_CAPACITY_UNITS: str
+STRING_SHORT: str
+STRING_SET_SHORT: str
+NUMBER_SHORT: str
+NUMBER_SET_SHORT: str
+BINARY_SHORT: str
+BINARY_SET_SHORT: str
+MAP_SHORT: str
+LIST_SHORT: str
+BOOLEAN: str
+BOOLEAN_SHORT: str
+STRING: str
+STRING_SET: str
+NUMBER: str
+NUMBER_SET: str
+BINARY: str
+BINARY_SET: str
+MAP: str
+LIST: str
+SHORT_ATTR_TYPES: Any
+ATTR_TYPE_MAP: Any
+LOCAL_SECONDARY_INDEX: str
+LOCAL_SECONDARY_INDEXES: str
+GLOBAL_SECONDARY_INDEX: str
+GLOBAL_SECONDARY_INDEXES: str
+PROJECTION: str
+PROJECTION_TYPE: str
+NON_KEY_ATTRIBUTES: str
+KEYS_ONLY: str
+ALL: str
+INCLUDE: str
+STREAM_VIEW_TYPE: str
+STREAM_SPECIFICATION: str
+STREAM_ENABLED: str
+STREAM_NEW_IMAGE: str
+STREAM_OLD_IMAGE: str
+STREAM_NEW_AND_OLD_IMAGE: str
+STREAM_KEYS_ONLY: str
+EXCLUSIVE_START_KEY: str
+LAST_EVALUATED_KEY: str
+QUERY_FILTER: str
+BEGINS_WITH: str
+BETWEEN: str
+EQ: str
+NE: str
+LE: str
+LT: str
+GE: str
+GT: str
+IN: str
+KEY_CONDITIONS: str
+COMPARISON_OPERATOR_VALUES: Any
+QUERY_OPERATOR_MAP: Any
+NOT_NULL: str
+NULL: str
+CONTAINS: str
+NOT_CONTAINS: str
+ALL_ATTRIBUTES: str
+ALL_PROJECTED_ATTRIBUTES: str
+SPECIFIC_ATTRIBUTES: str
+COUNT: str
+SELECT_VALUES: Any
+SCAN_OPERATOR_MAP: Any
+QUERY_FILTER_OPERATOR_MAP: Any
+DELETE_FILTER_OPERATOR_MAP: Any
+UPDATE_FILTER_OPERATOR_MAP: Any
+PUT_FILTER_OPERATOR_MAP: Any
+SEGMENT: str
+TOTAL_SEGMENTS: str
+SCAN_FILTER_VALUES: Any
+QUERY_FILTER_VALUES: Any
+DELETE_FILTER_VALUES: Any
+VALUE: str
+EXPECTED: str
+CONSUMED_CAPACITY: str
+CAPACITY_UNITS: str
+INDEXES: str
+TOTAL: str
+NONE: str
+RETURN_CONSUMED_CAPACITY_VALUES: Any
+SIZE: str
+RETURN_ITEM_COLL_METRICS_VALUES: Any
+ALL_OLD: str
+UPDATED_OLD: str
+ALL_NEW: str
+UPDATED_NEW: str
+RETURN_VALUES_VALUES: Any
+PUT: str
+DELETE: str
+ADD: str
+ATTR_UPDATE_ACTIONS: Any
+BATCH_GET_PAGE_LIMIT: int
+BATCH_WRITE_PAGE_LIMIT: int
+META_CLASS_NAME: str
+REGION: str
+HOST: str
+CONDITIONAL_OPERATOR: str
+AND: str
+OR: str
+CONDITIONAL_OPERATORS: Any
diff --git a/shared/package-files/d8/d83fbf749f65e2e002fba7822b198827157d91bd700068be68d50aaffb22f256/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt b/shared/package-files/d8/d83fbf749f65e2e002fba7822b198827157d91bd700068be68d50aaffb22f256/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4d21c392989dd6c9716a2e82245127b24d055e76
--- /dev/null
+++ b/shared/package-files/d8/d83fbf749f65e2e002fba7822b198827157d91bd700068be68d50aaffb22f256/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt
@@ -0,0 +1,13 @@
+*** Mon, 25 Jul 2016 10:14:36 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "R" do not Granger-cause "Dp"
+
+Test statistic l = 2.8553
+pval-F( l; 4, 178) = 0.0251 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "R" and "Dp"
+
+Test statistic: c = 0.9519
+pval-Chi( c; 1) = 0.3292 
+
diff --git a/shared/package-files/d8/d84a71bc2fb4fdd039f0c05f19070bc87ce966029c31ffd6f5cef70bf32a9e89/gh25286.f90 b/shared/package-files/d8/d84a71bc2fb4fdd039f0c05f19070bc87ce966029c31ffd6f5cef70bf32a9e89/gh25286.f90
new file mode 100644
index 0000000000000000000000000000000000000000..db1c7100d2ab812de5d212c1bbd255cf2ae60be3
--- /dev/null
+++ b/shared/package-files/d8/d84a71bc2fb4fdd039f0c05f19070bc87ce966029c31ffd6f5cef70bf32a9e89/gh25286.f90
@@ -0,0 +1,14 @@
+subroutine charint(trans, info)
+    character, intent(in) :: trans
+    integer, intent(out) :: info
+    if (trans == 'N') then
+        info = 1
+    else if (trans == 'T') then
+        info = 2
+    else if (trans == 'C') then
+        info = 3
+    else
+        info = -1
+    end if
+
+end subroutine charint
diff --git a/shared/package-files/d8/d84f016e967b4d3b9710c4b3f90dc67889c7503cdf14a87ab4c324b47ce2b5cf/orgqr.h b/shared/package-files/d8/d84f016e967b4d3b9710c4b3f90dc67889c7503cdf14a87ab4c324b47ce2b5cf/orgqr.h
new file mode 100644
index 0000000000000000000000000000000000000000..19c0e2f7e6b26ddf93878d3247b44a8865bc7fef
--- /dev/null
+++ b/shared/package-files/d8/d84f016e967b4d3b9710c4b3f90dc67889c7503cdf14a87ab4c324b47ce2b5cf/orgqr.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/orgqr_ops.h>
+
+namespace at {
+
+
+// aten::orgqr(Tensor self, Tensor input2) -> Tensor
+inline at::Tensor orgqr(const at::Tensor & self, const at::Tensor & input2) {
+    return at::_ops::orgqr::call(self, input2);
+}
+
+// aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & orgqr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & input2) {
+    return at::_ops::orgqr_out::call(self, input2, out);
+}
+// aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & orgqr_outf(const at::Tensor & self, const at::Tensor & input2, at::Tensor & out) {
+    return at::_ops::orgqr_out::call(self, input2, out);
+}
+
+}
diff --git a/shared/package-files/d8/d85027e403be29f5a591f4f170c274cfd5c3af676616308869fb0b26f682ca24/load.py b/shared/package-files/d8/d85027e403be29f5a591f4f170c274cfd5c3af676616308869fb0b26f682ca24/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..c27c5fde7d4b1c5744ccd5025f580b0732aecdd3
--- /dev/null
+++ b/shared/package-files/d8/d85027e403be29f5a591f4f170c274cfd5c3af676616308869fb0b26f682ca24/load.py
@@ -0,0 +1,197 @@
+"""Load centrality."""
+from operator import itemgetter
+
+import networkx as nx
+
+__all__ = ["load_centrality", "edge_load_centrality"]
+
+
+def newman_betweenness_centrality(G, v=None, cutoff=None, normalized=True, weight=None):
+    """Compute load centrality for nodes.
+
+    The load centrality of a node is the fraction of all shortest
+    paths that pass through that node.
+
+    Parameters
+    ----------
+    G : graph
+      A networkx graph.
+
+    normalized : bool, optional (default=True)
+      If True the betweenness values are normalized by b=b/(n-1)(n-2) where
+      n is the number of nodes in G.
+
+    weight : None or string, optional (default=None)
+      If None, edge weights are ignored.
+      Otherwise holds the name of the edge attribute used as weight.
+      The weight of an edge is treated as the length or distance between the two sides.
+
+    cutoff : bool, optional (default=None)
+      If specified, only consider paths of length <= cutoff.
+
+    Returns
+    -------
+    nodes : dictionary
+       Dictionary of nodes with centrality as the value.
+
+    See Also
+    --------
+    betweenness_centrality
+
+    Notes
+    -----
+    Load centrality is slightly different than betweenness. It was originally
+    introduced by [2]_. For this load algorithm see [1]_.
+
+    References
+    ----------
+    .. [1] Mark E. J. Newman:
+       Scientific collaboration networks. II.
+       Shortest paths, weighted networks, and centrality.
+       Physical Review E 64, 016132, 2001.
+       http://journals.aps.org/pre/abstract/10.1103/PhysRevE.64.016132
+    .. [2] Kwang-Il Goh, Byungnam Kahng and Doochul Kim
+       Universal behavior of Load Distribution in Scale-Free Networks.
+       Physical Review Letters 87(27):1–4, 2001.
+       https://doi.org/10.1103/PhysRevLett.87.278701
+    """
+    if v is not None:  # only one node
+        betweenness = 0.0
+        for source in G:
+            ubetween = _node_betweenness(G, source, cutoff, False, weight)
+            betweenness += ubetween[v] if v in ubetween else 0
+        if normalized:
+            order = G.order()
+            if order <= 2:
+                return betweenness  # no normalization b=0 for all nodes
+            betweenness *= 1.0 / ((order - 1) * (order - 2))
+    else:
+        betweenness = {}.fromkeys(G, 0.0)
+        for source in betweenness:
+            ubetween = _node_betweenness(G, source, cutoff, False, weight)
+            for vk in ubetween:
+                betweenness[vk] += ubetween[vk]
+        if normalized:
+            order = G.order()
+            if order <= 2:
+                return betweenness  # no normalization b=0 for all nodes
+            scale = 1.0 / ((order - 1) * (order - 2))
+            for v in betweenness:
+                betweenness[v] *= scale
+    return betweenness  # all nodes
+
+
+def _node_betweenness(G, source, cutoff=False, normalized=True, weight=None):
+    """Node betweenness_centrality helper:
+
+    See betweenness_centrality for what you probably want.
+    This actually computes "load" and not betweenness.
+    See https://networkx.lanl.gov/ticket/103
+
+    This calculates the load of each node for paths from a single source.
+    (The fraction of number of shortests paths from source that go
+    through each node.)
+
+    To get the load for a node you need to do all-pairs shortest paths.
+
+    If weight is not None then use Dijkstra for finding shortest paths.
+    """
+    # get the predecessor and path length data
+    if weight is None:
+        (pred, length) = nx.predecessor(G, source, cutoff=cutoff, return_seen=True)
+    else:
+        (pred, length) = nx.dijkstra_predecessor_and_distance(G, source, cutoff, weight)
+
+    # order the nodes by path length
+    onodes = [(l, vert) for (vert, l) in length.items()]
+    onodes.sort()
+    onodes[:] = [vert for (l, vert) in onodes if l > 0]
+
+    # initialize betweenness
+    between = {}.fromkeys(length, 1.0)
+
+    while onodes:
+        v = onodes.pop()
+        if v in pred:
+            num_paths = len(pred[v])  # Discount betweenness if more than
+            for x in pred[v]:  # one shortest path.
+                if x == source:  # stop if hit source because all remaining v
+                    break  # also have pred[v]==[source]
+                between[x] += between[v] / num_paths
+    #  remove source
+    for v in between:
+        between[v] -= 1
+    # rescale to be between 0 and 1
+    if normalized:
+        l = len(between)
+        if l > 2:
+            # scale by 1/the number of possible paths
+            scale = 1 / ((l - 1) * (l - 2))
+            for v in between:
+                between[v] *= scale
+    return between
+
+
+load_centrality = newman_betweenness_centrality
+
+
+def edge_load_centrality(G, cutoff=False):
+    """Compute edge load.
+
+    WARNING: This concept of edge load has not been analysed
+    or discussed outside of NetworkX that we know of.
+    It is based loosely on load_centrality in the sense that
+    it counts the number of shortest paths which cross each edge.
+    This function is for demonstration and testing purposes.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    cutoff : bool, optional (default=False)
+        If specified, only consider paths of length <= cutoff.
+
+    Returns
+    -------
+    A dict keyed by edge 2-tuple to the number of shortest paths
+    which use that edge. Where more than one path is shortest
+    the count is divided equally among paths.
+    """
+    betweenness = {}
+    for u, v in G.edges():
+        betweenness[(u, v)] = 0.0
+        betweenness[(v, u)] = 0.0
+
+    for source in G:
+        ubetween = _edge_betweenness(G, source, cutoff=cutoff)
+        for e, ubetweenv in ubetween.items():
+            betweenness[e] += ubetweenv  # cumulative total
+    return betweenness
+
+
+def _edge_betweenness(G, source, nodes=None, cutoff=False):
+    """Edge betweenness helper."""
+    # get the predecessor data
+    (pred, length) = nx.predecessor(G, source, cutoff=cutoff, return_seen=True)
+    # order the nodes by path length
+    onodes = [n for n, d in sorted(length.items(), key=itemgetter(1))]
+    # initialize betweenness, doesn't account for any edge weights
+    between = {}
+    for u, v in G.edges(nodes):
+        between[(u, v)] = 1.0
+        between[(v, u)] = 1.0
+
+    while onodes:  # work through all paths
+        v = onodes.pop()
+        if v in pred:
+            # Discount betweenness if more than one shortest path.
+            num_paths = len(pred[v])
+            for w in pred[v]:
+                if w in pred:
+                    # Discount betweenness, mult path
+                    num_paths = len(pred[w])
+                    for x in pred[w]:
+                        between[(w, x)] += between[(v, w)] / num_paths
+                        between[(x, w)] += between[(w, v)] / num_paths
+    return between
diff --git a/shared/package-files/d8/d850a9557ad223588a970e78356dfe6f8b02970646bde55c1973fcca25340739/cubic_spline.py b/shared/package-files/d8/d850a9557ad223588a970e78356dfe6f8b02970646bde55c1973fcca25340739/cubic_spline.py
new file mode 100644
index 0000000000000000000000000000000000000000..476d7c5e0b261a1dc9720ea8562479de98fe4ae2
--- /dev/null
+++ b/shared/package-files/d8/d850a9557ad223588a970e78356dfe6f8b02970646bde55c1973fcca25340739/cubic_spline.py
@@ -0,0 +1,605 @@
+"""
+The code in this module is derived from code in the `patsy` package.
+The original license of the code is as follows:
+
+    Copyright (C) 2014 GDF Suez, http://www.gdfsuez.com/
+    Copyright (C) 2011-2012, Patsy Developers. All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+       1. Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+
+       2. Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from functools import partial
+from typing import Literal, cast
+
+import numpy
+import pandas
+
+from formulaic.materializers.types import FactorValues
+from formulaic.transforms.basis_spline import SplineExtrapolation
+from formulaic.utils.stateful_transforms import stateful_transform
+
+
+class ExtrapolationError(ValueError):
+    pass
+
+
+def _find_knots_lower_bounds(x: numpy.ndarray, knots: numpy.ndarray) -> numpy.ndarray:
+    """
+    Find knot lower bounds for given values.
+
+    Returns an array of indices ``I`` such that
+    ``0 <= I[i] <= knots.size - 2`` for all ``i``
+    and
+    ``knots[I[i]] < x[i] <= knots[I[i] + 1]`` if
+    ``numpy.min(knots) < x[i] <= numpy.max(knots)``,
+    ``I[i] = 0`` if ``x[i] <= numpy.min(knots)``
+    ``I[i] = knots.size - 2`` if ``numpy.max(knots) < x[i]``
+
+    Args:
+        x: The 1-d array values whose knots lower bounds are to be found.
+        knots: The 1-d array knots used for cubic spline parametrization,
+            must be sorted in ascending order.
+    """
+    lb = numpy.searchsorted(knots, x) - 1
+
+    # I[i] = 0 for x[i] <= numpy.min(knots)
+    lb[lb == -1] = 0
+
+    # I[i] = knots.size - 2 for x[i] > numpy.max(knots)
+    lb[lb == knots.size - 1] = knots.size - 2
+
+    return lb
+
+
+def _compute_base_functions(
+    x: numpy.ndarray, knots: numpy.ndarray
+) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]:
+    """
+    Compute the base functions used for building the cubic splines basis.
+
+    This function computes the 4 base functions ajm, ajp, cjm, and cjp; and the
+    1-d array of knot lower bounds indices corresponding to the given `x`
+    values.
+
+    Args:
+        x: The 1-d array values for which base functions should be computed.
+        knots: The 1-d array knots used for cubic spline parametrization,
+            must be sorted in ascending order.
+
+    Note: See 'Generalized Additive Models', Simon N. Wood, 2006, p. 146
+        and for the special treatment of ``x`` values outside ``knots`` range
+        see 'mgcv' source code, file 'mgcv.c', function 'crspl()', l.249
+    """
+    j = _find_knots_lower_bounds(x, knots)
+
+    h = knots[1:] - knots[:-1]
+    hj = h[j]
+    xj1_x = cast(numpy.ndarray, knots[j + 1] - x)
+    x_xj = cast(numpy.ndarray, x - knots[j])
+
+    ajm = xj1_x / hj
+    ajp = x_xj / hj
+
+    cjm_3 = xj1_x * xj1_x * xj1_x / (6.0 * hj)
+    cjm_3[x > numpy.max(knots)] = 0.0
+    cjm_1 = hj * xj1_x / 6.0
+    cjm = cjm_3 - cjm_1
+
+    cjp_3 = x_xj * x_xj * x_xj / (6.0 * hj)
+    cjp_3[x < numpy.min(knots)] = 0.0
+    cjp_1 = hj * x_xj / 6.0
+    cjp = cjp_3 - cjp_1
+
+    return ajm, ajp, cjm, cjp, j
+
+
+def _get_cyclic_f(knots: numpy.ndarray) -> numpy.ndarray:
+    """
+    Generate the mapping of cyclic cubic spline values to 2nd derivatives as a
+    2-d array.
+
+    Args:
+        knots: The 1-d array knots used for cubic spline parametrization,
+            must be sorted in ascending order.
+
+    Note: See 'Generalized Additive Models', Simon N. Wood, 2006, pp 146-147
+    """
+    h = knots[1:] - knots[:-1]
+    n = knots.size - 1
+    b = numpy.zeros((n, n))
+    d = numpy.zeros((n, n))
+
+    b[0, 0] = (h[n - 1] + h[0]) / 3.0
+    b[0, n - 1] = h[n - 1] / 6.0
+    b[n - 1, 0] = h[n - 1] / 6.0
+
+    d[0, 0] = -1.0 / h[0] - 1.0 / h[n - 1]
+    d[0, n - 1] = 1.0 / h[n - 1]
+    d[n - 1, 0] = 1.0 / h[n - 1]
+
+    for i in range(1, n):
+        b[i, i] = (h[i - 1] + h[i]) / 3.0
+        b[i, i - 1] = h[i - 1] / 6.0
+        b[i - 1, i] = h[i - 1] / 6.0
+
+        d[i, i] = -1.0 / h[i - 1] - 1.0 / h[i]
+        d[i, i - 1] = 1.0 / h[i - 1]
+        d[i - 1, i] = 1.0 / h[i - 1]
+
+    return numpy.linalg.solve(b, d)
+
+
+def _get_natural_f(knots: numpy.ndarray) -> numpy.ndarray:
+    """
+    Generate the mapping of natural cubic spline values to 2nd derivatives as a
+    2-d array.
+
+    Args:
+        knots: The 1-d array knots used for cubic spline parametrization,
+            must be sorted in ascending order.
+
+    Note: See 'Generalized Additive Models', Simon N. Wood, 2006, pp 145-146
+    """
+    from scipy import linalg
+
+    h = knots[1:] - knots[:-1]
+    diag = (h[:-1] + h[1:]) / 3.0
+    ul_diag = h[1:-1] / 6.0
+    banded_b = numpy.array([numpy.r_[0.0, ul_diag], diag, numpy.r_[ul_diag, 0.0]])
+    d = numpy.zeros((knots.size - 2, knots.size))
+    for i in range(knots.size - 2):
+        d[i, i] = 1.0 / h[i]
+        d[i, i + 2] = 1.0 / h[i + 1]
+        d[i, i + 1] = -d[i, i] - d[i, i + 2]
+
+    fm = linalg.solve_banded((1, 1), banded_b, d)
+
+    return numpy.vstack([numpy.zeros(knots.size), fm, numpy.zeros(knots.size)])
+
+
+def _map_cyclic(x: numpy.ndarray, lbound: float, ubound: float) -> numpy.ndarray:
+    """
+    Map values into the interval [lbound, ubound] in a cyclic fashion.
+
+    Args:
+        x: The 1-d array values to be mapped.
+        lbound: The lower bound of the interval.
+        ubound: The upper bound of the interval.
+    """
+    if lbound >= ubound:
+        raise ValueError(
+            f"Invalid argument: lbound ({lbound}) should be "
+            f"less than ubound ({ubound})."
+        )
+
+    x = numpy.copy(x)
+    x[x > ubound] = lbound + (x[x > ubound] - ubound) % (ubound - lbound)
+    x[x < lbound] = ubound - (lbound - x[x < lbound]) % (ubound - lbound)
+
+    return x
+
+
+def _get_free_cubic_spline_matrix(
+    x: numpy.ndarray, knots: numpy.ndarray, cyclic: bool = False
+) -> numpy.ndarray:
+    """
+    Build an unconstrained cubic regression spline design matrix.
+
+    The returned design matrix has dimensions `len(x) * n` for a cubic
+    regression spline smoother where
+     - `n = len(knots)` for natural CRS
+     - `n = len(knots) - 1` for cyclic CRS
+
+    Args:
+        x: The 1-d array values.
+        knots: The 1-d array knots used for cubic spline parameterization,
+            must be sorted in ascending order.
+        cyclic: Indicates whether used cubic regression splines should
+            be cyclic or not.
+
+    Note: See 'Generalized Additive Models', Simon N. Wood, 2006, p. 145
+    """
+    n = knots.size
+    if cyclic:
+        x = _map_cyclic(x, min(knots), max(knots))
+        n -= 1
+
+    ajm, ajp, cjm, cjp, j = _compute_base_functions(x, knots)
+
+    j1 = j + 1
+    if cyclic:
+        j1[j1 == n] = 0
+
+    i = numpy.identity(n)
+
+    if cyclic:
+        f = _get_cyclic_f(knots)
+    else:
+        f = _get_natural_f(knots)
+
+    mat_transposed = (
+        ajm * i[j, :].T + ajp * i[j1, :].T + cjm * f[j, :].T + cjp * f[j1, :].T
+    )
+
+    return mat_transposed.T
+
+
+def _get_all_sorted_knots(
+    x: numpy.ndarray,
+    lower_bound: float,
+    upper_bound: float,
+    n_inner_knots: int | None = None,
+    inner_knots: numpy.ndarray | None = None,
+) -> numpy.ndarray:
+    """
+    Get all knots locations with lower and upper exterior knots included
+    (resulting in an array of size `n_inner_knots + 2`).
+
+    If needed, inner knots are computed as equally spaced quantiles of the
+    input data falling between given lower and upper bounds.
+
+    Args:
+        x: The 1-d array data values.
+        lower_bound: The lower exterior knot location.
+        upper_bound: The upper exterior knot location.
+        n_inner_knots: Number of inner knots to compute.
+        inner_knots: Provided inner knots if any.
+    """
+    if upper_bound < lower_bound:
+        raise ValueError(f"lower_bound > upper_bound ({lower_bound} > {upper_bound})")
+
+    if inner_knots is None and n_inner_knots is not None:
+        if n_inner_knots < 0:
+            raise ValueError(
+                f"Invalid requested number of inner knots: {n_inner_knots}"
+            )
+
+        x = x[(lower_bound <= x) & (x <= upper_bound)]
+        x = numpy.unique(x)
+
+        if x.size != 0:
+            inner_knots_q = numpy.linspace(0, 100, n_inner_knots + 2)[1:-1]
+            # .tolist() is necessary to work around a bug in numpy 1.8
+            inner_knots = numpy.asarray(numpy.nanpercentile(x, inner_knots_q.tolist()))
+        elif n_inner_knots == 0:
+            inner_knots = numpy.array([])
+        else:
+            raise ValueError(
+                f"No data values between lower_bound ({lower_bound}) and "
+                f"upper_bound ({upper_bound}): cannot compute requested "
+                f"{n_inner_knots} inner knot(s)."
+            )
+    elif inner_knots is not None:
+        inner_knots = cast(numpy.ndarray, numpy.unique(inner_knots))
+        if n_inner_knots is not None and n_inner_knots != inner_knots.size:
+            raise ValueError(
+                f"Needed number of inner knots={n_inner_knots} does not match "
+                f"provided number of inner knots={inner_knots.size}."
+            )
+        n_inner_knots = inner_knots.size
+        if numpy.any(inner_knots < lower_bound):
+            raise ValueError(
+                f"Some knot values ({inner_knots[inner_knots < lower_bound]}) fall "
+                f"below lower bound ({lower_bound})."
+            )
+        if numpy.any(inner_knots > upper_bound):
+            raise ValueError(
+                f"Some knot values ({inner_knots[inner_knots > upper_bound]}) fall "
+                f"above upper bound ({upper_bound})."
+            )
+    else:
+        raise ValueError("Must specify either 'n_inner_knots' or 'inner_knots'.")
+
+    all_knots: numpy.ndarray = numpy.concatenate(
+        ([lower_bound, upper_bound], inner_knots)
+    )
+    all_knots = numpy.unique(all_knots)
+    if all_knots.size != n_inner_knots + 2:
+        raise ValueError(
+            f"Unable to compute n_inner_knots({n_inner_knots}) + 2 distinct "
+            f"knots: {x.size} data value(s) found between "
+            f"lower_bound({lower_bound}) and upper_bound({upper_bound})."
+        )
+
+    return all_knots
+
+
+def _get_centering_constraint_from_matrix(matrix: numpy.ndarray) -> numpy.ndarray:
+    """
+    Compute the centering constraint from the given design matrix.
+
+    We want to ensure that if `b` is the array of parameters, our
+    model is centered, ie `numpy.mean(numpy.dot(matrix, b))` is zero.
+    We can rewrite this as `numpy.dot(c, b)` being zero with `c` a 1-row
+    constraint matrix containing the mean of each column of `matrix`.
+
+    Args:
+        matrix: The 2-d array design matrix.
+    """
+    return matrix.mean(axis=0).reshape((1, matrix.shape[1]))
+
+
+def _absorb_constraints(
+    matrix: numpy.ndarray, constraints: numpy.ndarray
+) -> numpy.ndarray:
+    """
+    Absorb model parameters constraints into a new design matrix.
+
+    Args:
+        matrix: The (2-d array) initial design matrix.
+        constraints: The 2-d array defining initial model parameters
+     (``betas``) constraints (``numpy.dot(constraints, betas) = 0``).
+    """
+    m = constraints.shape[0]
+    q, r = numpy.linalg.qr(numpy.transpose(constraints), mode="complete")
+
+    return numpy.dot(matrix, q[:, m:])
+
+
+def _get_cubic_spline_matrix(
+    x: numpy.ndarray,
+    knots: numpy.ndarray,
+    constraints: numpy.ndarray | None = None,
+    cyclic: bool = False,
+) -> numpy.ndarray:
+    """
+    Build a constrained cubic regression spline design matrix with dimensions
+    `len(x) * n` where:
+     - `n = len(knots) - nrows(constraints)` for natural CRS
+     - `n = len(knots) - nrows(constraints) - 1` for cyclic CRS
+    for a cubic regression spline smoother.
+
+    Args:
+        x: The 1-d array values.
+        knots: The 1-d array knots used for cubic spline parametrization,
+            must be sorted in ascending order.
+        constraints: The 2-d array defining model parameters (``betas``)
+            constraints (``numpy.dot(constraints, betas) = 0``).
+        cyclic: Indicates whether used cubic regression splines should
+            be cyclic or not. Default is ``False``.
+    """
+    mat = _get_free_cubic_spline_matrix(x, knots, cyclic)
+    if constraints is not None:
+        mat = _absorb_constraints(mat, constraints)
+
+    return mat
+
+
+def parse_bounds(
+    x: pandas.Series | numpy.ndarray,
+    default: float | None,
+    edge: Literal["lower", "upper"],
+    state: dict,
+) -> float:
+    """
+    Extract the lower or upper bound from either the value passed, the data, or
+    the state dictionary.
+
+    Args:
+        x: The 1-d array values for which bounds should be computed.
+        default: The default value to use if the bound is not in the state.
+        edge: "lower" or "upper" for lower and upper bounds.
+        state: The state dictionary. Bounds are read from the state if
+            present, or set in the state if not.
+    """
+    key = f"{edge}_bound"
+    if key in state:
+        bound = float(state[key])
+    elif default is not None:
+        bound = state[key] = default
+    else:
+        func = numpy.nanmin if edge == "lower" else numpy.nanmax
+        bound = state[key] = float(func(x))
+    return bound
+
+
+def cubic_spline(  # pylint: disable=dangerous-default-value  # always replaced by stateful-transform
+    x: pandas.Series | numpy.ndarray,
+    df: int | None = None,
+    knots: Iterable[float] | None = None,
+    lower_bound: float | None = None,
+    upper_bound: float | None = None,
+    constraints: numpy.ndarray | Literal["center"] | None = None,
+    cyclic: bool = False,
+    extrapolation: str | SplineExtrapolation = "extend",
+    _state: dict = {},
+) -> FactorValues[dict]:
+    """
+    Evaluates cubic spline vectors for given inputs `x`, satisfying nominated
+    constraints. This implementation is compatible with natural (`cr`, `cc`) and
+    cyclic (`cs`) from R's `mgcv` package.
+
+    Args:
+        x: The vector for which the B-Spline basis should be computed.
+        df: The number of degrees of freedom to use for this spline. If
+            specified, `knots` will be automatically generated such that they
+            are `df` - `degree` (minus one if `include_intercept` is True)
+            equally spaced quantiles. You cannot specify both `df` and `knots`.
+        knots: The internal breakpoints of the B-Spline. If not specified, they
+            default to the empty list (unless `df` is specified), in which case
+            the ordinary polynomial (Bezier) basis is generated.
+        lower_bound: The lower bound for the domain for the B-Spline basis. If
+            not specified this is determined from `x`.
+        upper_bound: The upper bound for the domain for the B-Spline basis. If
+            not specified this is determined from `x`.
+        constraints: Either a 2-d array defining general linear constraints
+            (that is ``np.dot(constraints, betas)`` is zero, where ``betas`` denotes
+             the array of *initial* parameters, corresponding to the *initial*
+            unconstrained design matrix), or the string ``'center'`` indicating
+            that we should apply a centering constraint (this constraint
+            will be computed from the input data, remembered and re-used for
+            prediction from the fitted model). The constraints are absorbed
+            in the resulting design matrix which means that the model is
+            actually rewritten in terms of *unconstrained* parameters.
+        cyclic: Whether the spline should be constrained such that smoothness
+            conditions hold with cyclic boundary conditions.
+        extrapolation: Selects how extrapolation should be performed when values
+            in `x` extend beyond the lower and upper bounds. Valid values are:
+            - 'raise': Raises a `ValueError` if there are any values in `x`
+              outside the B-Spline domain.
+            - 'clip': Any values above/below the domain are set to the
+              upper/lower bounds.
+            - 'na': Any values outside of bounds are set to `numpy.nan`.
+            - 'zero': Any values outside of bounds are set to `0`.
+            - 'extend': Any values outside of bounds are computed by extending
+              the polynomials of the B-Spline (this is the same as the default
+              in R).
+
+    Returns:
+        A dictionary representing the encoded vectors ready for ingestion
+        by materializers (wrapped in a `FactorValues` instance providing
+        relevant metadata).
+    """
+    # Prepare and check arguments
+    if df is not None and knots is not None:
+        raise ValueError("You cannot specify both `df` and `knots`.")
+
+    # Check and reformat x
+    x = numpy.atleast_1d(x)
+    if x.ndim == 2 and x.shape[1] == 1:
+        x = x[:, 0]
+    if x.ndim > 1:
+        raise ValueError("Input to cubic_spline must be 1-d, or a 2-d column vector.")
+
+    lower_bound = parse_bounds(x, lower_bound, "lower", _state)
+    upper_bound = parse_bounds(x, upper_bound, "upper", _state)
+
+    if "constraints" in _state:
+        constraints = _state["constraints"]
+    else:
+        _state["constraints"] = constraints
+
+    if "cyclic" in _state:
+        cyclic = _state["cyclic"]
+    else:
+        _state["cyclic"] = cyclic
+
+    extrapolation = SplineExtrapolation(extrapolation)
+
+    # Check extrapolations and adjust x if necessary
+    # SplineExtrapolation.EXTEND is the natural default, so no need to do anything
+    knots_x = x
+    below_lower = x < lower_bound
+    above_upper = x > upper_bound
+    if extrapolation is SplineExtrapolation.RAISE and numpy.any(
+        below_lower | above_upper
+    ):
+        raise ExtrapolationError(
+            "Some field values extend beyond upper and/or lower bounds, which can "
+            "result in ill-conditioned bases. Pass a value for `extrapolation` to "
+            "control how extrapolation should be performed."
+        )
+    elif extrapolation in (
+        SplineExtrapolation.CLIP,
+        SplineExtrapolation.ZERO,
+        SplineExtrapolation.NA,
+    ):
+        out_of_bounds = below_lower | above_upper
+        if "knots" not in _state and numpy.any(out_of_bounds):
+            knots_x = x[~out_of_bounds]
+        if extrapolation is SplineExtrapolation.CLIP:
+            x = numpy.clip(x, lower_bound, upper_bound)
+        elif extrapolation is SplineExtrapolation.NA:
+            x = numpy.where(~out_of_bounds, x, numpy.nan)
+
+    # Prepare knots
+    if "knots" not in _state:
+        if df is None and knots is None:
+            raise ValueError("Must specify either 'df' or 'knots'.")
+
+        n_constraints = 0
+        centering_constraint = isinstance(constraints, str) and constraints == "center"
+        if centering_constraint:
+            # Here we collect only number of constraints,
+            # actual centering constraint will be computed after all_knots
+            n_constraints = 1
+        elif isinstance(constraints, str):
+            raise ValueError(
+                "Constraints must be 'center' when not passed as an array."
+            )
+        elif constraints is not None:
+            constraints_arr = numpy.atleast_2d(constraints)
+            if constraints_arr.ndim != 2:
+                raise ValueError("Constraints must be 2-d array or 1-d vector.")
+            n_constraints = constraints_arr.shape[0]
+
+        n_inner_knots = None
+        if df is not None:
+            min_df = 1
+            if not cyclic and n_constraints == 0:
+                min_df = 2
+            if df < min_df:
+                raise ValueError(
+                    f"'df'={df} must be greater than or equal to {min_df}."
+                )
+            n_inner_knots = df - 2 + n_constraints
+            if cyclic:
+                n_inner_knots += 1
+        _knots = numpy.array(knots) if knots is not None else None
+        all_knots = _get_all_sorted_knots(
+            knots_x,
+            lower_bound,
+            upper_bound,
+            n_inner_knots=n_inner_knots,
+            inner_knots=_knots,
+        )
+        if constraints is not None:
+            if centering_constraint:
+                # Now we can compute centering constraints
+                constraints_arr = _get_centering_constraint_from_matrix(
+                    _get_free_cubic_spline_matrix(x, all_knots, cyclic=cyclic)
+                )
+            df_before_constraints = all_knots.size
+            if cyclic:
+                df_before_constraints -= 1
+            if constraints_arr.shape[1] != df_before_constraints:
+                raise ValueError(
+                    f"Constraints array should have {df_before_constraints} columns "
+                    f"but {constraints_arr.shape[1]} found."
+                )
+            _state["constraints"] = constraints_arr
+        _state["knots"] = all_knots.tolist()
+    constraints = _state["constraints"]
+    knots = numpy.array(_state["knots"])
+
+    # Compute cubic splines
+    cs_mat = _get_cubic_spline_matrix(x, knots, constraints, cyclic=cyclic)
+    if extrapolation is SplineExtrapolation.ZERO:
+        cs_mat[below_lower | above_upper] = 0.0
+    return FactorValues(
+        {i + 1: cs_mat[:, i] for i in range(cs_mat.shape[1])},
+        kind="numerical",
+        spans_intercept=False,
+        drop_field=0,
+        format="{name}[{field}]",
+        encoded=False,
+    )
+
+
+natural_cubic_spline = stateful_transform(partial(cubic_spline, cyclic=False))
+cyclic_cubic_spline = stateful_transform(partial(cubic_spline, cyclic=True))
diff --git a/shared/package-files/d8/d851bd7a614f38980a05057f4c4546550b790b959abd304ef0f18cab141f919b/_c_v_a_r.py b/shared/package-files/d8/d851bd7a614f38980a05057f4c4546550b790b959abd304ef0f18cab141f919b/_c_v_a_r.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb082178f73d17b7121cb6ed85070152b887368d
--- /dev/null
+++ b/shared/package-files/d8/d851bd7a614f38980a05057f4c4546550b790b959abd304ef0f18cab141f919b/_c_v_a_r.py
@@ -0,0 +1,93 @@
+from . import DefaultTable
+from fontTools.misc import sstruct
+from fontTools.misc.textTools import bytesjoin
+from fontTools.ttLib.tables.TupleVariation import (
+    compileTupleVariationStore,
+    decompileTupleVariationStore,
+    TupleVariation,
+)
+
+# https://www.microsoft.com/typography/otspec/cvar.htm
+# https://www.microsoft.com/typography/otspec/otvarcommonformats.htm
+# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6cvar.html
+
+CVAR_HEADER_FORMAT = """
+    > # big endian
+    majorVersion:        H
+    minorVersion:        H
+    tupleVariationCount: H
+    offsetToData:        H
+"""
+
+CVAR_HEADER_SIZE = sstruct.calcsize(CVAR_HEADER_FORMAT)
+
+
+class table__c_v_a_r(DefaultTable.DefaultTable):
+    """Control Value Table (CVT) variations table
+
+    The ``cvar`` table contains variations for the values in a ``cvt``
+    table.
+
+    See also https://learn.microsoft.com/en-us/typography/opentype/spec/cvar
+    """
+
+    dependencies = ["cvt ", "fvar"]
+
+    def __init__(self, tag=None):
+        DefaultTable.DefaultTable.__init__(self, tag)
+        self.majorVersion, self.minorVersion = 1, 0
+        self.variations = []
+
+    def compile(self, ttFont, useSharedPoints=False):
+        tupleVariationCount, tuples, data = compileTupleVariationStore(
+            variations=[v for v in self.variations if v.hasImpact()],
+            pointCount=len(ttFont["cvt "].values),
+            axisTags=[axis.axisTag for axis in ttFont["fvar"].axes],
+            sharedTupleIndices={},
+            useSharedPoints=useSharedPoints,
+        )
+        header = {
+            "majorVersion": self.majorVersion,
+            "minorVersion": self.minorVersion,
+            "tupleVariationCount": tupleVariationCount,
+            "offsetToData": CVAR_HEADER_SIZE + len(tuples),
+        }
+        return b"".join([sstruct.pack(CVAR_HEADER_FORMAT, header), tuples, data])
+
+    def decompile(self, data, ttFont):
+        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
+        header = {}
+        sstruct.unpack(CVAR_HEADER_FORMAT, data[0:CVAR_HEADER_SIZE], header)
+        self.majorVersion = header["majorVersion"]
+        self.minorVersion = header["minorVersion"]
+        assert self.majorVersion == 1, self.majorVersion
+        self.variations = decompileTupleVariationStore(
+            tableTag=self.tableTag,
+            axisTags=axisTags,
+            tupleVariationCount=header["tupleVariationCount"],
+            pointCount=len(ttFont["cvt "].values),
+            sharedTuples=None,
+            data=data,
+            pos=CVAR_HEADER_SIZE,
+            dataPos=header["offsetToData"],
+        )
+
+    def fromXML(self, name, attrs, content, ttFont):
+        if name == "version":
+            self.majorVersion = int(attrs.get("major", "1"))
+            self.minorVersion = int(attrs.get("minor", "0"))
+        elif name == "tuple":
+            valueCount = len(ttFont["cvt "].values)
+            var = TupleVariation({}, [None] * valueCount)
+            self.variations.append(var)
+            for tupleElement in content:
+                if isinstance(tupleElement, tuple):
+                    tupleName, tupleAttrs, tupleContent = tupleElement
+                    var.fromXML(tupleName, tupleAttrs, tupleContent)
+
+    def toXML(self, writer, ttFont):
+        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
+        writer.simpletag("version", major=self.majorVersion, minor=self.minorVersion)
+        writer.newline()
+        for var in self.variations:
+            var.toXML(writer, axisTags)
diff --git a/shared/package-files/d8/d851c6db7b1ca1c96c7d96409e02bdb1eb276bfdf181b8e048aa14ce53311eba/test_overlaps.py b/shared/package-files/d8/d851c6db7b1ca1c96c7d96409e02bdb1eb276bfdf181b8e048aa14ce53311eba/test_overlaps.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fcf59d7bb4afc0077884de68dc335aff25c2cc5
--- /dev/null
+++ b/shared/package-files/d8/d851c6db7b1ca1c96c7d96409e02bdb1eb276bfdf181b8e048aa14ce53311eba/test_overlaps.py
@@ -0,0 +1,67 @@
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.fixture(
+    params=[
+        (Timedelta("0 days"), Timedelta("1 day")),
+        (Timestamp("2018-01-01"), Timedelta("1 day")),
+        (0, 1),
+    ],
+    ids=lambda x: type(x[0]).__name__,
+)
+def start_shift(request):
+    """
+    Fixture for generating intervals of types from a start value and a shift
+    value that can be added to start to generate an endpoint
+    """
+    return request.param
+
+
+class TestOverlaps:
+    def test_overlaps_self(self, start_shift, closed):
+        start, shift = start_shift
+        interval = Interval(start, start + shift, closed)
+        assert interval.overlaps(interval)
+
+    def test_overlaps_nested(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + 3 * shift, other_closed)
+        interval2 = Interval(start + shift, start + 2 * shift, closed)
+
+        # nested intervals should always overlap
+        assert interval1.overlaps(interval2)
+
+    def test_overlaps_disjoint(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + shift, other_closed)
+        interval2 = Interval(start + 2 * shift, start + 3 * shift, closed)
+
+        # disjoint intervals should never overlap
+        assert not interval1.overlaps(interval2)
+
+    def test_overlaps_endpoint(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + shift, other_closed)
+        interval2 = Interval(start + shift, start + 2 * shift, closed)
+
+        # overlap if shared endpoint is closed for both (overlap at a point)
+        result = interval1.overlaps(interval2)
+        expected = interval1.closed_right and interval2.closed_left
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "other",
+        [10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_overlaps_invalid_type(self, other):
+        interval = Interval(0, 1)
+        msg = f"`other` must be an Interval, got {type(other).__name__}"
+        with pytest.raises(TypeError, match=msg):
+            interval.overlaps(other)
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diff --git a/shared/package-files/d8/d85661f996534fb2a2938cdd45487d9d8a92694e77e95a6b12911cf6a6afa0c0/script_call.h b/shared/package-files/d8/d85661f996534fb2a2938cdd45487d9d8a92694e77e95a6b12911cf6a6afa0c0/script_call.h
new file mode 100644
index 0000000000000000000000000000000000000000..2fc0efb8cdc717b27c3adc31103fff3e5e86a783
--- /dev/null
+++ b/shared/package-files/d8/d85661f996534fb2a2938cdd45487d9d8a92694e77e95a6b12911cf6a6afa0c0/script_call.h
@@ -0,0 +1,71 @@
+#pragma once
+
+#include <c10/util/Optional.h>
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/rpc_command_base.h>
+#include <torch/csrc/jit/runtime/operator.h>
+#include <torch/csrc/jit/serialization/pickler.h>
+#include <vector>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+using torch::jit::Operator;
+
+// A ScriptCall instance represents an invocation of a builtin operator for a
+// TorchScript function. If it is a builtin operator, it
+// contains a shared ptr to the `Operator` and a list of arguments.
+// If it is a TorchScript function, it contains a non empty qualifiedName string
+// to the TorchScript function schema name and a list of arguments.
+class TORCH_API ScriptCall : public RpcCommandBase {
+ public:
+  // Constructor for builitin operator call.
+  ScriptCall(std::shared_ptr<Operator> op, std::vector<at::IValue>&& stack);
+  // Constructor for TorchScript function call.
+  ScriptCall(
+      const c10::QualifiedName& qualifiedName,
+      std::vector<at::IValue>&& stack,
+      const bool isAsyncExecution = false);
+
+  bool hasOp() const;
+  std::shared_ptr<Operator> op() const;
+  bool hasQualifiedName() const;
+  const c10::QualifiedName& qualifiedName() const;
+  // return the argument stack of this builtin operator
+  const std::vector<at::IValue>& stack() const;
+  std::vector<at::IValue>& stackRef();
+  inline bool isAsyncExecution() const {
+    return isAsyncExecution_;
+  }
+
+  c10::intrusive_ptr<Message> toMessageImpl() && override;
+  static std::unique_ptr<ScriptCall> fromMessage(const Message& message);
+
+  ~ScriptCall() override = default;
+
+ protected:
+  virtual void toIValues(std::vector<at::IValue>& ivalues) const;
+  static std::unique_ptr<ScriptCall> fromIValues(
+      std::vector<at::IValue>& ivalues);
+
+ private:
+  // Given an operator symbol and a string schema, return the matched operator.
+  static std::shared_ptr<Operator> matchOperator(const std::string& str_schema);
+
+  static const std::string BUILTIN_OP_NAMESPACE_;
+  static const std::string ATEN_PREFIX_;
+
+  // This field has value if this ScriptCall represents invocation of a builtin
+  // operator.
+  c10::optional<std::shared_ptr<Operator>> op_;
+  // This field has non empty string if this ScriptCall represents invocation of
+  // an annotated torchscript function defined by users.
+  c10::optional<const c10::QualifiedName> qualifiedName_;
+  std::vector<at::IValue> stack_;
+  const bool isAsyncExecution_;
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch
diff --git a/shared/package-files/d8/d85def2e6a7b3939b864c48bc72215410ea25de19490a28f7947621a6a95bae2/Madeira b/shared/package-files/d8/d85def2e6a7b3939b864c48bc72215410ea25de19490a28f7947621a6a95bae2/Madeira
new file mode 100644
index 0000000000000000000000000000000000000000..21e84571ee1694758dd244ed0702fbae962648e6
Binary files /dev/null and b/shared/package-files/d8/d85def2e6a7b3939b864c48bc72215410ea25de19490a28f7947621a6a95bae2/Madeira differ
diff --git a/shared/package-files/d8/d85ff1b258e15c994ab58ff76b2ee24c1cce5a4a605f31e873311b85230f78cb/addcmul_ops.h b/shared/package-files/d8/d85ff1b258e15c994ab58ff76b2ee24c1cce5a4a605f31e873311b85230f78cb/addcmul_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6384b01ae714dcebe80661c6bc862552c88e9e47
--- /dev/null
+++ b/shared/package-files/d8/d85ff1b258e15c994ab58ff76b2ee24c1cce5a4a605f31e873311b85230f78cb/addcmul_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API addcmul_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::addcmul")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out);
+};
+
+struct TORCH_API addcmul {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::addcmul")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value);
+};
+
+struct TORCH_API addcmul_ {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::addcmul_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/d8/d8600a60c66f7058153543f0dcace9a9e5c548b750521a738669721f753d0c04/_foreach_lerp_ops.h b/shared/package-files/d8/d8600a60c66f7058153543f0dcace9a9e5c548b750521a738669721f753d0c04/_foreach_lerp_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..c51274a7ae94d01774f3997df525d8c9a5399f41
--- /dev/null
+++ b/shared/package-files/d8/d8600a60c66f7058153543f0dcace9a9e5c548b750521a738669721f753d0c04/_foreach_lerp_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_lerp_List {
+  using schema = ::std::vector<at::Tensor> (at::TensorList, at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "List")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self, at::TensorList tensors1, at::TensorList weights);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights);
+};
+
+struct TORCH_API _foreach_lerp__List {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "List")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> ()")
+  static void call(at::TensorList self, at::TensorList tensors1, at::TensorList weights);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights);
+};
+
+struct TORCH_API _foreach_lerp_Scalar {
+  using schema = ::std::vector<at::Tensor> (at::TensorList, at::TensorList, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight);
+};
+
+struct TORCH_API _foreach_lerp__Scalar {
+  using schema = void (at::TensorList, at::TensorList, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> ()")
+  static void call(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight);
+};
+
+struct TORCH_API _foreach_lerp_List_out {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "List_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out);
+};
+
+struct TORCH_API _foreach_lerp_Scalar_out {
+  using schema = void (at::TensorList, at::TensorList, const at::Scalar &, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_lerp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/d8/d86245bfe36a930232b688395507b4c0fb592a84b4d7abb261f1bbea5a9fb6da/METADATA b/shared/package-files/d8/d86245bfe36a930232b688395507b4c0fb592a84b4d7abb261f1bbea5a9fb6da/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..129184e398b0279ace02602aba51ba240210726f
--- /dev/null
+++ b/shared/package-files/d8/d86245bfe36a930232b688395507b4c0fb592a84b4d7abb261f1bbea5a9fb6da/METADATA
@@ -0,0 +1,147 @@
+Metadata-Version: 2.1
+Name: dnspython
+Version: 2.6.1
+Summary: DNS toolkit
+Project-URL: homepage, https://www.dnspython.org
+Project-URL: repository, https://github.com/rthalley/dnspython.git
+Project-URL: documentation, https://dnspython.readthedocs.io/en/stable/
+Project-URL: issues, https://github.com/rthalley/dnspython/issues
+Author-email: Bob Halley <halley@dnspython.org>
+License-Expression: ISC
+License-File: LICENSE
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: System Administrators
+Classifier: License :: OSI Approved :: ISC License (ISCL)
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Internet :: Name Service (DNS)
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.8
+Provides-Extra: dev
+Requires-Dist: black>=23.1.0; extra == 'dev'
+Requires-Dist: coverage>=7.0; extra == 'dev'
+Requires-Dist: flake8>=7; extra == 'dev'
+Requires-Dist: mypy>=1.8; extra == 'dev'
+Requires-Dist: pylint>=3; extra == 'dev'
+Requires-Dist: pytest-cov>=4.1.0; extra == 'dev'
+Requires-Dist: pytest>=7.4; extra == 'dev'
+Requires-Dist: sphinx>=7.2.0; extra == 'dev'
+Requires-Dist: twine>=4.0.0; extra == 'dev'
+Requires-Dist: wheel>=0.42.0; extra == 'dev'
+Provides-Extra: dnssec
+Requires-Dist: cryptography>=41; extra == 'dnssec'
+Provides-Extra: doh
+Requires-Dist: h2>=4.1.0; extra == 'doh'
+Requires-Dist: httpcore>=1.0.0; extra == 'doh'
+Requires-Dist: httpx>=0.26.0; extra == 'doh'
+Provides-Extra: doq
+Requires-Dist: aioquic>=0.9.25; extra == 'doq'
+Provides-Extra: idna
+Requires-Dist: idna>=3.6; extra == 'idna'
+Provides-Extra: trio
+Requires-Dist: trio>=0.23; extra == 'trio'
+Provides-Extra: wmi
+Requires-Dist: wmi>=1.5.1; extra == 'wmi'
+Description-Content-Type: text/markdown
+
+# dnspython
+
+[![Build Status](https://github.com/rthalley/dnspython/actions/workflows/python-package.yml/badge.svg)](https://github.com/rthalley/dnspython/actions/)
+[![Documentation Status](https://readthedocs.org/projects/dnspython/badge/?version=latest)](https://dnspython.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/dnspython.svg)](https://badge.fury.io/py/dnspython)
+[![License: ISC](https://img.shields.io/badge/License-ISC-brightgreen.svg)](https://opensource.org/licenses/ISC)
+[![Coverage](https://codecov.io/github/rthalley/dnspython/coverage.svg?branch=master)](https://codecov.io/github/rthalley/dnspython)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+## INTRODUCTION
+
+dnspython is a DNS toolkit for Python. It supports almost all record types. It
+can be used for queries, zone transfers, and dynamic updates. It supports TSIG
+authenticated messages and EDNS0.
+
+dnspython provides both high and low level access to DNS. The high level classes
+perform queries for data of a given name, type, and class, and return an answer
+set. The low level classes allow direct manipulation of DNS zones, messages,
+names, and records.
+
+To see a few of the ways dnspython can be used, look in the `examples/`
+directory.
+
+dnspython is a utility to work with DNS, `/etc/hosts` is thus not used. For
+simple forward DNS lookups, it's better to use `socket.getaddrinfo()` or
+`socket.gethostbyname()`.
+
+dnspython originated at Nominum where it was developed
+to facilitate the testing of DNS software.
+
+## ABOUT THIS RELEASE
+
+This is dnspython 2.6.1.
+Please read
+[What's New](https://dnspython.readthedocs.io/en/stable/whatsnew.html) for
+information about the changes in this release.
+
+## INSTALLATION
+
+* Many distributions have dnspython packaged for you, so you should
+  check there first.
+* To use a wheel downloaded from PyPi, run:
+
+    pip install dnspython
+
+* To install from the source code, go into the top-level of the source code
+  and run:
+
+```
+    pip install --upgrade pip build
+    python -m build
+    pip install dist/*.whl
+```
+
+* To install the latest from the master branch, run `pip install git+https://github.com/rthalley/dnspython.git`
+
+Dnspython's default installation does not depend on any modules other than
+those in the Python standard library.  To use some features, additional modules
+must be installed.  For convenience, pip options are defined for the
+requirements.
+
+If you want to use DNS-over-HTTPS, run
+`pip install dnspython[doh]`.
+
+If you want to use DNSSEC functionality, run
+`pip install dnspython[dnssec]`.
+
+If you want to use internationalized domain names (IDNA)
+functionality, you must run
+`pip install dnspython[idna]`
+
+If you want to use the Trio asynchronous I/O package, run
+`pip install dnspython[trio]`.
+
+If you want to use WMI on Windows to determine the active DNS settings
+instead of the default registry scanning method, run
+`pip install dnspython[wmi]`.
+
+If you want to try the experimental DNS-over-QUIC code, run
+`pip install dnspython[doq]`.
+
+Note that you can install any combination of the above, e.g.:
+`pip install dnspython[doh,dnssec,idna]`
+
+### Notices
+
+Python 2.x support ended with the release of 1.16.0.  Dnspython 2.0.0 through
+2.2.x support Python 3.6 and later.  For dnspython 2.3.x, the minimum
+supported Python version is 3.7, and for 2.4.x the minimum supported verison is 3.8.
+We plan to align future support with the lifetime of the Python 3 versions.
+
+Documentation has moved to
+[dnspython.readthedocs.io](https://dnspython.readthedocs.io).
diff --git a/shared/package-files/d8/d864023410385076be63c6e8f421362e6eaaec79ce94eb31bb504fb7dc5f15d6/psi.h b/shared/package-files/d8/d864023410385076be63c6e8f421362e6eaaec79ce94eb31bb504fb7dc5f15d6/psi.h
new file mode 100644
index 0000000000000000000000000000000000000000..c028e9ea14e0066c3b8c13d2c26c2773e61f767a
--- /dev/null
+++ b/shared/package-files/d8/d864023410385076be63c6e8f421362e6eaaec79ce94eb31bb504fb7dc5f15d6/psi.h
@@ -0,0 +1,194 @@
+/* Translated into C++ by SciPy developers in 2024.
+ * Original header with Copyright information appears below.
+ */
+
+/*                                                     psi.c
+ *
+ *     Psi (digamma) function
+ *
+ *
+ * SYNOPSIS:
+ *
+ * double x, y, psi();
+ *
+ * y = psi( x );
+ *
+ *
+ * DESCRIPTION:
+ *
+ *              d      -
+ *   psi(x)  =  -- ln | (x)
+ *              dx
+ *
+ * is the logarithmic derivative of the gamma function.
+ * For integer x,
+ *                   n-1
+ *                    -
+ * psi(n) = -EUL  +   >  1/k.
+ *                    -
+ *                   k=1
+ *
+ * This formula is used for 0 < n <= 10.  If x is negative, it
+ * is transformed to a positive argument by the reflection
+ * formula  psi(1-x) = psi(x) + pi cot(pi x).
+ * For general positive x, the argument is made greater than 10
+ * using the recurrence  psi(x+1) = psi(x) + 1/x.
+ * Then the following asymptotic expansion is applied:
+ *
+ *                           inf.   B
+ *                            -      2k
+ * psi(x) = log(x) - 1/2x -   >   -------
+ *                            -        2k
+ *                           k=1   2k x
+ *
+ * where the B2k are Bernoulli numbers.
+ *
+ * ACCURACY:
+ *    Relative error (except absolute when |psi| < 1):
+ * arithmetic   domain     # trials      peak         rms
+ *    IEEE      0,30        30000       1.3e-15     1.4e-16
+ *    IEEE      -30,0       40000       1.5e-15     2.2e-16
+ *
+ * ERROR MESSAGES:
+ *     message         condition      value returned
+ * psi singularity    x integer <=0      INFINITY
+ */
+
+/*
+ * Cephes Math Library Release 2.8:  June, 2000
+ * Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
+ */
+
+/*
+ * Code for the rational approximation on [1, 2] is:
+ *
+ * (C) Copyright John Maddock 2006.
+ * Use, modification and distribution are subject to the
+ * Boost Software License, Version 1.0. (See accompanying file
+ * LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt)
+ */
+#pragma once
+
+#include "../config.h"
+#include "../error.h"
+#include "const.h"
+#include "polevl.h"
+
+namespace xsf {
+namespace cephes {
+    namespace detail {
+        constexpr double psi_A[] = {8.33333333333333333333E-2,  -2.10927960927960927961E-2, 7.57575757575757575758E-3,
+                                    -4.16666666666666666667E-3, 3.96825396825396825397E-3,  -8.33333333333333333333E-3,
+                                    8.33333333333333333333E-2};
+
+        constexpr float psi_Y = 0.99558162689208984f;
+
+        constexpr double psi_root1 = 1569415565.0 / 1073741824.0;
+        constexpr double psi_root2 = (381566830.0 / 1073741824.0) / 1073741824.0;
+        constexpr double psi_root3 = 0.9016312093258695918615325266959189453125e-19;
+
+        constexpr double psi_P[] = {-0.0020713321167745952, -0.045251321448739056, -0.28919126444774784,
+                                    -0.65031853770896507,   -0.32555031186804491,  0.25479851061131551};
+        constexpr double psi_Q[] = {-0.55789841321675513e-6,
+                                    0.0021284987017821144,
+                                    0.054151797245674225,
+                                    0.43593529692665969,
+                                    1.4606242909763515,
+                                    2.0767117023730469,
+                                    1.0};
+
+        XSF_HOST_DEVICE double digamma_imp_1_2(double x) {
+            /*
+             * Rational approximation on [1, 2] taken from Boost.
+             *
+             * Now for the approximation, we use the form:
+             *
+             * digamma(x) = (x - root) * (Y + R(x-1))
+             *
+             * Where root is the location of the positive root of digamma,
+             * Y is a constant, and R is optimised for low absolute error
+             * compared to Y.
+             *
+             * Maximum Deviation Found:               1.466e-18
+             * At double precision, max error found:  2.452e-17
+             */
+            double r, g;
+
+            g = x - psi_root1;
+            g -= psi_root2;
+            g -= psi_root3;
+            r = xsf::cephes::polevl(x - 1.0, psi_P, 5) / xsf::cephes::polevl(x - 1.0, psi_Q, 6);
+
+            return g * psi_Y + g * r;
+        }
+
+        XSF_HOST_DEVICE double psi_asy(double x) {
+            double y, z;
+
+            if (x < 1.0e17) {
+                z = 1.0 / (x * x);
+                y = z * xsf::cephes::polevl(z, psi_A, 6);
+            } else {
+                y = 0.0;
+            }
+
+            return std::log(x) - (0.5 / x) - y;
+        }
+    } // namespace detail
+
+    XSF_HOST_DEVICE double psi(double x) {
+        double y = 0.0;
+        double q, r;
+        int i, n;
+
+        if (std::isnan(x)) {
+            return x;
+        } else if (x == std::numeric_limits<double>::infinity()) {
+            return x;
+        } else if (x == -std::numeric_limits<double>::infinity()) {
+            return std::numeric_limits<double>::quiet_NaN();
+        } else if (x == 0) {
+            set_error("psi", SF_ERROR_SINGULAR, NULL);
+            return std::copysign(std::numeric_limits<double>::infinity(), -x);
+        } else if (x < 0.0) {
+            /* argument reduction before evaluating tan(pi * x) */
+            r = std::modf(x, &q);
+            if (r == 0.0) {
+                set_error("psi", SF_ERROR_SINGULAR, NULL);
+                return std::numeric_limits<double>::quiet_NaN();
+            }
+            y = -M_PI / std::tan(M_PI * r);
+            x = 1.0 - x;
+        }
+
+        /* check for positive integer up to 10 */
+        if ((x <= 10.0) && (x == std::floor(x))) {
+            n = static_cast<int>(x);
+            for (i = 1; i < n; i++) {
+                y += 1.0 / i;
+            }
+            y -= detail::SCIPY_EULER;
+            return y;
+        }
+
+        /* use the recurrence relation to move x into [1, 2] */
+        if (x < 1.0) {
+            y -= 1.0 / x;
+            x += 1.0;
+        } else if (x < 10.0) {
+            while (x > 2.0) {
+                x -= 1.0;
+                y += 1.0 / x;
+            }
+        }
+        if ((1.0 <= x) && (x <= 2.0)) {
+            y += detail::digamma_imp_1_2(x);
+            return y;
+        }
+
+        /* x is large, use the asymptotic series */
+        y += detail::psi_asy(x);
+        return y;
+    }
+} // namespace cephes
+} // namespace xsf
diff --git a/shared/package-files/d8/d864f75f1b9c588ea5f6bf634f0330ec085ad86ef716c4978173456773bf6720/utils.py b/shared/package-files/d8/d864f75f1b9c588ea5f6bf634f0330ec085ad86ef716c4978173456773bf6720/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..83e26873e04846e258f09aa9dfb69830c856f014
--- /dev/null
+++ b/shared/package-files/d8/d864f75f1b9c588ea5f6bf634f0330ec085ad86ef716c4978173456773bf6720/utils.py
@@ -0,0 +1,6 @@
+# Re-export some functions from `_utils` to make them public.
+from __future__ import annotations
+
+from narwhals._utils import Implementation, Version, parse_version
+
+__all__ = ["Implementation", "Version", "parse_version"]
diff --git a/shared/package-files/d8/d86dd8559bcd12baba40584771d548caea95c3b4d6b2363d3f88e68996a14a25/_sysconfigdata__linux_x86_64-linux-gnu.py.orig b/shared/package-files/d8/d86dd8559bcd12baba40584771d548caea95c3b4d6b2363d3f88e68996a14a25/_sysconfigdata__linux_x86_64-linux-gnu.py.orig
new file mode 100644
index 0000000000000000000000000000000000000000..617c77ce748acfb4ea999252375e2b6ac3ad3df5
--- /dev/null
+++ b/shared/package-files/d8/d86dd8559bcd12baba40584771d548caea95c3b4d6b2363d3f88e68996a14a25/_sysconfigdata__linux_x86_64-linux-gnu.py.orig
@@ -0,0 +1,986 @@
+# system configuration generated and used by the sysconfig module
+build_time_vars = {'ABIFLAGS': '',
+ 'AC_APPLE_UNIVERSAL_BUILD': 0,
+ 'AIX_BUILDDATE': 0,
+ 'AIX_GENUINE_CPLUSPLUS': 0,
+ 'ALIGNOF_LONG': 8,
+ 'ALIGNOF_SIZE_T': 8,
+ 'ALT_SOABI': 0,
+ 'ANDROID_API_LEVEL': 0,
+ 'AR': 'x86_64-conda-linux-gnu-ar',
+ 'ARFLAGS': 'rcs',
+ 'BASECFLAGS': '-Wno-unused-result -Wsign-compare',
+ 'BASECPPFLAGS': '-IObjects -IInclude -IPython',
+ 'BASEMODLIBS': '',
+ 'BINDIR': '/opt/conda/bin',
+ 'BINLIBDEST': '/opt/conda/lib/python3.10',
+ 'BLDLIBRARY': 'libpython3.10.a',
+ 'BLDSHARED': 'x86_64-conda-linux-gnu-gcc -pthread -shared -Wl,-O2 '
+              '-Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now '
+              '-Wl,--disable-new-dtags -Wl,--gc-sections '
+              '-Wl,-rpath,/opt/conda/lib '
+              '-Wl,-rpath-link,/opt/conda/lib '
+              '-L/opt/conda/lib '
+              '-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro '
+              '-Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections '
+              '-Wl,-rpath,/opt/conda/lib '
+              '-Wl,-rpath-link,/opt/conda/lib '
+              '-L/opt/conda/lib',
+ 'BUILDEXE': '',
+ 'BUILDPYTHON': 'python',
+ 'BUILD_GNU_TYPE': 'x86_64-conda-linux-gnu',
+ 'BYTESTR_DEPS': '\\',
+ 'CC': 'x86_64-conda-linux-gnu-gcc -pthread',
+ 'CCSHARED': '-fPIC',
+ 'CFLAGS': '-Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall '
+           '-march=nocona -mtune=haswell -ftree-vectorize -fPIC '
+           '-fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe '
+           '-isystem '
+           '/opt/conda/include '
+           '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+           '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+           '    '
+           '-march=nocona -mtune=haswell -ftree-vectorize -fPIC '
+           '-fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe '
+           '-isystem '
+           '/opt/conda/include '
+           '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+           '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+           '   ',
+ 'CFLAGSFORSHARED': '',
+ 'CFLAGS_ALIASING': '',
+ 'CONFIGFILES': 'configure configure.ac acconfig.h pyconfig.h.in '
+                'Makefile.pre.in',
+ 'CONFIGURE_CFLAGS': '-march=nocona -mtune=haswell -ftree-vectorize -fPIC '
+                     '-fstack-protector-strong -fno-plt -O2 '
+                     '-ffunction-sections -pipe -isystem '
+                     '/opt/conda/include '
+                     '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+                     '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+                     '  '
+                     ' ',
+ 'CONFIGURE_CFLAGS_NODIST': '-fno-semantic-interposition  '
+                            '  '
+                            ' -g -std=c99 -Wextra '
+                            '-Wno-unused-result -Wno-unused-parameter '
+                            '-Wno-missing-field-initializers '
+                            '-Werror=implicit-function-declaration '
+                            '-fvisibility=hidden',
+ 'CONFIGURE_CPPFLAGS': '-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem '
+                       '/opt/conda/include '
+                       '-I/opt/conda/include',
+ 'CONFIGURE_LDFLAGS': '-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro '
+                      '-Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections '
+                      '-Wl,-rpath,/opt/conda/lib '
+                      '-Wl,-rpath-link,/opt/conda/lib '
+                      '-L/opt/conda/lib',
+ 'CONFIGURE_LDFLAGS_NODIST': '-fno-semantic-interposition  '
+                             '  '
+                             ' -g',
+ 'CONFIG_ARGS': "'--prefix=/opt/conda' "
+                "'--build=x86_64-conda-linux-gnu' "
+                "'--host=x86_64-conda-linux-gnu' '--enable-ipv6' "
+                "'--with-ensurepip=no' "
+                "'--with-tzpath=/opt/conda/share/zoneinfo' "
+                "'--with-computed-gotos' '--with-system-ffi' "
+                "'--enable-loadable-sqlite-extensions' "
+                "'--with-tcltk-includes=-I/opt/conda/include' "
+                "'--with-tcltk-libs=-L/opt/conda/lib "
+                "-ltcl8.6 -ltk8.6' '--with-platlibdir=lib' '--with-lto' "
+                "'--enable-optimizations' "
+                "'-oldincludedir=/croot/python-split_1711037784081/_build_env/x86_64-conda-linux-gnu/sysroot/usr/include' "
+                "'--disable-shared' 'PROFILE_TASK=-m test --pgo' "
+                "'build_alias=x86_64-conda-linux-gnu' "
+                "'host_alias=x86_64-conda-linux-gnu' 'MACHDEP=linux' "
+                "'CC=x86_64-conda-linux-gnu-gcc' 'CFLAGS=-march=nocona "
+                '-mtune=haswell -ftree-vectorize -fPIC '
+                '-fstack-protector-strong -fno-plt -O2 -ffunction-sections '
+                '-pipe -isystem '
+                '/opt/conda/include '
+                '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+                '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+                '   '
+                "' 'LDFLAGS=-Wl,-O2 -Wl,--sort-common -Wl,--as-needed "
+                '-Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags '
+                '-Wl,--gc-sections '
+                '-Wl,-rpath,/opt/conda/lib '
+                '-Wl,-rpath-link,/opt/conda/lib '
+                "-L/opt/conda/lib' "
+                "'CPPFLAGS=-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem "
+                '/opt/conda/include '
+                "-I/opt/conda/include' "
+                "'CPP=/croot/python-split_1711037784081/_build_env/bin/x86_64-conda-linux-gnu-cpp' "
+                "'PKG_CONFIG_PATH=/opt/conda/lib/pkgconfig'",
+ 'CONFINCLUDEDIR': '/opt/conda/include',
+ 'CONFINCLUDEPY': '/opt/conda/include/python3.10',
+ 'COREPYTHONPATH': '',
+ 'COVERAGE_INFO': '/croot/python-split_1711037784081/work/build-static/coverage.info',
+ 'COVERAGE_REPORT': '/croot/python-split_1711037784081/work/build-static/lcov-report',
+ 'COVERAGE_REPORT_OPTIONS': '--no-branch-coverage --title "CPython lcov '
+                            'report"',
+ 'CPPFLAGS': '-IObjects -IInclude -IPython -I. '
+             '-I/croot/python-split_1711037784081/work/Include -DNDEBUG '
+             '-D_FORTIFY_SOURCE=2 -O2 -isystem '
+             '/opt/conda/include '
+             '-I/opt/conda/include '
+             '-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem '
+             '/opt/conda/include '
+             '-I/opt/conda/include',
+ 'CXX': 'x86_64-conda-linux-gnu-c++ -pthread',
+ 'DESTDIRS': '/opt/conda '
+             '/opt/conda/lib '
+             '/opt/conda/lib/python3.10 '
+             '/opt/conda/lib/python3.10/lib-dynload',
+ 'DESTLIB': '/opt/conda/lib/python3.10',
+ 'DESTPATH': '',
+ 'DESTSHARED': '/opt/conda/lib/python3.10/lib-dynload',
+ 'DFLAGS': '',
+ 'DIRMODE': 755,
+ 'DIST': 'README.rst ChangeLog configure configure.ac acconfig.h pyconfig.h.in '
+         'Makefile.pre.in Include Lib Misc Ext-dummy',
+ 'DISTDIRS': 'Include Lib Misc Ext-dummy',
+ 'DISTFILES': 'README.rst ChangeLog configure configure.ac acconfig.h '
+              'pyconfig.h.in Makefile.pre.in',
+ 'DLINCLDIR': '.',
+ 'DLLLIBRARY': '',
+ 'DOUBLE_IS_ARM_MIXED_ENDIAN_IEEE754': 0,
+ 'DOUBLE_IS_BIG_ENDIAN_IEEE754': 0,
+ 'DOUBLE_IS_LITTLE_ENDIAN_IEEE754': 1,
+ 'DTRACE': '',
+ 'DTRACE_DEPS': '\\',
+ 'DTRACE_HEADERS': '',
+ 'DTRACE_OBJS': '',
+ 'DYNLOADFILE': 'dynload_shlib.o',
+ 'ENABLE_IPV6': 1,
+ 'ENSUREPIP': 'no',
+ 'EXE': '',
+ 'EXEMODE': 755,
+ 'EXPERIMENTAL_ISOLATED_SUBINTERPRETERS': 0,
+ 'EXPORTSFROM': '',
+ 'EXPORTSYMS': '',
+ 'EXTRATESTOPTS': '',
+ 'EXT_SUFFIX': '.cpython-310-x86_64-linux-gnu.so',
+ 'FILEMODE': 644,
+ 'FLOAT_WORDS_BIGENDIAN': 0,
+ 'FLOCK_NEEDS_LIBBSD': 0,
+ 'GETPGRP_HAVE_ARG': 0,
+ 'GITBRANCH': '',
+ 'GITTAG': '',
+ 'GITVERSION': '',
+ 'GNULD': 'yes',
+ 'HAVE_ACCEPT4': 1,
+ 'HAVE_ACOSH': 1,
+ 'HAVE_ADDRINFO': 1,
+ 'HAVE_ALARM': 1,
+ 'HAVE_ALIGNED_REQUIRED': 0,
+ 'HAVE_ALLOCA_H': 1,
+ 'HAVE_ALTZONE': 0,
+ 'HAVE_ASINH': 1,
+ 'HAVE_ASM_TYPES_H': 1,
+ 'HAVE_ATANH': 1,
+ 'HAVE_BIND_TEXTDOMAIN_CODESET': 1,
+ 'HAVE_BLUETOOTH_BLUETOOTH_H': 0,
+ 'HAVE_BLUETOOTH_H': 0,
+ 'HAVE_BROKEN_MBSTOWCS': 0,
+ 'HAVE_BROKEN_NICE': 0,
+ 'HAVE_BROKEN_PIPE_BUF': 0,
+ 'HAVE_BROKEN_POLL': 0,
+ 'HAVE_BROKEN_POSIX_SEMAPHORES': 0,
+ 'HAVE_BROKEN_PTHREAD_SIGMASK': 0,
+ 'HAVE_BROKEN_SEM_GETVALUE': 0,
+ 'HAVE_BROKEN_UNSETENV': 0,
+ 'HAVE_BUILTIN_ATOMIC': 1,
+ 'HAVE_CHFLAGS': 0,
+ 'HAVE_CHOWN': 1,
+ 'HAVE_CHROOT': 1,
+ 'HAVE_CLOCK': 1,
+ 'HAVE_CLOCK_GETRES': 1,
+ 'HAVE_CLOCK_GETTIME': 1,
+ 'HAVE_CLOCK_SETTIME': 1,
+ 'HAVE_CLOSE_RANGE': 0,
+ 'HAVE_COMPUTED_GOTOS': 1,
+ 'HAVE_CONFSTR': 1,
+ 'HAVE_CONIO_H': 0,
+ 'HAVE_COPYSIGN': 1,
+ 'HAVE_COPY_FILE_RANGE': 0,
+ 'HAVE_CRYPT_H': 1,
+ 'HAVE_CRYPT_R': 1,
+ 'HAVE_CTERMID': 1,
+ 'HAVE_CTERMID_R': 0,
+ 'HAVE_CURSES_FILTER': 1,
+ 'HAVE_CURSES_H': 1,
+ 'HAVE_CURSES_HAS_KEY': 1,
+ 'HAVE_CURSES_IMMEDOK': 1,
+ 'HAVE_CURSES_IS_PAD': 1,
+ 'HAVE_CURSES_IS_TERM_RESIZED': 1,
+ 'HAVE_CURSES_RESIZETERM': 1,
+ 'HAVE_CURSES_RESIZE_TERM': 1,
+ 'HAVE_CURSES_SYNCOK': 1,
+ 'HAVE_CURSES_TYPEAHEAD': 1,
+ 'HAVE_CURSES_USE_ENV': 1,
+ 'HAVE_CURSES_WCHGAT': 1,
+ 'HAVE_DECL_ISFINITE': 1,
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+ 'HAVE_DECL_ISNAN': 1,
+ 'HAVE_DECL_RTLD_DEEPBIND': 1,
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+ 'HAVE_DECL_RTLD_MEMBER': 0,
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+ 'HAVE_DECL_TZNAME': 0,
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+ 'HAVE_GAMMA': 1,
+ 'HAVE_GCC_ASM_FOR_MC68881': 0,
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+ 'HAVE_GCC_UINT128_T': 1,
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+ 'HAVE_GETC_UNLOCKED': 1,
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+ 'HAVE_GETLOADAVG': 1,
+ 'HAVE_GETLOGIN': 1,
+ 'HAVE_GETNAMEINFO': 1,
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+ 'HAVE_GETPRIORITY': 1,
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+ 'HAVE_IO_H': 0,
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+                '-I/opt/conda/include',
+ 'PY_ENABLE_SHARED': 0,
+ 'PY_FORMAT_SIZE_T': '"z"',
+ 'PY_LDFLAGS': '-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro '
+               '-Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections '
+               '-Wl,-rpath,/opt/conda/lib '
+               '-Wl,-rpath-link,/opt/conda/lib '
+               '-L/opt/conda/lib '
+               '-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro '
+               '-Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections '
+               '-Wl,-rpath,/opt/conda/lib '
+               '-Wl,-rpath-link,/opt/conda/lib '
+               '-L/opt/conda/lib',
+ 'PY_LDFLAGS_NODIST': '-fno-semantic-interposition   '
+                      '  -g',
+ 'PY_SSL_DEFAULT_CIPHERS': 1,
+ 'PY_SSL_DEFAULT_CIPHER_STRING': 0,
+ 'PY_STDMODULE_CFLAGS': '-Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv '
+                        '-O2 -Wall -march=nocona -mtune=haswell '
+                        '-ftree-vectorize -fPIC -fstack-protector-strong '
+                        '-fno-plt -O2 -ffunction-sections -pipe -isystem '
+                        '/opt/conda/include '
+                        '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+                        '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+                        '  '
+                        '  -march=nocona '
+                        '-mtune=haswell -ftree-vectorize -fPIC '
+                        '-fstack-protector-strong -fno-plt -O2 '
+                        '-ffunction-sections -pipe -isystem '
+                        '/opt/conda/include '
+                        '-fdebug-prefix-map=/croot/python-split_1711037784081/work=/usr/local/src/conda/python-3.10.14 '
+                        '-fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix '
+                        '  '
+                        '  '
+                        '-fno-semantic-interposition   '
+                        '  -g -std=c99 '
+                        '-Wextra -Wno-unused-result -Wno-unused-parameter '
+                        '-Wno-missing-field-initializers '
+                        '-Werror=implicit-function-declaration '
+                        '-fvisibility=hidden  '
+                        ' '
+                        '-I/croot/python-split_1711037784081/work/Include/internal '
+                        '-IObjects -IInclude -IPython -I. '
+                        '-I/croot/python-split_1711037784081/work/Include '
+                        '-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem '
+                        '/opt/conda/include '
+                        '-I/opt/conda/include '
+                        '-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem '
+                        '/opt/conda/include '
+                        '-I/opt/conda/include',
+ 'Py_DEBUG': 0,
+ 'Py_ENABLE_SHARED': 0,
+ 'Py_HASH_ALGORITHM': 0,
+ 'Py_TRACE_REFS': 0,
+ 'QUICKTESTOPTS': '-x test_subprocess test_io test_lib2to3 \\',
+ 'READELF': 'x86_64-conda-linux-gnu-readelf',
+ 'RESSRCDIR': 'Mac/Resources/framework',
+ 'RETSIGTYPE': 'void',
+ 'RUNSHARED': '',
+ 'SCRIPTDIR': '/opt/conda/lib',
+ 'SETPGRP_HAVE_ARG': 0,
+ 'SHELL': '/bin/sh',
+ 'SHLIBS': '-lcrypt -lpthread -ldl  -lutil -lm',
+ 'SHLIB_SUFFIX': '.so',
+ 'SHM_NEEDS_LIBRT': 1,
+ 'SIGNED_RIGHT_SHIFT_ZERO_FILLS': 0,
+ 'SITEPATH': '',
+ 'SIZEOF_DOUBLE': 8,
+ 'SIZEOF_FLOAT': 4,
+ 'SIZEOF_FPOS_T': 16,
+ 'SIZEOF_INT': 4,
+ 'SIZEOF_LONG': 8,
+ 'SIZEOF_LONG_DOUBLE': 16,
+ 'SIZEOF_LONG_LONG': 8,
+ 'SIZEOF_OFF_T': 8,
+ 'SIZEOF_PID_T': 4,
+ 'SIZEOF_PTHREAD_KEY_T': 4,
+ 'SIZEOF_PTHREAD_T': 8,
+ 'SIZEOF_SHORT': 2,
+ 'SIZEOF_SIZE_T': 8,
+ 'SIZEOF_TIME_T': 8,
+ 'SIZEOF_UINTPTR_T': 8,
+ 'SIZEOF_VOID_P': 8,
+ 'SIZEOF_WCHAR_T': 4,
+ 'SIZEOF__BOOL': 1,
+ 'SOABI': 'cpython-310-x86_64-linux-gnu',
+ 'SRCDIRS': 'Parser Objects Python Modules Modules/_io Programs',
+ 'SRC_GDB_HOOKS': '/croot/python-split_1711037784081/work/Tools/gdb/libpython.py',
+ 'STATIC_LIBPYTHON': 1,
+ 'STDC_HEADERS': 1,
+ 'STRICT_SYSV_CURSES': "/* Don't use ncurses extensions */",
+ 'STRIPFLAG': '-s',
+ 'SUBDIRS': '',
+ 'SUBDIRSTOO': 'Include Lib Misc',
+ 'SYSLIBS': '-lm',
+ 'SYS_SELECT_WITH_SYS_TIME': 1,
+ 'TCLTK_INCLUDES': '-I/opt/conda/include',
+ 'TCLTK_LIBS': '-L/opt/conda/lib '
+               '-ltcl8.6 -ltk8.6',
+ 'TESTOPTS': '',
+ 'TESTPATH': '',
+ 'TESTPYTHON': './python',
+ 'TESTPYTHONOPTS': '',
+ 'TESTRUNNER': './python '
+               '/croot/python-split_1711037784081/work/Tools/scripts/run_tests.py',
+ 'TESTSUBDIRS': 'ctypes/test \\',
+ 'TESTTIMEOUT': 1200,
+ 'TEST_MODULES': 'yes',
+ 'THREAD_STACK_SIZE': 0,
+ 'TIMEMODULE_LIB': 0,
+ 'TIME_WITH_SYS_TIME': 1,
+ 'TM_IN_SYS_TIME': 0,
+ 'TZPATH': '/opt/conda/share/zoneinfo',
+ 'UNICODE_DEPS': '\\',
+ 'UNIVERSALSDK': '',
+ 'UPDATE_FILE': '/croot/python-split_1711037784081/work/Tools/scripts/update_file.py',
+ 'USE_COMPUTED_GOTOS': 1,
+ 'VERSION': '3.10',
+ 'VPATH': '/croot/python-split_1711037784081/work',
+ 'WHEEL_PKG_DIR': '',
+ 'WINDOW_HAS_FLAGS': 1,
+ 'WITH_DECIMAL_CONTEXTVAR': 1,
+ 'WITH_DOC_STRINGS': 1,
+ 'WITH_DTRACE': 0,
+ 'WITH_DYLD': 0,
+ 'WITH_EDITLINE': 0,
+ 'WITH_LIBINTL': 0,
+ 'WITH_NEXT_FRAMEWORK': 0,
+ 'WITH_PYMALLOC': 1,
+ 'WITH_VALGRIND': 0,
+ 'X87_DOUBLE_ROUNDING': 0,
+ 'XMLLIBSUBDIRS': 'xml xml/dom xml/etree xml/parsers xml/sax',
+ 'abs_builddir': '/croot/python-split_1711037784081/work/build-static',
+ 'abs_srcdir': '/croot/python-split_1711037784081/work',
+ 'datarootdir': '/opt/conda/share',
+ 'exec_prefix': '/opt/conda',
+ 'prefix': '/opt/conda',
+ 'srcdir': '/croot/python-split_1711037784081/work'}
diff --git a/shared/package-files/d8/d870e68375722e033f64fc375ec1a990e0a51a2427c939543474b0553b62cc0d/__init__.py b/shared/package-files/d8/d870e68375722e033f64fc375ec1a990e0a51a2427c939543474b0553b62cc0d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7048bd548cca04780479539339dd8cc49a21990f
--- /dev/null
+++ b/shared/package-files/d8/d870e68375722e033f64fc375ec1a990e0a51a2427c939543474b0553b62cc0d/__init__.py
@@ -0,0 +1,8 @@
+from .products import product, Product
+from .summations import summation, Sum
+
+__all__ = [
+    'product', 'Product',
+
+    'summation', 'Sum',
+]
diff --git a/shared/package-files/d8/d876d68330916368cb2a511ffa2185d22b0c60a4a1350a47de2716c970c85e42/_propagate_xla_data_native.h b/shared/package-files/d8/d876d68330916368cb2a511ffa2185d22b0c60a4a1350a47de2716c970c85e42/_propagate_xla_data_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e44043454be2912547c5cf59340e4dcc463c5ad
--- /dev/null
+++ b/shared/package-files/d8/d876d68330916368cb2a511ffa2185d22b0c60a4a1350a47de2716c970c85e42/_propagate_xla_data_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void _propagate_xla_data(const at::Tensor & input, const at::Tensor & output);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/d8/d87adceac25e944da8442e65f64e8af8cdad0a30f00fb5fa4ceb2265b7802e56/xnuppc+f2 b/shared/package-files/d8/d87adceac25e944da8442e65f64e8af8cdad0a30f00fb5fa4ceb2265b7802e56/xnuppc+f2
new file mode 100644
index 0000000000000000000000000000000000000000..b08ee9b493099c739c53709b981dbdd552261f78
Binary files /dev/null and b/shared/package-files/d8/d87adceac25e944da8442e65f64e8af8cdad0a30f00fb5fa4ceb2265b7802e56/xnuppc+f2 differ
diff --git a/shared/package-files/d8/d87d41ab0926cdebbb55d841aff643e181982df1427103ed7ebb3c55cf3e0742/_importlib_modulespec.pyi b/shared/package-files/d8/d87d41ab0926cdebbb55d841aff643e181982df1427103ed7ebb3c55cf3e0742/_importlib_modulespec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..114b78e1061dc3d8f11c0f0595a2c840b90dd1a2
--- /dev/null
+++ b/shared/package-files/d8/d87d41ab0926cdebbb55d841aff643e181982df1427103ed7ebb3c55cf3e0742/_importlib_modulespec.pyi
@@ -0,0 +1,50 @@
+# ModuleSpec, ModuleType, Loader are part of a dependency cycle.
+# They are officially defined/exported in other places:
+#
+# - ModuleType in types
+# - Loader in importlib.abc
+# - ModuleSpec in importlib.machinery (3.4 and later only)
+#
+# _Loader is the PEP-451-defined interface for a loader type/object.
+
+from abc import ABCMeta
+from typing import Any, Dict, List, Optional, Protocol
+
+class _Loader(Protocol):
+    def load_module(self, fullname: str) -> ModuleType: ...
+
+class ModuleSpec:
+    def __init__(
+        self,
+        name: str,
+        loader: Optional[Loader],
+        *,
+        origin: Optional[str] = ...,
+        loader_state: Any = ...,
+        is_package: Optional[bool] = ...,
+    ) -> None: ...
+    name: str
+    loader: Optional[_Loader]
+    origin: Optional[str]
+    submodule_search_locations: Optional[List[str]]
+    loader_state: Any
+    cached: Optional[str]
+    parent: Optional[str]
+    has_location: bool
+
+class ModuleType:
+    __name__: str
+    __file__: str
+    __dict__: Dict[str, Any]
+    __loader__: Optional[_Loader]
+    __package__: Optional[str]
+    __spec__: Optional[ModuleSpec]
+    def __init__(self, name: str, doc: Optional[str] = ...) -> None: ...
+
+class Loader(metaclass=ABCMeta):
+    def load_module(self, fullname: str) -> ModuleType: ...
+    def module_repr(self, module: ModuleType) -> str: ...
+    def create_module(self, spec: ModuleSpec) -> Optional[ModuleType]: ...
+    # Not defined on the actual class for backwards-compatibility reasons,
+    # but expected in new code.
+    def exec_module(self, module: ModuleType) -> None: ...
diff --git a/shared/package-files/d8/d87f5d43c0428ddd9415e9e0616bb824618b56bc67cbd269f92717716133f9a7/_make_dual_copy_native.h b/shared/package-files/d8/d87f5d43c0428ddd9415e9e0616bb824618b56bc67cbd269f92717716133f9a7/_make_dual_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..1bd3e5d06d8ef622644e261b6fc91f18d3f338f1
--- /dev/null
+++ b/shared/package-files/d8/d87f5d43c0428ddd9415e9e0616bb824618b56bc67cbd269f92717716133f9a7/_make_dual_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & _make_dual_copy_out(const at::Tensor & primal, const at::Tensor & tangent, int64_t level, at::Tensor & out);
+TORCH_API at::Tensor _make_dual_copy(const at::Tensor & primal, const at::Tensor & tangent, int64_t level);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/d8/d8814a0f5aec581144df51d38b2520a44e72ec0261f3f0143dd73e6ed350b8ac/download.py b/shared/package-files/d8/d8814a0f5aec581144df51d38b2520a44e72ec0261f3f0143dd73e6ed350b8ac/download.py
new file mode 100644
index 0000000000000000000000000000000000000000..2081877d15a13e91a6fcb87905634addd23cc712
--- /dev/null
+++ b/shared/package-files/d8/d8814a0f5aec581144df51d38b2520a44e72ec0261f3f0143dd73e6ed350b8ac/download.py
@@ -0,0 +1,89 @@
+import hashlib
+import logging
+from os import PathLike
+from pathlib import Path
+from typing import Union
+
+import torch
+from torchaudio._internal import download_url_to_file
+
+_LG = logging.getLogger(__name__)
+
+
+def _get_local_path(key):
+    path = Path(torch.hub.get_dir()) / "torchaudio" / Path(key)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def _download(key, path, progress):
+    url = f"https://download.pytorch.org/torchaudio/{key}"
+    download_url_to_file(url, path, progress=progress)
+
+
+def _get_hash(path, hash, chunk_size=1028):
+    m = hashlib.sha256()
+    with open(path, "rb") as file:
+        data = file.read(chunk_size)
+        while data:
+            m.update(data)
+            data = file.read(chunk_size)
+    return m.hexdigest()
+
+
+def download_asset(
+    key: str,
+    hash: str = "",
+    path: Union[str, PathLike] = "",
+    *,
+    progress: bool = True,
+) -> str:
+    """Download and store torchaudio assets to local file system.
+
+    If a file exists at the download path, then that path is returned with or without
+    hash validation.
+
+    Args:
+        key (str): The asset identifier.
+        hash (str, optional):
+            The value of SHA256 hash of the asset. If provided, it is used to verify
+            the downloaded / cached object. If not provided, then no hash validation
+            is performed. This means if a file exists at the download path, then the path
+            is returned as-is without verifying the identity of the file.
+        path (path-like object, optional):
+            By default, the downloaded asset is saved in a directory under
+            :py:func:`torch.hub.get_dir` and intermediate directories based on the given `key`
+            are created.
+            This argument can be used to overwrite the target location.
+            When this argument is provided, all the intermediate directories have to be
+            created beforehand.
+        progress (bool): Whether to show progress bar for downloading. Default: ``True``.
+
+    Note:
+        Currently the valid key values are the route on ``download.pytorch.org/torchaudio``,
+        but this is an implementation detail.
+
+    Returns:
+        str: The path to the asset on the local file system.
+    """
+    path = path or _get_local_path(key)
+
+    if path.exists():
+        _LG.info("The local file (%s) exists. Skipping the download.", path)
+    else:
+        _LG.info("Downloading %s to %s", key, path)
+        _download(key, path, progress=progress)
+
+    if hash:
+        _LG.info("Verifying the hash value.")
+        digest = _get_hash(path, hash)
+
+        if digest != hash:
+            raise ValueError(
+                f"The hash value of the downloaded file ({path}), '{digest}' does not match "
+                f"the provided hash value, '{hash}'."
+            )
+
+        _LG.info("Hash validated.")
+
+    return str(path)
diff --git a/shared/package-files/d8/d8853646b4e7f79f34e2838c959a4362cf96ebab8f388663dd75c88194b2cbd5/test_array_attr.py b/shared/package-files/d8/d8853646b4e7f79f34e2838c959a4362cf96ebab8f388663dd75c88194b2cbd5/test_array_attr.py
new file mode 100644
index 0000000000000000000000000000000000000000..404ea72731b021d063b0136b25f756f40a80ff25
--- /dev/null
+++ b/shared/package-files/d8/d8853646b4e7f79f34e2838c959a4362cf96ebab8f388663dd75c88194b2cbd5/test_array_attr.py
@@ -0,0 +1,410 @@
+import numpy as np
+
+import unittest
+from numba.np.numpy_support import from_dtype
+from numba import njit, typeof
+from numba.core import types
+from numba.tests.support import (TestCase, MemoryLeakMixin,
+                                 skip_parfors_unsupported)
+from numba.core.errors import TypingError
+from numba.experimental import jitclass
+
+
+def array_dtype(a):
+    return a.dtype
+
+
+def use_dtype(a, b):
+    return a.view(b.dtype)
+
+
+def dtype_eq_int64(a):
+    return a.dtype == np.dtype('int64')
+
+
+def array_itemsize(a):
+    return a.itemsize
+
+
+def array_nbytes(a):
+    return a.nbytes
+
+
+def array_shape(a, i):
+    return a.shape[i]
+
+
+def array_strides(a, i):
+    return a.strides[i]
+
+
+def array_ndim(a):
+    return a.ndim
+
+
+def array_size(a):
+    return a.size
+
+
+def array_flags_contiguous(a):
+    return a.flags.contiguous
+
+def array_flags_c_contiguous(a):
+    return a.flags.c_contiguous
+
+def array_flags_f_contiguous(a):
+    return a.flags.f_contiguous
+
+
+def nested_array_itemsize(a):
+    return a.f.itemsize
+
+def nested_array_nbytes(a):
+    return a.f.nbytes
+
+def nested_array_shape(a):
+    return a.f.shape
+
+
+def nested_array_strides(a):
+    return a.f.strides
+
+
+def nested_array_ndim(a):
+    return a.f.ndim
+
+
+def nested_array_size(a):
+    return a.f.size
+
+
+def size_after_slicing_usecase(buf, i):
+    sliced = buf[i]
+    # Make sure size attribute is not lost
+    return sliced.size
+
+
+def array_ctypes_data(arr):
+    return arr.ctypes.data
+
+
+def array_real(arr):
+    return arr.real
+
+
+def array_imag(arr):
+    return arr.imag
+
+
+class TestArrayAttr(MemoryLeakMixin, TestCase):
+
+    def setUp(self):
+        super(TestArrayAttr, self).setUp()
+        self.a = np.arange(20, dtype=np.int32).reshape(4, 5)
+
+    def check_unary(self, pyfunc, arr):
+        aryty = typeof(arr)
+        cfunc = self.get_cfunc(pyfunc, (aryty,))
+        expected = pyfunc(arr)
+        self.assertPreciseEqual(cfunc(arr), expected)
+        # Retry with forced any layout
+        cfunc = self.get_cfunc(pyfunc, (aryty.copy(layout='A'),))
+        self.assertPreciseEqual(cfunc(arr), expected)
+
+    def check_unary_with_arrays(self, pyfunc,):
+        self.check_unary(pyfunc, self.a)
+        self.check_unary(pyfunc, self.a.T)
+        self.check_unary(pyfunc, self.a[::2])
+        # 0-d array
+        arr = np.array([42]).reshape(())
+        self.check_unary(pyfunc, arr)
+        # array with an empty dimension
+        arr = np.zeros(0)
+        self.check_unary(pyfunc, arr)
+
+        # check with reshape
+        self.check_unary(pyfunc, arr.reshape((1, 0, 2)))
+
+    def get_cfunc(self, pyfunc, argspec):
+        return njit(argspec)(pyfunc)
+
+    def test_shape(self):
+        pyfunc = array_shape
+        cfunc = self.get_cfunc(pyfunc, (types.int32[:,:], types.int32))
+
+        for i in range(self.a.ndim):
+            self.assertEqual(pyfunc(self.a, i), cfunc(self.a, i))
+
+    def test_strides(self):
+        pyfunc = array_strides
+        cfunc = self.get_cfunc(pyfunc, (types.int32[:,:], types.int32))
+
+        for i in range(self.a.ndim):
+            self.assertEqual(pyfunc(self.a, i), cfunc(self.a, i))
+
+    def test_ndim(self):
+        self.check_unary_with_arrays(array_ndim)
+
+    def test_size(self):
+        self.check_unary_with_arrays(array_size)
+
+    def test_itemsize(self):
+        self.check_unary_with_arrays(array_itemsize)
+
+    def test_nbytes(self):
+        self.check_unary_with_arrays(array_nbytes)
+
+    def test_dtype(self):
+        pyfunc = array_dtype
+        self.check_unary(pyfunc, self.a)
+        dtype = np.dtype([('x', np.int8), ('y', np.int8)])
+        arr = np.zeros(4, dtype=dtype)
+        self.check_unary(pyfunc, arr)
+
+    def test_use_dtype(self):
+        # Test using the dtype attribute inside the Numba function itself
+        b = np.empty(1, dtype=np.int16)
+        pyfunc = use_dtype
+        cfunc = self.get_cfunc(pyfunc, (typeof(self.a), typeof(b)))
+        expected = pyfunc(self.a, b)
+        self.assertPreciseEqual(cfunc(self.a, b), expected)
+
+    def test_dtype_equal(self):
+        # Test checking if a dtype is equal to another dtype
+        pyfunc = dtype_eq_int64
+        self.check_unary(pyfunc, np.empty(1, dtype=np.int16))
+        self.check_unary(pyfunc, np.empty(1, dtype=np.int64))
+
+    def test_flags_contiguous(self):
+        self.check_unary_with_arrays(array_flags_contiguous)
+
+    def test_flags_c_contiguous(self):
+        self.check_unary_with_arrays(array_flags_c_contiguous)
+
+    def test_flags_f_contiguous(self):
+        self.check_unary_with_arrays(array_flags_f_contiguous)
+
+
+class TestNestedArrayAttr(MemoryLeakMixin, unittest.TestCase):
+    def setUp(self):
+        super(TestNestedArrayAttr, self).setUp()
+        dtype = np.dtype([('a', np.int32), ('f', np.int32, (2, 5))])
+        self.a = np.recarray(1, dtype)[0]
+        self.nbrecord = from_dtype(self.a.dtype)
+
+    def get_cfunc(self, pyfunc):
+        return njit((self.nbrecord,))(pyfunc)
+
+    def test_shape(self):
+        pyfunc = nested_array_shape
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_strides(self):
+        pyfunc = nested_array_strides
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_ndim(self):
+        pyfunc = nested_array_ndim
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_nbytes(self):
+        pyfunc = nested_array_nbytes
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_size(self):
+        pyfunc = nested_array_size
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_itemsize(self):
+        pyfunc = nested_array_itemsize
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+
+class TestSlicedArrayAttr(MemoryLeakMixin, unittest.TestCase):
+    def test_size_after_slicing(self):
+        pyfunc = size_after_slicing_usecase
+        cfunc = njit(pyfunc)
+        arr = np.arange(2 * 5).reshape(2, 5)
+        for i in range(arr.shape[0]):
+            self.assertEqual(pyfunc(arr, i), cfunc(arr, i))
+        arr = np.arange(2 * 5 * 3).reshape(2, 5, 3)
+        for i in range(arr.shape[0]):
+            self.assertEqual(pyfunc(arr, i), cfunc(arr, i))
+
+
+class TestArrayCTypes(MemoryLeakMixin, TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_array_ctypes_data(self):
+        pyfunc = array_ctypes_data
+        cfunc = njit(pyfunc)
+        arr = np.arange(3)
+        self.assertEqual(pyfunc(arr), cfunc(arr))
+
+    @skip_parfors_unsupported
+    def test_array_ctypes_ref_error_in_parallel(self):
+        # Issue #2887
+        from ctypes import CFUNCTYPE, c_void_p, c_int32, c_double, c_bool
+
+        @CFUNCTYPE(c_bool, c_void_p, c_int32, c_void_p)
+        def callback(inptr, size, outptr):
+            # A ctypes callback that manipulate the incoming pointers.
+            try:
+                inbuf = (c_double * size).from_address(inptr)
+                outbuf = (c_double * 1).from_address(outptr)
+                a = np.ndarray(size, buffer=inbuf, dtype=np.float64)
+                b = np.ndarray(1, buffer=outbuf, dtype=np.float64)
+                b[0] = (a + a.size)[0]
+                return True
+            except Exception:
+                import traceback
+                traceback.print_exception()
+                return False
+
+
+        # parallel=True is required to reproduce the error.
+        @njit(parallel=True)
+        def foo(size):
+            arr = np.ones(size)
+            out = np.empty(1)
+            # Exercise array.ctypes
+            inct = arr.ctypes
+            outct = out.ctypes
+            # The reference to `arr` is dead by now
+            status = callback(inct.data, size, outct.data)
+            return status, out[0]
+
+        size = 3
+        status, got = foo(size)
+        self.assertTrue(status)
+        self.assertPreciseEqual(got, (np.ones(size) + size)[0])
+
+
+class TestRealImagAttr(MemoryLeakMixin, TestCase):
+    def check_complex(self, pyfunc):
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size) + np.arange(size) * 10j
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+
+    def test_complex_real(self):
+        self.check_complex(array_real)
+
+    def test_complex_imag(self):
+        self.check_complex(array_imag)
+
+    def check_number_real(self, dtype):
+        pyfunc = array_real
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size, dtype=dtype)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test identity
+        self.assertEqual(arr.data, pyfunc(arr).data)
+        self.assertEqual(arr.data, cfunc(arr).data)
+        # test writable
+        real = cfunc(arr)
+        self.assertNotEqual(arr[0, 0], 5)
+        real[0, 0] = 5
+        self.assertEqual(arr[0, 0], 5)
+
+    def test_number_real(self):
+        """
+        Testing .real of non-complex dtypes
+        """
+        for dtype in [np.uint8, np.int32, np.float32, np.float64]:
+            self.check_number_real(dtype)
+
+    def check_number_imag(self, dtype):
+        pyfunc = array_imag
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size, dtype=dtype)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test are zeros
+        self.assertEqual(cfunc(arr).tolist(), np.zeros_like(arr).tolist())
+        # test readonly
+        imag = cfunc(arr)
+        with self.assertRaises(ValueError) as raises:
+            imag[0] = 1
+        self.assertEqual('assignment destination is read-only',
+                         str(raises.exception))
+
+    def test_number_imag(self):
+        """
+        Testing .imag of non-complex dtypes
+        """
+        for dtype in [np.uint8, np.int32, np.float32, np.float64]:
+            self.check_number_imag(dtype)
+
+    def test_record_real(self):
+        rectyp = np.dtype([('real', np.float32), ('imag', np.complex64)])
+        arr = np.zeros(3, dtype=rectyp)
+        arr['real'] = np.random.random(arr.size)
+        arr['imag'] = np.random.random(arr.size) * 1.3j
+
+        # check numpy behavior
+        # .real is identity
+        self.assertIs(array_real(arr), arr)
+        # .imag is zero_like
+        self.assertEqual(array_imag(arr).tolist(), np.zeros_like(arr).tolist())
+
+        # check numba behavior
+        # it's most likely a user error, anyway
+        jit_array_real = njit(array_real)
+        jit_array_imag = njit(array_imag)
+
+        with self.assertRaises(TypingError) as raises:
+            jit_array_real(arr)
+        self.assertIn("cannot access .real of array of Record",
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            jit_array_imag(arr)
+        self.assertIn("cannot access .imag of array of Record",
+                      str(raises.exception))
+
+class TestJitclassFlagsSegfault(MemoryLeakMixin, TestCase):
+    """Regression test for: https://github.com/numba/numba/issues/4775 """
+
+    def test(self):
+
+        @jitclass(dict())
+        class B(object):
+
+            def __init__(self):
+                pass
+
+            def foo(self, X):
+                X.flags
+
+        Z = B()
+        Z.foo(np.ones(4))
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/d8/d885502e23d2d29155251bcafb4d274bfc60430332ed387262d5f78b49350be6/_dtypes.py b/shared/package-files/d8/d885502e23d2d29155251bcafb4d274bfc60430332ed387262d5f78b49350be6/_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..96845950fa9d5c4503d93b28840d310f72acd50e
--- /dev/null
+++ b/shared/package-files/d8/d885502e23d2d29155251bcafb4d274bfc60430332ed387262d5f78b49350be6/_dtypes.py
@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from narwhals._utils import inherit_doc
+from narwhals.dtypes import (
+    Array,
+    Binary,
+    Boolean,
+    Categorical,
+    Date,
+    Datetime as NwDatetime,
+    Decimal,
+    DType,
+    DTypeClass,
+    Duration as NwDuration,
+    Enum as NwEnum,
+    Field,
+    Float16,
+    Float32,
+    Float64,
+    FloatType,
+    Int8,
+    Int16,
+    Int32,
+    Int64,
+    Int128,
+    IntegerType,
+    List,
+    NestedType,
+    NumericType,
+    Object,
+    SignedIntegerType,
+    String,
+    Struct,
+    Time,
+    UInt8,
+    UInt16,
+    UInt32,
+    UInt64,
+    UInt128,
+    Unknown,
+    UnsignedIntegerType,
+)
+
+if TYPE_CHECKING:
+    from datetime import timezone
+
+    from narwhals.typing import TimeUnit
+
+
+class Datetime(NwDatetime):
+    @inherit_doc(NwDatetime)
+    def __init__(
+        self, time_unit: TimeUnit = "us", time_zone: str | timezone | None = None
+    ) -> None:
+        super().__init__(time_unit, time_zone)
+
+    def __hash__(self) -> int:
+        return hash(self.__class__)
+
+
+class Duration(NwDuration):
+    @inherit_doc(NwDuration)
+    def __init__(self, time_unit: TimeUnit = "us") -> None:
+        super().__init__(time_unit)
+
+    def __hash__(self) -> int:
+        return hash(self.__class__)
+
+
+class Enum(NwEnum):
+    """A fixed categorical encoding of a unique set of strings.
+
+    Polars has an Enum data type, while pandas and PyArrow do not.
+
+    Examples:
+       >>> import polars as pl
+       >>> import narwhals.stable.v1 as nw
+       >>> data = ["beluga", "narwhal", "orca"]
+       >>> s_native = pl.Series(data, dtype=pl.Enum(data))
+       >>> nw.from_native(s_native, series_only=True).dtype
+       Enum
+    """
+
+    def __init__(self) -> None:
+        super(NwEnum, self).__init__()
+
+    def __eq__(self, other: DType | type[DType]) -> bool:  # type: ignore[override]
+        if type(other) is DTypeClass:
+            return other in {type(self), NwEnum}
+        return isinstance(other, type(self))
+
+    def __hash__(self) -> int:  # pragma: no cover
+        return super(NwEnum, self).__hash__()
+
+    def __repr__(self) -> str:  # pragma: no cover
+        return super(NwEnum, self).__repr__()
+
+
+__all__ = [
+    "Array",
+    "Binary",
+    "Boolean",
+    "Categorical",
+    "DType",
+    "Date",
+    "Datetime",
+    "Decimal",
+    "Duration",
+    "Enum",
+    "Field",
+    "Float16",
+    "Float32",
+    "Float64",
+    "FloatType",
+    "Int8",
+    "Int16",
+    "Int32",
+    "Int64",
+    "Int128",
+    "IntegerType",
+    "List",
+    "NestedType",
+    "NumericType",
+    "Object",
+    "SignedIntegerType",
+    "String",
+    "Struct",
+    "Time",
+    "UInt8",
+    "UInt16",
+    "UInt32",
+    "UInt64",
+    "UInt128",
+    "Unknown",
+    "UnsignedIntegerType",
+]
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diff --git a/shared/package-files/d8/d88af9c72206e1c4c2e03820d5c71e2d768416238227ad194c30e7df7bf3d0db/test_cuts.py b/shared/package-files/d8/d88af9c72206e1c4c2e03820d5c71e2d768416238227ad194c30e7df7bf3d0db/test_cuts.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d8656e330e205c61d9f469560697a3875a0555b
--- /dev/null
+++ b/shared/package-files/d8/d88af9c72206e1c4c2e03820d5c71e2d768416238227ad194c30e7df7bf3d0db/test_cuts.py
@@ -0,0 +1,172 @@
+"""Unit tests for the :mod:`networkx.algorithms.cuts` module."""
+
+
+import networkx as nx
+
+
+class TestCutSize:
+    """Unit tests for the :func:`~networkx.cut_size` function."""
+
+    def test_symmetric(self):
+        """Tests that the cut size is symmetric."""
+        G = nx.barbell_graph(3, 0)
+        S = {0, 1, 4}
+        T = {2, 3, 5}
+        assert nx.cut_size(G, S, T) == 4
+        assert nx.cut_size(G, T, S) == 4
+
+    def test_single_edge(self):
+        """Tests for a cut of a single edge."""
+        G = nx.barbell_graph(3, 0)
+        S = {0, 1, 2}
+        T = {3, 4, 5}
+        assert nx.cut_size(G, S, T) == 1
+        assert nx.cut_size(G, T, S) == 1
+
+    def test_directed(self):
+        """Tests that each directed edge is counted once in the cut."""
+        G = nx.barbell_graph(3, 0).to_directed()
+        S = {0, 1, 2}
+        T = {3, 4, 5}
+        assert nx.cut_size(G, S, T) == 2
+        assert nx.cut_size(G, T, S) == 2
+
+    def test_directed_symmetric(self):
+        """Tests that a cut in a directed graph is symmetric."""
+        G = nx.barbell_graph(3, 0).to_directed()
+        S = {0, 1, 4}
+        T = {2, 3, 5}
+        assert nx.cut_size(G, S, T) == 8
+        assert nx.cut_size(G, T, S) == 8
+
+    def test_multigraph(self):
+        """Tests that parallel edges are each counted for a cut."""
+        G = nx.MultiGraph(["ab", "ab"])
+        assert nx.cut_size(G, {"a"}, {"b"}) == 2
+
+
+class TestVolume:
+    """Unit tests for the :func:`~networkx.volume` function."""
+
+    def test_graph(self):
+        G = nx.cycle_graph(4)
+        assert nx.volume(G, {0, 1}) == 4
+
+    def test_digraph(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0)])
+        assert nx.volume(G, {0, 1}) == 2
+
+    def test_multigraph(self):
+        edges = list(nx.cycle_graph(4).edges())
+        G = nx.MultiGraph(edges * 2)
+        assert nx.volume(G, {0, 1}) == 8
+
+    def test_multidigraph(self):
+        edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
+        G = nx.MultiDiGraph(edges * 2)
+        assert nx.volume(G, {0, 1}) == 4
+
+    def test_barbell(self):
+        G = nx.barbell_graph(3, 0)
+        assert nx.volume(G, {0, 1, 2}) == 7
+        assert nx.volume(G, {3, 4, 5}) == 7
+
+
+class TestNormalizedCutSize:
+    """Unit tests for the :func:`~networkx.normalized_cut_size` function."""
+
+    def test_graph(self):
+        G = nx.path_graph(4)
+        S = {1, 2}
+        T = set(G) - S
+        size = nx.normalized_cut_size(G, S, T)
+        # The cut looks like this: o-{-o--o-}-o
+        expected = 2 * ((1 / 4) + (1 / 2))
+        assert expected == size
+        # Test with no input T
+        assert expected == nx.normalized_cut_size(G, S)
+
+    def test_directed(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+        S = {1, 2}
+        T = set(G) - S
+        size = nx.normalized_cut_size(G, S, T)
+        # The cut looks like this: o-{->o-->o-}->o
+        expected = 2 * ((1 / 2) + (1 / 1))
+        assert expected == size
+        # Test with no input T
+        assert expected == nx.normalized_cut_size(G, S)
+
+
+class TestConductance:
+    """Unit tests for the :func:`~networkx.conductance` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        # Consider the singleton sets containing the "bridge" nodes.
+        # There is only one cut edge, and each set has volume five.
+        S = {4}
+        T = {5}
+        conductance = nx.conductance(G, S, T)
+        expected = 1 / 5
+        assert expected == conductance
+        # Test with no input T
+        G2 = nx.barbell_graph(3, 0)
+        # There is only one cut edge, and each set has volume seven.
+        S2 = {0, 1, 2}
+        assert nx.conductance(G2, S2) == 1 / 7
+
+
+class TestEdgeExpansion:
+    """Unit tests for the :func:`~networkx.edge_expansion` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        S = set(range(5))
+        T = set(G) - S
+        expansion = nx.edge_expansion(G, S, T)
+        expected = 1 / 5
+        assert expected == expansion
+        # Test with no input T
+        assert expected == nx.edge_expansion(G, S)
+
+
+class TestNodeExpansion:
+    """Unit tests for the :func:`~networkx.node_expansion` function."""
+
+    def test_graph(self):
+        G = nx.path_graph(8)
+        S = {3, 4, 5}
+        expansion = nx.node_expansion(G, S)
+        # The neighborhood of S has cardinality five, and S has
+        # cardinality three.
+        expected = 5 / 3
+        assert expected == expansion
+
+
+class TestBoundaryExpansion:
+    """Unit tests for the :func:`~networkx.boundary_expansion` function."""
+
+    def test_graph(self):
+        G = nx.complete_graph(10)
+        S = set(range(4))
+        expansion = nx.boundary_expansion(G, S)
+        # The node boundary of S has cardinality six, and S has
+        # cardinality three.
+        expected = 6 / 4
+        assert expected == expansion
+
+
+class TestMixingExpansion:
+    """Unit tests for the :func:`~networkx.mixing_expansion` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        S = set(range(5))
+        T = set(G) - S
+        expansion = nx.mixing_expansion(G, S, T)
+        # There is one cut edge, and the total number of edges in the
+        # graph is twice the total number of edges in a clique of size
+        # five, plus one more for the bridge.
+        expected = 1 / (2 * (5 * 4 + 1))
+        assert expected == expansion
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diff --git a/shared/package-files/d8/d88e071aebf499d164f62a152c9fcb42231496046c26f8f6e4f639499990a123/aminmax_cpu_dispatch.h b/shared/package-files/d8/d88e071aebf499d164f62a152c9fcb42231496046c26f8f6e4f639499990a123/aminmax_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee29be6ffc8a097b1eef9bb8e69fa002f28030f0
--- /dev/null
+++ b/shared/package-files/d8/d88e071aebf499d164f62a152c9fcb42231496046c26f8f6e4f639499990a123/aminmax_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> aminmax(const at::Tensor & self, c10::optional<int64_t> dim=c10::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> aminmax_out(at::Tensor & min, at::Tensor & max, const at::Tensor & self, c10::optional<int64_t> dim=c10::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> aminmax_outf(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & min, at::Tensor & max);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/d8/d88e7a2a3b63ba318e037c3bc283c334d7130dcec7b9066bd133010290db8fa4/test_pickle.py b/shared/package-files/d8/d88e7a2a3b63ba318e037c3bc283c334d7130dcec7b9066bd133010290db8fa4/test_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..05f4a20ee42d8dcaa2a5ef57435b50f9e89846aa
--- /dev/null
+++ b/shared/package-files/d8/d88e7a2a3b63ba318e037c3bc283c334d7130dcec7b9066bd133010290db8fa4/test_pickle.py
@@ -0,0 +1,658 @@
+"""
+manage legacy pickle tests
+
+How to add pickle tests:
+
+1. Install pandas version intended to output the pickle.
+
+2. Execute "generate_legacy_storage_files.py" to create the pickle.
+$ python generate_legacy_storage_files.py <output_dir> pickle
+
+3. Move the created pickle to "data/legacy_pickle/<version>" directory.
+"""
+from __future__ import annotations
+
+from array import array
+import bz2
+import datetime
+import functools
+from functools import partial
+import gzip
+import io
+import os
+from pathlib import Path
+import pickle
+import shutil
+import tarfile
+from typing import Any
+import uuid
+import zipfile
+
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    get_lzma_file,
+    is_platform_little_endian,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.compressors import flatten_buffer
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.tests.io.generate_legacy_storage_files import create_pickle_data
+
+import pandas.io.common as icom
+from pandas.tseries.offsets import (
+    Day,
+    MonthEnd,
+)
+
+
+# ---------------------
+# comparison functions
+# ---------------------
+def compare_element(result, expected, typ):
+    if isinstance(expected, Index):
+        tm.assert_index_equal(expected, result)
+        return
+
+    if typ.startswith("sp_"):
+        tm.assert_equal(result, expected)
+    elif typ == "timestamp":
+        if expected is pd.NaT:
+            assert result is pd.NaT
+        else:
+            assert result == expected
+    else:
+        comparator = getattr(tm, f"assert_{typ}_equal", tm.assert_almost_equal)
+        comparator(result, expected)
+
+
+# ---------------------
+# tests
+# ---------------------
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        b"123",
+        b"123456",
+        bytearray(b"123"),
+        memoryview(b"123"),
+        pickle.PickleBuffer(b"123"),
+        array("I", [1, 2, 3]),
+        memoryview(b"123456").cast("B", (3, 2)),
+        memoryview(b"123456").cast("B", (3, 2))[::2],
+        np.arange(12).reshape((3, 4), order="C"),
+        np.arange(12).reshape((3, 4), order="F"),
+        np.arange(12).reshape((3, 4), order="C")[:, ::2],
+    ],
+)
+def test_flatten_buffer(data):
+    result = flatten_buffer(data)
+    expected = memoryview(data).tobytes("A")
+    assert result == expected
+    if isinstance(data, (bytes, bytearray)):
+        assert result is data
+    elif isinstance(result, memoryview):
+        assert result.ndim == 1
+        assert result.format == "B"
+        assert result.contiguous
+        assert result.shape == (result.nbytes,)
+
+
+def test_pickles(datapath):
+    if not is_platform_little_endian():
+        pytest.skip("known failure on non-little endian")
+
+    # For loop for compat with --strict-data-files
+    for legacy_pickle in Path(__file__).parent.glob("data/legacy_pickle/*/*.p*kl*"):
+        legacy_pickle = datapath(legacy_pickle)
+
+        data = pd.read_pickle(legacy_pickle)
+
+        for typ, dv in data.items():
+            for dt, result in dv.items():
+                expected = data[typ][dt]
+
+                if typ == "series" and dt == "ts":
+                    # GH 7748
+                    tm.assert_series_equal(result, expected)
+                    assert result.index.freq == expected.index.freq
+                    assert not result.index.freq.normalize
+                    tm.assert_series_equal(result > 0, expected > 0)
+
+                    # GH 9291
+                    freq = result.index.freq
+                    assert freq + Day(1) == Day(2)
+
+                    res = freq + pd.Timedelta(hours=1)
+                    assert isinstance(res, pd.Timedelta)
+                    assert res == pd.Timedelta(days=1, hours=1)
+
+                    res = freq + pd.Timedelta(nanoseconds=1)
+                    assert isinstance(res, pd.Timedelta)
+                    assert res == pd.Timedelta(days=1, nanoseconds=1)
+                elif typ == "index" and dt == "period":
+                    tm.assert_index_equal(result, expected)
+                    assert isinstance(result.freq, MonthEnd)
+                    assert result.freq == MonthEnd()
+                    assert result.freqstr == "M"
+                    tm.assert_index_equal(result.shift(2), expected.shift(2))
+                elif typ == "series" and dt in ("dt_tz", "cat"):
+                    tm.assert_series_equal(result, expected)
+                elif typ == "frame" and dt in (
+                    "dt_mixed_tzs",
+                    "cat_onecol",
+                    "cat_and_float",
+                ):
+                    tm.assert_frame_equal(result, expected)
+                else:
+                    compare_element(result, expected, typ)
+
+
+def python_pickler(obj, path):
+    with open(path, "wb") as fh:
+        pickle.dump(obj, fh, protocol=-1)
+
+
+def python_unpickler(path):
+    with open(path, "rb") as fh:
+        fh.seek(0)
+        return pickle.load(fh)
+
+
+def flatten(data: dict) -> list[tuple[str, Any]]:
+    """Flatten create_pickle_data"""
+    return [
+        (typ, example)
+        for typ, examples in data.items()
+        for example in examples.values()
+    ]
+
+
+@pytest.mark.parametrize(
+    "pickle_writer",
+    [
+        pytest.param(python_pickler, id="python"),
+        pytest.param(pd.to_pickle, id="pandas_proto_default"),
+        pytest.param(
+            functools.partial(pd.to_pickle, protocol=pickle.HIGHEST_PROTOCOL),
+            id="pandas_proto_highest",
+        ),
+        pytest.param(functools.partial(pd.to_pickle, protocol=4), id="pandas_proto_4"),
+        pytest.param(
+            functools.partial(pd.to_pickle, protocol=5),
+            id="pandas_proto_5",
+        ),
+    ],
+)
+@pytest.mark.parametrize("writer", [pd.to_pickle, python_pickler])
+@pytest.mark.parametrize("typ, expected", flatten(create_pickle_data()))
+def test_round_trip_current(typ, expected, pickle_writer, writer):
+    with tm.ensure_clean() as path:
+        # test writing with each pickler
+        pickle_writer(expected, path)
+
+        # test reading with each unpickler
+        result = pd.read_pickle(path)
+        compare_element(result, expected, typ)
+
+        result = python_unpickler(path)
+        compare_element(result, expected, typ)
+
+        # and the same for file objects (GH 35679)
+        with open(path, mode="wb") as handle:
+            writer(expected, path)
+            handle.seek(0)  # shouldn't close file handle
+        with open(path, mode="rb") as handle:
+            result = pd.read_pickle(handle)
+            handle.seek(0)  # shouldn't close file handle
+        compare_element(result, expected, typ)
+
+
+def test_pickle_path_pathlib():
+    df = DataFrame(
+        1.1 * np.arange(120).reshape((30, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=Index([f"i-{i}" for i in range(30)], dtype=object),
+    )
+    result = tm.round_trip_pathlib(df.to_pickle, pd.read_pickle)
+    tm.assert_frame_equal(df, result)
+
+
+def test_pickle_path_localpath():
+    df = DataFrame(
+        1.1 * np.arange(120).reshape((30, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=Index([f"i-{i}" for i in range(30)], dtype=object),
+    )
+    result = tm.round_trip_localpath(df.to_pickle, pd.read_pickle)
+    tm.assert_frame_equal(df, result)
+
+
+# ---------------------
+# test pickle compression
+# ---------------------
+
+
+@pytest.fixture
+def get_random_path():
+    return f"__{uuid.uuid4()}__.pickle"
+
+
+class TestCompression:
+    _extension_to_compression = icom.extension_to_compression
+
+    def compress_file(self, src_path, dest_path, compression):
+        if compression is None:
+            shutil.copyfile(src_path, dest_path)
+            return
+
+        if compression == "gzip":
+            f = gzip.open(dest_path, "w")
+        elif compression == "bz2":
+            f = bz2.BZ2File(dest_path, "w")
+        elif compression == "zip":
+            with zipfile.ZipFile(dest_path, "w", compression=zipfile.ZIP_DEFLATED) as f:
+                f.write(src_path, os.path.basename(src_path))
+        elif compression == "tar":
+            with open(src_path, "rb") as fh:
+                with tarfile.open(dest_path, mode="w") as tar:
+                    tarinfo = tar.gettarinfo(src_path, os.path.basename(src_path))
+                    tar.addfile(tarinfo, fh)
+        elif compression == "xz":
+            f = get_lzma_file()(dest_path, "w")
+        elif compression == "zstd":
+            f = import_optional_dependency("zstandard").open(dest_path, "wb")
+        else:
+            msg = f"Unrecognized compression type: {compression}"
+            raise ValueError(msg)
+
+        if compression not in ["zip", "tar"]:
+            with open(src_path, "rb") as fh:
+                with f:
+                    f.write(fh.read())
+
+    def test_write_explicit(self, compression, get_random_path):
+        base = get_random_path
+        path1 = base + ".compressed"
+        path2 = base + ".raw"
+
+        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
+            df = DataFrame(
+                1.1 * np.arange(120).reshape((30, 4)),
+                columns=Index(list("ABCD"), dtype=object),
+                index=Index([f"i-{i}" for i in range(30)], dtype=object),
+            )
+
+            # write to compressed file
+            df.to_pickle(p1, compression=compression)
+
+            # decompress
+            with tm.decompress_file(p1, compression=compression) as f:
+                with open(p2, "wb") as fh:
+                    fh.write(f.read())
+
+            # read decompressed file
+            df2 = pd.read_pickle(p2, compression=None)
+
+            tm.assert_frame_equal(df, df2)
+
+    @pytest.mark.parametrize("compression", ["", "None", "bad", "7z"])
+    def test_write_explicit_bad(self, compression, get_random_path):
+        with pytest.raises(ValueError, match="Unrecognized compression type"):
+            with tm.ensure_clean(get_random_path) as path:
+                df = DataFrame(
+                    1.1 * np.arange(120).reshape((30, 4)),
+                    columns=Index(list("ABCD"), dtype=object),
+                    index=Index([f"i-{i}" for i in range(30)], dtype=object),
+                )
+                df.to_pickle(path, compression=compression)
+
+    def test_write_infer(self, compression_ext, get_random_path):
+        base = get_random_path
+        path1 = base + compression_ext
+        path2 = base + ".raw"
+        compression = self._extension_to_compression.get(compression_ext.lower())
+
+        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
+            df = DataFrame(
+                1.1 * np.arange(120).reshape((30, 4)),
+                columns=Index(list("ABCD"), dtype=object),
+                index=Index([f"i-{i}" for i in range(30)], dtype=object),
+            )
+
+            # write to compressed file by inferred compression method
+            df.to_pickle(p1)
+
+            # decompress
+            with tm.decompress_file(p1, compression=compression) as f:
+                with open(p2, "wb") as fh:
+                    fh.write(f.read())
+
+            # read decompressed file
+            df2 = pd.read_pickle(p2, compression=None)
+
+            tm.assert_frame_equal(df, df2)
+
+    def test_read_explicit(self, compression, get_random_path):
+        base = get_random_path
+        path1 = base + ".raw"
+        path2 = base + ".compressed"
+
+        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
+            df = DataFrame(
+                1.1 * np.arange(120).reshape((30, 4)),
+                columns=Index(list("ABCD"), dtype=object),
+                index=Index([f"i-{i}" for i in range(30)], dtype=object),
+            )
+
+            # write to uncompressed file
+            df.to_pickle(p1, compression=None)
+
+            # compress
+            self.compress_file(p1, p2, compression=compression)
+
+            # read compressed file
+            df2 = pd.read_pickle(p2, compression=compression)
+            tm.assert_frame_equal(df, df2)
+
+    def test_read_infer(self, compression_ext, get_random_path):
+        base = get_random_path
+        path1 = base + ".raw"
+        path2 = base + compression_ext
+        compression = self._extension_to_compression.get(compression_ext.lower())
+
+        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
+            df = DataFrame(
+                1.1 * np.arange(120).reshape((30, 4)),
+                columns=Index(list("ABCD"), dtype=object),
+                index=Index([f"i-{i}" for i in range(30)], dtype=object),
+            )
+
+            # write to uncompressed file
+            df.to_pickle(p1, compression=None)
+
+            # compress
+            self.compress_file(p1, p2, compression=compression)
+
+            # read compressed file by inferred compression method
+            df2 = pd.read_pickle(p2)
+            tm.assert_frame_equal(df, df2)
+
+
+# ---------------------
+# test pickle compression
+# ---------------------
+
+
+class TestProtocol:
+    @pytest.mark.parametrize("protocol", [-1, 0, 1, 2])
+    def test_read(self, protocol, get_random_path):
+        with tm.ensure_clean(get_random_path) as path:
+            df = DataFrame(
+                1.1 * np.arange(120).reshape((30, 4)),
+                columns=Index(list("ABCD"), dtype=object),
+                index=Index([f"i-{i}" for i in range(30)], dtype=object),
+            )
+            df.to_pickle(path, protocol=protocol)
+            df2 = pd.read_pickle(path)
+            tm.assert_frame_equal(df, df2)
+
+
+@pytest.mark.parametrize(
+    ["pickle_file", "excols"],
+    [
+        ("test_py27.pkl", Index(["a", "b", "c"], dtype=object)),
+        (
+            "test_mi_py27.pkl",
+            pd.MultiIndex(
+                [
+                    Index(["a", "b", "c"], dtype=object),
+                    Index(["A", "B", "C"], dtype=object),
+                ],
+                [np.array([0, 1, 2]), np.array([0, 1, 2])],
+            ),
+        ),
+    ],
+)
+def test_unicode_decode_error(datapath, pickle_file, excols):
+    # pickle file written with py27, should be readable without raising
+    #  UnicodeDecodeError, see GH#28645 and GH#31988
+    path = datapath("io", "data", "pickle", pickle_file)
+    df = pd.read_pickle(path)
+
+    # just test the columns are correct since the values are random
+    tm.assert_index_equal(df.columns, excols)
+
+
+# ---------------------
+# tests for buffer I/O
+# ---------------------
+
+
+def test_pickle_buffer_roundtrip():
+    with tm.ensure_clean() as path:
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )
+        with open(path, "wb") as fh:
+            df.to_pickle(fh)
+        with open(path, "rb") as fh:
+            result = pd.read_pickle(fh)
+        tm.assert_frame_equal(df, result)
+
+
+# ---------------------
+# tests for URL I/O
+# ---------------------
+
+
+@pytest.mark.parametrize(
+    "mockurl", ["http://url.com", "ftp://test.com", "http://gzip.com"]
+)
+def test_pickle_generalurl_read(monkeypatch, mockurl):
+    def python_pickler(obj, path):
+        with open(path, "wb") as fh:
+            pickle.dump(obj, fh, protocol=-1)
+
+    class MockReadResponse:
+        def __init__(self, path) -> None:
+            self.file = open(path, "rb")
+            if "gzip" in path:
+                self.headers = {"Content-Encoding": "gzip"}
+            else:
+                self.headers = {"Content-Encoding": ""}
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, *args):
+            self.close()
+
+        def read(self):
+            return self.file.read()
+
+        def close(self):
+            return self.file.close()
+
+    with tm.ensure_clean() as path:
+
+        def mock_urlopen_read(*args, **kwargs):
+            return MockReadResponse(path)
+
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )
+        python_pickler(df, path)
+        monkeypatch.setattr("urllib.request.urlopen", mock_urlopen_read)
+        result = pd.read_pickle(mockurl)
+        tm.assert_frame_equal(df, result)
+
+
+def test_pickle_fsspec_roundtrip():
+    pytest.importorskip("fsspec")
+    with tm.ensure_clean():
+        mockurl = "memory://mockfile"
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )
+        df.to_pickle(mockurl)
+        result = pd.read_pickle(mockurl)
+        tm.assert_frame_equal(df, result)
+
+
+class MyTz(datetime.tzinfo):
+    def __init__(self) -> None:
+        pass
+
+
+def test_read_pickle_with_subclass():
+    # GH 12163
+    expected = Series(dtype=object), MyTz()
+    result = tm.round_trip_pickle(expected)
+
+    tm.assert_series_equal(result[0], expected[0])
+    assert isinstance(result[1], MyTz)
+
+
+def test_pickle_binary_object_compression(compression):
+    """
+    Read/write from binary file-objects w/wo compression.
+
+    GH 26237, GH 29054, and GH 29570
+    """
+    df = DataFrame(
+        1.1 * np.arange(120).reshape((30, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=Index([f"i-{i}" for i in range(30)], dtype=object),
+    )
+
+    # reference for compression
+    with tm.ensure_clean() as path:
+        df.to_pickle(path, compression=compression)
+        reference = Path(path).read_bytes()
+
+    # write
+    buffer = io.BytesIO()
+    df.to_pickle(buffer, compression=compression)
+    buffer.seek(0)
+
+    # gzip  and zip safe the filename: cannot compare the compressed content
+    assert buffer.getvalue() == reference or compression in ("gzip", "zip", "tar")
+
+    # read
+    read_df = pd.read_pickle(buffer, compression=compression)
+    buffer.seek(0)
+    tm.assert_frame_equal(df, read_df)
+
+
+def test_pickle_dataframe_with_multilevel_index(
+    multiindex_year_month_day_dataframe_random_data,
+    multiindex_dataframe_random_data,
+):
+    ymd = multiindex_year_month_day_dataframe_random_data
+    frame = multiindex_dataframe_random_data
+
+    def _test_roundtrip(frame):
+        unpickled = tm.round_trip_pickle(frame)
+        tm.assert_frame_equal(frame, unpickled)
+
+    _test_roundtrip(frame)
+    _test_roundtrip(frame.T)
+    _test_roundtrip(ymd)
+    _test_roundtrip(ymd.T)
+
+
+def test_pickle_timeseries_periodindex():
+    # GH#2891
+    prng = period_range("1/1/2011", "1/1/2012", freq="M")
+    ts = Series(np.random.default_rng(2).standard_normal(len(prng)), prng)
+    new_ts = tm.round_trip_pickle(ts)
+    assert new_ts.index.freqstr == "M"
+
+
+@pytest.mark.parametrize(
+    "name", [777, 777.0, "name", datetime.datetime(2001, 11, 11), (1, 2)]
+)
+def test_pickle_preserve_name(name):
+    unpickled = tm.round_trip_pickle(Series(np.arange(10, dtype=np.float64), name=name))
+    assert unpickled.name == name
+
+
+def test_pickle_datetimes(datetime_series):
+    unp_ts = tm.round_trip_pickle(datetime_series)
+    tm.assert_series_equal(unp_ts, datetime_series)
+
+
+def test_pickle_strings(string_series):
+    unp_series = tm.round_trip_pickle(string_series)
+    tm.assert_series_equal(unp_series, string_series)
+
+
+@td.skip_array_manager_invalid_test
+def test_pickle_preserves_block_ndim():
+    # GH#37631
+    ser = Series(list("abc")).astype("category").iloc[[0]]
+    res = tm.round_trip_pickle(ser)
+
+    assert res._mgr.blocks[0].ndim == 1
+    assert res._mgr.blocks[0].shape == (1,)
+
+    # GH#37631 OP issue was about indexing, underlying problem was pickle
+    tm.assert_series_equal(res[[True]], ser)
+
+
+@pytest.mark.parametrize("protocol", [pickle.DEFAULT_PROTOCOL, pickle.HIGHEST_PROTOCOL])
+def test_pickle_big_dataframe_compression(protocol, compression):
+    # GH#39002
+    df = DataFrame(range(100000))
+    result = tm.round_trip_pathlib(
+        partial(df.to_pickle, protocol=protocol, compression=compression),
+        partial(pd.read_pickle, compression=compression),
+    )
+    tm.assert_frame_equal(df, result)
+
+
+def test_pickle_frame_v124_unpickle_130(datapath):
+    # GH#42345 DataFrame created in 1.2.x, unpickle in 1.3.x
+    path = datapath(
+        Path(__file__).parent,
+        "data",
+        "legacy_pickle",
+        "1.2.4",
+        "empty_frame_v1_2_4-GH#42345.pkl",
+    )
+    with open(path, "rb") as fd:
+        df = pickle.load(fd)
+
+    expected = DataFrame(index=[], columns=[])
+    tm.assert_frame_equal(df, expected)
+
+
+def test_pickle_pos_args_deprecation():
+    # GH-54229
+    df = DataFrame({"a": [1, 2, 3]})
+    msg = (
+        r"Starting with pandas version 3.0 all arguments of to_pickle except for the "
+        r"argument 'path' will be keyword-only."
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        buffer = io.BytesIO()
+        df.to_pickle(buffer, "infer")
diff --git a/shared/package-files/d8/d8928bc983c6345942c083a55319086a6a97b02de0f24a457d68b70b5aaa6ae4/_remove_batch_dim.h b/shared/package-files/d8/d8928bc983c6345942c083a55319086a6a97b02de0f24a457d68b70b5aaa6ae4/_remove_batch_dim.h
new file mode 100644
index 0000000000000000000000000000000000000000..a864fb1672799a233cd12cde95297f01ba7e3cc8
--- /dev/null
+++ b/shared/package-files/d8/d8928bc983c6345942c083a55319086a6a97b02de0f24a457d68b70b5aaa6ae4/_remove_batch_dim.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_remove_batch_dim_ops.h>
+
+namespace at {
+
+
+// aten::_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor
+inline at::Tensor _remove_batch_dim(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) {
+    return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim);
+}
+
+}
diff --git a/shared/package-files/d8/d894ca40ca9c79ffa9bea65a722bd38c4924da5270dc263b8578dcd5b80d4af3/_foreach_zero_native.h b/shared/package-files/d8/d894ca40ca9c79ffa9bea65a722bd38c4924da5270dc263b8578dcd5b80d4af3/_foreach_zero_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e98e622b3b846997481ef15a98ac27b95233a2a0
--- /dev/null
+++ b/shared/package-files/d8/d894ca40ca9c79ffa9bea65a722bd38c4924da5270dc263b8578dcd5b80d4af3/_foreach_zero_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> _foreach_zero(at::TensorList self);
+TORCH_API void _foreach_zero_out(at::TensorList self, at::TensorList out);
+TORCH_API void foreach_tensor_zero_slow_(at::TensorList self);
+TORCH_API void foreach_tensor_zero_cuda_(at::TensorList self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/d8/d8960d798b6b3f3d49ccb48b3b77781ac4bccc953c8d8fc8fc2475548f605ab0/ygen.py b/shared/package-files/d8/d8960d798b6b3f3d49ccb48b3b77781ac4bccc953c8d8fc8fc2475548f605ab0/ygen.py
new file mode 100644
index 0000000000000000000000000000000000000000..acf5ca1a37b69389256227c570c65eed96e3228e
--- /dev/null
+++ b/shared/package-files/d8/d8960d798b6b3f3d49ccb48b3b77781ac4bccc953c8d8fc8fc2475548f605ab0/ygen.py
@@ -0,0 +1,74 @@
+# ply: ygen.py
+#
+# This is a support program that auto-generates different versions of the YACC parsing
+# function with different features removed for the purposes of performance.
+#
+# Users should edit the method LParser.parsedebug() in yacc.py.   The source code 
+# for that method is then used to create the other methods.   See the comments in
+# yacc.py for further details.
+
+import os.path
+import shutil
+
+def get_source_range(lines, tag):
+    srclines = enumerate(lines)
+    start_tag = '#--! %s-start' % tag
+    end_tag = '#--! %s-end' % tag
+
+    for start_index, line in srclines:
+        if line.strip().startswith(start_tag):
+            break
+
+    for end_index, line in srclines:
+        if line.strip().endswith(end_tag):
+            break
+
+    return (start_index + 1, end_index)
+
+def filter_section(lines, tag):
+    filtered_lines = []
+    include = True
+    tag_text = '#--! %s' % tag
+    for line in lines:
+        if line.strip().startswith(tag_text):
+            include = not include
+        elif include:
+            filtered_lines.append(line)
+    return filtered_lines
+
+def main():
+    dirname = os.path.dirname(__file__)
+    shutil.copy2(os.path.join(dirname, 'yacc.py'), os.path.join(dirname, 'yacc.py.bak'))
+    with open(os.path.join(dirname, 'yacc.py'), 'r') as f:
+        lines = f.readlines()
+
+    parse_start, parse_end = get_source_range(lines, 'parsedebug')
+    parseopt_start, parseopt_end = get_source_range(lines, 'parseopt')
+    parseopt_notrack_start, parseopt_notrack_end = get_source_range(lines, 'parseopt-notrack')
+
+    # Get the original source
+    orig_lines = lines[parse_start:parse_end]
+
+    # Filter the DEBUG sections out
+    parseopt_lines = filter_section(orig_lines, 'DEBUG')
+
+    # Filter the TRACKING sections out
+    parseopt_notrack_lines = filter_section(parseopt_lines, 'TRACKING')
+
+    # Replace the parser source sections with updated versions
+    lines[parseopt_notrack_start:parseopt_notrack_end] = parseopt_notrack_lines
+    lines[parseopt_start:parseopt_end] = parseopt_lines
+
+    lines = [line.rstrip()+'\n' for line in lines]
+    with open(os.path.join(dirname, 'yacc.py'), 'w') as f:
+        f.writelines(lines)
+
+    print('Updated yacc.py')
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+
diff --git a/shared/package-files/d8/d898761ba09ed6acfd226cefe5eb7fbfeb0cc58eb68f7ac5767a75519fcf181f/sets.py b/shared/package-files/d8/d898761ba09ed6acfd226cefe5eb7fbfeb0cc58eb68f7ac5767a75519fcf181f/sets.py
new file mode 100644
index 0000000000000000000000000000000000000000..b53bcfedef30681ea4450c6dad9120bc2ea9016e
--- /dev/null
+++ b/shared/package-files/d8/d898761ba09ed6acfd226cefe5eb7fbfeb0cc58eb68f7ac5767a75519fcf181f/sets.py
@@ -0,0 +1,772 @@
+"""
+Handlers for predicates related to set membership: integer, rational, etc.
+"""
+
+from sympy.assumptions import Q, ask
+from sympy.core import Add, Basic, Expr, Mul, Pow, S
+from sympy.core.numbers import (AlgebraicNumber, ComplexInfinity, Exp1, Float,
+    GoldenRatio, ImaginaryUnit, Infinity, Integer, NaN, NegativeInfinity,
+    Number, NumberSymbol, Pi, pi, Rational, TribonacciConstant, E)
+from sympy.core.logic import fuzzy_bool
+from sympy.functions import (Abs, acos, acot, asin, atan, cos, cot, exp, im,
+    log, re, sin, tan)
+from sympy.core.numbers import I
+from sympy.core.relational import Eq
+from sympy.functions.elementary.complexes import conjugate
+from sympy.matrices import Determinant, MatrixBase, Trace
+from sympy.matrices.expressions.matexpr import MatrixElement
+
+from sympy.multipledispatch import MDNotImplementedError
+
+from .common import test_closed_group
+from ..predicates.sets import (IntegerPredicate, RationalPredicate,
+    IrrationalPredicate, RealPredicate, ExtendedRealPredicate,
+    HermitianPredicate, ComplexPredicate, ImaginaryPredicate,
+    AntihermitianPredicate, AlgebraicPredicate)
+
+
+# IntegerPredicate
+
+def _IntegerPredicate_number(expr, assumptions):
+    # helper function
+        try:
+            i = int(expr.round())
+            if not (expr - i).equals(0):
+                raise TypeError
+            return True
+        except TypeError:
+            return False
+
+@IntegerPredicate.register_many(int, Integer) # type:ignore
+def _(expr, assumptions):
+    return True
+
+@IntegerPredicate.register_many(Exp1, GoldenRatio, ImaginaryUnit, Infinity,
+        NegativeInfinity, Pi, Rational, TribonacciConstant)
+def _(expr, assumptions):
+    return False
+
+@IntegerPredicate.register(Expr)
+def _(expr, assumptions):
+    ret = expr.is_integer
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@IntegerPredicate.register_many(Add, Pow)
+def _(expr, assumptions):
+    """
+    * Integer + Integer       -> Integer
+    * Integer + !Integer      -> !Integer
+    * !Integer + !Integer -> ?
+    """
+    if expr.is_number:
+        return _IntegerPredicate_number(expr, assumptions)
+    return test_closed_group(expr, assumptions, Q.integer)
+
+@IntegerPredicate.register(Mul)
+def _(expr, assumptions):
+    """
+    * Integer*Integer      -> Integer
+    * Integer*Irrational   -> !Integer
+    * Odd/Even             -> !Integer
+    * Integer*Rational     -> ?
+    """
+    if expr.is_number:
+        return _IntegerPredicate_number(expr, assumptions)
+    _output = True
+    for arg in expr.args:
+        if not ask(Q.integer(arg), assumptions):
+            if arg.is_Rational:
+                if arg.q == 2:
+                    return ask(Q.even(2*expr), assumptions)
+                if ~(arg.q & 1):
+                    return None
+            elif ask(Q.irrational(arg), assumptions):
+                if _output:
+                    _output = False
+                else:
+                    return
+            else:
+                return
+
+    return _output
+
+@IntegerPredicate.register(Abs)
+def _(expr, assumptions):
+    return ask(Q.integer(expr.args[0]), assumptions)
+
+@IntegerPredicate.register_many(Determinant, MatrixElement, Trace)
+def _(expr, assumptions):
+    return ask(Q.integer_elements(expr.args[0]), assumptions)
+
+
+# RationalPredicate
+
+@RationalPredicate.register(Rational)
+def _(expr, assumptions):
+    return True
+
+@RationalPredicate.register(Float)
+def _(expr, assumptions):
+    return None
+
+@RationalPredicate.register_many(Exp1, GoldenRatio, ImaginaryUnit, Infinity,
+    NegativeInfinity, Pi, TribonacciConstant)
+def _(expr, assumptions):
+    return False
+
+@RationalPredicate.register(Expr)
+def _(expr, assumptions):
+    ret = expr.is_rational
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@RationalPredicate.register_many(Add, Mul)
+def _(expr, assumptions):
+    """
+    * Rational + Rational     -> Rational
+    * Rational + !Rational    -> !Rational
+    * !Rational + !Rational   -> ?
+    """
+    if expr.is_number:
+        if expr.as_real_imag()[1]:
+            return False
+    return test_closed_group(expr, assumptions, Q.rational)
+
+@RationalPredicate.register(Pow)
+def _(expr, assumptions):
+    """
+    * Rational ** Integer      -> Rational
+    * Irrational ** Rational   -> Irrational
+    * Rational ** Irrational   -> ?
+    """
+    if expr.base == E:
+        x = expr.exp
+        if ask(Q.rational(x), assumptions):
+            return ask(~Q.nonzero(x), assumptions)
+        return
+
+    if ask(Q.integer(expr.exp), assumptions):
+        return ask(Q.rational(expr.base), assumptions)
+    elif ask(Q.rational(expr.exp), assumptions):
+        if ask(Q.prime(expr.base), assumptions):
+            return False
+
+@RationalPredicate.register_many(asin, atan, cos, sin, tan)
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.rational(x), assumptions):
+        return ask(~Q.nonzero(x), assumptions)
+
+@RationalPredicate.register(exp)
+def _(expr, assumptions):
+    x = expr.exp
+    if ask(Q.rational(x), assumptions):
+        return ask(~Q.nonzero(x), assumptions)
+
+@RationalPredicate.register_many(acot, cot)
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.rational(x), assumptions):
+        return False
+
+@RationalPredicate.register_many(acos, log)
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.rational(x), assumptions):
+        return ask(~Q.nonzero(x - 1), assumptions)
+
+
+# IrrationalPredicate
+
+@IrrationalPredicate.register(Expr)
+def _(expr, assumptions):
+    ret = expr.is_irrational
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@IrrationalPredicate.register(Basic)
+def _(expr, assumptions):
+    _real = ask(Q.real(expr), assumptions)
+    if _real:
+        _rational = ask(Q.rational(expr), assumptions)
+        if _rational is None:
+            return None
+        return not _rational
+    else:
+        return _real
+
+
+# RealPredicate
+
+def _RealPredicate_number(expr, assumptions):
+    # let as_real_imag() work first since the expression may
+    # be simpler to evaluate
+    i = expr.as_real_imag()[1].evalf(2)
+    if i._prec != 1:
+        return not i
+    # allow None to be returned if we couldn't show for sure
+    # that i was 0
+
+@RealPredicate.register_many(Abs, Exp1, Float, GoldenRatio, im, Pi, Rational,
+    re, TribonacciConstant)
+def _(expr, assumptions):
+    return True
+
+@RealPredicate.register_many(ImaginaryUnit, Infinity, NegativeInfinity)
+def _(expr, assumptions):
+    return False
+
+@RealPredicate.register(Expr)
+def _(expr, assumptions):
+    ret = expr.is_real
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@RealPredicate.register(Add)
+def _(expr, assumptions):
+    """
+    * Real + Real              -> Real
+    * Real + (Complex & !Real) -> !Real
+    """
+    if expr.is_number:
+        return _RealPredicate_number(expr, assumptions)
+    return test_closed_group(expr, assumptions, Q.real)
+
+@RealPredicate.register(Mul)
+def _(expr, assumptions):
+    """
+    * Real*Real               -> Real
+    * Real*Imaginary          -> !Real
+    * Imaginary*Imaginary     -> Real
+    """
+    if expr.is_number:
+        return _RealPredicate_number(expr, assumptions)
+    result = True
+    for arg in expr.args:
+        if ask(Q.real(arg), assumptions):
+            pass
+        elif ask(Q.imaginary(arg), assumptions):
+            result = result ^ True
+        else:
+            break
+    else:
+        return result
+
+@RealPredicate.register(Pow)
+def _(expr, assumptions):
+    """
+    * Real**Integer              -> Real
+    * Positive**Real             -> Real
+    * Real**(Integer/Even)       -> Real if base is nonnegative
+    * Real**(Integer/Odd)        -> Real
+    * Imaginary**(Integer/Even)  -> Real
+    * Imaginary**(Integer/Odd)   -> not Real
+    * Imaginary**Real            -> ? since Real could be 0 (giving real)
+                                    or 1 (giving imaginary)
+    * b**Imaginary               -> Real if log(b) is imaginary and b != 0
+                                    and exponent != integer multiple of
+                                    I*pi/log(b)
+    * Real**Real                 -> ? e.g. sqrt(-1) is imaginary and
+                                    sqrt(2) is not
+    """
+    if expr.is_number:
+        return _RealPredicate_number(expr, assumptions)
+
+    if expr.base == E:
+        return ask(
+            Q.integer(expr.exp/I/pi) | Q.real(expr.exp), assumptions
+        )
+
+    if expr.base.func == exp or (expr.base.is_Pow and expr.base.base == E):
+        if ask(Q.imaginary(expr.base.exp), assumptions):
+            if ask(Q.imaginary(expr.exp), assumptions):
+                return True
+        # If the i = (exp's arg)/(I*pi) is an integer or half-integer
+        # multiple of I*pi then 2*i will be an integer. In addition,
+        # exp(i*I*pi) = (-1)**i so the overall realness of the expr
+        # can be determined by replacing exp(i*I*pi) with (-1)**i.
+        i = expr.base.exp/I/pi
+        if ask(Q.integer(2*i), assumptions):
+            return ask(Q.real((S.NegativeOne**i)**expr.exp), assumptions)
+        return
+
+    if ask(Q.imaginary(expr.base), assumptions):
+        if ask(Q.integer(expr.exp), assumptions):
+            odd = ask(Q.odd(expr.exp), assumptions)
+            if odd is not None:
+                return not odd
+            return
+
+    if ask(Q.imaginary(expr.exp), assumptions):
+        imlog = ask(Q.imaginary(log(expr.base)), assumptions)
+        if imlog is not None:
+            # I**i -> real, log(I) is imag;
+            # (2*I)**i -> complex, log(2*I) is not imag
+            return imlog
+
+    if ask(Q.real(expr.base), assumptions):
+        if ask(Q.real(expr.exp), assumptions):
+            if expr.exp.is_Rational and \
+                    ask(Q.even(expr.exp.q), assumptions):
+                return ask(Q.positive(expr.base), assumptions)
+            elif ask(Q.integer(expr.exp), assumptions):
+                return True
+            elif ask(Q.positive(expr.base), assumptions):
+                return True
+            elif ask(Q.negative(expr.base), assumptions):
+                return False
+
+@RealPredicate.register_many(cos, sin)
+def _(expr, assumptions):
+    if ask(Q.real(expr.args[0]), assumptions):
+            return True
+
+@RealPredicate.register(exp)
+def _(expr, assumptions):
+    return ask(
+        Q.integer(expr.exp/I/pi) | Q.real(expr.exp), assumptions
+    )
+
+@RealPredicate.register(log)
+def _(expr, assumptions):
+    return ask(Q.positive(expr.args[0]), assumptions)
+
+@RealPredicate.register_many(Determinant, MatrixElement, Trace)
+def _(expr, assumptions):
+    return ask(Q.real_elements(expr.args[0]), assumptions)
+
+
+# ExtendedRealPredicate
+
+@ExtendedRealPredicate.register(object)
+def _(expr, assumptions):
+    return ask(Q.negative_infinite(expr)
+               | Q.negative(expr)
+               | Q.zero(expr)
+               | Q.positive(expr)
+               | Q.positive_infinite(expr),
+            assumptions)
+
+@ExtendedRealPredicate.register_many(Infinity, NegativeInfinity)
+def _(expr, assumptions):
+    return True
+
+@ExtendedRealPredicate.register_many(Add, Mul, Pow) # type:ignore
+def _(expr, assumptions):
+    return test_closed_group(expr, assumptions, Q.extended_real)
+
+
+# HermitianPredicate
+
+@HermitianPredicate.register(object) # type:ignore
+def _(expr, assumptions):
+    if isinstance(expr, MatrixBase):
+        return None
+    return ask(Q.real(expr), assumptions)
+
+@HermitianPredicate.register(Add) # type:ignore
+def _(expr, assumptions):
+    """
+    * Hermitian + Hermitian  -> Hermitian
+    * Hermitian + !Hermitian -> !Hermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    return test_closed_group(expr, assumptions, Q.hermitian)
+
+@HermitianPredicate.register(Mul) # type:ignore
+def _(expr, assumptions):
+    """
+    As long as there is at most only one noncommutative term:
+
+    * Hermitian*Hermitian         -> Hermitian
+    * Hermitian*Antihermitian     -> !Hermitian
+    * Antihermitian*Antihermitian -> Hermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    nccount = 0
+    result = True
+    for arg in expr.args:
+        if ask(Q.antihermitian(arg), assumptions):
+            result = result ^ True
+        elif not ask(Q.hermitian(arg), assumptions):
+            break
+        if ask(~Q.commutative(arg), assumptions):
+            nccount += 1
+            if nccount > 1:
+                break
+    else:
+        return result
+
+@HermitianPredicate.register(Pow) # type:ignore
+def _(expr, assumptions):
+    """
+    * Hermitian**Integer -> Hermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    if expr.base == E:
+        if ask(Q.hermitian(expr.exp), assumptions):
+            return True
+        raise MDNotImplementedError
+    if ask(Q.hermitian(expr.base), assumptions):
+        if ask(Q.integer(expr.exp), assumptions):
+            return True
+    raise MDNotImplementedError
+
+@HermitianPredicate.register_many(cos, sin) # type:ignore
+def _(expr, assumptions):
+    if ask(Q.hermitian(expr.args[0]), assumptions):
+        return True
+    raise MDNotImplementedError
+
+@HermitianPredicate.register(exp) # type:ignore
+def _(expr, assumptions):
+    if ask(Q.hermitian(expr.exp), assumptions):
+        return True
+    raise MDNotImplementedError
+
+@HermitianPredicate.register(MatrixBase) # type:ignore
+def _(mat, assumptions):
+    rows, cols = mat.shape
+    ret_val = True
+    for i in range(rows):
+        for j in range(i, cols):
+            cond = fuzzy_bool(Eq(mat[i, j], conjugate(mat[j, i])))
+            if cond is None:
+                ret_val = None
+            if cond == False:
+                return False
+    if ret_val is None:
+        raise MDNotImplementedError
+    return ret_val
+
+
+# ComplexPredicate
+
+@ComplexPredicate.register_many(Abs, cos, exp, im, ImaginaryUnit, log, Number, # type:ignore
+    NumberSymbol, re, sin)
+def _(expr, assumptions):
+    return True
+
+@ComplexPredicate.register_many(Infinity, NegativeInfinity) # type:ignore
+def _(expr, assumptions):
+    return False
+
+@ComplexPredicate.register(Expr) # type:ignore
+def _(expr, assumptions):
+    ret = expr.is_complex
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@ComplexPredicate.register_many(Add, Mul) # type:ignore
+def _(expr, assumptions):
+    return test_closed_group(expr, assumptions, Q.complex)
+
+@ComplexPredicate.register(Pow) # type:ignore
+def _(expr, assumptions):
+    if expr.base == E:
+        return True
+    return test_closed_group(expr, assumptions, Q.complex)
+
+@ComplexPredicate.register_many(Determinant, MatrixElement, Trace) # type:ignore
+def _(expr, assumptions):
+    return ask(Q.complex_elements(expr.args[0]), assumptions)
+
+@ComplexPredicate.register(NaN) # type:ignore
+def _(expr, assumptions):
+    return None
+
+
+# ImaginaryPredicate
+
+def _Imaginary_number(expr, assumptions):
+    # let as_real_imag() work first since the expression may
+    # be simpler to evaluate
+    r = expr.as_real_imag()[0].evalf(2)
+    if r._prec != 1:
+        return not r
+    # allow None to be returned if we couldn't show for sure
+    # that r was 0
+
+@ImaginaryPredicate.register(ImaginaryUnit) # type:ignore
+def _(expr, assumptions):
+    return True
+
+@ImaginaryPredicate.register(Expr) # type:ignore
+def _(expr, assumptions):
+    ret = expr.is_imaginary
+    if ret is None:
+        raise MDNotImplementedError
+    return ret
+
+@ImaginaryPredicate.register(Add) # type:ignore
+def _(expr, assumptions):
+    """
+    * Imaginary + Imaginary -> Imaginary
+    * Imaginary + Complex   -> ?
+    * Imaginary + Real      -> !Imaginary
+    """
+    if expr.is_number:
+        return _Imaginary_number(expr, assumptions)
+
+    reals = 0
+    for arg in expr.args:
+        if ask(Q.imaginary(arg), assumptions):
+            pass
+        elif ask(Q.real(arg), assumptions):
+            reals += 1
+        else:
+            break
+    else:
+        if reals == 0:
+            return True
+        if reals in (1, len(expr.args)):
+            # two reals could sum 0 thus giving an imaginary
+            return False
+
+@ImaginaryPredicate.register(Mul) # type:ignore
+def _(expr, assumptions):
+    """
+    * Real*Imaginary      -> Imaginary
+    * Imaginary*Imaginary -> Real
+    """
+    if expr.is_number:
+        return _Imaginary_number(expr, assumptions)
+    result = False
+    reals = 0
+    for arg in expr.args:
+        if ask(Q.imaginary(arg), assumptions):
+            result = result ^ True
+        elif not ask(Q.real(arg), assumptions):
+            break
+    else:
+        if reals == len(expr.args):
+            return False
+        return result
+
+@ImaginaryPredicate.register(Pow) # type:ignore
+def _(expr, assumptions):
+    """
+    * Imaginary**Odd        -> Imaginary
+    * Imaginary**Even       -> Real
+    * b**Imaginary          -> !Imaginary if exponent is an integer
+                               multiple of I*pi/log(b)
+    * Imaginary**Real       -> ?
+    * Positive**Real        -> Real
+    * Negative**Integer     -> Real
+    * Negative**(Integer/2) -> Imaginary
+    * Negative**Real        -> not Imaginary if exponent is not Rational
+    """
+    if expr.is_number:
+        return _Imaginary_number(expr, assumptions)
+
+    if expr.base == E:
+        a = expr.exp/I/pi
+        return ask(Q.integer(2*a) & ~Q.integer(a), assumptions)
+
+    if expr.base.func == exp or (expr.base.is_Pow and expr.base.base == E):
+        if ask(Q.imaginary(expr.base.exp), assumptions):
+            if ask(Q.imaginary(expr.exp), assumptions):
+                return False
+            i = expr.base.exp/I/pi
+            if ask(Q.integer(2*i), assumptions):
+                return ask(Q.imaginary((S.NegativeOne**i)**expr.exp), assumptions)
+
+    if ask(Q.imaginary(expr.base), assumptions):
+        if ask(Q.integer(expr.exp), assumptions):
+            odd = ask(Q.odd(expr.exp), assumptions)
+            if odd is not None:
+                return odd
+            return
+
+    if ask(Q.imaginary(expr.exp), assumptions):
+        imlog = ask(Q.imaginary(log(expr.base)), assumptions)
+        if imlog is not None:
+            # I**i -> real; (2*I)**i -> complex ==> not imaginary
+            return False
+
+    if ask(Q.real(expr.base) & Q.real(expr.exp), assumptions):
+        if ask(Q.positive(expr.base), assumptions):
+            return False
+        else:
+            rat = ask(Q.rational(expr.exp), assumptions)
+            if not rat:
+                return rat
+            if ask(Q.integer(expr.exp), assumptions):
+                return False
+            else:
+                half = ask(Q.integer(2*expr.exp), assumptions)
+                if half:
+                    return ask(Q.negative(expr.base), assumptions)
+                return half
+
+@ImaginaryPredicate.register(log) # type:ignore
+def _(expr, assumptions):
+    if ask(Q.real(expr.args[0]), assumptions):
+        if ask(Q.positive(expr.args[0]), assumptions):
+            return False
+        return
+    # XXX it should be enough to do
+    # return ask(Q.nonpositive(expr.args[0]), assumptions)
+    # but ask(Q.nonpositive(exp(x)), Q.imaginary(x)) -> None;
+    # it should return True since exp(x) will be either 0 or complex
+    if expr.args[0].func == exp or (expr.args[0].is_Pow and expr.args[0].base == E):
+        if expr.args[0].exp in [I, -I]:
+            return True
+    im = ask(Q.imaginary(expr.args[0]), assumptions)
+    if im is False:
+        return False
+
+@ImaginaryPredicate.register(exp) # type:ignore
+def _(expr, assumptions):
+    a = expr.exp/I/pi
+    return ask(Q.integer(2*a) & ~Q.integer(a), assumptions)
+
+@ImaginaryPredicate.register_many(Number, NumberSymbol) # type:ignore
+def _(expr, assumptions):
+    return not (expr.as_real_imag()[1] == 0)
+
+@ImaginaryPredicate.register(NaN) # type:ignore
+def _(expr, assumptions):
+    return None
+
+
+# AntihermitianPredicate
+
+@AntihermitianPredicate.register(object) # type:ignore
+def _(expr, assumptions):
+    if isinstance(expr, MatrixBase):
+        return None
+    if ask(Q.zero(expr), assumptions):
+        return True
+    return ask(Q.imaginary(expr), assumptions)
+
+@AntihermitianPredicate.register(Add) # type:ignore
+def _(expr, assumptions):
+    """
+    * Antihermitian + Antihermitian  -> Antihermitian
+    * Antihermitian + !Antihermitian -> !Antihermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    return test_closed_group(expr, assumptions, Q.antihermitian)
+
+@AntihermitianPredicate.register(Mul) # type:ignore
+def _(expr, assumptions):
+    """
+    As long as there is at most only one noncommutative term:
+
+    * Hermitian*Hermitian         -> !Antihermitian
+    * Hermitian*Antihermitian     -> Antihermitian
+    * Antihermitian*Antihermitian -> !Antihermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    nccount = 0
+    result = False
+    for arg in expr.args:
+        if ask(Q.antihermitian(arg), assumptions):
+            result = result ^ True
+        elif not ask(Q.hermitian(arg), assumptions):
+            break
+        if ask(~Q.commutative(arg), assumptions):
+            nccount += 1
+            if nccount > 1:
+                break
+    else:
+        return result
+
+@AntihermitianPredicate.register(Pow) # type:ignore
+def _(expr, assumptions):
+    """
+    * Hermitian**Integer  -> !Antihermitian
+    * Antihermitian**Even -> !Antihermitian
+    * Antihermitian**Odd  -> Antihermitian
+    """
+    if expr.is_number:
+        raise MDNotImplementedError
+    if ask(Q.hermitian(expr.base), assumptions):
+        if ask(Q.integer(expr.exp), assumptions):
+            return False
+    elif ask(Q.antihermitian(expr.base), assumptions):
+        if ask(Q.even(expr.exp), assumptions):
+            return False
+        elif ask(Q.odd(expr.exp), assumptions):
+            return True
+    raise MDNotImplementedError
+
+@AntihermitianPredicate.register(MatrixBase) # type:ignore
+def _(mat, assumptions):
+    rows, cols = mat.shape
+    ret_val = True
+    for i in range(rows):
+        for j in range(i, cols):
+            cond = fuzzy_bool(Eq(mat[i, j], -conjugate(mat[j, i])))
+            if cond is None:
+                ret_val = None
+            if cond == False:
+                return False
+    if ret_val is None:
+        raise MDNotImplementedError
+    return ret_val
+
+
+# AlgebraicPredicate
+
+@AlgebraicPredicate.register_many(AlgebraicNumber, Float, GoldenRatio, # type:ignore
+    ImaginaryUnit, TribonacciConstant)
+def _(expr, assumptions):
+    return True
+
+@AlgebraicPredicate.register_many(ComplexInfinity, Exp1, Infinity, # type:ignore
+    NegativeInfinity, Pi)
+def _(expr, assumptions):
+    return False
+
+@AlgebraicPredicate.register_many(Add, Mul) # type:ignore
+def _(expr, assumptions):
+    return test_closed_group(expr, assumptions, Q.algebraic)
+
+@AlgebraicPredicate.register(Pow) # type:ignore
+def _(expr, assumptions):
+    if expr.base == E:
+        if ask(Q.algebraic(expr.exp), assumptions):
+            return ask(~Q.nonzero(expr.exp), assumptions)
+        return
+    return expr.exp.is_Rational and ask(Q.algebraic(expr.base), assumptions)
+
+@AlgebraicPredicate.register(Rational) # type:ignore
+def _(expr, assumptions):
+    return expr.q != 0
+
+@AlgebraicPredicate.register_many(asin, atan, cos, sin, tan) # type:ignore
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.algebraic(x), assumptions):
+        return ask(~Q.nonzero(x), assumptions)
+
+@AlgebraicPredicate.register(exp) # type:ignore
+def _(expr, assumptions):
+    x = expr.exp
+    if ask(Q.algebraic(x), assumptions):
+        return ask(~Q.nonzero(x), assumptions)
+
+@AlgebraicPredicate.register_many(acot, cot) # type:ignore
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.algebraic(x), assumptions):
+        return False
+
+@AlgebraicPredicate.register_many(acos, log) # type:ignore
+def _(expr, assumptions):
+    x = expr.args[0]
+    if ask(Q.algebraic(x), assumptions):
+        return ask(~Q.nonzero(x - 1), assumptions)
diff --git a/shared/package-files/d8/d89941484eb33b9f757c5dfa84f2fb0e6c97b7865b4c96c9deeeb1892e1e040e/__init__.py b/shared/package-files/d8/d89941484eb33b9f757c5dfa84f2fb0e6c97b7865b4c96c9deeeb1892e1e040e/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..670a654efb95096487f85de1d16e72ab55db37e8
--- /dev/null
+++ b/shared/package-files/d8/d89941484eb33b9f757c5dfa84f2fb0e6c97b7865b4c96c9deeeb1892e1e040e/__init__.py
@@ -0,0 +1,31 @@
+from .fused import _FusedModule  # noqa: F401
+from .fused import BNReLU2d
+from .fused import BNReLU3d
+from .fused import ConvBn1d
+from .fused import ConvBn2d
+from .fused import ConvBn3d
+from .fused import ConvBnReLU1d
+from .fused import ConvBnReLU2d
+from .fused import ConvBnReLU3d
+from .fused import ConvReLU1d
+from .fused import ConvReLU2d
+from .fused import ConvReLU3d
+from .fused import LinearBn1d
+from .fused import LinearReLU
+
+
+__all__ = [
+    'BNReLU2d',
+    'BNReLU3d',
+    'ConvBn1d',
+    'ConvBn2d',
+    'ConvBn3d',
+    'ConvBnReLU1d',
+    'ConvBnReLU2d',
+    'ConvBnReLU3d',
+    'ConvReLU1d',
+    'ConvReLU2d',
+    'ConvReLU3d',
+    'LinearBn1d',
+    'LinearReLU',
+]
diff --git a/shared/package-files/d8/d89b7ae8009dd279e3c0e9e72dc5d11949b34e4ea820bb4906dc21d4a76d8466/deprecations.py b/shared/package-files/d8/d89b7ae8009dd279e3c0e9e72dc5d11949b34e4ea820bb4906dc21d4a76d8466/deprecations.py
new file mode 100644
index 0000000000000000000000000000000000000000..67ceb59c7f7589d5d38cc424fc665af83795dac5
--- /dev/null
+++ b/shared/package-files/d8/d89b7ae8009dd279e3c0e9e72dc5d11949b34e4ea820bb4906dc21d4a76d8466/deprecations.py
@@ -0,0 +1,384 @@
+# Copyright (C) 2014 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tools to aid in deprecating code."""
+from __future__ import annotations
+
+import sys
+import warnings
+from functools import wraps
+from types import ModuleType
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from argparse import Action
+    from typing import Any, Callable
+
+    from packaging.version import Version
+
+from . import __version__
+
+
+class DeprecatedError(RuntimeError):
+    pass
+
+
+# inspired by deprecation (https://deprecation.readthedocs.io/en/latest/) and
+# CPython's warnings._deprecated
+class DeprecationHandler:
+    _version: str | None
+    _version_tuple: tuple[int, ...] | None
+    _version_object: Version | None
+
+    def __init__(self, version: str):
+        """Factory to create a deprecation handle for the specified version.
+
+        :param version: The version to compare against when checking deprecation statuses.
+        """
+        self._version = version
+        # Try to parse the version string as a simple tuple[int, ...] to avoid
+        # packaging.version import and costlier version comparisons.
+        self._version_tuple = self._get_version_tuple(version)
+        self._version_object = None
+
+    @staticmethod
+    def _get_version_tuple(version: str) -> tuple[int, ...] | None:
+        """Return version as non-empty tuple of ints if possible, else None.
+
+        :param version: Version string to parse.
+        """
+        try:
+            return tuple(int(part) for part in version.strip().split(".")) or None
+        except (AttributeError, ValueError):
+            return None
+
+    def _version_less_than(self, version: str) -> bool:
+        """Test whether own version is less than the given version.
+
+        :param version: Version string to compare against.
+        """
+        if self._version_tuple:
+            if version_tuple := self._get_version_tuple(version):
+                return self._version_tuple < version_tuple
+
+        # If self._version or version could not be represented by a simple
+        # tuple[int, ...], do a more elaborate version parsing and comparison.
+        # Avoid this import otherwise to reduce import time for conda activate.
+        from packaging.version import parse
+
+        if self._version_object is None:
+            try:
+                self._version_object = parse(self._version)
+            except TypeError:
+                self._version_object = parse("0.0.0.dev0+placeholder")
+        return self._version_object < parse(version)
+
+    def __call__(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        *,
+        addendum: str | None = None,
+        stack: int = 0,
+    ) -> Callable[[Callable], Callable]:
+        """Deprecation decorator for functions, methods, & classes.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param addendum: Optional additional messaging. Useful to indicate what to do instead.
+        :param stack: Optional stacklevel increment.
+        """
+
+        def deprecated_decorator(func: Callable) -> Callable:
+            # detect function name and generate message
+            category, message = self._generate_message(
+                deprecate_in,
+                remove_in,
+                f"{func.__module__}.{func.__qualname__}",
+                addendum=addendum,
+            )
+
+            # alert developer that it's time to remove something
+            if not category:
+                raise DeprecatedError(message)
+
+            # alert user that it's time to remove something
+            @wraps(func)
+            def inner(*args, **kwargs):
+                warnings.warn(message, category, stacklevel=2 + stack)
+
+                return func(*args, **kwargs)
+
+            return inner
+
+        return deprecated_decorator
+
+    def argument(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        argument: str,
+        *,
+        rename: str | None = None,
+        addendum: str | None = None,
+        stack: int = 0,
+    ) -> Callable[[Callable], Callable]:
+        """Deprecation decorator for keyword arguments.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param argument: The argument to deprecate.
+        :param rename: Optional new argument name.
+        :param addendum: Optional additional messaging. Useful to indicate what to do instead.
+        :param stack: Optional stacklevel increment.
+        """
+
+        def deprecated_decorator(func: Callable) -> Callable:
+            # detect function name and generate message
+            category, message = self._generate_message(
+                deprecate_in,
+                remove_in,
+                f"{func.__module__}.{func.__qualname__}({argument})",
+                # provide a default addendum if renaming and no addendum is provided
+                addendum=f"Use '{rename}' instead."
+                if rename and not addendum
+                else addendum,
+            )
+
+            # alert developer that it's time to remove something
+            if not category:
+                raise DeprecatedError(message)
+
+            # alert user that it's time to remove something
+            @wraps(func)
+            def inner(*args, **kwargs):
+                # only warn about argument deprecations if the argument is used
+                if argument in kwargs:
+                    warnings.warn(message, category, stacklevel=2 + stack)
+
+                    # rename argument deprecations as needed
+                    value = kwargs.pop(argument, None)
+                    if rename:
+                        kwargs.setdefault(rename, value)
+
+                return func(*args, **kwargs)
+
+            return inner
+
+        return deprecated_decorator
+
+    def action(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        action: type[Action],
+        *,
+        addendum: str | None = None,
+        stack: int = 0,
+    ):
+        class DeprecationMixin:
+            def __init__(inner_self, *args, **kwargs):
+                super().__init__(*args, **kwargs)
+
+                category, message = self._generate_message(
+                    deprecate_in,
+                    remove_in,
+                    (
+                        # option_string are ordered shortest to longest,
+                        # use the longest as it's the most descriptive
+                        f"`{inner_self.option_strings[-1]}`"
+                        if inner_self.option_strings
+                        # if not a flag/switch, use the destination itself
+                        else f"`{inner_self.dest}`"
+                    ),
+                    addendum=addendum,
+                )
+
+                # alert developer that it's time to remove something
+                if not category:
+                    raise DeprecatedError(message)
+
+                inner_self.category = category
+                inner_self.help = message
+
+            def __call__(inner_self, parser, namespace, values, option_string=None):
+                # alert user that it's time to remove something
+                warnings.warn(
+                    inner_self.help, inner_self.category, stacklevel=7 + stack
+                )
+
+                super().__call__(parser, namespace, values, option_string)
+
+        return type(action.__name__, (DeprecationMixin, action), {})
+
+    def module(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        *,
+        addendum: str | None = None,
+        stack: int = 0,
+    ) -> None:
+        """Deprecation function for modules.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param addendum: Optional additional messaging. Useful to indicate what to do instead.
+        :param stack: Optional stacklevel increment.
+        """
+        self.topic(
+            deprecate_in=deprecate_in,
+            remove_in=remove_in,
+            topic=self._get_module(stack)[1],
+            addendum=addendum,
+            stack=2 + stack,
+        )
+
+    def constant(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        constant: str,
+        value: Any,
+        *,
+        addendum: str | None = None,
+        stack: int = 0,
+    ) -> None:
+        """Deprecation function for module constant/global.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param constant:
+        :param value:
+        :param addendum: Optional additional messaging. Useful to indicate what to do instead.
+        :param stack: Optional stacklevel increment.
+        """
+        # detect calling module
+        module, fullname = self._get_module(stack)
+        # detect function name and generate message
+        category, message = self._generate_message(
+            deprecate_in,
+            remove_in,
+            f"{fullname}.{constant}",
+            addendum,
+        )
+
+        # alert developer that it's time to remove something
+        if not category:
+            raise DeprecatedError(message)
+
+        # patch module level __getattr__ to alert user that it's time to remove something
+        super_getattr = getattr(module, "__getattr__", None)
+
+        def __getattr__(name: str) -> Any:
+            if name == constant:
+                warnings.warn(message, category, stacklevel=2 + stack)
+                return value
+
+            if super_getattr:
+                return super_getattr(name)
+
+            raise AttributeError(f"module '{fullname}' has no attribute '{name}'")
+
+        module.__getattr__ = __getattr__
+
+    def topic(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        *,
+        topic: str,
+        addendum: str | None = None,
+        stack: int = 0,
+    ) -> None:
+        """Deprecation function for a topic.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param topic: The topic being deprecated.
+        :param addendum: Optional additional messaging. Useful to indicate what to do instead.
+        :param stack: Optional stacklevel increment.
+        """
+        # detect function name and generate message
+        category, message = self._generate_message(
+            deprecate_in,
+            remove_in,
+            topic,
+            addendum,
+        )
+
+        # alert developer that it's time to remove something
+        if not category:
+            raise DeprecatedError(message)
+
+        # alert user that it's time to remove something
+        warnings.warn(message, category, stacklevel=2 + stack)
+
+    def _get_module(self, stack: int) -> tuple[ModuleType, str]:
+        """Detect the module from which we are being called.
+
+        :param stack: The stacklevel increment.
+        :return: The module and module name.
+        """
+        try:
+            frame = sys._getframe(2 + stack)
+        except IndexError:
+            # IndexError: 2 + stack is out of range
+            pass
+        else:
+            # Shortcut finding the module by manually inspecting loaded modules.
+            try:
+                filename = frame.f_code.co_filename
+            except AttributeError:
+                # AttributeError: frame.f_code.co_filename is undefined
+                pass
+            else:
+                for module in sys.modules.values():
+                    if not isinstance(module, ModuleType):
+                        continue
+                    if not hasattr(module, "__file__"):
+                        continue
+                    if module.__file__ == filename:
+                        return (module, module.__name__)
+
+            # If above failed, do an expensive import and costly getmodule call.
+            import inspect
+
+            module = inspect.getmodule(frame)
+            if module is not None:
+                return (module, module.__name__)
+
+        raise DeprecatedError("unable to determine the calling module")
+
+    def _generate_message(
+        self,
+        deprecate_in: str,
+        remove_in: str,
+        prefix: str,
+        addendum: str | None,
+    ) -> tuple[type[Warning] | None, str]:
+        """Deprecation decorator for functions, methods, & classes.
+
+        :param deprecate_in: Version in which code will be marked as deprecated.
+        :param remove_in: Version in which code is expected to be removed.
+        :param prefix: The message prefix, usually the function name.
+        :param addendum: Additional messaging. Useful to indicate what to do instead.
+        :return: The warning category (if applicable) and the message.
+        """
+        category: type[Warning] | None
+        if self._version_less_than(deprecate_in):
+            category = PendingDeprecationWarning
+            warning = f"is pending deprecation and will be removed in {remove_in}."
+        elif self._version_less_than(remove_in):
+            category = DeprecationWarning
+            warning = f"is deprecated and will be removed in {remove_in}."
+        else:
+            category = None
+            warning = f"was slated for removal in {remove_in}."
+
+        return (
+            category,
+            " ".join(filter(None, [prefix, warning, addendum])),  # message
+        )
+
+
+deprecated = DeprecationHandler(__version__)
diff --git a/shared/package-files/d8/d8a248e22c2f06e1b914136cd8b08d4dfe09251d8b522da5d6c63fe0c41b2f22/qconfig_multi_mapping.py b/shared/package-files/d8/d8a248e22c2f06e1b914136cd8b08d4dfe09251d8b522da5d6c63fe0c41b2f22/qconfig_multi_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a005d0c8bf9441554113e9a1bb49754b415ee1
--- /dev/null
+++ b/shared/package-files/d8/d8a248e22c2f06e1b914136cd8b08d4dfe09251d8b522da5d6c63fe0c41b2f22/qconfig_multi_mapping.py
@@ -0,0 +1,243 @@
+from __future__ import annotations
+
+import copy
+from typing import Any, Callable, Dict, List, Union
+
+import torch
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.qconfig_mapping import _QCONFIG_STYLE_ORDER
+from torch.ao.quantization.qconfig import QConfigAny
+
+__all__ = ["QConfigMultiMapping"]
+
+_QCONFIG_STYLE_TO_METHOD: Dict[str, str] = {
+    "global_qconfig": "set_global",
+    "object_type_qconfigs": "set_object_type",
+    "module_name_regex_qconfigs": "set_module_name_regex",
+    "module_name_qconfigs": "set_module_name",
+    "module_name_object_type_order_qconfigs": "set_module_name_object_type_order",
+}
+
+def _remove_duplicates_and_none(qconfig_list: List[QConfigAny]) -> None:
+    to_remove = []
+    for index, cur_qconfig in enumerate(qconfig_list):
+        if cur_qconfig is None:
+            to_remove.append(index)
+            break
+        for checked_qconfig in qconfig_list[:index]:
+            if torch.ao.quantization.qconfig_equals(cur_qconfig, checked_qconfig):
+                to_remove.append(index)
+                break
+    for index in to_remove[::-1]:
+        qconfig_list.pop(index)
+
+class QConfigMultiMapping:
+    """
+    This class, used with the prepare_n_shadows_model API, stores a list of :class:`torch.ao.quantization.QConfigMapping`s
+    so that multiple QConfigs can be specified for each QConfig matching style.
+
+    The user can specify QConfigs using the following methods (in increasing match priority):
+
+        ``set_global`` : sets the global (default) QConfigs
+
+        ``set_object_type`` : sets the QConfigs for a given module type, function, or method name
+
+        ``set_module_name_regex`` : sets the QConfigs for modules matching the given regex string
+
+        ``set_module_name`` : sets the QConfigs for modules matching the given module name
+
+        ``set_module_name_object_type_order`` : sets the QConfigs for modules matching a combination
+        of the given module name, object type, and the index at which the module appears
+
+    Note: Usage of set methods is the same as in QConfigMapping except with a passed in list of QConfigs rather than a
+    single QConfig.
+
+    Example usage::
+
+        qconfig_mapping = QConfigMultiMapping()
+            .set_global([qconfig1, qconfig2])
+            .set_object_type(torch.nn.Linear, [qconfig2, qconfig3])
+            .set_object_type(torch.nn.ReLU, [qconfig1])
+            .set_module_name_regex("foo.*bar.*conv[0-9]+", [qconfig2])
+            .set_module_name_regex("foo.*", [qconfig1, qconfig2, qconfig3])
+            .set_module_name("module1", [None])
+            .set_module_name("module2", [qconfig2])
+            .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, [qconfig3])
+
+    """
+
+    def __init__(self):
+        # initialize this with 1 QConfigMapping to avoid corner cases
+        self.qconfig_mappings_list: List[QConfigMapping] = [QConfigMapping()]
+
+    def _handle_list_size_mismatch(
+        self, qconfig_list: List[QConfigAny], style: str
+    ) -> None:
+        # this method handles cases where the size of qconfig_list does not match
+        # the size of qconfig_mappings_list.
+        # Issue: Consider a user inserting global_qconfig A and B first, then inserting
+        # qconfig C as an object_type_qconfig for conv ops. If we internally store
+        # 1 QConfigMapping with A and C and another with just B, then the
+        # second QConfigMapping will match B to conv ops (which is not wanted), since B is global.
+
+        # we avoid this by maintaining the invariant that if any QConfigMapping
+        # has a qconfig style+key with a qconfig in it, all QConfigMappings must
+        # have either a qconfig or None for that same style+key. In the above
+        # example, a None qconfig would prevent the unwanted match in the
+        # second QConfigMapping
+
+        if len(qconfig_list) > len(self.qconfig_mappings_list):
+            # Case: we have more qconfigs (in qconfig_list) than QConfigMappings
+
+            # Add new QConfigMappings (initialized so we maintain the `invariant`)
+
+            new_qconfig_mapping = QConfigMapping()
+            # searches other QConfigMappings for qconfig style+keys
+            # that need to be inserted as `None` into the new QConfigMapping
+            for qconfig_mapping in self.qconfig_mappings_list:
+
+                # global_qconfig has None by default
+                for check_style in _QCONFIG_STYLE_ORDER[1:]:
+                    qconfigs_dict = getattr(qconfig_mapping, check_style)
+                    target_qconfigs_dict = getattr(new_qconfig_mapping, check_style)
+                    for key in qconfigs_dict:
+                        target_qconfigs_dict[key] = None
+                break
+
+            # insert copies of this new QConfigMapping until all entires
+            # in qconfig_list can fit among the QConfigMappings
+            while len(qconfig_list) > len(self.qconfig_mappings_list):
+                self.qconfig_mappings_list.append(copy.deepcopy(new_qconfig_mapping))
+        else:
+            # Case: we have fewer qconfigs in qconfig_list than QConfigMappings
+
+            # pad qconfig_list with `None` until length is same
+            while len(qconfig_list) < len(self.qconfig_mappings_list):
+                qconfig_list.append(None)
+
+    # this function applies the insertion method across each QConfigMapping
+    def _insert_qconfig_list(
+        self,
+        style: str,
+        args: List[Union[str, int, Callable]],
+        qconfig_list: List[QConfigAny],
+    ) -> None:
+
+        # we remove duplicates and None to make the ordering of qconfigs
+        # deterministic upon insertion.
+        _remove_duplicates_and_none(qconfig_list)
+
+        self._handle_list_size_mismatch(qconfig_list, style)
+        method_name = _QCONFIG_STYLE_TO_METHOD[style]
+        for qconfig_mapping, qconfig in zip(self.qconfig_mappings_list, qconfig_list):
+            # uses QConfigMapping set method to insert qconfig
+            set_method = getattr(qconfig_mapping, method_name)
+            set_method(*args, qconfig)
+
+    def set_global(self, global_qconfig_list: List[QConfigAny]) -> QConfigMultiMapping:
+        """
+        Set global QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_global()` for more info
+        """
+        self._insert_qconfig_list("global_qconfig", [], global_qconfig_list)
+        return self
+
+    def set_object_type(
+        self, object_type: Union[Callable, str], qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set object type QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_object_type()` for more info
+        """
+        self._insert_qconfig_list("object_type_qconfigs", [object_type], qconfig_list)
+        return self
+
+    def set_module_name_regex(
+        self, module_name_regex: str, qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name_regex QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_regex()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_regex_qconfigs", [module_name_regex], qconfig_list
+        )
+        return self
+
+    def set_module_name(
+        self, module_name: str, qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name()` for more info
+        """
+        self._insert_qconfig_list("module_name_qconfigs", [module_name], qconfig_list)
+        return self
+
+    def set_module_name_object_type_order(
+        self,
+        module_name: str,
+        object_type: Callable,
+        index: int,
+        qconfig_list: List[QConfigAny],
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_object_type_order()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_object_type_order_qconfigs",
+            [module_name, object_type, index],
+            qconfig_list,
+        )
+        return self
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__ +
+            " [" +
+            "".join(f"\n{qconfig_mapping.__repr__()}," for qconfig_mapping in self.qconfig_mappings_list) +
+            "\n]"
+        )
+
+    @classmethod
+    def from_list_qconfig_mapping(
+        cls, qconfig_mapping_list: List[QConfigMapping]
+    ) -> QConfigMultiMapping:
+        """
+        Creates a QConfigMultiMapping from a list of QConfigMappings
+        """
+        new_qconfig_multi_mapping = cls()
+
+        new_qconfig_multi_mapping.qconfig_mappings_list = copy.deepcopy(
+            qconfig_mapping_list
+        )
+
+        # we need to avoid the issue described in _handle_list_size_mismatch,
+        # so we reinsert all the qconfigs using the QConfigMultiMapping
+        # set methods
+
+        # go through all qconfig styles
+        # note: global can be ignored since it is None by default
+        for style in _QCONFIG_STYLE_ORDER[1:]:
+
+            # gather all key+qconfigs for current style
+            # into qconfig_dict_list
+            qconfig_dict_list: Dict[Any, List[QConfigAny]] = {}
+            for qconfig_mapping in qconfig_mapping_list:
+                qconfig_dict = getattr(qconfig_mapping, style)
+                for key, qconfig in qconfig_dict.items():
+                    if key not in qconfig_dict_list:
+                        qconfig_dict_list[key] = []
+                    qconfig_dict_list[key].append(qconfig)
+
+            # reinsert all gathered key+qconfigs
+            set_method_name = _QCONFIG_STYLE_TO_METHOD[style]
+            set_method = getattr(new_qconfig_multi_mapping, set_method_name)
+            for key, qconfig_list in qconfig_dict_list.items():
+                if isinstance(key, tuple):
+                    set_method(*key, qconfig_list)
+                else:
+                    set_method(key, qconfig_list)
+
+        return new_qconfig_multi_mapping
diff --git a/shared/package-files/d8/d8a5c62cfdaac233756be95125453bd08cba7c209284d0502bacd2a86e7b788f/vecm_e6_jmulti_colo_inst_causality_dp_r.txt b/shared/package-files/d8/d8a5c62cfdaac233756be95125453bd08cba7c209284d0502bacd2a86e7b788f/vecm_e6_jmulti_colo_inst_causality_dp_r.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b20d63cdb66c37920edbb79d5089544596ea2ad0
--- /dev/null
+++ b/shared/package-files/d8/d8a5c62cfdaac233756be95125453bd08cba7c209284d0502bacd2a86e7b788f/vecm_e6_jmulti_colo_inst_causality_dp_r.txt
@@ -0,0 +1,13 @@
+*** Fri, 26 Aug 2016 11:40:57 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "Dp" do not Granger-cause "R"
+
+Test statistic l = 0.8349
+pval-F( l; 3, 186) = 0.4763 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "Dp" and "R"
+
+Test statistic: c = 0.8433
+pval-Chi( c; 1) = 0.3585 
+
diff --git a/shared/package-files/d8/d8a5d8adf8b73efe7363d8faf8f8bd537ab90f457efdb9868d8f9875e50a9b35/test_set_value.py b/shared/package-files/d8/d8a5d8adf8b73efe7363d8faf8f8bd537ab90f457efdb9868d8f9875e50a9b35/test_set_value.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d23e13264911c52dabd58c12ed133f8cf1766a6
--- /dev/null
+++ b/shared/package-files/d8/d8a5d8adf8b73efe7363d8faf8f8bd537ab90f457efdb9868d8f9875e50a9b35/test_set_value.py
@@ -0,0 +1,77 @@
+import numpy as np
+
+from pandas.core.dtypes.common import is_float_dtype
+
+from pandas import (
+    DataFrame,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestSetValue:
+    def test_set_value(self, float_frame):
+        for idx in float_frame.index:
+            for col in float_frame.columns:
+                float_frame._set_value(idx, col, 1)
+                assert float_frame[col][idx] == 1
+
+    def test_set_value_resize(self, float_frame, using_infer_string):
+        res = float_frame._set_value("foobar", "B", 0)
+        assert res is None
+        assert float_frame.index[-1] == "foobar"
+        assert float_frame._get_value("foobar", "B") == 0
+
+        float_frame.loc["foobar", "qux"] = 0
+        assert float_frame._get_value("foobar", "qux") == 0
+
+        res = float_frame.copy()
+        res._set_value("foobar", "baz", "sam")
+        if using_infer_string:
+            assert res["baz"].dtype == "str"
+        else:
+            assert res["baz"].dtype == np.object_
+        res = float_frame.copy()
+        res._set_value("foobar", "baz", True)
+        assert res["baz"].dtype == np.object_
+
+        res = float_frame.copy()
+        res._set_value("foobar", "baz", 5)
+        assert is_float_dtype(res["baz"])
+        assert isna(res["baz"].drop(["foobar"])).all()
+
+        with tm.assert_produces_warning(
+            FutureWarning, match="Setting an item of incompatible dtype"
+        ):
+            res._set_value("foobar", "baz", "sam")
+        assert res.loc["foobar", "baz"] == "sam"
+
+    def test_set_value_with_index_dtype_change(self):
+        df_orig = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 3)),
+            index=range(3),
+            columns=list("ABC"),
+        )
+
+        # this is actually ambiguous as the 2 is interpreted as a positional
+        # so column is not created
+        df = df_orig.copy()
+        df._set_value("C", 2, 1.0)
+        assert list(df.index) == list(df_orig.index) + ["C"]
+        # assert list(df.columns) == list(df_orig.columns) + [2]
+
+        df = df_orig.copy()
+        df.loc["C", 2] = 1.0
+        assert list(df.index) == list(df_orig.index) + ["C"]
+        # assert list(df.columns) == list(df_orig.columns) + [2]
+
+        # create both new
+        df = df_orig.copy()
+        df._set_value("C", "D", 1.0)
+        assert list(df.index) == list(df_orig.index) + ["C"]
+        assert list(df.columns) == list(df_orig.columns) + ["D"]
+
+        df = df_orig.copy()
+        df.loc["C", "D"] = 1.0
+        assert list(df.index) == list(df_orig.index) + ["C"]
+        assert list(df.columns) == list(df_orig.columns) + ["D"]
diff --git a/shared/package-files/d8/d8a70cdbde81dbbbe05d83e6fd09cdfa4fbab35e9792ff0d3f03fbdbde63e2c1/run_nvcc.cmake b/shared/package-files/d8/d8a70cdbde81dbbbe05d83e6fd09cdfa4fbab35e9792ff0d3f03fbdbde63e2c1/run_nvcc.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..9293df3aafbdefdd8664ae2860d1b5b7fc9bfbfb
--- /dev/null
+++ b/shared/package-files/d8/d8a70cdbde81dbbbe05d83e6fd09cdfa4fbab35e9792ff0d3f03fbdbde63e2c1/run_nvcc.cmake
@@ -0,0 +1,303 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+
+##########################################################################
+# This file runs the nvcc commands to produce the desired output file along with
+# the dependency file needed by CMake to compute dependencies.  In addition the
+# file checks the output of each command and if the command fails it deletes the
+# output files.
+
+# Input variables
+#
+# verbose:BOOL=<>          OFF: Be as quiet as possible (default)
+#                          ON : Describe each step
+#
+# build_configuration:STRING=<> Typically one of Debug, MinSizeRel, Release, or
+#                               RelWithDebInfo, but it should match one of the
+#                               entries in CUDA_HOST_FLAGS. This is the build
+#                               configuration used when compiling the code.  If
+#                               blank or unspecified Debug is assumed as this is
+#                               what CMake does.
+#
+# generated_file:STRING=<> File to generate.  This argument must be passed in.
+#
+# generated_cubin_file:STRING=<> File to generate.  This argument must be passed
+#                                                   in if build_cubin is true.
+
+cmake_policy(PUSH)
+cmake_policy(SET CMP0007 NEW)
+cmake_policy(SET CMP0010 NEW)
+if(NOT generated_file)
+  message(FATAL_ERROR "You must specify generated_file on the command line")
+endif()
+
+# Set these up as variables to make reading the generated file easier
+set(CMAKE_COMMAND "@CMAKE_COMMAND@") # path
+set(source_file "@source_file@") # path
+set(NVCC_generated_dependency_file "@NVCC_generated_dependency_file@") # path
+set(cmake_dependency_file "@cmake_dependency_file@") # path
+set(CUDA_make2cmake "@CUDA_make2cmake@") # path
+set(CUDA_parse_cubin "@CUDA_parse_cubin@") # path
+set(build_cubin @build_cubin@) # bool
+set(CUDA_HOST_COMPILER "@CUDA_HOST_COMPILER@") # path
+# We won't actually use these variables for now, but we need to set this, in
+# order to force this file to be run again if it changes.
+set(generated_file_path "@generated_file_path@") # path
+set(generated_file_internal "@generated_file@") # path
+set(generated_cubin_file_internal "@generated_cubin_file@") # path
+
+set(CUDA_NVCC_EXECUTABLE "@CUDA_NVCC_EXECUTABLE@") # path
+set(CUDA_NVCC_FLAGS @CUDA_NVCC_FLAGS@ ;; @CUDA_WRAP_OPTION_NVCC_FLAGS@) # list
+@CUDA_NVCC_FLAGS_CONFIG@
+set(nvcc_flags @nvcc_flags@) # list
+set(CUDA_NVCC_INCLUDE_DIRS [==[@CUDA_NVCC_INCLUDE_DIRS@]==]) # list (needs to be in lua quotes to address backslashes)
+string(REPLACE "\\" "/" CUDA_NVCC_INCLUDE_DIRS "${CUDA_NVCC_INCLUDE_DIRS}")
+set(CUDA_NVCC_COMPILE_DEFINITIONS [==[@CUDA_NVCC_COMPILE_DEFINITIONS@]==]) # list (needs to be in lua quotes see #16510 ).
+set(format_flag "@format_flag@") # string
+set(cuda_language_flag @cuda_language_flag@) # list
+
+# Clean up list of include directories and add -I flags
+list(REMOVE_DUPLICATES CUDA_NVCC_INCLUDE_DIRS)
+set(CUDA_NVCC_INCLUDE_ARGS)
+foreach(dir ${CUDA_NVCC_INCLUDE_DIRS})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  list(APPEND CUDA_NVCC_INCLUDE_ARGS "-I${dir}")
+endforeach()
+
+# Clean up list of compile definitions, add -D flags, and append to nvcc_flags
+list(REMOVE_DUPLICATES CUDA_NVCC_COMPILE_DEFINITIONS)
+foreach(def ${CUDA_NVCC_COMPILE_DEFINITIONS})
+  list(APPEND nvcc_flags "-D${def}")
+endforeach()
+
+if(build_cubin AND NOT generated_cubin_file)
+  message(FATAL_ERROR "You must specify generated_cubin_file on the command line")
+endif()
+
+# This is the list of host compilation flags.  It C or CXX should already have
+# been chosen by FindCUDA.cmake.
+@CUDA_HOST_FLAGS@
+
+# Take the compiler flags and package them up to be sent to the compiler via -Xcompiler
+set(nvcc_host_compiler_flags "")
+# If we weren't given a build_configuration, use Debug.
+if(NOT build_configuration)
+  set(build_configuration Debug)
+endif()
+string(TOUPPER "${build_configuration}" build_configuration)
+#message("CUDA_NVCC_HOST_COMPILER_FLAGS = ${CUDA_NVCC_HOST_COMPILER_FLAGS}")
+foreach(flag ${CMAKE_HOST_FLAGS} ${CMAKE_HOST_FLAGS_${build_configuration}})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  string(APPEND nvcc_host_compiler_flags ",\"${flag}\"")
+endforeach()
+if (nvcc_host_compiler_flags)
+  set(nvcc_host_compiler_flags "-Xcompiler" ${nvcc_host_compiler_flags})
+endif()
+#message("nvcc_host_compiler_flags = \"${nvcc_host_compiler_flags}\"")
+# Add the build specific configuration flags
+list(APPEND CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS_${build_configuration}})
+
+# Any -ccbin existing in CUDA_NVCC_FLAGS gets highest priority
+list( FIND CUDA_NVCC_FLAGS "-ccbin" ccbin_found0 )
+list( FIND CUDA_NVCC_FLAGS "--compiler-bindir" ccbin_found1 )
+if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
+  if (CUDA_HOST_COMPILER STREQUAL "@_CUDA_MSVC_HOST_COMPILER@" AND DEFINED CCBIN)
+    set(CCBIN -ccbin "${CCBIN}")
+  else()
+    set(CCBIN -ccbin "${CUDA_HOST_COMPILER}")
+  endif()
+endif()
+
+# cuda_execute_process - Executes a command with optional command echo and status message.
+#
+#   status  - Status message to print if verbose is true
+#   command - COMMAND argument from the usual execute_process argument structure
+#   ARGN    - Remaining arguments are the command with arguments
+#
+#   CUDA_result - return value from running the command
+#
+# Make this a macro instead of a function, so that things like RESULT_VARIABLE
+# and other return variables are present after executing the process.
+macro(cuda_execute_process status command)
+  set(_command ${command})
+  if(NOT "x${_command}" STREQUAL "xCOMMAND")
+    message(FATAL_ERROR "Malformed call to cuda_execute_process.  Missing COMMAND as second argument. (command = ${command})")
+  endif()
+  if(verbose)
+    execute_process(COMMAND "${CMAKE_COMMAND}" -E echo -- ${status})
+    # Now we need to build up our command string.  We are accounting for quotes
+    # and spaces, anything else is left up to the user to fix if they want to
+    # copy and paste a runnable command line.
+    set(cuda_execute_process_string)
+    foreach(arg ${ARGN})
+      # If there are quotes, excape them, so they come through.
+      string(REPLACE "\"" "\\\"" arg ${arg})
+      # Args with spaces need quotes around them to get them to be parsed as a single argument.
+      if(arg MATCHES " ")
+        list(APPEND cuda_execute_process_string "\"${arg}\"")
+      else()
+        list(APPEND cuda_execute_process_string ${arg})
+      endif()
+    endforeach()
+    # Echo the command
+    execute_process(COMMAND ${CMAKE_COMMAND} -E echo ${cuda_execute_process_string})
+  endif()
+  # Run the command
+  execute_process(COMMAND ${ARGN} RESULT_VARIABLE CUDA_result )
+endmacro()
+
+# Delete the target file
+cuda_execute_process(
+  "Removing ${generated_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+  )
+
+# For CUDA 2.3 and below, -G -M doesn't work, so remove the -G flag
+# for dependency generation and hope for the best.
+set(depends_CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}")
+set(CUDA_VERSION @CUDA_VERSION@)
+
+# nvcc doesn't define __CUDACC__ for some reason when generating dependency files.  This
+# can cause incorrect dependencies when #including files based on this macro which is
+# defined in the generating passes of nvcc invocation.  We will go ahead and manually
+# define this for now until a future version fixes this bug.
+set(CUDACC_DEFINE -D__CUDACC__)
+
+# Generate the dependency file
+cuda_execute_process(
+  "Generating dependency file: ${NVCC_generated_dependency_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  -M
+  ${CUDACC_DEFINE}
+  "${source_file}"
+  -o "${NVCC_generated_dependency_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${depends_CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the cmake readable dependency file to a temp file.  Don't put the
+# quotes just around the filenames for the input_file and output_file variables.
+# CMake will pass the quotes through and not be able to find the file.
+cuda_execute_process(
+  "Generating temporary cmake readable file: ${cmake_dependency_file}.tmp"
+  COMMAND "${CMAKE_COMMAND}"
+  -D "input_file:FILEPATH=${NVCC_generated_dependency_file}"
+  -D "output_file:FILEPATH=${cmake_dependency_file}.tmp"
+  -D "verbose=${verbose}"
+  -P "${CUDA_make2cmake}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Copy the file if it is different
+cuda_execute_process(
+  "Copy if different ${cmake_dependency_file}.tmp to ${cmake_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${cmake_dependency_file}.tmp" "${cmake_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Delete the temporary file
+cuda_execute_process(
+  "Removing ${cmake_dependency_file}.tmp and ${NVCC_generated_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${cmake_dependency_file}.tmp" "${NVCC_generated_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the code
+cuda_execute_process(
+  "Generating ${generated_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  "${source_file}"
+  ${cuda_language_flag}
+  ${format_flag} -o "${generated_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  # Since nvcc can sometimes leave half done files make sure that we delete the output file.
+  cuda_execute_process(
+    "Removing ${generated_file}"
+    COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+    )
+  message(FATAL_ERROR "Error generating file ${generated_file}")
+else()
+  if(verbose)
+    message("Generated ${generated_file} successfully.")
+  endif()
+endif()
+
+# Cubin resource report commands.
+if( build_cubin )
+  # Run with -cubin to produce resource usage report.
+  cuda_execute_process(
+    "Generating ${generated_cubin_file}"
+    COMMAND "${CUDA_NVCC_EXECUTABLE}"
+    "${source_file}"
+    ${CUDA_NVCC_FLAGS}
+    ${nvcc_flags}
+    ${CCBIN}
+    ${nvcc_host_compiler_flags}
+    -DNVCC
+    -cubin
+    -o "${generated_cubin_file}"
+    ${CUDA_NVCC_INCLUDE_ARGS}
+    )
+
+  # Execute the parser script.
+  cuda_execute_process(
+    "Executing the parser script"
+    COMMAND  "${CMAKE_COMMAND}"
+    -D "input_file:STRING=${generated_cubin_file}"
+    -P "${CUDA_parse_cubin}"
+    )
+
+endif()
+
+cmake_policy(POP)
diff --git a/shared/package-files/d8/d8a89054d48243bed0700724e5f74eb0be29d507e862b22b6330bfb26734ba99/trunc_cuda_dispatch.h b/shared/package-files/d8/d8a89054d48243bed0700724e5f74eb0be29d507e862b22b6330bfb26734ba99/trunc_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..81f92307c4aead7aa1f466dd68962c87a2383794
--- /dev/null
+++ b/shared/package-files/d8/d8a89054d48243bed0700724e5f74eb0be29d507e862b22b6330bfb26734ba99/trunc_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor trunc(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/d8/d8a95920aa81c4d0119c3ec4a8cf7f97a358004b0829929db7b5f24ed7838c72/pixel_shuffle_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/d8/d8a95920aa81c4d0119c3ec4a8cf7f97a358004b0829929db7b5f24ed7838c72/pixel_shuffle_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..71456b97c5c3c871e009f80cf4250402efd76dfa
--- /dev/null
+++ b/shared/package-files/d8/d8a95920aa81c4d0119c3ec4a8cf7f97a358004b0829929db7b5f24ed7838c72/pixel_shuffle_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/d8/d8ab0763e8ed62088492a5334571930fe52e4006ac4f6c61dd526787e6395263/_has_same_storage_numel_compositeexplicitautograd_dispatch.h b/shared/package-files/d8/d8ab0763e8ed62088492a5334571930fe52e4006ac4f6c61dd526787e6395263/_has_same_storage_numel_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f7c9ed1a692caaad53f4998ae533a8a2686e89e
--- /dev/null
+++ b/shared/package-files/d8/d8ab0763e8ed62088492a5334571930fe52e4006ac4f6c61dd526787e6395263/_has_same_storage_numel_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/d8/d8add7bce4fac1d4ee4f3b1d86b586c866562eb5eff9e9fd0c6fdfc13a6abc6d/model.py b/shared/package-files/d8/d8add7bce4fac1d4ee4f3b1d86b586c866562eb5eff9e9fd0c6fdfc13a6abc6d/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..97292b55d64ee34089594305d9a66c50af28c2ea
--- /dev/null
+++ b/shared/package-files/d8/d8add7bce4fac1d4ee4f3b1d86b586c866562eb5eff9e9fd0c6fdfc13a6abc6d/model.py
@@ -0,0 +1,1710 @@
+"""
+Holt-Winters exponential smoothing models
+
+Notes
+-----
+Code written using below textbook as a reference.
+Results are checked against the expected outcomes in the text book.
+
+Properties:
+Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and
+practice. OTexts, 2014.
+
+Author: Terence L van Zyl
+Modified: Kevin Sheppard
+"""
+
+from collections.abc import Hashable, Sequence
+import contextlib
+from typing import Any
+import warnings
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import basinhopping, least_squares, minimize
+from scipy.special import inv_boxcox
+from scipy.stats import boxcox
+
+from statsmodels.tools.validation import (
+    array_like,
+    bool_like,
+    dict_like,
+    float_like,
+    int_like,
+    string_like,
+)
+from statsmodels.tsa.base.tsa_model import TimeSeriesModel
+from statsmodels.tsa.exponential_smoothing.ets import (
+    _initialization_heuristic,
+    _initialization_simple,
+)
+from statsmodels.tsa.holtwinters import (
+    _exponential_smoothers as smoothers,
+    _smoothers as py_smoothers,
+)
+from statsmodels.tsa.holtwinters._exponential_smoothers import HoltWintersArgs
+from statsmodels.tsa.holtwinters._smoothers import (
+    to_restricted,
+    to_unrestricted,
+)
+from statsmodels.tsa.holtwinters.results import (
+    HoltWintersResults,
+    HoltWintersResultsWrapper,
+)
+from statsmodels.tsa.tsatools import freq_to_period
+
+SMOOTHERS = {
+    ("mul", "add"): smoothers.holt_win_add_mul_dam,
+    ("mul", "mul"): smoothers.holt_win_mul_mul_dam,
+    ("mul", None): smoothers.holt_win__mul,
+    ("add", "add"): smoothers.holt_win_add_add_dam,
+    ("add", "mul"): smoothers.holt_win_mul_add_dam,
+    ("add", None): smoothers.holt_win__add,
+    (None, "add"): smoothers.holt_add_dam,
+    (None, "mul"): smoothers.holt_mul_dam,
+    (None, None): smoothers.holt__,
+}
+
+PY_SMOOTHERS = {
+    ("mul", "add"): py_smoothers.holt_win_add_mul_dam,
+    ("mul", "mul"): py_smoothers.holt_win_mul_mul_dam,
+    ("mul", None): py_smoothers.holt_win__mul,
+    ("add", "add"): py_smoothers.holt_win_add_add_dam,
+    ("add", "mul"): py_smoothers.holt_win_mul_add_dam,
+    ("add", None): py_smoothers.holt_win__add,
+    (None, "add"): py_smoothers.holt_add_dam,
+    (None, "mul"): py_smoothers.holt_mul_dam,
+    (None, None): py_smoothers.holt__,
+}
+
+
+def opt_wrapper(func):
+    def f(*args, **kwargs):
+        err = func(*args, **kwargs)
+        if isinstance(err, np.ndarray):
+            return err.T @ err
+        return err
+
+    return f
+
+
+class _OptConfig:
+    alpha: float
+    beta: float
+    phi: float
+    gamma: float
+    level: float
+    trend: float
+    seasonal: np.ndarray
+    y: np.ndarray
+    params: np.ndarray
+    mask: np.ndarray
+    mle_retvals: Any
+
+    def unpack_parameters(self, params) -> "_OptConfig":
+        self.alpha = params[0]
+        self.beta = params[1]
+        self.gamma = params[2]
+        self.level = params[3]
+        self.trend = params[4]
+        self.phi = params[5]
+        self.seasonal = params[6:]
+
+        return self
+
+
+class ExponentialSmoothing(TimeSeriesModel):
+    """
+    Holt Winters' Exponential Smoothing
+
+    Parameters
+    ----------
+    endog : array_like
+        The time series to model.
+    trend : {"add", "mul", "additive", "multiplicative"} or None, optional
+        Type of trend component.
+    damped_trend : bool, optional
+        Should the trend component be damped.
+    seasonal : {"add", "mul", "additive", "multiplicative"} or None, optional
+        Type of seasonal component.
+    seasonal_periods : int, optional
+        The number of periods in a complete seasonal cycle, e.g., 4 for
+        quarterly data or 7 for daily data with a weekly cycle.
+    initialization_method : str, optional
+        Method for initialize the recursions. One of:
+
+        * None
+        * 'estimated'
+        * 'heuristic'
+        * 'legacy-heuristic'
+        * 'known'
+
+        None defaults to the pre-0.12 behavior where initial values
+        are passed as part of ``fit``. If any of the other values are
+        passed, then the initial values must also be set when constructing
+        the model. If 'known' initialization is used, then `initial_level`
+        must be passed, as well as `initial_trend` and `initial_seasonal` if
+        applicable. Default is 'estimated'. "legacy-heuristic" uses the same
+        values that were used in statsmodels 0.11 and earlier.
+    initial_level : float, optional
+        The initial level component. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+    initial_trend : float, optional
+        The initial trend component. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+    initial_seasonal : array_like, optional
+        The initial seasonal component. An array of length `seasonal`
+        or length `seasonal - 1` (in which case the last initial value
+        is computed to make the average effect zero). Only used if
+        initialization is 'known'. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+    use_boxcox : {True, False, 'log', float}, optional
+        Should the Box-Cox transform be applied to the data first? If 'log'
+        then apply the log. If float then use the value as lambda.
+    bounds : dict of str to tuple of float, optional
+        A dictionary containing bounds for the parameters in the model,
+        excluding the initial values if estimated. The keys of the dictionary
+        are the variable names, e.g., smoothing_level or initial_slope.
+        The initial seasonal variables are labeled initial_seasonal.<j>
+        for j=0,...,m-1 where m is the number of period in a full season.
+        Use None to indicate a non-binding constraint, e.g., (0, None)
+        constrains a parameter to be non-negative.
+    dates : array_like of datetime, optional
+        An array-like object of datetime objects. If a Pandas object is given
+        for endog, it is assumed to have a DateIndex.
+    freq : str, optional
+        The frequency of the time-series. A Pandas offset or 'B', 'D', 'W',
+        'M', 'A', or 'Q'. This is optional if dates are given.
+    missing : str, optional
+        Available options are 'none', 'drop', and 'raise'. If 'none', no nan
+        checking is done. If 'drop', any observations with nans are dropped.
+        If 'raise', an error is raised. Default is 'none'.
+
+    Notes
+    -----
+    This is a full implementation of the holt winters exponential smoothing as
+    per [1]_. This includes all the unstable methods as well as the stable
+    methods. The implementation of the library covers the functionality of the
+    R library as much as possible whilst still being Pythonic.
+
+    See the notebook `Exponential Smoothing
+    <../examples/notebooks/generated/exponential_smoothing.html>`__
+    for an overview.
+
+    References
+    ----------
+    .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+        and practice. OTexts, 2014.
+    """
+
+    def __init__(
+        self,
+        endog,
+        trend=None,
+        damped_trend=False,
+        seasonal=None,
+        *,
+        seasonal_periods=None,
+        initialization_method="estimated",
+        initial_level=None,
+        initial_trend=None,
+        initial_seasonal=None,
+        use_boxcox=False,
+        bounds=None,
+        dates=None,
+        freq=None,
+        missing="none",
+    ):
+        super().__init__(endog, None, dates, freq, missing=missing)
+        self._y = self._data = array_like(
+            endog, "endog", ndim=1, contiguous=True, order="C"
+        )
+        options = ("add", "mul", "additive", "multiplicative")
+        trend = string_like(trend, "trend", options=options, optional=True)
+        if trend in ["additive", "multiplicative"]:
+            trend = {"additive": "add", "multiplicative": "mul"}[trend]
+        self.trend = trend
+        self.damped_trend = bool_like(damped_trend, "damped_trend")
+        seasonal = string_like(seasonal, "seasonal", options=options, optional=True)
+        if seasonal in ["additive", "multiplicative"]:
+            seasonal = {"additive": "add", "multiplicative": "mul"}[seasonal]
+        self.seasonal = seasonal
+        self.has_trend = trend in ["mul", "add"]
+        self.has_seasonal = seasonal in ["mul", "add"]
+        if (self.trend == "mul" or self.seasonal == "mul") and not np.all(
+            self._data > 0.0
+        ):
+            raise ValueError(
+                "endog must be strictly positive when using"
+                "multiplicative trend or seasonal components."
+            )
+        if self.damped_trend and not self.has_trend:
+            raise ValueError("Can only dampen the trend component")
+        if self.has_seasonal:
+            self.seasonal_periods = int_like(
+                seasonal_periods, "seasonal_periods", optional=True
+            )
+            if seasonal_periods is None:
+                try:
+                    self.seasonal_periods = freq_to_period(self._index_freq)
+                except Exception as exc:
+                    raise ValueError(
+                        "seasonal_periods has not been provided and index "
+                        "does not have a known freq. You must provide "
+                        "seasonal_periods"
+                    ) from exc
+            if self.seasonal_periods <= 1:
+                raise ValueError("seasonal_periods must be larger than 1.")
+            assert self.seasonal_periods is not None
+        else:
+            self.seasonal_periods = 0
+        self.nobs = len(self.endog)
+        options = ("known", "estimated", "heuristic", "legacy-heuristic")
+        self._initialization_method = string_like(
+            initialization_method,
+            "initialization_method",
+            optional=False,
+            options=options,
+        )
+        self._initial_level = float_like(initial_level, "initial_level", optional=True)
+        self._initial_trend = float_like(initial_trend, "initial_trend", optional=True)
+        self._initial_seasonal = array_like(
+            initial_seasonal, "initial_seasonal", optional=True
+        )
+        estimated = self._initialization_method == "estimated"
+        self._estimate_level = estimated
+        self._estimate_trend = estimated and self.trend is not None
+        self._estimate_seasonal = estimated and self.seasonal is not None
+        self._bounds = self._check_bounds(bounds)
+        self._use_boxcox = use_boxcox
+        self._lambda = np.nan
+        self._y = self._boxcox()
+        self._initialize()
+        self._fixed_parameters = {}
+
+    def _check_bounds(self, bounds):
+        bounds = dict_like(bounds, "bounds", optional=True)
+        if bounds is None:
+            return None
+        msg = (
+            "bounds must be a dictionary of 2-element tuples of the form"
+            " (lb, ub) where lb < ub, lb>=0 and ub<=1"
+        )
+        variables = self._ordered_names()
+        for key in bounds:
+            if key not in variables:
+                supported = ", ".join(variables[:-1])
+                supported += ", and " + variables[-1]
+                raise KeyError(
+                    f"{key} does not match the list of supported variables "
+                    f"names: {supported}."
+                )
+            bound = bounds[key]
+            if not isinstance(bound, tuple):
+                raise TypeError(msg)
+            lb = bound[0] if bound[0] is not None else -np.inf
+            ub = bound[1] if bound[1] is not None else np.inf
+            if len(bound) != 2 or lb >= ub:
+                raise ValueError(msg)
+            if ("smoothing" in key or "damp" in key) and (
+                bound[0] < 0.0 or bound[1] > 1.0
+            ):
+                raise ValueError(f"{key} must have a lower bound >= 0.0 and <= 1.0")
+        return bounds
+
+    def _boxcox(self):
+        if self._use_boxcox is None or self._use_boxcox is False:
+            self._lambda = np.nan
+            return self._y
+        if self._use_boxcox is True:
+            y, self._lambda = boxcox(self._y)
+        elif isinstance(self._use_boxcox, (int, float)):
+            self._lambda = float(self._use_boxcox)
+            y = boxcox(self._y, self._use_boxcox)
+        else:
+            raise TypeError("use_boxcox must be True, False or a float.")
+        return y
+
+    @contextlib.contextmanager
+    def fix_params(self, values):
+        """
+        Temporarily fix parameters for estimation
+
+        Parameters
+        ----------
+        values : dict
+            Values to fix. The key is the parameter name and the value is the
+            fixed value.
+
+        Yields
+        ------
+        None
+            No value returned.
+
+        Examples
+        --------
+        >>> from statsmodels.datasets.macrodata import load_pandas
+        >>> data = load_pandas()
+        >>> import statsmodels.tsa.api as tsa
+        >>> mod = tsa.ExponentialSmoothing(data.data.realcons, trend="add",
+        ...                                initialization_method="estimated")
+        >>> with mod.fix_params({"smoothing_level": 0.2}):
+        ...     mod.fit()
+        """
+        values = dict_like(values, "values")
+        valid_keys = ("smoothing_level",)
+        if self.has_trend:
+            valid_keys += ("smoothing_trend",)
+        if self.has_seasonal:
+            valid_keys += ("smoothing_seasonal",)
+            m = self.seasonal_periods
+            valid_keys += tuple([f"initial_seasonal.{i}" for i in range(m)])
+        if self.damped_trend:
+            valid_keys += ("damping_trend",)
+        if self._initialization_method in ("estimated", None):
+            extra_keys = [
+                key.replace("smoothing_", "initial_")
+                for key in valid_keys
+                if "smoothing_" in key
+            ]
+            valid_keys += tuple(extra_keys)
+
+        for key in values:
+            if key not in valid_keys:
+                valid = ", ".join(valid_keys[:-1]) + ", and " + valid_keys[-1]
+                raise KeyError(
+                    f"{key} if not allowed. Only {valid} are supported in "
+                    "this specification."
+                )
+
+        if "smoothing_level" in values:
+            alpha = values["smoothing_level"]
+            if alpha <= 0.0:
+                raise ValueError("smoothing_level must be in (0, 1)")
+            beta = values.get("smoothing_trend", 0.0)
+            if beta > alpha:
+                raise ValueError("smoothing_trend must be <= smoothing_level")
+            gamma = values.get("smoothing_seasonal", 0.0)
+            if gamma > 1 - alpha:
+                raise ValueError("smoothing_seasonal must be <= 1 - smoothing_level")
+
+        try:
+            self._fixed_parameters = values
+            yield
+        finally:
+            self._fixed_parameters = {}
+
+    def _initialize(self):
+        if self._initialization_method == "known":
+            return self._initialize_known()
+        msg = (
+            f"initialization method is {self._initialization_method} but "
+            "initial_{0} has been set."
+        )
+        if self._initial_level is not None:
+            raise ValueError(msg.format("level"))
+        if self._initial_trend is not None:
+            raise ValueError(msg.format("trend"))
+        if self._initial_seasonal is not None:
+            raise ValueError(msg.format("seasonal"))
+        if self._initialization_method == "legacy-heuristic":
+            return self._initialize_legacy()
+        elif self._initialization_method == "heuristic":
+            return self._initialize_heuristic()
+        elif self._initialization_method == "estimated":
+            if self.nobs < 10 + 2 * (self.seasonal_periods // 2):
+                return self._initialize_simple()
+            else:
+                return self._initialize_heuristic()
+
+    def _initialize_simple(self):
+        trend = self.trend if self.has_trend else False
+        seasonal = self.seasonal if self.has_seasonal else False
+        lvl, trend, seas = _initialization_simple(
+            self._y, trend, seasonal, self.seasonal_periods
+        )
+        self._initial_level = lvl
+        self._initial_trend = trend
+        self._initial_seasonal = seas
+
+    def _initialize_heuristic(self):
+        trend = self.trend if self.has_trend else False
+        seasonal = self.seasonal if self.has_seasonal else False
+        lvl, trend, seas = _initialization_heuristic(
+            self._y, trend, seasonal, self.seasonal_periods
+        )
+        self._initial_level = lvl
+        self._initial_trend = trend
+        self._initial_seasonal = seas
+
+    def _initialize_legacy(self):
+        lvl, trend, seasonal = self.initial_values(force=True)
+        self._initial_level = lvl
+        self._initial_trend = trend
+        self._initial_seasonal = seasonal
+
+    def _initialize_known(self):
+        msg = "initialization is 'known' but initial_{0} not given"
+        if self._initial_level is None:
+            raise ValueError(msg.format("level"))
+        excess = "initial_{0} set but model has no {0} component"
+        if self.has_trend and self._initial_trend is None:
+            raise ValueError(msg.format("trend"))
+        elif not self.has_trend and self._initial_trend is not None:
+            raise ValueError(excess.format("trend"))
+        if self.has_seasonal and self._initial_seasonal is None:
+            raise ValueError(msg.format("seasonal"))
+        elif not self.has_seasonal and self._initial_seasonal is not None:
+            raise ValueError(excess.format("seasonal"))
+
+    def predict(self, params, start=None, end=None):
+        """
+        In-sample and out-of-sample prediction
+
+        Parameters
+        ----------
+        params : dict
+            The fitted model parameters.
+        start : int, str, or datetime, optional
+            Zero-indexed observation number at which to start forecasting, ie.,
+            the first forecast is start. Can also be a date string to
+            parse or a datetime type.
+        end : int, str, or datetime, optional
+            Zero-indexed observation number at which to end forecasting, ie.,
+            the last forecast is end. Can also be a date string to
+            parse or a datetime type.
+
+        Returns
+        -------
+        ndarray
+            The predicted values.
+        """
+        if start is None:
+            freq = getattr(self._index, "freq", 1)
+            if isinstance(freq, int):
+                start = self._index.shape[0]
+            else:
+                start = self._index[-1] + freq
+        start, end, out_of_sample, _ = self._get_prediction_index(start=start, end=end)
+        if out_of_sample > 0:
+            res = self._predict(h=out_of_sample, **params)
+        else:
+            res = self._predict(h=0, **params)
+        return res.fittedfcast[start : end + out_of_sample + 1]
+
+    def _enforce_bounds(self, p, sel, lb, ub):
+        initial_p = p[sel]
+
+        # Ensure strictly inbounds
+        loc = initial_p <= lb
+        upper = ub[loc].copy()
+        upper[~np.isfinite(upper)] = 100.0
+        eps = 1e-4
+        initial_p[loc] = lb[loc] + eps * (upper - lb[loc])
+
+        loc = initial_p >= ub
+        lower = lb[loc].copy()
+        lower[~np.isfinite(lower)] = -100.0
+        eps = 1e-4
+        initial_p[loc] = ub[loc] - eps * (ub[loc] - lower)
+
+        return initial_p
+
+    @staticmethod
+    def _check_blocked_keywords(d: dict, keys: Sequence[Hashable], name="kwargs"):
+        for key in keys:
+            if key in d:
+                raise ValueError(f"{name} must not contain '{key}'")
+
+    def _check_bound_feasibility(self, bounds):
+        if bounds[1][0] > bounds[0][1]:
+            raise ValueError(
+                "The bounds for smoothing_trend and smoothing_level are "
+                "incompatible since smoothing_trend <= smoothing_level."
+            )
+        if bounds[2][0] > (1 - bounds[0][1]):
+            raise ValueError(
+                "The bounds for smoothing_seasonal and smoothing_level "
+                "are incompatible since smoothing_seasonal <= "
+                "1 - smoothing_level."
+            )
+
+    @staticmethod
+    def _setup_brute(sel, bounds, alpha):
+        # More points when fewer parameters
+        ns = 87 // sel[:3].sum()
+
+        if not sel[0]:
+            # Easy case since no cross-constraints
+            nparams = int(sel[1]) + int(sel[2])
+            args = []
+            for i in range(1, 3):
+                if sel[i]:
+                    bound = bounds[i]
+                    step = bound[1] - bound[0]
+                    lb = bound[0] + 0.005 * step
+                    if i == 1:
+                        ub = min(bound[1], alpha) - 0.005 * step
+                    else:
+                        ub = min(bound[1], 1 - alpha) - 0.005 * step
+                    args.append(np.linspace(lb, ub, ns))
+            points = np.stack(np.meshgrid(*args))
+            points = points.reshape((nparams, -1)).T
+            return np.ascontiguousarray(points)
+
+        bound = bounds[0]
+        step = 0.005 * (bound[1] - bound[0])
+        points = np.linspace(bound[0] + step, bound[1] - step, ns)
+        if not sel[1] and not sel[2]:
+            return points[:, None]
+
+        combined = []
+        b_bounds = bounds[1]
+        g_bounds = bounds[2]
+        if sel[1] and sel[2]:
+            for a in points:
+                b_lb = b_bounds[0]
+                b_ub = min(b_bounds[1], a)
+                g_lb = g_bounds[0]
+                g_ub = min(g_bounds[1], 1 - a)
+                if b_lb > b_ub or g_lb > g_ub:
+                    # infeasible point
+                    continue
+                nb = int(np.ceil(ns * np.sqrt(a)))
+                ng = int(np.ceil(ns * np.sqrt(1 - a)))
+                b = np.linspace(b_lb, b_ub, nb)
+                g = np.linspace(g_lb, g_ub, ng)
+                both = np.stack(np.meshgrid(b, g)).reshape(2, -1).T
+                final = np.empty((both.shape[0], 3))
+                final[:, 0] = a
+                final[:, 1:] = both
+                combined.append(final)
+        elif sel[1]:
+            for a in points:
+                b_lb = b_bounds[0]
+                b_ub = min(b_bounds[1], a)
+                if b_lb > b_ub:
+                    # infeasible point
+                    continue
+                nb = int(np.ceil(ns * np.sqrt(a)))
+                final = np.empty((nb, 2))
+                final[:, 0] = a
+                final[:, 1] = np.linspace(b_lb, b_ub, nb)
+                combined.append(final)
+        else:  # sel[2]
+            for a in points:
+                g_lb = g_bounds[0]
+                g_ub = min(g_bounds[1], 1 - a)
+                if g_lb > g_ub:
+                    # infeasible point
+                    continue
+                ng = int(np.ceil(ns * np.sqrt(1 - a)))
+                final = np.empty((ng, 2))
+                final[:, 1] = np.linspace(g_lb, g_ub, ng)
+                final[:, 0] = a
+                combined.append(final)
+
+        return np.vstack(combined)
+
+    def _ordered_names(self):
+        names = (
+            "smoothing_level",
+            "smoothing_trend",
+            "smoothing_seasonal",
+            "initial_level",
+            "initial_trend",
+            "damping_trend",
+        )
+        m = self.seasonal_periods
+        names += tuple([f"initial_seasonal.{i}" for i in range(m)])
+        return names
+
+    def _update_for_fixed(self, sel, alpha, beta, gamma, phi, l0, b0, s0):
+        if self._fixed_parameters:
+            fixed = self._fixed_parameters
+            names = self._ordered_names()
+            not_fixed = np.array([name not in fixed for name in names])
+            if (~sel[~not_fixed]).any():
+                invalid = []
+                for name, s, nf in zip(names, sel, not_fixed, strict=True):
+                    if not s and not nf:
+                        invalid.append(name)
+                invalid_names = ", ".join(invalid)
+                raise ValueError(
+                    "Cannot fix a parameter that is not being "
+                    f"estimated: {invalid_names}"
+                )
+
+            sel &= not_fixed
+            alpha = fixed.get("smoothing_level", alpha)
+            beta = fixed.get("smoothing_trend", beta)
+            gamma = fixed.get("smoothing_seasonal", gamma)
+            phi = fixed.get("damping_trend", phi)
+            l0 = fixed.get("initial_level", l0)
+            b0 = fixed.get("initial_trend", b0)
+            for i in range(self.seasonal_periods):
+                s0[i] = fixed.get(f"initial_seasonal.{i}", s0[i])
+        return sel, alpha, beta, gamma, phi, l0, b0, s0
+
+    def _construct_bounds(self):
+        trend_lb = 0.0 if self.trend == "mul" else None
+        season_lb = 0.0 if self.seasonal == "mul" else None
+        lvl_lb = None if trend_lb is None and season_lb is None else 0.0
+        bounds = [
+            (0.0, 1.0),  # alpha
+            (0.0, 1.0),  # beta
+            (0.0, 1.0),  # gamma
+            (lvl_lb, None),  # level
+            (trend_lb, None),  # trend
+            (0.8, 0.995),  # phi
+        ]
+        bounds += [(season_lb, None)] * self.seasonal_periods
+        if self._bounds is not None:
+            assert isinstance(self._bounds, dict)
+            for i, name in enumerate(self._ordered_names()):
+                bounds[i] = self._bounds.get(name, bounds[i])
+        # Update bounds to account for fixed parameters
+        fixed = self._fixed_parameters
+        if "smoothing_level" in fixed:
+            # Update bounds if fixed alpha
+            alpha = fixed["smoothing_level"]
+            # beta <= alpha
+            if bounds[1][1] > alpha:
+                bounds[1] = (bounds[1][0], alpha)
+            # gamma <= 1 - alpha
+            if bounds[2][1] > (1 - alpha):
+                bounds[2] = (bounds[2][0], 1 - alpha)
+            # gamma <= 1 - alpha
+        if "smoothing_trend" in fixed:
+            # beta <= alpha
+            beta = fixed["smoothing_trend"]
+            bounds[0] = (max(beta, bounds[0][0]), bounds[0][1])
+        if "smoothing_seasonal" in fixed:
+            gamma = fixed["smoothing_seasonal"]
+            # gamma <= 1 - alpha => alpha <= 1 - gamma
+            bounds[0] = (bounds[0][0], min(1 - gamma, bounds[0][1]))
+        # Ensure bounds are feasible
+        for i, name in enumerate(self._ordered_names()):
+            lb = bounds[i][0] if bounds[i][0] is not None else -np.inf
+            ub = bounds[i][1] if bounds[i][1] is not None else np.inf
+            if lb >= ub:
+                raise ValueError(
+                    "After adjusting for user-provided bounds fixed values, "
+                    f"the resulting set of bounds for {name}, {bounds[i]}, "
+                    "are infeasible."
+                )
+        self._check_bound_feasibility(bounds)
+        return bounds
+
+    def _get_starting_values(
+        self,
+        params,
+        start_params,
+        use_brute,
+        sel,
+        hw_args,
+        bounds,
+        alpha,
+        func,
+    ):
+        if start_params is None and use_brute and np.any(sel[:3]):
+            # Have a quick look in the region for a good starting place for
+            # alpha, beta & gamma using fixed values for initial
+            m = self.seasonal_periods
+            sv_sel = np.array([False] * (6 + m))
+            sv_sel[:3] = True
+            sv_sel &= sel
+            hw_args.xi = sv_sel.astype(np.int64)
+            hw_args.transform = False
+            # Setup the grid points, respecting constraints
+            points = self._setup_brute(sv_sel, bounds, alpha)
+            opt = opt_wrapper(func)
+            best_val = np.inf
+            best_params = points[0]
+            for point in points:
+                val = opt(point, hw_args)
+                if val < best_val:
+                    best_params = point
+                    best_val = val
+            params[sv_sel] = best_params
+        elif start_params is not None:
+            if len(start_params) != sel.sum():
+                msg = "start_params must have {0} values but has {1}."
+                nxi, nsp = len(sel), len(start_params)
+                raise ValueError(msg.format(nxi, nsp))
+            params[sel] = start_params
+        return params
+
+    def _optimize_parameters(
+        self, data: _OptConfig, use_brute, method, kwargs
+    ) -> _OptConfig:
+        # Prepare starting values
+        alpha = data.alpha
+        beta = data.beta
+        phi = data.phi
+        gamma = data.gamma
+        y = data.y
+        start_params = data.params
+
+        has_seasonal = self.has_seasonal
+        has_trend = self.has_trend
+        trend = self.trend
+        seasonal = self.seasonal
+        damped_trend = self.damped_trend
+
+        m = self.seasonal_periods
+        params = np.zeros(6 + m)
+        l0, b0, s0 = self.initial_values(
+            initial_level=data.level, initial_trend=data.trend
+        )
+
+        init_alpha = alpha if alpha is not None else 0.5 / max(m, 1)
+        init_beta = beta
+        if beta is None and has_trend:
+            init_beta = 0.1 * init_alpha
+        init_gamma = gamma
+        if has_seasonal and gamma is None:
+            init_gamma = 0.05 * (1 - init_alpha)
+        init_phi = phi if phi is not None else 0.99
+        # Selection of parameters to optimize
+        sel = np.array(
+            [
+                alpha is None,
+                has_trend and beta is None,
+                has_seasonal and gamma is None,
+                self._estimate_level,
+                self._estimate_trend,
+                damped_trend and phi is None,
+            ]
+            + [has_seasonal and self._estimate_seasonal] * m,
+        )
+        (
+            sel,
+            init_alpha,
+            init_beta,
+            init_gamma,
+            init_phi,
+            l0,
+            b0,
+            s0,
+        ) = self._update_for_fixed(
+            sel, init_alpha, init_beta, init_gamma, init_phi, l0, b0, s0
+        )
+
+        func = SMOOTHERS[(seasonal, trend)]
+        params[:6] = [init_alpha, init_beta, init_gamma, l0, b0, init_phi]
+        if m:
+            params[-m:] = s0
+        if not np.any(sel):
+            from statsmodels.tools.sm_exceptions import EstimationWarning
+
+            message = (
+                "Model has no free parameters to estimate. Set "
+                "optimized=False to suppress this warning"
+            )
+            warnings.warn(message, EstimationWarning, stacklevel=3)
+            data = data.unpack_parameters(params)
+            data.params = params
+            data.mask = sel
+
+            return data
+        orig_bounds = self._construct_bounds()
+
+        bounds = np.array(orig_bounds[:3], dtype=float)
+        hw_args = HoltWintersArgs(sel.astype(np.int64), params, bounds, y, m, self.nobs)
+        params = self._get_starting_values(
+            params,
+            start_params,
+            use_brute,
+            sel,
+            hw_args,
+            bounds,
+            init_alpha,
+            func,
+        )
+
+        # We always use [0, 1] for a, b and g and handle transform inside
+        mod_bounds = [(0, 1)] * 3 + orig_bounds[3:]
+        relevant_bounds = [bnd for bnd, flag in zip(mod_bounds, sel, strict=True) if flag]
+        bounds = np.array(relevant_bounds, dtype=float)
+        lb, ub = bounds.T
+        lb[np.isnan(lb)] = -np.inf
+        ub[np.isnan(ub)] = np.inf
+        hw_args.xi = sel.astype(np.int64)
+
+        # Ensure strictly inbounds
+        initial_p = self._enforce_bounds(params, sel, lb, ub)
+        # Transform to unrestricted space
+        params[sel] = initial_p
+        params[:3] = to_unrestricted(params, sel, hw_args.bounds)
+        initial_p = params[sel]
+        # Ensure parameters are transformed internally
+        hw_args.transform = True
+        if method in ("least_squares", "ls"):
+            # Least squares uses a different format for bounds
+            ls_bounds = lb, ub
+            self._check_blocked_keywords(kwargs, ("args", "bounds"))
+            res = least_squares(
+                func, initial_p, bounds=ls_bounds, args=(hw_args,), **kwargs
+            )
+            success = res.success
+        elif method in ("basinhopping", "bh"):
+            # Take a deeper look in the local minimum we are in to find the
+            # best solution to parameters, maybe hop around to try escape the
+            # local minimum we may be in.
+            minimizer_kwargs = {"args": (hw_args,), "bounds": relevant_bounds}
+            kwargs = kwargs.copy()
+            if "minimizer_kwargs" in kwargs:
+                self._check_blocked_keywords(
+                    kwargs["minimizer_kwargs"],
+                    ("args", "bounds"),
+                    name="kwargs['minimizer_kwargs']",
+                )
+                minimizer_kwargs.update(kwargs["minimizer_kwargs"])
+                del kwargs["minimizer_kwargs"]
+            default_kwargs = {
+                "minimizer_kwargs": minimizer_kwargs,
+                "stepsize": 0.01,
+            }
+            default_kwargs.update(kwargs)
+            obj = opt_wrapper(func)
+            res = basinhopping(obj, initial_p, **default_kwargs)
+            success = res.lowest_optimization_result.success
+        else:
+            obj = opt_wrapper(func)
+            self._check_blocked_keywords(kwargs, ("args", "bounds", "method"))
+            res = minimize(
+                obj,
+                initial_p,
+                args=(hw_args,),
+                bounds=relevant_bounds,
+                method=method,
+                **kwargs,
+            )
+            success = res.success
+        # finally transform to restricted space
+        params[sel] = res.x
+        params[:3] = to_restricted(params, sel, hw_args.bounds)
+        res.x = params[sel]
+
+        if not success:
+            from statsmodels.tools.sm_exceptions import ConvergenceWarning
+
+            warnings.warn(
+                "Optimization failed to converge. Check mle_retvals.",
+                ConvergenceWarning,
+                stacklevel=2,
+            )
+        params[sel] = res.x
+
+        data.unpack_parameters(params)
+        data.params = params
+        data.mask = sel
+        data.mle_retvals = res
+
+        return data
+
+    def fit(
+        self,
+        smoothing_level=None,
+        smoothing_trend=None,
+        smoothing_seasonal=None,
+        damping_trend=None,
+        *,
+        optimized=True,
+        remove_bias=False,
+        start_params=None,
+        method=None,
+        minimize_kwargs=None,
+        use_brute=True,
+    ):
+        """
+        Fit the model
+
+        Parameters
+        ----------
+        smoothing_level : float, optional
+            The alpha value of the simple exponential smoothing, if the value
+            is set then this value will be used as the value.
+        smoothing_trend : float, optional
+            The beta value of the Holt's trend method, if the value is
+            set then this value will be used as the value.
+        smoothing_seasonal : float, optional
+            The gamma value of the holt winters seasonal method, if the value
+            is set then this value will be used as the value.
+        damping_trend : float, optional
+            The phi value of the damped method, if the value is
+            set then this value will be used as the value.
+        optimized : bool, optional
+            Estimate model parameters by maximizing the log-likelihood.
+        remove_bias : bool, optional
+            Remove bias from forecast values and fitted values by enforcing
+            that the average residual is equal to zero.
+        start_params : array_like, optional
+            Starting values to used when optimizing the fit.  If not provided,
+            starting values are determined using a combination of grid search
+            and reasonable values based on the initial values of the data. See
+            the notes for the structure of the model parameters.
+        method : str, optional
+            The minimizer used. Valid options are "L-BFGS-B" (default),
+            "TNC", "SLSQP", "Powell", "trust-constr", "basinhopping" (also
+            "bh") and "least_squares" (also "ls"). basinhopping tries multiple
+            starting values in an attempt to find a global minimizer in
+            non-convex problems, and so is slower than the others.
+        minimize_kwargs : dict, optional
+            A dictionary of keyword arguments passed to SciPy's minimize
+            function if method is one of "L-BFGS-B", "TNC",
+            "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
+            or least_squares functions. The valid keywords are optimizer
+            specific. Consult SciPy's documentation for the full set of
+            options.
+        use_brute : bool, optional
+            Search for good starting values using a brute force (grid)
+            optimizer. If False, a naive set of starting values is used.
+
+        Returns
+        -------
+        HoltWintersResults
+            See statsmodels.tsa.holtwinters.HoltWintersResults.
+
+        Notes
+        -----
+        This is a full implementation of the holt winters exponential smoothing
+        as per [1]_. This includes all the unstable methods as well as the
+        stable methods. The implementation of the library covers the
+        functionality of the R library as much as possible whilst still
+        being Pythonic.
+
+        The parameters are ordered
+
+        [alpha, beta, gamma, initial_level, initial_trend, phi]
+
+        which are then followed by m seasonal values if the model contains
+        a seasonal smoother. Any parameter not relevant for the model is
+        omitted. For example, a model that has a level and a seasonal
+        component, but no trend and is not damped, would have starting
+        values
+
+        [alpha, gamma, initial_level, s0, s1, ..., s<m-1>]
+
+        where sj is the initial value for seasonal component j.
+
+        References
+        ----------
+        .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+            and practice. OTexts, 2014.
+        """
+        # Variable renames to alpha,beta, etc as this helps with following the
+        # mathematical notation in general
+        alpha = float_like(smoothing_level, "smoothing_level", True)
+        beta = float_like(smoothing_trend, "smoothing_trend", True)
+        gamma = float_like(smoothing_seasonal, "smoothing_seasonal", True)
+        phi = float_like(damping_trend, "damping_trend", True)
+        start_params = array_like(start_params, "start_params", optional=True)
+        minimize_kwargs = dict_like(minimize_kwargs, "minimize_kwargs", optional=True)
+        minimize_kwargs = {} if minimize_kwargs is None else minimize_kwargs
+        supported_methods = ("basinhopping", "bh")
+        supported_methods += ("least_squares", "ls")
+        supported_methods += (
+            "L-BFGS-B",
+            "TNC",
+            "SLSQP",
+            "Powell",
+            "trust-constr",
+        )
+        method = string_like(
+            method,
+            "method",
+            options=supported_methods,
+            lower=False,
+            optional=True,
+        )
+        use_boxcox = False if self._use_boxcox is None else self._use_boxcox
+
+        data = self._data
+        damped = self.damped_trend
+        phi = phi if damped else 1.0
+        if self._use_boxcox is not None:
+            if use_boxcox == "log":
+                lamda = 0.0
+                y = boxcox(data, lamda)
+            elif isinstance(use_boxcox, float):
+                lamda = use_boxcox
+                y = boxcox(data, lamda)
+            elif use_boxcox:
+                y, lamda = boxcox(data)
+                # use_boxcox = lamda
+            else:
+                y = data.squeeze()
+        else:
+            y = self._y
+
+        self._y = y
+        res = _OptConfig()
+        res.alpha = alpha
+        res.beta = beta
+        res.phi = phi
+        res.gamma = gamma
+        res.level = None
+        res.trend = None
+        res.seasonal = None
+        res.y = y
+        res.params = start_params
+        res.mle_retvals = res.mask = None
+        method = "L-BFGS-B" if method is None else method
+        if optimized:
+            res = self._optimize_parameters(res, use_brute, method, minimize_kwargs)
+        else:
+            l0, b0, s0 = self.initial_values()
+            res.level = l0
+            res.trend = b0
+            res.seasonal = s0
+            if self._fixed_parameters:
+                fp = self._fixed_parameters
+                res.alpha = fp.get("smoothing_level", res.alpha)
+                res.beta = fp.get("smoothing_trend", res.beta)
+                res.gamma = fp.get("smoothing_seasonal", res.gamma)
+                res.phi = fp.get("damping_trend", res.phi)
+                res.level = fp.get("initial_level", res.level)
+                res.trend = fp.get("initial_trend", res.trend)
+                res.seasonal = fp.get("initial_seasonal", res.seasonal)
+
+        hwfit = self._predict(
+            h=0,
+            smoothing_level=res.alpha,
+            smoothing_trend=res.beta,
+            smoothing_seasonal=res.gamma,
+            damping_trend=res.phi,
+            initial_level=res.level,
+            initial_trend=res.trend,
+            initial_seasons=res.seasonal,
+            use_boxcox=use_boxcox,
+            remove_bias=remove_bias,
+            is_optimized=res.mask,
+        )
+        hwfit._results.mle_retvals = res.mle_retvals
+        return hwfit
+
+    def initial_values(self, initial_level=None, initial_trend=None, force=False):
+        """
+        Compute initial values used in the exponential smoothing recursions
+
+        Parameters
+        ----------
+        initial_level : float, optional
+            The initial value used for the level component.
+        initial_trend : float, optional
+            The initial value used for the trend component.
+        force : bool, optional
+            Force the calculation even if initial values exist.
+
+        Returns
+        -------
+        initial_level : float
+            The initial value used for the level component.
+        initial_trend : float or None
+            The initial value used for the trend component.
+        initial_seasons : list of float
+            The initial values used for the seasonal components.
+
+        Notes
+        -----
+        Convenience function the exposes the values used to initialize the
+        recursions. When optimizing parameters these are used as starting
+        values.
+
+        Method used to compute the initial value depends on when components
+        are included in the model.  In a simple exponential smoothing model
+        without trend or a seasonal components, the initial value is set to the
+        first observation. When a trend is added, the trend is initialized
+        either using y[1]/y[0], if multiplicative, or y[1]-y[0]. When the
+        seasonal component is added the initialization adapts to account for
+        the modified structure.
+        """
+        if self._initialization_method is not None and not force:
+            return (
+                self._initial_level,
+                self._initial_trend,
+                self._initial_seasonal,
+            )
+        y = self._y
+        trend = self.trend
+        seasonal = self.seasonal
+        has_seasonal = self.has_seasonal
+        has_trend = self.has_trend
+        m = self.seasonal_periods
+        l0 = initial_level
+        b0 = initial_trend
+        if has_seasonal:
+            l0 = y[np.arange(self.nobs) % m == 0].mean() if l0 is None else l0
+            if b0 is None and has_trend:
+                # TODO: Fix for short m
+                lead, lag = y[m : m + m], y[:m]
+                if trend == "mul":
+                    b0 = np.exp((np.log(lead.mean()) - np.log(lag.mean())) / m)
+                else:
+                    b0 = ((lead - lag) / m).mean()
+            s0 = list(y[:m] / l0) if seasonal == "mul" else list(y[:m] - l0)
+        elif has_trend:
+            l0 = y[0] if l0 is None else l0
+            if b0 is None:
+                b0 = y[1] / y[0] if trend == "mul" else y[1] - y[0]
+            s0 = []
+        else:
+            if l0 is None:
+                l0 = y[0]
+            b0 = None
+            s0 = []
+
+        return l0, b0, s0
+
+    def _predict(
+        self,
+        h=None,
+        smoothing_level=None,
+        smoothing_trend=None,
+        smoothing_seasonal=None,
+        initial_level=None,
+        initial_trend=None,
+        damping_trend=None,
+        initial_seasons=None,
+        use_boxcox=None,
+        lamda=None,
+        remove_bias=None,
+        is_optimized=None,
+    ):
+        """
+        Helper prediction function
+
+        Parameters
+        ----------
+        h : int, optional
+            The number of time steps to forecast ahead.
+        smoothing_level : float, optional
+            The alpha value of the simple exponential smoothing.
+        smoothing_trend : float, optional
+            The beta value of the Holt's trend method.
+        smoothing_seasonal : float, optional
+            The gamma value of the holt winters seasonal method.
+        initial_level : float, optional
+            The initial value used for the level component.
+        initial_trend : float, optional
+            The initial value used for the trend component.
+        damping_trend : float, optional
+            The phi value of the damped method.
+        initial_seasons : array_like, optional
+            The initial values used for the seasonal components.
+        use_boxcox : {True, False, 'log', float}, optional
+            Should the Box-Cox transform be applied to the data first? If
+            'log' then apply the log. If float then use the value as lambda.
+        lamda : float, optional
+            The Box-Cox transformation parameter to use if `use_boxcox` is
+            a float.
+        remove_bias : bool, optional
+            Remove bias from forecast values and fitted values by enforcing
+            that the average residual is equal to zero.
+        is_optimized : ndarray, optional
+            Boolean array indicating which parameters were optimized.
+        """
+        # Variable renames to alpha, beta, etc as this helps with following the
+        # mathematical notation in general
+        alpha = smoothing_level
+        beta = smoothing_trend
+        gamma = smoothing_seasonal
+        phi = damping_trend
+
+        # Start in sample and out of sample predictions
+        data = self.endog
+        damped = self.damped_trend
+        has_seasonal = self.has_seasonal
+        has_trend = self.has_trend
+        trend = self.trend
+        seasonal = self.seasonal
+        m = self.seasonal_periods
+        phi = phi if damped else 1.0
+        if use_boxcox == "log":
+            lamda = 0.0
+            y = boxcox(data, 0.0)
+        elif isinstance(use_boxcox, float):
+            lamda = use_boxcox
+            y = boxcox(data, lamda)
+        elif use_boxcox:
+            y, lamda = boxcox(data)
+        else:
+            lamda = None
+            y = data.squeeze()
+            if np.ndim(y) != 1:
+                raise NotImplementedError("Only 1 dimensional data supported")
+        y_alpha = np.zeros((self.nobs,))
+        y_gamma = np.zeros((self.nobs,))
+        alphac = 1 - alpha
+        y_alpha[:] = alpha * y
+        betac = 1 - beta if beta is not None else 0
+        gammac = 1 - gamma if gamma is not None else 0
+        if has_seasonal:
+            y_gamma[:] = gamma * y
+        lvls = np.zeros((self.nobs + h + 1,))
+        b = np.zeros((self.nobs + h + 1,))
+        s = np.zeros((self.nobs + h + m + 1,))
+        lvls[0] = initial_level
+        b[0] = initial_trend
+        s[:m] = initial_seasons
+        phi_h = (
+            np.cumsum(np.repeat(phi, h + 1) ** np.arange(1, h + 1 + 1))
+            if damped
+            else np.arange(1, h + 1 + 1)
+        )
+        trended = {"mul": np.multiply, "add": np.add, None: lambda lvl, b: lvl}[trend]
+        detrend = {"mul": np.divide, "add": np.subtract, None: lambda lvl, b: 0}[trend]
+        dampen = {"mul": np.power, "add": np.multiply, None: lambda b, phi: 0}[trend]
+        nobs = self.nobs
+        if seasonal == "mul":
+            for i in range(1, nobs + 1):
+                lvls[i] = y_alpha[i - 1] / s[i - 1] + (
+                    alphac * trended(lvls[i - 1], dampen(b[i - 1], phi))
+                )
+                if has_trend:
+                    b[i] = (beta * detrend(lvls[i], lvls[i - 1])) + (
+                        betac * dampen(b[i - 1], phi)
+                    )
+                s[i + m - 1] = y_gamma[i - 1] / trended(
+                    lvls[i - 1], dampen(b[i - 1], phi)
+                ) + (gammac * s[i - 1])
+            _trend = b[1 : nobs + 1].copy()
+            season = s[m : nobs + m].copy()
+            lvls[nobs:] = lvls[nobs]
+            if has_trend:
+                b[:nobs] = dampen(b[:nobs], phi)
+                b[nobs:] = dampen(b[nobs], phi_h)
+            trend = trended(lvls, b)
+            s[nobs + m - 1 :] = [s[(nobs - 1) + j % m] for j in range(h + 1 + 1)]
+            fitted = trend * s[:-m]
+        elif seasonal == "add":
+            for i in range(1, nobs + 1):
+                lvls[i] = (
+                    y_alpha[i - 1]
+                    - (alpha * s[i - 1])
+                    + (alphac * trended(lvls[i - 1], dampen(b[i - 1], phi)))
+                )
+                if has_trend:
+                    b[i] = (beta * detrend(lvls[i], lvls[i - 1])) + (
+                        betac * dampen(b[i - 1], phi)
+                    )
+                s[i + m - 1] = (
+                    y_gamma[i - 1]
+                    - (gamma * trended(lvls[i - 1], dampen(b[i - 1], phi)))
+                    + (gammac * s[i - 1])
+                )
+            _trend = b[1 : nobs + 1].copy()
+            season = s[m : nobs + m].copy()
+            lvls[nobs:] = lvls[nobs]
+            if has_trend:
+                b[:nobs] = dampen(b[:nobs], phi)
+                b[nobs:] = dampen(b[nobs], phi_h)
+            trend = trended(lvls, b)
+            s[nobs + m - 1 :] = [s[(nobs - 1) + j % m] for j in range(h + 1 + 1)]
+            fitted = trend + s[:-m]
+        else:
+            for i in range(1, nobs + 1):
+                lvls[i] = y_alpha[i - 1] + (
+                    alphac * trended(lvls[i - 1], dampen(b[i - 1], phi))
+                )
+                if has_trend:
+                    b[i] = (beta * detrend(lvls[i], lvls[i - 1])) + (
+                        betac * dampen(b[i - 1], phi)
+                    )
+            _trend = b[1 : nobs + 1].copy()
+            season = s[m : nobs + m].copy()
+            lvls[nobs:] = lvls[nobs]
+            if has_trend:
+                b[:nobs] = dampen(b[:nobs], phi)
+                b[nobs:] = dampen(b[nobs], phi_h)
+            trend = trended(lvls, b)
+            fitted = trend
+        level = lvls[1 : nobs + 1].copy()
+        if use_boxcox or use_boxcox == "log" or isinstance(use_boxcox, float):
+            fitted = inv_boxcox(fitted, lamda)
+        err = fitted[: -h - 1] - data
+        sse = err.T @ err
+        # (s0 + gamma) + (b0 + beta) + (l0 + alpha) + phi
+        k = m * has_seasonal + 2 * has_trend + 2 + 1 * damped
+        aic = self.nobs * np.log(sse / self.nobs) + k * 2
+        dof_eff = self.nobs - k - 3
+        if dof_eff > 0:
+            aicc_penalty = (2 * (k + 2) * (k + 3)) / dof_eff
+        else:
+            aicc_penalty = np.inf
+        aicc = aic + aicc_penalty
+        bic = self.nobs * np.log(sse / self.nobs) + k * np.log(self.nobs)
+        resid = data - fitted[: -h - 1]
+        if remove_bias:
+            fitted += resid.mean()
+        self.params = {
+            "smoothing_level": alpha,
+            "smoothing_trend": beta,
+            "smoothing_seasonal": gamma,
+            "damping_trend": phi if damped else np.nan,
+            "initial_level": lvls[0],
+            "initial_trend": b[0] / phi if phi > 0 else 0,
+            "initial_seasons": s[:m],
+            "use_boxcox": use_boxcox,
+            "lamda": lamda,
+            "remove_bias": remove_bias,
+        }
+
+        # Format parameters into a DataFrame
+        codes = ["alpha", "beta", "gamma", "l.0", "b.0", "phi"]
+        codes += [f"s.{i}" for i in range(m)]
+        idx = [
+            "smoothing_level",
+            "smoothing_trend",
+            "smoothing_seasonal",
+            "initial_level",
+            "initial_trend",
+            "damping_trend",
+        ]
+        idx += [f"initial_seasons.{i}" for i in range(m)]
+
+        formatted = [alpha, beta, gamma, lvls[0], b[0], phi]
+        formatted += s[:m].tolist()
+        formatted = [np.nan if v is None else v for v in formatted]
+        formatted = np.array(formatted)
+        if is_optimized is None:
+            optimized = np.zeros(len(codes), dtype=bool)
+        else:
+            optimized = is_optimized.astype(bool)
+        included = [True, has_trend, has_seasonal, True, has_trend, damped]
+        included += [True] * m
+        formatted = pd.DataFrame(
+            [[c, f, o] for c, f, o in zip(codes, formatted, optimized, strict=True)],
+            columns=["name", "param", "optimized"],
+            index=idx,
+        )
+        formatted = formatted.loc[included]
+
+        hwfit = HoltWintersResults(
+            self,
+            self.params,
+            fittedfcast=fitted,
+            fittedvalues=fitted[: -h - 1],
+            fcastvalues=fitted[-h - 1 :],
+            sse=sse,
+            level=level,
+            trend=_trend,
+            season=season,
+            aic=aic,
+            bic=bic,
+            aicc=aicc,
+            resid=resid,
+            k=k,
+            params_formatted=formatted,
+            optimized=optimized,
+        )
+        return HoltWintersResultsWrapper(hwfit)
+
+
+class SimpleExpSmoothing(ExponentialSmoothing):
+    """
+    Simple Exponential Smoothing
+
+    Parameters
+    ----------
+    endog : array_like
+        The time series to model.
+    initialization_method : str, optional
+        Method for initialize the recursions. One of:
+
+        * None
+        * 'estimated'
+        * 'heuristic'
+        * 'legacy-heuristic'
+        * 'known'
+
+        None defaults to the pre-0.12 behavior where initial values
+        are passed as part of ``fit``. If any of the other values are
+        passed, then the initial values must also be set when constructing
+        the model. If 'known' initialization is used, then `initial_level`
+        must be passed, as well as `initial_trend` and `initial_seasonal` if
+        applicable. Default is 'estimated'. "legacy-heuristic" uses the same
+        values that were used in statsmodels 0.11 and earlier.
+    initial_level : float, optional
+        The initial level component. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+
+    See Also
+    --------
+    ExponentialSmoothing
+        Exponential smoothing with trend and seasonal components.
+    Holt
+        Exponential smoothing with a trend component.
+
+    Notes
+    -----
+    This is a full implementation of the simple exponential smoothing as
+    per [1]_.  `SimpleExpSmoothing` is a restricted version of
+    :class:`ExponentialSmoothing`.
+
+    See the notebook `Exponential Smoothing
+    <../examples/notebooks/generated/exponential_smoothing.html>`__
+    for an overview.
+
+    References
+    ----------
+    .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+        and practice. OTexts, 2014.
+    """
+
+    def __init__(
+        self,
+        endog,
+        initialization_method=None,  # Future: 'estimated',
+        initial_level=None,
+    ):
+        super().__init__(
+            endog,
+            initialization_method=initialization_method,
+            initial_level=initial_level,
+        )
+
+    def fit(
+        self,
+        smoothing_level=None,
+        *,
+        optimized=True,
+        start_params=None,
+        use_brute=True,
+        remove_bias=False,
+        method=None,
+        minimize_kwargs=None,
+    ):
+        """
+        Fit the model
+
+        Parameters
+        ----------
+        smoothing_level : float, optional
+            The smoothing_level value of the simple exponential smoothing, if
+            the value is set then this value will be used as the value.
+        optimized : bool, optional
+            Estimate model parameters by maximizing the log-likelihood.
+        start_params : array_like, optional
+            Starting values to used when optimizing the fit.  If not provided,
+            starting values are determined using a combination of grid search
+            and reasonable values based on the initial values of the data.
+        use_brute : bool, optional
+            Search for good starting values using a brute force (grid)
+            optimizer. If False, a naive set of starting values is used.
+        remove_bias : bool, optional
+            Remove bias from forecast values and fitted values by enforcing
+            that the average residual is equal to zero.
+        method : str, optional
+            The minimizer used. Valid options are "L-BFGS-B" (default), "TNC",
+            "SLSQP", "Powell", "trust-constr", "basinhopping" (also "bh") and
+            "least_squares" (also "ls"). basinhopping tries multiple starting
+            values in an attempt to find a global minimizer in non-convex
+            problems, and so is slower than the others.
+        minimize_kwargs : dict, optional
+            A dictionary of keyword arguments passed to SciPy's minimize
+            function if method is one of "L-BFGS-B" (default), "TNC",
+            "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
+            or least_squares. The valid keywords are optimizer specific.
+            Consult SciPy's documentation for the full set of options.
+
+        Returns
+        -------
+        HoltWintersResults
+            See statsmodels.tsa.holtwinters.HoltWintersResults.
+
+        Notes
+        -----
+        This is a full implementation of the simple exponential smoothing as
+        per [1]_.
+
+        References
+        ----------
+        .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+            and practice. OTexts, 2014.
+        """
+        return super().fit(
+            smoothing_level=smoothing_level,
+            optimized=optimized,
+            start_params=start_params,
+            use_brute=use_brute,
+            remove_bias=remove_bias,
+            method=method,
+            minimize_kwargs=minimize_kwargs,
+        )
+
+
+class Holt(ExponentialSmoothing):
+    """
+    Holt's Exponential Smoothing
+
+    Parameters
+    ----------
+    endog : array_like
+        The time series to model.
+    exponential : bool, optional
+        Type of trend component.
+    damped_trend : bool, optional
+        Should the trend component be damped.
+    initialization_method : str, optional
+        Method for initialize the recursions. One of:
+
+        * None
+        * 'estimated'
+        * 'heuristic'
+        * 'legacy-heuristic'
+        * 'known'
+
+        None defaults to the pre-0.12 behavior where initial values
+        are passed as part of ``fit``. If any of the other values are
+        passed, then the initial values must also be set when constructing
+        the model. If 'known' initialization is used, then `initial_level`
+        must be passed, as well as `initial_trend` and `initial_seasonal` if
+        applicable. Default is 'estimated'. "legacy-heuristic" uses the same
+        values that were used in statsmodels 0.11 and earlier.
+    initial_level : float, optional
+        The initial level component. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+    initial_trend : float, optional
+        The initial trend component. Required if estimation method is "known".
+        If set using either "estimated" or "heuristic" this value is used.
+        This allows one or more of the initial values to be set while
+        deferring to the heuristic for others or estimating the unset
+        parameters.
+
+    See Also
+    --------
+    ExponentialSmoothing
+        Exponential smoothing with trend and seasonal components.
+    SimpleExpSmoothing
+        Basic exponential smoothing with only a level component.
+
+    Notes
+    -----
+    This is a full implementation of the Holt's exponential smoothing as
+    per [1]_. `Holt` is a restricted version of :class:`ExponentialSmoothing`.
+
+    See the notebook `Exponential Smoothing
+    <../examples/notebooks/generated/exponential_smoothing.html>`__
+    for an overview.
+
+    References
+    ----------
+    .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+        and practice. OTexts, 2014.
+    """
+
+    def __init__(
+        self,
+        endog,
+        exponential=False,
+        damped_trend=False,
+        initialization_method=None,  # Future: 'estimated',
+        initial_level=None,
+        initial_trend=None,
+    ):
+        trend = "mul" if exponential else "add"
+        super().__init__(
+            endog,
+            trend=trend,
+            damped_trend=damped_trend,
+            initialization_method=initialization_method,
+            initial_level=initial_level,
+            initial_trend=initial_trend,
+        )
+
+    def fit(
+        self,
+        smoothing_level=None,
+        smoothing_trend=None,
+        *,
+        damping_trend=None,
+        optimized=True,
+        start_params=None,
+        use_brute=True,
+        remove_bias=False,
+        method=None,
+        minimize_kwargs=None,
+    ):
+        """
+        Fit the model
+
+        Parameters
+        ----------
+        smoothing_level : float, optional
+            The alpha value of the simple exponential smoothing, if the value
+            is set then this value will be used as the value.
+        smoothing_trend : float, optional
+            The beta value of the Holt's trend method, if the value is
+            set then this value will be used as the value.
+        damping_trend : float, optional
+            The phi value of the damped method, if the value is
+            set then this value will be used as the value.
+        optimized : bool, optional
+            Estimate model parameters by maximizing the log-likelihood.
+        start_params : array_like, optional
+            Starting values to used when optimizing the fit.  If not provided,
+            starting values are determined using a combination of grid search
+            and reasonable values based on the initial values of the data.
+        use_brute : bool, optional
+            Search for good starting values using a brute force (grid)
+            optimizer. If False, a naive set of starting values is used.
+        remove_bias : bool, optional
+            Remove bias from forecast values and fitted values by enforcing
+            that the average residual is equal to zero.
+        method : str, optional
+            The minimizer used. Valid options are "L-BFGS-B" (default), "TNC",
+            "SLSQP", "Powell", "trust-constr", "basinhopping" (also "bh") and
+            "least_squares" (also "ls"). basinhopping tries multiple starting
+            values in an attempt to find a global minimizer in non-convex
+            problems, and so is slower than the others.
+        minimize_kwargs : dict, optional
+            A dictionary of keyword arguments passed to SciPy's minimize
+            function if method is one of "L-BFGS-B" (default), "TNC",
+            "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
+            or least_squares. The valid keywords are optimizer specific.
+            Consult SciPy's documentation for the full set of options.
+
+        Returns
+        -------
+        HoltWintersResults
+            See statsmodels.tsa.holtwinters.HoltWintersResults.
+
+        Notes
+        -----
+        This is a full implementation of the Holt's exponential smoothing as
+        per [1]_.
+
+        References
+        ----------
+        .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
+            and practice. OTexts, 2014.
+        """
+        return super().fit(
+            smoothing_level=smoothing_level,
+            smoothing_trend=smoothing_trend,
+            damping_trend=damping_trend,
+            optimized=optimized,
+            start_params=start_params,
+            use_brute=use_brute,
+            remove_bias=remove_bias,
+            method=method,
+            minimize_kwargs=minimize_kwargs,
+        )
diff --git a/shared/package-files/d8/d8ae13e6d2693088d8ae4920d4baa1dc2d992a59ce86732d626b70cfdda5ff2f/types.py b/shared/package-files/d8/d8ae13e6d2693088d8ae4920d4baa1dc2d992a59ce86732d626b70cfdda5ff2f/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..e704b98eac0736d83586c66803db6b79cdfc5967
--- /dev/null
+++ b/shared/package-files/d8/d8ae13e6d2693088d8ae4920d4baa1dc2d992a59ce86732d626b70cfdda5ff2f/types.py
@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+import numpy as np
+
+# dtypes of arrays returned by ContourPy.
+point_dtype = np.float64
+code_dtype = np.uint8
+offset_dtype = np.uint32
+
+# Kind codes used in Matplotlib Paths.
+MOVETO = 1
+LINETO = 2
+CLOSEPOLY = 79
diff --git a/shared/package-files/d8/d8b2214bff715de004d7fa2737cebc81978037c1378d35eda67605d16f8bd8e1/test_utils.py b/shared/package-files/d8/d8b2214bff715de004d7fa2737cebc81978037c1378d35eda67605d16f8bd8e1/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a0eabaa2378f9e20bed8ab933e08410e6a9883
--- /dev/null
+++ b/shared/package-files/d8/d8b2214bff715de004d7fa2737cebc81978037c1378d35eda67605d16f8bd8e1/test_utils.py
@@ -0,0 +1,6 @@
+from toolz.utils import raises
+
+
+def test_raises():
+    assert raises(ZeroDivisionError, lambda: 1 / 0)
+    assert not raises(ZeroDivisionError, lambda: 1)
diff --git a/shared/package-files/d8/d8ba3a497d074120a216e4919bee7ea6a2560b7ac9a2f8c5b27e4dba200efea1/test_internals.py b/shared/package-files/d8/d8ba3a497d074120a216e4919bee7ea6a2560b7ac9a2f8c5b27e4dba200efea1/test_internals.py
new file mode 100644
index 0000000000000000000000000000000000000000..30c5d3177c5a569676c20b47e2776f266bb308cc
--- /dev/null
+++ b/shared/package-files/d8/d8ba3a497d074120a216e4919bee7ea6a2560b7ac9a2f8c5b27e4dba200efea1/test_internals.py
@@ -0,0 +1,1422 @@
+from datetime import (
+    date,
+    datetime,
+)
+import itertools
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs.internals import BlockPlacement
+from pandas.compat import IS64
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    period_range,
+)
+import pandas._testing as tm
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    DatetimeArray,
+    SparseArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import (
+    BlockManager,
+    SingleBlockManager,
+    make_block,
+)
+from pandas.core.internals.blocks import (
+    ensure_block_shape,
+    maybe_coerce_values,
+    new_block,
+)
+
+# this file contains BlockManager specific tests
+# TODO(ArrayManager) factor out interleave_dtype tests
+pytestmark = td.skip_array_manager_invalid_test
+
+
+@pytest.fixture(params=[new_block, make_block])
+def block_maker(request):
+    """
+    Fixture to test both the internal new_block and pseudo-public make_block.
+    """
+    return request.param
+
+
+@pytest.fixture
+def mgr():
+    return create_mgr(
+        "a: f8; b: object; c: f8; d: object; e: f8;"
+        "f: bool; g: i8; h: complex; i: datetime-1; j: datetime-2;"
+        "k: M8[ns, US/Eastern]; l: M8[ns, CET];"
+    )
+
+
+def assert_block_equal(left, right):
+    tm.assert_numpy_array_equal(left.values, right.values)
+    assert left.dtype == right.dtype
+    assert isinstance(left.mgr_locs, BlockPlacement)
+    assert isinstance(right.mgr_locs, BlockPlacement)
+    tm.assert_numpy_array_equal(left.mgr_locs.as_array, right.mgr_locs.as_array)
+
+
+def get_numeric_mat(shape):
+    arr = np.arange(shape[0])
+    return np.lib.stride_tricks.as_strided(
+        x=arr, shape=shape, strides=(arr.itemsize,) + (0,) * (len(shape) - 1)
+    ).copy()
+
+
+N = 10
+
+
+def create_block(typestr, placement, item_shape=None, num_offset=0, maker=new_block):
+    """
+    Supported typestr:
+
+        * float, f8, f4, f2
+        * int, i8, i4, i2, i1
+        * uint, u8, u4, u2, u1
+        * complex, c16, c8
+        * bool
+        * object, string, O
+        * datetime, dt, M8[ns], M8[ns, tz]
+        * timedelta, td, m8[ns]
+        * sparse (SparseArray with fill_value=0.0)
+        * sparse_na (SparseArray with fill_value=np.nan)
+        * category, category2
+
+    """
+    placement = BlockPlacement(placement)
+    num_items = len(placement)
+
+    if item_shape is None:
+        item_shape = (N,)
+
+    shape = (num_items,) + item_shape
+
+    mat = get_numeric_mat(shape)
+
+    if typestr in (
+        "float",
+        "f8",
+        "f4",
+        "f2",
+        "int",
+        "i8",
+        "i4",
+        "i2",
+        "i1",
+        "uint",
+        "u8",
+        "u4",
+        "u2",
+        "u1",
+    ):
+        values = mat.astype(typestr) + num_offset
+    elif typestr in ("complex", "c16", "c8"):
+        values = 1.0j * (mat.astype(typestr) + num_offset)
+    elif typestr in ("object", "string", "O"):
+        values = np.reshape([f"A{i:d}" for i in mat.ravel() + num_offset], shape)
+    elif typestr in ("b", "bool"):
+        values = np.ones(shape, dtype=np.bool_)
+    elif typestr in ("datetime", "dt", "M8[ns]"):
+        values = (mat * 1e9).astype("M8[ns]")
+    elif typestr.startswith("M8[ns"):
+        # datetime with tz
+        m = re.search(r"M8\[ns,\s*(\w+\/?\w*)\]", typestr)
+        assert m is not None, f"incompatible typestr -> {typestr}"
+        tz = m.groups()[0]
+        assert num_items == 1, "must have only 1 num items for a tz-aware"
+        values = DatetimeIndex(np.arange(N) * 10**9, tz=tz)._data
+        values = ensure_block_shape(values, ndim=len(shape))
+    elif typestr in ("timedelta", "td", "m8[ns]"):
+        values = (mat * 1).astype("m8[ns]")
+    elif typestr in ("category",):
+        values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4])
+    elif typestr in ("category2",):
+        values = Categorical(["a", "a", "a", "a", "b", "b", "c", "c", "c", "d"])
+    elif typestr in ("sparse", "sparse_na"):
+        if shape[-1] != 10:
+            # We also are implicitly assuming this in the category cases above
+            raise NotImplementedError
+
+        assert all(s == 1 for s in shape[:-1])
+        if typestr.endswith("_na"):
+            fill_value = np.nan
+        else:
+            fill_value = 0.0
+        values = SparseArray(
+            [fill_value, fill_value, 1, 2, 3, fill_value, 4, 5, fill_value, 6],
+            fill_value=fill_value,
+        )
+        arr = values.sp_values.view()
+        arr += num_offset - 1
+    else:
+        raise ValueError(f'Unsupported typestr: "{typestr}"')
+
+    values = maybe_coerce_values(values)
+    return maker(values, placement=placement, ndim=len(shape))
+
+
+def create_single_mgr(typestr, num_rows=None):
+    if num_rows is None:
+        num_rows = N
+
+    return SingleBlockManager(
+        create_block(typestr, placement=slice(0, num_rows), item_shape=()),
+        Index(np.arange(num_rows)),
+    )
+
+
+def create_mgr(descr, item_shape=None):
+    """
+    Construct BlockManager from string description.
+
+    String description syntax looks similar to np.matrix initializer.  It looks
+    like this::
+
+        a,b,c: f8; d,e,f: i8
+
+    Rules are rather simple:
+
+    * see list of supported datatypes in `create_block` method
+    * components are semicolon-separated
+    * each component is `NAME,NAME,NAME: DTYPE_ID`
+    * whitespace around colons & semicolons are removed
+    * components with same DTYPE_ID are combined into single block
+    * to force multiple blocks with same dtype, use '-SUFFIX'::
+
+        'a:f8-1; b:f8-2; c:f8-foobar'
+
+    """
+    if item_shape is None:
+        item_shape = (N,)
+
+    offset = 0
+    mgr_items = []
+    block_placements = {}
+    for d in descr.split(";"):
+        d = d.strip()
+        if not len(d):
+            continue
+        names, blockstr = d.partition(":")[::2]
+        blockstr = blockstr.strip()
+        names = names.strip().split(",")
+
+        mgr_items.extend(names)
+        placement = list(np.arange(len(names)) + offset)
+        try:
+            block_placements[blockstr].extend(placement)
+        except KeyError:
+            block_placements[blockstr] = placement
+        offset += len(names)
+
+    mgr_items = Index(mgr_items)
+
+    blocks = []
+    num_offset = 0
+    for blockstr, placement in block_placements.items():
+        typestr = blockstr.split("-")[0]
+        blocks.append(
+            create_block(
+                typestr, placement, item_shape=item_shape, num_offset=num_offset
+            )
+        )
+        num_offset += len(placement)
+
+    sblocks = sorted(blocks, key=lambda b: b.mgr_locs[0])
+    return BlockManager(
+        tuple(sblocks),
+        [mgr_items] + [Index(np.arange(n)) for n in item_shape],
+    )
+
+
+@pytest.fixture
+def fblock():
+    return create_block("float", [0, 2, 4])
+
+
+class TestBlock:
+    def test_constructor(self):
+        int32block = create_block("i4", [0])
+        assert int32block.dtype == np.int32
+
+    @pytest.mark.parametrize(
+        "typ, data",
+        [
+            ["float", [0, 2, 4]],
+            ["complex", [7]],
+            ["object", [1, 3]],
+            ["bool", [5]],
+        ],
+    )
+    def test_pickle(self, typ, data):
+        blk = create_block(typ, data)
+        assert_block_equal(tm.round_trip_pickle(blk), blk)
+
+    def test_mgr_locs(self, fblock):
+        assert isinstance(fblock.mgr_locs, BlockPlacement)
+        tm.assert_numpy_array_equal(
+            fblock.mgr_locs.as_array, np.array([0, 2, 4], dtype=np.intp)
+        )
+
+    def test_attrs(self, fblock):
+        assert fblock.shape == fblock.values.shape
+        assert fblock.dtype == fblock.values.dtype
+        assert len(fblock) == len(fblock.values)
+
+    def test_copy(self, fblock):
+        cop = fblock.copy()
+        assert cop is not fblock
+        assert_block_equal(fblock, cop)
+
+    def test_delete(self, fblock):
+        newb = fblock.copy()
+        locs = newb.mgr_locs
+        nb = newb.delete(0)[0]
+        assert newb.mgr_locs is locs
+
+        assert nb is not newb
+
+        tm.assert_numpy_array_equal(
+            nb.mgr_locs.as_array, np.array([2, 4], dtype=np.intp)
+        )
+        assert not (newb.values[0] == 1).all()
+        assert (nb.values[0] == 1).all()
+
+        newb = fblock.copy()
+        locs = newb.mgr_locs
+        nb = newb.delete(1)
+        assert len(nb) == 2
+        assert newb.mgr_locs is locs
+
+        tm.assert_numpy_array_equal(
+            nb[0].mgr_locs.as_array, np.array([0], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            nb[1].mgr_locs.as_array, np.array([4], dtype=np.intp)
+        )
+        assert not (newb.values[1] == 2).all()
+        assert (nb[1].values[0] == 2).all()
+
+        newb = fblock.copy()
+        nb = newb.delete(2)
+        assert len(nb) == 1
+        tm.assert_numpy_array_equal(
+            nb[0].mgr_locs.as_array, np.array([0, 2], dtype=np.intp)
+        )
+        assert (nb[0].values[1] == 1).all()
+
+        newb = fblock.copy()
+
+        with pytest.raises(IndexError, match=None):
+            newb.delete(3)
+
+    def test_delete_datetimelike(self):
+        # dont use np.delete on values, as that will coerce from DTA/TDA to ndarray
+        arr = np.arange(20, dtype="i8").reshape(5, 4).view("m8[ns]")
+        df = DataFrame(arr)
+        blk = df._mgr.blocks[0]
+        assert isinstance(blk.values, TimedeltaArray)
+
+        nb = blk.delete(1)
+        assert len(nb) == 2
+        assert isinstance(nb[0].values, TimedeltaArray)
+        assert isinstance(nb[1].values, TimedeltaArray)
+
+        df = DataFrame(arr.view("M8[ns]"))
+        blk = df._mgr.blocks[0]
+        assert isinstance(blk.values, DatetimeArray)
+
+        nb = blk.delete([1, 3])
+        assert len(nb) == 2
+        assert isinstance(nb[0].values, DatetimeArray)
+        assert isinstance(nb[1].values, DatetimeArray)
+
+    def test_split(self):
+        # GH#37799
+        values = np.random.default_rng(2).standard_normal((3, 4))
+        blk = new_block(values, placement=BlockPlacement([3, 1, 6]), ndim=2)
+        result = blk._split()
+
+        # check that we get views, not copies
+        values[:] = -9999
+        assert (blk.values == -9999).all()
+
+        assert len(result) == 3
+        expected = [
+            new_block(values[[0]], placement=BlockPlacement([3]), ndim=2),
+            new_block(values[[1]], placement=BlockPlacement([1]), ndim=2),
+            new_block(values[[2]], placement=BlockPlacement([6]), ndim=2),
+        ]
+        for res, exp in zip(result, expected):
+            assert_block_equal(res, exp)
+
+
+class TestBlockManager:
+    def test_attrs(self):
+        mgr = create_mgr("a,b,c: f8-1; d,e,f: f8-2")
+        assert mgr.nblocks == 2
+        assert len(mgr) == 6
+
+    def test_duplicate_ref_loc_failure(self):
+        tmp_mgr = create_mgr("a:bool; a: f8")
+
+        axes, blocks = tmp_mgr.axes, tmp_mgr.blocks
+
+        blocks[0].mgr_locs = BlockPlacement(np.array([0]))
+        blocks[1].mgr_locs = BlockPlacement(np.array([0]))
+
+        # test trying to create block manager with overlapping ref locs
+
+        msg = "Gaps in blk ref_locs"
+
+        with pytest.raises(AssertionError, match=msg):
+            mgr = BlockManager(blocks, axes)
+            mgr._rebuild_blknos_and_blklocs()
+
+        blocks[0].mgr_locs = BlockPlacement(np.array([0]))
+        blocks[1].mgr_locs = BlockPlacement(np.array([1]))
+        mgr = BlockManager(blocks, axes)
+        mgr.iget(1)
+
+    def test_pickle(self, mgr):
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(
+            DataFrame._from_mgr(mgr, axes=mgr.axes),
+            DataFrame._from_mgr(mgr2, axes=mgr2.axes),
+        )
+
+        # GH2431
+        assert hasattr(mgr2, "_is_consolidated")
+        assert hasattr(mgr2, "_known_consolidated")
+
+        # reset to False on load
+        assert not mgr2._is_consolidated
+        assert not mgr2._known_consolidated
+
+    @pytest.mark.parametrize("mgr_string", ["a,a,a:f8", "a: f8; a: i8"])
+    def test_non_unique_pickle(self, mgr_string):
+        mgr = create_mgr(mgr_string)
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(
+            DataFrame._from_mgr(mgr, axes=mgr.axes),
+            DataFrame._from_mgr(mgr2, axes=mgr2.axes),
+        )
+
+    def test_categorical_block_pickle(self):
+        mgr = create_mgr("a: category")
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(
+            DataFrame._from_mgr(mgr, axes=mgr.axes),
+            DataFrame._from_mgr(mgr2, axes=mgr2.axes),
+        )
+
+        smgr = create_single_mgr("category")
+        smgr2 = tm.round_trip_pickle(smgr)
+        tm.assert_series_equal(
+            Series()._constructor_from_mgr(smgr, axes=smgr.axes),
+            Series()._constructor_from_mgr(smgr2, axes=smgr2.axes),
+        )
+
+    def test_iget(self):
+        cols = Index(list("abc"))
+        values = np.random.default_rng(2).random((3, 3))
+        block = new_block(
+            values=values.copy(),
+            placement=BlockPlacement(np.arange(3, dtype=np.intp)),
+            ndim=values.ndim,
+        )
+        mgr = BlockManager(blocks=(block,), axes=[cols, Index(np.arange(3))])
+
+        tm.assert_almost_equal(mgr.iget(0).internal_values(), values[0])
+        tm.assert_almost_equal(mgr.iget(1).internal_values(), values[1])
+        tm.assert_almost_equal(mgr.iget(2).internal_values(), values[2])
+
+    def test_set(self):
+        mgr = create_mgr("a,b,c: int", item_shape=(3,))
+
+        mgr.insert(len(mgr.items), "d", np.array(["foo"] * 3))
+        mgr.iset(1, np.array(["bar"] * 3))
+        tm.assert_numpy_array_equal(mgr.iget(0).internal_values(), np.array([0] * 3))
+        tm.assert_numpy_array_equal(
+            mgr.iget(1).internal_values(), np.array(["bar"] * 3, dtype=np.object_)
+        )
+        tm.assert_numpy_array_equal(mgr.iget(2).internal_values(), np.array([2] * 3))
+        tm.assert_numpy_array_equal(
+            mgr.iget(3).internal_values(), np.array(["foo"] * 3, dtype=np.object_)
+        )
+
+    def test_set_change_dtype(self, mgr):
+        mgr.insert(len(mgr.items), "baz", np.zeros(N, dtype=bool))
+
+        mgr.iset(mgr.items.get_loc("baz"), np.repeat("foo", N))
+        idx = mgr.items.get_loc("baz")
+        assert mgr.iget(idx).dtype == np.object_
+
+        mgr2 = mgr.consolidate()
+        mgr2.iset(mgr2.items.get_loc("baz"), np.repeat("foo", N))
+        idx = mgr2.items.get_loc("baz")
+        assert mgr2.iget(idx).dtype == np.object_
+
+        mgr2.insert(
+            len(mgr2.items),
+            "quux",
+            np.random.default_rng(2).standard_normal(N).astype(int),
+        )
+        idx = mgr2.items.get_loc("quux")
+        assert mgr2.iget(idx).dtype == np.dtype(int)
+
+        mgr2.iset(
+            mgr2.items.get_loc("quux"), np.random.default_rng(2).standard_normal(N)
+        )
+        assert mgr2.iget(idx).dtype == np.float64
+
+    def test_copy(self, mgr):
+        cp = mgr.copy(deep=False)
+        for blk, cp_blk in zip(mgr.blocks, cp.blocks):
+            # view assertion
+            tm.assert_equal(cp_blk.values, blk.values)
+            if isinstance(blk.values, np.ndarray):
+                assert cp_blk.values.base is blk.values.base
+            else:
+                # DatetimeTZBlock has DatetimeIndex values
+                assert cp_blk.values._ndarray.base is blk.values._ndarray.base
+
+        # copy(deep=True) consolidates, so the block-wise assertions will
+        #  fail is mgr is not consolidated
+        mgr._consolidate_inplace()
+        cp = mgr.copy(deep=True)
+        for blk, cp_blk in zip(mgr.blocks, cp.blocks):
+            bvals = blk.values
+            cpvals = cp_blk.values
+
+            tm.assert_equal(cpvals, bvals)
+
+            if isinstance(cpvals, np.ndarray):
+                lbase = cpvals.base
+                rbase = bvals.base
+            else:
+                lbase = cpvals._ndarray.base
+                rbase = bvals._ndarray.base
+
+            # copy assertion we either have a None for a base or in case of
+            # some blocks it is an array (e.g. datetimetz), but was copied
+            if isinstance(cpvals, DatetimeArray):
+                assert (lbase is None and rbase is None) or (lbase is not rbase)
+            elif not isinstance(cpvals, np.ndarray):
+                assert lbase is not rbase
+            else:
+                assert lbase is None and rbase is None
+
+    def test_sparse(self):
+        mgr = create_mgr("a: sparse-1; b: sparse-2")
+        assert mgr.as_array().dtype == np.float64
+
+    def test_sparse_mixed(self):
+        mgr = create_mgr("a: sparse-1; b: sparse-2; c: f8")
+        assert len(mgr.blocks) == 3
+        assert isinstance(mgr, BlockManager)
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [("c: f4; d: f2", np.float32), ("c: f4; d: f2; e: f8", np.float64)],
+    )
+    def test_as_array_float(self, mgr_string, dtype):
+        mgr = create_mgr(mgr_string)
+        assert mgr.as_array().dtype == dtype
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [
+            ("a: bool-1; b: bool-2", np.bool_),
+            ("a: i8-1; b: i8-2; c: i4; d: i2; e: u1", np.int64),
+            ("c: i4; d: i2; e: u1", np.int32),
+        ],
+    )
+    def test_as_array_int_bool(self, mgr_string, dtype):
+        mgr = create_mgr(mgr_string)
+        assert mgr.as_array().dtype == dtype
+
+    def test_as_array_datetime(self):
+        mgr = create_mgr("h: datetime-1; g: datetime-2")
+        assert mgr.as_array().dtype == "M8[ns]"
+
+    def test_as_array_datetime_tz(self):
+        mgr = create_mgr("h: M8[ns, US/Eastern]; g: M8[ns, CET]")
+        assert mgr.iget(0).dtype == "datetime64[ns, US/Eastern]"
+        assert mgr.iget(1).dtype == "datetime64[ns, CET]"
+        assert mgr.as_array().dtype == "object"
+
+    @pytest.mark.parametrize("t", ["float16", "float32", "float64", "int32", "int64"])
+    def test_astype(self, t):
+        # coerce all
+        mgr = create_mgr("c: f4; d: f2; e: f8")
+
+        t = np.dtype(t)
+        tmgr = mgr.astype(t)
+        assert tmgr.iget(0).dtype.type == t
+        assert tmgr.iget(1).dtype.type == t
+        assert tmgr.iget(2).dtype.type == t
+
+        # mixed
+        mgr = create_mgr("a,b: object; c: bool; d: datetime; e: f4; f: f2; g: f8")
+
+        t = np.dtype(t)
+        tmgr = mgr.astype(t, errors="ignore")
+        assert tmgr.iget(2).dtype.type == t
+        assert tmgr.iget(4).dtype.type == t
+        assert tmgr.iget(5).dtype.type == t
+        assert tmgr.iget(6).dtype.type == t
+
+        assert tmgr.iget(0).dtype.type == np.object_
+        assert tmgr.iget(1).dtype.type == np.object_
+        if t != np.int64:
+            assert tmgr.iget(3).dtype.type == np.datetime64
+        else:
+            assert tmgr.iget(3).dtype.type == t
+
+    def test_convert(self, using_infer_string):
+        def _compare(old_mgr, new_mgr):
+            """compare the blocks, numeric compare ==, object don't"""
+            old_blocks = set(old_mgr.blocks)
+            new_blocks = set(new_mgr.blocks)
+            assert len(old_blocks) == len(new_blocks)
+
+            # compare non-numeric
+            for b in old_blocks:
+                found = False
+                for nb in new_blocks:
+                    if (b.values == nb.values).all():
+                        found = True
+                        break
+                assert found
+
+            for b in new_blocks:
+                found = False
+                for ob in old_blocks:
+                    if (b.values == ob.values).all():
+                        found = True
+                        break
+                assert found
+
+        # noops
+        mgr = create_mgr("f: i8; g: f8")
+        new_mgr = mgr.convert(copy=True)
+        _compare(mgr, new_mgr)
+
+        # convert
+        mgr = create_mgr("a,b,foo: object; f: i8; g: f8")
+        mgr.iset(0, np.array(["1"] * N, dtype=np.object_))
+        mgr.iset(1, np.array(["2."] * N, dtype=np.object_))
+        mgr.iset(2, np.array(["foo."] * N, dtype=np.object_))
+        new_mgr = mgr.convert(copy=True)
+        dtype = "str" if using_infer_string else np.object_
+        assert new_mgr.iget(0).dtype == dtype
+        assert new_mgr.iget(1).dtype == dtype
+        assert new_mgr.iget(2).dtype == dtype
+        assert new_mgr.iget(3).dtype == np.int64
+        assert new_mgr.iget(4).dtype == np.float64
+
+        mgr = create_mgr(
+            "a,b,foo: object; f: i4; bool: bool; dt: datetime; i: i8; g: f8; h: f2"
+        )
+        mgr.iset(0, np.array(["1"] * N, dtype=np.object_))
+        mgr.iset(1, np.array(["2."] * N, dtype=np.object_))
+        mgr.iset(2, np.array(["foo."] * N, dtype=np.object_))
+        new_mgr = mgr.convert(copy=True)
+        assert new_mgr.iget(0).dtype == dtype
+        assert new_mgr.iget(1).dtype == dtype
+        assert new_mgr.iget(2).dtype == dtype
+        assert new_mgr.iget(3).dtype == np.int32
+        assert new_mgr.iget(4).dtype == np.bool_
+        assert new_mgr.iget(5).dtype.type, np.datetime64
+        assert new_mgr.iget(6).dtype == np.int64
+        assert new_mgr.iget(7).dtype == np.float64
+        assert new_mgr.iget(8).dtype == np.float16
+
+    def test_interleave(self):
+        # self
+        for dtype in ["f8", "i8", "object", "bool", "complex", "M8[ns]", "m8[ns]"]:
+            mgr = create_mgr(f"a: {dtype}")
+            assert mgr.as_array().dtype == dtype
+            mgr = create_mgr(f"a: {dtype}; b: {dtype}")
+            assert mgr.as_array().dtype == dtype
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [
+            ("a: category", "i8"),
+            ("a: category; b: category", "i8"),
+            ("a: category; b: category2", "object"),
+            ("a: category2", "object"),
+            ("a: category2; b: category2", "object"),
+            ("a: f8", "f8"),
+            ("a: f8; b: i8", "f8"),
+            ("a: f4; b: i8", "f8"),
+            ("a: f4; b: i8; d: object", "object"),
+            ("a: bool; b: i8", "object"),
+            ("a: complex", "complex"),
+            ("a: f8; b: category", "object"),
+            ("a: M8[ns]; b: category", "object"),
+            ("a: M8[ns]; b: bool", "object"),
+            ("a: M8[ns]; b: i8", "object"),
+            ("a: m8[ns]; b: bool", "object"),
+            ("a: m8[ns]; b: i8", "object"),
+            ("a: M8[ns]; b: m8[ns]", "object"),
+        ],
+    )
+    def test_interleave_dtype(self, mgr_string, dtype):
+        # will be converted according the actual dtype of the underlying
+        mgr = create_mgr("a: category")
+        assert mgr.as_array().dtype == "i8"
+        mgr = create_mgr("a: category; b: category2")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: category2")
+        assert mgr.as_array().dtype == "object"
+
+        # combinations
+        mgr = create_mgr("a: f8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f8; b: i8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f4; b: i8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f4; b: i8; d: object")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: bool; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: complex")
+        assert mgr.as_array().dtype == "complex"
+        mgr = create_mgr("a: f8; b: category")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: M8[ns]; b: category")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: bool")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: m8[ns]; b: bool")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: m8[ns]; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: m8[ns]")
+        assert mgr.as_array().dtype == "object"
+
+    def test_consolidate_ordering_issues(self, mgr):
+        mgr.iset(mgr.items.get_loc("f"), np.random.default_rng(2).standard_normal(N))
+        mgr.iset(mgr.items.get_loc("d"), np.random.default_rng(2).standard_normal(N))
+        mgr.iset(mgr.items.get_loc("b"), np.random.default_rng(2).standard_normal(N))
+        mgr.iset(mgr.items.get_loc("g"), np.random.default_rng(2).standard_normal(N))
+        mgr.iset(mgr.items.get_loc("h"), np.random.default_rng(2).standard_normal(N))
+
+        # we have datetime/tz blocks in mgr
+        cons = mgr.consolidate()
+        assert cons.nblocks == 4
+        cons = mgr.consolidate().get_numeric_data()
+        assert cons.nblocks == 1
+        assert isinstance(cons.blocks[0].mgr_locs, BlockPlacement)
+        tm.assert_numpy_array_equal(
+            cons.blocks[0].mgr_locs.as_array, np.arange(len(cons.items), dtype=np.intp)
+        )
+
+    def test_reindex_items(self):
+        # mgr is not consolidated, f8 & f8-2 blocks
+        mgr = create_mgr("a: f8; b: i8; c: f8; d: i8; e: f8; f: bool; g: f8-2")
+
+        reindexed = mgr.reindex_axis(["g", "c", "a", "d"], axis=0)
+        # reindex_axis does not consolidate_inplace, as that risks failing to
+        #  invalidate _item_cache
+        assert not reindexed.is_consolidated()
+
+        tm.assert_index_equal(reindexed.items, Index(["g", "c", "a", "d"]))
+        tm.assert_almost_equal(
+            mgr.iget(6).internal_values(), reindexed.iget(0).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(2).internal_values(), reindexed.iget(1).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(0).internal_values(), reindexed.iget(2).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(3).internal_values(), reindexed.iget(3).internal_values()
+        )
+
+    def test_get_numeric_data(self, using_copy_on_write):
+        mgr = create_mgr(
+            "int: int; float: float; complex: complex;"
+            "str: object; bool: bool; obj: object; dt: datetime",
+            item_shape=(3,),
+        )
+        mgr.iset(5, np.array([1, 2, 3], dtype=np.object_))
+
+        numeric = mgr.get_numeric_data()
+        tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"]))
+        tm.assert_almost_equal(
+            mgr.iget(mgr.items.get_loc("float")).internal_values(),
+            numeric.iget(numeric.items.get_loc("float")).internal_values(),
+        )
+
+        # Check sharing
+        numeric.iset(
+            numeric.items.get_loc("float"),
+            np.array([100.0, 200.0, 300.0]),
+            inplace=True,
+        )
+        if using_copy_on_write:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([1.0, 1.0, 1.0]),
+            )
+        else:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([100.0, 200.0, 300.0]),
+            )
+
+    def test_get_bool_data(self, using_copy_on_write):
+        mgr = create_mgr(
+            "int: int; float: float; complex: complex;"
+            "str: object; bool: bool; obj: object; dt: datetime",
+            item_shape=(3,),
+        )
+        mgr.iset(6, np.array([True, False, True], dtype=np.object_))
+
+        bools = mgr.get_bool_data()
+        tm.assert_index_equal(bools.items, Index(["bool"]))
+        tm.assert_almost_equal(
+            mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+            bools.iget(bools.items.get_loc("bool")).internal_values(),
+        )
+
+        bools.iset(0, np.array([True, False, True]), inplace=True)
+        if using_copy_on_write:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, True, True]),
+            )
+        else:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, False, True]),
+            )
+
+    def test_unicode_repr_doesnt_raise(self):
+        repr(create_mgr("b,\u05d0: object"))
+
+    @pytest.mark.parametrize(
+        "mgr_string", ["a,b,c: i8-1; d,e,f: i8-2", "a,a,a: i8-1; b,b,b: i8-2"]
+    )
+    def test_equals(self, mgr_string):
+        # unique items
+        bm1 = create_mgr(mgr_string)
+        bm2 = BlockManager(bm1.blocks[::-1], bm1.axes)
+        assert bm1.equals(bm2)
+
+    @pytest.mark.parametrize(
+        "mgr_string",
+        [
+            "a:i8;b:f8",  # basic case
+            "a:i8;b:f8;c:c8;d:b",  # many types
+            "a:i8;e:dt;f:td;g:string",  # more types
+            "a:i8;b:category;c:category2",  # categories
+            "c:sparse;d:sparse_na;b:f8",  # sparse
+        ],
+    )
+    def test_equals_block_order_different_dtypes(self, mgr_string):
+        # GH 9330
+        bm = create_mgr(mgr_string)
+        block_perms = itertools.permutations(bm.blocks)
+        for bm_perm in block_perms:
+            bm_this = BlockManager(tuple(bm_perm), bm.axes)
+            assert bm.equals(bm_this)
+            assert bm_this.equals(bm)
+
+    def test_single_mgr_ctor(self):
+        mgr = create_single_mgr("f8", num_rows=5)
+        assert mgr.external_values().tolist() == [0.0, 1.0, 2.0, 3.0, 4.0]
+
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, value):
+        bm1 = create_mgr("a,b,c: i8-1; d,e,f: i8-2")
+
+        msg = (
+            'For argument "inplace" expected type bool, '
+            f"received type {type(value).__name__}."
+        )
+        with pytest.raises(ValueError, match=msg):
+            bm1.replace_list([1], [2], inplace=value)
+
+    def test_iset_split_block(self):
+        bm = create_mgr("a,b,c: i8; d: f8")
+        bm._iset_split_block(0, np.array([0]))
+        tm.assert_numpy_array_equal(
+            bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32")
+        )
+        # First indexer currently does not have a block associated with it in case
+        tm.assert_numpy_array_equal(
+            bm.blknos, np.array([0, 0, 0, 1], dtype="int64" if IS64 else "int32")
+        )
+        assert len(bm.blocks) == 2
+
+    def test_iset_split_block_values(self):
+        bm = create_mgr("a,b,c: i8; d: f8")
+        bm._iset_split_block(0, np.array([0]), np.array([list(range(10))]))
+        tm.assert_numpy_array_equal(
+            bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32")
+        )
+        # First indexer currently does not have a block associated with it in case
+        tm.assert_numpy_array_equal(
+            bm.blknos, np.array([0, 2, 2, 1], dtype="int64" if IS64 else "int32")
+        )
+        assert len(bm.blocks) == 3
+
+
+def _as_array(mgr):
+    if mgr.ndim == 1:
+        return mgr.external_values()
+    return mgr.as_array().T
+
+
+class TestIndexing:
+    # Nosetests-style data-driven tests.
+    #
+    # This test applies different indexing routines to block managers and
+    # compares the outcome to the result of same operations on np.ndarray.
+    #
+    # NOTE: sparse (SparseBlock with fill_value != np.nan) fail a lot of tests
+    #       and are disabled.
+
+    MANAGERS = [
+        create_single_mgr("f8", N),
+        create_single_mgr("i8", N),
+        # 2-dim
+        create_mgr("a,b,c,d,e,f: f8", item_shape=(N,)),
+        create_mgr("a,b,c,d,e,f: i8", item_shape=(N,)),
+        create_mgr("a,b: f8; c,d: i8; e,f: string", item_shape=(N,)),
+        create_mgr("a,b: f8; c,d: i8; e,f: f8", item_shape=(N,)),
+    ]
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    def test_get_slice(self, mgr):
+        def assert_slice_ok(mgr, axis, slobj):
+            mat = _as_array(mgr)
+
+            # we maybe using an ndarray to test slicing and
+            # might not be the full length of the axis
+            if isinstance(slobj, np.ndarray):
+                ax = mgr.axes[axis]
+                if len(ax) and len(slobj) and len(slobj) != len(ax):
+                    slobj = np.concatenate(
+                        [slobj, np.zeros(len(ax) - len(slobj), dtype=bool)]
+                    )
+
+            if isinstance(slobj, slice):
+                sliced = mgr.get_slice(slobj, axis=axis)
+            elif (
+                mgr.ndim == 1
+                and axis == 0
+                and isinstance(slobj, np.ndarray)
+                and slobj.dtype == bool
+            ):
+                sliced = mgr.get_rows_with_mask(slobj)
+            else:
+                # BlockManager doesn't support non-slice, SingleBlockManager
+                #  doesn't support axis > 0
+                raise TypeError(slobj)
+
+            mat_slobj = (slice(None),) * axis + (slobj,)
+            tm.assert_numpy_array_equal(
+                mat[mat_slobj], _as_array(sliced), check_dtype=False
+            )
+            tm.assert_index_equal(mgr.axes[axis][slobj], sliced.axes[axis])
+
+        assert mgr.ndim <= 2, mgr.ndim
+        for ax in range(mgr.ndim):
+            # slice
+            assert_slice_ok(mgr, ax, slice(None))
+            assert_slice_ok(mgr, ax, slice(3))
+            assert_slice_ok(mgr, ax, slice(100))
+            assert_slice_ok(mgr, ax, slice(1, 4))
+            assert_slice_ok(mgr, ax, slice(3, 0, -2))
+
+            if mgr.ndim < 2:
+                # 2D only support slice objects
+
+                # boolean mask
+                assert_slice_ok(mgr, ax, np.ones(mgr.shape[ax], dtype=np.bool_))
+                assert_slice_ok(mgr, ax, np.zeros(mgr.shape[ax], dtype=np.bool_))
+
+                if mgr.shape[ax] >= 3:
+                    assert_slice_ok(mgr, ax, np.arange(mgr.shape[ax]) % 3 == 0)
+                    assert_slice_ok(
+                        mgr, ax, np.array([True, True, False], dtype=np.bool_)
+                    )
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    def test_take(self, mgr):
+        def assert_take_ok(mgr, axis, indexer):
+            mat = _as_array(mgr)
+            taken = mgr.take(indexer, axis)
+            tm.assert_numpy_array_equal(
+                np.take(mat, indexer, axis), _as_array(taken), check_dtype=False
+            )
+            tm.assert_index_equal(mgr.axes[axis].take(indexer), taken.axes[axis])
+
+        for ax in range(mgr.ndim):
+            # take/fancy indexer
+            assert_take_ok(mgr, ax, indexer=np.array([], dtype=np.intp))
+            assert_take_ok(mgr, ax, indexer=np.array([0, 0, 0], dtype=np.intp))
+            assert_take_ok(
+                mgr, ax, indexer=np.array(list(range(mgr.shape[ax])), dtype=np.intp)
+            )
+
+            if mgr.shape[ax] >= 3:
+                assert_take_ok(mgr, ax, indexer=np.array([0, 1, 2], dtype=np.intp))
+                assert_take_ok(mgr, ax, indexer=np.array([-1, -2, -3], dtype=np.intp))
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0])
+    def test_reindex_axis(self, fill_value, mgr):
+        def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value):
+            mat = _as_array(mgr)
+            indexer = mgr.axes[axis].get_indexer_for(new_labels)
+
+            reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value)
+            tm.assert_numpy_array_equal(
+                algos.take_nd(mat, indexer, axis, fill_value=fill_value),
+                _as_array(reindexed),
+                check_dtype=False,
+            )
+            tm.assert_index_equal(reindexed.axes[axis], new_labels)
+
+        for ax in range(mgr.ndim):
+            assert_reindex_axis_is_ok(mgr, ax, Index([]), fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax], fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][[0, 0, 0]], fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, Index(["foo", "bar", "baz"]), fill_value)
+            assert_reindex_axis_is_ok(
+                mgr, ax, Index(["foo", mgr.axes[ax][0], "baz"]), fill_value
+            )
+
+            if mgr.shape[ax] >= 3:
+                assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][:-3], fill_value)
+                assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][-3::-1], fill_value)
+                assert_reindex_axis_is_ok(
+                    mgr, ax, mgr.axes[ax][[0, 1, 2, 0, 1, 2]], fill_value
+                )
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0])
+    def test_reindex_indexer(self, fill_value, mgr):
+        def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value):
+            mat = _as_array(mgr)
+            reindexed_mat = algos.take_nd(mat, indexer, axis, fill_value=fill_value)
+            reindexed = mgr.reindex_indexer(
+                new_labels, indexer, axis, fill_value=fill_value
+            )
+            tm.assert_numpy_array_equal(
+                reindexed_mat, _as_array(reindexed), check_dtype=False
+            )
+            tm.assert_index_equal(reindexed.axes[axis], new_labels)
+
+        for ax in range(mgr.ndim):
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index([]), np.array([], dtype=np.intp), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr,
+                ax,
+                Index(["foo"] * mgr.shape[ax]),
+                np.arange(mgr.shape[ax]),
+                fill_value,
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax][::-1], np.arange(mgr.shape[ax]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax])[::-1], fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index(["foo", "bar", "baz"]), np.array([0, 0, 0]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index(["foo", "bar", "baz"]), np.array([-1, 0, -1]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr,
+                ax,
+                Index(["foo", mgr.axes[ax][0], "baz"]),
+                np.array([-1, -1, -1]),
+                fill_value,
+            )
+
+            if mgr.shape[ax] >= 3:
+                assert_reindex_indexer_is_ok(
+                    mgr,
+                    ax,
+                    Index(["foo", "bar", "baz"]),
+                    np.array([0, 1, 2]),
+                    fill_value,
+                )
+
+
+class TestBlockPlacement:
+    @pytest.mark.parametrize(
+        "slc, expected",
+        [
+            (slice(0, 4), 4),
+            (slice(0, 4, 2), 2),
+            (slice(0, 3, 2), 2),
+            (slice(0, 1, 2), 1),
+            (slice(1, 0, -1), 1),
+        ],
+    )
+    def test_slice_len(self, slc, expected):
+        assert len(BlockPlacement(slc)) == expected
+
+    @pytest.mark.parametrize("slc", [slice(1, 1, 0), slice(1, 2, 0)])
+    def test_zero_step_raises(self, slc):
+        msg = "slice step cannot be zero"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(slc)
+
+    def test_slice_canonize_negative_stop(self):
+        # GH#37524 negative stop is OK with negative step and positive start
+        slc = slice(3, -1, -2)
+
+        bp = BlockPlacement(slc)
+        assert bp.indexer == slice(3, None, -2)
+
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            slice(None, None),
+            slice(10, None),
+            slice(None, None, -1),
+            slice(None, 10, -1),
+            # These are "unbounded" because negative index will
+            #  change depending on container shape.
+            slice(-1, None),
+            slice(None, -1),
+            slice(-1, -1),
+            slice(-1, None, -1),
+            slice(None, -1, -1),
+            slice(-1, -1, -1),
+        ],
+    )
+    def test_unbounded_slice_raises(self, slc):
+        msg = "unbounded slice"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(slc)
+
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            slice(0, 0),
+            slice(100, 0),
+            slice(100, 100),
+            slice(100, 100, -1),
+            slice(0, 100, -1),
+        ],
+    )
+    def test_not_slice_like_slices(self, slc):
+        assert not BlockPlacement(slc).is_slice_like
+
+    @pytest.mark.parametrize(
+        "arr, slc",
+        [
+            ([0], slice(0, 1, 1)),
+            ([100], slice(100, 101, 1)),
+            ([0, 1, 2], slice(0, 3, 1)),
+            ([0, 5, 10], slice(0, 15, 5)),
+            ([0, 100], slice(0, 200, 100)),
+            ([2, 1], slice(2, 0, -1)),
+        ],
+    )
+    def test_array_to_slice_conversion(self, arr, slc):
+        assert BlockPlacement(arr).as_slice == slc
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            [],
+            [-1],
+            [-1, -2, -3],
+            [-10],
+            [-1],
+            [-1, 0, 1, 2],
+            [-2, 0, 2, 4],
+            [1, 0, -1],
+            [1, 1, 1],
+        ],
+    )
+    def test_not_slice_like_arrays(self, arr):
+        assert not BlockPlacement(arr).is_slice_like
+
+    @pytest.mark.parametrize(
+        "slc, expected",
+        [(slice(0, 3), [0, 1, 2]), (slice(0, 0), []), (slice(3, 0), [])],
+    )
+    def test_slice_iter(self, slc, expected):
+        assert list(BlockPlacement(slc)) == expected
+
+    @pytest.mark.parametrize(
+        "slc, arr",
+        [
+            (slice(0, 3), [0, 1, 2]),
+            (slice(0, 0), []),
+            (slice(3, 0), []),
+            (slice(3, 0, -1), [3, 2, 1]),
+        ],
+    )
+    def test_slice_to_array_conversion(self, slc, arr):
+        tm.assert_numpy_array_equal(
+            BlockPlacement(slc).as_array, np.asarray(arr, dtype=np.intp)
+        )
+
+    def test_blockplacement_add(self):
+        bpl = BlockPlacement(slice(0, 5))
+        assert bpl.add(1).as_slice == slice(1, 6, 1)
+        assert bpl.add(np.arange(5)).as_slice == slice(0, 10, 2)
+        assert list(bpl.add(np.arange(5, 0, -1))) == [5, 5, 5, 5, 5]
+
+    @pytest.mark.parametrize(
+        "val, inc, expected",
+        [
+            (slice(0, 0), 0, []),
+            (slice(1, 4), 0, [1, 2, 3]),
+            (slice(3, 0, -1), 0, [3, 2, 1]),
+            ([1, 2, 4], 0, [1, 2, 4]),
+            (slice(0, 0), 10, []),
+            (slice(1, 4), 10, [11, 12, 13]),
+            (slice(3, 0, -1), 10, [13, 12, 11]),
+            ([1, 2, 4], 10, [11, 12, 14]),
+            (slice(0, 0), -1, []),
+            (slice(1, 4), -1, [0, 1, 2]),
+            ([1, 2, 4], -1, [0, 1, 3]),
+        ],
+    )
+    def test_blockplacement_add_int(self, val, inc, expected):
+        assert list(BlockPlacement(val).add(inc)) == expected
+
+    @pytest.mark.parametrize("val", [slice(1, 4), [1, 2, 4]])
+    def test_blockplacement_add_int_raises(self, val):
+        msg = "iadd causes length change"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(val).add(-10)
+
+
+class TestCanHoldElement:
+    @pytest.fixture(
+        params=[
+            lambda x: x,
+            lambda x: x.to_series(),
+            lambda x: x._data,
+            lambda x: list(x),
+            lambda x: x.astype(object),
+            lambda x: np.asarray(x),
+            lambda x: x[0],
+            lambda x: x[:0],
+        ]
+    )
+    def element(self, request):
+        """
+        Functions that take an Index and return an element that should have
+        blk._can_hold_element(element) for a Block with this index's dtype.
+        """
+        return request.param
+
+    def test_datetime_block_can_hold_element(self):
+        block = create_block("datetime", [0])
+
+        assert block._can_hold_element([])
+
+        # We will check that block._can_hold_element iff arr.__setitem__ works
+        arr = pd.array(block.values.ravel())
+
+        # coerce None
+        assert block._can_hold_element(None)
+        arr[0] = None
+        assert arr[0] is pd.NaT
+
+        # coerce different types of datetime objects
+        vals = [np.datetime64("2010-10-10"), datetime(2010, 10, 10)]
+        for val in vals:
+            assert block._can_hold_element(val)
+            arr[0] = val
+
+        val = date(2010, 10, 10)
+        assert not block._can_hold_element(val)
+
+        msg = (
+            "value should be a 'Timestamp', 'NaT', "
+            "or array of those. Got 'date' instead."
+        )
+        with pytest.raises(TypeError, match=msg):
+            arr[0] = val
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_interval_can_hold_element_emptylist(self, dtype, element):
+        arr = np.array([1, 3, 4], dtype=dtype)
+        ii = IntervalIndex.from_breaks(arr)
+        blk = new_block(ii._data, BlockPlacement([1]), ndim=2)
+
+        assert blk._can_hold_element([])
+        # TODO: check this holds for all blocks
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_interval_can_hold_element(self, dtype, element):
+        arr = np.array([1, 3, 4, 9], dtype=dtype)
+        ii = IntervalIndex.from_breaks(arr)
+        blk = new_block(ii._data, BlockPlacement([1]), ndim=2)
+
+        elem = element(ii)
+        self.check_series_setitem(elem, ii, True)
+        assert blk._can_hold_element(elem)
+
+        # Careful: to get the expected Series-inplace behavior we need
+        # `elem` to not have the same length as `arr`
+        ii2 = IntervalIndex.from_breaks(arr[:-1], closed="neither")
+        elem = element(ii2)
+        with tm.assert_produces_warning(FutureWarning):
+            self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+        ii3 = IntervalIndex.from_breaks([Timestamp(1), Timestamp(3), Timestamp(4)])
+        elem = element(ii3)
+        with tm.assert_produces_warning(FutureWarning):
+            self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+        ii4 = IntervalIndex.from_breaks([Timedelta(1), Timedelta(3), Timedelta(4)])
+        elem = element(ii4)
+        with tm.assert_produces_warning(FutureWarning):
+            self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+    def test_period_can_hold_element_emptylist(self):
+        pi = period_range("2016", periods=3, freq="Y")
+        blk = new_block(pi._data.reshape(1, 3), BlockPlacement([1]), ndim=2)
+
+        assert blk._can_hold_element([])
+
+    def test_period_can_hold_element(self, element):
+        pi = period_range("2016", periods=3, freq="Y")
+
+        elem = element(pi)
+        self.check_series_setitem(elem, pi, True)
+
+        # Careful: to get the expected Series-inplace behavior we need
+        # `elem` to not have the same length as `arr`
+        pi2 = pi.asfreq("D")[:-1]
+        elem = element(pi2)
+        with tm.assert_produces_warning(FutureWarning):
+            self.check_series_setitem(elem, pi, False)
+
+        dti = pi.to_timestamp("s")[:-1]
+        elem = element(dti)
+        with tm.assert_produces_warning(FutureWarning):
+            self.check_series_setitem(elem, pi, False)
+
+    def check_can_hold_element(self, obj, elem, inplace: bool):
+        blk = obj._mgr.blocks[0]
+        if inplace:
+            assert blk._can_hold_element(elem)
+        else:
+            assert not blk._can_hold_element(elem)
+
+    def check_series_setitem(self, elem, index: Index, inplace: bool):
+        arr = index._data.copy()
+        ser = Series(arr, copy=False)
+
+        self.check_can_hold_element(ser, elem, inplace)
+
+        if is_scalar(elem):
+            ser[0] = elem
+        else:
+            ser[: len(elem)] = elem
+
+        if inplace:
+            assert ser.array is arr  # i.e. setting was done inplace
+        else:
+            assert ser.dtype == object
+
+
+class TestShouldStore:
+    def test_should_store_categorical(self):
+        cat = Categorical(["A", "B", "C"])
+        df = DataFrame(cat)
+        blk = df._mgr.blocks[0]
+
+        # matching dtype
+        assert blk.should_store(cat)
+        assert blk.should_store(cat[:-1])
+
+        # different dtype
+        assert not blk.should_store(cat.as_ordered())
+
+        # ndarray instead of Categorical
+        assert not blk.should_store(np.asarray(cat))
+
+
+def test_validate_ndim():
+    values = np.array([1.0, 2.0])
+    placement = BlockPlacement(slice(2))
+    msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]"
+
+    with pytest.raises(ValueError, match=msg):
+        make_block(values, placement, ndim=2)
+
+
+def test_block_shape():
+    idx = Index([0, 1, 2, 3, 4])
+    a = Series([1, 2, 3]).reindex(idx)
+    b = Series(Categorical([1, 2, 3])).reindex(idx)
+
+    assert a._mgr.blocks[0].mgr_locs.indexer == b._mgr.blocks[0].mgr_locs.indexer
+
+
+def test_make_block_no_pandas_array(block_maker):
+    # https://github.com/pandas-dev/pandas/pull/24866
+    arr = pd.arrays.NumpyExtensionArray(np.array([1, 2]))
+
+    # NumpyExtensionArray, no dtype
+    result = block_maker(arr, BlockPlacement(slice(len(arr))), ndim=arr.ndim)
+    assert result.dtype.kind in ["i", "u"]
+
+    if block_maker is make_block:
+        # new_block requires caller to unwrap NumpyExtensionArray
+        assert result.is_extension is False
+
+        # NumpyExtensionArray, NumpyEADtype
+        result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim)
+        assert result.dtype.kind in ["i", "u"]
+        assert result.is_extension is False
+
+        # new_block no longer taked dtype keyword
+        # ndarray, NumpyEADtype
+        result = block_maker(
+            arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim
+        )
+        assert result.dtype.kind in ["i", "u"]
+        assert result.is_extension is False
diff --git a/shared/package-files/d8/d8be1a677cc2e62d9e5af60724d7d6efa1d68b29b5213652cfaaf6eccc356bd8/perimeterPen.py b/shared/package-files/d8/d8be1a677cc2e62d9e5af60724d7d6efa1d68b29b5213652cfaaf6eccc356bd8/perimeterPen.py
new file mode 100644
index 0000000000000000000000000000000000000000..059569fea8f4349c82420e08912c739a95c12fba
--- /dev/null
+++ b/shared/package-files/d8/d8be1a677cc2e62d9e5af60724d7d6efa1d68b29b5213652cfaaf6eccc356bd8/perimeterPen.py
@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+"""Calculate the perimeter of a glyph."""
+
+from fontTools.pens.basePen import BasePen
+from fontTools.misc.bezierTools import (
+    approximateQuadraticArcLengthC,
+    calcQuadraticArcLengthC,
+    approximateCubicArcLengthC,
+    calcCubicArcLengthC,
+)
+import math
+
+__all__ = ["PerimeterPen"]
+
+
+def _distance(p0, p1):
+    return math.hypot(p0[0] - p1[0], p0[1] - p1[1])
+
+
+class PerimeterPen(BasePen):
+    def __init__(self, glyphset=None, tolerance=0.005):
+        BasePen.__init__(self, glyphset)
+        self.value = 0
+        self.tolerance = tolerance
+
+        # Choose which algorithm to use for quadratic and for cubic.
+        # Quadrature is faster but has fixed error characteristic with no strong
+        # error bound.  The cutoff points are derived empirically.
+        self._addCubic = (
+            self._addCubicQuadrature if tolerance >= 0.0015 else self._addCubicRecursive
+        )
+        self._addQuadratic = (
+            self._addQuadraticQuadrature
+            if tolerance >= 0.00075
+            else self._addQuadraticExact
+        )
+
+    def _moveTo(self, p0):
+        self.__startPoint = p0
+
+    def _closePath(self):
+        p0 = self._getCurrentPoint()
+        if p0 != self.__startPoint:
+            self._lineTo(self.__startPoint)
+
+    def _lineTo(self, p1):
+        p0 = self._getCurrentPoint()
+        self.value += _distance(p0, p1)
+
+    def _addQuadraticExact(self, c0, c1, c2):
+        self.value += calcQuadraticArcLengthC(c0, c1, c2)
+
+    def _addQuadraticQuadrature(self, c0, c1, c2):
+        self.value += approximateQuadraticArcLengthC(c0, c1, c2)
+
+    def _qCurveToOne(self, p1, p2):
+        p0 = self._getCurrentPoint()
+        self._addQuadratic(complex(*p0), complex(*p1), complex(*p2))
+
+    def _addCubicRecursive(self, c0, c1, c2, c3):
+        self.value += calcCubicArcLengthC(c0, c1, c2, c3, self.tolerance)
+
+    def _addCubicQuadrature(self, c0, c1, c2, c3):
+        self.value += approximateCubicArcLengthC(c0, c1, c2, c3)
+
+    def _curveToOne(self, p1, p2, p3):
+        p0 = self._getCurrentPoint()
+        self._addCubic(complex(*p0), complex(*p1), complex(*p2), complex(*p3))
diff --git a/shared/package-files/d8/d8be7597b97d04a2951ad2ef1ef633deed113c939c5e16762f4f9a5751506853/_dia.py b/shared/package-files/d8/d8be7597b97d04a2951ad2ef1ef633deed113c939c5e16762f4f9a5751506853/_dia.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2944e080b6abc1705d3f597dda7478335b2bcf3
--- /dev/null
+++ b/shared/package-files/d8/d8be7597b97d04a2951ad2ef1ef633deed113c939c5e16762f4f9a5751506853/_dia.py
@@ -0,0 +1,590 @@
+"""Sparse DIAgonal format"""
+
+__docformat__ = "restructuredtext en"
+
+__all__ = ['dia_array', 'dia_matrix', 'isspmatrix_dia']
+
+import numpy as np
+
+from .._lib._util import copy_if_needed
+from ._matrix import spmatrix
+from ._base import issparse, _formats, _spbase, sparray
+from ._data import _data_matrix
+from ._sputils import (
+    isshape, upcast_char, getdtype, get_sum_dtype, validateaxis, check_shape
+)
+from ._sparsetools import dia_matvec
+
+
+class _dia_base(_data_matrix):
+    _format = 'dia'
+
+    def __init__(self, arg1, shape=None, dtype=None, copy=False, *, maxprint=None):
+        _data_matrix.__init__(self, arg1, maxprint=maxprint)
+
+        if issparse(arg1):
+            if arg1.format == "dia":
+                if copy:
+                    arg1 = arg1.copy()
+                self.data = arg1.data
+                self.offsets = arg1.offsets
+                self._shape = check_shape(arg1.shape)
+            else:
+                if arg1.format == self.format and copy:
+                    A = arg1.copy()
+                else:
+                    A = arg1.todia()
+                self.data = A.data
+                self.offsets = A.offsets
+                self._shape = check_shape(A.shape)
+        elif isinstance(arg1, tuple):
+            if isshape(arg1):
+                # It's a tuple of matrix dimensions (M, N)
+                # create empty matrix
+                self._shape = check_shape(arg1)
+                self.data = np.zeros((0,0), getdtype(dtype, default=float))
+                idx_dtype = self._get_index_dtype(maxval=max(self.shape))
+                self.offsets = np.zeros((0), dtype=idx_dtype)
+            else:
+                try:
+                    # Try interpreting it as (data, offsets)
+                    data, offsets = arg1
+                except Exception as e:
+                    message = 'unrecognized form for dia_array constructor'
+                    raise ValueError(message) from e
+                else:
+                    if shape is None:
+                        raise ValueError('expected a shape argument')
+                    if not copy:
+                        copy = copy_if_needed
+                    self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy))
+                    offsets = np.array(arg1[1],
+                                       dtype=self._get_index_dtype(maxval=max(shape)),
+                                       copy=copy)
+                    self.offsets = np.atleast_1d(offsets)
+                    self._shape = check_shape(shape)
+        else:
+            # must be dense, convert to COO first, then to DIA
+            try:
+                arg1 = np.asarray(arg1)
+            except Exception as e:
+                raise ValueError("unrecognized form for "
+                                 f"{self.format}_matrix constructor") from e
+            if isinstance(self, sparray) and arg1.ndim != 2:
+                raise ValueError(f"DIA arrays don't support {arg1.ndim}D input. Use 2D")
+            A = self._coo_container(arg1, dtype=dtype, shape=shape).todia()
+            self.data = A.data
+            self.offsets = A.offsets
+            self._shape = check_shape(A.shape)
+
+        if dtype is not None:
+            newdtype = getdtype(dtype)
+            self.data = self.data.astype(newdtype)
+
+        # check format
+        if self.offsets.ndim != 1:
+            raise ValueError('offsets array must have rank 1')
+
+        if self.data.ndim != 2:
+            raise ValueError('data array must have rank 2')
+
+        if self.data.shape[0] != len(self.offsets):
+            raise ValueError('number of diagonals (%d) '
+                    'does not match the number of offsets (%d)'
+                    % (self.data.shape[0], len(self.offsets)))
+
+        if len(np.unique(self.offsets)) != len(self.offsets):
+            raise ValueError('offset array contains duplicate values')
+
+    def __repr__(self):
+        _, fmt = _formats[self.format]
+        sparse_cls = 'array' if isinstance(self, sparray) else 'matrix'
+        d = self.data.shape[0]
+        return (
+            f"<{fmt} sparse {sparse_cls} of dtype '{self.dtype}'\n"
+            f"\twith {self.nnz} stored elements ({d} diagonals) and shape {self.shape}>"
+        )
+
+    def _data_mask(self):
+        """Returns a mask of the same shape as self.data, where
+        mask[i,j] is True when data[i,j] corresponds to a stored element."""
+        num_rows, num_cols = self.shape
+        offset_inds = np.arange(self.data.shape[1])
+        row = offset_inds - self.offsets[:,None]
+        mask = (row >= 0)
+        mask &= (row < num_rows)
+        mask &= (offset_inds < num_cols)
+        return mask
+
+    def count_nonzero(self, axis=None):
+        if axis is not None:
+            raise NotImplementedError(
+                "count_nonzero over an axis is not implemented for DIA format"
+            )
+        mask = self._data_mask()
+        return np.count_nonzero(self.data[mask])
+
+    count_nonzero.__doc__ = _spbase.count_nonzero.__doc__
+
+    def _getnnz(self, axis=None):
+        if axis is not None:
+            raise NotImplementedError("_getnnz over an axis is not implemented "
+                                      "for DIA format")
+        M,N = self.shape
+        nnz = 0
+        for k in self.offsets:
+            if k > 0:
+                nnz += min(M,N-k)
+            else:
+                nnz += min(M+k,N)
+        return int(nnz)
+
+    _getnnz.__doc__ = _spbase._getnnz.__doc__
+
+    def sum(self, axis=None, dtype=None, out=None):
+        validateaxis(axis)
+
+        if axis is not None and axis < 0:
+            axis += 2
+
+        res_dtype = get_sum_dtype(self.dtype)
+        num_rows, num_cols = self.shape
+        ret = None
+
+        if axis == 0:
+            mask = self._data_mask()
+            x = (self.data * mask).sum(axis=0)
+            if x.shape[0] == num_cols:
+                res = x
+            else:
+                res = np.zeros(num_cols, dtype=x.dtype)
+                res[:x.shape[0]] = x
+            ret = self._ascontainer(res, dtype=res_dtype)
+
+        else:
+            row_sums = np.zeros((num_rows, 1), dtype=res_dtype)
+            one = np.ones(num_cols, dtype=res_dtype)
+            dia_matvec(num_rows, num_cols, len(self.offsets),
+                       self.data.shape[1], self.offsets, self.data, one, row_sums)
+
+            row_sums = self._ascontainer(row_sums)
+
+            if axis is None:
+                return row_sums.sum(dtype=dtype, out=out)
+
+            ret = self._ascontainer(row_sums.sum(axis=axis))
+
+        return ret.sum(axis=(), dtype=dtype, out=out)
+
+    sum.__doc__ = _spbase.sum.__doc__
+
+    def _add_sparse(self, other):
+        # If other is not DIA format, let them handle us instead.
+        if not isinstance(other, _dia_base):
+            return other._add_sparse(self)
+
+        # Fast path for exact equality of the sparsity structure.
+        if np.array_equal(self.offsets, other.offsets):
+            return self._with_data(self.data + other.data)
+
+        # Find the union of the offsets (which will be sorted and unique).
+        new_offsets = np.union1d(self.offsets, other.offsets)
+        self_idx = np.searchsorted(new_offsets, self.offsets)
+        other_idx = np.searchsorted(new_offsets, other.offsets)
+
+        self_d = self.data.shape[1]
+        other_d = other.data.shape[1]
+        # Fast path for a sparsity structure where the final offsets are a
+        # permutation of the existing offsets and the diagonal lengths match.
+        if self_d == other_d and len(new_offsets) == len(self.offsets):
+            new_data = self.data[_invert_index(self_idx)]
+            new_data[other_idx, :] += other.data
+        elif self_d == other_d and len(new_offsets) == len(other.offsets):
+            new_data = other.data[_invert_index(other_idx)]
+            new_data[self_idx, :] += self.data
+        else:
+            # Maximum diagonal length of the result.
+            d = min(self.shape[0] + new_offsets[-1], self.shape[1])
+
+            # Add all diagonals to a freshly-allocated data array.
+            new_data = np.zeros(
+                (len(new_offsets), d),
+                dtype=np.result_type(self.data, other.data),
+            )
+            new_data[self_idx, :self_d] += self.data[:, :d]
+            new_data[other_idx, :other_d] += other.data[:, :d]
+        return self._dia_container((new_data, new_offsets), shape=self.shape)
+
+    def _mul_scalar(self, other):
+        return self._with_data(self.data * other)
+
+    def _matmul_vector(self, other):
+        x = other
+
+        y = np.zeros(self.shape[0], dtype=upcast_char(self.dtype.char,
+                                                       x.dtype.char))
+
+        L = self.data.shape[1]
+
+        M,N = self.shape
+
+        dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data,
+                   x.ravel(), y.ravel())
+
+        return y
+
+    def _setdiag(self, values, k=0):
+        M, N = self.shape
+
+        if values.ndim == 0:
+            # broadcast
+            values_n = np.inf
+        else:
+            values_n = len(values)
+
+        if k < 0:
+            n = min(M + k, N, values_n)
+            min_index = 0
+            max_index = n
+        else:
+            n = min(M, N - k, values_n)
+            min_index = k
+            max_index = k + n
+
+        if values.ndim != 0:
+            # allow also longer sequences
+            values = values[:n]
+
+        data_rows, data_cols = self.data.shape
+        if k in self.offsets:
+            if max_index > data_cols:
+                data = np.zeros((data_rows, max_index), dtype=self.data.dtype)
+                data[:, :data_cols] = self.data
+                self.data = data
+            self.data[self.offsets == k, min_index:max_index] = values
+        else:
+            self.offsets = np.append(self.offsets, self.offsets.dtype.type(k))
+            m = max(max_index, data_cols)
+            data = np.zeros((data_rows + 1, m), dtype=self.data.dtype)
+            data[:-1, :data_cols] = self.data
+            data[-1, min_index:max_index] = values
+            self.data = data
+
+    def todia(self, copy=False):
+        if copy:
+            return self.copy()
+        else:
+            return self
+
+    todia.__doc__ = _spbase.todia.__doc__
+
+    def transpose(self, axes=None, copy=False):
+        if axes is not None and axes != (1, 0):
+            raise ValueError("Sparse arrays/matrices do not support "
+                              "an 'axes' parameter because swapping "
+                              "dimensions is the only logical permutation.")
+
+        num_rows, num_cols = self.shape
+        max_dim = max(self.shape)
+
+        # flip diagonal offsets
+        offsets = -self.offsets
+
+        # re-align the data matrix
+        r = np.arange(len(offsets), dtype=np.intc)[:, None]
+        c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]
+        pad_amount = max(0, max_dim-self.data.shape[1])
+        data = np.hstack((self.data, np.zeros((self.data.shape[0], pad_amount),
+                                              dtype=self.data.dtype)))
+        data = data[r, c]
+        return self._dia_container((data, offsets), shape=(
+            num_cols, num_rows), copy=copy)
+
+    transpose.__doc__ = _spbase.transpose.__doc__
+
+    def diagonal(self, k=0):
+        rows, cols = self.shape
+        if k <= -rows or k >= cols:
+            return np.empty(0, dtype=self.data.dtype)
+        idx, = np.nonzero(self.offsets == k)
+        first_col = max(0, k)
+        last_col = min(rows + k, cols)
+        result_size = last_col - first_col
+        if idx.size == 0:
+            return np.zeros(result_size, dtype=self.data.dtype)
+        result = self.data[idx[0], first_col:last_col]
+        padding = result_size - len(result)
+        if padding > 0:
+            result = np.pad(result, (0, padding), mode='constant')
+        return result
+
+    diagonal.__doc__ = _spbase.diagonal.__doc__
+
+    def tocsc(self, copy=False):
+        if self.nnz == 0:
+            return self._csc_container(self.shape, dtype=self.dtype)
+
+        num_rows, num_cols = self.shape
+        num_offsets, offset_len = self.data.shape
+        offset_inds = np.arange(offset_len)
+
+        row = offset_inds - self.offsets[:,None]
+        mask = (row >= 0)
+        mask &= (row < num_rows)
+        mask &= (offset_inds < num_cols)
+        mask &= (self.data != 0)
+
+        idx_dtype = self._get_index_dtype(maxval=max(self.shape))
+        indptr = np.zeros(num_cols + 1, dtype=idx_dtype)
+        indptr[1:offset_len+1] = np.cumsum(mask.sum(axis=0)[:num_cols])
+        if offset_len < num_cols:
+            indptr[offset_len+1:] = indptr[offset_len]
+        indices = row.T[mask.T].astype(idx_dtype, copy=False)
+        data = self.data.T[mask.T]
+        return self._csc_container((data, indices, indptr), shape=self.shape,
+                                   dtype=self.dtype)
+
+    tocsc.__doc__ = _spbase.tocsc.__doc__
+
+    def tocoo(self, copy=False):
+        num_rows, num_cols = self.shape
+        num_offsets, offset_len = self.data.shape
+        offset_inds = np.arange(offset_len)
+
+        row = offset_inds - self.offsets[:,None]
+        mask = (row >= 0)
+        mask &= (row < num_rows)
+        mask &= (offset_inds < num_cols)
+        mask &= (self.data != 0)
+        row = row[mask]
+        col = np.tile(offset_inds, num_offsets)[mask.ravel()]
+        idx_dtype = self._get_index_dtype(
+            arrays=(self.offsets,), maxval=max(self.shape)
+        )
+        row = row.astype(idx_dtype, copy=False)
+        col = col.astype(idx_dtype, copy=False)
+        data = self.data[mask]
+        # Note: this cannot set has_canonical_format=True, because despite the
+        # lack of duplicates, we do not generate sorted indices.
+        return self._coo_container(
+            (data, (row, col)), shape=self.shape, dtype=self.dtype, copy=False
+        )
+
+    tocoo.__doc__ = _spbase.tocoo.__doc__
+
+    # needed by _data_matrix
+    def _with_data(self, data, copy=True):
+        """Returns a matrix with the same sparsity structure as self,
+        but with different data.  By default the structure arrays are copied.
+        """
+        if copy:
+            return self._dia_container(
+                (data, self.offsets.copy()), shape=self.shape
+            )
+        else:
+            return self._dia_container(
+                (data, self.offsets), shape=self.shape
+            )
+
+    def resize(self, *shape):
+        shape = check_shape(shape)
+        M, N = shape
+        # we do not need to handle the case of expanding N
+        self.data = self.data[:, :N]
+
+        if (M > self.shape[0] and
+                np.any(self.offsets + self.shape[0] < self.data.shape[1])):
+            # explicitly clear values that were previously hidden
+            mask = (self.offsets[:, None] + self.shape[0] <=
+                    np.arange(self.data.shape[1]))
+            self.data[mask] = 0
+
+        self._shape = shape
+
+    resize.__doc__ = _spbase.resize.__doc__
+
+
+def _invert_index(idx):
+    """Helper function to invert an index array."""
+    inv = np.zeros_like(idx)
+    inv[idx] = np.arange(len(idx))
+    return inv
+
+
+def isspmatrix_dia(x):
+    """Is `x` of dia_matrix type?
+
+    Parameters
+    ----------
+    x
+        object to check for being a dia matrix
+
+    Returns
+    -------
+    bool
+        True if `x` is a dia matrix, False otherwise
+
+    Examples
+    --------
+    >>> from scipy.sparse import dia_array, dia_matrix, coo_matrix, isspmatrix_dia
+    >>> isspmatrix_dia(dia_matrix([[5]]))
+    True
+    >>> isspmatrix_dia(dia_array([[5]]))
+    False
+    >>> isspmatrix_dia(coo_matrix([[5]]))
+    False
+    """
+    return isinstance(x, dia_matrix)
+
+
+# This namespace class separates array from matrix with isinstance
+class dia_array(_dia_base, sparray):
+    """
+    Sparse array with DIAgonal storage.
+
+    This can be instantiated in several ways:
+        dia_array(D)
+            where D is a 2-D ndarray
+
+        dia_array(S)
+            with another sparse array or matrix S (equivalent to S.todia())
+
+        dia_array((M, N), [dtype])
+            to construct an empty array with shape (M, N),
+            dtype is optional, defaulting to dtype='d'.
+
+        dia_array((data, offsets), shape=(M, N))
+            where the ``data[k,:]`` stores the diagonal entries for
+            diagonal ``offsets[k]`` (See example below)
+
+    Attributes
+    ----------
+    dtype : dtype
+        Data type of the array
+    shape : 2-tuple
+        Shape of the array
+    ndim : int
+        Number of dimensions (this is always 2)
+    nnz
+    size
+    data
+        DIA format data array of the array
+    offsets
+        DIA format offset array of the array
+    T
+
+    Notes
+    -----
+
+    Sparse arrays can be used in arithmetic operations: they support
+    addition, subtraction, multiplication, division, and matrix power.
+    Sparse arrays with DIAgonal storage do not support slicing.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy.sparse import dia_array
+    >>> dia_array((3, 4), dtype=np.int8).toarray()
+    array([[0, 0, 0, 0],
+           [0, 0, 0, 0],
+           [0, 0, 0, 0]], dtype=int8)
+
+    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
+    >>> offsets = np.array([0, -1, 2])
+    >>> dia_array((data, offsets), shape=(4, 4)).toarray()
+    array([[1, 0, 3, 0],
+           [1, 2, 0, 4],
+           [0, 2, 3, 0],
+           [0, 0, 3, 4]])
+
+    >>> from scipy.sparse import dia_array
+    >>> n = 10
+    >>> ex = np.ones(n)
+    >>> data = np.array([ex, 2 * ex, ex])
+    >>> offsets = np.array([-1, 0, 1])
+    >>> dia_array((data, offsets), shape=(n, n)).toarray()
+    array([[2., 1., 0., ..., 0., 0., 0.],
+           [1., 2., 1., ..., 0., 0., 0.],
+           [0., 1., 2., ..., 0., 0., 0.],
+           ...,
+           [0., 0., 0., ..., 2., 1., 0.],
+           [0., 0., 0., ..., 1., 2., 1.],
+           [0., 0., 0., ..., 0., 1., 2.]])
+    """
+
+
+class dia_matrix(spmatrix, _dia_base):
+    """
+    Sparse matrix with DIAgonal storage.
+
+    This can be instantiated in several ways:
+        dia_matrix(D)
+            where D is a 2-D ndarray
+
+        dia_matrix(S)
+            with another sparse array or matrix S (equivalent to S.todia())
+
+        dia_matrix((M, N), [dtype])
+            to construct an empty matrix with shape (M, N),
+            dtype is optional, defaulting to dtype='d'.
+
+        dia_matrix((data, offsets), shape=(M, N))
+            where the ``data[k,:]`` stores the diagonal entries for
+            diagonal ``offsets[k]`` (See example below)
+
+    Attributes
+    ----------
+    dtype : dtype
+        Data type of the matrix
+    shape : 2-tuple
+        Shape of the matrix
+    ndim : int
+        Number of dimensions (this is always 2)
+    nnz
+    size
+    data
+        DIA format data array of the matrix
+    offsets
+        DIA format offset array of the matrix
+    T
+
+    Notes
+    -----
+
+    Sparse matrices can be used in arithmetic operations: they support
+    addition, subtraction, multiplication, division, and matrix power.
+    Sparse matrices with DIAgonal storage do not support slicing.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy.sparse import dia_matrix
+    >>> dia_matrix((3, 4), dtype=np.int8).toarray()
+    array([[0, 0, 0, 0],
+           [0, 0, 0, 0],
+           [0, 0, 0, 0]], dtype=int8)
+
+    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
+    >>> offsets = np.array([0, -1, 2])
+    >>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
+    array([[1, 0, 3, 0],
+           [1, 2, 0, 4],
+           [0, 2, 3, 0],
+           [0, 0, 3, 4]])
+
+    >>> from scipy.sparse import dia_matrix
+    >>> n = 10
+    >>> ex = np.ones(n)
+    >>> data = np.array([ex, 2 * ex, ex])
+    >>> offsets = np.array([-1, 0, 1])
+    >>> dia_matrix((data, offsets), shape=(n, n)).toarray()
+    array([[2., 1., 0., ..., 0., 0., 0.],
+           [1., 2., 1., ..., 0., 0., 0.],
+           [0., 1., 2., ..., 0., 0., 0.],
+           ...,
+           [0., 0., 0., ..., 2., 1., 0.],
+           [0., 0., 0., ..., 1., 2., 1.],
+           [0., 0., 0., ..., 0., 1., 2.]])
+    """
diff --git a/shared/package-files/d8/d8be82063f31b57f628a9f7c34f6cab0bb70b518f18b9ef5c1899330715e2fdd/differentiation.py b/shared/package-files/d8/d8be82063f31b57f628a9f7c34f6cab0bb70b518f18b9ef5c1899330715e2fdd/differentiation.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8186bebd4476476eded9e4b2f8dc3eb23ef5ff9
--- /dev/null
+++ b/shared/package-files/d8/d8be82063f31b57f628a9f7c34f6cab0bb70b518f18b9ef5c1899330715e2fdd/differentiation.py
@@ -0,0 +1,647 @@
+from ..libmp.backend import xrange
+from .calculus import defun
+
+try:
+    iteritems = dict.iteritems
+except AttributeError:
+    iteritems = dict.items
+
+#----------------------------------------------------------------------------#
+#                                Differentiation                             #
+#----------------------------------------------------------------------------#
+
+@defun
+def difference(ctx, s, n):
+    r"""
+    Given a sequence `(s_k)` containing at least `n+1` items, returns the
+    `n`-th forward difference,
+
+    .. math ::
+
+        \Delta^n = \sum_{k=0}^{\infty} (-1)^{k+n} {n \choose k} s_k.
+    """
+    n = int(n)
+    d = ctx.zero
+    b = (-1) ** (n & 1)
+    for k in xrange(n+1):
+        d += b * s[k]
+        b = (b * (k-n)) // (k+1)
+    return d
+
+def hsteps(ctx, f, x, n, prec, **options):
+    singular = options.get('singular')
+    addprec = options.get('addprec', 10)
+    direction = options.get('direction', 0)
+    workprec = (prec+2*addprec) * (n+1)
+    orig = ctx.prec
+    try:
+        ctx.prec = workprec
+        h = options.get('h')
+        if h is None:
+            if options.get('relative'):
+                hextramag = int(ctx.mag(x))
+            else:
+                hextramag = 0
+            h = ctx.ldexp(1, -prec-addprec-hextramag)
+        else:
+            h = ctx.convert(h)
+        # Directed: steps x, x+h, ... x+n*h
+        direction = options.get('direction', 0)
+        if direction:
+            h *= ctx.sign(direction)
+            steps = xrange(n+1)
+            norm = h
+        # Central: steps x-n*h, x-(n-2)*h ..., x, ..., x+(n-2)*h, x+n*h
+        else:
+            steps = xrange(-n, n+1, 2)
+            norm = (2*h)
+        # Perturb
+        if singular:
+            x += 0.5*h
+        values = [f(x+k*h) for k in steps]
+        return values, norm, workprec
+    finally:
+        ctx.prec = orig
+
+
+@defun
+def diff(ctx, f, x, n=1, **options):
+    r"""
+    Numerically computes the derivative of `f`, `f'(x)`, or generally for
+    an integer `n \ge 0`, the `n`-th derivative `f^{(n)}(x)`.
+    A few basic examples are::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> diff(lambda x: x**2 + x, 1.0)
+        3.0
+        >>> diff(lambda x: x**2 + x, 1.0, 2)
+        2.0
+        >>> diff(lambda x: x**2 + x, 1.0, 3)
+        0.0
+        >>> nprint([diff(exp, 3, n) for n in range(5)])   # exp'(x) = exp(x)
+        [20.0855, 20.0855, 20.0855, 20.0855, 20.0855]
+
+    Even more generally, given a tuple of arguments `(x_1, \ldots, x_k)`
+    and order `(n_1, \ldots, n_k)`, the partial derivative
+    `f^{(n_1,\ldots,n_k)}(x_1,\ldots,x_k)` is evaluated. For example::
+
+        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (0,1))
+        2.75
+        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (1,1))
+        3.0
+
+    **Options**
+
+    The following optional keyword arguments are recognized:
+
+    ``method``
+        Supported methods are ``'step'`` or ``'quad'``: derivatives may be
+        computed using either a finite difference with a small step
+        size `h` (default), or numerical quadrature.
+    ``direction``
+        Direction of finite difference: can be -1 for a left
+        difference, 0 for a central difference (default), or +1
+        for a right difference; more generally can be any complex number.
+    ``addprec``
+        Extra precision for `h` used to account for the function's
+        sensitivity to perturbations (default = 10).
+    ``relative``
+        Choose `h` relative to the magnitude of `x`, rather than an
+        absolute value; useful for large or tiny `x` (default = False).
+    ``h``
+        As an alternative to ``addprec`` and ``relative``, manually
+        select the step size `h`.
+    ``singular``
+        If True, evaluation exactly at the point `x` is avoided; this is
+        useful for differentiating functions with removable singularities.
+        Default = False.
+    ``radius``
+        Radius of integration contour (with ``method = 'quad'``).
+        Default = 0.25. A larger radius typically is faster and more
+        accurate, but it must be chosen so that `f` has no
+        singularities within the radius from the evaluation point.
+
+    A finite difference requires `n+1` function evaluations and must be
+    performed at `(n+1)` times the target precision. Accordingly, `f` must
+    support fast evaluation at high precision.
+
+    With integration, a larger number of function evaluations is
+    required, but not much extra precision is required. For high order
+    derivatives, this method may thus be faster if f is very expensive to
+    evaluate at high precision.
+
+    **Further examples**
+
+    The direction option is useful for computing left- or right-sided
+    derivatives of nonsmooth functions::
+
+        >>> diff(abs, 0, direction=0)
+        0.0
+        >>> diff(abs, 0, direction=1)
+        1.0
+        >>> diff(abs, 0, direction=-1)
+        -1.0
+
+    More generally, if the direction is nonzero, a right difference
+    is computed where the step size is multiplied by sign(direction).
+    For example, with direction=+j, the derivative from the positive
+    imaginary direction will be computed::
+
+        >>> diff(abs, 0, direction=j)
+        (0.0 - 1.0j)
+
+    With integration, the result may have a small imaginary part
+    even even if the result is purely real::
+
+        >>> diff(sqrt, 1, method='quad')    # doctest:+ELLIPSIS
+        (0.5 - 4.59...e-26j)
+        >>> chop(_)
+        0.5
+
+    Adding precision to obtain an accurate value::
+
+        >>> diff(cos, 1e-30)
+        0.0
+        >>> diff(cos, 1e-30, h=0.0001)
+        -9.99999998328279e-31
+        >>> diff(cos, 1e-30, addprec=100)
+        -1.0e-30
+
+    """
+    partial = False
+    try:
+        orders = list(n)
+        x = list(x)
+        partial = True
+    except TypeError:
+        pass
+    if partial:
+        x = [ctx.convert(_) for _ in x]
+        return _partial_diff(ctx, f, x, orders, options)
+    method = options.get('method', 'step')
+    if n == 0 and method != 'quad' and not options.get('singular'):
+        return f(ctx.convert(x))
+    prec = ctx.prec
+    try:
+        if method == 'step':
+            values, norm, workprec = hsteps(ctx, f, x, n, prec, **options)
+            ctx.prec = workprec
+            v = ctx.difference(values, n) / norm**n
+        elif method == 'quad':
+            ctx.prec += 10
+            radius = ctx.convert(options.get('radius', 0.25))
+            def g(t):
+                rei = radius*ctx.expj(t)
+                z = x + rei
+                return f(z) / rei**n
+            d = ctx.quadts(g, [0, 2*ctx.pi])
+            v = d * ctx.factorial(n) / (2*ctx.pi)
+        else:
+            raise ValueError("unknown method: %r" % method)
+    finally:
+        ctx.prec = prec
+    return +v
+
+def _partial_diff(ctx, f, xs, orders, options):
+    if not orders:
+        return f()
+    if not sum(orders):
+        return f(*xs)
+    i = 0
+    for i in range(len(orders)):
+        if orders[i]:
+            break
+    order = orders[i]
+    def fdiff_inner(*f_args):
+        def inner(t):
+            return f(*(f_args[:i] + (t,) + f_args[i+1:]))
+        return ctx.diff(inner, f_args[i], order, **options)
+    orders[i] = 0
+    return _partial_diff(ctx, fdiff_inner, xs, orders, options)
+
+@defun
+def diffs(ctx, f, x, n=None, **options):
+    r"""
+    Returns a generator that yields the sequence of derivatives
+
+    .. math ::
+
+        f(x), f'(x), f''(x), \ldots, f^{(k)}(x), \ldots
+
+    With ``method='step'``, :func:`~mpmath.diffs` uses only `O(k)`
+    function evaluations to generate the first `k` derivatives,
+    rather than the roughly `O(k^2)` evaluations
+    required if one calls :func:`~mpmath.diff` `k` separate times.
+
+    With `n < \infty`, the generator stops as soon as the
+    `n`-th derivative has been generated. If the exact number of
+    needed derivatives is known in advance, this is further
+    slightly more efficient.
+
+    Options are the same as for :func:`~mpmath.diff`.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 15
+        >>> nprint(list(diffs(cos, 1, 5)))
+        [0.540302, -0.841471, -0.540302, 0.841471, 0.540302, -0.841471]
+        >>> for i, d in zip(range(6), diffs(cos, 1)):
+        ...     print("%s %s" % (i, d))
+        ...
+        0 0.54030230586814
+        1 -0.841470984807897
+        2 -0.54030230586814
+        3 0.841470984807897
+        4 0.54030230586814
+        5 -0.841470984807897
+
+    """
+    if n is None:
+        n = ctx.inf
+    else:
+        n = int(n)
+    if options.get('method', 'step') != 'step':
+        k = 0
+        while k < n + 1:
+            yield ctx.diff(f, x, k, **options)
+            k += 1
+        return
+    singular = options.get('singular')
+    if singular:
+        yield ctx.diff(f, x, 0, singular=True)
+    else:
+        yield f(ctx.convert(x))
+    if n < 1:
+        return
+    if n == ctx.inf:
+        A, B = 1, 2
+    else:
+        A, B = 1, n+1
+    while 1:
+        callprec = ctx.prec
+        y, norm, workprec = hsteps(ctx, f, x, B, callprec, **options)
+        for k in xrange(A, B):
+            try:
+                ctx.prec = workprec
+                d = ctx.difference(y, k) / norm**k
+            finally:
+                ctx.prec = callprec
+            yield +d
+            if k >= n:
+                return
+        A, B = B, int(A*1.4+1)
+        B = min(B, n)
+
+def iterable_to_function(gen):
+    gen = iter(gen)
+    data = []
+    def f(k):
+        for i in xrange(len(data), k+1):
+            data.append(next(gen))
+        return data[k]
+    return f
+
+@defun
+def diffs_prod(ctx, factors):
+    r"""
+    Given a list of `N` iterables or generators yielding
+    `f_k(x), f'_k(x), f''_k(x), \ldots` for `k = 1, \ldots, N`,
+    generate `g(x), g'(x), g''(x), \ldots` where
+    `g(x) = f_1(x) f_2(x) \cdots f_N(x)`.
+
+    At high precision and for large orders, this is typically more efficient
+    than numerical differentiation if the derivatives of each `f_k(x)`
+    admit direct computation.
+
+    Note: This function does not increase the working precision internally,
+    so guard digits may have to be added externally for full accuracy.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> f = lambda x: exp(x)*cos(x)*sin(x)
+        >>> u = diffs(f, 1)
+        >>> v = mp.diffs_prod([diffs(exp,1), diffs(cos,1), diffs(sin,1)])
+        >>> next(u); next(v)
+        1.23586333600241
+        1.23586333600241
+        >>> next(u); next(v)
+        0.104658952245596
+        0.104658952245596
+        >>> next(u); next(v)
+        -5.96999877552086
+        -5.96999877552086
+        >>> next(u); next(v)
+        -12.4632923122697
+        -12.4632923122697
+
+    """
+    N = len(factors)
+    if N == 1:
+        for c in factors[0]:
+            yield c
+    else:
+        u = iterable_to_function(ctx.diffs_prod(factors[:N//2]))
+        v = iterable_to_function(ctx.diffs_prod(factors[N//2:]))
+        n = 0
+        while 1:
+            #yield sum(binomial(n,k)*u(n-k)*v(k) for k in xrange(n+1))
+            s = u(n) * v(0)
+            a = 1
+            for k in xrange(1,n+1):
+                a = a * (n-k+1) // k
+                s += a * u(n-k) * v(k)
+            yield s
+            n += 1
+
+def dpoly(n, _cache={}):
+    """
+    nth differentiation polynomial for exp (Faa di Bruno's formula).
+
+    TODO: most exponents are zero, so maybe a sparse representation
+    would be better.
+    """
+    if n in _cache:
+        return _cache[n]
+    if not _cache:
+        _cache[0] = {(0,):1}
+    R = dpoly(n-1)
+    R = dict((c+(0,),v) for (c,v) in iteritems(R))
+    Ra = {}
+    for powers, count in iteritems(R):
+        powers1 = (powers[0]+1,) + powers[1:]
+        if powers1 in Ra:
+            Ra[powers1] += count
+        else:
+            Ra[powers1] = count
+    for powers, count in iteritems(R):
+        if not sum(powers):
+            continue
+        for k,p in enumerate(powers):
+            if p:
+                powers2 = powers[:k] + (p-1,powers[k+1]+1) + powers[k+2:]
+                if powers2 in Ra:
+                    Ra[powers2] += p*count
+                else:
+                    Ra[powers2] = p*count
+    _cache[n] = Ra
+    return _cache[n]
+
+@defun
+def diffs_exp(ctx, fdiffs):
+    r"""
+    Given an iterable or generator yielding `f(x), f'(x), f''(x), \ldots`
+    generate `g(x), g'(x), g''(x), \ldots` where `g(x) = \exp(f(x))`.
+
+    At high precision and for large orders, this is typically more efficient
+    than numerical differentiation if the derivatives of `f(x)`
+    admit direct computation.
+
+    Note: This function does not increase the working precision internally,
+    so guard digits may have to be added externally for full accuracy.
+
+    **Examples**
+
+    The derivatives of the gamma function can be computed using
+    logarithmic differentiation::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>>
+        >>> def diffs_loggamma(x):
+        ...     yield loggamma(x)
+        ...     i = 0
+        ...     while 1:
+        ...         yield psi(i,x)
+        ...         i += 1
+        ...
+        >>> u = diffs_exp(diffs_loggamma(3))
+        >>> v = diffs(gamma, 3)
+        >>> next(u); next(v)
+        2.0
+        2.0
+        >>> next(u); next(v)
+        1.84556867019693
+        1.84556867019693
+        >>> next(u); next(v)
+        2.49292999190269
+        2.49292999190269
+        >>> next(u); next(v)
+        3.44996501352367
+        3.44996501352367
+
+    """
+    fn = iterable_to_function(fdiffs)
+    f0 = ctx.exp(fn(0))
+    yield f0
+    i = 1
+    while 1:
+        s = ctx.mpf(0)
+        for powers, c in iteritems(dpoly(i)):
+            s += c*ctx.fprod(fn(k+1)**p for (k,p) in enumerate(powers) if p)
+        yield s * f0
+        i += 1
+
+@defun
+def differint(ctx, f, x, n=1, x0=0):
+    r"""
+    Calculates the Riemann-Liouville differintegral, or fractional
+    derivative, defined by
+
+    .. math ::
+
+        \,_{x_0}{\mathbb{D}}^n_xf(x) = \frac{1}{\Gamma(m-n)} \frac{d^m}{dx^m}
+        \int_{x_0}^{x}(x-t)^{m-n-1}f(t)dt
+
+    where `f` is a given (presumably well-behaved) function,
+    `x` is the evaluation point, `n` is the order, and `x_0` is
+    the reference point of integration (`m` is an arbitrary
+    parameter selected automatically).
+
+    With `n = 1`, this is just the standard derivative `f'(x)`; with `n = 2`,
+    the second derivative `f''(x)`, etc. With `n = -1`, it gives
+    `\int_{x_0}^x f(t) dt`, with `n = -2`
+    it gives `\int_{x_0}^x \left( \int_{x_0}^t f(u) du \right) dt`, etc.
+
+    As `n` is permitted to be any number, this operator generalizes
+    iterated differentiation and iterated integration to a single
+    operator with a continuous order parameter.
+
+    **Examples**
+
+    There is an exact formula for the fractional derivative of a
+    monomial `x^p`, which may be used as a reference. For example,
+    the following gives a half-derivative (order 0.5)::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> x = mpf(3); p = 2; n = 0.5
+        >>> differint(lambda t: t**p, x, n)
+        7.81764019044672
+        >>> gamma(p+1)/gamma(p-n+1) * x**(p-n)
+        7.81764019044672
+
+    Another useful test function is the exponential function, whose
+    integration / differentiation formula easy generalizes
+    to arbitrary order. Here we first compute a third derivative,
+    and then a triply nested integral. (The reference point `x_0`
+    is set to `-\infty` to avoid nonzero endpoint terms.)::
+
+        >>> differint(lambda x: exp(pi*x), -1.5, 3)
+        0.278538406900792
+        >>> exp(pi*-1.5) * pi**3
+        0.278538406900792
+        >>> differint(lambda x: exp(pi*x), 3.5, -3, -inf)
+        1922.50563031149
+        >>> exp(pi*3.5) / pi**3
+        1922.50563031149
+
+    However, for noninteger `n`, the differentiation formula for the
+    exponential function must be modified to give the same result as the
+    Riemann-Liouville differintegral::
+
+        >>> x = mpf(3.5)
+        >>> c = pi
+        >>> n = 1+2*j
+        >>> differint(lambda x: exp(c*x), x, n)
+        (-123295.005390743 + 140955.117867654j)
+        >>> x**(-n) * exp(c)**x * (x*c)**n * gammainc(-n, 0, x*c) / gamma(-n)
+        (-123295.005390743 + 140955.117867654j)
+
+
+    """
+    m = max(int(ctx.ceil(ctx.re(n)))+1, 1)
+    r = m-n-1
+    g = lambda x: ctx.quad(lambda t: (x-t)**r * f(t), [x0, x])
+    return ctx.diff(g, x, m) / ctx.gamma(m-n)
+
+@defun
+def diffun(ctx, f, n=1, **options):
+    r"""
+    Given a function `f`, returns a function `g(x)` that evaluates the nth
+    derivative `f^{(n)}(x)`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> cos2 = diffun(sin)
+        >>> sin2 = diffun(sin, 4)
+        >>> cos(1.3), cos2(1.3)
+        (0.267498828624587, 0.267498828624587)
+        >>> sin(1.3), sin2(1.3)
+        (0.963558185417193, 0.963558185417193)
+
+    The function `f` must support arbitrary precision evaluation.
+    See :func:`~mpmath.diff` for additional details and supported
+    keyword options.
+    """
+    if n == 0:
+        return f
+    def g(x):
+        return ctx.diff(f, x, n, **options)
+    return g
+
+@defun
+def taylor(ctx, f, x, n, **options):
+    r"""
+    Produces a degree-`n` Taylor polynomial around the point `x` of the
+    given function `f`. The coefficients are returned as a list.
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> nprint(chop(taylor(sin, 0, 5)))
+        [0.0, 1.0, 0.0, -0.166667, 0.0, 0.00833333]
+
+    The coefficients are computed using high-order numerical
+    differentiation. The function must be possible to evaluate
+    to arbitrary precision. See :func:`~mpmath.diff` for additional details
+    and supported keyword options.
+
+    Note that to evaluate the Taylor polynomial as an approximation
+    of `f`, e.g. with :func:`~mpmath.polyval`, the coefficients must be reversed,
+    and the point of the Taylor expansion must be subtracted from
+    the argument:
+
+        >>> p = taylor(exp, 2.0, 10)
+        >>> polyval(p[::-1], 2.5 - 2.0)
+        12.1824939606092
+        >>> exp(2.5)
+        12.1824939607035
+
+    """
+    gen = enumerate(ctx.diffs(f, x, n, **options))
+    if options.get("chop", True):
+        return [ctx.chop(d)/ctx.factorial(i) for i, d in gen]
+    else:
+        return [d/ctx.factorial(i) for i, d in gen]
+
+@defun
+def pade(ctx, a, L, M):
+    r"""
+    Computes a Pade approximation of degree `(L, M)` to a function.
+    Given at least `L+M+1` Taylor coefficients `a` approximating
+    a function `A(x)`, :func:`~mpmath.pade` returns coefficients of
+    polynomials `P, Q` satisfying
+
+    .. math ::
+
+        P = \sum_{k=0}^L p_k x^k
+
+        Q = \sum_{k=0}^M q_k x^k
+
+        Q_0 = 1
+
+        A(x) Q(x) = P(x) + O(x^{L+M+1})
+
+    `P(x)/Q(x)` can provide a good approximation to an analytic function
+    beyond the radius of convergence of its Taylor series (example
+    from G.A. Baker 'Essentials of Pade Approximants' Academic Press,
+    Ch.1A)::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> one = mpf(1)
+        >>> def f(x):
+        ...     return sqrt((one + 2*x)/(one + x))
+        ...
+        >>> a = taylor(f, 0, 6)
+        >>> p, q = pade(a, 3, 3)
+        >>> x = 10
+        >>> polyval(p[::-1], x)/polyval(q[::-1], x)
+        1.38169105566806
+        >>> f(x)
+        1.38169855941551
+
+    """
+    # To determine L+1 coefficients of P and M coefficients of Q
+    # L+M+1 coefficients of A must be provided
+    if len(a) < L+M+1:
+        raise ValueError("L+M+1 Coefficients should be provided")
+
+    if M == 0:
+        if L == 0:
+            return [ctx.one], [ctx.one]
+        else:
+            return a[:L+1], [ctx.one]
+
+    # Solve first
+    # a[L]*q[1] + ... + a[L-M+1]*q[M] = -a[L+1]
+    # ...
+    # a[L+M-1]*q[1] + ... + a[L]*q[M] = -a[L+M]
+    A = ctx.matrix(M)
+    for j in range(M):
+        for i in range(min(M, L+j+1)):
+            A[j, i] = a[L+j-i]
+    v = -ctx.matrix(a[(L+1):(L+M+1)])
+    x = ctx.lu_solve(A, v)
+    q = [ctx.one] + list(x)
+    # compute p
+    p = [0]*(L+1)
+    for i in range(L+1):
+        s = a[i]
+        for j in range(1, min(M,i) + 1):
+            s += q[j]*a[i-j]
+        p[i] = s
+    return p, q
diff --git a/shared/package-files/d8/d8c347a7bedc4ebf744573c8c6245f4b6b37ca631443a33576bf73e2113b3c5f/teken b/shared/package-files/d8/d8c347a7bedc4ebf744573c8c6245f4b6b37ca631443a33576bf73e2113b3c5f/teken
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diff --git a/shared/package-files/d8/d8c534af75cbdead9040c4922d1b238971bee19f7d2fbc5d58f9cbb0560de680/method.h b/shared/package-files/d8/d8c534af75cbdead9040c4922d1b238971bee19f7d2fbc5d58f9cbb0560de680/method.h
new file mode 100644
index 0000000000000000000000000000000000000000..c4a957d00113360b1da71ef06aff455123df5bf8
--- /dev/null
+++ b/shared/package-files/d8/d8c534af75cbdead9040c4922d1b238971bee19f7d2fbc5d58f9cbb0560de680/method.h
@@ -0,0 +1,45 @@
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <torch/csrc/jit/mobile/function.h>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+
+class Module;
+
+struct TORCH_API Method {
+  Method(const Module* owner, Function* function);
+
+  void run(Stack& stack) const;
+  void run(Stack&& stack) const {
+    run(stack);
+  }
+
+  c10::IValue operator()(std::vector<c10::IValue> stack) const;
+
+  const std::string& name() const {
+    return function_->name();
+  }
+
+  int64_t get_debug_handle(size_t pc) const {
+    return function_->get_debug_handle(pc);
+  }
+
+  Function& function() const {
+    return *function_;
+  }
+
+ private:
+  // Methods are uniquely owned by a single module.
+  // This raw pointer allows referencing the module
+  const Module* owner_;
+
+  // Underlying unbound function
+  Function* function_;
+};
+
+} // namespace mobile
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/d8/d8c8e2150455194a79a71d477beb30415ea8fb87568301e1ea872def18f98b77/generalized_linear_model.py b/shared/package-files/d8/d8c8e2150455194a79a71d477beb30415ea8fb87568301e1ea872def18f98b77/generalized_linear_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b99ccadafd651ce35e5f2c322180d7d06517b15
--- /dev/null
+++ b/shared/package-files/d8/d8c8e2150455194a79a71d477beb30415ea8fb87568301e1ea872def18f98b77/generalized_linear_model.py
@@ -0,0 +1,2862 @@
+"""
+Generalized linear models currently supports estimation using the one-parameter
+exponential families
+
+References
+----------
+Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach.
+    SAGE QASS Series.
+
+Green, PJ. 1984.  "Iteratively reweighted least squares for maximum
+    likelihood estimation, and some robust and resistant alternatives."
+    Journal of the Royal Statistical Society, Series B, 46, 149-192.
+
+Hardin, J.W. and Hilbe, J.M. 2007.  "Generalized Linear Models and
+    Extensions."  2nd ed.  Stata Press, College Station, TX.
+
+McCullagh, P. and Nelder, J.A.  1989.  "Generalized Linear Models." 2nd ed.
+    Chapman & Hall, Boca Rotan.
+"""
+
+import warnings
+
+import numpy as np
+from numpy.linalg import LinAlgError
+
+from statsmodels.base import _prediction_inference as pred
+import statsmodels.base._parameter_inference as pinfer
+from statsmodels.base._prediction_inference import PredictionResultsMean
+import statsmodels.base.model as base
+import statsmodels.base.wrapper as wrap
+from statsmodels.graphics._regressionplots_doc import (
+    _plot_added_variable_doc,
+    _plot_ceres_residuals_doc,
+    _plot_partial_residuals_doc,
+)
+import statsmodels.regression._tools as reg_tools
+import statsmodels.regression.linear_model as lm
+from statsmodels.tools._decorators import (
+    cache_readonly,
+    cached_data,
+    cached_value,
+)
+from statsmodels.tools.data import _as_array_with_name
+from statsmodels.tools.docstring import Docstring
+from statsmodels.tools.docstring_helpers import Appender
+from statsmodels.tools.sm_exceptions import (
+    ConvergenceWarning,
+    DomainWarning,
+    HessianInversionWarning,
+    PerfectSeparationWarning,
+)
+from statsmodels.tools.validation import float_like, string_like
+
+# need import in module instead of lazily to copy `__doc__`
+from . import families
+
+__all__ = ["GLM", "PredictionResultsMean"]
+
+
+def _check_convergence(criterion, iteration, atol, rtol):
+    return np.allclose(
+        criterion[iteration], criterion[iteration + 1], atol=atol, rtol=rtol
+    )
+
+
+class GLM(base.LikelihoodModel):
+    __doc__ = f"""
+    Generalized Linear Models
+
+    GLM inherits from statsmodels.base.model.LikelihoodModel
+
+    Parameters
+    ----------
+    endog : array_like
+        1d array of endogenous response variable.  This array can be 1d or 2d.
+        Binomial family models accept a 2d array with two columns. If
+        supplied, each observation is expected to be [success, failure].
+    exog : array_like
+        A nobs x k array where `nobs` is the number of observations and `k`
+        is the number of regressors. An intercept is not included by default
+        and should be added by the user (models specified using a formula
+        include an intercept by default). See `statsmodels.tools.add_constant`.
+    family : family class instance, optional
+        The default is Gaussian.  To specify the binomial distribution, use
+        family = sm.families.Binomial().
+        Each family can take a link instance as an argument.  See
+        sm.families.family for more information.
+    offset : array_like, optional
+        An offset to be included in the model.  If provided, must be
+        an array whose length is the number of rows in exog.
+    exposure : array_like, optional
+        Log(exposure) will be added to the linear prediction in the model.
+        Exposure is only valid if the log link is used. If provided, it must be
+        an array with the same length as endog.
+    freq_weights : array_like, optional
+        1d array of frequency weights. The default is None. If None is selected
+        or a blank value, then the algorithm will replace with an array of 1's
+        with length equal to the endog.
+        WARNING: Using weights is not verified yet for all possible options
+        and results, see Notes.
+    var_weights : array_like, optional
+        1d array of variance (analytic) weights. The default is None. If None
+        is selected or a blank value, then the algorithm will replace with an
+        array of 1's with length equal to the endog.
+        WARNING: Using weights is not verified yet for all possible options
+        and results, see Notes.
+    {base._missing_param_doc}
+
+    Attributes
+    ----------
+    df_model : float
+        Model degrees of freedom is equal to p - 1, where p is the number
+        of regressors.  Note that the intercept is not reported as a
+        degree of freedom.
+    df_resid : float
+        Residual degrees of freedom is equal to the number of observation n
+        minus the number of regressors p.
+    endog : ndarray
+        See Notes.  Note that `endog` is a reference to the data so that if
+        data is already an array and it is changed, then `endog` changes
+        as well.
+    exposure : ndarray or None
+        Include ln(exposure) in model with coefficient constrained to 1. Can
+        only be used if the link is the logarithm function.
+    exog : ndarray
+        See Notes.  Note that `exog` is a reference to the data so that if
+        data is already an array and it is changed, then `exog` changes
+        as well.
+    freq_weights : ndarray
+        See Notes. Note that `freq_weights` is a reference to the data so that
+        if data is already an array and it is changed, then `freq_weights`
+        changes as well.
+    var_weights : ndarray
+        See Notes. Note that `var_weights` is a reference to the data so that
+        if data is already an array and it is changed, then `var_weights`
+        changes as well.
+    iteration : int
+        The number of iterations that fit has run.  Initialized at 0.
+    family : family class instance
+        The distribution family of the model. Can be any family in
+        sm.families.  Default is Gaussian.
+    mu : ndarray
+        The mean response of the transformed variable.  `mu` is the value of
+        the inverse of the link function at lin_pred, where lin_pred is the
+        linear predicted value of the WLS fit of the transformed variable.
+        `mu` is only available after fit is called.  See
+        sm.families.family.fitted of the distribution family for more
+        information.
+    n_trials : ndarray
+        See Notes. Note that `n_trials` is a reference to the data so that if
+        data is already an array and it is changed, then `n_trials` changes
+        as well. `n_trials` is the number of binomial trials and only available
+        with that distribution. See sm.families.Binomial for more
+        information.
+    normalized_cov_params : ndarray
+        The p x p normalized covariance of the design / exogenous data.
+        This is approximately equal to (X.T X)^(-1)
+    offset : ndarray or None
+        Include offset in model with coefficient constrained to 1.
+    scale : float
+        The estimate of the scale / dispersion of the model fit.  Only
+        available after fit is called.  See GLM.fit and GLM.estimate_scale
+        for more information.
+    scaletype : str, float, or None
+        The scaling used for fitting the model.  This is only available after
+        fit is called.  The default is None.  See GLM.fit for more information.
+    weights : ndarray
+        The value of the weights after the last iteration of fit.  Only
+        available after fit is called.  See sm.families.family for
+        the specific distribution weighting functions.
+
+    Examples
+    --------
+    >>> import statsmodels.api as sm
+    >>> data = sm.datasets.scotland.load()
+    >>> data.exog = sm.add_constant(data.exog)
+
+    Instantiate a gamma family model with the default link function.
+
+    >>> gamma_model = sm.GLM(data.endog, data.exog,
+    ...                      family=sm.families.Gamma())
+
+    >>> gamma_results = gamma_model.fit()
+    >>> gamma_results.params
+    array([-0.01776527,  0.00004962,  0.00203442, -0.00007181,  0.00011185,
+           -0.00000015, -0.00051868, -0.00000243])
+    >>> gamma_results.scale
+    0.0035842831734919055
+    >>> gamma_results.deviance
+    0.087388516416999198
+    >>> gamma_results.pearson_chi2
+    0.086022796163805704
+    >>> gamma_results.llf
+    -83.017202161073527
+
+    See Also
+    --------
+    statsmodels.genmod.families.family.Family
+    :ref:`families`
+    :ref:`links`
+
+    Notes
+    -----
+    Note: PerfectSeparationError exception has been converted to a
+    PerfectSeparationWarning and perfect separation or perfect prediction will
+    not raise an exception by default. (changed in version 0.14)
+
+    Only the following combinations make sense for family and link:
+
+     ============= ===== === ===== ====== ======= === ==== ====== ====== ====
+     Family        ident log logit probit cloglog pow opow nbinom loglog logc
+     ============= ===== === ===== ====== ======= === ==== ====== ====== ====
+     Gaussian      x     x   x     x      x       x   x     x      x
+     inv Gaussian  x     x                        x
+     binomial      x     x   x     x      x       x   x           x      x
+     Poisson       x     x                        x
+     neg binomial  x     x                        x        x
+     gamma         x     x                        x
+     Tweedie       x     x                        x
+     ============= ===== === ===== ====== ======= === ==== ====== ====== ====
+
+    Not all of these link functions are currently available.
+
+    Endog and exog are references so that if the data they refer to are already
+    arrays and these arrays are changed, endog and exog will change.
+
+    statsmodels supports two separate definitions of weights: frequency weights
+    and variance weights.
+
+    Frequency weights produce the same results as repeating observations by the
+    frequencies (if those are integers). Frequency weights will keep the number
+    of observations consistent, but the degrees of freedom will change to
+    reflect the new weights.
+
+    Variance weights (referred to in other packages as analytic weights) are
+    used when ``endog`` represents an average or mean. This relies on the
+    assumption that the inverse variance scales proportionally to the
+    weight--an observation that is deemed more credible should have less
+    variance and therefore have more weight. For the ``Poisson`` family--which
+    assumes that occurrences scale proportionally with time--a natural practice
+    would be to use the amount of time as the variance weight and set ``endog``
+    to be a rate (occurrences per period of time). Similarly, using a
+    compound Poisson family, namely ``Tweedie``, makes a similar assumption
+    about the rate (or frequency) of occurrences having variance proportional to
+    time.
+
+    Both frequency and variance weights are verified for all basic results with
+    nonrobust or heteroscedasticity robust ``cov_type``. Other robust
+    covariance types have not yet been verified, and at least the small sample
+    correction is currently not based on the correct total frequency count.
+
+    Currently, all residuals are not weighted by frequency, although they may
+    incorporate ``n_trials`` for ``Binomial`` and ``var_weights``
+
+    +---------------+----------------------------------+
+    | Residual Type | Applicable weights               |
+    +===============+==================================+
+    | Anscombe      | ``var_weights``                  |
+    +---------------+----------------------------------+
+    | Deviance      | ``var_weights``                  |
+    +---------------+----------------------------------+
+    | Pearson       | ``var_weights`` and ``n_trials`` |
+    +---------------+----------------------------------+
+    | Reponse       | ``n_trials``                     |
+    +---------------+----------------------------------+
+    | Working       | ``n_trials``                     |
+    +---------------+----------------------------------+
+
+    WARNING: Log-likelihood and deviance are not valid in models where
+    scale is equal to 1 (i.e., ``Binomial``, ``NegativeBinomial``, and
+    ``Poisson``). If variance weights are specified, then results such as
+    ``loglike`` and ``deviance`` are based on a quasi-likelihood
+    interpretation. The log-likelihood is not correctly specified in this case,
+    and statistics based on it, such AIC or likelihood ratio tests, are not
+    appropriate.
+    """
+    # Maximum number of endogenous variables when using a formula
+    _formula_max_endog = 2
+
+    def __init__(
+        self,
+        endog,
+        exog,
+        family=None,
+        offset=None,
+        exposure=None,
+        freq_weights=None,
+        var_weights=None,
+        missing="none",
+        **kwargs,
+    ):
+
+        if type(self) is GLM:
+            self._check_kwargs(kwargs, ["n_trials"])
+
+        if (family is not None) and not isinstance(
+            family.link, tuple(family.safe_links)
+        ):
+
+            warnings.warn(
+                (
+                    f"The {type(family.link).__name__} link function "
+                    "does not respect the domain of the "
+                    f"{type(family).__name__} family."
+                ),
+                DomainWarning,
+                stacklevel=2,
+            )
+
+        self._exposure_name = None
+        self._offset_name = None
+        self._freq_weights_name = None
+        self._var_weights_name = None
+
+        if exposure is not None:
+            exposure_array, self._exposure_name = _as_array_with_name(
+                exposure, "exposure"
+            )
+            exposure = np.log(exposure_array)
+        if offset is not None:  # this should probably be done upstream
+            offset, self._offset_name = _as_array_with_name(offset, "offset")
+
+        if freq_weights is not None:
+            freq_weights, self._freq_weights_name = _as_array_with_name(
+                freq_weights, "freq_weights"
+            )
+        if var_weights is not None:
+            var_weights, self._var_weights_name = _as_array_with_name(
+                var_weights, "var_weights"
+            )
+
+        self.freq_weights = freq_weights
+        self.var_weights = var_weights
+
+        super().__init__(
+            endog,
+            exog,
+            missing=missing,
+            offset=offset,
+            exposure=exposure,
+            freq_weights=freq_weights,
+            var_weights=var_weights,
+            **kwargs,
+        )
+        self._check_inputs(
+            family,
+            self.offset,
+            self.exposure,
+            self.endog,
+            self.freq_weights,
+            self.var_weights,
+        )
+        # Track whether offset/exposure were actually provided, since
+        # `self.offset`/`self.exposure` are always set (possibly to None)
+        # by the base class's generic kwargs handling.
+        self._has_offset = offset is not None
+        self._has_exposure = exposure is not None
+
+        self.nobs = self.endog.shape[0]
+
+        # things to remove_data
+        self._data_attr.extend(
+            [
+                "weights",
+                "mu",
+                "freq_weights",
+                "var_weights",
+                "iweights",
+                "_offset_exposure",
+                "n_trials",
+                "_tmp_like_exog",
+            ]
+        )
+        # register kwds for __init__, offset and exposure are added by super
+        self._init_keys.append("family")
+
+        self._setup_binomial()
+        # internal usage for recreating a model
+        if "n_trials" in kwargs:
+            self.n_trials = kwargs["n_trials"]
+
+        # Construct a combined offset/exposure term.  Note that
+        # exposure has already been logged if present.
+        offset_exposure = 0.0
+        if self._has_offset:
+            offset_exposure = self.offset
+        if self._has_exposure:
+            offset_exposure = offset_exposure + self.exposure
+        self._offset_exposure = offset_exposure
+
+        self.scaletype = None
+
+        # Scratch state used by `hessian` when fitting with a gradient-based
+        # optimizer (see `fit`/`_fit_gradient`). Initialized here so that
+        # `fit` never needs to delete them afterwards.
+        self._optim_hessian = None
+        self._tmp_like_exog = np.empty_like(self.exog, dtype=float)
+
+    def initialize(self):
+        """
+        Initialize a generalized linear model.
+        """
+        self.df_model = np.linalg.matrix_rank(self.exog) - 1
+
+        if (self.freq_weights is not None) and (
+            self.freq_weights.shape[0] == self.endog.shape[0]
+        ):
+            self.wnobs = self.freq_weights.sum()
+            self.df_resid = self.wnobs - self.df_model - 1
+        else:
+            self.wnobs = self.exog.shape[0]
+            self.df_resid = self.exog.shape[0] - self.df_model - 1
+
+    def _check_inputs(self, family, offset, exposure, endog, freq_weights, var_weights):
+
+        # Default family is Gaussian
+        if family is None:
+            family = families.Gaussian()
+        self.family = family
+
+        if exposure is not None:
+            if not isinstance(self.family.link, families.links.Log):
+                raise ValueError("exposure can only be used with the log link function")
+            elif exposure.shape[0] != endog.shape[0]:
+                raise ValueError("exposure is not the same length as endog")
+
+        if offset is not None:
+            if offset.shape[0] != endog.shape[0]:
+                raise ValueError("offset is not the same length as endog")
+
+        if freq_weights is not None:
+            if freq_weights.shape[0] != endog.shape[0]:
+                raise ValueError("freq weights not the same length as endog")
+            if len(freq_weights.shape) > 1:
+                raise ValueError("freq weights has too many dimensions")
+
+        # internal flag to store whether freq_weights were not None
+        self._has_freq_weights = self.freq_weights is not None
+        if self.freq_weights is None:
+            self.freq_weights = np.ones(endog.shape[0])
+            # TODO: check do we want to keep None as sentinel for freq_weights
+
+        if np.shape(self.freq_weights) == () and self.freq_weights > 1:
+            self.freq_weights = self.freq_weights * np.ones(endog.shape[0])
+
+        if var_weights is not None:
+            if var_weights.shape[0] != endog.shape[0]:
+                raise ValueError("var weights not the same length as endog")
+            if len(var_weights.shape) > 1:
+                raise ValueError("var weights has too many dimensions")
+
+        # internal flag to store whether var_weights were not None
+        self._has_var_weights = var_weights is not None
+        if var_weights is None:
+            self.var_weights = np.ones(endog.shape[0])
+            # TODO: check do we want to keep None as sentinel for var_weights
+        self.iweights = np.asarray(self.freq_weights * self.var_weights)
+
+    def _get_init_kwds(self):
+        # this is a temporary fixup because exposure has been transformed
+        # see #1609, copied from discrete_model.CountModel
+        kwds = super()._get_init_kwds()
+        if "exposure" in kwds and kwds["exposure"] is not None:
+            kwds["exposure"] = np.exp(kwds["exposure"])
+        return kwds
+
+    def loglike_mu(self, mu, scale=1.0):
+        """
+        Evaluate the log-likelihood for a generalized linear model.
+        """
+        scale = float_like(scale, "scale")
+        return self.family.loglike(
+            self.endog, mu, self.var_weights, self.freq_weights, scale
+        )
+
+    def loglike(self, params, scale=None):
+        """
+        Evaluate the log-likelihood for a generalized linear model.
+        """
+        scale = float_like(scale, "scale", optional=True)
+        lin_pred = np.dot(self.exog, params) + self._offset_exposure
+        expval = self.family.link.inverse(lin_pred)
+        if scale is None:
+            scale = self.estimate_scale(expval)
+        llf = self.family.loglike(
+            self.endog, expval, self.var_weights, self.freq_weights, scale
+        )
+        return llf
+
+    def score_obs(self, params, scale=None):
+        """
+        Score first derivative of the log-likelihood for each observation
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score_obs : ndarray, 2d
+            The first derivative of the log-likelihood function evaluated at
+            params for each observation.
+        """
+        scale = float_like(scale, "scale", optional=True)
+        score_factor = self.score_factor(params, scale=scale)
+        return score_factor[:, None] * self.exog
+
+    def score(self, params, scale=None):
+        """
+        Score, first derivative of the log-likelihood function
+
+        Parameters
+        ----------
+        params : ndarray
+            Parameter at which score is evaluated.
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score : ndarray, 1d
+            The first derivative of the log-likelihood function calculated as
+            the sum of `score_obs`
+        """
+        scale = float_like(scale, "scale", optional=True)
+        score_factor = self.score_factor(params, scale=scale)
+        return np.dot(score_factor, self.exog)
+
+    def score_factor(self, params, scale=None):
+        """
+        Weights for score for each observation
+
+        This can be considered as score residuals.
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score_factor : ndarray, 1d
+            A 1d weight vector used in the calculation of the score_obs.
+            The score_obs are obtained by `score_factor[:, None] * exog`
+        """
+        scale = float_like(scale, "scale", optional=True)
+        mu = self.predict(params)
+        if scale is None:
+            scale = self.estimate_scale(mu)
+
+        score_factor = (self.endog - mu) / self.family.link.deriv(mu)
+        score_factor /= self.family.variance(mu)
+        score_factor *= self.iweights * self.n_trials
+
+        if not scale == 1:
+            score_factor /= scale
+
+        return score_factor
+
+    def hessian_factor(self, params, scale=None, observed=True):
+        """
+        Weights for calculating Hessian
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which Hessian is evaluated
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+        observed : bool, optional
+            If True, then the observed Hessian is returned. If false then the
+            expected information matrix is returned.
+
+        Returns
+        -------
+        hessian_factor : ndarray, 1d
+            A 1d weight vector used in the calculation of the Hessian.
+            The hessian is obtained by `(exog.T * hessian_factor).dot(exog)`
+        """
+
+        # calculating eim_factor
+        mu = self.predict(params)
+        if scale is None:
+            scale = self.estimate_scale(mu)
+
+        eim_factor = 1 / (self.family.link.deriv(mu) ** 2 * self.family.variance(mu))
+        eim_factor *= self.iweights * self.n_trials
+
+        if not observed:
+            if not scale == 1:
+                eim_factor /= scale
+            return eim_factor
+
+        # calculating oim_factor, eim_factor is with scale=1
+
+        score_factor = self.score_factor(params, scale=1.0)
+        if eim_factor.ndim > 1 or score_factor.ndim > 1:
+            raise RuntimeError("something wrong")
+
+        tmp = self.family.variance(mu) * self.family.link.deriv2(mu)
+        tmp += self.family.variance.deriv(mu) * self.family.link.deriv(mu)
+
+        tmp = score_factor * tmp
+        # correct for duplicatee iweights in oim_factor and score_factor
+        tmp /= self.iweights * self.n_trials
+        oim_factor = eim_factor * (1 + tmp)
+
+        if tmp.ndim > 1:
+            raise RuntimeError("something wrong")
+
+        if not scale == 1:
+            oim_factor /= scale
+
+        return oim_factor
+
+    def hessian(self, params, scale=None, observed=None):
+        """
+        Hessian, second derivative of log-likelihood function
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which Hessian is evaluated
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+        observed : bool, optional
+            If True, then the observed Hessian is returned (default).
+            If False, then the expected information matrix is returned.
+
+        Returns
+        -------
+        hessian : ndarray
+            Hessian, i.e., observed information, or expected information matrix.
+        """
+        if observed is None:
+            if self._optim_hessian == "eim":
+                observed = False
+            else:
+                observed = True
+        scale = float_like(scale, "scale", optional=True)
+        tmp = self._tmp_like_exog
+
+        factor = self.hessian_factor(params, scale=scale, observed=observed)
+        np.multiply(self.exog.T, factor, out=tmp.T)
+        return -tmp.T.dot(self.exog)
+
+    def information(self, params, scale=None):
+        """
+        Fisher information matrix.
+        """
+        scale = float_like(scale, "scale", optional=True)
+        return self.hessian(params, scale=scale, observed=False)
+
+    def _derivative_exog(
+        self,
+        params,
+        exog=None,
+        transform="dydx",
+        dummy_idx=None,
+        count_idx=None,
+        offset=None,
+        exposure=None,
+    ):
+        """
+        Derivative of mean, expected endog with respect to the parameters
+        """
+        if exog is None:
+            exog = self.exog
+        if (offset is not None) or (exposure is not None):
+            raise NotImplementedError("offset and exposure not supported")
+
+        lin_pred = self.predict(
+            params, exog, which="linear", offset=offset, exposure=exposure
+        )
+
+        k_extra = getattr(self, "k_extra", 0)
+        params_exog = params if k_extra == 0 else params[:-k_extra]
+
+        margeff = self.family.link.inverse_deriv(lin_pred)[:, None] * params_exog
+        if "ex" in transform:
+            margeff *= exog
+        if "ey" in transform:
+            mean = self.family.link.inverse(lin_pred)
+            margeff /= mean[:, None]
+
+        return self._derivative_exog_helper(
+            margeff, params, exog, dummy_idx, count_idx, transform
+        )
+
+    def _derivative_exog_helper(
+        self, margeff, params, exog, dummy_idx, count_idx, transform
+    ):
+        """
+        Helper for _derivative_exog to wrap results appropriately
+        """
+        from statsmodels.discrete.discrete_margins import (
+            _get_count_effects,
+            _get_dummy_effects,
+        )
+
+        if count_idx is not None:
+            margeff = _get_count_effects(
+                margeff, exog, count_idx, transform, self, params
+            )
+        if dummy_idx is not None:
+            margeff = _get_dummy_effects(
+                margeff, exog, dummy_idx, transform, self, params
+            )
+
+        return margeff
+
+    def _derivative_predict(
+        self, params, exog=None, transform="dydx", offset=None, exposure=None
+    ):
+        """
+        Derivative of the expected endog with respect to the parameters.
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+        exog : ndarray, optional
+            Explanatory variables at which derivative are computed.
+            If None, then the estimation exog is used.
+        transform : str, optional
+            The marginal effects transformation.
+        offset, exposure : None
+            Not yet implemented.
+
+        Returns
+        -------
+        The value of the derivative of the expected endog with respect
+        to the parameter vector.
+        """
+        # core part is same as derivative_mean_params
+        # additionally handles exog and transform
+        if exog is None:
+            exog = self.exog
+        if (
+            (offset is not None)
+            or (exposure is not None)
+            or (getattr(self, "offset", None) is not None)
+        ):
+            raise NotImplementedError("offset and exposure not supported")
+
+        lin_pred = self.predict(params, exog=exog, which="linear")
+        idl = self.family.link.inverse_deriv(lin_pred)
+        dmat = exog * idl[:, None]
+        if "ey" in transform:
+            mean = self.family.link.inverse(lin_pred)
+            dmat /= mean[:, None]
+
+        return dmat
+
+    def _deriv_mean_dparams(self, params):
+        """
+        Derivative of the expected endog with respect to the parameters.
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+
+        Returns
+        -------
+        The value of the derivative of the expected endog with respect
+        to the parameter vector.
+        """
+        lin_pred = self.predict(params, which="linear")
+        idl = self.family.link.inverse_deriv(lin_pred)
+        dmat = self.exog * idl[:, None]
+        return dmat
+
+    def _deriv_score_obs_dendog(self, params, scale=None):
+        """
+        Derivative of score_obs w.r.t. endog
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+        scale : float, optional
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        derivative : ndarray, 2d
+            The derivative of the score_obs with respect to endog. This
+            can is given by `score_factor0[:, None] * exog` where
+            `score_factor0` is the score_factor without the residual.
+        """
+        scale = float_like(scale, "scale", optional=True)
+        mu = self.predict(params)
+        if scale is None:
+            scale = self.estimate_scale(mu)
+
+        score_factor = 1 / self.family.link.deriv(mu)
+        score_factor /= self.family.variance(mu)
+        score_factor *= self.iweights * self.n_trials
+
+        if not scale == 1:
+            score_factor /= scale
+
+        return score_factor[:, None] * self.exog
+
+    def score_test(
+        self, params_constrained, k_constraints=None, exog_extra=None, observed=True
+    ):
+        """
+        Score test for restrictions or for omitted variables
+
+        The covariance matrix for the score is based on the Hessian, i.e.
+        observed information matrix or optionally on the expected information
+        matrix..
+
+        Parameters
+        ----------
+        params_constrained : array_like
+            estimated parameter of the restricted model. This can be the
+            parameter estimate for the current when testing for omitted
+            variables.
+        k_constraints : int, optional
+            Number of constraints that were used in the estimation of params
+            restricted relative to the number of exog in the model.
+            This must be provided if no exog_extra are given. If exog_extra is
+            not None, then k_constraints is assumed to be zero if it is None.
+        exog_extra : array_like, optional
+            Explanatory variables that are jointly tested for inclusion in the
+            model, i.e., omitted variables.
+        observed : bool, optional
+            If True, then the observed Hessian is used in calculating the
+            covariance matrix of the score. If false then the expected
+            information matrix is used.
+
+        Returns
+        -------
+        chi2_stat : float
+            chisquare statistic for the score test
+        p-value : float
+            P-value of the score test based on the chisquare distribution.
+        df : int
+            Degrees of freedom used in the p-value calculation. This is equal
+            to the number of constraints.
+
+        Notes
+        -----
+        not yet verified for case with scale not equal to 1.
+        """
+
+        if exog_extra is None:
+            if k_constraints is None:
+                raise ValueError(
+                    "if exog_extra is None, then k_constraintsneeds to be given"
+                )
+
+            score = self.score(params_constrained)
+            hessian = self.hessian(params_constrained, observed=observed)
+
+        else:
+            # exog_extra = np.asarray(exog_extra)
+            if k_constraints is None:
+                k_constraints = 0
+
+            ex = np.column_stack((self.exog, exog_extra))
+            k_constraints += ex.shape[1] - self.exog.shape[1]
+
+            score_factor = self.score_factor(params_constrained)
+            score = (score_factor[:, None] * ex).sum(0)
+            hessian_factor = self.hessian_factor(params_constrained, observed=observed)
+            hessian = -np.dot(ex.T * hessian_factor, ex)
+
+        from scipy import stats
+
+        # TODO check sign, why minus?
+        chi2stat = -score.dot(np.linalg.solve(hessian, score[:, None]))
+        pval = stats.chi2.sf(chi2stat, k_constraints)
+        # return a stats results instance instead?  Contrast?
+        return chi2stat, pval, k_constraints
+
+    def _update_history(self, tmp_result, mu, history):
+        """
+        Helper method to update history during iterative fit.
+        """
+        history["params"].append(tmp_result.params)
+        history["deviance"].append(
+            self.family.deviance(
+                self.endog, mu, self.var_weights, self.freq_weights, self.scale
+            )
+        )
+        return history
+
+    def estimate_scale(self, mu):
+        """
+        Estimate the dispersion/scale.
+
+        Type of scale can be chosen in the fit method.
+
+        Parameters
+        ----------
+        mu : ndarray
+            mu is the mean response estimate
+
+        Returns
+        -------
+        float
+            Estimate of scale
+
+        See Also
+        --------
+        statsmodels.genmod.generalized_linear_model.GLM.fit
+
+        Notes
+        -----
+        The default scale for Binomial, Poisson and Negative Binomial
+        families is 1.  The default for the other families is Pearson's
+        Chi-Square estimate.
+        """
+        if not self.scaletype:
+            if isinstance(
+                self.family,
+                (families.Binomial, families.Poisson, families.NegativeBinomial),
+            ):
+                return 1.0
+            else:
+                return self._estimate_x2_scale(mu)
+
+        if isinstance(self.scaletype, float):
+            return np.array(self.scaletype)
+
+        if isinstance(self.scaletype, str):
+            scaletype = string_like(self.scaletype, "scaletype", options=("x2", "dev"))
+            if scaletype == "x2":
+                return self._estimate_x2_scale(mu)
+            else:  # scaletype == "dev"
+                return self.family.deviance(
+                    self.endog, mu, self.var_weights, self.freq_weights, 1.0
+                ) / (self.df_resid)
+        else:
+            raise ValueError(
+                f"Scale {self.scaletype} with type {type(self.scaletype)} not understood"
+            )
+
+    def _estimate_x2_scale(self, mu):
+        resid = np.power(self.endog - mu, 2) * self.iweights
+        return np.sum(resid / self.family.variance(mu)) / self.df_resid
+
+    def estimate_tweedie_power(self, mu, method="brentq", low=1.01, high=5.0):
+        """
+        Tweedie specific function to estimate scale and the variance parameter.
+        The variance parameter is also referred to as p, xi, or shape.
+
+        Parameters
+        ----------
+        mu : array_like
+            Fitted mean response variable
+        method : str, optional
+            Scipy optimizer used to solve the Pearson equation. Only brentq
+            currently supported. The default is 'brentq'.
+        low : float, optional
+            Low end of the bracketing interval [a,b] to be used in the search
+            for the power. Defaults to 1.01.
+        high : float, optional
+            High end of the bracketing interval [a,b] to be used in the search
+            for the power. Defaults to 5.
+
+        Returns
+        -------
+        power : float
+            The estimated shape or power.
+        """
+        if method == "brentq":
+            from scipy.optimize import brentq
+
+            def psi_p(power, mu):
+                scale = (
+                    self.iweights * (self.endog - mu) ** 2 / (mu**power)
+                ).sum() / self.df_resid
+                return (
+                    np.sum(
+                        self.iweights
+                        * ((self.endog - mu) ** 2 / (scale * (mu**power)) - 1)
+                        * np.log(mu)
+                    )
+                    / self.freq_weights.sum()
+                )
+
+            power = brentq(psi_p, low, high, args=(mu))
+        else:
+            raise NotImplementedError("Only brentq can currently be used")
+        return power
+
+    def predict(
+        self, params, exog=None, exposure=None, offset=None, which="mean"
+    ):
+        """
+        Return predicted values for a design matrix
+
+        Parameters
+        ----------
+        params : array_like
+            Parameters / coefficients of a GLM.
+        exog : array_like, optional
+            Design / exogenous data. Is exog is None, model exog is used.
+        exposure : array_like, optional
+            Exposure time values, only can be used with the log link
+            function.  See notes for details.
+        offset : array_like, optional
+            Offset values.  See notes for details.
+        which : {'mean', 'linear', 'var_unscaled'}, optional
+            Statistic to predict. Default is 'mean'.
+
+            - 'mean' returns the conditional expectation of endog E(y | x),
+              i.e., inverse of the model's link function of linear predictor.
+            - 'linear' returns the linear predictor of the mean function.
+            - 'var_unscaled' variance of endog implied by the likelihood model.
+              This does not include scale or var_weights.
+
+        Returns
+        -------
+        ndarray
+            An array of fitted values
+
+        Notes
+        -----
+        Any `exposure` and `offset` provided here take precedence over
+        the `exposure` and `offset` used in the model fit.  If `exog`
+        is passed as an argument here, then any `exposure` and
+        `offset` values in the fit will be ignored.
+
+        Exposure values must be strictly positive.
+        """
+        # Use fit offset if appropriate
+        if offset is None and exog is None and self._has_offset:
+            offset = self.offset
+        elif offset is None:
+            offset = 0.0
+
+        if exposure is not None and not isinstance(
+            self.family.link, families.links.Log
+        ):
+            raise ValueError("exposure can only be used with the log link function")
+
+        # Use fit exposure if appropriate
+        if exposure is None and exog is None and self._has_exposure:
+            # Already logged
+            exposure = self.exposure
+        elif exposure is None:
+            exposure = 0.0
+        else:
+            exposure = np.log(np.asarray(exposure))
+
+        which = string_like(
+            which, "which", options=("mean", "linear", "var_unscaled"), lower=False
+        )
+        if exog is None:
+            exog = self.exog
+
+        linpred = np.dot(exog, params) + offset + exposure
+
+        if which == "mean":
+            return self.family.fitted(linpred)
+        elif which == "linear":
+            return linpred
+        else:  # which == "var_unscaled"
+            mean = self.family.fitted(linpred)
+            var_ = self.family.variance(mean)
+            return var_
+
+    def get_distribution(
+        self,
+        params,
+        scale=None,
+        exog=None,
+        exposure=None,
+        offset=None,
+        var_weights=1.0,
+        n_trials=1.0,
+    ):
+        """
+        Return a instance of the predictive distribution.
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters.
+        scale : scalar, optional
+            The scale parameter.
+        exog : array_like, optional
+            The predictor variable matrix.
+        offset : array_like, optional
+            Offset variable for predicted mean.
+        exposure : array_like, optional
+            Log(exposure) will be added to the linear prediction.
+        var_weights : array_like, optional
+            1d array of variance (analytic) weights. The default is 1.
+        n_trials : int, optional
+            Number of trials for the binomial distribution. The default is 1
+            which corresponds to a Bernoulli random variable.
+
+        Returns
+        -------
+        gen
+            Instance of a scipy frozen distribution based on estimated
+            parameters.
+            Use the ``rvs`` method to generate random values.
+
+        Notes
+        -----
+        Due to the behavior of ``scipy.stats.distributions objects``, the
+        returned random number generator must be called with ``gen.rvs(n)``
+        where ``n`` is the number of observations in the data set used
+        to fit the model.  If any other value is used for ``n``, misleading
+        results will be produced.
+        """
+        scale = float_like(scale, "scale", optional=True)
+        # use scale=1, independent of QMLE scale for discrete
+        if isinstance(
+            self.family,
+            (families.Binomial, families.Poisson, families.NegativeBinomial),
+        ):
+            scale = 1.0
+
+        mu = self.predict(params, exog, exposure, offset, which="mean")
+
+        kwds = {}
+        if np.any(n_trials != 1) and isinstance(self.family, families.Binomial):
+
+            kwds["n_trials"] = n_trials
+
+        distr = self.family.get_distribution(mu, scale, var_weights=var_weights, **kwds)
+        return distr
+
+    def _setup_binomial(self):
+        # this checks what kind of data is given for Binomial.
+        # family will need a reference to endog if this is to be removed from
+        # preprocessing
+        self.n_trials = np.ones(self.endog.shape[0])  # For binomial
+        if isinstance(self.family, families.Binomial):
+            tmp = self.family.initialize(self.endog, self.freq_weights)
+            self.endog = tmp[0]
+            self.n_trials = tmp[1]
+            self._init_keys.append("n_trials")
+
+    def fit(
+        self,
+        start_params=None,
+        maxiter=100,
+        method="IRLS",
+        tol=1e-8,
+        scale=None,
+        cov_type="nonrobust",
+        cov_kwds=None,
+        use_t=None,
+        full_output=True,
+        disp=False,
+        max_start_irls=3,
+        **kwargs,
+    ):
+        """
+        Fits a generalized linear model for a given family.
+
+        Parameters
+        ----------
+        start_params : array_like, optional
+            Initial guess of the solution for the log-likelihood maximization.
+            The default is family-specific and is given by the
+            ``family.starting_mu(endog)``. If start_params is given then the
+            initial mean will be calculated as ``np.dot(exog, start_params)``.
+        maxiter : int, optional
+            Default is 100.
+        method : str, optional
+            Default is 'IRLS' for iteratively reweighted least squares.
+            Otherwise gradient optimization is used.
+        tol : float, optional
+            Convergence tolerance.  Default is 1e-8.
+        scale : {'X2', 'dev'} or float, optional
+            `scale` can be 'X2', 'dev', or a float
+            The default value is None, which uses `X2` for Gamma, Gaussian,
+            and Inverse Gaussian.
+            `X2` is Pearson's chi-square divided by `df_resid`.
+            The default is 1 for the Binomial and Poisson families.
+            `dev` is the deviance divided by df_resid
+        cov_type : str, optional
+            The type of parameter estimate covariance matrix to compute.
+        cov_kwds : dict-like, optional
+            Extra arguments for calculating the covariance of the parameter
+            estimates.
+        use_t : bool, optional
+            If True, the Student t-distribution is used for inference.
+        full_output : bool, optional
+            Set to True to have all available output in the Results object's
+            mle_retvals attribute. The output is dependent on the solver.
+            See LikelihoodModelResults notes section for more information.
+            Not used if method is IRLS.
+        disp : bool, optional
+            Set to True to print convergence messages.  Not used if method is
+            IRLS.
+        max_start_irls : int, optional
+            The number of IRLS iterations used to obtain starting
+            values for gradient optimization.  Only relevant if
+            `method` is set to something other than 'IRLS'.
+        atol : float, optional
+            (available with IRLS fits) The absolute tolerance criterion that
+            must be satisfied. Defaults to ``tol``. Convergence is attained
+            when: :math:`rtol * prior + atol > abs(current - prior)`
+        rtol : float, optional
+            (available with IRLS fits) The relative tolerance criterion that
+            must be satisfied. Defaults to 0 which means ``rtol`` is not used.
+            Convergence is attained when:
+            :math:`rtol * prior + atol > abs(current - prior)`
+        tol_criterion : str, optional
+            (available with IRLS fits) Defaults to ``'deviance'``. Can
+            optionally be ``'params'``.
+        wls_method : str, optional
+            (available with IRLS fits) options are 'lstsq', 'pinv' and 'qr'
+            specifies which linear algebra function to use for the irls
+            optimization. Default is `lstsq` which uses the same underlying
+            svd based approach as 'pinv', but is faster during iterations.
+            'lstsq' and 'pinv' regularize the estimate in singular and
+            near-singular cases by truncating small singular values based
+            on `rcond` of the respective numpy.linalg function. 'qr' is
+            only valid for cases that are not singular nor near-singular.
+        optim_hessian : {'eim', 'oim'}, optional
+            (available with scipy optimizer fits) When 'oim'--the default--the
+            observed Hessian is used in fitting. 'eim' is the expected Hessian.
+            This may provide more stable fits, but adds assumption that the
+            Hessian is correctly specified.
+
+        Notes
+        -----
+        If method is 'IRLS', then an additional keyword 'attach_wls' is
+        available. This is currently for internal use only and might change
+        in future versions. If attach_wls' is true, then the final WLS
+        instance of the IRLS iteration is attached to the results instance
+        as `results_wls` attribute.
+        """
+        if isinstance(scale, str):
+            scale = string_like(scale, "scale", options=("x2", "dev"))
+        elif scale is not None:
+            # GH-6627
+            try:
+                scale = float(scale)
+            except Exception as exc:
+                raise type(exc)(
+                    "scale must be a float if given and not a string."
+                ) from exc
+        self.scaletype = scale
+
+        if method.lower() == "irls":
+            if cov_type.lower() == "eim":
+                cov_type = "nonrobust"
+            return self._fit_irls(
+                start_params=start_params,
+                maxiter=maxiter,
+                tol=tol,
+                scale=scale,
+                cov_type=cov_type,
+                cov_kwds=cov_kwds,
+                use_t=use_t,
+                **kwargs,
+            )
+        else:
+            self._optim_hessian = kwargs.get("optim_hessian")
+            if self._optim_hessian is not None:
+                del kwargs["optim_hessian"]
+            self._tmp_like_exog = np.empty_like(self.exog, dtype=float)
+            fit_ = self._fit_gradient(
+                start_params=start_params,
+                method=method,
+                maxiter=maxiter,
+                tol=tol,
+                scale=scale,
+                full_output=full_output,
+                disp=disp,
+                cov_type=cov_type,
+                cov_kwds=cov_kwds,
+                use_t=use_t,
+                max_start_irls=max_start_irls,
+                **kwargs,
+            )
+            # NOTE: `_optim_hessian`/`_tmp_like_exog` are reset (not deleted)
+            # to their __init__ defaults so `hessian` behaves consistently
+            # for anyone calling it directly after this fit() call.
+            self._optim_hessian = None
+            self._tmp_like_exog = np.empty_like(self.exog, dtype=float)
+            return fit_
+
+    def _fit_gradient(
+        self,
+        start_params=None,
+        method="newton",
+        maxiter=100,
+        tol=1e-8,
+        full_output=True,
+        disp=True,
+        scale=None,
+        cov_type="nonrobust",
+        cov_kwds=None,
+        use_t=None,
+        max_start_irls=3,
+        **kwargs,
+    ):
+        """
+        Fits a generalized linear model for a given family iteratively
+        using the scipy gradient optimizers.
+        """
+
+        # fix scale during optimization, see #4616
+        scaletype = self.scaletype
+        self.scaletype = 1.0
+
+        if (max_start_irls > 0) and (start_params is None):
+            irls_rslt = self._fit_irls(
+                start_params=start_params,
+                maxiter=max_start_irls,
+                tol=tol,
+                scale=1.0,
+                cov_type="nonrobust",
+                cov_kwds=None,
+                use_t=None,
+                **kwargs,
+            )
+            start_params = irls_rslt.params
+            del irls_rslt
+        rslt = super().fit(
+            start_params=start_params,
+            maxiter=maxiter,
+            full_output=full_output,
+            method=method,
+            disp=disp,
+            **kwargs,
+        )
+
+        # reset scaletype to original
+        self.scaletype = scaletype
+
+        mu = self.predict(rslt.params)
+        scale = self.estimate_scale(mu)
+
+        if cov_type.lower() == "eim":
+            oim = False
+            cov_type = "nonrobust"
+        else:
+            oim = True
+
+        try:
+            hessian = -self.hessian(rslt.params, observed=oim)
+            cov_p = np.linalg.inv(hessian) / scale
+        except LinAlgError:
+            warnings.warn(
+                "Inverting hessian failed, no bse or cov_params available",
+                HessianInversionWarning,
+                stacklevel=2,
+            )
+            if rslt.normalized_cov_params is not None:
+                cov_p = rslt.normalized_cov_params / scale
+            else:
+                cov_p = None
+
+        results_class = getattr(self, "_results_class", GLMResults)
+        results_class_wrapper = getattr(
+            self, "_results_class_wrapper", GLMResultsWrapper
+        )
+        glm_results = results_class(
+            self,
+            rslt.params,
+            cov_p,
+            scale,
+            cov_type=cov_type,
+            cov_kwds=cov_kwds,
+            use_t=use_t,
+        )
+
+        # TODO: iteration count is not always available
+        history = {"iteration": 0}
+        if full_output:
+            glm_results.mle_retvals = rslt.mle_retvals
+            if "iterations" in rslt.mle_retvals:
+                history["iteration"] = rslt.mle_retvals["iterations"]
+        glm_results.method = method
+        glm_results.fit_history = history
+
+        return results_class_wrapper(glm_results)
+
+    def _fit_irls(
+        self,
+        start_params=None,
+        maxiter=100,
+        tol=1e-8,
+        scale=None,
+        cov_type="nonrobust",
+        cov_kwds=None,
+        use_t=None,
+        **kwargs,
+    ):
+        """
+        Fits a generalized linear model for a given family using
+        iteratively reweighted least squares (IRLS).
+        """
+        attach_wls = kwargs.pop("attach_wls", False)
+        atol = kwargs.get("atol")
+        rtol = kwargs.get("rtol", 0.0)
+        tol_criterion = kwargs.get("tol_criterion", "deviance")
+        wls_method = kwargs.get("wls_method", "lstsq")
+        atol = tol if atol is None else atol
+
+        endog = self.endog
+        wlsexog = self.exog
+        if start_params is None:
+            start_params = np.zeros(self.exog.shape[1])
+            mu = self.family.starting_mu(self.endog)
+            lin_pred = self.family.predict(mu)
+        else:
+            lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
+            mu = self.family.fitted(lin_pred)
+        self.scale = self.estimate_scale(mu)
+        dev = self.family.deviance(
+            self.endog, mu, self.var_weights, self.freq_weights, self.scale
+        )
+        if np.isnan(dev):
+            raise ValueError(
+                "The first guess on the deviance function "
+                "returned a nan.  This could be a boundary "
+                " problem and should be reported."
+            )
+
+        # first guess on the deviance is assumed to be scaled by 1.
+        # params are none to start, so they line up with the deviance
+        history = dict(params=[np.inf, start_params], deviance=[np.inf, dev])
+        converged = False
+        criterion = history[tol_criterion]
+        # This special case is used to get the likelihood for a specific
+        # params vector.
+        if maxiter == 0:
+            mu = self.family.fitted(lin_pred)
+            self.scale = self.estimate_scale(mu)
+            wls_results = lm.RegressionResults(self, start_params, None)
+            iteration = 0
+        for iteration in range(maxiter):
+            self.weights = self.iweights * self.n_trials * self.family.weights(mu)
+            wlsendog = (
+                lin_pred
+                + self.family.link.deriv(mu) * (self.endog - mu)
+                - self._offset_exposure
+            )
+            wls_mod = reg_tools._MinimalWLS(
+                wlsendog, wlsexog, self.weights, check_endog=True, check_weights=True
+            )
+            wls_results = wls_mod.fit(method=wls_method)
+            lin_pred = np.dot(self.exog, wls_results.params)
+            lin_pred += self._offset_exposure
+            mu = self.family.fitted(lin_pred)
+            history = self._update_history(wls_results, mu, history)
+            self.scale = self.estimate_scale(mu)
+            if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0):
+                msg = (
+                    "Perfect separation or prediction detected, "
+                    "parameter may not be identified"
+                )
+                warnings.warn(msg, category=PerfectSeparationWarning, stacklevel=2)
+            converged = _check_convergence(criterion, iteration + 1, atol, rtol)
+            if converged:
+                break
+        self.mu = mu
+
+        if maxiter > 0:  # Only if iterative used
+            wls_method2 = "pinv" if wls_method == "lstsq" else wls_method
+            wls_model = lm.WLS(wlsendog, wlsexog, self.weights)
+            wls_results = wls_model.fit(method=wls_method2)
+
+        glm_results = GLMResults(
+            self,
+            wls_results.params,
+            wls_results.normalized_cov_params,
+            self.scale,
+            cov_type=cov_type,
+            cov_kwds=cov_kwds,
+            use_t=use_t,
+        )
+
+        glm_results.method = "IRLS"
+        glm_results.mle_settings = {}
+        glm_results.mle_settings["wls_method"] = wls_method
+        glm_results.mle_settings["optimizer"] = glm_results.method
+        if (maxiter > 0) and (attach_wls is True):
+            glm_results.results_wls = wls_results
+        history["iteration"] = iteration + 1
+        glm_results.fit_history = history
+        glm_results.converged = converged
+        return GLMResultsWrapper(glm_results)
+
+    def fit_regularized(
+        self,
+        method="elastic_net",
+        alpha=0.0,
+        start_params=None,
+        refit=False,
+        opt_method="bfgs",
+        **kwargs,
+    ):
+        r"""
+        Return a regularized fit to a linear regression model.
+
+        Parameters
+        ----------
+        method : {'elastic_net', 'l1_slsqp'}, optional
+            'elastic_net' uses coordinate descent and supports the full
+            elastic net penalty.  'l1_slsqp' solves a smooth constrained
+            reformulation of the L1 problem with slsqp, an interior
+            point style method, and only supports the lasso penalty
+            (L1_wt must be 1).
+        alpha : scalar or array_like, optional
+            The penalty weight.  If a scalar, the same penalty weight
+            applies to all variables in the model.  If a vector, it
+            must have the same length as `params`, and contains a
+            penalty weight for each coefficient.
+        start_params : array_like, optional
+            Starting values for `params`.
+        refit : bool, optional
+            If True, the model is refit using only the variables that
+            have non-zero coefficients in the regularized fit.  The
+            refitted model is not regularized.
+        opt_method : str, optional
+            The method used for numerical optimization.
+        **kwargs
+            Additional keyword arguments used when fitting the model.
+
+        Returns
+        -------
+        GLMResults
+            An array or a GLMResults object, same type returned by `fit`.
+
+        Notes
+        -----
+        The penalty is the ``elastic net`` penalty, which is a
+        combination of L1 and L2 penalties.
+
+        The function that is minimized is:
+
+        .. math::
+
+            -loglike/n + alpha*((1-L1\_wt)*|params|_2^2/2 + L1\_wt*|params|_1)
+
+        where :math:`|*|_1` and :math:`|*|_2` are the L1 and L2 norms.
+
+        Post-estimation results are based on the same data used to
+        select variables, hence may be subject to overfitting biases.
+
+        The elastic_net method uses the following keyword arguments:
+
+        maxiter : int
+            Maximum number of iterations
+        L1_wt  : float
+            Must be in [0, 1].  The L1 penalty has weight L1_wt and the
+            L2 penalty has weight 1 - L1_wt.
+        cnvrg_tol : float
+            Convergence threshold for maximum parameter change after
+            one sweep through all coefficients.
+        zero_tol : float
+            Coefficients below this threshold are treated as zero.
+
+        The l1_slsqp method uses the following keyword arguments:
+
+        maxiter : int
+            Maximum number of iterations (default 1000).
+        trim_mode : {'auto', 'size', 'off'}
+            If not 'off', trim (set to zero) parameters that would have
+            been zero if the solver reached the theoretical minimum.
+            If 'auto', trim params using the theoretical optimality
+            conditions.  If 'size', trim params if they have very small
+            absolute value.
+        size_trim_tol : float
+            Tolerance used when trim_mode is 'size'.
+        auto_trim_tol : float
+            Tolerance used when trim_mode is 'auto'.
+        qc_tol : float
+            Print warning and do not allow auto trim when the
+            optimality conditions are violated by this much.
+        qc_verbose : bool
+            If True, print out a full QC report upon failure.
+        acc : float
+            Requested accuracy as used by slsqp (default 1e-10).
+        """
+
+        method = string_like(
+            method, "method", options=("elastic_net", "l1_slsqp"), lower=False
+        )
+
+        if method == "elastic_net" and kwargs.get("L1_wt", 1) == 0:
+            return self._fit_ridge(alpha, start_params, opt_method)
+
+        from statsmodels.base.elastic_net import fit_elasticnet
+
+        if method == "elastic_net":
+            defaults = {
+                "maxiter": 50,
+                "L1_wt": 1,
+                "cnvrg_tol": 1e-10,
+                "zero_tol": 1e-10,
+            }
+        else:
+            # l1_slsqp uses an interior point style method, in contrast
+            # to the coordinate descent used by elastic_net.
+            defaults = {"maxiter": 1000}
+
+        defaults.update(kwargs)
+
+        llkw = kwargs.get("loglike_kwds", {})
+        sckw = kwargs.get("score_kwds", {})
+        hekw = kwargs.get("hess_kwds", {})
+        llkw["scale"] = 1
+        sckw["scale"] = 1
+        hekw["scale"] = 1
+        defaults["loglike_kwds"] = llkw
+        defaults["score_kwds"] = sckw
+        defaults["hess_kwds"] = hekw
+
+        result = fit_elasticnet(
+            self,
+            method=method,
+            alpha=alpha,
+            start_params=start_params,
+            refit=refit,
+            **defaults,
+        )
+
+        self.mu = self.predict(result.params)
+        self.scale = self.estimate_scale(self.mu)
+
+        if not result.converged:
+            warnings.warn(
+                "Regularized fitting did not converge",
+                ConvergenceWarning,
+                stacklevel=2,
+            )
+
+        return result
+
+    def _fit_ridge(self, alpha, start_params, method):
+
+        if start_params is None:
+            start_params = np.zeros(self.exog.shape[1])
+
+        def fun(x):
+            return -(self.loglike(x) / self.nobs - np.sum(alpha * x**2) / 2)
+
+        def grad(x):
+            return -(self.score(x) / self.nobs - alpha * x)
+
+        from scipy.optimize import minimize
+
+        from statsmodels.base.elastic_net import (
+            RegularizedResults,
+            RegularizedResultsWrapper,
+        )
+
+        mr = minimize(fun, start_params, jac=grad, method=method)
+        params = mr.x
+
+        if not mr.success:
+            ngrad = np.sqrt(np.sum(mr.jac**2))
+            msg = f"GLM ridge optimization may have failed, |grad|={ngrad:f}"
+            warnings.warn(msg, ConvergenceWarning, stacklevel=2)
+
+        results = RegularizedResults(self, params)
+        results = RegularizedResultsWrapper(results)
+
+        return results
+
+    def fit_constrained(self, constraints, start_params=None, **fit_kwds):
+        """
+        Fit the model subject to linear equality constraints
+
+        The constraints are of the form   `R params = q`
+        where R is the constraint_matrix and q is the vector of
+        constraint_values.
+
+        The estimation creates a new model with transformed design matrix,
+        exog, and converts the results back to the original parameterization.
+
+
+        Parameters
+        ----------
+        constraints : formula expression or tuple
+            If it is a tuple, then the constraint needs to be given by two
+            arrays (constraint_matrix, constraint_value), i.e., (R, q).
+            Otherwise, the constraints can be given as strings or list of
+            strings.
+            see t_test for details
+        start_params : array_like, optional
+            starting values for the optimization. `start_params` needs to be
+            given in the original parameter space and are internally
+            transformed.
+        **fit_kwds : keyword arguments
+            fit_kwds are used in the optimization of the transformed model.
+
+        Returns
+        -------
+        results : Results instance
+        """
+
+        from statsmodels.base._constraints import (
+            LinearConstraints,
+            fit_constrained,
+        )
+        from statsmodels.formula._manager import FormulaManager
+
+        # same pattern as in base.LikelihoodModel.t_test
+        lc = FormulaManager().get_linear_constraints(constraints, self.exog_names)
+        R, q = lc.constraint_matrix, lc.constraint_values
+
+        # TODO: add start_params option, need access to tranformation
+        #       fit_constrained needs to do the transformation
+        params, cov, res_constr = fit_constrained(
+            self, R, q, start_params=start_params, fit_kwds=fit_kwds
+        )
+        # create dummy results Instance, TODO: wire up properly
+        res = self.fit(start_params=params, maxiter=0)  # we get a wrapper back
+        res._results.params = params
+        res._results.cov_params_default = cov
+        cov_type = fit_kwds.get("cov_type", "nonrobust")
+        if cov_type != "nonrobust":
+            res._results.normalized_cov_params = cov / res_constr.scale
+        else:
+            res._results.normalized_cov_params = None
+        res._results.scale = res_constr.scale
+        k_constr = len(q)
+        res._results.df_resid += k_constr
+        res._results.df_model -= k_constr
+        res._results.constraints = LinearConstraints.from_formula_parser(lc)
+        res._results.k_constr = k_constr
+        res._results.results_constrained = res_constr
+        return res
+
+    @property
+    def offset_name(self):
+        """
+        Name of the offset variable if available. If offset is not a Series,
+        defaults to 'offset'.
+        """
+        return self._offset_name
+
+    @property
+    def exposure_name(self):
+        """
+        Name of the exposure variable if available. If exposure is not a Series,
+        defaults to 'exposure'.
+        """
+        return self._exposure_name
+
+    @property
+    def freq_weights_name(self):
+        """
+        Name of the freq weights variable if available. If freq_weights is not a
+        Series, defaults to 'freq_weights'.
+        """
+        return self._freq_weights_name
+
+    @property
+    def var_weights_name(self):
+        """
+        Name of var weights variable if available. If var_weights is not a Series,
+        defaults to 'var_weights'.
+
+        """
+        return self._var_weights_name
+
+
+get_prediction_doc = Docstring(pred.get_prediction_glm.__doc__)
+get_prediction_doc.remove_parameters("pred_kwds")
+
+
+class GLMResults(base.LikelihoodModelResults):
+    """
+    Class to contain GLM results.
+
+    GLMResults inherits from statsmodels.LikelihoodModelResults
+
+    Attributes
+    ----------
+    df_model : float
+        See GLM.df_model
+    df_resid : float
+        See GLM.df_resid
+    fit_history : dict
+        Contains information about the iterations. Its keys are `iteration`,
+        `deviance` and `params`.
+    model : class instance
+        Pointer to GLM model instance that called fit.
+    nobs : float
+        The number of observations n.
+    normalized_cov_params : ndarray
+        See GLM docstring
+    params : ndarray
+        The coefficients of the fitted model.  Note that interpretation
+        of the coefficients often depends on the distribution family and the
+        data.
+    pvalues : ndarray
+        The two-tailed p-values for the parameters.
+    scale : float
+        The estimate of the scale / dispersion for the model fit.
+        See GLM.fit and GLM.estimate_scale for more information.
+    bse : ndarray
+        The standard errors of the fitted GLM.
+
+    See Also
+    --------
+    statsmodels.base.model.LikelihoodModelResults
+    """
+
+    def __init__(
+        self,
+        model,
+        params,
+        normalized_cov_params,
+        scale,
+        cov_type="nonrobust",
+        cov_kwds=None,
+        use_t=None,
+    ):
+        super().__init__(
+            model, params, normalized_cov_params=normalized_cov_params, scale=scale
+        )
+        self.family = model.family
+        self._endog = model.endog
+        self.nobs = model.endog.shape[0]
+        self._freq_weights = model.freq_weights
+        self._var_weights = model.var_weights
+        self._iweights = model.iweights
+        if isinstance(self.family, families.Binomial):
+            self._n_trials = self.model.n_trials
+        else:
+            self._n_trials = 1
+        self.df_resid = model.df_resid
+        self.df_model = model.df_model
+        self._cache = {}
+        # are these intermediate results needed or can we just
+        # call the model's attributes?
+
+        # for remove data and pickle without large arrays
+        self._data_attr.extend(
+            ["results_constrained", "_freq_weights", "_var_weights", "_iweights"]
+        )
+        self._data_in_cache.extend(["null", "mu"])
+        self._data_attr_model = getattr(self, "_data_attr_model", [])
+        self._data_attr_model.append("mu")
+
+        # robust covariance
+        from statsmodels.base.covtype import get_robustcov_results
+
+        if use_t is None:
+            self.use_t = False  # TODO: class default
+        else:
+            self.use_t = use_t
+
+        # temporary warning
+        ct = (cov_type == "nonrobust") or (cov_type.upper().startswith("HC"))
+        if self.model._has_freq_weights and not ct:
+
+            from statsmodels.tools.sm_exceptions import SpecificationWarning
+
+            warnings.warn(
+                "cov_type not fully supported with freq_weights",
+                SpecificationWarning,
+                stacklevel=2,
+            )
+
+        if self.model._has_var_weights and not ct:
+
+            from statsmodels.tools.sm_exceptions import SpecificationWarning
+
+            warnings.warn(
+                "cov_type not fully supported with var_weights",
+                SpecificationWarning,
+                stacklevel=2,
+            )
+
+        if cov_type == "nonrobust":
+            self.cov_type = "nonrobust"
+            self.cov_kwds = {
+                "description": "Standard Errors assume that the covariance matrix of "
+                "the errors is correctly specified."
+            }
+
+        else:
+            if cov_kwds is None:
+                cov_kwds = {}
+            get_robustcov_results(
+                self, cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds
+            )
+
+    @cached_data
+    def resid_response(self):
+        """
+        Response residuals.  The response residuals are defined as
+        `endog` - `fittedvalues`
+        """
+        return self._n_trials * (self._endog - self.mu)
+
+    @cached_data
+    def resid_pearson(self):
+        """
+        Pearson residuals.  The Pearson residuals are defined as
+        (`endog` - `mu`)/sqrt(VAR(`mu`)) where VAR is the distribution
+        specific variance function.  See sm.families.family and
+        sm.families.varfuncs for more information.
+        """
+        return (
+            np.sqrt(self._n_trials)
+            * (self._endog - self.mu)
+            * np.sqrt(self._var_weights)
+            / np.sqrt(self.family.variance(self.mu))
+        )
+
+    @cached_data
+    def resid_working(self):
+        """
+        Working residuals.  The working residuals are defined as
+        `resid_response`/link'(`mu`).  See sm.family.links for the
+        derivatives of the link functions.  They are defined analytically.
+        """
+        # Isn't self.resid_response is already adjusted by _n_trials?
+        val = self.resid_response * self.family.link.deriv(self.mu)
+        val *= self._n_trials
+        return val
+
+    @cached_data
+    def resid_anscombe(self):
+        """
+        Anscombe residuals.  See sm.families.family for distribution-
+        specific Anscombe residuals. Currently, the unscaled residuals are
+        provided. In a future version, the scaled residuals will be provided.
+        """
+        return self.resid_anscombe_scaled
+
+    @cached_data
+    def resid_anscombe_scaled(self):
+        """
+        Scaled Anscombe residuals.  See sm.families.family for
+        distribution-specific Anscombe residuals.
+        """
+        return self.family.resid_anscombe(
+            self._endog,
+            self.fittedvalues,
+            var_weights=self._var_weights,
+            scale=self.scale,
+        )
+
+    @cached_data
+    def resid_anscombe_unscaled(self):
+        """
+        Unscaled Anscombe residuals.  See sm.families.family for
+        distribution-specific Anscombe residuals.
+        """
+        return self.family.resid_anscombe(
+            self._endog, self.fittedvalues, var_weights=self._var_weights, scale=1.0
+        )
+
+    @cached_data
+    def resid_deviance(self):
+        """
+        Deviance residuals.  See sm.families.family for distribution-
+        specific deviance residuals.
+        """
+        dev = self.family.resid_dev(
+            self._endog, self.fittedvalues, var_weights=self._var_weights, scale=1.0
+        )
+        return dev
+
+    @cached_value
+    def pearson_chi2(self):
+        """
+        Pearson's Chi-Squared statistic is defined as the sum of the squares
+        of the Pearson residuals.
+        """
+        chisq = (self._endog - self.mu) ** 2 / self.family.variance(self.mu)
+        chisq *= self._iweights * self._n_trials
+        chisqsum = np.sum(chisq)
+        return chisqsum
+
+    @cached_data
+    def fittedvalues(self):
+        """
+        The estimated mean response.
+
+        This is the value of the inverse of the link function at
+        lin_pred, where lin_pred is the linear predicted value
+        obtained by multiplying the design matrix by the coefficient
+        vector.
+        """
+        return self.mu
+
+    @cached_data
+    def mu(self):
+        """
+        See GLM docstring.
+        """
+        return self.model.predict(self.params)
+
+    @cache_readonly
+    def null(self):
+        """
+        Fitted values of the null model
+        """
+        endog = self._endog
+        model = self.model
+        exog = np.ones((len(endog), 1))
+
+        kwargs = model._get_init_kwds().copy()
+        kwargs.pop("family")
+
+        for key in getattr(model, "_null_drop_keys", []):
+            del kwargs[key]
+        start_params = np.atleast_1d(self.family.link(endog.mean()))
+        oe = self.model._offset_exposure
+        if not (np.size(oe) == 1 and oe == 0):
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", DomainWarning)
+                mod = GLM(endog, exog, family=self.family, **kwargs)
+                fitted = mod.fit(start_params=start_params).fittedvalues
+        else:
+            # correct if fitted is identical across observations
+            wls_model = lm.WLS(endog, exog, weights=self._iweights * self._n_trials)
+            fitted = wls_model.fit().fittedvalues
+
+        return fitted
+
+    @cache_readonly
+    def deviance(self):
+        """
+        See sm.families.family for the distribution-specific deviance
+        functions.
+        """
+        return self.family.deviance(
+            self._endog, self.mu, self._var_weights, self._freq_weights
+        )
+
+    @cache_readonly
+    def null_deviance(self):
+        """
+        The value of the deviance function for the model fit with a constant
+        as the only regressor
+        """
+        return self.family.deviance(
+            self._endog, self.null, self._var_weights, self._freq_weights
+        )
+
+    @cache_readonly
+    def llnull(self):
+        """
+        Log-likelihood of the model fit with a constant as the only regressor
+        """
+        return self.family.loglike(
+            self._endog,
+            self.null,
+            var_weights=self._var_weights,
+            freq_weights=self._freq_weights,
+            scale=self.scale,
+        )
+
+    def llf_scaled(self, scale=None):
+        """
+        Return the log-likelihood at the given scale, using the
+        estimated scale if the provided scale is None.  In the Gaussian
+        case with linear link, the concentrated log-likelihood is
+        returned.
+        """
+
+        _modelfamily = self.family
+        if scale is None:
+            if (
+                isinstance(self.family, families.Gaussian)
+                and isinstance(self.family.link, families.links.Power)
+                and (self.family.link.power == 1.0)
+            ):
+                # Scale for the concentrated Gaussian log likelihood
+                # (profile log likelihood with the scale parameter
+                # profiled out).
+                scale = (np.power(self._endog - self.mu, 2) * self._iweights).sum()
+                scale /= self.model.wnobs
+            else:
+                scale = self.scale
+        val = _modelfamily.loglike(
+            self._endog,
+            self.mu,
+            var_weights=self._var_weights,
+            freq_weights=self._freq_weights,
+            scale=scale,
+        )
+        return val
+
+    @cached_value
+    def llf(self):
+        """
+        Value of the log-likelihood function evaluated at params.
+        See sm.families.family for distribution-specific
+        log-likelihoods.  The result uses the concentrated
+        log-likelihood if the family is Gaussian and the link is linear,
+        otherwise it uses the non-concentrated log-likelihood evaluated
+        at the estimated scale.
+        """
+        return self._summary_cache("llf", self.llf_scaled)
+
+    def pseudo_rsquared(self, kind="cs"):
+        """
+        Pseudo R-squared
+
+        Cox-Snell likelihood ratio pseudo R-squared is valid for both discrete
+        and continuous data. McFadden's pseudo R-squared is only valid for
+        discrete data.
+
+        Cox & Snell's pseudo-R-squared:  1 - exp((llnull - llf)*(2/nobs))
+
+        McFadden's pseudo-R-squared: 1 - (llf / llnull)
+
+        Parameters
+        ----------
+        kind : {'cs', 'mcf'}, optional
+            Type of pseudo R-square to return
+
+        Returns
+        -------
+        float
+            Pseudo R-squared
+        """
+        kind = kind.lower()
+        if kind.startswith("mcf"):
+            prsq = 1 - self.llf / self.llnull
+        elif kind.startswith("cox") or kind in ["cs", "lr"]:
+            prsq = 1 - np.exp((self.llnull - self.llf) * (2 / self.nobs))
+        else:
+            raise ValueError("only McFadden and Cox-Snell are available")
+        return prsq
+
+    @cached_value
+    def aic(self):
+        """
+        Akaike Information Criterion
+        -2 * `llf` + 2 * (`df_model` + 1)
+        """
+        return self.info_criteria("aic")
+
+    @property
+    def bic(self):
+        """
+        Bayes Information Criterion
+
+        Based on the log-likelihood,
+        -2 * `llf` + (`df_model` + 1)*log(n)
+
+        See Also
+        --------
+        bic_llf
+        bic_deviance
+        """
+        return self.bic_llf
+
+    @cached_value
+    def bic_deviance(self):
+        """
+        Bayes Information Criterion
+
+        Based on the deviance,
+        `deviance` - `df_resid` * log(`nobs`)
+        """
+        return self.deviance - (self.model.wnobs - self.df_model - 1) * np.log(
+            self.model.wnobs
+        )
+
+    @cached_value
+    def bic_llf(self):
+        """
+        Bayes Information Criterion
+
+        Based on the log-likelihood,
+        -2 * `llf` + log(n) * (`df_model` + 1)
+        """
+        return self.info_criteria("bic")
+
+    def info_criteria(self, crit, scale=None, dk_params=0):
+        """
+        Return an information criterion for the model
+
+        Parameters
+        ----------
+        crit : str
+            One of 'aic', 'bic', or 'qaic'.
+        scale : float, optional
+            The scale parameter estimated using the parent model,
+            used only for qaic.
+        dk_params : int or float, optional
+            Correction to the number of parameters used in the information
+            criterion. By default, only mean parameters are included, the
+            scale parameter is not included in the parameter count.
+            Use ``dk_params=1`` to include scale in the parameter count.
+
+        Returns
+        -------
+        float
+            Value of information criterion.
+
+        Notes
+        -----
+        The quasi-Akaike Information criterion (qaic) is -2 *
+        `llf`/`scale` + 2 * (`df_model` + 1).  It may not give
+        meaningful results except for Poisson and related models.
+
+        The QAIC (ic_type='qaic') must be evaluated with a provided
+        scale parameter.  Two QAIC values are only comparable if they
+        are calculated using the same scale parameter.  The scale
+        parameter should be estimated using the largest model among
+        all models being compared.
+
+        References
+        ----------
+        .. [BurnhamAnderson2002] Burnham KP, Anderson KR (2002). Model Selection
+           and Multimodel Inference; Springer New York.
+        """
+        crit = crit.lower()
+        k_params = self.df_model + 1 + dk_params
+
+        if crit == "aic":
+            return -2 * self.llf + 2 * k_params
+        elif crit == "bic":
+            nobs = self.df_model + self.df_resid + 1
+            bic = -2 * self.llf + k_params * np.log(nobs)
+            return bic
+        elif crit == "qaic":
+            f = self.model.family
+            fl = (families.Poisson, families.NegativeBinomial, families.Binomial)
+            if not isinstance(f, fl):
+                msg = "QAIC is only valid for Binomial, Poisson and "
+                msg += "Negative Binomial families."
+                warnings.warn(msg, stacklevel=2)
+            llf = self.llf_scaled(scale=1)
+            return -2 * llf / scale + 2 * k_params
+
+    # now explicit docs, old and new behavior, copied from generic classes
+    # @Appender(str(get_prediction_doc))
+    def get_prediction(
+        self,
+        exog=None,
+        exposure=None,
+        offset=None,
+        transform=True,
+        which=None,
+        average=False,
+        agg_weights=None,
+        row_labels=None,
+    ):
+        """
+        Compute prediction results for GLM compatible models.
+
+        Options and return class depend on whether "which" is None or not.
+
+        Parameters
+        ----------
+        exog : array_like, optional
+            The values for which you want to predict.
+        exposure : array_like, optional
+            Exposure time values, only can be used with the log link
+            function.
+        offset : array_like, optional
+            Offset values.
+        transform : bool, optional
+            If the model was fit via a formula, do you want to pass
+            exog through the formula. Default is True. E.g., if you fit
+            a model y ~ log(x1) + log(x2), and transform is True, then
+            you can pass a data structure that contains x1 and x2 in
+            their original form. Otherwise, you'd need to log the data
+            first.
+        which : None or {'mean', 'linear', 'var_unscaled'}, optional
+            Statistic to predict. Default is 'mean'.
+            If which is None, then the pre-0.14 backwards compatible
+            prediction results class is returned.
+            If which is not None, then a generic Prediction results class will
+            be returned. Some options are only available if which is not None.
+            See notes.
+
+            - 'mean' returns the conditional expectation of endog E(y | x),
+              i.e., inverse of the model's link function of linear predictor.
+            - 'linear' returns the linear predictor of the mean function.
+            - 'var_unscaled' variance of endog implied by the likelihood model.
+              This does not include scale or var_weights.
+
+        average : bool, optional
+            Keyword is only used if ``which`` is not None.
+            If average is True, then the mean prediction is computed, that is,
+            predictions are computed for individual exog and then the average
+            over observation is used.
+            If average is False, then the results are the predictions for all
+            observations, i.e., same length as ``exog``.
+        agg_weights : ndarray, optional
+            Keyword is only used if ``which`` is not None.
+            Aggregation weights, only used if average is True.
+        row_labels : list of str, optional
+            If row_labels are provided, then they will replace the generated
+            labels.
+
+        Returns
+        -------
+        prediction_results : instance of a PredictionResults class.
+            The prediction results instance contains prediction and prediction
+            variance and can calculate confidence intervals and summary tables
+            for the prediction of the mean and of new observations on demand.
+            The Results class of the return depends on the value of ``which``.
+
+        See Also
+        --------
+        GLM.predict
+        GLMResults.predict
+
+        Notes
+        -----
+        Changes in statsmodels 0.14: The ``which`` keyword has been added.
+        If ``which`` is None, then the behavior is the same as in previous
+        versions, and returns the mean and linear prediction results.
+        If the ``which`` keyword is not None, then a generic prediction results
+        class is returned and is not backwards compatible with the old prediction
+        results class, e.g., column names of summary_frame differ.
+        There are more choices for the returned predicted statistic using
+        ``which``. More choices will be added in the next release.
+        Two additional keywords, average and agg_weights, are now also
+        available if ``which`` is not None.
+        In a future version ``which`` will become not None and the backwards
+        compatible prediction results class will be removed.
+
+        """
+
+        import statsmodels.regression._prediction as linpred
+
+        pred_kwds = {"exposure": exposure, "offset": offset, "which": "linear"}
+
+        if which is None:
+            # two calls to a get_prediction duplicates exog generation if patsy
+            res_linpred = linpred.get_prediction(
+                self,
+                exog=exog,
+                transform=transform,
+                row_labels=row_labels,
+                pred_kwds=pred_kwds,
+            )
+
+            pred_kwds["which"] = "mean"
+            res = pred.get_prediction_glm(
+                self,
+                exog=exog,
+                transform=transform,
+                row_labels=row_labels,
+                linpred=res_linpred,
+                link=self.model.family.link,
+                pred_kwds=pred_kwds,
+            )
+        else:
+            # new generic version, if 'which' is specified
+
+            pred_kwds = {"exposure": exposure, "offset": offset}
+            # not yet, only applies to count families
+            # y_values is explicit so we can add it to the docstring
+            # if y_values is not None:
+            #    pred_kwds["y_values"] = y_values
+
+            res = pred.get_prediction(
+                self,
+                exog=exog,
+                which=which,
+                transform=transform,
+                row_labels=row_labels,
+                average=average,
+                agg_weights=agg_weights,
+                pred_kwds=pred_kwds,
+            )
+
+        return res
+
+    @Appender(pinfer.score_test.__doc__)
+    def score_test(
+        self,
+        exog_extra=None,
+        params_constrained=None,
+        hypothesis="joint",
+        cov_type=None,
+        cov_kwds=None,
+        k_constraints=None,
+        observed=True,
+        *,
+        return_object: bool | None = None,
+    ):
+
+        if self.model._has_freq_weights is True:
+            warnings.warn(
+                "score test has not been verified with freq_weights",
+                UserWarning,
+                stacklevel=2,
+            )
+        if self.model._has_var_weights is True:
+            warnings.warn(
+                "score test has not been verified with var_weights",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        # We need to temporarily change model.df_resid for scale computation
+        # TODO: find a nicer way. gh #7840
+        mod_df_resid = self.model.df_resid
+        self.model.df_resid = self.df_resid
+        if k_constraints is not None:
+            self.model.df_resid += k_constraints
+        res = pinfer.score_test(
+            self,
+            exog_extra=exog_extra,
+            params_constrained=params_constrained,
+            hypothesis=hypothesis,
+            cov_type=cov_type,
+            cov_kwds=cov_kwds,
+            k_constraints=k_constraints,
+            scale=None,
+            observed=observed,
+            return_object=return_object,
+        )
+
+        self.model.df_resid = mod_df_resid
+        return res
+
+    def get_hat_matrix_diag(self, observed=True):
+        """
+        Compute the diagonal of the hat matrix
+
+        Parameters
+        ----------
+        observed : bool, optional
+            If true, then observed hessian is used in the hat matrix
+            computation. If false, then the expected hessian is used.
+            In the case of a canonical link function both are the same.
+
+        Returns
+        -------
+        hat_matrix_diag : ndarray
+            The diagonal of the hat matrix computed from the observed
+            or expected hessian.
+        """
+        weights = self.model.hessian_factor(self.params, observed=observed)
+        wexog = np.sqrt(weights)[:, None] * self.model.exog
+
+        hd = (wexog * np.linalg.pinv(wexog).T).sum(1)
+        return hd
+
+    def get_influence(self, observed=True):
+        """
+        Get an instance of GLMInfluence with influence and outlier measures
+
+        Parameters
+        ----------
+        observed : bool, optional
+            If true, then observed hessian is used in the hat matrix
+            computation. If false, then the expected hessian is used.
+            In the case of a canonical link function both are the same.
+
+        Returns
+        -------
+        infl : GLMInfluence instance
+            The instance has methods to calculate the main influence and
+            outlier measures as attributes.
+
+        See Also
+        --------
+        statsmodels.stats.outliers_influence.GLMInfluence
+        """
+        from statsmodels.stats.outliers_influence import GLMInfluence
+
+        weights = self.model.hessian_factor(self.params, observed=observed)
+        weights_sqrt = np.sqrt(weights)
+        wexog = weights_sqrt[:, None] * self.model.exog
+        wendog = weights_sqrt * self.model.endog
+
+        # using get_hat_matrix_diag has duplicated computation
+        hat_matrix_diag = self.get_hat_matrix_diag(observed=observed)
+        infl = GLMInfluence(
+            self,
+            endog=wendog,
+            exog=wexog,
+            resid=self.resid_pearson / np.sqrt(self.scale),
+            hat_matrix_diag=hat_matrix_diag,
+        )
+        return infl
+
+    def get_distribution(
+        self, exog=None, exposure=None, offset=None, var_weights=1.0, n_trials=1.0
+    ):
+        """
+        Return an instance of the predictive distribution
+
+        Parameters
+        ----------
+        exog : array_like, optional
+            The predictor variable matrix.
+        offset : array_like, optional
+            Offset variable for predicted mean.
+        exposure : array_like, optional
+            Log(exposure) will be added to the linear prediction.
+        var_weights : array_like, optional
+            1d array of variance (analytic) weights. The default is 1.
+        n_trials : int, optional
+            Number of trials for the binomial distribution. The default is 1
+            which corresponds to a Bernoulli random variable.
+
+        Returns
+        -------
+        gen
+            Instance of a scipy frozen distribution based on estimated
+            parameters.
+            Use the ``rvs`` method to generate random values.
+
+        Notes
+        -----
+        Due to the behavior of ``scipy.stats.distributions objects``, the
+        returned random number generator must be called with ``gen.rvs(n)``
+        where ``n`` is the number of observations in the data set used
+        to fit the model.  If any other value is used for ``n``, misleading
+        results will be produced.
+        """
+        # Note this is mostly a copy of GLM.get_prediction
+        # calling here results.predict avoids the exog check and trasnform
+
+        if isinstance(
+            self.model.family,
+            (families.Binomial, families.Poisson, families.NegativeBinomial),
+        ):
+            # use scale=1, independent of QMLE scale for discrete
+            scale = 1.0
+            if self.scale != 1.0:
+                msg = "using scale=1, no exess dispersion in distribution"
+                warnings.warn(msg, UserWarning, stacklevel=2)
+        else:
+            scale = self.scale
+
+        mu = self.predict(exog, exposure, offset, which="mean")
+
+        kwds = {}
+        if np.any(n_trials != 1) and isinstance(self.model.family, families.Binomial):
+
+            kwds["n_trials"] = n_trials
+
+        distr = self.model.family.get_distribution(
+            mu, scale, var_weights=var_weights, **kwds
+        )
+        return distr
+
+    def get_margeff(
+        self, at="overall", method="dydx", atexog=None, dummy=False, count=False
+    ):
+        """
+        Get marginal effects of the fitted model
+
+        Warning: offset, exposure and weights (var_weights and freq_weights)
+        are not supported by margeff.
+
+        Parameters
+        ----------
+        at : str, optional
+            Options are:
+
+            - 'overall', The average of the marginal effects at each
+              observation.
+            - 'mean', The marginal effects at the mean of each regressor.
+            - 'median', The marginal effects at the median of each regressor.
+            - 'zero', The marginal effects at zero for each regressor.
+            - 'all', The marginal effects at each observation. If `at` is all
+              only margeff will be available from the returned object.
+
+            Note that if `exog` is specified, then marginal effects for all
+            variables not specified by `exog` are calculated using the `at`
+            option.
+        method : str, optional
+            Options are:
+
+            - 'dydx' - dy/dx - No transformation is made and marginal effects
+              are returned.  This is the default.
+            - 'eyex' - estimate elasticities of variables in `exog` --
+              d(lny)/d(lnx)
+            - 'dyex' - estimate semi-elasticity -- dy/d(lnx)
+            - 'eydx' - estimate semi-elasticity -- d(lny)/dx
+
+            Note that transformations are done after each observation is
+            calculated.  Semi-elasticities for binary variables are computed
+            using the midpoint method. 'dyex' and 'eyex' do not make sense
+            for discrete variables. For interpretations of these methods
+            see notes below.
+        atexog : array_like, optional
+            Optionally, you can provide the exogenous variables over which to
+            get the marginal effects.  This should be a dictionary with the key
+            as the zero-indexed column number and the value of the dictionary.
+            Default is None for all independent variables less the constant.
+        dummy : bool, optional
+            If False, treats binary variables (if present) as continuous.  This
+            is the default.  Else if True, treats binary variables as
+            changing from 0 to 1.  Note that any variable that is either 0 or 1
+            is treated as binary.  Each binary variable is treated separately
+            for now.
+        count : bool, optional
+            If False, treats count variables (if present) as continuous.  This
+            is the default.  Else if True, the marginal effect is the
+            change in probabilities when each observation is increased by one.
+
+        Returns
+        -------
+        DiscreteMargins : marginal effects instance
+            Returns an object that holds the marginal effects, standard
+            errors, confidence intervals, etc. See
+            `statsmodels.discrete.discrete_margins.DiscreteMargins` for more
+            information.
+
+        Notes
+        -----
+        Interpretations of methods:
+
+        - 'dydx' - change in `endog` for a change in `exog`.
+        - 'eyex' - proportional change in `endog` for a proportional change
+          in `exog`.
+        - 'dyex' - change in `endog` for a proportional change in `exog`.
+        - 'eydx' - proportional change in `endog` for a change in `exog`.
+
+        When using after Poisson, returns the expected number of events per
+        period, assuming that the model is loglinear.
+
+        Status : unsupported features offset, exposure and weights. Default
+        handling of freq_weights for average effect "overall" might change.
+
+        """
+        if getattr(self.model, "offset", None) is not None:
+            raise NotImplementedError("Margins with offset are not available.")
+        if np.any(self.model.var_weights != 1) or np.any(self.model.freq_weights != 1):
+            warnings.warn("weights are not taken into account by margeff", stacklevel=2)
+        from statsmodels.discrete.discrete_margins import DiscreteMargins
+
+        return DiscreteMargins(self, (at, method, atexog, dummy, count))
+
+    @Appender(base.LikelihoodModelResults.remove_data.__doc__)
+    def remove_data(self):
+        # GLM has alias/reference in result instance
+        self._data_attr.extend([i for i in self.model._data_attr if "_data." not in i])
+        super().remove_data()
+
+        # TODO: what are these in results?
+        self._endog = None
+        self._freq_weights = None
+        self._var_weights = None
+        self._iweights = None
+        self._n_trials = None
+
+    @Appender(_plot_added_variable_doc % {"extra_params_doc": ""})
+    def plot_added_variable(
+        self,
+        focus_exog,
+        resid_type=None,
+        use_glm_weights=True,
+        fit_kwargs=None,
+        ax=None,
+    ):
+
+        from statsmodels.graphics.regressionplots import plot_added_variable
+
+        fig = plot_added_variable(
+            self,
+            focus_exog,
+            resid_type=resid_type,
+            use_glm_weights=use_glm_weights,
+            fit_kwargs=fit_kwargs,
+            ax=ax,
+        )
+
+        return fig
+
+    @Appender(_plot_partial_residuals_doc % {"extra_params_doc": ""})
+    def plot_partial_residuals(self, focus_exog, ax=None):
+
+        from statsmodels.graphics.regressionplots import plot_partial_residuals
+
+        return plot_partial_residuals(self, focus_exog, ax=ax)
+
+    @Appender(_plot_ceres_residuals_doc % {"extra_params_doc": ""})
+    def plot_ceres_residuals(self, focus_exog, frac=0.66, cond_means=None, ax=None):
+
+        from statsmodels.graphics.regressionplots import plot_ceres_residuals
+
+        return plot_ceres_residuals(
+            self, focus_exog, frac, cond_means=cond_means, ax=ax
+        )
+
+    def summary(self, yname=None, xname=None, title=None, alpha=0.05):
+        """
+        Summarize the Regression Results
+
+        Parameters
+        ----------
+        yname : str, optional
+            Default is `y`
+        xname : list of str, optional
+            Names for the exogenous variables, default is `var_#` for ## in
+            the number of regressors. Must match the number of parameters in
+            the model
+        title : str, optional
+            Title for the top table. If not None, then this replaces the
+            default title
+        alpha : float, optional
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary results
+        """
+
+        top_left = [
+            ("Dep. Variable:", None),
+            ("Model:", None),
+            ("Model Family:", [self.family.__class__.__name__]),
+            ("Link Function:", [self.family.link.__class__.__name__]),
+            ("Method:", [self.method]),
+            ("Date:", None),
+            ("Time:", None),
+            ("No. Iterations:", ["{:d}".format(self.fit_history["iteration"])]),
+        ]
+
+        def _prsquared():
+            try:
+                return self.pseudo_rsquared(kind="cs")
+            except ValueError:
+                return np.nan
+
+        prsquared = self._summary_cache("prsquared", _prsquared)
+        deviance = self._summary_cache("deviance", lambda: self.deviance)
+        pearson_chi2 = self._summary_cache(
+            "pearson_chi2", lambda: self.pearson_chi2
+        )
+
+        top_right = [
+            ("No. Observations:", None),
+            ("Df Residuals:", None),
+            ("Df Model:", None),
+            ("Scale:", [f"{self.scale:#8.5g}"]),
+            ("Log-Likelihood:", None),
+            ("Deviance:", [f"{deviance:#8.5g}"]),
+            ("Pearson chi2:", [f"{pearson_chi2:#6.3g}"]),
+            ("Pseudo R-squ. (CS):", [f"{prsquared:#6.4g}"]),
+        ]
+
+        if hasattr(self, "cov_type"):
+            top_left.append(("Covariance Type:", [self.cov_type]))
+
+        if title is None:
+            title = "Generalized Linear Model Regression Results"
+
+        # create summary tables
+        from statsmodels.iolib.summary import Summary
+
+        smry = Summary()
+        smry.add_table_2cols(
+            self,
+            gleft=top_left,
+            gright=top_right,
+            yname=yname,
+            xname=xname,
+            title=title,
+        )
+        smry.add_table_params(
+            self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t
+        )
+
+        if hasattr(self, "constraints"):
+            smry.add_extra_txt(
+                ["Model has been estimated subject to linear equality constraints."]
+            )
+        return smry
+
+    def summary2(
+        self, yname=None, xname=None, title=None, alpha=0.05, float_format="%.4f"
+    ):
+        """
+        Experimental summary for regression Results
+
+        Parameters
+        ----------
+        yname : str, optional
+            Name of the dependent variable
+        xname : list of str, optional
+            Names for the exogenous variables, default is `var_#` for ## in
+            the number of regressors. Must match the number of parameters in
+            the model
+        title : str, optional
+            Title for the top table. If not None, then this replaces the
+            default title
+        alpha : float, optional
+            significance level for the confidence intervals
+        float_format : str, optional
+            print format for floats in parameters summary
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary2.Summary : class to hold summary results
+        """
+        from statsmodels.iolib import summary2
+
+        smry = summary2.Summary()
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", FutureWarning)
+            smry.add_base(
+                results=self,
+                alpha=alpha,
+                float_format=float_format,
+                xname=xname,
+                yname=yname,
+                title=title,
+            )
+        if hasattr(self, "constraints"):
+            smry.add_text(
+                "Model has been estimated subject to linear equality constraints."
+            )
+
+        return smry
+
+
+class GLMResultsWrapper(lm.RegressionResultsWrapper):
+    _attrs = {
+        "resid_anscombe": "rows",
+        "resid_deviance": "rows",
+        "resid_pearson": "rows",
+        "resid_response": "rows",
+        "resid_working": "rows",
+    }
+    _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs)
+
+
+wrap.populate_wrapper(GLMResultsWrapper, GLMResults)
diff --git a/shared/package-files/d8/d8cc57138adec7fc12521a4bbb0c4e15e7a8afe5b164b8e2b15bd75b270d6aae/misc_util.py b/shared/package-files/d8/d8cc57138adec7fc12521a4bbb0c4e15e7a8afe5b164b8e2b15bd75b270d6aae/misc_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e226b47448153e34487def3176d5991319312363
--- /dev/null
+++ b/shared/package-files/d8/d8cc57138adec7fc12521a4bbb0c4e15e7a8afe5b164b8e2b15bd75b270d6aae/misc_util.py
@@ -0,0 +1,2493 @@
+import os
+import re
+import sys
+import copy
+import glob
+import atexit
+import tempfile
+import subprocess
+import shutil
+import multiprocessing
+import textwrap
+import importlib.util
+from threading import local as tlocal
+from functools import reduce
+
+import distutils
+from distutils.errors import DistutilsError
+
+# stores temporary directory of each thread to only create one per thread
+_tdata = tlocal()
+
+# store all created temporary directories so they can be deleted on exit
+_tmpdirs = []
+def clean_up_temporary_directory():
+    if _tmpdirs is not None:
+        for d in _tmpdirs:
+            try:
+                shutil.rmtree(d)
+            except OSError:
+                pass
+
+atexit.register(clean_up_temporary_directory)
+
+__all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict',
+           'dict_append', 'appendpath', 'generate_config_py',
+           'get_cmd', 'allpath', 'get_mathlibs',
+           'terminal_has_colors', 'red_text', 'green_text', 'yellow_text',
+           'blue_text', 'cyan_text', 'cyg2win32', 'mingw32', 'all_strings',
+           'has_f_sources', 'has_cxx_sources', 'filter_sources',
+           'get_dependencies', 'is_local_src_dir', 'get_ext_source_files',
+           'get_script_files', 'get_lib_source_files', 'get_data_files',
+           'dot_join', 'get_frame', 'minrelpath', 'njoin',
+           'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language',
+           'get_build_architecture', 'get_info', 'get_pkg_info',
+           'get_num_build_jobs', 'sanitize_cxx_flags',
+           'exec_mod_from_location']
+
+class InstallableLib:
+    """
+    Container to hold information on an installable library.
+
+    Parameters
+    ----------
+    name : str
+        Name of the installed library.
+    build_info : dict
+        Dictionary holding build information.
+    target_dir : str
+        Absolute path specifying where to install the library.
+
+    See Also
+    --------
+    Configuration.add_installed_library
+
+    Notes
+    -----
+    The three parameters are stored as attributes with the same names.
+
+    """
+    def __init__(self, name, build_info, target_dir):
+        self.name = name
+        self.build_info = build_info
+        self.target_dir = target_dir
+
+
+def get_num_build_jobs():
+    """
+    Get number of parallel build jobs set by the --parallel command line
+    argument of setup.py
+    If the command did not receive a setting the environment variable
+    NPY_NUM_BUILD_JOBS is checked. If that is unset, return the number of
+    processors on the system, with a maximum of 8 (to prevent
+    overloading the system if there a lot of CPUs).
+
+    Returns
+    -------
+    out : int
+        number of parallel jobs that can be run
+
+    """
+    from numpy.distutils.core import get_distribution
+    try:
+        cpu_count = len(os.sched_getaffinity(0))
+    except AttributeError:
+        cpu_count = multiprocessing.cpu_count()
+    cpu_count = min(cpu_count, 8)
+    envjobs = int(os.environ.get("NPY_NUM_BUILD_JOBS", cpu_count))
+    dist = get_distribution()
+    # may be None during configuration
+    if dist is None:
+        return envjobs
+
+    # any of these three may have the job set, take the largest
+    cmdattr = (getattr(dist.get_command_obj('build'), 'parallel', None),
+               getattr(dist.get_command_obj('build_ext'), 'parallel', None),
+               getattr(dist.get_command_obj('build_clib'), 'parallel', None))
+    if all(x is None for x in cmdattr):
+        return envjobs
+    else:
+        return max(x for x in cmdattr if x is not None)
+
+def quote_args(args):
+    """Quote list of arguments.
+
+    .. deprecated:: 1.22.
+    """
+    import warnings
+    warnings.warn('"quote_args" is deprecated.',
+                  DeprecationWarning, stacklevel=2)
+    # don't used _nt_quote_args as it does not check if
+    # args items already have quotes or not.
+    args = list(args)
+    for i in range(len(args)):
+        a = args[i]
+        if ' ' in a and a[0] not in '"\'':
+            args[i] = '"%s"' % (a)
+    return args
+
+def allpath(name):
+    "Convert a /-separated pathname to one using the OS's path separator."
+    split = name.split('/')
+    return os.path.join(*split)
+
+def rel_path(path, parent_path):
+    """Return path relative to parent_path."""
+    # Use realpath to avoid issues with symlinked dirs (see gh-7707)
+    pd = os.path.realpath(os.path.abspath(parent_path))
+    apath = os.path.realpath(os.path.abspath(path))
+    if len(apath) < len(pd):
+        return path
+    if apath == pd:
+        return ''
+    if pd == apath[:len(pd)]:
+        assert apath[len(pd)] in [os.sep], repr((path, apath[len(pd)]))
+        path = apath[len(pd)+1:]
+    return path
+
+def get_path_from_frame(frame, parent_path=None):
+    """Return path of the module given a frame object from the call stack.
+
+    Returned path is relative to parent_path when given,
+    otherwise it is absolute path.
+    """
+
+    # First, try to find if the file name is in the frame.
+    try:
+        caller_file = eval('__file__', frame.f_globals, frame.f_locals)
+        d = os.path.dirname(os.path.abspath(caller_file))
+    except NameError:
+        # __file__ is not defined, so let's try __name__. We try this second
+        # because setuptools spoofs __name__ to be '__main__' even though
+        # sys.modules['__main__'] might be something else, like easy_install(1).
+        caller_name = eval('__name__', frame.f_globals, frame.f_locals)
+        __import__(caller_name)
+        mod = sys.modules[caller_name]
+        if hasattr(mod, '__file__'):
+            d = os.path.dirname(os.path.abspath(mod.__file__))
+        else:
+            # we're probably running setup.py as execfile("setup.py")
+            # (likely we're building an egg)
+            d = os.path.abspath('.')
+
+    if parent_path is not None:
+        d = rel_path(d, parent_path)
+
+    return d or '.'
+
+def njoin(*path):
+    """Join two or more pathname components +
+    - convert a /-separated pathname to one using the OS's path separator.
+    - resolve `..` and `.` from path.
+
+    Either passing n arguments as in njoin('a','b'), or a sequence
+    of n names as in njoin(['a','b']) is handled, or a mixture of such arguments.
+    """
+    paths = []
+    for p in path:
+        if is_sequence(p):
+            # njoin(['a', 'b'], 'c')
+            paths.append(njoin(*p))
+        else:
+            assert is_string(p)
+            paths.append(p)
+    path = paths
+    if not path:
+        # njoin()
+        joined = ''
+    else:
+        # njoin('a', 'b')
+        joined = os.path.join(*path)
+    if os.path.sep != '/':
+        joined = joined.replace('/', os.path.sep)
+    return minrelpath(joined)
+
+def get_mathlibs(path=None):
+    """Return the MATHLIB line from numpyconfig.h
+    """
+    if path is not None:
+        config_file = os.path.join(path, '_numpyconfig.h')
+    else:
+        # Look for the file in each of the numpy include directories.
+        dirs = get_numpy_include_dirs()
+        for path in dirs:
+            fn = os.path.join(path, '_numpyconfig.h')
+            if os.path.exists(fn):
+                config_file = fn
+                break
+        else:
+            raise DistutilsError('_numpyconfig.h not found in numpy include '
+                'dirs %r' % (dirs,))
+
+    with open(config_file) as fid:
+        mathlibs = []
+        s = '#define MATHLIB'
+        for line in fid:
+            if line.startswith(s):
+                value = line[len(s):].strip()
+                if value:
+                    mathlibs.extend(value.split(','))
+    return mathlibs
+
+def minrelpath(path):
+    """Resolve `..` and '.' from path.
+    """
+    if not is_string(path):
+        return path
+    if '.' not in path:
+        return path
+    l = path.split(os.sep)
+    while l:
+        try:
+            i = l.index('.', 1)
+        except ValueError:
+            break
+        del l[i]
+    j = 1
+    while l:
+        try:
+            i = l.index('..', j)
+        except ValueError:
+            break
+        if l[i-1]=='..':
+            j += 1
+        else:
+            del l[i], l[i-1]
+            j = 1
+    if not l:
+        return ''
+    return os.sep.join(l)
+
+def sorted_glob(fileglob):
+    """sorts output of python glob for https://bugs.python.org/issue30461
+    to allow extensions to have reproducible build results"""
+    return sorted(glob.glob(fileglob))
+
+def _fix_paths(paths, local_path, include_non_existing):
+    assert is_sequence(paths), repr(type(paths))
+    new_paths = []
+    assert not is_string(paths), repr(paths)
+    for n in paths:
+        if is_string(n):
+            if '*' in n or '?' in n:
+                p = sorted_glob(n)
+                p2 = sorted_glob(njoin(local_path, n))
+                if p2:
+                    new_paths.extend(p2)
+                elif p:
+                    new_paths.extend(p)
+                else:
+                    if include_non_existing:
+                        new_paths.append(n)
+                    print('could not resolve pattern in %r: %r' %
+                            (local_path, n))
+            else:
+                n2 = njoin(local_path, n)
+                if os.path.exists(n2):
+                    new_paths.append(n2)
+                else:
+                    if os.path.exists(n):
+                        new_paths.append(n)
+                    elif include_non_existing:
+                        new_paths.append(n)
+                    if not os.path.exists(n):
+                        print('non-existing path in %r: %r' %
+                                (local_path, n))
+
+        elif is_sequence(n):
+            new_paths.extend(_fix_paths(n, local_path, include_non_existing))
+        else:
+            new_paths.append(n)
+    return [minrelpath(p) for p in new_paths]
+
+def gpaths(paths, local_path='', include_non_existing=True):
+    """Apply glob to paths and prepend local_path if needed.
+    """
+    if is_string(paths):
+        paths = (paths,)
+    return _fix_paths(paths, local_path, include_non_existing)
+
+def make_temp_file(suffix='', prefix='', text=True):
+    if not hasattr(_tdata, 'tempdir'):
+        _tdata.tempdir = tempfile.mkdtemp()
+        _tmpdirs.append(_tdata.tempdir)
+    fid, name = tempfile.mkstemp(suffix=suffix,
+                                 prefix=prefix,
+                                 dir=_tdata.tempdir,
+                                 text=text)
+    fo = os.fdopen(fid, 'w')
+    return fo, name
+
+# Hooks for colored terminal output.
+# See also https://web.archive.org/web/20100314204946/http://www.livinglogic.de/Python/ansistyle
+def terminal_has_colors():
+    if sys.platform=='cygwin' and 'USE_COLOR' not in os.environ:
+        # Avoid importing curses that causes illegal operation
+        # with a message:
+        #  PYTHON2 caused an invalid page fault in
+        #  module CYGNURSES7.DLL as 015f:18bbfc28
+        # Details: Python 2.3.3 [GCC 3.3.1 (cygming special)]
+        #          ssh to Win32 machine from debian
+        #          curses.version is 2.2
+        #          CYGWIN_98-4.10, release 1.5.7(0.109/3/2))
+        return 0
+    if hasattr(sys.stdout, 'isatty') and sys.stdout.isatty():
+        try:
+            import curses
+            curses.setupterm()
+            if (curses.tigetnum("colors") >= 0
+                and curses.tigetnum("pairs") >= 0
+                and ((curses.tigetstr("setf") is not None
+                      and curses.tigetstr("setb") is not None)
+                     or (curses.tigetstr("setaf") is not None
+                         and curses.tigetstr("setab") is not None)
+                     or curses.tigetstr("scp") is not None)):
+                return 1
+        except Exception:
+            pass
+    return 0
+
+if terminal_has_colors():
+    _colour_codes = dict(black=0, red=1, green=2, yellow=3,
+                         blue=4, magenta=5, cyan=6, white=7, default=9)
+    def colour_text(s, fg=None, bg=None, bold=False):
+        seq = []
+        if bold:
+            seq.append('1')
+        if fg:
+            fgcode = 30 + _colour_codes.get(fg.lower(), 0)
+            seq.append(str(fgcode))
+        if bg:
+            bgcode = 40 + _colour_codes.get(bg.lower(), 7)
+            seq.append(str(bgcode))
+        if seq:
+            return '\x1b[%sm%s\x1b[0m' % (';'.join(seq), s)
+        else:
+            return s
+else:
+    def colour_text(s, fg=None, bg=None):
+        return s
+
+def default_text(s):
+    return colour_text(s, 'default')
+def red_text(s):
+    return colour_text(s, 'red')
+def green_text(s):
+    return colour_text(s, 'green')
+def yellow_text(s):
+    return colour_text(s, 'yellow')
+def cyan_text(s):
+    return colour_text(s, 'cyan')
+def blue_text(s):
+    return colour_text(s, 'blue')
+
+#########################
+
+def cyg2win32(path: str) -> str:
+    """Convert a path from Cygwin-native to Windows-native.
+
+    Uses the cygpath utility (part of the Base install) to do the
+    actual conversion.  Falls back to returning the original path if
+    this fails.
+
+    Handles the default ``/cygdrive`` mount prefix as well as the
+    ``/proc/cygdrive`` portable prefix, custom cygdrive prefixes such
+    as ``/`` or ``/mnt``, and absolute paths such as ``/usr/src/`` or
+    ``/home/username``
+
+    Parameters
+    ----------
+    path : str
+       The path to convert
+
+    Returns
+    -------
+    converted_path : str
+        The converted path
+
+    Notes
+    -----
+    Documentation for cygpath utility:
+    https://cygwin.com/cygwin-ug-net/cygpath.html
+    Documentation for the C function it wraps:
+    https://cygwin.com/cygwin-api/func-cygwin-conv-path.html
+
+    """
+    if sys.platform != "cygwin":
+        return path
+    return subprocess.check_output(
+        ["/usr/bin/cygpath", "--windows", path], text=True
+    )
+
+
+def mingw32():
+    """Return true when using mingw32 environment.
+    """
+    if sys.platform=='win32':
+        if os.environ.get('OSTYPE', '')=='msys':
+            return True
+        if os.environ.get('MSYSTEM', '')=='MINGW32':
+            return True
+    return False
+
+def msvc_runtime_version():
+    "Return version of MSVC runtime library, as defined by __MSC_VER__ macro"
+    msc_pos = sys.version.find('MSC v.')
+    if msc_pos != -1:
+        msc_ver = int(sys.version[msc_pos+6:msc_pos+10])
+    else:
+        msc_ver = None
+    return msc_ver
+
+def msvc_runtime_library():
+    "Return name of MSVC runtime library if Python was built with MSVC >= 7"
+    ver = msvc_runtime_major ()
+    if ver:
+        if ver < 140:
+            return "msvcr%i" % ver
+        else:
+            return "vcruntime%i" % ver
+    else:
+        return None
+
+def msvc_runtime_major():
+    "Return major version of MSVC runtime coded like get_build_msvc_version"
+    major = {1300:  70,  # MSVC 7.0
+             1310:  71,  # MSVC 7.1
+             1400:  80,  # MSVC 8
+             1500:  90,  # MSVC 9  (aka 2008)
+             1600: 100,  # MSVC 10 (aka 2010)
+             1900: 140,  # MSVC 14 (aka 2015)
+    }.get(msvc_runtime_version(), None)
+    return major
+
+#########################
+
+#XXX need support for .C that is also C++
+cxx_ext_match = re.compile(r'.*\.(cpp|cxx|cc)\Z', re.I).match
+fortran_ext_match = re.compile(r'.*\.(f90|f95|f77|for|ftn|f)\Z', re.I).match
+f90_ext_match = re.compile(r'.*\.(f90|f95)\Z', re.I).match
+f90_module_name_match = re.compile(r'\s*module\s*(?P<name>[\w_]+)', re.I).match
+def _get_f90_modules(source):
+    """Return a list of Fortran f90 module names that
+    given source file defines.
+    """
+    if not f90_ext_match(source):
+        return []
+    modules = []
+    with open(source) as f:
+        for line in f:
+            m = f90_module_name_match(line)
+            if m:
+                name = m.group('name')
+                modules.append(name)
+                # break  # XXX can we assume that there is one module per file?
+    return modules
+
+def is_string(s):
+    return isinstance(s, str)
+
+def all_strings(lst):
+    """Return True if all items in lst are string objects. """
+    for item in lst:
+        if not is_string(item):
+            return False
+    return True
+
+def is_sequence(seq):
+    if is_string(seq):
+        return False
+    try:
+        len(seq)
+    except Exception:
+        return False
+    return True
+
+def is_glob_pattern(s):
+    return is_string(s) and ('*' in s or '?' in s)
+
+def as_list(seq):
+    if is_sequence(seq):
+        return list(seq)
+    else:
+        return [seq]
+
+def get_language(sources):
+    # not used in numpy/scipy packages, use build_ext.detect_language instead
+    """Determine language value (c,f77,f90) from sources """
+    language = None
+    for source in sources:
+        if isinstance(source, str):
+            if f90_ext_match(source):
+                language = 'f90'
+                break
+            elif fortran_ext_match(source):
+                language = 'f77'
+    return language
+
+def has_f_sources(sources):
+    """Return True if sources contains Fortran files """
+    for source in sources:
+        if fortran_ext_match(source):
+            return True
+    return False
+
+def has_cxx_sources(sources):
+    """Return True if sources contains C++ files """
+    for source in sources:
+        if cxx_ext_match(source):
+            return True
+    return False
+
+def filter_sources(sources):
+    """Return four lists of filenames containing
+    C, C++, Fortran, and Fortran 90 module sources,
+    respectively.
+    """
+    c_sources = []
+    cxx_sources = []
+    f_sources = []
+    fmodule_sources = []
+    for source in sources:
+        if fortran_ext_match(source):
+            modules = _get_f90_modules(source)
+            if modules:
+                fmodule_sources.append(source)
+            else:
+                f_sources.append(source)
+        elif cxx_ext_match(source):
+            cxx_sources.append(source)
+        else:
+            c_sources.append(source)
+    return c_sources, cxx_sources, f_sources, fmodule_sources
+
+
+def _get_headers(directory_list):
+    # get *.h files from list of directories
+    headers = []
+    for d in directory_list:
+        head = sorted_glob(os.path.join(d, "*.h")) #XXX: *.hpp files??
+        headers.extend(head)
+    return headers
+
+def _get_directories(list_of_sources):
+    # get unique directories from list of sources.
+    direcs = []
+    for f in list_of_sources:
+        d = os.path.split(f)
+        if d[0] != '' and not d[0] in direcs:
+            direcs.append(d[0])
+    return direcs
+
+def _commandline_dep_string(cc_args, extra_postargs, pp_opts):
+    """
+    Return commandline representation used to determine if a file needs
+    to be recompiled
+    """
+    cmdline = 'commandline: '
+    cmdline += ' '.join(cc_args)
+    cmdline += ' '.join(extra_postargs)
+    cmdline += ' '.join(pp_opts) + '\n'
+    return cmdline
+
+
+def get_dependencies(sources):
+    #XXX scan sources for include statements
+    return _get_headers(_get_directories(sources))
+
+def is_local_src_dir(directory):
+    """Return true if directory is local directory.
+    """
+    if not is_string(directory):
+        return False
+    abs_dir = os.path.abspath(directory)
+    c = os.path.commonprefix([os.getcwd(), abs_dir])
+    new_dir = abs_dir[len(c):].split(os.sep)
+    if new_dir and not new_dir[0]:
+        new_dir = new_dir[1:]
+    if new_dir and new_dir[0]=='build':
+        return False
+    new_dir = os.sep.join(new_dir)
+    return os.path.isdir(new_dir)
+
+def general_source_files(top_path):
+    pruned_directories = {'CVS':1, '.svn':1, 'build':1}
+    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
+    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
+        pruned = [ d for d in dirnames if d not in pruned_directories ]
+        dirnames[:] = pruned
+        for f in filenames:
+            if not prune_file_pat.search(f):
+                yield os.path.join(dirpath, f)
+
+def general_source_directories_files(top_path):
+    """Return a directory name relative to top_path and
+    files contained.
+    """
+    pruned_directories = ['CVS', '.svn', 'build']
+    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
+    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
+        pruned = [ d for d in dirnames if d not in pruned_directories ]
+        dirnames[:] = pruned
+        for d in dirnames:
+            dpath = os.path.join(dirpath, d)
+            rpath = rel_path(dpath, top_path)
+            files = []
+            for f in os.listdir(dpath):
+                fn = os.path.join(dpath, f)
+                if os.path.isfile(fn) and not prune_file_pat.search(fn):
+                    files.append(fn)
+            yield rpath, files
+    dpath = top_path
+    rpath = rel_path(dpath, top_path)
+    filenames = [os.path.join(dpath, f) for f in os.listdir(dpath) \
+                 if not prune_file_pat.search(f)]
+    files = [f for f in filenames if os.path.isfile(f)]
+    yield rpath, files
+
+
+def get_ext_source_files(ext):
+    # Get sources and any include files in the same directory.
+    filenames = []
+    sources = [_m for _m in ext.sources if is_string(_m)]
+    filenames.extend(sources)
+    filenames.extend(get_dependencies(sources))
+    for d in ext.depends:
+        if is_local_src_dir(d):
+            filenames.extend(list(general_source_files(d)))
+        elif os.path.isfile(d):
+            filenames.append(d)
+    return filenames
+
+def get_script_files(scripts):
+    scripts = [_m for _m in scripts if is_string(_m)]
+    return scripts
+
+def get_lib_source_files(lib):
+    filenames = []
+    sources = lib[1].get('sources', [])
+    sources = [_m for _m in sources if is_string(_m)]
+    filenames.extend(sources)
+    filenames.extend(get_dependencies(sources))
+    depends = lib[1].get('depends', [])
+    for d in depends:
+        if is_local_src_dir(d):
+            filenames.extend(list(general_source_files(d)))
+        elif os.path.isfile(d):
+            filenames.append(d)
+    return filenames
+
+def get_shared_lib_extension(is_python_ext=False):
+    """Return the correct file extension for shared libraries.
+
+    Parameters
+    ----------
+    is_python_ext : bool, optional
+        Whether the shared library is a Python extension.  Default is False.
+
+    Returns
+    -------
+    so_ext : str
+        The shared library extension.
+
+    Notes
+    -----
+    For Python shared libs, `so_ext` will typically be '.so' on Linux and OS X,
+    and '.pyd' on Windows.  For Python >= 3.2 `so_ext` has a tag prepended on
+    POSIX systems according to PEP 3149.
+
+    """
+    confvars = distutils.sysconfig.get_config_vars()
+    so_ext = confvars.get('EXT_SUFFIX', '')
+
+    if not is_python_ext:
+        # hardcode known values, config vars (including SHLIB_SUFFIX) are
+        # unreliable (see #3182)
+        # darwin, windows and debug linux are wrong in 3.3.1 and older
+        if (sys.platform.startswith('linux') or
+            sys.platform.startswith('gnukfreebsd')):
+            so_ext = '.so'
+        elif sys.platform.startswith('darwin'):
+            so_ext = '.dylib'
+        elif sys.platform.startswith('win'):
+            so_ext = '.dll'
+        else:
+            # fall back to config vars for unknown platforms
+            # fix long extension for Python >=3.2, see PEP 3149.
+            if 'SOABI' in confvars:
+                # Does nothing unless SOABI config var exists
+                so_ext = so_ext.replace('.' + confvars.get('SOABI'), '', 1)
+
+    return so_ext
+
+def get_data_files(data):
+    if is_string(data):
+        return [data]
+    sources = data[1]
+    filenames = []
+    for s in sources:
+        if hasattr(s, '__call__'):
+            continue
+        if is_local_src_dir(s):
+            filenames.extend(list(general_source_files(s)))
+        elif is_string(s):
+            if os.path.isfile(s):
+                filenames.append(s)
+            else:
+                print('Not existing data file:', s)
+        else:
+            raise TypeError(repr(s))
+    return filenames
+
+def dot_join(*args):
+    return '.'.join([a for a in args if a])
+
+def get_frame(level=0):
+    """Return frame object from call stack with given level.
+    """
+    try:
+        return sys._getframe(level+1)
+    except AttributeError:
+        frame = sys.exc_info()[2].tb_frame
+        for _ in range(level+1):
+            frame = frame.f_back
+        return frame
+
+
+######################
+
+class Configuration:
+
+    _list_keys = ['packages', 'ext_modules', 'data_files', 'include_dirs',
+                  'libraries', 'headers', 'scripts', 'py_modules',
+                  'installed_libraries', 'define_macros']
+    _dict_keys = ['package_dir', 'installed_pkg_config']
+    _extra_keys = ['name', 'version']
+
+    numpy_include_dirs = []
+
+    def __init__(self,
+                 package_name=None,
+                 parent_name=None,
+                 top_path=None,
+                 package_path=None,
+                 caller_level=1,
+                 setup_name='setup.py',
+                 **attrs):
+        """Construct configuration instance of a package.
+
+        package_name -- name of the package
+                        Ex.: 'distutils'
+        parent_name  -- name of the parent package
+                        Ex.: 'numpy'
+        top_path     -- directory of the toplevel package
+                        Ex.: the directory where the numpy package source sits
+        package_path -- directory of package. Will be computed by magic from the
+                        directory of the caller module if not specified
+                        Ex.: the directory where numpy.distutils is
+        caller_level -- frame level to caller namespace, internal parameter.
+        """
+        self.name = dot_join(parent_name, package_name)
+        self.version = None
+
+        caller_frame = get_frame(caller_level)
+        self.local_path = get_path_from_frame(caller_frame, top_path)
+        # local_path -- directory of a file (usually setup.py) that
+        #               defines a configuration() function.
+        # local_path -- directory of a file (usually setup.py) that
+        #               defines a configuration() function.
+        if top_path is None:
+            top_path = self.local_path
+            self.local_path = ''
+        if package_path is None:
+            package_path = self.local_path
+        elif os.path.isdir(njoin(self.local_path, package_path)):
+            package_path = njoin(self.local_path, package_path)
+        if not os.path.isdir(package_path or '.'):
+            raise ValueError("%r is not a directory" % (package_path,))
+        self.top_path = top_path
+        self.package_path = package_path
+        # this is the relative path in the installed package
+        self.path_in_package = os.path.join(*self.name.split('.'))
+
+        self.list_keys = self._list_keys[:]
+        self.dict_keys = self._dict_keys[:]
+
+        for n in self.list_keys:
+            v = copy.copy(attrs.get(n, []))
+            setattr(self, n, as_list(v))
+
+        for n in self.dict_keys:
+            v = copy.copy(attrs.get(n, {}))
+            setattr(self, n, v)
+
+        known_keys = self.list_keys + self.dict_keys
+        self.extra_keys = self._extra_keys[:]
+        for n in attrs.keys():
+            if n in known_keys:
+                continue
+            a = attrs[n]
+            setattr(self, n, a)
+            if isinstance(a, list):
+                self.list_keys.append(n)
+            elif isinstance(a, dict):
+                self.dict_keys.append(n)
+            else:
+                self.extra_keys.append(n)
+
+        if os.path.exists(njoin(package_path, '__init__.py')):
+            self.packages.append(self.name)
+            self.package_dir[self.name] = package_path
+
+        self.options = dict(
+            ignore_setup_xxx_py = False,
+            assume_default_configuration = False,
+            delegate_options_to_subpackages = False,
+            quiet = False,
+            )
+
+        caller_instance = None
+        for i in range(1, 3):
+            try:
+                f = get_frame(i)
+            except ValueError:
+                break
+            try:
+                caller_instance = eval('self', f.f_globals, f.f_locals)
+                break
+            except NameError:
+                pass
+        if isinstance(caller_instance, self.__class__):
+            if caller_instance.options['delegate_options_to_subpackages']:
+                self.set_options(**caller_instance.options)
+
+        self.setup_name = setup_name
+
+    def todict(self):
+        """
+        Return a dictionary compatible with the keyword arguments of distutils
+        setup function.
+
+        Examples
+        --------
+        >>> setup(**config.todict())                           #doctest: +SKIP
+        """
+
+        self._optimize_data_files()
+        d = {}
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        for n in known_keys:
+            a = getattr(self, n)
+            if a:
+                d[n] = a
+        return d
+
+    def info(self, message):
+        if not self.options['quiet']:
+            print(message)
+
+    def warn(self, message):
+        sys.stderr.write('Warning: %s\n' % (message,))
+
+    def set_options(self, **options):
+        """
+        Configure Configuration instance.
+
+        The following options are available:
+         - ignore_setup_xxx_py
+         - assume_default_configuration
+         - delegate_options_to_subpackages
+         - quiet
+
+        """
+        for key, value in options.items():
+            if key in self.options:
+                self.options[key] = value
+            else:
+                raise ValueError('Unknown option: '+key)
+
+    def get_distribution(self):
+        """Return the distutils distribution object for self."""
+        from numpy.distutils.core import get_distribution
+        return get_distribution()
+
+    def _wildcard_get_subpackage(self, subpackage_name,
+                                 parent_name,
+                                 caller_level = 1):
+        l = subpackage_name.split('.')
+        subpackage_path = njoin([self.local_path]+l)
+        dirs = [_m for _m in sorted_glob(subpackage_path) if os.path.isdir(_m)]
+        config_list = []
+        for d in dirs:
+            if not os.path.isfile(njoin(d, '__init__.py')):
+                continue
+            if 'build' in d.split(os.sep):
+                continue
+            n = '.'.join(d.split(os.sep)[-len(l):])
+            c = self.get_subpackage(n,
+                                    parent_name = parent_name,
+                                    caller_level = caller_level+1)
+            config_list.extend(c)
+        return config_list
+
+    def _get_configuration_from_setup_py(self, setup_py,
+                                         subpackage_name,
+                                         subpackage_path,
+                                         parent_name,
+                                         caller_level = 1):
+        # In case setup_py imports local modules:
+        sys.path.insert(0, os.path.dirname(setup_py))
+        try:
+            setup_name = os.path.splitext(os.path.basename(setup_py))[0]
+            n = dot_join(self.name, subpackage_name, setup_name)
+            setup_module = exec_mod_from_location(
+                                '_'.join(n.split('.')), setup_py)
+            if not hasattr(setup_module, 'configuration'):
+                if not self.options['assume_default_configuration']:
+                    self.warn('Assuming default configuration '\
+                              '(%s does not define configuration())'\
+                              % (setup_module))
+                config = Configuration(subpackage_name, parent_name,
+                                       self.top_path, subpackage_path,
+                                       caller_level = caller_level + 1)
+            else:
+                pn = dot_join(*([parent_name] + subpackage_name.split('.')[:-1]))
+                args = (pn,)
+                if setup_module.configuration.__code__.co_argcount > 1:
+                    args = args + (self.top_path,)
+                config = setup_module.configuration(*args)
+            if config.name!=dot_join(parent_name, subpackage_name):
+                self.warn('Subpackage %r configuration returned as %r' % \
+                          (dot_join(parent_name, subpackage_name), config.name))
+        finally:
+            del sys.path[0]
+        return config
+
+    def get_subpackage(self,subpackage_name,
+                       subpackage_path=None,
+                       parent_name=None,
+                       caller_level = 1):
+        """Return list of subpackage configurations.
+
+        Parameters
+        ----------
+        subpackage_name : str or None
+            Name of the subpackage to get the configuration. '*' in
+            subpackage_name is handled as a wildcard.
+        subpackage_path : str
+            If None, then the path is assumed to be the local path plus the
+            subpackage_name. If a setup.py file is not found in the
+            subpackage_path, then a default configuration is used.
+        parent_name : str
+            Parent name.
+        """
+        if subpackage_name is None:
+            if subpackage_path is None:
+                raise ValueError(
+                    "either subpackage_name or subpackage_path must be specified")
+            subpackage_name = os.path.basename(subpackage_path)
+
+        # handle wildcards
+        l = subpackage_name.split('.')
+        if subpackage_path is None and '*' in subpackage_name:
+            return self._wildcard_get_subpackage(subpackage_name,
+                                                 parent_name,
+                                                 caller_level = caller_level+1)
+        assert '*' not in subpackage_name, repr((subpackage_name, subpackage_path, parent_name))
+        if subpackage_path is None:
+            subpackage_path = njoin([self.local_path] + l)
+        else:
+            subpackage_path = njoin([subpackage_path] + l[:-1])
+            subpackage_path = self.paths([subpackage_path])[0]
+        setup_py = njoin(subpackage_path, self.setup_name)
+        if not self.options['ignore_setup_xxx_py']:
+            if not os.path.isfile(setup_py):
+                setup_py = njoin(subpackage_path,
+                                 'setup_%s.py' % (subpackage_name))
+        if not os.path.isfile(setup_py):
+            if not self.options['assume_default_configuration']:
+                self.warn('Assuming default configuration '\
+                          '(%s/{setup_%s,setup}.py was not found)' \
+                          % (os.path.dirname(setup_py), subpackage_name))
+            config = Configuration(subpackage_name, parent_name,
+                                   self.top_path, subpackage_path,
+                                   caller_level = caller_level+1)
+        else:
+            config = self._get_configuration_from_setup_py(
+                setup_py,
+                subpackage_name,
+                subpackage_path,
+                parent_name,
+                caller_level = caller_level + 1)
+        if config:
+            return [config]
+        else:
+            return []
+
+    def add_subpackage(self,subpackage_name,
+                       subpackage_path=None,
+                       standalone = False):
+        """Add a sub-package to the current Configuration instance.
+
+        This is useful in a setup.py script for adding sub-packages to a
+        package.
+
+        Parameters
+        ----------
+        subpackage_name : str
+            name of the subpackage
+        subpackage_path : str
+            if given, the subpackage path such as the subpackage is in
+            subpackage_path / subpackage_name. If None,the subpackage is
+            assumed to be located in the local path / subpackage_name.
+        standalone : bool
+        """
+
+        if standalone:
+            parent_name = None
+        else:
+            parent_name = self.name
+        config_list = self.get_subpackage(subpackage_name, subpackage_path,
+                                          parent_name = parent_name,
+                                          caller_level = 2)
+        if not config_list:
+            self.warn('No configuration returned, assuming unavailable.')
+        for config in config_list:
+            d = config
+            if isinstance(config, Configuration):
+                d = config.todict()
+            assert isinstance(d, dict), repr(type(d))
+
+            self.info('Appending %s configuration to %s' \
+                      % (d.get('name'), self.name))
+            self.dict_append(**d)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add a subpackage '+ subpackage_name)
+
+    def add_data_dir(self, data_path):
+        """Recursively add files under data_path to data_files list.
+
+        Recursively add files under data_path to the list of data_files to be
+        installed (and distributed). The data_path can be either a relative
+        path-name, or an absolute path-name, or a 2-tuple where the first
+        argument shows where in the install directory the data directory
+        should be installed to.
+
+        Parameters
+        ----------
+        data_path : seq or str
+            Argument can be either
+
+                * 2-sequence (<datadir suffix>, <path to data directory>)
+                * path to data directory where python datadir suffix defaults
+                  to package dir.
+
+        Notes
+        -----
+        Rules for installation paths::
+
+            foo/bar -> (foo/bar, foo/bar) -> parent/foo/bar
+            (gun, foo/bar) -> parent/gun
+            foo/* -> (foo/a, foo/a), (foo/b, foo/b) -> parent/foo/a, parent/foo/b
+            (gun, foo/*) -> (gun, foo/a), (gun, foo/b) -> gun
+            (gun/*, foo/*) -> parent/gun/a, parent/gun/b
+            /foo/bar -> (bar, /foo/bar) -> parent/bar
+            (gun, /foo/bar) -> parent/gun
+            (fun/*/gun/*, sun/foo/bar) -> parent/fun/foo/gun/bar
+
+        Examples
+        --------
+        For example suppose the source directory contains fun/foo.dat and
+        fun/bar/car.dat:
+
+        >>> self.add_data_dir('fun')                       #doctest: +SKIP
+        >>> self.add_data_dir(('sun', 'fun'))              #doctest: +SKIP
+        >>> self.add_data_dir(('gun', '/full/path/to/fun'))#doctest: +SKIP
+
+        Will install data-files to the locations::
+
+            <package install directory>/
+              fun/
+                foo.dat
+                bar/
+                  car.dat
+              sun/
+                foo.dat
+                bar/
+                  car.dat
+              gun/
+                foo.dat
+                car.dat
+
+        """
+        if is_sequence(data_path):
+            d, data_path = data_path
+        else:
+            d = None
+        if is_sequence(data_path):
+            [self.add_data_dir((d, p)) for p in data_path]
+            return
+        if not is_string(data_path):
+            raise TypeError("not a string: %r" % (data_path,))
+        if d is None:
+            if os.path.isabs(data_path):
+                return self.add_data_dir((os.path.basename(data_path), data_path))
+            return self.add_data_dir((data_path, data_path))
+        paths = self.paths(data_path, include_non_existing=False)
+        if is_glob_pattern(data_path):
+            if is_glob_pattern(d):
+                pattern_list = allpath(d).split(os.sep)
+                pattern_list.reverse()
+                # /a/*//b/ -> /a/*/b
+                rl = list(range(len(pattern_list)-1)); rl.reverse()
+                for i in rl:
+                    if not pattern_list[i]:
+                        del pattern_list[i]
+                #
+                for path in paths:
+                    if not os.path.isdir(path):
+                        print('Not a directory, skipping', path)
+                        continue
+                    rpath = rel_path(path, self.local_path)
+                    path_list = rpath.split(os.sep)
+                    path_list.reverse()
+                    target_list = []
+                    i = 0
+                    for s in pattern_list:
+                        if is_glob_pattern(s):
+                            if i>=len(path_list):
+                                raise ValueError('cannot fill pattern %r with %r' \
+                                      % (d, path))
+                            target_list.append(path_list[i])
+                        else:
+                            assert s==path_list[i], repr((s, path_list[i], data_path, d, path, rpath))
+                            target_list.append(s)
+                        i += 1
+                    if path_list[i:]:
+                        self.warn('mismatch of pattern_list=%s and path_list=%s'\
+                                  % (pattern_list, path_list))
+                    target_list.reverse()
+                    self.add_data_dir((os.sep.join(target_list), path))
+            else:
+                for path in paths:
+                    self.add_data_dir((d, path))
+            return
+        assert not is_glob_pattern(d), repr(d)
+
+        dist = self.get_distribution()
+        if dist is not None and dist.data_files is not None:
+            data_files = dist.data_files
+        else:
+            data_files = self.data_files
+
+        for path in paths:
+            for d1, f in list(general_source_directories_files(path)):
+                target_path = os.path.join(self.path_in_package, d, d1)
+                data_files.append((target_path, f))
+
+    def _optimize_data_files(self):
+        data_dict = {}
+        for p, files in self.data_files:
+            if p not in data_dict:
+                data_dict[p] = set()
+            for f in files:
+                data_dict[p].add(f)
+        self.data_files[:] = [(p, list(files)) for p, files in data_dict.items()]
+
+    def add_data_files(self,*files):
+        """Add data files to configuration data_files.
+
+        Parameters
+        ----------
+        files : sequence
+            Argument(s) can be either
+
+                * 2-sequence (<datadir prefix>,<path to data file(s)>)
+                * paths to data files where python datadir prefix defaults
+                  to package dir.
+
+        Notes
+        -----
+        The form of each element of the files sequence is very flexible
+        allowing many combinations of where to get the files from the package
+        and where they should ultimately be installed on the system. The most
+        basic usage is for an element of the files argument sequence to be a
+        simple filename. This will cause that file from the local path to be
+        installed to the installation path of the self.name package (package
+        path). The file argument can also be a relative path in which case the
+        entire relative path will be installed into the package directory.
+        Finally, the file can be an absolute path name in which case the file
+        will be found at the absolute path name but installed to the package
+        path.
+
+        This basic behavior can be augmented by passing a 2-tuple in as the
+        file argument. The first element of the tuple should specify the
+        relative path (under the package install directory) where the
+        remaining sequence of files should be installed to (it has nothing to
+        do with the file-names in the source distribution). The second element
+        of the tuple is the sequence of files that should be installed. The
+        files in this sequence can be filenames, relative paths, or absolute
+        paths. For absolute paths the file will be installed in the top-level
+        package installation directory (regardless of the first argument).
+        Filenames and relative path names will be installed in the package
+        install directory under the path name given as the first element of
+        the tuple.
+
+        Rules for installation paths:
+
+          #. file.txt -> (., file.txt)-> parent/file.txt
+          #. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt
+          #. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt
+          #. ``*``.txt -> parent/a.txt, parent/b.txt
+          #. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt
+          #. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt
+          #. (sun, file.txt) -> parent/sun/file.txt
+          #. (sun, bar/file.txt) -> parent/sun/file.txt
+          #. (sun, /foo/bar/file.txt) -> parent/sun/file.txt
+          #. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+          #. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+          #. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt
+
+        An additional feature is that the path to a data-file can actually be
+        a function that takes no arguments and returns the actual path(s) to
+        the data-files. This is useful when the data files are generated while
+        building the package.
+
+        Examples
+        --------
+        Add files to the list of data_files to be included with the package.
+
+            >>> self.add_data_files('foo.dat',
+            ...     ('fun', ['gun.dat', 'nun/pun.dat', '/tmp/sun.dat']),
+            ...     'bar/cat.dat',
+            ...     '/full/path/to/can.dat')                   #doctest: +SKIP
+
+        will install these data files to::
+
+            <package install directory>/
+             foo.dat
+             fun/
+               gun.dat
+               nun/
+                 pun.dat
+             sun.dat
+             bar/
+               car.dat
+             can.dat
+
+        where <package install directory> is the package (or sub-package)
+        directory such as '/usr/lib/python2.4/site-packages/mypackage' ('C:
+        \\Python2.4 \\Lib \\site-packages \\mypackage') or
+        '/usr/lib/python2.4/site- packages/mypackage/mysubpackage' ('C:
+        \\Python2.4 \\Lib \\site-packages \\mypackage \\mysubpackage').
+        """
+
+        if len(files)>1:
+            for f in files:
+                self.add_data_files(f)
+            return
+        assert len(files)==1
+        if is_sequence(files[0]):
+            d, files = files[0]
+        else:
+            d = None
+        if is_string(files):
+            filepat = files
+        elif is_sequence(files):
+            if len(files)==1:
+                filepat = files[0]
+            else:
+                for f in files:
+                    self.add_data_files((d, f))
+                return
+        else:
+            raise TypeError(repr(type(files)))
+
+        if d is None:
+            if hasattr(filepat, '__call__'):
+                d = ''
+            elif os.path.isabs(filepat):
+                d = ''
+            else:
+                d = os.path.dirname(filepat)
+            self.add_data_files((d, files))
+            return
+
+        paths = self.paths(filepat, include_non_existing=False)
+        if is_glob_pattern(filepat):
+            if is_glob_pattern(d):
+                pattern_list = d.split(os.sep)
+                pattern_list.reverse()
+                for path in paths:
+                    path_list = path.split(os.sep)
+                    path_list.reverse()
+                    path_list.pop() # filename
+                    target_list = []
+                    i = 0
+                    for s in pattern_list:
+                        if is_glob_pattern(s):
+                            target_list.append(path_list[i])
+                            i += 1
+                        else:
+                            target_list.append(s)
+                    target_list.reverse()
+                    self.add_data_files((os.sep.join(target_list), path))
+            else:
+                self.add_data_files((d, paths))
+            return
+        assert not is_glob_pattern(d), repr((d, filepat))
+
+        dist = self.get_distribution()
+        if dist is not None and dist.data_files is not None:
+            data_files = dist.data_files
+        else:
+            data_files = self.data_files
+
+        data_files.append((os.path.join(self.path_in_package, d), paths))
+
+    ### XXX Implement add_py_modules
+
+    def add_define_macros(self, macros):
+        """Add define macros to configuration
+
+        Add the given sequence of macro name and value duples to the beginning
+        of the define_macros list This list will be visible to all extension
+        modules of the current package.
+        """
+        dist = self.get_distribution()
+        if dist is not None:
+            if not hasattr(dist, 'define_macros'):
+                dist.define_macros = []
+            dist.define_macros.extend(macros)
+        else:
+            self.define_macros.extend(macros)
+
+
+    def add_include_dirs(self,*paths):
+        """Add paths to configuration include directories.
+
+        Add the given sequence of paths to the beginning of the include_dirs
+        list. This list will be visible to all extension modules of the
+        current package.
+        """
+        include_dirs = self.paths(paths)
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.include_dirs is None:
+                dist.include_dirs = []
+            dist.include_dirs.extend(include_dirs)
+        else:
+            self.include_dirs.extend(include_dirs)
+
+    def add_headers(self,*files):
+        """Add installable headers to configuration.
+
+        Add the given sequence of files to the beginning of the headers list.
+        By default, headers will be installed under <python-
+        include>/<self.name.replace('.','/')>/ directory. If an item of files
+        is a tuple, then its first argument specifies the actual installation
+        location relative to the <python-include> path.
+
+        Parameters
+        ----------
+        files : str or seq
+            Argument(s) can be either:
+
+                * 2-sequence (<includedir suffix>,<path to header file(s)>)
+                * path(s) to header file(s) where python includedir suffix will
+                  default to package name.
+        """
+        headers = []
+        for path in files:
+            if is_string(path):
+                [headers.append((self.name, p)) for p in self.paths(path)]
+            else:
+                if not isinstance(path, (tuple, list)) or len(path) != 2:
+                    raise TypeError(repr(path))
+                [headers.append((path[0], p)) for p in self.paths(path[1])]
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.headers is None:
+                dist.headers = []
+            dist.headers.extend(headers)
+        else:
+            self.headers.extend(headers)
+
+    def paths(self,*paths,**kws):
+        """Apply glob to paths and prepend local_path if needed.
+
+        Applies glob.glob(...) to each path in the sequence (if needed) and
+        pre-pends the local_path if needed. Because this is called on all
+        source lists, this allows wildcard characters to be specified in lists
+        of sources for extension modules and libraries and scripts and allows
+        path-names be relative to the source directory.
+
+        """
+        include_non_existing = kws.get('include_non_existing', True)
+        return gpaths(paths,
+                      local_path = self.local_path,
+                      include_non_existing=include_non_existing)
+
+    def _fix_paths_dict(self, kw):
+        for k in kw.keys():
+            v = kw[k]
+            if k in ['sources', 'depends', 'include_dirs', 'library_dirs',
+                     'module_dirs', 'extra_objects']:
+                new_v = self.paths(v)
+                kw[k] = new_v
+
+    def add_extension(self,name,sources,**kw):
+        """Add extension to configuration.
+
+        Create and add an Extension instance to the ext_modules list. This
+        method also takes the following optional keyword arguments that are
+        passed on to the Extension constructor.
+
+        Parameters
+        ----------
+        name : str
+            name of the extension
+        sources : seq
+            list of the sources. The list of sources may contain functions
+            (called source generators) which must take an extension instance
+            and a build directory as inputs and return a source file or list of
+            source files or None. If None is returned then no sources are
+            generated. If the Extension instance has no sources after
+            processing all source generators, then no extension module is
+            built.
+        include_dirs :
+        define_macros :
+        undef_macros :
+        library_dirs :
+        libraries :
+        runtime_library_dirs :
+        extra_objects :
+        extra_compile_args :
+        extra_link_args :
+        extra_f77_compile_args :
+        extra_f90_compile_args :
+        export_symbols :
+        swig_opts :
+        depends :
+            The depends list contains paths to files or directories that the
+            sources of the extension module depend on. If any path in the
+            depends list is newer than the extension module, then the module
+            will be rebuilt.
+        language :
+        f2py_options :
+        module_dirs :
+        extra_info : dict or list
+            dict or list of dict of keywords to be appended to keywords.
+
+        Notes
+        -----
+        The self.paths(...) method is applied to all lists that may contain
+        paths.
+        """
+        ext_args = copy.copy(kw)
+        ext_args['name'] = dot_join(self.name, name)
+        ext_args['sources'] = sources
+
+        if 'extra_info' in ext_args:
+            extra_info = ext_args['extra_info']
+            del ext_args['extra_info']
+            if isinstance(extra_info, dict):
+                extra_info = [extra_info]
+            for info in extra_info:
+                assert isinstance(info, dict), repr(info)
+                dict_append(ext_args,**info)
+
+        self._fix_paths_dict(ext_args)
+
+        # Resolve out-of-tree dependencies
+        libraries = ext_args.get('libraries', [])
+        libnames = []
+        ext_args['libraries'] = []
+        for libname in libraries:
+            if isinstance(libname, tuple):
+                self._fix_paths_dict(libname[1])
+
+            # Handle library names of the form libname@relative/path/to/library
+            if '@' in libname:
+                lname, lpath = libname.split('@', 1)
+                lpath = os.path.abspath(njoin(self.local_path, lpath))
+                if os.path.isdir(lpath):
+                    c = self.get_subpackage(None, lpath,
+                                            caller_level = 2)
+                    if isinstance(c, Configuration):
+                        c = c.todict()
+                    for l in [l[0] for l in c.get('libraries', [])]:
+                        llname = l.split('__OF__', 1)[0]
+                        if llname == lname:
+                            c.pop('name', None)
+                            dict_append(ext_args,**c)
+                            break
+                    continue
+            libnames.append(libname)
+
+        ext_args['libraries'] = libnames + ext_args['libraries']
+        ext_args['define_macros'] = \
+            self.define_macros + ext_args.get('define_macros', [])
+
+        from numpy.distutils.core import Extension
+        ext = Extension(**ext_args)
+        self.ext_modules.append(ext)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add an extension '+name)
+        return ext
+
+    def add_library(self,name,sources,**build_info):
+        """
+        Add library to configuration.
+
+        Parameters
+        ----------
+        name : str
+            Name of the extension.
+        sources : sequence
+            List of the sources. The list of sources may contain functions
+            (called source generators) which must take an extension instance
+            and a build directory as inputs and return a source file or list of
+            source files or None. If None is returned then no sources are
+            generated. If the Extension instance has no sources after
+            processing all source generators, then no extension module is
+            built.
+        build_info : dict, optional
+            The following keys are allowed:
+
+                * depends
+                * macros
+                * include_dirs
+                * extra_compiler_args
+                * extra_f77_compile_args
+                * extra_f90_compile_args
+                * f2py_options
+                * language
+
+        """
+        self._add_library(name, sources, None, build_info)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add a library '+ name)
+
+    def _add_library(self, name, sources, install_dir, build_info):
+        """Common implementation for add_library and add_installed_library. Do
+        not use directly"""
+        build_info = copy.copy(build_info)
+        build_info['sources'] = sources
+
+        # Sometimes, depends is not set up to an empty list by default, and if
+        # depends is not given to add_library, distutils barfs (#1134)
+        if not 'depends' in build_info:
+            build_info['depends'] = []
+
+        self._fix_paths_dict(build_info)
+
+        # Add to libraries list so that it is build with build_clib
+        self.libraries.append((name, build_info))
+
+    def add_installed_library(self, name, sources, install_dir, build_info=None):
+        """
+        Similar to add_library, but the specified library is installed.
+
+        Most C libraries used with `distutils` are only used to build python
+        extensions, but libraries built through this method will be installed
+        so that they can be reused by third-party packages.
+
+        Parameters
+        ----------
+        name : str
+            Name of the installed library.
+        sources : sequence
+            List of the library's source files. See `add_library` for details.
+        install_dir : str
+            Path to install the library, relative to the current sub-package.
+        build_info : dict, optional
+            The following keys are allowed:
+
+                * depends
+                * macros
+                * include_dirs
+                * extra_compiler_args
+                * extra_f77_compile_args
+                * extra_f90_compile_args
+                * f2py_options
+                * language
+
+        Returns
+        -------
+        None
+
+        See Also
+        --------
+        add_library, add_npy_pkg_config, get_info
+
+        Notes
+        -----
+        The best way to encode the options required to link against the specified
+        C libraries is to use a "libname.ini" file, and use `get_info` to
+        retrieve the required options (see `add_npy_pkg_config` for more
+        information).
+
+        """
+        if not build_info:
+            build_info = {}
+
+        install_dir = os.path.join(self.package_path, install_dir)
+        self._add_library(name, sources, install_dir, build_info)
+        self.installed_libraries.append(InstallableLib(name, build_info, install_dir))
+
+    def add_npy_pkg_config(self, template, install_dir, subst_dict=None):
+        """
+        Generate and install a npy-pkg config file from a template.
+
+        The config file generated from `template` is installed in the
+        given install directory, using `subst_dict` for variable substitution.
+
+        Parameters
+        ----------
+        template : str
+            The path of the template, relatively to the current package path.
+        install_dir : str
+            Where to install the npy-pkg config file, relatively to the current
+            package path.
+        subst_dict : dict, optional
+            If given, any string of the form ``@key@`` will be replaced by
+            ``subst_dict[key]`` in the template file when installed. The install
+            prefix is always available through the variable ``@prefix@``, since the
+            install prefix is not easy to get reliably from setup.py.
+
+        See also
+        --------
+        add_installed_library, get_info
+
+        Notes
+        -----
+        This works for both standard installs and in-place builds, i.e. the
+        ``@prefix@`` refer to the source directory for in-place builds.
+
+        Examples
+        --------
+        ::
+
+            config.add_npy_pkg_config('foo.ini.in', 'lib', {'foo': bar})
+
+        Assuming the foo.ini.in file has the following content::
+
+            [meta]
+            Name=@foo@
+            Version=1.0
+            Description=dummy description
+
+            [default]
+            Cflags=-I@prefix@/include
+            Libs=
+
+        The generated file will have the following content::
+
+            [meta]
+            Name=bar
+            Version=1.0
+            Description=dummy description
+
+            [default]
+            Cflags=-Iprefix_dir/include
+            Libs=
+
+        and will be installed as foo.ini in the 'lib' subpath.
+
+        When cross-compiling with numpy distutils, it might be necessary to
+        use modified npy-pkg-config files.  Using the default/generated files
+        will link with the host libraries (i.e. libnpymath.a).  For
+        cross-compilation you of-course need to link with target libraries,
+        while using the host Python installation.
+
+        You can copy out the numpy/core/lib/npy-pkg-config directory, add a
+        pkgdir value to the .ini files and set NPY_PKG_CONFIG_PATH environment
+        variable to point to the directory with the modified npy-pkg-config
+        files.
+
+        Example npymath.ini modified for cross-compilation::
+
+            [meta]
+            Name=npymath
+            Description=Portable, core math library implementing C99 standard
+            Version=0.1
+
+            [variables]
+            pkgname=numpy.core
+            pkgdir=/build/arm-linux-gnueabi/sysroot/usr/lib/python3.7/site-packages/numpy/core
+            prefix=${pkgdir}
+            libdir=${prefix}/lib
+            includedir=${prefix}/include
+
+            [default]
+            Libs=-L${libdir} -lnpymath
+            Cflags=-I${includedir}
+            Requires=mlib
+
+            [msvc]
+            Libs=/LIBPATH:${libdir} npymath.lib
+            Cflags=/INCLUDE:${includedir}
+            Requires=mlib
+
+        """
+        if subst_dict is None:
+            subst_dict = {}
+        template = os.path.join(self.package_path, template)
+
+        if self.name in self.installed_pkg_config:
+            self.installed_pkg_config[self.name].append((template, install_dir,
+                subst_dict))
+        else:
+            self.installed_pkg_config[self.name] = [(template, install_dir,
+                subst_dict)]
+
+
+    def add_scripts(self,*files):
+        """Add scripts to configuration.
+
+        Add the sequence of files to the beginning of the scripts list.
+        Scripts will be installed under the <prefix>/bin/ directory.
+
+        """
+        scripts = self.paths(files)
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.scripts is None:
+                dist.scripts = []
+            dist.scripts.extend(scripts)
+        else:
+            self.scripts.extend(scripts)
+
+    def dict_append(self,**dict):
+        for key in self.list_keys:
+            a = getattr(self, key)
+            a.extend(dict.get(key, []))
+        for key in self.dict_keys:
+            a = getattr(self, key)
+            a.update(dict.get(key, {}))
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        for key in dict.keys():
+            if key not in known_keys:
+                a = getattr(self, key, None)
+                if a and a==dict[key]: continue
+                self.warn('Inheriting attribute %r=%r from %r' \
+                          % (key, dict[key], dict.get('name', '?')))
+                setattr(self, key, dict[key])
+                self.extra_keys.append(key)
+            elif key in self.extra_keys:
+                self.info('Ignoring attempt to set %r (from %r to %r)' \
+                          % (key, getattr(self, key), dict[key]))
+            elif key in known_keys:
+                # key is already processed above
+                pass
+            else:
+                raise ValueError("Don't know about key=%r" % (key))
+
+    def __str__(self):
+        from pprint import pformat
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        s = '<'+5*'-' + '\n'
+        s += 'Configuration of '+self.name+':\n'
+        known_keys.sort()
+        for k in known_keys:
+            a = getattr(self, k, None)
+            if a:
+                s += '%s = %s\n' % (k, pformat(a))
+        s += 5*'-' + '>'
+        return s
+
+    def get_config_cmd(self):
+        """
+        Returns the numpy.distutils config command instance.
+        """
+        cmd = get_cmd('config')
+        cmd.ensure_finalized()
+        cmd.dump_source = 0
+        cmd.noisy = 0
+        old_path = os.environ.get('PATH')
+        if old_path:
+            path = os.pathsep.join(['.', old_path])
+            os.environ['PATH'] = path
+        return cmd
+
+    def get_build_temp_dir(self):
+        """
+        Return a path to a temporary directory where temporary files should be
+        placed.
+        """
+        cmd = get_cmd('build')
+        cmd.ensure_finalized()
+        return cmd.build_temp
+
+    def have_f77c(self):
+        """Check for availability of Fortran 77 compiler.
+
+        Use it inside source generating function to ensure that
+        setup distribution instance has been initialized.
+
+        Notes
+        -----
+        True if a Fortran 77 compiler is available (because a simple Fortran 77
+        code was able to be compiled successfully).
+        """
+        simple_fortran_subroutine = '''
+        subroutine simple
+        end
+        '''
+        config_cmd = self.get_config_cmd()
+        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f77')
+        return flag
+
+    def have_f90c(self):
+        """Check for availability of Fortran 90 compiler.
+
+        Use it inside source generating function to ensure that
+        setup distribution instance has been initialized.
+
+        Notes
+        -----
+        True if a Fortran 90 compiler is available (because a simple Fortran
+        90 code was able to be compiled successfully)
+        """
+        simple_fortran_subroutine = '''
+        subroutine simple
+        end
+        '''
+        config_cmd = self.get_config_cmd()
+        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f90')
+        return flag
+
+    def append_to(self, extlib):
+        """Append libraries, include_dirs to extension or library item.
+        """
+        if is_sequence(extlib):
+            lib_name, build_info = extlib
+            dict_append(build_info,
+                        libraries=self.libraries,
+                        include_dirs=self.include_dirs)
+        else:
+            from numpy.distutils.core import Extension
+            assert isinstance(extlib, Extension), repr(extlib)
+            extlib.libraries.extend(self.libraries)
+            extlib.include_dirs.extend(self.include_dirs)
+
+    def _get_svn_revision(self, path):
+        """Return path's SVN revision number.
+        """
+        try:
+            output = subprocess.check_output(['svnversion'], cwd=path)
+        except (subprocess.CalledProcessError, OSError):
+            pass
+        else:
+            m = re.match(rb'(?P<revision>\d+)', output)
+            if m:
+                return int(m.group('revision'))
+
+        if sys.platform=='win32' and os.environ.get('SVN_ASP_DOT_NET_HACK', None):
+            entries = njoin(path, '_svn', 'entries')
+        else:
+            entries = njoin(path, '.svn', 'entries')
+        if os.path.isfile(entries):
+            with open(entries) as f:
+                fstr = f.read()
+            if fstr[:5] == '<?xml':  # pre 1.4
+                m = re.search(r'revision="(?P<revision>\d+)"', fstr)
+                if m:
+                    return int(m.group('revision'))
+            else:  # non-xml entries file --- check to be sure that
+                m = re.search(r'dir[\n\r]+(?P<revision>\d+)', fstr)
+                if m:
+                    return int(m.group('revision'))
+        return None
+
+    def _get_hg_revision(self, path):
+        """Return path's Mercurial revision number.
+        """
+        try:
+            output = subprocess.check_output(
+                ['hg', 'identify', '--num'], cwd=path)
+        except (subprocess.CalledProcessError, OSError):
+            pass
+        else:
+            m = re.match(rb'(?P<revision>\d+)', output)
+            if m:
+                return int(m.group('revision'))
+
+        branch_fn = njoin(path, '.hg', 'branch')
+        branch_cache_fn = njoin(path, '.hg', 'branch.cache')
+
+        if os.path.isfile(branch_fn):
+            branch0 = None
+            with open(branch_fn) as f:
+                revision0 = f.read().strip()
+
+            branch_map = {}
+            with open(branch_cache_fn) as f:
+                for line in f:
+                    branch1, revision1  = line.split()[:2]
+                    if revision1==revision0:
+                        branch0 = branch1
+                    try:
+                        revision1 = int(revision1)
+                    except ValueError:
+                        continue
+                    branch_map[branch1] = revision1
+
+            return branch_map.get(branch0)
+
+        return None
+
+
+    def get_version(self, version_file=None, version_variable=None):
+        """Try to get version string of a package.
+
+        Return a version string of the current package or None if the version
+        information could not be detected.
+
+        Notes
+        -----
+        This method scans files named
+        __version__.py, <packagename>_version.py, version.py, and
+        __svn_version__.py for string variables version, __version__, and
+        <packagename>_version, until a version number is found.
+        """
+        version = getattr(self, 'version', None)
+        if version is not None:
+            return version
+
+        # Get version from version file.
+        if version_file is None:
+            files = ['__version__.py',
+                     self.name.split('.')[-1]+'_version.py',
+                     'version.py',
+                     '__svn_version__.py',
+                     '__hg_version__.py']
+        else:
+            files = [version_file]
+        if version_variable is None:
+            version_vars = ['version',
+                            '__version__',
+                            self.name.split('.')[-1]+'_version']
+        else:
+            version_vars = [version_variable]
+        for f in files:
+            fn = njoin(self.local_path, f)
+            if os.path.isfile(fn):
+                info = ('.py', 'U', 1)
+                name = os.path.splitext(os.path.basename(fn))[0]
+                n = dot_join(self.name, name)
+                try:
+                    version_module = exec_mod_from_location(
+                                        '_'.join(n.split('.')), fn)
+                except ImportError as e:
+                    self.warn(str(e))
+                    version_module = None
+                if version_module is None:
+                    continue
+
+                for a in version_vars:
+                    version = getattr(version_module, a, None)
+                    if version is not None:
+                        break
+
+                # Try if versioneer module
+                try:
+                    version = version_module.get_versions()['version']
+                except AttributeError:
+                    pass
+
+                if version is not None:
+                    break
+
+        if version is not None:
+            self.version = version
+            return version
+
+        # Get version as SVN or Mercurial revision number
+        revision = self._get_svn_revision(self.local_path)
+        if revision is None:
+            revision = self._get_hg_revision(self.local_path)
+
+        if revision is not None:
+            version = str(revision)
+            self.version = version
+
+        return version
+
+    def make_svn_version_py(self, delete=True):
+        """Appends a data function to the data_files list that will generate
+        __svn_version__.py file to the current package directory.
+
+        Generate package __svn_version__.py file from SVN revision number,
+        it will be removed after python exits but will be available
+        when sdist, etc commands are executed.
+
+        Notes
+        -----
+        If __svn_version__.py existed before, nothing is done.
+
+        This is
+        intended for working with source directories that are in an SVN
+        repository.
+        """
+        target = njoin(self.local_path, '__svn_version__.py')
+        revision = self._get_svn_revision(self.local_path)
+        if os.path.isfile(target) or revision is None:
+            return
+        else:
+            def generate_svn_version_py():
+                if not os.path.isfile(target):
+                    version = str(revision)
+                    self.info('Creating %s (version=%r)' % (target, version))
+                    with open(target, 'w') as f:
+                        f.write('version = %r\n' % (version))
+
+                def rm_file(f=target,p=self.info):
+                    if delete:
+                        try: os.remove(f); p('removed '+f)
+                        except OSError: pass
+                        try: os.remove(f+'c'); p('removed '+f+'c')
+                        except OSError: pass
+
+                atexit.register(rm_file)
+
+                return target
+
+            self.add_data_files(('', generate_svn_version_py()))
+
+    def make_hg_version_py(self, delete=True):
+        """Appends a data function to the data_files list that will generate
+        __hg_version__.py file to the current package directory.
+
+        Generate package __hg_version__.py file from Mercurial revision,
+        it will be removed after python exits but will be available
+        when sdist, etc commands are executed.
+
+        Notes
+        -----
+        If __hg_version__.py existed before, nothing is done.
+
+        This is intended for working with source directories that are
+        in an Mercurial repository.
+        """
+        target = njoin(self.local_path, '__hg_version__.py')
+        revision = self._get_hg_revision(self.local_path)
+        if os.path.isfile(target) or revision is None:
+            return
+        else:
+            def generate_hg_version_py():
+                if not os.path.isfile(target):
+                    version = str(revision)
+                    self.info('Creating %s (version=%r)' % (target, version))
+                    with open(target, 'w') as f:
+                        f.write('version = %r\n' % (version))
+
+                def rm_file(f=target,p=self.info):
+                    if delete:
+                        try: os.remove(f); p('removed '+f)
+                        except OSError: pass
+                        try: os.remove(f+'c'); p('removed '+f+'c')
+                        except OSError: pass
+
+                atexit.register(rm_file)
+
+                return target
+
+            self.add_data_files(('', generate_hg_version_py()))
+
+    def make_config_py(self,name='__config__'):
+        """Generate package __config__.py file containing system_info
+        information used during building the package.
+
+        This file is installed to the
+        package installation directory.
+
+        """
+        self.py_modules.append((self.name, name, generate_config_py))
+
+    def get_info(self,*names):
+        """Get resources information.
+
+        Return information (from system_info.get_info) for all of the names in
+        the argument list in a single dictionary.
+        """
+        from .system_info import get_info, dict_append
+        info_dict = {}
+        for a in names:
+            dict_append(info_dict,**get_info(a))
+        return info_dict
+
+
+def get_cmd(cmdname, _cache={}):
+    if cmdname not in _cache:
+        import distutils.core
+        dist = distutils.core._setup_distribution
+        if dist is None:
+            from distutils.errors import DistutilsInternalError
+            raise DistutilsInternalError(
+                  'setup distribution instance not initialized')
+        cmd = dist.get_command_obj(cmdname)
+        _cache[cmdname] = cmd
+    return _cache[cmdname]
+
+def get_numpy_include_dirs():
+    # numpy_include_dirs are set by numpy/core/setup.py, otherwise []
+    include_dirs = Configuration.numpy_include_dirs[:]
+    if not include_dirs:
+        import numpy
+        include_dirs = [ numpy.get_include() ]
+    # else running numpy/core/setup.py
+    return include_dirs
+
+def get_npy_pkg_dir():
+    """Return the path where to find the npy-pkg-config directory.
+
+    If the NPY_PKG_CONFIG_PATH environment variable is set, the value of that
+    is returned.  Otherwise, a path inside the location of the numpy module is
+    returned.
+
+    The NPY_PKG_CONFIG_PATH can be useful when cross-compiling, maintaining
+    customized npy-pkg-config .ini files for the cross-compilation
+    environment, and using them when cross-compiling.
+
+    """
+    d = os.environ.get('NPY_PKG_CONFIG_PATH')
+    if d is not None:
+        return d
+    spec = importlib.util.find_spec('numpy')
+    d = os.path.join(os.path.dirname(spec.origin),
+            'core', 'lib', 'npy-pkg-config')
+    return d
+
+def get_pkg_info(pkgname, dirs=None):
+    """
+    Return library info for the given package.
+
+    Parameters
+    ----------
+    pkgname : str
+        Name of the package (should match the name of the .ini file, without
+        the extension, e.g. foo for the file foo.ini).
+    dirs : sequence, optional
+        If given, should be a sequence of additional directories where to look
+        for npy-pkg-config files. Those directories are searched prior to the
+        NumPy directory.
+
+    Returns
+    -------
+    pkginfo : class instance
+        The `LibraryInfo` instance containing the build information.
+
+    Raises
+    ------
+    PkgNotFound
+        If the package is not found.
+
+    See Also
+    --------
+    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
+    get_info
+
+    """
+    from numpy.distutils.npy_pkg_config import read_config
+
+    if dirs:
+        dirs.append(get_npy_pkg_dir())
+    else:
+        dirs = [get_npy_pkg_dir()]
+    return read_config(pkgname, dirs)
+
+def get_info(pkgname, dirs=None):
+    """
+    Return an info dict for a given C library.
+
+    The info dict contains the necessary options to use the C library.
+
+    Parameters
+    ----------
+    pkgname : str
+        Name of the package (should match the name of the .ini file, without
+        the extension, e.g. foo for the file foo.ini).
+    dirs : sequence, optional
+        If given, should be a sequence of additional directories where to look
+        for npy-pkg-config files. Those directories are searched prior to the
+        NumPy directory.
+
+    Returns
+    -------
+    info : dict
+        The dictionary with build information.
+
+    Raises
+    ------
+    PkgNotFound
+        If the package is not found.
+
+    See Also
+    --------
+    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
+    get_pkg_info
+
+    Examples
+    --------
+    To get the necessary information for the npymath library from NumPy:
+
+    >>> npymath_info = np.distutils.misc_util.get_info('npymath')
+    >>> npymath_info                                    #doctest: +SKIP
+    {'define_macros': [], 'libraries': ['npymath'], 'library_dirs':
+    ['.../numpy/core/lib'], 'include_dirs': ['.../numpy/core/include']}
+
+    This info dict can then be used as input to a `Configuration` instance::
+
+      config.add_extension('foo', sources=['foo.c'], extra_info=npymath_info)
+
+    """
+    from numpy.distutils.npy_pkg_config import parse_flags
+    pkg_info = get_pkg_info(pkgname, dirs)
+
+    # Translate LibraryInfo instance into a build_info dict
+    info = parse_flags(pkg_info.cflags())
+    for k, v in parse_flags(pkg_info.libs()).items():
+        info[k].extend(v)
+
+    # add_extension extra_info argument is ANAL
+    info['define_macros'] = info['macros']
+    del info['macros']
+    del info['ignored']
+
+    return info
+
+def is_bootstrapping():
+    import builtins
+
+    try:
+        builtins.__NUMPY_SETUP__
+        return True
+    except AttributeError:
+        return False
+
+
+#########################
+
+def default_config_dict(name = None, parent_name = None, local_path=None):
+    """Return a configuration dictionary for usage in
+    configuration() function defined in file setup_<name>.py.
+    """
+    import warnings
+    warnings.warn('Use Configuration(%r,%r,top_path=%r) instead of '\
+                  'deprecated default_config_dict(%r,%r,%r)'
+                  % (name, parent_name, local_path,
+                     name, parent_name, local_path,
+                     ), stacklevel=2)
+    c = Configuration(name, parent_name, local_path)
+    return c.todict()
+
+
+def dict_append(d, **kws):
+    for k, v in kws.items():
+        if k in d:
+            ov = d[k]
+            if isinstance(ov, str):
+                d[k] = v
+            else:
+                d[k].extend(v)
+        else:
+            d[k] = v
+
+def appendpath(prefix, path):
+    if os.path.sep != '/':
+        prefix = prefix.replace('/', os.path.sep)
+        path = path.replace('/', os.path.sep)
+    drive = ''
+    if os.path.isabs(path):
+        drive = os.path.splitdrive(prefix)[0]
+        absprefix = os.path.splitdrive(os.path.abspath(prefix))[1]
+        pathdrive, path = os.path.splitdrive(path)
+        d = os.path.commonprefix([absprefix, path])
+        if os.path.join(absprefix[:len(d)], absprefix[len(d):]) != absprefix \
+           or os.path.join(path[:len(d)], path[len(d):]) != path:
+            # Handle invalid paths
+            d = os.path.dirname(d)
+        subpath = path[len(d):]
+        if os.path.isabs(subpath):
+            subpath = subpath[1:]
+    else:
+        subpath = path
+    return os.path.normpath(njoin(drive + prefix, subpath))
+
+def generate_config_py(target):
+    """Generate config.py file containing system_info information
+    used during building the package.
+
+    Usage:
+        config['py_modules'].append((packagename, '__config__',generate_config_py))
+    """
+    from numpy.distutils.system_info import system_info
+    from distutils.dir_util import mkpath
+    mkpath(os.path.dirname(target))
+    with open(target, 'w') as f:
+        f.write('# This file is generated by numpy\'s %s\n' % (os.path.basename(sys.argv[0])))
+        f.write('# It contains system_info results at the time of building this package.\n')
+        f.write('__all__ = ["get_info","show"]\n\n')
+
+        # For gfortran+msvc combination, extra shared libraries may exist
+        f.write(textwrap.dedent("""
+            import os
+            import sys
+
+            extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
+
+            if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
+                os.add_dll_directory(extra_dll_dir)
+
+            """))
+
+        for k, i in system_info.saved_results.items():
+            f.write('%s=%r\n' % (k, i))
+        f.write(textwrap.dedent(r'''
+            def get_info(name):
+                g = globals()
+                return g.get(name, g.get(name + "_info", {}))
+
+            def show():
+                """
+                Show libraries in the system on which NumPy was built.
+
+                Print information about various resources (libraries, library
+                directories, include directories, etc.) in the system on which
+                NumPy was built.
+
+                See Also
+                --------
+                get_include : Returns the directory containing NumPy C
+                              header files.
+
+                Notes
+                -----
+                1. Classes specifying the information to be printed are defined
+                   in the `numpy.distutils.system_info` module.
+
+                   Information may include:
+
+                   * ``language``: language used to write the libraries (mostly
+                     C or f77)
+                   * ``libraries``: names of libraries found in the system
+                   * ``library_dirs``: directories containing the libraries
+                   * ``include_dirs``: directories containing library header files
+                   * ``src_dirs``: directories containing library source files
+                   * ``define_macros``: preprocessor macros used by
+                     ``distutils.setup``
+                   * ``baseline``: minimum CPU features required
+                   * ``found``: dispatched features supported in the system
+                   * ``not found``: dispatched features that are not supported
+                     in the system
+
+                2. NumPy BLAS/LAPACK Installation Notes
+
+                   Installing a numpy wheel (``pip install numpy`` or force it
+                   via ``pip install numpy --only-binary :numpy: numpy``) includes
+                   an OpenBLAS implementation of the BLAS and LAPACK linear algebra
+                   APIs. In this case, ``library_dirs`` reports the original build
+                   time configuration as compiled with gcc/gfortran; at run time
+                   the OpenBLAS library is in
+                   ``site-packages/numpy.libs/`` (linux), or
+                   ``site-packages/numpy/.dylibs/`` (macOS), or
+                   ``site-packages/numpy/.libs/`` (windows).
+
+                   Installing numpy from source
+                   (``pip install numpy --no-binary numpy``) searches for BLAS and
+                   LAPACK dynamic link libraries at build time as influenced by
+                   environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
+                   NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
+                   or the optional file ``~/.numpy-site.cfg``.
+                   NumPy remembers those locations and expects to load the same
+                   libraries at run-time.
+                   In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
+                   library) is in the default build-time search order after
+                   'openblas'.
+
+                Examples
+                --------
+                >>> import numpy as np
+                >>> np.show_config()
+                blas_opt_info:
+                    language = c
+                    define_macros = [('HAVE_CBLAS', None)]
+                    libraries = ['openblas', 'openblas']
+                    library_dirs = ['/usr/local/lib']
+                """
+                from numpy.core._multiarray_umath import (
+                    __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+                )
+                for name,info_dict in globals().items():
+                    if name[0] == "_" or type(info_dict) is not type({}): continue
+                    print(name + ":")
+                    if not info_dict:
+                        print("  NOT AVAILABLE")
+                    for k,v in info_dict.items():
+                        v = str(v)
+                        if k == "sources" and len(v) > 200:
+                            v = v[:60] + " ...\n... " + v[-60:]
+                        print("    %s = %s" % (k,v))
+
+                features_found, features_not_found = [], []
+                for feature in __cpu_dispatch__:
+                    if __cpu_features__[feature]:
+                        features_found.append(feature)
+                    else:
+                        features_not_found.append(feature)
+
+                print("Supported SIMD extensions in this NumPy install:")
+                print("    baseline = %s" % (','.join(__cpu_baseline__)))
+                print("    found = %s" % (','.join(features_found)))
+                print("    not found = %s" % (','.join(features_not_found)))
+
+                    '''))
+
+    return target
+
+def msvc_version(compiler):
+    """Return version major and minor of compiler instance if it is
+    MSVC, raise an exception otherwise."""
+    if not compiler.compiler_type == "msvc":
+        raise ValueError("Compiler instance is not msvc (%s)"\
+                         % compiler.compiler_type)
+    return compiler._MSVCCompiler__version
+
+def get_build_architecture():
+    # Importing distutils.msvccompiler triggers a warning on non-Windows
+    # systems, so delay the import to here.
+    from distutils.msvccompiler import get_build_architecture
+    return get_build_architecture()
+
+
+_cxx_ignore_flags = {'-Werror=implicit-function-declaration', '-std=c99'}
+
+
+def sanitize_cxx_flags(cxxflags):
+    '''
+    Some flags are valid for C but not C++. Prune them.
+    '''
+    return [flag for flag in cxxflags if flag not in _cxx_ignore_flags]
+
+
+def exec_mod_from_location(modname, modfile):
+    '''
+    Use importlib machinery to import a module `modname` from the file
+    `modfile`. Depending on the `spec.loader`, the module may not be
+    registered in sys.modules.
+    '''
+    spec = importlib.util.spec_from_file_location(modname, modfile)
+    foo = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(foo)
+    return foo
diff --git a/shared/package-files/d8/d8cf7f21c15f04713e14b3ce1a89e60ecf2cd4f69a2c328e901eb70426a6be2d/nonzero_numpy_compositeimplicitautograd_dispatch.h b/shared/package-files/d8/d8cf7f21c15f04713e14b3ce1a89e60ecf2cd4f69a2c328e901eb70426a6be2d/nonzero_numpy_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b1a2ff600d2752711597d3cfbea4fdb0c3b0493
--- /dev/null
+++ b/shared/package-files/d8/d8cf7f21c15f04713e14b3ce1a89e60ecf2cd4f69a2c328e901eb70426a6be2d/nonzero_numpy_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> nonzero_numpy(const at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/d8/d8d208e5a8560c9193927b3e21c2f5a8e8a06c3362574ce7d16c50f4972e53e4/ast_mod.py b/shared/package-files/d8/d8d208e5a8560c9193927b3e21c2f5a8e8a06c3362574ce7d16c50f4972e53e4/ast_mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..e28b9f1231fe41721dfcc58aa98c718689ac4407
--- /dev/null
+++ b/shared/package-files/d8/d8d208e5a8560c9193927b3e21c2f5a8e8a06c3362574ce7d16c50f4972e53e4/ast_mod.py
@@ -0,0 +1,320 @@
+"""
+This module contains utility function and classes to inject simple ast
+transformations based on code strings into IPython. While it is already possible
+with ast-transformers it is not easy to directly manipulate ast.
+
+
+IPython has pre-code and post-code hooks, but are ran from within the IPython
+machinery so may be inappropriate, for example for performance mesurement.
+
+This module give you tools to simplify this, and expose 2 classes:
+
+- `ReplaceCodeTransformer` which is a simple ast transformer based on code
+  template,
+
+and for advance case:
+
+- `Mangler` which is a simple ast transformer that mangle names in the ast.
+
+
+Example, let's try to make a simple version of the ``timeit`` magic, that run a
+code snippet 10 times and print the average time taken.
+
+Basically we want to run :
+
+.. code-block:: python
+
+    from time import perf_counter
+    now = perf_counter()
+    for i in range(10):
+        __code__ # our code
+    print(f"Time taken: {(perf_counter() - now)/10}")
+    __ret__ # the result of the last statement
+
+Where ``__code__`` is the code snippet we want to run, and ``__ret__`` is the
+result, so that if we for example run `dataframe.head()` IPython still display
+the head of dataframe instead of nothing.
+
+Here is a complete example of a file `timit2.py` that define such a magic:
+
+.. code-block:: python
+
+    from IPython.core.magic import (
+        Magics,
+        magics_class,
+        line_cell_magic,
+    )
+    from IPython.core.magics.ast_mod import ReplaceCodeTransformer
+    from textwrap import dedent
+    import ast
+
+    template = template = dedent('''
+        from time import perf_counter
+        now = perf_counter()
+        for i in range(10):
+            __code__
+        print(f"Time taken: {(perf_counter() - now)/10}")
+        __ret__
+    '''
+    )
+
+
+    @magics_class
+    class AstM(Magics):
+        @line_cell_magic
+        def t2(self, line, cell):
+            transformer = ReplaceCodeTransformer.from_string(template)
+            transformer.debug = True
+            transformer.mangler.debug = True
+            new_code = transformer.visit(ast.parse(cell))
+            return exec(compile(new_code, "<ast>", "exec"))
+
+
+    def load_ipython_extension(ip):
+        ip.register_magics(AstM)
+
+
+
+.. code-block:: python
+
+    In [1]: %load_ext timit2
+
+    In [2]: %%t2
+       ...: import time
+       ...: time.sleep(0.05)
+       ...:
+       ...:
+    Time taken: 0.05435649999999441
+
+
+If you wish to ran all the code enter in IPython in an ast transformer, you can
+do so as well:
+
+.. code-block:: python
+
+    In [1]: from IPython.core.magics.ast_mod import ReplaceCodeTransformer
+       ...:
+       ...: template = '''
+       ...: from time import perf_counter
+       ...: now = perf_counter()
+       ...: __code__
+       ...: print(f"Code ran in {perf_counter()-now}")
+       ...: __ret__'''
+       ...:
+       ...: get_ipython().ast_transformers.append(ReplaceCodeTransformer.from_string(template))
+
+    In [2]: 1+1
+    Code ran in 3.40410006174352e-05
+    Out[2]: 2
+
+
+
+Hygiene and Mangling
+--------------------
+
+The ast transformer above is not hygienic, it may not work if the user code use
+the same variable names as the ones used in the template. For example.
+
+To help with this by default the `ReplaceCodeTransformer` will mangle all names
+staring with 3 underscores. This is a simple heuristic that should work in most
+case, but can be cumbersome in some case. We provide a `Mangler` class that can
+be overridden to change the mangling heuristic, or simply use the `mangle_all`
+utility function. It will _try_ to mangle all names (except `__ret__` and
+`__code__`), but this include builtins (``print``, ``range``, ``type``) and
+replace those by invalid identifiers py prepending ``mangle-``:
+``mangle-print``, ``mangle-range``, ``mangle-type`` etc. This is not a problem
+as currently Python AST support invalid identifiers, but it may not be the case
+in the future.
+
+You can set `ReplaceCodeTransformer.debug=True` and
+`ReplaceCodeTransformer.mangler.debug=True` to see the code after mangling and
+transforming:
+
+.. code-block:: python
+
+
+    In [1]: from IPython.core.magics.ast_mod import ReplaceCodeTransformer, mangle_all
+       ...:
+       ...: template = '''
+       ...: from builtins import type, print
+       ...: from time import perf_counter
+       ...: now = perf_counter()
+       ...: __code__
+       ...: print(f"Code ran in {perf_counter()-now}")
+       ...: __ret__'''
+       ...:
+       ...: transformer = ReplaceCodeTransformer.from_string(template, mangling_predicate=mangle_all)
+
+
+    In [2]: transformer.debug = True
+       ...: transformer.mangler.debug = True
+       ...: get_ipython().ast_transformers.append(transformer)
+
+    In [3]: 1+1
+    Mangling Alias mangle-type
+    Mangling Alias mangle-print
+    Mangling Alias mangle-perf_counter
+    Mangling now
+    Mangling perf_counter
+    Not mangling __code__
+    Mangling print
+    Mangling perf_counter
+    Mangling now
+    Not mangling __ret__
+    ---- Transformed code ----
+    from builtins import type as mangle-type, print as mangle-print
+    from time import perf_counter as mangle-perf_counter
+    mangle-now = mangle-perf_counter()
+    ret-tmp = 1 + 1
+    mangle-print(f'Code ran in {mangle-perf_counter() - mangle-now}')
+    ret-tmp
+    ---- ---------------- ----
+    Code ran in 0.00013654199938173406
+    Out[3]: 2
+
+
+"""
+
+__skip_doctest__ = True
+
+
+from ast import NodeTransformer, Store, Load, Name, Expr, Assign, Module
+import ast
+import copy
+
+from typing import Dict, Optional
+
+
+mangle_all = lambda name: False if name in ("__ret__", "__code__") else True
+
+
+class Mangler(NodeTransformer):
+    """
+    Mangle given names in and ast tree to make sure they do not conflict with
+    user code.
+    """
+
+    enabled: bool = True
+    debug: bool = False
+
+    def log(self, *args, **kwargs):
+        if self.debug:
+            print(*args, **kwargs)
+
+    def __init__(self, predicate=None):
+        if predicate is None:
+            predicate = lambda name: name.startswith("___")
+        self.predicate = predicate
+
+    def visit_Name(self, node):
+        if self.predicate(node.id):
+            self.log("Mangling", node.id)
+            # Once in the ast we do not need
+            # names to be valid identifiers.
+            node.id = "mangle-" + node.id
+        else:
+            self.log("Not mangling", node.id)
+        return node
+
+    def visit_FunctionDef(self, node):
+        if self.predicate(node.name):
+            self.log("Mangling", node.name)
+            node.name = "mangle-" + node.name
+        else:
+            self.log("Not mangling", node.name)
+
+        for arg in node.args.args:
+            if self.predicate(arg.arg):
+                self.log("Mangling function arg", arg.arg)
+                arg.arg = "mangle-" + arg.arg
+            else:
+                self.log("Not mangling function arg", arg.arg)
+        return self.generic_visit(node)
+
+    def visit_ImportFrom(self, node):
+        return self._visit_Import_and_ImportFrom(node)
+
+    def visit_Import(self, node):
+        return self._visit_Import_and_ImportFrom(node)
+
+    def _visit_Import_and_ImportFrom(self, node):
+        for alias in node.names:
+            asname = alias.name if alias.asname is None else alias.asname
+            if self.predicate(asname):
+                new_name: str = "mangle-" + asname
+                self.log("Mangling Alias", new_name)
+                alias.asname = new_name
+            else:
+                self.log("Not mangling Alias", alias.asname)
+        return node
+
+
+class ReplaceCodeTransformer(NodeTransformer):
+    enabled: bool = True
+    debug: bool = False
+    mangler: Mangler
+
+    def __init__(
+        self, template: Module, mapping: Optional[Dict] = None, mangling_predicate=None
+    ):
+        assert isinstance(mapping, (dict, type(None)))
+        assert isinstance(mangling_predicate, (type(None), type(lambda: None)))
+        assert isinstance(template, ast.Module)
+        self.template = template
+        self.mangler = Mangler(predicate=mangling_predicate)
+        if mapping is None:
+            mapping = {}
+        self.mapping = mapping
+
+    @classmethod
+    def from_string(
+        cls, template: str, mapping: Optional[Dict] = None, mangling_predicate=None
+    ):
+        return cls(
+            ast.parse(template), mapping=mapping, mangling_predicate=mangling_predicate
+        )
+
+    def visit_Module(self, code):
+        if not self.enabled:
+            return code
+        # if not isinstance(code, ast.Module):
+        # recursively called...
+        #    return generic_visit(self, code)
+        last = code.body[-1]
+        if isinstance(last, Expr):
+            code.body.pop()
+            code.body.append(Assign([Name("ret-tmp", ctx=Store())], value=last.value))
+            ast.fix_missing_locations(code)
+            ret = Expr(value=Name("ret-tmp", ctx=Load()))
+            ret = ast.fix_missing_locations(ret)
+            self.mapping["__ret__"] = ret
+        else:
+            self.mapping["__ret__"] = ast.parse("None").body[0]
+        self.mapping["__code__"] = code.body
+        tpl = ast.fix_missing_locations(self.template)
+
+        tx = copy.deepcopy(tpl)
+        tx = self.mangler.visit(tx)
+        node = self.generic_visit(tx)
+        node_2 = ast.fix_missing_locations(node)
+        if self.debug:
+            print("---- Transformed code ----")
+            print(ast.unparse(node_2))
+            print("---- ---------------- ----")
+        return node_2
+
+    # this does not work as the name might be in a list and one might want to extend the list.
+    # def visit_Name(self, name):
+    #     if name.id in self.mapping and name.id == "__ret__":
+    #         print(name, "in mapping")
+    #         if isinstance(name.ctx, ast.Store):
+    #             return Name("tmp", ctx=Store())
+    #         else:
+    #             return copy.deepcopy(self.mapping[name.id])
+    #     return name
+
+    def visit_Expr(self, expr):
+        if isinstance(expr.value, Name) and expr.value.id in self.mapping:
+            if self.mapping[expr.value.id] is not None:
+                return copy.deepcopy(self.mapping[expr.value.id])
+        return self.generic_visit(expr)
diff --git a/shared/package-files/d8/d8d4622f7235843ace74d965b9b750fa043e52769aa9bda62c05c6e3f24be706/rendered_sst2.py b/shared/package-files/d8/d8d4622f7235843ace74d965b9b750fa043e52769aa9bda62c05c6e3f24be706/rendered_sst2.py
new file mode 100644
index 0000000000000000000000000000000000000000..48b0ddfc4fb3394505c1125e67cc026b3fd14fc8
--- /dev/null
+++ b/shared/package-files/d8/d8d4622f7235843ace74d965b9b750fa043e52769aa9bda62c05c6e3f24be706/rendered_sst2.py
@@ -0,0 +1,86 @@
+from pathlib import Path
+from typing import Any, Callable, Optional, Tuple, Union
+
+import PIL.Image
+
+from .folder import make_dataset
+from .utils import download_and_extract_archive, verify_str_arg
+from .vision import VisionDataset
+
+
+class RenderedSST2(VisionDataset):
+    """`The Rendered SST2 Dataset <https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md>`_.
+
+    Rendered SST2 is an image classification dataset used to evaluate the models capability on optical
+    character recognition. This dataset was generated by rendering sentences in the Standford Sentiment
+    Treebank v2 dataset.
+
+    This dataset contains two classes (positive and negative) and is divided in three splits: a  train
+    split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images
+    (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative).
+
+    Args:
+        root (str or ``pathlib.Path``): Root directory of the dataset.
+        split (string, optional): The dataset split, supports ``"train"`` (default), `"val"` and ``"test"``.
+        transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
+            version. E.g, ``transforms.RandomCrop``.
+        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
+        download (bool, optional): If True, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again. Default is False.
+    """
+
+    _URL = "https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz"
+    _MD5 = "2384d08e9dcfa4bd55b324e610496ee5"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        split: str = "train",
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        download: bool = False,
+    ) -> None:
+        super().__init__(root, transform=transform, target_transform=target_transform)
+        self._split = verify_str_arg(split, "split", ("train", "val", "test"))
+        self._split_to_folder = {"train": "train", "val": "valid", "test": "test"}
+        self._base_folder = Path(self.root) / "rendered-sst2"
+        self.classes = ["negative", "positive"]
+        self.class_to_idx = {"negative": 0, "positive": 1}
+
+        if download:
+            self._download()
+
+        if not self._check_exists():
+            raise RuntimeError("Dataset not found. You can use download=True to download it")
+
+        self._samples = make_dataset(str(self._base_folder / self._split_to_folder[self._split]), extensions=("png",))
+
+    def __len__(self) -> int:
+        return len(self._samples)
+
+    def __getitem__(self, idx: int) -> Tuple[Any, Any]:
+        image_file, label = self._samples[idx]
+        image = PIL.Image.open(image_file).convert("RGB")
+
+        if self.transform:
+            image = self.transform(image)
+
+        if self.target_transform:
+            label = self.target_transform(label)
+
+        return image, label
+
+    def extra_repr(self) -> str:
+        return f"split={self._split}"
+
+    def _check_exists(self) -> bool:
+        for class_label in set(self.classes):
+            if not (self._base_folder / self._split_to_folder[self._split] / class_label).is_dir():
+                return False
+        return True
+
+    def _download(self) -> None:
+        if self._check_exists():
+            return
+        download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5)
diff --git a/shared/package-files/d8/d8d9947e27df6e3f53f64c02a8b1be1fe192b6a246a28a787c5793deacb51a79/_crypt.cpython-310-x86_64-linux-gnu.so b/shared/package-files/d8/d8d9947e27df6e3f53f64c02a8b1be1fe192b6a246a28a787c5793deacb51a79/_crypt.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..612f65ddd15676295e4d1eb5934a33807a119c9c
Binary files /dev/null and b/shared/package-files/d8/d8d9947e27df6e3f53f64c02a8b1be1fe192b6a246a28a787c5793deacb51a79/_crypt.cpython-310-x86_64-linux-gnu.so differ
diff --git a/shared/package-files/d8/d8dcd8e85872ad5170c361460d8da5047d47d4044b3d7d9a8de1ea7a1a365d8e/vt100+enq b/shared/package-files/d8/d8dcd8e85872ad5170c361460d8da5047d47d4044b3d7d9a8de1ea7a1a365d8e/vt100+enq
new file mode 100644
index 0000000000000000000000000000000000000000..3a9a348397033e1e3a056ea89368d120d496887f
Binary files /dev/null and b/shared/package-files/d8/d8dcd8e85872ad5170c361460d8da5047d47d4044b3d7d9a8de1ea7a1a365d8e/vt100+enq differ
diff --git a/shared/package-files/d8/d8ece0adfe809717975532a2281e4df36d3364895d4bc0f1e9f161f68be3ccc8/special_modified_bessel_i0_native.h b/shared/package-files/d8/d8ece0adfe809717975532a2281e4df36d3364895d4bc0f1e9f161f68be3ccc8/special_modified_bessel_i0_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..94e2978df3a17f278bf40e63c79e54b0bcc10fd7
--- /dev/null
+++ b/shared/package-files/d8/d8ece0adfe809717975532a2281e4df36d3364895d4bc0f1e9f161f68be3ccc8/special_modified_bessel_i0_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_modified_bessel_i0_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_modified_bessel_i0_out : public at::meta::structured_special_modified_bessel_i0 {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/d8/d8f2fa68b0dcccceb9370524ed84d75dd8ec90b2660c2829543fb14c7af9e5ba/test_fourier.py b/shared/package-files/d8/d8f2fa68b0dcccceb9370524ed84d75dd8ec90b2660c2829543fb14c7af9e5ba/test_fourier.py
new file mode 100644
index 0000000000000000000000000000000000000000..be544eaab9ce00b2e9802cf8f9a4819c4f1d2731
--- /dev/null
+++ b/shared/package-files/d8/d8f2fa68b0dcccceb9370524ed84d75dd8ec90b2660c2829543fb14c7af9e5ba/test_fourier.py
@@ -0,0 +1,189 @@
+import math
+import numpy as np
+
+from scipy._lib._array_api import (
+    xp_assert_equal,
+    assert_array_almost_equal,
+    assert_almost_equal,
+    is_cupy,
+)
+
+import pytest
+
+from scipy import ndimage
+
+from scipy.conftest import array_api_compatible
+skip_xp_backends = pytest.mark.skip_xp_backends
+pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends"),
+              skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'],)]
+
+
+@skip_xp_backends('jax.numpy', reason="jax-ml/jax#23827")
+class TestNdimageFourier:
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [("float32", 6), ("float64", 14)])
+    def test_fourier_gaussian_real01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.rfft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.irfft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(a), xp.asarray(1), decimal=dec,
+                            check_0d=False)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [("complex64", 6), ("complex128", 14)])
+    def test_fourier_gaussian_complex01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.fft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.ifft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
+                            check_0d=False)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [("float32", 6), ("float64", 14)])
+    def test_fourier_uniform_real01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.rfft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.irfft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(a), xp.asarray(1.0), decimal=dec,
+                            check_0d=False)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [("complex64", 6), ("complex128", 14)])
+    def test_fourier_uniform_complex01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.fft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.ifft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
+                            check_0d=False)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [("float32", 4), ("float64", 11)])
+    def test_fourier_shift_real01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        expected = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
+        expected = xp.asarray(expected)
+
+        a = fft.rfft(expected, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.irfft(a, n=shape[0], axis=0)
+        assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1], decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [("complex64", 4), ("complex128", 11)])
+    def test_fourier_shift_complex01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        expected = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
+        expected = xp.asarray(expected)
+
+        a = fft.fft(expected, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_shift(a, [1, 1], -1, 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.ifft(a, n=shape[0], axis=0)
+        assert_array_almost_equal(xp.real(a)[1:, 1:], expected[:-1, :-1], decimal=dec)
+        assert_array_almost_equal(xp.imag(a), xp.zeros(shape), decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [("float32", 5), ("float64", 14)])
+    def test_fourier_ellipsoid_real01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.rfft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.irfft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(a), xp.asarray(1.0), decimal=dec,
+                            check_0d=False)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [("complex64", 5), ("complex128", 14)])
+    def test_fourier_ellipsoid_complex01(self, shape, dtype, dec, xp):
+        fft = getattr(xp, 'fft')
+
+        a = np.zeros(shape, dtype=dtype)
+        a[0, 0] = 1.0
+        a = xp.asarray(a)
+
+        a = fft.fft(a, n=shape[0], axis=0)
+        a = fft.fft(a, n=shape[1], axis=1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, n=shape[1], axis=1)
+        a = fft.ifft(a, n=shape[0], axis=0)
+        assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
+                            check_0d=False)
+
+    def test_fourier_ellipsoid_unimplemented_ndim(self, xp):
+        # arrays with ndim > 3 raise NotImplementedError
+        x = xp.ones((4, 6, 8, 10), dtype=xp.complex128)
+        with pytest.raises(NotImplementedError):
+            ndimage.fourier_ellipsoid(x, 3)
+
+    def test_fourier_ellipsoid_1d_complex(self, xp):
+        # expected result of 1d ellipsoid is the same as for fourier_uniform
+        for shape in [(32, ), (31, )]:
+            for type_, dec in zip([xp.complex64, xp.complex128], [5, 14]):
+                x = xp.ones(shape, dtype=type_)
+                a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
+                b = ndimage.fourier_uniform(x, 5, -1, 0)
+                assert_array_almost_equal(a, b, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
+    @pytest.mark.parametrize('dtype', ["float32", "float64",
+                                       "complex64", "complex128"])
+    @pytest.mark.parametrize('test_func',
+                             [ndimage.fourier_ellipsoid,
+                              ndimage.fourier_gaussian,
+                              ndimage.fourier_uniform])
+    def test_fourier_zero_length_dims(self, shape, dtype, test_func, xp):
+        if is_cupy(xp):
+           if (test_func.__name__ == "fourier_ellipsoid" and
+               math.prod(shape) == 0):
+               pytest.xfail(
+                   "CuPy's fourier_ellipsoid does not accept size==0 arrays"
+               )
+        dtype = getattr(xp, dtype)
+        a = xp.ones(shape, dtype=dtype)
+        b = test_func(a, 3)
+        xp_assert_equal(a, b)
diff --git a/shared/package-files/d8/d8f7293a301acca252ce3e8be46d9f483b79991d5ea6f41d00161ba6d5717692/any_value.h b/shared/package-files/d8/d8f7293a301acca252ce3e8be46d9f483b79991d5ea6f41d00161ba6d5717692/any_value.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e6c23ef977ca88237b2999a43eaa08309a43c29
--- /dev/null
+++ b/shared/package-files/d8/d8f7293a301acca252ce3e8be46d9f483b79991d5ea6f41d00161ba6d5717692/any_value.h
@@ -0,0 +1,124 @@
+#pragma once
+
+#include <torch/detail/static.h>
+#include <torch/nn/module.h>
+#include <torch/nn/pimpl.h>
+#include <torch/types.h>
+
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/utils/variadic.h>
+
+#include <memory>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+
+namespace torch {
+namespace nn {
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AnyValue ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// An implementation of `std::any` which stores
+/// a type erased object, whose concrete value can be retrieved at runtime by
+/// checking if the `typeid()` of a requested type matches the `typeid()` of
+/// the object stored.
+class AnyValue {
+ public:
+  /// Move construction and assignment is allowed, and follows the default
+  /// behavior of move for `std::unique_ptr`.
+  AnyValue(AnyValue&&) = default;
+  AnyValue& operator=(AnyValue&&) = default;
+
+  /// Copy construction and assignment is allowed.
+  AnyValue(const AnyValue& other) : content_(other.content_->clone()) {}
+  AnyValue& operator=(const AnyValue& other) {
+    content_ = other.content_->clone();
+    return *this;
+  }
+
+  /// Constructs the `AnyValue` from value type.
+  template <typename T>
+  // NOLINTNEXTLINE(bugprone-forwarding-reference-overload)
+  explicit AnyValue(T&& value)
+      : content_(std::make_unique<Holder<decay_t<T>>>(std::forward<T>(value))) {
+  }
+
+  /// Returns a pointer to the value contained in the `AnyValue` if the type
+  /// passed as template parameter matches the type of the value stored, and
+  /// returns a null pointer otherwise.
+  template <typename T>
+  T* try_get() {
+    static_assert(
+        !std::is_reference<T>::value,
+        "AnyValue stores decayed types, you cannot cast it to a reference type");
+    static_assert(
+        !std::is_array<T>::value,
+        "AnyValue stores decayed types, you must cast it to T* instead of T[]");
+    if (typeid(T).hash_code() == type_info().hash_code()) {
+      return &static_cast<Holder<T>&>(*content_).value;
+    }
+    return nullptr;
+  }
+
+  /// Returns the value contained in the `AnyValue` if the type passed as
+  /// template parameter matches the type of the value stored, and throws an
+  /// exception otherwise.
+  template <typename T>
+  T get() {
+    if (auto* maybe_value = try_get<T>()) {
+      return *maybe_value;
+    }
+    AT_ERROR(
+        "Attempted to cast AnyValue to ",
+        c10::demangle(typeid(T).name()),
+        ", but its actual type is ",
+        c10::demangle(type_info().name()));
+  }
+
+  /// Returns the `type_info` object of the contained value.
+  const std::type_info& type_info() const noexcept {
+    return content_->type_info;
+  }
+
+ private:
+  friend struct AnyModulePlaceholder;
+  friend struct TestAnyValue;
+
+  /// \internal
+  /// The static type of the object we store in the `AnyValue`, which erases the
+  /// actual object's type, allowing us only to check the `type_info` of the
+  /// type stored in the dynamic type.
+  struct Placeholder {
+    explicit Placeholder(const std::type_info& type_info_) noexcept
+        : type_info(type_info_) {}
+    Placeholder(const Placeholder&) = default;
+    Placeholder(Placeholder&&) = default;
+    virtual ~Placeholder() = default;
+    virtual std::unique_ptr<Placeholder> clone() const {
+      TORCH_CHECK(false, "clone() should only be called on `AnyValue::Holder`");
+    }
+    const std::type_info& type_info;
+  };
+
+  /// \internal
+  /// The dynamic type of the object we store in the `AnyValue`, which hides the
+  /// actual object we have erased in this `AnyValue`.
+  template <typename T>
+  struct Holder : public Placeholder {
+    /// A template because T&& would not be universal reference here.
+    template <typename U>
+    // NOLINTNEXTLINE(bugprone-forwarding-reference-overload)
+    explicit Holder(U&& value_) noexcept
+        : Placeholder(typeid(T)), value(std::forward<U>(value_)) {}
+    std::unique_ptr<Placeholder> clone() const override {
+      return std::make_unique<Holder<T>>(value);
+    }
+    T value;
+  };
+
+  /// The type erased object.
+  std::unique_ptr<Placeholder> content_;
+};
+
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/d8/d8fb5d79e26d0faab212fb7d1c536fc939cc33ca61159463b1c8276f87c79dd5/bench_limit.py b/shared/package-files/d8/d8fb5d79e26d0faab212fb7d1c536fc939cc33ca61159463b1c8276f87c79dd5/bench_limit.py
new file mode 100644
index 0000000000000000000000000000000000000000..eafc28328848dad4b3ea433537971f5785253afe
--- /dev/null
+++ b/shared/package-files/d8/d8fb5d79e26d0faab212fb7d1c536fc939cc33ca61159463b1c8276f87c79dd5/bench_limit.py
@@ -0,0 +1,9 @@
+from sympy.core.numbers import oo
+from sympy.core.symbol import Symbol
+from sympy.series.limits import limit
+
+x = Symbol('x')
+
+
+def timeit_limit_1x():
+    limit(1/x, x, oo)
diff --git a/shared/package-files/e4/e40c557e82bb947396aa9c474f2f530efaf8a35b4cd1ba0afd43e80e36ee667b/klone+koi8acs b/shared/package-files/e4/e40c557e82bb947396aa9c474f2f530efaf8a35b4cd1ba0afd43e80e36ee667b/klone+koi8acs
new file mode 100644
index 0000000000000000000000000000000000000000..23edc40ef6a34f73e3243fbab209fcca4f71edea
Binary files /dev/null and b/shared/package-files/e4/e40c557e82bb947396aa9c474f2f530efaf8a35b4cd1ba0afd43e80e36ee667b/klone+koi8acs differ
diff --git a/shared/package-files/e4/e4123f7ede995833a56e90c8f94d4c33e6efa87e46dacb0681f2c9583e5ba335/MurmurHash3.cpp b/shared/package-files/e4/e4123f7ede995833a56e90c8f94d4c33e6efa87e46dacb0681f2c9583e5ba335/MurmurHash3.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..6c42316121e246acc676563969c74db2f103fa5b
--- /dev/null
+++ b/shared/package-files/e4/e4123f7ede995833a56e90c8f94d4c33e6efa87e46dacb0681f2c9583e5ba335/MurmurHash3.cpp
@@ -0,0 +1,345 @@
+//-----------------------------------------------------------------------------
+// MurmurHash3 was written by Austin Appleby, and is placed in the public
+// domain. The author hereby disclaims copyright to this source code.
+
+// Note - The x86 and x64 versions do _not_ produce the same results, as the
+// algorithms are optimized for their respective platforms. You can still
+// compile and run any of them on any platform, but your performance with the
+// non-native version will be less than optimal.
+
+#include "MurmurHash3.h"
+
+//-----------------------------------------------------------------------------
+// Platform-specific functions and macros
+
+// Microsoft Visual Studio
+
+#if defined(_MSC_VER)
+
+#define FORCE_INLINE	__forceinline
+
+#include <stdlib.h>
+
+#define ROTL32(x,y)	_rotl(x,y)
+#define ROTL64(x,y)	_rotl64(x,y)
+
+#define BIG_CONSTANT(x) (x)
+
+// Other compilers
+
+#else	// defined(_MSC_VER)
+
+#if defined(GNUC) && ((GNUC > 4) || (GNUC == 4 && GNUC_MINOR >= 4))
+
+/* gcc version >= 4.4 4.1 = RHEL 5, 4.4 = RHEL 6.
+ * Don't inline for RHEL 5 gcc which is 4.1 */
+#define FORCE_INLINE attribute((always_inline))
+
+#else
+
+#define FORCE_INLINE
+
+#endif
+
+
+inline uint32_t rotl32 ( uint32_t x, int8_t r )
+{
+  return (x << r) | (x >> (32 - r));
+}
+
+inline uint64_t rotl64 ( uint64_t x, int8_t r )
+{
+  return (x << r) | (x >> (64 - r));
+}
+
+#define	ROTL32(x,y)	rotl32(x,y)
+#define ROTL64(x,y)	rotl64(x,y)
+
+#define BIG_CONSTANT(x) (x##LLU)
+
+#endif // !defined(_MSC_VER)
+
+//-----------------------------------------------------------------------------
+// Block read - if your platform needs to do endian-swapping or can only
+// handle aligned reads, do the conversion here
+
+FORCE_INLINE uint32_t getblock ( const uint32_t * p, int i )
+{
+  return p[i];
+}
+
+FORCE_INLINE uint64_t getblock ( const uint64_t * p, int i )
+{
+  return p[i];
+}
+
+//-----------------------------------------------------------------------------
+// Finalization mix - force all bits of a hash block to avalanche
+
+FORCE_INLINE uint32_t fmix ( uint32_t h )
+{
+  h ^= h >> 16;
+  h *= 0x85ebca6b;
+  h ^= h >> 13;
+  h *= 0xc2b2ae35;
+  h ^= h >> 16;
+
+  return h;
+}
+
+//----------
+
+FORCE_INLINE uint64_t fmix ( uint64_t k )
+{
+  k ^= k >> 33;
+  k *= BIG_CONSTANT(0xff51afd7ed558ccd);
+  k ^= k >> 33;
+  k *= BIG_CONSTANT(0xc4ceb9fe1a85ec53);
+  k ^= k >> 33;
+
+  return k;
+}
+
+//-----------------------------------------------------------------------------
+
+void MurmurHash3_x86_32 ( const void * key, int len,
+                          uint32_t seed, void * out )
+{
+  const uint8_t * data = (const uint8_t*)key;
+  const int nblocks = len / 4;
+
+  uint32_t h1 = seed;
+
+  uint32_t c1 = 0xcc9e2d51;
+  uint32_t c2 = 0x1b873593;
+
+  //----------
+  // body
+
+  const uint32_t * blocks = (const uint32_t *)(data + nblocks*4);
+
+  for(int i = -nblocks; i; i++)
+  {
+    uint32_t k1 = getblock(blocks,i);
+
+    k1 *= c1;
+    k1 = ROTL32(k1,15);
+    k1 *= c2;
+
+    h1 ^= k1;
+    h1 = ROTL32(h1,13);
+    h1 = h1*5+0xe6546b64;
+  }
+
+  //----------
+  // tail
+
+  const uint8_t * tail = (const uint8_t*)(data + nblocks*4);
+
+  uint32_t k1 = 0;
+
+  switch(len & 3)
+  {
+  case 3: k1 ^= tail[2] << 16;
+  case 2: k1 ^= tail[1] << 8;
+  case 1: k1 ^= tail[0];
+          k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
+  }
+
+  //----------
+  // finalization
+
+  h1 ^= len;
+
+  h1 = fmix(h1);
+
+  *(uint32_t*)out = h1;
+}
+
+//-----------------------------------------------------------------------------
+
+void MurmurHash3_x86_128 ( const void * key, const int len,
+                           uint32_t seed, void * out )
+{
+  const uint8_t * data = (const uint8_t*)key;
+  const int nblocks = len / 16;
+
+  uint32_t h1 = seed;
+  uint32_t h2 = seed;
+  uint32_t h3 = seed;
+  uint32_t h4 = seed;
+
+  uint32_t c1 = 0x239b961b;
+  uint32_t c2 = 0xab0e9789;
+  uint32_t c3 = 0x38b34ae5;
+  uint32_t c4 = 0xa1e38b93;
+
+  //----------
+  // body
+
+  const uint32_t * blocks = (const uint32_t *)(data + nblocks*16);
+
+  for(int i = -nblocks; i; i++)
+  {
+    uint32_t k1 = getblock(blocks,i*4+0);
+    uint32_t k2 = getblock(blocks,i*4+1);
+    uint32_t k3 = getblock(blocks,i*4+2);
+    uint32_t k4 = getblock(blocks,i*4+3);
+
+    k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
+
+    h1 = ROTL32(h1,19); h1 += h2; h1 = h1*5+0x561ccd1b;
+
+    k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
+
+    h2 = ROTL32(h2,17); h2 += h3; h2 = h2*5+0x0bcaa747;
+
+    k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
+
+    h3 = ROTL32(h3,15); h3 += h4; h3 = h3*5+0x96cd1c35;
+
+    k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
+
+    h4 = ROTL32(h4,13); h4 += h1; h4 = h4*5+0x32ac3b17;
+  }
+
+  //----------
+  // tail
+
+  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
+
+  uint32_t k1 = 0;
+  uint32_t k2 = 0;
+  uint32_t k3 = 0;
+  uint32_t k4 = 0;
+
+  switch(len & 15)
+  {
+  case 15: k4 ^= tail[14] << 16;
+  case 14: k4 ^= tail[13] << 8;
+  case 13: k4 ^= tail[12] << 0;
+           k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
+
+  case 12: k3 ^= tail[11] << 24;
+  case 11: k3 ^= tail[10] << 16;
+  case 10: k3 ^= tail[ 9] << 8;
+  case  9: k3 ^= tail[ 8] << 0;
+           k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
+
+  case  8: k2 ^= tail[ 7] << 24;
+  case  7: k2 ^= tail[ 6] << 16;
+  case  6: k2 ^= tail[ 5] << 8;
+  case  5: k2 ^= tail[ 4] << 0;
+           k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
+
+  case  4: k1 ^= tail[ 3] << 24;
+  case  3: k1 ^= tail[ 2] << 16;
+  case  2: k1 ^= tail[ 1] << 8;
+  case  1: k1 ^= tail[ 0] << 0;
+           k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
+  }
+
+  //----------
+  // finalization
+
+  h1 ^= len; h2 ^= len; h3 ^= len; h4 ^= len;
+
+  h1 += h2; h1 += h3; h1 += h4;
+  h2 += h1; h3 += h1; h4 += h1;
+
+  h1 = fmix(h1);
+  h2 = fmix(h2);
+  h3 = fmix(h3);
+  h4 = fmix(h4);
+
+  h1 += h2; h1 += h3; h1 += h4;
+  h2 += h1; h3 += h1; h4 += h1;
+
+  ((uint32_t*)out)[0] = h1;
+  ((uint32_t*)out)[1] = h2;
+  ((uint32_t*)out)[2] = h3;
+  ((uint32_t*)out)[3] = h4;
+}
+
+//-----------------------------------------------------------------------------
+
+void MurmurHash3_x64_128 ( const void * key, const int len,
+                           const uint32_t seed, void * out )
+{
+  const uint8_t * data = (const uint8_t*)key;
+  const int nblocks = len / 16;
+
+  uint64_t h1 = seed;
+  uint64_t h2 = seed;
+
+  uint64_t c1 = BIG_CONSTANT(0x87c37b91114253d5);
+  uint64_t c2 = BIG_CONSTANT(0x4cf5ad432745937f);
+
+  //----------
+  // body
+
+  const uint64_t * blocks = (const uint64_t *)(data);
+
+  for(int i = 0; i < nblocks; i++)
+  {
+    uint64_t k1 = getblock(blocks,i*2+0);
+    uint64_t k2 = getblock(blocks,i*2+1);
+
+    k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
+
+    h1 = ROTL64(h1,27); h1 += h2; h1 = h1*5+0x52dce729;
+
+    k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
+
+    h2 = ROTL64(h2,31); h2 += h1; h2 = h2*5+0x38495ab5;
+  }
+
+  //----------
+  // tail
+
+  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
+
+  uint64_t k1 = 0;
+  uint64_t k2 = 0;
+
+  switch(len & 15)
+  {
+  case 15: k2 ^= uint64_t(tail[14]) << 48;
+  case 14: k2 ^= uint64_t(tail[13]) << 40;
+  case 13: k2 ^= uint64_t(tail[12]) << 32;
+  case 12: k2 ^= uint64_t(tail[11]) << 24;
+  case 11: k2 ^= uint64_t(tail[10]) << 16;
+  case 10: k2 ^= uint64_t(tail[ 9]) << 8;
+  case  9: k2 ^= uint64_t(tail[ 8]) << 0;
+           k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
+
+  case  8: k1 ^= uint64_t(tail[ 7]) << 56;
+  case  7: k1 ^= uint64_t(tail[ 6]) << 48;
+  case  6: k1 ^= uint64_t(tail[ 5]) << 40;
+  case  5: k1 ^= uint64_t(tail[ 4]) << 32;
+  case  4: k1 ^= uint64_t(tail[ 3]) << 24;
+  case  3: k1 ^= uint64_t(tail[ 2]) << 16;
+  case  2: k1 ^= uint64_t(tail[ 1]) << 8;
+  case  1: k1 ^= uint64_t(tail[ 0]) << 0;
+           k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
+  }
+
+  //----------
+  // finalization
+
+  h1 ^= len; h2 ^= len;
+
+  h1 += h2;
+  h2 += h1;
+
+  h1 = fmix(h1);
+  h2 = fmix(h2);
+
+  h1 += h2;
+  h2 += h1;
+
+  ((uint64_t*)out)[0] = h1;
+  ((uint64_t*)out)[1] = h2;
+}
+
+//-----------------------------------------------------------------------------
diff --git a/shared/package-files/e4/e41d2cbaba795b43d1a51201ce4a1004ccb5271c714a35109f61ad284a4e481e/_foreach_log1p_ops.h b/shared/package-files/e4/e41d2cbaba795b43d1a51201ce4a1004ccb5271c714a35109f61ad284a4e481e/_foreach_log1p_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..31dc0c795e312675dbce4848f951672e96abd0c3
--- /dev/null
+++ b/shared/package-files/e4/e41d2cbaba795b43d1a51201ce4a1004ccb5271c714a35109f61ad284a4e481e/_foreach_log1p_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_log1p {
+  using schema = ::std::vector<at::Tensor> (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_log1p")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_log1p(Tensor[] self) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_log1p_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_log1p_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_log1p_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_log1p_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_log1p")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/e4/e41dde4f338dd4106e38ba1bd6f09f97211bda549deaeb17410f82bfe85791e0/__init__.pyi b/shared/package-files/e4/e41dde4f338dd4106e38ba1bd6f09f97211bda549deaeb17410f82bfe85791e0/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..96f6e36c7f4ac9ea0aebdcd9e11b8d1ff092d2ef
--- /dev/null
+++ b/shared/package-files/e4/e41dde4f338dd4106e38ba1bd6f09f97211bda549deaeb17410f82bfe85791e0/__init__.pyi
@@ -0,0 +1,2 @@
+from .more import *
+from .recipes import *
diff --git a/shared/package-files/e4/e427c2ebd6a3b1a4c2b65a7e20936676b924e82f4748888b32e8dc82ffa15a1c/interface.h b/shared/package-files/e4/e427c2ebd6a3b1a4c2b65a7e20936676b924e82f4748888b32e8dc82ffa15a1c/interface.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf30c3742d523a18fce73bbdde40be55a7608190
--- /dev/null
+++ b/shared/package-files/e4/e427c2ebd6a3b1a4c2b65a7e20936676b924e82f4748888b32e8dc82ffa15a1c/interface.h
@@ -0,0 +1,182 @@
+#pragma once
+
+// WARNING: Be careful when adding new includes here. This header will be used
+// in model.so, and should not refer to any aten/c10 headers except the stable
+// C ABI defined in torch/csrc/inductor/aoti_torch/c/shim.h. The same rule
+// applies to other files under torch/csrc/inductor/aoti_runtime/.
+#include <torch/csrc/inductor/aoti_runtime/utils.h>
+
+extern "C" {
+struct AOTInductorModelOpaque;
+using AOTInductorModelHandle = AOTInductorModelOpaque*;
+
+struct AOTInductorModelContainerOpaque;
+using AOTInductorModelContainerHandle = AOTInductorModelContainerOpaque*;
+
+struct AOTInductorStreamOpaque;
+using AOTInductorStreamHandle = AOTInductorStreamOpaque*;
+
+struct AOTInductorConstantMap;
+using AOTInductorConstantMapHandle = AOTInductorConstantMap*;
+
+// TODO: Deprecate this API. This was kept for BC compatibility.
+// Please use AOTInductorModelContainerCreateWithDevice instead.
+AOTIRuntimeError AOTInductorModelContainerCreate(
+    AOTInductorModelContainerHandle* container_handle,
+    size_t num_models,
+    bool is_cpu,
+    const char* cubin_dir);
+
+// Creates an AOTInductor model container. The parameter num_models
+// specifies the number of model instances that may be run concurrently for
+// the same input model.
+// `device_str` MUST NOT be nullptr. It must be a valid device string, e.g.
+// "cpu", "cuda", "cuda:0", etc. If the device index is not specified for CUDA
+// device, runtime will use the device index returned by
+// "cudaGetDevice(&device_idx)"
+AOTIRuntimeError AOTInductorModelContainerCreateWithDevice(
+    AOTInductorModelContainerHandle* container_handle,
+    size_t num_models,
+    const char* device_str,
+    const char* cubin_dir);
+
+// Deletes the AOTInductor model container.
+AOTIRuntimeError AOTInductorModelContainerDelete(
+    AOTInductorModelContainerHandle container_handle);
+
+// Runs the inference.
+AOTIRuntimeError AOTInductorModelContainerRun(
+    AOTInductorModelContainerHandle container_handle,
+    AtenTensorHandle* input_handles, // array of input AtenTensorHandle; handles
+                                     // are stolen; the array itself is borrowed
+    size_t num_inputs,
+    AtenTensorHandle*
+        output_handles, // array for writing output AtenTensorHandle; handles
+                        // will be stolen by the caller; the array itself is
+                        // borrowed
+    size_t num_outputs,
+    AOTInductorStreamHandle stream_handle,
+    AOTIProxyExecutorHandle proxy_executor_handle);
+
+// Retrieves the number of constants for the model.
+AOTIRuntimeError AOTInductorModelContainerGetNumConstants(
+    AOTInductorModelContainerHandle container_handle,
+    size_t* num_constants);
+
+// Retrieves a constant's name.
+// idx is the index of the internal's constants.
+// Need idx < num_constants from AOTInductorModelContainerGetNumConstants
+AOTIRuntimeError AOTInductorModelContainerGetConstantName(
+    AOTInductorModelContainerHandle container_handle,
+    size_t idx,
+    const char** name);
+
+// Retrieves a constant's original FQN.
+// idx is the index of the internal's constants.
+// Need idx < num_constants from AOTInductorModelContainerGetNumConstants
+AOTIRuntimeError AOTInductorModelContainerGetConstantOriginalFQN(
+    AOTInductorModelContainerHandle container_handle,
+    size_t idx,
+    const char** original_fqn);
+
+// Retrieves whether a constant is from folded.
+// idx is the index of the internal's constants.
+// Need idx < num_constants from AOTInductorModelContainerGetNumConstants
+AOTIRuntimeError AOTInductorModelContainerGetConstantFromFolded(
+    AOTInductorModelContainerHandle container_handle,
+    size_t idx,
+    bool* from_folded);
+
+// Retrieves a constant's dtype.
+// idx is the index of the internal's constants.
+// Need idx < num_constants from AOTInductorModelContainerGetNumConstants
+AOTIRuntimeError AOTInductorModelContainerGetConstantDtype(
+    AOTInductorModelContainerHandle container_handle,
+    size_t idx,
+    int32_t* dtype);
+
+// Setup the constant buffer in model container with provided ConstantMap
+// use_inactive should be set as true if the inactive buffer is to be updated.
+// validate_full_update checks if all constants are included in the ConstantMap
+AOTIRuntimeError AOTInductorModelContainerUpdateConstantBuffer(
+    AOTInductorModelContainerHandle container_handle,
+    AOTInductorConstantMapHandle constant_map_handle,
+    bool use_inactive,
+    bool validate_full_update);
+
+// Setup the inactive constant buffer in model container with provided
+// ConstantMap
+AOTIRuntimeError AOTInductorModelContainerUpdateInactiveConstantBuffer(
+    AOTInductorModelContainerHandle container_handle,
+    AOTInductorConstantMapHandle constant_map_handle);
+
+// Run constant folding on constant buffer.
+AOTIRuntimeError AOTInductorModelContainerRunConstantFolding(
+    AOTInductorModelContainerHandle container_handle,
+    bool use_inactive,
+    AOTInductorStreamHandle stream_handle,
+    AOTIProxyExecutorHandle proxy_executor_handle);
+
+// Swap the constant buffer being used to the inactive one.
+AOTIRuntimeError AOTInductorModelContainerSwapConstantBuffer(
+    AOTInductorModelContainerHandle container_handle);
+
+// Retrieves the number of inputs for the model.
+AOTIRuntimeError AOTInductorModelContainerGetNumInputs(
+    AOTInductorModelContainerHandle container_handle,
+    size_t* ret_num_inputs);
+
+// Retrieves the input name at the given index.
+AOTIRuntimeError AOTInductorModelContainerGetInputName(
+    AOTInductorModelContainerHandle container_handle,
+    size_t input_idx,
+    const char** ret_input_names);
+
+// Retrieves the number of outputs for the model.
+AOTIRuntimeError AOTInductorModelContainerGetNumOutputs(
+    AOTInductorModelContainerHandle container_handle,
+    size_t* ret_num_outputs);
+
+// Retrieves the output name at the given index.
+AOTIRuntimeError AOTInductorModelContainerGetOutputName(
+    AOTInductorModelContainerHandle container_handle,
+    size_t output_idx,
+    const char** ret_output_names);
+
+// Creates an AOTInductorModel instance.  This is a thin and light wrapper
+// around the compiled model; it doesn't handle concurrency, queueing, device
+// management, etc.  Use this if bare-metal performance is needed and you are
+// willing to handle other "management" aspects yourself.
+//
+// constant_map_handle is an opaque type to satisfy the C ABI.  It should be a
+// std::unordered_map<std::string, at::Tensor*>*.
+AOTIRuntimeError AOTInductorModelCreate(
+    AOTInductorModelHandle* model_handle,
+    AOTInductorConstantMapHandle constant_map_handle);
+
+// Run an AOTInductorModel (see AOTInductorModelCreate for when one should use
+// this function versus AOTInductorModelContainerRun).
+AOTIRuntimeError AOTInductorModelRun(
+    AOTInductorModelHandle model_handle,
+    AtenTensorHandle* input_handles,
+    AtenTensorHandle* output_handles);
+
+// Replace AOTInductorModel's constant map. Note it doesn't handle concurrency
+// so be sure to handle ordering if AOTInductorModelRun is ran concurrently.
+AOTIRuntimeError AOTInductorModelUpdateConstantsMap(
+    AOTInductorModelHandle model_handle,
+    AOTInductorConstantMapHandle constant_map_handle);
+
+// Delete an AOTInductorModel created by AOTInductorModelCreate.
+AOTIRuntimeError AOTInductorModelDelete(AOTInductorModelHandle model_handle);
+
+AOTIRuntimeError AOTInductorModelGetNumOutputs(
+    AOTInductorModelHandle model_handle,
+    size_t* ret_num_outputs);
+
+AOTIRuntimeError AOTInductorModelContainerGetCallSpec(
+    AOTInductorModelContainerHandle container_handle,
+    const char** in_spec,
+    const char** out_spec);
+
+} // extern "C"
diff --git a/shared/package-files/e4/e43062242d5c718ff609f8251d199e744c0b07f3edf562bd98cfed3ded31ad6e/upsample_bilinear2d_backward_meta.h b/shared/package-files/e4/e43062242d5c718ff609f8251d199e744c0b07f3edf562bd98cfed3ded31ad6e/upsample_bilinear2d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..78d4f79857fd8ff41d0d27b195e1e29625401e4c
--- /dev/null
+++ b/shared/package-files/e4/e43062242d5c718ff609f8251d199e744c0b07f3edf562bd98cfed3ded31ad6e/upsample_bilinear2d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_bilinear2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/e4/e431b6fdaabd96f796c2284654c8d6685b22ec82fdb07105d74e3e27dacb6661/is_nonzero_native.h b/shared/package-files/e4/e431b6fdaabd96f796c2284654c8d6685b22ec82fdb07105d74e3e27dacb6661/is_nonzero_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4aa71bb97dee0b3a15c86c3638ecd8c5ce03dd79
--- /dev/null
+++ b/shared/package-files/e4/e431b6fdaabd96f796c2284654c8d6685b22ec82fdb07105d74e3e27dacb6661/is_nonzero_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API bool is_nonzero(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/e4/e43693655b61559ab21a73cba4a12fe9f62ec8cfec92c927a5d4d9354e412965/configuration.py b/shared/package-files/e4/e43693655b61559ab21a73cba4a12fe9f62ec8cfec92c927a5d4d9354e412965/configuration.py
new file mode 100644
index 0000000000000000000000000000000000000000..c688494b6695215d7235b8660c572602f8b4eaea
--- /dev/null
+++ b/shared/package-files/e4/e43693655b61559ab21a73cba4a12fe9f62ec8cfec92c927a5d4d9354e412965/configuration.py
@@ -0,0 +1,1549 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+A generalized application configuration utility.
+
+Features include:
+  - lazy eval
+  - merges configuration files
+  - parameter type validation, with custom validation
+  - parameter aliases
+
+Easily extensible to other source formats, e.g. json and ini
+
+"""
+from __future__ import annotations
+
+import copy
+import sys
+from abc import ABCMeta, abstractmethod
+from collections import defaultdict
+from collections.abc import Mapping
+from enum import Enum, EnumMeta
+from itertools import chain
+from logging import getLogger
+from os import environ, scandir, stat
+from os.path import basename, expandvars
+from stat import S_IFDIR, S_IFMT, S_IFREG
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from typing import Sequence
+
+try:
+    from boltons.setutils import IndexedSet
+except ImportError:  # pragma: no cover
+    from .._vendor.boltons.setutils import IndexedSet
+
+from .. import CondaError, CondaMultiError
+from .._vendor.frozendict import frozendict
+from ..auxlib.collection import AttrDict, first, last, make_immutable
+from ..auxlib.exceptions import ThisShouldNeverHappenError
+from ..auxlib.type_coercion import TypeCoercionError, typify, typify_data_structure
+from ..common.iterators import unique
+from .compat import isiterable, primitive_types
+from .constants import NULL
+from .path import expand
+from .serialize import yaml_round_trip_load
+
+try:
+    from ruamel.yaml.comments import CommentedMap, CommentedSeq
+    from ruamel.yaml.reader import ReaderError
+    from ruamel.yaml.scanner import ScannerError
+except ImportError:  # pragma: no cover
+    try:
+        from ruamel_yaml.comments import CommentedMap, CommentedSeq
+        from ruamel_yaml.reader import ReaderError
+        from ruamel_yaml.scanner import ScannerError
+    except ImportError:
+        raise ImportError(
+            "No yaml library available. To proceed, conda install ruamel.yaml"
+        )
+
+log = getLogger(__name__)
+
+EMPTY_MAP = frozendict()
+
+
+def pretty_list(iterable, padding="  "):  # TODO: move elsewhere in conda.common
+    if not isiterable(iterable):
+        iterable = [iterable]
+    try:
+        return "\n".join(f"{padding}- {item}" for item in iterable)
+    except TypeError:
+        return pretty_list([iterable], padding)
+
+
+def pretty_map(dictionary, padding="  "):
+    return "\n".join(f"{padding}{key}: {value}" for key, value in dictionary.items())
+
+
+def expand_environment_variables(unexpanded):
+    if isinstance(unexpanded, (str, bytes)):
+        return expandvars(unexpanded)
+    else:
+        return unexpanded
+
+
+class ConfigurationError(CondaError):
+    pass
+
+
+class ConfigurationLoadError(ConfigurationError):
+    def __init__(self, path, message_addition="", **kwargs):
+        message = "Unable to load configuration file.\n  path: %(path)s\n"
+        super().__init__(message + message_addition, path=path, **kwargs)
+
+
+class ValidationError(ConfigurationError):
+    def __init__(self, parameter_name, parameter_value, source, msg=None, **kwargs):
+        self.parameter_name = parameter_name
+        self.parameter_value = parameter_value
+        self.source = source
+        super().__init__(msg, **kwargs)
+
+
+class MultipleKeysError(ValidationError):
+    def __init__(self, source, keys, preferred_key):
+        self.source = source
+        self.keys = keys
+        msg = (
+            "Multiple aliased keys in file %s:\n"
+            "%s\n"
+            "Must declare only one. Prefer '%s'"
+            % (source, pretty_list(keys), preferred_key)
+        )
+        super().__init__(preferred_key, None, source, msg=msg)
+
+
+class InvalidTypeError(ValidationError):
+    def __init__(
+        self, parameter_name, parameter_value, source, wrong_type, valid_types, msg=None
+    ):
+        self.wrong_type = wrong_type
+        self.valid_types = valid_types
+        if msg is None:
+            msg = (
+                "Parameter %s = %r declared in %s has type %s.\n"
+                "Valid types:\n%s"
+                % (
+                    parameter_name,
+                    parameter_value,
+                    source,
+                    wrong_type,
+                    pretty_list(valid_types),
+                )
+            )
+        super().__init__(parameter_name, parameter_value, source, msg=msg)
+
+
+class CustomValidationError(ValidationError):
+    def __init__(self, parameter_name, parameter_value, source, custom_message):
+        msg = "Parameter %s = %r declared in %s is invalid.\n" "%s" % (
+            parameter_name,
+            parameter_value,
+            source,
+            custom_message,
+        )
+        super().__init__(parameter_name, parameter_value, source, msg=msg)
+
+
+class MultiValidationError(CondaMultiError, ConfigurationError):
+    def __init__(self, errors, *args, **kwargs):
+        super().__init__(errors, *args, **kwargs)
+
+
+def raise_errors(errors):
+    if not errors:
+        return True
+    elif len(errors) == 1:
+        raise errors[0]
+    else:
+        raise MultiValidationError(errors)
+
+
+class ParameterFlag(Enum):
+    final = "final"
+    top = "top"
+    bottom = "bottom"
+
+    def __str__(self):
+        return "%s" % self.value
+
+    @classmethod
+    def from_name(cls, name):
+        return cls[name]
+
+    @classmethod
+    def from_value(cls, value):
+        return cls(value)
+
+    @classmethod
+    def from_string(cls, string):
+        try:
+            string = string.strip("!#")
+            return cls.from_value(string)
+        except (ValueError, AttributeError):
+            return None
+
+
+class RawParameter(metaclass=ABCMeta):
+    def __init__(self, source, key, raw_value):
+        self.source = source
+        self.key = key
+        try:
+            # ignore flake8 on this because it finds an error on py3 even though it is guarded
+            self._raw_value = unicode(raw_value.decode("utf-8"))  # NOQA
+        except:
+            self._raw_value = raw_value
+
+    def __repr__(self):
+        return str(vars(self))
+
+    @abstractmethod
+    def value(self, parameter_obj):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def keyflag(self):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def valueflags(self, parameter_obj):
+        raise NotImplementedError()
+
+    @classmethod
+    def make_raw_parameters(cls, source, from_map):
+        if from_map:
+            return {key: cls(source, key, from_map[key]) for key in from_map}
+        return EMPTY_MAP
+
+
+class EnvRawParameter(RawParameter):
+    source = "envvars"
+
+    def value(self, parameter_obj):
+        # note: this assumes that EnvRawParameters will only have flat configuration of either
+        # primitive or sequential type
+        if hasattr(parameter_obj, "string_delimiter"):
+            assert isinstance(self._raw_value, str)
+            string_delimiter = getattr(parameter_obj, "string_delimiter")
+            # TODO: add stripping of !important, !top, and !bottom
+            return tuple(
+                EnvRawParameter(EnvRawParameter.source, self.key, v)
+                for v in (vv.strip() for vv in self._raw_value.split(string_delimiter))
+                if v
+            )
+        else:
+            return self.__important_split_value[0].strip()
+
+    def keyflag(self):
+        return ParameterFlag.final if len(self.__important_split_value) >= 2 else None
+
+    def valueflags(self, parameter_obj):
+        if hasattr(parameter_obj, "string_delimiter"):
+            string_delimiter = getattr(parameter_obj, "string_delimiter")
+            # TODO: add stripping of !important, !top, and !bottom
+            return tuple("" for _ in self._raw_value.split(string_delimiter))
+        else:
+            return self.__important_split_value[0].strip()
+
+    @property
+    def __important_split_value(self):
+        return self._raw_value.split("!important")
+
+    @classmethod
+    def make_raw_parameters(cls, appname):
+        keystart = f"{appname.upper()}_"
+        raw_env = {
+            k.replace(keystart, "", 1).lower(): v
+            for k, v in environ.items()
+            if k.startswith(keystart)
+        }
+        return super().make_raw_parameters(EnvRawParameter.source, raw_env)
+
+
+class ArgParseRawParameter(RawParameter):
+    source = "cmd_line"
+
+    def value(self, parameter_obj):
+        # note: this assumes ArgParseRawParameter will only have flat configuration of either
+        # primitive or sequential type
+        if isiterable(self._raw_value):
+            children_values = []
+            for i in range(len(self._raw_value)):
+                children_values.append(
+                    ArgParseRawParameter(self.source, self.key, self._raw_value[i])
+                )
+            return tuple(children_values)
+        else:
+            return make_immutable(self._raw_value)
+
+    def keyflag(self):
+        return None
+
+    def valueflags(self, parameter_obj):
+        return None if isinstance(parameter_obj, PrimitiveLoadedParameter) else ()
+
+    @classmethod
+    def make_raw_parameters(cls, args_from_argparse):
+        return super().make_raw_parameters(
+            ArgParseRawParameter.source, args_from_argparse
+        )
+
+
+class YamlRawParameter(RawParameter):
+    # this class should encapsulate all direct use of ruamel.yaml in this module
+
+    def __init__(self, source, key, raw_value, key_comment):
+        self._key_comment = key_comment
+        super().__init__(source, key, raw_value)
+
+        if isinstance(self._raw_value, CommentedSeq):
+            value_comments = self._get_yaml_list_comments(self._raw_value)
+            self._value_flags = tuple(
+                ParameterFlag.from_string(s) for s in value_comments
+            )
+            children_values = []
+            for i in range(len(self._raw_value)):
+                children_values.append(
+                    YamlRawParameter(
+                        self.source, self.key, self._raw_value[i], value_comments[i]
+                    )
+                )
+            self._value = tuple(children_values)
+        elif isinstance(self._raw_value, CommentedMap):
+            value_comments = self._get_yaml_map_comments(self._raw_value)
+            self._value_flags = {
+                k: ParameterFlag.from_string(v)
+                for k, v in value_comments.items()
+                if v is not None
+            }
+            children_values = {}
+            for k, v in self._raw_value.items():
+                children_values[k] = YamlRawParameter(
+                    self.source, self.key, v, value_comments[k]
+                )
+            self._value = frozendict(children_values)
+        elif isinstance(self._raw_value, primitive_types):
+            self._value_flags = None
+            self._value = self._raw_value
+        else:
+            print(type(self._raw_value), self._raw_value, file=sys.stderr)
+            raise ThisShouldNeverHappenError()  # pragma: no cover
+
+    def value(self, parameter_obj):
+        return self._value
+
+    def keyflag(self):
+        return ParameterFlag.from_string(self._key_comment)
+
+    def valueflags(self, parameter_obj):
+        return self._value_flags
+
+    @staticmethod
+    def _get_yaml_key_comment(commented_dict, key):
+        try:
+            return commented_dict.ca.items[key][2].value.strip()
+        except (AttributeError, KeyError):
+            return None
+
+    @classmethod
+    def _get_yaml_list_comments(cls, value):
+        # value is a ruamel.yaml CommentedSeq, len(value) is the number of lines in the sequence,
+        # value.ca is the comment object for the sequence and the comments themselves are stored as
+        # a sparse dict
+        list_comments = []
+        for i in range(len(value)):
+            try:
+                list_comments.append(cls._get_yaml_list_comment_item(value.ca.items[i]))
+            except (AttributeError, IndexError, KeyError, TypeError):
+                list_comments.append(None)
+        return tuple(list_comments)
+
+    @staticmethod
+    def _get_yaml_list_comment_item(item):
+        # take the pre_item comment if available
+        # if not, take the first post_item comment if available
+        if item[0]:
+            return item[0].value.strip() or None
+        else:
+            return item[1][0].value.strip() or None
+
+    @staticmethod
+    def _get_yaml_map_comments(value):
+        map_comments = {}
+        for key in value:
+            try:
+                map_comments[key] = value.ca.items[key][2].value.strip() or None
+            except (AttributeError, KeyError):
+                map_comments[key] = None
+        return map_comments
+
+    @classmethod
+    def make_raw_parameters(cls, source, from_map):
+        if from_map:
+            return {
+                key: cls(
+                    source, key, from_map[key], cls._get_yaml_key_comment(from_map, key)
+                )
+                for key in from_map
+            }
+        return EMPTY_MAP
+
+    @classmethod
+    def make_raw_parameters_from_file(cls, filepath):
+        with open(filepath) as fh:
+            try:
+                yaml_obj = yaml_round_trip_load(fh)
+            except ScannerError as err:
+                mark = err.problem_mark
+                raise ConfigurationLoadError(
+                    filepath,
+                    "  reason: invalid yaml at line %(line)s, column %(column)s",
+                    line=mark.line,
+                    column=mark.column,
+                )
+            except ReaderError as err:
+                raise ConfigurationLoadError(
+                    filepath,
+                    "  reason: invalid yaml at position %(position)s",
+                    position=err.position,
+                )
+            return cls.make_raw_parameters(filepath, yaml_obj) or EMPTY_MAP
+
+
+class DefaultValueRawParameter(RawParameter):
+    """Wraps a default value as a RawParameter, for usage in ParameterLoader."""
+
+    def __init__(self, source, key, raw_value):
+        super().__init__(source, key, raw_value)
+
+        if isinstance(self._raw_value, Mapping):
+            children_values = {}
+            for k, v in self._raw_value.items():
+                children_values[k] = DefaultValueRawParameter(self.source, self.key, v)
+            self._value = frozendict(children_values)
+        elif isiterable(self._raw_value):
+            children_values = []
+            for i in range(len(self._raw_value)):
+                children_values.append(
+                    DefaultValueRawParameter(self.source, self.key, self._raw_value[i])
+                )
+            self._value = tuple(children_values)
+        elif isinstance(self._raw_value, ConfigurationObject):
+            self._value = self._raw_value
+            for attr_name, attr_value in vars(self._raw_value).items():
+                self._value.__setattr__(
+                    attr_name,
+                    DefaultValueRawParameter(self.source, self.key, attr_value),
+                )
+        elif isinstance(self._raw_value, Enum):
+            self._value = self._raw_value
+        elif isinstance(self._raw_value, primitive_types):
+            self._value = self._raw_value
+        else:
+            raise ThisShouldNeverHappenError()  # pragma: no cover
+
+    def value(self, parameter_obj):
+        return self._value
+
+    def keyflag(self):
+        return None
+
+    def valueflags(self, parameter_obj):
+        if isinstance(self._raw_value, Mapping):
+            return frozendict()
+        elif isiterable(self._raw_value):
+            return ()
+        elif isinstance(self._raw_value, ConfigurationObject):
+            return None
+        elif isinstance(self._raw_value, Enum):
+            return None
+        elif isinstance(self._raw_value, primitive_types):
+            return None
+        else:
+            raise ThisShouldNeverHappenError()  # pragma: no cover
+
+
+def load_file_configs(search_path):
+    # returns an ordered map of filepath and dict of raw parameter objects
+
+    def _file_loader(fullpath):
+        assert fullpath.endswith((".yml", ".yaml")) or "condarc" in basename(
+            fullpath
+        ), fullpath
+        yield fullpath, YamlRawParameter.make_raw_parameters_from_file(fullpath)
+
+    def _dir_loader(fullpath):
+        for filepath in sorted(
+            p
+            for p in (entry.path for entry in scandir(fullpath))
+            if p[-4:] == ".yml" or p[-5:] == ".yaml"
+        ):
+            yield filepath, YamlRawParameter.make_raw_parameters_from_file(filepath)
+
+    # map a stat result to a file loader or a directory loader
+    _loader = {
+        S_IFREG: _file_loader,
+        S_IFDIR: _dir_loader,
+    }
+
+    def _get_st_mode(path):
+        # stat the path for file type, or None if path doesn't exist
+        try:
+            return S_IFMT(stat(path).st_mode)
+        except OSError:
+            return None
+
+    expanded_paths = tuple(expand(path) for path in search_path)
+    stat_paths = (_get_st_mode(path) for path in expanded_paths)
+    load_paths = (
+        _loader[st_mode](path)
+        for path, st_mode in zip(expanded_paths, stat_paths)
+        if st_mode is not None
+    )
+    raw_data = dict(kv for kv in chain.from_iterable(load_paths))
+    return raw_data
+
+
+class LoadedParameter(metaclass=ABCMeta):
+    # (type) describes the type of parameter
+    _type = None
+    # (Parameter or type) if the LoadedParameter holds a collection, describes the element held in
+    # the collection. if not, describes the primitive type held by the LoadedParameter.
+    _element_type = None
+
+    def __init__(self, name, value, key_flag, value_flags, validation=None):
+        """
+        Represents a Parameter that has been loaded with configuration value.
+
+        Args:
+            name (str): name of the loaded parameter
+            value (LoadedParameter or primitive): the value of the loaded parameter
+            key_flag (ParameterFlag or None): priority flag for the parameter itself
+            value_flags (Any or None): priority flags for the parameter values
+            validation (callable): Given a parameter value as input, return a boolean indicating
+                validity, or alternately return a string describing an invalid value.
+        """
+        self._name = name
+        self.value = value
+        self.key_flag = key_flag
+        self.value_flags = value_flags
+        self._validation = validation
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self.value == other.value
+        return False
+
+    def __hash__(self):
+        return hash(self.value)
+
+    def collect_errors(self, instance, typed_value, source="<<merged>>"):
+        """
+        Validate a LoadedParameter typed value.
+
+        Args:
+            instance (Configuration): the instance object used to create the LoadedParameter.
+            typed_value (Any): typed value to validate.
+            source (str): string description for the source of the typed_value.
+        """
+        errors = []
+        if not isinstance(typed_value, self._type):
+            errors.append(
+                InvalidTypeError(
+                    self._name, typed_value, source, type(self.value), self._type
+                )
+            )
+        elif self._validation is not None:
+            result = self._validation(typed_value)
+            if result is False:
+                errors.append(ValidationError(self._name, typed_value, source))
+            elif isinstance(result, str):
+                errors.append(
+                    CustomValidationError(self._name, typed_value, source, result)
+                )
+        return errors
+
+    def expand(self):
+        """
+        Recursively expands any environment values in the Loaded Parameter.
+
+        Returns: LoadedParameter
+        """
+        # This is similar to conda.auxlib.type_coercion.typify_data_structure
+        # It could be DRY-er but that would break SRP.
+        if isinstance(self.value, Mapping):
+            new_value = type(self.value)((k, v.expand()) for k, v in self.value.items())
+        elif isiterable(self.value):
+            new_value = type(self.value)(v.expand() for v in self.value)
+        elif isinstance(self.value, ConfigurationObject):
+            for attr_name, attr_value in vars(self.value).items():
+                if isinstance(attr_value, LoadedParameter):
+                    self.value.__setattr__(attr_name, attr_value.expand())
+            return self.value
+        else:
+            new_value = expand_environment_variables(self.value)
+        self.value = new_value
+        return self
+
+    @abstractmethod
+    def merge(self, matches):
+        """
+        Recursively merges matches into one LoadedParameter.
+
+        Args:
+            matches (List<LoadedParameter>): list of matches of this parameter.
+
+        Returns: LoadedParameter
+        """
+        raise NotImplementedError()
+
+    def typify(self, source):
+        """
+        Recursively types a LoadedParameter.
+
+        Args:
+            source (str): string describing the source of the LoadedParameter.
+
+        Returns: a primitive, sequence, or map representing the typed value.
+        """
+        element_type = self._element_type
+        try:
+            return LoadedParameter._typify_data_structure(
+                self.value, source, element_type
+            )
+        except TypeCoercionError as e:
+            msg = str(e)
+            if issubclass(element_type, Enum):
+                choices = ", ".join(
+                    map("'{}'".format, element_type.__members__.values())
+                )
+                msg += f"\nValid choices for {self._name}: {choices}"
+            raise CustomValidationError(self._name, e.value, source, msg)
+
+    @staticmethod
+    def _typify_data_structure(value, source, type_hint=None):
+        if isinstance(value, Mapping):
+            return type(value)((k, v.typify(source)) for k, v in value.items())
+        elif isiterable(value):
+            return type(value)(v.typify(source) for v in value)
+        elif isinstance(value, ConfigurationObject):
+            for attr_name, attr_value in vars(value).items():
+                if isinstance(attr_value, LoadedParameter):
+                    value.__setattr__(attr_name, attr_value.typify(source))
+            return value
+        elif (
+            isinstance(value, str)
+            and isinstance(type_hint, type)
+            and issubclass(type_hint, str)
+        ):
+            # This block is necessary because if we fall through to typify(), we end up calling
+            # .strip() on the str, when sometimes we want to preserve preceding and trailing
+            # whitespace.
+            return type_hint(value)
+        else:
+            return typify(value, type_hint)
+
+    @staticmethod
+    def _match_key_is_important(loaded_parameter):
+        return loaded_parameter.key_flag is ParameterFlag.final
+
+    @staticmethod
+    def _first_important_matches(matches):
+        idx = first(
+            enumerate(matches),
+            lambda x: LoadedParameter._match_key_is_important(x[1]),
+            apply=lambda x: x[0],
+        )
+        return matches if idx is None else matches[: idx + 1]
+
+
+class PrimitiveLoadedParameter(LoadedParameter):
+    """
+    LoadedParameter type that holds a single python primitive value.
+
+    The python primitive types are str, int, float, complex, bool, and NoneType. In addition,
+    python 2 has long and unicode types.
+    """
+
+    def __init__(
+        self, name, element_type, value, key_flag, value_flags, validation=None
+    ):
+        """
+        Args:
+            element_type (type or tuple[type]): Type-validation of parameter's value.
+            value (primitive value): primitive python value.
+        """
+        self._type = element_type
+        self._element_type = element_type
+        super().__init__(name, value, key_flag, value_flags, validation)
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self.value == other.value
+        return False
+
+    def __hash__(self):
+        return hash(self.value)
+
+    def merge(self, matches):
+        important_match = first(
+            matches, LoadedParameter._match_key_is_important, default=None
+        )
+        if important_match is not None:
+            return important_match
+
+        last_match = last(matches, lambda x: x is not None, default=None)
+        if last_match is not None:
+            return last_match
+        raise ThisShouldNeverHappenError()  # pragma: no cover
+
+
+class MapLoadedParameter(LoadedParameter):
+    """LoadedParameter type that holds a map (i.e. dict) of LoadedParameters."""
+
+    _type = frozendict
+
+    def __init__(
+        self, name, value, element_type, key_flag, value_flags, validation=None
+    ):
+        """
+        Args:
+            value (Mapping): Map of string keys to LoadedParameter values.
+            element_type (Parameter): The Parameter type that is held in value.
+            value_flags (Mapping): Map of priority value flags.
+        """
+        self._element_type = element_type
+        super().__init__(name, value, key_flag, value_flags, validation)
+
+    def collect_errors(self, instance, typed_value, source="<<merged>>"):
+        errors = super().collect_errors(instance, typed_value, self.value)
+
+        # recursively validate the values in the map
+        if isinstance(self.value, Mapping):
+            for key, value in self.value.items():
+                errors.extend(value.collect_errors(instance, typed_value[key], source))
+        return errors
+
+    def merge(self, parameters: Sequence[MapLoadedParameter]) -> MapLoadedParameter:
+        # get all values up to and including first important_match
+        # but if no important_match, then all matches are important_matches
+        parameters = LoadedParameter._first_important_matches(parameters)
+
+        # ensure all parameter values are Mappings
+        for parameter in parameters:
+            if not isinstance(parameter.value, Mapping):
+                raise InvalidTypeError(
+                    self.name,
+                    parameter.value,
+                    parameter.source,
+                    parameter.value.__class__.__name__,
+                    self._type.__name__,
+                )
+
+        # map keys with final values,
+        # first key has higher precedence than later ones
+        final_map = {
+            key: value
+            for parameter in reversed(parameters)
+            for key, value in parameter.value.items()
+            if parameter.value_flags.get(key) == ParameterFlag.final
+        }
+
+        # map each value by recursively calling merge on any entries with the same key,
+        # last key has higher precedence than earlier ones
+        grouped_map = {}
+        for parameter in parameters:
+            for key, value in parameter.value.items():
+                grouped_map.setdefault(key, []).append(value)
+        merged_map = {
+            key: values[0].merge(values) for key, values in grouped_map.items()
+        }
+
+        # update merged_map with final_map values
+        merged_value = frozendict({**merged_map, **final_map})
+
+        # create new parameter for the merged values
+        return MapLoadedParameter(
+            self._name,
+            merged_value,
+            self._element_type,
+            self.key_flag,
+            self.value_flags,
+            validation=self._validation,
+        )
+
+
+class SequenceLoadedParameter(LoadedParameter):
+    """LoadedParameter type that holds a sequence (i.e. list) of LoadedParameters."""
+
+    _type = tuple
+
+    def __init__(
+        self, name, value, element_type, key_flag, value_flags, validation=None
+    ):
+        """
+        Args:
+            value (Sequence): Sequence of LoadedParameter values.
+            element_type (Parameter): The Parameter type that is held in the sequence.
+            value_flags (Sequence): Sequence of priority value_flags.
+        """
+        self._element_type = element_type
+        super().__init__(name, value, key_flag, value_flags, validation)
+
+    def collect_errors(self, instance, typed_value, source="<<merged>>"):
+        errors = super().collect_errors(instance, typed_value, self.value)
+        # recursively collect errors on the elements in the sequence
+        for idx, element in enumerate(self.value):
+            errors.extend(element.collect_errors(instance, typed_value[idx], source))
+        return errors
+
+    def merge(self, matches):
+        # get matches up to and including first important_match
+        # but if no important_match, then all matches are important_matches
+        relevant_matches_and_values = tuple(
+            (match, match.value)
+            for match in LoadedParameter._first_important_matches(matches)
+        )
+        for match, value in relevant_matches_and_values:
+            if not isinstance(value, tuple):
+                raise InvalidTypeError(
+                    self.name,
+                    value,
+                    match.source,
+                    value.__class__.__name__,
+                    self._type.__name__,
+                )
+
+        # get individual lines from important_matches that were marked important
+        # these will be prepended to the final result
+        def get_marked_lines(match, marker):
+            return (
+                tuple(
+                    line
+                    for line, flag in zip(match.value, match.value_flags)
+                    if flag is marker
+                )
+                if match
+                else ()
+            )
+
+        top_lines = chain.from_iterable(
+            get_marked_lines(m, ParameterFlag.top)
+            for m, _ in relevant_matches_and_values
+        )
+
+        # also get lines that were marked as bottom, but reverse the match order so that lines
+        # coming earlier will ultimately be last
+        bottom_lines = tuple(
+            chain.from_iterable(
+                get_marked_lines(match, ParameterFlag.bottom)
+                for match, _ in reversed(relevant_matches_and_values)
+            )
+        )
+
+        # now, concat all lines, while reversing the matches
+        #   reverse because elements closer to the end of search path take precedence
+        all_lines = chain.from_iterable(
+            v for _, v in reversed(relevant_matches_and_values)
+        )
+
+        # stack top_lines + all_lines, then de-dupe
+        top_deduped = tuple(unique((*top_lines, *all_lines)))
+
+        # take the top-deduped lines, reverse them, and concat with reversed bottom_lines
+        # this gives us the reverse of the order we want, but almost there
+        # NOTE: for a line value marked both top and bottom, the bottom marker will win out
+        #       for the top marker to win out, we'd need one additional de-dupe step
+        bottom_deduped = tuple(
+            unique((*reversed(bottom_lines), *reversed(top_deduped)))
+        )
+        # just reverse, and we're good to go
+        merged_values = tuple(reversed(bottom_deduped))
+
+        return SequenceLoadedParameter(
+            self._name,
+            merged_values,
+            self._element_type,
+            self.key_flag,
+            self.value_flags,
+            validation=self._validation,
+        )
+
+
+class ObjectLoadedParameter(LoadedParameter):
+    """LoadedParameter type that holds a mapping (i.e. object) of LoadedParameters."""
+
+    _type = object
+
+    def __init__(
+        self, name, value, element_type, key_flag, value_flags, validation=None
+    ):
+        """
+        Args:
+            value (Sequence): Object with LoadedParameter fields.
+            element_type (object): The Parameter type that is held in the sequence.
+            value_flags (Sequence): Sequence of priority value_flags.
+        """
+        self._element_type = element_type
+        super().__init__(name, value, key_flag, value_flags, validation)
+
+    def collect_errors(self, instance, typed_value, source="<<merged>>"):
+        errors = super().collect_errors(instance, typed_value, self.value)
+
+        # recursively validate the values in the object fields
+        if isinstance(self.value, ConfigurationObject):
+            for key, value in vars(self.value).items():
+                if isinstance(value, LoadedParameter):
+                    errors.extend(
+                        value.collect_errors(instance, typed_value[key], source)
+                    )
+        return errors
+
+    def merge(
+        self, parameters: Sequence[ObjectLoadedParameter]
+    ) -> ObjectLoadedParameter:
+        # get all parameters up to and including first important_match
+        # but if no important_match, then all parameters are important_matches
+        parameters = LoadedParameter._first_important_matches(parameters)
+
+        # map keys with final values,
+        # first key has higher precedence than later ones
+        final_map = {
+            key: value
+            for parameter in reversed(parameters)
+            for key, value in vars(parameter.value).items()
+            if (
+                isinstance(value, LoadedParameter)
+                and parameter.value_flags.get(key) == ParameterFlag.final
+            )
+        }
+
+        # map each value by recursively calling merge on any entries with the same key,
+        # last key has higher precedence than earlier ones
+        grouped_map = {}
+        for parameter in parameters:
+            for key, value in vars(parameter.value).items():
+                grouped_map.setdefault(key, []).append(value)
+        merged_map = {
+            key: values[0].merge(values) for key, values in grouped_map.items()
+        }
+
+        # update merged_map with final_map values
+        merged_value = copy.deepcopy(self._element_type)
+        for key, value in {**merged_map, **final_map}.items():
+            merged_value.__setattr__(key, value)
+
+        # create new parameter for the merged values
+        return ObjectLoadedParameter(
+            self._name,
+            merged_value,
+            self._element_type,
+            self.key_flag,
+            self.value_flags,
+            validation=self._validation,
+        )
+
+
+class ConfigurationObject:
+    """Dummy class to mark whether a Python object has config parameters within."""
+
+
+class Parameter(metaclass=ABCMeta):
+    # (type) describes the type of parameter
+    _type = None
+    # (Parameter or type) if the Parameter is holds a collection, describes the element held in
+    # the collection. if not, describes the primitive type held by the Parameter.
+    _element_type = None
+
+    def __init__(self, default, validation=None):
+        """
+        The Parameter class represents an unloaded configuration parameter, holding type, default
+        and validation information until the parameter is loaded with a configuration.
+
+        Args:
+            default (Any): the typed, python representation default value given if the Parameter
+                is not found in a Configuration.
+            validation (callable): Given a parameter value as input, return a boolean indicating
+                validity, or alternately return a string describing an invalid value.
+        """
+        self._default = default
+        self._validation = validation
+
+    @property
+    def default(self):
+        """Returns a DefaultValueRawParameter that wraps the actual default value."""
+        wrapped_default = DefaultValueRawParameter("default", "default", self._default)
+        return self.load("default", wrapped_default)
+
+    def get_all_matches(self, name, names, instance):
+        """
+        Finds all matches of a Parameter in a Configuration instance
+
+        Args:
+            name (str): canonical name of the parameter to search for
+            names (tuple(str)): alternative aliases of the parameter
+            instance (Configuration): instance of the configuration to search within
+
+        Returns (List(RawParameter)): matches of the parameter found in the configuration.
+        """
+        matches = []
+        multikey_exceptions = []
+        for filepath, raw_parameters in instance.raw_data.items():
+            match, error = ParameterLoader.raw_parameters_from_single_source(
+                name, names, raw_parameters
+            )
+            if match is not None:
+                matches.append(match)
+            if error:
+                multikey_exceptions.append(error)
+        return matches, multikey_exceptions
+
+    @abstractmethod
+    def load(self, name, match):
+        """
+        Loads a Parameter with the value in a RawParameter.
+
+        Args:
+            name (str): name of the parameter to pass through
+            match (RawParameter): the value of the RawParameter match
+
+        Returns a LoadedParameter
+        """
+        raise NotImplementedError()
+
+    def typify(self, name, source, value):
+        element_type = self._element_type
+        try:
+            return typify_data_structure(value, element_type)
+        except TypeCoercionError as e:
+            msg = str(e)
+            if issubclass(element_type, Enum):
+                choices = ", ".join(
+                    map("'{}'".format, element_type.__members__.values())
+                )
+                msg += f"\nValid choices for {name}: {choices}"
+            raise CustomValidationError(name, e.value, source, msg)
+
+
+class PrimitiveParameter(Parameter):
+    """
+    Parameter type for a Configuration class that holds a single python primitive value.
+
+    The python primitive types are str, int, float, complex, bool, and NoneType. In addition,
+    python 2 has long and unicode types.
+    """
+
+    def __init__(self, default, element_type=None, validation=None):
+        """
+        Args:
+            default (primitive value): default value if the Parameter is not found.
+            element_type (type or tuple[type]): Type-validation of parameter's value. If None,
+                type(default) is used.
+        """
+        self._type = type(default) if element_type is None else element_type
+        self._element_type = self._type
+        super().__init__(default, validation)
+
+    def load(self, name, match):
+        return PrimitiveLoadedParameter(
+            name,
+            self._type,
+            match.value(self._element_type),
+            match.keyflag(),
+            match.valueflags(self._element_type),
+            validation=self._validation,
+        )
+
+
+class MapParameter(Parameter):
+    """Parameter type for a Configuration class that holds a map (i.e. dict) of Parameters."""
+
+    _type = frozendict
+
+    def __init__(self, element_type, default=frozendict(), validation=None):
+        """
+        Args:
+            element_type (Parameter): The Parameter type held in the MapParameter.
+            default (Mapping):  The parameter's default value. If None, will be an empty dict.
+        """
+        self._element_type = element_type
+        default = default and frozendict(default) or frozendict()
+        super().__init__(default, validation=validation)
+
+    def get_all_matches(self, name, names, instance):
+        # it also config settings like `proxy_servers: ~`
+        matches, exceptions = super().get_all_matches(name, names, instance)
+        matches = tuple(m for m in matches if m._raw_value is not None)
+        return matches, exceptions
+
+    def load(self, name, match):
+        value = match.value(self._element_type)
+        if value is None:
+            return MapLoadedParameter(
+                name,
+                frozendict(),
+                self._element_type,
+                match.keyflag(),
+                frozendict(),
+                validation=self._validation,
+            )
+
+        if not isinstance(value, Mapping):
+            raise InvalidTypeError(
+                name, value, match.source, value.__class__.__name__, self._type.__name__
+            )
+
+        loaded_map = {}
+        for key, child_value in match.value(self._element_type).items():
+            loaded_child_value = self._element_type.load(name, child_value)
+            loaded_map[key] = loaded_child_value
+
+        return MapLoadedParameter(
+            name,
+            frozendict(loaded_map),
+            self._element_type,
+            match.keyflag(),
+            match.valueflags(self._element_type),
+            validation=self._validation,
+        )
+
+
+class SequenceParameter(Parameter):
+    """Parameter type for a Configuration class that holds a sequence (i.e. list) of Parameters."""
+
+    _type = tuple
+
+    def __init__(self, element_type, default=(), validation=None, string_delimiter=","):
+        """
+        Args:
+            element_type (Parameter): The Parameter type that is held in the sequence.
+            default (Sequence): default value, empty tuple if not given.
+            string_delimiter (str): separation string used to parse string into sequence.
+        """
+        self._element_type = element_type
+        self.string_delimiter = string_delimiter
+        super().__init__(default, validation)
+
+    def get_all_matches(self, name, names, instance):
+        # this is necessary to handle argparse `action="append"`, which can't be set to a
+        #   default value of NULL
+        # it also config settings like `channels: ~`
+        matches, exceptions = super().get_all_matches(name, names, instance)
+        matches = tuple(m for m in matches if m._raw_value is not None)
+        return matches, exceptions
+
+    def load(self, name, match):
+        value = match.value(self)
+        if value is None:
+            return SequenceLoadedParameter(
+                name,
+                (),
+                self._element_type,
+                match.keyflag(),
+                (),
+                validation=self._validation,
+            )
+
+        if not isiterable(value):
+            raise InvalidTypeError(
+                name, value, match.source, value.__class__.__name__, self._type.__name__
+            )
+
+        loaded_sequence = []
+        for child_value in value:
+            loaded_child_value = self._element_type.load(name, child_value)
+            loaded_sequence.append(loaded_child_value)
+
+        return SequenceLoadedParameter(
+            name,
+            tuple(loaded_sequence),
+            self._element_type,
+            match.keyflag(),
+            match.valueflags(self._element_type),
+            validation=self._validation,
+        )
+
+
+class ObjectParameter(Parameter):
+    """Parameter type for a Configuration class that holds an object with Parameter fields."""
+
+    _type = object
+
+    def __init__(self, element_type, default=ConfigurationObject(), validation=None):
+        """
+        Args:
+            element_type (object): The object type with parameter fields held in ObjectParameter.
+            default (Sequence): default value, empty tuple if not given.
+        """
+        self._element_type = element_type
+        super().__init__(default, validation)
+
+    def get_all_matches(self, name, names, instance):
+        # it also config settings like `proxy_servers: ~`
+        matches, exceptions = super().get_all_matches(name, names, instance)
+        matches = tuple(m for m in matches if m._raw_value is not None)
+        return matches, exceptions
+
+    def load(self, name, match):
+        value = match.value(self._element_type)
+        if value is None:
+            return ObjectLoadedParameter(
+                name,
+                None,
+                self._element_type,
+                match.keyflag(),
+                None,
+                validation=self._validation,
+            )
+
+        if not isinstance(value, (Mapping, ConfigurationObject)):
+            raise InvalidTypeError(
+                name, value, match.source, value.__class__.__name__, self._type.__name__
+            )
+
+        # for a default object, extract out the instance variables
+        if isinstance(value, ConfigurationObject):
+            value = vars(value)
+
+        object_parameter_attrs = {
+            attr_name: parameter_type
+            for attr_name, parameter_type in vars(self._element_type).items()
+            if isinstance(parameter_type, Parameter) and attr_name in value.keys()
+        }
+
+        # recursively load object fields
+        loaded_attrs = {}
+        for attr_name, parameter_type in object_parameter_attrs.items():
+            raw_child_value = value.get(attr_name)
+            loaded_child_value = parameter_type.load(name, raw_child_value)
+            loaded_attrs[attr_name] = loaded_child_value
+
+        # copy object and replace Parameter with LoadedParameter fields
+        object_copy = copy.deepcopy(self._element_type)
+        for attr_name, loaded_child_parameter in loaded_attrs.items():
+            object_copy.__setattr__(attr_name, loaded_child_parameter)
+
+        return ObjectLoadedParameter(
+            name,
+            object_copy,
+            self._element_type,
+            match.keyflag(),
+            match.valueflags(self._element_type),
+            validation=self._validation,
+        )
+
+
+class ParameterLoader:
+    """
+    ParameterLoader class contains the top level logic needed to load a parameter from start to
+    finish.
+    """
+
+    def __init__(self, parameter_type, aliases=(), expandvars=False):
+        """
+        Args:
+            parameter_type (Parameter): the type of Parameter that is stored in the loader.
+            aliases (tuple(str)): alternative aliases for the Parameter
+            expandvars (bool): whether or not to recursively expand environmental variables.
+        """
+        self._name = None
+        self._names = None
+        self.type = parameter_type
+        self.aliases = aliases
+        self._expandvars = expandvars
+
+    def _set_name(self, name):
+        # this is an explicit method, and not a descriptor/setter
+        # it's meant to be called by the Configuration metaclass
+        self._name = name
+        _names = frozenset(x for x in chain(self.aliases, (name,)))
+        self._names = _names
+        return name
+
+    @property
+    def name(self):
+        if self._name is None:
+            # The Configuration metaclass should call the `_set_name` method.
+            raise ThisShouldNeverHappenError()  # pragma: no cover
+        return self._name
+
+    @property
+    def names(self):
+        if self._names is None:
+            # The Configuration metaclass should call the `_set_name` method.
+            raise ThisShouldNeverHappenError()  # pragma: no cover
+        return self._names
+
+    def __get__(self, instance, instance_type):
+        # strategy is "extract and merge," which is actually just map and reduce
+        # extract matches from each source in SEARCH_PATH
+        # then merge matches together
+        if self.name in instance._cache_:
+            return instance._cache_[self.name]
+
+        # step 1/2: load config and find top level matches
+        raw_matches, errors = self.type.get_all_matches(self.name, self.names, instance)
+
+        # step 3: parse RawParameters into LoadedParameters
+        matches = [self.type.load(self.name, match) for match in raw_matches]
+
+        # step 4: merge matches
+        merged = matches[0].merge(matches) if matches else self.type.default
+
+        # step 5: typify
+        # We need to expand any environment variables before type casting.
+        # Otherwise e.g. `my_bool_var: $BOOL` with BOOL=True would raise a TypeCoercionError.
+        expanded = merged.expand() if self._expandvars else merged
+        try:
+            result = expanded.typify("<<merged>>")
+        except CustomValidationError as e:
+            errors.append(e)
+        else:
+            errors.extend(expanded.collect_errors(instance, result, "<<merged>>"))
+        raise_errors(errors)
+        instance._cache_[self.name] = result
+        return result
+
+    def _raw_parameters_from_single_source(self, raw_parameters):
+        return ParameterLoader.raw_parameters_from_single_source(
+            self.name, self.names, raw_parameters
+        )
+
+    @staticmethod
+    def raw_parameters_from_single_source(name, names, raw_parameters):
+        # while supporting parameter name aliases, we enforce that only one definition is given
+        # per data source
+        keys = names & frozenset(raw_parameters.keys())
+        matches = {key: raw_parameters[key] for key in keys}
+        numkeys = len(keys)
+        if numkeys == 0:
+            return None, None
+        elif numkeys == 1:
+            return next(iter(matches.values())), None
+        elif name in keys:
+            return matches[name], MultipleKeysError(
+                raw_parameters[next(iter(keys))].source, keys, name
+            )
+        else:
+            return None, MultipleKeysError(
+                raw_parameters[next(iter(keys))].source, keys, name
+            )
+
+
+class ConfigurationType(type):
+    """metaclass for Configuration"""
+
+    def __init__(cls, name, bases, attr):
+        super().__init__(name, bases, attr)
+
+        # call _set_name for each parameter
+        cls.parameter_names = tuple(
+            p._set_name(name)
+            for name, p in cls.__dict__.items()
+            if isinstance(p, ParameterLoader)
+        )
+
+
+class Configuration(metaclass=ConfigurationType):
+    def __init__(self, search_path=(), app_name=None, argparse_args=None):
+        # Currently, __init__ does a **full** disk reload of all files.
+        # A future improvement would be to cache files that are already loaded.
+        self.raw_data = {}
+        self._cache_ = {}
+        self._reset_callbacks = IndexedSet()
+        self._validation_errors = defaultdict(list)
+
+        self._set_search_path(search_path)
+        self._set_env_vars(app_name)
+        self._set_argparse_args(argparse_args)
+
+    def _set_search_path(self, search_path):
+        self._search_path = IndexedSet(search_path)
+        self._set_raw_data(load_file_configs(search_path))
+        self._reset_cache()
+        return self
+
+    def _set_env_vars(self, app_name=None):
+        self._app_name = app_name
+        if not app_name:
+            return self
+        self.raw_data[EnvRawParameter.source] = EnvRawParameter.make_raw_parameters(
+            app_name
+        )
+        self._reset_cache()
+        return self
+
+    def _set_argparse_args(self, argparse_args):
+        # the argparse_args we store internally in this class as self._argparse_args
+        #   will be a mapping type, not a non-`dict` object like argparse_args is natively
+        if hasattr(argparse_args, "__dict__"):
+            # the argparse_args from argparse will be an object with a __dict__ attribute
+            #   and not a mapping type like this method will turn it into
+            self._argparse_args = AttrDict(
+                (k, v) for k, v, in vars(argparse_args).items() if v is not NULL
+            )
+        elif not argparse_args:
+            # argparse_args can be initialized as `None`
+            self._argparse_args = AttrDict()
+        else:
+            # we're calling this method with argparse_args that are a mapping type, likely
+            #   already having been processed by this method before
+            self._argparse_args = AttrDict(
+                (k, v) for k, v, in argparse_args.items() if v is not NULL
+            )
+
+        source = ArgParseRawParameter.source
+        self.raw_data[source] = ArgParseRawParameter.make_raw_parameters(
+            self._argparse_args
+        )
+        self._reset_cache()
+        return self
+
+    def _set_raw_data(self, raw_data):
+        self.raw_data.update(raw_data)
+        self._reset_cache()
+        return self
+
+    def _reset_cache(self):
+        self._cache_ = {}
+        for callback in self._reset_callbacks:
+            callback()
+        return self
+
+    def register_reset_callaback(self, callback):
+        self._reset_callbacks.add(callback)
+
+    def check_source(self, source):
+        # this method ends up duplicating much of the logic of Parameter.__get__
+        # I haven't yet found a way to make it more DRY though
+        typed_values = {}
+        validation_errors = []
+        raw_parameters = self.raw_data[source]
+        for key in self.parameter_names:
+            parameter = self.__class__.__dict__[key]
+            match, multikey_error = parameter._raw_parameters_from_single_source(
+                raw_parameters
+            )
+            if multikey_error:
+                validation_errors.append(multikey_error)
+
+            if match is not None:
+                loaded_parameter = parameter.type.load(key, match)
+                # untyped_value = loaded_parameter.value
+                # if untyped_value is None:
+                #     if isinstance(parameter, SequenceLoadedParameter):
+                #         untyped_value = ()
+                #     elif isinstance(parameter, MapLoadedParameter):
+                #         untyped_value = {}
+                try:
+                    typed_value = loaded_parameter.typify(match.source)
+                except CustomValidationError as e:
+                    validation_errors.append(e)
+                else:
+                    collected_errors = loaded_parameter.collect_errors(
+                        self, typed_value, match.source
+                    )
+                    if collected_errors:
+                        validation_errors.extend(collected_errors)
+                    else:
+                        typed_values[match.key] = typed_value
+            else:
+                # this situation will happen if there is a multikey_error and none of the
+                # matched keys is the primary key
+                pass
+        return typed_values, validation_errors
+
+    def validate_all(self):
+        validation_errors = list(
+            chain.from_iterable(
+                self.check_source(source)[1] for source in self.raw_data
+            )
+        )
+        raise_errors(validation_errors)
+        self.validate_configuration()
+
+    @staticmethod
+    def _collect_validation_error(func, *args, **kwargs):
+        try:
+            func(*args, **kwargs)
+        except ConfigurationError as e:
+            return (e.errors if hasattr(e, "errors") else e,)
+        return ()
+
+    def validate_configuration(self):
+        errors = chain.from_iterable(
+            Configuration._collect_validation_error(getattr, self, name)
+            for name in self.parameter_names
+        )
+        post_errors = self.post_build_validation()
+        raise_errors(tuple(chain.from_iterable((errors, post_errors))))
+
+    def post_build_validation(self):
+        return ()
+
+    def collect_all(self):
+        typed_values = {}
+        validation_errors = {}
+        for source in self.raw_data:
+            typed_values[source], validation_errors[source] = self.check_source(source)
+        raise_errors(tuple(chain.from_iterable(validation_errors.values())))
+        return {k: v for k, v in typed_values.items() if v}
+
+    def describe_parameter(self, parameter_name):
+        # TODO, in Parameter base class, rename element_type to value_type
+        if parameter_name not in self.parameter_names:
+            parameter_name = "_" + parameter_name
+        parameter_loader = self.__class__.__dict__[parameter_name]
+        parameter = parameter_loader.type
+        assert isinstance(parameter, Parameter)
+
+        # dedupe leading underscore from name
+        name = parameter_loader.name.lstrip("_")
+        aliases = tuple(alias for alias in parameter_loader.aliases if alias != name)
+
+        description = self.get_descriptions().get(name, "")
+        et = parameter._element_type
+        if type(et) == EnumMeta:
+            et = [et]
+        if not isiterable(et):
+            et = [et]
+
+        if isinstance(parameter._element_type, Parameter):
+            element_types = tuple(
+                _et.__class__.__name__.lower().replace("parameter", "") for _et in et
+            )
+        else:
+            element_types = tuple(_et.__name__ for _et in et)
+
+        details = {
+            "parameter_type": parameter.__class__.__name__.lower().replace(
+                "parameter", ""
+            ),
+            "name": name,
+            "aliases": aliases,
+            "element_types": element_types,
+            "default_value": parameter.default.typify("<<describe>>"),
+            "description": description.replace("\n", " ").strip(),
+        }
+        if isinstance(parameter, SequenceParameter):
+            details["string_delimiter"] = parameter.string_delimiter
+        return details
+
+    def list_parameters(self):
+        return tuple(sorted(name.lstrip("_") for name in self.parameter_names))
+
+    def typify_parameter(self, parameter_name, value, source):
+        # return a tuple with correct parameter name and typed-value
+        if parameter_name not in self.parameter_names:
+            parameter_name = "_" + parameter_name
+        parameter_loader = self.__class__.__dict__[parameter_name]
+        parameter = parameter_loader.type
+        assert isinstance(parameter, Parameter)
+
+        return parameter.typify(parameter_name, source, value)
+
+    def get_descriptions(self):
+        raise NotImplementedError()
diff --git a/shared/package-files/e4/e4381a6af6bd99b2caafced781b7ca64faee9d83c168df1e8cdce999b5baaf8b/RP.py b/shared/package-files/e4/e4381a6af6bd99b2caafced781b7ca64faee9d83c168df1e8cdce999b5baaf8b/RP.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b74549db90e97a990070423cb1b1554fb24b28f
--- /dev/null
+++ b/shared/package-files/e4/e4381a6af6bd99b2caafced781b7ca64faee9d83c168df1e8cdce999b5baaf8b/RP.py
@@ -0,0 +1,58 @@
+# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
+
+# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc.
+#
+# Permission to use, copy, modify, and distribute this software and its
+# documentation for any purpose with or without fee is hereby granted,
+# provided that the above copyright notice and this permission notice
+# appear in all copies.
+#
+# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
+# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
+# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+import dns.exception
+import dns.immutable
+import dns.name
+import dns.rdata
+
+
+@dns.immutable.immutable
+class RP(dns.rdata.Rdata):
+    """RP record"""
+
+    # see: RFC 1183
+
+    __slots__ = ["mbox", "txt"]
+
+    def __init__(self, rdclass, rdtype, mbox, txt):
+        super().__init__(rdclass, rdtype)
+        self.mbox = self._as_name(mbox)
+        self.txt = self._as_name(txt)
+
+    def to_text(self, origin=None, relativize=True, **kw):
+        mbox = self.mbox.choose_relativity(origin, relativize)
+        txt = self.txt.choose_relativity(origin, relativize)
+        return "{} {}".format(str(mbox), str(txt))
+
+    @classmethod
+    def from_text(
+        cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None
+    ):
+        mbox = tok.get_name(origin, relativize, relativize_to)
+        txt = tok.get_name(origin, relativize, relativize_to)
+        return cls(rdclass, rdtype, mbox, txt)
+
+    def _to_wire(self, file, compress=None, origin=None, canonicalize=False):
+        self.mbox.to_wire(file, None, origin, canonicalize)
+        self.txt.to_wire(file, None, origin, canonicalize)
+
+    @classmethod
+    def from_wire_parser(cls, rdclass, rdtype, parser, origin=None):
+        mbox = parser.get_name(origin)
+        txt = parser.get_name(origin)
+        return cls(rdclass, rdtype, mbox, txt)
diff --git a/shared/package-files/e4/e43cb7054cdfbe58ce53b8272567954e522ba9a2a86557b8a4f729add0b7591c/lstm_cell.h b/shared/package-files/e4/e43cb7054cdfbe58ce53b8272567954e522ba9a2a86557b8a4f729add0b7591c/lstm_cell.h
new file mode 100644
index 0000000000000000000000000000000000000000..14ae857ce4c98b46c147ba95333ae13fca71cd33
--- /dev/null
+++ b/shared/package-files/e4/e43cb7054cdfbe58ce53b8272567954e522ba9a2a86557b8a4f729add0b7591c/lstm_cell.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/lstm_cell_ops.h>
+
+namespace at {
+
+
+// aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional<at::Tensor> & b_ih={}, const c10::optional<at::Tensor> & b_hh={}) {
+    return at::_ops::lstm_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh);
+}
+
+}
diff --git a/shared/package-files/e4/e4416632f97af0a2880b43bb046ef0e1ff821262efb65c4f1ecc070bc02d1d30/files.pyi b/shared/package-files/e4/e4416632f97af0a2880b43bb046ef0e1ff821262efb65c4f1ecc070bc02d1d30/files.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..225d8fd19880a3c124fe1b46d8257b0bdcfcbb55
--- /dev/null
+++ b/shared/package-files/e4/e4416632f97af0a2880b43bb046ef0e1ff821262efb65c4f1ecc070bc02d1d30/files.pyi
@@ -0,0 +1,101 @@
+from pathlib import Path
+from typing import Any, Callable, Iterable, Optional, Type, TypeVar, Union, overload
+
+from django.core.files.base import File
+from django.core.files.images import ImageFile
+from django.core.files.storage import FileSystemStorage, Storage
+from django.db.models.base import Model
+
+from django.db.models.fields import Field, _FieldChoices, _ValidatorCallable, _ErrorMessagesToOverride
+
+class FieldFile(File):
+    instance: Model = ...
+    field: FileField = ...
+    storage: FileSystemStorage = ...
+    def __init__(self, instance: Model, field: FileField, name: Optional[str]) -> None: ...
+    file: Any = ...
+    @property
+    def path(self) -> str: ...
+    @property
+    def url(self) -> str: ...
+    @property
+    def size(self) -> int: ...
+    def save(self, name: str, content: File, save: bool = ...) -> None: ...
+    def delete(self, save: bool = ...) -> None: ...
+    @property
+    def closed(self) -> bool: ...
+
+class FileDescriptor:
+    field: FileField = ...
+    def __init__(self, field: FileField) -> None: ...
+    def __set__(self, instance: Model, value: Optional[Any]) -> None: ...
+    def __get__(self, instance: Optional[Model], cls: Type[Model] = ...) -> Union[FieldFile, FileDescriptor]: ...
+
+_T = TypeVar("_T", bound="Field")
+
+class FileField(Field):
+    storage: Any = ...
+    upload_to: Union[str, Callable] = ...
+    def __init__(
+        self,
+        upload_to: Union[str, Callable, Path] = ...,
+        storage: Optional[Storage] = ...,
+        verbose_name: Optional[Union[str, bytes]] = ...,
+        name: Optional[str] = ...,
+        max_length: Optional[int] = ...,
+        unique: bool = ...,
+        blank: bool = ...,
+        null: bool = ...,
+        db_index: bool = ...,
+        default: Any = ...,
+        editable: bool = ...,
+        auto_created: bool = ...,
+        serialize: bool = ...,
+        unique_for_date: Optional[str] = ...,
+        unique_for_month: Optional[str] = ...,
+        unique_for_year: Optional[str] = ...,
+        choices: Optional[_FieldChoices] = ...,
+        help_text: str = ...,
+        db_column: Optional[str] = ...,
+        db_tablespace: Optional[str] = ...,
+        validators: Iterable[_ValidatorCallable] = ...,
+        error_messages: Optional[_ErrorMessagesToOverride] = ...,
+    ): ...
+    # class access
+    @overload  # type: ignore
+    def __get__(self, instance: None, owner) -> FileDescriptor: ...
+    # Model instance access
+    @overload
+    def __get__(self, instance: Model, owner) -> Any: ...
+    # non-Model instances
+    @overload
+    def __get__(self: _T, instance, owner) -> _T: ...
+    def generate_filename(self, instance: Optional[Model], filename: str) -> str: ...
+
+class ImageFileDescriptor(FileDescriptor):
+    field: ImageField
+    def __set__(self, instance: Model, value: Optional[str]) -> None: ...
+
+class ImageFieldFile(ImageFile, FieldFile):
+    field: ImageField
+    def delete(self, save: bool = ...) -> None: ...
+
+class ImageField(FileField):
+    def __init__(
+        self,
+        verbose_name: Optional[str] = ...,
+        name: Optional[str] = ...,
+        width_field: Optional[str] = ...,
+        height_field: Optional[str] = ...,
+        **kwargs: Any
+    ) -> None: ...
+    # class access
+    @overload  # type: ignore
+    def __get__(self, instance: None, owner) -> ImageFileDescriptor: ...
+    # Model instance access
+    @overload
+    def __get__(self, instance: Model, owner) -> Any: ...
+    # non-Model instances
+    @overload
+    def __get__(self: _T, instance, owner) -> _T: ...
+    def update_dimension_fields(self, instance: Model, force: bool = ..., *args: Any, **kwargs: Any) -> None: ...
diff --git a/shared/package-files/e4/e456533ee7635704d1503d8140984eaf4a72c44c937975316268ffc6dd550b41/getpass.pyi b/shared/package-files/e4/e456533ee7635704d1503d8140984eaf4a72c44c937975316268ffc6dd550b41/getpass.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..784eb1a1d760eaaa12434b512bcf78dc56c393e8
--- /dev/null
+++ b/shared/package-files/e4/e456533ee7635704d1503d8140984eaf4a72c44c937975316268ffc6dd550b41/getpass.pyi
@@ -0,0 +1,6 @@
+from typing import IO, Any
+
+class GetPassWarning(UserWarning): ...
+
+def getpass(prompt: str = ..., stream: IO[Any] = ...) -> str: ...
+def getuser() -> str: ...
diff --git a/shared/package-files/e4/e45b199106608c7c981c149d3b4ccf092e7a2e7e9430cc76887cd769b9aaf533/test_textview.py b/shared/package-files/e4/e45b199106608c7c981c149d3b4ccf092e7a2e7e9430cc76887cd769b9aaf533/test_textview.py
new file mode 100644
index 0000000000000000000000000000000000000000..7189378ab3dd615e04cf17c2869e1fd611cb4eb1
--- /dev/null
+++ b/shared/package-files/e4/e45b199106608c7c981c149d3b4ccf092e7a2e7e9430cc76887cd769b9aaf533/test_textview.py
@@ -0,0 +1,233 @@
+"""Test textview, coverage 100%.
+
+Since all methods and functions create (or destroy) a ViewWindow, which
+is a widget containing a widget, etcetera, all tests must be gui tests.
+Using mock Text would not change this.  Other mocks are used to retrieve
+information about calls.
+"""
+from idlelib import textview as tv
+from test.support import requires
+requires('gui')
+
+import os
+import unittest
+from tkinter import Tk, TclError, CHAR, NONE, WORD
+from tkinter.ttk import Button
+from idlelib.idle_test.mock_idle import Func
+from idlelib.idle_test.mock_tk import Mbox_func
+
+def setUpModule():
+    global root
+    root = Tk()
+    root.withdraw()
+
+def tearDownModule():
+    global root
+    root.update_idletasks()
+    root.destroy()
+    del root
+
+# If we call ViewWindow or wrapper functions with defaults
+# modal=True, _utest=False, test hangs on call to wait_window.
+# Have also gotten tk error 'can't invoke "event" command'.
+
+
+class VW(tv.ViewWindow):  # Used in ViewWindowTest.
+    transient = Func()
+    grab_set = Func()
+    wait_window = Func()
+
+
+# Call wrapper class VW with mock wait_window.
+class ViewWindowTest(unittest.TestCase):
+
+    def setUp(self):
+        VW.transient.__init__()
+        VW.grab_set.__init__()
+        VW.wait_window.__init__()
+
+    def test_init_modal(self):
+        view = VW(root, 'Title', 'test text')
+        self.assertTrue(VW.transient.called)
+        self.assertTrue(VW.grab_set.called)
+        self.assertTrue(VW.wait_window.called)
+        view.ok()
+
+    def test_init_nonmodal(self):
+        view = VW(root, 'Title', 'test text', modal=False)
+        self.assertFalse(VW.transient.called)
+        self.assertFalse(VW.grab_set.called)
+        self.assertFalse(VW.wait_window.called)
+        view.ok()
+
+    def test_ok(self):
+        view = VW(root, 'Title', 'test text', modal=False)
+        view.destroy = Func()
+        view.ok()
+        self.assertTrue(view.destroy.called)
+        del view.destroy  # Unmask real function.
+        view.destroy()
+
+
+class AutoHideScrollbarTest(unittest.TestCase):
+    # Method set is tested in ScrollableTextFrameTest
+    def test_forbidden_geometry(self):
+        scroll = tv.AutoHideScrollbar(root)
+        self.assertRaises(TclError, scroll.pack)
+        self.assertRaises(TclError, scroll.place)
+
+
+class ScrollableTextFrameTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root = root = Tk()
+        root.withdraw()
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.root.update_idletasks()
+        cls.root.destroy()
+        del cls.root
+
+    def make_frame(self, wrap=NONE, **kwargs):
+        frame = tv.ScrollableTextFrame(self.root, wrap=wrap, **kwargs)
+        def cleanup_frame():
+            frame.update_idletasks()
+            frame.destroy()
+        self.addCleanup(cleanup_frame)
+        return frame
+
+    def test_line1(self):
+        frame = self.make_frame()
+        frame.text.insert('1.0', 'test text')
+        self.assertEqual(frame.text.get('1.0', '1.end'), 'test text')
+
+    def test_horiz_scrollbar(self):
+        # The horizontal scrollbar should be shown/hidden according to
+        # the 'wrap' setting: It should only be shown when 'wrap' is
+        # set to NONE.
+
+        # wrap = NONE -> with horizontal scrolling
+        frame = self.make_frame(wrap=NONE)
+        self.assertEqual(frame.text.cget('wrap'), NONE)
+        self.assertIsNotNone(frame.xscroll)
+
+        # wrap != NONE -> no horizontal scrolling
+        for wrap in [CHAR, WORD]:
+            with self.subTest(wrap=wrap):
+                frame = self.make_frame(wrap=wrap)
+                self.assertEqual(frame.text.cget('wrap'), wrap)
+                self.assertIsNone(frame.xscroll)
+
+
+class ViewFrameTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root = root = Tk()
+        root.withdraw()
+        cls.frame = tv.ViewFrame(root, 'test text')
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.frame
+        cls.root.update_idletasks()
+        cls.root.destroy()
+        del cls.root
+
+    def test_line1(self):
+        get = self.frame.text.get
+        self.assertEqual(get('1.0', '1.end'), 'test text')
+
+
+# Call ViewWindow with modal=False.
+class ViewFunctionTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.orig_error = tv.showerror
+        tv.showerror = Mbox_func()
+
+    @classmethod
+    def tearDownClass(cls):
+        tv.showerror = cls.orig_error
+        del cls.orig_error
+
+    def test_view_text(self):
+        view = tv.view_text(root, 'Title', 'test text', modal=False)
+        self.assertIsInstance(view, tv.ViewWindow)
+        self.assertIsInstance(view.viewframe, tv.ViewFrame)
+        view.viewframe.ok()
+
+    def test_view_file(self):
+        view = tv.view_file(root, 'Title', __file__, 'ascii', modal=False)
+        self.assertIsInstance(view, tv.ViewWindow)
+        self.assertIsInstance(view.viewframe, tv.ViewFrame)
+        get = view.viewframe.textframe.text.get
+        self.assertIn('Test', get('1.0', '1.end'))
+        view.ok()
+
+    def test_bad_file(self):
+        # Mock showerror will be used; view_file will return None.
+        view = tv.view_file(root, 'Title', 'abc.xyz', 'ascii', modal=False)
+        self.assertIsNone(view)
+        self.assertEqual(tv.showerror.title, 'File Load Error')
+
+    def test_bad_encoding(self):
+        p = os.path
+        fn = p.abspath(p.join(p.dirname(__file__), '..', 'CREDITS.txt'))
+        view = tv.view_file(root, 'Title', fn, 'ascii', modal=False)
+        self.assertIsNone(view)
+        self.assertEqual(tv.showerror.title, 'Unicode Decode Error')
+
+    def test_nowrap(self):
+        view = tv.view_text(root, 'Title', 'test', modal=False, wrap='none')
+        text_widget = view.viewframe.textframe.text
+        self.assertEqual(text_widget.cget('wrap'), 'none')
+
+
+# Call ViewWindow with _utest=True.
+class ButtonClickTest(unittest.TestCase):
+
+    def setUp(self):
+        self.view = None
+        self.called = False
+
+    def tearDown(self):
+        if self.view:
+            self.view.destroy()
+
+    def test_view_text_bind_with_button(self):
+        def _command():
+            self.called = True
+            self.view = tv.view_text(root, 'TITLE_TEXT', 'COMMAND', _utest=True)
+        button = Button(root, text='BUTTON', command=_command)
+        button.invoke()
+        self.addCleanup(button.destroy)
+
+        self.assertEqual(self.called, True)
+        self.assertEqual(self.view.title(), 'TITLE_TEXT')
+        self.assertEqual(self.view.viewframe.textframe.text.get('1.0', '1.end'),
+                         'COMMAND')
+
+    def test_view_file_bind_with_button(self):
+        def _command():
+            self.called = True
+            self.view = tv.view_file(root, 'TITLE_FILE', __file__,
+                                     encoding='ascii', _utest=True)
+        button = Button(root, text='BUTTON', command=_command)
+        button.invoke()
+        self.addCleanup(button.destroy)
+
+        self.assertEqual(self.called, True)
+        self.assertEqual(self.view.title(), 'TITLE_FILE')
+        get = self.view.viewframe.textframe.text.get
+        with open(__file__) as f:
+            self.assertEqual(get('1.0', '1.end'), f.readline().strip())
+            f.readline()
+            self.assertEqual(get('3.0', '3.end'), f.readline().strip())
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)
diff --git a/shared/package-files/e4/e45bf9dccb4d0513e8b3b84730cdb86e20b078071f060d319b853e108dfab0cd/triu_ops.h b/shared/package-files/e4/e45bf9dccb4d0513e8b3b84730cdb86e20b078071f060d319b853e108dfab0cd/triu_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..dbe6ebb4c0d9ba16df2939abb84647f282216d10
--- /dev/null
+++ b/shared/package-files/e4/e45bf9dccb4d0513e8b3b84730cdb86e20b078071f060d319b853e108dfab0cd/triu_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API triu_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::triu_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, int64_t diagonal);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal);
+};
+
+struct TORCH_API triu_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::triu")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+};
+
+struct TORCH_API triu {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::triu")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "triu(Tensor self, int diagonal=0) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, int64_t diagonal);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/e4/e45d150182533c351396a6f9cc334479cf81781636e598e78ea607150ab918f5/test_tolist.py b/shared/package-files/e4/e45d150182533c351396a6f9cc334479cf81781636e598e78ea607150ab918f5/test_tolist.py
new file mode 100644
index 0000000000000000000000000000000000000000..4af473528e23850794139ac563cc04c6d3c54617
--- /dev/null
+++ b/shared/package-files/e4/e45d150182533c351396a6f9cc334479cf81781636e598e78ea607150ab918f5/test_tolist.py
@@ -0,0 +1,36 @@
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "values, dtype, expected_dtype",
+    (
+        ([1], "int64", int),
+        ([1], "Int64", int),
+        ([1.0], "float64", float),
+        ([1.0], "Float64", float),
+        (["abc"], "object", str),
+        (["abc"], "string", str),
+        ([Interval(1, 3)], "interval", Interval),
+        ([Period("2000-01-01", "D")], "period[D]", Period),
+        ([Timedelta(days=1)], "timedelta64[ns]", Timedelta),
+        ([Timestamp("2000-01-01")], "datetime64[ns]", Timestamp),
+        pytest.param([1], "int64[pyarrow]", int, marks=td.skip_if_no("pyarrow")),
+        pytest.param([1.0], "float64[pyarrow]", float, marks=td.skip_if_no("pyarrow")),
+        pytest.param(["abc"], "string[pyarrow]", str, marks=td.skip_if_no("pyarrow")),
+    ),
+)
+def test_tolist_scalar_dtype(values, dtype, expected_dtype):
+    # GH49890
+    ser = Series(values, dtype=dtype)
+    result_dtype = type(ser.tolist()[0])
+    assert result_dtype == expected_dtype
diff --git a/shared/package-files/e4/e45e7fee706ba78533e260b9bcfdcc53ba1feddcbbaebd19416a498a79521d52/fmod_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/e4/e45e7fee706ba78533e260b9bcfdcc53ba1feddcbbaebd19416a498a79521d52/fmod_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a93af795ba6130deb5c016110ec8d823fe028b72
--- /dev/null
+++ b/shared/package-files/e4/e45e7fee706ba78533e260b9bcfdcc53ba1feddcbbaebd19416a498a79521d52/fmod_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor fmod(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmod_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/e4/e4672feabfa8842734eb267b73a90105b350f3ecc09bbde85a78a132de4a367c/fmt-targets-release.cmake b/shared/package-files/e4/e4672feabfa8842734eb267b73a90105b350f3ecc09bbde85a78a132de4a367c/fmt-targets-release.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..3b7cde1800a7d7190b1ec757d0c92568130abe3b
--- /dev/null
+++ b/shared/package-files/e4/e4672feabfa8842734eb267b73a90105b350f3ecc09bbde85a78a132de4a367c/fmt-targets-release.cmake
@@ -0,0 +1,19 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "fmt::fmt" for configuration "Release"
+set_property(TARGET fmt::fmt APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(fmt::fmt PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libfmt.so.9.1.0"
+  IMPORTED_SONAME_RELEASE "libfmt.so.9"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS fmt::fmt )
+list(APPEND _IMPORT_CHECK_FILES_FOR_fmt::fmt "${_IMPORT_PREFIX}/lib/libfmt.so.9.1.0" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/shared/package-files/e4/e4766c3ba8f683ff51a4c8acec73915fa3573bb91768ad6bc138eb6a4da5d9ed/communicate.h b/shared/package-files/e4/e4766c3ba8f683ff51a4c8acec73915fa3573bb91768ad6bc138eb6a4da5d9ed/communicate.h
new file mode 100644
index 0000000000000000000000000000000000000000..79c27eaeba7faee58662bcd002326bf65ccb1efc
--- /dev/null
+++ b/shared/package-files/e4/e4766c3ba8f683ff51a4c8acec73915fa3573bb91768ad6bc138eb6a4da5d9ed/communicate.h
@@ -0,0 +1,65 @@
+#pragma once
+#include <ext/stdio_filebuf.h>
+#include <sys/wait.h>
+#include <torch/csrc/profiler/unwind/unwind_error.h>
+#include <unistd.h>
+#include <memory>
+
+// helper to open a process with stdin/stdout/stderr streams.
+struct Communicate {
+  Communicate(const char* command, const char** args) {
+    if (pipe(inpipe_) < 0 || pipe(outpipe_) < 0 || pipe(errpipe_) < 0) {
+      throw UnwindError("pipe() failed");
+    }
+    pid_t pid = fork();
+    if (pid < 0) {
+      throw UnwindError("fork() failed");
+    } else if (pid == 0) { // child process
+      close(inpipe_[1]);
+      close(outpipe_[0]);
+      close(errpipe_[0]);
+
+      dup2(inpipe_[0], STDIN_FILENO);
+      dup2(outpipe_[1], STDOUT_FILENO);
+      dup2(errpipe_[1], STDERR_FILENO);
+      execvp(command, (char* const*)args);
+      throw UnwindError("failed execvp");
+    } else { // parent process
+      close(inpipe_[0]);
+      close(outpipe_[1]);
+      close(errpipe_[1]);
+      outbuf_.reset(
+          new __gnu_cxx::stdio_filebuf<char>(inpipe_[1], std::ios::out));
+      inbuf_.reset(
+          new __gnu_cxx::stdio_filebuf<char>(outpipe_[0], std::ios::in));
+      errbuf_.reset(
+          new __gnu_cxx::stdio_filebuf<char>(errpipe_[0], std::ios::in));
+      in_.reset(new std::istream(inbuf_.get()));
+      out_.reset(new std::ostream(outbuf_.get()));
+      err_.reset(new std::ostream(errbuf_.get()));
+    }
+  }
+  ~Communicate() {
+    close(inpipe_[1]);
+    close(outpipe_[0]);
+    close(errpipe_[0]);
+  }
+  std::ostream& out() {
+    return *out_;
+  }
+  std::ostream& err() {
+    return *err_;
+  }
+  std::istream& in() {
+    return *in_;
+  }
+
+ private:
+  int inpipe_[2];
+  int outpipe_[2];
+  int errpipe_[2];
+  std::unique_ptr<__gnu_cxx::stdio_filebuf<char>> outbuf_, inbuf_, errbuf_;
+  std::unique_ptr<std::istream> in_;
+  std::unique_ptr<std::ostream> out_;
+  std::unique_ptr<std::ostream> err_;
+};
diff --git a/shared/package-files/e4/e47690018e53a09dc519d0ae82b0031496166a81bbd549473f19706d0d92c22e/ansi77 b/shared/package-files/e4/e47690018e53a09dc519d0ae82b0031496166a81bbd549473f19706d0d92c22e/ansi77
new file mode 100644
index 0000000000000000000000000000000000000000..b7c11b18531c53e262ffdd38c2e73217d9857b28
Binary files /dev/null and b/shared/package-files/e4/e47690018e53a09dc519d0ae82b0031496166a81bbd549473f19706d0d92c22e/ansi77 differ
diff --git a/shared/package-files/e4/e47b5fc62423d697e813659914b914211806ff7bbe5fa22877f4852ec0ce4391/sub_meta_dispatch.h b/shared/package-files/e4/e47b5fc62423d697e813659914b914211806ff7bbe5fa22877f4852ec0ce4391/sub_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..073a975dc47b02606018ea63e65c3125d8cb4c36
--- /dev/null
+++ b/shared/package-files/e4/e47b5fc62423d697e813659914b914211806ff7bbe5fa22877f4852ec0ce4391/sub_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/e4/e47cc994e837d40e83b28ae262ac528dbb72ab8c757bc29a9d12d52542a00031/scatter_reduce_meta_dispatch.h b/shared/package-files/e4/e47cc994e837d40e83b28ae262ac528dbb72ab8c757bc29a9d12d52542a00031/scatter_reduce_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dba6685f9cd4f1a64bd32688981acd777631822
--- /dev/null
+++ b/shared/package-files/e4/e47cc994e837d40e83b28ae262ac528dbb72ab8c757bc29a9d12d52542a00031/scatter_reduce_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true);
+TORCH_API at::Tensor & scatter_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true);
+TORCH_API at::Tensor & scatter_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out);
+TORCH_API at::Tensor & scatter_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/e4/e48430787a0a8719117601bba1d63ed136c39db9c99212f75bb5bab88dca8263/data.py b/shared/package-files/e4/e48430787a0a8719117601bba1d63ed136c39db9c99212f75bb5bab88dca8263/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8021b1a6ea20ad1967b43d0abc107da39740959
--- /dev/null
+++ b/shared/package-files/e4/e48430787a0a8719117601bba1d63ed136c39db9c99212f75bb5bab88dca8263/data.py
@@ -0,0 +1,76 @@
+"""Yearly sunspots data 1700-2008"""
+from statsmodels.datasets import utils as du
+
+__docformat__ = "restructuredtext"
+
+COPYRIGHT = """This data is public domain."""
+TITLE = __doc__
+SOURCE = """
+http://www.ngdc.noaa.gov/stp/solar/solarda3.html
+
+The original dataset contains monthly data on sunspot activity in the file
+./src/sunspots_yearly.dat.  There is also sunspots_monthly.dat.
+"""
+
+DESCRSHORT = """Yearly (1700-2008) data on sunspots from the National
+Geophysical Data Center."""
+
+DESCRLONG = DESCRSHORT
+
+NOTE = """::
+
+    Number of Observations - 309 (Annual 1700 - 2008)
+    Number of Variables - 1
+    Variable name definitions::
+
+        SUNACTIVITY - Number of sunspots for each year
+
+    The data file contains a 'YEAR' variable that is not returned by load.
+"""
+
+
+def load_pandas():
+    """
+    Load the yearly sunspot data and returns a data class.
+
+    Returns
+    -------
+    Dataset
+        A `Dataset` instance with ``data``, ``names``, ``endog`` and ``endog_name``
+        attributes.
+
+    Notes
+    -----
+    This dataset only contains data for one variable, so the attributes
+    data, raw_data, and endog are all the same variable.  There is no exog
+    attribute defined.
+    """
+    data = _get_data()
+    # TODO: time series
+    endog = data.set_index(data.YEAR).SUNACTIVITY
+    dataset = du.Dataset(data=data, names=list(data.columns),
+                         endog=endog, endog_name="SUNACTIVITY")
+    return dataset
+
+
+def load():
+    """
+    Load the yearly sunspot data and returns a data class.
+
+    Returns
+    -------
+    Dataset
+        A `Dataset` instance with ``data``, ``names``, ``endog`` and ``endog_name``
+        attributes.
+
+    Notes
+    -----
+    This dataset only contains data for one variable, so the attributes
+    data, raw_data, and endog are all the same variable.  There is no exog
+    attribute defined.
+    """
+    return load_pandas()
+
+
+def _get_data():
+    return du.load_csv(__file__, "sunspots.csv").astype(float)
diff --git a/shared/package-files/e4/e48d5fd9d871a34b20841afb57b6ff55e9aea24f792340fa73cfc4075f7e32c3/_decorators.py b/shared/package-files/e4/e48d5fd9d871a34b20841afb57b6ff55e9aea24f792340fa73cfc4075f7e32c3/_decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..b50422eb941d2a874d80c22872b56818e25aaf7c
--- /dev/null
+++ b/shared/package-files/e4/e48d5fd9d871a34b20841afb57b6ff55e9aea24f792340fa73cfc4075f7e32c3/_decorators.py
@@ -0,0 +1,202 @@
+"""Decorators and descriptors used for cached attributes"""
+
+import warnings
+
+from statsmodels.tools.sm_exceptions import CacheWriteWarning
+
+__all__ = [
+    "ResettableCache",
+    "cache_readonly",
+    "cache_writable",
+    "cached_data",
+    "cached_value",
+    "deprecated_alias",
+]
+
+
+class ResettableCache(dict):
+    """Dictionary cache retained for backward compatibility"""
+
+    def __init__(self, *args, **kwargs):
+        """Initialize the cache"""
+        super().__init__(*args, **kwargs)
+        self.__dict__ = self
+
+
+def deprecated_alias(
+    old_name, new_name, remove_version=None, msg=None, warning=FutureWarning
+):
+    """
+    Deprecate attribute in favor of alternative name
+
+    Parameters
+    ----------
+    old_name : str
+        Old, deprecated name
+    new_name : str
+        New name
+    remove_version : str, optional
+        Version that the alias will be removed
+    msg : str, optional
+        Message to show.  Default is
+        `old_name` is a deprecated alias for `new_name`
+    warning : Warning, optional
+        Warning class to give.  Default is FutureWarning.
+
+    Notes
+    -----
+    Older or less-used classes may not conform to statsmodels naming
+    conventions.  `deprecated_alias` lets us bring them into conformance
+    without breaking backward-compatibility.
+
+    Examples
+    --------
+    Instances of the `Foo` class have a `nvars` attribute, but it _should_
+    be called `neqs`:
+
+    class Foo:
+        nvars = deprecated_alias('nvars', 'neqs')
+        def __init__(self, neqs):
+            self.neqs = neqs
+
+    >>> foo = Foo(3)
+    >>> foo.nvars
+    __main__:1: FutureWarning: nvars is a deprecated alias for neqs
+    3
+
+    """
+    if msg is None:
+        msg = f"{old_name} is a deprecated alias for {new_name}"
+        if remove_version is not None:
+            msg += f", will be removed in version {remove_version}"
+
+    def fget(self):
+        warnings.warn(msg, warning, stacklevel=2)
+        return getattr(self, new_name)
+
+    def fset(self, value):
+        warnings.warn(msg, warning, stacklevel=2)
+        setattr(self, new_name, value)
+
+    res = property(fget=fget, fset=fset)
+    return res
+
+
+class CachedAttribute:
+    """Descriptor that caches a read-only attribute on first access"""
+
+    def __init__(self, func, cachename=None):
+        self.fget = func
+        self.name = func.__name__
+        self.cachename = cachename or "_cache"
+
+    def __get__(self, obj, type=None):
+        if obj is None:
+            return self.fget
+        # Get the cache or set a default one if needed
+        _cachename = self.cachename
+        _cache = getattr(obj, _cachename, None)
+        if _cache is None:
+            setattr(obj, _cachename, {})
+            _cache = getattr(obj, _cachename)
+        # Get the name of the attribute to set and cache
+        name = self.name
+        _cachedval = _cache.get(name, None)
+        if _cachedval is None:
+            _cachedval = self.fget(obj)
+            _cache[name] = _cachedval
+
+        return _cachedval
+
+    def __set__(self, obj, value):
+        errmsg = f"The attribute '{self.name}' cannot be overwritten"
+        warnings.warn(errmsg, CacheWriteWarning, stacklevel=2)
+
+
+class CachedWritableAttribute(CachedAttribute):
+    """Descriptor that caches an attribute and permits reassignment"""
+
+    def __set__(self, obj, value):
+        _cache = getattr(obj, self.cachename)
+        name = self.name
+        _cache[name] = value
+
+
+class _cache_readonly(property):
+    """Decorate a method as a CachedAttribute"""
+
+    def __init__(self, cachename=None):
+        self.func = None
+        self.cachename = cachename
+
+    def __call__(self, func):
+        return CachedAttribute(func, cachename=self.cachename)
+
+
+class cache_writable(_cache_readonly):
+    """Decorate a method as a CachedWritableAttribute"""
+
+    def __call__(self, func):
+        """Return a writable cached descriptor for func"""
+        return CachedWritableAttribute(func, cachename=self.cachename)
+
+
+class cache_readonly:
+    """
+    Decorator for read-only, cached properties
+
+    Acts as a property that is computed once and then stored in the
+    instance's ``_cache`` dictionary. This allows ``remove_data``
+    to identify and clear cached values.
+
+    Vendored to replace pandas._libs.properties.cache_readonly
+    which is a private C extension.
+
+    Parameters
+    ----------
+    func : callable
+        The function to cache.
+
+    Examples
+    --------
+    >>> class Foo:
+    ...     @cache_readonly
+    ...     def bar(self):
+    ...         return 42
+    >>> f = Foo()
+    >>> f.bar
+    42
+    """
+
+    def __init__(self, func):
+        self.func = func
+        self.fget = func
+        self.__doc__ = getattr(func, "__doc__", None)
+        self.__name__ = func.__name__
+        self.__module__ = func.__module__
+
+    def __get__(self, obj, objtype=None):
+        if obj is None:
+            return self
+        # Use _cache dict — same as pandas implementation
+        # This is required for remove_data() to work correctly
+        try:
+            cache = obj._cache
+        except AttributeError:
+            cache = obj._cache = {}
+        val = cache.get(self.__name__, None)
+        if val is None:
+            val = self.func(obj)
+            cache[self.__name__] = val
+        return val
+
+    def __set__(self, obj, values):
+        raise AttributeError(f"The attribute '{self.__name__}' cannot be set.")
+
+
+# cached_value and cached_data behave identically to cache_readonly, but
+# are used by `remove_data` to
+#   a) identify array-like attributes to remove (cached_data)
+#   b) make sure certain values are evaluated before caching (cached_value)
+cached_data = cache_readonly
+cached_value = cache_readonly
diff --git a/shared/package-files/e4/e494442f6ba10f36b6e23cbe53f4f518f8afe6eb30fbf9dcc0d4f0049ed4bb6f/_conventional.pxd b/shared/package-files/e4/e494442f6ba10f36b6e23cbe53f4f518f8afe6eb30fbf9dcc0d4f0049ed4bb6f/_conventional.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..dd0392ec25ef13e34cfef007bbc5b7fe37a85ff9
--- /dev/null
+++ b/shared/package-files/e4/e494442f6ba10f36b6e23cbe53f4f518f8afe6eb30fbf9dcc0d4f0049ed4bb6f/_conventional.pxd
@@ -0,0 +1,71 @@
+#cython: boundscheck=False
+#cython: wraparound=False
+#cython: cdivision=False
+"""
+State Space Models - Conventional Kalman Filter declarations
+
+Author: Chad Fulton 
+License: Simplified-BSD
+"""
+
+cimport numpy as np
+
+from statsmodels.tsa.statespace._kalman_filter cimport (
+    cKalmanFilter,
+    dKalmanFilter,
+    sKalmanFilter,
+    zKalmanFilter,
+)
+from statsmodels.tsa.statespace._kalman_smoother cimport (
+    cKalmanSmoother,
+    dKalmanSmoother,
+    sKalmanSmoother,
+    zKalmanSmoother,
+)
+from statsmodels.tsa.statespace._representation cimport (
+    cStatespace,
+    dStatespace,
+    sStatespace,
+    zStatespace,
+)
+
+
+# Single precision
+cdef int ssmoothed_estimators_missing_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model) except *
+cdef int ssmoothed_disturbances_missing_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model)
+
+cdef int ssmoothed_estimators_measurement_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model) except *
+cdef int ssmoothed_estimators_time_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model)
+cdef int ssmoothed_state_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model)
+cdef int ssmoothed_state_autocov_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model)
+cdef int ssmoothed_disturbances_conventional(sKalmanSmoother smoother, sKalmanFilter kfilter, sStatespace model)
+
+# Double precision
+cdef int dsmoothed_estimators_missing_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model) except *
+cdef int dsmoothed_disturbances_missing_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model)
+
+cdef int dsmoothed_estimators_measurement_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model) except *
+cdef int dsmoothed_estimators_time_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model)
+cdef int dsmoothed_state_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model)
+cdef int dsmoothed_state_autocov_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model)
+cdef int dsmoothed_disturbances_conventional(dKalmanSmoother smoother, dKalmanFilter kfilter, dStatespace model)
+
+# Single precision complex
+cdef int csmoothed_estimators_missing_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model) except *
+cdef int csmoothed_disturbances_missing_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model)
+
+cdef int csmoothed_estimators_measurement_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model) except *
+cdef int csmoothed_estimators_time_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model)
+cdef int csmoothed_state_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model)
+cdef int csmoothed_state_autocov_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model)
+cdef int csmoothed_disturbances_conventional(cKalmanSmoother smoother, cKalmanFilter kfilter, cStatespace model)
+
+# Double precision complex
+cdef int zsmoothed_estimators_missing_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model) except *
+cdef int zsmoothed_disturbances_missing_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model)
+
+cdef int zsmoothed_estimators_measurement_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model) except *
+cdef int zsmoothed_estimators_time_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model)
+cdef int zsmoothed_state_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model)
+cdef int zsmoothed_state_autocov_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model)
+cdef int zsmoothed_disturbances_conventional(zKalmanSmoother smoother, zKalmanFilter kfilter, zStatespace model)
diff --git a/shared/package-files/e4/e49d1789f3b706ad4b84cb1873d6f83363d936e7e82a4fae7da2656d1a375c1b/embedding_backward_compositeimplicitautograd_dispatch.h b/shared/package-files/e4/e49d1789f3b706ad4b84cb1873d6f83363d936e7e82a4fae7da2656d1a375c1b/embedding_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff48ede40e3e4121ab8d5c00ff3b47ab7c9d8813
--- /dev/null
+++ b/shared/package-files/e4/e49d1789f3b706ad4b84cb1873d6f83363d936e7e82a4fae7da2656d1a375c1b/embedding_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse);
+TORCH_API at::Tensor embedding_backward_symint(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/e4/e4a4629a1ea3c6cec1203a80a38f1dd358bcff55a491c915d31556910eb8733b/ismags.py b/shared/package-files/e4/e4a4629a1ea3c6cec1203a80a38f1dd358bcff55a491c915d31556910eb8733b/ismags.py
new file mode 100644
index 0000000000000000000000000000000000000000..25ce94c87ec9b76ed81a5650f67ecfb5cda130ba
--- /dev/null
+++ b/shared/package-files/e4/e4a4629a1ea3c6cec1203a80a38f1dd358bcff55a491c915d31556910eb8733b/ismags.py
@@ -0,0 +1,1169 @@
+"""
+ISMAGS Algorithm
+================
+
+Provides a Python implementation of the ISMAGS algorithm. [1]_
+
+It is capable of finding (subgraph) isomorphisms between two graphs, taking the
+symmetry of the subgraph into account. In most cases the VF2 algorithm is
+faster (at least on small graphs) than this implementation, but in some cases
+there is an exponential number of isomorphisms that are symmetrically
+equivalent. In that case, the ISMAGS algorithm will provide only one solution
+per symmetry group.
+
+>>> petersen = nx.petersen_graph()
+>>> ismags = nx.isomorphism.ISMAGS(petersen, petersen)
+>>> isomorphisms = list(ismags.isomorphisms_iter(symmetry=False))
+>>> len(isomorphisms)
+120
+>>> isomorphisms = list(ismags.isomorphisms_iter(symmetry=True))
+>>> answer = [{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}]
+>>> answer == isomorphisms
+True
+
+In addition, this implementation also provides an interface to find the
+largest common induced subgraph [2]_ between any two graphs, again taking
+symmetry into account. Given `graph` and `subgraph` the algorithm will remove
+nodes from the `subgraph` until `subgraph` is isomorphic to a subgraph of
+`graph`. Since only the symmetry of `subgraph` is taken into account it is
+worth thinking about how you provide your graphs:
+
+>>> graph1 = nx.path_graph(4)
+>>> graph2 = nx.star_graph(3)
+>>> ismags = nx.isomorphism.ISMAGS(graph1, graph2)
+>>> ismags.is_isomorphic()
+False
+>>> largest_common_subgraph = list(ismags.largest_common_subgraph())
+>>> answer = [{1: 0, 0: 1, 2: 2}, {2: 0, 1: 1, 3: 2}]
+>>> answer == largest_common_subgraph
+True
+>>> ismags2 = nx.isomorphism.ISMAGS(graph2, graph1)
+>>> largest_common_subgraph = list(ismags2.largest_common_subgraph())
+>>> answer = [
+...     {1: 0, 0: 1, 2: 2},
+...     {1: 0, 0: 1, 3: 2},
+...     {2: 0, 0: 1, 1: 2},
+...     {2: 0, 0: 1, 3: 2},
+...     {3: 0, 0: 1, 1: 2},
+...     {3: 0, 0: 1, 2: 2},
+... ]
+>>> answer == largest_common_subgraph
+True
+
+However, when not taking symmetry into account, it doesn't matter:
+
+>>> largest_common_subgraph = list(ismags.largest_common_subgraph(symmetry=False))
+>>> answer = [
+...     {1: 0, 0: 1, 2: 2},
+...     {1: 0, 2: 1, 0: 2},
+...     {2: 0, 1: 1, 3: 2},
+...     {2: 0, 3: 1, 1: 2},
+...     {1: 0, 0: 1, 2: 3},
+...     {1: 0, 2: 1, 0: 3},
+...     {2: 0, 1: 1, 3: 3},
+...     {2: 0, 3: 1, 1: 3},
+...     {1: 0, 0: 2, 2: 3},
+...     {1: 0, 2: 2, 0: 3},
+...     {2: 0, 1: 2, 3: 3},
+...     {2: 0, 3: 2, 1: 3},
+... ]
+>>> answer == largest_common_subgraph
+True
+>>> largest_common_subgraph = list(ismags2.largest_common_subgraph(symmetry=False))
+>>> answer = [
+...     {1: 0, 0: 1, 2: 2},
+...     {1: 0, 0: 1, 3: 2},
+...     {2: 0, 0: 1, 1: 2},
+...     {2: 0, 0: 1, 3: 2},
+...     {3: 0, 0: 1, 1: 2},
+...     {3: 0, 0: 1, 2: 2},
+...     {1: 1, 0: 2, 2: 3},
+...     {1: 1, 0: 2, 3: 3},
+...     {2: 1, 0: 2, 1: 3},
+...     {2: 1, 0: 2, 3: 3},
+...     {3: 1, 0: 2, 1: 3},
+...     {3: 1, 0: 2, 2: 3},
+... ]
+>>> answer == largest_common_subgraph
+True
+
+Notes
+-----
+- The current implementation works for undirected graphs only. The algorithm
+  in general should work for directed graphs as well though.
+- Node keys for both provided graphs need to be fully orderable as well as
+  hashable.
+- Node and edge equality is assumed to be transitive: if A is equal to B, and
+  B is equal to C, then A is equal to C.
+
+References
+----------
+.. [1] M. Houbraken, S. Demeyer, T. Michoel, P. Audenaert, D. Colle,
+   M. Pickavet, "The Index-Based Subgraph Matching Algorithm with General
+   Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph
+   Enumeration", PLoS One 9(5): e97896, 2014.
+   https://doi.org/10.1371/journal.pone.0097896
+.. [2] https://en.wikipedia.org/wiki/Maximum_common_induced_subgraph
+"""
+
+__all__ = ["ISMAGS"]
+
+import itertools
+from collections import Counter, defaultdict
+from functools import reduce, wraps
+
+
+def are_all_equal(iterable):
+    """
+    Returns ``True`` if and only if all elements in `iterable` are equal; and
+    ``False`` otherwise.
+
+    Parameters
+    ----------
+    iterable: collections.abc.Iterable
+        The container whose elements will be checked.
+
+    Returns
+    -------
+    bool
+        ``True`` iff all elements in `iterable` compare equal, ``False``
+        otherwise.
+    """
+    try:
+        shape = iterable.shape
+    except AttributeError:
+        pass
+    else:
+        if len(shape) > 1:
+            message = "The function does not works on multidimensional arrays."
+            raise NotImplementedError(message) from None
+
+    iterator = iter(iterable)
+    first = next(iterator, None)
+    return all(item == first for item in iterator)
+
+
+def make_partitions(items, test):
+    """
+    Partitions items into sets based on the outcome of ``test(item1, item2)``.
+    Pairs of items for which `test` returns `True` end up in the same set.
+
+    Parameters
+    ----------
+    items : collections.abc.Iterable[collections.abc.Hashable]
+        Items to partition
+    test : collections.abc.Callable[collections.abc.Hashable, collections.abc.Hashable]
+        A function that will be called with 2 arguments, taken from items.
+        Should return `True` if those 2 items need to end up in the same
+        partition, and `False` otherwise.
+
+    Returns
+    -------
+    list[set]
+        A list of sets, with each set containing part of the items in `items`,
+        such that ``all(test(*pair) for pair in  itertools.combinations(set, 2))
+        == True``
+
+    Notes
+    -----
+    The function `test` is assumed to be transitive: if ``test(a, b)`` and
+    ``test(b, c)`` return ``True``, then ``test(a, c)`` must also be ``True``.
+    """
+    partitions = []
+    for item in items:
+        for partition in partitions:
+            p_item = next(iter(partition))
+            if test(item, p_item):
+                partition.add(item)
+                break
+        else:  # No break
+            partitions.append({item})
+    return partitions
+
+
+def partition_to_color(partitions):
+    """
+    Creates a dictionary that maps each item in each partition to the index of
+    the partition to which it belongs.
+
+    Parameters
+    ----------
+    partitions: collections.abc.Sequence[collections.abc.Iterable]
+        As returned by :func:`make_partitions`.
+
+    Returns
+    -------
+    dict
+    """
+    colors = {}
+    for color, keys in enumerate(partitions):
+        for key in keys:
+            colors[key] = color
+    return colors
+
+
+def intersect(collection_of_sets):
+    """
+    Given an collection of sets, returns the intersection of those sets.
+
+    Parameters
+    ----------
+    collection_of_sets: collections.abc.Collection[set]
+        A collection of sets.
+
+    Returns
+    -------
+    set
+        An intersection of all sets in `collection_of_sets`. Will have the same
+        type as the item initially taken from `collection_of_sets`.
+    """
+    collection_of_sets = list(collection_of_sets)
+    first = collection_of_sets.pop()
+    out = reduce(set.intersection, collection_of_sets, set(first))
+    return type(first)(out)
+
+
+class ISMAGS:
+    """
+    Implements the ISMAGS subgraph matching algorithm. [1]_ ISMAGS stands for
+    "Index-based Subgraph Matching Algorithm with General Symmetries". As the
+    name implies, it is symmetry aware and will only generate non-symmetric
+    isomorphisms.
+
+    Notes
+    -----
+    The implementation imposes additional conditions compared to the VF2
+    algorithm on the graphs provided and the comparison functions
+    (:attr:`node_equality` and :attr:`edge_equality`):
+
+     - Node keys in both graphs must be orderable as well as hashable.
+     - Equality must be transitive: if A is equal to B, and B is equal to C,
+       then A must be equal to C.
+
+    Attributes
+    ----------
+    graph: networkx.Graph
+    subgraph: networkx.Graph
+    node_equality: collections.abc.Callable
+        The function called to see if two nodes should be considered equal.
+        It's signature looks like this:
+        ``f(graph1: networkx.Graph, node1, graph2: networkx.Graph, node2) -> bool``.
+        `node1` is a node in `graph1`, and `node2` a node in `graph2`.
+        Constructed from the argument `node_match`.
+    edge_equality: collections.abc.Callable
+        The function called to see if two edges should be considered equal.
+        It's signature looks like this:
+        ``f(graph1: networkx.Graph, edge1, graph2: networkx.Graph, edge2) -> bool``.
+        `edge1` is an edge in `graph1`, and `edge2` an edge in `graph2`.
+        Constructed from the argument `edge_match`.
+
+    References
+    ----------
+    .. [1] M. Houbraken, S. Demeyer, T. Michoel, P. Audenaert, D. Colle,
+       M. Pickavet, "The Index-Based Subgraph Matching Algorithm with General
+       Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph
+       Enumeration", PLoS One 9(5): e97896, 2014.
+       https://doi.org/10.1371/journal.pone.0097896
+    """
+
+    def __init__(self, graph, subgraph, node_match=None, edge_match=None, cache=None):
+        """
+        Parameters
+        ----------
+        graph: networkx.Graph
+        subgraph: networkx.Graph
+        node_match: collections.abc.Callable or None
+            Function used to determine whether two nodes are equivalent. Its
+            signature should look like ``f(n1: dict, n2: dict) -> bool``, with
+            `n1` and `n2` node property dicts. See also
+            :func:`~networkx.algorithms.isomorphism.categorical_node_match` and
+            friends.
+            If `None`, all nodes are considered equal.
+        edge_match: collections.abc.Callable or None
+            Function used to determine whether two edges are equivalent. Its
+            signature should look like ``f(e1: dict, e2: dict) -> bool``, with
+            `e1` and `e2` edge property dicts. See also
+            :func:`~networkx.algorithms.isomorphism.categorical_edge_match` and
+            friends.
+            If `None`, all edges are considered equal.
+        cache: collections.abc.Mapping
+            A cache used for caching graph symmetries.
+        """
+        # TODO: graph and subgraph setter methods that invalidate the caches.
+        # TODO: allow for precomputed partitions and colors
+        self.graph = graph
+        self.subgraph = subgraph
+        self._symmetry_cache = cache
+        # Naming conventions are taken from the original paper. For your
+        # sanity:
+        #   sg: subgraph
+        #   g: graph
+        #   e: edge(s)
+        #   n: node(s)
+        # So: sgn means "subgraph nodes".
+        self._sgn_partitions_ = None
+        self._sge_partitions_ = None
+
+        self._sgn_colors_ = None
+        self._sge_colors_ = None
+
+        self._gn_partitions_ = None
+        self._ge_partitions_ = None
+
+        self._gn_colors_ = None
+        self._ge_colors_ = None
+
+        self._node_compat_ = None
+        self._edge_compat_ = None
+
+        if node_match is None:
+            self.node_equality = self._node_match_maker(lambda n1, n2: True)
+            self._sgn_partitions_ = [set(self.subgraph.nodes)]
+            self._gn_partitions_ = [set(self.graph.nodes)]
+            self._node_compat_ = {0: 0}
+        else:
+            self.node_equality = self._node_match_maker(node_match)
+        if edge_match is None:
+            self.edge_equality = self._edge_match_maker(lambda e1, e2: True)
+            self._sge_partitions_ = [set(self.subgraph.edges)]
+            self._ge_partitions_ = [set(self.graph.edges)]
+            self._edge_compat_ = {0: 0}
+        else:
+            self.edge_equality = self._edge_match_maker(edge_match)
+
+    @property
+    def _sgn_partitions(self):
+        if self._sgn_partitions_ is None:
+
+            def nodematch(node1, node2):
+                return self.node_equality(self.subgraph, node1, self.subgraph, node2)
+
+            self._sgn_partitions_ = make_partitions(self.subgraph.nodes, nodematch)
+        return self._sgn_partitions_
+
+    @property
+    def _sge_partitions(self):
+        if self._sge_partitions_ is None:
+
+            def edgematch(edge1, edge2):
+                return self.edge_equality(self.subgraph, edge1, self.subgraph, edge2)
+
+            self._sge_partitions_ = make_partitions(self.subgraph.edges, edgematch)
+        return self._sge_partitions_
+
+    @property
+    def _gn_partitions(self):
+        if self._gn_partitions_ is None:
+
+            def nodematch(node1, node2):
+                return self.node_equality(self.graph, node1, self.graph, node2)
+
+            self._gn_partitions_ = make_partitions(self.graph.nodes, nodematch)
+        return self._gn_partitions_
+
+    @property
+    def _ge_partitions(self):
+        if self._ge_partitions_ is None:
+
+            def edgematch(edge1, edge2):
+                return self.edge_equality(self.graph, edge1, self.graph, edge2)
+
+            self._ge_partitions_ = make_partitions(self.graph.edges, edgematch)
+        return self._ge_partitions_
+
+    @property
+    def _sgn_colors(self):
+        if self._sgn_colors_ is None:
+            self._sgn_colors_ = partition_to_color(self._sgn_partitions)
+        return self._sgn_colors_
+
+    @property
+    def _sge_colors(self):
+        if self._sge_colors_ is None:
+            self._sge_colors_ = partition_to_color(self._sge_partitions)
+        return self._sge_colors_
+
+    @property
+    def _gn_colors(self):
+        if self._gn_colors_ is None:
+            self._gn_colors_ = partition_to_color(self._gn_partitions)
+        return self._gn_colors_
+
+    @property
+    def _ge_colors(self):
+        if self._ge_colors_ is None:
+            self._ge_colors_ = partition_to_color(self._ge_partitions)
+        return self._ge_colors_
+
+    @property
+    def _node_compatibility(self):
+        if self._node_compat_ is not None:
+            return self._node_compat_
+        self._node_compat_ = {}
+        for sgn_part_color, gn_part_color in itertools.product(
+            range(len(self._sgn_partitions)), range(len(self._gn_partitions))
+        ):
+            sgn = next(iter(self._sgn_partitions[sgn_part_color]))
+            gn = next(iter(self._gn_partitions[gn_part_color]))
+            if self.node_equality(self.subgraph, sgn, self.graph, gn):
+                self._node_compat_[sgn_part_color] = gn_part_color
+        return self._node_compat_
+
+    @property
+    def _edge_compatibility(self):
+        if self._edge_compat_ is not None:
+            return self._edge_compat_
+        self._edge_compat_ = {}
+        for sge_part_color, ge_part_color in itertools.product(
+            range(len(self._sge_partitions)), range(len(self._ge_partitions))
+        ):
+            sge = next(iter(self._sge_partitions[sge_part_color]))
+            ge = next(iter(self._ge_partitions[ge_part_color]))
+            if self.edge_equality(self.subgraph, sge, self.graph, ge):
+                self._edge_compat_[sge_part_color] = ge_part_color
+        return self._edge_compat_
+
+    @staticmethod
+    def _node_match_maker(cmp):
+        @wraps(cmp)
+        def comparer(graph1, node1, graph2, node2):
+            return cmp(graph1.nodes[node1], graph2.nodes[node2])
+
+        return comparer
+
+    @staticmethod
+    def _edge_match_maker(cmp):
+        @wraps(cmp)
+        def comparer(graph1, edge1, graph2, edge2):
+            return cmp(graph1.edges[edge1], graph2.edges[edge2])
+
+        return comparer
+
+    def find_isomorphisms(self, symmetry=True):
+        """Find all subgraph isomorphisms between subgraph and graph
+
+        Finds isomorphisms where :attr:`subgraph` <= :attr:`graph`.
+
+        Parameters
+        ----------
+        symmetry: bool
+            Whether symmetry should be taken into account. If False, found
+            isomorphisms may be symmetrically equivalent.
+
+        Yields
+        ------
+        dict
+            The found isomorphism mappings of {graph_node: subgraph_node}.
+        """
+        # The networkx VF2 algorithm is slightly funny in when it yields an
+        # empty dict and when not.
+        if not self.subgraph:
+            yield {}
+            return
+        elif not self.graph:
+            return
+        elif len(self.graph) < len(self.subgraph):
+            return
+
+        if symmetry:
+            _, cosets = self.analyze_symmetry(
+                self.subgraph, self._sgn_partitions, self._sge_colors
+            )
+            constraints = self._make_constraints(cosets)
+        else:
+            constraints = []
+
+        candidates = self._find_nodecolor_candidates()
+        la_candidates = self._get_lookahead_candidates()
+        for sgn in self.subgraph:
+            extra_candidates = la_candidates[sgn]
+            if extra_candidates:
+                candidates[sgn] = candidates[sgn] | {frozenset(extra_candidates)}
+
+        if any(candidates.values()):
+            start_sgn = min(candidates, key=lambda n: min(candidates[n], key=len))
+            candidates[start_sgn] = (intersect(candidates[start_sgn]),)
+            yield from self._map_nodes(start_sgn, candidates, constraints)
+        else:
+            return
+
+    @staticmethod
+    def _find_neighbor_color_count(graph, node, node_color, edge_color):
+        """
+        For `node` in `graph`, count the number of edges of a specific color
+        it has to nodes of a specific color.
+        """
+        counts = Counter()
+        neighbors = graph[node]
+        for neighbor in neighbors:
+            n_color = node_color[neighbor]
+            if (node, neighbor) in edge_color:
+                e_color = edge_color[node, neighbor]
+            else:
+                e_color = edge_color[neighbor, node]
+            counts[e_color, n_color] += 1
+        return counts
+
+    def _get_lookahead_candidates(self):
+        """
+        Returns a mapping of {subgraph node: collection of graph nodes} for
+        which the graph nodes are feasible candidates for the subgraph node, as
+        determined by looking ahead one edge.
+        """
+        g_counts = {}
+        for gn in self.graph:
+            g_counts[gn] = self._find_neighbor_color_count(
+                self.graph, gn, self._gn_colors, self._ge_colors
+            )
+        candidates = defaultdict(set)
+        for sgn in self.subgraph:
+            sg_count = self._find_neighbor_color_count(
+                self.subgraph, sgn, self._sgn_colors, self._sge_colors
+            )
+            new_sg_count = Counter()
+            for (sge_color, sgn_color), count in sg_count.items():
+                try:
+                    ge_color = self._edge_compatibility[sge_color]
+                    gn_color = self._node_compatibility[sgn_color]
+                except KeyError:
+                    pass
+                else:
+                    new_sg_count[ge_color, gn_color] = count
+
+            for gn, g_count in g_counts.items():
+                if all(new_sg_count[x] <= g_count[x] for x in new_sg_count):
+                    # Valid candidate
+                    candidates[sgn].add(gn)
+        return candidates
+
+    def largest_common_subgraph(self, symmetry=True):
+        """
+        Find the largest common induced subgraphs between :attr:`subgraph` and
+        :attr:`graph`.
+
+        Parameters
+        ----------
+        symmetry: bool
+            Whether symmetry should be taken into account. If False, found
+            largest common subgraphs may be symmetrically equivalent.
+
+        Yields
+        ------
+        dict
+            The found isomorphism mappings of {graph_node: subgraph_node}.
+        """
+        # The networkx VF2 algorithm is slightly funny in when it yields an
+        # empty dict and when not.
+        if not self.subgraph:
+            yield {}
+            return
+        elif not self.graph:
+            return
+
+        if symmetry:
+            _, cosets = self.analyze_symmetry(
+                self.subgraph, self._sgn_partitions, self._sge_colors
+            )
+            constraints = self._make_constraints(cosets)
+        else:
+            constraints = []
+
+        candidates = self._find_nodecolor_candidates()
+
+        if any(candidates.values()):
+            yield from self._largest_common_subgraph(candidates, constraints)
+        else:
+            return
+
+    def analyze_symmetry(self, graph, node_partitions, edge_colors):
+        """
+        Find a minimal set of permutations and corresponding co-sets that
+        describe the symmetry of `graph`, given the node and edge equalities
+        given by `node_partitions` and `edge_colors`, respectively.
+
+        Parameters
+        ----------
+        graph : networkx.Graph
+            The graph whose symmetry should be analyzed.
+        node_partitions : list of sets
+            A list of sets containing node keys. Node keys in the same set
+            are considered equivalent. Every node key in `graph` should be in
+            exactly one of the sets. If all nodes are equivalent, this should
+            be ``[set(graph.nodes)]``.
+        edge_colors : dict mapping edges to their colors
+            A dict mapping every edge in `graph` to its corresponding color.
+            Edges with the same color are considered equivalent. If all edges
+            are equivalent, this should be ``{e: 0 for e in graph.edges}``.
+
+
+        Returns
+        -------
+        set[frozenset]
+            The found permutations. This is a set of frozensets of pairs of node
+            keys which can be exchanged without changing :attr:`subgraph`.
+        dict[collections.abc.Hashable, set[collections.abc.Hashable]]
+            The found co-sets. The co-sets is a dictionary of
+            ``{node key: set of node keys}``.
+            Every key-value pair describes which ``values`` can be interchanged
+            without changing nodes less than ``key``.
+        """
+        if self._symmetry_cache is not None:
+            key = hash(
+                (
+                    tuple(graph.nodes),
+                    tuple(graph.edges),
+                    tuple(map(tuple, node_partitions)),
+                    tuple(edge_colors.items()),
+                )
+            )
+            if key in self._symmetry_cache:
+                return self._symmetry_cache[key]
+        node_partitions = list(
+            self._refine_node_partitions(graph, node_partitions, edge_colors)
+        )
+        assert len(node_partitions) == 1
+        node_partitions = node_partitions[0]
+        permutations, cosets = self._process_ordered_pair_partitions(
+            graph, node_partitions, node_partitions, edge_colors
+        )
+        if self._symmetry_cache is not None:
+            self._symmetry_cache[key] = permutations, cosets
+        return permutations, cosets
+
+    def is_isomorphic(self, symmetry=False):
+        """
+        Returns True if :attr:`graph` is isomorphic to :attr:`subgraph` and
+        False otherwise.
+
+        Returns
+        -------
+        bool
+        """
+        return len(self.subgraph) == len(self.graph) and self.subgraph_is_isomorphic(
+            symmetry
+        )
+
+    def subgraph_is_isomorphic(self, symmetry=False):
+        """
+        Returns True if a subgraph of :attr:`graph` is isomorphic to
+        :attr:`subgraph` and False otherwise.
+
+        Returns
+        -------
+        bool
+        """
+        # symmetry=False, since we only need to know whether there is any
+        # example; figuring out all symmetry elements probably costs more time
+        # than it gains.
+        isom = next(self.subgraph_isomorphisms_iter(symmetry=symmetry), None)
+        return isom is not None
+
+    def isomorphisms_iter(self, symmetry=True):
+        """
+        Does the same as :meth:`find_isomorphisms` if :attr:`graph` and
+        :attr:`subgraph` have the same number of nodes.
+        """
+        if len(self.graph) == len(self.subgraph):
+            yield from self.subgraph_isomorphisms_iter(symmetry=symmetry)
+
+    def subgraph_isomorphisms_iter(self, symmetry=True):
+        """Alternative name for :meth:`find_isomorphisms`."""
+        return self.find_isomorphisms(symmetry)
+
+    def _find_nodecolor_candidates(self):
+        """
+        Per node in subgraph find all nodes in graph that have the same color.
+        """
+        candidates = defaultdict(set)
+        for sgn in self.subgraph.nodes:
+            sgn_color = self._sgn_colors[sgn]
+            if sgn_color in self._node_compatibility:
+                gn_color = self._node_compatibility[sgn_color]
+                candidates[sgn].add(frozenset(self._gn_partitions[gn_color]))
+            else:
+                candidates[sgn].add(frozenset())
+        candidates = dict(candidates)
+        for sgn, options in candidates.items():
+            candidates[sgn] = frozenset(options)
+        return candidates
+
+    @staticmethod
+    def _make_constraints(cosets):
+        """
+        Turn cosets into constraints.
+        """
+        constraints = []
+        for node_i, node_ts in cosets.items():
+            for node_t in node_ts:
+                if node_i != node_t:
+                    # Node i must be smaller than node t.
+                    constraints.append((node_i, node_t))
+        return constraints
+
+    @staticmethod
+    def _find_node_edge_color(graph, node_colors, edge_colors):
+        """
+        For every node in graph, come up with a color that combines 1) the
+        color of the node, and 2) the number of edges of a color to each type
+        of node.
+        """
+        counts = defaultdict(lambda: defaultdict(int))
+        for node1, node2 in graph.edges:
+            if (node1, node2) in edge_colors:
+                # FIXME directed graphs
+                ecolor = edge_colors[node1, node2]
+            else:
+                ecolor = edge_colors[node2, node1]
+            # Count per node how many edges it has of what color to nodes of
+            # what color
+            counts[node1][ecolor, node_colors[node2]] += 1
+            counts[node2][ecolor, node_colors[node1]] += 1
+
+        node_edge_colors = {}
+        for node in graph.nodes:
+            node_edge_colors[node] = node_colors[node], set(counts[node].items())
+
+        return node_edge_colors
+
+    @staticmethod
+    def _get_permutations_by_length(items):
+        """
+        Get all permutations of items, but only permute items with the same
+        length.
+
+        >>> found = list(ISMAGS._get_permutations_by_length([[1], [2], [3, 4], [4, 5]]))
+        >>> answer = [
+        ...     (([1], [2]), ([3, 4], [4, 5])),
+        ...     (([1], [2]), ([4, 5], [3, 4])),
+        ...     (([2], [1]), ([3, 4], [4, 5])),
+        ...     (([2], [1]), ([4, 5], [3, 4])),
+        ... ]
+        >>> found == answer
+        True
+        """
+        by_len = defaultdict(list)
+        for item in items:
+            by_len[len(item)].append(item)
+
+        yield from itertools.product(
+            *(itertools.permutations(by_len[l]) for l in sorted(by_len))
+        )
+
+    @classmethod
+    def _refine_node_partitions(cls, graph, node_partitions, edge_colors, branch=False):
+        """
+        Given a partition of nodes in graph, make the partitions smaller such
+        that all nodes in a partition have 1) the same color, and 2) the same
+        number of edges to specific other partitions.
+        """
+
+        def equal_color(node1, node2):
+            return node_edge_colors[node1] == node_edge_colors[node2]
+
+        node_partitions = list(node_partitions)
+        node_colors = partition_to_color(node_partitions)
+        node_edge_colors = cls._find_node_edge_color(graph, node_colors, edge_colors)
+        if all(
+            are_all_equal(node_edge_colors[node] for node in partition)
+            for partition in node_partitions
+        ):
+            yield node_partitions
+            return
+
+        new_partitions = []
+        output = [new_partitions]
+        for partition in node_partitions:
+            if not are_all_equal(node_edge_colors[node] for node in partition):
+                refined = make_partitions(partition, equal_color)
+                if (
+                    branch
+                    and len(refined) != 1
+                    and len({len(r) for r in refined}) != len([len(r) for r in refined])
+                ):
+                    # This is where it breaks. There are multiple new cells
+                    # in refined with the same length, and their order
+                    # matters.
+                    # So option 1) Hit it with a big hammer and simply make all
+                    # orderings.
+                    permutations = cls._get_permutations_by_length(refined)
+                    new_output = []
+                    for n_p in output:
+                        for permutation in permutations:
+                            new_output.append(n_p + list(permutation[0]))
+                    output = new_output
+                else:
+                    for n_p in output:
+                        n_p.extend(sorted(refined, key=len))
+            else:
+                for n_p in output:
+                    n_p.append(partition)
+        for n_p in output:
+            yield from cls._refine_node_partitions(graph, n_p, edge_colors, branch)
+
+    def _edges_of_same_color(self, sgn1, sgn2):
+        """
+        Returns all edges in :attr:`graph` that have the same colour as the
+        edge between sgn1 and sgn2 in :attr:`subgraph`.
+        """
+        if (sgn1, sgn2) in self._sge_colors:
+            # FIXME directed graphs
+            sge_color = self._sge_colors[sgn1, sgn2]
+        else:
+            sge_color = self._sge_colors[sgn2, sgn1]
+        if sge_color in self._edge_compatibility:
+            ge_color = self._edge_compatibility[sge_color]
+            g_edges = self._ge_partitions[ge_color]
+        else:
+            g_edges = []
+        return g_edges
+
+    def _map_nodes(self, sgn, candidates, constraints, mapping=None, to_be_mapped=None):
+        """
+        Find all subgraph isomorphisms honoring constraints.
+        """
+        if mapping is None:
+            mapping = {}
+        else:
+            mapping = mapping.copy()
+        if to_be_mapped is None:
+            to_be_mapped = set(self.subgraph.nodes)
+
+        # Note, we modify candidates here. Doesn't seem to affect results, but
+        # remember this.
+        # candidates = candidates.copy()
+        sgn_candidates = intersect(candidates[sgn])
+        candidates[sgn] = frozenset([sgn_candidates])
+        for gn in sgn_candidates:
+            # We're going to try to map sgn to gn.
+            if gn in mapping.values() or sgn not in to_be_mapped:
+                # gn is already mapped to something
+                continue  # pragma: no cover
+
+            # REDUCTION and COMBINATION
+            mapping[sgn] = gn
+            # BASECASE
+            if to_be_mapped == set(mapping.keys()):
+                yield {v: k for k, v in mapping.items()}
+                continue
+            left_to_map = to_be_mapped - set(mapping.keys())
+
+            new_candidates = candidates.copy()
+            sgn_neighbours = set(self.subgraph[sgn])
+            not_gn_neighbours = set(self.graph.nodes) - set(self.graph[gn])
+            for sgn2 in left_to_map:
+                if sgn2 not in sgn_neighbours:
+                    gn2_options = not_gn_neighbours
+                else:
+                    # Get all edges to gn of the right color:
+                    g_edges = self._edges_of_same_color(sgn, sgn2)
+                    # FIXME directed graphs
+                    # And all nodes involved in those which are connected to gn
+                    gn2_options = {n for e in g_edges for n in e if gn in e}
+                # Node color compatibility should be taken care of by the
+                # initial candidate lists made by find_subgraphs
+
+                # Add gn2_options to the right collection. Since new_candidates
+                # is a dict of frozensets of frozensets of node indices it's
+                # a bit clunky. We can't do .add, and + also doesn't work. We
+                # could do |, but I deem union to be clearer.
+                new_candidates[sgn2] = new_candidates[sgn2].union(
+                    [frozenset(gn2_options)]
+                )
+
+                if (sgn, sgn2) in constraints:
+                    gn2_options = {gn2 for gn2 in self.graph if gn2 > gn}
+                elif (sgn2, sgn) in constraints:
+                    gn2_options = {gn2 for gn2 in self.graph if gn2 < gn}
+                else:
+                    continue  # pragma: no cover
+                new_candidates[sgn2] = new_candidates[sgn2].union(
+                    [frozenset(gn2_options)]
+                )
+
+            # The next node is the one that is unmapped and has fewest
+            # candidates
+            # Pylint disables because it's a one-shot function.
+            next_sgn = min(
+                left_to_map, key=lambda n: min(new_candidates[n], key=len)
+            )  # pylint: disable=cell-var-from-loop
+            yield from self._map_nodes(
+                next_sgn,
+                new_candidates,
+                constraints,
+                mapping=mapping,
+                to_be_mapped=to_be_mapped,
+            )
+            # Unmap sgn-gn. Strictly not necessary since it'd get overwritten
+            # when making a new mapping for sgn.
+            # del mapping[sgn]
+
+    def _largest_common_subgraph(self, candidates, constraints, to_be_mapped=None):
+        """
+        Find all largest common subgraphs honoring constraints.
+        """
+        if to_be_mapped is None:
+            to_be_mapped = {frozenset(self.subgraph.nodes)}
+
+        # The LCS problem is basically a repeated subgraph isomorphism problem
+        # with smaller and smaller subgraphs. We store the nodes that are
+        # "part of" the subgraph in to_be_mapped, and we make it a little
+        # smaller every iteration.
+
+        # pylint disable because it's guarded against by default value
+        current_size = len(
+            next(iter(to_be_mapped), [])
+        )  # pylint: disable=stop-iteration-return
+
+        found_iso = False
+        if current_size <= len(self.graph):
+            # There's no point in trying to find isomorphisms of
+            # graph >= subgraph if subgraph has more nodes than graph.
+
+            # Try the isomorphism first with the nodes with lowest ID. So sort
+            # them. Those are more likely to be part of the final
+            # correspondence. This makes finding the first answer(s) faster. In
+            # theory.
+            for nodes in sorted(to_be_mapped, key=sorted):
+                # Find the isomorphism between subgraph[to_be_mapped] <= graph
+                next_sgn = min(nodes, key=lambda n: min(candidates[n], key=len))
+                isomorphs = self._map_nodes(
+                    next_sgn, candidates, constraints, to_be_mapped=nodes
+                )
+
+                # This is effectively `yield from isomorphs`, except that we look
+                # whether an item was yielded.
+                try:
+                    item = next(isomorphs)
+                except StopIteration:
+                    pass
+                else:
+                    yield item
+                    yield from isomorphs
+                    found_iso = True
+
+        # BASECASE
+        if found_iso or current_size == 1:
+            # Shrinking has no point because either 1) we end up with a smaller
+            # common subgraph (and we want the largest), or 2) there'll be no
+            # more subgraph.
+            return
+
+        left_to_be_mapped = set()
+        for nodes in to_be_mapped:
+            for sgn in nodes:
+                # We're going to remove sgn from to_be_mapped, but subject to
+                # symmetry constraints. We know that for every constraint we
+                # have those subgraph nodes are equal. So whenever we would
+                # remove the lower part of a constraint, remove the higher
+                # instead. This is all dealth with by _remove_node. And because
+                # left_to_be_mapped is a set, we don't do double work.
+
+                # And finally, make the subgraph one node smaller.
+                # REDUCTION
+                new_nodes = self._remove_node(sgn, nodes, constraints)
+                left_to_be_mapped.add(new_nodes)
+        # COMBINATION
+        yield from self._largest_common_subgraph(
+            candidates, constraints, to_be_mapped=left_to_be_mapped
+        )
+
+    @staticmethod
+    def _remove_node(node, nodes, constraints):
+        """
+        Returns a new set where node has been removed from nodes, subject to
+        symmetry constraints. We know, that for every constraint we have
+        those subgraph nodes are equal. So whenever we would remove the
+        lower part of a constraint, remove the higher instead.
+        """
+        while True:
+            for low, high in constraints:
+                if low == node and high in nodes:
+                    node = high
+                    break
+            else:  # no break, couldn't find node in constraints
+                break
+        return frozenset(nodes - {node})
+
+    @staticmethod
+    def _find_permutations(top_partitions, bottom_partitions):
+        """
+        Return the pairs of top/bottom partitions where the partitions are
+        different. Ensures that all partitions in both top and bottom
+        partitions have size 1.
+        """
+        # Find permutations
+        permutations = set()
+        for top, bot in zip(top_partitions, bottom_partitions):
+            # top and bot have only one element
+            if len(top) != 1 or len(bot) != 1:
+                raise IndexError(
+                    "Not all nodes are coupled. This is"
+                    f" impossible: {top_partitions}, {bottom_partitions}"
+                )
+            if top != bot:
+                permutations.add(frozenset((next(iter(top)), next(iter(bot)))))
+        return permutations
+
+    @staticmethod
+    def _update_orbits(orbits, permutations):
+        """
+        Update orbits based on permutations. Orbits is modified in place.
+        For every pair of items in permutations their respective orbits are
+        merged.
+        """
+        for permutation in permutations:
+            node, node2 = permutation
+            # Find the orbits that contain node and node2, and replace the
+            # orbit containing node with the union
+            first = second = None
+            for idx, orbit in enumerate(orbits):
+                if first is not None and second is not None:
+                    break
+                if node in orbit:
+                    first = idx
+                if node2 in orbit:
+                    second = idx
+            if first != second:
+                orbits[first].update(orbits[second])
+                del orbits[second]
+
+    def _couple_nodes(
+        self,
+        top_partitions,
+        bottom_partitions,
+        pair_idx,
+        t_node,
+        b_node,
+        graph,
+        edge_colors,
+    ):
+        """
+        Generate new partitions from top and bottom_partitions where t_node is
+        coupled to b_node. pair_idx is the index of the partitions where t_ and
+        b_node can be found.
+        """
+        t_partition = top_partitions[pair_idx]
+        b_partition = bottom_partitions[pair_idx]
+        assert t_node in t_partition and b_node in b_partition
+        # Couple node to node2. This means they get their own partition
+        new_top_partitions = [top.copy() for top in top_partitions]
+        new_bottom_partitions = [bot.copy() for bot in bottom_partitions]
+        new_t_groups = {t_node}, t_partition - {t_node}
+        new_b_groups = {b_node}, b_partition - {b_node}
+        # Replace the old partitions with the coupled ones
+        del new_top_partitions[pair_idx]
+        del new_bottom_partitions[pair_idx]
+        new_top_partitions[pair_idx:pair_idx] = new_t_groups
+        new_bottom_partitions[pair_idx:pair_idx] = new_b_groups
+
+        new_top_partitions = self._refine_node_partitions(
+            graph, new_top_partitions, edge_colors
+        )
+        new_bottom_partitions = self._refine_node_partitions(
+            graph, new_bottom_partitions, edge_colors, branch=True
+        )
+        new_top_partitions = list(new_top_partitions)
+        assert len(new_top_partitions) == 1
+        new_top_partitions = new_top_partitions[0]
+        for bot in new_bottom_partitions:
+            yield list(new_top_partitions), bot
+
+    def _process_ordered_pair_partitions(
+        self,
+        graph,
+        top_partitions,
+        bottom_partitions,
+        edge_colors,
+        orbits=None,
+        cosets=None,
+    ):
+        """
+        Processes ordered pair partitions as per the reference paper. Finds and
+        returns all permutations and cosets that leave the graph unchanged.
+        """
+        if orbits is None:
+            orbits = [{node} for node in graph.nodes]
+        else:
+            # Note that we don't copy orbits when we are given one. This means
+            # we leak information between the recursive branches. This is
+            # intentional!
+            orbits = orbits
+        if cosets is None:
+            cosets = {}
+        else:
+            cosets = cosets.copy()
+
+        assert all(
+            len(t_p) == len(b_p) for t_p, b_p in zip(top_partitions, bottom_partitions)
+        )
+
+        # BASECASE
+        if all(len(top) == 1 for top in top_partitions):
+            # All nodes are mapped
+            permutations = self._find_permutations(top_partitions, bottom_partitions)
+            self._update_orbits(orbits, permutations)
+            if permutations:
+                return [permutations], cosets
+            else:
+                return [], cosets
+
+        permutations = []
+        unmapped_nodes = {
+            (node, idx)
+            for idx, t_partition in enumerate(top_partitions)
+            for node in t_partition
+            if len(t_partition) > 1
+        }
+        node, pair_idx = min(unmapped_nodes)
+        b_partition = bottom_partitions[pair_idx]
+
+        for node2 in sorted(b_partition):
+            if len(b_partition) == 1:
+                # Can never result in symmetry
+                continue
+            if node != node2 and any(
+                node in orbit and node2 in orbit for orbit in orbits
+            ):
+                # Orbit prune branch
+                continue
+            # REDUCTION
+            # Couple node to node2
+            partitions = self._couple_nodes(
+                top_partitions,
+                bottom_partitions,
+                pair_idx,
+                node,
+                node2,
+                graph,
+                edge_colors,
+            )
+            for opp in partitions:
+                new_top_partitions, new_bottom_partitions = opp
+
+                new_perms, new_cosets = self._process_ordered_pair_partitions(
+                    graph,
+                    new_top_partitions,
+                    new_bottom_partitions,
+                    edge_colors,
+                    orbits,
+                    cosets,
+                )
+                # COMBINATION
+                permutations += new_perms
+                cosets.update(new_cosets)
+
+        mapped = {
+            k
+            for top, bottom in zip(top_partitions, bottom_partitions)
+            for k in top
+            if len(top) == 1 and top == bottom
+        }
+        ks = {k for k in graph.nodes if k < node}
+        # Have all nodes with ID < node been mapped?
+        find_coset = ks <= mapped and node not in cosets
+        if find_coset:
+            # Find the orbit that contains node
+            for orbit in orbits:
+                if node in orbit:
+                    cosets[node] = orbit.copy()
+        return permutations, cosets
diff --git a/shared/package-files/e4/e4b5d4084e3dccb7d537353557ed07aec1d6905b0d7b5eb0cd9f5cbb62ee6d6d/_basic.py b/shared/package-files/e4/e4b5d4084e3dccb7d537353557ed07aec1d6905b0d7b5eb0cd9f5cbb62ee6d6d/_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..70841fa80976c62ed7d894d824fdfbbcb4673273
--- /dev/null
+++ b/shared/package-files/e4/e4b5d4084e3dccb7d537353557ed07aec1d6905b0d7b5eb0cd9f5cbb62ee6d6d/_basic.py
@@ -0,0 +1,2119 @@
+#
+# Author: Pearu Peterson, March 2002
+#
+# w/ additions by Travis Oliphant, March 2002
+#              and Jake Vanderplas, August 2012
+
+import warnings
+from warnings import warn
+from itertools import product
+import numpy as np
+from numpy import atleast_1d, atleast_2d
+from .lapack import get_lapack_funcs, _compute_lwork
+from ._misc import LinAlgError, _datacopied, LinAlgWarning
+from ._decomp import _asarray_validated
+from . import _decomp, _decomp_svd
+from ._solve_toeplitz import levinson
+from ._cythonized_array_utils import (find_det_from_lu, bandwidth, issymmetric,
+                                      ishermitian)
+
+__all__ = ['solve', 'solve_triangular', 'solveh_banded', 'solve_banded',
+           'solve_toeplitz', 'solve_circulant', 'inv', 'det', 'lstsq',
+           'pinv', 'pinvh', 'matrix_balance', 'matmul_toeplitz']
+
+
+# The numpy facilities for type-casting checks are too slow for small sized
+# arrays and eat away the time budget for the checkups. Here we set a
+# precomputed dict container of the numpy.can_cast() table.
+
+# It can be used to determine quickly what a dtype can be cast to LAPACK
+# compatible types, i.e., 'float32, float64, complex64, complex128'.
+# Then it can be checked via "casting_dict[arr.dtype.char]"
+lapack_cast_dict = {x: ''.join([y for y in 'fdFD' if np.can_cast(x, y)])
+                    for x in np.typecodes['All']}
+
+
+# Linear equations
+def _solve_check(n, info, lamch=None, rcond=None):
+    """ Check arguments during the different steps of the solution phase """
+    if info < 0:
+        raise ValueError(f'LAPACK reported an illegal value in {-info}-th argument.')
+    elif 0 < info:
+        raise LinAlgError('Matrix is singular.')
+
+    if lamch is None:
+        return
+    E = lamch('E')
+    if rcond < E:
+        warn(f'Ill-conditioned matrix (rcond={rcond:.6g}): '
+             'result may not be accurate.',
+             LinAlgWarning, stacklevel=3)
+
+
+def _find_matrix_structure(a):
+    n = a.shape[0]
+    n_below, n_above = bandwidth(a)
+
+    if n_below == n_above == 0:
+        kind = 'diagonal'
+    elif n_above == 0:
+        kind = 'lower triangular'
+    elif n_below == 0:
+        kind = 'upper triangular'
+    elif n_above <= 1 and n_below <= 1 and n > 3:
+        kind = 'tridiagonal'
+    elif np.issubdtype(a.dtype, np.complexfloating) and ishermitian(a):
+        kind = 'hermitian'
+    elif issymmetric(a):
+        kind = 'symmetric'
+    else:
+        kind = 'general'
+
+    return kind, n_below, n_above
+
+
+def solve(a, b, lower=False, overwrite_a=False,
+          overwrite_b=False, check_finite=True, assume_a=None,
+          transposed=False):
+    """
+    Solves the linear equation set ``a @ x == b`` for the unknown ``x``
+    for square `a` matrix.
+
+    If the data matrix is known to be a particular type then supplying the
+    corresponding string to ``assume_a`` key chooses the dedicated solver.
+    The available options are
+
+    ===================  ================================
+     diagonal             'diagonal'
+     tridiagonal          'tridiagonal'
+     banded               'banded'
+     upper triangular     'upper triangular'
+     lower triangular     'lower triangular'
+     symmetric            'symmetric' (or 'sym')
+     hermitian            'hermitian' (or 'her')
+     positive definite    'positive definite' (or 'pos')
+     general              'general' (or 'gen')
+    ===================  ================================
+
+    Parameters
+    ----------
+    a : (N, N) array_like
+        Square input data
+    b : (N, NRHS) array_like
+        Input data for the right hand side.
+    lower : bool, default: False
+        Ignored unless ``assume_a`` is one of ``'sym'``, ``'her'``, or ``'pos'``.
+        If True, the calculation uses only the data in the lower triangle of `a`;
+        entries above the diagonal are ignored. If False (default), the
+        calculation uses only the data in the upper triangle of `a`; entries
+        below the diagonal are ignored.
+    overwrite_a : bool, default: False
+        Allow overwriting data in `a` (may enhance performance).
+    overwrite_b : bool, default: False
+        Allow overwriting data in `b` (may enhance performance).
+    check_finite : bool, default: True
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+    assume_a : str, optional
+        Valid entries are described above.
+        If omitted or ``None``, checks are performed to identify structure so the
+        appropriate solver can be called.
+    transposed : bool, default: False
+        If True, solve ``a.T @ x == b``. Raises `NotImplementedError`
+        for complex `a`.
+
+    Returns
+    -------
+    x : (N, NRHS) ndarray
+        The solution array.
+
+    Raises
+    ------
+    ValueError
+        If size mismatches detected or input a is not square.
+    LinAlgError
+        If the matrix is singular.
+    LinAlgWarning
+        If an ill-conditioned input a is detected.
+    NotImplementedError
+        If transposed is True and input a is a complex matrix.
+
+    Notes
+    -----
+    If the input b matrix is a 1-D array with N elements, when supplied
+    together with an NxN input a, it is assumed as a valid column vector
+    despite the apparent size mismatch. This is compatible with the
+    numpy.dot() behavior and the returned result is still 1-D array.
+
+    The general, symmetric, Hermitian and positive definite solutions are
+    obtained via calling ?GESV, ?SYSV, ?HESV, and ?POSV routines of
+    LAPACK respectively.
+
+    The datatype of the arrays define which solver is called regardless
+    of the values. In other words, even when the complex array entries have
+    precisely zero imaginary parts, the complex solver will be called based
+    on the data type of the array.
+
+    Examples
+    --------
+    Given `a` and `b`, solve for `x`:
+
+    >>> import numpy as np
+    >>> a = np.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]])
+    >>> b = np.array([2, 4, -1])
+    >>> from scipy import linalg
+    >>> x = linalg.solve(a, b)
+    >>> x
+    array([ 2., -2.,  9.])
+    >>> np.dot(a, x) == b
+    array([ True,  True,  True], dtype=bool)
+
+    """
+    # Flags for 1-D or N-D right-hand side
+    b_is_1D = False
+
+    # check finite after determining structure
+    a1 = atleast_2d(_asarray_validated(a, check_finite=False))
+    b1 = atleast_1d(_asarray_validated(b, check_finite=False))
+    a1, b1 = _ensure_dtype_cdsz(a1, b1)
+    n = a1.shape[0]
+
+    overwrite_a = overwrite_a or _datacopied(a1, a)
+    overwrite_b = overwrite_b or _datacopied(b1, b)
+
+    if a1.shape[0] != a1.shape[1]:
+        raise ValueError('Input a needs to be a square matrix.')
+
+    if n != b1.shape[0]:
+        # Last chance to catch 1x1 scalar a and 1-D b arrays
+        if not (n == 1 and b1.size != 0):
+            raise ValueError('Input b has to have same number of rows as '
+                             'input a')
+
+    # accommodate empty arrays
+    if b1.size == 0:
+        dt = solve(np.eye(2, dtype=a1.dtype), np.ones(2, dtype=b1.dtype)).dtype
+        return np.empty_like(b1, dtype=dt)
+
+    # regularize 1-D b arrays to 2D
+    if b1.ndim == 1:
+        if n == 1:
+            b1 = b1[None, :]
+        else:
+            b1 = b1[:, None]
+        b_is_1D = True
+
+    if assume_a not in {None, 'diagonal', 'tridiagonal', 'banded', 'lower triangular',
+                        'upper triangular', 'symmetric', 'hermitian',
+                        'positive definite', 'general', 'sym', 'her', 'pos', 'gen'}:
+        raise ValueError(f'{assume_a} is not a recognized matrix structure')
+
+    # for a real matrix, describe it as "symmetric", not "hermitian"
+    # (lapack doesn't know what to do with real hermitian matrices)
+    if assume_a in {'hermitian', 'her'} and not np.iscomplexobj(a1):
+        assume_a = 'symmetric'
+
+    n_below, n_above = None, None
+    if assume_a is None:
+        assume_a, n_below, n_above = _find_matrix_structure(a1)
+
+    # Get the correct lamch function.
+    # The LAMCH functions only exists for S and D
+    # So for complex values we have to convert to real/double.
+    if a1.dtype.char in 'fF':  # single precision
+        lamch = get_lapack_funcs('lamch', dtype='f')
+    else:
+        lamch = get_lapack_funcs('lamch', dtype='d')
+
+    # Currently we do not have the other forms of the norm calculators
+    #   lansy, lanpo, lanhe.
+    # However, in any case they only reduce computations slightly...
+    if assume_a == 'diagonal':
+        _matrix_norm = _matrix_norm_diagonal
+    elif assume_a == 'tridiagonal':
+        _matrix_norm = _matrix_norm_tridiagonal
+    elif assume_a in {'lower triangular', 'upper triangular'}:
+        _matrix_norm = _matrix_norm_triangular(assume_a)
+    else:
+        _matrix_norm = _matrix_norm_general
+
+    # Since the I-norm and 1-norm are the same for symmetric matrices
+    # we can collect them all in this one call
+    # Note however, that when issuing 'gen' and form!='none', then
+    # the I-norm should be used
+    if transposed:
+        trans = 1
+        norm = 'I'
+        if np.iscomplexobj(a1):
+            raise NotImplementedError('scipy.linalg.solve can currently '
+                                      'not solve a^T x = b or a^H x = b '
+                                      'for complex matrices.')
+    else:
+        trans = 0
+        norm = '1'
+
+    anorm = _matrix_norm(norm, a1, check_finite)
+
+    info, rcond = 0, np.inf
+
+    # Generalized case 'gesv'
+    if assume_a in {'general', 'gen'}:
+        gecon, getrf, getrs = get_lapack_funcs(('gecon', 'getrf', 'getrs'),
+                                               (a1, b1))
+        lu, ipvt, info = getrf(a1, overwrite_a=overwrite_a)
+        _solve_check(n, info)
+        x, info = getrs(lu, ipvt, b1,
+                        trans=trans, overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        rcond, info = gecon(lu, anorm, norm=norm)
+    # Hermitian case 'hesv'
+    elif assume_a in {'hermitian', 'her'}:
+        hecon, hesv, hesv_lw = get_lapack_funcs(('hecon', 'hesv',
+                                                 'hesv_lwork'), (a1, b1))
+        lwork = _compute_lwork(hesv_lw, n, lower)
+        lu, ipvt, x, info = hesv(a1, b1, lwork=lwork,
+                                 lower=lower,
+                                 overwrite_a=overwrite_a,
+                                 overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        rcond, info = hecon(lu, ipvt, anorm)
+    # Symmetric case 'sysv'
+    elif assume_a in {'symmetric', 'sym'}:
+        sycon, sysv, sysv_lw = get_lapack_funcs(('sycon', 'sysv',
+                                                 'sysv_lwork'), (a1, b1))
+        lwork = _compute_lwork(sysv_lw, n, lower)
+        lu, ipvt, x, info = sysv(a1, b1, lwork=lwork,
+                                 lower=lower,
+                                 overwrite_a=overwrite_a,
+                                 overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        rcond, info = sycon(lu, ipvt, anorm)
+    # Diagonal case
+    elif assume_a == 'diagonal':
+        diag_a = np.diag(a1)
+        x = (b1.T / diag_a).T
+        abs_diag_a = np.abs(diag_a)
+        rcond = abs_diag_a.min() / abs_diag_a.max()
+    # Tri-diagonal case
+    elif assume_a == 'tridiagonal':
+        a1 = a1.T if transposed else a1
+        dl, d, du = np.diag(a1, -1), np.diag(a1, 0), np.diag(a1, 1)
+        _gttrf, _gttrs, _gtcon = get_lapack_funcs(('gttrf', 'gttrs', 'gtcon'), (a1, b1))
+        dl, d, du, du2, ipiv, info = _gttrf(dl, d, du)
+        _solve_check(n, info)
+        x, info = _gttrs(dl, d, du, du2, ipiv, b1, overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        rcond, info = _gtcon(dl, d, du, du2, ipiv, anorm)
+    # Banded case
+    elif assume_a == 'banded':
+        a1, n_below, n_above = ((a1.T, n_above, n_below) if transposed
+                                else (a1, n_below, n_above))
+        n_below, n_above = bandwidth(a1) if n_below is None else (n_below, n_above)
+        ab = _to_banded(n_below, n_above, a1)
+        gbsv, = get_lapack_funcs(('gbsv',), (a1, b1))
+        # Next two lines copied from `solve_banded`
+        a2 = np.zeros((2*n_below + n_above + 1, ab.shape[1]), dtype=gbsv.dtype)
+        a2[n_below:, :] = ab
+        _, _, x, info = gbsv(n_below, n_above, a2, b1,
+                             overwrite_ab=True, overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        # TODO: wrap gbcon and use to get rcond
+    # Triangular case
+    elif assume_a in {'lower triangular', 'upper triangular'}:
+        lower = assume_a == 'lower triangular'
+        x, info = _solve_triangular(a1, b1, lower=lower, overwrite_b=overwrite_b,
+                                    trans=transposed)
+        _solve_check(n, info)
+        _trcon = get_lapack_funcs(('trcon'), (a1, b1))
+        rcond, info = _trcon(a1, uplo='L' if lower else 'U')
+    # Positive definite case 'posv'
+    else:
+        pocon, posv = get_lapack_funcs(('pocon', 'posv'),
+                                       (a1, b1))
+        lu, x, info = posv(a1, b1, lower=lower,
+                           overwrite_a=overwrite_a,
+                           overwrite_b=overwrite_b)
+        _solve_check(n, info)
+        rcond, info = pocon(lu, anorm)
+
+    _solve_check(n, info, lamch, rcond)
+
+    if b_is_1D:
+        x = x.ravel()
+
+    return x
+
+
+def _matrix_norm_diagonal(_, a, check_finite):
+    # Equivalent of dlange for diagonal matrix, assuming
+    # norm is either 'I' or '1' (really just not the Frobenius norm)
+    d = np.diag(a)
+    d = np.asarray_chkfinite(d) if check_finite else d
+    return np.abs(d).max()
+
+
+def _matrix_norm_tridiagonal(norm, a, check_finite):
+    # Equivalent of dlange for tridiagonal matrix, assuming
+    # norm is either 'I' or '1'
+    if norm == 'I':
+        a = a.T
+    # Context to avoid warning before error in cases like -inf + inf
+    with np.errstate(invalid='ignore'):
+        d = np.abs(np.diag(a))
+        d[1:] += np.abs(np.diag(a, 1))
+        d[:-1] += np.abs(np.diag(a, -1))
+    d = np.asarray_chkfinite(d) if check_finite else d
+    return d.max()
+
+
+def _matrix_norm_triangular(structure):
+    def fun(norm, a, check_finite):
+        a = np.asarray_chkfinite(a) if check_finite else a
+        lantr = get_lapack_funcs('lantr', (a,))
+        return lantr(norm, a, 'L' if structure == 'lower triangular' else 'U' )
+    return fun
+
+
+def _matrix_norm_general(norm, a, check_finite):
+    a = np.asarray_chkfinite(a) if check_finite else a
+    lange = get_lapack_funcs('lange', (a,))
+    return lange(norm, a)
+
+
+def _to_banded(n_below, n_above, a):
+    n = a.shape[0]
+    rows = n_above + n_below + 1
+    ab = np.zeros((rows, n), dtype=a.dtype)
+    ab[n_above] = np.diag(a)
+    for i in range(1, n_above + 1):
+        ab[n_above - i, i:] = np.diag(a, i)
+    for i in range(1, n_below + 1):
+        ab[n_above + i, :-i] = np.diag(a, -i)
+    return ab
+
+
+def _ensure_dtype_cdsz(*arrays):
+    # Ensure that the dtype of arrays is one of the standard types
+    # compatible with LAPACK functions (single or double precision
+    # real or complex).
+    dtype = np.result_type(*arrays)
+    if not np.issubdtype(dtype, np.inexact):
+        return (array.astype(np.float64) for array in arrays)
+    complex = np.issubdtype(dtype, np.complexfloating)
+    if np.finfo(dtype).bits <= 32:
+        dtype = np.complex64 if complex else np.float32
+    elif np.finfo(dtype).bits >= 64:
+        dtype = np.complex128 if complex else np.float64
+    return (array.astype(dtype, copy=False) for array in arrays)
+
+
+def solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False,
+                     overwrite_b=False, check_finite=True):
+    """
+    Solve the equation ``a x = b`` for `x`, assuming a is a triangular matrix.
+
+    Parameters
+    ----------
+    a : (M, M) array_like
+        A triangular matrix
+    b : (M,) or (M, N) array_like
+        Right-hand side matrix in ``a x = b``
+    lower : bool, optional
+        Use only data contained in the lower triangle of `a`.
+        Default is to use upper triangle.
+    trans : {0, 1, 2, 'N', 'T', 'C'}, optional
+        Type of system to solve:
+
+        ========  =========
+        trans     system
+        ========  =========
+        0 or 'N'  a x  = b
+        1 or 'T'  a^T x = b
+        2 or 'C'  a^H x = b
+        ========  =========
+    unit_diagonal : bool, optional
+        If True, diagonal elements of `a` are assumed to be 1 and
+        will not be referenced.
+    overwrite_b : bool, optional
+        Allow overwriting data in `b` (may enhance performance)
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    x : (M,) or (M, N) ndarray
+        Solution to the system ``a x = b``.  Shape of return matches `b`.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular
+
+    Notes
+    -----
+    .. versionadded:: 0.9.0
+
+    Examples
+    --------
+    Solve the lower triangular system a x = b, where::
+
+             [3  0  0  0]       [4]
+        a =  [2  1  0  0]   b = [2]
+             [1  0  1  0]       [4]
+             [1  1  1  1]       [2]
+
+    >>> import numpy as np
+    >>> from scipy.linalg import solve_triangular
+    >>> a = np.array([[3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]])
+    >>> b = np.array([4, 2, 4, 2])
+    >>> x = solve_triangular(a, b, lower=True)
+    >>> x
+    array([ 1.33333333, -0.66666667,  2.66666667, -1.33333333])
+    >>> a.dot(x)  # Check the result
+    array([ 4.,  2.,  4.,  2.])
+
+    """
+
+    a1 = _asarray_validated(a, check_finite=check_finite)
+    b1 = _asarray_validated(b, check_finite=check_finite)
+
+    if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
+        raise ValueError('expected square matrix')
+
+    if a1.shape[0] != b1.shape[0]:
+        raise ValueError(f'shapes of a {a1.shape} and b {b1.shape} are incompatible')
+
+    # accommodate empty arrays
+    if b1.size == 0:
+        dt_nonempty = solve_triangular(
+            np.eye(2, dtype=a1.dtype), np.ones(2, dtype=b1.dtype)
+        ).dtype
+        return np.empty_like(b1, dtype=dt_nonempty)
+
+    overwrite_b = overwrite_b or _datacopied(b1, b)
+
+    x, _ = _solve_triangular(a1, b1, trans, lower, unit_diagonal, overwrite_b)
+    return x
+
+
+# solve_triangular without the input validation
+def _solve_triangular(a1, b1, trans=0, lower=False, unit_diagonal=False,
+                      overwrite_b=False):
+
+    trans = {'N': 0, 'T': 1, 'C': 2}.get(trans, trans)
+    trtrs, = get_lapack_funcs(('trtrs',), (a1, b1))
+    if a1.flags.f_contiguous or trans == 2:
+        x, info = trtrs(a1, b1, overwrite_b=overwrite_b, lower=lower,
+                        trans=trans, unitdiag=unit_diagonal)
+    else:
+        # transposed system is solved since trtrs expects Fortran ordering
+        x, info = trtrs(a1.T, b1, overwrite_b=overwrite_b, lower=not lower,
+                        trans=not trans, unitdiag=unit_diagonal)
+
+    if info == 0:
+        return x, info
+    if info > 0:
+        raise LinAlgError("singular matrix: resolution failed at diagonal %d" %
+                          (info-1))
+    raise ValueError('illegal value in %dth argument of internal trtrs' %
+                     (-info))
+
+
+def solve_banded(l_and_u, ab, b, overwrite_ab=False, overwrite_b=False,
+                 check_finite=True):
+    """
+    Solve the equation a x = b for x, assuming a is banded matrix.
+
+    The matrix a is stored in `ab` using the matrix diagonal ordered form::
+
+        ab[u + i - j, j] == a[i,j]
+
+    Example of `ab` (shape of a is (6,6), `u` =1, `l` =2)::
+
+        *    a01  a12  a23  a34  a45
+        a00  a11  a22  a33  a44  a55
+        a10  a21  a32  a43  a54   *
+        a20  a31  a42  a53   *    *
+
+    Parameters
+    ----------
+    (l, u) : (integer, integer)
+        Number of non-zero lower and upper diagonals
+    ab : (`l` + `u` + 1, M) array_like
+        Banded matrix
+    b : (M,) or (M, K) array_like
+        Right-hand side
+    overwrite_ab : bool, optional
+        Discard data in `ab` (may enhance performance)
+    overwrite_b : bool, optional
+        Discard data in `b` (may enhance performance)
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    x : (M,) or (M, K) ndarray
+        The solution to the system a x = b. Returned shape depends on the
+        shape of `b`.
+
+    Examples
+    --------
+    Solve the banded system a x = b, where::
+
+            [5  2 -1  0  0]       [0]
+            [1  4  2 -1  0]       [1]
+        a = [0  1  3  2 -1]   b = [2]
+            [0  0  1  2  2]       [2]
+            [0  0  0  1  1]       [3]
+
+    There is one nonzero diagonal below the main diagonal (l = 1), and
+    two above (u = 2). The diagonal banded form of the matrix is::
+
+             [*  * -1 -1 -1]
+        ab = [*  2  2  2  2]
+             [5  4  3  2  1]
+             [1  1  1  1  *]
+
+    >>> import numpy as np
+    >>> from scipy.linalg import solve_banded
+    >>> ab = np.array([[0,  0, -1, -1, -1],
+    ...                [0,  2,  2,  2,  2],
+    ...                [5,  4,  3,  2,  1],
+    ...                [1,  1,  1,  1,  0]])
+    >>> b = np.array([0, 1, 2, 2, 3])
+    >>> x = solve_banded((1, 2), ab, b)
+    >>> x
+    array([-2.37288136,  3.93220339, -4.        ,  4.3559322 , -1.3559322 ])
+
+    """
+
+    a1 = _asarray_validated(ab, check_finite=check_finite, as_inexact=True)
+    b1 = _asarray_validated(b, check_finite=check_finite, as_inexact=True)
+
+    # Validate shapes.
+    if a1.shape[-1] != b1.shape[0]:
+        raise ValueError("shapes of ab and b are not compatible.")
+
+    (nlower, nupper) = l_and_u
+    if nlower + nupper + 1 != a1.shape[0]:
+        raise ValueError("invalid values for the number of lower and upper "
+                         "diagonals: l+u+1 (%d) does not equal ab.shape[0] "
+                         "(%d)" % (nlower + nupper + 1, ab.shape[0]))
+
+    # accommodate empty arrays
+    if b1.size == 0:
+        dt = solve(np.eye(1, dtype=a1.dtype), np.ones(1, dtype=b1.dtype)).dtype
+        return np.empty_like(b1, dtype=dt)
+
+    overwrite_b = overwrite_b or _datacopied(b1, b)
+    if a1.shape[-1] == 1:
+        b2 = np.array(b1, copy=(not overwrite_b))
+        # a1.shape[-1] == 1 -> original matrix is 1x1. Typically, the user
+        # will pass u = l = 0 and `a1` will be 1x1. However, the rest of the
+        # function works with unnecessary rows in `a1` as long as
+        # `a1[u + i - j, j] == a[i,j]`. In the 1x1 case, we want i = j = 0,
+        # so the diagonal is in row `u` of `a1`. See gh-8906.
+        b2 /= a1[nupper, 0]
+        return b2
+    if nlower == nupper == 1:
+        overwrite_ab = overwrite_ab or _datacopied(a1, ab)
+        gtsv, = get_lapack_funcs(('gtsv',), (a1, b1))
+        du = a1[0, 1:]
+        d = a1[1, :]
+        dl = a1[2, :-1]
+        du2, d, du, x, info = gtsv(dl, d, du, b1, overwrite_ab, overwrite_ab,
+                                   overwrite_ab, overwrite_b)
+    else:
+        gbsv, = get_lapack_funcs(('gbsv',), (a1, b1))
+        a2 = np.zeros((2*nlower + nupper + 1, a1.shape[1]), dtype=gbsv.dtype)
+        a2[nlower:, :] = a1
+        lu, piv, x, info = gbsv(nlower, nupper, a2, b1, overwrite_ab=True,
+                                overwrite_b=overwrite_b)
+    if info == 0:
+        return x
+    if info > 0:
+        raise LinAlgError("singular matrix")
+    raise ValueError('illegal value in %d-th argument of internal '
+                     'gbsv/gtsv' % -info)
+
+
+def solveh_banded(ab, b, overwrite_ab=False, overwrite_b=False, lower=False,
+                  check_finite=True):
+    """
+    Solve equation a x = b. a is Hermitian positive-definite banded matrix.
+
+    Uses Thomas' Algorithm, which is more efficient than standard LU
+    factorization, but should only be used for Hermitian positive-definite
+    matrices.
+
+    The matrix ``a`` is stored in `ab` either in lower diagonal or upper
+    diagonal ordered form:
+
+        ab[u + i - j, j] == a[i,j]        (if upper form; i <= j)
+        ab[    i - j, j] == a[i,j]        (if lower form; i >= j)
+
+    Example of `ab` (shape of ``a`` is (6, 6), number of upper diagonals,
+    ``u`` =2)::
+
+        upper form:
+        *   *   a02 a13 a24 a35
+        *   a01 a12 a23 a34 a45
+        a00 a11 a22 a33 a44 a55
+
+        lower form:
+        a00 a11 a22 a33 a44 a55
+        a10 a21 a32 a43 a54 *
+        a20 a31 a42 a53 *   *
+
+    Cells marked with * are not used.
+
+    Parameters
+    ----------
+    ab : (``u`` + 1, M) array_like
+        Banded matrix
+    b : (M,) or (M, K) array_like
+        Right-hand side
+    overwrite_ab : bool, optional
+        Discard data in `ab` (may enhance performance)
+    overwrite_b : bool, optional
+        Discard data in `b` (may enhance performance)
+    lower : bool, optional
+        Is the matrix in the lower form. (Default is upper form)
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    x : (M,) or (M, K) ndarray
+        The solution to the system ``a x = b``. Shape of return matches shape
+        of `b`.
+
+    Notes
+    -----
+    In the case of a non-positive definite matrix ``a``, the solver
+    `solve_banded` may be used.
+
+    Examples
+    --------
+    Solve the banded system ``A x = b``, where::
+
+            [ 4  2 -1  0  0  0]       [1]
+            [ 2  5  2 -1  0  0]       [2]
+        A = [-1  2  6  2 -1  0]   b = [2]
+            [ 0 -1  2  7  2 -1]       [3]
+            [ 0  0 -1  2  8  2]       [3]
+            [ 0  0  0 -1  2  9]       [3]
+
+    >>> import numpy as np
+    >>> from scipy.linalg import solveh_banded
+
+    ``ab`` contains the main diagonal and the nonzero diagonals below the
+    main diagonal. That is, we use the lower form:
+
+    >>> ab = np.array([[ 4,  5,  6,  7, 8, 9],
+    ...                [ 2,  2,  2,  2, 2, 0],
+    ...                [-1, -1, -1, -1, 0, 0]])
+    >>> b = np.array([1, 2, 2, 3, 3, 3])
+    >>> x = solveh_banded(ab, b, lower=True)
+    >>> x
+    array([ 0.03431373,  0.45938375,  0.05602241,  0.47759104,  0.17577031,
+            0.34733894])
+
+
+    Solve the Hermitian banded system ``H x = b``, where::
+
+            [ 8   2-1j   0     0  ]        [ 1  ]
+        H = [2+1j  5     1j    0  ]    b = [1+1j]
+            [ 0   -1j    9   -2-1j]        [1-2j]
+            [ 0    0   -2+1j   6  ]        [ 0  ]
+
+    In this example, we put the upper diagonals in the array ``hb``:
+
+    >>> hb = np.array([[0, 2-1j, 1j, -2-1j],
+    ...                [8,  5,    9,   6  ]])
+    >>> b = np.array([1, 1+1j, 1-2j, 0])
+    >>> x = solveh_banded(hb, b)
+    >>> x
+    array([ 0.07318536-0.02939412j,  0.11877624+0.17696461j,
+            0.10077984-0.23035393j, -0.00479904-0.09358128j])
+
+    """
+    a1 = _asarray_validated(ab, check_finite=check_finite)
+    b1 = _asarray_validated(b, check_finite=check_finite)
+
+    # Validate shapes.
+    if a1.shape[-1] != b1.shape[0]:
+        raise ValueError("shapes of ab and b are not compatible.")
+
+    # accommodate empty arrays
+    if b1.size == 0:
+        dt = solve(np.eye(1, dtype=a1.dtype), np.ones(1, dtype=b1.dtype)).dtype
+        return np.empty_like(b1, dtype=dt)
+
+    overwrite_b = overwrite_b or _datacopied(b1, b)
+    overwrite_ab = overwrite_ab or _datacopied(a1, ab)
+
+    if a1.shape[0] == 2:
+        ptsv, = get_lapack_funcs(('ptsv',), (a1, b1))
+        if lower:
+            d = a1[0, :].real
+            e = a1[1, :-1]
+        else:
+            d = a1[1, :].real
+            e = a1[0, 1:].conj()
+        d, du, x, info = ptsv(d, e, b1, overwrite_ab, overwrite_ab,
+                              overwrite_b)
+    else:
+        pbsv, = get_lapack_funcs(('pbsv',), (a1, b1))
+        c, x, info = pbsv(a1, b1, lower=lower, overwrite_ab=overwrite_ab,
+                          overwrite_b=overwrite_b)
+    if info > 0:
+        raise LinAlgError("%dth leading minor not positive definite" % info)
+    if info < 0:
+        raise ValueError('illegal value in %dth argument of internal '
+                         'pbsv' % -info)
+    return x
+
+
+def solve_toeplitz(c_or_cr, b, check_finite=True):
+    r"""Solve a Toeplitz system using Levinson Recursion
+
+    The Toeplitz matrix has constant diagonals, with c as its first column
+    and r as its first row. If r is not given, ``r == conjugate(c)`` is
+    assumed.
+
+    .. warning::
+
+        Beginning in SciPy 1.17, multidimensional input will be treated as a batch,
+        not ``ravel``\ ed. To preserve the existing behavior, ``ravel`` arguments
+        before passing them to `solve_toeplitz`.
+
+    Parameters
+    ----------
+    c_or_cr : array_like or tuple of (array_like, array_like)
+        The vector ``c``, or a tuple of arrays (``c``, ``r``). If not
+        supplied, ``r = conjugate(c)`` is assumed; in this case, if c[0] is
+        real, the Toeplitz matrix is Hermitian. r[0] is ignored; the first row
+        of the Toeplitz matrix is ``[c[0], r[1:]]``.
+    b : (M,) or (M, K) array_like
+        Right-hand side in ``T x = b``.
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (result entirely NaNs) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    x : (M,) or (M, K) ndarray
+        The solution to the system ``T x = b``. Shape of return matches shape
+        of `b`.
+
+    See Also
+    --------
+    toeplitz : Toeplitz matrix
+
+    Notes
+    -----
+    The solution is computed using Levinson-Durbin recursion, which is faster
+    than generic least-squares methods, but can be less numerically stable.
+
+    Examples
+    --------
+    Solve the Toeplitz system T x = b, where::
+
+            [ 1 -1 -2 -3]       [1]
+        T = [ 3  1 -1 -2]   b = [2]
+            [ 6  3  1 -1]       [2]
+            [10  6  3  1]       [5]
+
+    To specify the Toeplitz matrix, only the first column and the first
+    row are needed.
+
+    >>> import numpy as np
+    >>> c = np.array([1, 3, 6, 10])    # First column of T
+    >>> r = np.array([1, -1, -2, -3])  # First row of T
+    >>> b = np.array([1, 2, 2, 5])
+
+    >>> from scipy.linalg import solve_toeplitz, toeplitz
+    >>> x = solve_toeplitz((c, r), b)
+    >>> x
+    array([ 1.66666667, -1.        , -2.66666667,  2.33333333])
+
+    Check the result by creating the full Toeplitz matrix and
+    multiplying it by `x`.  We should get `b`.
+
+    >>> T = toeplitz(c, r)
+    >>> T.dot(x)
+    array([ 1.,  2.,  2.,  5.])
+
+    """
+    # If numerical stability of this algorithm is a problem, a future
+    # developer might consider implementing other O(N^2) Toeplitz solvers,
+    # such as GKO (https://www.jstor.org/stable/2153371) or Bareiss.
+
+    r, c, b, dtype, b_shape = _validate_args_for_toeplitz_ops(
+        c_or_cr, b, check_finite, keep_b_shape=True)
+
+    # accommodate empty arrays
+    if b.size == 0:
+        return np.empty_like(b)
+
+    # Form a 1-D array of values to be used in the matrix, containing a
+    # reversed copy of r[1:], followed by c.
+    vals = np.concatenate((r[-1:0:-1], c))
+    if b is None:
+        raise ValueError('illegal value, `b` is a required argument')
+
+    if b.ndim == 1:
+        x, _ = levinson(vals, np.ascontiguousarray(b))
+    else:
+        x = np.column_stack([levinson(vals, np.ascontiguousarray(b[:, i]))[0]
+                             for i in range(b.shape[1])])
+        x = x.reshape(*b_shape)
+
+    return x
+
+
+def _get_axis_len(aname, a, axis):
+    ax = axis
+    if ax < 0:
+        ax += a.ndim
+    if 0 <= ax < a.ndim:
+        return a.shape[ax]
+    raise ValueError(f"'{aname}axis' entry is out of bounds")
+
+
+def solve_circulant(c, b, singular='raise', tol=None,
+                    caxis=-1, baxis=0, outaxis=0):
+    """Solve C x = b for x, where C is a circulant matrix.
+
+    `C` is the circulant matrix associated with the vector `c`.
+
+    The system is solved by doing division in Fourier space. The
+    calculation is::
+
+        x = ifft(fft(b) / fft(c))
+
+    where `fft` and `ifft` are the fast Fourier transform and its inverse,
+    respectively. For a large vector `c`, this is *much* faster than
+    solving the system with the full circulant matrix.
+
+    Parameters
+    ----------
+    c : array_like
+        The coefficients of the circulant matrix.
+    b : array_like
+        Right-hand side matrix in ``a x = b``.
+    singular : str, optional
+        This argument controls how a near singular circulant matrix is
+        handled.  If `singular` is "raise" and the circulant matrix is
+        near singular, a `LinAlgError` is raised. If `singular` is
+        "lstsq", the least squares solution is returned. Default is "raise".
+    tol : float, optional
+        If any eigenvalue of the circulant matrix has an absolute value
+        that is less than or equal to `tol`, the matrix is considered to be
+        near singular. If not given, `tol` is set to::
+
+            tol = abs_eigs.max() * abs_eigs.size * np.finfo(np.float64).eps
+
+        where `abs_eigs` is the array of absolute values of the eigenvalues
+        of the circulant matrix.
+    caxis : int
+        When `c` has dimension greater than 1, it is viewed as a collection
+        of circulant vectors. In this case, `caxis` is the axis of `c` that
+        holds the vectors of circulant coefficients.
+    baxis : int
+        When `b` has dimension greater than 1, it is viewed as a collection
+        of vectors. In this case, `baxis` is the axis of `b` that holds the
+        right-hand side vectors.
+    outaxis : int
+        When `c` or `b` are multidimensional, the value returned by
+        `solve_circulant` is multidimensional. In this case, `outaxis` is
+        the axis of the result that holds the solution vectors.
+
+    Returns
+    -------
+    x : ndarray
+        Solution to the system ``C x = b``.
+
+    Raises
+    ------
+    LinAlgError
+        If the circulant matrix associated with `c` is near singular.
+
+    See Also
+    --------
+    circulant : circulant matrix
+
+    Notes
+    -----
+    For a 1-D vector `c` with length `m`, and an array `b`
+    with shape ``(m, ...)``,
+
+        solve_circulant(c, b)
+
+    returns the same result as
+
+        solve(circulant(c), b)
+
+    where `solve` and `circulant` are from `scipy.linalg`.
+
+    .. versionadded:: 0.16.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.linalg import solve_circulant, solve, circulant, lstsq
+
+    >>> c = np.array([2, 2, 4])
+    >>> b = np.array([1, 2, 3])
+    >>> solve_circulant(c, b)
+    array([ 0.75, -0.25,  0.25])
+
+    Compare that result to solving the system with `scipy.linalg.solve`:
+
+    >>> solve(circulant(c), b)
+    array([ 0.75, -0.25,  0.25])
+
+    A singular example:
+
+    >>> c = np.array([1, 1, 0, 0])
+    >>> b = np.array([1, 2, 3, 4])
+
+    Calling ``solve_circulant(c, b)`` will raise a `LinAlgError`.  For the
+    least square solution, use the option ``singular='lstsq'``:
+
+    >>> solve_circulant(c, b, singular='lstsq')
+    array([ 0.25,  1.25,  2.25,  1.25])
+
+    Compare to `scipy.linalg.lstsq`:
+
+    >>> x, resid, rnk, s = lstsq(circulant(c), b)
+    >>> x
+    array([ 0.25,  1.25,  2.25,  1.25])
+
+    A broadcasting example:
+
+    Suppose we have the vectors of two circulant matrices stored in an array
+    with shape (2, 5), and three `b` vectors stored in an array with shape
+    (3, 5).  For example,
+
+    >>> c = np.array([[1.5, 2, 3, 0, 0], [1, 1, 4, 3, 2]])
+    >>> b = np.arange(15).reshape(-1, 5)
+
+    We want to solve all combinations of circulant matrices and `b` vectors,
+    with the result stored in an array with shape (2, 3, 5). When we
+    disregard the axes of `c` and `b` that hold the vectors of coefficients,
+    the shapes of the collections are (2,) and (3,), respectively, which are
+    not compatible for broadcasting. To have a broadcast result with shape
+    (2, 3), we add a trivial dimension to `c`: ``c[:, np.newaxis, :]`` has
+    shape (2, 1, 5). The last dimension holds the coefficients of the
+    circulant matrices, so when we call `solve_circulant`, we can use the
+    default ``caxis=-1``. The coefficients of the `b` vectors are in the last
+    dimension of the array `b`, so we use ``baxis=-1``. If we use the
+    default `outaxis`, the result will have shape (5, 2, 3), so we'll use
+    ``outaxis=-1`` to put the solution vectors in the last dimension.
+
+    >>> x = solve_circulant(c[:, np.newaxis, :], b, baxis=-1, outaxis=-1)
+    >>> x.shape
+    (2, 3, 5)
+    >>> np.set_printoptions(precision=3)  # For compact output of numbers.
+    >>> x
+    array([[[-0.118,  0.22 ,  1.277, -0.142,  0.302],
+            [ 0.651,  0.989,  2.046,  0.627,  1.072],
+            [ 1.42 ,  1.758,  2.816,  1.396,  1.841]],
+           [[ 0.401,  0.304,  0.694, -0.867,  0.377],
+            [ 0.856,  0.758,  1.149, -0.412,  0.831],
+            [ 1.31 ,  1.213,  1.603,  0.042,  1.286]]])
+
+    Check by solving one pair of `c` and `b` vectors (cf. ``x[1, 1, :]``):
+
+    >>> solve_circulant(c[1], b[1, :])
+    array([ 0.856,  0.758,  1.149, -0.412,  0.831])
+
+    """
+    c = np.atleast_1d(c)
+    nc = _get_axis_len("c", c, caxis)
+    b = np.atleast_1d(b)
+    nb = _get_axis_len("b", b, baxis)
+    if nc != nb:
+        raise ValueError(f'Shapes of c {c.shape} and b {b.shape} are incompatible')
+
+    # accommodate empty arrays
+    if b.size == 0:
+        dt = solve_circulant(np.arange(3, dtype=c.dtype),
+                             np.ones(3, dtype=b.dtype)).dtype
+        return np.empty_like(b, dtype=dt)
+
+    fc = np.fft.fft(np.moveaxis(c, caxis, -1), axis=-1)
+    abs_fc = np.abs(fc)
+    if tol is None:
+        # This is the same tolerance as used in np.linalg.matrix_rank.
+        tol = abs_fc.max(axis=-1) * nc * np.finfo(np.float64).eps
+        if tol.shape != ():
+            tol.shape = tol.shape + (1,)
+        else:
+            tol = np.atleast_1d(tol)
+
+    near_zeros = abs_fc <= tol
+    is_near_singular = np.any(near_zeros)
+    if is_near_singular:
+        if singular == 'raise':
+            raise LinAlgError("near singular circulant matrix.")
+        else:
+            # Replace the small values with 1 to avoid errors in the
+            # division fb/fc below.
+            fc[near_zeros] = 1
+
+    fb = np.fft.fft(np.moveaxis(b, baxis, -1), axis=-1)
+
+    q = fb / fc
+
+    if is_near_singular:
+        # `near_zeros` is a boolean array, same shape as `c`, that is
+        # True where `fc` is (near) zero. `q` is the broadcasted result
+        # of fb / fc, so to set the values of `q` to 0 where `fc` is near
+        # zero, we use a mask that is the broadcast result of an array
+        # of True values shaped like `b` with `near_zeros`.
+        mask = np.ones_like(b, dtype=bool) & near_zeros
+        q[mask] = 0
+
+    x = np.fft.ifft(q, axis=-1)
+    if not (np.iscomplexobj(c) or np.iscomplexobj(b)):
+        x = x.real
+    if outaxis != -1:
+        x = np.moveaxis(x, -1, outaxis)
+    return x
+
+
+# matrix inversion
+def inv(a, overwrite_a=False, check_finite=True):
+    """
+    Compute the inverse of a matrix.
+
+    Parameters
+    ----------
+    a : array_like
+        Square matrix to be inverted.
+    overwrite_a : bool, optional
+        Discard data in `a` (may improve performance). Default is False.
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    ainv : ndarray
+        Inverse of the matrix `a`.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular.
+    ValueError
+        If `a` is not square, or not 2D.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    >>> linalg.inv(a)
+    array([[-2. ,  1. ],
+           [ 1.5, -0.5]])
+    >>> np.dot(a, linalg.inv(a))
+    array([[ 1.,  0.],
+           [ 0.,  1.]])
+
+    """
+    a1 = _asarray_validated(a, check_finite=check_finite)
+    if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
+        raise ValueError('expected square matrix')
+
+    # accommodate empty square matrices
+    if a1.size == 0:
+        dt = inv(np.eye(2, dtype=a1.dtype)).dtype
+        return np.empty_like(a1, dtype=dt)
+
+    overwrite_a = overwrite_a or _datacopied(a1, a)
+    getrf, getri, getri_lwork = get_lapack_funcs(('getrf', 'getri',
+                                                  'getri_lwork'),
+                                                 (a1,))
+    lu, piv, info = getrf(a1, overwrite_a=overwrite_a)
+    if info == 0:
+        lwork = _compute_lwork(getri_lwork, a1.shape[0])
+
+        # XXX: the following line fixes curious SEGFAULT when
+        # benchmarking 500x500 matrix inverse. This seems to
+        # be a bug in LAPACK ?getri routine because if lwork is
+        # minimal (when using lwork[0] instead of lwork[1]) then
+        # all tests pass. Further investigation is required if
+        # more such SEGFAULTs occur.
+        lwork = int(1.01 * lwork)
+        inv_a, info = getri(lu, piv, lwork=lwork, overwrite_lu=1)
+    if info > 0:
+        raise LinAlgError("singular matrix")
+    if info < 0:
+        raise ValueError('illegal value in %d-th argument of internal '
+                         'getrf|getri' % -info)
+    return inv_a
+
+
+# Determinant
+
+def det(a, overwrite_a=False, check_finite=True):
+    """
+    Compute the determinant of a matrix
+
+    The determinant is a scalar that is a function of the associated square
+    matrix coefficients. The determinant value is zero for singular matrices.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Input array to compute determinants for.
+    overwrite_a : bool, optional
+        Allow overwriting data in a (may enhance performance).
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    det : (...) float or complex
+        Determinant of `a`. For stacked arrays, a scalar is returned for each
+        (m, m) slice in the last two dimensions of the input. For example, an
+        input of shape (p, q, m, m) will produce a result of shape (p, q). If
+        all dimensions are 1 a scalar is returned regardless of ndim.
+
+    Notes
+    -----
+    The determinant is computed by performing an LU factorization of the
+    input with LAPACK routine 'getrf', and then calculating the product of
+    diagonal entries of the U factor.
+
+    Even if the input array is single precision (float32 or complex64), the
+    result will be returned in double precision (float64 or complex128) to
+    prevent overflows.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> a = np.array([[1,2,3], [4,5,6], [7,8,9]])  # A singular matrix
+    >>> linalg.det(a)
+    0.0
+    >>> b = np.array([[0,2,3], [4,5,6], [7,8,9]])
+    >>> linalg.det(b)
+    3.0
+    >>> # An array with the shape (3, 2, 2, 2)
+    >>> c = np.array([[[[1., 2.], [3., 4.]],
+    ...                [[5., 6.], [7., 8.]]],
+    ...               [[[9., 10.], [11., 12.]],
+    ...                [[13., 14.], [15., 16.]]],
+    ...               [[[17., 18.], [19., 20.]],
+    ...                [[21., 22.], [23., 24.]]]])
+    >>> linalg.det(c)  # The resulting shape is (3, 2)
+    array([[-2., -2.],
+           [-2., -2.],
+           [-2., -2.]])
+    >>> linalg.det(c[0, 0])  # Confirm the (0, 0) slice, [[1, 2], [3, 4]]
+    -2.0
+    """
+    # The goal is to end up with a writable contiguous array to pass to Cython
+
+    # First we check and make arrays.
+    a1 = np.asarray_chkfinite(a) if check_finite else np.asarray(a)
+    if a1.ndim < 2:
+        raise ValueError('The input array must be at least two-dimensional.')
+    if a1.shape[-1] != a1.shape[-2]:
+        raise ValueError('Last 2 dimensions of the array must be square'
+                         f' but received shape {a1.shape}.')
+
+    # Also check if dtype is LAPACK compatible
+    if a1.dtype.char not in 'fdFD':
+        dtype_char = lapack_cast_dict[a1.dtype.char]
+        if not dtype_char:  # No casting possible
+            raise TypeError(f'The dtype "{a1.dtype.name}" cannot be cast '
+                            'to float(32, 64) or complex(64, 128).')
+
+        a1 = a1.astype(dtype_char[0])  # makes a copy, free to scratch
+        overwrite_a = True
+
+    # Empty array has determinant 1 because math.
+    if min(*a1.shape) == 0:
+        dtyp = np.float64 if a1.dtype.char not in 'FD' else np.complex128
+        if a1.ndim == 2:
+            return dtyp(1.0)
+        else:
+            return np.ones(shape=a1.shape[:-2], dtype=dtyp)
+
+    # Scalar case
+    if a1.shape[-2:] == (1, 1):
+        a1 = a1[..., 0, 0]
+        if a1.ndim == 0:
+            a1 = a1[()]
+        # Convert float32 to float64, and complex64 to complex128.
+        if a1.dtype.char in 'dD':
+            return a1
+        return a1.astype('d') if a1.dtype.char == 'f' else a1.astype('D')
+
+    # Then check overwrite permission
+    if not _datacopied(a1, a):  # "a"  still alive through "a1"
+        if not overwrite_a:
+            # Data belongs to "a" so make a copy
+            a1 = a1.copy(order='C')
+        #  else: Do nothing we'll use "a" if possible
+    # else:  a1 has its own data thus free to scratch
+
+    # Then layout checks, might happen that overwrite is allowed but original
+    # array was read-only or non-C-contiguous.
+    if not (a1.flags['C_CONTIGUOUS'] and a1.flags['WRITEABLE']):
+        a1 = a1.copy(order='C')
+
+    if a1.ndim == 2:
+        det = find_det_from_lu(a1)
+        # Convert float, complex to NumPy scalars
+        return (np.float64(det) if np.isrealobj(det) else np.complex128(det))
+
+    # loop over the stacked array, and avoid overflows for single precision
+    # Cf. np.linalg.det(np.diag([1e+38, 1e+38]).astype(np.float32))
+    dtype_char = a1.dtype.char
+    if dtype_char in 'fF':
+        dtype_char = 'd' if dtype_char.islower() else 'D'
+
+    det = np.empty(a1.shape[:-2], dtype=dtype_char)
+    for ind in product(*[range(x) for x in a1.shape[:-2]]):
+        det[ind] = find_det_from_lu(a1[ind])
+    return det
+
+
+# Linear Least Squares
+def lstsq(a, b, cond=None, overwrite_a=False, overwrite_b=False,
+          check_finite=True, lapack_driver=None):
+    """
+    Compute least-squares solution to equation Ax = b.
+
+    Compute a vector x such that the 2-norm ``|b - A x|`` is minimized.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        Left-hand side array
+    b : (M,) or (M, K) array_like
+        Right hand side array
+    cond : float, optional
+        Cutoff for 'small' singular values; used to determine effective
+        rank of a. Singular values smaller than
+        ``cond * largest_singular_value`` are considered zero.
+    overwrite_a : bool, optional
+        Discard data in `a` (may enhance performance). Default is False.
+    overwrite_b : bool, optional
+        Discard data in `b` (may enhance performance). Default is False.
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+    lapack_driver : str, optional
+        Which LAPACK driver is used to solve the least-squares problem.
+        Options are ``'gelsd'``, ``'gelsy'``, ``'gelss'``. Default
+        (``'gelsd'``) is a good choice.  However, ``'gelsy'`` can be slightly
+        faster on many problems.  ``'gelss'`` was used historically.  It is
+        generally slow but uses less memory.
+
+        .. versionadded:: 0.17.0
+
+    Returns
+    -------
+    x : (N,) or (N, K) ndarray
+        Least-squares solution.
+    residues : (K,) ndarray or float
+        Square of the 2-norm for each column in ``b - a x``, if ``M > N`` and
+        ``rank(A) == n`` (returns a scalar if ``b`` is 1-D). Otherwise a
+        (0,)-shaped array is returned.
+    rank : int
+        Effective rank of `a`.
+    s : (min(M, N),) ndarray or None
+        Singular values of `a`. The condition number of ``a`` is
+        ``s[0] / s[-1]``.
+
+    Raises
+    ------
+    LinAlgError
+        If computation does not converge.
+
+    ValueError
+        When parameters are not compatible.
+
+    See Also
+    --------
+    scipy.optimize.nnls : linear least squares with non-negativity constraint
+
+    Notes
+    -----
+    When ``'gelsy'`` is used as a driver, `residues` is set to a (0,)-shaped
+    array and `s` is always ``None``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.linalg import lstsq
+    >>> import matplotlib.pyplot as plt
+
+    Suppose we have the following data:
+
+    >>> x = np.array([1, 2.5, 3.5, 4, 5, 7, 8.5])
+    >>> y = np.array([0.3, 1.1, 1.5, 2.0, 3.2, 6.6, 8.6])
+
+    We want to fit a quadratic polynomial of the form ``y = a + b*x**2``
+    to this data.  We first form the "design matrix" M, with a constant
+    column of 1s and a column containing ``x**2``:
+
+    >>> M = x[:, np.newaxis]**[0, 2]
+    >>> M
+    array([[  1.  ,   1.  ],
+           [  1.  ,   6.25],
+           [  1.  ,  12.25],
+           [  1.  ,  16.  ],
+           [  1.  ,  25.  ],
+           [  1.  ,  49.  ],
+           [  1.  ,  72.25]])
+
+    We want to find the least-squares solution to ``M.dot(p) = y``,
+    where ``p`` is a vector with length 2 that holds the parameters
+    ``a`` and ``b``.
+
+    >>> p, res, rnk, s = lstsq(M, y)
+    >>> p
+    array([ 0.20925829,  0.12013861])
+
+    Plot the data and the fitted curve.
+
+    >>> plt.plot(x, y, 'o', label='data')
+    >>> xx = np.linspace(0, 9, 101)
+    >>> yy = p[0] + p[1]*xx**2
+    >>> plt.plot(xx, yy, label='least squares fit, $y = a + bx^2$')
+    >>> plt.xlabel('x')
+    >>> plt.ylabel('y')
+    >>> plt.legend(framealpha=1, shadow=True)
+    >>> plt.grid(alpha=0.25)
+    >>> plt.show()
+
+    """
+    a1 = _asarray_validated(a, check_finite=check_finite)
+    b1 = _asarray_validated(b, check_finite=check_finite)
+    if len(a1.shape) != 2:
+        raise ValueError('Input array a should be 2D')
+    m, n = a1.shape
+    if len(b1.shape) == 2:
+        nrhs = b1.shape[1]
+    else:
+        nrhs = 1
+    if m != b1.shape[0]:
+        raise ValueError('Shape mismatch: a and b should have the same number'
+                         f' of rows ({m} != {b1.shape[0]}).')
+    if m == 0 or n == 0:  # Zero-sized problem, confuses LAPACK
+        x = np.zeros((n,) + b1.shape[1:], dtype=np.common_type(a1, b1))
+        if n == 0:
+            residues = np.linalg.norm(b1, axis=0)**2
+        else:
+            residues = np.empty((0,))
+        return x, residues, 0, np.empty((0,))
+
+    driver = lapack_driver
+    if driver is None:
+        driver = lstsq.default_lapack_driver
+    if driver not in ('gelsd', 'gelsy', 'gelss'):
+        raise ValueError(f'LAPACK driver "{driver}" is not found')
+
+    lapack_func, lapack_lwork = get_lapack_funcs((driver,
+                                                 f'{driver}_lwork'),
+                                                 (a1, b1))
+    real_data = True if (lapack_func.dtype.kind == 'f') else False
+
+    if m < n:
+        # need to extend b matrix as it will be filled with
+        # a larger solution matrix
+        if len(b1.shape) == 2:
+            b2 = np.zeros((n, nrhs), dtype=lapack_func.dtype)
+            b2[:m, :] = b1
+        else:
+            b2 = np.zeros(n, dtype=lapack_func.dtype)
+            b2[:m] = b1
+        b1 = b2
+
+    overwrite_a = overwrite_a or _datacopied(a1, a)
+    overwrite_b = overwrite_b or _datacopied(b1, b)
+
+    if cond is None:
+        cond = np.finfo(lapack_func.dtype).eps
+
+    if driver in ('gelss', 'gelsd'):
+        if driver == 'gelss':
+            lwork = _compute_lwork(lapack_lwork, m, n, nrhs, cond)
+            v, x, s, rank, work, info = lapack_func(a1, b1, cond, lwork,
+                                                    overwrite_a=overwrite_a,
+                                                    overwrite_b=overwrite_b)
+
+        elif driver == 'gelsd':
+            if real_data:
+                lwork, iwork = _compute_lwork(lapack_lwork, m, n, nrhs, cond)
+                x, s, rank, info = lapack_func(a1, b1, lwork,
+                                               iwork, cond, False, False)
+            else:  # complex data
+                lwork, rwork, iwork = _compute_lwork(lapack_lwork, m, n,
+                                                     nrhs, cond)
+                x, s, rank, info = lapack_func(a1, b1, lwork, rwork, iwork,
+                                               cond, False, False)
+        if info > 0:
+            raise LinAlgError("SVD did not converge in Linear Least Squares")
+        if info < 0:
+            raise ValueError('illegal value in %d-th argument of internal %s'
+                             % (-info, lapack_driver))
+        resids = np.asarray([], dtype=x.dtype)
+        if m > n:
+            x1 = x[:n]
+            if rank == n:
+                resids = np.sum(np.abs(x[n:])**2, axis=0)
+            x = x1
+        return x, resids, rank, s
+
+    elif driver == 'gelsy':
+        lwork = _compute_lwork(lapack_lwork, m, n, nrhs, cond)
+        jptv = np.zeros((a1.shape[1], 1), dtype=np.int32)
+        v, x, j, rank, info = lapack_func(a1, b1, jptv, cond,
+                                          lwork, False, False)
+        if info < 0:
+            raise ValueError("illegal value in %d-th argument of internal "
+                             "gelsy" % -info)
+        if m > n:
+            x1 = x[:n]
+            x = x1
+        return x, np.array([], x.dtype), rank, None
+
+
+lstsq.default_lapack_driver = 'gelsd'
+
+
+def pinv(a, *, atol=None, rtol=None, return_rank=False, check_finite=True):
+    """
+    Compute the (Moore-Penrose) pseudo-inverse of a matrix.
+
+    Calculate a generalized inverse of a matrix using its
+    singular-value decomposition ``U @ S @ V`` in the economy mode and picking
+    up only the columns/rows that are associated with significant singular
+    values.
+
+    If ``s`` is the maximum singular value of ``a``, then the
+    significance cut-off value is determined by ``atol + rtol * s``. Any
+    singular value below this value is assumed insignificant.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        Matrix to be pseudo-inverted.
+    atol : float, optional
+        Absolute threshold term, default value is 0.
+
+        .. versionadded:: 1.7.0
+
+    rtol : float, optional
+        Relative threshold term, default value is ``max(M, N) * eps`` where
+        ``eps`` is the machine precision value of the datatype of ``a``.
+
+        .. versionadded:: 1.7.0
+
+    return_rank : bool, optional
+        If True, return the effective rank of the matrix.
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    B : (N, M) ndarray
+        The pseudo-inverse of matrix `a`.
+    rank : int
+        The effective rank of the matrix. Returned if `return_rank` is True.
+
+    Raises
+    ------
+    LinAlgError
+        If SVD computation does not converge.
+
+    See Also
+    --------
+    pinvh : Moore-Penrose pseudoinverse of a hermitian matrix.
+
+    Notes
+    -----
+    If ``A`` is invertible then the Moore-Penrose pseudoinverse is exactly
+    the inverse of ``A`` [1]_. If ``A`` is not invertible then the
+    Moore-Penrose pseudoinverse computes the ``x`` solution to ``Ax = b`` such
+    that ``||Ax - b||`` is minimized [1]_.
+
+    References
+    ----------
+    .. [1] Penrose, R. (1956). On best approximate solutions of linear matrix
+           equations. Mathematical Proceedings of the Cambridge Philosophical
+           Society, 52(1), 17-19. doi:10.1017/S0305004100030929
+
+    Examples
+    --------
+
+    Given an ``m x n`` matrix ``A`` and an ``n x m`` matrix ``B`` the four
+    Moore-Penrose conditions are:
+
+    1. ``ABA = A`` (``B`` is a generalized inverse of ``A``),
+    2. ``BAB = B`` (``A`` is a generalized inverse of ``B``),
+    3. ``(AB)* = AB`` (``AB`` is hermitian),
+    4. ``(BA)* = BA`` (``BA`` is hermitian) [1]_.
+
+    Here, ``A*`` denotes the conjugate transpose. The Moore-Penrose
+    pseudoinverse is a unique ``B`` that satisfies all four of these
+    conditions and exists for any ``A``. Note that, unlike the standard
+    matrix inverse, ``A`` does not have to be a square matrix or have
+    linearly independent columns/rows.
+
+    As an example, we can calculate the Moore-Penrose pseudoinverse of a
+    random non-square matrix and verify it satisfies the four conditions.
+
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> rng = np.random.default_rng()
+    >>> A = rng.standard_normal((9, 6))
+    >>> B = linalg.pinv(A)
+    >>> np.allclose(A @ B @ A, A)  # Condition 1
+    True
+    >>> np.allclose(B @ A @ B, B)  # Condition 2
+    True
+    >>> np.allclose((A @ B).conj().T, A @ B)  # Condition 3
+    True
+    >>> np.allclose((B @ A).conj().T, B @ A)  # Condition 4
+    True
+
+    """
+    a = _asarray_validated(a, check_finite=check_finite)
+    u, s, vh = _decomp_svd.svd(a, full_matrices=False, check_finite=False)
+    t = u.dtype.char.lower()
+    maxS = np.max(s, initial=0.)
+
+    atol = 0. if atol is None else atol
+    rtol = max(a.shape) * np.finfo(t).eps if (rtol is None) else rtol
+
+    if (atol < 0.) or (rtol < 0.):
+        raise ValueError("atol and rtol values must be positive.")
+
+    val = atol + maxS * rtol
+    rank = np.sum(s > val)
+
+    u = u[:, :rank]
+    u /= s[:rank]
+    B = (u @ vh[:rank]).conj().T
+
+    if return_rank:
+        return B, rank
+    else:
+        return B
+
+
+def pinvh(a, atol=None, rtol=None, lower=True, return_rank=False,
+          check_finite=True):
+    """
+    Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.
+
+    Calculate a generalized inverse of a complex Hermitian/real symmetric
+    matrix using its eigenvalue decomposition and including all eigenvalues
+    with 'large' absolute value.
+
+    Parameters
+    ----------
+    a : (N, N) array_like
+        Real symmetric or complex hermetian matrix to be pseudo-inverted
+
+    atol : float, optional
+        Absolute threshold term, default value is 0.
+
+        .. versionadded:: 1.7.0
+
+    rtol : float, optional
+        Relative threshold term, default value is ``N * eps`` where
+        ``eps`` is the machine precision value of the datatype of ``a``.
+
+        .. versionadded:: 1.7.0
+
+    lower : bool, optional
+        Whether the pertinent array data is taken from the lower or upper
+        triangle of `a`. (Default: lower)
+    return_rank : bool, optional
+        If True, return the effective rank of the matrix.
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    B : (N, N) ndarray
+        The pseudo-inverse of matrix `a`.
+    rank : int
+        The effective rank of the matrix.  Returned if `return_rank` is True.
+
+    Raises
+    ------
+    LinAlgError
+        If eigenvalue algorithm does not converge.
+
+    See Also
+    --------
+    pinv : Moore-Penrose pseudoinverse of a matrix.
+
+    Examples
+    --------
+
+    For a more detailed example see `pinv`.
+
+    >>> import numpy as np
+    >>> from scipy.linalg import pinvh
+    >>> rng = np.random.default_rng()
+    >>> a = rng.standard_normal((9, 6))
+    >>> a = np.dot(a, a.T)
+    >>> B = pinvh(a)
+    >>> np.allclose(a, a @ B @ a)
+    True
+    >>> np.allclose(B, B @ a @ B)
+    True
+
+    """
+    a = _asarray_validated(a, check_finite=check_finite)
+    s, u = _decomp.eigh(a, lower=lower, check_finite=False, driver='ev')
+    t = u.dtype.char.lower()
+    maxS = np.max(np.abs(s), initial=0.)
+
+    atol = 0. if atol is None else atol
+    rtol = max(a.shape) * np.finfo(t).eps if (rtol is None) else rtol
+
+    if (atol < 0.) or (rtol < 0.):
+        raise ValueError("atol and rtol values must be positive.")
+
+    val = atol + maxS * rtol
+    above_cutoff = (abs(s) > val)
+
+    psigma_diag = 1.0 / s[above_cutoff]
+    u = u[:, above_cutoff]
+
+    B = (u * psigma_diag) @ u.conj().T
+
+    if return_rank:
+        return B, len(psigma_diag)
+    else:
+        return B
+
+
+def matrix_balance(A, permute=True, scale=True, separate=False,
+                   overwrite_a=False):
+    """
+    Compute a diagonal similarity transformation for row/column balancing.
+
+    The balancing tries to equalize the row and column 1-norms by applying
+    a similarity transformation such that the magnitude variation of the
+    matrix entries is reflected to the scaling matrices.
+
+    Moreover, if enabled, the matrix is first permuted to isolate the upper
+    triangular parts of the matrix and, again if scaling is also enabled,
+    only the remaining subblocks are subjected to scaling.
+
+    The balanced matrix satisfies the following equality
+
+    .. math::
+
+                        B = T^{-1} A T
+
+    The scaling coefficients are approximated to the nearest power of 2
+    to avoid round-off errors.
+
+    Parameters
+    ----------
+    A : (n, n) array_like
+        Square data matrix for the balancing.
+    permute : bool, optional
+        The selector to define whether permutation of A is also performed
+        prior to scaling.
+    scale : bool, optional
+        The selector to turn on and off the scaling. If False, the matrix
+        will not be scaled.
+    separate : bool, optional
+        This switches from returning a full matrix of the transformation
+        to a tuple of two separate 1-D permutation and scaling arrays.
+    overwrite_a : bool, optional
+        This is passed to xGEBAL directly. Essentially, overwrites the result
+        to the data. It might increase the space efficiency. See LAPACK manual
+        for details. This is False by default.
+
+    Returns
+    -------
+    B : (n, n) ndarray
+        Balanced matrix
+    T : (n, n) ndarray
+        A possibly permuted diagonal matrix whose nonzero entries are
+        integer powers of 2 to avoid numerical truncation errors.
+    scale, perm : (n,) ndarray
+        If ``separate`` keyword is set to True then instead of the array
+        ``T`` above, the scaling and the permutation vectors are given
+        separately as a tuple without allocating the full array ``T``.
+
+    Notes
+    -----
+    This algorithm is particularly useful for eigenvalue and matrix
+    decompositions and in many cases it is already called by various
+    LAPACK routines.
+
+    The algorithm is based on the well-known technique of [1]_ and has
+    been modified to account for special cases. See [2]_ for details
+    which have been implemented since LAPACK v3.5.0. Before this version
+    there are corner cases where balancing can actually worsen the
+    conditioning. See [3]_ for such examples.
+
+    The code is a wrapper around LAPACK's xGEBAL routine family for matrix
+    balancing.
+
+    .. versionadded:: 0.19.0
+
+    References
+    ----------
+    .. [1] B.N. Parlett and C. Reinsch, "Balancing a Matrix for
+       Calculation of Eigenvalues and Eigenvectors", Numerische Mathematik,
+       Vol.13(4), 1969, :doi:`10.1007/BF02165404`
+    .. [2] R. James, J. Langou, B.R. Lowery, "On matrix balancing and
+       eigenvector computation", 2014, :arxiv:`1401.5766`
+    .. [3] D.S. Watkins. A case where balancing is harmful.
+       Electron. Trans. Numer. Anal, Vol.23, 2006.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> x = np.array([[1,2,0], [9,1,0.01], [1,2,10*np.pi]])
+
+    >>> y, permscale = linalg.matrix_balance(x)
+    >>> np.abs(x).sum(axis=0) / np.abs(x).sum(axis=1)
+    array([ 3.66666667,  0.4995005 ,  0.91312162])
+
+    >>> np.abs(y).sum(axis=0) / np.abs(y).sum(axis=1)
+    array([ 1.2       ,  1.27041742,  0.92658316])  # may vary
+
+    >>> permscale  # only powers of 2 (0.5 == 2^(-1))
+    array([[  0.5,   0. ,  0. ],  # may vary
+           [  0. ,   1. ,  0. ],
+           [  0. ,   0. ,  1. ]])
+
+    """
+
+    A = np.atleast_2d(_asarray_validated(A, check_finite=True))
+
+    if not np.equal(*A.shape):
+        raise ValueError('The data matrix for balancing should be square.')
+
+    # accommodate empty arrays
+    if A.size == 0:
+        b_n, t_n = matrix_balance(np.eye(2, dtype=A.dtype))
+        B = np.empty_like(A, dtype=b_n.dtype)
+        if separate:
+            scaling = np.ones_like(A, shape=len(A))
+            perm = np.arange(len(A))
+            return B, (scaling, perm)
+        return B, np.empty_like(A, dtype=t_n.dtype)
+
+    gebal = get_lapack_funcs(('gebal'), (A,))
+    B, lo, hi, ps, info = gebal(A, scale=scale, permute=permute,
+                                overwrite_a=overwrite_a)
+
+    if info < 0:
+        raise ValueError('xGEBAL exited with the internal error '
+                         f'"illegal value in argument number {-info}.". See '
+                         'LAPACK documentation for the xGEBAL error codes.')
+
+    # Separate the permutations from the scalings and then convert to int
+    scaling = np.ones_like(ps, dtype=float)
+    scaling[lo:hi+1] = ps[lo:hi+1]
+
+    # gebal uses 1-indexing
+    ps = ps.astype(int, copy=False) - 1
+    n = A.shape[0]
+    perm = np.arange(n)
+
+    # LAPACK permutes with the ordering n --> hi, then 0--> lo
+    if hi < n:
+        for ind, x in enumerate(ps[hi+1:][::-1], 1):
+            if n-ind == x:
+                continue
+            perm[[x, n-ind]] = perm[[n-ind, x]]
+
+    if lo > 0:
+        for ind, x in enumerate(ps[:lo]):
+            if ind == x:
+                continue
+            perm[[x, ind]] = perm[[ind, x]]
+
+    if separate:
+        return B, (scaling, perm)
+
+    # get the inverse permutation
+    iperm = np.empty_like(perm)
+    iperm[perm] = np.arange(n)
+
+    return B, np.diag(scaling)[iperm, :]
+
+
+def _validate_args_for_toeplitz_ops(c_or_cr, b, check_finite, keep_b_shape,
+                                    enforce_square=True):
+    """Validate arguments and format inputs for toeplitz functions
+
+    Parameters
+    ----------
+    c_or_cr : array_like or tuple of (array_like, array_like)
+        The vector ``c``, or a tuple of arrays (``c``, ``r``). Whatever the
+        actual shape of ``c``, it will be converted to a 1-D array. If not
+        supplied, ``r = conjugate(c)`` is assumed; in this case, if c[0] is
+        real, the Toeplitz matrix is Hermitian. r[0] is ignored; the first row
+        of the Toeplitz matrix is ``[c[0], r[1:]]``. Whatever the actual shape
+        of ``r``, it will be converted to a 1-D array.
+    b : (M,) or (M, K) array_like
+        Right-hand side in ``T x = b``.
+    check_finite : bool
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (result entirely NaNs) if the inputs do contain infinities or NaNs.
+    keep_b_shape : bool
+        Whether to convert a (M,) dimensional b into a (M, 1) dimensional
+        matrix.
+    enforce_square : bool, optional
+        If True (default), this verifies that the Toeplitz matrix is square.
+
+    Returns
+    -------
+    r : array
+        1d array corresponding to the first row of the Toeplitz matrix.
+    c: array
+        1d array corresponding to the first column of the Toeplitz matrix.
+    b: array
+        (M,), (M, 1) or (M, K) dimensional array, post validation,
+        corresponding to ``b``.
+    dtype: numpy datatype
+        ``dtype`` stores the datatype of ``r``, ``c`` and ``b``. If any of
+        ``r``, ``c`` or ``b`` are complex, ``dtype`` is ``np.complex128``,
+        otherwise, it is ``np.float``.
+    b_shape: tuple
+        Shape of ``b`` after passing it through ``_asarray_validated``.
+
+    """
+
+    if isinstance(c_or_cr, tuple):
+        c, r = c_or_cr
+        c = _asarray_validated(c, check_finite=check_finite)
+        r = _asarray_validated(r, check_finite=check_finite)
+    else:
+        c = _asarray_validated(c_or_cr, check_finite=check_finite)
+        r = c.conjugate()
+
+    if c.ndim > 1 or r.ndim > 1:
+        msg = ("Beginning in SciPy 1.17, multidimensional input will be treated as a "
+               "batch, not `ravel`ed. To preserve the existing behavior and silence "
+               "this warning, `ravel` arguments before passing them to "
+               "`toeplitz`, `matmul_toeplitz`, and `solve_toeplitz`.")
+        warnings.warn(msg, FutureWarning, stacklevel=2)
+        c = c.ravel()
+        r = r.ravel()
+
+    if b is None:
+        raise ValueError('`b` must be an array, not None.')
+
+    b = _asarray_validated(b, check_finite=check_finite)
+    b_shape = b.shape
+
+    is_not_square = r.shape[0] != c.shape[0]
+    if (enforce_square and is_not_square) or b.shape[0] != r.shape[0]:
+        raise ValueError('Incompatible dimensions.')
+
+    is_cmplx = np.iscomplexobj(r) or np.iscomplexobj(c) or np.iscomplexobj(b)
+    dtype = np.complex128 if is_cmplx else np.float64
+    r, c, b = (np.asarray(i, dtype=dtype) for i in (r, c, b))
+
+    if b.ndim == 1 and not keep_b_shape:
+        b = b.reshape(-1, 1)
+    elif b.ndim != 1:
+        b = b.reshape(b.shape[0], -1 if b.size > 0 else 0)
+
+    return r, c, b, dtype, b_shape
+
+
+def matmul_toeplitz(c_or_cr, x, check_finite=False, workers=None):
+    r"""Efficient Toeplitz Matrix-Matrix Multiplication using FFT
+
+    This function returns the matrix multiplication between a Toeplitz
+    matrix and a dense matrix.
+
+    The Toeplitz matrix has constant diagonals, with c as its first column
+    and r as its first row. If r is not given, ``r == conjugate(c)`` is
+    assumed.
+
+    .. warning::
+
+        Beginning in SciPy 1.17, multidimensional input will be treated as a batch,
+        not ``ravel``\ ed. To preserve the existing behavior, ``ravel`` arguments
+        before passing them to `matmul_toeplitz`.
+
+    Parameters
+    ----------
+    c_or_cr : array_like or tuple of (array_like, array_like)
+        The vector ``c``, or a tuple of arrays (``c``, ``r``). If not
+        supplied, ``r = conjugate(c)`` is assumed; in this case, if c[0] is
+        real, the Toeplitz matrix is Hermitian. r[0] is ignored; the first row
+        of the Toeplitz matrix is ``[c[0], r[1:]]``.
+    x : (M,) or (M, K) array_like
+        Matrix with which to multiply.
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (result entirely NaNs) if the inputs do contain infinities or NaNs.
+    workers : int, optional
+        To pass to scipy.fft.fft and ifft. Maximum number of workers to use
+        for parallel computation. If negative, the value wraps around from
+        ``os.cpu_count()``. See scipy.fft.fft for more details.
+
+    Returns
+    -------
+    T @ x : (M,) or (M, K) ndarray
+        The result of the matrix multiplication ``T @ x``. Shape of return
+        matches shape of `x`.
+
+    See Also
+    --------
+    toeplitz : Toeplitz matrix
+    solve_toeplitz : Solve a Toeplitz system using Levinson Recursion
+
+    Notes
+    -----
+    The Toeplitz matrix is embedded in a circulant matrix and the FFT is used
+    to efficiently calculate the matrix-matrix product.
+
+    Because the computation is based on the FFT, integer inputs will
+    result in floating point outputs.  This is unlike NumPy's `matmul`,
+    which preserves the data type of the input.
+
+    This is partly based on the implementation that can be found in [1]_,
+    licensed under the MIT license. More information about the method can be
+    found in reference [2]_. References [3]_ and [4]_ have more reference
+    implementations in Python.
+
+    .. versionadded:: 1.6.0
+
+    References
+    ----------
+    .. [1] Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian
+       Q Weinberger, Andrew Gordon Wilson, "GPyTorch: Blackbox Matrix-Matrix
+       Gaussian Process Inference with GPU Acceleration" with contributions
+       from Max Balandat and Ruihan Wu. Available online:
+       https://github.com/cornellius-gp/gpytorch
+
+    .. [2] J. Demmel, P. Koev, and X. Li, "A Brief Survey of Direct Linear
+       Solvers". In Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der
+       Vorst, editors. Templates for the Solution of Algebraic Eigenvalue
+       Problems: A Practical Guide. SIAM, Philadelphia, 2000. Available at:
+       http://www.netlib.org/utk/people/JackDongarra/etemplates/node384.html
+
+    .. [3] R. Scheibler, E. Bezzam, I. Dokmanic, Pyroomacoustics: A Python
+       package for audio room simulations and array processing algorithms,
+       Proc. IEEE ICASSP, Calgary, CA, 2018.
+       https://github.com/LCAV/pyroomacoustics/blob/pypi-release/
+       pyroomacoustics/adaptive/util.py
+
+    .. [4] Marano S, Edwards B, Ferrari G and Fah D (2017), "Fitting
+       Earthquake Spectra: Colored Noise and Incomplete Data", Bulletin of
+       the Seismological Society of America., January, 2017. Vol. 107(1),
+       pp. 276-291.
+
+    Examples
+    --------
+    Multiply the Toeplitz matrix T with matrix x::
+
+            [ 1 -1 -2 -3]       [1 10]
+        T = [ 3  1 -1 -2]   x = [2 11]
+            [ 6  3  1 -1]       [2 11]
+            [10  6  3  1]       [5 19]
+
+    To specify the Toeplitz matrix, only the first column and the first
+    row are needed.
+
+    >>> import numpy as np
+    >>> c = np.array([1, 3, 6, 10])    # First column of T
+    >>> r = np.array([1, -1, -2, -3])  # First row of T
+    >>> x = np.array([[1, 10], [2, 11], [2, 11], [5, 19]])
+
+    >>> from scipy.linalg import toeplitz, matmul_toeplitz
+    >>> matmul_toeplitz((c, r), x)
+    array([[-20., -80.],
+           [ -7.,  -8.],
+           [  9.,  85.],
+           [ 33., 218.]])
+
+    Check the result by creating the full Toeplitz matrix and
+    multiplying it by ``x``.
+
+    >>> toeplitz(c, r) @ x
+    array([[-20, -80],
+           [ -7,  -8],
+           [  9,  85],
+           [ 33, 218]])
+
+    The full matrix is never formed explicitly, so this routine
+    is suitable for very large Toeplitz matrices.
+
+    >>> n = 1000000
+    >>> matmul_toeplitz([1] + [0]*(n-1), np.ones(n))
+    array([1., 1., 1., ..., 1., 1., 1.], shape=(1000000,))
+
+    """
+
+    from ..fft import fft, ifft, rfft, irfft
+
+    r, c, x, dtype, x_shape = _validate_args_for_toeplitz_ops(
+        c_or_cr, x, check_finite, keep_b_shape=False, enforce_square=False)
+    n, m = x.shape
+
+    T_nrows = len(c)
+    T_ncols = len(r)
+    p = T_nrows + T_ncols - 1  # equivalent to len(embedded_col)
+    return_shape = (T_nrows,) if len(x_shape) == 1 else (T_nrows, m)
+
+    # accommodate empty arrays
+    if x.size == 0:
+        return np.empty_like(x, shape=return_shape)
+
+    embedded_col = np.concatenate((c, r[-1:0:-1]))
+
+    if np.iscomplexobj(embedded_col) or np.iscomplexobj(x):
+        fft_mat = fft(embedded_col, axis=0, workers=workers).reshape(-1, 1)
+        fft_x = fft(x, n=p, axis=0, workers=workers)
+
+        mat_times_x = ifft(fft_mat*fft_x, axis=0,
+                           workers=workers)[:T_nrows, :]
+    else:
+        # Real inputs; using rfft is faster
+        fft_mat = rfft(embedded_col, axis=0, workers=workers).reshape(-1, 1)
+        fft_x = rfft(x, n=p, axis=0, workers=workers)
+
+        mat_times_x = irfft(fft_mat*fft_x, axis=0,
+                            workers=workers, n=p)[:T_nrows, :]
+
+    return mat_times_x.reshape(*return_shape)
diff --git a/shared/package-files/e4/e4b653bc1188b5749210fa23e477b4bef0cfaf17d2e75fa45a4c99ea8f4f8ee2/sgn_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/e4/e4b653bc1188b5749210fa23e477b4bef0cfaf17d2e75fa45a4c99ea8f4f8ee2/sgn_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d9c8bfcdedc007857a313bdcd3cbccc003cf7842
--- /dev/null
+++ b/shared/package-files/e4/e4b653bc1188b5749210fa23e477b4bef0cfaf17d2e75fa45a4c99ea8f4f8ee2/sgn_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor sgn(const at::Tensor & self);
+TORCH_API at::Tensor & sgn_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/e4/e4b732e5020ac5db76ea83c551e652ea2c7558693b525b052e760b14280cf861/base.pyi b/shared/package-files/e4/e4b732e5020ac5db76ea83c551e652ea2c7558693b525b052e760b14280cf861/base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a910132f6a61cda125d43127f04d3cab5569f158
--- /dev/null
+++ b/shared/package-files/e4/e4b732e5020ac5db76ea83c551e652ea2c7558693b525b052e760b14280cf861/base.pyi
@@ -0,0 +1,87 @@
+from datetime import date
+from io import BufferedReader, StringIO, TextIOWrapper
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Type, Union, Collection
+from uuid import UUID
+
+from django.core.management.base import OutputWrapper
+from django.db.models.base import Model
+from django.db.models.fields.related import ForeignKey, ManyToManyField
+
+from django.db.models.fields import Field
+
+class SerializerDoesNotExist(KeyError): ...
+class SerializationError(Exception): ...
+
+class DeserializationError(Exception):
+    @classmethod
+    def WithData(
+        cls, original_exc: Exception, model: str, fk: Union[int, str], field_value: Optional[Union[List[str], str]]
+    ) -> DeserializationError: ...
+
+class M2MDeserializationError(Exception):
+    original_exc: Exception = ...
+    pk: List[str] = ...
+    def __init__(self, original_exc: Exception, pk: Union[List[str], str]) -> None: ...
+
+class ProgressBar:
+    progress_width: int = ...
+    output: None = ...
+    total_count: int = ...
+    prev_done: int = ...
+    def __init__(self, output: Optional[Union[StringIO, OutputWrapper]], total_count: int) -> None: ...
+    def update(self, count: int) -> None: ...
+
+class Serializer:
+    internal_use_only: bool = ...
+    progress_class: Any = ...
+    stream_class: Any = ...
+    options: Dict[str, Any] = ...
+    stream: Any = ...
+    selected_fields: Optional[Collection[str]] = ...
+    use_natural_foreign_keys: bool = ...
+    use_natural_primary_keys: bool = ...
+    first: bool = ...
+    def serialize(
+        self,
+        queryset: Iterable[Model],
+        *,
+        stream: Optional[Any] = ...,
+        fields: Optional[Collection[str]] = ...,
+        use_natural_foreign_keys: bool = ...,
+        use_natural_primary_keys: bool = ...,
+        progress_output: Optional[Any] = ...,
+        object_count: int = ...,
+        **options: Any
+    ) -> Any: ...
+    def start_serialization(self) -> None: ...
+    def end_serialization(self) -> None: ...
+    def start_object(self, obj: Any) -> None: ...
+    def end_object(self, obj: Any) -> None: ...
+    def handle_field(self, obj: Any, field: Any) -> None: ...
+    def handle_fk_field(self, obj: Any, field: Any) -> None: ...
+    def handle_m2m_field(self, obj: Any, field: Any) -> None: ...
+    def getvalue(self) -> Optional[Union[bytes, str]]: ...
+
+class Deserializer:
+    options: Dict[str, Any] = ...
+    stream: Any = ...
+    def __init__(self, stream_or_string: Union[BufferedReader, TextIOWrapper, str], **options: Any) -> None: ...
+    def __iter__(self) -> Deserializer: ...
+    def __next__(self) -> None: ...
+
+class DeserializedObject:
+    object: Any = ...
+    m2m_data: Dict[str, List[int]] = ...
+    deferred_fields: Mapping[Field, Any]
+    def __init__(
+        self,
+        obj: Model,
+        m2m_data: Optional[Dict[str, List[int]]] = ...,
+        deferred_fields: Optional[Mapping[Field, Any]] = ...,
+    ) -> None: ...
+    def save(self, save_m2m: bool = ..., using: Optional[str] = ..., **kwargs: Any) -> None: ...
+    def save_deferred_fields(self, using: Optional[str] = ...) -> None: ...
+
+def build_instance(Model: Type[Model], data: Dict[str, Optional[Union[date, int, str, UUID]]], db: str) -> Model: ...
+def deserialize_m2m_values(field: ManyToManyField, field_value: Any, using: str) -> List[Any]: ...
+def deserialize_fk_value(field: ForeignKey, field_value: Any, using: str) -> Any: ...
diff --git a/shared/package-files/e4/e4bc33fe745cfbfdc19a83ffe9df6c9f637454a7d6545cf1aa0028bc815b4521/_sparse_csr_prod_ops.h b/shared/package-files/e4/e4bc33fe745cfbfdc19a83ffe9df6c9f637454a7d6545cf1aa0028bc815b4521/_sparse_csr_prod_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f465c6a58d2b8efa48c3aae1d3899a41bdd636ab
--- /dev/null
+++ b/shared/package-files/e4/e4bc33fe745cfbfdc19a83ffe9df6c9f637454a7d6545cf1aa0028bc815b4521/_sparse_csr_prod_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _sparse_csr_prod_dim_dtype {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef, bool, c10::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_sparse_csr_prod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dim_dtype")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API _sparse_csr_prod_dim_dtype_out {
+  using schema = at::Tensor & (const at::Tensor &, at::IntArrayRef, bool, c10::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_sparse_csr_prod")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dim_dtype_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/e4/e4bc760753d6dbd2b1067d93d3190dd420604416b780654904aa10a11a201159/connection.py b/shared/package-files/e4/e4bc760753d6dbd2b1067d93d3190dd420604416b780654904aa10a11a201159/connection.py
new file mode 100644
index 0000000000000000000000000000000000000000..6af1138f260e4eaaa0aa242f7f50b918a283b49f
--- /dev/null
+++ b/shared/package-files/e4/e4bc760753d6dbd2b1067d93d3190dd420604416b780654904aa10a11a201159/connection.py
@@ -0,0 +1,149 @@
+from __future__ import absolute_import
+
+import socket
+
+from ..contrib import _appengine_environ
+from ..exceptions import LocationParseError
+from ..packages import six
+from .wait import NoWayToWaitForSocketError, wait_for_read
+
+
+def is_connection_dropped(conn):  # Platform-specific
+    """
+    Returns True if the connection is dropped and should be closed.
+
+    :param conn:
+        :class:`http.client.HTTPConnection` object.
+
+    Note: For platforms like AppEngine, this will always return ``False`` to
+    let the platform handle connection recycling transparently for us.
+    """
+    sock = getattr(conn, "sock", False)
+    if sock is False:  # Platform-specific: AppEngine
+        return False
+    if sock is None:  # Connection already closed (such as by httplib).
+        return True
+    try:
+        # Returns True if readable, which here means it's been dropped
+        return wait_for_read(sock, timeout=0.0)
+    except NoWayToWaitForSocketError:  # Platform-specific: AppEngine
+        return False
+
+
+# This function is copied from socket.py in the Python 2.7 standard
+# library test suite. Added to its signature is only `socket_options`.
+# One additional modification is that we avoid binding to IPv6 servers
+# discovered in DNS if the system doesn't have IPv6 functionality.
+def create_connection(
+    address,
+    timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
+    source_address=None,
+    socket_options=None,
+):
+    """Connect to *address* and return the socket object.
+
+    Convenience function.  Connect to *address* (a 2-tuple ``(host,
+    port)``) and return the socket object.  Passing the optional
+    *timeout* parameter will set the timeout on the socket instance
+    before attempting to connect.  If no *timeout* is supplied, the
+    global default timeout setting returned by :func:`socket.getdefaulttimeout`
+    is used.  If *source_address* is set it must be a tuple of (host, port)
+    for the socket to bind as a source address before making the connection.
+    An host of '' or port 0 tells the OS to use the default.
+    """
+
+    host, port = address
+    if host.startswith("["):
+        host = host.strip("[]")
+    err = None
+
+    # Using the value from allowed_gai_family() in the context of getaddrinfo lets
+    # us select whether to work with IPv4 DNS records, IPv6 records, or both.
+    # The original create_connection function always returns all records.
+    family = allowed_gai_family()
+
+    try:
+        host.encode("idna")
+    except UnicodeError:
+        return six.raise_from(
+            LocationParseError(u"'%s', label empty or too long" % host), None
+        )
+
+    for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
+        af, socktype, proto, canonname, sa = res
+        sock = None
+        try:
+            sock = socket.socket(af, socktype, proto)
+
+            # If provided, set socket level options before connecting.
+            _set_socket_options(sock, socket_options)
+
+            if timeout is not socket._GLOBAL_DEFAULT_TIMEOUT:
+                sock.settimeout(timeout)
+            if source_address:
+                sock.bind(source_address)
+            sock.connect(sa)
+            return sock
+
+        except socket.error as e:
+            err = e
+            if sock is not None:
+                sock.close()
+                sock = None
+
+    if err is not None:
+        raise err
+
+    raise socket.error("getaddrinfo returns an empty list")
+
+
+def _set_socket_options(sock, options):
+    if options is None:
+        return
+
+    for opt in options:
+        sock.setsockopt(*opt)
+
+
+def allowed_gai_family():
+    """This function is designed to work in the context of
+    getaddrinfo, where family=socket.AF_UNSPEC is the default and
+    will perform a DNS search for both IPv6 and IPv4 records."""
+
+    family = socket.AF_INET
+    if HAS_IPV6:
+        family = socket.AF_UNSPEC
+    return family
+
+
+def _has_ipv6(host):
+    """Returns True if the system can bind an IPv6 address."""
+    sock = None
+    has_ipv6 = False
+
+    # App Engine doesn't support IPV6 sockets and actually has a quota on the
+    # number of sockets that can be used, so just early out here instead of
+    # creating a socket needlessly.
+    # See https://github.com/urllib3/urllib3/issues/1446
+    if _appengine_environ.is_appengine_sandbox():
+        return False
+
+    if socket.has_ipv6:
+        # has_ipv6 returns true if cPython was compiled with IPv6 support.
+        # It does not tell us if the system has IPv6 support enabled. To
+        # determine that we must bind to an IPv6 address.
+        # https://github.com/urllib3/urllib3/pull/611
+        # https://bugs.python.org/issue658327
+        try:
+            sock = socket.socket(socket.AF_INET6)
+            sock.bind((host, 0))
+            has_ipv6 = True
+        except Exception:
+            pass
+
+    if sock:
+        sock.close()
+    return has_ipv6
+
+
+HAS_IPV6 = _has_ipv6("::1")
diff --git a/shared/package-files/e4/e4bf68f1311482d075d69a086a0f39bd176ad3c2cc0d9999e833e7ed4a8f2ff8/GMT+1 b/shared/package-files/e4/e4bf68f1311482d075d69a086a0f39bd176ad3c2cc0d9999e833e7ed4a8f2ff8/GMT+1
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diff --git a/shared/package-files/e4/e4c4a21e23e45b0d171e56e19132485b2c57aec2209f3d13d4367e1388783d65/wyse60-42-w b/shared/package-files/e4/e4c4a21e23e45b0d171e56e19132485b2c57aec2209f3d13d4367e1388783d65/wyse60-42-w
new file mode 100644
index 0000000000000000000000000000000000000000..d06a50a68bca6ec5fbcfd10cc04982090446c14c
Binary files /dev/null and b/shared/package-files/e4/e4c4a21e23e45b0d171e56e19132485b2c57aec2209f3d13d4367e1388783d65/wyse60-42-w differ
diff --git a/shared/package-files/e4/e4c5b38a42a8310124a215b119bcd950fc18e84fe0d7e8be9ef3d0c35dd9c709/mailcap.pyi b/shared/package-files/e4/e4c5b38a42a8310124a215b119bcd950fc18e84fe0d7e8be9ef3d0c35dd9c709/mailcap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a65cc3a329d31e87edffc04cbc15d014944593e9
--- /dev/null
+++ b/shared/package-files/e4/e4c5b38a42a8310124a215b119bcd950fc18e84fe0d7e8be9ef3d0c35dd9c709/mailcap.pyi
@@ -0,0 +1,8 @@
+from typing import Dict, List, Mapping, Optional, Sequence, Tuple, Union
+
+_Cap = Dict[str, Union[str, int]]
+
+def findmatch(
+    caps: Mapping[str, List[_Cap]], MIMEtype: str, key: str = ..., filename: str = ..., plist: Sequence[str] = ...
+) -> Tuple[Optional[str], Optional[_Cap]]: ...
+def getcaps() -> Dict[str, List[_Cap]]: ...
diff --git a/shared/package-files/e4/e4c5de2164cd6d62a1a903a044d881465fbd5c13ce0036c0451e9ab1e882f607/results_exact_initial_dfm_R.csv b/shared/package-files/e4/e4c5de2164cd6d62a1a903a044d881465fbd5c13ce0036c0451e9ab1e882f607/results_exact_initial_dfm_R.csv
new file mode 100644
index 0000000000000000000000000000000000000000..80b2a03d1019e2eb9cf6dcad697a5174ddd6ef56
--- /dev/null
+++ b/shared/package-files/e4/e4c5de2164cd6d62a1a903a044d881465fbd5c13ce0036c0451e9ab1e882f607/results_exact_initial_dfm_R.csv
@@ -0,0 +1,22 @@
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diff --git a/shared/package-files/e4/e4c837e9991ee8cf142f8b7dd50b830d8e4b5fa907c2096e530af8cc649ecc19/selectors.pyi b/shared/package-files/e4/e4c837e9991ee8cf142f8b7dd50b830d8e4b5fa907c2096e530af8cc649ecc19/selectors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b019c4f9c4427f9a68314a43466fa1fc86bc2cf9
--- /dev/null
+++ b/shared/package-files/e4/e4c837e9991ee8cf142f8b7dd50b830d8e4b5fa907c2096e530af8cc649ecc19/selectors.pyi
@@ -0,0 +1,69 @@
+import sys
+from _typeshed import FileDescriptor, FileDescriptorLike
+from abc import ABCMeta, abstractmethod
+from typing import Any, List, Mapping, NamedTuple, Optional, Tuple
+
+_EventMask = int
+
+EVENT_READ: _EventMask
+EVENT_WRITE: _EventMask
+
+class SelectorKey(NamedTuple):
+    fileobj: FileDescriptorLike
+    fd: FileDescriptor
+    events: _EventMask
+    data: Any
+
+class BaseSelector(metaclass=ABCMeta):
+    @abstractmethod
+    def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    @abstractmethod
+    def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    def modify(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    @abstractmethod
+    def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+    def close(self) -> None: ...
+    def get_key(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    @abstractmethod
+    def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+    def __enter__(self) -> BaseSelector: ...
+    def __exit__(self, *args: Any) -> None: ...
+
+class SelectSelector(BaseSelector):
+    def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+    def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+
+if sys.platform != "win32":
+    class PollSelector(BaseSelector):
+        def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+        def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+        def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+        def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+    class EpollSelector(BaseSelector):
+        def fileno(self) -> int: ...
+        def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+        def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+        def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+        def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+
+class DevpollSelector(BaseSelector):
+    def fileno(self) -> int: ...
+    def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+    def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+
+class KqueueSelector(BaseSelector):
+    def fileno(self) -> int: ...
+    def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+    def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
+
+class DefaultSelector(BaseSelector):
+    def register(self, fileobj: FileDescriptorLike, events: _EventMask, data: Any = ...) -> SelectorKey: ...
+    def unregister(self, fileobj: FileDescriptorLike) -> SelectorKey: ...
+    def select(self, timeout: Optional[float] = ...) -> List[Tuple[SelectorKey, _EventMask]]: ...
+    def get_map(self) -> Mapping[FileDescriptorLike, SelectorKey]: ...
diff --git a/shared/package-files/e4/e4c893b3e99e6309deb1d155c4bafd3faa7c8be343b08a0c7065f7eed0c5506c/GTestConfig.cmake b/shared/package-files/e4/e4c893b3e99e6309deb1d155c4bafd3faa7c8be343b08a0c7065f7eed0c5506c/GTestConfig.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..771cb7e3e110dbe1d590cdbba83a17ec754ee19a
--- /dev/null
+++ b/shared/package-files/e4/e4c893b3e99e6309deb1d155c4bafd3faa7c8be343b08a0c7065f7eed0c5506c/GTestConfig.cmake
@@ -0,0 +1,33 @@
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was Config.cmake.in                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+macro(check_required_components _NAME)
+  foreach(comp ${${_NAME}_FIND_COMPONENTS})
+    if(NOT ${_NAME}_${comp}_FOUND)
+      if(${_NAME}_FIND_REQUIRED_${comp})
+        set(${_NAME}_FOUND FALSE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+####################################################################################
+include(CMakeFindDependencyMacro)
+if (ON)
+  set(THREADS_PREFER_PTHREAD_FLAG )
+  find_dependency(Threads)
+endif()
+
+include("${CMAKE_CURRENT_LIST_DIR}/GTestTargets.cmake")
+check_required_components("")
diff --git a/shared/package-files/e4/e4d0cf6d6aa488890fd45277b0b36a99ec368e43636b041fd86fc1a99346feee/legend.py b/shared/package-files/e4/e4d0cf6d6aa488890fd45277b0b36a99ec368e43636b041fd86fc1a99346feee/legend.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d8238c307ef3825ac1d9ffde26ff0c4874b20b8
--- /dev/null
+++ b/shared/package-files/e4/e4d0cf6d6aa488890fd45277b0b36a99ec368e43636b041fd86fc1a99346feee/legend.py
@@ -0,0 +1,1371 @@
+"""
+The legend module defines the Legend class, which is responsible for
+drawing legends associated with Axes and/or figures.
+
+.. important::
+
+    It is unlikely that you would ever create a Legend instance manually.
+    Most users would normally create a legend via the `~.Axes.legend`
+    function. For more details on legends there is also a :ref:`legend guide
+    <legend_guide>`.
+
+The `Legend` class is a container of legend handles and legend texts.
+
+The legend handler map specifies how to create legend handles from artists
+(lines, patches, etc.) in the Axes or figures. Default legend handlers are
+defined in the :mod:`~matplotlib.legend_handler` module. While not all artist
+types are covered by the default legend handlers, custom legend handlers can be
+defined to support arbitrary objects.
+
+See the :ref`<legend_guide>` for more
+information.
+"""
+
+import itertools
+import logging
+import numbers
+import time
+
+import numpy as np
+
+import matplotlib as mpl
+from matplotlib import _api, _docstring, cbook, colors, offsetbox
+from matplotlib.artist import Artist, allow_rasterization
+from matplotlib.cbook import silent_list
+from matplotlib.font_manager import FontProperties
+from matplotlib.lines import Line2D
+from matplotlib.patches import (Patch, Rectangle, Shadow, FancyBboxPatch,
+                                StepPatch)
+from matplotlib.collections import (
+    Collection, CircleCollection, LineCollection, PathCollection,
+    PolyCollection, RegularPolyCollection)
+from matplotlib.text import Text
+from matplotlib.transforms import Bbox, BboxBase, TransformedBbox
+from matplotlib.transforms import BboxTransformTo, BboxTransformFrom
+from matplotlib.offsetbox import (
+    AnchoredOffsetbox, DraggableOffsetBox,
+    HPacker, VPacker,
+    DrawingArea, TextArea,
+)
+from matplotlib.container import ErrorbarContainer, BarContainer, StemContainer
+from . import legend_handler
+
+
+class DraggableLegend(DraggableOffsetBox):
+    def __init__(self, legend, use_blit=False, update="loc"):
+        """
+        Wrapper around a `.Legend` to support mouse dragging.
+
+        Parameters
+        ----------
+        legend : `.Legend`
+            The `.Legend` instance to wrap.
+        use_blit : bool, optional
+            Use blitting for faster image composition. For details see
+            :ref:`func-animation`.
+        update : {'loc', 'bbox'}, optional
+            If "loc", update the *loc* parameter of the legend upon finalizing.
+            If "bbox", update the *bbox_to_anchor* parameter.
+        """
+        self.legend = legend
+
+        _api.check_in_list(["loc", "bbox"], update=update)
+        self._update = update
+
+        super().__init__(legend, legend._legend_box, use_blit=use_blit)
+
+    def finalize_offset(self):
+        if self._update == "loc":
+            self._update_loc(self.get_loc_in_canvas())
+        elif self._update == "bbox":
+            self._update_bbox_to_anchor(self.get_loc_in_canvas())
+
+    def _update_loc(self, loc_in_canvas):
+        bbox = self.legend.get_bbox_to_anchor()
+        # if bbox has zero width or height, the transformation is
+        # ill-defined. Fall back to the default bbox_to_anchor.
+        if bbox.width == 0 or bbox.height == 0:
+            self.legend.set_bbox_to_anchor(None)
+            bbox = self.legend.get_bbox_to_anchor()
+        _bbox_transform = BboxTransformFrom(bbox)
+        self.legend._loc = tuple(_bbox_transform.transform(loc_in_canvas))
+
+    def _update_bbox_to_anchor(self, loc_in_canvas):
+        loc_in_bbox = self.legend.axes.transAxes.transform(loc_in_canvas)
+        self.legend.set_bbox_to_anchor(loc_in_bbox)
+
+
+_legend_kw_doc_base = """
+bbox_to_anchor : `.BboxBase`, 2-tuple, or 4-tuple of floats
+    Box that is used to position the legend in conjunction with *loc*.
+    Defaults to ``axes.bbox`` (if called as a method to `.Axes.legend`) or
+    ``figure.bbox`` (if ``figure.legend``).  This argument allows arbitrary
+    placement of the legend.
+
+    Bbox coordinates are interpreted in the coordinate system given by
+    *bbox_transform*, with the default transform
+    Axes or Figure coordinates, depending on which ``legend`` is called.
+
+    If a 4-tuple or `.BboxBase` is given, then it specifies the bbox
+    ``(x, y, width, height)`` that the legend is placed in.
+    To put the legend in the best location in the bottom right
+    quadrant of the Axes (or figure)::
+
+        loc='best', bbox_to_anchor=(0.5, 0., 0.5, 0.5)
+
+    A 2-tuple ``(x, y)`` places the corner of the legend specified by *loc* at
+    x, y.  For example, to put the legend's upper right-hand corner in the
+    center of the Axes (or figure) the following keywords can be used::
+
+        loc='upper right', bbox_to_anchor=(0.5, 0.5)
+
+ncols : int, default: 1
+    The number of columns that the legend has.
+
+    For backward compatibility, the spelling *ncol* is also supported
+    but it is discouraged. If both are given, *ncols* takes precedence.
+
+prop : None or `~matplotlib.font_manager.FontProperties` or dict
+    The font properties of the legend. If None (default), the current
+    :data:`matplotlib.rcParams` will be used.
+
+fontsize : int or {'xx-small', 'x-small', 'small', 'medium', 'large', \
+'x-large', 'xx-large'}
+    The font size of the legend. If the value is numeric the size will be the
+    absolute font size in points. String values are relative to the current
+    default font size. This argument is only used if *prop* is not specified.
+
+labelcolor : str or list, default: :rc:`legend.labelcolor`
+    The color of the text in the legend. Either a valid color string
+    (for example, 'red'), or a list of color strings. The labelcolor can
+    also be made to match the color of the line or marker using 'linecolor',
+    'markerfacecolor' (or 'mfc'), or 'markeredgecolor' (or 'mec').
+
+    Labelcolor can be set globally using :rc:`legend.labelcolor`. If None,
+    use :rc:`text.color`.
+
+numpoints : int, default: :rc:`legend.numpoints`
+    The number of marker points in the legend when creating a legend
+    entry for a `.Line2D` (line).
+
+scatterpoints : int, default: :rc:`legend.scatterpoints`
+    The number of marker points in the legend when creating
+    a legend entry for a `.PathCollection` (scatter plot).
+
+scatteryoffsets : iterable of floats, default: ``[0.375, 0.5, 0.3125]``
+    The vertical offset (relative to the font size) for the markers
+    created for a scatter plot legend entry. 0.0 is at the base the
+    legend text, and 1.0 is at the top. To draw all markers at the
+    same height, set to ``[0.5]``.
+
+markerscale : float, default: :rc:`legend.markerscale`
+    The relative size of legend markers compared to the originally drawn ones.
+
+markerfirst : bool, default: True
+    If *True*, legend marker is placed to the left of the legend label.
+    If *False*, legend marker is placed to the right of the legend label.
+
+reverse : bool, default: False
+    If *True*, the legend labels are displayed in reverse order from the input.
+    If *False*, the legend labels are displayed in the same order as the input.
+
+    .. versionadded:: 3.7
+
+frameon : bool, default: :rc:`legend.frameon`
+    Whether the legend should be drawn on a patch (frame).
+
+fancybox : bool, default: :rc:`legend.fancybox`
+    Whether round edges should be enabled around the `.FancyBboxPatch` which
+    makes up the legend's background.
+
+shadow : None, bool or dict, default: :rc:`legend.shadow`
+    Whether to draw a shadow behind the legend.
+    The shadow can be configured using `.Patch` keywords.
+    Customization via :rc:`legend.shadow` is currently not supported.
+
+framealpha : float, default: :rc:`legend.framealpha`
+    The alpha transparency of the legend's background.
+    If *shadow* is activated and *framealpha* is ``None``, the default value is
+    ignored.
+
+facecolor : "inherit" or color, default: :rc:`legend.facecolor`
+    The legend's background color.
+    If ``"inherit"``, use :rc:`axes.facecolor`.
+
+edgecolor : "inherit" or color, default: :rc:`legend.edgecolor`
+    The legend's background patch edge color.
+    If ``"inherit"``, use :rc:`axes.edgecolor`.
+
+mode : {"expand", None}
+    If *mode* is set to ``"expand"`` the legend will be horizontally
+    expanded to fill the Axes area (or *bbox_to_anchor* if defines
+    the legend's size).
+
+bbox_transform : None or `~matplotlib.transforms.Transform`
+    The transform for the bounding box (*bbox_to_anchor*). For a value
+    of ``None`` (default) the Axes'
+    :data:`~matplotlib.axes.Axes.transAxes` transform will be used.
+
+title : str or None
+    The legend's title. Default is no title (``None``).
+
+title_fontproperties : None or `~matplotlib.font_manager.FontProperties` or dict
+    The font properties of the legend's title. If None (default), the
+    *title_fontsize* argument will be used if present; if *title_fontsize* is
+    also None, the current :rc:`legend.title_fontsize` will be used.
+
+title_fontsize : int or {'xx-small', 'x-small', 'small', 'medium', 'large', \
+'x-large', 'xx-large'}, default: :rc:`legend.title_fontsize`
+    The font size of the legend's title.
+    Note: This cannot be combined with *title_fontproperties*. If you want
+    to set the fontsize alongside other font properties, use the *size*
+    parameter in *title_fontproperties*.
+
+alignment : {'center', 'left', 'right'}, default: 'center'
+    The alignment of the legend title and the box of entries. The entries
+    are aligned as a single block, so that markers always lined up.
+
+borderpad : float, default: :rc:`legend.borderpad`
+    The fractional whitespace inside the legend border, in font-size units.
+
+labelspacing : float, default: :rc:`legend.labelspacing`
+    The vertical space between the legend entries, in font-size units.
+
+handlelength : float, default: :rc:`legend.handlelength`
+    The length of the legend handles, in font-size units.
+
+handleheight : float, default: :rc:`legend.handleheight`
+    The height of the legend handles, in font-size units.
+
+handletextpad : float, default: :rc:`legend.handletextpad`
+    The pad between the legend handle and text, in font-size units.
+
+borderaxespad : float, default: :rc:`legend.borderaxespad`
+    The pad between the Axes and legend border, in font-size units.
+
+columnspacing : float, default: :rc:`legend.columnspacing`
+    The spacing between columns, in font-size units.
+
+handler_map : dict or None
+    The custom dictionary mapping instances or types to a legend
+    handler. This *handler_map* updates the default handler map
+    found at `matplotlib.legend.Legend.get_legend_handler_map`.
+
+draggable : bool, default: False
+    Whether the legend can be dragged with the mouse.
+"""
+
+_loc_doc_base = """
+loc : str or pair of floats, default: {default}
+    The location of the legend.
+
+    The strings ``'upper left'``, ``'upper right'``, ``'lower left'``,
+    ``'lower right'`` place the legend at the corresponding corner of the
+    {parent}.
+
+    The strings ``'upper center'``, ``'lower center'``, ``'center left'``,
+    ``'center right'`` place the legend at the center of the corresponding edge
+    of the {parent}.
+
+    The string ``'center'`` places the legend at the center of the {parent}.
+{best}
+    The location can also be a 2-tuple giving the coordinates of the lower-left
+    corner of the legend in {parent} coordinates (in which case *bbox_to_anchor*
+    will be ignored).
+
+    For back-compatibility, ``'center right'`` (but no other location) can also
+    be spelled ``'right'``, and each "string" location can also be given as a
+    numeric value:
+
+    ==================   =============
+    Location String      Location Code
+    ==================   =============
+    'best' (Axes only)   0
+    'upper right'        1
+    'upper left'         2
+    'lower left'         3
+    'lower right'        4
+    'right'              5
+    'center left'        6
+    'center right'       7
+    'lower center'       8
+    'upper center'       9
+    'center'             10
+    ==================   =============
+    {outside}"""
+
+_loc_doc_best = """
+    The string ``'best'`` places the legend at the location, among the nine
+    locations defined so far, with the minimum overlap with other drawn
+    artists.  This option can be quite slow for plots with large amounts of
+    data; your plotting speed may benefit from providing a specific location.
+"""
+
+_legend_kw_axes_st = (
+    _loc_doc_base.format(parent='axes', default=':rc:`legend.loc`',
+                         best=_loc_doc_best, outside='') +
+    _legend_kw_doc_base)
+_docstring.interpd.register(_legend_kw_axes=_legend_kw_axes_st)
+
+_outside_doc = """
+    If a figure is using the constrained layout manager, the string codes
+    of the *loc* keyword argument can get better layout behaviour using the
+    prefix 'outside'. There is ambiguity at the corners, so 'outside
+    upper right' will make space for the legend above the rest of the
+    axes in the layout, and 'outside right upper' will make space on the
+    right side of the layout.  In addition to the values of *loc*
+    listed above, we have 'outside right upper', 'outside right lower',
+    'outside left upper', and 'outside left lower'.  See
+    :ref:`legend_guide` for more details.
+"""
+
+_legend_kw_figure_st = (
+    _loc_doc_base.format(parent='figure', default="'upper right'",
+                         best='', outside=_outside_doc) +
+    _legend_kw_doc_base)
+_docstring.interpd.register(_legend_kw_figure=_legend_kw_figure_st)
+
+_legend_kw_both_st = (
+    _loc_doc_base.format(parent='axes/figure',
+                         default=":rc:`legend.loc` for Axes, 'upper right' for Figure",
+                         best=_loc_doc_best, outside=_outside_doc) +
+    _legend_kw_doc_base)
+_docstring.interpd.register(_legend_kw_doc=_legend_kw_both_st)
+
+_legend_kw_set_loc_st = (
+    _loc_doc_base.format(parent='axes/figure',
+                         default=":rc:`legend.loc` for Axes, 'upper right' for Figure",
+                         best=_loc_doc_best, outside=_outside_doc))
+_docstring.interpd.register(_legend_kw_set_loc_doc=_legend_kw_set_loc_st)
+
+
+class Legend(Artist):
+    """
+    Place a legend on the figure/axes.
+    """
+
+    # 'best' is only implemented for Axes legends
+    codes = {'best': 0, **AnchoredOffsetbox.codes}
+    zorder = 5
+
+    def __str__(self):
+        return "Legend"
+
+    @_docstring.interpd
+    def __init__(
+        self, parent, handles, labels,
+        *,
+        loc=None,
+        numpoints=None,      # number of points in the legend line
+        markerscale=None,    # relative size of legend markers vs. original
+        markerfirst=True,    # left/right ordering of legend marker and label
+        reverse=False,       # reverse ordering of legend marker and label
+        scatterpoints=None,  # number of scatter points
+        scatteryoffsets=None,
+        prop=None,           # properties for the legend texts
+        fontsize=None,       # keyword to set font size directly
+        labelcolor=None,     # keyword to set the text color
+
+        # spacing & pad defined as a fraction of the font-size
+        borderpad=None,      # whitespace inside the legend border
+        labelspacing=None,   # vertical space between the legend entries
+        handlelength=None,   # length of the legend handles
+        handleheight=None,   # height of the legend handles
+        handletextpad=None,  # pad between the legend handle and text
+        borderaxespad=None,  # pad between the Axes and legend border
+        columnspacing=None,  # spacing between columns
+
+        ncols=1,     # number of columns
+        mode=None,  # horizontal distribution of columns: None or "expand"
+
+        fancybox=None,  # True: fancy box, False: rounded box, None: rcParam
+        shadow=None,
+        title=None,           # legend title
+        title_fontsize=None,  # legend title font size
+        framealpha=None,      # set frame alpha
+        edgecolor=None,       # frame patch edgecolor
+        facecolor=None,       # frame patch facecolor
+
+        bbox_to_anchor=None,  # bbox to which the legend will be anchored
+        bbox_transform=None,  # transform for the bbox
+        frameon=None,         # draw frame
+        handler_map=None,
+        title_fontproperties=None,  # properties for the legend title
+        alignment="center",       # control the alignment within the legend box
+        ncol=1,  # synonym for ncols (backward compatibility)
+        draggable=False  # whether the legend can be dragged with the mouse
+    ):
+        """
+        Parameters
+        ----------
+        parent : `~matplotlib.axes.Axes` or `.Figure`
+            The artist that contains the legend.
+
+        handles : list of (`.Artist` or tuple of `.Artist`)
+            A list of Artists (lines, patches) to be added to the legend.
+
+        labels : list of str
+            A list of labels to show next to the artists. The length of handles
+            and labels should be the same. If they are not, they are truncated
+            to the length of the shorter list.
+
+        Other Parameters
+        ----------------
+        %(_legend_kw_doc)s
+
+        Attributes
+        ----------
+        legend_handles
+            List of `.Artist` objects added as legend entries.
+
+            .. versionadded:: 3.7
+        """
+        # local import only to avoid circularity
+        from matplotlib.axes import Axes
+        from matplotlib.figure import FigureBase
+
+        super().__init__()
+
+        if prop is None:
+            self.prop = FontProperties(size=mpl._val_or_rc(fontsize, "legend.fontsize"))
+        else:
+            self.prop = FontProperties._from_any(prop)
+            if isinstance(prop, dict) and "size" not in prop:
+                self.prop.set_size(mpl.rcParams["legend.fontsize"])
+
+        self._fontsize = self.prop.get_size_in_points()
+
+        self.texts = []
+        self.legend_handles = []
+        self._legend_title_box = None
+
+        #: A dictionary with the extra handler mappings for this Legend
+        #: instance.
+        self._custom_handler_map = handler_map
+
+        self.numpoints = mpl._val_or_rc(numpoints, 'legend.numpoints')
+        self.markerscale = mpl._val_or_rc(markerscale, 'legend.markerscale')
+        self.scatterpoints = mpl._val_or_rc(scatterpoints, 'legend.scatterpoints')
+        self.borderpad = mpl._val_or_rc(borderpad, 'legend.borderpad')
+        self.labelspacing = mpl._val_or_rc(labelspacing, 'legend.labelspacing')
+        self.handlelength = mpl._val_or_rc(handlelength, 'legend.handlelength')
+        self.handleheight = mpl._val_or_rc(handleheight, 'legend.handleheight')
+        self.handletextpad = mpl._val_or_rc(handletextpad, 'legend.handletextpad')
+        self.borderaxespad = mpl._val_or_rc(borderaxespad, 'legend.borderaxespad')
+        self.columnspacing = mpl._val_or_rc(columnspacing, 'legend.columnspacing')
+        self.shadow = mpl._val_or_rc(shadow, 'legend.shadow')
+
+        if reverse:
+            labels = [*reversed(labels)]
+            handles = [*reversed(handles)]
+
+        handles = list(handles)
+        if len(handles) < 2:
+            ncols = 1
+        self._ncols = ncols if ncols != 1 else ncol
+
+        if self.numpoints <= 0:
+            raise ValueError("numpoints must be > 0; it was %d" % numpoints)
+
+        # introduce y-offset for handles of the scatter plot
+        if scatteryoffsets is None:
+            self._scatteryoffsets = np.array([3. / 8., 4. / 8., 2.5 / 8.])
+        else:
+            self._scatteryoffsets = np.asarray(scatteryoffsets)
+        reps = self.scatterpoints // len(self._scatteryoffsets) + 1
+        self._scatteryoffsets = np.tile(self._scatteryoffsets,
+                                        reps)[:self.scatterpoints]
+
+        # _legend_box is a VPacker instance that contains all
+        # legend items and will be initialized from _init_legend_box()
+        # method.
+        self._legend_box = None
+
+        if isinstance(parent, Axes):
+            self.isaxes = True
+            self.axes = parent
+            self.set_figure(parent.get_figure(root=False))
+        elif isinstance(parent, FigureBase):
+            self.isaxes = False
+            self.set_figure(parent)
+        else:
+            raise TypeError(
+                "Legend needs either Axes or FigureBase as parent"
+            )
+        self.parent = parent
+
+        self._mode = mode
+        self.set_bbox_to_anchor(bbox_to_anchor, bbox_transform)
+
+        # Figure out if self.shadow is valid
+        # If shadow was None, rcParams loads False
+        # So it shouldn't be None here
+
+        self._shadow_props = {'ox': 2, 'oy': -2}  # default location offsets
+        if isinstance(self.shadow, dict):
+            self._shadow_props.update(self.shadow)
+            self.shadow = True
+        elif self.shadow in (0, 1, True, False):
+            self.shadow = bool(self.shadow)
+        else:
+            raise ValueError(
+                'Legend shadow must be a dict or bool, not '
+                f'{self.shadow!r} of type {type(self.shadow)}.'
+            )
+
+        # We use FancyBboxPatch to draw a legend frame. The location
+        # and size of the box will be updated during the drawing time.
+
+        facecolor = mpl._val_or_rc(facecolor, "legend.facecolor")
+        if facecolor == 'inherit':
+            facecolor = mpl.rcParams["axes.facecolor"]
+
+        edgecolor = mpl._val_or_rc(edgecolor, "legend.edgecolor")
+        if edgecolor == 'inherit':
+            edgecolor = mpl.rcParams["axes.edgecolor"]
+
+        fancybox = mpl._val_or_rc(fancybox, "legend.fancybox")
+
+        self.legendPatch = FancyBboxPatch(
+            xy=(0, 0), width=1, height=1,
+            facecolor=facecolor, edgecolor=edgecolor,
+            # If shadow is used, default to alpha=1 (#8943).
+            alpha=(framealpha if framealpha is not None
+                   else 1 if shadow
+                   else mpl.rcParams["legend.framealpha"]),
+            # The width and height of the legendPatch will be set (in draw())
+            # to the length that includes the padding. Thus we set pad=0 here.
+            boxstyle=("round,pad=0,rounding_size=0.2" if fancybox
+                      else "square,pad=0"),
+            mutation_scale=self._fontsize,
+            snap=True,
+            visible=mpl._val_or_rc(frameon, "legend.frameon")
+        )
+        self._set_artist_props(self.legendPatch)
+
+        _api.check_in_list(["center", "left", "right"], alignment=alignment)
+        self._alignment = alignment
+
+        # init with null renderer
+        self._init_legend_box(handles, labels, markerfirst)
+
+        # Set legend location
+        self.set_loc(loc)
+
+        # figure out title font properties:
+        if title_fontsize is not None and title_fontproperties is not None:
+            raise ValueError(
+                "title_fontsize and title_fontproperties can't be specified "
+                "at the same time. Only use one of them. ")
+        title_prop_fp = FontProperties._from_any(title_fontproperties)
+        if isinstance(title_fontproperties, dict):
+            if "size" not in title_fontproperties:
+                title_fontsize = mpl.rcParams["legend.title_fontsize"]
+                title_prop_fp.set_size(title_fontsize)
+        elif title_fontsize is not None:
+            title_prop_fp.set_size(title_fontsize)
+        elif not isinstance(title_fontproperties, FontProperties):
+            title_fontsize = mpl.rcParams["legend.title_fontsize"]
+            title_prop_fp.set_size(title_fontsize)
+
+        self.set_title(title, prop=title_prop_fp)
+
+        self._draggable = None
+        self.set_draggable(state=draggable)
+
+        # set the text color
+
+        color_getters = {  # getter function depends on line or patch
+            'linecolor':       ['get_color',           'get_facecolor'],
+            'markerfacecolor': ['get_markerfacecolor', 'get_facecolor'],
+            'mfc':             ['get_markerfacecolor', 'get_facecolor'],
+            'markeredgecolor': ['get_markeredgecolor', 'get_edgecolor'],
+            'mec':             ['get_markeredgecolor', 'get_edgecolor'],
+        }
+        labelcolor = mpl._val_or_rc(labelcolor, 'legend.labelcolor')
+        if labelcolor is None:
+            labelcolor = mpl.rcParams['text.color']
+        if isinstance(labelcolor, str) and labelcolor in color_getters:
+            getter_names = color_getters[labelcolor]
+            for handle, text in zip(self.legend_handles, self.texts):
+                try:
+                    if handle.get_array() is not None:
+                        continue
+                except AttributeError:
+                    pass
+                for getter_name in getter_names:
+                    try:
+                        color = getattr(handle, getter_name)()
+                        if isinstance(color, np.ndarray):
+                            if (
+                                    color.shape[0] == 1
+                                    or np.isclose(color, color[0]).all()
+                            ):
+                                text.set_color(color[0])
+                            else:
+                                pass
+                        else:
+                            text.set_color(color)
+                        break
+                    except AttributeError:
+                        pass
+        elif cbook._str_equal(labelcolor, 'none'):
+            for text in self.texts:
+                text.set_color(labelcolor)
+        elif np.iterable(labelcolor):
+            for text, color in zip(self.texts,
+                                   itertools.cycle(
+                                       colors.to_rgba_array(labelcolor))):
+                text.set_color(color)
+        else:
+            raise ValueError(f"Invalid labelcolor: {labelcolor!r}")
+
+    def _set_artist_props(self, a):
+        """
+        Set the boilerplate props for artists added to Axes.
+        """
+        a.set_figure(self.get_figure(root=False))
+        if self.isaxes:
+            a.axes = self.axes
+
+        a.set_transform(self.get_transform())
+
+    @_docstring.interpd
+    def set_loc(self, loc=None):
+        """
+        Set the location of the legend.
+
+        .. versionadded:: 3.8
+
+        Parameters
+        ----------
+        %(_legend_kw_set_loc_doc)s
+        """
+        loc0 = loc
+        self._loc_used_default = loc is None
+        if loc is None:
+            loc = mpl.rcParams["legend.loc"]
+            if not self.isaxes and loc in [0, 'best']:
+                loc = 'upper right'
+
+        type_err_message = ("loc must be string, coordinate tuple, or"
+                            f" an integer 0-10, not {loc!r}")
+
+        # handle outside legends:
+        self._outside_loc = None
+        if isinstance(loc, str):
+            if loc.split()[0] == 'outside':
+                # strip outside:
+                loc = loc.split('outside ')[1]
+                # strip "center" at the beginning
+                self._outside_loc = loc.replace('center ', '')
+                # strip first
+                self._outside_loc = self._outside_loc.split()[0]
+                locs = loc.split()
+                if len(locs) > 1 and locs[0] in ('right', 'left'):
+                    # locs doesn't accept "left upper", etc, so swap
+                    if locs[0] != 'center':
+                        locs = locs[::-1]
+                    loc = locs[0] + ' ' + locs[1]
+            # check that loc is in acceptable strings
+            loc = _api.check_getitem(self.codes, loc=loc)
+        elif np.iterable(loc):
+            # coerce iterable into tuple
+            loc = tuple(loc)
+            # validate the tuple represents Real coordinates
+            if len(loc) != 2 or not all(isinstance(e, numbers.Real) for e in loc):
+                raise ValueError(type_err_message)
+        elif isinstance(loc, int):
+            # validate the integer represents a string numeric value
+            if loc < 0 or loc > 10:
+                raise ValueError(type_err_message)
+        else:
+            # all other cases are invalid values of loc
+            raise ValueError(type_err_message)
+
+        if self.isaxes and self._outside_loc:
+            raise ValueError(
+                f"'outside' option for loc='{loc0}' keyword argument only "
+                "works for figure legends")
+
+        if not self.isaxes and loc == 0:
+            raise ValueError(
+                "Automatic legend placement (loc='best') not implemented for "
+                "figure legend")
+
+        tmp = self._loc_used_default
+        self._set_loc(loc)
+        self._loc_used_default = tmp  # ignore changes done by _set_loc
+
+    def _set_loc(self, loc):
+        # find_offset function will be provided to _legend_box and
+        # _legend_box will draw itself at the location of the return
+        # value of the find_offset.
+        self._loc_used_default = False
+        self._loc_real = loc
+        self.stale = True
+        self._legend_box.set_offset(self._findoffset)
+
+    def set_ncols(self, ncols):
+        """Set the number of columns."""
+        self._ncols = ncols
+
+    def _get_loc(self):
+        return self._loc_real
+
+    _loc = property(_get_loc, _set_loc)
+
+    def _findoffset(self, width, height, xdescent, ydescent, renderer):
+        """Helper function to locate the legend."""
+
+        if self._loc == 0:  # "best".
+            x, y = self._find_best_position(width, height, renderer)
+        elif self._loc in Legend.codes.values():  # Fixed location.
+            bbox = Bbox.from_bounds(0, 0, width, height)
+            x, y = self._get_anchored_bbox(self._loc, bbox,
+                                           self.get_bbox_to_anchor(),
+                                           renderer)
+        else:  # Axes or figure coordinates.
+            fx, fy = self._loc
+            bbox = self.get_bbox_to_anchor()
+            x, y = bbox.x0 + bbox.width * fx, bbox.y0 + bbox.height * fy
+
+        return x + xdescent, y + ydescent
+
+    @allow_rasterization
+    def draw(self, renderer):
+        # docstring inherited
+        if not self.get_visible():
+            return
+
+        renderer.open_group('legend', gid=self.get_gid())
+
+        fontsize = renderer.points_to_pixels(self._fontsize)
+
+        # if mode == fill, set the width of the legend_box to the
+        # width of the parent (minus pads)
+        if self._mode in ["expand"]:
+            pad = 2 * (self.borderaxespad + self.borderpad) * fontsize
+            self._legend_box.set_width(self.get_bbox_to_anchor().width - pad)
+
+        # update the location and size of the legend. This needs to
+        # be done in any case to clip the figure right.
+        bbox = self._legend_box.get_window_extent(renderer)
+        self.legendPatch.set_bounds(bbox.bounds)
+        self.legendPatch.set_mutation_scale(fontsize)
+
+        # self.shadow is validated in __init__
+        # So by here it is a bool and self._shadow_props contains any configs
+
+        if self.shadow:
+            Shadow(self.legendPatch, **self._shadow_props).draw(renderer)
+
+        self.legendPatch.draw(renderer)
+        self._legend_box.draw(renderer)
+
+        renderer.close_group('legend')
+        self.stale = False
+
+    # _default_handler_map defines the default mapping between plot
+    # elements and the legend handlers.
+
+    _default_handler_map = {
+        StemContainer: legend_handler.HandlerStem(),
+        ErrorbarContainer: legend_handler.HandlerErrorbar(),
+        Line2D: legend_handler.HandlerLine2D(),
+        Patch: legend_handler.HandlerPatch(),
+        StepPatch: legend_handler.HandlerStepPatch(),
+        LineCollection: legend_handler.HandlerLineCollection(),
+        RegularPolyCollection: legend_handler.HandlerRegularPolyCollection(),
+        CircleCollection: legend_handler.HandlerCircleCollection(),
+        BarContainer: legend_handler.HandlerPatch(
+            update_func=legend_handler.update_from_first_child),
+        tuple: legend_handler.HandlerTuple(),
+        PathCollection: legend_handler.HandlerPathCollection(),
+        PolyCollection: legend_handler.HandlerPolyCollection()
+        }
+
+    # (get|set|update)_default_handler_maps are public interfaces to
+    # modify the default handler map.
+
+    @classmethod
+    def get_default_handler_map(cls):
+        """Return the global default handler map, shared by all legends."""
+        return cls._default_handler_map
+
+    @classmethod
+    def set_default_handler_map(cls, handler_map):
+        """Set the global default handler map, shared by all legends."""
+        cls._default_handler_map = handler_map
+
+    @classmethod
+    def update_default_handler_map(cls, handler_map):
+        """Update the global default handler map, shared by all legends."""
+        cls._default_handler_map.update(handler_map)
+
+    def get_legend_handler_map(self):
+        """Return this legend instance's handler map."""
+        default_handler_map = self.get_default_handler_map()
+        return ({**default_handler_map, **self._custom_handler_map}
+                if self._custom_handler_map else default_handler_map)
+
+    @staticmethod
+    def get_legend_handler(legend_handler_map, orig_handle):
+        """
+        Return a legend handler from *legend_handler_map* that
+        corresponds to *orig_handler*.
+
+        *legend_handler_map* should be a dictionary object (that is
+        returned by the get_legend_handler_map method).
+
+        It first checks if the *orig_handle* itself is a key in the
+        *legend_handler_map* and return the associated value.
+        Otherwise, it checks for each of the classes in its
+        method-resolution-order. If no matching key is found, it
+        returns ``None``.
+        """
+        try:
+            return legend_handler_map[orig_handle]
+        except (TypeError, KeyError):  # TypeError if unhashable.
+            pass
+        for handle_type in type(orig_handle).mro():
+            try:
+                return legend_handler_map[handle_type]
+            except KeyError:
+                pass
+        return None
+
+    def _init_legend_box(self, handles, labels, markerfirst=True):
+        """
+        Initialize the legend_box. The legend_box is an instance of
+        the OffsetBox, which is packed with legend handles and
+        texts. Once packed, their location is calculated during the
+        drawing time.
+        """
+
+        fontsize = self._fontsize
+
+        # legend_box is a HPacker, horizontally packed with columns.
+        # Each column is a VPacker, vertically packed with legend items.
+        # Each legend item is a HPacker packed with:
+        # - handlebox: a DrawingArea which contains the legend handle.
+        # - labelbox: a TextArea which contains the legend text.
+
+        text_list = []  # the list of text instances
+        handle_list = []  # the list of handle instances
+        handles_and_labels = []
+
+        # The approximate height and descent of text. These values are
+        # only used for plotting the legend handle.
+        descent = 0.35 * fontsize * (self.handleheight - 0.7)  # heuristic.
+        height = fontsize * self.handleheight - descent
+        # each handle needs to be drawn inside a box of (x, y, w, h) =
+        # (0, -descent, width, height).  And their coordinates should
+        # be given in the display coordinates.
+
+        # The transformation of each handle will be automatically set
+        # to self.get_transform(). If the artist does not use its
+        # default transform (e.g., Collections), you need to
+        # manually set their transform to the self.get_transform().
+        legend_handler_map = self.get_legend_handler_map()
+
+        for orig_handle, label in zip(handles, labels):
+            handler = self.get_legend_handler(legend_handler_map, orig_handle)
+            if handler is None:
+                _api.warn_external(
+                             "Legend does not support handles for "
+                             f"{type(orig_handle).__name__} "
+                             "instances.\nA proxy artist may be used "
+                             "instead.\nSee: https://matplotlib.org/"
+                             "stable/users/explain/axes/legend_guide.html"
+                             "#controlling-the-legend-entries")
+                # No handle for this artist, so we just defer to None.
+                handle_list.append(None)
+            else:
+                textbox = TextArea(label, multilinebaseline=True,
+                                   textprops=dict(
+                                       verticalalignment='baseline',
+                                       horizontalalignment='left',
+                                       fontproperties=self.prop))
+                handlebox = DrawingArea(width=self.handlelength * fontsize,
+                                        height=height,
+                                        xdescent=0., ydescent=descent)
+
+                text_list.append(textbox._text)
+                # Create the artist for the legend which represents the
+                # original artist/handle.
+                handle_list.append(handler.legend_artist(self, orig_handle,
+                                                         fontsize, handlebox))
+                handles_and_labels.append((handlebox, textbox))
+
+        columnbox = []
+        # array_split splits n handles_and_labels into ncols columns, with the
+        # first n%ncols columns having an extra entry.  filter(len, ...)
+        # handles the case where n < ncols: the last ncols-n columns are empty
+        # and get filtered out.
+        for handles_and_labels_column in filter(
+                len, np.array_split(handles_and_labels, self._ncols)):
+            # pack handlebox and labelbox into itembox
+            itemboxes = [HPacker(pad=0,
+                                 sep=self.handletextpad * fontsize,
+                                 children=[h, t] if markerfirst else [t, h],
+                                 align="baseline")
+                         for h, t in handles_and_labels_column]
+            # pack columnbox
+            alignment = "baseline" if markerfirst else "right"
+            columnbox.append(VPacker(pad=0,
+                                     sep=self.labelspacing * fontsize,
+                                     align=alignment,
+                                     children=itemboxes))
+
+        mode = "expand" if self._mode == "expand" else "fixed"
+        sep = self.columnspacing * fontsize
+        self._legend_handle_box = HPacker(pad=0,
+                                          sep=sep, align="baseline",
+                                          mode=mode,
+                                          children=columnbox)
+        self._legend_title_box = TextArea("")
+        self._legend_box = VPacker(pad=self.borderpad * fontsize,
+                                   sep=self.labelspacing * fontsize,
+                                   align=self._alignment,
+                                   children=[self._legend_title_box,
+                                             self._legend_handle_box])
+        self._legend_box.set_figure(self.get_figure(root=False))
+        self._legend_box.axes = self.axes
+        self.texts = text_list
+        self.legend_handles = handle_list
+
+    def _auto_legend_data(self, renderer):
+        """
+        Return display coordinates for hit testing for "best" positioning.
+
+        Returns
+        -------
+        bboxes
+            List of bounding boxes of all patches.
+        lines
+            List of `.Path` corresponding to each line.
+        offsets
+            List of (x, y) offsets of all collection.
+        """
+        assert self.isaxes  # always holds, as this is only called internally
+        bboxes = []
+        lines = []
+        offsets = []
+        for artist in self.parent._children:
+            if isinstance(artist, Line2D):
+                lines.append(
+                    artist.get_transform().transform_path(artist.get_path()))
+            elif isinstance(artist, Rectangle):
+                bboxes.append(
+                    artist.get_bbox().transformed(artist.get_data_transform()))
+            elif isinstance(artist, Patch):
+                lines.append(
+                    artist.get_transform().transform_path(artist.get_path()))
+            elif isinstance(artist, PolyCollection):
+                lines.extend(artist.get_transform().transform_path(path)
+                             for path in artist.get_paths())
+            elif isinstance(artist, Collection):
+                transform, transOffset, hoffsets, _ = artist._prepare_points()
+                if len(hoffsets):
+                    offsets.extend(transOffset.transform(hoffsets))
+            elif isinstance(artist, Text):
+                bboxes.append(artist.get_window_extent(renderer))
+
+        return bboxes, lines, offsets
+
+    def get_children(self):
+        # docstring inherited
+        return [self._legend_box, self.get_frame()]
+
+    def get_frame(self):
+        """Return the `~.patches.Rectangle` used to frame the legend."""
+        return self.legendPatch
+
+    def get_lines(self):
+        r"""Return the list of `~.lines.Line2D`\s in the legend."""
+        return [h for h in self.legend_handles if isinstance(h, Line2D)]
+
+    def get_patches(self):
+        r"""Return the list of `~.patches.Patch`\s in the legend."""
+        return silent_list('Patch',
+                           [h for h in self.legend_handles
+                            if isinstance(h, Patch)])
+
+    def get_texts(self):
+        r"""Return the list of `~.text.Text`\s in the legend."""
+        return silent_list('Text', self.texts)
+
+    def set_alignment(self, alignment):
+        """
+        Set the alignment of the legend title and the box of entries.
+
+        The entries are aligned as a single block, so that markers always
+        lined up.
+
+        Parameters
+        ----------
+        alignment : {'center', 'left', 'right'}.
+
+        """
+        _api.check_in_list(["center", "left", "right"], alignment=alignment)
+        self._alignment = alignment
+        self._legend_box.align = alignment
+
+    def get_alignment(self):
+        """Get the alignment value of the legend box"""
+        return self._legend_box.align
+
+    def set_title(self, title, prop=None):
+        """
+        Set legend title and title style.
+
+        Parameters
+        ----------
+        title : str
+            The legend title.
+
+        prop : `.font_manager.FontProperties` or `str` or `pathlib.Path`
+            The font properties of the legend title.
+            If a `str`, it is interpreted as a fontconfig pattern parsed by
+            `.FontProperties`.  If a `pathlib.Path`, it is interpreted as the
+            absolute path to a font file.
+
+        """
+        self._legend_title_box._text.set_text(title)
+        if title:
+            self._legend_title_box._text.set_visible(True)
+            self._legend_title_box.set_visible(True)
+        else:
+            self._legend_title_box._text.set_visible(False)
+            self._legend_title_box.set_visible(False)
+
+        if prop is not None:
+            self._legend_title_box._text.set_fontproperties(prop)
+
+        self.stale = True
+
+    def get_title(self):
+        """Return the `.Text` instance for the legend title."""
+        return self._legend_title_box._text
+
+    def get_window_extent(self, renderer=None):
+        # docstring inherited
+        if renderer is None:
+            renderer = self.get_figure(root=True)._get_renderer()
+        return self._legend_box.get_window_extent(renderer=renderer)
+
+    def get_tightbbox(self, renderer=None):
+        # docstring inherited
+        return self._legend_box.get_window_extent(renderer)
+
+    def get_frame_on(self):
+        """Get whether the legend box patch is drawn."""
+        return self.legendPatch.get_visible()
+
+    def set_frame_on(self, b):
+        """
+        Set whether the legend box patch is drawn.
+
+        Parameters
+        ----------
+        b : bool
+        """
+        self.legendPatch.set_visible(b)
+        self.stale = True
+
+    draw_frame = set_frame_on  # Backcompat alias.
+
+    def get_bbox_to_anchor(self):
+        """Return the bbox that the legend will be anchored to."""
+        if self._bbox_to_anchor is None:
+            return self.parent.bbox
+        else:
+            return self._bbox_to_anchor
+
+    def set_bbox_to_anchor(self, bbox, transform=None):
+        """
+        Set the bbox that the legend will be anchored to.
+
+        Parameters
+        ----------
+        bbox : `~matplotlib.transforms.BboxBase` or tuple
+            The bounding box can be specified in the following ways:
+
+            - A `.BboxBase` instance
+            - A tuple of ``(left, bottom, width, height)`` in the given
+              transform (normalized axes coordinate if None)
+            - A tuple of ``(left, bottom)`` where the width and height will be
+              assumed to be zero.
+            - *None*, to remove the bbox anchoring, and use the parent bbox.
+
+        transform : `~matplotlib.transforms.Transform`, optional
+            A transform to apply to the bounding box. If not specified, this
+            will use a transform to the bounding box of the parent.
+        """
+        if bbox is None:
+            self._bbox_to_anchor = None
+            return
+        elif isinstance(bbox, BboxBase):
+            self._bbox_to_anchor = bbox
+        else:
+            try:
+                l = len(bbox)
+            except TypeError as err:
+                raise ValueError(f"Invalid bbox: {bbox}") from err
+
+            if l == 2:
+                bbox = [bbox[0], bbox[1], 0, 0]
+
+            self._bbox_to_anchor = Bbox.from_bounds(*bbox)
+
+        if transform is None:
+            transform = BboxTransformTo(self.parent.bbox)
+
+        self._bbox_to_anchor = TransformedBbox(self._bbox_to_anchor,
+                                               transform)
+        self.stale = True
+
+    def _get_anchored_bbox(self, loc, bbox, parentbbox, renderer):
+        """
+        Place the *bbox* inside the *parentbbox* according to a given
+        location code. Return the (x, y) coordinate of the bbox.
+
+        Parameters
+        ----------
+        loc : int
+            A location code in range(1, 11). This corresponds to the possible
+            values for ``self._loc``, excluding "best".
+        bbox : `~matplotlib.transforms.Bbox`
+            bbox to be placed, in display coordinates.
+        parentbbox : `~matplotlib.transforms.Bbox`
+            A parent box which will contain the bbox, in display coordinates.
+        """
+        return offsetbox._get_anchored_bbox(
+            loc, bbox, parentbbox,
+            self.borderaxespad * renderer.points_to_pixels(self._fontsize))
+
+    def _find_best_position(self, width, height, renderer):
+        """Determine the best location to place the legend."""
+        assert self.isaxes  # always holds, as this is only called internally
+
+        start_time = time.perf_counter()
+
+        bboxes, lines, offsets = self._auto_legend_data(renderer)
+
+        bbox = Bbox.from_bounds(0, 0, width, height)
+
+        candidates = []
+        for idx in range(1, len(self.codes)):
+            l, b = self._get_anchored_bbox(idx, bbox,
+                                           self.get_bbox_to_anchor(),
+                                           renderer)
+            legendBox = Bbox.from_bounds(l, b, width, height)
+            # XXX TODO: If markers are present, it would be good to take them
+            # into account when checking vertex overlaps in the next line.
+            badness = (sum(legendBox.count_contains(line.vertices)
+                           for line in lines)
+                       + legendBox.count_contains(offsets)
+                       + legendBox.count_overlaps(bboxes)
+                       + sum(line.intersects_bbox(legendBox, filled=False)
+                             for line in lines))
+            # Include the index to favor lower codes in case of a tie.
+            candidates.append((badness, idx, (l, b)))
+            if badness == 0:
+                break
+
+        _, _, (l, b) = min(candidates)
+
+        if self._loc_used_default and time.perf_counter() - start_time > 1:
+            _api.warn_external(
+                'Creating legend with loc="best" can be slow with large '
+                'amounts of data.')
+
+        return l, b
+
+    def contains(self, mouseevent):
+        return self.legendPatch.contains(mouseevent)
+
+    def set_draggable(self, state, use_blit=False, update='loc'):
+        """
+        Enable or disable mouse dragging support of the legend.
+
+        Parameters
+        ----------
+        state : bool
+            Whether mouse dragging is enabled.
+        use_blit : bool, optional
+            Use blitting for faster image composition. For details see
+            :ref:`func-animation`.
+        update : {'loc', 'bbox'}, optional
+            The legend parameter to be changed when dragged:
+
+            - 'loc': update the *loc* parameter of the legend
+            - 'bbox': update the *bbox_to_anchor* parameter of the legend
+
+        Returns
+        -------
+        `.DraggableLegend` or *None*
+            If *state* is ``True`` this returns the `.DraggableLegend` helper
+            instance. Otherwise this returns *None*.
+        """
+        if state:
+            if self._draggable is None:
+                self._draggable = DraggableLegend(self,
+                                                  use_blit,
+                                                  update=update)
+        else:
+            if self._draggable is not None:
+                self._draggable.disconnect()
+            self._draggable = None
+        return self._draggable
+
+    def get_draggable(self):
+        """Return ``True`` if the legend is draggable, ``False`` otherwise."""
+        return self._draggable is not None
+
+
+# Helper functions to parse legend arguments for both `figure.legend` and
+# `axes.legend`:
+def _get_legend_handles(axs, legend_handler_map=None):
+    """Yield artists that can be used as handles in a legend."""
+    handles_original = []
+    for ax in axs:
+        handles_original += [
+            *(a for a in ax._children
+              if isinstance(a, (Line2D, Patch, Collection, Text))),
+            *ax.containers]
+        # support parasite Axes:
+        if hasattr(ax, 'parasites'):
+            for axx in ax.parasites:
+                handles_original += [
+                    *(a for a in axx._children
+                      if isinstance(a, (Line2D, Patch, Collection, Text))),
+                    *axx.containers]
+
+    handler_map = {**Legend.get_default_handler_map(),
+                   **(legend_handler_map or {})}
+    has_handler = Legend.get_legend_handler
+    for handle in handles_original:
+        label = handle.get_label()
+        if label != '_nolegend_' and has_handler(handler_map, handle):
+            yield handle
+        elif (label and not label.startswith('_') and
+                not has_handler(handler_map, handle)):
+            _api.warn_external(
+                             "Legend does not support handles for "
+                             f"{type(handle).__name__} "
+                             "instances.\nSee: https://matplotlib.org/stable/"
+                             "tutorials/intermediate/legend_guide.html"
+                             "#implementing-a-custom-legend-handler")
+            continue
+
+
+def _get_legend_handles_labels(axs, legend_handler_map=None):
+    """Return handles and labels for legend."""
+    handles = []
+    labels = []
+    for handle in _get_legend_handles(axs, legend_handler_map):
+        label = handle.get_label()
+        if label and not label.startswith('_'):
+            handles.append(handle)
+            labels.append(label)
+    return handles, labels
+
+
+def _parse_legend_args(axs, *args, handles=None, labels=None, **kwargs):
+    """
+    Get the handles and labels from the calls to either ``figure.legend``
+    or ``axes.legend``.
+
+    The parser is a bit involved because we support::
+
+        legend()
+        legend(labels)
+        legend(handles, labels)
+        legend(labels=labels)
+        legend(handles=handles)
+        legend(handles=handles, labels=labels)
+
+    The behavior for a mixture of positional and keyword handles and labels
+    is undefined and issues a warning; it will be an error in the future.
+
+    Parameters
+    ----------
+    axs : list of `.Axes`
+        If handles are not given explicitly, the artists in these Axes are
+        used as handles.
+    *args : tuple
+        Positional parameters passed to ``legend()``.
+    handles
+        The value of the keyword argument ``legend(handles=...)``, or *None*
+        if that keyword argument was not used.
+    labels
+        The value of the keyword argument ``legend(labels=...)``, or *None*
+        if that keyword argument was not used.
+    **kwargs
+        All other keyword arguments passed to ``legend()``.
+
+    Returns
+    -------
+    handles : list of (`.Artist` or tuple of `.Artist`)
+        The legend handles.
+    labels : list of str
+        The legend labels.
+    kwargs : dict
+        *kwargs* with keywords handles and labels removed.
+
+    """
+    log = logging.getLogger(__name__)
+
+    handlers = kwargs.get('handler_map')
+
+    if (handles is not None or labels is not None) and args:
+        _api.warn_deprecated("3.9", message=(
+            "You have mixed positional and keyword arguments, some input may "
+            "be discarded.  This is deprecated since %(since)s and will "
+            "become an error in %(removal)s."))
+
+    if (hasattr(handles, "__len__") and
+            hasattr(labels, "__len__") and
+            len(handles) != len(labels)):
+        _api.warn_external(f"Mismatched number of handles and labels: "
+                           f"len(handles) = {len(handles)} "
+                           f"len(labels) = {len(labels)}")
+    # if got both handles and labels as kwargs, make same length
+    if handles and labels:
+        handles, labels = zip(*zip(handles, labels))
+
+    elif handles is not None and labels is None:
+        labels = [handle.get_label() for handle in handles]
+
+    elif labels is not None and handles is None:
+        # Get as many handles as there are labels.
+        handles = [handle for handle, label
+                   in zip(_get_legend_handles(axs, handlers), labels)]
+
+    elif len(args) == 0:  # 0 args: automatically detect labels and handles.
+        handles, labels = _get_legend_handles_labels(axs, handlers)
+        if not handles:
+            _api.warn_external(
+                "No artists with labels found to put in legend.  Note that "
+                "artists whose label start with an underscore are ignored "
+                "when legend() is called with no argument.")
+
+    elif len(args) == 1:  # 1 arg: user defined labels, automatic handle detection.
+        labels, = args
+        if any(isinstance(l, Artist) for l in labels):
+            raise TypeError("A single argument passed to legend() must be a "
+                            "list of labels, but found an Artist in there.")
+
+        # Get as many handles as there are labels.
+        handles = [handle for handle, label
+                   in zip(_get_legend_handles(axs, handlers), labels)]
+
+    elif len(args) == 2:  # 2 args: user defined handles and labels.
+        handles, labels = args[:2]
+
+    else:
+        raise _api.nargs_error('legend', '0-2', len(args))
+
+    return handles, labels, kwargs
diff --git a/shared/package-files/e4/e4d604f8949236cc54b764f6d192b74c8156acd837707dc44ef393c1a93ee9c0/shapes.py b/shared/package-files/e4/e4d604f8949236cc54b764f6d192b74c8156acd837707dc44ef393c1a93ee9c0/shapes.py
new file mode 100644
index 0000000000000000000000000000000000000000..112d17fd4eaae885f82dd2ef85e3024215655531
--- /dev/null
+++ b/shared/package-files/e4/e4d604f8949236cc54b764f6d192b74c8156acd837707dc44ef393c1a93ee9c0/shapes.py
@@ -0,0 +1,185 @@
+import re
+
+
+def _prefer_non_zero(*args):
+    for arg in args:
+        if arg != 0:
+            return arg
+    return 0.0
+
+
+def _ntos(n):
+    # %f likes to add unnecessary 0's, %g isn't consistent about # decimals
+    return ("%.3f" % n).rstrip("0").rstrip(".")
+
+
+def _strip_xml_ns(tag):
+    # ElementTree API doesn't provide a way to ignore XML namespaces in tags
+    # so we here strip them ourselves: cf. https://bugs.python.org/issue18304
+    return tag.split("}", 1)[1] if "}" in tag else tag
+
+
+def _transform(raw_value):
+    # TODO assumes a 'matrix' transform.
+    # No other transform functions are supported at the moment.
+    # https://developer.mozilla.org/en-US/docs/Web/SVG/Attribute/transform
+    # start simple: if you aren't exactly matrix(...) then no love
+    match = re.match(r"matrix\((.*)\)", raw_value)
+    if not match:
+        raise NotImplementedError
+    matrix = tuple(float(p) for p in re.split(r"\s+|,", match.group(1)))
+    if len(matrix) != 6:
+        raise ValueError("wrong # of terms in %s" % raw_value)
+    return matrix
+
+
+class PathBuilder(object):
+    def __init__(self):
+        self.paths = []
+        self.transforms = []
+
+    def _start_path(self, initial_path=""):
+        self.paths.append(initial_path)
+        self.transforms.append(None)
+
+    def _end_path(self):
+        self._add("z")
+
+    def _add(self, path_snippet):
+        path = self.paths[-1]
+        if path:
+            path += " " + path_snippet
+        else:
+            path = path_snippet
+        self.paths[-1] = path
+
+    def _move(self, c, x, y):
+        self._add("%s%s,%s" % (c, _ntos(x), _ntos(y)))
+
+    def M(self, x, y):
+        self._move("M", x, y)
+
+    def m(self, x, y):
+        self._move("m", x, y)
+
+    def _arc(self, c, rx, ry, x, y, large_arc):
+        self._add(
+            "%s%s,%s 0 %d 1 %s,%s"
+            % (c, _ntos(rx), _ntos(ry), large_arc, _ntos(x), _ntos(y))
+        )
+
+    def A(self, rx, ry, x, y, large_arc=0):
+        self._arc("A", rx, ry, x, y, large_arc)
+
+    def a(self, rx, ry, x, y, large_arc=0):
+        self._arc("a", rx, ry, x, y, large_arc)
+
+    def _vhline(self, c, x):
+        self._add("%s%s" % (c, _ntos(x)))
+
+    def H(self, x):
+        self._vhline("H", x)
+
+    def h(self, x):
+        self._vhline("h", x)
+
+    def V(self, y):
+        self._vhline("V", y)
+
+    def v(self, y):
+        self._vhline("v", y)
+
+    def _line(self, c, x, y):
+        self._add("%s%s,%s" % (c, _ntos(x), _ntos(y)))
+
+    def L(self, x, y):
+        self._line("L", x, y)
+
+    def l(self, x, y):
+        self._line("l", x, y)
+
+    def _parse_line(self, line):
+        x1 = float(line.attrib.get("x1", 0))
+        y1 = float(line.attrib.get("y1", 0))
+        x2 = float(line.attrib.get("x2", 0))
+        y2 = float(line.attrib.get("y2", 0))
+
+        self._start_path()
+        self.M(x1, y1)
+        self.L(x2, y2)
+
+    def _parse_rect(self, rect):
+        x = float(rect.attrib.get("x", 0))
+        y = float(rect.attrib.get("y", 0))
+        w = float(rect.attrib.get("width"))
+        h = float(rect.attrib.get("height"))
+        rx = float(rect.attrib.get("rx", 0))
+        ry = float(rect.attrib.get("ry", 0))
+
+        rx = _prefer_non_zero(rx, ry)
+        ry = _prefer_non_zero(ry, rx)
+        # TODO there are more rules for adjusting rx, ry
+
+        self._start_path()
+        self.M(x + rx, y)
+        self.H(x + w - rx)
+        if rx > 0:
+            self.A(rx, ry, x + w, y + ry)
+        self.V(y + h - ry)
+        if rx > 0:
+            self.A(rx, ry, x + w - rx, y + h)
+        self.H(x + rx)
+        if rx > 0:
+            self.A(rx, ry, x, y + h - ry)
+        self.V(y + ry)
+        if rx > 0:
+            self.A(rx, ry, x + rx, y)
+        self._end_path()
+
+    def _parse_path(self, path):
+        if "d" in path.attrib:
+            self._start_path(initial_path=path.attrib["d"])
+
+    def _parse_polygon(self, poly):
+        if "points" in poly.attrib:
+            self._start_path("M" + poly.attrib["points"])
+            self._end_path()
+
+    def _parse_polyline(self, poly):
+        if "points" in poly.attrib:
+            self._start_path("M" + poly.attrib["points"])
+
+    def _parse_circle(self, circle):
+        cx = float(circle.attrib.get("cx", 0))
+        cy = float(circle.attrib.get("cy", 0))
+        r = float(circle.attrib.get("r"))
+
+        # arc doesn't seem to like being a complete shape, draw two halves
+        self._start_path()
+        self.M(cx - r, cy)
+        self.A(r, r, cx + r, cy, large_arc=1)
+        self.A(r, r, cx - r, cy, large_arc=1)
+
+    def _parse_ellipse(self, ellipse):
+        cx = float(ellipse.attrib.get("cx", 0))
+        cy = float(ellipse.attrib.get("cy", 0))
+        rx = float(ellipse.attrib.get("rx"))
+        ry = float(ellipse.attrib.get("ry"))
+
+        # arc doesn't seem to like being a complete shape, draw two halves
+        self._start_path()
+        self.M(cx - rx, cy)
+        self.A(rx, ry, cx + rx, cy, large_arc=1)
+        self.A(rx, ry, cx - rx, cy, large_arc=1)
+
+    def add_path_from_element(self, el):
+        if not isinstance(el.tag, str):
+            return False
+        tag = _strip_xml_ns(el.tag)
+        parse_fn = getattr(self, "_parse_%s" % tag.lower(), None)
+        if not callable(parse_fn):
+            return False
+        parse_fn(el)
+        if "transform" in el.attrib:
+            self.transforms[-1] = _transform(el.attrib["transform"])
+        return True
diff --git a/shared/package-files/e4/e4d6d2e408efbe9a452eaeb5acb43e4c4aa8b8e2724a009859c35b4bdac9c243/tvi950-rv-4p b/shared/package-files/e4/e4d6d2e408efbe9a452eaeb5acb43e4c4aa8b8e2724a009859c35b4bdac9c243/tvi950-rv-4p
new file mode 100644
index 0000000000000000000000000000000000000000..8f664ef4b3513f70b3af6918407e915f8b05e777
Binary files /dev/null and b/shared/package-files/e4/e4d6d2e408efbe9a452eaeb5acb43e4c4aa8b8e2724a009859c35b4bdac9c243/tvi950-rv-4p differ
diff --git a/shared/package-files/e4/e4e0357061007c730d7189c0c6b87e92171fe71bc1044788f0d9967991ef1df0/_traverse.py b/shared/package-files/e4/e4e0357061007c730d7189c0c6b87e92171fe71bc1044788f0d9967991ef1df0/_traverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..604b5e1a80c17e65a0683643a395f0a21d50e2eb
--- /dev/null
+++ b/shared/package-files/e4/e4e0357061007c730d7189c0c6b87e92171fe71bc1044788f0d9967991ef1df0/_traverse.py
@@ -0,0 +1,167 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing import (
+    Callable,
+    cast,
+    Collection,
+    List,
+    Mapping,
+    MutableMapping,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+import torch
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._tensor import DTensor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+PATH_ITEM = Union[str, int]
+OBJ_PATH = Tuple[PATH_ITEM, ...]
+T = TypeVar("T")
+
+STATE_DICT_ITEM = object
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, STATE_DICT_ITEM]
+
+__all__ = ["traverse_state_dict", "set_element", "get_element", "print_tensor"]
+
+
+def _keep_visiting_tensors(value: STATE_DICT_ITEM) -> bool:
+    return isinstance(value, torch.Tensor)
+
+
+# TODO: update docstring for traverse.py
+def traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+
+    Traversal is short-circuited when if finds a collection for which ``keep_visiting_tensors`` evaluates
+    to false for all elements.
+    By default, all collections with at least one ``torch.Tensor`` element are traversed.
+    Visitor takes a path argument that is a tuple of the keys used to reach it.
+    """
+
+    # a value is terminal if it has no other containers values inside it
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            values = value.values()
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, list):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def set_element(
+    root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: STATE_DICT_ITEM
+) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: List[STATE_DICT_ITEM], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val = cast(STATE_DICT_ITEM, {} if type(key) == str else [])
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) == int:
+        extend_list(cast(List[STATE_DICT_ITEM], cur_container), key)
+
+    cur_container[key] = value
+
+
+def get_element(
+    root_dict: STATE_DICT_TYPE,
+    path: OBJ_PATH,
+    default_value: Optional[T] = None,
+) -> Optional[T]:
+    """Retrieve the value at ``path``from ``root_dict``, returning ``default_value`` if not found."""
+    cur_value = cast(CONTAINER_TYPE, root_dict)
+    for part in path:
+        if type(part) is int:
+            if not isinstance(cur_value, list) or len(cur_value) < part:
+                return default_value
+        elif not isinstance(cur_value, Mapping) or part not in cur_value:
+            return default_value
+
+        cur_value = cast(CONTAINER_TYPE, cur_value[part])
+    return cast(Optional[T], cur_value)
+
+
+def _print_nested(
+    value: STATE_DICT_ITEM,
+    prefix: str = "",
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    if type(value) is ShardedTensor:
+        print_fun(f"{prefix} ShardedTensor size: {value.size()}")
+        for shard in value.local_shards():
+            _print_nested(
+                shard.tensor,
+                f"{shard.metadata.shard_offsets} ",
+                print_fun=print_fun,
+            )
+    elif type(value) is (DTensor):
+        print_fun(f"{prefix} DistributedTensor size: {value.size()}")
+        # TODO: add local offset for _local_tensor in print_nested.
+        _print_nested(
+            value._local_tensor,
+            print_fun=print_fun,
+        )
+    elif isinstance(value, torch.Tensor):
+        print_fun(f"{prefix} Tensor size: {value.size()}")
+    else:
+        print_fun(f"{prefix} Type: {type(value)}")
+
+
+def print_tensor(
+    path: OBJ_PATH,
+    value: STATE_DICT_ITEM,
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    """
+    Use this callback with traverse_state_dict to print its content.
+
+    By default the content is printed using the builtin ``print`` but this can
+    be change by passing a different ``print_fun` callable.
+    """
+    _print_nested(value, prefix=str(path), print_fun=print_fun)
diff --git a/shared/package-files/e4/e4e314df6c9b3c5ea470a51d6dc7cd3640ce2bc849cb407b7ea8ba6fe19fc2ac/parser.py b/shared/package-files/e4/e4e314df6c9b3c5ea470a51d6dc7cd3640ce2bc849cb407b7ea8ba6fe19fc2ac/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa45e8b1530a433a8e8e529a5376b6ff2f5612e7
--- /dev/null
+++ b/shared/package-files/e4/e4e314df6c9b3c5ea470a51d6dc7cd3640ce2bc849cb407b7ea8ba6fe19fc2ac/parser.py
@@ -0,0 +1,206 @@
+from parso.python import tree
+from parso.python.token import PythonTokenTypes
+from parso.parser import BaseParser
+
+
+NAME = PythonTokenTypes.NAME
+INDENT = PythonTokenTypes.INDENT
+DEDENT = PythonTokenTypes.DEDENT
+
+
+class Parser(BaseParser):
+    """
+    This class is used to parse a Python file, it then divides them into a
+    class structure of different scopes.
+
+    :param pgen_grammar: The grammar object of pgen2. Loaded by load_grammar.
+    """
+
+    node_map = {
+        'expr_stmt': tree.ExprStmt,
+        'classdef': tree.Class,
+        'funcdef': tree.Function,
+        'file_input': tree.Module,
+        'import_name': tree.ImportName,
+        'import_from': tree.ImportFrom,
+        'break_stmt': tree.KeywordStatement,
+        'continue_stmt': tree.KeywordStatement,
+        'return_stmt': tree.ReturnStmt,
+        'raise_stmt': tree.KeywordStatement,
+        'yield_expr': tree.YieldExpr,
+        'del_stmt': tree.KeywordStatement,
+        'pass_stmt': tree.KeywordStatement,
+        'global_stmt': tree.GlobalStmt,
+        'nonlocal_stmt': tree.KeywordStatement,
+        'print_stmt': tree.KeywordStatement,
+        'assert_stmt': tree.AssertStmt,
+        'if_stmt': tree.IfStmt,
+        'with_stmt': tree.WithStmt,
+        'for_stmt': tree.ForStmt,
+        'while_stmt': tree.WhileStmt,
+        'try_stmt': tree.TryStmt,
+        'sync_comp_for': tree.SyncCompFor,
+        # Not sure if this is the best idea, but IMO it's the easiest way to
+        # avoid extreme amounts of work around the subtle difference of 2/3
+        # grammar in list comoprehensions.
+        'decorator': tree.Decorator,
+        'lambdef': tree.Lambda,
+        'lambdef_nocond': tree.Lambda,
+        'namedexpr_test': tree.NamedExpr,
+    }
+    default_node = tree.PythonNode
+
+    # Names/Keywords are handled separately
+    _leaf_map = {
+        PythonTokenTypes.STRING: tree.String,
+        PythonTokenTypes.NUMBER: tree.Number,
+        PythonTokenTypes.NEWLINE: tree.Newline,
+        PythonTokenTypes.ENDMARKER: tree.EndMarker,
+        PythonTokenTypes.FSTRING_STRING: tree.FStringString,
+        PythonTokenTypes.FSTRING_START: tree.FStringStart,
+        PythonTokenTypes.FSTRING_END: tree.FStringEnd,
+    }
+
+    def __init__(self, pgen_grammar, error_recovery=True, start_nonterminal='file_input'):
+        super().__init__(pgen_grammar, start_nonterminal,
+                         error_recovery=error_recovery)
+
+        self.syntax_errors = []
+        self._omit_dedent_list = []
+        self._indent_counter = 0
+
+    def parse(self, tokens):
+        if self._error_recovery:
+            if self._start_nonterminal != 'file_input':
+                raise NotImplementedError
+
+            tokens = self._recovery_tokenize(tokens)
+
+        return super().parse(tokens)
+
+    def convert_node(self, nonterminal, children):
+        """
+        Convert raw node information to a PythonBaseNode instance.
+
+        This is passed to the parser driver which calls it whenever a reduction of a
+        grammar rule produces a new complete node, so that the tree is build
+        strictly bottom-up.
+        """
+        try:
+            node = self.node_map[nonterminal](children)
+        except KeyError:
+            if nonterminal == 'suite':
+                # We don't want the INDENT/DEDENT in our parser tree. Those
+                # leaves are just cancer. They are virtual leaves and not real
+                # ones and therefore have pseudo start/end positions and no
+                # prefixes. Just ignore them.
+                children = [children[0]] + children[2:-1]
+            node = self.default_node(nonterminal, children)
+        return node
+
+    def convert_leaf(self, type, value, prefix, start_pos):
+        # print('leaf', repr(value), token.tok_name[type])
+        if type == NAME:
+            if value in self._pgen_grammar.reserved_syntax_strings:
+                return tree.Keyword(value, start_pos, prefix)
+            else:
+                return tree.Name(value, start_pos, prefix)
+
+        return self._leaf_map.get(type, tree.Operator)(value, start_pos, prefix)
+
+    def error_recovery(self, token):
+        tos_nodes = self.stack[-1].nodes
+        if tos_nodes:
+            last_leaf = tos_nodes[-1].get_last_leaf()
+        else:
+            last_leaf = None
+
+        if self._start_nonterminal == 'file_input' and \
+                (token.type == PythonTokenTypes.ENDMARKER
+                 or token.type == DEDENT and not last_leaf.value.endswith('\n')
+                 and not last_leaf.value.endswith('\r')):
+            # In Python statements need to end with a newline. But since it's
+            # possible (and valid in Python) that there's no newline at the
+            # end of a file, we have to recover even if the user doesn't want
+            # error recovery.
+            if self.stack[-1].dfa.from_rule == 'simple_stmt':
+                try:
+                    plan = self.stack[-1].dfa.transitions[PythonTokenTypes.NEWLINE]
+                except KeyError:
+                    pass
+                else:
+                    if plan.next_dfa.is_final and not plan.dfa_pushes:
+                        # We are ignoring here that the newline would be
+                        # required for a simple_stmt.
+                        self.stack[-1].dfa = plan.next_dfa
+                        self._add_token(token)
+                        return
+
+        if not self._error_recovery:
+            return super().error_recovery(token)
+
+        def current_suite(stack):
+            # For now just discard everything that is not a suite or
+            # file_input, if we detect an error.
+            for until_index, stack_node in reversed(list(enumerate(stack))):
+                # `suite` can sometimes be only simple_stmt, not stmt.
+                if stack_node.nonterminal == 'file_input':
+                    break
+                elif stack_node.nonterminal == 'suite':
+                    # In the case where we just have a newline we don't want to
+                    # do error recovery here. In all other cases, we want to do
+                    # error recovery.
+                    if len(stack_node.nodes) != 1:
+                        break
+            return until_index
+
+        until_index = current_suite(self.stack)
+
+        if self._stack_removal(until_index + 1):
+            self._add_token(token)
+        else:
+            typ, value, start_pos, prefix = token
+            if typ == INDENT:
+                # For every deleted INDENT we have to delete a DEDENT as well.
+                # Otherwise the parser will get into trouble and DEDENT too early.
+                self._omit_dedent_list.append(self._indent_counter)
+
+            error_leaf = tree.PythonErrorLeaf(typ.name, value, start_pos, prefix)
+            self.stack[-1].nodes.append(error_leaf)
+
+        tos = self.stack[-1]
+        if tos.nonterminal == 'suite':
+            # Need at least one statement in the suite. This happend with the
+            # error recovery above.
+            try:
+                tos.dfa = tos.dfa.arcs['stmt']
+            except KeyError:
+                # We're already in a final state.
+                pass
+
+    def _stack_removal(self, start_index):
+        all_nodes = [node for stack_node in self.stack[start_index:] for node in stack_node.nodes]
+
+        if all_nodes:
+            node = tree.PythonErrorNode(all_nodes)
+            self.stack[start_index - 1].nodes.append(node)
+
+        self.stack[start_index:] = []
+        return bool(all_nodes)
+
+    def _recovery_tokenize(self, tokens):
+        for token in tokens:
+            typ = token[0]
+            if typ == DEDENT:
+                # We need to count indents, because if we just omit any DEDENT,
+                # we might omit them in the wrong place.
+                o = self._omit_dedent_list
+                if o and o[-1] == self._indent_counter:
+                    o.pop()
+                    self._indent_counter -= 1
+                    continue
+
+                self._indent_counter -= 1
+            elif typ == INDENT:
+                self._indent_counter += 1
+            yield token
diff --git a/shared/package-files/e4/e4e33f3b2407212d3d3d6e93ffc90024d4baec6bdca6f3fe94a7276b62dc499d/reports.py b/shared/package-files/e4/e4e33f3b2407212d3d3d6e93ffc90024d4baec6bdca6f3fe94a7276b62dc499d/reports.py
new file mode 100644
index 0000000000000000000000000000000000000000..77cbf773e237a6ecab941ecdd68f685e4f06bf06
--- /dev/null
+++ b/shared/package-files/e4/e4e33f3b2407212d3d3d6e93ffc90024d4baec6bdca6f3fe94a7276b62dc499d/reports.py
@@ -0,0 +1,636 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+from io import StringIO
+import os
+from pprint import pprint
+from typing import Any
+from typing import cast
+from typing import final
+from typing import Iterable
+from typing import Iterator
+from typing import Literal
+from typing import Mapping
+from typing import NoReturn
+from typing import Sequence
+from typing import TYPE_CHECKING
+
+from _pytest._code.code import ExceptionChainRepr
+from _pytest._code.code import ExceptionInfo
+from _pytest._code.code import ExceptionRepr
+from _pytest._code.code import ReprEntry
+from _pytest._code.code import ReprEntryNative
+from _pytest._code.code import ReprExceptionInfo
+from _pytest._code.code import ReprFileLocation
+from _pytest._code.code import ReprFuncArgs
+from _pytest._code.code import ReprLocals
+from _pytest._code.code import ReprTraceback
+from _pytest._code.code import TerminalRepr
+from _pytest._io import TerminalWriter
+from _pytest.config import Config
+from _pytest.nodes import Collector
+from _pytest.nodes import Item
+from _pytest.outcomes import fail
+from _pytest.outcomes import skip
+
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from _pytest.runner import CallInfo
+
+
+def getworkerinfoline(node):
+    try:
+        return node._workerinfocache
+    except AttributeError:
+        d = node.workerinfo
+        ver = "{}.{}.{}".format(*d["version_info"][:3])
+        node._workerinfocache = s = "[{}] {} -- Python {} {}".format(
+            d["id"], d["sysplatform"], ver, d["executable"]
+        )
+        return s
+
+
+class BaseReport:
+    when: str | None
+    location: tuple[str, int | None, str] | None
+    longrepr: (
+        None | ExceptionInfo[BaseException] | tuple[str, int, str] | str | TerminalRepr
+    )
+    sections: list[tuple[str, str]]
+    nodeid: str
+    outcome: Literal["passed", "failed", "skipped"]
+
+    def __init__(self, **kw: Any) -> None:
+        self.__dict__.update(kw)
+
+    if TYPE_CHECKING:
+        # Can have arbitrary fields given to __init__().
+        def __getattr__(self, key: str) -> Any: ...
+
+    def toterminal(self, out: TerminalWriter) -> None:
+        if hasattr(self, "node"):
+            worker_info = getworkerinfoline(self.node)
+            if worker_info:
+                out.line(worker_info)
+
+        longrepr = self.longrepr
+        if longrepr is None:
+            return
+
+        if hasattr(longrepr, "toterminal"):
+            longrepr_terminal = cast(TerminalRepr, longrepr)
+            longrepr_terminal.toterminal(out)
+        else:
+            try:
+                s = str(longrepr)
+            except UnicodeEncodeError:
+                s = "<unprintable longrepr>"
+            out.line(s)
+
+    def get_sections(self, prefix: str) -> Iterator[tuple[str, str]]:
+        for name, content in self.sections:
+            if name.startswith(prefix):
+                yield prefix, content
+
+    @property
+    def longreprtext(self) -> str:
+        """Read-only property that returns the full string representation of
+        ``longrepr``.
+
+        .. versionadded:: 3.0
+        """
+        file = StringIO()
+        tw = TerminalWriter(file)
+        tw.hasmarkup = False
+        self.toterminal(tw)
+        exc = file.getvalue()
+        return exc.strip()
+
+    @property
+    def caplog(self) -> str:
+        """Return captured log lines, if log capturing is enabled.
+
+        .. versionadded:: 3.5
+        """
+        return "\n".join(
+            content for (prefix, content) in self.get_sections("Captured log")
+        )
+
+    @property
+    def capstdout(self) -> str:
+        """Return captured text from stdout, if capturing is enabled.
+
+        .. versionadded:: 3.0
+        """
+        return "".join(
+            content for (prefix, content) in self.get_sections("Captured stdout")
+        )
+
+    @property
+    def capstderr(self) -> str:
+        """Return captured text from stderr, if capturing is enabled.
+
+        .. versionadded:: 3.0
+        """
+        return "".join(
+            content for (prefix, content) in self.get_sections("Captured stderr")
+        )
+
+    @property
+    def passed(self) -> bool:
+        """Whether the outcome is passed."""
+        return self.outcome == "passed"
+
+    @property
+    def failed(self) -> bool:
+        """Whether the outcome is failed."""
+        return self.outcome == "failed"
+
+    @property
+    def skipped(self) -> bool:
+        """Whether the outcome is skipped."""
+        return self.outcome == "skipped"
+
+    @property
+    def fspath(self) -> str:
+        """The path portion of the reported node, as a string."""
+        return self.nodeid.split("::")[0]
+
+    @property
+    def count_towards_summary(self) -> bool:
+        """**Experimental** Whether this report should be counted towards the
+        totals shown at the end of the test session: "1 passed, 1 failure, etc".
+
+        .. note::
+
+            This function is considered **experimental**, so beware that it is subject to changes
+            even in patch releases.
+        """
+        return True
+
+    @property
+    def head_line(self) -> str | None:
+        """**Experimental** The head line shown with longrepr output for this
+        report, more commonly during traceback representation during
+        failures::
+
+            ________ Test.foo ________
+
+
+        In the example above, the head_line is "Test.foo".
+
+        .. note::
+
+            This function is considered **experimental**, so beware that it is subject to changes
+            even in patch releases.
+        """
+        if self.location is not None:
+            fspath, lineno, domain = self.location
+            return domain
+        return None
+
+    def _get_verbose_word_with_markup(
+        self, config: Config, default_markup: Mapping[str, bool]
+    ) -> tuple[str, Mapping[str, bool]]:
+        _category, _short, verbose = config.hook.pytest_report_teststatus(
+            report=self, config=config
+        )
+
+        if isinstance(verbose, str):
+            return verbose, default_markup
+
+        if isinstance(verbose, Sequence) and len(verbose) == 2:
+            word, markup = verbose
+            if isinstance(word, str) and isinstance(markup, Mapping):
+                return word, markup
+
+        fail(  # pragma: no cover
+            "pytest_report_teststatus() hook (from a plugin) returned "
+            f"an invalid verbose value: {verbose!r}.\nExpected either a string "
+            "or a tuple of (word, markup)."
+        )
+
+    def _to_json(self) -> dict[str, Any]:
+        """Return the contents of this report as a dict of builtin entries,
+        suitable for serialization.
+
+        This was originally the serialize_report() function from xdist (ca03269).
+
+        Experimental method.
+        """
+        return _report_to_json(self)
+
+    @classmethod
+    def _from_json(cls, reportdict: dict[str, object]) -> Self:
+        """Create either a TestReport or CollectReport, depending on the calling class.
+
+        It is the callers responsibility to know which class to pass here.
+
+        This was originally the serialize_report() function from xdist (ca03269).
+
+        Experimental method.
+        """
+        kwargs = _report_kwargs_from_json(reportdict)
+        return cls(**kwargs)
+
+
+def _report_unserialization_failure(
+    type_name: str, report_class: type[BaseReport], reportdict
+) -> NoReturn:
+    url = "https://github.com/pytest-dev/pytest/issues"
+    stream = StringIO()
+    pprint("-" * 100, stream=stream)
+    pprint(f"INTERNALERROR: Unknown entry type returned: {type_name}", stream=stream)
+    pprint(f"report_name: {report_class}", stream=stream)
+    pprint(reportdict, stream=stream)
+    pprint(f"Please report this bug at {url}", stream=stream)
+    pprint("-" * 100, stream=stream)
+    raise RuntimeError(stream.getvalue())
+
+
+@final
+class TestReport(BaseReport):
+    """Basic test report object (also used for setup and teardown calls if
+    they fail).
+
+    Reports can contain arbitrary extra attributes.
+    """
+
+    __test__ = False
+    # Defined by skipping plugin.
+    # xfail reason if xfailed, otherwise not defined. Use hasattr to distinguish.
+    wasxfail: str
+
+    def __init__(
+        self,
+        nodeid: str,
+        location: tuple[str, int | None, str],
+        keywords: Mapping[str, Any],
+        outcome: Literal["passed", "failed", "skipped"],
+        longrepr: None
+        | ExceptionInfo[BaseException]
+        | tuple[str, int, str]
+        | str
+        | TerminalRepr,
+        when: Literal["setup", "call", "teardown"],
+        sections: Iterable[tuple[str, str]] = (),
+        duration: float = 0,
+        start: float = 0,
+        stop: float = 0,
+        user_properties: Iterable[tuple[str, object]] | None = None,
+        **extra,
+    ) -> None:
+        #: Normalized collection nodeid.
+        self.nodeid = nodeid
+
+        #: A (filesystempath, lineno, domaininfo) tuple indicating the
+        #: actual location of a test item - it might be different from the
+        #: collected one e.g. if a method is inherited from a different module.
+        #: The filesystempath may be relative to ``config.rootdir``.
+        #: The line number is 0-based.
+        self.location: tuple[str, int | None, str] = location
+
+        #: A name -> value dictionary containing all keywords and
+        #: markers associated with a test invocation.
+        self.keywords: Mapping[str, Any] = keywords
+
+        #: Test outcome, always one of "passed", "failed", "skipped".
+        self.outcome = outcome
+
+        #: None or a failure representation.
+        self.longrepr = longrepr
+
+        #: One of 'setup', 'call', 'teardown' to indicate runtest phase.
+        self.when = when
+
+        #: User properties is a list of tuples (name, value) that holds user
+        #: defined properties of the test.
+        self.user_properties = list(user_properties or [])
+
+        #: Tuples of str ``(heading, content)`` with extra information
+        #: for the test report. Used by pytest to add text captured
+        #: from ``stdout``, ``stderr``, and intercepted logging events. May
+        #: be used by other plugins to add arbitrary information to reports.
+        self.sections = list(sections)
+
+        #: Time it took to run just the test.
+        self.duration: float = duration
+
+        #: The system time when the call started, in seconds since the epoch.
+        self.start: float = start
+        #: The system time when the call ended, in seconds since the epoch.
+        self.stop: float = stop
+
+        self.__dict__.update(extra)
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__} {self.nodeid!r} when={self.when!r} outcome={self.outcome!r}>"
+
+    @classmethod
+    def from_item_and_call(cls, item: Item, call: CallInfo[None]) -> TestReport:
+        """Create and fill a TestReport with standard item and call info.
+
+        :param item: The item.
+        :param call: The call info.
+        """
+        when = call.when
+        # Remove "collect" from the Literal type -- only for collection calls.
+        assert when != "collect"
+        duration = call.duration
+        start = call.start
+        stop = call.stop
+        keywords = {x: 1 for x in item.keywords}
+        excinfo = call.excinfo
+        sections = []
+        if not call.excinfo:
+            outcome: Literal["passed", "failed", "skipped"] = "passed"
+            longrepr: (
+                None
+                | ExceptionInfo[BaseException]
+                | tuple[str, int, str]
+                | str
+                | TerminalRepr
+            ) = None
+        else:
+            if not isinstance(excinfo, ExceptionInfo):
+                outcome = "failed"
+                longrepr = excinfo
+            elif isinstance(excinfo.value, skip.Exception):
+                outcome = "skipped"
+                r = excinfo._getreprcrash()
+                assert (
+                    r is not None
+                ), "There should always be a traceback entry for skipping a test."
+                if excinfo.value._use_item_location:
+                    path, line = item.reportinfo()[:2]
+                    assert line is not None
+                    longrepr = os.fspath(path), line + 1, r.message
+                else:
+                    longrepr = (str(r.path), r.lineno, r.message)
+            else:
+                outcome = "failed"
+                if call.when == "call":
+                    longrepr = item.repr_failure(excinfo)
+                else:  # exception in setup or teardown
+                    longrepr = item._repr_failure_py(
+                        excinfo, style=item.config.getoption("tbstyle", "auto")
+                    )
+        for rwhen, key, content in item._report_sections:
+            sections.append((f"Captured {key} {rwhen}", content))
+        return cls(
+            item.nodeid,
+            item.location,
+            keywords,
+            outcome,
+            longrepr,
+            when,
+            sections,
+            duration,
+            start,
+            stop,
+            user_properties=item.user_properties,
+        )
+
+
+@final
+class CollectReport(BaseReport):
+    """Collection report object.
+
+    Reports can contain arbitrary extra attributes.
+    """
+
+    when = "collect"
+
+    def __init__(
+        self,
+        nodeid: str,
+        outcome: Literal["passed", "failed", "skipped"],
+        longrepr: None
+        | ExceptionInfo[BaseException]
+        | tuple[str, int, str]
+        | str
+        | TerminalRepr,
+        result: list[Item | Collector] | None,
+        sections: Iterable[tuple[str, str]] = (),
+        **extra,
+    ) -> None:
+        #: Normalized collection nodeid.
+        self.nodeid = nodeid
+
+        #: Test outcome, always one of "passed", "failed", "skipped".
+        self.outcome = outcome
+
+        #: None or a failure representation.
+        self.longrepr = longrepr
+
+        #: The collected items and collection nodes.
+        self.result = result or []
+
+        #: Tuples of str ``(heading, content)`` with extra information
+        #: for the test report. Used by pytest to add text captured
+        #: from ``stdout``, ``stderr``, and intercepted logging events. May
+        #: be used by other plugins to add arbitrary information to reports.
+        self.sections = list(sections)
+
+        self.__dict__.update(extra)
+
+    @property
+    def location(  # type:ignore[override]
+        self,
+    ) -> tuple[str, int | None, str] | None:
+        return (self.fspath, None, self.fspath)
+
+    def __repr__(self) -> str:
+        return f"<CollectReport {self.nodeid!r} lenresult={len(self.result)} outcome={self.outcome!r}>"
+
+
+class CollectErrorRepr(TerminalRepr):
+    def __init__(self, msg: str) -> None:
+        self.longrepr = msg
+
+    def toterminal(self, out: TerminalWriter) -> None:
+        out.line(self.longrepr, red=True)
+
+
+def pytest_report_to_serializable(
+    report: CollectReport | TestReport,
+) -> dict[str, Any] | None:
+    if isinstance(report, (TestReport, CollectReport)):
+        data = report._to_json()
+        data["$report_type"] = report.__class__.__name__
+        return data
+    # TODO: Check if this is actually reachable.
+    return None  # type: ignore[unreachable]
+
+
+def pytest_report_from_serializable(
+    data: dict[str, Any],
+) -> CollectReport | TestReport | None:
+    if "$report_type" in data:
+        if data["$report_type"] == "TestReport":
+            return TestReport._from_json(data)
+        elif data["$report_type"] == "CollectReport":
+            return CollectReport._from_json(data)
+        assert False, "Unknown report_type unserialize data: {}".format(
+            data["$report_type"]
+        )
+    return None
+
+
+def _report_to_json(report: BaseReport) -> dict[str, Any]:
+    """Return the contents of this report as a dict of builtin entries,
+    suitable for serialization.
+
+    This was originally the serialize_report() function from xdist (ca03269).
+    """
+
+    def serialize_repr_entry(
+        entry: ReprEntry | ReprEntryNative,
+    ) -> dict[str, Any]:
+        data = dataclasses.asdict(entry)
+        for key, value in data.items():
+            if hasattr(value, "__dict__"):
+                data[key] = dataclasses.asdict(value)
+        entry_data = {"type": type(entry).__name__, "data": data}
+        return entry_data
+
+    def serialize_repr_traceback(reprtraceback: ReprTraceback) -> dict[str, Any]:
+        result = dataclasses.asdict(reprtraceback)
+        result["reprentries"] = [
+            serialize_repr_entry(x) for x in reprtraceback.reprentries
+        ]
+        return result
+
+    def serialize_repr_crash(
+        reprcrash: ReprFileLocation | None,
+    ) -> dict[str, Any] | None:
+        if reprcrash is not None:
+            return dataclasses.asdict(reprcrash)
+        else:
+            return None
+
+    def serialize_exception_longrepr(rep: BaseReport) -> dict[str, Any]:
+        assert rep.longrepr is not None
+        # TODO: Investigate whether the duck typing is really necessary here.
+        longrepr = cast(ExceptionRepr, rep.longrepr)
+        result: dict[str, Any] = {
+            "reprcrash": serialize_repr_crash(longrepr.reprcrash),
+            "reprtraceback": serialize_repr_traceback(longrepr.reprtraceback),
+            "sections": longrepr.sections,
+        }
+        if isinstance(longrepr, ExceptionChainRepr):
+            result["chain"] = []
+            for repr_traceback, repr_crash, description in longrepr.chain:
+                result["chain"].append(
+                    (
+                        serialize_repr_traceback(repr_traceback),
+                        serialize_repr_crash(repr_crash),
+                        description,
+                    )
+                )
+        else:
+            result["chain"] = None
+        return result
+
+    d = report.__dict__.copy()
+    if hasattr(report.longrepr, "toterminal"):
+        if hasattr(report.longrepr, "reprtraceback") and hasattr(
+            report.longrepr, "reprcrash"
+        ):
+            d["longrepr"] = serialize_exception_longrepr(report)
+        else:
+            d["longrepr"] = str(report.longrepr)
+    else:
+        d["longrepr"] = report.longrepr
+    for name in d:
+        if isinstance(d[name], os.PathLike):
+            d[name] = os.fspath(d[name])
+        elif name == "result":
+            d[name] = None  # for now
+    return d
+
+
+def _report_kwargs_from_json(reportdict: dict[str, Any]) -> dict[str, Any]:
+    """Return **kwargs that can be used to construct a TestReport or
+    CollectReport instance.
+
+    This was originally the serialize_report() function from xdist (ca03269).
+    """
+
+    def deserialize_repr_entry(entry_data):
+        data = entry_data["data"]
+        entry_type = entry_data["type"]
+        if entry_type == "ReprEntry":
+            reprfuncargs = None
+            reprfileloc = None
+            reprlocals = None
+            if data["reprfuncargs"]:
+                reprfuncargs = ReprFuncArgs(**data["reprfuncargs"])
+            if data["reprfileloc"]:
+                reprfileloc = ReprFileLocation(**data["reprfileloc"])
+            if data["reprlocals"]:
+                reprlocals = ReprLocals(data["reprlocals"]["lines"])
+
+            reprentry: ReprEntry | ReprEntryNative = ReprEntry(
+                lines=data["lines"],
+                reprfuncargs=reprfuncargs,
+                reprlocals=reprlocals,
+                reprfileloc=reprfileloc,
+                style=data["style"],
+            )
+        elif entry_type == "ReprEntryNative":
+            reprentry = ReprEntryNative(data["lines"])
+        else:
+            _report_unserialization_failure(entry_type, TestReport, reportdict)
+        return reprentry
+
+    def deserialize_repr_traceback(repr_traceback_dict):
+        repr_traceback_dict["reprentries"] = [
+            deserialize_repr_entry(x) for x in repr_traceback_dict["reprentries"]
+        ]
+        return ReprTraceback(**repr_traceback_dict)
+
+    def deserialize_repr_crash(repr_crash_dict: dict[str, Any] | None):
+        if repr_crash_dict is not None:
+            return ReprFileLocation(**repr_crash_dict)
+        else:
+            return None
+
+    if (
+        reportdict["longrepr"]
+        and "reprcrash" in reportdict["longrepr"]
+        and "reprtraceback" in reportdict["longrepr"]
+    ):
+        reprtraceback = deserialize_repr_traceback(
+            reportdict["longrepr"]["reprtraceback"]
+        )
+        reprcrash = deserialize_repr_crash(reportdict["longrepr"]["reprcrash"])
+        if reportdict["longrepr"]["chain"]:
+            chain = []
+            for repr_traceback_data, repr_crash_data, description in reportdict[
+                "longrepr"
+            ]["chain"]:
+                chain.append(
+                    (
+                        deserialize_repr_traceback(repr_traceback_data),
+                        deserialize_repr_crash(repr_crash_data),
+                        description,
+                    )
+                )
+            exception_info: ExceptionChainRepr | ReprExceptionInfo = ExceptionChainRepr(
+                chain
+            )
+        else:
+            exception_info = ReprExceptionInfo(
+                reprtraceback=reprtraceback,
+                reprcrash=reprcrash,
+            )
+
+        for section in reportdict["longrepr"]["sections"]:
+            exception_info.addsection(*section)
+        reportdict["longrepr"] = exception_info
+
+    return reportdict
diff --git a/shared/package-files/e4/e4e807b07ddae59fa440cb7311f062f7c56f34df309b920c31b072b4bd480744/log_sigmoid_forward_cpu_dispatch.h b/shared/package-files/e4/e4e807b07ddae59fa440cb7311f062f7c56f34df309b920c31b072b4bd480744/log_sigmoid_forward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e091a8b009506eedddc0ec194bd8dfe99422605f
--- /dev/null
+++ b/shared/package-files/e4/e4e807b07ddae59fa440cb7311f062f7c56f34df309b920c31b072b4bd480744/log_sigmoid_forward_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward(const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_outf(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/e4/e4ea9df50bec5b8311fde1fefb06e99a86b9d36ca1a0b33883ef603e1e5e60a7/factor_.py b/shared/package-files/e4/e4ea9df50bec5b8311fde1fefb06e99a86b9d36ca1a0b33883ef603e1e5e60a7/factor_.py
new file mode 100644
index 0000000000000000000000000000000000000000..67e1242dcced325578e0d7469b585ff41a1c2ed6
--- /dev/null
+++ b/shared/package-files/e4/e4ea9df50bec5b8311fde1fefb06e99a86b9d36ca1a0b33883ef603e1e5e60a7/factor_.py
@@ -0,0 +1,2654 @@
+"""
+Integer factorization
+"""
+
+from collections import defaultdict
+from functools import reduce
+import random
+import math
+
+from sympy.core import sympify
+from sympy.core.containers import Dict
+from sympy.core.evalf import bitcount
+from sympy.core.expr import Expr
+from sympy.core.function import Function
+from sympy.core.logic import fuzzy_and
+from sympy.core.mul import Mul
+from sympy.core.numbers import igcd, ilcm, Rational, Integer
+from sympy.core.power import integer_nthroot, Pow, integer_log
+from sympy.core.singleton import S
+from sympy.external.gmpy import SYMPY_INTS
+from .primetest import isprime
+from .generate import sieve, primerange, nextprime
+from .digits import digits
+from sympy.utilities.iterables import flatten
+from sympy.utilities.misc import as_int, filldedent
+from .ecm import _ecm_one_factor
+
+# Note: This list should be updated whenever new Mersenne primes are found.
+# Refer: https://www.mersenne.org/
+MERSENNE_PRIME_EXPONENTS = (2, 3, 5, 7, 13, 17, 19, 31, 61, 89, 107, 127, 521, 607, 1279, 2203,
+ 2281, 3217, 4253, 4423, 9689, 9941, 11213, 19937, 21701, 23209, 44497, 86243, 110503, 132049,
+ 216091, 756839, 859433, 1257787, 1398269, 2976221, 3021377, 6972593, 13466917, 20996011, 24036583,
+ 25964951, 30402457, 32582657, 37156667, 42643801, 43112609, 57885161, 74207281, 77232917, 82589933)
+
+# compute more when needed for i in Mersenne prime exponents
+PERFECT = [6]  # 2**(i-1)*(2**i-1)
+MERSENNES = [3]  # 2**i - 1
+
+
+def _ismersenneprime(n):
+    global MERSENNES
+    j = len(MERSENNES)
+    while n > MERSENNES[-1] and j < len(MERSENNE_PRIME_EXPONENTS):
+        # conservatively grow the list
+        MERSENNES.append(2**MERSENNE_PRIME_EXPONENTS[j] - 1)
+        j += 1
+    return n in MERSENNES
+
+
+def _isperfect(n):
+    global PERFECT
+    if n % 2 == 0:
+        j = len(PERFECT)
+        while n > PERFECT[-1] and j < len(MERSENNE_PRIME_EXPONENTS):
+            # conservatively grow the list
+            t = 2**(MERSENNE_PRIME_EXPONENTS[j] - 1)
+            PERFECT.append(t*(2*t - 1))
+            j += 1
+    return n in PERFECT
+
+
+small_trailing = [0] * 256
+for j in range(1,8):
+    small_trailing[1<<j::1<<(j+1)] = [j] * (1<<(7-j))
+
+
+def smoothness(n):
+    """
+    Return the B-smooth and B-power smooth values of n.
+
+    The smoothness of n is the largest prime factor of n; the power-
+    smoothness is the largest divisor raised to its multiplicity.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import smoothness
+    >>> smoothness(2**7*3**2)
+    (3, 128)
+    >>> smoothness(2**4*13)
+    (13, 16)
+    >>> smoothness(2)
+    (2, 2)
+
+    See Also
+    ========
+
+    factorint, smoothness_p
+    """
+
+    if n == 1:
+        return (1, 1)  # not prime, but otherwise this causes headaches
+    facs = factorint(n)
+    return max(facs), max(m**facs[m] for m in facs)
+
+
+def smoothness_p(n, m=-1, power=0, visual=None):
+    """
+    Return a list of [m, (p, (M, sm(p + m), psm(p + m)))...]
+    where:
+
+    1. p**M is the base-p divisor of n
+    2. sm(p + m) is the smoothness of p + m (m = -1 by default)
+    3. psm(p + m) is the power smoothness of p + m
+
+    The list is sorted according to smoothness (default) or by power smoothness
+    if power=1.
+
+    The smoothness of the numbers to the left (m = -1) or right (m = 1) of a
+    factor govern the results that are obtained from the p +/- 1 type factoring
+    methods.
+
+        >>> from sympy.ntheory.factor_ import smoothness_p, factorint
+        >>> smoothness_p(10431, m=1)
+        (1, [(3, (2, 2, 4)), (19, (1, 5, 5)), (61, (1, 31, 31))])
+        >>> smoothness_p(10431)
+        (-1, [(3, (2, 2, 2)), (19, (1, 3, 9)), (61, (1, 5, 5))])
+        >>> smoothness_p(10431, power=1)
+        (-1, [(3, (2, 2, 2)), (61, (1, 5, 5)), (19, (1, 3, 9))])
+
+    If visual=True then an annotated string will be returned:
+
+        >>> print(smoothness_p(21477639576571, visual=1))
+        p**i=4410317**1 has p-1 B=1787, B-pow=1787
+        p**i=4869863**1 has p-1 B=2434931, B-pow=2434931
+
+    This string can also be generated directly from a factorization dictionary
+    and vice versa:
+
+        >>> factorint(17*9)
+        {3: 2, 17: 1}
+        >>> smoothness_p(_)
+        'p**i=3**2 has p-1 B=2, B-pow=2\\np**i=17**1 has p-1 B=2, B-pow=16'
+        >>> smoothness_p(_)
+        {3: 2, 17: 1}
+
+    The table of the output logic is:
+
+        ====== ====== ======= =======
+        |              Visual
+        ------ ----------------------
+        Input  True   False   other
+        ====== ====== ======= =======
+        dict    str    tuple   str
+        str     str    tuple   dict
+        tuple   str    tuple   str
+        n       str    tuple   tuple
+        mul     str    tuple   tuple
+        ====== ====== ======= =======
+
+    See Also
+    ========
+
+    factorint, smoothness
+    """
+
+    # visual must be True, False or other (stored as None)
+    if visual in (1, 0):
+        visual = bool(visual)
+    elif visual not in (True, False):
+        visual = None
+
+    if isinstance(n, str):
+        if visual:
+            return n
+        d = {}
+        for li in n.splitlines():
+            k, v = [int(i) for i in
+                    li.split('has')[0].split('=')[1].split('**')]
+            d[k] = v
+        if visual is not True and visual is not False:
+            return d
+        return smoothness_p(d, visual=False)
+    elif not isinstance(n, tuple):
+        facs = factorint(n, visual=False)
+
+    if power:
+        k = -1
+    else:
+        k = 1
+    if isinstance(n, tuple):
+        rv = n
+    else:
+        rv = (m, sorted([(f,
+                          tuple([M] + list(smoothness(f + m))))
+                         for f, M in list(facs.items())],
+                        key=lambda x: (x[1][k], x[0])))
+
+    if visual is False or (visual is not True) and (type(n) in [int, Mul]):
+        return rv
+    lines = []
+    for dat in rv[1]:
+        dat = flatten(dat)
+        dat.insert(2, m)
+        lines.append('p**i=%i**%i has p%+i B=%i, B-pow=%i' % tuple(dat))
+    return '\n'.join(lines)
+
+
+def trailing(n):
+    """Count the number of trailing zero digits in the binary
+    representation of n, i.e. determine the largest power of 2
+    that divides n.
+
+    Examples
+    ========
+
+    >>> from sympy import trailing
+    >>> trailing(128)
+    7
+    >>> trailing(63)
+    0
+    """
+    n = abs(int(n))
+    if not n:
+        return 0
+    low_byte = n & 0xff
+    if low_byte:
+        return small_trailing[low_byte]
+
+    # 2**m is quick for z up through 2**30
+    z = bitcount(n) - 1
+    if isinstance(z, SYMPY_INTS):
+        if n == 1 << z:
+            return z
+
+    if z < 300:
+        # fixed 8-byte reduction
+        t = 8
+        n >>= 8
+        while not n & 0xff:
+            n >>= 8
+            t += 8
+        return t + small_trailing[n & 0xff]
+
+    # binary reduction important when there might be a large
+    # number of trailing 0s
+    t = 0
+    p = 8
+    while not n & 1:
+        while not n & ((1 << p) - 1):
+            n >>= p
+            t += p
+            p *= 2
+        p //= 2
+    return t
+
+
+def multiplicity(p, n):
+    """
+    Find the greatest integer m such that p**m divides n.
+
+    Examples
+    ========
+
+    >>> from sympy import multiplicity, Rational
+    >>> [multiplicity(5, n) for n in [8, 5, 25, 125, 250]]
+    [0, 1, 2, 3, 3]
+    >>> multiplicity(3, Rational(1, 9))
+    -2
+
+    Note: when checking for the multiplicity of a number in a
+    large factorial it is most efficient to send it as an unevaluated
+    factorial or to call ``multiplicity_in_factorial`` directly:
+
+    >>> from sympy.ntheory import multiplicity_in_factorial
+    >>> from sympy import factorial
+    >>> p = factorial(25)
+    >>> n = 2**100
+    >>> nfac = factorial(n, evaluate=False)
+    >>> multiplicity(p, nfac)
+    52818775009509558395695966887
+    >>> _ == multiplicity_in_factorial(p, n)
+    True
+
+    """
+    try:
+        p, n = as_int(p), as_int(n)
+    except ValueError:
+        from sympy.functions.combinatorial.factorials import factorial
+        if all(isinstance(i, (SYMPY_INTS, Rational)) for i in (p, n)):
+            p = Rational(p)
+            n = Rational(n)
+            if p.q == 1:
+                if n.p == 1:
+                    return -multiplicity(p.p, n.q)
+                return multiplicity(p.p, n.p) - multiplicity(p.p, n.q)
+            elif p.p == 1:
+                return multiplicity(p.q, n.q)
+            else:
+                like = min(
+                    multiplicity(p.p, n.p),
+                    multiplicity(p.q, n.q))
+                cross = min(
+                    multiplicity(p.q, n.p),
+                    multiplicity(p.p, n.q))
+                return like - cross
+        elif (isinstance(p, (SYMPY_INTS, Integer)) and
+                isinstance(n, factorial) and
+                isinstance(n.args[0], Integer) and
+                n.args[0] >= 0):
+            return multiplicity_in_factorial(p, n.args[0])
+        raise ValueError('expecting ints or fractions, got %s and %s' % (p, n))
+
+    if n == 0:
+        raise ValueError('no such integer exists: multiplicity of %s is not-defined' %(n))
+    if p == 2:
+        return trailing(n)
+    if p < 2:
+        raise ValueError('p must be an integer, 2 or larger, but got %s' % p)
+    if p == n:
+        return 1
+
+    m = 0
+    n, rem = divmod(n, p)
+    while not rem:
+        m += 1
+        if m > 5:
+            # The multiplicity could be very large. Better
+            # to increment in powers of two
+            e = 2
+            while 1:
+                ppow = p**e
+                if ppow < n:
+                    nnew, rem = divmod(n, ppow)
+                    if not rem:
+                        m += e
+                        e *= 2
+                        n = nnew
+                        continue
+                return m + multiplicity(p, n)
+        n, rem = divmod(n, p)
+    return m
+
+
+def multiplicity_in_factorial(p, n):
+    """return the largest integer ``m`` such that ``p**m`` divides ``n!``
+    without calculating the factorial of ``n``.
+
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import multiplicity_in_factorial
+    >>> from sympy import factorial
+
+    >>> multiplicity_in_factorial(2, 3)
+    1
+
+    An instructive use of this is to tell how many trailing zeros
+    a given factorial has. For example, there are 6 in 25!:
+
+    >>> factorial(25)
+    15511210043330985984000000
+    >>> multiplicity_in_factorial(10, 25)
+    6
+
+    For large factorials, it is much faster/feasible to use
+    this function rather than computing the actual factorial:
+
+    >>> multiplicity_in_factorial(factorial(25), 2**100)
+    52818775009509558395695966887
+
+    """
+
+    p, n = as_int(p), as_int(n)
+
+    if p <= 0:
+        raise ValueError('expecting positive integer got %s' % p )
+
+    if n < 0:
+        raise ValueError('expecting non-negative integer got %s' % n )
+
+    factors = factorint(p)
+
+    # keep only the largest of a given multiplicity since those
+    # of a given multiplicity will be goverened by the behavior
+    # of the largest factor
+    test = defaultdict(int)
+    for k, v in factors.items():
+        test[v] = max(k, test[v])
+    keep = set(test.values())
+    # remove others from factors
+    for k in list(factors.keys()):
+        if k not in keep:
+            factors.pop(k)
+
+    mp = S.Infinity
+    for i in factors:
+        # multiplicity of i in n! is
+        mi = (n - (sum(digits(n, i)) - i))//(i - 1)
+        # multiplicity of p in n! depends on multiplicity
+        # of prime `i` in p, so we floor divide by factors[i]
+        # and keep it if smaller than the multiplicity of p
+        # seen so far
+        mp = min(mp, mi//factors[i])
+
+    return mp
+
+
+def perfect_power(n, candidates=None, big=True, factor=True):
+    """
+    Return ``(b, e)`` such that ``n`` == ``b**e`` if ``n`` is a unique
+    perfect power with ``e > 1``, else ``False`` (e.g. 1 is not a
+    perfect power). A ValueError is raised if ``n`` is not Rational.
+
+    By default, the base is recursively decomposed and the exponents
+    collected so the largest possible ``e`` is sought. If ``big=False``
+    then the smallest possible ``e`` (thus prime) will be chosen.
+
+    If ``factor=True`` then simultaneous factorization of ``n`` is
+    attempted since finding a factor indicates the only possible root
+    for ``n``. This is True by default since only a few small factors will
+    be tested in the course of searching for the perfect power.
+
+    The use of ``candidates`` is primarily for internal use; if provided,
+    False will be returned if ``n`` cannot be written as a power with one
+    of the candidates as an exponent and factoring (beyond testing for
+    a factor of 2) will not be attempted.
+
+    Examples
+    ========
+
+    >>> from sympy import perfect_power, Rational
+    >>> perfect_power(16)
+    (2, 4)
+    >>> perfect_power(16, big=False)
+    (4, 2)
+
+    Negative numbers can only have odd perfect powers:
+
+    >>> perfect_power(-4)
+    False
+    >>> perfect_power(-8)
+    (-2, 3)
+
+    Rationals are also recognized:
+
+    >>> perfect_power(Rational(1, 2)**3)
+    (1/2, 3)
+    >>> perfect_power(Rational(-3, 2)**3)
+    (-3/2, 3)
+
+    Notes
+    =====
+
+    To know whether an integer is a perfect power of 2 use
+
+        >>> is2pow = lambda n: bool(n and not n & (n - 1))
+        >>> [(i, is2pow(i)) for i in range(5)]
+        [(0, False), (1, True), (2, True), (3, False), (4, True)]
+
+    It is not necessary to provide ``candidates``. When provided
+    it will be assumed that they are ints. The first one that is
+    larger than the computed maximum possible exponent will signal
+    failure for the routine.
+
+        >>> perfect_power(3**8, [9])
+        False
+        >>> perfect_power(3**8, [2, 4, 8])
+        (3, 8)
+        >>> perfect_power(3**8, [4, 8], big=False)
+        (9, 4)
+
+    See Also
+    ========
+    sympy.core.power.integer_nthroot
+    sympy.ntheory.primetest.is_square
+    """
+    if isinstance(n, Rational) and not n.is_Integer:
+        p, q = n.as_numer_denom()
+        if p is S.One:
+            pp = perfect_power(q)
+            if pp:
+                pp = (n.func(1, pp[0]), pp[1])
+        else:
+            pp = perfect_power(p)
+            if pp:
+                num, e = pp
+                pq = perfect_power(q, [e])
+                if pq:
+                    den, _ = pq
+                    pp = n.func(num, den), e
+        return pp
+
+    n = as_int(n)
+    if n < 0:
+        pp = perfect_power(-n)
+        if pp:
+            b, e = pp
+            if e % 2:
+                return -b, e
+        return False
+
+    if n <= 3:
+        # no unique exponent for 0, 1
+        # 2 and 3 have exponents of 1
+        return False
+    logn = math.log(n, 2)
+    max_possible = int(logn) + 2  # only check values less than this
+    not_square = n % 10 in [2, 3, 7, 8]  # squares cannot end in 2, 3, 7, 8
+    min_possible = 2 + not_square
+    if not candidates:
+        candidates = primerange(min_possible, max_possible)
+    else:
+        candidates = sorted([i for i in candidates
+            if min_possible <= i < max_possible])
+        if n%2 == 0:
+            e = trailing(n)
+            candidates = [i for i in candidates if e%i == 0]
+        if big:
+            candidates = reversed(candidates)
+        for e in candidates:
+            r, ok = integer_nthroot(n, e)
+            if ok:
+                return (r, e)
+        return False
+
+    def _factors():
+        rv = 2 + n % 2
+        while True:
+            yield rv
+            rv = nextprime(rv)
+
+    for fac, e in zip(_factors(), candidates):
+        # see if there is a factor present
+        if factor and n % fac == 0:
+            # find what the potential power is
+            if fac == 2:
+                e = trailing(n)
+            else:
+                e = multiplicity(fac, n)
+            # if it's a trivial power we are done
+            if e == 1:
+                return False
+
+            # maybe the e-th root of n is exact
+            r, exact = integer_nthroot(n, e)
+            if not exact:
+                # Having a factor, we know that e is the maximal
+                # possible value for a root of n.
+                # If n = fac**e*m can be written as a perfect
+                # power then see if m can be written as r**E where
+                # gcd(e, E) != 1 so n = (fac**(e//E)*r)**E
+                m = n//fac**e
+                rE = perfect_power(m, candidates=divisors(e, generator=True))
+                if not rE:
+                    return False
+                else:
+                    r, E = rE
+                    r, e = fac**(e//E)*r, E
+            if not big:
+                e0 = primefactors(e)
+                if e0[0] != e:
+                    r, e = r**(e//e0[0]), e0[0]
+            return r, e
+
+        # Weed out downright impossible candidates
+        if logn/e < 40:
+            b = 2.0**(logn/e)
+            if abs(int(b + 0.5) - b) > 0.01:
+                continue
+
+        # now see if the plausible e makes a perfect power
+        r, exact = integer_nthroot(n, e)
+        if exact:
+            if big:
+                m = perfect_power(r, big=big, factor=factor)
+                if m:
+                    r, e = m[0], e*m[1]
+            return int(r), e
+
+    return False
+
+
+def pollard_rho(n, s=2, a=1, retries=5, seed=1234, max_steps=None, F=None):
+    r"""
+    Use Pollard's rho method to try to extract a nontrivial factor
+    of ``n``. The returned factor may be a composite number. If no
+    factor is found, ``None`` is returned.
+
+    The algorithm generates pseudo-random values of x with a generator
+    function, replacing x with F(x). If F is not supplied then the
+    function x**2 + ``a`` is used. The first value supplied to F(x) is ``s``.
+    Upon failure (if ``retries`` is > 0) a new ``a`` and ``s`` will be
+    supplied; the ``a`` will be ignored if F was supplied.
+
+    The sequence of numbers generated by such functions generally have a
+    a lead-up to some number and then loop around back to that number and
+    begin to repeat the sequence, e.g. 1, 2, 3, 4, 5, 3, 4, 5 -- this leader
+    and loop look a bit like the Greek letter rho, and thus the name, 'rho'.
+
+    For a given function, very different leader-loop values can be obtained
+    so it is a good idea to allow for retries:
+
+    >>> from sympy.ntheory.generate import cycle_length
+    >>> n = 16843009
+    >>> F = lambda x:(2048*pow(x, 2, n) + 32767) % n
+    >>> for s in range(5):
+    ...     print('loop length = %4i; leader length = %3i' % next(cycle_length(F, s)))
+    ...
+    loop length = 2489; leader length =  42
+    loop length =   78; leader length = 120
+    loop length = 1482; leader length =  99
+    loop length = 1482; leader length = 285
+    loop length = 1482; leader length = 100
+
+    Here is an explicit example where there is a two element leadup to
+    a sequence of 3 numbers (11, 14, 4) that then repeat:
+
+    >>> x=2
+    >>> for i in range(9):
+    ...     x=(x**2+12)%17
+    ...     print(x)
+    ...
+    16
+    13
+    11
+    14
+    4
+    11
+    14
+    4
+    11
+    >>> next(cycle_length(lambda x: (x**2+12)%17, 2))
+    (3, 2)
+    >>> list(cycle_length(lambda x: (x**2+12)%17, 2, values=True))
+    [16, 13, 11, 14, 4]
+
+    Instead of checking the differences of all generated values for a gcd
+    with n, only the kth and 2*kth numbers are checked, e.g. 1st and 2nd,
+    2nd and 4th, 3rd and 6th until it has been detected that the loop has been
+    traversed. Loops may be many thousands of steps long before rho finds a
+    factor or reports failure. If ``max_steps`` is specified, the iteration
+    is cancelled with a failure after the specified number of steps.
+
+    Examples
+    ========
+
+    >>> from sympy import pollard_rho
+    >>> n=16843009
+    >>> F=lambda x:(2048*pow(x,2,n) + 32767) % n
+    >>> pollard_rho(n, F=F)
+    257
+
+    Use the default setting with a bad value of ``a`` and no retries:
+
+    >>> pollard_rho(n, a=n-2, retries=0)
+
+    If retries is > 0 then perhaps the problem will correct itself when
+    new values are generated for a:
+
+    >>> pollard_rho(n, a=n-2, retries=1)
+    257
+
+    References
+    ==========
+
+    .. [1] Richard Crandall & Carl Pomerance (2005), "Prime Numbers:
+           A Computational Perspective", Springer, 2nd edition, 229-231
+
+    """
+    n = int(n)
+    if n < 5:
+        raise ValueError('pollard_rho should receive n > 4')
+    prng = random.Random(seed + retries)
+    V = s
+    for i in range(retries + 1):
+        U = V
+        if not F:
+            F = lambda x: (pow(x, 2, n) + a) % n
+        j = 0
+        while 1:
+            if max_steps and (j > max_steps):
+                break
+            j += 1
+            U = F(U)
+            V = F(F(V))  # V is 2x further along than U
+            g = igcd(U - V, n)
+            if g == 1:
+                continue
+            if g == n:
+                break
+            return int(g)
+        V = prng.randint(0, n - 1)
+        a = prng.randint(1, n - 3)  # for x**2 + a, a%n should not be 0 or -2
+        F = None
+    return None
+
+
+def pollard_pm1(n, B=10, a=2, retries=0, seed=1234):
+    """
+    Use Pollard's p-1 method to try to extract a nontrivial factor
+    of ``n``. Either a divisor (perhaps composite) or ``None`` is returned.
+
+    The value of ``a`` is the base that is used in the test gcd(a**M - 1, n).
+    The default is 2.  If ``retries`` > 0 then if no factor is found after the
+    first attempt, a new ``a`` will be generated randomly (using the ``seed``)
+    and the process repeated.
+
+    Note: the value of M is lcm(1..B) = reduce(ilcm, range(2, B + 1)).
+
+    A search is made for factors next to even numbers having a power smoothness
+    less than ``B``. Choosing a larger B increases the likelihood of finding a
+    larger factor but takes longer. Whether a factor of n is found or not
+    depends on ``a`` and the power smoothness of the even number just less than
+    the factor p (hence the name p - 1).
+
+    Although some discussion of what constitutes a good ``a`` some
+    descriptions are hard to interpret. At the modular.math site referenced
+    below it is stated that if gcd(a**M - 1, n) = N then a**M % q**r is 1
+    for every prime power divisor of N. But consider the following:
+
+        >>> from sympy.ntheory.factor_ import smoothness_p, pollard_pm1
+        >>> n=257*1009
+        >>> smoothness_p(n)
+        (-1, [(257, (1, 2, 256)), (1009, (1, 7, 16))])
+
+    So we should (and can) find a root with B=16:
+
+        >>> pollard_pm1(n, B=16, a=3)
+        1009
+
+    If we attempt to increase B to 256 we find that it does not work:
+
+        >>> pollard_pm1(n, B=256)
+        >>>
+
+    But if the value of ``a`` is changed we find that only multiples of
+    257 work, e.g.:
+
+        >>> pollard_pm1(n, B=256, a=257)
+        1009
+
+    Checking different ``a`` values shows that all the ones that did not
+    work had a gcd value not equal to ``n`` but equal to one of the
+    factors:
+
+        >>> from sympy import ilcm, igcd, factorint, Pow
+        >>> M = 1
+        >>> for i in range(2, 256):
+        ...     M = ilcm(M, i)
+        ...
+        >>> set([igcd(pow(a, M, n) - 1, n) for a in range(2, 256) if
+        ...      igcd(pow(a, M, n) - 1, n) != n])
+        {1009}
+
+    But does aM % d for every divisor of n give 1?
+
+        >>> aM = pow(255, M, n)
+        >>> [(d, aM%Pow(*d.args)) for d in factorint(n, visual=True).args]
+        [(257**1, 1), (1009**1, 1)]
+
+    No, only one of them. So perhaps the principle is that a root will
+    be found for a given value of B provided that:
+
+    1) the power smoothness of the p - 1 value next to the root
+       does not exceed B
+    2) a**M % p != 1 for any of the divisors of n.
+
+    By trying more than one ``a`` it is possible that one of them
+    will yield a factor.
+
+    Examples
+    ========
+
+    With the default smoothness bound, this number cannot be cracked:
+
+        >>> from sympy.ntheory import pollard_pm1
+        >>> pollard_pm1(21477639576571)
+
+    Increasing the smoothness bound helps:
+
+        >>> pollard_pm1(21477639576571, B=2000)
+        4410317
+
+    Looking at the smoothness of the factors of this number we find:
+
+        >>> from sympy.ntheory.factor_ import smoothness_p, factorint
+        >>> print(smoothness_p(21477639576571, visual=1))
+        p**i=4410317**1 has p-1 B=1787, B-pow=1787
+        p**i=4869863**1 has p-1 B=2434931, B-pow=2434931
+
+    The B and B-pow are the same for the p - 1 factorizations of the divisors
+    because those factorizations had a very large prime factor:
+
+        >>> factorint(4410317 - 1)
+        {2: 2, 617: 1, 1787: 1}
+        >>> factorint(4869863-1)
+        {2: 1, 2434931: 1}
+
+    Note that until B reaches the B-pow value of 1787, the number is not cracked;
+
+        >>> pollard_pm1(21477639576571, B=1786)
+        >>> pollard_pm1(21477639576571, B=1787)
+        4410317
+
+    The B value has to do with the factors of the number next to the divisor,
+    not the divisors themselves. A worst case scenario is that the number next
+    to the factor p has a large prime divisisor or is a perfect power. If these
+    conditions apply then the power-smoothness will be about p/2 or p. The more
+    realistic is that there will be a large prime factor next to p requiring
+    a B value on the order of p/2. Although primes may have been searched for
+    up to this level, the p/2 is a factor of p - 1, something that we do not
+    know. The modular.math reference below states that 15% of numbers in the
+    range of 10**15 to 15**15 + 10**4 are 10**6 power smooth so a B of 10**6
+    will fail 85% of the time in that range. From 10**8 to 10**8 + 10**3 the
+    percentages are nearly reversed...but in that range the simple trial
+    division is quite fast.
+
+    References
+    ==========
+
+    .. [1] Richard Crandall & Carl Pomerance (2005), "Prime Numbers:
+           A Computational Perspective", Springer, 2nd edition, 236-238
+    .. [2] https://web.archive.org/web/20150716201437/http://modular.math.washington.edu/edu/2007/spring/ent/ent-html/node81.html
+    .. [3] https://www.cs.toronto.edu/~yuvalf/Factorization.pdf
+    """
+
+    n = int(n)
+    if n < 4 or B < 3:
+        raise ValueError('pollard_pm1 should receive n > 3 and B > 2')
+    prng = random.Random(seed + B)
+
+    # computing a**lcm(1,2,3,..B) % n for B > 2
+    # it looks weird, but it's right: primes run [2, B]
+    # and the answer's not right until the loop is done.
+    for i in range(retries + 1):
+        aM = a
+        for p in sieve.primerange(2, B + 1):
+            e = int(math.log(B, p))
+            aM = pow(aM, pow(p, e), n)
+        g = igcd(aM - 1, n)
+        if 1 < g < n:
+            return int(g)
+
+        # get a new a:
+        # since the exponent, lcm(1..B), is even, if we allow 'a' to be 'n-1'
+        # then (n - 1)**even % n will be 1 which will give a g of 0 and 1 will
+        # give a zero, too, so we set the range as [2, n-2]. Some references
+        # say 'a' should be coprime to n, but either will detect factors.
+        a = prng.randint(2, n - 2)
+
+
+def _trial(factors, n, candidates, verbose=False):
+    """
+    Helper function for integer factorization. Trial factors ``n`
+    against all integers given in the sequence ``candidates``
+    and updates the dict ``factors`` in-place. Returns the reduced
+    value of ``n`` and a flag indicating whether any factors were found.
+    """
+    if verbose:
+        factors0 = list(factors.keys())
+    nfactors = len(factors)
+    for d in candidates:
+        if n % d == 0:
+            m = multiplicity(d, n)
+            n //= d**m
+            factors[d] = m
+    if verbose:
+        for k in sorted(set(factors).difference(set(factors0))):
+            print(factor_msg % (k, factors[k]))
+    return int(n), len(factors) != nfactors
+
+
+def _check_termination(factors, n, limitp1, use_trial, use_rho, use_pm1,
+                       verbose):
+    """
+    Helper function for integer factorization. Checks if ``n``
+    is a prime or a perfect power, and in those cases updates
+    the factorization and raises ``StopIteration``.
+    """
+
+    if verbose:
+        print('Check for termination')
+
+    # since we've already been factoring there is no need to do
+    # simultaneous factoring with the power check
+    p = perfect_power(n, factor=False)
+    if p is not False:
+        base, exp = p
+        if limitp1:
+            limit = limitp1 - 1
+        else:
+            limit = limitp1
+        facs = factorint(base, limit, use_trial, use_rho, use_pm1,
+                         verbose=False)
+        for b, e in facs.items():
+            if verbose:
+                print(factor_msg % (b, e))
+            factors[b] = exp*e
+        raise StopIteration
+
+    if isprime(n):
+        factors[int(n)] = 1
+        raise StopIteration
+
+    if n == 1:
+        raise StopIteration
+
+trial_int_msg = "Trial division with ints [%i ... %i] and fail_max=%i"
+trial_msg = "Trial division with primes [%i ... %i]"
+rho_msg = "Pollard's rho with retries %i, max_steps %i and seed %i"
+pm1_msg = "Pollard's p-1 with smoothness bound %i and seed %i"
+ecm_msg = "Elliptic Curve with B1 bound %i, B2 bound %i, num_curves %i"
+factor_msg = '\t%i ** %i'
+fermat_msg = 'Close factors satisying Fermat condition found.'
+complete_msg = 'Factorization is complete.'
+
+
+def _factorint_small(factors, n, limit, fail_max):
+    """
+    Return the value of n and either a 0 (indicating that factorization up
+    to the limit was complete) or else the next near-prime that would have
+    been tested.
+
+    Factoring stops if there are fail_max unsuccessful tests in a row.
+
+    If factors of n were found they will be in the factors dictionary as
+    {factor: multiplicity} and the returned value of n will have had those
+    factors removed. The factors dictionary is modified in-place.
+
+    """
+
+    def done(n, d):
+        """return n, d if the sqrt(n) was not reached yet, else
+           n, 0 indicating that factoring is done.
+        """
+        if d*d <= n:
+            return n, d
+        return n, 0
+
+    d = 2
+    m = trailing(n)
+    if m:
+        factors[d] = m
+        n >>= m
+    d = 3
+    if limit < d:
+        if n > 1:
+            factors[n] = 1
+        return done(n, d)
+    # reduce
+    m = 0
+    while n % d == 0:
+        n //= d
+        m += 1
+        if m == 20:
+            mm = multiplicity(d, n)
+            m += mm
+            n //= d**mm
+            break
+    if m:
+        factors[d] = m
+
+    # when d*d exceeds maxx or n we are done; if limit**2 is greater
+    # than n then maxx is set to zero so the value of n will flag the finish
+    if limit*limit > n:
+        maxx = 0
+    else:
+        maxx = limit*limit
+
+    dd = maxx or n
+    d = 5
+    fails = 0
+    while fails < fail_max:
+        if d*d > dd:
+            break
+        # d = 6*i - 1
+        # reduce
+        m = 0
+        while n % d == 0:
+            n //= d
+            m += 1
+            if m == 20:
+                mm = multiplicity(d, n)
+                m += mm
+                n //= d**mm
+                break
+        if m:
+            factors[d] = m
+            dd = maxx or n
+            fails = 0
+        else:
+            fails += 1
+        d += 2
+        if d*d > dd:
+            break
+        # d = 6*i - 1
+        # reduce
+        m = 0
+        while n % d == 0:
+            n //= d
+            m += 1
+            if m == 20:
+                mm = multiplicity(d, n)
+                m += mm
+                n //= d**mm
+                break
+        if m:
+            factors[d] = m
+            dd = maxx or n
+            fails = 0
+        else:
+            fails += 1
+        # d = 6*(i + 1) - 1
+        d += 4
+
+    return done(n, d)
+
+
+def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True,
+              use_ecm=True, verbose=False, visual=None, multiple=False):
+    r"""
+    Given a positive integer ``n``, ``factorint(n)`` returns a dict containing
+    the prime factors of ``n`` as keys and their respective multiplicities
+    as values. For example:
+
+    >>> from sympy.ntheory import factorint
+    >>> factorint(2000)    # 2000 = (2**4) * (5**3)
+    {2: 4, 5: 3}
+    >>> factorint(65537)   # This number is prime
+    {65537: 1}
+
+    For input less than 2, factorint behaves as follows:
+
+        - ``factorint(1)`` returns the empty factorization, ``{}``
+        - ``factorint(0)`` returns ``{0:1}``
+        - ``factorint(-n)`` adds ``-1:1`` to the factors and then factors ``n``
+
+    Partial Factorization:
+
+    If ``limit`` (> 3) is specified, the search is stopped after performing
+    trial division up to (and including) the limit (or taking a
+    corresponding number of rho/p-1 steps). This is useful if one has
+    a large number and only is interested in finding small factors (if
+    any). Note that setting a limit does not prevent larger factors
+    from being found early; it simply means that the largest factor may
+    be composite. Since checking for perfect power is relatively cheap, it is
+    done regardless of the limit setting.
+
+    This number, for example, has two small factors and a huge
+    semi-prime factor that cannot be reduced easily:
+
+    >>> from sympy.ntheory import isprime
+    >>> a = 1407633717262338957430697921446883
+    >>> f = factorint(a, limit=10000)
+    >>> f == {991: 1, int(202916782076162456022877024859): 1, 7: 1}
+    True
+    >>> isprime(max(f))
+    False
+
+    This number has a small factor and a residual perfect power whose
+    base is greater than the limit:
+
+    >>> factorint(3*101**7, limit=5)
+    {3: 1, 101: 7}
+
+    List of Factors:
+
+    If ``multiple`` is set to ``True`` then a list containing the
+    prime factors including multiplicities is returned.
+
+    >>> factorint(24, multiple=True)
+    [2, 2, 2, 3]
+
+    Visual Factorization:
+
+    If ``visual`` is set to ``True``, then it will return a visual
+    factorization of the integer.  For example:
+
+    >>> from sympy import pprint
+    >>> pprint(factorint(4200, visual=True))
+     3  1  2  1
+    2 *3 *5 *7
+
+    Note that this is achieved by using the evaluate=False flag in Mul
+    and Pow. If you do other manipulations with an expression where
+    evaluate=False, it may evaluate.  Therefore, you should use the
+    visual option only for visualization, and use the normal dictionary
+    returned by visual=False if you want to perform operations on the
+    factors.
+
+    You can easily switch between the two forms by sending them back to
+    factorint:
+
+    >>> from sympy import Mul
+    >>> regular = factorint(1764); regular
+    {2: 2, 3: 2, 7: 2}
+    >>> pprint(factorint(regular))
+     2  2  2
+    2 *3 *7
+
+    >>> visual = factorint(1764, visual=True); pprint(visual)
+     2  2  2
+    2 *3 *7
+    >>> print(factorint(visual))
+    {2: 2, 3: 2, 7: 2}
+
+    If you want to send a number to be factored in a partially factored form
+    you can do so with a dictionary or unevaluated expression:
+
+    >>> factorint(factorint({4: 2, 12: 3})) # twice to toggle to dict form
+    {2: 10, 3: 3}
+    >>> factorint(Mul(4, 12, evaluate=False))
+    {2: 4, 3: 1}
+
+    The table of the output logic is:
+
+        ====== ====== ======= =======
+                       Visual
+        ------ ----------------------
+        Input  True   False   other
+        ====== ====== ======= =======
+        dict    mul    dict    mul
+        n       mul    dict    dict
+        mul     mul    dict    dict
+        ====== ====== ======= =======
+
+    Notes
+    =====
+
+    Algorithm:
+
+    The function switches between multiple algorithms. Trial division
+    quickly finds small factors (of the order 1-5 digits), and finds
+    all large factors if given enough time. The Pollard rho and p-1
+    algorithms are used to find large factors ahead of time; they
+    will often find factors of the order of 10 digits within a few
+    seconds:
+
+    >>> factors = factorint(12345678910111213141516)
+    >>> for base, exp in sorted(factors.items()):
+    ...     print('%s %s' % (base, exp))
+    ...
+    2 2
+    2507191691 1
+    1231026625769 1
+
+    Any of these methods can optionally be disabled with the following
+    boolean parameters:
+
+        - ``use_trial``: Toggle use of trial division
+        - ``use_rho``: Toggle use of Pollard's rho method
+        - ``use_pm1``: Toggle use of Pollard's p-1 method
+
+    ``factorint`` also periodically checks if the remaining part is
+    a prime number or a perfect power, and in those cases stops.
+
+    For unevaluated factorial, it uses Legendre's formula(theorem).
+
+
+    If ``verbose`` is set to ``True``, detailed progress is printed.
+
+    See Also
+    ========
+
+    smoothness, smoothness_p, divisors
+
+    """
+    if isinstance(n, Dict):
+        n = dict(n)
+    if multiple:
+        fac = factorint(n, limit=limit, use_trial=use_trial,
+                           use_rho=use_rho, use_pm1=use_pm1,
+                           verbose=verbose, visual=False, multiple=False)
+        factorlist = sum(([p] * fac[p] if fac[p] > 0 else [S.One/p]*(-fac[p])
+                               for p in sorted(fac)), [])
+        return factorlist
+
+    factordict = {}
+    if visual and not isinstance(n, (Mul, dict)):
+        factordict = factorint(n, limit=limit, use_trial=use_trial,
+                               use_rho=use_rho, use_pm1=use_pm1,
+                               verbose=verbose, visual=False)
+    elif isinstance(n, Mul):
+        factordict = {int(k): int(v) for k, v in
+            n.as_powers_dict().items()}
+    elif isinstance(n, dict):
+        factordict = n
+    if factordict and isinstance(n, (Mul, dict)):
+        # check it
+        for key in list(factordict.keys()):
+            if isprime(key):
+                continue
+            e = factordict.pop(key)
+            d = factorint(key, limit=limit, use_trial=use_trial, use_rho=use_rho,
+                          use_pm1=use_pm1, verbose=verbose, visual=False)
+            for k, v in d.items():
+                if k in factordict:
+                    factordict[k] += v*e
+                else:
+                    factordict[k] = v*e
+    if visual or (type(n) is dict and
+                  visual is not True and
+                  visual is not False):
+        if factordict == {}:
+            return S.One
+        if -1 in factordict:
+            factordict.pop(-1)
+            args = [S.NegativeOne]
+        else:
+            args = []
+        args.extend([Pow(*i, evaluate=False)
+                     for i in sorted(factordict.items())])
+        return Mul(*args, evaluate=False)
+    elif isinstance(n, (dict, Mul)):
+        return factordict
+
+    assert use_trial or use_rho or use_pm1 or use_ecm
+
+    from sympy.functions.combinatorial.factorials import factorial
+    if isinstance(n, factorial):
+        x = as_int(n.args[0])
+        if x >= 20:
+            factors = {}
+            m = 2 # to initialize the if condition below
+            for p in sieve.primerange(2, x + 1):
+                if m > 1:
+                    m, q = 0, x // p
+                    while q != 0:
+                        m += q
+                        q //= p
+                factors[p] = m
+            if factors and verbose:
+                for k in sorted(factors):
+                    print(factor_msg % (k, factors[k]))
+            if verbose:
+                print(complete_msg)
+            return factors
+        else:
+            # if n < 20!, direct computation is faster
+            # since it uses a lookup table
+            n = n.func(x)
+
+    n = as_int(n)
+    if limit:
+        limit = int(limit)
+        use_ecm = False
+
+    # special cases
+    if n < 0:
+        factors = factorint(
+            -n, limit=limit, use_trial=use_trial, use_rho=use_rho,
+            use_pm1=use_pm1, verbose=verbose, visual=False)
+        factors[-1] = 1
+        return factors
+
+    if limit and limit < 2:
+        if n == 1:
+            return {}
+        return {n: 1}
+    elif n < 10:
+        # doing this we are assured of getting a limit > 2
+        # when we have to compute it later
+        return [{0: 1}, {}, {2: 1}, {3: 1}, {2: 2}, {5: 1},
+                {2: 1, 3: 1}, {7: 1}, {2: 3}, {3: 2}][n]
+
+    factors = {}
+
+    # do simplistic factorization
+    if verbose:
+        sn = str(n)
+        if len(sn) > 50:
+            print('Factoring %s' % sn[:5] + \
+                  '..(%i other digits)..' % (len(sn) - 10) + sn[-5:])
+        else:
+            print('Factoring', n)
+
+    if use_trial:
+        # this is the preliminary factorization for small factors
+        small = 2**15
+        fail_max = 600
+        small = min(small, limit or small)
+        if verbose:
+            print(trial_int_msg % (2, small, fail_max))
+        n, next_p = _factorint_small(factors, n, small, fail_max)
+    else:
+        next_p = 2
+    if factors and verbose:
+        for k in sorted(factors):
+            print(factor_msg % (k, factors[k]))
+    if next_p == 0:
+        if n > 1:
+            factors[int(n)] = 1
+        if verbose:
+            print(complete_msg)
+        return factors
+
+    # continue with more advanced factorization methods
+
+    # first check if the simplistic run didn't finish
+    # because of the limit and check for a perfect
+    # power before exiting
+    try:
+        if limit and next_p > limit:
+            if verbose:
+                print('Exceeded limit:', limit)
+
+            _check_termination(factors, n, limit, use_trial, use_rho, use_pm1,
+                               verbose)
+
+            if n > 1:
+                factors[int(n)] = 1
+            return factors
+        else:
+            # Before quitting (or continuing on)...
+
+            # ...do a Fermat test since it's so easy and we need the
+            # square root anyway. Finding 2 factors is easy if they are
+            # "close enough." This is the big root equivalent of dividing by
+            # 2, 3, 5.
+            sqrt_n = integer_nthroot(n, 2)[0]
+            a = sqrt_n + 1
+            a2 = a**2
+            b2 = a2 - n
+            for i in range(3):
+                b, fermat = integer_nthroot(b2, 2)
+                if fermat:
+                    break
+                b2 += 2*a + 1  # equiv to (a + 1)**2 - n
+                a += 1
+            if fermat:
+                if verbose:
+                    print(fermat_msg)
+                if limit:
+                    limit -= 1
+                for r in [a - b, a + b]:
+                    facs = factorint(r, limit=limit, use_trial=use_trial,
+                                     use_rho=use_rho, use_pm1=use_pm1,
+                                     verbose=verbose)
+                    for k, v in facs.items():
+                        factors[k] = factors.get(k, 0) + v
+                raise StopIteration
+
+            # ...see if factorization can be terminated
+            _check_termination(factors, n, limit, use_trial, use_rho, use_pm1,
+                               verbose)
+
+    except StopIteration:
+        if verbose:
+            print(complete_msg)
+        return factors
+
+    # these are the limits for trial division which will
+    # be attempted in parallel with pollard methods
+    low, high = next_p, 2*next_p
+
+    limit = limit or sqrt_n
+    # add 1 to make sure limit is reached in primerange calls
+    limit += 1
+    iteration = 0
+    while 1:
+
+        try:
+            high_ = high
+            if limit < high_:
+                high_ = limit
+
+            # Trial division
+            if use_trial:
+                if verbose:
+                    print(trial_msg % (low, high_))
+                ps = sieve.primerange(low, high_)
+                n, found_trial = _trial(factors, n, ps, verbose)
+                if found_trial:
+                    _check_termination(factors, n, limit, use_trial, use_rho,
+                                       use_pm1, verbose)
+            else:
+                found_trial = False
+
+            if high > limit:
+                if verbose:
+                    print('Exceeded limit:', limit)
+                if n > 1:
+                    factors[int(n)] = 1
+                raise StopIteration
+
+            # Only used advanced methods when no small factors were found
+            if not found_trial:
+                if (use_pm1 or use_rho):
+                    high_root = max(int(math.log(high_**0.7)), low, 3)
+
+                    # Pollard p-1
+                    if use_pm1:
+                        if verbose:
+                            print(pm1_msg % (high_root, high_))
+                        c = pollard_pm1(n, B=high_root, seed=high_)
+                        if c:
+                            # factor it and let _trial do the update
+                            ps = factorint(c, limit=limit - 1,
+                                           use_trial=use_trial,
+                                           use_rho=use_rho,
+                                           use_pm1=use_pm1,
+                                           use_ecm=use_ecm,
+                                           verbose=verbose)
+                            n, _ = _trial(factors, n, ps, verbose=False)
+                            _check_termination(factors, n, limit, use_trial,
+                                               use_rho, use_pm1, verbose)
+
+                    # Pollard rho
+                    if use_rho:
+                        max_steps = high_root
+                        if verbose:
+                            print(rho_msg % (1, max_steps, high_))
+                        c = pollard_rho(n, retries=1, max_steps=max_steps,
+                                        seed=high_)
+                        if c:
+                            # factor it and let _trial do the update
+                            ps = factorint(c, limit=limit - 1,
+                                           use_trial=use_trial,
+                                           use_rho=use_rho,
+                                           use_pm1=use_pm1,
+                                           use_ecm=use_ecm,
+                                           verbose=verbose)
+                            n, _ = _trial(factors, n, ps, verbose=False)
+                            _check_termination(factors, n, limit, use_trial,
+                                               use_rho, use_pm1, verbose)
+
+        except StopIteration:
+            if verbose:
+                print(complete_msg)
+            return factors
+        #Use subexponential algorithms if use_ecm
+        #Use pollard algorithms for finding small factors for 3 iterations
+        #if after small factors the number of digits of n is >= 20 then use ecm
+        iteration += 1
+        if use_ecm and iteration >= 3 and len(str(n)) >= 25:
+            break
+        low, high = high, high*2
+    B1 = 10000
+    B2 = 100*B1
+    num_curves = 50
+    while(1):
+        if verbose:
+            print(ecm_msg % (B1, B2, num_curves))
+        while(1):
+            try:
+                factor = _ecm_one_factor(n, B1, B2, num_curves)
+                ps = factorint(factor, limit=limit - 1,
+                               use_trial=use_trial,
+                               use_rho=use_rho,
+                               use_pm1=use_pm1,
+                               use_ecm=use_ecm,
+                               verbose=verbose)
+                n, _ = _trial(factors, n, ps, verbose=False)
+                _check_termination(factors, n, limit, use_trial,
+                                       use_rho, use_pm1, verbose)
+            except ValueError:
+                break
+            except StopIteration:
+                if verbose:
+                    print(complete_msg)
+                return factors
+        B1 *= 5
+        B2 = 100*B1
+        num_curves *= 4
+
+
+def factorrat(rat, limit=None, use_trial=True, use_rho=True, use_pm1=True,
+              verbose=False, visual=None, multiple=False):
+    r"""
+    Given a Rational ``r``, ``factorrat(r)`` returns a dict containing
+    the prime factors of ``r`` as keys and their respective multiplicities
+    as values. For example:
+
+    >>> from sympy import factorrat, S
+    >>> factorrat(S(8)/9)    # 8/9 = (2**3) * (3**-2)
+    {2: 3, 3: -2}
+    >>> factorrat(S(-1)/987)    # -1/789 = -1 * (3**-1) * (7**-1) * (47**-1)
+    {-1: 1, 3: -1, 7: -1, 47: -1}
+
+    Please see the docstring for ``factorint`` for detailed explanations
+    and examples of the following keywords:
+
+        - ``limit``: Integer limit up to which trial division is done
+        - ``use_trial``: Toggle use of trial division
+        - ``use_rho``: Toggle use of Pollard's rho method
+        - ``use_pm1``: Toggle use of Pollard's p-1 method
+        - ``verbose``: Toggle detailed printing of progress
+        - ``multiple``: Toggle returning a list of factors or dict
+        - ``visual``: Toggle product form of output
+    """
+    if multiple:
+        fac = factorrat(rat, limit=limit, use_trial=use_trial,
+                  use_rho=use_rho, use_pm1=use_pm1,
+                  verbose=verbose, visual=False, multiple=False)
+        factorlist = sum(([p] * fac[p] if fac[p] > 0 else [S.One/p]*(-fac[p])
+                               for p, _ in sorted(fac.items(),
+                                                        key=lambda elem: elem[0]
+                                                        if elem[1] > 0
+                                                        else 1/elem[0])), [])
+        return factorlist
+
+    f = factorint(rat.p, limit=limit, use_trial=use_trial,
+                  use_rho=use_rho, use_pm1=use_pm1,
+                  verbose=verbose).copy()
+    f = defaultdict(int, f)
+    for p, e in factorint(rat.q, limit=limit,
+                          use_trial=use_trial,
+                          use_rho=use_rho,
+                          use_pm1=use_pm1,
+                          verbose=verbose).items():
+        f[p] += -e
+
+    if len(f) > 1 and 1 in f:
+        del f[1]
+    if not visual:
+        return dict(f)
+    else:
+        if -1 in f:
+            f.pop(-1)
+            args = [S.NegativeOne]
+        else:
+            args = []
+        args.extend([Pow(*i, evaluate=False)
+                     for i in sorted(f.items())])
+        return Mul(*args, evaluate=False)
+
+
+
+def primefactors(n, limit=None, verbose=False):
+    """Return a sorted list of n's prime factors, ignoring multiplicity
+    and any composite factor that remains if the limit was set too low
+    for complete factorization. Unlike factorint(), primefactors() does
+    not return -1 or 0.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import primefactors, factorint, isprime
+    >>> primefactors(6)
+    [2, 3]
+    >>> primefactors(-5)
+    [5]
+
+    >>> sorted(factorint(123456).items())
+    [(2, 6), (3, 1), (643, 1)]
+    >>> primefactors(123456)
+    [2, 3, 643]
+
+    >>> sorted(factorint(10000000001, limit=200).items())
+    [(101, 1), (99009901, 1)]
+    >>> isprime(99009901)
+    False
+    >>> primefactors(10000000001, limit=300)
+    [101]
+
+    See Also
+    ========
+
+    divisors
+    """
+    n = int(n)
+    factors = sorted(factorint(n, limit=limit, verbose=verbose).keys())
+    s = [f for f in factors[:-1:] if f not in [-1, 0, 1]]
+    if factors and isprime(factors[-1]):
+        s += [factors[-1]]
+    return s
+
+
+def _divisors(n, proper=False):
+    """Helper function for divisors which generates the divisors."""
+
+    factordict = factorint(n)
+    ps = sorted(factordict.keys())
+
+    def rec_gen(n=0):
+        if n == len(ps):
+            yield 1
+        else:
+            pows = [1]
+            for j in range(factordict[ps[n]]):
+                pows.append(pows[-1] * ps[n])
+            for q in rec_gen(n + 1):
+                for p in pows:
+                    yield p * q
+
+    if proper:
+        for p in rec_gen():
+            if p != n:
+                yield p
+    else:
+        yield from rec_gen()
+
+
+def divisors(n, generator=False, proper=False):
+    r"""
+    Return all divisors of n sorted from 1..n by default.
+    If generator is ``True`` an unordered generator is returned.
+
+    The number of divisors of n can be quite large if there are many
+    prime factors (counting repeated factors). If only the number of
+    factors is desired use divisor_count(n).
+
+    Examples
+    ========
+
+    >>> from sympy import divisors, divisor_count
+    >>> divisors(24)
+    [1, 2, 3, 4, 6, 8, 12, 24]
+    >>> divisor_count(24)
+    8
+
+    >>> list(divisors(120, generator=True))
+    [1, 2, 4, 8, 3, 6, 12, 24, 5, 10, 20, 40, 15, 30, 60, 120]
+
+    Notes
+    =====
+
+    This is a slightly modified version of Tim Peters referenced at:
+    https://stackoverflow.com/questions/1010381/python-factorization
+
+    See Also
+    ========
+
+    primefactors, factorint, divisor_count
+    """
+
+    n = as_int(abs(n))
+    if isprime(n):
+        if proper:
+            return [1]
+        return [1, n]
+    if n == 1:
+        if proper:
+            return []
+        return [1]
+    if n == 0:
+        return []
+    rv = _divisors(n, proper)
+    if not generator:
+        return sorted(rv)
+    return rv
+
+
+def divisor_count(n, modulus=1, proper=False):
+    """
+    Return the number of divisors of ``n``. If ``modulus`` is not 1 then only
+    those that are divisible by ``modulus`` are counted. If ``proper`` is True
+    then the divisor of ``n`` will not be counted.
+
+    Examples
+    ========
+
+    >>> from sympy import divisor_count
+    >>> divisor_count(6)
+    4
+    >>> divisor_count(6, 2)
+    2
+    >>> divisor_count(6, proper=True)
+    3
+
+    See Also
+    ========
+
+    factorint, divisors, totient, proper_divisor_count
+
+    """
+
+    if not modulus:
+        return 0
+    elif modulus != 1:
+        n, r = divmod(n, modulus)
+        if r:
+            return 0
+    if n == 0:
+        return 0
+    n = Mul(*[v + 1 for k, v in factorint(n).items() if k > 1])
+    if n and proper:
+        n -= 1
+    return n
+
+
+def proper_divisors(n, generator=False):
+    """
+    Return all divisors of n except n, sorted by default.
+    If generator is ``True`` an unordered generator is returned.
+
+    Examples
+    ========
+
+    >>> from sympy import proper_divisors, proper_divisor_count
+    >>> proper_divisors(24)
+    [1, 2, 3, 4, 6, 8, 12]
+    >>> proper_divisor_count(24)
+    7
+    >>> list(proper_divisors(120, generator=True))
+    [1, 2, 4, 8, 3, 6, 12, 24, 5, 10, 20, 40, 15, 30, 60]
+
+    See Also
+    ========
+
+    factorint, divisors, proper_divisor_count
+
+    """
+    return divisors(n, generator=generator, proper=True)
+
+
+def proper_divisor_count(n, modulus=1):
+    """
+    Return the number of proper divisors of ``n``.
+
+    Examples
+    ========
+
+    >>> from sympy import proper_divisor_count
+    >>> proper_divisor_count(6)
+    3
+    >>> proper_divisor_count(6, modulus=2)
+    1
+
+    See Also
+    ========
+
+    divisors, proper_divisors, divisor_count
+
+    """
+    return divisor_count(n, modulus=modulus, proper=True)
+
+
+def _udivisors(n):
+    """Helper function for udivisors which generates the unitary divisors."""
+
+    factorpows = [p**e for p, e in factorint(n).items()]
+    for i in range(2**len(factorpows)):
+        d, j, k = 1, i, 0
+        while j:
+            if (j & 1):
+                d *= factorpows[k]
+            j >>= 1
+            k += 1
+        yield d
+
+
+def udivisors(n, generator=False):
+    r"""
+    Return all unitary divisors of n sorted from 1..n by default.
+    If generator is ``True`` an unordered generator is returned.
+
+    The number of unitary divisors of n can be quite large if there are many
+    prime factors. If only the number of unitary divisors is desired use
+    udivisor_count(n).
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import udivisors, udivisor_count
+    >>> udivisors(15)
+    [1, 3, 5, 15]
+    >>> udivisor_count(15)
+    4
+
+    >>> sorted(udivisors(120, generator=True))
+    [1, 3, 5, 8, 15, 24, 40, 120]
+
+    See Also
+    ========
+
+    primefactors, factorint, divisors, divisor_count, udivisor_count
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Unitary_divisor
+    .. [2] https://mathworld.wolfram.com/UnitaryDivisor.html
+
+    """
+
+    n = as_int(abs(n))
+    if isprime(n):
+        return [1, n]
+    if n == 1:
+        return [1]
+    if n == 0:
+        return []
+    rv = _udivisors(n)
+    if not generator:
+        return sorted(rv)
+    return rv
+
+
+def udivisor_count(n):
+    """
+    Return the number of unitary divisors of ``n``.
+
+    Parameters
+    ==========
+
+    n : integer
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import udivisor_count
+    >>> udivisor_count(120)
+    8
+
+    See Also
+    ========
+
+    factorint, divisors, udivisors, divisor_count, totient
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/UnitaryDivisorFunction.html
+
+    """
+
+    if n == 0:
+        return 0
+    return 2**len([p for p in factorint(n) if p > 1])
+
+
+def _antidivisors(n):
+    """Helper function for antidivisors which generates the antidivisors."""
+
+    for d in _divisors(n):
+        y = 2*d
+        if n > y and n % y:
+            yield y
+    for d in _divisors(2*n-1):
+        if n > d >= 2 and n % d:
+            yield d
+    for d in _divisors(2*n+1):
+        if n > d >= 2 and n % d:
+            yield d
+
+
+def antidivisors(n, generator=False):
+    r"""
+    Return all antidivisors of n sorted from 1..n by default.
+
+    Antidivisors [1]_ of n are numbers that do not divide n by the largest
+    possible margin.  If generator is True an unordered generator is returned.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import antidivisors
+    >>> antidivisors(24)
+    [7, 16]
+
+    >>> sorted(antidivisors(128, generator=True))
+    [3, 5, 15, 17, 51, 85]
+
+    See Also
+    ========
+
+    primefactors, factorint, divisors, divisor_count, antidivisor_count
+
+    References
+    ==========
+
+    .. [1] definition is described in https://oeis.org/A066272/a066272a.html
+
+    """
+
+    n = as_int(abs(n))
+    if n <= 2:
+        return []
+    rv = _antidivisors(n)
+    if not generator:
+        return sorted(rv)
+    return rv
+
+
+def antidivisor_count(n):
+    """
+    Return the number of antidivisors [1]_ of ``n``.
+
+    Parameters
+    ==========
+
+    n : integer
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import antidivisor_count
+    >>> antidivisor_count(13)
+    4
+    >>> antidivisor_count(27)
+    5
+
+    See Also
+    ========
+
+    factorint, divisors, antidivisors, divisor_count, totient
+
+    References
+    ==========
+
+    .. [1] formula from https://oeis.org/A066272
+
+    """
+
+    n = as_int(abs(n))
+    if n <= 2:
+        return 0
+    return divisor_count(2*n - 1) + divisor_count(2*n + 1) + \
+        divisor_count(n) - divisor_count(n, 2) - 5
+
+
+class totient(Function):
+    r"""
+    Calculate the Euler totient function phi(n)
+
+    ``totient(n)`` or `\phi(n)` is the number of positive integers `\leq` n
+    that are relatively prime to n.
+
+    Parameters
+    ==========
+
+    n : integer
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import totient
+    >>> totient(1)
+    1
+    >>> totient(25)
+    20
+    >>> totient(45) == totient(5)*totient(9)
+    True
+
+    See Also
+    ========
+
+    divisor_count
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Euler%27s_totient_function
+    .. [2] https://mathworld.wolfram.com/TotientFunction.html
+
+    """
+    @classmethod
+    def eval(cls, n):
+        if n.is_Integer:
+            if n < 1:
+                raise ValueError("n must be a positive integer")
+            factors = factorint(n)
+            return cls._from_factors(factors)
+        elif not isinstance(n, Expr) or (n.is_integer is False) or (n.is_positive is False):
+            raise ValueError("n must be a positive integer")
+
+    def _eval_is_integer(self):
+        return fuzzy_and([self.args[0].is_integer, self.args[0].is_positive])
+
+    @classmethod
+    def _from_distinct_primes(self, *args):
+        """Subroutine to compute totient from the list of assumed
+        distinct primes
+
+        Examples
+        ========
+
+        >>> from sympy.ntheory.factor_ import totient
+        >>> totient._from_distinct_primes(5, 7)
+        24
+        """
+        return reduce(lambda i, j: i * (j-1), args, 1)
+
+    @classmethod
+    def _from_factors(self, factors):
+        """Subroutine to compute totient from already-computed factors
+
+        Examples
+        ========
+
+        >>> from sympy.ntheory.factor_ import totient
+        >>> totient._from_factors({5: 2})
+        20
+        """
+        t = 1
+        for p, k in factors.items():
+            t *= (p - 1) * p**(k - 1)
+        return t
+
+
+class reduced_totient(Function):
+    r"""
+    Calculate the Carmichael reduced totient function lambda(n)
+
+    ``reduced_totient(n)`` or `\lambda(n)` is the smallest m > 0 such that
+    `k^m \equiv 1 \mod n` for all k relatively prime to n.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import reduced_totient
+    >>> reduced_totient(1)
+    1
+    >>> reduced_totient(8)
+    2
+    >>> reduced_totient(30)
+    4
+
+    See Also
+    ========
+
+    totient
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Carmichael_function
+    .. [2] https://mathworld.wolfram.com/CarmichaelFunction.html
+
+    """
+    @classmethod
+    def eval(cls, n):
+        if n.is_Integer:
+            if n < 1:
+                raise ValueError("n must be a positive integer")
+            factors = factorint(n)
+            return cls._from_factors(factors)
+
+    @classmethod
+    def _from_factors(self, factors):
+        """Subroutine to compute totient from already-computed factors
+        """
+        t = 1
+        for p, k in factors.items():
+            if p == 2 and k > 2:
+                t = ilcm(t, 2**(k - 2))
+            else:
+                t = ilcm(t, (p - 1) * p**(k - 1))
+        return t
+
+    @classmethod
+    def _from_distinct_primes(self, *args):
+        """Subroutine to compute totient from the list of assumed
+        distinct primes
+        """
+        args = [p - 1 for p in args]
+        return ilcm(*args)
+
+    def _eval_is_integer(self):
+        return fuzzy_and([self.args[0].is_integer, self.args[0].is_positive])
+
+
+class divisor_sigma(Function):
+    r"""
+    Calculate the divisor function `\sigma_k(n)` for positive integer n
+
+    ``divisor_sigma(n, k)`` is equal to ``sum([x**k for x in divisors(n)])``
+
+    If n's prime factorization is:
+
+    .. math ::
+        n = \prod_{i=1}^\omega p_i^{m_i},
+
+    then
+
+    .. math ::
+        \sigma_k(n) = \prod_{i=1}^\omega (1+p_i^k+p_i^{2k}+\cdots
+        + p_i^{m_ik}).
+
+    Parameters
+    ==========
+
+    n : integer
+
+    k : integer, optional
+        power of divisors in the sum
+
+        for k = 0, 1:
+        ``divisor_sigma(n, 0)`` is equal to ``divisor_count(n)``
+        ``divisor_sigma(n, 1)`` is equal to ``sum(divisors(n))``
+
+        Default for k is 1.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import divisor_sigma
+    >>> divisor_sigma(18, 0)
+    6
+    >>> divisor_sigma(39, 1)
+    56
+    >>> divisor_sigma(12, 2)
+    210
+    >>> divisor_sigma(37)
+    38
+
+    See Also
+    ========
+
+    divisor_count, totient, divisors, factorint
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Divisor_function
+
+    """
+
+    @classmethod
+    def eval(cls, n, k=S.One):
+        k = sympify(k)
+
+        if n.is_prime:
+            return 1 + n**k
+
+        if n.is_Integer:
+            if n <= 0:
+                raise ValueError("n must be a positive integer")
+            elif k.is_Integer:
+                k = int(k)
+                return Integer(math.prod(
+                    (p**(k*(e + 1)) - 1)//(p**k - 1) if k != 0
+                    else e + 1 for p, e in factorint(n).items()))
+            else:
+                return Mul(*[(p**(k*(e + 1)) - 1)/(p**k - 1) if k != 0
+                           else e + 1 for p, e in factorint(n).items()])
+
+        if n.is_integer:  # symbolic case
+            args = []
+            for p, e in (_.as_base_exp() for _ in Mul.make_args(n)):
+                if p.is_prime and e.is_positive:
+                    args.append((p**(k*(e + 1)) - 1)/(p**k - 1) if
+                                k != 0 else e + 1)
+                else:
+                    return
+            return Mul(*args)
+
+
+def core(n, t=2):
+    r"""
+    Calculate core(n, t) = `core_t(n)` of a positive integer n
+
+    ``core_2(n)`` is equal to the squarefree part of n
+
+    If n's prime factorization is:
+
+    .. math ::
+        n = \prod_{i=1}^\omega p_i^{m_i},
+
+    then
+
+    .. math ::
+        core_t(n) = \prod_{i=1}^\omega p_i^{m_i \mod t}.
+
+    Parameters
+    ==========
+
+    n : integer
+
+    t : integer
+        core(n, t) calculates the t-th power free part of n
+
+        ``core(n, 2)`` is the squarefree part of ``n``
+        ``core(n, 3)`` is the cubefree part of ``n``
+
+        Default for t is 2.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import core
+    >>> core(24, 2)
+    6
+    >>> core(9424, 3)
+    1178
+    >>> core(379238)
+    379238
+    >>> core(15**11, 10)
+    15
+
+    See Also
+    ========
+
+    factorint, sympy.solvers.diophantine.diophantine.square_factor
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Square-free_integer#Squarefree_core
+
+    """
+
+    n = as_int(n)
+    t = as_int(t)
+    if n <= 0:
+        raise ValueError("n must be a positive integer")
+    elif t <= 1:
+        raise ValueError("t must be >= 2")
+    else:
+        y = 1
+        for p, e in factorint(n).items():
+            y *= p**(e % t)
+        return y
+
+
+class udivisor_sigma(Function):
+    r"""
+    Calculate the unitary divisor function `\sigma_k^*(n)` for positive integer n
+
+    ``udivisor_sigma(n, k)`` is equal to ``sum([x**k for x in udivisors(n)])``
+
+    If n's prime factorization is:
+
+    .. math ::
+        n = \prod_{i=1}^\omega p_i^{m_i},
+
+    then
+
+    .. math ::
+        \sigma_k^*(n) = \prod_{i=1}^\omega (1+ p_i^{m_ik}).
+
+    Parameters
+    ==========
+
+    k : power of divisors in the sum
+
+        for k = 0, 1:
+        ``udivisor_sigma(n, 0)`` is equal to ``udivisor_count(n)``
+        ``udivisor_sigma(n, 1)`` is equal to ``sum(udivisors(n))``
+
+        Default for k is 1.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import udivisor_sigma
+    >>> udivisor_sigma(18, 0)
+    4
+    >>> udivisor_sigma(74, 1)
+    114
+    >>> udivisor_sigma(36, 3)
+    47450
+    >>> udivisor_sigma(111)
+    152
+
+    See Also
+    ========
+
+    divisor_count, totient, divisors, udivisors, udivisor_count, divisor_sigma,
+    factorint
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/UnitaryDivisorFunction.html
+
+    """
+
+    @classmethod
+    def eval(cls, n, k=S.One):
+        k = sympify(k)
+        if n.is_prime:
+            return 1 + n**k
+        if n.is_Integer:
+            if n <= 0:
+                raise ValueError("n must be a positive integer")
+            else:
+                return Mul(*[1+p**(k*e) for p, e in factorint(n).items()])
+
+
+class primenu(Function):
+    r"""
+    Calculate the number of distinct prime factors for a positive integer n.
+
+    If n's prime factorization is:
+
+    .. math ::
+        n = \prod_{i=1}^k p_i^{m_i},
+
+    then ``primenu(n)`` or `\nu(n)` is:
+
+    .. math ::
+        \nu(n) = k.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import primenu
+    >>> primenu(1)
+    0
+    >>> primenu(30)
+    3
+
+    See Also
+    ========
+
+    factorint
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/PrimeFactor.html
+
+    """
+
+    @classmethod
+    def eval(cls, n):
+        if n.is_Integer:
+            if n <= 0:
+                raise ValueError("n must be a positive integer")
+            else:
+                return len(factorint(n).keys())
+
+
+class primeomega(Function):
+    r"""
+    Calculate the number of prime factors counting multiplicities for a
+    positive integer n.
+
+    If n's prime factorization is:
+
+    .. math ::
+        n = \prod_{i=1}^k p_i^{m_i},
+
+    then ``primeomega(n)``  or `\Omega(n)` is:
+
+    .. math ::
+        \Omega(n) = \sum_{i=1}^k m_i.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import primeomega
+    >>> primeomega(1)
+    0
+    >>> primeomega(20)
+    3
+
+    See Also
+    ========
+
+    factorint
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/PrimeFactor.html
+
+    """
+
+    @classmethod
+    def eval(cls, n):
+        if n.is_Integer:
+            if n <= 0:
+                raise ValueError("n must be a positive integer")
+            else:
+                return sum(factorint(n).values())
+
+
+def mersenne_prime_exponent(nth):
+    """Returns the exponent ``i`` for the nth Mersenne prime (which
+    has the form `2^i - 1`).
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import mersenne_prime_exponent
+    >>> mersenne_prime_exponent(1)
+    2
+    >>> mersenne_prime_exponent(20)
+    4423
+    """
+    n = as_int(nth)
+    if n < 1:
+        raise ValueError("nth must be a positive integer; mersenne_prime_exponent(1) == 2")
+    if n > 51:
+        raise ValueError("There are only 51 perfect numbers; nth must be less than or equal to 51")
+    return MERSENNE_PRIME_EXPONENTS[n - 1]
+
+
+def is_perfect(n):
+    """Returns True if ``n`` is a perfect number, else False.
+
+    A perfect number is equal to the sum of its positive, proper divisors.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import is_perfect, divisors, divisor_sigma
+    >>> is_perfect(20)
+    False
+    >>> is_perfect(6)
+    True
+    >>> 6 == divisor_sigma(6) - 6 == sum(divisors(6)[:-1])
+    True
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/PerfectNumber.html
+    .. [2] https://en.wikipedia.org/wiki/Perfect_number
+
+    """
+
+    n = as_int(n)
+    if _isperfect(n):
+        return True
+
+    # all perfect numbers for Mersenne primes with exponents
+    # less than or equal to 43112609 are known
+    iknow = MERSENNE_PRIME_EXPONENTS.index(43112609)
+    if iknow <= len(PERFECT) - 1 and n <= PERFECT[iknow]:
+        # there may be gaps between this and larger known values
+        # so only conclude in the range for which all values
+        # are known
+        return False
+    if n%2 == 0:
+        last2 = n % 100
+        if last2 != 28 and last2 % 10 != 6:
+            return False
+        r, b = integer_nthroot(1 + 8*n, 2)
+        if not b:
+            return False
+        m, x = divmod(1 + r, 4)
+        if x:
+            return False
+        e, b = integer_log(m, 2)
+        if not b:
+            return False
+    else:
+        if n < 10**2000:  # https://www.lirmm.fr/~ochem/opn/
+            return False
+        if n % 105 == 0:  # not divis by 105
+            return False
+        if not any(n%m == r for m, r in [(12, 1), (468, 117), (324, 81)]):
+            return False
+        # there are many criteria that the factor structure of n
+        # must meet; since we will have to factor it to test the
+        # structure we will have the factors and can then check
+        # to see whether it is a perfect number or not. So we
+        # skip the structure checks and go straight to the final
+        # test below.
+    rv = divisor_sigma(n) - n
+    if rv == n:
+        if n%2 == 0:
+            raise ValueError(filldedent('''
+                This even number is perfect and is associated with a
+                Mersenne Prime, 2^%s - 1. It should be
+                added to SymPy.''' % (e + 1)))
+        else:
+            raise ValueError(filldedent('''In 1888, Sylvester stated: "
+                ...a prolonged meditation on the subject has satisfied
+                me that the existence of any one such [odd perfect number]
+                -- its escape, so to say, from the complex web of conditions
+                which hem it in on all sides -- would be little short of a
+                miracle." I guess SymPy just found that miracle and it
+                factors like this: %s''' % factorint(n)))
+
+
+def is_mersenne_prime(n):
+    """Returns True if  ``n`` is a Mersenne prime, else False.
+
+    A Mersenne prime is a prime number having the form `2^i - 1`.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import is_mersenne_prime
+    >>> is_mersenne_prime(6)
+    False
+    >>> is_mersenne_prime(127)
+    True
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/MersennePrime.html
+
+    """
+
+    n = as_int(n)
+    if _ismersenneprime(n):
+        return True
+    if not isprime(n):
+        return False
+    r, b = integer_log(n + 1, 2)
+    if not b:
+        return False
+    raise ValueError(filldedent('''
+        This Mersenne Prime, 2^%s - 1, should
+        be added to SymPy's known values.''' % r))
+
+
+def abundance(n):
+    """Returns the difference between the sum of the positive
+    proper divisors of a number and the number.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory import abundance, is_perfect, is_abundant
+    >>> abundance(6)
+    0
+    >>> is_perfect(6)
+    True
+    >>> abundance(10)
+    -2
+    >>> is_abundant(10)
+    False
+    """
+    return divisor_sigma(n, 1) - 2 * n
+
+
+def is_abundant(n):
+    """Returns True if ``n`` is an abundant number, else False.
+
+    A abundant number is smaller than the sum of its positive proper divisors.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import is_abundant
+    >>> is_abundant(20)
+    True
+    >>> is_abundant(15)
+    False
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/AbundantNumber.html
+
+    """
+    n = as_int(n)
+    if is_perfect(n):
+        return False
+    return n % 6 == 0 or bool(abundance(n) > 0)
+
+
+def is_deficient(n):
+    """Returns True if ``n`` is a deficient number, else False.
+
+    A deficient number is greater than the sum of its positive proper divisors.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import is_deficient
+    >>> is_deficient(20)
+    False
+    >>> is_deficient(15)
+    True
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/DeficientNumber.html
+
+    """
+    n = as_int(n)
+    if is_perfect(n):
+        return False
+    return bool(abundance(n) < 0)
+
+
+def is_amicable(m, n):
+    """Returns True if the numbers `m` and `n` are "amicable", else False.
+
+    Amicable numbers are two different numbers so related that the sum
+    of the proper divisors of each is equal to that of the other.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import is_amicable, divisor_sigma
+    >>> is_amicable(220, 284)
+    True
+    >>> divisor_sigma(220) == divisor_sigma(284)
+    True
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Amicable_numbers
+
+    """
+    if m == n:
+        return False
+    a, b = (divisor_sigma(i) for i in (m, n))
+    return a == b == (m + n)
+
+
+def dra(n, b):
+    """
+    Returns the additive digital root of a natural number ``n`` in base ``b``
+    which is a single digit value obtained by an iterative process of summing
+    digits, on each iteration using the result from the previous iteration to
+    compute a digit sum.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import dra
+    >>> dra(3110, 12)
+    8
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Digital_root
+
+    """
+
+    num = abs(as_int(n))
+    b = as_int(b)
+    if b <= 1:
+        raise ValueError("Base should be an integer greater than 1")
+
+    if num == 0:
+        return 0
+
+    return (1 + (num - 1) % (b - 1))
+
+
+def drm(n, b):
+    """
+    Returns the multiplicative digital root of a natural number ``n`` in a given
+    base ``b`` which is a single digit value obtained by an iterative process of
+    multiplying digits, on each iteration using the result from the previous
+    iteration to compute the digit multiplication.
+
+    Examples
+    ========
+
+    >>> from sympy.ntheory.factor_ import drm
+    >>> drm(9876, 10)
+    0
+
+    >>> drm(49, 10)
+    8
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/MultiplicativeDigitalRoot.html
+
+    """
+
+    n = abs(as_int(n))
+    b = as_int(b)
+    if b <= 1:
+        raise ValueError("Base should be an integer greater than 1")
+    while n > b:
+        mul = 1
+        while n > 1:
+            n, r = divmod(n, b)
+            if r == 0:
+                return 0
+            mul *= r
+        n = mul
+    return n
diff --git a/shared/package-files/e4/e4ebd5e4a2d903f79451ea13e696e9cc6048997baf09ae615d23a3b8aa6367ed/_grid_sampler_2d_cpu_fallback_backward_native.h b/shared/package-files/e4/e4ebd5e4a2d903f79451ea13e696e9cc6048997baf09ae615d23a3b8aa6367ed/_grid_sampler_2d_cpu_fallback_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a410c3b42250c0a3c9b6fc2948b68a1fdddf2afc
--- /dev/null
+++ b/shared/package-files/e4/e4ebd5e4a2d903f79451ea13e696e9cc6048997baf09ae615d23a3b8aa6367ed/_grid_sampler_2d_cpu_fallback_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _grid_sampler_2d_cpu_fallback_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/e4/e4ec0b4cc802065c6c0df8845339558ea7126d5406f97e66230e497d6df04170/testobject_7.4_GLNX86.mat b/shared/package-files/e4/e4ec0b4cc802065c6c0df8845339558ea7126d5406f97e66230e497d6df04170/testobject_7.4_GLNX86.mat
new file mode 100644
index 0000000000000000000000000000000000000000..59d243c4de4fbb3fa653753e40651a6d0a4f4967
Binary files /dev/null and b/shared/package-files/e4/e4ec0b4cc802065c6c0df8845339558ea7126d5406f97e66230e497d6df04170/testobject_7.4_GLNX86.mat differ
diff --git a/shared/package-files/e4/e4ee7eb28b653cac444323b3e38b74f2678cc52810ff8dfbd1d6fb5eec1448b4/deactivate.bat b/shared/package-files/e4/e4ee7eb28b653cac444323b3e38b74f2678cc52810ff8dfbd1d6fb5eec1448b4/deactivate.bat
new file mode 100644
index 0000000000000000000000000000000000000000..0ec2c4324569af150280f197eb2431f9bcbeda80
--- /dev/null
+++ b/shared/package-files/e4/e4ee7eb28b653cac444323b3e38b74f2678cc52810ff8dfbd1d6fb5eec1448b4/deactivate.bat
@@ -0,0 +1,2 @@
+@ECHO OFF
+set PATH=%PATH_PRE_ACTIVATE_DEACTIVATE%
diff --git a/shared/package-files/f2/f200b6f7410b924d66e931cee43d830db7a27d39886295fbaca778e79729d653/cpunish.csv b/shared/package-files/f2/f200b6f7410b924d66e931cee43d830db7a27d39886295fbaca778e79729d653/cpunish.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6e9e90d30a1291bc41bf90b1e562141886e13784
--- /dev/null
+++ b/shared/package-files/f2/f200b6f7410b924d66e931cee43d830db7a27d39886295fbaca778e79729d653/cpunish.csv
@@ -0,0 +1,18 @@
+"STATE","EXECUTIONS","INCOME","PERPOVERTY","PERBLACK","VC100k96","SOUTH","DEGREE"
+"Texas",37,34453,16.7,12.2,644,1,0.16
+"Virginia",9,41534,12.5,20,351,1,0.27
+"Missouri",6,35802,10.6,11.2,591,0,0.21
+"Arkansas",4,26954,18.4,16.1,524,1,0.16
+"Alabama",3,31468,14.8,25.9,565,1,0.19
+"Arizona",2,32552,18.8,3.5,632,0,0.25
+"Illinois",2,40873,11.6,15.3,886,0,0.25
+"South_Carolina",2,34861,13.1,30.1,997,1,0.21
+"Colorado",1,42562,9.4,4.3,405,0,0.31
+"Florida",1,31900,14.3,15.4,1051,1,0.24
+"Indiana",1,37421,8.2,8.2,537,0,0.19
+"Kentucky",1,33305,16.4,7.2,321,0,0.16
+"Louisiana",1,32108,18.4,32.1,929,1,0.18
+"Maryland",1,45844,9.3,27.4,931,0,0.29
+"Nebraska",1,34743,10,4,435,0,0.24
+"Oklahoma",1,29709,15.2,7.7,597,0,0.21
+"Oregon",1,36777,11.7,1.8,463,0,0.25
diff --git a/shared/package-files/f2/f2097a703044b5f3d83798de75e0e8c035c0ce10a258c98fa71f139037dfceeb/arima112nc_css_results.py b/shared/package-files/f2/f2097a703044b5f3d83798de75e0e8c035c0ce10a258c98fa71f139037dfceeb/arima112nc_css_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9498b0adcadaf0c49d792e2a80d65f950bcfdfb
--- /dev/null
+++ b/shared/package-files/f2/f2097a703044b5f3d83798de75e0e8c035c0ce10a258c98fa71f139037dfceeb/arima112nc_css_results.py
@@ -0,0 +1,1305 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+llf = np.array([-239.75290561974])
+
+nobs = np.array([202])
+
+k = np.array([4])
+
+k_exog = np.array([1])
+
+sigma = np.array([.79291203639424])
+
+chi2 = np.array([35036.682546665])
+
+df_model = np.array([3])
+
+k_ar = np.array([1])
+
+k_ma = np.array([2])
+
+params = np.array([
+    .99954097483478,
+    -.69022779461512,
+    -.20477682541104,
+    .62870949745886])
+
+cov_params = np.array([
+    .00007344276568,
+    -.00016074342677,
+    -.00018478942445,
+    8.040251506e-06,
+    -.00016074342677,
+    .00094099304774,
+    .00017233777676,
+    -.0000145011098,
+    -.00018478942445,
+    .00017233777676,
+    .00103352686916,
+    .00030686101903,
+    8.040251506e-06,
+    -.0000145011098,
+    .00030686101903,
+    .00067796985496]).reshape(4, 4)
+
+xb = np.array([
+    0,
+    0,
+    .05104803293943,
+    .06663129478693,
+    .02164618112147,
+    .0773858949542,
+    .02606418170035,
+    .09391833096743,
+    .05710592120886,
+    .03083370067179,
+    .07319989800453,
+    .05287836492062,
+    .05776296555996,
+    .09105986356735,
+    .04293738678098,
+    .09576436132193,
+    .06068528071046,
+    .06157376244664,
+    .11172580718994,
+    .06527806818485,
+    .11443704366684,
+    .05653077363968,
+    .08205550909042,
+    .08481238037348,
+    .10436166077852,
+    .0875685736537,
+    .12320486456156,
+    .08366665989161,
+    .13979130983353,
+    .1902572363615,
+    .1306214183569,
+    .21803694963455,
+    .11079790443182,
+    .17274764180183,
+    .1937662512064,
+    .20047917962074,
+    .24034893512726,
+    .21783453226089,
+    .29279819130898,
+    .26804205775261,
+    .28678458929062,
+    .35651323199272,
+    .33659368753433,
+    .35760068893433,
+    .39895334839821,
+    .41131839156151,
+    .36645981669426,
+    .40991494059563,
+    .41024547815323,
+    .32657703757286,
+    .42312324047089,
+    .34933325648308,
+    .35912537574768,
+    .35077446699142,
+    .34701564908028,
+    .37364318966866,
+    .40170526504517,
+    .56070649623871,
+    .41915491223335,
+    .73478156328201,
+    .67748892307281,
+    .7744625210762,
+    .77825599908829,
+    .97586625814438,
+    .88692498207092,
+    .76232481002808,
+    .87376874685287,
+    .83281141519547,
+    .84783887863159,
+    .66423743963242,
+    .84904235601425,
+    .81613594293594,
+    .80033475160599,
+    .95782464742661,
+    .80624777078629,
+    .83626395463943,
+    .91873735189438,
+    .95130664110184,
+    1.0939226150513,
+    1.1171194314957,
+    1.1004731655121,
+    1.3512066602707,
+    1.4703129529953,
+    1.4805699586868,
+    1.7385860681534,
+    1.8268398046494,
+    1.5489361286163,
+    1.7446503639221,
+    1.864644408226,
+    1.7200467586517,
+    1.9223358631134,
+    1.775306224823,
+    1.5392524003983,
+    1.4067870378494,
+    1.9366238117218,
+    1.2984343767166,
+    1.1080636978149,
+    1.3500427007675,
+    1.2837564945221,
+    1.2670782804489,
+    1.3347851037979,
+    1.2857422828674,
+    1.1625040769577,
+    1.2111755609512,
+    1.0548515319824,
+    1.2553508281708,
+    1.0327949523926,
+    1.0740388631821,
+    1.222040772438,
+    .40555971860886,
+    1.0233588218689,
+    .84209614992142,
+    1.0186324119568,
+    1.0319027900696,
+    .99487775564194,
+    1.0439211130142,
+    .98785293102264,
+    1.0620124340057,
+    1.0916963815689,
+    1.1378232240677,
+    1.1243290901184,
+    1.3305295705795,
+    1.1925677061081,
+    1.0872994661331,
+    1.4599523544312,
+    1.2333589792252,
+    1.3584797382355,
+    1.7595859766006,
+    1.3009568452835,
+    1.1157965660095,
+    1.2948887348175,
+    1.2063180208206,
+    1.2332669496536,
+    1.2132470607758,
+    1.2049551010132,
+    1.2260574102402,
+    1.1875206232071,
+    1.1547852754593,
+    1.0519831180573,
+    1.1594845056534,
+    1.0069926977158,
+    1.0675266981125,
+    1.1299223899841,
+    1.0620901584625,
+    1.0999356508255,
+    1.1535499095917,
+    1.0026944875717,
+    1.0428657531738,
+    1.1120204925537,
+    1.1684119701385,
+    1.0258769989014,
+    1.1342295408249,
+    1.1183958053589,
+    .91313683986664,
+    .91156214475632,
+    1.0540328025818,
+    .84359037876129,
+    .75758427381516,
+    .96401190757751,
+    .83226495981216,
+    .8759680390358,
+    .98239886760712,
+    .85917687416077,
+    1.0634194612503,
+    .99442666769028,
+    1.153311252594,
+    1.2288066148758,
+    1.0869039297104,
+    1.281947016716,
+    1.0067318677902,
+    1.1028815507889,
+    .82448446750641,
+    .78489726781845,
+    1.1850204467773,
+    .86753690242767,
+    1.0692945718765,
+    1.1030179262161,
+    .8791960477829,
+    .86451041698456,
+    1.0455346107483,
+    1.085998415947,
+    1.0172398090363,
+    1.2250980138779,
+    1.2316122055054,
+    1.062157869339,
+    1.3991860151291,
+    1.0520887374878,
+    2.2203133106232,
+    .88833123445511,
+    1.4289729595184,
+    1.5206423997879,
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+    .6287094950676,
+    .6287094950676,
+    .6287094950676,
+    .6287094950676,
+    .6287094950676])
+
+stdp = np.array([
+    0,
+    0,
+    .05104803293943,
+    .06663129478693,
+    .02164618112147,
+    .0773858949542,
+    .02606418170035,
+    .09391833096743,
+    .05710592120886,
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+    .05287836492062,
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+    .06068528071046,
+    .06157376244664,
+    .11172580718994,
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+    .11443704366684,
+    .05653077363968,
+    .08205550909042,
+    .08481238037348,
+    .10436166077852,
+    .0875685736537,
+    .12320486456156,
+    .08366665989161,
+    .13979130983353,
+    .1902572363615,
+    .1306214183569,
+    .21803694963455,
+    .11079790443182,
+    .17274764180183,
+    .1937662512064,
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+    .24034893512726,
+    .21783453226089,
+    .29279819130898,
+    .26804205775261,
+    .28678458929062,
+    .35651323199272,
+    .33659368753433,
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+    .40991494059563,
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+    .35912537574768,
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+    1.3347851037979,
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+    1.1625040769577,
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+    1.2948887348175,
+    1.2063180208206,
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+    1.2132470607758,
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+    1.0519831180573,
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+    1.1299223899841,
+    1.0620901584625,
+    1.0999356508255,
+    1.1535499095917,
+    1.0026944875717,
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+    1.0258769989014,
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+    1.1183958053589,
+    .91313683986664,
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+    1.0540328025818,
+    .84359037876129,
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+    .96401190757751,
+    .83226495981216,
+    .8759680390358,
+    .98239886760712,
+    .85917687416077,
+    1.0634194612503,
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+    1.153311252594,
+    1.2288066148758,
+    1.0869039297104,
+    1.281947016716,
+    1.0067318677902,
+    1.1028815507889,
+    .82448446750641,
+    .78489726781845,
+    1.1850204467773,
+    .86753690242767,
+    1.0692945718765,
+    1.1030179262161,
+    .8791960477829,
+    .86451041698456,
+    1.0455346107483,
+    1.085998415947,
+    1.0172398090363,
+    1.2250980138779,
+    1.2316122055054,
+    1.062157869339,
+    1.3991860151291,
+    1.0520887374878,
+    2.2203133106232,
+    .88833123445511,
+    1.4289729595184,
+    1.5206423997879,
+    .68520504236221,
+    1.4659557342529,
+    1.5350053310394,
+    1.3178979158401,
+    1.4888265132904,
+    1.9698411226273,
+    1.4406447410583,
+    2.517040014267,
+    .55537897348404,
+    -.20722626149654,
+    1.0899519920349,
+    1.164245724678])
+
+icstats = np.array([
+    202,
+    np.nan,
+    -239.75290561974,
+    4,
+    487.50581123949,
+    500.73888202909])
+
+
+results = Bunch(
+    llf=llf,
+    nobs=nobs,
+    k=k,
+    k_exog=k_exog,
+    sigma=sigma,
+    chi2=chi2,
+    df_model=df_model,
+    k_ar=k_ar,
+    k_ma=k_ma,
+    params=params,
+    cov_params=cov_params,
+    xb=xb,
+    y=y,
+    resid=resid,
+    yr=yr,
+    mse=mse,
+    stdp=stdp,
+    icstats=icstats
+)
diff --git a/shared/package-files/f2/f20c997a98c64f45b7ca2cb7f91d7da7b02f04d36b9094f0590b6ec18655eede/_utils.py b/shared/package-files/f2/f20c997a98c64f45b7ca2cb7f91d7da7b02f04d36b9094f0590b6ec18655eede/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6662eb58f361f1d650bb5f217d7d72571d6652a1
--- /dev/null
+++ b/shared/package-files/f2/f20c997a98c64f45b7ca2cb7f91d7da7b02f04d36b9094f0590b6ec18655eede/_utils.py
@@ -0,0 +1,57 @@
+"""Defines utilities for interacting with scaled_dot_product_attention"""
+import math
+from typing import List, Optional
+
+import torch
+
+__all__: List[str] = []
+
+
+def _input_requires_grad(*tensors: torch.Tensor) -> bool:
+    """Returns True if any of the tensors requires grad"""
+    return any(t.requires_grad for t in tensors)
+
+
+def _postprocess_flash_output(inpt_tensor: torch.Tensor, og_size: int) -> torch.Tensor:
+    """Handles the unpad of the last dimension"""
+    if inpt_tensor.size(-1) != og_size:
+        return inpt_tensor[..., :og_size]
+    return inpt_tensor
+
+
+def _calculate_scale(head_dim_size: int, scale: Optional[float]) -> float:
+    """
+    For FlashAttention we pad the head dimension to be a multiple of 8 so we need to scale the output
+    by the original head size and not the padded.
+    """
+    if scale is not None:
+        return scale
+    return 1.0 / math.sqrt(head_dim_size)
+
+
+def _validate_sdpa_input(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p=0.0,
+    is_causal=False,
+    scale=None,
+):
+    if query.dtype != key.dtype or query.dtype != value.dtype:
+        raise ValueError(
+            f"Expected query, key, and value to have the same dtype, "
+            f"but got query.dtype: {query.dtype}, key.dtype: {key.dtype}, "
+            f"and value.dtype: {value.dtype} instead."
+        )
+    if query.device != key.device or query.device != value.device:
+        raise ValueError(
+            f"Expected query, key, and value to have the same device type, "
+            f"but got query.device: {query.device}, key.device: {key.device}, "
+            f"and value.device: {value.device} instead."
+        )
+    if query.dim() < 2 or key.dim() < 2 or value.dim() < 2:
+        raise ValueError(
+            f"Expected query, key, and value to all be  at least 2 dimensional, but got query.dim: "
+            f"{query.dim()}, key.dim: {key.dim()} and value.dim: {value.dim()} instead."
+        )
diff --git a/shared/package-files/f2/f2127c23b5286f9672745df1a00f2b5ea6952a1a6101ff7180a142b3571053e0/__init__.py b/shared/package-files/f2/f2127c23b5286f9672745df1a00f2b5ea6952a1a6101ff7180a142b3571053e0/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ea419566044ef8cdddcd9f1ccb370b4ece2e6a
--- /dev/null
+++ b/shared/package-files/f2/f2127c23b5286f9672745df1a00f2b5ea6952a1a6101ff7180a142b3571053e0/__init__.py
@@ -0,0 +1,28 @@
+"""
+:mod:`torch.optim._multi_tensor` is a package implementing various optimization algorithms.
+Most commonly used methods are already supported, and the interface is general
+enough, so that more sophisticated ones can be also easily integrated in the
+future.
+"""
+from functools import partialmethod
+from torch import optim
+
+def partialclass(cls, *args, **kwargs):
+
+    class NewCls(cls):
+        __init__ = partialmethod(cls.__init__, *args, **kwargs)
+
+    return NewCls
+
+
+Adam = partialclass(optim.Adam, foreach=True)
+AdamW = partialclass(optim.AdamW, foreach=True)
+NAdam = partialclass(optim.NAdam, foreach=True)
+SGD = partialclass(optim.SGD, foreach=True)
+RAdam = partialclass(optim.RAdam, foreach=True)
+RMSprop = partialclass(optim.RMSprop, foreach=True)
+Rprop = partialclass(optim.Rprop, foreach=True)
+ASGD = partialclass(optim.ASGD, foreach=True)
+Adamax = partialclass(optim.Adamax, foreach=True)
+Adadelta = partialclass(optim.Adadelta, foreach=True)
+Adagrad = partialclass(optim.Adagrad, foreach=True)
diff --git a/shared/package-files/f2/f21839490f81d81356491390517c1d0a628c782a2c4f1ac623a389eb32b4fa0e/cauchy_cpu_dispatch.h b/shared/package-files/f2/f21839490f81d81356491390517c1d0a628c782a2c4f1ac623a389eb32b4fa0e/cauchy_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..93b5ab745d94dcc7c80ae4df99d3b71c2d6dc63f
--- /dev/null
+++ b/shared/package-files/f2/f21839490f81d81356491390517c1d0a628c782a2c4f1ac623a389eb32b4fa0e/cauchy_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & cauchy_(at::Tensor & self, double median=0, double sigma=1, c10::optional<at::Generator> generator=c10::nullopt);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/f2/f22358eceae2ab13bf3e42e3cda3df728087d3133e717a397ca4a91aeadcd794/logging.h b/shared/package-files/f2/f22358eceae2ab13bf3e42e3cda3df728087d3133e717a397ca4a91aeadcd794/logging.h
new file mode 100644
index 0000000000000000000000000000000000000000..b0b67c68088389bdd35e72c00cd7d1005399cb1c
--- /dev/null
+++ b/shared/package-files/f2/f22358eceae2ab13bf3e42e3cda3df728087d3133e717a397ca4a91aeadcd794/logging.h
@@ -0,0 +1,86 @@
+#pragma once
+
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+
+namespace torch::jit::logging {
+
+class LoggerBase {
+ public:
+  TORCH_API virtual void addStatValue(
+      const std::string& stat_name,
+      int64_t val) = 0;
+  virtual ~LoggerBase() = default;
+};
+
+TORCH_API LoggerBase* getLogger();
+TORCH_API LoggerBase* setLogger(LoggerBase* logger);
+
+// No-op logger. This is the default and is meant to incur almost no runtime
+// overhead.
+
+class NoopLogger : public LoggerBase {
+ public:
+  void addStatValue(const std::string& stat_name, int64_t val) override {}
+  ~NoopLogger() override = default;
+};
+
+// Trivial locking logger. Pass in an instance of this to setLogger() to use it.
+// This keeps track of the sum of all statistics.
+//
+// NOTE: this is not written in a scalable way and should probably only be used
+// in the single-threaded case or for testing.
+class TORCH_API LockingLogger : public LoggerBase {
+ public:
+  void addStatValue(const std::string& stat_name, int64_t val) override;
+  virtual int64_t getCounterValue(const std::string& name) const;
+  enum class AggregationType { SUM = 0, AVG = 1 };
+  void setAggregationType(const std::string& stat_name, AggregationType type);
+  ~LockingLogger() override = default;
+
+ private:
+  mutable std::mutex m;
+  struct RawCounter {
+    RawCounter() : sum(0), count(0) {}
+    int64_t sum;
+    size_t count;
+  };
+  std::unordered_map<std::string, RawCounter> raw_counters;
+  std::unordered_map<std::string, AggregationType> agg_types;
+};
+
+// Make this struct so the timer internals are opaque to the user.
+struct JITTimePoint {
+  std::chrono::time_point<std::chrono::high_resolution_clock> point;
+};
+
+TORCH_API JITTimePoint timePoint();
+TORCH_API void recordDurationSince(
+    const std::string& name,
+    const JITTimePoint& tp);
+
+namespace runtime_counters {
+constexpr const char* GRAPH_EXECUTORS_CONSTRUCTED =
+    "pytorch_runtime.graph_executors_constructed";
+constexpr const char* GRAPH_EXECUTOR_INVOCATIONS =
+    "pytorch_runtime.graph_executor_invocations";
+constexpr const char* EXECUTION_PLAN_CACHE_HIT =
+    "pytorch_runtime.execution_plan_cache_hit";
+constexpr const char* EXECUTION_PLAN_CACHE_MISS =
+    "pytorch_runtime.execution_plan_cache_miss";
+
+inline std::vector<const char*> allRuntimeCounters() {
+  return {
+      GRAPH_EXECUTORS_CONSTRUCTED,
+      GRAPH_EXECUTOR_INVOCATIONS,
+      EXECUTION_PLAN_CACHE_HIT,
+      EXECUTION_PLAN_CACHE_MISS};
+}
+
+} // namespace runtime_counters
+
+} // namespace torch::jit::logging
diff --git a/shared/package-files/f2/f22379592a4f35e471054d19b07410a10cd2f66f9d3c9fededc48e6ac6912e2d/results_varmax.py b/shared/package-files/f2/f22379592a4f35e471054d19b07410a10cd2f66f9d3c9fededc48e6ac6912e2d/results_varmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..e57736f3cb31d5cccf957c1e6087c412b0634cc2
--- /dev/null
+++ b/shared/package-files/f2/f22379592a4f35e471054d19b07410a10cd2f66f9d3c9fededc48e6ac6912e2d/results_varmax.py
@@ -0,0 +1,234 @@
+"""
+Results for VARMAX tests
+
+Results from Stata using script `test_varmax_stata.do`.
+See also Stata time series documentation, in particular `dfactor`.
+
+Data from:
+
+http://www.jmulti.de/download/datasets/e1.dat
+
+Author: Chad Fulton
+License: Simplified-BSD
+"""
+
+# See http://www.jmulti.de/download/datasets/e1.dat
+# 1960:Q1 - 1982Q4
+lutkepohl_data = [
+    [180, 451, 415], [179, 465, 421], [185, 485, 434], [192, 493, 448],
+    [211, 509, 459], [202, 520, 458], [207, 521, 479], [214, 540, 487],
+    [231, 548, 497], [229, 558, 510], [234, 574, 516], [237, 583, 525],
+    [206, 591, 529], [250, 599, 538], [259, 610, 546], [263, 627, 555],
+    [264, 642, 574], [280, 653, 574], [282, 660, 586], [292, 694, 602],
+    [286, 709, 617], [302, 734, 639], [304, 751, 653], [307, 763, 668],
+    [317, 766, 679], [314, 779, 686], [306, 808, 697], [304, 785, 688],
+    [292, 794, 704], [275, 799, 699], [273, 799, 709], [301, 812, 715],
+    [280, 837, 724], [289, 853, 746], [303, 876, 758], [322, 897, 779],
+    [315, 922, 798], [339, 949, 816], [364, 979, 837], [371, 988, 858],
+    [375, 1025, 881], [432, 1063, 905], [453, 1104, 934], [460, 1131, 968],
+    [475, 1137, 983], [496, 1178, 1013], [494, 1211, 1034], [498, 1256, 1064],
+    [526, 1290, 1101], [519, 1314, 1102], [516, 1346, 1145], [531, 1385, 1173],
+    [573, 1416, 1216], [551, 1436, 1229], [538, 1462, 1242], [532, 1493, 1267],
+    [558, 1516, 1295], [524, 1557, 1317], [525, 1613, 1355], [519, 1642, 1371],
+    [526, 1690, 1402], [510, 1759, 1452], [519, 1756, 1485], [538, 1780, 1516],
+    [549, 1807, 1549], [570, 1831, 1567], [559, 1873, 1588], [584, 1897, 1631],
+    [611, 1910, 1650], [597, 1943, 1685], [603, 1976, 1722], [619, 2018, 1752],
+    [635, 2040, 1774], [658, 2070, 1807], [675, 2121, 1831], [700, 2132, 1842],
+    [692, 2199, 1890], [759, 2253, 1958], [782, 2276, 1948], [816, 2318, 1994],
+    [844, 2369, 2061], [830, 2423, 2056], [853, 2457, 2102], [852, 2470, 2121],
+    [833, 2521, 2145], [860, 2545, 2164], [870, 2580, 2206], [830, 2620, 2225],
+    [801, 2639, 2235], [824, 2618, 2237], [831, 2628, 2250], [830, 2651, 2271],
+]
+
+lutkepohl_var1 = {
+    "params": [
+        -0.25034303,  0.28759168,  0.81626475,  # Phi, row 1
+        0.023383,     0.19048278,  0.66502259,  # Phi, row 2
+        -0.01492992,  0.53796097,  0.28114733,  # Phi, row 3
+        # .00199294,                              # Covariance, lower triangle
+        # .00006096, .00012986,
+        # .00018523, .00011695, .00016188,
+        # Note: the following are the Cholesky of the covariance
+        # matrix defined just above
+        0.04464236,                             # Cholesky, lower triangle
+        0.00136552,  0.01354125,
+        0.0029089,   0.00834324,  0.00915471
+    ],
+    "var_oim": [
+        .01319669, .19413864, .2386643,
+        .0012437,  .01829378, .02234399,
+        .00107749, .01584584, .01938099,
+        1.061e-07,
+        4.981e-09, 4.549e-09,
+        9.211e-10, 5.825e-10, 7.016e-10],
+    "loglike": 587.8481018831948,
+    "aic": -1145.696,
+    "bic": -1110.934,
+}
+
+lutkepohl_var1_diag = {
+    "params": [
+        -0.24817904,  0.29283012,  0.80794938,  # Phi, row 1
+        0.02282985,   0.19672157,  0.66329776,  # Phi, row 2
+        -0.01522531,  0.53500874,  0.28859213,  # Phi, row 3
+        0.00199106,  0.00018529,  0.00016179    # Variances, diagonal
+    ],
+    "var_oim": [
+        .01314245, .1902972,  .23400828,
+        .00124336, .01840132, .02229946,
+        .00107322, .01558391, .01909303,
+        1.057e-07, 9.233e-10, 7.011e-10
+    ],
+    "loglike": 562.8168476509002,
+    "aic": -1101.634,
+    "bic": -1073.824
+}
+
+lutkepohl_var1_diag_meas = {
+    "params": [
+        -0.24817904,  0.29283012,  0.80794938,  # Phi, row 1
+        0.02282985,   0.19672157,  0.66329776,  # Phi, row 2
+        -0.01522531,  0.53500874,  0.28859213,  # Phi, row 3
+        0.00199106,   0.00018529,  0.00016179,  # Variances, diagonal
+        0,            0,           0            # Measurement error variances
+    ],
+    "var_oim": [
+        .01314245, .1902972,  .23400828,
+        .00124336, .01840132, .02229946,
+        .00107322, .01558391, .01909303,
+        1.057e-07, 9.233e-10, 7.011e-10,
+        None, None, None
+    ],
+    "loglike": 562.8168476509002,
+    "aic": None,
+    "bic": None
+}
+
+lutkepohl_var1_obs_intercept = {
+    "params": [
+        -.24762,   .25961003,  .75992623,   # Phi, row 1
+        .03186854, -.07271862, .23697765,   # Phi, row 2
+        -.0053055, .2362571,   -.19438311,  # Phi, row 3
+        .00199116, .00013515,  .00009937    # Variances, diagonal
+    ],
+    "obs_intercept": [.01799302, .02065458,  .01987525],  # Intercepts
+    "var_oim": [
+        .01317874, .2311403,  .33481866,
+        .00090084, .0157839,  .0229119,
+        .00065737, .01149729, .01661236,
+        # .00001802, 1.818e-06, 1.086e-06,  # Intercept parameters
+        1.057e-07, 4.869e-10, 2.630e-10],
+    "loglike": 593.5252693885262,
+    "aic": -1101.634,
+    "bic": -1073.824
+}
+
+lutkepohl_var1_exog = {
+    "params": [
+        -.25549409, .31149462, .92674046,  # Phi, row 1
+        .02935715,  .13699757, .5059042,   # Phi, row 2
+        -.00540021, .4598014,  .06065565,  # Phi, row 3
+        -.00007533, .00012771, .00018224,  # exog
+        # .00200617,                         # Covariance, lower triangle
+        # .00007053,  .00017216,
+        # .00013934,  .00010021, .00013833
+        # Note: the following are the Cholesky of the covariance
+        # matrix defined just above
+        .04479029,                         # Cholesky, lower triangle
+        .00157467,  .01302614,
+        .00311094,  .00731692, .00866687
+    ],
+    "var_oim": [
+        .01350243, .20429977, .29684366,  # Phi, row 1
+        .00115871, .01753203, .02547371,  # Phi, row 2
+        .000931,   .01408662, .02046759,  # Phi, row 3
+        3.720e-08, 3.192e-09, 2.565e-09   # exog
+    ],
+    "loglike": 587.4157014188437,
+    "aic": None,
+    "bic": None
+}
+
+lutkepohl_var1_exog2 = {
+    "params": [
+        -.2552236,  .21722691,  .81525457,  # Phi, row 1
+        .02998355,  -.08130972, .24772266,  # Phi, row 2
+        -.00476998, .24016112, -.19910237,  # Phi, row 3
+        .00811096, -.00015244,  # exog, y1
+        .01878355, -.00005086,  # exog, y2
+        .01889825, 2.577e-06,   # exog, y3
+        # .00199918,                         # Covariance, lower triangle
+        # .00005435,  .00013469,
+        # .00012306,  .00006251,   .00010039
+        # Note: the following are the Cholesky of the covariance
+        # matrix defined just above
+        .04471219,                         # Cholesky, lower triangle
+        .00121555,  .01102644,
+        .00275227,  .00536569,  .00800152
+    ],
+    "var_oim": None,
+    # 'loglike': 600.9801664685759,  # From Stata
+    "loglike": 600.65449034396283,   # From VARMAX (regression test)
+    "aic": None,
+    "bic": None
+}
+
+lutkepohl_var2 = {
+    "params": [
+        -.25244981, .62528114,  # Phi_1, row 1
+        -.13011679, .58173748,  # Phi_1, row 2
+        .05369178,  .35716349,  # Phi_2, row 1
+        .03861472,  .43812606,  # Phi_2, row 2
+        # .00197786,              # Covariance, lower triangle
+        # .00008091,  .00018269
+        0.04447314,             # Covariance cholesky, lower triangle
+        0.0018193, 0.01339329
+    ],
+    "var_oim": [
+        .01315844, .11805816,  # Phi_1, row 1
+        .01321036, .11300702,  # Phi_1, row 2
+        .00122666, .01064478,  # Phi_2, row 1
+        .0012571,  .0106738,   # Phi_2, row 2
+        1.048e-07,             # Covariance, lower triangle
+        4.994e-09, 8.940e-10
+    ],
+    "loglike": 343.3149718445623,
+    "aic": -664.6299,
+    "bic": -639.1376
+}
+
+fred_varma11 = {
+    "params": [
+        .80580312,  0,           # Phi_1, row 1
+        .17348681,  -.48093755,  # Phi_1, row 2
+        -.51890703, 0,           # Theta_1, row 1
+        0,          0,           # Theta_1, row 2
+        .0000582,   .00003815,   # Variances
+    ],
+    "var_oim": [
+        .00272999,  0,          # Phi_1, row 1
+        .00164152, .00248576,   # Phi_1, row 2
+        .0049259,  0,           # Theta_1, row 1
+        0,         0,           # Theta_1, row 2
+        1.529e-11, 6.572e-12,   # Variances
+    ],
+    "loglike": 3156.056423235071,
+    "aic": -6300.113,
+    "bic": -6275.551
+}
+
+fred_vma1 = {
+    "params": [
+        .24803941, 0,          # Theta_1, row 1
+        0,         0,          # Theta_1, row 2
+        .00006514, .00004621,  # Variances
+    ],
+    "var_oim": [
+        .00154773, 0,           # Theta_1, row 1
+        0,         0,           # Theta_1, row 2
+        1.916e-11, 9.639e-12,   # Variances
+    ],
+    "loglike": 3088.909619417645,
+    "aic": -6171.819,
+    "bic": -6159.539
+}
diff --git a/shared/package-files/f2/f22693a4cc995c4e5577e922287791c4a5b9ef41f65c77fd20c41f238b095f51/test_x13.py b/shared/package-files/f2/f22693a4cc995c4e5577e922287791c4a5b9ef41f65c77fd20c41f238b095f51/test_x13.py
new file mode 100644
index 0000000000000000000000000000000000000000..142b313202b747cb69ab3833d9e35ff23fa1465c
--- /dev/null
+++ b/shared/package-files/f2/f22693a4cc995c4e5577e922287791c4a5b9ef41f65c77fd20c41f238b095f51/test_x13.py
@@ -0,0 +1,275 @@
+from statsmodels.compat.pandas import MONTH_END
+
+from pathlib import Path
+import tempfile
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from statsmodels.datasets import co2, macrodata
+from statsmodels.tools.sm_exceptions import X13Error
+from statsmodels.tsa.x13 import (
+    _find_x12,
+    x13_arima_analysis,
+    x13_arima_select_order,
+)
+
+x13path = _find_x12()
+
+pytestmark = pytest.mark.skipif(x13path is False, reason="X13/X12 not available")
+
+dta = macrodata.load_pandas().data
+index = pd.period_range(start="1959Q1", end="2009Q3", freq="Q")
+dta.index = index
+quarterly_data = dta.dropna()
+
+dta = co2.load_pandas().data
+dta["co2"] = dta.co2.interpolate()
+monthly_data = dta.resample(MONTH_END)
+# change in pandas 0.18 resample is deferred object
+if not isinstance(monthly_data, (pd.DataFrame, pd.Series)):
+    monthly_data = monthly_data.mean()
+
+monthly_start_data = dta.resample("MS")
+if not isinstance(monthly_start_data, (pd.DataFrame, pd.Series)):
+    monthly_start_data = monthly_start_data.mean()
+
+data = (
+    monthly_data,
+    monthly_start_data,
+    monthly_data.co2,
+    monthly_start_data.co2,
+    quarterly_data.realgdp,
+    quarterly_data[["realgdp"]],
+)
+ids = (
+    "monthly",
+    "monthly_start",
+    "monthly_co2",
+    "monthly_start_co2",
+    "series",
+    "dataframe",
+)
+
+
+@pytest.fixture(params=data, ids=ids)
+def dataset(request):
+    return request.param
+
+
+@pytest.mark.parametrize("use_numpy", [True, False])
+def test_x13_arima_select_order(dataset, use_numpy):
+    if use_numpy:
+        index = dataset.index
+        dataset = np.squeeze(np.asarray(dataset))
+        start = index[0]
+        if isinstance(index, pd.DatetimeIndex):
+            try:
+                with pd.option_context("future.infer_freq_returns_offset", True):
+                    freq = index.inferred_freq.freqstr
+            except (AttributeError, ImportError):
+                freq = index.inferred_freq
+        elif isinstance(index, pd.PeriodIndex):
+            start = start.to_timestamp()
+            freq = index.freq
+        else:
+            raise NotImplementedError
+        assert freq is not None
+    else:
+        freq = start = None
+    res = x13_arima_select_order(dataset, start=start, freq=freq)
+    assert isinstance(res.order, tuple)
+    assert isinstance(res.sorder, tuple)
+
+
+@pytest.mark.thread_unsafe(reason="Uses matplotlib")
+@pytest.mark.matplotlib
+def test_x13_arima_plot(dataset, close_figures):
+    res = x13_arima_analysis(dataset)
+    res.plot()
+
+
+def test_x13_arima_plot_no_pandas(dataset, close_figures):
+    res = x13_arima_analysis(dataset)
+    res.plot()
+
+
+@pytest.mark.smoke
+def test_log_diagnostics(dataset):
+    res = x13_arima_analysis(dataset, log_diagnostics=True)
+    assert isinstance(res.x13_diagnostic, dict)
+    assert next(iter(res.x13_diagnostic.keys())) == "F-D8"
+    assert isinstance(next(iter(res.x13_diagnostic.values())), float)
+
+
+@pytest.mark.smoke
+def test_log_diagnostics_false(dataset):
+    res = x13_arima_analysis(dataset, log_diagnostics=False)
+    assert isinstance(res.x13_diagnostic, dict)
+    assert next(iter(res.x13_diagnostic.keys())) == "F-D8"
+    assert next(iter(res.x13_diagnostic.values())) == "Log diagnostics not retrieved."
+
+
+def test_x13_arima_rawspec_arg():
+    with pytest.raises(ValueError):
+        # error because both param and rawspec are specified
+        x13_arima_analysis(dataset, outlier=True, rawspec="/fake/path.spc")
+
+    with pytest.raises(ValueError):
+        # error because of fake path
+        x13_arima_analysis(dataset, rawspec="/fake/path.spc")
+
+
+def test_x13_arima_rawspec_run(dataset):
+    # example rawspec file string
+    raw_spec_file = """
+series {
+    modelspan=(,)
+    save=(B1)
+    span=(,)
+    type=(flow)
+}
+x11 {
+    seasonalma=(  msr)
+    appendfcst=yes
+    mode=(mult)
+    print=( seasadj seasonal adjustfac)
+    save=(seasadj seasonal adjustfac)
+    savelog=(  alldiagnostics)
+}
+arima {model=(0 1 0)(1 0 1)}
+transform {
+    function=log
+}
+regression {
+    savelog=(  aictest)
+ }
+estimate {save=mdl}
+slidingspans { }
+history {
+    estimates=(sadj seasonal fcst)
+    fixmdl=yes
+}
+"""
+
+    # pass rawspec as string
+    x13_arima_analysis(dataset, rawspec=raw_spec_file)
+
+    # pass rawspec as file path
+    with tempfile.NamedTemporaryFile(suffix=".spc", delete_on_close=False) as ft:
+        ft.write(raw_spec_file.encode("utf8"))
+        ft.seek(0)
+        ft.close()
+
+        x13_arima_analysis(dataset, rawspec=ft.name)
+
+
+def test_x13_arima_rawspec_no_save(dataset):
+    # example rawspec file string
+    raw_spec_file = """
+series {
+    modelspan=(,)
+    save=(B1)
+    span=(,)
+    type=(flow)
+}
+x11 {
+    seasonalma=(  msr)
+    appendfcst=yes
+    mode=(mult)
+    print=( seasadj seasonal adjustfac)
+    savelog=(  alldiagnostics)
+}
+arima {model=(0 1 0)(1 0 1)}
+transform {
+    function=log
+}
+regression {
+    savelog=(  aictest)
+ }
+estimate {save=mdl}
+slidingspans { }
+history {
+    estimates=(sadj seasonal fcst)
+    fixmdl=yes
+}
+"""
+
+    # pass rawspec as string
+    x13_arima_analysis(dataset, rawspec=raw_spec_file)
+
+    # pass rawspec as file path
+    with tempfile.NamedTemporaryFile(suffix=".spc", delete_on_close=False) as ft:
+        ft.write(raw_spec_file.encode("utf8"))
+        ft.seek(0)
+        ft.close()
+
+        x13_arima_analysis(dataset, rawspec=ft.name)
+
+
+def test_x13_arima_rawspec_no_x11(dataset):
+    # example rawspec file string
+    raw_spec_file = """
+series {
+    modelspan=(,)
+    save=(B1)
+    span=(,)
+    type=(flow)
+}
+arima {model=(0 1 0)(1 0 1)}
+transform {
+    function=log
+}
+regression {
+    savelog=(  aictest)
+ }
+estimate {save=mdl}
+slidingspans { }
+history {
+    estimates=(sadj seasonal fcst)
+    fixmdl=yes
+}
+"""
+
+    # pass rawspec as string
+    x13_arima_analysis(dataset, rawspec=raw_spec_file)
+
+    # pass rawspec as file path
+    with tempfile.NamedTemporaryFile(suffix=".spc", delete_on_close=False) as ft:
+        ft.write(raw_spec_file.encode("utf8"))
+        ft.seek(0)
+        ft.close()
+
+        x13_arima_analysis(dataset, rawspec=ft.name)
+        try:
+            Path(ft.name).unlink()
+        except OSError:
+            # While it best ot unlink, not strictly required
+            pass
+
+
+def test_x13_arima_invalid_rawspec(dataset):
+    # bad rawspec file string ("series" misspelled, no closing "}" on "x11")
+    raw_spec_file = """
+seri es {
+    modelspan=(,)
+}
+x11 {
+    seasonalma=(  msr)
+"""
+
+    # pass rawspec as string
+    with pytest.raises(X13Error):
+
+        x13_arima_analysis(dataset, rawspec=raw_spec_file)
+
+    # pass rawspec as file path
+    with tempfile.NamedTemporaryFile(suffix=".spc", delete_on_close=False) as ft:
+        ft.write(raw_spec_file.encode("utf8"))
+        ft.seek(0)
+        ft.close()
+
+        with pytest.raises(X13Error):
+
+            x13_arima_analysis(dataset, rawspec=ft.name)
diff --git a/shared/package-files/f2/f22ac597132e96e847666b9b06c19454d0b147db7b4dc6027b61ac8c3a29323c/RECORD b/shared/package-files/f2/f22ac597132e96e847666b9b06c19454d0b147db7b4dc6027b61ac8c3a29323c/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..ce0124addc4d7551db3aa5220ea0dd5eb9a1db66
--- /dev/null
+++ b/shared/package-files/f2/f22ac597132e96e847666b9b06c19454d0b147db7b4dc6027b61ac8c3a29323c/RECORD
@@ -0,0 +1,22 @@
+OpenSSL/SSL.py,sha256=F4d6UYDFeeGiKpZdO6AFcpHq_L_HoAAMuEvg9N5wT28,102221
+OpenSSL/__init__.py,sha256=35kHGKn8c6hnSehguA69WoPvXy0eUPzhC80QDfImwZE,497
+OpenSSL/__pycache__/SSL.cpython-310.pyc,,
+OpenSSL/__pycache__/__init__.cpython-310.pyc,,
+OpenSSL/__pycache__/_util.cpython-310.pyc,,
+OpenSSL/__pycache__/crypto.cpython-310.pyc,,
+OpenSSL/__pycache__/debug.cpython-310.pyc,,
+OpenSSL/__pycache__/rand.cpython-310.pyc,,
+OpenSSL/__pycache__/version.cpython-310.pyc,,
+OpenSSL/_util.py,sha256=eo8NTmsyF9ND3rqXB6iJ5da7BRVLiB8PI1El8wgiQr0,3539
+OpenSSL/crypto.py,sha256=bjnriIGKo9RwmtWsOyqvSsrleAcyu_gVt0z90-2U8Ag,106021
+OpenSSL/debug.py,sha256=vCl77f2MslUoTRSu9fqP5DL_9DK_RSC7Jpu07Ip9gQM,1008
+OpenSSL/rand.py,sha256=arqFZUdw5YyI4z7TNBTJi_dyevzx6krLyB6zLbwjrVI,1069
+OpenSSL/version.py,sha256=hv4Xt_BXa-EkhqnUzO34t8MK4XUrD635rZKTcab8kmE,641
+pyOpenSSL-24.0.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+pyOpenSSL-24.0.0.dist-info/LICENSE,sha256=z8d0m5b2O9McPEK1xHG_dWgUBT6EfBDz6wA0F7xSPTA,11358
+pyOpenSSL-24.0.0.dist-info/METADATA,sha256=my_xXLPSF85-8LBlR_FnQ6721TYwcsUFv_hdA-dpWW4,12360
+pyOpenSSL-24.0.0.dist-info/RECORD,,
+pyOpenSSL-24.0.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+pyOpenSSL-24.0.0.dist-info/WHEEL,sha256=yQN5g4mg4AybRjkgi-9yy4iQEFibGQmlz78Pik5Or-A,92
+pyOpenSSL-24.0.0.dist-info/direct_url.json,sha256=oV5UDjDJj5HOP8V9ugD5F7-LXV-o7yMks9WyfgxYlCY,69
+pyOpenSSL-24.0.0.dist-info/top_level.txt,sha256=NNxWqS8hKNJh2cUXa1RZOMX62VJfyd8URo1TsYnR_MU,8
diff --git a/shared/package-files/f2/f22e7243fac42a9f8801fea0c0e3dddc8d5e872069f7c1d6d9129d1567a885e1/vecm_e6_jmulti_co_granger_causality_dp_r.txt b/shared/package-files/f2/f22e7243fac42a9f8801fea0c0e3dddc8d5e872069f7c1d6d9129d1567a885e1/vecm_e6_jmulti_co_granger_causality_dp_r.txt
new file mode 100644
index 0000000000000000000000000000000000000000..faf725ae42ab8b97b5032392be8413a46ccd2531
--- /dev/null
+++ b/shared/package-files/f2/f22e7243fac42a9f8801fea0c0e3dddc8d5e872069f7c1d6d9129d1567a885e1/vecm_e6_jmulti_co_granger_causality_dp_r.txt
@@ -0,0 +1,13 @@
+*** Mon, 25 Jul 2016 10:26:21 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "Dp" do not Granger-cause "R"
+
+Test statistic l = 1.4382
+pval-F( l; 4, 182) = 0.2231 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "Dp" and "R"
+
+Test statistic: c = 0.9998
+pval-Chi( c; 1) = 0.3174 
+
diff --git a/shared/package-files/f2/f232d14d6146890caa90759f2aba06fa82c164435a4b920275172fc85f820e2b/P12-M-W b/shared/package-files/f2/f232d14d6146890caa90759f2aba06fa82c164435a4b920275172fc85f820e2b/P12-M-W
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diff --git a/shared/package-files/f2/f232d14d6146890caa90759f2aba06fa82c164435a4b920275172fc85f820e2b/prism12-m-w b/shared/package-files/f2/f232d14d6146890caa90759f2aba06fa82c164435a4b920275172fc85f820e2b/prism12-m-w
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diff --git a/shared/package-files/f2/f2421f4d25769ae214a905748d9cd953e5d670b3a125e6ccd559a2383aaf4980/entry_points.txt b/shared/package-files/f2/f2421f4d25769ae214a905748d9cd953e5d670b3a125e6ccd559a2383aaf4980/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b26481e6a0ae7224837e33038a4ddb0dfac3af7d
--- /dev/null
+++ b/shared/package-files/f2/f2421f4d25769ae214a905748d9cd953e5d670b3a125e6ccd559a2383aaf4980/entry_points.txt
@@ -0,0 +1,5 @@
+[console_scripts]
+conda-content-trust = conda_content_trust.cli:cli
+
+[conda]
+conda-content-trust = conda_content_trust.plugin
diff --git a/shared/package-files/f2/f249abb3909b8014c196dfe797b47dfc0dba8b758736d0138501730f3f7963bb/portal.py b/shared/package-files/f2/f249abb3909b8014c196dfe797b47dfc0dba8b758736d0138501730f3f7963bb/portal.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3484a1b69d57b087787badb2915b5efc94adeb8
--- /dev/null
+++ b/shared/package-files/f2/f249abb3909b8014c196dfe797b47dfc0dba8b758736d0138501730f3f7963bb/portal.py
@@ -0,0 +1,231 @@
+# Copyright 2019 Kakao Brain
+#
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+"""Portal keeps a tensor in the pocket plane. The tensor becomes hidden to the
+autograd engine. The shared context of three functions (:class:`PortalBlue`,
+:class:`PortalOrange`, and :class:`PortalCopy`) out of the computation graph is
+one of the most important feature of :mod:`torchpipe.skip`.
+
+The metaphor is inspired by Portal™ from Valve.
+
+"""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import Tensor
+
+from ..copy import Context as CopyContext
+from ..copy import Copy
+from ..phony import get_phony
+from ..stream import AbstractStream, get_device
+
+__all__: List[str] = []
+
+
+class Portal:
+    """A portal for a tensor."""
+
+    def __init__(self, tensor: Optional[Tensor], tensor_life: int) -> None:
+        self.put_tensor(tensor, tensor_life)
+        self.grad: Optional[Tensor] = None
+
+    def blue(self) -> Tensor:
+        """Creates a :class:`PortalBlue` which hides the underlying tensor from
+        the autograd engine.
+
+        Join the returning phony to the main lane of the autograd graph to
+        assure the correct backpropagation::
+
+            PortalBlue --+
+                         |
+            ---------- Join --
+
+        """
+        tensor = self.use_tensor()
+
+        if tensor is None:
+            return get_phony(torch.device("cpu"), requires_grad=False)
+
+        return PortalBlue.apply(self, tensor)
+
+    def orange(self, phony: Tensor) -> Optional[Tensor]:
+        """Creates a :class:`PortalOrange` which retrieves the hidden tensor
+        without losing ability of backpropagation.
+
+        Give a phony forked from the main lane of an autograd graph::
+
+                +-- PortalOrange --+
+                |                  |
+            -- Fork --------- f(a, b) --
+
+        """
+        self.check_tensor_life()
+
+        if self.tensor is None:
+            return self.use_tensor()
+
+        return PortalOrange.apply(self, phony)
+
+    def copy(self, prev_stream: AbstractStream, next_stream: AbstractStream, phony: Tensor,) -> Tensor:
+        """Copies the hidden tensor by a :class:`PortalCopy`.
+
+        Give a phony and use the returning phony to keep backpropagation::
+
+                +-- PortalCopy --+
+                |                |
+            -- Fork ---------- Join --
+
+        """
+        if self.tensor is None:
+            return get_phony(torch.device("cpu"), requires_grad=False)
+
+        return PortalCopy.apply(self, prev_stream, next_stream, phony)
+
+    def check_tensor_life(self) -> None:
+        if self.tensor_life <= 0:
+            raise RuntimeError("tensor in portal has been removed")
+
+    def put_tensor(self, tensor: Optional[Tensor], tensor_life: int) -> None:
+        """Stores a tensor into this portal."""
+        # [Life of Tensor through Portal]
+        #
+        # The tensor can be retrieved by use_tensor() up to 'tensor_life'
+        # times. When the life becomes 0, the tensor will be deleted for
+        # deallocation in CUDA memory.
+        #
+        # The below events participate in a tensor through a portal.
+        # Note that [x] denotes the events which call use_tensor():
+        #
+        #  1. [x] blue()
+        #  2. [ ]   PortalBlue.forward
+        #  3. [ ] copy()
+        #  4. [ ]   PortalCopy.forward
+        #  5. [ ] orange()
+        #  6. [x]   PortalOrange.forward
+        # - - - - - - - - - - - - - - - - - - - - - - - - - - -
+        #  7. [ ] orange() (recomputed)
+        #  8. [x]   PortalOrange.forward (recomputed)
+        #  9. [ ]   PortalOrange.backward
+        # 10. [ ] PortalCopy.backward
+        # 11. [x] blue() (recomputed)
+        # 12. [ ]   PortalBlue.forward (recomputed)
+        # 13. [ ]   PortalBlue.backward
+        #
+        self.tensor_life = tensor_life
+
+        if tensor_life > 0:
+            self.tensor = tensor
+        else:
+            self.tensor = None
+
+    def use_tensor(self) -> Optional[Tensor]:
+        """Retrieves the underlying tensor and decreases the tensor  life. When
+        the life becomes 0, it the tensor will be removed.
+        """
+        self.check_tensor_life()
+
+        tensor = self.tensor
+
+        self.tensor_life -= 1
+
+        if self.tensor_life <= 0:
+            self.tensor = None
+
+        return tensor
+
+    def put_grad(self, grad: Tensor) -> None:
+        """Stores a gradient into this portal."""
+        self.grad = grad
+
+    def use_grad(self) -> Tensor:
+        """Retrieves and removes the underlying gradient. The gradient is
+        always ephemeral.
+        """
+        if self.grad is None:
+            raise RuntimeError("grad in portal has been removed or never set")
+
+        grad = self.grad
+        self.grad = None
+        return grad
+
+
+# Common interface between :class:`PortalBlue`, :class:`PortalOrange`, and
+# :class:`PortalCopy`.
+class Context(CopyContext):
+    portal: Portal
+
+
+class PortalBlue(torch.autograd.Function):
+    """Hides a tensor from the autograd engine by a :class:`Portal`."""
+
+    @staticmethod
+    # type: ignore[override]
+    def forward(
+        ctx: Context,
+        portal: Portal,
+        # This tensor must be retrieved by portal.use_tensor().
+        tensor: Tensor,
+    ) -> Tensor:
+        ctx.portal = portal
+
+        phony = get_phony(tensor.device, requires_grad=False)
+        return phony.detach()
+
+    @staticmethod
+    # type: ignore[override]
+    def backward(ctx: Context, grad_phony: Tensor,) -> Tuple[None, Tensor]:
+        # The paired PortalOrange should keep the gradient.
+        grad = ctx.portal.use_grad()
+        return None, grad
+
+
+class PortalOrange(torch.autograd.Function):
+    """Retrieves the hidden tensor from a :class:`Portal`."""
+
+    @staticmethod
+    # type: ignore[override]
+    def forward(ctx: Context, portal: Portal, phony: Tensor) -> Tensor:
+        ctx.portal = portal
+
+        tensor = portal.use_tensor()
+        assert tensor is not None
+
+        return tensor.detach()
+
+    @staticmethod
+    def backward(ctx: Context, grad: Tensor) -> Tuple[None, None]:  # type: ignore[override]
+        # The paired PortalBlue will use the gradient.
+        ctx.portal.put_grad(grad)
+        return None, None
+
+
+class PortalCopy(torch.autograd.Function):
+    """Copies the hidden tensor in a :class:`Portal`. It replaces the hidden
+    tensor with copied one.
+    """
+
+    @staticmethod
+    # type: ignore[override]
+    def forward(
+        ctx: Context, portal: Portal, prev_stream: AbstractStream, next_stream: AbstractStream, phony: Tensor,
+    ) -> Tensor:
+        ctx.portal = portal
+
+        assert portal.tensor is not None
+        (portal.tensor,) = Copy.forward(ctx, prev_stream, next_stream, portal.tensor)
+
+        phony = get_phony(get_device(next_stream), requires_grad=False)
+        return phony.detach()
+
+    @staticmethod
+    # type: ignore[override]
+    def backward(ctx: Context, grad_phony: Tensor,) -> Tuple[None, None, None, None]:
+        portal = ctx.portal
+
+        assert portal.grad is not None
+        _, _, portal.grad = Copy.backward(ctx, portal.grad)
+
+        return None, None, None, None
diff --git a/shared/package-files/f2/f2507f4b9485817d8ab1f964b0af19ca093a5c35d3a1062d2259a0777ec8f5a6/_next_gen.py b/shared/package-files/f2/f2507f4b9485817d8ab1f964b0af19ca093a5c35d3a1062d2259a0777ec8f5a6/_next_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f7c0b9a46b7a0ee008f94b8054baf5807df043a
--- /dev/null
+++ b/shared/package-files/f2/f2507f4b9485817d8ab1f964b0af19ca093a5c35d3a1062d2259a0777ec8f5a6/_next_gen.py
@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: MIT
+
+"""
+These are keyword-only APIs that call `attr.s` and `attr.ib` with different
+default values.
+"""
+
+
+from functools import partial
+
+from . import setters
+from ._funcs import asdict as _asdict
+from ._funcs import astuple as _astuple
+from ._make import (
+    NOTHING,
+    _frozen_setattrs,
+    _ng_default_on_setattr,
+    attrib,
+    attrs,
+)
+from .exceptions import UnannotatedAttributeError
+
+
+def define(
+    maybe_cls=None,
+    *,
+    these=None,
+    repr=None,
+    unsafe_hash=None,
+    hash=None,
+    init=None,
+    slots=True,
+    frozen=False,
+    weakref_slot=True,
+    str=False,
+    auto_attribs=None,
+    kw_only=False,
+    cache_hash=False,
+    auto_exc=True,
+    eq=None,
+    order=False,
+    auto_detect=True,
+    getstate_setstate=None,
+    on_setattr=None,
+    field_transformer=None,
+    match_args=True,
+):
+    r"""
+    Define an *attrs* class.
+
+    Differences to the classic `attr.s` that it uses underneath:
+
+    - Automatically detect whether or not *auto_attribs* should be `True` (c.f.
+      *auto_attribs* parameter).
+    - If *frozen* is `False`, run converters and validators when setting an
+      attribute by default.
+    - *slots=True*
+
+      .. caution::
+
+         Usually this has only upsides and few visible effects in everyday
+         programming. But it *can* lead to some suprising behaviors, so please
+         make sure to read :term:`slotted classes`.
+    - *auto_exc=True*
+    - *auto_detect=True*
+    - *order=False*
+    - Some options that were only relevant on Python 2 or were kept around for
+      backwards-compatibility have been removed.
+
+    Please note that these are all defaults and you can change them as you
+    wish.
+
+    :param Optional[bool] auto_attribs: If set to `True` or `False`, it behaves
+       exactly like `attr.s`. If left `None`, `attr.s` will try to guess:
+
+       1. If any attributes are annotated and no unannotated `attrs.fields`\ s
+          are found, it assumes *auto_attribs=True*.
+       2. Otherwise it assumes *auto_attribs=False* and tries to collect
+          `attrs.fields`\ s.
+
+    For now, please refer to `attr.s` for the rest of the parameters.
+
+    .. versionadded:: 20.1.0
+    .. versionchanged:: 21.3.0 Converters are also run ``on_setattr``.
+    .. versionadded:: 22.2.0
+       *unsafe_hash* as an alias for *hash* (for :pep:`681` compliance).
+    """
+
+    def do_it(cls, auto_attribs):
+        return attrs(
+            maybe_cls=cls,
+            these=these,
+            repr=repr,
+            hash=hash,
+            unsafe_hash=unsafe_hash,
+            init=init,
+            slots=slots,
+            frozen=frozen,
+            weakref_slot=weakref_slot,
+            str=str,
+            auto_attribs=auto_attribs,
+            kw_only=kw_only,
+            cache_hash=cache_hash,
+            auto_exc=auto_exc,
+            eq=eq,
+            order=order,
+            auto_detect=auto_detect,
+            collect_by_mro=True,
+            getstate_setstate=getstate_setstate,
+            on_setattr=on_setattr,
+            field_transformer=field_transformer,
+            match_args=match_args,
+        )
+
+    def wrap(cls):
+        """
+        Making this a wrapper ensures this code runs during class creation.
+
+        We also ensure that frozen-ness of classes is inherited.
+        """
+        nonlocal frozen, on_setattr
+
+        had_on_setattr = on_setattr not in (None, setters.NO_OP)
+
+        # By default, mutable classes convert & validate on setattr.
+        if frozen is False and on_setattr is None:
+            on_setattr = _ng_default_on_setattr
+
+        # However, if we subclass a frozen class, we inherit the immutability
+        # and disable on_setattr.
+        for base_cls in cls.__bases__:
+            if base_cls.__setattr__ is _frozen_setattrs:
+                if had_on_setattr:
+                    raise ValueError(
+                        "Frozen classes can't use on_setattr "
+                        "(frozen-ness was inherited)."
+                    )
+
+                on_setattr = setters.NO_OP
+                break
+
+        if auto_attribs is not None:
+            return do_it(cls, auto_attribs)
+
+        try:
+            return do_it(cls, True)
+        except UnannotatedAttributeError:
+            return do_it(cls, False)
+
+    # maybe_cls's type depends on the usage of the decorator.  It's a class
+    # if it's used as `@attrs` but ``None`` if used as `@attrs()`.
+    if maybe_cls is None:
+        return wrap
+    else:
+        return wrap(maybe_cls)
+
+
+mutable = define
+frozen = partial(define, frozen=True, on_setattr=None)
+
+
+def field(
+    *,
+    default=NOTHING,
+    validator=None,
+    repr=True,
+    hash=None,
+    init=True,
+    metadata=None,
+    type=None,
+    converter=None,
+    factory=None,
+    kw_only=False,
+    eq=None,
+    order=None,
+    on_setattr=None,
+    alias=None,
+):
+    """
+    Identical to `attr.ib`, except keyword-only and with some arguments
+    removed.
+
+    .. versionadded:: 23.1.0
+       The *type* parameter has been re-added; mostly for
+       {func}`attrs.make_class`. Please note that type checkers ignore this
+       metadata.
+    .. versionadded:: 20.1.0
+    """
+    return attrib(
+        default=default,
+        validator=validator,
+        repr=repr,
+        hash=hash,
+        init=init,
+        metadata=metadata,
+        type=type,
+        converter=converter,
+        factory=factory,
+        kw_only=kw_only,
+        eq=eq,
+        order=order,
+        on_setattr=on_setattr,
+        alias=alias,
+    )
+
+
+def asdict(inst, *, recurse=True, filter=None, value_serializer=None):
+    """
+    Same as `attr.asdict`, except that collections types are always retained
+    and dict is always used as *dict_factory*.
+
+    .. versionadded:: 21.3.0
+    """
+    return _asdict(
+        inst=inst,
+        recurse=recurse,
+        filter=filter,
+        value_serializer=value_serializer,
+        retain_collection_types=True,
+    )
+
+
+def astuple(inst, *, recurse=True, filter=None):
+    """
+    Same as `attr.astuple`, except that collections types are always retained
+    and `tuple` is always used as the *tuple_factory*.
+
+    .. versionadded:: 21.3.0
+    """
+    return _astuple(
+        inst=inst, recurse=recurse, filter=filter, retain_collection_types=True
+    )
diff --git a/shared/package-files/f2/f25449fc55351ed1d306655f2dbda378c65bb0e191695061e8aedc32262beaac/cumprod_native.h b/shared/package-files/f2/f25449fc55351ed1d306655f2dbda378c65bb0e191695061e8aedc32262beaac/cumprod_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2270c63e5d028429a7bb03fb3db8717d129ebae8
--- /dev/null
+++ b/shared/package-files/f2/f25449fc55351ed1d306655f2dbda378c65bb0e191695061e8aedc32262beaac/cumprod_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/cumprod_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_cumprod_out : public at::meta::structured_cumprod {
+void impl(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, const at::Tensor & out);
+};
+TORCH_API at::Tensor cumprod(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & cumprod_out(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+TORCH_API at::Tensor & cumprod_(at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/f2/f25dcfa6fcba30f2914d0f8fd8b728207e71f76b8d359a44bfd613e04d5eb895/_efficientzerotensor.h b/shared/package-files/f2/f25dcfa6fcba30f2914d0f8fd8b728207e71f76b8d359a44bfd613e04d5eb895/_efficientzerotensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..d7d38b326b539a9dd0613ab166a40a32a826a0b3
--- /dev/null
+++ b/shared/package-files/f2/f25dcfa6fcba30f2914d0f8fd8b728207e71f76b8d359a44bfd613e04d5eb895/_efficientzerotensor.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_efficientzerotensor_ops.h>
+
+namespace at {
+
+
+// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _efficientzerotensor(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _efficientzerotensor(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _efficientzerotensor(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _efficientzerotensor(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _efficientzerotensor_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_efficientzerotensor::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _efficientzerotensor(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_efficientzerotensor::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _efficientzerotensor_symint(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_efficientzerotensor::call(size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _efficientzerotensor(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_efficientzerotensor::call(size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _efficientzerotensor_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _efficientzerotensor_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _efficientzerotensor_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & _efficientzerotensor_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _efficientzerotensor_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::_efficientzerotensor_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _efficientzerotensor_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::_efficientzerotensor_out::call(size, out);
+  }
+}
+
+// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _efficientzerotensor_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::_efficientzerotensor_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & _efficientzerotensor_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::_efficientzerotensor_out::call(size, out);
+  }
+}
+
+}
diff --git a/shared/package-files/f2/f25e08acd46b419264f8b2db1f7cc65aa8294e6057036fab52c3fbe8682963f6/operations.pyi b/shared/package-files/f2/f25e08acd46b419264f8b2db1f7cc65aa8294e6057036fab52c3fbe8682963f6/operations.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..359719cf5dcdd4d9fa5e0a1f4b602f3070d05e45
--- /dev/null
+++ b/shared/package-files/f2/f25e08acd46b419264f8b2db1f7cc65aa8294e6057036fab52c3fbe8682963f6/operations.pyi
@@ -0,0 +1,3 @@
+from django.db.backends.base.operations import BaseDatabaseOperations
+
+class DatabaseOperations(BaseDatabaseOperations): ...
diff --git a/shared/package-files/f2/f2609271635c7b439d1b76fabd6089191dfbd02c9d6cad88cbc5bde837ec9742/kitti.py b/shared/package-files/f2/f2609271635c7b439d1b76fabd6089191dfbd02c9d6cad88cbc5bde837ec9742/kitti.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e603c76f22bb6387a4ab6e42bb4d92f49a0bee
--- /dev/null
+++ b/shared/package-files/f2/f2609271635c7b439d1b76fabd6089191dfbd02c9d6cad88cbc5bde837ec9742/kitti.py
@@ -0,0 +1,158 @@
+import csv
+import os
+from pathlib import Path
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+from PIL import Image
+
+from .utils import download_and_extract_archive
+from .vision import VisionDataset
+
+
+class Kitti(VisionDataset):
+    """`KITTI <http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark>`_ Dataset.
+
+    It corresponds to the "left color images of object" dataset, for object detection.
+
+    Args:
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
+            Expects the following folder structure if download=False:
+
+            .. code::
+
+                <root>
+                    └── Kitti
+                        └─ raw
+                            ├── training
+                            |   ├── image_2
+                            |   └── label_2
+                            └── testing
+                                └── image_2
+        train (bool, optional): Use ``train`` split if true, else ``test`` split.
+            Defaults to ``train``.
+        transform (callable, optional): A function/transform that takes in a PIL image
+            and returns a transformed version. E.g, ``transforms.PILToTensor``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        transforms (callable, optional): A function/transform that takes input sample
+            and its target as entry and returns a transformed version.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+
+    """
+
+    data_url = "https://s3.eu-central-1.amazonaws.com/avg-kitti/"
+    resources = [
+        "data_object_image_2.zip",
+        "data_object_label_2.zip",
+    ]
+    image_dir_name = "image_2"
+    labels_dir_name = "label_2"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        train: bool = True,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        transforms: Optional[Callable] = None,
+        download: bool = False,
+    ):
+        super().__init__(
+            root,
+            transform=transform,
+            target_transform=target_transform,
+            transforms=transforms,
+        )
+        self.images = []
+        self.targets = []
+        self.train = train
+        self._location = "training" if self.train else "testing"
+
+        if download:
+            self.download()
+        if not self._check_exists():
+            raise RuntimeError("Dataset not found. You may use download=True to download it.")
+
+        image_dir = os.path.join(self._raw_folder, self._location, self.image_dir_name)
+        if self.train:
+            labels_dir = os.path.join(self._raw_folder, self._location, self.labels_dir_name)
+        for img_file in os.listdir(image_dir):
+            self.images.append(os.path.join(image_dir, img_file))
+            if self.train:
+                self.targets.append(os.path.join(labels_dir, f"{img_file.split('.')[0]}.txt"))
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """Get item at a given index.
+
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target), where
+            target is a list of dictionaries with the following keys:
+
+            - type: str
+            - truncated: float
+            - occluded: int
+            - alpha: float
+            - bbox: float[4]
+            - dimensions: float[3]
+            - locations: float[3]
+            - rotation_y: float
+
+        """
+        image = Image.open(self.images[index])
+        target = self._parse_target(index) if self.train else None
+        if self.transforms:
+            image, target = self.transforms(image, target)
+        return image, target
+
+    def _parse_target(self, index: int) -> List:
+        target = []
+        with open(self.targets[index]) as inp:
+            content = csv.reader(inp, delimiter=" ")
+            for line in content:
+                target.append(
+                    {
+                        "type": line[0],
+                        "truncated": float(line[1]),
+                        "occluded": int(line[2]),
+                        "alpha": float(line[3]),
+                        "bbox": [float(x) for x in line[4:8]],
+                        "dimensions": [float(x) for x in line[8:11]],
+                        "location": [float(x) for x in line[11:14]],
+                        "rotation_y": float(line[14]),
+                    }
+                )
+        return target
+
+    def __len__(self) -> int:
+        return len(self.images)
+
+    @property
+    def _raw_folder(self) -> str:
+        return os.path.join(self.root, self.__class__.__name__, "raw")
+
+    def _check_exists(self) -> bool:
+        """Check if the data directory exists."""
+        folders = [self.image_dir_name]
+        if self.train:
+            folders.append(self.labels_dir_name)
+        return all(os.path.isdir(os.path.join(self._raw_folder, self._location, fname)) for fname in folders)
+
+    def download(self) -> None:
+        """Download the KITTI data if it doesn't exist already."""
+
+        if self._check_exists():
+            return
+
+        os.makedirs(self._raw_folder, exist_ok=True)
+
+        # download files
+        for fname in self.resources:
+            download_and_extract_archive(
+                url=f"{self.data_url}{fname}",
+                download_root=self._raw_folder,
+                filename=fname,
+            )
diff --git a/shared/package-files/f2/f262a0bfab64a87f69793a671733721f7f84e49d604d36fa1e58ba4449260204/log1p.h b/shared/package-files/f2/f262a0bfab64a87f69793a671733721f7f84e49d604d36fa1e58ba4449260204/log1p.h
new file mode 100644
index 0000000000000000000000000000000000000000..f596630ed82b0d07ab90fe630d693bb8f7d2b2ed
--- /dev/null
+++ b/shared/package-files/f2/f262a0bfab64a87f69793a671733721f7f84e49d604d36fa1e58ba4449260204/log1p.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/log1p_ops.h>
+
+namespace at {
+
+
+// aten::log1p(Tensor self) -> Tensor
+inline at::Tensor log1p(const at::Tensor & self) {
+    return at::_ops::log1p::call(self);
+}
+
+// aten::log1p_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & log1p_(at::Tensor & self) {
+    return at::_ops::log1p_::call(self);
+}
+
+// aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log1p_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::log1p_out::call(self, out);
+}
+// aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log1p_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::log1p_out::call(self, out);
+}
+
+}
diff --git a/shared/package-files/f2/f2668b485954c9e3f74a995bc4710d9f0390d012ac48a889a266864151aa5d4c/_result.py b/shared/package-files/f2/f2668b485954c9e3f74a995bc4710d9f0390d012ac48a889a266864151aa5d4c/_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9d5dbe8f83c3e9fa7b5afec6ebde7631a4a5fc8
--- /dev/null
+++ b/shared/package-files/f2/f2668b485954c9e3f74a995bc4710d9f0390d012ac48a889a266864151aa5d4c/_result.py
@@ -0,0 +1,107 @@
+"""
+Hook wrapper "result" utilities.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from types import TracebackType
+from typing import cast
+from typing import final
+from typing import Generic
+from typing import TypeAlias
+from typing import TypeVar
+
+
+_ExcInfo: TypeAlias = tuple[type[BaseException], BaseException, TracebackType | None]
+ResultType = TypeVar("ResultType")
+
+
+class HookCallError(Exception):
+    """Hook was called incorrectly."""
+
+
+@final
+class Result(Generic[ResultType]):
+    """An object used to inspect and set the result in a :ref:`hook wrapper
+    <hookwrappers>`."""
+
+    __slots__ = ("_exception", "_result", "_traceback")
+
+    def __init__(
+        self,
+        result: ResultType | None,
+        exception: BaseException | None,
+    ) -> None:
+        """:meta private:"""
+        self._result = result
+        self._exception = exception
+        # Exception __traceback__ is mutable, this keeps the original.
+        self._traceback = exception.__traceback__ if exception is not None else None
+
+    @property
+    def excinfo(self) -> _ExcInfo | None:
+        """:meta private:"""
+        exc = self._exception
+        if exc is None:
+            return None
+        else:
+            return (type(exc), exc, self._traceback)
+
+    @property
+    def exception(self) -> BaseException | None:
+        """:meta private:"""
+        return self._exception
+
+    @classmethod
+    def from_call(cls, func: Callable[[], ResultType]) -> Result[ResultType]:
+        """:meta private:"""
+        __tracebackhide__ = True
+        result = exception = None
+        try:
+            result = func()
+        except BaseException as exc:
+            exception = exc
+        return cls(result, exception)
+
+    def force_result(self, result: ResultType) -> None:
+        """Force the result(s) to ``result``.
+
+        If the hook was marked as a ``firstresult`` a single value should
+        be set, otherwise set a (modified) list of results. Any exceptions
+        found during invocation will be deleted.
+
+        This overrides any previous result or exception.
+        """
+        self._result = result
+        self._exception = None
+        self._traceback = None
+
+    def force_exception(self, exception: BaseException) -> None:
+        """Force the result to fail with ``exception``.
+
+        This overrides any previous result or exception.
+
+        .. versionadded:: 1.1.0
+        """
+        self._result = None
+        self._exception = exception
+        self._traceback = exception.__traceback__ if exception is not None else None
+
+    def get_result(self) -> ResultType:
+        """Get the result(s) for this hook call.
+
+        If the hook was marked as a ``firstresult`` only a single value
+        will be returned, otherwise a list of results.
+        """
+        __tracebackhide__ = True
+        exc = self._exception
+        tb = self._traceback
+        if exc is None:
+            return cast(ResultType, self._result)
+        else:
+            raise exc.with_traceback(tb)
+
+
+# Historical name (pluggy<=1.2), kept for backward compatibility.
+_Result = Result
diff --git a/shared/package-files/f2/f26d3c51be78f741f88d0e8b617bc5cac1ad80aa0ab0751ddb31ff8bcfd39d5c/utf8prober.py b/shared/package-files/f2/f26d3c51be78f741f88d0e8b617bc5cac1ad80aa0ab0751ddb31ff8bcfd39d5c/utf8prober.py
new file mode 100644
index 0000000000000000000000000000000000000000..d96354d97c2195320d0acc1717a5876eafbea2af
--- /dev/null
+++ b/shared/package-files/f2/f26d3c51be78f741f88d0e8b617bc5cac1ad80aa0ab0751ddb31ff8bcfd39d5c/utf8prober.py
@@ -0,0 +1,82 @@
+######################## BEGIN LICENSE BLOCK ########################
+# The Original Code is mozilla.org code.
+#
+# The Initial Developer of the Original Code is
+# Netscape Communications Corporation.
+# Portions created by the Initial Developer are Copyright (C) 1998
+# the Initial Developer. All Rights Reserved.
+#
+# Contributor(s):
+#   Mark Pilgrim - port to Python
+#
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
+# 02110-1301  USA
+######################### END LICENSE BLOCK #########################
+
+from typing import Union
+
+from .charsetprober import CharSetProber
+from .codingstatemachine import CodingStateMachine
+from .enums import MachineState, ProbingState
+from .mbcssm import UTF8_SM_MODEL
+
+
+class UTF8Prober(CharSetProber):
+    ONE_CHAR_PROB = 0.5
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.coding_sm = CodingStateMachine(UTF8_SM_MODEL)
+        self._num_mb_chars = 0
+        self.reset()
+
+    def reset(self) -> None:
+        super().reset()
+        self.coding_sm.reset()
+        self._num_mb_chars = 0
+
+    @property
+    def charset_name(self) -> str:
+        return "utf-8"
+
+    @property
+    def language(self) -> str:
+        return ""
+
+    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
+        for c in byte_str:
+            coding_state = self.coding_sm.next_state(c)
+            if coding_state == MachineState.ERROR:
+                self._state = ProbingState.NOT_ME
+                break
+            if coding_state == MachineState.ITS_ME:
+                self._state = ProbingState.FOUND_IT
+                break
+            if coding_state == MachineState.START:
+                if self.coding_sm.get_current_charlen() >= 2:
+                    self._num_mb_chars += 1
+
+        if self.state == ProbingState.DETECTING:
+            if self.get_confidence() > self.SHORTCUT_THRESHOLD:
+                self._state = ProbingState.FOUND_IT
+
+        return self.state
+
+    def get_confidence(self) -> float:
+        unlike = 0.99
+        if self._num_mb_chars < 6:
+            unlike *= self.ONE_CHAR_PROB**self._num_mb_chars
+            return 1.0 - unlike
+        return unlike
diff --git a/shared/package-files/f2/f2728aa5d53cd73a2a2f167d59e789292785eb58d7f0aab44266918c1a82a54c/palettes.py b/shared/package-files/f2/f2728aa5d53cd73a2a2f167d59e789292785eb58d7f0aab44266918c1a82a54c/palettes.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7f4298436f6fd83d819f314c0ad2cbe0db4b257
--- /dev/null
+++ b/shared/package-files/f2/f2728aa5d53cd73a2a2f167d59e789292785eb58d7f0aab44266918c1a82a54c/palettes.py
@@ -0,0 +1,841 @@
+import colorsys
+from itertools import cycle
+
+import numpy as np
+import matplotlib as mpl
+
+from .external import husl
+
+from .utils import desaturate, get_color_cycle
+from .colors import xkcd_rgb, crayons
+from ._compat import get_colormap
+
+
+__all__ = ["color_palette", "hls_palette", "husl_palette", "mpl_palette",
+           "dark_palette", "light_palette", "diverging_palette",
+           "blend_palette", "xkcd_palette", "crayon_palette",
+           "cubehelix_palette", "set_color_codes"]
+
+
+SEABORN_PALETTES = dict(
+    deep=["#4C72B0", "#DD8452", "#55A868", "#C44E52", "#8172B3",
+          "#937860", "#DA8BC3", "#8C8C8C", "#CCB974", "#64B5CD"],
+    deep6=["#4C72B0", "#55A868", "#C44E52",
+           "#8172B3", "#CCB974", "#64B5CD"],
+    muted=["#4878D0", "#EE854A", "#6ACC64", "#D65F5F", "#956CB4",
+           "#8C613C", "#DC7EC0", "#797979", "#D5BB67", "#82C6E2"],
+    muted6=["#4878D0", "#6ACC64", "#D65F5F",
+            "#956CB4", "#D5BB67", "#82C6E2"],
+    pastel=["#A1C9F4", "#FFB482", "#8DE5A1", "#FF9F9B", "#D0BBFF",
+            "#DEBB9B", "#FAB0E4", "#CFCFCF", "#FFFEA3", "#B9F2F0"],
+    pastel6=["#A1C9F4", "#8DE5A1", "#FF9F9B",
+             "#D0BBFF", "#FFFEA3", "#B9F2F0"],
+    bright=["#023EFF", "#FF7C00", "#1AC938", "#E8000B", "#8B2BE2",
+            "#9F4800", "#F14CC1", "#A3A3A3", "#FFC400", "#00D7FF"],
+    bright6=["#023EFF", "#1AC938", "#E8000B",
+             "#8B2BE2", "#FFC400", "#00D7FF"],
+    dark=["#001C7F", "#B1400D", "#12711C", "#8C0800", "#591E71",
+          "#592F0D", "#A23582", "#3C3C3C", "#B8850A", "#006374"],
+    dark6=["#001C7F", "#12711C", "#8C0800",
+           "#591E71", "#B8850A", "#006374"],
+    colorblind=["#0173B2", "#DE8F05", "#029E73", "#D55E00", "#CC78BC",
+                "#CA9161", "#FBAFE4", "#949494", "#ECE133", "#56B4E9"],
+    colorblind6=["#0173B2", "#029E73", "#D55E00",
+                 "#CC78BC", "#ECE133", "#56B4E9"]
+)
+
+
+MPL_QUAL_PALS = {
+    "tab10": 10, "tab20": 20, "tab20b": 20, "tab20c": 20,
+    "Set1": 9, "Set2": 8, "Set3": 12,
+    "Accent": 8, "Paired": 12,
+    "Pastel1": 9, "Pastel2": 8, "Dark2": 8,
+}
+
+
+QUAL_PALETTE_SIZES = MPL_QUAL_PALS.copy()
+QUAL_PALETTE_SIZES.update({k: len(v) for k, v in SEABORN_PALETTES.items()})
+QUAL_PALETTES = list(QUAL_PALETTE_SIZES.keys())
+
+
+class _ColorPalette(list):
+    """Set the color palette in a with statement, otherwise be a list."""
+    def __enter__(self):
+        """Open the context."""
+        from .rcmod import set_palette
+        self._orig_palette = color_palette()
+        set_palette(self)
+        return self
+
+    def __exit__(self, *args):
+        """Close the context."""
+        from .rcmod import set_palette
+        set_palette(self._orig_palette)
+
+    def as_hex(self):
+        """Return a color palette with hex codes instead of RGB values."""
+        hex = [mpl.colors.rgb2hex(rgb) for rgb in self]
+        return _ColorPalette(hex)
+
+    def _repr_html_(self):
+        """Rich display of the color palette in an HTML frontend."""
+        s = 55
+        n = len(self)
+        html = f'<svg  width="{n * s}" height="{s}">'
+        for i, c in enumerate(self.as_hex()):
+            html += (
+                f'<rect x="{i * s}" y="0" width="{s}" height="{s}" style="fill:{c};'
+                'stroke-width:2;stroke:rgb(255,255,255)"/>'
+            )
+        html += '</svg>'
+        return html
+
+
+def _patch_colormap_display():
+    """Simplify the rich display of matplotlib color maps in a notebook."""
+    def _repr_png_(self):
+        """Generate a PNG representation of the Colormap."""
+        import io
+        from PIL import Image
+        import numpy as np
+        IMAGE_SIZE = (400, 50)
+        X = np.tile(np.linspace(0, 1, IMAGE_SIZE[0]), (IMAGE_SIZE[1], 1))
+        pixels = self(X, bytes=True)
+        png_bytes = io.BytesIO()
+        Image.fromarray(pixels).save(png_bytes, format='png')
+        return png_bytes.getvalue()
+
+    def _repr_html_(self):
+        """Generate an HTML representation of the Colormap."""
+        import base64
+        png_bytes = self._repr_png_()
+        png_base64 = base64.b64encode(png_bytes).decode('ascii')
+        return ('<img '
+                + 'alt="' + self.name + ' color map" '
+                + 'title="' + self.name + '"'
+                + 'src="data:image/png;base64,' + png_base64 + '">')
+
+    mpl.colors.Colormap._repr_png_ = _repr_png_
+    mpl.colors.Colormap._repr_html_ = _repr_html_
+
+
+def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
+    """Return a list of colors or continuous colormap defining a palette.
+
+    Possible ``palette`` values include:
+        - Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
+        - Name of matplotlib colormap
+        - 'husl' or 'hls'
+        - 'ch:<cubehelix arguments>'
+        - 'light:<color>', 'dark:<color>', 'blend:<color>,<color>',
+        - A sequence of colors in any format matplotlib accepts
+
+    Calling this function with ``palette=None`` will return the current
+    matplotlib color cycle.
+
+    This function can also be used in a ``with`` statement to temporarily
+    set the color cycle for a plot or set of plots.
+
+    See the :ref:`tutorial <palette_tutorial>` for more information.
+
+    Parameters
+    ----------
+    palette : None, string, or sequence, optional
+        Name of palette or None to return current palette. If a sequence, input
+        colors are used but possibly cycled and desaturated.
+    n_colors : int, optional
+        Number of colors in the palette. If ``None``, the default will depend
+        on how ``palette`` is specified. Named palettes default to 6 colors,
+        but grabbing the current palette or passing in a list of colors will
+        not change the number of colors unless this is specified. Asking for
+        more colors than exist in the palette will cause it to cycle. Ignored
+        when ``as_cmap`` is True.
+    desat : float, optional
+        Proportion to desaturate each color by.
+    as_cmap : bool
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+
+    Returns
+    -------
+    list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    set_palette : Set the default color cycle for all plots.
+    set_color_codes : Reassign color codes like ``"b"``, ``"g"``, etc. to
+                      colors from one of the seaborn palettes.
+
+    Examples
+    --------
+
+    .. include:: ../docstrings/color_palette.rst
+
+    """
+    if palette is None:
+        palette = get_color_cycle()
+        if n_colors is None:
+            n_colors = len(palette)
+
+    elif not isinstance(palette, str):
+        palette = palette
+        if n_colors is None:
+            n_colors = len(palette)
+    else:
+
+        if n_colors is None:
+            # Use all colors in a qualitative palette or 6 of another kind
+            n_colors = QUAL_PALETTE_SIZES.get(palette, 6)
+
+        if palette in SEABORN_PALETTES:
+            # Named "seaborn variant" of matplotlib default color cycle
+            palette = SEABORN_PALETTES[palette]
+
+        elif palette == "hls":
+            # Evenly spaced colors in cylindrical RGB space
+            palette = hls_palette(n_colors, as_cmap=as_cmap)
+
+        elif palette == "husl":
+            # Evenly spaced colors in cylindrical Lab space
+            palette = husl_palette(n_colors, as_cmap=as_cmap)
+
+        elif palette.lower() == "jet":
+            # Paternalism
+            raise ValueError("No.")
+
+        elif palette.startswith("ch:"):
+            # Cubehelix palette with params specified in string
+            args, kwargs = _parse_cubehelix_args(palette)
+            palette = cubehelix_palette(n_colors, *args, **kwargs, as_cmap=as_cmap)
+
+        elif palette.startswith("light:"):
+            # light palette to color specified in string
+            _, color = palette.split(":")
+            reverse = color.endswith("_r")
+            if reverse:
+                color = color[:-2]
+            palette = light_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
+
+        elif palette.startswith("dark:"):
+            # light palette to color specified in string
+            _, color = palette.split(":")
+            reverse = color.endswith("_r")
+            if reverse:
+                color = color[:-2]
+            palette = dark_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
+
+        elif palette.startswith("blend:"):
+            # blend palette between colors specified in string
+            _, colors = palette.split(":")
+            colors = colors.split(",")
+            palette = blend_palette(colors, n_colors, as_cmap=as_cmap)
+
+        else:
+            try:
+                # Perhaps a named matplotlib colormap?
+                palette = mpl_palette(palette, n_colors, as_cmap=as_cmap)
+            except (ValueError, KeyError):  # Error class changed in mpl36
+                raise ValueError(f"{palette!r} is not a valid palette name")
+
+    if desat is not None:
+        palette = [desaturate(c, desat) for c in palette]
+
+    if not as_cmap:
+
+        # Always return as many colors as we asked for
+        pal_cycle = cycle(palette)
+        palette = [next(pal_cycle) for _ in range(n_colors)]
+
+        # Always return in r, g, b tuple format
+        try:
+            palette = map(mpl.colors.colorConverter.to_rgb, palette)
+            palette = _ColorPalette(palette)
+        except ValueError:
+            raise ValueError(f"Could not generate a palette for {palette}")
+
+    return palette
+
+
+def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False):  # noqa
+    """
+    Return hues with constant lightness and saturation in the HLS system.
+
+    The hues are evenly sampled along a circular path. The resulting palette will be
+    appropriate for categorical or cyclical data.
+
+    The `h`, `l`, and `s` values should be between 0 and 1.
+
+    .. note::
+        While the separation of the resulting colors will be mathematically
+        constant, the HLS system does not construct a perceptually-uniform space,
+        so their apparent intensity will vary.
+
+    Parameters
+    ----------
+    n_colors : int
+        Number of colors in the palette.
+    h : float
+        The value of the first hue.
+    l : float
+        The lightness value.
+    s : float
+        The saturation intensity.
+    as_cmap : bool
+        If True, return a matplotlib colormap object.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    husl_palette : Make a palette using evenly spaced hues in the HUSL system.
+
+    Examples
+    --------
+    .. include:: ../docstrings/hls_palette.rst
+
+    """
+    if as_cmap:
+        n_colors = 256
+    hues = np.linspace(0, 1, int(n_colors) + 1)[:-1]
+    hues += h
+    hues %= 1
+    hues -= hues.astype(int)
+    palette = [colorsys.hls_to_rgb(h_i, l, s) for h_i in hues]
+    if as_cmap:
+        return mpl.colors.ListedColormap(palette, "hls")
+    else:
+        return _ColorPalette(palette)
+
+
+def husl_palette(n_colors=6, h=.01, s=.9, l=.65, as_cmap=False):  # noqa
+    """
+    Return hues with constant lightness and saturation in the HUSL system.
+
+    The hues are evenly sampled along a circular path. The resulting palette will be
+    appropriate for categorical or cyclical data.
+
+    The `h`, `l`, and `s` values should be between 0 and 1.
+
+    This function is similar to :func:`hls_palette`, but it uses a nonlinear color
+    space that is more perceptually uniform.
+
+    Parameters
+    ----------
+    n_colors : int
+        Number of colors in the palette.
+    h : float
+        The value of the first hue.
+    l : float
+        The lightness value.
+    s : float
+        The saturation intensity.
+    as_cmap : bool
+        If True, return a matplotlib colormap object.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    hls_palette : Make a palette using evenly spaced hues in the HSL system.
+
+    Examples
+    --------
+    .. include:: ../docstrings/husl_palette.rst
+
+    """
+    if as_cmap:
+        n_colors = 256
+    hues = np.linspace(0, 1, int(n_colors) + 1)[:-1]
+    hues += h
+    hues %= 1
+    hues *= 359
+    s *= 99
+    l *= 99  # noqa
+    palette = [_color_to_rgb((h_i, s, l), input="husl") for h_i in hues]
+    if as_cmap:
+        return mpl.colors.ListedColormap(palette, "hsl")
+    else:
+        return _ColorPalette(palette)
+
+
+def mpl_palette(name, n_colors=6, as_cmap=False):
+    """
+    Return a palette or colormap from the matplotlib registry.
+
+    For continuous palettes, evenly-spaced discrete samples are chosen while
+    excluding the minimum and maximum value in the colormap to provide better
+    contrast at the extremes.
+
+    For qualitative palettes (e.g. those from colorbrewer), exact values are
+    indexed (rather than interpolated), but fewer than `n_colors` can be returned
+    if the palette does not define that many.
+
+    Parameters
+    ----------
+    name : string
+        Name of the palette. This should be a named matplotlib colormap.
+    n_colors : int
+        Number of discrete colors in the palette.
+
+    Returns
+    -------
+    list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    Examples
+    --------
+    .. include:: ../docstrings/mpl_palette.rst
+
+    """
+    if name.endswith("_d"):
+        sub_name = name[:-2]
+        if sub_name.endswith("_r"):
+            reverse = True
+            sub_name = sub_name[:-2]
+        else:
+            reverse = False
+        pal = color_palette(sub_name, 2) + ["#333333"]
+        if reverse:
+            pal = pal[::-1]
+        cmap = blend_palette(pal, n_colors, as_cmap=True)
+    else:
+        cmap = get_colormap(name)
+
+    if name in MPL_QUAL_PALS:
+        bins = np.linspace(0, 1, MPL_QUAL_PALS[name])[:n_colors]
+    else:
+        bins = np.linspace(0, 1, int(n_colors) + 2)[1:-1]
+    palette = list(map(tuple, cmap(bins)[:, :3]))
+
+    if as_cmap:
+        return cmap
+    else:
+        return _ColorPalette(palette)
+
+
+def _color_to_rgb(color, input):
+    """Add some more flexibility to color choices."""
+    if input == "hls":
+        color = colorsys.hls_to_rgb(*color)
+    elif input == "husl":
+        color = husl.husl_to_rgb(*color)
+        color = tuple(np.clip(color, 0, 1))
+    elif input == "xkcd":
+        color = xkcd_rgb[color]
+
+    return mpl.colors.to_rgb(color)
+
+
+def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
+    """Make a sequential palette that blends from dark to ``color``.
+
+    This kind of palette is good for data that range between relatively
+    uninteresting low values and interesting high values.
+
+    The ``color`` parameter can be specified in a number of ways, including
+    all options for defining a color in matplotlib and several additional
+    color spaces that are handled by seaborn. You can also use the database
+    of named colors from the XKCD color survey.
+
+    If you are using the IPython notebook, you can also choose this palette
+    interactively with the :func:`choose_dark_palette` function.
+
+    Parameters
+    ----------
+    color : base color for high values
+        hex, rgb-tuple, or html color name
+    n_colors : int, optional
+        number of colors in the palette
+    reverse : bool, optional
+        if True, reverse the direction of the blend
+    as_cmap : bool, optional
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+    input : {'rgb', 'hls', 'husl', xkcd'}
+        Color space to interpret the input color. The first three options
+        apply to tuple inputs and the latter applies to string inputs.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    light_palette : Create a sequential palette with bright low values.
+    diverging_palette : Create a diverging palette with two colors.
+
+    Examples
+    --------
+    .. include:: ../docstrings/dark_palette.rst
+
+    """
+    rgb = _color_to_rgb(color, input)
+    hue, sat, _ = husl.rgb_to_husl(*rgb)
+    gray_s, gray_l = .15 * sat, 15
+    gray = _color_to_rgb((hue, gray_s, gray_l), input="husl")
+    colors = [rgb, gray] if reverse else [gray, rgb]
+    return blend_palette(colors, n_colors, as_cmap)
+
+
+def light_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
+    """Make a sequential palette that blends from light to ``color``.
+
+    The ``color`` parameter can be specified in a number of ways, including
+    all options for defining a color in matplotlib and several additional
+    color spaces that are handled by seaborn. You can also use the database
+    of named colors from the XKCD color survey.
+
+    If you are using a Jupyter notebook, you can also choose this palette
+    interactively with the :func:`choose_light_palette` function.
+
+    Parameters
+    ----------
+    color : base color for high values
+        hex code, html color name, or tuple in `input` space.
+    n_colors : int, optional
+        number of colors in the palette
+    reverse : bool, optional
+        if True, reverse the direction of the blend
+    as_cmap : bool, optional
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+    input : {'rgb', 'hls', 'husl', xkcd'}
+        Color space to interpret the input color. The first three options
+        apply to tuple inputs and the latter applies to string inputs.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    dark_palette : Create a sequential palette with dark low values.
+    diverging_palette : Create a diverging palette with two colors.
+
+    Examples
+    --------
+    .. include:: ../docstrings/light_palette.rst
+
+    """
+    rgb = _color_to_rgb(color, input)
+    hue, sat, _ = husl.rgb_to_husl(*rgb)
+    gray_s, gray_l = .15 * sat, 95
+    gray = _color_to_rgb((hue, gray_s, gray_l), input="husl")
+    colors = [rgb, gray] if reverse else [gray, rgb]
+    return blend_palette(colors, n_colors, as_cmap)
+
+
+def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6,  # noqa
+                      center="light", as_cmap=False):
+    """Make a diverging palette between two HUSL colors.
+
+    If you are using the IPython notebook, you can also choose this palette
+    interactively with the :func:`choose_diverging_palette` function.
+
+    Parameters
+    ----------
+    h_neg, h_pos : float in [0, 359]
+        Anchor hues for negative and positive extents of the map.
+    s : float in [0, 100], optional
+        Anchor saturation for both extents of the map.
+    l : float in [0, 100], optional
+        Anchor lightness for both extents of the map.
+    sep : int, optional
+        Size of the intermediate region.
+    n : int, optional
+        Number of colors in the palette (if not returning a cmap)
+    center : {"light", "dark"}, optional
+        Whether the center of the palette is light or dark
+    as_cmap : bool, optional
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    dark_palette : Create a sequential palette with dark values.
+    light_palette : Create a sequential palette with light values.
+
+    Examples
+    --------
+    .. include: ../docstrings/diverging_palette.rst
+
+    """
+    palfunc = dict(dark=dark_palette, light=light_palette)[center]
+    n_half = int(128 - (sep // 2))
+    neg = palfunc((h_neg, s, l), n_half, reverse=True, input="husl")
+    pos = palfunc((h_pos, s, l), n_half, input="husl")
+    midpoint = dict(light=[(.95, .95, .95)], dark=[(.133, .133, .133)])[center]
+    mid = midpoint * sep
+    pal = blend_palette(np.concatenate([neg, mid, pos]), n, as_cmap=as_cmap)
+    return pal
+
+
+def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
+    """Make a palette that blends between a list of colors.
+
+    Parameters
+    ----------
+    colors : sequence of colors in various formats interpreted by `input`
+        hex code, html color name, or tuple in `input` space.
+    n_colors : int, optional
+        Number of colors in the palette.
+    as_cmap : bool, optional
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    Examples
+    --------
+    .. include: ../docstrings/blend_palette.rst
+
+    """
+    colors = [_color_to_rgb(color, input) for color in colors]
+    name = "blend"
+    pal = mpl.colors.LinearSegmentedColormap.from_list(name, colors)
+    if not as_cmap:
+        rgb_array = pal(np.linspace(0, 1, int(n_colors)))[:, :3]  # no alpha
+        pal = _ColorPalette(map(tuple, rgb_array))
+    return pal
+
+
+def xkcd_palette(colors):
+    """Make a palette with color names from the xkcd color survey.
+
+    See xkcd for the full list of colors: https://xkcd.com/color/rgb/
+
+    This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary.
+
+    Parameters
+    ----------
+    colors : list of strings
+        List of keys in the `seaborn.xkcd_rgb` dictionary.
+
+    Returns
+    -------
+    palette
+        A list of colors as RGB tuples.
+
+    See Also
+    --------
+    crayon_palette : Make a palette with Crayola crayon colors.
+
+    """
+    palette = [xkcd_rgb[name] for name in colors]
+    return color_palette(palette, len(palette))
+
+
+def crayon_palette(colors):
+    """Make a palette with color names from Crayola crayons.
+
+    Colors are taken from here:
+    https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
+
+    This is just a simple wrapper around the `seaborn.crayons` dictionary.
+
+    Parameters
+    ----------
+    colors : list of strings
+        List of keys in the `seaborn.crayons` dictionary.
+
+    Returns
+    -------
+    palette
+        A list of colors as RGB tuples.
+
+    See Also
+    --------
+    xkcd_palette : Make a palette with named colors from the XKCD color survey.
+
+    """
+    palette = [crayons[name] for name in colors]
+    return color_palette(palette, len(palette))
+
+
+def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
+                      light=.85, dark=.15, reverse=False, as_cmap=False):
+    """Make a sequential palette from the cubehelix system.
+
+    This produces a colormap with linearly-decreasing (or increasing)
+    brightness. That means that information will be preserved if printed to
+    black and white or viewed by someone who is colorblind.  "cubehelix" is
+    also available as a matplotlib-based palette, but this function gives the
+    user more control over the look of the palette and has a different set of
+    defaults.
+
+    In addition to using this function, it is also possible to generate a
+    cubehelix palette generally in seaborn using a string starting with
+    `ch:` and containing other parameters (e.g. `"ch:s=.25,r=-.5"`).
+
+    Parameters
+    ----------
+    n_colors : int
+        Number of colors in the palette.
+    start : float, 0 <= start <= 3
+        The hue value at the start of the helix.
+    rot : float
+        Rotations around the hue wheel over the range of the palette.
+    gamma : float 0 <= gamma
+        Nonlinearity to emphasize dark (gamma < 1) or light (gamma > 1) colors.
+    hue : float, 0 <= hue <= 1
+        Saturation of the colors.
+    dark : float 0 <= dark <= 1
+        Intensity of the darkest color in the palette.
+    light : float 0 <= light <= 1
+        Intensity of the lightest color in the palette.
+    reverse : bool
+        If True, the palette will go from dark to light.
+    as_cmap : bool
+        If True, return a :class:`matplotlib.colors.ListedColormap`.
+
+    Returns
+    -------
+    palette
+        list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
+
+    See Also
+    --------
+    choose_cubehelix_palette : Launch an interactive widget to select cubehelix
+                               palette parameters.
+    dark_palette : Create a sequential palette with dark low values.
+    light_palette : Create a sequential palette with bright low values.
+
+    References
+    ----------
+    Green, D. A. (2011). "A colour scheme for the display of astronomical
+    intensity images". Bulletin of the Astromical Society of India, Vol. 39,
+    p. 289-295.
+
+    Examples
+    --------
+    .. include:: ../docstrings/cubehelix_palette.rst
+
+    """
+    def get_color_function(p0, p1):
+        # Copied from matplotlib because it lives in private module
+        def color(x):
+            # Apply gamma factor to emphasise low or high intensity values
+            xg = x ** gamma
+
+            # Calculate amplitude and angle of deviation from the black
+            # to white diagonal in the plane of constant
+            # perceived intensity.
+            a = hue * xg * (1 - xg) / 2
+
+            phi = 2 * np.pi * (start / 3 + rot * x)
+
+            return xg + a * (p0 * np.cos(phi) + p1 * np.sin(phi))
+        return color
+
+    cdict = {
+        "red": get_color_function(-0.14861, 1.78277),
+        "green": get_color_function(-0.29227, -0.90649),
+        "blue": get_color_function(1.97294, 0.0),
+    }
+
+    cmap = mpl.colors.LinearSegmentedColormap("cubehelix", cdict)
+
+    x = np.linspace(light, dark, int(n_colors))
+    pal = cmap(x)[:, :3].tolist()
+    if reverse:
+        pal = pal[::-1]
+
+    if as_cmap:
+        x_256 = np.linspace(light, dark, 256)
+        if reverse:
+            x_256 = x_256[::-1]
+        pal_256 = cmap(x_256)
+        cmap = mpl.colors.ListedColormap(pal_256, "seaborn_cubehelix")
+        return cmap
+    else:
+        return _ColorPalette(pal)
+
+
+def _parse_cubehelix_args(argstr):
+    """Turn stringified cubehelix params into args/kwargs."""
+
+    if argstr.startswith("ch:"):
+        argstr = argstr[3:]
+
+    if argstr.endswith("_r"):
+        reverse = True
+        argstr = argstr[:-2]
+    else:
+        reverse = False
+
+    if not argstr:
+        return [], {"reverse": reverse}
+
+    all_args = argstr.split(",")
+
+    args = [float(a.strip(" ")) for a in all_args if "=" not in a]
+
+    kwargs = [a.split("=") for a in all_args if "=" in a]
+    kwargs = {k.strip(" "): float(v.strip(" ")) for k, v in kwargs}
+
+    kwarg_map = dict(
+        s="start", r="rot", g="gamma",
+        h="hue", l="light", d="dark",  # noqa: E741
+    )
+
+    kwargs = {kwarg_map.get(k, k): v for k, v in kwargs.items()}
+
+    if reverse:
+        kwargs["reverse"] = True
+
+    return args, kwargs
+
+
+def set_color_codes(palette="deep"):
+    """Change how matplotlib color shorthands are interpreted.
+
+    Calling this will change how shorthand codes like "b" or "g"
+    are interpreted by matplotlib in subsequent plots.
+
+    Parameters
+    ----------
+    palette : {deep, muted, pastel, dark, bright, colorblind}
+        Named seaborn palette to use as the source of colors.
+
+    See Also
+    --------
+    set : Color codes can be set through the high-level seaborn style
+          manager.
+    set_palette : Color codes can also be set through the function that
+                  sets the matplotlib color cycle.
+
+    """
+    if palette == "reset":
+        colors = [
+            (0., 0., 1.),
+            (0., .5, 0.),
+            (1., 0., 0.),
+            (.75, 0., .75),
+            (.75, .75, 0.),
+            (0., .75, .75),
+            (0., 0., 0.)
+        ]
+    elif not isinstance(palette, str):
+        err = "set_color_codes requires a named seaborn palette"
+        raise TypeError(err)
+    elif palette in SEABORN_PALETTES:
+        if not palette.endswith("6"):
+            palette = palette + "6"
+        colors = SEABORN_PALETTES[palette] + [(.1, .1, .1)]
+    else:
+        err = f"Cannot set colors with palette '{palette}'"
+        raise ValueError(err)
+
+    for code, color in zip("bgrmyck", colors):
+        rgb = mpl.colors.colorConverter.to_rgb(color)
+        mpl.colors.colorConverter.colors[code] = rgb
diff --git a/shared/package-files/f2/f2757ca91a5b52b9c565ec84e95710d6846e64f2e29765bbe0cd94dfb0cd30e1/seaborn-v0_8-pastel.mplstyle b/shared/package-files/f2/f2757ca91a5b52b9c565ec84e95710d6846e64f2e29765bbe0cd94dfb0cd30e1/seaborn-v0_8-pastel.mplstyle
new file mode 100644
index 0000000000000000000000000000000000000000..dff67482c085da8d5d508a4dc5ce946b7230b513
--- /dev/null
+++ b/shared/package-files/f2/f2757ca91a5b52b9c565ec84e95710d6846e64f2e29765bbe0cd94dfb0cd30e1/seaborn-v0_8-pastel.mplstyle
@@ -0,0 +1,3 @@
+# Seaborn pastel palette
+axes.prop_cycle: cycler('color', ['92C6FF', '97F0AA', 'FF9F9A', 'D0BBFF', 'FFFEA3', 'B0E0E6'])
+patch.facecolor: 92C6FF
diff --git a/shared/package-files/f2/f2777ab3fc6e061e6257322f43ea8d95fa2bba4673b82c77e83caf0a61caef8f/__init__.py b/shared/package-files/f2/f2777ab3fc6e061e6257322f43ea8d95fa2bba4673b82c77e83caf0a61caef8f/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5486424dd21a0f1aeb8ea2cead737309fb7f9a7a
--- /dev/null
+++ b/shared/package-files/f2/f2777ab3fc6e061e6257322f43ea8d95fa2bba4673b82c77e83caf0a61caef8f/__init__.py
@@ -0,0 +1,13 @@
+from torio.io import CodecConfig, StreamingMediaDecoder as StreamReader, StreamingMediaEncoder as StreamWriter
+
+from ._effector import AudioEffector
+from ._playback import play_audio
+
+
+__all__ = [
+    "AudioEffector",
+    "StreamReader",
+    "StreamWriter",
+    "CodecConfig",
+    "play_audio",
+]
diff --git a/shared/package-files/f2/f278c342a969b83db646369a0b203be455d0b124036398591894abc45a6f1865/chunk.py b/shared/package-files/f2/f278c342a969b83db646369a0b203be455d0b124036398591894abc45a6f1865/chunk.py
new file mode 100644
index 0000000000000000000000000000000000000000..94fded1b161d64adb23748b067630370d8fa2f3c
--- /dev/null
+++ b/shared/package-files/f2/f278c342a969b83db646369a0b203be455d0b124036398591894abc45a6f1865/chunk.py
@@ -0,0 +1,95 @@
+from __future__ import annotations
+
+import math
+
+
+def calc_chunk_sizes(
+    chunk_size: int | tuple[int, int] | None,
+    chunk_count: int | tuple[int, int] | None,
+    total_chunk_count: int | None,
+    ny: int,
+    nx: int,
+) -> tuple[int, int]:
+    """Calculate chunk sizes.
+
+    Args:
+        chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
+            size in both directions if only one is specified. Cannot be negative.
+        chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
+            same count in both directions if only one is specified. If less than 1, set to 1.
+        total_chunk_count (int, optional): Total number of chunks. If less than 1, set to 1.
+        ny (int): Number of grid points in y-direction.
+        nx (int): Number of grid points in x-direction.
+
+    Return:
+        tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size).
+
+    Note:
+        Zero or one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` should be
+        specified.
+    """
+    if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1:
+        raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set")
+
+    if nx < 2 or ny < 2:
+        raise ValueError(f"(ny, nx) must be at least (2, 2), not ({ny}, {nx})")
+
+    if total_chunk_count is not None:
+        max_chunk_count = (nx-1)*(ny-1)
+        total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count)
+        if total_chunk_count == 1:
+            chunk_size = 0
+        elif total_chunk_count == max_chunk_count:
+            chunk_size = (1, 1)
+        else:
+            factors = two_factors(total_chunk_count)
+            if ny > nx:
+                chunk_count = factors
+            else:
+                chunk_count = (factors[1], factors[0])
+
+    if chunk_count is not None:
+        if isinstance(chunk_count, tuple):
+            y_chunk_count, x_chunk_count = chunk_count
+        else:
+            y_chunk_count = x_chunk_count = chunk_count
+        x_chunk_count = min(max(x_chunk_count, 1), nx-1)
+        y_chunk_count = min(max(y_chunk_count, 1), ny-1)
+        chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count))
+
+    if chunk_size is None:
+        y_chunk_size = x_chunk_size = 0
+    elif isinstance(chunk_size, tuple):
+        y_chunk_size, x_chunk_size = chunk_size
+    else:
+        y_chunk_size = x_chunk_size = chunk_size
+
+    if x_chunk_size < 0 or y_chunk_size < 0:
+        raise ValueError("chunk_size cannot be negative")
+
+    return y_chunk_size, x_chunk_size
+
+
+def two_factors(n: int) -> tuple[int, int]:
+    """Split an integer into two integer factors.
+
+    The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
+    order.  Worst case returns (n, 1).
+
+    Args:
+        n (int): The integer to factorize, must be positive.
+
+    Return:
+        tuple(int, int): The two factors of n, in decreasing order.
+    """
+    if n < 0:
+        raise ValueError(f"two_factors expects positive integer not {n}")
+
+    i = math.ceil(math.sqrt(n))
+    while n % i != 0:
+        i -= 1
+    j = n // i
+    if i > j:
+        return i, j
+    else:
+        return j, i
diff --git a/shared/package-files/f2/f27aee5637c9b53214f35ea9961987b062f9551b9b6ec59e9b986f5a2ca238d7/sunau.pyi b/shared/package-files/f2/f27aee5637c9b53214f35ea9961987b062f9551b9b6ec59e9b986f5a2ca238d7/sunau.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b9475937c012f1a202dae205570ad69f27fc0144
--- /dev/null
+++ b/shared/package-files/f2/f27aee5637c9b53214f35ea9961987b062f9551b9b6ec59e9b986f5a2ca238d7/sunau.pyi
@@ -0,0 +1,86 @@
+import sys
+from typing import IO, Any, NamedTuple, NoReturn, Optional, Text, Tuple, Union
+
+_File = Union[Text, IO[bytes]]
+
+class Error(Exception): ...
+
+AUDIO_FILE_MAGIC: int
+AUDIO_FILE_ENCODING_MULAW_8: int
+AUDIO_FILE_ENCODING_LINEAR_8: int
+AUDIO_FILE_ENCODING_LINEAR_16: int
+AUDIO_FILE_ENCODING_LINEAR_24: int
+AUDIO_FILE_ENCODING_LINEAR_32: int
+AUDIO_FILE_ENCODING_FLOAT: int
+AUDIO_FILE_ENCODING_DOUBLE: int
+AUDIO_FILE_ENCODING_ADPCM_G721: int
+AUDIO_FILE_ENCODING_ADPCM_G722: int
+AUDIO_FILE_ENCODING_ADPCM_G723_3: int
+AUDIO_FILE_ENCODING_ADPCM_G723_5: int
+AUDIO_FILE_ENCODING_ALAW_8: int
+AUDIO_UNKNOWN_SIZE: int
+
+if sys.version_info < (3, 0):
+    _sunau_params = Tuple[int, int, int, int, str, str]
+else:
+    class _sunau_params(NamedTuple):
+        nchannels: int
+        sampwidth: int
+        framerate: int
+        nframes: int
+        comptype: str
+        compname: str
+
+class Au_read:
+    def __init__(self, f: _File) -> None: ...
+    if sys.version_info >= (3, 3):
+        def __enter__(self) -> Au_read: ...
+        def __exit__(self, *args: Any) -> None: ...
+    def getfp(self) -> Optional[IO[bytes]]: ...
+    def rewind(self) -> None: ...
+    def close(self) -> None: ...
+    def tell(self) -> int: ...
+    def getnchannels(self) -> int: ...
+    def getnframes(self) -> int: ...
+    def getsampwidth(self) -> int: ...
+    def getframerate(self) -> int: ...
+    def getcomptype(self) -> str: ...
+    def getcompname(self) -> str: ...
+    def getparams(self) -> _sunau_params: ...
+    def getmarkers(self) -> None: ...
+    def getmark(self, id: Any) -> NoReturn: ...
+    def setpos(self, pos: int) -> None: ...
+    def readframes(self, nframes: int) -> Optional[bytes]: ...
+
+class Au_write:
+    def __init__(self, f: _File) -> None: ...
+    if sys.version_info >= (3, 3):
+        def __enter__(self) -> Au_write: ...
+        def __exit__(self, *args: Any) -> None: ...
+    def setnchannels(self, nchannels: int) -> None: ...
+    def getnchannels(self) -> int: ...
+    def setsampwidth(self, sampwidth: int) -> None: ...
+    def getsampwidth(self) -> int: ...
+    def setframerate(self, framerate: float) -> None: ...
+    def getframerate(self) -> int: ...
+    def setnframes(self, nframes: int) -> None: ...
+    def getnframes(self) -> int: ...
+    def setcomptype(self, comptype: str, compname: str) -> None: ...
+    def getcomptype(self) -> str: ...
+    def getcompname(self) -> str: ...
+    def setparams(self, params: _sunau_params) -> None: ...
+    def getparams(self) -> _sunau_params: ...
+    def setmark(self, id: Any, pos: Any, name: Any) -> NoReturn: ...
+    def getmark(self, id: Any) -> NoReturn: ...
+    def getmarkers(self) -> None: ...
+    def tell(self) -> int: ...
+    # should be any bytes-like object after 3.4, but we don't have a type for that
+    def writeframesraw(self, data: bytes) -> None: ...
+    def writeframes(self, data: bytes) -> None: ...
+    def close(self) -> None: ...
+
+# Returns a Au_read if mode is rb and Au_write if mode is wb
+def open(f: _File, mode: Optional[str] = ...) -> Any: ...
+
+if sys.version_info < (3, 9):
+    openfp = open
diff --git a/shared/package-files/f2/f27af56827986c31842cdb42dd7ef32d063352bad639eeb4449e4a57a5df308e/uniform_ops.h b/shared/package-files/f2/f27af56827986c31842cdb42dd7ef32d063352bad639eeb4449e4a57a5df308e/uniform_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..54010e4be4a12db9cb53128dc1836da99ee956b6
--- /dev/null
+++ b/shared/package-files/f2/f27af56827986c31842cdb42dd7ef32d063352bad639eeb4449e4a57a5df308e/uniform_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API uniform_ {
+  using schema = at::Tensor & (at::Tensor &, double, double, c10::optional<at::Generator>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::uniform_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, double from, double to, c10::optional<at::Generator> generator);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double from, double to, c10::optional<at::Generator> generator);
+};
+
+struct TORCH_API uniform_out {
+  using schema = at::Tensor & (const at::Tensor &, double, double, c10::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::uniform")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, double from, double to, c10::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, c10::optional<at::Generator> generator, at::Tensor & out);
+};
+
+struct TORCH_API uniform {
+  using schema = at::Tensor (const at::Tensor &, double, double, c10::optional<at::Generator>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::uniform")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, double from, double to, c10::optional<at::Generator> generator);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, c10::optional<at::Generator> generator);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/f2/f28cd537769aa930070af623d1eb1c14ea49a8f22094372eaa8794cfbad0af5f/_exact.py b/shared/package-files/f2/f28cd537769aa930070af623d1eb1c14ea49a8f22094372eaa8794cfbad0af5f/_exact.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd47ea4ee45825d5017c05d951000ab3d1159598
--- /dev/null
+++ b/shared/package-files/f2/f28cd537769aa930070af623d1eb1c14ea49a8f22094372eaa8794cfbad0af5f/_exact.py
@@ -0,0 +1,379 @@
+import logging
+
+import numpy as np
+from numba import njit
+
+from .. import links
+from ..models import Model
+from ..utils import (
+    MaskedModel,
+    delta_minimization_order,
+    make_masks,
+    shapley_coefficients,
+)
+from ._explainer import Explainer
+
+log = logging.getLogger("shap")
+
+
+class ExactExplainer(Explainer):
+    """Computes SHAP values via an optimized exact enumeration.
+
+    This works well for standard Shapley value maskers for models with less than ~15 features that vary
+    from the background per sample. It also works well for Owen values from hclustering structured
+    maskers when there are less than ~100 features that vary from the background per sample. This
+    explainer minimizes the number of function evaluations needed by ordering the masking sets to
+    minimize sequential differences. This is done using gray codes for standard Shapley values
+    and a greedy sorting method for hclustering structured maskers.
+    """
+
+    def __init__(self, model, masker, link=links.identity, linearize_link=True, feature_names=None):
+        """Build an explainers.Exact object for the given model using the given masker object.
+
+        Parameters
+        ----------
+        model : function
+            A callable python object that executes the model given a set of input data samples.
+
+        masker : function or numpy.array or pandas.DataFrame
+            A callable python object used to "mask" out hidden features of the form `masker(mask, *fargs)`.
+            It takes a single a binary mask and an input sample and returns a matrix of masked samples. These
+            masked samples are evaluated using the model function and the outputs are then averaged.
+            As a shortcut for the standard masking used by SHAP you can pass a background data matrix
+            instead of a function and that matrix will be used for masking. To use a clustering
+            game structure you can pass a shap.maskers.TabularPartitions(data) object.
+
+        link : function
+            The link function used to map between the output units of the model and the SHAP value units. By
+            default it is shap.links.identity, but shap.links.logit can be useful so that expectations are
+            computed in probability units while explanations remain in the (more naturally additive) log-odds
+            units. For more details on how link functions work see any overview of link functions for generalized
+            linear models.
+
+        linearize_link : bool
+            If we use a non-linear link function to take expectations then models that are additive with respect to that
+            link function for a single background sample will no longer be additive when using a background masker with
+            many samples. This for example means that a linear logistic regression model would have interaction effects
+            that arise from the non-linear changes in expectation averaging. To retain the additively of the model with
+            still respecting the link function we linearize the link function by default.
+
+        """  # TODO link to the link linearization paper when done
+        super().__init__(model, masker, link=link, linearize_link=linearize_link, feature_names=feature_names)
+
+        self.model = Model(model)
+
+        if getattr(masker, "clustering", None) is not None:
+            self._partition_masks, self._partition_masks_inds = partition_masks(masker.clustering)
+            self._partition_delta_indexes = partition_delta_indexes(masker.clustering, self._partition_masks)
+
+        self._gray_code_cache = {}  # used to avoid regenerating the same gray code patterns
+
+    def __call__(
+        self,
+        *args,
+        max_evals=100000,
+        main_effects=False,
+        error_bounds=False,
+        batch_size="auto",
+        interactions=1,
+        silent=False,
+    ):
+        """Explains the output of model(*args), where args represents one or more parallel iterators."""
+        # we entirely rely on the general call implementation, we override just to remove **kwargs
+        # from the function signature
+        return super().__call__(
+            *args,
+            max_evals=max_evals,
+            main_effects=main_effects,
+            error_bounds=error_bounds,
+            batch_size=batch_size,
+            interactions=interactions,
+            silent=silent,
+        )
+
+    def _cached_gray_codes(self, n):
+        if n not in self._gray_code_cache:
+            self._gray_code_cache[n] = gray_code_indexes(n)
+        return self._gray_code_cache[n]
+
+    def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_size, outputs, interactions, silent):
+        """Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes)."""
+        # build a masked version of the model for the current input sample
+        fm = MaskedModel(self.model, self.masker, self.link, self.linearize_link, *row_args)
+
+        # do the standard Shapley values
+        inds = None
+        if getattr(self.masker, "clustering", None) is None:
+            # see which elements we actually need to perturb
+            inds = fm.varying_inputs()
+
+            # make sure we have enough evals
+            if max_evals is not None and max_evals != "auto" and max_evals < 2 ** len(inds):
+                raise ValueError(
+                    f"It takes {2 ** len(inds)} masked evaluations to run the Exact explainer on this instance, but max_evals={max_evals}!"
+                )
+
+            # generate the masks in gray code order (so that we change the inputs as little
+            # as possible while we iterate to minimize the need to re-eval when the inputs
+            # don't vary from the background)
+            delta_indexes = self._cached_gray_codes(len(inds))
+
+            # map to a larger mask that includes the invariant entries
+            extended_delta_indexes = np.zeros(2 ** len(inds), dtype=int)
+            for i in range(2 ** len(inds)):
+                if delta_indexes[i] == MaskedModel.delta_mask_noop_value:
+                    extended_delta_indexes[i] = delta_indexes[i]
+                else:
+                    extended_delta_indexes[i] = inds[delta_indexes[i]]
+
+            # run the model
+            outputs = fm(extended_delta_indexes, zero_index=0, batch_size=batch_size)
+
+            # Shapley values
+            # Care: Need to distinguish between `True` and `1`
+            if interactions is False or (interactions == 1 and interactions is not True):
+                # loop over all the outputs to update the rows
+                coeff = shapley_coefficients(len(inds))
+                row_values = np.zeros((len(fm),) + outputs.shape[1:])
+                mask = np.zeros(len(fm), dtype=bool)
+                _compute_grey_code_row_values(
+                    row_values, mask, inds, outputs, coeff, extended_delta_indexes, MaskedModel.delta_mask_noop_value
+                )
+
+            # Shapley-Taylor interaction values
+            elif interactions is True or interactions == 2:
+                # loop over all the outputs to update the rows
+                coeff = shapley_coefficients(len(inds))
+                row_values = np.zeros((len(fm), len(fm)) + outputs.shape[1:])
+                mask = np.zeros(len(fm), dtype=bool)
+                _compute_grey_code_row_values_st(
+                    row_values, mask, inds, outputs, coeff, extended_delta_indexes, MaskedModel.delta_mask_noop_value
+                )
+
+            elif interactions > 2:
+                raise NotImplementedError(
+                    "Currently the Exact explainer does not support interactions higher than order 2!"
+                )
+
+        # do a partition tree constrained version of Shapley values
+        else:
+            # make sure we have enough evals
+            if max_evals is not None and max_evals != "auto" and max_evals < len(fm) ** 2:
+                raise ValueError(
+                    f"It takes {len(fm) ** 2} masked evaluations to run the Exact explainer on this instance, but max_evals={max_evals}!"
+                )
+
+            # generate the masks in a hclust order (so that we change the inputs as little
+            # as possible while we iterate to minimize the need to re-eval when the inputs
+            # don't vary from the background)
+            delta_indexes = self._partition_delta_indexes
+
+            # run the model
+            outputs = fm(delta_indexes, batch_size=batch_size)
+
+            # loop over each output feature
+            row_values = np.zeros((len(fm),) + outputs.shape[1:])
+            for i in range(len(fm)):
+                on_outputs = outputs[self._partition_masks_inds[i][1]]
+                off_outputs = outputs[self._partition_masks_inds[i][0]]
+                row_values[i] = (on_outputs - off_outputs).mean(0)
+
+        # compute the main effects if we need to
+        main_effect_values = None
+        if main_effects or interactions is True or interactions == 2:
+            if inds is None:
+                inds = np.arange(len(fm))
+            main_effect_values = fm.main_effects(inds)
+            if interactions is True or interactions == 2:
+                for i in range(len(fm)):
+                    row_values[i, i] = main_effect_values[i]
+
+        return {
+            "values": row_values,
+            "expected_values": outputs[0],
+            "mask_shapes": fm.mask_shapes,
+            "main_effects": main_effect_values if main_effects else None,
+            "clustering": getattr(self.masker, "clustering", None),
+        }
+
+
+@njit
+def _compute_grey_code_row_values(row_values, mask, inds, outputs, shapley_coeff, extended_delta_indexes, noop_code):
+    set_size = 0
+    M = len(inds)
+    for i in range(2**M):
+        # update the mask
+        delta_ind = extended_delta_indexes[i]
+        if delta_ind != noop_code:
+            mask[delta_ind] = ~mask[delta_ind]
+            if mask[delta_ind]:
+                set_size += 1
+            else:
+                set_size -= 1
+
+        # update the output row values
+        on_coeff = shapley_coeff[set_size - 1]
+        if set_size < M:
+            off_coeff = shapley_coeff[set_size]
+        out = outputs[i]
+        for j in inds:
+            if mask[j]:
+                row_values[j] += out * on_coeff
+            else:
+                row_values[j] -= out * off_coeff
+
+
+@njit
+def _compute_grey_code_row_values_st(row_values, mask, inds, outputs, shapley_coeff, extended_delta_indexes, noop_code):
+    set_size = 0
+    M = len(inds)
+    for i in range(2**M):
+        # update the mask
+        delta_ind = extended_delta_indexes[i]
+        if delta_ind != noop_code:
+            mask[delta_ind] = ~mask[delta_ind]
+            if mask[delta_ind]:
+                set_size += 1
+            else:
+                set_size -= 1
+
+        # distribute the effect of this mask set over all the terms it impacts
+        out = outputs[i]
+        for j in range(M):
+            for k in range(j + 1, M):
+                if not mask[j] and not mask[k]:
+                    delta = out * shapley_coeff[set_size]  # * 2
+                elif (not mask[j] and mask[k]) or (mask[j] and not mask[k]):
+                    delta = -out * shapley_coeff[set_size - 1]  # * 2
+                else:  # both true
+                    delta = out * shapley_coeff[set_size - 2]  # * 2
+                row_values[j, k] += delta
+                row_values[k, j] += delta
+
+
+def partition_delta_indexes(partition_tree, all_masks):
+    """Return an delta index encoded array of all the masks possible while following the given partition tree."""
+    # convert the masks to delta index format
+    mask = np.zeros(all_masks.shape[1], dtype=bool)
+    delta_inds = []
+    for i in range(len(all_masks)):
+        inds = np.where(mask ^ all_masks[i, :])[0]
+
+        for j in inds[:-1]:
+            delta_inds.append(-j - 1)  # negative + (-1) means we have more inds still to change...
+        if len(inds) == 0:
+            delta_inds.append(MaskedModel.delta_mask_noop_value)
+        else:
+            delta_inds.extend(inds[-1:])
+        mask = all_masks[i, :]
+
+    return np.array(delta_inds)
+
+
+def partition_masks(partition_tree):
+    """Return an array of all the masks possible while following the given partition tree."""
+    M = partition_tree.shape[0] + 1
+    mask_matrix = make_masks(partition_tree)
+    all_masks = []
+    m00 = np.zeros(M, dtype=bool)
+    all_masks.append(m00)
+    all_masks.append(~m00)
+    # inds_stack = [0,1]
+    inds_lists = [[[], []] for i in range(M)]
+    _partition_masks_recurse(len(partition_tree) - 1, m00, 0, 1, inds_lists, mask_matrix, partition_tree, M, all_masks)
+
+    all_masks = np.array(all_masks)
+
+    # we resort the clustering matrix to minimize the sequential difference between the masks
+    # this minimizes the number of model evaluations we need to run when the background sometimes
+    # matches the foreground. We seem to average about 1.5 feature changes per mask with this
+    # approach. This is not as clean as the grey code ordering, but a perfect 1 feature change
+    # ordering is not possible with a clustering tree
+    order = delta_minimization_order(all_masks)
+    inverse_order = np.arange(len(order))[np.argsort(order)]
+
+    for inds_list0, inds_list1 in inds_lists:
+        for i in range(len(inds_list0)):
+            inds_list0[i] = inverse_order[inds_list0[i]]
+            inds_list1[i] = inverse_order[inds_list1[i]]
+
+    # Care: inds_lists have different lengths, so partition_masks_inds is a "ragged" array. See GH #3063
+    partition_masks = all_masks[order]
+    partition_masks_inds = [[np.array(on), np.array(off)] for on, off in inds_lists]
+    return partition_masks, partition_masks_inds
+
+
+# TODO: this should be a jit function... which would require preallocating the inds_lists (sizes are 2**depth of that ind)
+# TODO: we could also probable avoid making the masks at all and just record the deltas if we want...
+def _partition_masks_recurse(index, m00, ind00, ind11, inds_lists, mask_matrix, partition_tree, M, all_masks):
+    if index < 0:
+        inds_lists[index + M][0].append(ind00)
+        inds_lists[index + M][1].append(ind11)
+        return
+
+    # get our children indexes
+    left_index = int(partition_tree[index, 0] - M)
+    right_index = int(partition_tree[index, 1] - M)
+
+    # build more refined masks
+    m10 = m00.copy()  # we separate the copy from the add so as to not get converted to a matrix
+    m10[:] += mask_matrix[left_index + M, :]
+    m01 = m00.copy()
+    m01[:] += mask_matrix[right_index + M, :]
+
+    # record the new masks we made
+    ind01 = len(all_masks)
+    all_masks.append(m01)
+    ind10 = len(all_masks)
+    all_masks.append(m10)
+
+    # inds_stack.append(len(all_masks) - 2)
+    # inds_stack.append(len(all_masks) - 1)
+
+    # recurse left and right with both 1 (True) and 0 (False) contexts
+    _partition_masks_recurse(left_index, m00, ind00, ind10, inds_lists, mask_matrix, partition_tree, M, all_masks)
+    _partition_masks_recurse(right_index, m10, ind10, ind11, inds_lists, mask_matrix, partition_tree, M, all_masks)
+    _partition_masks_recurse(left_index, m01, ind01, ind11, inds_lists, mask_matrix, partition_tree, M, all_masks)
+    _partition_masks_recurse(right_index, m00, ind00, ind01, inds_lists, mask_matrix, partition_tree, M, all_masks)
+
+
+def gray_code_masks(nbits):
+    """Produces an array of all binary patterns of size nbits in gray code order.
+
+    This is based on code from: http://code.activestate.com/recipes/576592-gray-code-generatoriterator/
+    """
+    out = np.zeros((2**nbits, nbits), dtype=bool)
+    li = np.zeros(nbits, dtype=bool)
+
+    for term in range(2, (1 << nbits) + 1):
+        if term % 2 == 1:  # odd
+            for i in range(-1, -nbits, -1):
+                if li[i] == 1:
+                    li[i - 1] = li[i - 1] ^ 1
+                    break
+        else:  # even
+            li[-1] = li[-1] ^ 1
+
+        out[term - 1, :] = li
+    return out
+
+
+def gray_code_indexes(nbits):
+    """Produces an array of which bits flip at which position.
+
+    We assume the masks start at all zero and -1 means don't do a flip.
+    This is a more efficient representation of the gray_code_masks version.
+    """
+    out = np.ones(2**nbits, dtype=int) * MaskedModel.delta_mask_noop_value
+    li = np.zeros(nbits, dtype=bool)
+    for term in range((1 << nbits) - 1):
+        if term % 2 == 1:  # odd
+            for i in range(-1, -nbits, -1):
+                if li[i] == 1:
+                    li[i - 1] = li[i - 1] ^ 1
+                    out[term + 1] = nbits + (i - 1)
+                    break
+        else:  # even
+            li[-1] = li[-1] ^ 1
+            out[term + 1] = nbits - 1
+    return out
diff --git a/shared/package-files/f2/f28db73030cfb696d0bd930b6e7a9aaf22540d28e1ee2c31582c77fa612caa5e/_trilinear_ops.h b/shared/package-files/f2/f28db73030cfb696d0bd930b6e7a9aaf22540d28e1ee2c31582c77fa612caa5e/_trilinear_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d80dfd46d517cb5a23dda429fae8d5ea7b618a2
--- /dev/null
+++ b/shared/package-files/f2/f28db73030cfb696d0bd930b6e7a9aaf22540d28e1ee2c31582c77fa612caa5e/_trilinear_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _trilinear {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_trilinear")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor")
+  static at::Tensor call(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim);
+};
+
+struct TORCH_API _trilinear_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_trilinear")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/f2/f29792b7bad19b9b7a6096aae01e9a6a0a3ec28029576af74efc530ee331c412/pickle.py b/shared/package-files/f2/f29792b7bad19b9b7a6096aae01e9a6a0a3ec28029576af74efc530ee331c412/pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..f027e0432045b762f9661a90d380ebb9f8c1d8d8
--- /dev/null
+++ b/shared/package-files/f2/f29792b7bad19b9b7a6096aae01e9a6a0a3ec28029576af74efc530ee331c412/pickle.py
@@ -0,0 +1,1820 @@
+"""Create portable serialized representations of Python objects.
+
+See module copyreg for a mechanism for registering custom picklers.
+See module pickletools source for extensive comments.
+
+Classes:
+
+    Pickler
+    Unpickler
+
+Functions:
+
+    dump(object, file)
+    dumps(object) -> string
+    load(file) -> object
+    loads(bytes) -> object
+
+Misc variables:
+
+    __version__
+    format_version
+    compatible_formats
+
+"""
+
+from types import FunctionType
+from copyreg import dispatch_table
+from copyreg import _extension_registry, _inverted_registry, _extension_cache
+from itertools import islice
+from functools import partial
+import sys
+from sys import maxsize
+from struct import pack, unpack
+import re
+import io
+import codecs
+import _compat_pickle
+
+__all__ = ["PickleError", "PicklingError", "UnpicklingError", "Pickler",
+           "Unpickler", "dump", "dumps", "load", "loads"]
+
+try:
+    from _pickle import PickleBuffer
+    __all__.append("PickleBuffer")
+    _HAVE_PICKLE_BUFFER = True
+except ImportError:
+    _HAVE_PICKLE_BUFFER = False
+
+
+# Shortcut for use in isinstance testing
+bytes_types = (bytes, bytearray)
+
+# These are purely informational; no code uses these.
+format_version = "4.0"                  # File format version we write
+compatible_formats = ["1.0",            # Original protocol 0
+                      "1.1",            # Protocol 0 with INST added
+                      "1.2",            # Original protocol 1
+                      "1.3",            # Protocol 1 with BINFLOAT added
+                      "2.0",            # Protocol 2
+                      "3.0",            # Protocol 3
+                      "4.0",            # Protocol 4
+                      "5.0",            # Protocol 5
+                      ]                 # Old format versions we can read
+
+# This is the highest protocol number we know how to read.
+HIGHEST_PROTOCOL = 5
+
+# The protocol we write by default.  May be less than HIGHEST_PROTOCOL.
+# Only bump this if the oldest still supported version of Python already
+# includes it.
+DEFAULT_PROTOCOL = 4
+
+class PickleError(Exception):
+    """A common base class for the other pickling exceptions."""
+    pass
+
+class PicklingError(PickleError):
+    """This exception is raised when an unpicklable object is passed to the
+    dump() method.
+
+    """
+    pass
+
+class UnpicklingError(PickleError):
+    """This exception is raised when there is a problem unpickling an object,
+    such as a security violation.
+
+    Note that other exceptions may also be raised during unpickling, including
+    (but not necessarily limited to) AttributeError, EOFError, ImportError,
+    and IndexError.
+
+    """
+    pass
+
+# An instance of _Stop is raised by Unpickler.load_stop() in response to
+# the STOP opcode, passing the object that is the result of unpickling.
+class _Stop(Exception):
+    def __init__(self, value):
+        self.value = value
+
+# Jython has PyStringMap; it's a dict subclass with string keys
+try:
+    from org.python.core import PyStringMap
+except ImportError:
+    PyStringMap = None
+
+# Pickle opcodes.  See pickletools.py for extensive docs.  The listing
+# here is in kind-of alphabetical order of 1-character pickle code.
+# pickletools groups them by purpose.
+
+MARK           = b'('   # push special markobject on stack
+STOP           = b'.'   # every pickle ends with STOP
+POP            = b'0'   # discard topmost stack item
+POP_MARK       = b'1'   # discard stack top through topmost markobject
+DUP            = b'2'   # duplicate top stack item
+FLOAT          = b'F'   # push float object; decimal string argument
+INT            = b'I'   # push integer or bool; decimal string argument
+BININT         = b'J'   # push four-byte signed int
+BININT1        = b'K'   # push 1-byte unsigned int
+LONG           = b'L'   # push long; decimal string argument
+BININT2        = b'M'   # push 2-byte unsigned int
+NONE           = b'N'   # push None
+PERSID         = b'P'   # push persistent object; id is taken from string arg
+BINPERSID      = b'Q'   #  "       "         "  ;  "  "   "     "  stack
+REDUCE         = b'R'   # apply callable to argtuple, both on stack
+STRING         = b'S'   # push string; NL-terminated string argument
+BINSTRING      = b'T'   # push string; counted binary string argument
+SHORT_BINSTRING= b'U'   #  "     "   ;    "      "       "      " < 256 bytes
+UNICODE        = b'V'   # push Unicode string; raw-unicode-escaped'd argument
+BINUNICODE     = b'X'   #   "     "       "  ; counted UTF-8 string argument
+APPEND         = b'a'   # append stack top to list below it
+BUILD          = b'b'   # call __setstate__ or __dict__.update()
+GLOBAL         = b'c'   # push self.find_class(modname, name); 2 string args
+DICT           = b'd'   # build a dict from stack items
+EMPTY_DICT     = b'}'   # push empty dict
+APPENDS        = b'e'   # extend list on stack by topmost stack slice
+GET            = b'g'   # push item from memo on stack; index is string arg
+BINGET         = b'h'   #   "    "    "    "   "   "  ;   "    " 1-byte arg
+INST           = b'i'   # build & push class instance
+LONG_BINGET    = b'j'   # push item from memo on stack; index is 4-byte arg
+LIST           = b'l'   # build list from topmost stack items
+EMPTY_LIST     = b']'   # push empty list
+OBJ            = b'o'   # build & push class instance
+PUT            = b'p'   # store stack top in memo; index is string arg
+BINPUT         = b'q'   #   "     "    "   "   " ;   "    " 1-byte arg
+LONG_BINPUT    = b'r'   #   "     "    "   "   " ;   "    " 4-byte arg
+SETITEM        = b's'   # add key+value pair to dict
+TUPLE          = b't'   # build tuple from topmost stack items
+EMPTY_TUPLE    = b')'   # push empty tuple
+SETITEMS       = b'u'   # modify dict by adding topmost key+value pairs
+BINFLOAT       = b'G'   # push float; arg is 8-byte float encoding
+
+TRUE           = b'I01\n'  # not an opcode; see INT docs in pickletools.py
+FALSE          = b'I00\n'  # not an opcode; see INT docs in pickletools.py
+
+# Protocol 2
+
+PROTO          = b'\x80'  # identify pickle protocol
+NEWOBJ         = b'\x81'  # build object by applying cls.__new__ to argtuple
+EXT1           = b'\x82'  # push object from extension registry; 1-byte index
+EXT2           = b'\x83'  # ditto, but 2-byte index
+EXT4           = b'\x84'  # ditto, but 4-byte index
+TUPLE1         = b'\x85'  # build 1-tuple from stack top
+TUPLE2         = b'\x86'  # build 2-tuple from two topmost stack items
+TUPLE3         = b'\x87'  # build 3-tuple from three topmost stack items
+NEWTRUE        = b'\x88'  # push True
+NEWFALSE       = b'\x89'  # push False
+LONG1          = b'\x8a'  # push long from < 256 bytes
+LONG4          = b'\x8b'  # push really big long
+
+_tuplesize2code = [EMPTY_TUPLE, TUPLE1, TUPLE2, TUPLE3]
+
+# Protocol 3 (Python 3.x)
+
+BINBYTES       = b'B'   # push bytes; counted binary string argument
+SHORT_BINBYTES = b'C'   #  "     "   ;    "      "       "      " < 256 bytes
+
+# Protocol 4
+
+SHORT_BINUNICODE = b'\x8c'  # push short string; UTF-8 length < 256 bytes
+BINUNICODE8      = b'\x8d'  # push very long string
+BINBYTES8        = b'\x8e'  # push very long bytes string
+EMPTY_SET        = b'\x8f'  # push empty set on the stack
+ADDITEMS         = b'\x90'  # modify set by adding topmost stack items
+FROZENSET        = b'\x91'  # build frozenset from topmost stack items
+NEWOBJ_EX        = b'\x92'  # like NEWOBJ but work with keyword only arguments
+STACK_GLOBAL     = b'\x93'  # same as GLOBAL but using names on the stacks
+MEMOIZE          = b'\x94'  # store top of the stack in memo
+FRAME            = b'\x95'  # indicate the beginning of a new frame
+
+# Protocol 5
+
+BYTEARRAY8       = b'\x96'  # push bytearray
+NEXT_BUFFER      = b'\x97'  # push next out-of-band buffer
+READONLY_BUFFER  = b'\x98'  # make top of stack readonly
+
+__all__.extend([x for x in dir() if re.match("[A-Z][A-Z0-9_]+$", x)])
+
+
+class _Framer:
+
+    _FRAME_SIZE_MIN = 4
+    _FRAME_SIZE_TARGET = 64 * 1024
+
+    def __init__(self, file_write):
+        self.file_write = file_write
+        self.current_frame = None
+
+    def start_framing(self):
+        self.current_frame = io.BytesIO()
+
+    def end_framing(self):
+        if self.current_frame and self.current_frame.tell() > 0:
+            self.commit_frame(force=True)
+            self.current_frame = None
+
+    def commit_frame(self, force=False):
+        if self.current_frame:
+            f = self.current_frame
+            if f.tell() >= self._FRAME_SIZE_TARGET or force:
+                data = f.getbuffer()
+                write = self.file_write
+                if len(data) >= self._FRAME_SIZE_MIN:
+                    # Issue a single call to the write method of the underlying
+                    # file object for the frame opcode with the size of the
+                    # frame. The concatenation is expected to be less expensive
+                    # than issuing an additional call to write.
+                    write(FRAME + pack("<Q", len(data)))
+
+                # Issue a separate call to write to append the frame
+                # contents without concatenation to the above to avoid a
+                # memory copy.
+                write(data)
+
+                # Start the new frame with a new io.BytesIO instance so that
+                # the file object can have delayed access to the previous frame
+                # contents via an unreleased memoryview of the previous
+                # io.BytesIO instance.
+                self.current_frame = io.BytesIO()
+
+    def write(self, data):
+        if self.current_frame:
+            return self.current_frame.write(data)
+        else:
+            return self.file_write(data)
+
+    def write_large_bytes(self, header, payload):
+        write = self.file_write
+        if self.current_frame:
+            # Terminate the current frame and flush it to the file.
+            self.commit_frame(force=True)
+
+        # Perform direct write of the header and payload of the large binary
+        # object. Be careful not to concatenate the header and the payload
+        # prior to calling 'write' as we do not want to allocate a large
+        # temporary bytes object.
+        # We intentionally do not insert a protocol 4 frame opcode to make
+        # it possible to optimize file.read calls in the loader.
+        write(header)
+        write(payload)
+
+
+class _Unframer:
+
+    def __init__(self, file_read, file_readline, file_tell=None):
+        self.file_read = file_read
+        self.file_readline = file_readline
+        self.current_frame = None
+
+    def readinto(self, buf):
+        if self.current_frame:
+            n = self.current_frame.readinto(buf)
+            if n == 0 and len(buf) != 0:
+                self.current_frame = None
+                n = len(buf)
+                buf[:] = self.file_read(n)
+                return n
+            if n < len(buf):
+                raise UnpicklingError(
+                    "pickle exhausted before end of frame")
+            return n
+        else:
+            n = len(buf)
+            buf[:] = self.file_read(n)
+            return n
+
+    def read(self, n):
+        if self.current_frame:
+            data = self.current_frame.read(n)
+            if not data and n != 0:
+                self.current_frame = None
+                return self.file_read(n)
+            if len(data) < n:
+                raise UnpicklingError(
+                    "pickle exhausted before end of frame")
+            return data
+        else:
+            return self.file_read(n)
+
+    def readline(self):
+        if self.current_frame:
+            data = self.current_frame.readline()
+            if not data:
+                self.current_frame = None
+                return self.file_readline()
+            if data[-1] != b'\n'[0]:
+                raise UnpicklingError(
+                    "pickle exhausted before end of frame")
+            return data
+        else:
+            return self.file_readline()
+
+    def load_frame(self, frame_size):
+        if self.current_frame and self.current_frame.read() != b'':
+            raise UnpicklingError(
+                "beginning of a new frame before end of current frame")
+        self.current_frame = io.BytesIO(self.file_read(frame_size))
+
+
+# Tools used for pickling.
+
+def _getattribute(obj, name):
+    for subpath in name.split('.'):
+        if subpath == '<locals>':
+            raise AttributeError("Can't get local attribute {!r} on {!r}"
+                                 .format(name, obj))
+        try:
+            parent = obj
+            obj = getattr(obj, subpath)
+        except AttributeError:
+            raise AttributeError("Can't get attribute {!r} on {!r}"
+                                 .format(name, obj)) from None
+    return obj, parent
+
+def whichmodule(obj, name):
+    """Find the module an object belong to."""
+    module_name = getattr(obj, '__module__', None)
+    if module_name is not None:
+        return module_name
+    # Protect the iteration by using a list copy of sys.modules against dynamic
+    # modules that trigger imports of other modules upon calls to getattr.
+    for module_name, module in sys.modules.copy().items():
+        if (module_name == '__main__'
+            or module_name == '__mp_main__'  # bpo-42406
+            or module is None):
+            continue
+        try:
+            if _getattribute(module, name)[0] is obj:
+                return module_name
+        except AttributeError:
+            pass
+    return '__main__'
+
+def encode_long(x):
+    r"""Encode a long to a two's complement little-endian binary string.
+    Note that 0 is a special case, returning an empty string, to save a
+    byte in the LONG1 pickling context.
+
+    >>> encode_long(0)
+    b''
+    >>> encode_long(255)
+    b'\xff\x00'
+    >>> encode_long(32767)
+    b'\xff\x7f'
+    >>> encode_long(-256)
+    b'\x00\xff'
+    >>> encode_long(-32768)
+    b'\x00\x80'
+    >>> encode_long(-128)
+    b'\x80'
+    >>> encode_long(127)
+    b'\x7f'
+    >>>
+    """
+    if x == 0:
+        return b''
+    nbytes = (x.bit_length() >> 3) + 1
+    result = x.to_bytes(nbytes, byteorder='little', signed=True)
+    if x < 0 and nbytes > 1:
+        if result[-1] == 0xff and (result[-2] & 0x80) != 0:
+            result = result[:-1]
+    return result
+
+def decode_long(data):
+    r"""Decode a long from a two's complement little-endian binary string.
+
+    >>> decode_long(b'')
+    0
+    >>> decode_long(b"\xff\x00")
+    255
+    >>> decode_long(b"\xff\x7f")
+    32767
+    >>> decode_long(b"\x00\xff")
+    -256
+    >>> decode_long(b"\x00\x80")
+    -32768
+    >>> decode_long(b"\x80")
+    -128
+    >>> decode_long(b"\x7f")
+    127
+    """
+    return int.from_bytes(data, byteorder='little', signed=True)
+
+
+# Pickling machinery
+
+class _Pickler:
+
+    def __init__(self, file, protocol=None, *, fix_imports=True,
+                 buffer_callback=None):
+        """This takes a binary file for writing a pickle data stream.
+
+        The optional *protocol* argument tells the pickler to use the
+        given protocol; supported protocols are 0, 1, 2, 3, 4 and 5.
+        The default protocol is 4. It was introduced in Python 3.4, and
+        is incompatible with previous versions.
+
+        Specifying a negative protocol version selects the highest
+        protocol version supported.  The higher the protocol used, the
+        more recent the version of Python needed to read the pickle
+        produced.
+
+        The *file* argument must have a write() method that accepts a
+        single bytes argument. It can thus be a file object opened for
+        binary writing, an io.BytesIO instance, or any other custom
+        object that meets this interface.
+
+        If *fix_imports* is True and *protocol* is less than 3, pickle
+        will try to map the new Python 3 names to the old module names
+        used in Python 2, so that the pickle data stream is readable
+        with Python 2.
+
+        If *buffer_callback* is None (the default), buffer views are
+        serialized into *file* as part of the pickle stream.
+
+        If *buffer_callback* is not None, then it can be called any number
+        of times with a buffer view.  If the callback returns a false value
+        (such as None), the given buffer is out-of-band; otherwise the
+        buffer is serialized in-band, i.e. inside the pickle stream.
+
+        It is an error if *buffer_callback* is not None and *protocol*
+        is None or smaller than 5.
+        """
+        if protocol is None:
+            protocol = DEFAULT_PROTOCOL
+        if protocol < 0:
+            protocol = HIGHEST_PROTOCOL
+        elif not 0 <= protocol <= HIGHEST_PROTOCOL:
+            raise ValueError("pickle protocol must be <= %d" % HIGHEST_PROTOCOL)
+        if buffer_callback is not None and protocol < 5:
+            raise ValueError("buffer_callback needs protocol >= 5")
+        self._buffer_callback = buffer_callback
+        try:
+            self._file_write = file.write
+        except AttributeError:
+            raise TypeError("file must have a 'write' attribute")
+        self.framer = _Framer(self._file_write)
+        self.write = self.framer.write
+        self._write_large_bytes = self.framer.write_large_bytes
+        self.memo = {}
+        self.proto = int(protocol)
+        self.bin = protocol >= 1
+        self.fast = 0
+        self.fix_imports = fix_imports and protocol < 3
+
+    def clear_memo(self):
+        """Clears the pickler's "memo".
+
+        The memo is the data structure that remembers which objects the
+        pickler has already seen, so that shared or recursive objects
+        are pickled by reference and not by value.  This method is
+        useful when re-using picklers.
+        """
+        self.memo.clear()
+
+    def dump(self, obj):
+        """Write a pickled representation of obj to the open file."""
+        # Check whether Pickler was initialized correctly. This is
+        # only needed to mimic the behavior of _pickle.Pickler.dump().
+        if not hasattr(self, "_file_write"):
+            raise PicklingError("Pickler.__init__() was not called by "
+                                "%s.__init__()" % (self.__class__.__name__,))
+        if self.proto >= 2:
+            self.write(PROTO + pack("<B", self.proto))
+        if self.proto >= 4:
+            self.framer.start_framing()
+        self.save(obj)
+        self.write(STOP)
+        self.framer.end_framing()
+
+    def memoize(self, obj):
+        """Store an object in the memo."""
+
+        # The Pickler memo is a dictionary mapping object ids to 2-tuples
+        # that contain the Unpickler memo key and the object being memoized.
+        # The memo key is written to the pickle and will become
+        # the key in the Unpickler's memo.  The object is stored in the
+        # Pickler memo so that transient objects are kept alive during
+        # pickling.
+
+        # The use of the Unpickler memo length as the memo key is just a
+        # convention.  The only requirement is that the memo values be unique.
+        # But there appears no advantage to any other scheme, and this
+        # scheme allows the Unpickler memo to be implemented as a plain (but
+        # growable) array, indexed by memo key.
+        if self.fast:
+            return
+        assert id(obj) not in self.memo
+        idx = len(self.memo)
+        self.write(self.put(idx))
+        self.memo[id(obj)] = idx, obj
+
+    # Return a PUT (BINPUT, LONG_BINPUT) opcode string, with argument i.
+    def put(self, idx):
+        if self.proto >= 4:
+            return MEMOIZE
+        elif self.bin:
+            if idx < 256:
+                return BINPUT + pack("<B", idx)
+            else:
+                return LONG_BINPUT + pack("<I", idx)
+        else:
+            return PUT + repr(idx).encode("ascii") + b'\n'
+
+    # Return a GET (BINGET, LONG_BINGET) opcode string, with argument i.
+    def get(self, i):
+        if self.bin:
+            if i < 256:
+                return BINGET + pack("<B", i)
+            else:
+                return LONG_BINGET + pack("<I", i)
+
+        return GET + repr(i).encode("ascii") + b'\n'
+
+    def save(self, obj, save_persistent_id=True):
+        self.framer.commit_frame()
+
+        # Check for persistent id (defined by a subclass)
+        pid = self.persistent_id(obj)
+        if pid is not None and save_persistent_id:
+            self.save_pers(pid)
+            return
+
+        # Check the memo
+        x = self.memo.get(id(obj))
+        if x is not None:
+            self.write(self.get(x[0]))
+            return
+
+        rv = NotImplemented
+        reduce = getattr(self, "reducer_override", None)
+        if reduce is not None:
+            rv = reduce(obj)
+
+        if rv is NotImplemented:
+            # Check the type dispatch table
+            t = type(obj)
+            f = self.dispatch.get(t)
+            if f is not None:
+                f(self, obj)  # Call unbound method with explicit self
+                return
+
+            # Check private dispatch table if any, or else
+            # copyreg.dispatch_table
+            reduce = getattr(self, 'dispatch_table', dispatch_table).get(t)
+            if reduce is not None:
+                rv = reduce(obj)
+            else:
+                # Check for a class with a custom metaclass; treat as regular
+                # class
+                if issubclass(t, type):
+                    self.save_global(obj)
+                    return
+
+                # Check for a __reduce_ex__ method, fall back to __reduce__
+                reduce = getattr(obj, "__reduce_ex__", None)
+                if reduce is not None:
+                    rv = reduce(self.proto)
+                else:
+                    reduce = getattr(obj, "__reduce__", None)
+                    if reduce is not None:
+                        rv = reduce()
+                    else:
+                        raise PicklingError("Can't pickle %r object: %r" %
+                                            (t.__name__, obj))
+
+        # Check for string returned by reduce(), meaning "save as global"
+        if isinstance(rv, str):
+            self.save_global(obj, rv)
+            return
+
+        # Assert that reduce() returned a tuple
+        if not isinstance(rv, tuple):
+            raise PicklingError("%s must return string or tuple" % reduce)
+
+        # Assert that it returned an appropriately sized tuple
+        l = len(rv)
+        if not (2 <= l <= 6):
+            raise PicklingError("Tuple returned by %s must have "
+                                "two to six elements" % reduce)
+
+        # Save the reduce() output and finally memoize the object
+        self.save_reduce(obj=obj, *rv)
+
+    def persistent_id(self, obj):
+        # This exists so a subclass can override it
+        return None
+
+    def save_pers(self, pid):
+        # Save a persistent id reference
+        if self.bin:
+            self.save(pid, save_persistent_id=False)
+            self.write(BINPERSID)
+        else:
+            try:
+                self.write(PERSID + str(pid).encode("ascii") + b'\n')
+            except UnicodeEncodeError:
+                raise PicklingError(
+                    "persistent IDs in protocol 0 must be ASCII strings")
+
+    def save_reduce(self, func, args, state=None, listitems=None,
+                    dictitems=None, state_setter=None, *, obj=None):
+        # This API is called by some subclasses
+
+        if not isinstance(args, tuple):
+            raise PicklingError("args from save_reduce() must be a tuple")
+        if not callable(func):
+            raise PicklingError("func from save_reduce() must be callable")
+
+        save = self.save
+        write = self.write
+
+        func_name = getattr(func, "__name__", "")
+        if self.proto >= 2 and func_name == "__newobj_ex__":
+            cls, args, kwargs = args
+            if not hasattr(cls, "__new__"):
+                raise PicklingError("args[0] from {} args has no __new__"
+                                    .format(func_name))
+            if obj is not None and cls is not obj.__class__:
+                raise PicklingError("args[0] from {} args has the wrong class"
+                                    .format(func_name))
+            if self.proto >= 4:
+                save(cls)
+                save(args)
+                save(kwargs)
+                write(NEWOBJ_EX)
+            else:
+                func = partial(cls.__new__, cls, *args, **kwargs)
+                save(func)
+                save(())
+                write(REDUCE)
+        elif self.proto >= 2 and func_name == "__newobj__":
+            # A __reduce__ implementation can direct protocol 2 or newer to
+            # use the more efficient NEWOBJ opcode, while still
+            # allowing protocol 0 and 1 to work normally.  For this to
+            # work, the function returned by __reduce__ should be
+            # called __newobj__, and its first argument should be a
+            # class.  The implementation for __newobj__
+            # should be as follows, although pickle has no way to
+            # verify this:
+            #
+            # def __newobj__(cls, *args):
+            #     return cls.__new__(cls, *args)
+            #
+            # Protocols 0 and 1 will pickle a reference to __newobj__,
+            # while protocol 2 (and above) will pickle a reference to
+            # cls, the remaining args tuple, and the NEWOBJ code,
+            # which calls cls.__new__(cls, *args) at unpickling time
+            # (see load_newobj below).  If __reduce__ returns a
+            # three-tuple, the state from the third tuple item will be
+            # pickled regardless of the protocol, calling __setstate__
+            # at unpickling time (see load_build below).
+            #
+            # Note that no standard __newobj__ implementation exists;
+            # you have to provide your own.  This is to enforce
+            # compatibility with Python 2.2 (pickles written using
+            # protocol 0 or 1 in Python 2.3 should be unpicklable by
+            # Python 2.2).
+            cls = args[0]
+            if not hasattr(cls, "__new__"):
+                raise PicklingError(
+                    "args[0] from __newobj__ args has no __new__")
+            if obj is not None and cls is not obj.__class__:
+                raise PicklingError(
+                    "args[0] from __newobj__ args has the wrong class")
+            args = args[1:]
+            save(cls)
+            save(args)
+            write(NEWOBJ)
+        else:
+            save(func)
+            save(args)
+            write(REDUCE)
+
+        if obj is not None:
+            # If the object is already in the memo, this means it is
+            # recursive. In this case, throw away everything we put on the
+            # stack, and fetch the object back from the memo.
+            if id(obj) in self.memo:
+                write(POP + self.get(self.memo[id(obj)][0]))
+            else:
+                self.memoize(obj)
+
+        # More new special cases (that work with older protocols as
+        # well): when __reduce__ returns a tuple with 4 or 5 items,
+        # the 4th and 5th item should be iterators that provide list
+        # items and dict items (as (key, value) tuples), or None.
+
+        if listitems is not None:
+            self._batch_appends(listitems)
+
+        if dictitems is not None:
+            self._batch_setitems(dictitems)
+
+        if state is not None:
+            if state_setter is None:
+                save(state)
+                write(BUILD)
+            else:
+                # If a state_setter is specified, call it instead of load_build
+                # to update obj's with its previous state.
+                # First, push state_setter and its tuple of expected arguments
+                # (obj, state) onto the stack.
+                save(state_setter)
+                save(obj)  # simple BINGET opcode as obj is already memoized.
+                save(state)
+                write(TUPLE2)
+                # Trigger a state_setter(obj, state) function call.
+                write(REDUCE)
+                # The purpose of state_setter is to carry-out an
+                # inplace modification of obj. We do not care about what the
+                # method might return, so its output is eventually removed from
+                # the stack.
+                write(POP)
+
+    # Methods below this point are dispatched through the dispatch table
+
+    dispatch = {}
+
+    def save_none(self, obj):
+        self.write(NONE)
+    dispatch[type(None)] = save_none
+
+    def save_bool(self, obj):
+        if self.proto >= 2:
+            self.write(NEWTRUE if obj else NEWFALSE)
+        else:
+            self.write(TRUE if obj else FALSE)
+    dispatch[bool] = save_bool
+
+    def save_long(self, obj):
+        if self.bin:
+            # If the int is small enough to fit in a signed 4-byte 2's-comp
+            # format, we can store it more efficiently than the general
+            # case.
+            # First one- and two-byte unsigned ints:
+            if obj >= 0:
+                if obj <= 0xff:
+                    self.write(BININT1 + pack("<B", obj))
+                    return
+                if obj <= 0xffff:
+                    self.write(BININT2 + pack("<H", obj))
+                    return
+            # Next check for 4-byte signed ints:
+            if -0x80000000 <= obj <= 0x7fffffff:
+                self.write(BININT + pack("<i", obj))
+                return
+        if self.proto >= 2:
+            encoded = encode_long(obj)
+            n = len(encoded)
+            if n < 256:
+                self.write(LONG1 + pack("<B", n) + encoded)
+            else:
+                self.write(LONG4 + pack("<i", n) + encoded)
+            return
+        if -0x80000000 <= obj <= 0x7fffffff:
+            self.write(INT + repr(obj).encode("ascii") + b'\n')
+        else:
+            self.write(LONG + repr(obj).encode("ascii") + b'L\n')
+    dispatch[int] = save_long
+
+    def save_float(self, obj):
+        if self.bin:
+            self.write(BINFLOAT + pack('>d', obj))
+        else:
+            self.write(FLOAT + repr(obj).encode("ascii") + b'\n')
+    dispatch[float] = save_float
+
+    def save_bytes(self, obj):
+        if self.proto < 3:
+            if not obj: # bytes object is empty
+                self.save_reduce(bytes, (), obj=obj)
+            else:
+                self.save_reduce(codecs.encode,
+                                 (str(obj, 'latin1'), 'latin1'), obj=obj)
+            return
+        n = len(obj)
+        if n <= 0xff:
+            self.write(SHORT_BINBYTES + pack("<B", n) + obj)
+        elif n > 0xffffffff and self.proto >= 4:
+            self._write_large_bytes(BINBYTES8 + pack("<Q", n), obj)
+        elif n >= self.framer._FRAME_SIZE_TARGET:
+            self._write_large_bytes(BINBYTES + pack("<I", n), obj)
+        else:
+            self.write(BINBYTES + pack("<I", n) + obj)
+        self.memoize(obj)
+    dispatch[bytes] = save_bytes
+
+    def save_bytearray(self, obj):
+        if self.proto < 5:
+            if not obj:  # bytearray is empty
+                self.save_reduce(bytearray, (), obj=obj)
+            else:
+                self.save_reduce(bytearray, (bytes(obj),), obj=obj)
+            return
+        n = len(obj)
+        if n >= self.framer._FRAME_SIZE_TARGET:
+            self._write_large_bytes(BYTEARRAY8 + pack("<Q", n), obj)
+        else:
+            self.write(BYTEARRAY8 + pack("<Q", n) + obj)
+        self.memoize(obj)
+    dispatch[bytearray] = save_bytearray
+
+    if _HAVE_PICKLE_BUFFER:
+        def save_picklebuffer(self, obj):
+            if self.proto < 5:
+                raise PicklingError("PickleBuffer can only pickled with "
+                                    "protocol >= 5")
+            with obj.raw() as m:
+                if not m.contiguous:
+                    raise PicklingError("PickleBuffer can not be pickled when "
+                                        "pointing to a non-contiguous buffer")
+                in_band = True
+                if self._buffer_callback is not None:
+                    in_band = bool(self._buffer_callback(obj))
+                if in_band:
+                    # Write data in-band
+                    # XXX The C implementation avoids a copy here
+                    if m.readonly:
+                        self.save_bytes(m.tobytes())
+                    else:
+                        self.save_bytearray(m.tobytes())
+                else:
+                    # Write data out-of-band
+                    self.write(NEXT_BUFFER)
+                    if m.readonly:
+                        self.write(READONLY_BUFFER)
+
+        dispatch[PickleBuffer] = save_picklebuffer
+
+    def save_str(self, obj):
+        if self.bin:
+            encoded = obj.encode('utf-8', 'surrogatepass')
+            n = len(encoded)
+            if n <= 0xff and self.proto >= 4:
+                self.write(SHORT_BINUNICODE + pack("<B", n) + encoded)
+            elif n > 0xffffffff and self.proto >= 4:
+                self._write_large_bytes(BINUNICODE8 + pack("<Q", n), encoded)
+            elif n >= self.framer._FRAME_SIZE_TARGET:
+                self._write_large_bytes(BINUNICODE + pack("<I", n), encoded)
+            else:
+                self.write(BINUNICODE + pack("<I", n) + encoded)
+        else:
+            obj = obj.replace("\\", "\\u005c")
+            obj = obj.replace("\0", "\\u0000")
+            obj = obj.replace("\n", "\\u000a")
+            obj = obj.replace("\r", "\\u000d")
+            obj = obj.replace("\x1a", "\\u001a")  # EOF on DOS
+            self.write(UNICODE + obj.encode('raw-unicode-escape') +
+                       b'\n')
+        self.memoize(obj)
+    dispatch[str] = save_str
+
+    def save_tuple(self, obj):
+        if not obj: # tuple is empty
+            if self.bin:
+                self.write(EMPTY_TUPLE)
+            else:
+                self.write(MARK + TUPLE)
+            return
+
+        n = len(obj)
+        save = self.save
+        memo = self.memo
+        if n <= 3 and self.proto >= 2:
+            for element in obj:
+                save(element)
+            # Subtle.  Same as in the big comment below.
+            if id(obj) in memo:
+                get = self.get(memo[id(obj)][0])
+                self.write(POP * n + get)
+            else:
+                self.write(_tuplesize2code[n])
+                self.memoize(obj)
+            return
+
+        # proto 0 or proto 1 and tuple isn't empty, or proto > 1 and tuple
+        # has more than 3 elements.
+        write = self.write
+        write(MARK)
+        for element in obj:
+            save(element)
+
+        if id(obj) in memo:
+            # Subtle.  d was not in memo when we entered save_tuple(), so
+            # the process of saving the tuple's elements must have saved
+            # the tuple itself:  the tuple is recursive.  The proper action
+            # now is to throw away everything we put on the stack, and
+            # simply GET the tuple (it's already constructed).  This check
+            # could have been done in the "for element" loop instead, but
+            # recursive tuples are a rare thing.
+            get = self.get(memo[id(obj)][0])
+            if self.bin:
+                write(POP_MARK + get)
+            else:   # proto 0 -- POP_MARK not available
+                write(POP * (n+1) + get)
+            return
+
+        # No recursion.
+        write(TUPLE)
+        self.memoize(obj)
+
+    dispatch[tuple] = save_tuple
+
+    def save_list(self, obj):
+        if self.bin:
+            self.write(EMPTY_LIST)
+        else:   # proto 0 -- can't use EMPTY_LIST
+            self.write(MARK + LIST)
+
+        self.memoize(obj)
+        self._batch_appends(obj)
+
+    dispatch[list] = save_list
+
+    _BATCHSIZE = 1000
+
+    def _batch_appends(self, items):
+        # Helper to batch up APPENDS sequences
+        save = self.save
+        write = self.write
+
+        if not self.bin:
+            for x in items:
+                save(x)
+                write(APPEND)
+            return
+
+        it = iter(items)
+        while True:
+            tmp = list(islice(it, self._BATCHSIZE))
+            n = len(tmp)
+            if n > 1:
+                write(MARK)
+                for x in tmp:
+                    save(x)
+                write(APPENDS)
+            elif n:
+                save(tmp[0])
+                write(APPEND)
+            # else tmp is empty, and we're done
+            if n < self._BATCHSIZE:
+                return
+
+    def save_dict(self, obj):
+        if self.bin:
+            self.write(EMPTY_DICT)
+        else:   # proto 0 -- can't use EMPTY_DICT
+            self.write(MARK + DICT)
+
+        self.memoize(obj)
+        self._batch_setitems(obj.items())
+
+    dispatch[dict] = save_dict
+    if PyStringMap is not None:
+        dispatch[PyStringMap] = save_dict
+
+    def _batch_setitems(self, items):
+        # Helper to batch up SETITEMS sequences; proto >= 1 only
+        save = self.save
+        write = self.write
+
+        if not self.bin:
+            for k, v in items:
+                save(k)
+                save(v)
+                write(SETITEM)
+            return
+
+        it = iter(items)
+        while True:
+            tmp = list(islice(it, self._BATCHSIZE))
+            n = len(tmp)
+            if n > 1:
+                write(MARK)
+                for k, v in tmp:
+                    save(k)
+                    save(v)
+                write(SETITEMS)
+            elif n:
+                k, v = tmp[0]
+                save(k)
+                save(v)
+                write(SETITEM)
+            # else tmp is empty, and we're done
+            if n < self._BATCHSIZE:
+                return
+
+    def save_set(self, obj):
+        save = self.save
+        write = self.write
+
+        if self.proto < 4:
+            self.save_reduce(set, (list(obj),), obj=obj)
+            return
+
+        write(EMPTY_SET)
+        self.memoize(obj)
+
+        it = iter(obj)
+        while True:
+            batch = list(islice(it, self._BATCHSIZE))
+            n = len(batch)
+            if n > 0:
+                write(MARK)
+                for item in batch:
+                    save(item)
+                write(ADDITEMS)
+            if n < self._BATCHSIZE:
+                return
+    dispatch[set] = save_set
+
+    def save_frozenset(self, obj):
+        save = self.save
+        write = self.write
+
+        if self.proto < 4:
+            self.save_reduce(frozenset, (list(obj),), obj=obj)
+            return
+
+        write(MARK)
+        for item in obj:
+            save(item)
+
+        if id(obj) in self.memo:
+            # If the object is already in the memo, this means it is
+            # recursive. In this case, throw away everything we put on the
+            # stack, and fetch the object back from the memo.
+            write(POP_MARK + self.get(self.memo[id(obj)][0]))
+            return
+
+        write(FROZENSET)
+        self.memoize(obj)
+    dispatch[frozenset] = save_frozenset
+
+    def save_global(self, obj, name=None):
+        write = self.write
+        memo = self.memo
+
+        if name is None:
+            name = getattr(obj, '__qualname__', None)
+        if name is None:
+            name = obj.__name__
+
+        module_name = whichmodule(obj, name)
+        try:
+            __import__(module_name, level=0)
+            module = sys.modules[module_name]
+            obj2, parent = _getattribute(module, name)
+        except (ImportError, KeyError, AttributeError):
+            raise PicklingError(
+                "Can't pickle %r: it's not found as %s.%s" %
+                (obj, module_name, name)) from None
+        else:
+            if obj2 is not obj:
+                raise PicklingError(
+                    "Can't pickle %r: it's not the same object as %s.%s" %
+                    (obj, module_name, name))
+
+        if self.proto >= 2:
+            code = _extension_registry.get((module_name, name))
+            if code:
+                assert code > 0
+                if code <= 0xff:
+                    write(EXT1 + pack("<B", code))
+                elif code <= 0xffff:
+                    write(EXT2 + pack("<H", code))
+                else:
+                    write(EXT4 + pack("<i", code))
+                return
+        lastname = name.rpartition('.')[2]
+        if parent is module:
+            name = lastname
+        # Non-ASCII identifiers are supported only with protocols >= 3.
+        if self.proto >= 4:
+            self.save(module_name)
+            self.save(name)
+            write(STACK_GLOBAL)
+        elif parent is not module:
+            self.save_reduce(getattr, (parent, lastname))
+        elif self.proto >= 3:
+            write(GLOBAL + bytes(module_name, "utf-8") + b'\n' +
+                  bytes(name, "utf-8") + b'\n')
+        else:
+            if self.fix_imports:
+                r_name_mapping = _compat_pickle.REVERSE_NAME_MAPPING
+                r_import_mapping = _compat_pickle.REVERSE_IMPORT_MAPPING
+                if (module_name, name) in r_name_mapping:
+                    module_name, name = r_name_mapping[(module_name, name)]
+                elif module_name in r_import_mapping:
+                    module_name = r_import_mapping[module_name]
+            try:
+                write(GLOBAL + bytes(module_name, "ascii") + b'\n' +
+                      bytes(name, "ascii") + b'\n')
+            except UnicodeEncodeError:
+                raise PicklingError(
+                    "can't pickle global identifier '%s.%s' using "
+                    "pickle protocol %i" % (module, name, self.proto)) from None
+
+        self.memoize(obj)
+
+    def save_type(self, obj):
+        if obj is type(None):
+            return self.save_reduce(type, (None,), obj=obj)
+        elif obj is type(NotImplemented):
+            return self.save_reduce(type, (NotImplemented,), obj=obj)
+        elif obj is type(...):
+            return self.save_reduce(type, (...,), obj=obj)
+        return self.save_global(obj)
+
+    dispatch[FunctionType] = save_global
+    dispatch[type] = save_type
+
+
+# Unpickling machinery
+
+class _Unpickler:
+
+    def __init__(self, file, *, fix_imports=True,
+                 encoding="ASCII", errors="strict", buffers=None):
+        """This takes a binary file for reading a pickle data stream.
+
+        The protocol version of the pickle is detected automatically, so
+        no proto argument is needed.
+
+        The argument *file* must have two methods, a read() method that
+        takes an integer argument, and a readline() method that requires
+        no arguments.  Both methods should return bytes.  Thus *file*
+        can be a binary file object opened for reading, an io.BytesIO
+        object, or any other custom object that meets this interface.
+
+        The file-like object must have two methods, a read() method
+        that takes an integer argument, and a readline() method that
+        requires no arguments.  Both methods should return bytes.
+        Thus file-like object can be a binary file object opened for
+        reading, a BytesIO object, or any other custom object that
+        meets this interface.
+
+        If *buffers* is not None, it should be an iterable of buffer-enabled
+        objects that is consumed each time the pickle stream references
+        an out-of-band buffer view.  Such buffers have been given in order
+        to the *buffer_callback* of a Pickler object.
+
+        If *buffers* is None (the default), then the buffers are taken
+        from the pickle stream, assuming they are serialized there.
+        It is an error for *buffers* to be None if the pickle stream
+        was produced with a non-None *buffer_callback*.
+
+        Other optional arguments are *fix_imports*, *encoding* and
+        *errors*, which are used to control compatibility support for
+        pickle stream generated by Python 2.  If *fix_imports* is True,
+        pickle will try to map the old Python 2 names to the new names
+        used in Python 3.  The *encoding* and *errors* tell pickle how
+        to decode 8-bit string instances pickled by Python 2; these
+        default to 'ASCII' and 'strict', respectively. *encoding* can be
+        'bytes' to read these 8-bit string instances as bytes objects.
+        """
+        self._buffers = iter(buffers) if buffers is not None else None
+        self._file_readline = file.readline
+        self._file_read = file.read
+        self.memo = {}
+        self.encoding = encoding
+        self.errors = errors
+        self.proto = 0
+        self.fix_imports = fix_imports
+
+    def load(self):
+        """Read a pickled object representation from the open file.
+
+        Return the reconstituted object hierarchy specified in the file.
+        """
+        # Check whether Unpickler was initialized correctly. This is
+        # only needed to mimic the behavior of _pickle.Unpickler.dump().
+        if not hasattr(self, "_file_read"):
+            raise UnpicklingError("Unpickler.__init__() was not called by "
+                                  "%s.__init__()" % (self.__class__.__name__,))
+        self._unframer = _Unframer(self._file_read, self._file_readline)
+        self.read = self._unframer.read
+        self.readinto = self._unframer.readinto
+        self.readline = self._unframer.readline
+        self.metastack = []
+        self.stack = []
+        self.append = self.stack.append
+        self.proto = 0
+        read = self.read
+        dispatch = self.dispatch
+        try:
+            while True:
+                key = read(1)
+                if not key:
+                    raise EOFError
+                assert isinstance(key, bytes_types)
+                dispatch[key[0]](self)
+        except _Stop as stopinst:
+            return stopinst.value
+
+    # Return a list of items pushed in the stack after last MARK instruction.
+    def pop_mark(self):
+        items = self.stack
+        self.stack = self.metastack.pop()
+        self.append = self.stack.append
+        return items
+
+    def persistent_load(self, pid):
+        raise UnpicklingError("unsupported persistent id encountered")
+
+    dispatch = {}
+
+    def load_proto(self):
+        proto = self.read(1)[0]
+        if not 0 <= proto <= HIGHEST_PROTOCOL:
+            raise ValueError("unsupported pickle protocol: %d" % proto)
+        self.proto = proto
+    dispatch[PROTO[0]] = load_proto
+
+    def load_frame(self):
+        frame_size, = unpack('<Q', self.read(8))
+        if frame_size > sys.maxsize:
+            raise ValueError("frame size > sys.maxsize: %d" % frame_size)
+        self._unframer.load_frame(frame_size)
+    dispatch[FRAME[0]] = load_frame
+
+    def load_persid(self):
+        try:
+            pid = self.readline()[:-1].decode("ascii")
+        except UnicodeDecodeError:
+            raise UnpicklingError(
+                "persistent IDs in protocol 0 must be ASCII strings")
+        self.append(self.persistent_load(pid))
+    dispatch[PERSID[0]] = load_persid
+
+    def load_binpersid(self):
+        pid = self.stack.pop()
+        self.append(self.persistent_load(pid))
+    dispatch[BINPERSID[0]] = load_binpersid
+
+    def load_none(self):
+        self.append(None)
+    dispatch[NONE[0]] = load_none
+
+    def load_false(self):
+        self.append(False)
+    dispatch[NEWFALSE[0]] = load_false
+
+    def load_true(self):
+        self.append(True)
+    dispatch[NEWTRUE[0]] = load_true
+
+    def load_int(self):
+        data = self.readline()
+        if data == FALSE[1:]:
+            val = False
+        elif data == TRUE[1:]:
+            val = True
+        else:
+            val = int(data, 0)
+        self.append(val)
+    dispatch[INT[0]] = load_int
+
+    def load_binint(self):
+        self.append(unpack('<i', self.read(4))[0])
+    dispatch[BININT[0]] = load_binint
+
+    def load_binint1(self):
+        self.append(self.read(1)[0])
+    dispatch[BININT1[0]] = load_binint1
+
+    def load_binint2(self):
+        self.append(unpack('<H', self.read(2))[0])
+    dispatch[BININT2[0]] = load_binint2
+
+    def load_long(self):
+        val = self.readline()[:-1]
+        if val and val[-1] == b'L'[0]:
+            val = val[:-1]
+        self.append(int(val, 0))
+    dispatch[LONG[0]] = load_long
+
+    def load_long1(self):
+        n = self.read(1)[0]
+        data = self.read(n)
+        self.append(decode_long(data))
+    dispatch[LONG1[0]] = load_long1
+
+    def load_long4(self):
+        n, = unpack('<i', self.read(4))
+        if n < 0:
+            # Corrupt or hostile pickle -- we never write one like this
+            raise UnpicklingError("LONG pickle has negative byte count")
+        data = self.read(n)
+        self.append(decode_long(data))
+    dispatch[LONG4[0]] = load_long4
+
+    def load_float(self):
+        self.append(float(self.readline()[:-1]))
+    dispatch[FLOAT[0]] = load_float
+
+    def load_binfloat(self):
+        self.append(unpack('>d', self.read(8))[0])
+    dispatch[BINFLOAT[0]] = load_binfloat
+
+    def _decode_string(self, value):
+        # Used to allow strings from Python 2 to be decoded either as
+        # bytes or Unicode strings.  This should be used only with the
+        # STRING, BINSTRING and SHORT_BINSTRING opcodes.
+        if self.encoding == "bytes":
+            return value
+        else:
+            return value.decode(self.encoding, self.errors)
+
+    def load_string(self):
+        data = self.readline()[:-1]
+        # Strip outermost quotes
+        if len(data) >= 2 and data[0] == data[-1] and data[0] in b'"\'':
+            data = data[1:-1]
+        else:
+            raise UnpicklingError("the STRING opcode argument must be quoted")
+        self.append(self._decode_string(codecs.escape_decode(data)[0]))
+    dispatch[STRING[0]] = load_string
+
+    def load_binstring(self):
+        # Deprecated BINSTRING uses signed 32-bit length
+        len, = unpack('<i', self.read(4))
+        if len < 0:
+            raise UnpicklingError("BINSTRING pickle has negative byte count")
+        data = self.read(len)
+        self.append(self._decode_string(data))
+    dispatch[BINSTRING[0]] = load_binstring
+
+    def load_binbytes(self):
+        len, = unpack('<I', self.read(4))
+        if len > maxsize:
+            raise UnpicklingError("BINBYTES exceeds system's maximum size "
+                                  "of %d bytes" % maxsize)
+        self.append(self.read(len))
+    dispatch[BINBYTES[0]] = load_binbytes
+
+    def load_unicode(self):
+        self.append(str(self.readline()[:-1], 'raw-unicode-escape'))
+    dispatch[UNICODE[0]] = load_unicode
+
+    def load_binunicode(self):
+        len, = unpack('<I', self.read(4))
+        if len > maxsize:
+            raise UnpicklingError("BINUNICODE exceeds system's maximum size "
+                                  "of %d bytes" % maxsize)
+        self.append(str(self.read(len), 'utf-8', 'surrogatepass'))
+    dispatch[BINUNICODE[0]] = load_binunicode
+
+    def load_binunicode8(self):
+        len, = unpack('<Q', self.read(8))
+        if len > maxsize:
+            raise UnpicklingError("BINUNICODE8 exceeds system's maximum size "
+                                  "of %d bytes" % maxsize)
+        self.append(str(self.read(len), 'utf-8', 'surrogatepass'))
+    dispatch[BINUNICODE8[0]] = load_binunicode8
+
+    def load_binbytes8(self):
+        len, = unpack('<Q', self.read(8))
+        if len > maxsize:
+            raise UnpicklingError("BINBYTES8 exceeds system's maximum size "
+                                  "of %d bytes" % maxsize)
+        self.append(self.read(len))
+    dispatch[BINBYTES8[0]] = load_binbytes8
+
+    def load_bytearray8(self):
+        len, = unpack('<Q', self.read(8))
+        if len > maxsize:
+            raise UnpicklingError("BYTEARRAY8 exceeds system's maximum size "
+                                  "of %d bytes" % maxsize)
+        b = bytearray(len)
+        self.readinto(b)
+        self.append(b)
+    dispatch[BYTEARRAY8[0]] = load_bytearray8
+
+    def load_next_buffer(self):
+        if self._buffers is None:
+            raise UnpicklingError("pickle stream refers to out-of-band data "
+                                  "but no *buffers* argument was given")
+        try:
+            buf = next(self._buffers)
+        except StopIteration:
+            raise UnpicklingError("not enough out-of-band buffers")
+        self.append(buf)
+    dispatch[NEXT_BUFFER[0]] = load_next_buffer
+
+    def load_readonly_buffer(self):
+        buf = self.stack[-1]
+        with memoryview(buf) as m:
+            if not m.readonly:
+                self.stack[-1] = m.toreadonly()
+    dispatch[READONLY_BUFFER[0]] = load_readonly_buffer
+
+    def load_short_binstring(self):
+        len = self.read(1)[0]
+        data = self.read(len)
+        self.append(self._decode_string(data))
+    dispatch[SHORT_BINSTRING[0]] = load_short_binstring
+
+    def load_short_binbytes(self):
+        len = self.read(1)[0]
+        self.append(self.read(len))
+    dispatch[SHORT_BINBYTES[0]] = load_short_binbytes
+
+    def load_short_binunicode(self):
+        len = self.read(1)[0]
+        self.append(str(self.read(len), 'utf-8', 'surrogatepass'))
+    dispatch[SHORT_BINUNICODE[0]] = load_short_binunicode
+
+    def load_tuple(self):
+        items = self.pop_mark()
+        self.append(tuple(items))
+    dispatch[TUPLE[0]] = load_tuple
+
+    def load_empty_tuple(self):
+        self.append(())
+    dispatch[EMPTY_TUPLE[0]] = load_empty_tuple
+
+    def load_tuple1(self):
+        self.stack[-1] = (self.stack[-1],)
+    dispatch[TUPLE1[0]] = load_tuple1
+
+    def load_tuple2(self):
+        self.stack[-2:] = [(self.stack[-2], self.stack[-1])]
+    dispatch[TUPLE2[0]] = load_tuple2
+
+    def load_tuple3(self):
+        self.stack[-3:] = [(self.stack[-3], self.stack[-2], self.stack[-1])]
+    dispatch[TUPLE3[0]] = load_tuple3
+
+    def load_empty_list(self):
+        self.append([])
+    dispatch[EMPTY_LIST[0]] = load_empty_list
+
+    def load_empty_dictionary(self):
+        self.append({})
+    dispatch[EMPTY_DICT[0]] = load_empty_dictionary
+
+    def load_empty_set(self):
+        self.append(set())
+    dispatch[EMPTY_SET[0]] = load_empty_set
+
+    def load_frozenset(self):
+        items = self.pop_mark()
+        self.append(frozenset(items))
+    dispatch[FROZENSET[0]] = load_frozenset
+
+    def load_list(self):
+        items = self.pop_mark()
+        self.append(items)
+    dispatch[LIST[0]] = load_list
+
+    def load_dict(self):
+        items = self.pop_mark()
+        d = {items[i]: items[i+1]
+             for i in range(0, len(items), 2)}
+        self.append(d)
+    dispatch[DICT[0]] = load_dict
+
+    # INST and OBJ differ only in how they get a class object.  It's not
+    # only sensible to do the rest in a common routine, the two routines
+    # previously diverged and grew different bugs.
+    # klass is the class to instantiate, and k points to the topmost mark
+    # object, following which are the arguments for klass.__init__.
+    def _instantiate(self, klass, args):
+        if (args or not isinstance(klass, type) or
+            hasattr(klass, "__getinitargs__")):
+            try:
+                value = klass(*args)
+            except TypeError as err:
+                raise TypeError("in constructor for %s: %s" %
+                                (klass.__name__, str(err)), sys.exc_info()[2])
+        else:
+            value = klass.__new__(klass)
+        self.append(value)
+
+    def load_inst(self):
+        module = self.readline()[:-1].decode("ascii")
+        name = self.readline()[:-1].decode("ascii")
+        klass = self.find_class(module, name)
+        self._instantiate(klass, self.pop_mark())
+    dispatch[INST[0]] = load_inst
+
+    def load_obj(self):
+        # Stack is ... markobject classobject arg1 arg2 ...
+        args = self.pop_mark()
+        cls = args.pop(0)
+        self._instantiate(cls, args)
+    dispatch[OBJ[0]] = load_obj
+
+    def load_newobj(self):
+        args = self.stack.pop()
+        cls = self.stack.pop()
+        obj = cls.__new__(cls, *args)
+        self.append(obj)
+    dispatch[NEWOBJ[0]] = load_newobj
+
+    def load_newobj_ex(self):
+        kwargs = self.stack.pop()
+        args = self.stack.pop()
+        cls = self.stack.pop()
+        obj = cls.__new__(cls, *args, **kwargs)
+        self.append(obj)
+    dispatch[NEWOBJ_EX[0]] = load_newobj_ex
+
+    def load_global(self):
+        module = self.readline()[:-1].decode("utf-8")
+        name = self.readline()[:-1].decode("utf-8")
+        klass = self.find_class(module, name)
+        self.append(klass)
+    dispatch[GLOBAL[0]] = load_global
+
+    def load_stack_global(self):
+        name = self.stack.pop()
+        module = self.stack.pop()
+        if type(name) is not str or type(module) is not str:
+            raise UnpicklingError("STACK_GLOBAL requires str")
+        self.append(self.find_class(module, name))
+    dispatch[STACK_GLOBAL[0]] = load_stack_global
+
+    def load_ext1(self):
+        code = self.read(1)[0]
+        self.get_extension(code)
+    dispatch[EXT1[0]] = load_ext1
+
+    def load_ext2(self):
+        code, = unpack('<H', self.read(2))
+        self.get_extension(code)
+    dispatch[EXT2[0]] = load_ext2
+
+    def load_ext4(self):
+        code, = unpack('<i', self.read(4))
+        self.get_extension(code)
+    dispatch[EXT4[0]] = load_ext4
+
+    def get_extension(self, code):
+        nil = []
+        obj = _extension_cache.get(code, nil)
+        if obj is not nil:
+            self.append(obj)
+            return
+        key = _inverted_registry.get(code)
+        if not key:
+            if code <= 0: # note that 0 is forbidden
+                # Corrupt or hostile pickle.
+                raise UnpicklingError("EXT specifies code <= 0")
+            raise ValueError("unregistered extension code %d" % code)
+        obj = self.find_class(*key)
+        _extension_cache[code] = obj
+        self.append(obj)
+
+    def find_class(self, module, name):
+        # Subclasses may override this.
+        sys.audit('pickle.find_class', module, name)
+        if self.proto < 3 and self.fix_imports:
+            if (module, name) in _compat_pickle.NAME_MAPPING:
+                module, name = _compat_pickle.NAME_MAPPING[(module, name)]
+            elif module in _compat_pickle.IMPORT_MAPPING:
+                module = _compat_pickle.IMPORT_MAPPING[module]
+        __import__(module, level=0)
+        if self.proto >= 4:
+            return _getattribute(sys.modules[module], name)[0]
+        else:
+            return getattr(sys.modules[module], name)
+
+    def load_reduce(self):
+        stack = self.stack
+        args = stack.pop()
+        func = stack[-1]
+        stack[-1] = func(*args)
+    dispatch[REDUCE[0]] = load_reduce
+
+    def load_pop(self):
+        if self.stack:
+            del self.stack[-1]
+        else:
+            self.pop_mark()
+    dispatch[POP[0]] = load_pop
+
+    def load_pop_mark(self):
+        self.pop_mark()
+    dispatch[POP_MARK[0]] = load_pop_mark
+
+    def load_dup(self):
+        self.append(self.stack[-1])
+    dispatch[DUP[0]] = load_dup
+
+    def load_get(self):
+        i = int(self.readline()[:-1])
+        try:
+            self.append(self.memo[i])
+        except KeyError:
+            msg = f'Memo value not found at index {i}'
+            raise UnpicklingError(msg) from None
+    dispatch[GET[0]] = load_get
+
+    def load_binget(self):
+        i = self.read(1)[0]
+        try:
+            self.append(self.memo[i])
+        except KeyError as exc:
+            msg = f'Memo value not found at index {i}'
+            raise UnpicklingError(msg) from None
+    dispatch[BINGET[0]] = load_binget
+
+    def load_long_binget(self):
+        i, = unpack('<I', self.read(4))
+        try:
+            self.append(self.memo[i])
+        except KeyError as exc:
+            msg = f'Memo value not found at index {i}'
+            raise UnpicklingError(msg) from None
+    dispatch[LONG_BINGET[0]] = load_long_binget
+
+    def load_put(self):
+        i = int(self.readline()[:-1])
+        if i < 0:
+            raise ValueError("negative PUT argument")
+        self.memo[i] = self.stack[-1]
+    dispatch[PUT[0]] = load_put
+
+    def load_binput(self):
+        i = self.read(1)[0]
+        if i < 0:
+            raise ValueError("negative BINPUT argument")
+        self.memo[i] = self.stack[-1]
+    dispatch[BINPUT[0]] = load_binput
+
+    def load_long_binput(self):
+        i, = unpack('<I', self.read(4))
+        if i > maxsize:
+            raise ValueError("negative LONG_BINPUT argument")
+        self.memo[i] = self.stack[-1]
+    dispatch[LONG_BINPUT[0]] = load_long_binput
+
+    def load_memoize(self):
+        memo = self.memo
+        memo[len(memo)] = self.stack[-1]
+    dispatch[MEMOIZE[0]] = load_memoize
+
+    def load_append(self):
+        stack = self.stack
+        value = stack.pop()
+        list = stack[-1]
+        list.append(value)
+    dispatch[APPEND[0]] = load_append
+
+    def load_appends(self):
+        items = self.pop_mark()
+        list_obj = self.stack[-1]
+        try:
+            extend = list_obj.extend
+        except AttributeError:
+            pass
+        else:
+            extend(items)
+            return
+        # Even if the PEP 307 requires extend() and append() methods,
+        # fall back on append() if the object has no extend() method
+        # for backward compatibility.
+        append = list_obj.append
+        for item in items:
+            append(item)
+    dispatch[APPENDS[0]] = load_appends
+
+    def load_setitem(self):
+        stack = self.stack
+        value = stack.pop()
+        key = stack.pop()
+        dict = stack[-1]
+        dict[key] = value
+    dispatch[SETITEM[0]] = load_setitem
+
+    def load_setitems(self):
+        items = self.pop_mark()
+        dict = self.stack[-1]
+        for i in range(0, len(items), 2):
+            dict[items[i]] = items[i + 1]
+    dispatch[SETITEMS[0]] = load_setitems
+
+    def load_additems(self):
+        items = self.pop_mark()
+        set_obj = self.stack[-1]
+        if isinstance(set_obj, set):
+            set_obj.update(items)
+        else:
+            add = set_obj.add
+            for item in items:
+                add(item)
+    dispatch[ADDITEMS[0]] = load_additems
+
+    def load_build(self):
+        stack = self.stack
+        state = stack.pop()
+        inst = stack[-1]
+        setstate = getattr(inst, "__setstate__", None)
+        if setstate is not None:
+            setstate(state)
+            return
+        slotstate = None
+        if isinstance(state, tuple) and len(state) == 2:
+            state, slotstate = state
+        if state:
+            inst_dict = inst.__dict__
+            intern = sys.intern
+            for k, v in state.items():
+                if type(k) is str:
+                    inst_dict[intern(k)] = v
+                else:
+                    inst_dict[k] = v
+        if slotstate:
+            for k, v in slotstate.items():
+                setattr(inst, k, v)
+    dispatch[BUILD[0]] = load_build
+
+    def load_mark(self):
+        self.metastack.append(self.stack)
+        self.stack = []
+        self.append = self.stack.append
+    dispatch[MARK[0]] = load_mark
+
+    def load_stop(self):
+        value = self.stack.pop()
+        raise _Stop(value)
+    dispatch[STOP[0]] = load_stop
+
+
+# Shorthands
+
+def _dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None):
+    _Pickler(file, protocol, fix_imports=fix_imports,
+             buffer_callback=buffer_callback).dump(obj)
+
+def _dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None):
+    f = io.BytesIO()
+    _Pickler(f, protocol, fix_imports=fix_imports,
+             buffer_callback=buffer_callback).dump(obj)
+    res = f.getvalue()
+    assert isinstance(res, bytes_types)
+    return res
+
+def _load(file, *, fix_imports=True, encoding="ASCII", errors="strict",
+          buffers=None):
+    return _Unpickler(file, fix_imports=fix_imports, buffers=buffers,
+                     encoding=encoding, errors=errors).load()
+
+def _loads(s, /, *, fix_imports=True, encoding="ASCII", errors="strict",
+           buffers=None):
+    if isinstance(s, str):
+        raise TypeError("Can't load pickle from unicode string")
+    file = io.BytesIO(s)
+    return _Unpickler(file, fix_imports=fix_imports, buffers=buffers,
+                      encoding=encoding, errors=errors).load()
+
+# Use the faster _pickle if possible
+try:
+    from _pickle import (
+        PickleError,
+        PicklingError,
+        UnpicklingError,
+        Pickler,
+        Unpickler,
+        dump,
+        dumps,
+        load,
+        loads
+    )
+except ImportError:
+    Pickler, Unpickler = _Pickler, _Unpickler
+    dump, dumps, load, loads = _dump, _dumps, _load, _loads
+
+# Doctest
+def _test():
+    import doctest
+    return doctest.testmod()
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(
+        description='display contents of the pickle files')
+    parser.add_argument(
+        'pickle_file', type=argparse.FileType('br'),
+        nargs='*', help='the pickle file')
+    parser.add_argument(
+        '-t', '--test', action='store_true',
+        help='run self-test suite')
+    parser.add_argument(
+        '-v', action='store_true',
+        help='run verbosely; only affects self-test run')
+    args = parser.parse_args()
+    if args.test:
+        _test()
+    else:
+        if not args.pickle_file:
+            parser.print_help()
+        else:
+            import pprint
+            for f in args.pickle_file:
+                obj = load(f)
+                pprint.pprint(obj)
diff --git a/shared/package-files/f2/f2a6ad2010e46c021b0b5dfb8c0e7344f54ef4f46021ef2b0d4c1a71e3915576/imp.pyi b/shared/package-files/f2/f2a6ad2010e46c021b0b5dfb8c0e7344f54ef4f46021ef2b0d4c1a71e3915576/imp.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3cd37648b9683a6b3c23828f38626d6286bc5124
--- /dev/null
+++ b/shared/package-files/f2/f2a6ad2010e46c021b0b5dfb8c0e7344f54ef4f46021ef2b0d4c1a71e3915576/imp.pyi
@@ -0,0 +1,33 @@
+import types
+from typing import IO, Any, Iterable, List, Optional, Tuple
+
+C_BUILTIN: int
+C_EXTENSION: int
+IMP_HOOK: int
+PKG_DIRECTORY: int
+PY_CODERESOURCE: int
+PY_COMPILED: int
+PY_FROZEN: int
+PY_RESOURCE: int
+PY_SOURCE: int
+SEARCH_ERROR: int
+
+def acquire_lock() -> None: ...
+def find_module(name: str, path: Iterable[str] = ...) -> Optional[Tuple[IO[Any], str, Tuple[str, str, int]]]: ...
+def get_magic() -> str: ...
+def get_suffixes() -> List[Tuple[str, str, int]]: ...
+def init_builtin(name: str) -> types.ModuleType: ...
+def init_frozen(name: str) -> types.ModuleType: ...
+def is_builtin(name: str) -> int: ...
+def is_frozen(name: str) -> bool: ...
+def load_compiled(name: str, pathname: str, file: IO[Any] = ...) -> types.ModuleType: ...
+def load_dynamic(name: str, pathname: str, file: IO[Any] = ...) -> types.ModuleType: ...
+def load_module(name: str, file: str, pathname: str, description: Tuple[str, str, int]) -> types.ModuleType: ...
+def load_source(name: str, pathname: str, file: IO[Any] = ...) -> types.ModuleType: ...
+def lock_held() -> bool: ...
+def new_module(name: str) -> types.ModuleType: ...
+def release_lock() -> None: ...
+
+class NullImporter:
+    def __init__(self, path_string: str) -> None: ...
+    def find_module(self, fullname: str, path: str = ...) -> None: ...
diff --git a/shared/package-files/f2/f2adf318d01f461f3f24b697607e538abb789fa58dfbe3aa33256bc29cfe5f8d/interpreter.py b/shared/package-files/f2/f2adf318d01f461f3f24b697607e538abb789fa58dfbe3aa33256bc29cfe5f8d/interpreter.py
new file mode 100644
index 0000000000000000000000000000000000000000..47a6f5a5bfc9135cb4adbc468ebf60ac5f655925
--- /dev/null
+++ b/shared/package-files/f2/f2adf318d01f461f3f24b697607e538abb789fa58dfbe3aa33256bc29cfe5f8d/interpreter.py
@@ -0,0 +1,512 @@
+from .graph_module import GraphModule
+from ._lazy_graph_module import _make_graph_module
+from .graph import Graph
+from .node import Argument, Node, Target, map_arg, map_aggregate
+from .proxy import Proxy
+from ._symbolic_trace import Tracer
+from ._compatibility import compatibility
+from . import config
+import torch.fx.traceback as fx_traceback
+import torch
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+import inspect
+from contextlib import contextmanager
+from torch.hub import tqdm
+
+__all__ = ['Interpreter', 'Transformer']
+
+@compatibility(is_backward_compatible=True)
+class Interpreter:
+    """
+    An Interpreter executes an FX graph Node-by-Node. This pattern
+    can be useful for many things, including writing code
+    transformations as well as analysis passes.
+
+    Methods in the Interpreter class can be overridden to customize
+    the behavior of execution. The map of overrideable methods
+    in terms of call hierarchy::
+
+        run()
+            +-- run_node
+                +-- placeholder()
+                +-- get_attr()
+                +-- call_function()
+                +-- call_method()
+                +-- call_module()
+                +-- output()
+
+    Example:
+
+        Suppose we want to swap all instances of ``torch.neg`` with
+        ``torch.sigmoid`` and vice versa (including their ``Tensor``
+        method equivalents). We could subclass Interpreter like so::
+
+            class NegSigmSwapInterpreter(Interpreter):
+                def call_function(self, target : Target,
+                                  args : Tuple, kwargs : Dict) -> Any:
+                    if target == torch.sigmoid:
+                        return torch.neg(*args, **kwargs)
+                    return super().call_function(n)
+
+                def call_method(self, target : Target,
+                                args : Tuple, kwargs : Dict) -> Any:
+                    if target == 'neg':
+                        call_self, *args_tail = args
+                        return call_self.sigmoid(*args_tail, **kwargs)
+                    return super().call_method(n)
+
+            def fn(x):
+                return torch.sigmoid(x).neg()
+
+            gm = torch.fx.symbolic_trace(fn)
+            input = torch.randn(3, 4)
+            result = NegSigmSwapInterpreter(gm).run(input)
+            torch.testing.assert_close(result, torch.neg(input).sigmoid())
+
+    Args:
+        module (torch.nn.Module): The module to be executed
+        garbage_collect_values (bool): Whether to delete values after their last
+            use within the Module's execution. This ensures optimal memory usage during
+            execution. This can be disabled to, for example, examine all of the intermediate
+            values in the execution by looking at the ``Interpreter.env`` attribute.
+        graph (Optional[Graph]): If passed, the interpreter will execute this
+            graph instead of `module.graph`, using the provided `module`
+            argument to satisfy any requests for state.
+    """
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, module: torch.nn.Module, garbage_collect_values: bool = True, graph: Optional[Graph] = None):
+        self.module = module
+        self.submodules = dict(self.module.named_modules())
+        if graph is not None:
+            self.graph = graph
+        else:
+            self.graph = self.module.graph
+        self.env : Dict[Node, Any] = {}
+        self.name = "Interpreter"
+        self.garbage_collect_values = garbage_collect_values
+        self.extra_traceback = True
+
+        if self.garbage_collect_values:
+            # Run through reverse nodes and record the first instance of a use
+            # of a given node. This represents the *last* use of the node in the
+            # execution order of the program, which we will use to free unused
+            # values
+            node_to_last_use : Dict[Node, Node] = {}
+            self.user_to_last_uses : Dict[Node, List[Node]] = {}
+
+            def register_last_uses(n : Node, user : Node):
+                if n not in node_to_last_use:
+                    node_to_last_use[n] = user
+                    self.user_to_last_uses.setdefault(user, []).append(n)
+
+            for node in reversed(self.graph.nodes):
+                map_arg(node.args, lambda n: register_last_uses(n, node))
+                map_arg(node.kwargs, lambda n: register_last_uses(n, node))
+
+    @compatibility(is_backward_compatible=True)
+    def run(self, *args, initial_env : Optional[Dict[Node, Any]] = None, enable_io_processing : bool = True) -> Any:
+        """
+        Run `module` via interpretation and return the result.
+
+        Args:
+            *args: The arguments to the Module to run, in positional order
+            initial_env (Optional[Dict[Node, Any]]): An optional starting environment for execution.
+                This is a dict mapping `Node` to any value. This can be used, for example, to
+                pre-populate results for certain `Nodes` so as to do only partial evaluation within
+                the interpreter.
+            enable_io_processing (bool): If true, we process the inputs and outputs with graph's process_inputs and
+                process_outputs function first before using them.
+
+        Returns:
+            Any: The value returned from executing the Module
+        """
+        self.env = initial_env if initial_env is not None else {}
+
+        # Positional function args are consumed left-to-right by
+        # `placeholder` nodes. Use an iterator to keep track of
+        # position and extract those values.
+        if enable_io_processing:
+            args = self.graph.process_inputs(*args)
+        self.args_iter : Iterator[Any] = iter(args)
+        pbar = tqdm(total=len(self.graph.nodes),
+                    desc=f"{self.name}: {str(list(self.graph.nodes)) if config.verbose_progress else ''}",
+                    initial=0, position=0, leave=True, disable=config.disable_progress, delay=0)
+
+        for node in self.graph.nodes:
+            pbar.update(1)
+            if node in self.env:
+                # Short circuit if we have this value. This could
+                # be used, for example, for partial evaluation
+                # where the caller has pre-populated `env` with
+                # values for a subset of the program.
+                continue
+
+            try:
+                self.env[node] = self.run_node(node)
+            except Exception as e:
+                if self.extra_traceback:
+                    msg = f"While executing {node.format_node()}"
+                    msg = f'{e.args[0]}\n\n{msg}' if e.args else str(msg)
+                    msg += f"\nOriginal traceback:\n{node.stack_trace}"
+                    e.args = (msg,) + e.args[1:]
+                    if isinstance(e, KeyError):
+                        raise RuntimeError(*e.args) from e
+                raise
+
+            if self.garbage_collect_values:
+                for to_delete in self.user_to_last_uses.get(node, []):
+                    del self.env[to_delete]
+
+            if node.op == 'output':
+                output_val = self.env[node]
+                return self.graph.process_outputs(output_val) if enable_io_processing else output_val
+
+    @compatibility(is_backward_compatible=True)
+    def boxed_run(self, args_list):
+        """
+        Run `module` via interpretation and return the result.  This uses the "boxed"
+        calling convention, where you pass a list of arguments, which will be cleared
+        by the interpreter.  This ensures that input tensors are promptly deallocated.
+        """
+        args_iter = iter(args_list)
+        env = {}
+        for n in self.graph.nodes:
+            if n.op == "placeholder":
+                env[n] = next(args_iter)
+        args_list.clear()
+        return self.run(initial_env=env)
+
+    @contextmanager
+    def _set_current_node(self, node):
+        with fx_traceback.set_current_meta(node):
+            yield
+
+    @compatibility(is_backward_compatible=True)
+    def run_node(self, n : Node) -> Any:
+        """
+        Run a specific node ``n`` and return the result.
+        Calls into placeholder, get_attr, call_function,
+        call_method, call_module, or output depending
+        on ``node.op``
+
+        Args:
+            n (Node): The Node to execute
+
+        Returns:
+            Any: The result of executing ``n``
+        """
+        with self._set_current_node(n):
+            args, kwargs = self.fetch_args_kwargs_from_env(n)
+            assert isinstance(args, tuple)
+            assert isinstance(kwargs, dict)
+            return getattr(self, n.op)(n.target, args, kwargs)
+
+    # Main Node running APIs
+    @compatibility(is_backward_compatible=True)
+    def placeholder(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``placeholder`` node. Note that this is stateful:
+        ``Interpreter`` maintains an internal iterator over
+        arguments passed to ``run`` and this method returns
+        next() on that iterator.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Returns:
+            Any: The argument value that was retrieved.
+        """
+        assert isinstance(target, str)
+        if target.startswith('*'):
+            # For a starred parameter e.g. `*args`, retrieve all
+            # remaining values from the args list.
+            return list(self.args_iter)
+        else:
+            try:
+                return next(self.args_iter)
+            except StopIteration as si:
+                if len(args) > 0:
+                    return args[0]
+                else:
+                    raise RuntimeError(f'Expected positional argument for parameter {target}, but one was not passed in!') from si
+
+    @compatibility(is_backward_compatible=True)
+    def get_attr(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``get_attr`` node. Will retrieve an attribute
+        value from the ``Module`` hierarchy of ``self.module``.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return:
+            Any: The value of the attribute that was retrieved
+        """
+        assert isinstance(target, str)
+        return self.fetch_attr(target)
+
+    @compatibility(is_backward_compatible=True)
+    def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_function`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the function invocation
+        """
+        assert not isinstance(target, str)
+
+        # Execute the function and return the result
+        return target(*args, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_method`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the method invocation
+        """
+        # args[0] is the `self` object for this method call
+        self_obj, *args_tail = args
+
+        # Execute the method and return the result
+        assert isinstance(target, str)
+        return getattr(self_obj, target)(*args_tail, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_module`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the module invocation
+        """
+        # Retrieve executed args and kwargs values from the environment
+
+        # Execute the method and return the result
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+
+        return submod(*args, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def output(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute an ``output`` node. This really just retrieves
+        the value referenced by the ``output`` node and returns it.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return:
+            Any: The return value referenced by the output node
+        """
+        return args[0]
+
+    # Helper methods
+    @compatibility(is_backward_compatible=True)
+    def fetch_attr(self, target : str):
+        """
+        Fetch an attribute from the ``Module`` hierarchy of ``self.module``.
+
+        Args:
+            target (str): The fully-qualified name of the attribute to fetch
+
+        Return:
+            Any: The value of the attribute.
+        """
+        target_atoms = target.split('.')
+        attr_itr = self.module
+        for i, atom in enumerate(target_atoms):
+            if not hasattr(attr_itr, atom):
+                raise RuntimeError(f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}")
+            attr_itr = getattr(attr_itr, atom)
+        return attr_itr
+
+    @compatibility(is_backward_compatible=True)
+    def fetch_args_kwargs_from_env(self, n : Node) -> Tuple[Tuple, Dict]:
+        """
+        Fetch the concrete values of ``args`` and ``kwargs`` of node ``n``
+        from the current execution environment.
+
+        Args:
+            n (Node): The node for which ``args`` and ``kwargs`` should be fetched.
+
+        Return:
+            Tuple[Tuple, Dict]: ``args`` and ``kwargs`` with concrete values for ``n``.
+        """
+        args = self.map_nodes_to_values(n.args, n)
+        assert isinstance(args, tuple)
+        kwargs = self.map_nodes_to_values(n.kwargs, n)
+        assert isinstance(kwargs, dict)
+        return args, kwargs
+
+    @compatibility(is_backward_compatible=True)
+    def map_nodes_to_values(self, args : Argument, n : Node) -> Argument:
+        """
+        Recursively descend through ``args`` and look up the concrete value
+        for each ``Node`` in the current execution environment.
+
+        Args:
+            args (Argument): Data structure within which to look up concrete values
+
+            n (Node): Node to which ``args`` belongs. This is only used for error reporting.
+        """
+        def load_arg(n_arg : Node) -> Any:
+            if n_arg not in self.env:
+                raise RuntimeError(f'Node {n} referenced nonexistent value {n_arg}! Run Graph.lint() '
+                                   f'to diagnose such issues')
+            return self.env[n_arg]
+        return map_arg(args, load_arg)
+
+@compatibility(is_backward_compatible=True)
+class Transformer(Interpreter):
+    """
+    ``Transformer`` is a special type of interpreter that produces a
+    new ``Module``. It exposes a ``transform()`` method that returns
+    the transformed ``Module``. ``Transformer`` does not require
+    arguments to run, as ``Interpreter`` does. ``Transformer`` works
+    entirely symbolically.
+
+    Example:
+
+        Suppose we want to swap all instances of ``torch.neg`` with
+        ``torch.sigmoid`` and vice versa (including their ``Tensor``
+        method equivalents). We could subclass ``Transformer`` like so::
+
+            class NegSigmSwapXformer(Transformer):
+                def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+                    if target == torch.sigmoid:
+                        return torch.neg(*args, **kwargs)
+                    return super().call_function(n)
+
+                def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+                    if target == 'neg':
+                        call_self, *args_tail = args
+                        return call_self.sigmoid(*args_tail, **kwargs)
+                    return super().call_method(n)
+
+            def fn(x):
+                return torch.sigmoid(x).neg()
+
+            gm = torch.fx.symbolic_trace(fn)
+
+            transformed : torch.nn.Module = NegSigmSwapXformer(gm).transform()
+            input = torch.randn(3, 4)
+            torch.testing.assert_close(transformed(input), torch.neg(input).sigmoid())
+
+    Args:
+        module (GraphModule): The ``Module`` to be transformed.
+    """
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, module):
+        super().__init__(module)
+        self.new_graph = Graph()
+        self.new_graph.set_codegen(module.graph._codegen)
+
+        class TransformerTracer(Tracer):
+            def __init__(self, graph: Graph):
+                super().__init__()
+                self.graph = graph
+                self.tensor_attrs: Dict[torch.Tensor, str] = {}  # type: ignore[assignment]
+
+            def is_leaf_module(self, _, __) -> bool:
+                return True
+
+        self.tracer = TransformerTracer(self.new_graph)
+        self.tracer.root = module
+
+    @compatibility(is_backward_compatible=True)
+    def placeholder(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Proxy:
+        """
+        Execute a ``placeholder`` node. In ``Transformer``, this is
+        overridden to insert a new ``placeholder`` into the output
+        graph.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+        """
+        assert isinstance(target, str)
+        default_value = next(iter(args)) if args else inspect.Signature.empty
+        return Proxy(self.new_graph.placeholder(target, default_value=default_value), self.tracer)
+
+    @compatibility(is_backward_compatible=True)
+    def get_attr(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Proxy:
+        """
+        Execute a ``get_attr`` node. In ``Transformer``, this is
+        overridden to insert a new ``get_attr`` node into the output
+        graph.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+        """
+        assert isinstance(target, str)
+        return self.tracer.create_proxy("get_attr", target, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        # Override so that the leaf module policy from `self.tracer` is respected.
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+        return self.tracer.call_module(submod, submod.forward, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        # Override so that functions that were wrapped are still wrapped.
+        return self.tracer.create_proxy('call_function', target, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def transform(self) -> GraphModule:
+        """
+        Transform ``self.module`` and return the transformed
+        ``GraphModule``.
+        """
+        with fx_traceback.preserve_node_meta():
+            result = super().run(enable_io_processing=False)
+        if result is not None:
+            def strip_proxy(a : Union[Argument, Proxy]) -> Any:
+                return a.node if isinstance(a, Proxy) else a
+            self.new_graph.output(map_aggregate(result, strip_proxy))
+        return _make_graph_module(self.module, self.new_graph)
diff --git a/shared/package-files/f2/f2b6fbaecb2abc8a12cedcc27c56f9a58e28207fc68c34ad2ab5e40ba6e75199/test_astype.py b/shared/package-files/f2/f2b6fbaecb2abc8a12cedcc27c56f9a58e28207fc68c34ad2ab5e40ba6e75199/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a2140f817f3a8e5689d001768cf5642118b105
--- /dev/null
+++ b/shared/package-files/f2/f2b6fbaecb2abc8a12cedcc27c56f9a58e28207fc68c34ad2ab5e40ba6e75199/test_astype.py
@@ -0,0 +1,28 @@
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    Index,
+    IntervalIndex,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype_categorical_retains_ordered(self, ordered):
+        index = IntervalIndex.from_breaks(range(5))
+        arr = index._data
+
+        dtype = CategoricalDtype(None, ordered=ordered)
+
+        expected = Categorical(list(arr), ordered=ordered)
+        result = arr.astype(dtype)
+        assert result.ordered is ordered
+        tm.assert_categorical_equal(result, expected)
+
+        # test IntervalIndex.astype while we're at it.
+        result = index.astype(dtype)
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
diff --git a/shared/package-files/f2/f2b87a890b76eb3174a8acc795fa865ca6732ae198e98e6df6104d5461bdbf93/ideals.py b/shared/package-files/f2/f2b87a890b76eb3174a8acc795fa865ca6732ae198e98e6df6104d5461bdbf93/ideals.py
new file mode 100644
index 0000000000000000000000000000000000000000..862604d4fbaba9d85fac38c74ef976b3f3fbb809
--- /dev/null
+++ b/shared/package-files/f2/f2b87a890b76eb3174a8acc795fa865ca6732ae198e98e6df6104d5461bdbf93/ideals.py
@@ -0,0 +1,394 @@
+"""Computations with ideals of polynomial rings."""
+
+from sympy.polys.polyerrors import CoercionFailed
+from sympy.polys.polyutils import IntegerPowerable
+
+
+class Ideal(IntegerPowerable):
+    """
+    Abstract base class for ideals.
+
+    Do not instantiate - use explicit constructors in the ring class instead:
+
+    >>> from sympy import QQ
+    >>> from sympy.abc import x
+    >>> QQ.old_poly_ring(x).ideal(x+1)
+    <x + 1>
+
+    Attributes
+
+    - ring - the ring this ideal belongs to
+
+    Non-implemented methods:
+
+    - _contains_elem
+    - _contains_ideal
+    - _quotient
+    - _intersect
+    - _union
+    - _product
+    - is_whole_ring
+    - is_zero
+    - is_prime, is_maximal, is_primary, is_radical
+    - is_principal
+    - height, depth
+    - radical
+
+    Methods that likely should be overridden in subclasses:
+
+    - reduce_element
+    """
+
+    def _contains_elem(self, x):
+        """Implementation of element containment."""
+        raise NotImplementedError
+
+    def _contains_ideal(self, I):
+        """Implementation of ideal containment."""
+        raise NotImplementedError
+
+    def _quotient(self, J):
+        """Implementation of ideal quotient."""
+        raise NotImplementedError
+
+    def _intersect(self, J):
+        """Implementation of ideal intersection."""
+        raise NotImplementedError
+
+    def is_whole_ring(self):
+        """Return True if ``self`` is the whole ring."""
+        raise NotImplementedError
+
+    def is_zero(self):
+        """Return True if ``self`` is the zero ideal."""
+        raise NotImplementedError
+
+    def _equals(self, J):
+        """Implementation of ideal equality."""
+        return self._contains_ideal(J) and J._contains_ideal(self)
+
+    def is_prime(self):
+        """Return True if ``self`` is a prime ideal."""
+        raise NotImplementedError
+
+    def is_maximal(self):
+        """Return True if ``self`` is a maximal ideal."""
+        raise NotImplementedError
+
+    def is_radical(self):
+        """Return True if ``self`` is a radical ideal."""
+        raise NotImplementedError
+
+    def is_primary(self):
+        """Return True if ``self`` is a primary ideal."""
+        raise NotImplementedError
+
+    def is_principal(self):
+        """Return True if ``self`` is a principal ideal."""
+        raise NotImplementedError
+
+    def radical(self):
+        """Compute the radical of ``self``."""
+        raise NotImplementedError
+
+    def depth(self):
+        """Compute the depth of ``self``."""
+        raise NotImplementedError
+
+    def height(self):
+        """Compute the height of ``self``."""
+        raise NotImplementedError
+
+    # TODO more
+
+    # non-implemented methods end here
+
+    def __init__(self, ring):
+        self.ring = ring
+
+    def _check_ideal(self, J):
+        """Helper to check ``J`` is an ideal of our ring."""
+        if not isinstance(J, Ideal) or J.ring != self.ring:
+            raise ValueError(
+                'J must be an ideal of %s, got %s' % (self.ring, J))
+
+    def contains(self, elem):
+        """
+        Return True if ``elem`` is an element of this ideal.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ
+        >>> QQ.old_poly_ring(x).ideal(x+1, x-1).contains(3)
+        True
+        >>> QQ.old_poly_ring(x).ideal(x**2, x**3).contains(x)
+        False
+        """
+        return self._contains_elem(self.ring.convert(elem))
+
+    def subset(self, other):
+        """
+        Returns True if ``other`` is is a subset of ``self``.
+
+        Here ``other`` may be an ideal.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ
+        >>> I = QQ.old_poly_ring(x).ideal(x+1)
+        >>> I.subset([x**2 - 1, x**2 + 2*x + 1])
+        True
+        >>> I.subset([x**2 + 1, x + 1])
+        False
+        >>> I.subset(QQ.old_poly_ring(x).ideal(x**2 - 1))
+        True
+        """
+        if isinstance(other, Ideal):
+            return self._contains_ideal(other)
+        return all(self._contains_elem(x) for x in other)
+
+    def quotient(self, J, **opts):
+        r"""
+        Compute the ideal quotient of ``self`` by ``J``.
+
+        That is, if ``self`` is the ideal `I`, compute the set
+        `I : J = \{x \in R | xJ \subset I \}`.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y
+        >>> from sympy import QQ
+        >>> R = QQ.old_poly_ring(x, y)
+        >>> R.ideal(x*y).quotient(R.ideal(x))
+        <y>
+        """
+        self._check_ideal(J)
+        return self._quotient(J, **opts)
+
+    def intersect(self, J):
+        """
+        Compute the intersection of self with ideal J.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y
+        >>> from sympy import QQ
+        >>> R = QQ.old_poly_ring(x, y)
+        >>> R.ideal(x).intersect(R.ideal(y))
+        <x*y>
+        """
+        self._check_ideal(J)
+        return self._intersect(J)
+
+    def saturate(self, J):
+        r"""
+        Compute the ideal saturation of ``self`` by ``J``.
+
+        That is, if ``self`` is the ideal `I`, compute the set
+        `I : J^\infty = \{x \in R | xJ^n \subset I \text{ for some } n\}`.
+        """
+        raise NotImplementedError
+        # Note this can be implemented using repeated quotient
+
+    def union(self, J):
+        """
+        Compute the ideal generated by the union of ``self`` and ``J``.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ
+        >>> QQ.old_poly_ring(x).ideal(x**2 - 1).union(QQ.old_poly_ring(x).ideal((x+1)**2)) == QQ.old_poly_ring(x).ideal(x+1)
+        True
+        """
+        self._check_ideal(J)
+        return self._union(J)
+
+    def product(self, J):
+        r"""
+        Compute the ideal product of ``self`` and ``J``.
+
+        That is, compute the ideal generated by products `xy`, for `x` an element
+        of ``self`` and `y \in J`.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x, y
+        >>> from sympy import QQ
+        >>> QQ.old_poly_ring(x, y).ideal(x).product(QQ.old_poly_ring(x, y).ideal(y))
+        <x*y>
+        """
+        self._check_ideal(J)
+        return self._product(J)
+
+    def reduce_element(self, x):
+        """
+        Reduce the element ``x`` of our ring modulo the ideal ``self``.
+
+        Here "reduce" has no specific meaning: it could return a unique normal
+        form, simplify the expression a bit, or just do nothing.
+        """
+        return x
+
+    def __add__(self, e):
+        if not isinstance(e, Ideal):
+            R = self.ring.quotient_ring(self)
+            if isinstance(e, R.dtype):
+                return e
+            if isinstance(e, R.ring.dtype):
+                return R(e)
+            return R.convert(e)
+        self._check_ideal(e)
+        return self.union(e)
+
+    __radd__ = __add__
+
+    def __mul__(self, e):
+        if not isinstance(e, Ideal):
+            try:
+                e = self.ring.ideal(e)
+            except CoercionFailed:
+                return NotImplemented
+        self._check_ideal(e)
+        return self.product(e)
+
+    __rmul__ = __mul__
+
+    def _zeroth_power(self):
+        return self.ring.ideal(1)
+
+    def _first_power(self):
+        # Raising to any power but 1 returns a new instance. So we mult by 1
+        # here so that the first power is no exception.
+        return self * 1
+
+    def __eq__(self, e):
+        if not isinstance(e, Ideal) or e.ring != self.ring:
+            return False
+        return self._equals(e)
+
+    def __ne__(self, e):
+        return not (self == e)
+
+
+class ModuleImplementedIdeal(Ideal):
+    """
+    Ideal implementation relying on the modules code.
+
+    Attributes:
+
+    - _module - the underlying module
+    """
+
+    def __init__(self, ring, module):
+        Ideal.__init__(self, ring)
+        self._module = module
+
+    def _contains_elem(self, x):
+        return self._module.contains([x])
+
+    def _contains_ideal(self, J):
+        if not isinstance(J, ModuleImplementedIdeal):
+            raise NotImplementedError
+        return self._module.is_submodule(J._module)
+
+    def _intersect(self, J):
+        if not isinstance(J, ModuleImplementedIdeal):
+            raise NotImplementedError
+        return self.__class__(self.ring, self._module.intersect(J._module))
+
+    def _quotient(self, J, **opts):
+        if not isinstance(J, ModuleImplementedIdeal):
+            raise NotImplementedError
+        return self._module.module_quotient(J._module, **opts)
+
+    def _union(self, J):
+        if not isinstance(J, ModuleImplementedIdeal):
+            raise NotImplementedError
+        return self.__class__(self.ring, self._module.union(J._module))
+
+    @property
+    def gens(self):
+        """
+        Return generators for ``self``.
+
+        Examples
+        ========
+
+        >>> from sympy import QQ
+        >>> from sympy.abc import x, y
+        >>> list(QQ.old_poly_ring(x, y).ideal(x, y, x**2 + y).gens)
+        [x, y, x**2 + y]
+        """
+        return (x[0] for x in self._module.gens)
+
+    def is_zero(self):
+        """
+        Return True if ``self`` is the zero ideal.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ
+        >>> QQ.old_poly_ring(x).ideal(x).is_zero()
+        False
+        >>> QQ.old_poly_ring(x).ideal().is_zero()
+        True
+        """
+        return self._module.is_zero()
+
+    def is_whole_ring(self):
+        """
+        Return True if ``self`` is the whole ring, i.e. one generator is a unit.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ, ilex
+        >>> QQ.old_poly_ring(x).ideal(x).is_whole_ring()
+        False
+        >>> QQ.old_poly_ring(x).ideal(3).is_whole_ring()
+        True
+        >>> QQ.old_poly_ring(x, order=ilex).ideal(2 + x).is_whole_ring()
+        True
+        """
+        return self._module.is_full_module()
+
+    def __repr__(self):
+        from sympy.printing.str import sstr
+        return '<' + ','.join(sstr(x) for [x] in self._module.gens) + '>'
+
+    # NOTE this is the only method using the fact that the module is a SubModule
+    def _product(self, J):
+        if not isinstance(J, ModuleImplementedIdeal):
+            raise NotImplementedError
+        return self.__class__(self.ring, self._module.submodule(
+            *[[x*y] for [x] in self._module.gens for [y] in J._module.gens]))
+
+    def in_terms_of_generators(self, e):
+        """
+        Express ``e`` in terms of the generators of ``self``.
+
+        Examples
+        ========
+
+        >>> from sympy.abc import x
+        >>> from sympy import QQ
+        >>> I = QQ.old_poly_ring(x).ideal(x**2 + 1, x)
+        >>> I.in_terms_of_generators(1)
+        [1, -x]
+        """
+        return self._module.in_terms_of_generators([e])
+
+    def reduce_element(self, x, **options):
+        return self._module.reduce_element([x], **options)[0]
diff --git a/shared/package-files/f2/f2c1c3b4ef198bca776b57902845b1b4298aad7987a03deac65228920d802005/test_regression.py b/shared/package-files/f2/f2c1c3b4ef198bca776b57902845b1b4298aad7987a03deac65228920d802005/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54d44020a70ebf7832443458a6c3483513243ab
--- /dev/null
+++ b/shared/package-files/f2/f2c1c3b4ef198bca776b57902845b1b4298aad7987a03deac65228920d802005/test_regression.py
@@ -0,0 +1,31 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_raises
+
+
+class TestRegression:
+    def test_kron_matrix(self):
+        # Ticket #71
+        x = np.matrix('[1 0; 1 0]')
+        assert_equal(type(np.kron(x, x)), type(x))
+
+    def test_matrix_properties(self):
+        # Ticket #125
+        a = np.matrix([1.0], dtype=float)
+        assert_(type(a.real) is np.matrix)
+        assert_(type(a.imag) is np.matrix)
+        c, d = np.matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+    def test_matrix_multiply_by_1d_vector(self):
+        # Ticket #473
+        def mul():
+            np.mat(np.eye(2))*np.ones(2)
+
+        assert_raises(ValueError, mul)
+
+    def test_matrix_std_argmax(self):
+        # Ticket #83
+        x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
+        assert_equal(x.std().shape, ())
+        assert_equal(x.argmax().shape, ())
diff --git a/shared/package-files/f2/f2c220caaba7b0707b2d8f5e257ad2f5e2e0cf6c75632a8611b197bbcda8c44f/miopen_rnn_backward_native.h b/shared/package-files/f2/f2c220caaba7b0707b2d8f5e257ad2f5e2e0cf6c75632a8611b197bbcda8c44f/miopen_rnn_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..af7bb9dde5b148e01e75cba9afd41094b2d7ed3a
--- /dev/null
+++ b/shared/package-files/f2/f2c220caaba7b0707b2d8f5e257ad2f5e2e0cf6c75632a8611b197bbcda8c44f/miopen_rnn_backward_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void miopen_rnn_backward_out(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, const at::Tensor & output, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> miopen_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, const at::Tensor & output, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/f2/f2ca34052d720622d300023472ef8ae94fe5b57d69acbb28a8667f6dd0e13497/METADATA b/shared/package-files/f2/f2ca34052d720622d300023472ef8ae94fe5b57d69acbb28a8667f6dd0e13497/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..01e913b8644e06be5d9235f9d1e2e50cb3f7f748
--- /dev/null
+++ b/shared/package-files/f2/f2ca34052d720622d300023472ef8ae94fe5b57d69acbb28a8667f6dd0e13497/METADATA
@@ -0,0 +1,147 @@
+Metadata-Version: 2.5
+Name: pyparsing
+Version: 3.3.3
+Summary: pyparsing - Classes and methods to define and execute parsing grammars
+Author-email: Paul McGuire <ptmcg.gm+pyparsing@gmail.com>
+Requires-Python: >=3.9
+Description-Content-Type: text/x-rst
+License-Expression: MIT
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Information Technology
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3.15
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Compilers
+Classifier: Topic :: Text Processing
+Classifier: Typing :: Typed
+License-File: LICENSE
+Requires-Dist: railroad-diagrams ; extra == "diagrams"
+Requires-Dist: jinja2 ; extra == "diagrams"
+Project-URL: Documentation, https://pyparsing-docs.readthedocs.io/en/latest/
+Project-URL: Homepage, https://github.com/pyparsing/pyparsing/
+Project-URL: Source, https://github.com/pyparsing/pyparsing.git
+Provides-Extra: diagrams
+Import-Name: pyparsing
+
+PyParsing -- A Python Parsing Module
+====================================
+
+|Version| |Build Status| |Coverage| |License| |Python Versions| |Snyk Score|
+
+Introduction
+============
+
+The pyparsing module is an alternative approach to creating and
+executing simple grammars, vs. the traditional lex/yacc approach, or the
+use of regular expressions. The pyparsing module provides a library of
+classes that client code uses to construct the grammar directly in
+Python code.
+
+*[Since first writing this description of pyparsing in late 2003, this
+technique for developing parsers has become more widespread, under the
+name Parsing Expression Grammars - PEGs. See more information on PEGs*
+`here <https://en.wikipedia.org/wiki/Parsing_expression_grammar>`__
+*.]*
+
+Here is a program to parse ``"Hello, World!"`` (or any greeting of the form
+``"salutation, addressee!"``):
+
+.. code:: python
+
+    from pyparsing import Word, alphas
+    greet = Word(alphas) + "," + Word(alphas) + "!"
+    hello = "Hello, World!"
+    print(hello, "->", greet.parse_string(hello))
+
+The program outputs the following::
+
+    Hello, World! -> ['Hello', ',', 'World', '!']
+
+The Python representation of the grammar is quite readable, owing to the
+self-explanatory class names, and the use of '+', '|' and '^' operator
+definitions.
+
+The parsed results returned from ``parse_string()`` is a collection of type
+``ParseResults``, which can be accessed as a
+nested list, a dictionary, or an object with named attributes.
+
+The pyparsing module handles some of the problems that are typically
+vexing when writing text parsers:
+
+- extra or missing whitespace (the above program will also handle ``"Hello,World!"``, ``"Hello , World !"``, etc.)
+- quoted strings
+- embedded comments
+
+The examples directory includes a simple SQL parser, simple CORBA IDL
+parser, a config file parser, a chemical formula parser, and a four-
+function algebraic notation parser, among many others.
+
+Documentation
+=============
+
+There are many examples in the online docstrings of the classes
+and methods in pyparsing. You can find them compiled into `online docs <https://pyparsing-docs.readthedocs.io/en/latest/>`__. Additional
+documentation resources and project info are listed in the online
+`GitHub wiki <https://github.com/pyparsing/pyparsing/wiki>`__. An
+entire directory of examples can be found `here <https://github.com/pyparsing/pyparsing/tree/master/examples>`__.
+
+AI Instructions
+===============
+
+There are also instructions for AI agents to use when helping you to create your parser. They can
+be pulled from the GitHub project repository, at pyparsing/ai/best_practices.md. You can also tell
+the AI to access them programmatically after installing pyparsing, either from the CLI with
+``python -m pyparsing.ai.show_best_practices`` or within python with
+``import pyparsing; pyparsing.show_best_practices()``.
+
+
+License
+=======
+
+MIT License. See header of the `pyparsing __init__.py <https://github.com/pyparsing/pyparsing/blob/master/pyparsing/__init__.py#L1-L23>`__ file.
+
+History
+=======
+
+See `CHANGES <https://github.com/pyparsing/pyparsing/blob/master/CHANGES>`__ file.
+
+
+Performance benchmarks
+======================
+
+For usage instructions and details on the performance benchmark suite, see
+``tests/README.md`` in this repository.
+
+.. |Build Status| image:: https://github.com/pyparsing/pyparsing/actions/workflows/ci.yml/badge.svg
+   :target: https://github.com/pyparsing/pyparsing/actions/workflows/ci.yml
+
+.. |Coverage| image:: https://codecov.io/gh/pyparsing/pyparsing/branch/master/graph/badge.svg
+  :target: https://codecov.io/gh/pyparsing/pyparsing
+
+.. |Version| image:: https://img.shields.io/pypi/v/pyparsing?style=flat-square
+    :target: https://pypi.org/project/pyparsing/
+    :alt: Version
+
+.. |License| image:: https://img.shields.io/pypi/l/pyparsing.svg?style=flat-square
+    :target: https://pypi.org/project/pyparsing/
+    :alt: License
+
+.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/pyparsing.svg?style=flat-square
+    :target: https://pypi.org/project/python-liquid/
+    :alt: Python versions
+
+.. |Snyk Score| image:: https://snyk.io//advisor/python/pyparsing/badge.svg
+   :target: https://snyk.io//advisor/python/pyparsing
+   :alt: pyparsing
+
diff --git a/shared/package-files/f2/f2d1a08f53738bbe87fd61791942e4c6a58bed8bbf5dfd25bd32593b03812ac2/test_array_interface.py b/shared/package-files/f2/f2d1a08f53738bbe87fd61791942e4c6a58bed8bbf5dfd25bd32593b03812ac2/test_array_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c719c5a5b934dd9669a6f0c3f3692a9e83525e
--- /dev/null
+++ b/shared/package-files/f2/f2d1a08f53738bbe87fd61791942e4c6a58bed8bbf5dfd25bd32593b03812ac2/test_array_interface.py
@@ -0,0 +1,219 @@
+import sys
+import pytest
+import numpy as np
+from numpy.testing import extbuild
+
+
+@pytest.fixture
+def get_module(tmp_path):
+    """ Some codes to generate data and manage temporary buffers use when
+    sharing with numpy via the array interface protocol.
+    """
+
+    if not sys.platform.startswith('linux'):
+        pytest.skip('link fails on cygwin')
+
+    prologue = '''
+        #include <Python.h>
+        #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+        #include <numpy/arrayobject.h>
+        #include <stdio.h>
+        #include <math.h>
+
+        NPY_NO_EXPORT
+        void delete_array_struct(PyObject *cap) {
+
+            /* get the array interface structure */
+            PyArrayInterface *inter = (PyArrayInterface*)
+                PyCapsule_GetPointer(cap, NULL);
+
+            /* get the buffer by which data was shared */
+            double *ptr = (double*)PyCapsule_GetContext(cap);
+
+            /* for the purposes of the regression test set the elements
+               to nan */
+            for (npy_intp i = 0; i < inter->shape[0]; ++i)
+                ptr[i] = nan("");
+
+            /* free the shared buffer */
+            free(ptr);
+
+            /* free the array interface structure */
+            free(inter->shape);
+            free(inter);
+
+            fprintf(stderr, "delete_array_struct\\ncap = %ld inter = %ld"
+                " ptr = %ld\\n", (long)cap, (long)inter, (long)ptr);
+        }
+        '''
+
+    functions = [
+        ("new_array_struct", "METH_VARARGS", """
+
+            long long n_elem = 0;
+            double value = 0.0;
+
+            if (!PyArg_ParseTuple(args, "Ld", &n_elem, &value)) {
+                Py_RETURN_NONE;
+            }
+
+            /* allocate and initialize the data to share with numpy */
+            long long n_bytes = n_elem*sizeof(double);
+            double *data = (double*)malloc(n_bytes);
+
+            if (!data) {
+                PyErr_Format(PyExc_MemoryError,
+                    "Failed to malloc %lld bytes", n_bytes);
+
+                Py_RETURN_NONE;
+            }
+
+            for (long long i = 0; i < n_elem; ++i) {
+                data[i] = value;
+            }
+
+            /* calculate the shape and stride */
+            int nd = 1;
+
+            npy_intp *ss = (npy_intp*)malloc(2*nd*sizeof(npy_intp));
+            npy_intp *shape = ss;
+            npy_intp *stride = ss + nd;
+
+            shape[0] = n_elem;
+            stride[0] = sizeof(double);
+
+            /* construct the array interface */
+            PyArrayInterface *inter = (PyArrayInterface*)
+                malloc(sizeof(PyArrayInterface));
+
+            memset(inter, 0, sizeof(PyArrayInterface));
+
+            inter->two = 2;
+            inter->nd = nd;
+            inter->typekind = 'f';
+            inter->itemsize = sizeof(double);
+            inter->shape = shape;
+            inter->strides = stride;
+            inter->data = data;
+            inter->flags = NPY_ARRAY_WRITEABLE | NPY_ARRAY_NOTSWAPPED |
+                           NPY_ARRAY_ALIGNED | NPY_ARRAY_C_CONTIGUOUS;
+
+            /* package into a capsule */
+            PyObject *cap = PyCapsule_New(inter, NULL, delete_array_struct);
+
+            /* save the pointer to the data */
+            PyCapsule_SetContext(cap, data);
+
+            fprintf(stderr, "new_array_struct\\ncap = %ld inter = %ld"
+                " ptr = %ld\\n", (long)cap, (long)inter, (long)data);
+
+            return cap;
+        """)
+        ]
+
+    more_init = "import_array();"
+
+    try:
+        import array_interface_testing
+        return array_interface_testing
+    except ImportError:
+        pass
+
+    # if it does not exist, build and load it
+    return extbuild.build_and_import_extension('array_interface_testing',
+                                               functions,
+                                               prologue=prologue,
+                                               include_dirs=[np.get_include()],
+                                               build_dir=tmp_path,
+                                               more_init=more_init)
+
+
+# FIXME: numpy.testing.extbuild uses `numpy.distutils`, so this won't work on
+# Python 3.12 and up.
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+@pytest.mark.slow
+def test_cstruct(get_module):
+
+    class data_source:
+        """
+        This class is for testing the timing of the PyCapsule destructor
+        invoked when numpy release its reference to the shared data as part of
+        the numpy array interface protocol. If the PyCapsule destructor is
+        called early the shared data is freed and invalid memory accesses will
+        occur.
+        """
+
+        def __init__(self, size, value):
+            self.size = size
+            self.value = value
+
+        @property
+        def __array_struct__(self):
+            return get_module.new_array_struct(self.size, self.value)
+
+    # write to the same stream as the C code
+    stderr = sys.__stderr__
+
+    # used to validate the shared data.
+    expected_value = -3.1415
+    multiplier = -10000.0
+
+    # create some data to share with numpy via the array interface
+    # assign the data an expected value.
+    stderr.write(' ---- create an object to share data ---- \n')
+    buf = data_source(256, expected_value)
+    stderr.write(' ---- OK!\n\n')
+
+    # share the data
+    stderr.write(' ---- share data via the array interface protocol ---- \n')
+    arr = np.array(buf, copy=False)
+    stderr.write('arr.__array_interface___ = %s\n' % (
+                 str(arr.__array_interface__)))
+    stderr.write('arr.base = %s\n' % (str(arr.base)))
+    stderr.write(' ---- OK!\n\n')
+
+    # release the source of the shared data. this will not release the data
+    # that was shared with numpy, that is done in the PyCapsule destructor.
+    stderr.write(' ---- destroy the object that shared data ---- \n')
+    buf = None
+    stderr.write(' ---- OK!\n\n')
+
+    # check that we got the expected data. If the PyCapsule destructor we
+    # defined was prematurely called then this test will fail because our
+    # destructor sets the elements of the array to NaN before free'ing the
+    # buffer. Reading the values here may also cause a SEGV
+    assert np.allclose(arr, expected_value)
+
+    # read the data. If the PyCapsule destructor we defined was prematurely
+    # called then reading the values here may cause a SEGV and will be reported
+    # as invalid reads by valgrind
+    stderr.write(' ---- read shared data ---- \n')
+    stderr.write('arr = %s\n' % (str(arr)))
+    stderr.write(' ---- OK!\n\n')
+
+    # write to the shared buffer. If the shared data was prematurely deleted
+    # this will may cause a SEGV and valgrind will report invalid writes
+    stderr.write(' ---- modify shared data ---- \n')
+    arr *= multiplier
+    expected_value *= multiplier
+    stderr.write('arr.__array_interface___ = %s\n' % (
+                 str(arr.__array_interface__)))
+    stderr.write('arr.base = %s\n' % (str(arr.base)))
+    stderr.write(' ---- OK!\n\n')
+
+    # read the data. If the shared data was prematurely deleted this
+    # will may cause a SEGV and valgrind will report invalid reads
+    stderr.write(' ---- read modified shared data ---- \n')
+    stderr.write('arr = %s\n' % (str(arr)))
+    stderr.write(' ---- OK!\n\n')
+
+    # check that we got the expected data. If the PyCapsule destructor we
+    # defined was prematurely called then this test will fail because our
+    # destructor sets the elements of the array to NaN before free'ing the
+    # buffer. Reading the values here may also cause a SEGV
+    assert np.allclose(arr, expected_value)
+
+    # free the shared data, the PyCapsule destructor should run here
+    stderr.write(' ---- free shared data ---- \n')
+    arr = None
+    stderr.write(' ---- OK!\n\n')
diff --git a/shared/package-files/f2/f2d2cea04cad71aa6f5abaf525f09fec3a20fb0ba506d4b562b0ecf12ec97cca/plyparser.py b/shared/package-files/f2/f2d2cea04cad71aa6f5abaf525f09fec3a20fb0ba506d4b562b0ecf12ec97cca/plyparser.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8f4c4395ea7ed07572aeb3a9d9064c0373b504b
--- /dev/null
+++ b/shared/package-files/f2/f2d2cea04cad71aa6f5abaf525f09fec3a20fb0ba506d4b562b0ecf12ec97cca/plyparser.py
@@ -0,0 +1,133 @@
+#-----------------------------------------------------------------
+# plyparser.py
+#
+# PLYParser class and other utilities for simplifying programming
+# parsers with PLY
+#
+# Eli Bendersky [https://eli.thegreenplace.net/]
+# License: BSD
+#-----------------------------------------------------------------
+
+import warnings
+
+class Coord(object):
+    """ Coordinates of a syntactic element. Consists of:
+            - File name
+            - Line number
+            - (optional) column number, for the Lexer
+    """
+    __slots__ = ('file', 'line', 'column', '__weakref__')
+    def __init__(self, file, line, column=None):
+        self.file = file
+        self.line = line
+        self.column = column
+
+    def __str__(self):
+        str = "%s:%s" % (self.file, self.line)
+        if self.column: str += ":%s" % self.column
+        return str
+
+
+class ParseError(Exception): pass
+
+
+class PLYParser(object):
+    def _create_opt_rule(self, rulename):
+        """ Given a rule name, creates an optional ply.yacc rule
+            for it. The name of the optional rule is
+            <rulename>_opt
+        """
+        optname = rulename + '_opt'
+
+        def optrule(self, p):
+            p[0] = p[1]
+
+        optrule.__doc__ = '%s : empty\n| %s' % (optname, rulename)
+        optrule.__name__ = 'p_%s' % optname
+        setattr(self.__class__, optrule.__name__, optrule)
+
+    def _coord(self, lineno, column=None):
+        return Coord(
+                file=self.clex.filename,
+                line=lineno,
+                column=column)
+
+    def _token_coord(self, p, token_idx):
+        """ Returns the coordinates for the YaccProduction object 'p' indexed
+            with 'token_idx'. The coordinate includes the 'lineno' and
+            'column'. Both follow the lex semantic, starting from 1.
+        """
+        last_cr = p.lexer.lexer.lexdata.rfind('\n', 0, p.lexpos(token_idx))
+        if last_cr < 0:
+            last_cr = -1
+        column = (p.lexpos(token_idx) - (last_cr))
+        return self._coord(p.lineno(token_idx), column)
+
+    def _parse_error(self, msg, coord):
+        raise ParseError("%s: %s" % (coord, msg))
+
+
+def parameterized(*params):
+    """ Decorator to create parameterized rules.
+
+    Parameterized rule methods must be named starting with 'p_' and contain
+    'xxx', and their docstrings may contain 'xxx' and 'yyy'. These will be
+    replaced by the given parameter tuples. For example, ``p_xxx_rule()`` with
+    docstring 'xxx_rule  : yyy' when decorated with
+    ``@parameterized(('id', 'ID'))`` produces ``p_id_rule()`` with the docstring
+    'id_rule  : ID'. Using multiple tuples produces multiple rules.
+    """
+    def decorate(rule_func):
+        rule_func._params = params
+        return rule_func
+    return decorate
+
+
+def template(cls):
+    """ Class decorator to generate rules from parameterized rule templates.
+
+    See `parameterized` for more information on parameterized rules.
+    """
+    issued_nodoc_warning = False
+    for attr_name in dir(cls):
+        if attr_name.startswith('p_'):
+            method = getattr(cls, attr_name)
+            if hasattr(method, '_params'):
+                # Remove the template method
+                delattr(cls, attr_name)
+                # Create parameterized rules from this method; only run this if
+                # the method has a docstring. This is to address an issue when
+                # pycparser's users are installed in -OO mode which strips
+                # docstrings away.
+                # See: https://github.com/eliben/pycparser/pull/198/ and
+                #      https://github.com/eliben/pycparser/issues/197
+                # for discussion.
+                if method.__doc__ is not None:
+                    _create_param_rules(cls, method)
+                elif not issued_nodoc_warning:
+                    warnings.warn(
+                        'parsing methods must have __doc__ for pycparser to work properly',
+                        RuntimeWarning,
+                        stacklevel=2)
+                    issued_nodoc_warning = True
+    return cls
+
+
+def _create_param_rules(cls, func):
+    """ Create ply.yacc rules based on a parameterized rule function
+
+    Generates new methods (one per each pair of parameters) based on the
+    template rule function `func`, and attaches them to `cls`. The rule
+    function's parameters must be accessible via its `_params` attribute.
+    """
+    for xxx, yyy in func._params:
+        # Use the template method's body for each new method
+        def param_rule(self, p):
+            func(self, p)
+
+        # Substitute in the params for the grammar rule and function name
+        param_rule.__doc__ = func.__doc__.replace('xxx', xxx).replace('yyy', yyy)
+        param_rule.__name__ = func.__name__.replace('xxx', xxx)
+
+        # Attach the new method to the class
+        setattr(cls, param_rule.__name__, param_rule)
diff --git a/shared/package-files/f2/f2dbcf8dc618d5c500e8de2c5784dd7b6c8dca2e7d9ec06d0b6d1bcc50827d2c/direct_url.json b/shared/package-files/f2/f2dbcf8dc618d5c500e8de2c5784dd7b6c8dca2e7d9ec06d0b6d1bcc50827d2c/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb1d6f7cb71f20272da7ab9cfe24f904e47ce8e7
--- /dev/null
+++ b/shared/package-files/f2/f2dbcf8dc618d5c500e8de2c5784dd7b6c8dca2e7d9ec06d0b6d1bcc50827d2c/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///opt/conda/conda-bld/parso_1641458642106/work"}
\ No newline at end of file
diff --git a/shared/package-files/f2/f2ea34304be8aa08095f226e83a5f588eb6b1c48c4b911c4cbc5caf44104b151/_check_build.pyx b/shared/package-files/f2/f2ea34304be8aa08095f226e83a5f588eb6b1c48c4b911c4cbc5caf44104b151/_check_build.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..0409e73f5e96dc3a4c27889fa44eda8a17d36ef9
--- /dev/null
+++ b/shared/package-files/f2/f2ea34304be8aa08095f226e83a5f588eb6b1c48c4b911c4cbc5caf44104b151/_check_build.pyx
@@ -0,0 +1,2 @@
+def check_build():
+    return
diff --git a/shared/package-files/f2/f2eaf267382ab466a4c950f203b386cc8f6eaaf4a86e02281abb23e4c5a6c1be/runtime.pyi b/shared/package-files/f2/f2eaf267382ab466a4c950f203b386cc8f6eaaf4a86e02281abb23e4c5a6c1be/runtime.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..66ffe9d3e367f94c1c1fc80dec0838f2c6e3c0b0
--- /dev/null
+++ b/shared/package-files/f2/f2eaf267382ab466a4c950f203b386cc8f6eaaf4a86e02281abb23e4c5a6c1be/runtime.pyi
@@ -0,0 +1,132 @@
+from typing import Any, Dict, Optional, Text, Union
+
+from jinja2.environment import Environment
+from jinja2.exceptions import TemplateNotFound as TemplateNotFound, TemplateRuntimeError as TemplateRuntimeError
+from jinja2.utils import Markup as Markup, concat as concat, escape as escape, missing as missing
+
+to_string: Any
+identity: Any
+
+def markup_join(seq): ...
+def unicode_join(seq): ...
+
+class TemplateReference:
+    def __init__(self, context) -> None: ...
+    def __getitem__(self, name): ...
+
+class Context:
+    parent: Union[Context, Dict[str, Any]]
+    vars: Dict[str, Any]
+    environment: Environment
+    eval_ctx: Any
+    exported_vars: Any
+    name: Text
+    blocks: Dict[str, Any]
+    def __init__(
+        self, environment: Environment, parent: Union[Context, Dict[str, Any]], name: Text, blocks: Dict[str, Any]
+    ) -> None: ...
+    def super(self, name, current): ...
+    def get(self, key, default: Optional[Any] = ...): ...
+    def resolve(self, key): ...
+    def get_exported(self): ...
+    def get_all(self): ...
+    def call(__self, __obj, *args, **kwargs): ...
+    def derived(self, locals: Optional[Any] = ...): ...
+    keys: Any
+    values: Any
+    items: Any
+    iterkeys: Any
+    itervalues: Any
+    iteritems: Any
+    def __contains__(self, name): ...
+    def __getitem__(self, key): ...
+
+class BlockReference:
+    name: Any
+    def __init__(self, name, context, stack, depth) -> None: ...
+    @property
+    def super(self): ...
+    def __call__(self): ...
+
+class LoopContext:
+    index0: int
+    depth0: Any
+    def __init__(self, iterable, recurse: Optional[Any] = ..., depth0: int = ...) -> None: ...
+    def cycle(self, *args): ...
+    first: Any
+    last: Any
+    index: Any
+    revindex: Any
+    revindex0: Any
+    depth: Any
+    def __len__(self): ...
+    def __iter__(self): ...
+    def loop(self, iterable): ...
+    __call__: Any
+    @property
+    def length(self): ...
+
+class LoopContextIterator:
+    context: Any
+    def __init__(self, context) -> None: ...
+    def __iter__(self): ...
+    def __next__(self): ...
+
+class Macro:
+    name: Any
+    arguments: Any
+    defaults: Any
+    catch_kwargs: Any
+    catch_varargs: Any
+    caller: Any
+    def __init__(self, environment, func, name, arguments, defaults, catch_kwargs, catch_varargs, caller) -> None: ...
+    def __call__(self, *args, **kwargs): ...
+
+class Undefined:
+    def __init__(self, hint: Optional[Any] = ..., obj: Any = ..., name: Optional[Any] = ..., exc: Any = ...) -> None: ...
+    def __getattr__(self, name): ...
+    __add__: Any
+    __radd__: Any
+    __mul__: Any
+    __rmul__: Any
+    __div__: Any
+    __rdiv__: Any
+    __truediv__: Any
+    __rtruediv__: Any
+    __floordiv__: Any
+    __rfloordiv__: Any
+    __mod__: Any
+    __rmod__: Any
+    __pos__: Any
+    __neg__: Any
+    __call__: Any
+    __getitem__: Any
+    __lt__: Any
+    __le__: Any
+    __gt__: Any
+    __ge__: Any
+    __int__: Any
+    __float__: Any
+    __complex__: Any
+    __pow__: Any
+    __rpow__: Any
+    def __eq__(self, other): ...
+    def __ne__(self, other): ...
+    def __hash__(self): ...
+    def __len__(self): ...
+    def __iter__(self): ...
+    def __nonzero__(self): ...
+    __bool__: Any
+
+def make_logging_undefined(logger: Optional[Any] = ..., base: Optional[Any] = ...): ...
+
+class DebugUndefined(Undefined): ...
+
+class StrictUndefined(Undefined):
+    __iter__: Any
+    __len__: Any
+    __nonzero__: Any
+    __eq__: Any
+    __ne__: Any
+    __bool__: Any
+    __hash__: Any
diff --git a/shared/package-files/f2/f2eba3e9795ab4dc8820b3a98c96a9d7708c6660f0ac2c862a7f4b8885222d5d/test_multinomial.py b/shared/package-files/f2/f2eba3e9795ab4dc8820b3a98c96a9d7708c6660f0ac2c862a7f4b8885222d5d/test_multinomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..b455c5cc979b9ba9756c9da88c1694471115cd5d
--- /dev/null
+++ b/shared/package-files/f2/f2eba3e9795ab4dc8820b3a98c96a9d7708c6660f0ac2c862a7f4b8885222d5d/test_multinomial.py
@@ -0,0 +1,48 @@
+from sympy.ntheory.multinomial import (binomial_coefficients, binomial_coefficients_list, multinomial_coefficients)
+from sympy.ntheory.multinomial import multinomial_coefficients_iterator
+
+
+def test_binomial_coefficients_list():
+    assert binomial_coefficients_list(0) == [1]
+    assert binomial_coefficients_list(1) == [1, 1]
+    assert binomial_coefficients_list(2) == [1, 2, 1]
+    assert binomial_coefficients_list(3) == [1, 3, 3, 1]
+    assert binomial_coefficients_list(4) == [1, 4, 6, 4, 1]
+    assert binomial_coefficients_list(5) == [1, 5, 10, 10, 5, 1]
+    assert binomial_coefficients_list(6) == [1, 6, 15, 20, 15, 6, 1]
+
+
+def test_binomial_coefficients():
+    for n in range(15):
+        c = binomial_coefficients(n)
+        l = [c[k] for k in sorted(c)]
+        assert l == binomial_coefficients_list(n)
+
+
+def test_multinomial_coefficients():
+    assert multinomial_coefficients(1, 1) == {(1,): 1}
+    assert multinomial_coefficients(1, 2) == {(2,): 1}
+    assert multinomial_coefficients(1, 3) == {(3,): 1}
+    assert multinomial_coefficients(2, 0) == {(0, 0): 1}
+    assert multinomial_coefficients(2, 1) == {(0, 1): 1, (1, 0): 1}
+    assert multinomial_coefficients(2, 2) == {(2, 0): 1, (0, 2): 1, (1, 1): 2}
+    assert multinomial_coefficients(2, 3) == {(3, 0): 1, (1, 2): 3, (0, 3): 1,
+            (2, 1): 3}
+    assert multinomial_coefficients(3, 1) == {(1, 0, 0): 1, (0, 1, 0): 1,
+            (0, 0, 1): 1}
+    assert multinomial_coefficients(3, 2) == {(0, 1, 1): 2, (0, 0, 2): 1,
+            (1, 1, 0): 2, (0, 2, 0): 1, (1, 0, 1): 2, (2, 0, 0): 1}
+    mc = multinomial_coefficients(3, 3)
+    assert mc == {(2, 1, 0): 3, (0, 3, 0): 1,
+            (1, 0, 2): 3, (0, 2, 1): 3, (0, 1, 2): 3, (3, 0, 0): 1,
+            (2, 0, 1): 3, (1, 2, 0): 3, (1, 1, 1): 6, (0, 0, 3): 1}
+    assert dict(multinomial_coefficients_iterator(2, 0)) == {(0, 0): 1}
+    assert dict(
+        multinomial_coefficients_iterator(2, 1)) == {(0, 1): 1, (1, 0): 1}
+    assert dict(multinomial_coefficients_iterator(2, 2)) == \
+        {(2, 0): 1, (0, 2): 1, (1, 1): 2}
+    assert dict(multinomial_coefficients_iterator(3, 3)) == mc
+    it = multinomial_coefficients_iterator(7, 2)
+    assert [next(it) for i in range(4)] == \
+        [((2, 0, 0, 0, 0, 0, 0), 1), ((1, 1, 0, 0, 0, 0, 0), 2),
+      ((0, 2, 0, 0, 0, 0, 0), 1), ((1, 0, 1, 0, 0, 0, 0), 2)]
diff --git a/shared/package-files/f2/f2f327cc3083e53987a448dd3959b0f79b4d7e7e60e87e0565ac80ca33088ce9/direct_url.json b/shared/package-files/f2/f2f327cc3083e53987a448dd3959b0f79b4d7e7e60e87e0565ac80ca33088ce9/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa47d2b27cbc67cac09e8d2d9866f29bb584a793
--- /dev/null
+++ b/shared/package-files/f2/f2f327cc3083e53987a448dd3959b0f79b4d7e7e60e87e0565ac80ca33088ce9/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///croot/ruamel.yaml_1666304550667/work"}
\ No newline at end of file
diff --git a/shared/package-files/f2/f2f50943cae268db445e690fe17bd7879a83a19bb8e48ad7607f3c3076b51225/linalg_lu_factor_compositeimplicitautograd_dispatch.h b/shared/package-files/f2/f2f50943cae268db445e690fe17bd7879a83a19bb8e48ad7607f3c3076b51225/linalg_lu_factor_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e06853c7cd10ba29e532f96be0e71d2765c4d6e7
--- /dev/null
+++ b/shared/package-files/f2/f2f50943cae268db445e690fe17bd7879a83a19bb8e48ad7607f3c3076b51225/linalg_lu_factor_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> linalg_lu_factor(const at::Tensor & A, bool pivot=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_out(at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A, bool pivot=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_outf(const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/f2/f2fd508e69258bdc0886152eab382a0b3cacaf40b56760b7a0930252836f818e/test_ego.py b/shared/package-files/f2/f2fd508e69258bdc0886152eab382a0b3cacaf40b56760b7a0930252836f818e/test_ego.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6fc779548a3fd2e049679987f941b2bc211c2d0
--- /dev/null
+++ b/shared/package-files/f2/f2fd508e69258bdc0886152eab382a0b3cacaf40b56760b7a0930252836f818e/test_ego.py
@@ -0,0 +1,39 @@
+"""
+ego graph
+---------
+"""
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestGeneratorEgo:
+    def test_ego(self):
+        G = nx.star_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(G, H)
+        G.add_edge(1, 11)
+        G.add_edge(2, 22)
+        G.add_edge(3, 33)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(nx.star_graph(3), H)
+        G = nx.path_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, undirected=True)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, center=False)
+        assert edges_equal(H.edges(), [])
+
+    def test_ego_distance(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=2, distance=1)
+        G.add_edge(1, 2, weight=2, distance=2)
+        G.add_edge(2, 3, weight=2, distance=1)
+        assert nodes_equal(nx.ego_graph(G, 0, radius=3).nodes(), [0, 1, 2, 3])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight")
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight", undirected=True)
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="distance")
+        assert nodes_equal(eg.nodes(), [0, 1, 2])